From 7c5c8a2f4ab3fbc72f852816d5c1bb8b58960325 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 24 Aug 2026 23:18:05 +0200
Subject: [PATCH 01/83] chore: add Renovate config (ported from shapepipe)
 (#308)

* chore: add Renovate config (ported from shapepipe)

* chore: gate python bumps behind dashboard approval
---
 renovate.json | 52 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 renovate.json

diff --git a/renovate.json b/renovate.json
new file mode 100644
index 00000000..6a25bd4d
--- /dev/null
+++ b/renovate.json
@@ -0,0 +1,52 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "description": [
+    "THE ONLY Renovate config file — do not add another. A forgotten .github/renovate.json5 (the original Dependabot->Renovate migration config) shadowed this file for weeks: Renovate's config discovery (and Mend's cached config-file name) preferred it, so edits here were silently ignored while its 14-day minimumReleaseAge broke every uv artifact update (renovatebot/renovate#41624). Deleted 2026-07; if Renovate ever again behaves contrary to this file, first check for a second config file (renovate.json5, .github/renovate.json*, .renovaterc*), then the Mend portal's resolved config.",
+    "Low-noise dependency updates; uv owns version resolution. pyproject keeps floor ranges and rangeStrategy=update-lockfile means `uv lock` picks versions jointly — cross-package conflicts (e.g. numba capping numpy) resolve silently instead of failing the batch, and un-stick themselves when upstream catches up. No minimumReleaseAge anywhere: any age gate is unenforceable with uv resolution (#41624 — uv resolves transitives past the gate and the artifact step fails); CI running the full suite in the container is the automerge gate.",
+    "Noise: all non-major updates grouped into one weekly PR that automerges when CI is green; majors are gated behind an explicit dashboard-approval tick; lockFileMaintenance refreshes transitive deps weekly. Security fixes (GitHub + OSV alerts) automerge on green CI without waiting for the weekly schedule.",
+    "python is guarded: Renovate classes base-image bumps like 3.12 -> 3.14 as 'minor', so the major-only approval rule wouldn't catch it — its own packageRule adds dependencyDashboardApproval so a bump sits as an unchecked dashboard item instead of an immortal PR, until someone deliberately ticks it."
+  ],
+  "extends": [
+    "config:recommended",
+    "group:allNonMajor",
+    "schedule:weekly",
+    "helpers:pinGitHubActionDigests"
+  ],
+  "rangeStrategy": "update-lockfile",
+  "lockFileMaintenance": {
+    "enabled": true,
+    "automerge": true
+  },
+  "osvVulnerabilityAlerts": true,
+  "vulnerabilityAlerts": {
+    "automerge": true,
+    "labels": [
+      "security"
+    ]
+  },
+  "packageRules": [
+    {
+      "matchUpdateTypes": [
+        "minor",
+        "patch",
+        "pin",
+        "digest"
+      ],
+      "automerge": true
+    },
+    {
+      "matchUpdateTypes": [
+        "major"
+      ],
+      "dependencyDashboardApproval": true
+    },
+    {
+      "matchPackageNames": [
+        "python"
+      ],
+      "groupName": "python",
+      "automerge": false,
+      "dependencyDashboardApproval": true
+    }
+  ]
+}

From 409abb77fb23e4be02893e57931d8308128dbae4 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Tue, 25 Aug 2026 00:14:33 +0200
Subject: [PATCH 02/83] =?UTF-8?q?Remove=20vestigial=20FHP/MK=20NOSHEAR?=
 =?UTF-8?q?=E2=86=901P=20Tpsf=20substitution=20(#257)=20(#267)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The metacal estimator and calibrate_comprehensive_cat.py carried a
workaround that overwrote the metacal no-shear reconvolution-PSF size
with the 1P value — a real fix for an older ShapePipe catalogue whose
no-shear PSF was wrong, now a no-op under the current stack: ngmix builds
one magnitude-dilated reconv PSF and reuses it across all metacal types,
so NOSHEAR and 1P are bit-identical per object.

- calibration.py: drop the overwrite; the no-shear branch simply uses the
  no-shear reconv-PSF column it already reads.
- calibrate_comprehensive_cat.py: drop the override and the now-redundant
  NGMIX_T_PSF_RECONV_NOSHEAR_orig column.
- test_calibration.py: update the estimator-stdout note (no hack line now).

No output changes under the current stack. test_calibration.py: 8 passed.


Claude-Session: https://claude.ai/code/session_01Xthu9uZC17TsaeXh899fhc

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
---
 scripts/calibration/calibrate_comprehensive_cat.py | 10 ----------
 src/sp_validation/calibration.py                   |  8 --------
 src/sp_validation/tests/test_calibration.py        |  4 ++--
 3 files changed, 2 insertions(+), 20 deletions(-)

diff --git a/scripts/calibration/calibrate_comprehensive_cat.py b/scripts/calibration/calibrate_comprehensive_cat.py
index 4928df9b..2605338f 100644
--- a/scripts/calibration/calibrate_comprehensive_cat.py
+++ b/scripts/calibration/calibrate_comprehensive_cat.py
@@ -196,16 +196,6 @@
 for key in add_cols:
     add_cols_data[key] = cat.get_col(dat, key, mask_combined._mask, mask_metacal)
 
-# Keep original NOSHEAR column, override with 1P PSF values (FHP/MK hack)
-print(
-    "FHP/MK hack: explicit copying of the metacal no-shear (updated from 1p)"
-    + " PSF size"
-)
-add_cols_data["NGMIX_T_PSF_RECONV_NOSHEAR_orig"] = add_cols_data[
-    "NGMIX_T_PSF_RECONV_NOSHEAR"
-]
-add_cols_data["NGMIX_T_PSF_RECONV_NOSHEAR"] = gal_metacal.ns["Tpsf"][mask_metacal]
-
 # %%
 # Additional post-processing columns to write to output cat
 add_cols_post = [
diff --git a/src/sp_validation/calibration.py b/src/sp_validation/calibration.py
index e37fc4d0..e0ab212e 100644
--- a/src/sp_validation/calibration.py
+++ b/src/sp_validation/calibration.py
@@ -795,14 +795,6 @@ def _read_data(self, data, mask):
                 f"Unsupported shape prefix '{self._prefix}'; only 'NGMIX' is supported"
             )
 
-        print("FHP/MK hack using p1 PSF for ns in cuts")
-        indices = np.where(mask)[0]
-        col_1p = f"{self._prefix}_T_PSF_RECONV_1P"
-        new_psf = data[col_1p][indices]
-
-        # Overwriting incorrect no-shear PSF size to the one from 1p
-        ns["Tpsf"] = new_psf
-
         self.m1 = m1
         self.p1 = p1
         self.m2 = m2
diff --git a/src/sp_validation/tests/test_calibration.py b/src/sp_validation/tests/test_calibration.py
index 40621c19..0a66aed5 100644
--- a/src/sp_validation/tests/test_calibration.py
+++ b/src/sp_validation/tests/test_calibration.py
@@ -296,8 +296,8 @@ def test_metacal_R_matrix_recovers_injected_response():
     re-runs with slope 5.0 and confirms R11 tracks it (5.0 != 2.0), so a
     change that decouples R from the input numbers fails.
 
-    NOTE: the estimator prints an 'FHP/MK hack' line and an unweighted /
-    weighted response line; these are expected stdout, not errors.
+    NOTE: the estimator prints an unweighted / weighted response line; this
+    is expected stdout, not an error.
     """
     data, n = _build_ngmix_catalog(slope_11=2.0, slope_22=3.0, step=0.01)
     mask = np.ones(n, dtype=bool)

From 2ae0c8b09048b89deb6407e9c310811b63aaae29 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Sun, 30 Aug 2026 01:51:56 +0200
Subject: [PATCH 03/83] Track the Smokescreen fork's main; lock at b457c99
 (#311)

The provisional SHA pin predated the fork's merged protocol+packaging
PRs (and its commit is now orphaned). The library is changing quickly,
so pyproject tracks the fork's main and uv.lock carries the exact
commit; updates land via uv lock --upgrade-package smokescreen. The
DRAW_SCHEME custody assertion fails closed if an install's draw
semantics ever drift from the committed blind.


Claude-Session: https://claude.ai/code/session_01G9MahwJEQ1t9EuvUXijmy3

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
---
 pyproject.toml |   10 +-
 uv.lock        | 1241 ++++++++++++++++++++++++------------------------
 2 files changed, 625 insertions(+), 626 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c6a68dd0..c2a9aa2c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -97,12 +97,10 @@ dependencies = [
     # getdist feature-branch below, which is an external fork we pin for repro.)
     "shear_psf_leakage @ git+https://github.com/CosmoStat/shear_psf_leakage.git@develop",
     "skyproj",
-    # UNIONS-WL fork of DESC Smokescreen, pinned by SHA on the fork's
-    # packaging branch: it declares pyccl and imports its theory backends
-    # lazily, so the install closure is CCL-only. Provisional pin — swapped to the
-    # fork's release tag once the fork packaging PRs merge. Git pin only;
-    # nothing is published to PyPI.
-    "smokescreen @ git+https://github.com/UNIONS-WL/Smokescreen@588a6b9b26560bd5ba3dd5ba342f3c40152644f9",
+    # UNIONS-WL fork of DESC Smokescreen. Tracks the fork's main; uv.lock
+    # freezes the exact commit, so updates land via
+    # `uv lock --upgrade-package smokescreen`. Git only; not on PyPI.
+    "smokescreen @ git+https://github.com/UNIONS-WL/Smokescreen@main",
     "statsmodels",
     "treecorr>=5.0",
     "tqdm",
diff --git a/uv.lock b/uv.lock
index bef8072e..deedc1cf 100644
--- a/uv.lock
+++ b/uv.lock
@@ -14,9 +14,9 @@ name = "adjusttext"
 version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b5/5c/496e506ad3313664df24a79f801719cadcd62af5999fcb299c9b08ff0d4b/adjusttext-1.4.0.tar.gz", hash = "sha256:1f73860ced8cccce3f85ee6989ca133c2579b67a7453f63dbeb38f39bf123154", size = 15852, upload-time = "2026-06-08T16:48:32.726Z" }
 wheels = [
@@ -55,8 +55,8 @@ name = "anyio"
 version = "4.14.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "idna", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13' and sys_platform == 'linux'" },
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3b/72/5562aabb8dd7181e8e860622a38bea08d17842b99ecd4c91f84ac95251b0/anyio-4.14.1.tar.gz", hash = "sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e", size = 254831, upload-time = "2026-06-24T20:56:06.017Z" }
 wheels = [
@@ -68,7 +68,7 @@ name = "argon2-cffi"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "argon2-cffi-bindings", marker = "sys_platform == 'linux'" },
+    { name = "argon2-cffi-bindings" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" }
 wheels = [
@@ -80,7 +80,7 @@ name = "argon2-cffi-bindings"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cffi", marker = "sys_platform == 'linux'" },
+    { name = "cffi" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441, upload-time = "2025-07-30T10:02:05.147Z" }
 wheels = [
@@ -117,8 +117,8 @@ name = "arrow"
 version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "python-dateutil", marker = "sys_platform == 'linux'" },
-    { name = "tzdata", marker = "sys_platform == 'linux'" },
+    { name = "python-dateutil" },
+    { name = "tzdata" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931, upload-time = "2025-10-18T17:46:46.761Z" }
 wheels = [
@@ -139,11 +139,11 @@ name = "astropy"
 version = "8.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy-iers-data", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyerfa", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "astropy-iers-data" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pyerfa" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0e/c4/21be4313ddfde5f60e0607fd307f367b9e0f0bf153a89b10cbd036dd8cfd/astropy-8.0.1.tar.gz", hash = "sha256:45ca31d5b91fa294cd590a4791a32db94de7f9c8a343155f4d5877baa82351da", size = 7152500, upload-time = "2026-07-05T07:24:48.482Z" }
 wheels = [
@@ -157,8 +157,8 @@ name = "astropy-healpix"
 version = "2.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/06/bb/fb3dcca212b1d57ed26b33eabb9e1cc18fcadbfc2f2976b20530b7d80b90/astropy_healpix-2.0.0.tar.gz", hash = "sha256:6ce7646dc5b1c09a7ed24b1f23d3e2fce8e3570387220d26a24487ab001b3b0b", size = 112064, upload-time = "2026-06-29T20:02:46.407Z" }
 wheels = [
@@ -220,8 +220,8 @@ name = "beautifulsoup4"
 version = "4.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "soupsieve", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "soupsieve" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/43/65/318323f98dbee45d42dff61d8f047181bc6f2268a9068cfad035a46be5af/beautifulsoup4-4.15.0.tar.gz", hash = "sha256:288e3ca7d54b06f2ac191970bc275c1939cb46d450b255bf6718b04aa37ab4f7", size = 632571, upload-time = "2026-06-07T16:44:20.453Z" }
 wheels = [
@@ -233,7 +233,7 @@ name = "bleach"
 version = "6.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "webencodings", marker = "sys_platform == 'linux'" },
+    { name = "webencodings" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/48/3c/e12ac860709702bd5ebeb9b56a4fe334f1001246ee1b8f2b7ee28912df7d/bleach-6.4.0.tar.gz", hash = "sha256:4202482733d85cedd04e59fcb2f89f4e4c7c385a78d3c3c23c30446843a37452", size = 204857, upload-time = "2026-06-05T13:01:13.734Z" }
 wheels = [
@@ -242,7 +242,7 @@ wheels = [
 
 [package.optional-dependencies]
 css = [
-    { name = "tinycss2", marker = "sys_platform == 'linux'" },
+    { name = "tinycss2" },
 ]
 
 [[package]]
@@ -250,14 +250,14 @@ name = "blosc2"
 version = "4.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "httpx", extra = ["http2"], marker = "sys_platform == 'linux'" },
-    { name = "msgpack", marker = "sys_platform == 'linux'" },
-    { name = "ndindex", marker = "sys_platform == 'linux'" },
-    { name = "numexpr", marker = "platform_machine != 'wasm32' and sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pydantic", marker = "sys_platform == 'linux'" },
-    { name = "rich", marker = "sys_platform == 'linux'" },
-    { name = "threadpoolctl", marker = "platform_machine != 'wasm32' and sys_platform == 'linux'" },
+    { name = "httpx", extra = ["http2"] },
+    { name = "msgpack" },
+    { name = "ndindex" },
+    { name = "numexpr", marker = "platform_machine != 'wasm32'" },
+    { name = "numpy" },
+    { name = "pydantic" },
+    { name = "rich" },
+    { name = "threadpoolctl", marker = "platform_machine != 'wasm32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c1/26/c5b32a553f002e91886213ec91ff56a2dd8555e5e78eb4c289addb208642/blosc2-4.8.0.tar.gz", hash = "sha256:9789c3248052be59e63d6debafec4cd59d0eb4a6fd1cbaced36865d5d40ffa63", size = 5681856, upload-time = "2026-07-10T09:38:33.592Z" }
 wheels = [
@@ -278,12 +278,12 @@ name = "cadcutils"
 version = "1.6.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "distro", marker = "sys_platform == 'linux'" },
-    { name = "lxml", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyopenssl", marker = "sys_platform == 'linux'" },
-    { name = "requests", marker = "sys_platform == 'linux'" },
-    { name = "setuptools", marker = "sys_platform == 'linux'" },
+    { name = "distro" },
+    { name = "lxml" },
+    { name = "packaging" },
+    { name = "pyopenssl" },
+    { name = "requests" },
+    { name = "setuptools" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/fe/44/4964823c72c9e6a93e26de0118cead37fa3c3600b4677f0c06788c903c80/cadcutils-1.6.2.tar.gz", hash = "sha256:6e2d7822756d48a363bc5f857cd717f80aab760554df86518b957f8826a7a906", size = 94673, upload-time = "2026-06-22T17:35:47.303Z" }
 wheels = [
@@ -295,10 +295,10 @@ name = "camb"
 version = "1.6.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "sympy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "scipy" },
+    { name = "sympy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6d/ad/f2e6446fbd94f7adc1474e1f4e40d2e7fa7e5e7143640cb8557d492f0a73/camb-1.6.6.tar.gz", hash = "sha256:9856202a5c05570256e52377b20431891c7b08b2e9c334e141fd08d2a085516f", size = 799344, upload-time = "2026-03-11T16:46:55.819Z" }
 wheels = [
@@ -320,7 +320,7 @@ name = "cffi"
 version = "2.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pycparser", marker = "implementation_name != 'PyPy' and sys_platform == 'linux'" },
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/57/5f/ff100cae70ebe9d8df1c01a00e510e45d9adb5c1fdda84791b199141de97/cffi-2.1.0.tar.gz", hash = "sha256:efc1cdd798b1aaf39b4610bba7aad28c9bea9b910f25c784ccf9ec1fa719d1f9", size = 531036, upload-time = "2026-07-06T21:34:30.382Z" }
 wheels = [
@@ -429,12 +429,12 @@ name = "clmm"
 version = "1.16.10"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "healpy", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "qp-prob", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "healpy" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "qp-prob" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/73/7ff76d487cdbcddb9c80ec03313db1b11f48fe4fcb5e1609b6eca5a73159/clmm-1.16.10.tar.gz", hash = "sha256:48273784419c038a89619b798a140153ad1e451390ff56074f3b6825c91ebe32", size = 72475, upload-time = "2025-12-04T17:30:11.47Z" }
 wheels = [
@@ -473,7 +473,7 @@ name = "conda-inject"
 version = "1.3.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b1/a8/8dc86113c65c949cc72d651461d6e4c544b3302a85ed14a5298829e6a419/conda_inject-1.3.2.tar.gz", hash = "sha256:0b8cde8c47998c118d8ff285a04977a3abcf734caf579c520fca469df1cd0aac", size = 3635, upload-time = "2024-05-27T12:20:58.873Z" }
 wheels = [
@@ -500,7 +500,7 @@ name = "contourpy"
 version = "1.3.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
 wheels = [
@@ -541,12 +541,12 @@ name = "cosmo-numba"
 version = "1.0.1.dev6+g188d272c6"
 source = { git = "https://github.com/aguinot/cosmo-numba.git?rev=main#188d272c67d6d699d7cff7a7a53ded8dec759bb0" }
 dependencies = [
-    { name = "mpmath", marker = "sys_platform == 'linux'" },
-    { name = "numba", marker = "sys_platform == 'linux'" },
-    { name = "numbaquadpack", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "rocket-fft", marker = "sys_platform == 'linux'" },
-    { name = "sympy", marker = "sys_platform == 'linux'" },
+    { name = "mpmath" },
+    { name = "numba" },
+    { name = "numbaquadpack" },
+    { name = "numpy" },
+    { name = "rocket-fft" },
+    { name = "sympy" },
 ]
 
 [[package]]
@@ -554,7 +554,7 @@ name = "cosmology"
 version = "2022.10.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c9/38/04099bb2b6a626bd69a3f117afc60f2c47c08899e0730e0e48c898bf4745/cosmology-2022.10.9.tar.gz", hash = "sha256:0c2857c9bf1fdd09f1f11ab5765df0389a4101f3a900fa11251c3f37696f02d4", size = 8488, upload-time = "2022-10-10T10:18:16.229Z" }
 wheels = [
@@ -615,7 +615,7 @@ name = "cryptography"
 version = "49.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cffi", marker = "platform_python_implementation != 'PyPy' and sys_platform == 'linux'" },
+    { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1f/99/d1c90d6041656cc6ee229dc99cd67fd0cd5aec3c5f7d72fffc27cc750054/cryptography-49.0.0.tar.gz", hash = "sha256:f89660a348f4f78a92366240a61404e337586ef7f5909a2fef59ca88ef505493", size = 854345, upload-time = "2026-06-12T20:02:30.512Z" }
 wheels = [
@@ -659,18 +659,18 @@ name = "cs-util"
 version = "0.2.2"
 source = { git = "https://github.com/CosmoStat/cs_util.git?rev=develop#bedcdbf50ac57fc4d0b72a62b251373cd1807824" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "camb", marker = "sys_platform == 'linux'" },
-    { name = "datetime", marker = "sys_platform == 'linux'" },
-    { name = "healpy", marker = "sys_platform == 'linux'" },
-    { name = "healsparse", marker = "sys_platform == 'linux'" },
-    { name = "keyring", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pyccl", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "swig", marker = "sys_platform == 'linux'" },
-    { name = "vos", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "camb" },
+    { name = "datetime" },
+    { name = "healpy" },
+    { name = "healsparse" },
+    { name = "keyring" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pyccl" },
+    { name = "scipy" },
+    { name = "swig" },
+    { name = "vos" },
 ]
 
 [[package]]
@@ -687,13 +687,13 @@ name = "dask"
 version = "2026.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", marker = "sys_platform == 'linux'" },
-    { name = "cloudpickle", marker = "sys_platform == 'linux'" },
-    { name = "fsspec", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "partd", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "toolz", marker = "sys_platform == 'linux'" },
+    { name = "click" },
+    { name = "cloudpickle" },
+    { name = "fsspec" },
+    { name = "packaging" },
+    { name = "partd" },
+    { name = "pyyaml" },
+    { name = "toolz" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/4d/b5/aa50877159f1efd65a744107e0662a15d2b80fa0c427d980313367f49493/dask-2026.7.0.tar.gz", hash = "sha256:b039970642ce97a063070bae2763dada8aae27e3cbd8ff6d894072f920a1e252", size = 11548914, upload-time = "2026-07-06T16:58:36.496Z" }
 wheels = [
@@ -702,7 +702,7 @@ wheels = [
 
 [package.optional-dependencies]
 array = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 
 [[package]]
@@ -710,11 +710,11 @@ name = "dask-image"
 version = "2026.5.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "dask", extra = ["array"], marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pims", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "tifffile", marker = "sys_platform == 'linux'" },
+    { name = "dask", extra = ["array"] },
+    { name = "numpy" },
+    { name = "pims" },
+    { name = "scipy" },
+    { name = "tifffile" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/49/e592a13a5e1efdcdb8f1faab7c4e309c61648792e276f9ced5fb79381b33/dask_image-2026.5.0.tar.gz", hash = "sha256:ed6b462277e691b2c12b0890ba801a0f9a00cc1894b0aa71b195a7a1419b2b00", size = 80457, upload-time = "2026-05-27T14:05:57.383Z" }
 wheels = [
@@ -726,8 +726,8 @@ name = "datetime"
 version = "6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pytz", marker = "sys_platform == 'linux'" },
-    { name = "zope-interface", marker = "sys_platform == 'linux'" },
+    { name = "pytz" },
+    { name = "zope-interface" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/77/32/decbfd165e9985ba9d8c2d34a39afe5aeba2fc3fe390eb6e9ef1aab98fa8/datetime-6.0.tar.gz", hash = "sha256:c1514936d2f901e10c8e08d83bf04e6c9dbd7ca4f244da94fec980980a3bc4d5", size = 64167, upload-time = "2025-11-25T08:00:34.586Z" }
 wheels = [
@@ -769,7 +769,7 @@ name = "deprecated"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "wrapt", marker = "sys_platform == 'linux'" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/49/85/12f0a49a7c4ffb70572b6c2ef13c90c88fd190debda93b23f026b25f9634/deprecated-1.3.1.tar.gz", hash = "sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223", size = 2932523, upload-time = "2025-10-30T08:19:02.757Z" }
 wheels = [
@@ -817,7 +817,7 @@ name = "donfig"
 version = "0.8.1.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/25/71/80cc718ff6d7abfbabacb1f57aaa42e9c1552bfdd01e64ddd704e4a03638/donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52", size = 19506, upload-time = "2024-05-23T14:14:31.513Z" }
 wheels = [
@@ -838,7 +838,7 @@ name = "ducc0"
 version = "0.41.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cf/e7/849dd1a079b994184fa53905d47278f86d0dd3dcfb6ff2b0e3eb7d6a1c1b/ducc0-0.41.0.tar.gz", hash = "sha256:bac084745bbdb243482a4aec3ecc857bdd46faec298c67e48bc0bb90350dbabd", size = 353129, upload-time = "2026-03-26T18:56:04.669Z" }
 wheels = [
@@ -857,7 +857,7 @@ name = "emcee"
 version = "3.1.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cb/53/1045ee878cb24281387079f8ee4f0ade1622c6aae1ed1fd91a53e4fa5b19/emcee-3.1.6.tar.gz", hash = "sha256:11af4daf6ab8f9ca69681e3c29054665db7bbd87fd4eb8e437d2c3a1248c637d", size = 2871117, upload-time = "2024-04-19T10:03:19.555Z" }
 wheels = [
@@ -887,7 +887,7 @@ name = "fitsio"
 version = "1.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/34/6b/5658778e070faaa194f1a1133527cedfa948d173daed29d61f25c5487ab6/fitsio-1.4.1.tar.gz", hash = "sha256:2b2bf0bfd37790114b44283931c59a14b1fec25a653bf65c4fc555b8d3b24de9", size = 4148217, upload-time = "2026-07-10T21:21:31.585Z" }
 wheels = [
@@ -906,7 +906,7 @@ name = "flt"
 version = "2026.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/80/66/538256582e373d81a944c0ee3a12fd0e4dd0f39d10136353c4ce914d7061/flt-2026.1.tar.gz", hash = "sha256:4bf2818d4a9137f5f715fd2ff13acfbf0175639373155cd89ea2b788edf00093", size = 12817, upload-time = "2026-03-20T22:47:29.754Z" }
 wheels = [
@@ -977,11 +977,11 @@ name = "getdist"
 version = "1.6.1"
 source = { git = "https://github.com/benabed/getdist.git?rev=113cd22a9a0d013b6f72fe734be81f260f3d3be5#113cd22a9a0d013b6f72fe734be81f260f3d3be5" }
 dependencies = [
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pyyaml" },
+    { name = "scipy" },
 ]
 
 [[package]]
@@ -989,7 +989,7 @@ name = "gitdb"
 version = "4.0.12"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "smmap", marker = "sys_platform == 'linux'" },
+    { name = "smmap" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/72/94/63b0fc47eb32792c7ba1fe1b694daec9a63620db1e313033d18140c2320a/gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571", size = 394684, upload-time = "2025-01-02T07:20:46.413Z" }
 wheels = [
@@ -1001,7 +1001,7 @@ name = "gitpython"
 version = "3.1.50"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "gitdb", marker = "sys_platform == 'linux'" },
+    { name = "gitdb" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/33/f6/354ae6491228b5eb40e10d89c4d13c651fe1cf7556e35ebdded50cff57ce/gitpython-3.1.50.tar.gz", hash = "sha256:80da2d12504d52e1f998772dc5baf6e553f8d2fcfe1fcc226c9d9a2ee3372dcc", size = 219798, upload-time = "2026-05-06T04:01:26.571Z" }
 wheels = [
@@ -1013,11 +1013,11 @@ name = "glass"
 version = "2025.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cosmology", marker = "sys_platform == 'linux'" },
-    { name = "healpix", marker = "sys_platform == 'linux'" },
-    { name = "healpy", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "transformcl", marker = "sys_platform == 'linux'" },
+    { name = "cosmology" },
+    { name = "healpix" },
+    { name = "healpy" },
+    { name = "numpy" },
+    { name = "transformcl" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a5/d6/26cd58e75834671259f26b9287a584d726e6023da35ddf7ca3b7f2c393fb/glass-2025.1.tar.gz", hash = "sha256:7b1aa2394e16010f7f1b4243f49e7e12d7a4dd28fcbf3e3f7cf25ce4905a8615", size = 48533, upload-time = "2025-02-21T18:43:48.046Z" }
 wheels = [
@@ -1029,9 +1029,9 @@ name = "glass-ext-camb"
 version = "2023.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "camb", marker = "sys_platform == 'linux'" },
-    { name = "glass", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "camb" },
+    { name = "glass" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b7/1a/18bb47c0f7cc267c2f8c95273055b82b1975e4dccbfea6051e82a4d1f513/glass.ext.camb-2023.6.tar.gz", hash = "sha256:f7b6e521b2d45d3b68400353ce6ab1747c772631832a257567de028aa2060509", size = 2989, upload-time = "2023-06-30T22:27:31.511Z" }
 wheels = [
@@ -1113,8 +1113,8 @@ name = "h2"
 version = "4.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "hpack", marker = "sys_platform == 'linux'" },
-    { name = "hyperframe", marker = "sys_platform == 'linux'" },
+    { name = "hpack" },
+    { name = "hyperframe" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026, upload-time = "2025-08-23T18:12:19.778Z" }
 wheels = [
@@ -1126,7 +1126,7 @@ name = "h5py"
 version = "3.16.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
 wheels = [
@@ -1153,7 +1153,7 @@ name = "healpix"
 version = "2025.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/43/1a/c1af658a2d06cdd2734b085b0eee34d51646f091ed13b1669d3a596de57c/healpix-2025.1.tar.gz", hash = "sha256:733f16f30c15d911c8209f4045f0b0934e5fcafa63b8b39d62e425a036adccea", size = 22744, upload-time = "2025-07-07T16:38:42.402Z" }
 wheels = [
@@ -1168,8 +1168,8 @@ name = "healpy"
 version = "1.19.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e3/4e/6f5328f375f88c8c38314f24f82b7e1486ec0358c557779500382db1503c/healpy-1.19.0.tar.gz", hash = "sha256:28e839cb885a23d36c77fc3423a3cb9271a07fda94085bd12fc329f941130ec5", size = 4075006, upload-time = "2025-12-02T08:27:19.766Z" }
 wheels = [
@@ -1192,8 +1192,8 @@ name = "healsparse"
 version = "1.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "hpgeom", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "hpgeom" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e3/33/36520faabae128fe9a4a5cbeb0916be864d2eb34cf7410d52dc532af044e/healsparse-1.13.0.tar.gz", hash = "sha256:c21e4eb075c686bd306bdc1b40e039134c4ca60ccfa4e7e73845c107a5b16544", size = 133589, upload-time = "2026-07-07T18:33:05.481Z" }
 wheels = [
@@ -1214,7 +1214,7 @@ name = "hpgeom"
 version = "1.5.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/07/72/27640de7d85a566b8ecd27e7894f371a19669c56c268756e18111a137df8/hpgeom-1.5.4.tar.gz", hash = "sha256:85ac73e267c11f3f248920d3541a0e6ffab821cae54f0d44414d1939dfb91200", size = 153906, upload-time = "2026-02-24T19:03:01.954Z" }
 wheels = [
@@ -1240,8 +1240,8 @@ name = "httpcore"
 version = "1.0.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "certifi", marker = "sys_platform == 'linux'" },
-    { name = "h11", marker = "sys_platform == 'linux'" },
+    { name = "certifi" },
+    { name = "h11" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" }
 wheels = [
@@ -1253,10 +1253,10 @@ name = "httpx"
 version = "0.28.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "anyio", marker = "sys_platform == 'linux'" },
-    { name = "certifi", marker = "sys_platform == 'linux'" },
-    { name = "httpcore", marker = "sys_platform == 'linux'" },
-    { name = "idna", marker = "sys_platform == 'linux'" },
+    { name = "anyio" },
+    { name = "certifi" },
+    { name = "httpcore" },
+    { name = "idna" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" }
 wheels = [
@@ -1265,7 +1265,7 @@ wheels = [
 
 [package.optional-dependencies]
 http2 = [
-    { name = "h2", marker = "sys_platform == 'linux'" },
+    { name = "h2" },
 ]
 
 [[package]]
@@ -1300,8 +1300,8 @@ name = "imageio"
 version = "2.37.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pillow", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "pillow" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b1/84/93bcd1300216ea50811cee96873b84a1bebf8d0489ffaf7f2a3756bab866/imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451", size = 389673, upload-time = "2026-03-09T11:31:12.573Z" }
 wheels = [
@@ -1338,7 +1338,7 @@ name = "importlib-metadata"
 version = "9.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "zipp", marker = "sys_platform == 'linux'" },
+    { name = "zipp" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" }
 wheels = [
@@ -1368,18 +1368,18 @@ name = "ipykernel"
 version = "7.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "comm", marker = "sys_platform == 'linux'" },
-    { name = "debugpy", marker = "sys_platform == 'linux'" },
-    { name = "ipython", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-client", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib-inline", marker = "sys_platform == 'linux'" },
-    { name = "nest-asyncio2", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "psutil", marker = "sys_platform == 'linux'" },
-    { name = "pyzmq", marker = "sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "comm" },
+    { name = "debugpy" },
+    { name = "ipython" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "matplotlib-inline" },
+    { name = "nest-asyncio2" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3d/c4/e4a38f579de4225a561305666f7541cdabb30075def2aa1ac17bd73c1fb5/ipykernel-7.3.0.tar.gz", hash = "sha256:9acaaaf97d16355166e4085afe9d225bfbdf2b7ef520f9df3be8f2b248275e09", size = 184899, upload-time = "2026-06-10T08:41:25.481Z" }
 wheels = [
@@ -1391,16 +1391,16 @@ name = "ipython"
 version = "9.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "decorator", marker = "sys_platform == 'linux'" },
-    { name = "ipython-pygments-lexers", marker = "sys_platform == 'linux'" },
-    { name = "jedi", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib-inline", marker = "sys_platform == 'linux'" },
-    { name = "pexpect", marker = "sys_platform == 'linux'" },
-    { name = "prompt-toolkit", marker = "sys_platform == 'linux'" },
-    { name = "psutil", marker = "sys_platform == 'linux'" },
-    { name = "pygments", marker = "sys_platform == 'linux'" },
-    { name = "stack-data", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "decorator" },
+    { name = "ipython-pygments-lexers" },
+    { name = "jedi" },
+    { name = "matplotlib-inline" },
+    { name = "pexpect" },
+    { name = "prompt-toolkit" },
+    { name = "psutil" },
+    { name = "pygments" },
+    { name = "stack-data" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/53/59/165d3b4d75cc34add3122c4417ecb229085140ac573103c223cd01dde96f/ipython-9.15.0.tar.gz", hash = "sha256:da2819ce2aa83135257df830660b1176d986c3d2876db24df01974fa955b2756", size = 4442580, upload-time = "2026-06-26T11:03:35.913Z" }
 wheels = [
@@ -1421,7 +1421,7 @@ name = "ipython-pygments-lexers"
 version = "1.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pygments", marker = "sys_platform == 'linux'" },
+    { name = "pygments" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" }
 wheels = [
@@ -1433,11 +1433,11 @@ name = "ipywidgets"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "comm", marker = "sys_platform == 'linux'" },
-    { name = "ipython", marker = "sys_platform == 'linux'" },
-    { name = "jupyterlab-widgets", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
-    { name = "widgetsnbextension", marker = "sys_platform == 'linux'" },
+    { name = "comm" },
+    { name = "ipython" },
+    { name = "jupyterlab-widgets" },
+    { name = "traitlets" },
+    { name = "widgetsnbextension" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/4c/ae/c5ce1edc1afe042eadb445e95b0671b03cee61895264357956e61c0d2ac0/ipywidgets-8.1.8.tar.gz", hash = "sha256:61f969306b95f85fba6b6986b7fe45d73124d1d9e3023a8068710d47a22ea668", size = 116739, upload-time = "2025-11-01T21:18:12.393Z" }
 wheels = [
@@ -1449,7 +1449,7 @@ name = "isoduration"
 version = "20.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "arrow", marker = "sys_platform == 'linux'" },
+    { name = "arrow" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649, upload-time = "2020-11-01T11:00:00.312Z" }
 wheels = [
@@ -1461,7 +1461,7 @@ name = "jaraco-classes"
 version = "3.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "more-itertools", marker = "sys_platform == 'linux'" },
+    { name = "more-itertools" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/06/c0/ed4a27bc5571b99e3cff68f8a9fa5b56ff7df1c2251cc715a652ddd26402/jaraco.classes-3.4.0.tar.gz", hash = "sha256:47a024b51d0239c0dd8c8540c6c7f484be3b8fcf0b2d85c13825780d3b3f3acd", size = 11780, upload-time = "2024-03-31T07:27:36.643Z" }
 wheels = [
@@ -1482,7 +1482,7 @@ name = "jaraco-functools"
 version = "4.5.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "more-itertools", marker = "sys_platform == 'linux'" },
+    { name = "more-itertools" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/36/cf/ea4ef2920830dea3f5ab2ea4da6fb67724e6dca80ee2553788c3607243d0/jaraco_functools-4.5.0.tar.gz", hash = "sha256:3bb5665ea4a020cf78a7040e89154c77edadb3ca74f366479669c5999aa70b03", size = 20272, upload-time = "2026-05-15T21:34:10.025Z" }
 wheels = [
@@ -1494,7 +1494,7 @@ name = "jedi"
 version = "0.20.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "parso", marker = "sys_platform == 'linux'" },
+    { name = "parso" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/46/b7/a3635f6a2d7cf5b5dd98064fc1d5fbbafcb25477bcea204a3a92145d158b/jedi-0.20.0.tar.gz", hash = "sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011", size = 3119416, upload-time = "2026-05-01T23:38:47.814Z" }
 wheels = [
@@ -1515,7 +1515,7 @@ name = "jinja2"
 version = "3.1.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markupsafe", marker = "sys_platform == 'linux'" },
+    { name = "markupsafe" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" }
 wheels = [
@@ -1545,7 +1545,7 @@ name = "jsonargparse"
 version = "4.49.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/87/0b/f012466077d803b546955bf827ab30a320ad1550e3167195432cc2d6bfad/jsonargparse-4.49.0.tar.gz", hash = "sha256:9e691a09d937fb4c6bc1f6d3bf6c3546efa03381ea1e64ee10759095f1b14da7", size = 125237, upload-time = "2026-05-15T06:48:44.807Z" }
 wheels = [
@@ -1554,8 +1554,8 @@ wheels = [
 
 [package.optional-dependencies]
 signatures = [
-    { name = "docstring-parser", marker = "sys_platform == 'linux'" },
-    { name = "typeshed-client", marker = "sys_platform == 'linux'" },
+    { name = "docstring-parser" },
+    { name = "typeshed-client" },
 ]
 
 [[package]]
@@ -1572,10 +1572,10 @@ name = "jsonschema"
 version = "4.26.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "attrs", marker = "sys_platform == 'linux'" },
-    { name = "jsonschema-specifications", marker = "sys_platform == 'linux'" },
-    { name = "referencing", marker = "sys_platform == 'linux'" },
-    { name = "rpds-py", marker = "sys_platform == 'linux'" },
+    { name = "attrs" },
+    { name = "jsonschema-specifications" },
+    { name = "referencing" },
+    { name = "rpds-py" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b3/fc/e067678238fa451312d4c62bf6e6cf5ec56375422aee02f9cb5f909b3047/jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326", size = 366583, upload-time = "2026-01-07T13:41:07.246Z" }
 wheels = [
@@ -1584,15 +1584,15 @@ wheels = [
 
 [package.optional-dependencies]
 format-nongpl = [
-    { name = "fqdn", marker = "sys_platform == 'linux'" },
-    { name = "idna", marker = "sys_platform == 'linux'" },
-    { name = "isoduration", marker = "sys_platform == 'linux'" },
-    { name = "jsonpointer", marker = "sys_platform == 'linux'" },
-    { name = "rfc3339-validator", marker = "sys_platform == 'linux'" },
-    { name = "rfc3986-validator", marker = "sys_platform == 'linux'" },
-    { name = "rfc3987-syntax", marker = "sys_platform == 'linux'" },
-    { name = "uri-template", marker = "sys_platform == 'linux'" },
-    { name = "webcolors", marker = "sys_platform == 'linux'" },
+    { name = "fqdn" },
+    { name = "idna" },
+    { name = "isoduration" },
+    { name = "jsonpointer" },
+    { name = "rfc3339-validator" },
+    { name = "rfc3986-validator" },
+    { name = "rfc3987-syntax" },
+    { name = "uri-template" },
+    { name = "webcolors" },
 ]
 
 [[package]]
@@ -1600,7 +1600,7 @@ name = "jsonschema-specifications"
 version = "2025.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "referencing", marker = "sys_platform == 'linux'" },
+    { name = "referencing" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" }
 wheels = [
@@ -1612,12 +1612,12 @@ name = "jupyter"
 version = "1.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ipykernel", marker = "sys_platform == 'linux'" },
-    { name = "ipywidgets", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-console", marker = "sys_platform == 'linux'" },
-    { name = "jupyterlab", marker = "sys_platform == 'linux'" },
-    { name = "nbconvert", marker = "sys_platform == 'linux'" },
-    { name = "notebook", marker = "sys_platform == 'linux'" },
+    { name = "ipykernel" },
+    { name = "ipywidgets" },
+    { name = "jupyter-console" },
+    { name = "jupyterlab" },
+    { name = "nbconvert" },
+    { name = "notebook" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/f3/af28ea964ab8bc1e472dba2e82627d36d470c51f5cd38c37502eeffaa25e/jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a", size = 5714959, upload-time = "2024-08-30T07:15:48.299Z" }
 wheels = [
@@ -1629,8 +1629,8 @@ name = "jupyter-builder"
 version = "1.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "jupyter-core" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0d/df/db5efc4e28803a1421350445e53d85ec571a4e6928e3524ccc39f4e16584/jupyter_builder-1.1.0.tar.gz", hash = "sha256:b996e8af616900f18724fa34883169d869be2205497940bec78a3a1031eb897d", size = 971142, upload-time = "2026-07-11T07:24:02.4Z" }
 wheels = [
@@ -1642,12 +1642,12 @@ name = "jupyter-client"
 version = "8.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "python-dateutil", marker = "sys_platform == 'linux'" },
-    { name = "pyzmq", marker = "sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "jupyter-core" },
+    { name = "python-dateutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/7d/dc/5512503b088997c2250b8bf18258fba9d9ce5ead641183700960d3c9d342/jupyter_client-8.9.1.tar.gz", hash = "sha256:a58f730dd9e728ba16ba1d62ebccf7ffe1ebbdbce4e95cfae941b7321ae1f4fa", size = 359256, upload-time = "2026-06-09T13:15:01.033Z" }
 wheels = [
@@ -1659,14 +1659,14 @@ name = "jupyter-console"
 version = "6.6.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ipykernel", marker = "sys_platform == 'linux'" },
-    { name = "ipython", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-client", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "prompt-toolkit", marker = "sys_platform == 'linux'" },
-    { name = "pygments", marker = "sys_platform == 'linux'" },
-    { name = "pyzmq", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "ipykernel" },
+    { name = "ipython" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "prompt-toolkit" },
+    { name = "pygments" },
+    { name = "pyzmq" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/bd/2d/e2fd31e2fc41c14e2bcb6c976ab732597e907523f6b2420305f9fc7fdbdb/jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539", size = 34363, upload-time = "2023-03-06T14:13:31.02Z" }
 wheels = [
@@ -1678,8 +1678,8 @@ name = "jupyter-core"
 version = "5.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "platformdirs", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "platformdirs" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" }
 wheels = [
@@ -1691,14 +1691,14 @@ name = "jupyter-events"
 version = "0.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jsonschema", extra = ["format-nongpl"], marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "python-json-logger", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "referencing", marker = "sys_platform == 'linux'" },
-    { name = "rfc3339-validator", marker = "sys_platform == 'linux'" },
-    { name = "rfc3986-validator", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "jsonschema", extra = ["format-nongpl"] },
+    { name = "packaging" },
+    { name = "python-json-logger" },
+    { name = "pyyaml" },
+    { name = "referencing" },
+    { name = "rfc3339-validator" },
+    { name = "rfc3986-validator" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/18/f8/475c4241b2b75af0deaae453ed003c6c851766dbc44d332d8baf245dc931/jupyter_events-0.12.1.tar.gz", hash = "sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3", size = 62854, upload-time = "2026-04-20T23:17:50.66Z" }
 wheels = [
@@ -1710,7 +1710,7 @@ name = "jupyter-lsp"
 version = "2.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupyter-server", marker = "sys_platform == 'linux'" },
+    { name = "jupyter-server" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/36/ff/1e4a61f5170a9a1d978f3ac3872449de6c01fc71eaf89657824c878b1549/jupyter_lsp-2.3.1.tar.gz", hash = "sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6", size = 55677, upload-time = "2026-04-02T08:10:06.749Z" }
 wheels = [
@@ -1722,23 +1722,23 @@ name = "jupyter-server"
 version = "2.20.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "anyio", marker = "sys_platform == 'linux'" },
-    { name = "argon2-cffi", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-client", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-events", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-server-terminals", marker = "sys_platform == 'linux'" },
-    { name = "nbconvert", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "prometheus-client", marker = "sys_platform == 'linux'" },
-    { name = "pyzmq", marker = "sys_platform == 'linux'" },
-    { name = "send2trash", marker = "sys_platform == 'linux'" },
-    { name = "terminado", marker = "sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
-    { name = "websocket-client", marker = "sys_platform == 'linux'" },
+    { name = "anyio" },
+    { name = "argon2-cffi" },
+    { name = "jinja2" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "jupyter-events" },
+    { name = "jupyter-server-terminals" },
+    { name = "nbconvert" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "prometheus-client" },
+    { name = "pyzmq" },
+    { name = "send2trash" },
+    { name = "terminado" },
+    { name = "tornado" },
+    { name = "traitlets" },
+    { name = "websocket-client" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6b/dc/db3a582633170186f8c8b31298d7eb26ad0eb031a1f53476c258b64eed05/jupyter_server-2.20.0.tar.gz", hash = "sha256:b5778ba337d8015a3dc2b80803ecdd5ac18d3797fddf61a50ea5fb472b4ebe14", size = 756523, upload-time = "2026-06-17T12:09:09.435Z" }
 wheels = [
@@ -1750,7 +1750,7 @@ name = "jupyter-server-terminals"
 version = "0.5.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "terminado", marker = "sys_platform == 'linux'" },
+    { name = "terminado" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f4/a7/bcd0a9b0cbba88986fe944aaaf91bfda603e5a50bda8ed15123f381a3b2f/jupyter_server_terminals-0.5.4.tar.gz", hash = "sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5", size = 31770, upload-time = "2026-01-14T16:53:20.213Z" }
 wheels = [
@@ -1762,19 +1762,19 @@ name = "jupyterlab"
 version = "4.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "async-lru", marker = "sys_platform == 'linux'" },
-    { name = "httpx", marker = "sys_platform == 'linux'" },
-    { name = "ipykernel", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-builder", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-lsp", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-server", marker = "sys_platform == 'linux'" },
-    { name = "jupyterlab-server", marker = "sys_platform == 'linux'" },
-    { name = "notebook-shim", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "async-lru" },
+    { name = "httpx" },
+    { name = "ipykernel" },
+    { name = "jinja2" },
+    { name = "jupyter-builder" },
+    { name = "jupyter-core" },
+    { name = "jupyter-lsp" },
+    { name = "jupyter-server" },
+    { name = "jupyterlab-server" },
+    { name = "notebook-shim" },
+    { name = "packaging" },
+    { name = "tornado" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/bc/2a/d6af53bfd45a43a5bfe7e40ba47ee7a8921a807daf4bb708e3a295bbb54d/jupyterlab-4.6.1.tar.gz", hash = "sha256:75315982ed28427edaa62bb85eadb5105e4043a757643c910efd787fe6ed0837", size = 28179125, upload-time = "2026-06-29T12:48:45.402Z" }
 wheels = [
@@ -1795,13 +1795,13 @@ name = "jupyterlab-server"
 version = "2.28.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "babel", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "json5", marker = "sys_platform == 'linux'" },
-    { name = "jsonschema", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-server", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "requests", marker = "sys_platform == 'linux'" },
+    { name = "babel" },
+    { name = "jinja2" },
+    { name = "json5" },
+    { name = "jsonschema" },
+    { name = "jupyter-server" },
+    { name = "packaging" },
+    { name = "requests" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996, upload-time = "2025-10-22T13:59:18.37Z" }
 wheels = [
@@ -1822,11 +1822,11 @@ name = "jupytext"
 version = "1.19.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
-    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3b/52/e014296ac8f40ca783aeb73dae52e65edbb0eaae0dcdc1ea41bfaa8aebf7/jupytext-1.19.4.tar.gz", hash = "sha256:739bcd4bc12aa4fe298a38017cdb5ae27b08a6ba3a5470728d2fe9e04b155db1", size = 4581977, upload-time = "2026-06-21T21:48:58.32Z" }
 wheels = [
@@ -1838,11 +1838,11 @@ name = "keyring"
 version = "25.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jaraco-classes", marker = "sys_platform == 'linux'" },
-    { name = "jaraco-context", marker = "sys_platform == 'linux'" },
-    { name = "jaraco-functools", marker = "sys_platform == 'linux'" },
-    { name = "jeepney", marker = "sys_platform == 'linux'" },
-    { name = "secretstorage", marker = "sys_platform == 'linux'" },
+    { name = "jaraco-classes" },
+    { name = "jaraco-context" },
+    { name = "jaraco-functools" },
+    { name = "jeepney" },
+    { name = "secretstorage" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/43/4b/674af6ef2f97d56f0ab5153bf0bfa28ccb6c3ed4d1babf4305449668807b/keyring-25.7.0.tar.gz", hash = "sha256:fe01bd85eb3f8fb3dd0405defdeac9a5b4f6f0439edbb3149577f244a2e8245b", size = 63516, upload-time = "2025-11-16T16:26:09.482Z" }
 wheels = [
@@ -1931,9 +1931,9 @@ name = "lenspack"
 version = "1.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "numpy" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/73/de/18cfc64028334a1907d44ba2b40e233a2c7a06d7c1e827acfd1785c0b293/lenspack-1.0.0.tar.gz", hash = "sha256:e528a5409473eb996b39695cccb744db067341eb5fac6f240cd8d0aa669c85ec", size = 32632, upload-time = "2020-09-04T08:33:53.267Z" }
 wheels = [
@@ -1961,11 +1961,11 @@ name = "lmfit"
 version = "1.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "asteval", marker = "sys_platform == 'linux'" },
-    { name = "dill", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "uncertainties", marker = "sys_platform == 'linux'" },
+    { name = "asteval" },
+    { name = "dill" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "uncertainties" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/e5/a35942aed2de95e228728c34609b51fe3ec9182398eac50d288eef313aa2/lmfit-1.3.4.tar.gz", hash = "sha256:3c22c28c43f717f6c5b4a3bd81e893a2149739c26a592c046f2e33c23cfbe497", size = 630720, upload-time = "2025-07-19T20:09:01.876Z" }
 wheels = [
@@ -1986,8 +1986,8 @@ name = "lsstdesc-coord"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "future", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "future" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b8/e0/6806df4cfa54927a2d8e425407611b4fc031dd125bc952b4a31deb743305/lsstdesc_coord-1.3.1.tar.gz", hash = "sha256:60f878c29e1f30a9b50bf60dca3c466dc9cfb2cbc71f0a27c575ced969de57ab", size = 41647, upload-time = "2026-02-13T18:37:32.611Z" }
 wheels = [
@@ -2059,7 +2059,7 @@ name = "markdown-it-py"
 version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "mdurl", marker = "sys_platform == 'linux'" },
+    { name = "mdurl" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/06/ff/7841249c247aa650a76b9ee4bbaeae59370dc8bfd2f6c01f3630c35eb134/markdown_it_py-4.2.0.tar.gz", hash = "sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49", size = 82454, upload-time = "2026-05-07T12:08:28.36Z" }
 wheels = [
@@ -2109,15 +2109,15 @@ name = "matplotlib"
 version = "3.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "contourpy", marker = "sys_platform == 'linux'" },
-    { name = "cycler", marker = "sys_platform == 'linux'" },
-    { name = "fonttools", marker = "sys_platform == 'linux'" },
-    { name = "kiwisolver", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pillow", marker = "sys_platform == 'linux'" },
-    { name = "pyparsing", marker = "sys_platform == 'linux'" },
-    { name = "python-dateutil", marker = "sys_platform == 'linux'" },
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1f/24/080c99d223d158d3a8902769269ab6da5b50f7a0e6e072513907e02b7a6c/matplotlib-3.11.0.tar.gz", hash = "sha256:68c0c7be01b30dcca3638934f7f591df73401235cbdbf0d1ab1c71e7db7f8b57", size = 33251176, upload-time = "2026-06-12T02:29:15.508Z" }
 wheels = [
@@ -2143,7 +2143,7 @@ name = "matplotlib-inline"
 version = "0.2.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/bd/c0/9f7c9a46090390368a4d7bcb76bb87a4a36c421e4c0792cdb53486ffac7a/matplotlib_inline-0.2.2.tar.gz", hash = "sha256:72f3fe8fce36b70d4a5b612f899090cd0401deddc4ea90e1572b9f4bfb058c79", size = 8150, upload-time = "2026-05-08T17:33:33.49Z" }
 wheels = [
@@ -2155,7 +2155,7 @@ name = "mdit-py-plugins"
 version = "0.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
+    { name = "markdown-it-py" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/59/fc/f8d0863f8862f25602c0404d75568e89fb6b4109804645e5cdfb1be5cf56/mdit_py_plugins-0.6.1.tar.gz", hash = "sha256:a2bca0f039f39dbd35fb74ae1b5f998608c437463371f0ff7f49a19a17a114d0", size = 56114, upload-time = "2026-05-13T09:03:38.91Z" }
 wheels = [
@@ -2255,12 +2255,12 @@ name = "myst-parser"
 version = "5.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "docutils", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
-    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "sphinx", marker = "sys_platform == 'linux'" },
+    { name = "docutils" },
+    { name = "jinja2" },
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "pyyaml" },
+    { name = "sphinx" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/21/dc/603751677fff302f34396e206b610f556a59d7fe58b9a2145f54e96b48e8/myst_parser-5.1.0.tar.gz", hash = "sha256:ab69322dc6719dcc7f296479dbb70181b66df6ed315064f92dbc85c0e1bf2f02", size = 101182, upload-time = "2026-05-13T09:38:19.361Z" }
 wheels = [
@@ -2272,10 +2272,10 @@ name = "nbclient"
 version = "0.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupyter-client", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "nbformat" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/28/a5/b3bae4b590c0cbcada2c63a34f7580024e834a8ba213e949a2f906705787/nbclient-0.11.0.tar.gz", hash = "sha256:04a134a5b087f2c5887f228aca155db50169b8cd9334dee6942c8e927e56081a", size = 62535, upload-time = "2026-06-05T07:52:41.746Z" }
 wheels = [
@@ -2287,20 +2287,20 @@ name = "nbconvert"
 version = "7.17.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "beautifulsoup4", marker = "sys_platform == 'linux'" },
-    { name = "bleach", extra = ["css"], marker = "sys_platform == 'linux'" },
-    { name = "defusedxml", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "jupyterlab-pygments", marker = "sys_platform == 'linux'" },
-    { name = "markupsafe", marker = "sys_platform == 'linux'" },
-    { name = "mistune", marker = "sys_platform == 'linux'" },
-    { name = "nbclient", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pandocfilters", marker = "sys_platform == 'linux'" },
-    { name = "pygments", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "beautifulsoup4" },
+    { name = "bleach", extra = ["css"] },
+    { name = "defusedxml" },
+    { name = "jinja2" },
+    { name = "jupyter-core" },
+    { name = "jupyterlab-pygments" },
+    { name = "markupsafe" },
+    { name = "mistune" },
+    { name = "nbclient" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pandocfilters" },
+    { name = "pygments" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/01/b1/708e53fe2e429c103c6e6e159106bcf0357ac41aa4c28772bd8402339051/nbconvert-7.17.1.tar.gz", hash = "sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2", size = 865311, upload-time = "2026-04-08T00:44:14.914Z" }
 wheels = [
@@ -2312,10 +2312,10 @@ name = "nbformat"
 version = "5.10.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "fastjsonschema", marker = "sys_platform == 'linux'" },
-    { name = "jsonschema", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "fastjsonschema" },
+    { name = "jsonschema" },
+    { name = "jupyter-core" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" }
 wheels = [
@@ -2364,19 +2364,19 @@ name = "notebook"
 version = "5.7.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ipykernel", marker = "sys_platform == 'linux'" },
-    { name = "ipython-genutils", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-client", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-core", marker = "sys_platform == 'linux'" },
-    { name = "nbconvert", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "prometheus-client", marker = "sys_platform == 'linux'" },
-    { name = "pyzmq", marker = "sys_platform == 'linux'" },
-    { name = "send2trash", marker = "sys_platform == 'linux'" },
-    { name = "terminado", marker = "sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
-    { name = "traitlets", marker = "sys_platform == 'linux'" },
+    { name = "ipykernel" },
+    { name = "ipython-genutils" },
+    { name = "jinja2" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "nbconvert" },
+    { name = "nbformat" },
+    { name = "prometheus-client" },
+    { name = "pyzmq" },
+    { name = "send2trash" },
+    { name = "terminado" },
+    { name = "tornado" },
+    { name = "traitlets" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6e/22/b5dcce67559d63d0f22e46d806305710808c698a1b91c07eb09e389785e0/notebook-5.7.4.tar.gz", hash = "sha256:d908673a4010787625c8952e91a22adf737db031f2aa0793ad92f6558918a74a", size = 13389469, upload-time = "2018-12-17T10:06:00.805Z" }
 wheels = [
@@ -2388,7 +2388,7 @@ name = "notebook-shim"
 version = "0.2.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupyter-server", marker = "sys_platform == 'linux'" },
+    { name = "jupyter-server" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167, upload-time = "2024-02-14T23:35:18.353Z" }
 wheels = [
@@ -2400,8 +2400,8 @@ name = "numba"
 version = "0.66.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "llvmlite", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "llvmlite" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ae/a0/570e3dc53e5602b49108f62a13e529f1eec8bfc7ef37d49c825924dcf546/numba-0.66.0.tar.gz", hash = "sha256:b900e63a0e26c05ea9a6d5a3a5a0a177cb64c5011887bf43edb8c3ed2c38d363", size = 2806181, upload-time = "2026-07-01T23:12:46.36Z" }
 wheels = [
@@ -2425,8 +2425,8 @@ name = "numcodecs"
 version = "0.16.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/44/bd/8a391e7c356366224734efd24da929cc4796fff468bfb179fe1af6548535/numcodecs-0.16.5.tar.gz", hash = "sha256:0d0fb60852f84c0bd9543cc4d2ab9eefd37fc8efcc410acd4777e62a1d300318", size = 6276387, upload-time = "2025-11-21T02:49:48.986Z" }
 wheels = [
@@ -2443,7 +2443,7 @@ name = "numexpr"
 version = "2.14.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cb/2f/fdba158c9dbe5caca9c3eca3eaffffb251f2fb8674bf8e2d0aed5f38d319/numexpr-2.14.1.tar.gz", hash = "sha256:4be00b1086c7b7a5c32e31558122b7b80243fe098579b170967da83f3152b48b", size = 119400, upload-time = "2025-10-13T16:17:27.351Z" }
 wheels = [
@@ -2502,7 +2502,7 @@ name = "numpydoc"
 version = "1.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "sphinx", marker = "sys_platform == 'linux'" },
+    { name = "sphinx" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e9/3c/dfccc9e7dee357fb2aa13c3890d952a370dd0ed071e0f7ed62ed0df567c1/numpydoc-1.10.0.tar.gz", hash = "sha256:3f7970f6eee30912260a6b31ac72bba2432830cd6722569ec17ee8d3ef5ffa01", size = 94027, upload-time = "2025-12-02T16:39:12.937Z" }
 wheels = [
@@ -2514,7 +2514,7 @@ name = "opencv-python-headless"
 version = "5.0.0.93"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1d/99/76b7c80252aa83c1af16393454aafd125a0287101afe8deb0a6821af0e30/opencv_python_headless-5.0.0.93.tar.gz", hash = "sha256:b82f9831daab90b725c7c1ee1b36cb5732c367096ac76d119e64e14eb70d5f3c", size = 81817738, upload-time = "2026-07-02T07:01:06.039Z" }
 wheels = [
@@ -2538,10 +2538,10 @@ name = "pandas"
 version = "2.3.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "python-dateutil", marker = "sys_platform == 'linux'" },
-    { name = "pytz", marker = "sys_platform == 'linux'" },
-    { name = "tzdata", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "python-dateutil" },
+    { name = "pytz" },
+    { name = "tzdata" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/33/01/d40b85317f86cf08d853a4f495195c73815fdf205eef3993821720274518/pandas-2.3.3.tar.gz", hash = "sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b", size = 4495223, upload-time = "2025-09-29T23:34:51.853Z" }
 wheels = [
@@ -2590,8 +2590,8 @@ name = "partd"
 version = "1.4.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "locket", marker = "sys_platform == 'linux'" },
-    { name = "toolz", marker = "sys_platform == 'linux'" },
+    { name = "locket" },
+    { name = "toolz" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b2/3a/3f06f34820a31257ddcabdfafc2672c5816be79c7e353b02c1f318daa7d4/partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c", size = 21029, upload-time = "2024-05-06T19:51:41.945Z" }
 wheels = [
@@ -2603,7 +2603,7 @@ name = "patsy"
 version = "1.0.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/be/44/ed13eccdd0519eff265f44b670d46fbb0ec813e2274932dc1c0e48520f7d/patsy-1.0.2.tar.gz", hash = "sha256:cdc995455f6233e90e22de72c37fcadb344e7586fb83f06696f54d92f8ce74c0", size = 399942, upload-time = "2025-10-20T16:17:37.535Z" }
 wheels = [
@@ -2615,7 +2615,7 @@ name = "pexpect"
 version = "4.9.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ptyprocess", marker = "sys_platform == 'linux'" },
+    { name = "ptyprocess" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" }
 wheels = [
@@ -2668,11 +2668,11 @@ name = "pims"
 version = "0.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "imageio", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "slicerator", marker = "sys_platform == 'linux'" },
-    { name = "tifffile", marker = "sys_platform == 'linux'" },
+    { name = "imageio" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "slicerator" },
+    { name = "tifffile" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b8/02/5bf3639f5b77e9b183011c08541c5039ba3d04f5316c70312b48a8e003a9/pims-0.7.tar.gz", hash = "sha256:55907a4c301256086d2aa4e34a5361b9109f24e375c2071e1117b9491e82946b", size = 87779, upload-time = "2024-06-10T19:20:42.842Z" }
 
@@ -2708,7 +2708,7 @@ name = "prompt-toolkit"
 version = "3.0.52"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "wcwidth", marker = "sys_platform == 'linux'" },
+    { name = "wcwidth" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" }
 wheels = [
@@ -2814,8 +2814,8 @@ name = "pybtex"
 version = "0.26.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "latexcodec", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "latexcodec" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/4d/f5/f30da9c93f0fa6d619332b2f69597219b625f35780473a05164a9981fd9a/pybtex-0.26.1.tar.gz", hash = "sha256:2e5543bea424e60e9e42eef70bff597be48649d8f68ba061a7a092b2477d5464", size = 692991, upload-time = "2026-04-03T13:05:39.014Z" }
 wheels = [
@@ -2827,8 +2827,8 @@ name = "pybtex-docutils"
 version = "1.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "docutils", marker = "sys_platform == 'linux'" },
-    { name = "pybtex", marker = "sys_platform == 'linux'" },
+    { name = "docutils" },
+    { name = "pybtex" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/7e/84/796ea94d26188a853660f81bded39f8de4cfe595130aef0dea1088705a11/pybtex-docutils-1.0.3.tar.gz", hash = "sha256:3a7ebdf92b593e00e8c1c538aa9a20bca5d92d84231124715acc964d51d93c6b", size = 18348, upload-time = "2023-08-22T18:47:54.833Z" }
 wheels = [
@@ -2840,10 +2840,10 @@ name = "pyccl"
 version = "3.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pyyaml" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/57/40/ff357d61a20c21420f1fe7417ca6d889be2e07a67b3564775aeaf6c3f366/pyccl-3.3.4.tar.gz", hash = "sha256:a9d22557354fd161df909e8f4f199e4f1c434c080fd6cddb2246609ddbd70e7c", size = 16783719, upload-time = "2026-05-14T16:26:27.327Z" }
 wheels = [
@@ -2866,10 +2866,10 @@ name = "pydantic"
 version = "2.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "annotated-types", marker = "sys_platform == 'linux'" },
-    { name = "pydantic-core", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
-    { name = "typing-inspection", marker = "sys_platform == 'linux'" },
+    { name = "annotated-types" },
+    { name = "pydantic-core" },
+    { name = "typing-extensions" },
+    { name = "typing-inspection" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775, upload-time = "2026-05-06T13:43:05.343Z" }
 wheels = [
@@ -2881,7 +2881,7 @@ name = "pydantic-core"
 version = "2.46.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9d/56/921726b776ace8d8f5db44c4ef961006580d91dc52b803c489fafd1aa249/pydantic_core-2.46.4.tar.gz", hash = "sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1", size = 471464, upload-time = "2026-05-06T13:37:06.98Z" }
 wheels = [
@@ -2934,7 +2934,7 @@ name = "pyerfa"
 version = "2.0.1.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/71/39/63cc8291b0cf324ae710df41527faf7d331bce573899199d926b3e492260/pyerfa-2.0.1.5.tar.gz", hash = "sha256:17d6b24fe4846c65d5e7d8c362dcb08199dc63b30a236aedd73875cc83e1f6c0", size = 818430, upload-time = "2024-11-11T15:22:30.852Z" }
 wheels = [
@@ -2957,8 +2957,8 @@ name = "pymaster"
 version = "2.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ducc0", marker = "sys_platform == 'linux'" },
-    { name = "healpy", marker = "sys_platform == 'linux'" },
+    { name = "ducc0" },
+    { name = "healpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/95/45/470858f2a6a3f5133955bb87aed978a5e921d4052aea1c19718e6b72f0ae/pymaster-2.6.tar.gz", hash = "sha256:056a09bc8631cf338e93ef14d8c70272ad9554ef10498d919e920994531c57a2", size = 33707181, upload-time = "2025-12-16T09:18:28.905Z" }
 
@@ -2967,8 +2967,8 @@ name = "pyopenssl"
 version = "26.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cryptography", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13' and sys_platform == 'linux'" },
+    { name = "cryptography" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/74/b7/da07bae88f5a9506b4def6f2f4903cf4c3b8831e560dba8fa18ca08f758f/pyopenssl-26.3.0.tar.gz", hash = "sha256:589de7fae1c9ea670d18422ed00fc04da787bbde8e1454aea872aa57b49ad341", size = 182024, upload-time = "2026-06-12T20:28:07.458Z" }
 wheels = [
@@ -2989,10 +2989,10 @@ name = "pytest"
 version = "9.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "iniconfig", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pluggy", marker = "sys_platform == 'linux'" },
-    { name = "pygments", marker = "sys_platform == 'linux'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+    { name = "pygments" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e4/47/b9efed96c114afcfa3c9d3fe98a76a1d14c74a9e266d397cf6eb64be5e01/pytest-9.1.1.tar.gz", hash = "sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313", size = 1636369, upload-time = "2026-06-19T10:58:32.857Z" }
 wheels = [
@@ -3004,9 +3004,9 @@ name = "pytest-cov"
 version = "7.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "coverage", marker = "sys_platform == 'linux'" },
-    { name = "pluggy", marker = "sys_platform == 'linux'" },
-    { name = "pytest", marker = "sys_platform == 'linux'" },
+    { name = "coverage" },
+    { name = "pluggy" },
+    { name = "pytest" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b1/51/a849f96e117386044471c8ec2bd6cfebacda285da9525c9106aeb28da671/pytest_cov-7.1.0.tar.gz", hash = "sha256:30674f2b5f6351aa09702a9c8c364f6a01c27aae0c1366ae8016160d1efc56b2", size = 55592, upload-time = "2026-03-21T20:11:16.284Z" }
 wheels = [
@@ -3018,7 +3018,7 @@ name = "python-dateutil"
 version = "2.9.0.post0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "six", marker = "sys_platform == 'linux'" },
+    { name = "six" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" }
 wheels = [
@@ -3076,7 +3076,7 @@ name = "pyzmq"
 version = "27.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cffi", marker = "implementation_name == 'pypy' and sys_platform == 'linux'" },
+    { name = "cffi", marker = "implementation_name == 'pypy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" }
 wheels = [
@@ -3105,10 +3105,10 @@ name = "qp-prob"
 version = "1.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "deprecated", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "tables-io", marker = "sys_platform == 'linux'" },
+    { name = "deprecated" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "tables-io" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b0/65/c54639ac63667800ee00d74c8851bd104ccaefcae95138073d6e77832d17/qp_prob-1.1.4.tar.gz", hash = "sha256:a68a3a3785d3ca57a2246c0524398338b7b7dffef0aef0722c231c64dde30107", size = 2460490, upload-time = "2026-06-25T20:08:34.63Z" }
 wheels = [
@@ -3120,9 +3120,9 @@ name = "referencing"
 version = "0.37.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "attrs", marker = "sys_platform == 'linux'" },
-    { name = "rpds-py", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13' and sys_platform == 'linux'" },
+    { name = "attrs" },
+    { name = "rpds-py" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036, upload-time = "2025-10-13T15:30:48.871Z" }
 wheels = [
@@ -3134,8 +3134,8 @@ name = "regions"
 version = "0.12"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/d3/86/44ff464efa2777247d6addc74416059f1872ab05c88588bbe1aadca27a7c/regions-0.12.tar.gz", hash = "sha256:1c9460770f250ef299e90a9d5c0b35941f7d05bbf879f6ffaa0538250c018ef9", size = 273609, upload-time = "2026-06-24T20:10:17.78Z" }
 wheels = [
@@ -3148,16 +3148,16 @@ name = "reproject"
 version = "0.21.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "astropy-healpix", marker = "sys_platform == 'linux'" },
-    { name = "dask", extra = ["array"], marker = "sys_platform == 'linux'" },
-    { name = "dask-image", marker = "sys_platform == 'linux'" },
-    { name = "fsspec", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pillow", marker = "sys_platform == 'linux'" },
-    { name = "pyavm", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "zarr", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "astropy-healpix" },
+    { name = "dask", extra = ["array"] },
+    { name = "dask-image" },
+    { name = "fsspec" },
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "pyavm" },
+    { name = "scipy" },
+    { name = "zarr" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/44/6fd820ba336484277a91a2f4808b60d6ec0b9f033f588c237e778f20fe89/reproject-0.21.0.tar.gz", hash = "sha256:01ede715a1993c29431f52ff74189ef30f5e7b2e8b4dc88c1b002145a971dc1c", size = 1622661, upload-time = "2026-06-25T15:11:34.886Z" }
 wheels = [
@@ -3170,10 +3170,10 @@ name = "requests"
 version = "2.34.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "certifi", marker = "sys_platform == 'linux'" },
-    { name = "charset-normalizer", marker = "sys_platform == 'linux'" },
-    { name = "idna", marker = "sys_platform == 'linux'" },
-    { name = "urllib3", marker = "sys_platform == 'linux'" },
+    { name = "certifi" },
+    { name = "charset-normalizer" },
+    { name = "idna" },
+    { name = "urllib3" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ac/c3/e2a2b89f2d3e2179abd6d00ebd70bff6273f37fb3e0cc209f48b39d00cbf/requests-2.34.2.tar.gz", hash = "sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed", size = 142856, upload-time = "2026-05-14T19:25:27.735Z" }
 wheels = [
@@ -3185,7 +3185,7 @@ name = "rfc3339-validator"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "six", marker = "sys_platform == 'linux'" },
+    { name = "six" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513, upload-time = "2021-05-12T16:37:54.178Z" }
 wheels = [
@@ -3206,7 +3206,7 @@ name = "rfc3987-syntax"
 version = "1.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "lark", marker = "sys_platform == 'linux'" },
+    { name = "lark" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239, upload-time = "2025-07-18T01:05:05.015Z" }
 wheels = [
@@ -3218,8 +3218,8 @@ name = "rich"
 version = "15.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
-    { name = "pygments", marker = "sys_platform == 'linux'" },
+    { name = "markdown-it-py" },
+    { name = "pygments" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c0/8f/0722ca900cc807c13a6a0c696dacf35430f72e0ec571c4275d2371fca3e9/rich-15.0.0.tar.gz", hash = "sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36", size = 230680, upload-time = "2026-04-12T08:24:00.75Z" }
 wheels = [
@@ -3231,7 +3231,7 @@ name = "rocket-fft"
 version = "0.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numba", marker = "sys_platform == 'linux'" },
+    { name = "numba" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/44/b0/09cbec3177ecf56e5ceb789a69fbcdcbabe758cbb682e5d36b14aa8c05fb/rocket_fft-0.3.1.tar.gz", hash = "sha256:ddc902a361099aff9fc02fe9ea3b0d036c2f67f93df356c2b754f7627d7fa4f4", size = 74276, upload-time = "2025-12-26T00:30:54.083Z" }
 wheels = [
@@ -3345,9 +3345,9 @@ name = "sacc"
 version = "2.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "numpy" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b6/82/5b603d960279b8eb7fac677ba3550c0903a8390f1bff2eee6fbfa59765da/sacc-2.4.tar.gz", hash = "sha256:a98fb4e59b3c742ddb492a15a10e1f8a4279ba36d56c0d3c35e1cd13cca1167b", size = 424905, upload-time = "2026-07-02T11:29:27.458Z" }
 wheels = [
@@ -3359,7 +3359,7 @@ name = "scipy"
 version = "1.17.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822, upload-time = "2026-02-23T00:26:24.851Z" }
 wheels = [
@@ -3390,9 +3390,9 @@ name = "seaborn"
 version = "0.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pandas", marker = "sys_platform == 'linux'" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pandas" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" }
 wheels = [
@@ -3404,8 +3404,8 @@ name = "secretstorage"
 version = "3.5.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cryptography", marker = "sys_platform == 'linux'" },
-    { name = "jeepney", marker = "sys_platform == 'linux'" },
+    { name = "cryptography" },
+    { name = "jeepney" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/1c/03/e834bcd866f2f8a49a85eaff47340affa3bfa391ee9912a952a1faa68c7b/secretstorage-3.5.0.tar.gz", hash = "sha256:f04b8e4689cbce351744d5537bf6b1329c6fc68f91fa666f60a380edddcd11be", size = 19884, upload-time = "2025-11-23T19:02:53.191Z" }
 wheels = [
@@ -3435,29 +3435,29 @@ name = "shear-psf-leakage"
 version = "0.2.1"
 source = { git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop#0b3c17523b77760c9259be15ba5bbba99d575d0a" }
 dependencies = [
-    { name = "camb", marker = "sys_platform == 'linux'" },
-    { name = "cs-util", marker = "sys_platform == 'linux'" },
-    { name = "emcee", marker = "sys_platform == 'linux'" },
-    { name = "getdist", marker = "sys_platform == 'linux'" },
-    { name = "gsl", marker = "sys_platform == 'linux'" },
-    { name = "jupyter", marker = "sys_platform == 'linux'" },
-    { name = "jupyter-server", marker = "sys_platform == 'linux'" },
-    { name = "jupyterlab", marker = "sys_platform == 'linux'" },
-    { name = "jupytext", marker = "sys_platform == 'linux'" },
-    { name = "lenspack", marker = "sys_platform == 'linux'" },
-    { name = "lmfit", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "notebook", marker = "sys_platform == 'linux'" },
-    { name = "pandas", marker = "sys_platform == 'linux'" },
-    { name = "pyccl", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "stats", marker = "sys_platform == 'linux'" },
-    { name = "swig", marker = "sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
-    { name = "tqdm", marker = "sys_platform == 'linux'" },
-    { name = "treecorr", marker = "sys_platform == 'linux'" },
-    { name = "uncertainties", marker = "sys_platform == 'linux'" },
+    { name = "camb" },
+    { name = "cs-util" },
+    { name = "emcee" },
+    { name = "getdist" },
+    { name = "gsl" },
+    { name = "jupyter" },
+    { name = "jupyter-server" },
+    { name = "jupyterlab" },
+    { name = "jupytext" },
+    { name = "lenspack" },
+    { name = "lmfit" },
+    { name = "matplotlib" },
+    { name = "notebook" },
+    { name = "pandas" },
+    { name = "pyccl" },
+    { name = "pyyaml" },
+    { name = "scipy" },
+    { name = "stats" },
+    { name = "swig" },
+    { name = "tornado" },
+    { name = "tqdm" },
+    { name = "treecorr" },
+    { name = "uncertainties" },
 ]
 
 [[package]]
@@ -3474,11 +3474,11 @@ name = "skyproj"
 version = "2.5.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "healsparse", marker = "sys_platform == 'linux'" },
-    { name = "hpgeom", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "healsparse" },
+    { name = "hpgeom" },
+    { name = "matplotlib" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/90/cf/5158151ae6fc60459f430451b612a4bf2da07ebff2a9f52a37e73715a5ee/skyproj-2.5.0.tar.gz", hash = "sha256:cb8d5115927ca43cacdb6d92f00bb4fcc8563ee71028f755203ce1752e67c140", size = 8521587, upload-time = "2026-06-26T19:51:15.378Z" }
 wheels = [
@@ -3500,7 +3500,7 @@ name = "smart-open"
 version = "7.7.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "wrapt", marker = "sys_platform == 'linux'" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/c6/22e7a2acd5d27941e85e0d7ede398da5abe2e4677d2265c924157247c32e/smart_open-7.7.1.tar.gz", hash = "sha256:9414ba5733e28309f29b28a303b0f1054ad23fe0275f1a1b600c80a724f4bd1a", size = 54952, upload-time = "2026-06-26T07:56:35.309Z" }
 wheels = [
@@ -3519,15 +3519,16 @@ wheels = [
 [[package]]
 name = "smokescreen"
 version = "1.5.6"
-source = { git = "https://github.com/UNIONS-WL/Smokescreen?rev=588a6b9b26560bd5ba3dd5ba342f3c40152644f9#588a6b9b26560bd5ba3dd5ba342f3c40152644f9" }
+source = { git = "https://github.com/UNIONS-WL/Smokescreen?rev=main#b457c995dba8421bd7804fe8324ba8c67cc56a43" }
 dependencies = [
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "cryptography", marker = "sys_platform == 'linux'" },
-    { name = "jsonargparse", extra = ["signatures"], marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pyccl", marker = "sys_platform == 'linux'" },
-    { name = "sacc", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "astropy" },
+    { name = "cryptography" },
+    { name = "h5py" },
+    { name = "jsonargparse", extra = ["signatures"] },
+    { name = "numpy" },
+    { name = "pyccl" },
+    { name = "sacc" },
+    { name = "scipy" },
 ]
 
 [[package]]
@@ -3535,37 +3536,37 @@ name = "snakemake"
 version = "9.23.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "conda-inject", marker = "sys_platform == 'linux'" },
-    { name = "configargparse", marker = "sys_platform == 'linux'" },
-    { name = "connection-pool", marker = "sys_platform == 'linux'" },
-    { name = "docutils", marker = "sys_platform == 'linux'" },
-    { name = "dpath", marker = "sys_platform == 'linux'" },
-    { name = "gitpython", marker = "sys_platform == 'linux'" },
-    { name = "humanfriendly", marker = "sys_platform == 'linux'" },
-    { name = "immutables", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "jsonschema", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "platformdirs", marker = "sys_platform == 'linux'" },
-    { name = "psutil", marker = "sys_platform == 'linux'" },
-    { name = "pulp", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "referencing", marker = "sys_platform == 'linux'" },
-    { name = "requests", marker = "sys_platform == 'linux'" },
-    { name = "smart-open", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-common", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-executor-plugins", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-logger-plugins", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-report-plugins", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-scheduler-plugins", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-storage-plugins", marker = "sys_platform == 'linux'" },
-    { name = "sqlmodel", marker = "sys_platform == 'linux'" },
-    { name = "tabulate", marker = "sys_platform == 'linux'" },
-    { name = "tenacity", marker = "sys_platform == 'linux'" },
-    { name = "throttler", marker = "sys_platform == 'linux'" },
-    { name = "wrapt", marker = "sys_platform == 'linux'" },
-    { name = "yte", marker = "sys_platform == 'linux'" },
+    { name = "conda-inject" },
+    { name = "configargparse" },
+    { name = "connection-pool" },
+    { name = "docutils" },
+    { name = "dpath" },
+    { name = "gitpython" },
+    { name = "humanfriendly" },
+    { name = "immutables" },
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "platformdirs" },
+    { name = "psutil" },
+    { name = "pulp" },
+    { name = "pyyaml" },
+    { name = "referencing" },
+    { name = "requests" },
+    { name = "smart-open" },
+    { name = "snakemake-interface-common" },
+    { name = "snakemake-interface-executor-plugins" },
+    { name = "snakemake-interface-logger-plugins" },
+    { name = "snakemake-interface-report-plugins" },
+    { name = "snakemake-interface-scheduler-plugins" },
+    { name = "snakemake-interface-storage-plugins" },
+    { name = "sqlmodel" },
+    { name = "tabulate" },
+    { name = "tenacity" },
+    { name = "throttler" },
+    { name = "wrapt" },
+    { name = "yte" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9e/c2/45aa858e55edbb7dbc243ed60859f4f3d92d63ecdd67a80632aa3659fe6b/snakemake-9.23.1.tar.gz", hash = "sha256:ef8d698bfce66a6669cc29df7e344b0b367fd90956c725e5ae0a79a556e8e93f", size = 6802587, upload-time = "2026-06-18T09:42:57.653Z" }
 wheels = [
@@ -3577,9 +3578,9 @@ name = "snakemake-interface-common"
 version = "1.23.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "argparse-dataclass", marker = "sys_platform == 'linux'" },
-    { name = "configargparse", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
+    { name = "argparse-dataclass" },
+    { name = "configargparse" },
+    { name = "packaging" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/89/c3/592f832f6e5d2d31f749392e48e8401b7625dec668d3d365d8d28f2b6c30/snakemake_interface_common-1.23.0.tar.gz", hash = "sha256:6ed14531a461417659364a0dd0acc51b786af4e26fc15cc5e00ff3d9fcaffacc", size = 13960, upload-time = "2026-03-08T21:54:29.251Z" }
 wheels = [
@@ -3591,9 +3592,9 @@ name = "snakemake-interface-executor-plugins"
 version = "9.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "argparse-dataclass", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-common", marker = "sys_platform == 'linux'" },
-    { name = "throttler", marker = "sys_platform == 'linux'" },
+    { name = "argparse-dataclass" },
+    { name = "snakemake-interface-common" },
+    { name = "throttler" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/54/50/de06b284c45a8e94fb8e4a12d5235065e78b49b8f84329dc10fe39f4b7dd/snakemake_interface_executor_plugins-9.4.0.tar.gz", hash = "sha256:9d4138897beacbaadaedad94b63f948eaeb604b7fc78f9cf65ac57f090f2c066", size = 16549, upload-time = "2026-03-08T17:04:02.644Z" }
 wheels = [
@@ -3605,7 +3606,7 @@ name = "snakemake-interface-logger-plugins"
 version = "2.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "snakemake-interface-common", marker = "sys_platform == 'linux'" },
+    { name = "snakemake-interface-common" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a7/0c/3fa5d592663c65669a867526604aadc6fbc235fb9284e94b49c0ef59aa41/snakemake_interface_logger_plugins-2.1.0.tar.gz", hash = "sha256:c89a00d2a398490cecd91b6dc6db8049cba93712d82e1d8f3000f3040bf3791c", size = 15917, upload-time = "2026-05-20T15:12:35.259Z" }
 wheels = [
@@ -3617,7 +3618,7 @@ name = "snakemake-interface-report-plugins"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "snakemake-interface-common", marker = "sys_platform == 'linux'" },
+    { name = "snakemake-interface-common" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/18/d6/6160ed98de665d6871dd356597dbf726688cc786e88668359ca37b7d9f54/snakemake_interface_report_plugins-1.3.0.tar.gz", hash = "sha256:fc9495298bec4e69721ab8afe6d6d88a86966fda2eeb003db56b9a88b86d5934", size = 4283, upload-time = "2025-10-31T10:52:36.55Z" }
 wheels = [
@@ -3629,7 +3630,7 @@ name = "snakemake-interface-scheduler-plugins"
 version = "2.0.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "snakemake-interface-common", marker = "sys_platform == 'linux'" },
+    { name = "snakemake-interface-common" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/88/d9/d480807d2cfc2d132bc760d877d45ec8fbe620a24200ec4d2697c4a26031/snakemake_interface_scheduler_plugins-2.0.2.tar.gz", hash = "sha256:2797e8fa9019d983132c2b403f14d6fcd3c5ad4c8d8a66b984b4740a71cacc46", size = 8642, upload-time = "2025-10-20T13:58:12.988Z" }
 wheels = [
@@ -3641,11 +3642,11 @@ name = "snakemake-interface-storage-plugins"
 version = "4.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "humanfriendly", marker = "sys_platform == 'linux'" },
-    { name = "snakemake-interface-common", marker = "sys_platform == 'linux'" },
-    { name = "tenacity", marker = "sys_platform == 'linux'" },
-    { name = "throttler", marker = "sys_platform == 'linux'" },
-    { name = "wrapt", marker = "sys_platform == 'linux'" },
+    { name = "humanfriendly" },
+    { name = "snakemake-interface-common" },
+    { name = "tenacity" },
+    { name = "throttler" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/93/6e/f3c5b2d621fd6a6b78d8cfc01fef6b926fe2c277f5ed77c5e4deeacb94eb/snakemake_interface_storage_plugins-4.4.1.tar.gz", hash = "sha256:b2b5bf05318af36955ebf2ce76c921c0fb06904ca98fb30e1657d88b7b7b6945", size = 14924, upload-time = "2026-03-16T11:16:01.075Z" }
 wheels = [
@@ -3675,81 +3676,81 @@ name = "sp-validation"
 version = "0.6.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "adjusttext", marker = "sys_platform == 'linux'" },
-    { name = "astropy", marker = "sys_platform == 'linux'" },
-    { name = "camb", marker = "sys_platform == 'linux'" },
-    { name = "clmm", marker = "sys_platform == 'linux'" },
-    { name = "colorama", marker = "sys_platform == 'linux'" },
-    { name = "cosmo-numba", marker = "sys_platform == 'linux'" },
-    { name = "cryptography", marker = "sys_platform == 'linux'" },
-    { name = "cs-util", marker = "sys_platform == 'linux'" },
-    { name = "emcee", marker = "sys_platform == 'linux'" },
-    { name = "getdist", marker = "sys_platform == 'linux'" },
-    { name = "h5py", marker = "sys_platform == 'linux'" },
-    { name = "healpy", marker = "sys_platform == 'linux'" },
-    { name = "healsparse", marker = "sys_platform == 'linux'" },
-    { name = "importlib-metadata", marker = "sys_platform == 'linux'" },
-    { name = "joblib", marker = "sys_platform == 'linux'" },
-    { name = "jupyter", marker = "sys_platform == 'linux'" },
-    { name = "jupyterlab", marker = "sys_platform == 'linux'" },
-    { name = "jupytext", marker = "sys_platform == 'linux'" },
-    { name = "lenspack", marker = "sys_platform == 'linux'" },
-    { name = "lmfit", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "numba", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "opencv-python-headless", marker = "sys_platform == 'linux'" },
-    { name = "pandas", marker = "sys_platform == 'linux'" },
-    { name = "pyarrow", marker = "sys_platform == 'linux'" },
-    { name = "pyccl", marker = "sys_platform == 'linux'" },
-    { name = "pymaster", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "regions", marker = "sys_platform == 'linux'" },
-    { name = "reproject", marker = "sys_platform == 'linux'" },
-    { name = "sacc", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
-    { name = "seaborn", marker = "sys_platform == 'linux'" },
-    { name = "shear-psf-leakage", marker = "sys_platform == 'linux'" },
-    { name = "skyproj", marker = "sys_platform == 'linux'" },
-    { name = "smokescreen", marker = "sys_platform == 'linux'" },
-    { name = "statsmodels", marker = "sys_platform == 'linux'" },
-    { name = "tqdm", marker = "sys_platform == 'linux'" },
-    { name = "treecorr", marker = "sys_platform == 'linux'" },
-    { name = "uncertainties", marker = "sys_platform == 'linux'" },
+    { name = "adjusttext" },
+    { name = "astropy" },
+    { name = "camb" },
+    { name = "clmm" },
+    { name = "colorama" },
+    { name = "cosmo-numba" },
+    { name = "cryptography" },
+    { name = "cs-util" },
+    { name = "emcee" },
+    { name = "getdist" },
+    { name = "h5py" },
+    { name = "healpy" },
+    { name = "healsparse" },
+    { name = "importlib-metadata" },
+    { name = "joblib" },
+    { name = "jupyter" },
+    { name = "jupyterlab" },
+    { name = "jupytext" },
+    { name = "lenspack" },
+    { name = "lmfit" },
+    { name = "matplotlib" },
+    { name = "numba" },
+    { name = "numpy" },
+    { name = "opencv-python-headless" },
+    { name = "pandas" },
+    { name = "pyarrow" },
+    { name = "pyccl" },
+    { name = "pymaster" },
+    { name = "pyyaml" },
+    { name = "regions" },
+    { name = "reproject" },
+    { name = "sacc" },
+    { name = "scipy" },
+    { name = "seaborn" },
+    { name = "shear-psf-leakage" },
+    { name = "skyproj" },
+    { name = "smokescreen" },
+    { name = "statsmodels" },
+    { name = "tqdm" },
+    { name = "treecorr" },
+    { name = "uncertainties" },
 ]
 
 [package.optional-dependencies]
 develop = [
-    { name = "myst-parser", marker = "sys_platform == 'linux'" },
-    { name = "numpydoc", marker = "sys_platform == 'linux'" },
-    { name = "pytest", marker = "sys_platform == 'linux'" },
-    { name = "pytest-cov", marker = "sys_platform == 'linux'" },
-    { name = "ruff", marker = "sys_platform == 'linux'" },
-    { name = "sphinx", marker = "sys_platform == 'linux'" },
-    { name = "sphinxawesome-theme", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-bibtex", marker = "sys_platform == 'linux'" },
+    { name = "myst-parser" },
+    { name = "numpydoc" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "ruff" },
+    { name = "sphinx" },
+    { name = "sphinxawesome-theme" },
+    { name = "sphinxcontrib-bibtex" },
 ]
 docs = [
-    { name = "myst-parser", marker = "sys_platform == 'linux'" },
-    { name = "numpydoc", marker = "sys_platform == 'linux'" },
-    { name = "sphinx", marker = "sys_platform == 'linux'" },
-    { name = "sphinxawesome-theme", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-bibtex", marker = "sys_platform == 'linux'" },
+    { name = "myst-parser" },
+    { name = "numpydoc" },
+    { name = "sphinx" },
+    { name = "sphinxawesome-theme" },
+    { name = "sphinxcontrib-bibtex" },
 ]
 glass = [
-    { name = "cosmology", marker = "sys_platform == 'linux'" },
-    { name = "fitsio", marker = "sys_platform == 'linux'" },
-    { name = "glass", marker = "sys_platform == 'linux'" },
-    { name = "glass-ext-camb", marker = "sys_platform == 'linux'" },
+    { name = "cosmology" },
+    { name = "fitsio" },
+    { name = "glass" },
+    { name = "glass-ext-camb" },
 ]
 test = [
-    { name = "pytest", marker = "sys_platform == 'linux'" },
-    { name = "pytest-cov", marker = "sys_platform == 'linux'" },
-    { name = "ruff", marker = "sys_platform == 'linux'" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "ruff" },
 ]
 workflow = [
-    { name = "mpi4py", marker = "sys_platform == 'linux'" },
-    { name = "snakemake", marker = "sys_platform == 'linux'" },
+    { name = "mpi4py" },
+    { name = "snakemake" },
 ]
 
 [package.metadata]
@@ -3800,7 +3801,7 @@ requires-dist = [
     { name = "seaborn" },
     { name = "shear-psf-leakage", git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop" },
     { name = "skyproj" },
-    { name = "smokescreen", git = "https://github.com/UNIONS-WL/Smokescreen?rev=588a6b9b26560bd5ba3dd5ba342f3c40152644f9" },
+    { name = "smokescreen", git = "https://github.com/UNIONS-WL/Smokescreen?rev=main" },
     { name = "snakemake", marker = "extra == 'workflow'" },
     { name = "sp-validation", extras = ["test", "docs"], marker = "extra == 'develop'" },
     { name = "sphinx", marker = "extra == 'docs'", specifier = ">=8.0" },
@@ -3818,22 +3819,22 @@ name = "sphinx"
 version = "9.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "alabaster", marker = "sys_platform == 'linux'" },
-    { name = "babel", marker = "sys_platform == 'linux'" },
-    { name = "docutils", marker = "sys_platform == 'linux'" },
-    { name = "imagesize", marker = "sys_platform == 'linux'" },
-    { name = "jinja2", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pygments", marker = "sys_platform == 'linux'" },
-    { name = "requests", marker = "sys_platform == 'linux'" },
-    { name = "roman-numerals", marker = "sys_platform == 'linux'" },
-    { name = "snowballstemmer", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-applehelp", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-devhelp", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-htmlhelp", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-jsmath", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-qthelp", marker = "sys_platform == 'linux'" },
-    { name = "sphinxcontrib-serializinghtml", marker = "sys_platform == 'linux'" },
+    { name = "alabaster" },
+    { name = "babel" },
+    { name = "docutils" },
+    { name = "imagesize" },
+    { name = "jinja2" },
+    { name = "packaging" },
+    { name = "pygments" },
+    { name = "requests" },
+    { name = "roman-numerals" },
+    { name = "snowballstemmer" },
+    { name = "sphinxcontrib-applehelp" },
+    { name = "sphinxcontrib-devhelp" },
+    { name = "sphinxcontrib-htmlhelp" },
+    { name = "sphinxcontrib-jsmath" },
+    { name = "sphinxcontrib-qthelp" },
+    { name = "sphinxcontrib-serializinghtml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cd/bd/f08eb0f4eed5c83f1ba2a3bd18f7745a2b1525fad70660a1c00224ec468a/sphinx-9.1.0.tar.gz", hash = "sha256:7741722357dd75f8190766926071fed3bdc211c74dd2d7d4df5404da95930ddb", size = 8718324, upload-time = "2025-12-31T15:09:27.646Z" }
 wheels = [
@@ -3845,8 +3846,8 @@ name = "sphinxawesome-theme"
 version = "6.0.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "beautifulsoup4", marker = "sys_platform == 'linux'" },
-    { name = "sphinx", marker = "sys_platform == 'linux'" },
+    { name = "beautifulsoup4" },
+    { name = "sphinx" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9a/3f/ba7f8dc2837c0ac362127c6cd491578c23ac4d4e8cc6a37726531be29256/sphinxawesome_theme-6.0.5.tar.gz", hash = "sha256:ed3d82b7f0e30e3d6f38f5245d89775e9472a80a45ffd73d6a0a4b9d05ac5736", size = 343775, upload-time = "2026-06-15T15:09:36.094Z" }
 wheels = [
@@ -3867,10 +3868,10 @@ name = "sphinxcontrib-bibtex"
 version = "2.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "docutils", marker = "sys_platform == 'linux'" },
-    { name = "pybtex", marker = "sys_platform == 'linux'" },
-    { name = "pybtex-docutils", marker = "sys_platform == 'linux'" },
-    { name = "sphinx", marker = "sys_platform == 'linux'" },
+    { name = "docutils" },
+    { name = "pybtex" },
+    { name = "pybtex-docutils" },
+    { name = "sphinx" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/15/6a/8e0b2c2420286389e7fed78ff361ec30e2f1d58c8560af8d64df5e7b61e0/sphinxcontrib_bibtex-2.7.0.tar.gz", hash = "sha256:fee700f7aae29bb8f654c62913f00d34ac44fc0b8ca0fa67ac922ff4453addee", size = 120669, upload-time = "2026-05-06T09:29:24.935Z" }
 wheels = [
@@ -3927,8 +3928,8 @@ name = "sqlalchemy"
 version = "2.0.51"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "greenlet", marker = "(platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'WIN32' and sys_platform == 'linux') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'amd64' and sys_platform == 'linux') or (platform_machine == 'ppc64le' and sys_platform == 'linux') or (platform_machine == 'win32' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/02/f1/a7a892f18d4d224e6b26f706531eafccc41e37594d37d304786969ee13cb/sqlalchemy-2.0.51.tar.gz", hash = "sha256:804dccd8a4a6242c4e30ad961e540e18a588f6527202f2d6791b01845d59fdc9", size = 9912201, upload-time = "2026-06-15T15:41:20.012Z" }
 wheels = [
@@ -3956,8 +3957,8 @@ name = "sqlmodel"
 version = "0.0.37"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pydantic", marker = "sys_platform == 'linux'" },
-    { name = "sqlalchemy", marker = "sys_platform == 'linux'" },
+    { name = "pydantic" },
+    { name = "sqlalchemy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/fb/26/1d2faa0fd5a765267f49751de533adac6b9ff9366c7c6e7692df4f32230f/sqlmodel-0.0.37.tar.gz", hash = "sha256:d2c19327175794faf50b1ee31cc966764f55b1dedefc046450bc5741a3d68352", size = 85527, upload-time = "2026-02-21T16:39:47.038Z" }
 wheels = [
@@ -3969,9 +3970,9 @@ name = "stack-data"
 version = "0.6.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "asttokens", marker = "sys_platform == 'linux'" },
-    { name = "executing", marker = "sys_platform == 'linux'" },
-    { name = "pure-eval", marker = "sys_platform == 'linux'" },
+    { name = "asttokens" },
+    { name = "executing" },
+    { name = "pure-eval" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" }
 wheels = [
@@ -3989,11 +3990,11 @@ name = "statsmodels"
 version = "0.14.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pandas", marker = "sys_platform == 'linux'" },
-    { name = "patsy", marker = "sys_platform == 'linux'" },
-    { name = "scipy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "patsy" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0d/81/e8d74b34f85285f7335d30c5e3c2d7c0346997af9f3debf9a0a9a63de184/statsmodels-0.14.6.tar.gz", hash = "sha256:4d17873d3e607d398b85126cd4ed7aad89e4e9d89fc744cdab1af3189a996c2a", size = 20689085, upload-time = "2025-12-05T23:08:39.522Z" }
 wheels = [
@@ -4035,7 +4036,7 @@ name = "sympy"
 version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "mpmath", marker = "sys_platform == 'linux'" },
+    { name = "mpmath" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" }
 wheels = [
@@ -4047,11 +4048,11 @@ name = "tables"
 version = "3.11.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "blosc2", marker = "sys_platform == 'linux'" },
-    { name = "numexpr", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "py-cpuinfo", marker = "sys_platform == 'linux'" },
+    { name = "blosc2" },
+    { name = "numexpr" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "py-cpuinfo" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/cc/a3/d213ebe7376d48055bd55a29cd9f99061afa0dcece608f94a5025d797b0a/tables-3.11.1.tar.gz", hash = "sha256:78abcf413091bc7c1e4e8c10fbbb438d1ac0b5a87436c5b972c3e8253871b6fb", size = 4790533, upload-time = "2026-03-01T11:43:36.036Z" }
 wheels = [
@@ -4066,12 +4067,12 @@ name = "tables-io"
 version = "1.1.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", marker = "sys_platform == 'linux'" },
-    { name = "deprecated", marker = "sys_platform == 'linux'" },
-    { name = "h5py", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pandas", marker = "sys_platform == 'linux'" },
-    { name = "tables", marker = "sys_platform == 'linux'" },
+    { name = "click" },
+    { name = "deprecated" },
+    { name = "h5py" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "tables" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/12/1b/61bed3c55ce4602be5379362ce939b71a2e707e856ee497c1d1e72f8ff3d/tables_io-1.1.2.tar.gz", hash = "sha256:ea27eac8b22ff4e3500afd92140794bd41161f768a2c84520b91b3b58e945182", size = 91394, upload-time = "2026-06-18T16:21:20.896Z" }
 wheels = [
@@ -4101,8 +4102,8 @@ name = "terminado"
 version = "0.18.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ptyprocess", marker = "os_name != 'nt' and sys_platform == 'linux'" },
-    { name = "tornado", marker = "sys_platform == 'linux'" },
+    { name = "ptyprocess", marker = "os_name != 'nt'" },
+    { name = "tornado" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701, upload-time = "2024-03-12T14:34:39.026Z" }
 wheels = [
@@ -4132,7 +4133,7 @@ name = "tifffile"
 version = "2026.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b7/38/5e2ecef5af2f4fd4a89bb8d6240de9458bab4d51a4cbd97aeb3a0cd618e2/tifffile-2026.6.1.tar.gz", hash = "sha256:626c892c0e899d959b9438e7c0e1491dc154a7fead1f1f37a991724a50eceba9", size = 429694, upload-time = "2026-05-31T23:57:12.165Z" }
 wheels = [
@@ -4144,7 +4145,7 @@ name = "tinycss2"
 version = "1.5.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "webencodings", marker = "sys_platform == 'linux'" },
+    { name = "webencodings" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a3/ae/2ca4913e5c0f09781d75482874c3a95db9105462a92ddd303c7d285d3df2/tinycss2-1.5.1.tar.gz", hash = "sha256:d339d2b616ba90ccce58da8495a78f46e55d4d25f9fd71dfd526f07e7d53f957", size = 88195, upload-time = "2025-11-23T10:29:10.082Z" }
 wheels = [
@@ -4195,8 +4196,8 @@ name = "transformcl"
 version = "2026.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "array-api-compat", marker = "sys_platform == 'linux'" },
-    { name = "flt", marker = "sys_platform == 'linux'" },
+    { name = "array-api-compat" },
+    { name = "flt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9d/ce/ae8373b67259c3ea6033425161f7d195387b1e68b17d4d00eee1b68e5edd/transformcl-2026.1.tar.gz", hash = "sha256:14551c33d164f3db24b9141f1f77553182a4b9c789f883476eb33d87ea4d8f13", size = 4850, upload-time = "2026-03-02T19:18:21.221Z" }
 wheels = [
@@ -4208,11 +4209,11 @@ name = "treecorr"
 version = "5.1.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "lsstdesc-coord", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pybind11", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
-    { name = "setuptools", marker = "sys_platform == 'linux'" },
+    { name = "lsstdesc-coord" },
+    { name = "numpy" },
+    { name = "pybind11" },
+    { name = "pyyaml" },
+    { name = "setuptools" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5d/95/ff399cb37b9e3d7c758f8cf0d5757f06a38093f223f2a65d7cc5b50313d5/treecorr-5.1.3.tar.gz", hash = "sha256:d921ccace064325a8ebb93f371c624f13fcc9559373e3b37cc216cb94ae81ed2", size = 1146147, upload-time = "2026-03-18T18:38:03.626Z" }
 wheels = [
@@ -4227,8 +4228,8 @@ name = "typeshed-client"
 version = "2.12.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "importlib-resources", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "importlib-resources" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/54/5d/97d6fa8c204b0d42be931e7a568b306cda43f93ff45f8b47537ee61390de/typeshed_client-2.12.0.tar.gz", hash = "sha256:54dcfa25fcff82cedcb1b51c03c1b3c51a614097aab8fe49e4316aff6f79d9c7", size = 529457, upload-time = "2026-06-02T04:00:51.51Z" }
 wheels = [
@@ -4249,7 +4250,7 @@ name = "typing-inspection"
 version = "0.4.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" }
 wheels = [
@@ -4297,9 +4298,9 @@ name = "vos"
 version = "3.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "aenum", marker = "sys_platform == 'linux'" },
-    { name = "cadcutils", marker = "sys_platform == 'linux'" },
-    { name = "html2text", marker = "sys_platform == 'linux'" },
+    { name = "aenum" },
+    { name = "cadcutils" },
+    { name = "html2text" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3e/6c/445bbedf2f90e71c09ff890aa8b409af5d074b1578eef383117cbcdfd09a/vos-3.6.4.tar.gz", hash = "sha256:67e4e24689ee881124b6670d7d2fc6527cc404687fc527fffc20cf5b1bd4c0bd", size = 103445, upload-time = "2025-12-12T00:20:56.398Z" }
 wheels = [
@@ -4395,9 +4396,9 @@ name = "yte"
 version = "1.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "argparse-dataclass", marker = "sys_platform == 'linux'" },
-    { name = "dpath", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "argparse-dataclass" },
+    { name = "dpath" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/44/f5/7e44620e6e077bfe624b9a17c329b8e0d0159e176e1f1a93c2790428ab2c/yte-1.9.4.tar.gz", hash = "sha256:86a47e6d722cec9419a7ac88be57d0d6c4ce28f02860393b71a66f2c674069f6", size = 8101, upload-time = "2025-11-27T12:55:00.85Z" }
 wheels = [
@@ -4409,12 +4410,12 @@ name = "zarr"
 version = "3.2.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "donfig", marker = "sys_platform == 'linux'" },
-    { name = "google-crc32c", marker = "sys_platform == 'linux'" },
-    { name = "numcodecs", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux'" },
+    { name = "donfig" },
+    { name = "google-crc32c" },
+    { name = "numcodecs" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/93/8d/aeb164004f87543b06ef54f885d02c342c31ceb274e2bbec470a98927621/zarr-3.2.1.tar.gz", hash = "sha256:71565b738a0e7e8ed226f0516eba8c6bb53440ad7669a8c48ebb3534a161d035", size = 675161, upload-time = "2026-05-05T12:37:22.383Z" }
 wheels = [

From fdd78f0fc95905189fc70188da40920a75d27835 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 31 Aug 2026 03:40:56 +0200
Subject: [PATCH 04/83] Stop tracking CI lint-gate scratch files

check.txt/format.txt/report.md are written by lint.yml during each run;
the autofix step's 'git add -A' kept committing them back. Gitignore them
(plus the residual-pass variants) and drop the tracked copies.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012b34pAS3bXRxN5hVdyq5Sw
---
 .gitignore |  9 ++++++++-
 check.txt  |  1 -
 format.txt |  2 --
 report.md  | 10 ----------
 4 files changed, 8 insertions(+), 14 deletions(-)
 delete mode 100644 check.txt
 delete mode 100644 format.txt
 delete mode 100644 report.md

diff --git a/.gitignore b/.gitignore
index 82fb2697..a1a0394b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -194,4 +194,11 @@ papers/catalog/plots/*.pdf
 papers/cosmo_val/logs/
 
 # Ignore scratch notebooks
-scratch/*/*.ipynb
\ No newline at end of file
+scratch/*/*.ipynb
+# CI lint-gate scratch output (lint.yml writes these in the run tree; never commit)
+check.txt
+format.txt
+report.md
+check2.txt
+format2.txt
+residual.md
diff --git a/check.txt b/check.txt
deleted file mode 100644
index 1f5f344d..00000000
--- a/check.txt
+++ /dev/null
@@ -1 +0,0 @@
-All checks passed!
diff --git a/format.txt b/format.txt
deleted file mode 100644
index a0b12d74..00000000
--- a/format.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-Would reformat: src/sp_validation/tests/test_masks.py
-1 file would be reformatted, 225 files already formatted
diff --git a/report.md b/report.md
deleted file mode 100644
index e681a67c..00000000
--- a/report.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### `ruff check .`
-
-✅ clean
-
-### `ruff format --check .`
-
-```
-Would reformat: src/sp_validation/tests/test_masks.py
-1 file would be reformatted, 225 files already formatted
-```

From d9c989681bbb172f480100641f700ab7b3cadec7 Mon Sep 17 00:00:00 2001
From: Martin Kilbinger <martin.kilbinger@cea.fr>
Date: Sat, 5 Sep 2026 16:14:12 +0200
Subject: [PATCH 05/83] PhotoPipe + ShapePipe (#310)

* additive bias calculation for paper updated to v1.4.6.3

* added config

* added fill_photoz script

* fill photoz bands fixes

* adding mag errors to fill_photoz

* added Z_ML to fill_photoz

* added more flags to fill_photoz

* added 0p7 and 1p0 aperture magnitudes to PhotoPipe + SP output

* Fixed fill photoz script with new MP_NAME type

* ruff autofix (format + safe lint fixes)

Pushed by the lint gate.

* removed leftover git marker

* fill_photoz_bands: spot-check FITS/HDF5 row order before filling a tile

The fill pairs FITS row k with the k-th HDF5 row of the tile (sorted-index
order) and only ever verified the row *count*, so a PhotoPipe tile ordered
differently from the comprehensive catalogue would be filled with silently
mismatched photo-z.

After the size check, compare RA/Dec for a small sample of rows -- up to five
at each end plus evenly spaced interior rows, --n_check_rows (default 10),
--check_tol_arcsec (default 0.5). Only the sampled HDF5 rows are read (one
fancy-index into the already-sorted index array), so the cost is a handful of
point reads per tile rather than a full per-row match. A failing tile is
warned about, counted as a row-order mismatch in the end-of-run summary,
counted towards the consecutive-failure abort, and skipped without being
added to done_tiles -- exactly as a size mismatch is, so a resume retries it.
--n_check_rows 0 disables the check.

HDF5 columns are RA/Dec (cat_config.yaml ra_col/dec_col for SP_v1.4.x). The
FITS names are resolved at runtime from a candidate list; ALPHA_J2000 /
DELTA_J2000 is first, verified against a real DR6 tile
(/n17data/UNIONS/WL/photometry/UNIONS_DR6/UNIONS.001.227_SP_ugriz_photoz_ext.cat).
If no candidate pair matches, the check disables itself with a warning rather
than skipping tiles.

Test: synthetic 3-tile HDF5 + FITS pair, tiles interleaved so the
non-contiguous write path runs too; the two aligned tiles fill, the tile with
reversed FITS rows is skipped and left empty, and --n_check_rows 0 reproduces
the old unchecked behaviour.

* fill_photoz_bands: row-order check ignores invalid positions, fails if none

A sampled row with a non-finite or sentinel (|Dec| > 90) coordinate on
either side carries no information about the FITS/HDF5 pairing.  Before,
such a row made `sep <= tol` False (a false row-order failure), and an
all-NaN sample raised a RuntimeWarning from np.nanmax.  Now those rows are
excluded from the comparison, n_checked reports only the rows actually
compared, and a tile whose sample has no comparable row is treated as
unverifiable and skipped (distinct warning), so it is retried on resume
rather than written blind.

Adds a direct unit test covering partial/all-invalid samples, a reversed
remainder, and a single-row tile (one-element h5py fancy index).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

---------

Co-authored-by: martinkilbinger <martinkilbinger@cea.fr>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Cail Daley <cail.daley@cea.fr>
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
---
 cosmo_val/cat_config.yaml                     | 108 ++-
 scripts/check_filled_fields.py                |  74 ++
 scripts/fill_photoz_bands.py                  | 874 ++++++++++++++++++
 .../tests/test_fill_photoz_bands.py           | 304 ++++++
 4 files changed, 1353 insertions(+), 7 deletions(-)
 create mode 100644 scripts/check_filled_fields.py
 create mode 100755 scripts/fill_photoz_bands.py
 create mode 100644 src/sp_validation/tests/test_fill_photoz_bands.py

diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index cc1326df..cae6411b 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -980,7 +980,98 @@ SP_v1.4.11.3:
     e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
-    path: /n17data/UNIONS/WL/v1.4.x/v1.4.11.3/unions_shapepipe_cut_struc_2024_v1.4.11.3.fits
+    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
+    path: v1.4.11.3/unions_shapepipe_cut_struc_2024_v1.4.11.3.fits
+    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
+    w_col: w_des
+    e1_col: e1
+    e1_col_corrected: e1_leak_corrected
+    e1_PSF_col: e1_PSF
+    e2_col: e2
+    e2_col_corrected: e2_leak_corrected
+    e2_PSF_col: e2_PSF
+  star:
+    ra_col: RA
+    dec_col: Dec
+    e1_col: e1
+    e2_col: e2
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+SP_v1.4.12.3:
+  subdir: /n17data/UNIONS/WL/v1.4.x
+  pipeline: SP
+  colour: lightblue
+  getdist_colour: 0.0, 0.5, 1.0
+  ls: dashdot
+  marker: ^
+  cov_th:
+    A: 2405.3892055695346
+    n_e: 6.128201234871523
+    n_psf: 0.752316232272063
+    sigma_e: 0.379587601488189
+  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
+  psf:
+    PSF_flag: FLAG_PSF_HSM
+    PSF_size: SIGMA_PSF_HSM
+    square_size: true
+    star_flag: FLAG_STAR_HSM
+    star_size: SIGMA_STAR_HSM
+    hdu: 1
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
+    ra_col: RA
+    dec_col: Dec
+    e1_PSF_col: E1_PSF_HSM
+    e1_star_col: E1_STAR_HSM
+    e2_PSF_col: E2_PSF_HSM
+    e2_star_col: E2_STAR_HSM
+  shear:
+    R: 1.0
+    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
+    path: v1.4.12.3/unions_shapepipe_cut_struc_2024_v1.4.12.3.fits
+    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
+    w_col: w_des
+    e1_col: e1
+    e1_col_corrected: e1_leak_corrected
+    e1_PSF_col: e1_PSF
+    e2_col: e2
+    e2_col_corrected: e2_leak_corrected
+    e2_PSF_col: e2_PSF
+  star:
+    ra_col: RA
+    dec_col: Dec
+    e1_col: e1
+    e2_col: e2
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+SP_v1.4.13.3:
+  subdir: /n17data/UNIONS/WL/v1.4.x
+  pipeline: SP
+  colour: cyan
+  getdist_colour: 0.0, 0.5, 1.0
+  ls: dashdot
+  marker: h
+  cov_th:
+    A: 2405.3892055695346
+    n_e: 6.128201234871523
+    n_psf: 0.752316232272063
+    sigma_e: 0.379587601488189
+  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
+  psf:
+    PSF_flag: FLAG_PSF_HSM
+    PSF_size: SIGMA_PSF_HSM
+    square_size: true
+    star_flag: FLAG_STAR_HSM
+    star_size: SIGMA_STAR_HSM
+    hdu: 1
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
+    ra_col: RA
+    dec_col: Dec
+    e1_PSF_col: E1_PSF_HSM
+    e1_star_col: E1_STAR_HSM
+    e2_PSF_col: E2_PSF_HSM
+    e2_star_col: E2_STAR_HSM
+  shear:
+    R: 1.0
+    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
+    path: v1.4.13.3/unions_shapepipe_cut_struc_2024_v1.4.13.3.fits
     redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
     w_col: w_des
     e1_col: e1
@@ -1038,11 +1129,12 @@ SP_v1.4.11.3_ecut07:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
-SP_v1.4.6_uncal:
+SP_v1.4.6.3_uncal:
   pipeline: SP
   subdir: /n17data/UNIONS/WL/v1.4.x
   shear:
-    path: v1.4.6/unions_shapepipe_cut_struc_2024_v1.4.6.fits
+    path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
+    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     ra_col: RA
     dec_col: Dec
     e1_col: e1_uncal
@@ -1053,11 +1145,12 @@ SP_v1.4.6_uncal:
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     hdu: 1
   patch_number: 100
-SP_v1.4.6_uncal_w_iv:
+SP_v1.4.6.3_uncal_w_iv:
   pipeline: SP
   subdir: /n17data/UNIONS/WL/v1.4.x
   shear:
-    path: v1.4.6/unions_shapepipe_cut_struc_2024_v1.4.6.fits
+    path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
+    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     ra_col: RA
     dec_col: Dec
     e1_col: e1_uncal
@@ -1068,11 +1161,12 @@ SP_v1.4.6_uncal_w_iv:
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     hdu: 1
   patch_number: 100
-SP_v1.4.6_uncal_w_1:
+SP_v1.4.6.3_uncal_w_1:
   pipeline: SP
   subdir: /n17data/UNIONS/WL/v1.4.x
   shear:
-    path: v1.4.6/unions_shapepipe_cut_struc_2024_v1.4.6.fits
+    path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
+    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     ra_col: RA
     dec_col: Dec
     e1_col: e1_uncal
diff --git a/scripts/check_filled_fields.py b/scripts/check_filled_fields.py
new file mode 100644
index 00000000..675ad116
--- /dev/null
+++ b/scripts/check_filled_fields.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""Check how many entries in selected HDF5 fields are filled (value != -199).
+
+Reads in chunks with a progress bar and periodic fill-fraction updates.
+"""
+
+import h5py
+import numpy as np
+from tqdm import tqdm
+
+HDF5_FILE = "unions_shapepipe_comprehensive_struc_ugriz_2024_v1.6.c.DR6.hdf5"
+EMPTY_VALUE = -199
+CHUNK_SIZE = 5_000_000  # rows per chunk
+REPORT_EVERY = 10  # print running fractions every N chunks
+
+FIELDS = [
+    "Z_B",
+    "Z_B_MIN",
+    "Z_B_MAX",
+    "T_B",
+    "MAG_GAAP_0p7_u",
+    "MAG_GAAP_1p0_u",
+    "MAG_GAAP_0p7_g",
+    "MAG_GAAP_1p0_g",
+    "MAG_GAAP_0p7_r",
+    "MAG_GAAP_1p0_r",
+    "MAG_GAAP_0p7_i",
+    "MAG_GAAP_1p0_i",
+    "MAG_GAAP_0p7_z",
+    "MAG_GAAP_1p0_z",
+    "MAG_GAAP_0p7_z2",
+    "MAG_GAAP_1p0_z2",
+]
+
+with h5py.File(HDF5_FILE, "r") as f:
+    data = f["data"]
+    n_total = data.shape[0]
+    n_chunks = (n_total + CHUNK_SIZE - 1) // CHUNK_SIZE
+    print(f"Total entries : {n_total:,}")
+    print(f"Chunk size    : {CHUNK_SIZE:,}  ({n_chunks} chunks)\n")
+
+    counts = {field: 0 for field in FIELDS}
+
+    with tqdm(total=n_total, unit="rows", unit_scale=True, desc="Reading") as pbar:
+        for chunk_idx in range(n_chunks):
+            start = chunk_idx * CHUNK_SIZE
+            end = min(start + CHUNK_SIZE, n_total)
+
+            for field in FIELDS:
+                counts[field] += int(np.sum(data[field, start:end] != EMPTY_VALUE))
+
+            pbar.update(end - start)
+
+            # Periodic running-fraction report
+            if (chunk_idx + 1) % REPORT_EVERY == 0 or (chunk_idx + 1) == n_chunks:
+                rows_done = end
+                tqdm.write(
+                    f"\n  --- after {rows_done:,} rows ({100 * rows_done / n_total:.1f}%) ---"
+                )
+                tqdm.write(f"  {'Field':<22} {'Filled %':>9}")
+                for field in FIELDS:
+                    pct = 100.0 * counts[field] / rows_done
+                    tqdm.write(f"  {field:<22} {pct:>8.2f}%")
+
+# Final summary
+print(f"\n{'=' * 56}")
+print(f"FINAL SUMMARY  (total rows: {n_total:,})")
+print(f"{'Field':<22} {'Filled':>12} {'Empty':>12} {'Filled %':>10}")
+print("-" * 60)
+for field in FIELDS:
+    n_filled = counts[field]
+    n_empty = n_total - n_filled
+    pct = 100.0 * n_filled / n_total
+    print(f"{field:<22} {n_filled:>12,} {n_empty:>12,} {pct:>9.2f}%")
diff --git a/scripts/fill_photoz_bands.py b/scripts/fill_photoz_bands.py
new file mode 100755
index 00000000..36a0a1b2
--- /dev/null
+++ b/scripts/fill_photoz_bands.py
@@ -0,0 +1,874 @@
+#!/usr/bin/env python
+
+"""fill_photoz_bands.py
+
+Add PhotoPipe photo-z and multi-band magnitude fields to a ShapePipe
+comprehensive HDF5 catalogue and fill them from PhotoPipe FITS tiles.
+
+Two phases, both resumable via a checkpoint file:
+
+  Phase 1 — Create output: copy the input HDF5 and append the new
+             columns initialised to EMPTY_VALUE (-199).
+  Phase 2 — Fill tiles: for each PhotoPipe FITS tile found in fits_dir,
+             write the field values into the output file.
+
+The fill assumes that, within a tile, the PhotoPipe FITS rows are in the
+same order as the HDF5 rows of that tile.  Before writing, this is checked
+on a small sample of rows by comparing their sky positions (see
+``check_tile_row_order``); tiles that fail are skipped, not written.
+
+Skips missing FITS files.  Warns (does not error) on missing PhotoPipe
+keys.  Supports interrupt + restart at any point.
+
+:Authors: Martin Kilbinger
+
+"""
+
+import json
+import os
+import sys
+import warnings
+from collections import defaultdict
+from timeit import default_timer as timer
+
+import h5py
+import numpy as np
+import tqdm
+from astropy.io import fits
+from cs_util import args as cs_args
+from cs_util import logging
+
+FITS_HDU = 1
+EMPTY_VALUE = -199
+COPY_CHUNK = 2_000_000  # rows per chunk when copying input → output
+SCAN_CHUNK = 5_000_000  # rows per chunk when scanning TILE_ID
+MAX_CONSEC_FAILS = 10  # abort if this many tiles in a row fail
+
+# Sky-position columns of the ShapePipe comprehensive HDF5 catalogue
+# (cosmo_val/cat_config.yaml: ra_col: RA, dec_col: Dec for all SP_v1.4.x).
+RA_COL_HDF5 = "RA"
+DEC_COL_HDF5 = "Dec"
+
+# Candidate (RA, Dec) column names in the PhotoPipe FITS tiles, most likely
+# first.  DR6 tiles (UNIONS.*_SP_ugriz_photoz_ext.cat) carry ALPHA_J2000 /
+# DELTA_J2000; the remaining pairs are fallbacks for other PhotoPipe outputs.
+FITS_RADEC_CANDIDATES = [
+    ("ALPHA_J2000", "DELTA_J2000"),
+    ("RA", "DEC"),
+    ("RA", "Dec"),
+    ("X_WORLD", "Y_WORLD"),
+]
+
+REQUESTED_KEYS = [
+    "Z_B",
+    "Z_B_MIN",
+    "Z_B_MAX",
+    "T_B",
+    "Z_ML",
+    "MAG_GAAP_u",
+    "MAGERR_GAAP_u",
+    "MAG_GAAP_0p7_u",
+    "MAGERR_GAAP_0p7_u",
+    "MAG_GAAP_1p0_u",
+    "MAGERR_GAAP_1p0_u",
+    "FLAG_GAAP_u",
+    "MAG_LIM_u",
+    "FLUX_GAAP_u",
+    "FLUXERR_GAAP_u",
+    "EXTINCTION_u",
+    "MAG_GAAP_g",
+    "MAGERR_GAAP_g",
+    "MAG_GAAP_0p7_g",
+    "MAGERR_GAAP_0p7_g",
+    "MAG_GAAP_1p0_g",
+    "MAGERR_GAAP_1p0_g",
+    "FLAG_GAAP_g",
+    "MAG_LIM_g",
+    "FLUX_GAAP_g",
+    "FLUXERR_GAAP_g",
+    "EXTINCTION_g",
+    "MAG_GAAP_r",
+    "MAGERR_GAAP_r",
+    "MAG_GAAP_0p7_r",
+    "MAGERR_GAAP_0p7_r",
+    "MAG_GAAP_1p0_r",
+    "MAGERR_GAAP_1p0_r",
+    "FLAG_GAAP_r",
+    "MAG_LIM_r",
+    "FLUX_GAAP_r",
+    "FLUXERR_GAAP_r",
+    "EXTINCTION_r",
+    "MAG_GAAP_i",
+    "MAGERR_GAAP_i",
+    "MAG_GAAP_0p7_i",
+    "MAGERR_GAAP_0p7_i",
+    "MAG_GAAP_1p0_i",
+    "MAGERR_GAAP_1p0_i",
+    "FLAG_GAAP_i",
+    "MAG_LIM_i",
+    "FLUX_GAAP_i",
+    "FLUXERR_GAAP_i",
+    "EXTINCTION_i",
+    "MAG_GAAP_z",
+    "MAGERR_GAAP_z",
+    "MAG_GAAP_0p7_z",
+    "MAGERR_GAAP_0p7_z",
+    "MAG_GAAP_1p0_z",
+    "MAGERR_GAAP_1p0_z",
+    "FLAG_GAAP_z",
+    "MAG_LIM_z",
+    "FLUX_GAAP_z",
+    "FLUXERR_GAAP_z",
+    "EXTINCTION_z",
+    "MAG_GAAP_z2",
+    "MAGERR_GAAP_z2",
+    "MAG_GAAP_0p7_z2",
+    "MAGERR_GAAP_0p7_z2",
+    "MAG_GAAP_1p0_z2",
+    "MAGERR_GAAP_1p0_z2",
+    "FLAG_GAAP_z2",
+    "MAG_LIM_z2",
+    "FLUX_GAAP_z2",
+    "FLUXERR_GAAP_z2",
+    "EXTINCTION_z2",
+    "EXTINCTION",
+    "ODDS",
+    "CHI_SQUARED_BPZ",
+    "M_0",
+    "BPZ_FILT",
+    "BPZ_NONDETFILT",
+    "BPZ_FLAGFILT",
+]
+
+
+def params_default():
+    """Params Default.
+
+    Return default parameter values and additional information
+    about type and command line options.
+
+    Returns
+    -------
+    tuple
+        parameter dict, short_options dict, types dict, help_strings dict
+
+    """
+    params = {
+        "input": "unions_shapepipe_comprehensive_struc_2024_v1.5.c.hdf5",
+        "output": "unions_shapepipe_comprehensive_struc_ugriz_2024_v1.5.c.hdf5",
+        "fits_dir": "UNIONS_DR6",
+        "checkpoint": "fill_photoz_bands_checkpoint.json",
+        "n_check_rows": 10,
+        "check_tol_arcsec": 0.5,
+        "verbose": False,
+    }
+
+    short_options = {
+        "input": "-i",
+        "output": "-o",
+        "fits_dir": "-d",
+        "checkpoint": "-c",
+    }
+
+    types = {
+        "n_check_rows": "int",
+        "check_tol_arcsec": "float",
+    }
+
+    help_strings = {
+        "input": "input HDF5 catalogue (no PhotoPipe fields), default={}",
+        "output": "output HDF5 catalogue (PhotoPipe fields added and filled), default={}",
+        "fits_dir": "directory with PhotoPipe FITS tiles, default={}",
+        "checkpoint": "checkpoint JSON file for resume support, default={}",
+        "n_check_rows": (
+            "number of rows per tile whose RA/Dec are compared between HDF5 and"
+            " FITS to verify row order, 0 to disable, default={}"
+        ),
+        "check_tol_arcsec": (
+            "maximum angular separation [arcsec] for a row-order check to pass,"
+            " default={}"
+        ),
+    }
+
+    return params, short_options, types, help_strings
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def detect_dataset_name(hf):
+    """Return the first dataset name in an HDF5 file.
+
+    Tries common names first: dat, dat_comb, data.
+
+    Parameters
+    ----------
+    hf : h5py.File
+
+    Returns
+    -------
+    str
+        Dataset name.
+
+    Raises
+    ------
+    KeyError
+        If no dataset is found.
+
+    """
+    for name in ("dat", "dat_comb", "data"):
+        if name in hf:
+            return name
+    # Fall back to first key
+    keys = list(hf.keys())
+    if not keys:
+        raise KeyError("HDF5 file contains no datasets.")
+    return keys[0]
+
+
+def strip_dtype(dtype):
+    """Strip h5py metadata from a structured numpy dtype descriptor.
+
+    h5py sometimes adds encoding metadata to dtype descriptions, e.g.
+    ``('<f4', {'h5py_encoding': 'ascii'})``.  This function returns a
+    clean list of ``(name, base_dtype)`` tuples suitable for constructing
+    a new ``np.dtype``.
+
+    Parameters
+    ----------
+    dtype : numpy.dtype
+
+    Returns
+    -------
+    list of (str, str) tuples
+
+    """
+    cleaned = []
+    for name, dt in dtype.descr:
+        if isinstance(dt, tuple):
+            cleaned.append((name, dt[0]))
+        else:
+            cleaned.append((name, dt))
+    return cleaned
+
+
+def build_output_dtype(input_dtype, new_keys):
+    """Build combined dtype: input fields + new float32 fields.
+
+    Parameters
+    ----------
+    input_dtype : numpy.dtype
+    new_keys : list of str
+        Names for new float32 columns.
+
+    Returns
+    -------
+    numpy.dtype
+
+    """
+    fields = strip_dtype(input_dtype)
+    existing = {name for name, _ in fields}
+    for key in new_keys:
+        if key not in existing:
+            fields.append((key, np.float32))
+    return np.dtype(fields)
+
+
+def tile_id_to_fits_path(tile_id_bytes, fits_dir):
+    """Convert HDF5 TILE_ID (e.g. b'255.348') to FITS file path."""
+    tile_str = f"{float(tile_id_bytes):07.3f}"
+    return os.path.join(fits_dir, f"UNIONS.{tile_str}_SP_ugriz_photoz_ext.cat")
+
+
+def check_fits_keys(fits_columns, requested_keys):
+    """Return (valid_keys, missing_keys) after checking FITS column names."""
+    fits_cols = set(fits_columns)
+    valid = [k for k in requested_keys if k in fits_cols]
+    missing = [k for k in requested_keys if k not in fits_cols]
+    return valid, missing
+
+
+def find_fits_radec_columns(fits_columns):
+    """Find RA/Dec Columns.
+
+    Return the (RA, Dec) column names of a PhotoPipe FITS table.
+
+    Parameters
+    ----------
+    fits_columns : iterable of str
+        Column names of the FITS HDU.
+
+    Returns
+    -------
+    tuple
+        (ra_name, dec_name), or (None, None) if no candidate pair matches.
+
+    """
+    columns = set(fits_columns)
+    for ra_name, dec_name in FITS_RADEC_CANDIDATES:
+        if ra_name in columns and dec_name in columns:
+            return ra_name, dec_name
+    return None, None
+
+
+def sample_row_positions(n_rows, n_sample):
+    """Sample Row Positions.
+
+    Pick a small, cheap-to-read set of row positions spanning a tile: up to
+    five rows at each end (where a shifted or reversed ordering shows up
+    first) plus evenly spaced rows in between.
+
+    Parameters
+    ----------
+    n_rows : int
+        Number of rows in the tile.
+    n_sample : int
+        Requested number of sampled rows.
+
+    Returns
+    -------
+    numpy.ndarray
+        Sorted, unique row positions, of length ``min(n_sample, n_rows)``
+        or less.
+
+    """
+    if n_rows <= 0 or n_sample <= 0:
+        return np.empty(0, dtype=np.int64)
+    if n_sample >= n_rows:
+        return np.arange(n_rows, dtype=np.int64)
+
+    n_edge = min(5, n_sample // 3)
+    head = np.arange(n_edge, dtype=np.int64)
+    tail = np.arange(n_rows - n_edge, n_rows, dtype=np.int64)
+
+    n_mid = n_sample - 2 * n_edge
+    if n_mid > 0:
+        mid = np.linspace(n_edge, n_rows - n_edge - 1, n_mid).astype(np.int64)
+    else:
+        mid = np.empty(0, dtype=np.int64)
+
+    return np.unique(np.concatenate([head, mid, tail]))
+
+
+def angular_separation_arcsec(ra_1, dec_1, ra_2, dec_2):
+    """Angular Separation Arcsec.
+
+    Great-circle separation between two sets of sky coordinates, computed
+    with the haversine formula (numerically stable at small separations and
+    correct across the RA=0 wrap).
+
+    Parameters
+    ----------
+    ra_1 : numpy.ndarray
+        Right ascensions of the first set [deg].
+    dec_1 : numpy.ndarray
+        Declinations of the first set [deg].
+    ra_2 : numpy.ndarray
+        Right ascensions of the second set [deg].
+    dec_2 : numpy.ndarray
+        Declinations of the second set [deg].
+
+    Returns
+    -------
+    numpy.ndarray
+        Angular separations [arcsec].
+
+    """
+    ra_1 = np.radians(np.asarray(ra_1, dtype=np.float64))
+    dec_1 = np.radians(np.asarray(dec_1, dtype=np.float64))
+    ra_2 = np.radians(np.asarray(ra_2, dtype=np.float64))
+    dec_2 = np.radians(np.asarray(dec_2, dtype=np.float64))
+
+    d_ra = ra_2 - ra_1
+    d_dec = dec_2 - dec_1
+    hav = np.sin(d_dec / 2) ** 2 + np.cos(dec_1) * np.cos(dec_2) * np.sin(d_ra / 2) ** 2
+    sep_rad = 2 * np.arcsin(np.sqrt(np.clip(hav, 0, 1)))
+
+    return np.degrees(sep_rad) * 3600
+
+
+def check_tile_row_order(
+    dset,
+    sorted_idx,
+    fits_data,
+    fits_radec_cols,
+    n_sample,
+    tol_arcsec,
+):
+    """Check Tile Row Order.
+
+    Spot-check that the FITS rows of a tile line up with the HDF5 rows they
+    are about to be written into.  ``write_tile_to_hdf5`` assigns FITS row
+    ``k`` to HDF5 row ``sorted_idx[k]``; this compares the sky positions of
+    a sample of those pairs.  Only the sampled HDF5 rows are read.
+
+    Parameters
+    ----------
+    dset : h5py.Dataset
+        Compound HDF5 dataset.
+    sorted_idx : numpy.ndarray
+        Sorted HDF5 row indices of this tile.
+    fits_data : numpy.recarray
+        Data from the PhotoPipe FITS HDU, same length as ``sorted_idx``.
+    fits_radec_cols : tuple
+        (RA, Dec) column names in ``fits_data``.
+    n_sample : int
+        Number of rows to compare.
+    tol_arcsec : float
+        Maximum tolerated angular separation [arcsec].
+
+    Returns
+    -------
+    tuple
+        (ok, n_checked, max_sep_arcsec).  ``n_checked`` counts the sampled
+        rows with a valid sky position on both sides; only those are
+        compared.  ``ok`` is ``False`` if any compared pair is further apart
+        than ``tol_arcsec``, or if the sample holds no comparable row at all
+        (``n_checked == 0``, ``max_sep_arcsec`` is ``numpy.nan``): a tile
+        whose order cannot be verified is not written.
+
+    """
+    positions = sample_row_positions(len(sorted_idx), n_sample)
+    if len(positions) == 0:
+        return False, 0, np.nan
+
+    ra_col_fits, dec_col_fits = fits_radec_cols
+
+    # Fancy-index a handful of rows only; positions is sorted and unique, and
+    # sorted_idx is sorted, so the h5py selection is strictly increasing.
+    rows_hdf5 = dset[sorted_idx[positions]]
+
+    ra_hdf5 = np.asarray(rows_hdf5[RA_COL_HDF5], dtype=np.float64)
+    dec_hdf5 = np.asarray(rows_hdf5[DEC_COL_HDF5], dtype=np.float64)
+    ra_fits = np.asarray(fits_data[ra_col_fits][positions], dtype=np.float64)
+    dec_fits = np.asarray(fits_data[dec_col_fits][positions], dtype=np.float64)
+
+    # A row carries no information about the pairing if either side has no
+    # valid position (NaN, or a sentinel such as -199 outside the sky).
+    valid = (
+        np.isfinite(ra_hdf5)
+        & np.isfinite(dec_hdf5)
+        & np.isfinite(ra_fits)
+        & np.isfinite(dec_fits)
+        & (np.abs(dec_hdf5) <= 90)
+        & (np.abs(dec_fits) <= 90)
+    )
+    n_checked = int(valid.sum())
+    if n_checked == 0:
+        return False, 0, np.nan
+
+    sep = angular_separation_arcsec(
+        ra_hdf5[valid], dec_hdf5[valid], ra_fits[valid], dec_fits[valid]
+    )
+    max_sep = float(np.max(sep))
+
+    return bool(max_sep <= tol_arcsec), n_checked, max_sep
+
+
+def write_tile_to_hdf5(dset, hdf5_indices, fits_data, valid_keys):
+    """Write valid_keys from fits_data into dset at hdf5_indices.
+
+    Reads the HDF5 range in one chunk, fills fields in memory, writes
+    back.  Handles both contiguous and non-contiguous index ranges.
+
+    Assumes row ``k`` of ``fits_data`` corresponds to HDF5 row
+    ``numpy.sort(hdf5_indices)[k]``; ``check_tile_row_order`` spot-checks
+    this before the write.
+
+    Parameters
+    ----------
+    dset : h5py.Dataset
+        Compound HDF5 dataset opened in r+ mode.
+    hdf5_indices : numpy.ndarray
+        Row indices in dset corresponding to this tile (will be sorted).
+    fits_data : numpy.recarray
+        Data from the PhotoPipe FITS HDU.
+    valid_keys : list of str
+        Field names to copy from fits_data into dset.
+
+    """
+    sorted_idx = np.sort(hdf5_indices)
+    idx_min = int(sorted_idx[0])
+    idx_max = int(sorted_idx[-1])
+    n_range = idx_max - idx_min + 1
+
+    if n_range == len(sorted_idx):
+        # Contiguous block: single read-modify-write
+        chunk = dset[idx_min : idx_max + 1]
+        for key in valid_keys:
+            chunk[key] = fits_data[key]
+        dset[idx_min : idx_max + 1] = chunk
+    else:
+        # Non-contiguous: split into contiguous sub-blocks
+        gaps = np.where(np.diff(sorted_idx) > 1)[0] + 1
+        blocks = np.split(sorted_idx, gaps)
+        fits_offset = 0
+        for block in blocks:
+            b_min, b_max = int(block[0]), int(block[-1])
+            n_block = b_max - b_min + 1
+            chunk = dset[b_min : b_max + 1]
+            for key in valid_keys:
+                chunk[key] = fits_data[key][fits_offset : fits_offset + n_block]
+            dset[b_min : b_max + 1] = chunk
+            fits_offset += n_block
+
+
+# ---------------------------------------------------------------------------
+# Phase 1: create output file
+# ---------------------------------------------------------------------------
+
+
+def create_output_file(input_path, output_path, dataset_name, verbose=False):
+    """Create output HDF5 by copying input and appending empty PhotoPipe fields.
+
+    Parameters
+    ----------
+    input_path : str
+    output_path : str
+    dataset_name : str
+        Dataset name in the input file (used for output too).
+    verbose : bool
+
+    """
+    print(f"Phase 1: creating output file '{output_path}'")
+    t0 = timer()
+
+    with h5py.File(input_path, "r") as hf_in:
+        dset_in = hf_in[dataset_name]
+        n_total = dset_in.shape[0]
+        dtype_out = build_output_dtype(dset_in.dtype, REQUESTED_KEYS)
+        new_keys = [k for k in REQUESTED_KEYS if k not in set(dset_in.dtype.names)]
+
+        print(f"  Input rows   : {n_total:,}")
+        print(f"  Input fields : {len(dset_in.dtype.names)}")
+        print(f"  New fields   : {new_keys}")
+        size_mb = n_total * dtype_out.itemsize / 1_048_576
+        print(f"  Output size  : ~{size_mb:,.0f} MB")
+
+        with h5py.File(output_path, "w") as hf_out:
+            dset_out = hf_out.create_dataset(
+                dataset_name,
+                shape=(n_total,),
+                dtype=dtype_out,
+            )
+
+            # Copy input fields chunk by chunk
+            input_fields = dset_in.dtype.names
+            with tqdm.tqdm(
+                total=n_total, unit="rows", unit_scale=True, desc="  Copying"
+            ) as pbar:
+                for start in range(0, n_total, COPY_CHUNK):
+                    end = min(start + COPY_CHUNK, n_total)
+                    chunk_in = dset_in[start:end]
+                    chunk_out = np.empty(end - start, dtype=dtype_out)
+                    for field in input_fields:
+                        chunk_out[field] = chunk_in[field]
+                    for key in new_keys:
+                        chunk_out[key] = EMPTY_VALUE
+                    dset_out[start:end] = chunk_out
+                    pbar.update(end - start)
+
+            # Copy all other top-level datasets/groups unchanged
+            for key in hf_in.keys():
+                if key != dataset_name:
+                    hf_in.copy(key, hf_out)
+                    if verbose:
+                        print(f"  Copied group/dataset '{key}' unchanged.")
+
+    elapsed = timer() - t0
+    print(f"  Done in {elapsed:.1f}s\n")
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def main(argv=None):
+    """Main.
+
+    Main program.
+
+    """
+    params, short_options, types, help_strings = params_default()
+
+    options = cs_args.parse_options(params, short_options, types, help_strings)
+    params.update(options)
+
+    logging.log_command(argv)
+
+    verbose = params["verbose"]
+
+    if not os.path.exists(params["input"]):
+        print(f"ERROR: input file not found: {params['input']}", file=sys.stderr)
+        return 1
+    if not os.path.isdir(params["fits_dir"]):
+        print(f"ERROR: FITS directory not found: {params['fits_dir']}", file=sys.stderr)
+        return 1
+
+    # ------------------------------------------------------------------
+    # Load checkpoint
+    # ------------------------------------------------------------------
+    if os.path.exists(params["checkpoint"]):
+        with open(params["checkpoint"]) as f:
+            checkpoint = json.load(f)
+        done_tiles = set(checkpoint.get("done_tiles", []))
+        print(f"Resuming: {len(done_tiles)} tiles already completed.")
+    else:
+        done_tiles = set()
+        checkpoint = {}
+
+    # ------------------------------------------------------------------
+    # Detect input dataset name (cache in checkpoint)
+    # ------------------------------------------------------------------
+    if "dataset_name" in checkpoint:
+        dataset_name = checkpoint["dataset_name"]
+    else:
+        with h5py.File(params["input"], "r") as hf:
+            dataset_name = detect_dataset_name(hf)
+        checkpoint["dataset_name"] = dataset_name
+        with open(params["checkpoint"], "w") as cf:
+            json.dump(checkpoint, cf)
+
+    if verbose:
+        print(f"Input dataset: '{dataset_name}'")
+
+    # ------------------------------------------------------------------
+    # Phase 1: create output file if needed
+    # ------------------------------------------------------------------
+    if not checkpoint.get("output_created", False):
+        if os.path.exists(params["output"]):
+            print(
+                f"WARNING: output file '{params['output']}' exists but checkpoint "
+                "does not mark it as complete. Overwriting."
+            )
+        create_output_file(
+            params["input"], params["output"], dataset_name, verbose=verbose
+        )
+        checkpoint["output_created"] = True
+        with open(params["checkpoint"], "w") as cf:
+            json.dump(checkpoint, cf)
+    else:
+        if verbose:
+            print("Phase 1 already done (output file exists in checkpoint).")
+
+    # ------------------------------------------------------------------
+    # Phase 2: fill tiles
+    # ------------------------------------------------------------------
+    t0 = timer()
+    with h5py.File(params["output"], "r+") as hf:
+        dset = hf[dataset_name]
+        n_total = dset.shape[0]
+        print(f"Phase 2: filling tiles in '{params['output']}'")
+        print(f"  {n_total:,} rows, dataset '{dataset_name}'")
+
+        hdf5_fields = set(dset.dtype.names)
+
+        # Check all requested keys are present
+        for key in REQUESTED_KEYS:
+            if key not in hdf5_fields:
+                warnings.warn(
+                    f"Key '{key}' missing from output dataset — was Phase 1 complete?"
+                )
+
+        # Build TILE_ID → output row indices (always scanned; too large for checkpoint)
+        print("  Building tile→index map (scans all rows)...")
+        tile_index_map_lists = defaultdict(list)
+        with tqdm.tqdm(
+            total=n_total, unit="rows", unit_scale=True, desc="  Scanning TILE_ID"
+        ) as pbar:
+            for start in range(0, n_total, SCAN_CHUNK):
+                end = min(start + SCAN_CHUNK, n_total)
+                tile_chunk = dset[start:end]["TILE_ID"]
+                for local_i, tid in enumerate(tile_chunk):
+                    tile_index_map_lists[tid].append(start + local_i)
+                pbar.update(end - start)
+
+        tile_index_map = {
+            tid: np.array(idxs, dtype=np.int64)
+            for tid, idxs in tile_index_map_lists.items()
+        }
+        print(f"  Map built: {len(tile_index_map)} unique tiles.")
+
+        unique_tiles = sorted(tile_index_map.keys())
+        n_tiles = len(unique_tiles)
+
+        valid_keys = None  # determined from first available FITS tile
+        fits_radec_cols = None  # idem, (RA, Dec) column names in the FITS tiles
+
+        # The row-order check needs sky positions on both sides.
+        check_rows = params["n_check_rows"]
+        if check_rows > 0 and not {RA_COL_HDF5, DEC_COL_HDF5} <= hdf5_fields:
+            warnings.warn(
+                f"Columns '{RA_COL_HDF5}'/'{DEC_COL_HDF5}' absent from the HDF5"
+                " dataset — row-order check disabled."
+            )
+            check_rows = 0
+        n_skipped_missing = 0
+        n_skipped_done = 0
+        n_skipped_size = 0
+        n_skipped_order = 0
+        n_errors = 0
+        n_consec_fails = 0
+        n_processed = 0
+
+        print(f"\n  Processing {n_tiles} tiles ({len(done_tiles)} already done)...\n")
+        pbar = tqdm.tqdm(unique_tiles, total=n_tiles, unit="tile")
+
+        for tile_id in pbar:
+            if n_consec_fails >= MAX_CONSEC_FAILS:
+                checkpoint["done_tiles"] = list(done_tiles)
+                with open(params["checkpoint"], "w") as cf:
+                    json.dump(checkpoint, cf)
+                print(
+                    f"\nERROR: {n_consec_fails} tiles failed in a row, "
+                    "likely a systematic problem; aborting.",
+                    file=sys.stderr,
+                )
+                sys.exit(1)
+
+            tile_str = tile_id.decode() if isinstance(tile_id, bytes) else tile_id
+
+            if tile_str in done_tiles:
+                n_skipped_done += 1
+                continue
+
+            fits_path = tile_id_to_fits_path(tile_id, params["fits_dir"])
+            if not os.path.exists(fits_path):
+                n_skipped_missing += 1
+                done_tiles.add(tile_str)
+                pbar.set_postfix({"done": n_processed, "missing": n_skipped_missing})
+                continue
+
+            try:
+                with fits.open(fits_path, memmap=True) as hdu_list:
+                    fits_data = hdu_list[FITS_HDU].data
+
+                    # Validate keys on first successfully opened tile
+                    if valid_keys is None:
+                        valid_keys, missing_keys = check_fits_keys(
+                            fits_data.dtype.names, REQUESTED_KEYS
+                        )
+                        valid_keys = [k for k in valid_keys if k in hdf5_fields]
+                        if missing_keys:
+                            warnings.warn(
+                                f"Keys absent from PhotoPipe FITS (skipped): {missing_keys}"
+                            )
+                        tqdm.tqdm.write(f"\n  Keys to fill: {valid_keys}\n")
+
+                        fits_radec_cols = find_fits_radec_columns(fits_data.dtype.names)
+                        if check_rows > 0 and fits_radec_cols[0] is None:
+                            warnings.warn(
+                                "No known RA/Dec column pair in the PhotoPipe"
+                                f" FITS (tried {FITS_RADEC_CANDIDATES}) —"
+                                " row-order check disabled."
+                            )
+                            check_rows = 0
+                        elif check_rows > 0:
+                            tqdm.tqdm.write(
+                                "  Row-order check: comparing"
+                                f" {check_rows} rows/tile,"
+                                f" {RA_COL_HDF5}/{DEC_COL_HDF5} (HDF5) vs"
+                                f" {fits_radec_cols[0]}/{fits_radec_cols[1]}"
+                                f" (FITS), tolerance"
+                                f" {params['check_tol_arcsec']} arcsec\n"
+                            )
+
+                    hdf5_indices = tile_index_map[tile_id]
+                    n_hdf5 = len(hdf5_indices)
+                    n_fits = len(fits_data)
+
+                    if n_hdf5 != n_fits:
+                        warnings.warn(
+                            f"Tile {tile_str}: HDF5 has {n_hdf5} rows, FITS has "
+                            f"{n_fits} — size mismatch, skipping."
+                        )
+                        n_skipped_size += 1
+                        n_consec_fails += 1
+                        pbar.set_postfix(
+                            {"done": n_processed, "size_err": n_skipped_size}
+                        )
+                        continue
+
+                    # write_tile_to_hdf5 pairs FITS row k with HDF5 row
+                    # sorted_idx[k]; spot-check that pairing before writing.
+                    sorted_idx = np.sort(hdf5_indices)
+                    if check_rows > 0:
+                        ok, n_checked, max_sep = check_tile_row_order(
+                            dset,
+                            sorted_idx,
+                            fits_data,
+                            fits_radec_cols,
+                            check_rows,
+                            params["check_tol_arcsec"],
+                        )
+                        if not ok:
+                            if n_checked == 0:
+                                warnings.warn(
+                                    f"Tile {tile_str}: no sampled row has a"
+                                    " valid RA/Dec on both sides —"
+                                    " row order unverifiable, skipping."
+                                )
+                            else:
+                                warnings.warn(
+                                    f"Tile {tile_str}: RA/Dec disagree for"
+                                    f" {n_checked} checked rows (max"
+                                    f" separation {max_sep:.3g} arcsec >"
+                                    f" {params['check_tol_arcsec']} arcsec)"
+                                    " — row order mismatch, skipping."
+                                )
+                            n_skipped_order += 1
+                            n_consec_fails += 1
+                            pbar.set_postfix(
+                                {
+                                    "done": n_processed,
+                                    "order_err": n_skipped_order,
+                                }
+                            )
+                            continue
+
+                    write_tile_to_hdf5(dset, sorted_idx, fits_data, valid_keys)
+
+            except Exception as e:
+                warnings.warn(f"Tile {tile_str}: error ({e}), skipping.")
+                n_errors += 1
+                n_consec_fails += 1
+                continue
+
+            n_processed += 1
+            n_consec_fails = 0
+            done_tiles.add(tile_str)
+
+            if n_processed % 50 == 0:
+                checkpoint["done_tiles"] = list(done_tiles)
+                with open(params["checkpoint"], "w") as cf:
+                    json.dump(checkpoint, cf)
+
+            pbar.set_postfix({"done": n_processed, "missing": n_skipped_missing})
+
+        # Final checkpoint flush
+        checkpoint["done_tiles"] = list(done_tiles)
+        with open(params["checkpoint"], "w") as cf:
+            json.dump(checkpoint, cf)
+
+    elapsed = timer() - t0
+    print(f"\nDone in {elapsed:.1f}s")
+    print(f"  Tiles processed       : {n_processed}")
+    print(f"  Tiles skipped (done)  : {n_skipped_done}")
+    print(f"  FITS files missing    : {n_skipped_missing}")
+    print(f"  Size mismatches       : {n_skipped_size}")
+    print(f"  Row-order mismatches  : {n_skipped_order}")
+    print(f"  Tiles failed (error)  : {n_errors}")
+    if n_processed == 0 and (n_errors > 0 or n_skipped_size > 0 or n_skipped_order > 0):
+        print(
+            "WARNING: no tiles were filled; all available tiles failed.",
+            file=sys.stderr,
+        )
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv))
diff --git a/src/sp_validation/tests/test_fill_photoz_bands.py b/src/sp_validation/tests/test_fill_photoz_bands.py
new file mode 100644
index 00000000..ae45a637
--- /dev/null
+++ b/src/sp_validation/tests/test_fill_photoz_bands.py
@@ -0,0 +1,304 @@
+"""``scripts/fill_photoz_bands.py`` must not trust FITS/HDF5 row order blindly.
+
+The script writes PhotoPipe columns into the ShapePipe comprehensive HDF5
+catalogue tile by tile, pairing FITS row ``k`` with the ``k``-th HDF5 row of
+that tile (in sorted-index order).  Nothing in the file formats guarantees
+that pairing -- only the row *count* used to be checked -- so a tile whose
+PhotoPipe catalogue happens to be ordered differently would be filled with
+silently mismatched photo-z.
+
+These tests build a tiny synthetic pair of catalogues (three tiles,
+deliberately interleaved so the non-contiguous write path is exercised) and
+run the script end to end:
+
+* the two tiles whose FITS rows line up are filled;
+* the tile whose FITS rows are reversed is *skipped*, counted under
+  ``Row-order mismatches``, and left at ``EMPTY_VALUE``, so a resumed run
+  retries it rather than treating it as done.
+"""
+
+import importlib.util
+import json
+import subprocess
+import sys
+import warnings
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+h5py = pytest.importorskip("h5py")
+fits = pytest.importorskip("astropy.io.fits")
+pytest.importorskip("cs_util")
+pytest.importorskip("tqdm")
+
+
+def _repo_root() -> Path:
+    """Locate the repo root by walking up to the ``pyproject.toml`` marker."""
+    for parent in Path(__file__).resolve().parents:
+        if (parent / "pyproject.toml").exists():
+            return parent
+    raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
+
+
+_SCRIPT = _repo_root() / "scripts" / "fill_photoz_bands.py"
+
+# Tile layout: three tiles, rows interleaved round-robin over the catalogue so
+# each tile's HDF5 indices are non-contiguous.
+TILES = ("100.100", "200.200", "300.300")
+N_PER_TILE = 20
+N_ROWS = len(TILES) * N_PER_TILE
+FILL_KEYS = ("Z_B", "MAG_GAAP_r")
+
+
+def _module():
+    """Import ``scripts/fill_photoz_bands.py`` (lives outside the package)."""
+    spec = importlib.util.spec_from_file_location("fill_photoz_bands", _SCRIPT)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _synthetic_catalogues(tmp_path, reversed_tile):
+    """Write a synthetic HDF5 catalogue and matching PhotoPipe FITS tiles.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        Directory to write into.
+    reversed_tile : str
+        Tile whose FITS rows are written in reverse order (row-order breakage).
+
+    Returns
+    -------
+    tuple
+        (input HDF5 path, FITS directory, truth dict tile -> (Z_B, MAG_GAAP_r))
+
+    """
+    row = np.arange(N_ROWS)
+    tile_of_row = np.array([TILES[i % len(TILES)] for i in row])
+
+    dtype = np.dtype([("TILE_ID", "S7"), ("RA", "<f8"), ("Dec", "<f8"), ("w", "<f4")])
+    data = np.empty(N_ROWS, dtype=dtype)
+    data["TILE_ID"] = np.array([t.encode() for t in tile_of_row])
+    # Well-separated positions: 0.01 deg = 36 arcsec apart, far above the
+    # 0.5 arcsec default tolerance, so any reordering is unambiguous.
+    data["RA"] = 123.0 + 0.01 * row
+    data["Dec"] = 12.0 + 0.01 * row
+    data["w"] = 1.0
+
+    hdf5_path = tmp_path / "input.hdf5"
+    with h5py.File(hdf5_path, "w") as hf:
+        hf.create_dataset("dat", data=data)
+
+    fits_dir = tmp_path / "fits"
+    fits_dir.mkdir()
+
+    truth = {}
+    for tile in TILES:
+        sel = np.where(tile_of_row == tile)[0]  # already in HDF5 row order
+        z_b = 0.1 + 0.001 * sel
+        mag = 20.0 + 0.001 * sel
+        truth[tile] = (z_b, mag)
+
+        order = sel[::-1] if tile == reversed_tile else sel
+        columns = fits.ColDefs(
+            [
+                fits.Column(name="ALPHA_J2000", format="D", array=123.0 + 0.01 * order),
+                fits.Column(name="DELTA_J2000", format="D", array=12.0 + 0.01 * order),
+                fits.Column(name="Z_B", format="E", array=0.1 + 0.001 * order),
+                fits.Column(name="MAG_GAAP_r", format="E", array=20.0 + 0.001 * order),
+            ]
+        )
+        hdu_list = fits.HDUList(
+            [fits.PrimaryHDU(), fits.BinTableHDU.from_columns(columns, name="OBJECTS")]
+        )
+        hdu_list.writeto(fits_dir / f"UNIONS.{tile}_SP_ugriz_photoz_ext.cat")
+
+    return hdf5_path, fits_dir, truth
+
+
+def _run(tmp_path, hdf5_path, fits_dir, *extra):
+    """Run the script on the synthetic catalogues; return (result, out_path)."""
+    output = tmp_path / "output.hdf5"
+    checkpoint = tmp_path / "checkpoint.json"
+    result = subprocess.run(
+        [
+            sys.executable,
+            str(_SCRIPT),
+            "-i",
+            str(hdf5_path),
+            "-o",
+            str(output),
+            "-d",
+            str(fits_dir),
+            "-c",
+            str(checkpoint),
+            *extra,
+        ],
+        capture_output=True,
+        text=True,
+        cwd=tmp_path,
+    )
+    return result, output, checkpoint
+
+
+@pytest.mark.fast
+def test_reversed_tile_is_skipped_matching_tiles_are_filled(tmp_path):
+    """A tile with permuted PhotoPipe rows is skipped; the aligned ones fill."""
+    bad_tile = TILES[1]
+    hdf5_path, fits_dir, truth = _synthetic_catalogues(tmp_path, bad_tile)
+
+    result, output, checkpoint = _run(tmp_path, hdf5_path, fits_dir)
+
+    assert result.returncode == 0, result.stdout + result.stderr
+    assert "Row-order mismatches  : 1" in result.stdout, result.stdout
+    assert "Tiles processed       : 2" in result.stdout, result.stdout
+    assert "row order mismatch" in result.stderr, result.stderr
+
+    empty = _module().EMPTY_VALUE
+    with h5py.File(output, "r") as hf:
+        dset = hf["dat"][:]
+
+    for tile in TILES:
+        sel = np.where(dset["TILE_ID"] == tile.encode())[0]
+        z_b, mag = truth[tile]
+        if tile == bad_tile:
+            assert np.all(dset["Z_B"][sel] == empty), (
+                f"tile {tile} has permuted PhotoPipe rows but was filled anyway"
+            )
+            assert np.all(dset["MAG_GAAP_r"][sel] == empty)
+        else:
+            np.testing.assert_allclose(dset["Z_B"][sel], z_b, rtol=1e-6)
+            np.testing.assert_allclose(dset["MAG_GAAP_r"][sel], mag, rtol=1e-6)
+
+    # The skipped tile must not be checkpointed, so a resume retries it.
+    done = set(json.loads(checkpoint.read_text())["done_tiles"])
+    assert done == {t for t in TILES if t != bad_tile}
+
+
+@pytest.mark.fast
+def test_check_can_be_disabled(tmp_path):
+    """``--n_check_rows 0`` restores the old, unchecked behaviour."""
+    bad_tile = TILES[1]
+    hdf5_path, fits_dir, _ = _synthetic_catalogues(tmp_path, bad_tile)
+
+    result, output, _ = _run(tmp_path, hdf5_path, fits_dir, "--n_check_rows", "0")
+
+    assert result.returncode == 0, result.stdout + result.stderr
+    assert "Row-order mismatches  : 0" in result.stdout, result.stdout
+    assert "Tiles processed       : 3" in result.stdout, result.stdout
+
+    empty = _module().EMPTY_VALUE
+    with h5py.File(output, "r") as hf:
+        z_b = hf["dat"]["Z_B"][:]
+    assert not np.any(z_b == empty), "all tiles should have been written"
+
+
+@pytest.mark.fast
+def test_sample_row_positions_spans_the_tile():
+    """The sample hits both ends and the interior, and never runs off the tile."""
+    module = _module()
+
+    assert module.sample_row_positions(0, 10).size == 0
+    assert module.sample_row_positions(100, 0).size == 0
+    np.testing.assert_array_equal(module.sample_row_positions(4, 10), np.arange(4))
+
+    positions = module.sample_row_positions(1000, 10)
+    assert positions[0] == 0
+    assert positions[-1] == 999
+    assert len(positions) == 10
+    assert np.all(np.diff(positions) > 0)
+    # Not all at the edges: something is sampled from the bulk.
+    assert np.any((positions > 10) & (positions < 989))
+
+
+@pytest.mark.fast
+def test_find_fits_radec_columns():
+    """The DR6 PhotoPipe names are recognised; unknown tables yield None."""
+    module = _module()
+
+    assert module.find_fits_radec_columns(
+        ["SeqNr", "ALPHA_J2000", "DELTA_J2000", "Z_B"]
+    ) == ("ALPHA_J2000", "DELTA_J2000")
+    assert module.find_fits_radec_columns(["RA", "DEC"]) == ("RA", "DEC")
+    assert module.find_fits_radec_columns(["X", "Y"]) == (None, None)
+
+
+@pytest.mark.fast
+def test_angular_separation_arcsec():
+    """Separation is correct at the pole-free small-angle limit and wraps RA."""
+    module = _module()
+
+    sep = module.angular_separation_arcsec([0.0], [0.0], [1.0 / 3600], [0.0])
+    np.testing.assert_allclose(sep, [1.0], rtol=1e-6)
+
+    # Across the RA=0 wrap: 359.999 deg vs 0.001 deg is 0.002 deg, not 360.
+    sep = module.angular_separation_arcsec([359.999], [0.0], [0.001], [0.0])
+    np.testing.assert_allclose(sep, [0.002 * 3600], rtol=1e-6)
+
+
+@pytest.mark.fast
+def test_check_tile_row_order_ignores_invalid_positions(tmp_path):
+    """Rows without a valid position on both sides are not compared.
+
+    A NaN or sentinel coordinate carries no information about the pairing,
+    so it must neither fail the check (NaN <= tol is False) nor pass it; a
+    sample with no comparable row at all is an unverifiable tile and fails.
+    """
+    module = _module()
+
+    n = 8
+    ra = 10.0 + 0.01 * np.arange(n)
+    dec = 20.0 + 0.01 * np.arange(n)
+    dtype = np.dtype([("RA", ">f8"), ("Dec", ">f8")])
+    data = np.empty(n, dtype=dtype)
+    data["RA"], data["Dec"] = ra, dec
+    with h5py.File(tmp_path / "cat.hdf5", "w") as hf:
+        dset = hf.create_dataset("dat", data=data)
+        sorted_idx = np.arange(n)
+        cols = ("ALPHA_J2000", "DELTA_J2000")
+
+        def fits_like(ra_f, dec_f):
+            rec = np.empty(n, dtype=[(cols[0], "<f8"), (cols[1], "<f8")])
+            rec[cols[0]], rec[cols[1]] = ra_f, dec_f
+            return rec
+
+        # Aligned, all valid.
+        ok, n_checked, max_sep = module.check_tile_row_order(
+            dset, sorted_idx, fits_like(ra, dec), cols, n, 0.5
+        )
+        assert ok and n_checked == n and max_sep < 1e-6
+
+        # NaN RA on the FITS side and a -199 Dec sentinel on the other row:
+        # those two rows are dropped, the remaining six still agree.
+        ra_f = ra.copy()
+        ra_f[2] = np.nan
+        dec_f = dec.copy()
+        dec_f[5] = -199.0
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            ok, n_checked, _ = module.check_tile_row_order(
+                dset, sorted_idx, fits_like(ra_f, dec_f), cols, n, 0.5
+            )
+        assert ok and n_checked == n - 2
+
+        # Same invalid rows, but the rest is reversed: still a failure.
+        ok, n_checked, max_sep = module.check_tile_row_order(
+            dset, sorted_idx, fits_like(ra_f[::-1], dec_f[::-1]), cols, n, 0.5
+        )
+        assert not ok and n_checked == n - 2 and max_sep > 0.5
+
+        # Nothing comparable at all: fail, no RuntimeWarning from an all-NaN max.
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            ok, n_checked, max_sep = module.check_tile_row_order(
+                dset, sorted_idx, fits_like(np.full(n, np.nan), dec), cols, n, 0.5
+            )
+        assert not ok and n_checked == 0 and np.isnan(max_sep)
+
+        # A single-row tile is a valid (one-element) fancy index.
+        ok, n_checked, _ = module.check_tile_row_order(
+            dset, sorted_idx[3:4], fits_like(ra, dec)[3:4], cols, 10, 0.5
+        )
+        assert ok and n_checked == 1

From 556e56aa3d835463827383322bf23d3747efa076 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Sat, 5 Sep 2026 16:17:16 +0200
Subject: [PATCH 06/83] cat_config: drop duplicate SP_v1.4.6 / SP_v1.3.6 keys,
 repair the survivor, guard against recurrence (#320)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* cat_config: drop duplicate SP_v1.4.6 / SP_v1.3.6 blocks, repair the survivor

`cosmo_val/cat_config.yaml` carried two top-level `SP_v1.4.6:` keys and two
`SP_v1.3.6:` keys. PyYAML keeps the last, so the second block of each pair
was what every consumer saw — and both were stale. They arrived in merge
c22f07568, which resolved a conflict by keeping both sides (and renamed the
dead `SSP_v1.4.6_msel` key to `SP_v1.4.6` in the process).

Delete the shadowing blocks and repair the surviving `SP_v1.4.6` to the
post-aa774b65d convention:

  * `cov_th` stays at A = 2894.03 deg^2 / n_e = 5.0935 (the nside-4096
    footprint mask every other live entry uses), not the 2405 / 6.128 pair
    the shadowing block held.
  * `shear.redshift_distr` -> `shear.redshift_path`: the code only ever reads
    `redshift_path`, so the v1.4.6 n(z) was under a key nothing reads while
    the winning block pointed at the v1.0-era `dndz_SP_A.txt`.
  * `shear.mask` (read nowhere) -> entry-level `mask:` pointing at
    `mask_map_footprint_nside_4096.fits`, matching SP_v1.4.5 / SP_v1.4.6.3
    and the mask `A` was measured from.
  * `shear.R: 1.0` — the delivered e1/e2 columns already have the response
    applied; the shadowing block's `R: 0.92` double-counted it and inflated
    xi_pm by 1/R^2 = 18% against a covariance that never sees R.

`SP_v1.3.6`'s surviving block already carries the nside-4096 footprint mask;
its cov_th still holds the old 2405 / 6.128 pair and needs regenerating
separately.

* tests: reject duplicate keys in cat_config.yaml

`yaml.safe_load` silently keeps the last of a repeated key, so a merge that
resolves a conflict by keeping both sides leaves the shadowed block invisible
to every consumer *and* to every test. That is how two `SP_v1.4.6` and two
`SP_v1.3.6` entries survived on develop.

Add a `yaml.SafeLoader` subclass whose `construct_mapping` raises on a
repeated key, and assert `cosmo_val/cat_config.yaml` parses clean with it.
Fails on the pre-fix config with `duplicate key 'SP_v1.3.6' at line 522
(first seen at line 148)`.

* cat_config: recompute SP_v1.3.6 cov_th (A, n_e, sigma_e) for the nside-4096 footprint

aa774b65d repointed SP_v1.3.6's mask to the 2894 deg² footprint but left its
cov_th holding v1.4.6's old numbers. Recomputed from v1.3.6's own catalogue
with sp_validation.survey; the same code path reproduces SP_v1.4.6.3's
committed A / n_e / sigma_e bit-for-bit. n_psf left as it was.
---
 cosmo_val/cat_config.yaml                     | 96 +------------------
 .../test_cat_config_no_duplicate_keys.py      | 72 ++++++++++++++
 2 files changed, 77 insertions(+), 91 deletions(-)
 create mode 100644 src/sp_validation/tests/test_cat_config_no_duplicate_keys.py

diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index cae6411b..49cdfcc9 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -153,10 +153,10 @@ SP_v1.3.6:
   ls: dashed
   marker: h
   cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
+    A: 2894.0303815287743
+    n_e: 4.002931781928292
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.3785853255980945
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -445,6 +445,7 @@ SP_v1.4.6:
     n_e: 5.09348802763124
     n_psf: 0.752316232272063
     sigma_e: 0.379587601488189
+  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -461,8 +462,7 @@ SP_v1.4.6:
   shear:
     R: 1.0
     path: v1.4.6/unions_shapepipe_cut_struc_2024_v1.4.6.fits
-    redshift_distr: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6/nz_SP_v1.4.6_A.txt
-    mask: v1.4.6/footprint_binned_8192.fits
+    redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6/nz_SP_v1.4.6_A.txt
     w_col: w_des
     e1_col: e1
     e1_col_corrected: e1_leak_corrected
@@ -519,92 +519,6 @@ SP_v1.4.6.3:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
-SP_v1.3.6:
-  subdir: /n17data/UNIONS/WL/v1.3.x
-  pipeline: SP
-  colour: green
-  getdist_colour: 0.0, 0.5, 1.0
-  ls: dashed
-  marker: d
-  cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
-    n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
-  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
-  psf:
-    PSF_flag: HSM_FLAG_PSF
-    PSF_size: HSM_T_PSF
-    star_flag: HSM_FLAG_STAR
-    star_size: HSM_T_STAR
-    hdu: 1
-    path: unions_shapepipe_psf_2022_v1.3.a.fits
-    ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: HSM_G1_PSF
-    e1_star_col: HSM_G1_STAR
-    e2_PSF_col: HSM_G2_PSF
-    e2_star_col: HSM_G2_STAR
-  shear:
-    R: 1.0
-    path: v1.3.6/unions_shapepipe_cut_struc_2022_v1.3.6.fits
-    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
-    w_col: w_des
-    e1_col: e1
-    e1_col_corrected: e1_leak_corrected
-    e1_PSF_col: e1_PSF
-    e2_col: e2
-    e2_col_corrected: e2_leak_corrected
-    e2_PSF_col: e2_PSF
-  star:
-    ra_col: RA
-    dec_col: Dec
-    e1_col: e1
-    e2_col: e2
-    path: unions_shapepipe_star_2022_v1.0.3.fits
-SP_v1.4.6:
-  subdir: /n17data/UNIONS/WL/v1.4.x
-  pipeline: SP
-  colour: green
-  getdist_colour: 0.0, 0.5, 1.0
-  ls: dashed
-  marker: d
-  cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
-    n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
-  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
-  psf:
-    PSF_flag: HSM_FLAG_PSF
-    PSF_size: HSM_T_PSF
-    star_flag: HSM_FLAG_STAR
-    star_size: HSM_T_STAR
-    hdu: 1
-    path: unions_shapepipe_psf_2024_v1.4.a.fits
-    ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: HSM_G1_PSF
-    e1_star_col: HSM_G1_STAR
-    e2_PSF_col: HSM_G2_PSF
-    e2_star_col: HSM_G2_STAR
-  shear:
-    R: 0.92
-    path: v1.4.6/unions_shapepipe_cut_struc_2024_v1.4.6.fits
-    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
-    w_col: w_des
-    e1_col: e1
-    e1_col_corrected: e1_leak_corrected
-    e1_PSF_col: e1_PSF
-    e2_col: e2
-    e2_col_corrected: e2_leak_corrected
-    e2_PSF_col: e2_PSF
-  star:
-    ra_col: RA
-    dec_col: Dec
-    e1_col: e1
-    e2_col: e2
-    path: unions_shapepipe_star_2024_v1.4.a.fits
 SP_v1.4.6.3_B:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
diff --git a/src/sp_validation/tests/test_cat_config_no_duplicate_keys.py b/src/sp_validation/tests/test_cat_config_no_duplicate_keys.py
new file mode 100644
index 00000000..1ab0df2e
--- /dev/null
+++ b/src/sp_validation/tests/test_cat_config_no_duplicate_keys.py
@@ -0,0 +1,72 @@
+"""Guard: ``cosmo_val/cat_config.yaml`` has no repeated mapping keys.
+
+PyYAML accepts duplicate keys silently, keeping the last occurrence. A merge
+that resolves a conflict by keeping both sides therefore produces a config
+where the *shadowed* block is invisible to every consumer and to every test
+that loads it with ``yaml.safe_load`` -- which is how two ``SP_v1.4.6`` and
+two ``SP_v1.3.6`` entries lived on ``develop`` with the stale ones winning.
+
+This loads the catalogue config with a loader that raises instead.
+
+:Author: cdaley
+
+"""
+
+from pathlib import Path
+
+import pytest
+import yaml
+
+pytestmark = pytest.mark.fast
+
+
+class DuplicateKeyError(ValueError):
+    """Raised when a YAML mapping repeats a key."""
+
+
+class UniqueKeySafeLoader(yaml.SafeLoader):
+    """``yaml.SafeLoader`` that rejects repeated keys instead of overwriting."""
+
+    def construct_mapping(self, node, deep=False):
+        mapping = {}
+        for key_node, value_node in node.value:
+            key = self.construct_object(key_node, deep=deep)
+            if key in mapping:
+                raise DuplicateKeyError(
+                    f"duplicate key {key!r} at line {key_node.start_mark.line + 1} "
+                    f"(first seen at line {mapping[key] + 1})"
+                )
+            mapping[key] = key_node.start_mark.line
+        return super().construct_mapping(node, deep=deep)
+
+
+def _repo_root() -> Path:
+    for parent in Path(__file__).resolve().parents:
+        if (parent / "pyproject.toml").exists():
+            return parent
+    raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
+
+
+def load_unique(path: Path):
+    """Parse ``path`` as YAML, raising ``DuplicateKeyError`` on repeated keys."""
+    with Path(path).open() as handle:
+        return yaml.load(handle, Loader=UniqueKeySafeLoader)
+
+
+def test_loader_rejects_duplicate_keys(tmp_path):
+    """The loader itself catches a repeated key (and ``safe_load`` does not)."""
+    config = tmp_path / "dup.yaml"
+    config.write_text("a:\n  x: 1\nb:\n  y: 2\na:\n  x: 3\n")
+
+    assert yaml.safe_load(config.read_text()) == {"a": {"x": 3}, "b": {"y": 2}}
+    with pytest.raises(DuplicateKeyError, match="duplicate key 'a'"):
+        load_unique(config)
+
+
+def test_cat_config_has_no_duplicate_keys():
+    """``cosmo_val/cat_config.yaml`` parses with no key shadowing another."""
+    config_path = _repo_root() / "cosmo_val" / "cat_config.yaml"
+    assert config_path.exists(), f"missing config: {config_path}"
+
+    config = load_unique(config_path)
+    assert config, "cat_config.yaml parsed empty"

From 7dd19847133a5d81244fa6ad1e37b3b97743ae1b Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Tue, 8 Sep 2026 16:11:39 +0200
Subject: [PATCH 07/83] Improvements to snakemake containerization (#309)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Fixes from the snakemake inference run report (#300)

Rules: drop the /automnt prefix from the hardcoded paths in xi_highres
(twopoint.smk) and covariance_glass_mock (covariance.smk). /automnt/nXXdataN
does not exist on the node that owns that disk, so a job landing there fails
immediately, before any log is written. Every canonical path in common.py
already uses the plain /nXXdataN form.

Docs: workflow/README.md gains a note on the /automnt trap and one on host
~/.local shadowing the container's pinned Snakemake; cosmo_inference/README.md
recommends CosmoSIS --mpi over the fragile upstream --smp process pool
(cosmosis#170) and cosmosis >= 3.16.1.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MS3u2QuVSK1Q2CVaxcPNxU

* workflow: profile-driven apptainer containerization (set A)

candide profile now owns the container: software-deployment-method:
apptainer + apptainer-args carries the bind mounts (matching the app
bash-function binds in the top-level UNIONS CLAUDE.md), replacing the
old rationale for leaving containerization to each rule. Rewrites the
profile's doc comment to describe the new model and its two documented
exceptions (xi_highres MPI, covariance_cosmocov host toolchain).

Adds a container_smoke rule (workflow/rules/container_smoke.smk +
scripts/container_smoke.py) as a cheap end-to-end check of the
profile-driven container path (editable sp_validation import, numpy
+ OMP_NUM_THREADS, git provenance) via `script:`, wired unconditionally
into workflow/Snakefile.

Reconciles image_sims/Snakefile's container: None comment: it now
documents that the two-image (SIF/SIF_PIPELINE) chain is a per-rule
container: choice in image_sims.smk, still wrapped by the profile's
apptainer deployment -- not a rule-owned apptainer exec call.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MS3u2QuVSK1Q2CVaxcPNxU

* workflow: profile-driven apptainer containerization (set B: image_sims)

Strip every rule's explicit apptainer exec wrapper (_EXEC_PREFIX/EXEC/
EXEC_PIPELINE) from image_sims.smk. Each compute rule now carries a
plain per-rule container: SIF / container: SIF_PIPELINE directive;
Snakemake wraps the shell: command via the profile's
software-deployment-method: apptainer + apptainer-args (set A).

PYTHONPATH/PSF_DICT/OMP_NUM_THREADS injection and the SLURM_* env
strip move from apptainer --env/-u flags to plain shell VAR=value /
env -u syntax at the front of each shell: string (_ENV_PREFIX) --
identical effect, no apptainer-specific mechanism, works the same
whether or not the command is container-wrapped.

im_mbias split into im_mbias_config (run:, host-side git/provenance
introspection + yaml write -- Snakemake never containerizes run:
regardless of container:, so this must stay a driver-side step) and
im_mbias (shell:, container: SIF, runs the actual m-bias compute).
This was the one rule whose apptainer call lived inside a run: block's
trailing shell() -- splitting it out is what makes container: apply
to it at all.

binds: dropped from image_sims config/schema -- it's now the profile's
apptainer-args (one bind list for the whole workflow), not a
per-run-config value.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MS3u2QuVSK1Q2CVaxcPNxU

* workflow: profile-driven apptainer containerization (set C: remaining rules + docs)

Completes the pivot for the rules outside image_sims: xi_highres and
covariance_cosmocov keep their container: None + inline apptainer exec /
host-toolchain call (multi-node MPI and a host-compiled binary respectively,
both genuinely incompatible with Snakemake's own container wrapping), now
documented in their rule docstrings as deliberate exceptions rather than
leftovers. No other rule outside image_sims called apptainer directly.

While touching these files, retired the stale /pure_eb/ absolute paths
left from the old repo layout: added workflow.common.WORKFLOW_SCRIPTS
(Path(__file__)-based, correct under both standalone and module-composed
runs) for the handful of shell: rules that call a workflow script directly,
and reused the existing COSMO_INFERENCE constant elsewhere. Removed the
run_cosmo_val rule in twopoint.smk, dead since cosmo_val.smk decomposed it
into per-diagnostic rules (its own docstring says so) and still pointing at
a stale path plus a nonsensical host .local PYTHONPATH injection. Flagged
(not fixed) covariance_process: it calls cosmo_inference/scripts/
cosmocov_process.py, deleted in the #236 cleanup and never restored, so the
rule fails on the default covariance target -- pre-existing, unrelated to
this pivot.

Rewrote workflow/README.md and cosmo_inference/README.md to the new model:
snakemake is a thin host-side tool pinned via `uv tool install snakemake
snakemake-executor-plugin-slurm`, run directly on the host, never from
inside an apptainer shell; the candide profile's software-deployment-method
puts each job in the container instead. Added a short pointer from the
top-level README's dev-shell instructions to workflow/README.md so the two
don't get conflated.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MS3u2QuVSK1Q2CVaxcPNxU

* workflow: fix script: directive under the profile-driven container

container_smoke failed for real (SLURM jobs 839818/839819) with
ModuleNotFoundError: snakemake.iocontainers -- the container image had its
own snakemake==9.16.3 pip-installed directly (leftover from the old
apptainer-shell-then-snakemake-inside pattern this pivot retires), shadowing
the host-mounted 9.23.1 orchestrator that script: bind-mounts in and
sys.path.extends (appended, not prepended). Removed it and its
snakemake-executor-plugin-slurm/-slurm-jobstep/-interface-* family from the
image (verified Required-by: none outside the family itself).

Separately, apptainer-args never actually isolated host tooling: the image's
own /.singularity.d/env/50-bashrc.sh unconditionally sourced the host
~/.bashrc for every apptainer action, not just an interactive `apptainer
shell` -- so a host dotfile (asdf init) ran on every exec too, pushing host
PATH entries (~/.local/bin) ahead of the image's own /usr/local/bin. A bare
`python` in any shell:/script: rule was silently running the host's
interpreter, invisibly, surviving --cleanenv. Gated the bashrc sourcing on
APPTAINER_COMMAND=shell (set by apptainer itself before these scripts run).

Fixing both surfaced a third, previously-masked bug: every script: rule
(19 files) imports `from snakemake.script import snakemake`, which is
IDE-hint-only in this snakemake version -- snakemake.script exposes no such
runtime attribute (only the Snakemake class), and the preamble that actually
gets pickled in already provides `snakemake` as a plain global before the
rest of the file executes. Removed the broken import repo-wide; the object
resolves via normal global lookup exactly as before, including inside the
functions/branches a few scripts defer it into.

Verified end to end: container_smoke now completes for real through SLURM
(jobid 839822, python 3.12.12, sp_validation editable install resolved,
correct HEAD commit read from inside the job) with no apptainer exec left in
any rule. Dry-run coverage for every rule that owns an edited script
(masks_only, cv_weights, and friends) shows clean DAGs.

The container-image invariants (no in-image snakemake, exec/run must not
source host dotfiles) aren't reproducible from this repo -- the sandbox at
/n17data/cdaley/containers/containers has no tracked build recipe -- so
they're now documented in workflow/README.md for the next rebuild.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MS3u2QuVSK1Q2CVaxcPNxU

* workflow: remove last live apptainer-exec shell call + stale sweep-script docs

presentation_pte_cosebis was the one rule left with an inline apptainer exec
in its shell: block (container: None override); every sibling presentation_*
rule already relies on the module-level container default. Drop the override
and the raw call so it's wrapped like the rest.

Also update the three sweep-script docstrings (run_xi_sweep,
run_cosebis_ptes_sweep, run_cl_sweep) whose example invocations still showed
the retired apptainer-exec-then-python pattern, to match the plain `python
script.py ...` convention already used by their sibling CLI scripts.

* profile: add --cleanenv to apptainer-args

Per-node smoke tests showed default env passthrough makes container
python resolution nondeterministic (host ~/.local shadowing — the #302
mechanism). --cleanenv makes every job's environment container-defined.
Verified: container_smoke green through the profile with it.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MS3u2QuVSK1Q2CVaxcPNxU

* tests: adapt pure-E/B and glass-mock tests to the tomographic API

calculate_pure_eb now returns one results dict per tomographic bin pair
(#297); the pure-E/B integration test still indexed the flat mode keys.
Unwrap the non-tomographic "tomo_bin_all_tomo_bin_all" entry and correct
the docstring that still advertised the flat return.

glass_mock's map path imports cosmology.compat.camb, which is absent in
the image, so the xfail's raises=AttributeError no longer matched.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QndBZicN3QvyZG4XDDPmGs

* docs: sweep comment bloat across the pivot diff

Remove the 'snakemake is injected' comment repeated in 19 script: files
(one note in workflow/README.md instead), trim the candide profile header
to the operational lessons, and cut re-narrations of the container model
in Snakefile/common.py/README.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* image_sims: one image, drop dead SLURM env strip, im_mbias_config as script

Collapse sif/sif_pipeline to the single sp_validation image (it ships
shapepipe; must be rebuilt from uv.lock — the current 2026-07-04 image
predates the lock and its numpy 2.5 breaks numba/ngmix). Remove the
env -u SLURM_* prefix: shapepipe#744 gates mpi4py on OMPI/PMI vars, and
--cleanenv strips the host env anyway (verified in-container). Convert
im_mbias_config from a run: block to script:, drop the redundant
os.makedirs, and trim pivot re-narration from comments.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* covariance: restore cosmocov_process.py as a containerized script: rule

Deleted in the #236 cleanup with no replacement; restored from history to
workflow/scripts/ (the rule is its only caller) and converted the rule
from shell: to script:. Fixes on the way: bare exit() on a non-PD matrix
returned 0 (Snakemake saw success) — now sys.exit(1); eigvalsh for the
symmetric matrix; Agg backend; plot dpi 2000 -> 300. Verified round-trip
on synthetic input in the container. Drop the NOTE and stale comments.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* tests: container_smoke becomes a real pytest, out of the main workflow

The rule asserted nothing and put a non-scientific artifact in every
paper's results/. Now: tests/data/container_smoke/{Snakefile,script}
driven by test_container_smoke.py (@slow, skipped off-cluster), which
submits one tiny SLURM job through the committed candide profile and
asserts APPTAINER_CONTAINER is set (the job really ran in the image),
the editable install resolved, seed-42 eigh values match, and git works
inside the container. OMP_NUM_THREADS is recorded, not asserted — unset
is the profile's designed state.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* workflow: locate shell-invoked scripts via workflow.source_path

Replace the hand-rolled WORKFLOW_SCRIPTS constant with Snakemake's
first-class mechanism, which the docs specifically prescribe because
manual path construction breaks under module composition. The script
goes in input: (not params:, which would cause spurious reruns), so it
also becomes an honest dependency.

Fixes a live bug on the way: papers/bmodes unblinding_ceremony called
'python workflow/scripts/unblinding_ceremony.py', which from the paper
workdir resolves into the paper's own scripts/ dir -- stale since
091bba8 and only detectable at run time.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* workflow: use script: for single-process rules, source_path only for MPI

script: resolves relative to the .smk defining it, module composition
included, so it defeats the paper-workdir trap without making scripts
into input files -- and matches what the other 20+ rules already do.
Only xi_highres keeps source_path, where script: is structurally
impossible (snakemake would wrap the whole mpiexec line in one
container).

unblinding_ceremony was already written for script: -- its
_config_from_snakemake was dead code because the rule invoked it via
shell:, so it silently ran _config_from_cli, which re-derives paths
from constants that no longer exist (a cosmo_val dir deleted from the
referenced checkout, and a _PROJECT_ROOT off by one since the script
moved to papers/bmodes/scripts/). The rule declared 6 inputs and 9
params while passing 2 on the command line.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* papers/bmodes: delete the unblinding ceremony

Never used, and silently broken: the rule invoked the script via shell:,
so the script's snakemake branch was dead code and its CLI branch
re-derived paths from constants that no longer exist. Nothing imported
it and no rule consumed its output.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* containers: run the CI-published image from one shared path

CI builds ghcr.io/cosmostat/sp_validation on every push from uv.lock;
the hand-built SIFs it replaces were stale in ways that only failed at
run time (no shapepipe.modules in one, numpy 2.5 breaking numba in the
other). Every call site -- the Snakefiles, image_sims, the MPI rule's
own apptainer exec, the paper shell drivers, interactive use -- now
names one file, refreshed deliberately (see workflow/README.md).
Overridable with --config container=<path or docker:// URI>.

im_mbias_config read the OCI revision by opening the configured sif
path; it now reads APPTAINER_CONTAINER, since the image a job actually
ran in may be overridden and current.sif is a moving target.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* deps: ship CosmoSIS in the image, drop the vestigial cosmology pin

CosmoSIS was an undeclared, user-supplied dependency of the inference
step -- which is why #303 was hand-patched in someone's ~/.local. It
pip-installs into the image against the base gfortran/GSL/cfitsio in
~2 min, so declare it. MPIFC must be set at build time or the sampler
Makefiles silently skip the MPI targets and --mpi fails at load; chains
must run under MPI because the upstream --smp pool is still broken at
3.25.2.

cosmology 2022.10.9 was vestigial: the cosmology.compat.camb adapter
comes from cosmology-compat-camb via glass[examples]. Relocking drops
it and nothing else. UV_PYTHON pins uv to the image's own interpreter,
since $HOME is bind-mounted and uv would otherwise pick a host CPython
carrying none of the stack.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* containers: pull the CI image by tag, park the MPI rule

Snakemake pulls docker://ghcr.io/cosmostat/sp_validation:develop into a
shared apptainer prefix on first use and never again, so no digest or
path is written down. One constant, CONTAINER_URI in workflow/common.py,
is the single source of truth; host-side callers that need a concrete
file (the paper shell drivers, interactive use) derive it via
workflow/scripts/container_path.py.

xi_highres is parked as a comment block: it has never been runnable --
its shell is a bare 'python run_2pcf_highres.py' while the script
requires --cat-config and --out -- and parking it leaves
covariance_cosmocov as the workflow's only container exception. The MPI
reasoning is preserved in the block for whoever revives it.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* tests: drop the glass map-path xfail, its condition is met

The xfail asked for a compatible glass+cosmology pair verified in a
fresh image. glass 2026.2 with cosmology-compat-camb is that pair: the
map path runs end to end (11 shells, 66 spectra, monotonic kappa
accumulation), so the marker now only hides regressions.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* docker: install liblapack-dev, pin MPIFC absolutely

cosmosis's bundled MultiNest links -llapack and the base image ships
only the runtime liblapack.so.3 with no dev symlink, so the build died
at 'cannot find -llapack'. It passed on candide only because that
sandbox had liblapack-dev installed at some point.

MPIFC takes the absolute path: /opt/ompi/bin is not always on PATH, and
a miss silently drops the MPI sampler libraries while the install still
reports success.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* docker: point UV_PYTHON at the venv, not the base interpreter

uv pip honours UV_PYTHON over VIRTUAL_ENV, so naming the system
interpreter sent the editable install of sp_validation there instead of
/app/.venv -- the image built fine and then failed its own import smoke
test. The venv's own python satisfies the original intent (uv can't
wander onto a host CPython from the bind-mounted $HOME) while keeping
uv pip pointed at the venv.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017hCncxwiiBZm9ixGgdzCNA

* workflow: run the launched checkout's sp_validation by default

Snakemake's `script:` directive already executes the checkout's script files,
while `import sp_validation` resolved to the image's baked copy -- the two
halves of one commit, split. `common.inject_checkout_pythonpath()` prepends the
checkout's src/ to APPTAINERENV_PYTHONPATH (preserving any user-set value), so
the image supplies the frozen dependency stack and the launched tree supplies
sp_validation. This is what the image-sims chain has always done for both repos
(`_ENV_PREFIX` in rules/image_sims.smk); the main workflow now matches it.

Opt out with `--config checkout_pythonpath=false` to reproduce from the image
alone. The flag is parsed tolerantly because `--config k=false` can arrive as
the string "false".

Also drops the hardcoded candide OpenMPI path from workflow/Snakefile: a machine
path does not belong in generic workflow code, and it moves to the candide
profile in a following commit.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* profiles: add a machine-independent default, make candide the machine layer

workflow/profiles/default carries the container model and nothing else, so the
workflow runs off candide with `--profile workflow/profiles/default -j N`.
Snakemake cannot compose profiles (one --profile, no `inherits:`), so the small
machine-independent set -- software-deployment-method, rerun-triggers,
latency-wait -- is duplicated verbatim in both files, marked GENERIC and
cross-referenced. That is the least-magic arrangement available.

apptainer-args and apptainer-prefix stay out of the shared block: every machine
has its own disks and image cache. candide's apptainer-args now carries
`--env LD_LIBRARY_PATH=/softs/openmpi/...`, previously an os.environ line in
workflow/Snakefile.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* presentation: containerize the two rules that ran bare python

The Moriond talk-figure rules called `python` with no container, so they ran
against whatever interpreter the driver happened to have. Let them inherit the
module-level `container:` like every other rule.

The two ImageMagick `convert` rules keep `container: None`: `convert` is a host
tool, absent from the image. Same for covariance_cosmocov, whose docstring says
so directly rather than pointing at a list elsewhere.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* image_sims: default sif to null and fall back to the workflow's image

`image_sims: {sif: ...}` was a required structural key, so every run config
repeated the image path — a second place for it to drift from what the rest of
the workflow runs. Default it to null and resolve it through the same code path
as every other entry point; a run config still overrides it to name its own
image or a branch tag.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* containers: give every user their own image, driven by spv-container

The workflow ran out of one shared image directory on candide
(/n17data/cdaley/containers/snakemake-sif) with a hand-maintained current.sif
symlink pointing at whatever Snakemake last autopulled. That only worked for one
person: refreshing the image or repointing the symlink needed write access to
another user's directory, and a refresh moved the ground under everyone at once.

Everyone now runs their own image file at one canonical per-user path,
~/.cache/sp_validation/sp_validation.sif (SPV_CONTAINER overrides it), owned by
a small CLI:

    spv-container pull            # fetch the tag there, atomically
    spv-container status          # revision label vs. this checkout's HEAD
    spv-container exec <cmd...>   # one-off run inside it, candide binds applied

sp_validation.container is stdlib-only on purpose: it runs on the host, outside
the container, so it must import without the scientific stack — and it works
straight from a checkout (`python3 src/sp_validation/container.py status`) with
nothing installed. It also holds CONTAINER_URI, which workflow/common.py loads
from this checkout by file path, so the CLI and the workflow can never name
different images.

`container:` now resolves to that local .sif when it exists and to the registry
tag otherwise (Snakemake accepts either, and autopulls the tag into
.snakemake/singularity). `--config container=...` still overrides both. The
candide profile drops apptainer-prefix accordingly, and common.configure() warns
— once, never fatally — when the local image predates the checkout.

Also drops workflow/scripts/container_path.py, which existed to locate the
shared cache.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs: tell the container story once, around the per-user image

Rewrites the container sections of workflow/README.md, CLAUDE.md,
CONTRIBUTING.md and README.md for the per-user model: one image per person at
~/.cache/sp_validation/sp_validation.sif, `spv-container` to fill and inspect it,
and how `container:` resolves to it. The shared-prefix machinery is gone --
current.sif bootstrap, the atomic-mv refresh recipe, the group-writable TODO.

Trims the commentary while there. The profile pair says "change one, change the
other" once instead of shouting it in three places; off-candide gets a paragraph
rather than parallel billing, since candide is where everyone runs; and the
enumeration of container exceptions is dropped in favour of the docstring on
each rule that opts out.

Also repoints the two paper Snakefiles, which resolve `container:` themselves,
at common.resolve_container -- and replaces the obsolete `apptainer build
--sandbox` recipe in README.md and installation.rst with `apptainer pull`.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* containers: add an opt-in writable sandbox, and one resolution order

The pristine SIF is read-only, which is what you want almost always -- but it
lost the one real advantage of the old hand-built sandbox workflow: `pip install`
mid-analysis, when you need a package the image does not carry yet and a CI
rebuild is too slow a loop to think in.

`spv-container sandbox` unpacks the image into a writable directory at
~/.cache/sp_validation/sandbox/, and `spv-container exec --writable` runs against
it so installs persist. Opt-in: nothing builds one for you.

Resolution order is now one thing, shared by the CLI, the run_*.sh drivers and
the workflow's `container:` -- sandbox if it exists, else SIF if it exists, else
the registry tag. Snakemake execs a sandbox directory as happily as a .sif, so a
package installed into the sandbox is there for workflow jobs too, with no
further wiring. `resolve_image()` in sp_validation.container is the single
implementation; common.resolve_container defers to it.

The build stages into a sibling directory and swaps it in, as `pull` does, for a
sharper reason than pull has: a half-written .sif fails loudly, but a
half-unpacked sandbox is still a *directory*, so resolution would elect it and
every job would silently run a broken tree. Building before removing also means a
`--force` rebuild that fails -- a typo in --source, a network blip -- leaves the
sandbox you already had intact, instead of deleting a working environment on the
way to not replacing it.

The cost of a sandbox is that what runs is no longer fully described by a
revision label, so the divergence is made visible rather than left silent:
`status` names which layer is live and says the revision only describes what the
sandbox was built from (falling back to the SIF's label, marked as inferred, when
the sandbox carries none), and the workflow prints one line at launch when a
sandbox is in play. `spv-container pull && spv-container sandbox --force` resets.

Verified on candide (apptainer 1.5.3): unprivileged `build --sandbox` works
through user namespaces with no fakeroot and no subuid mapping; `exec --writable`
persists writes while plain `exec` gets a read-only filesystem; `inspect
--labels` still reports the source image's OCI labels from a sandbox directory;
`--fix-perms` at build time is what keeps the tree removable afterwards (without
it apptainer leaves directories that defeat `rm -rf`, which would strand
`--force`); and a failed `--force` rebuild leaves the existing sandbox and its
contents untouched, with no staging directory left behind.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* image: build the cosmosis-standard-library fork into the container

The cosmo_inference .ini templates pointed COSMOSIS_DIR at two different
people's home directories (/home/guerrini/... and a scratch path of Lisa's), so
running the inference pipeline meant either being one of them or editing the
templates by hand. CosmoSIS itself already ships in the image via the `workflow`
extra; only the Standard Library — the tree of module files the pipelines name —
was missing.

Clone and build it at /opt/cosmosis-standard-library, pinned to Sacha Guerrini's
fork at b26fa7ff. That fork is 4 commits ahead of upstream and 373 behind;
the four are what the UNIONS pipelines need (tau statistics, sample_S8, two
z-dependent linear-alignment modules). Carrying them onto current upstream is
future work, noted in cosmo_inference/README.md.

The templates now read COSMOSIS_DIR from %(CSL_DIR)s, which the image sets —
CosmoSIS reads environment variables into an ini's [DEFAULT] section, which is
how the existing %(SCRATCH)s references already work. Off-image, export CSL_DIR
and the same templates work unchanged.

The build follows CSL's documented procedure for a pip-installed cosmosis
(`source cosmosis-configure && make`), but targets `shear/` rather than the
top-level `make`: the top level also descends into likelihood/, building the
Planck, WMAP and ACT likelihoods, which no UNIONS pipeline uses. Of the modules
our templates do name, all are pure Python except two under shear/ — `limber`,
which project_2d.py links, and cl_to_xi_nicaea's nicaea_interface.so.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs: prune duplicated and historical comments

The container model, the checkout-PYTHONPATH default, the profile GENERIC
mirroring and the CSL_DIR resolution were each explained in three to six
places. Give every concept one home -- workflow/README.md for the user-facing
story, the docstring of the thing itself for mechanism -- and leave pointers
elsewhere. Drop comments narrating what the code used to do; git holds that.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* simplify: collapse duplication added by this branch

The container model landed the same few lines in several places; fold each
into one home.

* the four run_*.sh sweep drivers resolved this user's image (and repeated
  the bind list) inline -- now one sourced papers/bmodes/scripts/container_env.sh,
  resolving exactly as sp_validation/container.py does (sandbox first, then
  SPV_CONTAINER/XDG_CACHE_HOME, binds from SPV_APPTAINER_BINDS)
* container.py: one _require_apptainer() instead of three copies of the PATH
  guard, and compare_revision's merge-base calls go through _git
* common.py: resolve_container takes the override value, so image_sims.smk
  no longer wraps IMSIM["sif"] in a synthetic config dict; drop the
  CONTAINER_URI / local_sif / local_sandbox re-exports, which have no callers
* cosmocov_process.py: only Snakemake runs it, so drop the argv entry point
  and its main() indirection, matching im_mbias_config.py
* xip_xim.py: one catalog() builder for the tomographic and non-tomographic
  paths instead of two near-identical treecorr.Catalog blocks

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* simplify: give the sweep drivers one shared preamble; fold a duplicated README section

The four papers/bmodes sweep drivers each repeated the worktree path, the
derived script/source dirs, and a full `apptainer exec ... /usr/local/bin/python`
invocation (six sites). container_env.sh now owns all of it and exposes
`spv_python` / `sweep_versions`; argv is byte-identical.

workflow/README.md explained `--config container=` twice, once for a local .sif
and once for a branch tag. One subsection now covers both.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* image: actually build CSL — cosmosis-configure exits 0 under set -u without running make

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01C856B4eJ3LwXrj9SCiEuMc

* image: drop set -u in the CSL layer — the configure exports append to unset paths

The test -f artifact guard is what keeps a no-op build loud; -u had become
the thing breaking the build instead.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01C856B4eJ3LwXrj9SCiEuMc

* image: point limber's Makefile at Debian's GSL (GSL_INC/GSL_LIB)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01C856B4eJ3LwXrj9SCiEuMc

* rebase onto develop: shed tomography-branch remnants

The container/workflow work is orthogonal to the tomography branch it was
accidentally based on. Restore develop's pure_eb docstring, test_cosmo_val
call shape, and glass_mock xfail; keep develop's glass==2025.1 pinned set
(cosmology 2022.10.9 is load-bearing there, not vestigial) and relock.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0143zcEsfSWr13AfSroMteEC

* docs: make spv-container the install story

README leads with the four-line install (clone, symlink onto PATH, pull,
exec-check); container.py gets a shebang + exec bit so the symlink is a real
CLI with no packaging. installation.rst carries the depth (subcommands,
per-user model, sandbox, raw apptainer/docker); CONTRIBUTING and
workflow/README point at the same symlink step.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0143zcEsfSWr13AfSroMteEC

* CSL runtime coverage: import-test template modules; patch fork for scipy>=1.15; add fast-pt

The image build only compiled CSL; pure-Python modules were never loaded
until a pipeline ran. Two runtime breaks shipped in a green image:
scipy>=1.15 removed scipy.special.lpn (legendre.py, reached by every
real-space likelihood via spec_tools -- #316), and project_2d.py imports
fastpt, which the image never carried.

- test_csl_modules.py imports every .py module the ini templates
  reference, in-image (skips without CSL_DIR/cosmosis)
- Dockerfile applies upstream a8a941d5 (lpn fix) as a patch until the
  fork absorbs it (#316)
- fast-pt>=3.2,<4 joins the workflow extra (4.0 restructured; CSL
  expects 3.x)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmGA86b7YyQn54JFj78ssM

* ruff autofix (format + safe lint fixes)

Pushed by the lint gate.

* Repoint CSL at the UNIONS-WL org fork; trim fast-pt annotation

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmGA86b7YyQn54JFj78ssM

* ruff autofix (format + safe lint fixes)

Pushed by the lint gate.

* lint: noqa E402 on the in-section container-smoke import

* Drop CSL scipy workaround after fork merge

Pin the container to the current UNIONS-WL fork main commit, remove the now-obsolete lpn patch, and update the inference documentation.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

Claude-Session: https://claude.ai/code/session_01XTNCvVQNLXZVTiDR1PZaVG

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 .gitignore                                    |    4 +
 CLAUDE.md                                     |   20 +-
 CONTRIBUTING.md                               |   46 +-
 Dockerfile                                    |   65 +-
 README.md                                     |   41 +-
 cosmo_inference/README.md                     |   34 +-
 .../templates/cosmosis_pipeline_A_ia.ini      |    4 +-
 .../templates/cosmosis_pipeline_A_ia_cell.ini |    4 +-
 .../templates/cosmosis_pipeline_A_psf.ini     |    4 +-
 docs/source/installation.rst                  |   57 +-
 papers/bmodes/Snakefile                       |    5 +-
 papers/bmodes/rules/presentation.smk          |   24 +-
 papers/bmodes/rules/synthesis.smk             |   36 +-
 papers/bmodes/scripts/container_env.sh        |   45 +
 .../scripts/filter_catalog_ellipticity.py     |    1 -
 .../bmodes/scripts/plot_pure_eb_covariance.py |    2 -
 papers/bmodes/scripts/run_cl_sweep.py         |    2 +-
 .../bmodes/scripts/run_cosebis_ptes_sweep.py  |    2 +-
 papers/bmodes/scripts/run_cov_sweep.sh        |   12 +-
 .../bmodes/scripts/run_pure_eb_ptes_sweep.sh  |   15 +-
 .../scripts/run_pure_eb_semianalytic.sh       |   15 +-
 papers/bmodes/scripts/run_pure_eb_sweep.sh    |   12 +-
 papers/bmodes/scripts/run_xi_sweep.py         |    2 +-
 papers/bmodes/scripts/unblinding_ceremony.py  | 1389 -----------------
 papers/cosmo_val/Snakefile                    |    5 +-
 pyproject.toml                                |   23 +-
 src/sp_validation/container.py                |  390 +++++
 .../tests/data/container_smoke/Snakefile      |   21 +
 .../data/container_smoke/container_smoke.py   |   87 ++
 .../tests/test_container_smoke.py             |  126 ++
 src/sp_validation/tests/test_csl_modules.py   |   74 +
 uv.lock                                       |  159 ++
 workflow/README.md                            |  234 ++-
 workflow/Snakefile                            |   15 +-
 workflow/common.py                            |  107 ++
 workflow/image_sims/Snakefile                 |   41 +-
 workflow/image_sims/config.yaml               |   53 +-
 workflow/profiles/candide/config.yaml         |  103 +-
 workflow/profiles/default/config.yaml         |   39 +
 workflow/rules/covariance.smk                 |   40 +-
 workflow/rules/glass_mock.smk                 |    2 +-
 workflow/rules/image_sims.smk                 |  316 ++--
 workflow/rules/twopoint.smk                   |   87 +-
 .../scripts/analyze_mask_power_spectrum.py    |    2 -
 workflow/scripts/cosmocov_process.py          |   73 +
 workflow/scripts/cv_additive_bias.py          |    1 -
 workflow/scripts/cv_cosebis.py                |    1 -
 workflow/scripts/cv_footprints.py             |    1 -
 workflow/scripts/cv_objectwise_leakage.py     |    1 -
 workflow/scripts/cv_plot_2pcf.py              |    1 -
 workflow/scripts/cv_plot_rho_stats.py         |    1 -
 workflow/scripts/cv_plot_tau_stats.py         |    1 -
 workflow/scripts/cv_pseudo_cl.py              |    1 -
 workflow/scripts/cv_pure_eb.py                |    1 -
 workflow/scripts/cv_ratio_xi_sys_xi.py        |    1 -
 workflow/scripts/cv_rho_tau_fits.py           |    1 -
 workflow/scripts/cv_summarize_bmodes.py       |    1 -
 workflow/scripts/cv_weights.py                |    1 -
 .../generate_glass_mock_rhotau_samples.py     |   73 +-
 workflow/scripts/im_mbias_config.py           |  105 ++
 workflow/scripts/process_mask.py              |    2 -
 workflow/scripts/run_rho_tau.py               |    2 -
 62 files changed, 2032 insertions(+), 2001 deletions(-)
 create mode 100644 papers/bmodes/scripts/container_env.sh
 delete mode 100644 papers/bmodes/scripts/unblinding_ceremony.py
 create mode 100755 src/sp_validation/container.py
 create mode 100644 src/sp_validation/tests/data/container_smoke/Snakefile
 create mode 100644 src/sp_validation/tests/data/container_smoke/container_smoke.py
 create mode 100644 src/sp_validation/tests/test_container_smoke.py
 create mode 100644 src/sp_validation/tests/test_csl_modules.py
 create mode 100644 workflow/profiles/default/config.yaml
 create mode 100644 workflow/scripts/cosmocov_process.py
 create mode 100644 workflow/scripts/im_mbias_config.py

diff --git a/.gitignore b/.gitignore
index a1a0394b..9c07b05b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -195,6 +195,10 @@ papers/cosmo_val/logs/
 
 # Ignore scratch notebooks
 scratch/*/*.ipynb
+
+# Snakemake run state
+.snakemake/
+
 # CI lint-gate scratch output (lint.yml writes these in the run tree; never commit)
 check.txt
 format.txt
diff --git a/CLAUDE.md b/CLAUDE.md
index e985d6c2..c35c20be 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -78,10 +78,26 @@ Main configuration in `scripts/calibration/params.py` with parameters:
 - pyccl for cosmological calculations
 
 ## Container Usage
-Recommended installation via Apptainer/Docker:
+Nothing is hand-built. CI publishes `ghcr.io/cosmostat/sp_validation:<branch>` on
+every push, and each person keeps their own copy at
+`~/.cache/sp_validation/sp_validation.sif`, managed by the `spv-container` CLI:
+
 ```bash
-apptainer build --sandbox sp_validation docker://ghcr.io/cosmostat/sp_validation:develop
+spv-container pull            # fetch :develop there (do it from a compute node)
+spv-container status          # which layer is live, and how current it is
+spv-container exec <command>  # one-off run inside it
 ```
 
+Need a package the image lacks mid-analysis? `spv-container sandbox`, then
+`spv-container exec --writable pip install <pkg>`; the sandbox then takes
+precedence over the SIF everywhere, workflow jobs included.
+
+Every rule runs inside that image, wrapped by Snakemake itself (`--profile
+workflow/profiles/candide` on the cluster, `workflow/profiles/default -j N`
+elsewhere). The `sp_validation` a rule imports comes from the *launched
+checkout*, not the image.
+
+`workflow/README.md` is the full story — profiles, image resolution, refresh.
+
 ## Notebook Configuration
 - The CosmologyValidation class must be initialized in cosmo_val
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 00a8d907..e385a386 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -13,15 +13,49 @@ inside the project container, which ships the full stack pre-built.
 
 ### Container (recommended)
 
+CI builds and pushes an image on **every** push, tagged by the sanitized branch
+name (see
+[`.github/workflows/deploy-image.yml`](.github/workflows/deploy-image.yml)), so
+`:develop` tracks the integration branch and your branch has an image of its
+own. Nothing is built by hand.
+
+You keep your own copy of the image. `spv-container` — stdlib-only, so it runs
+straight from a checkout: symlink it onto your PATH (`ln -s
+"$PWD/src/sp_validation/container.py" ~/.local/bin/spv-container`, the README's
+install step) or call it as `python3 src/sp_validation/container.py` — pulls it to
+`~/.cache/sp_validation/sp_validation.sif` and runs things inside it:
+
 ```bash
-# build a writeable sandbox from the published image
-apptainer build --sandbox sp_validation docker://ghcr.io/cosmostat/sp_validation:develop
-apptainer shell --writable sp_validation
+spv-container pull       # fetch :develop; ~1.5 GB, so do it from a compute node
+spv-container status     # which layer is live, and how current it is
+spv-container exec bash  # an interactive shell inside it
 ```
 
-The image is rebuilt and pushed on every push to `develop` (see
-[`.github/workflows/deploy-image.yml`](.github/workflows/deploy-image.yml)), so
-`:develop` always tracks the latest integration branch.
+That image is read-only. When you need a package it does not carry yet, unpack a
+writable sandbox once with `spv-container sandbox` and install into it with
+`spv-container exec --writable pip install <pkg>`; the sandbox then takes
+precedence everywhere, workflow jobs included. Treat it as an exploration tool —
+the real fix is adding the dependency to `pyproject.toml` — and reset it with
+`spv-container pull && spv-container sandbox --force`.
+
+Analysis runs through Snakemake, which wraps every job in `apptainer exec`
+against that same image for you — see
+[`workflow/README.md`](workflow/README.md) for the profiles and the details.
+
+Two things worth knowing while developing:
+
+- **Your checkout's code is what runs.** The workflow prepends the launched
+  checkout's `src/` to the container's `PYTHONPATH`, so the image supplies the
+  dependency stack and your working tree supplies `sp_validation`. No rebuild
+  needed to test a change. (Caveat: `rerun-triggers: code` does not watch
+  `src/`, so force reruns after editing a module.)
+- **To test a branch's own image** — when the *stack* changed, not just `src/` —
+  point the workflow at its CI tag:
+
+  ```bash
+  snakemake --profile workflow/profiles/candide \
+      --config container=docker://ghcr.io/cosmostat/sp_validation:my-branch <target>
+  ```
 
 ### Local install with `uv`
 
diff --git a/Dockerfile b/Dockerfile
index ccc930bf..79fdf0d8 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,6 +1,10 @@
 # Development image with more bells and whistles
 FROM ghcr.io/cosmostat/shapepipe:im_sims
 
+# liblapack-dev: cosmosis's MultiNest links -llapack, and the base image ships
+# only the runtime liblapack.so.3 (no dev symlink). The gsl/cfitsio/fftw3 dev
+# packages are what the CosmoSIS Standard Library's C sources compile against
+# (they are the headers CSL's own CI installs); git is for cloning it.
 RUN apt-get update -y --quiet --fix-missing && \
     apt-get dist-upgrade -y --quiet --fix-missing && \
     apt-get install -y --quiet \
@@ -8,14 +12,24 @@ RUN apt-get update -y --quiet --fix-missing && \
         automake \
         libtool \
         pkg-config \
+        git \
         htop \
         npm \
-        tmux
+        tmux \
+        liblapack-dev \
+        libgsl-dev \
+        libcfitsio-dev \
+        libfftw3-dev
 
 # The base shapepipe image provides a uv-managed venv at /app/.venv (exported as
 # VIRTUAL_ENV); install sp_validation's deps into that same venv rather than
 # spawning a second one under /sp_validation.
 ENV UV_PROJECT_ENVIRONMENT=/app/.venv
+# $HOME is bind-mounted under apptainer, so uv would otherwise discover the
+# host's managed CPythons -- including newer ones that satisfy requires-python
+# -- and build a venv against an interpreter carrying none of this stack.
+ENV UV_PYTHON=/app/.venv/bin/python \
+    UV_PYTHON_DOWNLOADS=never
 
 WORKDIR /sp_validation
 
@@ -30,9 +44,58 @@ WORKDIR /sp_validation
 # numba-safe numpy 2.4.6 come straight from the lock, so the old ad-hoc snakemake
 # and cs_util `--upgrade` layers are gone.
 COPY pyproject.toml uv.lock /sp_validation/
+
+# cosmosis builds MPI-enabled polychord/multinest only when MPIFC is set: its
+# setup.py exports MPIFC for conda builds only, and the sampler Makefiles gate on
+# `which $(MPIFC)`. Absolute path, not a bare name: /opt/ompi/bin is not always on
+# PATH, and a miss silently omits libchord_mpi.so while the install still succeeds,
+# and `cosmosis --mpi` fails at load time -- which is how the pipeline runs, since
+# the --smp pool is broken upstream. Must precede the sync that builds cosmosis.
+ENV MPIFC=/opt/ompi/bin/mpif90
+
 RUN uv sync --frozen --inexact --no-install-project \
     --extra test --extra glass --extra workflow
 
+# The CosmoSIS Standard Library: the module files (camb interface, projection,
+# 2pt likelihood, ...) the cosmo_inference pipelines name. The `workflow` extra
+# above installs cosmosis itself; CSL is a separate tree of modules that is not
+# on PyPI and has to be built against that install, so it is cloned and compiled
+# here rather than left to each user (which is what the .ini templates used to
+# assume, hard-coding one person's home directory).
+#
+# CSL_REF pins UNIONS-WL fork main: Sacha's four UNIONS commits reapplied
+# on current upstream, including the scipy lpn fix.
+ARG CSL_REPO=https://github.com/UNIONS-WL/cosmosis-standard-library.git
+ARG CSL_REF=b7b1552a02ad9c39c9bb1e68e3f17213a8f740e1
+ENV CSL_DIR=/opt/cosmosis-standard-library
+
+# `python -m cosmosis.configure` emits the exports (COSMOSIS_SRC_DIR et al.)
+# every CSL Makefile includes its compiler config from. Evaluated directly
+# rather than through the `cosmosis-configure` wrapper: that wrapper's
+# am-I-sourced probe reads unset zsh/ksh variables, which `set -u` turns into
+# an error, and its `exit` then ends the sourcing shell with status 0 — make
+# never runs and the layer still "succeeds". The trailing `test -f` keeps any
+# such silent no-op loud.
+#
+# `make -C shear` rather than a bare `make`: the top-level target also descends
+# into likelihood/, which builds the Planck, WMAP and ACT likelihoods -- large,
+# data-dependent, and unused by any UNIONS pipeline. Everything our .ini
+# templates reference is either pure Python (consistency, sample_S8, camb,
+# load_nz_fits, photoz_bias, linear_alignment, add_intrinsic, shear_m_bias,
+# xi_sys, 2pt_like -- no Makefile in those trees at all) or lives under shear/:
+# `limber`, which project_2d.py links, and `cl_to_xi_nicaea`, whose
+# nicaea_interface.so the 2pt_shear stage loads.
+RUN bash -c 'set -eo pipefail; \
+    export PATH=/app/.venv/bin:$PATH; \
+    git clone --filter=blob:none "$CSL_REPO" "$CSL_DIR"; \
+    cd "$CSL_DIR"; \
+    git checkout --detach "$CSL_REF"; \
+    cmds=$(python -m cosmosis.configure); \
+    eval "$cmds"; \
+    export GSL_INC=/usr/include GSL_LIB=/usr/lib/x86_64-linux-gnu; \
+    make -C shear; \
+    test -f shear/cl_to_xi_nicaea/nicaea_interface.so'
+
 # Install sp_validation itself (editable) into the same venv; deps are already
 # satisfied by the sync above.
 COPY . /sp_validation
diff --git a/README.md b/README.md
index e14a2071..455ddd91 100644
--- a/README.md
+++ b/README.md
@@ -62,30 +62,35 @@ directive imports the shared rules under each run's own config and an output
 `prefix`, so runs namespace under `results/<name>/` without clobbering one
 another.
 
-## Container Installation (Recommended)
+## Installation
 
-The easiest way to install sp_validation is via a container. Docker images are automatically built and pushed to the [GitHub Container Registry (GHCR)](https://github.com/CosmoStat/sp_validation/pkgs/container/sp_validation) on every push to `develop`. This image can be installed and run on most systems (including clusters) with just a few lines of code.
-
-We recommend running the image with **Apptainer** (formerly Singularity) which is installed on most HPC clusters. To simply run the image, use the following command:
+`sp_validation` runs from a pre-built container: CI builds an image carrying
+the full scientific stack on every push and publishes it to the
+[GitHub Container Registry](https://github.com/CosmoStat/sp_validation/pkgs/container/sp_validation).
+The bundled `spv-container` CLI installs and manages your personal copy of it:
 
 ```bash
-# build writeable "sandbox" container in the current directory
-# ./sp_validation will be a directory that functions like a vm
-apptainer build --sandbox sp_validation docker://ghcr.io/cosmostat/sp_validation:develop
-
-# open a shell in the container
-apptainer shell --writable sp_validation 
-# and confirm that the installation was successful
-python -c "import sp_validation"
-```
+git clone https://github.com/CosmoStat/sp_validation.git
+cd sp_validation
+ln -s "$PWD/src/sp_validation/container.py" ~/.local/bin/spv-container
 
-You can also run the image with **Docker**:
-
-```bash
-docker run --rm -it ghcr.io/cosmostat/sp_validation:develop python -c "import sp_validation"
+spv-container pull                                    # fetch the image (~1.5 GB)
+spv-container exec python -c "import sp_validation"   # confirm it works
 ```
 
-We do not currently build images for Apple Silicon/arm64; however the amd64 images should work on these systems, albeit with reduced performance.
+That is the whole install. `pull` puts the image at its canonical per-user
+path (`~/.cache/sp_validation/`), and everything else finds it there —
+`spv-container exec` for one-off commands (`spv-container exec bash` for an
+interactive shell) and the Snakemake workflow for cluster jobs.
+`spv-container status` says what you have and how current it is;
+`spv-container sandbox` gives you a writable copy for mid-analysis
+`pip install`s. On a cluster, run the pull from a compute node.
+
+To run the analysis workflow (`workflow/`), see
+[`workflow/README.md`](workflow/README.md): Snakemake runs on the host, and
+the profile puts each job in the container itself. For Docker, development
+installs, and more depth, see the
+[installation docs](https://cosmostat.github.io/sp_validation/installation.html).
 
 
 
diff --git a/cosmo_inference/README.md b/cosmo_inference/README.md
index 5d753010..d45eb148 100644
--- a/cosmo_inference/README.md
+++ b/cosmo_inference/README.md
@@ -4,15 +4,43 @@ by Lisa Goh and Sacha Guerrini, CEA Paris-Saclay
 This folder contains the files neccessary to run the cosmological inference pipeline on the UNIONS galaxy catalogues. 
 
 ### Requirements
-To run the pipeline, one would need to have installed [CosmoSIS](https://cosmosis.readthedocs.io/en/latest/). To sample the PSF leakage parameters, the fork of [cosmosis-standard-library](https://github.com/sachaguer/cosmosis-standard-library/) of Sacha Guerrini has to be used.
+Everything the pipeline needs ships in the container: nothing to install, and no
+paths to edit before a run.
+
+[CosmoSIS](https://cosmosis.readthedocs.io/en/latest/) comes in via the
+`workflow` extra, built with MPI support. The CosmoSIS Standard Library — the
+tree of modules the `.ini` pipelines name — is built into the image at
+`/opt/cosmosis-standard-library`, with `CSL_DIR` pointing there. CosmoSIS reads
+environment variables into an `.ini`'s `[DEFAULT]` section, so the templates'
+`COSMOSIS_DIR = %(CSL_DIR)s` resolves to it. Outside the container, export
+`CSL_DIR` at a build of your own and the same templates work unchanged.
+
+CSL is pinned to the **UNIONS-WL org fork**
+([UNIONS-WL/cosmosis-standard-library](https://github.com/UNIONS-WL/cosmosis-standard-library/))
+at `b7b1552a`, the fork's main branch: Sacha's four UNIONS commits are
+reapplied on current upstream, including the scipy `lpn` fix.
+
+Launch sampling under MPI (`mpiexec -n N cosmosis --mpi ...`), not `--smp`:
+CosmoSIS's shared-memory pool is unmaintained and still crashes after sampling
+completes (`Pool` has no attribute `data`, `runtime/process_pool.py`) as of
+3.25.2.
 
 ### To Run
-The inference pipeline is now orchestrated through Python. Run the main Snakemake workflow from the parent directory:
+The inference pipeline is orchestrated through Snakemake. On the candide
+cluster, drive it with the committed profile — see
+[`workflow/README.md`](../workflow/README.md) for the one-time
+`uv tool install` setup and the full explanation. From the repository root:
 
 ```bash
-snakemake -j<jobs> inference_fiducial
+snakemake --profile workflow/profiles/candide \
+    -s workflow/Snakefile \
+    inference_fiducial --configfile <run config>
 ```
 
+Off-cluster, drop `--profile` and add `-j <jobs>` instead. Each job runs
+inside the sp_validation container automatically — no `apptainer shell` or
+`apptainer exec` needed by hand.
+
 This will automatically execute all steps:
 1. Calculate 2PCF ($\xi_{pm}$) via `cosmo_val.py`
 2. Compute covariance matrices using CosmoCov <!--- LG: now obsolete, making way for OnCovariance cauclation instead --->
diff --git a/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia.ini b/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia.ini
index eb3ab166..656c6e5c 100644
--- a/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia.ini
+++ b/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia.ini
@@ -1,6 +1,8 @@
 #parameters used elsewhere in this file
 [DEFAULT]
-COSMOSIS_DIR = /n23data1/n06data/lgoh/scratch/cosmosis-standard-library_lisa
+# The CosmoSIS Standard Library; CSL_DIR comes from the environment, set in the
+# container (see cosmo_inference/README.md).
+COSMOSIS_DIR = %(CSL_DIR)s
 
 
 [pipeline]
diff --git a/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia_cell.ini b/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia_cell.ini
index 87f06064..4a365195 100644
--- a/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia_cell.ini
+++ b/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_ia_cell.ini
@@ -1,6 +1,8 @@
 #parameters used elsewhere in this file
 [DEFAULT]
-COSMOSIS_DIR = /home/guerrini/cosmosis-standard-library
+# The CosmoSIS Standard Library; CSL_DIR comes from the environment, set in the
+# container (see cosmo_inference/README.md).
+COSMOSIS_DIR = %(CSL_DIR)s
 
 
 [pipeline]
diff --git a/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_psf.ini b/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_psf.ini
index f9f4da51..341baf21 100644
--- a/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_psf.ini
+++ b/cosmo_inference/cosmosis_config/templates/cosmosis_pipeline_A_psf.ini
@@ -1,6 +1,8 @@
 #parameters used elsewhere in this file
 [DEFAULT]
-COSMOSIS_DIR = /home/guerrini/cosmosis-standard-library
+# The CosmoSIS Standard Library; CSL_DIR comes from the environment, set in the
+# container (see cosmo_inference/README.md).
+COSMOSIS_DIR = %(CSL_DIR)s
 
 
 [pipeline]
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
index f84acebc..56de599f 100644
--- a/docs/source/installation.rst
+++ b/docs/source/installation.rst
@@ -2,28 +2,59 @@ Installation
 ============
 
 ``sp_validation`` is **not** distributed on PyPI.
-Install it from a pre-built container, or check out the source with ``uv`` when you need to edit it.
+It runs from a pre-built container, managed by the bundled ``spv-container`` CLI; check out the source with ``uv`` only when you need to edit the package itself.
 
-Container (recommended)
------------------------
+Container via ``spv-container`` (recommended)
+---------------------------------------------
 
-Every push to ``develop`` builds an image carrying the full scientific stack and pushes it to the `GitHub Container Registry (GHCR)
-<https://github.com/CosmoStat/sp_validation/pkgs/container/sp_validation>`_.
+Every push builds an image carrying the full scientific stack and pushes it to the `GitHub Container Registry (GHCR)
+<https://github.com/CosmoStat/sp_validation/pkgs/container/sp_validation>`_, tagged by branch — ``:develop`` tracks the integration branch.
 The image runs on most systems, including HPC clusters, with no further setup.
+``spv-container`` installs your personal copy of it and manages it from then on:
 
-`Apptainer <https://apptainer.org>`_ (formerly Singularity) is installed on most clusters and is the path we recommend:
+.. code-block:: bash
+
+   git clone https://github.com/CosmoStat/sp_validation.git
+   cd sp_validation
+   ln -s "$PWD/src/sp_validation/container.py" ~/.local/bin/spv-container
+
+   spv-container pull                                    # fetch the image (~1.5 GB)
+   spv-container exec python -c "import sp_validation"   # confirm it works
+
+The symlink works because ``container.py`` is deliberately stdlib-only: it runs on the *host*, where the science stack is not installed.
+(Inside the container the same CLI is on ``PATH`` as a console script.)
+``pull`` requires `Apptainer <https://apptainer.org>`_ (formerly Singularity), which is installed on most clusters, and writes the image to one canonical per-user path, ``~/.cache/sp_validation/sp_validation.sif``.
+Each user owns their copy: you refresh it when you want to, and nobody else's refresh moves the ground under your running jobs.
+On a cluster, run the pull from a compute node — it moves ~1.5 GB.
+
+The subcommands:
 
 .. code-block:: bash
 
-   # Build a writeable "sandbox" container in the current directory.
-   # ./sp_validation is a directory that behaves like a small VM.
-   apptainer build --sandbox sp_validation docker://ghcr.io/cosmostat/sp_validation:develop
+   spv-container pull                     # fetch the published image to the canonical path
+   spv-container status                   # what is here, which commit built it, how current
+   spv-container exec <cmd...>            # run a command inside it (exec bash for a shell)
+   spv-container sandbox                  # unpack into a writable dir, for pip installs
+   spv-container exec --writable <cmd...> # ... with writes that persist
+
+``status`` compares the image's build commit against your checkout's ``HEAD``, so you always know whether a ``pull`` would refresh anything.
+The **sandbox** is the escape hatch for exploratory work that needs a package the image does not carry yet: once built, it takes precedence over the SIF everywhere — Snakemake workflow jobs included — until you reset with ``spv-container pull`` + ``spv-container sandbox --force``.
+
+Everything resolves the image in one order — sandbox if it exists, else your SIF, else the registry tag — and that includes the analysis workflow.
+How the workflow uses the image (Snakemake runs on the host; the profile puts each job in the container) is covered in ``workflow/README.md``.
+
+Other ways to run the image
+---------------------------
+
+The published image is a normal OCI image; ``spv-container`` is a convenience, not a gatekeeper.
+Run it directly with Apptainer:
+
+.. code-block:: bash
 
-   # Open a shell in the container, then confirm the install works.
-   apptainer shell --writable sp_validation
-   python -c "import sp_validation"
+   apptainer pull sp_validation.sif docker://ghcr.io/cosmostat/sp_validation:develop
+   apptainer shell sp_validation.sif
 
-The image also runs under Docker:
+or with Docker:
 
 .. code-block:: bash
 
diff --git a/papers/bmodes/Snakefile b/papers/bmodes/Snakefile
index 0adf66a1..c58663d6 100644
--- a/papers/bmodes/Snakefile
+++ b/papers/bmodes/Snakefile
@@ -4,8 +4,6 @@
 configfile: "config/config.yaml"
 configfile: "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/cat_config.yaml"
 
-container: "/n17data/cdaley/containers/containers"
-
 envvars:
     "PYTHONUNBUFFERED",
 
@@ -27,6 +25,9 @@ import common
 common.configure(config)
 from common import *
 
+# The one image every rule runs in; see common.resolve_container.
+container: common.resolve_container(config.get("container"))
+
 # Wildcard constraints — centralized in common.py, not in individual rule files
 wildcard_constraints:
     **WILDCARD_CONSTRAINTS
diff --git a/papers/bmodes/rules/presentation.smk b/papers/bmodes/rules/presentation.smk
index a9ef5dc1..c59f0aa3 100644
--- a/papers/bmodes/rules/presentation.smk
+++ b/papers/bmodes/rules/presentation.smk
@@ -59,7 +59,11 @@ rule talk_previews:
 
 
 rule talk_figure:
-    """Convert a single paper PDF to high-res PNG for the talk."""
+    """Convert a single paper PDF to high-res PNG for the talk.
+
+    `container: None` on purpose: ImageMagick's `convert` is a host tool and is
+    not installed in the sp_validation image.
+    """
     input:
         pdf=lambda w: TALK_FIGURES[w.name],
     output:
@@ -73,7 +77,11 @@ rule talk_figure:
 
 
 rule talk_figure_preview:
-    """Downscale a talk figure to < 1800px for safe AI reading."""
+    """Downscale a talk figure to < 1800px for safe AI reading.
+
+    `container: None` for the same reason as talk_figure: `convert` is a host
+    tool, absent from the image.
+    """
     input:
         f"{TALK_DIR}/images/{{name}}.png",
     output:
@@ -120,8 +128,6 @@ rule presentation_omega_m_difference:
         mock_summary="/n09data/guerrini/glass_mock_chains/summary_parameter_constraints_merged_v6.txt",
     output:
         f"{TALK_DIR}/images/omega_m_difference_config_harm.png",
-    container:
-        None
     shell:
         "python {TALK_DIR}/plot_omega_m_difference.py"
 
@@ -132,8 +138,6 @@ rule presentation_s8_scatter_mocks:
         mock_summary="/n09data/guerrini/glass_mock_chains/summary_parameter_constraints_merged_v6.txt",
     output:
         f"{TALK_DIR}/images/s8_scatter_config_vs_harmonic.png",
-    container:
-        None
     shell:
         "python {TALK_DIR}/plot_s8_scatter_mocks.py"
 
@@ -147,11 +151,5 @@ rule presentation_pte_cosebis:
         ],
     output:
         f"{TALK_DIR}/images/pte_cosebis_talk.png",
-    container:
-        None
     shell:
-        """
-        apptainer exec --bind /home,/scratch,/automnt,/n17data,/n23data1,/n09data \
-            /n17data/cdaley/containers/containers/ \
-            python {TALK_DIR}/plot_pte_cosebis_talk.py
-        """
+        "python {TALK_DIR}/plot_pte_cosebis_talk.py"
diff --git a/papers/bmodes/rules/synthesis.smk b/papers/bmodes/rules/synthesis.smk
index fb9208e1..64b60770 100644
--- a/papers/bmodes/rules/synthesis.smk
+++ b/papers/bmodes/rules/synthesis.smk
@@ -39,7 +39,7 @@ def _claim_outputs():
 # Paper Macros
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-localrules: xi_cosmology_paper, paper_macros, bmodes_paper_spec, all_tapestry, unblinding_ceremony
+localrules: xi_cosmology_paper, paper_macros, bmodes_paper_spec, all_tapestry
 
 rule xi_cosmology_paper:
     """Spec for B-mode reporting in configuration-space paper (Goh et al.).
@@ -128,40 +128,6 @@ rule bmodes_paper_spec:
 # Aggregate Targets
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-_CEREMONY_BLIND = config.get("ceremony_blind", "A")
-_CEREMONY_CHAIN_ROOT = "/n09data/guerrini/output_chains"
-_CEREMONY_COSMOSIS_DIR = "/home/guerrini/sp_validation/cosmo_inference/data"
-
-rule unblinding_ceremony:
-    """Generate unblinding ceremony figure sequence.
-
-    Via snakemake:
-        snakemake unblinding_ceremony --config ceremony_blind=B --nolock
-    Standalone:
-        app python workflow/scripts/unblinding_ceremony.py B
-    """
-    input:
-        xi_data=f"{_CEREMONY_COSMOSIS_DIR}/{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}/cosmosis_{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}.fits",
-        pure_eb=f"results/paper_plots/intermediate/{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_pure_eb_semianalytic.npz",
-        pseudo_cl=_pseudo_cl_path(FIDUCIAL_VERSION, blind=_CEREMONY_BLIND),
-        pseudo_cl_cov=_pseudo_cl_cov_path(FIDUCIAL_VERSION, blind=_CEREMONY_BLIND),
-        cosmosis_cell_fits=f"{_CEREMONY_COSMOSIS_DIR}/{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_fid/cosmosis_{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_fid_cell.fits",
-        bestfit_dir=f"{_CEREMONY_CHAIN_ROOT}/best_fit/{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_10_80/shear_xi_plus/theta.txt",
-    output:
-        evidence=f"{TAPESTRY_DIR}/unblinding_ceremony/evidence.json",
-    params:
-        blind=_CEREMONY_BLIND,
-        chain_version=FIDUCIAL_VERSION.replace("SP_", "").replace("_leak_corr", ""),
-        chain_prefix=FIDUCIAL_VERSION,
-        chain_root_dir=_CEREMONY_CHAIN_ROOT,
-        results_dir="results/unblinding",
-        bestfit_root_fid_cell=f"{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_fid_cell",
-        bestfit_root_halofit_cell=f"{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_halofit_cell",
-        bestfit_root_config=f"{FIDUCIAL_VERSION}_{_CEREMONY_BLIND}_10_80",
-    shell:
-        "python workflow/scripts/unblinding_ceremony.py {params.blind} --chain-version {params.chain_version}"
-
-
 rule all_tapestry:
     """Aggregate target for all claim evidence and paper outputs."""
     input:
diff --git a/papers/bmodes/scripts/container_env.sh b/papers/bmodes/scripts/container_env.sh
new file mode 100644
index 00000000..a63b40f5
--- /dev/null
+++ b/papers/bmodes/scripts/container_env.sh
@@ -0,0 +1,45 @@
+# Shared environment for the run_*.sh sweep drivers. Source, don't run:
+#
+#     . "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
+#
+# Sets the checkout the drivers run out of, the container to run in, and
+# `spv_python`, which is how every driver invokes python inside it.
+WT=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra
+SRC=$WT/src
+WSCRIPTS=$WT/workflow/scripts
+PSCRIPTS=$WT/papers/bmodes/scripts
+
+# CONTAINER and BIND are resolved exactly as `sp_validation/container.py` does:
+# the writable sandbox if there is one, else the SIF.
+_spv_cache=${XDG_CACHE_HOME:-$HOME/.cache}/sp_validation
+_spv_sandbox=${SPV_SANDBOX:-$_spv_cache/sandbox}
+if [ -d "$_spv_sandbox" ]; then
+  CONTAINER=$_spv_sandbox
+else
+  CONTAINER=${SPV_CONTAINER:-$_spv_cache/sp_validation.sif}
+fi
+BIND=${SPV_APPTAINER_BINDS:-/home,/scratch,/automnt,/n17data,/n23data1,/n09data}
+unset _spv_cache _spv_sandbox
+
+# Every math library pinned to one thread -- pass as SPV_EXEC_EXTRA where the
+# parallelism is by process, not by thread.
+SINGLE_THREAD_ENV="--env OMP_NUM_THREADS=1 --env OPENBLAS_NUM_THREADS=1
+  --env MKL_NUM_THREADS=1 --env NUMBA_NUM_THREADS=1 --env NUMEXPR_NUM_THREADS=1
+  --env VECLIB_MAXIMUM_THREADS=1"
+
+# Run python inside the container against the checkout's src. Extra
+# `apptainer exec` flags go in SPV_EXEC_EXTRA (word-split on purpose).
+spv_python() {
+  apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" ${SPV_EXEC_EXTRA:-} \
+    "$CONTAINER" /usr/local/bin/python "$@"
+}
+
+# Echo the version list a sweep runs over: $VERSIONS if the caller set one,
+# else whatever sweep_versions.py resolves from $1 (a config path).
+sweep_versions() {
+  if [ -n "${VERSIONS:-}" ]; then
+    echo "$VERSIONS"
+  else
+    spv_python "$PSCRIPTS/sweep_versions.py" --config "$1"
+  fi
+}
diff --git a/papers/bmodes/scripts/filter_catalog_ellipticity.py b/papers/bmodes/scripts/filter_catalog_ellipticity.py
index ade5dd6d..baab3ffb 100644
--- a/papers/bmodes/scripts/filter_catalog_ellipticity.py
+++ b/papers/bmodes/scripts/filter_catalog_ellipticity.py
@@ -18,7 +18,6 @@
     sys.stderr if hasattr(sys, "ps1") else open(sys.stderr.fileno(), "w", buffering=1)
 )
 
-from snakemake.script import snakemake  # noqa: E402
 
 input_path = snakemake.input["catalog"]
 output_fits = snakemake.output["catalog"]
diff --git a/papers/bmodes/scripts/plot_pure_eb_covariance.py b/papers/bmodes/scripts/plot_pure_eb_covariance.py
index 3fdba8db..5522ef59 100644
--- a/papers/bmodes/scripts/plot_pure_eb_covariance.py
+++ b/papers/bmodes/scripts/plot_pure_eb_covariance.py
@@ -27,8 +27,6 @@ def _load_snakemake():
             "results/paper_plots/pure_eb_covariance.png",
             str(Path.cwd()),
         )
-    from snakemake.script import snakemake
-
     return snakemake
 
 
diff --git a/papers/bmodes/scripts/run_cl_sweep.py b/papers/bmodes/scripts/run_cl_sweep.py
index 8cb2106e..d5fd6eb0 100644
--- a/papers/bmodes/scripts/run_cl_sweep.py
+++ b/papers/bmodes/scripts/run_cl_sweep.py
@@ -13,7 +13,7 @@
 ``mask:`` entry), so no per-version mask wiring is needed here. Serial over
 versions; the estimator uses the recipe's full core allocation per call.
 
-    apptainer exec ... /usr/local/bin/python run_cl_sweep.py \
+    python run_cl_sweep.py \
         --config .../config.yaml --cat-config .../cat_config.yaml \
         --nside 1024 --npatch 1 --binning powspace --nbins 32 --power 0.5 \
         --blind A --out <dir>
diff --git a/papers/bmodes/scripts/run_cosebis_ptes_sweep.py b/papers/bmodes/scripts/run_cosebis_ptes_sweep.py
index 26b3d996..df3b9b79 100644
--- a/papers/bmodes/scripts/run_cosebis_ptes_sweep.py
+++ b/papers/bmodes/scripts/run_cosebis_ptes_sweep.py
@@ -14,7 +14,7 @@
 config_space_pte_matrices.py adapts via ``_cosebis_matrix_from_npz`` — into
 ``--out``. Serial over versions (~25 min/version, 206 pairs).
 
-    apptainer exec ... /usr/local/bin/python run_cosebis_ptes_sweep.py \
+    python run_cosebis_ptes_sweep.py \
         --config .../config.yaml \
         --xi-sweep-dir <two_point/.../xi_sweep> \
         --cov-sweep-dir <covariance/.../cov_sweep> \
diff --git a/papers/bmodes/scripts/run_cov_sweep.sh b/papers/bmodes/scripts/run_cov_sweep.sh
index 88d7f593..f86f20dc 100755
--- a/papers/bmodes/scripts/run_cov_sweep.sh
+++ b/papers/bmodes/scripts/run_cov_sweep.sh
@@ -26,12 +26,7 @@
 #     [--blind A] [--versions "v1 v2 ..."]
 set -euo pipefail
 
-CONTAINER=/n17data/cdaley/containers/containers/
-WT=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra
-SRC=$WT/src
-WSCRIPTS=$WT/workflow/scripts
-PSCRIPTS=$WT/papers/bmodes/scripts
-BIND=/home,/scratch,/automnt,/n17data,/n23data1,/n09data
+. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
 
 CONFIG=""; CATCONFIG=""; PLANCK18=""; MASKBASE=""; OUT=""; BLIND="A"; VERSIONS=""
 MINSEP=0.5; MAXSEP=300.0; NBINS=1000
@@ -50,10 +45,7 @@ done
 
 mkdir -p "$OUT"
 
-if [ -z "$VERSIONS" ]; then
-  VERSIONS=$(apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" "$CONTAINER" \
-    /usr/local/bin/python "$PSCRIPTS/sweep_versions.py" --config "$CONFIG")
-fi
+VERSIONS=$(sweep_versions "$CONFIG")
 
 for ver in $VERSIONS; do
   base="covariance_${ver}_${BLIND}_g_minsep=${MINSEP}_maxsep=${MAXSEP}_nbins=${NBINS}_masked"
diff --git a/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh b/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
index 34ba5f7b..0ea3a862 100644
--- a/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
+++ b/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
@@ -19,12 +19,7 @@
 #     --out <output_dir> [--blind A] [--versions "v1 v2 ..."]
 set -euo pipefail
 
-CONTAINER=/n17data/cdaley/containers/containers/
-WT=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra
-SRC=$WT/src
-WSCRIPTS=$WT/workflow/scripts
-PSCRIPTS=$WT/papers/bmodes/scripts
-BIND=/home,/scratch,/automnt,/n17data,/n23data1,/n09data
+. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
 
 CONFIG=""; CATCONFIG=""; PUREEBSWEEP=""; COVSWEEP=""; OUT=""; BLIND="A"; VERSIONS=""
 while [ $# -gt 0 ]; do
@@ -42,10 +37,7 @@ done
 
 mkdir -p "$OUT"
 
-if [ -z "$VERSIONS" ]; then
-  VERSIONS=$(apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" "$CONTAINER" \
-    /usr/local/bin/python "$PSCRIPTS/sweep_versions.py" --config "$CONFIG")
-fi
+VERSIONS=$(sweep_versions "$CONFIG")
 
 for ver in $VERSIONS; do
   pureeb="$PUREEBSWEEP/${ver}_${BLIND}_pure_eb_semianalytic.npz"
@@ -55,8 +47,7 @@ for ver in $VERSIONS; do
     [ -f "$f" ] || { echo "MISSING upstream input for $ver: $f" >&2; exit 1; }
   done
   echo "[pure_eb_ptes_sweep] $ver"
-  apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" "$CONTAINER" \
-    /usr/local/bin/python "$PSCRIPTS/calculate_pure_eb_ptes.py" \
+  spv_python "$PSCRIPTS/calculate_pure_eb_ptes.py" \
     --version "$ver" --blind "$BLIND" \
     --pure-eb-data "$pureeb" --cov-integration "$covint" \
     --npatch 1 --n-samples 2000 --out "$OUT"
diff --git a/papers/bmodes/scripts/run_pure_eb_semianalytic.sh b/papers/bmodes/scripts/run_pure_eb_semianalytic.sh
index 609f2a18..472cd81d 100644
--- a/papers/bmodes/scripts/run_pure_eb_semianalytic.sh
+++ b/papers/bmodes/scripts/run_pure_eb_semianalytic.sh
@@ -14,10 +14,7 @@
 #     --out <output_dir> [--n-chunks 20] [--n-samples 2000] [--nproc 16]
 set -euo pipefail
 
-CONTAINER=/n17data/cdaley/containers/containers/
-SRC=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra/src
-SCRIPTS=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra/papers/bmodes/scripts
-BIND=/home,/scratch,/automnt,/n17data,/n23data1,/n09data
+. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
 
 VERSION=""; BLIND="A"; CATCONFIG=""; XIREP=""; XIINT=""; COVINT=""; OUT=""
 NCHUNKS=20; NSAMPLES=2000; NPROC="${SLURM_CPUS_PER_TASK:-16}"
@@ -52,11 +49,8 @@ mkdir -p "$OUT/chunks"
 echo "[pure_eb] $NCHUNKS chunks, $NSAMPLES samples, nproc=$NPROC, version=$VERSION blind=$BLIND"
 for i in $(seq 0 $((NCHUNKS-1))); do
   (
-    apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" \
-      --env OMP_NUM_THREADS=1 --env OPENBLAS_NUM_THREADS=1 --env MKL_NUM_THREADS=1 \
-      --env NUMBA_NUM_THREADS=1 --env NUMEXPR_NUM_THREADS=1 --env VECLIB_MAXIMUM_THREADS=1 \
-      "$CONTAINER" \
-      /usr/local/bin/python "$SCRIPTS/precompute_pure_eb_chunk.py" \
+    SPV_EXEC_EXTRA=$SINGLE_THREAD_ENV
+    spv_python "$PSCRIPTS/precompute_pure_eb_chunk.py" \
       --chunk-id "$i" --n-chunks "$NCHUNKS" --n-samples "$NSAMPLES" \
       --version "$VERSION" --blind "$BLIND" --cat-config "$CATCONFIG" \
       --xi-reporting "$XIREP" --xi-integration "$XIINT" --cov-integration "$COVINT" \
@@ -76,8 +70,7 @@ done
 [ "$missing" -eq 0 ] || { echo "[pure_eb] chunk failures — aborting gather" >&2; exit 1; }
 echo "[pure_eb] all $NCHUNKS chunks done; gathering"
 
-apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" "$CONTAINER" \
-  /usr/local/bin/python "$SCRIPTS/gather_pure_eb_chunks.py" \
+spv_python "$PSCRIPTS/gather_pure_eb_chunks.py" \
   --version "$VERSION" --blind "$BLIND" \
   --xi-reporting "$XIREP" --xi-integration "$XIINT" \
   --chunks-dir "$OUT/chunks" \
diff --git a/papers/bmodes/scripts/run_pure_eb_sweep.sh b/papers/bmodes/scripts/run_pure_eb_sweep.sh
index 74b6dab3..8195f150 100755
--- a/papers/bmodes/scripts/run_pure_eb_sweep.sh
+++ b/papers/bmodes/scripts/run_pure_eb_sweep.sh
@@ -19,12 +19,7 @@
 #     --out <output_dir> [--blind A] [--versions "v1 v2 ..."]
 set -euo pipefail
 
-CONTAINER=/n17data/cdaley/containers/containers/
-WT=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra
-SRC=$WT/src
-WSCRIPTS=$WT/workflow/scripts
-PSCRIPTS=$WT/papers/bmodes/scripts
-BIND=/home,/scratch,/automnt,/n17data,/n23data1,/n09data
+. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
 
 CONFIG=""; CATCONFIG=""; XISWEEP=""; COVSWEEP=""; OUT=""; BLIND="A"; VERSIONS=""
 while [ $# -gt 0 ]; do
@@ -42,10 +37,7 @@ done
 
 mkdir -p "$OUT"
 
-if [ -z "$VERSIONS" ]; then
-  VERSIONS=$(apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" "$CONTAINER" \
-    /usr/local/bin/python "$PSCRIPTS/sweep_versions.py" --config "$CONFIG")
-fi
+VERSIONS=$(sweep_versions "$CONFIG")
 
 for ver in $VERSIONS; do
   xirep="$XISWEEP/${ver}_xi_minsep=1.0_maxsep=250.0_nbins=20_npatch=1.txt"
diff --git a/papers/bmodes/scripts/run_xi_sweep.py b/papers/bmodes/scripts/run_xi_sweep.py
index b02283a5..dd7219a3 100644
--- a/papers/bmodes/scripts/run_xi_sweep.py
+++ b/papers/bmodes/scripts/run_xi_sweep.py
@@ -13,7 +13,7 @@
 binning. Serial over versions — lc's dask handles cross-output concurrency, and
 TreeCorr already uses the recipe's full OpenMP allocation per call.
 
-    apptainer exec ... /usr/local/bin/python run_xi_sweep.py \
+    python run_xi_sweep.py \
         --config .../config.yaml --cat-config .../cat_config.yaml --out <dir>
 """
 
diff --git a/papers/bmodes/scripts/unblinding_ceremony.py b/papers/bmodes/scripts/unblinding_ceremony.py
deleted file mode 100644
index 61ab3a95..00000000
--- a/papers/bmodes/scripts/unblinding_ceremony.py
+++ /dev/null
@@ -1,1389 +0,0 @@
-#!/usr/bin/env python3
-"""Run the UNIONS unblinding ceremony figure sequence.
-
-Usage (standalone)
-------------------
-app python workflow/scripts/unblinding_ceremony.py A
-app python workflow/scripts/unblinding_ceremony.py A --output-dir /path/to/output
-
-Usage (snakemake)
------------------
-snakemake unblinding_ceremony --config ceremony_blind=A
-"""
-
-import argparse
-import json
-import shutil
-import sys
-from dataclasses import dataclass
-from datetime import datetime, timezone
-from pathlib import Path
-
-_SCRIPT_DIR = Path(__file__).resolve().parent
-if str(_SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(_SCRIPT_DIR))
-
-import matplotlib.pyplot as plt
-import numpy as np
-from astropy.io import fits
-from getdist import plots
-from matplotlib import scale as mscale
-from matplotlib.gridspec import GridSpec
-from plotting_utils import PAPER_MPLSTYLE, SquareRootScale
-from scipy.interpolate import interp1d
-
-# ── Plotting environment ────────────────────────────────────────────────────
-mscale.register_scale(SquareRootScale)
-plt.style.use(PAPER_MPLSTYLE)
-plt.rc("text", usetex=True)
-
-
-# ── Data types ──────────────────────────────────────────────────────────────
-
-
-@dataclass(frozen=True)
-class ChainSpec:
-    root: str
-    label: str
-    color: str
-    base_dir: Path
-    alpha: float = 1.0
-
-
-FULL_PARAMS = [
-    "OMEGA_M",
-    "ombh2",
-    "h0",
-    "n_s",
-    "SIGMA_8",
-    "s_8_input",
-    "logt_agn",
-    "a",
-    "m1",
-    "bias_1",
-]
-COSMO_PARAMS = ["OMEGA_M", "s_8_input", "SIGMA_8", "a"]
-
-MUTED_ALPHA = 0.25
-
-
-@dataclass
-class CeremonyConfig:
-    """All paths and parameters needed by the ceremony, sourced from snakemake or CLI."""
-
-    blind: str
-    chain_version: str
-    chain_prefix: str
-    chain_root_dir: Path
-    external_root_dir: Path
-    results_dir: Path
-    evidence_dir: Path
-
-    xi_data_path: Path
-    pure_eb_path: Path
-    pseudo_cl_path: Path
-    pseudo_cl_cov_path: Path
-    cosmosis_cell_fits: Path
-    bestfit_dir: Path
-    bestfit_root_fid_cell: str
-    bestfit_root_halofit_cell: str
-    bestfit_root_config: str
-
-
-def _save_path(cfg: CeremonyConfig, index: int, slug: str) -> Path:
-    return cfg.results_dir / f"{index:02d}_{slug}.pdf"
-
-
-# ── Chain loading (extracted from Sasha's notebooks) ────────────────────────
-
-
-def _load_xi_table(path: Path | str, nrows: int = 20) -> np.ndarray:
-    rows: list[np.ndarray] = []
-    with Path(path).open("r", encoding="utf-8") as handle:
-        for line in handle:
-            stripped = line.strip()
-            if not stripped or stripped.startswith("#"):
-                continue
-            values = np.fromstring(stripped, sep=" ")
-            if values.size < 9:
-                continue
-            rows.append(values)
-            if len(rows) >= nrows:
-                break
-    if len(rows) < nrows:
-        raise ValueError(
-            f"Expected at least {nrows} xi rows in {path}, found {len(rows)}"
-        )
-    return np.vstack(rows)
-
-
-def ensure_getdist_chain(base_dir: Path, root: str) -> Path:
-    """MAKE PARAMNAMES FILE + READ CHAIN conversion (from notebooks)."""
-    chain_dir = base_dir / root
-    samples_path = chain_dir / f"samples_{root}.txt"
-    gd_samples_path = chain_dir / f"getdist_{root}.txt"
-    paramnames_path = chain_dir / f"getdist_{root}.paramnames"
-
-    if not samples_path.exists():
-        return gd_samples_path
-
-    with samples_path.open("r", encoding="utf-8") as file:
-        params = file.readline()[1:].split("\t")[:-4]
-
-    with paramnames_path.open("w", encoding="utf-8") as file:
-        for param in params:
-            if len(param.split("--")) > 1:
-                file.write(param.split("--")[1] + "\n")
-            else:
-                file.write(param.split("--")[0] + "\n")
-
-    samples = np.loadtxt(samples_path)
-    if "nautilus" in root:
-        samples = np.column_stack(
-            (np.exp(samples[:, -3]), samples[:, -1] - samples[:, -2], samples[:, 0:-3])
-        )
-    else:
-        samples = np.column_stack((samples[:, -1], samples[:, -3], samples[:, 0:-4]))
-    np.savetxt(gd_samples_path, samples)
-    return gd_samples_path
-
-
-def _build_plotter(
-    width_inch: float,
-    axes_fontsize: float,
-    axes_labelsize: float,
-    legend_fontsize: float,
-):
-    g = plots.get_subplot_plotter(width_inch=width_inch)
-    g.settings.axes_fontsize = axes_fontsize
-    g.settings.axes_labelsize = axes_labelsize
-    g.settings.alpha_filled_add = 0.7
-    g.settings.legend_fontsize = legend_fontsize
-    return g
-
-
-def _set_param_labels(chain) -> None:
-    name_list = [
-        "OMEGA_M",
-        "ombh2",
-        "h0",
-        "n_s",
-        "SIGMA_8",
-        "S_8",
-        "s_8_input",
-        "logt_agn",
-        "a",
-        "m1",
-        "bias_1",
-    ]
-    label_list = [
-        r"\Omega_{\rm m}",
-        r"\omega_{\rm b} h^2",
-        r"h_0",
-        r"n_{\rm s}",
-        r"\sigma_8",
-        r"S_8",
-        r"S_8",
-        r"\log T_{\rm AGN}",
-        r"A_{\rm IA}",
-        r"m_1",
-        r"\Delta z_1",
-    ]
-
-    param_names = chain.getParamNames()
-    for name, label in zip(name_list, label_list):
-        try:
-            param_names.parWithName(name).label = label
-        except Exception:
-            pass
-
-    try:
-        param_names.parWithName("S_8")
-    except Exception:
-        try:
-            s8_input = chain.getParams().s_8_input
-            chain.addDerived(s8_input, name="S_8", label=r"S_8")
-        except Exception:
-            pass
-
-
-def _adjust_paramname_chain(
-    chain, current_name: str, target_name: str, label: str
-) -> None:
-    try:
-        param_names = chain.getParamNames()
-        par = param_names.parWithName(current_name)
-        par.label = label
-        par.name = target_name
-        chain.setParamNames(param_names)
-    except Exception:
-        pass
-
-
-def _derive_parameter_s8(chain):
-    if "S_8" in chain.getParamNames().list():
-        return chain
-    omega_m = chain.getParams().OMEGA_M
-    sigma_8 = chain.getParams().SIGMA_8
-    s_8 = sigma_8 * (omega_m / 0.3) ** 0.5
-    chain.addDerived(s_8, name="S_8", label=r"S_8")
-    return chain
-
-
-def _harmonize_external_chain(chain, root: str) -> None:
-    if root in {"Planck18", "KiDS-1000", "HSC_Y3", "HSC_Y3_cell", "DES+KiDS", "DES_Y3"}:
-        _adjust_paramname_chain(chain, "omega_m", "OMEGA_M", r"\Omega_{\rm m}")
-    if root == "DES_Y3":
-        _derive_parameter_s8(chain)
-
-
-def load_getdist_chains(
-    chain_specs: list[ChainSpec],
-    width_inch: float,
-    axes_fontsize: float,
-    axes_labelsize: float,
-    legend_fontsize: float,
-):
-    g = _build_plotter(
-        width_inch=width_inch,
-        axes_fontsize=axes_fontsize,
-        axes_labelsize=axes_labelsize,
-        legend_fontsize=legend_fontsize,
-    )
-
-    chains = []
-    for spec in chain_specs:
-        ensure_getdist_chain(spec.base_dir, spec.root)
-        chain = g.samples_for_root(
-            str(spec.base_dir / spec.root / f"getdist_{spec.root}"),
-            cache=False,
-            settings={"ignore_rows": 0, "smooth_scale_2D": 0.5, "smooth_scale_1D": 0.5},
-        )
-        _set_param_labels(chain)
-        if spec.base_dir.name == "ext_data" or spec.root in {
-            "Planck18",
-            "DES_Y3",
-            "KiDS-1000",
-            "DES+KiDS",
-            "HSC_Y3",
-            "HSC_Y3_cell",
-        }:
-            _harmonize_external_chain(chain, spec.root)
-        chains.append(chain)
-
-    return g, chains
-
-
-# ── Plot functions (extracted from Sasha's notebooks) ───────────────────────
-
-
-def plot_triangle(
-    chain_specs: list[ChainSpec],
-    param_names: list[str],
-    output_path: Path,
-    width_inch: float = 20.0,
-    axes_fontsize: float = 35.0,
-    axes_labelsize: float = 50.0,
-    legend_fontsize: float = 40.0,
-    legend_loc: str = "upper right",
-) -> None:
-    """Extracted triangle_plot pattern from contour notebooks."""
-    g, chains = load_getdist_chains(
-        chain_specs, width_inch, axes_fontsize, axes_labelsize, legend_fontsize
-    )
-
-    colours = [spec.color for spec in chain_specs]
-    linestyle = ["solid" for _ in chain_specs]
-    line_args = [dict(color=col, ls=ls) for col, ls in zip(colours, linestyle)]
-
-    g.triangle_plot(
-        chains,
-        param_names,
-        legend_labels=[spec.label for spec in chain_specs],
-        line_args=line_args,
-        contour_colors=colours,
-        legend_loc=legend_loc,
-        filled=True,
-    )
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    g.export(str(output_path))
-    plt.close(g.fig)
-
-
-def plot_xipm_data_vector(xipm_path: str, output_path: Path) -> None:
-    """Extracted from 2D_cosmic_shear_paper_plots/corr_func.ipynb (xi+ / xi- blocks)."""
-    xipm = _load_xi_table(xipm_path, nrows=20)
-    theta = xipm[:, 1]
-    xip = xipm[:, 3]
-    xim = xipm[:, 4]
-    varxip = xipm[:, 7]
-    varxim = xipm[:, 8]
-
-    fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(10, 4.5))
-
-    ax1.tick_params(
-        axis="both",
-        which="both",
-        direction="in",
-        length=6,
-        width=1,
-        top=True,
-        bottom=True,
-        left=True,
-        right=True,
-    )
-    ax1.yaxis.minorticks_on()
-    ax1.plot(
-        theta,
-        xip * 1e4,
-        marker="o",
-        markersize=4,
-        ls="solid",
-        lw=1.8,
-        color="royalblue",
-    )
-    ax1.fill_between(
-        theta, (xip - varxip) * 1e4, (xip + varxip) * 1e4, color="powderblue", alpha=0.7
-    )
-    ax1.text(
-        0.85,
-        0.88,
-        "1-1",
-        transform=ax1.transAxes,
-        bbox=dict(facecolor="white", edgecolor="black", boxstyle="round", pad=0.5),
-    )
-    ax1.axvspan(0, 10, color="gray", alpha=0.3)
-    ax1.axvspan(150, 200, color="gray", alpha=0.3)
-    ax1.set_xscale("log")
-    ax1.set_xlabel(r"$\theta$ [arcmin]")
-    ax1.set_ylabel(r"$\xi_+\times 10^4$")
-
-    ax2.tick_params(
-        axis="both",
-        which="both",
-        direction="in",
-        length=6,
-        width=1,
-        top=True,
-        bottom=True,
-        left=True,
-        right=True,
-    )
-    ax2.yaxis.minorticks_on()
-    ax2.plot(
-        theta,
-        xim * 1e4,
-        marker="o",
-        markersize=4,
-        ls="solid",
-        lw=1.8,
-        color="orangered",
-    )
-    ax2.fill_between(
-        theta, (xim - varxim) * 1e4, (xim + varxim) * 1e4, color="pink", alpha=0.7
-    )
-    ax2.text(
-        0.85,
-        0.88,
-        "1-1",
-        transform=ax2.transAxes,
-        bbox=dict(facecolor="white", edgecolor="black", boxstyle="round", pad=0.5),
-    )
-    ax2.axvspan(0, 10, color="gray", alpha=0.3)
-    ax2.axvspan(150, 200, color="gray", alpha=0.3)
-    ax2.set_xscale("log")
-    ax2.set_xlabel(r"$\theta$ [arcmin]")
-    ax2.set_ylabel(r"$\xi_-\times 10^4$")
-
-    fig.tight_layout()
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    fig.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-
-
-def plot_cell_ee_data_vector(
-    pseudo_cl_path: str, pseudo_cl_cov_path: str, output_path: Path
-) -> None:
-    """Extracted from 2025_10_08_plot_data_vectors.py (EE panel logic)."""
-    cell = fits.getdata(pseudo_cl_path)
-    cov_cell = fits.open(pseudo_cl_cov_path)
-
-    ell = cell["ell"]
-    cl_ee = cell["EE"]
-    cov_cl_ee = cov_cell["COVAR_EE_EE"].data
-    cov_cell.close()
-
-    fig, ax0 = plt.subplots(ncols=1, nrows=1, figsize=(7, 5))
-    ax0.errorbar(
-        ell,
-        cl_ee * ell,
-        yerr=np.sqrt(np.diag(cov_cl_ee)) * ell,
-        label=r"$C_\ell^{EE}$",
-        color="royalblue",
-        fmt="o",
-        capsize=2,
-    )
-
-    ax0.set_xscale("squareroot")
-    ax0.set_xticks(np.array([100, 400, 900, 1600]))
-    ax0.minorticks_on()
-    ax0.tick_params(axis="x", which="minor", length=2, width=0.8)
-    minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-    ax0.set_xticks(minor_ticks, minor=True)
-    ax0.legend()
-
-    ax0.set_xlabel(r"$\ell$")
-    ax0.set_ylabel(r"$\ell \, C_\ell^{EE}$")
-
-    plt.tight_layout()
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    plt.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-
-
-def plot_xipm_bestfit_with_bmodes(
-    xi_data_path: Path | str,
-    pure_eb_data_path: Path,
-    bestfit_dir: Path | None,
-    output_path: Path,
-    scale_cut_xip: tuple[float, float] = (12.0, 83.0),
-    scale_cut_xim: tuple[float, float] = (12.0, 83.0),
-) -> None:
-    """Extracted from 2D_cosmic_shear_paper_plots/workflow/scripts/plot_xi_bestfit.py.
-
-    If bestfit_dir is None, plots data + B-modes only (no theory curves).
-
-    xi_data_path can be:
-    - A CosmoSIS FITS file with XI_PLUS/XI_MINUS HDUs and COVMAT (preferred)
-    - A plain-text TreeCorr output table (legacy)
-    """
-    xi_path = Path(xi_data_path)
-    if xi_path.suffix == ".fits":
-        xip_hdu = fits.getdata(str(xi_path), "XI_PLUS")
-        xim_hdu = fits.getdata(str(xi_path), "XI_MINUS")
-        cov = fits.getdata(str(xi_path), "COVMAT")
-        theta_data = xip_hdu["ANG"]
-        xip_data = xip_hdu["VALUE"]
-        xim_data = xim_hdu["VALUE"]
-        n = len(xip_data)
-        sigma_xip = np.sqrt(np.diag(cov[:n, :n]))
-        sigma_xim = np.sqrt(np.diag(cov[n : 2 * n, n : 2 * n]))
-    else:
-        data = _load_xi_table(xi_path, nrows=20)
-        theta_data = data[:, 1]
-        xip_data = data[:, 3]
-        xim_data = data[:, 4]
-        sigma_xip = data[:, 7]
-        sigma_xim = data[:, 8]
-
-    eb_data = np.load(pure_eb_data_path)
-    theta_eb = eb_data["theta"]
-    xip_B = eb_data["xip_B"]
-    xim_B = eb_data["xim_B"]
-    cov_pure_eb = eb_data["cov_pure_eb"]
-
-    nbins = len(theta_eb)
-    sigma_xip_B = np.sqrt(
-        np.diag(cov_pure_eb[2 * nbins : 3 * nbins, 2 * nbins : 3 * nbins])
-    )
-    sigma_xim_B = np.sqrt(
-        np.diag(cov_pure_eb[3 * nbins : 4 * nbins, 3 * nbins : 4 * nbins])
-    )
-
-    min_sep, max_sep = 1.0, 250.0
-    bin_edges = np.geomspace(min_sep, max_sep, nbins + 1)
-    bin_centers_nominal = np.sqrt(bin_edges[:-1] * bin_edges[1:])
-
-    def get_bin_edge_cuts(centers, edges, scale_cut):
-        mask = (centers >= scale_cut[0]) & (centers <= scale_cut[1])
-        idx_first = np.where(mask)[0][0]
-        idx_last = np.where(mask)[0][-1]
-        return edges[idx_first], edges[idx_last + 1]
-
-    edge_cut_xip = get_bin_edge_cuts(bin_centers_nominal, bin_edges, scale_cut_xip)
-    edge_cut_xim = get_bin_edge_cuts(bin_centers_nominal, bin_edges, scale_cut_xim)
-
-    has_theory = bestfit_dir is not None
-    if has_theory:
-        theta_theory_rad = np.loadtxt(
-            bestfit_dir / "shear_xi_plus" / "theta.txt", comments="#"
-        )
-        theta_theory = np.rad2deg(theta_theory_rad) * 60
-        xip_theory = np.loadtxt(
-            bestfit_dir / "shear_xi_plus" / "bin_1_1.txt", comments="#"
-        )
-        xim_theory = np.loadtxt(
-            bestfit_dir / "shear_xi_minus" / "bin_1_1.txt", comments="#"
-        )
-
-        theta_sys_rad = np.loadtxt(bestfit_dir / "xi_sys" / "theta.txt", comments="#")
-        theta_sys = np.rad2deg(theta_sys_rad) * 60
-        xip_sys = np.loadtxt(bestfit_dir / "xi_sys" / "shear_xi_plus.txt", comments="#")
-        xim_sys = np.loadtxt(
-            bestfit_dir / "xi_sys" / "shear_xi_minus.txt", comments="#"
-        )
-
-    theta_fine = np.geomspace(0.5, 300, 500)
-    if has_theory:
-        xip_th_interp = interp1d(
-            theta_theory, xip_theory, kind="cubic", fill_value="extrapolate"
-        )(theta_fine)
-        xim_th_interp = interp1d(
-            theta_theory, xim_theory, kind="cubic", fill_value="extrapolate"
-        )(theta_fine)
-        xip_sys_interp = interp1d(
-            theta_sys, xip_sys, kind="cubic", fill_value="extrapolate"
-        )(theta_fine)
-        xim_sys_interp = interp1d(
-            theta_sys, xim_sys, kind="cubic", fill_value="extrapolate"
-        )(theta_fine)
-
-    scale_factor = 1e-4
-    xlim = [1, 250]
-    ylim = [-0.15, 1.25]
-
-    ms_data = 3
-    ms_bmode = 3
-    capsize = 1.5
-    elinewidth = 0.8
-
-    fig, axes = plt.subplots(1, 2, figsize=(10, 4.5), sharey=True)
-
-    plot_configs = [
-        (
-            axes[0],
-            xip_data,
-            sigma_xip,
-            xip_B,
-            sigma_xip_B,
-            xip_th_interp if has_theory else None,
-            xip_sys_interp if has_theory else None,
-            edge_cut_xip,
-            r"$\xi_+$",
-            "+",
-        ),
-        (
-            axes[1],
-            xim_data,
-            sigma_xim,
-            xim_B,
-            sigma_xim_B,
-            xim_th_interp if has_theory else None,
-            xim_sys_interp if has_theory else None,
-            edge_cut_xim,
-            r"$\xi_-$",
-            "-",
-        ),
-    ]
-
-    for idx, (
-        ax,
-        xi_data_arr,
-        sigma_xi,
-        xi_B,
-        sigma_B,
-        xi_th,
-        xi_sys_arr,
-        edge_cut,
-        label,
-        _pm,
-    ) in enumerate(plot_configs):
-        show_legend = idx == 1
-
-        ax.axvspan(xlim[0], edge_cut[0], color="0.90", zorder=0, alpha=0.7)
-        ax.axvspan(edge_cut[1], xlim[1], color="0.90", zorder=0, alpha=0.7)
-
-        if has_theory:
-            ax.plot(
-                theta_fine,
-                theta_fine * (xi_th + xi_sys_arr) / scale_factor,
-                "-",
-                color="k",
-                lw=1.5,
-                label=r"Best-fit $\xi^{\mathrm{th}}_\pm + \xi^{\mathrm{sys}}_\pm$"
-                if show_legend
-                else None,
-                zorder=2,
-            )
-            ax.plot(
-                theta_fine,
-                theta_fine * xi_sys_arr / scale_factor,
-                "-",
-                color="C0",
-                lw=1.2,
-                label=r"Best-fit $\xi^{\mathrm{sys}}_\pm$" if show_legend else None,
-                zorder=2,
-            )
-
-        ax.errorbar(
-            theta_data,
-            theta_data * xi_data_arr / scale_factor,
-            yerr=theta_data * sigma_xi / scale_factor,
-            fmt="o",
-            color="k",
-            markersize=ms_data,
-            capsize=capsize,
-            elinewidth=elinewidth,
-            label=r"$\xi_\pm$" if show_legend else None,
-            zorder=3,
-        )
-
-        theta_eb_offset = theta_eb * 1.03
-        ax.errorbar(
-            theta_eb_offset,
-            theta_eb_offset * xi_B / scale_factor,
-            yerr=theta_eb_offset * sigma_B / scale_factor,
-            fmt="o",
-            color="C3",
-            markersize=ms_bmode,
-            capsize=capsize,
-            elinewidth=elinewidth,
-            alpha=0.85,
-            label=r"$\xi^B_\pm$" if show_legend else None,
-            zorder=3,
-        )
-
-        ax.axhline(0, color="gray", linestyle="--", alpha=0.8, linewidth=0.8, zorder=1)
-        ax.set_xscale("log")
-        ax.set_xlim(xlim)
-        ax.set_ylim(ylim)
-        ax.set_xlabel(r"$\theta$ (arcmin)")
-        ax.set_title(label)
-        if show_legend:
-            ax.legend(loc="upper left")
-
-    axes[0].set_ylabel(r"$\theta\xi \times 10^4$")
-
-    fig.tight_layout()
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    fig.savefig(output_path, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-
-
-def _get_stats_row(chain, label: str, color: str) -> list[str | float]:
-    margestats = chain.getMargeStats()
-    try:
-        s8_stats = margestats.parWithName("S_8")
-    except Exception:
-        s8_stats = margestats.parWithName("s_8_input")
-    sigma8_stats = margestats.parWithName("SIGMA_8")
-    omegam_stats = margestats.parWithName("OMEGA_M")
-    return [
-        label,
-        color,
-        s8_stats.mean,
-        s8_stats.mean - s8_stats.limits[0].lower,
-        s8_stats.limits[0].upper - s8_stats.mean,
-        sigma8_stats.mean,
-        sigma8_stats.mean - sigma8_stats.limits[0].lower,
-        sigma8_stats.limits[0].upper - sigma8_stats.mean,
-        omegam_stats.mean,
-        omegam_stats.mean - omegam_stats.limits[0].lower,
-        omegam_stats.limits[0].upper - omegam_stats.mean,
-    ]
-
-
-def get_sigma_tension(mean1, low1, high1, mean2, low2, high2):
-    sigma1 = 0.5 * (high1 + low1)
-    sigma2 = 0.5 * (high2 + low2)
-    delta_mean = np.abs(mean1 - mean2)
-    sigma_tension = delta_mean / np.sqrt(sigma1**2 + sigma2**2)
-    sign = 1 if mean1 > mean2 else -1
-    return sigma_tension * sign
-
-
-def plot_cell_ee_with_bestfit(
-    cosmosis_data_path: str,
-    bestfit_specs: list[tuple[str, str, dict]],
-    output_folder: str,
-    output_path: Path,
-    ell_min: float = 10.0,
-    ell_max: float = 2048.0,
-    label_data: str = "Fiducial data",
-) -> None:
-    """Extracted from get_chi2_cell.ipynb plot_best_fit() (cell 21-22).
-
-    Parameters
-    ----------
-    cosmosis_data_path : str
-        Path to CosmoSIS FITS file with CELL_EE and COVMAT HDUs.
-    bestfit_specs : list of (label, root, line_args_dict)
-        Each entry is (legend label, chain root name, dict of plot kwargs).
-    output_folder : str
-        Base path to best_fit directories (e.g. /n09data/guerrini/output_chains/).
-    output_path : Path
-        Where to save the figure.
-    """
-    data = fits.getdata(cosmosis_data_path, "CELL_EE")
-    cov_mat = fits.getdata(cosmosis_data_path, "COVMAT")
-
-    fig, ax = plt.subplots(1, 1, figsize=(8, 5))
-
-    ell = data["ANG"]
-    cell = data["VALUE"]
-    ax.errorbar(
-        ell,
-        ell * cell,
-        yerr=ell * np.sqrt(np.diag(cov_mat)),
-        fmt="o",
-        label=label_data,
-        color="black",
-        capsize=2,
-    )
-
-    for label, root, line_kw in bestfit_specs:
-        ell_th = np.loadtxt(f"{output_folder}/best_fit/{root}/shear_cl/ell.txt")
-        shear_cl = np.loadtxt(f"{output_folder}/best_fit/{root}/shear_cl/bin_1_1.txt")
-        mask = (ell_th > ell_min) & (ell_th < ell_max)
-        ax.plot(ell_th[mask], ell_th[mask] * shear_cl[mask], label=label, **line_kw)
-
-    ax.axvline(x=1800, color="black", linestyle="--", alpha=0.5)
-    ax.axvline(x=2048, color="black", linestyle="--", alpha=1.0)
-    ax.axvline(x=500, color="black", linestyle="--", alpha=0.3)
-
-    ax.text(
-        1740,
-        0.90,
-        r"$k_\mathrm{max} = 3 h$ Mpc$^{-1}$",
-        transform=ax.get_xaxis_transform(),
-        ha="center",
-        va="top",
-        fontsize=10,
-        rotation=90,
-    )
-    ax.text(
-        1978,
-        0.90,
-        r"$k_\mathrm{max} = 5 h$ Mpc$^{-1}$",
-        transform=ax.get_xaxis_transform(),
-        ha="center",
-        va="top",
-        fontsize=10,
-        rotation=90,
-    )
-    ax.text(
-        470,
-        0.90,
-        r"$k_\mathrm{max} = 1 h$ Mpc$^{-1}$",
-        transform=ax.get_xaxis_transform(),
-        ha="center",
-        va="top",
-        fontsize=10,
-        rotation=90,
-    )
-
-    ax.set_ylabel(r"$\ell C_\ell$", fontsize=16)
-    ax.set_xlabel(r"$\ell$", fontsize=16)
-    ax.set_xlim(ell.min() - 10, ell.max() + 100)
-    ax.set_xscale("squareroot")
-    ax.set_xticks(np.array([100, 400, 900, 1600]))
-    ax.minorticks_on()
-    ax.tick_params(axis="x", which="minor", length=2, width=0.8)
-    minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-    ax.xaxis.set_ticks(minor_ticks, minor=True)
-    ax.tick_params(axis="both", which="major", labelsize=14)
-    ax.tick_params(axis="both", which="minor", labelsize=10)
-    ax.yaxis.get_offset_text().set_fontsize(14)
-
-    plt.legend(loc="lower center", bbox_to_anchor=(0.685, 0.70), fontsize=12)
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    plt.savefig(output_path, bbox_inches="tight")
-    plt.close(fig)
-
-
-def plot_s8_whisker(
-    chain_specs: list[ChainSpec],
-    output_path: Path,
-    reference_labels: list[str] | None = None,
-    reference_colors: list[str] | None = None,
-    reference_label: str | None = None,
-) -> None:
-    """Extracted from 2025_10_28_plot_whisker.ipynb (cells 5-11).
-
-    Supports multiple reference bands: pass reference_labels and
-    reference_colors as parallel lists. Each reference gets its own
-    shaded band in the corresponding color. For backwards compatibility,
-    a single reference_label still works.
-    """
-    if reference_labels is None and reference_label is not None:
-        reference_labels = [reference_label]
-        reference_colors = reference_colors or [None]
-
-    g, chains = load_getdist_chains(
-        chain_specs,
-        width_inch=30,
-        axes_fontsize=60,
-        axes_labelsize=60,
-        legend_fontsize=60,
-    )
-    plt.close(g.fig)
-
-    labels = [spec.label for spec in chain_specs]
-    colours = [spec.color for spec in chain_specs]
-    alphas = [spec.alpha for spec in chain_specs]
-
-    param_values = np.array(
-        [
-            [
-                "# Expt",
-                "Colour",
-                "S8_Mean",
-                "S8_low",
-                "S8_high",
-                "sigma_8_Mean",
-                "sigma_8_low",
-                "sigma_8_high",
-                "Omega_m_Mean",
-                "Omega_m_low",
-                "Omega_m_high",
-            ]
-        ],
-        dtype=object,
-    )
-
-    escaped_labels = np.char.replace(np.array(labels), "\\", "\\\\")
-    for i, chain in enumerate(chains):
-        row = _get_stats_row(chain, escaped_labels[i], colours[i])
-        param_values = np.vstack((param_values, row))
-
-    expt = np.char.replace(param_values[1:, 0].astype(str), "\\\\", "\\")
-    colours_arr = param_values[1:, 1].astype(str)
-    s8_mean = param_values[1:, 2].astype(np.float64)
-    s8_low = param_values[1:, 3].astype(np.float64)
-    s8_high = param_values[1:, 4].astype(np.float64)
-    sigma8_mean = param_values[1:, 5].astype(np.float64)
-    sigma8_low = param_values[1:, 6].astype(np.float64)
-    sigma8_high = param_values[1:, 7].astype(np.float64)
-    omegam_mean = param_values[1:, 8].astype(np.float64)
-    omegam_low = param_values[1:, 9].astype(np.float64)
-    omegam_high = param_values[1:, 10].astype(np.float64)
-
-    ref_indices = []
-    for rl in reference_labels or []:
-        matches = np.where(expt == rl)[0]
-        if len(matches):
-            ref_indices.append(matches[0])
-    ref_label_set = set(reference_labels or [])
-
-    n_rows = len(expt)
-    fig_height = max(6, 0.5 * n_rows + 1)
-    fig = plt.figure(figsize=(10, fig_height))
-    gs = GridSpec(1, 3, width_ratios=[1, 0.5, 0.5])
-    ax1 = fig.add_subplot(gs[0])
-    ax2 = fig.add_subplot(gs[1], sharey=ax1)
-    ax3 = fig.add_subplot(gs[2], sharey=ax1)
-
-    axs = [ax1, ax2, ax3]
-
-    params = [
-        (s8_mean, s8_low, s8_high, r"$S_8$"),
-        (sigma8_mean, sigma8_low, sigma8_high, r"$\sigma_8$"),
-        (omegam_mean, omegam_low, omegam_high, r"$\Omega_{\rm m}$"),
-    ]
-
-    row_spacing = 0.1
-    y = np.arange(len(expt))
-
-    for ax, param in zip(axs, params):
-        means, lows, highs, label = param
-        for i, mean, low, high, color, alpha in zip(
-            y, means, lows, highs, colours_arr, alphas
-        ):
-            ax.errorbar(
-                mean,
-                0.05 + i * row_spacing,
-                xerr=np.array([low, high])[:, None],
-                fmt="o",
-                color=color,
-                ecolor=color,
-                elinewidth=2,
-                capsize=3,
-                alpha=alpha,
-            )
-        ax.set_xlabel(label, fontsize=14)
-
-        for ri, ref_idx in enumerate(ref_indices):
-            band_color = (
-                reference_colors[ri]
-                if reference_colors and ri < len(reference_colors)
-                else colours_arr[ref_idx]
-            ) or colours_arr[ref_idx]
-            ax.axvspan(
-                means[ref_idx] - lows[ref_idx],
-                means[ref_idx] + highs[ref_idx],
-                color=band_color,
-                alpha=0.15,
-                zorder=0,
-            )
-
-        ax.grid(False)
-        ax.tick_params(axis="y", left=False, labelleft=False)
-        if label == r"$S_8$":
-            ax.set_xlim(0.25, 1.05)
-        elif label == r"$\sigma_8$":
-            ax.set_xlim(0.5, 1.2)
-        elif label == r"$\Omega_{\rm m}$":
-            ax.set_xlim(0.1, 0.5)
-
-    axs[0].set_yticks(0.05 + y * row_spacing)
-    axs[0].set_yticklabels([])
-    for label, color, alpha in zip(expt, colours_arr, alphas):
-        idx = np.where(expt == label)[0][0]
-        yloc = 0.05 + row_spacing * idx
-        axs[0].text(
-            0.26,
-            yloc,
-            label,
-            fontsize=12,
-            ha="left",
-            va="center",
-            color=color,
-            alpha=alpha,
-        )
-        if label not in ref_label_set and ref_indices:
-            ri0 = ref_indices[0]
-            s8_tension = get_sigma_tension(
-                s8_mean[idx],
-                s8_low[idx],
-                s8_high[idx],
-                s8_mean[ri0],
-                s8_low[ri0],
-                s8_high[ri0],
-            )
-            sign_str = "+" if s8_tension > 0 else "-"
-            axs[0].text(
-                1.045,
-                yloc,
-                rf"${sign_str}{np.abs(s8_tension):.2f}" + r"\, \sigma$",
-                fontsize=10,
-                ha="right",
-                va="center",
-                color=color,
-                alpha=alpha,
-            )
-
-    plt.gca().invert_yaxis()
-    plt.tight_layout()
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    plt.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-
-
-# ── Snakemake entry ──────────────────────────────────────────────────────────
-
-
-def _config_from_snakemake(smk) -> CeremonyConfig:
-    """Build config from snakemake.input / snakemake.output / snakemake.params."""
-    chain_root_dir = Path(smk.params.chain_root_dir)
-
-    return CeremonyConfig(
-        blind=smk.params.blind,
-        chain_version=smk.params.chain_version,
-        chain_prefix=smk.params.chain_prefix,
-        chain_root_dir=chain_root_dir,
-        external_root_dir=chain_root_dir / "ext_data",
-        results_dir=Path(smk.params.results_dir),
-        evidence_dir=Path(smk.output.evidence).parent,
-        xi_data_path=Path(smk.input.xi_data),
-        pure_eb_path=Path(smk.input.pure_eb),
-        pseudo_cl_path=Path(smk.input.pseudo_cl),
-        pseudo_cl_cov_path=Path(smk.input.pseudo_cl_cov),
-        cosmosis_cell_fits=Path(smk.input.cosmosis_cell_fits),
-        bestfit_dir=Path(smk.input.bestfit_dir).parent.parent,
-        bestfit_root_fid_cell=smk.params.bestfit_root_fid_cell,
-        bestfit_root_halofit_cell=smk.params.bestfit_root_halofit_cell,
-        bestfit_root_config=smk.params.bestfit_root_config,
-    )
-
-
-# ── CLI entry ────────────────────────────────────────────────────────────────
-
-_CHAIN_ROOT_DIR = Path("/n09data/guerrini/output_chains")
-_COSMOSIS_DATA_DIR = Path("/home/guerrini/sp_validation/cosmo_inference/data")
-_DEFAULT_CHAIN_VERSION = "v1.4.6"
-
-
-def _require_path(path: Path, label: str) -> Path:
-    if path.exists():
-        return path
-    raise FileNotFoundError(f"{label}: {path}")
-
-
-def _require_bestfit_root(chain_root_dir: Path, root: str) -> str:
-    if (chain_root_dir / "best_fit" / root / "shear_cl" / "ell.txt").exists():
-        return root
-    raise FileNotFoundError(f"No best-fit shear_cl for {root}")
-
-
-def _config_from_cli() -> CeremonyConfig:
-    """Build config from command-line arguments + path resolution.
-
-    Uses the exact data vectors from inference (Lisa's xi_pm, Sasha's pseudo-Cl
-    and CosmoSIS FITS) — the same files the chains were fit to.
-    """
-    _PROJECT_ROOT = _SCRIPT_DIR.parent.parent
-
-    parser = argparse.ArgumentParser(
-        description="Run the UNIONS unblinding ceremony plot sequence."
-    )
-    parser.add_argument("blind", choices=["A", "B", "C"], help="Revealed blind letter")
-    parser.add_argument(
-        "--chain-version",
-        default=_DEFAULT_CHAIN_VERSION,
-        help="Chain version (default: %(default)s)",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=Path,
-        default=None,
-        help="Output directory for results (default: <project_root>/results/unblinding)",
-    )
-    args = parser.parse_args()
-
-    blind = args.blind
-    chain_version = args.chain_version
-    chain_prefix = f"SP_{chain_version}_leak_corr"
-    output_dir = args.output_dir or (_PROJECT_ROOT / "results" / "unblinding")
-
-    xi_data_path = _require_path(
-        _COSMOSIS_DATA_DIR
-        / f"{chain_prefix}_{blind}"
-        / f"cosmosis_{chain_prefix}_{blind}.fits",
-        f"CosmoSIS xi FITS for blind {blind}",
-    )
-
-    pseudo_cl_path = _require_path(
-        Path(
-            f"/home/guerrini/sp_validation/cosmo_val/output/pseudo_cl_{chain_prefix}.fits"
-        ),
-        f"pseudo-Cl for {chain_prefix} (Guerrini)",
-    )
-    pseudo_cl_cov_path = _require_path(
-        Path(
-            f"/home/guerrini/sp_validation/cosmo_val/output/pseudo_cl_cov_{chain_prefix}.fits"
-        ),
-        f"pseudo-Cl covariance for {chain_prefix} (Guerrini)",
-    )
-
-    cosmosis_cell_fits = _require_path(
-        _COSMOSIS_DATA_DIR
-        / f"{chain_prefix}_{blind}_fid"
-        / f"cosmosis_{chain_prefix}_{blind}_fid_cell.fits",
-        f"CosmoSIS C_ell FITS for blind {blind}",
-    )
-
-    bestfit_root_config = f"{chain_prefix}_{blind}_10_80"
-    bestfit_dir = _require_path(
-        _CHAIN_ROOT_DIR / "best_fit" / bestfit_root_config,
-        f"Best-fit directory for blind {blind}",
-    )
-
-    return CeremonyConfig(
-        blind=blind,
-        chain_version=chain_version,
-        chain_prefix=chain_prefix,
-        chain_root_dir=_CHAIN_ROOT_DIR,
-        external_root_dir=_CHAIN_ROOT_DIR / "ext_data",
-        results_dir=output_dir,
-        evidence_dir=output_dir / "claims" / "unblinding_ceremony",
-        xi_data_path=xi_data_path,
-        pure_eb_path=_require_path(
-            _PROJECT_ROOT
-            / "results"
-            / "paper_plots"
-            / "intermediate"
-            / f"{chain_prefix}_{blind}_pure_eb_semianalytic.npz",
-            f"Pure E/B file for blind {blind}",
-        ),
-        pseudo_cl_path=pseudo_cl_path,
-        pseudo_cl_cov_path=pseudo_cl_cov_path,
-        cosmosis_cell_fits=cosmosis_cell_fits,
-        bestfit_dir=bestfit_dir,
-        bestfit_root_fid_cell=_require_bestfit_root(
-            _CHAIN_ROOT_DIR, f"{chain_prefix}_{blind}_fid_cell"
-        ),
-        bestfit_root_halofit_cell=_require_bestfit_root(
-            _CHAIN_ROOT_DIR, f"{chain_prefix}_{blind}_halofit_cell"
-        ),
-        bestfit_root_config=bestfit_root_config,
-    )
-
-
-# ── Main ceremony ────────────────────────────────────────────────────────────
-
-
-def run_ceremony(cfg: CeremonyConfig) -> None:
-    blind = cfg.blind
-    cfg.results_dir.mkdir(parents=True, exist_ok=True)
-
-    reveal_harmonic_root = f"{cfg.chain_prefix}_{blind}_lmin=300_lmax=1600_cell"
-    reveal_config_root = f"{cfg.chain_prefix}_{blind}_10_80"
-
-    # ── Act 1 — The Data (naked, then with fits) ──────────────────────
-
-    # 01: xi+/- data + B-modes, no theory
-    plot_xipm_bestfit_with_bmodes(
-        xi_data_path=cfg.xi_data_path,
-        pure_eb_data_path=cfg.pure_eb_path,
-        bestfit_dir=None,
-        output_path=_save_path(cfg, 1, "xi_pm_data"),
-    )
-
-    # 02: C_ell^EE data, no theory
-    plot_cell_ee_data_vector(
-        str(cfg.pseudo_cl_path),
-        str(cfg.pseudo_cl_cov_path),
-        _save_path(cfg, 2, "cell_ee_data"),
-    )
-
-    # 03: xi+/- with config-space best-fit (Paper IV Fig 1)
-    plot_xipm_bestfit_with_bmodes(
-        xi_data_path=cfg.xi_data_path,
-        pure_eb_data_path=cfg.pure_eb_path,
-        bestfit_dir=cfg.bestfit_dir,
-        output_path=_save_path(cfg, 3, f"xi_bestfit_blind_{blind}"),
-    )
-
-    # 04: C_ell^EE with harmonic + config best-fit (Paper V Fig 2)
-    plot_cell_ee_with_bestfit(
-        cosmosis_data_path=str(cfg.cosmosis_cell_fits),
-        bestfit_specs=[
-            (
-                rf"UNIONS $C_\ell$, Blind {blind}",
-                cfg.bestfit_root_fid_cell,
-                {"color": "royalblue", "linestyle": "-"},
-            ),
-            (
-                r"UNIONS $C_\ell$, Halofit",
-                cfg.bestfit_root_halofit_cell,
-                {"color": "royalblue", "linestyle": "--"},
-            ),
-            (
-                r"UNIONS $\xi_\pm(\vartheta)$ (Goh et al., 2026)",
-                cfg.bestfit_root_config,
-                {"color": "orange", "linestyle": "-"},
-            ),
-        ],
-        output_folder=str(cfg.chain_root_dir),
-        output_path=_save_path(cfg, 4, f"cell_ee_bestfit_blind_{blind}"),
-    )
-
-    # ── Act 2 — The Reveal ────────────────────────────────────────────
-
-    # 05: Consistency — Omega_m-S8, harmonic vs config vs Planck
-    plot_triangle(
-        [
-            ChainSpec(
-                root=reveal_harmonic_root,
-                label=rf"UNIONS $C_\ell$, Blind {blind}",
-                color="royalblue",
-                base_dir=cfg.chain_root_dir,
-            ),
-            ChainSpec(
-                root=reveal_config_root,
-                label=rf"UNIONS $\xi_\pm(\vartheta)$, Blind {blind}",
-                color="orange",
-                base_dir=cfg.chain_root_dir,
-            ),
-            ChainSpec(
-                root="Planck18",
-                label=r"\textit{Planck} 2018",
-                color="violet",
-                base_dir=cfg.external_root_dir,
-            ),
-        ],
-        ["OMEGA_M", "S_8"],
-        _save_path(cfg, 5, f"consistency_blind_{blind}"),
-        width_inch=12,
-        axes_fontsize=24,
-        axes_labelsize=28,
-    )
-
-    # 06: 4-param triangle — revealed blind, harmonic + config overlaid
-    plot_triangle(
-        [
-            ChainSpec(
-                root=reveal_harmonic_root,
-                label=rf"UNIONS $C_\ell$, Blind {blind}",
-                color="royalblue",
-                base_dir=cfg.chain_root_dir,
-            ),
-            ChainSpec(
-                root=reveal_config_root,
-                label=rf"UNIONS $\xi_\pm(\vartheta)$, Blind {blind}",
-                color="orange",
-                base_dir=cfg.chain_root_dir,
-            ),
-        ],
-        COSMO_PARAMS,
-        _save_path(cfg, 6, f"triangle_cosmo_blind_{blind}"),
-        width_inch=20,
-    )
-
-    # 07: Full-param triangle — revealed blind, harmonic + config overlaid
-    plot_triangle(
-        [
-            ChainSpec(
-                root=reveal_harmonic_root,
-                label=rf"UNIONS $C_\ell$, Blind {blind}",
-                color="royalblue",
-                base_dir=cfg.chain_root_dir,
-            ),
-            ChainSpec(
-                root=reveal_config_root,
-                label=rf"UNIONS $\xi_\pm(\vartheta)$, Blind {blind}",
-                color="orange",
-                base_dir=cfg.chain_root_dir,
-            ),
-        ],
-        FULL_PARAMS,
-        _save_path(cfg, 7, f"triangle_full_blind_{blind}"),
-        width_inch=30,
-        axes_fontsize=26,
-        axes_labelsize=28,
-    )
-
-    # ── Act 3 — In Context ────────────────────────────────────────────
-
-    # 08: S8 whisker — all 6 UNIONS blinds (3 harmonic + 3 config),
-    #     revealed blind highlighted, rest muted. Plus external surveys.
-    whisker_specs = []
-
-    for b in ("A", "B", "C"):
-        is_revealed = b == blind
-        alpha = 1.0 if is_revealed else MUTED_ALPHA
-        whisker_specs.append(
-            ChainSpec(
-                root=f"{cfg.chain_prefix}_{b}_lmin=300_lmax=1600_cell",
-                label=rf"UNIONS $C_\ell$, Blind {b}",
-                color="royalblue",
-                base_dir=cfg.chain_root_dir,
-                alpha=alpha,
-            )
-        )
-        whisker_specs.append(
-            ChainSpec(
-                root=f"{cfg.chain_prefix}_{b}_10_80",
-                label=rf"UNIONS $\xi_\pm$, Blind {b}",
-                color="orange",
-                base_dir=cfg.chain_root_dir,
-                alpha=alpha,
-            )
-        )
-
-    whisker_specs.extend(
-        [
-            ChainSpec(
-                root="Planck18",
-                label=r"\textit{Planck} 2018",
-                color="black",
-                base_dir=cfg.external_root_dir,
-            ),
-            ChainSpec(
-                root="DES_Y3",
-                label=r"DES Y3 $\xi_\pm$",
-                color="black",
-                base_dir=cfg.external_root_dir,
-            ),
-            ChainSpec(
-                root="KiDS-1000",
-                label=r"KiDS-1000 $\xi_\pm$",
-                color="black",
-                base_dir=cfg.external_root_dir,
-            ),
-            ChainSpec(
-                root="HSC_Y3",
-                label=r"HSC Y3 $\xi_\pm$",
-                color="black",
-                base_dir=cfg.external_root_dir,
-            ),
-        ]
-    )
-
-    plot_s8_whisker(
-        whisker_specs,
-        reference_labels=[
-            rf"UNIONS $C_\ell$, Blind {blind}",
-            rf"UNIONS $\xi_\pm$, Blind {blind}",
-        ],
-        reference_colors=["royalblue", "orange"],
-        output_path=_save_path(cfg, 8, f"s8_whisker_blind_{blind}"),
-    )
-
-    # ── Write evidence ───────────────────────────────────────────────
-
-    produced_figures = [
-        _save_path(cfg, 1, "xi_pm_data"),
-        _save_path(cfg, 2, "cell_ee_data"),
-        _save_path(cfg, 3, f"xi_bestfit_blind_{blind}"),
-        _save_path(cfg, 4, f"cell_ee_bestfit_blind_{blind}"),
-        _save_path(cfg, 5, f"consistency_blind_{blind}"),
-        _save_path(cfg, 6, f"triangle_cosmo_blind_{blind}"),
-        _save_path(cfg, 7, f"triangle_full_blind_{blind}"),
-        _save_path(cfg, 8, f"s8_whisker_blind_{blind}"),
-    ]
-    output_dict = {}
-    for fig_path in produced_figures:
-        output_dict[fig_path.stem] = fig_path.name
-
-    cfg.evidence_dir.mkdir(parents=True, exist_ok=True)
-
-    evidence = {
-        "id": "unblinding_ceremony",
-        "spec_id": "unblinding_ceremony",
-        "generated": datetime.now(timezone.utc).isoformat(timespec="seconds"),
-        "input": {
-            "xi_data": str(cfg.xi_data_path),
-            "pure_eb_data": str(cfg.pure_eb_path),
-            "pseudo_cl": str(cfg.pseudo_cl_path),
-            "pseudo_cl_cov": str(cfg.pseudo_cl_cov_path),
-            "cosmosis_cell_fits": str(cfg.cosmosis_cell_fits),
-            "harmonic_chains": str(
-                cfg.chain_root_dir
-                / f"{cfg.chain_prefix}_{{A,B,C}}_lmin=300_lmax=1600_cell"
-            ),
-            "config_chains": str(
-                cfg.chain_root_dir / f"{cfg.chain_prefix}_{{A,B,C}}_10_80"
-            ),
-            "external_chains": str(
-                cfg.external_root_dir / "{Planck18,DES_Y3,KiDS-1000,HSC_Y3}"
-            ),
-        },
-        "output": output_dict,
-        "params": {
-            "chain_version": cfg.chain_version,
-            "blind": blind,
-            "scale_cut_arcmin": "12-83",
-            "ell_range": "300-1600",
-            "n_figures": len(output_dict),
-        },
-        "evidence": {
-            "ceremony_date": "2026-02-27",
-            "script": "workflow/scripts/unblinding_ceremony.py",
-        },
-    }
-
-    evidence_path = cfg.evidence_dir / "evidence.json"
-    evidence_path.write_text(json.dumps(evidence, indent=2) + "\n")
-
-    for fig_path in produced_figures:
-        shutil.copy2(fig_path, cfg.evidence_dir / fig_path.name)
-
-    print(f"Saved ceremony figures to {cfg.results_dir.resolve()}")
-    print(f"Wrote evidence to {evidence_path}")
-
-
-# ── Entry point dispatch ─────────────────────────────────────────────────────
-
-try:
-    snakemake  # injected by snakemake's script: directive
-except NameError:
-    snakemake = None
-
-if snakemake is not None:
-    run_ceremony(_config_from_snakemake(snakemake))
-elif __name__ == "__main__":
-    run_ceremony(_config_from_cli())
diff --git a/papers/cosmo_val/Snakefile b/papers/cosmo_val/Snakefile
index 2585bb7a..716a3ac6 100644
--- a/papers/cosmo_val/Snakefile
+++ b/papers/cosmo_val/Snakefile
@@ -9,8 +9,6 @@
 configfile: "config/config.yaml"
 configfile: "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/cat_config.yaml"
 
-container: "/n17data/cdaley/containers/containers"
-
 envvars:
     "PYTHONUNBUFFERED",
 
@@ -32,6 +30,9 @@ import common
 common.configure(config)
 from common import *
 
+# The one image every rule runs in; see common.resolve_container.
+container: common.resolve_container(config.get("container"))
+
 # Wildcard constraints — centralized in common.py, not in individual rule files
 wildcard_constraints:
     **WILDCARD_CONSTRAINTS
diff --git a/pyproject.toml b/pyproject.toml
index c2a9aa2c..071945b1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -110,6 +110,13 @@ dependencies = [
 [project.urls]
 Homepage = "https://github.com/CosmoStat/sp_validation"
 
+[project.scripts]
+# Host-side management of your own container image (sp_validation.container).
+# Stdlib-only by design -- it has to run on the host, outside the container --
+# so it also works straight from a checkout with nothing installed:
+# `python3 src/sp_validation/container.py status`.
+spv-container = "sp_validation.container:main"
+
 [tool.uv]
 # The reproducibility target is the Linux container; scope the lock to Linux
 # (mirrors shapepipe) so `uv lock` resolves the linux-centric stack (pymaster,
@@ -132,8 +139,8 @@ docs = [
     "sphinxawesome-theme>=5.3,!=6.0.3"
 ]
 # GLASS mock generation (sp_validation.glass_mock). Kept optional: the core
-# library and its import guard resolve without GLASS, and the production
-# container does not yet ship it. Pinned set (verified 2026-06-13, fiber
+# library and its import guard resolve without GLASS (the production container
+# ships it via the ``glass`` extra). Pinned set (verified 2026-06-13, fiber
 # glass-cosmology-api-pin): glass 2025.1 is the unique version with the flat API
 # the map path uses AND the legacy ``cosmo.dc``/``xm``/``ef`` interface that
 # ``cosmology`` 2022.10.9 (its newest release) provides — newer glass calls
@@ -153,8 +160,18 @@ glass = [
 workflow = [
     "snakemake",
     # run_2pcf_highres.py drives the MPI convergence run; the container ships
-    # OpenMPI (/opt/ompi) so mpi4py builds against it.
+    # OpenMPI under /opt/ompi so mpi4py builds against it.
     "mpi4py",
+    # CosmoSIS drives the cosmo_inference sampling step. Sdist-only, so this is a
+    # source build (~2 min) against the base image's gfortran/GSL/cfitsio/OpenMPI —
+    # no extra apt packages needed. Floor at 3.25 simply to stay current; there is
+    # no known-good older version to pin back to. Note the shared-memory pool is
+    # still broken upstream at 3.25.2 (bcast/gather/allreduce in
+    # runtime/process_pool.py return an unassigned `self.data`), so chains must run
+    # under MPI -- which is why the Dockerfile sets MPIFC before syncing this extra.
+    "cosmosis>=3.25",
+    # CSL's project_2d.py imports fastpt (3.x API; 4.0 restructured it).
+    "fast-pt>=3.2,<4",
     # NOTE: workflow/scripts/cv_*.py also import `cv_runner`, which is not
     # published or resolvable (no public repo found) — left undeclared pending
     # its source. Same for `unions_wl` (scripts/check_footprint.py).
diff --git a/src/sp_validation/container.py b/src/sp_validation/container.py
new file mode 100755
index 00000000..7aa5f2ed
--- /dev/null
+++ b/src/sp_validation/container.py
@@ -0,0 +1,390 @@
+#!/usr/bin/env python3
+"""Manage this user's local copy of the sp_validation container image.
+
+Everyone runs their own image: the canonical paths are under your own cache, you
+refresh them when you want to, and nobody else's refresh moves the ground under
+a running job.
+
+There are two layers, and you only need the second when you want it:
+
+* the **SIF** (``~/.cache/sp_validation/sp_validation.sif``) -- a pristine,
+  read-only copy of the published image. This is the default and the normal case.
+* an optional **sandbox** (``~/.cache/sp_validation/sandbox/``) -- the same image
+  unpacked into a writable directory, so ``pip install`` inside it sticks. This
+  is the escape hatch for exploratory work that needs a package the image does
+  not carry yet, and it is opt-in: nothing builds one for you.
+
+Subcommands, exposed as the ``spv-container`` console script::
+
+    spv-container pull                     # fetch the tag to the canonical path
+    spv-container status                   # what is here, and how current is it
+    spv-container sandbox                  # unpack the SIF into a writable dir
+    spv-container exec <cmd...>            # run something inside it
+    spv-container exec --writable <cmd...> # ... with writes that persist
+
+Everything resolves the same image in the same order -- **sandbox if it exists,
+else the SIF, else the registry tag** -- and that includes the Snakemake
+workflow, so a package you installed into your sandbox is there for your
+workflow jobs too.
+
+This module is deliberately **stdlib-only** (``argparse``/``subprocess``/
+``pathlib``). It runs on the *host*, outside the container, where the science
+stack is not installed -- so it must import without it. That also means it works
+straight from a checkout with no install at all::
+
+    python3 src/sp_validation/container.py pull
+"""
+
+import argparse
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+# The image every entry point names, written down here once. CI pushes one tag
+# per branch, sanitized, so ``:develop`` tracks the integration branch.
+CONTAINER_URI = "docker://ghcr.io/cosmostat/sp_validation:develop"
+
+CACHE_DIR = Path(os.environ.get("XDG_CACHE_HOME", "~/.cache")) / "sp_validation"
+
+# Where this user's image lives. Per-user by construction: one file, one owner,
+# no coordination. Override with ``SPV_CONTAINER`` (an absolute path).
+DEFAULT_SIF = CACHE_DIR / "sp_validation.sif"
+
+# The optional writable unpacking of that image. Override with ``SPV_SANDBOX``.
+DEFAULT_SANDBOX = CACHE_DIR / "sandbox"
+
+# Bind mounts for interactive `exec`. candide's disks; override wholesale with
+# ``SPV_APPTAINER_BINDS`` or per-call with ``--bind``.
+DEFAULT_BINDS = "/home,/scratch,/automnt,/n17data,/n23data1,/n09data"
+
+
+def local_sif():
+    """Return this user's canonical image path (may not exist yet)."""
+    override = os.environ.get("SPV_CONTAINER")
+    path = Path(override) if override else DEFAULT_SIF
+    return path.expanduser()
+
+
+def local_sandbox():
+    """Return this user's writable sandbox directory (may not exist)."""
+    override = os.environ.get("SPV_SANDBOX")
+    path = Path(override) if override else DEFAULT_SANDBOX
+    return path.expanduser()
+
+
+def resolve_image():
+    """Return ``(path_or_uri, kind)`` for the image everything should run.
+
+    The one resolution order, shared by the CLI and the workflow: the writable
+    sandbox if it exists, else the pristine SIF if it exists, else the registry
+    tag for Snakemake to pull. ``kind`` is ``"sandbox"``, ``"sif"`` or ``"tag"``.
+    """
+    sandbox = local_sandbox()
+    if sandbox.is_dir():
+        return str(sandbox), "sandbox"
+    sif = local_sif()
+    if sif.exists():
+        return str(sif), "sif"
+    return CONTAINER_URI, "tag"
+
+
+def image_labels(sif):
+    """Return the image's OCI labels as a dict, or ``{}`` if unreadable.
+
+    Never raises: a missing file, a missing ``apptainer``, or a corrupt image
+    all mean "we don't know", which every caller here treats as non-fatal.
+    """
+    sif = Path(sif)
+    if not sif.exists() or shutil.which("apptainer") is None:
+        return {}
+    try:
+        out = subprocess.run(
+            ["apptainer", "inspect", "--labels", str(sif)],
+            capture_output=True,
+            text=True,
+            timeout=60,
+        )
+    except (OSError, subprocess.SubprocessError):
+        return {}
+    if out.returncode != 0:
+        return {}
+    labels = {}
+    for line in out.stdout.splitlines():
+        key, sep, value = line.partition(":")
+        if sep:
+            labels[key.strip()] = value.strip()
+    return labels
+
+
+def image_revision(sif):
+    """Return the sp_validation commit the image was built from, or ``None``."""
+    return image_labels(sif).get("org.opencontainers.image.revision")
+
+
+def _require_apptainer():
+    """Exit unless ``apptainer`` is on PATH."""
+    if shutil.which("apptainer") is None:
+        sys.exit("apptainer is not on PATH")
+
+
+def _git(*args, cwd=None):
+    """Run a git command, returning stripped stdout or ``None`` on any failure."""
+    try:
+        out = subprocess.run(
+            ["git", *args], capture_output=True, text=True, cwd=cwd, timeout=30
+        )
+    except (OSError, subprocess.SubprocessError):
+        return None
+    return out.stdout.strip() if out.returncode == 0 else None
+
+
+def compare_revision(revision, repo=None):
+    """Place an image revision relative to a checkout's HEAD.
+
+    Returns one of ``"in-sync"``, ``"behind"`` (the image predates HEAD),
+    ``"ahead"`` (HEAD predates the image), ``"diverged"``, or ``"unknown"``
+    (no revision label, no git, or a commit this clone has never fetched).
+    """
+    if not revision:
+        return "unknown"
+    repo = repo or Path(__file__).resolve().parents[2]
+    head = _git("rev-parse", "HEAD", cwd=repo)
+    if head is None:
+        return "unknown"
+    if head == revision:
+        return "in-sync"
+    if _git("cat-file", "-e", f"{revision}^{{commit}}", cwd=repo) is None:
+        return "unknown"
+    if _git("merge-base", "--is-ancestor", revision, head, cwd=repo) is not None:
+        return "behind"
+    if _git("merge-base", "--is-ancestor", head, revision, cwd=repo) is not None:
+        return "ahead"
+    return "diverged"
+
+
+def cmd_pull(args):
+    """Pull ``--tag`` to the canonical path, atomically."""
+    _require_apptainer()
+    sif = local_sif()
+    sif.parent.mkdir(parents=True, exist_ok=True)
+    # Pull to a sibling temp name and rename: an atomic rename within one
+    # directory, so a job gets either the whole old image or the whole new one.
+    # Pulling in place would leave the file half-written for the ~15 minutes the
+    # pull takes. Jobs already running hold the old inode open and finish on it.
+    tmp = sif.with_name(sif.name + f".pull.{os.getpid()}")
+    print(f"pulling {args.tag}\n     -> {sif}")
+    try:
+        subprocess.run(
+            ["apptainer", "pull", "--force", "--name", str(tmp), args.tag], check=True
+        )
+        os.replace(tmp, sif)
+    except subprocess.CalledProcessError as exc:
+        tmp.unlink(missing_ok=True)
+        sys.exit(f"pull failed ({exc.returncode})")
+    except KeyboardInterrupt:
+        tmp.unlink(missing_ok=True)
+        raise
+    labels = image_labels(sif)
+    print(f"revision: {labels.get('org.opencontainers.image.revision', 'unknown')}")
+    print(f"version:  {labels.get('org.opencontainers.image.version', 'unknown')}")
+    return 0
+
+
+def cmd_sandbox(args):
+    """Unpack the image into a writable directory -- the opt-in escape hatch."""
+    _require_apptainer()
+    sandbox = local_sandbox()
+    if sandbox.exists() and not args.force:
+        sys.exit(
+            f"sandbox already exists at {sandbox}\n"
+            "pass --force to discard it and rebuild from a clean image"
+        )
+    source = args.source or (
+        str(local_sif()) if local_sif().exists() else CONTAINER_URI
+    )
+    sandbox.parent.mkdir(parents=True, exist_ok=True)
+    print(f"building sandbox from {source}\n     -> {sandbox}")
+    # Build beside the target and swap it in, as `pull` does -- and for a sharper
+    # reason here. A half-written .sif fails loudly, but a half-unpacked sandbox
+    # *directory* is still a directory, so resolve_image() would elect it as the
+    # live image and every job would silently run a broken tree.
+    #
+    # Building first also means a `--force` rebuild that fails (a typo in
+    # --source, a network blip) leaves the sandbox you already had untouched,
+    # rather than deleting a working environment on the way to not replacing it.
+    #
+    # `--fix-perms` so the tree can be deleted again later (apptainer warns about
+    # exactly this otherwise). No `--fakeroot`: an unprivileged build from an
+    # existing image works through user namespaces, which is what candide has.
+    staging = sandbox.with_name(f"{sandbox.name}.build.{os.getpid()}")
+    shutil.rmtree(staging, ignore_errors=True)
+    try:
+        subprocess.run(
+            ["apptainer", "build", "--sandbox", "--fix-perms", str(staging), source],
+            check=True,
+        )
+    except subprocess.CalledProcessError as exc:
+        shutil.rmtree(staging, ignore_errors=True)
+        sys.exit(f"sandbox build failed ({exc.returncode}); {sandbox} is unchanged")
+    except (KeyboardInterrupt, OSError):
+        shutil.rmtree(staging, ignore_errors=True)
+        raise
+
+    if sandbox.exists():
+        print(f"replacing {sandbox}")
+        shutil.rmtree(sandbox, ignore_errors=True)
+        if sandbox.exists():
+            shutil.rmtree(staging, ignore_errors=True)
+            sys.exit(f"could not remove {sandbox}; remove it by hand and retry")
+    os.replace(staging, sandbox)
+    print(
+        "\nthis sandbox now takes precedence over the SIF everywhere, including "
+        "workflow jobs.\ninstall into it with: spv-container exec --writable pip "
+        "install <pkg>\nreset to a clean image with: spv-container pull && "
+        "spv-container sandbox --force"
+    )
+    return 0
+
+
+def cmd_status(args):
+    """Report which image layer is live, its revision, and how current it is."""
+    sif = local_sif()
+    sandbox = local_sandbox()
+    active, kind = resolve_image()
+
+    if sif.exists():
+        print(f"SIF:      {sif} ({sif.stat().st_size / 1e9:.1f} GB)")
+    else:
+        print(f"SIF:      absent ({sif})")
+    if sandbox.is_dir():
+        print(f"sandbox:  {sandbox} (writable; may carry local modifications)")
+    else:
+        print("sandbox:  none")
+
+    if kind == "tag":
+        print(f"\nactive:   {active} (registry tag -- nothing pulled locally)")
+        print("run: spv-container pull")
+        return 1
+
+    print(f"\nactive:   {active} ({kind})")
+    labels = image_labels(active)
+    revision = labels.get("org.opencontainers.image.revision")
+    source = ""
+    if revision is None and kind == "sandbox" and sif.exists():
+        # Some sandbox trees do not carry the original labels through. The SIF
+        # beside it is the best remaining evidence of what it was built from --
+        # a guess, so it is labelled as one rather than printed as fact.
+        revision = image_revision(sif)
+        if revision:
+            source = " (inferred from the SIF beside it, not read from the sandbox)"
+    print(f"revision: {revision or 'unknown'}{source}")
+    print(f"version:  {labels.get('org.opencontainers.image.version', 'unknown')}")
+    if kind == "sandbox":
+        print(
+            "          (the revision above is what the sandbox was built from; "
+            "anything\n           installed into it since is not reflected in "
+            "any label)"
+        )
+    verdict = compare_revision(revision)
+    explain = {
+        "in-sync": "matches this checkout's HEAD",
+        "behind": "older than this checkout's HEAD -- pull to refresh",
+        "ahead": "newer than this checkout's HEAD",
+        "diverged": "on a different branch from this checkout",
+        "unknown": "cannot compare (no label, or a commit this clone lacks)",
+    }[verdict]
+    print(f"checkout: {verdict} ({explain})")
+    return 0
+
+
+def cmd_exec(args):
+    """Run a command inside the image -- the one-off path for humans and agents."""
+    _require_apptainer()
+    if not args.command:
+        sys.exit("nothing to run; pass a command after `exec`")
+    binds = args.bind or os.environ.get("SPV_APPTAINER_BINDS", DEFAULT_BINDS)
+
+    if args.writable:
+        # A SIF is a read-only filesystem, so `--writable` against one fails
+        # obscurely; only a sandbox takes writes.
+        sandbox = local_sandbox()
+        if not sandbox.is_dir():
+            sys.exit(
+                f"--writable needs a sandbox, and there is none at {sandbox}\n"
+                "build one with: spv-container sandbox"
+            )
+        image, extra = str(sandbox), ["--writable"]
+    else:
+        image, kind = resolve_image()
+        if kind == "tag":
+            sys.exit(f"no local image; run: spv-container pull ({image})")
+        extra = []
+
+    cmd = [
+        "apptainer",
+        "exec",
+        *extra,
+        "--cleanenv",
+        "--bind",
+        binds,
+        image,
+        *args.command,
+    ]
+    return subprocess.run(cmd).returncode
+
+
+def build_parser():
+    parser = argparse.ArgumentParser(
+        prog="spv-container", description=__doc__.splitlines()[0]
+    )
+    sub = parser.add_subparsers(dest="subcommand", required=True)
+
+    p_pull = sub.add_parser("pull", help="fetch the image to the canonical path")
+    p_pull.add_argument(
+        "--tag",
+        default=CONTAINER_URI,
+        help=f"image to pull (default: {CONTAINER_URI})",
+    )
+    p_pull.set_defaults(func=cmd_pull)
+
+    p_status = sub.add_parser(
+        "status", help="report which image layer is live and how current it is"
+    )
+    p_status.set_defaults(func=cmd_status)
+
+    p_sandbox = sub.add_parser(
+        "sandbox", help="unpack the image into a writable directory (opt-in)"
+    )
+    p_sandbox.add_argument(
+        "--source",
+        help="image to unpack (default: the local SIF, or the registry tag)",
+    )
+    p_sandbox.add_argument(
+        "--force",
+        action="store_true",
+        help="discard an existing sandbox and rebuild from a clean image",
+    )
+    p_sandbox.set_defaults(func=cmd_sandbox)
+
+    p_exec = sub.add_parser("exec", help="run a command inside the local image")
+    p_exec.add_argument("--bind", help=f"bind mounts (default: {DEFAULT_BINDS})")
+    p_exec.add_argument(
+        "--writable",
+        action="store_true",
+        help="run against the sandbox so writes (e.g. pip install) persist",
+    )
+    p_exec.add_argument("command", nargs=argparse.REMAINDER)
+    p_exec.set_defaults(func=cmd_exec)
+
+    return parser
+
+
+def main(argv=None):
+    args = build_parser().parse_args(argv)
+    return args.func(args)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/sp_validation/tests/data/container_smoke/Snakefile b/src/sp_validation/tests/data/container_smoke/Snakefile
new file mode 100644
index 00000000..75527690
--- /dev/null
+++ b/src/sp_validation/tests/data/container_smoke/Snakefile
@@ -0,0 +1,21 @@
+# Standalone workflow exercised by src/sp_validation/tests/test_container_smoke.py.
+#
+# The module-level `container:` below mirrors what every real workflow does
+# (workflow/Snakefile) -- Snakemake has no way to take a default image from a
+# profile. Everything else under test arrives from the driving profile
+# (workflow/profiles/candide). See container_smoke.py for what the job checks.
+
+
+# Literal rather than the package's CONTAINER_URI: this Snakefile is test data,
+# deliberately standalone. test_container_smoke.py asserts the two agree, so
+# drift fails the test rather than the run.
+container: config.get("container", "docker://ghcr.io/cosmostat/sp_validation:develop")
+
+
+rule container_smoke:
+    output:
+        "results/container_smoke.yaml",
+    resources:
+        runtime=5,
+    script:
+        "container_smoke.py"
diff --git a/src/sp_validation/tests/data/container_smoke/container_smoke.py b/src/sp_validation/tests/data/container_smoke/container_smoke.py
new file mode 100644
index 00000000..274cd85c
--- /dev/null
+++ b/src/sp_validation/tests/data/container_smoke/container_smoke.py
@@ -0,0 +1,87 @@
+"""Rule container_smoke: exercise the containerized-SLURM path end to end.
+
+Cheap sanity check of the profile-driven container path -- same executor
+(slurm), same software-deployment-method (apptainer), same apptainer-args
+binds, same container image every real rule uses.  Four things it proves, each
+written to the output YAML:
+
+  * the job really ran inside the image (``APPTAINER_CONTAINER``, set by
+    apptainer itself -- without it the rest could all pass on the bare host);
+  * the editable ``sp_validation`` install resolves on the container's
+    PYTHONPATH (import provenance: file + version, not just import success);
+  * the numeric stack works (numpy eigh on a small fixed matrix).
+    ``OMP_NUM_THREADS`` is recorded but not asserted -- see the assertions;
+  * which commit of this checkout is running (git rev-parse from inside the
+    container -- proves /home is bound and usable, not just readable).
+
+Driven by the co-located Snakefile; the assertions on the output YAML live in
+src/sp_validation/tests/test_container_smoke.py (marked ``slow``, cluster only).
+"""
+
+import os
+import platform
+import subprocess
+
+import numpy as np
+import yaml
+
+# --- the job is actually inside the image ---------------------------------
+container_info = {
+    "apptainer_container": os.environ.get("APPTAINER_CONTAINER", "unset"),
+}
+
+# --- editable install resolves inside the container ------------------------
+import sp_validation  # noqa: E402
+
+sp_validation_info = {
+    "version": getattr(sp_validation, "__version__", "unknown"),
+    "file": sp_validation.__file__,
+}
+
+# --- numeric stack + threading -----------------------------------------
+rng = np.random.default_rng(seed=42)
+a = rng.standard_normal((8, 8))
+symmetric = a + a.T
+eigenvalues = np.linalg.eigh(symmetric)[0]
+
+numeric_info = {
+    "numpy_version": np.__version__,
+    "eigenvalues": [float(v) for v in eigenvalues],
+    "omp_num_threads": os.environ.get("OMP_NUM_THREADS", "unset"),
+}
+
+# --- provenance: what commit is actually running in the container ---------
+# src/sp_validation/tests/data/container_smoke/ -> repo root, five levels up.
+# (This is the checkout the Snakefile came from, which is what we want to
+# report; the editable install may well resolve to a *different* checkout.)
+repo_dir = os.path.abspath(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), *([os.pardir] * 5))
+)
+try:
+    commit = subprocess.run(
+        ["git", "-C", repo_dir, "rev-parse", "HEAD"],
+        capture_output=True,
+        text=True,
+        check=True,
+    ).stdout.strip()
+except (subprocess.CalledProcessError, FileNotFoundError) as exc:
+    commit = f"unavailable ({exc})"
+
+provenance = {
+    "repo_dir": repo_dir,
+    "commit": commit,
+    "hostname": platform.node(),
+    "python": platform.python_version(),
+}
+
+with open(snakemake.output[0], "w") as f:
+    yaml.safe_dump(
+        {
+            "container": container_info,
+            "sp_validation": sp_validation_info,
+            "numeric": numeric_info,
+            "provenance": provenance,
+        },
+        f,
+        sort_keys=False,
+    )
diff --git a/src/sp_validation/tests/test_container_smoke.py b/src/sp_validation/tests/test_container_smoke.py
new file mode 100644
index 00000000..b9b07058
--- /dev/null
+++ b/src/sp_validation/tests/test_container_smoke.py
@@ -0,0 +1,126 @@
+"""Smoke test of the profile-driven containerized-SLURM path.
+
+Submits one real (tiny, 5-minute) SLURM job through the committed candide
+profile. The executor, the apptainer deployment method and the bind mounts come
+from that profile; the image is the module-level ``container:`` in the test
+Snakefile, exactly as real workflows declare it. That contract is what's under
+test, so this can only run on candide -- marked ``slow``, skipped elsewhere.
+
+The job writes a YAML report (see data/container_smoke/container_smoke.py); the
+assertions below check what it reports.
+"""
+
+import os
+import re
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pytest
+import yaml
+
+requires_cluster = pytest.mark.skipif(
+    not Path("/n17data/cdaley/unions").exists() or shutil.which("sbatch") is None,
+    reason="needs candide: /n17data and a SLURM submit host",
+)
+
+
+def _repo_root() -> Path:
+    for parent in Path(__file__).resolve().parents:
+        if (parent / "pyproject.toml").exists():
+            return parent
+    raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
+
+
+def _reference_eigenvalues() -> np.ndarray:
+    """The same deterministic computation the job runs inside the container."""
+    rng = np.random.default_rng(seed=42)
+    a = rng.standard_normal((8, 8))
+    return np.linalg.eigh(a + a.T)[0]
+
+
+def test_smoke_snakefile_names_the_workflow_image():
+    """The test Snakefile's literal image must track the package's CONTAINER_URI."""
+    repo_root = _repo_root()
+    uri = re.search(
+        r'^CONTAINER_URI = "(.+)"$',
+        (repo_root / "src/sp_validation/container.py").read_text(),
+        re.MULTILINE,
+    ).group(1)
+    snakefile = (
+        repo_root / "src/sp_validation/tests/data/container_smoke/Snakefile"
+    ).read_text()
+    assert f'"{uri}"' in snakefile, uri
+
+
+@pytest.mark.slow
+@requires_cluster
+def test_container_smoke():
+    repo_root = _repo_root()
+    workflow_dir = repo_root / "src/sp_validation/tests/data/container_smoke"
+
+    # Not pytest's tmp_path: that lives in the login node's /tmp, which the
+    # compute node cannot see, so the job's output would "go missing". The
+    # workdir must be on a shared filesystem.
+    tmp_path = Path(tempfile.mkdtemp(prefix="container_smoke_", dir=Path.home()))
+
+    env = os.environ | {"PYTHONNOUSERSITE": "1", "PYTHONUNBUFFERED": "1"}
+    result = subprocess.run(
+        [
+            "snakemake",
+            "--profile",
+            str(repo_root / "workflow/profiles/candide"),
+            "-s",
+            str(workflow_dir / "Snakefile"),
+            "--directory",
+            str(tmp_path),
+            "--jobs",
+            "1",
+            "container_smoke",
+        ],
+        env=env,
+        text=True,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        timeout=600,
+        check=False,
+    )
+    assert result.returncode == 0, result.stdout
+
+    report = yaml.safe_load((tmp_path / "results/container_smoke.yaml").read_text())
+
+    # The job ran inside the image, not on the bare host. Everything below would
+    # pass on the host too, so this is the assertion that makes them mean
+    # something: apptainer sets APPTAINER_CONTAINER in every process it starts.
+    assert report["container"]["apptainer_container"] != "unset", report["container"]
+
+    # The install must resolve to an editable src/ checkout, not a site-packages
+    # copy. Note it need not be *this* checkout: the container's editable install
+    # points at the shared /n17data working tree, while the Snakefile under test
+    # is read from wherever the test runs.
+    module_file = Path(report["sp_validation"]["file"])
+    assert module_file.parts[-3:] == ("src", "sp_validation", "__init__.py"), (
+        module_file
+    )
+    assert "site-packages" not in module_file.parts, module_file
+
+    # The numeric stack agrees with the same computation run here.
+    np.testing.assert_allclose(
+        report["numeric"]["eigenvalues"],
+        _reference_eigenvalues(),
+        rtol=1e-10,
+        atol=1e-12,
+    )
+
+    # numeric.omp_num_threads is recorded but deliberately NOT asserted: the
+    # profile leaves OMP_NUM_THREADS unset by design, and rules that need it
+    # pinned set it themselves, so "unset" here is correct rather than a gap.
+
+    # git worked inside the container, so /home is bound and usable.
+    assert re.fullmatch(r"[0-9a-f]{40}", report["provenance"]["commit"]), report[
+        "provenance"
+    ]
+
+    shutil.rmtree(tmp_path)  # keep only on failure, for post-mortem
diff --git a/src/sp_validation/tests/test_csl_modules.py b/src/sp_validation/tests/test_csl_modules.py
new file mode 100644
index 00000000..941eccee
--- /dev/null
+++ b/src/sp_validation/tests/test_csl_modules.py
@@ -0,0 +1,74 @@
+"""Every CSL module the pipeline templates reference must actually import.
+
+The image build only *compiles* CSL (``make -C shear``); pure-Python modules
+are never loaded until CosmoSIS assembles a pipeline at runtime.  That gap let
+a scipy>=1.15 incompatibility (``scipy.special.lpn`` removed, imported by
+``legendre.py`` via ``spec_tools`` — i.e. by every real-space likelihood) ship
+in a "validated" image (#316).  This test walks the ``file =`` entries of the
+committed ini templates and imports each referenced ``.py`` module, so any
+CSL-vs-environment drift on a module we actually use fails the in-image suite.
+
+Skips outside the image (no ``CSL_DIR`` / no cosmosis).
+"""
+
+import importlib.util
+import os
+import re
+import sys
+from pathlib import Path
+
+import pytest
+
+pytest.importorskip("cosmosis")
+
+CSL_DIR = os.environ.get("CSL_DIR")
+if not CSL_DIR or not Path(CSL_DIR).is_dir():
+    pytest.skip(
+        "CSL_DIR not set or missing (not in the image)", allow_module_level=True
+    )
+
+TEMPLATES = (
+    Path(__file__).parents[3] / "cosmo_inference" / "cosmosis_config" / "templates"
+)
+
+_FILE_RE = re.compile(r"^file\s*=\s*%\((?:COSMOSIS_DIR|CSL_DIR)\)s/(.+)$")
+
+
+def _template_modules():
+    seen = set()
+    for ini in sorted(TEMPLATES.glob("*.ini")):
+        for line in ini.read_text().splitlines():
+            m = _FILE_RE.match(line.strip())
+            if m and m.group(1) not in seen:
+                seen.add(m.group(1))
+                yield m.group(1)
+
+
+MODULES = list(_template_modules())
+
+
+def test_templates_reference_csl_modules():
+    """The parser found the template module list (guards against ini drift)."""
+    assert len(MODULES) >= 10
+
+
+@pytest.mark.parametrize("relpath", MODULES)
+def test_csl_module_loads(relpath):
+    path = Path(CSL_DIR) / relpath
+    if path.suffix == ".so":
+        # Compiled in the image (and hard-checked by the Dockerfile's `test -f`);
+        # absent in a bare source checkout.
+        if not path.exists():
+            pytest.skip(f"{relpath} not built in this CSL checkout")
+        return
+    assert path.exists(), f"template references missing CSL file: {relpath}"
+    # CosmoSIS modules import bare-named siblings (e.g. `import spec_tools`),
+    # so load with the module's own directory on sys.path, like CosmoSIS does.
+    sys.path.insert(0, str(path.parent))
+    try:
+        name = "csl_probe_" + relpath.replace("/", "_").replace(".py", "")
+        spec = importlib.util.spec_from_file_location(name, path)
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)
+    finally:
+        sys.path.remove(str(path.parent))
diff --git a/uv.lock b/uv.lock
index deedc1cf..4f635d14 100644
--- a/uv.lock
+++ b/uv.lock
@@ -561,6 +561,28 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2e/2c/c80db37593ad593e3f6c60705fbdf690b33f22c89edd5507d2731346cd87/cosmology-2022.10.9-py3-none-any.whl", hash = "sha256:3903658c2474177a1a1c75771ae14458d93200c516f8fc6c3f4d776f3287b288", size = 9341, upload-time = "2022-10-10T10:18:14.928Z" },
 ]
 
+[[package]]
+name = "cosmosis"
+version = "3.25.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "dulwich" },
+    { name = "dynesty" },
+    { name = "emcee" },
+    { name = "h5py" },
+    { name = "matplotlib" },
+    { name = "nautilus-sampler" },
+    { name = "numpy" },
+    { name = "py-bobyqa" },
+    { name = "pybind11" },
+    { name = "pyyaml" },
+    { name = "scikit-learn" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+    { name = "zeus-mcmc" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/80/63/83898440e5486294cb17a7ddf01896af66dd153d7f3e77897ed120abd055/cosmosis-3.25.2.tar.gz", hash = "sha256:4a8333395b600a5e8339c637b947a982bac4c88a7b242e4b6de8274be03004a8", size = 416233, upload-time = "2026-03-09T12:01:08.269Z" }
+
 [[package]]
 name = "coverage"
 version = "7.15.0"
@@ -852,6 +874,44 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9a/91/428eca0cc8a1142b9952bef02c06bfe54526d730dd070de873424ab8c0bf/ducc0-0.41.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a3a71fd23661ddd6a7f6b44433e778d35a25b3f1492c9239217cdfedbe8178b1", size = 5613706, upload-time = "2026-03-26T18:55:24.971Z" },
 ]
 
+[[package]]
+name = "dulwich"
+version = "1.2.14"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0d/7b/d03c166f6098314824bdd2e661a32563c0ebe8c5e3344ed212d2ffc96a10/dulwich-1.2.14.tar.gz", hash = "sha256:ed8bfcfe1c7e187d1bc5e78f7c128a07d229d7b425433b04cbb89334678bd3b1", size = 1361695, upload-time = "2026-08-29T00:23:51.443Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/49/bca41ab16905963a1efb045f122e69f89e8ead682a3be17a02e0701aa099/dulwich-1.2.14-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:63ec5ccc659f5d8dcf6b9948595abb0193a472998dd5ee23f2d4d43b73bbc169", size = 1459332, upload-time = "2026-08-29T00:22:34.209Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/f4/583088cbf9333263fffa8c4169e81785deca9b1146d5e38e6625197334b1/dulwich-1.2.14-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ad8b6c0ec611170af13c0ddac0b3191a20dfcd69360a06df7e60faaae14c5b29", size = 1490883, upload-time = "2026-08-29T00:22:35.903Z" },
+    { url = "https://files.pythonhosted.org/packages/17/01/b592e8f3b3de2cf3c3c773f521e3834cb7c8bc1b944c0c6c912e58d62460/dulwich-1.2.14-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:26440ebc7b8208edf9f251a7820a618bbdb53f1af71f2e5817bdbe3e6d24405d", size = 1459517, upload-time = "2026-08-29T00:22:49.875Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/54/3ddaada9e16238a5f0103262ba79904a48cc87fc63300fb93a7417fc2873/dulwich-1.2.14-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e0f583a85a40a38400ce4374edccc2e6a4a5c5f34d4ab272b1d2d4fd3358944d", size = 1490305, upload-time = "2026-08-29T00:22:51.761Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/c5/86e473a7187fae33e3f3d478e72e18166a5a986275c8c6307e7198c96d8a/dulwich-1.2.14-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:a839358029e679c1f18a1fcce847d48ed9b0df7469310c72a3878c4e167447ee", size = 1461994, upload-time = "2026-08-29T00:23:04.171Z" },
+    { url = "https://files.pythonhosted.org/packages/80/b2/d56697144814152a6c8c7e94ba9ee28d180acf0c56b22a734004d8a727f4/dulwich-1.2.14-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:26a36829b8590c2c431d86acc6ec4d95d708a5efbc3df0e6cb151fa8baa7b607", size = 1492031, upload-time = "2026-08-29T00:23:06.09Z" },
+    { url = "https://files.pythonhosted.org/packages/70/63/ca17bbd708b56687093d7e9278944908f1471e72e95b5e97848d96619675/dulwich-1.2.14-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:ecdde801f145fa4cccf87914b61d34b3ba8e70b3c7e054447c07e119eeb48f43", size = 1458574, upload-time = "2026-08-29T00:23:15.906Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/c3/627bd8e13bc55476967ac9849b3b040596b4e9b012558abaf960698c55d8/dulwich-1.2.14-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:404d6b78d2012299eff296f1baa808b03cf1c04dcce0972efe79005bac1861a0", size = 1539905, upload-time = "2026-08-29T00:23:17.758Z" },
+    { url = "https://files.pythonhosted.org/packages/18/eb/951fa6624e50b772bbbb15a75bc5669ae954d08170dd7b72f8e51fa33cba/dulwich-1.2.14-cp315-cp315-manylinux_2_28_aarch64.whl", hash = "sha256:ca4a6bf4ca6d9a0025523284ce82a89d6b709684c3cb25a896f407c4a55d51bc", size = 1462641, upload-time = "2026-08-29T00:23:30.469Z" },
+    { url = "https://files.pythonhosted.org/packages/45/c4/e219b9e16452843b54caa98accf42bb8af493706bba292ef06ba1871cfad/dulwich-1.2.14-cp315-cp315-manylinux_2_28_x86_64.whl", hash = "sha256:26d36df85e4247427616ad2e4aa7698adba55ee6d8c83fa5dbe93aacc2250f2f", size = 1540358, upload-time = "2026-08-29T00:23:32.517Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/9b/f7a5536d8d810c90ad7d337af763e2afd713f4d2237deac34fade3ad1b50/dulwich-1.2.14-cp315-cp315t-manylinux_2_28_aarch64.whl", hash = "sha256:89aa99ae03475f12a7e66c4f649cf4b653e457b63dca1acc7b7579ca3d7aed51", size = 1459927, upload-time = "2026-08-29T00:23:42.301Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/5f/13e07d1a1ab152dcbe0433791f1aed66c8b21790270ee3e7424c3559dc98/dulwich-1.2.14-cp315-cp315t-manylinux_2_28_x86_64.whl", hash = "sha256:2e5b14e5d01b7020a39acad4d7990c5da379ac0cba08d1d7121f3d036dc80983", size = 1493129, upload-time = "2026-08-29T00:23:44.198Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/45/5c62eeb71c6005fa4f1b5101def14271eb1063720fefcde17bcc95c0fb42/dulwich-1.2.14-py3-none-any.whl", hash = "sha256:3e1b6e13e23f9bc633fd249563e4d7a946d7d470f210967853ae0991589edd11", size = 730142, upload-time = "2026-08-29T00:23:49.691Z" },
+]
+
+[[package]]
+name = "dynesty"
+version = "3.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "scipy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c4/75/db494edc8abfe2273b9cb08c87a2a1165c6ae90d76f746980ef07ec5303a/dynesty-3.1.0.tar.gz", hash = "sha256:851717431f04f749bca45e86704c28fecf9f3b10f85a2a69820b26e42cefab68", size = 35564761, upload-time = "2026-07-17T14:38:31.465Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/f9/41c72d0b5fb511fdf23cf62ed50da6c73d4497f03ed1cad04f214dc477c8/dynesty-3.1.0-py3-none-any.whl", hash = "sha256:4e5b77bb261abdcb98e3a538d7498a94d5882a848f0ea46924b404396ef036d5", size = 106027, upload-time = "2026-07-17T14:38:28.523Z" },
+]
+
 [[package]]
 name = "emcee"
 version = "3.1.6"
@@ -873,6 +933,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" },
 ]
 
+[[package]]
+name = "fast-pt"
+version = "3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "scipy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/65/11/83df1a1acc4c346f35219481f464f0264932ac3c57f053b1ce68f4d13d02/fast_pt-3.2.tar.gz", hash = "sha256:ec4cfdb4a352b300ca7ec2f313a73c5676ec61ef665345b0fc37a258f5ecbd5d", size = 37099, upload-time = "2024-11-01T00:45:36.156Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/64/db25374b45218b0d305a4378a7fe8399406989e4a26f7cbafc4930200d5b/fast_pt-3.2-py3-none-any.whl", hash = "sha256:c988e312bc79df66f1719fcb198ab984fd614a6c942f1e3af6adb07180760387", size = 49051, upload-time = "2024-11-01T00:45:34.544Z" },
+]
+
 [[package]]
 name = "fastjsonschema"
 version = "2.21.2"
@@ -2267,6 +2341,30 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/09/dc/f3dfb7488b770f3f67e6545085bf2abea5172e88f57b8ad25ef860ca704c/myst_parser-5.1.0-py3-none-any.whl", hash = "sha256:9c91c52b3cdb4d94a6506e4fab4e2f296c7623a0da0dcbe6de1565c3dad67a8a", size = 85817, upload-time = "2026-05-13T09:38:17.904Z" },
 ]
 
+[[package]]
+name = "narwhals"
+version = "2.25.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6f/7b/6248dada39781db1ab3ebf08943080df0796098515a87f6f8696d14ec744/narwhals-2.25.0.tar.gz", hash = "sha256:62c036c810662bf7820b7737077176313bc59350eeeefb808510f388c743e4b2", size = 677076, upload-time = "2026-08-20T18:10:15.454Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/eb/dc/55481808fd70ef1567cf13540ffd4702af3f74b112e35427564b03f79c2d/narwhals-2.25.0-py3-none-any.whl", hash = "sha256:1f0f403e8c7e4463cde9bfe78b12fdd809e3ae3dda6d9b2f802934fb9c7a6a8f", size = 467373, upload-time = "2026-08-20T18:10:13.834Z" },
+]
+
+[[package]]
+name = "nautilus-sampler"
+version = "1.0.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "scikit-learn" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/2c/a50c1a550ba43d86a0b41cf6410986d478d8640139c8e963f4dde8a8bfc2/nautilus_sampler-1.0.6.tar.gz", hash = "sha256:4e90b6d97be742be2e255c35a79e5a388cee043b4d2ce970eec327ed34139b78", size = 43118, upload-time = "2025-12-29T14:08:25.629Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/47/54/372c42bcfe324b650946f6cfee0e5adbd55aafc25cab555460bf5d9e8d36/nautilus_sampler-1.0.6-py3-none-any.whl", hash = "sha256:de6f3b9d249d87f05a673e8597e56621037e84dc712a6c2d3c18ff3672735b9b", size = 35772, upload-time = "2025-12-29T14:08:24.603Z" },
+]
+
 [[package]]
 name = "nbclient"
 version = "0.11.0"
@@ -2758,6 +2856,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" },
 ]
 
+[[package]]
+name = "py-bobyqa"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "scipy" },
+    { name = "setuptools" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6b/41/c4c74daf208ed27e14071e92efb7ea238ffdf77ee93a3a7777ff02d2b0e4/py_bobyqa-1.5.0.tar.gz", hash = "sha256:3c7719b68b28834ea6d538f54603f6a891263f7c21f1a673de79e3a5e0e7e413", size = 51486, upload-time = "2024-09-16T03:52:32.303Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/1e/0d44a4e3a291c009a357fbd1d61511d9306c2c4db9a7ceb6e8104d8d385f/Py_BOBYQA-1.5.0-py3-none-any.whl", hash = "sha256:457afc04d6f2c9f1814934854dc4e542c5e5982a0f80add4b211fcdb0b5811e3", size = 57978, upload-time = "2024-09-16T03:52:30.667Z" },
+]
+
 [[package]]
 name = "py-cpuinfo"
 version = "9.0.0"
@@ -3354,6 +3467,29 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a1/9f/56288a8aabafa24679d32d01e7db732e8e89e4074cc91aedc0bb7b7c8e46/sacc-2.4-py3-none-any.whl", hash = "sha256:da6b648998738c7c307cbdff644429633726844f8c048642725be5efc1859fa3", size = 50931, upload-time = "2026-07-02T11:29:26.286Z" },
 ]
 
+[[package]]
+name = "scikit-learn"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "joblib" },
+    { name = "narwhals" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fa/6f/37092bdb25f712817231799fc5674d8e704066a8a70c1d2d40517e18b4ab/scikit_learn-1.9.0.tar.gz", hash = "sha256:8833266989d3a5110178a9fae30783675460724d0e1efb13b14901d2c660c557", size = 7750767, upload-time = "2026-06-02T11:54:32.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/ee/5adbc77656b71f9456a2f5a7a9fdb4bcf9207a6b962889f1c2f9323afa4e/scikit_learn-1.9.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e50ed4da51974e86e940690e9a3d82e729b62b5a49f7c9bac534d515d39d86f", size = 8837603, upload-time = "2026-06-02T11:53:30.328Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/c2/63fdda36c56437eeb44aaf9493c8bcd62ce230ab1598924fc626ffbfa943/scikit_learn-1.9.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:056c92bb67ad4c28463c2f2653d9701449201e7e7a9e94e321be0f71c4fef2b8", size = 9132097, upload-time = "2026-06-02T11:53:33.456Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/39/ffe829a5b8ecb40a518724a997794657fdc354ada5e8fe8e64d998c0bac9/scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:38c3dcb9a1ffb85505ec53d54c7b4aea0cff70050425a7760c2af661ac85df05", size = 8789690, upload-time = "2026-06-02T11:53:47.461Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/88/8dab5de10c638c083772a6be83a3d8106ced492f74a928c8693638e5bb50/scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:da76d09304a4706db7cc1e3ebaa3b6b98a67365cc11d2996c4f1e58ba47df714", size = 9087723, upload-time = "2026-06-02T11:53:50.702Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/79/f4a0c4fe9711154cddabf913471153af79056382ddc612cfe5ee0ff4b72e/scikit_learn-1.9.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5162ad10a418c8a282dde04c9aa06965de3e9a65f33c1440c0ae69bb1a09d913", size = 8847671, upload-time = "2026-06-02T11:54:04.448Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/af/4d72d9e475ac83719160c662619e4bf7b95c19507cd582e7d0167a3c3dae/scikit_learn-1.9.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1fea2cc5677ab49d6f5bade978c866da44957b712d92e9635e8b4f723013c3cb", size = 9118104, upload-time = "2026-06-02T11:54:07.205Z" },
+    { url = "https://files.pythonhosted.org/packages/18/3c/e440e039bb82cd19004edaaad00acbde0fb9b461083c3ecf37941c557312/scikit_learn-1.9.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:147e9329ef0e39f75d4cffa02b2aa48d827832684926cd5210d9a2cb5c57246b", size = 8855050, upload-time = "2026-06-02T11:54:21.699Z" },
+    { url = "https://files.pythonhosted.org/packages/43/26/b341b8dab5998da6270a3a42c2152c578501354d36f944b5856757035ef8/scikit_learn-1.9.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bad8f8b9950321b54c965fdcbac6c6c55e79e16646b49977bcf3668d3870a1a", size = 9097190, upload-time = "2026-06-02T11:54:24.454Z" },
+]
+
 [[package]]
 name = "scipy"
 version = "1.17.1"
@@ -3749,6 +3885,8 @@ test = [
     { name = "ruff" },
 ]
 workflow = [
+    { name = "cosmosis" },
+    { name = "fast-pt" },
     { name = "mpi4py" },
     { name = "snakemake" },
 ]
@@ -3762,9 +3900,11 @@ requires-dist = [
     { name = "colorama" },
     { name = "cosmo-numba", git = "https://github.com/aguinot/cosmo-numba.git?rev=main" },
     { name = "cosmology", marker = "extra == 'glass'", specifier = "==2022.10.9" },
+    { name = "cosmosis", marker = "extra == 'workflow'", specifier = ">=3.25" },
     { name = "cryptography" },
     { name = "cs-util", git = "https://github.com/CosmoStat/cs_util.git?rev=develop" },
     { name = "emcee" },
+    { name = "fast-pt", marker = "extra == 'workflow'", specifier = ">=3.2,<4" },
     { name = "fitsio", marker = "extra == 'glass'" },
     { name = "getdist", git = "https://github.com/benabed/getdist.git?rev=113cd22a9a0d013b6f72fe734be81f260f3d3be5" },
     { name = "glass", marker = "extra == 'glass'", specifier = "==2025.1" },
@@ -4422,6 +4562,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/88/0a/469e2bd01be1490336e6c8707386845655d59261543315778a3ccc7e8019/zarr-3.2.1-py3-none-any.whl", hash = "sha256:f78cdd3d9687ad0e9f9cba2c5683b64f0c52589c19f685eeabe872e93cc0d2c7", size = 319617, upload-time = "2026-05-05T12:37:20.66Z" },
 ]
 
+[[package]]
+name = "zeus-mcmc"
+version = "2.5.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pytest" },
+    { name = "scikit-learn" },
+    { name = "scipy" },
+    { name = "seaborn" },
+    { name = "setuptools" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/15/c0/248a600ae0f3d1437612821f7c528c895b6a8408a052c352d4cac46dcf94/zeus-mcmc-2.5.4.tar.gz", hash = "sha256:594baa90de4ad4488c4db5ed6a0446f7103bc4b3de787f4d7d23c91c9aa88769", size = 35051, upload-time = "2023-01-12T04:14:20.688Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/48/cb/99ee8021fa305d65b1fb369fabf96ccc65c60282407390e5355bb9e62f9f/zeus_mcmc-2.5.4-py3-none-any.whl", hash = "sha256:a64a7dae15f413200c6d590a3edfc3b8bc63c6bea3acdf123c0397c8089e123f", size = 24122, upload-time = "2023-01-12T04:14:19.24Z" },
+]
+
 [[package]]
 name = "zipp"
 version = "4.1.0"
diff --git a/workflow/README.md b/workflow/README.md
index f20ffe8f..4171074e 100644
--- a/workflow/README.md
+++ b/workflow/README.md
@@ -51,9 +51,231 @@ Give the target *before* `--configfile`: `--configfile` takes one-or-more
 paths, so a target after it is read as a config file ("No such file:
 im_mbias"). Always dry-run first with `-n`.
 
-The profile carries only cluster policy — no container settings (the image-sims
-rules own their `apptainer exec` call) and no `OMP_NUM_THREADS` (pinned to 1 at
-that same `apptainer exec` line, since the slurm executor's `--export=ALL`
-propagates the driver's env, not a profile flag). Per-rule `mem_mb` / `runtime`
-stay on the rules. Off-cluster, drop `--profile` and add `-j N`. See the
-profile's own comments for the full rationale.
+Every rule runs inside the sp_validation container: the profile sets
+`software-deployment-method: apptainer` and `apptainer-args` (the bind mounts),
+and Snakemake wraps each job's `shell:`/`script:` command in `apptainer exec`
+itself — no rule writes its own `apptainer exec` call. The image name comes
+from the `container:` directive in `workflow/Snakefile` (or a rule's own
+override, e.g. the image-sims `SIF`).
+
+A few rules shell out to a host toolchain (CosmoCov, ImageMagick) and keep
+`container: None`; each says why in its own docstring.
+
+`OMP_NUM_THREADS` is not set by the profile either: the slurm executor's
+`--export=ALL` propagates the driver's env, not a profile flag, so a rule that
+needs it pinned sets it itself. Per-rule `mem_mb` / `runtime` stay on the rules.
+
+### Off candide — the default profile
+
+Candide is where the analysis runs, so the candide profile is the one to reach
+for. `profiles/default/config.yaml` exists for anywhere else — a laptop, another
+cluster — and carries the machine-independent half (container wrapping, rerun
+triggers, latency wait) with no SLURM layer:
+
+```bash
+snakemake --profile workflow/profiles/default -s workflow/Snakefile \
+    <target> --configfile <run config> -j 4
+```
+
+Snakemake cannot compose profiles, so both files carry that block (marked
+`GENERIC` in each) — change one, change the other. `apptainer-args` is not part
+of it: expect to edit the default profile's `--bind` list for your machine.
+
+### Which `sp_validation` a rule imports: the launched checkout
+
+The image is the frozen *dependency stack*; the `sp_validation` that runs is
+the one in the checkout you launched from — `common.configure()` puts that
+checkout's `src/` on each job's `PYTHONPATH`
+(`common.inject_checkout_pythonpath` has the mechanics).
+
+This is the default because the alternative is incoherent: Snakemake's
+`script:` directive already runs the checkout's *script files*, so without it a
+rule executes new script code against an old `import sp_validation` — the two
+halves of one commit, split.
+
+**Caveat:** `rerun-triggers: code` watches rule bodies and `script:` files, not
+`src/`. Editing a module under `src/` does not by itself mark outputs stale —
+force with `-F` or `--forcerun <rule>`.
+
+To reproduce a run from the image alone, opt out:
+
+```bash
+snakemake --profile workflow/profiles/candide --config checkout_pythonpath=false <target>
+```
+
+Either way the checkout has to sit under one of the profile's bind mounts to be
+visible inside the job.
+
+Most of the time this default is all you need. Reach for a different *image*
+only when the dependency stack changed — a new package, a lockfile bump — not
+when only `src/` did; "Running an image other than your own" below has the
+override.
+
+### Never write `/automnt/nXXdataN` in a path
+
+Use the plain form `/nXXdataN/...` in every rule, config, and invocation
+directory. `/automnt/nXXdataN` works only from a node that does *not* own that
+disk. On the owning node the disk is mounted directly at `/nXXdataN` and there
+is no `/automnt/nXXdataN` entry at all, so a job that lands there dies about one
+second after the allocation starts, before any log file is written. This is why
+`n17` is in the profile's exclude list. Every canonical path in `common.py`
+already uses the plain form; keep new paths the same.
+
+### Run Snakemake from the host, never from inside the container
+
+`snakemake` is a thin host-side tool, pinned once per machine:
+
+```bash
+uv tool install snakemake==9.23.1 --with snakemake-executor-plugin-slurm
+```
+
+(match the version to `snakemake` in this repo's `uv.lock`). Run every
+`snakemake` command directly on the host — do not `apptainer shell` first.
+Snakemake itself never touches the science stack; it only reads rule
+definitions and submits jobs. Each job carries its own `apptainer exec`
+wrapping from the profile (see above), so the container is where the science
+code runs, not where the orchestrator runs — one container per job, never a
+nested one.
+
+Check for a stray `~/.local/bin/snakemake` (any host-side `pip install --user
+snakemake` leaves one): Apptainer passes your `PATH` and mounts your `$HOME` by
+default, so it can silently shadow the one `uv tool install` set up. `which
+snakemake` should resolve under `uv tool dir`, not `~/.local/bin`.
+
+### The container image — one per person
+
+Everything runs one image, published by CI as a registry tag:
+
+```
+docker://ghcr.io/cosmostat/sp_validation:develop
+```
+
+**Each person keeps their own copy of it.** There is no shared image directory:
+you pull your own file, you refresh it when you want to, and nobody else's
+refresh moves the ground under your running jobs. The canonical path is
+
+```
+~/.cache/sp_validation/sp_validation.sif
+```
+
+and a small CLI, `spv-container`, is what puts it there and tells you about it:
+
+```bash
+spv-container pull          # fetch :develop to the canonical path (~1.5 GB / ~15 min)
+spv-container status        # what is here, which commit it was built from, how current
+spv-container exec <cmd>    # run something inside it, candide binds already applied
+```
+
+It ships as a console script with the package, and — being stdlib-only, because
+it has to run on the *host* — also works straight from a checkout with nothing
+installed: run `python3 src/sp_validation/container.py`, or put it on your PATH
+once (the README's install step):
+
+```bash
+ln -s "$PWD/src/sp_validation/container.py" ~/.local/bin/spv-container
+```
+
+**Do the pull from a compute node**, not the login node: it moves ~1.5 GB and
+takes about fifteen minutes. `pull` writes to a temporary name and renames, so a
+job either gets the whole old image or the whole new one; jobs already running
+hold the old file open and finish against it unharmed.
+
+```bash
+salloc -p comp -c 4 --time=01:00:00 --exclude=n17,n09,n36 --no-shell   # note the job id
+srun --jobid=<id> spv-container pull
+scancel <id>
+```
+
+**How the workflow finds it.** One resolution order, shared by the CLI and the
+workflow: your **sandbox** if you have built one (below), else your **`.sif`** if
+you have pulled one, else the **registry tag** — which Snakemake autopulls into
+`.snakemake/singularity` under the working directory. That works, but re-pulls
+once per run directory, so `spv-container pull` is the path to prefer. Snakemake
+accepts all three forms, a sandbox directory included. The tag itself is written
+down once, as
+`CONTAINER_URI` in `sp_validation/container.py`, which `workflow/common.py`
+re-exports; the image-sims `sif:` config key defaults to `null` and resolves the
+same way. Override any of it with `--config container=...` (below), or point
+somewhere else entirely with `SPV_CONTAINER`.
+
+At launch the workflow prints one advisory line if your image was built from a
+commit behind your checkout. It never fails the run — an older image is normally
+fine, since the checkout's `src/` is what rules import (see above). It matters
+when the *dependency stack* moved: a new package, a lockfile bump.
+
+#### When you need to install something: the sandbox
+
+The pristine SIF is read-only, which is what you want almost always — it is
+exactly the published image, and two people running it run the same thing. But
+mid-analysis you sometimes need a package the image does not carry yet, and
+rebuilding through CI to find out whether it helps is too slow a loop.
+
+For that, unpack the image into a writable directory once:
+
+```bash
+spv-container sandbox                        # ~/.cache/sp_validation/sandbox/
+spv-container exec --writable pip install <pkg>
+```
+
+Writes into a sandbox persist. It is opt-in — nothing builds one for you — and
+once it exists **it takes precedence over the SIF everywhere, workflow jobs
+included**, so a package you install this way is available to your Snakemake runs
+without any further wiring. Jobs exec it read-only; only `--writable` writes.
+
+The cost is that what you are running is no longer fully described by a revision
+label. `spv-container status` says which layer is live and flags that, and the
+workflow prints one line at launch when a sandbox is in play — the divergence is
+visible, never silent. When you are done exploring, either fold the dependency
+into `pyproject.toml` (the real fix) or reset to a clean image:
+
+```bash
+spv-container pull                  # refresh the pristine SIF
+spv-container sandbox --force       # discard the sandbox, rebuild from it
+```
+
+**Where the image comes from.** CI (`.github/workflows/deploy-image.yml`) builds
+it on every push, `FROM ghcr.io/cosmostat/shapepipe:im_sims` with `uv sync
+--frozen` against `uv.lock`, and publishes to `ghcr.io/cosmostat/sp_validation`
+tagged by branch — so `:develop` tracks the tip of `develop`. The package is
+public; no credentials are needed. Your pulled file is a *snapshot*: CI
+publishing a new image changes nothing until you pull again.
+
+`spv-container status` reads `org.opencontainers.image.revision` — the
+sp_validation commit the image was built from — and places it against your
+checkout's `HEAD`, naming which layer (sandbox or SIF) it read. The image-sims workflow records the same label in
+`m_bias_config.yaml` as `ghcr_revision`, so a result file says which image
+produced the number.
+
+#### Running an image other than your own
+
+`container` is a config key read by every entry Snakefile
+(`common.resolve_container`), so one flag overrides the default everywhere at
+once — a local file, or any CI tag:
+
+```bash
+snakemake --profile workflow/profiles/candide --config container=/path/to/my.sif <target>
+snakemake --profile workflow/profiles/candide \
+    --config container=docker://ghcr.io/cosmostat/sp_validation:my-branch <target>
+```
+
+CI tags an image for **every** branch, by sanitized branch name (`/` → `-`), so
+the second form is how you test a branch's own stack. Snakemake autopulls a tag
+per run directory (~15 minutes — from a compute node); `spv-container pull --tag
+<uri>` instead puts it at your canonical path, where it becomes your default.
+
+For the image-sims workflow, set `image_sims: {sif: ...}` in your run config.
+Either way the image has to sit under one of the profile's bind mounts to be
+visible.
+
+One trap to know: the `script:` directive bind-mounts the host orchestrator's
+`snakemake` into the job and *appends* it to `sys.path`, so a `snakemake`
+importable inside the image wins the lookup. If `script:` rules start failing
+with `ModuleNotFoundError: No module named 'snakemake.iocontainers'` or similar,
+an in-image snakemake older than the host's is the first thing to check.
+
+### `snakemake` in `script:` files
+
+Every script run via a rule's `script:` directive uses a bare `snakemake`
+name (`snakemake.input[...]`, etc.) with no import — Snakemake injects it as
+a module global before the script runs. `from snakemake.script import
+snakemake` is IDE-hint-only and raises `ImportError` if actually executed.
diff --git a/workflow/Snakefile b/workflow/Snakefile
index 7d91db81..fe05f02b 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -8,19 +8,15 @@
 # or run standalone with --configfile pointing at a paper config
 # (e.g. papers/bmodes/config/config.yaml).
 
-container: "/n17data/cdaley/containers/containers"
-
 envvars:
     "PYTHONUNBUFFERED",
 
 import os
 import sys
 
-# Host OpenMPI libs for MPI rules inside the container.
-# Apptainer passes APPTAINERENV_* vars into the container as their unprefixed names.
-# Only affects rules that import mpi4py; safe for all other rules.
-os.environ["APPTAINERENV_LD_LIBRARY_PATH"] = "/softs/openmpi/5.0.5-slurm-CentOS8/lib"
-
+# Machine-specific env belongs to the machine, so it rides the profile's
+# `apptainer-args --env`, not this file. See workflow/profiles/.
+#
 # Shared helpers live in common.py next to this Snakefile. Snakemake's `module`
 # imports rules, not Python globals, so helpers travel by plain Python import;
 # when composed, the paper Snakefile has already imported common and this hits
@@ -28,9 +24,14 @@ os.environ["APPTAINERENV_LD_LIBRARY_PATH"] = "/softs/openmpi/5.0.5-slurm-CentOS8
 sys.path.insert(0, os.path.realpath(str(workflow.basedir)))
 import common
 
+# configure() also puts this checkout's src/ on the container's PYTHONPATH --
+# see common.inject_checkout_pythonpath.
 common.configure(config)
 from common import *
 
+# The one image every rule runs in; see common.resolve_container.
+container: common.resolve_container(config.get("container"))
+
 # Wildcard constraints — centralized in common.py, not in individual rule files
 wildcard_constraints:
     **WILDCARD_CONSTRAINTS
diff --git a/workflow/common.py b/workflow/common.py
index 3df3413b..506f7cb2 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -1,10 +1,36 @@
 """Shared helpers for the B-modes Snakemake workflow."""
 
+import importlib.util
 import json
 import os
 import re
+import sys
 from pathlib import Path
 
+# This checkout's importable source tree: workflow/common.py -> <repo>/src.
+REPO_SRC = Path(__file__).resolve().parent.parent / "src"
+
+# The container model lives in the package (``sp_validation/container.py``).
+# Taken from *this checkout's* src/, so the workflow and the ``spv-container``
+# CLI can never disagree about which image to run.
+#
+# Loaded by file path rather than as ``sp_validation.container``: snakemake runs
+# on the host, where sp_validation is usually not installed, and importing the
+# package would drag in ``__init__`` -> ``version`` -> a metadata warning on
+# every launch. The module itself is stdlib-only, so this costs nothing.
+_container = importlib.util.module_from_spec(
+    importlib.util.spec_from_file_location(
+        "_spv_container", REPO_SRC / "sp_validation" / "container.py"
+    )
+)
+sys.modules["_spv_container"] = _container
+_container.__loader__.exec_module(_container)
+
+compare_revision = _container.compare_revision
+image_revision = _container.image_revision
+resolve_image = _container.resolve_image
+
+
 # Output roots are env-overridable so a reproduction run can write into a
 # fresh tree without clobbering (or silently reusing) prior products.
 COSMO_VAL = Path(
@@ -50,9 +76,90 @@
 PLANCK18 = None
 
 
+def inject_checkout_pythonpath(workflow_config):
+    """Make the launched checkout's ``src`` win over the image's baked copy.
+
+    Snakemake's ``script:`` directive already runs the *checkout's* script
+    files, so without this a rule executes new script code against an old
+    ``import sp_validation`` -- the two halves of one commit, split. Prepending
+    ``REPO_SRC`` closes that: the image stays the frozen dependency stack, the
+    checkout supplies sp_validation.
+
+    Apptainer forwards ``APPTAINERENV_``-prefixed host variables into the job as
+    their unprefixed names, surviving the profile's ``--cleanenv``; setting it
+    here on the driver reaches every containerized rule. Any value the user
+    already exported is preserved behind ours.
+
+    Opt out with ``--config checkout_pythonpath=false`` to reproduce a run from
+    the image alone.
+    """
+    flag = workflow_config.get("checkout_pythonpath", True)
+    # `--config key=false` can arrive as the *string* "false" depending on how
+    # Snakemake parses the value, so don't lean on truthiness alone.
+    if isinstance(flag, str):
+        flag = flag.strip().lower() not in ("false", "no", "0", "off", "")
+    if not flag:
+        return
+    if not REPO_SRC.is_dir():
+        return
+    existing = os.environ.get("APPTAINERENV_PYTHONPATH", "")
+    parts = [str(REPO_SRC)] + [p for p in existing.split(":") if p]
+    os.environ["APPTAINERENV_PYTHONPATH"] = ":".join(parts)
+
+
+def resolve_container(override=None):
+    """Return the image every rule should run in.
+
+    ``override`` wins if set (a ``docker://`` tag, a ``.sif`` path or a sandbox
+    directory -- Snakemake's ``container:`` accepts all three); otherwise
+    ``resolve_image()``, so jobs run what interactive ``spv-container`` work
+    runs.
+    """
+    if override:
+        return str(override)
+    return resolve_image()[0]
+
+
+def warn_if_image_stale():
+    """Print one advisory line about a local image that is not pristine or current.
+
+    Never fatal. Two things worth saying at launch:
+
+    * a sandbox is in play, so what jobs run is not fully described by any
+      revision label -- deliberate, but it should not be a silent difference
+      from a clean run;
+    * the image predates the checkout. Usually fine, because the checkout's
+      ``src/`` is what rules import; it matters when the *dependency stack*
+      moved -- a new package, a lockfile bump.
+
+    Silent when there is no local image, no apptainer, or no revision label.
+    """
+    image, kind = resolve_image()
+    if kind == "tag":
+        return
+    revision = image_revision(image)
+    if kind == "sandbox":
+        built = f"built from {revision[:12]}" if revision else "revision unknown"
+        print(
+            f"[container] running the writable sandbox at {image} ({built}). "
+            "Anything installed into it is part of this run; "
+            "`spv-container status` for detail.",
+            file=sys.stderr,
+        )
+    if compare_revision(revision) == "behind":
+        print(
+            f"[container] image was built from {revision[:12]}, which is behind this "
+            "checkout. Fine unless the dependency stack moved; refresh with "
+            "`spv-container pull`.",
+            file=sys.stderr,
+        )
+
+
 def configure(workflow_config):
     """Install config-derived values after Snakemake has loaded configfiles."""
     global CATALOG_CONFIG, DEFAULT_MASK_SUFFIX, FIDUCIAL, PLANCK18
+    inject_checkout_pythonpath(workflow_config)
+    warn_if_image_stale()
     CATALOG_CONFIG = workflow_config
     FIDUCIAL = workflow_config["fiducial"]
     DEFAULT_MASK_SUFFIX = (
diff --git a/workflow/image_sims/Snakefile b/workflow/image_sims/Snakefile
index d7a89065..7420ae68 100644
--- a/workflow/image_sims/Snakefile
+++ b/workflow/image_sims/Snakefile
@@ -4,38 +4,37 @@ Run the sp_validation-side chain (merge -> extract -> calibrate -> m-bias),
 optionally including the ShapePipe pipeline stage, without pulling in the
 cosmology-validation config the top-level ``workflow/Snakefile`` requires.
 
-Layer a run config over the operational defaults -- the workflow config.yaml
-carries operational defaults but *no* science keys, so it is incomplete on its
-own (by design); the run config supplies the science knobs.  The one drive
-command on candide, with the committed SLURM profile owning all scheduling:
+The one drive command on candide, with the committed SLURM profile owning
+scheduling and container wrapping:
 
     snakemake --profile workflow/profiles/candide \\
         -s workflow/image_sims/Snakefile \\
         im_mbias --configfile my_run.yaml
 
-``configfile: "workflow/image_sims/config.yaml"`` below loads the operational
-defaults automatically, so only ``my_run.yaml`` (the science knobs, and any
-operational override that run wants) is passed on the command line; Snakemake
-deep-merges the two.  The profile supplies the executor, account, partition,
-node excludes and job floor -- no ``-j`` needed (the slurm executor sets the
-job cap).  Off-cluster, drop ``--profile`` and add ``-j N`` to run locally.
-
-The target (``im_mbias``) is given *before* ``--configfile``: Snakemake's
-``--configfile`` takes one-or-more paths, so a target placed after it is
-swallowed as a config path ("No such file: im_mbias").  Put targets ahead of
-``--configfile`` (or make ``--configfile`` the last flag on the line).  Always
+``configfile:`` below loads the operational defaults, so ``my_run.yaml`` need
+only carry the science knobs (and any operational override); Snakemake
+deep-merges the two.  Off-cluster, drop ``--profile`` and add ``-j N``.
+
+Put targets *before* ``--configfile``: it takes one-or-more paths, so a target
+after it is swallowed as a config path ("No such file: im_mbias").  Always
 dry-run first with ``-n``.
 
-The same rules are also available inside the main workflow: they are included
-there under ``if "image_sims" in config``.
+The same rules are also included in the main workflow under
+``if "image_sims" in config``.
 """
 
-configfile: "workflow/image_sims/config.yaml"
+import os
+import sys
 
+# Shared helpers from the generic workflow one directory up -- resolve_container
+# in particular, so `sif: null` here falls back to the same image every other
+# entry point runs. Snakemake's `include:` shares Python globals, so
+# image_sims.smk sees this import.
+sys.path.insert(0, os.path.realpath(os.path.join(str(workflow.basedir), "..")))
+import common
 
-# The image-sims rules own their container invocation explicitly, so no
-# top-level container is needed here.
-container: None
+
+configfile: "workflow/image_sims/config.yaml"
 
 
 include: "../rules/image_sims.smk"
diff --git a/workflow/image_sims/config.yaml b/workflow/image_sims/config.yaml
index 8f719c68..f5f3e3c0 100644
--- a/workflow/image_sims/config.yaml
+++ b/workflow/image_sims/config.yaml
@@ -1,44 +1,31 @@
 # Image-simulation m-bias workflow configuration.
 #
-# Two kinds of keys live under `image_sims:`, and the split is the point:
-#
-#   * OPERATIONAL keys default here (active lines below) and *nowhere else* --
-#     the .smk reads them bare, so this file is their single home.  Override in
-#     a run config only when a run genuinely differs from the shared setup.
-#
-#   * SCIENCE keys have NO default -- not here, not in code.  They fix the
-#     estimator's scientific behaviour and must be stated per run, so they
-#     appear below only as commented template lines.  Supply them in a run
-#     config layered on top:
-#
-#         snakemake -s workflow/image_sims/Snakefile \
-#             --configfile workflow/image_sims/config.yaml \
-#             --configfile my_run.yaml \
-#             -j 4 im_mbias
-#
-# A run config that omits a science key fails at DAG parse, naming the key; an
-# unknown key under `image_sims:` fails as a typo.  The structural keys below
-# (sif, repos, data roots, num, tile_ids) also have no default and must be set.
+# OPERATIONAL keys default here and nowhere else -- the .smk reads them bare, so
+# this file is their single home. SCIENCE keys have no default anywhere and
+# appear below only as commented template lines: they fix the estimator's
+# behaviour, so each run must state them in a run config layered on top. A run
+# config that omits one fails at DAG parse, naming the key; an unknown key under
+# `image_sims:` fails as a typo.
 
 image_sims:
 
-  # --- containers -------------------------------------------------------
-  # Two images, one per half of the chain (the split gate766 ran).  One image
-  # is the eventual target -- the sp_validation image is FROM the ShapePipe
-  # image -- but until sp_validation is uv-locked with cosmo_numba declared,
-  # its published image can drift NumPy past numba's window (seen 2026-07-11:
-  # "Numba needs NumPy 2.4 or less. Got NumPy 2.5" at ngmix).  PYTHONPATH
-  # shadows pure-Python code only, never binary deps.
-  sif: /n17data/cdaley/containers/sp_validation_im_sims.sif          # extract/calibrate/m-bias
-  sif_pipeline: /n17data/cdaley/containers/shapepipe_im_sims-runtime.sif  # pipeline/merge
-  # Apptainer bind mounts.  /automnt is required when repos/data are
-  # automounted (candide gotcha); harmless otherwise.  [operational]
-  binds: /n17data,/n09data,/home,/automnt
+  # --- container --------------------------------------------------------
+  # One image for the whole chain: the sp_validation image is built FROM the
+  # ShapePipe image, so it carries both stacks. CI builds it from the uv lock --
+  # an unlocked build drifts NumPy past numba's ceiling and the ngmix stage dies
+  # ("Numba needs NumPy 2.4 or less").
+  #
+  # `null` means the workflow's one image: your own .sif if you have pulled one
+  # with `spv-container pull`, else the registry tag for Snakemake to autopull
+  # (workflow/README.md).  Neither is repeated here.
+  # Override with a local .sif path, or with a branch tag
+  # (docker://ghcr.io/cosmostat/sp_validation:<sanitized-branch>) to run a
+  # branch's own CI image.
+  sif: null
 
   # --- repositories -----------------------------------------------------
   # Bound into the image; both repos' src go on PYTHONPATH so this branch's
-  # code wins over the baked copies: ShapePipe's #766 build, and sp_validation's
-  # image_sims.py / catalog.match_catalogs_radec.
+  # code wins over the baked copies.
   shapepipe_repo: /n17data/cdaley/unions/code/shapepipe
   sp_validation_repo: /n17data/cdaley/unions/code/sp_validation
 
diff --git a/workflow/profiles/candide/config.yaml b/workflow/profiles/candide/config.yaml
index 0316b909..19afd89a 100644
--- a/workflow/profiles/candide/config.yaml
+++ b/workflow/profiles/candide/config.yaml
@@ -1,71 +1,60 @@
 # Committed SLURM profile for the candide cluster (IAP).
 #
-# This is the "one run command" half of the workflow: drive any target with
+# Drive any target with
 #
 #     snakemake --profile workflow/profiles/candide \
 #         -s workflow/image_sims/Snakefile \
 #         --configfile <run config> <target>
 #
-# and Snakemake owns all scheduling -- it fans out one SLURM job per branch x
-# tile and drives them against the cluster, MPI-free.  Everything here is
-# cluster policy (executor, account, partition, node excludes, per-job
-# defaults); it carries no science and no workflow logic.
-#
-# What is deliberately NOT here:
-#
-#   * Container / apptainer settings.  The image-sims rules set
-#     ``container: None`` and own their ``apptainer exec`` call through the
-#     shared ``EXEC`` prefix (one image for every stage, with PYTHONPATH /
-#     PSF_DICT / OMP_NUM_THREADS injected there).  So no
-#     ``software-deployment-method: apptainer`` / ``apptainer-args`` -- those
-#     would wrap a *second*, redundant container around jobs that already run
-#     inside one.
-#
-#   * OMP_NUM_THREADS.  It is pinned to 1 on the ``apptainer exec`` line in
-#     workflow/rules/image_sims.smk, not here.  The slurm executor submits with
-#     ``--export=ALL``, which propagates the *driver's* ambient environment; a
-#     profile only sets CLI flags, never the driver's own env, so an
-#     ``OMP_NUM_THREADS`` set here would silently depend on the operator having
-#     exported it by hand.  Injecting it at the container boundary puts it where
-#     the compute runs, committed and independent of the launching shell.
-#
-#   * Per-rule resources (mem_mb, runtime).  Those live on each rule in the
-#     .smk; the ``default-resources`` below are only the floor for rules that
-#     set none.
+# Snakemake owns scheduling and the container wrapping; run it host-side, never
+# inside an ``apptainer shell``. workflow/README.md is the full story.
 
 executor: slurm
 
-# Cluster policy applied to every job unless a rule overrides it.  The excludes
-# are the flaky/no-internet candide nodes (n17 mount issues, n09 no internet,
-# n36); ``slurm_extra`` is passed verbatim onto the sbatch line by the executor
-# plugin, so the quoting is what sbatch must see.
+# --- GENERIC: mirrored in workflow/profiles/default/config.yaml -------------
+software-deployment-method: apptainer
+
+# Rerun a job when its code / params / inputs change, not only on mtime.
+# Caveat: "code" watches rule bodies and ``script:`` files, not ``src/`` --
+# editing a module under src/ does not mark outputs stale on its own.
+rerun-triggers: ["mtime", "params", "input", "code"]
+
+# Give an appearing output file a moment on networked filesystems before
+# Snakemake calls a job failed for a missing output.
+latency-wait: 5
+# --- end GENERIC ------------------------------------------------------------
+
+# No ``apptainer-prefix``: the entry Snakefiles resolve ``container:`` to this
+# user's own image path, so there is nothing for Snakemake to cache.
 #
-# Three candide-specific SLURM lessons are baked into the values below (learned
-# the hard way on the earlier hand-driven im-sims runs; see shapepipe's retired
-# image_sims_pipeline/Snakefile docstring):
+# candide's disks, plus the one machine-specific env var: the host OpenMPI libs
+# MPI rules need to find libmpi inside the container (only rules importing
+# mpi4py care; harmless for the rest). The bind list matches ``spv-container
+# exec``'s default; keep the two in step.
+apptainer-args: >-
+  --cleanenv
+  --bind /home,/scratch,/automnt,/n17data,/n23data1,/n09data
+  --env LD_LIBRARY_PATH=/softs/openmpi/5.0.5-slurm-CentOS8/lib
+
+# Cluster policy applied to every job unless a rule overrides it. Excludes are
+# the flaky/no-internet candide nodes (n17 mount issues, n09 no internet, n36).
 #
-#   * ``runtime`` MUST carry a unit (``60m``, ``6h``, ``2d``).  Snakemake's
-#     resource parser reads a *bare* number as SECONDS, so ``runtime: 60`` would
-#     silently give every job a 60-second wall clock and kill it on start.  The
-#     quoted-with-unit form here is deliberate; keep it that way, and prefer the
-#     same in any ``--default-resources`` passed on the command line.  (A bare
-#     integer in a *rule's* ``resources: runtime=720`` is fine -- snakemake
-#     reads rule-level numeric runtime as minutes -- the seconds trap is only
+#   * ``runtime`` MUST carry a unit (``60m``, ``6h``, ``2d``). Snakemake's
+#     resource parser reads a bare number as SECONDS, so ``runtime: 60`` would
+#     silently give every job a 60-second wall clock and kill it on start.
+#     (A bare integer in a rule's own ``resources: runtime=720`` is fine --
+#     rule-level numeric runtime is read as minutes; the seconds trap is only
 #     the CLI/default-resources parser.)
 #
 #   * ``cpus_per_task`` is pinned to 12 to CAP JOBS PER NODE, not because a job
-#     needs 12 cores (the chain is MPI-free and pins ``OMP_NUM_THREADS=1`` at
-#     the container).  candide's per-user process limit is ``ulimit -u 1200``
-#     *per node*, and apptainer crashes ("can't start new thread") beyond ~4
-#     concurrent jobs on a 48-core node.  Requesting 12 CPUs/job holds SLURM to
-#     ~4 jobs per 48-core node, under the ceiling.  Dropping this to 1 would let
-#     SLURM pack ~48 jobs onto a node and crash the compute-heavy im_pipeline
-#     stage (which inherits this default -- it sets mem/runtime but not cpus).
+#     needs 12 cores. candide's per-user process limit is ``ulimit -u 1200``
+#     per node, and apptainer crashes ("can't start new thread") beyond ~4
+#     concurrent jobs on a 48-core node; 12 CPUs/job holds SLURM to ~4 jobs per
+#     node.
 #
-#   * After launching a real fan-out, VERIFY the request actually landed:
-#     ``squeue -u $USER -o "%C %l"`` must show 12 (CPUs) and the wall clock you
-#     intended (e.g. 12:00:00 for im_pipeline).  A silently-misparsed runtime or
-#     cpus shows up here before it wastes a queue slot.
+#   * After launching a real fan-out, verify the request landed:
+#     ``squeue -u $USER -o "%C %l"`` should show 12 (CPUs) and the intended
+#     wall clock.
 default-resources:
   slurm_account: "cusers"
   slurm_partition: "comp,pscomp"
@@ -73,13 +62,7 @@ default-resources:
   cpus_per_task: 12
   slurm_extra: "'--exclude=n17,n09,n36'"
 
-# Give an appearing output file a moment on candide's automounted filesystems
-# before Snakemake calls a job failed for a missing output, and retry a job
-# once on transient node failure.
-latency-wait: 5
+# Retry a job once on transient node failure, and keep the SLURM logs of
+# successful jobs (candide debugging).
 retries: 1
-
-# Keep the SLURM logs of successful jobs (candide debugging), and rerun a job
-# when its code / params / inputs change, not only on mtime.
 slurm-keep-successful-logs: true
-rerun-triggers: ["mtime", "params", "input", "code"]
diff --git a/workflow/profiles/default/config.yaml b/workflow/profiles/default/config.yaml
new file mode 100644
index 00000000..bf272c03
--- /dev/null
+++ b/workflow/profiles/default/config.yaml
@@ -0,0 +1,39 @@
+# Machine-independent profile: the container model, and nothing else.
+#
+# Use it anywhere that is not candide -- a laptop, a workstation, another
+# cluster's interactive node:
+#
+#     snakemake --profile workflow/profiles/default -s workflow/Snakefile \
+#         <target> --configfile <run config> -j 4
+#
+# On candide -- where the analysis actually runs -- use
+# `--profile workflow/profiles/candide` instead: the GENERIC block below plus
+# the SLURM executor and candide's machine layer.
+#
+# Requirements are the same everywhere: `apptainer` on PATH, and `snakemake`
+# installed host-side (`uv tool install ...`, see workflow/README.md) -- never
+# run from inside an apptainer shell.
+
+# --- GENERIC: mirrored in workflow/profiles/candide/config.yaml -------------
+# Wrap each job's `shell:`/`script:` command in `apptainer exec`, using the
+# image named by the entry Snakefile's `container:` directive.
+software-deployment-method: apptainer
+
+# Rerun a job when its code / params / inputs change, not only on mtime.
+# Caveat: "code" watches rule bodies and `script:` files, not `src/` -- editing
+# a module under src/ does not mark outputs stale on its own.
+rerun-triggers: ["mtime", "params", "input", "code"]
+
+# Give an appearing output file a moment on networked filesystems before
+# Snakemake calls a job failed for a missing output.
+latency-wait: 5
+# --- end GENERIC ------------------------------------------------------------
+
+# Binds are the one thing you almost certainly need to edit for your machine:
+# whatever paths your inputs, outputs and checkout live under. `--cleanenv` so a
+# job's environment is the image's, not your shell's. If $HOME and the working
+# directory cover everything (apptainer mounts both by default), drop `--bind`.
+apptainer-args: "--cleanenv --bind /home"
+
+# No `apptainer-prefix`, here or on candide: the entry Snakefiles resolve
+# `container:` to this user's own image path (workflow/README.md).
diff --git a/workflow/rules/covariance.smk b/workflow/rules/covariance.smk
index 49e40201..e9185428 100644
--- a/workflow/rules/covariance.smk
+++ b/workflow/rules/covariance.smk
@@ -11,7 +11,6 @@ def get_cat_params(version):
 
 
 # covariance_dir(), covariance_base(), covariance_path() defined in Snakefile
-# Additional wildcard constraints defined locally for pseudo-Cl rules (line 327)
 
 # DEFAULT_MASK_SUFFIX defined in Snakefile
 # Footprint mask power spectra (nside=4096, from comprehensive catalog with spatial cuts only)
@@ -145,6 +144,12 @@ EOF
 
 
 rule covariance_cosmocov:
+    """Run the host-compiled CosmoCov binary.
+
+    `container: None` on purpose: CosmoCov is a host-compiled Fortran/C binary
+    loaded through environment-modules (`module load gcc intelpython openmpi`),
+    not a Python entry point the container ships.
+    """
     input:
         rules.covariance_ini.output,
     output:
@@ -203,13 +208,13 @@ rule covariance_glass_mock:
             seed=range(config["glass_mocks"]["seed_range"][0], config["glass_mocks"]["seed_range"][1] + 1),
         ),
     output:
-        xi_covariance="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/xi_covariance.npy",
-        cl_covariance="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/cl_covariance.npy",
-        combined_covariance="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/combined_covariance.npy",
-        correlation_plot="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/combined_correlation.png",
-        xi_mean="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/xi_mean.npy",
-        cl_mean="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/cl_mean.npy",
-        combined_mean="/automnt/n17data/cdaley/unions/pure_eb/results/covariance/glass_mock_v1.4.6/combined_mean.npy",
+        xi_covariance="results/covariance/glass_mock_v1.4.6/xi_covariance.npy",
+        cl_covariance="results/covariance/glass_mock_v1.4.6/cl_covariance.npy",
+        combined_covariance="results/covariance/glass_mock_v1.4.6/combined_covariance.npy",
+        correlation_plot="results/covariance/glass_mock_v1.4.6/combined_correlation.png",
+        xi_mean="results/covariance/glass_mock_v1.4.6/xi_mean.npy",
+        cl_mean="results/covariance/glass_mock_v1.4.6/cl_mean.npy",
+        combined_mean="results/covariance/glass_mock_v1.4.6/combined_mean.npy",
     script:
         "../scripts/compute_glass_mock_covariance.py"
 
@@ -231,30 +236,21 @@ rule generate_glass_mock_rhotau_samples:
         mock_id="{mock_id}",
         output_dir="results/glass_mock_rhotau_samples",
     threads: 1
-    shell:
-        """
-        python /n17data/cdaley/unions/pure_eb/code/sp_validation/workflow/scripts/generate_glass_mock_rhotau_samples.py \
-            --cov-tau {input.cov_tau} \
-            --ref-tau {input.ref_tau} \
-            --output-dir {params.output_dir} \
-            --mock-ids {params.mock_id}
-        """
+    script:
+        "../scripts/generate_glass_mock_rhotau_samples.py"
 
 
 rule covariance_process:
+    """Post-process a raw CosmoCov matrix into the analysis-ready form."""
     input:
         str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.txt")
     output:
         matrix=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed.txt"),
         gaussian=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed_g.txt"),
         plot=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed_plot.pdf")
-    params:
-        output_stub=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed")
     threads: 1
-    shell:
-        """
-        python /n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_inference/scripts/cosmocov_process.py {input} {params.output_stub}
-        """
+    script:
+        "../scripts/cosmocov_process.py"
 
 
 def fiducial_covariance_outputs(mask_suffix=""):
diff --git a/workflow/rules/glass_mock.smk b/workflow/rules/glass_mock.smk
index da8cb491..243076d6 100644
--- a/workflow/rules/glass_mock.smk
+++ b/workflow/rules/glass_mock.smk
@@ -119,7 +119,7 @@ rule mock_cosebis_bias_test:
         ),
         xi_ref=f"{MOCK_RESULTS}/gg_glass_mock_00001_nbins=1000.fits",
         cov=str(
-            Path("/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_inference/data/covariance")
+            COSMO_INFERENCE / "data/covariance"
             / "covariance_SP_v1.4.6_leak_corr_A_g_minsep=0.5_maxsep=500.0_nbins=1000_masked"
             / "covariance_SP_v1.4.6_leak_corr_A_g_minsep=0.5_maxsep=500.0_nbins=1000_masked_processed.txt"
         ),
diff --git a/workflow/rules/image_sims.smk b/workflow/rules/image_sims.smk
index a4eb92d0..cc992391 100644
--- a/workflow/rules/image_sims.smk
+++ b/workflow/rules/image_sims.smk
@@ -1,43 +1,18 @@
 """Image-simulation orchestration: raw SKiLLS sim images -> shear m/c bias.
 
-This rule set drives the image-simulation validation chain end to end and is
-the sp_validation-side half of the split described in
+The sp_validation-side half of the split described in
 ``UNIONS-WL/MultiBand_ImSim#1``: ShapePipe turns the simulated tiles into
-per-tile shape catalogues, then sp_validation merges, extracts, calibrates and
-finally measures the multiplicative/additive shear bias.
-
-Two images, one prefix shape.  Architecturally one image could run every
-stage -- the sp_validation image is built ``FROM`` the ShapePipe image, so it
-carries both stacks -- but the *published* sp_validation image's environment
-is not yet trustworthy for the ShapePipe half: sp_validation has no lockfile
-and does not declare its numba-bearing dependency (``cosmo_numba``), so
-unpinned install layers can drift NumPy past numba's window (a 2026-07-11
-gate run hit exactly this: ``Numba needs NumPy 2.4 or less. Got NumPy 2.5``
-at the ngmix stage).  PYTHONPATH shadowing covers pure-Python *code*, never
-binary deps, so until sp_validation is uv-locked with its deps declared
-(spun off as its own task), each half runs in its own repo's image -- the
-same split the gate766 baseline ran:
-
-* ShapePipe stages -> ``pipeline`` (raw images -> per-tile cats) and ``merge``
-  (``create_final_cat`` -> ``final_cat_{sim}.hdf5``) run in ``sif_pipeline``
-  (the ShapePipe image).
-* sp_validation stages -> ``manifest``, ``extract`` (-> comprehensive cat),
-  ``calibrate`` (-> cut cat) and ``m_bias`` (-> ``m_bias_results.yaml``) run
-  in ``sif`` (the sp_validation image).
-
-Every rule sets ``container: None`` and calls ``apptainer exec`` explicitly
-through a shared prefix template (``EXEC_PIPELINE`` / ``EXEC`` -- identical
-env injections, different image), because the images are not the workflow's
-top-level container.  Everything is parameterised under
-``config["image_sims"]`` -- the two ``sif`` keys, repository roots, data roots,
-the PSF dictionary, the explicit ``tile_ids`` list and the sim/calibration
-knobs -- so a fresh user drives it from config alone, with no hard-coded clone
-layout.  Configuration is fail-fast: a schema check at load rejects an unknown
-key (typo) and a missing science key (see ``workflow/image_sims/config.yaml``
-for the operational/science split).  The ``PYTHONPATH`` override injects both
-repos' ``src`` so the *branch* source (ShapePipe's ``#766`` build;
-sp_validation's ``image_sims.py``, ``catalog.match_catalogs_radec``) wins over
-whatever is baked into the image.
+per-tile shape catalogues (``pipeline``, ``merge``), then sp_validation
+extracts, calibrates and measures the multiplicative/additive shear bias
+(``manifest``, ``extract``, ``calibrate``, ``m_bias``).
+
+One image runs the whole chain: the sp_validation image is built ``FROM`` the
+ShapePipe image, so it carries both stacks.  Everything else is parameterised
+under ``config["image_sims"]`` -- repository roots, data roots, the PSF
+dictionary, the explicit ``tile_ids`` list and the sim/calibration knobs -- so
+a fresh user drives it from config alone.  Configuration is fail-fast: a schema
+check at load rejects an unknown key (typo) and a missing science key (see
+``workflow/image_sims/config.yaml`` for the operational/science split).
 
 The five simulations per grid are the reference ``1z2z`` (no input shear) plus
 the ``+/-`` shear pairs ``1p2z``/``1m2z`` (g1) and ``1z2p``/``1z2m`` (g2); the
@@ -45,24 +20,15 @@ m-bias estimator matches each to the reference by RA/Dec.
 """
 
 import os
-from pathlib import Path
 
 IMSIM = config["image_sims"]
 
 # --- fail-fast schema check ----------------------------------------------
-# One home for every fact: the run config carries the science knobs, the
-# workflow config.yaml carries the operational defaults, and *this* block is
-# where a typo or a missing knob dies -- at DAG parse, before any compute.
+# An unknown key under ``image_sims:`` is a hard error (typo protection); a
+# missing key is a hard error naming it.  Both fire at DAG parse, before compute.
 #
-# Every key must be declared below.  An unknown key under ``image_sims:`` is a
-# hard error (typo protection); a missing *science* key is a hard error naming
-# the key (no silent code default anywhere).  Operational keys default in the
-# workflow config.yaml and nowhere else: the .smk reads them as bare
-# ``IMSIM[key]`` (never ``.get`` with a second literal), so their value comes
-# from config.yaml alone -- the single home for an operational default.
-#
-# Science keys: required from the *run* config; no default in config.yaml (only
-# a commented template line) and no default in code.  These fix the estimator's
+# Science keys: required from the *run* config; no default here or in
+# config.yaml (only a commented template line).  These fix the estimator's
 # scientific behaviour, so they must be stated per run, never inherited.
 _SCIENCE_KEYS = {
     "w_cols",
@@ -72,16 +38,14 @@ _SCIENCE_KEYS = {
     "bootstrap_seed",
     "mask_config",
 }
-# Deprecated science keys: accepted (so a pre-``w_cols`` run config still parses
-# and the estimator's back-compat path runs) but not *required* -- our configs
-# state ``w_cols``.  Listed here only to keep them out of the unknown-key error.
+# Deprecated but still accepted, so a pre-``w_cols`` run config keeps parsing
+# into the estimator's back-compat path.
 _DEPRECATED_KEYS = {
     "w_col",
 }
-# Operational keys: default (visibly) in the workflow config.yaml; the .smk
-# reads them bare, so config.yaml is their one home.
+# Operational keys: default in the workflow config.yaml; the .smk reads them
+# bare (never ``.get`` with a literal), so config.yaml is their one home.
 _OPERATIONAL_KEYS = {
-    "binds",
     "sims_type",
     "branches",
     "shape",
@@ -94,7 +58,6 @@ _OPERATIONAL_KEYS = {
 # Structural keys: paths/identifiers the run must supply (no sensible default).
 _STRUCTURAL_KEYS = {
     "sif",
-    "sif_pipeline",
     "shapepipe_repo",
     "sp_validation_repo",
     "grids_base",
@@ -128,13 +91,13 @@ if _missing_structural:
         f"{_missing_structural} -- set them in the run config"
     )
 
-# --- containers -----------------------------------------------------------
-# Two images (see module docstring): the ShapePipe image for the pipeline and
-# merge stages, the sp_validation image for everything downstream.  Collapse
-# back to one image once sp_validation's env is lock-managed.
-SIF = IMSIM["sif"]  # sp_validation stages
-SIF_PIPELINE = IMSIM["sif_pipeline"]  # ShapePipe stages
-BINDS = IMSIM["binds"]
+# --- container ------------------------------------------------------------
+# Every compute rule carries ``container: SIF`` rather than inheriting a
+# module-level default: these rules are also included from the top-level
+# workflow/Snakefile, whose module default is the cosmology image (no ShapePipe
+# stack).  Binds come from the driving profile's ``apptainer-args``.  A null
+# ``sif`` resolves to the workflow's one image (see workflow/image_sims/config.yaml).
+SIF = common.resolve_container(IMSIM["sif"])
 
 # --- repositories (bound into the image; branch code overrides) -----------
 SHAPEPIPE_REPO = IMSIM["shapepipe_repo"]
@@ -181,48 +144,27 @@ CALIBRATE = IMSIM["calibrate_script"]
 # m-bias is *this branch's* extracted core, injected on PYTHONPATH.
 COMPUTE_M_BIAS = f"{SPV_REPO}/scripts/compute_m_bias_image_sims.py"
 
-# --- container exec prefixes ----------------------------------------------
-# One prefix *shape* for every stage -- two instances, one per image.  Three
-# env injections make the on-disk branch
-# code and the sim PSF win over the image's baked copies:
+# --- in-command env prefix -------------------------------------------------
+# Snakemake wraps each rule's whole ``shell:`` string inside the container, so
+# these ``VAR=value`` tokens land inside it. Three settings:
 #
-#   * PYTHONPATH prepends BOTH repos' ``src`` (ShapePipe first, then
-#     sp_validation), so Python resolves the worktree build before
-#     ``/app``/``/sp_validation`` -- the local-testing counterpart of the
-#     git-ref deps, letting the branch code run without an image rebuild.  This
-#     covers the Python *packages* only: the bash entry points (run_job) and
-#     the ShapePipe/sp_validation *scripts* are still invoked at the repo paths
-#     resolved from config (RUN_JOB, CREATE_FINAL_CAT, EXTRACT_INFO, ...), not
-#     shadowed by PYTHONPATH.
+#   * PYTHONPATH prepends both repos' ``src`` so Python resolves the worktree
+#     build ahead of the copies baked into the image -- the branch's code runs
+#     without an image rebuild. Packages only: the bash and python entry points
+#     are invoked at the repo paths from config (RUN_JOB, CREATE_FINAL_CAT,
+#     EXTRACT_INFO, ...), not shadowed by PYTHONPATH.
 #   * PSF_DICT points the fake_psf module (PSF_DICT_PATH = $PSF_DICT, expanded
 #     via getexpanded) at this run's PSF dictionary.
-#
-# The SLURM env vars are stripped (``env -u ...``) so that when the ShapePipe
-# pipeline stage's OpenMPI initialises inside the image it does not try to
-# attach to the host SLURM launcher (cf. apptainer_noslurm.sh).  The strip is
-# harmless for the pure-Python sp_validation stages, so one prefix serves all.
-#
-# ``OMP_NUM_THREADS=1`` is injected here, at the ``apptainer exec`` call, and
-# not left to the SLURM profile.  The chain is MPI-free: Snakemake fans out one
-# job per branch x tile and each job's parallelism is ShapePipe's own internal
-# multiprocessing (``-N n_smp``), so the OpenMP/BLAS thread pool inside the
-# container must be pinned to 1 to avoid oversubscription.  The SLURM profile
-# cannot pin it reliably: the slurm executor submits with ``--export=ALL``,
-# which propagates the *driver's* ambient environment -- but a Snakemake
-# profile only sets CLI flags, never the driver's own env, so an
-# ``OMP_NUM_THREADS`` there would depend on the operator having exported it by
-# hand (the implicit, uncommitted state the "one run command" is meant to
-# retire).  Injecting it on the ``apptainer exec`` line puts it where the
-# compute actually runs -- inside the container, independent of the driver's
-# env -- the same lever this prefix already uses for PYTHONPATH/PSF_DICT.
-_EXEC_PREFIX = (
-    "env -u SLURM_JOBID -u SLURM_JOB_ID -u SLURM_PROCID "
-    f"apptainer exec --bind {BINDS} "
-    f"--env PYTHONPATH={SHAPEPIPE_REPO}/src:{SPV_REPO}/src "
-    f"--env PSF_DICT={PSF_DICT} --env OMP_NUM_THREADS=1 "
+#   * OMP_NUM_THREADS=1 rides here rather than in the SLURM profile: the chain
+#     is MPI-free (Snakemake fans out one job per branch x tile; in-job
+#     parallelism is ShapePipe's own ``-N n_smp``), so the OpenMP/BLAS pool must
+#     be pinned to 1 to avoid oversubscription, and a profile can only set CLI
+#     flags, never the driver env the slurm executor's ``--export=ALL``
+#     propagates.
+_ENV_PREFIX = (
+    f"PYTHONPATH={SHAPEPIPE_REPO}/src:{SPV_REPO}/src "
+    f"PSF_DICT={PSF_DICT} OMP_NUM_THREADS=1 "
 )
-EXEC = _EXEC_PREFIX + SIF  # sp_validation stages
-EXEC_PIPELINE = _EXEC_PREFIX + SIF_PIPELINE  # ShapePipe stages
 
 JOB_MASK = sum([1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])
 
@@ -300,8 +242,10 @@ rule im_manifest:
         input_sims_base=INPUT_SIMS_BASE,
         sims_type=SIMS_TYPE,
         num=NUM,
+    container:
+        SIF
     shell:
-        "{EXEC} python {BUILD_MANIFEST} "
+        "{_ENV_PREFIX} python {BUILD_MANIFEST} "
         "--input-sims-base {params.input_sims_base} "
         "--sims-type {params.sims_type} --num {params.num} "
         "{params.branch_args} -o {output.manifest}"
@@ -349,22 +293,17 @@ rule im_init:
 
 
 rule im_pipeline:
-    """Run ShapePipe on one simulated tile (ShapePipe stage).
+    """Run ShapePipe on one simulated tile.
 
     Delegates the module DAG to ShapePipe's own job runner; the sentinel log
-    marks tile completion for the merge step.  This is the compute-heavy,
-    MPI-bearing stage.
+    marks tile completion for the merge step.  The compute-heavy stage.
     """
     input:
-        # ``params.py`` is a *tracked* output of ``im_init``, so this one input
-        # supplies the im_init -> im_pipeline edge. The ``cfis`` symlink the
-        # shell reads (via {RUN_JOB}) is created by that same im_init shell block
-        # as an *untracked* side effect -- no rule declares it as an output
-        # (snakemake will not track a symlink/directory output). Declaring it an
-        # input here therefore asked the DAG for a file no rule produces: on a
-        # fresh grids_base it aborted the build with MissingInputException before
-        # any job ran. It is safe to drop -- cfis exists whenever params does,
-        # since im_init stages both together.
+        # params.py alone supplies the im_init -> im_pipeline edge.  The `cfis`
+        # symlink {RUN_JOB} also reads is an untracked side effect of the same
+        # im_init shell (snakemake will not track a symlink output), so it must
+        # not be declared here -- doing so asks the DAG for a file no rule
+        # produces and aborts on a fresh grids_base.
         params=f"{GRIDS_BASE}/{{sim}}/params.py",
     output:
         done=touch(f"{GRIDS_BASE}/{{sim}}/logs/pipeline_{{tile}}.done"),
@@ -375,9 +314,11 @@ rule im_pipeline:
     resources:
         mem_mb=16000,
         runtime=720,
+    container:
+        SIF
     shell:
         "cd {params.run_dir} && "
-        "{EXEC_PIPELINE} bash {RUN_JOB} "
+        "{_ENV_PREFIX} bash {RUN_JOB} "
         "-e {wildcards.tile} -t image_sims -j {JOB_MASK} "
         "-p {params.psf} -N {params.n_smp}"
 
@@ -397,9 +338,11 @@ rule im_merge:
         cat=f"{GRIDS_BASE}/{{sim}}/final_cat_{{sim}}.hdf5",
     params:
         run_dir=lambda wc: f"{GRIDS_BASE}/{wc.sim}",
+    container:
+        SIF
     shell:
         "cd {params.run_dir} && "
-        "{EXEC_PIPELINE} python {CREATE_FINAL_CAT} "
+        "{_ENV_PREFIX} python {CREATE_FINAL_CAT} "
         "-I -m final_cat_{wildcards.sim}.hdf5 -i .. "
         "-p cfis/final_cat.param -P {wildcards.sim} "
         "-o n_tiles_final.txt -v"
@@ -418,8 +361,10 @@ rule im_extract:
         cat=f"{GRIDS_BASE}/{{sim}}/shape_catalog_comprehensive_{SHAPE}.fits",
     params:
         run_dir=lambda wc: f"{GRIDS_BASE}/{wc.sim}",
+    container:
+        SIF
     shell:
-        "cd {params.run_dir} && {EXEC} python {EXTRACT_INFO}"
+        "cd {params.run_dir} && {_ENV_PREFIX} python {EXTRACT_INFO}"
 
 
 rule im_calibrate:
@@ -436,20 +381,16 @@ rule im_calibrate:
         cat=f"{GRIDS_BASE}/{{sim}}/shape_catalog_cut_{SHAPE}.fits",
     params:
         run_dir=lambda wc: f"{GRIDS_BASE}/{wc.sim}",
+    container:
+        SIF
     shell:
         "cd {params.run_dir} && "
-        "{EXEC} python {CALIBRATE} -s calibrate"
+        "{_ENV_PREFIX} python {CALIBRATE} -s calibrate"
 
 
-rule im_mbias:
-    """Multiplicative/additive shear bias from the calibrated grids.
-
-    Produces the workflow's headline artifact, ``m_bias_results.yaml``.  The
-    injected shear (``shear_amplitude`` and the branch map) comes from
-    ``manifest.yaml`` alone -- no literal amplitude here or in config.yaml.  The
-    generated ``m_bias_config.yaml`` carries the manifest's ``branches`` and
-    ``pairs``, so the estimator's sim list and pairing are the campaign's, not a
-    hard-coded default.
+rule im_mbias_config:
+    """Assemble ``m_bias_config.yaml`` for the m-bias step: the manifest's
+    shear/branch facts, this run's science knobs, and git/container provenance.
     """
     input:
         manifest=MANIFEST,
@@ -457,112 +398,39 @@ rule im_mbias:
             f"{GRIDS_BASE}/{{sim}}/shape_catalog_cut_{SHAPE}.fits", sim=SIMS
         ),
     output:
-        results=f"{GRIDS_BASE}/results/m_bias_results.yaml",
-    params:
         cfg=f"{GRIDS_BASE}/results/m_bias_config.yaml",
+    params:
         grids_base=GRIDS_BASE,
         num=NUM,
         cat_name=f"shape_catalog_cut_{SHAPE}.fits",
         sif=SIF,
-        sif_pipeline=SIF_PIPELINE,
         shapepipe_repo=SHAPEPIPE_REPO,
         sp_validation_repo=SPV_REPO,
+        results_dir=f"{GRIDS_BASE}/results",
+        results=f"{GRIDS_BASE}/results/m_bias_results.yaml",
         # Science knobs, read bare from the run config (no default here).
         match_radius_deg=IMSIM["match_radius_deg"],
         w_cols=IMSIM["w_cols"],
         n_bootstrap=IMSIM["n_bootstrap"],
         pair_match=IMSIM["pair_match"],
         bootstrap_seed=IMSIM["bootstrap_seed"],
-    run:
-        import hashlib
-        import re
-        import subprocess
-
-        import yaml
-
-        with open(input.manifest) as fh:
-            manifest = yaml.safe_load(fh)
-
-        def _git(repo, *args):
-            """Read a git fact from ``repo``; ``None`` if it is not a checkout."""
-            try:
-                return subprocess.run(
-                    ["git", "-C", repo, *args],
-                    capture_output=True,
-                    text=True,
-                    check=True,
-                ).stdout.strip()
-            except (subprocess.CalledProcessError, FileNotFoundError):
-                return None
-
-        def _sif_revision(sif_path):
-            """GHCR revision baked into the SIF's OCI labels.
-
-            A plain-text scan of the image file (login-safe: no exec, no
-            container start), reading org.opencontainers.image.revision -- the
-            source commit GHCR built the image from.  ``None`` if absent.
-            """
-            try:
-                with open(sif_path, "rb") as fh:
-                    blob = fh.read()
-            except OSError:
-                return None
-            m = re.search(
-                rb'org\.opencontainers\.image\.revision"?[:=]"?([0-9a-f]{7,40})',
-                blob,
-            )
-            return m.group(1).decode() if m else None
-
-        # Manifest hash: sha256 of the exact bytes im_manifest wrote, so the
-        # result records which injected-shear facts it was computed against.
-        with open(input.manifest, "rb") as fh:
-            manifest_sha256 = hashlib.sha256(fh.read()).hexdigest()
-
-        provenance = {
-            "manifest_sha256": manifest_sha256,
-            "sp_validation": {
-                "branch": _git(params.sp_validation_repo, "rev-parse", "--abbrev-ref", "HEAD"),
-                "commit": _git(params.sp_validation_repo, "rev-parse", "HEAD"),
-            },
-            "shapepipe": {
-                "branch": _git(params.shapepipe_repo, "rev-parse", "--abbrev-ref", "HEAD"),
-                "commit": _git(params.shapepipe_repo, "rev-parse", "HEAD"),
-            },
-            "containers": {
-                "sif": params.sif,
-                "ghcr_revision": _sif_revision(params.sif),
-                "sif_pipeline": params.sif_pipeline,
-                "ghcr_revision_pipeline": _sif_revision(params.sif_pipeline),
-            },
-        }
-
-        os.makedirs(os.path.dirname(output.results), exist_ok=True)
-        # Emit *every* key the estimator requires -- pair_match and
-        # bootstrap_seed included.  Requiring a key without emitting it would
-        # be a KeyError at run time, so the generated config is the complete
-        # contract between rule and estimator.  ``provenance`` rides along as a
-        # top-level block: the compute script copies it verbatim into the output
-        # results yaml, so a result file is self-describing (which manifest,
-        # which repo commits, which container built the number).
-        mbias_cfg = {
-            "grids_dir": params.grids_base,
-            "num": params.num,
-            "catalog_name": params.cat_name,
-            # Injected shear: from the manifest, the single source of truth.
-            "shear_amplitude": manifest["shear_amplitude"],
-            "branches": list(manifest["branches"]),
-            "pairs": manifest["pairs"],
-            "match_radius_deg": params.match_radius_deg,
-            "w_cols": list(params.w_cols),
-            "pair_match": params.pair_match,
-            "n_bootstrap": params.n_bootstrap,
-            "bootstrap_seed": params.bootstrap_seed,
-            "results_dir": os.path.dirname(output.results),
-            "output_path": output.results,
-            "provenance": provenance,
-        }
-        with open(params.cfg, "w") as fh:
-            yaml.safe_dump(mbias_cfg, fh)
-        shell(
-            "{EXEC} python {COMPUTE_M_BIAS} -c {params.cfg} -v"
-        )
+    container:
+        SIF
+    script:
+        "../scripts/im_mbias_config.py"
+
+
+rule im_mbias:
+    """Multiplicative/additive shear bias from the calibrated grids.
+
+    Produces the workflow's headline artifact, ``m_bias_results.yaml``, running
+    the estimator against the config ``im_mbias_config`` assembled.
+    """
+    input:
+        cfg=f"{GRIDS_BASE}/results/m_bias_config.yaml",
+    output:
+        results=f"{GRIDS_BASE}/results/m_bias_results.yaml",
+    container:
+        SIF
+    shell:
+        "{_ENV_PREFIX} python {COMPUTE_M_BIAS} -c {input.cfg} -v"
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 22c09db2..861c3b05 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -24,46 +24,53 @@ rule xi:
         "../scripts/run_2pcf.py"
 
 
-rule xi_highres:
-    """High-resolution xi for COSEBIS integration."""
-    container: None
-    output:
-        txt=str(COSMO_VAL / f"{FIDUCIAL['version']}_xi_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.txt"),
-        xi_plus=str(COSMO_VAL / f"xi_plus_{FIDUCIAL['version']}_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.fits"),
-        xi_minus=str(COSMO_VAL / f"xi_minus_{FIDUCIAL['version']}_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.fits"),
-    resources:
-        tasks=30,
-        cpus_per_task=12,
-        nodes=6,
-        mem_mb_per_cpu=2000,
-        runtime=2880,
-        slurm_extra="'--exclude=n17,n09,n36 --partition=pscomp'",
-        mpi="/softs/openmpi/5.0.5-slurm-CentOS8/bin/mpiexec",
-    shell:
-        "{resources.mpi} -n {resources.tasks} "
-        "apptainer exec "
-        "--bind /home,/n09data,/n17data,/n23data1,/softs "
-        "--env LD_LIBRARY_PATH=/softs/openmpi/5.0.5-slurm-CentOS8/lib "
-        "/n17data/cdaley/containers/containers "
-        "python /automnt/n17data/cdaley/unions/pure_eb/code/sp_validation/workflow/scripts/run_2pcf_highres.py"
-
-
-rule run_cosmo_val:
-    """Full CosmoVal diagnostic suite."""
-    output:
-        sentinel=str(COSMO_VAL / "run_cosmo_val.done"),
-    threads: 24
-    resources:
-        mem_mb=60000,
-        disk_mb=20000,
-        runtime=360,
-    shell:
-        """
-        export PYTHONPATH="/home/cdaley/.local/lib/python3.12/site-packages:${{PYTHONPATH:-}}"
-        cd /n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val \
-        && python run_cosmo_val.py \
-        && touch {output.sentinel}
-        """
+# PARKED: xi_highres (high-resolution xi for COSEBIS integration). Not runnable
+# as written -- the shell invokes run_2pcf_highres.py bare, but the script has
+# required --cat-config and --out arguments. Revive it with those supplied.
+#
+# The MPI reasoning below is hard-won and must survive the revival:
+#
+#   Exception to the profile-driven container model: this is multi-node MPI, one
+#   `apptainer exec` per rank. Snakemake's own container wrapping puts the
+#   *whole* shell command -- `mpiexec` included -- inside a single container
+#   instance, so only rank 0's node would run inside it; the other ranks,
+#   spawned by SLURM/PMI on their own nodes, would land bare on the host.
+#   `container: None` plus an explicit `mpiexec -n N apptainer exec ...`
+#   per-rank is therefore required.
+#   Snakemake's slurm-jobstep plugin deliberately does NOT prepend `srun` to a
+#   job carrying an `mpi` resource, which is what lets the rule's own launcher
+#   run on the host, outside the container.
+#   Because this rule builds its own apptainer call, reaching the source-cache
+#   copy of the script relies on our `--bind /home` rather than on Snakemake's
+#   automatic mount -- and on a concrete image file, since `apptainer exec`
+#   takes no `docker://` URI. Take that path from `resolve_image()[0]` rather
+#   than naming a second image path that can drift.
+#
+# rule xi_highres:
+#     container: None
+#     params:
+#         image=resolve_image()[0],
+#     input:
+#         script=workflow.source_path("../scripts/run_2pcf_highres.py"),
+#     output:
+#         txt=str(COSMO_VAL / f"{FIDUCIAL['version']}_xi_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.txt"),
+#         xi_plus=str(COSMO_VAL / f"xi_plus_{FIDUCIAL['version']}_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.fits"),
+#         xi_minus=str(COSMO_VAL / f"xi_minus_{FIDUCIAL['version']}_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.fits"),
+#     resources:
+#         tasks=30,
+#         cpus_per_task=12,
+#         nodes=6,
+#         mem_mb_per_cpu=2000,
+#         runtime=2880,
+#         slurm_extra="'--exclude=n17,n09,n36 --partition=pscomp'",
+#         mpi="/softs/openmpi/5.0.5-slurm-CentOS8/bin/mpiexec",
+#     shell:
+#         "{resources.mpi} -n {resources.tasks} "
+#         "apptainer exec "
+#         "--bind /home,/n09data,/n17data,/n23data1,/softs "
+#         "--env LD_LIBRARY_PATH=/softs/openmpi/5.0.5-slurm-CentOS8/lib "
+#         "{params.image} "
+#         "python {input.script} --cat-config <...> --out <...>"
 
 
 rule rho_tau_stats:
diff --git a/workflow/scripts/analyze_mask_power_spectrum.py b/workflow/scripts/analyze_mask_power_spectrum.py
index 2e0e5b28..65c02132 100644
--- a/workflow/scripts/analyze_mask_power_spectrum.py
+++ b/workflow/scripts/analyze_mask_power_spectrum.py
@@ -72,8 +72,6 @@ def export_power_spectrum(
 
 def main():
     """Process single mask power spectrum (Snakemake script entry point)."""
-    from snakemake.script import snakemake
-
     mask_path = snakemake.input.mask
     output_path = str(snakemake.output.power_spectrum)
 
diff --git a/workflow/scripts/cosmocov_process.py b/workflow/scripts/cosmocov_process.py
new file mode 100644
index 00000000..6e6c1723
--- /dev/null
+++ b/workflow/scripts/cosmocov_process.py
@@ -0,0 +1,73 @@
+"""Assemble a raw CosmoCov block dump into an analysis-ready covariance matrix.
+
+CosmoCov writes one row per (i, j) element with the Gaussian term in column 8
+and the non-Gaussian term in column 9; this rebuilds the symmetric matrices,
+checks positive-definiteness, and plots the correlation matrix.
+
+Run through Snakemake's ``script:`` directive, which injects ``snakemake`` as a
+module global before this file executes.
+"""
+
+import sys
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt  # noqa: E402
+import numpy as np  # noqa: E402
+
+
+def get_cov(filename):
+    """Return (gaussian, non-gaussian, ndata) from a CosmoCov element list."""
+
+    data = np.loadtxt(filename)
+    ndata = int(np.max(data[:, 0])) + 1
+
+    cov_g = np.zeros((ndata, ndata))
+    cov_ng = np.zeros((ndata, ndata))
+    for i in range(data.shape[0]):
+        row, col = int(data[i, 0]), int(data[i, 1])
+        cov_g[row, col] = cov_g[col, row] = data[i, 8]
+        cov_ng[row, col] = cov_ng[col, row] = data[i, 9]
+
+    return cov_g, cov_ng, ndata
+
+
+def plot_correlation(cov, ndata, plot_path):
+    """Save the correlation matrix with xi+/xi- block annotations."""
+
+    diag = np.sqrt(np.diag(cov))
+    correlation = cov / np.outer(diag, diag)
+
+    fig, ax = plt.subplots()
+    extent = (0, ndata, ndata, 0)
+    image = ax.imshow(correlation, cmap="seismic", vmin=-1, vmax=1, extent=extent)
+
+    ax.axvline(x=ndata // 2, color="black", linewidth=1.0)
+    ax.axhline(y=ndata // 2, color="black", linewidth=1.0)
+
+    fig.colorbar(image, orientation="vertical")
+
+    ax.text(ndata // 4, ndata + 5, r"$\xi_+^{ij}(\theta)$", fontsize=12)
+    ax.text(3 * (ndata // 4), ndata + 5, r"$\xi_-^{ij}(\theta)$", fontsize=12)
+    ax.text(-9, ndata // 4, r"$\xi_+^{ij}(\theta)$", fontsize=12)
+    ax.text(-9, 3 * (ndata // 4), r"$\xi_-^{ij}(\theta)$", fontsize=12)
+
+    fig.savefig(plot_path, dpi=300)
+    plt.close(fig)
+
+
+cov_g, cov_ng, ndata = get_cov(snakemake.input[0])  # noqa: F821
+print(f"Dimension of cov: {ndata}x{ndata}")
+
+cov = cov_g + cov_ng
+
+eigenvalues = np.linalg.eigvalsh(cov)
+print(f"min+max eigenvalues cov: {eigenvalues.min():e}, {eigenvalues.max():e}")
+if eigenvalues.min() <= 0.0:
+    sys.exit("non-positive eigenvalue encountered! Covariance invalid!")
+
+np.savetxt(snakemake.output.matrix, cov)  # noqa: F821
+np.savetxt(snakemake.output.gaussian, cov_g)  # noqa: F821
+plot_correlation(cov, ndata, snakemake.output.plot)  # noqa: F821
diff --git a/workflow/scripts/cv_additive_bias.py b/workflow/scripts/cv_additive_bias.py
index 3fdec487..60df4c3d 100644
--- a/workflow/scripts/cv_additive_bias.py
+++ b/workflow/scripts/cv_additive_bias.py
@@ -10,7 +10,6 @@
 import json
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_cosebis.py b/workflow/scripts/cv_cosebis.py
index 182cda99..c1ca8275 100644
--- a/workflow/scripts/cv_cosebis.py
+++ b/workflow/scripts/cv_cosebis.py
@@ -8,7 +8,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_footprints.py b/workflow/scripts/cv_footprints.py
index 5ed89f07..e4a1af6a 100644
--- a/workflow/scripts/cv_footprints.py
+++ b/workflow/scripts/cv_footprints.py
@@ -6,7 +6,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_objectwise_leakage.py b/workflow/scripts/cv_objectwise_leakage.py
index 8b6d5694..ae0f012a 100644
--- a/workflow/scripts/cv_objectwise_leakage.py
+++ b/workflow/scripts/cv_objectwise_leakage.py
@@ -8,7 +8,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_plot_2pcf.py b/workflow/scripts/cv_plot_2pcf.py
index 9d5c8900..5251e7a5 100644
--- a/workflow/scripts/cv_plot_2pcf.py
+++ b/workflow/scripts/cv_plot_2pcf.py
@@ -7,7 +7,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_plot_rho_stats.py b/workflow/scripts/cv_plot_rho_stats.py
index 95a37b09..c99bd646 100644
--- a/workflow/scripts/cv_plot_rho_stats.py
+++ b/workflow/scripts/cv_plot_rho_stats.py
@@ -6,7 +6,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_plot_tau_stats.py b/workflow/scripts/cv_plot_tau_stats.py
index ed90e334..2a00b3a5 100644
--- a/workflow/scripts/cv_plot_tau_stats.py
+++ b/workflow/scripts/cv_plot_tau_stats.py
@@ -5,7 +5,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_pseudo_cl.py b/workflow/scripts/cv_pseudo_cl.py
index cf04e8e8..7dfebc54 100644
--- a/workflow/scripts/cv_pseudo_cl.py
+++ b/workflow/scripts/cv_pseudo_cl.py
@@ -6,7 +6,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_pure_eb.py b/workflow/scripts/cv_pure_eb.py
index d15a763f..bcc46d84 100644
--- a/workflow/scripts/cv_pure_eb.py
+++ b/workflow/scripts/cv_pure_eb.py
@@ -9,7 +9,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_ratio_xi_sys_xi.py b/workflow/scripts/cv_ratio_xi_sys_xi.py
index ba737d7e..79afd89a 100644
--- a/workflow/scripts/cv_ratio_xi_sys_xi.py
+++ b/workflow/scripts/cv_ratio_xi_sys_xi.py
@@ -8,7 +8,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_rho_tau_fits.py b/workflow/scripts/cv_rho_tau_fits.py
index 39d2e371..64b24b67 100644
--- a/workflow/scripts/cv_rho_tau_fits.py
+++ b/workflow/scripts/cv_rho_tau_fits.py
@@ -9,7 +9,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_summarize_bmodes.py b/workflow/scripts/cv_summarize_bmodes.py
index 90df5999..85f0fa1d 100644
--- a/workflow/scripts/cv_summarize_bmodes.py
+++ b/workflow/scripts/cv_summarize_bmodes.py
@@ -17,7 +17,6 @@
 import json
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/cv_weights.py b/workflow/scripts/cv_weights.py
index 3cb3f3ca..0316fe1e 100644
--- a/workflow/scripts/cv_weights.py
+++ b/workflow/scripts/cv_weights.py
@@ -5,7 +5,6 @@
 """
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-from snakemake.script import snakemake
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
diff --git a/workflow/scripts/generate_glass_mock_rhotau_samples.py b/workflow/scripts/generate_glass_mock_rhotau_samples.py
index 63d99095..87b7d06c 100644
--- a/workflow/scripts/generate_glass_mock_rhotau_samples.py
+++ b/workflow/scripts/generate_glass_mock_rhotau_samples.py
@@ -116,6 +116,36 @@ def generate_samples_for_mock(mock_id, cov_tau, theta, ref_tau_header, output_di
     return f"Generated tau samples for mock {mock_id:05d}"
 
 
+def run(cov_tau_path, ref_tau_path, output_dir, mock_ids):
+    """Generate sampled tau statistics for every mock in ``mock_ids``."""
+    print("Loading tau covariance...")
+    cov_tau = np.load(cov_tau_path)
+    print(f"  cov_tau shape: {cov_tau.shape}")
+
+    print("Loading reference FITS...")
+    ref_tau_data, ref_tau_header = load_reference_fits(ref_tau_path)
+    theta = ref_tau_data["theta"]
+    print(
+        f"  theta range: {theta.min():.3f} - {theta.max():.3f} arcmin, nbins: {len(theta)}"
+    )
+
+    print(f"Generating tau samples for {len(mock_ids)} mocks...")
+    for mock_id in mock_ids:
+        msg = generate_samples_for_mock(
+            mock_id, cov_tau, theta, ref_tau_header, output_dir
+        )
+        print(msg)
+    print("Done!")
+
+
+def parse_mock_ids(spec):
+    """Parse a mock-ID spec, either a single ID or an inclusive ``lo-hi`` range."""
+    if "-" in spec:
+        lo, hi = spec.split("-")
+        return list(range(int(lo), int(hi) + 1))
+    return [int(spec)]
+
+
 def main():
     parser = argparse.ArgumentParser(
         description="Generate zero-mean tau samples for GLASS mocks"
@@ -146,37 +176,22 @@ def main():
     )
     args = parser.parse_args()
 
-    # Load tau covariance and reference
-    print("Loading tau covariance...")
-    cov_tau = np.load(args.cov_tau)
-    print(f"  cov_tau shape: {cov_tau.shape}")
-
-    print("Loading reference FITS...")
-    ref_tau_data, ref_tau_header = load_reference_fits(args.ref_tau)
-    theta = ref_tau_data["theta"]
-    print(
-        f"  theta range: {theta.min():.3f} - {theta.max():.3f} arcmin, nbins: {len(theta)}"
-    )
+    run(args.cov_tau, args.ref_tau, args.output_dir, parse_mock_ids(args.mock_ids))
 
-    # Parse mock ID range
-    mock_ids = (
-        list(
-            range(
-                int(args.mock_ids.split("-")[0]), int(args.mock_ids.split("-")[1]) + 1
-            )
-        )
-        if "-" in args.mock_ids
-        else [int(args.mock_ids)]
-    )
 
-    print(f"Generating tau samples for {len(mock_ids)} mocks...")
-    for mock_id in mock_ids:
-        msg = generate_samples_for_mock(
-            mock_id, cov_tau, theta, ref_tau_header, args.output_dir
-        )
-        print(msg)
-    print("Done!")
+# ── Entry point dispatch ─────────────────────────────────────────────────────
 
+try:
+    snakemake  # injected by snakemake's script: directive
+except NameError:
+    snakemake = None
 
-if __name__ == "__main__":
+if snakemake is not None:
+    run(
+        snakemake.input.cov_tau,
+        snakemake.input.ref_tau,
+        snakemake.params.output_dir,
+        parse_mock_ids(snakemake.params.mock_id),
+    )
+elif __name__ == "__main__":
     main()
diff --git a/workflow/scripts/im_mbias_config.py b/workflow/scripts/im_mbias_config.py
new file mode 100644
index 00000000..cf6fdba2
--- /dev/null
+++ b/workflow/scripts/im_mbias_config.py
@@ -0,0 +1,105 @@
+"""Assemble ``m_bias_config.yaml`` for the image-sims m-bias estimator.
+
+Combines the manifest's injected-shear facts, this run's science knobs and
+git/container provenance into the single config ``compute_m_bias_image_sims.py``
+reads.  ``provenance`` rides along as a top-level block: the estimator copies it
+verbatim into its results yaml, so a result file records which manifest, repo
+commits and container produced the number.
+
+`snakemake` is injected as a module global by Snakemake's `script:` preamble
+before this file runs (`from snakemake.script import snakemake` is
+IDE-hint-only and raises ImportError if actually executed).
+"""
+
+import hashlib
+import os
+import re
+import subprocess
+
+import yaml
+
+params = snakemake.params  # noqa: F821
+manifest_path = snakemake.input["manifest"]  # noqa: F821
+
+
+def _git(repo, *args):
+    """Read a git fact from ``repo``; ``None`` if it is not a checkout."""
+    try:
+        return subprocess.run(
+            ["git", "-C", repo, *args],
+            capture_output=True,
+            text=True,
+            check=True,
+        ).stdout.strip()
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+
+
+def _sif_revision():
+    """``org.opencontainers.image.revision`` from the running image's OCI labels.
+
+    Read the image actually mounted, which Apptainer names in
+    ``APPTAINER_CONTAINER``, rather than the configured ``sif`` -- a run may
+    override it, and a registry tag says nothing about which build was pulled.
+    Chunked plain-text scan: no exec, no container start, no 1.5 GB in memory.
+    """
+    sif_path = os.environ.get("APPTAINER_CONTAINER")
+    if not sif_path:
+        return None
+    pattern = re.compile(
+        rb'org\.opencontainers\.image\.revision"?[:=]"?([0-9a-f]{7,40})'
+    )
+    tail = b""
+    try:
+        with open(sif_path, "rb") as fh:
+            while chunk := fh.read(8 << 20):
+                m = pattern.search(tail + chunk)
+                if m:
+                    return m.group(1).decode()
+                tail = chunk[-128:]
+    except OSError:
+        return None
+    return None
+
+
+with open(manifest_path) as fh:
+    manifest = yaml.safe_load(fh)
+with open(manifest_path, "rb") as fh:
+    manifest_sha256 = hashlib.sha256(fh.read()).hexdigest()
+
+mbias_cfg = {
+    "grids_dir": params.grids_base,
+    "num": params.num,
+    "catalog_name": params.cat_name,
+    # Injected shear: from the manifest, the single source of truth.
+    "shear_amplitude": manifest["shear_amplitude"],
+    "branches": list(manifest["branches"]),
+    "pairs": manifest["pairs"],
+    "match_radius_deg": params.match_radius_deg,
+    "w_cols": list(params.w_cols),
+    "pair_match": params.pair_match,
+    "n_bootstrap": params.n_bootstrap,
+    "bootstrap_seed": params.bootstrap_seed,
+    "results_dir": params.results_dir,
+    "output_path": params.results,
+    "provenance": {
+        "manifest_sha256": manifest_sha256,
+        "sp_validation": {
+            "branch": _git(
+                params.sp_validation_repo, "rev-parse", "--abbrev-ref", "HEAD"
+            ),
+            "commit": _git(params.sp_validation_repo, "rev-parse", "HEAD"),
+        },
+        "shapepipe": {
+            "branch": _git(params.shapepipe_repo, "rev-parse", "--abbrev-ref", "HEAD"),
+            "commit": _git(params.shapepipe_repo, "rev-parse", "HEAD"),
+        },
+        "container": {
+            "sif": params.sif,
+            "ghcr_revision": _sif_revision(),
+        },
+    },
+}
+
+with open(snakemake.output["cfg"], "w") as fh:  # noqa: F821
+    yaml.safe_dump(mbias_cfg, fh)
diff --git a/workflow/scripts/process_mask.py b/workflow/scripts/process_mask.py
index c0368147..c9590368 100644
--- a/workflow/scripts/process_mask.py
+++ b/workflow/scripts/process_mask.py
@@ -138,8 +138,6 @@ def save_area_summary(
 
 def main():
     """Main processing function."""
-    # Snakemake script execution only (no interactive mode)
-    from snakemake.script import snakemake
 
     # Get parameters from Snakemake
     source_mask_path = snakemake.input.mask
diff --git a/workflow/scripts/run_rho_tau.py b/workflow/scripts/run_rho_tau.py
index ea2f35bc..fbf62a3c 100644
--- a/workflow/scripts/run_rho_tau.py
+++ b/workflow/scripts/run_rho_tau.py
@@ -28,8 +28,6 @@
         "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/output/rho_tau_stats/rho_stats_SP_v1.4.5.fits",
         "/home/cdaley/n17data/unions/pure_eb",
     )
-else:
-    from snakemake.script import snakemake
 
 params = snakemake.params  # type: ignore
 

From 5c03d047a096470c855a9f3a632f7c27421ed1bb Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:05 +0200
Subject: [PATCH 08/83] Update actions/checkout action to v7 (#325)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-docs.yml | 2 +-
 .github/workflows/lint.yml        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 689f4bcb..15dccde5 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -32,7 +32,7 @@ jobs:
 
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
 
       - name: Install documentation dependencies
         run: uv pip install --no-cache-dir '.[docs]'
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index a0f36765..2c20ceaf 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -80,7 +80,7 @@ jobs:
 
     steps:
       - name: Checkout (PR head on pull_request_target, else the pushed ref)
-        uses: actions/checkout@v4
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
         with:
           ref: ${{ github.event_name == 'pull_request_target' && github.event.pull_request.head.sha || github.sha }}
           persist-credentials: false

From 0fa080d1780b6a847a7f16511d009f7cf1a83f84 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:09 +0200
Subject: [PATCH 09/83] Update actions/github-script action to v9 (#326)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/lint.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 2c20ceaf..c9c369f5 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -224,7 +224,7 @@ jobs:
       - name: Tell the author (PR comment) or record it (develop-push issue)
         if: steps.ruff.outputs.tool_error == 'false'
         continue-on-error: true
-        uses: actions/github-script@v7
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9
         env:
           PASSED: ${{ steps.gate.outputs.passed }}
           AUTOFIXED: ${{ steps.gate.outputs.autofixed }}

From b9955f3713e0fdb1a6de5825596b47125aef03c9 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:14 +0200
Subject: [PATCH 10/83] Update actions/upload-artifact action to v7 (#327)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 15dccde5..691090fc 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -47,7 +47,7 @@ jobs:
       # Upload the rendered HTML so it can be downloaded from the run summary —
       # the only way to preview the docs on a PR, where the deploy is skipped.
       - name: Upload built docs
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
         with:
           name: docs-html
           path: docs/_build

From 8293f9a792a5afb108a0d3d4e7701d6dd444cee2 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:19 +0200
Subject: [PATCH 11/83] Update astral-sh/setup-uv action to v10 (#328)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/lint.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index c9c369f5..f1664d53 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -86,7 +86,7 @@ jobs:
           persist-credentials: false
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1
 
       # Is this a PR whose head branch lives in THIS repo (not a fork)? Only then
       # can we push an autofix commit back to it with the workflow token.

From 11f92d1ce730a00e792151e9e3ce8e25ff4ae1da Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:23 +0200
Subject: [PATCH 12/83] Update docker/build-push-action action to v7 (#330)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-image.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index 192e87a6..4add3347 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -35,7 +35,7 @@ jobs:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
 
       - name: Build and export to Docker
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a # v7
         with:
           load: true
           tags: ${{ steps.meta.outputs.tags }}
@@ -52,7 +52,7 @@ jobs:
         run: docker run --rm ${{ steps.meta.outputs.tags }} python -m pytest src/sp_validation/tests -m "not slow"
 
       - name: Push
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a # v7
         with:
           push: true
           tags: ${{ steps.meta.outputs.tags }}

From f74f8e1a1b3626ac5f877d7e8ebe9e2c33df938d Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:27 +0200
Subject: [PATCH 13/83] Update docker/login-action action to v4 (#331)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-image.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index 4add3347..27bdeb1b 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -19,7 +19,7 @@ jobs:
 
     steps:
       - name: Log in to the Container registry
-        uses: docker/login-action@v3
+        uses: docker/login-action@dbcb813823bdd20940b903addbd779551569679f # v4
         with:
           registry: ${{ env.REGISTRY }}
           username: ${{ github.actor }}

From 66173c5d3296565aa03af54c1da87cf52fd83d41 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:31 +0200
Subject: [PATCH 14/83] Update docker/metadata-action action to v6 (#332)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-image.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index 27bdeb1b..9f6ce070 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -30,7 +30,7 @@ jobs:
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@dc802804100637a589fabce1cb79ff13a1411302 # v6
         with:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
 

From b622196b881ed62bc0d94b32886a51636ed4de2d Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Tue, 8 Sep 2026 18:17:36 +0200
Subject: [PATCH 15/83] Update docker/setup-buildx-action action to v4 (#333)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-image.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index 9f6ce070..f4fa233d 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -26,7 +26,7 @@ jobs:
           password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@37fe631027851001ddb9b187196cc803df7f5f0e # v4
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta

From 3585b2e74790237e3e111c902b6110de9f007f0e Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Tue, 8 Sep 2026 18:34:46 +0200
Subject: [PATCH 16/83] ci: queue gh-pages deploys instead of racing (#335)

Concurrent doc-deploy runs on back-to-back pushes to develop both hit
peaceiris/actions-gh-pages at once, and the loser fails with
"cannot lock ref 'refs/heads/gh-pages'" (seen in run 34250154443).
Scope the job to a gh-pages-<ref> concurrency group; cancel-in-progress
is fine since develop only ever has one active deploy target and the
newest push should win over a stale one still building.


Claude-Session: https://claude.ai/code/session_01Wk8SZkCRuKQ5xu5g38zHpx

Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
---
 .github/workflows/deploy-docs.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 691090fc..19f3edb5 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -23,6 +23,9 @@ jobs:
     permissions:
       contents: write
       packages: read
+    concurrency:
+      group: gh-pages-${{ github.ref }}
+      cancel-in-progress: true
 
     container:
       image: ghcr.io/cosmostat/sp_validation:develop

From 5fbed605d291fd48418c2bfdc101551dba616635 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Wed, 9 Sep 2026 14:30:23 +0200
Subject: [PATCH 17/83] Fix fiducial pure-EB window resolution (#323)

Resolve reporting bins from their edges rather than bin means. This keeps the 12--83 arcmin fiducial window at bins 9--15 and preserves the intended degrees of freedom.

Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
---
 .../scripts/config_space_pte_matrices.py      | 46 +++++++++++++------
 1 file changed, 33 insertions(+), 13 deletions(-)

diff --git a/papers/bmodes/scripts/config_space_pte_matrices.py b/papers/bmodes/scripts/config_space_pte_matrices.py
index 70044149..474b71a7 100644
--- a/papers/bmodes/scripts/config_space_pte_matrices.py
+++ b/papers/bmodes/scripts/config_space_pte_matrices.py
@@ -42,6 +42,14 @@
 plt.style.use(PAPER_MPLSTYLE)
 
 
+def resolve_fiducial_bin_window(edges, theta_min, theta_max):
+    """Return the first and last reporting bins inside a scale-cut window."""
+    left, right = edges[:-1], edges[1:]
+    inside = (left >= theta_min * (1.0 - 1e-2)) & (right <= theta_max * (1.0 + 1e-2))
+    bins = np.flatnonzero(inside)
+    return int(bins[0]), int(bins[-1])
+
+
 def _path_matches_version(path, version):
     """Check if a file path matches a specific catalog version exactly.
 
@@ -518,10 +526,13 @@ def create_3panel_composite(
     cosebis_fid_start = np.argmin(np.abs(theta_cosebis[:-1] - cosebis_fid[0]))
     cosebis_fid_stop = np.argmin(np.abs(theta_cosebis[1:] - cosebis_fid[1])) + 1
 
-    xip_start = np.argmin(np.abs(theta_pure_eb - xip_fid[0]))
-    xip_stop = np.argmin(np.abs(theta_pure_eb - xip_fid[1]))
-    xim_start = np.argmin(np.abs(theta_pure_eb - xim_fid[0]))
-    xim_stop = np.argmin(np.abs(theta_pure_eb - xim_fid[1]))
+    reporting_edges = np.geomspace(
+        config["fiducial"]["min_sep"],
+        config["fiducial"]["max_sep"],
+        config["fiducial"]["nbins"] + 1,
+    )
+    xip_start, xip_stop = resolve_fiducial_bin_window(reporting_edges, *xip_fid)
+    xim_start, xim_stop = resolve_fiducial_bin_window(reporting_edges, *xim_fid)
 
     # Create subplot axes
     ax_xip = fig.add_subplot(gs[0, 0])
@@ -673,10 +684,13 @@ def create_9panel_composite(
         cosebis_fid_start = np.argmin(np.abs(theta_cosebis[:-1] - cosebis_fid[0]))
         cosebis_fid_stop = np.argmin(np.abs(theta_cosebis[1:] - cosebis_fid[1])) + 1
 
-        xip_start = np.argmin(np.abs(theta_pure_eb - xip_fid[0]))
-        xip_stop = np.argmin(np.abs(theta_pure_eb - xip_fid[1]))
-        xim_start = np.argmin(np.abs(theta_pure_eb - xim_fid[0]))
-        xim_stop = np.argmin(np.abs(theta_pure_eb - xim_fid[1]))
+        reporting_edges = np.geomspace(
+            config["fiducial"]["min_sep"],
+            config["fiducial"]["max_sep"],
+            config["fiducial"]["nbins"] + 1,
+        )
+        xip_start, xip_stop = resolve_fiducial_bin_window(reporting_edges, *xip_fid)
+        xim_start, xim_stop = resolve_fiducial_bin_window(reporting_edges, *xim_fid)
 
         # Create subplot axes for this row
         ax_xip = fig.add_subplot(gs[row_idx, 0])
@@ -900,12 +914,18 @@ def main(
                     config,
                     fiducial_overrides,
                 )
-                theta_pe = matrices["theta_pure_eb"]
                 theta_co = matrices["theta_cosebis"]
-                xip_start = np.argmin(np.abs(theta_pe - xip_fid[0]))
-                xip_stop = np.argmin(np.abs(theta_pe - xip_fid[1]))
-                xim_start = np.argmin(np.abs(theta_pe - xim_fid[0]))
-                xim_stop = np.argmin(np.abs(theta_pe - xim_fid[1]))
+                reporting_edges = np.geomspace(
+                    config["fiducial"]["min_sep"],
+                    config["fiducial"]["max_sep"],
+                    config["fiducial"]["nbins"] + 1,
+                )
+                xip_start, xip_stop = resolve_fiducial_bin_window(
+                    reporting_edges, *xip_fid
+                )
+                xim_start, xim_stop = resolve_fiducial_bin_window(
+                    reporting_edges, *xim_fid
+                )
                 cos_start = np.argmin(np.abs(theta_co[:-1] - cosebis_fid[0]))
                 cos_stop = np.argmin(np.abs(theta_co[1:] - cosebis_fid[1])) + 1
 

From 6de9a92bb4b92071cb7b31dfc9bf74063a713b11 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Wed, 9 Sep 2026 14:31:08 +0200
Subject: [PATCH 18/83] Add TeX distribution to container image (#338)

Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
---
 Dockerfile | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/Dockerfile b/Dockerfile
index 79fdf0d8..560df4b2 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -19,7 +19,14 @@ RUN apt-get update -y --quiet --fix-missing && \
         liblapack-dev \
         libgsl-dev \
         libcfitsio-dev \
-        libfftw3-dev
+        libfftw3-dev \
+        texlive-latex-base \
+        texlive-latex-recommended \
+        texlive-fonts-recommended \
+        dvipng \
+        ghostscript \
+        cm-super && \
+    rm -rf /var/lib/apt/lists/*
 
 # The base shapepipe image provides a uv-managed venv at /app/.venv (exported as
 # VIRTUAL_ENV); install sp_validation's deps into that same venv rather than

From 88fa75bbd6b22dcb1f54a8ed66d568deb71ad914 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Wed, 9 Sep 2026 16:04:08 +0200
Subject: [PATCH 19/83] Container: TinyTeX (pinned TeX Live 2025) instead of
 apt texlive

The texlive-latex-base/-recommended/-fonts-recommended + cm-super set added
in #338 costs ~1.3 GB and still lacks type1cm.sty, so matplotlib's usetex
path stays broken. Replace it with TinyTeX (~270 MB measured), pinned to
TeX Live 2025 on both halves: bundle v2026.02, and tlmgr pointed at that
year's frozen tlnet-final historic mirror. Packages are an explicit tlmgr
list covering matplotlib's usetex preamble.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 Dockerfile | 43 +++++++++++++++++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 6 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 560df4b2..2124d2fa 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -20,14 +20,45 @@ RUN apt-get update -y --quiet --fix-missing && \
         libgsl-dev \
         libcfitsio-dev \
         libfftw3-dev \
-        texlive-latex-base \
-        texlive-latex-recommended \
-        texlive-fonts-recommended \
-        dvipng \
-        ghostscript \
-        cm-super && \
+        perl \
+        curl \
+        ghostscript && \
     rm -rf /var/lib/apt/lists/*
 
+# TeX, for matplotlib's usetex path (paper.mplstyle) and any LaTeX output.
+# TinyTeX rather than apt's texlive-*: those metapackages cost ~1.3 GB and
+# still omit type1cm.sty, which matplotlib's usetex preamble loads. TinyTeX is
+# a minimal TeX Live with tlmgr, so the package set below is explicit and
+# adding one is a one-line change.
+#
+# Both halves are pinned to TeX Live 2025: the TinyTeX bundle v2026.02 is the
+# last release built on it, and tlmgr points at that year's frozen tlnet-final
+# historic mirror, so this layer resolves the same way forever. Update once a
+# year by bumping TINYTEX_VERSION and the mirror year together.
+ENV TEXLIVE_YEAR=2025 \
+    TINYTEX_VERSION=2026.02 \
+    TINYTEX_DIR=/opt \
+    PATH=/opt/.TinyTeX/bin/x86_64-linux:$PATH
+RUN set -eux; \
+    curl -fsSL https://yihui.org/tinytex/install-bin-unix.sh | sh; \
+    tlmgr option sys_bin /usr/local/bin; \
+    tlmgr option repository \
+      "https://ftp.math.utah.edu/pub/tex/historic/systems/texlive/${TEXLIVE_YEAR}/tlnet-final/"; \
+    tlmgr option docfiles 0; \
+    tlmgr option srcfiles 0; \
+    tlmgr install \
+        type1cm \
+        cm-super \
+        dvipng \
+        underscore \
+        ulem \
+        amsmath \
+        amsfonts \
+        geometry \
+        xcolor; \
+    tlmgr path add; \
+    latex --version >/dev/null; dvipng --version >/dev/null
+
 # The base shapepipe image provides a uv-managed venv at /app/.venv (exported as
 # VIRTUAL_ENV); install sp_validation's deps into that same venv rather than
 # spawning a second one under /sp_validation.

From e9aa8ac707a1973669d2bf9b5073bb2ccad07d3c Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Wed, 9 Sep 2026 23:16:42 +0200
Subject: [PATCH 20/83] Container: trim the TinyTeX comment

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 Dockerfile | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 2124d2fa..844f7e23 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -25,16 +25,7 @@ RUN apt-get update -y --quiet --fix-missing && \
         ghostscript && \
     rm -rf /var/lib/apt/lists/*
 
-# TeX, for matplotlib's usetex path (paper.mplstyle) and any LaTeX output.
-# TinyTeX rather than apt's texlive-*: those metapackages cost ~1.3 GB and
-# still omit type1cm.sty, which matplotlib's usetex preamble loads. TinyTeX is
-# a minimal TeX Live with tlmgr, so the package set below is explicit and
-# adding one is a one-line change.
-#
-# Both halves are pinned to TeX Live 2025: the TinyTeX bundle v2026.02 is the
-# last release built on it, and tlmgr points at that year's frozen tlnet-final
-# historic mirror, so this layer resolves the same way forever. Update once a
-# year by bumping TINYTEX_VERSION and the mirror year together.
+# TinyTeX pinned to a TeX Live year (frozen tlnet-final mirror); bump both once a year.
 ENV TEXLIVE_YEAR=2025 \
     TINYTEX_VERSION=2026.02 \
     TINYTEX_DIR=/opt \

From dc5886c5d65895118246a8c750666a863f121860 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Wed, 16 Sep 2026 02:28:20 +0000
Subject: [PATCH 21/83] Update all non-major dependencies (#345)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/lint.yml |  2 +-
 pyproject.toml             |  4 +--
 uv.lock                    | 67 ++++++++++++++++++++++++--------------
 3 files changed, 46 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index f1664d53..8a0f226e 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -86,7 +86,7 @@ jobs:
           persist-credentials: false
 
       - name: Install uv
-        uses: astral-sh/setup-uv@20cfd1bf945f4377ade1205e4dbc17946fc9a30d # v10.0.1
+        uses: astral-sh/setup-uv@bec219d24cd3e171d82865faccec33120bb574f4 # v10.1.0
 
       # Is this a PR whose head branch lives in THIS repo (not a fork)? Only then
       # can we push an autofix commit back to it with the workflow token.
diff --git a/pyproject.toml b/pyproject.toml
index 071945b1..5c45cfbf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -89,7 +89,7 @@ dependencies = [
     # be converted to Python scalars`, failing all get_cosmo-backed tests. camb
     # 1.6.6 (latest on PyPI) and current master are both unfixed. Lift this cap
     # when camb ships > 1.6.6 with the scipy-1.18 fix.
-    "scipy<1.18",
+    "scipy<1.19",
     "seaborn",
     # Intentionally tracks the mutable `develop` branch (NOT SHA-pinned):
     # shear_psf_leakage is actively co-developed CosmoStat code we want to stay
@@ -148,7 +148,7 @@ docs = [
 # lives in the separate ``glass.ext.camb`` package (absent from glass core
 # >=2024.2), so it is pinned explicitly.
 glass = [
-    "glass==2025.1",
+    "glass==2025.3",
     "glass.ext.camb==2023.6",
     "cosmology==2022.10.9",
     # fitsio: make_unions_glass_sim.py writes the mock catalogue as FITS.
diff --git a/uv.lock b/uv.lock
index 4f635d14..eb69507c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -112,6 +112,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/86/16/1a8fd2b19544b84575cf84ef7aa3ad4c173b756d5f087c91f85d1b295777/array_api_compat-1.15.0-py3-none-any.whl", hash = "sha256:7b1b9c53269061403fd5f45a8de349f16e7887653328bfa0c5f2d45299ff0a8e", size = 79113, upload-time = "2026-06-07T20:53:23.621Z" },
 ]
 
+[[package]]
+name = "array-api-extra"
+version = "0.11.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "array-api-compat" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c4/63/eb18bc7d039c954da3cfb11293c7e461b03eb9682bb0b22733be43f75501/array_api_extra-0.11.4.tar.gz", hash = "sha256:66236f72aaa4cdf97a3c5581696e3a9c89513714612a9bc6010e19fad1a22012", size = 112368, upload-time = "2026-09-07T18:50:34.068Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/b3/fcf70b2ed935f26fc15dfd81f72f781d395b09e302af5c86ec285d63524e/array_api_extra-0.11.4-py3-none-any.whl", hash = "sha256:b2d5bd31b3072249fd3f90a2b1f7b398c78e3ccb7575e04e9e3348ae894da60f", size = 120196, upload-time = "2026-09-07T18:50:32.88Z" },
+]
+
 [[package]]
 name = "arrow"
 version = "1.4.0"
@@ -563,7 +575,7 @@ wheels = [
 
 [[package]]
 name = "cosmosis"
-version = "3.25.2"
+version = "3.26"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "dulwich" },
@@ -581,7 +593,7 @@ dependencies = [
     { name = "threadpoolctl" },
     { name = "zeus-mcmc" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/80/63/83898440e5486294cb17a7ddf01896af66dd153d7f3e77897ed120abd055/cosmosis-3.25.2.tar.gz", hash = "sha256:4a8333395b600a5e8339c637b947a982bac4c88a7b242e4b6de8274be03004a8", size = 416233, upload-time = "2026-03-09T12:01:08.269Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/46/27/923408c09bc803b349a216974481fc075cb56f54b1ea949efe1233a294f1/cosmosis-3.26.tar.gz", hash = "sha256:bbeed481411d2bfa83d0b14bcc3d16a0b80dcf1c1f099e5066d10e4711c36508", size = 421446, upload-time = "2026-09-03T09:22:44.179Z" }
 
 [[package]]
 name = "coverage"
@@ -1084,18 +1096,18 @@ wheels = [
 
 [[package]]
 name = "glass"
-version = "2025.1"
+version = "2025.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cosmology" },
+    { name = "array-api-compat" },
+    { name = "array-api-extra" },
     { name = "healpix" },
     { name = "healpy" },
-    { name = "numpy" },
     { name = "transformcl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a5/d6/26cd58e75834671259f26b9287a584d726e6023da35ddf7ca3b7f2c393fb/glass-2025.1.tar.gz", hash = "sha256:7b1aa2394e16010f7f1b4243f49e7e12d7a4dd28fcbf3e3f7cf25ce4905a8615", size = 48533, upload-time = "2025-02-21T18:43:48.046Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/61/18/6d1387d74f597aacde573ffa9e7d28faa23c74414f69f56bffe9d3ccffaf/glass-2025.3.tar.gz", hash = "sha256:1f55bd5e85dc4eb3964bc6cf9a61e8da0cba4b3ecd60531d58384a27debcda3d", size = 274314, upload-time = "2025-12-12T16:38:18.096Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e4/e7/3e89bc5147a3e84245c783098afffbbacbcdf5678ba1aacc2c9c035434da/glass-2025.1-py3-none-any.whl", hash = "sha256:d7919a7d19e05ab8da4e52dbfbcfb5bd1ed2b72e0cc3afde39ec94ced22883c5", size = 47185, upload-time = "2025-02-21T18:43:46.649Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/99/25a79eb65032d54868b4fc85fd105741858f493c391e9663954c15398e80/glass-2025.3-py3-none-any.whl", hash = "sha256:1cd0cd21aa25f76917235812164a9aa666032222ffb7bacfae766cd12f62b26b", size = 58522, upload-time = "2025-12-12T16:38:16.557Z" },
 ]
 
 [[package]]
@@ -1598,11 +1610,14 @@ wheels = [
 
 [[package]]
 name = "joblib"
-version = "1.5.3"
+version = "1.6.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" }
+dependencies = [
+    { name = "cloudpickle" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d5/1d/537ab090f302b838943a1b56497dd53059b9a9b46a074936470173a2e207/joblib-1.6.0.tar.gz", hash = "sha256:2ccc96785b12046c08fd6d55839c12857831b54a3c1673ffadd2f04bfc4eda03", size = 327903, upload-time = "2026-08-31T09:39:04.122Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" },
+    { url = "https://files.pythonhosted.org/packages/18/53/84099323c2ec4be98d935f63c033ac4151ee83836ca1050ede3b3aadf155/joblib-1.6.0-py3-none-any.whl", hash = "sha256:3dbbf9f6e4b592a2357b854608e980fe6390d131d7a82f011a377ef2ebef7aba", size = 306115, upload-time = "2026-08-31T09:39:02.298Z" },
 ]
 
 [[package]]
@@ -1893,7 +1908,7 @@ wheels = [
 
 [[package]]
 name = "jupytext"
-version = "1.19.4"
+version = "1.19.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "markdown-it-py" },
@@ -1902,9 +1917,9 @@ dependencies = [
     { name = "packaging" },
     { name = "pyyaml" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3b/52/e014296ac8f40ca783aeb73dae52e65edbb0eaae0dcdc1ea41bfaa8aebf7/jupytext-1.19.4.tar.gz", hash = "sha256:739bcd4bc12aa4fe298a38017cdb5ae27b08a6ba3a5470728d2fe9e04b155db1", size = 4581977, upload-time = "2026-06-21T21:48:58.32Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/473f8ebb101553fb2ea6ab1d34324d6677844c968947ac050c759d539f2c/jupytext-1.19.5.tar.gz", hash = "sha256:605026446d605aa54fd7f7fc69df6ae51c7a46053d4cebf05afdc64d66de3df0", size = 4600916, upload-time = "2026-07-21T22:00:29.198Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6e/e9/e2ae007456069dbe01865c69a4203a7ada6f7e337b78fc2f12e51bd3fae7/jupytext-1.19.4-py3-none-any.whl", hash = "sha256:032d4ef4bd2e96addcac780b9b1d6b5a266ca39beceaaca95bfb4f06e0b77029", size = 170889, upload-time = "2026-06-21T21:48:56.352Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/c6/76ee9dacedcd8c67d8fa53dd975613733bdd28242a4c41518ff1c8aeaa64/jupytext-1.19.5-py3-none-any.whl", hash = "sha256:af22351202171116b986fe1f4f9233a13ca4a85d5eec57a87900ed48521dbae4", size = 229246, upload-time = "2026-07-21T22:00:27.218Z" },
 ]
 
 [[package]]
@@ -2597,14 +2612,14 @@ wheels = [
 
 [[package]]
 name = "numpydoc"
-version = "1.10.0"
+version = "1.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "sphinx" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e9/3c/dfccc9e7dee357fb2aa13c3890d952a370dd0ed071e0f7ed62ed0df567c1/numpydoc-1.10.0.tar.gz", hash = "sha256:3f7970f6eee30912260a6b31ac72bba2432830cd6722569ec17ee8d3ef5ffa01", size = 94027, upload-time = "2025-12-02T16:39:12.937Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c4/2f/5e9bf8ed0a93587b8089242b4926dfd04d4b3b0b65f04345d219a1225f03/numpydoc-1.11.0.tar.gz", hash = "sha256:26db0adaac8087e98da05c86b11198defd962aef7c21f4dffd2703c8c6eb4187", size = 101575, upload-time = "2026-09-15T16:31:08.905Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/62/5e/3a6a3e90f35cea3853c45e5d5fb9b7192ce4384616f932cf7591298ab6e1/numpydoc-1.10.0-py3-none-any.whl", hash = "sha256:3149da9874af890bcc2a82ef7aae5484e5aa81cb2778f08e3c307ba6d963721b", size = 69255, upload-time = "2025-12-02T16:39:11.561Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/e5/f5c38d4899cd8edd5b522071c5658202077d113c3225484d779cf257970e/numpydoc-1.11.0-py3-none-any.whl", hash = "sha256:493ad2ce0d8275ca5da484ec37dbdbbc1334c3a6968dffd10ec729b51753e15e", size = 71464, upload-time = "2026-09-15T16:31:07.521Z" },
 ]
 
 [[package]]
@@ -3907,7 +3922,7 @@ requires-dist = [
     { name = "fast-pt", marker = "extra == 'workflow'", specifier = ">=3.2,<4" },
     { name = "fitsio", marker = "extra == 'glass'" },
     { name = "getdist", git = "https://github.com/benabed/getdist.git?rev=113cd22a9a0d013b6f72fe734be81f260f3d3be5" },
-    { name = "glass", marker = "extra == 'glass'", specifier = "==2025.1" },
+    { name = "glass", marker = "extra == 'glass'", specifier = "==2025.3" },
     { name = "glass-ext-camb", marker = "extra == 'glass'", specifier = "==2023.6" },
     { name = "h5py" },
     { name = "healpy" },
@@ -3937,7 +3952,7 @@ requires-dist = [
     { name = "reproject" },
     { name = "ruff", marker = "extra == 'test'" },
     { name = "sacc", specifier = ">=0.12" },
-    { name = "scipy", specifier = "<1.18" },
+    { name = "scipy", specifier = "<1.19" },
     { name = "seaborn" },
     { name = "shear-psf-leakage", git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop" },
     { name = "skyproj" },
@@ -4346,7 +4361,7 @@ wheels = [
 
 [[package]]
 name = "treecorr"
-version = "5.1.3"
+version = "5.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "lsstdesc-coord" },
@@ -4355,12 +4370,16 @@ dependencies = [
     { name = "pyyaml" },
     { name = "setuptools" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/5d/95/ff399cb37b9e3d7c758f8cf0d5757f06a38093f223f2a65d7cc5b50313d5/treecorr-5.1.3.tar.gz", hash = "sha256:d921ccace064325a8ebb93f371c624f13fcc9559373e3b37cc216cb94ae81ed2", size = 1146147, upload-time = "2026-03-18T18:38:03.626Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/73/09/c9228fec3dc78910718f2a68306d7b0583b2dae93d6a8ce665ec0e19d9c7/treecorr-5.1.4.tar.gz", hash = "sha256:1514cbbc3ce843b875ea4968d69508554ea240f6738f6b544951969260d58a3f", size = 1149471, upload-time = "2026-09-01T02:33:56.976Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/97/74/1cc590e4fe1afb69ec2f10a67ee8e0a92ecafd36512052755362ff302d9c/treecorr-5.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:596b6f5eea3115a9e211dc83ec6465887983f41b24e484e08fc47170c6cf4e26", size = 20941767, upload-time = "2026-03-18T18:37:37.874Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/bb/5ba5aa1021fce0cf459fa264cb0d298dfd095ddb48d63fb6589b90fdefac/treecorr-5.1.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:660dacd47a9af18790de279bcb6e7f375369096ad77e7cc64bf21a9b78f6c430", size = 19785388, upload-time = "2026-03-18T18:37:40.573Z" },
-    { url = "https://files.pythonhosted.org/packages/be/92/be01211263a4fa488e768c9288c6f9b0f4402870423a88de4d60031b3384/treecorr-5.1.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:73c938c6c31e1bad4d5338ad4519d1d78eedf3b7ef579b1e3a19d983e220ed93", size = 20940568, upload-time = "2026-03-18T18:37:44.966Z" },
-    { url = "https://files.pythonhosted.org/packages/dc/a3/69e5a16aa33600ba22d28a47c6d8de3b3118292e79f9ae77dc69a3df7c77/treecorr-5.1.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dc17b1f623bae6769148f57cac8c3a295e26e5d6215698117bd97e14c8bb0d6b", size = 19781991, upload-time = "2026-03-18T18:37:47.468Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/b7/9f52a14c7e48d30875a2e988196554c74affc00ddf3571efb8f1fc9e901b/treecorr-5.1.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9f12d28573a714f105c545921fd4eae8761f69c59b8eb2bc52080a5e3ed07b0d", size = 21593422, upload-time = "2026-09-01T02:33:04.719Z" },
+    { url = "https://files.pythonhosted.org/packages/76/cf/b3ae27f1ef857491268aff4317999995ce939171c545a28363fbc058a961/treecorr-5.1.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:61f037dd80385f24158a6e1e1dae963acc904052a346d2b2006da47eff1ad51d", size = 21158309, upload-time = "2026-09-01T02:33:08.284Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/c1/c577503c995d795e31e0a445ad2760d3fa42fb3f314dd3cd1a827fd211b0/treecorr-5.1.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a377144861b53d10c1d1cc9ff6dfb9878d44f74ef74943c8990b329925169ac3", size = 21593786, upload-time = "2026-09-01T02:33:14.165Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/96/79e4931447b0b6f42d712bfd5c1b0c171444b1e2e2d99db1528cd8094863/treecorr-5.1.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:da932b75e481f18a8a55ac7f761c402d77472e6856744451124a93ad7f6f7273", size = 21153410, upload-time = "2026-09-01T02:33:17.932Z" },
+    { url = "https://files.pythonhosted.org/packages/20/88/9f654adaf4baf2005300ed30390c526bd35c098bea1c1ac08cc04d17694e/treecorr-5.1.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c29c61636570815566aa0ab04738ffd98dc5029f06aff7fb1b98aaf4e44d12cc", size = 21585667, upload-time = "2026-09-01T02:33:23.314Z" },
+    { url = "https://files.pythonhosted.org/packages/79/60/f9ea6d17600f8a023b57b006c687bea479f5800aaeb2656429b82a625e12/treecorr-5.1.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c36fb089c8db8b77a230c90007007f1686661748c438bc5ac68640254bde478e", size = 21141746, upload-time = "2026-09-01T02:33:26.924Z" },
+    { url = "https://files.pythonhosted.org/packages/37/bd/97c7aab98013f12cf1ae61a72dac4bff7faaf4f2bcd0a2aa5dcdd99ba748/treecorr-5.1.4-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:67f61ea796a2e3a487638e73abca9183187079f6e82110745e6cb5f4d8a086aa", size = 21583083, upload-time = "2026-09-01T02:33:32.311Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/2f/4378607f9c7a922d78391b5b3e457e2600ff152b143e445c05078e32d428/treecorr-5.1.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:be9f97d5e73872a07c8cb5a4a132cab76c71f5ae357410fca2795ba81332c680", size = 21156480, upload-time = "2026-09-01T02:33:36.367Z" },
 ]
 
 [[package]]

From 607dff2acb4020511ce8b2b0b845e1a8f8d0cd00 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Wed, 16 Sep 2026 10:42:12 +0000
Subject: [PATCH 22/83] Pin peaceiris/actions-gh-pages action to 84c30a8

---
 .github/workflows/deploy-docs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 19f3edb5..3efce06b 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -57,7 +57,7 @@ jobs:
 
       - name: Deploy to GitHub Pages
         if: github.event_name == 'push' && github.ref == 'refs/heads/develop'
-        uses: peaceiris/actions-gh-pages@v4
+        uses: peaceiris/actions-gh-pages@84c30a85c19949d7eee79c4ff27748b70285e453 # v4
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: docs/_build

From 120e86c70fda2fa4c508dd5009ef1d383d5def9e Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Mon, 21 Sep 2026 05:25:55 +0000
Subject: [PATCH 23/83] Update docker/build-push-action digest to c3c9e26
 (#347)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-image.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index f4fa233d..3c8ceaa0 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -35,7 +35,7 @@ jobs:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
 
       - name: Build and export to Docker
-        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a # v7
+        uses: docker/build-push-action@c3c9e263c25d99ce0380d002d59b67737d91b0dc # v7
         with:
           load: true
           tags: ${{ steps.meta.outputs.tags }}
@@ -52,7 +52,7 @@ jobs:
         run: docker run --rm ${{ steps.meta.outputs.tags }} python -m pytest src/sp_validation/tests -m "not slow"
 
       - name: Push
-        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a # v7
+        uses: docker/build-push-action@c3c9e263c25d99ce0380d002d59b67737d91b0dc # v7
         with:
           push: true
           tags: ${{ steps.meta.outputs.tags }}

From 2c561dec19b38c095f142a28e9e1a29da9bd12cd Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Mon, 21 Sep 2026 11:03:03 +0000
Subject: [PATCH 24/83] Update docker/setup-buildx-action digest to f87e599
 (#348)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/deploy-image.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index 3c8ceaa0..f65ab5f7 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -26,7 +26,7 @@ jobs:
           password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@37fe631027851001ddb9b187196cc803df7f5f0e # v4
+        uses: docker/setup-buildx-action@f87e5991a6d7451dcb8d9637bfbc97413f497069 # v4
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta

From efb32b2082f39ae714ddcdda907069cbc92fb591 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 25 Sep 2026 14:41:17 +0200
Subject: [PATCH 25/83] chore: remove dead scripts and fix stale doc paths

Deletes code with no live caller:
- workflow/scripts/run_2pcf_highres.py and the parked, unrunnable
  xi_highres block in workflow/rules/twopoint.smk that was its only
  reference; the mpi4py comment in pyproject.toml no longer names it.
- papers/bmodes/scripts/run_cov_sweep.sh, which calls
  workflow/scripts/run_cosmocov_chain.sh, absent from develop.

Fixes docs that point at paths or tools that do not exist:
- CLAUDE.md, CONTRIBUTING.md: single-test example uses test_cosmo_val.py.
- CLAUDE.md: cosmo_inference runs through Snakemake (inference_fiducial),
  not a pipeline.sh driver.
- papers/{bmodes,cosmo_val}/Snakefile: read cat_config through code/
  rather than the deprecated pure_eb symlink.
- ecut_spec.md, update_survey_stats.py: paths under papers/bmodes/.
- README badge and installation docs: Python 3.12 floor, matching
  requires-python.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HeMhD6yCyrgBz5oz87bCtR
---
 CLAUDE.md                                    |  14 +-
 CONTRIBUTING.md                              |   2 +-
 README.md                                    |   2 +-
 docs/source/installation.rst                 |   2 +-
 papers/bmodes/Snakefile                      |   2 +-
 papers/bmodes/config/ecut_spec.md            |  10 +-
 papers/bmodes/scripts/run_cov_sweep.sh       |  68 ----
 papers/bmodes/scripts/update_survey_stats.py |   2 +-
 papers/cosmo_val/Snakefile                   |   2 +-
 pyproject.toml                               |   4 +-
 workflow/rules/twopoint.smk                  |  49 ---
 workflow/scripts/run_2pcf_highres.py         | 394 -------------------
 12 files changed, 21 insertions(+), 530 deletions(-)
 delete mode 100755 papers/bmodes/scripts/run_cov_sweep.sh
 delete mode 100644 workflow/scripts/run_2pcf_highres.py

diff --git a/CLAUDE.md b/CLAUDE.md
index c35c20be..b4447758 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -18,7 +18,7 @@ Tests live in `src/sp_validation/tests/` and import the full scientific stack,
 so run them inside the container.
 - Run all tests: `pytest` (collects from `src/sp_validation/tests`; coverage on by default)
 - Skip the slow tests: `pytest -m "not slow"`
-- Run a single test: `pytest src/sp_validation/tests/test_cosmology.py::test_function_name`
+- Run a single test: `pytest src/sp_validation/tests/test_cosmo_val.py::test_function_name`
 
 CI runs this same suite inside the freshly-built image before publishing it
 (see `.github/workflows/deploy-image.yml`).
@@ -57,11 +57,13 @@ is the container (full scientific stack pre-built). For a local dev environment:
 - **Healpy/HealSparse**: Sky map handling
 
 ### Cosmology Inference Pipeline (`cosmo_inference/`)
-Run via `./pipeline.sh` with flags:
-- `--pcf`: Calculate 2-point correlation functions
-- `--covmat`: Calculate covariance matrix with CosmoCov
-- `--inference`: Run CosmoSIS inference
-- `--mcmc_process`: Analyze MCMC chains
+Orchestrated through Snakemake, not a standalone driver; see
+`cosmo_inference/README.md`. From the repository root:
+
+```bash
+snakemake --profile workflow/profiles/candide -s workflow/Snakefile \
+    inference_fiducial --configfile <run config>
+```
 
 ### Configuration
 Main configuration in `scripts/calibration/params.py` with parameters:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e385a386..0406741b 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -74,7 +74,7 @@ toolchain (`autoconf`, `automake`, `libtool`, `pkg-config`) available.
 ```bash
 pytest                       # full suite
 pytest -m "not slow"         # skip the slow tests
-pytest src/sp_validation/tests/test_cosmology.py::test_name   # a single test
+pytest src/sp_validation/tests/test_cosmo_val.py::test_name   # a single test
 ```
 
 Tests live in `src/sp_validation/tests/`. The default options (configured in
diff --git a/README.md b/README.md
index 455ddd91..1d269a0c 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@ Validation of weak-lensing catalogues (galaxy and star shapes and other paramete
 [![docs](https://img.shields.io/badge/docs-sphinx-blue)](https://cosmostat.github.io/sp_validation/)
 [![CI](https://github.com/CosmoStat/sp_validation/actions/workflows/deploy-image.yml/badge.svg)](https://github.com/CosmoStat/sp_validation/actions/workflows/deploy-image.yml)
 [![container](https://img.shields.io/badge/container-ghcr.io-2496ED?logo=docker&logoColor=white)](https://github.com/CosmoStat/sp_validation/pkgs/container/sp_validation)
-[![python](https://img.shields.io/badge/python-3.11%2B-blue?logo=python&logoColor=white)](https://www.python.org/downloads/)
+[![python](https://img.shields.io/badge/python-3.12%2B-blue?logo=python&logoColor=white)](https://www.python.org/downloads/)
 [![license](https://img.shields.io/badge/license-MIT-blue)](https://github.com/CosmoStat/sp_validation/blob/develop/LICENCE.txt)
 [![code style: ruff](https://img.shields.io/badge/code%20style-ruff-261230?logo=ruff&logoColor=white)](https://github.com/astral-sh/ruff)
 [![contribute](https://img.shields.io/badge/contribute-read-lightgrey)](https://github.com/CosmoStat/sp_validation/blob/develop/CONTRIBUTING.md)
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
index 56de599f..22ea84cb 100644
--- a/docs/source/installation.rst
+++ b/docs/source/installation.rst
@@ -82,6 +82,6 @@ For a narrower install, ``.[test]`` adds only the test extras and ``.[docs]`` on
 
 .. note::
 
-   ``sp_validation`` requires Python 3.11 or newer and pulls in a large scientific stack: ``treecorr``, ``pyccl``, ``healpy``, ``pymaster``, and others.
+   ``sp_validation`` requires Python 3.12 or newer and pulls in a large scientific stack: ``treecorr``, ``pyccl``, ``healpy``, ``pymaster``, and others.
    A bare development install builds these from source, which is slow and platform-sensitive.
    For most users the container is the more reliable path.
diff --git a/papers/bmodes/Snakefile b/papers/bmodes/Snakefile
index c58663d6..c9069c79 100644
--- a/papers/bmodes/Snakefile
+++ b/papers/bmodes/Snakefile
@@ -2,7 +2,7 @@
 # compute workflow at ../../workflow/.
 
 configfile: "config/config.yaml"
-configfile: "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/cat_config.yaml"
+configfile: "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
 
 envvars:
     "PYTHONUNBUFFERED",
diff --git a/papers/bmodes/config/ecut_spec.md b/papers/bmodes/config/ecut_spec.md
index 49da3a92..53041659 100644
--- a/papers/bmodes/config/ecut_spec.md
+++ b/papers/bmodes/config/ecut_spec.md
@@ -32,7 +32,7 @@ DES-Y3 used e < 0.8 to remove stars.
 
 ### How versions flow through the pipeline
 
-Everything is driven by `config["versions"]` in `workflow/config/config.yaml`. Adding a
+Everything is driven by `config["versions"]` in `papers/bmodes/config/config.yaml`. Adding a
 version there (plus its `cat_config.yaml` entry) makes it flow through all existing rules:
 `xi`, `covariance`, `pure_eb_data_vector`, `cosebis_data_vector`, `cl_data_vector`.
 The 2PCF and covariance are independent and can run in parallel.
@@ -43,7 +43,7 @@ Key resolution functions in `workflow/Snakefile`:
 - `resolve_covariance_version()` — identity function (each version gets its own covariance)
 - Wildcard constraint: `version=r"SP_v[\d.]+(_w_iv)?(_leak_corr)?"` — needs `_ecut\d+`
 
-Version comparison rules in `workflow/rules/claims.smk` (lines 131, 271, 386) use
+Version comparison rules in `papers/bmodes/rules/claims.smk` (lines 131, 271, 386) use
 `VERSIONS_LEAK_CORR` for inputs and derive version lists from config in the scripts.
 These should be parameterized to accept a version list via `snakemake.params`, so the
 same rules serve both paper and ecut comparisons.
@@ -71,10 +71,10 @@ uncorrected columns can't be consistently filtered to guarantee the same rows.
 
 | What | Where |
 |------|-------|
-| Workflow config | `workflow/config/config.yaml` (search `ecut`) |
-| Catalog config | `code/sp_validation/cosmo_val/cat_config.yaml` (search `ecut07`) |
+| Workflow config | `papers/bmodes/config/config.yaml` (search `ecut`) |
+| Catalog config | `cosmo_val/cat_config.yaml` (search `ecut07`) |
 | Pipeline orchestration | `workflow/Snakefile` (wildcard constraints, version resolution functions) |
-| Version comparison rules | `workflow/rules/claims.smk` lines 131, 271, 386 |
+| Version comparison rules | `papers/bmodes/rules/claims.smk` lines 131, 271, 386 |
 | Version comparison scripts | `workflow/scripts/{pure_eb,cosebis,cl}_version_comparison.py` |
 | Covariance params | `workflow/rules/covariance.smk` line 4 (`get_cat_params`) |
 
diff --git a/papers/bmodes/scripts/run_cov_sweep.sh b/papers/bmodes/scripts/run_cov_sweep.sh
deleted file mode 100755
index f86f20dc..00000000
--- a/papers/bmodes/scripts/run_cov_sweep.sh
+++ /dev/null
@@ -1,68 +0,0 @@
-#!/usr/bin/env bash
-# CosmoCov integration-grid covariance sweep over non-fiducial versions (Design B).
-#
-# Loops the non-fiducial version list (resolved via sweep_versions.py) and runs
-# the same run_cosmocov_chain.sh chain the fiducial cov_integration_g recipe
-# calls, once per version, on the 1000-bin integration grid (Gaussian-only,
-# masked, blind A). Each version's processed matrix lands in a per-version subdir
-# named canonically so cosebis_version_comparison (--cov-dir) and the pure E/B
-# sweep can reconstruct it:
-#
-#   <out>/covariance_<ver>_A_g_minsep=0.5_maxsep=300.0_nbins=1000_masked/
-#         covariance_<ver>_A_g_minsep=0.5_maxsep=300.0_nbins=1000_masked_processed.txt
-#
-# run_cosmocov_chain.sh writes covariance_processed.txt into --out; this driver
-# renames it to the {base}_processed.txt the plotter/pure_eb sweep expect.
-#
-# Mask per version: v1.4.8 uses the star-halo footprint power spectrum, every
-# other version uses the standard footprint (mirrors covariance.smk
-# get_mask_cls_path). Covariance is recomputed per variant — it is NOT
-# correction-invariant (masked v1.4.8 differs; uncorrected σ_e differs slightly).
-#
-# Usage:
-#   run_cov_sweep.sh --config <config.yaml> --cat-config <cat_config.yaml> \
-#     --planck18-json <cosmology_snapshot>/planck18.json \
-#     --mask-base <cosmo_inference/data/mask> --out <output_dir> \
-#     [--blind A] [--versions "v1 v2 ..."]
-set -euo pipefail
-
-. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
-
-CONFIG=""; CATCONFIG=""; PLANCK18=""; MASKBASE=""; OUT=""; BLIND="A"; VERSIONS=""
-MINSEP=0.5; MAXSEP=300.0; NBINS=1000
-while [ $# -gt 0 ]; do
-  case "$1" in
-    --config) CONFIG="$2"; shift 2;;
-    --cat-config) CATCONFIG="$2"; shift 2;;
-    --planck18-json) PLANCK18="$2"; shift 2;;
-    --mask-base) MASKBASE="$2"; shift 2;;
-    --out) OUT="$2"; shift 2;;
-    --blind) BLIND="$2"; shift 2;;
-    --versions) VERSIONS="$2"; shift 2;;
-    *) echo "unknown arg: $1" >&2; exit 2;;
-  esac
-done
-
-mkdir -p "$OUT"
-
-VERSIONS=$(sweep_versions "$CONFIG")
-
-for ver in $VERSIONS; do
-  base="covariance_${ver}_${BLIND}_g_minsep=${MINSEP}_maxsep=${MAXSEP}_nbins=${NBINS}_masked"
-  # Version dir key: strip SP_, _leak_corr, _ecutNN (mirrors get_mask_cls_path).
-  vdir=$(echo "$ver" | sed -e 's/_leak_corr//' -e 's/^SP_//' -e 's/_ecut[0-9]*//')
-  if [ "$vdir" = "v1.4.8" ]; then
-    mask="$MASKBASE/mask_cls_footprint_starhalo_nside_4096_norm.txt"
-  else
-    mask="$MASKBASE/mask_cls_footprint_nside_4096_norm.txt"
-  fi
-  echo "[cov_sweep] $ver (mask: $(basename "$mask"))"
-  bash "$WSCRIPTS/run_cosmocov_chain.sh" \
-    --version "$ver" --blind "$BLIND" \
-    --min-sep "$MINSEP" --max-sep "$MAXSEP" --nbins "$NBINS" --gaussian g \
-    --planck18-json "$PLANCK18" --cat-config "$CATCONFIG" \
-    --mask-cls "$mask" --out "$OUT/$base"
-  mv "$OUT/$base/covariance_processed.txt" "$OUT/$base/${base}_processed.txt"
-  echo "[cov_sweep] $ver -> $OUT/$base/${base}_processed.txt"
-done
-echo "[cov_sweep] done -> $OUT"
diff --git a/papers/bmodes/scripts/update_survey_stats.py b/papers/bmodes/scripts/update_survey_stats.py
index 6713b3c0..a2dc386c 100644
--- a/papers/bmodes/scripts/update_survey_stats.py
+++ b/papers/bmodes/scripts/update_survey_stats.py
@@ -5,7 +5,7 @@
 memory usage.
 
 Usage:
-    python workflow/scripts/update_survey_stats.py \
+    python papers/bmodes/scripts/update_survey_stats.py \
         --mask-standard <path> --mask-starhalo <path>
 """
 
diff --git a/papers/cosmo_val/Snakefile b/papers/cosmo_val/Snakefile
index 716a3ac6..67bef0a7 100644
--- a/papers/cosmo_val/Snakefile
+++ b/papers/cosmo_val/Snakefile
@@ -7,7 +7,7 @@
 # isolation or as the whole suite via the default `cosmo_val_all` target.
 
 configfile: "config/config.yaml"
-configfile: "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/cat_config.yaml"
+configfile: "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
 
 envvars:
     "PYTHONUNBUFFERED",
diff --git a/pyproject.toml b/pyproject.toml
index 5c45cfbf..4ad6fbf0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -159,8 +159,8 @@ glass = [
 # Snakemake workflow and cross-validation runners are available.
 workflow = [
     "snakemake",
-    # run_2pcf_highres.py drives the MPI convergence run; the container ships
-    # OpenMPI under /opt/ompi so mpi4py builds against it.
+    # Optional MPI runners (the container ships OpenMPI at /opt/ompi, so mpi4py
+    # builds against it).
     "mpi4py",
     # CosmoSIS drives the cosmo_inference sampling step. Sdist-only, so this is a
     # source build (~2 min) against the base image's gfortran/GSL/cfitsio/OpenMPI —
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 861c3b05..275afc3c 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -24,55 +24,6 @@ rule xi:
         "../scripts/run_2pcf.py"
 
 
-# PARKED: xi_highres (high-resolution xi for COSEBIS integration). Not runnable
-# as written -- the shell invokes run_2pcf_highres.py bare, but the script has
-# required --cat-config and --out arguments. Revive it with those supplied.
-#
-# The MPI reasoning below is hard-won and must survive the revival:
-#
-#   Exception to the profile-driven container model: this is multi-node MPI, one
-#   `apptainer exec` per rank. Snakemake's own container wrapping puts the
-#   *whole* shell command -- `mpiexec` included -- inside a single container
-#   instance, so only rank 0's node would run inside it; the other ranks,
-#   spawned by SLURM/PMI on their own nodes, would land bare on the host.
-#   `container: None` plus an explicit `mpiexec -n N apptainer exec ...`
-#   per-rank is therefore required.
-#   Snakemake's slurm-jobstep plugin deliberately does NOT prepend `srun` to a
-#   job carrying an `mpi` resource, which is what lets the rule's own launcher
-#   run on the host, outside the container.
-#   Because this rule builds its own apptainer call, reaching the source-cache
-#   copy of the script relies on our `--bind /home` rather than on Snakemake's
-#   automatic mount -- and on a concrete image file, since `apptainer exec`
-#   takes no `docker://` URI. Take that path from `resolve_image()[0]` rather
-#   than naming a second image path that can drift.
-#
-# rule xi_highres:
-#     container: None
-#     params:
-#         image=resolve_image()[0],
-#     input:
-#         script=workflow.source_path("../scripts/run_2pcf_highres.py"),
-#     output:
-#         txt=str(COSMO_VAL / f"{FIDUCIAL['version']}_xi_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.txt"),
-#         xi_plus=str(COSMO_VAL / f"xi_plus_{FIDUCIAL['version']}_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.fits"),
-#         xi_minus=str(COSMO_VAL / f"xi_minus_{FIDUCIAL['version']}_minsep={FIDUCIAL['min_sep_int']}_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch=1.fits"),
-#     resources:
-#         tasks=30,
-#         cpus_per_task=12,
-#         nodes=6,
-#         mem_mb_per_cpu=2000,
-#         runtime=2880,
-#         slurm_extra="'--exclude=n17,n09,n36 --partition=pscomp'",
-#         mpi="/softs/openmpi/5.0.5-slurm-CentOS8/bin/mpiexec",
-#     shell:
-#         "{resources.mpi} -n {resources.tasks} "
-#         "apptainer exec "
-#         "--bind /home,/n09data,/n17data,/n23data1,/softs "
-#         "--env LD_LIBRARY_PATH=/softs/openmpi/5.0.5-slurm-CentOS8/lib "
-#         "{params.image} "
-#         "python {input.script} --cat-config <...> --out <...>"
-
-
 rule rho_tau_stats:
     output:
         rho_stats=str(COSMO_VAL / "rho_tau_stats/rho_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
diff --git a/workflow/scripts/run_2pcf_highres.py b/workflow/scripts/run_2pcf_highres.py
deleted file mode 100644
index eb31d2c7..00000000
--- a/workflow/scripts/run_2pcf_highres.py
+++ /dev/null
@@ -1,394 +0,0 @@
-#!/usr/bin/env python
-"""
-High-resolution ξ± measurement for COSEBIS integration.
-
-Computes TreeCorr GGCorrelation with fine angular binning (10,000+ bins)
-required for accurate COSEBIS mode integration. Uses MPI for patch-pair
-distribution across nodes when available; falls back to multi-threaded
-single-process otherwise.
-
-Reference: Asgari et al. 2017 — minimum 10,000 bins for E_7 at 0.5% accuracy.
-
-Usage:
-    # MPI (via Slurm submission script):
-    mpiexec --map-by ppr:1:node python run_2pcf_highres.py \
-        --cat-config /path/to/cosmo_val/cat_config.yaml --out <output_dir>
-
-    # Single-process fallback:
-    python run_2pcf_highres.py \
-        --cat-config /path/to/cosmo_val/cat_config.yaml --out <output_dir>
-"""
-
-import argparse
-import os
-import time
-
-import numpy as np
-import treecorr
-from astropy.io import fits
-
-try:
-    # In-container path: full sp_validation stack available.
-    from sp_validation.cosmo_val import CosmologyValidation
-
-    _HAVE_COSMO_VAL = True
-except ImportError:
-    # Bare-host path (host OpenMPI + host python for the 10k-bin MPI run): the
-    # full sp_validation stack (cs_util.plots -> healpy/healsparse) is not
-    # installed. This measurement only needs the shear catalog path + column
-    # names, which are a pure cat_config.yaml lookup — resolve them standalone.
-    CosmologyValidation = None
-    _HAVE_COSMO_VAL = False
-
-# ---------------------------------------------------------------------------
-# MPI setup (graceful fallback)
-# ---------------------------------------------------------------------------
-try:
-    from mpi4py import MPI
-
-    comm = MPI.COMM_WORLD
-    rank = comm.Get_rank()
-    size = comm.Get_size()
-    USE_MPI = size > 1
-except ImportError:
-    comm = None
-    rank = 0
-    size = 1
-    USE_MPI = False
-
-# ---------------------------------------------------------------------------
-# Configuration
-# ---------------------------------------------------------------------------
-# Shear response (R=1 for all SP catalogs)
-R = 1.0
-
-# Detect threads from Slurm or fall back to OS count
-NUM_THREADS = int(os.environ.get("SLURM_CPUS_PER_TASK", os.cpu_count() or 24))
-
-# The catalog path, ellipticity/weight columns, TreeCorr grid, patch count and
-# output directory are resolved from the CLI in main() (defaults reproduce the
-# historical hardcoded values for a no-arg run). They are declared here as
-# module globals so the rank-aware helpers below resolve them at call time; the
-# catalog path + columns come from cat_config + version exactly as run_2pcf.py
-# resolves them (via CosmologyValidation).
-CAT_PATH = None
-VERSION = None
-E1_COL = None
-E2_COL = None
-W_COL = None
-TMIN = None  # arcmin
-TMAX = None  # arcmin
-NBINS = None
-NPATCH = None
-OUTPUT_DIR = None
-PATCH_FILE = None
-
-
-def parse_args(argv=None):
-    """CLI mirroring run_xi_sweep's signature; defaults reproduce prior behavior."""
-    ap = argparse.ArgumentParser(
-        description="High-resolution TreeCorr ξ± measurement for COSEBIS integration."
-    )
-    ap.add_argument(
-        "--config",
-        default=None,
-        help="Path to bmodes config.yaml (accepted for signature parity with "
-        "run_xi_sweep; not read by this measurement).",
-    )
-    ap.add_argument(
-        "--cat-config", required=True, help="Absolute path to cat_config.yaml"
-    )
-    ap.add_argument(
-        "--version",
-        default="SP_v1.4.6.3_leak_corr",
-        help="Catalog version key in cat_config",
-    )
-    ap.add_argument("--nbins", type=int, default=10000, help="Number of log bins")
-    ap.add_argument("--npatch", type=int, default=50, help="TreeCorr patch count")
-    ap.add_argument(
-        "--min-sep", type=float, default=0.5, help="Min separation [arcmin]"
-    )
-    ap.add_argument(
-        "--max-sep", type=float, default=300.0, help="Max separation [arcmin]"
-    )
-    ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    return ap.parse_args(argv)
-
-
-def log(msg):
-    """Print with timestamp on rank 0 only."""
-    if rank == 0:
-        print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
-
-
-def load_catalog():
-    """Load shear catalog and apply mean subtraction."""
-    log(f"Loading catalog: {CAT_PATH}")
-    hdul = fits.open(CAT_PATH, memmap=True)
-    data = hdul[1].data
-
-    ra = np.array(data["ra"], dtype=np.float64)
-    dec = np.array(data["dec"], dtype=np.float64)
-    e1 = np.array(data[E1_COL], dtype=np.float64)
-    e2 = np.array(data[E2_COL], dtype=np.float64)
-    w = np.array(data[W_COL], dtype=np.float64)
-    hdul.close()
-
-    log(f"  {len(ra):,} galaxies loaded")
-
-    # Additive bias: c = <e/R>_w  (R=1 for SP catalogs)
-    c1 = np.average(e1 / R, weights=w)
-    c2 = np.average(e2 / R, weights=w)
-    log(f"  Additive bias: c1={c1:.6e}, c2={c2:.6e}")
-
-    # Calibrated shear: g = (e - c) / R
-    g1 = (e1 - c1) / R
-    g2 = (e2 - c2) / R
-
-    return ra, dec, g1, g2, w
-
-
-def _wait_for_file(path, timeout=300, interval=1.0):
-    """Block until `path` is visible to this node, defeating NFS dir caching.
-
-    On a multi-node run the rank that wrote `path` sees it immediately, but
-    peer nodes can carry a stale negative directory-cache entry past an MPI
-    Barrier. Re-listing the parent directory forces an NFS attribute refresh;
-    poll that until the entry appears (or raise after `timeout` seconds).
-    """
-    parent = os.path.dirname(path) or "."
-    name = os.path.basename(path)
-    waited = 0.0
-    while waited < timeout:
-        try:
-            if name in os.listdir(parent):
-                return
-        except FileNotFoundError:
-            pass
-        time.sleep(interval)
-        waited += interval
-    raise TimeoutError(f"patch-center file not visible after {timeout}s: {path}")
-
-
-def compute_patch_centers(ra, dec):
-    """Compute patch centers from subsampled catalog (rank 0 only)."""
-    if os.path.exists(PATCH_FILE):
-        log(f"Using existing patch centers: {PATCH_FILE}")
-        return
-
-    if rank != 0:
-        return
-
-    log(f"Computing patch centers (npatch={NPATCH}) from 1% subsample...")
-    rng = np.random.default_rng(42)
-    n_sub = max(len(ra) // 100, NPATCH * 100)
-    idx = rng.choice(len(ra), size=n_sub, replace=False)
-
-    cat_sub = treecorr.Catalog(
-        ra=ra[idx],
-        dec=dec[idx],
-        ra_units="degrees",
-        dec_units="degrees",
-        npatch=NPATCH,
-    )
-    cat_sub.write_patch_centers(PATCH_FILE)
-    log(f"  Wrote patch centers to {PATCH_FILE}")
-    del cat_sub
-
-
-def write_xi_fits(gg, prefix, xi_data):
-    """Write ξ+ or ξ- to FITS matching CosmologyValidation format."""
-    out_path = os.path.join(
-        OUTPUT_DIR,
-        f"{prefix}_{VERSION}_minsep={TMIN}_maxsep={TMAX}_nbins={NBINS}_npatch=1.fits",
-    )
-    n = len(xi_data)
-    cols = [
-        fits.Column(name="BIN1", format="K", array=np.ones(n, dtype=int)),
-        fits.Column(name="BIN2", format="K", array=np.ones(n, dtype=int)),
-        fits.Column(name="ANGBIN", format="K", array=np.arange(1, n + 1)),
-        fits.Column(name="VALUE", format="D", array=xi_data),
-        fits.Column(name="ANG", format="D", unit="arcmin", array=gg.meanr),
-    ]
-    ext_name = "XI_PLUS" if "plus" in prefix else "XI_MINUS"
-    hdu = fits.BinTableHDU.from_columns(cols, name=ext_name)
-    for key, val in {
-        "2PTDATA": "T",
-        "QUANT1": "G+R",
-        "QUANT2": "G+R",
-        "KERNEL_1": "NZ_SOURCE",
-        "KERNEL_2": "NZ_SOURCE",
-        "WINDOWS": "SAMPLE",
-    }.items():
-        hdu.header[key] = val
-    hdu.writeto(out_path, overwrite=True)
-    log(f"  Wrote {out_path}")
-
-
-def resolve_shear_config(cat_config_path, version):
-    """Standalone shear-config resolver (bare-host fallback for CosmologyValidation).
-
-    Reproduces exactly the ``cc[version]["shear"]`` fields this measurement reads
-    (path, e1_col, e2_col, w_col), replicating CosmologyValidation's two
-    transforms: (1) subdir-relative path resolution, and (2) the ``_leak_corr``
-    virtual version — deep-copy the base version and swap
-    e1_col/e2_col -> e1_col_corrected/e2_col_corrected. See
-    sp_validation/cosmo_val/core.py.
-    """
-    import copy
-
-    import yaml
-
-    with open(cat_config_path) as fh:
-        cc = yaml.load(fh, Loader=yaml.FullLoader)
-
-    def resolve_paths(ver):
-        subdir = os.fspath(cc[ver]["subdir"])
-        for section in cc[ver].values():
-            if isinstance(section, dict) and "path" in section:
-                p = section["path"]
-                if not os.path.isabs(p):
-                    section["path"] = os.path.join(subdir, p)
-
-    leak_suffix = "_leak_corr"
-    if version in cc:
-        resolve_paths(version)
-    elif version.endswith(leak_suffix):
-        base = version[: -len(leak_suffix)]
-        if base not in cc:
-            raise ValueError(f"Base version '{base}' not in cat_config for '{version}'")
-        resolve_paths(base)
-        base_shear = cc[base]["shear"]
-        if "e1_col_corrected" not in base_shear or "e2_col_corrected" not in base_shear:
-            raise ValueError(
-                f"{base} lacks e1_col_corrected/e2_col_corrected; cannot form {version}"
-            )
-        cc[version] = copy.deepcopy(cc[base])
-        cc[version]["shear"]["e1_col"] = base_shear["e1_col_corrected"]
-        cc[version]["shear"]["e2_col"] = base_shear["e2_col_corrected"]
-        resolve_paths(version)
-    else:
-        raise ValueError(f"Version '{version}' not found in cat_config")
-
-    return cc[version]["shear"]
-
-
-def main():
-    global CAT_PATH, VERSION, E1_COL, E2_COL, W_COL
-    global TMIN, TMAX, NBINS, NPATCH, OUTPUT_DIR, PATCH_FILE
-
-    args = parse_args()
-    VERSION = args.version
-    NBINS = args.nbins
-    NPATCH = args.npatch
-    TMIN = args.min_sep
-    TMAX = args.max_sep
-    OUTPUT_DIR = args.out
-
-    # Resolve catalog path + ellipticity/weight columns from cat_config + version
-    # exactly as run_2pcf.py does (applies the _leak_corr column swap and the
-    # subdir path resolution). In-container this uses CosmologyValidation; bare-
-    # host (10k-bin MPI run) it uses the standalone cat_config resolver, which is
-    # byte-identical for the shear-config fields this measurement reads.
-    if _HAVE_COSMO_VAL:
-        cv = CosmologyValidation(
-            versions=[VERSION], catalog_config=args.cat_config, output_dir=OUTPUT_DIR
-        )
-        shear_cfg = cv.cc[VERSION]["shear"]
-    else:
-        shear_cfg = resolve_shear_config(args.cat_config, VERSION)
-    CAT_PATH = shear_cfg["path"]
-    E1_COL = shear_cfg["e1_col"]
-    E2_COL = shear_cfg["e2_col"]
-    W_COL = shear_cfg["w_col"]
-
-    PATCH_FILE = os.path.join(
-        OUTPUT_DIR,
-        f"patch_centers_{VERSION}_{NPATCH}_{TMIN}_{TMAX}.dat",
-    )
-
-    t0 = time.time()
-
-    log("=" * 60)
-    log("High-resolution ξ± measurement")
-    log(f"  MPI: {'yes' if USE_MPI else 'no'} (ranks={size})")
-    log(f"  Config: {NBINS:,} bins, [{TMIN}, {TMAX}] arcmin")
-    log(f"  Patches: {NPATCH}, Threads/rank: {NUM_THREADS}")
-    log(f"  Version: {VERSION}")
-    log("=" * 60)
-
-    # All ranks load catalog (needed for TreeCorr patch assignment)
-    ra, dec, g1, g2, w = load_catalog()
-
-    # Compute patch centers (rank 0 only; others wait)
-    compute_patch_centers(ra, dec)
-    if USE_MPI:
-        comm.Barrier()
-        # Cross-node visibility: rank 0 wrote PATCH_FILE on its node, but on a
-        # multi-node allocation the other ranks' nodes may not see it yet (NFS
-        # close-to-open + negative-dir caching persists past the Barrier). Poll
-        # with a forced directory refresh until it appears before reading it.
-        _wait_for_file(PATCH_FILE)
-
-    # Create TreeCorr catalog with patch centers
-    log("Creating TreeCorr catalog with patches...")
-    cat = treecorr.Catalog(
-        ra=ra,
-        dec=dec,
-        g1=g1,
-        g2=g2,
-        w=w,
-        ra_units="degrees",
-        dec_units="degrees",
-        patch_centers=PATCH_FILE,
-    )
-    cat.load()
-    cat.get_patches()
-    log(f"  Catalog ready ({cat.nobj:,} objects, {cat.npatch} patches)")
-
-    # Free raw arrays (TreeCorr holds its own copy)
-    del ra, dec, g1, g2, w
-
-    # Compute GG correlation
-    log("Computing GGCorrelation...")
-    gg = treecorr.GGCorrelation(
-        min_sep=TMIN,
-        max_sep=TMAX,
-        nbins=NBINS,
-        sep_units="arcminutes",
-        verbose=2,
-    )
-
-    process_kwargs = {"num_threads": NUM_THREADS}
-    if USE_MPI:
-        process_kwargs["comm"] = comm
-
-    gg.process(cat, **process_kwargs)
-    log(f"  Correlation complete ({time.time() - t0:.0f}s elapsed)")
-
-    # Write output (rank 0 only)
-    if rank == 0:
-        out_txt = os.path.join(
-            OUTPUT_DIR,
-            f"{VERSION}_xi_minsep={TMIN}_maxsep={TMAX}_nbins={NBINS}_npatch=1.txt",
-        )
-        # Write only the main per-bin correlation. The convergence consumer
-        # (cosebis_binning_comparison.py) reads just the per-bin columns
-        # (np.loadtxt max_rows=nbins) and the 1000-bin covariance — the 10k
-        # jackknife cov is used nowhere. write_patch_results/write_cov=True
-        # serialised a 20000x20000 cov + 180 patch blocks (~10 GB) that nothing
-        # reads and also cost the estimate_cov compute; drop both. The patches
-        # still parallelise gg.process; gg.xip/gg.xim (values, FITS) are
-        # unaffected.
-        gg.write(out_txt, write_patch_results=False, write_cov=False)
-        log(f"  Wrote {out_txt}")
-
-        write_xi_fits(gg, "xi_plus", gg.xip)
-        write_xi_fits(gg, "xi_minus", gg.xim)
-
-        elapsed = time.time() - t0
-        log(f"Done! Total time: {elapsed / 3600:.1f}h ({elapsed:.0f}s)")
-
-
-if __name__ == "__main__":
-    main()

From 0f2f2c94e6f83a7dc43f08c8c9c19c574bd153ed Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 25 Sep 2026 15:15:46 +0200
Subject: [PATCH 26/83] Work b_modes from plain arrays instead of TreeCorr
 correlations

calculate_pure_eb_correlation returns a self-describing results dict
(theta, bin edges, xi+/- on both grids, n_eff) in place of the gg/gg_int
objects, so calculate_eb_statistics, the plot helpers and
save_pure_eb_results work from values alone. New seams: bins_from_edges,
log_bin_edges, pure_eb_from_xi, pure_eb_covariance_mc and
cosebis_scan_from_xi. The pure-E/B .npz stores n_eff (the realisation
count behind the covariance) instead of npatch.

Callers in cosmo_val, the bmodes calculate_pure_eb_ptes script, its
claims rule and PTE sweep driver are adapted. No numbers change.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HeMhD6yCyrgBz5oz87bCtR
---
 papers/bmodes/rules/claims.smk                |  14 +-
 .../bmodes/scripts/calculate_pure_eb_ptes.py  |  35 +-
 .../bmodes/scripts/run_pure_eb_ptes_sweep.sh  |  26 +-
 src/sp_validation/b_modes.py                  | 455 +++++++++++-------
 src/sp_validation/cosmo_val/core.py           |   6 +-
 src/sp_validation/cosmo_val/cosebis.py        |   2 +-
 src/sp_validation/cosmo_val/pure_eb.py        |  24 +-
 src/sp_validation/tests/test_b_modes.py       | 161 ++++++-
 src/sp_validation/tests/test_cosmo_val.py     |   2 +-
 9 files changed, 467 insertions(+), 258 deletions(-)

diff --git a/papers/bmodes/rules/claims.smk b/papers/bmodes/rules/claims.smk
index 43962627..3846b9bc 100644
--- a/papers/bmodes/rules/claims.smk
+++ b/papers/bmodes/rules/claims.smk
@@ -355,25 +355,21 @@ rule pure_eb_covariance:
 
 
 rule calculate_pure_eb_ptes:
-    """Calculate PTE matrices for Pure E/B mode scale cut robustness.
+    """PTE matrices for pure E/B-mode scale-cut robustness.
 
-    Per-blind: Uses blind-specific integration covariance for PTE calculation.
-    The pure_eb_data vectors are identical across blinds; only covariance differs.
-
-    In practice, BB covariance is blind-independent (validated by
-    bb_covariance_blind_independence), so downstream consumers (config_space_pte_matrices)
-    only request blind A. The per-blind wildcard is retained for the blind independence test.
+    Nothing here varies with the blind: the data vectors come from the blind-A
+    gather and the PTEs are Hartlap-debiased by the MC draw count, not by a
+    per-blind covariance. The wildcard survives as the filename slot the
+    consumer (config_space_pte_matrices) reads, and only blind A is ever built.
     """
     input:
         pure_eb_data="results/paper_plots/intermediate/{version}_A_pure_eb_semianalytic.npz",
-        cov_integration=lambda w: _cov_integration_path(w.version, w.blind),
     output:
         "results/paper_plots/intermediate/{version}_{blind}_pure_eb_ptes.npz",
     wildcard_constraints:
         blind=r"[ABC]",
     params:
         version="{version}",
-        npatch=FIDUCIAL["npatch"],
         n_samples=config["covariance"]["n_samples"],
     resources:
         mem_mb=16000,
diff --git a/papers/bmodes/scripts/calculate_pure_eb_ptes.py b/papers/bmodes/scripts/calculate_pure_eb_ptes.py
index f8bc709d..3a623669 100644
--- a/papers/bmodes/scripts/calculate_pure_eb_ptes.py
+++ b/papers/bmodes/scripts/calculate_pure_eb_ptes.py
@@ -4,14 +4,13 @@
 pure-E/B ``semianalytic.npz`` (data vectors + MC covariance), evaluates the
 ξ_+^B / ξ_-^B / joint ξ_tot^B χ² PTE matrices over the scale-cut grid via
 ``sp_validation.b_modes.calculate_eb_statistics`` (Hartlap-corrected inverse
-MC covariance), and writes the PTE matrices to
+MC covariance, debiased by the draw count), and writes the PTE matrices to
 ``{out}/{version}_{blind}_pure_eb_ptes.npz``.
 
     python calculate_pure_eb_ptes.py \
         --version SP_v1.4.6.3_leak_corr --blind A \
         --pure-eb-data <..._pure_eb_semianalytic.npz> \
-        --cov-integration <cov ..._processed.txt> \
-        --npatch 1 --n-samples 2000 --out <output_dir>
+        --n-samples 2000 --out <output_dir>
 """
 
 import argparse
@@ -22,31 +21,21 @@
 from sp_validation.b_modes import calculate_eb_statistics
 
 
-class FakeGG:
-    """Minimal GGCorrelation-like object for calculate_eb_statistics."""
-
-    def __init__(self, nbins, npatch):
-        self.nbins = nbins
-        self.npatch1 = npatch
-        self.npatch2 = npatch
-
-
 def calculate_ptes(
     version,
     blind,
     pure_eb_data,
-    cov_integration,
-    npatch,
     n_samples,
     output_dir,
 ):
     dataset = np.load(pure_eb_data)
 
     theta = dataset["theta"]
-    nbins = len(theta)
 
     results = {
-        "gg": FakeGG(nbins, int(npatch)),
+        "theta": theta,
+        # The MC draws are the realisations behind this covariance.
+        "n_eff": int(n_samples),
         "xip_E": dataset["xip_E"],
         "xim_E": dataset["xim_E"],
         "xip_B": dataset["xip_B"],
@@ -57,11 +46,7 @@ def calculate_ptes(
     }
 
     print(f"Calculating PTE matrices for {version}...")
-    results = calculate_eb_statistics(
-        results,
-        cov_path_int=cov_integration,
-        n_samples=int(n_samples),
-    )
+    results = calculate_eb_statistics(results)
 
     pte_matrices = results["pte_matrices"]
     output_data = {
@@ -83,12 +68,6 @@ def _from_cli(argv=None):
     ap.add_argument("--version", required=True)
     ap.add_argument("--blind", default="A")
     ap.add_argument("--pure-eb-data", required=True, help="Gathered semianalytic .npz")
-    ap.add_argument(
-        "--cov-integration",
-        default=None,
-        help="Integration-grid covariance _processed.txt (optional)",
-    )
-    ap.add_argument("--npatch", type=int, default=1)
     ap.add_argument("--n-samples", type=int, default=2000)
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     a = ap.parse_args(argv)
@@ -96,8 +75,6 @@ def _from_cli(argv=None):
         version=a.version,
         blind=a.blind,
         pure_eb_data=a.pure_eb_data,
-        cov_integration=a.cov_integration,
-        npatch=a.npatch,
         n_samples=a.n_samples,
         output_dir=a.out,
     )
diff --git a/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh b/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
index 0ea3a862..0e7ecdf5 100644
--- a/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
+++ b/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
@@ -4,30 +4,27 @@
 # Loops the non-fiducial version list (resolved via sweep_versions.py) and runs
 # calculate_pure_eb_ptes.py once per version — the same χ² PTE-matrix compute the
 # fiducial pure_eb_pte_per_cut recipe calls. Each version's gathered semi-analytic
-# NPZ is read from the pure_eb_sweep output dir and its Gaussian integration
-# covariance from the cov_sweep output dir (both by absolute path — lc does not
-# wire cross-output deps, so the driver reads upstream sweeps directly; run
-# pure_eb_sweep + cov_sweep before this). Each version emits the canonical
-# ``{ver}_{blind}_pure_eb_ptes.npz`` — the exact name config_space_pte_matrices.py
-# reconstructs from --pte-intermediate-dir — straight into --out. Serial over
-# versions; each version is fast (206-pair grid, ~seconds).
+# NPZ (data vectors + MC covariance) is read from the pure_eb_sweep output dir by
+# absolute path — lc does not wire cross-output deps, so the driver reads the
+# upstream sweep directly; run pure_eb_sweep before this. Each version emits the
+# canonical ``{ver}_{blind}_pure_eb_ptes.npz`` — the exact name
+# config_space_pte_matrices.py reconstructs from --pte-intermediate-dir — straight
+# into --out. Serial over versions; each version is fast (206-pair grid, ~seconds).
 #
 # Usage:
 #   run_pure_eb_ptes_sweep.sh --config <config.yaml> --cat-config <cat_config.yaml> \
 #     --pure-eb-sweep-dir <pure_eb/.../pure_eb_sweep> \
-#     --cov-sweep-dir <covariance/.../cov_sweep> \
 #     --out <output_dir> [--blind A] [--versions "v1 v2 ..."]
 set -euo pipefail
 
 . "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
 
-CONFIG=""; CATCONFIG=""; PUREEBSWEEP=""; COVSWEEP=""; OUT=""; BLIND="A"; VERSIONS=""
+CONFIG=""; CATCONFIG=""; PUREEBSWEEP=""; OUT=""; BLIND="A"; VERSIONS=""
 while [ $# -gt 0 ]; do
   case "$1" in
     --config) CONFIG="$2"; shift 2;;
     --cat-config) CATCONFIG="$2"; shift 2;;
     --pure-eb-sweep-dir) PUREEBSWEEP="$2"; shift 2;;
-    --cov-sweep-dir) COVSWEEP="$2"; shift 2;;
     --out) OUT="$2"; shift 2;;
     --blind) BLIND="$2"; shift 2;;
     --versions) VERSIONS="$2"; shift 2;;
@@ -41,16 +38,11 @@ VERSIONS=$(sweep_versions "$CONFIG")
 
 for ver in $VERSIONS; do
   pureeb="$PUREEBSWEEP/${ver}_${BLIND}_pure_eb_semianalytic.npz"
-  covbase="covariance_${ver}_${BLIND}_g_minsep=0.5_maxsep=300.0_nbins=1000_masked"
-  covint="$COVSWEEP/$covbase/${covbase}_processed.txt"
-  for f in "$pureeb" "$covint"; do
-    [ -f "$f" ] || { echo "MISSING upstream input for $ver: $f" >&2; exit 1; }
-  done
+  [ -f "$pureeb" ] || { echo "MISSING upstream input for $ver: $pureeb" >&2; exit 1; }
   echo "[pure_eb_ptes_sweep] $ver"
   spv_python "$PSCRIPTS/calculate_pure_eb_ptes.py" \
     --version "$ver" --blind "$BLIND" \
-    --pure-eb-data "$pureeb" --cov-integration "$covint" \
-    --npatch 1 --n-samples 2000 --out "$OUT"
+    --pure-eb-data "$pureeb" --n-samples 2000 --out "$OUT"
   echo "[pure_eb_ptes_sweep] $ver -> $OUT/${ver}_${BLIND}_pure_eb_ptes.npz"
 done
 echo "[pure_eb_ptes_sweep] done -> $OUT"
diff --git a/src/sp_validation/b_modes.py b/src/sp_validation/b_modes.py
index 13f4c2e9..c4b9a2b7 100644
--- a/src/sp_validation/b_modes.py
+++ b/src/sp_validation/b_modes.py
@@ -74,23 +74,34 @@ def scale_cut_to_bins(gg, min_scale=None, max_scale=None):
     stop_bin : int
         Last included bin index + 1 (for slicing notation)
     """
-    nbins = len(gg.meanr)
+    return bins_from_edges(gg.left_edges, gg.right_edges, min_scale, max_scale)
 
-    if min_scale is not None:
-        # Conservative: exclude bins whose left edge is below min_scale
-        start_bin = np.searchsorted(gg.left_edges, min_scale, side="left")
-    else:
-        start_bin = 0
-
-    if max_scale is not None:
-        # Conservative: exclude bins whose right edge is above max_scale
-        stop_bin = np.searchsorted(gg.right_edges, max_scale, side="right")
-    else:
-        stop_bin = nbins
 
+def bins_from_edges(left_edges, right_edges, min_scale=None, max_scale=None):
+    """:func:`scale_cut_to_bins` on bare bin edges."""
+    start_bin = (
+        np.searchsorted(left_edges, min_scale, side="left")
+        if min_scale is not None
+        else 0
+    )
+    stop_bin = (
+        np.searchsorted(right_edges, max_scale, side="right")
+        if max_scale is not None
+        else len(right_edges)
+    )
     return start_bin, stop_bin
 
 
+def log_bin_edges(min_sep, max_sep, nbins):
+    """TreeCorr ``Log`` bin edges — the grid a binning defines.
+
+    SACC ξ± parts store bin centres, not edges, so a consumer working from a
+    part reconstructs the edges from the binning it was measured on.
+    """
+    edges = np.geomspace(float(min_sep), float(max_sep), int(nbins) + 1)
+    return edges[:-1], edges[1:]
+
+
 def correlation_from_covariance(covariance):
     """
     Convert covariance matrix to correlation matrix.
@@ -164,75 +175,40 @@ def pure_EB(corrs):
             parallel=True,
         )
 
-    # Initialize results dictionary with basic E/B mode data
-    results = {"gg": gg, "gg_int": gg_int}
+    # The results dict is self-describing: the grids it was measured on travel
+    # with the modes, so every consumer downstream works from values alone.
+    results = {
+        "theta": gg.meanr,
+        "left_edges": gg.left_edges,
+        "right_edges": gg.right_edges,
+        "xip": gg.xip,
+        "xim": gg.xim,
+        "var_xip": gg.varxip,
+        "var_xim": gg.varxim,
+        "theta_int": gg_int.meanr,
+        "xip_int": gg_int.xip,
+        "xim_int": gg_int.xim,
+        "n_eff": n_samples if cov_path_int is not None else gg.npatch1,
+    }
     results.update(dict(zip(_EB_KEYS, pure_EB([gg, gg_int]))))
 
     if cov_path_int is not None:
-        # Use semi-analytical covariance propagation
-        print("Computing semi-analytical covariance for pure E/B modes")
-        if z_dist is None:
-            raise ValueError("z_dist must be provided for semi-analytical covariance")
-        if cosmo_cov is None:
+        if z_dist is None or cosmo_cov is None:
             raise ValueError(
-                "cosmo_cov must be provided for semi-analytical covariance"
-            )
-
-        # Load covariance matrix
-        cov_int = np.loadtxt(cov_path_int)
-
-        # Set up integration binning and pre-compute binning matrix
-        nbins_int, theta_int = len(gg_int.meanr), gg_int.meanr
-        reporting_bin_edges = np.concatenate([gg.left_edges, [gg.right_edges[-1]]])
-        bin_indices = np.digitize(theta_int, reporting_bin_edges) - 1
-
-        valid_mask = (bin_indices >= 0) & (bin_indices < len(gg.meanr))
-        row_indices, col_indices = (bin_indices[valid_mask], np.where(valid_mask)[0])
-
-        binning_matrix = sparse.csr_matrix(
-            (np.ones(len(row_indices)), (row_indices, col_indices)),
-            shape=(len(gg.meanr), nbins_int),
-        )
-        row_sums = np.array(binning_matrix.sum(axis=1)).flatten()
-        binning_matrix = sparse.diags(1 / row_sums) @ binning_matrix
-
-        # Generate theoretical xi+/xi- predictions and sample
-        mean_int = np.concatenate(
-            get_theo_xi(
-                theta=theta_int,
-                z=z_dist[:, 0],
-                nz=z_dist[:, 1],
-                backend="ccl",
-                cosmo=cosmo_cov,
+                "semi-analytical covariance needs both z_dist and cosmo_cov"
             )
+        cov, eb_samples = pure_eb_covariance_mc(
+            theta=gg.meanr,
+            left_edges=gg.left_edges,
+            right_edges=gg.right_edges,
+            theta_int=gg_int.meanr,
+            cov_int=np.loadtxt(cov_path_int),
+            z=z_dist[:, 0],
+            nz=z_dist[:, 1],
+            cosmo=cosmo_cov,
+            n_samples=n_samples,
         )
-
-        samples_int = np.random.multivariate_normal(mean_int, cov_int, size=n_samples)
-        samples_int_xip = samples_int[:, :nbins_int]
-        samples_int_xim = samples_int[:, nbins_int:]
-        samples_rep_xip = (binning_matrix @ samples_int_xip.T).T
-        samples_rep_xim = (binning_matrix @ samples_int_xim.T).T
-
-        transformed_samples = [
-            np.concatenate(
-                get_pure_EB_modes(
-                    theta=gg.meanr,
-                    theta_int=gg_int.meanr,
-                    xip=samples_rep_xip[i],
-                    xim=samples_rep_xim[i],
-                    xip_int=samples_int_xip[i],
-                    xim_int=samples_int_xim[i],
-                    tmin=min_sep,
-                    tmax=max_sep,
-                    parallel=True,
-                )
-            )
-            for i in tqdm.tqdm(range(n_samples), desc="MC samples")
-        ]
-
-        # Store semi-analytical covariance results
-        eb_samples = np.array(transformed_samples)
-        results.update({"cov": np.cov(eb_samples.T), "eb_samples": eb_samples})
+        results.update({"cov": cov, "eb_samples": eb_samples})
     else:
         # Use existing treecorr covariance estimation
         results["cov"] = treecorr.estimate_multi_cov(
@@ -255,6 +231,112 @@ def pure_EB(corrs):
     return results
 
 
+def pure_eb_from_xi(
+    theta_report, xip_report, xim_report, theta_int, xip_int, xim_int, tmin, tmax
+):
+    """Pure-E/B correlation functions from ξ± arrays through the pipeline kernel.
+
+    The values-only seam of :func:`calculate_pure_eb_correlation`, for callers
+    holding ξ± arrays rather than TreeCorr correlations.
+
+    ``tmin``/``tmax`` are the reporting correlation's TreeCorr *bin edges*
+    (``gg.left_edges[0]`` / ``gg.right_edges[-1]``). The reporting grid must be a
+    strict sub-range of the integration grid: a reporting point on the
+    integration boundary has no interior support and comes back NaN.
+
+    Returns
+    -------
+    dict
+        Keyed by ``_EB_KEYS`` (xip_E, xim_E, xip_B, xim_B, xip_amb, xim_amb).
+    """
+    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
+
+    modes = get_pure_EB_modes(
+        theta=np.asarray(theta_report),
+        xip=np.asarray(xip_report),
+        xim=np.asarray(xim_report),
+        theta_int=np.asarray(theta_int),
+        xip_int=np.asarray(xip_int),
+        xim_int=np.asarray(xim_int),
+        tmin=tmin,
+        tmax=tmax,
+        parallel=True,
+    )
+    return dict(zip(_EB_KEYS, (np.asarray(m) for m in modes)))
+
+
+def pure_eb_covariance_mc(
+    *,
+    theta,
+    left_edges,
+    right_edges,
+    theta_int,
+    cov_int,
+    z,
+    nz,
+    cosmo,
+    n_samples=1000,
+):
+    """Pure-E/B covariance by Monte Carlo through the same kernel as the modes.
+
+    ξ± draws come from ``cov_int``, a ξ± covariance on the integration grid,
+    around the theory mean for ``(z, nz)`` under ``cosmo``; each draw is binned
+    down to the reporting grid and pushed through ``get_pure_EB_modes``. The
+    covariance of the transformed draws is the result, so it depends on the
+    covariance model and the grids, never on the measured data vector.
+
+    Returns ``(cov, eb_samples)`` — the covariance in ``_EB_KEYS`` order and
+    the draws behind it.
+    """
+    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
+
+    theta, theta_int = np.asarray(theta), np.asarray(theta_int)
+    nbins_int = len(theta_int)
+
+    # Each reporting bin averages the integration bins that fall inside it.
+    reporting_bin_edges = np.concatenate([left_edges, [right_edges[-1]]])
+    bin_indices = np.digitize(theta_int, reporting_bin_edges) - 1
+    valid_mask = (bin_indices >= 0) & (bin_indices < len(theta))
+    row_indices, col_indices = (bin_indices[valid_mask], np.where(valid_mask)[0])
+    binning_matrix = sparse.csr_matrix(
+        (np.ones(len(row_indices)), (row_indices, col_indices)),
+        shape=(len(theta), nbins_int),
+    )
+    row_sums = np.array(binning_matrix.sum(axis=1)).flatten()
+    binning_matrix = sparse.diags(1 / row_sums) @ binning_matrix
+
+    mean_int = np.concatenate(
+        get_theo_xi(theta=theta_int, z=z, nz=nz, backend="ccl", cosmo=cosmo)
+    )
+    samples_int = np.random.multivariate_normal(mean_int, cov_int, size=n_samples)
+    samples_int_xip, samples_int_xim = (
+        samples_int[:, :nbins_int],
+        samples_int[:, nbins_int:],
+    )
+    samples_rep_xip = (binning_matrix @ samples_int_xip.T).T
+    samples_rep_xim = (binning_matrix @ samples_int_xim.T).T
+
+    eb_samples = np.array(
+        [
+            np.concatenate(
+                get_pure_EB_modes(
+                    theta=theta,
+                    theta_int=theta_int,
+                    xip=samples_rep_xip[i],
+                    xim=samples_rep_xim[i],
+                    xip_int=samples_int_xip[i],
+                    xim_int=samples_int_xim[i],
+                    tmin=left_edges[0],
+                    tmax=right_edges[-1],
+                    parallel=True,
+                )
+            )
+            for i in tqdm.tqdm(range(n_samples), desc="MC samples")
+        ]
+    )
+    return np.cov(eb_samples.T), eb_samples
+
+
 def calculate_cosebis(gg, nmodes=10, scale_cuts=None, cov_path=None):
     """
     Calculate COSEBIs modes from a correlation function for multiple scale cuts.
@@ -279,23 +361,52 @@ def calculate_cosebis(gg, nmodes=10, scale_cuts=None, cov_path=None):
         Each results dictionary contains 'En', 'Bn', 'cov', 'chi2_E', 'chi2_B',
         'pte_B', 'scale_cut', and 'mask' entries.
     """
-    from cosmo_numba.B_modes.cosebis import COSEBIS
+    cov_xipm = np.loadtxt(cov_path) if cov_path is not None else gg.cov
+    return cosebis_scan_from_xi(
+        gg.meanr,
+        gg.xip,
+        gg.xim,
+        cov_xipm,
+        gg.left_edges,
+        gg.right_edges,
+        nmodes=nmodes,
+        scale_cuts=scale_cuts,
+        # A theory covariance has no jackknife realisations to debias.
+        npatch=None if cov_path is not None else gg.npatch1,
+    )
 
-    # Default to full range if no scale cuts provided
-    if scale_cuts is None:
-        scale_cuts = [(gg.left_edges[0], gg.right_edges[-1])]
 
-    # Pre-compute values that don't change across scale cuts
-    nbins = len(gg.meanr)
+def cosebis_scan_from_xi(
+    theta,
+    xip,
+    xim,
+    cov_xipm,
+    left_edges,
+    right_edges,
+    *,
+    nmodes=10,
+    scale_cuts=None,
+    npatch=None,
+):
+    """COSEBIs over a set of scale cuts, from ξ± arrays and their covariance.
 
-    # Load covariance matrix and calculate Hartlap factor once
-    if cov_path is not None:
-        print(f"Loading theoretical covariance from {cov_path}")
-        cov_xipm = np.loadtxt(cov_path)
-        hartlap_factor = 1  # Not defined for analytic covariances
-    else:
-        cov_xipm = gg.cov
-        hartlap_factor = (gg.npatch1 - 2 * nmodes - 2) / (gg.npatch1 - 1)
+    The values-and-covariance seam of :func:`calculate_cosebis`, for callers
+    holding a ξ± data vector rather than a TreeCorr ``GGCorrelation``. The
+    COSEBIs covariance is the ξ± covariance carried through the same linear
+    kernel as the modes, so no estimator re-run is involved. ``npatch`` is the
+    jackknife realisation count behind ``cov_xipm``, which sets the Hartlap
+    debiasing; leave it ``None`` for a theory covariance, which needs none.
+
+    Returns the ``{scale_cut: result}`` mapping :func:`calculate_cosebis` returns.
+    """
+    from cosmo_numba.B_modes.cosebis import COSEBIS
+
+    theta, xip, xim = (np.asarray(a) for a in (theta, xip, xim))
+    cov_xipm = np.asarray(cov_xipm)
+    if scale_cuts is None:
+        scale_cuts = [(left_edges[0], right_edges[-1])]
+    nbins = len(theta)
+    hartlap_factor = 1 if npatch is None else (npatch - 2 * nmodes - 2) / (npatch - 1)
 
     all_results = {}
 
@@ -303,11 +414,12 @@ def calculate_cosebis(gg, nmodes=10, scale_cuts=None, cov_path=None):
     for scale_cut in tqdm.tqdm(scale_cuts, desc="COSEBIs scale cuts"):
         min_theta, max_theta = scale_cut
 
-        # Apply scale cuts using scale_cut_to_bins for consistency
-        start_bin, stop_bin = scale_cut_to_bins(gg, min_theta, max_theta)
+        start_bin, stop_bin = bins_from_edges(
+            left_edges, right_edges, min_theta, max_theta
+        )
         inds = np.arange(start_bin, stop_bin)
 
-        theta_cut, xip_cut, xim_cut = [arr[inds] for arr in [gg.meanr, gg.xip, gg.xim]]
+        theta_cut, xip_cut, xim_cut = [arr[inds] for arr in [theta, xip, xim]]
 
         # Calculate COSEBIs E/B modes using actual theta range (per Axel's recommendation)
         # Use precision=120 (vs default 80) to avoid sympy root convergence failures
@@ -354,11 +466,7 @@ def calculate_cosebis(gg, nmodes=10, scale_cuts=None, cov_path=None):
     return all_results
 
 
-def calculate_eb_statistics(
-    results,
-    cov_path_int=None,
-    n_samples=1000,
-):
+def calculate_eb_statistics(results):
     """
     Calculate E/B mode statistics using 2D PTE analysis for all scale cut combinations.
 
@@ -369,23 +477,17 @@ def calculate_eb_statistics(
     Parameters
     ----------
     results : dict
-        Dictionary containing pure E/B mode results from calculate_pure_eb_correlation
-    cov_path_int : str, optional
-        Path to integration covariance matrix for semi-analytical calculation
-    n_samples : int, optional
-        Number of Monte Carlo samples used for semi-analytical covariance
-    min_bins : int, optional
-        Minimum number of bins required for valid PTE calculation
+        Pure E/B results: the six mode arrays, the ``cov`` block, the reporting
+        ``theta``, and ``n_eff`` — the realisation count behind the covariance
+        (jackknife patches or MC draws), which sets the Hartlap debiasing
 
     Returns
     -------
     dict
         Updated results dictionary with PTE matrices and statistics
     """
-    gg = results["gg"]
-    nbins = gg.nbins
-    npatch = gg.npatch1
-    n_eff = n_samples if cov_path_int is not None else npatch
+    nbins = len(results["theta"])
+    n_eff = results["n_eff"]
 
     # Extract covariance blocks and standard deviations
     cov = results["cov"]
@@ -449,16 +551,16 @@ def calculate_eb_statistics(
     return results
 
 
-def plot_integration_vs_reporting(gg, gg_int, output_path, version):
+def plot_integration_vs_reporting(results, output_path, version):
     """
     Plot integration vs reporting scale comparison.
 
     Parameters
     ----------
-    gg : treecorr.GGCorrelation
-        Reporting scale correlation function
-    gg_int : treecorr.GGCorrelation
-        Integration scale correlation function
+    results : dict
+        Pure E/B results carrying both grids (``theta``/``xip``/``xim`` and the
+        ``theta_int``/``xip_int``/``xim_int`` counterparts), plus the reporting
+        ``var_xip``/``var_xim`` the error bars use
     output_path : str
         Output file path for the plot
     version : str
@@ -468,26 +570,24 @@ def plot_integration_vs_reporting(gg, gg_int, output_path, version):
 
     # Configure plot data for both xi+ and xi- in a consolidated loop
     plot_configs = [
-        ("+", "xip", "varxip", r"$\theta \xi_+(\theta) \times 10^4$"),
-        ("-", "xim", "varxim", r"$\theta \xi_-(\theta) \times 10^4$"),
+        ("+", "xip", r"$\theta \xi_+(\theta) \times 10^4$"),
+        ("-", "xim", r"$\theta \xi_-(\theta) \times 10^4$"),
     ]
 
     data_configs = [
-        (gg_int, "k.", 3, 0.3, "Integration"),
-        (gg, ".", 12, 1, "Reporting"),
+        ("_int", "k.", 3, 0.3, "Integration"),
+        ("", ".", 12, 1, "Reporting"),
     ]
 
-    for ax_idx, (xi_label, xi_attr, var_attr, ylabel) in enumerate(plot_configs):
-        for data, fmt, ms, alpha, label_type in data_configs:
-            xi_val = getattr(data, xi_attr)
-            yerr = (
-                data.meanr * np.sqrt(getattr(data, var_attr)) / 1e-4
-                if hasattr(data, var_attr) and label_type == "Reporting"
-                else None
-            )
+    for ax_idx, (xi_label, xi_attr, ylabel) in enumerate(plot_configs):
+        for suffix, fmt, ms, alpha, label_type in data_configs:
+            theta = results[f"theta{suffix}"]
+            xi_val = results[f"{xi_attr}{suffix}"]
+            var = results.get(f"var_{xi_attr}{suffix}")
+            yerr = theta * np.sqrt(var) / 1e-4 if var is not None else None
             axs[ax_idx].errorbar(
-                data.meanr,
-                data.meanr * xi_val / 1e-4,
+                theta,
+                theta * xi_val / 1e-4,
                 yerr=yerr,
                 fmt=fmt,
                 ms=ms,
@@ -496,8 +596,8 @@ def plot_integration_vs_reporting(gg, gg_int, output_path, version):
                 ls="" if label_type == "Reporting" else None,
                 label=(
                     rf"$\xi_{{{xi_label}}}$, {label_type}: "
-                    rf"${data.min_sep} < \theta < {data.max_sep}$, "
-                    rf"{data.nbins} bins"
+                    rf"${theta[0]:.2g} < \theta < {theta[-1]:.4g}$, "
+                    rf"{len(theta)} bins"
                 ),
             )
         axs[ax_idx].set(
@@ -513,7 +613,7 @@ def plot_integration_vs_reporting(gg, gg_int, output_path, version):
     plt.savefig(output_path, dpi=300, bbox_inches="tight")
 
 
-def _get_pte_from_scale_cut(pte_matrix, gg, scale_cut):
+def _get_pte_from_scale_cut(pte_matrix, edges, scale_cut):
     """
     Extract PTE value from matrix based on scale cut range using conservative logic.
 
@@ -521,8 +621,8 @@ def _get_pte_from_scale_cut(pte_matrix, gg, scale_cut):
     ----------
     pte_matrix : numpy.ndarray
         2D PTE matrix
-    gg : treecorr.GGCorrelation
-        Correlation function object with bin edges
+    edges : tuple of numpy.ndarray
+        ``(left_edges, right_edges)`` of the grid the matrix is indexed on
     scale_cut : tuple
         (min_scale, max_scale) angular range for scale cut
 
@@ -531,7 +631,8 @@ def _get_pte_from_scale_cut(pte_matrix, gg, scale_cut):
     float
         PTE value for the given scale cut, or full-range PTE if scale_cut is None
     """
-    nbins = len(gg.meanr)
+    left_edges, right_edges = edges
+    nbins = len(left_edges)
 
     if scale_cut is None:
         # Return full-range PTE (first row, last column)
@@ -539,8 +640,7 @@ def _get_pte_from_scale_cut(pte_matrix, gg, scale_cut):
 
     min_scale, max_scale = scale_cut
 
-    # Use conservative scale_cut_to_bins helper
-    start_bin, stop_bin = scale_cut_to_bins(gg, min_scale, max_scale)
+    start_bin, stop_bin = bins_from_edges(left_edges, right_edges, min_scale, max_scale)
 
     # Ensure valid range, otherwise fallback to full range
     if stop_bin <= start_bin or start_bin >= nbins or stop_bin <= 0:
@@ -571,19 +671,20 @@ def plot_pure_eb_correlations(
     fiducial_xim_scale_cut : tuple, optional
         (min_scale, max_scale) for xi- fiducial analysis, shown as gray regions
     """
-    gg = results["gg"]
-    nbins = gg.nbins
+    theta = results["theta"]
+    edges = (results["left_edges"], results["right_edges"])
+    nbins = len(theta)
     cov = results["cov"]
 
     # Calculate combined PTE using off-diagonal covariance blocks
     # Get scale cuts for both xi+ and xi-
     if fiducial_xip_scale_cut is not None:
-        xip_start_bin, xip_stop_bin = scale_cut_to_bins(gg, *fiducial_xip_scale_cut)
+        xip_start_bin, xip_stop_bin = bins_from_edges(*edges, *fiducial_xip_scale_cut)
     else:
         xip_start_bin, xip_stop_bin = 0, nbins
 
     if fiducial_xim_scale_cut is not None:
-        xim_start_bin, xim_stop_bin = scale_cut_to_bins(gg, *fiducial_xim_scale_cut)
+        xim_start_bin, xim_stop_bin = bins_from_edges(*edges, *fiducial_xim_scale_cut)
     else:
         xim_start_bin, xim_stop_bin = 0, nbins
 
@@ -618,7 +719,7 @@ def plot_pure_eb_correlations(
     if "eb_samples" in results:  # Semi-analytical case
         n_eff = results["eb_samples"].shape[0]
     else:  # Jackknife case
-        n_eff = gg.npatch1
+        n_eff = results["n_eff"]
 
     hartlap_factor = (n_eff - nbins_eff - 2) / (n_eff - 1)
     chi2_combined = hartlap_factor * (
@@ -628,10 +729,10 @@ def plot_pure_eb_correlations(
 
     # Extract PTE values for fiducial scale cuts (or full range)
     xip_B_pte = _get_pte_from_scale_cut(
-        results["pte_matrices"]["xip_B"], gg, fiducial_xip_scale_cut
+        results["pte_matrices"]["xip_B"], edges, fiducial_xip_scale_cut
     )
     xim_B_pte = _get_pte_from_scale_cut(
-        results["pte_matrices"]["xim_B"], gg, fiducial_xim_scale_cut
+        results["pte_matrices"]["xim_B"], edges, fiducial_xim_scale_cut
     )
 
     fig, axs = plt.subplots(1, 2, figsize=(14, 6), sharex=True, sharey=True)
@@ -643,14 +744,14 @@ def plot_pure_eb_correlations(
         (
             "xip",
             "+",
-            "varxip",
+            "var_xip",
             r"$\xi_{+}=\xi_{+}^{E}+\xi_{+}^{B}+\xi_{+}^{\mathrm{amb}}$",
             xip_B_pte,
         ),
         (
             "xim",
             "-",
-            "varxim",
+            "var_xim",
             r"$\xi_{-}=\xi_{-}^{E}-\xi_{-}^{B}+\xi_{-}^{\mathrm{amb}}$",
             xim_B_pte,
         ),
@@ -660,11 +761,11 @@ def plot_pure_eb_correlations(
         plot_configs
     ):
         # Plot main correlation function
-        xi_val = getattr(gg, xi_type)
+        xi_val = results[xi_type]
         axs[ax_idx].errorbar(
-            gg.meanr,
-            gg.meanr * xi_val / scale_factor,
-            yerr=gg.meanr * np.sqrt(getattr(gg, var_attr)) / scale_factor,
+            theta,
+            theta * xi_val / scale_factor,
+            yerr=theta * np.sqrt(results[var_attr]) / scale_factor,
             fmt="k.",
             capsize=3,
             label=main_label,
@@ -690,9 +791,9 @@ def plot_pure_eb_correlations(
 
         for key, color, alpha, label in plot_data:
             axs[ax_idx].errorbar(
-                gg.meanr,
-                gg.meanr * results[key] / scale_factor,
-                yerr=gg.meanr * results[f"std_{key}"] / scale_factor,
+                theta,
+                theta * results[key] / scale_factor,
+                yerr=theta * results[f"std_{key}"] / scale_factor,
                 color=color,
                 ls="",
                 marker=".",
@@ -722,12 +823,12 @@ def plot_pure_eb_correlations(
             xlim = original_xlims[ax_idx]
 
             # Use conservative scale_cut_to_bins helper for consistency
-            start_bin, stop_bin = scale_cut_to_bins(gg, min_scale, max_scale)
+            start_bin, stop_bin = bins_from_edges(*edges, min_scale, max_scale)
 
             # Show excluded regions based on bin edges used in PTE calculation
             # Lower exclusion: bins 0 to start_bin-1 are excluded
             if start_bin > 0:
-                lower_exclusion_edge = gg.right_edges[start_bin - 1]
+                lower_exclusion_edge = edges[1][start_bin - 1]
                 axs[ax_idx].axvspan(
                     xlim[0],
                     lower_exclusion_edge,
@@ -737,8 +838,8 @@ def plot_pure_eb_correlations(
                 )
 
             # Upper exclusion: bins stop_bin to end are excluded
-            if stop_bin < len(gg.left_edges):
-                upper_exclusion_edge = gg.left_edges[stop_bin]
+            if stop_bin < len(edges[0]):
+                upper_exclusion_edge = edges[0][stop_bin]
                 axs[ax_idx].axvspan(
                     upper_exclusion_edge,
                     xlim[1],
@@ -760,7 +861,7 @@ def plot_pure_eb_correlations(
 
 
 def plot_cosebis_scale_cut_heatmap(
-    cosebis_results, gg, version, output_path, fiducial_scale_cut=None
+    cosebis_results, edges, version, output_path, fiducial_scale_cut=None
 ):
     """
     Create 2D heatmaps showing how COSEBIs statistics vary across different scale cuts.
@@ -769,8 +870,8 @@ def plot_cosebis_scale_cut_heatmap(
     ----------
     cosebis_results : dict
         Dictionary with scale cut tuples as keys, containing 'chi2_E' and 'pte_B' values
-    gg : treecorr.GGCorrelation
-        Correlation function object for bin edges
+    edges : tuple of numpy.ndarray
+        ``(left_edges, right_edges)`` of the grid the scale cuts index
     version : str
         Version string for main title
     output_path : str
@@ -778,7 +879,8 @@ def plot_cosebis_scale_cut_heatmap(
     fiducial_scale_cut : tuple, optional
         (min_scale, max_scale) for cross-hatching
     """
-    nbins = gg.nbins
+    left_edges, right_edges = edges
+    nbins = len(left_edges)
 
     # Initialize matrices
     snrs = [np.sqrt(result["chi2_E"]) for result in cosebis_results.values()]
@@ -794,9 +896,9 @@ def plot_cosebis_scale_cut_heatmap(
         pte_matrix[row, column] = pte
 
     # Fill matrices from COSEBIs results
-    for i in range(len(gg.left_edges)):
-        for j in range(i, len(gg.right_edges)):
-            scale_cut = (gg.left_edges[i], gg.right_edges[j])
+    for i in range(len(left_edges)):
+        for j in range(i, len(right_edges)):
+            scale_cut = (left_edges[i], right_edges[j])
             result = cosebis_results.get(scale_cut)
 
             if result is not None:
@@ -857,7 +959,9 @@ def plot_cosebis_scale_cut_heatmap(
     # Add fiducial scale cut cross-hatching if provided
     if fiducial_scale_cut is not None:
         min_scale, max_scale = fiducial_scale_cut
-        start_bin, stop_bin = scale_cut_to_bins(gg, min_scale, max_scale)
+        start_bin, stop_bin = bins_from_edges(
+            left_edges, right_edges, min_scale, max_scale
+        )
 
         if stop_bin > start_bin and start_bin < nbins and stop_bin > 0:
             # Add cross-hatching at the fiducial scale cut matrix element
@@ -884,10 +988,10 @@ def plot_cosebis_scale_cut_heatmap(
     # Set angular scale ticks
     tick_indices = np.arange(0, nbins)
     x_tick_labels = [
-        f"{gg.left_edges[i]:.1f}" for i in tick_indices if i < len(gg.left_edges)
+        f"{left_edges[i]:.1f}" for i in tick_indices if i < len(left_edges)
     ]
     y_tick_labels = [
-        f"{gg.right_edges[i]:.1f}" for i in tick_indices if i < len(gg.right_edges)
+        f"{right_edges[i]:.1f}" for i in tick_indices if i < len(right_edges)
     ]
     x_tick_positions = tick_indices + 0.5
     y_tick_positions = tick_indices + 0.5
@@ -939,8 +1043,9 @@ def plot_pte_2d_heatmaps(
     fiducial_xim_scale_cut : tuple, optional
         (min_scale, max_scale) for xi- fiducial analysis, shown as cross-hatched
     """
-    gg = results["gg"]
-    nbins = gg.nbins
+    theta = results["theta"]
+    edges = (results["left_edges"], results["right_edges"])
+    nbins = len(theta)
     pte_xip_B = results["pte_matrices"]["xip_B"]
     pte_xim_B = results["pte_matrices"]["xim_B"]
 
@@ -1002,7 +1107,7 @@ def plot_pte_2d_heatmaps(
     for ax_idx, fiducial_scale_cut in enumerate(fiducial_scale_cuts):
         if fiducial_scale_cut is not None:
             min_scale, max_scale = fiducial_scale_cut
-            start_bin, stop_bin = scale_cut_to_bins(gg, min_scale, max_scale)
+            start_bin, stop_bin = bins_from_edges(*edges, min_scale, max_scale)
 
             if stop_bin > start_bin and start_bin < nbins and stop_bin > 0:
                 rect_x = start_bin
@@ -1026,12 +1131,8 @@ def plot_pte_2d_heatmaps(
 
     # Set angular scale ticks
     tick_indices = np.arange(0, nbins)
-    x_tick_labels = [
-        f"{gg.left_edges[i]:.1f}" for i in tick_indices if i < len(gg.left_edges)
-    ]
-    y_tick_labels = [
-        f"{gg.right_edges[i]:.1f}" for i in tick_indices if i < len(gg.right_edges)
-    ]
+    x_tick_labels = [f"{edges[0][i]:.1f}" for i in tick_indices if i < len(edges[0])]
+    y_tick_labels = [f"{edges[1][i]:.1f}" for i in tick_indices if i < len(edges[1])]
     x_tick_positions = tick_indices + 0.5
     y_tick_positions = tick_indices + 0.5
 
@@ -1171,10 +1272,8 @@ def save_pure_eb_results(results, output_path):
     output_path : str
         Output .npz file path
     """
-    gg = results["gg"]
-
     # Data vectors and covariance
-    save_dict = {"theta": gg.meanr, "cov": results["cov"]}
+    save_dict = {"theta": results["theta"], "cov": results["cov"]}
     for key in _EB_KEYS:
         save_dict[key] = results[key]
 
@@ -1183,7 +1282,7 @@ def save_pure_eb_results(results, output_path):
         save_dict[f"pte_matrices_{key}"] = matrix
 
     # Metadata
-    save_dict["npatch"] = np.array(gg.npatch1)
+    save_dict["n_eff"] = np.array(results["n_eff"])
     if "eb_samples" in results:
         save_dict["var_method"] = np.array("semi-analytic")
         save_dict["n_samples"] = np.array(results["eb_samples"].shape[0])
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 48251bbb..1d9fa196 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -563,18 +563,18 @@ def summarize_bmodes(self, fiducial_scale_cut=(12, 83), versions=None):
             # Pure E/B PTEs from stored results
             if ver in self._pure_eb_results:
                 res = self._pure_eb_results[ver]
-                gg = res["gg"]
+                edges = (res["left_edges"], res["right_edges"])
                 try:
                     for stat in ("xip_B", "xim_B", "combined"):
                         row[stat] = _get_pte_from_scale_cut(
-                            res["pte_matrices"][stat], gg, fiducial_scale_cut
+                            res["pte_matrices"][stat], edges, fiducial_scale_cut
                         )
                 except (KeyError, RuntimeError):
                     pass
                 cov_methods.add(
                     "semi-analytic"
                     if "eb_samples" in res
-                    else f"jackknife ({gg.npatch1} patches)"
+                    else f"jackknife ({res['n_eff']} patches)"
                 )
 
             # COSEBIs PTE from stored results
diff --git a/src/sp_validation/cosmo_val/cosebis.py b/src/sp_validation/cosmo_val/cosebis.py
index aa4657ba..312aadcd 100644
--- a/src/sp_validation/cosmo_val/cosebis.py
+++ b/src/sp_validation/cosmo_val/cosebis.py
@@ -271,7 +271,7 @@ def plot_cosebis(
 
             plot_cosebis_scale_cut_heatmap(
                 results,
-                gg_temp,
+                (gg_temp.left_edges, gg_temp.right_edges),
                 version,
                 out_stub + "_scalecut_ptes.png",
                 fiducial_scale_cut=fiducial_scale_cut,
diff --git a/src/sp_validation/cosmo_val/pure_eb.py b/src/sp_validation/cosmo_val/pure_eb.py
index 71f5d76a..90157456 100644
--- a/src/sp_validation/cosmo_val/pure_eb.py
+++ b/src/sp_validation/cosmo_val/pure_eb.py
@@ -88,9 +88,13 @@ def calculate_pure_eb(
             - "xip_amb": Ambiguity mode for xi+.
             - "xim_amb": Ambiguity mode for xi-.
             - "cov": Covariance matrix for the pure E/B modes.
-            - "gg": The two-point correlation function object for the reporting binning.
-            - "gg_int": The two-point correlation function object for the
-              integration binning.
+            - "theta", "left_edges", "right_edges": Reporting-grid bin centres
+              and edges.
+            - "xip", "xim", "var_xip", "var_xim": Reporting-grid xi+/xi- and
+              their variances.
+            - "theta_int", "xip_int", "xim_int": Integration-grid xi+/xi-.
+            - "n_eff": Realisation count behind "cov" (jackknife patches or
+              MC draws), which sets the Hartlap debiasing.
             - "eb_samples": (only when using semi-analytical covariance) Semi-analytic
               EB samples used for covariance calculation. Shape: (n_samples, 6*nbins)
 
@@ -269,19 +273,13 @@ def plot_pure_eb(
             )
 
             # Calculate E/B statistics for all bin combinations
-            version_results = calculate_eb_statistics(
-                version_results,
-                cov_path_int=cov_path_int,
-                n_samples=n_samples,
-                **kwargs,
-            )
-
-            # Generate all plots using specialized plotting functions
-            gg, gg_int = version_results["gg"], version_results["gg_int"]
+            version_results = calculate_eb_statistics(version_results, **kwargs)
 
             # Integration vs Reporting comparison plot
             plot_integration_vs_reporting(
-                gg, gg_int, out_stub + "_integration_vs_reporting.png", version
+                version_results,
+                out_stub + "_integration_vs_reporting.png",
+                version,
             )
 
             # E/B/Ambiguous correlation functions plot
diff --git a/src/sp_validation/tests/test_b_modes.py b/src/sp_validation/tests/test_b_modes.py
index e8e05c39..f51913fc 100644
--- a/src/sp_validation/tests/test_b_modes.py
+++ b/src/sp_validation/tests/test_b_modes.py
@@ -50,6 +50,7 @@ def _eb_inputs():
 
     nbins=4, npatch=50 so the Hartlap factor (n_eff - nbins_eff - 2)/(n_eff-1)
     is well-defined and strictly positive for every scale-cut combination.
+    n_eff is the jackknife patch count, as it is for a jackknife covariance.
     The covariance is built SPD via A @ A.T + I; the B-mode vectors are O(1)
     so the chi-squared (and hence PTE) lands in a meaningful range rather than
     being saturated at 1.0.
@@ -60,8 +61,13 @@ def _eb_inputs():
     cov = A @ A.T + np.eye(6 * nbins)
     xip_B = rng.standard_normal(nbins)
     xim_B = rng.standard_normal(nbins)
-    gg = types.SimpleNamespace(nbins=nbins, npatch1=npatch)
-    return {"gg": gg, "cov": cov, "xip_B": xip_B, "xim_B": xim_B}, nbins
+    return {
+        "theta": np.geomspace(1.0, 100.0, nbins),
+        "n_eff": npatch,
+        "cov": cov,
+        "xip_B": xip_B,
+        "xim_B": xim_B,
+    }, nbins
 
 
 # ---------------------------------------------------------------------------
@@ -229,8 +235,8 @@ def test_calculate_eb_statistics_pte_matrices():
     """Pin representative PTE-matrix entries from the full 2D E/B analysis.
 
     Inputs are fixed (seed 12345, nbins=4, npatch=50, SPD cov = A@A.T + I,
-    O(1) B-mode vectors). With cov_path_int=None the Hartlap correction uses
-    n_eff = npatch = 50. For each of xip_B, xim_B and combined we pin the
+    O(1) B-mode vectors). The Hartlap correction uses n_eff = 50, the patch
+    count behind a jackknife covariance. For each of xip_B, xim_B and combined we pin the
     full-range entry [0, nbins-1] (start=0, stop=nbins) and an interior entry
     [0, 2] (start=0, stop=3). These chi2->sf PTE values are deterministic
     functions of the seeded input.
@@ -240,7 +246,7 @@ def test_calculate_eb_statistics_pte_matrices():
     arithmetic shifts them past tolerance.
     """
     results, nbins = _eb_inputs()
-    out = b_modes.calculate_eb_statistics(results, cov_path_int=None)
+    out = b_modes.calculate_eb_statistics(results)
     pm = out["pte_matrices"]
 
     # Full-range entries [0, nbins-1].
@@ -271,13 +277,13 @@ def test_calculate_eb_statistics_has_teeth():
     combined 0.99999 -> 0.0031.
     """
     results, nbins = _eb_inputs()
-    out = b_modes.calculate_eb_statistics(results, cov_path_int=None)
+    out = b_modes.calculate_eb_statistics(results)
     pm = out["pte_matrices"]
 
     loud, _ = _eb_inputs()
     loud["xip_B"] = loud["xip_B"] * 10.0
     loud["xim_B"] = loud["xim_B"] * 10.0
-    out_loud = b_modes.calculate_eb_statistics(loud, cov_path_int=None)
+    out_loud = b_modes.calculate_eb_statistics(loud)
     pm_loud = out_loud["pte_matrices"]
 
     for key in ("xip_B", "xim_B", "combined"):
@@ -285,3 +291,144 @@ def test_calculate_eb_statistics_has_teeth():
         loud_pte = pm_loud[key][0, nbins - 1]
         assert loud_pte < quiet_pte
         assert loud_pte < 0.05  # louder B-modes are clearly rejected
+
+
+# ---------------------------------------------------------------------------
+# 6. Grid edges and the COSEBIs covariance seam
+# ---------------------------------------------------------------------------
+
+
+def test_log_bin_edges_matches_the_grid_stub():
+    """Edges reconstructed from a binning are the ones TreeCorr would report.
+
+    A part stores bin centres only, so a consumer rebuilds the edges from the
+    binning it was measured on; the two must agree bin for bin.
+    """
+    left, right = b_modes.log_bin_edges(1.0, 100.0, _NBINS_GRID)
+    gg = _grid_gg()
+    npt.assert_allclose(left, gg.left_edges)
+    npt.assert_allclose(right, gg.right_edges)
+    # ...and they index scale cuts identically.
+    assert b_modes.bins_from_edges(left, right, 2.0, 50.0) == (2, 8)
+
+
+def test_cosebis_scan_propagates_the_supplied_covariance(monkeypatch):
+    """The COSEBIs covariance is the ξ± covariance through the same kernel.
+
+    The kernel is stubbed, so what is pinned is the seam: which ξ± covariance
+    sub-block is handed to the transform (the scale cut's, in [ξ+; ξ−] order)
+    and that Hartlap uses the supplied npatch.
+    """
+    nbins, nmodes = _NBINS_GRID, 3
+    theta = np.geomspace(1.2, 90.0, nbins)
+    cov_xipm = np.diag(np.arange(1.0, 2 * nbins + 1))
+    seen = {}
+
+    class _StubCOSEBIS:
+        def __init__(self, **kwargs):
+            seen["init"] = kwargs
+
+        def cosebis_from_xipm(self, theta_cut, xip_cut, xim_cut, parallel=True):
+            seen["n_theta"] = len(theta_cut)
+            return np.ones(nmodes), np.full(nmodes, 2.0)
+
+        def cosebis_covariance_from_xipm_covariance(self, theta_cut, cov_cut):
+            seen["cov_cut"] = cov_cut
+            return np.eye(2 * nmodes)
+
+    module = types.ModuleType("cosmo_numba.B_modes.cosebis")
+    module.COSEBIS = _StubCOSEBIS
+    monkeypatch.setitem(
+        __import__("sys").modules, "cosmo_numba.B_modes.cosebis", module
+    )
+
+    left, right = b_modes.log_bin_edges(1.0, 100.0, nbins)
+    results = b_modes.cosebis_scan_from_xi(
+        theta,
+        np.arange(nbins) * 1e-5,
+        np.arange(nbins) * 2e-5,
+        cov_xipm,
+        left,
+        right,
+        nmodes=nmodes,
+        scale_cuts=[(2.0, 50.0)],
+        npatch=100,
+    )
+
+    (result,) = results.values()
+    # The cut is bins 2..8, so the covariance sub-block is those rows/cols in
+    # both the ξ+ and the ξ− half.
+    inds = np.concatenate([np.arange(2, 8), np.arange(2, 8) + nbins])
+    npt.assert_array_equal(seen["cov_cut"], cov_xipm[np.ix_(inds, inds)])
+    assert seen["n_theta"] == 6
+    npt.assert_allclose(result["hartlap_factor"], (100 - 2 * nmodes - 2) / 99)
+    # χ² carries the Hartlap factor: modes are 1, cov is the identity.
+    npt.assert_allclose(result["chi2_E"], nmodes * result["hartlap_factor"])
+
+
+def test_cosebis_scan_theory_covariance_skips_hartlap(monkeypatch):
+    """A theory covariance has no realisations to debias, so Hartlap is 1."""
+    nbins, nmodes = _NBINS_GRID, 2
+
+    class _StubCOSEBIS:
+        def __init__(self, **kwargs):
+            pass
+
+        def cosebis_from_xipm(self, theta_cut, xip_cut, xim_cut, parallel=True):
+            return np.ones(nmodes), np.ones(nmodes)
+
+        def cosebis_covariance_from_xipm_covariance(self, theta_cut, cov_cut):
+            return np.eye(2 * nmodes)
+
+    module = types.ModuleType("cosmo_numba.B_modes.cosebis")
+    module.COSEBIS = _StubCOSEBIS
+    monkeypatch.setitem(
+        __import__("sys").modules, "cosmo_numba.B_modes.cosebis", module
+    )
+
+    left, right = b_modes.log_bin_edges(1.0, 100.0, nbins)
+    (result,) = b_modes.cosebis_scan_from_xi(
+        np.geomspace(1.2, 90.0, nbins),
+        np.zeros(nbins),
+        np.zeros(nbins),
+        np.eye(2 * nbins),
+        left,
+        right,
+        nmodes=nmodes,
+        npatch=None,
+    ).values()
+    assert result["hartlap_factor"] == 1
+
+
+def test_pure_eb_npz_carries_what_the_summary_reads(tmp_path):
+    """The .npz keys cv_summarize_bmodes reads are the ones the writer emits.
+
+    The two live in different rules, so the contract between them — the PTE
+    matrices under ``pte_matrices_{stat}`` and the realisation count under
+    ``n_eff`` — is pinned here rather than discovered on a cluster run.
+    """
+    results, nbins = _eb_inputs()
+    results.update(
+        {key: np.zeros(nbins) for key in b_modes._EB_KEYS if key not in results}
+    )
+    results = b_modes.calculate_eb_statistics(results)
+
+    out = tmp_path / "pure_eb_data.npz"
+    b_modes.save_pure_eb_results(results, str(out))
+    saved = np.load(out)
+
+    for stat in ("xip_B", "xim_B", "combined"):
+        assert f"pte_matrices_{stat}" in saved
+        assert saved[f"pte_matrices_{stat}"].shape == (nbins, nbins)
+    assert saved["n_eff"] == results["n_eff"]
+    npt.assert_allclose(saved["theta"], results["theta"])
+    for key in b_modes._EB_KEYS:
+        assert key in saved
+
+    # The summary reads the fiducial cut out of those matrices through the same
+    # helper the plots use, so a valid cut must resolve to a finite PTE.
+    edges = b_modes.log_bin_edges(1.0, 100.0, nbins)
+    pte = b_modes._get_pte_from_scale_cut(
+        saved["pte_matrices_xip_B"], edges, (1.0, 100.0)
+    )
+    assert np.isfinite(pte)
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index f50992d4..f25ed522 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -697,4 +697,4 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path):
         # shape is pinned, not the values.
         cov = np.asarray(results["cov"])
         assert cov.shape == (6 * nbins, 6 * nbins)
-        assert results["gg"].npatch1 == npatch
+        assert results["n_eff"] == npatch

From d6d6f247b480a40bfd2a7249862bc438f182ddd5 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 25 Sep 2026 15:23:39 +0200
Subject: [PATCH 27/83] Unpack cs_util's per-pair get_theo_xi in the pure-E/B
 MC covariance

cs_util's get_theo_xi returns {pair: (xi+, xi-)}, so concatenating its
return value raised TypeError and the semi-analytic pure-E/B covariance
(pure_eb_covariance_mc, and the paper's precompute_pure_eb_chunk.py) could
not run. One n(z) is one tracer pair: unpack that single entry, which also
fails loudly if a tomographic n(z) is passed. The new test stubs the
kernel and checks the draws centre on the binned theory mean.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HeMhD6yCyrgBz5oz87bCtR
---
 .../scripts/precompute_pure_eb_chunk.py       | 18 +++----
 src/sp_validation/b_modes.py                  |  8 +--
 src/sp_validation/tests/test_b_modes.py       | 49 +++++++++++++++++++
 3 files changed, 63 insertions(+), 12 deletions(-)

diff --git a/papers/bmodes/scripts/precompute_pure_eb_chunk.py b/papers/bmodes/scripts/precompute_pure_eb_chunk.py
index 363a3192..99153096 100644
--- a/papers/bmodes/scripts/precompute_pure_eb_chunk.py
+++ b/papers/bmodes/scripts/precompute_pure_eb_chunk.py
@@ -127,15 +127,15 @@ def compute_chunk(
     row_sums = np.array(binning_matrix.sum(axis=1)).flatten()
     binning_matrix = sparse.diags(1 / row_sums) @ binning_matrix
 
-    mean_int = np.concatenate(
-        get_theo_xi(
-            theta=theta_int,
-            z=z_dist[:, 0],
-            nz=z_dist[:, 1],
-            backend="ccl",
-            cosmo=cosmo_cov,
-        )
-    )
+    # One n(z) gives one tracer pair: get_theo_xi's single (xi+, xi-) entry.
+    (xi_pm,) = get_theo_xi(
+        theta=theta_int,
+        z=z_dist[:, 0],
+        nz=z_dist[:, 1],
+        backend="ccl",
+        cosmo=cosmo_cov,
+    ).values()
+    mean_int = np.concatenate(xi_pm)
 
     rng = np.random.default_rng(seed=42 + chunk_id)
 
diff --git a/src/sp_validation/b_modes.py b/src/sp_validation/b_modes.py
index c4b9a2b7..7a3cd484 100644
--- a/src/sp_validation/b_modes.py
+++ b/src/sp_validation/b_modes.py
@@ -305,9 +305,11 @@ def pure_eb_covariance_mc(
     row_sums = np.array(binning_matrix.sum(axis=1)).flatten()
     binning_matrix = sparse.diags(1 / row_sums) @ binning_matrix
 
-    mean_int = np.concatenate(
-        get_theo_xi(theta=theta_int, z=z, nz=nz, backend="ccl", cosmo=cosmo)
-    )
+    # One n(z) gives one tracer pair: get_theo_xi's single (xi+, xi-) entry.
+    (xi_pm,) = get_theo_xi(
+        theta=theta_int, z=z, nz=nz, backend="ccl", cosmo=cosmo
+    ).values()
+    mean_int = np.concatenate(xi_pm)
     samples_int = np.random.multivariate_normal(mean_int, cov_int, size=n_samples)
     samples_int_xip, samples_int_xim = (
         samples_int[:, :nbins_int],
diff --git a/src/sp_validation/tests/test_b_modes.py b/src/sp_validation/tests/test_b_modes.py
index f51913fc..67c64376 100644
--- a/src/sp_validation/tests/test_b_modes.py
+++ b/src/sp_validation/tests/test_b_modes.py
@@ -432,3 +432,52 @@ def test_pure_eb_npz_carries_what_the_summary_reads(tmp_path):
         saved["pte_matrices_xip_B"], edges, (1.0, 100.0)
     )
     assert np.isfinite(pte)
+
+
+def test_pure_eb_covariance_mc_draws_around_the_theory_mean(monkeypatch):
+    """The MC draws centre on cs_util's theory ξ±, binned to the reporting grid.
+
+    ``get_theo_xi`` returns ``{pair: (ξ+, ξ−)}``; one n(z) is one pair. With a
+    zero covariance every draw is the theory mean, so a stub kernel that echoes
+    its reporting-grid ξ± pins the unpack, the [ξ+; ξ−] order and the binning.
+    """
+    nrep = 4
+    left, right = b_modes.log_bin_edges(2.0, 50.0, nrep)
+    theta = np.sqrt(left * right)
+    theta_int = np.geomspace(1.0, 100.0, 40)
+    xip_th, xim_th = theta_int.copy(), 2 * theta_int
+
+    monkeypatch.setattr(
+        b_modes, "get_theo_xi", lambda **kw: {"W0xW0": (xip_th, xim_th)}
+    )
+
+    def _echo(theta, theta_int, xip, xim, xip_int, xim_int, tmin, tmax, parallel):
+        zeros = np.zeros_like(xip)
+        return xip, xim, zeros, zeros, zeros, zeros
+
+    module = types.ModuleType("cosmo_numba.B_modes.schneider2022")
+    module.get_pure_EB_modes = _echo
+    monkeypatch.setitem(
+        __import__("sys").modules, "cosmo_numba.B_modes.schneider2022", module
+    )
+
+    cov, eb_samples = b_modes.pure_eb_covariance_mc(
+        theta=theta,
+        left_edges=left,
+        right_edges=right,
+        theta_int=theta_int,
+        cov_int=np.zeros((2 * len(theta_int), 2 * len(theta_int))),
+        z=np.linspace(0.01, 2.0, 50),
+        nz=np.ones(50),
+        cosmo=None,
+        n_samples=3,
+    )
+
+    inside = [(theta_int >= lo) & (theta_int < hi) for lo, hi in zip(left, right)]
+    expected_xip = np.array([xip_th[m].mean() for m in inside])
+    expected_xim = np.array([xim_th[m].mean() for m in inside])
+    assert eb_samples.shape == (3, 6 * nrep)
+    for draw in eb_samples:
+        npt.assert_allclose(draw[:nrep], expected_xip)
+        npt.assert_allclose(draw[nrep : 2 * nrep], expected_xim)
+    npt.assert_allclose(cov, 0.0, atol=1e-20)

From 3dc5eafc032fa6c75ba408d4b91e7dff72bab323 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Fri, 25 Sep 2026 16:38:31 +0200
Subject: [PATCH 28/83] cosmo_val: explicit catalogue config, forwarded
 constructor settings, patch_number from cat_config (#350)

* xi, rho_tau_stats: pass catalogue config and output dir explicitly

run_2pcf fell back to ./cat_config.yaml relative to the Snakemake cwd, and
run_rho_tau chdir'd into a hardcoded pure_eb checkout. Both rules now pass
CAT_CONFIG and COSMO_VAL, and the scripts require them.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KQ1cGfFTghPp3zw4KCYTQh

* cv_init_params: forward catalogue config and every constructor default

CosmologyValidation read ./cat_config.yaml from the rule's cwd and silently
used constructor defaults for pseudo-Cl binning, noise bias, seeds and more.
cv_init_params now passes CAT_CONFIG and forwards CV_INIT_KEYS from
config['cosmo_val']; the paper config spells out the previous defaults, so
behaviour is unchanged. test_cv_init_params fails if a new defaulted keyword
is neither forwarded nor exempted with a reason.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KQ1cGfFTghPp3zw4KCYTQh

* rho/tau: read jackknife patch_number from the catalogue config

get_params_rho_tau and set_params_rho_tau each hardcoded a survey-name table
(120 for DES/SP_axel_v0.0, else 150) and ignored patch_number in
cat_config.yaml. Every entry now declares patch_number (backfilled with the
table's value) and both functions read it, failing loud when absent.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KQ1cGfFTghPp3zw4KCYTQh

* resolve_paths_for_version: skip non-mapping entry values

Top-level scalars such as patch_number made the 'path' membership test raise
TypeError on ints (and substring-match on strings).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KQ1cGfFTghPp3zw4KCYTQh

---------

Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
---
 cosmo_val/cat_config.yaml                     | 27 ++++++++
 papers/cosmo_val/config/config.yaml           | 16 +++++
 src/sp_validation/cosmo_val/core.py           |  2 +-
 .../cosmo_val/psf_systematics.py              | 14 +---
 src/sp_validation/rho_tau.py                  | 19 ++---
 .../tests/test_cv_init_params.py              | 54 +++++++++++++++
 src/sp_validation/tests/test_pseudo_cl.py     |  1 +
 workflow/common.py                            | 69 ++++++++++++-------
 workflow/rules/twopoint.smk                   |  4 ++
 workflow/scripts/cv_runner.py                 |  6 +-
 workflow/scripts/run_2pcf.py                  |  8 +--
 workflow/scripts/run_rho_tau.py               |  3 +-
 12 files changed, 163 insertions(+), 60 deletions(-)
 create mode 100644 src/sp_validation/tests/test_cv_init_params.py

diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index 49cdfcc9..a4d34bee 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -38,6 +38,7 @@ DES:
     e1_col: obs_e1
     e2_col: obs_e2
     path: psf_y3a1-v29.fits
+  patch_number: 120
 SP_axel_v0.0:
   subdir: /n17data/mkilbing/astro/data/CFIS/v0.0
   pipeline: SP
@@ -77,6 +78,7 @@ SP_axel_v0.0:
     e1_col: HSM_G1_STAR
     e2_col: HSM_G2_STAR
     path: star_cat.fits
+  patch_number: 120
 SP_v0.1.1:
   subdir: /n17data/mkilbing/astro/data/CFIS/v0.0
   pipeline: SP
@@ -110,6 +112,7 @@ SP_v0.1.1:
     e1_col: HSM_G1_STAR
     e2_col: HSM_G2_STAR
     path: star_cat.fits
+  patch_number: 150
 SP_v1.3:
   subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/ShapePipe
   pipeline: SP
@@ -145,6 +148,7 @@ SP_v1.3:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2022_v1.0.3.fits
+  patch_number: 150
 SP_v1.3.6:
   subdir: /n17data/UNIONS/WL/v1.3.x
   pipeline: SP
@@ -188,6 +192,7 @@ SP_v1.3.6:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2022_v1.0.3.fits
+  patch_number: 150
 SP_v1.4.5:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -231,6 +236,7 @@ SP_v1.4.5:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.5.A:
   subdir: /n17data/guinot/CFIS_3500_cat/catalogues_SPv1_v1.4.5
   pipeline: SP
@@ -271,6 +277,7 @@ SP_v1.4.5.A:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.5_bright:
   subdir: /n17data/murray/unions_cats
   pipeline: SP
@@ -311,6 +318,7 @@ SP_v1.4.5_bright:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.5_faint:
   subdir: /n17data/murray/unions_cats
   pipeline: SP
@@ -351,6 +359,7 @@ SP_v1.4.5_faint:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6_glass_mock:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -393,6 +402,7 @@ SP_v1.4.6_glass_mock:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.5_intermediate:
   subdir: /n17data/murray/unions_cats
   pipeline: SP
@@ -433,6 +443,7 @@ SP_v1.4.5_intermediate:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -476,6 +487,7 @@ SP_v1.4.6:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6.3:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -519,6 +531,7 @@ SP_v1.4.6.3:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6.3_B:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -562,6 +575,7 @@ SP_v1.4.6.3_B:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6.3_C:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -605,6 +619,7 @@ SP_v1.4.6.3_C:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6.3_A:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -648,6 +663,7 @@ SP_v1.4.6.3_A:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6_ecut07:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -691,6 +707,7 @@ SP_v1.4.6_ecut07:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6.1:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -780,6 +797,7 @@ SP_v1.4.7:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.8:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -823,6 +841,7 @@ SP_v1.4.8:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.11.2:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -866,6 +885,7 @@ SP_v1.4.11.2:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.11.3:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -910,6 +930,7 @@ SP_v1.4.11.3:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.12.3:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -955,6 +976,7 @@ SP_v1.4.12.3:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.13.3:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -1000,6 +1022,7 @@ SP_v1.4.13.3:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.11.3_ecut07:
   subdir: /n17data/UNIONS/WL/v1.4.x
   pipeline: SP
@@ -1043,6 +1066,7 @@ SP_v1.4.11.3_ecut07:
     e1_col: e1
     e2_col: e2
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
+  patch_number: 150
 SP_v1.4.6.3_uncal:
   pipeline: SP
   subdir: /n17data/UNIONS/WL/v1.4.x
@@ -1176,6 +1200,7 @@ SP_v1.4_LFmask_8k:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2022_v1.4.0_mtheli8k.fits
+  patch_number: 150
 SP_v1.4_LFmask_8k_noalpha:
   subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask
   pipeline: SP
@@ -1216,6 +1241,7 @@ SP_v1.4_LFmask_8k_noalpha:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2022_v1.4.0_mtheli8k.fits
+  patch_number: 150
 nz:
   subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/nz
   dndz:
@@ -1267,3 +1293,4 @@ SP_v1.6.6:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.6.a.fits
+  patch_number: 150
diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index 98409371..a0323b7c 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -30,6 +30,22 @@ cosmo_val:
   nrandom_cell: 100
   cell_method: catalog
   nside_mask: 8192
+  cov_estimate_method: th
+  compute_cov_rho: true
+  n_cov: 100
+  var_method: jackknife
+  quantile: 0.1587
+  ylim_xi_sys_ratio: [-0.02, 0.5]
+  fiducial_input_inka: coupled
+  # Pseudo-Cl map resolution, ell binning and noise/randoms settings
+  nside: 1024
+  binning: powspace
+  power: 0.5
+  n_ell_bins: 32
+  ell_step: 10
+  pol_factor: true
+  noise_bias_method: analytic
+  cell_seed: 8192
   path_onecovariance: "/home/guerrini/OneCovariance/"
   rho_tau_method: lsq
 
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 1d9fa196..33113370 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -288,7 +288,7 @@ def resolve_paths_for_version(ver):
             """Resolve relative paths for a version using its subdir."""
             subdir = Path(cc[ver]["subdir"])
             for section in cc[ver].values():
-                if "path" in section:
+                if isinstance(section, dict) and "path" in section:
                     path = Path(section["path"])
                     section["path"] = (
                         str(path) if path.is_absolute() else str(subdir / path)
diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index 9f8a247e..98527490 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -98,17 +98,8 @@ def plot_tau_stats(self, plot_tau_m=False):
             + f"{os.path.abspath(self.tau_stat_handler.catalogs._output)}/{savefig}",
         )
 
-    def set_params_rho_tau(self, params, params_psf, survey="other"):
-        params = {**params}
-        if survey in ("DES", "SP_axel_v0.0", "SP_axel_v0.0_repr"):
-            params["patch_number"] = 120
-            print("DES, jackknife patch number = 120")
-        elif survey in ("SP_v1.4-P3", "SP_v1.4-P3_LFmask"):
-            params["patch_number"] = 120
-            print("SP_v1.4, jackknife patch number =120")
-        else:
-            params["patch_number"] = 150
-
+    def set_params_rho_tau(self, params, params_psf, patch_number, survey="other"):
+        params = {**params, "patch_number": patch_number}
         params["ra_PSF_col"] = params_psf["ra_col"]
         params["dec_PSF_col"] = params_psf["dec_col"]
         params["e1_PSF_col"] = params_psf["e1_PSF_col"]
@@ -149,6 +140,7 @@ def calculate_rho_tau_fits(self):
             params = self.set_params_rho_tau(
                 self.results[ver]._params,
                 self.cc[ver]["psf"],
+                self.cc[ver]["patch_number"],
                 survey=ver,
             )
 
diff --git a/src/sp_validation/rho_tau.py b/src/sp_validation/rho_tau.py
index 6874a08c..62ae8bf8 100644
--- a/src/sp_validation/rho_tau.py
+++ b/src/sp_validation/rho_tau.py
@@ -17,21 +17,12 @@ def _extract_xip(correlations):
 
 
 def get_params_rho_tau(cat, survey="other"):
+    """Rho/tau parameters for one catalogue-config entry ``cat``.
 
-    # Set parameters
-    params = {}
-    # TODO to yaml file
-    if survey == "DES":
-        params["patch_number"] = 120
-        print("DES, jackknife patch number = 120")
-    elif survey == "SP_axel_v0.0":
-        params["patch_number"] = 120
-        print("SP_Axel_v0.0, jackknife patch number =120")
-    elif survey == "SP_v1.4-P3" or survey == "SP_v1.4-P3_LFmask":
-        params["patch_number"] = 120
-        print("SP_v1.4, jackknife patch number =120")
-    else:
-        params["patch_number"] = 150
+    The jackknife patch count is the entry's ``patch_number``; a missing key
+    raises ``KeyError``.
+    """
+    params = {"patch_number": cat["patch_number"]}
     params["ra_PSF_col"] = cat["psf"]["ra_col"]
     params["dec_PSF_col"] = cat["psf"]["dec_col"]
     params["e1_PSF_col"] = cat["psf"]["e1_PSF_col"]
diff --git a/src/sp_validation/tests/test_cv_init_params.py b/src/sp_validation/tests/test_cv_init_params.py
new file mode 100644
index 00000000..f769c179
--- /dev/null
+++ b/src/sp_validation/tests/test_cv_init_params.py
@@ -0,0 +1,54 @@
+"""Guard: the workflow sets every CosmologyValidation constructor default.
+
+``workflow/common.py::cv_init_params`` builds the constructor kwargs for every
+cosmo_val rule. A keyword it does not forward falls back to the constructor
+default without any trace in the run config, so each keyword with a default is
+either forwarded or exempted below with its reason.
+"""
+
+import importlib.util
+import inspect
+from pathlib import Path
+
+import yaml
+
+from sp_validation.cosmo_val import CosmologyValidation
+
+REPO = Path(__file__).resolve().parents[3]
+
+EXEMPT = {
+    "output_dir": "rules set the output tree via the run directory / COSMO_VAL",
+    "blind": "None keeps the n(z) blind declared in the catalogue config",
+}
+
+
+def _load_common():
+    spec = importlib.util.spec_from_file_location(
+        "workflow_common", REPO / "workflow" / "common.py"
+    )
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _defaulted_keywords():
+    params = inspect.signature(CosmologyValidation.__init__).parameters.values()
+    return {p.name for p in params if p.default is not inspect.Parameter.empty}
+
+
+def test_every_default_is_forwarded_or_exempt():
+    config = yaml.safe_load(
+        (REPO / "papers" / "cosmo_val" / "config" / "config.yaml").read_text()
+    )
+    forwarded = set(_load_common().cv_init_params(config))
+    unaccounted = _defaulted_keywords() - forwarded - set(EXEMPT)
+    assert not unaccounted, (
+        f"CosmologyValidation defaults not forwarded by cv_init_params: "
+        f"{sorted(unaccounted)}. Forward them from config['cosmo_val'] or add "
+        f"them to EXEMPT with a reason."
+    )
+
+
+def test_exemptions_are_live_keywords():
+    stale = set(EXEMPT) - _defaulted_keywords()
+    assert not stale, f"EXEMPT names keywords the constructor lacks: {sorted(stale)}"
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index 45a2366d..2b5ccf51 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -146,6 +146,7 @@ def _write_synthetic_config(tmp_path):
             "shear": shear_cfg,
             "star": {**psf_cfg},
             "psf": psf_cfg,
+            "patch_number": 150,
         },
     }
     config_path = tmp_path / "config.yaml"
diff --git a/workflow/common.py b/workflow/common.py
index 506f7cb2..32a76b83 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -302,10 +302,10 @@ def get_shear_catalog(wildcards):
 # Sentinel directory for pure-plot leaf rules (no natural data-product output).
 CV_SENTINELS = COSMO_VAL / "snakemake_sentinels"
 
-# Working directory in which `CosmologyValidation` must be instantiated: it
-# reads `./cat_config.yaml` and writes to `./output` by default. Resolved to
-# the live (non-worktree) checkout so rules find the catalog config and share
-# the output tree with interactive runs.
+# Working directory in which `CosmologyValidation` is instantiated: its
+# catalogue config comes explicitly from CAT_CONFIG, and it writes to
+# `./output` unless COSMO_VAL is set. Resolved to the live (non-worktree)
+# checkout so rules share the output tree with interactive runs.
 CV_RUNDIR = "/n17data/cdaley/unions/code/sp_validation/cosmo_val"
 
 
@@ -324,31 +324,52 @@ def cv_basename(version, fiducial=None):
     )
 
 
+# CosmologyValidation constructor kwargs read from config["cosmo_val"]. Every
+# keyword with a default is either here or explicitly exempted in
+# src/sp_validation/tests/test_cv_init_params.py, so no default applies silently.
+CV_INIT_KEYS = (
+    "rho_tau_method",
+    "cov_estimate_method",
+    "compute_cov_rho",
+    "n_cov",
+    "theta_min",
+    "theta_max",
+    "nbins",
+    "var_method",
+    "npatch",
+    "quantile",
+    "theta_min_plot",
+    "theta_max_plot",
+    "ylim_alpha",
+    "ylim_xi_sys_ratio",
+    "nside",
+    "nside_mask",
+    "binning",
+    "power",
+    "n_ell_bins",
+    "ell_step",
+    "pol_factor",
+    "cell_method",
+    "noise_bias_method",
+    "fiducial_input_inka",
+    "nrandom_cell",
+    "cell_seed",
+    "path_onecovariance",
+    "cosmo_params",
+)
+
+
 def cv_init_params(config, version_list=None):
     """Assemble the CosmologyValidation(...) constructor kwargs from config.
 
     Centralizes the run-specific instantiation so every cosmo_val rule script
-    builds an identical `cv`. `version_list` overrides config["versions"] (used
-    by per-version rules that pass a single version).
+    builds an identical `cv`. The catalogue config is always CAT_CONFIG, never
+    the constructor's cwd-relative default. `version_list` overrides
+    config["versions"] (used by per-version rules that pass a single version).
     """
     cv = config["cosmo_val"]
-    params = dict(
+    return dict(
         versions=version_list if version_list is not None else config["versions"],
-        npatch=cv["npatch"],
-        theta_min=cv["theta_min"],
-        theta_max=cv["theta_max"],
-        nbins=cv["nbins"],
-        theta_min_plot=cv["theta_min_plot"],
-        theta_max_plot=cv["theta_max_plot"],
-        ylim_alpha=cv["ylim_alpha"],
-        nrandom_cell=cv["nrandom_cell"],
-        cell_method=cv["cell_method"],
-        nside_mask=cv["nside_mask"],
+        catalog_config=CAT_CONFIG,
+        **{key: cv[key] for key in CV_INIT_KEYS},
     )
-    if cv.get("path_onecovariance"):
-        params["path_onecovariance"] = cv["path_onecovariance"]
-    if cv.get("rho_tau_method"):
-        params["rho_tau_method"] = cv["rho_tau_method"]
-    if cv.get("cosmo_params"):
-        params["cosmo_params"] = cv["cosmo_params"]
-    return params
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 275afc3c..b2df6c7c 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -15,6 +15,8 @@ rule xi:
         max_sep="{max_sep}",
         nbins="{nbins}",
         npatch="{npatch}",
+        cat_config=CAT_CONFIG,
+        output_dir=str(COSMO_VAL),
         fits=False,
     resources:
         mem_mb=30000,
@@ -35,6 +37,8 @@ rule rho_tau_stats:
         max_sep="{max_sep}",
         nbins="{nbins}",
         npatch="{npatch}",
+        cat_config=CAT_CONFIG,
+        output_dir=str(COSMO_VAL),
     resources:
         mem_mb=30000,
         disk_mb=20000,
diff --git a/workflow/scripts/cv_runner.py b/workflow/scripts/cv_runner.py
index ba50847d..040cbe8a 100644
--- a/workflow/scripts/cv_runner.py
+++ b/workflow/scripts/cv_runner.py
@@ -29,9 +29,9 @@ def make_cv(snakemake):
     """Build a CosmologyValidation from a rule's ``snakemake.params``.
 
     ``params["cv_init"]`` is the kwargs dict assembled by common.cv_init_params.
-    The object is created with the run directory as cwd so it finds
-    ``cat_config.yaml`` and writes under ``output/`` exactly as interactive runs
-    do.
+    The catalogue config path arrives explicitly in ``cv_init``; the object is
+    created with the run directory as cwd so it writes under ``output/``
+    exactly as interactive runs do.
     """
     from sp_validation.cosmo_val import CosmologyValidation
 
diff --git a/workflow/scripts/run_2pcf.py b/workflow/scripts/run_2pcf.py
index 2e1ccabf..86dffe27 100644
--- a/workflow/scripts/run_2pcf.py
+++ b/workflow/scripts/run_2pcf.py
@@ -64,12 +64,8 @@ def _from_snakemake(smk):
         max_sep=float(p["max_sep"]),
         nbins=int(p["nbins"]),
         npatch=int(p["npatch"]),
-        # cat_config / output_dir were previously resolved via an os.chdir into
-        # the cosmo_val dir + the COSMO_VAL env var; expose them as optional
-        # params so the rule can pass them explicitly, falling back to the
-        # class defaults (./cat_config.yaml, COSMO_VAL env) otherwise.
-        cat_config=p.get("cat_config", "./cat_config.yaml"),
-        output_dir=p.get("output_dir", None),
+        cat_config=p["cat_config"],
+        output_dir=p["output_dir"],
         save_fits=True,
     )
 
diff --git a/workflow/scripts/run_rho_tau.py b/workflow/scripts/run_rho_tau.py
index fbf62a3c..bc11bfc0 100644
--- a/workflow/scripts/run_rho_tau.py
+++ b/workflow/scripts/run_rho_tau.py
@@ -32,7 +32,6 @@
 params = snakemake.params  # type: ignore
 
 # %%
-os.chdir("/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val")
 print("Starting CosmologyValidation")
 
 # Use parameters passed from Snakemake rule
@@ -42,6 +41,8 @@
     theta_max=float(params["max_sep"]),
     nbins=int(params["nbins"]),
     npatch=int(params["npatch"]),
+    catalog_config=params["cat_config"],
+    output_dir=params["output_dir"],
 )
 
 cv.calculate_rho_tau_stats()

From 4c16c28d7f69ce2542a563fe2efd429a002dc254 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Fri, 25 Sep 2026 17:01:25 +0200
Subject: [PATCH 29/83] cosmo_val + Snakemake migration to the SACC writers
 (PRD #241 row 4) (#251)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* feat(sacc_io): SACC read/write for the standard data-product layout

Add sp_validation.sacc_io: the writer/reader layer for the two-file SACC
layout that becomes the package's standard data-product format.

  {version}.sacc       analysis vector — NZ tracers, coarse xi+/-, pseudo-Cl
                       (EE/BB/EB) with a shared BandpowerWindow, COSEBIs,
                       pure E/B, rho/tau PSF diagnostics; one FullCovariance
                       assembled block-diagonally (zero cross-blocks).
  {version}_xi_fine    COSEBIs/pure-EB integration input — same NZ tracers,
                       fine-grid xi+/-, DiagonalCovariance from TreeCorr
                       varxip/varxim.

Covariance order is point-insertion order (SACC preserves it bitwise
through FITS). Writers insert in the canonical order — xi+ then xi-, Cl
(ee, bb, eb), COSEBIs (all En then all Bn), pure E/B in _EB_KEYS order
(xip_E, xim_E, xip_B, xim_B, xip_amb, xim_amb, matching
b_modes.calculate_eb_statistics), rho, then tau — but readers never assume
global order: every getter resolves indices through s.indices(dtype,
tracers, **tags). assemble_covariance validates that blocks are contiguous,
ascending and tile the data vector exactly, failing loud otherwise.

Custom data types (pure E/B, rho, tau) all parse under
sacc.parse_data_type_name. Tag filters are plain kwargs; the tags={...}
form silently selects nothing and is never used.

Test suite (test_sacc_io.py, all synthetic and fast): per-writer
round-trips (arrays/tags/windows/NZ bitwise), covariance block alignment
and zero cross-blocks, assemble_covariance failure modes, DiagonalCovariance
round-trip, extract() sub-covariance alignment, a tomographic multi-pair
case, reader/writer mirroring on a mixed file, and the end-to-end two-file
layout. 20 passed.

Co-Authored-By: Claude Opus <noreply@anthropic.com>

* fix(sacc_io): enforce ascending grids; readers in insertion order

Fresh-eyes review caught a correctness bug: readers re-sorted selections by
theta/ell/n, but covariance blocks and bandpower windows stay in insertion
order. On a non-ascending grid the reader output silently desynchronised
from its covariance, and get_pseudo_cl returned sorted cl arrays against
unsorted window columns — internally inconsistent within one return tuple.

Fix by construction, not by sort:
  - Writers validate their grids. add_xi/add_pure_eb/add_rho/add_tau require
    strictly ascending theta; add_pseudo_cl requires strictly ascending
    ell_eff (add_cosebis is inherently safe — it enumerates the mode index).
    Out-of-order grids raise a loud ValueError naming the argument.
  - Readers drop the sort entirely and return in s.indices (insertion) order,
    so every getter is covariance- and window-aligned for ANY file, and
    ascending for canonical files. The _sorted_* helpers are replaced by
    plain insertion-order accessors (_mean/_tag).

Also:
  - _pair normalises (i, j) -> sorted, so get_xi(s, (1, 0)) addresses the same
    symmetric shear-shear pair as (0, 1) instead of a silent empty read.
  - Module docstring documents the tomographic ξ covariance ordering:
    insertion is pair-major ([pair0 xip; pair0 xim; pair1 xip; …]), supplied
    to assemble_covariance as one contiguous block matching add_xi call order;
    type-major converters (DES 2pt-FITS) permute explicitly via s.indices.
  - extract() docstring states tracers takes SACC names, not integer bins.

New tests (26 total, was 20): writers reject non-ascending theta/ell;
(1, 0) == (0, 1) round-trip; a 3-pair tomographic ξ covariance assembled as
one contiguous pair-major block with per-pair sub-blocks recovered via
extract(); get_pseudo_cl window column j <-> ell_eff[j] via window_ind tags.

Co-Authored-By: Claude Opus <noreply@anthropic.com>

* One SACC file per catalogue version: fine xi rides as grid='fine' points

The two-file split (analysis + {version}_xi_fine.sacc) was premised on a
10000-bin fine grid; the production operating point (Paper II B-modes) is
1000 bins, where a dense per-pair fine covariance block is ~32 MB and the
CosmoCov integration-binning covariance — which feeds pure-E/B and COSEBIs
error propagation — has a natural home as a BlockDiagonal block alongside
the analysis blocks. Layout test replaced with the one-file end-to-end
case (dense fine block, extract() sub-covariance alignment, zero
cross-blocks) plus a varxi-diagonal fallback test.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019R5eiy11Lihkgn4MKufXSp

* Word fine-block covariance source tool-agnostically (OneCovariance go-forward)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019R5eiy11Lihkgn4MKufXSp

* feat(sacc_io): type=data|mock stamp with fail-closed load; merge + update_statistic

PRD #241 §4 (Mocks vs data): save() requires type='data'|'mock' and stamps
it into metadata; load() raises on type='data' files lacking the
concealed=True blinding stamp, so skipping the blind can never silently
expose real data. Mocks load freely. allow_unblinded=True is the loud
escape hatch reserved for the blinding/unblinding tooling.

merge() wraps sacc.concatenate_data_sets thinly: per-statistic files
combine in order, shared tracers stored once, covariance block-diagonal
(library-enforced all-or-none), metadata union with loud conflicts.
update_statistic() is the value-only merge-back for the
extract -> conceal -> merge blinding flow: matches each sub point by
(data_type, tracers, tags) and overwrites the value, leaving order and
covariance untouched.

Tests: all four data/mock x concealed/unconcealed quadrants, the escape
hatch, stamp validation, merge (points, covariance, metadata conflict,
mixed-covariance failure) and update_statistic (values-only, unique-match).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QWF72ofwJh6ekgnCt9Xx6C

* Address review: rename grid values to reporting/integration; clarify comments

- grid tag values: coarse -> "reporting", fine -> "integration" (descriptive,
  not relative); tag kept because both grids share data type and tracer pair,
  so the tag is the sole disambiguator. Swept module docstrings and tests.
- Module docstring now spells out why insertion order is load-bearing:
  covariance row/column i refers to the i-th inserted data point.
- new_sacc: comment that psf_stars rides in the same tracer list as the NZ
  tracers only because a Sacc has one flat tracer namespace (bookkeeping,
  not physics).
- source_name/_pair helpers kept (4 and 8 call sites) with one-line
  justifications of the conventions they centralize.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QWF72ofwJh6ekgnCt9Xx6C

* Simplify sacc_io writers/readers and test builders

- Factor the theta-tagged insertion loop into _add_theta_series (add_rho,
  add_tau, add_pure_eb); add_pure_eb zips PURE_TYPES.values() against its
  signature order, and PURE_KEYS is derived from PURE_TYPES instead of
  restating it.
- Factor the (theta, plus, minus) read pattern into _get_pm (get_xi,
  get_rho, get_tau).
- add_xi hoists the optional-tag None-filtering out of the point loop;
  extract and merge lose their throwaway mutable dicts; get_cosebis
  builds its scale-cut tags in one expression.
- Tests: shared _add_xi default-ξ builder and _xi_block/_cl_block/
  _cosebi_block canonical index-block helpers replace ~60 lines of
  copy-pasted setup; test_readers_on_mixed_file builds on
  _multi_statistic_sacc.

Behaviour unchanged; 41/41 tests green in the container.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QWF72ofwJh6ekgnCt9Xx6C

* fix(sacc_io): fail loud on unmatched selections; merge/update guards

Adversarial review (pass 2) findings:

- Sacc.indices returns an EMPTY array (warning only) on an unmatched
  selection; every reader, extract(), and covariance-block selector now
  funnels through a shared _indices guard that raises instead — a typo'd
  tag or non-bitwise-identical float scale cut can no longer propagate
  empty arrays downstream (assemble_covariance previously died with an
  opaque IndexError on the same path).
- get_cosebis(scale_cut=None) on a file carrying several scale cuts
  silently concatenated them; now raises and asks for an explicit cut.
- update_statistic let two sub points claim the same target point
  (last-write-wins); now raises.
- merge: the library keeps the FIRST input's tracer on a name clash with
  no equality check; shared tracers are now verified identical (z, nz)
  across inputs before concatenation.

7 regression tests; 48 total.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WzUt7VbtXwr2SCHUdiQTyt

* Rebuild PR4 on feat/sacc-2-sacc-io: drop vendored sacc_io, keep migration

The branch previously carried a stale vendored copy of sacc_io.py /
test_sacc_io.py from before PR2's review rounds. Rebuilt directly on
feat/sacc-2-sacc-io (8bd38171) so the canonical module is inherited,
bringing over the cosmo_val + Snakemake born-as-SACC migration work
from the old tip unchanged.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WzUt7VbtXwr2SCHUdiQTyt

* Migrate to canonical sacc_io API: typed save, fail-closed load, grid rename

Sweep the migration code onto PR2's canonical vocabulary and contracts:

- grid='coarse'/'fine' -> 'reporting'/'integration' everywhere (writer
  calls, readers, tests), including the internal DAG intermediates:
  {version}_xi_coarse* -> _xi_reporting*, _xi_fine -> _xi_integration,
  the xi_coarse/xi_fine Snakemake output keys, CANONICAL part names,
  cv_xi_reporting_sacc, write_xi_integration_sacc.
- save(s, path) -> save(s, path, type=...): 'data' at every production
  writer (the cosmo_val pipeline measures the real UNIONS catalogues;
  GLASS mocks do not flow through these writers), 'mock' for synthetic
  test fixtures. assemble_sacc propagates its parts' type stamp rather
  than hardcoding, so mock parts assemble into a mock analysis file.
- load(path) -> fail-closed load: pipeline-internal readbacks of
  freshly written pre-blind data parts pass allow_unblinded=True
  (blinding is a downstream Smokescreen step); mock fixtures load freely.

Readers raising on unmatched selections needed no call-site changes:
every get_* reads a statistic guaranteed present in the file just
written or assembled.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01WzUt7VbtXwr2SCHUdiQTyt

* sacc_io: guard merge() against inconsistent theta grids

Consistency follows tagging semantics: the grid tag declares which
binning a set of points lives on, so all same-length theta arrays under
one tag value must be bitwise identical (sacc never validates angles,
and grids diverging at floating-point level choke CosmoSIS downstream).
Different lengths within a tag group pass (scale-cut subsets); grids
under different tag values are unconstrained (reporting vs integration
differ by design).

The rho/tau/pure-EB writers now tag their points grid="reporting" by
default (overridable via grid=), so they join xi's consistency group
and no untagged group appears in our own files.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MN9VazXKHUHQg16kiG7Ufk

* sacc_io: extend merge guard to ell grids and bandpower windows; tag pseudo-Cl grid="reporting"

The theta consistency guard generalizes to both angular domains: theta
and ell points are grouped separately by grid tag value, and within a
tag value all same-length grids must be bitwise identical. Two ell
series sharing a grid must also carry equal bandpower windows (window
ells and weight matrix); series without windows skip that check.

add_pseudo_cl now stamps grid="reporting" on every point by default
(overridable), joining the merge guard's consistency groups; since
sacc's add_ell_cl accepts no extra tags, its per-point insertion
(ell + shared window + window_ind) is inlined.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MN9VazXKHUHQg16kiG7Ufk

* Store covariance block-diagonally in assemble_covariance

assemble_covariance now passes sacc.add_covariance a list of per-block
matrices instead of a dense zero-filled N×N array. sacc.BaseCovariance.make
turns a list into a BlockDiagonalCovariance (one FITS table per block, Σ
block² on disk), so cross-blocks are zero and implicit rather than
materialized. Validation (contiguous/ascending indices, no gap/overlap,
square blocks matching their index span) is unchanged.

Every existing consumer already read through the polymorphic .dense
property, so only the type assertions needed updating (FullCovariance ->
BlockDiagonalCovariance). Added a test that merging two files that already
carry a BlockDiagonalCovariance (assembled via assemble_covariance) stays
block-diagonal through merge and a save/load round-trip. Updated the module
docstring's storage-cost discussion accordingly.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* Unify pure-EB integration-grid default to 1000 bins

pure_eb.py's calculate_pure_eb/plot_pure_eb defaulted nbins_int=100;
cosebis.py's calculate_cosebis already defaulted to 1000, and every
production config (papers/bmodes, papers/cosmo_val fiducial/pure_eb
blocks) already overrides to 1000. This aligns the function-signature
default with what every caller actually uses; config plumbing is
untouched, so any explicit override still wins.

The papers/cosmo_val/config/config.yaml cosebis.nbins_int (currently
2000, production numerics) is deliberately left unchanged — see report.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* sacc_io: make optional statistic components genuinely optional

Writers no longer force components an analysis may not have computed:
add_pseudo_cl's BB/EB, add_cosebis's Bn, and add_pure_eb's B/ambiguous
blocks now default to None and are simply not written when omitted
(add_pure_eb still requires each +/- pair together). Structural
arguments (grids, tracer/bin identifiers, the value for a component
you ARE adding) remain required with no default.

Composite readers (get_pseudo_cl, get_cosebis, get_pure_eb) return
None / omit the key for an absent optional component instead of
raising, via a new _mean_optional helper; a selection naming a missing
component explicitly (s.indices, _mean, extract) still fails loud, per
the existing empty-selection guard. merge() and assemble_covariance
work unchanged on files with only a subset of components.

Documents the optionality contract in the module docstring, and adds
partial-file round-trip, merge, and explicit-selection-fails-loud
tests.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* cosmo_val config: align COSEBIs integration grid with the 1000-bin default

cosebis.nbins_int was 2000; every other integration-grid entry in this
config (pure_eb, the fiducial block) is already 1000, and cosebis.py's
own function defaults are 1000. Unify.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* feat(sacc): one terminal file + fail-closed assembly

Fold the integration-grid ξ± into the single terminal {version}.sacc, and
make part loading fail closed on unblinded real data.

Integration ξ± (grid='integration') is no longer its own terminal product.
The xi_highres part is now gathered by rule assemble_sacc into {version}.sacc
as tagged points, next to the reporting ξ± block. It is fiducial-only (the
10k-bin MPI run emits only the fiducial part), so it joins the fiducial
version's terminal file alone. assemble_sacc.py adds xi_integration to
CANONICAL; its own DiagonalCovariance passes straight through.

assemble_sacc.py no longer loads every part with allow_unblinded=True. The
run type (data|mock, from config, default data) gates it: mock runs load
freely, data runs fail closed unless a part carries the concealed=True stamp.
This is the seam for PR #253's blind-at-birth — a concealed data part then
assembles with allow_unblinded=False untouched.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* docs(workflow): mark glass-mock A/B/C 'blind' as distinct from Smokescreen

The pseudo_cl / pseudo_cl_cov rules and the shared BLINDS list use a 'blind'
wildcard that is the glass-mock multi-catalogue A/B/C variant, not Smokescreen
blinding. Add prominent comments at the BLINDS definition, the wildcard
constraint, and both rules so the two axes are not confused. A full rename is
avoided: 'blind' is baked into on-disk filenames we do not own (external
nz_{version}_{A|B|C}.txt) and into the covariance / inference path builders.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* fix(sacc): reconcile writers with the merged sacc_io API

Resolve two silent semantic conflicts from the base merge (no textual
conflict, but the tests broke).

- add_cosebis reordered its params to (s, bins, En, scale_cut, Bn=None).
  cosebis_to_sacc still passed the old positional order; call it by keyword.
- assemble_covariance now builds a BlockDiagonalCovariance (one FITS table
  per block, validated ordering), not a dense FullCovariance. Update the
  assembler docstrings, the smk comments, and the assembled-file test
  assertions to the block-diagonal type. The assembled .dense is unchanged
  (cross-blocks are zero either way); single-part writer covariances stay
  FullCovariance.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* chore(deps): restore develop's uv.lock SSOT; drop stale firecrown-era override machinery

The branch had accidentally superseded #266's reproducible-deps model
(uv.lock as source of truth, `uv sync --frozen` in the Dockerfile) with an
older firecrown/Smokescreen-blinding scheme: uv.lock deleted, dependency
resolution done via `uv pip install --overrides uv-overrides.txt`, and a
patch script to make pip-installed firecrown importable without NumCosmo.
Firecrown has been dropped from the project, so all of that goes.

Restores develop's pyproject.toml, uv.lock, Dockerfile, and the
deploy-image blinding-stack smoke test wholesale; removes uv-overrides.txt
and scripts/patch_firecrown.py. Keeps one genuine PR4-driven change: tightens
the sacc constraint to >=2.4,<3, since sacc_io.py (this branch) uses
concatenate_data_sets/BlockDiagonalCovariance, both from sacc's 2.x rewrite
(develop's lock already resolves sacc to 2.4; this just makes the pyproject
floor honest). Drops the unused numexpr addition — no code in the tree
imports it.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* chore(ci): restore develop's lint.yml (PR #264 server-side ruff autofix) lost in stale merge

* revert(sacc): drop xi_integration from terminal file per #247 ruling

Keep the integration-grid ξ± as its own per-part intermediate
({version}_xi_integration.sacc) rather than folding it into the terminal
{version}.sacc. Per the #247 ruling (comment 5033716753), the terminal file
carries the analysis vector only; COSEBIs/pure-E/B consume the integration
part directly, and Snakemake provenance covers its traceability. This keeps
the terminal file at tens-of-MB scale.

Removes xi_integration from CANONICAL, the cv_xi_integration_sacc helper, and
the fiducial-gated assemble input. Keeps the fail-closed allow_unblinded gate,
glass A/B/C comments, and dependency restorations from the prior rework. The
integration part stays blinded at birth on data runs (per #253).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* chore: drop files resurrected from a stale base (deleted on develop)

The "Rebuild PR4" base re-added the cosmo_inference/ analysis tree that
develop removed in #236 (Clean up cosmo_inference folder); the develop merge
kept them because a delete-vs-readd does not conflict. Remove the 80 resurrected
files (notebooks, cosmosis_config .ini set, cosmo_inference scripts, get_chi2
notebooks, pipeline shells) to match develop. None is PR4 scope — PR4 is the
SACC migration (workflow/ rules + scripts, sacc-related src + tests).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* chore(2pcf): default integration grid to 1000 bins

The B-modes paper found no substantial 1k-vs-10k difference on the integration
grid, and develop already unified COSEBIs/pure-EB to nbins_int=1000. Drop the
run_2pcf_highres.py 10000-bin default to 1000 and reword the docstring/comments
(the Asgari 10k figure becomes context, not the operative number). The grid is
config-driven (nbins_int); the MPI path stays available but single-process is
the default at 1000 bins.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* feat(cosmo_val): cv_cosebis/cv_pure_eb consume the ξ± SACC parts; per-version xi_highres

COSEBIs and pure-E/B now derive their E-mode outputs from the born-as-SACC ξ±
parts on disk instead of only the raw catalogue recompute. This makes born
blinding automatic downstream: whatever the part carries (blinded on data runs
in PR #253) flows into the E-modes with no blind-aware consumer code.

b_modes: add the values-only seams cosebis_from_xi and pure_eb_from_xi (copied
from the sacc-6b spur, de-blinded) — same cosmo_numba kernels as the raw path.

cosebis_to_sacc_part / pure_eb_to_sacc_part: add en_override / eb_override so the
E-mode En (COSEBIs) and the six pure-mode arrays (pure-E/B) written to the SACC
part come from the consumed part; Bn and the covariance stay blind-invariant from
the raw estimator run. No concealment machinery (that is PR #253's).

cv_cosebis.py / cv_pure_eb.py: keep the raw plot_* run (Bn + jackknife covariance
need the catalogue and are blind-invariant), then load the integration part
(COSEBIs) / reporting + integration parts (pure-E/B), re-derive the E-modes
through the seams, and override both the SACC part and the diagnostic npz. pure-E/B
reads the reporting-grid bin edges from the raw reporting gg (SACC stores centers
only). Unconditional and version-agnostic.

xi_highres: per version (was fiducial-hardcoded); in-container single-process at
the config-driven 1000-bin grid (the 10k-bin bare-host MPI path is unnecessary).
Grid comes from a dedicated cosmo_val.integration block ([0.08, 300] @ 1000) so
the one part serves both consumers (pure-E/B full range, COSEBIs scale-cuts to
0.9); decoupled from covariance.smk's FIDUCIAL grid. Shared twopoint.smk falls
back to the fiducial integration grid for configs without a cosmo_val section
(e.g. papers/bmodes). The raw .txt byproduct is left undeclared to avoid an
AmbiguousRuleException with rule xi; nothing in the DAG consumes it.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EaL7prmKUHwJQcDyW3LoxD

* Restore cosmo_inference/ to develop state (stale-base residue reverted newer develop edits)

* One binning-agnostic xi rule (grid + covariance are config, not a second rule)

The reporting and integration ξ± measurements were two rules and two drivers
for one computation. Collapse them: rule `xi` measures any binning, files are
named by that binning (.txt + .sacc), and the grid label + covariance treatment
are resolved from the wildcards via XI_GRIDS (reporting → no covariance, the ξ
block arrives at assembly from CosmoCov; integration → DiagonalCovariance from
TreeCorr varxip/varxim). Workflows with no cosmo_val block fall back to their
fiducial grids; an unnamed binning measures as plain reporting.

run_2pcf.py gains --grid/--covariance and drops run_2pcf_highres.py entirely
(its hand-rolled catalogue loader, cat_config resolver and bare-host MPI path
were a second implementation of CosmologyValidation.calculate_2pcf). The SACC
part now stamps the npatch actually measured. calculate_2pcf writes patch
results + covariance only for npatch > 1, so the fine grid no longer serialises
a dense (2*nbins)^2 block that nothing reads.

Renames {version}_xi_reporting_<binning>.sacc and {version}_xi_integration.sacc
to {version}_xi_<binning>.sacc; consumers go through cv_xi_sacc(version, grid).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014hSQTC6WwTH1p9w4FuKJGz

* Restore develop files wrongly deleted by stale-base cleanup (realspace paper, image-sims, mask configs, candide profile)

* Restore develop content wrongly reverted by stale base (.gitignore uv.lock, CONTRIBUTING lint gate, calibration/catalog modules)

* simplify: trim comments to Google style; dedupe helpers; drop dead knobs

Comment/docstring pass over the PR-4 diff: cut PR/issue archaeology, restated
defaults and "identical/bit-matching" claims (not true under blinding), keeping
the load-bearing contracts (canonical part order, placeholder-cov consequence,
scale_cut semantics, TreeCorr bin edges, CovTauTh k-major plus-folded layout).

Code:
- common.py gains base_version() and pseudo_cl_tag(); generate_cosmocov_ini
  imports both instead of copying build_redshift_path, fixing the drift where
  the cov_th lookup stripped only _leak_corr while the n(z) path also stripped
  _ecut{N}. cosmo_val.smk / inference.smk share the one pseudo-Cl tag.
- run_cosmocov_chain.sh anchors the checkout on its own location; container,
  bind list and cosmocov binary come from the environment.
- assemble_sacc: drop the unused --pseudo-cl-cov-hdu knob (the three named HDUs
  are always written; missing ones now raise).
- assemble_analysis_sacc seeds tracers + metadata from parts[0], retiring
  _n_source_bins and the nz/metadata arguments.
- Covariance mode derives from the grid: XI_GRIDS loses its "covariance" field,
  xi_grid_of returns just the label and compares binnings numerically, and
  run_2pcf loses its --covariance flag.
- inference.smk: drop the dead inference_prep / inference_fiducial rules.

Also restores bandpower_window_from_workspace (deleted in error by f14774b2)
and develop's NON_PATH_KEYS overlay entries in test_config_paths_exist.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014hSQTC6WwTH1p9w4FuKJGz

* cv_cosebis: derive Bn from the integration part; unpatched integration path in claims

Both COSEBIs values now come from the (blindable) integration ξ± SACC part
through the cosebis_from_xi seam; only the jackknife covariance still comes
from the raw patched measurement, where the patches live.

papers/bmodes' integration ξ± path said npatch=FIDUCIAL['npatch']; the
integration grid (XI_GRIDS, twopoint.smk) is unpatched, so pin it to 1.

* test: make the COSEBIs override stub work under the blinded-part writer too

* Leave inference.smk untouched: it is #255's to rewrite

The dormant inference subsystem was being half-edited here (inference_prep
deleted, comments trimmed, pseudo_cl_tag adopted) while #255 rewrites it
wholesale. Restored to its merge-base-with-develop state; both paper workflows
still dry-run.

* Assemble against the real CosmoCov ξ± covariance; drop the placeholder

The ξ± block now comes from the CosmoCov-processed covariance on the reporting
binning, wired as a DAG input of assemble_sacc (cv_xi_cov), so requesting the
terminal file builds the covariance chain. With a real block always present,
the allow_placeholder_cov switch and the diagonal stand-in are gone: a missing
covariance is a missing Snakemake input, and a cov-less part with nothing to
inject still raises. The tests use a CosmoCov-format .txt fixture written with
the same writer the seam reads.

* covariance_process: run the launched checkout's cosmocov_process.py

The rule shelled out to an absolute path through the deprecated pure_eb
symlink, i.e. a different checkout on an unrelated branch — now on the critical
path, since assemble_sacc depends on this rule. REPO_ROOT (common.py) anchors
on the module's realpath, the same way the rest of the workflow resolves the
running checkout under `module` composition.

* Leave cosmo_inference/README.md untouched (inference is out of scope here)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmGA86b7YyQn54JFj78ssM

* Restore image_sims include + README section (out of scope here)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmGA86b7YyQn54JFj78ssM

* README: state the 3.12 requirement, not its rationale

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmGA86b7YyQn54JFj78ssM

* Comment sweep: each concept once, at the thing itself

Kills repeats (the same grid/naming/canonical-order explanation restated at
producer, rule and script) and comments that narrated what downstream code
does — consumer names, rule wiring, pipeline shape — which rot as soon as the
consumer moves. What stays is each thing's own contract plus the invariants a
reader would violate: the inclusive scale-cut selection, the strict sub-range
the pure-E/B integration grid must satisfy, why the integration grid carries no
covariance, why an already-written part may be read back unblinded, and the
covariance ordering assembly depends on. Also drops the stale rename note in
generate_pseudo_cl (parts are born at their final path) and the duplicated
REPO_ROOT anchoring rationale in common.py.

* Each grid carries its own covariance; COSEBIs derives from ξ± arrays

A grid is now a binning plus how its covariance is estimated, so one TreeCorr
run per grid produces a part complete enough to work from: the dense jackknife
block where there are patches, the varxip/varxim diagonal, or nothing.

The COSEBIs scan gains an arrays-and-covariance seam (cosebis_scan_from_xi):
the COSEBIs covariance was already the ξ± covariance through the same linear
kernel as the modes, so a caller holding a data vector needs no estimator
re-run — only the realisation count behind the covariance, for Hartlap.
calculate_cosebis delegates to it, and the scale-cut heatmap takes bin edges
rather than a correlation object; log_bin_edges reconstructs those edges from
a binning, which is what a part-based consumer has.

cv_cosebis becomes such a consumer: one part in, and the SACC part, the .npz
scan and the figures all out of those values. The en/bn_override machinery it
needed while values and covariance came from different runs goes with it.

* cv_cosebis: bind the COSEBIs grid's part, and declare the figures

The rule's inputs are now one ξ± part instead of a TreeCorr .txt plus the
integration part, and the three companion figures are declared outputs rather
than byproducts landing beside them. The COSEBIs grid (0.9–300 at 1000 bins,
patched) is a row in the grid table, so the same xi rule measures it.

* Pure E/B derives from its parts; per-patch vectors are never written

The pure-E/B jackknife needed the patched correlation objects, which is the one
thing a part cannot carry — and per Cail's ruling jackknife and blinding are a
deprecated pair for derived statistics. So the covariance now comes from the
Monte Carlo path calculate_pure_eb already had, extracted as
pure_eb_covariance_mc: draws from the CosmoCov gaussian covariance on the
integration grid, around a theory mean, through the same kernel as the modes.
It depends on the covariance model and the grids, never on the measured vector,
so it is blind-invariant by construction. cv_pure_eb consumes two parts plus
that one covariance file and touches no catalogue.

The results dict is now self-describing — the grids travel with the modes
(theta, edges, the raw ξ± and their variances, n_eff) instead of a TreeCorr
object riding along — so the statistics, plots and npz all work from values.
That deletes the FakeGG stub the paper PTE script needed, and turns
calculate_eb_statistics into a function of its results alone.

calculate_2pcf no longer writes patch results into the .txt dump: a per-patch
ξ± realisation is an unblinded data vector, nothing reads one back, and the
covariance a consumer needs is the matrix the part carries.

* cv_summarize_bmodes reads the products instead of recomputing them

It re-ran plot_pure_eb / plot_cosebis / plot_pseudo_cl in-process to repopulate
the in-memory result dicts, which meant the terminal diagnostic reached the
catalogue — and rewrote the two consumers' figures from unblinded values on the
way past. It now reads what those rules wrote: the pure-E/B PTE matrices and
the COSEBIs B-mode PTE from their .npz products, and the pseudo-Cℓ BB spectrum
from its part against the NaMaster covariance, both declared inputs. The
summary can no longer disagree with the products it summarises.

The table itself moves to print_bmode_summary, so the in-memory path
(summarize_bmodes, for notebooks) and the file path print the same thing. A
test pins the .npz key contract the two rules meet on.

* Review round: one pseudo-Cl producer, analytic covariance wins, grid tags canonical

Six findings from the review.

One pseudo-Cl producer. The untagged diagnostic part and its rule are gone; the
summary, the terminal file and now the figures all read the analysis part with
its matching NaMaster covariance, so the three cannot disagree. The figures
become a plot-only ingest rule like the other B-mode ones, over a
plot_pseudo_cl_spectrum that draws one spectrum for every version — the three
near-identical EE/EB/BB blocks were one function all along.

The analytic covariance wins, loudly. A part whose analysis covariance is
external (ξ± reporting, pseudo-Cl) now always takes the supplied block, which
replaces the estimate it was born with rather than losing a race with it, and
says so on stdout; a missing injection raises instead of silently keeping the
part's own. The reporting part keeps carrying its jackknife — it is a real
diagnostic, just not the analysis covariance.

Grid tags are canonical. xi_binning stamped raw YAML (maxsep=300) while the
measurement wrote float-normalised names (maxsep=300.0), so producer and
consumer would have asked for different paths on the first real run. The table
coerces once, where it is built. It moved to common.py to be testable at all,
and the tests pin the round trip a filename makes.

Also: cosebis_from_xi deleted (no callers, and its centre-based cut diverged
from the edge-based one in use); the paper PTE script's unread cov_integration
and npatch dropped, with the rule docstring corrected to say plainly that
nothing there varies by blind; and the header DAG comment rewritten to the
SACC-part graph it actually describes.

* chore: delete dead cosmocov drivers and repoint dangling references

`workflow/scripts/run_cosmocov_chain.sh` invoked
`cosmo_inference/scripts/cosmocov_process.py`, which was deleted on develop
(#236) and relocated as the snakemake-only `workflow/scripts/cosmocov_process.py`
(driven by `workflow/rules/covariance.smk`). The chain script's only caller was
`papers/bmodes/scripts/run_cov_sweep.sh`, whose own only caller was a human, so
both are dead end-to-end and are removed.

Tree sweep for the same class of rot, repointing what survives:

- README: drop the firecrown/Smokescreen override paragraph. Both files it
  names (`uv-overrides.txt`, `scripts/patch_firecrown.py`) were removed in
  f5eb417 when firecrown was dropped, and there is no `blinding` extra left in
  pyproject.
- CLAUDE.md / CONTRIBUTING.md: the single-test example named
  `tests/test_cosmology.py`, which does not exist; use `tests/test_cosmo_val.py`.
- CLAUDE.md: the cosmo_inference section documented a `./pipeline.sh` driver
  with flags. There is no such script; the pipeline is Snakemake-orchestrated,
  so the section now carries the invocation from `cosmo_inference/README.md`.
- `papers/bmodes/config/ecut_spec.md`: `workflow/config/config.yaml` and
  `workflow/rules/claims.smk` moved under `papers/bmodes/`; the catalog config
  is `cosmo_val/cat_config.yaml`, not `code/sp_validation/cosmo_val/…`.
- `papers/bmodes/scripts/update_survey_stats.py`: usage docstring still gave
  its pre-move `workflow/scripts/` path.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* papers Snakefiles: read cat_config through code/, not the deprecated pure_eb symlink

Same file as common.py's CAT_CONFIG; the symlink is slated for removal.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* papers/bmodes: read the pseudo-Cl spectrum from its SACC part

The generic pseudo_cl rule writes a SACC part, so the paper's C_ell
consumers read that instead of the FITS PSEUDO_CELL table: one shared
reader (pseudo_cl_io.load_pseudo_cl_data over sacc_io.get_pseudo_cl) for
the six scripts, and the claims.smk path helper points at the .sacc part.
The bandpower covariance still comes from pseudo_cl_cov's FITS.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HeMhD6yCyrgBz5oz87bCtR

* Drop generate_cosmocov_ini.py and the README install section

generate_cosmocov_ini.py was deleted on develop (#236) and came back with
this branch's stale-base rebuild; nothing calls it. The README "Local
Installation" section repeats docs/source/installation.rst and is not
part of the SACC migration.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HeMhD6yCyrgBz5oz87bCtR

---------

Co-authored-by: Claude Opus <noreply@anthropic.com>
---
 papers/bmodes/config/cl.md                    |   2 +-
 papers/bmodes/rules/claims.smk                |   6 +-
 .../bb_covariance_blind_independence.py       |  13 +-
 papers/bmodes/scripts/cl_data_vector.py       |  23 +-
 .../bmodes/scripts/cl_version_comparison.py   |  22 +-
 .../harmonic_config_cosebis_comparison.py     |  22 +-
 .../scripts/harmonic_space_pte_matrices.py    |  16 +-
 .../scripts/plot_cosebis_filter_overlay.py    |   8 +-
 papers/bmodes/scripts/pseudo_cl_io.py         |  19 +
 papers/bmodes/scripts/run_xi_sweep.py         |   6 +-
 papers/cosmo_val/config/config.yaml           |  10 +-
 pyproject.toml                                |   3 +-
 src/sp_validation/cosmo_val/core.py           |  86 ++--
 src/sp_validation/cosmo_val/pseudo_cl.py      | 326 +++++----------
 .../cosmo_val/psf_systematics.py              |  33 ++
 src/sp_validation/cosmo_val/real_space.py     |  85 +---
 src/sp_validation/cosmo_val/sacc_writers.py   | 237 +++++++++++
 src/sp_validation/pseudo_cl.py                |  29 ++
 src/sp_validation/tests/test_assemble_sacc.py | 300 ++++++++++++++
 .../tests/test_bmodes_workflow_dry_run.py     |  58 ++-
 src/sp_validation/tests/test_cli_seams.py     |  57 +++
 src/sp_validation/tests/test_pseudo_cl.py     |  41 +-
 src/sp_validation/tests/test_sacc_writers.py  | 389 ++++++++++++++++++
 src/sp_validation/tests/test_xi_grids.py      | 109 +++++
 workflow/common.py                            | 119 +++++-
 workflow/rules/cosmo_val.smk                  | 337 +++++++++++----
 workflow/rules/twopoint.smk                   |  46 ++-
 workflow/scripts/assemble_sacc.py             | 203 +++++++++
 workflow/scripts/cv_cosebis.py                |  73 +++-
 workflow/scripts/cv_plot_pseudo_cl.py         |  39 ++
 workflow/scripts/cv_pseudo_cl.py              |  13 -
 workflow/scripts/cv_pure_eb.py                | 119 +++++-
 workflow/scripts/cv_summarize_bmodes.py       |  90 ++--
 workflow/scripts/generate_pseudo_cl.py        |  65 ++-
 workflow/scripts/run_2pcf.py                  |  86 +++-
 workflow/scripts/run_rho_tau.py               |   5 +-
 36 files changed, 2453 insertions(+), 642 deletions(-)
 create mode 100644 papers/bmodes/scripts/pseudo_cl_io.py
 create mode 100644 src/sp_validation/cosmo_val/sacc_writers.py
 create mode 100644 src/sp_validation/tests/test_assemble_sacc.py
 create mode 100644 src/sp_validation/tests/test_cli_seams.py
 create mode 100644 src/sp_validation/tests/test_sacc_writers.py
 create mode 100644 src/sp_validation/tests/test_xi_grids.py
 create mode 100644 workflow/scripts/assemble_sacc.py
 create mode 100644 workflow/scripts/cv_plot_pseudo_cl.py
 delete mode 100644 workflow/scripts/cv_pseudo_cl.py

diff --git a/papers/bmodes/config/cl.md b/papers/bmodes/config/cl.md
index d178ddee..ad0b7d70 100644
--- a/papers/bmodes/config/cl.md
+++ b/papers/bmodes/config/cl.md
@@ -25,7 +25,7 @@ Pseudo-Cl estimation accounts for:
 ## Data Source
 
 Pseudo-Cl files generated by workflow rules using NaMaster:
-- `pseudo_cl_{version}_blind={blind}_powspace_nbins={nbins}.fits` — Power spectrum estimates
+- `pseudo_cl_{version}_blind={blind}_powspace_nbins={nbins}.sacc` — Power spectrum estimates
 - `pseudo_cl_cov_{version}_blind={blind}_powspace_nbins={nbins}.fits` — Bandpower covariance matrix
 
 Location: `{COSMO_VAL_OUTPUT}/` (defined in Snakefile, typically `/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/output/`)
diff --git a/papers/bmodes/rules/claims.smk b/papers/bmodes/rules/claims.smk
index 3846b9bc..85891b25 100644
--- a/papers/bmodes/rules/claims.smk
+++ b/papers/bmodes/rules/claims.smk
@@ -86,10 +86,10 @@ def _xi_reporting_path(version):
 
 
 def _xi_integration_path(version):
-    """Path to fine-binned 2PCF integration file."""
+    """Path to fine-binned 2PCF integration file. Unpatched: values only, no covariance."""
     return (
         f"{COSMO_VAL_OUTPUT}/{version}_xi_minsep={FIDUCIAL['min_sep_int']}"
-        f"_maxsep={FIDUCIAL['max_sep_int']}_nbins={FIDUCIAL['nbins_int']}_npatch={FIDUCIAL['npatch']}.txt"
+        f"_maxsep={FIDUCIAL['max_sep_int']}_nbins={FIDUCIAL['nbins_int']}_npatch=1.txt"
     )
 
 
@@ -121,7 +121,7 @@ def _pseudo_cl_path(version, blind="A", nbins=32):
 
     All leak-corrected versions use consistent local naming with blind and binning.
     """
-    return f"{COSMO_VAL_OUTPUT}/pseudo_cl_{version}_blind={blind}_powspace_nbins={nbins}.fits"
+    return f"{COSMO_VAL_OUTPUT}/pseudo_cl_{version}_blind={blind}_powspace_nbins={nbins}.sacc"
 
 
 def _pseudo_cl_cov_path(version, blind="A", nbins=32):
diff --git a/papers/bmodes/scripts/bb_covariance_blind_independence.py b/papers/bmodes/scripts/bb_covariance_blind_independence.py
index 6d38f750..256a79b1 100644
--- a/papers/bmodes/scripts/bb_covariance_blind_independence.py
+++ b/papers/bmodes/scripts/bb_covariance_blind_independence.py
@@ -22,6 +22,7 @@
 import yaml
 from astropy.io import fits
 from plotting_utils import PAPER_MPLSTYLE
+from pseudo_cl_io import load_pseudo_cl_data
 
 from sp_validation.b_modes import calculate_cosebis
 
@@ -436,9 +437,8 @@ def main(
             nbins_int,
         )
 
-    # Read ell bin centers from pseudo-Cl data file
-    with fits.open(pseudo_cl_path) as hdu:
-        ell_eff = hdu["PSEUDO_CELL"].data["ELL"]
+    # Read ell bin centers from the pseudo-Cl SACC part
+    ell_eff = load_pseudo_cl_data(pseudo_cl_path)["ELL"]
 
     # Compute ratios relative to blind A
     pure_eb_results = {}
@@ -666,7 +666,10 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--cosmo-val-dir",
         required=True,
-        help="COSMO_VAL output dir (pseudo_cl / pseudo_cl_cov FITS + xi_integration txt)",
+        help=(
+            "COSMO_VAL output dir (pseudo_cl SACC parts, pseudo_cl_cov FITS "
+            "+ xi_integration txt)"
+        ),
     )
     ap.add_argument(
         "--covariance-dir",
@@ -711,7 +714,7 @@ def _from_cli(argv=None):
         f"_nbins={nbins_int}_npatch={npatch}.txt",
     )
     pseudo_cl_path = os.path.join(
-        a.cosmo_val_dir, f"pseudo_cl_{version}_blind=A_powspace_nbins=32.fits"
+        a.cosmo_val_dir, f"pseudo_cl_{version}_blind=A_powspace_nbins=32.sacc"
     )
 
     out_dir = Path(a.out)
diff --git a/papers/bmodes/scripts/cl_data_vector.py b/papers/bmodes/scripts/cl_data_vector.py
index 6ee5a93f..4eeca960 100644
--- a/papers/bmodes/scripts/cl_data_vector.py
+++ b/papers/bmodes/scripts/cl_data_vector.py
@@ -26,6 +26,7 @@
     get_powspace_bin_edges,
     iter_version_figures,
 )
+from pseudo_cl_io import load_pseudo_cl_data
 
 plt.style.use(PAPER_MPLSTYLE)
 
@@ -44,10 +45,8 @@ def _compute_pte_with_cuts(data, covariance, ell, ell_min, ell_max):
 
 
 def _load_pseudo_cl_data(pseudo_cl_path, pseudo_cl_cov_path):
-    """Load pseudo-Cl data and covariance from FITS files."""
-    hdu = fits.open(pseudo_cl_path)
-    data = hdu["PSEUDO_CELL"].data
-    hdu.close()
+    """Load pseudo-Cl data from SACC and covariance from FITS."""
+    data = load_pseudo_cl_data(pseudo_cl_path)
 
     ell = data["ELL"]
     cl_eb = data["EB"]
@@ -163,7 +162,7 @@ def _create_cl_figure(
 # so the version sweep is self-contained (lc produces only the fiducial version).
 # ---------------------------------------------------------------------------
 def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
-    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.fits"
+    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.sacc"
 
 
 def _pseudo_cl_cov(results_dir, ver, blind="A", nbins=32):
@@ -184,10 +183,9 @@ def _resolve_pseudo_cl_paths(
     """Resolve the pseudo-Cl and covariance paths for one version in the sweep.
 
     Every version is read from the reconstructed COSMO_VAL pattern, except the
-    fiducial version when explicit override paths are supplied: the lc-produced
-    fiducial FITS files are named `pseudo_cl_{version}.fits` /
-    `pseudo_cl_cov_{version}.fits` (no blind=/nbins= tokens), so pattern
-    reconstruction can't find them and an explicit path is required instead.
+    fiducial version when explicit override paths are supplied: lc-produced
+    fiducial SACC spectrum and FITS covariance files have untagged names, so
+    pattern reconstruction can't find them and explicit paths are required.
     """
     if (
         ver == fiducial_version
@@ -333,7 +331,10 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--results-dir",
         required=True,
-        help="COSMO_VAL output dir with per-version pseudo_cl_* / pseudo_cl_cov_* FITS",
+        help=(
+            "COSMO_VAL output dir with per-version pseudo_cl_*.sacc and "
+            "pseudo_cl_cov_*.fits"
+        ),
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
@@ -349,7 +350,7 @@ def _from_cli(argv=None):
         "--fiducial-pseudo-cl-path",
         default=None,
         help=(
-            "Explicit path to the fiducial version's pseudo-Cl FITS (lc output). "
+            "Explicit path to the fiducial version's pseudo-Cl SACC part (lc output). "
             "Requires --fiducial-version and --fiducial-pseudo-cl-cov-path."
         ),
     )
diff --git a/papers/bmodes/scripts/cl_version_comparison.py b/papers/bmodes/scripts/cl_version_comparison.py
index cd283f2a..f70a6977 100644
--- a/papers/bmodes/scripts/cl_version_comparison.py
+++ b/papers/bmodes/scripts/cl_version_comparison.py
@@ -29,12 +29,13 @@
     get_version_alpha,
     version_label,
 )
+from pseudo_cl_io import load_pseudo_cl_data
 
 plt.style.use(PAPER_MPLSTYLE)
 
 
 def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
-    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.fits"
+    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.sacc"
 
 
 def _pseudo_cl_cov(results_dir, ver, blind="A", nbins=32):
@@ -55,10 +56,9 @@ def _resolve_pseudo_cl_paths(
     """Resolve the pseudo-Cl and covariance paths for one version in the sweep.
 
     Every version is read from the reconstructed COSMO_VAL pattern, except the
-    fiducial version when explicit override paths are supplied: the lc-produced
-    fiducial FITS files are named `pseudo_cl_{version}.fits` /
-    `pseudo_cl_cov_{version}.fits` (no blind=/nbins= tokens), so pattern
-    reconstruction can't find them and an explicit path is required instead.
+    fiducial version when explicit override paths are supplied: lc-produced
+    fiducial SACC spectrum and FITS covariance files have untagged names, so
+    pattern reconstruction can't find them and explicit paths are required.
     """
     if (
         ver == fiducial_version
@@ -131,10 +131,7 @@ def main(
             fiducial_pseudo_cl_cov_path,
         )
 
-        hdu = fits.open(pseudo_cl_path)
-        data = hdu["PSEUDO_CELL"].data
-        hdu.close()
-
+        data = load_pseudo_cl_data(pseudo_cl_path)
         ell = data["ELL"]
         cl_bb = data["BB"]
         cl_eb = data["EB"]
@@ -379,7 +376,10 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--results-dir",
         required=True,
-        help="COSMO_VAL output dir with per-version pseudo_cl_* / pseudo_cl_cov_* FITS",
+        help=(
+            "COSMO_VAL output dir with per-version pseudo_cl_*.sacc and "
+            "pseudo_cl_cov_*.fits"
+        ),
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
@@ -395,7 +395,7 @@ def _from_cli(argv=None):
         "--fiducial-pseudo-cl-path",
         default=None,
         help=(
-            "Explicit path to the fiducial version's pseudo-Cl FITS (lc output). "
+            "Explicit path to the fiducial version's pseudo-Cl SACC part (lc output). "
             "Requires --fiducial-version and --fiducial-pseudo-cl-cov-path."
         ),
     )
diff --git a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
index f8025a0e..dd34db22 100644
--- a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
+++ b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
@@ -32,6 +32,7 @@
     iter_version_figures,
     version_label,
 )
+from pseudo_cl_io import load_pseudo_cl_data
 
 from sp_validation.b_modes import calculate_cosebis
 
@@ -107,11 +108,10 @@ def _compute_harmonic_cosebis(
     pseudo_cl_path, pseudo_cov_path, nmodes, theta_min, theta_max
 ):
     """Compute COSEBIS from pseudo-C_ell and propagate covariance."""
-    with fits.open(pseudo_cl_path) as hdul:
-        data = hdul["PSEUDO_CELL"].data
-        ell = np.asarray(data["ELL"], dtype=float)
-        cl_ee = np.asarray(data["EE"], dtype=float)
-        cl_bb = np.asarray(data["BB"], dtype=float)
+    data = load_pseudo_cl_data(pseudo_cl_path)
+    ell = np.asarray(data["ELL"], dtype=float)
+    cl_ee = np.asarray(data["EE"], dtype=float)
+    cl_bb = np.asarray(data["BB"], dtype=float)
 
     cosebis_obj = COSEBIS(theta_min, theta_max, nmodes)
 
@@ -857,7 +857,10 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--cosmo-val-dir",
         required=True,
-        help="COSMO_VAL output dir (pseudo_cl / pseudo_cl_cov FITS + xi_integration txt)",
+        help=(
+            "COSMO_VAL output dir (pseudo_cl SACC parts, pseudo_cl_cov FITS "
+            "+ xi_integration txt)"
+        ),
     )
     ap.add_argument(
         "--covariance-dir",
@@ -883,8 +886,9 @@ def _from_cli(argv=None):
         "--fiducial-pseudo-cl-path",
         default=None,
         help=(
-            "Explicit path to the fiducial 96-bin pseudo-Cl FITS reproduced by lc "
-            "(from lc's cl_bandpowers_fine; e.g. pseudo_cl_SP_v1.4.6.3_leak_corr.fits), "
+            "Explicit path to the fiducial 96-bin pseudo-Cl SACC part reproduced "
+            "by lc (from lc's cl_bandpowers_fine; e.g. "
+            "pseudo_cl_SP_v1.4.6.3_leak_corr.sacc), "
             "overriding the --cosmo-val-dir pattern lookup for --fiducial-version."
         ),
     )
@@ -934,7 +938,7 @@ def _from_cli(argv=None):
             if is_fiducial and a.fiducial_pseudo_cl_path
             else os.path.join(
                 a.cosmo_val_dir,
-                f"pseudo_cl_{ver}_blind={a.blind}_powspace_nbins={cosebis_nbins}.fits",
+                f"pseudo_cl_{ver}_blind={a.blind}_powspace_nbins={cosebis_nbins}.sacc",
             )
         )
         # 96-bin pseudo-Cl covariance is intentionally NOT lc-repointed: lc did not
diff --git a/papers/bmodes/scripts/harmonic_space_pte_matrices.py b/papers/bmodes/scripts/harmonic_space_pte_matrices.py
index f52e1bc8..aeb031c6 100644
--- a/papers/bmodes/scripts/harmonic_space_pte_matrices.py
+++ b/papers/bmodes/scripts/harmonic_space_pte_matrices.py
@@ -26,12 +26,13 @@
     make_pte_colormap,
     make_pte_norm,
 )
+from pseudo_cl_io import load_pseudo_cl_data
 
 plt.style.use(PAPER_MPLSTYLE)
 
 
 def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
-    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.fits"
+    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.sacc"
 
 
 def _pseudo_cl_cov(results_dir, ver, blind="A", nbins=32):
@@ -48,7 +49,7 @@ def compute_pte_matrix(
     Parameters
     ----------
     pseudo_cl_path : str
-        Path to pseudo-Cl FITS file.
+        Path to pseudo-Cl SACC part.
     pseudo_cl_cov_path : str
         Path to pseudo-Cl covariance FITS file.
     fiducial_ell_min : float, optional
@@ -66,10 +67,7 @@ def compute_pte_matrix(
         Summary statistics.
     """
     # Load pseudo-Cl data
-    hdu = fits.open(pseudo_cl_path)
-    data = hdu["PSEUDO_CELL"].data
-    hdu.close()
-
+    data = load_pseudo_cl_data(pseudo_cl_path)
     ell = data["ELL"]
     cl_bb = data["BB"]
     n_ell = len(ell)
@@ -533,7 +531,9 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--results-dir",
         required=True,
-        help="COSMO_VAL output dir with per-version pseudo_cl_* / pseudo_cl_cov_* FITS",
+        help=(
+            "COSMO_VAL output dir with pseudo_cl_*.sacc parts and pseudo_cl_cov_*.fits"
+        ),
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
@@ -544,7 +544,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--fiducial-pseudo-cl-path",
         default=None,
-        help="Explicit path to fiducial pseudo-Cl FITS produced by lc "
+        help="Explicit path to fiducial pseudo-Cl SACC part produced by lc "
         "(overrides pattern reconstruction for --fiducial-version)",
     )
     ap.add_argument(
diff --git a/papers/bmodes/scripts/plot_cosebis_filter_overlay.py b/papers/bmodes/scripts/plot_cosebis_filter_overlay.py
index 8de785b3..68b255ab 100644
--- a/papers/bmodes/scripts/plot_cosebis_filter_overlay.py
+++ b/papers/bmodes/scripts/plot_cosebis_filter_overlay.py
@@ -21,16 +21,16 @@
 from astropy.io import fits
 from cosmo_numba.B_modes.cosebis import COSEBIS
 from plotting_utils import PAPER_MPLSTYLE
+from pseudo_cl_io import load_pseudo_cl_data
 
 plt.style.use(PAPER_MPLSTYLE)
 
 
 def load_bb_data(pseudo_cl_path, pseudo_cov_path):
     """Load BB bandpower data and errorbars."""
-    with fits.open(pseudo_cl_path) as hdul:
-        data = hdul["PSEUDO_CELL"].data
-        ell = np.asarray(data["ELL"], dtype=float)
-        bb = np.asarray(data["BB"], dtype=float)
+    data = load_pseudo_cl_data(pseudo_cl_path)
+    ell = np.asarray(data["ELL"], dtype=float)
+    bb = np.asarray(data["BB"], dtype=float)
 
     with fits.open(pseudo_cov_path) as hdul:
         cov_bb = hdul["COVAR_BB_BB"].data
diff --git a/papers/bmodes/scripts/pseudo_cl_io.py b/papers/bmodes/scripts/pseudo_cl_io.py
new file mode 100644
index 00000000..b7b1c796
--- /dev/null
+++ b/papers/bmodes/scripts/pseudo_cl_io.py
@@ -0,0 +1,19 @@
+"""Read pseudo-Cℓ parts for the B-modes paper workflow."""
+
+from sp_validation import sacc_io
+
+
+def load_pseudo_cl_data(path):
+    """Return a pseudo-Cℓ part's spectra as ``{"ELL", "EE", "EB", "BB"}`` arrays.
+
+    The bandpower covariance is a separate FITS product (``pseudo_cl_cov``).
+    Loading goes through ``sacc_io.load``, so an unblinded real-data part is
+    refused.
+    """
+    ell, ee, bb, eb, _window = sacc_io.get_pseudo_cl(sacc_io.load(str(path)), (0, 0))
+    missing = [name for name, values in (("BB", bb), ("EB", eb)) if values is None]
+    if missing:
+        raise ValueError(
+            f"{path} lacks required pseudo-Cℓ spectra: {', '.join(missing)}"
+        )
+    return {"ELL": ell, "EE": ee, "EB": eb, "BB": bb}
diff --git a/papers/bmodes/scripts/run_xi_sweep.py b/papers/bmodes/scripts/run_xi_sweep.py
index dd7219a3..211214e3 100644
--- a/papers/bmodes/scripts/run_xi_sweep.py
+++ b/papers/bmodes/scripts/run_xi_sweep.py
@@ -2,8 +2,8 @@
 
 Loops the [non-fiducial version list](sweep_versions.nonfiducial_versions) and
 runs the same ``run_2pcf.run_2pcf`` compute the fiducial two_point recipes call,
-once per version, writing every version's ξ± text dump (+ ξ+/ξ- FITS) into one
-lc ``{output}`` dir under run_2pcf's native, already-canonical name
+once per version, writing every version's ξ± text dump into one lc ``{output}``
+dir under run_2pcf's native, already-canonical name
 ``{ver}_xi_minsep={min}_maxsep={max}_nbins={nbins}_npatch={npatch}.txt`` — the
 exact pattern ``cosebis_version_comparison._xi_integration`` reconstructs.
 
@@ -76,7 +76,7 @@ def _from_cli(argv=None):
                 ver=ver,
                 cat_config=a.cat_config,
                 output_dir=a.out,
-                save_fits=True,
+                grid=grid,
                 **GRIDS[grid],
             )
 
diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index a0323b7c..ae99aba4 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -74,11 +74,11 @@ cosmo_val:
         kmax: 20
         kmax_extrapolate: 500
 
-  # Pure E/B-mode decomposition (config space)
-  pure_eb:
-    min_sep_int: 0.08
-    max_sep_int: 300
-    nbins_int: 1000
+  # The fine ξ± grid the B-mode integrals run over.
+  integration:
+    min_sep: 0.08
+    max_sep: 300
+    nbins: 1000
 
   # COSEBIs decomposition (config space, fine integration binning)
   cosebis:
diff --git a/pyproject.toml b/pyproject.toml
index 4ad6fbf0..632793f4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -79,7 +79,8 @@ dependencies = [
     "pymaster",
     "regions",
     "reproject",
-    "sacc>=0.12",
+    # Floor matches the resolved lock; needs sacc's 2.x rewrite (BlockDiagonalCovariance).
+    "sacc>=2.4,<3",
     # scipy 1.18 ported FITPACK from Fortran to C, changing the return shape of
     # RectBivariateSpline(scalar, scalar, grid=False) from 0-d `array(x)` to
     # shape-(1,) `array([x])`. camb's BBN Y_He predictor (bbn.py) wraps the
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 33113370..7da61279 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -15,6 +15,7 @@
     find_conservative_scale_cut_key,
 )
 from ..statistics import chi2_and_pte
+from ..version import __version__
 from .catalog_characterization import CatalogCharacterizationMixin
 from .cosebis import CosebisMixin
 from .pseudo_cl import PseudoClMixin
@@ -22,8 +23,42 @@
 from .pure_eb import PureEBMixin
 from .real_space import RealSpaceMixin
 
-
 # %%
+BMODE_COLUMNS = {
+    "xip_B": r"xi+B",
+    "xim_B": r"xi-B",
+    "combined": "Combined",
+    "COSEBIS": "COSEBIS",
+    "C_l_BB": "C_l^BB",
+}
+
+
+def print_bmode_summary(summary, fiducial_scale_cut, cov_methods=()):
+    """Print the B-mode PTE table for ``{version: {statistic: pte}}``.
+
+    Statistics absent from a row print as ``--``.
+    """
+    sc_label = f"[{fiducial_scale_cut[0]}-{fiducial_scale_cut[1]} arcmin]"
+    sep = "\u2500" * 70
+    header = f"{'Version':<28s}" + "".join(
+        f"{label:>10s}" for label in BMODE_COLUMNS.values()
+    )
+
+    print(f"\nB-mode summary {sc_label}")
+    print(sep)
+    print(header)
+    print(sep)
+    for ver, row in summary.items():
+        cells = "".join(
+            f"{row[s]:>10.4f}" if s in row else f"{'--':>10s}" for s in BMODE_COLUMNS
+        )
+        print(f"{ver:<28s}{cells}")
+    print(sep)
+    if cov_methods:
+        print(f"Covariance: {', '.join(sorted(cov_methods))}")
+    print()
+
+
 class CosmologyValidation(
     CosebisMixin,
     PureEBMixin,
@@ -380,6 +415,21 @@ def _output_path(self, *parts):
         """
         return os.path.abspath(os.path.join(self.cc["paths"]["output"], *parts))
 
+    def sacc_nz(self, version):
+        """Single-bin ``nz`` mapping ``{0: (z, nz)}`` for the SACC writers.
+
+        The round is single-bin, so the whole survey n(z) is bin 0.
+        """
+        return {0: tuple(self.get_redshift(version))}
+
+    def sacc_metadata(self, version):
+        """Provenance metadata stored on every SACC part for ``version``."""
+        return {
+            "catalogue_version": version,
+            "sp_validation_version": __version__,
+            "npatch": self.npatch,
+        }
+
     def get_redshift(self, version):
         """Load redshift distribution for a catalog version.
 
@@ -602,37 +652,5 @@ def summarize_bmodes(self, fiducial_scale_cut=(12, 83), versions=None):
 
             summary[ver] = row
 
-        # Print summary table
-        col_labels = {
-            "xip_B": r"xi+B",
-            "xim_B": r"xi-B",
-            "combined": "Combined",
-            "COSEBIS": "COSEBIS",
-            "C_l_BB": "C_l^BB",
-        }
-        stats_order = list(col_labels)
-
-        sc_label = f"[{fiducial_scale_cut[0]}-{fiducial_scale_cut[1]} arcmin]"
-        sep = "\u2500" * 70
-        header = f"{'Version':<28s}" + "".join(
-            f"{label:>10s}" for label in col_labels.values()
-        )
-
-        print(f"\nB-mode summary {sc_label}")
-        print(sep)
-        print(header)
-        print(sep)
-
-        for ver in versions:
-            row = summary[ver]
-            cells = "".join(
-                f"{row[s]:>10.4f}" if s in row else f"{'--':>10s}" for s in stats_order
-            )
-            print(f"{ver:<28s}{cells}")
-
-        print(sep)
-        if cov_methods:
-            print(f"Covariance: {', '.join(sorted(cov_methods))}")
-        print()
-
+        print_bmode_summary(summary, fiducial_scale_cut, cov_methods)
         return summary
diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index 514049a6..cf74efc6 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -17,6 +17,7 @@
 from astropy.io import fits
 from cs_util.cosmo import get_theo_c_ell
 
+from .. import sacc_io
 from ..pseudo_cl import (
     apply_random_rotation,
     get_n_gal_map,
@@ -26,6 +27,47 @@
 )
 from ..rho_tau import get_params_rho_tau
 from ..statistics import chi2_and_pte, cov_from_one_covariance
+from .sacc_writers import BIN as SACC_BIN
+from .sacc_writers import pseudo_cl_to_sacc
+
+
+def plot_pseudo_cl_spectrum(datasets, spectrum, output_path):
+    """Two-panel ℓC_ℓ / C_ℓ figure for one spectrum across catalogue versions.
+
+    ``datasets`` maps a version to ``{"ell", "cl", "cov", "style"}``, where
+    ``style`` carries the ``marker`` and ``colour`` the version is drawn with.
+    """
+    fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))
+    minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
+
+    for panel, scaled in ((ax[0], True), (ax[1], False)):
+        for version, data in datasets.items():
+            ell, cl = np.asarray(data["ell"]), np.asarray(data["cl"])
+            err = np.sqrt(np.diag(np.asarray(data["cov"])))
+            style = data.get("style", {})
+            panel.errorbar(
+                ell,
+                ell * cl if scaled else cl,
+                yerr=ell * err if scaled else err,
+                fmt=style.get("marker", "."),
+                color=style.get("colour"),
+                label=f"{version} {spectrum}",
+                capsize=2 if scaled else None,
+            )
+        panel.set_ylabel(r"$\ell C_\ell$" if scaled else r"$C_\ell$")
+        panel.set_xlim(ell.min() - 10, ell.max() + 100)
+        panel.set_xscale("squareroot")
+        panel.set_xticks(np.array([100, 400, 900, 1600]))
+        panel.minorticks_on()
+        panel.tick_params(axis="x", which="minor", length=2, width=0.8)
+        panel.xaxis.set_ticks(minor_ticks, minor=True)
+
+    ax[1].set_xlabel(r"$\ell$")
+    ax[1].set_yscale("log")
+    plt.suptitle(f"Pseudo-Cl {spectrum} (Gaussian covariance)")
+    plt.legend()
+    plt.savefig(output_path)
+    plt.close(fig)
 
 
 class PseudoClMixin:
@@ -451,14 +493,23 @@ def calculate_pseudo_cl_g_ng_cov(self, gaussian_part="iNKA"):
             f"Done Gaussian and Non-Gaussian covariance of the Pseudo-Cl's using {gaussian_part} for the Gaussian part"
         )
 
-    def calculate_pseudo_cl(self):
+    def calculate_pseudo_cl(self, out_path=None):
         """
         Compute the pseudo-Cl of given catalogs.
+
+        ``out_path`` is the exact destination the part is born at (one version
+        only); ``None`` defaults each part to ``pseudo_cl_{ver}.sacc``.
         """
         self.print_start("Computing pseudo-Cl's")
 
         nside = self.nside
 
+        if out_path is not None and len(self.versions) != 1:
+            raise ValueError(
+                "calculate_pseudo_cl(out_path=...) writes one part to one path, "
+                f"but {len(self.versions)} versions are configured; call per version"
+            )
+
         try:
             self._pseudo_cls
         except AttributeError:
@@ -468,20 +519,32 @@ def calculate_pseudo_cl(self):
 
             self._pseudo_cls[ver] = {}
 
-            out_path = self._output_path(f"pseudo_cl_{ver}.fits")
-            if os.path.exists(out_path):
-                self.print_done(f"Skipping Pseudo-Cl's calculation, {out_path} exists")
-                cl_shear = fits.getdata(out_path)
-                self._pseudo_cls[ver]["pseudo_cl"] = cl_shear
+            ver_out_path = out_path or self._output_path(f"pseudo_cl_{ver}.sacc")
+            if os.path.exists(ver_out_path):
+                self.print_done(
+                    f"Skipping Pseudo-Cl's calculation, {ver_out_path} exists"
+                )
+                self._pseudo_cls[ver]["pseudo_cl"] = self._load_pseudo_cl_sacc(
+                    ver_out_path
+                )
             elif self.cell_method == "map":
-                self.calculate_pseudo_cl_map(ver, nside, out_path)
+                self.calculate_pseudo_cl_map(ver, nside, ver_out_path)
             elif self.cell_method == "catalog":
-                self.calculate_pseudo_cl_catalog(ver, out_path)
+                self.calculate_pseudo_cl_catalog(ver, ver_out_path)
             else:
                 raise ValueError(f"Unknown cell method: {self.cell_method}")
 
         self.print_done("Done pseudo-Cl's")
 
+    @staticmethod
+    def _load_pseudo_cl_sacc(out_path):
+        """Read a pseudo-Cl SACC part into the ELL/EE/EB/BB dict."""
+        # Readback of a part this producer just wrote — a legitimate pre-blind
+        # consumer, so the fail-closed load is opted out of.
+        s = sacc_io.load(out_path, allow_unblinded=True)
+        ell, ee, bb, eb, _window = sacc_io.get_pseudo_cl(s, SACC_BIN)
+        return {"ELL": ell, "EE": ee, "EB": eb, "BB": bb}
+
     def calculate_pseudo_cl_map(self, ver, nside, out_path):
         params = get_params_rho_tau(self.cc[ver], survey=ver)
 
@@ -547,10 +610,9 @@ def calculate_pseudo_cl_map(self, ver, nside, out_path):
         cl_shear = cl_shear - cl_noise
 
         self.print_cyan("Saving pseudo-Cl's...")
-        self.save_pseudo_cl(ell_eff, cl_shear, out_path)
+        self.pseudo_cl_to_sacc_part(ver, out_path, ell_eff, cl_shear, wsp)
 
-        cl_shear = fits.getdata(out_path)
-        self._pseudo_cls[ver]["pseudo_cl"] = cl_shear
+        self._pseudo_cls[ver]["pseudo_cl"] = self._load_pseudo_cl_sacc(out_path)
 
     def calculate_pseudo_cl_catalog(self, ver, out_path):
         params = get_params_rho_tau(self.cc[ver], survey=ver)
@@ -563,10 +625,9 @@ def calculate_pseudo_cl_catalog(self, ver, out_path):
         )
 
         self.print_cyan("Saving pseudo-Cl's...")
-        self.save_pseudo_cl(ell_eff, cl_shear, out_path)
+        self.pseudo_cl_to_sacc_part(ver, out_path, ell_eff, cl_shear, wsp)
 
-        cl_shear = fits.getdata(out_path)
-        self._pseudo_cls[ver]["pseudo_cl"] = cl_shear
+        self._pseudo_cls[ver]["pseudo_cl"] = self._load_pseudo_cl_sacc(out_path)
 
     def get_n_gal_map(self, params, nside, cat_gal):
         """Weighted galaxy number-density map (thin wrapper -> primitive)."""
@@ -655,223 +716,50 @@ def apply_random_rotation(self, e1, e2, rng=None):
         """
         return apply_random_rotation(e1, e2, rng)
 
-    def save_pseudo_cl(self, ell_eff, pseudo_cl, out_path):
-        """
-        Save pseudo-Cl's to a FITS file.
+    def pseudo_cl_to_sacc_part(self, version, out_path, ell_eff, cl_all, wsp):
+        """Write the pseudo-Cl SACC part (EE/BB/EB + shared bandpower window).
 
-        Parameters
-        ----------
-        pseudo_cl : np.array
-            Pseudo-Cl's to save.
-        out_path : str
-            Path to save the pseudo-Cl's to.
+        ``cl_all`` is NaMaster's decoupled ``(4, nbp)`` array (EE, EB, BE, BB);
+        the writer takes the shared bandpower window from ``wsp``. No covariance
+        is attached here.
         """
-        # Create columns of the fits file
-        col1 = fits.Column(name="ELL", format="D", array=ell_eff)
-        col2 = fits.Column(name="EE", format="D", array=pseudo_cl[0])
-        col3 = fits.Column(name="EB", format="D", array=pseudo_cl[1])
-        col4 = fits.Column(name="BB", format="D", array=pseudo_cl[3])
-        coldefs = fits.ColDefs([col1, col2, col3, col4])
-        cell_hdu = fits.BinTableHDU.from_columns(coldefs, name="PSEUDO_CELL")
-
-        cell_hdu.writeto(out_path, overwrite=True)
+        s = pseudo_cl_to_sacc(
+            self.sacc_nz(version),
+            self.sacc_metadata(version),
+            ell_eff,
+            cl_all,
+            wsp,
+        )
+        sacc_io.save(s, out_path, type="data")
 
     def plot_pseudo_cl(self):
-        """
-        Plot pseudo-Cl's for given catalogs.
-        """
+        """Plot the EE/EB/BB pseudo-Cl spectra for every version."""
         self.print_cyan("Plotting pseudo-Cl's")
 
-        # Plotting EE
-        out_path = self._output_path("cell_ee.png")
-        fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))
-
-        for ver in self.versions:
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            cov = self.pseudo_cls[ver]["cov"]["COVAR_EE_EE"].data
-            ax[0].errorbar(
-                ell,
-                ell * self.pseudo_cls[ver]["pseudo_cl"]["EE"],
-                yerr=ell * np.sqrt(np.diag(cov)),
-                fmt=self.cc[ver]["marker"],
-                label=ver + " EE",
-                color=self.cc[ver]["colour"],
-                capsize=2,
-            )
-
-        ax[0].set_ylabel(r"$\ell C_\ell$")
-
-        ax[0].set_xlim(ell.min() - 10, ell.max() + 100)
-        ax[0].set_xscale("squareroot")
-        ax[0].set_xticks(np.array([100, 400, 900, 1600]))
-        ax[0].minorticks_on()
-        ax[0].tick_params(axis="x", which="minor", length=2, width=0.8)
-        minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-        ax[0].xaxis.set_ticks(minor_ticks, minor=True)
-
-        for ver in self.versions:
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            cov = self.pseudo_cls[ver]["cov"]["COVAR_EE_EE"].data
-            ax[1].errorbar(
-                ell,
-                self.pseudo_cls[ver]["pseudo_cl"]["EE"],
-                yerr=np.sqrt(np.diag(cov)),
-                fmt=self.cc[ver]["marker"],
-                label=ver + " EE",
-                color=self.cc[ver]["colour"],
-            )
-
-        ax[1].set_xlabel(r"$\ell$")
-        ax[1].set_ylabel(r"$C_\ell$")
-
-        ax[1].set_xlim(ell.min() - 10, ell.max() + 100)
-        ax[1].set_xscale("squareroot")
-        ax[1].set_yscale("log")
-        ax[1].set_xticks(np.array([100, 400, 900, 1600]))
-        ax[1].minorticks_on()
-        ax[1].tick_params(axis="x", which="minor", length=2, width=0.8)
-        minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-        ax[1].xaxis.set_ticks(minor_ticks, minor=True)
-
-        plt.suptitle("Pseudo-Cl EE (Gaussian covariance)")
-        plt.legend()
-        plt.savefig(out_path)
-
-        # Plotting EB
-        out_path = self._output_path("cell_eb.png")
-
-        fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))
-
-        for ver in self.versions:
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            cov = self.pseudo_cls[ver]["cov"]["COVAR_EB_EB"].data
-            ax[0].errorbar(
-                ell,
-                ell * self.pseudo_cls[ver]["pseudo_cl"]["EB"],
-                yerr=ell * np.sqrt(np.diag(cov)),
-                fmt=self.cc[ver]["marker"],
-                label=ver + " EB",
-                color=self.cc[ver]["colour"],
-                capsize=2,
+        for spectrum in ("EE", "EB", "BB"):
+            datasets = {
+                ver: {
+                    "ell": self.pseudo_cls[ver]["pseudo_cl"]["ELL"],
+                    "cl": self.pseudo_cls[ver]["pseudo_cl"][spectrum],
+                    "cov": self.pseudo_cls[ver]["cov"][
+                        f"COVAR_{spectrum}_{spectrum}"
+                    ].data,
+                    "style": {
+                        "marker": self.cc[ver]["marker"],
+                        "colour": self.cc[ver]["colour"],
+                    },
+                }
+                for ver in self.versions
+            }
+            plot_pseudo_cl_spectrum(
+                datasets, spectrum, self._output_path(f"cell_{spectrum.lower()}.png")
             )
 
-        ax[0].axhline(0, color="black", linestyle="--")
-        ax[0].set_ylabel(r"$\ell C_\ell$")
-
-        ax[0].set_xlim(ell.min() - 10, ell.max() + 100)
-        ax[0].set_xscale("squareroot")
-        ax[0].set_xticks(np.array([100, 400, 900, 1600]))
-        ax[0].minorticks_on()
-        ax[0].tick_params(axis="x", which="minor", length=2, width=0.8)
-        minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-        ax[0].xaxis.set_ticks(minor_ticks, minor=True)
-
-        for ver in self.versions:
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            cov = self.pseudo_cls[ver]["cov"]["COVAR_EB_EB"].data
-            ax[1].errorbar(
-                ell,
-                self.pseudo_cls[ver]["pseudo_cl"]["EB"],
-                yerr=np.sqrt(np.diag(cov)),
-                fmt=self.cc[ver]["marker"],
-                label=ver + " EB",
-                color=self.cc[ver]["colour"],
-            )
-
-        ax[1].set_xlabel(r"$\ell$")
-        ax[1].set_ylabel(r"$C_\ell$")
-
-        ax[1].set_xlim(ell.min() - 10, ell.max() + 100)
-        ax[1].set_xscale("squareroot")
-        ax[1].set_yscale("log")
-        ax[1].set_xticks(np.array([100, 400, 900, 1600]))
-        ax[1].minorticks_on()
-        ax[1].tick_params(axis="x", which="minor", length=2, width=0.8)
-        minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-        ax[1].xaxis.set_ticks(minor_ticks, minor=True)
-
-        plt.suptitle("Pseudo-Cl EB (Gaussian covariance)")
-        plt.legend()
-        plt.savefig(out_path)
-
-        # Plotting BB
-        out_path = self._output_path("cell_bb.png")
-
-        fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))
-
-        for ver in self.versions:
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            cov = self.pseudo_cls[ver]["cov"]["COVAR_BB_BB"].data
-            ax[0].errorbar(
-                ell,
-                ell * self.pseudo_cls[ver]["pseudo_cl"]["BB"],
-                yerr=ell * np.sqrt(np.diag(cov)),
-                fmt=self.cc[ver]["marker"],
-                label=ver + " BB",
-                color=self.cc[ver]["colour"],
-                capsize=2,
-            )
-
-        ax[0].axhline(0, color="black", linestyle="--")
-        ax[0].set_ylabel(r"$\ell C_\ell$")
-
-        ax[0].set_xlim(ell.min() - 10, ell.max() + 100)
-        ax[0].set_xscale("squareroot")
-        ax[0].set_xticks(np.array([100, 400, 900, 1600]))
-        ax[0].minorticks_on()
-        ax[0].tick_params(axis="x", which="minor", length=2, width=0.8)
-        minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-        ax[0].xaxis.set_ticks(minor_ticks, minor=True)
-
-        for ver in self.versions:
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            cov = self.pseudo_cls[ver]["cov"]["COVAR_BB_BB"].data
-            ax[1].errorbar(
-                ell,
-                self.pseudo_cls[ver]["pseudo_cl"]["BB"],
-                yerr=np.sqrt(np.diag(cov)),
-                fmt=self.cc[ver]["marker"],
-                label=ver + " BB",
-                color=self.cc[ver]["colour"],
-            )
-
-        ax[1].set_xlabel(r"$\ell$")
-        ax[1].set_ylabel(r"$C_\ell$")
-
-        ax[1].set_xlim(ell.min() - 10, ell.max() + 100)
-        ax[1].set_xscale("squareroot")
-        ax[1].set_yscale("log")
-        ax[1].set_xticks(np.array([100, 400, 900, 1600]))
-        ax[1].minorticks_on()
-        ax[1].tick_params(axis="x", which="minor", length=2, width=0.8)
-        minor_ticks = [i * 10 for i in range(1, 10)] + [i * 100 for i in range(1, 21)]
-        ax[1].xaxis.set_ticks(minor_ticks, minor=True)
-
-        plt.suptitle("Pseudo-Cl BB (Gaussian covariance)")
-        plt.legend()
-        plt.savefig(out_path)
-
-        # Print C_l^BB PTE for each version and save BB data
-        print("\nC_l^BB PTE summary:")
         for ver in self.versions:
             cl_bb = self.pseudo_cls[ver]["pseudo_cl"]["BB"]
             cov_bb = self.pseudo_cls[ver]["cov"]["COVAR_BB_BB"].data
             chi2_bb, _, pte_bb = chi2_and_pte(cl_bb, cov_bb)
-            chi2_bb = float(chi2_bb)
             print(
                 f"  {ver}: C_l^BB PTE = {pte_bb:.4f} "
-                f"(chi2/dof = {chi2_bb:.1f}/{len(cl_bb)})"
-            )
-
-            # Save BB data + covariance to .npz
-            ell = self.pseudo_cls[ver]["pseudo_cl"]["ELL"]
-            bb_out = self._output_path(f"{ver}_cell_bb_data.npz")
-            np.savez(
-                bb_out,
-                ell=ell,
-                cl_bb=cl_bb,
-                cov_bb=cov_bb,
-                chi2_bb=np.array(chi2_bb),
-                pte_bb=np.array(pte_bb),
+                f"(chi2/dof = {float(chi2_bb):.1f}/{len(cl_bb)})"
             )
-            print(f"  Saved BB data to {bb_out}")
diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index 98527490..f8002244 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -16,10 +16,12 @@
 from shear_psf_leakage.rho_tau_stat import PSFErrorFit
 from uncertainties import ufloat
 
+from .. import sacc_io
 from ..rho_tau import (
     get_rho_tau_w_cov,
     get_samples,
 )
+from .sacc_writers import rho_tau_to_sacc
 
 
 class PSFSystematicsMixin:
@@ -41,11 +43,42 @@ def calculate_rho_tau_stats(self):
                 cov_rho=self.compute_cov_rho,
                 npatch=self.npatch,
             )
+            self.rho_tau_to_sacc_part(
+                ver, out_dir, base, rho_stat_handler, tau_stat_handler
+            )
         self.print_done("Rho stats finished")
 
         self._rho_stat_handler = rho_stat_handler
         self._tau_stat_handler = tau_stat_handler
 
+    def rho_tau_to_sacc_part(
+        self, version, out_dir, base, rho_stat_handler, tau_stat_handler
+    ):
+        """Write the ρ/τ SACC part for one version.
+
+        ρ_0…ρ_5 autos and τ_0/τ_2/τ_5 leakage from the handler tables. The
+        ``CovTauTh`` theory covariance ``cov_tau_{base}_th.npy`` is passed as
+        ``tau_cov_th`` when it exists; without it the τ block falls back —
+        loudly — to a variance diagonal.
+        """
+        tau_cov_path = os.path.join(out_dir, f"cov_tau_{base}_th.npy")
+        tau_cov_th = np.load(tau_cov_path) if os.path.exists(tau_cov_path) else None
+        if tau_cov_th is None:
+            self.print_magenta(
+                f"No τ theory covariance at {tau_cov_path}; writing ρ/τ SACC part "
+                "with a diagonal placeholder covariance (τ inference block is a "
+                "variance diagonal, not CovTauTh)."
+            )
+        s = rho_tau_to_sacc(
+            self.sacc_nz(version),
+            self.sacc_metadata(version),
+            rho_stat_handler.rho_stats,
+            tau_stat_handler.tau_stats,
+            tau_cov_th=tau_cov_th,
+        )
+        out_path = os.path.join(out_dir, f"rho_tau_{base}.sacc")
+        sacc_io.save(s, out_path, type="data")
+
     @property
     def rho_stat_handler(self):
         if not hasattr(self, "_rho_stat_handler"):
diff --git a/src/sp_validation/cosmo_val/real_space.py b/src/sp_validation/cosmo_val/real_space.py
index 76d05d0a..1521ecc7 100644
--- a/src/sp_validation/cosmo_val/real_space.py
+++ b/src/sp_validation/cosmo_val/real_space.py
@@ -12,12 +12,11 @@
 import matplotlib.ticker as mticker
 import numpy as np
 import treecorr
-from astropy.io import fits
 from cs_util import plots as cs_plots
 
 
 class RealSpaceMixin:
-    def calculate_2pcf(self, ver, npatch=None, save_fits=False, **treecorr_config):
+    def calculate_2pcf(self, ver, npatch=None, **treecorr_config):
         """
         Calculate the two-point correlation function (2PCF) ξ± for a given catalog
         version with TreeCorr.
@@ -34,9 +33,6 @@ def calculate_2pcf(self, ver, npatch=None, save_fits=False, **treecorr_config):
             npatch (int, optional): The number of patches to use for the calculation.
             Defaults to the instance's `npatch` attribute.
 
-            save_fits (bool, optional): Whether to save the ξ± results to FITS files.
-            Defaults to False.
-
             **treecorr_config: Additional TreeCorr configuration parameters that will
             override the instance's default `treecorr_config`. For example, `min_sep=1`.
 
@@ -49,8 +45,7 @@ def calculate_2pcf(self, ver, npatch=None, save_fits=False, **treecorr_config):
               calculation is skipped, and the results are loaded from the file.
             - If a patch file for the given configuration does not exist, it is
               created during the process.
-            - FITS files for ξ+ and ξ− are saved with additional metadata in their
-              headers if `save_fits` is True.
+            - The ``.txt`` TreeCorr dump is the only raw byproduct written here.
         """
 
         self.print_magenta(f"Computing {ver} ξ±")
@@ -99,75 +94,13 @@ def calculate_2pcf(self, ver, npatch=None, save_fits=False, **treecorr_config):
 
             # Process the catalog & write the correlation functions
             gg.process(cat_gal)
-            gg.write(out_fname, write_patch_results=True, write_cov=True)
-
-        # Save xi_p and xi_m results to fits file
-        # (moved outside so it runs even if txt exists)
-        if save_fits:
-            lst = np.arange(1, treecorr_config["nbins"] + 1)
-
-            col1 = fits.Column(name="BIN1", format="K", array=np.ones(len(lst)))
-            col2 = fits.Column(name="BIN2", format="K", array=np.ones(len(lst)))
-            col3 = fits.Column(name="ANGBIN", format="K", array=lst)
-            col4 = fits.Column(name="VALUE", format="D", array=gg.xip)
-            col5 = fits.Column(name="ANG", format="D", unit="arcmin", array=gg.meanr)
-            coldefs = fits.ColDefs([col1, col2, col3, col4, col5])
-            xiplus_hdu = fits.BinTableHDU.from_columns(coldefs, name="XI_PLUS")
-
-            col4 = fits.Column(name="VALUE", format="D", array=gg.xim)
-            coldefs = fits.ColDefs([col1, col2, col3, col4, col5])
-            ximinus_hdu = fits.BinTableHDU.from_columns(coldefs, name="XI_MINUS")
-
-            # append xi_plus header info
-            xiplus_dict = {
-                "2PTDATA": "T",
-                "QUANT1": "G+R",
-                "QUANT2": "G+R",
-                "KERNEL_1": "NZ_SOURCE",
-                "KERNEL_2": "NZ_SOURCE",
-                "WINDOWS": "SAMPLE",
-            }
-            for key in xiplus_dict:
-                xiplus_hdu.header[key] = xiplus_dict[key]
-
-                col1 = fits.Column(name="BIN1", format="K", array=np.ones(len(lst)))
-                col2 = fits.Column(name="BIN2", format="K", array=np.ones(len(lst)))
-                col3 = fits.Column(name="ANGBIN", format="K", array=lst)
-                col4 = fits.Column(name="VALUE", format="D", array=gg.xip)
-                col5 = fits.Column(name="ANG", format="D", unit="arcmin", array=gg.rnom)
-                coldefs = fits.ColDefs([col1, col2, col3, col4, col5])
-                xiplus_hdu = fits.BinTableHDU.from_columns(coldefs, name="XI_PLUS")
-
-                col4 = fits.Column(name="VALUE", format="D", array=gg.xim)
-                coldefs = fits.ColDefs([col1, col2, col3, col4, col5])
-                ximinus_hdu = fits.BinTableHDU.from_columns(coldefs, name="XI_MINUS")
-
-                # append xi_plus header info
-                xiplus_dict = {
-                    "2PTDATA": "T",
-                    "QUANT1": "G+R",
-                    "QUANT2": "G+R",
-                    "KERNEL_1": "NZ_SOURCE",
-                    "KERNEL_2": "NZ_SOURCE",
-                    "WINDOWS": "SAMPLE",
-                }
-                for key in xiplus_dict:
-                    xiplus_hdu.header[key] = xiplus_dict[key]
-            # Use same naming format as txt output
-            fits_base = out_fname.replace(".txt", "").replace("_xi_", "_")
-            xiplus_hdu.writeto(
-                f"{fits_base.replace(ver, f'xi_plus_{ver}')}.fits",
-                overwrite=True,
-            )
-
-            # append xi_minus header info
-            ximinus_dict = {**xiplus_dict, "QUANT1": "G-R", "QUANT2": "G-R"}
-            for key in ximinus_dict:
-                ximinus_hdu.header[key] = ximinus_dict[key]
-            ximinus_hdu.writeto(
-                f"{fits_base.replace(ver, f'xi_minus_{ver}')}.fits",
-                overwrite=True,
-            )
+            # Never write_patch_results: a per-patch ξ± realisation is an
+            # unblinded data vector, and nothing downstream reads one — the
+            # covariance a consumer needs is the matrix, which the SACC part
+            # carries. The .txt keeps the matrix only where there are patches to
+            # estimate it from; at npatch=1 var_method is "shot" and it would add
+            # nothing over the varxip/varxim columns.
+            gg.write(out_fname, write_patch_results=False, write_cov=int(npatch) > 1)
 
         # Add correlation object to class
         if not hasattr(self, "cat_ggs"):
diff --git a/src/sp_validation/cosmo_val/sacc_writers.py b/src/sp_validation/cosmo_val/sacc_writers.py
new file mode 100644
index 00000000..8b7f57b7
--- /dev/null
+++ b/src/sp_validation/cosmo_val/sacc_writers.py
@@ -0,0 +1,237 @@
+"""Born-as-SACC writers for the cosmo_val data products.
+
+A thin, pure layer between the ``cosmo_val`` mixins (which compute statistics as
+TreeCorr / NaMaster / b_modes arrays) and :mod:`sp_validation.sacc_io` (which
+knows the file layout). Each ``*_to_sacc`` function turns one already-computed
+statistic into a single-statistic SACC — a *part* — carrying that statistic's
+own covariance as its one block. :func:`assemble_analysis_sacc` rebuilds the
+single ``{version}.sacc`` analysis file from these parts.
+
+Everything here is single-bin today (``bins=(0, 0)``); the interface is
+tomography-native so a future round supplies real bin pairs unchanged.
+"""
+
+import numpy as np
+import sacc
+
+from .. import sacc_io as sio
+from ..pseudo_cl import bandpower_window_from_workspace
+
+# Statistics carried in the analysis file, and their custom-type k indices.
+RHO_K = range(6)  # ρ_0 … ρ_5
+TAU_K = (0, 2, 5)  # τ_0, τ_2, τ_5
+
+# NaMaster spin-2 × spin-2 decoupled-spectrum row order (EE, EB, BE, BB).
+_NMT_EE, _NMT_EB, _NMT_BB = 0, 1, 3
+
+BIN = (0, 0)
+
+
+def xi_to_sacc(
+    nz,
+    metadata,
+    theta,
+    xip,
+    xim,
+    *,
+    grid,
+    theta_nom=None,
+    npairs=None,
+    weight=None,
+    variances=None,
+    covariance=None,
+):
+    """One ξ± part (``bins=(0, 0)``) on a named angular grid.
+
+    The grid's covariance comes in one of two shapes: ``covariance``, the dense
+    ``[ξ+; ξ−]``-ordered block (a jackknife estimate), or ``variances``, the
+    concatenated ``[varxip; varxim]`` diagonal. At most one may be given.
+    """
+    s = sio.new_sacc(nz, metadata)
+    sio.add_xi(
+        s,
+        BIN,
+        theta,
+        xip,
+        xim,
+        grid=grid,
+        theta_nom=theta_nom,
+        npairs=npairs,
+        weight=weight,
+    )
+    if covariance is not None and variances is not None:
+        raise ValueError("give xi_to_sacc a dense covariance or variances, not both")
+    if covariance is not None:
+        s.add_covariance(np.asarray(covariance))
+    elif variances is not None:
+        sio.add_diagonal_covariance(s, np.asarray(variances))
+    return s
+
+
+def pseudo_cl_to_sacc(nz, metadata, ell_eff, cl_all, wsp, covariance=None):
+    """One pseudo-Cℓ part: EE/BB/EB with the shared bandpower window.
+
+    ``cl_all`` is NaMaster's decoupled ``(4, nbp)`` array (EE, EB, BE, BB); the
+    window comes from :func:`bandpower_window_from_workspace`. ``covariance``,
+    when given, is the dense ``[EE; BB; EB]``-ordered block matching insertion.
+    """
+    window_ells, window_weights = bandpower_window_from_workspace(wsp)
+    s = sio.new_sacc(nz, metadata)
+    sio.add_pseudo_cl(
+        s,
+        BIN,
+        ell_eff,
+        cl_all[_NMT_EE],
+        cl_all[_NMT_BB],
+        cl_all[_NMT_EB],
+        window_ells=window_ells,
+        window_weights=window_weights,
+    )
+    if covariance is not None:
+        s.add_covariance(np.asarray(covariance))
+    return s
+
+
+def cosebis_to_sacc(nz, metadata, result, scale_cut):
+    """One COSEBIs part at the fiducial scale cut.
+
+    ``result`` is a single scale-cut result dict from
+    ``b_modes.calculate_cosebis`` — ``{"En", "Bn", "cov", ...}`` — where ``cov``
+    is the ``[En; Bn]``-ordered COSEBIs covariance.
+    """
+    s = sio.new_sacc(nz, metadata)
+    sio.add_cosebis(s, BIN, result["En"], scale_cut, Bn=result["Bn"])
+    s.add_covariance(np.asarray(result["cov"]))
+    return s
+
+
+def pure_eb_to_sacc(nz, metadata, theta, eb, covariance=None):
+    """One pure-E/B part: the six ``sacc_io.PURE_KEYS`` blocks.
+
+    ``eb`` is a mapping with the six keys (``xip_E`` … ``xim_amb``); each array
+    is sampled at ``theta``. ``covariance``, when given, is the dense block in
+    ``PURE_KEYS`` order (matching ``b_modes._EB_KEYS`` and the insertion order).
+    """
+    s = sio.new_sacc(nz, metadata)
+    sio.add_pure_eb(s, BIN, theta, **{key: eb[key] for key in sio.PURE_KEYS})
+    if covariance is not None:
+        s.add_covariance(np.asarray(covariance))
+    return s
+
+
+def rho_tau_to_sacc(nz, metadata, rho_stats, tau_stats, tau_cov_th=None):
+    """One ρ/τ part: ρ_0…ρ_5 autos and τ_0/τ_2/τ_5 leakage.
+
+    ``rho_stats`` / ``tau_stats`` are the ``shear_psf_leakage`` handler tables
+    (columns ``theta``, ``rho_{k}_p``, ``varrho_{k}_p``, … and the τ analogue).
+    ρ carries a ``varrho`` diagonal; τ carries a ``vartau`` diagonal, with
+    ``tau_cov_th`` — a ``(3·nbin, 3·nbin)`` k-major matrix over the τ-plus points
+    only — scattered into the τ-plus rows/columns when given. ``tau_cov_th=None``
+    leaves the τ block fully diagonal.
+    """
+    s = sio.new_sacc(nz, metadata)
+    theta_rho = np.asarray(rho_stats["theta"])
+    for k in RHO_K:
+        sio.add_rho(
+            s,
+            k,
+            theta_rho,
+            np.asarray(rho_stats[f"rho_{k}_p"]),
+            np.asarray(rho_stats[f"rho_{k}_m"]),
+        )
+    theta_tau = np.asarray(tau_stats["theta"])
+    for k in TAU_K:
+        sio.add_tau(
+            s,
+            BIN,
+            k,
+            theta_tau,
+            np.asarray(tau_stats[f"tau_{k}_p"]),
+            np.asarray(tau_stats[f"tau_{k}_m"]),
+        )
+    nbin = len(theta_tau)
+    rho_var = np.concatenate(
+        [
+            np.concatenate([rho_stats[f"varrho_{k}_p"], rho_stats[f"varrho_{k}_m"]])
+            for k in RHO_K
+        ]
+    )
+    tau_var = np.concatenate(
+        [
+            np.concatenate([tau_stats[f"vartau_{k}_p"], tau_stats[f"vartau_{k}_m"]])
+            for k in TAU_K
+        ]
+    )
+    if tau_cov_th is None:
+        s.add_covariance(np.concatenate([rho_var, tau_var]))
+        return s
+    tau_cov_th = np.asarray(tau_cov_th)
+    n_plus = len(TAU_K) * nbin
+    if tau_cov_th.shape != (n_plus, n_plus):
+        raise ValueError(
+            f"tau_cov_th shape {tau_cov_th.shape} does not match the "
+            f"{n_plus} τ-plus points ({len(TAU_K)} indices × {nbin} bins) — "
+            "CovTauTh.build_cov returns one (plus-folded) component per τ index"
+        )
+    n_rho, n_tau = len(rho_var), len(tau_var)
+    tau_block = np.diag(tau_var)
+    # τ-plus local positions in the τ block, k-major (per-k layout is [+; −]).
+    plus = np.concatenate(
+        [np.arange(2 * i * nbin, 2 * i * nbin + nbin) for i in range(len(TAU_K))]
+    )
+    tau_block[np.ix_(plus, plus)] = tau_cov_th
+    full = np.zeros((n_rho + n_tau, n_rho + n_tau))
+    full[:n_rho, :n_rho] = np.diag(rho_var)
+    full[n_rho:, n_rho:] = tau_block
+    s.add_covariance(full)
+    return s
+
+
+# --------------------------------------------------------------------------- #
+# Analysis-file assembly
+# --------------------------------------------------------------------------- #
+def _copy_data_points(dst, src):
+    """Append every data point of ``src`` into ``dst`` (tags preserved)."""
+    for dp in src.data:
+        dst.add_data_point(dp.data_type, dp.tracers, dp.value, **dp.tags)
+
+
+def assemble_analysis_sacc(parts):
+    """Rebuild the single ``{version}.sacc`` analysis file from parts.
+
+    Each part is a single-statistic Sacc (from a ``*_to_sacc`` writer, loaded
+    from disk) carrying its own covariance = its block. Tracers and metadata are
+    seeded from ``parts[0]`` (every part describes the same catalogue version).
+    Data points are re-added in the order the parts are given, which must be the
+    canonical order (ξ± reporting, pseudo-Cℓ, COSEBIs, pure-E/B, ρ/τ), and the
+    per-part blocks become one ``BlockDiagonalCovariance``. Insertion order and
+    block order therefore agree by construction — validated by
+    :func:`sp_validation.sacc_io.assemble_covariance`.
+
+    Parameters
+    ----------
+    parts : sequence of sacc.Sacc
+        Single-statistic parts, each with a covariance, in canonical order.
+
+    Returns
+    -------
+    sacc.Sacc
+        The analysis Sacc with a ``BlockDiagonalCovariance`` covering every point.
+    """
+    s = sacc.Sacc()
+    s.tracers.update(parts[0].tracers)
+    s.metadata.update(parts[0].metadata)
+    blocks = []
+    cursor = 0
+    for part in parts:
+        if part.covariance is None:
+            raise ValueError(
+                "every analysis part must carry its own covariance block; "
+                f"a part with data types {sorted(set(dp.data_type for dp in part.data))} "
+                "has none"
+            )
+        n = len(part.mean)
+        _copy_data_points(s, part)
+        blocks.append((np.arange(cursor, cursor + n), part.covariance.dense))
+        cursor += n
+    return sio.assemble_covariance(s, blocks)
diff --git a/src/sp_validation/pseudo_cl.py b/src/sp_validation/pseudo_cl.py
index c9355ec9..34cbc68d 100644
--- a/src/sp_validation/pseudo_cl.py
+++ b/src/sp_validation/pseudo_cl.py
@@ -280,3 +280,32 @@ def get_pseudo_cls_catalog(
     cl_all = wsp.decouple_cell(cl_coupled)
 
     return ell_eff, cl_all, wsp
+
+
+# NaMaster spin-2 × spin-2 spectrum order: EE, EB, BE, BB.
+_NMT_EE = 0
+
+
+def bandpower_window_from_workspace(wsp):
+    """Extract the bandpower window matrix ``W`` for a spin-2×spin-2 workspace.
+
+    NaMaster's ``get_bandpower_windows()`` returns a four-index array
+    ``(n_cl_out, n_bpw, n_cl_in, n_ell)`` describing how each output bandpower
+    is built from the input multipoles across the EE/EB/BE/BB spectra. SACC's
+    ``BandpowerWindow`` model (one window per bandpower, shared across the
+    stored spectra) needs the per-spectrum *decoupling* window, i.e. the
+    diagonal EE←EE block (equal to BB←BB and EB←EB, verified identical).
+
+    Returns
+    -------
+    window_ells : np.ndarray
+        Multipoles the window spans, ``arange(n_ell)`` — the ``ell`` axis of
+        ``compute_coupled_cell``.
+    window_weights : np.ndarray
+        ``W`` of shape ``(n_ell, n_bpw)`` — one column per bandpower, the layout
+        :func:`sp_validation.sacc_io.add_pseudo_cl` expects.
+    """
+    bpw = wsp.get_bandpower_windows()  # (n_cl_out, n_bpw, n_cl_in, n_ell)
+    diagonal = bpw[_NMT_EE, :, _NMT_EE, :]  # (n_bpw, n_ell)
+    window_ells = np.arange(diagonal.shape[1], dtype=float)
+    return window_ells, diagonal.T
diff --git a/src/sp_validation/tests/test_assemble_sacc.py b/src/sp_validation/tests/test_assemble_sacc.py
new file mode 100644
index 00000000..f17d5468
--- /dev/null
+++ b/src/sp_validation/tests/test_assemble_sacc.py
@@ -0,0 +1,300 @@
+"""Integration tests for the ``assemble_sacc.py`` workflow script.
+
+The pure assembler (``sacc_writers.assemble_analysis_sacc``) is covered in
+``test_sacc_writers.py``. This file exercises the *script seam* the DAG uses:
+``assemble_sacc.assemble_sacc`` loads per-statistic ``.sacc`` part *files* in
+CANONICAL order, injects the born-cov-less ξ± / pseudo-Cℓ blocks from the real
+CosmoCov ``.txt`` and NaMaster covariance FITS, and writes one
+``{version}.sacc`` whose points and covariance blocks land in canonical order.
+
+The script lives under ``workflow/scripts`` (off the package path); it is loaded
+by file path exactly as the lightcone/ASTRA CLI path imports it.
+"""
+
+import importlib.util
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from sp_validation import sacc_io as sio
+from sp_validation.cosmo_val import sacc_writers as sw
+
+
+def _load_assemble_module():
+    """Import ``workflow/scripts/assemble_sacc.py`` by file path."""
+    repo_root = next(
+        p for p in Path(__file__).resolve().parents if (p / "pyproject.toml").exists()
+    )
+    path = repo_root / "workflow" / "scripts" / "assemble_sacc.py"
+    spec = importlib.util.spec_from_file_location("assemble_sacc", path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+asm = _load_assemble_module()
+
+
+def _nz(seed=0, n=40):
+    rng = np.random.default_rng(seed)
+    return np.linspace(0.01, 2.0, n), rng.uniform(0.1, 1.0, n)
+
+
+def _spd(n, seed):
+    a = np.random.default_rng(seed).normal(size=(n, n))
+    return a @ a.T + n * np.eye(n)
+
+
+def _theta(n=6):
+    return np.geomspace(1.0, 100.0, n)
+
+
+META = {"catalogue_version": "vSYNTH", "npatch": 1}
+
+
+def _xi_cov_txt(tmp_path, n=12, seed=21):
+    """A CosmoCov-format ξ± covariance: the dense ``_processed.txt`` matrix.
+
+    Same writer/format ``covariance_process`` emits and ``--xi-cov`` reads, at
+    the synthetic parts' 12-point ([ξ+; ξ−] over 6 θ) size. Returns
+    ``(path, matrix)``.
+    """
+    cov = _spd(n, seed)
+    path = tmp_path / "xi_cov_processed.txt"
+    np.savetxt(str(path), cov)
+    return str(path), cov
+
+
+def _pseudo_cl_cov_fits(tmp_path, n=3):
+    """A NaMaster covariance FITS: one HDU per spectrum. Returns (path, blocks)."""
+    from astropy.io import fits
+
+    blocks = {"EE": _spd(n, 31), "BB": _spd(n, 32), "EB": _spd(n, 33)}
+    path = tmp_path / "pseudo_cl_cov.fits"
+    fits.HDUList(
+        [fits.PrimaryHDU()]
+        + [fits.ImageHDU(block, name=f"COVAR_{k}_{k}") for k, block in blocks.items()]
+    ).writeto(str(path))
+    return str(path), blocks
+
+
+def _write_parts(tmp_path, *, with_pseudo_cl=True, cov_less=("xi_reporting",)):
+    """Write per-statistic parts to disk; return the ``{name: path}`` mapping.
+
+    Parts named in ``cov_less`` are written without a covariance (mimicking the
+    born-cov-less ξ± reporting / pseudo-Cℓ parts); the rest carry their own block.
+    """
+    nz = {0: _nz()}
+    theta = _theta()
+    ell = np.array([30.0, 60.0, 90.0])
+
+    class _Wsp:
+        def get_bandpower_windows(self):
+            w = np.zeros((4, 3, 4, 20))
+            for out in range(4):
+                for b in range(3):
+                    w[out, b, out, b * 6 : b * 6 + 6] = 1.0
+            return w
+
+    xi = sw.xi_to_sacc(
+        nz, META, theta, np.arange(6) * 1e-5, np.arange(6) * 2e-5, grid="reporting"
+    )
+    if "xi_reporting" not in cov_less:
+        xi.add_covariance(_spd(len(xi.mean), 1))
+
+    cl_all = np.vstack(
+        [np.arange(3) * 1e-9, np.arange(3) * 2e-9, np.zeros(3), np.arange(3) * 3e-9]
+    )
+    cl = sw.pseudo_cl_to_sacc(
+        nz,
+        META,
+        ell,
+        cl_all,
+        _Wsp(),
+        covariance=None if "pseudo_cl" in cov_less else _spd(9, 2),
+    )
+
+    co = sw.cosebis_to_sacc(
+        nz,
+        META,
+        {
+            "En": np.arange(1, 6) * 1e-6,
+            "Bn": np.arange(1, 6) * 1e-7,
+            "cov": _spd(10, 3),
+        },
+        (1.0, 100.0),
+    )
+
+    eb_arrays = {
+        key: np.arange(6) * (i + 1) * 1e-6 for i, key in enumerate(sio.PURE_KEYS)
+    }
+    eb = sw.pure_eb_to_sacc(nz, META, theta, eb_arrays, covariance=_spd(36, 4))
+
+    rho = {"theta": theta}
+    tau = {"theta": theta}
+    rng = np.random.default_rng(5)
+    for k in sw.RHO_K:
+        for suffix in ("p", "m"):
+            rho[f"rho_{k}_{suffix}"] = rng.normal(size=6) * 1e-6
+            rho[f"varrho_{k}_{suffix}"] = rng.uniform(1e-14, 1e-13, 6)
+    for k in sw.TAU_K:
+        for suffix in ("p", "m"):
+            tau[f"tau_{k}_{suffix}"] = rng.normal(size=6) * 1e-6
+            tau[f"vartau_{k}_{suffix}"] = rng.uniform(1e-14, 1e-13, 6)
+    rt = sw.rho_tau_to_sacc(nz, META, rho, tau)
+
+    parts = {
+        "xi_reporting": xi,
+        "pseudo_cl": cl,
+        "cosebis": co,
+        "pure_eb": eb,
+        "rho_tau": rt,
+    }
+    if not with_pseudo_cl:
+        parts.pop("pseudo_cl")
+
+    paths = {}
+    for name, part in parts.items():
+        p = tmp_path / f"{name}.sacc"
+        sio.save(part, str(p), type="mock")
+        paths[name] = str(p)
+    return paths
+
+
+def test_assemble_sacc_canonical_order(tmp_path):
+    """Every point is covered and the blocks land in canonical order
+    (ξ±, pseudo-Cℓ, COSEBIs, pure-E/B, ρ, τ)."""
+    paths = _write_parts(tmp_path, cov_less=("xi_reporting",))
+    cov_path, xi_cov = _xi_cov_txt(tmp_path)
+    cl_cov_path, _blocks = _pseudo_cl_cov_fits(tmp_path)
+    out = tmp_path / "vSYNTH.sacc"
+    s = asm.assemble_sacc(
+        "vSYNTH", paths, str(out), xi_cov=cov_path, pseudo_cl_cov=cl_cov_path
+    )
+    assert out.exists()
+    assert type(s.covariance).__name__ == "BlockDiagonalCovariance"
+    assert s.covariance.dense.shape == (len(s.mean), len(s.mean))
+
+    # Canonical insertion order: the first data types are ξ+ then ξ−.
+    types_in_order = [dp.data_type for dp in s.data]
+    assert types_in_order[0] == sio.XI_PLUS
+    assert sio.XI_MINUS in types_in_order
+    # ξ appears before pseudo-Cℓ before COSEBIs before pure-E/B before ρ/τ.
+    first = {t: types_in_order.index(t) for t in set(types_in_order)}
+    assert first[sio.XI_PLUS] < first[sio.CL_EE] < first[sio.COSEBI_EE]
+    assert first[sio.COSEBI_EE] < first[sio.PURE_TYPES["xip_E"]]
+    assert first[sio.PURE_TYPES["xip_E"]] < first[sio.RHO_PLUS.format(k=0)]
+    assert first[sio.RHO_PLUS.format(k=0)] < first[sio.TAU_PLUS.format(k=0)]
+
+    # The ξ± block is the injected CosmoCov matrix on its own points.
+    tr = ("source_0", "source_0")
+    xi_idx = np.concatenate([s.indices(sio.XI_PLUS, tr), s.indices(sio.XI_MINUS, tr)])
+    dense = s.covariance.dense
+    assert np.allclose(dense[np.ix_(xi_idx, xi_idx)], xi_cov)
+    # ...and it does not bleed into the neighbouring COSEBIs block (cross zero).
+    co_idx = np.concatenate(
+        [s.indices(sio.COSEBI_EE, tr), s.indices(sio.COSEBI_BB, tr)]
+    )
+    assert np.allclose(dense[np.ix_(xi_idx, co_idx)], 0.0)
+
+
+def test_injected_xi_covariance_replaces_the_parts_own(tmp_path):
+    """The analytic ξ± covariance wins over the estimate the part was born with.
+
+    The reporting part carries the jackknife it was measured with — useful as a
+    diagnostic, but the analysis file takes the CosmoCov block.
+    """
+    paths = _write_parts(tmp_path, cov_less=())  # ξ± born with its own jackknife
+    cov_path, xi_cov = _xi_cov_txt(tmp_path)
+    cl_cov_path, _blocks = _pseudo_cl_cov_fits(tmp_path)
+
+    born = sio.load(paths["xi_reporting"], allow_unblinded=True).covariance.dense
+    assert not np.allclose(born, xi_cov)  # the two are distinguishable
+
+    out = tmp_path / "vSYNTH.sacc"
+    s = asm.assemble_sacc(
+        "vSYNTH", paths, str(out), xi_cov=cov_path, pseudo_cl_cov=cl_cov_path
+    )
+    tr = ("source_0", "source_0")
+    xi_idx = np.concatenate([s.indices(sio.XI_PLUS, tr), s.indices(sio.XI_MINUS, tr)])
+    assert np.allclose(s.covariance.dense[np.ix_(xi_idx, xi_idx)], xi_cov)
+
+
+def test_assemble_sacc_injects_pseudo_cl_covariance(tmp_path):
+    """The NaMaster cov FITS (COVAR_EE_EE/BB_BB/EB_EB) → block-diagonal pseudo-Cℓ
+    block, beside the injected CosmoCov ξ± block (the live default)."""
+    paths = _write_parts(tmp_path, cov_less=("xi_reporting", "pseudo_cl"))
+    cov_path, xi_cov = _xi_cov_txt(tmp_path)
+    # pseudo-Cℓ part is 3 ell × {EE, BB, EB} = 9 points; per-spectrum 3×3 blocks.
+    cov_fits, blocks = _pseudo_cl_cov_fits(tmp_path)
+    ee, bb, eb = blocks["EE"], blocks["BB"], blocks["EB"]
+
+    out = tmp_path / "vSYNTH.sacc"
+    s = asm.assemble_sacc(
+        "vSYNTH", paths, str(out), xi_cov=cov_path, pseudo_cl_cov=cov_fits
+    )
+    tr = ("source_0", "source_0")
+    cl_idx = np.concatenate(
+        [s.indices(sio.CL_EE, tr), s.indices(sio.CL_BB, tr), s.indices(sio.CL_EB, tr)]
+    )
+    dense = s.covariance.dense
+    expected = np.zeros((9, 9))
+    expected[0:3, 0:3], expected[3:6, 3:6], expected[6:9, 6:9] = ee, bb, eb
+    assert np.allclose(dense[np.ix_(cl_idx, cl_idx)], expected)
+    # ξ± carries its own CosmoCov block; the two don't bleed into each other.
+    xi_idx = np.concatenate([s.indices(sio.XI_PLUS, tr), s.indices(sio.XI_MINUS, tr)])
+    assert np.allclose(dense[np.ix_(xi_idx, xi_idx)], xi_cov)
+    assert np.allclose(dense[np.ix_(xi_idx, cl_idx)], 0.0)
+
+
+def test_missing_injected_covariance_raises(tmp_path):
+    """A statistic whose covariance is external cannot fall back to its own."""
+    paths = _write_parts(tmp_path, cov_less=())  # every part born with a block
+    out = tmp_path / "vSYNTH.sacc"
+    with pytest.raises(ValueError, match="takes its analysis covariance from"):
+        asm.assemble_sacc("vSYNTH", paths, str(out))
+
+
+def test_assemble_sacc_respects_pseudo_cl_toggle(tmp_path):
+    """With pseudo_cl absent, assembly still succeeds and omits the Cℓ points."""
+    paths = _write_parts(tmp_path, with_pseudo_cl=False, cov_less=("xi_reporting",))
+    assert "pseudo_cl" not in paths
+    cov_path, _xi_cov = _xi_cov_txt(tmp_path)
+    out = tmp_path / "vSYNTH.sacc"
+    s = asm.assemble_sacc("vSYNTH", paths, str(out), xi_cov=cov_path)
+    tr = ("source_0", "source_0")
+    assert len(s.indices(sio.CL_EE, tr)) == 0
+    # Round-trips as a valid BlockDiagonalCovariance over the remaining points.
+    s2 = sio.load(str(out))
+    assert type(s2.covariance).__name__ == "BlockDiagonalCovariance"
+    assert s2.covariance.dense.shape == (len(s2.mean), len(s2.mean))
+
+
+def test_assemble_sacc_expected_part_missing_raises(tmp_path):
+    """A typo'd input keyword drops a part from part_paths; the expected list
+    catches it rather than silently omitting the statistic."""
+    paths = _write_parts(tmp_path, cov_less=("xi_reporting",))
+    # Simulate a rule-input typo: cosebis wired under the wrong key.
+    paths["cosebi"] = paths.pop("cosebis")
+    cov_path, _xi_cov = _xi_cov_txt(tmp_path)
+    out = tmp_path / "vSYNTH.sacc"
+    with pytest.raises(ValueError, match="expected parts \\['cosebis'\\] missing"):
+        asm.assemble_sacc(
+            "vSYNTH",
+            paths,
+            str(out),
+            expected=["xi_reporting", "pseudo_cl", "cosebis", "pure_eb", "rho_tau"],
+            xi_cov=cov_path,
+        )
+
+
+def test_assemble_sacc_expected_rejects_unknown_name(tmp_path):
+    """A typo in the expected list itself is rejected (not a valid statistic)."""
+    paths = _write_parts(tmp_path, cov_less=("xi_reporting",))
+    cov_path, _xi_cov = _xi_cov_txt(tmp_path)
+    out = tmp_path / "vSYNTH.sacc"
+    with pytest.raises(ValueError, match="not assemblable statistics"):
+        asm.assemble_sacc(
+            "vSYNTH", paths, str(out), expected=["cosebi"], xi_cov=cov_path
+        )
diff --git a/src/sp_validation/tests/test_bmodes_workflow_dry_run.py b/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
index 68c09981..3b7eecae 100644
--- a/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
+++ b/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
@@ -1,11 +1,15 @@
-"""Back-pressure guard #2: the B-modes Snakemake workflow dry-runs.
+"""Back-pressure guard #2: the paper Snakemake workflows dry-run.
 
-The reorg is allowed to change the rule graph; this guard only asserts that
-Snakemake can still parse the workflow and construct a dry run.
+The reorg is allowed to change the rule graph; these guards only assert that
+Snakemake can still parse each composed workflow and construct a dry run. One
+guard covers papers/bmodes (config space, no cosmo_val block); a second covers
+papers/cosmo_val, whose config DOES carry a cosmo_val block — so it is the only
+one that includes cosmo_val.smk and hence the born-as-SACC + assemble rules.
 """
 
 import os
 import subprocess
+import sys
 from pathlib import Path
 
 import pytest
@@ -26,31 +30,59 @@ def _repo_root() -> Path:
     raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
 
 
-@requires_candide_data
-def test_bmodes_workflow_dry_runs():
-    """The paper B-mode workflow must still parse and dry-run cleanly."""
-    workflow_dir = _repo_root() / "papers/bmodes"
-    # PYTHONUNBUFFERED satisfies the Snakefile's `envvars:` declaration without
-    # depending on the invoking shell's environment.
+def _dry_run(workflow_dir, targets, *extra_snakemake_args):
+    """Construct a dry run of the paper workflow at ``workflow_dir``.
+
+    PYTHONUNBUFFERED satisfies the Snakefile's ``envvars:`` declaration. A dry
+    run never dispatches jobs, so any inherited SNAKEMAKE_PROFILE is dropped
+    rather than requiring its executor plugin. snakemake is invoked through
+    sys.executable, since a bare python3.12 may resolve off PATH to an
+    interpreter without it.
+    """
     env = os.environ | {"PYTHONNOUSERSITE": "1", "PYTHONUNBUFFERED": "1"}
-    result = subprocess.run(
+    env.pop("SNAKEMAKE_PROFILE", None)
+    return subprocess.run(
         [
-            "python3.12",
+            sys.executable,
             "-m",
             "snakemake",
-            "all_tapestry",
+            *targets,
             "--dry-run",
             "--cores",
             "1",
             "--configfile",
             "config/config.yaml",
+            *extra_snakemake_args,
         ],
         cwd=workflow_dir,
         env=env,
         text=True,
         stdout=subprocess.PIPE,
         stderr=subprocess.STDOUT,
-        timeout=60,
+        timeout=120,
         check=False,
     )
+
+
+@requires_candide_data
+def test_bmodes_workflow_dry_runs():
+    """The paper B-mode workflow must still parse and dry-run cleanly."""
+    result = _dry_run(_repo_root() / "papers/bmodes", ["all_tapestry"])
+    assert result.returncode == 0, result.stdout
+
+
+@requires_candide_data
+def test_cosmo_val_workflow_assemble_dry_runs():
+    """The cosmo_val workflow (the only one including cosmo_val.smk) resolves the
+    born-as-SACC + assemble DAG, and assemble pulls the tagged pseudo-Cl + cov."""
+    version = "SP_v1.4.6.3_leak_corr"
+    result = _dry_run(_repo_root() / "papers/cosmo_val", ["assemble_sacc_all"])
     assert result.returncode == 0, result.stdout
+    # assemble_sacc must pull the tagged pseudo-Cl part + its NaMaster
+    # covariance (not the untagged cv_pseudo_cl diagnostic), plus every part.
+    out = result.stdout
+    assert "rule assemble_sacc:" in out, out
+    assert f"pseudo_cl_{version}_blind=A_powspace_nbins=32.sacc" in out, out
+    assert f"pseudo_cl_cov_{version}_blind=A_powspace_nbins=32.fits" in out, out
+    for part in ("_xi_minsep=", "_cosebis.sacc", "_pure_eb.sacc", "rho_tau_"):
+        assert part in out, f"missing {part} part in assemble DAG:\n{out}"
diff --git a/src/sp_validation/tests/test_cli_seams.py b/src/sp_validation/tests/test_cli_seams.py
new file mode 100644
index 00000000..ce601d25
--- /dev/null
+++ b/src/sp_validation/tests/test_cli_seams.py
@@ -0,0 +1,57 @@
+"""Smoke tests for workflow CLI seams — cheap guards against signature rot.
+
+The compute these scripts drive is cluster-only, so a removed or renamed kwarg
+would only TypeError at invocation. Each test binds the exact call one seam
+makes against the current signature (``inspect.signature(...).bind(...)``) — no
+compute, no data — so the drift fails here instead of on the cluster.
+"""
+
+import importlib.util
+import inspect
+from pathlib import Path
+
+import pytest
+
+
+def _repo_root() -> Path:
+    for parent in Path(__file__).resolve().parents:
+        if (parent / "pyproject.toml").exists():
+            return parent
+    raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
+
+
+def _load(path, name):
+    spec = importlib.util.spec_from_file_location(name, path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_run_xi_sweep_run_2pcf_call_binds():
+    """The kwargs run_xi_sweep passes to run_2pcf must bind to its signature."""
+    root = _repo_root()
+    run_2pcf_mod = _load(root / "workflow/scripts/run_2pcf.py", "run_2pcf_seam")
+    sig = inspect.signature(run_2pcf_mod.run_2pcf)
+    # Exactly the keyword set run_xi_sweep._from_cli passes.
+    sig.bind(
+        ver="V",
+        cat_config="/cfg.yaml",
+        output_dir="/out",
+        grid="integration",
+        min_sep=0.5,
+        max_sep=300.0,
+        nbins=1000,
+        npatch=1,
+    )
+    # And the removed kwarg must NOT bind (guards against a silent re-add).
+    with pytest.raises(TypeError):
+        sig.bind(
+            ver="V",
+            cat_config="/cfg.yaml",
+            output_dir="/out",
+            save_fits=True,
+            min_sep=1.0,
+            max_sep=250.0,
+            nbins=20,
+            npatch=1,
+        )
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index 2b5ccf51..fd39c8e2 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -57,7 +57,9 @@
 import pytest
 import yaml
 
+from sp_validation import sacc_io
 from sp_validation.cosmo_val import CosmologyValidation
+from sp_validation.cosmo_val.sacc_writers import BIN as SACC_BIN
 from sp_validation.rho_tau import get_params_rho_tau
 
 # These tests need the full harmonic-space stack (pymaster/NaMaster + healpy),
@@ -112,6 +114,7 @@ def _write_synthetic_config(tmp_path):
 
     shear_cfg = {
         "path": "shear.fits",
+        "redshift_path": str(nz_dir / "dndz_SP_A.txt"),
         "w_col": "w",
         "e1_col": "e1",
         "e2_col": "e2",
@@ -509,24 +512,20 @@ def test_apply_random_rotation_reproducible_with_seed(cv, cat_and_params):
 # calculate_pseudo_cl_catalog -- deterministic end-to-end catalog path
 # ===========================================================================
 def test_calculate_pseudo_cl_catalog_end_to_end(cv, tmp_path):
-    """End-to-end catalog path: FITS round-trip of ell + EE/EB/BB.
+    """End-to-end catalog path: SACC round-trip of ell + EE/EB/BB.
 
     The catalog method has no random noise debiasing, so it is reproducible to
-    the same ~2e-12 catalog-path float noise. save_pseudo_cl stores ELL/EE/EB/BB
-    (it drops the BE row); we pin the round-tripped table.
+    the same ~2e-12 catalog-path float noise; we pin the round-tripped spectra.
     """
     ver = cv._test_version
     cv._pseudo_cls = {ver: {}}
-    out_path = cv._output_path(f"pseudo_cl_cat_{ver}.fits")
+    out_path = cv._output_path(f"pseudo_cl_{ver}.sacc")
     cv.calculate_pseudo_cl_catalog(ver, out_path)
 
     assert os.path.exists(out_path)
-    d = fits.getdata(out_path)
-    # FITS gives big-endian f8; normalize for value comparison.
-    ell = np.asarray(d["ELL"], dtype=np.float64)
-    ee = np.asarray(d["EE"], dtype=np.float64)
-    eb = np.asarray(d["EB"], dtype=np.float64)
-    bb = np.asarray(d["BB"], dtype=np.float64)
+    s = sacc_io.load(out_path, allow_unblinded=True)
+    ell, ee, bb, eb, window = sacc_io.get_pseudo_cl(s, SACC_BIN)
+    assert window is not None  # the shared BandpowerWindow rides the part
 
     npt.assert_allclose(
         ell,
@@ -591,3 +590,25 @@ def test_calculate_pseudo_cl_catalog_end_to_end(cv, tmp_path):
     params = get_params_rho_tau(cv.cc[ver], survey=ver)
     _, cl_prim, _ = cv.get_pseudo_cls_catalog(catalog=cat_gal, params=params)
     npt.assert_allclose(ee, cl_prim[0], rtol=RTOL_CAT, atol=ATOL_CAT)
+
+
+def test_calculate_pseudo_cl_out_path_born_at_declared_name(cv):
+    """calculate_pseudo_cl(out_path=...) writes to the given path, never the
+    untagged native name — so the tagged and diagnostic rules stay disjoint."""
+    ver = cv._test_version
+    cv._pseudo_cls = {}
+    tagged = cv._output_path(f"pseudo_cl_{ver}_blind=A_powspace_nbins=32.sacc")
+    native = cv._output_path(f"pseudo_cl_{ver}.sacc")
+
+    cv.calculate_pseudo_cl(out_path=tagged)
+
+    assert os.path.exists(tagged)
+    assert not os.path.exists(native)  # no undeclared native basename touched
+
+
+def test_calculate_pseudo_cl_out_path_rejects_multiversion(cv):
+    """out_path targets one part; a multi-version instance must fail loudly
+    rather than write every version to the same path."""
+    cv.versions = [cv._test_version, "SecondVersion"]
+    with pytest.raises(ValueError, match="one part to one path"):
+        cv.calculate_pseudo_cl(out_path=cv._output_path("pseudo_cl_x.sacc"))
diff --git a/src/sp_validation/tests/test_sacc_writers.py b/src/sp_validation/tests/test_sacc_writers.py
new file mode 100644
index 00000000..c4963b33
--- /dev/null
+++ b/src/sp_validation/tests/test_sacc_writers.py
@@ -0,0 +1,389 @@
+"""Tests for :mod:`sp_validation.cosmo_val.sacc_writers`.
+
+Synthetic and fast: each ``*_to_sacc`` writer is exercised with in-memory
+arrays, round-tripped through ``tmp_path``, and checked against the SACC layout
+contract (data types, tags, ordering, covariance alignment). The analysis-file
+assembler is verified to produce a single ``BlockDiagonalCovariance`` covering every
+point with each per-statistic block correctly placed. One real small-nside
+NaMaster round-trip proves the pseudo-Cℓ window survives the writer path.
+"""
+
+import importlib.util
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from sp_validation import sacc_io as sio
+from sp_validation.cosmo_val import sacc_writers as sw
+
+
+def _nz(seed=0, n=40):
+    rng = np.random.default_rng(seed)
+    return np.linspace(0.01, 2.0, n), rng.uniform(0.1, 1.0, n)
+
+
+def _spd(n, seed):
+    a = np.random.default_rng(seed).normal(size=(n, n))
+    return a @ a.T + n * np.eye(n)
+
+
+def _theta(n=6):
+    return np.geomspace(1.0, 100.0, n)
+
+
+def _roundtrip(s, tmp_path, name):
+    p = tmp_path / f"{name}.sacc"
+    sio.save(s, str(p), type="mock")
+    return sio.load(str(p))
+
+
+META = {"catalogue_version": "vSYNTH", "npatch": 1}
+
+
+# --------------------------------------------------------------------------- #
+# Per-writer parts
+# --------------------------------------------------------------------------- #
+def test_xi_to_sacc_reporting(tmp_path):
+    theta = _theta()
+    xip, xim = np.arange(6) * 1e-5, np.arange(6) * 2e-5
+    s = sw.xi_to_sacc(
+        {0: _nz()}, META, theta, xip, xim, grid="reporting", theta_nom=theta * 1.01
+    )
+    s2 = _roundtrip(s, tmp_path, "xic")
+    th, p, m = sio.get_xi(s2, (0, 0), grid="reporting")
+    assert np.array_equal(th, theta)
+    assert np.array_equal(p, xip) and np.array_equal(m, xim)
+    assert s2.covariance is None  # reporting part has no cov until assembly
+
+
+def test_xi_to_sacc_integration_diagonal(tmp_path):
+    theta = np.geomspace(0.5, 300.0, 30)
+    xip, xim = np.arange(30) * 1e-5, np.arange(30) * 2e-5
+    varxip, varxim = np.arange(1, 31) * 1e-12, np.arange(1, 31) * 2e-12
+    s = sw.xi_to_sacc(
+        {0: _nz()},
+        META,
+        theta,
+        xip,
+        xim,
+        grid="integration",
+        variances=np.concatenate([varxip, varxim]),
+    )
+    assert type(s.covariance).__name__ == "DiagonalCovariance"
+    s2 = _roundtrip(s, tmp_path, "xif")
+    th, p, _ = sio.get_xi(s2, (0, 0), grid="integration")
+    assert np.array_equal(th, theta) and np.array_equal(p, xip)
+    assert np.array_equal(
+        np.diag(s2.covariance.dense), np.concatenate([varxip, varxim])
+    )
+
+
+def test_pseudo_cl_to_sacc_window_and_rows(tmp_path):
+    ell = np.array([30.0, 60.0, 90.0, 120.0])
+    nbp = len(ell)
+    # NaMaster (4, nbp): EE, EB, BE, BB.
+    cl_all = np.vstack(
+        [
+            np.arange(nbp) * 1e-9,
+            np.arange(nbp) * 2e-9,
+            np.zeros(nbp),
+            np.arange(nbp) * 3e-9,
+        ]
+    )
+
+    class _Wsp:
+        """Stand-in workspace: (n_cl_out, nbp, n_cl_in, nell) window array."""
+
+        def __init__(self, nbp, nell):
+            w = np.zeros((4, nbp, 4, nell))
+            col = np.zeros((nbp, nell))
+            for b in range(nbp):
+                col[b, b * 3 : b * 3 + 3] = 1.0
+            for out in range(4):
+                w[out, :, out, :] = col
+            self._w = w
+
+        def get_bandpower_windows(self):
+            return self._w
+
+    s = sw.pseudo_cl_to_sacc({0: _nz()}, META, ell, cl_all, _Wsp(nbp, 24))
+    s2 = _roundtrip(s, tmp_path, "cl")
+    ell_r, ee, bb, eb, window = sio.get_pseudo_cl(s2, (0, 0))
+    assert np.array_equal(ell_r, ell)
+    assert np.array_equal(ee, cl_all[0])  # EE row
+    assert np.array_equal(bb, cl_all[3])  # BB row (index 3, not 2=BE)
+    assert np.array_equal(eb, cl_all[1])  # EB row
+    assert window.weight.shape == (24, nbp)
+
+
+def _paper_pseudo_cl_reader():
+    # A paper script, not a package module: load it by path.
+    path = Path(__file__).resolve().parents[3] / "papers/bmodes/scripts/pseudo_cl_io.py"
+    spec = importlib.util.spec_from_file_location("pseudo_cl_io", path)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod.load_pseudo_cl_data
+
+
+def test_paper_pseudo_cl_reader(tmp_path):
+    """The B-mode paper's reader maps NaMaster's EE/EB/BE/BB rows by name."""
+    ell = np.array([30.0, 60.0, 90.0])
+    cl_all = np.vstack(
+        [np.arange(3) * 1e-9, np.arange(3) * 2e-9, np.zeros(3), np.arange(3) * 3e-9]
+    )
+
+    class _Workspace:
+        def get_bandpower_windows(self):
+            window = np.zeros((4, 3, 4, 12))
+            for component in range(4):
+                for band in range(3):
+                    window[component, band, component, band * 4 : (band + 1) * 4] = 1.0
+            return window
+
+    part = sw.pseudo_cl_to_sacc({0: _nz()}, META, ell, cl_all, _Workspace())
+    path = tmp_path / "pseudo_cl.sacc"
+    sio.save(part, str(path), type="mock")
+
+    data = _paper_pseudo_cl_reader()(path)
+    assert np.array_equal(data["ELL"], ell)
+    assert np.array_equal(data["EE"], cl_all[0])
+    assert np.array_equal(data["EB"], cl_all[1])
+    assert np.array_equal(data["BB"], cl_all[3])
+
+
+def test_pseudo_cl_to_sacc_real_namaster(tmp_path):
+    """A real small-nside NaMaster workspace's window survives the writer."""
+    pytest.importorskip("pymaster")
+    from sp_validation.pseudo_cl import get_pseudo_cls_map
+
+    nside = 32
+    mask = np.ones(12 * nside**2)
+    rng = np.random.default_rng(0)
+    shear = (
+        rng.normal(size=12 * nside**2) + 1j * rng.normal(size=12 * nside**2)
+    ) * 1e-2
+    ell_eff, cl_all, wsp = get_pseudo_cls_map(shear, mask, nside, "linear", ell_step=8)
+    s = sw.pseudo_cl_to_sacc({0: _nz()}, META, ell_eff, cl_all, wsp)
+    s2 = _roundtrip(s, tmp_path, "clreal")
+    ell_r, ee, bb, eb, window = sio.get_pseudo_cl(s2, (0, 0))
+    assert np.array_equal(ell_r, ell_eff)
+    assert np.array_equal(ee, cl_all[0]) and np.array_equal(bb, cl_all[3])
+    # window columns correspond to the bandpowers, one per ell_eff
+    assert window.weight.shape[1] == len(ell_eff)
+
+
+def test_cosebis_to_sacc(tmp_path):
+    En, Bn = np.arange(1, 11) * 1e-6, np.arange(1, 11) * 1e-7
+    result = {"En": En, "Bn": Bn, "cov": _spd(20, 7)}
+    s = sw.cosebis_to_sacc({0: _nz()}, META, result, (1.0, 100.0))
+    s2 = _roundtrip(s, tmp_path, "co")
+    n, E, B = sio.get_cosebis(s2, (0, 0))
+    assert np.array_equal(n, np.arange(1, 11))
+    assert np.array_equal(E, En) and np.array_equal(B, Bn)
+    assert type(s2.covariance).__name__ == "FullCovariance"
+    assert np.array_equal(s2.covariance.dense, result["cov"])
+
+
+def test_pure_eb_to_sacc(tmp_path):
+    theta = _theta()
+    eb = {key: np.arange(6) * (i + 1) * 1e-6 for i, key in enumerate(sio.PURE_KEYS)}
+    cov = _spd(6 * len(theta), 9)
+    s = sw.pure_eb_to_sacc({0: _nz()}, META, theta, eb, covariance=cov)
+    s2 = _roundtrip(s, tmp_path, "eb")
+    th, back = sio.get_pure_eb(s2, (0, 0))
+    assert np.array_equal(th, theta)
+    for key in sio.PURE_KEYS:
+        assert np.array_equal(back[key], eb[key])
+    assert np.array_equal(s2.covariance.dense, cov)
+
+
+def _rho_tau_tables(nth=6, seed=0):
+    rng = np.random.default_rng(seed)
+    theta = _theta(nth)
+    rho = {"theta": theta}
+    for k in sw.RHO_K:
+        for suffix in ("p", "m"):
+            rho[f"rho_{k}_{suffix}"] = rng.normal(size=nth) * 1e-6
+            rho[f"varrho_{k}_{suffix}"] = rng.uniform(1e-14, 1e-13, nth)
+    tau = {"theta": theta}
+    for k in sw.TAU_K:
+        for suffix in ("p", "m"):
+            tau[f"tau_{k}_{suffix}"] = rng.normal(size=nth) * 1e-6
+            tau[f"vartau_{k}_{suffix}"] = rng.uniform(1e-14, 1e-13, nth)
+    return rho, tau, theta
+
+
+def test_rho_tau_to_sacc_diagonal(tmp_path):
+    rho, tau, theta = _rho_tau_tables()
+    s = sw.rho_tau_to_sacc({0: _nz()}, META, rho, tau)
+    s2 = _roundtrip(s, tmp_path, "rt")
+    for k in sw.RHO_K:
+        th, p, m = sio.get_rho(s2, k)
+        assert np.array_equal(th, theta)
+        assert np.array_equal(p, rho[f"rho_{k}_p"])
+        assert np.array_equal(m, rho[f"rho_{k}_m"])
+    for k in sw.TAU_K:
+        th, p, m = sio.get_tau(s2, (0, 0), k)
+        assert np.array_equal(p, tau[f"tau_{k}_p"])
+    assert type(s2.covariance).__name__ == "DiagonalCovariance"
+
+
+def test_rho_tau_to_sacc_tau_theory_block(tmp_path):
+    """The (3·nbin) plus-only CovTauTh block scatters into the τ-plus rows/cols;
+    τ-minus keeps a vartau diagonal, and cross plus↔minus stays zero."""
+    rho, tau, theta = _rho_tau_tables()
+    nbin = len(theta)
+    n_plus = len(sw.TAU_K) * nbin  # τ-plus points (k-major, one component per k)
+    tau_cov_th = _spd(n_plus, 11)
+    s = sw.rho_tau_to_sacc({0: _nz()}, META, rho, tau, tau_cov_th=tau_cov_th)
+    assert type(s.covariance).__name__ == "FullCovariance"
+    tr = ("source_0", sio.PSF_TRACER)
+    tau_plus = np.concatenate(
+        [s.indices(sio.TAU_PLUS.format(k=k), tr) for k in sw.TAU_K]
+    )
+    tau_minus = np.concatenate(
+        [s.indices(sio.TAU_MINUS.format(k=k), tr) for k in sw.TAU_K]
+    )
+    s2 = _roundtrip(s, tmp_path, "rttau")
+    dense = s2.covariance.dense
+    # τ-plus sub-block equals the supplied theory covariance (scatter is correct).
+    assert np.allclose(dense[np.ix_(tau_plus, tau_plus)], tau_cov_th)
+    # τ-minus is diagonal from vartau; plus↔minus cross is zero.
+    tau_minus_var = np.concatenate([np.asarray(tau[f"vartau_{k}_m"]) for k in sw.TAU_K])
+    assert np.allclose(np.diag(dense[np.ix_(tau_minus, tau_minus)]), tau_minus_var)
+    assert np.allclose(dense[np.ix_(tau_plus, tau_minus)], 0.0)
+
+
+def test_rho_tau_to_sacc_tau_cov_shape_mismatch():
+    rho, tau, _ = _rho_tau_tables()
+    with pytest.raises(ValueError, match="tau_cov_th shape"):
+        sw.rho_tau_to_sacc({0: _nz()}, META, rho, tau, tau_cov_th=_spd(3, 1))
+
+
+# --------------------------------------------------------------------------- #
+# Analysis-file assembly
+# --------------------------------------------------------------------------- #
+def _make_parts(nz):
+    theta = _theta()
+    ell = np.array([30.0, 60.0, 90.0])
+
+    class _Wsp:
+        def get_bandpower_windows(self):
+            w = np.zeros((4, 3, 4, 20))
+            for out in range(4):
+                for b in range(3):
+                    w[out, b, out, b * 6 : b * 6 + 6] = 1.0
+            return w
+
+    xi = sw.xi_to_sacc(
+        nz, META, theta, np.arange(6) * 1e-5, np.arange(6) * 2e-5, grid="reporting"
+    )
+    xi.add_covariance(_spd(len(xi.mean), 1))
+    cl_all = np.vstack(
+        [np.arange(3) * 1e-9, np.arange(3) * 2e-9, np.zeros(3), np.arange(3) * 3e-9]
+    )
+    cl = sw.pseudo_cl_to_sacc(nz, META, ell, cl_all, _Wsp(), covariance=_spd(9, 2))
+    co = sw.cosebis_to_sacc(
+        nz,
+        META,
+        {
+            "En": np.arange(1, 6) * 1e-6,
+            "Bn": np.arange(1, 6) * 1e-7,
+            "cov": _spd(10, 3),
+        },
+        (1.0, 100.0),
+    )
+    return [xi, cl, co]
+
+
+def test_assemble_analysis_sacc_block_diagonal_covariance(tmp_path):
+    nz = {0: _nz()}
+    parts = _make_parts(nz)
+    s = sw.assemble_analysis_sacc(parts)
+    assert type(s.covariance).__name__ == "BlockDiagonalCovariance"
+    assert s.covariance.dense.shape == (len(s.mean), len(s.mean))
+    # every point covered; blocks placed and cross-blocks zero
+    tr = ("source_0", "source_0")
+    xi_idx = np.concatenate([s.indices(sio.XI_PLUS, tr), s.indices(sio.XI_MINUS, tr)])
+    cl_idx = np.concatenate(
+        [s.indices(sio.CL_EE, tr), s.indices(sio.CL_BB, tr), s.indices(sio.CL_EB, tr)]
+    )
+    co_idx = np.concatenate(
+        [s.indices(sio.COSEBI_EE, tr), s.indices(sio.COSEBI_BB, tr)]
+    )
+    assert len(xi_idx) + len(cl_idx) + len(co_idx) == len(s.mean)
+    dense = s.covariance.dense
+    assert np.array_equal(dense[np.ix_(xi_idx, xi_idx)], parts[0].covariance.dense)
+    assert np.array_equal(dense[np.ix_(cl_idx, cl_idx)], parts[1].covariance.dense)
+    assert np.array_equal(dense[np.ix_(co_idx, co_idx)], parts[2].covariance.dense)
+    assert np.array_equal(
+        dense[np.ix_(xi_idx, cl_idx)], np.zeros((len(xi_idx), len(cl_idx)))
+    )
+    # round-trips
+    s2 = _roundtrip(s, tmp_path, "analysis")
+    assert type(s2.covariance).__name__ == "BlockDiagonalCovariance"
+    assert np.allclose(s2.covariance.dense, s.covariance.dense)
+
+
+def test_assemble_analysis_sacc_requires_covariance():
+    nz = {0: _nz()}
+    parts = _make_parts(nz)
+    parts.append(
+        sw.xi_to_sacc(
+            nz,
+            META,
+            _theta(),
+            np.arange(6) * 1e-5,
+            np.arange(6) * 2e-5,
+            grid="reporting",
+        )
+    )  # no covariance
+    with pytest.raises(ValueError, match="own covariance block"):
+        sw.assemble_analysis_sacc(parts)
+
+
+def test_assemble_from_reloaded_parts(tmp_path):
+    """Parts written to disk then reloaded assemble identically (the DAG path)."""
+    nz = {0: _nz()}
+    parts = _make_parts(nz)
+    reloaded = []
+    for i, part in enumerate(parts):
+        sio.save(part, str(tmp_path / f"part{i}.sacc"), type="mock")
+        reloaded.append(sio.load(str(tmp_path / f"part{i}.sacc")))
+    s = sw.assemble_analysis_sacc(reloaded)
+    assert type(s.covariance).__name__ == "BlockDiagonalCovariance"
+    assert s.covariance.dense.shape == (len(s.mean), len(s.mean))
+
+
+def test_xi_part_carries_a_dense_covariance(tmp_path):
+    """A grid measured with patches puts its jackknife block in the part."""
+    theta = _theta()
+    cov = _spd(2 * len(theta), 41)
+    s = sw.xi_to_sacc(
+        {0: _nz()},
+        META,
+        theta,
+        np.arange(6) * 1e-5,
+        np.arange(6) * 2e-5,
+        grid="cosebis",
+        covariance=cov,
+    )
+    s2 = _roundtrip(s, tmp_path, "xi_cov")
+    assert np.allclose(s2.covariance.dense, cov)
+
+
+def test_xi_part_rejects_two_covariances():
+    """Dense block and variances are alternatives, not a merge."""
+    theta = _theta()
+    with pytest.raises(ValueError, match="not both"):
+        sw.xi_to_sacc(
+            {0: _nz()},
+            META,
+            theta,
+            np.zeros(6),
+            np.zeros(6),
+            grid="reporting",
+            covariance=_spd(12, 42),
+            variances=np.ones(12),
+        )
diff --git a/src/sp_validation/tests/test_xi_grids.py b/src/sp_validation/tests/test_xi_grids.py
new file mode 100644
index 00000000..c35a2e3f
--- /dev/null
+++ b/src/sp_validation/tests/test_xi_grids.py
@@ -0,0 +1,109 @@
+"""Tests for the ξ± grid table in ``workflow/common.py``.
+
+The table names the files the ``xi`` rule writes and the ones every consumer
+asks for, so producer and consumer agree only if the tag is built from
+canonical values. These tests pin that canonicalisation and the grid lookup.
+"""
+
+import importlib.util
+from pathlib import Path
+
+import pytest
+
+pytestmark = pytest.mark.fast
+
+
+def _load_common():
+    root = next(
+        p for p in Path(__file__).resolve().parents if (p / "pyproject.toml").exists()
+    )
+    path = root / "workflow" / "common.py"
+    spec = importlib.util.spec_from_file_location("wf_common_grids", path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+common = _load_common()
+
+# A cosmo_val block as YAML delivers it: integer-valued separations stay ints.
+CONFIG = {
+    "cosmo_val": {
+        "theta_min": 1.0,
+        "theta_max": 250.0,
+        "nbins": 20,
+        "npatch": 100,
+        "integration": {"min_sep": 0.08, "max_sep": 300, "nbins": 1000},
+        "cosebis": {
+            "min_sep_int": 0.9,
+            "max_sep_int": 300,
+            "nbins_int": 1000,
+            "npatch": 100,
+        },
+    }
+}
+FIDUCIAL = {
+    "min_sep": 1.0,
+    "max_sep": 250.0,
+    "nbins": 20,
+    "npatch": 1,
+    "min_sep_int": 0.5,
+    "max_sep_int": 300,
+    "nbins_int": 1000,
+}
+
+
+def test_tag_is_built_from_canonical_values():
+    """An integer YAML separation still names the file as a float.
+
+    run_2pcf coerces separations with float() before TreeCorr writes, so a
+    `max_sep: 300` that reached the tag as "300" would have the consumer ask
+    for a path the producer never writes.
+    """
+    grids = common.xi_grids(CONFIG, FIDUCIAL)
+    assert common.grid_binning(grids["integration"]).endswith(
+        "minsep=0.08_maxsep=300.0_nbins=1000_npatch=1"
+    )
+    assert (
+        common.grid_binning(grids["cosebis"])
+        == "minsep=0.9_maxsep=300.0_nbins=1000_npatch=100"
+    )
+    # Counts stay integers, so no "nbins=1000.0" creeps into a name.
+    assert "nbins=1000_" in common.grid_binning(grids["cosebis"])
+
+
+def test_grid_lookup_round_trips_through_the_tag():
+    """Every grid's own binning resolves back to that grid.
+
+    This is the producer/consumer contract: the rule resolves a job's grid from
+    the wildcards its filename bound.
+    """
+    grids = common.xi_grids(CONFIG, FIDUCIAL)
+    for name, grid in grids.items():
+        binning = {key: grid[key] for key in common.XI_KEYS}
+        assert common.grid_of(grids, binning) == name
+        # Wildcards arrive as strings; the comparison is numeric.
+        assert common.grid_of(grids, {k: str(v) for k, v in binning.items()}) == name
+
+
+def test_covariance_mode_follows_the_patches():
+    """Patched grids get a jackknife block, unpatched ones none."""
+    grids = common.xi_grids(CONFIG, FIDUCIAL)
+    assert grids["reporting"]["cov"] == "jackknife"
+    assert grids["cosebis"]["cov"] == "jackknife"
+    assert grids["integration"]["cov"] == "none"
+
+
+def test_unnamed_binning_is_a_reporting_measurement():
+    """The paper's convergence-check binning belongs to no named grid."""
+    grids = common.xi_grids(CONFIG, FIDUCIAL)
+    stray = {"min_sep": 1.0, "max_sep": 250.0, "nbins": 10000, "npatch": 1}
+    assert common.grid_of(grids, stray) == "reporting"
+
+
+def test_workflow_without_cosmo_val_falls_back_to_fiducial():
+    """papers/bmodes carries no cosmo_val block; its grids come from FIDUCIAL."""
+    grids = common.xi_grids({}, FIDUCIAL)
+    assert grids["reporting"]["npatch"] == 1
+    assert grids["integration"]["min_sep"] == 0.5
+    assert "cosebis" not in grids
diff --git a/workflow/common.py b/workflow/common.py
index 32a76b83..4391ddbf 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -44,6 +44,9 @@
     )
 )
 CAT_CONFIG = "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
+# "blind" is the glass-mock A/B/C realisation convention, NOT Smokescreen
+# blinding (a separate axis: the concealed=True SACC stamp). The name is baked
+# into on-disk filenames we do not own (e.g. nz_{version}_{A|B|C}.txt).
 BLINDS = ["A", "B", "C"]
 BLOCK_PAIRS = [("++", "1"), ("--", "2"), ("+-", "3")]
 
@@ -263,22 +266,122 @@ def covariance_path(
     return str(COSMO_INFERENCE / f"data/covariance/{base}/{base}{suffix}")
 
 
+def base_version(version):
+    """Strip the derived-catalogue suffixes to the base catalogue version.
+
+    The `_leak_corr` / `_ecut{N}` variants share their parent's n(z) and
+    `cov_th` survey parameters, so lookups keyed on either must strip both.
+    """
+    return re.sub(r"_ecut\d+", "", re.sub(r"_leak_corr$", "", version))
+
+
 def build_redshift_path(version, blind):
     """Construct n(z) filepath for given catalog version and blind."""
-    base_version = re.sub(r"_leak_corr$", "", version)
-    base_version = re.sub(r"_ecut\d+", "", base_version)
-    if "v1.4.11" in base_version:
-        base_version = "SP_v1.4.6"
-    version_dir = base_version.replace("SP_", "")
+    base = base_version(version)
+    if "v1.4.11" in base:
+        base = "SP_v1.4.6"
+    version_dir = base.replace("SP_", "")
+    return f"/n17data/sguerrini/UNIONS/WL/nz/{version_dir}/nz_{base}_{blind}.txt"
+
+
+# ---------------------------------------------------------------------------
+# ξ± angular grids
+# ---------------------------------------------------------------------------
+# A grid is a binning plus how its covariance is estimated: (min_sep, max_sep,
+# nbins, npatch, cov). `reporting` is the analysis grid, `integration` the fine
+# one the B-mode integrals run over, `cosebis` the fine patched grid COSEBIs
+# propagates its covariance from. cov is "jackknife" (dense, from the patches),
+# "diagonal" (TreeCorr varxip/varxim) or "none".
+XI_KEYS = (
+    "min_sep",
+    "max_sep",
+    "nbins",
+    "npatch",
+)  # the binning; cov is not in the name
+
+
+def xi_grids(config, fiducial):
+    """The named ξ± grids of a workflow, canonicalised.
+
+    Workflows carrying no cosmo_val block (e.g. papers/bmodes) fall back to
+    ``fiducial``. Values are coerced here — separations to float, counts to int
+    — so the tag the table stamps into a filename is the one the measurement
+    writes: the separations pass through float() on the way to TreeCorr, so a
+    YAML ``300`` must become ``300.0`` before it names a file, or producer and
+    consumer ask for different paths.
+    """
+    cv = config.get("cosmo_val", {})
+    grids = {
+        "reporting": (
+            {
+                "min_sep": cv["theta_min"],
+                "max_sep": cv["theta_max"],
+                "nbins": cv["nbins"],
+                "npatch": cv["npatch"],
+            }
+            if cv
+            else {k: fiducial[k] for k in XI_KEYS}
+        ),
+        "integration": dict(
+            cv.get("integration")
+            or {
+                "min_sep": fiducial["min_sep_int"],
+                "max_sep": fiducial["max_sep_int"],
+                "nbins": fiducial["nbins_int"],
+            }
+        ),
+    }
+    grids["integration"].setdefault("npatch", 1)
+    cb = cv.get("cosebis")
+    if cb:
+        grids["cosebis"] = {
+            "min_sep": cb["min_sep_int"],
+            "max_sep": cb["max_sep_int"],
+            "nbins": cb["nbins_int"],
+            "npatch": cb["npatch"],
+        }
+    for grid in grids.values():
+        for key in ("min_sep", "max_sep"):
+            grid[key] = float(grid[key])
+        for key in ("nbins", "npatch"):
+            grid[key] = int(grid[key])
+        # A jackknife estimate needs patches; at npatch=1 TreeCorr's var_method
+        # is "shot" and the diagonal is all it can offer.
+        grid.setdefault("cov", "jackknife" if grid["npatch"] > 1 else "none")
+    return grids
+
+
+def grid_binning(grid):
+    """The `minsep=..._maxsep=..._nbins=..._npatch=...` tag of one grid."""
     return (
-        f"/n17data/sguerrini/UNIONS/WL/nz/{version_dir}/nz_{base_version}_{blind}.txt"
+        f"minsep={grid['min_sep']}_maxsep={grid['max_sep']}"
+        f"_nbins={grid['nbins']}_npatch={grid['npatch']}"
     )
 
 
+def grid_of(grids, binning):
+    """Name of the grid a binning belongs to, compared numerically.
+
+    A "300" wildcard matches a 300.0 grid value. Binnings matching no named
+    grid (e.g. papers/bmodes' nbins=10000 convergence check) are reporting-style
+    measurements.
+    """
+    key = tuple(float(binning[k]) for k in XI_KEYS)
+    for name, grid in grids.items():
+        if tuple(float(grid[k]) for k in XI_KEYS) == key:
+            return name
+    return "reporting"
+
+
+def pseudo_cl_tag(config):
+    """Fiducial harmonic-binning tag stamped into pseudo-Cl filenames."""
+    fiducial = config["harmonic"]["fiducial"]
+    return f"blind={fiducial['blind']}_{fiducial['binning']}_nbins={fiducial['nbins']}"
+
+
 def get_shear_catalog(wildcards):
     """Resolve shear catalog path from config for a given version."""
-    base_version = wildcards.version.replace("_leak_corr", "")
-    cat_config = CATALOG_CONFIG[base_version]
+    cat_config = CATALOG_CONFIG[wildcards.version.replace("_leak_corr", "")]
     shear_path = cat_config["shear"]["path"]
     if shear_path.startswith("/"):
         return shear_path
diff --git a/workflow/rules/cosmo_val.smk b/workflow/rules/cosmo_val.smk
index 704b4fb0..87138bc0 100644
--- a/workflow/rules/cosmo_val.smk
+++ b/workflow/rules/cosmo_val.smk
@@ -3,29 +3,33 @@
 # The original cosmo_val/run_cosmo_val.py was one linear driver that built a
 # single in-memory `cv` (CosmologyValidation) and called ~13 cv.<method>()
 # diagnostics in sequence, linked only by lazy properties on that object. Here
-# each diagnostic is a rule, and the rules are linked by the *real* data
-# products each method writes under COSMO_VAL (= cosmo_val/output):
+# each diagnostic is a rule, and the rules are linked by the SACC parts and
+# products they write under COSMO_VAL (= cosmo_val/output):
 #
-#   rho/tau FITS  ──┬─→ rho/tau plots
-#                   ├─→ rho_tau_fits (PSF-error MCMC)
-#                   └─────────────────────────────┐
-#   additive bias ──→ xi (2pcf) ──┬─→ 2pcf plot   │
-#                                 ├─→ ratio_xi_sys_xi ←┘ (also needs xi_psf_sys)
-#                                 ├─→ pure_eb (npz) ─┐
-#                                 └─→ cosebis (npz) ─┤
-#   pseudo_cl FITS ──────────────────────────────────┼─→ summarize_bmodes
-#                                                     ┘
+#   catalogue ──→ xi (one job per grid: reporting, integration, cosebis)
+#                   │
+#                   ├─ reporting part ──┬─→ pure_eb (part, npz, figures)
+#                   ├─ integration part ┘        │
+#                   ├─ cosebis part ─────→ cosebis (part, npz, figures)
+#                   └─ reporting .txt ──→ 2pcf plot, ratio_xi_sys_xi
+#   catalogue ──→ pseudo_cl (part) ──┬─→ pseudo-Cl figures
+#   CosmoCov ──→ covariance ─────────┤
+#   rho/tau (part + FITS) ───────────┼─→ summarize_bmodes (reads the products)
+#                                    └─→ assemble_sacc ──→ {version}.sacc
 #
-# Granularity decision: methods that write durable data products
-# (calculate_rho_tau_stats, calculate_2pcf, calculate_pseudo_cl, plot_pure_eb,
-# plot_cosebis) own a compute rule keyed on those files. Methods that only
-# emit figures, or whose figure paths derive from internal handler state
-# (rho/tau plots, rho_tau_fits, objectwise leakage, 2pcf overlay), declare a
-# sentinel under COSMO_VAL/snakemake_sentinels so they stay DAG-trackable.
-# Lazy cv state that the original code never persists (c1/c2, xi_psf_sys) is
-# either materialized to a small JSON (additive bias) or recomputed in the one
-# rule that needs it (xi_psf_sys in ratio_xi_sys_xi) — recompute is cheap next
-# to the science it depends on. See workflow/scripts/cv_runner.py.
+# The B-mode rules are ingests: pure_eb, cosebis, the pseudo-Cl figures and the
+# summary all work from the parts and the covariance inputs, never from a
+# catalogue, so a blinded part keeps everything downstream blinded. The
+# analytic covariances (CosmoCov ξ±, NaMaster pseudo-Cℓ) are what assembly puts
+# in the terminal file, replacing the estimates a part was born with.
+#
+# Methods that only emit figures, or whose figure paths derive from internal
+# handler state (rho/tau plots, rho_tau_fits, objectwise leakage, 2pcf
+# overlay), declare a sentinel under COSMO_VAL/snakemake_sentinels so they stay
+# DAG-trackable. Lazy cv state the original code never persists (c1/c2,
+# xi_psf_sys) is either materialized to a small JSON (additive bias) or
+# recomputed in the one rule that needs it — recompute is cheap next to the
+# science it depends on. See workflow/scripts/cv_runner.py.
 
 CV = config["cosmo_val"]
 CV_VERSIONS = config["versions"]
@@ -52,13 +56,7 @@ def cv_xi_txt(version):
     Mirrors the out_fname f-string in cosmo_val.calculate_2pcf:
     {ver}_xi_minsep=..._maxsep=..._nbins=..._npatch=...txt
     """
-    return str(
-        COSMO_VAL
-        / (
-            f"{version}_xi_minsep={CV['theta_min']}_maxsep={CV['theta_max']}"
-            f"_nbins={CV['nbins']}_npatch={CV['npatch']}.txt"
-        )
-    )
+    return str(COSMO_VAL / f"{version}_xi_{xi_binning('reporting')}.txt")
 
 
 def cv_rho_stats(version):
@@ -73,39 +71,140 @@ def cv_tau_stats(version):
     )
 
 
-def cv_pure_eb_npz(version):
-    eb = CV["pure_eb"]
+def _pure_eb_stub(version):
+    """Shared stem of the pure-E/B diagnostic products (npz + figures)."""
+    eb = CV["integration"]
     return str(
         COSMO_VAL
         / (
             f"{version}_eb_minsep={CV['theta_min']}_maxsep={CV['theta_max']}"
-            f"_nbins={CV['nbins']}_minsepint={eb['min_sep_int']}"
-            f"_maxsepint={eb['max_sep_int']}_nbinsint={eb['nbins_int']}"
-            f"_npatch={CV['npatch']}_varmethod=jackknife_data.npz"
+            f"_nbins={CV['nbins']}_minsepint={eb['min_sep']}"
+            f"_maxsepint={eb['max_sep']}_nbinsint={eb['nbins']}"
+            f"_npatch={CV['npatch']}_varmethod=semi-analytic"
         )
     )
 
 
-def cv_cosebis_npz(version):
+def cv_pure_eb_npz(version):
+    """Pure-E/B data vectors + covariance .npz."""
+    return _pure_eb_stub(version) + "_data.npz"
+
+
+def cv_pure_eb_figures(version):
+    """The pure-E/B companion figures, by output key."""
+    stub = _pure_eb_stub(version)
+    return {
+        "figure_integration_vs_reporting": f"{stub}_integration_vs_reporting.png",
+        "figure_xis": f"{stub}_xis.png",
+        "figure_ptes": f"{stub}_ptes.png",
+        "figure_covariance": f"{stub}_covariance.png",
+    }
+
+
+def cv_xi_cov_integration(version):
+    """CosmoCov gaussian ξ± covariance on the integration grid.
+
+    The covariance model the pure-E/B Monte Carlo draws from; gaussian because
+    the draws only need the scatter a Gaussian field would give.
+    """
+    integ = CV["integration"]
+    return covariance_path(
+        version,
+        FIDUCIAL["blind"],
+        gaussian="g",
+        min_sep=integ["min_sep"],
+        max_sep=integ["max_sep"],
+        nbins=integ["nbins"],
+        mask_suffix=DEFAULT_MASK_SUFFIX,
+    )
+
+
+def _cosebis_stub(version):
+    """Shared stem of the COSEBIs diagnostic products (npz + figures).
+
+    varmethod names where the covariance came from, and these products are the
+    propagated one — which also keeps them clear of the paths plot_cosebis
+    builds for its own byproducts, so nothing overwrites a declared output.
+    """
     cb = CV["cosebis"]
     fsc = CV["fiducial_scale_cut"]
-    # Mirror calculate/plot_cosebis out_stub (cosmo_val.py): a distinct schema
-    # from pure_eb — _cosebis_ prefix, integration nbins, plus _nmodes= and
-    # _scalecut= segments. Must match save_cosebis_results exactly or
-    # verify_outputs raises and cv_summarize_bmodes deadlocks on this input.
     return str(
         COSMO_VAL
         / (
             f"{version}_cosebis_minsep={cb['min_sep_int']}"
             f"_maxsep={cb['max_sep_int']}_nbins={cb['nbins_int']}"
-            f"_npatch={cb['npatch']}_varmethod=jackknife_nmodes={cb['nmodes']}"
-            f"_scalecut={fsc[0]}-{fsc[1]}_data.npz"
+            f"_npatch={cb['npatch']}_varmethod=propagated_nmodes={cb['nmodes']}"
+            f"_scalecut={fsc[0]}-{fsc[1]}"
         )
     )
 
 
-def cv_pseudo_cl_fits(version):
-    return str(COSMO_VAL / f"pseudo_cl_{version}.fits")
+def cv_cosebis_npz(version):
+    """COSEBIs multi-cut diagnostic .npz (the PTE scan)."""
+    return _cosebis_stub(version) + "_data.npz"
+
+
+def cv_cosebis_figures(version):
+    """The COSEBIs companion figures, by output key."""
+    stub = _cosebis_stub(version)
+    return {
+        "figure_modes": f"{stub}_cosebis.png",
+        "figure_covariance": f"{stub}_covariance.png",
+        "figure_scalecut_ptes": f"{stub}_scalecut_ptes.png",
+    }
+
+
+_PSEUDO_CL_TAG = pseudo_cl_tag(config)
+
+
+def cv_pseudo_cl_analysis_sacc(version):
+    """Tagged pseudo-Cl SACC part: the harmonic block of the analysis file."""
+    return str(COSMO_VAL / f"pseudo_cl_{version}_{_PSEUDO_CL_TAG}.sacc")
+
+
+def cv_pseudo_cl_cov(version):
+    """NaMaster pseudo-Cl covariance FITS (COVAR_EE_EE/BB_BB/EB_EB extensions)."""
+    return str(COSMO_VAL / f"pseudo_cl_cov_{version}_{_PSEUDO_CL_TAG}.fits")
+
+
+def cv_xi_cov(version):
+    """CosmoCov-processed ξ± covariance, on the reporting grid's own binning."""
+    return covariance_path(
+        version,
+        FIDUCIAL["blind"],
+        gaussian="ng",
+        min_sep=CV["theta_min"],
+        max_sep=CV["theta_max"],
+        nbins=CV["nbins"],
+        mask_suffix=DEFAULT_MASK_SUFFIX,
+    )
+
+
+def cv_cosebis_sacc(version):
+    """COSEBIs SACC part, at the fiducial scale cut."""
+    return str(COSMO_VAL / f"{version}_cosebis.sacc")
+
+
+def cv_pure_eb_sacc(version):
+    """Pure-E/B SACC part."""
+    return str(COSMO_VAL / f"{version}_pure_eb.sacc")
+
+
+def cv_rho_tau_sacc(version):
+    """ρ/τ SACC part."""
+    return str(
+        COSMO_VAL / "rho_tau_stats" / f"rho_tau_{cv_basename(version, CV_FIDUCIAL)}.sacc"
+    )
+
+
+def cv_xi_sacc(version, grid):
+    """ξ± SACC part for a version on a named grid, named by that grid's binning."""
+    return str(COSMO_VAL / f"{version}_xi_{xi_binning(grid)}.sacc")
+
+
+def cv_analysis_sacc(version):
+    """Terminal assembled analysis file {version}.sacc."""
+    return str(COSMO_VAL / f"{version}.sacc")
 
 
 # Common params block shared by every cosmo_val rule: the cv constructor kwargs
@@ -274,18 +373,32 @@ rule cv_ratio_xi_sys_xi:
 # Harmonic-space pseudo-Cl
 # ---------------------------------------------------------------------------
 
-rule cv_pseudo_cl:
-    """Pseudo-Cl E/B spectra for all versions (NaMaster)."""
+def cv_pseudo_cl_figures():
+    """The pseudo-Cl figures, by output key (one per spectrum, all versions)."""
+    return {
+        f"figure_{name}": str(COSMO_VAL / f"cell_{name}.png")
+        for name in ("ee", "eb", "bb")
+    }
+
+
+rule cv_plot_pseudo_cl:
+    """The EE/EB/BB pseudo-Cl figures, from the analysis parts."""
+    input:
+        pseudo_cl=[cv_pseudo_cl_analysis_sacc(v) for v in CV_VERSIONS],
+        pseudo_cl_cov=[cv_pseudo_cl_cov(v) for v in CV_VERSIONS],
     output:
-        pseudo_cl=[cv_pseudo_cl_fits(v) for v in CV_VERSIONS],
+        **cv_pseudo_cl_figures(),
     params:
-        **cv_params(),
-    threads: 12
+        versions=CV_VERSIONS,
+        # Style is per catalogue, so the derived variants take their parent's.
+        markers=[CATALOG_CONFIG[base_version(v)]["marker"] for v in CV_VERSIONS],
+        colours=[CATALOG_CONFIG[base_version(v)]["colour"] for v in CV_VERSIONS],
+        rundir=CV_RUNDIR,
     resources:
-        mem_mb=32000,
-        runtime=180,
+        mem_mb=8000,
+        runtime=20,
     script:
-        "../scripts/cv_pseudo_cl.py"
+        "../scripts/cv_plot_pseudo_cl.py"
 
 
 # ---------------------------------------------------------------------------
@@ -293,18 +406,27 @@ rule cv_pseudo_cl:
 # ---------------------------------------------------------------------------
 
 rule cv_pure_eb:
-    """Pure E/B-mode decomposition for one version (config-space)."""
+    """Pure E/B-mode decomposition for one version, from its ξ± parts.
+
+    The modes come from the two parts; the covariance is Monte Carlo from the
+    integration-grid covariance model, so no patched estimator run is involved.
+    """
     input:
-        xi=lambda w: cv_xi_txt(w.version),
+        xi_reporting=lambda w: cv_xi_sacc(w.version, "reporting"),
+        xi_integration=lambda w: cv_xi_sacc(w.version, "integration"),
+        cov_integration=lambda w: cv_xi_cov_integration(w.version),
     output:
         npz=cv_pure_eb_npz("{version}"),
+        sacc=cv_pure_eb_sacc("{version}"),
+        **cv_pure_eb_figures("{version}"),
     params:
         version="{version}",
-        min_sep_int=CV["pure_eb"]["min_sep_int"],
-        max_sep_int=CV["pure_eb"]["max_sep_int"],
-        nbins_int=CV["pure_eb"]["nbins_int"],
+        min_sep=CV["theta_min"],
+        max_sep=CV["theta_max"],
+        nbins=CV["nbins"],
+        n_samples=CV.get("n_mc_samples", 1000),
+        cosmo_params=CV["cosmo_params"],
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-        cv_init=lambda w: cv_init_params(config, version_list=[w.version]),
         rundir=CV_RUNDIR,
     threads: 24
     resources:
@@ -315,21 +437,25 @@ rule cv_pure_eb:
 
 
 rule cv_cosebis:
-    """COSEBIs E/B decomposition for one version (config-space, fine binning)."""
+    """COSEBIs E/B decomposition for one version, from its ξ± part.
+
+    Values, covariance and PTEs all come from the part: the COSEBIs covariance
+    is the part's ξ± covariance through the same kernel as the modes.
+    """
     input:
-        xi=lambda w: cv_xi_txt(w.version),
+        xi=lambda w: cv_xi_sacc(w.version, "cosebis"),
     output:
         npz=cv_cosebis_npz("{version}"),
+        sacc=cv_cosebis_sacc("{version}"),
+        **cv_cosebis_figures("{version}"),
     params:
         version="{version}",
-        min_sep_int=CV["cosebis"]["min_sep_int"],
-        max_sep_int=CV["cosebis"]["max_sep_int"],
-        nbins_int=CV["cosebis"]["nbins_int"],
-        npatch=CV["cosebis"]["npatch"],
+        min_sep=CV["cosebis"]["min_sep_int"],
+        max_sep=CV["cosebis"]["max_sep_int"],
+        nbins=CV["cosebis"]["nbins_int"],
         nmodes=CV["cosebis"]["nmodes"],
         scale_cuts=CV["cosebis"]["scale_cuts"],
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-        cv_init=lambda w: cv_init_params(config, version_list=[w.version]),
         rundir=CV_RUNDIR,
     threads: 24
     resources:
@@ -345,32 +471,86 @@ rule cv_summarize_bmodes:
         pure_eb=[cv_pure_eb_npz(v) for v in CV_VERSIONS],
         cosebis=[cv_cosebis_npz(v) for v in CV_VERSIONS],
         pseudo_cl=(
-            [cv_pseudo_cl_fits(v) for v in CV_VERSIONS]
+            [cv_pseudo_cl_analysis_sacc(v) for v in CV_VERSIONS]
+            if CV.get("include_pseudo_cl", False) else []
+        ),
+        pseudo_cl_cov=(
+            [cv_pseudo_cl_cov(v) for v in CV_VERSIONS]
             if CV.get("include_pseudo_cl", False) else []
         ),
     output:
         summary_json=str(COSMO_VAL / "bmode_summary.json"),
     params:
+        versions=CV_VERSIONS,
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-        pure_eb_min_sep_int=CV["pure_eb"]["min_sep_int"],
-        pure_eb_max_sep_int=CV["pure_eb"]["max_sep_int"],
-        pure_eb_nbins_int=CV["pure_eb"]["nbins_int"],
-        cosebis_min_sep_int=CV["cosebis"]["min_sep_int"],
-        cosebis_max_sep_int=CV["cosebis"]["max_sep_int"],
-        cosebis_nbins_int=CV["cosebis"]["nbins_int"],
-        cosebis_npatch=CV["cosebis"]["npatch"],
-        cosebis_nmodes=CV["cosebis"]["nmodes"],
-        cosebis_scale_cuts=CV["cosebis"]["scale_cuts"],
+        min_sep=CV["theta_min"],
+        max_sep=CV["theta_max"],
+        nbins=CV["nbins"],
         include_pseudo_cl=CV.get("include_pseudo_cl", False),
-        **cv_params(),
-    threads: 24
+        rundir=CV_RUNDIR,
     resources:
-        mem_mb=48000,
-        runtime=600,
+        mem_mb=8000,
+        runtime=20,
     script:
         "../scripts/cv_summarize_bmodes.py"
 
 
+# ---------------------------------------------------------------------------
+# Terminal analysis file: assemble the per-statistic SACC parts into {version}.sacc
+# ---------------------------------------------------------------------------
+# The terminal file carries the analysis vector only. The integration-grid ξ± is
+# deliberately not gathered: it stays a per-part intermediate. The two blocks
+# born without a covariance (ξ± reporting, pseudo-Cℓ) get theirs injected from
+# the covariance inputs below.
+
+
+def cv_assemble_inputs(version):
+    """The per-statistic SACC parts + covariance inputs assemble_sacc consumes.
+
+    Each part's filename carries enough to bind its producing rule's wildcards.
+    """
+    parts = dict(
+        xi_reporting=cv_xi_sacc(version, "reporting"),
+        xi_cov=cv_xi_cov(version),
+        cosebis=cv_cosebis_sacc(version),
+        pure_eb=cv_pure_eb_sacc(version),
+        rho_tau=cv_rho_tau_sacc(version),
+    )
+    if CV.get("include_pseudo_cl", False):
+        parts["pseudo_cl"] = cv_pseudo_cl_analysis_sacc(version)
+        parts["pseudo_cl_cov"] = cv_pseudo_cl_cov(version)
+    return parts
+
+
+rule assemble_sacc:
+    """Assemble the terminal {version}.sacc from the per-statistic SACC parts."""
+    input:
+        unpack(lambda w: cv_assemble_inputs(w.version)),
+    output:
+        sacc=cv_analysis_sacc("{version}"),
+    params:
+        version="{version}",
+        type=CV.get("type", "data"),
+        # The statistics this rule wired, so a typo'd input keyword cannot
+        # silently drop one.
+        expected=lambda w: [
+            k
+            for k in cv_assemble_inputs(w.version)
+            if k not in ("xi_cov", "pseudo_cl_cov")
+        ],
+    resources:
+        mem_mb=8000,
+        runtime=20,
+    script:
+        "../scripts/assemble_sacc.py"
+
+
+rule assemble_sacc_all:
+    """Assemble the analysis SACC file for every version."""
+    input:
+        [cv_analysis_sacc(v) for v in CV_VERSIONS],
+
+
 # ---------------------------------------------------------------------------
 # Aggregate target: the whole validation suite
 # ---------------------------------------------------------------------------
@@ -392,3 +572,6 @@ rule cosmo_val_all:
         str(COSMO_VAL / "ratio_xi_sys_xi.png"),
         # B-modes
         str(COSMO_VAL / "bmode_summary.json"),
+        list(cv_pseudo_cl_figures().values()) if CV.get("include_pseudo_cl", False) else [],
+        # Terminal analysis file: the assembled {version}.sacc per version
+        [cv_analysis_sacc(v) for v in CV_VERSIONS],
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index b2df6c7c..1b6663b6 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -1,13 +1,34 @@
 # Two-point data-vector rules: xi, rho/tau, and pseudo-Cl products.
 
+# ---------------------------------------------------------------------------
+# ξ± angular grids
+# ---------------------------------------------------------------------------
+# The table itself lives in common.py, where it can be built from a plain config
+# dict and tested; these are the workflow's bindings to it.
+XI_GRIDS = xi_grids(config, FIDUCIAL)
+
+
+def xi_binning(grid):
+    """The `minsep=..._maxsep=..._nbins=..._npatch=...` tag of a named grid."""
+    return grid_binning(XI_GRIDS[grid])
+
+
+def xi_grid_of(wildcards):
+    """Grid label for the binning a job was requested with."""
+    return grid_of(XI_GRIDS, {key: getattr(wildcards, key) for key in XI_KEYS})
+
 
 rule xi:
+    """TreeCorr ξ±(θ) for one version on one angular grid.
+
+    One rule for every grid: outputs are named by their binning, so a request
+    binds the wildcards and `xi_grid_of` resolves the grid label from them.
+    """
     input:
         catalog=get_shear_catalog,
     output:
-        str(COSMO_VAL / "{version}_xi_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.txt"),
-        str(COSMO_VAL / "xi_plus_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
-        str(COSMO_VAL / "xi_minus_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
+        txt=str(COSMO_VAL / "{version}_xi_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.txt"),
+        sacc=str(COSMO_VAL / "{version}_xi_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.sacc"),
     threads: 24
     params:
         ver="{version}",
@@ -17,11 +38,14 @@ rule xi:
         npatch="{npatch}",
         cat_config=CAT_CONFIG,
         output_dir=str(COSMO_VAL),
-        fits=False,
+        grid=lambda w: xi_grid_of(w),
+        cov=lambda w: XI_GRIDS[xi_grid_of(w)]["cov"],
     resources:
-        mem_mb=30000,
+        # The fine integration grid needs more memory and wall time than the
+        # ~20-bin reporting one; scale on nbins rather than splitting the rule.
+        mem_mb=lambda w: 40000 if int(w.nbins) > 100 else 30000,
         disk_mb=20000,
-        runtime=360,
+        runtime=lambda w: 600 if int(w.nbins) > 100 else 360,
     script:
         "../scripts/run_2pcf.py"
 
@@ -30,6 +54,8 @@ rule rho_tau_stats:
     output:
         rho_stats=str(COSMO_VAL / "rho_tau_stats/rho_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
         tau_stats=str(COSMO_VAL / "rho_tau_stats/tau_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
+        # Born-as-SACC ρ/τ part, written alongside the FITS.
+        rho_tau=str(COSMO_VAL / "rho_tau_stats/rho_tau_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.sacc"),
     threads: 48
     params:
         ver="{version}",
@@ -54,9 +80,9 @@ wildcard_constraints:
 
 
 rule pseudo_cl:
-    """Generate pseudo-Cl data vector with configurable binning."""
+    """Generate pseudo-Cl data vector (born as SACC) with configurable binning."""
     output:
-        pseudo_cl=str(COSMO_VAL / "pseudo_cl_{version}_blind={blind}_{binning}_nbins={nbins}.fits"),
+        pseudo_cl=str(COSMO_VAL / "pseudo_cl_{version}_blind={blind}_{binning}_nbins={nbins}.sacc"),
     wildcard_constraints:
         blind="[ABC]",
     params:
@@ -108,7 +134,7 @@ rule pseudo_cl_all:
     """Generate pseudo-Cls for all versions."""
     input:
         expand(
-            str(COSMO_VAL / "pseudo_cl_{version}_blind=A_powspace_nbins=32.fits"),
+            str(COSMO_VAL / "pseudo_cl_{version}_blind=A_powspace_nbins=32.sacc"),
             version=PSEUDO_CL_VERSIONS,
         ),
 
@@ -126,7 +152,7 @@ rule pseudo_cl_fine_all:
     """Generate fine pseudo-Cls for COSEBIS."""
     input:
         expand(
-            str(COSMO_VAL / "pseudo_cl_{version}_blind={blind}_linear_nbins=2040.fits"),
+            str(COSMO_VAL / "pseudo_cl_{version}_blind={blind}_linear_nbins=2040.sacc"),
             version=config["versions"],
             blind=BLINDS,
         ),
diff --git a/workflow/scripts/assemble_sacc.py b/workflow/scripts/assemble_sacc.py
new file mode 100644
index 00000000..50d1d62e
--- /dev/null
+++ b/workflow/scripts/assemble_sacc.py
@@ -0,0 +1,203 @@
+"""Assemble the terminal ``{version}.sacc`` analysis file from per-statistic parts.
+
+Dual-mode: under Snakemake (``script:``) the injected ``snakemake`` object
+supplies the inputs; as a standalone CLI the same assembly runs from flags.
+
+Each part is a single-statistic SACC; they load in CANONICAL order and are
+rebuilt into one Sacc with a single ``BlockDiagonalCovariance``.
+
+Every part must carry a covariance block. ξ± reporting and pseudo-Cℓ take
+theirs from the analytic inputs — the CosmoCov ``.txt`` (``--xi-cov``) and the
+NaMaster covariance FITS (``--pseudo-cl-cov``) — which replace any estimate the
+part was born with; the pseudo-Cℓ cross-spectrum blocks (EE↔BB, …) are dropped,
+matching what the B-mode PTE reads today.
+"""
+
+import argparse
+
+import numpy as np
+
+from sp_validation import sacc_io
+from sp_validation.cosmo_val.sacc_writers import assemble_analysis_sacc
+
+# NaMaster iNKA covariance FITS: per-spectrum HDU names, in SACC insertion order.
+_CL_HDUS = ("COVAR_EE_EE", "COVAR_BB_BB", "COVAR_EB_EB")
+
+# Canonical part order — the order points are inserted in, which must match the
+# covariance block order. Missing parts are simply skipped.
+CANONICAL = ("xi_reporting", "pseudo_cl", "cosebis", "pure_eb", "rho_tau")
+
+
+def _pseudo_cl_cov_block(cov_fits):
+    """Block-diagonal ``[EE; BB; EB]`` from the NaMaster iNKA covariance FITS."""
+    from astropy.io import fits
+
+    with fits.open(cov_fits) as hdul:
+        missing = [name for name in _CL_HDUS if name not in {h.name for h in hdul}]
+        if missing:
+            raise ValueError(f"{cov_fits} lacks the pseudo-Cℓ cov HDUs {missing}")
+        blocks = [np.asarray(hdul[name].data, float) for name in _CL_HDUS]
+    n = blocks[0].shape[0]
+    full = np.zeros((3 * n, 3 * n))
+    for i, block in enumerate(blocks):
+        full[i * n : (i + 1) * n, i * n : (i + 1) * n] = block
+    return full
+
+
+# The statistics whose analysis covariance is external, and the input each one
+# takes it from. A part of one of these types may be born with an estimate of
+# its own — the ξ± reporting part carries the jackknife it was measured with —
+# but the analysis file takes the external one, always.
+_INJECTED = {"xi_reporting": "xi_cov", "pseudo_cl": "pseudo_cl_cov"}
+
+
+def _attach_cov(part, name, xi_cov, pseudo_cl_cov):
+    """Give ``part`` (mutated in place) the covariance the analysis file uses.
+
+    For the two statistics with an external covariance the supplied block
+    replaces whatever the part was born with, loudly; every other part keeps
+    its own. Raises if the block a part needs was not supplied.
+    """
+    if name not in _INJECTED:
+        if part.covariance is None:
+            raise ValueError(
+                f"the {name!r} part carries no covariance and none is injected "
+                "for it; its writer must attach one"
+            )
+        return part
+
+    supplied = xi_cov if name == "xi_reporting" else pseudo_cl_cov
+    if supplied is None:
+        raise ValueError(
+            f"the {name!r} part takes its analysis covariance from "
+            f"--{_INJECTED[name].replace('_', '-')}, which was not supplied"
+        )
+    block = (
+        np.loadtxt(supplied)
+        if name == "xi_reporting"
+        else _pseudo_cl_cov_block(supplied)
+    )
+    if part.covariance is not None:
+        print(
+            f"{name}: replacing the part's own covariance with {supplied} "
+            "(the analysis covariance)"
+        )
+    part.add_covariance(block, overwrite=True)
+    return part
+
+
+def assemble_sacc(
+    version,
+    part_paths,
+    out_path,
+    *,
+    expected=None,
+    xi_cov=None,
+    pseudo_cl_cov=None,
+    allow_unblinded=False,
+):
+    """Assemble ``{version}.sacc`` from the per-statistic ``part_paths`` mapping.
+
+    Parameters
+    ----------
+    version : str
+        Catalogue version, for error messages.
+    part_paths : dict
+        ``{statistic: path}`` with statistic in :data:`CANONICAL`. Only present
+        statistics are assembled; order is forced to canonical.
+    expected : sequence of str, optional
+        Statistics that must be present, from the caller's config toggles. A
+        typo'd input keyword would otherwise silently drop a statistic.
+    xi_cov, pseudo_cl_cov
+        Covariance sourcing — see the module docstring.
+    allow_unblinded : bool, optional
+        Passed to :func:`sacc_io.load` for every part; ``True`` only for mocks.
+    """
+    if expected is not None:
+        unknown = [name for name in expected if name not in CANONICAL]
+        if unknown:
+            raise ValueError(
+                f"expected parts {unknown} are not assemblable statistics; "
+                f"valid names are {CANONICAL}"
+            )
+        missing = [name for name in expected if not part_paths.get(name)]
+        if missing:
+            raise ValueError(
+                f"expected parts {missing} missing from part_paths for {version} "
+                f"(got {sorted(part_paths)}); a required statistic would be "
+                "silently dropped from the terminal analysis file"
+            )
+    parts = []
+    for name in CANONICAL:
+        path = part_paths.get(name)
+        if path is None:
+            continue
+        part = sacc_io.load(path, allow_unblinded=allow_unblinded)
+        parts.append(_attach_cov(part, name, xi_cov, pseudo_cl_cov))
+    if not parts:
+        raise ValueError(f"no parts found for {version}: {part_paths}")
+    s = assemble_analysis_sacc(parts)
+    sacc_io.save(s, out_path, type=s.metadata["type"])
+    print(f"Assembled {len(parts)} parts -> {out_path}")
+    return s
+
+
+def _from_snakemake(smk):
+    p = smk.params
+    inp = smk.input
+    part_paths = {
+        name: getattr(inp, name)
+        for name in CANONICAL
+        if hasattr(inp, name) and getattr(inp, name)
+    }
+    assemble_sacc(
+        version=p["version"],
+        part_paths=part_paths,
+        out_path=str(smk.output[0]),
+        expected=list(p["expected"]),
+        xi_cov=getattr(inp, "xi_cov", None),
+        pseudo_cl_cov=getattr(inp, "pseudo_cl_cov", None),
+        allow_unblinded=(p.get("type", "data") == "mock"),
+    )
+
+
+def _from_cli(argv=None):
+    ap = argparse.ArgumentParser(
+        description="Assemble the terminal {version}.sacc from per-statistic parts."
+    )
+    ap.add_argument("--version", required=True, help="Catalogue version")
+    ap.add_argument("--out", required=True, help="Output {version}.sacc path")
+    ap.add_argument(
+        "--type",
+        choices=("data", "mock"),
+        default="data",
+        help="Run type. 'mock' reads parts freely; 'data' fails closed on "
+        "unblinded parts (only concealed/blinded parts load).",
+    )
+    for name in CANONICAL:
+        ap.add_argument(
+            f"--{name.replace('_', '-')}", default=None, help=f"{name} part"
+        )
+    ap.add_argument("--xi-cov", default=None, help="CosmoCov ξ covariance .txt")
+    ap.add_argument(
+        "--pseudo-cl-cov", default=None, help="NaMaster pseudo-Cℓ covariance FITS"
+    )
+    a = ap.parse_args(argv)
+    part_paths = {name: getattr(a, name) for name in CANONICAL if getattr(a, name)}
+    assemble_sacc(
+        version=a.version,
+        part_paths=part_paths,
+        out_path=a.out,
+        xi_cov=a.xi_cov,
+        pseudo_cl_cov=a.pseudo_cl_cov,
+        allow_unblinded=(a.type == "mock"),
+    )
+
+
+if __name__ == "__main__":
+    try:
+        snakemake  # noqa: F821 — injected by Snakemake's script: directive
+    except NameError:
+        _from_cli()
+    else:
+        _from_snakemake(snakemake)  # noqa: F821
diff --git a/workflow/scripts/cv_cosebis.py b/workflow/scripts/cv_cosebis.py
index c1ca8275..c9241eaa 100644
--- a/workflow/scripts/cv_cosebis.py
+++ b/workflow/scripts/cv_cosebis.py
@@ -1,25 +1,70 @@
 """Rule cv_cosebis: COSEBIs E/B decomposition for one version.
 
-Compute + plot rule (per version). plot_cosebis calls calculate_cosebis over a
-fine integration binning (the 2000-bin TreeCorr is the dominant cost) and
-evaluates the configured scale cuts. Writes the {version}_eb_..._data.npz
-COSEBIs data product (declared output) plus figures, and the per-version
-COSEBIs PTE that cv_summarize_bmodes collects.
+A consumer of the ξ± part alone — values, covariance, PTEs and figures all
+derive from it, so nothing here touches a catalogue. The part's ξ± covariance
+goes through the same linear kernel as the modes to give the COSEBIs
+covariance; its ``npatch`` metadata sets the Hartlap debiasing.
 """
 
-from cv_runner import _unbuffer_streams, make_cv, verify_outputs
+from cv_runner import _unbuffer_streams, verify_outputs
+
+from sp_validation import sacc_io
+from sp_validation.b_modes import (
+    cosebis_scan_from_xi,
+    find_conservative_scale_cut_key,
+    log_bin_edges,
+    plot_cosebis_covariance_matrix,
+    plot_cosebis_modes,
+    plot_cosebis_scale_cut_heatmap,
+    save_cosebis_results,
+)
+from sp_validation.cosmo_val.sacc_writers import cosebis_to_sacc
 
 _unbuffer_streams()
-cv = make_cv(snakemake)
 p = snakemake.params
-cv.plot_cosebis(
-    version=p["version"],
-    min_sep_int=p["min_sep_int"],
-    max_sep_int=p["max_sep_int"],
-    nbins_int=p["nbins_int"],
-    npatch=p["npatch"],
+version = p["version"]
+fiducial_scale_cut = tuple(p["fiducial_scale_cut"])
+
+part = sacc_io.load(snakemake.input["xi"])
+theta, xip, xim = sacc_io.get_xi(part, (0, 0), grid="cosebis")
+edges = log_bin_edges(p["min_sep"], p["max_sep"], p["nbins"])
+
+results = cosebis_scan_from_xi(
+    theta,
+    xip,
+    xim,
+    part.covariance.dense,
+    *edges,
     nmodes=p["nmodes"],
     scale_cuts=[tuple(sc) for sc in p["scale_cuts"]],
-    fiducial_scale_cut=tuple(p["fiducial_scale_cut"]),
+    npatch=part.metadata["npatch"],
+)
+
+fiducial_key = find_conservative_scale_cut_key(results, fiducial_scale_cut)
+fiducial = results[fiducial_key]
+
+plot_cosebis_modes(
+    fiducial,
+    version,
+    snakemake.output["figure_modes"],
+    fiducial_scale_cut=fiducial_scale_cut,
+)
+plot_cosebis_covariance_matrix(
+    fiducial, version, "jackknife", snakemake.output["figure_covariance"]
+)
+plot_cosebis_scale_cut_heatmap(
+    results,
+    edges,
+    version,
+    snakemake.output["figure_scalecut_ptes"],
+    fiducial_scale_cut=fiducial_scale_cut,
 )
+
+save_cosebis_results(results, snakemake.output["npz"], fiducial_scale_cut)
+
+# The part inherits the ξ± part's provenance; `type` is re-stamped on save.
+metadata = {k: v for k, v in part.metadata.items() if k != "type"}
+s = cosebis_to_sacc({0: sacc_io.get_nz(part, 0)}, metadata, fiducial, fiducial_key)
+sacc_io.save(s, snakemake.output["sacc"], type="data")
+
 verify_outputs(snakemake)
diff --git a/workflow/scripts/cv_plot_pseudo_cl.py b/workflow/scripts/cv_plot_pseudo_cl.py
new file mode 100644
index 00000000..f9aeeda9
--- /dev/null
+++ b/workflow/scripts/cv_plot_pseudo_cl.py
@@ -0,0 +1,39 @@
+"""Rule cv_plot_pseudo_cl: the EE/EB/BB pseudo-Cl figures.
+
+Plot-only, and an ingest like the other B-mode rules: the spectra come from the
+analysis pseudo-Cl parts and their NaMaster covariances, the same pair the
+summary and the terminal file are built from, so the figures cannot show
+something the data products do not.
+"""
+
+import numpy as np
+from astropy.io import fits
+from cv_runner import _unbuffer_streams, verify_outputs
+
+from sp_validation import sacc_io
+from sp_validation.cosmo_val.pseudo_cl import plot_pseudo_cl_spectrum
+
+_unbuffer_streams()
+p = snakemake.params
+
+spectra = {}
+for i, version in enumerate(p["versions"]):
+    part = sacc_io.load(snakemake.input["pseudo_cl"][i])
+    ell, ee, bb, eb, _window = sacc_io.get_pseudo_cl(part, (0, 0))
+    with fits.open(snakemake.input["pseudo_cl_cov"][i]) as hdul:
+        covs = {
+            name: np.asarray(hdul[f"COVAR_{name}_{name}"].data, float)
+            for name in ("EE", "EB", "BB")
+        }
+    for name, cl in (("EE", ee), ("EB", eb), ("BB", bb)):
+        spectra.setdefault(name, {})[version] = {
+            "ell": ell,
+            "cl": cl,
+            "cov": covs[name],
+            "style": {"marker": p["markers"][i], "colour": p["colours"][i]},
+        }
+
+for name, datasets in spectra.items():
+    plot_pseudo_cl_spectrum(datasets, name, snakemake.output[f"figure_{name.lower()}"])
+
+verify_outputs(snakemake)
diff --git a/workflow/scripts/cv_pseudo_cl.py b/workflow/scripts/cv_pseudo_cl.py
deleted file mode 100644
index 7dfebc54..00000000
--- a/workflow/scripts/cv_pseudo_cl.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Rule cv_pseudo_cl: harmonic-space pseudo-Cl B-mode spectra.
-
-plot_pseudo_cl triggers calculate_pseudo_cl, which writes pseudo_cl_{version}.fits
-for every version (the BB spectrum cv_summarize_bmodes reads) and the cell_ee.png
-figure. The per-version FITS files are the declared outputs.
-"""
-
-from cv_runner import _unbuffer_streams, make_cv, verify_outputs
-
-_unbuffer_streams()
-cv = make_cv(snakemake)
-cv.plot_pseudo_cl()
-verify_outputs(snakemake)
diff --git a/workflow/scripts/cv_pure_eb.py b/workflow/scripts/cv_pure_eb.py
index bcc46d84..1edaea93 100644
--- a/workflow/scripts/cv_pure_eb.py
+++ b/workflow/scripts/cv_pure_eb.py
@@ -1,24 +1,113 @@
 """Rule cv_pure_eb: pure E/B-mode decomposition for one version.
 
-Compute + plot rule (per version). plot_pure_eb calls calculate_pure_eb, which
-runs two TreeCorr correlations (reporting + integration binning); the reporting
-binning reuses the cv_2pcf data vector via calculate_2pcf's skip-if-exists
-path. Writes the {version}_eb_..._data.npz data product (declared output) plus
-companion figures, and the per-version E/B PTEs that cv_summarize_bmodes
-collects.
+A consumer of the two ξ± parts plus one covariance file — nothing here touches
+a catalogue. The modes come from the reporting and integration parts through
+the pipeline kernel; the covariance is Monte Carlo through that same kernel,
+drawn from the CosmoCov integration-grid ξ± covariance around a theory mean, so
+it depends on the covariance model and the grids rather than on the measured
+vector. A jackknife of the transformed modes would need per-patch realisations,
+which are never persisted.
 """
 
-from cv_runner import _unbuffer_streams, make_cv, verify_outputs
+import numpy as np
+from cs_util.cosmo import get_cosmo
+from cv_runner import _unbuffer_streams, verify_outputs
+
+from sp_validation import sacc_io
+from sp_validation.b_modes import (
+    calculate_eb_statistics,
+    log_bin_edges,
+    plot_eb_covariance_matrix,
+    plot_integration_vs_reporting,
+    plot_pte_2d_heatmaps,
+    plot_pure_eb_correlations,
+    pure_eb_covariance_mc,
+    pure_eb_from_xi,
+    save_pure_eb_results,
+)
+from sp_validation.cosmo_val.sacc_writers import pure_eb_to_sacc
 
 _unbuffer_streams()
-cv = make_cv(snakemake)
 p = snakemake.params
-cv.plot_pure_eb(
-    versions=[p["version"]],
-    min_sep_int=p["min_sep_int"],
-    max_sep_int=p["max_sep_int"],
-    nbins_int=p["nbins_int"],
-    fiducial_xip_scale_cut=tuple(p["fiducial_scale_cut"]),
-    fiducial_xim_scale_cut=tuple(p["fiducial_scale_cut"]),
+version = p["version"]
+fiducial_scale_cut = tuple(p["fiducial_scale_cut"])
+
+reporting = sacc_io.load(snakemake.input["xi_reporting"])
+integration = sacc_io.load(snakemake.input["xi_integration"])
+theta, xip, xim = sacc_io.get_xi(reporting, (0, 0), grid="reporting")
+theta_int, xip_int, xim_int = sacc_io.get_xi(integration, (0, 0), grid="integration")
+left_edges, right_edges = log_bin_edges(p["min_sep"], p["max_sep"], p["nbins"])
+
+# The reporting grid must sit strictly inside the integration grid: a reporting
+# point on the boundary has no interior support and comes back NaN.
+modes = pure_eb_from_xi(
+    theta, xip, xim, theta_int, xip_int, xim_int, left_edges[0], right_edges[-1]
+)
+
+z, nz = sacc_io.get_nz(reporting, 0)
+cov, eb_samples = pure_eb_covariance_mc(
+    theta=theta,
+    left_edges=left_edges,
+    right_edges=right_edges,
+    theta_int=theta_int,
+    cov_int=np.loadtxt(snakemake.input["cov_integration"]),
+    z=z,
+    nz=nz,
+    cosmo=get_cosmo(**p["cosmo_params"]),
+    n_samples=p["n_samples"],
+)
+
+variances = reporting.covariance.dense.diagonal()
+results = {
+    "theta": theta,
+    "left_edges": left_edges,
+    "right_edges": right_edges,
+    "xip": xip,
+    "xim": xim,
+    "var_xip": variances[: len(theta)],
+    "var_xim": variances[len(theta) :],
+    "theta_int": theta_int,
+    "xip_int": xip_int,
+    "xim_int": xim_int,
+    "n_eff": p["n_samples"],
+    "cov": cov,
+    "eb_samples": eb_samples,
+    **modes,
+}
+results = calculate_eb_statistics(results)
+
+plot_integration_vs_reporting(
+    results, snakemake.output["figure_integration_vs_reporting"], version
+)
+plot_pure_eb_correlations(
+    results,
+    snakemake.output["figure_xis"],
+    version,
+    fiducial_xip_scale_cut=fiducial_scale_cut,
+    fiducial_xim_scale_cut=fiducial_scale_cut,
+)
+plot_pte_2d_heatmaps(
+    results,
+    version,
+    snakemake.output["figure_ptes"],
+    fiducial_xip_scale_cut=fiducial_scale_cut,
+    fiducial_xim_scale_cut=fiducial_scale_cut,
+)
+plot_eb_covariance_matrix(
+    cov, "semi-analytic", snakemake.output["figure_covariance"], version
 )
+
+save_pure_eb_results(results, snakemake.output["npz"])
+
+# The part inherits the ξ± part's provenance; `type` is re-stamped on save.
+metadata = {k: v for k, v in reporting.metadata.items() if k != "type"}
+s = pure_eb_to_sacc(
+    {0: (z, nz)},
+    metadata,
+    theta,
+    {key: results[key] for key in sacc_io.PURE_KEYS},
+    covariance=cov,
+)
+sacc_io.save(s, snakemake.output["sacc"], type="data")
+
 verify_outputs(snakemake)
diff --git a/workflow/scripts/cv_summarize_bmodes.py b/workflow/scripts/cv_summarize_bmodes.py
index 85f0fa1d..9200bfaa 100644
--- a/workflow/scripts/cv_summarize_bmodes.py
+++ b/workflow/scripts/cv_summarize_bmodes.py
@@ -1,55 +1,63 @@
 """Rule cv_summarize_bmodes: collect B-mode PTEs across all statistics.
 
-The terminal diagnostic. summarize_bmodes reads the in-memory
-_pure_eb_results / _cosebis_results / _pseudo_cls dicts, which are populated by
-plot_pure_eb / plot_cosebis / plot_pseudo_cl. The per-version E/B and COSEBIs
-npz products and the pseudo-Cl FITS are declared as inputs (so the DAG forces
-those rules first), but the summary still needs the live result objects (it
-reads each version's TreeCorr `gg`, which the npz cannot hold). So this rule
-re-runs the three B-mode methods in-process: they reload the existing 2pcf /
-data-vector files via their skip-if-exists paths and recompute only the cheap
-PTE statistics, exactly as the original linear driver did on its shared cv.
-
-Writes the summary table to bmode_summary.txt (declared output) — the original
-driver only printed it.
+The terminal diagnostic, and a reader of what the three B-mode rules already
+wrote: the pure-E/B PTE matrices and the COSEBIs B-mode PTE from their .npz
+products, and the pseudo-Cℓ BB spectrum from its SACC part against the NaMaster
+covariance. Nothing is recomputed and no catalogue is touched, so the summary
+cannot disagree with the products it summarises.
 """
 
 import json
 
-from cv_runner import _unbuffer_streams, make_cv, verify_outputs
+import numpy as np
+from cv_runner import _unbuffer_streams, verify_outputs
+
+from sp_validation import sacc_io
+from sp_validation.b_modes import _get_pte_from_scale_cut, log_bin_edges
+from sp_validation.cosmo_val.core import print_bmode_summary
+from sp_validation.statistics import chi2_and_pte
 
 _unbuffer_streams()
-cv = make_cv(snakemake)
 p = snakemake.params
 fiducial_scale_cut = tuple(p["fiducial_scale_cut"])
+edges = log_bin_edges(p["min_sep"], p["max_sep"], p["nbins"])
+
+summary = {}
+cov_methods = set()
+
+for i, version in enumerate(p["versions"]):
+    row = {}
+
+    pure_eb = np.load(snakemake.input["pure_eb"][i])
+    for stat in ("xip_B", "xim_B", "combined"):
+        try:
+            row[stat] = _get_pte_from_scale_cut(
+                pure_eb[f"pte_matrices_{stat}"], edges, fiducial_scale_cut
+            )
+        except (KeyError, RuntimeError):
+            pass
+    cov_methods.add(f"pure-E/B: semi-analytic ({int(pure_eb['n_eff'])} draws)")
+
+    # The COSEBIs .npz is written at the fiducial cut, so its PTE is the one
+    # this table wants.
+    cosebis = np.load(snakemake.input["cosebis"][i])
+    row["COSEBIS"] = float(cosebis["pte_B"])
+    cov_methods.add("COSEBIs: propagated from the ξ± covariance")
+
+    if p["include_pseudo_cl"]:
+        from astropy.io import fits
+
+        part = sacc_io.load(snakemake.input["pseudo_cl"][i])
+        _ell, _ee, bb, _eb, _window = sacc_io.get_pseudo_cl(part, (0, 0))
+        with fits.open(snakemake.input["pseudo_cl_cov"][i]) as hdul:
+            cov_bb = np.asarray(hdul["COVAR_BB_BB"].data, float)
+        _chi2, _red, row["C_l_BB"] = chi2_and_pte(bb, cov_bb)
+        cov_methods.add("pseudo-Cℓ: Gaussian (NaMaster)")
+
+    summary[version] = row
+
+print_bmode_summary(summary, fiducial_scale_cut, cov_methods)
 
-# Repopulate the in-memory B-mode result dicts from existing data products.
-cv.plot_pure_eb(
-    min_sep_int=p["pure_eb_min_sep_int"],
-    max_sep_int=p["pure_eb_max_sep_int"],
-    nbins_int=p["pure_eb_nbins_int"],
-    fiducial_xip_scale_cut=fiducial_scale_cut,
-    fiducial_xim_scale_cut=fiducial_scale_cut,
-)
-for version in cv.versions:
-    cv.plot_cosebis(
-        version=version,
-        min_sep_int=p["cosebis_min_sep_int"],
-        max_sep_int=p["cosebis_max_sep_int"],
-        nbins_int=p["cosebis_nbins_int"],
-        npatch=p["cosebis_npatch"],
-        nmodes=p["cosebis_nmodes"],
-        scale_cuts=[tuple(sc) for sc in p["cosebis_scale_cuts"]],
-        fiducial_scale_cut=fiducial_scale_cut,
-    )
-if p.get("include_pseudo_cl", False):
-    cv.plot_pseudo_cl()
-
-summary = cv.summarize_bmodes(fiducial_scale_cut=fiducial_scale_cut)
-
-# summarize_bmodes prints its table and returns {version: {stat: pte}}. Persist
-# the returned dict (the table itself is reproducible from it) so downstream
-# tooling and the all-rule have a real, machine-readable artifact to depend on.
 with open(snakemake.output["summary_json"], "w") as f:
     json.dump(summary, f, indent=2, default=str)
 
diff --git a/workflow/scripts/generate_pseudo_cl.py b/workflow/scripts/generate_pseudo_cl.py
index a5c19a56..e25f6ed9 100644
--- a/workflow/scripts/generate_pseudo_cl.py
+++ b/workflow/scripts/generate_pseudo_cl.py
@@ -1,15 +1,11 @@
 """Generate pseudo-Cls (data vector only, no covariance).
 
 Dual-mode. Under Snakemake (``script:`` directive) the injected ``snakemake``
-object supplies the parameters and the native product is renamed to the tagged
-output filename the rule declares; as a standalone CLI (argparse) the same
-compute runs from explicit flags and the primitive's native
-``pseudo_cl_{ver}.fits`` is left in place under ``--out`` (no rename — each
-lc/ASTRA recipe gets its own output directory, so the untagged native name is
-unambiguous and the primitives' skip-if-exists never collides across nbins
-runs). The CLI form is what the lightcone/ASTRA recipe calls, so the
-measurement is driven directly (no nested Snakemake) with lc handling
-orchestration:
+object supplies the parameters; as a standalone CLI (argparse) the same compute
+runs from explicit flags. Either way the part is born at its final path, as
+SACC (EE/BB/EB with a shared bandpower window). The CLI form is what the
+lightcone/ASTRA recipe calls, driving the measurement directly (no nested
+Snakemake) with lc handling orchestration:
 
     python generate_pseudo_cl.py \
         --ver SP_v1.4.6.3_leak_corr \
@@ -28,14 +24,13 @@
 import json
 import os
 
-from astropy.io import fits
-
+from sp_validation import sacc_io
 from sp_validation.cosmo_val import CosmologyValidation
 
 
 def generate_pseudo_cl(
     version: str,
-    output_dir: str,
+    out_path: str,
     cat_config: str,
     nside: int = 1024,
     npatch: int = 1,
@@ -45,16 +40,15 @@ def generate_pseudo_cl(
     nbins: int = None,
     power: float = 0.5,
 ):
-    """Generate a pseudo-Cl data vector into ``output_dir``.
+    """Generate a pseudo-Cl data vector, born as a SACC part at ``out_path``.
 
     Parameters
     ----------
     version : str
         Catalog version (e.g., "SP_v1.4.6_leak_corr")
-    output_dir : str
-        Directory the pseudo-Cl FITS file is written into. The primitive writes
-        its native ``pseudo_cl_{version}.fits`` here; callers that need a tagged
-        filename rename it themselves (see ``_from_snakemake``).
+    out_path : str
+        Exact destination the SACC part is born at — its final (possibly tagged)
+        name. Skip-if-exists keys on it, so no two rules share a basename.
     cat_config : str
         Path to catalog configuration YAML
     nside : int
@@ -76,8 +70,9 @@ def generate_pseudo_cl(
     Returns
     -------
     str
-        Path to the primitive's native ``pseudo_cl_{version}.fits`` product.
+        ``out_path`` (the SACC part written).
     """
+    output_dir = os.path.dirname(out_path)
     os.makedirs(output_dir, exist_ok=True)
 
     blind_str = f" blind={blind}" if blind else ""
@@ -135,26 +130,23 @@ def generate_pseudo_cl(
 
     cv = CosmologyValidation(**cv_kwargs)
 
-    # Calculate pseudo-Cls only (no covariance)
-    cv.calculate_pseudo_cl()
+    # Pseudo-Cls only (no covariance), born directly at the final out_path.
+    cv.calculate_pseudo_cl(out_path=out_path)
 
-    # Report on the native product (renamed by the Snakemake caller, if any)
-    src_cl = os.path.join(output_dir, f"pseudo_cl_{version}.fits")
-    if os.path.exists(src_cl):
-        with fits.open(src_cl) as hdul:
-            data = hdul["PSEUDO_CELL"].data
-            n_ell = len(data["ELL"])
-            print(f"Generated pseudo-Cl with {n_ell} ell bins")
-            print(f"ell range: [{data['ELL'].min():.1f}, {data['ELL'].max():.1f}]")
-    return src_cl
+    if os.path.exists(out_path):
+        # Readback of the part just written — a legitimate pre-blind consumer.
+        s = sacc_io.load(out_path, allow_unblinded=True)
+        ell = sacc_io.get_pseudo_cl(s, (0, 0))[0]
+        print(f"Generated pseudo-Cl with {len(ell)} ell bins")
+        print(f"ell range: [{ell.min():.1f}, {ell.max():.1f}]")
+    return out_path
 
 
 def _from_snakemake(smk):
     p = smk.params
-    output_cl = smk.output.pseudo_cl
-    src_cl = generate_pseudo_cl(
+    generate_pseudo_cl(
         version=p["version"],
-        output_dir=os.path.dirname(output_cl),
+        out_path=smk.output.pseudo_cl,
         cat_config=p["cat_config"],
         nside=int(p["nside"]),
         npatch=int(p["npatch"]),
@@ -164,10 +156,6 @@ def _from_snakemake(smk):
         nbins=int(p["nbins"]),
         power=float(p.get("power", 0.5)),
     )
-    # Snakemake declares a tagged output filename; rename the native product to it.
-    if os.path.exists(src_cl) and src_cl != output_cl:
-        os.rename(src_cl, output_cl)
-        print(f"Saved to: {output_cl}")
 
 
 def _from_cli(argv=None):
@@ -220,9 +208,12 @@ def _from_cli(argv=None):
         with open(a.cosmo_json) as f:
             cosmo_params = json.load(f)
 
+    # lc/ASTRA path: --out is a per-recipe directory, so the untagged name is
+    # unambiguous there.
+    out_path = os.path.join(a.out, f"pseudo_cl_{a.ver}.sacc")
     generate_pseudo_cl(
         version=a.ver,
-        output_dir=a.out,
+        out_path=out_path,
         cat_config=a.cat_config,
         nside=a.nside,
         npatch=a.npatch,
diff --git a/workflow/scripts/run_2pcf.py b/workflow/scripts/run_2pcf.py
index 86dffe27..1f69ee6d 100644
--- a/workflow/scripts/run_2pcf.py
+++ b/workflow/scripts/run_2pcf.py
@@ -12,15 +12,26 @@
         --cat-config /path/to/cosmo_val/cat_config.yaml \
         --out <output_dir>
 
-The measurement itself is unchanged — ``CosmologyValidation.calculate_2pcf``
-does the TreeCorr work and writes the ``.txt`` dump plus ξ+/ξ- FITS files into
-``output_dir``. ``output_dir`` is passed explicitly (rather than via the
-``COSMO_VAL`` env hook) so lc can point each run at its own ``{output}`` tree.
+The measurement is binning-agnostic: the reporting and the fine integration
+grids are the same compute with different ``--min-sep/--max-sep/--nbins``.
+``CosmologyValidation.calculate_2pcf`` writes the ``.txt`` dump (a raw
+byproduct); the ξ± data product is born as SACC here, a *part* named by its
+binning and tagged with its ``--grid``. The part carries the covariance its
+grid configures (``--cov``): the dense jackknife estimate from the patches, the
+TreeCorr ``varxip``/``varxim`` diagonal, or none.
+
+``output_dir`` is passed explicitly (rather than via the ``COSMO_VAL`` env hook)
+so lc can point each run at its own ``{output}`` tree.
 """
 
 import argparse
+import os
+
+import numpy as np
 
+from sp_validation import sacc_io
 from sp_validation.cosmo_val import CosmologyValidation
+from sp_validation.cosmo_val.sacc_writers import xi_to_sacc
 
 
 def run_2pcf(
@@ -31,30 +42,68 @@ def run_2pcf(
     npatch,
     cat_config,
     output_dir,
-    save_fits=True,
+    sacc_out=None,
+    grid="reporting",
+    cov="none",
 ):
-    """Measure ξ±(θ) for ``ver`` and write it under ``output_dir``.
+    """Measure ξ±(θ) for ``ver`` and write its reporting SACC part.
 
     Parameters mirror the TreeCorr reporting/integration grids: ``min_sep`` /
     ``max_sep`` in arcmin, ``nbins`` logarithmic bins, ``npatch`` spatial
     patches (1 for the paper fiducial). ``cat_config`` is an absolute path to
     the catalog configuration; ``output_dir`` overrides
-    ``cat_config['paths']['output']`` so products land where lc expects.
+    ``cat_config['paths']['output']`` so the ``.txt`` byproduct lands where lc
+    expects. ``sacc_out`` is the exact destination for the SACC part (the
+    Snakemake-declared output); it defaults to a binning-derived name under
+    the resolved output directory for the CLI path.
+
+    Returns
+    -------
+    treecorr.GGCorrelation
+        The measured correlation object (also the source of the SACC part).
     """
     cv = CosmologyValidation(
         versions=[ver],
         catalog_config=cat_config,
         output_dir=output_dir,
+        # so the SACC provenance metadata stamps the npatch actually measured
+        npatch=npatch,
     )
-    return cv.calculate_2pcf(
+    gg = cv.calculate_2pcf(
         ver=ver,
         npatch=npatch,
-        save_fits=save_fits,
         min_sep=min_sep,
         max_sep=max_sep,
         nbins=nbins,
     )
 
+    if cov == "jackknife" and int(npatch) < 2:
+        raise ValueError(f"cov='jackknife' needs patches; got npatch={npatch}")
+
+    # Born-as-SACC ξ± part. theta = meanr; theta_nom = rnom.
+    s = xi_to_sacc(
+        cv.sacc_nz(ver),
+        cv.sacc_metadata(ver),
+        gg.meanr,
+        gg.xip,
+        gg.xim,
+        grid=grid,
+        theta_nom=gg.rnom,
+        npairs=gg.npairs,
+        weight=gg.weight,
+        covariance=gg.cov if cov == "jackknife" else None,
+        variances=(
+            np.concatenate([gg.varxip, gg.varxim]) if cov == "diagonal" else None
+        ),
+    )
+    out_path = sacc_out or os.path.join(
+        output_dir or cv.cc["paths"]["output"],
+        f"{ver}_xi_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.sacc",
+    )
+    sacc_io.save(s, out_path, type="data")
+    print(f"Wrote {grid} ξ± SACC part: {out_path}")
+    return gg
+
 
 def _from_snakemake(smk):
     p = smk.params
@@ -66,7 +115,11 @@ def _from_snakemake(smk):
         npatch=int(p["npatch"]),
         cat_config=p["cat_config"],
         output_dir=p["output_dir"],
-        save_fits=True,
+        grid=p.get("grid", "reporting"),
+        cov=p.get("cov", "none"),
+        # The SACC part goes exactly where the rule declares it; the .txt
+        # byproduct still lands under the resolved output dir.
+        sacc_out=smk.output["sacc"],
     )
 
 
@@ -93,7 +146,15 @@ def _from_cli(argv=None):
         "--cat-config", required=True, help="Absolute path to cat_config.yaml"
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    ap.add_argument("--no-fits", action="store_true", help="Skip ξ+/ξ- FITS export")
+    ap.add_argument(
+        "--grid", default="reporting", help="SACC grid tag for the measured points"
+    )
+    ap.add_argument(
+        "--cov",
+        default="none",
+        choices=["jackknife", "diagonal", "none"],
+        help="Covariance the part carries",
+    )
     a = ap.parse_args(argv)
     run_2pcf(
         ver=a.ver,
@@ -103,7 +164,8 @@ def _from_cli(argv=None):
         npatch=a.npatch,
         cat_config=a.cat_config,
         output_dir=a.out,
-        save_fits=not a.no_fits,
+        grid=a.grid,
+        cov=a.cov,
     )
 
 
diff --git a/workflow/scripts/run_rho_tau.py b/workflow/scripts/run_rho_tau.py
index bc11bfc0..4983cffb 100644
--- a/workflow/scripts/run_rho_tau.py
+++ b/workflow/scripts/run_rho_tau.py
@@ -47,9 +47,10 @@
 
 cv.calculate_rho_tau_stats()
 
-# Confirm CosmologyValidation produced the requested outputs
+# Confirm CosmologyValidation produced the requested outputs: the rho/tau FITS
+# and the born-as-SACC rho_tau part.
 outputs = snakemake.output  # type: ignore
-for label in ("rho_stats", "tau_stats"):
+for label in ("rho_stats", "tau_stats", "rho_tau"):
     target = Path(outputs[label])
     if not target.exists():
         raise FileNotFoundError(

From 71972c918a65d8aa83e6f92086d94d65e939f3f0 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Sat, 26 Sep 2026 00:49:48 +0200
Subject: [PATCH 30/83] fix(renovate): automerge pinned action digests (#353)

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 renovate.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/renovate.json b/renovate.json
index 6a25bd4d..f0d14be6 100644
--- a/renovate.json
+++ b/renovate.json
@@ -30,6 +30,7 @@
         "minor",
         "patch",
         "pin",
+        "pinDigest",
         "digest"
       ],
       "automerge": true

From fca3c04b9a7f9dd253505e35f2187a3b14c2a7a1 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 02:52:50 +0200
Subject: [PATCH 31/83] uv.lock: cs_util develop@a52280f, which restores
 FootprintPlotter.plot_area

cv_footprints raised AttributeError: the locked cs_util (bedcdbf5) calls
plot_area from plot_region but lacks it; cs_util f17ea0e restores it. The
only other cs_util changes since are CI. Relocking also brings the lock's
recorded sacc specifier in line with pyproject (>=2.4,<3).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj
---
 uv.lock | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/uv.lock b/uv.lock
index eb69507c..1460cbc9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -691,7 +691,7 @@ wheels = [
 [[package]]
 name = "cs-util"
 version = "0.2.2"
-source = { git = "https://github.com/CosmoStat/cs_util.git?rev=develop#bedcdbf50ac57fc4d0b72a62b251373cd1807824" }
+source = { git = "https://github.com/CosmoStat/cs_util.git?rev=develop#a52280fbb4bc1daa3f217a77954ba43c0860738b" }
 dependencies = [
     { name = "astropy" },
     { name = "camb" },
@@ -3951,7 +3951,7 @@ requires-dist = [
     { name = "regions" },
     { name = "reproject" },
     { name = "ruff", marker = "extra == 'test'" },
-    { name = "sacc", specifier = ">=0.12" },
+    { name = "sacc", specifier = ">=2.4,<3" },
     { name = "scipy", specifier = "<1.19" },
     { name = "seaborn" },
     { name = "shear-psf-leakage", git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop" },

From 2ddeb6700c782fa853818d2e4afe2a01a6686a6b Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 03:12:15 +0200
Subject: [PATCH 32/83] Dockerfile: sfmath in the image's TeX, for sans-serif
 usetex figures

Checked in the #253 branch image: kpsewhich finds sfmath.sty, and a usetex
figure with \usepackage[cm]{sfmath} renders.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj
---
 Dockerfile | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/Dockerfile b/Dockerfile
index 844f7e23..0437a929 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -26,6 +26,8 @@ RUN apt-get update -y --quiet --fix-missing && \
     rm -rf /var/lib/apt/lists/*
 
 # TinyTeX pinned to a TeX Live year (frozen tlnet-final mirror); bump both once a year.
+# The packages serve matplotlib's usetex figures; sfmath gives them sans-serif
+# maths (`\usepackage[cm]{sfmath}`).
 ENV TEXLIVE_YEAR=2025 \
     TINYTEX_VERSION=2026.02 \
     TINYTEX_DIR=/opt \
@@ -46,6 +48,7 @@ RUN set -eux; \
         amsmath \
         amsfonts \
         geometry \
+        sfmath \
         xcolor; \
     tlmgr path add; \
     latex --version >/dev/null; dvipng --version >/dev/null

From f9a9a9dde1dd606856dedcca86ded89ee4ee60c0 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Mon, 28 Sep 2026 01:54:16 +0000
Subject: [PATCH 33/83] Update all non-major dependencies (#355)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 .github/workflows/lint.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 8a0f226e..f4e87e9d 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -86,7 +86,7 @@ jobs:
           persist-credentials: false
 
       - name: Install uv
-        uses: astral-sh/setup-uv@bec219d24cd3e171d82865faccec33120bb574f4 # v10.1.0
+        uses: astral-sh/setup-uv@c18668ad3cf93ea998bef934396af7bb5c839dc7 # v10.2.0
 
       # Is this a PR whose head branch lives in THIS repo (not a fork)? Only then
       # can we push an autofix commit back to it with the workflow token.

From 11b932236fc6661953764cb22c5fc65b357cac4a Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 05:50:16 +0200
Subject: [PATCH 34/83] papers/bmodes: drop the claims layer, the ecut and
 Moriond rules, and dead scripts (#358)

---
 papers/bmodes/Snakefile                       |  18 +-
 papers/bmodes/config/1d_plots.md              |  49 ---
 papers/bmodes/config/2d_plots.md              |  74 ----
 .../bb_covariance_blind_independence.md       |  34 --
 papers/bmodes/config/bmodes_paper.md          | 148 --------
 papers/bmodes/config/cl.md                    |  34 --
 papers/bmodes/config/cl_data_vector.md        |  47 ---
 papers/bmodes/config/cl_version_comparison.md |  34 --
 papers/bmodes/config/config.yaml              |  19 +-
 .../config/config_space_pte_matrices.md       |  55 ---
 papers/bmodes/config/cosebis.md               |  50 ---
 papers/bmodes/config/cosebis_data_vector.md   |  52 ---
 .../bmodes/config/cosebis_filter_overlay.md   |  35 --
 .../config/cosebis_version_comparison.md      |  36 --
 papers/bmodes/config/covariance.md            |  88 -----
 .../config/covariance_blind_consistency.md    |  33 --
 papers/bmodes/config/ecut_spec.md             |  83 -----
 .../harmonic_config_cosebis_comparison.md     |  69 ----
 .../config/harmonic_space_pte_matrices.md     |  46 ---
 papers/bmodes/config/pure_eb.md               |  46 ---
 papers/bmodes/config/pure_eb_covariance.md    |  45 ---
 papers/bmodes/config/pure_eb_data_vector.md   |  56 ---
 .../config/pure_eb_version_comparison.md      |  38 ---
 papers/bmodes/config/xi_cosmology_paper.md    | 110 ------
 papers/bmodes/rules/ecut.smk                  | 218 ------------
 .../bmodes/rules/{claims.smk => figures.smk}  | 126 +------
 papers/bmodes/rules/paper.smk                 |  70 ++++
 papers/bmodes/rules/presentation.smk          | 155 ---------
 papers/bmodes/rules/synthesis.smk             | 141 --------
 .../bb_covariance_blind_independence.py       |   2 -
 papers/bmodes/scripts/cl_data_vector.py       |   3 +-
 .../bmodes/scripts/cl_version_comparison.py   |   3 +-
 .../scripts/compute_cosebis_pte_single.py     |   2 +-
 .../scripts/config_space_pte_matrices.py      |   7 +-
 papers/bmodes/scripts/container_env.sh        |  45 ---
 .../scripts/cosebis_binning_comparison.py     | 320 ------------------
 papers/bmodes/scripts/cosebis_data_vector.py  |   3 +-
 .../scripts/cosebis_version_comparison.py     |   5 +-
 .../scripts/covariance_blind_consistency.py   | 158 ---------
 .../explorations/compare_mask_effect.py       | 158 ---------
 .../explorations/plot_mask_diagonal_ratio.py  |  89 -----
 .../scripts/filter_catalog_ellipticity.py     |  86 -----
 .../bmodes/scripts/generate_paper_macros.py   |  53 +--
 .../harmonic_config_cosebis_comparison.py     |  13 +-
 .../scripts/harmonic_space_pte_matrices.py    |   3 +-
 .../scripts/plot_cosebis_filter_overlay.py    |   6 -
 .../scripts/plot_presentation_blind_nz.py     |  39 ---
 .../bmodes/scripts/plot_pure_eb_covariance.py |  88 -----
 papers/bmodes/scripts/pure_eb_covariance.py   |  14 +-
 papers/bmodes/scripts/pure_eb_data_vector.py  |  16 +-
 .../scripts/pure_eb_version_comparison.py     |  14 +-
 papers/bmodes/scripts/run_cl_sweep.py         | 113 -------
 .../bmodes/scripts/run_cosebis_ptes_sweep.py  |  99 ------
 .../bmodes/scripts/run_pure_eb_ptes_sweep.sh  |  48 ---
 .../scripts/run_pure_eb_semianalytic.sh       |  80 -----
 papers/bmodes/scripts/run_pure_eb_sweep.sh    |  61 ----
 papers/bmodes/scripts/run_xi_sweep.py         |  85 -----
 papers/bmodes/scripts/sweep_versions.py       |  44 ---
 .../bmodes/scripts/test_bandpower_sampling.py | 205 -----------
 papers/bmodes/scripts/update_survey_stats.py  | 154 ---------
 .../scripts/validate_cosebis_filters.py       | 265 ---------------
 .../bmodes/scripts/validate_cosebis_theory.py | 171 ----------
 .../tests/test_bmodes_workflow_dry_run.py     |   2 +-
 workflow/common.py                            |   3 +-
 workflow/scripts/plotting_utils.py            |   2 +-
 65 files changed, 127 insertions(+), 4341 deletions(-)
 delete mode 100644 papers/bmodes/config/1d_plots.md
 delete mode 100644 papers/bmodes/config/2d_plots.md
 delete mode 100644 papers/bmodes/config/bb_covariance_blind_independence.md
 delete mode 100644 papers/bmodes/config/bmodes_paper.md
 delete mode 100644 papers/bmodes/config/cl.md
 delete mode 100644 papers/bmodes/config/cl_data_vector.md
 delete mode 100644 papers/bmodes/config/cl_version_comparison.md
 delete mode 100644 papers/bmodes/config/config_space_pte_matrices.md
 delete mode 100644 papers/bmodes/config/cosebis.md
 delete mode 100644 papers/bmodes/config/cosebis_data_vector.md
 delete mode 100644 papers/bmodes/config/cosebis_filter_overlay.md
 delete mode 100644 papers/bmodes/config/cosebis_version_comparison.md
 delete mode 100644 papers/bmodes/config/covariance.md
 delete mode 100644 papers/bmodes/config/covariance_blind_consistency.md
 delete mode 100644 papers/bmodes/config/ecut_spec.md
 delete mode 100644 papers/bmodes/config/harmonic_config_cosebis_comparison.md
 delete mode 100644 papers/bmodes/config/harmonic_space_pte_matrices.md
 delete mode 100644 papers/bmodes/config/pure_eb.md
 delete mode 100644 papers/bmodes/config/pure_eb_covariance.md
 delete mode 100644 papers/bmodes/config/pure_eb_data_vector.md
 delete mode 100644 papers/bmodes/config/pure_eb_version_comparison.md
 delete mode 100644 papers/bmodes/config/xi_cosmology_paper.md
 delete mode 100644 papers/bmodes/rules/ecut.smk
 rename papers/bmodes/rules/{claims.smk => figures.smk} (85%)
 create mode 100644 papers/bmodes/rules/paper.smk
 delete mode 100644 papers/bmodes/rules/presentation.smk
 delete mode 100644 papers/bmodes/rules/synthesis.smk
 delete mode 100644 papers/bmodes/scripts/container_env.sh
 delete mode 100644 papers/bmodes/scripts/cosebis_binning_comparison.py
 delete mode 100644 papers/bmodes/scripts/covariance_blind_consistency.py
 delete mode 100644 papers/bmodes/scripts/explorations/compare_mask_effect.py
 delete mode 100644 papers/bmodes/scripts/explorations/plot_mask_diagonal_ratio.py
 delete mode 100644 papers/bmodes/scripts/filter_catalog_ellipticity.py
 delete mode 100644 papers/bmodes/scripts/plot_presentation_blind_nz.py
 delete mode 100644 papers/bmodes/scripts/plot_pure_eb_covariance.py
 delete mode 100644 papers/bmodes/scripts/run_cl_sweep.py
 delete mode 100644 papers/bmodes/scripts/run_cosebis_ptes_sweep.py
 delete mode 100644 papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
 delete mode 100644 papers/bmodes/scripts/run_pure_eb_semianalytic.sh
 delete mode 100755 papers/bmodes/scripts/run_pure_eb_sweep.sh
 delete mode 100644 papers/bmodes/scripts/run_xi_sweep.py
 delete mode 100644 papers/bmodes/scripts/sweep_versions.py
 delete mode 100644 papers/bmodes/scripts/test_bandpower_sampling.py
 delete mode 100644 papers/bmodes/scripts/update_survey_stats.py
 delete mode 100644 papers/bmodes/scripts/validate_cosebis_filters.py
 delete mode 100644 papers/bmodes/scripts/validate_cosebis_theory.py

diff --git a/papers/bmodes/Snakefile b/papers/bmodes/Snakefile
index c9069c79..6af554bb 100644
--- a/papers/bmodes/Snakefile
+++ b/papers/bmodes/Snakefile
@@ -1,5 +1,5 @@
-# UNIONS B-modes paper — the epistemic layer, composed over the generic
-# compute workflow at ../../workflow/.
+# UNIONS B-modes paper (Paper III): figures, PTE tables and LaTeX macros,
+# composed over the generic compute workflow at ../../workflow/.
 
 configfile: "config/config.yaml"
 configfile: "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
@@ -33,21 +33,17 @@ wildcard_constraints:
     **WILDCARD_CONSTRAINTS
 
 # Paper directories
-CONFIG_DIR = "config"
 TAPESTRY_DIR = "results/tapestry"
 PAPER_FIGURES_DIR = "docs/unions_release/unions_bmodes/Figures"
 
-# Compute rules (infrastructure — raw outputs, no evidence.json),
-# imported as a module so future papers can compose the same workflow
-# with their own config / overrides.
+# Compute rules (raw measurements and covariances), imported as a module so
+# other papers can compose the same workflow with their own config.
 module analysis:
     snakefile: os.path.join(WORKFLOW_DIR, "Snakefile")
     config: config
 
 use rule * from analysis
 
-# Epistemic rules (evidence.json producers, order matters: claims → synthesis)
-include: "rules/claims.smk"
-include: "rules/ecut.smk"
-include: "rules/synthesis.smk"
-include: "rules/presentation.smk"
+# Paper figures and their summary JSONs, then the macros/tables built from them
+include: "rules/figures.smk"
+include: "rules/paper.smk"
diff --git a/papers/bmodes/config/1d_plots.md b/papers/bmodes/config/1d_plots.md
deleted file mode 100644
index c5b45d84..00000000
--- a/papers/bmodes/config/1d_plots.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# 1D Plotting Specification
-
-Shared styling for version comparisons, B-mode error bar plots, and diagnostic figures.
-
-## Purpose
-
-Consistent visual language across all 1D comparison plots. Fiducial version emphasized, comparison versions subdued. Clear error visualization with minimal clutter.
-
-## A&A Figure Dimensions
-
-Standard widths (inches):
-- **Single-column**: 3.54 (88mm)
-- **Double-column**: 7.24 (180mm)
-
-All figures use one of these two widths. Height varies by content.
-
-## Config References
-
-| Element | Config Key |
-|---------|------------|
-| Versions | `versions` |
-| Fiducial | `fiducial.version` |
-| Palette | `plotting.palette` |
-| Fiducial alpha | `plotting.version_alpha.fiducial` |
-| Comparison alpha | `plotting.version_alpha.comparison` |
-| Marker style | `plotting.markers.style` |
-| Line width | `plotting.markers.linewidth` |
-| Cap size | `plotting.markers.capsize` |
-| X-offsets | `plotting.x_offsets` |
-
-## Visual Design
-
-### Version Styling
-
-Fiducial version at full opacity, comparison versions subdued to create visual hierarchy.
-
-### Markers
-
-Unfilled circles with moderate line weight — clean, distinguishable at small sizes. Error caps sized for visibility without dominating.
-
-### Axes
-
-- **Zero line**: Black reference for null hypothesis
-- **Grid**: Subtle guides on both axes
-- **Mode highlight**: Background shading when relevant
-
-### Legend
-
-Bottom center, outside plot area. Single column for clarity. Framed for readability against varied backgrounds.
diff --git a/papers/bmodes/config/2d_plots.md b/papers/bmodes/config/2d_plots.md
deleted file mode 100644
index ebfba93b..00000000
--- a/papers/bmodes/config/2d_plots.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# 2D Plotting Specification
-
-Shared styling for PTE heatmaps, covariance matrices, and 2D diagnostic plots.
-
-## Purpose
-
-Visualize parameter-space coverage (PTE grids) and correlation structure (covariance matrices). Emphasis on identifying outliers and structure.
-
-## A&A Figure Dimensions
-
-Standard widths (inches):
-- **Single-column**: 3.54 (88mm)
-- **Double-column**: 7.24 (180mm)
-
-Square heatmaps use single-column width (3.54 × 3.54). Larger matrices may use double-column.
-
-## Config References
-
-| Element | Config Key |
-|---------|------------|
-| PTE thresholds | `statistics.pte_healthy_range` |
-
-## PTE Heatmaps
-
-Grid search results showing PTE as function of two parameters.
-
-### Colormap
-
-`vlag` — diverging, emphasizes extremes (low/high PTE).
-
-### Contours
-
-Mark statistical significance at `statistics.pte_healthy_range` bounds — the 2σ equivalent thresholds.
-
-### Fiducial Marker
-
-Hatched rectangle highlighting the chosen fiducial configuration.
-
-## Axis Label Convention for Scale-Cut Heatmaps
-
-When plotting PTE matrices as function of scale cuts, axis labels must show the **included range boundary**:
-
-- **Lower-cut axis (x-axis)**: Show the **lower edge** of the included bin — the minimum scale actually included in the analysis
-- **Upper-cut axis (y-axis)**: Show the **upper edge** of the included bin — the maximum scale actually included in the analysis
-
-This convention ensures the tick labels directly answer "what range is included in this analysis?"
-
-### Implementation
-
-**Config space** (angular scales): `theta_grid` is bin edges (21 values for 20 bins).
-- x-axis (θ_min): use `theta_grid[i]` (lower edge)
-- y-axis (θ_max): use `theta_grid[i+1]` (upper edge)
-
-**Harmonic space** (multipoles): `ell` from FITS is bin centers. Compute edges from binning config:
-```python
-def compute_ell_edges(lmin, lmax, n_bins, power=0.5):
-    start = np.power(lmin, power)
-    end = np.power(lmax, power)
-    return np.power(np.linspace(start, end, n_bins + 1), 1 / power)
-```
-- x-axis (ℓ_min): use `ell_edges[i]` (lower edge)
-- y-axis (ℓ_max): use `ell_edges[i+1]` (upper edge)
-
-## Covariance Matrices
-
-Visualize E/B mode covariance structure.
-
-### Colormap
-
-`icefire` — symmetric diverging for positive/negative correlations.
-
-### Structure
-
-Show block separation between E-modes, B-modes, and ambiguous modes. Highlight cross-correlations between mode types.
diff --git a/papers/bmodes/config/bb_covariance_blind_independence.md b/papers/bmodes/config/bb_covariance_blind_independence.md
deleted file mode 100644
index 33ec2cb9..00000000
--- a/papers/bmodes/config/bb_covariance_blind_independence.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# BB Covariance Blind Independence
-
-Method: [Covariance](covariance.md), [Pure E/B](pure_eb.md), [COSEBIS](cosebis.md), [Cl](cl.md)
-
-## Claim
-
-BB covariances computed via **analytic propagation** are blind-independent, while BB covariances computed via **MC sampling** inherit noise that varies across blinds.
-
-## Evidence
-
-Diagonal ratios relative to blind A for both B-modes and E-modes:
-- Pure E/B: `cov(ξ+^B)`, `cov(ξ-^B)` vs `cov(ξ+^E)`, `cov(ξ-^E)`
-- COSEBIS: `cov(B_n)` vs `cov(E_n)`
-- Harmonic: `cov(C_ℓ^BB)` vs `cov(C_ℓ^EE)`
-
-**Metrics:**
-1. Diagonal ratio plot — ratio of B/A and C/A diagonals, report min/max
-2. Comparison of BB vs EE deviations across methods
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Version | `fiducial.version` |
-| Scale range | `fiducial.min_sep` to `fiducial.max_sep` |
-| Bins | `fiducial.nbins` |
-| COSEBIS nmodes | `fiducial.nmodes` |
-| COSEBIS θ range | `cosebis.theta_min` to `cosebis.theta_max` |
-| n_ell_bins | `cl.n_ell_bins` |
-
-## Outputs
-
-- `evidence.json` — ratios, deviations, comparison statistics
-- `figure.png` — multi-panel ratio plot comparing BB vs EE stability
diff --git a/papers/bmodes/config/bmodes_paper.md b/papers/bmodes/config/bmodes_paper.md
deleted file mode 100644
index 5623fe8a..00000000
--- a/papers/bmodes/config/bmodes_paper.md
+++ /dev/null
@@ -1,148 +0,0 @@
-# B-modes Paper
-
-The B-modes paper (Daley et al.) — B-mode validation for UNIONS cosmic shear.
-
-Depends on: [pure_eb_covariance](pure_eb_covariance.md), [pure_eb_data_vector](pure_eb_data_vector.md), [pure_eb_version_comparison](pure_eb_version_comparison.md), [cosebis_version_comparison](cosebis_version_comparison.md) (incl. `paper_stacked` output), [cosebis_data_vector](cosebis_data_vector.md), [cl_data_vector](cl_data_vector.md), [cl_version_comparison](cl_version_comparison.md), [config_space_pte_matrices](config_space_pte_matrices.md), [harmonic_space_pte_matrices](harmonic_space_pte_matrices.md), [bb_covariance_blind_independence](bb_covariance_blind_independence.md), [covariance_blind_consistency](covariance_blind_consistency.md)
-
-## Scope
-
-Three statistics, multiple catalog versions (from `config.versions`), one question: are B-modes consistent with noise?
-
-The config-space paper (Goh et al.) reports the answer for the fiducial catalog. This paper shows the work — methodology, version comparison, scale cut selection.
-
-## Figure Mapping
-
-### Main Text — Methods
-
-| Figure | Claim | File | Section | Label |
-|--------|-------|------|---------|-------|
-| Pure E/B decomposition | `pure_eb_data_vector` | `pure_eb_data_vector.png` | §2.3 | `fig:pure_eb_decomposition` |
-| COSEBIS B-modes | `cosebis_data_vector` | `cosebis_data_vector.png` | §2.3 | `fig:cosebis_fiducial` |
-| Harmonic fiducial | `cl_data_vector` | `cl_fiducial.png` | §2.3 | `fig:cl_fiducial` |
-| Pure E/B covariance | `pure_eb_covariance` | `eb_covariance.png` | §2.4 | `fig:eb_covariance` |
-
-### Main Text — Results
-
-| Figure | Claim | File | Section | Label |
-|--------|-------|------|---------|-------|
-| Config-space PTE heatmaps | `config_space_pte_matrices` | `config_space_pte_fiducial.png` | §3 | `fig:pte_heatmaps` |
-| Harmonic PTE heatmap | `harmonic_space_pte_matrices` | `cl_pte_heatmap.png` | §3 | `fig:pte_cl` |
-| Pure E/B version comparison | `pure_eb_version_comparison` | `pure_eb_versions.png` | §3.1 | `fig:pure_eb_versions` |
-| Harmonic version comparison | `cl_version_comparison` | `cl_versions.png` | §3.1 | `fig:cl_versions` |
-
-### Appendix
-
-| Figure | Claim | File | Section | Label |
-|--------|-------|------|---------|-------|
-| Config-space PTE (all versions) | `config_space_pte_matrices` | `config_space_pte_composite_appendix.png` | Appendix A | `fig:pte_appendix` |
-| Harmonic PTE (all versions) | `harmonic_space_pte_matrices` | `cl_pte_composite_appendix.png` | Appendix A | `fig:pte_cl_appendix` |
-
-Figures copy from `results/tapestry/{claim}/` to `docs/unions_release/unions_bmodes/Figures/`.
-
-## Pure E/B Covariance
-
-Evidence: `results/tapestry/pure_eb_covariance/evidence.json`
-
-### Block Structure
-
-Six blocks of 20 bins each, ordered: ξ+^E, ξ-^E, ξ+^B, ξ-^B, ξ+^amb, ξ-^amb.
-
-The evidence groups these into three 40×40 analysis blocks:
-- `xi_E`: ξ+^E and ξ-^E combined
-- `xi_B`: ξ+^B and ξ-^B combined
-- `xi_amb`: ξ+^amb and ξ-^amb combined
-
-### Figure Context
-
-Appears in §2.4 after COSEBIS covariance discussion:
-
-> Figure~\ref{fig:eb_covariance} shows the pure E/B covariance structure.
-
-Caption references `covariance.n_samples` (currently 2000).
-
-## PTE Reporting
-
-### Format
-
-Two significant figures: "PTE of 0.55"
-
-Scientific notation below 0.01: "PTE of 2.6 × 10⁻⁴"
-
-### Scale Cuts
-
-En-dash, arcmin units: "6–85 arcmin"
-
-### Tables
-
-Bold failures (< 0.01). Values from evidence files, not hardcoded.
-
-Example structure (values populated from evidence):
-
-| Version | ξ+^B | ξ-^B | Joint |
-|---------|------|------|-------|
-| v1.4.X | `config_space_pte_matrices.evidence.versions[ver].xip_stats...` | ... | ... |
-
-Note: Per-version PTEs come from `config_space_pte_matrices`, not `pure_eb_data_vector`.
-
-### Blind Handling
-
-PTEs report the **minimum across blinds** (`pte_joint_min`) as the conservative estimate. This ensures reported values remain valid regardless of which blind is eventually unblinded. The fiducial blind `config["fiducial"]["blind"]` determines covariance matrix selection.
-
-## Macros and Tables
-
-Generated by `paper_macros` rule:
-
-| Output | Purpose |
-|--------|---------|
-| `claims_macros.tex` | LaTeX macro definitions (`\ebfiducialPte`, etc.) |
-| `pte_table_results.tex` | PTE results table for main text |
-| `pte_table_appendix.tex` | PTE table for appendix (all versions) |
-
-All outputs written to `docs/unions_release/unions_bmodes/`.
-
-### Pure E/B Covariance
-
-| Macro | Evidence Path |
-|-------|---------------|
-| `\ebcovCondE` | `pure_eb_covariance` → `block_analysis.xi_E.condition_number` |
-| `\ebcovCondB` | `pure_eb_covariance` → `block_analysis.xi_B.condition_number` |
-| `\ebcovCondAmb` | `pure_eb_covariance` → `block_analysis.xi_amb.condition_number` |
-| `\ebcovCondFull` | `pure_eb_covariance` → `condition_number` |
-| `\ebcovNbins` | `pure_eb_covariance` → `n_bins` |
-
-### Pure E/B Decomposition
-
-| Macro | Evidence Path |
-|-------|---------------|
-| `\ebfiducialPte` | `pure_eb_data_vector` → `fiducial.pte_joint_min` |
-| `\ebfullPte` | `pure_eb_data_vector` → `full.pte_joint_min` |
-| `\ebthetaXipMin`, `\ebthetaXipMax` | `pure_eb_data_vector` → `fiducial.scale_cut_xip` |
-| `\ebthetaXimMin`, `\ebthetaXimMax` | `pure_eb_data_vector` → `fiducial.scale_cut_xim` |
-
-### COSEBIS
-
-| Macro | Evidence Path |
-|-------|---------------|
-| `\cosebisfiducialPte` | `cosebis_version_comparison` → `fiducial.versions.{fiducial_version}.pte_6_min` |
-| `\cosebisfullPte` | `cosebis_version_comparison` → `full.versions.{fiducial_version}.pte_6_min` |
-| `\cosebisthetaMin`, `\cosebisthetaMax` | `cosebis_version_comparison` → `fiducial.scale_cut_arcmin` |
-
-### Covariance Consistency
-
-| Macro | Evidence Path |
-|-------|---------------|
-| `\covXipMaxDev` | `covariance_blind_consistency` → max deviation across blinds |
-| `\covXimMaxDev` | `covariance_blind_consistency` → max deviation across blinds |
-| `\ebJointPteDelta` | `pure_eb_data_vector` → joint PTE variation (max − min) |
-
-Never hardcode values in text. Use macros.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Fiducial version | `fiducial.version` |
-| MC samples | `covariance.n_samples` |
-| Reporting bins | `fiducial.nbins` |
-| ξ+ scale cut | `fiducial.fiducial_xip_scale_cut` |
-| ξ- scale cut | `fiducial.fiducial_xim_scale_cut` |
diff --git a/papers/bmodes/config/cl.md b/papers/bmodes/config/cl.md
deleted file mode 100644
index ad0b7d70..00000000
--- a/papers/bmodes/config/cl.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Harmonic-Space Power Spectra
-
-Pseudo-Cl estimation of cosmic shear E/B-mode power spectra using NaMaster.
-
-## Method
-
-Angular power spectra C_ell are the harmonic-space analog of configuration-space correlation functions. For cosmic shear:
-
-- **C_ell^EE**: E-mode auto-power spectrum (cosmological signal)
-- **C_ell^BB**: B-mode auto-power spectrum (null test for systematics)
-- **C_ell^EB**: E-B cross-power spectrum (should be zero)
-
-Pseudo-Cl estimation accounts for:
-1. Survey mask geometry via mode-coupling matrices
-2. Bandpower binning (sqrt(ell)-linear spacing)
-3. Noise bias subtraction
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| ell bins | `cl.n_ell_bins` |
-| Fiducial version | `fiducial.version` |
-
-## Data Source
-
-Pseudo-Cl files generated by workflow rules using NaMaster:
-- `pseudo_cl_{version}_blind={blind}_powspace_nbins={nbins}.sacc` — Power spectrum estimates
-- `pseudo_cl_cov_{version}_blind={blind}_powspace_nbins={nbins}.fits` — Bandpower covariance matrix
-
-Location: `{COSMO_VAL_OUTPUT}/` (defined in Snakefile, typically `/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/output/`)
-
-Default parameters: `blind=A` (from `fiducial.blind`), `nbins=32` (from `cl.n_ell_bins`).
-
diff --git a/papers/bmodes/config/cl_data_vector.md b/papers/bmodes/config/cl_data_vector.md
deleted file mode 100644
index 4863f064..00000000
--- a/papers/bmodes/config/cl_data_vector.md
+++ /dev/null
@@ -1,47 +0,0 @@
-# Harmonic-Space Fiducial
-
-Depends: [Harmonic-Space Power Spectra](cl.md), [2D Plots](2d_plots.md)
-Method: [Harmonic-Space Power Spectra](cl.md)
-
-## Claim
-
-The fiducial catalog shows B-mode power spectra consistent with zero across all ell bins, validating the absence of significant systematic contamination in harmonic space. PTEs are reported both for the full ell range and with scale cuts (ell_min=300, ell_max=1600) from the harmonic space paper.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Fiducial version | `fiducial.version` |
-| ell bins | `cl.n_ell_bins` |
-| Scale cuts | `cl.fiducial_ell_min`, `cl.fiducial_ell_max` (from the harmonic paper, Guerrini et al.) |
-
-## Evidence
-
-Power spectra and PTEs for BB and EB components, with and without scale cuts:
-
-| Metric | Description |
-|--------|-------------|
-| `pte_bb_full` | B-mode PTE, full ell range |
-| `pte_eb_full` | E-B cross PTE, full ell range |
-| `pte_bb_cut` | B-mode PTE with scale cuts |
-| `pte_eb_cut` | E-B cross PTE with scale cuts |
-| `version` | Fiducial catalog version |
-
-## Outputs
-
-Produces 9 figures: 1 paper figure + 4 per-version leak-corrected + 4 per-version uncorrected.
-
-**Paper figure (leak-corrected, fiducial version):**
-- `figure.png` — Two-panel figure (BB top, EB bottom), no title
-- BB: filled circles, EB: unfilled squares
-- Data normalized by errors (C_ell / sigma)
-- Scale cuts marked with shaded excluded regions
-- sqrt(ell) x-axis scaling matches bandpower binning
-
-**Per-version figures (leak-corrected, with title):**
-- `figure_v{X.Y.Z}.png` — One per leak-corrected version, with version title
-
-**Per-version figures (uncorrected, with title):**
-- `figure_v{X.Y.Z}_uncorrected.png` — One per uncorrected version, with version title
-- Labeled "(uncorrected)" in legend
-- For validation/comparison purposes, not included in paper
diff --git a/papers/bmodes/config/cl_version_comparison.md b/papers/bmodes/config/cl_version_comparison.md
deleted file mode 100644
index 07c4e0c8..00000000
--- a/papers/bmodes/config/cl_version_comparison.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Harmonic-Space Version Comparison
-
-Depends: [Harmonic-Space Power Spectra](cl.md), [2D Plots](2d_plots.md)
-Method: [Harmonic-Space Power Spectra](cl.md)
-Plotting: [2D Plots](2d_plots.md)
-
-## Claim
-
-B-mode power spectra C_ell^BB are consistent with zero for all leak-corrected versions.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Versions | `versions` |
-| ell bins | `cl.n_ell_bins` |
-| PTE healthy range | `statistics.pte_healthy_range` |
-
-## Evidence
-
-Per-version B-mode PTEs:
-
-| Metric | Description |
-|--------|-------------|
-| `{version}.pte_bb` | B-mode power spectrum PTE |
-| `{version}.pte_eb` | E-B cross PTE |
-| `{version}.chi2_bb` | B-mode chi-squared |
-| `{version}.dof` | Degrees of freedom |
-
-## Outputs
-
-- `figure.png` — Two-panel plot (BB top, EB bottom) with all versions overlaid
-- Version comparison uses distinct colors/markers
-- sqrt(ell) x-axis scaling
diff --git a/papers/bmodes/config/config.yaml b/papers/bmodes/config/config.yaml
index 47b385bd..ea51a89f 100644
--- a/papers/bmodes/config/config.yaml
+++ b/papers/bmodes/config/config.yaml
@@ -1,12 +1,9 @@
 versions: [
   "SP_v1.4.5", "SP_v1.4.5_leak_corr",
   "SP_v1.4.6.3", "SP_v1.4.6.3_leak_corr",
-  "SP_v1.4.6_ecut07", "SP_v1.4.6_ecut07_leak_corr",
   "SP_v1.4.8", "SP_v1.4.8_leak_corr",
-  "SP_v1.4.11.3", "SP_v1.4.11.3_leak_corr",
-  "SP_v1.4.11.3_ecut07", "SP_v1.4.11.3_ecut07_leak_corr"
+  "SP_v1.4.11.3", "SP_v1.4.11.3_leak_corr"
 ]
-# IV weight variants removed — one-off test (see fiber v1-4-11-2-iv-weights-harmonic-b-2a2ed88b)
 ellipticity_suffix: "_leak_corrected"
 
 # grid search parameters
@@ -153,17 +150,3 @@ glass_mocks:
 # External tools
 tools:
   cosmocov_executable: "/n23data1/n06data/lgoh/scratch/UNIONS/CosmoCov/covs/cov"
-
-# Ellipticity cut investigation
-ecut:
-  versions_for_comparison:
-    - SP_v1.4.6.3_leak_corr
-    - SP_v1.4.6_ecut07_leak_corr
-    - SP_v1.4.11.3_leak_corr
-    - SP_v1.4.11.3_ecut07_leak_corr
-  version_labels:
-    SP_v1.4.6.3_leak_corr: "v1.4.6.3"
-    SP_v1.4.6_ecut07_leak_corr: "v1.4.6 $|e|<0.7$"
-    SP_v1.4.11.3_leak_corr: "v1.4.11.3"
-    SP_v1.4.11.3_ecut07_leak_corr: "v1.4.11.3 $|e|<0.7$"
-  fiducial_for_comparison: "SP_v1.4.6.3_leak_corr"
diff --git a/papers/bmodes/config/config_space_pte_matrices.md b/papers/bmodes/config/config_space_pte_matrices.md
deleted file mode 100644
index 40073add..00000000
--- a/papers/bmodes/config/config_space_pte_matrices.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Configuration-Space PTE Matrices
-
-Depends: [Pure E/B](pure_eb.md), [COSEBIS](cosebis.md), [2D Plots](2d_plots.md)
-Method: [Pure E/B](pure_eb.md), [COSEBIS](cosebis.md)
-Plotting: [2D Plots](2d_plots.md)
-
-## Claim
-
-Fiducial angular scale cuts are justified by PTE heatmaps across all (theta_min, theta_max) combinations. The main text presents a 1x3 composite for the fiducial version showing xi+^B, xi-^B, and COSEBIS B_n. The appendix presents an Nx3 composite showing all catalog versions (from `config.versions`) with each of the three B-mode statistics. Rows represent versions (labeled via `config.plotting.version_labels`), columns represent statistics.
-
-Scale cuts from `fiducial.fiducial_xip_scale_cut` and `fiducial.fiducial_xim_scale_cut`. COSEBIS uses the same unified range.
-
-## Blind Handling
-
-Uses fiducial blind from `config["fiducial"]["blind"]`. Data vectors (ξ+^B, ξ-^B, COSEBIS B_n) are identical across blinds; covariances vary with blind via n(z)-dependent theoretical predictions.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Versions | `versions` |
-| Fiducial version | `fiducial.version` |
-| xi+ scale cut | `fiducial.fiducial_xip_scale_cut` |
-| xi- scale cut | `fiducial.fiducial_xim_scale_cut` |
-| COSEBIS scale cut | `fiducial.fiducial_min_scale`, `fiducial.fiducial_max_scale` |
-
-## Evidence
-
-Per-version statistics for each statistic:
-
-| Metric | Description |
-|--------|-------------|
-| `{version}.role` | "fiducial" or "appendix" |
-| `{version}.xip_stats.pte_at_fiducial` | xi+^B PTE at fiducial scale cut |
-| `{version}.xip_stats.pte_at_full_range` | xi+^B PTE at full theta range |
-| `{version}.xim_stats.pte_at_fiducial` | xi-^B PTE at fiducial scale cut |
-| `{version}.xim_stats.pte_at_full_range` | xi-^B PTE at full theta range |
-| `{version}.cosebis_stats.pte_at_fiducial` | COSEBIS B_n PTE at fiducial |
-| `{version}.cosebis_stats.pte_at_full_range` | COSEBIS B_n PTE at full theta range |
-
-## Outputs
-
-- `figure_fiducial.png` — 1x3 composite for fiducial version (main text)
-  - Single row: xi+^B, xi-^B, COSEBIS B_n
-  - Y-axis label on leftmost panel
-  - Single shared colorbar on right
-
-- `figure_appendix.png` — Nx3 composite for all versions (appendix)
-  - Rows: All versions from `config.versions` with labels from `config.plotting.version_labels`
-  - Columns: xi+^B, xi-^B, COSEBIS B_n
-  - Y-axis label on each row's leftmost panel
-  - Version labels on right side of each row
-  - Single shared colorbar on right
-
-Each panel uses a discrete PTE colormap (`make_pte_colormap` from `plotting_utils.py`) with solid blue below 0.05, solid red above 0.95, and a gradient between. No contour overlays. Fiducial scale cut marked with a plain black-edged rectangle (no hatching).
diff --git a/papers/bmodes/config/cosebis.md b/papers/bmodes/config/cosebis.md
deleted file mode 100644
index 25756887..00000000
--- a/papers/bmodes/config/cosebis.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# COSEBIS Specification
-
-Complete Orthogonal Sets of E/B-Integrals — a model-independent B-mode null test.
-
-## Purpose
-
-COSEBIS compress 2PCF information into discrete modes with clean E/B separation. Unlike band powers, they're complete and orthogonal over a finite angular range. B-modes should be consistent with zero for a pure lensing signal.
-
-## Config References
-
-| Parameter | Config Key | Description |
-|-----------|------------|-------------|
-| n_modes | `fiducial.nmodes` | Number of COSEBIS modes to compute |
-| θ_min | `cosebis.theta_min` | Minimum angular scale (arcmin) |
-| θ_max | `cosebis.theta_max` | Maximum angular scale (arcmin) |
-| mode_subsets | `cosebis.mode_subsets` | Mode counts for PTE evaluation |
-| scale_cut | `fiducial.fiducial_min_scale` to `fiducial.fiducial_max_scale` | Fiducial scale range |
-| pte_range | `statistics.pte_healthy_range` | Healthy PTE bounds |
-
-## Integration Grid
-
-High-resolution 2PCF for accurate mode integration:
-
-| Parameter | Config Key |
-|-----------|------------|
-| min_sep | `fiducial.min_sep_int` |
-| max_sep | `fiducial.max_sep_int` |
-| nbins | `fiducial.nbins_int` |
-
-## Versions
-
-Compare across catalog versions from `config.versions`:
-- Fiducial: `fiducial.version`
-- All versions tested for consistency
-
-## Blind Handling
-
-Uses fiducial blind from `config["fiducial"]["blind"]`. COSEBIS B_n data vectors are identical across blinds; covariances vary with blind via n(z)-dependent theoretical predictions.
-
-## Analysis Decisions
-
-- **No Hartlap correction**: Using theoretical (CosmoCov) covariance, not jackknife
-- **Conservative scale cuts**: Require full bin containment within θ range
-- **PTE thresholds**: Values outside healthy range flag potential systematics
-
-## Outputs
-
-- B-mode plot with fiducial scale cuts
-- B-mode plot without scale cuts (full range)
-- PTE values for each mode subset
diff --git a/papers/bmodes/config/cosebis_data_vector.md b/papers/bmodes/config/cosebis_data_vector.md
deleted file mode 100644
index 064ae63a..00000000
--- a/papers/bmodes/config/cosebis_data_vector.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# COSEBIs Data Vector
-
-Depends: [COSEBIS](cosebis.md), [1D Plots](1d_plots.md)
-Method: [COSEBIS](cosebis.md)
-Plotting: [1D Plots](1d_plots.md)
-
-## Claim
-
-COSEBIS B-modes at fiducial version (`fiducial.version`) are consistent with zero across the full angular range and at fiducial scale cuts.
-
-## Blind Handling
-
-Uses fiducial blind from `config["fiducial"]["blind"]`. COSEBIS B_n data vectors are identical across blinds; covariances vary with blind via n(z)-dependent theoretical predictions. Statistical evidence (PTEs) is in [Config-Space PTE Matrices](config_space_pte_matrices.md).
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Fiducial version | `fiducial.version` |
-| Fiducial scale cut | `fiducial.fiducial_min_scale` to `fiducial.fiducial_max_scale` |
-| Number of modes | `fiducial.nmodes` |
-
-## Evidence
-
-This claim produces visualizations only. Statistical evidence (PTEs) is in [Config-Space PTE Matrices](config_space_pte_matrices.md).
-
-| Metric | Description |
-|--------|-------------|
-| `fiducial_scale_cut` | Angular range for fiducial |
-| `full_scale_cut` | Full angular range |
-| `nmodes` | Number of COSEBIS modes |
-
-## Outputs
-
-Produces 9 figures: 1 paper figure + 4 per-version leak-corrected + 4 per-version uncorrected.
-
-**Paper figure (leak-corrected, fiducial version):**
-- `figure.png` — Single-panel figure showing $B_n / \sigma_n$ for fiducial version, no title
-  - Both scale cuts (fiducial and full) overplotted with different colors
-  - Error bars are unity by construction (normalized)
-  - Paper figure for main text B-mode validation
-
-**Per-version figures (leak-corrected, with title):**
-- `figure_v{X.Y.Z}.png` — One per leak-corrected version, with version title
-
-**Per-version figures (uncorrected, with title):**
-- `figure_v{X.Y.Z}_uncorrected.png` — One per uncorrected version, with version title
-- For validation/comparison purposes, not included in paper
-
-## Notes
-
-This is the paper-ready data vector figure. For multi-version comparison, see [COSEBIS Version Comparison](cosebis_version_comparison.md).
diff --git a/papers/bmodes/config/cosebis_filter_overlay.md b/papers/bmodes/config/cosebis_filter_overlay.md
deleted file mode 100644
index 9e263e3d..00000000
--- a/papers/bmodes/config/cosebis_filter_overlay.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# COSEBIS Filter Overlay
-
-## Claim
-
-The COSEBIS harmonic-space filter functions W_n(ell) become increasingly oscillatory
-for higher modes n, making them impossible to resolve with 32 coarse bandpower bins.
-
-## Method
-
-Overlay the continuous W_n(ell) filter functions (modes n = 1 through 6) on the
-measured 32-bin BB bandpower spectrum from the fiducial catalog. Show both the full
-[1', 250'] and fiducial [12', 83'] angular scale cuts.
-
-W_n(ell) computed via cosmo_numba's FFT-log Hankel transform on a dense ell grid
-(2000 points). Each filter peak-normalized for visual comparison.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Full theta range | `cosebis.theta_min`, `cosebis.theta_max` |
-| Fiducial theta range | `fiducial.fiducial_min_scale`, `fiducial.fiducial_max_scale` |
-| Number of ell bins | `cl.n_ell_bins` |
-
-## Evidence
-
-| Metric | Description |
-|--------|-------------|
-| `nmodes_shown` | Number of COSEBIS modes displayed |
-| `n_bandpower_bins` | Number of bandpower bins in data |
-| `ell_range` | Effective ell range of the bandpowers |
-
-## Outputs
-
-- `figure.png` — Two-panel overlay: W_n filters + BB data for full and fiducial scale cuts
diff --git a/papers/bmodes/config/cosebis_version_comparison.md b/papers/bmodes/config/cosebis_version_comparison.md
deleted file mode 100644
index e02e3db8..00000000
--- a/papers/bmodes/config/cosebis_version_comparison.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# COSEBIS Version Comparison
-
-Depends: [COSEBIS](cosebis.md), [1D Plots](1d_plots.md)
-Method: [COSEBIS](cosebis.md)
-Plotting: [1D Plots](1d_plots.md)
-
-## Claim
-
-B-mode COSEBIS magnitudes are small relative to measurement uncertainty across catalog versions. Visualization shows $B_n / \sigma_n$ (in units of standard deviation from zero) at fiducial and full angular ranges.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Versions | `versions` |
-| Fiducial | `fiducial.version` |
-| Scale cut | `fiducial.fiducial_min_scale` to `fiducial.fiducial_max_scale` |
-
-## Evidence
-
-This claim produces visualizations only. Statistical evidence (PTEs) is in [Config-Space PTE Matrices](config_space_pte_matrices.md).
-
-| Metric | Description |
-|--------|-------------|
-| `scale_cuts` | Angular ranges shown |
-| `versions_plotted` | Catalog versions included |
-
-## Outputs
-
-Main figure shows leak_corr catalog versions from `config.versions` for catalog evolution comparison.
-
-- `figure_stacked.png` — Two-panel figure showing $B_n / \sigma_n$ (catalog evolution)
-  - Top: Full range (no scale cuts)
-  - Bottom: Fiducial scale cut
-  - Error bars are unity by construction (normalized)
-  - Legend labels from `config.version_labels`
diff --git a/papers/bmodes/config/covariance.md b/papers/bmodes/config/covariance.md
deleted file mode 100644
index 54e7ad42..00000000
--- a/papers/bmodes/config/covariance.md
+++ /dev/null
@@ -1,88 +0,0 @@
-# Covariance
-
-Semi-analytical covariance estimation using CosmoCov.
-
-## Purpose
-
-Provides theoretical covariance matrices for ξ± correlation functions. Avoids jackknife noise by propagating analytical Gaussian covariance through the analysis pipeline.
-
-## Method
-
-1. **CosmoCov** computes Gaussian-only covariance for integration binning
-2. **Sampling** draws realizations from integration covariance
-3. **Propagation** bins samples to reporting scale, propagates through E/B decomposition
-
-Gaussian-only approximation: non-Gaussian contributions computationally prohibitive at 1000 bins.
-
-## Config References
-
-| Parameter | Source | Description |
-|-----------|--------|-------------|
-| Samples | `config.yaml: covariance.n_samples` | MC samples for propagation (default 2000) |
-| Use masked | `config.yaml: covariance.default_masked` | Whether to use masked covariance |
-| Cosmology | `Snakefile: PLANCK18` | astropy Planck18 via cs_util.cosmo |
-| Mask Cls | `covariance.smk: MASK_CLS_FILES` | Per-version mask power spectra |
-
-## Survey Properties
-
-Extracted from catalog config (`cat_config.yaml`) per version:
-- Area (deg²)
-- n_e (gal/arcmin²)
-- σ_e (shape noise)
-
-## Binning
-
-| Scale | min | max | bins |
-|-------|-----|-----|------|
-| Integration | 0.5' | 500' | 1000 |
-| Reporting | 1' | 250' | 20 |
-
-## File Naming
-
-```
-covariance_{version}_{blind}_{gaussian}_minsep={min}_maxsep={max}_nbins={n}{mask_suffix}_processed.txt
-```
-
-- `version`: SP_v1.4.6_leak_corr, etc.
-- `blind`: A, B, or C
-- `gaussian`: g (Gaussian-only) or ng (non-Gaussian)
-- `mask_suffix`: empty or `_masked`
-
-## Blind Handling
-
-B-mode claims use the fiducial blind from `config["fiducial"]["blind"]`. Covariances are computed for the fiducial blind only.
-
-## Covariance Usage Policy
-
-Official results use specific covariance sources for consistency and correctness:
-
-| Quantity | Source | Gaussian | Binning | Notes |
-|----------|--------|----------|---------|-------|
-| Total ξ± errors | CosmoCov | ng | 20-bin (reporting) | Per-blind, masked |
-| Pure E/B mode errors | MC propagation | g | 1000→20 bin | Conservative: underestimates uncertainty |
-| COSEBIS errors | MC propagation | g | 1000→scales | Per scale cut |
-
-**Not used:**
-- TreeCorr jackknife covariance — too noisy for official results
-- NPZ `cov_xip_xim` field — deprecated (was jackknife)
-
-### Footprint Masks
-
-Two footprint masks at nside=4096, generated from the comprehensive catalog with
-only spatially-structured cuts (no galaxy selection cuts):
-
-| Mask | Area | Used by |
-|------|------|---------|
-| Standard footprint | 2894 deg² | v1.4.5, v1.4.6, v1.4.11.3 (and ecut variants) |
-| Star-halo footprint | 2517 deg² | v1.4.8 |
-
-Each version gets its own covariance from its own survey properties (A, n_e, sigma_e).
-`resolve_covariance_version()` is the identity function — no cross-version covariance sharing.
-`MASK_CLS_FILES` (covariance.smk) maps to two mask power spectrum files based on whether
-the version is in `STARHALO_VERSIONS`.
-
-## Related Specs
-
-- [Pure E/B](pure_eb.md) — uses semi-analytic covariance propagation
-- [COSEBIS](cosebis.md) — covariance propagation for bandpowers
-- [Covariance Blind Consistency](covariance_blind_consistency.md) — validates diagonal agreement across blinds
diff --git a/papers/bmodes/config/covariance_blind_consistency.md b/papers/bmodes/config/covariance_blind_consistency.md
deleted file mode 100644
index 0e9ac650..00000000
--- a/papers/bmodes/config/covariance_blind_consistency.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Covariance Blind Consistency
-
-Method: [Covariance](covariance.md)
-
-## Claim
-
-Reporting covariance diagonals are consistent across blinds A, B, and C. Differences arise only from n(z) variations between blinds; survey geometry and cosmology are identical.
-
-## Evidence
-
-Diagonal ratios relative to blind A:
-- `diag(Cov_B) / diag(Cov_A)`
-- `diag(Cov_C) / diag(Cov_A)`
-
-Computed separately for ξ+ and ξ- blocks.
-
-**Metrics:**
-- Max absolute deviation from unity
-- Mean absolute deviation
-- Pass/fail at 1% and 10% thresholds
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Version | `fiducial.version` |
-| Scale range | `fiducial.min_sep` to `fiducial.max_sep` |
-| Bins | `fiducial.nbins` |
-
-## Outputs
-
-- `evidence.json` — ratios, deviations, pass/fail flags
-- `figure.png` — two-panel (ξ+, ξ-) ratio plot with ±1%, ±10% bands
diff --git a/papers/bmodes/config/ecut_spec.md b/papers/bmodes/config/ecut_spec.md
deleted file mode 100644
index 53041659..00000000
--- a/papers/bmodes/config/ecut_spec.md
+++ /dev/null
@@ -1,83 +0,0 @@
-This is your spec for a Ralph loop, a meditative iteration toward a desired state.
-
-## Desired State
-
-Three version comparison figures (pure E/B, COSEBIS, harmonic) showing B-modes for 4
-leak-corrected catalog versions side by side:
-
-- v1.4.6 (all galaxies)
-- v1.4.6 with |e| < 0.7
-- v1.4.11.3 (all galaxies)
-- v1.4.11.3 with |e| < 0.7
-
-Each ecut version has its own covariance computed from the filtered catalog's n_eff and
-sigma_e (with area inherited from the parent version). The full pipeline runs through
-existing rules — ecut versions are just new entries in `config["versions"]` and
-`cat_config.yaml`.
-
-**Done when:**
-- `snakemake ecut_version_comparisons --dry-run` resolves cleanly (convenience target)
-- Filtered catalogs exist at `results/ecut/SP_v1.4.{6,11.3}_ecut07.fits`
-- `cat_config.yaml` has correct (non-zero) n_e and sigma_e for both ecut versions
-- Existing version comparison rules accept a `{comparison}` wildcard in the output path
-  (e.g., `results/tapestry/{comparison}_pure_eb_version_comparison/`), with an input
-  function mapping `paper` → current versions, `ecut` → ecut versions
-- `snakemake paper_pure_eb_version_comparison --dry-run` still works (backward compat via convenience target or alias)
-
-**Why:** Calum reports that |e| < 0.7 significantly reduces B-modes in v1.4.11 and v1.4.6,
-but without trustable error bars. We need the full pipeline's covariances to quantify this.
-DES-Y3 used e < 0.8 to remove stars.
-
-## Context
-
-### How versions flow through the pipeline
-
-Everything is driven by `config["versions"]` in `papers/bmodes/config/config.yaml`. Adding a
-version there (plus its `cat_config.yaml` entry) makes it flow through all existing rules:
-`xi`, `covariance`, `pure_eb_data_vector`, `cosebis_data_vector`, `cl_data_vector`.
-The 2PCF and covariance are independent and can run in parallel.
-
-Key resolution functions in `workflow/Snakefile`:
-- `get_shear_catalog()` — resolves catalog path from cat_config, strips `_leak_corr`
-- `build_redshift_path()` — v1.4.11.x uses v1.4.6's n(z)
-- `resolve_covariance_version()` — identity function (each version gets its own covariance)
-- Wildcard constraint: `version=r"SP_v[\d.]+(_w_iv)?(_leak_corr)?"` — needs `_ecut\d+`
-
-Version comparison rules in `papers/bmodes/rules/claims.smk` (lines 131, 271, 386) use
-`VERSIONS_LEAK_CORR` for inputs and derive version lists from config in the scripts.
-These should be parameterized to accept a version list via `snakemake.params`, so the
-same rules serve both paper and ecut comparisons.
-
-### Covariance depends on survey properties
-
-`get_cat_params()` in `workflow/rules/covariance.smk` (line 4) reads `cov_th.{A, n_e, sigma_e}`
-from `cat_config.yaml`. These flow into CosmoCov INI files. Ecut versions need their own
-values — same area as parent, but recomputed n_e and sigma_e.
-
-### Catalog filtering
-
-A preprocessing rule writes filtered FITS files (`results/ecut/`). Filter:
-`sqrt(e1_leak_corrected² + e2_leak_corrected²) < 0.7`. Only `_leak_corr` versions — the
-uncorrected columns can't be consistently filtered to guarantee the same rows.
-
-### Caveats
-
-- **n(z)**: Ellipticity cut preferentially removes faint/noisy objects. We reuse parent
-  n(z) — acceptable for B-mode null test, not for cosmological inference.
-- **x_offsets**: Paper comparison has 4 versions with specific spacing. Ecut comparison
-  also has 4, so same offsets work.
-
-### Key files
-
-| What | Where |
-|------|-------|
-| Workflow config | `papers/bmodes/config/config.yaml` (search `ecut`) |
-| Catalog config | `cosmo_val/cat_config.yaml` (search `ecut07`) |
-| Pipeline orchestration | `workflow/Snakefile` (wildcard constraints, version resolution functions) |
-| Version comparison rules | `papers/bmodes/rules/claims.smk` lines 131, 271, 386 |
-| Version comparison scripts | `workflow/scripts/{pure_eb,cosebis,cl}_version_comparison.py` |
-| Covariance params | `workflow/rules/covariance.smk` line 4 (`get_cat_params`) |
-
-## Skills
-
-`/snakemake` for DAG operations and job submission.
diff --git a/papers/bmodes/config/harmonic_config_cosebis_comparison.md b/papers/bmodes/config/harmonic_config_cosebis_comparison.md
deleted file mode 100644
index f3122e04..00000000
--- a/papers/bmodes/config/harmonic_config_cosebis_comparison.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Harmonic vs Configuration-Space COSEBIS Cross-Validation
-
-Cross-validate COSEBIS E_n and B_n modes computed from two independent paths.
-
-## Purpose
-
-Validate consistency between harmonic-space and configuration-space COSEBIS estimates. Both paths should yield the same E_n and B_n modes since they measure the same underlying shear field. Disagreement would indicate a problem in one of the estimation pipelines.
-
-## Methods
-
-### Path 1: Harmonic (pseudo-C_ell -> COSEBIS)
-
-1. Pseudo-C_ell from NaMaster (powspace binning, `cl.cosebis_nbins` bins — default 96)
-2. `COSEBIS.cosebis_from_Cell(ell, Cell_E, Cell_B, theta)` transforms to E_n, B_n
-3. Covariance propagated from C_ell covariance via linear transform T: `Cov_COSEBIS = T @ Cov_Cell @ T^T`
-
-### Path 2: Configuration (xi_pm -> COSEBIS)
-
-1. Fine-binned 2PCF from TreeCorr (integration grid: min_sep_int to max_sep_int, nbins_int bins)
-2. `sp_validation.b_modes.calculate_cosebis()` integrates xi_pm with COSEBIS filter functions
-3. Covariance from `COSEBIS.cosebis_covariance_from_xipm_covariance()` applied to theoretical xi_pm covariance
-
-## Angular Range
-
-Parameterized by `{angular_range}` wildcard:
-- **full**: `cosebis.theta_min` to `cosebis.theta_max` (1–250 arcmin)
-- **fiducial**: `fiducial.fiducial_min_scale` to `fiducial.fiducial_max_scale` (12–83 arcmin)
-
-The rule runs once per angular range, producing separate evidence and figures.
-
-## Reliable Modes
-
-At 96-bin powspace, modes n = 1–8 are quantitatively reliable from the harmonic path (validated on GLASS mocks: E_n/config within 2% for modes 1–5, <1% at the dense-ell harmonic ceiling for modes 1–7). Higher modes (n > 8) are shown grayed out for completeness but excluded from PTE calculations. The root cause for mode 9+ is W_n(ell) numerical precision at ~10^-14 amplitudes, not binning. The reliable mode count depends on `cl.cosebis_nbins` (6 at 32 bins, 8 at 96+ bins).
-
-## B-mode PTEs
-
-Chi-squared PTEs are computed for B-modes using reliable modes only (modes 1–8 at 96-bin), from both paths independently:
-- **Harmonic-space**: propagated from pseudo-C_ell Gaussian covariance via linear transform
-- **Config-space**: from CosmoCov theoretical xi_pm covariance
-
-At fiducial scale cuts, both methods find B-modes consistent with zero. At full range, both fail due to the known small-scale contamination.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| n_modes | `fiducial.nmodes` |
-| theta_min (full) | `cosebis.theta_min` |
-| theta_max (full) | `cosebis.theta_max` |
-| theta_min (fiducial) | `fiducial.fiducial_min_scale` |
-| theta_max (fiducial) | `fiducial.fiducial_max_scale` |
-| powspace_nbins | `cl.cosebis_nbins` (default 96; separate from `cl.n_ell_bins` used for BB PTEs) |
-
-## Figures
-
-Per angular range:
-1. **Data vector** (`figure.png`): Fiducial catalog. E-modes (top) and B-modes (bottom, B_n/sigma_n). Config vs harmonic overlaid. Unreliable modes grayed.
-2. **Version comparison** (`figure_versions.png`): All leak-corrected versions, B_n/sigma_n.
-3. **Paper figure**: `harmonic_config_cosebis_{angular_range}.png`
-
-## Known Limitations
-
-- Cross-covariance between harmonic and configuration-space methods is unknown, so no formal chi2/PTE is computed on the difference between the two paths.
-- At 96 bins, modes n > 8 remain unreliable due to W_n(ell) numerical precision limits (COSEBIS amplitudes ~10^-14 at high modes), not binning. This is a fundamental ceiling validated on GLASS mocks with dense integer-ell sampling.
-
-## Depends on
-
-- cosebis (COSEBIS methodology)
-- cl (harmonic-space pseudo-Cl estimation)
diff --git a/papers/bmodes/config/harmonic_space_pte_matrices.md b/papers/bmodes/config/harmonic_space_pte_matrices.md
deleted file mode 100644
index 0f17b6fc..00000000
--- a/papers/bmodes/config/harmonic_space_pte_matrices.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Harmonic-Space PTE Matrices
-
-Depends: [Harmonic-Space Power Spectra](cl.md), [2D Plots](2d_plots.md)
-Method: [Harmonic-Space Power Spectra](cl.md)
-Plotting: [2D Plots](2d_plots.md)
-
-## Claim
-
-Harmonic-space B-mode PTEs are consistent with noise at fiducial multipole range for the fiducial catalog version. PTE heatmaps across all (ell_min, ell_max) combinations show where B-modes become significant. The appendix presents all catalog versions (from `config.versions`) for comparison.
-
-Fiducial scale cuts from `cl.fiducial_ell_min` and `cl.fiducial_ell_max`. Full B-mode test range spans all multipole bins present in the input pseudo-Cℓ file.
-
-## Blind Handling
-
-Uses fiducial blind from `config["fiducial"]["blind"]`. The C_ℓ^BB data vector is identical across blinds; covariances vary with blind via n(z)-dependent theoretical predictions.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Versions | `versions` |
-| Fiducial version | `fiducial.version` |
-| Fiducial multipole range | `cl.fiducial_ell_min`, `cl.fiducial_ell_max` |
-
-Note: Number of multipole bins and full multipole range are determined by the input pseudo-Cℓ file, not config.
-
-## Evidence
-
-Per-version statistics:
-
-| Metric | Description |
-|--------|-------------|
-| `{version}.role` | "fiducial" or "appendix" |
-| `{version}.pte_at_fiducial` | C_l^BB PTE at fiducial multipole range |
-| `{version}.pte_at_full_range` | C_l^BB PTE at full multipole range |
-| `{version}.n_evaluated` | Number of (ell_min, ell_max) pairs |
-| `{version}.ell_range` | [ell_min, ell_max] of full range |
-| `{version}.fiducial_ell_range` | [ell_min, ell_max] of fiducial range |
-| `{version}.n_ell_bins` | Number of multipole bins |
-
-## Outputs
-
-- `figure_fiducial.png` — PTE heatmap for fiducial version (main text)
-- `figure_appendix.png` — N-panel composite for all versions from `config.versions` (appendix)
-
-Heatmaps use a discrete PTE colormap (`make_pte_colormap` from `plotting_utils.py`) with solid blue below 0.05, solid red above 0.95, and a gradient between. No contour overlays. Fiducial multipole range marked with a plain black-edged rectangle (no hatching).
diff --git a/papers/bmodes/config/pure_eb.md b/papers/bmodes/config/pure_eb.md
deleted file mode 100644
index 6800b149..00000000
--- a/papers/bmodes/config/pure_eb.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Pure E/B Mode Decomposition
-
-Schneider et al. decomposition of shear correlation functions into pure E-mode, B-mode, and ambiguous components.
-
-## Purpose
-
-Traditional ξ± mix E and B modes. Pure-mode decomposition cleanly separates:
-- **E-modes**: Cosmological lensing signal
-- **B-modes**: Should be zero for pure lensing; non-zero indicates systematics
-- **Ambiguous modes**: Modes that cannot be uniquely assigned to E or B
-
-## Config References
-
-| Parameter | Config Key | Description |
-|-----------|------------|-------------|
-| ξ+ scale cut | `fiducial.fiducial_xip_scale_cut` | Angular range for ξ+ |
-| ξ- scale cut | `fiducial.fiducial_xim_scale_cut` | Angular range for ξ- |
-| version | `fiducial.version` | Catalog version |
-
-## Method
-
-Following Schneider et al. (2002), decompose:
-- ξ+(θ) → ξ+^E(θ) + ξ+^B(θ) + ξ+^amb(θ)
-- ξ-(θ) → ξ-^E(θ) + ξ-^B(θ) + ξ-^amb(θ)
-
-Uses semi-analytical covariance propagation through the decomposition.
-
-## Data Products
-
-Precomputed decomposition stored in:
-`results/paper_plots/intermediate/{version}_{blind}_pure_eb_semianalytic.npz`
-
-Uses fiducial blind (A) from config. Each NPZ contains:
-- `theta`: Angular bins
-- `xip_E`, `xim_E`: Pure E-mode components
-- `xip_B`, `xim_B`: Pure B-mode components
-- `xip_amb`, `xim_amb`: Ambiguous components
-- `cov_pure_eb`: Full covariance matrix for decomposed modes (MC propagation)
-
-## Plotting Conventions
-
-- Total ξ± shown with filled markers
-- E-modes in teal (secondary, lower alpha)
-- B-modes in crimson (primary, unfilled markers, full opacity)
-- Ambiguous in purple (secondary, lower alpha)
-- Fiducial scale range highlighted
diff --git a/papers/bmodes/config/pure_eb_covariance.md b/papers/bmodes/config/pure_eb_covariance.md
deleted file mode 100644
index 05453d23..00000000
--- a/papers/bmodes/config/pure_eb_covariance.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Pure E/B Covariance
-
-Depends: [Pure E/B](pure_eb.md), [Covariance](covariance.md), [2D Plots](2d_plots.md), [Pure E/B Data Vector](pure_eb_data_vector.md), [Pure E/B Version Comparison](pure_eb_version_comparison.md)
-Method: [Pure E/B](pure_eb.md), [Covariance](covariance.md)
-Plotting: [2D Plots](2d_plots.md)
-
-## Claim
-
-Pure E/B covariance blocks are well-conditioned; ill-conditioning is localized to ambiguous modes. The 6-block correlation structure validates the semi-analytic covariance propagation for B-mode tests.
-
-## Evidence
-
-Block-wise condition numbers for the 120×120 pure E/B covariance (6 blocks of 20 bins each for ξ+/ξ- × E/B/amb):
-
-| Block | Description | Condition Number |
-|-------|-------------|------------------|
-| ξ_E | ξ+^E and ξ-^E combined | ~10^5 (well-conditioned) |
-| ξ_B | ξ+^B and ξ-^B combined | ~10^5 (well-conditioned) |
-| ξ_amb | ξ+^amb and ξ-^amb combined | ~10^15 (ill-conditioned) |
-
-**Key metrics:**
-- Full matrix positive definite
-- E and B blocks stable for PTE calculation
-- Ill-conditioning confined to ambiguous modes (expected)
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Version | `fiducial.version` |
-| Blind | `fiducial.blind` |
-| Integration bins | `fiducial.nbins_int` |
-| Reporting bins | `fiducial.nbins` |
-
-## Outputs
-
-- `pure_eb_covariance.png` — 6-block correlation matrix heatmap (icefire diverging colormap)
-- `evidence.json` — condition numbers, eigenvalue bounds, positive definiteness
-
-## Visualization
-
-Correlation matrix with:
-- icefire diverging colormap (−1 to +1)
-- Block boundaries marked
-- Labels for E/B/amb blocks
diff --git a/papers/bmodes/config/pure_eb_data_vector.md b/papers/bmodes/config/pure_eb_data_vector.md
deleted file mode 100644
index 6e334d9f..00000000
--- a/papers/bmodes/config/pure_eb_data_vector.md
+++ /dev/null
@@ -1,56 +0,0 @@
-# Pure E/B Data Vector
-
-Depends: [Pure E/B](pure_eb.md), [Pure E/B Covariance](pure_eb_covariance.md), [1D Plots](1d_plots.md)
-Method: [Pure E/B](pure_eb.md)
-Covariance: [Pure E/B Covariance](pure_eb_covariance.md)
-Plotting: [1D Plots](1d_plots.md)
-
-## Claim
-
-B-mode signals in UNIONS cosmic shear are consistent with zero at fiducial scale cuts, validating the absence of significant systematic contamination in the shear measurement pipeline.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Fiducial version | `fiducial.version` |
-| xi+ scale cut | `fiducial.fiducial_xip_scale_cut` |
-| xi- scale cut | `fiducial.fiducial_xim_scale_cut` |
-| PTE range | `statistics.pte_healthy_range` |
-
-## Evidence
-
-PTE values for B-mode null tests at two scale ranges, using fiducial blind (A):
-
-1. **Fiducial scale cuts**: Angular range used for cosmological inference
-2. **Full theta range**: All measured angular bins (no cuts)
-
-For each range, report:
-- xi+^B PTE
-- xi-^B PTE
-- Joint [xi+^B, xi-^B] PTE using full cross-covariance between components
-
-Fiducial PTEs should fall within healthy range for null hypothesis consistency. Full-range PTEs may show tension at scales excluded from analysis.
-
-| Metric | Description |
-|--------|-------------|
-| `fiducial.pte_xip_B` | xi+ B-mode PTE at fiducial cuts |
-| `fiducial.pte_xim_B` | xi- B-mode PTE at fiducial cuts |
-| `fiducial.pte_joint` | Joint PTE at fiducial cuts |
-| `full.pte_xip_B` | xi+ B-mode PTE, full range |
-| `full.pte_xim_B` | xi- B-mode PTE, full range |
-| `full.pte_joint` | Joint PTE, full range |
-
-## Outputs
-
-**Main figure (leak-corrected, paper):**
-- `figure.png` — Pure E/B decomposition showing xi+^B and xi-^B consistent with zero
-- 1x2 layout: left panel (ξ+), right panel (ξ-)
-- Each panel shows all decomposition components stacked: total, E-modes, ambiguous, B-modes
-- Color coding: total (black), E-modes (teal), ambiguous (purple), B-modes (crimson)
-- Excluded scale regions shaded gray (outside fiducial range)
-
-**Companion figure (uncorrected, dashboard only):**
-- `figure_uncorrected.png` — Same layout, using uncorrected shear measurements
-- Labeled "(uncorrected)" in title
-- Not included in paper, for validation/comparison purposes
diff --git a/papers/bmodes/config/pure_eb_version_comparison.md b/papers/bmodes/config/pure_eb_version_comparison.md
deleted file mode 100644
index 583ce554..00000000
--- a/papers/bmodes/config/pure_eb_version_comparison.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# Pure E/B Version Comparison
-
-Depends: [Pure E/B](pure_eb.md), [1D Plots](1d_plots.md)
-Method: [Pure E/B](pure_eb.md)
-Plotting: [1D Plots](1d_plots.md)
-
-## Claim
-
-B-mode correlation functions $\xi_{\pm}^B$ are consistent with zero across catalog versions. Total correlation functions $\xi_{\pm}$ show cosmological signal stability. Data outside fiducial scale cuts displayed greyed out.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Versions | `versions` |
-| Fiducial | `fiducial.version` |
-| Scale cut (xi+) | `fiducial.fiducial_xip_scale_cut` |
-| Scale cut (xi-) | `fiducial.fiducial_xim_scale_cut` |
-
-## Evidence
-
-This claim produces visualizations only. Statistical evidence (PTEs) is in [Config Space PTE Matrices](config_space_pte_matrices.md).
-
-| Metric | Description |
-|--------|-------------|
-| `scale_cuts` | Angular ranges (fiducial cuts shown; excluded data greyed) |
-| `versions_plotted` | Catalog versions included |
-
-## Outputs
-
-Main figure shows leak_corr catalog versions from `config.versions` for catalog evolution comparison.
-
-- `figure.png` — Four-panel figure with asymmetric row heights (catalog evolution)
-  - Top row (2/3 height): $\xi_+$ (left), $\xi_-$ (right) as $\theta \xi \times 10^4$
-  - Bottom row (1/3 height): $\xi_+^B / \sigma$ (left), $\xi_-^B / \sigma$ (right)
-  - Data outside fiducial scale cuts shown greyed out
-  - B-mode error bars are unity by construction (normalized)
-  - Legend labels from `config.version_labels`
diff --git a/papers/bmodes/config/xi_cosmology_paper.md b/papers/bmodes/config/xi_cosmology_paper.md
deleted file mode 100644
index 10acd58d..00000000
--- a/papers/bmodes/config/xi_cosmology_paper.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# ξ Cosmology Paper B-mode Reporting
-
-Spec for B-mode validation reporting in the configuration-space cosmology paper (Goh et al., unions_2d_shear_xi).
-
-Depends on: [pure_eb_data_vector](pure_eb_data_vector.md), [cosebis_version_comparison](cosebis_version_comparison.md), [covariance_blind_consistency](covariance_blind_consistency.md)
-
-## Scope
-
-This spec defines what B-mode evidence appears in the config-space paper (Goh et al.). The B-modes paper (Daley et al.) contains the full version comparison and methodological details.
-
-## Reporting Choices
-
-### Catalog Version
-
-Report only the **fiducial catalog** (with leakage correction, see `fiducial.version` in config). Version comparisons belong in the B-modes paper.
-
-### COSEBIS Mode Count
-
-Use **n=6** modes, not n=20. Rationale:
-- Fewer modes = more conservative test (less prone to noise fluctuations)
-- n=6 captures the dominant B-mode signal at small scales
-- Consistent with scale cuts that exclude small and large angular scales
-
-### Statistics to Report
-
-Report both full-range and fiducial scale cut PTEs for both statistics. All PTEs are the **minimum across blinds** (conservative).
-
-| Statistic | Macro |
-|-----------|-------|
-| COSEBIS full PTE | `\cosebisfullPte` |
-| COSEBIS fiducial PTE | `\cosebisfiducialPte` |
-| Joint ξ±^B full PTE | `\ebfullPte` |
-| Joint ξ±^B fiducial PTE | `\ebfiducialPte` |
-
-Values read from `evidence.json` at build time; see `generate_paper_macros.py`.
-
-The joint test combines ξ+^B and ξ-^B using the full cross-covariance matrix.
-
-### Scale Cuts
-
-Scale cuts from config (`fiducial.fiducial_xip_scale_cut`, `fiducial.fiducial_xim_scale_cut`):
-
-| Correlation | Min (arcmin) | Max (arcmin) |
-|-------------|--------------|--------------|
-| ξ+^B | 12 | 83 |
-| ξ-^B | 12 | 83 |
-| COSEBIS | 12 | 83 |
-
-## Text Requirements
-
-The E/B mode section should:
-1. State that significant B-modes appear at extreme scales (motivating scale cuts)
-2. Report passing PTEs at fiducial scale cuts
-3. Reference the B-modes paper for methodology and version comparison
-4. Use auto-generated macros (never hardcode values)
-
-## Blinding and Covariance
-
-The cosmological parameters (Ωm, σ8) are blinded with three independent blinds (A, B, C). Blinding affects the theoretical ξ± predictions, which propagate into the CosmoCov semi-analytical covariance matrices.
-
-### Covariance Variation Between Blinds
-
-From `covariance_blind_consistency`:
-- ξ+ covariance diagonals vary by up to **9%** between blinds
-- ξ- covariance diagonals vary by up to **8.6%** between blinds
-- All blinds pass the 10% consistency threshold
-
-### PTE Variation Between Blinds
-
-The covariance variations propagate into PTE estimates differently for the two statistics. Example values (illustrative, see evidence.json for current):
-
-**Pure E/B (fiducial scale cuts):**
-
-| Blind | ξ+^B PTE | ξ-^B PTE | Joint PTE |
-|-------|----------|----------|-----------|
-| A | 0.48 | 0.10 | 0.28 |
-| B | 0.49 | 0.10 | 0.31 |
-| C | 0.50 | 0.08 | 0.29 |
-| **Δ** | ~0.02 | ~0.02 | ~0.03 |
-
-**COSEBIS (fiducial scale cuts, n=6):**
-
-| Blind | PTE |
-|-------|-----|
-| A, B, C | ~0.29 |
-| **Δ** | <0.001 |
-
-The near-identical COSEBIS PTEs reflect the compressed information in mode space — the integration over angular scales averages out the blind-dependent covariance variations. Pure E/B PTEs show more sensitivity (ΔPTE ≈ 0.03) because the test operates directly on angular bins where covariance differences are localized.
-
-### Reporting Strategy
-
-Report the **minimum PTE across blinds** as the conservative estimate. This ensures reported PTEs remain valid regardless of which blind is eventually unblinded.
-
-**Additional macros for blinding discussion:**
-
-| Macro | Description |
-|-------|-------------|
-| `\covXipMaxDev` | Max ξ+ covariance deviation between blinds |
-| `\covXimMaxDev` | Max ξ- covariance deviation between blinds |
-| `\ebJointPteDelta` | Joint PTE variation across blinds (max − min) |
-
-Values read from `covariance_blind_consistency/evidence.json` at build time.
-
-## Config References
-
-| Parameter | Config Key |
-|-----------|------------|
-| Fiducial version | `fiducial.version` |
-| COSEBIS modes | `cosebis.mode_subsets` (use n=6 subset) |
-| Scale cuts | `fiducial.fiducial_min_scale`, `fiducial.fiducial_max_scale` |
diff --git a/papers/bmodes/rules/ecut.smk b/papers/bmodes/rules/ecut.smk
deleted file mode 100644
index 9f941534..00000000
--- a/papers/bmodes/rules/ecut.smk
+++ /dev/null
@@ -1,218 +0,0 @@
-# workflow/rules/ecut.smk
-"""
-Ellipticity cut investigation: filter catalogs by |e| < threshold,
-recompute survey properties, and run through the full B-mode pipeline.
-"""
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Configuration
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-ECUT_CONFIG = config.get("ecut", {})
-ECUT_VERSIONS = ECUT_CONFIG.get("versions_for_comparison", [])
-ECUT_VERSION_LABELS = ECUT_CONFIG.get("version_labels", {})
-ECUT_FIDUCIAL = ECUT_CONFIG.get("fiducial_for_comparison", config["fiducial"]["version"])
-
-# Parent version mapping: ecut version → parent version for catalog source
-# e.g., SP_v1.4.6_ecut07 → SP_v1.4.6
-ECUT_PARENT_VERSIONS = {
-    ver: ver.split("_ecut")[0]
-    for ver in config.get("versions", [])
-    if "_ecut" in ver and "_leak_corr" not in ver
-}
-
-
-def _ecut_parent_catalog(wildcards):
-    """Resolve parent catalog path for an ecut version."""
-    parent = ECUT_PARENT_VERSIONS.get(wildcards.version)
-    if parent is None:
-        raise ValueError(f"No parent version found for {wildcards.version}")
-    cat_config = config[parent]
-    shear_path = cat_config["shear"]["path"]
-    if shear_path.startswith("/"):
-        return shear_path
-    return str(Path(cat_config.get("subdir", "")) / shear_path)
-
-
-def _ecut_parent_area(version):
-    """Get parent version's area for an ecut version."""
-    base = version.replace("_leak_corr", "")
-    parent = ECUT_PARENT_VERSIONS.get(base, base)
-    return config[parent]["cov_th"]["A"]
-
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Catalog filtering
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-rule filter_catalog_ellipticity:
-    """Filter a shear catalog by |e_leak_corrected| < threshold."""
-    input:
-        catalog=_ecut_parent_catalog,
-    params:
-        e_max=lambda w: float(w.version.split("ecut")[1]) / 10,  # ecut07 → 0.7
-        e1_col="e1_leak_corrected",
-        e2_col="e2_leak_corrected",
-        w_col="w_des",
-        parent_area_deg2=lambda w: _ecut_parent_area(w.version),
-    output:
-        catalog="results/ecut/{version}.fits",
-        survey_props="results/ecut/{version}_survey_props.json",
-    wildcard_constraints:
-        version=r"SP_v[\d.]+_ecut\d+",
-    resources:
-        mem_mb=16000,
-    script:
-        "../scripts/filter_catalog_ellipticity.py"
-
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Version comparison figures (parameterized by {comparison} wildcard)
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-# Comparison sets: maps {comparison} wildcard → (versions, labels, fiducial)
-COMPARISON_SETS = {
-    "paper": {
-        "versions": VERSIONS_LEAK_CORR,
-        "labels": VERSION_LABELS,
-        "fiducial": config["plotting"].get("fiducial_for_comparison", config["fiducial"]["version"]),
-    },
-    "ecut": {
-        "versions": ECUT_VERSIONS,
-        "labels": ECUT_VERSION_LABELS,
-        "fiducial": ECUT_FIDUCIAL,
-    },
-}
-
-
-def _comparison_versions(comparison):
-    return COMPARISON_SETS[comparison]["versions"]
-
-
-def _comparison_labels(comparison):
-    return COMPARISON_SETS[comparison]["labels"]
-
-
-def _comparison_fiducial(comparison):
-    return COMPARISON_SETS[comparison]["fiducial"]
-
-
-# --- Pure E/B version comparison ---
-
-def _pure_eb_comparison_inputs(wildcards):
-    versions = _comparison_versions(wildcards.comparison)
-    return {
-        "specs": [
-            f"{CONFIG_DIR}/pure_eb_version_comparison.md",
-            f"{CONFIG_DIR}/pure_eb.md",
-            f"{CONFIG_DIR}/1d_plots.md",
-        ],
-        "config": f"{CONFIG_DIR}/config.yaml",
-        "pure_eb_data": [
-            f"results/paper_plots/intermediate/{ver}_A_pure_eb_semianalytic.npz"
-            for ver in versions
-        ],
-    }
-
-
-rule pure_eb_comparison:
-    """B-mode visualization: Pure E/B across catalog versions (parameterized)."""
-    input:
-        unpack(_pure_eb_comparison_inputs),
-    params:
-        version_labels=lambda w: _comparison_labels(w.comparison),
-        versions=lambda w: _comparison_versions(w.comparison),
-        fiducial_for_comparison=lambda w: _comparison_fiducial(w.comparison),
-    output:
-        evidence=f"{TAPESTRY_DIR}/{{comparison}}_pure_eb_version_comparison/evidence.json",
-        figure=f"{TAPESTRY_DIR}/{{comparison}}_pure_eb_version_comparison/figure.png",
-        paper_figure=f"{TAPESTRY_DIR}/{{comparison}}_pure_eb_version_comparison/paper_figure.pdf",
-    wildcard_constraints:
-        comparison=r"(paper|ecut)",
-    script:
-        "../scripts/pure_eb_version_comparison.py"
-
-
-# --- COSEBIS version comparison ---
-
-def _cosebis_comparison_inputs(wildcards):
-    versions = _comparison_versions(wildcards.comparison)
-    return {
-        "specs": [
-            f"{CONFIG_DIR}/cosebis_version_comparison.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/1d_plots.md",
-        ],
-        "config": f"{CONFIG_DIR}/config.yaml",
-        "xi_integration": [_xi_integration_path(ver) for ver in versions],
-        "cov_integration": [_cov_integration_path(ver, "A") for ver in versions],
-    }
-
-
-rule cosebis_comparison:
-    """B-mode visualization: COSEBIS across catalog versions (parameterized)."""
-    input:
-        unpack(_cosebis_comparison_inputs),
-    params:
-        version_labels=lambda w: _comparison_labels(w.comparison),
-        versions=lambda w: _comparison_versions(w.comparison),
-        fiducial_for_comparison=lambda w: _comparison_fiducial(w.comparison),
-    output:
-        evidence=f"{TAPESTRY_DIR}/{{comparison}}_cosebis_version_comparison/evidence.json",
-        figure_stacked=f"{TAPESTRY_DIR}/{{comparison}}_cosebis_version_comparison/figure_stacked.png",
-        paper_stacked=f"{TAPESTRY_DIR}/{{comparison}}_cosebis_version_comparison/paper_figure.pdf",
-    wildcard_constraints:
-        comparison=r"(paper|ecut)",
-    script:
-        "../scripts/cosebis_version_comparison.py"
-
-
-# --- Cl version comparison ---
-
-def _cl_comparison_inputs(wildcards):
-    versions = _comparison_versions(wildcards.comparison)
-    return {
-        "specs": [
-            f"{CONFIG_DIR}/cl_version_comparison.md",
-            f"{CONFIG_DIR}/cl.md",
-            f"{CONFIG_DIR}/cl_data_vector.md",
-        ],
-        "config": f"{CONFIG_DIR}/config.yaml",
-        "pseudo_cl": [_pseudo_cl_path(ver) for ver in versions],
-        "pseudo_cl_cov": [_pseudo_cl_cov_path(ver) for ver in versions],
-    }
-
-
-rule cl_comparison:
-    """C_ell^BB version comparison (parameterized)."""
-    input:
-        unpack(_cl_comparison_inputs),
-    params:
-        version_labels=lambda w: _comparison_labels(w.comparison),
-        versions=lambda w: _comparison_versions(w.comparison),
-        fiducial_for_comparison=lambda w: _comparison_fiducial(w.comparison),
-        ell_min_cut=config["cl"]["fiducial_ell_min"],
-        ell_max_cut=config["cl"]["fiducial_ell_max"],
-    output:
-        evidence=f"{TAPESTRY_DIR}/{{comparison}}_cl_version_comparison/evidence.json",
-        figure=f"{TAPESTRY_DIR}/{{comparison}}_cl_version_comparison/figure.png",
-        paper_figure=f"{TAPESTRY_DIR}/{{comparison}}_cl_version_comparison/paper_figure.pdf",
-    wildcard_constraints:
-        comparison=r"(paper|ecut)",
-    script:
-        "../scripts/cl_version_comparison.py"
-
-
-localrules: pure_eb_comparison, cosebis_comparison, cl_comparison
-
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Convenience targets
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-rule ecut_version_comparisons:
-    """All three ecut version comparison figures."""
-    input:
-        rules.pure_eb_comparison.output[0].format(comparison="ecut"),
-        rules.cosebis_comparison.output[0].format(comparison="ecut"),
-        rules.cl_comparison.output[0].format(comparison="ecut"),
diff --git a/papers/bmodes/rules/claims.smk b/papers/bmodes/rules/figures.smk
similarity index 85%
rename from papers/bmodes/rules/claims.smk
rename to papers/bmodes/rules/figures.smk
index 85891b25..322423d7 100644
--- a/papers/bmodes/rules/claims.smk
+++ b/papers/bmodes/rules/figures.smk
@@ -1,14 +1,14 @@
-# workflow/rules/claims.smk
 """
-Claims — testable assertions that produce evidence.
-Claims depend on methods (for technique definitions) and compute outputs (for data).
+Paper figures. Each rule plots from compute-workflow outputs and writes an
+evidence.json of summary statistics (PTEs, chi2) that paper.smk turns into
+LaTeX macros and tables.
 """
 
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 # Configuration
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-# CONFIG_DIR, TAPESTRY_DIR, PAPER_FIGURES_DIR, BLINDS, FIDUCIAL, PLANCK18 defined in Snakefile
+# TAPESTRY_DIR, PAPER_FIGURES_DIR, BLINDS, FIDUCIAL, PLANCK18 defined in Snakefile
 # COSMO_VAL, COSMO_INFERENCE, covariance_path() defined in Snakefile
 COSMO_VAL_OUTPUT = str(COSMO_VAL)  # String version for f-string interpolation
 
@@ -33,7 +33,7 @@ MOCK_VERSION = f"{FIDUCIAL['mock_version']}_leak_corr"
 
 # Filter versions for different analysis types
 # Pure E/B and PTEs only apply to leak-corrected versions
-VERSIONS_LEAK_CORR = [v for v in config["versions"] if "_leak_corr" in v and "_ecut" not in v]
+VERSIONS_LEAK_CORR = [v for v in config["versions"] if "_leak_corr" in v]
 
 # Uncorrected counterparts (bare catalog, no leakage correction)
 VERSIONS_UNCORRECTED = [v.replace("_leak_corr", "") for v in VERSIONS_LEAK_CORR]
@@ -52,7 +52,7 @@ def _extract_version_number(version_string):
     return match.group(1) if match else version_string
 
 
-def _per_version_figure_outputs(claim_dir):
+def _per_version_figure_outputs(fig_dir):
     """Generate output dict for 9 per-version figures.
 
     Returns dict mapping output keys to paths for all 9 figures:
@@ -60,11 +60,11 @@ def _per_version_figure_outputs(claim_dir):
     - figure_v{X.Y.Z}.png for each leak-corrected version
     - figure_v{X.Y.Z}_uncorrected.png for each uncorrected version
     """
-    outputs = {"figure": f"{claim_dir}/figure.png"}
+    outputs = {"figure": f"{fig_dir}/figure.png"}
     for ver_lc in sorted(VERSION_LABELS.keys(), key=lambda v: -len(v)):
         ver_num = _extract_version_number(ver_lc)
-        outputs[f"figure_{ver_num.replace('.', '_')}"] = f"{claim_dir}/figure_{ver_num}.png"
-        outputs[f"figure_{ver_num.replace('.', '_')}_uncorrected"] = f"{claim_dir}/figure_{ver_num}_uncorrected.png"
+        outputs[f"figure_{ver_num.replace('.', '_')}"] = f"{fig_dir}/figure_{ver_num}.png"
+        outputs[f"figure_{ver_num.replace('.', '_')}_uncorrected"] = f"{fig_dir}/figure_{ver_num}_uncorrected.png"
     return outputs
 
 
@@ -130,7 +130,7 @@ def _pseudo_cl_cov_path(version, blind="A", nbins=32):
 
 
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# COSEBIS Claims
+# COSEBIS
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
 rule cosebis_version_comparison:
@@ -139,12 +139,6 @@ rule cosebis_version_comparison:
     Plotting only - statistical PTEs are in cosebis_pte_matrix.
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/cosebis_version_comparison.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/1d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # COSEBIs only for leak-corrected versions
         xi_integration=[_xi_integration_path(ver) for ver in VERSIONS_LEAK_CORR],
         cov_integration=[_cov_integration_path(ver, "A") for ver in VERSIONS_LEAK_CORR],
@@ -171,12 +165,6 @@ rule cosebis_data_vector:
     - figure_v{X.Y.Z}_uncorrected.png: each version, uncorrected, with title
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/cosebis_data_vector.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/1d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # Per-version inputs: xi_{version} and cov_{version} for all versions
         **{f"xi_{ver}": _xi_integration_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"cov_{ver}": _cov_integration_path(ver, "A") for ver in VERSIONS_ALL_FOR_PLOTS},
@@ -190,32 +178,8 @@ rule cosebis_data_vector:
         "../scripts/cosebis_data_vector.py"
 
 
-rule cosebis_binning_comparison:
-    """COSEBIS angular binning convergence: 1,000 vs 10,000 ξ± bins.
-
-    Tests whether the numerical integration of T±n(θ) × ξ±(θ) is converged
-    at 1,000 bins by comparing B_n values and PTEs against 10,000-bin results.
-    Uses the 1,000-bin COSEBIS covariance for both (integration-independent
-    if converged). Ref: Asgari et al. 2017.
-    """
-    input:
-        xi_1k=_xi_integration_path(FIDUCIAL_VERSION),
-        xi_10k=(
-            f"{COSMO_VAL_OUTPUT}/{FIDUCIAL_VERSION}_xi_minsep={FIDUCIAL['min_sep_int']}"
-            f"_maxsep={FIDUCIAL['max_sep_int']}_nbins=10000_npatch={FIDUCIAL['npatch']}.txt"
-        ),
-        cov_1k=_cov_integration_path(FIDUCIAL_VERSION, "A"),
-    output:
-        evidence=f"{TAPESTRY_DIR}/cosebis_binning_comparison/evidence.json",
-        figure=f"{TAPESTRY_DIR}/cosebis_binning_comparison/figure.png",
-    resources:
-        mem_mb=8000,
-    script:
-        "../scripts/cosebis_binning_comparison.py"
-
-
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Pure E/B Claims
+# Pure E/B
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
 # Number of parallel chunks for MC covariance estimation
@@ -247,7 +211,7 @@ rule precompute_pure_eb_chunk:
 rule precompute_pure_eb:
     """Gather MC sample chunks and compute final pure E/B covariance."""
     wildcard_constraints:
-        version=r"[^_]+_v[\d.]+(_ecut\d+)?(_leak_corr)?",  # e.g. SP_v1.4.6, SP_v1.4.6_ecut07_leak_corr
+        version=r"[^_]+_v[\d.]+(_leak_corr)?",  # e.g. SP_v1.4.6.3, SP_v1.4.6.3_leak_corr
         blind=r"[ABC]",
     input:
         chunks=expand(
@@ -279,12 +243,6 @@ rule pure_eb_data_vector:
     - figure_v{X.Y.Z}_uncorrected.png: each version, uncorrected, with title
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/pure_eb_data_vector.md",
-            f"{CONFIG_DIR}/pure_eb.md",
-            f"{CONFIG_DIR}/1d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # Per-version inputs: pure_eb_{version} and cov_{version} for all versions
         **{f"pure_eb_{ver}": f"results/paper_plots/intermediate/{ver}_{FIDUCIAL['blind']}_pure_eb_semianalytic.npz"
            for ver in VERSIONS_ALL_FOR_PLOTS},
@@ -305,12 +263,6 @@ rule pure_eb_version_comparison:
     Uses E-mode errors from pure_eb covariance as proxy for total xi (E dominates).
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/pure_eb_version_comparison.md",
-            f"{CONFIG_DIR}/pure_eb.md",
-            f"{CONFIG_DIR}/1d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # Pure E/B only for leak-corrected versions
         pure_eb_data=[
             f"results/paper_plots/intermediate/{ver}_A_pure_eb_semianalytic.npz"
@@ -338,13 +290,6 @@ rule pure_eb_covariance:
     Uses blind A covariance for visualization (structure is similar across blinds).
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/pure_eb_covariance.md",
-            f"{CONFIG_DIR}/pure_eb.md",
-            f"{CONFIG_DIR}/covariance.md",
-            f"{CONFIG_DIR}/2d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         pure_eb_data=f"results/paper_plots/intermediate/{FIDUCIAL_VERSION}_A_pure_eb_semianalytic.npz",
     output:
         evidence=f"{TAPESTRY_DIR}/pure_eb_covariance/evidence.json",
@@ -379,7 +324,7 @@ rule calculate_pure_eb_ptes:
 
 
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Harmonic-Space Claims (Cl)
+# Harmonic space (Cl)
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
 rule cl_data_vector:
@@ -391,11 +336,6 @@ rule cl_data_vector:
     - figure_v{X.Y.Z}_uncorrected.png: each version, uncorrected, with title
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/cl_data_vector.md",
-            f"{CONFIG_DIR}/cl.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # Per-version inputs: pseudo_cl_{version} and pseudo_cl_cov_{version} for all versions
         **{f"pseudo_cl_{ver}": _pseudo_cl_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"pseudo_cl_cov_{ver}": _pseudo_cl_cov_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
@@ -413,12 +353,6 @@ rule cl_data_vector:
 rule cl_version_comparison:
     """C_ell^BB version comparison across catalog versions."""
     input:
-        specs=[
-            f"{CONFIG_DIR}/cl_version_comparison.md",
-            f"{CONFIG_DIR}/cl.md",
-            f"{CONFIG_DIR}/cl_data_vector.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         cl_data_vector_evidence=rules.cl_data_vector.output.evidence,
         # Cl version comparison only for leak-corrected versions
         pseudo_cl=[_pseudo_cl_path(ver) for ver in VERSIONS_LEAK_CORR],
@@ -472,14 +406,6 @@ rule config_space_pte_matrices:
     Appendix: 3x3 composite for all versions (3 rows x 3 statistics)
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/config_space_pte_matrices.md",
-            f"{CONFIG_DIR}/pure_eb.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/2d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
-        # Claim dependencies
         pure_eb_data_vector=f"{TAPESTRY_DIR}/pure_eb_data_vector/evidence.json",
         cosebis_data_vector=f"{TAPESTRY_DIR}/cosebis_data_vector/evidence.json",
         # Data inputs (fiducial blind only)
@@ -512,12 +438,6 @@ rule harmonic_space_pte_matrices:
     Uses fiducial blind covariance (blind independence validated in bb_covariance_blind_independence).
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/harmonic_space_pte_matrices.md",
-            f"{CONFIG_DIR}/cl.md",
-            f"{CONFIG_DIR}/2d_plots.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # Harmonic PTE matrices for both corrected and uncorrected versions
         pseudo_cl=[_pseudo_cl_path(ver) for ver in VERSIONS_CONFIG_SPACE_PTES],
         pseudo_cl_cov=[
@@ -549,14 +469,6 @@ rule bb_covariance_blind_independence:
     for the fiducial version.
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/bb_covariance_blind_independence.md",
-            f"{CONFIG_DIR}/covariance.md",
-            f"{CONFIG_DIR}/pure_eb.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/cl.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         # Per-blind MC-propagated pure E/B covariances (using mock_version for all blinds)
         **{f"pure_eb_{b}": f"results/paper_plots/intermediate/{MOCK_VERSION}_{b}_pure_eb_semianalytic.npz"
            for b in BLINDS},
@@ -603,12 +515,6 @@ rule harmonic_config_cosebis_comparison:
     wildcard_constraints:
         angular_range="full|fiducial",
     input:
-        specs=[
-            f"{CONFIG_DIR}/harmonic_config_cosebis_comparison.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/cl.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         **{f"pseudo_cl_{ver}": _pseudo_cl_path(ver, nbins=_COSEBIS_NBINS) for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"pseudo_cl_cov_{ver}": _pseudo_cl_cov_path(ver, nbins=_COSEBIS_NBINS) for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"xi_{ver}": _xi_integration_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
@@ -635,12 +541,6 @@ rule cosebis_filter_overlay:
     Shows why coarse bandpowers underresolve higher COSEBIS modes.
     """
     input:
-        specs=[
-            f"{CONFIG_DIR}/cosebis_filter_overlay.md",
-            f"{CONFIG_DIR}/cosebis.md",
-            f"{CONFIG_DIR}/cl.md",
-        ],
-        config=f"{CONFIG_DIR}/config.yaml",
         pseudo_cl=_pseudo_cl_path(FIDUCIAL_VERSION),
         pseudo_cl_cov=_pseudo_cl_cov_path(FIDUCIAL_VERSION),
     output:
diff --git a/papers/bmodes/rules/paper.smk b/papers/bmodes/rules/paper.smk
new file mode 100644
index 00000000..ead4e1d3
--- /dev/null
+++ b/papers/bmodes/rules/paper.smk
@@ -0,0 +1,70 @@
+"""
+Paper outputs: LaTeX macros and PTE tables built from the figure rules'
+evidence.json summaries, and the aggregate `paper` target.
+"""
+
+# Figure rules whose outputs the paper target builds
+FIGURE_RULES = [
+    "cosebis_version_comparison",
+    "cosebis_data_vector",
+    "pure_eb_data_vector",
+    "pure_eb_version_comparison",
+    "pure_eb_covariance",
+    "cl_data_vector",
+    "cl_version_comparison",
+    "config_space_pte_matrices",
+    "harmonic_space_pte_matrices",
+    "bb_covariance_blind_independence",
+    "cosebis_filter_overlay",
+]
+
+_HARMONIC_COSEBIS_ANGULAR_RANGES = ["full", "fiducial"]
+
+
+localrules: xi_cosmology_paper_macros, paper_macros, paper
+
+
+rule xi_cosmology_paper_macros:
+    """B-mode macros for the configuration-space cosmology paper (Goh et al.):
+    fiducial version, n=6 COSEBIS, joint pure-mode PTEs at full and fiducial
+    scales."""
+    input:
+        cosebis_evidence=rules.cosebis_version_comparison.output.evidence,
+        pure_eb_evidence=rules.pure_eb_data_vector.output.evidence,
+        bb_blind_evidence=rules.bb_covariance_blind_independence.output.evidence,
+    output:
+        macros="docs/unions_release/unions_2d_shear_xi/claims_macros.tex",
+    params:
+        tapestry_dir=TAPESTRY_DIR,
+    script:
+        "../scripts/generate_paper_macros.py"
+
+
+rule paper_macros:
+    """LaTeX macros and PTE tables for the B-modes paper (Daley et al.)."""
+    input:
+        cosebis_evidence=rules.cosebis_version_comparison.output.evidence,
+        pure_eb_evidence=rules.pure_eb_data_vector.output.evidence,
+        pure_eb_covariance=rules.pure_eb_covariance.output.evidence,
+        config_space_pte=rules.config_space_pte_matrices.output.evidence,
+        harmonic_space_pte=rules.harmonic_space_pte_matrices.output.evidence,
+    output:
+        bmodes="docs/unions_release/unions_bmodes/claims_macros.tex",
+        pte_table_results="docs/unions_release/unions_bmodes/pte_table_results.tex",
+        pte_table_appendix="docs/unions_release/unions_bmodes/pte_table_appendix.tex",
+    params:
+        tapestry_dir=TAPESTRY_DIR,
+    script:
+        "../scripts/generate_paper_macros.py"
+
+
+rule paper:
+    """Every paper figure, macro file and PTE table."""
+    input:
+        rules.xi_cosmology_paper_macros.output,
+        rules.paper_macros.output,
+        expand(
+            rules.harmonic_config_cosebis_comparison.output,
+            angular_range=_HARMONIC_COSEBIS_ANGULAR_RANGES,
+        ),
+        *(getattr(rules, name).output for name in FIGURE_RULES),
diff --git a/papers/bmodes/rules/presentation.smk b/papers/bmodes/rules/presentation.smk
deleted file mode 100644
index c59f0aa3..00000000
--- a/papers/bmodes/rules/presentation.smk
+++ /dev/null
@@ -1,155 +0,0 @@
-"""Snakemake rules for Moriond 2026 talk figures.
-
-Converts paper-repo PDFs to PNGs for the reveal.js deck. Whenever a
-collaborator updates a figure in their paper directory, re-running
-``snakemake talk_figures`` picks up the change automatically.
-"""
-
-TALK_DIR = "docs/talks/26_Moriond_UNIONS"
-PAPER_III = "docs/unions_release/unions_bmodes/Figures"
-PAPER_IV  = "docs/unions_release/unions_2d_shear_xi/Figures"
-PAPER_V   = "docs/unions_release/unions_harmonic/Figures"
-
-# output name (without .png) → source PDF
-TALK_FIGURES = {
-    # Paper III — B-mode data vectors
-    "pure_eb_data_vector":       f"{PAPER_III}/pure_eb_data_vector.pdf",
-    "cosebis_data_vector":       f"{PAPER_III}/cosebis_data_vector.pdf",
-    "cl_data_vector":            f"{PAPER_III}/cl_data_vector.pdf",
-    "config_space_pte_fiducial": f"{PAPER_III}/config_space_pte_fiducial.pdf",
-    "harmonic_config_cosebis_full": f"{PAPER_III}/harmonic_config_cosebis_full.pdf",
-    "harmonic_config_cosebis_fiducial": f"{PAPER_III}/harmonic_config_cosebis_fiducial.pdf",
-    # Paper IV — configuration-space cosmology
-    "best_fit_xipm":                    f"{PAPER_IV}/best_fit_xipm_SP_v1.4.6.3_B.pdf",
-    "contour_survey_comparison":        f"{PAPER_IV}/SP_v1.4.6.3_B_fiducial_config_contour_plot.pdf",
-    "whisker_plot":                     f"{PAPER_IV}/S8_whisker_plot.pdf",
-    "Omega_m_sigma_8_joint_config":     f"{PAPER_IV}/Omega_m_sigma_8_joint_config.pdf",
-    "S8_comparison_config_harm":        f"{PAPER_IV}/S8_comparison_config_harm.pdf",
-    # Paper V — harmonic-space cosmology
-    "Cell_EE_bestfit":              f"{PAPER_V}/paperplot_Cell_EE_and_best_fit.pdf",
-    "S8_joint_config_harm":         f"{PAPER_V}/S8_joint_config_harm.pdf",
-    "S8_difference_config_harm":    f"{PAPER_V}/S8_difference_config_harm_map_2D.pdf",
-    "contours_blind_harmonic":      f"{PAPER_V}/cosmological_constraints_blind/contours_s8_omegam_cell.pdf",
-    "contours_config_vs_harmonic":  f"{PAPER_V}/cosmological_constraints_cl_vs_xi/contours_s8_omegam_cell.pdf",
-    "contours_weak_lensing_surveys": f"{PAPER_V}/cosmological_constraints_weak_lensing/contours_OMEGA_M_S8_2D_weak_lensing_all_surveys.pdf",
-}
-
-
-localrules: talk_figure, talk_figure_preview, talk_figures, talk_previews, presentation_blind_nz_plot, presentation_s8_convergence, presentation_s8_with_unions, presentation_omega_m_difference, presentation_s8_scatter_mocks, presentation_pte_cosebis
-
-ruleorder: presentation_blind_nz_plot > talk_figure
-
-
-rule talk_figures:
-    """Build all talk PNGs from paper-repo PDFs."""
-    input:
-        expand(f"{TALK_DIR}/images/{{name}}.png", name=TALK_FIGURES.keys()),
-        f"{TALK_DIR}/images/blind_nz_ABC.png",
-        f"{TALK_DIR}/images/s8_convergence_whisker.png",
-        f"{TALK_DIR}/images/omega_m_difference_config_harm.png",
-        f"{TALK_DIR}/images/s8_scatter_config_vs_harmonic.png",
-        f"{TALK_DIR}/images/pte_cosebis_talk.png",
-        f"{TALK_DIR}/images/s8_convergence_with_unions.png",
-
-
-rule talk_previews:
-    """Build low-res preview PNGs (< 1800px) safe for Claude to read."""
-    input:
-        expand(f"{TALK_DIR}/images/preview/{{name}}.png", name=TALK_FIGURES.keys()),
-
-
-rule talk_figure:
-    """Convert a single paper PDF to high-res PNG for the talk.
-
-    `container: None` on purpose: ImageMagick's `convert` is a host tool and is
-    not installed in the sp_validation image.
-    """
-    input:
-        pdf=lambda w: TALK_FIGURES[w.name],
-    output:
-        f"{TALK_DIR}/images/{{name}}.png",
-    wildcard_constraints:
-        name="|".join(TALK_FIGURES.keys()),
-    container:
-        None
-    shell:
-        "convert -density 300 {input.pdf} -quality 95 {output}"
-
-
-rule talk_figure_preview:
-    """Downscale a talk figure to < 1800px for safe AI reading.
-
-    `container: None` for the same reason as talk_figure: `convert` is a host
-    tool, absent from the image.
-    """
-    input:
-        f"{TALK_DIR}/images/{{name}}.png",
-    output:
-        f"{TALK_DIR}/images/preview/{{name}}.png",
-    wildcard_constraints:
-        name="|".join(TALK_FIGURES.keys()),
-    container:
-        None
-    shell:
-        "convert {input} -resize '1800x>' {output}"
-
-
-rule presentation_s8_convergence:
-    """S8 convergence whisker plot for Moriond slide 2."""
-    output:
-        f"{TALK_DIR}/images/s8_convergence_whisker.png",
-    shell:
-        "python {TALK_DIR}/plot_s8_convergence.py"
-
-
-rule presentation_s8_with_unions:
-    """S8 convergence whisker plot WITH UNIONS constraints for slide 14 animation."""
-    output:
-        f"{TALK_DIR}/images/s8_convergence_with_unions.png",
-    shell:
-        "python {TALK_DIR}/plot_s8_with_unions.py"
-
-
-rule presentation_blind_nz_plot:
-    """Plot all three blinded n(z) curves for Moriond presentation."""
-    input:
-        nz_A=lambda w: build_redshift_path(FIDUCIAL["version"], "A"),
-        nz_B=lambda w: build_redshift_path(FIDUCIAL["version"], "B"),
-        nz_C=lambda w: build_redshift_path(FIDUCIAL["version"], "C"),
-    output:
-        f"{TALK_DIR}/images/blind_nz_ABC.png",
-    script:
-        "../scripts/plot_presentation_blind_nz.py"
-
-
-rule presentation_omega_m_difference:
-    """Delta Omega_m histogram from 350 GLASS mocks (config vs harmonic)."""
-    input:
-        mock_summary="/n09data/guerrini/glass_mock_chains/summary_parameter_constraints_merged_v6.txt",
-    output:
-        f"{TALK_DIR}/images/omega_m_difference_config_harm.png",
-    shell:
-        "python {TALK_DIR}/plot_omega_m_difference.py"
-
-
-rule presentation_s8_scatter_mocks:
-    """S8(config) vs S8(harmonic) scatter from 350 GLASS mocks with data crosshair."""
-    input:
-        mock_summary="/n09data/guerrini/glass_mock_chains/summary_parameter_constraints_merged_v6.txt",
-    output:
-        f"{TALK_DIR}/images/s8_scatter_config_vs_harmonic.png",
-    shell:
-        "python {TALK_DIR}/plot_s8_scatter_mocks.py"
-
-
-rule presentation_pte_cosebis:
-    """COSEBIS B_n PTE heatmap for Moriond talk (single panel, talk-sized)."""
-    input:
-        pte_files=[
-            f"{TAPESTRY_DIR}/cosebis_pte_matrix/pte_values/{FIDUCIAL['version']}/{FIDUCIAL['blind']}/pte_{i:03d}_{j:03d}.json"
-            for i, j in PTE_SCALE_CUT_PAIRS
-        ],
-    output:
-        f"{TALK_DIR}/images/pte_cosebis_talk.png",
-    shell:
-        "python {TALK_DIR}/plot_pte_cosebis_talk.py"
diff --git a/papers/bmodes/rules/synthesis.smk b/papers/bmodes/rules/synthesis.smk
deleted file mode 100644
index 64b60770..00000000
--- a/papers/bmodes/rules/synthesis.smk
+++ /dev/null
@@ -1,141 +0,0 @@
-# workflow/rules/synthesis.smk
-"""
-Synthesis — paper specs and aggregate targets.
-Synthesis rules aggregate claims into papers and generate outputs for publication.
-"""
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Configuration
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-# Variables from included files: CONFIG_DIR, TAPESTRY_DIR (Snakefile)
-
-# Claim rules that produce evidence.json — single source of truth for all_tapestry
-# Each entry is a rule name; we access rules.X.output to get all outputs
-CLAIM_RULES = [
-    "cosebis_version_comparison",
-    "cosebis_data_vector",
-    "pure_eb_data_vector",
-    "pure_eb_version_comparison",
-    "pure_eb_covariance",
-    "cl_data_vector",
-    "cl_version_comparison",
-    "config_space_pte_matrices",
-    "harmonic_space_pte_matrices",
-    "bb_covariance_blind_independence",
-    "cosebis_filter_overlay",
-]
-
-# Wildcard claim rules expanded over their parameter values
-_HARMONIC_COSEBIS_ANGULAR_RANGES = ["full", "fiducial"]
-
-
-def _claim_outputs():
-    """Get all outputs from claim rules."""
-    return {name: getattr(rules, name).output for name in CLAIM_RULES}
-
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Paper Macros
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-localrules: xi_cosmology_paper, paper_macros, bmodes_paper_spec, all_tapestry
-
-rule xi_cosmology_paper:
-    """Spec for B-mode reporting in configuration-space paper (Goh et al.).
-
-    Depends on COSEBIS version comparison, pure E/B data vector, and covariance consistency.
-    Reports fiducial version, n=6 COSEBIS, joint pure-mode PTEs at both full and fiducial scales.
-    Also generates evidence.json for dashboard dependency tracking.
-    """
-    input:
-        spec=f"{CONFIG_DIR}/xi_cosmology_paper.md",
-        cosebis_evidence=rules.cosebis_version_comparison.output.evidence,
-        pure_eb_evidence=rules.pure_eb_data_vector.output.evidence,
-        bb_blind_evidence=rules.bb_covariance_blind_independence.output.evidence,
-    output:
-        macros="docs/unions_release/unions_2d_shear_xi/claims_macros.tex",
-        evidence=f"{TAPESTRY_DIR}/xi_cosmology_paper/evidence.json",
-    params:
-        tapestry_dir=TAPESTRY_DIR,
-    script:
-        "../scripts/generate_paper_macros.py"
-
-
-rule paper_macros:
-    """Generate LaTeX macros and tables for B-modes paper (Daley et al.)."""
-    input:
-        cosebis_evidence=rules.cosebis_version_comparison.output.evidence,
-        pure_eb_evidence=rules.pure_eb_data_vector.output.evidence,
-        pure_eb_covariance=rules.pure_eb_covariance.output.evidence,
-        # PTE composite evidence for table generation
-        config_space_pte=rules.config_space_pte_matrices.output.evidence,
-        harmonic_space_pte=rules.harmonic_space_pte_matrices.output.evidence,
-    output:
-        bmodes="docs/unions_release/unions_bmodes/claims_macros.tex",
-        pte_table_results="docs/unions_release/unions_bmodes/pte_table_results.tex",
-        pte_table_appendix="docs/unions_release/unions_bmodes/pte_table_appendix.tex",
-    params:
-        tapestry_dir=TAPESTRY_DIR,
-    script:
-        "../scripts/generate_paper_macros.py"
-
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Paper Spec
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-rule bmodes_paper_spec:
-    """Generate evidence.json for bmodes_paper spec.
-
-    Dependencies include both evidence files and figure outputs to ensure
-    all stale plots are regenerated.
-    """
-    input:
-        spec=f"{CONFIG_DIR}/bmodes_paper.md",
-        # Upstream evidence (using rules.X.output for single source of truth)
-        pure_eb_covariance=rules.pure_eb_covariance.output.evidence,
-        pure_eb_data_vector=rules.pure_eb_data_vector.output.evidence,
-        cosebis_data_vector=rules.cosebis_data_vector.output.evidence,
-        cosebis_version_comparison=rules.cosebis_version_comparison.output.evidence,
-        cl_data_vector=rules.cl_data_vector.output.evidence,
-        cl_version_comparison=rules.cl_version_comparison.output.evidence,
-        config_space_pte=rules.config_space_pte_matrices.output.evidence,
-        harmonic_space_pte=rules.harmonic_space_pte_matrices.output.evidence,
-        # Paper figure dependencies (ensures version comparison plots regenerate)
-        pure_eb_version_comparison=rules.pure_eb_version_comparison.output.evidence,
-        cosebis_bmode_stacked=rules.cosebis_version_comparison.output.paper_stacked,
-        # Consistency checks
-        bb_covariance_blind=rules.bb_covariance_blind_independence.output.evidence,
-    output:
-        evidence=f"{TAPESTRY_DIR}/bmodes_paper/evidence.json",
-    run:
-        import json
-        from datetime import datetime
-        from pathlib import Path
-
-        evidence = {
-            "id": "bmodes_paper",
-            "generated": datetime.now().isoformat(),
-            "evidence": {"type": "synthesis"},
-            "output": {},
-        }
-        with open(output.evidence, "w") as f:
-            json.dump(evidence, f, indent=2)
-
-
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-# Aggregate Targets
-# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-rule all_tapestry:
-    """Aggregate target for all claim evidence and paper outputs."""
-    input:
-        bmodes_paper=rules.bmodes_paper_spec.output,
-        xi_cosmology_paper=rules.xi_cosmology_paper.output,
-        paper_macros=rules.paper_macros.output,
-        harmonic_cosebis=expand(
-            f"{TAPESTRY_DIR}/harmonic_config_cosebis_comparison_{{angular_range}}/evidence.json",
-            angular_range=_HARMONIC_COSEBIS_ANGULAR_RANGES,
-        ),
-        **_claim_outputs(),
diff --git a/papers/bmodes/scripts/bb_covariance_blind_independence.py b/papers/bmodes/scripts/bb_covariance_blind_independence.py
index 256a79b1..c66607af 100644
--- a/papers/bmodes/scripts/bb_covariance_blind_independence.py
+++ b/papers/bmodes/scripts/bb_covariance_blind_independence.py
@@ -526,8 +526,6 @@ def main(
 
     # Build evidence
     evidence = {
-        "spec_id": "bb_covariance_blind_independence",
-        "spec_path": "workflow/config/bb_covariance_blind_independence.md",
         "depends_on": ["covariance", "pure_eb", "cosebis", "pseudo_cl"],
         "generated": datetime.now().isoformat(),
         "evidence": {
diff --git a/papers/bmodes/scripts/cl_data_vector.py b/papers/bmodes/scripts/cl_data_vector.py
index 4eeca960..e47c24d8 100644
--- a/papers/bmodes/scripts/cl_data_vector.py
+++ b/papers/bmodes/scripts/cl_data_vector.py
@@ -158,7 +158,7 @@ def _create_cl_figure(
 
 # ---------------------------------------------------------------------------
 # Canonical per-version file paths (COSMO_VAL results tree). The DAG read these
-# via _pseudo_cl_path() in claims.smk; the CLI reconstructs them from --results-dir
+# via _pseudo_cl_path() in figures.smk; the CLI reconstructs them from --results-dir
 # so the version sweep is self-contained (lc produces only the fiducial version).
 # ---------------------------------------------------------------------------
 def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
@@ -290,7 +290,6 @@ def main(
     )
 
     evidence_data = {
-        "spec_id": "cl_data_vector",
         "generated": datetime.now().isoformat(),
         "evidence": {
             # Full range PTEs
diff --git a/papers/bmodes/scripts/cl_version_comparison.py b/papers/bmodes/scripts/cl_version_comparison.py
index f70a6977..c3c77ddd 100644
--- a/papers/bmodes/scripts/cl_version_comparison.py
+++ b/papers/bmodes/scripts/cl_version_comparison.py
@@ -86,7 +86,7 @@ def main(
 
     plotting_config = config["plotting"]
     version_labels = plotting_config["version_labels"]
-    # Leak-corrected, non-ecut versions (matches VERSIONS_LEAK_CORR in claims.smk)
+    # Leak-corrected versions (matches VERSIONS_LEAK_CORR in figures.smk)
     versions = [v for v in config["versions"] if "_leak_corr" in v and "_ecut" not in v]
 
     # Which version gets the fiducial reference line in boxes
@@ -348,7 +348,6 @@ def main(
         evidence_versions[f"{v}_dof_eb_cut"] = int(data["dof_eb_cut"])
 
     evidence_data = {
-        "spec_id": "cl_version_comparison",
         "generated": datetime.now().isoformat(),
         "evidence": {
             "versions": evidence_versions,
diff --git a/papers/bmodes/scripts/compute_cosebis_pte_single.py b/papers/bmodes/scripts/compute_cosebis_pte_single.py
index 452fc514..73563a8c 100644
--- a/papers/bmodes/scripts/compute_cosebis_pte_single.py
+++ b/papers/bmodes/scripts/compute_cosebis_pte_single.py
@@ -23,7 +23,7 @@
 
 def _pte_scale_cut_pairs():
     """(i_min, i_max) index pairs for the PTE matrix, excluding the polynomial-
-    root-unstable subsets. Mirrors _pte_scale_cut_pairs() in claims.smk."""
+    root-unstable subsets. Mirrors _pte_scale_cut_pairs() in figures.smk."""
     unstable = {(9, 10), (10, 11), (11, 12), (13, 14)}
     return [
         (i, j) for i in range(20) for j in range(i + 1, 21) if (i, j) not in unstable
diff --git a/papers/bmodes/scripts/config_space_pte_matrices.py b/papers/bmodes/scripts/config_space_pte_matrices.py
index 474b71a7..3e3f9a67 100644
--- a/papers/bmodes/scripts/config_space_pte_matrices.py
+++ b/papers/bmodes/scripts/config_space_pte_matrices.py
@@ -789,7 +789,6 @@ def main(
     pure_eb_pte_files,
     cosebis_pte_files,
     output_dir,
-    spec_path=None,
     fiducial_overrides=None,
 ):
     # Both corrected and uncorrected versions (exclude ecut variants)
@@ -964,8 +963,6 @@ def main(
 
     # Build evidence
     evidence_data = {
-        "spec_id": "config_space_pte_matrices",
-        "spec_path": spec_path or "papers/bmodes/config/config_space_pte_matrices.md",
         "generated": datetime.now().isoformat(),
         "evidence": {
             "versions": {},
@@ -1016,7 +1013,7 @@ def main(
 
 
 def _versions_config_space(config):
-    """Reproduce VERSIONS_CONFIG_SPACE_PTES from the Snakemake claims.smk:
+    """Reproduce VERSIONS_CONFIG_SPACE_PTES from the Snakemake figures.smk:
     leak-corrected (non-ecut) versions plus their uncorrected counterparts."""
     leak_corr = [
         v for v in config["versions"] if "_leak_corr" in v and "_ecut" not in v
@@ -1032,13 +1029,11 @@ def _from_snakemake(smk):
     else:
         pure_eb_pte_files = list(pure_eb_pte_files)
     cosebis_pte_files = list(smk.input["cosebis_pte_files"])
-    spec_paths = smk.input["specs"]
     main(
         config=smk.config,
         pure_eb_pte_files=pure_eb_pte_files,
         cosebis_pte_files=cosebis_pte_files,
         output_dir=Path(smk.output["evidence"]).parent,
-        spec_path=spec_paths[0],
     )
 
 
diff --git a/papers/bmodes/scripts/container_env.sh b/papers/bmodes/scripts/container_env.sh
deleted file mode 100644
index a63b40f5..00000000
--- a/papers/bmodes/scripts/container_env.sh
+++ /dev/null
@@ -1,45 +0,0 @@
-# Shared environment for the run_*.sh sweep drivers. Source, don't run:
-#
-#     . "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
-#
-# Sets the checkout the drivers run out of, the container to run in, and
-# `spv_python`, which is how every driver invokes python inside it.
-WT=/n17data/cdaley/unions/code/sp_validation.worktrees/repro-paper-ii-astra
-SRC=$WT/src
-WSCRIPTS=$WT/workflow/scripts
-PSCRIPTS=$WT/papers/bmodes/scripts
-
-# CONTAINER and BIND are resolved exactly as `sp_validation/container.py` does:
-# the writable sandbox if there is one, else the SIF.
-_spv_cache=${XDG_CACHE_HOME:-$HOME/.cache}/sp_validation
-_spv_sandbox=${SPV_SANDBOX:-$_spv_cache/sandbox}
-if [ -d "$_spv_sandbox" ]; then
-  CONTAINER=$_spv_sandbox
-else
-  CONTAINER=${SPV_CONTAINER:-$_spv_cache/sp_validation.sif}
-fi
-BIND=${SPV_APPTAINER_BINDS:-/home,/scratch,/automnt,/n17data,/n23data1,/n09data}
-unset _spv_cache _spv_sandbox
-
-# Every math library pinned to one thread -- pass as SPV_EXEC_EXTRA where the
-# parallelism is by process, not by thread.
-SINGLE_THREAD_ENV="--env OMP_NUM_THREADS=1 --env OPENBLAS_NUM_THREADS=1
-  --env MKL_NUM_THREADS=1 --env NUMBA_NUM_THREADS=1 --env NUMEXPR_NUM_THREADS=1
-  --env VECLIB_MAXIMUM_THREADS=1"
-
-# Run python inside the container against the checkout's src. Extra
-# `apptainer exec` flags go in SPV_EXEC_EXTRA (word-split on purpose).
-spv_python() {
-  apptainer exec --bind "$BIND" --env PYTHONPATH="$SRC" ${SPV_EXEC_EXTRA:-} \
-    "$CONTAINER" /usr/local/bin/python "$@"
-}
-
-# Echo the version list a sweep runs over: $VERSIONS if the caller set one,
-# else whatever sweep_versions.py resolves from $1 (a config path).
-sweep_versions() {
-  if [ -n "${VERSIONS:-}" ]; then
-    echo "$VERSIONS"
-  else
-    spv_python "$PSCRIPTS/sweep_versions.py" --config "$1"
-  fi
-}
diff --git a/papers/bmodes/scripts/cosebis_binning_comparison.py b/papers/bmodes/scripts/cosebis_binning_comparison.py
deleted file mode 100644
index 1e9ac56f..00000000
--- a/papers/bmodes/scripts/cosebis_binning_comparison.py
+++ /dev/null
@@ -1,320 +0,0 @@
-"""COSEBIS angular binning convergence test.
-
-Computes COSEBIS B-modes from both 1,000-bin and 10,000-bin ξ± integration
-grids and compares. Tests whether the numerical integration is converged:
-if B_n values agree, the 1,000-bin results are reliable; if they differ,
-integration error may contaminate the anomalous PTE.
-
-Reference: Asgari et al. 2017 — ≥10,000 bins for E_7 at 0.5% accuracy.
-"""
-
-import argparse
-import json
-import types
-from datetime import datetime
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-import treecorr
-from cosmo_numba.B_modes.cosebis import COSEBIS
-from plotting_utils import (
-    FIG_WIDTH_SINGLE,
-    PAPER_MPLSTYLE,
-    compute_chi2_pte,
-)
-
-from sp_validation.b_modes import calculate_cosebis, scale_cut_to_bins
-
-plt.style.use(PAPER_MPLSTYLE)
-
-
-def _load_gg(xi_path, min_sep, max_sep, nbins, columns_only=False):
-    """Load a TreeCorr GGCorrelation from a text file.
-
-    If columns_only=True, read only the per-bin columns (meanr, xip, xim)
-    and skip the patch covariance. This avoids loading a 20000x20000
-    covariance matrix for high-nbins files.
-    """
-    gg = treecorr.GGCorrelation(
-        min_sep=min_sep,
-        max_sep=max_sep,
-        nbins=nbins,
-        sep_units="arcmin",
-    )
-    if columns_only:
-        # TreeCorr ASCII: ## comment, # col_names, then data rows.
-        # Read only per-bin columns, skip the huge patch covariance.
-        # cols: r_nom meanr meanlogr xip xim xip_im xim_im sigma_xip sigma_xim weight npairs
-        data = np.loadtxt(xi_path, max_rows=nbins)
-        # Compute bin edges from log-spaced binning (matching TreeCorr)
-        bin_edges = np.exp(np.linspace(np.log(min_sep), np.log(max_sep), nbins + 1))
-        return types.SimpleNamespace(
-            meanr=data[:, 1],
-            xip=data[:, 3],
-            xim=data[:, 4],
-            left_edges=bin_edges[:-1],
-            right_edges=bin_edges[1:],
-        )
-    else:
-        gg.read(xi_path)
-    return gg
-
-
-def _compute_Bn_only(gg, nmodes, scale_cut):
-    """Compute COSEBIS B_n from a GGCorrelation without covariance."""
-    min_theta, max_theta = scale_cut
-    start_bin, stop_bin = scale_cut_to_bins(gg, min_theta, max_theta)
-    inds = np.arange(start_bin, stop_bin)
-    theta_cut = gg.meanr[inds]
-    xip_cut = gg.xip[inds]
-    xim_cut = gg.xim[inds]
-
-    cosebis = COSEBIS(
-        theta_min=np.min(theta_cut),
-        theta_max=np.max(theta_cut),
-        N_max=nmodes,
-        precision=120,
-    )
-    En, Bn = cosebis.cosebis_from_xipm(theta_cut, xip_cut, xim_cut, parallel=True)
-    return En, Bn
-
-
-def main(config, xi_1k_path, xi_10k_path, cov_1k_path, out_dir):
-    nmodes = config["fiducial"]["nmodes"]
-
-    # Scale cuts
-    fiducial_scale_cut = (
-        float(config["fiducial"]["fiducial_min_scale"]),
-        float(config["fiducial"]["fiducial_max_scale"]),
-    )
-    full_scale_cut = (
-        float(config["cosebis"]["theta_min"]),
-        float(config["cosebis"]["theta_max"]),
-    )
-    scale_cuts = {"fiducial": fiducial_scale_cut, "full": full_scale_cut}
-
-    # Integration parameters
-    min_sep_int = float(config["fiducial"]["min_sep_int"])
-    max_sep_int = float(config["fiducial"]["max_sep_int"])
-    nbins_1k = int(config["fiducial"]["nbins_int"])
-    nbins_10k = 10_000
-
-    # Load both ξ± grids
-    print("Loading 1,000-bin ξ±...")
-    gg_1k = _load_gg(xi_1k_path, min_sep_int, max_sep_int, nbins_1k)
-    print("Loading 10,000-bin ξ±...")
-    gg_10k = _load_gg(
-        xi_10k_path, min_sep_int, max_sep_int, nbins_10k, columns_only=True
-    )
-
-    # Compute COSEBIS from 1,000-bin ξ± (with covariance for PTE baseline)
-    print("\nComputing COSEBIS from 1,000-bin ξ±...")
-    results_1k = calculate_cosebis(
-        gg_1k,
-        nmodes=nmodes,
-        scale_cuts=list(scale_cuts.values()),
-        cov_path=cov_1k_path,
-    )
-
-    # Compute COSEBIS from 10,000-bin ξ± (B_n only — no matching covariance)
-    print("\nComputing COSEBIS from 10,000-bin ξ±...")
-    results_10k = {}
-    for scale_key, scale_cut in scale_cuts.items():
-        En, Bn = _compute_Bn_only(gg_10k, nmodes, scale_cut)
-        results_10k[scale_key] = {"En": En, "Bn": Bn}
-        print(f"  {scale_key} {scale_cut}: done")
-
-    # Compare and compute PTEs using 1k covariance for both
-    output_dir = Path(out_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    evidence = {}
-    for scale_key, scale_cut in scale_cuts.items():
-        r1k = results_1k[scale_cut]
-        r10k = results_10k[scale_key]
-
-        Bn_1k = r1k["Bn"]
-        Bn_10k = r10k["Bn"]
-        cov_B = r1k["cov"][nmodes:, nmodes:]
-        sigma_B = np.sqrt(np.diag(cov_B))
-
-        # Fractional difference
-        delta_Bn = Bn_10k - Bn_1k
-        delta_Bn_sigma = delta_Bn / sigma_B
-
-        # PTEs using the same covariance
-        chi2_1k, pte_1k, dof = compute_chi2_pte(Bn_1k, cov_B)
-        chi2_10k, pte_10k, _ = compute_chi2_pte(Bn_10k, cov_B)
-
-        # Also for first 6 modes
-        cov_B_6 = cov_B[:6, :6]
-        chi2_1k_6, pte_1k_6, _ = compute_chi2_pte(Bn_1k[:6], cov_B_6)
-        chi2_10k_6, pte_10k_6, _ = compute_chi2_pte(Bn_10k[:6], cov_B_6)
-
-        prefix = scale_key
-        evidence[f"{prefix}_max_delta_Bn_over_sigma"] = float(
-            np.max(np.abs(delta_Bn_sigma))
-        )
-        evidence[f"{prefix}_rms_delta_Bn_over_sigma"] = float(
-            np.sqrt(np.mean(delta_Bn_sigma**2))
-        )
-        evidence[f"{prefix}_pte_1k_20"] = float(pte_1k)
-        evidence[f"{prefix}_pte_10k_20"] = float(pte_10k)
-        evidence[f"{prefix}_chi2_1k_20"] = float(chi2_1k)
-        evidence[f"{prefix}_chi2_10k_20"] = float(chi2_10k)
-        evidence[f"{prefix}_pte_1k_6"] = float(pte_1k_6)
-        evidence[f"{prefix}_pte_10k_6"] = float(pte_10k_6)
-        evidence[f"{prefix}_chi2_1k_6"] = float(chi2_1k_6)
-        evidence[f"{prefix}_chi2_10k_6"] = float(chi2_10k_6)
-
-        print(f"\n{scale_key} [{scale_cut[0]:.0f}–{scale_cut[1]:.0f}']:")
-        print(f"  max |ΔB_n/σ| = {np.max(np.abs(delta_Bn_sigma)):.4f}")
-        print(f"  RMS  ΔB_n/σ  = {np.sqrt(np.mean(delta_Bn_sigma**2)):.4f}")
-        print(f"  PTE (20 modes): 1k={pte_1k:.4e}  10k={pte_10k:.4e}")
-        print(f"  PTE ( 6 modes): 1k={pte_1k_6:.4e}  10k={pte_10k_6:.4e}")
-
-    # Figure: side-by-side comparison
-    fig, axes = plt.subplots(
-        1, 2, figsize=(FIG_WIDTH_SINGLE * 2, FIG_WIDTH_SINGLE * 0.6), sharey=True
-    )
-
-    colors = sns.color_palette("colorblind", 4)
-    modes = np.arange(1, nmodes + 1)
-
-    for ax, (scale_key, scale_cut) in zip(axes, scale_cuts.items()):
-        r1k = results_1k[scale_cut]
-        r10k = results_10k[scale_key]
-
-        cov_B = r1k["cov"][nmodes:, nmodes:]
-        sigma_B = np.sqrt(np.diag(cov_B))
-
-        Bn_1k_norm = r1k["Bn"] / sigma_B
-        Bn_10k_norm = r10k["Bn"] / sigma_B
-
-        ax.errorbar(
-            modes - 0.15,
-            Bn_1k_norm,
-            yerr=np.ones(nmodes),
-            fmt="o",
-            color=colors[0],
-            markerfacecolor=colors[0],
-            markeredgecolor="white",
-            markeredgewidth=0.5,
-            markersize=5,
-            capsize=2,
-            capthick=0.8,
-            linewidth=0.8,
-            elinewidth=0.8,
-            label=r"1,000 bins",
-        )
-        ax.errorbar(
-            modes + 0.15,
-            Bn_10k_norm,
-            yerr=np.ones(nmodes),
-            fmt="s",
-            color=colors[1],
-            markerfacecolor=colors[1],
-            markeredgecolor="white",
-            markeredgewidth=0.5,
-            markersize=5,
-            capsize=2,
-            capthick=0.8,
-            linewidth=0.8,
-            elinewidth=0.8,
-            label=r"10,000 bins",
-        )
-
-        ax.axhline(0, color="black", linewidth=0.8, alpha=0.6)
-        ax.axvspan(0.5, 6.5, color="0.95", alpha=0.5, zorder=0)
-
-        # Annotate PTEs
-        pte_1k = evidence[f"{scale_key}_pte_1k_20"]
-        pte_10k = evidence[f"{scale_key}_pte_10k_20"]
-        ax.text(
-            0.97,
-            0.97,
-            f"PTE(20): {pte_1k:.2e} → {pte_10k:.2e}",
-            transform=ax.transAxes,
-            ha="right",
-            va="top",
-            fontsize=7,
-            bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8),
-        )
-
-        ax.set_xlabel("COSEBIS mode $n$")
-        ax.set_xlim(0.5, nmodes + 0.5)
-        ax.set_xticks(np.arange(1, nmodes + 1))
-        ax.set_title(
-            rf"$\theta = {scale_cut[0]:.0f}$--${scale_cut[1]:.0f}'$", fontsize=10
-        )
-
-    axes[0].set_ylabel(r"$B_n / \sigma_n$")
-    axes[0].legend(loc="upper left", frameon=True, framealpha=0.9, fontsize=8)
-
-    plt.tight_layout()
-    fig_path = output_dir / "figure.png"
-    fig.savefig(fig_path, dpi=300, bbox_inches="tight")
-    print(f"\nSaved {fig_path}")
-    plt.close(fig)
-
-    # Write evidence
-    evidence_data = {
-        "id": "cosebis_binning_comparison",
-        "generated": datetime.now().isoformat(),
-        "input": {
-            "xi_1k": str(xi_1k_path),
-            "xi_10k": str(xi_10k_path),
-            "cov_1k": str(cov_1k_path),
-        },
-        "output": {"figure": "figure.png"},
-        "params": {
-            "nbins_1k": nbins_1k,
-            "nbins_10k": nbins_10k,
-            "nmodes": nmodes,
-            "fiducial_scale_cut": list(fiducial_scale_cut),
-            "full_scale_cut": list(full_scale_cut),
-        },
-        "evidence": evidence,
-    }
-
-    evidence_path = output_dir / "evidence.json"
-    with open(evidence_path, "w") as f:
-        json.dump(evidence_data, f, indent=2)
-    print(f"Saved evidence to {evidence_path}")
-
-
-def _from_cli(argv=None):
-    import yaml
-
-    ap = argparse.ArgumentParser(
-        description="COSEBI angular-binning convergence (1,000 vs 10,000-bin xi_pm) figure."
-    )
-    ap.add_argument(
-        "--config", required=True, help="Absolute path to bmodes config.yaml"
-    )
-    ap.add_argument(
-        "--xi-1k",
-        required=True,
-        help="Fiducial 1000-bin integration-grid TreeCorr xi_pm .txt",
-    )
-    ap.add_argument(
-        "--xi-10k",
-        required=True,
-        help="Fiducial 10000-bin integration-grid TreeCorr xi_pm .txt",
-    )
-    ap.add_argument(
-        "--cov-1k",
-        required=True,
-        help="Fiducial 1000-bin Gaussian covariance (processed .txt) used for both grids",
-    )
-    ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    a = ap.parse_args(argv)
-    with open(a.config) as f:
-        config = yaml.safe_load(f)
-    main(config, a.xi_1k, a.xi_10k, a.cov_1k, a.out)
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/cosebis_data_vector.py b/papers/bmodes/scripts/cosebis_data_vector.py
index dfa0383b..89631f83 100644
--- a/papers/bmodes/scripts/cosebis_data_vector.py
+++ b/papers/bmodes/scripts/cosebis_data_vector.py
@@ -1,4 +1,4 @@
-"""COSEBIs data vector claim.
+"""COSEBIs data vector figure.
 
 Single-panel figure showing B-mode COSEBIS for each catalog version.
 Overplots fiducial and full angular range scale cuts.
@@ -207,7 +207,6 @@ def main(config, xi_integration, cov_integration, out_dir):
     plt.close(fig)
 
     evidence_data = {
-        "spec_id": "cosebis_data_vector",
         "generated": datetime.now().isoformat(),
         "evidence": {
             "version": version,
diff --git a/papers/bmodes/scripts/cosebis_version_comparison.py b/papers/bmodes/scripts/cosebis_version_comparison.py
index 9942bb28..65543ac9 100644
--- a/papers/bmodes/scripts/cosebis_version_comparison.py
+++ b/papers/bmodes/scripts/cosebis_version_comparison.py
@@ -1,4 +1,4 @@
-"""COSEBIS version comparison claim.
+"""COSEBIS version comparison figure.
 
 Visualizes B-mode COSEBIS across catalog versions.
 Produces figures at fiducial scale cut and full range.
@@ -165,7 +165,7 @@ def main(
     fiducial_xi_path=None,
     fiducial_cov_path=None,
 ):
-    # Leak-corrected, non-ecut versions (matches VERSIONS_LEAK_CORR in claims.smk)
+    # Leak-corrected versions (matches VERSIONS_LEAK_CORR in figures.smk)
     versions = [v for v in config["versions"] if "_leak_corr" in v and "_ecut" not in v]
     nmodes = config["fiducial"]["nmodes"]
     plotting_config = config["plotting"]
@@ -342,7 +342,6 @@ def main(
             evidence_versions[f"{version}_{key}"] = val
 
     evidence_data = {
-        "spec_id": "cosebis_version_comparison",
         "generated": datetime.now().isoformat(),
         "evidence": {
             "scale_cuts": scale_cuts,
diff --git a/papers/bmodes/scripts/covariance_blind_consistency.py b/papers/bmodes/scripts/covariance_blind_consistency.py
deleted file mode 100644
index 99ff920c..00000000
--- a/papers/bmodes/scripts/covariance_blind_consistency.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""
-Covariance Blind Consistency Claim
-
-Compare reporting covariance diagonals across blinds A, B, C.
-Claim: diagonals agree at percent level.
-"""
-
-import json
-from datetime import datetime
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-
-plt.style.use(
-    "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_inference/notebooks/"
-    "2D_cosmic_shear_paper_plots/config/paper.mplstyle"
-)
-
-
-def load_covariance_diagonal(path, nbins=20):
-    """Load covariance and extract ξ+ and ξ- diagonals."""
-    cov = np.loadtxt(path)
-    # Covariance is 2*nbins x 2*nbins: [ξ+, ξ-]
-    diag = np.diag(cov)
-    return {
-        "xip": diag[:nbins],
-        "xim": diag[nbins:],
-    }
-
-
-def compute_ratios(diag_ref, diag_test):
-    """Compute ratio and deviation statistics."""
-    ratios = {}
-    for key in ["xip", "xim"]:
-        ratio = diag_test[key] / diag_ref[key]
-        dev = np.abs(ratio - 1.0)
-        ratios[key] = {
-            "ratio": ratio,
-            "max_dev": float(np.max(dev)),
-            "mean_dev": float(np.mean(dev)),
-        }
-    return ratios
-
-
-def make_figure(theta, ratios_B, ratios_C, output_path):
-    """Two-panel ratio plot with threshold bands."""
-    fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)
-
-    colors = sns.color_palette("colorblind", 2)
-
-    for ax, key, label in zip(axes, ["xip", "xim"], [r"$\xi_+$", r"$\xi_-$"]):
-        ax.axhline(1.0, color="gray", ls="-", lw=0.8, zorder=0)
-        ax.axhspan(0.99, 1.01, color="teal", alpha=0.15, label=r"$\pm 1\%$")
-        ax.axhspan(0.90, 1.10, color="orange", alpha=0.10, label=r"$\pm 10\%$")
-
-        ax.plot(
-            theta,
-            ratios_B[key]["ratio"],
-            "o-",
-            color=colors[0],
-            label="B / A",
-            markersize=5,
-        )
-        ax.plot(
-            theta,
-            ratios_C[key]["ratio"],
-            "s--",
-            color=colors[1],
-            label="C / A",
-            markersize=5,
-        )
-
-        ax.set_xscale("log")
-        ax.set_xlabel(r"$\theta$ [arcmin]")
-        ax.set_title(label)
-
-    axes[0].set_ylabel("Diagonal ratio")
-    axes[0].legend(loc="upper right", fontsize=8)
-
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=150, bbox_inches="tight")
-    plt.close(fig)
-
-
-def main(snakemake):
-    # Load covariances (lowercase names match rule definition)
-    diag_A = load_covariance_diagonal(snakemake.input.cov_a)
-    diag_B = load_covariance_diagonal(snakemake.input.cov_b)
-    diag_C = load_covariance_diagonal(snakemake.input.cov_c)
-
-    # Compute ratios
-    ratios_B = compute_ratios(diag_A, diag_B)
-    ratios_C = compute_ratios(diag_A, diag_C)
-
-    # Angular bins (log-spaced)
-    min_sep = snakemake.config["fiducial"]["min_sep"]
-    max_sep = snakemake.config["fiducial"]["max_sep"]
-    nbins = snakemake.config["fiducial"]["nbins"]
-    theta = np.logspace(np.log10(min_sep), np.log10(max_sep), nbins)
-
-    # Generate figure
-    make_figure(theta, ratios_B, ratios_C, snakemake.output.figure)
-
-    # Determine pass/fail
-    all_max_devs = [
-        ratios_B["xip"]["max_dev"],
-        ratios_B["xim"]["max_dev"],
-        ratios_C["xip"]["max_dev"],
-        ratios_C["xim"]["max_dev"],
-    ]
-    pass_1pct = all(d < 0.01 for d in all_max_devs)
-    pass_10pct = all(d < 0.10 for d in all_max_devs)
-
-    # Build evidence
-    evidence = {
-        "spec_id": "covariance_blind_consistency",
-        "spec_path": "workflow/config/covariance_blind_consistency.md",
-        "depends_on": ["covariance"],
-        "generated": datetime.now().isoformat(),
-        "evidence": {
-            "xip": {
-                "B_to_A": {
-                    "max_dev": ratios_B["xip"]["max_dev"],
-                    "mean_dev": ratios_B["xip"]["mean_dev"],
-                },
-                "C_to_A": {
-                    "max_dev": ratios_C["xip"]["max_dev"],
-                    "mean_dev": ratios_C["xip"]["mean_dev"],
-                },
-            },
-            "xim": {
-                "B_to_A": {
-                    "max_dev": ratios_B["xim"]["max_dev"],
-                    "mean_dev": ratios_B["xim"]["mean_dev"],
-                },
-                "C_to_A": {
-                    "max_dev": ratios_C["xim"]["max_dev"],
-                    "mean_dev": ratios_C["xim"]["mean_dev"],
-                },
-            },
-            "pass_1pct": pass_1pct,
-            "pass_10pct": pass_10pct,
-        },
-        "output": {
-            "figure": Path(snakemake.output.figure).name,
-        },
-    }
-
-    # Write evidence
-    Path(snakemake.output.evidence).parent.mkdir(parents=True, exist_ok=True)
-    with open(snakemake.output.evidence, "w") as f:
-        json.dump(evidence, f, indent=2)
-
-
-if __name__ == "__main__":
-    main(snakemake)  # noqa: F821
diff --git a/papers/bmodes/scripts/explorations/compare_mask_effect.py b/papers/bmodes/scripts/explorations/compare_mask_effect.py
deleted file mode 100644
index 0c5f8d6f..00000000
--- a/papers/bmodes/scripts/explorations/compare_mask_effect.py
+++ /dev/null
@@ -1,158 +0,0 @@
-#!/usr/bin/env python3
-# %%
-"""
-Visual comparison of CosmoCov covariance matrices with and without mask window.
-
-This script loads processed covariance matrices from the Snakemake exploration
-and produces diagnostic plots highlighting the impact of applying the CosmoCov
-mask power spectrum file.
-"""
-
-# %%
-from pathlib import Path
-from typing import Tuple
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-from IPython import get_ipython
-
-# %%
-ipython = get_ipython()
-if ipython is not None:
-    ipython.run_line_magic("matplotlib", "inline")
-
-plt.style.use(
-    "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_inference/"
-    "notebooks/2D_cosmic_shear_paper_plots/config/paper.mplstyle"
-)
-
-
-# %%
-def load_covariance_pair(
-    mask_path: Path, nomask_path: Path
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Load covariance matrices for mask/no-mask comparison."""
-    if not mask_path.exists():
-        raise FileNotFoundError(f"Mask covariance not found: {mask_path}")
-    if not nomask_path.exists():
-        raise FileNotFoundError(f"No-mask covariance not found: {nomask_path}")
-
-    mask_cov = np.loadtxt(mask_path)
-    nomask_cov = np.loadtxt(nomask_path)
-
-    if mask_cov.shape != nomask_cov.shape:
-        raise ValueError(
-            f"Covariance shapes differ: mask {mask_cov.shape}, no-mask {nomask_cov.shape}"
-        )
-
-    return mask_cov, nomask_cov
-
-
-# %%
-def create_diagnostics(
-    mask_cov: np.ndarray, nomask_cov: np.ndarray
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    """Compute standard deviations, variance ratios, and covariance differences."""
-    mask_std = np.sqrt(np.diag(mask_cov))
-    nomask_std = np.sqrt(np.diag(nomask_cov))
-
-    # Protect against divide-by-zero in variance ratio
-    with np.errstate(divide="ignore", invalid="ignore"):
-        variance_ratio = np.square(mask_std) / np.square(nomask_std)
-        variance_ratio[~np.isfinite(variance_ratio)] = np.nan
-
-    covariance_delta = mask_cov - nomask_cov
-
-    return mask_std, nomask_std, variance_ratio, covariance_delta
-
-
-# %%
-def plot_comparison(
-    mask_std: np.ndarray,
-    nomask_std: np.ndarray,
-    variance_ratio: np.ndarray,
-    covariance_delta: np.ndarray,
-    output_path: Path,
-) -> None:
-    """Generate diagnostic plots comparing masked vs nominal covariance."""
-    sns.set_palette("husl", 2)
-
-    n_points = mask_std.size
-    indices = np.arange(n_points)
-
-    fig = plt.figure(figsize=(12, 10))
-    grid = fig.add_gridspec(2, 2, height_ratios=[1.0, 1.2])
-
-    ax_std = fig.add_subplot(grid[0, 0])
-    ax_ratio = fig.add_subplot(grid[0, 1])
-    ax_delta = fig.add_subplot(grid[1, :])
-
-    ax_std.plot(indices, nomask_std, label="No mask", linewidth=2)
-    ax_std.plot(indices, mask_std, label="Mask applied", linewidth=2)
-    ax_std.set_xlabel("Data vector index")
-    ax_std.set_ylabel("Standard deviation")
-    ax_std.set_title("Marginal uncertainties")
-    ax_std.grid(alpha=0.3)
-    ax_std.legend()
-
-    ax_ratio.plot(indices, variance_ratio, linewidth=2, color="#ef8a62")
-    ax_ratio.set_xlabel("Data vector index")
-    ax_ratio.set_ylabel(
-        "$\\sigma^2_{\\mathrm{mask}} / \\sigma^2_{\\mathrm{no\\ mask}}$"
-    )
-    ax_ratio.set_title("Variance ratio")
-    ax_ratio.grid(alpha=0.3)
-
-    im = ax_delta.imshow(
-        covariance_delta,
-        cmap="PRGn",
-        vmin=-np.nanmax(np.abs(covariance_delta)),
-        vmax=np.nanmax(np.abs(covariance_delta)),
-        origin="upper",
-    )
-    ax_delta.set_title("Covariance difference (mask - no mask)")
-    ax_delta.set_xlabel("Data vector index")
-    ax_delta.set_ylabel("Data vector index")
-    fig.colorbar(im, ax=ax_delta, fraction=0.046, pad=0.04, label="$\\Delta C$")
-
-    variance_reduction = np.nanmedian(variance_ratio)
-    ax_ratio.text(
-        0.02,
-        0.95,
-        f"Median variance ratio: {variance_reduction:.3f}",
-        transform=ax_ratio.transAxes,
-        ha="left",
-        va="top",
-        fontsize=11,
-        bbox=dict(boxstyle="round", facecolor="white", alpha=0.6),
-    )
-
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=300, bbox_inches="tight")
-    plt.close(fig)
-
-
-# %%
-def main() -> None:
-    try:
-        mask_path = Path(snakemake.input["mask"])  # type: ignore[name-defined]
-        nomask_path = Path(snakemake.input["nomask"])  # type: ignore[name-defined]
-        output_path = Path(snakemake.output["plot"])  # type: ignore[name-defined]
-    except NameError as exc:  # pragma: no cover - executed only outside Snakemake
-        raise RuntimeError(
-            "This script is intended to be executed via Snakemake."
-        ) from exc
-
-    mask_cov, nomask_cov = load_covariance_pair(mask_path, nomask_path)
-    mask_std, nomask_std, variance_ratio, covariance_delta = create_diagnostics(
-        mask_cov, nomask_cov
-    )
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    plot_comparison(mask_std, nomask_std, variance_ratio, covariance_delta, output_path)
-
-
-# %%
-if __name__ == "__main__":
-    main()
diff --git a/papers/bmodes/scripts/explorations/plot_mask_diagonal_ratio.py b/papers/bmodes/scripts/explorations/plot_mask_diagonal_ratio.py
deleted file mode 100644
index 32f930b5..00000000
--- a/papers/bmodes/scripts/explorations/plot_mask_diagonal_ratio.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import argparse
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-
-STYLE_PATH = Path(
-    "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_inference/notebooks/2D_cosmic_shear_paper_plots/config/paper.mplstyle"
-)
-
-
-def compute_ratio(
-    mask_path: Path, nomask_path: Path, data_out: Path, plot_out: Path
-) -> None:
-    mask_cov = np.loadtxt(mask_path)
-    nomask_cov = np.loadtxt(nomask_path)
-
-    if mask_cov.shape != nomask_cov.shape:
-        raise ValueError(
-            f"Mask and no-mask covariances have mismatched shapes {mask_cov.shape} vs {nomask_cov.shape}"
-        )
-
-    nomask_diag = np.diag(nomask_cov)
-    mask_diag = np.diag(mask_cov)
-
-    if np.any(nomask_diag == 0):
-        zero_indices = np.where(nomask_diag == 0)[0]
-        raise ValueError(
-            f"Zero variance entries at indices {zero_indices.tolist()} prevent ratio evaluation."
-        )
-
-    ratio = mask_diag / nomask_diag
-
-    data_out.parent.mkdir(parents=True, exist_ok=True)
-    np.savetxt(data_out, ratio)
-
-    plt.style.use(STYLE_PATH)
-    sns.set_palette("husl", 1)
-
-    fig, ax = plt.subplots(figsize=(9, 4.5), constrained_layout=True)
-    x = np.arange(ratio.size)
-
-    ax.plot(x, ratio, linewidth=1.5)
-    ax.axhline(1.0, color="black", linestyle="--", linewidth=1.0)
-    ax.set_xlabel("Covariance diagonal element")
-    ax.set_ylabel(r"$C_{\mathrm{mask}} / C_{\mathrm{nomask}}$")
-    ax.set_title("Gaussian covariance diagonal ratio")
-    ax.set_xlim(0, ratio.size - 1)
-
-    plot_out.parent.mkdir(parents=True, exist_ok=True)
-    fig.savefig(plot_out, dpi=300)
-
-
-def run_from_snakemake() -> None:
-    mask_path = Path(snakemake.input["mask"])
-    nomask_path = Path(snakemake.input["nomask"])
-    data_out = Path(snakemake.output["data"])
-    plot_out = Path(snakemake.output["plot"])
-    compute_ratio(mask_path, nomask_path, data_out, plot_out)
-
-
-def run_from_cli() -> None:
-    parser = argparse.ArgumentParser(
-        description="Plot ratio of covariance diagonals with and without mask."
-    )
-    parser.add_argument(
-        "mask",
-        type=Path,
-        help="Path to Gaussian covariance matrix generated with mask.",
-    )
-    parser.add_argument(
-        "nomask",
-        type=Path,
-        help="Path to Gaussian covariance matrix generated without mask.",
-    )
-    parser.add_argument(
-        "data", type=Path, help="Output path for diagonal ratio text file."
-    )
-    parser.add_argument("plot", type=Path, help="Output path for diagonal ratio plot.")
-    args = parser.parse_args()
-
-    compute_ratio(args.mask, args.nomask, args.data, args.plot)
-
-
-if "snakemake" in globals():
-    run_from_snakemake()
-else:
-    run_from_cli()
diff --git a/papers/bmodes/scripts/filter_catalog_ellipticity.py b/papers/bmodes/scripts/filter_catalog_ellipticity.py
deleted file mode 100644
index baab3ffb..00000000
--- a/papers/bmodes/scripts/filter_catalog_ellipticity.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""Filter a shear catalog by ellipticity magnitude.
-
-Applies |e| < e_max using leak-corrected ellipticity columns,
-writes filtered FITS, and computes survey properties (n_eff, sigma_e)
-using the parent version's area.
-"""
-
-import json
-import sys
-
-import numpy as np
-from astropy.io import fits
-
-sys.stdout = (
-    sys.stdout if hasattr(sys, "ps1") else open(sys.stdout.fileno(), "w", buffering=1)
-)
-sys.stderr = (
-    sys.stderr if hasattr(sys, "ps1") else open(sys.stderr.fileno(), "w", buffering=1)
-)
-
-
-input_path = snakemake.input["catalog"]
-output_fits = snakemake.output["catalog"]
-output_props = snakemake.output["survey_props"]
-
-e_max = float(snakemake.params["e_max"])
-e1_col = snakemake.params["e1_col"]
-e2_col = snakemake.params["e2_col"]
-w_col = snakemake.params["w_col"]
-parent_area_deg2 = float(snakemake.params["parent_area_deg2"])
-
-print(f"Loading catalog: {input_path}")
-with fits.open(input_path, memmap=True) as hdul:
-    data = hdul[1].data
-    n_total = len(data)
-
-    e1 = np.asarray(data[e1_col], dtype=np.float32)
-    e2 = np.asarray(data[e2_col], dtype=np.float32)
-    e_mag = np.sqrt(e1**2 + e2**2)
-    del e1, e2
-
-    mask = e_mag < e_max
-    del e_mag
-    n_kept = int(np.sum(mask))
-    print(
-        f"Ellipticity cut |e| < {e_max}: {n_kept}/{n_total} galaxies kept ({100 * n_kept / n_total:.1f}%)"
-    )
-
-    # Compute survey properties from masked columns (memory-efficient)
-    w = np.asarray(data[w_col][mask], dtype=np.float64)
-    e1_filt = np.asarray(data[e1_col][mask], dtype=np.float64)
-    e2_filt = np.asarray(data[e2_col][mask], dtype=np.float64)
-
-    sum_w = float(np.sum(w))
-    sum_w2 = float(np.sum(w**2))
-    sum_w2_e2 = float(np.sum(w**2 * (e1_filt**2 + e2_filt**2)))
-    del w, e1_filt, e2_filt
-
-    filtered = data[mask]
-    del mask
-
-    print(f"Writing filtered catalog: {output_fits}")
-    fits.writeto(output_fits, filtered, overwrite=True)
-    del filtered
-
-area_arcmin2 = parent_area_deg2 * 3600.0
-n_eff = sum_w**2 / (area_arcmin2 * sum_w2) if sum_w2 > 0 else 0.0
-sigma_e = np.sqrt(sum_w2_e2 / sum_w2) if sum_w2 > 0 else 0.0
-
-props = {
-    "parent_area_deg2": parent_area_deg2,
-    "n_eff": n_eff,
-    "sigma_e": sigma_e,
-    "n_total": n_total,
-    "n_kept": n_kept,
-    "fraction_kept": n_kept / n_total,
-    "e_max": e_max,
-}
-
-print(f"Survey properties: n_eff={n_eff:.3f} gal/arcmin², sigma_e={sigma_e:.4f}")
-print("  (parent n_eff and sigma_e should be compared to assess impact)")
-
-with open(output_props, "w") as f:
-    json.dump(props, f, indent=2)
-
-print("Done.")
diff --git a/papers/bmodes/scripts/generate_paper_macros.py b/papers/bmodes/scripts/generate_paper_macros.py
index 35442b03..9e02c132 100644
--- a/papers/bmodes/scripts/generate_paper_macros.py
+++ b/papers/bmodes/scripts/generate_paper_macros.py
@@ -1,10 +1,10 @@
-"""Generate LaTeX macros from claim evidence.
+"""Generate LaTeX macros and PTE tables from the figure rules' evidence.json.
 
-Reads evidence.json files and produces:
+Produces:
 - claims_macros.tex: LaTeX macro definitions for paper values
 - pte_table_results.tex: PTE results table for main text
 - pte_table_appendix.tex: PTE table for appendix
-- evidence.json: Dashboard dependency tracking
+- evidence.json (CLI only): which inputs the tables were built from
 """
 
 import json
@@ -78,17 +78,16 @@ def generate_macros(
 ):
     """Generate LaTeX macros from evidence files.
 
-    Macro names are kept simple. The spec (bmodes_paper.md)
-    determines which values go into the paper. Fiducial version from config.
+    Macro names are kept simple. Fiducial version from config.
 
     ``config_pte_path`` / ``harmonic_pte_path`` override the
-    ``claims_dir/<spec_id>/evidence.json`` location for the two PTE-matrix
+    ``claims_dir/<rule>/evidence.json`` location for the two PTE-matrix
     evidence files (used by the CLI form, where each lc output lands at its own
     absolute results path rather than under a shared tapestry tree). When
-    ``None`` the original ``claims_dir``-relative layout is used.
+    ``None`` the ``claims_dir``-relative layout is used.
     """
     macros = []
-    macros.append("% Auto-generated from claim evidence")
+    macros.append("% Auto-generated from figure evidence.json summaries")
     macros.append("% Regenerate: snakemake paper_macros")
     macros.append("% See workflow/config/bmodes_paper.md for paper choices")
     macros.append("")
@@ -138,7 +137,7 @@ def generate_macros(
         with open(eb_path) as f:
             eb_ev = json.load(f).get("evidence", {})
 
-        macros.append("% pure_eb_data_vector (min across blinds per spec)")
+        macros.append("% pure_eb_data_vector (min across blinds)")
 
         # Fiducial PTEs - use pte_joint_min (conservative across blinds)
         eb_fid = eb_ev.get("fiducial", {})
@@ -592,14 +591,11 @@ def generate_pte_tables(
 
 
 def generate_evidence(
-    spec_id: str,
-    spec_path: str,
     depends_on: list[str],
     claims_dir: Path,
     output_path: Path,
 ):
-    """Generate evidence.json for dashboard dependency tracking."""
-    # Collect summary from dependent claims
+    """Record which evidence files the tables were built from."""
     summary = {}
     for dep in depends_on:
         dep_evidence = claims_dir / dep / "evidence.json"
@@ -614,8 +610,6 @@ def generate_evidence(
             summary[dep] = {"has_evidence": False}
 
     evidence = {
-        "spec_id": spec_id,
-        "spec_path": spec_path,
         "depends_on": depends_on,
         "generated": datetime.now().isoformat(),
         "evidence": {
@@ -640,40 +634,19 @@ def _from_snakemake(smk):
     versions = config["versions"]
     version_labels = config["plotting"]["version_labels"]
 
-    # Separate macro file from PTE tables and evidence
-    # Only claims_macros.tex gets macro content; PTE tables generated separately
     macro_file = [Path(p) for p in smk.output if p.endswith("claims_macros.tex")]
-    evidence_outputs = [Path(p) for p in smk.output if p.endswith("evidence.json")]
 
     print(f"Generating macros from {tapestry_dir}")
     generate_macros(tapestry_dir, macro_file, fiducial_version)
 
-    # Generate PTE tables (separate files, not macro content)
-    if macro_file:
+    # The B-modes paper rule also writes the two PTE tables beside its macros
+    if len(smk.output) > 1:
         paper_dir = macro_file[0].parent
         print(f"Generating PTE tables to {paper_dir}")
         generate_pte_tables(
             tapestry_dir, paper_dir, fiducial_version, versions, version_labels, config
         )
 
-    # Generate evidence.json if requested
-    # Dependencies derived from snakemake inputs (rules.X.output declarations)
-    rule_inputs = smk.input.keys()
-    input_deps = [
-        k for k in rule_inputs if k.endswith("_evidence") or k == "covariance_evidence"
-    ]
-    depends_on = [d.replace("_evidence", "") for d in input_deps]
-
-    for evidence_path in evidence_outputs:
-        spec_id = evidence_path.parent.name  # e.g., xi_cosmology_paper
-        generate_evidence(
-            spec_id=spec_id,
-            spec_path=f"workflow/config/{spec_id}.md",
-            depends_on=depends_on,
-            claims_dir=tapestry_dir,
-            output_path=evidence_path,
-        )
-
 
 def _from_cli(argv=None):
     import argparse
@@ -701,7 +674,7 @@ def _from_cli(argv=None):
         "--claims-dir",
         default=None,
         help=(
-            "Optional tapestry-style dir holding <spec_id>/evidence.json for the "
+            "Optional tapestry-style dir holding <rule>/evidence.json for the "
             "extra claims_macros.tex macros (cosebis/pure_eb/harmonic_config); "
             "the two PTE tables need only the two --*-evidence paths above."
         ),
@@ -746,8 +719,6 @@ def _from_cli(argv=None):
     )
 
     generate_evidence(
-        spec_id="pte_summary_evidence",
-        spec_path="analyses/null_tests/astra.yaml#pte_summary_evidence",
         depends_on=["config_space_pte_matrices", "harmonic_space_pte_matrices"],
         claims_dir=claims_dir,
         output_path=out_dir / "evidence.json",
diff --git a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
index dd34db22..b3b05c27 100644
--- a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
+++ b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
@@ -510,12 +510,12 @@ def _make_version_comparison_figure(
     return fig
 
 
-def main(config, inputs, scale_cut, output_dir, paper_figure_name=None, spec_path=None):
+def main(config, inputs, scale_cut, output_dir, paper_figure_name=None):
     """Harmonic-vs-config COSEBI cross-check for one angular range.
 
     ``inputs`` is a dict mirroring ``snakemake.input``: per-version keys
     ``pseudo_cl_{ver}`` / ``pseudo_cl_cov_{ver}`` / ``xi_{ver}`` / ``cov_{ver}``
-    (all absolute paths) plus ``specs``. All artifacts land under ``output_dir``;
+    (all absolute paths). All artifacts land under ``output_dir``;
     ``paper_figure_name`` (when set) is the combined 2×2 paper PDF filename.
     """
     nmodes = int(config["fiducial"]["nmodes"])
@@ -765,9 +765,6 @@ def main(config, inputs, scale_cut, output_dir, paper_figure_name=None, spec_pat
 
     # --- Evidence ---
     evidence = {
-        "spec_id": "harmonic_config_cosebis_comparison",
-        "spec_path": spec_path
-        or "papers/bmodes/config/harmonic_config_cosebis_comparison.md",
         "generated": datetime.now().isoformat(),
         "evidence": {
             "nmodes": nmodes,
@@ -818,7 +815,7 @@ def _cov_integration_path(cov_dir, version, blind, min_sep, max_sep, nbins):
 
 
 def _angular_ranges(config):
-    """Reproduce claims.smk _COSEBIS_ANGULAR_RANGES."""
+    """Reproduce figures.smk _COSEBIS_ANGULAR_RANGES."""
     return {
         "full": (
             float(config["cosebis"]["theta_min"]),
@@ -839,7 +836,6 @@ def _from_snakemake(smk):
             if "paper_figure" in smk.output.keys()
             else None
         ),
-        spec_path=smk.input["specs"][0],
     )
 
 
@@ -929,7 +925,7 @@ def _from_cli(argv=None):
     npatch = fid["npatch"]
 
     versions = _versions_all_for_plots(config)
-    inputs = {"specs": ["papers/bmodes/config/harmonic_config_cosebis_comparison.md"]}
+    inputs = {}
     for ver in versions:
         is_fiducial = ver == a.fiducial_version
 
@@ -974,7 +970,6 @@ def _from_cli(argv=None):
         scale_cut=scale_cut,
         output_dir=a.out,
         paper_figure_name=f"harmonic_config_cosebis_{a.angular_range}.pdf",
-        spec_path=inputs["specs"][0],
     )
 
 
diff --git a/papers/bmodes/scripts/harmonic_space_pte_matrices.py b/papers/bmodes/scripts/harmonic_space_pte_matrices.py
index aeb031c6..3be9644a 100644
--- a/papers/bmodes/scripts/harmonic_space_pte_matrices.py
+++ b/papers/bmodes/scripts/harmonic_space_pte_matrices.py
@@ -366,7 +366,7 @@ def main(
     output_dir.mkdir(parents=True, exist_ok=True)
 
     # Per-version pseudo-Cl / covariance files (canonical COSMO_VAL tree, blind A).
-    # Mirrors _pseudo_cl_path(ver) / _pseudo_cl_cov_path(ver, blind) in claims.smk.
+    # Mirrors _pseudo_cl_path(ver) / _pseudo_cl_cov_path(ver, blind) in figures.smk.
     # Fiducial-provenance repoint: when --fiducial-version matches and an explicit
     # lc override path is set, read that path instead of the reconstructed pattern
     # (lc files lack the blind=/powspace_nbins= tokens, distinguished by directory).
@@ -491,7 +491,6 @@ def main(
 
     # Build evidence
     evidence_data = {
-        "spec_id": "harmonic_space_pte_matrices",
         "generated": datetime.now().isoformat(),
         "evidence": {
             "blind": fiducial_blind,
diff --git a/papers/bmodes/scripts/plot_cosebis_filter_overlay.py b/papers/bmodes/scripts/plot_cosebis_filter_overlay.py
index 68b255ab..5f071bb6 100644
--- a/papers/bmodes/scripts/plot_cosebis_filter_overlay.py
+++ b/papers/bmodes/scripts/plot_cosebis_filter_overlay.py
@@ -183,13 +183,7 @@ def main():
     make_figure(ell_32, bb, sigma_bb, ell_dense, Wn_full, Wn_fid, output_figure)
 
     # --- Evidence ---
-    spec_paths = snakemake.input.specs  # noqa: F821
-    depends_on = [Path(p).stem for p in spec_paths[1:]]
-
     evidence = {
-        "id": Path(spec_paths[0]).stem,
-        "kind": "claim",
-        "depends_on": depends_on,
         "generated": datetime.now().isoformat(),
         "evidence": {
             "nmodes_shown": nmodes,
diff --git a/papers/bmodes/scripts/plot_presentation_blind_nz.py b/papers/bmodes/scripts/plot_presentation_blind_nz.py
deleted file mode 100644
index c71fbd4f..00000000
--- a/papers/bmodes/scripts/plot_presentation_blind_nz.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Plot the three blinded n(z) curves used in the Moriond slides."""
-
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-from plotting_utils import PAPER_MPLSTYLE
-
-
-def load_nz(path):
-    data = np.loadtxt(path)
-    return data[:, 0], data[:, 1]
-
-
-plt.style.use(PAPER_MPLSTYLE)
-sns.set_palette("husl", 3)
-
-fig, ax = plt.subplots(figsize=(4.5, 3.2))
-
-for label, path in [
-    ("Blind A", snakemake.input.nz_A),
-    ("Blind B", snakemake.input.nz_B),
-    ("Blind C", snakemake.input.nz_C),
-]:
-    z, nz = load_nz(path)
-    ax.plot(z, nz, linewidth=1.6, label=label)
-
-ax.set_xlabel(r"$z$", fontsize=18)
-ax.set_ylabel(r"$n(z)$", fontsize=18)
-ax.set_xlim(0, 2)
-ax.set_ylim(bottom=0)
-ax.legend(frameon=False, loc="upper right", fontsize=15)
-ax.tick_params(labelsize=15)
-ax.grid(False)
-
-fig.tight_layout()
-Path(snakemake.output[0]).parent.mkdir(parents=True, exist_ok=True)
-fig.savefig(snakemake.output[0], dpi=220, bbox_inches="tight")
diff --git a/papers/bmodes/scripts/plot_pure_eb_covariance.py b/papers/bmodes/scripts/plot_pure_eb_covariance.py
deleted file mode 100644
index 5522ef59..00000000
--- a/papers/bmodes/scripts/plot_pure_eb_covariance.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# %%
-"""Visualize Pure E/B covariance matrix structure."""
-
-import os
-import sys
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-from mpl_toolkits.axes_grid1 import make_axes_locatable
-from plotting_utils import PAPER_MPLSTYLE
-
-# Unbuffered output for Snakemake log streaming
-sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1)
-sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1)
-
-# Apply paper style
-plt.style.use(PAPER_MPLSTYLE)
-
-
-def _load_snakemake():
-    if hasattr(sys, "ps1"):
-        from snakemake_helpers import snakemake_interactive
-
-        return snakemake_interactive(
-            "results/paper_plots/pure_eb_covariance.png",
-            str(Path.cwd()),
-        )
-    return snakemake
-
-
-snakemake = _load_snakemake()
-
-params = snakemake.params
-
-
-def _correlation_from_covariance(cov):
-    """Convert covariance to correlation matrix."""
-    std = np.sqrt(np.diag(cov))
-    corr = cov / np.outer(std, std)
-    return corr
-
-
-def main():
-    # Load precomputed data
-    dataset = np.load(snakemake.input["pure_eb_data"])
-
-    cov_pure_eb = dataset["cov_pure_eb"]
-    theta = dataset["theta"]
-    nbins = len(theta)
-
-    # Create figure
-    fig, ax = plt.subplots(figsize=(9, 9))
-    vlag_cmap = sns.color_palette("vlag", as_cmap=True)
-    im = ax.matshow(_correlation_from_covariance(cov_pure_eb), cmap=vlag_cmap)
-
-    # Configure tick labels for E/B modes
-    tick_positions = np.arange(nbins / 2, nbins * 6, nbins)
-    tick_labels = [
-        r"$\xi_+^{\mathrm{E}}$",
-        r"$\xi_-^{\mathrm{E}}$",
-        r"$\xi_+^{\mathrm{B}}$",
-        r"$\xi_-^{\mathrm{B}}$",
-        r"$\xi_+^{\mathrm{amb}}$",
-        r"$\xi_-^{\mathrm{amb}}$",
-    ]
-
-    for ticks in (plt.xticks, plt.yticks):
-        ticks(tick_positions, tick_labels)
-        ticks(np.arange(0, nbins * 6 + 1, 20), minor=True)
-
-    ax.tick_params(axis="both", which="major", length=0)
-    im.set_clim(-1, 1)
-    divider = make_axes_locatable(ax)
-    cax = divider.append_axes("right", size="5%", pad=0.1)
-    plt.colorbar(im, cax=cax)
-    ax.set_title(f"{params['version']}: semi-analytic correlation matrix")
-
-    # Save
-    output_path = Path(snakemake.output[0])
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    plt.savefig(output_path, dpi=300, bbox_inches="tight")
-    print(f"Saved plot to {output_path}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/papers/bmodes/scripts/pure_eb_covariance.py b/papers/bmodes/scripts/pure_eb_covariance.py
index ee5ac77c..61b1967a 100644
--- a/papers/bmodes/scripts/pure_eb_covariance.py
+++ b/papers/bmodes/scripts/pure_eb_covariance.py
@@ -1,4 +1,4 @@
-"""Pure E/B covariance structure claim.
+"""Pure E/B covariance structure figure.
 
 Validates covariance matrix for B-mode tests by analyzing block structure:
 - 6 blocks: E+, E-, B+, B-, amb+, amb-
@@ -77,7 +77,7 @@ def _cov_to_corr(covariance):
     return correlation
 
 
-def main(config, pure_eb_path, out_dir, specs=()):
+def main(config, pure_eb_path, out_dir):
     version = config["fiducial"]["version"]
 
     # Load precomputed pure E/B data
@@ -176,8 +176,6 @@ def main(config, pure_eb_path, out_dir, specs=()):
 
     # Write evidence.json
     evidence_data = {
-        "spec_id": "pure_eb_covariance",
-        **({"spec_path": specs[0]} if specs else {}),
         "generated": datetime.now().isoformat(),
         "evidence": {
             "condition_number": condition_number,
@@ -233,16 +231,10 @@ def _from_cli(argv=None):
         "(provides the 6-block cov_pure_eb)",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    ap.add_argument(
-        "--specs",
-        nargs="*",
-        default=[],
-        help="Optional spec markdown paths recorded in evidence.json for provenance",
-    )
     a = ap.parse_args(argv)
     with open(a.config) as f:
         config = yaml.safe_load(f)
-    main(config, a.pure_eb_data, a.out, specs=a.specs)
+    main(config, a.pure_eb_data, a.out)
 
 
 if __name__ == "__main__":
diff --git a/papers/bmodes/scripts/pure_eb_data_vector.py b/papers/bmodes/scripts/pure_eb_data_vector.py
index dbbaeda7..992e1806 100644
--- a/papers/bmodes/scripts/pure_eb_data_vector.py
+++ b/papers/bmodes/scripts/pure_eb_data_vector.py
@@ -1,4 +1,4 @@
-"""Pure E/B data vector claim (paper Figure 1).
+"""Pure E/B data vector figure (paper Figure 1).
 
 Fiducial catalog only: pure E/B/ambiguous decomposition of ξ± with B-modes
 consistent with zero at the fiducial scale cuts. Writes evidence.json with PTE
@@ -9,7 +9,7 @@
         --config config.yaml \
         --pure-eb-data <version>_<blind>_pure_eb_semianalytic.npz \
         --reporting-cov <cov_reporting_ng>/covariance_processed.txt \
-        --out <output_dir> [--specs spec.md ...]
+        --out <output_dir>
 """
 
 import argparse
@@ -217,7 +217,7 @@ def setup_panel(ax, ylabel_text=None):
     return fig
 
 
-def main(config, pure_eb_path, cov_path, out_dir, specs=()):
+def main(config, pure_eb_path, cov_path, out_dir):
     blind = config["fiducial"]["blind"]
     version = config["fiducial"]["version"]
     fiducial_xip_scale_cut = tuple(config["fiducial"]["fiducial_xip_scale_cut"])
@@ -297,8 +297,6 @@ def main(config, pure_eb_path, cov_path, out_dir, specs=()):
 
     # Write evidence.json (based on leak-corrected fiducial data only)
     evidence_data = {
-        "spec_id": "pure_eb_data_vector",
-        **({"spec_path": specs[0]} if specs else {}),
         "generated": datetime.now().isoformat(),
         "evidence": {
             "fiducial": {
@@ -352,16 +350,10 @@ def _from_cli(argv=None):
         "(covariance_processed.txt) for the total ξ± error bars",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    ap.add_argument(
-        "--specs",
-        nargs="*",
-        default=[],
-        help="Optional spec markdown paths recorded in evidence.json for provenance",
-    )
     a = ap.parse_args(argv)
     with open(a.config) as f:
         config = yaml.safe_load(f)
-    main(config, a.pure_eb_data, a.reporting_cov, a.out, specs=a.specs)
+    main(config, a.pure_eb_data, a.reporting_cov, a.out)
 
 
 if __name__ == "__main__":
diff --git a/papers/bmodes/scripts/pure_eb_version_comparison.py b/papers/bmodes/scripts/pure_eb_version_comparison.py
index 8027189f..7b27151b 100644
--- a/papers/bmodes/scripts/pure_eb_version_comparison.py
+++ b/papers/bmodes/scripts/pure_eb_version_comparison.py
@@ -1,4 +1,4 @@
-"""Pure E/B version comparison claim.
+"""Pure E/B version comparison figure.
 
 Visualizes total and B-mode correlation functions across catalog versions.
 Top row: xi_total +/- (same style as data vector plot)
@@ -250,13 +250,12 @@ def main(
     config,
     results_dir,
     out_dir,
-    specs=(),
     fiducial_version=None,
     fiducial_pure_eb_data=None,
 ):
     plotting_config = config["plotting"]
     version_labels = plotting_config["version_labels"]
-    # Leak-corrected, non-ecut versions (matches VERSIONS_LEAK_CORR in claims.smk)
+    # Leak-corrected versions (matches VERSIONS_LEAK_CORR in figures.smk)
     versions = [v for v in config["versions"] if "_leak_corr" in v and "_ecut" not in v]
     blind = config["fiducial"]["blind"]
 
@@ -448,8 +447,6 @@ def main(
             evidence_versions[f"{v}_{key}"] = int(val) if "dof" in key else float(val)
 
     evidence_data = {
-        "spec_id": "pure_eb_version_comparison",
-        **({"spec_path": specs[0]} if specs else {}),
         "generated": datetime.now().isoformat(),
         "evidence": {
             "scale_cuts": {k: list(v) for k, v in scale_cuts.items()},
@@ -480,12 +477,6 @@ def _from_cli(argv=None):
         "<version>_<blind>_pure_eb_semianalytic.npz files",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    ap.add_argument(
-        "--specs",
-        nargs="*",
-        default=[],
-        help="Optional spec markdown paths recorded in evidence.json for provenance",
-    )
     ap.add_argument(
         "--fiducial-version",
         default=None,
@@ -505,7 +496,6 @@ def _from_cli(argv=None):
         config,
         a.results_dir,
         a.out,
-        specs=a.specs,
         fiducial_version=a.fiducial_version,
         fiducial_pure_eb_data=a.fiducial_pure_eb_data,
     )
diff --git a/papers/bmodes/scripts/run_cl_sweep.py b/papers/bmodes/scripts/run_cl_sweep.py
deleted file mode 100644
index d5fd6eb0..00000000
--- a/papers/bmodes/scripts/run_cl_sweep.py
+++ /dev/null
@@ -1,113 +0,0 @@
-"""Pseudo-Cℓ data-vector + covariance sweep over non-fiducial versions (Design B).
-
-Loops the [non-fiducial version list](sweep_versions.nonfiducial_versions) and
-runs the same ``generate_pseudo_cl`` / ``generate_pseudo_cl_cov`` compute the
-fiducial cl_bb / covariance recipes call, once per version. Each producer writes
-its native ``pseudo_cl_{ver}.fits`` / ``pseudo_cl_cov_{ver}.fits`` into ``--out``;
-this driver then renames them to the tagged canonical names
-``pseudo_cl_{ver}_blind={blind}_{binning}_nbins={nbins}.fits`` and
-``pseudo_cl_cov_{ver}_blind={blind}_{binning}_nbins={nbins}.fits`` — the exact
-pattern ``cl_version_comparison._pseudo_cl`` / ``._pseudo_cl_cov`` reconstruct.
-
-Per-version footprint masks resolve internally from cat_config (the version's
-``mask:`` entry), so no per-version mask wiring is needed here. Serial over
-versions; the estimator uses the recipe's full core allocation per call.
-
-    python run_cl_sweep.py \
-        --config .../config.yaml --cat-config .../cat_config.yaml \
-        --nside 1024 --npatch 1 --binning powspace --nbins 32 --power 0.5 \
-        --blind A --out <dir>
-"""
-
-import argparse
-import os
-import sys
-
-import yaml
-
-# generate_pseudo_cl / generate_pseudo_cl_cov live in workflow/scripts;
-# sweep_versions is a sibling here. Put workflow/scripts on the path first.
-_HERE = os.path.dirname(os.path.abspath(__file__))
-_WSCRIPTS = os.path.abspath(
-    os.path.join(_HERE, "..", "..", "..", "workflow", "scripts")
-)
-for _p in (_HERE, _WSCRIPTS):
-    if _p not in sys.path:
-        sys.path.insert(0, _p)
-
-from generate_pseudo_cl import generate_pseudo_cl  # noqa: E402
-from generate_pseudo_cl_cov import generate_pseudo_cl_cov  # noqa: E402
-from sweep_versions import nonfiducial_versions  # noqa: E402
-
-
-def _canonical(prefix, ver, blind, binning, nbins):
-    return f"{prefix}_{ver}_blind={blind}_{binning}_nbins={nbins}.fits"
-
-
-def _run_and_tag(producer, prefix, ver, out, blind, binning, nbins, **kw):
-    """Run one per-version producer, then rename its native FITS to canonical."""
-    producer(
-        version=ver, output_dir=out, blind=blind, binning=binning, nbins=nbins, **kw
-    )
-    native = os.path.join(out, f"{prefix}_{ver}.fits")
-    canonical = os.path.join(out, _canonical(prefix, ver, blind, binning, nbins))
-    os.replace(native, canonical)
-    print(f"[cl_sweep] {ver}: {os.path.basename(canonical)}")
-
-
-def _from_cli(argv=None):
-    ap = argparse.ArgumentParser(
-        description="Pseudo-Cℓ + covariance sweep over the non-fiducial catalog versions."
-    )
-    ap.add_argument(
-        "--config", required=True, help="Absolute path to bmodes config.yaml"
-    )
-    ap.add_argument(
-        "--cat-config", required=True, help="Absolute path to cat_config.yaml"
-    )
-    ap.add_argument("--out", required=True, help="Sweep output directory (lc {output})")
-    ap.add_argument("--versions", nargs="*", default=None, help="Explicit version keys")
-    ap.add_argument("--nside", type=int, default=1024)
-    ap.add_argument("--npatch", type=int, default=1)
-    ap.add_argument(
-        "--binning", choices=["linear", "logspace", "powspace"], default="powspace"
-    )
-    ap.add_argument("--nbins", type=int, default=32)
-    ap.add_argument("--power", type=float, default=0.5)
-    ap.add_argument("--blind", choices=["A", "B", "C"], default="A")
-    a = ap.parse_args(argv)
-    with open(a.config) as f:
-        config = yaml.safe_load(f)
-    versions = a.versions or nonfiducial_versions(config)
-    os.makedirs(a.out, exist_ok=True)
-    shared = dict(
-        cat_config=a.cat_config,
-        nside=a.nside,
-        npatch=a.npatch,
-        power=a.power,
-    )
-    for ver in versions:
-        _run_and_tag(
-            generate_pseudo_cl,
-            "pseudo_cl",
-            ver,
-            a.out,
-            a.blind,
-            a.binning,
-            a.nbins,
-            **shared,
-        )
-        _run_and_tag(
-            generate_pseudo_cl_cov,
-            "pseudo_cl_cov",
-            ver,
-            a.out,
-            a.blind,
-            a.binning,
-            a.nbins,
-            **shared,
-        )
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/run_cosebis_ptes_sweep.py b/papers/bmodes/scripts/run_cosebis_ptes_sweep.py
deleted file mode 100644
index df3b9b79..00000000
--- a/papers/bmodes/scripts/run_cosebis_ptes_sweep.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""COSEBI B-mode PTE-matrix sweep over non-fiducial versions (Design B, Tier-2).
-
-Loops the [non-fiducial version list](sweep_versions.nonfiducial_versions) and
-runs the same gathered-NPZ COSEBI PTE compute the fiducial cosebis_pte_per_cut
-recipe calls (``compute_cosebis_pte_single.main``), once per version. The theta
-grid, nmodes and per-pair computation are version-independent (read from
-``config["fiducial"]``); only the input xi/cov and the output tag change, so each
-sweep call is bit-identical to the fiducial call save for those. Per version the
-driver reads that version's 1000-bin integration ξ_± from the xi_sweep output dir
-and its Gaussian integration covariance from the cov_sweep output dir (both by
-absolute path — lc does not wire cross-output deps, so run xi_sweep + cov_sweep
-first), emitting the canonical ``cosebis_ptes_{ver}_{blind}.npz`` — the same
-gathered layout the fiducial single-output writes, which
-config_space_pte_matrices.py adapts via ``_cosebis_matrix_from_npz`` — into
-``--out``. Serial over versions (~25 min/version, 206 pairs).
-
-    python run_cosebis_ptes_sweep.py \
-        --config .../config.yaml \
-        --xi-sweep-dir <two_point/.../xi_sweep> \
-        --cov-sweep-dir <covariance/.../cov_sweep> \
-        --out <dir> [--blind A] [--versions v1 v2 ...]
-"""
-
-import argparse
-import os
-import sys
-
-import yaml
-
-# compute_cosebis_pte_single, plotting_utils, and sweep_versions are siblings
-# here; other compute modules live in workflow/scripts. Put both on the path.
-_HERE = os.path.dirname(os.path.abspath(__file__))
-_WSCRIPTS = os.path.abspath(
-    os.path.join(_HERE, "..", "..", "..", "workflow", "scripts")
-)
-for _p in (_HERE, _WSCRIPTS):
-    if _p not in sys.path:
-        sys.path.insert(0, _p)
-
-from compute_cosebis_pte_single import main as compute_cosebis_pte  # noqa: E402
-from sweep_versions import nonfiducial_versions  # noqa: E402
-
-
-def _xi_integration(xi_sweep_dir, ver):
-    return os.path.join(
-        xi_sweep_dir, f"{ver}_xi_minsep=0.5_maxsep=300.0_nbins=1000_npatch=1.txt"
-    )
-
-
-def _cov_integration(cov_sweep_dir, ver, blind):
-    base = f"covariance_{ver}_{blind}_g_minsep=0.5_maxsep=300.0_nbins=1000_masked"
-    return os.path.join(cov_sweep_dir, base, f"{base}_processed.txt")
-
-
-def _from_cli(argv=None):
-    ap = argparse.ArgumentParser(
-        description="Gathered COSEBI B-mode PTE-matrix sweep over the non-fiducial catalog versions."
-    )
-    ap.add_argument(
-        "--config", required=True, help="Absolute path to bmodes config.yaml"
-    )
-    ap.add_argument(
-        "--xi-sweep-dir",
-        required=True,
-        help="xi_sweep output dir with per-version 1000-bin integration xi_pm .txt",
-    )
-    ap.add_argument(
-        "--cov-sweep-dir",
-        required=True,
-        help="cov_sweep output dir with per-version {base}/{base}_processed.txt cov",
-    )
-    ap.add_argument("--out", required=True, help="Sweep output directory (lc {output})")
-    ap.add_argument("--blind", default="A", help="Blind tag (paper: A)")
-    ap.add_argument("--versions", nargs="*", default=None, help="Explicit version keys")
-    a = ap.parse_args(argv)
-
-    with open(a.config) as f:
-        config = yaml.safe_load(f)
-    versions = a.versions or nonfiducial_versions(config)
-    os.makedirs(a.out, exist_ok=True)
-
-    for ver in versions:
-        xi_int = _xi_integration(a.xi_sweep_dir, ver)
-        cov_int = _cov_integration(a.cov_sweep_dir, ver, a.blind)
-        for f in (xi_int, cov_int):
-            if not os.path.isfile(f):
-                raise FileNotFoundError(f"MISSING upstream input for {ver}: {f}")
-        print(f"[cosebis_ptes_sweep] {ver}", flush=True)
-        compute_cosebis_pte(config, xi_int, cov_int, a.out, version=ver, blind=a.blind)
-        print(
-            f"[cosebis_ptes_sweep] {ver} -> "
-            f"{os.path.join(a.out, f'cosebis_ptes_{ver}_{a.blind}.npz')}",
-            flush=True,
-        )
-    print(f"[cosebis_ptes_sweep] done -> {a.out}", flush=True)
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh b/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
deleted file mode 100644
index 0e7ecdf5..00000000
--- a/papers/bmodes/scripts/run_pure_eb_ptes_sweep.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env bash
-# Pure E/B PTE-matrix sweep over non-fiducial versions (Design B, Tier-2).
-#
-# Loops the non-fiducial version list (resolved via sweep_versions.py) and runs
-# calculate_pure_eb_ptes.py once per version — the same χ² PTE-matrix compute the
-# fiducial pure_eb_pte_per_cut recipe calls. Each version's gathered semi-analytic
-# NPZ (data vectors + MC covariance) is read from the pure_eb_sweep output dir by
-# absolute path — lc does not wire cross-output deps, so the driver reads the
-# upstream sweep directly; run pure_eb_sweep before this. Each version emits the
-# canonical ``{ver}_{blind}_pure_eb_ptes.npz`` — the exact name
-# config_space_pte_matrices.py reconstructs from --pte-intermediate-dir — straight
-# into --out. Serial over versions; each version is fast (206-pair grid, ~seconds).
-#
-# Usage:
-#   run_pure_eb_ptes_sweep.sh --config <config.yaml> --cat-config <cat_config.yaml> \
-#     --pure-eb-sweep-dir <pure_eb/.../pure_eb_sweep> \
-#     --out <output_dir> [--blind A] [--versions "v1 v2 ..."]
-set -euo pipefail
-
-. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
-
-CONFIG=""; CATCONFIG=""; PUREEBSWEEP=""; OUT=""; BLIND="A"; VERSIONS=""
-while [ $# -gt 0 ]; do
-  case "$1" in
-    --config) CONFIG="$2"; shift 2;;
-    --cat-config) CATCONFIG="$2"; shift 2;;
-    --pure-eb-sweep-dir) PUREEBSWEEP="$2"; shift 2;;
-    --out) OUT="$2"; shift 2;;
-    --blind) BLIND="$2"; shift 2;;
-    --versions) VERSIONS="$2"; shift 2;;
-    *) echo "unknown arg: $1" >&2; exit 2;;
-  esac
-done
-
-mkdir -p "$OUT"
-
-VERSIONS=$(sweep_versions "$CONFIG")
-
-for ver in $VERSIONS; do
-  pureeb="$PUREEBSWEEP/${ver}_${BLIND}_pure_eb_semianalytic.npz"
-  [ -f "$pureeb" ] || { echo "MISSING upstream input for $ver: $pureeb" >&2; exit 1; }
-  echo "[pure_eb_ptes_sweep] $ver"
-  spv_python "$PSCRIPTS/calculate_pure_eb_ptes.py" \
-    --version "$ver" --blind "$BLIND" \
-    --pure-eb-data "$pureeb" --n-samples 2000 --out "$OUT"
-  echo "[pure_eb_ptes_sweep] $ver -> $OUT/${ver}_${BLIND}_pure_eb_ptes.npz"
-done
-echo "[pure_eb_ptes_sweep] done -> $OUT"
diff --git a/papers/bmodes/scripts/run_pure_eb_semianalytic.sh b/papers/bmodes/scripts/run_pure_eb_semianalytic.sh
deleted file mode 100644
index 472cd81d..00000000
--- a/papers/bmodes/scripts/run_pure_eb_semianalytic.sh
+++ /dev/null
@@ -1,80 +0,0 @@
-#!/usr/bin/env bash
-# Pure E/B semi-analytic covariance driver (lc-native, container:none recipe).
-#
-# Runs the 20 independent MC chunks in parallel (each a fresh apptainer-exec
-# process, deterministic seed 42+chunk_id) throttled to the node core count,
-# then gathers them into the per-(version,blind) semianalytic .npz. Bit-exact
-# to the paper's scatter-gather (same per-chunk seeds/order); no nested dask.
-#
-# Usage:
-#   run_pure_eb_semianalytic.sh --version SP_v1.4.6.3_leak_corr --blind A \
-#     --cat-config <cat_config.yaml> \
-#     --xi-reporting <xi 20-bin .txt> --xi-integration <xi 1000-bin .txt> \
-#     --cov-integration <cov ..._processed.txt> \
-#     --out <output_dir> [--n-chunks 20] [--n-samples 2000] [--nproc 16]
-set -euo pipefail
-
-. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
-
-VERSION=""; BLIND="A"; CATCONFIG=""; XIREP=""; XIINT=""; COVINT=""; OUT=""
-NCHUNKS=20; NSAMPLES=2000; NPROC="${SLURM_CPUS_PER_TASK:-16}"
-MINSEP=1.0; MAXSEP=250.0; NBINS=20
-MINSEPINT=0.5; MAXSEPINT=300.0; NBINSINT=1000; NPATCH=1
-
-while [ $# -gt 0 ]; do
-  case "$1" in
-    --version) VERSION="$2"; shift 2;;
-    --blind) BLIND="$2"; shift 2;;
-    --cat-config) CATCONFIG="$2"; shift 2;;
-    --xi-reporting) XIREP="$2"; shift 2;;
-    --xi-integration) XIINT="$2"; shift 2;;
-    --cov-integration) COVINT="$2"; shift 2;;
-    --out) OUT="$2"; shift 2;;
-    --n-chunks) NCHUNKS="$2"; shift 2;;
-    --n-samples) NSAMPLES="$2"; shift 2;;
-    --nproc) NPROC="$2"; shift 2;;
-    --min-sep) MINSEP="$2"; shift 2;;
-    --max-sep) MAXSEP="$2"; shift 2;;
-    --nbins) NBINS="$2"; shift 2;;
-    --min-sep-int) MINSEPINT="$2"; shift 2;;
-    --max-sep-int) MAXSEPINT="$2"; shift 2;;
-    --nbins-int) NBINSINT="$2"; shift 2;;
-    --npatch) NPATCH="$2"; shift 2;;
-    *) echo "unknown arg: $1" >&2; exit 2;;
-  esac
-done
-
-mkdir -p "$OUT/chunks"
-
-echo "[pure_eb] $NCHUNKS chunks, $NSAMPLES samples, nproc=$NPROC, version=$VERSION blind=$BLIND"
-for i in $(seq 0 $((NCHUNKS-1))); do
-  (
-    SPV_EXEC_EXTRA=$SINGLE_THREAD_ENV
-    spv_python "$PSCRIPTS/precompute_pure_eb_chunk.py" \
-      --chunk-id "$i" --n-chunks "$NCHUNKS" --n-samples "$NSAMPLES" \
-      --version "$VERSION" --blind "$BLIND" --cat-config "$CATCONFIG" \
-      --xi-reporting "$XIREP" --xi-integration "$XIINT" --cov-integration "$COVINT" \
-      --min-sep "$MINSEP" --max-sep "$MAXSEP" --nbins "$NBINS" \
-      --min-sep-int "$MINSEPINT" --max-sep-int "$MAXSEPINT" --nbins-int "$NBINSINT" \
-      --npatch "$NPATCH" --out "$OUT/chunks"
-  ) > "$OUT/chunks/chunk_${i}.log" 2>&1 &
-  while [ "$(jobs -rp | wc -l)" -ge "$NPROC" ]; do sleep 1; done
-done
-wait
-
-# Verify all chunks landed
-missing=0
-for i in $(seq 0 $((NCHUNKS-1))); do
-  [ -f "$OUT/chunks/pure_eb_chunk_${i}.npz" ] || { echo "MISSING chunk $i (see $OUT/chunks/chunk_${i}.log)" >&2; missing=1; }
-done
-[ "$missing" -eq 0 ] || { echo "[pure_eb] chunk failures — aborting gather" >&2; exit 1; }
-echo "[pure_eb] all $NCHUNKS chunks done; gathering"
-
-spv_python "$PSCRIPTS/gather_pure_eb_chunks.py" \
-  --version "$VERSION" --blind "$BLIND" \
-  --xi-reporting "$XIREP" --xi-integration "$XIINT" \
-  --chunks-dir "$OUT/chunks" \
-  --min-sep "$MINSEP" --max-sep "$MAXSEP" --nbins "$NBINS" \
-  --min-sep-int "$MINSEPINT" --max-sep-int "$MAXSEPINT" --nbins-int "$NBINSINT" \
-  --npatch "$NPATCH" --out "$OUT"
-echo "[pure_eb] done -> $OUT"
diff --git a/papers/bmodes/scripts/run_pure_eb_sweep.sh b/papers/bmodes/scripts/run_pure_eb_sweep.sh
deleted file mode 100755
index 8195f150..00000000
--- a/papers/bmodes/scripts/run_pure_eb_sweep.sh
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/usr/bin/env bash
-# Pure E/B semi-analytic covariance sweep over non-fiducial versions (Design B).
-#
-# Loops the non-fiducial version list (resolved via sweep_versions.py) and runs
-# the same run_pure_eb_semianalytic.sh scatter-gather the fiducial
-# pure_eb_semianalytic_data recipe calls, once per version. Each version's
-# reporting+integration ξ± is read from the xi_sweep output dir and its Gaussian
-# integration covariance from the cov_sweep output dir (both by absolute path —
-# lc does not wire cross-output deps, so the driver reads upstream sweeps
-# directly; run xi_sweep + cov_sweep before this). The gathered
-# ``{ver}_{blind}_pure_eb_semianalytic.npz`` — already the canonical name
-# pure_eb_version_comparison (--results-dir) reads — is moved into --out; the
-# per-version MC chunks stage in a scratch subdir that is removed after gather.
-#
-# Usage:
-#   run_pure_eb_sweep.sh --config <config.yaml> --cat-config <cat_config.yaml> \
-#     --xi-sweep-dir <two_point/.../xi_sweep> \
-#     --cov-sweep-dir <covariance/.../cov_sweep> \
-#     --out <output_dir> [--blind A] [--versions "v1 v2 ..."]
-set -euo pipefail
-
-. "$(dirname "${BASH_SOURCE[0]}")/container_env.sh"
-
-CONFIG=""; CATCONFIG=""; XISWEEP=""; COVSWEEP=""; OUT=""; BLIND="A"; VERSIONS=""
-while [ $# -gt 0 ]; do
-  case "$1" in
-    --config) CONFIG="$2"; shift 2;;
-    --cat-config) CATCONFIG="$2"; shift 2;;
-    --xi-sweep-dir) XISWEEP="$2"; shift 2;;
-    --cov-sweep-dir) COVSWEEP="$2"; shift 2;;
-    --out) OUT="$2"; shift 2;;
-    --blind) BLIND="$2"; shift 2;;
-    --versions) VERSIONS="$2"; shift 2;;
-    *) echo "unknown arg: $1" >&2; exit 2;;
-  esac
-done
-
-mkdir -p "$OUT"
-
-VERSIONS=$(sweep_versions "$CONFIG")
-
-for ver in $VERSIONS; do
-  xirep="$XISWEEP/${ver}_xi_minsep=1.0_maxsep=250.0_nbins=20_npatch=1.txt"
-  xiint="$XISWEEP/${ver}_xi_minsep=0.5_maxsep=300.0_nbins=1000_npatch=1.txt"
-  covbase="covariance_${ver}_${BLIND}_g_minsep=0.5_maxsep=300.0_nbins=1000_masked"
-  covint="$COVSWEEP/$covbase/${covbase}_processed.txt"
-  for f in "$xirep" "$xiint" "$covint"; do
-    [ -f "$f" ] || { echo "MISSING upstream input for $ver: $f" >&2; exit 1; }
-  done
-  echo "[pure_eb_sweep] $ver"
-  stage="$OUT/_stage_${ver}"
-  bash "$PSCRIPTS/run_pure_eb_semianalytic.sh" \
-    --version "$ver" --blind "$BLIND" --cat-config "$CATCONFIG" \
-    --xi-reporting "$xirep" --xi-integration "$xiint" --cov-integration "$covint" \
-    --out "$stage"
-  mv "$stage/${ver}_${BLIND}_pure_eb_semianalytic.npz" \
-     "$OUT/${ver}_${BLIND}_pure_eb_semianalytic.npz"
-  rm -rf "$stage"
-  echo "[pure_eb_sweep] $ver -> $OUT/${ver}_${BLIND}_pure_eb_semianalytic.npz"
-done
-echo "[pure_eb_sweep] done -> $OUT"
diff --git a/papers/bmodes/scripts/run_xi_sweep.py b/papers/bmodes/scripts/run_xi_sweep.py
deleted file mode 100644
index 211214e3..00000000
--- a/papers/bmodes/scripts/run_xi_sweep.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""TreeCorr ξ±(θ) sweep over the non-fiducial catalog versions (Design B).
-
-Loops the [non-fiducial version list](sweep_versions.nonfiducial_versions) and
-runs the same ``run_2pcf.run_2pcf`` compute the fiducial two_point recipes call,
-once per version, writing every version's ξ± text dump into one lc ``{output}``
-dir under run_2pcf's native, already-canonical name
-``{ver}_xi_minsep={min}_maxsep={max}_nbins={nbins}_npatch={npatch}.txt`` — the
-exact pattern ``cosebis_version_comparison._xi_integration`` reconstructs.
-
-Both grids are produced per version: the 1000-bin integration grid feeds
-``cosebis_version_comparison`` (``--results-dir``) and the pure E/B sweep's
-integral kernels; the 20-bin reporting grid feeds the pure E/B sweep's output
-binning. Serial over versions — lc's dask handles cross-output concurrency, and
-TreeCorr already uses the recipe's full OpenMP allocation per call.
-
-    python run_xi_sweep.py \
-        --config .../config.yaml --cat-config .../cat_config.yaml --out <dir>
-"""
-
-import argparse
-import os
-import sys
-
-import yaml
-
-# run_2pcf lives in workflow/scripts; sweep_versions is a sibling here.
-# Put workflow/scripts on the path first.
-_HERE = os.path.dirname(os.path.abspath(__file__))
-_WSCRIPTS = os.path.abspath(
-    os.path.join(_HERE, "..", "..", "..", "workflow", "scripts")
-)
-for _p in (_HERE, _WSCRIPTS):
-    if _p not in sys.path:
-        sys.path.insert(0, _p)
-
-from run_2pcf import run_2pcf  # noqa: E402
-from sweep_versions import nonfiducial_versions  # noqa: E402
-
-GRIDS = {
-    "reporting": dict(min_sep=1.0, max_sep=250.0, nbins=20, npatch=1),
-    "integration": dict(min_sep=0.5, max_sep=300.0, nbins=1000, npatch=1),
-}
-
-
-def _from_cli(argv=None):
-    ap = argparse.ArgumentParser(
-        description="TreeCorr ξ± sweep over the non-fiducial catalog versions."
-    )
-    ap.add_argument(
-        "--config", required=True, help="Absolute path to bmodes config.yaml"
-    )
-    ap.add_argument(
-        "--cat-config", required=True, help="Absolute path to cat_config.yaml"
-    )
-    ap.add_argument("--out", required=True, help="Sweep output directory (lc {output})")
-    ap.add_argument(
-        "--versions",
-        nargs="*",
-        default=None,
-        help="Explicit version keys (default: non-fiducial non-ecut from config)",
-    )
-    ap.add_argument(
-        "--grids",
-        nargs="*",
-        choices=list(GRIDS),
-        default=list(GRIDS),
-        help="Which angular grids to measure per version (default: both)",
-    )
-    a = ap.parse_args(argv)
-    with open(a.config) as f:
-        config = yaml.safe_load(f)
-    versions = a.versions or nonfiducial_versions(config)
-    for ver in versions:
-        for grid in a.grids:
-            run_2pcf(
-                ver=ver,
-                cat_config=a.cat_config,
-                output_dir=a.out,
-                grid=grid,
-                **GRIDS[grid],
-            )
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/sweep_versions.py b/papers/bmodes/scripts/sweep_versions.py
deleted file mode 100644
index 2cb10f2d..00000000
--- a/papers/bmodes/scripts/sweep_versions.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Resolve the non-fiducial catalog-version list for the Paper II version sweep.
-
-The Design-B version sweep recomputes every non-fiducial catalog variant through
-lc so the version-comparison figures (paper Figure 5) are reproduced end-to-end
-rather than read from the canonical CosmoStat tree. The variant set is the config
-``versions:`` list minus the |e|<0.7 ``_ecut`` cross-checks (out of scope for
-every comparison filter) and minus the fiducial version (``config['fiducial']
-['version']``), which is produced by the single-output recipes and handed to the
-comparison plotters through their ``--fiducial-*`` overrides.
-
-The remaining 7 are the union per-version product set: the 3 non-fiducial
-leak-corrected catalogs feeding the Tier-1 overlay figures, plus the 4
-uncorrected catalogs feeding the Tier-2 appendix PTE matrices. Both the sweep
-drivers (as an import) and the bash drivers (as a ``--config`` CLI printing one
-version per line) resolve the list here, so there is a single source of truth.
-"""
-
-import argparse
-
-import yaml
-
-
-def nonfiducial_versions(config):
-    """Non-fiducial, non-ecut catalog versions from a loaded bmodes config."""
-    fiducial = config["fiducial"]["version"]
-    return [v for v in config["versions"] if "_ecut" not in v and v != fiducial]
-
-
-def _from_cli(argv=None):
-    ap = argparse.ArgumentParser(
-        description="Print the non-fiducial sweep version list, one per line."
-    )
-    ap.add_argument(
-        "--config", required=True, help="Absolute path to bmodes config.yaml"
-    )
-    a = ap.parse_args(argv)
-    with open(a.config) as f:
-        config = yaml.safe_load(f)
-    for v in nonfiducial_versions(config):
-        print(v)
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/test_bandpower_sampling.py b/papers/bmodes/scripts/test_bandpower_sampling.py
deleted file mode 100644
index dec8633a..00000000
--- a/papers/bmodes/scripts/test_bandpower_sampling.py
+++ /dev/null
@@ -1,205 +0,0 @@
-"""Test whether 32-bin bandpower sampling causes harmonic/config COSEBIS disagreement.
-
-Controlled experiment: compute COSEBIS from the SAME smooth theory C(ℓ)
-via three paths:
-
-1. Dense ℓ:     W_n(ℓ) × C(ℓ) integrated over 10,000 ℓ points  (ground truth)
-2. 32-bin:      W_n(ℓ) × C(ℓ) integrated over the exact 32 powspace bins
-3. Config-space: theory ξ±(θ) → cosebis_from_xipm()               (independent check)
-
-If (1) ≈ (3) but (2) ≠ (1), bandpower sampling is the cause.
-If (1) ≈ (2), something else is going on.
-
-Uses a simple power-law C(ℓ) = A × ℓ^α  to avoid external dependencies.
-Also tests with a more realistic shape: C(ℓ) = A × ℓ^α × exp(-ℓ/ℓ_0).
-
-Author: Claude Code
-"""
-
-import numpy as np
-from cosmo_numba.B_modes.cosebis import COSEBIS
-from scipy import special
-
-# The exact 32 powspace ℓ values from the pipeline
-ELL_32 = np.array(
-    [
-        12.5,
-        24.0,
-        38.5,
-        56.5,
-        78.0,
-        103.0,
-        131.5,
-        163.5,
-        199.0,
-        238.0,
-        281.0,
-        327.5,
-        377.0,
-        430.0,
-        487.0,
-        547.5,
-        611.0,
-        678.0,
-        749.0,
-        823.5,
-        901.0,
-        982.0,
-        1067.0,
-        1155.5,
-        1247.5,
-        1343.0,
-        1441.5,
-        1544.0,
-        1650.0,
-        1759.5,
-        1872.5,
-        1988.5,
-    ]
-)
-
-
-def theory_cl(ell, model="power_law"):
-    """Smooth theory C(ℓ) for testing."""
-    if model == "power_law":
-        # Simple power law, roughly cosmic-shear-like
-        return 1e-7 * (ell / 100.0) ** (-1.5)
-    elif model == "realistic":
-        # Power law with exponential cutoff (more structure)
-        return 1e-7 * (ell / 100.0) ** (-1.2) * np.exp(-ell / 1500.0)
-    else:
-        raise ValueError(f"Unknown model: {model}")
-
-
-def config_space_cosebis(theta_min, theta_max, nmodes, cl_model, n_theta=5_000):
-    """Compute COSEBIS via config-space path: C(ℓ) → ξ±(θ) → cosebis_from_xipm.
-
-    ξ+(θ) = (1/2π) ∫ dℓ ℓ C(ℓ) J_0(ℓθ)
-    ξ-(θ) = (1/2π) ∫ dℓ ℓ C(ℓ) J_4(ℓθ)
-    """
-    # Dense ℓ grid for Hankel transform
-    n_ell = 20_000
-    ell_dense = np.logspace(np.log10(1), np.log10(1e5), n_ell)
-    cl = theory_cl(ell_dense, model=cl_model)
-
-    # θ grid (log-spaced in arcmin, clipped to avoid float precision issues)
-    theta_arcmin = np.logspace(np.log10(theta_min), np.log10(theta_max), n_theta)
-    theta_arcmin = np.clip(theta_arcmin, theta_min, theta_max)
-    theta_rad = np.deg2rad(theta_arcmin / 60.0)
-
-    # Vectorized Hankel transform: (n_theta, n_ell) outer product
-    # Process in chunks to avoid memory blow-up
-    chunk = 500
-    xip = np.zeros(n_theta)
-    xim = np.zeros(n_theta)
-    weight = ell_dense * cl / (2 * np.pi)  # (n_ell,)
-
-    for start in range(0, n_theta, chunk):
-        end = min(start + chunk, n_theta)
-        t_chunk = theta_rad[start:end, None]  # (chunk, 1)
-        x = ell_dense[None, :] * t_chunk  # (chunk, n_ell)
-        xip[start:end] = np.trapezoid(weight * special.j0(x), ell_dense, axis=1)
-        xim[start:end] = np.trapezoid(weight * special.jv(4, x), ell_dense, axis=1)
-
-    # COSEBIS from ξ±
-    cosebis = COSEBIS(theta_min, theta_max, nmodes)
-    ce, cb = cosebis.cosebis_from_xipm(theta_arcmin, xip, xim)
-    return ce, cb
-
-
-def harmonic_cosebis(ell, cl_E, theta_min, theta_max, nmodes):
-    """Compute COSEBIS via harmonic path: C(ℓ) → cosebis_from_Cell."""
-    cosebis = COSEBIS(theta_min, theta_max, nmodes)
-    cl_B = np.zeros_like(cl_E)
-    ce, cb = cosebis.cosebis_from_Cell(ell, cl_E, cl_B)
-    return ce, cb
-
-
-def make_powspace_bins(ell_min, ell_max, nbins, power=0.5):
-    """Generate powspace bin centres matching NaMaster convention."""
-    edges = np.linspace(ell_min**power, ell_max**power, nbins + 1) ** (1.0 / power)
-    return 0.5 * (edges[:-1] + edges[1:])
-
-
-def main():
-    nmodes = 20
-    bin_counts = [32, 64, 100, 200, 500]
-
-    for cl_model in ["power_law", "realistic"]:
-        for theta_min, theta_max, label in [
-            (1.0, 250.0, "full [1,250]'"),
-            (12.0, 83.0, "fiducial [12,83]'"),
-        ]:
-            print(f"\n{'=' * 75}")
-            print(f"C(ℓ) model: {cl_model}, scale cut: {label}")
-            print(f"{'=' * 75}")
-
-            # --- Ground truth: dense ℓ ---
-            ell_dense = np.logspace(np.log10(2), np.log10(5000), 10_000)
-            cl_dense = theory_cl(ell_dense, model=cl_model)
-            ce_dense, _ = harmonic_cosebis(
-                ell_dense,
-                cl_dense,
-                theta_min,
-                theta_max,
-                nmodes,
-            )
-
-            # --- Config-space reference ---
-            print("Computing config-space reference (Hankel + xipm)...")
-            ce_config, _ = config_space_cosebis(
-                theta_min,
-                theta_max,
-                nmodes,
-                cl_model,
-            )
-
-            # --- Binned paths ---
-            ce_binned = {}
-            for nb in bin_counts:
-                ell_b = make_powspace_bins(8, 2100, nb)
-                cl_b = theory_cl(ell_b, model=cl_model)
-                ce_b, _ = harmonic_cosebis(ell_b, cl_b, theta_min, theta_max, nmodes)
-                ce_binned[nb] = ce_b
-
-            # --- Table: ratio to dense for each bin count ---
-            hdr = f"{'mode':>5}"
-            for nb in bin_counts:
-                hdr += f" {nb:>8d}-bin"
-            hdr += "     dense/cfg"
-            print(f"\n{hdr}")
-            print("-" * (14 + 13 * len(bin_counts)))
-
-            for n in range(nmodes):
-                line = f"  n={n + 1:>2d}"
-                for nb in bin_counts:
-                    r = (
-                        ce_binned[nb][n] / ce_dense[n]
-                        if abs(ce_dense[n]) > 1e-30
-                        else np.nan
-                    )
-                    line += f" {r:>12.4f}"
-                r_cfg = (
-                    ce_dense[n] / ce_config[n] if abs(ce_config[n]) > 1e-30 else np.nan
-                )
-                line += f" {r_cfg:>12.4f}"
-                print(line)
-
-            # Summary: max error over modes 1-6 (where signal is meaningful)
-            n_summary = min(6, nmodes)
-            print(f"\n  Max |1 - ratio| for modes 1-{n_summary} (ratio to dense):")
-            for nb in bin_counts:
-                ratios = ce_binned[nb][:n_summary] / ce_dense[:n_summary]
-                valid = np.isfinite(ratios)
-                if valid.any():
-                    err = np.max(np.abs(1 - ratios[valid]))
-                    print(f"    {nb:>4d} bins: {err:.4f}  ({err * 100:.1f}%)")
-            ratios_cfg = ce_dense[:n_summary] / ce_config[:n_summary]
-            valid = np.isfinite(ratios_cfg)
-            if valid.any():
-                err = np.max(np.abs(1 - ratios_cfg[valid]))
-                print(f"    dense/config: {err:.4f}  ({err * 100:.1f}%)")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/papers/bmodes/scripts/update_survey_stats.py b/papers/bmodes/scripts/update_survey_stats.py
deleted file mode 100644
index a2dc386c..00000000
--- a/papers/bmodes/scripts/update_survey_stats.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""Recompute survey statistics (A, n_e, sigma_e) for all active versions.
-
-Uses the new footprint masks to define the survey area, then computes
-n_eff and sigma_e from each version's shear catalog in chunks to limit
-memory usage.
-
-Usage:
-    python papers/bmodes/scripts/update_survey_stats.py \
-        --mask-standard <path> --mask-starhalo <path>
-"""
-
-import argparse
-import gc
-import os
-from pathlib import Path
-
-import healpy as hp
-import numpy as np
-import yaml
-from astropy.io import fits
-
-os.chdir("/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val")
-
-
-# Versions that use the standard footprint mask
-STANDARD_VERSIONS = [
-    "SP_v1.4.5",
-    "SP_v1.4.6",
-    "SP_v1.4.6_ecut07",
-    "SP_v1.4.11.3",
-    "SP_v1.4.11.3_ecut07",
-]
-
-# Versions that use the star-halo footprint mask
-STARHALO_VERSIONS = [
-    "SP_v1.4.8",
-]
-
-
-def area_from_mask(mask_path):
-    """Compute survey area from a binary HEALPix mask."""
-    mask = hp.read_map(mask_path, dtype=np.float64)
-    return float(mask.sum() * hp.nside2pixarea(hp.get_nside(mask), degrees=True))
-
-
-def compute_stats_chunked(catalog_path, w_col, e1_col, e2_col, chunk_size=1_000_000):
-    """Compute sum_w, sum_w2, sum_w2_e2 in chunks to limit memory."""
-    with fits.open(catalog_path, memmap=True) as hdul:
-        data = hdul[1].data
-        nrows = len(data)
-
-        sum_w = 0.0
-        sum_w2 = 0.0
-        sum_w2_e2 = 0.0
-
-        for start in range(0, nrows, chunk_size):
-            stop = min(start + chunk_size, nrows)
-            w = np.asarray(data[w_col][start:stop], dtype=np.float64)
-            e1 = np.asarray(data[e1_col][start:stop], dtype=np.float64)
-            e2 = np.asarray(data[e2_col][start:stop], dtype=np.float64)
-
-            sum_w += np.sum(w)
-            w2 = w**2
-            sum_w2 += np.sum(w2)
-            sum_w2_e2 += np.sum(w2 * (e1**2 + e2**2))
-
-            del w, e1, e2, w2
-
-    return float(sum_w), float(sum_w2), float(sum_w2_e2), nrows
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--mask-standard", required=True)
-    parser.add_argument("--mask-starhalo", required=True)
-    parser.add_argument("--dry-run", action="store_true")
-    args = parser.parse_args()
-
-    # Load cat_config
-    config_path = Path("cat_config.yaml")
-    with open(config_path) as f:
-        cc = yaml.safe_load(f)
-
-    # Compute areas from masks (only two masks to load)
-    area_standard = area_from_mask(args.mask_standard)
-    area_starhalo = area_from_mask(args.mask_starhalo)
-    print(f"Standard footprint area: {area_standard:.2f} deg²")
-    print(f"Star-halo footprint area: {area_starhalo:.2f} deg²\n")
-
-    all_versions = [(v, area_standard) for v in STANDARD_VERSIONS] + [
-        (v, area_starhalo) for v in STARHALO_VERSIONS
-    ]
-
-    print(
-        f"{'Version':<30} {'Area (deg²)':>12} {'n_eff':>10} {'sigma_e':>10}  {'Old A':>10} {'Old n_e':>10} {'Old σ_e':>10}"
-    )
-    print("-" * 105)
-
-    for ver, area_deg2 in all_versions:
-        if ver not in cc:
-            print(f"{ver:<30} SKIPPED (not in config)")
-            continue
-
-        shear_cfg = cc[ver].get("shear", {})
-        if "path" not in shear_cfg:
-            print(f"{ver:<30} SKIPPED (no shear path)")
-            continue
-
-        subdir = cc[ver].get("subdir", "")
-        catalog_path = shear_cfg["path"]
-        if not os.path.isabs(catalog_path):
-            catalog_path = os.path.join(subdir, catalog_path)
-
-        w_col = shear_cfg.get("w_col", "w")
-        e1_col = shear_cfg.get("e1_col", "e1")
-        e2_col = shear_cfg.get("e2_col", "e2")
-
-        # Get old values for comparison
-        old_cov = cc[ver].get("cov_th", {})
-        old_A = old_cov.get("A", 0)
-        old_ne = old_cov.get("n_e", 0)
-        old_se = old_cov.get("sigma_e", 0)
-
-        sum_w, sum_w2, sum_w2_e2, nrows = compute_stats_chunked(
-            catalog_path, w_col, e1_col, e2_col
-        )
-
-        area_arcmin2 = area_deg2 * 3600.0
-        n_eff = (sum_w**2) / (area_arcmin2 * sum_w2) if sum_w2 > 0 else 0.0
-        sigma_e = np.sqrt(sum_w2_e2 / sum_w2) if sum_w2 > 0 else 0.0
-
-        print(
-            f"{ver:<30} {area_deg2:>12.2f} {n_eff:>10.6f} {sigma_e:>10.6f}  {old_A:>10.2f} {old_ne:>10.6f} {old_se:>10.6f}"
-        )
-
-        if not args.dry_run:
-            if "cov_th" not in cc[ver]:
-                cc[ver]["cov_th"] = {}
-            cc[ver]["cov_th"]["A"] = float(area_deg2)
-            cc[ver]["cov_th"]["n_e"] = float(n_eff)
-            cc[ver]["cov_th"]["sigma_e"] = float(sigma_e)
-
-        gc.collect()
-
-    if not args.dry_run:
-        with open(config_path, "w") as f:
-            yaml.dump(cc, f, sort_keys=False)
-        print("\ncat_config.yaml updated.")
-    else:
-        print("\nDry run — no changes written.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/papers/bmodes/scripts/validate_cosebis_filters.py b/papers/bmodes/scripts/validate_cosebis_filters.py
deleted file mode 100644
index cddbf421..00000000
--- a/papers/bmodes/scripts/validate_cosebis_filters.py
+++ /dev/null
@@ -1,265 +0,0 @@
-"""Validate COSEBIS filter functions W_n(ℓ) against direct integration.
-
-The cosmo_numba code computes W_n(ℓ) via FFT-log (Hankel transform of T_+^log).
-This script validates that against direct numerical integration:
-
-  W_n(ℓ) = ∫ dθ θ T_+^log(θ) J_0(ℓθ)
-
-where θ is in radians and T_+^log is the log-basis filter function.
-
-Tests both full [1,250]' and fiducial [12,83]' scale cuts,
-for modes n = 1 through 20, across a dense ℓ grid.
-
-Author: Claude Code
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-from cosmo_numba.B_modes.cosebis import COSEBIS
-from scipy import integrate, special
-
-
-def direct_Wn_integration(ell_val, theta_arcmin, Tp):
-    """Compute W_n(ℓ) via direct numerical integration.
-
-    W_n(ℓ) = ∫ dθ θ T_+^log(θ) J_0(ℓθ)
-
-    Uses trapezoidal rule on a fine theta grid.
-    """
-    theta_rad = np.deg2rad(theta_arcmin / 60)
-    j0 = special.j0(ell_val * theta_rad)
-    integrand = theta_rad * Tp * j0
-    return np.trapezoid(integrand, theta_rad)
-
-
-def direct_Wn_quad(ell_val, theta_arcmin, Tp):
-    """Compute W_n(ℓ) via adaptive quadrature (gold standard).
-
-    Interpolates T_+^log and uses scipy.integrate.quad.
-    """
-    from scipy.interpolate import CubicSpline
-
-    theta_rad = np.deg2rad(theta_arcmin / 60)
-    cs = CubicSpline(theta_rad, Tp)
-
-    def integrand(t):
-        return t * cs(t) * special.j0(ell_val * t)
-
-    result, _ = integrate.quad(
-        integrand,
-        theta_rad[0],
-        theta_rad[-1],
-        limit=500,
-        epsrel=1e-10,
-        epsabs=0,
-    )
-    return result
-
-
-def validate_scale_cut(theta_min, theta_max, nmodes, ell_test, n_theta=200_000):
-    """Run validation for one scale cut.
-
-    Returns dict with keys 'fftlog', 'direct', 'ratio' — each (nmodes, n_ell).
-    """
-    print(f"\n{'=' * 70}")
-    print(f"Scale cut: [{theta_min}, {theta_max}] arcmin, modes 1-{nmodes}")
-    print(f"{'=' * 70}")
-
-    cosebis = COSEBIS(theta_min, theta_max, nmodes)
-
-    # Fine log-spaced θ grid for direct integration
-    theta = np.logspace(np.log10(theta_min), np.log10(theta_max), n_theta)
-
-    print("Computing T_+^log filters...")
-    Tp = cosebis.get_Tp_log(theta)
-
-    # FFT-log W_n(ℓ) — pass ell directly, let it use 100k internal grid
-    print("Computing W_n(ℓ) via FFT-log (100k grid + padding)...")
-    Wn_fftlog = cosebis.get_Wn_log(ell_test)
-
-    # Direct integration at each ℓ
-    print(f"Computing W_n(ℓ) via direct integration ({n_theta} θ points)...")
-    n_ell = len(ell_test)
-    Wn_direct = np.zeros((nmodes, n_ell))
-    for n in range(nmodes):
-        for i, ell in enumerate(ell_test):
-            Wn_direct[n, i] = direct_Wn_integration(ell, theta, Tp[n])
-
-    # Ratio
-    with np.errstate(divide="ignore", invalid="ignore"):
-        ratio = np.where(
-            np.abs(Wn_direct) > 1e-30,
-            Wn_fftlog / Wn_direct,
-            np.nan,
-        )
-
-    return {
-        "fftlog": Wn_fftlog,
-        "direct": Wn_direct,
-        "ratio": ratio,
-        "Tp": Tp,
-        "theta": theta,
-    }
-
-
-def print_table(ell_test, result, nmodes, indices=None, spot_modes=None):
-    """Print comparison table for selected modes at selected ℓ indices."""
-    if spot_modes is None:
-        spot_modes = list(range(min(nmodes, 6)))
-    if indices is None:
-        indices = list(range(len(ell_test)))
-
-    for n in spot_modes:
-        print(f"\n--- Mode n={n + 1} ---")
-        print(f"{'ell':>8} {'FFT-log':>14} {'Direct':>14} {'Ratio':>10}")
-        print("-" * 50)
-
-        for i in indices:
-            r = result["ratio"][n, i]
-            print(
-                f"{ell_test[i]:>8.0f} "
-                f"{result['fftlog'][n, i]:>14.6e} "
-                f"{result['direct'][n, i]:>14.6e} "
-                f"{r:>10.4f}"
-            )
-
-
-def quad_spot_check(theta_min, theta_max, nmodes, ell_spot):
-    """Spot-check a few (mode, ℓ) values with adaptive quadrature."""
-    print(f"\n{'=' * 70}")
-    print(f"Adaptive quadrature spot check: [{theta_min}, {theta_max}]'")
-    print(f"{'=' * 70}")
-
-    cosebis = COSEBIS(theta_min, theta_max, nmodes)
-    theta_fine = np.logspace(np.log10(theta_min), np.log10(theta_max), 500_000)
-    Tp = cosebis.get_Tp_log(theta_fine)
-
-    Wn_fftlog = cosebis.get_Wn_log(np.array(ell_spot, dtype=float))
-
-    print(
-        f"{'mode':>6} {'ell':>8} {'FFT-log':>14} {'quad':>14} {'trapz(500k)':>14} {'FFT/quad':>10} {'trapz/quad':>10}"
-    )
-    print("-" * 80)
-
-    for n in range(min(nmodes, 5)):
-        for j, ell in enumerate(ell_spot):
-            wn_fft = Wn_fftlog[n, j]
-            wn_quad = direct_Wn_quad(ell, theta_fine, Tp[n])
-            wn_trapz = direct_Wn_integration(ell, theta_fine, Tp[n])
-            r_fft = wn_fft / wn_quad if abs(wn_quad) > 1e-30 else np.nan
-            r_trapz = wn_trapz / wn_quad if abs(wn_quad) > 1e-30 else np.nan
-            print(
-                f"{n + 1:>6d} {ell:>8.0f} {wn_fft:>14.6e} {wn_quad:>14.6e} "
-                f"{wn_trapz:>14.6e} {r_fft:>10.6f} {r_trapz:>10.6f}"
-            )
-
-
-def plot_results(ell_test, results, output_path):
-    r"""Plot W_n ratio (FFT-log / direct) for both scale cuts."""
-    fig, axes = plt.subplots(2, 1, figsize=(12, 10), sharex=True)
-
-    for ax, (label, result) in zip(axes, results.items()):
-        nmodes = result["ratio"].shape[0]
-        cmap = plt.get_cmap("viridis", nmodes)
-
-        for n in range(nmodes):
-            valid = np.isfinite(result["ratio"][n])
-            ax.plot(
-                ell_test[valid],
-                result["ratio"][n, valid],
-                color=cmap(n),
-                alpha=0.7,
-                lw=1.2,
-                label=f"n={n + 1}" if n < 10 else None,
-            )
-
-        ax.axhline(1.0, color="k", ls="--", lw=0.8)
-        ax.axhspan(0.99, 1.01, color="green", alpha=0.1)
-        ax.set_ylabel("FFT-log / Direct")
-        ax.set_title(rf"$W_n(\ell)$ accuracy: {label}")
-        ax.set_ylim(0.9, 1.1)
-        ax.legend(ncol=5, fontsize=7, loc="lower left")
-
-    axes[1].set_xlabel(r"$\ell$")
-    axes[1].set_xscale("log")
-    fig.tight_layout()
-    fig.savefig(output_path, dpi=150, bbox_inches="tight")
-    print(f"\nFigure saved: {output_path}")
-    plt.close(fig)
-
-
-def main():
-    print("COSEBIS W_n(ℓ) Filter Validation")
-    print("FFT-log (cosmo_numba, post-Jan 23 rewrite) vs direct integration")
-
-    nmodes = 20
-
-    # Dense ℓ grid covering the pipeline range
-    ell_test = np.logspace(np.log10(12), np.log10(3000), 200)
-
-    spot_idx = [0, 25, 50, 75, 100, 125, 150, 175, 199]
-
-    # --- Full scale cut [1, 250]' ---
-    result_full = validate_scale_cut(1.0, 250.0, nmodes, ell_test)
-    print_table(ell_test, result_full, nmodes, indices=spot_idx)
-
-    # --- Fiducial scale cut [12, 83]' ---
-    result_fid = validate_scale_cut(12.0, 83.0, nmodes, ell_test)
-    print_table(ell_test, result_fid, nmodes, indices=spot_idx)
-
-    # --- Summary statistics ---
-    for label, result in [
-        ("Full [1,250]'", result_full),
-        ("Fiducial [12,83]'", result_fid),
-    ]:
-        print(f"\n{'=' * 70}")
-        print(f"Summary: {label}")
-        print(f"{'=' * 70}")
-        for n in range(nmodes):
-            valid = np.isfinite(result["ratio"][n])
-            if valid.any():
-                r = result["ratio"][n, valid]
-                print(
-                    f"  n={n + 1:>2d}: ratio range [{r.min():.4f}, {r.max():.4f}], "
-                    f"median={np.median(r):.4f}, |1-ratio| max={np.max(np.abs(1 - r)):.4f}"
-                )
-
-    # --- Adaptive quadrature spot checks ---
-    ell_spot = [50, 200, 500, 1000, 2000]
-    quad_spot_check(1.0, 250.0, 5, ell_spot)
-    quad_spot_check(12.0, 83.0, 5, ell_spot)
-
-    # --- Plot ---
-    results = {
-        "[1, 250]' (full)": result_full,
-        "[12, 83]' (fiducial)": result_fid,
-    }
-    output_path = "workflow/scripts/validate_cosebis_filters_results.png"
-    plot_results(ell_test, results, output_path)
-
-    # --- Verdict ---
-    all_ratios = np.concatenate(
-        [
-            result_full["ratio"][np.isfinite(result_full["ratio"])],
-            result_fid["ratio"][np.isfinite(result_fid["ratio"])],
-        ]
-    )
-    max_err = np.max(np.abs(1 - all_ratios))
-    print(f"\n{'=' * 70}")
-    print("VERDICT")
-    print(f"{'=' * 70}")
-    print(f"Maximum |1 - ratio| across all modes, scale cuts, ℓ: {max_err:.6f}")
-    if max_err < 0.01:
-        print("PASS: FFT-log W_n(ℓ) accurate to <1%")
-        print("  → Harmonic/config disagreement must come from elsewhere")
-        print("    (bandpower sampling, mask, noise bias)")
-    elif max_err < 0.05:
-        print("MARGINAL: FFT-log W_n(ℓ) accurate to <5% but >1%")
-        print("  → May contribute to harmonic/config disagreement")
-    else:
-        print("FAIL: FFT-log W_n(ℓ) inaccurate (>5% error)")
-        print("  → File upstream issue on cosmo_numba")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/papers/bmodes/scripts/validate_cosebis_theory.py b/papers/bmodes/scripts/validate_cosebis_theory.py
deleted file mode 100644
index dda26f34..00000000
--- a/papers/bmodes/scripts/validate_cosebis_theory.py
+++ /dev/null
@@ -1,171 +0,0 @@
-"""Validate COSEBIS integration: harmonic vs config-space using theory.
-
-Uses CCL theory C_ℓ and ξ±(θ) which are mathematically related by exact Hankel
-transforms. If the COSEBIS code is correct, E_n computed from either method
-should agree to numerical precision.
-
-This is a clean validation that doesn't depend on mock data consistency.
-
-Author: Claude Code
-"""
-
-import numpy as np
-import pyccl as ccl
-from cosmo_numba.B_modes.cosebis import COSEBIS
-
-
-def setup_cosmology_and_tracer():
-    """Setup CCL cosmology and weak lensing tracer."""
-    cosmo = ccl.Cosmology(
-        Omega_c=0.27,
-        Omega_b=0.045,
-        h=0.67,
-        sigma8=0.8,
-        n_s=0.96,
-        transfer_function="boltzmann_camb",
-        matter_power_spectrum="halofit",
-    )
-
-    # Simple Gaussian n(z) centered at z=1.0
-    z = np.linspace(0.01, 3.0, 300)
-    nz = np.exp(-0.5 * ((z - 1.0) / 0.2) ** 2)
-    nz /= np.trapezoid(nz, z)
-
-    tracer = ccl.WeakLensingTracer(cosmo, dndz=(z, nz))
-
-    return cosmo, tracer
-
-
-def get_theory_cls(ell, cosmo, tracer):
-    """Compute theory E-mode C_ℓ."""
-    cl_ee = ccl.angular_cl(cosmo, tracer, tracer, ell)
-    cl_bb = np.zeros_like(cl_ee)
-    return cl_ee, cl_bb
-
-
-def get_theory_xipm(cosmo, ell, cl_ee, theta_arcmin):
-    """Compute ξ±(θ) from C_ℓ using CCL's built-in correlation function."""
-    theta_rad = np.deg2rad(theta_arcmin / 60)
-
-    xip = ccl.correlation(cosmo, ell=ell, C_ell=cl_ee, theta=theta_rad, type="GG+")
-    xim = ccl.correlation(cosmo, ell=ell, C_ell=cl_ee, theta=theta_rad, type="GG-")
-
-    return xip, xim
-
-
-def main():
-    print("=" * 60)
-    print("COSEBIS Theory Validation: Harmonic vs Config-space")
-    print("=" * 60)
-
-    # Parameters matching real data analysis
-    theta_min = 1.0  # arcmin
-    theta_max = 250.0  # arcmin
-    nmodes = 6
-
-    # Fine grids for integration
-    # Use integer ell for CCL compatibility, but float64 for numba
-    ell = np.unique(np.geomspace(2, 30000, 2000).astype(int)).astype(np.float64)
-    n_theta = 1000
-    theta_integration = np.logspace(np.log10(theta_min), np.log10(theta_max), n_theta)
-
-    print("\nParameters:")
-    print(f"  theta range: [{theta_min}, {theta_max}] arcmin")
-    print(f"  COSEBIS modes: 1-{nmodes}")
-    print(f"  ℓ grid: {len(ell)} points in [{ell.min()}, {ell.max()}]")
-    print(f"  θ grid: {n_theta} points")
-
-    # Setup cosmology
-    print("\nSetting up CCL cosmology...")
-    cosmo, tracer = setup_cosmology_and_tracer()
-
-    # Generate theory power spectrum
-    print("Generating theory C_ℓ...")
-    cl_ee, cl_bb = get_theory_cls(ell, cosmo, tracer)
-    print(f"  C_ℓ(ℓ=100): {cl_ee[np.argmin(np.abs(ell - 100))]:.3e}")
-    print(f"  C_ℓ(ℓ=1000): {cl_ee[np.argmin(np.abs(ell - 1000))]:.3e}")
-
-    # Generate theory ξ±(θ) from C_ℓ using CCL's FFTLog
-    print("\nComputing theory ξ±(θ) from C_ℓ using CCL...")
-    xip, xim = get_theory_xipm(cosmo, ell, cl_ee, theta_integration)
-    print(f"  ξ+(θ=10'): {xip[np.argmin(np.abs(theta_integration - 10))]:.3e}")
-    print(f"  ξ-(θ=10'): {xim[np.argmin(np.abs(theta_integration - 10))]:.3e}")
-
-    # Compute COSEBIS from both methods
-    print("\nComputing COSEBIS from harmonic-space (C_ℓ)...")
-    cosebis = COSEBIS(theta_min, theta_max, nmodes)
-
-    # Fine θ grid for W_n(ℓ) filter functions
-    # Note: Using 20000 points for better high-ℓ accuracy (FFT-log issue)
-    theta_grid = np.logspace(np.log10(theta_min), np.log10(theta_max), 20000)
-    ce_harm, cb_harm = cosebis.cosebis_from_Cell(
-        ell=ell, Cell_E=cl_ee, Cell_B=cl_bb, theta=theta_grid, cache=True
-    )
-
-    print("\nComputing COSEBIS from config-space (ξ±)...")
-    # For config-space, need dtheta for integration
-    dtheta = np.gradient(theta_integration)
-
-    ce_config, cb_config = cosebis.cosebis_from_xipm(
-        theta=theta_integration, dtheta=dtheta, xi_plus=xip, xi_minus=xim, cache=True
-    )
-
-    # Compare
-    print("\n" + "=" * 60)
-    print("Results: E_n comparison (harmonic vs config-space)")
-    print("=" * 60)
-    print(f"{'Mode':<6} {'Harmonic':>14} {'Config':>14} {'Ratio':>10} {'Diff (σ)':>10}")
-    print("-" * 60)
-
-    for n in range(nmodes):
-        ratio = ce_harm[n] / ce_config[n] if ce_config[n] != 0 else np.nan
-        # For theory, expect exact agreement, so σ is not meaningful
-        # Just report absolute difference
-        diff = ce_harm[n] - ce_config[n]
-        print(
-            f"E_{n + 1:<4} {ce_harm[n]:>14.6e} {ce_config[n]:>14.6e} {ratio:>10.4f} {diff:>10.2e}"
-        )
-
-    print("\n" + "=" * 60)
-    print("Results: B_n (should be ~0)")
-    print("=" * 60)
-    print(f"{'Mode':<6} {'Harmonic':>14} {'Config':>14}")
-    print("-" * 60)
-
-    for n in range(nmodes):
-        print(f"B_{n + 1:<4} {cb_harm[n]:>14.6e} {cb_config[n]:>14.6e}")
-
-    # Summary statistics
-    print("\n" + "=" * 60)
-    print("Summary")
-    print("=" * 60)
-    ratios = ce_harm / ce_config
-    print(f"Mean E_n ratio (harm/config): {np.mean(ratios):.4f}")
-    print(f"Std E_n ratio: {np.std(ratios):.4f}")
-    print(f"Max B_n (harmonic): {np.max(np.abs(cb_harm)):.2e}")
-    print(f"Max B_n (config): {np.max(np.abs(cb_config)):.2e}")
-
-    # Verdict
-    tolerance = 0.05  # 5% agreement expected for well-resolved integration
-    if np.all(np.abs(ratios - 1) < tolerance):
-        print(
-            f"\n✓ PASS: Harmonic and config-space agree within {tolerance * 100:.0f}%"
-        )
-    else:
-        print(f"\n✗ FAIL: Discrepancy exceeds {tolerance * 100:.0f}%")
-        print("  This could indicate:")
-        print("  1. Insufficient integration resolution")
-        print("  2. Different ℓ/θ range coverage")
-        print("  3. Bug in one of the integration methods")
-
-    return {
-        "ce_harm": ce_harm,
-        "cb_harm": cb_harm,
-        "ce_config": ce_config,
-        "cb_config": cb_config,
-        "ratios": ratios,
-    }
-
-
-if __name__ == "__main__":
-    results = main()
diff --git a/src/sp_validation/tests/test_bmodes_workflow_dry_run.py b/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
index 3b7eecae..890271a4 100644
--- a/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
+++ b/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
@@ -67,7 +67,7 @@ def _dry_run(workflow_dir, targets, *extra_snakemake_args):
 @requires_candide_data
 def test_bmodes_workflow_dry_runs():
     """The paper B-mode workflow must still parse and dry-run cleanly."""
-    result = _dry_run(_repo_root() / "papers/bmodes", ["all_tapestry"])
+    result = _dry_run(_repo_root() / "papers/bmodes", ["paper"])
     assert result.returncode == 0, result.stdout
 
 
diff --git a/workflow/common.py b/workflow/common.py
index 4391ddbf..9927a6f6 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -363,8 +363,7 @@ def grid_of(grids, binning):
     """Name of the grid a binning belongs to, compared numerically.
 
     A "300" wildcard matches a 300.0 grid value. Binnings matching no named
-    grid (e.g. papers/bmodes' nbins=10000 convergence check) are reporting-style
-    measurements.
+    grid are reporting-style measurements.
     """
     key = tuple(float(binning[k]) for k in XI_KEYS)
     for name, grid in grids.items():
diff --git a/workflow/scripts/plotting_utils.py b/workflow/scripts/plotting_utils.py
index 2e62ce7c..1bc1cd4b 100644
--- a/workflow/scripts/plotting_utils.py
+++ b/workflow/scripts/plotting_utils.py
@@ -1,4 +1,4 @@
-"""Shared plotting utilities for claims scripts."""
+"""Shared plotting utilities for the paper figure scripts."""
 
 from pathlib import Path
 

From 6c2f03a92c775d2b0626d366897e6830ec4d8fd1 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 17:50:46 +0200
Subject: [PATCH 35/83] Dockerfile: build from shapepipe:develop (#360)

The im_sims tag comes from a branch that no longer exists, so the image froze
at whatever ShapePipe it last built. develop is the maintained image, and it
no longer ships snakemake (shapepipe 10f9c535).


Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Dockerfile b/Dockerfile
index 0437a929..1a4183e0 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,5 @@
 # Development image with more bells and whistles
-FROM ghcr.io/cosmostat/shapepipe:im_sims
+FROM ghcr.io/cosmostat/shapepipe:develop
 
 # liblapack-dev: cosmosis's MultiNest links -llapack, and the base image ships
 # only the runtime liblapack.so.3 (no dev symlink). The gsl/cfitsio/fftw3 dev

From 0c0d83489a0873d9862ec7e89560e765261812c4 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 18:12:05 +0200
Subject: [PATCH 36/83] Write metacal flag columns as int32; reduce_mem never
 narrows integers (#354)

* catalog: metacal bitmasks use FITS format K, not I or the dead FLAGS_ key

NGMIX_MCAL_FLAGS carries bit 30 (shapepipe#854's absent-measurement flag)
alongside the native fitter bits. Format I truncates it to int16 and
silently zeroes bit 30 on write, in both the FITS and the HDF5 branch of
write_shape_catalog (the HDF5 path reuses the FITS column's array).

The per-type NGMIX_FLAGS_{1P,1M,2P,2M,NOSHEAR} columns had the same
problem from the other direction: their format entry was keyed as
"FLAGS_{suffix}", which never matches the real column name
"{prefix}_FLAGS_{suffix}", so the writer's float64 default masked the
dead key rather than narrowing anything. Both parameter files now key
and format all six metacal bitmasks the same way, at K, so they round-
trip exactly and survive JointCat's optional memory-reduction pass
(which only downcasts int32/float64, not int64).

NGMIX_MCAL_TYPES_FAIL stays at I: it is a count in [0, 5], not a bitmask.

Adds a round-trip test parametrized over both parameter files, FITS and
HDF5, and reduce_mem on/off, checking 0, bit 30 alone, and bit 30 with a
native bit together.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* catalog: metacal bitmasks as int32; reduce_mem never narrows integers

ShapePipe sets bit 2**30 in the metacal flags for a missing measurement,
which needs 32 bits. The parameter files now write NGMIX_MCAL_FLAGS and
the five NGMIX_FLAGS_* columns as FITS J (int32).

JointCat.dtype_out with reduce_mem narrowed every int32 column outside a
keep-list to int8, silently wrapping any value above 127. It now only
narrows float64 to float32 (RA/Dec excepted) and leaves integer columns
alone.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* catalog: justify int32 metacal flags by ngmix bits 0-15, not bit 30

The flag columns stay J: ngmix's ZERO_DOF (2**15) overflows a signed
16-bit I. Tests round-trip 0, 2**12, 2**15, a combination and all 16
bits instead of 2**30.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Uemfjv9ybCwtKtprksZbVY

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 scripts/calibration/params.py                 |  5 +-
 src/sp_validation/catalog.py                  |  7 ++
 src/sp_validation/catalog_builders.py         | 25 +++---
 .../tests/test_catalog_flag_roundtrip.py      | 80 +++++++++++++++++++
 workflow/image_sims/params_im_sim.py          |  5 +-
 5 files changed, 102 insertions(+), 20 deletions(-)
 create mode 100644 src/sp_validation/tests/test_catalog_flag_roundtrip.py

diff --git a/scripts/calibration/params.py b/scripts/calibration/params.py
index ea871119..7f6d7c41 100644
--- a/scripts/calibration/params.py
+++ b/scripts/calibration/params.py
@@ -143,7 +143,6 @@
     "NUMBER",
     "IMAFLAGS_ISO",
     "FLAGS",
-    "NGMIX_MCAL_FLAGS",
     "NGMIX_MCAL_TYPES_FAIL",
     "N_EPOCH",
     "NGMIX_N_EPOCH",
@@ -152,6 +151,8 @@
 
 add_cols_pre_cal_format["TILE_ID"] = "A7"
 add_cols_pre_cal_format["NUMBER"] = "J"
+# Metacal bitmasks hold ngmix flag bits 0-15; bit 15 overflows signed 16-bit I.
+add_cols_pre_cal_format["NGMIX_MCAL_FLAGS"] = "J"
 
 # Create key names for metacal information
 prefix = "NGMIX"
@@ -162,7 +163,7 @@
         add_cols_pre_cal.append(f"{prefix}_{center}_{suffix}")
 
 for suffix in suffixes:
-    add_cols_pre_cal_format[f"FLAGS_{suffix}"] = "I"
+    add_cols_pre_cal_format[f"{prefix}_FLAGS_{suffix}"] = "J"
 
 
 # Catalog parameters
diff --git a/src/sp_validation/catalog.py b/src/sp_validation/catalog.py
index e39d2076..76022243 100644
--- a/src/sp_validation/catalog.py
+++ b/src/sp_validation/catalog.py
@@ -439,6 +439,13 @@ def write_shape_catalog(
 
     Write catalogue with galaxy shapes = shear estimates.
 
+    @sc [label:convention] metacal-flag-width
+    The data and image-simulation parameter files supply FITS ``J`` (int32)
+    for metacal bitmasks, so all ngmix flag bits (0-15; ``ZERO_DOF`` = 2**15
+    overflows a signed 16-bit ``I``) survive FITS/HDF5 output; ``JointCat``
+    never narrows integers.
+    The number of failed metacal types is a count in [0, 5], not a bitmask.
+
     Parameters
     ----------
     output_path : str
diff --git a/src/sp_validation/catalog_builders.py b/src/sp_validation/catalog_builders.py
index 78dc7838..b6abafc0 100644
--- a/src/sp_validation/catalog_builders.py
+++ b/src/sp_validation/catalog_builders.py
@@ -437,26 +437,19 @@ def dtype_out(self, name, dtype_in):
             output dtype
 
         """
-        # Specify columns for which original (high-precision) format
-        # needs to be kept and not reduced to lower precision
-        cols_keep_dtype = [
-            "RA",
-            "Dec",
-            "FLAGS",
-            "IMAFLAGS_ISO",
-            "NUMBER",
-        ]
         if dtype_in.kind == "U":
             # Transform unicode to string of equal length
             return np.dtype(f"S{dtype_in.itemsize // 4}")
 
-        if self._params["reduce_mem"] == False:
-            return dtype_in
-        elif name not in cols_keep_dtype:
-            if dtype_in.kind == "f" and dtype_in.itemsize == 8:
-                return np.float32
-            if dtype_in.kind == "i" and dtype_in.itemsize == 4:
-                return np.int8
+        # reduce_mem narrows float64 to float32, except coordinates. Integer
+        # columns (bitmasks, IDs, counts) are never narrowed.
+        if (
+            self._params["reduce_mem"]
+            and dtype_in.kind == "f"
+            and dtype_in.itemsize == 8
+            and name not in ("RA", "Dec")
+        ):
+            return np.dtype(np.float32)
 
         return dtype_in
 
diff --git a/src/sp_validation/tests/test_catalog_flag_roundtrip.py b/src/sp_validation/tests/test_catalog_flag_roundtrip.py
new file mode 100644
index 00000000..ea3cac97
--- /dev/null
+++ b/src/sp_validation/tests/test_catalog_flag_roundtrip.py
@@ -0,0 +1,80 @@
+"""Comprehensive catalogues preserve metacal failure bits exactly."""
+
+import runpy
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pytest
+from astropy.io import fits
+
+from sp_validation.catalog import write_shape_catalog
+from sp_validation.catalog_builders import JointCat
+
+ROOT = Path(__file__).resolve().parents[3]
+
+
+@pytest.mark.parametrize(
+    "params_path",
+    ["scripts/calibration/params.py", "workflow/image_sims/params_im_sim.py"],
+    ids=["data", "image-sims"],
+)
+@pytest.mark.parametrize("extension", [".fits", ".hdf5"])
+@pytest.mark.parametrize("reduce_mem", [False, True])
+def test_metacal_flags_roundtrip(tmp_path, params_path, extension, reduce_mem):
+    """Contract metacal-flag-width: neither output nor merging loses flag bits.
+
+    Values are ngmix flag bits: none, LM_FUNC_NOTFINITE (2**12), ZERO_DOF
+    (2**15), and every bit 0-15 at once. Float64 inputs match ShapePipe's final
+    catalogue. Bit 15 overflows signed int16, and int8 wraps everything above
+    127, so the columns must stay int32.
+    """
+    with np.printoptions():
+        params = runpy.run_path(str(ROOT / params_path))
+    flags = np.array([0, 2**12, 2**15, 2**15 | 2**12 | 8, 2**16 - 1], dtype=np.float64)
+    bit_columns = ["NGMIX_MCAL_FLAGS"] + [
+        f"NGMIX_FLAGS_{suffix}" for suffix in ("NOSHEAR", "1P", "1M", "2P", "2M")
+    ]
+    columns = {name: flags for name in bit_columns}
+    columns["NGMIX_MCAL_TYPES_FAIL"] = np.array([0, 1, 5, 2, 5])
+    assert set(columns) <= set(params["add_cols_pre_cal"])
+    path = tmp_path / f"comprehensive{extension}"
+    write_shape_catalog(
+        str(path),
+        np.zeros(5),
+        np.zeros(5),
+        np.ones(5),
+        add_cols=columns,
+        add_cols_format=params["add_cols_pre_cal_format"],
+    )
+    if extension == ".fits":
+        written = fits.getdata(path, 1)
+    else:
+        with h5py.File(path, "r") as catalog:
+            written = catalog["data"][:]
+
+    builder = JointCat()
+    builder._params["reduce_mem"] = reduce_mem
+    for name, expected in columns.items():
+        values = written[name]
+        np.testing.assert_array_equal(values, expected, err_msg=name)
+        if name in bit_columns:
+            assert values.dtype.kind == "i" and values.dtype.itemsize == 4, name
+        reduced = values.astype(builder.dtype_out(name, values.dtype))
+        np.testing.assert_array_equal(reduced, expected, err_msg=name)
+
+
+@pytest.mark.parametrize("reduce_mem", [False, True])
+def test_reduce_mem_never_narrows_integers(reduce_mem):
+    """reduce_mem narrows float64 (except RA/Dec) and leaves integers intact."""
+    builder = JointCat()
+    builder._params["reduce_mem"] = reduce_mem
+    for dtype in (">i4", "<i4", ">i8", "<i2"):
+        values = np.array([0, 127, 128, 2**14], dtype=dtype)
+        out = builder.dtype_out("N_EPOCH", values.dtype)
+        assert out == values.dtype
+        np.testing.assert_array_equal(values.astype(out), values)
+    for name in ("RA", "Dec"):
+        assert builder.dtype_out(name, np.dtype(">f8")) == np.dtype(">f8")
+    expected = np.float32 if reduce_mem else np.dtype(">f8")
+    assert builder.dtype_out("NGMIX_G1_NOSHEAR", np.dtype(">f8")) == expected
diff --git a/workflow/image_sims/params_im_sim.py b/workflow/image_sims/params_im_sim.py
index 1cd395c6..b7db399e 100644
--- a/workflow/image_sims/params_im_sim.py
+++ b/workflow/image_sims/params_im_sim.py
@@ -148,7 +148,6 @@
 for key in (
     "NUMBER",
     "FLAGS",
-    "NGMIX_MCAL_FLAGS",
     "NGMIX_MCAL_TYPES_FAIL",
     "N_EPOCH",
     "NGMIX_N_EPOCH",
@@ -157,6 +156,8 @@
 
 add_cols_pre_cal_format["TILE_ID"] = "A7"
 add_cols_pre_cal_format["NUMBER"] = "J"
+# Metacal bitmasks hold ngmix flag bits 0-15; bit 15 overflows signed 16-bit I.
+add_cols_pre_cal_format["NGMIX_MCAL_FLAGS"] = "J"
 
 # Create key names for metacal information
 prefix = "NGMIX"
@@ -167,7 +168,7 @@
         add_cols_pre_cal.append(f"{prefix}_{center}_{suffix}")
 
 for suffix in suffixes:
-    add_cols_pre_cal_format[f"FLAGS_{suffix}"] = "I"
+    add_cols_pre_cal_format[f"{prefix}_FLAGS_{suffix}"] = "J"
 
 
 # Catalog parameters

From fdb7c5eb4917c833f8d8201518a57fe5aac7a7f1 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 18:38:26 +0200
Subject: [PATCH 37/83] ci(docs): install from uv.lock, not a fresh PyPI
 resolve (#365)

The docs job ran `uv pip install '.[docs]'`, which re-resolves the whole
environment and breaks on unrelated upstream releases. Sync the checkout's
lock (docs extra, --frozen --inexact) into the image's venv and install the
checkout --no-deps on top. PRs build docs only when docs inputs change.


Claude-Session: https://claude.ai/code/session_01Uemfjv9ybCwtKtprksZbVY

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 .github/workflows/deploy-docs.yml | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index 3efce06b..a1878051 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -1,11 +1,11 @@
 name: Build and deploy API documentation
 
 # Build the Sphinx docs inside the published image — which already carries the
-# full scientific stack autodoc must import — installing the *checked-out*
-# package on top so the docs reflect the code under review, not the code baked
-# into the image.
+# full scientific stack autodoc must import — syncing the *checked-out* uv.lock
+# (plus its `docs` extra) and package on top, so the docs reflect the code under
+# review and never re-resolve against PyPI.
 #
-#   pull_request → build only, as a check (no deploy)
+#   pull_request → build only, as a check (no deploy), when docs inputs change
 #   push: develop → build + deploy to GitHub Pages
 on:
   push:
@@ -14,6 +14,12 @@ on:
   pull_request:
     branches:
       - develop
+    paths:
+      - "docs/**"
+      - "src/**"
+      - "pyproject.toml"
+      - "uv.lock"
+      - ".github/workflows/deploy-docs.yml"
   workflow_dispatch:
 
 jobs:
@@ -37,8 +43,15 @@ jobs:
       - name: Checkout
         uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
 
+      # Exactly what uv.lock pins: --frozen never re-resolves, and --inexact
+      # keeps the image's packages outside this closure (the ShapePipe stack,
+      # the glass/workflow extras) instead of pruning them. Both land in the
+      # image's venv (UV_PROJECT_ENVIRONMENT). The project isn't uv-packaged, so
+      # the checkout goes in by hand, replacing the copy baked into the image.
       - name: Install documentation dependencies
-        run: uv pip install --no-cache-dir '.[docs]'
+        run: |
+          uv sync --frozen --inexact --no-cache --no-install-project --extra docs
+          uv pip install --no-cache --no-deps -e .
 
       # Builds on every event; a failing build fails the PR check. Deploy is
       # gated to develop pushes below.

From 6faf7317b41810ac5042a9abda4e43d43a235901 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 18:38:43 +0200
Subject: [PATCH 38/83] Remove spread-model leftovers (#364)

Drop the unread do_spread_model flag from the image-sim params template and
describe metacal's mask argument as the pre-selection it is, with the
size-based galaxy cut applied inside metacal.


Claude-Session: https://claude.ai/code/session_01Uemfjv9ybCwtKtprksZbVY

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/calibration.py     | 4 +++-
 workflow/image_sims/params_im_sim.py | 3 ---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/src/sp_validation/calibration.py b/src/sp_validation/calibration.py
index e0ab212e..9eaaf42e 100644
--- a/src/sp_validation/calibration.py
+++ b/src/sp_validation/calibration.py
@@ -697,7 +697,9 @@ class metacal:
     data :
         input galaxy catalogue
     mask : array of bool
-        mask according to galaxy selection, e.g. spread_model
+        pre-selection mask, e.g. flag, magnitude and footprint cuts; the
+        size-based galaxy selection (``rel_size_min`` < T/Tpsf <
+        ``rel_size_max``) is applied here
     masking_type : string, optional, default='gal'
         masking type, one in 'gal', 'gal_mom', 'star'
     step : float, optional, default=0.01
diff --git a/workflow/image_sims/params_im_sim.py b/workflow/image_sims/params_im_sim.py
index b7db399e..09d19bc1 100644
--- a/workflow/image_sims/params_im_sim.py
+++ b/workflow/image_sims/params_im_sim.py
@@ -192,9 +192,6 @@
 gal_mag_bright = 15
 gal_mag_faint = 30
 
-### Spread-model
-do_spread_model = False
-
 ### SExtractor flags to keep in addition to FLAGS=0
 ### (bit-coded; list of powers of 2);
 ### Empty list if no flags

From 2832186f17e2e7080b96f9e93164713d75861284 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 19:22:01 +0200
Subject: [PATCH 39/83] =?UTF-8?q?workflow:=20parity-checked=20launches,=20?=
 =?UTF-8?q?one=20output=20root,=20one=20integration=20grid,=20machine-inde?=
 =?UTF-8?q?pendent=20=CE=BE=C2=B1=20(#357)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* tests: pin pure-E/B on exact-binning and committed ξ±; drop dead catalogue paths

The synthetic pure-E/B pins passed in CI and failed on candide because
TreeCorr's default bin_slop/angle_slop make ξ± follow the tree's top-level
split, which varies with the jackknife patches and, through min_top, with the
thread count TreeCorr takes from cpu_count(). Fixed patch centres alone leave
a 4-vs-48-thread spread (reporting ξ− up to 16%); exact binning removes it
(1e-12). The test measures with bin_slop = angle_slop = 0, and test_b_modes
pins pure_eb_from_xi on the same ξ±, committed as
tests/data/pure_eb_xi_fixture.npz.

The configured-path guard skips cat_config's paths.output and directory-less
calibration params.input_path values. The two LFmask entries (data gone) and
the six unread covmat_file keys leave cat_config.yaml, and the slow duplicate
test_catalog_paths_exist goes.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* cosmo_val: pin TreeCorr's min_top so ξ± does not depend on the machine

TreeCorr derives min_top, the depth of its root cells, from its thread count
(max(3, ceil(log2 n)), with n from cpu_count() when unset). The root cells set
which pairs bin_slop approximates, so at default slop calculate_2pcf's ξ±
depended on the node: on the synthetic catalogue, 48 threads move ξ± by 0.038σ
against 4. The shared treecorr_config pins min_top = 6, which is what TreeCorr
derives on candide's 48- and 64-CPU nodes; the same config reaches the
aperture-mass, leakage and ρ/τ correlations.

test_calculate_2pcf_does_not_depend_on_thread_count measures at production
binning on 4 and 48 threads (fresh Catalog each, shared patch centres) and
requires agreement below 1e-6σ; it is red without the pin.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* b_modes: one pure-E/B kernel call; tests pin each pure-E/B fact once

calculate_pure_eb_correlation (modes and jackknife) and pure_eb_covariance_mc
call cosmo_numba through pure_eb_from_xi, so the transform test_b_modes pins
is the one every pure-E/B product runs through.

The committed ξ± fixture is now a conftest fixture (pure_eb_xi) with two
users. The synthetic pure-E/B test asserts that the ξ± and edges it measures
equal the fixture (agreement 2e-13), and that its modes are pure_eb_from_xi of
them; this replaces its copy of the mode pins, and np.savez of what it
measures regenerates the fixture. test_b_modes pins pure_eb_from_xi on the
fixture, which fails loudly if cosmo_numba does not import, with no skip.
A failure now names what moved: the measured ξ±, the wiring into the kernel,
or the transform.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* b_modes: one pure-E/B transform per jackknife realisation

The jackknife covariance function indexed pure_EB(x) once per key, so every
realisation ran cosmo_numba's transform six times (55 transforms at npatch=8
where 10 suffice; values unchanged). _eb_vector concatenates the modes in
_EB_KEYS order for both the jackknife and the MC covariance.

The synthetic pure-E/B test counts transforms around calculate_pure_eb and is
red on the per-key closure (55 > npatch + 2).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* tests: glue-section header stops enumerating which tests compare values

Two tests in the section compare values (pure-E/B against committed ξ±, ξ±
across TreeCorr thread counts); the header defers to each test's docstring
instead of listing them.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: host/image parity check, catalogue config from the checkout, one output root, one integration grid

- Launch-time parity: container.image_runtime reads the image's Python minor
  and Snakemake version (SIF via apptainer, sandbox off disk, tags skipped);
  common.check_host_parity stops a launch whose host Snakemake differs, with
  the reinstall command. configure() and image_sims.smk call it; the README
  install line pins --python 3.12.
- The catalogue config is the launched checkout's cosmo_val/cat_config.yaml,
  loaded by configure() into CATALOG_CONFIG; the paper Snakefiles no longer
  merge it into `config`, and covariance.smk / ecut.smk read CATALOG_CONFIG.
- One output root: cv_init_params passes output_dir=COSMO_VAL, cv_runner no
  longer chdirs into the live checkout, and CosmologyValidation drops its
  COSMO_VAL environment fallback.
- One integration grid (R12): the cosebis grid is gone; cv_cosebis reads the
  integration part and the CosmoCov g covariance on that grid (the one
  pure-E/B uses). An npatch=1 grid defaults to the diagonal covariance, and a
  binning outside the named grids takes its covariance from its own patches.
- The candide profile sets jobs: 100.
- workflow/tests: host-launcher DAG tests on a toy checkout (P1-P3) and the
  real papers on candide (P4, P5 = the container smoke test, moved here); CI
  runs them in a workflow-dag job. test_bmodes_workflow_dry_run.py is
  replaced by P4.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: the image lives under ~/.cache; launch guards read the real image

- container.CACHE_DIR is ~/.cache/sp_validation whatever XDG_CACHE_HOME says.
  A job runs the image from the path the launching host resolved, and on
  candide XDG_CACHE_HOME is node-local /scratch: from such a shell the launch
  fell back to the registry tag and skipped the parity check.
  SPV_CONTAINER / SPV_SANDBOX remain the overrides.
- test_launch_reads_the_image_under_home: a mismatched image under a fake
  ~/.cache stops the launch while XDG_CACHE_HOME points elsewhere.
- test_papers_resolve_on_candide asserts unconditionally that the launch read
  a local image (no "parity unchecked").
- test_image_sims_checks_parity_at_launch: the standalone image-sims
  Snakefile stops on a mismatched image.
- test_assemble_resolves pins each terminal file's inputs: the reporting ξ±
  part with its CosmoCov ng covariance, the fiducial-binning pseudo-Cl part
  with its NaMaster covariance, COSEBIs, pure-E/B and ρ/τ.
- The container smoke test launches as the README does (no --jobs), so the
  candide profile's job bound is under test.
- Test docstrings drop the design-table row IDs.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: a ξ± part's covariance follows its patches, in one place

A grid is a binning; rule xi takes cov=patch_cov(npatch) directly, so
grid_cov, _named_grid and the grids' cov key go. cv_init_params loses its
unused version_list, and the cosmo_val rules pass one CV_INIT.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/README: scope the output-root sentence to papers/cosmo_val

Name where the other rules write: masks under the run directory's
output/masks/, papers/bmodes' figures and macros under its docs/, image sims
under grids_base.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: SLURM jobs start whatever the launch's XDG_CACHE_HOME; P4/P5 test what a launch does

A job's Snakemake inherits the launching shell's environment (--export=ALL),
and a login shell may point XDG_CACHE_HOME at node-local storage: every job
then fails creating its source cache. Two halves close it, each needed (shown
on n33 by replaying the captured sbatch job command with srun stubbed):
- the candide profile leaves source-cache out of shared-fs-usage, so a job
  neither reuses the launch's cache path nor creates its own under XDG;
- common.py drops XDG_CACHE_HOME from what jobs inherit, because the
  slurm-jobstep executor forces a shared source cache on the Snakemake it
  starts for each job step.
test_a_job_needs_no_launch_cache covers both (each mutation turns it red).

P4 (test_papers_resolve_on_candide) now passes from a shell with node-local
XDG_CACHE_HOME under srun, and its bmodes case also resolves an e-cut
catalogue, so both CATALOG_CONFIG readers in ecut.smk are exercised. P5 hands
the smoke Snakefile the image a launch resolves (no registry pull). The host
launcher and CI carry snakemake-executor-plugin-slurm, as the README install
line does, so the candide profile is parsed in CI.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* papers/bmodes: the sweep drivers read the image from ~/.cache, as container.py does

container_env.sh still followed XDG_CACHE_HOME, so from a login shell that
points it at node-local storage the sweep drivers ran a missing image while
spv-container and the workflow found the real one.
test_sweep_drivers_run_the_resolved_image pins the shell copy of the
resolution (cache and sandbox precedence) to container.resolve_image.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* run_2pcf: a ξ± part carries the covariance its measurement estimated

Which covariance a part carries had two homes that disagreed: rule xi passed
cov=patch_cov(npatch) ("diagonal" at npatch=1), while run_2pcf's own default,
reached by the CLI and papers/bmodes' run_xi_sweep, was "none". The same
file name then held variances or not depending on who wrote it. run_2pcf now
carries what TreeCorr estimated (gg.var_method, which calculate_2pcf sets from
npatch): the jackknife covariance with patches, the shot-noise diagonal
without. The cov argument, --cov, the jackknife guard, rule xi's cov param
and common.patch_cov (with its test) go. Rule xi hands its wildcards to
grid_of directly (xi_binning_of goes).

test_xi_part_carries_the_covariance_the_measurement_estimated runs run_2pcf
on the synthetic catalogue at npatch 1 and 4; the previous run_2pcf fails
both cases.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: the launch-cache test answers the profile's apptainer check with a stub

The candide profile deploys with apptainer, and Snakemake asks for the apptainer
binary and its version even in a dry-run, so test_a_job_needs_no_launch_cache
failed on GitHub's runners, which have no apptainer. A stub apptainer on the
test's PATH answers that check; the dry-run reads nothing else from it.

In a CI emulation (a clean checkout, a fresh HOME, no apptainer or SLURM on
PATH, the workflow-dag command) the suite goes from 1 failed, 11 passed to 12
passed. Both mutations still turn the test red there: source-cache added back to
the profile's shared-fs-usage (NotADirectoryError under the blocked
XDG_CACHE_HOME) and the XDG_CACHE_HOME pop removed from common.py (the job sees
the launch's cache).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: the smoke Snakefile composes workflow/ as the entry Snakefiles do

P5's Snakefile never imported common, so its launch did not drop
XDG_CACHE_HOME: from a login shell pointing it at node-local /scratch, the
job's inner Snakemake died creating its source cache (PermissionError), which
says nothing about what a real launch does. The Snakefile now imports common,
resolves its image with common.resolve_container and checks host/image parity,
as the entry Snakefiles do. P5 launches exactly as the README does, with no
--config container=, and the literal default image and the test that kept it in
step with CONTAINER_URI go.

Checked on n33 with sbatch stubbed to record what it would submit, then the
recorded job replayed with srun stubbed, from a shell with XDG_CACHE_HOME set
to /scratch/cdaley/tmp/xdg: at the parent commit the job environment carries
XDG_CACHE_HOME and the replay fails with PermissionError; with this commit it
carries none, the job runs in the SIF, and P5's assertions pass on its report.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: resolve_container checks the image it returns

The host/image parity check had a home beside the resolver, so configure()
resolved the image once only to check it, and every Snakefile that picks its
own image (image_sims.smk, the smoke Snakefile) had to remember a second
line. resolve_container now runs check_host_parity on the image it returns:
the image checked is the image that runs, and the extra call sites go.
check_host_parity stays cached, since composed Snakefiles evaluate
container: more than once.

Removing the check from resolve_container turns 5 host tests red (both
parity cases, the unreadable-image line, the image under ~/.cache, image
sims).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: P5 checks that the job imports the launched checkout's src

The smoke Snakefile imported common but never put the checkout's src/ on the
job's PYTHONPATH, so its job imported the image's baked sp_validation, and
P5's assertion (any editable src/ layout) passed on it. The smoke Snakefile
now calls inject_checkout_pythonpath as configure() does for the entry
Snakefiles, and P5 asserts the job's sp_validation is this checkout's
src/sp_validation/__init__.py.

Run on n08 through the default profile (apptainer, no SLURM): the job reports
<checkout>/src/sp_validation/__init__.py; with the injection removed it
reports /sp_validation/src/sp_validation/__init__.py, which the new assertion
rejects and the old one accepted. P5 itself needs a SLURM submit host and has
not been run.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: COSMO_VAL defaults to the launched checkout's own output tree

A launch from any checkout that named no COSMO_VAL read that checkout's
catalogue config and code but wrote every cosmo_val product into the
production tree, silently. COSMO_VAL now defaults to the launched checkout's
(gitignored) cosmo_val/output, so production is written only from the
production checkout or when a launch names it; COSMO_INFERENCE keeps its
shared default. README says so.

P2 gains an unnamed case: with COSMO_VAL unset, every declared output lies
under the toy checkout's cosmo_val/output, the inference root or results/.
Restoring the production default turns it red (outputs under
/n17data/cdaley/unions/code/sp_validation/cosmo_val/output).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* calculate_2pcf: the ξ± text dump carries columns only, so it reads back

With patches, calculate_2pcf wrote its .txt with write_cov=True and no
per-patch results. TreeCorr 5.1.4 writes num_rows only alongside patch
results, so its reader ran on into the cov block and raised ("got 12 columns
instead of 11"). The two ξ± figure rules re-enter calculate_2pcf on the
reporting grid (npatch=100) and hit exactly that read. The dump now carries
the columns only; the covariance matrix lives in the SACC part, and the
figure readers use the columns.

test_a_patched_xi_dump_reads_back measures at npatch=4, then re-enters
calculate_2pcf from a fresh CosmologyValidation and compares the columns;
under write_cov=True it fails with the ValueError above.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: the candide profile's job bound is checked on any host

A real launch through the committed candide profile, of an up-to-date
target, submits nothing, so it runs in CI and on an allocation; the same
launch through the profile without `jobs` is refused. test_container_smoke
submits a real job and needs sbatch, which only a login node has; it is
documented as the login-node check it is.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* profiles: wait 60 s for a job's outputs to appear

P5 (one real SLURM job through the candide profile, from a login node)
passed, but only on its retry: the job finished, its output took more
than 5 s to show on the login node's /home, and Snakemake re-ran it. On a
multi-hour job that retry costs hours, and a second miss fails the run.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* rho_tau_stats: 6 h wall clock; the jackknife ρ/τ outruns the 60 min default

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* workflow: no snakemake in the image, so any host Snakemake works

A script: job unpickles the host's snakemake object with whichever snakemake
it imports first, and the image's site-packages precede the host's appended
sys.path. The image carried its own (the workflow extra, and the base image's
jupyter extra), so the host had to match its version exactly. The Dockerfile
now uninstalls every snakemake* package after the sync and the workflow extra
drops snakemake; the job then reads the pickle with the package that wrote it.

The launch check keeps only the Python minor (check_host_python): the host's
snakemake and its compiled dependencies load into the image's interpreter.
The README install line, CI's DAG job and the test harness no longer pin a
Snakemake version. The container smoke job now reports which snakemake
unpickled its object and asserts it is the host's version.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* profiles: every key needed and non-default, each with its reason

Drops the candide LD_LIBRARY_PATH to /softs/openmpi: /softs is not bound, so
the path does not exist inside the container, and no containerized rule uses
MPI. Drops the default profile's --bind /home (apptainer mounts $HOME already).
States the real reasons for the rest: rerun-triggers leaves out software-env
because it hashes the per-person image path; shared-fs-usage leaves out
source-cache because jobs would be handed the launch's node-local cache path;
slurm_account because the executor's guess fails on candide; retries and
kept logs for fan-outs and their printed output.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* workflow: jobs read the repo's matplotlibrc, never the launching user's

Apptainer binds $HOME, so a job read the launcher's own matplotlibrc, and a
LaTeX preamble there the image cannot typeset stopped every figure rule.
common.py points each job's MATPLOTLIBRC (through APPTAINERENV_, past
--cleanenv) at an empty workflow/matplotlibrc. The container smoke job reports
the matplotlibrc it would read and the test asserts it is the repo's.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* CONTRACTS: the host-side workflow imports only stdlib and snakemake

workflow.common runs in the host Snakemake with no sp_validation installed,
and loads container.py by path; container.py imports only the standard
library, since it also runs as the spv-container CLI before any image exists.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* profiles/default: bind /home; Snakemake's --home <workdir> keeps apptainer from mounting it

A job's home is its working directory, so a checkout or output tree under
the launching user's home was invisible to the job: the toy run's jobs
imported the image's sp_validation instead of the checkout's src/.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* workflow: jobs read the user's matplotlibrc; no snakemake-uninstall step

The image's TeX now carries sfmath, so a usetex preamble typesets inside
jobs and the repo matplotlibrc override has nothing left to guard. The
shapepipe:develop base ships no snakemake, so the uninstall step is gone;
pyproject says why the workflow extra must not re-add it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* workflow: resolve the image without a host/image Python check; trim profile and README

The launch resolves its image and runs it; the README states that the host
Snakemake runs on the image's Python (3.12). The CONTRACTS notes go (the
workflow-dag CI job is what enforces host-importability). The candide profile
excludes n17 and n36 only, and the README's output-roots paragraph and the
profile's shared-fs-usage comment say what they need to in fewer lines. The DAG
tests drop the Python-check tests and the profile job-bound test, and
test_one_integration_grid checks only the shared COSEBIs/pure-E/B inputs.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* tests: drop the smoke test's numeric check and perf-pinning asserts

The container smoke job reports and checks placement, imports, Snakemake
version and provenance; the host-side suite no longer needs numpy.
test_xi_grids loses its grid-count test, and the pure-E/B cosmo_val test keeps
its value checks without counting kernel calls.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 .github/workflows/deploy-image.yml            |  20 +
 Dockerfile                                    |   5 +-
 cosmo_val/cat_config.yaml                     |  88 -----
 papers/bmodes/Snakefile                       |   1 -
 papers/cosmo_val/Snakefile                    |   1 -
 papers/cosmo_val/config/config.yaml           |   8 +-
 pyproject.toml                                |   4 +-
 src/sp_validation/b_modes.py                  |  57 ++-
 src/sp_validation/container.py                |   5 +-
 src/sp_validation/cosmo_val/core.py           |  13 +-
 src/sp_validation/cosmo_val/real_space.py     |  12 +-
 src/sp_validation/tests/conftest.py           |  19 +
 .../tests/data/container_smoke/Snakefile      |  21 -
 .../tests/data/pure_eb_xi_fixture.npz         | Bin 0 -> 16564 bytes
 src/sp_validation/tests/test_b_modes.py       |  77 +++-
 .../tests/test_bmodes_workflow_dry_run.py     |  88 -----
 .../tests/test_config_paths_exist.py          |  19 +
 .../tests/test_container_smoke.py             | 126 ------
 src/sp_validation/tests/test_cosmo_val.py     | 331 +++++++---------
 .../tests/test_cv_init_params.py              |   1 -
 src/sp_validation/tests/test_xi_grids.py      |  26 +-
 uv.lock                                       | 369 ------------------
 workflow/README.md                            |  55 ++-
 workflow/common.py                            |  91 ++---
 workflow/profiles/candide/config.yaml         |  84 ++--
 workflow/profiles/default/config.yaml         |  46 +--
 workflow/rules/cosmo_val.smk                  | 113 +++---
 workflow/rules/covariance.smk                 |   4 +-
 workflow/rules/twopoint.smk                   |  11 +-
 workflow/scripts/cv_cosebis.py                |  16 +-
 workflow/scripts/cv_runner.py                 |   8 +-
 workflow/scripts/cv_summarize_bmodes.py       |   2 +-
 workflow/scripts/run_2pcf.py                  |  29 +-
 workflow/tests/conftest.py                    | 222 +++++++++++
 workflow/tests/data/container_smoke/Snakefile |  27 ++
 .../data/container_smoke/container_smoke.py   |  41 +-
 workflow/tests/pytest.ini                     |   3 +
 workflow/tests/test_container_smoke.py        |  90 +++++
 workflow/tests/test_dag.py                    | 173 ++++++++
 39 files changed, 1066 insertions(+), 1240 deletions(-)
 create mode 100644 src/sp_validation/tests/conftest.py
 delete mode 100644 src/sp_validation/tests/data/container_smoke/Snakefile
 create mode 100644 src/sp_validation/tests/data/pure_eb_xi_fixture.npz
 delete mode 100644 src/sp_validation/tests/test_bmodes_workflow_dry_run.py
 delete mode 100644 src/sp_validation/tests/test_container_smoke.py
 create mode 100644 workflow/tests/conftest.py
 create mode 100644 workflow/tests/data/container_smoke/Snakefile
 rename {src/sp_validation => workflow}/tests/data/container_smoke/container_smoke.py (61%)
 create mode 100644 workflow/tests/pytest.ini
 create mode 100644 workflow/tests/test_container_smoke.py
 create mode 100644 workflow/tests/test_dag.py

diff --git a/.github/workflows/deploy-image.yml b/.github/workflows/deploy-image.yml
index f65ab5f7..19995403 100644
--- a/.github/workflows/deploy-image.yml
+++ b/.github/workflows/deploy-image.yml
@@ -9,6 +9,26 @@ env:
   BRANCH: ${{ github.ref }}
 
 jobs:
+  # The workflow's DAG properties, checked through the host launcher: Snakemake
+  # on the image's Python with sp_validation absent, as on a user's machine.
+  workflow-dag:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+        with:
+          persist-credentials: false
+
+      - uses: astral-sh/setup-uv@bec219d24cd3e171d82865faccec33120bb574f4 # v10.1.0
+
+      - name: DAG tests
+        run: >-
+          uv run --isolated --no-project --python 3.12
+          --with snakemake --with snakemake-executor-plugin-slurm
+          --with pytest
+          pytest workflow/tests -m "not candide"
+
   build-and-push-image:
     runs-on: 
       - ubuntu-latest
diff --git a/Dockerfile b/Dockerfile
index 1a4183e0..f9272fd3 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -72,9 +72,8 @@ WORKDIR /sp_validation
 # our lock — instead of pruning them. Copy the lock + manifest first so this
 # layer caches independently of source edits. Extras: test (CI unit suite),
 # glass (GLASS map-level mock — pulls glass.ext.camb + the cosmology wrapper),
-# workflow (Snakemake + mpi4py runners). cs_util 0.2.2 (with cs_util.size) and a
-# numba-safe numpy 2.4.6 come straight from the lock, so the old ad-hoc snakemake
-# and cs_util `--upgrade` layers are gone.
+# workflow (mpi4py, CosmoSIS and the other rule-script runners). cs_util and a
+# numba-safe numpy come straight from the lock.
 COPY pyproject.toml uv.lock /sp_validation/
 
 # cosmosis builds MPI-enabled polychord/multinest only when MPIFC is set: its
diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index a4d34bee..218ba413 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -914,7 +914,6 @@ SP_v1.4.11.3:
     e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
-    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     path: v1.4.11.3/unions_shapepipe_cut_struc_2024_v1.4.11.3.fits
     redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
     w_col: w_des
@@ -960,7 +959,6 @@ SP_v1.4.12.3:
     e2_star_col: E2_STAR_HSM
   shear:
     R: 1.0
-    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     path: v1.4.12.3/unions_shapepipe_cut_struc_2024_v1.4.12.3.fits
     redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
     w_col: w_des
@@ -1006,7 +1004,6 @@ SP_v1.4.13.3:
     e2_star_col: E2_STAR_HSM
   shear:
     R: 1.0
-    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     path: v1.4.13.3/unions_shapepipe_cut_struc_2024_v1.4.13.3.fits
     redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
     w_col: w_des
@@ -1072,7 +1069,6 @@ SP_v1.4.6.3_uncal:
   subdir: /n17data/UNIONS/WL/v1.4.x
   shear:
     path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
-    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     ra_col: RA
     dec_col: Dec
     e1_col: e1_uncal
@@ -1088,7 +1084,6 @@ SP_v1.4.6.3_uncal_w_iv:
   subdir: /n17data/UNIONS/WL/v1.4.x
   shear:
     path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
-    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     ra_col: RA
     dec_col: Dec
     e1_col: e1_uncal
@@ -1104,7 +1099,6 @@ SP_v1.4.6.3_uncal_w_1:
   subdir: /n17data/UNIONS/WL/v1.4.x
   shear:
     path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
-    covmat_file: ./covs/shapepipe_A/cov_shapepipe_A.txt
     ra_col: RA
     dec_col: Dec
     e1_col: e1_uncal
@@ -1160,88 +1154,6 @@ SP_v1.4.8_uncal:
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     hdu: 1
   patch_number: 100
-SP_v1.4_LFmask_8k:
-  subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask
-  pipeline: SP
-  colour: black
-  getdist_colour: 0.0, 0.5, 1.0
-  ls: solid
-  marker: d
-  cov_th:
-    A: 2137.7977618140676
-    n_e: 7.976462506484096
-    n_psf: 0.5434016250405327
-    sigma_e: 0.31509572849714534
-  psf:
-    PSF_flag: HSM_FLAG_PSF
-    PSF_size: HSM_T_PSF
-    star_flag: HSM_FLAG_STAR
-    star_size: HSM_T_STAR
-    hdu: 1
-    path: unions_shapepipe_psf_conv_2022_v1.4.0_mtheli8k.fits
-    ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: HSM_G1_PSF
-    e1_star_col: HSM_G1_STAR
-    e2_PSF_col: HSM_G2_PSF
-    e2_star_col: HSM_G2_STAR
-    label: SP_LFmask_psf
-  shear:
-    R: 1.0
-    path: unions_shapepipe_extended_2022_v1.4.0_mtheli8k.fits
-    w_col: w
-    e1_col: e1
-    e1_PSF_col: e1_PSF
-    e2_col: e2
-    e2_PSF_col: e2_PSF
-  star:
-    ra_col: RA
-    dec_col: Dec
-    e1_col: e1
-    e2_col: e2
-    path: unions_shapepipe_star_2022_v1.4.0_mtheli8k.fits
-  patch_number: 150
-SP_v1.4_LFmask_8k_noalpha:
-  subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/SP_LFmask
-  pipeline: SP
-  colour: brown
-  getdist_colour: 0.0, 0.5, 1.0
-  ls: solid
-  marker: d
-  cov_th:
-    A: 2137.7977618140676
-    n_e: 7.976462506484096
-    n_psf: 0.5434016250405327
-    sigma_e: 0.31509572849714534
-  psf:
-    PSF_flag: HSM_FLAG_PSF
-    PSF_size: HSM_T_PSF
-    star_flag: HSM_FLAG_STAR
-    star_size: HSM_T_STAR
-    hdu: 1
-    path: unions_shapepipe_psf_conv_2022_v1.4.0_mtheli8k.fits
-    ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: HSM_G1_PSF
-    e1_star_col: HSM_G1_STAR
-    e2_PSF_col: HSM_G2_PSF
-    e2_star_col: HSM_G2_STAR
-    label: SP_LFmask_psf
-  shear:
-    R: 1.0
-    path: unions_shapepipe_extended_rmalpha_2022_v1.4.0_mtheli8k.fits
-    w_col: w
-    e1_col: e1_cor
-    e1_PSF_col: e1_PSF
-    e2_col: e2_cor
-    e2_PSF_col: e2_PSF
-  star:
-    ra_col: RA
-    dec_col: Dec
-    e1_col: e1
-    e2_col: e2
-    path: unions_shapepipe_star_2022_v1.4.0_mtheli8k.fits
-  patch_number: 150
 nz:
   subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/nz
   dndz:
diff --git a/papers/bmodes/Snakefile b/papers/bmodes/Snakefile
index 6af554bb..144097d2 100644
--- a/papers/bmodes/Snakefile
+++ b/papers/bmodes/Snakefile
@@ -2,7 +2,6 @@
 # composed over the generic compute workflow at ../../workflow/.
 
 configfile: "config/config.yaml"
-configfile: "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
 
 envvars:
     "PYTHONUNBUFFERED",
diff --git a/papers/cosmo_val/Snakefile b/papers/cosmo_val/Snakefile
index 67bef0a7..083dd2a6 100644
--- a/papers/cosmo_val/Snakefile
+++ b/papers/cosmo_val/Snakefile
@@ -7,7 +7,6 @@
 # isolation or as the whole suite via the default `cosmo_val_all` target.
 
 configfile: "config/config.yaml"
-configfile: "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
 
 envvars:
     "PYTHONUNBUFFERED",
diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index ae99aba4..5d8416c6 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -74,18 +74,14 @@ cosmo_val:
         kmax: 20
         kmax_extrapolate: 500
 
-  # The fine ξ± grid the B-mode integrals run over.
+  # The fine ξ± grid both B-mode statistics (COSEBIs, pure-E/B) integrate over.
   integration:
     min_sep: 0.08
     max_sep: 300
     nbins: 1000
 
-  # COSEBIs decomposition (config space, fine integration binning)
+  # COSEBIs on the integration grid: mode count and the scale cuts scanned.
   cosebis:
-    min_sep_int: 0.9
-    max_sep_int: 300
-    nbins_int: 1000
-    npatch: 100
     nmodes: 20
     scale_cuts: [
       [1, 250], [2, 250], [5, 250], [10, 250],
diff --git a/pyproject.toml b/pyproject.toml
index 632793f4..a8335f10 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -157,9 +157,9 @@ glass = [
 ]
 # Cosmo-inference workflow runners (workflow/scripts/*). Kept optional: the core
 # library resolves without them, but the container installs this extra so the
-# Snakemake workflow and cross-validation runners are available.
+# workflow's rule scripts and cross-validation runners are available. No
+# snakemake: a `script:` job must unpickle with the host's (workflow/README.md).
 workflow = [
-    "snakemake",
     # Optional MPI runners (the container ships OpenMPI at /opt/ompi, so mpi4py
     # builds against it).
     "mpi4py",
diff --git a/src/sp_validation/b_modes.py b/src/sp_validation/b_modes.py
index 7a3cd484..fdc2ddf8 100644
--- a/src/sp_validation/b_modes.py
+++ b/src/sp_validation/b_modes.py
@@ -19,6 +19,11 @@
 _EB_KEYS = ("xip_E", "xim_E", "xip_B", "xim_B", "xip_amb", "xim_amb")
 
 
+def _eb_vector(modes):
+    """Pure-E/B modes concatenated in ``_EB_KEYS`` order, the covariance layout."""
+    return np.concatenate([modes[k] for k in _EB_KEYS])
+
+
 def find_conservative_scale_cut_key(results, requested_scale_cut):
     """
     Find scale cut key that conservatively fits within requested range.
@@ -156,23 +161,20 @@ def calculate_pure_eb_correlation(
     dict
         Dictionary containing pure E/B mode results and covariance
     """
-    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
-
     # Calculate min_sep and max_sep from gg object
     min_sep, max_sep = gg.left_edges[0], gg.right_edges[-1]
 
     def pure_EB(corrs):
         gg, gg_int = corrs
-        return get_pure_EB_modes(
-            theta=gg.meanr,
-            xip=gg.xip,
-            xim=gg.xim,
+        return pure_eb_from_xi(
+            theta_report=gg.meanr,
+            xip_report=gg.xip,
+            xim_report=gg.xim,
             theta_int=gg_int.meanr,
             xip_int=gg_int.xip,
             xim_int=gg_int.xim,
             tmin=min_sep,
             tmax=max_sep,
-            parallel=True,
         )
 
     # The results dict is self-describing: the grids it was measured on travel
@@ -190,7 +192,7 @@ def pure_EB(corrs):
         "xim_int": gg_int.xim,
         "n_eff": n_samples if cov_path_int is not None else gg.npatch1,
     }
-    results.update(dict(zip(_EB_KEYS, pure_EB([gg, gg_int]))))
+    results.update(pure_EB([gg, gg_int]))
 
     if cov_path_int is not None:
         if z_dist is None or cosmo_cov is None:
@@ -214,7 +216,7 @@ def pure_EB(corrs):
         results["cov"] = treecorr.estimate_multi_cov(
             [gg, gg_int],
             var_method,
-            func=lambda x: np.hstack(pure_EB(x)),
+            func=lambda x: _eb_vector(pure_EB(x)),
             cross_patch_weight="match" if var_method == "jackknife" else None,
         )
 
@@ -236,8 +238,7 @@ def pure_eb_from_xi(
 ):
     """Pure-E/B correlation functions from ξ± arrays through the pipeline kernel.
 
-    The values-only seam of :func:`calculate_pure_eb_correlation`, for callers
-    holding ξ± arrays rather than TreeCorr correlations.
+    The one place this module calls cosmo_numba's Schneider (2022) transform.
 
     ``tmin``/``tmax`` are the reporting correlation's TreeCorr *bin edges*
     (``gg.left_edges[0]`` / ``gg.right_edges[-1]``). The reporting grid must be a
@@ -281,15 +282,13 @@ def pure_eb_covariance_mc(
 
     ξ± draws come from ``cov_int``, a ξ± covariance on the integration grid,
     around the theory mean for ``(z, nz)`` under ``cosmo``; each draw is binned
-    down to the reporting grid and pushed through ``get_pure_EB_modes``. The
+    down to the reporting grid and pushed through :func:`pure_eb_from_xi`. The
     covariance of the transformed draws is the result, so it depends on the
     covariance model and the grids, never on the measured data vector.
 
     Returns ``(cov, eb_samples)`` — the covariance in ``_EB_KEYS`` order and
     the draws behind it.
     """
-    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
-
     theta, theta_int = np.asarray(theta), np.asarray(theta_int)
     nbins_int = len(theta_int)
 
@@ -318,23 +317,21 @@ def pure_eb_covariance_mc(
     samples_rep_xip = (binning_matrix @ samples_int_xip.T).T
     samples_rep_xim = (binning_matrix @ samples_int_xim.T).T
 
+    def eb_draw(i):
+        modes = pure_eb_from_xi(
+            theta_report=theta,
+            xip_report=samples_rep_xip[i],
+            xim_report=samples_rep_xim[i],
+            theta_int=theta_int,
+            xip_int=samples_int_xip[i],
+            xim_int=samples_int_xim[i],
+            tmin=left_edges[0],
+            tmax=right_edges[-1],
+        )
+        return _eb_vector(modes)
+
     eb_samples = np.array(
-        [
-            np.concatenate(
-                get_pure_EB_modes(
-                    theta=theta,
-                    theta_int=theta_int,
-                    xip=samples_rep_xip[i],
-                    xim=samples_rep_xim[i],
-                    xip_int=samples_int_xip[i],
-                    xim_int=samples_int_xim[i],
-                    tmin=left_edges[0],
-                    tmax=right_edges[-1],
-                    parallel=True,
-                )
-            )
-            for i in tqdm.tqdm(range(n_samples), desc="MC samples")
-        ]
+        [eb_draw(i) for i in tqdm.tqdm(range(n_samples), desc="MC samples")]
     )
     return np.cov(eb_samples.T), eb_samples
 
diff --git a/src/sp_validation/container.py b/src/sp_validation/container.py
index 7aa5f2ed..0bc58e11 100755
--- a/src/sp_validation/container.py
+++ b/src/sp_validation/container.py
@@ -46,7 +46,10 @@
 # per branch, sanitized, so ``:develop`` tracks the integration branch.
 CONTAINER_URI = "docker://ghcr.io/cosmostat/sp_validation:develop"
 
-CACHE_DIR = Path(os.environ.get("XDG_CACHE_HOME", "~/.cache")) / "sp_validation"
+# Under the home directory, which every node mounts: a job runs the image from
+# the path the launching host resolved, so the image cannot sit on node-local
+# storage -- where a cluster's XDG_CACHE_HOME often points.
+CACHE_DIR = Path("~/.cache/sp_validation")
 
 # Where this user's image lives. Per-user by construction: one file, one owner,
 # no coordination. Override with ``SPV_CONTAINER`` (an absolute path).
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 7da61279..1ea75134 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -83,8 +83,7 @@ class CosmologyValidation(
         Path to catalog configuration YAML defining survey metadata, file paths,
         and analysis settings for each version.
     output_dir : str, optional
-        Override for output directory. If None, falls back to the COSMO_VAL
-        environment variable, then to the catalog config's paths.output.
+        Output directory. If None, the catalog config's paths.output.
     rho_tau_method : {'lsq', 'mcmc'}, default 'lsq'
         Fitting method for PSF leakage systematics parameters.
     cov_estimate_method : {'th', 'jk'}, default 'th'
@@ -313,6 +312,11 @@ def __init__(
             "nbins": nbins,
             "var_method": var_method,
             "cross_patch_weight": "match" if var_method == "jackknife" else "simple",
+            # min_top sets the depth of TreeCorr's root cells, hence which pairs
+            # bin_slop approximates. Left unset, TreeCorr derives it from its
+            # thread count (max(3, ceil(log2 n))) and ξ± depends on the machine.
+            # 6 is what TreeCorr derives on candide's 48- and 64-CPU nodes.
+            "min_top": 6,
         }
 
         self.catalog_config_path = Path(catalog_config)
@@ -391,11 +395,6 @@ def ensure_version_exists(ver):
 
         self.versions = final_versions
 
-        # Override output directory: explicit arg > COSMO_VAL env var >
-        # catalog config's paths.output. The env hook lets a reproduction
-        # run redirect every product to a fresh tree without touching the
-        # per-script call sites (mirrors workflow/common.py's COSMO_VAL).
-        output_dir = output_dir or os.environ.get("COSMO_VAL")
         if output_dir is not None:
             cc["paths"]["output"] = output_dir
 
diff --git a/src/sp_validation/cosmo_val/real_space.py b/src/sp_validation/cosmo_val/real_space.py
index 1521ecc7..89dad2a9 100644
--- a/src/sp_validation/cosmo_val/real_space.py
+++ b/src/sp_validation/cosmo_val/real_space.py
@@ -94,13 +94,11 @@ def calculate_2pcf(self, ver, npatch=None, **treecorr_config):
 
             # Process the catalog & write the correlation functions
             gg.process(cat_gal)
-            # Never write_patch_results: a per-patch ξ± realisation is an
-            # unblinded data vector, and nothing downstream reads one — the
-            # covariance a consumer needs is the matrix, which the SACC part
-            # carries. The .txt keeps the matrix only where there are patches to
-            # estimate it from; at npatch=1 var_method is "shot" and it would add
-            # nothing over the varxip/varxim columns.
-            gg.write(out_fname, write_patch_results=False, write_cov=int(npatch) > 1)
+            # Columns only. The covariance matrix lives in the SACC part; a
+            # per-patch ξ± realisation is an unblinded data vector nothing reads;
+            # and TreeCorr cannot read back a text file carrying the matrix
+            # without the per-patch results.
+            gg.write(out_fname, write_patch_results=False, write_cov=False)
 
         # Add correlation object to class
         if not hasattr(self, "cat_ggs"):
diff --git a/src/sp_validation/tests/conftest.py b/src/sp_validation/tests/conftest.py
new file mode 100644
index 00000000..40424120
--- /dev/null
+++ b/src/sp_validation/tests/conftest.py
@@ -0,0 +1,19 @@
+"""Fixtures shared across test modules."""
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+PURE_EB_XI = Path(__file__).parent / "data" / "pure_eb_xi_fixture.npz"
+
+
+@pytest.fixture
+def pure_eb_xi():
+    """Committed ξ± of the synthetic coherent-shear catalogue.
+
+    Exact-binning reporting [15, 70]′ in 6 bins and integration [1, 300]′ in
+    600 bins, keyed by ``b_modes.pure_eb_from_xi``'s parameters.
+    """
+    with np.load(PURE_EB_XI) as npz:
+        return {k: (v.item() if v.ndim == 0 else v) for k, v in npz.items()}
diff --git a/src/sp_validation/tests/data/container_smoke/Snakefile b/src/sp_validation/tests/data/container_smoke/Snakefile
deleted file mode 100644
index 75527690..00000000
--- a/src/sp_validation/tests/data/container_smoke/Snakefile
+++ /dev/null
@@ -1,21 +0,0 @@
-# Standalone workflow exercised by src/sp_validation/tests/test_container_smoke.py.
-#
-# The module-level `container:` below mirrors what every real workflow does
-# (workflow/Snakefile) -- Snakemake has no way to take a default image from a
-# profile. Everything else under test arrives from the driving profile
-# (workflow/profiles/candide). See container_smoke.py for what the job checks.
-
-
-# Literal rather than the package's CONTAINER_URI: this Snakefile is test data,
-# deliberately standalone. test_container_smoke.py asserts the two agree, so
-# drift fails the test rather than the run.
-container: config.get("container", "docker://ghcr.io/cosmostat/sp_validation:develop")
-
-
-rule container_smoke:
-    output:
-        "results/container_smoke.yaml",
-    resources:
-        runtime=5,
-    script:
-        "container_smoke.py"
diff --git a/src/sp_validation/tests/data/pure_eb_xi_fixture.npz b/src/sp_validation/tests/data/pure_eb_xi_fixture.npz
new file mode 100644
index 0000000000000000000000000000000000000000..fa67b006d5fae626006bc7670bcafba9e7558242
GIT binary patch
literal 16564
zcmd6OXH*r-x-B{9oLS_YbLy|iIU|T58384W7zm016%Y)dhyep)LIgoUlvPSpK@b!a
zPy|sxlH?!)kA2VCZ@hiReZTIX+iUdtM)&Hus%F>M)m5{5y4X|Gs8IZCk)ZhYu)}`i
z-wOu?4@E+FXhNWWd}wS;e1d9p?0!lL4vMmWAN_Yc=d{Jq#ha2Mks?VhBy@LhyqqCP
z&UlBuoH9vn$0ja5Fxo#RJ|y(7{Z@gIyF)kk?+y=)4c*+9*Hu<fCZ&-62Mg0@Cd;5j
zYYoJsG|H;EMHd~j)iDbDVvG)|H}q_~V}|xEKXyqIv_V6uqmH67&dB8-jBS5j*8ZC@
z+a}|E5wZWv`F7Wj@~P@e!_nKPV>3mh!LW+WVRV56TSe9wPH&NbHo5h-Vk`nzQ0r5j
zT0!_HU#$wtrhoE{`d`jhLa)o`?d@fPGg2^p^wMMUg+0FrLwG@6`W#Ck|K&9~#o(7+
z*xg|=mwkD9@YG%6pM0~MM``}%%ldyaIwJc2rZeLHS3dt8|7Yo}uKqtvXeM<B)$Qqg
z)3BRfvyB~1!<SUCi|sq6A=M`Ajo7JakbO_P%2PQFQ@_XoT%V?)(^#pogl-1DnwW5|
zs?R`PvBn3&V+L|=X4Yu!n*kcWOpWF{GjMuMZbpA#25k3l(5F(&0&VKD?qm5`m{Ak)
zVhxyulT%X4Zke;tp0GCbyJ{AG+}BcZ@1KQ1xxy>V{BvNpU4g#WbPkXUMStVYIe4r6
zti<5P9LVXHUf1lJgPR>rW!I?Yp;7l?>3f}dxR}U0{xWDD>`h!o+Op^2OX*%&y_R`!
zS2j?sn4Jff*M<GG$_oH5h(nIs7vR^r3Z^}~0Ll1k?w>ac;9((k^$_(UJW2g-?x48{
zx0)FxJ9jO@z%An)@x_ZEwv;qK{b4f}hn3FoEy3fUT}e|`OOVJ`wjp+W2^5U7Z?@Jh
z!S83pZl0AT7(KeZwnk$a&iD)$<%ciB6x-;C+}&mHABm>w9a)AD?h686#aBQh(rLmr
zcm+6bZN0Yl(h5)%4jvBcS%IPXqe<C9zhS+plUHQhZxH-i5$=}%8-C2M`p}I028Q<;
z`bBc9aN8`Z%p+hG2AgEq_ZP3isp#3WAIDZfZ2Z!~cDX-rp~18~JoFDlOIsvXRQ`dL
zmi`^*Dc8W2^JFfa<r=&yrEjiITZ4d>5ryp?YhblAx8}IuI#_hoEhdGmLn7q^_2CEW
z;FA5+^VFYpIJAv(i-gSvppc@pmYfaf4j>sk|GWV$E+=()1u2lvkjXTgF$J<cqj39J
z7zL76Q@F^TO@Z9*Jv`V}MS%pv3(RN-DA0Zv6>my9N`xa4!aTGn(b3K-mp|T==xWr{
zr@|*Fk>9f=e%eY(R21Xt_~bJs;*BUCOk|`&EWiAz<#njgZN8xLRX-}k_vno<S0)u=
z9*|@Vd`^X`3pULBe^Q~-mtL~C@>8R0V`JyTZKzTAh5fAFd#RCL#8<iW>(uBMUwNtP
zM{3mTt|wVUPlMiH`o2)0OM^lV+KpV?NrRFbb5sspq(R%ewA=T*p+S;=R`wfi&>+^^
zR;FjQXpvs3V|i#8Ei$dWb-*T{7X5to*irL6Evi10U&%*9hs5hkbvupekeq$B1&*ad
zez$l+s_)Vvwnt(8-CyYt-#+5E7au(mkPff1aHdBd+Qwh|kI^Gfm%4@Q7xbvD^^$qw
zZ+cYoL&R5EmjNaHZY~~<XF&Zqx^qqs8Bnowh+D)@2IRc;<iffvBjSlKSos~sh~g3>
zm6r+`QE|HZ6#Eb(nx#dj<RzKV!*^cCPX#a`cAsN2dy1G4-$27NmO&;|aGmOtj07{%
zcq?AH9?FcG-)TtvC}u`+C2#iJnq)>?#ii;ink-1r<m74gy)5VwtFyZMa~3rFp*Cie
zk`=L4SzPe5V@2mUDC*vxWknPgEEbU;SW$@BBwLR-8xsCvb@f~%8?w2rjeH)nA+|+F
zt`-V*l<UhdDDT9MX!3vFE5FE&t{e~!CylbBk$l<8UxFOyNIkFO0^&e7j^Ejz7si45
zxC@(R&Tyc9MXm28PdN}@XJ(P(5C^)j{IIBnl@nQ=aX*-#!-<}1$iL_C<3ub&qa6NO
zoGAG4!k3jwPV{?+dPDzLP9(cWHvc#a7ixLB-{p)x7ka@l`Jg743-v@j^=CiJh32Jd
zRkJY{B8ga}j7)H$ux`Wcp9Hy4{7=iS`*z$Yi{Z0OLJ~LnWRla#bcY+6dQI5u>ET9}
zJue*{n0ZiML!OzeArCrrzuRMa7Y{0||0vddl?O>sXH96o<3XzzwhabQ^P<TIt-A?b
zUL@FK@6)=A7u`P*u=PM8FY*+0`LO!~FWPhR?a3fUJ_OtMJFo#C%C^7cFu8{h32n<8
z*Dc{gA!mMGd-aVE*?B&?ct(UDEpn+*(0THsJLq8ct}K4!(W<=mt(hOmW9iw0lmh6w
z6Md$EkpSAZ-GwSQK>#IvQJOHQ5J0&c4>QxI1<+JdTUW5MAWGC0p|OY%M6c-<<j`$F
z)ZQ%mym43%9Uf|EjFk~W6ONr~UqXe@`q29=9(RNg-52%u79&C^I%K%MUtSnJ5&tZW
zBZZOI_ViiWGGRn{P(I_ttT2*Qev}ueCxXCpo@G8s1eI1k@>Z`CL6QV8h%<?z&&q~n
z+)kosVV2|V@AIOFDfUzrXTK<-Y&py@BqxTXs9S?%;>D2sx^L%`8ZngX@A0UUP8`iD
zbRJE07e^wDmwcZTilexLICjgVIQsa#WWh|7gzje0sK2u$p_={Q)Ne<Tkn~Zq@Y*>N
z@-p5*#ZM-oTiu->ltxHM-+D2754!|P>@DV2F_1t3#@@yQArh#UvF=bvwgd{!cr?8A
zsRUZ%ZuUjrB~W3MmHJ0^N#v+K_uJT568&m8U2!;E66GGbcWp3F5(R{u*j8RIiQ4UR
ztAl1F5sjDe)E+S@Btp-{^U_fYeW=Q>IFTxaz8UbzM3+b*g+lh0>d#WhFSG9416FDD
zw!1-V2S_86Ju!WJanfigr#VadhBSI2xN%(SgESf!Y!KjMkU?G?I`le*GKhFl)ano;
zgYviiaG$;@gC5F995L^aLF8qaw_=k;ojfB$aptlp${<Gi@BvvQs+61OP%ev}#@{lT
zAC*Pp%<~e1BstXC5#B%MC5O&wN1U=aD~HlJMa~kfa_F&4dG{2ZJStc5P<f5yQ7c#U
z@WEtxbR}7tow`~cIoL8}o|u<MOG_7=%rz9yqEtm>R*V92vbEk9S)zb!b}3C?9alg{
zuTKYjS5id%ug{qG?NUUAKRl@=N)*x8AT|^*sfbX<d7UgZCA8{Xyu&3<2^DVtR9I1|
zgi>gIEhm<hP>oN2>A0~nQb?Djk2<W3q(ws;cQq@c_m&!5d2A}Ej<QVWrH2aAUKRhs
ze?<jJ-7cb3|Dl3t_2iwZR8-OGE24QIQ59uMbo&g~sUmvqwiF9iH6%vK^Etv>4S~4#
z<Xo{D^5i}m_H|ATC78Q^xIt1!Q*~l1AvWshc*@?F3o+`5_a5t{RK7au+f`(-@j@N>
zU7XoNF{X|z{7-jgb8Dc2cjSkrCK~7ubByN6Fb$;c#VWLLMgtjMiR(hO8fb35#M^6B
z16|V?H|gWnMD)}K@fVRM(mtcDJ`|~mR-f7q@4l#s+KuRcb2n=uEm!lr_=U|V{q%B&
zloq11r@egAO$$v(bwr*`(?V;OzVqhgT4=BBLRa{p7E&FNZ;<8EM$*Oyc59kzqY{OX
z=r;-4=)l_gAopfp++4bK4@-|WvN<MCBgdkH%7RTlHh>OlemJqno}hyQay>F#?`-aW
zZECXZiw>Fz@#fjXrHd4ltwot^bP@kowvPE!UGy_vmB;0YF3M8e8pb@Si-IB@1!QFO
z(534^`=+<+p%~gTKNT+MA&-$@?eq>k^dr6Xlqs`5@?#tgG_lf0*2nL>xtykta-P&m
z3e@VOUM}wn#ozkq#C9!}MqLAR%E|MzNP+>Pk!9~Gcw~UmP0-Mp83W|9Euvaj!w@~+
zLlhas8KSp>R);>78zMIS14(pqhG>!IZNznLBgAi~B))I25xU`qg6L|D(1^@{?b4bN
z!fm=$!sf<E#lp2v;Dj+6I~(iL_SP7EOd|E$@tB|^G>J`lJ|@U+Cr{n28zw07gl$jm
zxCsi)Pn2lTCXgNxE#Z+$pf@fFA@~)6j6YvZ*~<e+lU=GfKL8Nb%Exup2Y_}9u7BkI
z4XFHGqpgiJLdlX|Jbm^EHM<Dkc)1%PsY~zp>n|bHW850CwE-b!>-VdflL+l!Nsrg!
zH$@jNCg&s~Q)J~n;PHN^DYAUZX?7yd6fFhnJ#~1o8SUoIH;$X4nQt@))A`NNw0qt*
zb_+8kSP*wAE!GT$DQJgXykdqPlOn}TTFekM%XJ=GF++OK?sg=|nWOXbY*R{}=19nB
zP3Yb+bHwaNpR((*Il5TSyS?g%If{P9IxNj+f!6PG@x@tNpbyFsECKs1km$vyhqm0e
zK+Xk;X<1(^5L4vL{CN&bG%sSWB4S~QzJ`9k$G^`K@z7++<(F8ZjZ-I3$)F`VzSqo{
zhTjUMvs>%aIa;9$HBBrhj##0CU1NofHC8B2<Nlo6tQ9g?;B}-`v_=P+PpGK{TO+wr
z?-lLK)=1jEvsAp>8qq}W-;n39L5<Y~gHrZ3D8o_wQcSuHl3h;_JzZymzLNP?FR$C6
z4O3no31eIIQd62$=zuLcsLK-{U2Tg@u9fYWShPi!FPEPu>e`{GqCl!EiFW8GeHRr+
zl^qIIIm8>WY=`hgZ{Z?Cd(@PAEx{<o9+ltodmr_}9_>3>Bo;;Efbw6jh_u=`pt7=<
zexYm!BzbgE@BMoR^zP2qFH}N~NHF_qVOXFeveU49A$`{oP0}QANX<GT@w8WBGDc3Q
z@5`y%gGZfENn}`dYP%DPy_r;BCFG279K{q(m@_KON_~-2;fy{~?Qkrma6zicW{c0{
zTu`2Hi(#y@3%cP=^;2)J3!3N=3h^v-LBW1S-7K$MQ2%3}56Uwx=;D%D%RwPm^tJN!
z4?_!A^!wCBp3Bj$=$)#|Dea4{NaXb6ZAuNU=vK(F8;^duA{7Diyo17Sh#g#}YOUQ+
z+zXG7@d<9ImtknA;<_7BT;hGq)9!}MOf~q#*W3`r&8CYNl-!XZG1iy7%^hu2X(zup
z>5c+idd3Q$x})XBTEmH-?&$R6?8r<Z4<w2WnV;BuAhvNiPvH~~6j*5TJ?5bYs%Vck
z{xj%-(jq!-WqG%tzH>M1F4}BCmHLj}_ma1u%s<s3;^kYAHEr9P>F^fh3CFI#7xqN-
z5utA5uAZoe>xN3!2~VV2R=8AyJ<+T1J3aPGo~SD7RcO4L7ji4QrbZp^h5qarjSjx<
zg_f?`eI4!fLOPQfDek;mk<hh)5M!6E=#j#A_OF?n@$PQF#^$ZaV2byJ2aPvU*xPL!
zVCs#+51v0{f6yDH`Tsn2w$>X>y|}Zz;Ey+|F5T}yGTw$hJZAyr<ZVcxLDX*P`8G5b
z>hLLkeH;3nULhESe2{1Ju3p9?KBy_+4$IaiA9PeDTrY*m7s;`g@1A$^MbiyCkICoy
zqI6PG(?X9gx^RV-%bv6y9ViZ@d>OVKP295C-SKcc3g=SFHDBJ26r4ncPMi9n;Vuid
zf=oYzj@)#M>-Iy3nOIEDN%$jG6QVXZ${#V3oPyhG{Lz;O9`}W40?@c#a;vja06M2}
zHKD~V04eKM4%Hk8K+=9Haa=b8&<mkk^&eXT5T(G4(((BKR9mPz?<E$9cAK1uRk8_0
zkxlIyVex^;UuRbq!<9fZT=e2C->X0*9CnXCZ!Qp>zSwv(O*{xGoLP09un$57yG&Oz
z_64DYjGJ4@w}OxcTs-dlJ_v=Y*-Qjd2BZ62{Z1`v!RWd1@2>ZL!N^EDev&^s8112>
zr5UXaM&E-!Nm)(>qxp^3?E_*VNTh2&7vU0uI9u*oET@H_WXR59t_(rJRFl_AhC@(?
z0wZO;U?}R)`Bk{nF%$(*_eGyg3q=JxTc|~!gd$(IjAiBVP_({a_-2T-10@AH)irtU
zKxBFkn(R|M&<PPyr6&zL(3M{deyM+Upxr0fl`^!$Q07@5;iRZAWS6w?>2+}!VsK`-
zdUhZT<=h@+`XCgJN|(-+f7lX^1bn{59Xu0`K0d6si+UT5@S$#7Ev5+cHesdB);a<S
zov0&A9g9HWN!3?M>LZZ#;dAQ8sdl2*H$u+nnC(PQ=)x^c4(~+mQ&lF8^*a%tY4z?x
znq8>$i078)R=d#koU-YyCwC$7M3?B^)?H|R`S{Knj!1OTD$HDBOC*Y<4eKl_h(sGf
z1yzRzA`v~O;E6x7QHXMdHOnqK3h^DDv)=wB3T5|I-`rS_Li#Cmg$g#&NPOL6Z^_wc
z<m|(RQ@%u_BVKE))ABKB_(hFVenJd77->^RT^ED)Z>L;gV2DK>cP|^Ssm7vN8Wpv4
z&selkr(1S8H5S=MZkP-e$D$)|`=~nGW6|{P^L#nWv4}?flE+rbI5g2ZaZtq}4xQFD
zq>fCCL&>*C7=y0Ip@c9l-^A89L=z*U@?<Fvd99pqu#t{OGGDE{#+>8P*_f`G_>_2*
z_0-_<i@WjvA+N4Z)!qDCeq;W(y!yXbeEVK*RDG@894>A69`v=}4i1d9FB3m_L#xzV
z(~?FXh?ToL#bcueM`q0*lr3oBg3=4OKOS)hjpeaNV_vFwDbH6YL(c$&U!HsVs>>2n
zXjUaZP&R>wR>cgoBUU(~Z|YNboiDy^KQq`+>jFy06saG6(#2`7z5i?p3W6_bBTFAI
zo8a1AvK60PY;aO%>|;M&FE~>-qg;O%VM7bODC0Y}Ac*%nOiuWKbq()Q@wg>+8x%~h
z@Nfhk#gQ-HBRpU^a6A1iL3i-i=r0@ybB9Jje?NyfL9D7t*68fqf+>xco{K6=;zzDD
zS~oczL9E1!Qpj5tGuLQNnW$T1)2I4P>E=4H^z9qNjW`{APb5u)dY=YV3QaZCIdbDi
zJl~k3oi*|7gn35kxC3~{Py0B``hw}s(gYHZ0WgyEr%NRTaU;jx54h6}E@T#19QmvU
zC7t>|oGC1@%Po%2Pt#1H-|AUPRh2F5()soI9@4_BD-9DY=Mm<l<qA78U;_*pYLw2Z
z2qY@xrl0R}f&KZiYvK6-Kep*Rm9JXjq2;S9?V)b?M1Gc@pAsFGN;KTzaY+LoDajUj
zbjSoB9RG8GHVWax0a7pJe5`Q6l`GaG8C$T-6PIruw&uX}{pzvF0xzhN%jTu})lKFO
z9Bh=`%Z7veKhfCEX@J732XPbU0^x&ScGe#+bNCr8S1IExk2^}(6_jiUVAIUY<>)X2
zHg1+>9a&S*s!cpA{YMvThPbiBxR_(h8ylZ=ln{_O@=^|;7R7<A6Yeie?cw|nh368F
zHNcSa`+<#qGw_WU`QqSW1{$y4oxH2ahx2bGmAQD^;Hp0_+uRy7V1;@8jzp#@2w#!X
zc$cmV4@bFfN8Ypmo`T0dHlfo*G}GQ_ItwS9)Tc2_WoQI)tYp(x9xb?PAh&$fl7MmL
z{5MS(v>?*bfak;+E4Y^(smmF}14;t5`R4n0AmY+}F`FF}IP6M>5JL(hK3#iCeJDo(
z&c*BTxZSY;)fBI`kpsF|FqiH4;a8UUcs1R^c5PcsePZU8Ww<_c(PYjhKQje&4JL<4
zvN344HxGaEwt?y9v)7L1Xv0|*#oA+^=7_6t^qs<61%cPAP-5w*EEX>BIohv0LsTS3
z#UIGE1;uWCo}42F5I!W&9@ndZFR&#)efdZk&lS@x<g0Ljg-59>dEErOC}f<9ESO>C
z)T!@&lX_rLd-0ChJx!PqZsg^#v;k_?<42^A8RJ{^kFTF?Gsh>F*=}TTkzhNK&!2rs
z8DCWV8m%O$0_CKX-Pz|g@yV|SzU=~%xVGcu`${@3&{I)Q_Ea$k1=C)|16)clI+JlX
z##9qiQA}K;%5nuMhC((@cXMz(5vXOBZ-6Ivxt%=jp@ElGg|CQKS%B+vhu7Pd32^IP
z;J&z19yjn@e8SDdi;EqP4#yaha1{^N-X>uid}`&|t5i!XI4(L667Ru>B^=yekv_=6
zlxau&$MZax|LLn+nVZ)`c78$lK#DG?<@};fE>Z^XJujG&U+Y8EllU`x0}a7Yf8txt
z9D<yx-8a1L5q#`fi_9`I1Xhnn>$KAZWSeDOyFgkeXOs{9IFZW=Bh`a%XSCH}@5MWY
z%knzFbT{*<f($Lb!|f)S(yWb}6JmaUJirawZFc!Z-kMN$H*`;urZRjD?Vh@6u|n+d
z`aCoKUJ4T<H6~4ujiB*rpul9C4kTUveY=iQ1AgR5O>HA-!M)NgwGA!$AZ}<kkV(Bl
z=4CWI?c&IY6EgRf7t~0APm6!gyH-nZI)8HOfebtNV?++<sdz=4x+d3F`%Mg2o9jJk
z@)Lxrk`g7YTV`-)^(ptZCS4HyrIzt0kP~_ty(&95^M&5+#f*lh5y+1=C8wO|BMMHg
zsN5*lhhWu(n<Fy1AoRpvQqQ=L7&0fbQnX0}F&eBUEyWK7Pv7YtXI&$5cAR~}o+*jX
z$ZPEIF*SrDU{)2i)`c^H6*(U)IC1B3jfyw-Md8lVH~oaM{APP>7%vR~ylu$QA2YE8
zwht9teaQk4caSHK|F|5if2>|QE6NC$HM$1hXb8h__1@8|Gjh;-(JgMdkqL}@cA0s+
z`A+Z?aT+rBMd0)=zh6@g)DZvFyXSTdH>7ll=>6t=PcEwcR&!;ueLQW>E$^4pf+?nf
z@`fxD+$}4*5ST5Cy-o{xFMte4p9c%J6m>}PtfK^eZMdh$sBzZ^0B%)p%)aM^xe~72
zf};kIYm~iX%3J|HT^Ew^U8RC?dP!D&H64)59DMitu{3lYzJEBnR0j02`1M4ywc*IF
z7j?V`I3e&|qhG}tWjM$*vLDN+!uKa_j`6!FvBcR&_63{$(&O4K+=8ouV4I#d|FoY3
zzkIiwoK)b%gNg@fwD)L3;XJZ>m|_S*>nR_!H>hwU`N}@E&kC@zJpOT>TLT!uBxRaf
z4;0QUotR0H2J`6J$FF{|V4WWsTXg0%A%5zCpGO}XJUlrmINYfYqLC(BPF!P#!X%Hp
zIW=ae{!o_1Evx`{zP=v&C1U`tQt1jjO}yCSgWtNOoeunYp5q&H<{P=g;41Z_DJoo^
z-_0B$uK}IxN1LZa<)G;l``7^q63`A%2JYbDgIAwKZCT+pS$xNb%8ynOa6rm9h$_?&
zHkNn!Ot;biRhq=U<G)ETcT-!s{F*k%J>VvNAEAL)uPoAeuBgK4U8A2K?v;bq(+nIx
zH~DzbykWj(s1DS7RT+-dD#C?_u^z_HRN>K<>6hGJ6v1+<axq<k4lEZ^Q9E0+!M@bz
zmAP9qVLF;xx-p3tW_b&y?>|?DHtP#&Mybu@8Ww8xSIbIJ-h9|RwvhzP)eoFo0_5P~
zQPv(RHwvJQzTL`wOa@}nH&tFHX$aaTt2y^h15VnpoUbpC0WHsf_~K2z&YAZ|jdrR5
zdwD5~1-A@TN%q(6oKu4caRy!693?naF2~k<O9h0)QAF9<O+J<Kd#keP;ryvS+5`?c
zsNY$fDFQ-ptwZWlladTDtq2;}UFHMCckO#wqcjZfziNJYt0Xw9ORztAs{pMsz3(Cf
zHGrD=T<CETQOKiy!NwR&g3!_<mq1Vvo+{V$#VRO){jUYn7c9zP*2XMBt0WCeB!T=N
zH)-Lh&rsn_f*;5<Z!);=$bjj@((*)&G+ce_+~|8p0=9$=OQs5NLHnJqyEZZc@cwt_
zAum&PkaRmi7*Z<2aBBCH(K1EwtBbG<SmlD-p1W!pE961&nckbe?}`v~U^te}MG|`T
zp41%bQ3SfKv>q0A7FZLLJ{RCB0bGv+Nxr|OfLi}mw!v#@V3QkEjNfb*+tBx)Z(NcC
zzPRR<nMQdqz5FpMH$wv8C;eHZCIWH3w2nXXWZ-$3UVTT19Hhx!V_C432JYN+6%ST=
zaF!0ywAGOVZYJSmfpB3c7<Mj#Y$@p62&GBml!Nqrzvj2DE5Q8V9gFds{2)!!y;D}3
z1dp~#OtdU9K={M1(;CInV6kfKY%N9tQ^PUA=k}ZZQPEU2Z!9ysQKsr+a+QQTJVzWq
zKaqn9d;8}!4+Ozv|E`=^eRar@>fzb=$^!3#NEJJ$g<-_+{BmQf0^FFjy7^gG8eUXq
zS=VJSz&9zj=X1%@U{Ldn+aQ@A6iys`JAOzQEZ!Cd7SGYcjN(tVOUEVPaZB~|`(v_T
zc_7cof<+0=%6@;SC@2Hzu6w((gf`F5Bgu!nKEklZd^}I1L<-_Fxu2CXOF_c%(_2d<
zMPS5QJmZ))55%UIajJghhT9`fomV!`Kgrm~MJ=5K7Gm1UAs<PwXt^Gnvq*yGV}~ed
zd!*sKLhYK9jTA&gDaz6r$ihnt{T_}~E)aEUbC|CqLHWW0?Ey;3&4=bLYr~u40ZG4v
zm>-YC;aGvfmtRei5Ll*M+QK9Ovf20JWaLR8uH1VrH(L_&3<kWo9VB6Z94cr3O&)sI
zMcW(gCBV+IOE*DF9Da(3{;1KBfZJ}D$-bs?V1C+T^tH4Yu$r9?D0v|bsgm|@`6PMZ
zIVo}R$WJOz=<JzTp%#Jbv;v>@<?+DUcv6#en<&T<vx!v#B+%R$oBCyr1a(vV73WWg
zLUq(t1uI$+$T_Vc_A^ximSaz8QHqJd$HdQ){!JuMcRe9a))s;XVPlcCwIsOn;B&l%
zrv$W5#+lzqCBe0{w`vE(H?Py9bK+k2c;Qqq_eTazDR}s2^|*z)3@Cdfl=f6{gDg)P
zmA8=yY<IKCWj5gfg_WD+?=`IO`IGqf4n1+u)wNzY&MyH1XFTU#4@p8~<{l`mV26V@
z)8Ec+w&#N&HPy35!l3QVwm-O96qYq3+&Oo1!R0zcSL7}M8vDi4&wk|sU3Y6K_ZDHe
zIux%beSsU29{%Djyuc4iJI3`Cmjz(kn}PU;`rMFzMx6-Kl>{*jpQ5>EEO5y;Bhun4
zFErj}{zYpo2`{(~?f*!{2j`D`{dmoq1J3VN>Sy003@0gu+fNOVfROqWovc8DFm3NP
zM=M_7`@LXCq7Va(K~L(PYQm5{xc*j_fg4=oOz3?XmWbTCmj?{gXyLvX1$V?|`ydhG
z!t|UFoU7b1%8?=t&2$B?Jy*HGPW_QNLkS!7(w&*vP0I!v=!5jU<QgI25G_S4af34y
z@UU46!1$iW6P~eLuw`G<8I5BM5QX1sP&!h>_|^P<YcAZ-*b%SQvdRIw`~Az3uhYQH
ztBmPbUuIC-dDTrviyP8kn!HQrV}aKW-xc3#@B{ri^*i0qY~Xiq8)w-GMu>cz=Jch4
z8;0mD<n>%w;W~Ns;m27fcoP)JIeuxL*!iBXfABjsocl2Ms*Iin66F+c?j-2pXAIxR
zYjg|{AYAmi){_m^POnIOlB0zi?-vsf*^QCCD@WTeEi4eD?WqFSROukfIeg1h0xg(W
z3;)hr9VCWfzPinj3MMos_f*QQ66CC?NiiM{06`H!6Tx4^Vn?qVZ5Aa+{-jwJ6rCn^
z-0O_#F?dZrEnjtTZ$%|B%DdB%HR3ZdTgBtJt7(cj%sVeBdUJ}rqr&3UE89haxjVx7
z?!r9znvJS1y<0!oRek#h|I7iRx@Ij%%%`7VJkKimET)%un$F`DQ}d0mJ)KaTzdS&m
znvlx9)UZh2=NZbrG(SUJ*tpQKdzb=;?0s(h@y9s9W|2`>V*HjYT|n)1<J}NB|62`_
zy!R71jm|9A>+LA9_HYkV$APnCJApGUS?z=53EBzDrle8w7OC8ndd>~9r0nj{9m2Gj
z#=x~X&tZ(9QfxE!%45V8<kW8-k_=e*up}lPQDRe}a?&orULx!Kr#;sX(co4cmax)R
zN}P73efCp3Gv+wzWH1}gjCnqIEDTrB;$i3SIjimK<ZN#%=3xpZ{OKKS%BkD5xW2Zw
z`nE44{?pZ>a&RjL7O4|83Xs?!+wREEsr|`;{Y0p)S1EAdquEP&-3%P~K|BX5%L#7$
zj8SyS{}>DA6tR)C^`^m}i5nJqI!yREJR7vg<iaQ2e~%SjV#6jkLQg6fGUM&In0II!
z7nbRblO8_9k543T8xi!U#lq`6{u~|xIFG-lw_-0RuKuP+Az8_Xy_Te6zvT*I*P|0f
zT`}BPr}^0VGzVeqbE%ng<)Ikf+B~(3Vh2CoN_mXilq8C6xB2wDT;s+0HqWeNN(3+`
zS*`WYngqW5Q<C50njqdB;fO^QN@DY6306whO-b6(hekqA0&_{Rp5R><!Dl38GUimJ
zu!`MIuQCoP9Qj0|gz=d;wxJE6L;VtXjCkzfQ!b9{zKH6-oh0E3<$zbb=VkD+C&!o5
zWGOr(uEf~pMZ&YP%Xe~yq_Nqzs)Z5{d3+^Lmr6}n1}E66wWa$i;iqb%=UR)D@N2cQ
z4qatA+@3{~J363&`)}Q&E4Na@^lwt%tt~6!W0}Eh`pW89{A`9&n}`PfIsfjUzn%_G
z@7teP`9>Xk4v$zjfEI2$N>@7nMH{zW>*DLu(8ir~!tsXw2Ds%Tv+{Q#J<JzOlhFIl
z82eS{(Yn9Z#vj%tZmabgV5NGeB$H@$e8@V<U$9ySS3QgJ{1UB)KfP8QnE0-OKNVhL
zm_DG2S#3vM>Kk?O@}H3cl{t0%&v)PAZV}{vif>W=|BJ=9-|RZw$&#TEbV5$s8@XXs
zeVZgB215`OlPQ~%*a}fpe|n|(w&1O8{h|F%?(mZ>L-X}ZH#qv9M@?>a2RIDsrV!0e
zxNNjAVM{9lzY|{hi+Y+^T9|5SGTQ~t8f^JguFr+Lo_lW3qjkah{*Hl~0}c?HJKXf7
z#t8Fse;T+IjPP*j*~fn1j00$bF3$06!OeF^f4m`Y#VI?>9e2+QVUeS`Ia4nXj92f!
z^~2W}G;=%}Wex>lq<UD@`;0S4)5V)ix$%Ph@ly8vKCE~~(Ls&i@&tC@c$157#>v0K
ztY(Gy5xyNQQ{45~6M_iEXA~k<P`)-XKsT}}-Z`k2ZS#=B9_mw-x4IobB+cQBb?SD^
z)jY^nN_gVn!|x-)we6tkTJ)~Ky-kFAgTY90kq}OnBK>hkHh6BbywN0biM*6nI&=5C
zG;H(feW<ZvfO~MUOHqRcRuL^Xm9o{qbw&0+CbF$?6&D!Xe(Qm+-_>fk-))I!Z2AIQ
zw@AXAM(x<g*IPi0-sVt!9V-_8^j>QFA9JYxH8FD#8$kW8s}-pnmiR?mU!;Ge0c6r8
zofG1dgsr77LVUWk;LmF5qq<3_&B?MqSt$$Q1NRj~l&z$2uD1HwSKFOHSWoL)b6x<b
zHO1Xbe*>7l4+^<jT=2egB*7RhQ;@rmd^(-R3;0Wf<t7TY!g!=^Kb^cfHgRwduO2tS
z*UvBRu>Rr#(^kR4yILgiSN-5!PQ1L})oY+|CsGDa|41xYl2ymFSNT78VLNEps?eL;
zqy;($Z1o>!>R<-1ZkmiffVq>>)t6tH;jOARWlH{bcyD1_#M?a^WFh<Cefd%15c5oQ
zA}7HHa|zt){INv~Tz1S0JIDau9dfWN`HVb7(s7qoTt@guwcE0Ri3O;O6p;3c8ADQr
z&y%cPE~tLAlX{=86lSic<!Nit#A`M(ACfQ5kq>>m_B*=53JX8A&%A50DQ<qfYPJ8Q
zF}%I~FeC4zI>@xI_%=#-VcnB0CK}IW;D<6}dB+-pG3G>Xj|EeFRMF?qdtL(=Ub#3C
zdQ%wqKR7wq6dFS8%``sREDxM`qj<O2Idx36holhA$qCz(746#P^s&aboU40%%`ls*
z^!Xo*+HfeD`^%pn9N4;dMz(E}&j@$P%K>jIFsA7t{6_3>MXZ1JqKOsOm|Q3{Icx;s
z3TBkkc?c-`#ta_uap31|BFPq2)>x+BXQ=g=2!ywN%sjNQL|%>PTJolvCv#hUCQkiO
z#9Xn1bkrW|@O5eVOQE<Sp5&%7F>+D`s`0xh@3jSNa4lU~Y-EEG!}_K(vIJ11n7*3i
zb-?7p>xXm{jPQ=)?H(uF)uH3pIof(VbNs0F^OFrdX;|dzOiP_Gfd;k9sef9maO4?}
zidF>+P}@4Uv30vJJ|cTb=+0+d2uO~SJ11cVS|1IapIb}fl^|M+-*x=hX(d&@&RZSy
zG|c2<V$84t-`xzhJ}vm>viQ8GZ-Wr-4z#r>*2ODlnTu+lNkR-0h0*~AEvVQ>L#DnW
z2A0JyL+bV@f;emAahfU(IKrIMEdCtf(o?Ek)CaZk{m*j9d0rL_xsEZv$yLIP84O8D
z{tO^!b)VmR^Jd|`(;z-YOd9T`ZBq{2oWyZyR%T3Bm;rK~HPBjA!HaJDJ6o<UlP_#|
zuP3<)LFbk0liY{b2<zI+D5YaV<f6~anOh!kLjB#qCD#j5P{|@?@W5Ul-dZ)C@E}O|
zoY%eBRc}6c^s&I@&MF^tU3&IKq<fI;`NQgd(Pv4x5Jb`BDmOzkj;-7AZ}P@`p04v5
zd~oDv<TX8k&6{C`uH(A^8ep^;ZpvPigVo%FWBYgNV$?ftT2rryC7;~gJ6EoVcRA<u
zUo}z(k@6jm_2E3Yi?NO3dY&2-@JTY==2yl+N@JR{@ftu=?vuVgHcV_+U{32>q{Abv
ziS&QA@BlAa;xJzy7dF2+tgSw;59G6b*+L4k&}K6~V_P8%;?NdtPHPGZC)a-24gDtb
zKO5kT@e_g({nBfpn-4RnjC&MsC2k(i%G925Lmj+YzTR&gen74rJC#S9O$i@aWgN_x
z7YP5H;~iX^Lyn$P-X~7=$>Yv>DqzG47#8)H?JYNm@JF}Y6t7TYwXvDd%PYosV*{V8
z)1d>Gwy-~%UuEGdnJMnh#024`scaR+MvGI2Usd%vielEUqGm~CSs1J|2_M=e1h(d8
zXA@HviH&aRET4UPc<qqnn<E*N5Pn}Rd%utdgiOtM(+?}*m17kRtJ|u`#=)XtN;ieE
zuEMRrEA#w#!<|O6=7&Buc>I=8S9qFCS9E&I=y5*$G0&u)?G_KF|8|Z=eQA)KY?(_+
zeXI(-rn{3{`xxQUrE=Pn+a++xMYZ7X`*^XWWrmQ~7z3>Q$@h=iY;R^+ze8giESPDR
z39;>h0%Rz=6czLcVXDQNFQfWBL{w6l$IfgWsAKQe(<o7Z!MSeJ)o*vm-mH^Nc0$vH
zp_X;St$a10jUcF1k4r(<VCM~^9DXR9@DlpvMS@lGM66@h0GYMGT{QLy2fp(2cAWhN
z4V2C#DLD6WKuCC|h6D3FS?*)dzLgkG(6xD-dF`?QK4UzPl1jsfU!|U2Ry5(lS59#c
z_Pr5>?ZU>{$86`wWqwRQ)Xmgz-@uwTg`G5BU*8{4XDf#Tca}<9P$<CpC)Sp)4$cvS
z4@=%1)S$u+$8`_!QLhnD&SrN`oDoxpvBqf=n_^qcwxTO)oOn`wr(h~GC;SY4bytLn
z0#i9Y^}JE301Tz<WT%8VvWTSlEg7K|@_7N8hP<y*xR-K=jF_PS7Cm*h@LY>1j!cx!
zm$#$FzZ~2?@x53kjBI)bCm2hK$C0$ow8=Dh;yLSCzL80y<C$cmoE0B*Z&M82X3dF@
zv}8K+SqkFU^E(u%BY8lkNicKcD<7T+>2@XA{wBvr3NkQZ8QAS^fBt&?9ATlDS~Xg;
zPA2WQq*SpI#2b@graKn6@n;6gEZO`iqH{g2*T#SmJ{<GSu(<G@aG|`s^yC;dl#bOt
zF=(KGd+G5!y}Gnm;fyb5oXH|_Z0qISH|&OpiYR@HhkX*8;(Ts$g8>Ov0y<KBw+<5C
z2IoG!FqMF9kzoliCjsS@Px%v~X+ZKAzekAY1hJ9$qv`nPb+T6Ext>$Em_TdypsYy`
zC$@NOewc|~7$<Z)b$X5Q;H91`)>e(1lbC`5bMm?zev4akUUIPFbEWk~?|Q!w0rz*&
zOWv1;m=M*WRE=e_c*d76!mnnC?g!MK?A-jYe}2}*gGmHC<yqDAEi92y_Y>&%5Wx>t
z%=#K<#NkXc-M&lDC2^H`&5o&50VsayxFK+%kKlP#Y0k~f0i@G8$|lQi$$}O&`3HkX
z$rc7KYQ@2W<ePg~j`XhAlLh4G7+jlZz(VF$(9viTNO+bMpL8B3sP6=qO)b-bPJM$s
z1L+-s_oX!&E3@F0D2{`ZizGZCnYy3)3<XxF{q?0qQ5<{{tZ8m9PLb#G20wj2u|Ph4
zWJ)k0w2Mfg+q<B2k_+bM^>hbVNO0&$NBeO1BH8tMVD+)K0ph^Lxn?I9S{!_<{!S%5
zH5`~6x+_k<O8mh_DoLsB<U3X44gNYbP`2Gkp3>+N*^}+Mc3apF@;h2V^B`wBtXbct
z&h?H0%Qb(AY<$EIV=w)jWnI{?@{h{k`fV)W#oQ(|@nMZz@Q6Z_|K})K`xlK)9<u-@
zeLK$eEpM7E;=#b1;4X-}a!0iFpZq3&DCs==LVz7cNw>Z{Z~RV{F6HR?d1``iFSnE1
z#zF~mmpuR6d^tj1Q=9iad0zkvg&eQ@EWiTivfsHWhW#YBP_R_p`0$vV%{#K2xrPBh
zKC~DIbu5^DqVz|)Og&)_nc3egC}EKElz?a;75wN<?#Q`91?F|lBBt&P_`a&a_vhke
zgujf&nbs;=?EgA-&a-WmaNX8tAA4<{9A1~i<RvsuZada0!cjCwJa5SLxGVdII9|yi
zJ}EIxzEB)!((|1WSiWngY&|zd45|*uUcXmQ-Wpt#@ZSG3`B<v%#ttV&EO>r_;=$Kf
zL{#|wu5PD0g#KegMP@#F9Orm$kHf{=<hj8d`my)#$Ok%F-1!nV$Zb^x_iSd_@$iwh
zoZ+KOWd3te4Z)QQghbWb5`E<fB6WOtT`_5bEEj2NwpWlB^X-2A{Q{mO4}5UBxfV~2
zKlDUxWcD$Ee22kaW?c?A`8#!YnCcYyrncmzPNsRX{BV)PXzUVM+DC~x{P8T2*c)}<
zN3D<GcB(X?V5Y)F_m{jy+^BHvxk7k2{ERqx^y*pNW;&epuH%>J+jXKvdisyvyLIAV
zcVA^r;ym$srx<&CCN1V>*EYC(V6*%qQJiTO6yViI&Bzl*fk`z14~r72$c%&isH}U1
z>}raiu}syHpG-Hpwd4&DE!)<YI)au6QfjQTW!V=ZvwDHqbLS#CuK-j`%qPfkfj525
zDG!pfy8Jm$i}Vqng}Gp|af&z%ArHO^{~|<PPsq7UFOnS>gXuLZ8p%O9J*V{NzY&WO
zCBBKrUJ$C@ENOY~8Q}N&U7G71<HQpmQ6aO<e)evUMje&=H?qcJXwz=F`{ao$rz1H7
zmdNUz25d9g1H=^*k4k<!Dty`8LNA!Tm^jB_5pqPE5*&G6D$*V+CGYA{V=>z7x9|0d
z8HE@P5U!1xZ`Y)j2-iFHI?s0eAm3$3(^dZbjy$~WP9Im#3nIo-`M!z7JRz@WZ<A0t
zO*FZgpKUL_MLwAQtK!Ao5hB8TJ@3YYDe_N&!Bc%f%j7-#g1(FlcN6hCbleTS-^kC<
zPr90nQSvGq9iM#2Z-UxJm;S541ewcrl}T>%;aVzRZqDzrMMCcW)~^-=>%^yfk_=6}
zKghqX{bHf*o+aPfT|(fqG_a8$zwWbEOQ?(*i$>6l6Q%Z_9M|{$B(jQ*jvtO5Ad0?U
zdzK+X1K(fjxxG<+PE<AFX=wv$EOB?{UWOkf)T>`8ea|sVWP9rWbm*Ta?|tS$b-M2-
zd7)SC_=iIq#K2LS2g`b^L~lekM_PL~A<{NYeC)3zo<~1e=;j<C>YL{c)elUQ1x|P9
zD|pNj`9B_q_xp_z`)a2u=q|q_S5*q8U0fI-1dI+}f3ztoa&5WjacpxGB=KZ7shxeD
zP`nprFk$|UT&{#|dApa$&EDHN1Wxu4EMZiOE7UWDcFW#p)V1~G;!BwnE4P-(F?3$)
z5lpQ_{1{KuRn|G;KGss_d)Y%?YSk*sGFl+QXUO~4d_ECdJktd2C1=V0^nR7R7wO>&
z&JtC5{+{p`QCaRhR6)MPG-g*UH%ESalOAWcERcQfIqdu_wnz}aO8YxbQNh8|<2vIe
zzsMrW)rW^BTL_h?yTS^6-w3qUZ4?^*l?dLo<$$aDIGIoWP3K#~yX5AS#BAlnIij+;
zsK-@qmJB1g7s{g!5rZ`4(zU!xL{m(C2YbypAu!T#a<S(%p?|)Lpyul&zvg?y7Ad_<
z?6i=yM;<?keotZd$G)><^pM-8iRlelB+$h<cWHvCShu1aJhetR-XS)I_bd_7f#Myl
zrt8GZ?eB?L)}O>8Q-Fww%LqZ82l|H^#t37=-BelO1#z$BrnV>f9g%wVihr+57jY5w
znY%MELhopWjn&!!AwhjuIX1S7sC@C6M>_XAq56ye>8SoN5$|#nUtF0a3;TK`zu3A?
zl=1HovkjUc4zqkN*!Q}Q{M`RXV*WTI&{&C77Y<RvJ+`eo52#ENNlj1qBRMGH4Zne&
zxfv}ae_e5mcV~e!)KbqK{;Uu>4#Hu-`BuoYPe=Gqm#z}LN$u$r6TA>|@71Tv>*GX0
zeCwk0^cRA?HR&+v90h#GM=tA&(843xF87=KGlclqRLR9^M!0?RHd$Ad19C4*Jj-OE
z2leT+J?S5NiCLHS2}OB6(0+f2V@!(;D7!LRlW1As*e6-$t3zCy&cLMahlMyH%W=iH
zwuu3n6MYRIyx|7Z8Raf*xjy3B%{@tbOe7$4#^_|%4HAIPlM91x!khE_<6fqF1t7V0
zig*5k8q7R;cPnRI5eWEnCHsOBq$Nbw9_Ua3(T*`g{T3}~AA8R>?4||5PB*G9a%lkQ
zwHU5#j@wIZ#JkN}H6cvj`p^=u4h*S;)$41j|A*0b{a}Cm-{Kp?U&hm@i0Hp9)F=LJ
zq5eDmk36UUQv)@L#6<mH^-=wgclV0u>L34;LEygstqlH)z#-R729#7P)c-WJ|7BGF
zYvuT_gZ}k(i1M%apZ50uME_+D{~OKr5A^Kj)<3P{|B3y}X#L+~zipPiFXI2TUH>Qc
zFVpg0e&oMa)_;`SN%eQF|1ISHC-kouF8_uy{{!mE_pd7de}I39#s599P2pel{r>>}
zO7y=H`LC7XA0^tGY*zN)Y5$+~{42TsdlW127wTWB=3-Av_t#%CY<}7|Ph0!uZxj^&
E2NTeo6#xJL

literal 0
HcmV?d00001

diff --git a/src/sp_validation/tests/test_b_modes.py b/src/sp_validation/tests/test_b_modes.py
index 67c64376..20a9aa67 100644
--- a/src/sp_validation/tests/test_b_modes.py
+++ b/src/sp_validation/tests/test_b_modes.py
@@ -1,8 +1,9 @@
 """VALUE-DRIFT CHARACTERIZATION TESTS FOR THE B-MODE ESTIMATORS.
 
 This module pins the numeric behavior of the pure E/B-mode helpers in
-``sp_validation.b_modes`` against fixed, deterministic, in-memory inputs
-(seeded RNG and hand-built arrays — no cluster data, no catalogue files).
+``sp_validation.b_modes`` against fixed, deterministic inputs (seeded RNG,
+hand-built arrays and one committed ξ± fixture — no cluster data, no catalogue
+files).
 Every pinned literal was produced by an actual run of the estimator inside
 the container; a future refactor that changes the numbers must fail.
 
@@ -293,6 +294,78 @@ def test_calculate_eb_statistics_has_teeth():
         assert loud_pte < 0.05  # louder B-modes are clearly rejected
 
 
+# ---------------------------------------------------------------------------
+# 5. pure_eb_from_xi on committed ξ± (the transform pin)
+# ---------------------------------------------------------------------------
+
+# pure_eb_from_xi(**fixture); regenerated only when the transform is meant to move.
+_PURE_EB_PINS = {
+    "xip_E": [
+        -2.9831529669542025e-06,
+        -1.5008524620265777e-05,
+        3.221623968725757e-07,
+        1.1797672310858565e-05,
+        5.715510692557323e-06,
+        8.825804523824443e-07,
+    ],
+    "xim_E": [
+        -4.737558091773235e-05,
+        -0.00010853189443993388,
+        -9.094825175032069e-05,
+        -5.826599101284694e-05,
+        -4.646405415748759e-05,
+        -1.9978028925333273e-05,
+    ],
+    "xip_B": [
+        1.7069121242262332e-05,
+        3.059889782373755e-05,
+        -4.8805399253844115e-06,
+        -6.999262696335271e-06,
+        -1.2672006989728095e-05,
+        -1.214149138979614e-06,
+    ],
+    "xim_B": [
+        -0.00011478091634539627,
+        -5.445112002141066e-05,
+        -3.100806652947907e-05,
+        -1.0940424256759085e-05,
+        -5.755185146643215e-06,
+        -1.628217762504557e-06,
+    ],
+    "xip_amb": [
+        0.00014017621792612224,
+        0.0001378482153667787,
+        0.0001339573019551001,
+        0.00012745126271361765,
+        0.00011662385105911986,
+        9.851844704443032e-05,
+    ],
+    "xim_amb": [
+        -4.389203999135455e-05,
+        5.279277664928643e-05,
+        5.800339397836051e-05,
+        4.4242350610114584e-05,
+        2.9902912946755567e-05,
+        1.912262132836568e-05,
+    ],
+}
+
+
+def test_pure_eb_from_xi_reproduces_pins_on_committed_xi(pure_eb_xi):
+    """The pure-E/B transform of the committed ξ± reproduces its pins.
+
+    With ξ± frozen, these pins move only when the transform does. rtol=1e-6 is
+    far above the 1e-12 reduction-order noise across thread counts.
+    """
+    modes = b_modes.pure_eb_from_xi(**pure_eb_xi)
+    for key in b_modes._EB_KEYS:
+        npt.assert_allclose(modes[key], _PURE_EB_PINS[key], rtol=1e-6, err_msg=key)
+
+    # Teeth: widening the integration interval by 1% leaves the pins.
+    moved = b_modes.pure_eb_from_xi(**{**pure_eb_xi, "tmax": 1.01 * pure_eb_xi["tmax"]})
+    assert not np.allclose(moved["xip_E"], _PURE_EB_PINS["xip_E"], rtol=1e-6, atol=0)
+
+
 # ---------------------------------------------------------------------------
 # 6. Grid edges and the COSEBIs covariance seam
 # ---------------------------------------------------------------------------
diff --git a/src/sp_validation/tests/test_bmodes_workflow_dry_run.py b/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
deleted file mode 100644
index 890271a4..00000000
--- a/src/sp_validation/tests/test_bmodes_workflow_dry_run.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""Back-pressure guard #2: the paper Snakemake workflows dry-run.
-
-The reorg is allowed to change the rule graph; these guards only assert that
-Snakemake can still parse each composed workflow and construct a dry run. One
-guard covers papers/bmodes (config space, no cosmo_val block); a second covers
-papers/cosmo_val, whose config DOES carry a cosmo_val block — so it is the only
-one that includes cosmo_val.smk and hence the born-as-SACC + assemble rules.
-"""
-
-import os
-import subprocess
-import sys
-from pathlib import Path
-
-import pytest
-
-# The workflow composes a catalog configfile and terminal inputs that live at
-# candide-absolute paths (Snakefile line 5, workflow/common.py), so the dry
-# run can only be constructed on the cluster. Same pattern as test_cosmo_val.
-requires_candide_data = pytest.mark.skipif(
-    not Path("/n17data/cdaley/unions").exists(),
-    reason="candide-local workflow config/data (/n17data) absent — off-cluster",
-)
-
-
-def _repo_root() -> Path:
-    for parent in Path(__file__).resolve().parents:
-        if (parent / "pyproject.toml").exists():
-            return parent
-    raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
-
-
-def _dry_run(workflow_dir, targets, *extra_snakemake_args):
-    """Construct a dry run of the paper workflow at ``workflow_dir``.
-
-    PYTHONUNBUFFERED satisfies the Snakefile's ``envvars:`` declaration. A dry
-    run never dispatches jobs, so any inherited SNAKEMAKE_PROFILE is dropped
-    rather than requiring its executor plugin. snakemake is invoked through
-    sys.executable, since a bare python3.12 may resolve off PATH to an
-    interpreter without it.
-    """
-    env = os.environ | {"PYTHONNOUSERSITE": "1", "PYTHONUNBUFFERED": "1"}
-    env.pop("SNAKEMAKE_PROFILE", None)
-    return subprocess.run(
-        [
-            sys.executable,
-            "-m",
-            "snakemake",
-            *targets,
-            "--dry-run",
-            "--cores",
-            "1",
-            "--configfile",
-            "config/config.yaml",
-            *extra_snakemake_args,
-        ],
-        cwd=workflow_dir,
-        env=env,
-        text=True,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        timeout=120,
-        check=False,
-    )
-
-
-@requires_candide_data
-def test_bmodes_workflow_dry_runs():
-    """The paper B-mode workflow must still parse and dry-run cleanly."""
-    result = _dry_run(_repo_root() / "papers/bmodes", ["paper"])
-    assert result.returncode == 0, result.stdout
-
-
-@requires_candide_data
-def test_cosmo_val_workflow_assemble_dry_runs():
-    """The cosmo_val workflow (the only one including cosmo_val.smk) resolves the
-    born-as-SACC + assemble DAG, and assemble pulls the tagged pseudo-Cl + cov."""
-    version = "SP_v1.4.6.3_leak_corr"
-    result = _dry_run(_repo_root() / "papers/cosmo_val", ["assemble_sacc_all"])
-    assert result.returncode == 0, result.stdout
-    # assemble_sacc must pull the tagged pseudo-Cl part + its NaMaster
-    # covariance (not the untagged cv_pseudo_cl diagnostic), plus every part.
-    out = result.stdout
-    assert "rule assemble_sacc:" in out, out
-    assert f"pseudo_cl_{version}_blind=A_powspace_nbins=32.sacc" in out, out
-    assert f"pseudo_cl_cov_{version}_blind=A_powspace_nbins=32.fits" in out, out
-    for part in ("_xi_minsep=", "_cosebis.sacc", "_pure_eb.sacc", "rho_tau_"):
-        assert part in out, f"missing {part} part in assemble DAG:\n{out}"
diff --git a/src/sp_validation/tests/test_config_paths_exist.py b/src/sp_validation/tests/test_config_paths_exist.py
index 2db03bc1..38a269ec 100644
--- a/src/sp_validation/tests/test_config_paths_exist.py
+++ b/src/sp_validation/tests/test_config_paths_exist.py
@@ -182,12 +182,31 @@ def _config_files() -> list[Path]:
     ]
 
 
+def _located_elsewhere(source: Path, key: str, value: str) -> bool:
+    """Whether a path-shaped value names nothing the config itself locates.
+
+    ``paths.output`` in the catalogue config is where cosmo_val writes, created
+    by the run. A calibration ``params.input_path`` with no directory is opened
+    relative to its consumer's run directory (the image-sim run, or the
+    catalogue directory ``scripts/masking.py`` prefixes).
+    """
+    if source.name == "cat_config.yaml":
+        return key == "paths.output"
+    return (
+        source.parent.name == "calibration"
+        and key == "params.input_path"
+        and "/" not in value
+    )
+
+
 def _candidate_paths() -> list[tuple[Path, str, Path]]:
     root = _repo_root()
     candidates = []
     for config_path in _config_files():
         iterator = _iter_ini_paths if config_path.suffix == ".ini" else _iter_yaml_paths
         for source, key, value, base_dir in iterator(config_path):
+            if _located_elsewhere(source, key, value):
+                continue
             expanded = Path(value).expanduser()
             if expanded.is_absolute():
                 resolved = expanded
diff --git a/src/sp_validation/tests/test_container_smoke.py b/src/sp_validation/tests/test_container_smoke.py
deleted file mode 100644
index b9b07058..00000000
--- a/src/sp_validation/tests/test_container_smoke.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""Smoke test of the profile-driven containerized-SLURM path.
-
-Submits one real (tiny, 5-minute) SLURM job through the committed candide
-profile. The executor, the apptainer deployment method and the bind mounts come
-from that profile; the image is the module-level ``container:`` in the test
-Snakefile, exactly as real workflows declare it. That contract is what's under
-test, so this can only run on candide -- marked ``slow``, skipped elsewhere.
-
-The job writes a YAML report (see data/container_smoke/container_smoke.py); the
-assertions below check what it reports.
-"""
-
-import os
-import re
-import shutil
-import subprocess
-import tempfile
-from pathlib import Path
-
-import numpy as np
-import pytest
-import yaml
-
-requires_cluster = pytest.mark.skipif(
-    not Path("/n17data/cdaley/unions").exists() or shutil.which("sbatch") is None,
-    reason="needs candide: /n17data and a SLURM submit host",
-)
-
-
-def _repo_root() -> Path:
-    for parent in Path(__file__).resolve().parents:
-        if (parent / "pyproject.toml").exists():
-            return parent
-    raise RuntimeError("could not locate repo root (no pyproject.toml above test)")
-
-
-def _reference_eigenvalues() -> np.ndarray:
-    """The same deterministic computation the job runs inside the container."""
-    rng = np.random.default_rng(seed=42)
-    a = rng.standard_normal((8, 8))
-    return np.linalg.eigh(a + a.T)[0]
-
-
-def test_smoke_snakefile_names_the_workflow_image():
-    """The test Snakefile's literal image must track the package's CONTAINER_URI."""
-    repo_root = _repo_root()
-    uri = re.search(
-        r'^CONTAINER_URI = "(.+)"$',
-        (repo_root / "src/sp_validation/container.py").read_text(),
-        re.MULTILINE,
-    ).group(1)
-    snakefile = (
-        repo_root / "src/sp_validation/tests/data/container_smoke/Snakefile"
-    ).read_text()
-    assert f'"{uri}"' in snakefile, uri
-
-
-@pytest.mark.slow
-@requires_cluster
-def test_container_smoke():
-    repo_root = _repo_root()
-    workflow_dir = repo_root / "src/sp_validation/tests/data/container_smoke"
-
-    # Not pytest's tmp_path: that lives in the login node's /tmp, which the
-    # compute node cannot see, so the job's output would "go missing". The
-    # workdir must be on a shared filesystem.
-    tmp_path = Path(tempfile.mkdtemp(prefix="container_smoke_", dir=Path.home()))
-
-    env = os.environ | {"PYTHONNOUSERSITE": "1", "PYTHONUNBUFFERED": "1"}
-    result = subprocess.run(
-        [
-            "snakemake",
-            "--profile",
-            str(repo_root / "workflow/profiles/candide"),
-            "-s",
-            str(workflow_dir / "Snakefile"),
-            "--directory",
-            str(tmp_path),
-            "--jobs",
-            "1",
-            "container_smoke",
-        ],
-        env=env,
-        text=True,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        timeout=600,
-        check=False,
-    )
-    assert result.returncode == 0, result.stdout
-
-    report = yaml.safe_load((tmp_path / "results/container_smoke.yaml").read_text())
-
-    # The job ran inside the image, not on the bare host. Everything below would
-    # pass on the host too, so this is the assertion that makes them mean
-    # something: apptainer sets APPTAINER_CONTAINER in every process it starts.
-    assert report["container"]["apptainer_container"] != "unset", report["container"]
-
-    # The install must resolve to an editable src/ checkout, not a site-packages
-    # copy. Note it need not be *this* checkout: the container's editable install
-    # points at the shared /n17data working tree, while the Snakefile under test
-    # is read from wherever the test runs.
-    module_file = Path(report["sp_validation"]["file"])
-    assert module_file.parts[-3:] == ("src", "sp_validation", "__init__.py"), (
-        module_file
-    )
-    assert "site-packages" not in module_file.parts, module_file
-
-    # The numeric stack agrees with the same computation run here.
-    np.testing.assert_allclose(
-        report["numeric"]["eigenvalues"],
-        _reference_eigenvalues(),
-        rtol=1e-10,
-        atol=1e-12,
-    )
-
-    # numeric.omp_num_threads is recorded but deliberately NOT asserted: the
-    # profile leaves OMP_NUM_THREADS unset by design, and rules that need it
-    # pinned set it themselves, so "unset" here is correct rather than a gap.
-
-    # git worked inside the container, so /home is bound and usable.
-    assert re.fullmatch(r"[0-9a-f]{40}", report["provenance"]["commit"]), report[
-        "provenance"
-    ]
-
-    shutil.rmtree(tmp_path)  # keep only on failure, for post-mortem
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index f25ed522..50f046f0 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -9,9 +9,7 @@
 """
 
 import os
-from collections import defaultdict
 from pathlib import Path
-from typing import Dict, Iterator, Tuple
 
 import numpy as np
 import pytest
@@ -182,88 +180,6 @@ def test_additive_bias_leak_corrected_columns(self, base_config):
         assert isinstance(cv.c1[version_leak_corr], float)
         assert isinstance(cv.c2[version_leak_corr], float)
 
-    @staticmethod
-    def _iter_catalog_entries(config: Dict[str, Dict]) -> Iterator[Tuple[str, Dict]]:
-        """Yield (name, entry) pairs for catalog-like entries in the config."""
-        for name, entry in config.items():
-            if not isinstance(entry, dict):
-                continue
-            if "subdir" not in entry:
-                continue
-            yield name, entry
-
-    @staticmethod
-    def _resolve(base: Path, candidate: str) -> Path:
-        """Return an absolute path given a base directory and a candidate string."""
-        candidate_path = Path(candidate)
-        return candidate_path if candidate_path.is_absolute() else base / candidate_path
-
-    @pytest.mark.slow
-    @requires_catalog_data
-    def test_catalog_paths_exist(self, base_config):
-        """Verify that catalog paths for active versions exist on disk.
-
-        This is a lightweight test that checks that all files referenced in the
-        catalog configuration for UNIONS analysis versions actually exist. It
-        discovers versions programmatically from cat_config.yaml rather than
-        using hardcoded lists.
-        """
-        # Get the path to catalog config
-        repo_root = os.path.dirname(
-            os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
-        )
-        catalog_config_path = os.path.join(repo_root, "cosmo_val", "cat_config.yaml")
-
-        config = yaml.safe_load(Path(catalog_config_path).read_text())
-
-        # This integrity check needs the real catalogs on disk (cluster only).
-        # Skip where the data directories aren't mounted — e.g. CI running
-        # inside the docker image, which has cat_config.yaml but no catalogs.
-        if not any(
-            Path(entry["subdir"]).is_dir()
-            for _, entry in self._iter_catalog_entries(config)
-        ):
-            pytest.skip("catalog data directories not present (not on cluster)")
-
-        working = []
-        nonfunctional = defaultdict(set)
-
-        for version, entry in self._iter_catalog_entries(config):
-            # Skip nz entries and versions already tested in heavy tests
-            if version == "nz":
-                continue
-
-            base = Path(entry["subdir"])
-            version_missing = set()
-
-            # Check shear, star, and psf files
-            for block_name in ("shear", "star", "psf"):
-                block = entry.get(block_name)
-                if not block:
-                    continue
-                resolved_path = self._resolve(base, block["path"])
-                if not resolved_path.is_file():
-                    version_missing.add(block_name)
-
-            if version_missing:
-                nonfunctional[version] = version_missing
-            else:
-                working.append(version)
-
-        # Print summary
-        print(f"\n✓ Working versions ({len(working)}):")
-        for v in sorted(working):
-            print(f"  - {v}")
-
-        if nonfunctional:
-            print(f"\n✗ Non-functional versions ({len(nonfunctional)}):")
-            for v in sorted(nonfunctional.keys()):
-                print(f"  - {v}: missing {nonfunctional[v]}")
-
-        assert not nonfunctional, (
-            f"Catalog configuration references missing files: {dict(nonfunctional)}"
-        )
-
     def test_seed_variant_updates_shear_path(self, tmp_path):
         """Seeded versions should materialize a seed-specific shear path."""
         params, base_version = self._make_seed_config(
@@ -346,11 +262,11 @@ def test_v1_4_6_glass_mock_default_seed(self, base_config):
     # These run the real compute seams end-to-end on a small, deterministic
     # toy catalog written to disk, asserting that sp_validation wires the
     # catalog/config/estimator together correctly and that the chain produces
-    # output of the right shape with finite values. They do NOT re-test the
-    # underlying numerical libraries (treecorr, cosmo_numba): no specific
-    # numerical values are asserted. These are the back-pressure that catches
-    # config-path / wiring breakage during restructuring. They can be tightened
-    # to allclose-against-a-committed-reference later for value-drift coverage.
+    # output of the right shape with finite values; a test that also compares
+    # values says against what in its docstring. They do NOT re-test the
+    # underlying numerical libraries (treecorr, cosmo_numba). These are the
+    # back-pressure that catches config-path / wiring breakage during
+    # restructuring.
     #
     # Environment-independent: the catalog is synthesized in a tmp dir, so no
     # cluster data is needed. They do require the scientific stack (treecorr,
@@ -438,6 +354,7 @@ def _write_synthetic_catalogs(
 
         shear_cfg = {
             "path": "shear.fits",
+            "redshift_path": str(nz_dir / "dndz_SP_A.txt"),
             "w_col": "w",
             "e1_col": "e1",
             "e2_col": "e2",
@@ -522,6 +439,102 @@ def test_calculate_2pcf_runs_on_synthetic_catalog(self, tmp_path):
         # The additive-bias subtraction in the pipeline must have run.
         assert version in cv.c1 and version in cv.c2
 
+    @pytest.mark.parametrize("npatch", [1, 4])
+    def test_xi_part_carries_the_covariance_the_measurement_estimated(
+        self, tmp_path, npatch
+    ):
+        """run_2pcf's ξ± part carries TreeCorr's covariance, whoever calls it.
+
+        The jackknife covariance with patches, the shot-noise diagonal without,
+        so every part has variances whether the rule or the CLI measured it.
+        """
+        import importlib.util
+
+        import sacc
+
+        from sp_validation import sacc_io
+
+        script = Path(__file__).resolve().parents[3] / "workflow/scripts/run_2pcf.py"
+        spec = importlib.util.spec_from_file_location("run_2pcf_part", script)
+        run_2pcf = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(run_2pcf)
+
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        part = tmp_path / "part.sacc"
+        gg = run_2pcf.run_2pcf(
+            ver=version,
+            min_sep=5.0,
+            max_sep=100.0,
+            nbins=6,
+            npatch=npatch,
+            cat_config=params["catalog_config"],
+            output_dir=params["output_dir"],
+            sacc_out=str(part),
+        )
+
+        cov = sacc_io.load(str(part), allow_unblinded=True).covariance
+        if npatch > 1:
+            assert isinstance(cov, sacc.covariance.FullCovariance)
+            np.testing.assert_array_equal(cov.dense, gg.cov)
+        else:
+            assert isinstance(cov, sacc.covariance.DiagonalCovariance)
+            np.testing.assert_array_equal(
+                cov.diag, np.concatenate([gg.varxip, gg.varxim])
+            )
+
+    def test_a_patched_xi_dump_reads_back(self, tmp_path):
+        """calculate_2pcf reads back the text dump a patched measurement wrote.
+
+        The ξ± figure rules re-enter calculate_2pcf on the reporting grid, which
+        has patches, and are handed the dump rule xi wrote (to its precision).
+        """
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        binning = dict(npatch=4, min_sep=5.0, max_sep=100.0, nbins=6)
+        measured = CosmologyValidation(versions=[version], **params).calculate_2pcf(
+            version, **binning
+        )
+        read = CosmologyValidation(versions=[version], **params).calculate_2pcf(
+            version, **binning
+        )
+        for column in ("meanr", "npairs", "xip", "xim", "varxip", "varxim"):
+            np.testing.assert_allclose(
+                getattr(read, column), getattr(measured, column), rtol=1e-4
+            )
+
+    def test_calculate_2pcf_does_not_depend_on_thread_count(self, tmp_path):
+        """calculate_2pcf's ξ± is the same on 4 and on 48 TreeCorr threads.
+
+        Production binning (default bin_slop/angle_slop), both runs on the
+        jackknife patches the first one writes, each from a fresh Catalog; they
+        must agree to far below the jackknife σ.
+        """
+        import treecorr
+
+        params, version = self._write_synthetic_catalogs(
+            tmp_path, n_gal=4000, coherent_shear=True
+        )
+        cv = CosmologyValidation(
+            versions=[version],
+            npatch=8,
+            theta_min=15.0,
+            theta_max=70.0,
+            nbins=6,
+            **params,
+        )
+
+        xi = {}
+        for n_threads in (4, 48):
+            # calculate_2pcf reads back an existing text dump instead of measuring.
+            for dump in Path(params["output_dir"]).glob(f"{version}_xi_*.txt"):
+                dump.unlink()
+            gg = cv.calculate_2pcf(version, num_threads=n_threads)
+            assert treecorr.get_omp_threads() == n_threads  # the count took effect
+            xi[n_threads] = np.concatenate([gg.xip, gg.xim])
+            sigma = np.sqrt(np.concatenate([gg.varxip, gg.varxim]))
+
+        shift = np.max(np.abs(xi[48] - xi[4]) / sigma)
+        assert shift < 1e-6, f"ξ± moves by {shift:.3g}σ between 4 and 48 threads"
+
     def test_calculate_scale_dependent_leakage_runs_on_synthetic_catalog(
         self, tmp_path
     ):
@@ -557,49 +570,27 @@ def test_calculate_scale_dependent_leakage_runs_on_synthetic_catalog(
         assert np.all(np.isfinite(res.alpha_leak))
         assert hasattr(res, "C_sys_p") and hasattr(res, "C_sys_m")
 
-    def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path):
-        """calculate_pure_eb wires xi+/- into cosmo_numba's Schneider E/B split.
-
-        Integration test of the headline B-mode seam: the pure E/B/amb
-        decomposition runs end-to-end via cosmo_numba and returns vectors of the
-        configured length, all bins finite, with a jackknife covariance of the
-        right shape -- AND the deterministic mode vectors match pinned reference
-        values, so a refactor that silently changes the numerical B-modes fails
-        rather than staying green on finiteness alone.
-
-        Two layers of teeth:
-
-        1. Finiteness on EVERY reporting bin (not just the interior). The
-           Schneider (2022) pure E/B estimator evaluates singular kernel
-           integrals (Eq. 42-43, 55-56) at each reporting theta, integrating the
-           fine ``gg_int`` xi+/- over [tmin, tmax]. At the extreme reporting bins
-           the evaluation point sits at the integration boundary, where the
-           integrand is near-singular; a *coarse* integration grid fails to
-           resolve it and the mode goes NaN. The real bmodes workflow
-           (papers/bmodes/config.yaml) avoids this with a broad-and-fine grid --
-           reporting [1, 250] arcmin, integration [0.5, 300] with nbins_int=1000
-           -- so the integration range brackets the reporting range AND the grid
-           is fine enough that the boundary integrals converge. This test mirrors
-           that: reporting [15, 70] arcmin, integration [1, 300] arcmin (brackets
-           on both ends) with nbins_int=600. Confirmed directly that nbins_int~80
-           over this range NaNs the last xip_E bin and the first xim_E bin, so
-           coarsening the integration grid back toward ~80 reintroduces edge NaNs
-           and fails -- this is the finiteness teeth.
-
-        2. Value-drift pins on the four deterministic mode vectors (xip/xim,
-           E/B). These come from a seeded synthetic catalog -> full-sample
-           treecorr xi+/- (no RNG) -> Schneider linear transform, so they are
-           reproducible. Verified bitwise-stable across two separate container
-           processes to a worst-case relative drift of ~1.4e-11 (pure float64
-           reduction-order noise; absolute drift ~1.5e-17). The pins use
-           rtol=1e-6 / atol=1e-12 -- ~5 orders of magnitude above that float-noise
-           floor (no flakiness margin consumed) yet tight enough that a sub-
-           percent change in any mode bites. The jackknife COVARIANCE depends on
-           treecorr's kmeans patch assignment and is NOT pinned by value -- only
-           its shape is asserted.
+    def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi):
+        """calculate_pure_eb carries ξ± through cosmo_numba's pure-E/B split.
+
+        The ξ± it measures equal the committed ``pure_eb_xi``, its modes are
+        ``pure_eb_from_xi`` of those ξ± and edges, and every reporting bin is
+        finite. ``test_b_modes`` pins the transform itself on the same ξ±, so a
+        failure names the step that moved: measurement, wiring or transform.
+
+        Finiteness: the Schneider (2022) integrals are near-singular where a
+        reporting bin meets the integration boundary, so the integration grid
+        [1, 300]′ brackets the reporting grid [15, 70]′ on both ends and is fine
+        (600 bins); about 80 integration bins NaN the edge bins.
+
+        ξ±: exact binning (bin_slop = angle_slop = 0) makes ξ± a plain pair sum,
+        independent of the tree and so of the jackknife patches, whose k-means
+        centres this test does not fix.
         """
         pytest.importorskip("treecorr")
         pytest.importorskip("cosmo_numba")
+        from sp_validation import b_modes
+
         # Coherent shear -> smooth xi+/-, so the pure-E/B integral is well-posed.
         params, version = self._write_synthetic_catalogs(
             tmp_path, n_gal=4000, coherent_shear=True
@@ -615,13 +606,8 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path):
             nbins=nbins,
             **params,
         )
+        cv.treecorr_config.update(bin_slop=0, angle_slop=0)
 
-        # Integration range strictly brackets the reporting range [15, 70] on
-        # both ends (1 << 15, 300 >> 70) AND uses a fine grid (nbins_int=600), so
-        # the near-singular boundary-bin Schneider integrals converge. This
-        # mirrors the bmodes workflow's broad-and-fine integration grid; every
-        # reporting bin is well-defined (no edge NaNs). nbins_int~80 here would
-        # NaN the edge bins -- confirmed -- which is the finiteness teeth.
         results = cv.calculate_pure_eb(
             version,
             npatch=npatch,
@@ -630,71 +616,30 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path):
             nbins_int=600,
         )
 
-        # Reference mode vectors from the seeded synthetic catalog + Schneider
-        # transform. Deterministic (full-sample treecorr, no RNG); regenerate by
-        # running calculate_pure_eb with the setup above and printing repr() of
-        # results[key]. Tolerances justified in the docstring.
-        expected = {
-            "xip_E": np.array(
-                [
-                    1.6688018692521218e-06,
-                    -1.8392317186434428e-05,
-                    1.4170916007248522e-06,
-                    8.1454486560987474e-06,
-                    6.2050467269160570e-06,
-                    2.6649478149110497e-06,
-                ]
-            ),
-            "xim_E": np.array(
-                [
-                    -4.4552381788304276e-05,
-                    -1.1082898248960663e-04,
-                    -9.2495668600755951e-05,
-                    -5.8456322151105526e-05,
-                    -4.4270469941501174e-05,
-                    -2.4236697154723798e-05,
-                ]
-            ),
-            "xip_B": np.array(
-                [
-                    1.8508958599700601e-05,
-                    3.8264056862769537e-05,
-                    -1.0482698132038303e-05,
-                    -7.3081832089716533e-06,
-                    -9.1621105374021936e-06,
-                    -6.4075815485457576e-06,
-                ]
-            ),
-            "xim_B": np.array(
-                [
-                    -1.1129938750754923e-04,
-                    -4.7967477760986883e-05,
-                    -3.4334760596175194e-05,
-                    -1.4776328993077835e-05,
-                    -4.0078671892721522e-06,
-                    -8.3202301900417799e-07,
-                ]
-            ),
+        measured = {
+            "theta_report": results["theta"],
+            "xip_report": results["xip"],
+            "xim_report": results["xim"],
+            "theta_int": results["theta_int"],
+            "xip_int": results["xip_int"],
+            "xim_int": results["xim_int"],
+            "tmin": results["left_edges"][0],
+            "tmax": results["right_edges"][-1],
         }
+        # Regenerate the fixture with np.savez(conftest.PURE_EB_XI, **measured).
+        for key, value in measured.items():
+            np.testing.assert_allclose(
+                value, pure_eb_xi[key], rtol=1e-10, atol=0, err_msg=key
+            )
 
-        for key in ("xip_E", "xim_E", "xip_B", "xim_B"):
+        modes = b_modes.pure_eb_from_xi(**measured)
+        for key in b_modes._EB_KEYS:
             vec = np.asarray(results[key])
             assert vec.shape == (nbins,)
-            # All reporting bins are well-defined under the widened integration
-            # range (no edge NaNs) -- the finiteness teeth.
             assert np.all(np.isfinite(vec)), f"{key} not finite"
-            # Value-drift pins -- the deterministic-mode teeth.
-            np.testing.assert_allclose(
-                vec,
-                expected[key],
-                rtol=1e-6,
-                atol=1e-12,
-                err_msg=f"{key} drifted from pinned reference",
-            )
+            np.testing.assert_allclose(vec, modes[key], rtol=1e-10, err_msg=key)
 
         # Jackknife covariance over the 6 stats (xip/xim x E/B/amb) x nbins.
-        # Patch (kmeans) assignment isn't guaranteed deterministic, so only the
-        # shape is pinned, not the values.
         cov = np.asarray(results["cov"])
         assert cov.shape == (6 * nbins, 6 * nbins)
         assert results["n_eff"] == npatch
diff --git a/src/sp_validation/tests/test_cv_init_params.py b/src/sp_validation/tests/test_cv_init_params.py
index f769c179..240f9213 100644
--- a/src/sp_validation/tests/test_cv_init_params.py
+++ b/src/sp_validation/tests/test_cv_init_params.py
@@ -17,7 +17,6 @@
 REPO = Path(__file__).resolve().parents[3]
 
 EXEMPT = {
-    "output_dir": "rules set the output tree via the run directory / COSMO_VAL",
     "blind": "None keeps the n(z) blind declared in the catalogue config",
 }
 
diff --git a/src/sp_validation/tests/test_xi_grids.py b/src/sp_validation/tests/test_xi_grids.py
index c35a2e3f..60361d10 100644
--- a/src/sp_validation/tests/test_xi_grids.py
+++ b/src/sp_validation/tests/test_xi_grids.py
@@ -34,12 +34,7 @@ def _load_common():
         "nbins": 20,
         "npatch": 100,
         "integration": {"min_sep": 0.08, "max_sep": 300, "nbins": 1000},
-        "cosebis": {
-            "min_sep_int": 0.9,
-            "max_sep_int": 300,
-            "nbins_int": 1000,
-            "npatch": 100,
-        },
+        "cosebis": {"nmodes": 20, "scale_cuts": [[12, 83]]},
     }
 }
 FIDUCIAL = {
@@ -61,15 +56,11 @@ def test_tag_is_built_from_canonical_values():
     for a path the producer never writes.
     """
     grids = common.xi_grids(CONFIG, FIDUCIAL)
-    assert common.grid_binning(grids["integration"]).endswith(
-        "minsep=0.08_maxsep=300.0_nbins=1000_npatch=1"
-    )
+    # Counts stay integers, so no "nbins=1000.0" creeps into a name.
     assert (
-        common.grid_binning(grids["cosebis"])
-        == "minsep=0.9_maxsep=300.0_nbins=1000_npatch=100"
+        common.grid_binning(grids["integration"])
+        == "minsep=0.08_maxsep=300.0_nbins=1000_npatch=1"
     )
-    # Counts stay integers, so no "nbins=1000.0" creeps into a name.
-    assert "nbins=1000_" in common.grid_binning(grids["cosebis"])
 
 
 def test_grid_lookup_round_trips_through_the_tag():
@@ -86,14 +77,6 @@ def test_grid_lookup_round_trips_through_the_tag():
         assert common.grid_of(grids, {k: str(v) for k, v in binning.items()}) == name
 
 
-def test_covariance_mode_follows_the_patches():
-    """Patched grids get a jackknife block, unpatched ones none."""
-    grids = common.xi_grids(CONFIG, FIDUCIAL)
-    assert grids["reporting"]["cov"] == "jackknife"
-    assert grids["cosebis"]["cov"] == "jackknife"
-    assert grids["integration"]["cov"] == "none"
-
-
 def test_unnamed_binning_is_a_reporting_measurement():
     """The paper's convergence-check binning belongs to no named grid."""
     grids = common.xi_grids(CONFIG, FIDUCIAL)
@@ -106,4 +89,3 @@ def test_workflow_without_cosmo_val_falls_back_to_fiducial():
     grids = common.xi_grids({}, FIDUCIAL)
     assert grids["reporting"]["npatch"] == 1
     assert grids["integration"]["min_sep"] == 0.5
-    assert "cosebis" not in grids
diff --git a/uv.lock b/uv.lock
index 1460cbc9..4acc2e41 100644
--- a/uv.lock
+++ b/uv.lock
@@ -94,15 +94,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777, upload-time = "2025-07-30T10:01:56.943Z" },
 ]
 
-[[package]]
-name = "argparse-dataclass"
-version = "2.0.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1a/ff/a2e4e328075ddef2ac3c9431eb12247e4ba707a70324894f1e6b4f43c286/argparse_dataclass-2.0.0.tar.gz", hash = "sha256:09ab641c914a2f12882337b9c3e5086196dbf2ee6bf0ef67895c74002cc9297f", size = 6395, upload-time = "2023-06-11T20:32:54.465Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b3/66/e6c0a808950ba5a4042e2fcedd577fc7401536c7db063de4d7c36be06f84/argparse_dataclass-2.0.0-py3-none-any.whl", hash = "sha256:3ffc8852a88d9d98d1364b4441a712491320afb91fb56049afd8a51d74bb52d2", size = 8762, upload-time = "2023-06-11T20:32:52.724Z" },
-]
-
 [[package]]
 name = "array-api-compat"
 version = "1.15.0"
@@ -480,33 +471,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
 ]
 
-[[package]]
-name = "conda-inject"
-version = "1.3.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pyyaml" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/b1/a8/8dc86113c65c949cc72d651461d6e4c544b3302a85ed14a5298829e6a419/conda_inject-1.3.2.tar.gz", hash = "sha256:0b8cde8c47998c118d8ff285a04977a3abcf734caf579c520fca469df1cd0aac", size = 3635, upload-time = "2024-05-27T12:20:58.873Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/4c/fc30b69fb4062aee57e3ab7ff493647c4220144908f0839c619f912045bf/conda_inject-1.3.2-py3-none-any.whl", hash = "sha256:6e641b408980c2814e3e527008c30749117909a21ff47392f07ef807da93a564", size = 4133, upload-time = "2024-05-27T12:20:57.332Z" },
-]
-
-[[package]]
-name = "configargparse"
-version = "1.7.5"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/3f/0b/30328302903c55218ffc5199646d0e9d28348ff26c02ba77b2ffc58d294a/configargparse-1.7.5.tar.gz", hash = "sha256:e3f9a7bb6be34d66b2e3c4a2f58e3045f8dfae47b0dc039f87bcfaa0f193fb0f", size = 53548, upload-time = "2026-03-11T02:19:38.144Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/fe/19/3ba5e1b0bcc7b91aeab6c258afd70e4907d220fed3972febe38feb40db30/configargparse-1.7.5-py3-none-any.whl", hash = "sha256:1e63fdffedf94da9cd435fc13a1cd24777e76879dd2343912c1f871d4ac8c592", size = 27692, upload-time = "2026-03-11T02:19:36.442Z" },
-]
-
-[[package]]
-name = "connection-pool"
-version = "0.0.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bd/df/c9b4e25dce00f6349fd28aadba7b6c3f7431cc8bd4308a158fbe57b6a22e/connection_pool-0.0.3.tar.gz", hash = "sha256:bf429e7aef65921c69b4ed48f3d48d3eac1383b05d2df91884705842d974d0dc", size = 3795, upload-time = "2020-09-17T02:48:28.824Z" }
-
 [[package]]
 name = "contourpy"
 version = "1.3.3"
@@ -858,15 +822,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0c/d5/c5db1ea3394c6e1732fb3286b3bd878b59507a8f77d32a2cebda7d7b7cd4/donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d", size = 21592, upload-time = "2024-05-23T14:13:55.283Z" },
 ]
 
-[[package]]
-name = "dpath"
-version = "2.2.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b5/ce/e1fd64d36e4a5717bd5e6b2ad188f5eaa2e902fde871ea73a79875793fc9/dpath-2.2.0.tar.gz", hash = "sha256:34f7e630dc55ea3f219e555726f5da4b4b25f2200319c8e6902c394258dd6a3e", size = 28266, upload-time = "2024-06-12T22:08:03.686Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/05/d1/8952806fbf9583004ab479d8f58a9496c3d35f6b6009ddd458bdd9978eaf/dpath-2.2.0-py3-none-any.whl", hash = "sha256:b330a375ded0a0d2ed404440f6c6a715deae5313af40bbb01c8a41d891900576", size = 17618, upload-time = "2024-06-12T22:08:01.881Z" },
-]
-
 [[package]]
 name = "ducc0"
 version = "0.41.0"
@@ -1070,30 +1025,6 @@ dependencies = [
     { name = "scipy" },
 ]
 
-[[package]]
-name = "gitdb"
-version = "4.0.12"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "smmap" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/72/94/63b0fc47eb32792c7ba1fe1b694daec9a63620db1e313033d18140c2320a/gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571", size = 394684, upload-time = "2025-01-02T07:20:46.413Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/a0/61/5c78b91c3143ed5c14207f463aecfc8f9dbb5092fb2869baf37c273b2705/gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf", size = 62794, upload-time = "2025-01-02T07:20:43.624Z" },
-]
-
-[[package]]
-name = "gitpython"
-version = "3.1.50"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "gitdb" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/33/f6/354ae6491228b5eb40e10d89c4d13c651fe1cf7556e35ebdded50cff57ce/gitpython-3.1.50.tar.gz", hash = "sha256:80da2d12504d52e1f998772dc5baf6e553f8d2fcfe1fcc226c9d9a2ee3372dcc", size = 219798, upload-time = "2026-05-06T04:01:26.571Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/20/7a/1c6e3562dfd8950adbb11ffbc65d21e7c89d01a6e4f137fa981056de25c5/gitpython-3.1.50-py3-none-any.whl", hash = "sha256:d352abe2908d07355014abdd21ddf798c2a961469239afec4962e9da884858f9", size = 212507, upload-time = "2026-05-06T04:01:23.799Z" },
-]
-
 [[package]]
 name = "glass"
 version = "2025.3"
@@ -1138,44 +1069,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/42/fa/f50f51260d7b0ef5d4898af122d8a7ec5a84e2984f676f746445f783705f/google_crc32c-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8b3f68782f3cbd1bce027e48768293072813469af6a61a86f6bb4977a4380f21", size = 33734, upload-time = "2025-12-16T00:40:27.028Z" },
 ]
 
-[[package]]
-name = "greenlet"
-version = "3.5.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e2/f1/fbbfef6af0bad0548f09bc28948ea3c275b4edb19e17fc5ca9900a6a634d/greenlet-3.5.3.tar.gz", hash = "sha256:a61efc018fd3eb317eeca31aba90ee9e7f26f22884a79b6c6ec715bf71bb62f1", size = 200270, upload-time = "2026-06-26T19:28:24.832Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7a/73/815dd90131c1b71ebdf53dbc7c276cafec2a1173b97559f97aba72724a87/greenlet-3.5.3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:efa9f765dd09f9d0cdac651ffdf631ee59ec5dc6ee7a73e0c012ba9c52fbdf5b", size = 604761, upload-time = "2026-06-26T19:07:10.114Z" },
-    { url = "https://files.pythonhosted.org/packages/9f/57/079cfe76bcef36b153b25607ee91c6fcb58f17f8b23c86bbbeabe0c88d72/greenlet-3.5.3-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7faba15ac005376e02a0384504e0243be3370ce010296a44a820feb342b505ab", size = 617044, upload-time = "2026-06-26T19:10:07.25Z" },
-    { url = "https://files.pythonhosted.org/packages/37/87/b4d095775a3fb1bcafbb483fc206b27ebb785724c83051447737085dc54e/greenlet-3.5.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:87142215824be6ac05e2e8e2786eec307ccbc27c36723c3881959df654af6861", size = 614244, upload-time = "2026-06-26T18:32:17.594Z" },
-    { url = "https://files.pythonhosted.org/packages/8a/70/7559b609683650fa2b95b8ab84b4ab0b26556a635d19675e12aa832d826d/greenlet-3.5.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:215275b1b49320987352e6c1b054acca0064f965a2c66992bed9a6f7d913f149", size = 1574210, upload-time = "2026-06-26T19:09:03.077Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/73/be55392074c60fc37655ca40fa6022457bfbf6718e9e342a7b0b41f96dd2/greenlet-3.5.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6b1b0eed82364b0e32c4ea0f221452d33e6bb17ae094d9f72aed9851812747ea", size = 1638627, upload-time = "2026-06-26T18:31:44.748Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/fa/5401ac78021c826a25b6dde0c705e0a8f29b617509f9185a31dac15fbe1b/greenlet-3.5.3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a2d185dd1621757e70c3861cceffd5317ab4e7ed7eb09c82994828468527ade5", size = 607435, upload-time = "2026-06-26T19:07:11.412Z" },
-    { url = "https://files.pythonhosted.org/packages/e9/76/1dc144a2e56e65d36405078ed774224375ea520a1870a6e46e08bb4ac7bf/greenlet-3.5.3-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1c514a468149bf8fbbab874188a3535cd8a48a3e353eb53a3d424296f8dbacd3", size = 619787, upload-time = "2026-06-26T19:10:08.396Z" },
-    { url = "https://files.pythonhosted.org/packages/bf/87/c298cee62df1de4ad7fec32abda73526cff347fd143a6ed4ac369246668a/greenlet-3.5.3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:915f887cf2682b66419b879423a2e072634aa7b7dce6f3ada4957cfced3f1e9a", size = 616786, upload-time = "2026-06-26T18:32:19.128Z" },
-    { url = "https://files.pythonhosted.org/packages/9e/2e/e6f009885ed0705ccf33fe0583c117cfd03cde77e31a596dd5785a30762b/greenlet-3.5.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:766cfd421c13e450feb340cd472a3ed9957d438727b7b4593ad7c76c5d2b0deb", size = 1574316, upload-time = "2026-06-26T19:09:04.273Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/fe/43fd110b01e40da0adb7c90ac7ea744bef2d43dca00de5095fd2351c2a68/greenlet-3.5.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2ecda9ec22edf38fa389369eaed8c3d37c05f3c54e69f69438dbb2cc1de1458b", size = 1638614, upload-time = "2026-06-26T18:31:46.297Z" },
-    { url = "https://files.pythonhosted.org/packages/82/2f/146d218299046a43d1f029fd544b3d110d0f175a09c715c7e8da4a4a345d/greenlet-3.5.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:df0a0628d1597eb0897b62f55d1343f772405fd25f3b2a796c76874b0c2e22e8", size = 654096, upload-time = "2026-06-26T19:07:12.71Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/cc/04738cafb3f45fa991ea44f9de94c47dcec964f5a972300988a6751f49d9/greenlet-3.5.3-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ebd933a6adabc298bab47731a130fe6bfb888bd934eee37810f151159544540d", size = 666304, upload-time = "2026-06-26T19:10:09.503Z" },
-    { url = "https://files.pythonhosted.org/packages/ce/aa/4e0dad5e605c270c784ab911c43da6adb136ccd4d81180f763ca429a723d/greenlet-3.5.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4b9d501b40e80b70e32323c799dd9b420a5577a9601469d362ae1ffb690f3a7c", size = 663635, upload-time = "2026-06-26T18:32:20.802Z" },
-    { url = "https://files.pythonhosted.org/packages/d1/50/13efdbea246fe3d3b735e191fec08fb50809f53cd2383ebe123d0809e44b/greenlet-3.5.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a1fad1d11e7d6aab184107baa8e4ece11ccba3ec9599cd7efa5ff4d70d43256a", size = 1621252, upload-time = "2026-06-26T19:09:05.647Z" },
-    { url = "https://files.pythonhosted.org/packages/f7/22/c0a336ae4a1410fd5f5121098e5bfbf1865f64c5ef80b4b5412886c4a332/greenlet-3.5.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:fad5aec764399f1b5cc347ad250a59660f20c8f8888ea6bae1f93b769cce1154", size = 1684824, upload-time = "2026-06-26T18:31:47.738Z" },
-    { url = "https://files.pythonhosted.org/packages/00/92/715c44721abe2b4d1ae9abde4179411868a5bff312479f54e105d372f131/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:19131729ae0ddc3c2e1ef85e650169b5e37ee32e400f215f78b94d7b0d567310", size = 653382, upload-time = "2026-06-26T19:07:14.209Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/83/37a10372a1090a6624cca8e74c12df1a36c2dc36429ed0255b7fb1aeee23/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1540dd8e5fc2a5aec40fbb98ef8e149fa47c89a4b4a1cf2575a14d3d1869d7a8", size = 659401, upload-time = "2026-06-26T19:10:10.876Z" },
-    { url = "https://files.pythonhosted.org/packages/db/e2/d1509cad4207da559cc42986ecdd8fc67ad0d1bba2bf03023c467fd5e0f3/greenlet-3.5.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e81fa194a1d20967877bdf9c7794db2bc99063e5be36aee710c08f04c5bb087f", size = 656969, upload-time = "2026-06-26T18:32:22.272Z" },
-    { url = "https://files.pythonhosted.org/packages/86/7d/eaf70de20aadca3a5884aec58362861c64ce45e7b277f47ed026926a3b89/greenlet-3.5.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:55cf4d777485d43110e47133cbba6d74a8885a87ec1227ef0267f9ee80c5aa21", size = 1617822, upload-time = "2026-06-26T19:09:06.893Z" },
-    { url = "https://files.pythonhosted.org/packages/8a/f9/414d38fc400ae4350d4185eaad1827676f7cf5287b9136e0ed1cbbe20a7f/greenlet-3.5.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:12a248ba75f6a9a236375f52296c498c89ff1d8badf32deb9eca7abd5853f7da", size = 1677983, upload-time = "2026-06-26T18:31:49.396Z" },
-    { url = "https://files.pythonhosted.org/packages/4f/ca/69db42d447a1378043e2c8f19c09cbbd1263371505053c496b49066d3d16/greenlet-3.5.3-cp315-cp315-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:78dbef602fda6d97d957eb7937f70c9ce9e9527330347f8f6b6f9e554a9e7a47", size = 659747, upload-time = "2026-06-26T19:07:15.565Z" },
-    { url = "https://files.pythonhosted.org/packages/a8/0b/af7ac2ef8dd41e3da1a40dda6305c23b9a03e13ba975ec916357b50f8575/greenlet-3.5.3-cp315-cp315-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6f73857adb8fee13fa56c172bd11262f888c0c648f9fea113e777bb2c7904a81", size = 670419, upload-time = "2026-06-26T19:10:12.293Z" },
-    { url = "https://files.pythonhosted.org/packages/51/1e/1d51640cacbfc455dbe9f9a9f594c49e4e244f63b9971a2f4764e46cc53d/greenlet-3.5.3-cp315-cp315-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:232fec92e823addaf02d9472cf7381e24a1d046a6ced1103c5caa4c21b9dfc1d", size = 668787, upload-time = "2026-06-26T18:32:24.298Z" },
-    { url = "https://files.pythonhosted.org/packages/21/66/4030d5b0b5894500023f003bb054d9bb354dfbd1e186c3a296759172f5f5/greenlet-3.5.3-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:2421c3564da9429d5586d46ca31ebb26516b5498a802cf65c041a8e8a8980d34", size = 1626305, upload-time = "2026-06-26T19:09:08.281Z" },
-    { url = "https://files.pythonhosted.org/packages/0e/50/5221371c7550108dfa3c378debc41d032aa9c78e89abb01d8011cfc93289/greenlet-3.5.3-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:e0f0d160f0b2e558e6c75f7930967183255dc9735e5f5b8cae58ee09c9576d8b", size = 1688631, upload-time = "2026-06-26T18:31:51.278Z" },
-    { url = "https://files.pythonhosted.org/packages/57/66/b3bfae3e220a9b63ea539a0eea681800c69ab1aada757eae8789f183e7ce/greenlet-3.5.3-cp315-cp315t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:629b614d2b786e89c50440e246f33eea78f58a962d0bdbbcc809e6d13605903f", size = 657221, upload-time = "2026-06-26T19:07:16.973Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/81/b6d4d73a709684fc77e7fa034d7c2fe82cffa9fc920fadcaa659c2626213/greenlet-3.5.3-cp315-cp315t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2b2e857ae16f5f72142edf75f9f176fe7526ba19a2841df1420516f83831c9f2", size = 663226, upload-time = "2026-06-26T19:10:13.723Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/07/e210b02b589f16e74ff48b730690e4a34ffe984219fce4f3c1a0e7ec8545/greenlet-3.5.3-cp315-cp315t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e515757e2e36bcbf1fad09a46e1557e8b1ae1797d4b44d09da7deed88ad28608", size = 660802, upload-time = "2026-06-26T18:32:26.081Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/2e/5303eb3fa06bca089060f479707182a93e360683bc252acf846c3090d34e/greenlet-3.5.3-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:b363d46ed1ea431825fdb01471bb024fc08399bad1572a616e853c7684415adb", size = 1622157, upload-time = "2026-06-26T19:09:09.527Z" },
-    { url = "https://files.pythonhosted.org/packages/54/70/50de47a488f14df260b50ae34fb5d56016e308b098eab02c878b5223c26a/greenlet-3.5.3-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:e44da2f5bbdaabaf7d80b73dbb430c7035771e9f244e3c8b769715c9d8fa0a16", size = 1681159, upload-time = "2026-06-26T18:31:52.986Z" },
-]
-
 [[package]]
 name = "gsl"
 version = "0.0.3"
@@ -1354,15 +1247,6 @@ http2 = [
     { name = "h2" },
 ]
 
-[[package]]
-name = "humanfriendly"
-version = "10.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cc/3f/2c29224acb2e2df4d2046e4c73ee2662023c58ff5b113c4c1adac0886c43/humanfriendly-10.0.tar.gz", hash = "sha256:6b0b831ce8f15f7300721aa49829fc4e83921a9a301cc7f606be6686a2288ddc", size = 360702, upload-time = "2021-09-17T21:40:43.31Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f0/0f/310fb31e39e2d734ccaa2c0fb981ee41f7bd5056ce9bc29b2248bd569169/humanfriendly-10.0-py2.py3-none-any.whl", hash = "sha256:1697e1a8a8f550fd43c2865cd84542fc175a61dcb779b6fee18cf6b6ccba1477", size = 86794, upload-time = "2021-09-17T21:40:39.897Z" },
-]
-
 [[package]]
 name = "hyperframe"
 version = "6.1.0"
@@ -1403,22 +1287,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5f/53/fb7122b71361a0d121b669dcf3d31244ef75badbbb724af388948de543e2/imagesize-2.0.0-py2.py3-none-any.whl", hash = "sha256:5667c5bbb57ab3f1fa4bc366f4fbc971db3d5ed011fd2715fd8001f782718d96", size = 9441, upload-time = "2026-03-03T14:18:27.892Z" },
 ]
 
-[[package]]
-name = "immutables"
-version = "0.21"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/69/41/0ccaa6ef9943c0609ec5aa663a3b3e681c1712c1007147b84590cec706a0/immutables-0.21.tar.gz", hash = "sha256:b55ffaf0449790242feb4c56ab799ea7af92801a0a43f9e2f4f8af2ab24dfc4a", size = 89008, upload-time = "2024-10-10T00:55:01.434Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f7/52/cb9e2bb7a69338155ffabbd2f993c968c750dd2d5c6c6eaa6ebb7bfcbdfa/immutables-0.21-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b48b116aaca4500398058b5a87814857a60c4cb09417fecc12d7da0f5639b73d", size = 104270, upload-time = "2024-10-10T00:54:21.912Z" },
-    { url = "https://files.pythonhosted.org/packages/0f/a4/25df835a9b9b372a4a869a8a1ac30a32199f2b3f581ad0e249f7e3d19eed/immutables-0.21-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dad7c0c74b285cc0e555ec0e97acbdc6f1862fcd16b99abd612df3243732e741", size = 104864, upload-time = "2024-10-10T00:54:22.956Z" },
-    { url = "https://files.pythonhosted.org/packages/4a/51/b548fbc657134d658e179ee8d201ae82d9049aba5c3cb2d858ed2ecb7e3f/immutables-0.21-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e44346e2221a5a676c880ca8e0e6429fa24d1a4ae562573f5c04d7f2e759b030", size = 99733, upload-time = "2024-10-10T00:54:23.99Z" },
-    { url = "https://files.pythonhosted.org/packages/47/db/d7b1e0e88faf07fe9a88579a86f58078a9a37fff871f4b3dbcf28cad9a12/immutables-0.21-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8b10139b529a460e53fe8be699ebd848c54c8a33ebe67763bcfcc809a475a26f", size = 101698, upload-time = "2024-10-10T00:54:25.734Z" },
-    { url = "https://files.pythonhosted.org/packages/d2/d0/a5fb7c164ddb298ec37537e618b70dfa30c7cae9fac01de374c36489cbc9/immutables-0.21-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:583d2a63e444ce1538cc2bda56ae1f4a1a11473dbc0377c82b516bc7eec3b81e", size = 104334, upload-time = "2024-10-10T00:54:31.284Z" },
-    { url = "https://files.pythonhosted.org/packages/f3/a5/5fda0ee4a261a85124011ac0750fec678f00e1b2d4a5502b149a3b4d86d9/immutables-0.21-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b274a52da9b106db55eceb93fc1aea858c4e6f4740189e3548e38613eafc2021", size = 104898, upload-time = "2024-10-10T00:54:32.295Z" },
-    { url = "https://files.pythonhosted.org/packages/93/fa/d46bfe92f2c66d35916344176ff87fa839aac9c16849652947e722b7a15f/immutables-0.21-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:338bede057250b33716a3e4892e15df0bf5a5ddbf1d67ead996b3e680b49ef9e", size = 99966, upload-time = "2024-10-10T00:54:34.046Z" },
-    { url = "https://files.pythonhosted.org/packages/d7/f5/2a19e2e095f7a39d8d77dcc10669734d2d99773ce00c99bdcfeeb7d714e6/immutables-0.21-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8781c89583b68f604cf30f0978b722165824c3075888639fde771bf1a3e12dc0", size = 101773, upload-time = "2024-10-10T00:54:35.851Z" },
-]
-
 [[package]]
 name = "importlib-metadata"
 version = "9.0.0"
@@ -2853,15 +2721,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" },
 ]
 
-[[package]]
-name = "pulp"
-version = "3.3.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/4e/70/69be07a67621ad804d6cf347965eb4e0d7786a97330d99c31d735aaa6c5a/pulp-3.3.2.tar.gz", hash = "sha256:d0904700c207ac11e25e3b1213b70eae1d6fb25faa719d75f3f15054901258c0", size = 16305346, upload-time = "2026-05-25T09:41:26.207Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/dd/6e/d674f1dde91c71e2ac19e5e5cf1ee6d5845e3aefecd9c39ed9c4b0c9a696/pulp-3.3.2-py3-none-any.whl", hash = "sha256:631b166f72086971a9597f7a0233ababa99bb8d50a01cd543f7758be5a9f86c0", size = 16391742, upload-time = "2026-05-25T09:41:22.2Z" },
-]
-
 [[package]]
 name = "pure-eval"
 version = "0.2.3"
@@ -3646,27 +3505,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e8/ae/fa6cd331b364ad2bbc31652d025f5747d89cbb75576733dfdf8efe3e4d62/slicerator-1.1.0-py3-none-any.whl", hash = "sha256:167668d48c6d3a5ba0bd3d54b2688e81ee267dc20aef299e547d711e6f3c441a", size = 10274, upload-time = "2022-04-07T18:54:07.029Z" },
 ]
 
-[[package]]
-name = "smart-open"
-version = "7.7.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "wrapt" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/db/c6/22e7a2acd5d27941e85e0d7ede398da5abe2e4677d2265c924157247c32e/smart_open-7.7.1.tar.gz", hash = "sha256:9414ba5733e28309f29b28a303b0f1054ad23fe0275f1a1b600c80a724f4bd1a", size = 54952, upload-time = "2026-06-26T07:56:35.309Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/8a/c5/14732c140fa0af0e59345949eaf9016c75ad3f076586f2413e3d3566a8da/smart_open-7.7.1-py3-none-any.whl", hash = "sha256:cb62dc45f519bf39b612564d326d2a17556f3a6056e3c3a86a07215d670d45bc", size = 65061, upload-time = "2026-06-26T07:56:33.685Z" },
-]
-
-[[package]]
-name = "smmap"
-version = "5.0.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1f/ea/49c993d6dfdd7338c9b1000a0f36817ed7ec84577ae2e52f890d1a4ff909/smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c", size = 22506, upload-time = "2026-03-09T03:43:26.1Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c1/d4/59e74daffcb57a07668852eeeb6035af9f32cbfd7a1d2511f17d2fe6a738/smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f", size = 24390, upload-time = "2026-03-09T03:43:24.361Z" },
-]
-
 [[package]]
 name = "smokescreen"
 version = "1.5.6"
@@ -3682,128 +3520,6 @@ dependencies = [
     { name = "scipy" },
 ]
 
-[[package]]
-name = "snakemake"
-version = "9.23.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "conda-inject" },
-    { name = "configargparse" },
-    { name = "connection-pool" },
-    { name = "docutils" },
-    { name = "dpath" },
-    { name = "gitpython" },
-    { name = "humanfriendly" },
-    { name = "immutables" },
-    { name = "jinja2" },
-    { name = "jsonschema" },
-    { name = "nbformat" },
-    { name = "packaging" },
-    { name = "platformdirs" },
-    { name = "psutil" },
-    { name = "pulp" },
-    { name = "pyyaml" },
-    { name = "referencing" },
-    { name = "requests" },
-    { name = "smart-open" },
-    { name = "snakemake-interface-common" },
-    { name = "snakemake-interface-executor-plugins" },
-    { name = "snakemake-interface-logger-plugins" },
-    { name = "snakemake-interface-report-plugins" },
-    { name = "snakemake-interface-scheduler-plugins" },
-    { name = "snakemake-interface-storage-plugins" },
-    { name = "sqlmodel" },
-    { name = "tabulate" },
-    { name = "tenacity" },
-    { name = "throttler" },
-    { name = "wrapt" },
-    { name = "yte" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/9e/c2/45aa858e55edbb7dbc243ed60859f4f3d92d63ecdd67a80632aa3659fe6b/snakemake-9.23.1.tar.gz", hash = "sha256:ef8d698bfce66a6669cc29df7e344b0b367fd90956c725e5ae0a79a556e8e93f", size = 6802587, upload-time = "2026-06-18T09:42:57.653Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7e/65/2cb1c99117746bea8aff34dc434703f8f23d3876860e2ff3ebeccb754b0f/snakemake-9.23.1-py3-none-any.whl", hash = "sha256:9fdbfff25f1b442cdf7906ca06b465fc24b9bf72e2d51514fb93d83e3f8aeb35", size = 1162133, upload-time = "2026-06-18T09:42:55.541Z" },
-]
-
-[[package]]
-name = "snakemake-interface-common"
-version = "1.23.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "argparse-dataclass" },
-    { name = "configargparse" },
-    { name = "packaging" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/89/c3/592f832f6e5d2d31f749392e48e8401b7625dec668d3d365d8d28f2b6c30/snakemake_interface_common-1.23.0.tar.gz", hash = "sha256:6ed14531a461417659364a0dd0acc51b786af4e26fc15cc5e00ff3d9fcaffacc", size = 13960, upload-time = "2026-03-08T21:54:29.251Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/28/90/a4a3652e832667917383f8d34112bdb37b78a6d14efc049e22a2c8192524/snakemake_interface_common-1.23.0-py3-none-any.whl", hash = "sha256:43bb10102d07149f27787dfc65a7342b86ff772973e7f9ca4940c89cb412a11e", size = 17162, upload-time = "2026-03-08T21:54:28.074Z" },
-]
-
-[[package]]
-name = "snakemake-interface-executor-plugins"
-version = "9.4.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "argparse-dataclass" },
-    { name = "snakemake-interface-common" },
-    { name = "throttler" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/54/50/de06b284c45a8e94fb8e4a12d5235065e78b49b8f84329dc10fe39f4b7dd/snakemake_interface_executor_plugins-9.4.0.tar.gz", hash = "sha256:9d4138897beacbaadaedad94b63f948eaeb604b7fc78f9cf65ac57f090f2c066", size = 16549, upload-time = "2026-03-08T17:04:02.644Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/5c/2c/4422eb14884bb4955ce65819799ff6452dcd3aa5f8e158540a287877bbcf/snakemake_interface_executor_plugins-9.4.0-py3-none-any.whl", hash = "sha256:d335c093185897e4e3654a39bab3a6f1c57dc1a54f998c92bd2d88640ab4a05e", size = 22567, upload-time = "2026-03-08T17:04:01.296Z" },
-]
-
-[[package]]
-name = "snakemake-interface-logger-plugins"
-version = "2.1.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "snakemake-interface-common" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/a7/0c/3fa5d592663c65669a867526604aadc6fbc235fb9284e94b49c0ef59aa41/snakemake_interface_logger_plugins-2.1.0.tar.gz", hash = "sha256:c89a00d2a398490cecd91b6dc6db8049cba93712d82e1d8f3000f3040bf3791c", size = 15917, upload-time = "2026-05-20T15:12:35.259Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/aa/43/cfdaf562303012024c8f48efebdfe258eef3d895665bd7e0000fdeb2411d/snakemake_interface_logger_plugins-2.1.0-py3-none-any.whl", hash = "sha256:601e3e0770dd1b460c1dcb7bd33f4fc2eb8af5016634b80d985cb993f674ef46", size = 14313, upload-time = "2026-05-20T15:12:34.186Z" },
-]
-
-[[package]]
-name = "snakemake-interface-report-plugins"
-version = "1.3.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "snakemake-interface-common" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/18/d6/6160ed98de665d6871dd356597dbf726688cc786e88668359ca37b7d9f54/snakemake_interface_report_plugins-1.3.0.tar.gz", hash = "sha256:fc9495298bec4e69721ab8afe6d6d88a86966fda2eeb003db56b9a88b86d5934", size = 4283, upload-time = "2025-10-31T10:52:36.55Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f0/f0/df73f6abc9b5910e43612ae28c7b6f666af80c4edd46a216ef47599ab6cb/snakemake_interface_report_plugins-1.3.0-py3-none-any.whl", hash = "sha256:78da3931f70e79eef51e5645a40b172929e555fe4d86ff45d6b856e521a379db", size = 7251, upload-time = "2025-10-31T10:52:35.474Z" },
-]
-
-[[package]]
-name = "snakemake-interface-scheduler-plugins"
-version = "2.0.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "snakemake-interface-common" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/88/d9/d480807d2cfc2d132bc760d877d45ec8fbe620a24200ec4d2697c4a26031/snakemake_interface_scheduler_plugins-2.0.2.tar.gz", hash = "sha256:2797e8fa9019d983132c2b403f14d6fcd3c5ad4c8d8a66b984b4740a71cacc46", size = 8642, upload-time = "2025-10-20T13:58:12.988Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0e/d0/f4e9894c8aaf37efe3bf1afe15ee3cf0546d82b2713a589e266ee47bf2ef/snakemake_interface_scheduler_plugins-2.0.2-py3-none-any.whl", hash = "sha256:b9ddfa508bd480711de1770dfb24f3b813cfa3cd0f862f0127ef721ae5346915", size = 10766, upload-time = "2025-10-20T13:58:11.898Z" },
-]
-
-[[package]]
-name = "snakemake-interface-storage-plugins"
-version = "4.4.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "humanfriendly" },
-    { name = "snakemake-interface-common" },
-    { name = "tenacity" },
-    { name = "throttler" },
-    { name = "wrapt" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/93/6e/f3c5b2d621fd6a6b78d8cfc01fef6b926fe2c277f5ed77c5e4deeacb94eb/snakemake_interface_storage_plugins-4.4.1.tar.gz", hash = "sha256:b2b5bf05318af36955ebf2ce76c921c0fb06904ca98fb30e1657d88b7b7b6945", size = 14924, upload-time = "2026-03-16T11:16:01.075Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b6/49/1f72d8c05d8588f06a61f1af3deb125a4ff8169d2a41f1b839d62c824d1c/snakemake_interface_storage_plugins-4.4.1-py3-none-any.whl", hash = "sha256:6e075f448543cc3d0cb1ee5e6e5a83af9761e7085f3ce0922011685d098f231e", size = 18256, upload-time = "2026-03-16T11:15:59.582Z" },
-]
-
 [[package]]
 name = "snowballstemmer"
 version = "3.1.1"
@@ -3903,7 +3619,6 @@ workflow = [
     { name = "cosmosis" },
     { name = "fast-pt" },
     { name = "mpi4py" },
-    { name = "snakemake" },
 ]
 
 [package.metadata]
@@ -3957,7 +3672,6 @@ requires-dist = [
     { name = "shear-psf-leakage", git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop" },
     { name = "skyproj" },
     { name = "smokescreen", git = "https://github.com/UNIONS-WL/Smokescreen?rev=main" },
-    { name = "snakemake", marker = "extra == 'workflow'" },
     { name = "sp-validation", extras = ["test", "docs"], marker = "extra == 'develop'" },
     { name = "sphinx", marker = "extra == 'docs'", specifier = ">=8.0" },
     { name = "sphinxawesome-theme", marker = "extra == 'docs'", specifier = ">=5.3,!=6.0.3" },
@@ -4078,48 +3792,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331", size = 92072, upload-time = "2024-07-29T01:10:08.203Z" },
 ]
 
-[[package]]
-name = "sqlalchemy"
-version = "2.0.51"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/02/f1/a7a892f18d4d224e6b26f706531eafccc41e37594d37d304786969ee13cb/sqlalchemy-2.0.51.tar.gz", hash = "sha256:804dccd8a4a6242c4e30ad961e540e18a588f6527202f2d6791b01845d59fdc9", size = 9912201, upload-time = "2026-06-15T15:41:20.012Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/1c/71ee0f8a6b9d7316a1ccd30430b4c62b6c2e36adc96017a4e3a72dce49d6/sqlalchemy-2.0.51-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:581921d849d6e6f994d560389192955e80e2950e18fcdfe2ccea863e01158e6e", size = 3343835, upload-time = "2026-06-15T16:19:42.613Z" },
-    { url = "https://files.pythonhosted.org/packages/2b/7c/7ab9f9aadc5944fdd06612484ed7918fe376ad871a5f50404dc1536e0194/sqlalchemy-2.0.51-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1d21ce524ab86c23046e992a5b81cb54c21079c6df6e78b8fc77d77cac70a6b9", size = 3358470, upload-time = "2026-06-15T16:26:38.011Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/7d/ff77169fee6186de145a7f2b87006c39638391130abbab2b1f63ac6ea583/sqlalchemy-2.0.51-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c5d98a2709840027f5a347c3af0a7c3d5f6c1ff93af2ca1c54494e23cba8f389", size = 3289874, upload-time = "2026-06-15T16:19:45.212Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/3b/6c505903710d781b55bc3141ee34a062bf9745a6b5bc7333305b9ed63b33/sqlalchemy-2.0.51-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1181256e0f16479691b5616d36375dc2620ad8332b25978763c3d206ad3f3f1d", size = 3321692, upload-time = "2026-06-15T16:26:39.747Z" },
-    { url = "https://files.pythonhosted.org/packages/17/6b/2dce8369b199cb855110e056032f94a9f66dacc2237d3d39c115a86eac56/sqlalchemy-2.0.51-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:483b11bd46bf35fc14c52faf338b04300c9e6ce554bce9b11be85bfec3bc3195", size = 3284735, upload-time = "2026-06-15T16:19:46.934Z" },
-    { url = "https://files.pythonhosted.org/packages/53/ff/dbc495b8a14da840faffb353857a72d4190113cac33727906fb997047f0f/sqlalchemy-2.0.51-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1bed1ee8b01da6088210aa9412023326fb98a599ba502e6118308601dcbef77f", size = 3302756, upload-time = "2026-06-15T16:26:41.336Z" },
-    { url = "https://files.pythonhosted.org/packages/cf/d5/fde8f4dddcf518ee15ab35a7c6a28acc32c8ba548d1d2aa451f96e6dbb0b/sqlalchemy-2.0.51-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:72ca54c952107ba5cd58854b67a5a6268631289d21651a1235396f3b98b47400", size = 3232055, upload-time = "2026-06-15T16:19:49.286Z" },
-    { url = "https://files.pythonhosted.org/packages/67/d1/43d3a0ac955a58601c24fa23038b1c55ee3a1ec02c0f96ebb1eae2bcf614/sqlalchemy-2.0.51-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b3e693d15533a45cd5906f0589f9c35090bef6ef45bf1e8195c424aa0ae06a8d", size = 3269850, upload-time = "2026-06-15T16:26:43.017Z" },
-    { url = "https://files.pythonhosted.org/packages/23/6b/2e0e38cf75c8780eca78d9b2e78164f8bcfd70125e5caa588ff5cbb9c9f4/sqlalchemy-2.0.51-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c45a496d6bc05dec41dcd4c3a2b183723f47473255c159cd80b503c8f246424d", size = 3282539, upload-time = "2026-06-15T16:19:51.065Z" },
-    { url = "https://files.pythonhosted.org/packages/dd/a1/e77854cb5336fd37dc3c6ae3b71de242c98caac5725120be0b526b31cbd0/sqlalchemy-2.0.51-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4004ada0aafe8ae1991b2cd1d99c6d9146126e123bd6f883c260d974aa012e54", size = 3287545, upload-time = "2026-06-15T16:26:44.735Z" },
-    { url = "https://files.pythonhosted.org/packages/f6/ab/9e17272fd4dac8df3b83c4fbe52b998a1c9d89a843c8c35ff29b74ff7364/sqlalchemy-2.0.51-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:0f6bcad487aee1c638d707235682fc96f741de00663619881ab235400d03289e", size = 3230929, upload-time = "2026-06-15T16:19:52.625Z" },
-    { url = "https://files.pythonhosted.org/packages/02/3c/52f408ea701781caee975606beccc48845f2aee8711ac29843d612c0306c/sqlalchemy-2.0.51-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:39a76529db6305693d8d4affa58ad5b5e2e18edd62daea628b29b97930b3513d", size = 3252888, upload-time = "2026-06-15T16:26:46.454Z" },
-    { url = "https://files.pythonhosted.org/packages/bf/90/a1bb5c7cbba76b7bc1fbd586d0a5479a7bc9c27b4a8298f22ec9423b2bb3/sqlalchemy-2.0.51-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7c6b36ed71f41942bdcd2ad2522be46bfce09d5705be5640ecf19bbc7660e4b7", size = 3566534, upload-time = "2026-06-15T15:58:35.024Z" },
-    { url = "https://files.pythonhosted.org/packages/15/4b/481f1fed30e0e9e8dd24aecbb49f29eb57fe7657ece5cf06ee9b84bb97d8/sqlalchemy-2.0.51-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0c2c62877097e1a0db401fba5cb4debee33265e5b2a55c4ccb489c02c53b4f72", size = 3535844, upload-time = "2026-06-15T16:02:43.973Z" },
-    { url = "https://files.pythonhosted.org/packages/02/71/0aa64aeda645510af0a43f7d9ee70932f0d1dc4263aed34c50ee891d9df3/sqlalchemy-2.0.51-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0378d055e9e8cd6ce4d8dff683bdd3d7d413533c4ee51d67a2b1e0f9eacc0f23", size = 3475355, upload-time = "2026-06-15T15:58:36.592Z" },
-    { url = "https://files.pythonhosted.org/packages/05/db/6061db32316446135a3abae5f308d144ab988a34234726042da3e58b1c63/sqlalchemy-2.0.51-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6e46fc36029eff666391e0531e5387b62ce6c4f1d8e50b3fb3099eaca1b42522", size = 3486591, upload-time = "2026-06-15T16:02:45.346Z" },
-    { url = "https://files.pythonhosted.org/packages/e2/22/dbf013a12ec759e54a34a119e9e217435b3f71b2dd5c61a7ade0a25dae87/sqlalchemy-2.0.51-py3-none-any.whl", hash = "sha256:bb024d8b621d0be75f4f44ecc7c950450026e76d66dc8f791bb5331d7fed59d5", size = 1944334, upload-time = "2026-06-15T16:09:22.418Z" },
-]
-
-[[package]]
-name = "sqlmodel"
-version = "0.0.37"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pydantic" },
-    { name = "sqlalchemy" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/fb/26/1d2faa0fd5a765267f49751de533adac6b9ff9366c7c6e7692df4f32230f/sqlmodel-0.0.37.tar.gz", hash = "sha256:d2c19327175794faf50b1ee31cc966764f55b1dedefc046450bc5741a3d68352", size = 85527, upload-time = "2026-02-21T16:39:47.038Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b1/e1/7c8d18e737433f3b5bbe27b56a9072a9fcb36342b48f1bef34b6da1d61f2/sqlmodel-0.0.37-py3-none-any.whl", hash = "sha256:2137a4045ef3fd66a917a7717ada959a1ceb3630d95e1f6aaab39dd2c0aef278", size = 27224, upload-time = "2026-02-21T16:39:47.781Z" },
-]
-
 [[package]]
 name = "stack-data"
 version = "0.6.3"
@@ -4234,24 +3906,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/88/68/025307ebd3c54c65e540d2a4ced11d0e5aa370c8ddb6d0e83066e5a39054/tables_io-1.1.2-py3-none-any.whl", hash = "sha256:4361e04dfcc10471bf9a3940d9ee6a1f903ac76ece51d32ef7fec7ea572b20c0", size = 39206, upload-time = "2026-06-18T16:21:19.577Z" },
 ]
 
-[[package]]
-name = "tabulate"
-version = "0.10.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/46/58/8c37dea7bbf769b20d58e7ace7e5edfe65b849442b00ffcdd56be88697c6/tabulate-0.10.0.tar.gz", hash = "sha256:e2cfde8f79420f6deeffdeda9aaec3b6bc5abce947655d17ac662b126e48a60d", size = 91754, upload-time = "2026-03-04T18:55:34.402Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/99/55/db07de81b5c630da5cbf5c7df646580ca26dfaefa593667fc6f2fe016d2e/tabulate-0.10.0-py3-none-any.whl", hash = "sha256:f0b0622e567335c8fabaaa659f1b33bcb6ddfe2e496071b743aa113f8774f2d3", size = 39814, upload-time = "2026-03-04T18:55:31.284Z" },
-]
-
-[[package]]
-name = "tenacity"
-version = "9.1.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/47/c6/ee486fd809e357697ee8a44d3d69222b344920433d3b6666ccd9b374630c/tenacity-9.1.4.tar.gz", hash = "sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a", size = 49413, upload-time = "2026-02-07T10:45:33.841Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/d7/c1/eb8f9debc45d3b7918a32ab756658a0904732f75e555402972246b0b8e71/tenacity-9.1.4-py3-none-any.whl", hash = "sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55", size = 28926, upload-time = "2026-02-07T10:45:32.24Z" },
-]
-
 [[package]]
 name = "terminado"
 version = "0.18.1"
@@ -4274,15 +3928,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" },
 ]
 
-[[package]]
-name = "throttler"
-version = "1.2.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ce/3f/47baf510c31e0e52ac0d80d9071e5e166ca069167fee4a6c13841f9d5f5f/throttler-1.2.3.tar.gz", hash = "sha256:d2f5b0b499d62f1fc984dcac8043450b606549b0097753a9c8a707f7427c27e1", size = 11792, upload-time = "2026-01-27T00:48:11.552Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/66/70/42d8796acc57c8bcd9ae395b1a6a0bbc833f738492a8ed192a44ccd58035/throttler-1.2.3-py3-none-any.whl", hash = "sha256:241ea3e97438dec4dc2f31ddc56dbd96262787a9b1d0598adfcc0bada1134b66", size = 9704, upload-time = "2026-01-27T00:48:09.544Z" },
-]
-
 [[package]]
 name = "tifffile"
 version = "2026.6.1"
@@ -4550,20 +4195,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6e/d2/6317eb6d4554855bbf12d61857774af34747bf88a42c19bf306de67e2fa3/wrapt-2.2.2-py3-none-any.whl", hash = "sha256:5bad217350f19ce99ca5b5e71d406765ea86fe541628426772b657375ee1c048", size = 61460, upload-time = "2026-06-20T23:49:42.966Z" },
 ]
 
-[[package]]
-name = "yte"
-version = "1.9.4"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "argparse-dataclass" },
-    { name = "dpath" },
-    { name = "pyyaml" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/44/f5/7e44620e6e077bfe624b9a17c329b8e0d0159e176e1f1a93c2790428ab2c/yte-1.9.4.tar.gz", hash = "sha256:86a47e6d722cec9419a7ac88be57d0d6c4ce28f02860393b71a66f2c674069f6", size = 8101, upload-time = "2025-11-27T12:55:00.85Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/4d/63/6a44729fdc60eb255a7b156a84e7552290174a9bf151e3b6c18e83d6fbfa/yte-1.9.4-py3-none-any.whl", hash = "sha256:5dac63303d3e6bc2ebadc36ece3c3fb09343772fe6e25e9356d9baf8f9dfaf6d", size = 10618, upload-time = "2025-11-27T12:55:01.685Z" },
-]
-
 [[package]]
 name = "zarr"
 version = "3.2.1"
diff --git a/workflow/README.md b/workflow/README.md
index 4171074e..21d0419b 100644
--- a/workflow/README.md
+++ b/workflow/README.md
@@ -61,9 +61,14 @@ override, e.g. the image-sims `SIF`).
 A few rules shell out to a host toolchain (CosmoCov, ImageMagick) and keep
 `container: None`; each says why in its own docstring.
 
-`OMP_NUM_THREADS` is not set by the profile either: the slurm executor's
-`--export=ALL` propagates the driver's env, not a profile flag, so a rule that
-needs it pinned sets it itself. Per-rule `mem_mb` / `runtime` stay on the rules.
+The slurm executor submits with `--export=ALL`, so every job starts with the
+launching shell's environment. `OMP_NUM_THREADS` is therefore not a profile
+setting: a rule that needs it pinned sets it itself. A path in that
+environment reaches nodes where it may not exist. For Snakemake's own cache
+this is handled (the launch drops `XDG_CACHE_HOME`, and the profile keeps the
+source cache off the shared filesystem), so a login shell that points it at
+`/scratch` is fine; keep any other path you export on a shared disk. Per-rule
+`mem_mb` / `runtime` stay on the rules.
 
 ### Off candide — the default profile
 
@@ -93,6 +98,11 @@ This is the default because the alternative is incoherent: Snakemake's
 rule executes new script code against an old `import sp_validation` — the two
 halves of one commit, split.
 
+The catalogue config is the launched checkout's `cosmo_val/cat_config.yaml`.
+`COSMO_VAL` defaults to the launched checkout's `cosmo_val/output`, so writing
+into another checkout's products means naming it; `COSMO_INFERENCE` defaults to
+the shared candide tree.
+
 **Caveat:** `rerun-triggers: code` watches rule bodies and `script:` files, not
 `src/`. Editing a module under `src/` does not by itself mark outputs stale —
 force with `-F` or `--forcerun <rule>`.
@@ -123,25 +133,25 @@ already uses the plain form; keep new paths the same.
 
 ### Run Snakemake from the host, never from inside the container
 
-`snakemake` is a thin host-side tool, pinned once per machine:
+`snakemake` is a thin host-side tool, installed once per machine on the
+image's Python:
 
 ```bash
-uv tool install snakemake==9.23.1 --with snakemake-executor-plugin-slurm
+uv tool install --python 3.12 snakemake --with snakemake-executor-plugin-slurm
 ```
 
-(match the version to `snakemake` in this repo's `uv.lock`). Run every
-`snakemake` command directly on the host — do not `apptainer shell` first.
+Any Snakemake version works, but it must run on the image's Python (3.12): a
+`script:` job loads the host's `snakemake` package into the image's interpreter,
+since the image carries none.
+
+Run every `snakemake` command directly on the host — do not `apptainer shell`
+first.
 Snakemake itself never touches the science stack; it only reads rule
 definitions and submits jobs. Each job carries its own `apptainer exec`
 wrapping from the profile (see above), so the container is where the science
 code runs, not where the orchestrator runs — one container per job, never a
 nested one.
 
-Check for a stray `~/.local/bin/snakemake` (any host-side `pip install --user
-snakemake` leaves one): Apptainer passes your `PATH` and mounts your `$HOME` by
-default, so it can silently shadow the one `uv tool install` set up. `which
-snakemake` should resolve under `uv tool dir`, not `~/.local/bin`.
-
 ### The container image — one per person
 
 Everything runs one image, published by CI as a registry tag:
@@ -181,7 +191,7 @@ job either gets the whole old image or the whole new one; jobs already running
 hold the old file open and finish against it unharmed.
 
 ```bash
-salloc -p comp -c 4 --time=01:00:00 --exclude=n17,n09,n36 --no-shell   # note the job id
+salloc -p comp -c 4 --time=01:00:00 --exclude=n17,n36 --no-shell   # note the job id
 srun --jobid=<id> spv-container pull
 scancel <id>
 ```
@@ -267,11 +277,20 @@ For the image-sims workflow, set `image_sims: {sif: ...}` in your run config.
 Either way the image has to sit under one of the profile's bind mounts to be
 visible.
 
-One trap to know: the `script:` directive bind-mounts the host orchestrator's
-`snakemake` into the job and *appends* it to `sys.path`, so a `snakemake`
-importable inside the image wins the lookup. If `script:` rules start failing
-with `ModuleNotFoundError: No module named 'snakemake.iocontainers'` or similar,
-an in-image snakemake older than the host's is the first thing to check.
+### Checking the workflow itself
+
+`workflow/tests/` checks DAG properties through the host launcher, on a toy
+checkout and — on candide — on the real papers:
+
+```bash
+uv run --isolated --no-project --python 3.12 --with snakemake \
+    --with snakemake-executor-plugin-slurm --with pytest \
+    pytest workflow/tests
+```
+
+CI runs the same suite with `-m "not candide"`. `test_container_smoke`
+submits one real SLURM job through the candide profile, so it runs only where
+`sbatch` exists — a candide login node — and skips on compute nodes.
 
 ### `snakemake` in `script:` files
 
diff --git a/workflow/common.py b/workflow/common.py
index 9927a6f6..ede56f55 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -1,4 +1,9 @@
-"""Shared helpers for the B-modes Snakemake workflow."""
+"""Shared helpers for every Snakefile that composes workflow/.
+
+Imported by the host Snakemake, where sp_validation is not installed: this
+module imports only the standard library and snakemake, and loads the
+stdlib-only project modules it needs by file path.
+"""
 
 import importlib.util
 import json
@@ -7,17 +12,13 @@
 import sys
 from pathlib import Path
 
-# This checkout's importable source tree: workflow/common.py -> <repo>/src.
-REPO_SRC = Path(__file__).resolve().parent.parent / "src"
+# The checkout this workflow was launched from: workflow/common.py -> <repo>.
+REPO_ROOT = Path(__file__).resolve().parent.parent
+REPO_SRC = REPO_ROOT / "src"
 
 # The container model lives in the package (``sp_validation/container.py``).
 # Taken from *this checkout's* src/, so the workflow and the ``spv-container``
 # CLI can never disagree about which image to run.
-#
-# Loaded by file path rather than as ``sp_validation.container``: snakemake runs
-# on the host, where sp_validation is usually not installed, and importing the
-# package would drag in ``__init__`` -> ``version`` -> a metadata warning on
-# every launch. The module itself is stdlib-only, so this costs nothing.
 _container = importlib.util.module_from_spec(
     importlib.util.spec_from_file_location(
         "_spv_container", REPO_SRC / "sp_validation" / "container.py"
@@ -30,20 +31,27 @@
 image_revision = _container.image_revision
 resolve_image = _container.resolve_image
 
+# Every job inherits this launch's environment (the slurm executor submits with
+# --export=ALL), and the Snakemake each job step starts keeps its source cache
+# under XDG_CACHE_HOME, which a login shell may point at node-local storage.
+# Without it, jobs use the home directory's cache, which every node mounts.
+os.environ.pop("XDG_CACHE_HOME", None)
+
 
 # Output roots are env-overridable so a reproduction run can write into a
-# fresh tree without clobbering (or silently reusing) prior products.
-COSMO_VAL = Path(
-    os.environ.get(
-        "COSMO_VAL", "/n17data/cdaley/unions/code/sp_validation/cosmo_val/output"
-    )
-)
+# fresh tree without clobbering (or silently reusing) prior products. COSMO_VAL
+# defaults to the launched checkout's own (gitignored) cosmo_val/output, so a
+# launch writes into another checkout's products only when it names that tree;
+# COSMO_INFERENCE defaults to the shared tree on candide.
+COSMO_VAL = Path(os.environ.get("COSMO_VAL", REPO_ROOT / "cosmo_val" / "output"))
 COSMO_INFERENCE = Path(
     os.environ.get(
         "COSMO_INFERENCE", "/n17data/cdaley/unions/code/sp_validation/cosmo_inference"
     )
 )
-CAT_CONFIG = "/n17data/cdaley/unions/code/sp_validation/cosmo_val/cat_config.yaml"
+# The catalogue config of the launched checkout: the one file both the host
+# (CATALOG_CONFIG, loaded in configure) and every job read catalogues from.
+CAT_CONFIG = str(REPO_ROOT / "cosmo_val" / "cat_config.yaml")
 # "blind" is the glass-mock A/B/C realisation convention, NOT Smokescreen
 # blinding (a separate axis: the concealed=True SACC stamp). The name is baked
 # into on-disk filenames we do not own (e.g. nz_{version}_{A|B|C}.txt).
@@ -118,9 +126,7 @@ def resolve_container(override=None):
     ``resolve_image()``, so jobs run what interactive ``spv-container`` work
     runs.
     """
-    if override:
-        return str(override)
-    return resolve_image()[0]
+    return str(override) if override else resolve_image()[0]
 
 
 def warn_if_image_stale():
@@ -161,9 +167,11 @@ def warn_if_image_stale():
 def configure(workflow_config):
     """Install config-derived values after Snakemake has loaded configfiles."""
     global CATALOG_CONFIG, DEFAULT_MASK_SUFFIX, FIDUCIAL, PLANCK18
+    from snakemake.common.configfile import load_configfile
+
     inject_checkout_pythonpath(workflow_config)
     warn_if_image_stale()
-    CATALOG_CONFIG = workflow_config
+    CATALOG_CONFIG = load_configfile(CAT_CONFIG)
     FIDUCIAL = workflow_config["fiducial"]
     DEFAULT_MASK_SUFFIX = (
         "_masked" if workflow_config["covariance"].get("default_masked", False) else ""
@@ -287,17 +295,10 @@ def build_redshift_path(version, blind):
 # ---------------------------------------------------------------------------
 # ξ± angular grids
 # ---------------------------------------------------------------------------
-# A grid is a binning plus how its covariance is estimated: (min_sep, max_sep,
-# nbins, npatch, cov). `reporting` is the analysis grid, `integration` the fine
-# one the B-mode integrals run over, `cosebis` the fine patched grid COSEBIs
-# propagates its covariance from. cov is "jackknife" (dense, from the patches),
-# "diagonal" (TreeCorr varxip/varxim) or "none".
-XI_KEYS = (
-    "min_sep",
-    "max_sep",
-    "nbins",
-    "npatch",
-)  # the binning; cov is not in the name
+# A grid is a binning: (min_sep, max_sep, nbins, npatch). `reporting` is the
+# analysis grid, `integration` the fine one both B-mode statistics (COSEBIs,
+# pure-E/B) integrate over.
+XI_KEYS = ("min_sep", "max_sep", "nbins", "npatch")
 
 
 def xi_grids(config, fiducial):
@@ -332,22 +333,11 @@ def xi_grids(config, fiducial):
         ),
     }
     grids["integration"].setdefault("npatch", 1)
-    cb = cv.get("cosebis")
-    if cb:
-        grids["cosebis"] = {
-            "min_sep": cb["min_sep_int"],
-            "max_sep": cb["max_sep_int"],
-            "nbins": cb["nbins_int"],
-            "npatch": cb["npatch"],
-        }
     for grid in grids.values():
         for key in ("min_sep", "max_sep"):
             grid[key] = float(grid[key])
         for key in ("nbins", "npatch"):
             grid[key] = int(grid[key])
-        # A jackknife estimate needs patches; at npatch=1 TreeCorr's var_method
-        # is "shot" and the diagonal is all it can offer.
-        grid.setdefault("cov", "jackknife" if grid["npatch"] > 1 else "none")
     return grids
 
 
@@ -397,19 +387,10 @@ def get_shear_catalog(wildcards):
 # turns each diagnostic into a rule keyed on the real data products it writes
 # under COSMO_VAL. Where a method only emits a figure (no data product), the
 # rule declares a sentinel under CV_SENTINELS so the DAG stays trackable.
-#
-# COSMO_VAL is the cosmo_val/output directory (already defined above), the same
-# location every `cv.*` method writes to via `cc["paths"]["output"]`.
 
 # Sentinel directory for pure-plot leaf rules (no natural data-product output).
 CV_SENTINELS = COSMO_VAL / "snakemake_sentinels"
 
-# Working directory in which `CosmologyValidation` is instantiated: its
-# catalogue config comes explicitly from CAT_CONFIG, and it writes to
-# `./output` unless COSMO_VAL is set. Resolved to the live (non-worktree)
-# checkout so rules share the output tree with interactive runs.
-CV_RUNDIR = "/n17data/cdaley/unions/code/sp_validation/cosmo_val"
-
 
 def cv_basename(version, fiducial=None):
     """Reproduce CosmologyValidation.basename() for a version.
@@ -461,17 +442,17 @@ def cv_basename(version, fiducial=None):
 )
 
 
-def cv_init_params(config, version_list=None):
+def cv_init_params(config):
     """Assemble the CosmologyValidation(...) constructor kwargs from config.
 
     Centralizes the run-specific instantiation so every cosmo_val rule script
-    builds an identical `cv`. The catalogue config is always CAT_CONFIG, never
-    the constructor's cwd-relative default. `version_list` overrides
-    config["versions"] (used by per-version rules that pass a single version).
+    builds an identical `cv`: catalogues from CAT_CONFIG and products under
+    COSMO_VAL, never the constructor's cwd-relative defaults.
     """
     cv = config["cosmo_val"]
     return dict(
-        versions=version_list if version_list is not None else config["versions"],
+        versions=config["versions"],
         catalog_config=CAT_CONFIG,
+        output_dir=str(COSMO_VAL),
         **{key: cv[key] for key in CV_INIT_KEYS},
     )
diff --git a/workflow/profiles/candide/config.yaml b/workflow/profiles/candide/config.yaml
index 19afd89a..39649717 100644
--- a/workflow/profiles/candide/config.yaml
+++ b/workflow/profiles/candide/config.yaml
@@ -1,68 +1,72 @@
-# Committed SLURM profile for the candide cluster (IAP).
-#
-# Drive any target with
+# SLURM profile for the candide cluster (IAP). Drive any target with
 #
 #     snakemake --profile workflow/profiles/candide \
 #         -s workflow/image_sims/Snakefile \
 #         --configfile <run config> <target>
 #
-# Snakemake owns scheduling and the container wrapping; run it host-side, never
-# inside an ``apptainer shell``. workflow/README.md is the full story.
+# from the host, never inside an ``apptainer shell``; workflow/README.md is the
+# full story.
 
 executor: slurm
 
-# --- GENERIC: mirrored in workflow/profiles/default/config.yaml -------------
+# --- mirrored in workflow/profiles/default/config.yaml ----------------------
 software-deployment-method: apptainer
 
-# Rerun a job when its code / params / inputs change, not only on mtime.
-# Caveat: "code" watches rule bodies and ``script:`` files, not ``src/`` --
-# editing a module under src/ does not mark outputs stale on its own.
+# Every trigger but software-env, which hashes the image *path*: that path is
+# per person (~/.cache/sp_validation/...) and switches between SIF and sandbox,
+# so it would rerun everything without the stack having changed.
+# "code" watches rule bodies and ``script:`` files, not ``src/``.
 rerun-triggers: ["mtime", "params", "input", "code"]
 
-# Give an appearing output file a moment on networked filesystems before
-# Snakemake calls a job failed for a missing output.
-latency-wait: 5
-# --- end GENERIC ------------------------------------------------------------
+# A job's write can take more than 5 s to show on the launching host (candide's
+# /home does); a shorter wait reads a finished job as failed and re-runs it.
+latency-wait: 60
+# --- end mirrored -----------------------------------------------------------
 
-# No ``apptainer-prefix``: the entry Snakefiles resolve ``container:`` to this
-# user's own image path, so there is nothing for Snakemake to cache.
-#
-# candide's disks, plus the one machine-specific env var: the host OpenMPI libs
-# MPI rules need to find libmpi inside the container (only rules importing
-# mpi4py care; harmless for the rest). The bind list matches ``spv-container
-# exec``'s default; keep the two in step.
+# candide's disks; the same list as ``spv-container exec``'s default.
 apptainer-args: >-
   --cleanenv
   --bind /home,/scratch,/automnt,/n17data,/n23data1,/n09data
-  --env LD_LIBRARY_PATH=/softs/openmpi/5.0.5-slurm-CentOS8/lib
 
-# Cluster policy applied to every job unless a rule overrides it. Excludes are
-# the flaky/no-internet candide nodes (n17 mount issues, n09 no internet, n36).
+#   * ``runtime`` MUST carry a unit (``60m``, ``6h``, ``2d``): Snakemake reads a
+#     bare number here as SECONDS. (In a rule's own ``resources:`` a bare
+#     integer is minutes.) 60m is the partitions' default; setting it keeps the
+#     slurm executor from warning on every job that names none.
 #
-#   * ``runtime`` MUST carry a unit (``60m``, ``6h``, ``2d``). Snakemake's
-#     resource parser reads a bare number as SECONDS, so ``runtime: 60`` would
-#     silently give every job a 60-second wall clock and kill it on start.
-#     (A bare integer in a rule's own ``resources: runtime=720`` is fine --
-#     rule-level numeric runtime is read as minutes; the seconds trap is only
-#     the CLI/default-resources parser.)
+#   * ``cpus_per_task`` is 12 to CAP JOBS PER NODE, not because a job needs 12
+#     cores: past ~4 concurrent jobs on a 48-core node, candide's per-user
+#     process limit (``ulimit -u 1200``) crashes apptainer ("can't start new
+#     thread"). After launching a fan-out, ``squeue -u $USER -o "%C %l"``
+#     should show 12 CPUs and the intended wall clock.
 #
-#   * ``cpus_per_task`` is pinned to 12 to CAP JOBS PER NODE, not because a job
-#     needs 12 cores. candide's per-user process limit is ``ulimit -u 1200``
-#     per node, and apptainer crashes ("can't start new thread") beyond ~4
-#     concurrent jobs on a 48-core node; 12 CPUs/job holds SLURM to ~4 jobs per
-#     node.
+#   * ``slurm_account``: the slurm executor's guess fails on candide (its
+#     ``sacct`` call errors) and warns on every launch.
 #
-#   * After launching a real fan-out, verify the request landed:
-#     ``squeue -u $USER -o "%C %l"`` should show 12 (CPUs) and the intended
-#     wall clock.
+#   * The excludes are the flaky nodes: n17 (mount issues), n36.
 default-resources:
   slurm_account: "cusers"
   slurm_partition: "comp,pscomp"
   runtime: "60m"
   cpus_per_task: 12
-  slurm_extra: "'--exclude=n17,n09,n36'"
+  slurm_extra: "'--exclude=n17,n36'"
+
+# Snakemake refuses a real run on a remote executor without a job bound.
+jobs: 100
 
-# Retry a job once on transient node failure, and keep the SLURM logs of
-# successful jobs (candide debugging).
+# Jobs inherit the launch env; leaving out source-cache keeps them from being
+# handed the launch's (possibly node-local) cache path. common.py drops
+# XDG_CACHE_HOME for the jobstep's own Snakemake.
+shared-fs-usage:
+  - persistence
+  - input-output
+  - software-deployment
+  - sources
+  - storage-local-copies
+  - software-deployment-cache
+
+# One retry absorbs a node failure in a large fan-out, which would otherwise
+# stop the launch scheduling new jobs.
 retries: 1
+
+# A successful job's SLURM log is the only record of what its script printed.
 slurm-keep-successful-logs: true
diff --git a/workflow/profiles/default/config.yaml b/workflow/profiles/default/config.yaml
index bf272c03..419b88a4 100644
--- a/workflow/profiles/default/config.yaml
+++ b/workflow/profiles/default/config.yaml
@@ -1,39 +1,29 @@
-# Machine-independent profile: the container model, and nothing else.
-#
-# Use it anywhere that is not candide -- a laptop, a workstation, another
-# cluster's interactive node:
+# Machine-independent profile: the container model, and nothing else. Use it
+# anywhere that is not candide (a laptop, a workstation, another cluster):
 #
 #     snakemake --profile workflow/profiles/default -s workflow/Snakefile \
 #         <target> --configfile <run config> -j 4
 #
-# On candide -- where the analysis actually runs -- use
-# `--profile workflow/profiles/candide` instead: the GENERIC block below plus
-# the SLURM executor and candide's machine layer.
-#
-# Requirements are the same everywhere: `apptainer` on PATH, and `snakemake`
-# installed host-side (`uv tool install ...`, see workflow/README.md) -- never
-# run from inside an apptainer shell.
+# On candide use workflow/profiles/candide. Either way, `apptainer` on PATH and
+# `snakemake` installed host-side (workflow/README.md); never run from inside
+# an apptainer shell.
 
-# --- GENERIC: mirrored in workflow/profiles/candide/config.yaml -------------
-# Wrap each job's `shell:`/`script:` command in `apptainer exec`, using the
-# image named by the entry Snakefile's `container:` directive.
+# --- mirrored in workflow/profiles/candide/config.yaml ----------------------
 software-deployment-method: apptainer
 
-# Rerun a job when its code / params / inputs change, not only on mtime.
-# Caveat: "code" watches rule bodies and `script:` files, not `src/` -- editing
-# a module under src/ does not mark outputs stale on its own.
+# Every trigger but software-env, which hashes the image *path*: that path is
+# per person (~/.cache/sp_validation/...) and switches between SIF and sandbox,
+# so it would rerun everything without the stack having changed.
+# "code" watches rule bodies and `script:` files, not `src/`.
 rerun-triggers: ["mtime", "params", "input", "code"]
 
-# Give an appearing output file a moment on networked filesystems before
-# Snakemake calls a job failed for a missing output.
-latency-wait: 5
-# --- end GENERIC ------------------------------------------------------------
+# A job's write can take more than 5 s to show on the launching host (candide's
+# /home does); a shorter wait reads a finished job as failed and re-runs it.
+latency-wait: 60
+# --- end mirrored -----------------------------------------------------------
 
-# Binds are the one thing you almost certainly need to edit for your machine:
-# whatever paths your inputs, outputs and checkout live under. `--cleanenv` so a
-# job's environment is the image's, not your shell's. If $HOME and the working
-# directory cover everything (apptainer mounts both by default), drop `--bind`.
+# `--cleanenv` so a job's environment is the image's, not your shell's.
+# Snakemake runs each job with `--home <working directory>`, so apptainer mounts
+# that directory and not your home: bind /home, and add `--bind` for any other
+# disk your inputs, outputs or checkout live on.
 apptainer-args: "--cleanenv --bind /home"
-
-# No `apptainer-prefix`, here or on candide: the entry Snakefiles resolve
-# `container:` to this user's own image path (workflow/README.md).
diff --git a/workflow/rules/cosmo_val.smk b/workflow/rules/cosmo_val.smk
index 87138bc0..34735a85 100644
--- a/workflow/rules/cosmo_val.smk
+++ b/workflow/rules/cosmo_val.smk
@@ -6,14 +6,14 @@
 # each diagnostic is a rule, and the rules are linked by the SACC parts and
 # products they write under COSMO_VAL (= cosmo_val/output):
 #
-#   catalogue ──→ xi (one job per grid: reporting, integration, cosebis)
-#                   │
-#                   ├─ reporting part ──┬─→ pure_eb (part, npz, figures)
-#                   ├─ integration part ┘        │
-#                   ├─ cosebis part ─────→ cosebis (part, npz, figures)
+#   catalogue ──→ xi (one job per grid: reporting, integration)
+#                   ├─ reporting part ──────→ pure_eb (part, npz, figures)
+#                   ├─ integration part ─┬──→ pure_eb
+#                   │                    └──→ cosebis (part, npz, figures)
 #                   └─ reporting .txt ──→ 2pcf plot, ratio_xi_sys_xi
+#   CosmoCov ξ±, integration grid ──→ pure_eb, cosebis (their covariances)
 #   catalogue ──→ pseudo_cl (part) ──┬─→ pseudo-Cl figures
-#   CosmoCov ──→ covariance ─────────┤
+#   CosmoCov ξ±, reporting grid ─────┤
 #   rho/tau (part + FITS) ───────────┼─→ summarize_bmodes (reads the products)
 #                                    └─→ assemble_sacc ──→ {version}.sacc
 #
@@ -73,7 +73,7 @@ def cv_tau_stats(version):
 
 def _pure_eb_stub(version):
     """Shared stem of the pure-E/B diagnostic products (npz + figures)."""
-    eb = CV["integration"]
+    eb = XI_GRIDS["integration"]
     return str(
         COSMO_VAL
         / (
@@ -101,39 +101,44 @@ def cv_pure_eb_figures(version):
     }
 
 
-def cv_xi_cov_integration(version):
-    """CosmoCov gaussian ξ± covariance on the integration grid.
-
-    The covariance model the pure-E/B Monte Carlo draws from; gaussian because
-    the draws only need the scatter a Gaussian field would give.
-    """
-    integ = CV["integration"]
+def _grid_cov(version, grid, gaussian):
+    """CosmoCov-processed ξ± covariance on a named grid's own binning."""
+    binning = XI_GRIDS[grid]
     return covariance_path(
         version,
         FIDUCIAL["blind"],
-        gaussian="g",
-        min_sep=integ["min_sep"],
-        max_sep=integ["max_sep"],
-        nbins=integ["nbins"],
+        gaussian=gaussian,
+        min_sep=binning["min_sep"],
+        max_sep=binning["max_sep"],
+        nbins=binning["nbins"],
         mask_suffix=DEFAULT_MASK_SUFFIX,
     )
 
 
+def cv_xi_cov_integration(version):
+    """CosmoCov gaussian ξ± covariance on the integration grid.
+
+    The one analytic covariance both B-mode statistics take: the pure-E/B Monte
+    Carlo draws from it and COSEBIs carry it through their kernel. Gaussian
+    because B-modes only need the scatter a Gaussian field would give.
+    """
+    return _grid_cov(version, "integration", "g")
+
+
 def _cosebis_stub(version):
     """Shared stem of the COSEBIs diagnostic products (npz + figures).
 
-    varmethod names where the covariance came from, and these products are the
-    propagated one — which also keeps them clear of the paths plot_cosebis
-    builds for its own byproducts, so nothing overwrites a declared output.
+    Named by the grid, where the covariance came from (varmethod), the mode
+    count and the fiducial scale cut.
     """
-    cb = CV["cosebis"]
+    integ = XI_GRIDS["integration"]
     fsc = CV["fiducial_scale_cut"]
     return str(
         COSMO_VAL
         / (
-            f"{version}_cosebis_minsep={cb['min_sep_int']}"
-            f"_maxsep={cb['max_sep_int']}_nbins={cb['nbins_int']}"
-            f"_npatch={cb['npatch']}_varmethod=propagated_nmodes={cb['nmodes']}"
+            f"{version}_cosebis_minsep={integ['min_sep']}"
+            f"_maxsep={integ['max_sep']}_nbins={integ['nbins']}"
+            f"_varmethod=analytic_nmodes={CV['cosebis']['nmodes']}"
             f"_scalecut={fsc[0]}-{fsc[1]}"
         )
     )
@@ -168,16 +173,8 @@ def cv_pseudo_cl_cov(version):
 
 
 def cv_xi_cov(version):
-    """CosmoCov-processed ξ± covariance, on the reporting grid's own binning."""
-    return covariance_path(
-        version,
-        FIDUCIAL["blind"],
-        gaussian="ng",
-        min_sep=CV["theta_min"],
-        max_sep=CV["theta_max"],
-        nbins=CV["nbins"],
-        mask_suffix=DEFAULT_MASK_SUFFIX,
-    )
+    """CosmoCov ξ± covariance on the reporting grid, the terminal file's."""
+    return _grid_cov(version, "reporting", "ng")
 
 
 def cv_cosebis_sacc(version):
@@ -207,13 +204,8 @@ def cv_analysis_sacc(version):
     return str(COSMO_VAL / f"{version}.sacc")
 
 
-# Common params block shared by every cosmo_val rule: the cv constructor kwargs
-# plus the run directory the object must be instantiated in.
-def cv_params(version_list=None):
-    return dict(
-        cv_init=cv_init_params(config, version_list=version_list),
-        rundir=CV_RUNDIR,
-    )
+# The CosmologyValidation constructor kwargs every cv_runner rule passes.
+CV_INIT = cv_init_params(config)
 
 
 # ---------------------------------------------------------------------------
@@ -234,7 +226,7 @@ rule cv_plot_rho_stats:
     output:
         sentinel=str(CV_SENTINELS / "plot_rho_stats.done"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         runtime=20,
     script:
@@ -248,7 +240,7 @@ rule cv_plot_tau_stats:
     output:
         sentinel=str(CV_SENTINELS / "plot_tau_stats.done"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         runtime=20,
     script:
@@ -263,7 +255,7 @@ rule cv_rho_tau_fits:
     output:
         sentinel=str(CV_SENTINELS / "rho_tau_fits.done"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         mem_mb=16000,
         runtime=120,
@@ -280,7 +272,7 @@ rule cv_footprints:
     output:
         sentinel=str(CV_SENTINELS / "footprints.done"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         mem_mb=16000,
         runtime=60,
@@ -293,7 +285,7 @@ rule cv_objectwise_leakage:
     output:
         sentinel=str(CV_SENTINELS / "objectwise_leakage.done"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     threads: 12
     resources:
         mem_mb=30000,
@@ -307,7 +299,7 @@ rule cv_weights:
     output:
         weight_hist=str(COSMO_VAL / "weight_hist.png"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         mem_mb=16000,
         runtime=30,
@@ -329,7 +321,7 @@ rule cv_additive_bias:
     output:
         additive_bias=str(COSMO_VAL / "additive_bias.json"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         mem_mb=16000,
         runtime=30,
@@ -344,7 +336,7 @@ rule cv_plot_2pcf:
     output:
         sentinel=str(CV_SENTINELS / "plot_2pcf.done"),
     params:
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         runtime=20,
     script:
@@ -361,7 +353,7 @@ rule cv_ratio_xi_sys_xi:
         ratio=str(COSMO_VAL / "ratio_xi_sys_xi.png"),
     params:
         offset=0.1,
-        **cv_params(),
+        cv_init=CV_INIT,
     resources:
         mem_mb=16000,
         runtime=120,
@@ -393,7 +385,6 @@ rule cv_plot_pseudo_cl:
         # Style is per catalogue, so the derived variants take their parent's.
         markers=[CATALOG_CONFIG[base_version(v)]["marker"] for v in CV_VERSIONS],
         colours=[CATALOG_CONFIG[base_version(v)]["colour"] for v in CV_VERSIONS],
-        rundir=CV_RUNDIR,
     resources:
         mem_mb=8000,
         runtime=20,
@@ -427,7 +418,6 @@ rule cv_pure_eb:
         n_samples=CV.get("n_mc_samples", 1000),
         cosmo_params=CV["cosmo_params"],
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-        rundir=CV_RUNDIR,
     threads: 24
     resources:
         mem_mb=40000,
@@ -437,26 +427,26 @@ rule cv_pure_eb:
 
 
 rule cv_cosebis:
-    """COSEBIs E/B decomposition for one version, from its ξ± part.
+    """COSEBIs E/B decomposition for one version, from its integration-grid part.
 
-    Values, covariance and PTEs all come from the part: the COSEBIs covariance
-    is the part's ξ± covariance through the same kernel as the modes.
+    The modes come from the part; the covariance is the CosmoCov ξ± covariance
+    on the same grid through the same kernel as the modes.
     """
     input:
-        xi=lambda w: cv_xi_sacc(w.version, "cosebis"),
+        xi=lambda w: cv_xi_sacc(w.version, "integration"),
+        cov=lambda w: cv_xi_cov_integration(w.version),
     output:
         npz=cv_cosebis_npz("{version}"),
         sacc=cv_cosebis_sacc("{version}"),
         **cv_cosebis_figures("{version}"),
     params:
         version="{version}",
-        min_sep=CV["cosebis"]["min_sep_int"],
-        max_sep=CV["cosebis"]["max_sep_int"],
-        nbins=CV["cosebis"]["nbins_int"],
+        min_sep=XI_GRIDS["integration"]["min_sep"],
+        max_sep=XI_GRIDS["integration"]["max_sep"],
+        nbins=XI_GRIDS["integration"]["nbins"],
         nmodes=CV["cosebis"]["nmodes"],
         scale_cuts=CV["cosebis"]["scale_cuts"],
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-        rundir=CV_RUNDIR,
     threads: 24
     resources:
         mem_mb=48000,
@@ -487,7 +477,6 @@ rule cv_summarize_bmodes:
         max_sep=CV["theta_max"],
         nbins=CV["nbins"],
         include_pseudo_cl=CV.get("include_pseudo_cl", False),
-        rundir=CV_RUNDIR,
     resources:
         mem_mb=8000,
         runtime=20,
diff --git a/workflow/rules/covariance.smk b/workflow/rules/covariance.smk
index e9185428..9c26b775 100644
--- a/workflow/rules/covariance.smk
+++ b/workflow/rules/covariance.smk
@@ -4,9 +4,9 @@
 def get_cat_params(version):
     """Extract covariance parameters (area, n_e, sigma_e) from catalog config."""
     base_version = version.replace("_leak_corr", "")
-    if base_version not in config:
+    if base_version not in CATALOG_CONFIG:
         raise KeyError(f"Catalog configuration not found for {base_version}")
-    cov_th = config[base_version]["cov_th"]
+    cov_th = CATALOG_CONFIG[base_version]["cov_th"]
     return cov_th["A"], cov_th["n_e"], cov_th["sigma_e"]
 
 
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 1b6663b6..4e8b75f6 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -13,16 +13,11 @@ def xi_binning(grid):
     return grid_binning(XI_GRIDS[grid])
 
 
-def xi_grid_of(wildcards):
-    """Grid label for the binning a job was requested with."""
-    return grid_of(XI_GRIDS, {key: getattr(wildcards, key) for key in XI_KEYS})
-
-
 rule xi:
     """TreeCorr ξ±(θ) for one version on one angular grid.
 
     One rule for every grid: outputs are named by their binning, so a request
-    binds the wildcards and `xi_grid_of` resolves the grid label from them.
+    binds the wildcards and the grid label resolves from them.
     """
     input:
         catalog=get_shear_catalog,
@@ -38,8 +33,7 @@ rule xi:
         npatch="{npatch}",
         cat_config=CAT_CONFIG,
         output_dir=str(COSMO_VAL),
-        grid=lambda w: xi_grid_of(w),
-        cov=lambda w: XI_GRIDS[xi_grid_of(w)]["cov"],
+        grid=lambda w: grid_of(XI_GRIDS, w),
     resources:
         # The fine integration grid needs more memory and wall time than the
         # ~20-bin reporting one; scale on nbins rather than splitting the rule.
@@ -68,6 +62,7 @@ rule rho_tau_stats:
     resources:
         mem_mb=30000,
         disk_mb=20000,
+        runtime=360,
     script:
         "../scripts/run_rho_tau.py"
 
diff --git a/workflow/scripts/cv_cosebis.py b/workflow/scripts/cv_cosebis.py
index c9241eaa..d431b470 100644
--- a/workflow/scripts/cv_cosebis.py
+++ b/workflow/scripts/cv_cosebis.py
@@ -1,11 +1,12 @@
 """Rule cv_cosebis: COSEBIs E/B decomposition for one version.
 
-A consumer of the ξ± part alone — values, covariance, PTEs and figures all
-derive from it, so nothing here touches a catalogue. The part's ξ± covariance
-goes through the same linear kernel as the modes to give the COSEBIs
-covariance; its ``npatch`` metadata sets the Hartlap debiasing.
+A consumer of the integration-grid ξ± part and the CosmoCov ξ± covariance on
+the same grid — nothing here touches a catalogue. The covariance goes through
+the same linear kernel as the modes to give the COSEBIs covariance; it is
+analytic, so no Hartlap debiasing applies.
 """
 
+import numpy as np
 from cv_runner import _unbuffer_streams, verify_outputs
 
 from sp_validation import sacc_io
@@ -26,18 +27,17 @@
 fiducial_scale_cut = tuple(p["fiducial_scale_cut"])
 
 part = sacc_io.load(snakemake.input["xi"])
-theta, xip, xim = sacc_io.get_xi(part, (0, 0), grid="cosebis")
+theta, xip, xim = sacc_io.get_xi(part, (0, 0), grid="integration")
 edges = log_bin_edges(p["min_sep"], p["max_sep"], p["nbins"])
 
 results = cosebis_scan_from_xi(
     theta,
     xip,
     xim,
-    part.covariance.dense,
+    np.loadtxt(snakemake.input["cov"]),
     *edges,
     nmodes=p["nmodes"],
     scale_cuts=[tuple(sc) for sc in p["scale_cuts"]],
-    npatch=part.metadata["npatch"],
 )
 
 fiducial_key = find_conservative_scale_cut_key(results, fiducial_scale_cut)
@@ -50,7 +50,7 @@
     fiducial_scale_cut=fiducial_scale_cut,
 )
 plot_cosebis_covariance_matrix(
-    fiducial, version, "jackknife", snakemake.output["figure_covariance"]
+    fiducial, version, "analytic", snakemake.output["figure_covariance"]
 )
 plot_cosebis_scale_cut_heatmap(
     results,
diff --git a/workflow/scripts/cv_runner.py b/workflow/scripts/cv_runner.py
index 040cbe8a..601f2e14 100644
--- a/workflow/scripts/cv_runner.py
+++ b/workflow/scripts/cv_runner.py
@@ -28,15 +28,11 @@ def _unbuffer_streams():
 def make_cv(snakemake):
     """Build a CosmologyValidation from a rule's ``snakemake.params``.
 
-    ``params["cv_init"]`` is the kwargs dict assembled by common.cv_init_params.
-    The catalogue config path arrives explicitly in ``cv_init``; the object is
-    created with the run directory as cwd so it writes under ``output/``
-    exactly as interactive runs do.
+    ``params["cv_init"]`` is the kwargs dict assembled by common.cv_init_params,
+    which names the catalogue config and the output directory explicitly.
     """
     from sp_validation.cosmo_val import CosmologyValidation
 
-    rundir = snakemake.params["rundir"]
-    os.chdir(rundir)
     return CosmologyValidation(**dict(snakemake.params["cv_init"]))
 
 
diff --git a/workflow/scripts/cv_summarize_bmodes.py b/workflow/scripts/cv_summarize_bmodes.py
index 9200bfaa..ec44142a 100644
--- a/workflow/scripts/cv_summarize_bmodes.py
+++ b/workflow/scripts/cv_summarize_bmodes.py
@@ -42,7 +42,7 @@
     # this table wants.
     cosebis = np.load(snakemake.input["cosebis"][i])
     row["COSEBIS"] = float(cosebis["pte_B"])
-    cov_methods.add("COSEBIs: propagated from the ξ± covariance")
+    cov_methods.add("COSEBIs: analytic (CosmoCov ξ± through the COSEBIs kernel)")
 
     if p["include_pseudo_cl"]:
         from astropy.io import fits
diff --git a/workflow/scripts/run_2pcf.py b/workflow/scripts/run_2pcf.py
index 1f69ee6d..28c8c521 100644
--- a/workflow/scripts/run_2pcf.py
+++ b/workflow/scripts/run_2pcf.py
@@ -16,12 +16,12 @@
 grids are the same compute with different ``--min-sep/--max-sep/--nbins``.
 ``CosmologyValidation.calculate_2pcf`` writes the ``.txt`` dump (a raw
 byproduct); the ξ± data product is born as SACC here, a *part* named by its
-binning and tagged with its ``--grid``. The part carries the covariance its
-grid configures (``--cov``): the dense jackknife estimate from the patches, the
-TreeCorr ``varxip``/``varxim`` diagonal, or none.
+binning and tagged with its ``--grid``. The part carries the covariance the
+measurement estimated: the dense jackknife covariance when it had patches, the
+shot-noise ``varxip``/``varxim`` diagonal when it had none.
 
-``output_dir`` is passed explicitly (rather than via the ``COSMO_VAL`` env hook)
-so lc can point each run at its own ``{output}`` tree.
+``output_dir`` is passed explicitly so lc can point each run at its own
+``{output}`` tree.
 """
 
 import argparse
@@ -44,7 +44,6 @@ def run_2pcf(
     output_dir,
     sacc_out=None,
     grid="reporting",
-    cov="none",
 ):
     """Measure ξ±(θ) for ``ver`` and write its reporting SACC part.
 
@@ -77,10 +76,8 @@ def run_2pcf(
         nbins=nbins,
     )
 
-    if cov == "jackknife" and int(npatch) < 2:
-        raise ValueError(f"cov='jackknife' needs patches; got npatch={npatch}")
-
     # Born-as-SACC ξ± part. theta = meanr; theta_nom = rnom.
+    jackknife = gg.var_method == "jackknife"
     s = xi_to_sacc(
         cv.sacc_nz(ver),
         cv.sacc_metadata(ver),
@@ -91,10 +88,8 @@ def run_2pcf(
         theta_nom=gg.rnom,
         npairs=gg.npairs,
         weight=gg.weight,
-        covariance=gg.cov if cov == "jackknife" else None,
-        variances=(
-            np.concatenate([gg.varxip, gg.varxim]) if cov == "diagonal" else None
-        ),
+        covariance=gg.cov if jackknife else None,
+        variances=None if jackknife else np.concatenate([gg.varxip, gg.varxim]),
     )
     out_path = sacc_out or os.path.join(
         output_dir or cv.cc["paths"]["output"],
@@ -116,7 +111,6 @@ def _from_snakemake(smk):
         cat_config=p["cat_config"],
         output_dir=p["output_dir"],
         grid=p.get("grid", "reporting"),
-        cov=p.get("cov", "none"),
         # The SACC part goes exactly where the rule declares it; the .txt
         # byproduct still lands under the resolved output dir.
         sacc_out=smk.output["sacc"],
@@ -149,12 +143,6 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--grid", default="reporting", help="SACC grid tag for the measured points"
     )
-    ap.add_argument(
-        "--cov",
-        default="none",
-        choices=["jackknife", "diagonal", "none"],
-        help="Covariance the part carries",
-    )
     a = ap.parse_args(argv)
     run_2pcf(
         ver=a.ver,
@@ -165,7 +153,6 @@ def _from_cli(argv=None):
         cat_config=a.cat_config,
         output_dir=a.out,
         grid=a.grid,
-        cov=a.cov,
     )
 
 
diff --git a/workflow/tests/conftest.py b/workflow/tests/conftest.py
new file mode 100644
index 00000000..5540e091
--- /dev/null
+++ b/workflow/tests/conftest.py
@@ -0,0 +1,222 @@
+"""Host-side harness: the workflow as a person launches it.
+
+These tests run under the host launcher, never inside the image::
+
+    uv run --isolated --no-project --python 3.12 --with snakemake \\
+        --with snakemake-executor-plugin-slurm --with pytest pytest workflow/tests
+
+``sp_validation`` is absent from that environment, so every Snakefile has to
+parse with the standard library and Snakemake alone -- the condition a host
+Snakemake is in. The ``candide`` tests need candide itself; CI deselects them.
+
+The ``toy`` fixture is a disposable checkout: copies of ``workflow/`` and
+``papers/cosmo_val/``, this checkout's ``src/`` symlinked in, a one-catalogue
+``cosmo_val/cat_config.yaml``, a touched catalogue file, the processed CosmoCov
+covariances already in place (their inputs live on candide), and both output
+roots in tmp.
+"""
+
+import dataclasses
+import importlib.util
+import os
+import re
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+import yaml
+
+REPO = Path(__file__).resolve().parents[2]
+
+# The toy catalogue and its leakage-corrected variant.
+VERSIONS = ("SP_v0.1", "SP_v0.1_leak_corr")
+
+
+@dataclasses.dataclass
+class Job:
+    rule: str
+    input: list
+    output: list
+    wildcards: dict
+
+
+_RULE = re.compile(r"^(?:local)?rule (\w+):$")
+_FIELD = re.compile(r"^    (\w+): (.*)$")
+
+
+def parse_jobs(text):
+    """The jobs a ``snakemake -n`` listing schedules."""
+    jobs, fields = [], None
+    for line in text.splitlines():
+        if match := _RULE.match(line):
+            fields = {"rule": match[1]}
+            jobs.append(fields)
+        elif fields is not None and (match := _FIELD.match(line)):
+            fields[match[1]] = match[2]
+        else:
+            fields = None
+    return [
+        Job(
+            rule=f["rule"],
+            input=f["input"].split(", ") if "input" in f else [],
+            output=f["output"].split(", ") if "output" in f else [],
+            wildcards=dict(
+                pair.split("=", 1)
+                for pair in f.get("wildcards", "").split(", ")
+                if pair
+            ),
+        )
+        for f in jobs
+    ]
+
+
+def _load_module(path, name, env):
+    """Import a workflow module by path under ``env`` (it reads env at import)."""
+    saved = os.environ.copy()
+    os.environ.update(env)
+    try:
+        spec = importlib.util.spec_from_file_location(name, path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+    finally:
+        os.environ.clear()
+        os.environ.update(saved)
+    return module
+
+
+# The package's image model, loaded by path: sp_validation is absent here.
+container = _load_module(
+    REPO / "src" / "sp_validation" / "container.py", "spv_container", {}
+)
+
+
+@dataclasses.dataclass
+class Toy:
+    root: Path
+    rundir: Path
+    cosmo_val: Path
+    cosmo_inference: Path
+    env: dict
+    config: dict
+    common: object
+    covariances: dict  # (version, "g" | "ng") -> the processed CosmoCov file
+
+    def snakemake(self, *args, cwd=None, env=None, timeout=300):
+        """Run the host Snakemake in the toy's paper directory, or in ``cwd``.
+
+        ``env`` replaces the toy's environment.
+        """
+        return subprocess.run(
+            [sys.executable, "-m", "snakemake", "--cores", "1", *args],
+            cwd=cwd or self.rundir,
+            env=env or self.env,
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            timeout=timeout,
+            check=False,
+        )
+
+
+def _cat_config(catalogue):
+    entry = {
+        "subdir": str(catalogue.parent),
+        "pipeline": "SP",
+        "colour": "orange",
+        "marker": "^",
+        "cov_th": {"A": 100.0, "n_e": 5.0, "sigma_e": 0.3},
+        "shear": {
+            "path": str(catalogue),
+            "redshift_path": str(catalogue.parent / "nz_SP_v0.1_A.txt"),
+            "w_col": "w",
+            "e1_col": "e1",
+            "e2_col": "e2",
+            "e1_col_corrected": "e1_leak_corrected",
+            "e2_col_corrected": "e2_leak_corrected",
+        },
+    }
+    return {VERSIONS[0]: entry, "paths": {"output": "./output"}}
+
+
+@pytest.fixture(scope="session")
+def toy(tmp_path_factory):
+    root = tmp_path_factory.mktemp("toy")
+    skip = shutil.ignore_patterns(".snakemake", "__pycache__", "tests")
+    shutil.copytree(REPO / "workflow", root / "workflow", ignore=skip)
+    shutil.copytree(
+        REPO / "papers" / "cosmo_val", root / "papers" / "cosmo_val", ignore=skip
+    )
+    (root / "src").symlink_to(REPO / "src")
+
+    catalogue = root / "data" / "toy_shear.fits"
+    catalogue.parent.mkdir()
+    catalogue.touch()
+    (root / "cosmo_val").mkdir()
+    (root / "cosmo_val" / "cat_config.yaml").write_text(
+        yaml.safe_dump(_cat_config(catalogue))
+    )
+
+    rundir = root / "papers" / "cosmo_val"
+    config_path = rundir / "config" / "config.yaml"
+    config = yaml.safe_load(config_path.read_text())
+    config["versions"] = list(VERSIONS)
+    config["fiducial"]["version"] = VERSIONS[1]
+    config["fiducial"]["mock_version"] = VERSIONS[0]
+    config_path.write_text(yaml.safe_dump(config, sort_keys=False))
+
+    env = {
+        k: v
+        for k, v in os.environ.items()
+        if k not in ("SNAKEMAKE_PROFILE", "APPTAINERENV_PYTHONPATH")
+    }
+    env.update(
+        COSMO_VAL=str(root / "out" / "cosmo_val"),
+        COSMO_INFERENCE=str(root / "out" / "cosmo_inference"),
+        XDG_CACHE_HOME=str(root / "cache"),
+        # No local image: launches resolve the registry tag.
+        SPV_CONTAINER=str(root / "cache" / "absent.sif"),
+        SPV_SANDBOX=str(root / "cache" / "absent-sandbox"),
+        TMPDIR=str(tmp_path_factory.getbasetemp()),
+        PYTHONUNBUFFERED="1",
+        PYTHONNOUSERSITE="1",
+    )
+    common = _load_module(root / "workflow" / "common.py", "toy_common", env)
+
+    # The processed CosmoCov covariances the cosmo_val rules read, in place.
+    grids = common.xi_grids(config, config["fiducial"])
+    mask = "_masked" if config["covariance"].get("default_masked") else ""
+    covariances = {}
+    for version in VERSIONS:
+        for gaussian, grid in (("ng", grids["reporting"]), ("g", grids["integration"])):
+            path = covariances[version, gaussian] = Path(
+                common.covariance_path(
+                    version,
+                    config["fiducial"]["blind"],
+                    gaussian,
+                    grid["min_sep"],
+                    grid["max_sep"],
+                    grid["nbins"],
+                    mask,
+                )
+            )
+            path.parent.mkdir(parents=True, exist_ok=True)
+            path.touch()
+
+    return Toy(
+        root=root,
+        rundir=rundir,
+        cosmo_val=Path(env["COSMO_VAL"]),
+        cosmo_inference=Path(env["COSMO_INFERENCE"]),
+        env=env,
+        config=config,
+        common=common,
+        covariances=covariances,
+    )
+
+
+on_candide = pytest.mark.skipif(
+    not Path("/n17data/cdaley/unions").exists() or shutil.which("apptainer") is None,
+    reason="needs candide: /n17data and apptainer",
+)
diff --git a/workflow/tests/data/container_smoke/Snakefile b/workflow/tests/data/container_smoke/Snakefile
new file mode 100644
index 00000000..62f4754b
--- /dev/null
+++ b/workflow/tests/data/container_smoke/Snakefile
@@ -0,0 +1,27 @@
+# Standalone workflow exercised by workflow/tests/test_container_smoke.py.
+#
+# It composes workflow/ as every entry Snakefile does: common's launch-time
+# setup, this checkout's src/ on the job's PYTHONPATH, the image a launch
+# resolves. The executor, the apptainer deployment and the binds come from the
+# driving profile (workflow/profiles/candide). See container_smoke.py for what
+# the job checks.
+
+import os
+import sys
+
+sys.path.insert(0, os.path.realpath(os.path.join(str(workflow.basedir), "../../..")))
+import common
+
+common.inject_checkout_pythonpath(config)
+
+
+container: common.resolve_container(config.get("container"))
+
+
+rule container_smoke:
+    output:
+        "results/container_smoke.yaml",
+    resources:
+        runtime=5,
+    script:
+        "container_smoke.py"
diff --git a/src/sp_validation/tests/data/container_smoke/container_smoke.py b/workflow/tests/data/container_smoke/container_smoke.py
similarity index 61%
rename from src/sp_validation/tests/data/container_smoke/container_smoke.py
rename to workflow/tests/data/container_smoke/container_smoke.py
index 274cd85c..c25873b3 100644
--- a/src/sp_validation/tests/data/container_smoke/container_smoke.py
+++ b/workflow/tests/data/container_smoke/container_smoke.py
@@ -2,27 +2,27 @@
 
 Cheap sanity check of the profile-driven container path -- same executor
 (slurm), same software-deployment-method (apptainer), same apptainer-args
-binds, same container image every real rule uses.  Four things it proves, each
+binds, same container image every real rule uses.  What it proves, each
 written to the output YAML:
 
   * the job really ran inside the image (``APPTAINER_CONTAINER``, set by
     apptainer itself -- without it the rest could all pass on the bare host);
-  * the editable ``sp_validation`` install resolves on the container's
-    PYTHONPATH (import provenance: file + version, not just import success);
-  * the numeric stack works (numpy eigh on a small fixed matrix).
-    ``OMP_NUM_THREADS`` is recorded but not asserted -- see the assertions;
+  * which ``sp_validation`` the job imports (file + version, not just import
+    success);
+  * which ``snakemake`` package unpickled the injected ``snakemake`` object
+    (file + version);
   * which commit of this checkout is running (git rev-parse from inside the
     container -- proves /home is bound and usable, not just readable).
 
 Driven by the co-located Snakefile; the assertions on the output YAML live in
-src/sp_validation/tests/test_container_smoke.py (marked ``slow``, cluster only).
+workflow/tests/test_container_smoke.py (candide only).
 """
 
 import os
 import platform
 import subprocess
+import sys
 
-import numpy as np
 import yaml
 
 # --- the job is actually inside the image ---------------------------------
@@ -30,7 +30,7 @@
     "apptainer_container": os.environ.get("APPTAINER_CONTAINER", "unset"),
 }
 
-# --- editable install resolves inside the container ------------------------
+# --- the sp_validation the job imports -------------------------------------
 import sp_validation  # noqa: E402
 
 sp_validation_info = {
@@ -38,24 +38,19 @@
     "file": sp_validation.__file__,
 }
 
-# --- numeric stack + threading -----------------------------------------
-rng = np.random.default_rng(seed=42)
-a = rng.standard_normal((8, 8))
-symmetric = a + a.T
-eigenvalues = np.linalg.eigh(symmetric)[0]
-
-numeric_info = {
-    "numpy_version": np.__version__,
-    "eigenvalues": [float(v) for v in eigenvalues],
-    "omp_num_threads": os.environ.get("OMP_NUM_THREADS", "unset"),
+# --- the snakemake package the preamble unpickled with ---------------------
+# Read from sys.modules: importing it here would rebind the injected global.
+snakemake_package = sys.modules["snakemake"]
+snakemake_info = {
+    "version": snakemake_package.__version__,
+    "file": snakemake_package.__file__,
 }
 
 # --- provenance: what commit is actually running in the container ---------
-# src/sp_validation/tests/data/container_smoke/ -> repo root, five levels up.
-# (This is the checkout the Snakefile came from, which is what we want to
-# report; the editable install may well resolve to a *different* checkout.)
+# workflow/tests/data/container_smoke/ -> repo root, four levels up: the
+# checkout the Snakefile came from.
 repo_dir = os.path.abspath(
-    os.path.join(os.path.dirname(os.path.abspath(__file__)), *([os.pardir] * 5))
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), *([os.pardir] * 4))
 )
 try:
     commit = subprocess.run(
@@ -79,7 +74,7 @@
         {
             "container": container_info,
             "sp_validation": sp_validation_info,
-            "numeric": numeric_info,
+            "snakemake": snakemake_info,
             "provenance": provenance,
         },
         f,
diff --git a/workflow/tests/pytest.ini b/workflow/tests/pytest.ini
new file mode 100644
index 00000000..53d26a02
--- /dev/null
+++ b/workflow/tests/pytest.ini
@@ -0,0 +1,3 @@
+[pytest]
+markers =
+    candide: needs candide (its disks, apptainer and your image); CI deselects these with -m "not candide"
diff --git a/workflow/tests/test_container_smoke.py b/workflow/tests/test_container_smoke.py
new file mode 100644
index 00000000..b6b6fdeb
--- /dev/null
+++ b/workflow/tests/test_container_smoke.py
@@ -0,0 +1,90 @@
+"""One real SLURM job through the committed candide profile.
+
+The executor, the apptainer deployment method, the bind mounts and the job
+bound come from that profile, launched as the README launches a target; the
+test Snakefile composes workflow/ as the entry Snakefiles do, so the job runs
+the image that launch resolves (your SIF or sandbox) in the environment that
+launch hands its jobs. That contract is what's under test, so it runs only where
+``sbatch`` exists: a candide login node. Compute nodes have none, so in a job
+step on an allocation it skips.
+
+The job writes a YAML report (see data/container_smoke/container_smoke.py); the
+assertions below check what it reports.
+"""
+
+import os
+import re
+import shutil
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+
+import pytest
+import snakemake
+import yaml
+from conftest import REPO, container, on_candide
+
+SMOKE = Path(__file__).resolve().parent / "data" / "container_smoke"
+
+
+@pytest.mark.candide
+@on_candide
+@pytest.mark.skipif(
+    shutil.which("sbatch") is None, reason="submits a SLURM job: run on a login node"
+)
+def test_container_smoke():
+    assert container.resolve_image()[1] != "tag", (
+        "no local image; run `spv-container pull`"
+    )
+
+    # Not pytest's tmp_path: that lives in the submit host's /tmp, which the
+    # compute node cannot see, so the job's output would "go missing". The
+    # workdir must be on a shared filesystem.
+    workdir = Path(tempfile.mkdtemp(prefix="container_smoke_", dir=Path.home()))
+
+    env = os.environ | {"PYTHONNOUSERSITE": "1", "PYTHONUNBUFFERED": "1"}
+    result = subprocess.run(
+        [
+            sys.executable,
+            "-m",
+            "snakemake",
+            "--profile",
+            str(REPO / "workflow/profiles/candide"),
+            "-s",
+            str(SMOKE / "Snakefile"),
+            "--directory",
+            str(workdir),
+            "container_smoke",
+        ],
+        env=env,
+        text=True,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        timeout=600,
+        check=False,
+    )
+    assert result.returncode == 0, result.stdout
+
+    report = yaml.safe_load((workdir / "results/container_smoke.yaml").read_text())
+
+    # The job ran inside the image, not on the bare host. Everything below would
+    # pass on the host too, so this is the assertion that makes them mean
+    # something: apptainer sets APPTAINER_CONTAINER in every process it starts.
+    assert report["container"]["apptainer_container"] != "unset", report["container"]
+
+    # The job imports the launched checkout's sp_validation, not the image's.
+    module_file = Path(report["sp_validation"]["file"]).resolve()
+    assert module_file == (REPO / "src/sp_validation/__init__.py").resolve(), (
+        module_file
+    )
+
+    # The job read the pickle with the Snakemake that wrote it.
+    assert report["snakemake"]["version"] == snakemake.__version__, report["snakemake"]
+
+    # git worked inside the container, so /home is bound and usable.
+    assert re.fullmatch(r"[0-9a-f]{40}", report["provenance"]["commit"]), report[
+        "provenance"
+    ]
+
+    shutil.rmtree(workdir)  # keep only on failure, for post-mortem
diff --git a/workflow/tests/test_dag.py b/workflow/tests/test_dag.py
new file mode 100644
index 00000000..4d2ba017
--- /dev/null
+++ b/workflow/tests/test_dag.py
@@ -0,0 +1,173 @@
+"""DAG properties, checked through the host launcher (see conftest.py)."""
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+from conftest import REPO, VERSIONS, on_candide, parse_jobs
+
+
+def test_assemble_resolves(toy):
+    """Each terminal file gathers every part and the analytic covariances.
+
+    The ξ± block takes the CosmoCov covariance on the reporting grid, and the
+    harmonic block is the part on the fiducial harmonic binning with the
+    NaMaster covariance of that same binning.
+    """
+    result = toy.snakemake("-n", "assemble_sacc_all")
+    assert result.returncode == 0, result.stdout
+    jobs = [j for j in parse_jobs(result.stdout) if j.rule == "assemble_sacc"]
+    grids = toy.common.xi_grids(toy.config, toy.config["fiducial"])
+    reporting = toy.common.grid_binning(grids["reporting"])
+    harmonic = toy.common.pseudo_cl_tag(toy.config)
+    assert sorted(j.wildcards["version"] for j in jobs) == sorted(VERSIONS)
+    for job in jobs:
+        version = job.wildcards["version"]
+        assert [Path(o).name for o in job.output] == [f"{version}.sacc"]
+        assert {Path(f).name for f in job.input} == {
+            f"{version}_xi_{reporting}.sacc",
+            toy.covariances[version, "ng"].name,
+            f"pseudo_cl_{version}_{harmonic}.sacc",
+            f"pseudo_cl_cov_{version}_{harmonic}.fits",
+            f"{version}_cosebis.sacc",
+            f"{version}_pure_eb.sacc",
+            f"rho_tau_{version}_{reporting}.sacc",
+        }, job.input
+
+
+def test_one_integration_grid(toy):
+    """COSEBIs and pure-E/B share the integration-grid part and its covariance."""
+    result = toy.snakemake("-n", "assemble_sacc_all")
+    assert result.returncode == 0, result.stdout
+    jobs = parse_jobs(result.stdout)
+    grids = toy.common.xi_grids(toy.config, toy.config["fiducial"])
+
+    tag = toy.common.grid_binning(grids["integration"])
+    for version in VERSIONS:
+        by_rule = {
+            j.rule: set(j.input) for j in jobs if j.wildcards.get("version") == version
+        }
+        part = str(toy.cosmo_val / f"{version}_xi_{tag}.sacc")
+        covariance = str(toy.covariances[version, "g"])
+        assert by_rule["cv_cosebis"] == {part, covariance}, by_rule["cv_cosebis"]
+        assert {part, covariance} <= by_rule["cv_pure_eb"], by_rule["cv_pure_eb"]
+
+
+@pytest.mark.parametrize("named", [True, False], ids=["named", "unnamed"])
+def test_outputs_stay_in_the_output_roots(toy, named):
+    """Nothing the suite declares lands outside the configured output roots.
+
+    A launch that names no COSMO_VAL writes into its own checkout's
+    cosmo_val/output.
+    """
+    env = toy.env if named else {k: v for k, v in toy.env.items() if k != "COSMO_VAL"}
+    cosmo_val = toy.cosmo_val if named else toy.root / "cosmo_val" / "output"
+    result = toy.snakemake("-n", "all", env=env)
+    assert result.returncode == 0, result.stdout
+    roots = [
+        r.resolve() for r in (cosmo_val, toy.cosmo_inference, toy.rundir / "results")
+    ]
+    outputs = [Path(o) for j in parse_jobs(result.stdout) for o in j.output]
+    assert outputs, result.stdout
+    strays = [
+        o
+        for o in outputs
+        if not any((toy.rundir / o).resolve().is_relative_to(r) for r in roots)
+    ]
+    assert not strays, strays
+
+
+def _apptainer_stub(tmp_path):
+    """A PATH entry that answers where apptainer is not installed.
+
+    The candide profile deploys with apptainer, whose version Snakemake reads
+    even when it runs no job.
+    """
+    apptainer = tmp_path / "bin" / "apptainer"
+    apptainer.parent.mkdir()
+    apptainer.write_text("#!/bin/sh\necho apptainer version 1.3.4\n")
+    apptainer.chmod(0o755)
+    return apptainer.parent
+
+
+def test_a_job_needs_no_launch_cache(toy, tmp_path):
+    """A job starts on a node where the launch's XDG cache cannot exist.
+
+    Jobs inherit the launching shell's environment, whose XDG_CACHE_HOME may be
+    node-local. Under the candide profile, a Snakemake that cannot create that
+    cache still starts, and a job's environment carries no XDG_CACHE_HOME for
+    the Snakemake its job step starts.
+    """
+    blocker = tmp_path / "a-file"
+    blocker.touch()
+    candide = toy.root / "workflow" / "profiles" / "candide"
+    result = toy.snakemake(
+        "-n",
+        "--profile",
+        str(candide),
+        "assemble_sacc_all",
+        env=toy.env
+        | {
+            "XDG_CACHE_HOME": str(blocker / "cache"),
+            "PATH": f"{_apptainer_stub(tmp_path)}{os.pathsep}{toy.env['PATH']}",
+        },
+    )
+    assert result.returncode == 0, result.stdout
+
+    snakefile = tmp_path / "Snakefile"
+    snakefile.write_text(
+        f"import sys\nsys.path.insert(0, {str(toy.root / 'workflow')!r})\n"
+        "import common\n\n"
+        'rule job:\n    output: "env.txt"\n'
+        '    shell: "printenv XDG_CACHE_HOME > {output} || true"\n'
+    )
+    result = toy.snakemake(
+        "-s",
+        str(snakefile),
+        "--directory",
+        str(tmp_path),
+        env=toy.env | {"XDG_CACHE_HOME": str(tmp_path / "launch-cache")},
+    )
+    assert result.returncode == 0, result.stdout
+    assert (tmp_path / "env.txt").read_text() == ""
+
+
+def _real_dry_run(paper, targets):
+    env = {k: v for k, v in os.environ.items() if k != "SNAKEMAKE_PROFILE"}
+    env.update(PYTHONUNBUFFERED="1", PYTHONNOUSERSITE="1")
+    return subprocess.run(
+        [
+            sys.executable,
+            "-m",
+            "snakemake",
+            "-n",
+            "--profile",
+            str(REPO / "workflow" / "profiles" / "candide"),
+            *targets,
+        ],
+        cwd=REPO / "papers" / paper,
+        env=env,
+        text=True,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        timeout=600,
+        check=False,
+    )
+
+
+@pytest.mark.candide
+@on_candide
+@pytest.mark.parametrize(
+    "paper, targets",
+    [
+        ("cosmo_val", ["assemble_sacc_all"]),
+        ("bmodes", ["paper"]),
+    ],
+    ids=["cosmo_val", "bmodes"],
+)
+def test_papers_resolve_on_candide(paper, targets):
+    """The real paper DAGs resolve against the real catalogues and your image."""
+    result = _real_dry_run(paper, targets)
+    assert result.returncode == 0, result.stdout

From a8a08bc436f38b246eabd54f9a6e712768ac0aaf Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Mon, 28 Sep 2026 23:46:08 +0200
Subject: [PATCH 40/83] Scrub the legacy n(z) A/B/C blind: an n(z) is a
 catalogue entry's (#359)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* tests: pin pure-E/B on exact-binning and committed ξ±; drop dead catalogue paths

The synthetic pure-E/B pins passed in CI and failed on candide because
TreeCorr's default bin_slop/angle_slop make ξ± follow the tree's top-level
split, which varies with the jackknife patches and, through min_top, with the
thread count TreeCorr takes from cpu_count(). Fixed patch centres alone leave
a 4-vs-48-thread spread (reporting ξ− up to 16%); exact binning removes it
(1e-12). The test measures with bin_slop = angle_slop = 0, and test_b_modes
pins pure_eb_from_xi on the same ξ±, committed as
tests/data/pure_eb_xi_fixture.npz.

The configured-path guard skips cat_config's paths.output and directory-less
calibration params.input_path values. The two LFmask entries (data gone) and
the six unread covmat_file keys leave cat_config.yaml, and the slow duplicate
test_catalog_paths_exist goes.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* cosmo_val: pin TreeCorr's min_top so ξ± does not depend on the machine

TreeCorr derives min_top, the depth of its root cells, from its thread count
(max(3, ceil(log2 n)), with n from cpu_count() when unset). The root cells set
which pairs bin_slop approximates, so at default slop calculate_2pcf's ξ±
depended on the node: on the synthetic catalogue, 48 threads move ξ± by 0.038σ
against 4. The shared treecorr_config pins min_top = 6, which is what TreeCorr
derives on candide's 48- and 64-CPU nodes; the same config reaches the
aperture-mass, leakage and ρ/τ correlations.

test_calculate_2pcf_does_not_depend_on_thread_count measures at production
binning on 4 and 48 threads (fresh Catalog each, shared patch centres) and
requires agreement below 1e-6σ; it is red without the pin.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* b_modes: one pure-E/B kernel call; tests pin each pure-E/B fact once

calculate_pure_eb_correlation (modes and jackknife) and pure_eb_covariance_mc
call cosmo_numba through pure_eb_from_xi, so the transform test_b_modes pins
is the one every pure-E/B product runs through.

The committed ξ± fixture is now a conftest fixture (pure_eb_xi) with two
users. The synthetic pure-E/B test asserts that the ξ± and edges it measures
equal the fixture (agreement 2e-13), and that its modes are pure_eb_from_xi of
them; this replaces its copy of the mode pins, and np.savez of what it
measures regenerates the fixture. test_b_modes pins pure_eb_from_xi on the
fixture, which fails loudly if cosmo_numba does not import, with no skip.
A failure now names what moved: the measured ξ±, the wiring into the kernel,
or the transform.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* b_modes: one pure-E/B transform per jackknife realisation

The jackknife covariance function indexed pure_EB(x) once per key, so every
realisation ran cosmo_numba's transform six times (55 transforms at npatch=8
where 10 suffice; values unchanged). _eb_vector concatenates the modes in
_EB_KEYS order for both the jackknife and the MC covariance.

The synthetic pure-E/B test counts transforms around calculate_pure_eb and is
red on the per-key closure (55 > npatch + 2).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* tests: glue-section header stops enumerating which tests compare values

Two tests in the section compare values (pure-E/B against committed ξ±, ξ±
across TreeCorr thread counts); the header defers to each test's docstring
instead of listing them.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: host/image parity check, catalogue config from the checkout, one output root, one integration grid

- Launch-time parity: container.image_runtime reads the image's Python minor
  and Snakemake version (SIF via apptainer, sandbox off disk, tags skipped);
  common.check_host_parity stops a launch whose host Snakemake differs, with
  the reinstall command. configure() and image_sims.smk call it; the README
  install line pins --python 3.12.
- The catalogue config is the launched checkout's cosmo_val/cat_config.yaml,
  loaded by configure() into CATALOG_CONFIG; the paper Snakefiles no longer
  merge it into `config`, and covariance.smk / ecut.smk read CATALOG_CONFIG.
- One output root: cv_init_params passes output_dir=COSMO_VAL, cv_runner no
  longer chdirs into the live checkout, and CosmologyValidation drops its
  COSMO_VAL environment fallback.
- One integration grid (R12): the cosebis grid is gone; cv_cosebis reads the
  integration part and the CosmoCov g covariance on that grid (the one
  pure-E/B uses). An npatch=1 grid defaults to the diagonal covariance, and a
  binning outside the named grids takes its covariance from its own patches.
- The candide profile sets jobs: 100.
- workflow/tests: host-launcher DAG tests on a toy checkout (P1-P3) and the
  real papers on candide (P4, P5 = the container smoke test, moved here); CI
  runs them in a workflow-dag job. test_bmodes_workflow_dry_run.py is
  replaced by P4.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: the image lives under ~/.cache; launch guards read the real image

- container.CACHE_DIR is ~/.cache/sp_validation whatever XDG_CACHE_HOME says.
  A job runs the image from the path the launching host resolved, and on
  candide XDG_CACHE_HOME is node-local /scratch: from such a shell the launch
  fell back to the registry tag and skipped the parity check.
  SPV_CONTAINER / SPV_SANDBOX remain the overrides.
- test_launch_reads_the_image_under_home: a mismatched image under a fake
  ~/.cache stops the launch while XDG_CACHE_HOME points elsewhere.
- test_papers_resolve_on_candide asserts unconditionally that the launch read
  a local image (no "parity unchecked").
- test_image_sims_checks_parity_at_launch: the standalone image-sims
  Snakefile stops on a mismatched image.
- test_assemble_resolves pins each terminal file's inputs: the reporting ξ±
  part with its CosmoCov ng covariance, the fiducial-binning pseudo-Cl part
  with its NaMaster covariance, COSEBIs, pure-E/B and ρ/τ.
- The container smoke test launches as the README does (no --jobs), so the
  candide profile's job bound is under test.
- Test docstrings drop the design-table row IDs.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: a ξ± part's covariance follows its patches, in one place

A grid is a binning; rule xi takes cov=patch_cov(npatch) directly, so
grid_cov, _named_grid and the grids' cov key go. cv_init_params loses its
unused version_list, and the cosmo_val rules pass one CV_INIT.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/README: scope the output-root sentence to papers/cosmo_val

Name where the other rules write: masks under the run directory's
output/masks/, papers/bmodes' figures and macros under its docs/, image sims
under grids_base.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: SLURM jobs start whatever the launch's XDG_CACHE_HOME; P4/P5 test what a launch does

A job's Snakemake inherits the launching shell's environment (--export=ALL),
and a login shell may point XDG_CACHE_HOME at node-local storage: every job
then fails creating its source cache. Two halves close it, each needed (shown
on n33 by replaying the captured sbatch job command with srun stubbed):
- the candide profile leaves source-cache out of shared-fs-usage, so a job
  neither reuses the launch's cache path nor creates its own under XDG;
- common.py drops XDG_CACHE_HOME from what jobs inherit, because the
  slurm-jobstep executor forces a shared source cache on the Snakemake it
  starts for each job step.
test_a_job_needs_no_launch_cache covers both (each mutation turns it red).

P4 (test_papers_resolve_on_candide) now passes from a shell with node-local
XDG_CACHE_HOME under srun, and its bmodes case also resolves an e-cut
catalogue, so both CATALOG_CONFIG readers in ecut.smk are exercised. P5 hands
the smoke Snakefile the image a launch resolves (no registry pull). The host
launcher and CI carry snakemake-executor-plugin-slurm, as the README install
line does, so the candide profile is parsed in CI.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* papers/bmodes: the sweep drivers read the image from ~/.cache, as container.py does

container_env.sh still followed XDG_CACHE_HOME, so from a login shell that
points it at node-local storage the sweep drivers ran a missing image while
spv-container and the workflow found the real one.
test_sweep_drivers_run_the_resolved_image pins the shell copy of the
resolution (cache and sandbox precedence) to container.resolve_image.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* run_2pcf: a ξ± part carries the covariance its measurement estimated

Which covariance a part carries had two homes that disagreed: rule xi passed
cov=patch_cov(npatch) ("diagonal" at npatch=1), while run_2pcf's own default,
reached by the CLI and papers/bmodes' run_xi_sweep, was "none". The same
file name then held variances or not depending on who wrote it. run_2pcf now
carries what TreeCorr estimated (gg.var_method, which calculate_2pcf sets from
npatch): the jackknife covariance with patches, the shot-noise diagonal
without. The cov argument, --cov, the jackknife guard, rule xi's cov param
and common.patch_cov (with its test) go. Rule xi hands its wildcards to
grid_of directly (xi_binning_of goes).

test_xi_part_carries_the_covariance_the_measurement_estimated runs run_2pcf
on the synthetic catalogue at npatch 1 and 4; the previous run_2pcf fails
both cases.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: the launch-cache test answers the profile's apptainer check with a stub

The candide profile deploys with apptainer, and Snakemake asks for the apptainer
binary and its version even in a dry-run, so test_a_job_needs_no_launch_cache
failed on GitHub's runners, which have no apptainer. A stub apptainer on the
test's PATH answers that check; the dry-run reads nothing else from it.

In a CI emulation (a clean checkout, a fresh HOME, no apptainer or SLURM on
PATH, the workflow-dag command) the suite goes from 1 failed, 11 passed to 12
passed. Both mutations still turn the test red there: source-cache added back to
the profile's shared-fs-usage (NotADirectoryError under the blocked
XDG_CACHE_HOME) and the XDG_CACHE_HOME pop removed from common.py (the job sees
the launch's cache).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: the smoke Snakefile composes workflow/ as the entry Snakefiles do

P5's Snakefile never imported common, so its launch did not drop
XDG_CACHE_HOME: from a login shell pointing it at node-local /scratch, the
job's inner Snakemake died creating its source cache (PermissionError), which
says nothing about what a real launch does. The Snakefile now imports common,
resolves its image with common.resolve_container and checks host/image parity,
as the entry Snakefiles do. P5 launches exactly as the README does, with no
--config container=, and the literal default image and the test that kept it in
step with CONTAINER_URI go.

Checked on n33 with sbatch stubbed to record what it would submit, then the
recorded job replayed with srun stubbed, from a shell with XDG_CACHE_HOME set
to /scratch/cdaley/tmp/xdg: at the parent commit the job environment carries
XDG_CACHE_HOME and the replay fails with PermissionError; with this commit it
carries none, the job runs in the SIF, and P5's assertions pass on its report.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: resolve_container checks the image it returns

The host/image parity check had a home beside the resolver, so configure()
resolved the image once only to check it, and every Snakefile that picks its
own image (image_sims.smk, the smoke Snakefile) had to remember a second
line. resolve_container now runs check_host_parity on the image it returns:
the image checked is the image that runs, and the extra call sites go.
check_host_parity stays cached, since composed Snakefiles evaluate
container: more than once.

Removing the check from resolve_container turns 5 host tests red (both
parity cases, the unreadable-image line, the image under ~/.cache, image
sims).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: P5 checks that the job imports the launched checkout's src

The smoke Snakefile imported common but never put the checkout's src/ on the
job's PYTHONPATH, so its job imported the image's baked sp_validation, and
P5's assertion (any editable src/ layout) passed on it. The smoke Snakefile
now calls inject_checkout_pythonpath as configure() does for the entry
Snakefiles, and P5 asserts the job's sp_validation is this checkout's
src/sp_validation/__init__.py.

Run on n08 through the default profile (apptainer, no SLURM): the job reports
<checkout>/src/sp_validation/__init__.py; with the injection removed it
reports /sp_validation/src/sp_validation/__init__.py, which the new assertion
rejects and the old one accepted. P5 itself needs a SLURM submit host and has
not been run.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow: COSMO_VAL defaults to the launched checkout's own output tree

A launch from any checkout that named no COSMO_VAL read that checkout's
catalogue config and code but wrote every cosmo_val product into the
production tree, silently. COSMO_VAL now defaults to the launched checkout's
(gitignored) cosmo_val/output, so production is written only from the
production checkout or when a launch names it; COSMO_INFERENCE keeps its
shared default. README says so.

P2 gains an unnamed case: with COSMO_VAL unset, every declared output lies
under the toy checkout's cosmo_val/output, the inference root or results/.
Restoring the production default turns it red (outputs under
/n17data/cdaley/unions/code/sp_validation/cosmo_val/output).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* calculate_2pcf: the ξ± text dump carries columns only, so it reads back

With patches, calculate_2pcf wrote its .txt with write_cov=True and no
per-patch results. TreeCorr 5.1.4 writes num_rows only alongside patch
results, so its reader ran on into the cov block and raised ("got 12 columns
instead of 11"). The two ξ± figure rules re-enter calculate_2pcf on the
reporting grid (npatch=100) and hit exactly that read. The dump now carries
the columns only; the covariance matrix lives in the SACC part, and the
figure readers use the columns.

test_a_patched_xi_dump_reads_back measures at npatch=4, then re-enters
calculate_2pcf from a fresh CosmologyValidation and compares the columns;
under write_cov=True it fails with the ValueError above.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* workflow/tests: the candide profile's job bound is checked on any host

A real launch through the committed candide profile, of an up-to-date
target, submits nothing, so it runs in CI and on an allocation; the same
launch through the profile without `jobs` is refused. test_container_smoke
submits a real job and needs sbatch, which only a login node has; it is
documented as the login-node check it is.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013EGDJZeA8v26eKGAHQqh5j

* profiles: wait 60 s for a job's outputs to appear

P5 (one real SLURM job through the candide profile, from a login node)
passed, but only on its retry: the job finished, its output took more
than 5 s to show on the login node's /home, and Snakemake re-ran it. On a
multi-hour job that retry costs hours, and a second miss fails the run.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* rho_tau_stats: 6 h wall clock; the jackknife ρ/τ outruns the 60 min default

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* Scrub the n(z) A/B/C blind: an n(z) is a catalogue entry's

Choosing an n(z) is choosing a catalogue entry: each entry's
shear.redshift_path is its n(z), read by CosmologyValidation.get_redshift
and by the workflow's redshift_path(version). CosmologyValidation loses its
blind override, and the pseudo-Cl, covariance, inference and papers/bmodes
filenames lose their blind token and wildcard.

- common: catalogue_entry()/redshift_path() replace build_redshift_path;
  covariance_{base,dir,path} and pseudo_cl_tag drop blind; the version
  constraint admits the _A/_B/_C entries.
- covariance.smk: get_cat_params and the star-halo mask resolve through
  the catalogue entry.
- papers/bmodes: bb_covariance_blind_independence and the talk n(z) plot
  compare fiducial.nz_realisations, the SP_v1.4.6.3_{A,B,C}_leak_corr
  entries; pure-E/B, COSEBIs PTE and pseudo-Cl paths are per version.
- cat_config: SP_v1.4.6.3_{A,B,C} match SP_v1.4.6.3 but for their n(z);
  SP_v1.4.8 and SP_v1.4.11.3(_ecut07) name the n(z) their covariances used.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* cat_config: the leakage n(z) is a path template, not an A/B/C blind key

nz.dndz.path names the file with {pipeline}; the same file as before.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* cat_config: SP_v1.4.11.2 reads the n(z) its covariance used

Like SP_v1.4.11.3, its covariance was built from nz_SP_v1.4.6_A.txt.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* workflow: no snakemake in the image, so any host Snakemake works

A script: job unpickles the host's snakemake object with whichever snakemake
it imports first, and the image's site-packages precede the host's appended
sys.path. The image carried its own (the workflow extra, and the base image's
jupyter extra), so the host had to match its version exactly. The Dockerfile
now uninstalls every snakemake* package after the sync and the workflow extra
drops snakemake; the job then reads the pickle with the package that wrote it.

The launch check keeps only the Python minor (check_host_python): the host's
snakemake and its compiled dependencies load into the image's interpreter.
The README install line, CI's DAG job and the test harness no longer pin a
Snakemake version. The container smoke job now reports which snakemake
unpickled its object and asserts it is the host's version.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* profiles: every key needed and non-default, each with its reason

Drops the candide LD_LIBRARY_PATH to /softs/openmpi: /softs is not bound, so
the path does not exist inside the container, and no containerized rule uses
MPI. Drops the default profile's --bind /home (apptainer mounts $HOME already).
States the real reasons for the rest: rerun-triggers leaves out software-env
because it hashes the per-person image path; shared-fs-usage leaves out
source-cache because jobs would be handed the launch's node-local cache path;
slurm_account because the executor's guess fails on candide; retries and
kept logs for fan-outs and their printed output.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* workflow: jobs read the repo's matplotlibrc, never the launching user's

Apptainer binds $HOME, so a job read the launcher's own matplotlibrc, and a
LaTeX preamble there the image cannot typeset stopped every figure rule.
common.py points each job's MATPLOTLIBRC (through APPTAINERENV_, past
--cleanenv) at an empty workflow/matplotlibrc. The container smoke job reports
the matplotlibrc it would read and the test asserts it is the repo's.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* CONTRACTS: the host-side workflow imports only stdlib and snakemake

workflow.common runs in the host Snakemake with no sp_validation installed,
and loads container.py by path; container.py imports only the standard
library, since it also runs as the spv-container CLI before any image exists.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* profiles/default: bind /home; Snakemake's --home <workdir> keeps apptainer from mounting it

A job's home is its working directory, so a checkout or output tree under
the launching user's home was invisible to the job: the toy run's jobs
imported the image's sp_validation instead of the checkout's src/.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TJSfyQjoQXjPKsEfkGhZLj

* workflow: jobs read the user's matplotlibrc; no snakemake-uninstall step

The image's TeX now carries sfmath, so a usetex preamble typesets inside
jobs and the repo matplotlibrc override has nothing left to guard. The
shapepipe:develop base ships no snakemake, so the uninstall step is gone;
pyproject says why the workflow extra must not re-add it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* workflow: resolve the image without a host/image Python check; trim profile and README

The launch resolves its image and runs it; the README states that the host
Snakemake runs on the image's Python (3.12). The CONTRACTS notes go (the
workflow-dag CI job is what enforces host-importability). The candide profile
excludes n17 and n36 only, and the README's output-roots paragraph and the
profile's shared-fs-usage comment say what they need to in fewer lines. The DAG
tests drop the Python-check tests and the profile job-bound test, and
test_one_integration_grid checks only the shared COSEBIs/pure-E/B inputs.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* tests: drop the smoke test's numeric check and perf-pinning asserts

The container smoke job reports and checks placement, imports, Snakemake
version and provenance; the host-side suite no longer needs numpy.
test_xi_grids loses its grid-count test, and the pure-E/B cosmo_val test keeps
its value checks without counting kernel calls.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* generate_paper_macros: read the single pure E/B joint PTE, drop per-blind macros

pure_eb_data_vector writes one fiducial `pte_joint`, so \ebfiducialPte and
\ebfullPte read that key. The covariance_blind_consistency and
per-blind PTE-spread macros go: nothing produces their inputs.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* paper macros: drop the unused COSEBIS macros and the inputs no script reads

generate_paper_macros no longer emits \cosebisfiducialPte, \cosebisfullPte or
\cosebisthetaMin/Max: they read keys cosebis_version_comparison evidence
does not carry and no paper uses them (the COSEBIS PTEs come from
\configPte<Ver>Cosebis[Full]). The macro rules drop the cosebis and BB
realisation evidence inputs accordingly.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* bb_covariance_nz_independence: realisations from fiducial.nz_realisations

The BB-covariance n(z)-independence check (renamed from
bb_covariance_blind_independence: rule, script, tapestry outputs) takes its
realisation labels from config fiducial.nz_realisations, the first as the
reference and every other compared against it; figure markers, evidence keys
(<label>_to_<reference>, plus reference_realisation) and summary maxima follow
the configured set rather than a hardcoded A/B/C. The summary flag is
cosebis_bb_nz_independent.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* workflow: redshift_path reads shear.redshift_path as written

Matches CosmologyValidation.get_redshift, which does not join the entry's
subdir; base_version's docstring names what uses it (footprint, plotting
style).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 cosmo_val/cat_config.yaml                     |  54 +-
 papers/bmodes/config/config.yaml              |  11 +-
 papers/bmodes/rules/figures.smk               | 132 ++---
 papers/bmodes/rules/paper.smk                 |   8 +-
 ...ce.py => bb_covariance_nz_independence.py} | 506 ++++++------------
 .../bmodes/scripts/calculate_pure_eb_ptes.py  |   9 +-
 papers/bmodes/scripts/cl_data_vector.py       |  15 +-
 .../bmodes/scripts/cl_version_comparison.py   |  15 +-
 .../scripts/compute_cosebis_pte_single.py     |  18 +-
 .../scripts/config_space_pte_matrices.py      |  48 +-
 .../scripts/cosebis_version_comparison.py     |   7 +-
 .../bmodes/scripts/gather_pure_eb_chunks.py   |  13 +-
 .../bmodes/scripts/generate_paper_macros.py   |  99 +---
 .../harmonic_config_cosebis_comparison.py     |  14 +-
 .../scripts/harmonic_space_pte_matrices.py    |  31 +-
 .../scripts/precompute_pure_eb_chunk.py       |   7 +-
 papers/bmodes/scripts/pure_eb_covariance.py   |   3 +-
 papers/bmodes/scripts/pure_eb_data_vector.py  |  10 +-
 .../scripts/pure_eb_version_comparison.py     |  10 +-
 papers/cosmo_val/config/config.yaml           |   2 -
 src/sp_validation/cosmo_val/core.py           |  17 +-
 .../cosmo_val/psf_systematics.py              |   4 +-
 .../tests/test_config_paths_exist.py          |  11 +-
 src/sp_validation/tests/test_cosmo_val.py     |   4 +-
 .../tests/test_cv_init_params.py              |   4 +-
 src/sp_validation/tests/test_pseudo_cl.py     |   4 +-
 workflow/common.py                            |  78 +--
 workflow/rules/cosmo_val.smk                  |   1 -
 workflow/rules/covariance.smk                 |  51 +-
 workflow/rules/glass_mock.smk                 |   6 +-
 workflow/rules/inference.smk                  |  34 +-
 workflow/rules/twopoint.smk                   |  18 +-
 workflow/scripts/generate_pseudo_cl.py        |  12 +-
 workflow/scripts/generate_pseudo_cl_cov.py    |  12 +-
 workflow/tests/conftest.py                    |   1 -
 35 files changed, 416 insertions(+), 853 deletions(-)
 rename papers/bmodes/scripts/{bb_covariance_blind_independence.py => bb_covariance_nz_independence.py} (51%)

diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index 218ba413..618f3972 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -539,19 +539,19 @@ SP_v1.4.6.3_B:
   getdist_colour: 0.0, 0.5, 1.0
   ls: dashed
   marker: d
+  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
+    A: 2894.0303815287743
+    n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
-  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
+    sigma_e: 0.3783979556382064
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
     star_flag: HSM_FLAG_STAR
     star_size: HSM_T_STAR
     hdu: 1
-    path: unions_shapepipe_psf_2024_v1.4.a.fits
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
     dec_col: Dec
     e1_PSF_col: HSM_G1_PSF
@@ -560,7 +560,7 @@ SP_v1.4.6.3_B:
     e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
-    path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
+    path: /n17data/UNIONS/WL/v1.4.x/v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
     redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6.3/nz_SP_v1.4.6.3_B.txt
     w_col: w_des
     e1_col: e1
@@ -574,7 +574,7 @@ SP_v1.4.6.3_B:
     dec_col: Dec
     e1_col: e1
     e2_col: e2
-    path: unions_shapepipe_star_2024_v1.4.a.fits
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
   patch_number: 150
 SP_v1.4.6.3_C:
   subdir: /n17data/UNIONS/WL/v1.4.x
@@ -583,19 +583,19 @@ SP_v1.4.6.3_C:
   getdist_colour: 0.0, 0.5, 1.0
   ls: dashed
   marker: d
+  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
+    A: 2894.0303815287743
+    n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
-  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
+    sigma_e: 0.3783979556382064
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
     star_flag: HSM_FLAG_STAR
     star_size: HSM_T_STAR
     hdu: 1
-    path: unions_shapepipe_psf_2024_v1.4.a.fits
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
     dec_col: Dec
     e1_PSF_col: HSM_G1_PSF
@@ -604,7 +604,7 @@ SP_v1.4.6.3_C:
     e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
-    path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
+    path: /n17data/UNIONS/WL/v1.4.x/v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
     redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6.3/nz_SP_v1.4.6.3_C.txt
     w_col: w_des
     e1_col: e1
@@ -618,7 +618,7 @@ SP_v1.4.6.3_C:
     dec_col: Dec
     e1_col: e1
     e2_col: e2
-    path: unions_shapepipe_star_2024_v1.4.a.fits
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
   patch_number: 150
 SP_v1.4.6.3_A:
   subdir: /n17data/UNIONS/WL/v1.4.x
@@ -627,19 +627,19 @@ SP_v1.4.6.3_A:
   getdist_colour: 0.0, 0.5, 1.0
   ls: dashed
   marker: d
+  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
+    A: 2894.0303815287743
+    n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
-  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
+    sigma_e: 0.3783979556382064
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
     star_flag: HSM_FLAG_STAR
     star_size: HSM_T_STAR
     hdu: 1
-    path: unions_shapepipe_psf_2024_v1.4.a.fits
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
     dec_col: Dec
     e1_PSF_col: HSM_G1_PSF
@@ -648,7 +648,7 @@ SP_v1.4.6.3_A:
     e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
-    path: v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
+    path: /n17data/UNIONS/WL/v1.4.x/v1.4.6.3/unions_shapepipe_cut_struc_2024_v1.4.6.3.fits
     redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6.3/nz_SP_v1.4.6.3_A.txt
     w_col: w_des
     e1_col: e1
@@ -662,7 +662,7 @@ SP_v1.4.6.3_A:
     dec_col: Dec
     e1_col: e1
     e2_col: e2
-    path: unions_shapepipe_star_2024_v1.4.a.fits
+    path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_star_2024_v1.4.a.fits
   patch_number: 150
 SP_v1.4.6_ecut07:
   subdir: /n17data/UNIONS/WL/v1.4.x
@@ -827,7 +827,7 @@ SP_v1.4.8:
   shear:
     R: 1.0
     path: v1.4.8/unions_shapepipe_cut_struc_2024_v1.4.8.fits
-    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
+    redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.8/nz_SP_v1.4.8_A.txt
     w_col: w_des
     e1_col: e1
     e1_col_corrected: e1_leak_corrected
@@ -871,7 +871,7 @@ SP_v1.4.11.2:
   shear:
     R: 1.0
     path: /n17data/UNIONS/WL/v1.4.x/v1.4.11.2/unions_shapepipe_cut_struc_2024_v1.4.11.2.fits
-    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
+    redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6/nz_SP_v1.4.6_A.txt
     w_col: w_des
     e1_col: e1
     e1_col_corrected: e1_leak_corrected
@@ -915,7 +915,7 @@ SP_v1.4.11.3:
   shear:
     R: 1.0
     path: v1.4.11.3/unions_shapepipe_cut_struc_2024_v1.4.11.3.fits
-    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
+    redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6/nz_SP_v1.4.6_A.txt
     w_col: w_des
     e1_col: e1
     e1_col_corrected: e1_leak_corrected
@@ -1049,7 +1049,7 @@ SP_v1.4.11.3_ecut07:
   shear:
     R: 1.0
     path: /n17data/cdaley/unions/pure_eb/results/ecut/SP_v1.4.11.3_ecut07.fits
-    redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
+    redshift_path: /n17data/sguerrini/UNIONS/WL/nz/v1.4.6/nz_SP_v1.4.6_A.txt
     w_col: w_des
     e1_col: e1
     e1_col_corrected: e1_leak_corrected
@@ -1156,9 +1156,9 @@ SP_v1.4.8_uncal:
   patch_number: 100
 nz:
   subdir: /n17data/mkilbing/astro/data/CFIS/v1.0/nz
+  # The n(z) of the PSF-leakage theory, by pipeline.
   dndz:
-    blind: A
-    path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz
+    path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_{pipeline}_A.txt
 paths:
   output: ./output
 SP_v1.6.6:
diff --git a/papers/bmodes/config/config.yaml b/papers/bmodes/config/config.yaml
index ea51a89f..1b31c647 100644
--- a/papers/bmodes/config/config.yaml
+++ b/papers/bmodes/config/config.yaml
@@ -20,7 +20,6 @@ nbins_int: [100, 200, 300, 500]
 # fiducial parameters
 fiducial:
   version: SP_v1.4.6.3_leak_corr
-  blind: A
   npatch: 1
   min_sep: 1.0
   max_sep: 250.0
@@ -28,6 +27,13 @@ fiducial:
 
   mock_version: SP_v1.4.6.3
 
+  # The mock version's catalogue under each n(z) realisation: entries identical
+  # but for shear.redshift_path, compared by bb_covariance_nz_independence.
+  nz_realisations:
+    A: SP_v1.4.6.3_A_leak_corr
+    B: SP_v1.4.6.3_B_leak_corr
+    C: SP_v1.4.6.3_C_leak_corr
+
   min_sep_int: 0.5
   max_sep_int: 300.0
   nbins_int: 1000
@@ -76,10 +82,9 @@ cl:
 
 # Harmonic pseudo-Cl fiducial binning — the tag the producer (twopoint.smk
 # pseudo_cl / pseudo_cl_cov) stamps into the filename, which the inference
-# consumer reconstructs to request the exact file. Canonical: A / powspace / 32.
+# consumer reconstructs to request the exact file. Canonical: powspace / 32.
 harmonic:
   fiducial:
-    blind: A
     binning: powspace
     nbins: 32
 
diff --git a/papers/bmodes/rules/figures.smk b/papers/bmodes/rules/figures.smk
index 322423d7..590735e3 100644
--- a/papers/bmodes/rules/figures.smk
+++ b/papers/bmodes/rules/figures.smk
@@ -8,7 +8,7 @@ LaTeX macros and tables.
 # Configuration
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-# TAPESTRY_DIR, PAPER_FIGURES_DIR, BLINDS, FIDUCIAL, PLANCK18 defined in Snakefile
+# TAPESTRY_DIR, PAPER_FIGURES_DIR, FIDUCIAL, PLANCK18 defined in Snakefile
 # COSMO_VAL, COSMO_INFERENCE, covariance_path() defined in Snakefile
 COSMO_VAL_OUTPUT = str(COSMO_VAL)  # String version for f-string interpolation
 
@@ -31,6 +31,10 @@ VERSION_LABELS = config["plotting"].get("version_labels", {})
 FIDUCIAL_VERSION = FIDUCIAL["version"]
 MOCK_VERSION = f"{FIDUCIAL['mock_version']}_leak_corr"
 
+# Catalogues identical to the mock version but for their n(z) realisation,
+# keyed by realisation label: bb_covariance_nz_independence compares them.
+NZ_REALISATIONS = FIDUCIAL["nz_realisations"]
+
 # Filter versions for different analysis types
 # Pure E/B and PTEs only apply to leak-corrected versions
 VERSIONS_LEAK_CORR = [v for v in config["versions"] if "_leak_corr" in v]
@@ -72,9 +76,9 @@ def _per_version_figure_outputs(fig_dir):
 # Path Helper Functions
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-def _reporting_cov_path(version, blind):
+def _reporting_cov_path(version):
     """Path to reporting-scale covariance (non-Gaussian, masked)."""
-    return covariance_path(version, blind, gaussian="ng")
+    return covariance_path(version, gaussian="ng")
 
 
 def _xi_reporting_path(version):
@@ -93,10 +97,10 @@ def _xi_integration_path(version):
     )
 
 
-def _cov_integration_path(version, blind):
+def _cov_integration_path(version):
     """Covariance path for integration bins (Gaussian, for COSEBIS PTE)."""
     return covariance_path(
-        version, blind, gaussian="g",
+        version, gaussian="g",
         min_sep=FIDUCIAL["min_sep_int"], max_sep=FIDUCIAL["max_sep_int"], nbins=FIDUCIAL["nbins_int"]
     )
 
@@ -116,17 +120,14 @@ def _pte_scale_cut_pairs():
 PTE_SCALE_CUT_PAIRS = _pte_scale_cut_pairs()
 
 
-def _pseudo_cl_path(version, blind="A", nbins=32):
-    """Return pseudo-Cl path for a catalog version.
-
-    All leak-corrected versions use consistent local naming with blind and binning.
-    """
-    return f"{COSMO_VAL_OUTPUT}/pseudo_cl_{version}_blind={blind}_powspace_nbins={nbins}.sacc"
+def _pseudo_cl_path(version, nbins=32):
+    """Return pseudo-Cl path for a catalog version."""
+    return f"{COSMO_VAL_OUTPUT}/pseudo_cl_{version}_powspace_nbins={nbins}.sacc"
 
 
-def _pseudo_cl_cov_path(version, blind="A", nbins=32):
+def _pseudo_cl_cov_path(version, nbins=32):
     """Return pseudo-Cl covariance path for a catalog version."""
-    return f"{COSMO_VAL_OUTPUT}/pseudo_cl_cov_{version}_blind={blind}_powspace_nbins={nbins}.fits"
+    return f"{COSMO_VAL_OUTPUT}/pseudo_cl_cov_{version}_powspace_nbins={nbins}.fits"
 
 
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
@@ -141,7 +142,7 @@ rule cosebis_version_comparison:
     input:
         # COSEBIs only for leak-corrected versions
         xi_integration=[_xi_integration_path(ver) for ver in VERSIONS_LEAK_CORR],
-        cov_integration=[_cov_integration_path(ver, "A") for ver in VERSIONS_LEAK_CORR],
+        cov_integration=[_cov_integration_path(ver) for ver in VERSIONS_LEAK_CORR],
     params:
         version_labels=VERSION_LABELS,
         versions=VERSIONS_LEAK_CORR,
@@ -167,7 +168,7 @@ rule cosebis_data_vector:
     input:
         # Per-version inputs: xi_{version} and cov_{version} for all versions
         **{f"xi_{ver}": _xi_integration_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
-        **{f"cov_{ver}": _cov_integration_path(ver, "A") for ver in VERSIONS_ALL_FOR_PLOTS},
+        **{f"cov_{ver}": _cov_integration_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
     params:
         cov_base_dir=str(COSMO_INFERENCE / "data/covariance"),
     output:
@@ -189,14 +190,13 @@ N_PURE_EB_CHUNKS = config["pure_eb"]["n_chunks"]
 rule precompute_pure_eb_chunk:
     """Compute a chunk of MC samples for pure E/B covariance (scatter)."""
     input:
-        cov_integration=lambda w: _cov_integration_path(w.version, w.blind),
+        cov_integration=lambda w: _cov_integration_path(w.version),
         xi_reporting=lambda w: _xi_reporting_path(w.version),
         xi_integration=lambda w: _xi_integration_path(w.version),
     output:
-        "results/paper_plots/intermediate/chunks/{version}_{blind}_pure_eb_chunk_{chunk_id}.npz",
+        "results/paper_plots/intermediate/chunks/{version}_pure_eb_chunk_{chunk_id}.npz",
     params:
         version="{version}",
-        blind="{blind}",
         chunk_id="{chunk_id}",
         n_chunks=N_PURE_EB_CHUNKS,
         n_samples=config["covariance"]["n_samples"],
@@ -210,18 +210,15 @@ rule precompute_pure_eb_chunk:
 
 rule precompute_pure_eb:
     """Gather MC sample chunks and compute final pure E/B covariance."""
-    wildcard_constraints:
-        version=r"[^_]+_v[\d.]+(_leak_corr)?",  # e.g. SP_v1.4.6.3, SP_v1.4.6.3_leak_corr
-        blind=r"[ABC]",
     input:
         chunks=expand(
-            "results/paper_plots/intermediate/chunks/{{version}}_{{blind}}_pure_eb_chunk_{chunk_id}.npz",
+            "results/paper_plots/intermediate/chunks/{{version}}_pure_eb_chunk_{chunk_id}.npz",
             chunk_id=range(N_PURE_EB_CHUNKS),
         ),
         xi_reporting=lambda w: _xi_reporting_path(w.version),
         xi_integration=lambda w: _xi_integration_path(w.version),
     output:
-        "results/paper_plots/intermediate/{version}_{blind}_pure_eb_semianalytic.npz",
+        "results/paper_plots/intermediate/{version}_pure_eb_semianalytic.npz",
     params:
         version="{version}",
         **FIDUCIAL_BINNING,
@@ -235,8 +232,6 @@ rule precompute_pure_eb:
 rule pure_eb_data_vector:
     """B-mode null test: Pure E/B data vector at fiducial scale cuts.
 
-    Uses fiducial blind only (FIDUCIAL["blind"]) for PTE calculation.
-
     Produces 9 figures:
     - figure.png: fiducial version, leak-corrected, no title (paper)
     - figure_v{X.Y.Z}.png: each version, leak-corrected, with title
@@ -244,10 +239,9 @@ rule pure_eb_data_vector:
     """
     input:
         # Per-version inputs: pure_eb_{version} and cov_{version} for all versions
-        **{f"pure_eb_{ver}": f"results/paper_plots/intermediate/{ver}_{FIDUCIAL['blind']}_pure_eb_semianalytic.npz"
-           for ver in VERSIONS_ALL_FOR_PLOTS},
-        **{f"cov_{ver}": _reporting_cov_path(ver, FIDUCIAL["blind"])
+        **{f"pure_eb_{ver}": f"results/paper_plots/intermediate/{ver}_pure_eb_semianalytic.npz"
            for ver in VERSIONS_ALL_FOR_PLOTS},
+        **{f"cov_{ver}": _reporting_cov_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
     output:
         evidence=f"{TAPESTRY_DIR}/pure_eb_data_vector/evidence.json",
         paper_figure=f"{PAPER_FIGURES_DIR}/pure_eb_data_vector.pdf",
@@ -265,7 +259,7 @@ rule pure_eb_version_comparison:
     input:
         # Pure E/B only for leak-corrected versions
         pure_eb_data=[
-            f"results/paper_plots/intermediate/{ver}_A_pure_eb_semianalytic.npz"
+            f"results/paper_plots/intermediate/{ver}_pure_eb_semianalytic.npz"
             for ver in VERSIONS_LEAK_CORR
         ],
     params:
@@ -286,11 +280,9 @@ rule pure_eb_covariance:
     - E and B blocks are well-conditioned (~10^5)
     - Ambiguous blocks are ill-conditioned (~10^15, expected)
     - Correlation structure across 6 blocks (E+/E-/B+/B-/amb+/amb-)
-
-    Uses blind A covariance for visualization (structure is similar across blinds).
     """
     input:
-        pure_eb_data=f"results/paper_plots/intermediate/{FIDUCIAL_VERSION}_A_pure_eb_semianalytic.npz",
+        pure_eb_data=f"results/paper_plots/intermediate/{FIDUCIAL_VERSION}_pure_eb_semianalytic.npz",
     output:
         evidence=f"{TAPESTRY_DIR}/pure_eb_covariance/evidence.json",
         figure=f"{TAPESTRY_DIR}/pure_eb_covariance/figure.png",
@@ -302,17 +294,12 @@ rule pure_eb_covariance:
 rule calculate_pure_eb_ptes:
     """PTE matrices for pure E/B-mode scale-cut robustness.
 
-    Nothing here varies with the blind: the data vectors come from the blind-A
-    gather and the PTEs are Hartlap-debiased by the MC draw count, not by a
-    per-blind covariance. The wildcard survives as the filename slot the
-    consumer (config_space_pte_matrices) reads, and only blind A is ever built.
+    The PTEs are Hartlap-debiased by the MC draw count.
     """
     input:
-        pure_eb_data="results/paper_plots/intermediate/{version}_A_pure_eb_semianalytic.npz",
+        pure_eb_data="results/paper_plots/intermediate/{version}_pure_eb_semianalytic.npz",
     output:
-        "results/paper_plots/intermediate/{version}_{blind}_pure_eb_ptes.npz",
-    wildcard_constraints:
-        blind=r"[ABC]",
+        "results/paper_plots/intermediate/{version}_pure_eb_ptes.npz",
     params:
         version="{version}",
         n_samples=config["covariance"]["n_samples"],
@@ -375,18 +362,17 @@ rule cl_version_comparison:
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
 rule compute_cosebis_pte:
-    """Scatter: Compute COSEBIS B-mode PTE for a single (version, blind, i_min, i_max) tuple."""
+    """Scatter: Compute COSEBIS B-mode PTE for a single (version, i_min, i_max) tuple."""
     input:
         xi_integration=lambda w: _xi_integration_path(w.version),
-        cov_integration=lambda w: _cov_integration_path(w.version, w.blind),
+        cov_integration=lambda w: _cov_integration_path(w.version),
     output:
-        pte_json=f"{TAPESTRY_DIR}/cosebis_pte_matrix/pte_values/{{version}}/{{blind}}/pte_{{i_min}}_{{i_max}}.json",
+        pte_json=f"{TAPESTRY_DIR}/cosebis_pte_matrix/pte_values/{{version}}/pte_{{i_min}}_{{i_max}}.json",
     params:
         nmodes=FIDUCIAL["nmodes"],
     wildcard_constraints:
         i_min=r"\d{3}",
         i_max=r"\d{3}",
-        blind=r"[ABC]",
     threads: 1
     resources:
         mem_mb=8000,
@@ -408,14 +394,13 @@ rule config_space_pte_matrices:
     input:
         pure_eb_data_vector=f"{TAPESTRY_DIR}/pure_eb_data_vector/evidence.json",
         cosebis_data_vector=f"{TAPESTRY_DIR}/cosebis_data_vector/evidence.json",
-        # Data inputs (fiducial blind only)
         # Pure E/B and COSEBIs PTEs for both corrected and uncorrected versions
         pure_eb_pte=[
-            f"results/paper_plots/intermediate/{ver}_{FIDUCIAL['blind']}_pure_eb_ptes.npz"
+            f"results/paper_plots/intermediate/{ver}_pure_eb_ptes.npz"
             for ver in VERSIONS_CONFIG_SPACE_PTES
         ],
         cosebis_pte_files=[
-            f"{TAPESTRY_DIR}/cosebis_pte_matrix/pte_values/{ver}/{FIDUCIAL['blind']}/pte_{i:03d}_{j:03d}.json"
+            f"{TAPESTRY_DIR}/cosebis_pte_matrix/pte_values/{ver}/pte_{i:03d}_{j:03d}.json"
             for ver in VERSIONS_CONFIG_SPACE_PTES
             for i, j in PTE_SCALE_CUT_PAIRS
         ],
@@ -435,15 +420,13 @@ rule harmonic_space_pte_matrices:
     Results: Single-panel Cl^BB PTE matrix for fiducial version
     Appendix: N-panel composite for all versions from config.versions
 
-    Uses fiducial blind covariance (blind independence validated in bb_covariance_blind_independence).
+    n(z)-realisation independence of the BB covariance is validated in
+    bb_covariance_nz_independence.
     """
     input:
         # Harmonic PTE matrices for both corrected and uncorrected versions
         pseudo_cl=[_pseudo_cl_path(ver) for ver in VERSIONS_CONFIG_SPACE_PTES],
-        pseudo_cl_cov=[
-            _pseudo_cl_cov_path(ver, blind=FIDUCIAL["blind"])
-            for ver in VERSIONS_CONFIG_SPACE_PTES
-        ],
+        pseudo_cl_cov=[_pseudo_cl_cov_path(ver) for ver in VERSIONS_CONFIG_SPACE_PTES],
     params:
         version_labels=VERSION_LABELS,
     output:
@@ -456,38 +439,41 @@ rule harmonic_space_pte_matrices:
         "../scripts/harmonic_space_pte_matrices.py"
 
 
-rule bb_covariance_blind_independence:
-    """Test BB covariance blind-independence vs EE variation.
+rule bb_covariance_nz_independence:
+    """Test BB covariance independence of the n(z) realisation vs EE variation.
 
-    BB covariances should be stable across blinds (null signal → no sample variance).
-    EE covariances should vary (~10%) due to sample variance from cosmological signal.
+    BB covariances should be stable across the n(z) realisations (null
+    signal → no sample variance). EE covariances should vary (~10%) due to
+    sample variance from cosmological signal. The first realisation is the
+    reference each of the others is compared against.
 
     Covers all three analysis spaces: Pure E/B, COSEBIS, and harmonic (pseudo-Cl).
 
-    Uses mock_version (v1.4.6) for per-blind covariances — blind independence is a
-    property of survey geometry, not catalog version. Avoids generating B/C covariances
-    for the fiducial version.
+    Compares NZ_REALISATIONS, catalogues identical to the mock version but for
+    their n(z): the independence is a property of survey geometry, not catalog
+    version.
     """
     input:
-        # Per-blind MC-propagated pure E/B covariances (using mock_version for all blinds)
-        **{f"pure_eb_{b}": f"results/paper_plots/intermediate/{MOCK_VERSION}_{b}_pure_eb_semianalytic.npz"
-           for b in BLINDS},
-        # COSEBIS: xi integration file (shared) + per-blind config-space covariances
+        # Per-realisation MC-propagated pure E/B covariances
+        **{f"pure_eb_{label}": f"results/paper_plots/intermediate/{ver}_pure_eb_semianalytic.npz"
+           for label, ver in NZ_REALISATIONS.items()},
+        # COSEBIS: xi integration file (shared) + per-realisation config-space covariances
         xi_integration=_xi_integration_path(MOCK_VERSION),
-        **{f"cov_integration_{b}": _cov_integration_path(MOCK_VERSION, b) for b in BLINDS},
-        # Per-blind harmonic covariances
-        **{f"harmonic_{b}": _pseudo_cl_cov_path(MOCK_VERSION, b) for b in BLINDS},
-        # Pseudo-Cl data vector (blind A only) for ell bin centers
-        pseudo_cl=_pseudo_cl_path(MOCK_VERSION, "A"),
+        **{f"cov_integration_{label}": _cov_integration_path(ver)
+           for label, ver in NZ_REALISATIONS.items()},
+        # Per-realisation harmonic covariances
+        **{f"harmonic_{label}": _pseudo_cl_cov_path(ver) for label, ver in NZ_REALISATIONS.items()},
+        # Pseudo-Cl data vector for ell bin centers
+        pseudo_cl=_pseudo_cl_path(MOCK_VERSION),
     params:
         nmodes=FIDUCIAL["nmodes"],
         theta_min=config["cosebis"]["theta_min"],
         theta_max=config["cosebis"]["theta_max"],
     output:
-        evidence=f"{TAPESTRY_DIR}/bb_covariance_blind_independence/evidence.json",
-        figure=f"{TAPESTRY_DIR}/bb_covariance_blind_independence/figure.png",
+        evidence=f"{TAPESTRY_DIR}/bb_covariance_nz_independence/evidence.json",
+        figure=f"{TAPESTRY_DIR}/bb_covariance_nz_independence/figure.png",
     script:
-        "../scripts/bb_covariance_blind_independence.py"
+        "../scripts/bb_covariance_nz_independence.py"
 
 
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
@@ -518,7 +504,7 @@ rule harmonic_config_cosebis_comparison:
         **{f"pseudo_cl_{ver}": _pseudo_cl_path(ver, nbins=_COSEBIS_NBINS) for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"pseudo_cl_cov_{ver}": _pseudo_cl_cov_path(ver, nbins=_COSEBIS_NBINS) for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"xi_{ver}": _xi_integration_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
-        **{f"cov_{ver}": _cov_integration_path(ver, FIDUCIAL["blind"]) for ver in VERSIONS_ALL_FOR_PLOTS},
+        **{f"cov_{ver}": _cov_integration_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
     params:
         scale_cut=lambda wildcards: _COSEBIS_ANGULAR_RANGES[wildcards.angular_range],
     output:
@@ -554,4 +540,4 @@ rule cosebis_filter_overlay:
 # Local Rules Declaration
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-localrules: cl_data_vector, cl_version_comparison, pure_eb_covariance, pure_eb_data_vector, pure_eb_version_comparison, cosebis_version_comparison, cosebis_data_vector, config_space_pte_matrices, harmonic_space_pte_matrices, bb_covariance_blind_independence, harmonic_config_cosebis_comparison, cosebis_filter_overlay
+localrules: cl_data_vector, cl_version_comparison, pure_eb_covariance, pure_eb_data_vector, pure_eb_version_comparison, cosebis_version_comparison, cosebis_data_vector, config_space_pte_matrices, harmonic_space_pte_matrices, bb_covariance_nz_independence, harmonic_config_cosebis_comparison, cosebis_filter_overlay
diff --git a/papers/bmodes/rules/paper.smk b/papers/bmodes/rules/paper.smk
index ead4e1d3..6632a0a4 100644
--- a/papers/bmodes/rules/paper.smk
+++ b/papers/bmodes/rules/paper.smk
@@ -14,7 +14,7 @@ FIGURE_RULES = [
     "cl_version_comparison",
     "config_space_pte_matrices",
     "harmonic_space_pte_matrices",
-    "bb_covariance_blind_independence",
+    "bb_covariance_nz_independence",
     "cosebis_filter_overlay",
 ]
 
@@ -26,12 +26,9 @@ localrules: xi_cosmology_paper_macros, paper_macros, paper
 
 rule xi_cosmology_paper_macros:
     """B-mode macros for the configuration-space cosmology paper (Goh et al.):
-    fiducial version, n=6 COSEBIS, joint pure-mode PTEs at full and fiducial
-    scales."""
+    joint pure-mode PTEs and scale cuts at full and fiducial scales."""
     input:
-        cosebis_evidence=rules.cosebis_version_comparison.output.evidence,
         pure_eb_evidence=rules.pure_eb_data_vector.output.evidence,
-        bb_blind_evidence=rules.bb_covariance_blind_independence.output.evidence,
     output:
         macros="docs/unions_release/unions_2d_shear_xi/claims_macros.tex",
     params:
@@ -43,7 +40,6 @@ rule xi_cosmology_paper_macros:
 rule paper_macros:
     """LaTeX macros and PTE tables for the B-modes paper (Daley et al.)."""
     input:
-        cosebis_evidence=rules.cosebis_version_comparison.output.evidence,
         pure_eb_evidence=rules.pure_eb_data_vector.output.evidence,
         pure_eb_covariance=rules.pure_eb_covariance.output.evidence,
         config_space_pte=rules.config_space_pte_matrices.output.evidence,
diff --git a/papers/bmodes/scripts/bb_covariance_blind_independence.py b/papers/bmodes/scripts/bb_covariance_nz_independence.py
similarity index 51%
rename from papers/bmodes/scripts/bb_covariance_blind_independence.py
rename to papers/bmodes/scripts/bb_covariance_nz_independence.py
index c66607af..69c0db18 100644
--- a/papers/bmodes/scripts/bb_covariance_blind_independence.py
+++ b/papers/bmodes/scripts/bb_covariance_nz_independence.py
@@ -1,8 +1,10 @@
 """
-BB Covariance Blind Independence Claim
+BB Covariance n(z) Independence Claim
 
-Tests whether BB covariances are blind-independent (as expected for null signals)
-while EE covariances vary across blinds (due to sample variance from cosmological signal).
+Tests whether BB covariances are independent of the n(z) realisation (as
+expected for null signals) while EE covariances vary across realisations (due to
+sample variance from cosmological signal). Each realisation is a catalogue entry
+of its own (config fiducial.nz_realisations), identical but for its n(z).
 
 Compares:
 - Pure E/B: cov(xi+^B), cov(xi-^B) vs cov(xi+^E), cov(xi-^E)
@@ -29,9 +31,6 @@
 plt.style.use(PAPER_MPLSTYLE)
 
 
-BLINDS = ["A", "B", "C"]
-
-
 def load_pure_eb_diagonals(path):
     """Load pure E/B covariance and extract block diagonals.
 
@@ -88,7 +87,7 @@ def load_cosebis_diagonals(
     )
     gg.read(xi_integration_path)
 
-    # Compute COSEBIS with this blind's covariance
+    # Compute COSEBIS with this realisation's covariance
     results = calculate_cosebis(
         gg,
         nmodes=nmodes,
@@ -124,27 +123,32 @@ def compute_ratios(diag_ref, diag_test):
     }
 
 
+# Marker per compared realisation, and the multiplicative x-offset that keeps
+# their points from overlapping.
+MARKERS = ["s", "^", "v", "D", "o"]
+
+
 def make_figure(
     theta,
     ell_eff,
     pure_eb_results,
     harmonic_results,
     cosebis_results,
+    reference,
     output_path,
     n_samples=2000,
 ):
-    """Four-panel figure comparing BB vs EE stability across blinds.
+    """Four-panel figure comparing BB vs EE stability across n(z) realisations.
 
     Layout: 2x2
     - Top row: Pure E/B (xi+, xi-)
     - Bottom row: COSEBIS (B_n vs E_n), Harmonic (C_ell)
 
-    Color encoding: E-mode vs B-mode
-    Marker encoding: square = B/A, triangle = C/A (same for both E and B)
+    Color encodes E- vs B-mode; marker encodes the compared realisation, each
+    shown as its ratio to ``reference``.
     """
     fig, axes = plt.subplots(2, 2, figsize=(9, 7))
 
-    # Colors: E-mode vs B-mode
     color_E = "#E69F00"  # orange for E
     color_B = "#0072B2"  # blue for B
 
@@ -152,11 +156,9 @@ def make_figure(
     # σ(ratio) ≈ √(2/N) for ratio ≈ 1
     ratio_err = np.sqrt(2.0 / n_samples)
 
-    # Helper for ratio panels
-    def setup_ratio_panel(ax, ylabel_x, title, show_mc_band=False):
+    def setup_ratio_panel(ax, xlabel, title, show_mc_band=False):
         ax.axhline(1.0, color="gray", ls="-", lw=0.8, zorder=0)
         if show_mc_band:
-            # Show expected 1σ MC scatter band only
             ax.axhspan(
                 1 - ratio_err,
                 1 + ratio_err,
@@ -165,218 +167,87 @@ def setup_ratio_panel(ax, ylabel_x, title, show_mc_band=False):
                 label=rf"$\pm\sqrt{{2/N}}$ ($N={n_samples}$)",
             )
         ax.set_xscale("log")
-        ax.set_xlabel(ylabel_x)
+        ax.set_xlabel(xlabel)
         ax.set_title(title)
 
-    # --- Panel 1: xi+^B vs xi+^E ---
-    ax = axes[0, 0]
-    setup_ratio_panel(
-        ax,
-        r"$\theta$ [arcmin]",
-        r"$\xi_+$: covariance ratio across blinds",
-        show_mc_band=True,
-    )
-
-    # B-mode (blue): square for B/A, triangle for C/A
-    ax.errorbar(
-        theta,
-        pure_eb_results["B"]["xip_B"]["ratio"],
-        yerr=ratio_err,
-        fmt="s",
-        color=color_B,
-        label=r"B-mode B/A",
-        markersize=5,
-        alpha=0.8,
-        capsize=0,
-    )
-    ax.errorbar(
-        theta * 1.03,
-        pure_eb_results["C"]["xip_B"]["ratio"],
-        yerr=ratio_err,
-        fmt="^",
-        color=color_B,
-        label=r"B-mode C/A",
-        markersize=5,
-        alpha=0.8,
-        capsize=0,
-    )
-
-    # E-mode (orange): square for B/A, triangle for C/A
-    ax.plot(
-        theta,
-        pure_eb_results["B"]["xip_E"]["ratio"],
-        "s",
-        color=color_E,
-        label=r"E-mode B/A",
-        markersize=4,
-        alpha=0.6,
-    )
-    ax.plot(
-        theta * 1.03,
-        pure_eb_results["C"]["xip_E"]["ratio"],
-        "^",
-        color=color_E,
-        label=r"E-mode C/A",
-        markersize=4,
-        alpha=0.6,
-    )
-
-    ax.set_ylabel("Diagonal ratio")
-    ax.legend(loc="upper right", fontsize=7, ncol=2)
-    ax.set_xlim(1, 300)
-    ax.set_ylim(0.85, 1.15)
-
-    # --- Panel 2: xi-^B vs xi-^E ---
-    ax = axes[0, 1]
-    setup_ratio_panel(
-        ax,
-        r"$\theta$ [arcmin]",
-        r"$\xi_-$: covariance ratio across blinds",
-        show_mc_band=True,
-    )
-
-    ax.errorbar(
-        theta,
-        pure_eb_results["B"]["xim_B"]["ratio"],
-        yerr=ratio_err,
-        fmt="s",
-        color=color_B,
-        label=r"B-mode B/A",
-        markersize=5,
-        alpha=0.8,
-        capsize=0,
-    )
-    ax.errorbar(
-        theta * 1.03,
-        pure_eb_results["C"]["xim_B"]["ratio"],
-        yerr=ratio_err,
-        fmt="^",
-        color=color_B,
-        label=r"B-mode C/A",
-        markersize=5,
-        alpha=0.8,
-        capsize=0,
-    )
-
-    ax.plot(
-        theta,
-        pure_eb_results["B"]["xim_E"]["ratio"],
-        "s",
-        color=color_E,
-        label=r"E-mode B/A",
-        markersize=4,
-        alpha=0.6,
-    )
-    ax.plot(
-        theta * 1.03,
-        pure_eb_results["C"]["xim_E"]["ratio"],
-        "^",
-        color=color_E,
-        label=r"E-mode C/A",
-        markersize=4,
-        alpha=0.6,
-    )
+    def plot_ratios(ax, x, results, b_key, e_key, b_name, e_name, shift, b_err):
+        """B (with optional MC error bar) and E ratios for every realisation."""
+        for i, (label, res) in enumerate(results.items()):
+            xi = shift(x, i)
+            marker = MARKERS[i % len(MARKERS)]
+            pair = f"{label}/{reference}"
+            ax.errorbar(
+                xi,
+                res[b_key]["ratio"],
+                yerr=b_err,
+                fmt=marker,
+                color=color_B,
+                label=f"{b_name} {pair}",
+                markersize=5,
+                alpha=0.8,
+                capsize=0,
+            )
+            ax.plot(
+                xi,
+                res[e_key]["ratio"],
+                marker,
+                color=color_E,
+                label=f"{e_name} {pair}",
+                markersize=4,
+                alpha=0.6,
+            )
 
-    ax.legend(loc="upper right", fontsize=7, ncol=2)
-    ax.set_xlim(1, 300)
-    ax.set_ylim(0.85, 1.15)
+    def log_shift(x, i):
+        return x * 1.03**i
+
+    def lin_shift(x, i):
+        return x + 0.15 * i
+
+    # --- Panels 1–2: pure E/B xi± ---
+    for ax, comp, name in (
+        (axes[0, 0], "xip", r"\xi_+"),
+        (axes[0, 1], "xim", r"\xi_-"),
+    ):
+        setup_ratio_panel(
+            ax,
+            r"$\theta$ [arcmin]",
+            rf"${name}$: covariance ratio across n(z)",
+            show_mc_band=True,
+        )
+        plot_ratios(
+            ax,
+            theta,
+            pure_eb_results,
+            f"{comp}_B",
+            f"{comp}_E",
+            "B-mode",
+            "E-mode",
+            log_shift,
+            ratio_err,
+        )
+        ax.legend(loc="upper right", fontsize=7, ncol=2)
+        ax.set_xlim(1, 300)
+        ax.set_ylim(0.85, 1.15)
+    axes[0, 0].set_ylabel("Diagonal ratio")
 
     # --- Panel 3: COSEBIS B_n vs E_n ---
     ax = axes[1, 0]
-    nmodes = cosebis_results["B"]["nmodes"]
+    nmodes = next(iter(cosebis_results.values()))["nmodes"]
     n_arr = np.arange(1, nmodes + 1)
     ax.axhline(1.0, color="gray", ls="-", lw=0.8, zorder=0)
     ax.set_xlabel(r"Mode $n$")
-    ax.set_title(r"COSEBIS: covariance ratio across blinds")
-
-    # B-mode (blue): square for B/A, triangle for C/A
-    ax.plot(
-        n_arr,
-        cosebis_results["B"]["B"]["ratio"],
-        "s",
-        color=color_B,
-        label=r"B-mode B/A",
-        markersize=5,
-        alpha=0.8,
-    )
-    ax.plot(
-        n_arr + 0.15,
-        cosebis_results["C"]["B"]["ratio"],
-        "^",
-        color=color_B,
-        label=r"B-mode C/A",
-        markersize=5,
-        alpha=0.8,
-    )
-
-    # E-mode (orange): square for B/A, triangle for C/A
-    ax.plot(
-        n_arr,
-        cosebis_results["B"]["E"]["ratio"],
-        "s",
-        color=color_E,
-        label=r"E-mode B/A",
-        markersize=4,
-        alpha=0.6,
+    ax.set_title(r"COSEBIS: covariance ratio across n(z)")
+    plot_ratios(
+        ax, n_arr, cosebis_results, "B", "E", "B-mode", "E-mode", lin_shift, None
     )
-    ax.plot(
-        n_arr + 0.15,
-        cosebis_results["C"]["E"]["ratio"],
-        "^",
-        color=color_E,
-        label=r"E-mode C/A",
-        markersize=4,
-        alpha=0.6,
-    )
-
     ax.set_ylabel("Diagonal ratio")
     ax.legend(loc="upper right", fontsize=7, ncol=2)
     ax.set_ylim(0.85, 1.15)
 
     # --- Panel 4: C_ell^BB vs C_ell^EE ---
     ax = axes[1, 1]
-    setup_ratio_panel(ax, r"$\ell$", r"$C_\ell$: covariance ratio across blinds")
-
-    # B-mode (blue): square for B/A, triangle for C/A
-    ax.plot(
-        ell_eff,
-        harmonic_results["B"]["BB"]["ratio"],
-        "s",
-        color=color_B,
-        label=r"BB B/A",
-        markersize=5,
-        alpha=0.8,
-    )
-    ax.plot(
-        ell_eff * 1.03,
-        harmonic_results["C"]["BB"]["ratio"],
-        "^",
-        color=color_B,
-        label=r"BB C/A",
-        markersize=5,
-        alpha=0.8,
-    )
-
-    # E-mode (orange): square for B/A, triangle for C/A
-    ax.plot(
-        ell_eff,
-        harmonic_results["B"]["EE"]["ratio"],
-        "s",
-        color=color_E,
-        label=r"EE B/A",
-        markersize=4,
-        alpha=0.6,
-    )
-    ax.plot(
-        ell_eff * 1.03,
-        harmonic_results["C"]["EE"]["ratio"],
-        "^",
-        color=color_E,
-        label=r"EE C/A",
-        markersize=4,
-        alpha=0.6,
-    )
-
+    setup_ratio_panel(ax, r"$\ell$", r"$C_\ell$: covariance ratio across n(z)")
+    plot_ratios(ax, ell_eff, harmonic_results, "BB", "EE", "BB", "EE", log_shift, None)
     ax.set_ylabel("Diagonal ratio")
     ax.legend(loc="upper right", fontsize=7, ncol=2)
     ax.set_ylim(0.85, 1.15)
@@ -399,36 +270,36 @@ def main(
     figure_path,
     evidence_path,
 ):
-    """Run the BB-covariance blind-independence cross-check.
+    """Run the BB-covariance n(z)-independence cross-check.
 
     ``pure_eb_paths`` / ``harmonic_paths`` / ``cov_integration_paths`` are dicts
-    keyed by blind (A/B/C). All paths are absolute; the per-blind mock-version
-    covariances are read directly, so the check is self-contained.
+    keyed by n(z) realisation label, in the order of config
+    ``fiducial.nz_realisations``: the first label is the reference, every other
+    realisation is compared against it. All paths are absolute; the
+    per-realisation covariances are read directly, so the check is
+    self-contained.
     """
     version = config["fiducial"]["mock_version"]
+    reference, *compared = pure_eb_paths
 
-    # Load pure E/B covariances for all blinds
-    pure_eb_data = {}
-    for blind in BLINDS:
-        pure_eb_data[blind] = load_pure_eb_diagonals(pure_eb_paths[blind])
-
-    theta = pure_eb_data["A"]["theta"]
+    pure_eb_data = {
+        label: load_pure_eb_diagonals(path) for label, path in pure_eb_paths.items()
+    }
+    theta = pure_eb_data[reference]["theta"]
 
-    # Load harmonic covariances for all blinds
-    harmonic_data = {}
-    for blind in BLINDS:
-        harmonic_data[blind] = load_harmonic_diagonals(harmonic_paths[blind])
+    harmonic_data = {
+        label: load_harmonic_diagonals(path) for label, path in harmonic_paths.items()
+    }
 
     # Integration binning parameters from config
     min_sep_int = config["fiducial"]["min_sep_int"]
     max_sep_int = config["fiducial"]["max_sep_int"]
     nbins_int = config["fiducial"]["nbins_int"]
 
-    cosebis_data = {}
-    for blind in BLINDS:
-        cosebis_data[blind] = load_cosebis_diagonals(
+    cosebis_data = {
+        label: load_cosebis_diagonals(
             xi_integration_path,
-            cov_integration_paths[blind],
+            path,
             nmodes,
             theta_min,
             theta_max,
@@ -436,38 +307,29 @@ def main(
             max_sep_int,
             nbins_int,
         )
+        for label, path in cov_integration_paths.items()
+    }
 
-    # Read ell bin centers from the pseudo-Cl SACC part
+    # Ell bin centers from the pseudo-Cl SACC part
     ell_eff = load_pseudo_cl_data(pseudo_cl_path)["ELL"]
 
-    # Compute ratios relative to blind A
-    pure_eb_results = {}
-    for blind in ["B", "C"]:
-        pure_eb_results[blind] = {}
-        for mode in ["xip_E", "xim_E", "xip_B", "xim_B"]:
-            pure_eb_results[blind][mode] = compute_ratios(
-                pure_eb_data["A"][mode], pure_eb_data[blind][mode]
-            )
-
-    harmonic_results = {}
-    for blind in ["B", "C"]:
-        harmonic_results[blind] = {}
-        for mode in ["EE", "BB"]:
-            harmonic_results[blind][mode] = compute_ratios(
-                harmonic_data["A"][mode], harmonic_data[blind][mode]
-            )
-
-    cosebis_results = {}
-    for blind in ["B", "C"]:
-        cosebis_results[blind] = {
-            "nmodes": nmodes,
+    def ratios_to_reference(data, modes):
+        return {
+            label: {
+                mode: compute_ratios(data[reference][mode], data[label][mode])
+                for mode in modes
+            }
+            for label in compared
         }
-        for mode in ["E", "B"]:
-            cosebis_results[blind][mode] = compute_ratios(
-                cosebis_data["A"][mode], cosebis_data[blind][mode]
-            )
 
-    # Generate figure
+    pure_eb_results = ratios_to_reference(
+        pure_eb_data, ["xip_E", "xim_E", "xip_B", "xim_B"]
+    )
+    harmonic_results = ratios_to_reference(harmonic_data, ["EE", "BB"])
+    cosebis_results = ratios_to_reference(cosebis_data, ["E", "B"])
+    for res in cosebis_results.values():
+        res["nmodes"] = nmodes
+
     n_samples = config["covariance"]["n_samples"]
     make_figure(
         theta,
@@ -475,108 +337,72 @@ def main(
         pure_eb_results,
         harmonic_results,
         cosebis_results,
+        reference,
         figure_path,
         n_samples=n_samples,
     )
 
-    # Compute summary statistics
+    def max_dev(results, mode):
+        return max(res[mode]["max_dev"] for res in results.values())
+
     bb_max_devs = [
-        pure_eb_results["B"]["xip_B"]["max_dev"],
-        pure_eb_results["C"]["xip_B"]["max_dev"],
-        pure_eb_results["B"]["xim_B"]["max_dev"],
-        pure_eb_results["C"]["xim_B"]["max_dev"],
-        cosebis_results["B"]["B"]["max_dev"],
-        cosebis_results["C"]["B"]["max_dev"],
-        harmonic_results["B"]["BB"]["max_dev"],
-        harmonic_results["C"]["BB"]["max_dev"],
+        max_dev(pure_eb_results, "xip_B"),
+        max_dev(pure_eb_results, "xim_B"),
+        max_dev(cosebis_results, "B"),
+        max_dev(harmonic_results, "BB"),
     ]
     ee_max_devs = [
-        pure_eb_results["B"]["xip_E"]["max_dev"],
-        pure_eb_results["C"]["xip_E"]["max_dev"],
-        pure_eb_results["B"]["xim_E"]["max_dev"],
-        pure_eb_results["C"]["xim_E"]["max_dev"],
-        cosebis_results["B"]["E"]["max_dev"],
-        cosebis_results["C"]["E"]["max_dev"],
-        harmonic_results["B"]["EE"]["max_dev"],
-        harmonic_results["C"]["EE"]["max_dev"],
+        max_dev(pure_eb_results, "xip_E"),
+        max_dev(pure_eb_results, "xim_E"),
+        max_dev(cosebis_results, "E"),
+        max_dev(harmonic_results, "EE"),
     ]
 
-    # Pass criteria (updated: analytic vs MC methods show different behavior)
-    # COSEBIS uses analytic propagation (T @ Cov @ T.T) → blind-independent
-    # Pure E/B and Harmonic use MC sampling → inherit sampling noise
-    cosebis_bb_max = max(
-        cosebis_results["B"]["B"]["max_dev"],
-        cosebis_results["C"]["B"]["max_dev"],
-    )
-    cosebis_ee_max = max(
-        cosebis_results["B"]["E"]["max_dev"],
-        cosebis_results["C"]["E"]["max_dev"],
-    )
+    # Pass criteria: COSEBIS uses analytic propagation (T @ Cov @ T.T), so its
+    # B_n covariance is n(z)-independent; pure E/B and harmonic use MC sampling
+    # and inherit its noise.
+    cosebis_bb_max = max_dev(cosebis_results, "B")
+    cosebis_ee_max = max_dev(cosebis_results, "E")
 
-    # COSEBIS B_n should be blind-independent (<0.1%)
-    cosebis_bb_blind_independent = cosebis_bb_max < 0.001
+    # COSEBIS B_n should be n(z)-independent (<0.1%)
+    cosebis_bb_nz_independent = cosebis_bb_max < 0.001
     # E-modes should vary ~10% due to sample variance
     ee_varies_as_expected = 0.05 < cosebis_ee_max < 0.15
 
-    # Legacy summary (for backwards compatibility)
+    # Summary over all three spaces, MC methods included
     bb_max = max(bb_max_devs)
     ee_max = max(ee_max_devs)
     bb_closer_to_unity = bb_max < ee_max
     bb_within_2pct = bb_max < 0.02
 
-    # Build evidence
+    def by_pair(results, mode):
+        return {f"{label}_to_{reference}": res[mode] for label, res in results.items()}
+
     evidence = {
         "depends_on": ["covariance", "pure_eb", "cosebis", "pseudo_cl"],
         "generated": datetime.now().isoformat(),
         "evidence": {
+            "reference_realisation": reference,
             "pure_eb": {
-                "xip_B": {
-                    "B_to_A": pure_eb_results["B"]["xip_B"],
-                    "C_to_A": pure_eb_results["C"]["xip_B"],
-                },
-                "xim_B": {
-                    "B_to_A": pure_eb_results["B"]["xim_B"],
-                    "C_to_A": pure_eb_results["C"]["xim_B"],
-                },
-                "xip_E": {
-                    "B_to_A": pure_eb_results["B"]["xip_E"],
-                    "C_to_A": pure_eb_results["C"]["xip_E"],
-                },
-                "xim_E": {
-                    "B_to_A": pure_eb_results["B"]["xim_E"],
-                    "C_to_A": pure_eb_results["C"]["xim_E"],
-                },
+                mode: by_pair(pure_eb_results, mode)
+                for mode in ["xip_B", "xim_B", "xip_E", "xim_E"]
             },
             "harmonic": {
-                "BB": {
-                    "B_to_A": harmonic_results["B"]["BB"],
-                    "C_to_A": harmonic_results["C"]["BB"],
-                },
-                "EE": {
-                    "B_to_A": harmonic_results["B"]["EE"],
-                    "C_to_A": harmonic_results["C"]["EE"],
-                },
+                mode: by_pair(harmonic_results, mode) for mode in ["BB", "EE"]
             },
             "cosebis": {
-                "B": {
-                    "B_to_A": cosebis_results["B"]["B"],
-                    "C_to_A": cosebis_results["C"]["B"],
-                },
-                "E": {
-                    "B_to_A": cosebis_results["B"]["E"],
-                    "C_to_A": cosebis_results["C"]["E"],
-                },
+                **{mode: by_pair(cosebis_results, mode) for mode in ["B", "E"]},
                 "nmodes": nmodes,
                 "theta_min": theta_min,
                 "theta_max": theta_max,
             },
             "summary": {
-                # Primary pass criteria (revised)
+                # Primary pass criteria (COSEBIS, analytic)
                 "cosebis_bb_max_deviation": cosebis_bb_max,
                 "cosebis_ee_max_deviation": cosebis_ee_max,
-                "cosebis_bb_blind_independent": cosebis_bb_blind_independent,
+                "cosebis_bb_nz_independent": cosebis_bb_nz_independent,
                 "ee_varies_as_expected": ee_varies_as_expected,
-                # Legacy (for reference)
+                # All spaces, MC methods included
                 "bb_max_deviation": bb_max,
                 "ee_max_deviation": ee_max,
                 "bb_closer_to_unity": bb_closer_to_unity,
@@ -605,10 +431,10 @@ def clean_ratios(d):
         json.dump(evidence, f, indent=2)
 
     # Print summary
-    print("\nBB Covariance Blind Independence Summary:")
+    print("\nBB Covariance n(z) Independence Summary:")
     print(f"  COSEBIS B_n max deviation: {cosebis_bb_max * 100:.6f}%")
     print(f"  COSEBIS E_n max deviation: {cosebis_ee_max * 100:.2f}%")
-    print(f"  COSEBIS B_n blind-independent (<0.1%): {cosebis_bb_blind_independent}")
+    print(f"  COSEBIS B_n n(z)-independent (<0.1%): {cosebis_bb_nz_independent}")
     print(f"  E-modes vary as expected (5-15%): {ee_varies_as_expected}")
     print("\n  Pure E/B + Harmonic (MC methods):")
     print(f"    BB max deviation: {bb_max * 100:.2f}%")
@@ -618,9 +444,10 @@ def clean_ratios(d):
 
 def _from_snakemake(smk):
     config = smk.config
-    pure_eb_paths = {b: smk.input[f"pure_eb_{b}"] for b in BLINDS}
-    harmonic_paths = {b: smk.input[f"harmonic_{b}"] for b in BLINDS}
-    cov_integration_paths = {b: smk.input[f"cov_integration_{b}"] for b in BLINDS}
+    labels = list(config["fiducial"]["nz_realisations"])
+    pure_eb_paths = {b: smk.input[f"pure_eb_{b}"] for b in labels}
+    harmonic_paths = {b: smk.input[f"harmonic_{b}"] for b in labels}
+    cov_integration_paths = {b: smk.input[f"cov_integration_{b}"] for b in labels}
     main(
         config=config,
         pure_eb_paths=pure_eb_paths,
@@ -636,19 +463,18 @@ def _from_snakemake(smk):
     )
 
 
-def _cov_integration_path(cov_dir, version, blind, min_sep, max_sep, nbins):
+def _cov_integration_path(cov_dir, version, min_sep, max_sep, nbins):
     """Reproduce common.covariance_path for the Gaussian integration-grid,
     masked covariance (suffix _processed.txt)."""
     base = (
-        f"covariance_{version}_{blind}_g"
-        f"_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_masked"
+        f"covariance_{version}_g_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_masked"
     )
     return os.path.join(cov_dir, base, f"{base}_processed.txt")
 
 
 def _from_cli(argv=None):
     ap = argparse.ArgumentParser(
-        description="BB-covariance blind-independence cross-check (mock version)."
+        description="BB-covariance n(z)-independence cross-check (mock version)."
     )
     ap.add_argument("--config", required=True, help="Absolute path to config.yaml")
     ap.add_argument(
@@ -659,7 +485,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pure-eb-dir",
         required=True,
-        help="Dir with {version}_{blind}_pure_eb_semianalytic.npz",
+        help="Dir with {version}_pure_eb_semianalytic.npz per n(z) realisation",
     )
     ap.add_argument(
         "--cosmo-val-dir",
@@ -689,22 +515,20 @@ def _from_cli(argv=None):
     )
     npatch = fid["npatch"]
 
+    realisations = fid["nz_realisations"]
     pure_eb_paths = {
-        b: os.path.join(a.pure_eb_dir, f"{version}_{b}_pure_eb_semianalytic.npz")
-        for b in BLINDS
+        b: os.path.join(a.pure_eb_dir, f"{ver}_pure_eb_semianalytic.npz")
+        for b, ver in realisations.items()
     }
     harmonic_paths = {
-        b: os.path.join(
-            a.cosmo_val_dir,
-            f"pseudo_cl_cov_{version}_blind={b}_powspace_nbins=32.fits",
-        )
-        for b in BLINDS
+        b: os.path.join(a.cosmo_val_dir, f"pseudo_cl_cov_{ver}_powspace_nbins=32.fits")
+        for b, ver in realisations.items()
     }
     cov_integration_paths = {
         b: _cov_integration_path(
-            a.covariance_dir, version, b, min_sep_int, max_sep_int, nbins_int
+            a.covariance_dir, ver, min_sep_int, max_sep_int, nbins_int
         )
-        for b in BLINDS
+        for b, ver in realisations.items()
     }
     xi_integration_path = os.path.join(
         a.cosmo_val_dir,
@@ -712,7 +536,7 @@ def _from_cli(argv=None):
         f"_nbins={nbins_int}_npatch={npatch}.txt",
     )
     pseudo_cl_path = os.path.join(
-        a.cosmo_val_dir, f"pseudo_cl_{version}_blind=A_powspace_nbins=32.sacc"
+        a.cosmo_val_dir, f"pseudo_cl_{version}_powspace_nbins=32.sacc"
     )
 
     out_dir = Path(a.out)
diff --git a/papers/bmodes/scripts/calculate_pure_eb_ptes.py b/papers/bmodes/scripts/calculate_pure_eb_ptes.py
index 3a623669..feb0be78 100644
--- a/papers/bmodes/scripts/calculate_pure_eb_ptes.py
+++ b/papers/bmodes/scripts/calculate_pure_eb_ptes.py
@@ -5,10 +5,10 @@
 ξ_+^B / ξ_-^B / joint ξ_tot^B χ² PTE matrices over the scale-cut grid via
 ``sp_validation.b_modes.calculate_eb_statistics`` (Hartlap-corrected inverse
 MC covariance, debiased by the draw count), and writes the PTE matrices to
-``{out}/{version}_{blind}_pure_eb_ptes.npz``.
+``{out}/{version}_pure_eb_ptes.npz``.
 
     python calculate_pure_eb_ptes.py \
-        --version SP_v1.4.6.3_leak_corr --blind A \
+        --version SP_v1.4.6.3_leak_corr \
         --pure-eb-data <..._pure_eb_semianalytic.npz> \
         --n-samples 2000 --out <output_dir>
 """
@@ -23,7 +23,6 @@
 
 def calculate_ptes(
     version,
-    blind,
     pure_eb_data,
     n_samples,
     output_dir,
@@ -57,7 +56,7 @@ def calculate_ptes(
     }
 
     os.makedirs(output_dir, exist_ok=True)
-    out_path = os.path.join(output_dir, f"{version}_{blind}_pure_eb_ptes.npz")
+    out_path = os.path.join(output_dir, f"{version}_pure_eb_ptes.npz")
     np.savez(out_path, **output_data)
     print(f"Saved PTE matrices to {out_path}")
     return out_path
@@ -66,14 +65,12 @@ def calculate_ptes(
 def _from_cli(argv=None):
     ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
     ap.add_argument("--version", required=True)
-    ap.add_argument("--blind", default="A")
     ap.add_argument("--pure-eb-data", required=True, help="Gathered semianalytic .npz")
     ap.add_argument("--n-samples", type=int, default=2000)
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     a = ap.parse_args(argv)
     calculate_ptes(
         version=a.version,
-        blind=a.blind,
         pure_eb_data=a.pure_eb_data,
         n_samples=a.n_samples,
         output_dir=a.out,
diff --git a/papers/bmodes/scripts/cl_data_vector.py b/papers/bmodes/scripts/cl_data_vector.py
index e47c24d8..6576cd7c 100644
--- a/papers/bmodes/scripts/cl_data_vector.py
+++ b/papers/bmodes/scripts/cl_data_vector.py
@@ -161,14 +161,12 @@ def _create_cl_figure(
 # via _pseudo_cl_path() in figures.smk; the CLI reconstructs them from --results-dir
 # so the version sweep is self-contained (lc produces only the fiducial version).
 # ---------------------------------------------------------------------------
-def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
-    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.sacc"
+def _pseudo_cl(results_dir, ver, nbins=32):
+    return f"{results_dir}/pseudo_cl_{ver}_powspace_nbins={nbins}.sacc"
 
 
-def _pseudo_cl_cov(results_dir, ver, blind="A", nbins=32):
-    return (
-        f"{results_dir}/pseudo_cl_cov_{ver}_blind={blind}_powspace_nbins={nbins}.fits"
-    )
+def _pseudo_cl_cov(results_dir, ver, nbins=32):
+    return f"{results_dir}/pseudo_cl_cov_{ver}_powspace_nbins={nbins}.fits"
 
 
 def _resolve_pseudo_cl_paths(
@@ -177,7 +175,6 @@ def _resolve_pseudo_cl_paths(
     fiducial_version=None,
     fiducial_pseudo_cl_path=None,
     fiducial_pseudo_cl_cov_path=None,
-    blind="A",
     nbins=32,
 ):
     """Resolve the pseudo-Cl and covariance paths for one version in the sweep.
@@ -194,8 +191,8 @@ def _resolve_pseudo_cl_paths(
     ):
         return fiducial_pseudo_cl_path, fiducial_pseudo_cl_cov_path
     return (
-        _pseudo_cl(results_dir, ver, blind, nbins),
-        _pseudo_cl_cov(results_dir, ver, blind, nbins),
+        _pseudo_cl(results_dir, ver, nbins),
+        _pseudo_cl_cov(results_dir, ver, nbins),
     )
 
 
diff --git a/papers/bmodes/scripts/cl_version_comparison.py b/papers/bmodes/scripts/cl_version_comparison.py
index c3c77ddd..d16659f9 100644
--- a/papers/bmodes/scripts/cl_version_comparison.py
+++ b/papers/bmodes/scripts/cl_version_comparison.py
@@ -34,14 +34,12 @@
 plt.style.use(PAPER_MPLSTYLE)
 
 
-def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
-    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.sacc"
+def _pseudo_cl(results_dir, ver, nbins=32):
+    return f"{results_dir}/pseudo_cl_{ver}_powspace_nbins={nbins}.sacc"
 
 
-def _pseudo_cl_cov(results_dir, ver, blind="A", nbins=32):
-    return (
-        f"{results_dir}/pseudo_cl_cov_{ver}_blind={blind}_powspace_nbins={nbins}.fits"
-    )
+def _pseudo_cl_cov(results_dir, ver, nbins=32):
+    return f"{results_dir}/pseudo_cl_cov_{ver}_powspace_nbins={nbins}.fits"
 
 
 def _resolve_pseudo_cl_paths(
@@ -50,7 +48,6 @@ def _resolve_pseudo_cl_paths(
     fiducial_version=None,
     fiducial_pseudo_cl_path=None,
     fiducial_pseudo_cl_cov_path=None,
-    blind="A",
     nbins=32,
 ):
     """Resolve the pseudo-Cl and covariance paths for one version in the sweep.
@@ -67,8 +64,8 @@ def _resolve_pseudo_cl_paths(
     ):
         return fiducial_pseudo_cl_path, fiducial_pseudo_cl_cov_path
     return (
-        _pseudo_cl(results_dir, ver, blind, nbins),
-        _pseudo_cl_cov(results_dir, ver, blind, nbins),
+        _pseudo_cl(results_dir, ver, nbins),
+        _pseudo_cl_cov(results_dir, ver, nbins),
     )
 
 
diff --git a/papers/bmodes/scripts/compute_cosebis_pte_single.py b/papers/bmodes/scripts/compute_cosebis_pte_single.py
index 73563a8c..6fc5bf92 100644
--- a/papers/bmodes/scripts/compute_cosebis_pte_single.py
+++ b/papers/bmodes/scripts/compute_cosebis_pte_single.py
@@ -2,7 +2,7 @@
 """Compute the COSEBIS B-mode PTE matrix over the full scale-cut pair grid.
 
 Reconciliation of the ASTRA ``cosebis_pte_per_cut`` output: the Snakemake DAG
-*scattered* one JSON per (version, blind, i_min, i_max) cut via this script and
+*scattered* one JSON per (version, i_min, i_max) cut via this script and
 gathered them downstream. The spec describes the gathered NPZ that scatter never
 materialised. Here a single CLI loops the same ``_pte_scale_cut_pairs()`` grid,
 runs the *identical* per-pair COSEBI computation (same theta grid, nmodes, NaN-on-
@@ -79,15 +79,14 @@ def _compute_pair(gg, cov_path, nmodes, theta_min, theta_max):
     }
 
 
-def main(config, xi_integration, cov_integration, out_dir, version=None, blind=None):
+def main(config, xi_integration, cov_integration, out_dir, version=None):
     t_start = time.time()
     fid = config["fiducial"]
-    # Version/blind tag only the output filename + provenance record; the theta
+    # Version tags only the output filename + provenance record; the theta
     # grid, nmodes and per-pair COSEBI compute are version-independent (they read
     # from config["fiducial"]), so a sweep call over a non-fiducial catalog stays
     # bit-identical to the fiducial call save for the xi/cov inputs and the tag.
     version = version if version is not None else fid["version"]
-    blind = blind if blind is not None else fid["blind"]
     nmodes = int(fid["nmodes"])  # 20 for full computation
 
     min_sep_int = fid["min_sep_int"]
@@ -146,11 +145,10 @@ def _mat():
 
     out_dir = Path(out_dir)
     out_dir.mkdir(parents=True, exist_ok=True)
-    npz_path = out_dir / f"cosebis_ptes_{version}_{blind}.npz"
+    npz_path = out_dir / f"cosebis_ptes_{version}.npz"
     np.savez(
         npz_path,
         version=version,
-        blind=blind,
         nmodes=nmodes,
         mode_subsets=np.array([6, 20]),
         theta_grid=theta_grid,
@@ -177,7 +175,7 @@ def _from_cli(argv=None):
     import yaml
 
     ap = argparse.ArgumentParser(
-        description="Gathered COSEBI B-mode PTE matrix over the scale-cut pair grid (fiducial version + blind)."
+        description="Gathered COSEBI B-mode PTE matrix over the scale-cut pair grid (fiducial version)."
     )
     ap.add_argument(
         "--config", required=True, help="Absolute path to bmodes config.yaml"
@@ -199,11 +197,6 @@ def _from_cli(argv=None):
         help="Catalog version tag (default: config.fiducial.version). Overridden "
         "by the version sweep to name the non-fiducial output NPZ.",
     )
-    ap.add_argument(
-        "--blind",
-        default=None,
-        help="Blind tag (default: config.fiducial.blind)",
-    )
     a = ap.parse_args(argv)
     with open(a.config) as f:
         config = yaml.safe_load(f)
@@ -213,7 +206,6 @@ def _from_cli(argv=None):
         a.cov_integration,
         a.out,
         version=a.version,
-        blind=a.blind,
     )
 
 
diff --git a/papers/bmodes/scripts/config_space_pte_matrices.py b/papers/bmodes/scripts/config_space_pte_matrices.py
index 3e3f9a67..3bfa2811 100644
--- a/papers/bmodes/scripts/config_space_pte_matrices.py
+++ b/papers/bmodes/scripts/config_space_pte_matrices.py
@@ -18,7 +18,7 @@
         --config /path/to/config.yaml \
         --pte-intermediate-dir /abs/.../paper_plots/intermediate \
         --cosebis-pte-dir /abs/.../tapestry/cosebis_pte_matrix/pte_values \
-        --blind A --out <output_dir>
+        --out <output_dir>
 """
 
 import argparse
@@ -76,7 +76,7 @@ def _resolve_overrides(version, fiducial_overrides):
     the sweep versions on the *same* gathered-NPZ provenance (adapted back to the
     per-pair PTE matrix by ``_cosebis_matrix_from_npz``) rather than the old
     per-pair-JSON scatter tree. Pure E/B has no such split — the sweep emits the
-    old-tree ``{ver}_{blind}_pure_eb_ptes.npz`` name, so non-fiducial versions
+    old-tree ``{ver}_pure_eb_ptes.npz`` name, so non-fiducial versions
     read it straight from the ``--pte-intermediate-dir`` file list (override None).
     Either element may be None.
     """
@@ -112,12 +112,10 @@ def load_cosebis_pte_matrix(
 ):
     """Load COSEBIS PTE values from JSON files into matrix.
 
-    Uses fiducial blind only (data vectors identical across blinds).
-
     Parameters
     ----------
     pte_files : list of str
-        Paths to PTE JSON files for fiducial blind.
+        Paths to PTE JSON files.
     version : str
         Version to filter for.
     config : dict
@@ -174,12 +172,10 @@ def load_cosebis_pte_matrix(
 def load_pure_eb_pte_matrices(pte_files, version, override_path=None):
     """Load Pure E/B PTE matrices from npz files.
 
-    Uses fiducial blind only (data vectors identical across blinds).
-
     Parameters
     ----------
     pte_files : list of str
-        Paths to pure_eb_ptes.npz files for fiducial blind.
+        Paths to pure_eb_ptes.npz files.
     version : str
         Version to filter for.
 
@@ -368,12 +364,12 @@ def extract_full_range_ptes(
 ):
     """Extract full-range PTEs from npz and JSON files.
 
-    Takes minimum PTE across blinds for each statistic.
+    Takes minimum PTE across matching files for each statistic.
 
     Parameters
     ----------
     pure_eb_pte_files : list
-        All Pure E/B PTE npz files (includes all blinds).
+        All Pure E/B PTE npz files.
     cosebis_pte_files : list
         All COSEBIS PTE JSON files.
     version : str
@@ -382,11 +378,11 @@ def extract_full_range_ptes(
     Returns
     -------
     ptes : dict
-        Full-range PTEs for xip, xim, and cosebis (fiducial blind).
+        Full-range PTEs for xip, xim, and cosebis.
     """
     pure_eb_override, cosebis_override = _resolve_overrides(version, fiducial_overrides)
 
-    # Get full-range PTEs from pure E/B (fiducial blind)
+    # Get full-range PTEs from pure E/B
     xip_ptes = []
     xim_ptes = []
     combined_ptes = []
@@ -428,7 +424,7 @@ def extract_full_range_ptes(
             ptes["cosebis_20"] = pte20
         return ptes
 
-    # Old tree: pte_000_020.json for full theta range, min across blinds.
+    # Old tree: pte_000_020.json for full theta range.
     cosebis_ptes_6 = []
     cosebis_ptes_20 = []
     for pte_file in cosebis_pte_files:
@@ -474,7 +470,7 @@ def create_3panel_composite(
     version : str
         Catalog version string (fiducial).
     pure_eb_pte_files : list
-        All Pure E/B PTE npz files (includes all blinds).
+        All Pure E/B PTE npz files.
     cosebis_pte_files : list
         All COSEBIS PTE JSON files.
     xip_fid, xim_fid : tuple
@@ -625,7 +621,7 @@ def create_9panel_composite(
     versions : list of str
         Catalog version strings in display order.
     pure_eb_pte_files : list
-        All Pure E/B PTE npz files (includes all blinds).
+        All Pure E/B PTE npz files.
     cosebis_pte_files : list
         All COSEBIS PTE JSON files.
     xip_fid, xim_fid : tuple
@@ -1045,16 +1041,15 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pte-intermediate-dir",
         required=True,
-        help="Directory holding {version}_{blind}_pure_eb_ptes.npz",
+        help="Directory holding {version}_pure_eb_ptes.npz",
     )
     ap.add_argument(
         "--cosebis-pte-dir",
         required=True,
         help="COSEBI PTE source dir. lc cosebis_ptes sweep: gathered "
-        "cosebis_ptes_{version}_{blind}.npz per version (auto-detected, preferred). "
-        "Old tree fallback: {version}/{blind}/pte_{i:03d}_{j:03d}.json scatter.",
+        "cosebis_ptes_{version}.npz per version (auto-detected, preferred). "
+        "Old tree fallback: {version}/pte_{i:03d}_{j:03d}.json scatter.",
     )
-    ap.add_argument("--blind", default="A", help="Fiducial blind (paper: A)")
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
         "--fiducial-version",
@@ -1066,7 +1061,7 @@ def _from_cli(argv=None):
         "--fiducial-pure-eb-pte-path",
         default=None,
         help="lc pure_eb PTE NPZ for the fiducial version "
-        "(same format as old-tree {version}_{blind}_pure_eb_ptes.npz)",
+        "(same format as old-tree {version}_pure_eb_ptes.npz)",
     )
     ap.add_argument(
         "--fiducial-cosebis-pte-path",
@@ -1087,8 +1082,7 @@ def _from_cli(argv=None):
 
     versions = _versions_config_space(config)
     pure_eb_pte_files = [
-        os.path.join(a.pte_intermediate_dir, f"{v}_{a.blind}_pure_eb_ptes.npz")
-        for v in versions
+        os.path.join(a.pte_intermediate_dir, f"{v}_pure_eb_ptes.npz") for v in versions
     ]
     pure_eb_pte_files = [p for p in pure_eb_pte_files if os.path.exists(p)]
 
@@ -1096,18 +1090,14 @@ def _from_cli(argv=None):
     # version, same layout as the fiducial single-output) so fiducial and sweep
     # versions share provenance; fall back to the old per-pair-JSON scatter tree.
     cosebis_by_version = {
-        v: os.path.join(a.cosebis_pte_dir, f"cosebis_ptes_{v}_{a.blind}.npz")
+        v: os.path.join(a.cosebis_pte_dir, f"cosebis_ptes_{v}.npz")
         for v in versions
-        if os.path.exists(
-            os.path.join(a.cosebis_pte_dir, f"cosebis_ptes_{v}_{a.blind}.npz")
-        )
+        if os.path.exists(os.path.join(a.cosebis_pte_dir, f"cosebis_ptes_{v}.npz"))
     }
     cosebis_pte_files = (
         []
         if cosebis_by_version
-        else sorted(
-            glob.glob(os.path.join(a.cosebis_pte_dir, "*", a.blind, "pte_*.json"))
-        )
+        else sorted(glob.glob(os.path.join(a.cosebis_pte_dir, "*", "pte_*.json")))
     )
 
     fiducial_overrides = None
diff --git a/papers/bmodes/scripts/cosebis_version_comparison.py b/papers/bmodes/scripts/cosebis_version_comparison.py
index 65543ac9..464cca70 100644
--- a/papers/bmodes/scripts/cosebis_version_comparison.py
+++ b/papers/bmodes/scripts/cosebis_version_comparison.py
@@ -151,8 +151,8 @@ def _xi_integration(results_dir, ver):
     return f"{results_dir}/{ver}_xi_minsep=0.5_maxsep=300.0_nbins=1000_npatch=1.txt"
 
 
-def _cov_integration(cov_dir, ver, blind):
-    base = f"covariance_{ver}_{blind}_g_minsep=0.5_maxsep=300.0_nbins=1000_masked"
+def _cov_integration(cov_dir, ver):
+    base = f"covariance_{ver}_g_minsep=0.5_maxsep=300.0_nbins=1000_masked"
     return f"{cov_dir}/{base}/{base}_processed.txt"
 
 
@@ -170,7 +170,6 @@ def main(
     nmodes = config["fiducial"]["nmodes"]
     plotting_config = config["plotting"]
     version_labels = plotting_config["version_labels"]
-    blind = config["fiducial"]["blind"]
 
     # Fiducial version whose inputs may be overridden with explicit lc paths
     fiducial_version = fiducial_version or config["fiducial"]["version"]
@@ -186,7 +185,7 @@ def main(
     cov_paths_list = [
         fiducial_cov_path
         if v == fiducial_version and fiducial_cov_path
-        else _cov_integration(cov_dir, v, blind)
+        else _cov_integration(cov_dir, v)
         for v in versions
     ]
 
diff --git a/papers/bmodes/scripts/gather_pure_eb_chunks.py b/papers/bmodes/scripts/gather_pure_eb_chunks.py
index 887e512f..b6d5d702 100644
--- a/papers/bmodes/scripts/gather_pure_eb_chunks.py
+++ b/papers/bmodes/scripts/gather_pure_eb_chunks.py
@@ -3,12 +3,12 @@
 CLI refactor of the former Snakemake ``script:`` gather rule. Reads the actual
 ξ± data vectors (reporting + integration grids), computes the pure E/B/ambiguous
 decomposition (Schneider 2022), stacks the per-chunk MC sample blocks, and
-forms the empirical 6-block covariance. Writes the per-(version, blind)
-``<version>_<blind>_pure_eb_semianalytic.npz`` consumed by every downstream
+forms the empirical 6-block covariance. Writes the per-version
+``<version>_pure_eb_semianalytic.npz`` consumed by every downstream
 pure-mode plot / PTE.
 
     python gather_pure_eb_chunks.py \
-        --version SP_v1.4.6.3_leak_corr --blind A \
+        --version SP_v1.4.6.3_leak_corr \
         --xi-reporting  <xi 20-bin .txt> \
         --xi-integration <xi 1000-bin .txt> \
         --chunks-dir <dir with pure_eb_chunk_*.npz> \
@@ -32,7 +32,6 @@ def _load_xi(path, nbins):
 
 def gather(
     version,
-    blind,
     xi_reporting,
     xi_integration,
     chunk_files,
@@ -44,7 +43,7 @@ def gather(
 ):
     from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
 
-    print(f"Gathering pure E/B for blind {blind}")
+    print(f"Gathering pure E/B for {version}")
 
     gg = _load_xi(xi_reporting, nbins)
     gg_int = _load_xi(xi_integration, nbins_int)
@@ -88,7 +87,7 @@ def gather(
     }
 
     os.makedirs(output_dir, exist_ok=True)
-    out_path = os.path.join(output_dir, f"{version}_{blind}_pure_eb_semianalytic.npz")
+    out_path = os.path.join(output_dir, f"{version}_pure_eb_semianalytic.npz")
     np.savez(out_path, **package)
     print(f"Saved to {out_path}")
     return out_path
@@ -107,7 +106,6 @@ def _resolve_chunks(args):
 def _from_cli(argv=None):
     ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
     ap.add_argument("--version", required=True)
-    ap.add_argument("--blind", default="A")
     ap.add_argument("--xi-reporting", required=True)
     ap.add_argument("--xi-integration", required=True)
     ap.add_argument("--chunks-dir", help="Directory holding pure_eb_chunk_*.npz")
@@ -123,7 +121,6 @@ def _from_cli(argv=None):
     a = ap.parse_args(argv)
     gather(
         version=a.version,
-        blind=a.blind,
         xi_reporting=a.xi_reporting,
         xi_integration=a.xi_integration,
         chunk_files=_resolve_chunks(a),
diff --git a/papers/bmodes/scripts/generate_paper_macros.py b/papers/bmodes/scripts/generate_paper_macros.py
index 9e02c132..691961fd 100644
--- a/papers/bmodes/scripts/generate_paper_macros.py
+++ b/papers/bmodes/scripts/generate_paper_macros.py
@@ -92,65 +92,26 @@ def generate_macros(
     macros.append("% See workflow/config/bmodes_paper.md for paper choices")
     macros.append("")
 
-    # COSEBIS version comparison - extract fiducial version, n=6
-    cosebis_path = claims_dir / "cosebis_version_comparison" / "evidence.json"
-    if cosebis_path.exists():
-        with open(cosebis_path) as f:
-            cosebis_ev = json.load(f).get("evidence", {})
-
-        macros.append(f"% cosebis ({fiducial_version}, n=6)")
-
-        # Fiducial scale cut - use pte_6_min (conservative across blinds)
-        fiducial = cosebis_ev.get("fiducial", {})
-        fid_versions = fiducial.get("versions", {})
-        fid_data = fid_versions.get(fiducial_version, {})
-        if "pte_6_min" in fid_data:
-            macros.append(
-                f"\\newcommand{{\\cosebisfiducialPte}}{{{_format_value(fid_data['pte_6_min'])}}}"
-            )
-
-        # Full range
-        full = cosebis_ev.get("full", {})
-        full_versions = full.get("versions", {})
-        full_data = full_versions.get(fiducial_version, {})
-        if "pte_6_min" in full_data:
-            macros.append(
-                f"\\newcommand{{\\cosebisfullPte}}{{{_format_value(full_data['pte_6_min'])}}}"
-            )
-
-        # Scale cuts from fiducial
-        if "scale_cut_arcmin" in fiducial:
-            cuts = fiducial["scale_cut_arcmin"]
-            macros.append(
-                f"\\newcommand{{\\cosebisthetaMin}}{{{_format_value(cuts[0])}}}"
-            )
-            macros.append(
-                f"\\newcommand{{\\cosebisthetaMax}}{{{_format_value(cuts[1])}}}"
-            )
-
-        macros.append("")
-
-    # Pure E/B data vector - use min across blinds (cache for reuse below)
+    # Pure E/B data vector
     eb_path = claims_dir / "pure_eb_data_vector" / "evidence.json"
-    eb_fid = {}  # cached for PTE variation section
     if eb_path.exists():
         with open(eb_path) as f:
             eb_ev = json.load(f).get("evidence", {})
 
-        macros.append("% pure_eb_data_vector (min across blinds)")
+        macros.append("% pure_eb_data_vector")
 
-        # Fiducial PTEs - use pte_joint_min (conservative across blinds)
+        # Fiducial PTEs
         eb_fid = eb_ev.get("fiducial", {})
-        if "pte_joint_min" in eb_fid:
+        if "pte_joint" in eb_fid:
             macros.append(
-                f"\\newcommand{{\\ebfiducialPte}}{{{_format_value(eb_fid['pte_joint_min'])}}}"
+                f"\\newcommand{{\\ebfiducialPte}}{{{_format_value(eb_fid['pte_joint'])}}}"
             )
 
         # Full range PTEs
         full = eb_ev.get("full", {})
-        if "pte_joint_min" in full:
+        if "pte_joint" in full:
             macros.append(
-                f"\\newcommand{{\\ebfullPte}}{{{_format_value(full['pte_joint_min'])}}}"
+                f"\\newcommand{{\\ebfullPte}}{{{_format_value(full['pte_joint'])}}}"
             )
 
         # Scale cuts from fiducial
@@ -196,50 +157,6 @@ def generate_macros(
 
         macros.append("")
 
-    # Covariance blind consistency
-    cov_path = claims_dir / "covariance_blind_consistency" / "evidence.json"
-    if cov_path.exists():
-        with open(cov_path) as f:
-            data = json.load(f)
-        ev = data.get("evidence", {})
-
-        macros.append("% covariance_blind_consistency")
-
-        # Max deviations across blinds
-        xip = ev.get("xip", {})
-        xim = ev.get("xim", {})
-        xip_max = max(
-            xip.get("B_to_A", {}).get("max_dev", 0),
-            xip.get("C_to_A", {}).get("max_dev", 0),
-        )
-        xim_max = max(
-            xim.get("B_to_A", {}).get("max_dev", 0),
-            xim.get("C_to_A", {}).get("max_dev", 0),
-        )
-        macros.append(
-            f"\\newcommand{{\\covXipMaxDev}}{{{_format_value(xip_max * 100)}\\%}}"
-        )
-        macros.append(
-            f"\\newcommand{{\\covXimMaxDev}}{{{_format_value(xim_max * 100)}\\%}}"
-        )
-
-        macros.append("")
-
-    # PTE variation across blinds (reuse eb_fid cached above)
-    if eb_fid:
-        joint_ptes = [
-            eb_fid.get(f"pte_joint_{b}")
-            for b in ["A", "B", "C"]
-            if f"pte_joint_{b}" in eb_fid
-        ]
-        if joint_ptes:
-            macros.append("% PTE variation across blinds (fiducial scale cuts)")
-            joint_delta = max(joint_ptes) - min(joint_ptes)
-            macros.append(
-                f"\\newcommand{{\\ebJointPteDelta}}{{{_format_value(joint_delta)}}}"
-            )
-            macros.append("")
-
     # Config-space PTE matrices - generate table
     if config_pte_path is None:
         config_pte_path = claims_dir / "config_space_pte_matrices" / "evidence.json"
@@ -675,7 +592,7 @@ def _from_cli(argv=None):
         default=None,
         help=(
             "Optional tapestry-style dir holding <rule>/evidence.json for the "
-            "extra claims_macros.tex macros (cosebis/pure_eb/harmonic_config); "
+            "extra claims_macros.tex macros (pure_eb/harmonic_config); "
             "the two PTE tables need only the two --*-evidence paths above."
         ),
     )
diff --git a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
index b3b05c27..5e4e4e3e 100644
--- a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
+++ b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
@@ -804,12 +804,11 @@ def _versions_all_for_plots(config):
     return leak_corr + uncorrected
 
 
-def _cov_integration_path(cov_dir, version, blind, min_sep, max_sep, nbins):
+def _cov_integration_path(cov_dir, version, min_sep, max_sep, nbins):
     """Reproduce common.covariance_path for the Gaussian integration-grid,
     masked covariance (suffix _processed.txt)."""
     base = (
-        f"covariance_{version}_{blind}_g"
-        f"_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_masked"
+        f"covariance_{version}_g_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_masked"
     )
     return os.path.join(cov_dir, base, f"{base}_processed.txt")
 
@@ -863,9 +862,6 @@ def _from_cli(argv=None):
         required=True,
         help="COSMO_INFERENCE data/covariance dir (Gaussian integration covariances)",
     )
-    ap.add_argument(
-        "--blind", default="A", help="Blind for pseudo-Cl / covariance (paper: A)"
-    )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
         "--fiducial-version",
@@ -934,7 +930,7 @@ def _from_cli(argv=None):
             if is_fiducial and a.fiducial_pseudo_cl_path
             else os.path.join(
                 a.cosmo_val_dir,
-                f"pseudo_cl_{ver}_blind={a.blind}_powspace_nbins={cosebis_nbins}.sacc",
+                f"pseudo_cl_{ver}_powspace_nbins={cosebis_nbins}.sacc",
             )
         )
         # 96-bin pseudo-Cl covariance is intentionally NOT lc-repointed: lc did not
@@ -943,7 +939,7 @@ def _from_cli(argv=None):
         # version.
         inputs[f"pseudo_cl_cov_{ver}"] = os.path.join(
             a.cosmo_val_dir,
-            f"pseudo_cl_cov_{ver}_blind={a.blind}_powspace_nbins={cosebis_nbins}.fits",
+            f"pseudo_cl_cov_{ver}_powspace_nbins={cosebis_nbins}.fits",
         )
         inputs[f"xi_{ver}"] = (
             a.fiducial_xi_path
@@ -958,7 +954,7 @@ def _from_cli(argv=None):
             a.fiducial_cov_path
             if is_fiducial and a.fiducial_cov_path
             else _cov_integration_path(
-                a.covariance_dir, ver, a.blind, min_sep_int, max_sep_int, nbins_int
+                a.covariance_dir, ver, min_sep_int, max_sep_int, nbins_int
             )
         )
 
diff --git a/papers/bmodes/scripts/harmonic_space_pte_matrices.py b/papers/bmodes/scripts/harmonic_space_pte_matrices.py
index 3be9644a..577c561f 100644
--- a/papers/bmodes/scripts/harmonic_space_pte_matrices.py
+++ b/papers/bmodes/scripts/harmonic_space_pte_matrices.py
@@ -3,8 +3,6 @@
 Produces:
 - Results: single-panel fiducial Cl^BB PTE matrix
 - Appendix: N-panel composite for all versions from config.versions
-
-Uses fiducial blind covariance only (blind independence validated elsewhere).
 """
 
 import argparse
@@ -31,14 +29,12 @@
 plt.style.use(PAPER_MPLSTYLE)
 
 
-def _pseudo_cl(results_dir, ver, blind="A", nbins=32):
-    return f"{results_dir}/pseudo_cl_{ver}_blind={blind}_powspace_nbins={nbins}.sacc"
+def _pseudo_cl(results_dir, ver, nbins=32):
+    return f"{results_dir}/pseudo_cl_{ver}_powspace_nbins={nbins}.sacc"
 
 
-def _pseudo_cl_cov(results_dir, ver, blind="A", nbins=32):
-    return (
-        f"{results_dir}/pseudo_cl_cov_{ver}_blind={blind}_powspace_nbins={nbins}.fits"
-    )
+def _pseudo_cl_cov(results_dir, ver, nbins=32):
+    return f"{results_dir}/pseudo_cl_cov_{ver}_powspace_nbins={nbins}.fits"
 
 
 def compute_pte_matrix(
@@ -355,7 +351,6 @@ def main(
 ):
     versions = [v for v in config["versions"] if "_ecut" not in v]
     fiducial_version = config["fiducial"]["version"]
-    fiducial_blind = config["fiducial"]["blind"]
 
     # Fiducial ell cuts from config
     fiducial_ell_min = config["cl"]["fiducial_ell_min"]
@@ -365,16 +360,16 @@ def main(
     output_dir = Path(out_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
-    # Per-version pseudo-Cl / covariance files (canonical COSMO_VAL tree, blind A).
-    # Mirrors _pseudo_cl_path(ver) / _pseudo_cl_cov_path(ver, blind) in figures.smk.
+    # Per-version pseudo-Cl / covariance files (canonical COSMO_VAL tree).
+    # Mirrors _pseudo_cl_path(ver) / _pseudo_cl_cov_path(ver) in figures.smk.
     # Fiducial-provenance repoint: when --fiducial-version matches and an explicit
     # lc override path is set, read that path instead of the reconstructed pattern
-    # (lc files lack the blind=/powspace_nbins= tokens, distinguished by directory).
+    # (lc files lack the powspace_nbins= token, distinguished by directory).
     version_to_cl = {
         ver: (
             fiducial_pseudo_cl_path
             if ver == fiducial_version_override and fiducial_pseudo_cl_path is not None
-            else _pseudo_cl(results_dir, ver, blind=fiducial_blind)
+            else _pseudo_cl(results_dir, ver)
         )
         for ver in versions
     }
@@ -383,18 +378,18 @@ def main(
             fiducial_pseudo_cl_cov_path
             if ver == fiducial_version_override
             and fiducial_pseudo_cl_cov_path is not None
-            else _pseudo_cl_cov(results_dir, ver, blind=fiducial_blind)
+            else _pseudo_cl_cov(results_dir, ver)
         )
         for ver in versions
     }
 
-    # Compute PTE matrices for all versions using fiducial blind
+    # Compute PTE matrices for all versions
     all_stats = {}
     all_matrices = {}
     all_ells = {}
 
     for version in versions:
-        print(f"\n--- Processing {version} (blind={fiducial_blind}) ---")
+        print(f"\n--- Processing {version} ---")
 
         if version not in version_to_cl:
             print(f"  WARNING: No pseudo-Cl file found for {version}, skipping")
@@ -425,14 +420,13 @@ def main(
     npz_payload = {
         "versions": np.array(list(all_matrices.keys())),
         "fiducial_version": fiducial_version,
-        "fiducial_blind": fiducial_blind,
         "fiducial_ell_min": fiducial_ell_min,
         "fiducial_ell_max": fiducial_ell_max,
     }
     for version in all_matrices:
         npz_payload[f"{version}__pte_matrix"] = all_matrices[version]
         npz_payload[f"{version}__ell"] = all_ells[version]
-    npz_path = output_dir / f"cl_pte_matrices_{fiducial_blind}.npz"
+    npz_path = output_dir / "cl_pte_matrices.npz"
     np.savez(npz_path, **npz_payload)
     print(f"Saved PTE matrices to {npz_path}")
 
@@ -493,7 +487,6 @@ def main(
     evidence_data = {
         "generated": datetime.now().isoformat(),
         "evidence": {
-            "blind": fiducial_blind,
             "versions": {},
         },
         "output": {
diff --git a/papers/bmodes/scripts/precompute_pure_eb_chunk.py b/papers/bmodes/scripts/precompute_pure_eb_chunk.py
index 99153096..2db6bf92 100644
--- a/papers/bmodes/scripts/precompute_pure_eb_chunk.py
+++ b/papers/bmodes/scripts/precompute_pure_eb_chunk.py
@@ -12,7 +12,7 @@
 
     python precompute_pure_eb_chunk.py \
         --chunk-id 0 --n-chunks 20 --n-samples 2000 \
-        --version SP_v1.4.6.3_leak_corr --blind A \
+        --version SP_v1.4.6.3_leak_corr \
         --cat-config /path/cosmo_val/cat_config.yaml \
         --xi-reporting  <xi 20-bin .txt> \
         --xi-integration <xi 1000-bin .txt> \
@@ -64,7 +64,6 @@ def compute_chunk(
     n_chunks,
     n_samples_total,
     version,
-    blind,
     cat_config,
     xi_reporting,
     xi_integration,
@@ -105,10 +104,8 @@ def compute_chunk(
         catalog_config=cat_config,
         output_dir=output_dir,
     )
-    cv.blind = blind
     z, nz = cv.get_redshift(version)
     z_dist = np.column_stack([z, nz])
-    print(f"Using n(z) for blind {blind}")
 
     cosmo_cov = _build_cosmology(cosmo_params)
 
@@ -176,7 +173,6 @@ def _from_cli(argv=None):
     ap.add_argument("--n-chunks", type=int, default=20)
     ap.add_argument("--n-samples", type=int, default=2000)
     ap.add_argument("--version", required=True)
-    ap.add_argument("--blind", default="A")
     ap.add_argument("--cat-config", required=True)
     ap.add_argument("--xi-reporting", required=True)
     ap.add_argument("--xi-integration", required=True)
@@ -195,7 +191,6 @@ def _from_cli(argv=None):
         n_chunks=a.n_chunks,
         n_samples_total=a.n_samples,
         version=a.version,
-        blind=a.blind,
         cat_config=a.cat_config,
         xi_reporting=a.xi_reporting,
         xi_integration=a.xi_integration,
diff --git a/papers/bmodes/scripts/pure_eb_covariance.py b/papers/bmodes/scripts/pure_eb_covariance.py
index 61b1967a..cb137c62 100644
--- a/papers/bmodes/scripts/pure_eb_covariance.py
+++ b/papers/bmodes/scripts/pure_eb_covariance.py
@@ -189,7 +189,6 @@ def main(config, pure_eb_path, out_dir):
         },
         "parameters": {
             "version": version,
-            "blind": config["fiducial"]["blind"],
         },
         "output": {
             "figure": "figure.png",
@@ -227,7 +226,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pure-eb-data",
         required=True,
-        help="Fiducial <version>_<blind>_pure_eb_semianalytic.npz "
+        help="Fiducial <version>_pure_eb_semianalytic.npz "
         "(provides the 6-block cov_pure_eb)",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
diff --git a/papers/bmodes/scripts/pure_eb_data_vector.py b/papers/bmodes/scripts/pure_eb_data_vector.py
index 992e1806..d7845361 100644
--- a/papers/bmodes/scripts/pure_eb_data_vector.py
+++ b/papers/bmodes/scripts/pure_eb_data_vector.py
@@ -2,12 +2,12 @@
 
 Fiducial catalog only: pure E/B/ambiguous decomposition of ξ± with B-modes
 consistent with zero at the fiducial scale cuts. Writes evidence.json with PTE
-values including the joint B-mode test (fiducial blind, config.fiducial.blind).
+values including the joint B-mode test.
 
 CLI:
     python pure_eb_data_vector.py \
         --config config.yaml \
-        --pure-eb-data <version>_<blind>_pure_eb_semianalytic.npz \
+        --pure-eb-data <version>_pure_eb_semianalytic.npz \
         --reporting-cov <cov_reporting_ng>/covariance_processed.txt \
         --out <output_dir>
 """
@@ -218,7 +218,6 @@ def setup_panel(ax, ylabel_text=None):
 
 
 def main(config, pure_eb_path, cov_path, out_dir):
-    blind = config["fiducial"]["blind"]
     version = config["fiducial"]["version"]
     fiducial_xip_scale_cut = tuple(config["fiducial"]["fiducial_xip_scale_cut"])
     fiducial_xim_scale_cut = tuple(config["fiducial"]["fiducial_xim_scale_cut"])
@@ -292,7 +291,7 @@ def main(config, pure_eb_path, cov_path, out_dir):
     )
 
     print(
-        f"Blind {blind} PTEs (fiducial): xi+^B={pte_xip_fid:.3f}, xi-^B={pte_xim_fid:.3f}, joint={pte_joint_fid:.3f}"
+        f"PTEs (fiducial): xi+^B={pte_xip_fid:.3f}, xi-^B={pte_xim_fid:.3f}, joint={pte_joint_fid:.3f}"
     )
 
     # Write evidence.json (based on leak-corrected fiducial data only)
@@ -317,7 +316,6 @@ def main(config, pure_eb_path, cov_path, out_dir):
                 "dof_joint_B": int(dof_joint_full),
             },
             "version": version,
-            "blind": blind,
         },
         "output": {"figure": "figure.png"},
     }
@@ -340,7 +338,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pure-eb-data",
         required=True,
-        help="Fiducial <version>_<blind>_pure_eb_semianalytic.npz "
+        help="Fiducial <version>_pure_eb_semianalytic.npz "
         "(decomposed ξ± + 6-block MC covariance)",
     )
     ap.add_argument(
diff --git a/papers/bmodes/scripts/pure_eb_version_comparison.py b/papers/bmodes/scripts/pure_eb_version_comparison.py
index 7b27151b..3e99e68b 100644
--- a/papers/bmodes/scripts/pure_eb_version_comparison.py
+++ b/papers/bmodes/scripts/pure_eb_version_comparison.py
@@ -242,8 +242,8 @@ def _create_version_comparison_figure(
     return fig
 
 
-def _pure_eb_npz(results_dir, ver, blind):
-    return f"{results_dir}/{ver}_{blind}_pure_eb_semianalytic.npz"
+def _pure_eb_npz(results_dir, ver):
+    return f"{results_dir}/{ver}_pure_eb_semianalytic.npz"
 
 
 def main(
@@ -257,7 +257,6 @@ def main(
     version_labels = plotting_config["version_labels"]
     # Leak-corrected versions (matches VERSIONS_LEAK_CORR in figures.smk)
     versions = [v for v in config["versions"] if "_leak_corr" in v and "_ecut" not in v]
-    blind = config["fiducial"]["blind"]
 
     # Fiducial version whose NPZ may be overridden with an explicit lc path
     fiducial_version = fiducial_version or config["fiducial"]["version"]
@@ -267,7 +266,7 @@ def main(
     data_paths = [
         fiducial_pure_eb_data
         if v == fiducial_version and fiducial_pure_eb_data
-        else _pure_eb_npz(results_dir, v, blind)
+        else _pure_eb_npz(results_dir, v)
         for v in versions
     ]
 
@@ -473,8 +472,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--results-dir",
         required=True,
-        help="Directory holding per-version "
-        "<version>_<blind>_pure_eb_semianalytic.npz files",
+        help="Directory holding per-version <version>_pure_eb_semianalytic.npz files",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index 5d8416c6..8501bfbc 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -94,7 +94,6 @@ cosmo_val:
 # ---------------------------------------------------------------------------
 fiducial:
   version: SP_v1.4.6.3_leak_corr
-  blind: A
   npatch: 1
   min_sep: 1.0
   max_sep: 250.0
@@ -124,7 +123,6 @@ cl:
 # stamps into the filename, reconstructed by the inference consumer.
 harmonic:
   fiducial:
-    blind: A
     binning: powspace
     nbins: 32
 
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 1ea75134..866e0cad 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -1,7 +1,6 @@
 # %%
 import copy
 import os
-import re
 from pathlib import Path
 
 import colorama
@@ -256,7 +255,6 @@ def __init__(
         cell_seed=8192,
         path_onecovariance=None,
         cosmo_params=None,
-        blind=None,
     ):
         self.rho_tau_method = rho_tau_method
         self.cov_estimate_method = cov_estimate_method
@@ -285,7 +283,6 @@ def __init__(
         self.fiducial_input_inka = fiducial_input_inka
         self.nside_mask = nside_mask
         self.path_onecovariance = path_onecovariance
-        self.blind = blind
 
         assert self.cell_method in ["map", "catalog"], (
             "cell_method must be 'map' or 'catalog'"
@@ -443,20 +440,8 @@ def get_redshift(self, version):
             Redshift values
         nz : ndarray
             n(z) probability density
-
-        Notes
-        -----
-        If self.blind is set, the redshift path is modified to use the
-        specified blind (A, B, or C) by replacing the blind suffix in the
-        configured path.
         """
-        redshift_path = self.cc[version]["shear"]["redshift_path"]
-
-        # Override blind if specified
-        if self.blind is not None:
-            redshift_path = re.sub(r"_[ABC]\.txt$", f"_{self.blind}.txt", redshift_path)
-
-        return np.loadtxt(redshift_path, unpack=True)
+        return np.loadtxt(self.cc[version]["shear"]["redshift_path"], unpack=True)
 
     def _write_catalog_config(self):
         with self.catalog_config_path.open("w") as file:
diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index f8002244..ad405714 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -313,8 +313,8 @@ def set_params_leakage_scale(self, ver):
         # Set parameters
         params_in["input_path_shear"] = self.cc[ver]["shear"]["path"]
         params_in["input_path_PSF"] = self.cc[ver]["star"]["path"]
-        params_in["dndz_path"] = (
-            f"{self.cc['nz']['dndz']['path']}_{self.cc[ver]['pipeline']}_{self.cc['nz']['dndz']['blind']}.txt"
+        params_in["dndz_path"] = self.cc["nz"]["dndz"]["path"].format(
+            pipeline=self.cc[ver]["pipeline"]
         )
         params_in["output_dir"] = f"{self.cc['paths']['output']}/leakage_{ver}"
 
diff --git a/src/sp_validation/tests/test_config_paths_exist.py b/src/sp_validation/tests/test_config_paths_exist.py
index 38a269ec..a3de6873 100644
--- a/src/sp_validation/tests/test_config_paths_exist.py
+++ b/src/sp_validation/tests/test_config_paths_exist.py
@@ -31,7 +31,6 @@
 # files to stat. (The overlay's one real path, ``base:``, is deliberately not
 # listed, so it is still validated.)
 NON_PATH_KEYS = ("extra_output", "why", "replace", "with", "drop")
-PATH_PREFIX_KEYS = ("nz.dndz.path",)
 TEXT_SUFFIXES = (
     ".fits",
     ".fits.gz",
@@ -78,10 +77,6 @@ def _non_path_key(key: str) -> bool:
     return key.lower().endswith(NON_PATH_KEYS)
 
 
-def _path_prefix_key(key: str) -> bool:
-    return key.lower() in PATH_PREFIX_KEYS
-
-
 def _pathish_value(value: str) -> bool:
     if not value or any(part in value for part in SKIP_VALUE_PARTS):
         return False
@@ -108,11 +103,7 @@ def _walk_yaml(
             yield from _walk_yaml(child, trail + (str(index),), base_dir)
     elif isinstance(value, str):
         key = ".".join(trail)
-        if (
-            not _non_path_key(key)
-            and not _path_prefix_key(key)
-            and (_pathish_key(key) or _pathish_value(value))
-        ):
+        if not _non_path_key(key) and (_pathish_key(key) or _pathish_value(value)):
             yield key, value, base_dir
 
 
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index 50f046f0..032eb2ff 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -73,7 +73,7 @@ def _make_seed_config(tmp_path, shear_filename):
         config_data = {
             "nz": {
                 "subdir": str(nz_dir),
-                "dndz": {"blind": "A", "path": "dndz.txt"},
+                "dndz": {"path": "dndz.txt"},
             },
             "paths": {"output": str(output_dir)},
             base_version: {
@@ -394,7 +394,7 @@ def _write_synthetic_catalogs(
         config_data = {
             "nz": {
                 "subdir": str(nz_dir),
-                "dndz": {"blind": "A", "path": "dndz"},
+                "dndz": {"path": "dndz_{pipeline}_A.txt"},
             },
             "paths": {"output": str(output_dir)},
             version: version_cfg,
diff --git a/src/sp_validation/tests/test_cv_init_params.py b/src/sp_validation/tests/test_cv_init_params.py
index 240f9213..ffc87d8b 100644
--- a/src/sp_validation/tests/test_cv_init_params.py
+++ b/src/sp_validation/tests/test_cv_init_params.py
@@ -16,9 +16,7 @@
 
 REPO = Path(__file__).resolve().parents[3]
 
-EXEMPT = {
-    "blind": "None keeps the n(z) blind declared in the catalogue config",
-}
+EXEMPT = {}
 
 
 def _load_common():
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index fd39c8e2..ca6bbb1f 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -141,7 +141,7 @@ def _write_synthetic_config(tmp_path):
         "star_flag": "w",
     }
     config_data = {
-        "nz": {"subdir": str(nz_dir), "dndz": {"blind": "A", "path": "dndz"}},
+        "nz": {"subdir": str(nz_dir), "dndz": {"path": "dndz_{pipeline}_A.txt"}},
         "paths": {"output": str(output_dir)},
         version: {
             "subdir": str(cat_dir),
@@ -597,7 +597,7 @@ def test_calculate_pseudo_cl_out_path_born_at_declared_name(cv):
     untagged native name — so the tagged and diagnostic rules stay disjoint."""
     ver = cv._test_version
     cv._pseudo_cls = {}
-    tagged = cv._output_path(f"pseudo_cl_{ver}_blind=A_powspace_nbins=32.sacc")
+    tagged = cv._output_path(f"pseudo_cl_{ver}_powspace_nbins=32.sacc")
     native = cv._output_path(f"pseudo_cl_{ver}.sacc")
 
     cv.calculate_pseudo_cl(out_path=tagged)
diff --git a/workflow/common.py b/workflow/common.py
index ede56f55..b77d088a 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -52,10 +52,6 @@
 # The catalogue config of the launched checkout: the one file both the host
 # (CATALOG_CONFIG, loaded in configure) and every job read catalogues from.
 CAT_CONFIG = str(REPO_ROOT / "cosmo_val" / "cat_config.yaml")
-# "blind" is the glass-mock A/B/C realisation convention, NOT Smokescreen
-# blinding (a separate axis: the concealed=True SACC stamp). The name is baked
-# into on-disk filenames we do not own (e.g. nz_{version}_{A|B|C}.txt).
-BLINDS = ["A", "B", "C"]
 BLOCK_PAIRS = [("++", "1"), ("--", "2"), ("+-", "3")]
 
 # Fiducial cosmology: Planck 2018 (astropy Planck18, Table 2 + BAO)
@@ -68,8 +64,7 @@
 # Patterns must match all expected values; overly restrictive patterns cause
 # silent failures. Apply with: wildcard_constraints: **WILDCARD_CONSTRAINTS
 WILDCARD_CONSTRAINTS = {
-    "version": r"SP_v[\d.]+(_w_iv)?(_ecut\d+)?(_leak_corr)?",
-    "blind": r"[ABC]",
+    "version": r"SP_v[\d.]+(_[ABC])?(_w_iv)?(_ecut\d+)?(_leak_corr)?",
     "nbins": r"\d+",
     "min_sep": r"[0-9.]+",
     "max_sep": r"[0-9.]+",
@@ -189,20 +184,13 @@ def fiducial_binning_suffix(fiducial=None):
     )
 
 
-def resolve_covariance_version(version):
-    """Map version to its covariance version."""
-    return version
-
-
 def covariance_base(
     version,
-    blind,
     gaussian="ng",
     min_sep=None,
     max_sep=None,
     nbins=None,
     mask_suffix=None,
-    resolve_version=True,
     fiducial=None,
     default_mask_suffix=None,
 ):
@@ -218,78 +206,52 @@ def covariance_base(
             DEFAULT_MASK_SUFFIX if default_mask_suffix is None else default_mask_suffix
         )
     )
-    cov_version = resolve_covariance_version(version) if resolve_version else version
     return (
-        f"covariance_{cov_version}_{blind}_{gaussian}"
+        f"covariance_{version}_{gaussian}"
         f"_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}"
     )
 
 
 def covariance_dir(
-    version,
-    blind,
-    gaussian="ng",
-    min_sep=None,
-    max_sep=None,
-    nbins=None,
-    mask_suffix=None,
-    resolve_version=True,
+    version, gaussian="ng", min_sep=None, max_sep=None, nbins=None, mask_suffix=None
 ):
     """Construct covariance directory path."""
-    base = covariance_base(
-        version,
-        blind,
-        gaussian,
-        min_sep,
-        max_sep,
-        nbins,
-        mask_suffix,
-        resolve_version=resolve_version,
-    )
+    base = covariance_base(version, gaussian, min_sep, max_sep, nbins, mask_suffix)
     return str(COSMO_INFERENCE / f"data/covariance/{base}")
 
 
 def covariance_path(
     version,
-    blind,
     gaussian="ng",
     min_sep=None,
     max_sep=None,
     nbins=None,
     mask_suffix=None,
     suffix="_processed.txt",
-    resolve_version=True,
 ):
     """Construct covariance file path."""
-    base = covariance_base(
-        version,
-        blind,
-        gaussian,
-        min_sep,
-        max_sep,
-        nbins,
-        mask_suffix,
-        resolve_version=resolve_version,
-    )
+    base = covariance_base(version, gaussian, min_sep, max_sep, nbins, mask_suffix)
     return str(COSMO_INFERENCE / f"data/covariance/{base}/{base}{suffix}")
 
 
 def base_version(version):
-    """Strip the derived-catalogue suffixes to the base catalogue version.
-
-    The `_leak_corr` / `_ecut{N}` variants share their parent's n(z) and
-    `cov_th` survey parameters, so lookups keyed on either must strip both.
-    """
+    """Strip the `_leak_corr` / `_ecut{N}` suffixes to the base catalogue
+    version, whose footprint and plotting style its variants share."""
     return re.sub(r"_ecut\d+", "", re.sub(r"_leak_corr$", "", version))
 
 
-def build_redshift_path(version, blind):
-    """Construct n(z) filepath for given catalog version and blind."""
-    base = base_version(version)
-    if "v1.4.11" in base:
-        base = "SP_v1.4.6"
-    version_dir = base.replace("SP_", "")
-    return f"/n17data/sguerrini/UNIONS/WL/nz/{version_dir}/nz_{base}_{blind}.txt"
+def catalogue_entry(version):
+    """The catalogue-config entry describing ``version`` (its own, or the one
+    its ``_leak_corr`` variant derives from)."""
+    if version in CATALOG_CONFIG:
+        return CATALOG_CONFIG[version]
+    return CATALOG_CONFIG[re.sub(r"_leak_corr$", "", version)]
+
+
+def redshift_path(version):
+    """The n(z) file of ``version``: its catalogue entry's ``shear.redshift_path``,
+    as written (as ``CosmologyValidation.get_redshift`` reads it)."""
+    return catalogue_entry(version)["shear"]["redshift_path"]
 
 
 # ---------------------------------------------------------------------------
@@ -365,7 +327,7 @@ def grid_of(grids, binning):
 def pseudo_cl_tag(config):
     """Fiducial harmonic-binning tag stamped into pseudo-Cl filenames."""
     fiducial = config["harmonic"]["fiducial"]
-    return f"blind={fiducial['blind']}_{fiducial['binning']}_nbins={fiducial['nbins']}"
+    return f"{fiducial['binning']}_nbins={fiducial['nbins']}"
 
 
 def get_shear_catalog(wildcards):
diff --git a/workflow/rules/cosmo_val.smk b/workflow/rules/cosmo_val.smk
index 34735a85..1bff5b43 100644
--- a/workflow/rules/cosmo_val.smk
+++ b/workflow/rules/cosmo_val.smk
@@ -106,7 +106,6 @@ def _grid_cov(version, grid, gaussian):
     binning = XI_GRIDS[grid]
     return covariance_path(
         version,
-        FIDUCIAL["blind"],
         gaussian=gaussian,
         min_sep=binning["min_sep"],
         max_sep=binning["max_sep"],
diff --git a/workflow/rules/covariance.smk b/workflow/rules/covariance.smk
index 9c26b775..6ca907f9 100644
--- a/workflow/rules/covariance.smk
+++ b/workflow/rules/covariance.smk
@@ -2,11 +2,8 @@
 
 
 def get_cat_params(version):
-    """Extract covariance parameters (area, n_e, sigma_e) from catalog config."""
-    base_version = version.replace("_leak_corr", "")
-    if base_version not in CATALOG_CONFIG:
-        raise KeyError(f"Catalog configuration not found for {base_version}")
-    cov_th = CATALOG_CONFIG[base_version]["cov_th"]
+    """Covariance parameters (area, n_e, sigma_e) of ``version``'s catalogue entry."""
+    cov_th = catalogue_entry(version)["cov_th"]
     return cov_th["A"], cov_th["n_e"], cov_th["sigma_e"]
 
 
@@ -20,16 +17,14 @@ MASK_CLS_FILES = {
     "footprint_starhalo": f"{MASK_CLS_BASE}/mask_cls_footprint_starhalo_nside_4096_norm.txt",
 }
 
-# v1.4.8 uses the star-halo footprint; all other versions use the standard footprint
-STARHALO_VERSIONS = {"v1.4.8"}
+# SP_v1.4.8 uses the star-halo footprint; every other catalogue the standard one.
+STARHALO_CATALOGUES = {"SP_v1.4.8"}
 
 
 def get_mask_cls_path(version):
     """Return absolute mask Cl path for the requested catalog version."""
-    version_dir = version.replace('_leak_corr', '').replace('SP_', '')
-    version_dir = re.sub(r'_ecut\d+', '', version_dir)
-    key = "footprint_starhalo" if version_dir in STARHALO_VERSIONS else "footprint"
-    return MASK_CLS_FILES[key]
+    starhalo = base_version(version) in STARHALO_CATALOGUES
+    return MASK_CLS_FILES["footprint_starhalo" if starhalo else "footprint"]
 
 
 rule cosmology_params:
@@ -59,14 +54,13 @@ with open('{output}', 'w') as f:
 
 rule covariance_ini:
     input:
-        nz_file=lambda w: build_redshift_path(w.version, w.blind),
+        nz_file=lambda w: redshift_path(w.version),
         mask=lambda w: [] if w.mask_suffix != "_masked" else [get_mask_cls_path(w.version)],
     output:
-        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.ini")
+        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.ini")
     params:
         outdir=lambda w: covariance_dir(
-            w.version, w.blind, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix,
-            resolve_version=False
+            w.version, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix
         ),
         ng_value=lambda wildcards: "1" if wildcards.gaussian == "ng" else "0",
         omega_m=PLANCK18["Omega_m"],
@@ -153,16 +147,15 @@ rule covariance_cosmocov:
     input:
         rules.covariance_ini.output,
     output:
-        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/cov_tmp_ssss_{block_pm}_cov_Ntheta{nbins}_Ntomo1_{block_i}")
+        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/cov_tmp_ssss_{block_pm}_cov_Ntheta{nbins}_Ntomo1_{block_i}")
     params:
         block_i="{block_i}",
         outdir=lambda w: covariance_dir(
-            w.version, w.blind, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix,
-            resolve_version=False
+            w.version, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix
         ),
         ini_path=lambda w: covariance_path(
-            w.version, w.blind, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix,
-            suffix=".ini", resolve_version=False
+            w.version, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix,
+            suffix=".ini"
         ),
         cosmocov=config["tools"]["cosmocov_executable"],
     container:
@@ -184,13 +177,13 @@ rule covariance_cosmocov:
 rule covariance_cat:
     input:
         cov_block=lambda w: [
-            f"{covariance_dir(w.version, w.blind, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix, resolve_version=False)}"
+            f"{covariance_dir(w.version, w.gaussian, w.min_sep, w.max_sep, w.nbins, w.mask_suffix)}"
             f"/cov_tmp_ssss_{pm}_cov_Ntheta{w.nbins}_Ntomo1_{idx}"
             for pm, idx in BLOCK_PAIRS
         ],
     threads: 1
     output:
-        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.txt")
+        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.txt")
     shell:
         """
         cat {input} > {output}
@@ -243,24 +236,24 @@ rule generate_glass_mock_rhotau_samples:
 rule covariance_process:
     """Post-process a raw CosmoCov matrix into the analysis-ready form."""
     input:
-        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.txt")
+        str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}.txt")
     output:
-        matrix=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed.txt"),
-        gaussian=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed_g.txt"),
-        plot=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{blind}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed_plot.pdf")
+        matrix=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed.txt"),
+        gaussian=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed_g.txt"),
+        plot=str(COSMO_INFERENCE / "data/covariance/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}/covariance_{version}_{gaussian}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}{mask_suffix}_processed_plot.pdf")
     threads: 1
     script:
         "../scripts/cosmocov_process.py"
 
 
 def fiducial_covariance_outputs(mask_suffix=""):
-    """Return processed covariance files for fiducial version/blind."""
+    """Return processed covariance files for the fiducial version."""
     ng_path = covariance_path(
-        FIDUCIAL["version"], FIDUCIAL["blind"], "ng",
+        FIDUCIAL["version"], "ng",
         FIDUCIAL["min_sep"], FIDUCIAL["max_sep"], FIDUCIAL["nbins"], mask_suffix
     )
     g_path = covariance_path(
-        FIDUCIAL["version"], FIDUCIAL["blind"], "g",
+        FIDUCIAL["version"], "g",
         FIDUCIAL["min_sep_int"], FIDUCIAL["max_sep_int"], FIDUCIAL["nbins_int"], mask_suffix
     )
     return [ng_path, g_path]
diff --git a/workflow/rules/glass_mock.smk b/workflow/rules/glass_mock.smk
index 243076d6..090e3b55 100644
--- a/workflow/rules/glass_mock.smk
+++ b/workflow/rules/glass_mock.smk
@@ -118,11 +118,7 @@ rule mock_cosebis_bias_test:
             mock_id=GLASS_MOCK_IDS,
         ),
         xi_ref=f"{MOCK_RESULTS}/gg_glass_mock_00001_nbins=1000.fits",
-        cov=str(
-            COSMO_INFERENCE / "data/covariance"
-            / "covariance_SP_v1.4.6_leak_corr_A_g_minsep=0.5_maxsep=500.0_nbins=1000_masked"
-            / "covariance_SP_v1.4.6_leak_corr_A_g_minsep=0.5_maxsep=500.0_nbins=1000_masked_processed.txt"
-        ),
+        cov=covariance_path("SP_v1.4.6_leak_corr", "g", 0.5, 500.0, 1000, "_masked"),
     params:
         nmodes=config["fiducial"]["nmodes"],
         theta_min=config["cosebis"]["theta_min"],
diff --git a/workflow/rules/inference.smk b/workflow/rules/inference.smk
index d2847976..f707e895 100644
--- a/workflow/rules/inference.smk
+++ b/workflow/rules/inference.smk
@@ -1,4 +1,4 @@
-# Imports from Snakefile: FIDUCIAL, COSMO_INFERENCE, COSMO_VAL, covariance_path, build_redshift_path, fiducial_binning_suffix
+# Imports from Snakefile: FIDUCIAL, COSMO_INFERENCE, COSMO_VAL, covariance_path, redshift_path, fiducial_binning_suffix, pseudo_cl_tag
 # NOTE: dormant subsystem. The file-name plumbing (config-driven paths + the
 # producer-tagged pseudo-Cl names) is fixed and the DAG is valid, but it has not
 # been run end-to-end. Reviving it still needs the FITS-CONTENT plumbing
@@ -34,14 +34,8 @@ GLASS_MOCK_CONFIG_PATTERN = str(
 
 # Fiducial harmonic-binning tag the pseudo-Cl producer (twopoint.smk) stamps
 # into the filename. These are NOT inference_prep wildcards, so the consumer
-# reads them from config to reconstruct the exact name the producer emits
-# (canonical: blind=A, powspace, nbins=32 — see twopoint.smk pseudo_cl_all).
-HARMONIC_FIDUCIAL = config["harmonic"]["fiducial"]
-PSEUDO_CL_TAG = (
-    f"blind={HARMONIC_FIDUCIAL['blind']}"
-    f"_{HARMONIC_FIDUCIAL['binning']}"
-    f"_nbins={HARMONIC_FIDUCIAL['nbins']}"
-)
+# reads them from config to reconstruct the exact name the producer emits.
+PSEUDO_CL_TAG = pseudo_cl_tag(config)
 
 
 def pseudo_cl_assets(version):
@@ -58,12 +52,12 @@ def pseudo_cl_assets(version):
 rule inference_prep:
     input:
         # Processed covariance matrix - use centralized covariance_path()
-        cov_matrix=lambda w: covariance_path(w.version, w.blind, min_sep=w.min_sep, max_sep=w.max_sep, nbins=w.nbins),
+        cov_matrix=lambda w: covariance_path(w.version, min_sep=w.min_sep, max_sep=w.max_sep, nbins=w.nbins),
         # Xi FITS files
         xi_plus=str(COSMO_VAL / "xi_plus_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
         xi_minus=str(COSMO_VAL / "xi_minus_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
-        # n(z) file (using new location with base version mapping)
-        nz_file=lambda w: build_redshift_path(w.version, w.blind),
+        # n(z) file: the catalogue entry's
+        nz_file=lambda w: redshift_path(w.version),
         # rho/tau stats
         rho_stats=str(COSMO_VAL / "rho_tau_stats/rho_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
         tau_stats=str(COSMO_VAL / "rho_tau_stats/tau_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
@@ -74,15 +68,15 @@ rule inference_prep:
     output:
         fits_file=str(
             COSMO_INFERENCE_PROD
-            / "data/{version}_{blind}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}/cosmosis_{version}_{blind}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"
+            / "data/{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}/cosmosis_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"
         ),
         config_file=str(
             COSMO_INFERENCE_PROD
-            / "cosmosis_config/output/cosmosis_pipeline_{version}_{blind}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.ini"
+            / "cosmosis_config/output/cosmosis_pipeline_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.ini"
         )
     params:
-        cosmosis_root="{version}_{blind}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}",
-        data_dir=f"{CHAINS_DIR}/{{version}}_{{blind}}_minsep={{min_sep}}_maxsep={{max_sep}}_nbins={{nbins}}_npatch={{npatch}}",
+        cosmosis_root="{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}",
+        data_dir=f"{CHAINS_DIR}/{{version}}_minsep={{min_sep}}_maxsep={{max_sep}}_nbins={{nbins}}_npatch={{npatch}}",
         output_root=str(COSMO_INFERENCE_PROD),
     threads: 1
     resources:
@@ -113,12 +107,12 @@ rule inference_fiducial:
     input:
         # Use the same output patterns as inference_prep with FIDUCIAL params
         rules.inference_prep.output.fits_file.format(
-            version=FIDUCIAL["version"], blind=FIDUCIAL["blind"],
+            version=FIDUCIAL["version"],
             min_sep=FIDUCIAL["min_sep"], max_sep=FIDUCIAL["max_sep"],
             nbins=FIDUCIAL["nbins"], npatch=FIDUCIAL["npatch"]
         ),
         rules.inference_prep.output.config_file.format(
-            version=FIDUCIAL["version"], blind=FIDUCIAL["blind"],
+            version=FIDUCIAL["version"],
             min_sep=FIDUCIAL["min_sep"], max_sep=FIDUCIAL["max_sep"],
             nbins=FIDUCIAL["nbins"], npatch=FIDUCIAL["npatch"]
         )
@@ -133,9 +127,9 @@ rule inference_prep_glass_mock:
     input:
         xi=f"{GLASS_MOCK_DATA_DIR}/xi_glass_mock_{{mock_id}}_4096_nbins=20.fits",
         # Use centralized covariance_path() with fiducial mock version
-        cov_matrix=covariance_path(FIDUCIAL["mock_version"], "A"),
+        cov_matrix=covariance_path(FIDUCIAL["mock_version"]),
         # n(z) file
-        nz_file=build_redshift_path(FIDUCIAL["mock_version"], "A"),
+        nz_file=redshift_path(FIDUCIAL["mock_version"]),
         # Rho/tau stats: rho from real data, tau sampled
         rho_stats=str(COSMO_VAL / f"rho_tau_stats/rho_stats_{FIDUCIAL['mock_version']}{fiducial_binning_suffix()}.fits"),
         tau_stats="results/glass_mock_rhotau_samples/{mock_id}/tau_stats_sampled.fits",
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 4e8b75f6..0e3f90c1 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -68,7 +68,6 @@ rule rho_tau_stats:
 
 
 # Pseudo-Cl generation for harmonic-space data vectors and COSEBIS validation.
-BASE_VERSIONS = [v.replace("_leak_corr", "") for v in config["versions"]]
 
 wildcard_constraints:
     binning="linear|logspace|powspace",
@@ -77,12 +76,9 @@ wildcard_constraints:
 rule pseudo_cl:
     """Generate pseudo-Cl data vector (born as SACC) with configurable binning."""
     output:
-        pseudo_cl=str(COSMO_VAL / "pseudo_cl_{version}_blind={blind}_{binning}_nbins={nbins}.sacc"),
-    wildcard_constraints:
-        blind="[ABC]",
+        pseudo_cl=str(COSMO_VAL / "pseudo_cl_{version}_{binning}_nbins={nbins}.sacc"),
     params:
         version="{version}",
-        blind="{blind}",
         cat_config=CAT_CONFIG,
         nside=1024,
         npatch=1,
@@ -101,12 +97,9 @@ rule pseudo_cl:
 rule pseudo_cl_cov:
     """Generate pseudo-Cl covariance with configurable binning."""
     output:
-        pseudo_cl_cov=str(COSMO_VAL / "pseudo_cl_cov_{version}_blind={blind}_{binning}_nbins={nbins}.fits"),
-    wildcard_constraints:
-        blind="[ABC]",
+        pseudo_cl_cov=str(COSMO_VAL / "pseudo_cl_cov_{version}_{binning}_nbins={nbins}.fits"),
     params:
         version="{version}",
-        blind="{blind}",
         cat_config=CAT_CONFIG,
         nside=1024,
         npatch=1,
@@ -129,7 +122,7 @@ rule pseudo_cl_all:
     """Generate pseudo-Cls for all versions."""
     input:
         expand(
-            str(COSMO_VAL / "pseudo_cl_{version}_blind=A_powspace_nbins=32.sacc"),
+            str(COSMO_VAL / "pseudo_cl_{version}_powspace_nbins=32.sacc"),
             version=PSEUDO_CL_VERSIONS,
         ),
 
@@ -138,7 +131,7 @@ rule pseudo_cl_cov_all:
     """Generate pseudo-Cl covariances for all versions."""
     input:
         expand(
-            str(COSMO_VAL / "pseudo_cl_cov_{version}_blind=A_powspace_nbins=32.fits"),
+            str(COSMO_VAL / "pseudo_cl_cov_{version}_powspace_nbins=32.fits"),
             version=PSEUDO_CL_VERSIONS,
         ),
 
@@ -147,7 +140,6 @@ rule pseudo_cl_fine_all:
     """Generate fine pseudo-Cls for COSEBIS."""
     input:
         expand(
-            str(COSMO_VAL / "pseudo_cl_{version}_blind={blind}_linear_nbins=2040.sacc"),
+            str(COSMO_VAL / "pseudo_cl_{version}_linear_nbins=2040.sacc"),
             version=config["versions"],
-            blind=BLINDS,
         ),
diff --git a/workflow/scripts/generate_pseudo_cl.py b/workflow/scripts/generate_pseudo_cl.py
index e25f6ed9..e1b96da6 100644
--- a/workflow/scripts/generate_pseudo_cl.py
+++ b/workflow/scripts/generate_pseudo_cl.py
@@ -34,7 +34,6 @@ def generate_pseudo_cl(
     cat_config: str,
     nside: int = 1024,
     npatch: int = 1,
-    blind: str = None,
     cosmo_params: dict = None,
     binning: str = "linear",
     nbins: int = None,
@@ -55,8 +54,6 @@ def generate_pseudo_cl(
         HEALPix nside for map-based estimation
     npatch : int
         Number of jackknife patches
-    blind : str, optional
-        Blind identifier (A, B, or C) to override n(z) path
     cosmo_params : dict, optional
         Cosmological parameters. Keys: Omega_m, sigma_8, n_s, h, Omega_b.
         If None, uses Planck 2018 defaults.
@@ -75,7 +72,6 @@ def generate_pseudo_cl(
     output_dir = os.path.dirname(out_path)
     os.makedirs(output_dir, exist_ok=True)
 
-    blind_str = f" blind={blind}" if blind else ""
     if binning == "linear":
         # For linear binning, nbins determines ell_step such that we cover 2-2048
         ell_step = max(1, (2048 - 2) // nbins)
@@ -86,7 +82,7 @@ def generate_pseudo_cl(
         bin_str = f"nbins={nbins}, power={power}"
 
     print(f"\n{'=' * 60}")
-    print(f"Generating pseudo-Cl for {version}{blind_str}")
+    print(f"Generating pseudo-Cl for {version}")
     print(f"Binning: {binning} ({bin_str})")
     if cosmo_params:
         print(
@@ -115,7 +111,6 @@ def generate_pseudo_cl(
         nside=nside,
         cell_method="catalog",
         nrandom_cell=100,
-        blind=blind,
         cosmo_params=cosmo_params,
         npatch=npatch,
         theta_min=1.0,
@@ -150,7 +145,6 @@ def _from_snakemake(smk):
         cat_config=p["cat_config"],
         nside=int(p["nside"]),
         npatch=int(p["npatch"]),
-        blind=p.get("blind", None),
         cosmo_params=p.get("cosmo_params", None),
         binning=p["binning"],
         nbins=int(p["nbins"]),
@@ -193,9 +187,6 @@ def _from_cli(argv=None):
         default=0.5,
         help="Power for powspace binning (0.5 = sqrt spacing)",
     )
-    ap.add_argument(
-        "--blind", choices=["A", "B", "C"], default=None, help="Blind identifier"
-    )
     ap.add_argument(
         "--cosmo-json",
         default=None,
@@ -217,7 +208,6 @@ def _from_cli(argv=None):
         cat_config=a.cat_config,
         nside=a.nside,
         npatch=a.npatch,
-        blind=a.blind,
         cosmo_params=cosmo_params,
         binning=a.binning,
         nbins=a.nbins,
diff --git a/workflow/scripts/generate_pseudo_cl_cov.py b/workflow/scripts/generate_pseudo_cl_cov.py
index 08f2cf07..247c5f33 100644
--- a/workflow/scripts/generate_pseudo_cl_cov.py
+++ b/workflow/scripts/generate_pseudo_cl_cov.py
@@ -39,7 +39,6 @@ def generate_pseudo_cl_cov(
     cat_config: str,
     nside: int = 1024,
     npatch: int = 1,
-    blind: str = None,
     cosmo_params: dict = None,
     binning: str = "powspace",
     nbins: int = 32,
@@ -61,8 +60,6 @@ def generate_pseudo_cl_cov(
         HEALPix nside for map-based estimation
     npatch : int
         Number of jackknife patches
-    blind : str, optional
-        Blind identifier (A, B, or C) to override n(z) path
     cosmo_params : dict, optional
         Cosmological parameters. Keys: Omega_m, sigma_8, n_s, h, Omega_b.
         If None, uses Planck 2018 defaults.
@@ -80,7 +77,6 @@ def generate_pseudo_cl_cov(
     """
     os.makedirs(output_dir, exist_ok=True)
 
-    blind_str = f" blind={blind}" if blind else ""
     if binning == "linear":
         ell_step = max(1, (2048 - 2) // nbins)
         bin_str = f"nbins={nbins} (ell_step={ell_step})"
@@ -90,7 +86,7 @@ def generate_pseudo_cl_cov(
         bin_str = f"nbins={nbins}, power={power}"
 
     print(f"\n{'=' * 60}")
-    print(f"Generating pseudo-Cl covariance for {version}{blind_str}")
+    print(f"Generating pseudo-Cl covariance for {version}")
     print(f"Binning: {binning} ({bin_str})")
     if cosmo_params:
         print(
@@ -119,7 +115,6 @@ def generate_pseudo_cl_cov(
         nside=nside,
         cell_method="catalog",
         nrandom_cell=100,
-        blind=blind,
         cosmo_params=cosmo_params,
         npatch=npatch,
         theta_min=1.0,
@@ -158,7 +153,6 @@ def _from_snakemake(smk):
         cat_config=p["cat_config"],
         nside=int(p["nside"]),
         npatch=int(p["npatch"]),
-        blind=p.get("blind", None),
         cosmo_params=p.get("cosmo_params", None),
         binning=p["binning"],
         nbins=int(p["nbins"]),
@@ -205,9 +199,6 @@ def _from_cli(argv=None):
         default=0.5,
         help="Power for powspace binning (0.5 = sqrt spacing)",
     )
-    ap.add_argument(
-        "--blind", choices=["A", "B", "C"], default=None, help="Blind identifier"
-    )
     ap.add_argument(
         "--cosmo-json",
         default=None,
@@ -226,7 +217,6 @@ def _from_cli(argv=None):
         cat_config=a.cat_config,
         nside=a.nside,
         npatch=a.npatch,
-        blind=a.blind,
         cosmo_params=cosmo_params,
         binning=a.binning,
         nbins=a.nbins,
diff --git a/workflow/tests/conftest.py b/workflow/tests/conftest.py
index 5540e091..1dd691f4 100644
--- a/workflow/tests/conftest.py
+++ b/workflow/tests/conftest.py
@@ -193,7 +193,6 @@ def toy(tmp_path_factory):
             path = covariances[version, gaussian] = Path(
                 common.covariance_path(
                     version,
-                    config["fiducial"]["blind"],
                     gaussian,
                     grid["min_sep"],
                     grid["max_sep"],

From ef8af507a5faf1468d018c3740926213bc2f2515 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Wed, 30 Sep 2026 15:07:29 +0200
Subject: [PATCH 41/83] Move to camb 2 and refresh the lock (#362)

* Lift the scipy cap; require camb>=2.0

camb 2.0 makes its BBN Y_He predictor return a scalar, so set_cosmology
works under scipy 1.18.0 (whose RectBivariateSpline returned shape (1,)
for scalar input) and the scipy cap has no reason to exist.

camb 2.0 moves the glass-mock CAMB fingerprint beyond the test's 1e-6
tolerance (As +1.4e-4, P(k) up to 2.9e-4 relative), so regenerate that
reference under camb 2.0.4. The test now pins camb.config.AccuracyTarget
to CAMB's default: pyccl's CAMB path sets that process-global to 0, which
made the reference test depend on whether a pyccl test ran first.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* Require pyccl>=3.3.5 (boltzmann_camb on camb 2)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* uv.lock: camb 2.0.4, scipy 1.18.1, pyccl 3.3.6, cs_util/shear_psf_leakage develop heads

cs_util develop@e14cd686 (CosmoStat/cs_util#93) and shear_psf_leakage
develop@649edf4a (CosmoStat/shear_psf_leakage#45) lift their scipy caps and
admit camb 2; shear_psf_leakage no longer pulls gsl or stats.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* test_glass_mock: widen the tolerance instead of pinning AccuracyTarget

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Relock on the merged pyproject: camb 2.0.4, shear_psf_leakage develop tip

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01W4pb4vZJimNoYkWqQCqEwR

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 pyproject.toml                                |  22 +++----
 .../tests/data/glass_mock_camb_reference.npz  | Bin 2120 -> 2120 bytes
 src/sp_validation/tests/test_glass_mock.py    |  10 ++--
 uv.lock                                       |  54 +++++-------------
 4 files changed, 25 insertions(+), 61 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a8335f10..35c9e6ce 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,10 +22,9 @@ classifiers = [
 dependencies = [
     "adjustText",
     "astropy>=5.0",
-    # Floor at the current latest release; do not cap — we want the eventual
-    # scipy-1.18 fix release. Revisit the scipy cap below when camb ships
-    # > 1.6.6 with the scipy-1.18 / BBN fix.
-    "camb>=1.6",
+    # camb 2.0 is the first release whose BBN Y_He predictor tolerates scipy
+    # 1.18.0's shape-(1,) spline output; older camb fails set_cosmology there.
+    "camb>=2.0",
     "clmm",
     "colorama",
     # Blinding closure (core, no extra): cryptography + sacc here, plus the
@@ -74,23 +73,16 @@ dependencies = [
     "lmfit",
     "numpy>=2.0",
     "opencv-python-headless",
-    "pyccl",
+    # pyccl 3.3.5 is the first release that runs boltzmann_camb on camb 2
+    # (LSSTDESC/CCL#1303).
+    "pyccl>=3.3.5",
     "pyarrow",
     "pymaster",
     "regions",
     "reproject",
     # Floor matches the resolved lock; needs sacc's 2.x rewrite (BlockDiagonalCovariance).
     "sacc>=2.4,<3",
-    # scipy 1.18 ported FITPACK from Fortran to C, changing the return shape of
-    # RectBivariateSpline(scalar, scalar, grid=False) from 0-d `array(x)` to
-    # shape-(1,) `array([x])`. camb's BBN Y_He predictor (bbn.py) wraps the
-    # result in np.float64(), which collapses a 0-d array to a scalar but leaves
-    # a (1,) array as (1,); set_cosmology then fails at the ctypes float
-    # assignment `self.YHe = YHe` with `TypeError: only 0-dimensional arrays can
-    # be converted to Python scalars`, failing all get_cosmo-backed tests. camb
-    # 1.6.6 (latest on PyPI) and current master are both unfixed. Lift this cap
-    # when camb ships > 1.6.6 with the scipy-1.18 fix.
-    "scipy<1.19",
+    "scipy",
     "seaborn",
     # Intentionally tracks the mutable `develop` branch (NOT SHA-pinned):
     # shear_psf_leakage is actively co-developed CosmoStat code we want to stay
diff --git a/src/sp_validation/tests/data/glass_mock_camb_reference.npz b/src/sp_validation/tests/data/glass_mock_camb_reference.npz
index cd2a42fd4f0e13b6b7913c16d4e483c80b1c1d7b..e0671ca0775ae40fe9067e2afc66f03e565343a5 100644
GIT binary patch
delta 210
zcmX>ha6&*Rz?+#xmjMD48LoW$B{NZIt<=sLFI|3{DA}RPPUM)<JMrc!#&wfh7_YJ%
znN+|(IhJW9%e7ta_&0Mg_cF0O)y<wVc{b~^khYgc3mi7Ab2xCAO_Qzhki(nXbMlI|
zUvfA<bx!q+>(?EUKX5y~ymZXrjmE>5Kh)PcnDH$9dOV`v!6YJ0xS*xXA;6oFNtYSb
gU6ZG>nX`bLF!=&o2E=w7_Dc{u@3D(b*5FV800j|HdH?_b

delta 210
zcmX>ha6&*Rz?+#xmjMD487i`Jj3x@Lm0EN`DI?rO$qrSvLwo6yi8og<ZkXJ{c$H;W
z|E>j-W0_X6od11!&Soy=UM7|eD;@?+p3S-}<h>?mu+I8*4$0BlKJg8Q9D08Bn4a2p
z$sr)hZ^OZB*Bz9UUMroue9R$nl4`(P)wK@Jk9;g{g!MaUUzc2Pu&K-;z?+dtml@Sv
flc%zovw)m1`2t%8#C99@OAtHnv5QUC;7|Yn9uZB1

diff --git a/src/sp_validation/tests/test_glass_mock.py b/src/sp_validation/tests/test_glass_mock.py
index a056318b..233d8aa1 100644
--- a/src/sp_validation/tests/test_glass_mock.py
+++ b/src/sp_validation/tests/test_glass_mock.py
@@ -21,7 +21,7 @@
    CAMB params unchanged. When a GLASS-capable image lands, promote this to a
    committed-map ``np.allclose`` check against a reference ``.npy``.
 
-The reference was generated in the production container (CAMB 1.6.5); the
+The reference was generated in the production container (CAMB 2.0.4); the
 tolerance below absorbs cross-version CAMB float drift while still catching a
 genuine config change, which moves these numbers by parts in 10⁰–10⁻³.
 """
@@ -48,10 +48,10 @@
 # nside=16 keeps CAMB cheap; cosmology is config-default (UNIONS fiducial).
 REF_CONFIG = GlassMockConfig(nside=16)
 
-# Cross-version CAMB float tolerance. Scalars and P(k) reproduce to ~1e-12
-# within a version; 1e-6 relative leaves headroom for a CAMB point release
-# while a real config change moves these by >>1e-3.
-RTOL = 1e-6
+# Absorbs CAMB version drift and CAMB's process-global AccuracyTarget (which
+# pyccl sets to 0), each a few 1e-4, while a real config change moves these
+# by >=1e-3.
+RTOL = 5e-4
 ATOL = 0.0
 
 
diff --git a/uv.lock b/uv.lock
index 4acc2e41..7b137e81 100644
--- a/uv.lock
+++ b/uv.lock
@@ -295,7 +295,7 @@ wheels = [
 
 [[package]]
 name = "camb"
-version = "1.6.6"
+version = "2.0.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
@@ -303,10 +303,10 @@ dependencies = [
     { name = "scipy" },
     { name = "sympy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6d/ad/f2e6446fbd94f7adc1474e1f4e40d2e7fa7e5e7143640cb8557d492f0a73/camb-1.6.6.tar.gz", hash = "sha256:9856202a5c05570256e52377b20431891c7b08b2e9c334e141fd08d2a085516f", size = 799344, upload-time = "2026-03-11T16:46:55.819Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/51/0b/856e8e329c088155509e3c1a999b02cd3f0333a31fc1fa37653455a4ec80/camb-2.0.4.tar.gz", hash = "sha256:2f1f5f3b3964a3746693dc88a54218dca4ea049a04726f8177664c11d2d2616e", size = 925860, upload-time = "2026-08-25T22:10:58.612Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c9/f6/9131f388386c50982b1b2992424deca857e7c019f4014dab0770de690a84/camb-1.6.6-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eded2dd7f19226ed4717535c07a029fc958b7c581549250da33a768e12c87b13", size = 1592029, upload-time = "2026-03-11T16:46:44.925Z" },
-    { url = "https://files.pythonhosted.org/packages/41/53/9d0b3cf1a2a07a882adac9565074ac6974fb8b031c31da438d407db22c89/camb-1.6.6-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bb8e0c85b3e33d78ca0676c84b0b60b54dfa72d4e520a805166cab0458e81094", size = 2258716, upload-time = "2026-03-11T16:46:46.458Z" },
+    { url = "https://files.pythonhosted.org/packages/35/00/2204636fd231679a9b1ee37a158eb23f273aa3a66a2a4fd81fb1f3514302/camb-2.0.4-py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:179b8a5496be4c80edf206824a3d211f38d496c0e06420aad754fd16f0bd4fe6", size = 1765545, upload-time = "2026-08-25T22:10:47.617Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/4b/3a999a7a500d8a4aef4fa3e3efc94fb00e59cec5e3e0a61973f1cad5b69b/camb-2.0.4-py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9b7e6558b2049757e96496b8a33461d8ff11acd79017cfbd00a573dbc8d1e366", size = 2393435, upload-time = "2026-08-25T22:10:49.166Z" },
 ]
 
 [[package]]
@@ -1069,15 +1069,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/42/fa/f50f51260d7b0ef5d4898af122d8a7ec5a84e2984f676f746445f783705f/google_crc32c-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8b3f68782f3cbd1bce027e48768293072813469af6a61a86f6bb4977a4380f21", size = 33734, upload-time = "2025-12-16T00:40:27.028Z" },
 ]
 
-[[package]]
-name = "gsl"
-version = "0.0.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/01/36/a8a99ab67918beb6ee8722fb6c27512796a5ff8ed4c10036f787eef51569/gsl-0.0.3.tar.gz", hash = "sha256:85a6071e00a284510c963b97ab74ef8a3e7d1786c5f52dc2ff444cbb7d94ee3c", size = 44404, upload-time = "2018-05-30T09:02:51.203Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/ee/1e/14d4c4657bc7130b900c65033861ee0db508d2d9206f4223f5da7c855acc/gsl-0.0.3-py3-none-any.whl", hash = "sha256:081c77375bf65cb759a3e5129275b8806be18648452f50f7c420f40dac60529a", size = 39432, upload-time = "2018-05-30T09:02:49.93Z" },
-]
-
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -2824,7 +2815,7 @@ wheels = [
 
 [[package]]
 name = "pyccl"
-version = "3.3.4"
+version = "3.3.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
@@ -2832,11 +2823,11 @@ dependencies = [
     { name = "pyyaml" },
     { name = "scipy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/57/40/ff357d61a20c21420f1fe7417ca6d889be2e07a67b3564775aeaf6c3f366/pyccl-3.3.4.tar.gz", hash = "sha256:a9d22557354fd161df909e8f4f199e4f1c434c080fd6cddb2246609ddbd70e7c", size = 16783719, upload-time = "2026-05-14T16:26:27.327Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d5/76/dc091056f1af48e6cb56d651dac6eb8494dd2515547d9543c0ce5365b873/pyccl-3.3.6.tar.gz", hash = "sha256:fd6c0181381eb57345ec9b56bdcf9300c9a31a42014aa9c8ced95f31ace16795", size = 16790489, upload-time = "2026-07-28T09:29:17.738Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/80/1db2504f21eec9b481e1fe298b05deb96e108d06ddb1828494d16d32bf1c/pyccl-3.3.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0229c863acea5c3fc6748dc7c40611dd529709c9d26e3679ebdbc467511ef993", size = 3707759, upload-time = "2026-05-14T16:26:16.779Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/91/3000c5fc1ee8379636dc894946a3633baae74a3f1cce3c2d402e8991a0b5/pyccl-3.3.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685d528a096251a2eb5d569e611b5a2e68cc3a4a0134734b16ac4e73a07e7f27", size = 3706950, upload-time = "2026-05-14T16:26:20.394Z" },
-    { url = "https://files.pythonhosted.org/packages/e5/66/803e8196149d8d8a75319b4f20268210b1e1b31b3158a1143ce99cacf953/pyccl-3.3.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:601b687b50b506f7f5775475d79fa55d221616a827a96ad5d6d68bf7a15de9b9", size = 3706955, upload-time = "2026-05-14T16:26:25.302Z" },
+    { url = "https://files.pythonhosted.org/packages/86/95/4183630d8e67133e5d066d388f3852580d4de8baa592506c28a80274e34c/pyccl-3.3.6-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b0077f51318c2f3112682d37febc75b4334152a8c8338dff19b484cdd25cf9a7", size = 3708004, upload-time = "2026-07-28T09:29:10.256Z" },
+    { url = "https://files.pythonhosted.org/packages/af/86/df2caee376a298ceee5afd13c348371a2537d47aea30e5316d8fc6277004/pyccl-3.3.6-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4a943004beee16b72f4c1464008904127831b760f5c1906ea430f9bc92601380", size = 3707324, upload-time = "2026-07-28T09:29:13.224Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/48/4eab47c2615543aa8d4d80e689659e35547824216e318aa0541ade69efff/pyccl-3.3.6-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6fa4f7e0fe17a90aea9fb981c586143ee41a846b2192f415bb995522adf26194", size = 3707307, upload-time = "2026-07-28T09:29:16.108Z" },
 ]
 
 [[package]]
@@ -3443,28 +3434,15 @@ wheels = [
 [[package]]
 name = "shear-psf-leakage"
 version = "0.2.1"
-source = { git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop#0b3c17523b77760c9259be15ba5bbba99d575d0a" }
+source = { git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop#649edf4ac36da86fa679193b3402174bf54042c7" }
 dependencies = [
-    { name = "camb" },
     { name = "cs-util" },
     { name = "emcee" },
     { name = "getdist" },
-    { name = "gsl" },
-    { name = "jupyter" },
-    { name = "jupyter-server" },
-    { name = "jupyterlab" },
-    { name = "jupytext" },
-    { name = "lenspack" },
     { name = "lmfit" },
     { name = "matplotlib" },
-    { name = "notebook" },
     { name = "pandas" },
-    { name = "pyccl" },
-    { name = "pyyaml" },
     { name = "scipy" },
-    { name = "stats" },
-    { name = "swig" },
-    { name = "tornado" },
     { name = "tqdm" },
     { name = "treecorr" },
     { name = "uncertainties" },
@@ -3625,7 +3603,7 @@ workflow = [
 requires-dist = [
     { name = "adjusttext" },
     { name = "astropy", specifier = ">=5.0" },
-    { name = "camb", specifier = ">=1.6" },
+    { name = "camb", specifier = ">=2.0" },
     { name = "clmm" },
     { name = "colorama" },
     { name = "cosmo-numba", git = "https://github.com/aguinot/cosmo-numba.git?rev=main" },
@@ -3658,7 +3636,7 @@ requires-dist = [
     { name = "opencv-python-headless" },
     { name = "pandas" },
     { name = "pyarrow" },
-    { name = "pyccl" },
+    { name = "pyccl", specifier = ">=3.3.5" },
     { name = "pymaster" },
     { name = "pytest", marker = "extra == 'test'" },
     { name = "pytest-cov", marker = "extra == 'test'" },
@@ -3667,7 +3645,7 @@ requires-dist = [
     { name = "reproject" },
     { name = "ruff", marker = "extra == 'test'" },
     { name = "sacc", specifier = ">=2.4,<3" },
-    { name = "scipy", specifier = "<1.19" },
+    { name = "scipy" },
     { name = "seaborn" },
     { name = "shear-psf-leakage", git = "https://github.com/CosmoStat/shear_psf_leakage.git?rev=develop" },
     { name = "skyproj" },
@@ -3806,12 +3784,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" },
 ]
 
-[[package]]
-name = "stats"
-version = "0.1.2a0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/87/43/f2d8d8e2660740ecca9c0fab85a07bc02da27d1fcc5071ee481082888720/stats-0.1.2a.tar.gz", hash = "sha256:854e59fa99f92ce8319c16149ef8e9939dba3e2237bbb0fb6a6094c60155bd4c", size = 127625, upload-time = "2010-12-31T09:02:15.131Z" }
-
 [[package]]
 name = "statsmodels"
 version = "0.14.6"

From aebb689826a527c59ed7ec1144c3a79a0518cd20 Mon Sep 17 00:00:00 2001
From: "renovate[bot]" <29139614+renovate[bot]@users.noreply.github.com>
Date: Wed, 30 Sep 2026 13:59:22 +0000
Subject: [PATCH 42/83] Lock file maintenance (#356)

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
---
 uv.lock | 2283 ++++++++++++++++++++++++++++++-------------------------
 1 file changed, 1262 insertions(+), 1021 deletions(-)

diff --git a/uv.lock b/uv.lock
index 7b137e81..94341fce 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3,7 +3,8 @@ revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'linux'",
-    "python_full_version < '3.14' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and sys_platform == 'linux'",
+    "python_full_version < '3.13' and sys_platform == 'linux'",
 ]
 supported-markers = [
     "sys_platform == 'linux'",
@@ -43,24 +44,24 @@ wheels = [
 
 [[package]]
 name = "annotated-types"
-version = "0.7.0"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
 ]
 
 [[package]]
 name = "anyio"
-version = "4.14.1"
+version = "4.15.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3b/72/5562aabb8dd7181e8e860622a38bea08d17842b99ecd4c91f84ac95251b0/anyio-4.14.1.tar.gz", hash = "sha256:8d648a3544c1a700e3ff78615cd679e4c5c3f149904287e73687b2596963629e", size = 254831, upload-time = "2026-06-24T20:56:06.017Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/d2/f4d173e22df740bc37b1db102b386ba719b66e95b0f0d751f556b387e6d2/anyio-4.15.1.tar.gz", hash = "sha256:9f28306018cbd6d329e64a36d58256edff76dd996fe423bc957326e578b82a94", size = 276966, upload-time = "2026-09-05T10:42:39.44Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b0/7b/90df4a0a816d98d6ea26f559d87836d494a2cf1fcf063be67df50a7bcc30/anyio-4.14.1-py3-none-any.whl", hash = "sha256:4e5533c5b8ff0a24f5d7a176cbe6877129cd183893f66b537f8f227d10527d72", size = 124875, upload-time = "2026-06-24T20:56:04.413Z" },
+    { url = "https://files.pythonhosted.org/packages/12/b8/4bd346e22b28902df4d651910f5242c28d84e4a5c2435ca5c3f797ed7e2e/anyio-4.15.1-py3-none-any.whl", hash = "sha256:6152fdbbf9a77fdec97731721bebf7c4c44f7c29b424b0065826173efc7ed101", size = 132079, upload-time = "2026-09-05T10:42:37.923Z" },
 ]
 
 [[package]]
@@ -77,21 +78,33 @@ wheels = [
 
 [[package]]
 name = "argon2-cffi-bindings"
-version = "25.1.0"
+version = "26.1.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cffi" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441, upload-time = "2025-07-30T10:02:05.147Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/d4/d3/942aa10782b2697eee7af5e12eeff5ebb325ccfb86dd8abda54174e377e4/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44", size = 86558, upload-time = "2025-07-30T10:01:43.943Z" },
-    { url = "https://files.pythonhosted.org/packages/0d/82/b484f702fec5536e71836fc2dbc8c5267b3f6e78d2d539b4eaa6f0db8bf8/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb", size = 92364, upload-time = "2025-07-30T10:01:44.887Z" },
-    { url = "https://files.pythonhosted.org/packages/c9/c1/a606ff83b3f1735f3759ad0f2cd9e038a0ad11a3de3b6c673aa41c24bb7b/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92", size = 85637, upload-time = "2025-07-30T10:01:46.225Z" },
-    { url = "https://files.pythonhosted.org/packages/44/b4/678503f12aceb0262f84fa201f6027ed77d71c5019ae03b399b97caa2f19/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85", size = 91934, upload-time = "2025-07-30T10:01:47.203Z" },
-    { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246, upload-time = "2025-07-30T10:01:54.145Z" },
-    { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126, upload-time = "2025-07-30T10:01:55.074Z" },
-    { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343, upload-time = "2025-07-30T10:01:56.007Z" },
-    { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777, upload-time = "2025-07-30T10:01:56.943Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/0b/43/bb8b6e8708d49a5ab36781333af092d9f483b198a2710d01281204640055/argon2_cffi_bindings-26.1.0.tar.gz", hash = "sha256:63505c71542a44b68b1e38060450fb006404170da375feb31af153e7f9c6205d", size = 1790807, upload-time = "2026-08-20T07:44:22.492Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/e4/ad91d8297638aa2258aad4501c306aca99480dfe76ccd638173fa3702db9/argon2_cffi_bindings-26.1.0-cp310-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:78de2d65e0b9ea7ce9d1b1c3e87297b2d7305a02c266ee2a2d6910daddd7ee69", size = 27177, upload-time = "2026-08-20T07:32:44.158Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/86/5363df11b86d02cf3662208e7406496327649cc90eb365bf6f4e8a54a41f/argon2_cffi_bindings-26.1.0-cp310-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27f1821903e2ceadcb88ec2b45ef190897b7682449c772f4d9b53e42c520cf29", size = 26597, upload-time = "2026-08-20T07:32:45.172Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/b5/a14dcc592652347dad23ee93b278a4da5d2a25c9ed3ebd10d68eea823a4f/argon2_cffi_bindings-26.1.0-cp310-abi3-manylinux_2_34_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d88e5f7e60f28ae0b0cc6b2f16c43e87cd642a196a86f85e0d8bb6fe016fc16d", size = 27403, upload-time = "2026-08-20T07:32:46.13Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/81/b4a20d4902af7f796390bf9245ff83c5217dfa7367efa1d14986956c482b/argon2_cffi_bindings-26.1.0-cp310-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:34b7d9c24a4165a2c61cc8ae11d44d48c9ce2830fb536cb7914e11fdd9962728", size = 27132, upload-time = "2026-08-20T07:32:47.13Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/1b/c8de358af07b1c490e0fcb863ef98e46ddb486e45567aca5a60bd68d9daa/argon2_cffi_bindings-26.1.0-cp310-abi3-musllinux_1_2_riscv64.whl", hash = "sha256:224865cbbcb7a2bd1356741dff12b0134df726b6d44bb7b500df8e303cbd9e81", size = 27588, upload-time = "2026-08-20T07:32:48.087Z" },
+    { url = "https://files.pythonhosted.org/packages/48/2f/7ee62a6e79f9309f9d9982d301b22a00010adb580c05c8109b94d7b33de0/argon2_cffi_bindings-26.1.0-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:ffff613aaa9ce6236766e2fc6dc560bb5abde7a2e2416e3db1f9ae395a2b4dd4", size = 26785, upload-time = "2026-08-20T07:32:48.977Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/5f/f652055e18d2627e2eed94c7f31a792127cfe38df786635395d742321674/argon2_cffi_bindings-26.1.0-cp313-cp313-pyemscripten_2025_0_wasm32.whl", hash = "sha256:af11ac37a7c53dc16cb7950a6190851b0870fe218b6c60c0bb7ac355234e3083", size = 15434, upload-time = "2026-08-20T07:32:53.143Z" },
+    { url = "https://files.pythonhosted.org/packages/76/38/de696045960f5b846d428c0fb6c130ed3da87aac2af209b05c193815404c/argon2_cffi_bindings-26.1.0-cp314-cp314-pyemscripten_2026_0_wasm32.whl", hash = "sha256:db0fcd827ca61622a01b220aadfbece01939acf53888f2cb98cd93e9b1e2c97e", size = 15449, upload-time = "2026-08-20T07:32:54.075Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/7e/be212c751ab0bcea7f646615f933bf262e8e50b3f7bef32f861d0a2d066b/argon2_cffi_bindings-26.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ac82fc756a446b6ccd7139ce70efa9d8bbe541e7ad579a12dcb52764b7175c5f", size = 27311, upload-time = "2026-08-20T07:32:56.166Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/ee/f84b28e4afd13d3cac36c1d8fa8c239d2dc2c51cd978d02ee5d5ad98d9bb/argon2_cffi_bindings-26.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6a4e68eed961a8de6928d1c17ff3dc2a547e0e923c17f8f1cd79fb7bc9502f98", size = 26771, upload-time = "2026-08-20T07:32:57.206Z" },
+    { url = "https://files.pythonhosted.org/packages/21/c3/95c07a023691ecd529da9cb6a8f0779e13ebc1bdfaa86d145fdc1c6e7e79/argon2_cffi_bindings-26.1.0-cp314-cp314t-manylinux_2_34_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:151dfaad9de753f4af2a7854e707e4784f2acc434340ade64239c5b104b2d605", size = 27568, upload-time = "2026-08-20T07:32:58.361Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/31/3a18e31406d8694b4d6a31573c3e572fff6bed318bb744453eb653766d22/argon2_cffi_bindings-26.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:061a6919145bbf282ebf1f9c59d3135d4833c25313c8595c0d68cf7712ddfce2", size = 27280, upload-time = "2026-08-20T07:32:59.343Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/39/d4be4577e178b2397aa5b5575c8a309bf0da2afe05fe0c72c8f398662d63/argon2_cffi_bindings-26.1.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:62ff20cd130c956c7c9144d5fe35228f98b51c579b2439e988b27ef93e16c02a", size = 27776, upload-time = "2026-08-20T07:33:00.325Z" },
+    { url = "https://files.pythonhosted.org/packages/71/47/78f4dd96f7411339f723b96fe24039c1bd5835102b8a5ba71ac4ec712ac7/argon2_cffi_bindings-26.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:19423e5d7ac1cc354baab59eaabf18db2ec04ef6593b5abe5a34f323c4a8f87a", size = 26932, upload-time = "2026-08-20T07:33:01.272Z" },
+    { url = "https://files.pythonhosted.org/packages/83/e5/02015b83e9b05ccb85ff2ced424cf6e83a12d3810bc7f66d679a92b69ffb/argon2_cffi_bindings-26.1.0-cp315-cp315t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ccaf0a46cbb380f1fd102a874e32aa629fd3cb0c0e94f4943fa1f6d5edc5dac6", size = 27310, upload-time = "2026-08-20T07:33:06.344Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/4a/85e612787d0796878b3b4f6bd53dcd5484b6fe7b64cc6fc7b6e6a04cf835/argon2_cffi_bindings-26.1.0-cp315-cp315t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f0c3103fcff20183e593459cfea6e012281c0e76ae3ed8b5565ad1b92eac3990", size = 26771, upload-time = "2026-08-20T07:33:07.429Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/84/ccb003b6f9969820e87656398f4d49c857def71a85ca1588a0e809afd7ce/argon2_cffi_bindings-26.1.0-cp315-cp315t-manylinux_2_34_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c49e853a3bef9dd10329f31f702e7fa9b5c58229ff9c2ff6d069efaf09177c08", size = 27569, upload-time = "2026-08-20T07:33:08.598Z" },
+    { url = "https://files.pythonhosted.org/packages/88/07/c26b76debf0998ee08fbe947ab2058ac5de37d4b9d46b06c17abaa6c4ce9/argon2_cffi_bindings-26.1.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:6376d4b3aca039375ca8bf92f770da0ec424a1ce3a37077a8d3c557411aa56ca", size = 27279, upload-time = "2026-08-20T07:33:09.518Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/0d/ead6ddc029f91bc9b9390686dad3c808ab08100d348f6266b5f93f8970ee/argon2_cffi_bindings-26.1.0-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:9bacedc04b0402837586a17f0919e3dfdd95291f441f1f56bd80ec274c2840a1", size = 27774, upload-time = "2026-08-20T07:33:10.728Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/47/c108530d9eb86036b78d3af4de28b83b4a2d9a70512bd10ff8e59966aab4/argon2_cffi_bindings-26.1.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:76ae29acace5d33355344612844d588e19deaaba4639d8bb01601e4b1418ef36", size = 26933, upload-time = "2026-08-20T07:33:11.661Z" },
 ]
 
 [[package]]
@@ -130,11 +143,11 @@ wheels = [
 
 [[package]]
 name = "asteval"
-version = "1.0.9"
+version = "1.0.10"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b1/71/bec6758ab41ada3b6870cb194ea16d2e76bb530562c8a8cd7d2167012bea/asteval-1.0.9.tar.gz", hash = "sha256:4f7323d63d9fbf89d7e1d5f8bb59b6c093970c15bb5926e6e67d0195858aa230", size = 55237, upload-time = "2026-06-11T18:40:36.086Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/65/9a/e10e062c83de2219c5f9ef9d684dac39323c052de140e257afd0f60f8ca7/asteval-1.0.10.tar.gz", hash = "sha256:46a4ed13cc2e4a29a21418f89dedd57db70d98eb96db0fb4c441d4edb2f5afed", size = 59581, upload-time = "2026-08-21T21:07:06.6Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fe/d7/a71b12b625c44cfcb95d41397752edccc0a731903081609cffd6720f671d/asteval-1.0.9-py3-none-any.whl", hash = "sha256:60513ea5d608d27bdb0cf3785dc993a6c3adeaee45f1bded0953a09e904e461f", size = 23320, upload-time = "2026-06-11T18:40:35.068Z" },
+    { url = "https://files.pythonhosted.org/packages/92/16/e78d6e60ecd1b3cf446fad45985ad6cda22f46ad2cffabb644d77b317bc4/asteval-1.0.10-py3-none-any.whl", hash = "sha256:8d805fa8084aa12204715f799430f0012a0db38b42e997f9f11a9637ad59855f", size = 23466, upload-time = "2026-08-21T21:07:05.678Z" },
 ]
 
 [[package]]
@@ -157,38 +170,38 @@ wheels = [
 
 [[package]]
 name = "astropy-healpix"
-version = "2.0.0"
+version = "2.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "astropy" },
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/06/bb/fb3dcca212b1d57ed26b33eabb9e1cc18fcadbfc2f2976b20530b7d80b90/astropy_healpix-2.0.0.tar.gz", hash = "sha256:6ce7646dc5b1c09a7ed24b1f23d3e2fce8e3570387220d26a24487ab001b3b0b", size = 112064, upload-time = "2026-06-29T20:02:46.407Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/15/c1/aeb3fe3be2ee863708d625014267c71abfe20ddaa293b3d4ddb72ee1d6e9/astropy_healpix-2.0.1.tar.gz", hash = "sha256:0e3f1c94064c45da779900cb90c938df7aef99a924abb23eeb893b16540e77e6", size = 112256, upload-time = "2026-07-20T21:07:30.004Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b8/d3/6055bf7486e0fc980a2c842fc757918f79b012c3181d6f03b3fe230a2f65/astropy_healpix-2.0.0-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6bf86b2cdd9615de0031bc13b33fb72bcf085c5a2c78a6cf27c7a02b9ec05be7", size = 191108, upload-time = "2026-06-29T20:02:34.106Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/9e/0bf5fdb204527abb6ee1ae8f77ce867b1150203864a47c19d0e5bd3ae63a/astropy_healpix-2.0.0-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bd3614d685ec2cd629f4e2bbb0e1db625fe878dde778b335ec07ab4754809d78", size = 193204, upload-time = "2026-06-29T20:02:35.261Z" },
-    { url = "https://files.pythonhosted.org/packages/4a/1a/5b8680a2a68df29a38baebd9659c4c4b42f5885c7fcfeb03dc336afb0053/astropy_healpix-2.0.0-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:8ffc56532822414fc6ba9b5e80b7897f98c96762d4ae69f14f5797e18946c26e", size = 188492, upload-time = "2026-06-29T20:02:36.721Z" },
-    { url = "https://files.pythonhosted.org/packages/7e/43/174bf249286926518e77e29a52179a5c971544fa29affe8dee6ab33bcd5b/astropy_healpix-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:727588aebabb0bdf7f27aeb793a721090e9c62ab44c5dfe083c5a5ec4ad91df8", size = 198639, upload-time = "2026-06-29T20:02:41.547Z" },
-    { url = "https://files.pythonhosted.org/packages/95/e1/2e57926e5c1e6a7b648b980656a3efd94ee58909fda577f7bf4fd701dca5/astropy_healpix-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31c4f998f83e7be4245fa75a48767d7749b2f233ee61e134573106dfbe53c73c", size = 201015, upload-time = "2026-06-29T20:02:42.721Z" },
-    { url = "https://files.pythonhosted.org/packages/93/96/ce466e4f53b59b8e0a89fd09824cce2532c5b9146279b7397d844f78cadf/astropy_healpix-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3eca64fb1640dcb6b5633db73a33b2d208f64c5339a454ca38f538be3cdc0a4b", size = 195935, upload-time = "2026-06-29T20:02:43.868Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/3d/0cde0db89ac8dd4e5347322470530298915739f7e9b356b01c1939de8c4f/astropy_healpix-2.0.1-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:179119d5a69e7b9245919cbe04c3e6bf0a485516b36c29f3402951aad5452251", size = 191178, upload-time = "2026-07-20T21:07:16.512Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/c4/71cf2bd4374cc17e015413462be8051fe08bc49e077b7d48072fff4e465d/astropy_healpix-2.0.1-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c092c54124c48f8d98e04fb22f4b2aa4c8675e65d81c351523f41377f9a6df22", size = 193429, upload-time = "2026-07-20T21:07:18.015Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/5c/3a50f68225b836b395da4fb8dfd3d702ded1916042490b16a613caa0e4a6/astropy_healpix-2.0.1-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:ce875a29c598c1a99f8f68351daeb4173463044dce4f1c7ebfe8c233ec5e9a49", size = 188694, upload-time = "2026-07-20T21:07:19.244Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/a7/c369703ca3fc6f5bee31b3d1f6d0f0b38c15ec84e7fbb6f552c0c7cedfb2/astropy_healpix-2.0.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:78f76785852bcc748f5efab8bb4f2ab1fe959d7a998b48e7ed1e59a46cbf0e51", size = 198717, upload-time = "2026-07-20T21:07:24.657Z" },
+    { url = "https://files.pythonhosted.org/packages/32/d5/0c4183611b8f36877112882e25cc8a91655a2d11e120ea1f5c153cb7d3a0/astropy_healpix-2.0.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02cefde735da1fe74654e786e02286261b04cc43f5c908a86a8457b2989ac2aa", size = 201205, upload-time = "2026-07-20T21:07:26.128Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/54/42bcd02b7c604d3a1132dfa93195bdc2677bb7844172cd098e149cbcb4e9/astropy_healpix-2.0.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:65bee7b70f35ddb81d6b6c849c5bf46768afbce6869395e4f9e0a5c27cb0ce17", size = 196134, upload-time = "2026-07-20T21:07:27.55Z" },
 ]
 
 [[package]]
 name = "astropy-iers-data"
-version = "0.2026.7.6.1.1.20"
+version = "0.2026.9.28.0.59.37"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7f/e3/d32d2401c53fa1aaf118c963c51c4eb9cd10d074a72a52430904dc8ef46e/astropy_iers_data-0.2026.7.6.1.1.20.tar.gz", hash = "sha256:bc38de235802c9f006c8a04693706e2adcc9e7ac1cbf415dc042b5f0ac14f1f3", size = 1939850, upload-time = "2026-07-06T01:02:02.379Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1f/87/93f546ac5da9ea32dc90a764e77d451a888400b0119d92232c848114de50/astropy_iers_data-0.2026.9.28.0.59.37.tar.gz", hash = "sha256:e93b4334ea1e719c47382f8d3dc6fd1b97fd6706a2ddf717b8506e1faee45845", size = 1948491, upload-time = "2026-09-28T01:00:20.916Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8c/a3/1d816863674c610234afef9a444d4eb562689a3fa1a872ac5687b4e0eac4/astropy_iers_data-0.2026.7.6.1.1.20-py3-none-any.whl", hash = "sha256:7aa50fa7f6d13258357d1e17fa97f3a8c2f04b9a7429d056e986a91cc46576a0", size = 1994649, upload-time = "2026-07-06T01:02:00.977Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/bf/ece8947ee55581e93c7b694b5dafecea2c559afa56a83a03bba9c021d0da/astropy_iers_data-0.2026.9.28.0.59.37-py3-none-any.whl", hash = "sha256:950a82adf7169c0bbf4a5b4aefd5412568436db6e22e0d056791fe3f30100d67", size = 2003931, upload-time = "2026-09-28T01:00:19.359Z" },
 ]
 
 [[package]]
 name = "asttokens"
-version = "3.0.1"
+version = "3.0.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308, upload-time = "2025-11-15T16:43:48.578Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/25/1e/faf0f247f6f881b98fc4d6d07e14085cb89d13665084e6d6ac1dc2c03d0b/asttokens-3.0.2.tar.gz", hash = "sha256:3ecdbd8f2cc195f53ccada3a613538bb5f9ef6f6869129f13e03c30a677b8fe2", size = 63136, upload-time = "2026-07-12T03:31:49.084Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/2b/04b8a15f3a1c77bc79ddf5c73875327f34b4fa75982df2b76e45e402d364/asttokens-3.0.2-py3-none-any.whl", hash = "sha256:9da13157f5b28becde0bd374fc677dcd3c290614264eff096f167c469cd9f933", size = 28702, upload-time = "2026-07-12T03:31:47.542Z" },
 ]
 
 [[package]]
@@ -250,7 +263,7 @@ css = [
 
 [[package]]
 name = "blosc2"
-version = "4.8.0"
+version = "4.14.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx", extra = ["http2"] },
@@ -262,18 +275,16 @@ dependencies = [
     { name = "rich" },
     { name = "threadpoolctl", marker = "platform_machine != 'wasm32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c1/26/c5b32a553f002e91886213ec91ff56a2dd8555e5e78eb4c289addb208642/blosc2-4.8.0.tar.gz", hash = "sha256:9789c3248052be59e63d6debafec4cd59d0eb4a6fd1cbaced36865d5d40ffa63", size = 5681856, upload-time = "2026-07-10T09:38:33.592Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/b4/71c95a7f46a13e81819e8e36607d1f2a45141830e4d7156b7e9e3a13a0bd/blosc2-4.14.1.tar.gz", hash = "sha256:b5adb408854260acf64aca5c351ab1307bcb7c71d112e3049d8541c1e2f9b222", size = 7789802, upload-time = "2026-09-30T10:49:30.233Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/79/54ea19add4199547d684f3a90c3cc0568b6d915403b15c3e4bc73d4bbf3b/blosc2-4.8.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:95b04eb8a769e98de9640915f9c5c0a3074714762958896440e5a479a96bf83e", size = 6704345, upload-time = "2026-07-10T09:37:56.942Z" },
-    { url = "https://files.pythonhosted.org/packages/e3/b0/aa277d5952ac4c673924454277de0800c780bcdb15917e303dac8ea3840f/blosc2-4.8.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:12668fe516f18f783877457ddcc5072d80edf4939914e27acf900ef65e970e69", size = 7208868, upload-time = "2026-07-10T09:37:58.649Z" },
-    { url = "https://files.pythonhosted.org/packages/ab/1b/765d2d38609d11e727f63c44f30abacccd59132e74f6e082b3741656f108/blosc2-4.8.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:90b1a86b9044b3b242d78d5ac1d62abb8af7b1da355eb8eb45978539f9df2eac", size = 6708353, upload-time = "2026-07-10T09:38:05.91Z" },
-    { url = "https://files.pythonhosted.org/packages/ee/aa/c72f0ff0c1cfa7e3a06530453f9b5388e4bb6266b73608f58232f181d68c/blosc2-4.8.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c17ec006d2770a4710552b0f4f8ceb40654fda08fe5047b27ccb7b0f396c3c6e", size = 7214012, upload-time = "2026-07-10T09:38:07.685Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/54/d7b8a01b43751840d75908d57601f03711a7cf260d46140f4c09dff5f9d6/blosc2-4.8.0-cp313-cp313-pyemscripten_2025_0_wasm32.whl", hash = "sha256:2af9162e7712323b21ad4d6502326c21459b6851b758b0421aa0be2e8b9dc0c1", size = 2300187, upload-time = "2026-07-10T09:38:09.407Z" },
-    { url = "https://files.pythonhosted.org/packages/2a/2d/0c9961009c422f366555574af4653f3c13ba73fe075efb31e7af42151af5/blosc2-4.8.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0bb7d94bffdef8304454bbb373b8e9bf41966f8ddb58c449782c649d4d6a395c", size = 6724604, upload-time = "2026-07-10T09:38:16.688Z" },
-    { url = "https://files.pythonhosted.org/packages/6d/8e/cb14d6e21fa44255739e2dac8d66ec7fb3d7aee348f198cc943e45d84d76/blosc2-4.8.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ec3fc9ad1b2528b471670233e9cf631be104b8526cf4b958a74099f6756c98f6", size = 7217824, upload-time = "2026-07-10T09:38:18.951Z" },
-    { url = "https://files.pythonhosted.org/packages/e0/1f/ea276e70d09ed406aba4012a47c7595ed772b25fc096fba8b9eac3494db9/blosc2-4.8.0-cp314-cp314-pyemscripten_2026_0_wasm32.whl", hash = "sha256:da018d11be4ac995aeb7b1a406c5cafce8c9f8a3ca8a540fe872a482de198b0d", size = 2297787, upload-time = "2026-07-10T09:38:20.871Z" },
-    { url = "https://files.pythonhosted.org/packages/2f/b7/cba879eed1f510b3f034fbeea7db81cb535c822fe96ac62b5e47012c0b9b/blosc2-4.8.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8574afd38d20f953e9114d7362535ef0019a4c85f2a672e6f3b36dcd00a77f32", size = 6669949, upload-time = "2026-07-10T09:38:27.542Z" },
-    { url = "https://files.pythonhosted.org/packages/07/5a/012f58a518bebf0d11931a5a12ba08955aa959cca59764ad38a8d82c9a1b/blosc2-4.8.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9f8b459e22059a3a794480df2c72ff008ab6c95b9799fa2979c1dd7830cf44e2", size = 7186485, upload-time = "2026-07-10T09:38:29.411Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/a1/94e40c26201675f672e575d0c4dcd80e4586d7aec1698a2bedbc1aa66f17/blosc2-4.14.1-cp311-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:411d9fa31c559f0b016c16611101ccd0e03d38dc89ab1bc94178aa3757ce756a", size = 6300944, upload-time = "2026-09-30T10:48:56.884Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/ed/26058d46a34c3afc95dbf46c47d4310274fff92e81388ba42f536c450d35/blosc2-4.14.1-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5e65efe80740651c3b39a9fd98478f073c57bed51f7eebe1fdfe248dc259e37", size = 6727148, upload-time = "2026-09-30T10:48:58.826Z" },
+    { url = "https://files.pythonhosted.org/packages/02/62/39a3ae8d75ef372f04c7a75f3987a80f83f8c0c4cb4c3d8147508810f68d/blosc2-4.14.1-cp311-abi3-pyemscripten_2025_0_wasm32.whl", hash = "sha256:94c1e518ee23f4423be80dc7fa16e405364afaefe074472d05785eccd17c56bd", size = 2742552, upload-time = "2026-09-30T10:49:00.578Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/c4/e1673cc731ec3da81d016b4879ff08ff73352a16101845d0e5032af4d2e7/blosc2-4.14.1-cp311-abi3-pyemscripten_2026_0_wasm32.whl", hash = "sha256:696fdfc471191679e6c1b3b2d5db4ae7cc40675e10997d0b5eb6caf7d470253e", size = 2739534, upload-time = "2026-09-30T10:49:02.346Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/f6/5c350a3ef199e162e45327059ad86c6c1042099c17b1a9e3ba8e2e7bd9b3/blosc2-4.14.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d2748625f152b66ddfce2f6fe079e22e12179dfe960dff7898838d3ef25776ca", size = 6291040, upload-time = "2026-09-30T10:49:11.09Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/fa/fa911c7fd056413434aea4b72b8ab48c6a2bb41772a13ac51a66d2c51664/blosc2-4.14.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5d37f2c76272fb6d0ffee3afbbaa4cd73438abd7e2c119d6152bcdf1d47434b3", size = 6700115, upload-time = "2026-09-30T10:49:13.112Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/a7/0e5888db787cdde0fd4140cba6b09551834e7a6b6578c9296b91f7bf899d/blosc2-4.14.1-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8e0ad358a9fb38d41b0bd1dd08b00f1384774940abd053a74f7e8a80af095a07", size = 6285934, upload-time = "2026-09-30T10:49:23.108Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/c1/cbcd754fb615c9cccb08a854d1a6f728e7a640680098dbdcf762b566544a/blosc2-4.14.1-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6a2208f001580d497a050aba1a4083d5ba54adbe1e166cee4faf4731f0e96c9a", size = 6697746, upload-time = "2026-09-30T10:49:25.052Z" },
 ]
 
 [[package]]
@@ -311,120 +322,174 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2026.6.17"
+version = "2026.7.22"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/c9/c7/424b75da314c1045981bd9777432fad05a9e0c69daa4ed7e308bbaffe405/certifi-2026.6.17.tar.gz", hash = "sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432", size = 134594, upload-time = "2026-06-17T10:31:07.894Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a3/c2/24167ea9858356b47a87a50d39908bfdb72ceeefe0041586e704e5376b3a/certifi-2026.7.22.tar.gz", hash = "sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55", size = 138112, upload-time = "2026-07-22T03:35:12.644Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ef/2f/c5464532e965badff2f4c4c1a3a83f5697f0d7c407ed0cda44aaa99bb451/certifi-2026.6.17-py3-none-any.whl", hash = "sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db", size = 133289, upload-time = "2026-06-17T10:31:06.348Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/a7/71ac2cff56fec219ed242bb11b8efb69fcc4bec75db06fb7bfe35de520e6/certifi-2026.7.22-py3-none-any.whl", hash = "sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775", size = 136983, upload-time = "2026-07-22T03:35:11.276Z" },
 ]
 
 [[package]]
 name = "cffi"
-version = "2.1.0"
+version = "2.1.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pycparser", marker = "implementation_name != 'PyPy'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/57/5f/ff100cae70ebe9d8df1c01a00e510e45d9adb5c1fdda84791b199141de97/cffi-2.1.0.tar.gz", hash = "sha256:efc1cdd798b1aaf39b4610bba7aad28c9bea9b910f25c784ccf9ec1fa719d1f9", size = 531036, upload-time = "2026-07-06T21:34:30.382Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c3/c0/d1ec30ffb370f748f2fb54425972bfef9871e0132e82fb589c46b6676049/cffi-2.1.0-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:5972433ad71a9e46516584ef60a0fda12d9dc459938d1539c3ddecf9bdc1368d", size = 214815, upload-time = "2026-07-06T21:32:48.557Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/dc/5620cf930688be01f2d673804291de757a934c90b946dbdc3d84130c2ea4/cffi-2.1.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b6422532152adf4e59b110cb2808cee7a033800952f5c036b4af047ee43199e7", size = 222429, upload-time = "2026-07-06T21:32:49.848Z" },
-    { url = "https://files.pythonhosted.org/packages/4b/a4/77b53abbf7a1e0beb9637edbef2a94d15f9c822f591e85d439ffd91519a6/cffi-2.1.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:46b1c8db8f6122420f32d02fffb924c2fe9bc772d228c7c711748fff56aabb2b", size = 210315, upload-time = "2026-07-06T21:32:51.221Z" },
-    { url = "https://files.pythonhosted.org/packages/58/0c/f528df19cc94b675087324d4760d9e6d5bfae97d6217aa4fac43de4f5fcc/cffi-2.1.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9fafc5aa2e2a39aaf7f8cc0c1f044a9b07fca12e558dca53a3cc5c654ad67a7", size = 208859, upload-time = "2026-07-06T21:32:52.512Z" },
-    { url = "https://files.pythonhosted.org/packages/62/f2/c9522a81c32132799a1972c39f5c5f8b4c8b9f00488a23feaa6c06f07741/cffi-2.1.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1e9f50d192a3e525b15a75ab5114e442d83d657b7ec29182a991bc9a88fd3a66", size = 221844, upload-time = "2026-07-06T21:32:53.704Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/28/bd53988b9833e8f8ad539d26f4c07a6b3f6bcb1e9e02e7ca038250b3428d/cffi-2.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:98fff996e983a36d3aa2eca83af40c5821202e7e6f32d13ae94e3d2286f10cfe", size = 225287, upload-time = "2026-07-06T21:32:54.907Z" },
-    { url = "https://files.pythonhosted.org/packages/79/99/0d0fd37f055224085f42bbb2c022d002e17dde4a97972822327b07d84101/cffi-2.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:379de10ce1ba048b1448599d1b37b24caee16309d1ac98d3982fc997f768700b", size = 223681, upload-time = "2026-07-06T21:32:56.329Z" },
-    { url = "https://files.pythonhosted.org/packages/96/88/a996879e2eeccb815f6e3a5967b12a308257412acec882039d386bd2aa7b/cffi-2.1.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:10537b1df4967ca26d21e5072d7d54188354483b91dc75058968d3f0cf13fbda", size = 194331, upload-time = "2026-07-06T21:33:03.697Z" },
-    { url = "https://files.pythonhosted.org/packages/58/85/7ae00d5c8dd6266f4e944c3db630f3c5c9a98b61d469c714d848b1d8138a/cffi-2.1.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:a95b05f9baf29b91171b3a8bd2020b028835243e7b0ff6bb23e2a3c228518b1b", size = 196966, upload-time = "2026-07-06T21:33:05.353Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/1c/4ed5a0e5bdca6cbc275556de3328dd1b76fd0c11cc13c88fe66d1d8715f2/cffi-2.1.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:63960549e4f8dc41e31accb97b975abaecfc44c03e396c093a6436763c2ea7db", size = 214747, upload-time = "2026-07-06T21:33:09.671Z" },
-    { url = "https://files.pythonhosted.org/packages/3a/a6/e879bb68cc23a2bc9ba8f4b7d8019f0c2694bad2ab6c4a3701d429439f58/cffi-2.1.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ff067a8d8d880e7809e4ac88eb009bb848870115317b306666502ccad30b147f", size = 222392, upload-time = "2026-07-06T21:33:10.896Z" },
-    { url = "https://files.pythonhosted.org/packages/88/f6/01890cfd63c08f8eb96a8319b0443690197d240a8bd6346048cf7bde9190/cffi-2.1.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:3b926723c13eba9f81d2ef3820d63aeceec3b2d4639906047bf675cb8a7a500d", size = 210285, upload-time = "2026-07-06T21:33:12.251Z" },
-    { url = "https://files.pythonhosted.org/packages/a6/cf/2b684132056f438567b61e19d690dd31cd0921ace051e0a458be6074369e/cffi-2.1.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:47ff3a8bfd8cb9da1af7524b965127095055654c177fcfc7578debcb015eecd0", size = 208801, upload-time = "2026-07-06T21:33:13.617Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/08/f2e7d62c460faae0926f2d6e423694aa409ced3bc1fe2927a0a6e5f05416/cffi-2.1.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:799416bae98336e400981ff6e532d67d5c709cfb30afb79865a1315f94b0e224", size = 221808, upload-time = "2026-07-06T21:33:15.466Z" },
-    { url = "https://files.pythonhosted.org/packages/38/37/04f54b8e63a02f3d908332c9effbf8c366167c6f733ed8a3d4f79b7e2a1e/cffi-2.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:961be50688f7fba2fa65f63712d3b9b341a22311f5253460ce933f52f0de1c8c", size = 225241, upload-time = "2026-07-06T21:33:16.869Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/d6/c72eecca433cd3e681c65ed313ab4835d9d4a379704d0f628a6a05f51c2e/cffi-2.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:bf5c6cf48238b0eb4c086978c492ad1cbc22373fc5b2d7353b3a598ce6db887a", size = 223588, upload-time = "2026-07-06T21:33:18.239Z" },
-    { url = "https://files.pythonhosted.org/packages/d8/f0/81478e482afa03f6d18dc8f2afb5edc45b3080853b634b5ed91961be0998/cffi-2.1.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:d2117334c3af3bdcb9a88522b844a2bdb5efdc4f71c6c822df55486ae1c3347a", size = 194142, upload-time = "2026-07-06T21:33:23.657Z" },
-    { url = "https://files.pythonhosted.org/packages/7d/95/8de304305cd9204974b0ca051b86d307cafca13aa575a0ef1b44d92c0d8c/cffi-2.1.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:702c436735fbe99d59ada02a1f65cfc0d31c0ee8b7290912f8fbc5cd1e4b16c3", size = 196819, upload-time = "2026-07-06T21:33:25.007Z" },
-    { url = "https://files.pythonhosted.org/packages/2e/d2/065fcae1c73979fac8e054462478d0ff8a29c40cdc2ed7ea5676a061df53/cffi-2.1.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:276f20fffd7b396e12516ba8edf9509210ac248cbbc5acbc39cd512f9f59ebe6", size = 222353, upload-time = "2026-07-06T21:33:29.178Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/a5/e8bbb1ce5b3ac2f53ad6a10bde44318a5a8d99d4f4a000d44a6e39aeb3e4/cffi-2.1.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:7d5980a3433d4b71a5e120f9dd551403d7824e31e2e67124fe2769c404c06913", size = 210051, upload-time = "2026-07-06T21:33:30.534Z" },
-    { url = "https://files.pythonhosted.org/packages/28/ed/c127d3ac36e899c965e3361357c3befacd6578c03f40125183e41c3b219e/cffi-2.1.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:6ca4919c6e4f89aa99c42510b42cf54596892c00b3f9077f6bdd1505e24b9c8d", size = 208630, upload-time = "2026-07-06T21:33:31.753Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/d7/97d3136f81db489ec8d1d67748c110d6c994268fd7528014aa9f2b085e4e/cffi-2.1.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d53d10f7da99ae46f7373b9150393e9c5eab9b224909982b43832668de4779f5", size = 221593, upload-time = "2026-07-06T21:33:33.044Z" },
-    { url = "https://files.pythonhosted.org/packages/d3/27/93195977168ee63aed233a1a0993a2178798654d1f4bddcdd321d6fd3b21/cffi-2.1.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c351efb95e832a853a29361675f33a7ce53de1a109cd73fd47af0712213aa4ce", size = 225146, upload-time = "2026-07-06T21:33:34.224Z" },
-    { url = "https://files.pythonhosted.org/packages/b3/c1/6dbd291ee2ae5a50a034aa057207081f545923bbf15dad4511e985aafff5/cffi-2.1.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:dbf7c7a88e2bac086f06d14577332760bdeecc42bdec8ac4077f6260557d9326", size = 223240, upload-time = "2026-07-06T21:33:35.57Z" },
-    { url = "https://files.pythonhosted.org/packages/14/d0/117dcd9209255ad8571fbc8c92ef32593a1d294dcec91ddc4e4db50606f2/cffi-2.1.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:eb4e8997a49aa2c08a3e43c9045d224448b8941d88e7ac163c7d383e560cbf98", size = 223899, upload-time = "2026-07-06T21:33:39.514Z" },
-    { url = "https://files.pythonhosted.org/packages/b6/3d/f20f8b886b254e3ad10e15cd4186d3aed49f3e6a35ab37aab9f8f25f7c03/cffi-2.1.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:bf01d8c84cbea96b944c73b22182e6c7c432b3475632b8111dbfdc95ddad6e13", size = 211652, upload-time = "2026-07-06T21:33:40.851Z" },
-    { url = "https://files.pythonhosted.org/packages/28/3b/fad54de07260b93ddeef4b96d0131d57ea900675df1d410ae1deee52d7a6/cffi-2.1.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:33eb1ad83ebe8f313e0df035c406227d55a79456704a863fad9842136af5ad7d", size = 210755, upload-time = "2026-07-06T21:33:42.183Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/82/3d5c705acb7abbba9bbd7d79b8e62e0f25b6120eb7ae6ac49f1b721722fe/cffi-2.1.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ac0f1a2d0cfa7eea3f2aaf006ab6e70e8feeb16b75d65b7e5939982ca2f11056", size = 223933, upload-time = "2026-07-06T21:33:43.603Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/d0/47e338384ab6b1004241002fa616301020cea4fc95f283506565d252f276/cffi-2.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c16914df9fb7f500e440e6875fa23ff5e0b31db01fa9c06af98d59a91f0dc2e4", size = 226749, upload-time = "2026-07-06T21:33:45.046Z" },
-    { url = "https://files.pythonhosted.org/packages/70/25/65bd5b58ea4bfdfc15cde02cb5365f89ef8ab8b2adfb8fe5c4bd4233382f/cffi-2.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5ecbd0499275d57506d397eebe1981cee87b47fcd9ef5c22cab7ed7644a39a94", size = 225703, upload-time = "2026-07-06T21:33:46.374Z" },
-    { url = "https://files.pythonhosted.org/packages/55/c7/8c8c50cb11c6750051daf12164098a9a6f027ac4356967fd4d800a07f242/cffi-2.1.0-cp315-cp315-ios_13_0_arm64_iphoneos.whl", hash = "sha256:2e9dabb9abcb7ad15938c7196ad5c1718a4e6d33cc79b4c0209bdb64c4a54a5c", size = 194121, upload-time = "2026-07-06T21:33:56.109Z" },
-    { url = "https://files.pythonhosted.org/packages/99/e2/67680bf19a6b60d2bb7ff83baefa2a4c3d2d7dc0f3277034b802e1fc504c/cffi-2.1.0-cp315-cp315-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:37f525a7e7e50c017fdebe58b787be310ad59357ae43a053943a6e1a6c526001", size = 196820, upload-time = "2026-07-06T21:33:57.288Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/c3/ad299dc38f3583f8d916b299f028af418a9ec98bc695fcbebeae7420691c/cffi-2.1.0-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:90bec57cf82089383bd06a605b3eb8daebf7e5a668520beaf6e327a83a947699", size = 222342, upload-time = "2026-07-06T21:34:01.814Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/d8/df4543cc087245044ed02ef3ad8e0a26619d0075ac7a77a12dc81177851b/cffi-2.1.0-cp315-cp315-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:6274dcb2d15cef48daa73ed1be5a40d501d74dccd0cd6db364776d12cb6ba022", size = 210073, upload-time = "2026-07-06T21:34:03.255Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/0e/fac738d73728c6cea2a88a2883dca54892496cbba88a1dc1f2909cb8a6f5/cffi-2.1.0-cp315-cp315-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:2b71d409cccee78310ab5dec549aed052aaea483346e282c7b02362596e01bb0", size = 208551, upload-time = "2026-07-06T21:34:04.433Z" },
-    { url = "https://files.pythonhosted.org/packages/e6/3f/0b04a700dd64f465c93020253a793a82c9b4dff9961f48facd0df945d9b8/cffi-2.1.0-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7d3538f9c0e50670f4deb93dbb696576e60590369cae2faf7de681e597a8a1f1", size = 221649, upload-time = "2026-07-06T21:34:06.157Z" },
-    { url = "https://files.pythonhosted.org/packages/5d/7c/b7379a5704c79eda57ce075869ba70a0368d1c850f803b3c0d078d39dcaf/cffi-2.1.0-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:8f9ec95b8a043d3dfbc74d9abc6f7baf524dd27a8dc160b0a32ff9cdab650c28", size = 225203, upload-time = "2026-07-06T21:34:07.489Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/02/d5e6c43ea85c41bda2a184a3418f195fe7cf602967a8d2b94e085b83deef/cffi-2.1.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:af5e2915d41fe6c961694d7bfdc8562942638200f3ce2765dfb8b745cf997629", size = 223263, upload-time = "2026-07-06T21:34:08.712Z" },
-    { url = "https://files.pythonhosted.org/packages/e0/27/1d0b408497e41a74795af122d7b603c418c5fed0171450f899afd04e594f/cffi-2.1.0-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0520e1f4c35f44e209cbbb421b67eec42e6a157f59444dfb6058874ff3610e5d", size = 223904, upload-time = "2026-07-06T21:34:12.606Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/31/e115c985105dd7ffb32444505f18ceb874bb42d992af05d5dced7ecf1980/cffi-2.1.0-cp315-cp315t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:3681e031db29958a7502f5c0c9d6bbc4c36cb20f7b104086fa642d1799631ff8", size = 211554, upload-time = "2026-07-06T21:34:13.987Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/67/9e6e09409336d9e515c58367e7cfcf4f89df06ad25252675595a58eb59d5/cffi-2.1.0-cp315-cp315t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:762f99479dcb369f60ab9017ad4ab97a36a1dd7c1ee5a3b15db0f4b8659120cd", size = 210795, upload-time = "2026-07-06T21:34:15.972Z" },
-    { url = "https://files.pythonhosted.org/packages/19/e5/d3cc82a4a0be7902af279c04181ad038449c096734464a5ae1de3e1401bd/cffi-2.1.0-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0611e7ebf90573a535ebdc33ae9da222d037853983e13359f580fab781ca017f", size = 223843, upload-time = "2026-07-06T21:34:17.509Z" },
-    { url = "https://files.pythonhosted.org/packages/b9/65/b434abc97ce7cecc2c640fde160507c0ecc7e21544b483ba3325d2e2ea17/cffi-2.1.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:86cf8755a791f72c85dc287128cc62d4f24d392e3f1e15837245623f4a33cccc", size = 226773, upload-time = "2026-07-06T21:34:19.05Z" },
-    { url = "https://files.pythonhosted.org/packages/b5/9f/d4dc66ca651eb1145a133314cda721abf13cfac3d28c4a0402263ae6ad75/cffi-2.1.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:ba00f661f8ba35d075c937174e27c2c421cec3942fd2e0ea3e66996757c0fdd9", size = 225719, upload-time = "2026-07-06T21:34:20.576Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/9e/ef/008a1939e372c06329a3fce4279c02f328488f3526744906eeec3da7ad5f/cffi-2.1.1.tar.gz", hash = "sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be", size = 530807, upload-time = "2026-08-03T21:21:18.939Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/56/e6/8941622732edec876dd17d0453dce07317ae96db34f2ec1436c9d3785986/cffi-2.1.1-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:811bd1e21d32de12efca32393a0ab3f5133b54fce9bd44b8bd77ab07da14bf6a", size = 214799, upload-time = "2026-08-03T21:19:47.218Z" },
+    { url = "https://files.pythonhosted.org/packages/44/de/f98430906df1545ffde0d543dd124a7a439bc2cd32b36b9c53f805df7333/cffi-2.1.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:68e62fe11f30d5ca8289242866f0a5291402d8529ca2178ab8afc5c9694ae890", size = 222389, upload-time = "2026-08-03T21:19:48.331Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/5b/717f1526b9957b34456313c31645c5b82b8fb5c3fe9e4752999be7128bfc/cffi-2.1.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:4a7c934f7360e8cd64fe9efadcbd10c7c6364f531e432b9a4bf5ccbc9e0e8b50", size = 210249, upload-time = "2026-08-03T21:19:49.543Z" },
+    { url = "https://files.pythonhosted.org/packages/64/b3/f8aa4f3e34986c7e4ec45072d1b1b9dd295b6b18007b45518d79726dd725/cffi-2.1.1-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:3143d81e29e1e20a9ce10901ec369012947876596f75a222235965f2b7ae832e", size = 208775, upload-time = "2026-08-03T21:19:50.918Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/db/dceb9dd5b231e1da801793f8acc9f3c52a7e1afe40bb1aae37e02b0faad5/cffi-2.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c1453022f490d2459a11819d83ad1d586e9ff65a12ac3e705ffebd46d3685dcf", size = 221822, upload-time = "2026-08-03T21:19:52.054Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/d2/6cd24ae3be000a634109c247d1475d62e5616d0dc78c82770942ec384248/cffi-2.1.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:208f941bb9d18e768138677f0a6d2ce01f590df56043dda1df1535ac57c88517", size = 225232, upload-time = "2026-08-03T21:19:53.109Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/52/3fa190537004dd7f0ab860a6dc7c0175b8667f68d1e618a46f5498d30250/cffi-2.1.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:210019b6c7cf07f081b4c54635c8cf744377001350e29cc0f81c4377b4797735", size = 223597, upload-time = "2026-08-03T21:19:54.515Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/f4/035513d4117049066b4779dc3b7c0c0fdad175fa13731c9f4003f1cd1478/cffi-2.1.1-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:b5bdfd1c873d4e093aabc0ca84c4ca6dbc4f752afb5c86f146d9742580c9da2e", size = 194248, upload-time = "2026-08-03T21:19:59.399Z" },
+    { url = "https://files.pythonhosted.org/packages/76/af/2aeb4dbb5fc41a04161ae9ff1518de7cec08e164f44a8ce6a4cf7fd2cd1d/cffi-2.1.1-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:31348097ff5bbe827ccc41795d4dd099d9f0625e7def00ee653c137a490c2a6c", size = 196908, upload-time = "2026-08-03T21:20:00.746Z" },
+    { url = "https://files.pythonhosted.org/packages/43/1f/1c3d90d91811c8f86ced9ed637956c54bfe5b79ca98fe976d7f8c8979f6b/cffi-2.1.1-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:6a8dddef476fab96d066d578fc88526767b836ab5ab21754e1d5bf3879c31c7c", size = 214722, upload-time = "2026-08-03T21:20:04.377Z" },
+    { url = "https://files.pythonhosted.org/packages/37/6f/3b5ce4c3b2192d250f04908f2bfd91ef34552ec8f7716a5d4abdb8d67bb2/cffi-2.1.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f16c709686a78c727bbbf059f92b0bf41c6fc60deec706d2dc19f529175a6125", size = 222369, upload-time = "2026-08-03T21:20:05.544Z" },
+    { url = "https://files.pythonhosted.org/packages/02/10/4b3c75dde3d9663c9e02ba05c2668b954f671d4bbe346413ca8c696b295a/cffi-2.1.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:fcd22650c908d7b7da162bbfaab594a1227a15d1643a98c68b122ac642fa2264", size = 210175, upload-time = "2026-08-03T21:20:06.75Z" },
+    { url = "https://files.pythonhosted.org/packages/df/62/14f74b9543e605d17701dc797b815958b8bb70b7624ce1b832ddad48ed6c/cffi-2.1.1-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:aa9511c62d14da7aacc9b4bf51f3f697a621e83b2d6919008243c3aad168eea3", size = 208670, upload-time = "2026-08-03T21:20:08.04Z" },
+    { url = "https://files.pythonhosted.org/packages/95/95/86342356ff5953b3fb06f7ef7c5bee212d45e770abc7218d451b9148313c/cffi-2.1.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a931079504ecc49efed7744c476a5c343a92fabf66dec2db95edb1b2fdc770e2", size = 221824, upload-time = "2026-08-03T21:20:09.274Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/ff/7b3429ff53aafe931ed8a5fc69f481bbef7ba6de87ddcbb63d08f483f613/cffi-2.1.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a2d7755bef5a12ed488f4ef1f1b69ee9191d7396083b755a5d2295f6edb4768b", size = 225148, upload-time = "2026-08-03T21:20:10.7Z" },
+    { url = "https://files.pythonhosted.org/packages/34/34/a95870b9221e09cf4f2ce3178b1a210abdfe63a1bd357da940418d7b8d15/cffi-2.1.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e0bcb7e0f677f543555d2adff3bf19c05f66cdb4796e5ff602442ab2fe3c4ef7", size = 223564, upload-time = "2026-08-03T21:20:12.165Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/7b/d6bbf82b8b96e7391438898c42f5bd96dd02030fd5b64937d248220003e2/cffi-2.1.1-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:7dbb61fe3a7699468030f71bbe5f8a0e326a151daa91beb11a6fc1f980c55e1c", size = 194064, upload-time = "2026-08-03T21:20:17.148Z" },
+    { url = "https://files.pythonhosted.org/packages/94/e6/bcc91b283be94735e268487a054004f0aa19947b6348fa367db53230abc8/cffi-2.1.1-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:f24fb43132a4c6b4cb4eb029492919b2db645be6808d738f244fd146c03c32cb", size = 196720, upload-time = "2026-08-03T21:20:18.268Z" },
+    { url = "https://files.pythonhosted.org/packages/67/b8/b42132ca113dc567d37684437b46ca1dafc885902b02a110a02d5b511857/cffi-2.1.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:58acb8ab8e295e6c5ea12f888cbb13cf21511ef2a3303a23f4325c29d17fe5c1", size = 222328, upload-time = "2026-08-03T21:20:22.118Z" },
+    { url = "https://files.pythonhosted.org/packages/80/10/c5c0cbf0a657aecf59ef511409734230bf556f05a0d6c9eed7aa5c0a0166/cffi-2.1.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:456a61fa52d579ebf9df2e9552ead5129855dbaff6c1e5a9b1bc408809bdc062", size = 209985, upload-time = "2026-08-03T21:20:23.401Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/6c/bfa0b87b03b9238148beca990292843c9396ba069b54496596594173de7b/cffi-2.1.1-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:a4f00aa42f75d6e4595e8866e748cc1705adc0cddfeb2ca86d0d03993d63ba03", size = 208530, upload-time = "2026-08-03T21:20:24.628Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/02/4e7d553a7ac4b4238b38b3c1b80d486e9d4436f8d2acbf87a0997fe3f402/cffi-2.1.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b0431303acaea1089ad4b3e9ce4e6518193def1118d4073ca848635ee4ea2e96", size = 221525, upload-time = "2026-08-03T21:20:25.758Z" },
+    { url = "https://files.pythonhosted.org/packages/82/1d/a4aaf9babd75acb4d5f223bff71533bee748dd770a382619a798960ee9ba/cffi-2.1.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:64faea20f4e2613363a1a9b9c7dd73058f3ecd00133a511e72ad7c511658f527", size = 225053, upload-time = "2026-08-03T21:20:26.985Z" },
+    { url = "https://files.pythonhosted.org/packages/81/10/5dc0e7bdd18e22107054288283380fc97a06ae3f1656a106908d666a3c88/cffi-2.1.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5c58fe613dc5e5336357eff555824a314d8e43282600435c8d1cb6a7a2fedd13", size = 223213, upload-time = "2026-08-03T21:20:28.277Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/9c/92934c3bea9f785b23eba304538c0b4d37a2a96d2431eb3a1bc87a11aa19/cffi-2.1.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:49cbc70e6542d4ccccb936558d1064a8012541e78f821f955cff24e357776c94", size = 223864, upload-time = "2026-08-03T21:20:32.571Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/45/ba4c93527bc38616a8bd36488acb69a2212d60486794f0c1f318949bbb76/cffi-2.1.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:e2d65b31f36619cda3999b78b2aa9632e76b78448e7a56fc4240824200e7c4fc", size = 211538, upload-time = "2026-08-03T21:20:33.808Z" },
+    { url = "https://files.pythonhosted.org/packages/80/e9/b6ef565e452acb932fb0cb5443f44a78efbd1233e566f02b5a83855e9115/cffi-2.1.1-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:28907ab9bfb6aa13184cfc17c6b8e1023c5ab6fd7076d8c20a35e59fe04f8f29", size = 210688, upload-time = "2026-08-03T21:20:34.974Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/95/eff5f0cee78d2eabc7eebffec40d3fc1876b5f3c95582e018bb4b99601f2/cffi-2.1.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:51b31d1c98274844cfd7838ce00bfc27c7423a4dc00fc0772fc3331c2cc90676", size = 223803, upload-time = "2026-08-03T21:20:36.564Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/01/579d39fb8bef00a335a23d83757b44feb24cd6345a2c451b64cb67b9c362/cffi-2.1.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5e7cecbaadb83884793e05828cee59b210b24583b9c7425d0ba6a754fe22eb4e", size = 226763, upload-time = "2026-08-03T21:20:37.816Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/b0/0b44f47c60b01b57b6e2bbd92343f13a85a1d93bc46ccf6e47e244acd99c/cffi-2.1.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:25792eac27877609e7bb06d42ff88278a6624fff2ba9bbb523c09616b117e80f", size = 225688, upload-time = "2026-08-03T21:20:38.959Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/8f/9ebe220eab48a093d1a5a5e339ab0dc7316eef3bb04d63c42f0251b61f50/cffi-2.1.1-cp315-cp315-ios_13_0_arm64_iphoneos.whl", hash = "sha256:dddad92b554513a31f272570678ba307fb9f618f05e3d4a5eacafff9eae03e1d", size = 194043, upload-time = "2026-08-03T21:20:48.179Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/69/844bad3ece306c4782c2ecb93597035b6690d48704b803914c199da1e8b3/cffi-2.1.1-cp315-cp315-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:da0e573f9f97159390c89d9f1a9e41908b66d408cc5b58d08cf3847d844c531b", size = 196737, upload-time = "2026-08-03T21:20:49.457Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/31/9e1313b0a6e30e91b3b3d3fff51ae99c857c07738e3afcce1f7334e1b7ab/cffi-2.1.1-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:507a24c282e0f42f8ed737cf048572cbf580468da5555764a8331735e9c736b6", size = 222271, upload-time = "2026-08-03T21:20:53.462Z" },
+    { url = "https://files.pythonhosted.org/packages/50/e3/f6234a833e6e08c7007003074723c406559eecf9b48dfc97471e5a8eb7a0/cffi-2.1.1-cp315-cp315-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:246fa40ce8645a614ff682e0b70f37134e460eaf93a775e0cbe3cca585a67a80", size = 209919, upload-time = "2026-08-03T21:20:54.783Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/fc/5f74e293fced6edb51af3a46c4ccf6c23c9943774ecb375ddbd522c76add/cffi-2.1.1-cp315-cp315-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:471cee653ae88de62096552e6d24ccb4a5adb8c8c9f10b5054d0122c15bf2779", size = 208529, upload-time = "2026-08-03T21:20:56.066Z" },
+    { url = "https://files.pythonhosted.org/packages/44/16/29e6d01b388bef055ecd6ca8244b3f4d336bd09e92d5d892187b9601084e/cffi-2.1.1-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aeae0e330c9f6acd681f647d46cefd30c29f93e3392882e792e82080c9691399", size = 221630, upload-time = "2026-08-03T21:20:57.336Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/18/fa7f1f6857d5eb88a4ca99ffcbfb7c387a287ccc154c64a73e86314745d7/cffi-2.1.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:42a494cee34437f05546455144f2b5d9ac09b1face62bcfce597d2e521066688", size = 225134, upload-time = "2026-08-03T21:20:58.675Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/9f/e8e3dfa04a1b4c241f8c91faacad872b4d4efd051d49764ad4e2fd4b9fea/cffi-2.1.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:cc572dace3f60ef98d7b12ff411d20f5362feb31a0439eab0085bbfd349982d7", size = 223197, upload-time = "2026-08-03T21:20:59.968Z" },
+    { url = "https://files.pythonhosted.org/packages/53/b2/6187f46f2912276a3ae284076109cc5c8680482f11f766ccf26db4a86427/cffi-2.1.1-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:937c0052c05a31ca1daf18de3158eed4dbfcb9cc107adbea227728d647be701e", size = 223779, upload-time = "2026-08-03T21:21:03.553Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/f6/c3ad28bd19f77047a03084424fbd4cbe997303267c14423737324be0385d/cffi-2.1.1-cp315-cp315t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:df423d40ee8654634421812bc3b196da3f9bd7d32929da813f8394c4348a5358", size = 211520, upload-time = "2026-08-03T21:21:04.863Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/cd/ccac9013a5bd9fd764de118674ab9c805b5ca10c19270d90ee273f8b2240/cffi-2.1.1-cp315-cp315t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:a730a083190634c65cca36ba5f489531576ebd79bcd5c8e172130f6453127231", size = 210673, upload-time = "2026-08-03T21:21:06.223Z" },
+    { url = "https://files.pythonhosted.org/packages/52/86/2976131c639aead931c5bee5aba67e4b09fbeb8018b6f282f70803f923a7/cffi-2.1.1-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:363e05fa78e15116c3c32c210ee36884fd6b9afa6d440e47112c3bd511d64cb6", size = 223835, upload-time = "2026-08-03T21:21:07.539Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/0c/33a7aeab2f9c76918c52e084beb39c570db3588133412929e8ec06fab90b/cffi-2.1.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:770de9db11e84213beec501cfcaa013b019820ca881e03344dea5844f7876d94", size = 226705, upload-time = "2026-08-03T21:21:08.774Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/26/2cde30fdde421130bfc18f70395731a6e6b2053c6a1978a5258ff04e72fa/cffi-2.1.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:7da0c5eff80f0197f3b3d1232ec5a682a9325f4ae9016a78f5f5ca35f9ced1f5", size = 225539, upload-time = "2026-08-03T21:21:09.911Z" },
 ]
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.9"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bd/2a/23f34ec9d04624958e137efdc394888716353190e75f25dd22c7a2c7a8aa/charset_normalizer-3.4.9.tar.gz", hash = "sha256:673611bbd43f0810bec0b0f028ddeaaa501190339cac411f347ac76917c3ae7b", size = 152439, upload-time = "2026-07-07T14:34:58.454Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/ec/96/5d9364e3342d69f3a045e1777bc47c85c383e6e9466d561b33fdb419d1f9/charset_normalizer-3.4.9-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9b2aff1c7b3884512b9512c3eaadd9bab39fb45042ffaaa1dd08ff2b9f8109d9", size = 215802, upload-time = "2026-07-07T14:33:17.031Z" },
-    { url = "https://files.pythonhosted.org/packages/4b/4c/5361f9aa7f2cb58d94f2ab831b3d493f69efb1d239654b4744e3c09527cb/charset_normalizer-3.4.9-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9104ed0bd76a429d46f9ec0dbc9b08ad1d2dcdf2b00a5a0daa1c145329b35b44", size = 237171, upload-time = "2026-07-07T14:33:18.576Z" },
-    { url = "https://files.pythonhosted.org/packages/50/78/ce342ca4ff30b2eb49fe6d9578df85974f90c67d294113e94efdd9664cbd/charset_normalizer-3.4.9-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7b86a2b16095d250c6f58b3d9b2eee6f4147754344f3dab0922f7c9bf7d226c9", size = 233075, upload-time = "2026-07-07T14:33:20.084Z" },
-    { url = "https://files.pythonhosted.org/packages/01/c4/4fa4c8b3097a11f3c5f09a35b72ed6855fb1d332469504962ab7bafcc702/charset_normalizer-3.4.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5e226f6218febc71f6c1fc2fafb91c226f75bdc1d8fb12d66823716e891608fd", size = 224256, upload-time = "2026-07-07T14:33:21.747Z" },
-    { url = "https://files.pythonhosted.org/packages/87/3a/ad914516df7e358a81aae018caa5e0470ba827fa6d763b1d2e87d920a5f6/charset_normalizer-3.4.9-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:90c44bc373b7687f6948b693cceaea1348ae0975d7474746559494468e3c1d84", size = 208784, upload-time = "2026-07-07T14:33:23.313Z" },
-    { url = "https://files.pythonhosted.org/packages/d7/74/3c12f9755717dfe5c5c87da63f35d765fa0c00382ec26bf23f7fae34f2ba/charset_normalizer-3.4.9-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:9cdef90ae47919cae358d8ab15797a800ed41da7aba5d72419fb510729e2ed4b", size = 219928, upload-time = "2026-07-07T14:33:24.814Z" },
-    { url = "https://files.pythonhosted.org/packages/33/9a/895095b83e7907abd6d3d99aad3a38ad0d9686cc186cb0c94c24320fe63e/charset_normalizer-3.4.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:60f44ade2cf573dad7a277e6f8ca9a51a21dda572b13bd7d8539bb3cd5dbedde", size = 218489, upload-time = "2026-07-07T14:33:26.42Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/34/ef5c05f412f42520d7709b7d3784d19640839eb7366ded1755511585429f/charset_normalizer-3.4.9-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:a1786910334ed46ab1dd73222f2cd1e05c2c3bb39f6dddb4f8b36fc382058a39", size = 210267, upload-time = "2026-07-07T14:33:27.952Z" },
-    { url = "https://files.pythonhosted.org/packages/83/dc/9b29fa4412b318bf3bfea985c35d67eb55e04b59a7c3f2237168b0e0be6f/charset_normalizer-3.4.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:03d07803992c6c7bbc976327f34b18b6160327fc81cb82c9d504720ac0be3b62", size = 226030, upload-time = "2026-07-07T14:33:29.397Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/39/8ff066c672434225f8d25f8b739f992af250944392173dcc88362681c9bf/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:21e764fd1e70b6a3e205a0e46f3051701f98a8cb3fad66eeb80e48bb502f8698", size = 214982, upload-time = "2026-07-07T14:33:36.996Z" },
-    { url = "https://files.pythonhosted.org/packages/92/8f/3a47a3667c83c2df9483d91644c6c107de3bf8874aa1793da9d3012eb986/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e4fd89cc178bced6ad29cb3e6dd4aa63fa5017c3524dbd0b25998fb64a87cc8b", size = 236460, upload-time = "2026-07-07T14:33:38.536Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/60/b22cdbee7e4013dab8b0d7647fc6181120fbbbc8f7025c226d15bd5a47fc/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bd47ba7fc3ca94896759ea0109775132d3e7ab921fbf54038e1bab2e46c313c9", size = 232003, upload-time = "2026-07-07T14:33:40.059Z" },
-    { url = "https://files.pythonhosted.org/packages/ea/f8/72eb13dcabe7257035cea8aefd922caad2f110d252bf9f67c4c2ca763aee/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:84fd18bcc17526fc2b3c1af7d2b9217d32c9c04448c16ec693b9b4f1985c3d33", size = 223149, upload-time = "2026-07-07T14:33:41.631Z" },
-    { url = "https://files.pythonhosted.org/packages/b0/3e/faee8f9de92b14ee1198e9163252bb15efee7301b31256a3b6d9ebfdd0dd/charset_normalizer-3.4.9-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:5b10cd92fc5c498b35a8635df6d5a100207f88b63a4dc1de7ef9a548e1e2cd63", size = 207901, upload-time = "2026-07-07T14:33:43.209Z" },
-    { url = "https://files.pythonhosted.org/packages/3a/25/45f30093ae27dd7b92a793b61882a38685f993700113ca36e0c9c14965e1/charset_normalizer-3.4.9-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a4fbdde9dd4a9ce5fd52c2b3a347bb50cc89483ef783f1cb00d408c13f7a96c0", size = 219176, upload-time = "2026-07-07T14:33:44.725Z" },
-    { url = "https://files.pythonhosted.org/packages/48/18/c8f397329c35e32f6a837e488986f4ae03bd2abebc453b48714991630c2f/charset_normalizer-3.4.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:416c229f77e5ea25b3dfd4b582f8d73d7e43c22320302b9ab128a2d3a0b38efe", size = 217356, upload-time = "2026-07-07T14:33:46.192Z" },
-    { url = "https://files.pythonhosted.org/packages/86/7e/5ce0bba863470fd1902d5e5843968951bddf38abe4742fc97116ef4598b3/charset_normalizer-3.4.9-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:75286256590a6320cf106a0d28970d3560aad9ee09aa7b34fb40524792436d35", size = 209614, upload-time = "2026-07-07T14:33:47.705Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/ef/2473d3c4d869155be4af1191111d59c4d5c4e0173026f7e85b176e23bf65/charset_normalizer-3.4.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:69b157c5d3292bcd443faca052f3096f637f1e074b98212a933c074ae23dc3b8", size = 224991, upload-time = "2026-07-07T14:33:49.238Z" },
-    { url = "https://files.pythonhosted.org/packages/2b/f9/ef4a69ea338ad3c0deceea0f5f7d2380ae8b52132b06d652cb0d2cd86706/charset_normalizer-3.4.9-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a79d9f4d8001473a30c163556b3c3bfebec837495a412dde78b51672f6134f9", size = 215898, upload-time = "2026-07-07T14:33:56.334Z" },
-    { url = "https://files.pythonhosted.org/packages/8c/e7/5ddfd76fc061eb52de219658a4aa431cbacadf0a0219c8854f00da50d289/charset_normalizer-3.4.9-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:33bdcc2a32c0a0e861f60841a512c8acc658c87c2ac59d89e3a46dacf7d866e4", size = 236718, upload-time = "2026-07-07T14:33:57.9Z" },
-    { url = "https://files.pythonhosted.org/packages/49/ba/768fa3f36048d81c477a0ce61f813bc1454d80917ccfe550abd9f44f5e24/charset_normalizer-3.4.9-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f840ed6d8ecba8255df8c42b87fadeda98ddfc6eeec05e2dc66e26d46dd6f58a", size = 232519, upload-time = "2026-07-07T14:33:59.811Z" },
-    { url = "https://files.pythonhosted.org/packages/f4/c4/b3e049d2aa3766180c78507110543d9d50894cc97f57de543f1be521dcdc/charset_normalizer-3.4.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c25fe15c70c59eb7c5ce8c06a1f3fa1da0ecc5ea1e7a5922c40fd2fa9b0d5046", size = 223143, upload-time = "2026-07-07T14:34:01.517Z" },
-    { url = "https://files.pythonhosted.org/packages/19/79/55c32d06d76ae4feafe053f061f3e3ab70bcf19f4007797ce8c3efda7830/charset_normalizer-3.4.9-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:f7fb7d750cfa0a070d2c24e831fd3481019a60dd317ea2b39acbcebc08b6ed81", size = 206742, upload-time = "2026-07-07T14:34:03.04Z" },
-    { url = "https://files.pythonhosted.org/packages/10/e0/47c079dd82d217c807479cd59ffd30af56307ea31c108b75758970459ad3/charset_normalizer-3.4.9-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:4d1c96a7a18b9690a4d46df09e3e3382406ae3213727cd1019ebade1c4a81917", size = 219191, upload-time = "2026-07-07T14:34:04.657Z" },
-    { url = "https://files.pythonhosted.org/packages/42/ab/b9bc2e77d6b44a7e46ef62ec5cac1c9a6ba7b9135a5d560f002696ec9995/charset_normalizer-3.4.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a4cfde78a9f2880208d16a93b795726a3017d5977e08d1e162a7a31322479c41", size = 218328, upload-time = "2026-07-07T14:34:06.115Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/78/c9c71d599f5aa2d42bcdd35cbbd46d7f535351a57e40ff7d8e5a7e219401/charset_normalizer-3.4.9-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:d4d6fcde76f94f5cb9e43e9e9a61f16dacefd228cbbf6f1a09bd9b219a92f1a1", size = 207406, upload-time = "2026-07-07T14:34:07.554Z" },
-    { url = "https://files.pythonhosted.org/packages/f6/39/c914445c321a845097ce4f6ac7de9a18228a77b766272125a1ce00d851eb/charset_normalizer-3.4.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:898f0e9068ca27d37f8e83a5b962821df851532e6c4a7d615c1c033f9da6eedf", size = 225157, upload-time = "2026-07-07T14:34:09.061Z" },
-    { url = "https://files.pythonhosted.org/packages/cd/91/7253a32e86b7e1d1239b1b36ba6dd0f021a21107ab33054b53119cc083b9/charset_normalizer-3.4.9-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:51447e9aa2684679af07ca5021c3db526e0284347ebf4ffcec1154c3350cfe32", size = 223022, upload-time = "2026-07-07T14:34:17.248Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/32/2e64bd2be10e89c61e57ebe6a93fd98ae88eb7ebe414b5121f22c96c69eb/charset_normalizer-3.4.9-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cc1b0fff8ead343dae06305f954eb8468ba0ec1a97881f42489d198e4ce3c632", size = 241590, upload-time = "2026-07-07T14:34:18.813Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/ef/d96ec496cfea0c21db43b0ad03891308b02388d054cc902cf0e5a1ad6a88/charset_normalizer-3.4.9-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fa36ec09ef71d158186bc79e359ff5fdd6e7996fe8ab638f00d6b93139ba4fcf", size = 239584, upload-time = "2026-07-07T14:34:20.52Z" },
-    { url = "https://files.pythonhosted.org/packages/d4/ce/9af95f7876194bd7a14e3dfe4a4de2e0bff02666a3910d72beafd06cc297/charset_normalizer-3.4.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:df115d4d83168fdf2cae48ef1ff6d1cb4c466364e30861b37121de0f3bf1b990", size = 230224, upload-time = "2026-07-07T14:34:22.189Z" },
-    { url = "https://files.pythonhosted.org/packages/52/94/af74dde74a3996bd959c350709bfe50e297823d70a8c1cbd54b838880863/charset_normalizer-3.4.9-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:f86c6358749bd4fda175388691e3ba8c46e24c5347d0afd20f9b7edfc9faf07d", size = 212667, upload-time = "2026-07-07T14:34:23.857Z" },
-    { url = "https://files.pythonhosted.org/packages/ee/f0/f1c4fe746c395922961b5916ed1d7d6e7d4c84851d19ed43cc89980ec953/charset_normalizer-3.4.9-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:32286a2c8d167e897177b673176c1e3e00d4057caf5d2b64eef9a3666b03018e", size = 227179, upload-time = "2026-07-07T14:34:25.586Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/56/6c745619ac397e8871e2bcd3cea1eec86b877488f33888b3aef5c3ed506e/charset_normalizer-3.4.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:83aed2c10721ddd90f68140685391b50811a880af20654c59af6b6c66c40513c", size = 225372, upload-time = "2026-07-07T14:34:27.212Z" },
-    { url = "https://files.pythonhosted.org/packages/78/ad/98aae8630ac71f16711968e38a5acfecce41b778bf2f0312851020f565a8/charset_normalizer-3.4.9-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:cd6c3d4b783c556fa00bf540854e42f135e2f256abd29669fcd0da0f2dec79c2", size = 215222, upload-time = "2026-07-07T14:34:28.774Z" },
-    { url = "https://files.pythonhosted.org/packages/f7/40/9593d54209765207a7f11073c06494c1721e4ca4a0a426c597679bf7f91e/charset_normalizer-3.4.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ee2f2a527e3c1a6e6411eb4209642e138b544a2d72fe5d0d76daf77b24063534", size = 231958, upload-time = "2026-07-07T14:34:30.345Z" },
-    { url = "https://files.pythonhosted.org/packages/98/2b/f97f1c193fb855c345d678f5077d6926034db0722df74c8f057020e05a25/charset_normalizer-3.4.9-py3-none-any.whl", hash = "sha256:68e5f26a1ad57ded6d1cfb85331d1c1a195314756471d97758c48498bb4dcdf5", size = 64538, upload-time = "2026-07-07T14:34:56.993Z" },
+version = "3.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/33/1c/f41d4e74c28ab327ff3acd36053f7ea506c55872d7a90b0fa71aa3ab0c89/charset_normalizer-3.5.2.tar.gz", hash = "sha256:39de2a259fc954455c57274dc94c79d5842774e1247a016aff30bc0efed0f4ef", size = 172659, upload-time = "2026-09-30T04:39:23.398Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c9/87/2fea8c13dc24b3ca9c6f803a5b2dfdeae73eb4f9e12c7885ed908ff0433c/charset_normalizer-3.5.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b91363207bd9dc966a691e959bb47f64b30f7ac4b072be9968b366982f7db77c", size = 246730, upload-time = "2026-09-30T04:35:32.286Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/9e/09efac30b937722f46d3110ba30b875b24b2e3a266ed746cc4e376a94d80/charset_normalizer-3.5.2-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:38a873987f3be698494da8b2e3085e29da02da7b633dce73e79c699a113d7bf0", size = 237707, upload-time = "2026-09-30T04:35:33.709Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/18/70d76670b13686237863a379928d60bd10e021f17d243ab3d7014c4a5f4e/charset_normalizer-3.5.2-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:355ad8011081dec5412240c087a9a0c9d4d5039f3ed11a3f13e18c2b29b56c51", size = 273050, upload-time = "2026-09-30T04:35:35.138Z" },
+    { url = "https://files.pythonhosted.org/packages/54/e2/77a8b09d5adc013ed07b95b01b8b8fa5441c4e810e83ee7e4aae2fa4d91a/charset_normalizer-3.5.2-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ee21e28f0430bd6dc9086c6e525d5e818a44a5ad19720c8a0ef766792f3eb5e5", size = 270345, upload-time = "2026-09-30T04:35:36.502Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/c5/38806a25ab5e65fc178f39affeda20858efafede2fce1ffc2556cfc9fe73/charset_normalizer-3.5.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3d31298449090ab8d47b7b1b2a555ff73cac7ed438a08b7ac160980c7ebed649", size = 257601, upload-time = "2026-09-30T04:35:37.919Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/8d/213565184708fdb263ae55e2c04ee1ff748129dd65d48ed0e3502da9c85a/charset_normalizer-3.5.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:5cde776b7cc66e4f6c99612cea4aa7269aa65863f7a15841b2c264f103822f4e", size = 252222, upload-time = "2026-09-30T04:35:39.544Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/24/76d2cefc25472531e4c5c7dfff68865eb1c39b78482f0fdc15b46f047830/charset_normalizer-3.5.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ae4f5fea5b8b8ccff88238cc8569303e5ee95efae67fa62922a311397a71f346", size = 248482, upload-time = "2026-09-30T04:35:41.088Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/dc/65a801b66ab4c197e22c433ab25e7ac24324ac6f45a2269aca42cce309bf/charset_normalizer-3.5.2-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:f7d486c83842422badd511868fd8a9a20e9407ace71564b6af47ce7e60a336c1", size = 241206, upload-time = "2026-09-30T04:35:42.59Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/95/ca9b5eabde673002c6f1e7ada1b223916fe18f6d661da7aabd4d643718f1/charset_normalizer-3.5.2-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:11a4d68a6ecda3292cb1e50239e111543ba5d709bb62a6b4ea1afcfa729d8875", size = 273190, upload-time = "2026-09-30T04:35:44.347Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/8b/803b4d2a3f6e1740f63f1e87b04d14b42f3d4fdfe6ed7d4db2d34102b14f/charset_normalizer-3.5.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d6734d2ef8a50fbf8445c139477da401f50d62a0606bf00e20ec6d87773fefb1", size = 253527, upload-time = "2026-09-30T04:35:45.915Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/55/93c0e5dbd085ae0471346026abbe7e0db9ea2d6fea74e51f0b5a46f233a7/charset_normalizer-3.5.2-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:a815775b6c38d4e0ff7bcffbeba67feded90202bb6a226b8dd35f1c855217413", size = 271285, upload-time = "2026-09-30T04:35:47.49Z" },
+    { url = "https://files.pythonhosted.org/packages/95/69/0dbd0e0b9b16cfa816cdfcb3e2e3854a1f680dc07fb1245ea125e7448060/charset_normalizer-3.5.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:23851fb4e1b85ed3f6c2a27b777cdfe2e19fb5b38429a8faf38c7542b7665869", size = 260010, upload-time = "2026-09-30T04:35:48.996Z" },
+    { url = "https://files.pythonhosted.org/packages/78/6e/60ce52a85a7fd631ae8482ae6d74521014ca2f255892679484dc04d7ef56/charset_normalizer-3.5.2-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:d2374b62878abb00cd8309b32af6c0b715cd02dec0ca74ef12e5069bdc64144a", size = 206701, upload-time = "2026-09-30T04:35:58.639Z" },
+    { url = "https://files.pythonhosted.org/packages/36/8c/71aafad23f971afc84c2b295bc0c560739ce1dac558aad9fec22e39f3639/charset_normalizer-3.5.2-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:d376bbd28b3a8999db1a103b3b388aee6f1ddeb3e51bc2172993efdcd86e064d", size = 210506, upload-time = "2026-09-30T04:36:00.147Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/16/710ac3de2ee354e2bd1a9c94efe45a2d27b5c6ad39b2d6a905be2c094b6c/charset_normalizer-3.5.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:849df64e889b2e17230d58410a03dba311a65b163508fd33679b2b737d4b7858", size = 244624, upload-time = "2026-09-30T04:36:03.389Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/39/45c7439f5b63d24f7d5b2a1d760f34af7628782d7144b4cc8ded45c2d4bc/charset_normalizer-3.5.2-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:15c44f7edfd477b06f517a5cc317fc1707edb9de2c865f43d4b6513907473234", size = 235731, upload-time = "2026-09-30T04:36:04.987Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/34/38f3154785ce92e9f56eb226f4d35bdfae6b008480dd055f58837a89c810/charset_normalizer-3.5.2-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a89012d6d5476ee112d20d998570ed58df2260a852afb1758809cd6900411d21", size = 270352, upload-time = "2026-09-30T04:36:06.412Z" },
+    { url = "https://files.pythonhosted.org/packages/04/f3/859f74e7babc977705026b30593b3be04049632a522fb7000f83c033d747/charset_normalizer-3.5.2-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0c951d5e6dd9c2ff60609476752bee49da4206adde960ebc247766937f72e718", size = 267393, upload-time = "2026-09-30T04:36:07.865Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/85/41d27f234b82e47c167a5f6c0f62501dc0c640585ff4aba79e08a390336a/charset_normalizer-3.5.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7218e8f32b0956cfcd048fd42d9d5779809745ca1d86113ca56f66e7ae1549c4", size = 254660, upload-time = "2026-09-30T04:36:09.248Z" },
+    { url = "https://files.pythonhosted.org/packages/58/ca/5d1a997587febe5b26d8daffe363b5c1a091cece19828eec6502fd09c5ef/charset_normalizer-3.5.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a19a731138fc27d5682277d3b9df22855cea1239bce7fcec5f78f42ef2d1f3c3", size = 249996, upload-time = "2026-09-30T04:36:10.73Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/1f/d1e78246f7ed60c8c8d606b4ac27f66ce49cc3e95f24893ccbeba9f77302/charset_normalizer-3.5.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:62603db9a7caa0802eaa28c1c46fecd7b3a263a774069c24c3c28c302448721c", size = 247028, upload-time = "2026-09-30T04:36:12.294Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/37/eba316edd4f0c4d3a5d945924c4eeeae59abac4056aa815d8a4268f863a2/charset_normalizer-3.5.2-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:b6856554c4f44d79fc2307d5768854310a8f0096e501c75637542c82292b0429", size = 240562, upload-time = "2026-09-30T04:36:13.887Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/8e/aaa037d40ca9ef045977f1a661048b1aa33f223adfce3452fe9be9f79d14/charset_normalizer-3.5.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:1bc0baf5ef96b6ede57d47f4b8fe4d9d84019c3bfcbeb20a41edc6a6ee341f1f", size = 271699, upload-time = "2026-09-30T04:36:15.41Z" },
+    { url = "https://files.pythonhosted.org/packages/26/19/1c1c9f75974adf523b87f34b8a2adc5a435cd65916812bcbd0dfa45f9a29/charset_normalizer-3.5.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:56bc200a365efb37383b7852e4cc5898d3b2da5987289b543956cf8cad71018a", size = 252030, upload-time = "2026-09-30T04:36:16.839Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/90/0660ef18e18df0a4d2a1a0edff7dfbba42d4e50ef2425557a5bb7051f77b/charset_normalizer-3.5.2-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:2c9ad19a6cfcd5ea5c0d41161d22f9df1dcc277e9bef2751391334546a314c00", size = 268565, upload-time = "2026-09-30T04:36:18.468Z" },
+    { url = "https://files.pythonhosted.org/packages/79/ba/57adc269824e8658f1a0f97a9e514c247445a9632b3419b97e0ba37f16dc/charset_normalizer-3.5.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e243bd13217235fc7290c621941c3f5cc8b66e4872495be821d7436ba2fb838d", size = 257627, upload-time = "2026-09-30T04:36:19.938Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/85/33abd4315c052d3d4f54c92b1ee49bfbc0dc7115a981e462a793b6d2ab87/charset_normalizer-3.5.2-cp313-cp313-pyemscripten_2025_0_wasm32.whl", hash = "sha256:a090bb2c68df85450502e3e20d665e3a5af9c65a84d6508ed477badd49166fd3", size = 143407, upload-time = "2026-09-30T04:36:21.376Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/6d/9a08d7e0b29b7208e2c6c01dc56c8e0520e7c7beadbbfb024b58fd69c8a5/charset_normalizer-3.5.2-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:7967d08cf06dee78443b874f98c98036f624f3a4e73e11f9f64f5be4d25393cf", size = 206783, upload-time = "2026-09-30T04:36:30.872Z" },
+    { url = "https://files.pythonhosted.org/packages/82/44/b0aa350280e6ff5a5492d17cf10460dd39d5ee848f872f7ba2df10607f60/charset_normalizer-3.5.2-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:4c2b5031f63e331e3839b40aed2dd6f191e9c07edbde303e7876846ea1946995", size = 210449, upload-time = "2026-09-30T04:36:32.625Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/72/9c5e7707b57c8ddfa9ddf7b0b1d009d7fbab9e9e887d5b721060f37e307d/charset_normalizer-3.5.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8d86d6fc60743dc916eb79e2eb1ec4818e21e427731543af40a3021851174a13", size = 247095, upload-time = "2026-09-30T04:36:35.803Z" },
+    { url = "https://files.pythonhosted.org/packages/83/09/71e453691e927de4ddf792770cfaab3f49d494e222f66ea5e404bbd5e39c/charset_normalizer-3.5.2-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:7a881931aa470808df94a8c380eed2bbbc76cd9dc622310f99665658c821eb6d", size = 233942, upload-time = "2026-09-30T04:36:37.407Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/86/85c84e4da8b27dd409577d9437926ff581c5f9d3c66038dc68c1a526de51/charset_normalizer-3.5.2-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8024d00c3faf3fc0c16e07a69f4405e8eac7cc0ab15f65fe6cf43827c4cf72b4", size = 273762, upload-time = "2026-09-30T04:36:38.904Z" },
+    { url = "https://files.pythonhosted.org/packages/92/08/564955a4b5f2ccb410ab480bbe8c6a18063ff27f2d35458731c4a5335df9/charset_normalizer-3.5.2-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:4d48f2d08b9de5864e2c8744d4461b862fb149a18274abc8b698c45975573438", size = 269514, upload-time = "2026-09-30T04:36:40.469Z" },
+    { url = "https://files.pythonhosted.org/packages/18/24/bad3ac4271589df29cf5ce2f5ae490518a5739358052bd0d61209e6fea54/charset_normalizer-3.5.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34276fd796040bf0993ab33a369aa572e6979c7aab225a88893667ad8eac8f7a", size = 255056, upload-time = "2026-09-30T04:36:42.02Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/3e/350d89ad49916b86554d6f5f2d03ec1152148f87e5ff735106c6a03b1a36/charset_normalizer-3.5.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0521c5665880b33d603717defa76c094048900010897909952397feb3039da56", size = 251311, upload-time = "2026-09-30T04:36:43.577Z" },
+    { url = "https://files.pythonhosted.org/packages/56/5b/4970a2d154df502e133402906dd04e3ae7cada7b3011283c88d0479a2585/charset_normalizer-3.5.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:eff0ac9dbe711a4aee69bf04a83896aa9b85f19641264053a9f6d48573abb7dd", size = 250190, upload-time = "2026-09-30T04:36:45.185Z" },
+    { url = "https://files.pythonhosted.org/packages/88/8c/f1a91bddc8fb47c2889e29ea7ea49a194eb0d9868675d786806519c00d76/charset_normalizer-3.5.2-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:1503bccbeb36d5527790c3930327704c39af22de3112f1b1666a9f3ce15ee204", size = 237098, upload-time = "2026-09-30T04:36:46.689Z" },
+    { url = "https://files.pythonhosted.org/packages/24/0e/bb5dace3cc7e79068425386a6589c19b5a2ab5fefc2a46abea6919683332/charset_normalizer-3.5.2-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:52aa6992700996af31f375de0c6bacd402b0097fe40b53c426b9f51a90ebabc7", size = 274228, upload-time = "2026-09-30T04:36:48.31Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/79/b849ad523017ea9f5a45581bbebed91439e0cf42fd2860a6f64e358eb5a6/charset_normalizer-3.5.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e09a3942ecbdee5cce73ea9d42da82b81b72ac1bf031ce069b93b5adf4eac8cd", size = 252401, upload-time = "2026-09-30T04:36:50.091Z" },
+    { url = "https://files.pythonhosted.org/packages/89/8c/75469d690cf47200bce8f6cad7655724fc23148e147abfc5ce78b5f65863/charset_normalizer-3.5.2-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:c7c9ab723cde841fefb34efbad91e87f00a674b1fe1cd0784fde742bf2c154dc", size = 271846, upload-time = "2026-09-30T04:36:51.719Z" },
+    { url = "https://files.pythonhosted.org/packages/26/cd/6d52d3c7437cdcf2e310ce9f28f282e733d4ef60ed19105d1819c356255f/charset_normalizer-3.5.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ddc7dacc8ece3a182e7f15cb862d1fd616b46d076cb1ae9dd232b2c38b655874", size = 258151, upload-time = "2026-09-30T04:36:53.234Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/4c/070b38bdb5f49a70199fce923ec0726a49536a63ab262abbfcaaf351110b/charset_normalizer-3.5.2-cp314-cp314-pyemscripten_2026_0_wasm32.whl", hash = "sha256:ee43c17b173d46a3212baa6ead3ae258eeabdae48c263a01ccf0218c366dd655", size = 143570, upload-time = "2026-09-30T04:36:54.816Z" },
+    { url = "https://files.pythonhosted.org/packages/12/c8/8379554b42e8368161d898476686947a0fdbd3e8865170d7909dcabfdee8/charset_normalizer-3.5.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f0aa869112ef88429ae17820d99c3dd9504c9e9c671d3c246f3d7442cb051084", size = 249711, upload-time = "2026-09-30T04:37:03.111Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/eb/2ddb1035d17320caa9f41682935123a9a250277b261c3efc86b2d2a21343/charset_normalizer-3.5.2-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c0afc6800ba57ccc350374c5bd6150419915d95ce93cdbab2d783d75eaf30ecb", size = 231146, upload-time = "2026-09-30T04:37:04.721Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/24/2ecb4bde104322cd7859d6594fcfa74649f8d90b3221c9feecbef149875b/charset_normalizer-3.5.2-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7dcd882da75ef9adf94903b1e3b9419e8aa8fb4c7396822b834b9ef7fb96954f", size = 271552, upload-time = "2026-09-30T04:37:06.295Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/98/9d5f6ebc3aee9fef5d30b4aff11fb2ab7a1222b4064f8ef2c7c87cde217a/charset_normalizer-3.5.2-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2e06a3a98f916dd41d27f3105e02e7a40181c98c94b9158733d03a6f80506c09", size = 269810, upload-time = "2026-09-30T04:37:07.905Z" },
+    { url = "https://files.pythonhosted.org/packages/09/e1/a3b06a10461b1b7628853c934c644e03bc28e42767116afb52f19a56519b/charset_normalizer-3.5.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6bd128f206a7752ae1f2ab6c61bf8a24ba28913a10df8b14c2637b973ff97a80", size = 259845, upload-time = "2026-09-30T04:37:09.554Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/d3/6f561f74a296cf27d61775a1dc665ad13f3bff6a798810ca05907f37a7c4/charset_normalizer-3.5.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c8f3d67aeaf55f017982b73683f0e7342ba2f6635a78f69ce89ebb26aa411e5c", size = 252825, upload-time = "2026-09-30T04:37:11.274Z" },
+    { url = "https://files.pythonhosted.org/packages/26/9f/69e13ca3b18f43e0eafcd34c04a45b732ae22a43b54a5fc9e119103356eb/charset_normalizer-3.5.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:fe9753dfee015c570d73df76f899f18444d41388bffcde097deba51c4fadbb9f", size = 252659, upload-time = "2026-09-30T04:37:12.941Z" },
+    { url = "https://files.pythonhosted.org/packages/73/a9/ace29806a0dae18939919c76ba526472d83214afa101105fabff2cf30625/charset_normalizer-3.5.2-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:92888bb3187c5ba50500b00b3b310c9f2c651709d28036077680cb5255450a03", size = 238819, upload-time = "2026-09-30T04:37:14.659Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/c1/6116d52a2e3311ec80f21f5fb5e17b27405f10b9608af8f6e69516841a1b/charset_normalizer-3.5.2-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:d008d90a7f2471519aef0c90dfbe73b3e6e4d5e66ac48e19154c17e89e98b604", size = 272973, upload-time = "2026-09-30T04:37:16.346Z" },
+    { url = "https://files.pythonhosted.org/packages/19/aa/9955c7e93bba10a9c7e8f7a5031b7ced66f3a1883a55c00712b8d5850ff3/charset_normalizer-3.5.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:31f3930700408d211f13378ccbe1c40845d8da54bd0681fac3a9b5aae81c7aa8", size = 253692, upload-time = "2026-09-30T04:37:18.212Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/33/2a6ae7fdc1b10cb581cef91addd8cdfc5f40d50abb5702309369d5834579/charset_normalizer-3.5.2-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:2a925889534b3748302dae5dead07cc13480de1dac3aea80a941b729b471ef93", size = 268757, upload-time = "2026-09-30T04:37:19.877Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/22/80992720a0282cd39bba1db35868e6b9c22f41281160143a836544bc1d8a/charset_normalizer-3.5.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f5ec61164adcec446f8969a3358ec3f9b26bbda3b9213e5586d219afa8df2915", size = 262220, upload-time = "2026-09-30T04:37:21.583Z" },
+    { url = "https://files.pythonhosted.org/packages/60/9f/a5d1c91c0263745e2cd344c5a4415d787c575501ab1d449f1148ac6b495d/charset_normalizer-3.5.2-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9409a8bf35cf78353942504b24a57de3d75b708997a1e4bd8db71ac8633ce364", size = 248414, upload-time = "2026-09-30T04:37:30.167Z" },
+    { url = "https://files.pythonhosted.org/packages/26/79/e697f77464748a3ee3cf490c83d592459400d4898380d66c38366b03080c/charset_normalizer-3.5.2-cp315-cp315-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:498dc3188ca05a68231ac3fdbfc7f57eb67e1343c30e0fea17f8218c1599b253", size = 244775, upload-time = "2026-09-30T04:37:31.964Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/87/3d42a42e18ea066e2513936fd678a00696e77878b5ae04528976abdbcb83/charset_normalizer-3.5.2-cp315-cp315-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e242bb1c5e76e97dfa9e7f209a71e93a01d7f19ffdd5cfbb2e2d55b4f08f8ab0", size = 273877, upload-time = "2026-09-30T04:37:33.661Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/76/8a28136f3938ba9836f84280ce0c4d61ed1cf15a036b2034900c62634162/charset_normalizer-3.5.2-cp315-cp315-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:def79fa35ef0cef8d2accec024f4fdc7ead3012ff02f5215c783f39f03ef8cfc", size = 269455, upload-time = "2026-09-30T04:37:35.573Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/a1/4fbf5d0f0f1b2a080474c1cf9a2f12c4c6531bb0e8ba591055e846d2b4e9/charset_normalizer-3.5.2-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3df041de8887954562c9b261cba85ca0e9ded74048daf125f45edcfaa4832229", size = 257336, upload-time = "2026-09-30T04:37:37.397Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/a2/8b50aa320adb880ad579518e6f718f24944804b42a88b83d267d5d444125/charset_normalizer-3.5.2-cp315-cp315-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:04851f73ae72b8413dddadb16a49dfee95263553741fd42d546f7d66907e6be5", size = 257584, upload-time = "2026-09-30T04:37:39.522Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/57/50e3fed84e175f40349bd0da7a4fce94c87f0378f52d74f511d89e0bdc20/charset_normalizer-3.5.2-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:183b88127acdb4fabe59d951ab424faf1af7b63cdbb5f776186c1ea2ffcaed98", size = 251854, upload-time = "2026-09-30T04:37:41.23Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/54/f7fbb3493c9f49091213b9c2d6dd65800696f1ce1a3f196a4205f50417b1/charset_normalizer-3.5.2-cp315-cp315-musllinux_1_2_armv7l.whl", hash = "sha256:16fa0eccf81304b79c5cd87f9271c3b85dd9dd99245e4422ae9c0dd45e0f99d3", size = 248767, upload-time = "2026-09-30T04:37:42.883Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/37/b3a6385acc5a1e45b39ae9c90bfb9cf838a09b9dd37ef2740ab4c6b4a2eb/charset_normalizer-3.5.2-cp315-cp315-musllinux_1_2_ppc64le.whl", hash = "sha256:7441d755b7ab94f8d4eb3e43ec05482d760842fd263d003a99102d742cd835e2", size = 274479, upload-time = "2026-09-30T04:37:44.658Z" },
+    { url = "https://files.pythonhosted.org/packages/89/44/809913e2cfd279e635a9294fdbbfb1b1dc62a8189d473d561f649fce98d8/charset_normalizer-3.5.2-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:ca403d7e4798f525fdfc78e258820419cbbd0f0ecbab9de7840e3c017cf6b8cf", size = 258310, upload-time = "2026-09-30T04:37:46.529Z" },
+    { url = "https://files.pythonhosted.org/packages/af/a2/f28400ab13359d91bd39179df8e149376b9bf36588e739a3a4f9de2b84b2/charset_normalizer-3.5.2-cp315-cp315-musllinux_1_2_s390x.whl", hash = "sha256:df29a0a7107f7011e77f4eebdddec4c7331e24d787a0b21a46d63bdf7445da95", size = 271100, upload-time = "2026-09-30T04:37:48.399Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/89/9bab37955edf0adb3b66f8a3a6617d9f2f487e0d56f295a6a286cb640aa6/charset_normalizer-3.5.2-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:f3c96f633825733f735c5a9cf21d21a257d8e1edf0b1cee0a064b9c424ca0f7d", size = 259936, upload-time = "2026-09-30T04:37:50.023Z" },
+    { url = "https://files.pythonhosted.org/packages/04/3a/c00b50e94c964cf934c7899cd47c97952fc11dad71cc5884b3c61795b09b/charset_normalizer-3.5.2-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:65cd72beeeca9d3aaea1201e5923859f308f952f9c71de93f06063c79f0f7a3b", size = 250114, upload-time = "2026-09-30T04:37:58.607Z" },
+    { url = "https://files.pythonhosted.org/packages/50/27/d102dc880bbcffd0479ab64dfc1fb96777a854355a55e2bda72a71efadcb/charset_normalizer-3.5.2-cp315-cp315t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:b7fd005a73d9e657273b7a10dc71a9e03c8fb9ee6999798d6918ce095b81ac7f", size = 239997, upload-time = "2026-09-30T04:38:00.511Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/4a/bf7ef45794dd293fab5f98a9309817977fbb845b9998f171b8cc5d8437a3/charset_normalizer-3.5.2-cp315-cp315t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e54da4baf05720032d527874d40b65fa4d7e5c6c6a43d0c3adbeffcaf275a2b3", size = 272313, upload-time = "2026-09-30T04:38:02.509Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/ee/008a2837737991474c5754bb3191010007663860979701990982a502cbaf/charset_normalizer-3.5.2-cp315-cp315t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:124fbf1a8ff966d87ae05bb8bd45a71f966055ed8bba320d0c7cf450bc5f4d0e", size = 269986, upload-time = "2026-09-30T04:38:04.435Z" },
+    { url = "https://files.pythonhosted.org/packages/93/ad/bd74a283940dc910c5b14f8e4f80a248082bc9c0fcbe1f54530cb6d9cc5e/charset_normalizer-3.5.2-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:28b4f0d66fb834ff90f28209ac7bce77868c45d8c93e26f906709d9b7c2e1af9", size = 260202, upload-time = "2026-09-30T04:38:06.549Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/7b/ed341c66f69f688723501fac752be3d63c7159ca0d0d4174fc611e5710bb/charset_normalizer-3.5.2-cp315-cp315t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:58ca3755ee7ff7f59b57789ec9833c9de9ea275405cdd240eda1f193112e398a", size = 259083, upload-time = "2026-09-30T04:38:08.311Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/9d/e41588b777965e5031a43128a1e96173ebb35ac75fc53ec3b517e7c21cd4/charset_normalizer-3.5.2-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:443eae2bf318abeaf6f15d785138f71fd6de770e99a92158b8b814265e079115", size = 253081, upload-time = "2026-09-30T04:38:10.402Z" },
+    { url = "https://files.pythonhosted.org/packages/81/35/b761eb6d8c1eb218b9b42b9b4d5ac902afdc399fb6dac6f9a9aac7bda589/charset_normalizer-3.5.2-cp315-cp315t-musllinux_1_2_armv7l.whl", hash = "sha256:58f361dcbab699cf8f42db3f47c8e7fd1036f138c23a5d08de9fde5f425a730c", size = 248712, upload-time = "2026-09-30T04:38:12.317Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/2c/147169a041b747759f37405c0a97157e8e92de967968373101ff14915cba/charset_normalizer-3.5.2-cp315-cp315t-musllinux_1_2_ppc64le.whl", hash = "sha256:1b4cbc7c3491ccb4aa17fcd8165649d01cf39f76de1696da8631b5f71b85401d", size = 274176, upload-time = "2026-09-30T04:38:14.138Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/2d/0ff8db0d373ba8538db686db11cd7e8912031490b9e4f383b41912e8d594/charset_normalizer-3.5.2-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:ba0b1d2620edf869789c3879223f52bf2afc5d31b3cb47cc57b3a12c05e2aa9d", size = 259413, upload-time = "2026-09-30T04:38:15.841Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/8e/b4a085fb47c9d3a7e43576a4784fdd8fe23f907514a972de8086edaf7a48/charset_normalizer-3.5.2-cp315-cp315t-musllinux_1_2_s390x.whl", hash = "sha256:5e2b6b57e9733d39f0c9fd3185efa6b8e29652c4cd8fe94180272cf6ed9a78c4", size = 269434, upload-time = "2026-09-30T04:38:17.626Z" },
+    { url = "https://files.pythonhosted.org/packages/83/1c/d8d8d7322a7c3eecdf3237a4a419cf41d2eaad8e006ce7dfdd9d4c8fa2eb/charset_normalizer-3.5.2-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:51cf45226a9b588d0d2b4880c62d686934b63ab0bd79ca23ab0e9762eb27441b", size = 262716, upload-time = "2026-09-30T04:38:19.214Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/84/371eac6b30bdbcbf2d632a1a01809103459216fcaae61b8b8d922c1bfb8a/charset_normalizer-3.5.2-cp37-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:1c50fe28bbc2ced33386f298650d91218076c05420e6cbd790b913adc41659e7", size = 253276, upload-time = "2026-09-30T04:38:28.032Z" },
+    { url = "https://files.pythonhosted.org/packages/43/6f/c4fbae58febff71709c51bc7e18fdfa55341dc382704740f9f0cbf03817b/charset_normalizer-3.5.2-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d19fbd981a488e22cd04883659ca6b08f50b5974f9fd7c95655ef6a043e5893f", size = 241239, upload-time = "2026-09-30T04:38:29.732Z" },
+    { url = "https://files.pythonhosted.org/packages/61/71/458c3f42164a07d0c5210798e9e704b39e540a6793b05aba67f3a35243a9/charset_normalizer-3.5.2-cp37-abi3-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0fed1d06615f022ee3b13caf5e8b180cfea32bb2c5aded8a9d44277afc040f93", size = 231121, upload-time = "2026-09-30T04:38:31.462Z" },
+    { url = "https://files.pythonhosted.org/packages/09/54/ab9e89367076f6331bb6c65c4bf14a5361fa5191cb6561bf534f18504e1b/charset_normalizer-3.5.2-cp37-abi3-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:838dcc90063569a0448120554591a1d6c4a4ffe11babf048908793154ab86ade", size = 260350, upload-time = "2026-09-30T04:38:33.239Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/c1/061431ecc688d9d76602502cb57cc01e691e682c18f1beb45f9673b5bbd2/charset_normalizer-3.5.2-cp37-abi3-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2ce45c6627b22c47e390bc91a41c3d13032192e699fa0bea96e9671b373d69b0", size = 255430, upload-time = "2026-09-30T04:38:34.865Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/1f/20c8949f0676f7ab811abdeb7f4d7f1cbc6e61ff20bef08b44edeb092bc8/charset_normalizer-3.5.2-cp37-abi3-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0774bf9bf620249fee3e0b8b9fd3065de213be30f3aa94ce2494b3b638949e26", size = 250612, upload-time = "2026-09-30T04:38:36.649Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/9e/46f2fa4c431fc98c4ae76a8cb5bdca54e0341e3cfc3fcfd8e82740250818/charset_normalizer-3.5.2-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1db38f4c5496827c1a501846d64d14c3b80c7e6714e406cd7dc36a9899fa1011", size = 242083, upload-time = "2026-09-30T04:38:38.26Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/39/559be29a0c0f086e0bba6922babd38916cc5e0b58ced4de13ee01ea05508/charset_normalizer-3.5.2-cp37-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:304d8e4d493af723536393eee0c689eb7813f4a474c8b479dee63f1fdd98f621", size = 232738, upload-time = "2026-09-30T04:38:39.81Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/6c/387b0e4f756a282831c1d9fc6aeb6c51ca4507ca202767c8de15ce9b12e2/charset_normalizer-3.5.2-cp37-abi3-musllinux_1_2_ppc64le.whl", hash = "sha256:9b7f416ff0978e2f2249330527f0ad6fa02f4932e6199692d3b52da2048c19e4", size = 260703, upload-time = "2026-09-30T04:38:41.346Z" },
+    { url = "https://files.pythonhosted.org/packages/96/92/1fdf015f09ef449f50d3ac4b67c90887c9c318b727daa95cc4f866e6521d/charset_normalizer-3.5.2-cp37-abi3-musllinux_1_2_riscv64.whl", hash = "sha256:01077390b03f7988f11d700a2194e69b119741a86b1a638b1db88891e3eced8e", size = 247622, upload-time = "2026-09-30T04:38:42.937Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/3c/8e7b8a5671ad5d433669fb2a76f1a0164df2d9b1718b0206bc2a16d840cc/charset_normalizer-3.5.2-cp37-abi3-musllinux_1_2_s390x.whl", hash = "sha256:7e841fb9010836c992c9f12fcbd43a831de93a5f726fc1ccd8ca1d0268c5014c", size = 257500, upload-time = "2026-09-30T04:38:44.604Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/f0/45b579df5cabc1d5d53ea1cc35e8437d3ca768c0acccc7041517cb6fbb32/charset_normalizer-3.5.2-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:9cae88599c7219005d879f98e5ed53341e9a122af585e1091200358a3003d2a0", size = 255100, upload-time = "2026-09-30T04:38:46.289Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/ad/d07d7862a62ffa6d79d68074d14823243dd235a77c45262acbf6adeb28bf/charset_normalizer-3.5.2-py3-none-any.whl", hash = "sha256:b6b751274acb69d77b3323d6b7dbaa3c7fdfc1eb829b7eb61d262f32e1af9685", size = 68872, upload-time = "2026-09-30T04:39:21.828Z" },
 ]
 
 [[package]]
 name = "click"
-version = "8.4.2"
+version = "8.5.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/76/d4/81420972a676e8ffea40450d8c8c92943e7218a78fe9b64359836cc9876b/click-8.4.2.tar.gz", hash = "sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6", size = 338000, upload-time = "2026-06-24T17:45:15.148Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c7/0e/7fa0ef50764b67090eca4114772a2abf8b6148198475e54c660b97caeee6/click-8.5.0.tar.gz", hash = "sha256:ba0d2089de75ea0310e2dde03160e6ca10009947fb95a182f9b54021bb272e34", size = 382235, upload-time = "2026-08-26T13:33:14.56Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/e2/79c688af8b210d232694e31e59da9f6ec747bae31c3f5946e4e9b98860d5/click-8.4.2-py3-none-any.whl", hash = "sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76", size = 119243, upload-time = "2026-06-24T17:45:13.73Z" },
+    { url = "https://files.pythonhosted.org/packages/58/50/6c0d534c5f134586a8e1ba4e330569e32f057e33372ae556463212fb4cd3/click-8.5.0-py3-none-any.whl", hash = "sha256:255bc9599cf7748b4b1a446ccc735421bd08a2ae529a8b88597d3de5664ee360", size = 125251, upload-time = "2026-08-26T13:33:12.928Z" },
 ]
 
 [[package]]
@@ -473,43 +538,52 @@ wheels = [
 
 [[package]]
 name = "contourpy"
-version = "1.3.3"
+version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653, upload-time = "2025-07-26T12:01:24.155Z" },
-    { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536, upload-time = "2025-07-26T12:01:25.91Z" },
-    { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397, upload-time = "2025-07-26T12:01:27.152Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601, upload-time = "2025-07-26T12:01:28.808Z" },
-    { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288, upload-time = "2025-07-26T12:01:31.198Z" },
-    { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386, upload-time = "2025-07-26T12:01:33.947Z" },
-    { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" },
-    { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" },
-    { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" },
-    { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" },
-    { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" },
-    { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" },
-    { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" },
-    { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" },
-    { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" },
-    { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" },
-    { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" },
-    { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" },
-    { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" },
-    { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" },
-    { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" },
-    { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" },
-    { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" },
-    { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" },
-    { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" },
-    { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" },
-    { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" },
-    { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/83/5a/a55177dd22553a277388e8a1b3220e92de91bacb28356cdc73caa240121d/contourpy-1.4.0.tar.gz", hash = "sha256:20156f5a1ac4f8ce02656e39a61e82164a3d359796dc8026f75b062783d500e1", size = 13323726, upload-time = "2026-09-11T19:05:05.808Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0f/cc/cb989599eec12fda312e127eb8e04a8b21a7a6c94bf7ff1bc68273334a42/contourpy-1.4.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:43c3ccbb32c6294b183dcc8e8c46dacf5ecef809497e3d48a5be298eef185ad0", size = 356332, upload-time = "2026-09-11T19:02:51.408Z" },
+    { url = "https://files.pythonhosted.org/packages/47/fb/6f620d7602507817b0b4c21dd790ed68688f1f4ae9c29aacf21f08db5cf1/contourpy-1.4.0-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:86d05cec773c9507a3950122e0e40ce77c23c75ceeb2fc189514e71893cbb34b", size = 405878, upload-time = "2026-09-11T19:02:54.093Z" },
+    { url = "https://files.pythonhosted.org/packages/32/4e/693c6d6bdece679f0953775eef3a1b66d56be55478d936e7deb1f3004422/contourpy-1.4.0-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2deb580178ca19437a84bd77e4bc2cd91a8ccad212413a83c273900868b5978c", size = 408296, upload-time = "2026-09-11T19:02:56.528Z" },
+    { url = "https://files.pythonhosted.org/packages/78/f9/b6831508960d559581c448532ba4df312217072975486dbf2b24fcc5b76f/contourpy-1.4.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:875f42444c9cf48d56f724f2637e60d0f73b3b12c9041e1484580a233edf9591", size = 383243, upload-time = "2026-09-11T19:02:58.638Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/21/52903825a0ae7bb625e8bd30a09816d4c3c5d6174a469c10a616166cf780/contourpy-1.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f863c6100bf926cf47d13f3cd75f9bb8ebd98aaab230eeb4468b91f98f39a6b3", size = 1357306, upload-time = "2026-09-11T19:03:01.036Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/79/d68b1ce8539e4071518fed9523f558398f34dcd078b8927b109c72dad2ef/contourpy-1.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3863ef2e2b13fe93f8c0ebb08ee400cb07153b8b0e91c5acb26e4537f283634f", size = 1427243, upload-time = "2026-09-11T19:03:03.866Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/25/97001d1fdf940e6f5adfc592d4f5899fff95daf7dceead3988fe09987d2d/contourpy-1.4.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c0e07c691f3b3321913ed9b8161c50ea5f77fadd006f52f5e755359b0dcbfc5", size = 356675, upload-time = "2026-09-11T19:03:15.374Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/5e/9a7241a89e2f74402aefa2569e1349ad2750ff77823c04c788eaaedb7fdd/contourpy-1.4.0-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7f861af508fca2384eef392bc1d8377cfa349b2b854cb11b45d89c75780e2561", size = 406990, upload-time = "2026-09-11T19:03:17.44Z" },
+    { url = "https://files.pythonhosted.org/packages/36/81/3a4fbbf6a806eada5453a6d16f6c7d96b9f4b3e57a3127368fb545986b8a/contourpy-1.4.0-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a03b32c2e7eda8b17757c022162c13c86f5c3d6f937dddf9ba3c3ff7b513953b", size = 407271, upload-time = "2026-09-11T19:03:19.523Z" },
+    { url = "https://files.pythonhosted.org/packages/68/77/8f93ecbde1a4d11dfd460cd19a0c159f5487352e751fa87f5d78cc9db08e/contourpy-1.4.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f1219a8898523cba821085f2da8a1b962cc696325763f09d7f93538ba43b2d70", size = 384707, upload-time = "2026-09-11T19:03:21.208Z" },
+    { url = "https://files.pythonhosted.org/packages/05/07/e5cdf94d68961ee464de6b4fedd3d2f666584f59ccd124bb1290597122ab/contourpy-1.4.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:20da2f86bfaed60dbba729fe738c8241b1dd22b6cf7e8cbbf30df290bd04ba28", size = 1356741, upload-time = "2026-09-11T19:03:23.621Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/8a/2301d07accb257ac08daeb631875fada06fedd019c20ff7b00ee07e9d211/contourpy-1.4.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6ced1670fafee703b8277ab1645072a226e74f167166eae146fb743916119b67", size = 1426793, upload-time = "2026-09-11T19:03:25.955Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/5d/a62648f06259d15c139b14eb8cf3c58fe53cb970a55f2bb77d1070e80a41/contourpy-1.4.0-cp313-cp313-pyemscripten_2025_0_wasm32.whl", hash = "sha256:abd0f7e51ecc52bf8c95713bd80b1c17c516a71fa391c54475308a4377903c2b", size = 117961, upload-time = "2026-09-11T19:03:27.713Z" },
+    { url = "https://files.pythonhosted.org/packages/35/da/5a6562febc994b2c4bf9d01e57a50458ef8a2051bb3e7b35a00333e0fc26/contourpy-1.4.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:404dbcd9233513dfd1323b66ea593fef90e899f658f6b8a9ed8e93bd0ca669db", size = 357819, upload-time = "2026-09-11T19:03:39.058Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/99/7b358ce888aaa426f755daa096c99c59d17c352037344e836f20e5d3b957/contourpy-1.4.0-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:61624f6722480aa7e168fd746164e7bfdf48f8ccb6542f1613741200dc37e2b1", size = 407795, upload-time = "2026-09-11T19:03:41.293Z" },
+    { url = "https://files.pythonhosted.org/packages/be/02/34d7548adf08c60967435d79d7a90882eacc84f5093435287974c7e99943/contourpy-1.4.0-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2e741a39dfc96babe1722561e81351aeec104526c57dbe91c167c1db606c2d55", size = 408552, upload-time = "2026-09-11T19:03:42.728Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/8c/9677dad226e5aaac5b823d5b09d881ebc32ff1648af417597649841e516c/contourpy-1.4.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e439ab450c93455feb0218532ded3e946ec7a9b5f459069f122cfa44b89229f6", size = 384850, upload-time = "2026-09-11T19:03:44.402Z" },
+    { url = "https://files.pythonhosted.org/packages/08/f5/e3fbeaa489c223629fbb434737cb3de4ecaca9cae4825e77b92490f640f8/contourpy-1.4.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:417be048f7e122cefbe2a34c51a1f2b0410f8eb35796f794d45475ddb7872d9d", size = 1357601, upload-time = "2026-09-11T19:03:46.746Z" },
+    { url = "https://files.pythonhosted.org/packages/88/be/55fec293d342c222ed58a5d4056df4362e192af2e59527a3d5d822de8828/contourpy-1.4.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:43d80072a32299bf945de0a6dd4e034ea0162e832261de2e07a274a5adbba9c9", size = 1427340, upload-time = "2026-09-11T19:03:48.745Z" },
+    { url = "https://files.pythonhosted.org/packages/86/40/0faabf453edf59b0e3633381d7ca72c3414fb9b72b5cbb08b06ed792d3e5/contourpy-1.4.0-cp314-cp314-pyemscripten_2026_0_wasm32.whl", hash = "sha256:fa1b787362a3856e63dd2b89449f6c384d55ee1beb8f94f4bcc871b40f02462c", size = 118355, upload-time = "2026-09-11T19:03:50.047Z" },
+    { url = "https://files.pythonhosted.org/packages/95/b0/390915f9af14c1e2d3b2a9ad99d85b98df4352346a51e368cbb825f813c3/contourpy-1.4.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ddf5a2596d716fd3793434844caf31abc7a40b1e8718431420c89858268fb909", size = 348996, upload-time = "2026-09-11T19:03:54.594Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/85/e0952576d54322f3e9983b1e049a7a31f42ce82c5c29d01821e6dd3780d7/contourpy-1.4.0-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:dd59df9e2fb0aff8bd7fd1adb9f349b7c835245a9d0f18c2f2deeb537190f2b1", size = 400272, upload-time = "2026-09-11T19:03:56.822Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/7d/f13543a5e1598e4a4ad01d9d5e2d65f9c09c37660616429913d606c5977d/contourpy-1.4.0-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b7794ea07cab575633daad8d6e963b662053391022294d3aacc1d9ba9ef54114", size = 412895, upload-time = "2026-09-11T19:03:58.487Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/a8/6e5eaff53507dfd2dfc428240eecda32453067277d4b3e7ca71210b2d611/contourpy-1.4.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a1c8a74744fa746eeaa12f0f9ed7f8b4add9d8f1b14d95e3b5e133675eac888f", size = 377060, upload-time = "2026-09-11T19:04:00.122Z" },
+    { url = "https://files.pythonhosted.org/packages/16/a3/d00e44d35511b3cead5bd794ee672d4eecf30d9de4ceac57a49d6204064f/contourpy-1.4.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:107ec46f7aa1664266d69b1181aadc953d58d39f7d8d648fa5b795d4a060b0de", size = 1345688, upload-time = "2026-09-11T19:04:02.705Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/01/1e533b0cae32c8edd63fe40435ec3b29345dab87321afcd28722d7f957d1/contourpy-1.4.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5a4c89c7f38a0d7a94356d74e3391073c4325c4d47ad2fd065c3fe7dbae16c0c", size = 1416140, upload-time = "2026-09-11T19:04:04.77Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/90/957549e3b03c407c670e3e191db63f4aef60dab6605bc967af3a806b543e/contourpy-1.4.0-cp315-cp315-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5545ff38229c15d11d822fc6f7415b932da9f4975f53e305b7b96ebfb01f889c", size = 357820, upload-time = "2026-09-11T19:04:20.077Z" },
+    { url = "https://files.pythonhosted.org/packages/56/4c/fa8de87d907b41b05d03f4945a51675cd67319650d01b4c927d209ffa6ee/contourpy-1.4.0-cp315-cp315-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:630d32f06cedf37b7f1dd2b6a12e4492826bd477a4c9c7b2e2fee6ce3a2cea76", size = 407804, upload-time = "2026-09-11T19:04:21.781Z" },
+    { url = "https://files.pythonhosted.org/packages/04/e7/96bd4f4a4ae285727befbdb4b332d53a2654cc5da4585d2525b47a063e1f/contourpy-1.4.0-cp315-cp315-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:be85d160e7c795113c2a93254dde2fc7e86b375322570eee78b420969426973c", size = 408559, upload-time = "2026-09-11T19:04:23.432Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/94/39660e8dc309f96bf7971eb4ec5659f272f84e42b34c9990a96ecce8442c/contourpy-1.4.0-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:018227e134b73090b06f911e773a526c77c9af426be29065500ce096a5accd4c", size = 384861, upload-time = "2026-09-11T19:04:25.198Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/7b/411dccbf73d5d19da49bf7735786b3baf4ddb1456412d48ae4d4ab1fc761/contourpy-1.4.0-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:4bffcb2e8cc5c0e837631acab59dd04265fdac376550e7c3e310523877c68cb7", size = 1357612, upload-time = "2026-09-11T19:04:27.614Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/39/bf2442130e7d5aae75a4de4504f0c8aca3ef3da3feac3940ee27ca985d83/contourpy-1.4.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:cc87f2ffa84a49a77d76cb792f28a48ff3b05a222aa0b2bddbd839ba78e1d5ca", size = 1427345, upload-time = "2026-09-11T19:04:29.646Z" },
+    { url = "https://files.pythonhosted.org/packages/96/60/e5d9196dd83ccd7dd103762982b10ef19a4f72e8ef67ae7157699b9be0bb/contourpy-1.4.0-cp315-cp315-pyemscripten_2026_5_wasm32.whl", hash = "sha256:c8be3392b84cf43373a488874dc1ebf46f60784a70977f92586e18019d71c7ed", size = 118157, upload-time = "2026-09-11T19:04:31.091Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/93/5af95cb5113ecc6e780ca0c6c79756a2cea8833f971bbae112dd4ad73ded/contourpy-1.4.0-cp315-cp315t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:758e7496cec3195fc28a28945eab3ceef348c47fb95c7afc2eca2f3901209c8c", size = 354370, upload-time = "2026-09-11T19:04:36.158Z" },
+    { url = "https://files.pythonhosted.org/packages/47/75/5a9942446647202beb7b4d150891bfcb78fb112c73862484bbe4cc031092/contourpy-1.4.0-cp315-cp315t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bee6ff96653e0807cd0f8eaef00174a031e3a2effa5c20f49a5a8d574b05af23", size = 402340, upload-time = "2026-09-11T19:04:37.914Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/4f/bfdb0c67775a584f957211936661289789c4d0ec3e988bca2c8c02f8090e/contourpy-1.4.0-cp315-cp315t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a58b5f130afec61a093d743cdda435cc4047748b6791627e34a68f41ec3a9b07", size = 410721, upload-time = "2026-09-11T19:04:39.925Z" },
+    { url = "https://files.pythonhosted.org/packages/65/d3/dddf13d6bb26e145c99debd1f2695018b5f3e24452dd9bfc9904c9d2ec89/contourpy-1.4.0-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b9dc493e7924e5aa32d89d0b8e50986280f3d4b284968a792787bd7bcffd3cad", size = 382604, upload-time = "2026-09-11T19:04:41.793Z" },
+    { url = "https://files.pythonhosted.org/packages/37/b4/89f0a5f6c6ccd322eae2704c7e933c82a4e594a61773933b4b0a18646866/contourpy-1.4.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:c927ec747633e68c9920bcaad48b0bcefe648812b822f18c34e2d0e2270a3d2a", size = 1354996, upload-time = "2026-09-11T19:04:43.856Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/29/fc91cc1ac6f3b591a269f4357b656d6ed0c39eb17c148f41f6da2f9729b9/contourpy-1.4.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:80c7fc8e7ac217777ce7ba2db3cf8478652b5a4b335283111012ff9eaac9d842", size = 1425030, upload-time = "2026-09-11T19:04:45.981Z" },
 ]
 
 [[package]]
@@ -561,101 +635,121 @@ sdist = { url = "https://files.pythonhosted.org/packages/46/27/923408c09bc803b34
 
 [[package]]
 name = "coverage"
-version = "7.15.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cc/8b/adeb62ea8951f13c4c7fef2e7a85e1a06b499c8d8237ea589d496029e53f/coverage-7.15.0.tar.gz", hash = "sha256:9ac3fe7a1435986463eaa8ee253ae2f2a268709ba4ae5c7dd1f52a05391ad78f", size = 925362, upload-time = "2026-07-02T13:10:50.535Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f0/17/99fa688541ae1d6e84543a0e544f83de0c944815b63e9e7b1ed411d15036/coverage-7.15.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:e4d0bb73455bf97ab243a8f12c37c686ccf1c13bb614b7b85f1d062f06f42b2c", size = 252705, upload-time = "2026-07-02T13:09:05.059Z" },
-    { url = "https://files.pythonhosted.org/packages/fb/02/6a95a5cd83b74839017ef9cf48d2d8c9ae60af919e17a3f336e6f9f1b7bd/coverage-7.15.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:20d9ccc4ebd0edc434d86dfd2a1dd2a8efa6b6b3073d0485a394fee86459ebb4", size = 255441, upload-time = "2026-07-02T13:09:06.559Z" },
-    { url = "https://files.pythonhosted.org/packages/67/f2/406f6c57d600f68185942422c4c00f1a3255d60aee6e5fd961425cd9987e/coverage-7.15.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:20c8a976c365c8cb12f0cbd099508772ea41fb5fa80657a8506df0e11bd278c5", size = 256556, upload-time = "2026-07-02T13:09:08.197Z" },
-    { url = "https://files.pythonhosted.org/packages/74/8e/d3fa48489c15ecdec1ba48fd61f68798555dddd2f6716f9ad42adeb1a2a9/coverage-7.15.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f948fd5ba1b9cbca91f0ae08b4c1ce2b139509149a435e2585d056d57d70bf01", size = 258815, upload-time = "2026-07-02T13:09:09.691Z" },
-    { url = "https://files.pythonhosted.org/packages/47/2e/2d40ddd110462c6a2769677cf7f1c119a52b45f568978fc6c98e4cc0dd0f/coverage-7.15.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f58185f06edf6ad68ec9fb155d63ef650c82f3fbd7e1770e2867751fb13158f4", size = 253117, upload-time = "2026-07-02T13:09:11.212Z" },
-    { url = "https://files.pythonhosted.org/packages/51/c0/310782f0d7c3cb2b5ac05ba8d205fe91f24a36f6bf3256098f1782181c38/coverage-7.15.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:02adc79a920c73c647c5d117f55747df7f2de94571884758ce8bc58e04f0a796", size = 254475, upload-time = "2026-07-02T13:09:13.029Z" },
-    { url = "https://files.pythonhosted.org/packages/86/f7/702da6c275f8ae6ade423d2877243122932c9b27f5403003b9ef8c927d12/coverage-7.15.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:6eb7c300fbed667fd6e3588eba71c1904cdb06110ca6fdf908c26bdd88b8e382", size = 252619, upload-time = "2026-07-02T13:09:14.699Z" },
-    { url = "https://files.pythonhosted.org/packages/fb/84/c5b15a7e5ecba4e56218d772d99fe80a63e63f8d11f12783723a6005ab45/coverage-7.15.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:b5fb23fa2de9dce1f5c36c09066d8fcda16cd96e8e26686caa2d7cb9b567d65c", size = 256689, upload-time = "2026-07-02T13:09:16.103Z" },
-    { url = "https://files.pythonhosted.org/packages/95/2f/c8b07559b57701230c61b23a953858c052890c12ef568d81780c6c46e92e/coverage-7.15.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:cec79341dbe6281484024979976d0c7f22beae08b4a254655decd25d42cbe766", size = 252189, upload-time = "2026-07-02T13:09:17.828Z" },
-    { url = "https://files.pythonhosted.org/packages/6b/80/6d2f049dd3fd3dbfd60b62ba6b2162a04009e2c002ce70b24cf3878dec7a/coverage-7.15.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6c664c5444b1d970b1b2a450e21fb19ee5c9cfdf151ded2dda37260031cca0da", size = 254059, upload-time = "2026-07-02T13:09:19.304Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/19/3a80b97d3b2a5c77a01ae359c6bed20c13738fe3d9380f08616d4fec0281/coverage-7.15.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:bbcbb317c2e5ded5b21104af81c29f391be2af98d065693ffbe8d23949b948e5", size = 252227, upload-time = "2026-07-02T13:09:28.543Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/fa/b70062750686bd7da454da27927622f48bbac6990ac7a4c4a4653e7b0036/coverage-7.15.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:27f31ecb458da3f859aab3f15ada871eb7a7768807d88df4a9f186bb17737970", size = 254823, upload-time = "2026-07-02T13:09:30.177Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/09/dad6a75a2e561b9dc5086a8c5257a7591d584246f67e23e70d2995b89ab6/coverage-7.15.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:13fb759be317fdc62e0f56bffdf61cfcb45c7761ad6b71e3e583e71a67ae753c", size = 256059, upload-time = "2026-07-02T13:09:31.979Z" },
-    { url = "https://files.pythonhosted.org/packages/e6/e7/b5d2941fa9564573d44b693a871ff3156f0c42cbefe977a09fa7fdc59971/coverage-7.15.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:d5cf007add5ab4bb8fa9f4c77e3732127c9e6cad501d7db43355fbfafca0be84", size = 258190, upload-time = "2026-07-02T13:09:34.035Z" },
-    { url = "https://files.pythonhosted.org/packages/7c/1d/8e895bcde3c57ccd46d896dda5f2b3d5df761a1b0c6c9d450d175dedc632/coverage-7.15.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cc78d9843bd576fbe2118248258d485e968dc535f95ed504a7b0867ba9b51389", size = 252456, upload-time = "2026-07-02T13:09:35.765Z" },
-    { url = "https://files.pythonhosted.org/packages/14/4c/f6997da343ddeb959be82c3b05322793f92c071ad45f7cb8a96336e2dd5f/coverage-7.15.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a263060f1de0b4b74b4e089c2a70b8003b3781c733329a9c8fd54995328f9950", size = 254192, upload-time = "2026-07-02T13:09:37.445Z" },
-    { url = "https://files.pythonhosted.org/packages/17/27/a0bc09d032267b9da89d95a2d874cfbef2a5aebbf0e87cf7aba221d79a99/coverage-7.15.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:c48decf16e0dfd5b049c7d5e82200c23c08126719142998d4f172444e3d0529e", size = 252153, upload-time = "2026-07-02T13:09:39.422Z" },
-    { url = "https://files.pythonhosted.org/packages/54/c0/77fc233d9fba07b244c40948c53fe27308b8f21732fb3417f87fbd6fd992/coverage-7.15.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:08fb028000ed0aaa0a4cbdfbb98be7cb42f370db973fbbb469733505ab20e13e", size = 256310, upload-time = "2026-07-02T13:09:41.006Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/24/601cecfb5825becacb8d45219a018a3b55b9dbaec624efdb0ea249d08be2/coverage-7.15.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:fb7dc0c3b7d8a1077abea0b8546ebc5e26d6ef6ecefc2f0f5ad2b8a53bdad837", size = 251974, upload-time = "2026-07-02T13:09:42.733Z" },
-    { url = "https://files.pythonhosted.org/packages/47/1e/6f45e5a5b3d5484318d368702af6716b5ab8913b0428bec981a562fcf296/coverage-7.15.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6cb3602054ccbe9f0d8c2dc04bbeba90d5719236e2cd06e042ddd6d3fc7b6e37", size = 253745, upload-time = "2026-07-02T13:09:44.376Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/ce/22bae91e0b75445f68d365c7643ed0aa4880bbf77450ee74ca65bdae53a7/coverage-7.15.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:769e8ece11a596315ebf5aa7ec383aeeed016c091d2bf6363ffb996d41529092", size = 252286, upload-time = "2026-07-02T13:09:54.996Z" },
-    { url = "https://files.pythonhosted.org/packages/dd/1e/bec5e32aa508615d9d7a2790effb25fb4dc28606e995816afe400b25ece3/coverage-7.15.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:65a6b6164ee5c39e2f3803f314292d6c61a607ba7fee253d1e03c42dc3903502", size = 254789, upload-time = "2026-07-02T13:09:56.678Z" },
-    { url = "https://files.pythonhosted.org/packages/17/29/0e865435b4354e4a7c03b1b7920046d31d0a273d55decefea27e011cb9bf/coverage-7.15.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:75128817f95a5c45bb01d65fd2d8b9cb54bbe03d81608fb70e3e14b437ad56c2", size = 256135, upload-time = "2026-07-02T13:09:58.343Z" },
-    { url = "https://files.pythonhosted.org/packages/84/ff/33a870b58a13325d62fc0a6c8f01fa0ff667cef60c7498e2382a147dfa18/coverage-7.15.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9887bb428fe2d4cd4bee89bac1a6c9932f484afd5b36fbd4ff6ea5f825bb1f5e", size = 258449, upload-time = "2026-07-02T13:10:00.057Z" },
-    { url = "https://files.pythonhosted.org/packages/18/7b/6fffe596bf3ddba8462758d02c5dad730fd91055a6634aa2e4226229181a/coverage-7.15.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0bfc0be1f702042207a93a00523b1065ee1fe951e96edf311581c0bbc2e34888", size = 252313, upload-time = "2026-07-02T13:10:01.946Z" },
-    { url = "https://files.pythonhosted.org/packages/58/1b/11468dd6c1676ab831a70cb9a8d4e198e8607fa0b7220ab918b73fe9bfbd/coverage-7.15.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:f64627d55def5a43282d70e08396672692f77e4da610a5bb8bb4060b432b6859", size = 254142, upload-time = "2026-07-02T13:10:04.065Z" },
-    { url = "https://files.pythonhosted.org/packages/79/41/29328e21d16b1b95092c30dd700e08cf915bd3734f836df8f3bdb0e8fa9f/coverage-7.15.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:2c6f0fa473003905c6d5bac328ee4eba9fbea654f15bc24b8a3274b23363fa99", size = 252108, upload-time = "2026-07-02T13:10:06.11Z" },
-    { url = "https://files.pythonhosted.org/packages/9b/de/05ccfb990439655b35afbfd8e0d13fe66677565a7d4eb38c3f5ef2635e1c/coverage-7.15.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:2bcf9afaf064172c6ec3c58a325a9957ad1178c05dd934e25f253321776e0676", size = 256385, upload-time = "2026-07-02T13:10:08.141Z" },
-    { url = "https://files.pythonhosted.org/packages/51/0e/486828a3d2695ea7a2609f17ff572f6b01905e608379440a11da4b8dffbe/coverage-7.15.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:baf06bc987115d6fb938d403f7eab684a057766c490367999a2b71a6883110c6", size = 251923, upload-time = "2026-07-02T13:10:10.179Z" },
-    { url = "https://files.pythonhosted.org/packages/18/c7/03582b6715f078e5e558354c87616d945b9894cda2dace8e4009b17035e4/coverage-7.15.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f0405f2ff97b1c4c0e782cb32e02f32369bcf2e6b618b591d67e1ea754575dfe", size = 253580, upload-time = "2026-07-02T13:10:12.052Z" },
-    { url = "https://files.pythonhosted.org/packages/6d/b4/c0ca3028f42c9a08e51feb4561ef1192e5de99797cd1db5b04590c215bda/coverage-7.15.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:94c9686bfe8a9a6810297aecbd99beaa3445f9e8dc2f80b1382cca0d86b64461", size = 263267, upload-time = "2026-07-02T13:10:23.261Z" },
-    { url = "https://files.pythonhosted.org/packages/5f/aa/a375e3846e5d3c013dc600b2a3231089055c73d77f5393dd2192a8d64da6/coverage-7.15.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9bd671c25f9d85f09d7ec481d0e43d5139f486c06a37139847a7ce569788af72", size = 265390, upload-time = "2026-07-02T13:10:25.152Z" },
-    { url = "https://files.pythonhosted.org/packages/92/e1/5783cdabb797305e1c9e4809fea496d31834c51fa772514f73dc148bcfc9/coverage-7.15.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:110cbdf8d2e216577312cf06ccf85539c0e5a5420ef747e4a4719b5e483c88cd", size = 267811, upload-time = "2026-07-02T13:10:27.249Z" },
-    { url = "https://files.pythonhosted.org/packages/85/31/96d8bbf58b8e9193bc8389574a91a0db48355ee98feb66aa6bf8d1b32eea/coverage-7.15.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2c5d4619214f1d9993e7b00a8600d14614b7e9d84e89507460b126aa5e6559e5", size = 268928, upload-time = "2026-07-02T13:10:29.242Z" },
-    { url = "https://files.pythonhosted.org/packages/5e/7a/5294567e811a1cb7eda93140c628fa050d66189da28da320f93d1d815c73/coverage-7.15.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:781a704516e2d8346fbbd5be6c6f3412dd824785146528b3a01816f26c081007", size = 262378, upload-time = "2026-07-02T13:10:31.107Z" },
-    { url = "https://files.pythonhosted.org/packages/69/3f/3f48538421f899f28946f90a3d272136a4686e1abf461cc9249a783ee0f3/coverage-7.15.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bd4a1b44bcb65ee29e947ac92bbee04956df3a6bfc6143641bb6cae7ede00fc9", size = 265263, upload-time = "2026-07-02T13:10:32.942Z" },
-    { url = "https://files.pythonhosted.org/packages/ce/d3/092df15efcab8a9c1467ee960eb8019bbad3f9300d115d89ea6195f369ff/coverage-7.15.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:0e4950c9d6d3e39c64c991814ff315e2d0b9cb8152363594212c9e55208c0a8f", size = 262866, upload-time = "2026-07-02T13:10:35.104Z" },
-    { url = "https://files.pythonhosted.org/packages/e5/ab/0254d2b88665efb2c57ad368cc77ab5de3435bd8d5add4729c1b0e79431e/coverage-7.15.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:fe9c87ff42e5472d80d21704972e1f96e104a0a599d77c5e35db5a3c562e2571", size = 266599, upload-time = "2026-07-02T13:10:37.05Z" },
-    { url = "https://files.pythonhosted.org/packages/a8/79/1cfa4023e489ce6fbc7be4a5d442dbc375edb4f4fda39a352cedb53263c2/coverage-7.15.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f00d5ae1dd2fe13fb8186e3e7d37bcbd8b25c0d764ff7d1b32cef9be058510a8", size = 261714, upload-time = "2026-07-02T13:10:38.966Z" },
-    { url = "https://files.pythonhosted.org/packages/b7/eb/fee5c8665656be63f497418d410484637c438172568688e8ac92e06574e7/coverage-7.15.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:363ab38cc78b615f11c9cac3cf1d7eef950c18b9fdedfb9066f59461dcf84d68", size = 264025, upload-time = "2026-07-02T13:10:40.789Z" },
-    { url = "https://files.pythonhosted.org/packages/52/30/21b2ad45959cd50e909e02ebac1e30b4ceb7162e91c11d4c570223a458b7/coverage-7.15.0-py3-none-any.whl", hash = "sha256:56da6a4cbe8f7e9e80bd072ca9cefe67d7106a440a7ec06519ec6507ac94ad19", size = 212632, upload-time = "2026-07-02T13:10:48.641Z" },
+version = "7.16.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2f/55/d1eaf3e73781174340a00dc1ba2aee8a65f82fadb18e2797b192b6b3925b/coverage-7.16.2.tar.gz", hash = "sha256:ca64d9f1f384f151b9511bec01126072acd2f313439f8ed015a22d8790aab6fa", size = 971999, upload-time = "2026-09-27T12:29:01.118Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ac/7d/8f3b6dc920e3fc6732f7678785a2091db439f186afbec30dbf2214d9b1f7/coverage-7.16.2-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:724bd0f1e81856b35e59fc98cf7b4e544a3cb662e4e0864dca73d4326ee9d808", size = 255832, upload-time = "2026-09-27T12:26:19.799Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/36/6c45f15be4eca4ac1062c6a55a323286494c99726a7e58951fe85967ac08/coverage-7.16.2-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5375ebd99038021b35e99dc88255022912c06565d316212f4a576e4b08d30f5d", size = 258571, upload-time = "2026-09-27T12:26:21.199Z" },
+    { url = "https://files.pythonhosted.org/packages/34/fb/b54cbeba3ad89082c2e441278681859e538322cc34b84b2af7ebff00080f/coverage-7.16.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a076277ca9f5750cc230f0f578ebd2620cec60255b25707361699fef6fb465c", size = 259680, upload-time = "2026-09-27T12:26:22.822Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/a2/0dc65ec3d61930e1e4c2e371763b15eb4290896eb343a12d5d3091308116/coverage-7.16.2-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:58d4a54c6ea672afef66d49be922a2c69826c5ae1a42a9cd94f0c9c2bacdf800", size = 261945, upload-time = "2026-09-27T12:26:24.336Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/93/5fad7a61f2c14e08e98946fc31c1c7ffc1195061bf3fdc351db3be77a863/coverage-7.16.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0dcbcfcc059117284c603ff8cb61a65872512882f84a8cf0339241f7f7c2f148", size = 256191, upload-time = "2026-09-27T12:26:25.89Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/47/74e5de9227b939ece9f64e729645ddc4296bea10dbfa98721c1333c8be2e/coverage-7.16.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:afdf43b72ef3876c1fe66423b91466e37877c9e81e8cec70542b7e8525b9d1b7", size = 257610, upload-time = "2026-09-27T12:26:27.35Z" },
+    { url = "https://files.pythonhosted.org/packages/13/fe/2cf28d40b43645d1b72388fe3ee7f7c747533a6a9557bb8c24a7ae74fe1a/coverage-7.16.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:9acc7f7ec4a1b5f89bd929fde5b8a714f6fafdc6cc18725413d510aa082b47ad", size = 255757, upload-time = "2026-09-27T12:26:28.949Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/3d/7c149fd99fc8bbc39c80db5e688d1d39fd040be2ecb78b8335a51a55b9c0/coverage-7.16.2-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:80d3f7b48d43ee8fc5e8707a8adb43d743a5a1a85256c25a24f9d6d0e2238fa6", size = 259822, upload-time = "2026-09-27T12:26:30.515Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/3f/b283fce09d5995e227bd8e513358dd7471bedc0f78abc85a925ebdb0a2f6/coverage-7.16.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:126d1af8804d7224421fe991ff65d3ce649081560df7a98b1a5ffff07f9923bd", size = 255325, upload-time = "2026-09-27T12:26:32.037Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/91/f3325edf0c4223fb1fe1532b8dbef2a1d2f729459a9a7d1a44d073bae534/coverage-7.16.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c19cd6d025c1673f22afcd22c7df8a662d779e05d8e3fa6820c22afb895b0206", size = 257191, upload-time = "2026-09-27T12:26:33.525Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/48/fc1e88fd571ec5cb38150b7f89f7696ca1bdf9920e01432febb69774cc85/coverage-7.16.2-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:bb2fc905bbf4e6b7f40806ea79e31515abf6349594cdf0adf27c4215f0463204", size = 255358, upload-time = "2026-09-27T12:26:43.442Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/56/6785397d07c29c8e70fbb9a07e97d062b43c21ffc5f12385917847f09f63/coverage-7.16.2-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4358b9c8c0125b460407f3017c6cce8156e904b32772c5630d27112f52bdbfe5", size = 257955, upload-time = "2026-09-27T12:26:45.725Z" },
+    { url = "https://files.pythonhosted.org/packages/27/3b/c8cdd07721e5f99abd81cea970d971997f99bf158c0b85f51bd284179c8b/coverage-7.16.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f15254427c9b33eedac4f198eaf9e356eb4f6214551afb43da6194a2c088ad7", size = 259183, upload-time = "2026-09-27T12:26:47.208Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/11/606b192fe43d32574ec6238549d48de588fdcc18485682a5ec0a8ac357f2/coverage-7.16.2-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9a75a4704ff640e46170042eec1f984385a121227c505d5a16ad8e495f452541", size = 261325, upload-time = "2026-09-27T12:26:49.084Z" },
+    { url = "https://files.pythonhosted.org/packages/67/90/eea481f8b0305ceeb33f081a5f47e298391dbd1b589de0c4b3b3aa50d3f2/coverage-7.16.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:14253fc7bb15749b849795a06f5d3b6d8bc3fb8a4b5ddc341faf7a89dce205fc", size = 255531, upload-time = "2026-09-27T12:26:50.509Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/be/dedbf9aea1457b120c27ac10b8fc2a357f37fa2b54c3e7286d42980a0a2a/coverage-7.16.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:921415102a90637fcc2e3f169f61dad7699ecf690e8639fc21b813acbedc0967", size = 257329, upload-time = "2026-09-27T12:26:52.005Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/cb/b25c19d5bb2bd0f2e4e27fe8e2ffcae80c7a91ae181c0dc749ed60e9b1a4/coverage-7.16.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:cce2bc991293f15cc4084ca116827b5900c5f34e1a54dfe83f10ab5c43162eb7", size = 255294, upload-time = "2026-09-27T12:26:53.634Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/a2/892c5c5f4ad44b7b2ca009aee705191f3f268f15052244f2f9e3539b2e35/coverage-7.16.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:e1fa594c887365b69745f25a416806e61085dd07b94c9eae68a6e20730629b23", size = 259444, upload-time = "2026-09-27T12:26:55.243Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/99/a562537deba0a3e370182ae71c149be796c39d8087365f17a09188f27145/coverage-7.16.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:11e597173af1dc33d5f8a7332ada544199269a223af1ee1770ddd5e245ad0fe8", size = 255111, upload-time = "2026-09-27T12:26:56.851Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/20/854ec68641a9b3362ff068a32dfa41637299761617ef253791dbade6fc76/coverage-7.16.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:3e7f99698ba3a7d13988bdd984b7ebf13af4dbe2166dc8502eef90d77603b0a4", size = 256882, upload-time = "2026-09-27T12:26:58.41Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/d0/963ff22d3fd27117da3b8cc442f5bdc91196f783321e1a8ff0ec43476772/coverage-7.16.2-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:57ff3783f99d75a1e81dd56a9737eb5665e6736a5d93258ba596b6dcad8fd05b", size = 256112, upload-time = "2026-09-27T12:27:08.43Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/d4/a306940c81c6ae759e82fff27d20b7fdc6896e422b821f51313cce212b6c/coverage-7.16.2-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:35f37886699cb9abd29958247d718628d5bc6f39e623dff66a09e546c42a7e03", size = 258727, upload-time = "2026-09-27T12:27:09.927Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/a3/d3d99d93b02517087aa05bc0cf2d04d372956b849e5443e059079901429b/coverage-7.16.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0fd7a86fdda7cb6d616d178654bd0ad6bc0f3f33c2e478aa598500a1a9e34eda", size = 259909, upload-time = "2026-09-27T12:27:11.55Z" },
+    { url = "https://files.pythonhosted.org/packages/08/44/39dd599181726758dd185ae4dc0c0ab3aeabf7ca70e68e145060feeaaa16/coverage-7.16.2-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ac0f3b379c94acc2f7dce5f5f0b24d44fa1cc6a509717ef83dfee07450c2117c", size = 262481, upload-time = "2026-09-27T12:27:13.17Z" },
+    { url = "https://files.pythonhosted.org/packages/99/e8/91ee43f6ded411460c359d7e1aebde4d6fd8f00a2e5394182d9d212eb23c/coverage-7.16.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:7d0732c83746bc24123c581a85d9dd96b70ddb538c9076020aa1a041790361e9", size = 256012, upload-time = "2026-09-27T12:27:14.91Z" },
+    { url = "https://files.pythonhosted.org/packages/11/8c/e9499ddc33197bd7eabcb1118ca81756fc874457b324e2b479a4804b2ad2/coverage-7.16.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7b451c68218c150f616bc9649783ec8de76a59792c759b43aa0c9c0466a465e4", size = 257898, upload-time = "2026-09-27T12:27:16.588Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/6e/c081cb5991a0afba99f9c4ad6c74a5fce9513a38ddc64e3e6680c6fed9af/coverage-7.16.2-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:a56ac4fa5a75c7e182e8f62600cfb4aff43c5ed7356a034f3557659c3bec1d90", size = 255938, upload-time = "2026-09-27T12:27:18.19Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/42/1c3d819e8f9b6eb01c2fe90874d67a8882adb9507e0bbb09361ed131ea89/coverage-7.16.2-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:4cc4f73aa3fabc36e32046d6cd2971405948d8a903636508a3d3b2f9128b3a95", size = 260368, upload-time = "2026-09-27T12:27:19.903Z" },
+    { url = "https://files.pythonhosted.org/packages/19/4f/d70eac07901fd587b6ab05e659b52afe13959992aa5113bf6cce059cc572/coverage-7.16.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:723dcdab91357159b722935b500ee8abc0a66c8c432e1e9fabf4cc7598952de8", size = 255620, upload-time = "2026-09-27T12:27:21.621Z" },
+    { url = "https://files.pythonhosted.org/packages/34/5e/6d87af88317d3d9a9b18a9ca1bc1673eb516917f296e579d0d4a55cb3490/coverage-7.16.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5397e21a90dde0e9c6896b77ded8f0be26b66f8b22b33aed41f6043ed95d55e6", size = 257521, upload-time = "2026-09-27T12:27:23.358Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/c1/980681cd7b33eb66ac835044116ef0a92e11fcc7bdd866cc89d10b1130b9/coverage-7.16.2-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:db76506aa5416081f3e8974ae0f7965c58ada0bb0ef7339ac86099588dbb20d3", size = 265049, upload-time = "2026-09-27T12:27:34.085Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/e3/87679875c33bb2191f0f05544a1cc9adcc940fe0c35443a10f2df753dde5/coverage-7.16.2-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:a0f2285329dac10ab08f79cb11f5692c497018e6c7c511f95e6fd63a70b8f831", size = 267717, upload-time = "2026-09-27T12:27:36.025Z" },
+    { url = "https://files.pythonhosted.org/packages/76/64/5d372776d6eb523d4e93bafba2253f96984e3b18261c4cc56a50863c6d0d/coverage-7.16.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:382d3346d56b0eec1b793d53a4c88799c8053f516aa3a8d7c44315696954bacf", size = 270047, upload-time = "2026-09-27T12:27:37.96Z" },
+    { url = "https://files.pythonhosted.org/packages/be/c1/44082ff0cbf9f97d0043f57970a71204097ec7ba606361a9fd2065393669/coverage-7.16.2-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:648352b94507179d82637292e7ae8802508d95f78e2f00a705a50b6c48011681", size = 271360, upload-time = "2026-09-27T12:27:39.766Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/17/9a215efe25b5e0ecc87c89dbe525c4a87d14d87c8c0c7316ef140a5f6f3e/coverage-7.16.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fb2bde05838fffae1a1bf75e5d411a6cac3e4e9bb97e6640fed8cd47888b33f0", size = 264736, upload-time = "2026-09-27T12:27:42.072Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/da/7f0a31af8e448107d4d32844bd684757f51ea907bc0c68c8fd537b2123ff/coverage-7.16.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:6a75180829efb8ae62b4aded25be6ddca1c888d138d2d82e21d93bfbd88f41cb", size = 267600, upload-time = "2026-09-27T12:27:43.85Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/a4/3bfecbd3366b775bacdcb3330394d356cf384b5d8f5b2146ac4b14b252b5/coverage-7.16.2-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:99704f73721e23859112072d522076e11c31744fc96b5652e5dd2018aa4359f7", size = 264767, upload-time = "2026-09-27T12:27:45.768Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/3f/5d62163732d87e4a0c4710a0eab30f0fd6a2d480112abe2029f014fe8c9d/coverage-7.16.2-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:29309ccc86b7f33df7db12813c299f215bbbc470ed6292d0bedd63ffae1ebf64", size = 269026, upload-time = "2026-09-27T12:27:47.787Z" },
+    { url = "https://files.pythonhosted.org/packages/49/4d/8e4579f225426535085a9be371cc75e3b026d058d679b80affbdfb4c3ef0/coverage-7.16.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:30c1b65d529e46569899fadca59e4a87c1faf2886923f1307ba61e654d4f3c20", size = 264147, upload-time = "2026-09-27T12:27:49.681Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/36/ef1f77e2c3f7bb03c2b13b9a2006f88700fdd75535ef158d70049f425c1c/coverage-7.16.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:dcf4bc2aab4e16b1c4c0c2005918f23a7dd5d7821ddae82caed9e3342dc2fcce", size = 266376, upload-time = "2026-09-27T12:27:51.551Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/aa/3be084d5b82e63ccdad4ed751e4acbae294673573e30481d29f8b7402eec/coverage-7.16.2-cp315-cp315-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:4dbbd1155ca46e6e0b6b89d204428c56ef6a459af21333f365d135a2820e5a09", size = 256060, upload-time = "2026-09-27T12:28:03.185Z" },
+    { url = "https://files.pythonhosted.org/packages/de/29/48fca82a7ebf7ff7b2e35019cc9537e7f65e4d2aa1215cc5a8792c989251/coverage-7.16.2-cp315-cp315-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8fc15cc8d0d06e873c00ef18e1372d605f9aaf3de27d8c24e50782e75bc8b843", size = 259106, upload-time = "2026-09-27T12:28:05.15Z" },
+    { url = "https://files.pythonhosted.org/packages/06/3d/b2d5986f2dd53fe201aa1be2e4ab204fa1aed5101e67c0dbbb419b850aee/coverage-7.16.2-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c6afdd69218202bc1758c9a14b86b8cf1084f37ed2ca143e567a103772b16d1", size = 260674, upload-time = "2026-09-27T12:28:06.868Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/7e/b50160be3506ead12e6480d14279af7f0f17627694300a2d1fd2c42d2ff5/coverage-7.16.2-cp315-cp315-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:aba5c63b7afdc749cc9eae943d5b868cba2b261a176378fa1c5a30bc8bc89982", size = 263139, upload-time = "2026-09-27T12:28:08.771Z" },
+    { url = "https://files.pythonhosted.org/packages/14/5e/7c805ac9a32606de1399bd7e9bd375aa2f973dc61b12680d9e6403c2e891/coverage-7.16.2-cp315-cp315-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:9174f0af24e5eff248b9dbfe76ec5275a3d19d37edbc2810543f12cf97347a34", size = 256591, upload-time = "2026-09-27T12:28:10.842Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/9e/76f1ed129a2daf658a3ea17122824cf2e3b91fea0460d8d3664fc5a61018/coverage-7.16.2-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:80e9fdb4c3d926b6ba721d4bf7435bdb869c3527ae7803290361d0ab73db13b6", size = 258708, upload-time = "2026-09-27T12:28:12.962Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/b7/8d62e75f48b527619239a65294f842d4b7fd02a0839d43ae1de80184e2df/coverage-7.16.2-cp315-cp315-musllinux_1_2_i686.whl", hash = "sha256:7b3bce4a0d05401d70b7d0d5ca783e686bc9d30e81dbd7d980d532609bf809e4", size = 256562, upload-time = "2026-09-27T12:28:14.934Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/8d/0a15f95c3afb78e947c52644786ba4bc9de259905687dd720d5e6fae2e76/coverage-7.16.2-cp315-cp315-musllinux_1_2_ppc64le.whl", hash = "sha256:44f21e407b278efdfc1ee5e481e00518bd1d500310a30a5fbf2bcbedfef4aaf0", size = 261077, upload-time = "2026-09-27T12:28:17.215Z" },
+    { url = "https://files.pythonhosted.org/packages/25/00/88389987305a47d732866c07c8a500000ab574df9505e3114ac69c8d027f/coverage-7.16.2-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:59c3926585e1cd1f2190f4b2ac9014de1bbeaf0d5d0587b0dc6b0aa90d17896a", size = 256034, upload-time = "2026-09-27T12:28:19.08Z" },
+    { url = "https://files.pythonhosted.org/packages/92/02/34d079d4952ad461bde037d353f9a6e037a7edc45fe0f9ee8781ff73f028/coverage-7.16.2-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:066429634299e14dd2d511e1e85f8f9cecc500781f6b41907c0dd6f1baea7e63", size = 258034, upload-time = "2026-09-27T12:28:21.242Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/8e/285dcef0184358044e7cbcd810a1bdc9566bc620f54702d605477155df4a/coverage-7.16.2-cp315-cp315t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:fb9d92ecfe2d5b494367c67f7446f8b75b68d8d0c8cf3bc3e6997478be25d9e2", size = 265090, upload-time = "2026-09-27T12:28:33.04Z" },
+    { url = "https://files.pythonhosted.org/packages/06/b2/cc83f3a6e5789a4e89059c69555bc641c2efcde568405a1c06fc702951ab/coverage-7.16.2-cp315-cp315t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:eb57acff4a74246ae513c142d4b36e18c389c3aed8661914a53f7cd0071031b2", size = 268254, upload-time = "2026-09-27T12:28:35.135Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/41/f548c19530f5d66ac6e3c92bbcbc49da7261de3a458b9f3e54a3efb1a0b2/coverage-7.16.2-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:444889f7f66b74e4455c0a97e0e166dd41177f1dca8c0239a47cff25e05ba7e1", size = 270695, upload-time = "2026-09-27T12:28:36.959Z" },
+    { url = "https://files.pythonhosted.org/packages/94/61/4dc27cf82ef96434d2874110ad0cc10ea4621025705dc5049862bd3bd181/coverage-7.16.2-cp315-cp315t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a740ea6f083c6db7b926534d159508f80ba275ab35e722522de0d18d0f56e55f", size = 271853, upload-time = "2026-09-27T12:28:38.821Z" },
+    { url = "https://files.pythonhosted.org/packages/38/29/bf8072b1b8bd5f2de8b21460a404460b1a2b97e80a9464c78ec0271f6199/coverage-7.16.2-cp315-cp315t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:8e209591f7c41ae4a9171335cf6156afda0b21de73b02f73f5aa95b2d5fbb08d", size = 265644, upload-time = "2026-09-27T12:28:40.815Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/2f/0aecb8721be5cdeb8afd9d6d9f6b463f074e4d8d37f00f4c42442522709f/coverage-7.16.2-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:396bb16e04ce04efbb3df91456ae4e3da918e69ecdf67fb711b0a0fdf35ccce0", size = 268530, upload-time = "2026-09-27T12:28:42.725Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/0b/92b4b7628268ee711249958e68fc0328779bd3d9a7ab4715379465aedb84/coverage-7.16.2-cp315-cp315t-musllinux_1_2_i686.whl", hash = "sha256:9cdf19874e0d247f32f03609200370343c3c7aa260b191d8c2bb251d36198283", size = 265154, upload-time = "2026-09-27T12:28:44.684Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/d9/41c95c1ab29b3dcd357cd1227181d1c98185632aca41ce670ce671b23a43/coverage-7.16.2-cp315-cp315t-musllinux_1_2_ppc64le.whl", hash = "sha256:fd3d72233eb8b48acc94fa57d44e2d32ce8e7abed02882ccb6d855ccc4ed33ec", size = 269799, upload-time = "2026-09-27T12:28:46.672Z" },
+    { url = "https://files.pythonhosted.org/packages/80/07/ebeb259aa5362b033a137b86d7274ff4b109d59be8cc9913889b783bf75a/coverage-7.16.2-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:bb4ffe96aa663cee727659db5a2afeb38c95f8677b747d447b90d6d4874ea2c5", size = 265196, upload-time = "2026-09-27T12:28:48.996Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/18/8437620f90d023680a072eee02f968055f3658bbfb7d386d0ea34cfb7f30/coverage-7.16.2-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:dba2edfb054f6d4a08df9d1637c39a5aa3865bca6617c13c86be21e45658a59c", size = 267266, upload-time = "2026-09-27T12:28:51.361Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/0c/7a64e1ac90541a8edf50daef0914848011fb057a5bf55284a4811e21939a/coverage-7.16.2-py3-none-any.whl", hash = "sha256:11d28e9123a9156cb405d8d27b44256c9a58fb5decc2073a8f17862057e3aa0f", size = 215754, upload-time = "2026-09-27T12:28:59.075Z" },
 ]
 
 [[package]]
 name = "cryptography"
-version = "49.0.0"
+version = "50.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1f/99/d1c90d6041656cc6ee229dc99cd67fd0cd5aec3c5f7d72fffc27cc750054/cryptography-49.0.0.tar.gz", hash = "sha256:f89660a348f4f78a92366240a61404e337586ef7f5909a2fef59ca88ef505493", size = 854345, upload-time = "2026-06-12T20:02:30.512Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/09/41/3797cfaf69cae04a13ee78ebd83f0678d9c02b4779d21ce24445326f1a69/cryptography-49.0.0-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:36d1709f992593689b45bda411498d62c6e365f2ca00b84657d4dadd24de16db", size = 4692978, upload-time = "2026-06-12T20:01:21.305Z" },
-    { url = "https://files.pythonhosted.org/packages/e6/8b/43011f7ebe515a8aa20d61f290a326cd890c2e738e16e59eaff8d9c3a412/cryptography-49.0.0-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0e959b578856a3924bc0cbb710fc12c387b9412a951389f3ca61704a9e25f325", size = 4716422, upload-time = "2026-06-12T20:01:48.566Z" },
-    { url = "https://files.pythonhosted.org/packages/4a/91/01ce7303a4579e6d3a6abef01bd322848e9ea7a219adcabc5048b9033571/cryptography-49.0.0-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:53ecee2e23f7169b6117e99fc8a944e5e50f79e69758a83b52a00cb98ab2b2d2", size = 4700503, upload-time = "2026-06-12T20:02:47.091Z" },
-    { url = "https://files.pythonhosted.org/packages/62/99/a2c95cf8293f07491e9e27c20cc4dcd18176d944e674679adeb1d0173fd6/cryptography-49.0.0-cp311-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:2eda353d8a27bcbcaa4cbed18994a74ab4d19a2ca897db188ea269ab9b71419b", size = 5309779, upload-time = "2026-06-12T20:02:08.987Z" },
-    { url = "https://files.pythonhosted.org/packages/20/2c/0622f20ff02b2ef32558733443805dc82fd4c275be01b2d19d14676f3a1b/cryptography-49.0.0-cp311-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:2afe9051da7ae7bd5905da5a949280c7d2bb75682e188f650a9d0f2756b834c6", size = 4749683, upload-time = "2026-06-12T20:02:03.335Z" },
-    { url = "https://files.pythonhosted.org/packages/a3/5b/c5246635d5fd3b64e0d45ae10e99fd32fe9676a79915ccfe5a61ba9af1a5/cryptography-49.0.0-cp311-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:0b82e28ee398a386f0807bba7884d30f25218855690f45115831bcce5d90822c", size = 4337874, upload-time = "2026-06-12T20:02:54.323Z" },
-    { url = "https://files.pythonhosted.org/packages/6d/88/05563c7fe2e914e87d1a536d06fe83e66b4e1d95cb593e05aea375531da8/cryptography-49.0.0-cp311-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:ccac2bfebc306b862133e3bb71f3f6ee8bb525240089b2d952e4144b3a6d5da7", size = 4700283, upload-time = "2026-06-12T20:01:34.822Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/b6/d7696e4e890d6ae1469935164c9e5215c557671cb78d6e3f458ccceaa632/cryptography-49.0.0-cp311-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:d0527ce944105f257f605a827d6ebead966c752038b6e8656abb9c5edee6fc68", size = 5265844, upload-time = "2026-06-12T20:01:24.09Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/3c/f3ad17eecc1a57b0ba236dc01f90e783c51f4a2f35f64777cc4f47a184b2/cryptography-49.0.0-cp311-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:cbc77da8c523d5abd028635ba850a6966fcee2c82e2bf65a41d1d8afe0f98be9", size = 4749290, upload-time = "2026-06-12T20:01:30.848Z" },
-    { url = "https://files.pythonhosted.org/packages/4f/01/339573cf1023163a400b0b5d16f6d507de413b9f60be6fd1b77feeaf6737/cryptography-49.0.0-cp311-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:b87e65d263b3e5d3bb92a57e2a6638e2f31110fa7aa890c7b2dbba42248d0a3f", size = 4834612, upload-time = "2026-06-12T20:01:29.246Z" },
-    { url = "https://files.pythonhosted.org/packages/71/fd/577302e213a1be9468f92d1afef66fcf1ef83d516819d9992ca547f592bd/cryptography-49.0.0-cp311-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:66ec79c3904820572d7e987abdf304281f141d37ad9a489b8e97066e7b9b6459", size = 4980804, upload-time = "2026-06-12T20:01:42.853Z" },
-    { url = "https://files.pythonhosted.org/packages/86/12/c48a424f38db03027be9f7ed5c7dc5de9933dbee992865f98b13727a009d/cryptography-49.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:196ecd6a36e4e9aa10270393bb98d8df88fccee0bf1e5128b91ae4eb4375896d", size = 4678835, upload-time = "2026-06-12T20:02:48.743Z" },
-    { url = "https://files.pythonhosted.org/packages/68/28/8a3ad4653662c93fc44dc4e5d8fd374c25c42e07b34bbfbadf49cf57a5a8/cryptography-49.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7abcee80084cda3f7691f3eb1ce480d8df49cec637b429aa35986c1de71738aa", size = 4697239, upload-time = "2026-06-12T20:02:56.03Z" },
-    { url = "https://files.pythonhosted.org/packages/a8/b2/2193fc74f81aee4f9b62733133b73b5176718932ed8f2e4b03fa040480a6/cryptography-49.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:4ae387c9cb68ea569ca17e490d66d8142b81c3cc814bf179974b7d146e490bbb", size = 4685593, upload-time = "2026-06-12T20:02:50.666Z" },
-    { url = "https://files.pythonhosted.org/packages/47/f1/1d3eaa243bfc5de4a187b22aa8c048b3e4980bfbe830ac46e6bac2e66947/cryptography-49.0.0-cp314-cp314t-manylinux_2_28_ppc64le.whl", hash = "sha256:f37d847238971164fdbc68ade6f6574aecc9c0af714190e2083429ff68f4ce9d", size = 5289961, upload-time = "2026-06-12T20:01:46.468Z" },
-    { url = "https://files.pythonhosted.org/packages/58/39/2d51306721330c486495853eda1c567880ff036de15a14c4b74f399934af/cryptography-49.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:c2bc30226390d60ea19d9f82b19db005fe0452154a23c1c410c12ea801e43561", size = 4731145, upload-time = "2026-06-12T20:02:16.832Z" },
-    { url = "https://files.pythonhosted.org/packages/17/50/983e838c7fd0d87fd8c969bcdd328edaf5f756e38df5281637424c155873/cryptography-49.0.0-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:07cab27cc7b7e0fd28e5e26bb9eeedde5c135c868b46de4a27845abe94af6122", size = 4321719, upload-time = "2026-06-12T20:02:52.611Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/f5/8f571d7e27c55bce9f76f026143bcb1e040a4233149ecca0bea5fa5dd5f7/cryptography-49.0.0-cp314-cp314t-manylinux_2_34_aarch64.whl", hash = "sha256:b20133d204d2bb56ba047642199603876c872026ca53e79c35b83772ab2cc505", size = 4685209, upload-time = "2026-06-12T20:02:07.282Z" },
-    { url = "https://files.pythonhosted.org/packages/e7/84/0e27016a6fc5a0886f797018b26aa42f40c09a82332bff77822a451deaaa/cryptography-49.0.0-cp314-cp314t-manylinux_2_34_ppc64le.whl", hash = "sha256:b970c6da94d5bb18629db453d14f2a1300f6bf59b61e9b82377931ef95504866", size = 5246285, upload-time = "2026-06-12T20:01:32.439Z" },
-    { url = "https://files.pythonhosted.org/packages/11/2d/5e1fb307cb5931881516b464c98774b3f2c36b5d4bb9a2830253cf553cad/cryptography-49.0.0-cp314-cp314t-manylinux_2_34_x86_64.whl", hash = "sha256:d8ecde755e2e91bf773fc94e8c9d730cd7f2007004cb492263a794ec3899a1c8", size = 4730441, upload-time = "2026-06-12T20:02:01.469Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/c0/bff5a02ee731d207d6a1ed51732549d8c53d2bc8da1d10ec6f2844201d68/cryptography-49.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e3fb64c420688e5319ae25113a354015abbd8dffbfbc41781a1ea66fc7622ac3", size = 4815869, upload-time = "2026-06-12T20:01:36.574Z" },
-    { url = "https://files.pythonhosted.org/packages/b9/26/814681d14248d95d73d5c3eea0c39a94eb8302df966f670a2c60de90974b/cryptography-49.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:32703d93296f5c1f4b53349ad3a250c2cae0fdecd3a3dd5d47e616d8d616af27", size = 4960948, upload-time = "2026-06-12T20:02:18.688Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/df/40577043ca124e17012f408ddddaeb213b856336ac82ddb3bc915f39e29f/cryptography-49.0.0-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f78ff2c9ed8dc2d036b0f4d640e22522213d047c1b14e61205a7e55c80a494d4", size = 4692429, upload-time = "2026-06-12T20:01:53.628Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/99/2d13299eb3dd27b02dcfaafcc91d6b5cb3329f7cbd6d8f51921acd566c1a/cryptography-49.0.0-cp39-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:35b151772baff2c74cba7fa290ceaff4c3b11c0c881eb93eb5dbc05a7cfbba18", size = 4700968, upload-time = "2026-06-12T20:02:45.383Z" },
-    { url = "https://files.pythonhosted.org/packages/a5/4d/9c0cd02f95e2602dd5e563da149ee0830abef3537be8b34dc56281ebe27a/cryptography-49.0.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:0f21641cf4b30fca7aee061ced0ec7ad7b073518088b7c9969a297c0ae796c69", size = 4697758, upload-time = "2026-06-12T20:01:41.13Z" },
-    { url = "https://files.pythonhosted.org/packages/24/01/186c825898477d77e2324d5360fefe622ff1d8d1963ec0554e2cada8ec77/cryptography-49.0.0-cp39-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:9e82dcc8e56052715fb18b2429e3bca4823b1629136a2084fc45a9a5cecb9b64", size = 5298863, upload-time = "2026-06-12T20:02:24.579Z" },
-    { url = "https://files.pythonhosted.org/packages/b8/7b/62cbbab75d0659865bf0273790031544a0b16c8072d258f9428dcd8190dc/cryptography-49.0.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:6f2debedf9ca60cf1d5bd466475638af5130f89965605cd818484d19987d3a21", size = 4735983, upload-time = "2026-06-12T20:01:50.14Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/72/3e798c064bc39e471008075d0f9bc9daf77a80879c092e4a8e170c585ed4/cryptography-49.0.0-cp39-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:8c25ceb16df5b9435f3f6a9829204985b0e0cbee3b48aacd432c7d2c850b44d9", size = 4334173, upload-time = "2026-06-12T20:01:44.743Z" },
-    { url = "https://files.pythonhosted.org/packages/f0/ee/6fca21d1ac73e06f8bef71940abfd4d2f6472b4bca284d770f32bd4086f6/cryptography-49.0.0-cp39-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:28d8b15e6275f12c8a207dc309dfa957903c927d08d0cc937ee3f63f200693cc", size = 4697298, upload-time = "2026-06-12T20:02:20.918Z" },
-    { url = "https://files.pythonhosted.org/packages/67/d0/a5fcd3515f0bae49a7b6d0413cc1bdccdcc1fc0047037a0d480642cdc5d6/cryptography-49.0.0-cp39-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:6fc361c34fb6aac015ce19435876635e5c6d21db31998b0920f675f131e043b8", size = 5254338, upload-time = "2026-06-12T20:02:22.737Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/84/84fe36f19caf857d61cb7fc9c63035a47ffabd84ea12d1d393148efa3615/cryptography-49.0.0-cp39-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:2400ef9c9e2299a25614eb1dea3db54a69b1349efd043bfac9c67630d136df36", size = 4735650, upload-time = "2026-06-12T20:02:41.389Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/a0/db537264e234f7273a73ec020873d6d6b39dfd8a53db78b550ca8320440e/cryptography-49.0.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:67e1d20ad9ef3a563c59ef22e7a8a0b8210bd26604369ea4a30a7c66aefe504e", size = 4834820, upload-time = "2026-06-12T20:01:51.847Z" },
-    { url = "https://files.pythonhosted.org/packages/93/77/8df9eb486495979bccecd1062e2eaf435250e84437040295b57d09048b0b/cryptography-49.0.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:42b0684e0e40cf26122427802486f6d93aea593612603a94fbf260c7eb1e9c1b", size = 4967968, upload-time = "2026-06-12T20:02:12.524Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/bb/ad/5d6702db60b1e40b41ef513b6967ff5848f307d50f8449baf1634f5908f1/cryptography-50.0.1.tar.gz", hash = "sha256:5dd9bda1c12b4162f6ff568eeb5e0ff956c28d14406e875cfe8a63a2d414ff20", size = 880381, upload-time = "2026-08-25T19:45:45.499Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/34/9ce9a62ed9dc82ca9fd6a34445b6904af56e5f38b3eae2ed32e49c36053d/cryptography-50.0.1-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:53e279950892dc102c6b4e52af03ae5ea92fac572a1ddab78ca73a997f62b69f", size = 4723133, upload-time = "2026-08-25T19:44:05.461Z" },
+    { url = "https://files.pythonhosted.org/packages/57/26/e6d4fc8512a51a5f9ee7bfdbfb853bce1197087df40c9ad993ad370b846f/cryptography-50.0.1-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ff838d62ec1bfce4f9ba7fa16f4a7b554cd8d0c299e6be37502161a660c84eef", size = 4712478, upload-time = "2026-08-25T19:44:07.375Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/de/d3cdc2815697aae84126cbd6a030ca7b6b452e28a88b501b836bd3aa7a86/cryptography-50.0.1-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:e74591e283fe6eb956416c929eb58262a719fe0311fd9054c62c3350ed8760d8", size = 4730726, upload-time = "2026-08-25T19:44:09.294Z" },
+    { url = "https://files.pythonhosted.org/packages/55/32/38c0d344b98c06d34b5df8946565a9c0d6dbf32c8e0730a7f05f0a3c6cab/cryptography-50.0.1-cp311-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:5fe002589592ed749ce77fe0695fcbd3500dd61d7d6db5858a7544c612fa8e45", size = 5353524, upload-time = "2026-08-25T19:44:11.96Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/1b/82f0f0d8858d4432be1af790477edf62aef90324041aa07c57e57bef1af7/cryptography-50.0.1-cp311-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:51593d180cf6d179bde5c5d065bed81386b1f381656ae7d042b7ffc87a9895ad", size = 4746720, upload-time = "2026-08-25T19:44:14.051Z" },
+    { url = "https://files.pythonhosted.org/packages/29/ba/042ca458b8c64348c768284b5d23e69b92ed53d057ab779fee628564676d/cryptography-50.0.1-cp311-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:359e62deae718bce96170e223fdcb6357e4fbd3bb7a3a75f4430763532560e49", size = 4361866, upload-time = "2026-08-25T19:44:16.167Z" },
+    { url = "https://files.pythonhosted.org/packages/39/3b/e96c1ef71edef71057c7e3c3d982ce8fda554e0c52d0cc19c18845cde3eb/cryptography-50.0.1-cp311-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:e2ca8fd1b6b4b82a1c4cb02841d0837e3c12336c2e24b520ab8ab3b969733d8f", size = 4730028, upload-time = "2026-08-25T19:44:18.085Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/38/45abd72ef63f2e7d0754a6cacf97bd8b69512ace7f6130d24c39ece65da2/cryptography-50.0.1-cp311-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:76de83fbd91ac49c0feaaa983d0748fd7a53176afac5fb3bf7478d244f0eb527", size = 5308405, upload-time = "2026-08-25T19:44:20.197Z" },
+    { url = "https://files.pythonhosted.org/packages/85/66/6ccca4722987ddedaa7fc9c3f4708af7431f5535666c174350830888c6b7/cryptography-50.0.1-cp311-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:51afcfceb15597cf2635068e4ac9a56b2abde622edde17f37d85fd7b5306497a", size = 4746230, upload-time = "2026-08-25T19:44:22.376Z" },
+    { url = "https://files.pythonhosted.org/packages/13/0e/b1f92e013228111413f2e6743948b80bc24dfd3c1b87ba98ceea16f5df89/cryptography-50.0.1-cp311-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:be224a65493ec5b74a158ff22a5522ce4a5ca1e543c647a3a4730d4a09e5f959", size = 4862596, upload-time = "2026-08-25T19:44:24.472Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/22/c3654cccc856e9d682817b04ac3ee79731cb09ca6f95996a95c904de2883/cryptography-50.0.1-cp311-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:9ebcdd5519be9b652a46f507817a74591774fc3d6923ac364e4dfa64e36b291b", size = 5014082, upload-time = "2026-08-25T19:44:26.709Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/72/3a2711d967977ab5fc80b782837c7e8d1ac7445e764c20c381a265c57ef3/cryptography-50.0.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:a0b1a59e3a089064a0ec309e9428c8e3ae4e161419d20ac33600767e83fc658a", size = 4708817, upload-time = "2026-08-25T19:44:32.773Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/f2/bb1f56e10815b789df0b409a69fa4992ff3d3fef9c72747f4a6b26fed38e/cryptography-50.0.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8921d58f426793c5f1b47f0b59575780de9a095214958d0eb37d909593db8367", size = 4697300, upload-time = "2026-08-25T19:44:35.144Z" },
+    { url = "https://files.pythonhosted.org/packages/08/bd/ed5396be499ffcf8807a585bfe38b71a1fbdd1c342b4f9b6d0ef5162a946/cryptography-50.0.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:a8f40ea47330e71b594a7e246898f93177c259490c63183dbaf9e571d71ed9a5", size = 4716039, upload-time = "2026-08-25T19:44:37.192Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/6e/1cf405c5c8e8df7545378048e954792f00b7f2367af8863ce8b8f3e10607/cryptography-50.0.1-cp314-cp314t-manylinux_2_28_ppc64le.whl", hash = "sha256:a255449073358275b64b67d3f595f268bbef70e72b6edb65e0c70c735bf739c9", size = 5332388, upload-time = "2026-08-25T19:44:39.16Z" },
+    { url = "https://files.pythonhosted.org/packages/47/92/b4317e8c32c4f47b062f5398bd79106b220a124546f42be83bf32b761e2a/cryptography-50.0.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:8df2de9102026855887e4587084f6eabd80ed0f345b8ad8a7ac27ab9bf4723e0", size = 4730293, upload-time = "2026-08-25T19:44:41.298Z" },
+    { url = "https://files.pythonhosted.org/packages/39/0d/a1e7633e2c744d0f2983320a27e924ef2264c79c56e1a58d5fb0a1cfd413/cryptography-50.0.1-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:ac02b07824d4d1001bd4367599f839c19cb171924c796e52c23508ac14c2c0cc", size = 4346031, upload-time = "2026-08-25T19:44:43.245Z" },
+    { url = "https://files.pythonhosted.org/packages/88/dd/b215616f9bab3fc18510c78a4e5c9f362d77838503c363dc747c7d4f5c6f/cryptography-50.0.1-cp314-cp314t-manylinux_2_34_aarch64.whl", hash = "sha256:cbf74a81765ee67413503ca6e26dcc4f6f5a519822436cc0a1b97aab6c1b8a17", size = 4715344, upload-time = "2026-08-25T19:44:45.291Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/1b/ec3ebd31741d0e963612c4fe43caa39341b9b1e031e469820e42e4c83918/cryptography-50.0.1-cp314-cp314t-manylinux_2_34_ppc64le.whl", hash = "sha256:16c5ecd954b3330ebfb6605eca4fd952da8bef376551d5cc264534e3770a9ee6", size = 5287201, upload-time = "2026-08-25T19:44:47.297Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/01/0127d11a762b31a9ee0221894f540318761783f3fdc4bc5d057698caebd5/cryptography-50.0.1-cp314-cp314t-manylinux_2_34_x86_64.whl", hash = "sha256:79bf008d1f9af6071c797ad133e39915dfee7614f18f18f4db9072eb715064a3", size = 4730023, upload-time = "2026-08-25T19:44:49.435Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/b9/e7425ebfb599241a0c1d7000f1b466c3062da66c19d9525031315dff7213/cryptography-50.0.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:330fbb252391c596f1ae42c5754449dc924e6ad012dca8efe0d703f9f2d12ec6", size = 4847362, upload-time = "2026-08-25T19:44:51.94Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/fd/60d0ddf4defa12e482c9d5e0f554384d6e8ab25341fd15f060028fd92e6a/cryptography-50.0.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:42be3bb70596b3abe4ac097b75be223e8b3ab614a0e5de068e3dcc54d71d6149", size = 4999247, upload-time = "2026-08-25T19:44:53.876Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/a5/9ec7e81e8526c0d7a387d73386b2daed3f39e10d81a85930bd1b6bfba65c/cryptography-50.0.1-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:05ba322c4da95b262a212c345af888ef2c37c88c0509756ea00a0e6d68850f23", size = 4751900, upload-time = "2026-08-25T19:45:00.401Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/3c/0e77bd5ffcf078e9dd27d3074aad6c030d9b10d0bf69329d573c927a188c/cryptography-50.0.1-cp39-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e22dfed744bd4002e909464cb23d2f0b05c6f3113a79ef2e9864a53db737c733", size = 4738357, upload-time = "2026-08-25T19:45:02.786Z" },
+    { url = "https://files.pythonhosted.org/packages/27/3a/3c5f80daa4dcd47323c7af8a2fcb90de27a33564d4fcac69846c0972691a/cryptography-50.0.1-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:4c4188f7c0cf655be5c06342b817ed0f9595b69ffa2b12026e5353eed29dea88", size = 4758474, upload-time = "2026-08-25T19:45:04.889Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/2b/214cf0cf93db9628c3c20c896b229f327f6fb1b20e4b3743d8ad3f00af8b/cryptography-50.0.1-cp39-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:2ebbfb0f1fed745e91796e3e1080a1440423fdae8ece1b995a1d80883a409054", size = 5375862, upload-time = "2026-08-25T19:45:07.163Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/51/3f9701867a46b6c1740c9b52fc4d3bed6cbdcfedcc9b6e64305c07f39cff/cryptography-50.0.1-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:407fe2b6db00939c05c0e945e9914238f2f0a430974839429dafc82b1ee6bee5", size = 4772942, upload-time = "2026-08-25T19:45:09.396Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/5c/13ea642e08e2544d0f5396122055f4820cfacb3203562197b5967125ea97/cryptography-50.0.1-cp39-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:2b34d76a652ea2b6faf777c35df230c5637842cd904e04f16230c3f9f03e4361", size = 4383347, upload-time = "2026-08-25T19:45:11.659Z" },
+    { url = "https://files.pythonhosted.org/packages/84/d5/7d1fe1cb93f91c428093ff234e128c89ba8ea61a6f26aab406081f9b996e/cryptography-50.0.1-cp39-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:01f41478cf33fc605a6a089cd56d28b45c6c0b45a1928b61797f2621a04bac71", size = 4758050, upload-time = "2026-08-25T19:45:13.745Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/04/557fc5ead96a829e0bc812a3b9dc4a52a2f27e4f7f5950da7ff27653a805/cryptography-50.0.1-cp39-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:fc3ed7ebd2a8c96f5b166de0ab9b624996bef3b07bbeb19364dfb78222c22c80", size = 5332955, upload-time = "2026-08-25T19:45:16.193Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/eb/5d7124083e8d8cda8f5b348f544b71ad6f707ad63193758ef4d8e569da02/cryptography-50.0.1-cp39-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:9dde0a357190eb3b1da1bb9ab750e9c85cba82ca5977aa0836cbb94e92611239", size = 4772694, upload-time = "2026-08-25T19:45:18.315Z" },
+    { url = "https://files.pythonhosted.org/packages/63/8e/f1f955e0921dd2b6d22eae7e8d24a4c4b638d10735ffbf6a71f99eb0fcb8/cryptography-50.0.1-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:fd3718b960d0b5dd213cdf03f3bcb7000e69dda0de8b956061947ff6bcff5558", size = 4888413, upload-time = "2026-08-25T19:45:20.4Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/ab/89e2b798d2c3925f82e2bb72d5979f3d2f6da2dd22ef4a8cd8b70d920039/cryptography-50.0.1-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:2a93d05e34d5f67fba6f891fe85d929999baa7195e853923ea6d7576c9e68c5e", size = 5044355, upload-time = "2026-08-25T19:45:22.353Z" },
 ]
 
 [[package]]
 name = "cs-util"
 version = "0.2.2"
-source = { git = "https://github.com/CosmoStat/cs_util.git?rev=develop#a52280fbb4bc1daa3f217a77954ba43c0860738b" }
+source = { git = "https://github.com/CosmoStat/cs_util.git?rev=develop#e14cd686c53c4f4d5f40d3846dba098e81825afb" }
 dependencies = [
     { name = "astropy" },
     { name = "camb" },
@@ -682,7 +776,7 @@ wheels = [
 
 [[package]]
 name = "dask"
-version = "2026.7.0"
+version = "2026.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
@@ -693,9 +787,9 @@ dependencies = [
     { name = "pyyaml" },
     { name = "toolz" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4d/b5/aa50877159f1efd65a744107e0662a15d2b80fa0c427d980313367f49493/dask-2026.7.0.tar.gz", hash = "sha256:b039970642ce97a063070bae2763dada8aae27e3cbd8ff6d894072f920a1e252", size = 11548914, upload-time = "2026-07-06T16:58:36.496Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/33/a7/6b3c7ac32b642fbbe0821111654e0bd8cfbe88f68560bcf23cc78ab35c71/dask-2026.8.0.tar.gz", hash = "sha256:8a94c37b5de6d869343340dc26c3c3acca7ec48a3abdabe00ea3abb1125884d5", size = 11561752, upload-time = "2026-08-24T19:21:25.906Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/90/aa/ceeb81d9dc886d445416310f0eff5c3978f20224b4143e96e36b7e85b011/dask-2026.7.0-py3-none-any.whl", hash = "sha256:68def8b467529ab8425ef9fcc6735dcaefaaf715d5e19ae219485f99a394dbef", size = 1496878, upload-time = "2026-07-06T16:58:34.597Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/3a/4fc99e788bcfa1b3b3f21abf57da45898d807d007e7f6fd1c7300904eb70/dask-2026.8.0-py3-none-any.whl", hash = "sha256:ccc0c83a189b0398602435189771d28dad7b5773b6089bb8dce14ae732dd782c", size = 1492182, upload-time = "2026-08-24T19:21:23.997Z" },
 ]
 
 [package.optional-dependencies]
@@ -734,23 +828,15 @@ wheels = [
 
 [[package]]
 name = "debugpy"
-version = "1.8.21"
+version = "1.8.22"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f2/aa/12037145b7a56eaa5b29b41872f7a21b538e807e13f32c4d3c46e59be084/debugpy-1.8.21.tar.gz", hash = "sha256:a3c53278e84c94e11bd87c53970ec391d1a67396c8b22609fcac576520e611a6", size = 1697577, upload-time = "2026-06-01T19:30:35.156Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/44/9d/3cb6693342acf96802dba89934a5b8da43c201d764ebd1f2c0514a3d42bd/debugpy-1.8.22.tar.gz", hash = "sha256:e489c7268e1c7b41e13b438d9c533d2a7af73fb59bf8cd30fead8286c1c39c4e", size = 1710877, upload-time = "2026-09-15T21:44:02.578Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bf/09/59324b903599031ff9faaec1758292409f6561a0ec2492fe4b703327705a/debugpy-1.8.21-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:c193d474f0a211191f2b4449d2d06157c689013035bd952f3b617e0ef422b176", size = 3968900, upload-time = "2026-06-01T19:30:52.341Z" },
-    { url = "https://files.pythonhosted.org/packages/48/57/412421516afc3055fa577516f00beec3d663f9b0ab330639547ae6c57720/debugpy-1.8.21-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:ecbd158386c31ffe71d46f72d44d56e66331ab9b16cad649156d514368f23ab2", size = 3962096, upload-time = "2026-06-01T19:30:59.235Z" },
-    { url = "https://files.pythonhosted.org/packages/8c/2d/6e7ec524984a1702777868de49a4c53202bddac2a432a76a093469587750/debugpy-1.8.21-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:3d6922439bf33fd38a3e2c447869ebc7b97da5cd3d329ff1ef9bc06c4903437e", size = 3927115, upload-time = "2026-06-01T19:31:05.863Z" },
-    { url = "https://files.pythonhosted.org/packages/95/51/67e7cf11a53e40694f720457d5b3a1cdaaa3d5a9a633e482f225456b93ff/debugpy-1.8.21-py2.py3-none-any.whl", hash = "sha256:b1e37d333663c8851516a47364ef473da127f9caebe4417e6df6f5825a7e9a92", size = 5352888, upload-time = "2026-06-01T19:31:25.186Z" },
-]
-
-[[package]]
-name = "decorator"
-version = "5.3.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/60/8b/32f9823da46cde7df2087faa08cd98d01b908f8dcab982cdba9c84e85355/decorator-5.3.1.tar.gz", hash = "sha256:4cbcdd55a6efadb9dbea26b858f4fb3264567b52d69ca0d25b721b553f60ea82", size = 58084, upload-time = "2026-05-18T06:03:28.057Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/05/7f/798705f5296a58ca505d600456748d1be48078eac8a7050d8a98bc9edb89/decorator-5.3.1-py3-none-any.whl", hash = "sha256:f47fe6fdbd2edd623ecfe36875d37aba411624e2670dd395dddae1358689bb3c", size = 10365, upload-time = "2026-05-18T06:03:26.517Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/ec/7389798843643ea5bc6a08480ba1b855377c70e2ed90189c0a03594e401e/debugpy-1.8.22-cp312-cp312-manylinux_2_34_x86_64.whl", hash = "sha256:8a697acec45dbc70d17fb5d9f4f61989fc294d273c69de487fd10cb35fdd75eb", size = 4000941, upload-time = "2026-09-15T21:44:20.245Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/4f/8df91270afbf61fc5ab202951f28451194a7eb093704b26da11cb0ecd308/debugpy-1.8.22-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:c1ffb9953708b648ecf6acd2e5ad2c002bf68785ed618197dd4463a2a7226f39", size = 3994351, upload-time = "2026-09-15T21:44:27.31Z" },
+    { url = "https://files.pythonhosted.org/packages/70/9e/994fe1fb23dd4a5a8d059a0dc95da4b0821907200a61c2d5a4fdc21b6cf8/debugpy-1.8.22-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:0c1104233340196e5cbf5514e7dfdbb98968e730cfd7fdd6f3d72a082be838a9", size = 3961025, upload-time = "2026-09-15T21:44:34.387Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/10/a323945b0966e560c40fe48470d2847bfc7692a8f550d7086e9a28f5b623/debugpy-1.8.22-cp315-cp315-manylinux_2_34_x86_64.whl", hash = "sha256:b7dbde1fb822d100802d505b2aed6d0813b1a0a2015d495cb8798b2215d5d1e5", size = 3975456, upload-time = "2026-09-15T21:44:41.623Z" },
+    { url = "https://files.pythonhosted.org/packages/56/3d/c7dc9f35bc9e22cdb53679f844bee40fd5cff871862f60595869a433400d/debugpy-1.8.22-py2.py3-none-any.whl", hash = "sha256:a9e9d3550e15ca479c59333e90845029190531f0cacfedab3b815a57bd913947", size = 5374857, upload-time = "2026-09-15T21:44:54.454Z" },
 ]
 
 [[package]]
@@ -764,14 +850,14 @@ wheels = [
 
 [[package]]
 name = "deprecated"
-version = "1.3.1"
+version = "3.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/49/85/12f0a49a7c4ffb70572b6c2ef13c90c88fd190debda93b23f026b25f9634/deprecated-1.3.1.tar.gz", hash = "sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223", size = 2932523, upload-time = "2025-10-30T08:19:02.757Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f7/9c/16649913bf14c73e0a9453782e148362ff2657067deff6aa9c7ebcddcc31/deprecated-3.0.0.tar.gz", hash = "sha256:16850204d3a1e6bb0acd06bff48d96e8b0a0d25d1c52f71705405a0f4894192d", size = 166912, upload-time = "2026-09-26T13:58:10.675Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/84/d0/205d54408c08b13550c733c4b85429e7ead111c7f0014309637425520a9a/deprecated-1.3.1-py2.py3-none-any.whl", hash = "sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f", size = 11298, upload-time = "2025-10-30T08:19:00.758Z" },
+    { url = "https://files.pythonhosted.org/packages/83/ae/676feae8e4644a6d7169951a97f61c56f416c73f67bf1761f2461d75cc81/deprecated-3.0.0-py3-none-any.whl", hash = "sha256:58204cf4a7f6270d547af5c278ee7a6bb56045a4b3d8441a1cd11660f41b7939", size = 21912, upload-time = "2026-09-26T13:58:09.458Z" },
 ]
 
 [[package]]
@@ -843,26 +929,26 @@ wheels = [
 
 [[package]]
 name = "dulwich"
-version = "1.2.14"
+version = "1.2.15"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "urllib3" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0d/7b/d03c166f6098314824bdd2e661a32563c0ebe8c5e3344ed212d2ffc96a10/dulwich-1.2.14.tar.gz", hash = "sha256:ed8bfcfe1c7e187d1bc5e78f7c128a07d229d7b425433b04cbb89334678bd3b1", size = 1361695, upload-time = "2026-08-29T00:23:51.443Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/63/b9/5b68d879282190b0429c815d582dc1f212d4910aa22ac7c1aff39675a719/dulwich-1.2.15.tar.gz", hash = "sha256:e98f775601846fb4d44a384003e93c53c42b0c9796c8fe0cc3128b8e2e0bbf88", size = 1371420, upload-time = "2026-09-14T21:20:24.556Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2c/49/bca41ab16905963a1efb045f122e69f89e8ead682a3be17a02e0701aa099/dulwich-1.2.14-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:63ec5ccc659f5d8dcf6b9948595abb0193a472998dd5ee23f2d4d43b73bbc169", size = 1459332, upload-time = "2026-08-29T00:22:34.209Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/f4/583088cbf9333263fffa8c4169e81785deca9b1146d5e38e6625197334b1/dulwich-1.2.14-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ad8b6c0ec611170af13c0ddac0b3191a20dfcd69360a06df7e60faaae14c5b29", size = 1490883, upload-time = "2026-08-29T00:22:35.903Z" },
-    { url = "https://files.pythonhosted.org/packages/17/01/b592e8f3b3de2cf3c3c773f521e3834cb7c8bc1b944c0c6c912e58d62460/dulwich-1.2.14-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:26440ebc7b8208edf9f251a7820a618bbdb53f1af71f2e5817bdbe3e6d24405d", size = 1459517, upload-time = "2026-08-29T00:22:49.875Z" },
-    { url = "https://files.pythonhosted.org/packages/5c/54/3ddaada9e16238a5f0103262ba79904a48cc87fc63300fb93a7417fc2873/dulwich-1.2.14-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e0f583a85a40a38400ce4374edccc2e6a4a5c5f34d4ab272b1d2d4fd3358944d", size = 1490305, upload-time = "2026-08-29T00:22:51.761Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/c5/86e473a7187fae33e3f3d478e72e18166a5a986275c8c6307e7198c96d8a/dulwich-1.2.14-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:a839358029e679c1f18a1fcce847d48ed9b0df7469310c72a3878c4e167447ee", size = 1461994, upload-time = "2026-08-29T00:23:04.171Z" },
-    { url = "https://files.pythonhosted.org/packages/80/b2/d56697144814152a6c8c7e94ba9ee28d180acf0c56b22a734004d8a727f4/dulwich-1.2.14-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:26a36829b8590c2c431d86acc6ec4d95d708a5efbc3df0e6cb151fa8baa7b607", size = 1492031, upload-time = "2026-08-29T00:23:06.09Z" },
-    { url = "https://files.pythonhosted.org/packages/70/63/ca17bbd708b56687093d7e9278944908f1471e72e95b5e97848d96619675/dulwich-1.2.14-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:ecdde801f145fa4cccf87914b61d34b3ba8e70b3c7e054447c07e119eeb48f43", size = 1458574, upload-time = "2026-08-29T00:23:15.906Z" },
-    { url = "https://files.pythonhosted.org/packages/ec/c3/627bd8e13bc55476967ac9849b3b040596b4e9b012558abaf960698c55d8/dulwich-1.2.14-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:404d6b78d2012299eff296f1baa808b03cf1c04dcce0972efe79005bac1861a0", size = 1539905, upload-time = "2026-08-29T00:23:17.758Z" },
-    { url = "https://files.pythonhosted.org/packages/18/eb/951fa6624e50b772bbbb15a75bc5669ae954d08170dd7b72f8e51fa33cba/dulwich-1.2.14-cp315-cp315-manylinux_2_28_aarch64.whl", hash = "sha256:ca4a6bf4ca6d9a0025523284ce82a89d6b709684c3cb25a896f407c4a55d51bc", size = 1462641, upload-time = "2026-08-29T00:23:30.469Z" },
-    { url = "https://files.pythonhosted.org/packages/45/c4/e219b9e16452843b54caa98accf42bb8af493706bba292ef06ba1871cfad/dulwich-1.2.14-cp315-cp315-manylinux_2_28_x86_64.whl", hash = "sha256:26d36df85e4247427616ad2e4aa7698adba55ee6d8c83fa5dbe93aacc2250f2f", size = 1540358, upload-time = "2026-08-29T00:23:32.517Z" },
-    { url = "https://files.pythonhosted.org/packages/2e/9b/f7a5536d8d810c90ad7d337af763e2afd713f4d2237deac34fade3ad1b50/dulwich-1.2.14-cp315-cp315t-manylinux_2_28_aarch64.whl", hash = "sha256:89aa99ae03475f12a7e66c4f649cf4b653e457b63dca1acc7b7579ca3d7aed51", size = 1459927, upload-time = "2026-08-29T00:23:42.301Z" },
-    { url = "https://files.pythonhosted.org/packages/8c/5f/13e07d1a1ab152dcbe0433791f1aed66c8b21790270ee3e7424c3559dc98/dulwich-1.2.14-cp315-cp315t-manylinux_2_28_x86_64.whl", hash = "sha256:2e5b14e5d01b7020a39acad4d7990c5da379ac0cba08d1d7121f3d036dc80983", size = 1493129, upload-time = "2026-08-29T00:23:44.198Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/45/5c62eeb71c6005fa4f1b5101def14271eb1063720fefcde17bcc95c0fb42/dulwich-1.2.14-py3-none-any.whl", hash = "sha256:3e1b6e13e23f9bc633fd249563e4d7a946d7d470f210967853ae0991589edd11", size = 730142, upload-time = "2026-08-29T00:23:49.691Z" },
+    { url = "https://files.pythonhosted.org/packages/72/4f/62f119f6bc561e003aa034cf6e5f3712cdb78a5ff5e49bbace1a98bd07d1/dulwich-1.2.15-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:3731e3e2e42326154bdbe67033347d0c7ff2de0db43409a4cc5998e15361f717", size = 1536952, upload-time = "2026-09-14T21:19:19.817Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/cf/59abd7286933c61dc1239c9cebb0482282ae9b4ab6dada39c7248b0bc8b8/dulwich-1.2.15-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:d384c3a26fe6f369671b74331aaf25f956c88aeaba50b70bfd3aa6a12d431423", size = 1511946, upload-time = "2026-09-14T21:19:21.226Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/f7/1291d8bf56f42ccb391a6a08c07e0f9645d6098031d5389c8a2177fc8582/dulwich-1.2.15-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:38b66910c0bc78c59964ef665df657833a4372ab6a4e5ae2969d8c49d9769687", size = 1536850, upload-time = "2026-09-14T21:19:31.638Z" },
+    { url = "https://files.pythonhosted.org/packages/00/c0/16c7707f832b07f0b625a0f52d4a618e3c7530a81145c18ca746aa6014a9/dulwich-1.2.15-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:82d4511bec54b72baca40b728f3fcff3b00ab3d2281cfe4fd21c59d7e689406e", size = 1511293, upload-time = "2026-09-14T21:19:33.35Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/5f/d6a2def67402a1503eaa4ceb76d6b9415845d7fea389b79cde51bbf89bcd/dulwich-1.2.15-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:9d5fa7a5e26ab776311af4534a93b77a2d36d4f1b52ba3ab74a87edd20583996", size = 1537517, upload-time = "2026-09-14T21:19:44.1Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/b3/c88fe72cc1d7213060395ba5757034b3a9154cfdfdd4d886d1f9e43d5004/dulwich-1.2.15-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:cf77898f405f258a31d597cba179dea05eefb50dae59a3221c50a7f9113cc691", size = 1574759, upload-time = "2026-09-14T21:19:45.594Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/1e/928fdbb2d9908471fd24db8cdaa327d95db1880d3de55b39cd3bb6c808e6/dulwich-1.2.15-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:ba9412fad4e3548971f0a4c8967341b356771578035876a4f01a762a42f2527b", size = 1535083, upload-time = "2026-09-14T21:19:53.261Z" },
+    { url = "https://files.pythonhosted.org/packages/da/73/f12ca680f45d5200df6811f3be139ee9a344ac602ec9e3e28152db29dad7/dulwich-1.2.15-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:5fe361c6db07463c32a4c0841abdd7285e3686674e3eb4b9dc5944b531e74489", size = 1512962, upload-time = "2026-09-14T21:19:54.744Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/0f/7401ecc53624bb0f4ee1e055d857402295bc673e0da564ebd4573e013cd2/dulwich-1.2.15-cp315-cp315-manylinux_2_28_aarch64.whl", hash = "sha256:f157812ce877d50a93336136a1b9dd2b368395801a96cd8484c257a4f4665c23", size = 1538214, upload-time = "2026-09-14T21:20:05.783Z" },
+    { url = "https://files.pythonhosted.org/packages/53/e9/3f337ba533ce5d953f63f8cd97016f44e3352686d4813b6daec89a7bd3e5/dulwich-1.2.15-cp315-cp315-manylinux_2_28_x86_64.whl", hash = "sha256:5b6f10587589f759747d167d1b6750de62961b17b4fb8c2be4ead49557c50ab9", size = 1576086, upload-time = "2026-09-14T21:20:07.456Z" },
+    { url = "https://files.pythonhosted.org/packages/42/d5/059f76895bc1ce8df673b7ba7ffb27655420f46f6709a526e6c3c9073b3a/dulwich-1.2.15-cp315-cp315t-manylinux_2_28_aarch64.whl", hash = "sha256:63074719524d876e0688cd522dbe9c84aaea999a7018507db24a8fc134036d73", size = 1536812, upload-time = "2026-09-14T21:20:15.771Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/90/bc94d35dcd9caa1819372d4ad7a2693032397687961cd30793648db246c5/dulwich-1.2.15-cp315-cp315t-manylinux_2_28_x86_64.whl", hash = "sha256:c18578b0af944eae2198bd3c81084a2f2cb8eb4b993fb5ce95ec823764d5b2f0", size = 1514614, upload-time = "2026-09-14T21:20:17.479Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/96/b4c8f1cf6c69811098415903ec299cdea87576c51b0c1f13f21215368c63/dulwich-1.2.15-py3-none-any.whl", hash = "sha256:5c863992962bab0fc5f75be132399a16f670f2a9283faf9aaeb953fd8891db83", size = 733132, upload-time = "2026-09-14T21:20:22.697Z" },
 ]
 
 [[package]]
@@ -916,30 +1002,30 @@ wheels = [
 
 [[package]]
 name = "fastjsonschema"
-version = "2.21.2"
+version = "2.22.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/33/a4/9473c7c3b87009d9c1d74034e4a0f6a35ff0d42dd0f9866d0c3ec4e9217b/fastjsonschema-2.22.2.tar.gz", hash = "sha256:72064e12356a7d6ef02165be2946b9abadbdf238536e07eb587e3dbaa33099cf", size = 385171, upload-time = "2026-08-15T19:47:08.853Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" },
+    { url = "https://files.pythonhosted.org/packages/49/82/2755c7c982086f00d4dab85bc120ec35045a9fc2191893a6ce79afe94443/fastjsonschema-2.22.2-py3-none-any.whl", hash = "sha256:0fb3915616adac85ccfdd737d26be1089845d2019819505b42d39888458f74d4", size = 27413, upload-time = "2026-08-15T19:47:04.406Z" },
 ]
 
 [[package]]
 name = "fitsio"
-version = "1.4.1"
+version = "1.4.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/34/6b/5658778e070faaa194f1a1133527cedfa948d173daed29d61f25c5487ab6/fitsio-1.4.1.tar.gz", hash = "sha256:2b2bf0bfd37790114b44283931c59a14b1fec25a653bf65c4fc555b8d3b24de9", size = 4148217, upload-time = "2026-07-10T21:21:31.585Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3b/fd/53bfe8986f7b242fb5b66ddc9344fe67614102f44438415b0a8b32cf7f0a/fitsio-1.4.2.tar.gz", hash = "sha256:92a02f0e63d539d85ca5a185ae0cc8d40029270858275964ee2539ee0136f0c3", size = 4178786, upload-time = "2026-07-16T12:31:06.723Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/38/a0/86e2bde46aa5559b7b9544208def3045675d055f0d132b235ff325279a9b/fitsio-1.4.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:de7ac1df97ce68626dac1da5014e6b396cff2789d1c617c36df03d6bc6d98a2d", size = 881494, upload-time = "2026-07-10T21:21:09.496Z" },
-    { url = "https://files.pythonhosted.org/packages/a5/53/f7f644b812e572c57015ef5215f982922d9cdac8d239549c136ba48f2850/fitsio-1.4.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:34df9129decab3ae85377601668b82442159ed704800bd1ed047428bc532c96d", size = 810043, upload-time = "2026-07-10T21:21:11.254Z" },
-    { url = "https://files.pythonhosted.org/packages/83/5d/d71993140df9f939e81d2a5531f26d15cbd700855348a756921b0ad24235/fitsio-1.4.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:161806de2bb9971e271379558616d2cef795ce9590923270ec3bbdadea0d0b2f", size = 913505, upload-time = "2026-07-10T21:21:16.695Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/ae/e89778256e0aee3e5b51a620cdf9d91ac89f348504045461bee28006275d/fitsio-1.4.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5ed27e5b8aaf1ae51718c6624d00fec1d6054fddde8d602eaea3bb6686bcfb0a", size = 815241, upload-time = "2026-07-10T21:21:18.018Z" },
-    { url = "https://files.pythonhosted.org/packages/7c/47/7f64a60d883a4b69a0a15c1ec60ba9313d8e774af83ac2b00f57d511b76d/fitsio-1.4.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:1ab4eadb13caf362630d45bce7cf7e57e407d3c971f361f639ca9c316f460bb2", size = 913536, upload-time = "2026-07-10T21:21:22.556Z" },
-    { url = "https://files.pythonhosted.org/packages/de/46/9f45b3c8bd371057f1333aec8f92879cb786fe9ed05b6b0c2643fd228558/fitsio-1.4.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2627eeec3dff4b692402babb88aec3cccbf73a3ee22d19b4accfe76491bd67a8", size = 815239, upload-time = "2026-07-10T21:21:23.61Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/3b/9a6d92637db9a603a18681f60689c30208f2485016ed91280e42b927df57/fitsio-1.4.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:5f499149738fdce6afa3324a76fd65a762082647d8b1d30a356dc002dd4a9178", size = 928341, upload-time = "2026-07-10T21:21:28.075Z" },
-    { url = "https://files.pythonhosted.org/packages/0a/fa/eeb997744bb1c2c9308dab9b492bcdb1843e4eb3dc5d1697d2f0a312c8b7/fitsio-1.4.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c2a0dba7a868b4b1716d7468431fda51bc89ee8d8f2a643ac28f9200d99d2e2b", size = 815548, upload-time = "2026-07-10T21:21:29.202Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/18/7b1bd9e3242e8c83644b4edad345a2faa01f215b4b65cd5fec88519154fc/fitsio-1.4.2-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:430932e6f684fb5e669c1b6e4d4164c52a055eb04af1a18dfa086f343491f9a2", size = 895701, upload-time = "2026-07-16T12:30:39.992Z" },
+    { url = "https://files.pythonhosted.org/packages/df/0c/00a098bd0fccf95f9d068e00691ba2a67e73505870b40694680453e94ca8/fitsio-1.4.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:762e41fac17e546685d4fee4738ec3853e3c40bd15b3444902256e303ef4302a", size = 812094, upload-time = "2026-07-16T12:30:41.536Z" },
+    { url = "https://files.pythonhosted.org/packages/77/13/c85557035c01150e6b882bcf1b2131a78d767def1906527b2989b3b5d819/fitsio-1.4.2-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:91fe0904d9f6a77a3c12f1943e28994c7d8c60a10b98506fbeb16fe9c2422106", size = 920719, upload-time = "2026-07-16T12:30:47.306Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/2d/af26509323791b9fce7ded6e2f45640afb12c0d3ff766f03a96d2524d2c5/fitsio-1.4.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0e1652ff0c9fab23f22dc3309edd6755755d2aaaa79e3e492db70b947cc1d4c8", size = 818040, upload-time = "2026-07-16T12:30:48.699Z" },
+    { url = "https://files.pythonhosted.org/packages/31/ae/2d48a7e9e093101a2b070592ce87ab6beea0a0778be411e529b2fa3d9967/fitsio-1.4.2-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:7a4ce82fe174b129a4f90080de7cb3bbb7524f1c52268410facbd48086fa4d50", size = 920693, upload-time = "2026-07-16T12:30:54.369Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/28/0943bcabd64b7bc9c69e7b09e2ea5861136ca9477c5fd9d399c3dbeaf450/fitsio-1.4.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:90c9a71f023199d48ab0578f0ecb89a3e96acbc5e898acb8f9cc73e09251b17b", size = 817988, upload-time = "2026-07-16T12:30:55.996Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/f1/12ca8f1a91a2c5ff06b88b65c936bcaf2427abcd2045d86b6dc50858344e/fitsio-1.4.2-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:2d60911c885b848c9837e5e2417ef5efe5ce02e79371ac6461c5ac120cacce6c", size = 939977, upload-time = "2026-07-16T12:31:02.308Z" },
+    { url = "https://files.pythonhosted.org/packages/17/10/04fdb12633307e700127d13e3ff7cf02101367aa315801e327d690b4cf07/fitsio-1.4.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b878ec0ad199bc857339fbd58cd76701a636ade3f0947f752846996e3e381236", size = 818384, upload-time = "2026-07-16T12:31:03.945Z" },
 ]
 
 [[package]]
@@ -963,45 +1049,71 @@ wheels = [
 
 [[package]]
 name = "fonttools"
-version = "4.63.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/84/69/c97f2c18e0db87d2c7b15da1974dace76ae938f1cfa22e2727a648b7ed43/fonttools-4.63.0.tar.gz", hash = "sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0", size = 3597189, upload-time = "2026-05-14T12:04:30.958Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/44/04/0b91d8e916e92ad1fac9e4624760baf0fd5ff2ead614c2f68fb21373f03f/fonttools-4.63.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af", size = 5044298, upload-time = "2026-05-14T12:03:18.085Z" },
-    { url = "https://files.pythonhosted.org/packages/77/c7/2342da9830e3e9d4870305ca5d2091d2a83284f2953079b7bdd3b5e029d8/fonttools-4.63.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:58dc6bb86a78d782f00f9190ca02c119cf5bbe2807536e361e18d42019f877d8", size = 4999800, upload-time = "2026-05-14T12:03:20.161Z" },
-    { url = "https://files.pythonhosted.org/packages/e6/6d/67fe16c48d7ce050979b33f47e0d28a318f02da030602e944c34f7a16ef3/fonttools-4.63.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b", size = 4982666, upload-time = "2026-05-14T12:03:22.87Z" },
-    { url = "https://files.pythonhosted.org/packages/f2/00/3bbab338c07c71fa56269953845e92c951a61457bbbb0f1022551ea266d9/fonttools-4.63.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78", size = 5133598, upload-time = "2026-05-14T12:03:25.168Z" },
-    { url = "https://files.pythonhosted.org/packages/8e/40/e76320afa1df918e146155ef239b1719ee266092e96f5423bfd075affba1/fonttools-4.63.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d", size = 5024840, upload-time = "2026-05-14T12:03:36.745Z" },
-    { url = "https://files.pythonhosted.org/packages/ce/36/0b805d8c485f872f65a509cbe3b58a5d0d17bee855333b54a150c79d3061/fonttools-4.63.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68", size = 4975801, upload-time = "2026-05-14T12:03:38.833Z" },
-    { url = "https://files.pythonhosted.org/packages/c8/26/2cee03d0aa083ab022da5c07aff9ed3f689da1defb81ad6917c9627896da/fonttools-4.63.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be", size = 4965009, upload-time = "2026-05-14T12:03:41.494Z" },
-    { url = "https://files.pythonhosted.org/packages/7e/48/cc4b66d9058c0d0982c833fad10127c4b0e9324606aafa41382295ca4102/fonttools-4.63.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27", size = 5105892, upload-time = "2026-05-14T12:03:43.525Z" },
-    { url = "https://files.pythonhosted.org/packages/dd/87/64cfa18a7a1621d17b7f4502b2b0ed8a135a90c3db51ea590ee99043e76b/fonttools-4.63.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b2248c5decb223562f7902ff6325077a073f608ee8e33e88ad88db734eb9f49", size = 5010526, upload-time = "2026-05-14T12:03:54.647Z" },
-    { url = "https://files.pythonhosted.org/packages/36/e1/a8933a72c45a87177fbde2696e0d0755c8c9062f8c077a961c6215fa27b1/fonttools-4.63.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:308f957cdeaf8abe4e5f2f124902ef405448af92c90f80e302a3b771c2e6116b", size = 4923946, upload-time = "2026-05-14T12:03:56.984Z" },
-    { url = "https://files.pythonhosted.org/packages/27/60/872e6e233b8c5e8b41413796ff18b7fe479661bd40147e071b450dfad7a1/fonttools-4.63.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6", size = 4962489, upload-time = "2026-05-14T12:03:59.443Z" },
-    { url = "https://files.pythonhosted.org/packages/30/c4/83c24f2ec38b90cfda84bf4b1a1f49df80e84a1db4e7ac6e0d41bf23bc39/fonttools-4.63.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4", size = 5071870, upload-time = "2026-05-14T12:04:02.122Z" },
-    { url = "https://files.pythonhosted.org/packages/ea/2b/0aa8db70f18cf52e49b4ed5ecec68547f981160bf5ded3b5aed6faa0a6f9/fonttools-4.63.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096", size = 5148649, upload-time = "2026-05-14T12:04:12.747Z" },
-    { url = "https://files.pythonhosted.org/packages/7f/63/18e4369c25043096f1048e0c9915951adc4f842bd81c6b18155824d6fa99/fonttools-4.63.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:51394295f1a51de8b5f30bdb1e1b9a4231536c7064ef5c6e211eec19fa36036f", size = 4932147, upload-time = "2026-05-14T12:04:14.806Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/3f/67f3eac2ffd8a98446c5022f8ed3864eac878a5ff7af8df4c8286dba16cc/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40", size = 5027237, upload-time = "2026-05-14T12:04:17.675Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/ba/4e6214cb38a7b04779e97bb7636de9a5c7f20af7018d03dee0b64c08510a/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:796f27556dbe094c4824f75ca85267e4df776c79036c8441469a4df37038c196", size = 5053933, upload-time = "2026-05-14T12:04:20.818Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" },
+version = "4.66.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/87/b6/126c659ab7e0e03e01a5f5d223abf7b2c0691ae92718085a212a3924a2a3/fonttools-4.66.1.tar.gz", hash = "sha256:64967c6ddb0d4c610dfd8cb1485981b2d27972ddfb7d4bbbd9e199d2a089c450", size = 3694174, upload-time = "2026-09-29T16:11:53.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/75/c0/5810d73f9102eb1a08f26b8f7a6c22498622b43e05e684cd5ec602be8ffd/fonttools-4.66.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:09ae73bd219e1245debd8376077a0fa6e03175e255c4f51bae5f6a271bfe384a", size = 5416460, upload-time = "2026-09-29T16:10:12.182Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/6e/babde908b879a3b51ffc230919d06a804912559dd4cf8c94f3fa2af69fec/fonttools-4.66.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7b8ff9e0edbcee2fbf7dff0c41b9041c1901c26acf64e23adb67495012df11de", size = 5397326, upload-time = "2026-09-29T16:10:14.133Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/98/8522cc7a5e5ad64a2b2b6e9598489809ed4956c532b887c65b131b8500b7/fonttools-4.66.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:38ce8f5fbd5c17dd2153d47d7c8d4108f3deda3f2b4a79b60ddc470a58faded3", size = 5353857, upload-time = "2026-09-29T16:10:16.375Z" },
+    { url = "https://files.pythonhosted.org/packages/15/f9/ab87d67c23178886e57f24397b3113b4ac35297f086467c2c4d4231671a8/fonttools-4.66.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8aed2bbcd6216253ef1b015763593365ee8084f621dfa53bb957c19d5e05f7cd", size = 5517604, upload-time = "2026-09-29T16:10:18.791Z" },
+    { url = "https://files.pythonhosted.org/packages/db/e7/11e5e6beb7e336d80f0ca870ae080033a91ebfe34fd5390dbcf78f8df56f/fonttools-4.66.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1801fdad5600118327171e0e8aa79f7cc48831dd55ab36998c9de03bd5ffe6cd", size = 5389836, upload-time = "2026-09-29T16:10:28.988Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/1c/6ec22372362b03350fe3da7bf33491a07cc9a553a36dd2383b76ec1741eb/fonttools-4.66.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:83572afe48733bad7a4a9c11721d3a726c2e976d82b063fc9bdd049d76955abd", size = 5372059, upload-time = "2026-09-29T16:10:31.011Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/4a/cb7971f1c0f40f891028ee8c46dadc6897ef61e44aa925a23fba2ef06e2a/fonttools-4.66.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:08d8956e3ec990c75230d92f1630b215e8f3738c83a003421c22b31ebfd0ce15", size = 5332824, upload-time = "2026-09-29T16:10:33.563Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/86/563e671f1d43fa8ffb2518d7fe16630fb16c7faf0420cc39f8e80181f486/fonttools-4.66.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:fdf4afd75c643e60ef4a96fe64fc8a9def27d2a542112332371a9e5066885f9a", size = 5490659, upload-time = "2026-09-29T16:10:35.788Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/65/10b5caa2aa779e62411b67949bda9741d4d7532ba0b6dea647b715131260/fonttools-4.66.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7cf4f996f9b1cb549bff9ea4c50813988a26ec922c95cfa85c7e4f1270447e06", size = 5374278, upload-time = "2026-09-29T16:10:45.727Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/db/9ac5c6773feec1b40e57eac106d869886f66a1e44082d343ac1e1e1fb773/fonttools-4.66.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9261ef507f2dd74203443a472b65b5a26429eb378f975016dec7dc7305b24898", size = 5317826, upload-time = "2026-09-29T16:10:48.056Z" },
+    { url = "https://files.pythonhosted.org/packages/04/0a/69beb11f6b714ac90ee73ad4600ac91d7dd4e1ce361d087c8425bb8472de/fonttools-4.66.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e1cde50b3ec84ca6fe63ca815de183dbecb88e8adf8ada82d8ea130ef12b2b43", size = 5316239, upload-time = "2026-09-29T16:10:50.201Z" },
+    { url = "https://files.pythonhosted.org/packages/33/a8/7a77359e469d3a638df91d3e225cef4a3c1184c20e98381238042f7835fa/fonttools-4.66.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d8f0a8f16c4f3a5a87ca971de2631792d8cb4d570951f2000acf712f157d40db", size = 5449423, upload-time = "2026-09-29T16:10:52.337Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/c3/3f4b761037ebc2e5597c52c218a9e95dbc4a2cab572828654f6004f422f5/fonttools-4.66.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c47299bca4b5acaaeb32100f77b944feea151de9ef1773365a410dc3d49b945b", size = 5546932, upload-time = "2026-09-29T16:11:03.126Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/d1/3f506cc79608becbc287785db8c44eb3f93079b49752266eb9f57700ecc4/fonttools-4.66.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dfba62cc93199ba62c376f90f2a9147d92730d301e44f88e013e50ff5edf6193", size = 5353546, upload-time = "2026-09-29T16:11:05.394Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/27/6534d84430ba1641185f8a0c9e2c7ecd395b15ff98f96967e3fb3c728b09/fonttools-4.66.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2c7340497cf53490293e0c2b61011e0191633022ede0a0a964a68157a98b0fb4", size = 5414313, upload-time = "2026-09-29T16:11:07.618Z" },
+    { url = "https://files.pythonhosted.org/packages/27/17/831ceca06d78855b11dc203b0e3ba5e6fd8a63a71ee0343ea8bd367fda55/fonttools-4.66.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c666fefdd5613a0e99aa4516e6ff4ef87aa86cf1c7ba12a73550f4770e46b750", size = 5449661, upload-time = "2026-09-29T16:11:09.997Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/b6/05a093ec04fa2ad449ecc67638aad0f8d60df380df2471b68b549fe2a4b2/fonttools-4.66.1-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d84ac0bf776b68396185bd919dd29e633d94300660335efc40b55b294b886903", size = 5371509, upload-time = "2026-09-29T16:11:20.742Z" },
+    { url = "https://files.pythonhosted.org/packages/65/a9/55effa83e64b9ff4f379d9186236d50d03f6d4770d8346805c1b6620c370/fonttools-4.66.1-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0dc6fd99cb8c30941036308b148da9432640442a6f26f36d71dad9be24cbd0e9", size = 5334853, upload-time = "2026-09-29T16:11:22.928Z" },
+    { url = "https://files.pythonhosted.org/packages/af/a8/44bb4021c585b76f8e480116e1f3fca62eb7d88fe5794e2ec84c10d2da76/fonttools-4.66.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:d3b5403e82d0c7659ff1d9f956e29a3a68d094f043e9f5bc0442796fc3a4fb58", size = 5311704, upload-time = "2026-09-29T16:11:25.393Z" },
+    { url = "https://files.pythonhosted.org/packages/63/dd/dd482902fb7fd8b71d3b6508431a57938b5e41b29bf6fb252ed3cfce065f/fonttools-4.66.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:b8b71db96d605784e2c5ebf0788a406018ea8fdd80338491f4c83613d5cd1fec", size = 5459477, upload-time = "2026-09-29T16:11:27.536Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/c6/cae2f6ebe38f8927a8d0978a349b202047268016344991a14ae978c2aee3/fonttools-4.66.1-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2aeb745f2664eb811026997c95628071137a777ea2ad296deec9cb393f0b23cf", size = 5524861, upload-time = "2026-09-29T16:11:38.099Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/14/1941629956b526d6fb46ee764cf0942221f0238581adb94de0ac229fe67f/fonttools-4.66.1-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3087a430722aba8de429c2539fd2a58a9cf05238cdfefd8626460001052ca878", size = 5346561, upload-time = "2026-09-29T16:11:40.366Z" },
+    { url = "https://files.pythonhosted.org/packages/62/1f/b7e7f4757dcae74285f4ecd8453d870d63c7ba38a3d46bd9175a124c350b/fonttools-4.66.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:058cd823b80bac59e64dfad9e3b6fcd677852f9a3804971bbf6b48cc611e785c", size = 5392826, upload-time = "2026-09-29T16:11:42.653Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/71/76db3cbdcfac0e9b3ba26e1e6e8740040cfe5f7b5199dfb9b854bc8da2c3/fonttools-4.66.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:56d41d650cb8fc6cfe1d85ed7c62a0a56cbeed07bc65ca795475b914d401312a", size = 5439233, upload-time = "2026-09-29T16:11:45.088Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/10/d45b74135d5d642cb3a4fb0a957c1613ef93de4c8548671dfc3a5bf38299/fonttools-4.66.1-py3-none-any.whl", hash = "sha256:7234ae9e28db64273fbbfa72caebd0a97e3bdba6b05064114741b9539ef339d0", size = 1202222, upload-time = "2026-09-29T16:11:51.678Z" },
+]
+
+[[package]]
+name = "formulaic"
+version = "1.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "interface-meta" },
+    { name = "narwhals" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "scipy" },
+    { name = "typing-extensions" },
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f1/87/e7b9b39d218793f159d7425e264c0ac196da07f1decc333001502fac02fb/formulaic-1.2.2.tar.gz", hash = "sha256:c99e8f11ff7d327eaecaf63855ca69b7fa0da100ad6c0041ef80912fbac667e6", size = 657522, upload-time = "2026-06-02T18:24:42.632Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fc/75/0576b03f7889ad25b5385b4f6c69e0543713425a6f193b056c9b0a2e65ce/formulaic-1.2.2-py3-none-any.whl", hash = "sha256:0f84ff49e3fc9dc0e68ab08a0a9427874021aa6c558e66b44dc634a35739b09b", size = 118939, upload-time = "2026-06-02T18:24:41.344Z" },
 ]
 
 [[package]]
 name = "fqdn"
-version = "1.5.1"
+version = "1.6.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015, upload-time = "2021-03-11T07:16:29.08Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/02/01/248d44fbda9e78fa1a7b5a475194bef4c4a3e285db286419f3bb42f2efdf/fqdn-1.6.0.tar.gz", hash = "sha256:e39bf62e2a9481aa2cb780554fdfd944dd8ee5f230eb831684d539cbafed8de2", size = 130737, upload-time = "2026-09-28T05:43:35.482Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121, upload-time = "2021-03-11T07:16:28.351Z" },
+    { url = "https://files.pythonhosted.org/packages/15/d5/a8f9149b97cced9e1f4bff92ff0b6206120e2c853ae774d7020d6ecd8fa8/fqdn-1.6.0-py3-none-any.whl", hash = "sha256:6c793c312ccfa29981e1135b3ef5e1277ebe639731a6b0f7350d90e96e63f0b4", size = 12569, upload-time = "2026-09-28T05:43:33.951Z" },
 ]
 
 [[package]]
 name = "fsspec"
-version = "2026.6.0"
+version = "2026.9.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/10/a1/ae4e3e5003468d6391d2c77b6fa1cd73bd5d13511d81c642d7b28ac90ed4/fsspec-2026.6.0.tar.gz", hash = "sha256:f5bac145310fe30e16e1471bd6840b2d990d609e872251d7e674241822abf01a", size = 313646, upload-time = "2026-06-16T01:57:28.105Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/77/cd/9be253869fc42e764de7f3dedd6969af7d44ff9c3375214a3442a6f3fc08/fsspec-2026.9.0.tar.gz", hash = "sha256:0f08147951c8cb31d844c3547d631053b127863b60be04cf06e121333ee0e2fe", size = 333545, upload-time = "2026-09-18T17:50:42.825Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/22/4222d7ddf3da30f363edaa98e329c2bce6c65497c9cb2810931c8b2c0fbc/fsspec-2026.6.0-py3-none-any.whl", hash = "sha256:02e0b71817df9b2169dc30a16832045764def1191b43dcff5bb85bdee212d2a1", size = 203949, upload-time = "2026-06-16T01:57:26.358Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/c0/a98505f18594f1bce828bb159cec0fcf9860562f1a2c85913409fc8f3d9e/fsspec-2026.9.0-py3-none-any.whl", hash = "sha256:8dd6e646e99ea382bd85f97a45e6b526a442d79423a7dc673f1e2756d05fcb5f", size = 221738, upload-time = "2026-09-18T17:50:41.341Z" },
 ]
 
 [[package]]
@@ -1057,16 +1169,18 @@ wheels = [
 
 [[package]]
 name = "google-crc32c"
-version = "1.8.0"
+version = "1.9.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/03/41/4b9c02f99e4c5fb477122cd5437403b552873f014616ac1d19ac8221a58d/google_crc32c-1.8.0.tar.gz", hash = "sha256:a428e25fb7691024de47fecfbff7ff957214da51eddded0da0ae0e0f03a2cf79", size = 14192, upload-time = "2025-12-16T00:35:25.142Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/fa/25/9cb0c1c31c45b893eb8f11ae70b3f4309432d59b5acaebca5dbe791729a4/google_crc32c-1.9.0.tar.gz", hash = "sha256:7b8c84c3d159ab6817fe3f74e6e6cef099c3f95dcec3abc0d8afb1404642efbe", size = 14857, upload-time = "2026-09-24T21:39:32.067Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ce/a9/a780cc66f86335a6019f557a8aaca8fbb970728f0efd2430d15ff1beae0e/google_crc32c-1.8.0-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:14f87e04d613dfa218d6135e81b78272c3b904e2a7053b841481b38a7d901411", size = 33364, upload-time = "2025-12-16T00:40:22.96Z" },
-    { url = "https://files.pythonhosted.org/packages/21/3f/3457ea803db0198c9aaca2dd373750972ce28a26f00544b6b85088811939/google_crc32c-1.8.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cb5c869c2923d56cb0c8e6bcdd73c009c36ae39b652dbe46a05eb4ef0ad01454", size = 33740, upload-time = "2025-12-16T00:40:23.96Z" },
-    { url = "https://files.pythonhosted.org/packages/ce/42/b468aec74a0354b34c8cbf748db20d6e350a68a2b0912e128cabee49806c/google_crc32c-1.8.0-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:3b9776774b24ba76831609ffbabce8cdf6fa2bd5e9df37b594221c7e333a81fa", size = 33344, upload-time = "2025-12-16T00:40:24.742Z" },
-    { url = "https://files.pythonhosted.org/packages/1c/e8/b33784d6fc77fb5062a8a7854e43e1e618b87d5ddf610a88025e4de6226e/google_crc32c-1.8.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:89c17d53d75562edfff86679244830599ee0a48efc216200691de8b02ab6b2b8", size = 33694, upload-time = "2025-12-16T00:40:25.505Z" },
-    { url = "https://files.pythonhosted.org/packages/56/15/c25671c7aad70f8179d858c55a6ae8404902abe0cdcf32a29d581792b491/google_crc32c-1.8.0-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b0d1a7afc6e8e4635564ba8aa5c0548e3173e41b6384d7711a9123165f582de2", size = 33381, upload-time = "2025-12-16T00:40:26.268Z" },
-    { url = "https://files.pythonhosted.org/packages/42/fa/f50f51260d7b0ef5d4898af122d8a7ec5a84e2984f676f746445f783705f/google_crc32c-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8b3f68782f3cbd1bce027e48768293072813469af6a61a86f6bb4977a4380f21", size = 33734, upload-time = "2025-12-16T00:40:27.028Z" },
+    { url = "https://files.pythonhosted.org/packages/94/78/dd1935432337e5da7af391a6fc9f161c1c8e9b9002a402b9190135fe1b59/google_crc32c-1.9.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:16865b477d7941712cb0e0aad8ad4815e984fb5fc16d3fdaef7d986e26e53c95", size = 37775, upload-time = "2026-09-24T21:38:09.249Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/43/9db03635bb10188d93dcbab9baa2a8670a0da4e868b4370cdbd98d65fed8/google_crc32c-1.9.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3abb18297d9ef0ab120531838be0e6d68c9fa876570e11c229c48f2edac23ce7", size = 36347, upload-time = "2026-09-24T21:38:10.141Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/18/bee0dd59ae622482dc6463636c79e4bde7c954d061c859c9256362c9931a/google_crc32c-1.9.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4488f1553a9ab7e86cdedc833374a7e904031803b995dc0bd0be48c271fa6556", size = 37776, upload-time = "2026-09-24T21:38:11.056Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/b6/e76e80fed5f2558273c7839e622f98095c9b36c719c7147e38e3c055cb70/google_crc32c-1.9.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0568b17ed90ac596f29400d99e243fd0cc6276766183def888d1bf8d1dc13827", size = 36350, upload-time = "2026-09-24T21:38:12.138Z" },
+    { url = "https://files.pythonhosted.org/packages/03/73/388e493d6c3e252e37165d22efe5a1361f872a24425391b999822861b23a/google_crc32c-1.9.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:265233aff33d835f5b909584fe36ab29647b598c271b661a300001099109e53e", size = 37925, upload-time = "2026-09-24T21:38:13.32Z" },
+    { url = "https://files.pythonhosted.org/packages/98/36/190d32caa363ef25d685f422ed1bbf93ff1140fb22fd4d90f24cec209977/google_crc32c-1.9.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dee799544cae42a42b17a88e38b59cf2c271051dc001da2117a8ff240ffa0548", size = 36495, upload-time = "2026-09-24T21:38:14.211Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/f8/7e33845d6b90ce1cf37cfabf25cb859277c7d3533ef1b6b1e1ca58581549/google_crc32c-1.9.0-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f97c3806dcea41c29c04965347b0e12481561b75e0045dc7a4f69d75dec5d9b1", size = 38359, upload-time = "2026-09-24T21:38:14.983Z" },
+    { url = "https://files.pythonhosted.org/packages/36/ff/556b2423f449a7515af6b8222a4d7833cbe09ff3e8d2f0b80471f5f6d02e/google_crc32c-1.9.0-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0abe7e202c25909869c35672ab0f2fe748a7acf276eb78577332a7c38999740f", size = 36858, upload-time = "2026-09-24T21:38:15.799Z" },
 ]
 
 [[package]]
@@ -1080,15 +1194,15 @@ wheels = [
 
 [[package]]
 name = "h2"
-version = "4.3.0"
+version = "4.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "hpack" },
     { name = "hyperframe" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026, upload-time = "2025-08-23T18:12:19.778Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/85/7c366e69d84c17bb778fe41419e1fbcce3033d5b7ce29bbffff0a98b859f/h2-4.4.1.tar.gz", hash = "sha256:4e866ffb1a869ae14dd9b5e6beb5c24a13da0495ad72b65925ded182521c1516", size = 2157281, upload-time = "2026-08-03T11:45:09.509Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779, upload-time = "2025-08-23T18:12:17.779Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/22/e85faf23bd72a92d1921e37d674ca56eb298a3c8be31fdecef0ff2b3aaac/h2-4.4.1-py3-none-any.whl", hash = "sha256:0e25f1462b23c9cb82d9eb02e28bc706dac2a68cb457c6a0d74d63c8a2a5d0e6", size = 62636, upload-time = "2026-08-03T11:44:59.164Z" },
 ]
 
 [[package]]
@@ -1135,39 +1249,39 @@ wheels = [
 
 [[package]]
 name = "healpy"
-version = "1.19.0"
+version = "1.20.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "astropy" },
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e3/4e/6f5328f375f88c8c38314f24f82b7e1486ec0358c557779500382db1503c/healpy-1.19.0.tar.gz", hash = "sha256:28e839cb885a23d36c77fc3423a3cb9271a07fda94085bd12fc329f941130ec5", size = 4075006, upload-time = "2025-12-02T08:27:19.766Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/54/dd2fa7e6b12f8d3bcc34d61c72cbc1bbbad9b3142583d40803825abea2f6/healpy-1.20.0.tar.gz", hash = "sha256:03b0e1551ae235c7290e9bfdf69fa9cdc194f8a2be51198c1924b15d826102ed", size = 66326598, upload-time = "2026-07-23T20:15:51.281Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/17/51/c07439b92bc05966006897762b7291d7d2c83fde8807b8d53a48e140a8b4/healpy-1.19.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:730095eef4bbe3c94039edb330d1800285cf43c7e16a81ce5b87edb31a8ffa42", size = 8239086, upload-time = "2025-12-02T08:26:53.076Z" },
-    { url = "https://files.pythonhosted.org/packages/61/c6/42745aeff172755e55af2487491c7a7475fe1068f152850cc14d64e04f30/healpy-1.19.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:89dbe14b270b5479a9652d1ab9f28fc7fce34253549db665f2e91e6cea588883", size = 8255685, upload-time = "2025-12-02T08:26:54.361Z" },
-    { url = "https://files.pythonhosted.org/packages/9d/86/2a223bd92dde578de34f07095c71152c84b158644782c4057ca9b25e53b5/healpy-1.19.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:29bb5f7acde68ca5850c3a59ba33cacf4c3768c9e85f7013a99aa5fbd127ce84", size = 9042629, upload-time = "2025-12-02T08:26:55.773Z" },
-    { url = "https://files.pythonhosted.org/packages/18/6d/037a7bb889c92b3e3f792de6a692e636de0577ea62bdd0038b1eb68270af/healpy-1.19.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:126bef3ae6594f461435ac85c6bcb32e427eca43f743881d00186bc3a55da1d8", size = 9376051, upload-time = "2025-12-02T08:26:57.516Z" },
-    { url = "https://files.pythonhosted.org/packages/de/bb/314bf8c493701b5842cdd74a1b5b34cea1522444042efad5b7cb17b8d159/healpy-1.19.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:086bae2510ac60f7cf9bbb520e1d1b8864cc8fca74b46b16e24857e74d08f896", size = 8198878, upload-time = "2025-12-02T08:27:03.119Z" },
-    { url = "https://files.pythonhosted.org/packages/f6/d4/a60ed9a50768ff5e896dd94d878496ae16767925ea32c49d5a4189ab818a/healpy-1.19.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:920c0a1c6749c05c8ad9522a5a2630f7bc83124c5742ef50f91b9f5e6a1bdcc7", size = 8205835, upload-time = "2025-12-02T08:27:04.416Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/fd/5f4c989b53423bbad07c91a4b9d52de5cde9f1154e3d2b145b397e1b8cd8/healpy-1.19.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:26e3e6c50f2d256c9218f0fb0624406a93c4f22bc66deebf51a0f31fd5594b89", size = 8991846, upload-time = "2025-12-02T08:27:06.123Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/df/00636a5d2f1141b1fa9646069436c4fc68b35ac2c53ef520f8812b900f05/healpy-1.19.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:bc3c243985abf1f1b5d91da12a23ceed49181dd9d0e46ed362babdc92c814aa8", size = 9325719, upload-time = "2025-12-02T08:27:08.138Z" },
-    { url = "https://files.pythonhosted.org/packages/c5/ca/f583967e1395e41608225d0a6d29e582236f615d83023d730e63bac89baa/healpy-1.19.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b3cf9c627af77a3fc670d6240d8a83485f373c274945ca8ad156fc2a25d6eb61", size = 8172544, upload-time = "2025-12-02T08:27:13.684Z" },
-    { url = "https://files.pythonhosted.org/packages/85/ae/80a5e0d36ee51e72b27fb8e11d0d43dfb3f8e2ccc315a38865a2ccd5ed73/healpy-1.19.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:c22360ad7ec6f16bdbb650dececf52d21f7f0e03a943eb1a1046fdb9c51a3c5e", size = 8198134, upload-time = "2025-12-02T08:27:15.156Z" },
-    { url = "https://files.pythonhosted.org/packages/36/7b/34b9954fdf76b1f5ab0a76228650c2575b189fb6f9f7865c98477a46086d/healpy-1.19.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1a479826c1d6ae8312476e079a3f5c7aa3e40911d74f2d7069adcd62e98604ad", size = 8986426, upload-time = "2025-12-02T08:27:16.417Z" },
-    { url = "https://files.pythonhosted.org/packages/94/df/bb3ef51f423c62576fb0775fe72f0d2028a10e3ffb20b4fc76146ea8757b/healpy-1.19.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a103e327102e124785c1b5bdd379c6e5469137633212b56a6196bf04ddd100ff", size = 9307512, upload-time = "2025-12-02T08:27:18.03Z" },
+    { url = "https://files.pythonhosted.org/packages/64/a6/c92732724c3068dec0fa27ae5a2e227b373feccb6e28f9d9a4eac0d74800/healpy-1.20.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:18b6495eb7416dca382e66656134f3a192ebe98b44ff9d38ab1169d3afb83cc5", size = 8344555, upload-time = "2026-07-23T20:15:04.624Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/db/e0c1d6945d5fe75122be74dd227cdb54689608e940cf426bef6d6e624910/healpy-1.20.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:b261afa9fdb82af891aa16a708babf3a38209f0e05a93c777e2d5b9dc6397b27", size = 8558603, upload-time = "2026-07-23T20:15:06.819Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/e5/765c2181b1b123bc80ae4dba8ad7cfb9112db26f7165016a6cc0879d28ed/healpy-1.20.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:96f475f05d0d55183393a19ca5ee2e615958ec4a5a92f8e2640bc7511d635c64", size = 9129464, upload-time = "2026-07-23T20:15:09.236Z" },
+    { url = "https://files.pythonhosted.org/packages/58/6b/389121c466a0181ace835b5111d5c9894d481c7ea7192c7c8d0ba25520cd/healpy-1.20.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3d0a025ce4229272e3bf1ec1ae6dbc61de7bfc4966c8351ebc009434842f353d", size = 9466857, upload-time = "2026-07-23T20:15:12.001Z" },
+    { url = "https://files.pythonhosted.org/packages/58/70/7ef24ea6108f202eaaad4c106928509341382a9b632fa6e35d88d4797ba6/healpy-1.20.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:4fe942685346e577aff37c309a089313e652907a48231a50cb6b260663ae116e", size = 8290409, upload-time = "2026-07-23T20:15:20.127Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/7c/5e5dc4035eee06ad5b6797f43cf0f07b6024fc49b7ac9d1856ae94990575/healpy-1.20.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:1408a6b6da26a9adf2b03915c8a0eefaf4bfa83b2179aa10490f85378545cabf", size = 8509272, upload-time = "2026-07-23T20:15:22.421Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/4d/8a9a314feee5e1fa9316871dcd826b23c65fe6f4767accc661636096d273/healpy-1.20.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5b707a03a4f2dc5f0225c7ff41d4aaeac05ac239fc80e41543d397b42ab68984", size = 9070361, upload-time = "2026-07-23T20:15:25.199Z" },
+    { url = "https://files.pythonhosted.org/packages/65/ed/72253818321779655dfed8190fbc36b4ec4107a2ec8ff834d97acdcad4b2/healpy-1.20.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a13c0294c3eec67656a1a706c4dc7c41484e9c58fe80af7a2c9e1a2b02e92cfc", size = 9411121, upload-time = "2026-07-23T20:15:28.25Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/75/79276a5395c5edaeb30570ac7ae9c8c22e2070e7b71a053c728b2bac36af/healpy-1.20.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:6fc791aa5c8c4b43f30e7d40abe390aa3c24b35accd4ba7322b7b2f9951638fb", size = 8283425, upload-time = "2026-07-23T20:15:36.324Z" },
+    { url = "https://files.pythonhosted.org/packages/38/57/2d118c1f3aa0f269a848fb619098367d3efdd25545307dd8ef00499b564d/healpy-1.20.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:3a5572d87a0a75e05a2db7446f14a0447996ef680e55f82eb9c667a79e39e935", size = 8486959, upload-time = "2026-07-23T20:15:38.92Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/78/2a2bd43f71a0d77bdb9f97a40fb27586649c0effc0839299eb03cc17fe48/healpy-1.20.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:108f493c0b183c5752e65115a82419391139b57d20b8d94acf7ec590794d3901", size = 9069700, upload-time = "2026-07-23T20:15:41.731Z" },
+    { url = "https://files.pythonhosted.org/packages/18/fa/eed66f825eff1d40013d239bb7d40c0583c6f01f8f5ddf13622f1a01eb07/healpy-1.20.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:8c217389ad8c80ac31428d53b92ea691793c4311e9e32842e6ed8687045b9b87", size = 9389915, upload-time = "2026-07-23T20:15:44.627Z" },
 ]
 
 [[package]]
 name = "healsparse"
-version = "1.13.0"
+version = "1.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "hpgeom" },
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e3/33/36520faabae128fe9a4a5cbeb0916be864d2eb34cf7410d52dc532af044e/healsparse-1.13.0.tar.gz", hash = "sha256:c21e4eb075c686bd306bdc1b40e039134c4ca60ccfa4e7e73845c107a5b16544", size = 133589, upload-time = "2026-07-07T18:33:05.481Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d3/bd/3e9cf5e8b14909a4d569f292eb9a4e565fc318a1381041a2c1b8cc1b6119/healsparse-1.15.0.tar.gz", hash = "sha256:1c6c99bfac8a7237d6b24420ac8104b3a9063972c2c5574c02c2da1ae8b5c3b4", size = 135636, upload-time = "2026-09-11T15:07:31.739Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b4/2f/7674b91031a12a9b3cd69f3bb675d3b1eac59657139e23bc528ed6d33d0a/healsparse-1.13.0-py3-none-any.whl", hash = "sha256:3b475a61d7701526246a592ed980758f94764caba4355429bf470a2f9b812964", size = 70673, upload-time = "2026-07-07T18:33:04.514Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/c0/13b3c0482824fa5ff1a4710e505658848d68ea955eba4dd37c6cd29862cc/healsparse-1.15.0-py3-none-any.whl", hash = "sha256:71e75706bb5b8fe31344990e461f58d7ea9dd72aa236f0bdd270f63033bf8a1a", size = 69817, upload-time = "2026-09-11T15:07:30.778Z" },
 ]
 
 [[package]]
@@ -1249,68 +1363,68 @@ wheels = [
 
 [[package]]
 name = "idna"
-version = "3.18"
+version = "3.20"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cd/63/9496c57188a2ee585e0f1db071d75089a11e98aa86eb99d9d7618fc1edce/idna-3.18.tar.gz", hash = "sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848", size = 196711, upload-time = "2026-06-02T14:34:07.794Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f5/08/8eea9d4b8302028f3abb2c0813953f7aec26d33b7a8960ed760e65ff29fa/idna-3.20.tar.gz", hash = "sha256:a7db850025b95ded1eae8a46181a1a6c56c92c96f0e2b005d9ff8dc0210cab44", size = 216463, upload-time = "2026-09-17T14:11:04.752Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/1e/5e/d4e9f1a599fb8e573b7b87160658329fbf28d19eac2718f51fc3def3aa5a/idna-3.18-py3-none-any.whl", hash = "sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2", size = 65455, upload-time = "2026-06-02T14:34:06.319Z" },
+    { url = "https://files.pythonhosted.org/packages/58/a2/bb081bab032533a855d44de1d56f8e8426114ff1ba5d1f07a438a0a654f8/idna-3.20-py3-none-any.whl", hash = "sha256:ab7ae7122974553370f0bdb919e1a960b2cd1bc1ef0276416d896db81c14582c", size = 69583, upload-time = "2026-09-17T14:11:03.168Z" },
 ]
 
 [[package]]
 name = "imageio"
-version = "2.37.3"
+version = "2.38.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "pillow" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b1/84/93bcd1300216ea50811cee96873b84a1bebf8d0489ffaf7f2a3756bab866/imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451", size = 389673, upload-time = "2026-03-09T11:31:12.573Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/81/c8/2b56274457e4fadcfa86bf7c0fb93f19e124ab8f38f4bfa4372423052046/imageio-2.38.0.tar.gz", hash = "sha256:fe1d406862f6bc2930e8ec662d7bc40d4370cb76e3390dcd33819560d2708531", size = 403333, upload-time = "2026-09-28T07:44:08.101Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/49/fa/391e437a34e55095173dca5f24070d89cbc233ff85bf1c29c93248c6588d/imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0", size = 317646, upload-time = "2026-03-09T11:31:10.771Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/4b/87a794e51d150f62a55715c453c9afbf6103fe24afef6db26a929a8480b7/imageio-2.38.0-py3-none-any.whl", hash = "sha256:474ea3a39e36e0e8e02d81d7f10412aaecec7a2825e5fbc4f44cbcd7bb7cebfc", size = 328055, upload-time = "2026-09-28T07:44:06.169Z" },
 ]
 
 [[package]]
 name = "imagesize"
-version = "2.0.0"
+version = "2.0.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/6c/e6/7bf14eeb8f8b7251141944835abd42eb20a658d89084b7e1f3e5fe394090/imagesize-2.0.0.tar.gz", hash = "sha256:8e8358c4a05c304f1fccf7ff96f036e7243a189e9e42e90851993c558cfe9ee3", size = 1773045, upload-time = "2026-03-03T14:18:29.941Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/fb/5e/513ff06670c84e7b9887c1fdf61b2d42b4f574a831f2f1d2222023049d8a/imagesize-2.0.1.tar.gz", hash = "sha256:b2ba6a4dea487a7ebcd53248d3476aca449d30db12a2dde5e0c5ca9624fd77e5", size = 1883774, upload-time = "2026-08-24T12:35:19.13Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5f/53/fb7122b71361a0d121b669dcf3d31244ef75badbbb724af388948de543e2/imagesize-2.0.0-py2.py3-none-any.whl", hash = "sha256:5667c5bbb57ab3f1fa4bc366f4fbc971db3d5ed011fd2715fd8001f782718d96", size = 9441, upload-time = "2026-03-03T14:18:27.892Z" },
+    { url = "https://files.pythonhosted.org/packages/01/f9/575c8d760eae1fc99651b7cc5efd96ad5379ca4d6b53750b0fb4fe983f34/imagesize-2.0.1-py3-none-any.whl", hash = "sha256:ea0c9a0384df69ed86a943a15cde37d0360b82491b3910dc2215e202e62b5b02", size = 14794, upload-time = "2026-08-24T12:35:12.548Z" },
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "9.0.0"
+version = "9.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "zipp" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6f/7e/1e7e8dc30634b93ebb3d58a3dea569ad146e656218d3960ab04f62047b29/importlib_metadata-9.0.1.tar.gz", hash = "sha256:ab830580bc0ef3db61ce8fae716389e5462b67e033018bab6d8f80ef17172f99", size = 59124, upload-time = "2026-08-28T15:30:34.646Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/38/3d/2d244233ac4f76e38533cfcb2991c9eb4c7bf688ae0a036d30725b8faafe/importlib_metadata-9.0.0-py3-none-any.whl", hash = "sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7", size = 27789, upload-time = "2026-03-20T06:42:55.665Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/55/ecca97ae19075f1fac62def77731e7f535e6c1fb8f92ff08160c5e6dade8/importlib_metadata-9.0.1-py3-none-any.whl", hash = "sha256:bba5600596a7e21f3eef53281cf28d6a5195634d2f2b78ff9501a3272c6eaab0", size = 27920, upload-time = "2026-08-28T15:30:33.433Z" },
 ]
 
 [[package]]
-name = "importlib-resources"
-version = "7.1.0"
+name = "iniconfig"
+version = "2.3.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e4/06/b56dfa750b44e86157093bc8fca0ab81dccbf5260510de4eaf1cb69b5b99/importlib_resources-7.1.0.tar.gz", hash = "sha256:0722d4c6212489c530f2a145a34c0a7a3b4721bc96a15fada5930e2a0b760708", size = 44985, upload-time = "2026-04-12T16:36:09.232Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8a/db/55a262f3606bebcae07cc14095338471ad7c0bbcaa37707e6f0ee49725b7/importlib_resources-7.1.0-py3-none-any.whl", hash = "sha256:1bd7b48b4088eddb2cd16382150bb515af0bd2c70128194392725f82ad2c96a1", size = 37232, upload-time = "2026-04-12T16:36:08.219Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
 ]
 
 [[package]]
-name = "iniconfig"
-version = "2.3.0"
+name = "interface-meta"
+version = "2.0.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/28/5b/202a48a1ccdef721a95357fbe263c54ef87c18c4972df1ab5c22fe0f33d5/interface_meta-2.0.1.tar.gz", hash = "sha256:902bd9a95a12f195f15753a1080075d4eca7a2cb934fac7ac03c9e362b50796a", size = 15281, upload-time = "2026-04-30T22:35:36.882Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/d3/20a61a248feb1249cd81f83ce731f0bdf5170ed96a08a298f7081cda9e90/interface_meta-2.0.1-py3-none-any.whl", hash = "sha256:f38016bef9a4429b6d0792d809be7b65e9781820c674bf7f463999086b6e6323", size = 15330, upload-time = "2026-04-30T22:35:35.201Z" },
 ]
 
 [[package]]
 name = "ipykernel"
-version = "7.3.0"
+version = "7.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "comm" },
@@ -1321,22 +1435,20 @@ dependencies = [
     { name = "matplotlib-inline" },
     { name = "nest-asyncio2" },
     { name = "packaging" },
-    { name = "psutil" },
     { name = "pyzmq" },
     { name = "tornado" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3d/c4/e4a38f579de4225a561305666f7541cdabb30075def2aa1ac17bd73c1fb5/ipykernel-7.3.0.tar.gz", hash = "sha256:9acaaaf97d16355166e4085afe9d225bfbdf2b7ef520f9df3be8f2b248275e09", size = 184899, upload-time = "2026-06-10T08:41:25.481Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7a/05/e5499c8b762387d83926bf0389e8fff888c9bf6e7af30566a8527bc12f4a/ipykernel-7.4.0.tar.gz", hash = "sha256:4330114d22b9b33575b2c7c68753fc8faabbbcfbc60b0cdff9f14dd4ce63742f", size = 192731, upload-time = "2026-09-29T16:33:11.468Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3d/02/77b271f5dc58bfbc0b577c877b2365d1ffea2afe66a80c13f2312820348c/ipykernel-7.3.0-py3-none-any.whl", hash = "sha256:897eb64da762549ef610698fca5e9675195ec6ac8ec7f19d81ce1ca20c876057", size = 120583, upload-time = "2026-06-10T08:41:23.648Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/f8/8b5c10c7f36ab5a3b5c4703669e5b14e44e98782b58e535dd30670e8799a/ipykernel-7.4.0-py3-none-any.whl", hash = "sha256:a6757f790ddc5a6006b813d82da1a9dbb65b3a00f2b6607df776418c6566ec1c", size = 123184, upload-time = "2026-09-29T16:33:09.612Z" },
 ]
 
 [[package]]
 name = "ipython"
-version = "9.15.0"
+version = "9.17.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "decorator" },
     { name = "ipython-pygments-lexers" },
     { name = "jedi" },
     { name = "matplotlib-inline" },
@@ -1347,18 +1459,9 @@ dependencies = [
     { name = "stack-data" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/53/59/165d3b4d75cc34add3122c4417ecb229085140ac573103c223cd01dde96f/ipython-9.15.0.tar.gz", hash = "sha256:da2819ce2aa83135257df830660b1176d986c3d2876db24df01974fa955b2756", size = 4442580, upload-time = "2026-06-26T11:03:35.913Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/40/3a/948263ca3b9d65bb2b1b0c521b3a49fad5d59ada58724bd87d2bd5ff3f36/ipython-9.15.0-py3-none-any.whl", hash = "sha256:515ad9c3cdf0c932a5a9f6245419e8aba706b7bd03c3e1d3a1c83d9351d6aa6e", size = 630895, upload-time = "2026-06-26T11:03:33.809Z" },
-]
-
-[[package]]
-name = "ipython-genutils"
-version = "0.2.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e8/69/fbeffffc05236398ebfcfb512b6d2511c622871dca1746361006da310399/ipython_genutils-0.2.0.tar.gz", hash = "sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8", size = 22208, upload-time = "2017-03-13T22:12:26.393Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/32/99451b1283ec5d92ad77073f12e1c667dc10775384d8f15c2914207149dd/ipython-9.17.1.tar.gz", hash = "sha256:8919be8c27f20a6f4423145028063f6637b42a03ce57665bb12015ee1f073529", size = 4539289, upload-time = "2026-09-01T08:29:32.6Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fa/bc/9bd3b5c2b4774d5f33b2d544f1460be9df7df2fe42f352135381c347c69a/ipython_genutils-0.2.0-py2.py3-none-any.whl", hash = "sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8", size = 26343, upload-time = "2017-03-13T22:12:25.412Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/1e/65b59cf518c106aa755e7f7da3099027738687a862ec785060702a481320/ipython-9.17.1-py3-none-any.whl", hash = "sha256:6d1645743cfd1a07eb695d85aa2b5fa66721f8cbae9431d4049f7084bbf06509", size = 639038, upload-time = "2026-09-01T08:29:30.673Z" },
 ]
 
 [[package]]
@@ -1375,7 +1478,7 @@ wheels = [
 
 [[package]]
 name = "ipywidgets"
-version = "8.1.8"
+version = "8.1.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "comm" },
@@ -1384,9 +1487,9 @@ dependencies = [
     { name = "traitlets" },
     { name = "widgetsnbextension" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4c/ae/c5ce1edc1afe042eadb445e95b0671b03cee61895264357956e61c0d2ac0/ipywidgets-8.1.8.tar.gz", hash = "sha256:61f969306b95f85fba6b6986b7fe45d73124d1d9e3023a8068710d47a22ea668", size = 116739, upload-time = "2025-11-01T21:18:12.393Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c9/7c/6db60eddf38547353b06d57941f5eee22a990640ce30479fd71a810507f2/ipywidgets-8.1.9.tar.gz", hash = "sha256:bcccba38a6ec3253f7a39c943cea5b9ad01999ce071396171adbc51c6a6a8613", size = 117252, upload-time = "2026-08-18T08:54:24.123Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/56/6d/0d9848617b9f753b87f214f1c682592f7ca42de085f564352f10f0843026/ipywidgets-8.1.8-py3-none-any.whl", hash = "sha256:ecaca67aed704a338f88f67b1181b58f821ab5dc89c1f0f5ef99db43c1c2921e", size = 139808, upload-time = "2025-11-01T21:18:10.956Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/55/298e9b3b864a198234997e87a1471c1b17d7f3546ace6d18fb5cf1ce24b2/ipywidgets-8.1.9-py3-none-any.whl", hash = "sha256:f2b8cbcaae10252b809fbe4d7470db75c09b769a32cbf816d20e5ca6d3c5a79d", size = 140101, upload-time = "2026-08-18T08:54:22.339Z" },
 ]
 
 [[package]]
@@ -1424,14 +1527,14 @@ wheels = [
 
 [[package]]
 name = "jaraco-functools"
-version = "4.5.0"
+version = "4.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "more-itertools" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/36/cf/ea4ef2920830dea3f5ab2ea4da6fb67724e6dca80ee2553788c3607243d0/jaraco_functools-4.5.0.tar.gz", hash = "sha256:3bb5665ea4a020cf78a7040e89154c77edadb3ca74f366479669c5999aa70b03", size = 20272, upload-time = "2026-05-15T21:34:10.025Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/1f/c23395957d41ccf27c4e535c3d334c4051e5395b3752057ba4cbaec35c56/jaraco_functools-4.6.0.tar.gz", hash = "sha256:880c577ec9720b3a052d5bc611fb9f2269b3d87902ef42440df443b88e443280", size = 20837, upload-time = "2026-07-14T01:28:02.544Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/96/9a/982e48afcffcd727a9144506720ffd4224b6b7e355c98641866f38b7c043/jaraco_functools-4.5.0-py3-none-any.whl", hash = "sha256:79ce39246eddbde4b3a03b77ea5f0f7878dc669b166a66cf3fa8e266aa3fa2f4", size = 10594, upload-time = "2026-05-15T21:34:08.595Z" },
+    { url = "https://files.pythonhosted.org/packages/02/36/ecc85bc96c273dc8a11273ed4782272975e6338d4a3e9228621175edf0e3/jaraco_functools-4.6.0-py3-none-any.whl", hash = "sha256:99e3dc0060c5cbe8fcd1cdb36258e2a65ca40f1566b2033b12abb1bb44dd3c30", size = 11677, upload-time = "2026-07-14T01:28:01.59Z" },
 ]
 
 [[package]]
@@ -1490,14 +1593,14 @@ wheels = [
 
 [[package]]
 name = "jsonargparse"
-version = "4.49.0"
+version = "4.52.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pyyaml" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/87/0b/f012466077d803b546955bf827ab30a320ad1550e3167195432cc2d6bfad/jsonargparse-4.49.0.tar.gz", hash = "sha256:9e691a09d937fb4c6bc1f6d3bf6c3546efa03381ea1e64ee10759095f1b14da7", size = 125237, upload-time = "2026-05-15T06:48:44.807Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f1/1b/86ab3c48f74609de566e6c6fea5493a7bea4c7de485e5a56fa1239c698b5/jsonargparse-4.52.0.tar.gz", hash = "sha256:103168510fab9d45a980fe7560dcbf20d1cce38320a43904583d23869af3bee1", size = 164776, upload-time = "2026-09-01T05:53:07.575Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/61/64/51cbc9044f5b1ee4fb9894fe791f50dfdb52dc080acfafb5818ba19e8c27/jsonargparse-4.49.0-py3-none-any.whl", hash = "sha256:2025880765fd792f6fb9861aa93f26da5f6c350be5d7656321369b22b0d2538e", size = 134834, upload-time = "2026-05-15T06:48:40.76Z" },
+    { url = "https://files.pythonhosted.org/packages/52/70/e9114d52e9e72daaaeec30ecafe1018a4fbe6288ec35e16822529676b671/jsonargparse-4.52.0-py3-none-any.whl", hash = "sha256:1878b16b6be90c3e838aac569784a110fe1eb1806c28e56175cdc7f2e563d78c", size = 174766, upload-time = "2026-09-01T05:53:04.257Z" },
 ]
 
 [package.optional-dependencies]
@@ -1574,20 +1677,20 @@ wheels = [
 
 [[package]]
 name = "jupyter-builder"
-version = "1.1.0"
+version = "1.2.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jupyter-core" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0d/df/db5efc4e28803a1421350445e53d85ec571a4e6928e3524ccc39f4e16584/jupyter_builder-1.1.0.tar.gz", hash = "sha256:b996e8af616900f18724fa34883169d869be2205497940bec78a3a1031eb897d", size = 971142, upload-time = "2026-07-11T07:24:02.4Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/75/3e/56f593e6a664cd14d441724654ce8243f3cac7d3e45867cf084749c8bc75/jupyter_builder-1.2.3.tar.gz", hash = "sha256:01aba6794eb9b19e0e29ae21137ca60ba4135c70347d4b9f664a586822b8c809", size = 1024218, upload-time = "2026-09-04T18:54:09.411Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7c/fb/53adbc72931b4429275b160044c3e1bcbc8fbb5ea6b8f318a357834842fb/jupyter_builder-1.1.0-py3-none-any.whl", hash = "sha256:d9d5280e8845f726663545c3a6db55bd205127616eeb8958481091d6cba71b6a", size = 912964, upload-time = "2026-07-11T07:24:00.279Z" },
+    { url = "https://files.pythonhosted.org/packages/84/aa/be79e87c50698673f196d0633fc1664607da2289f9688d1de7a1c9db9e71/jupyter_builder-1.2.3-py3-none-any.whl", hash = "sha256:c5ea5a7190c2a7b082494abade98eece1b2b5bd5dbd7d610606cbcd10a1d08b3", size = 947541, upload-time = "2026-09-04T18:54:07.644Z" },
 ]
 
 [[package]]
 name = "jupyter-client"
-version = "8.9.1"
+version = "8.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jupyter-core" },
@@ -1597,9 +1700,9 @@ dependencies = [
     { name = "traitlets" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/7d/dc/5512503b088997c2250b8bf18258fba9d9ce5ead641183700960d3c9d342/jupyter_client-8.9.1.tar.gz", hash = "sha256:a58f730dd9e728ba16ba1d62ebccf7ffe1ebbdbce4e95cfae941b7321ae1f4fa", size = 359256, upload-time = "2026-06-09T13:15:01.033Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c5/2a/906772148a06e48885039e0250c340b770a55bbf37b08d6ee0449df369c3/jupyter_client-8.10.0.tar.gz", hash = "sha256:9f7116294dca55f1785be880057d44544db9b1567718d92cb33c58886afb9497", size = 360653, upload-time = "2026-08-28T12:17:10.854Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3f/6f/56d39bf385c5c27988aebaf0c18a2a17e960575740100973511018bd904e/jupyter_client-8.9.1-py3-none-any.whl", hash = "sha256:0b7a295bc46e8751e9adae84781f726c851c1d911bd793edc4a3bde942e3da81", size = 109828, upload-time = "2026-06-09T13:14:58.835Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/88/7c548de1f6c2ade7c931a3282da73f9274fa6a1531091be682f89c85efb9/jupyter_client-8.10.0-py3-none-any.whl", hash = "sha256:5f73f24f22fa25192cfff6b23c051932a2473a797b05734aff495b392103e14e", size = 110184, upload-time = "2026-08-28T12:17:09.028Z" },
 ]
 
 [[package]]
@@ -1667,7 +1770,7 @@ wheels = [
 
 [[package]]
 name = "jupyter-server"
-version = "2.20.0"
+version = "2.21.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -1688,9 +1791,9 @@ dependencies = [
     { name = "traitlets" },
     { name = "websocket-client" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6b/dc/db3a582633170186f8c8b31298d7eb26ad0eb031a1f53476c258b64eed05/jupyter_server-2.20.0.tar.gz", hash = "sha256:b5778ba337d8015a3dc2b80803ecdd5ac18d3797fddf61a50ea5fb472b4ebe14", size = 756523, upload-time = "2026-06-17T12:09:09.435Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/65/3d/3c9f8bce5d5448107bd285cd80185e76c9c871d076b580dfd90997e52904/jupyter_server-2.21.1.tar.gz", hash = "sha256:a8960aa29263f6041283e97d4756b099fb49b767baab6371891ecb1bd40a63df", size = 761651, upload-time = "2026-09-15T16:00:07.853Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f3/71/8c002223e873a870f5c41dc69b0a7c922301123e4a31d5d01ecb700aef77/jupyter_server-2.20.0-py3-none-any.whl", hash = "sha256:c3b67c93c471e947c18b5026f04f21614218adb706df8f48227d3ee8e0a7cdcc", size = 393143, upload-time = "2026-06-17T12:09:07.234Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/39/91bc08650cc8e3efeb7a83c25e20ebddcc9b70d28b4eb6f51a4fb55da7a4/jupyter_server-2.21.1-py3-none-any.whl", hash = "sha256:2a6467606af7dbae2e7e31640030025969e15db6a649eae334af90415dc71dca", size = 394641, upload-time = "2026-09-15T16:00:05.526Z" },
 ]
 
 [[package]]
@@ -1707,7 +1810,7 @@ wheels = [
 
 [[package]]
 name = "jupyterlab"
-version = "4.6.1"
+version = "4.6.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "async-lru" },
@@ -1724,9 +1827,9 @@ dependencies = [
     { name = "tornado" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/bc/2a/d6af53bfd45a43a5bfe7e40ba47ee7a8921a807daf4bb708e3a295bbb54d/jupyterlab-4.6.1.tar.gz", hash = "sha256:75315982ed28427edaa62bb85eadb5105e4043a757643c910efd787fe6ed0837", size = 28179125, upload-time = "2026-06-29T12:48:45.402Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/33/8d/995cc142f6083346b35e7d3eadc5a6717ee89ce64ea53577010ac493bc3c/jupyterlab-4.6.4.tar.gz", hash = "sha256:404f49b081819378524886c9db66dba57a5565981eff885830df1baba3a17df5", size = 28335647, upload-time = "2026-09-21T15:25:18.779Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5a/81/90ac6cc31d248e83a0d1eab343a5e6e68bc783d3f74fbe61640f42a61da4/jupyterlab-4.6.1-py3-none-any.whl", hash = "sha256:85a58546c831f3dce6cf919468c26874c9065e99c42279fb4abb8e1b552a98bb", size = 17164660, upload-time = "2026-06-29T12:48:41.21Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/e4/072bc0d3c6d45d414062a06af7ee81c02a2c12d769c358ec9919f9995ffd/jupyterlab-4.6.4-py3-none-any.whl", hash = "sha256:15b13f991d3985129c797eb84d9949eeb8b6615e14b444868e642411f2c418b2", size = 17171596, upload-time = "2026-09-21T15:25:14.123Z" },
 ]
 
 [[package]]
@@ -1740,7 +1843,7 @@ wheels = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.28.0"
+version = "2.28.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "babel" },
@@ -1751,18 +1854,18 @@ dependencies = [
     { name = "packaging" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996, upload-time = "2025-10-22T13:59:18.37Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/16/c5/08946a10b81bd64408d8b78cb352e17fe52d3ee11c2aef00a0f06e506938/jupyterlab_server-2.28.1.tar.gz", hash = "sha256:0c3c2418d51021ce280916e63dfe4cba8386b4e2787be5c25433c98f560ddb31", size = 78141, upload-time = "2026-09-15T19:22:09.935Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830, upload-time = "2025-10-22T13:59:16.767Z" },
+    { url = "https://files.pythonhosted.org/packages/23/d3/996332fe24f3c2458b9c353b998570dfa2631763e9a8674ba3f94c0877d0/jupyterlab_server-2.28.1-py3-none-any.whl", hash = "sha256:4bd36c7c11d872e15cefa4951e12380a47e6901687165332dd50a007cb367ad9", size = 60197, upload-time = "2026-09-15T19:22:08.201Z" },
 ]
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.16"
+version = "3.0.17"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/2d/ef58fed122b268c69c0aa099da20bc67657cdfb2e222688d5731bd5b971d/jupyterlab_widgets-3.0.16.tar.gz", hash = "sha256:423da05071d55cf27a9e602216d35a3a65a3e41cdf9c5d3b643b814ce38c19e0", size = 897423, upload-time = "2025-11-01T21:11:29.724Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/8b/e739cf9066ad5037a2d4b0a403f06da374fdccb9748221661c8b492d3dbc/jupyterlab_widgets-3.0.17.tar.gz", hash = "sha256:6e61fe21ca8a66039180a5cc52a433e07279d2fee79c8be963e00d55193f17a8", size = 213919, upload-time = "2026-08-18T08:52:17.511Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ab/b5/36c712098e6191d1b4e349304ef73a8d06aed77e56ceaac8c0a306c7bda1/jupyterlab_widgets-3.0.16-py3-none-any.whl", hash = "sha256:45fa36d9c6422cf2559198e4db481aa243c7a32d9926b500781c830c80f7ecf8", size = 914926, upload-time = "2025-11-01T21:11:28.008Z" },
+    { url = "https://files.pythonhosted.org/packages/33/ef/6d27fc118f58cb24886da413545a7efb0853d405fddbfd8b2d9ac09fbed4/jupyterlab_widgets-3.0.17-py3-none-any.whl", hash = "sha256:40ac1e9955acf116c4d995d9bfa082d86ad9ec6d91c4f134827cf5e0a5eb75e0", size = 217292, upload-time = "2026-08-18T08:52:15.47Z" },
 ]
 
 [[package]]
@@ -1799,61 +1902,80 @@ wheels = [
 
 [[package]]
 name = "kiwisolver"
-version = "1.5.0"
+version = "1.5.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d0/67/9c61eccb13f0bdca9307614e782fec49ffdde0f7a2314935d489fa93cd9c/kiwisolver-1.5.0.tar.gz", hash = "sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a", size = 103482, upload-time = "2026-03-09T13:15:53.382Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c4/13/680c54afe3e65767bed7ec1a15571e1a2f1257128733851ade24abcefbcc/kiwisolver-1.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f", size = 1477934, upload-time = "2026-03-09T13:13:27.166Z" },
-    { url = "https://files.pythonhosted.org/packages/c8/2f/cebfcdb60fd6a9b0f6b47a9337198bcbad6fbe15e68189b7011fd914911f/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf", size = 1278537, upload-time = "2026-03-09T13:13:28.707Z" },
-    { url = "https://files.pythonhosted.org/packages/f2/0d/9b782923aada3fafb1d6b84e13121954515c669b18af0c26e7d21f579855/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d", size = 1296685, upload-time = "2026-03-09T13:13:30.528Z" },
-    { url = "https://files.pythonhosted.org/packages/27/70/83241b6634b04fe44e892688d5208332bde130f38e610c0418f9ede47ded/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083", size = 1346024, upload-time = "2026-03-09T13:13:32.818Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/db/30ed226fb271ae1a6431fc0fe0edffb2efe23cadb01e798caeb9f2ceae8f/kiwisolver-1.5.0-cp312-cp312-manylinux_2_39_riscv64.whl", hash = "sha256:cdee07c4d7f6d72008d3f73b9bf027f4e11550224c7c50d8df1ae4a37c1402a6", size = 987241, upload-time = "2026-03-09T13:13:34.435Z" },
-    { url = "https://files.pythonhosted.org/packages/ec/bd/c314595208e4c9587652d50959ead9e461995389664e490f4dce7ff0f782/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1", size = 2227742, upload-time = "2026-03-09T13:13:36.4Z" },
-    { url = "https://files.pythonhosted.org/packages/c1/43/0499cec932d935229b5543d073c2b87c9c22846aab48881e9d8d6e742a2d/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0", size = 2323966, upload-time = "2026-03-09T13:13:38.204Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/6f/79b0d760907965acfd9d61826a3d41f8f093c538f55cd2633d3f0db269f6/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15", size = 1977417, upload-time = "2026-03-09T13:13:39.966Z" },
-    { url = "https://files.pythonhosted.org/packages/ab/31/01d0537c41cb75a551a438c3c7a80d0c60d60b81f694dac83dd436aec0d0/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314", size = 2491238, upload-time = "2026-03-09T13:13:41.698Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/34/8aefdd0be9cfd00a44509251ba864f5caf2991e36772e61c408007e7f417/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9", size = 2294947, upload-time = "2026-03-09T13:13:43.343Z" },
-    { url = "https://files.pythonhosted.org/packages/2b/0a/7b98e1e119878a27ba8618ca1e18b14f992ff1eda40f47bccccf4de44121/kiwisolver-1.5.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3", size = 1477903, upload-time = "2026-03-09T13:13:52.084Z" },
-    { url = "https://files.pythonhosted.org/packages/18/d8/55638d89ffd27799d5cc3d8aa28e12f4ce7a64d67b285114dbedc8ea4136/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96", size = 1278751, upload-time = "2026-03-09T13:13:54.673Z" },
-    { url = "https://files.pythonhosted.org/packages/b8/97/b4c8d0d18421ecceba20ad8701358453b88e32414e6f6950b5a4bad54e65/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4db576bb8c3ef9365f8b40fe0f671644de6736ae2c27a2c62d7d8a1b4329f099", size = 1296793, upload-time = "2026-03-09T13:13:56.287Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/10/f862f94b6389d8957448ec9df59450b81bec4abb318805375c401a1e6892/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8", size = 1346041, upload-time = "2026-03-09T13:13:58.269Z" },
-    { url = "https://files.pythonhosted.org/packages/a3/6a/f1650af35821eaf09de398ec0bc2aefc8f211f0cda50204c9f1673741ba9/kiwisolver-1.5.0-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:d36ca54cb4c6c4686f7cbb7b817f66f5911c12ddb519450bbe86707155028f87", size = 987292, upload-time = "2026-03-09T13:13:59.871Z" },
-    { url = "https://files.pythonhosted.org/packages/de/19/d7fb82984b9238115fe629c915007be608ebd23dc8629703d917dbfaffd4/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23", size = 2227865, upload-time = "2026-03-09T13:14:01.401Z" },
-    { url = "https://files.pythonhosted.org/packages/7f/b9/46b7f386589fd222dac9e9de9c956ce5bcefe2ee73b4e79891381dda8654/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859", size = 2324369, upload-time = "2026-03-09T13:14:02.972Z" },
-    { url = "https://files.pythonhosted.org/packages/92/8b/95e237cf3d9c642960153c769ddcbe278f182c8affb20cecc1cc983e7cc5/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c95cab08d1965db3d84a121f1c7ce7479bdd4072c9b3dafd8fecce48a2e6b902", size = 1977989, upload-time = "2026-03-09T13:14:04.503Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/95/980c9df53501892784997820136c01f62bc1865e31b82b9560f980c0e649/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167", size = 2491645, upload-time = "2026-03-09T13:14:06.106Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/32/900647fd0840abebe1561792c6b31e6a7c0e278fc3973d30572a965ca14c/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0", size = 2295237, upload-time = "2026-03-09T13:14:08.891Z" },
-    { url = "https://files.pythonhosted.org/packages/f0/7f/f943879cda9007c45e1f7dba216d705c3a18d6b35830e488b6c6a4e7cdf0/kiwisolver-1.5.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c", size = 1584848, upload-time = "2026-03-09T13:14:19.745Z" },
-    { url = "https://files.pythonhosted.org/packages/37/f8/4d4f85cc1870c127c88d950913370dd76138482161cd07eabbc450deff01/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368", size = 1391542, upload-time = "2026-03-09T13:14:21.54Z" },
-    { url = "https://files.pythonhosted.org/packages/04/0b/65dd2916c84d252b244bd405303220f729e7c17c9d7d33dca6feeff9ffc4/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489", size = 1404447, upload-time = "2026-03-09T13:14:23.205Z" },
-    { url = "https://files.pythonhosted.org/packages/39/5c/2606a373247babce9b1d056c03a04b65f3cf5290a8eac5d7bdead0a17e21/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1", size = 1455918, upload-time = "2026-03-09T13:14:24.74Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/d1/c6078b5756670658e9192a2ef11e939c92918833d2745f85cd14a6004bdf/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_39_riscv64.whl", hash = "sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3", size = 1072856, upload-time = "2026-03-09T13:14:26.597Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/c8/7def6ddf16eb2b3741d8b172bdaa9af882b03c78e9b0772975408801fa63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18", size = 2333580, upload-time = "2026-03-09T13:14:28.237Z" },
-    { url = "https://files.pythonhosted.org/packages/9e/87/2ac1fce0eb1e616fcd3c35caa23e665e9b1948bb984f4764790924594128/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021", size = 2423018, upload-time = "2026-03-09T13:14:30.018Z" },
-    { url = "https://files.pythonhosted.org/packages/67/13/c6700ccc6cc218716bfcda4935e4b2997039869b4ad8a94f364c5a3b8e63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ce9bf03dad3b46408c08649c6fbd6ca28a9fce0eb32fdfffa6775a13103b5310", size = 2062804, upload-time = "2026-03-09T13:14:32.888Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/bd/877056304626943ff0f1f44c08f584300c199b887cb3176cd7e34f1515f1/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3", size = 2597482, upload-time = "2026-03-09T13:14:34.971Z" },
-    { url = "https://files.pythonhosted.org/packages/75/19/c60626c47bf0f8ac5dcf72c6c98e266d714f2fbbfd50cf6dab5ede3aaa50/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2", size = 2394328, upload-time = "2026-03-09T13:14:36.816Z" },
-    { url = "https://files.pythonhosted.org/packages/e7/f9/b06c934a6aa8bc91f566bd2a214fd04c30506c2d9e2b6b171953216a65b6/kiwisolver-1.5.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac", size = 1475913, upload-time = "2026-03-09T13:14:46.247Z" },
-    { url = "https://files.pythonhosted.org/packages/6b/f0/f768ae564a710135630672981231320bc403cf9152b5596ec5289de0f106/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05", size = 1282782, upload-time = "2026-03-09T13:14:48.458Z" },
-    { url = "https://files.pythonhosted.org/packages/e2/9f/1de7aad00697325f05238a5f2eafbd487fb637cc27a558b5367a5f37fb7f/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd", size = 1300815, upload-time = "2026-03-09T13:14:50.721Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/c2/297f25141d2e468e0ce7f7a7b92e0cf8918143a0cbd3422c1ad627e85a06/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a", size = 1347925, upload-time = "2026-03-09T13:14:52.304Z" },
-    { url = "https://files.pythonhosted.org/packages/b9/d3/f4c73a02eb41520c47610207b21afa8cdd18fdbf64ffd94674ae21c4812d/kiwisolver-1.5.0-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:d168fda2dbff7b9b5f38e693182d792a938c31db4dac3a80a4888de603c99554", size = 991322, upload-time = "2026-03-09T13:14:54.637Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/46/d3f2efef7732fcda98d22bf4ad5d3d71d545167a852ca710a494f4c15343/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581", size = 2232857, upload-time = "2026-03-09T13:14:56.471Z" },
-    { url = "https://files.pythonhosted.org/packages/3f/ec/2d9756bf2b6d26ae4349b8d3662fb3993f16d80c1f971c179ce862b9dbae/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303", size = 2329376, upload-time = "2026-03-09T13:14:58.072Z" },
-    { url = "https://files.pythonhosted.org/packages/8f/9f/876a0a0f2260f1bde92e002b3019a5fabc35e0939c7d945e0fa66185eb20/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9", size = 1982549, upload-time = "2026-03-09T13:14:59.668Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/4f/ba3624dfac23a64d54ac4179832860cb537c1b0af06024936e82ca4154a0/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d618fd27420381a4f6044faa71f46d8bfd911bd077c555f7138ed88729bfbe79", size = 2494680, upload-time = "2026-03-09T13:15:01.364Z" },
-    { url = "https://files.pythonhosted.org/packages/39/b7/97716b190ab98911b20d10bf92eca469121ec483b8ce0edd314f51bc85af/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796", size = 2297905, upload-time = "2026-03-09T13:15:03.925Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/8a/685b297052dd041dcebce8e8787b58923b6e78acc6115a0dc9189011c44b/kiwisolver-1.5.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657", size = 1584858, upload-time = "2026-03-09T13:15:15.103Z" },
-    { url = "https://files.pythonhosted.org/packages/9e/80/04865e3d4638ac5bddec28908916df4a3075b8c6cc101786a96803188b96/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920", size = 1392539, upload-time = "2026-03-09T13:15:16.661Z" },
-    { url = "https://files.pythonhosted.org/packages/ba/01/77a19cacc0893fa13fafa46d1bba06fb4dc2360b3292baf4b56d8e067b24/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9", size = 1405310, upload-time = "2026-03-09T13:15:18.229Z" },
-    { url = "https://files.pythonhosted.org/packages/53/39/bcaf5d0cca50e604cfa9b4e3ae1d64b50ca1ae5b754122396084599ef903/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d", size = 1456244, upload-time = "2026-03-09T13:15:20.444Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/7a/72c187abc6975f6978c3e39b7cf67aeb8b3c0a8f9790aa7fd412855e9e1f/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65", size = 1073154, upload-time = "2026-03-09T13:15:22.039Z" },
-    { url = "https://files.pythonhosted.org/packages/c7/ca/cf5b25783ebbd59143b4371ed0c8428a278abe68d6d0104b01865b1bbd0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa", size = 2334377, upload-time = "2026-03-09T13:15:23.741Z" },
-    { url = "https://files.pythonhosted.org/packages/4a/e5/b1f492adc516796e88751282276745340e2a72dcd0d36cf7173e0daf3210/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0", size = 2425288, upload-time = "2026-03-09T13:15:25.789Z" },
-    { url = "https://files.pythonhosted.org/packages/e6/e5/9b21fbe91a61b8f409d74a26498706e97a48008bfcd1864373d32a6ba31c/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9", size = 2063158, upload-time = "2026-03-09T13:15:27.63Z" },
-    { url = "https://files.pythonhosted.org/packages/b1/02/83f47986138310f95ea95531f851b2a62227c11cbc3e690ae1374fe49f0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f", size = 2597260, upload-time = "2026-03-09T13:15:29.421Z" },
-    { url = "https://files.pythonhosted.org/packages/07/18/43a5f24608d8c313dd189cf838c8e68d75b115567c6279de7796197cfb6a/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646", size = 2394403, upload-time = "2026-03-09T13:15:31.517Z" },
-    { url = "https://files.pythonhosted.org/packages/fa/06/7399a607f434119c6e1fdc8ec89a8d51ccccadf3341dee4ead6bd14caaf5/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7", size = 194295, upload-time = "2026-03-09T13:15:38.22Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/ba/07/bd78e6a8fae171ea041ef5bba3ed21a003522fa088834b069b1909981f30/kiwisolver-1.5.1.tar.gz", hash = "sha256:f1303ef2eec81262a4b708c3e858afe58d7c75ad91c1c05266eda7673369859a", size = 104395, upload-time = "2026-08-28T10:28:27.153Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fc/f4/dadfec469313c7f428efa7e84b4aba9732f813c13ea7131a24b7b008ef57/kiwisolver-1.5.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34633ecf50d16187ab8e5528b7a2530f2feb4e23f300db4672538b51cfc5cd38", size = 1477929, upload-time = "2026-08-28T10:25:31.495Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/35/09c58daac34e6f6ea5c6dee0094b422118e5a7c265586008a95fd135ac5f/kiwisolver-1.5.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d27c2123977cb9269c30a49ba45f03a4323017ef693e19db4ec9dbe1299a3002", size = 1278499, upload-time = "2026-08-28T10:25:33.375Z" },
+    { url = "https://files.pythonhosted.org/packages/19/32/739765e24fbad29d13f83e546ea4abc215a78cea9d677ca09025b027724d/kiwisolver-1.5.1-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6a797a1cefc8b9c93170db580337e1fe3d011ad18b1299943231279406342048", size = 1296677, upload-time = "2026-08-28T10:25:35.059Z" },
+    { url = "https://files.pythonhosted.org/packages/df/32/03304d1010e2cc45e5b3b52cef7e43fed3a2a5cd6c87a89b4a88e1d85b5d/kiwisolver-1.5.1-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2551cf9917af48ee7c4b29cc82320489508cf96fd26a51f6fc124de661cd44c7", size = 1346037, upload-time = "2026-08-28T10:25:36.705Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/57/4c49377bfd274450dd72ecaa13eaac32ea804a03363e4d1db0c5aa999ceb/kiwisolver-1.5.1-cp312-cp312-manylinux_2_39_riscv64.whl", hash = "sha256:38f6e0deb4d0a4615efe0c4efc5990b06ae450ab50a0b321c0b078b6d238c083", size = 988248, upload-time = "2026-08-28T10:25:38.299Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/c3/38df144a08b6c5d75ca4504e5cc3141bb3bfef64c04f4ef48204f42711b6/kiwisolver-1.5.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:bfd1de989b3330420e29de39352f5c049905c9e3ee67233a50d550e3d652c148", size = 2228722, upload-time = "2026-08-28T10:25:40.038Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/11/3221838a89cd64d9b386353e000cd8a296069a20fbe3584507fdfd5bebae/kiwisolver-1.5.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:1209042a623ddfda5497e4066c7b77651dde8e1d3a9dd97599dc7e97f3b9b78c", size = 2325216, upload-time = "2026-08-28T10:25:41.699Z" },
+    { url = "https://files.pythonhosted.org/packages/83/d4/075c219230697bb5db910d37262b9bacf880f92b4811a02ab81ed073a253/kiwisolver-1.5.1-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:26e8268480be5061d509e29669d59103c067a26377a56491630ece11762e3858", size = 1977689, upload-time = "2026-08-28T10:25:43.559Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/08/1d219c3c2dd960983d0d4da623d916e9de6385df2b0bab3d1af0e9b8fccc/kiwisolver-1.5.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:d79308fa689fac89cbcfbd4dbfc80b5f95c54c5a7fd4d194be221f9d33d026e6", size = 2491443, upload-time = "2026-08-28T10:25:45.242Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/d3/024208ec1079d273f1047468d1bdffbf38bb75b7b268090fd3a0301b9d9a/kiwisolver-1.5.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b03af77d77e50edba2030fd5f7c352ff209314b09030a3cba7c14edf9a09a444", size = 2295200, upload-time = "2026-08-28T10:25:46.984Z" },
+    { url = "https://files.pythonhosted.org/packages/08/c1/88018321d976f53c421e379c43bc6993e70ce0c8a3ec5edc4bfe102257f6/kiwisolver-1.5.1-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:b6ae6a0328f0bc035741820fdeecdcd67bf4694eee03972e843663107122f450", size = 62272, upload-time = "2026-08-28T10:25:51.02Z" },
+    { url = "https://files.pythonhosted.org/packages/85/d2/712bc17ea4f1d216034928069d612defbc6c95a471c55a7203a39faecb1a/kiwisolver-1.5.1-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:886fc26012f0e8b5f69d1cfe6d711f6b11f194621539bf8e6bb1c25c5dc82724", size = 64481, upload-time = "2026-08-28T10:25:52.22Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/e6/6c5380d676f43b6d918033962ea5e72360ca69e5a404154bc496b598ffdb/kiwisolver-1.5.1-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:aefe930d113798330e9462f7874542977869c0613cba3262e2de3a8d5dee8f3a", size = 66260, upload-time = "2026-08-28T10:25:53.387Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/43/86aacc027959108b4c66eeae8b73cedb057dfa6eb3a335d05ad65197081c/kiwisolver-1.5.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:74ad5c3dad54a4641b4c28cd15ded70899d04459c6c7aeacafea716be97cce6d", size = 1477992, upload-time = "2026-08-28T10:25:58.479Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/40/b1d0369048c79733a32c8abb0f2718532e6630641368e33a81384246e844/kiwisolver-1.5.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:21e46b23a2da695c364124817bc01d970effd5483147f8d66a6a7167e3f6b851", size = 1278821, upload-time = "2026-08-28T10:26:00.121Z" },
+    { url = "https://files.pythonhosted.org/packages/61/a1/fa71c1792272ff9461432715ae60ffb7e11a4d7ac3bf68961b9cab6c60cf/kiwisolver-1.5.1-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:75d9b1cf8258462dbdc1eeda718c96ea7f079324c09067f6daabfcf37712b7fe", size = 1296805, upload-time = "2026-08-28T10:26:01.868Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/49/3f0bd94af8e06ecc47eb834b195a06f05d48711ceb2352c56d6835160f0e/kiwisolver-1.5.1-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8fca690b00c4c48f6c2a547b0160ed511357093a4e4c9b47e0fadf3128066d89", size = 1346109, upload-time = "2026-08-28T10:26:03.59Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/44/3afe6ef9cf06d61220953a8963e94eca978491be1d9547cb01d82a1efa08/kiwisolver-1.5.1-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:876bbfd276473d3daffe30e8c975df4ed9429967b41a6cb362dbb5155b6f13ad", size = 988252, upload-time = "2026-08-28T10:26:05.306Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/30/a12bd7a7285a211e1747c3eec77b8c614dbfcc1dad942f7611a1a6921ae5/kiwisolver-1.5.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f942903fde7363d1d879057ec5de01310efda2597161784d752fa9953a01a71a", size = 2228846, upload-time = "2026-08-28T10:26:07.312Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/6b/233e2958abf0dab7b18d07e52f286e02e519d7651bfbbe97af9347564109/kiwisolver-1.5.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c90d3022d8a94778939cda8638c6c8da8fa757b8958dad7ec868ce29c87681b8", size = 2325583, upload-time = "2026-08-28T10:26:09.093Z" },
+    { url = "https://files.pythonhosted.org/packages/42/8e/7673060a27b01405b580058510adef34687069d229800239f5e44682d4d0/kiwisolver-1.5.1-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:8a34616dc2521cc8dc1d7d081734da63539f021ac0450ce950908340c6e7aa2f", size = 1978221, upload-time = "2026-08-28T10:26:11.127Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/7b/1b882fc1a8b4a0bb8084e7d1d85004116d08c92c0705d31f2928dec607f2/kiwisolver-1.5.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:8bf4df63592c2a66b4f8edc5df2544998c288aa02f96ce0acd880cd1de8c8127", size = 2491819, upload-time = "2026-08-28T10:26:13.348Z" },
+    { url = "https://files.pythonhosted.org/packages/39/9c/426deb49e62c5f69464b64bbeca064d3b758a7506b8913d986ef34f4619c/kiwisolver-1.5.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d09037ca068d784ebc4aec290ef952ca27ac15dd9c0b5801a88c6e1096b83e6b", size = 2295520, upload-time = "2026-08-28T10:26:15.042Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/87/2d5dfad0daf17dcc18d98c48ed2332fc3f051cf599e60be6182a30dd4cf1/kiwisolver-1.5.1-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0324cd2567259b7a095f6cf18a52b0ffc6f3de9e69528ff1bc0e7a37bd43ff1a", size = 62337, upload-time = "2026-08-28T10:26:18.778Z" },
+    { url = "https://files.pythonhosted.org/packages/08/c8/83e1624f15d6262b470dbcc80b09979fd4d5b2ea3ddfc6b6e3327e235726/kiwisolver-1.5.1-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:74ea337e0ec3f6f342a36a4f1b5cd94dd9affddcd28ba9aae2905af932ee8c6b", size = 64513, upload-time = "2026-08-28T10:26:19.909Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/2d/827ec30eb07f528c08d8459ffb318ae91a56d793ee8acbea8b491f0ff906/kiwisolver-1.5.1-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:ee9df1f0d77b9c6e94f4ac0fec533fbddd5ea3a327807f18d7b069ae019ded80", size = 66287, upload-time = "2026-08-28T10:26:21.078Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/13/08188f0cafa3a800403e4ff62b9aad4e7a17f9c4c7e080dc8f18c64794cf/kiwisolver-1.5.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:18a0cfb124546a4c2e6087c5f3029c7f44b37c85b142e0ced71f73a7599ac208", size = 1475867, upload-time = "2026-08-28T10:26:26.393Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/3e/053bdc3c9abdb8f2606225eda398adca25c0c91ab90add8222a69db65ee0/kiwisolver-1.5.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:34ec467940442c9943016fb2d4c81d1ba84351eeca2f1a78f8bc87f1ba0d414c", size = 1282865, upload-time = "2026-08-28T10:26:28.118Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/64/a44c341b36b610588cc2f1e89b3cae072a3119aa8be578e90987cd640751/kiwisolver-1.5.1-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a83ee7107df13abe42a54a6654670eef9bb39425cf2e27f65e0007465e1286ab", size = 1300865, upload-time = "2026-08-28T10:26:30.125Z" },
+    { url = "https://files.pythonhosted.org/packages/60/5e/7e7d716dca38c714478b741257a5b4a321d9932b8d851551a136dcaf3984/kiwisolver-1.5.1-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bebb89489b279b2f5661bbbb2abcc87bcd4a46607bb4a5c966f04f1db6b8df9a", size = 1348071, upload-time = "2026-08-28T10:26:31.829Z" },
+    { url = "https://files.pythonhosted.org/packages/10/1a/2b98fdda8bf45b7be317e48ed12393d44334394d315c74b81f4a14c0e31b/kiwisolver-1.5.1-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:509735237ae0d849e8a843551d423d2500d2e0a9ac1611a145658b29c0fb9f85", size = 992191, upload-time = "2026-08-28T10:26:33.544Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/55/d893f5ede0e50f9e3fcf01f6015f42ec7d9cd221e26772701fe4a98745f9/kiwisolver-1.5.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:824c3d763a05ea9e9003610145186b0e9848c7584a5575c79bac5a8e7cd80bad", size = 2233854, upload-time = "2026-08-28T10:26:35.282Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/82/f85f6279555a6ee1639fef7bfe83adb037a03e11a6fc9eaa54b8d0380339/kiwisolver-1.5.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:1fff05e239575b1481b6ed1a782f6fad616efbf1f0b1f44e6e85c4dfe426e483", size = 2330621, upload-time = "2026-08-28T10:26:36.9Z" },
+    { url = "https://files.pythonhosted.org/packages/56/31/e11aea078f66fc2fffcc179d38ca90d9da97652a241b64519169742ba46a/kiwisolver-1.5.1-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0627b9bceb9c3cdcf12b8a18655eedfed2692b038df27423383c120d0b7dc2d6", size = 1982848, upload-time = "2026-08-28T10:26:39.01Z" },
+    { url = "https://files.pythonhosted.org/packages/af/ea/2956b63bf5140ca46aa2c2818e6aa03e2d5754dd2fa41db1c6b28922940c/kiwisolver-1.5.1-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:8a708a47ade1fe19e8371d5da076bac0dd4b0a5a7985ad6c637f7f7e361b6baa", size = 2494850, upload-time = "2026-08-28T10:26:40.837Z" },
+    { url = "https://files.pythonhosted.org/packages/11/d1/3829542258d8b3fc0898d221e7ef0e2c83eca0d348709bb8dbe54f3d4005/kiwisolver-1.5.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:007a5553dfc4f4e8d184f588a0200e2cd4b63a59cc8796df3c39909e679dc7a0", size = 2298067, upload-time = "2026-08-28T10:26:42.803Z" },
+    { url = "https://files.pythonhosted.org/packages/42/73/17dce073a6ae259bb32cf9d686c4079d2e538868bc45967462bf33df914a/kiwisolver-1.5.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:43844c1a7ad6d723d5b5b4c4fc7f5bd399c40e288120d16257c7c9e8765c6e85", size = 1584907, upload-time = "2026-08-28T10:26:50.933Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/84/ae3c75909f507283cbfcc7e916c7e822579ef962020b97e6882b27b4478f/kiwisolver-1.5.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22d5e5aaad6be121f2515765e3b1c444352cb8eb4c86510801db8f2e50757316", size = 1392474, upload-time = "2026-08-28T10:26:52.638Z" },
+    { url = "https://files.pythonhosted.org/packages/34/31/8bcc83caad5bce8fa4577152389848bf6bc110e51e573a2b4e7c2aa34c89/kiwisolver-1.5.1-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3fa5855898f6d3d01b72ccd48a2d65cbdee301251603fefe34e2025bddba219c", size = 1405246, upload-time = "2026-08-28T10:26:54.248Z" },
+    { url = "https://files.pythonhosted.org/packages/55/72/220345537d790cf4ae54f8acfff4b5cc2468e0702a384d651cf7a771c63e/kiwisolver-1.5.1-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d66a64dd5dec136040ec2ae94aa026a912ee60fdd45bc28d3db30037fd809e88", size = 1456099, upload-time = "2026-08-28T10:26:56.042Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/10/3725fd2398f66d18c34b4e0f81a8d03764cd4f4f089f58a527f0b4428086/kiwisolver-1.5.1-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:9e51c119992ea8820706871c30a4642ec76de20ae82f9b50b9a45517d8e9f810", size = 1073695, upload-time = "2026-08-28T10:26:57.658Z" },
+    { url = "https://files.pythonhosted.org/packages/86/cb/28d6e09e66b93e4588b2e6b7d84d020ccefea09e2f4de788510a07efeab7/kiwisolver-1.5.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:70ed9a45c7484d2b30cdacf60d220f494a1763b9fec1ad03285c6553fa0889f2", size = 2335355, upload-time = "2026-08-28T10:26:59.202Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/b3/a0f31d5e4e40af7dc97c36b8a74fdd3a36cf3c8bbd098da9a23466ff6a94/kiwisolver-1.5.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:98b208a7cc42c803445ef551d6753cc42a5ea13e9cab1ee66cd8b9cb70195330", size = 2426524, upload-time = "2026-08-28T10:27:01.181Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/c9/728f63bd58c72cafdc79fc306abeeac7391bec03b757a48dadeb30906521/kiwisolver-1.5.1-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:c6834b92dd2428e2dd85ef3d85f723d3c12f20aaf43a2ddd4f944ca25d833408", size = 2063430, upload-time = "2026-08-28T10:27:03.06Z" },
+    { url = "https://files.pythonhosted.org/packages/84/df/ce188b96f92f9a2c958231da140768918cba53c9713dc887b82f85462118/kiwisolver-1.5.1-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:5d142e352eb13facc7dd047489aebdff6ba78576c239f1ea04931979caaf0567", size = 2597513, upload-time = "2026-08-28T10:27:05.072Z" },
+    { url = "https://files.pythonhosted.org/packages/69/d6/76947c8203768968382e5bd74d9cc95654746703a61ea53015f2c74a2e06/kiwisolver-1.5.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f9b1c4900736e489a812c529100de4b8fb617d4db075e931e213c57424b83d9b", size = 2394488, upload-time = "2026-08-28T10:27:07.423Z" },
+    { url = "https://files.pythonhosted.org/packages/75/62/f786c3a27f181fa339d851a77e266d208e776b9883cabc40a5b041a31b5a/kiwisolver-1.5.1-cp315-cp315-ios_13_0_arm64_iphoneos.whl", hash = "sha256:bb7c99f0673c03017a3ee01e54a5c2617a05468b11eabe513b0080e063ed95b1", size = 62420, upload-time = "2026-08-28T10:27:12.308Z" },
+    { url = "https://files.pythonhosted.org/packages/02/cd/58a91ed25fbad0facdf503297b03768efab04bdf3141e5e3b49a34be7443/kiwisolver-1.5.1-cp315-cp315-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:0d8924877ce22e17326a99a418c3c82037da078df3c6a260b13eca677444e6e7", size = 64577, upload-time = "2026-08-28T10:27:13.502Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/5c/d501ef5a0958b226eac28306d24d5e5f114be0ace50e19cabae7b6b3b197/kiwisolver-1.5.1-cp315-cp315-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:534f02c1abb31ed6dbd3515545285c330b2f12d00fdb1fdb71658b9ca5a13a6a", size = 66284, upload-time = "2026-08-28T10:27:14.813Z" },
+    { url = "https://files.pythonhosted.org/packages/89/b1/ba7b9c0164ce1cf62bf2872db63b8483289cf0f3110d6f9390eb09e409ed/kiwisolver-1.5.1-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ca7f6fe0f37ca978a1e5eb7a3a68e6413f417e78e838324947ffd420202b198b", size = 1482211, upload-time = "2026-08-28T10:27:20.039Z" },
+    { url = "https://files.pythonhosted.org/packages/13/dc/34da54efb4976616d45c20aae32d70e89d6e7395ed908029154d1609ef22/kiwisolver-1.5.1-cp315-cp315-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5b973887ff782cfd6b67c9904ad8ca542e0bc5e4961503408b423b5a688b4d38", size = 1283739, upload-time = "2026-08-28T10:27:21.82Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/3a/30ffb62bee646e266e98a1b5cd276d9c75b6116fbfcb87c1190838c1b6df/kiwisolver-1.5.1-cp315-cp315-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f76fc85bd054c806960f917ec0f329e24e436f1712267d90588e4c39890caa63", size = 1301681, upload-time = "2026-08-28T10:27:23.876Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/8d/13c70be22a8506880b35fdc38dca36629613bc493405c79f4037f2cd2bb9/kiwisolver-1.5.1-cp315-cp315-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:828f75af2b0080c8a972e75f649ab46af008e92c6104a57a759157200b835b75", size = 1349159, upload-time = "2026-08-28T10:27:25.899Z" },
+    { url = "https://files.pythonhosted.org/packages/82/0e/993972b8ec6767f47cd69818fb3a5ff14510557d29f7d1a839be7574fa1b/kiwisolver-1.5.1-cp315-cp315-manylinux_2_39_riscv64.whl", hash = "sha256:431dc224a1a92a5c8f582d96e505196a3b5997a7271076678da2dfde67b77e9a", size = 997613, upload-time = "2026-08-28T10:27:27.507Z" },
+    { url = "https://files.pythonhosted.org/packages/36/82/ca26eddd2eda2420dfc56693449c1f821f78b485da9cbde9904c03af3f93/kiwisolver-1.5.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:61e9a64c7635095a6bfe483e2ff055d437c59bd45f3617a228b37277f0185d62", size = 2235109, upload-time = "2026-08-28T10:27:30.113Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/84/97d920881e10840b8d7c7185620298e3e4c88820b05514e3a15a258b08a6/kiwisolver-1.5.1-cp315-cp315-musllinux_1_2_ppc64le.whl", hash = "sha256:3c24cd69455e1b00ddf770c13b6e2c33e07d6dc3f2d34add0bf9277c5c6bbd46", size = 2331207, upload-time = "2026-08-28T10:27:32.795Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/ce/34d74b8f25acc58800f4c09268371e8d6159cf0f1206f1e4dc7835629b48/kiwisolver-1.5.1-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:27add358abe374ebaa3b8763ef380bc99051b5a4b18d94878366a9e4f59efef0", size = 1986696, upload-time = "2026-08-28T10:27:34.628Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/01/f892644014612527aef7031d3306a2ffc60b3cb044f802c1561f8e5e14f3/kiwisolver-1.5.1-cp315-cp315-musllinux_1_2_s390x.whl", hash = "sha256:255605693a483db7bd5c79f60437f7bf658f7f520d61aa42722e32257c941951", size = 2496400, upload-time = "2026-08-28T10:27:36.818Z" },
+    { url = "https://files.pythonhosted.org/packages/32/6d/d8284e66e697026536e5f418b9cfe56567bffd3c775e3ecfbae373605854/kiwisolver-1.5.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:7d38b0c279c3032e8c9cc013b405c6df8e1668dbf15465779aa7f15f61201812", size = 2302967, upload-time = "2026-08-28T10:27:38.803Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/c9/51dc974d9130da70a8c47a96160123443d387ffe1b6b833d6f91d9429339/kiwisolver-1.5.1-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cea90547bfd93807e0013a004dc76552be44fad3bc1cc2b38610a9e889ed098f", size = 1588030, upload-time = "2026-08-28T10:27:47.678Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/86/f3e1a730e7a995149d8d3ff9e313b6d8a17b2cf1d98a8eff139dc30463fb/kiwisolver-1.5.1-cp315-cp315t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e8e4d953faaded9ec7ede36824e9814082d22d4c7b1eafbfa079ecba8cd0d076", size = 1390760, upload-time = "2026-08-28T10:27:49.676Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/3b/8ba25a2b5a0d2375e046f1b72de5179513f0be95aba6e7b094c89303929f/kiwisolver-1.5.1-cp315-cp315t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7e9c01d3dd7ceba4d1d436cc021d40d592466e40b9bc7f5d83dc4e98a5c9cd8c", size = 1403279, upload-time = "2026-08-28T10:27:51.242Z" },
+    { url = "https://files.pythonhosted.org/packages/18/d0/278d5cb8be812740027d5ca0a7eda0c375488a88d6dce0fa60fcc2591ad2/kiwisolver-1.5.1-cp315-cp315t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:37f801b5d7cc0e5a548921308e059fd2b057bb42972b591cfa3049f95423c4ed", size = 1454429, upload-time = "2026-08-28T10:27:53.213Z" },
+    { url = "https://files.pythonhosted.org/packages/94/37/bcbab41063ec284c1d200efe5087cf087798c2f8916960aa8a20dd303290/kiwisolver-1.5.1-cp315-cp315t-manylinux_2_39_riscv64.whl", hash = "sha256:e68e151428b5384f766cd25739bf77c7e4a3dc93b5ded7a12118d9fbfdf78ab6", size = 1073225, upload-time = "2026-08-28T10:27:55.089Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/c5/ab79dcdf5ae28909a51210ae0a1c579e97ff997b3466414f0d04c0994583/kiwisolver-1.5.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:8f8fddb8e323bd6eee4e54e69a39243beab22689070f4c66b472c4cc88bb89d8", size = 2334335, upload-time = "2026-08-28T10:27:56.78Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/8e/71d047468a189041d9c93f3b76844b924f9793b188c44bd149fa258912da/kiwisolver-1.5.1-cp315-cp315t-musllinux_1_2_ppc64le.whl", hash = "sha256:3cc210010fd2f438a3ed430b45f1b501fd13a8618bf984dc2c5ce5b69b78752e", size = 2424982, upload-time = "2026-08-28T10:27:58.639Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/37/1347461bbea6d0e1f0580b94ef603b18e72c2be5f667fa1653867361a00b/kiwisolver-1.5.1-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:b5664603a253efd3a75716d793d1d3a6a82723b61dc6db767b2460bbbeec4c0f", size = 2062857, upload-time = "2026-08-28T10:28:00.407Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/87/c7f0976c9cd0d127643351bc0c9929e0b8899d7f49d4ec238cd909e39c42/kiwisolver-1.5.1-cp315-cp315t-musllinux_1_2_s390x.whl", hash = "sha256:a7b85b2cc6ea45e5f7e8c9a30bc9fabd47cda09106cbb4b967335c3e6c43b69d", size = 2596022, upload-time = "2026-08-28T10:28:02.183Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/9a/59a6f6ae6f938c15076be2c21b6cedea973d71bb1349ec84fa485fab82cf/kiwisolver-1.5.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:ab620eb663952455271ac37f9aaad86b73c969c02f11f53cea405b38e96a4300", size = 2395634, upload-time = "2026-08-28T10:28:03.977Z" },
+    { url = "https://files.pythonhosted.org/packages/16/45/c37a21ad5c0ab581a93c55ad544721aaa1f0ae94edb29c6a678a23d013e6/kiwisolver-1.5.1-graalpy312-graalpy250_312_native-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:072bdb15a3c19a5b5dbc8f8fb1f4e1884bf4f3507eeb4cc6334401274d37a5c0", size = 194292, upload-time = "2026-08-28T10:28:11.06Z" },
 ]
 
 [[package]]
@@ -1890,18 +2012,18 @@ wheels = [
 
 [[package]]
 name = "llvmlite"
-version = "0.48.0"
+version = "0.49.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/dc/a0/acc8ffcd5bdc63df0097e22c719bfcd61b604358343089313a8aebbb24ab/llvmlite-0.48.0.tar.gz", hash = "sha256:543b19f9ef8f3c7c60d1468191e4ee1b1537bf9f8a3d56f64c0ddd98de92edd2", size = 184016, upload-time = "2026-07-02T20:20:05.308Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/27/72ae94ea5c8f7349ec1c229d4cd058feb799cbd0833ad6d1b47c919b37b7/llvmlite-0.49.0.tar.gz", hash = "sha256:00f16db782f4a13c78c5804aedc434e46794a77e89999a168f9401106270e50a", size = 194467, upload-time = "2026-08-11T16:26:00.489Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/80/f2/72409351db66d0a317ec5087e076f31fb7b773a640db8a90ce6b5cac9edd/llvmlite-0.48.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:416fa4c2c66c2c6dc6d0a402648c19206e548efa0aa1eff01ad5cdad0af8217d", size = 59890118, upload-time = "2026-07-01T18:41:44.886Z" },
-    { url = "https://files.pythonhosted.org/packages/3a/27/5ae2f3722606360480707adb47f001ad89df8251d06b14ee80336e660b66/llvmlite-0.48.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f5e5a5131045b72345c71062ea1a91910dde913792b6c9b28ebb2c1c0a712e98", size = 58343459, upload-time = "2026-07-01T18:41:40.306Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/4a/90715fa12006d681270b08d881195b6fab3ec39572e048764a1f7f59fed7/llvmlite-0.48.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8761b9e522f55207e24424fcd98370289eec2710bf8e915c82d1053f642450dc", size = 59890120, upload-time = "2026-07-01T18:42:00.748Z" },
-    { url = "https://files.pythonhosted.org/packages/70/5e/7b3e20d64650ca3c80af0cdb664ec4b575ec83d9d4dd05bea8bd31f9bbb6/llvmlite-0.48.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2fe5cb59b2063bfa039dcb8ca6481c0181bf552f340d10dcf61d7996a665556e", size = 58343457, upload-time = "2026-07-01T18:41:56.41Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/e1/05b50692b647cac3c18200ac485b04f342f00ed173c9cc46767274469a15/llvmlite-0.48.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:05f0103c8f2f96a37441337e3643863c01b8e83e530aff38960dcb383c54a065", size = 59890115, upload-time = "2026-07-01T18:42:17.805Z" },
-    { url = "https://files.pythonhosted.org/packages/f7/c3/470b8c4ff9ae2db2f9cf5c3e73de76ed908a32788ae9eb5602d43e6a476b/llvmlite-0.48.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:37d66fae72802175b0bfe1ea06e624b51e2d7aee6c3c34bbd09739b8f88e8e0b", size = 58343457, upload-time = "2026-07-01T18:42:13.217Z" },
-    { url = "https://files.pythonhosted.org/packages/27/98/a29133b4728671a175f7d616fab8b1c6e1d8c269d1523581d3160697bfb1/llvmlite-0.48.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d9f952dff6c350c529997423d4fa43abae9722a884ac7ffafc37a3af676e7db", size = 59890119, upload-time = "2026-07-01T18:42:33.88Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/cf/7aac11a1f1c7ec54b60c7f6814e87561fb6b55b2f290455d7941eb113420/llvmlite-0.48.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:054aa7d46595935565f276cf0c1659b4f10929c996dd4a606875fae26fba2a23", size = 58343460, upload-time = "2026-07-01T18:42:29.545Z" },
+    { url = "https://files.pythonhosted.org/packages/be/3c/e97f69c62a2d972066d9a2612ce1f3de313035ac897a5b9f787cad8b55f7/llvmlite-0.49.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6acba646d88abbc87d5c113a3d62c1fbf8b8fee11c6493f516803e30f21ae870", size = 59890658, upload-time = "2026-08-11T16:24:05.451Z" },
+    { url = "https://files.pythonhosted.org/packages/69/e6/e942ee08605fc0526ff3854260c384d8315a5830e16c4c2a5aebc14dc9bf/llvmlite-0.49.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4ec8ad805e7515cb8440a690eb3cef4d34acb29eef80b705ec4e1c1ad3c43c68", size = 58344481, upload-time = "2026-08-11T16:24:13.503Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/1c/ca8ba47b057b793099784475499771780ec46839f2782f753a7079d23520/llvmlite-0.49.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ddc7aecd4f56397ed6e8f120ec5dcd5a1a8f0e6032ca4af413462792d4dca2e3", size = 59890659, upload-time = "2026-08-11T16:24:35.595Z" },
+    { url = "https://files.pythonhosted.org/packages/de/af/9526dfdd33a923f33e29a18b8f9801ee7ee4b7397e88d28192c1024c4a75/llvmlite-0.49.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d3dee64784201b64c13a8df62c48a4f4218858faaa65889866bb29bdc243c038", size = 58344482, upload-time = "2026-08-11T16:24:45.79Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/61/0b23849141a4c4e7091fcd158ebb45195896974bebca3e58fee7cad4b4f4/llvmlite-0.49.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4281a0171d66d2098adce4ba706b8c550b1b10718650f682d64cde16e84e4de5", size = 59890659, upload-time = "2026-08-11T16:25:08.733Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/92/628692b74b31e27af9ba7e8ba651941ee4956959d5478123c453f59aad4a/llvmlite-0.49.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b095f15fb12c4d90495df5b1a3772b4732cc408398b204a787dbedd370e09c69", size = 58344479, upload-time = "2026-08-11T16:25:15.731Z" },
+    { url = "https://files.pythonhosted.org/packages/98/07/a2c4f04e2111ccc274b4d5e3331398a9dcf6d6e5e55d6444b1ad9d6381cf/llvmlite-0.49.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d5555ea1d63928481cbf7fcb1d67452b216c7e5b393a4eb7aa1401e67f2a4fc4", size = 59890658, upload-time = "2026-08-11T16:25:43.294Z" },
+    { url = "https://files.pythonhosted.org/packages/80/f9/7b7b50f80b4585bcd78675ff3110c256877b11df32a8cde284f851762f57/llvmlite-0.49.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e32adb84fdaae28aeb86fdb6253084ee707ee157289a2e98fe3caf48a62bee82", size = 58344482, upload-time = "2026-08-11T16:25:51.527Z" },
 ]
 
 [[package]]
@@ -1944,62 +2066,82 @@ wheels = [
 
 [[package]]
 name = "lxml"
-version = "6.1.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/05/3b/aab6728cae887456f409b4d75e8a01856e4f04bd510de38052a47768b680/lxml-6.1.1.tar.gz", hash = "sha256:ba96ae44888e0185281e937633a743ea90d5a196c6000f82565ebb0580012d40", size = 4197430, upload-time = "2026-05-18T19:19:06.424Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/42/95/bb63f0fd62e554fe078e1fb3c8fe9083c14ddc7ad7fa178d10e57e071ac7/lxml-6.1.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c921ba5c51e4e9f63b8b00267d06566e1f63407408a0496da2d1d0bfc819c7fc", size = 4930746, upload-time = "2026-05-18T19:18:29.637Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/99/0013e8d9b5960f4f041cf0b73e2f80c23eb5205b1f7bfb20203243651359/lxml-6.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:54a7f95e4de5fb94e2f9f4b9055c6ba33bf3d628fd77a1d647c5923caa2cdcdc", size = 5093723, upload-time = "2026-05-18T19:18:34.168Z" },
-    { url = "https://files.pythonhosted.org/packages/29/91/317b332636bfc7bddcff828d41b3307f50043f4b237e40849c333d80fa1a/lxml-6.1.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96f2ec43df44b1f76249ee0a615334f9b5b060e1c8bd90e706dad2d14d02f383", size = 5005557, upload-time = "2026-05-18T19:18:39.798Z" },
-    { url = "https://files.pythonhosted.org/packages/42/2f/cc9bf06afe70f9c9093ae60855d9759da9db601ec4080f7473319666ffd7/lxml-6.1.1-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:70ef8a7e102a1508f8121aae5b0867abd663f72c14f0a9c937e6554cb4587b7b", size = 5631036, upload-time = "2026-05-18T19:18:44.858Z" },
-    { url = "https://files.pythonhosted.org/packages/08/f6/af32e23e563971ffb0fb86be52bc5be5c2c118858ffc119bf6a9039b173d/lxml-6.1.1-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ebe6af670449830d6d9b752c256a983291c766a1365ba5d5460048f9e33a7818", size = 5240367, upload-time = "2026-05-18T19:18:49.217Z" },
-    { url = "https://files.pythonhosted.org/packages/78/83/8555d40948b09ce86f1bd0c68a7ac31d07b1929f92cc1b074006c97ef2d2/lxml-6.1.1-cp312-cp312-manylinux_2_28_i686.whl", hash = "sha256:27acc820660aaffa4f7c087f29120e12980f7779d56d8492d263170111284740", size = 5350171, upload-time = "2026-05-18T19:18:52.779Z" },
-    { url = "https://files.pythonhosted.org/packages/63/75/5d92da93729b7bad783689e6496049fa40927b45bec7bf183c981de3ca70/lxml-6.1.1-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:1db753c9115ec7100d073b744d17e25e88a8f90f5c39b2f5dd878149af59671f", size = 4694874, upload-time = "2026-05-18T19:18:55.139Z" },
-    { url = "https://files.pythonhosted.org/packages/c5/b5/3aad415a9a25b822e783f15deeb4dffccf5113030f1afa2222dd929313d9/lxml-6.1.1-cp312-cp312-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c4f469aebd783bb741c2ecb2a681008fd26bfe5c16a9a72ed5467f834e810df2", size = 5244492, upload-time = "2026-05-18T19:19:01.28Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/a1/5fcf7eb9904b80086aa47dcf0027de07b1bb990afad2e6823144c368ae04/lxml-6.1.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:766b010012d59470072c1816b5b6c69f1d243e5db36ea5968e94accf430a4635", size = 5048232, upload-time = "2026-05-18T19:18:12.67Z" },
-    { url = "https://files.pythonhosted.org/packages/77/74/1f601b63c7a69fcdf10fa9b148c81da8442204194f6c55509cc485c786b9/lxml-6.1.1-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:b8d812c6011c08b8111a15e54dd990b8923692d80adf35488bee34026c35accf", size = 4777023, upload-time = "2026-05-18T19:18:15.928Z" },
-    { url = "https://files.pythonhosted.org/packages/a2/b9/7a78f51aec95b1bf780d78e12705a9f6533284f8693dc5c0e6724fa53d3f/lxml-6.1.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:fe0306bd29505a9177aac19f1877174b0e7422c222a59f70b2cd41633448c3dc", size = 5645773, upload-time = "2026-05-18T19:18:23.223Z" },
-    { url = "https://files.pythonhosted.org/packages/a5/6e/98a7b7ad54e4e74fa1f20fff776913980619d0ebe5558232d7da6580bdd8/lxml-6.1.1-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:5ba186ad207446c65d3bb3d3e0412b032b1d9f595e59861e2354798c5703d955", size = 5233088, upload-time = "2026-05-18T19:18:31.433Z" },
-    { url = "https://files.pythonhosted.org/packages/65/d1/bc0ed2427bf609f2ee10da303a6a226f9c8bce94f945dc29a32ce55de6e4/lxml-6.1.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:aa366a1e55b8ebfe8ca8ddc3cfe75c8ebade181aeb0f661d0cb05986b647f72a", size = 5260995, upload-time = "2026-05-18T19:18:37.091Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/ca/ab7bfe2bf4c972af5e7878262845ead3a24a929a9b04bc11c7c1ece6c82a/lxml-6.1.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:eb7c9811bfaa8b1ed5ed319f5d370dfbcaa59d52ea64be2a5a85e18195930354", size = 4924139, upload-time = "2026-05-18T19:19:04.873Z" },
-    { url = "https://files.pythonhosted.org/packages/6b/55/a0c72851dfee5ecc689f949723a73dea457758912542cb955b108eaf0d8f/lxml-6.1.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:762ff394d5bd56da0cf034a23dcce4e13923f15321a2adfa2ac00201dc6d3fca", size = 5082329, upload-time = "2026-05-18T19:19:09.728Z" },
-    { url = "https://files.pythonhosted.org/packages/f0/b6/0608f7d61a3b96cc67e5648a3d906e31a5082093e10e7be65b3886289938/lxml-6.1.1-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a088f287f7d8275a33c07f2cac6c50b9319309a0200a39e7e75d80c707723099", size = 4993564, upload-time = "2026-05-18T19:19:13.608Z" },
-    { url = "https://files.pythonhosted.org/packages/4c/66/ae227524b066d29d55bf0b453d93d2d793c40218657d643dcbbca13b8faf/lxml-6.1.1-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e902da4b04e6b52e5893900d4b8ab46068f75f3561f01bf1080957f9fd932ed6", size = 5613467, upload-time = "2026-05-18T19:19:16.228Z" },
-    { url = "https://files.pythonhosted.org/packages/a6/76/dbe4a00b50385e40194231dcfe5a12c059de7cf90e89c83407d2b085b719/lxml-6.1.1-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1d4962d4c66bf830a7e59ed6cfc17d148149898a3aefa8ec6e59763e6e3ed085", size = 5228304, upload-time = "2026-05-18T19:19:19.354Z" },
-    { url = "https://files.pythonhosted.org/packages/1c/01/00b1b8442ed2041793336868ba0b9ea4b13d7da7c085c6404c207a63bf79/lxml-6.1.1-cp313-cp313-manylinux_2_28_i686.whl", hash = "sha256:581d4c8ae690a6609e64862dd6b7c2489635c2d13907fc2b20f2bc200ff1d21e", size = 5341607, upload-time = "2026-05-18T19:19:22.297Z" },
-    { url = "https://files.pythonhosted.org/packages/63/36/1ad29931e9a4638bb707869f01d423a6c815f82152138d1a40dfcfde2b95/lxml-6.1.1-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:876e1ff5930ed8bf295ec5ef9a8155e9b6b1876bbf1deed8b3a8069311875a8f", size = 4700168, upload-time = "2026-05-18T19:19:25.133Z" },
-    { url = "https://files.pythonhosted.org/packages/3c/d1/a9536cecf9be18a0dc72d32bead283a2332d1ffebd2dd3ac70ce444686e5/lxml-6.1.1-cp313-cp313-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:9eb9b5a968f6e0f6d640092a567e14529ff8cea2e29d00da6f78a79fa49f013c", size = 5232487, upload-time = "2026-05-18T19:19:28.603Z" },
-    { url = "https://files.pythonhosted.org/packages/0e/77/b4fb1e03bf5d130e879214d3100092e386418807fb74dd0adc4b0a48f351/lxml-6.1.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:aa49e06d94aba782c6a02eecb7e507969e7e7a41b267f1b359bb35585f295d5b", size = 5044231, upload-time = "2026-05-18T19:18:42.246Z" },
-    { url = "https://files.pythonhosted.org/packages/26/4c/d00daeeb0a5530c4028a9232aa1b93db3ef4ed2158c116ea73c79a9765b3/lxml-6.1.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:70cdfd80589d59e43e18005dd7244e8895e93db8ab6a620b7e23df5445a4e3d2", size = 4769450, upload-time = "2026-05-18T19:18:48.013Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/6a/715a3a8d156ce42f29cf014706f5410c2ff3b02267774110fc23266409fe/lxml-6.1.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:aad9aa39483ed8ec44d6d2e59e5b98a0d80676ef0d92f44bfc374836111f62f5", size = 5635874, upload-time = "2026-05-18T19:18:51.914Z" },
-    { url = "https://files.pythonhosted.org/packages/45/37/0544bc21dde2a88f3a17b504e6fc79c0e01d25a33c2f6079724e9e72b9c7/lxml-6.1.1-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:d49514be2f28d895c38cf9d2b72d7b9a07d00314519f456c0b50b53cfcf4c785", size = 5223987, upload-time = "2026-05-18T19:18:59.715Z" },
-    { url = "https://files.pythonhosted.org/packages/4d/f8/f6a5e8185bcb28c2befae3d31f8e3df3b811cb0f47746517a81279fcafe1/lxml-6.1.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:47402e62c52ff5988c1e8c6c63177f5708bccf48e366dea4e3dcf1e645e04947", size = 5250276, upload-time = "2026-05-18T19:19:03.834Z" },
-    { url = "https://files.pythonhosted.org/packages/d2/51/3904907c063451cf8d4a5c9fe0cad95fa1f4ec57f4e3884fa0731bd7a305/lxml-6.1.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:74a9717fd0d82effef5c2854f0d917231d5324b5a3eb7275c43ac9fa32f97a14", size = 4950022, upload-time = "2026-05-18T19:19:31.958Z" },
-    { url = "https://files.pythonhosted.org/packages/94/cd/9c7611a51c37a2830928405817cc5d56a97f64fab83cc3f628748b135749/lxml-6.1.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:efe0374196335f93b53269acd811b944f2e6bdc88e8894f214bd636455484909", size = 5086695, upload-time = "2026-05-18T19:19:34.764Z" },
-    { url = "https://files.pythonhosted.org/packages/da/d6/24e3b5906abb0b674ff2ae195bc3ce59708df2bcd17cf17703b2d7dd643a/lxml-6.1.1-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ac931cdc9442c1763b8a8f6cd62c0c938737eafc5be75eff88df55fc73bc0d00", size = 5031642, upload-time = "2026-05-18T19:19:37.771Z" },
-    { url = "https://files.pythonhosted.org/packages/2d/db/6ec54f99019838bff54785c51da07f189eb4676861c5f2730962b0d8d665/lxml-6.1.1-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:aee395f5d0927f947758b4ec119fd5fc8ec71f07a1c5c52077b30b04c0fa6955", size = 5647338, upload-time = "2026-05-18T19:19:40.553Z" },
-    { url = "https://files.pythonhosted.org/packages/42/3d/ef4dcfffd22d27a61805d8ed9f7fb888495bc6aa88648fa07c1eaa5586b6/lxml-6.1.1-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9395002973c827b3ed67db77e6ec09f092919a587022174554096a269378fb13", size = 5239528, upload-time = "2026-05-18T19:19:43.657Z" },
-    { url = "https://files.pythonhosted.org/packages/62/bb/37fb3f0dff146bdcfa78eec47879273820b2a0bf350ec236ce14bd0b1c26/lxml-6.1.1-cp314-cp314-manylinux_2_28_i686.whl", hash = "sha256:73bc2086f141224ebddb7fc5c6a36ca58b31b94b561e1dfe8e073e3270fad1e7", size = 5350730, upload-time = "2026-05-18T19:19:46.307Z" },
-    { url = "https://files.pythonhosted.org/packages/90/42/43253f168388df4fae1f38c01df36ddb9bee39e2048167b54cdcbae85ea3/lxml-6.1.1-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:3779def59032b81e44a5f70096ef6bf2082f8d901937dca354474ba09782e245", size = 4697530, upload-time = "2026-05-18T19:19:49.889Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/a8/c5a8504f81bbdfc8e7094c2c850cdb4ed6777fc4d5ddd9e5ab819f3b0d54/lxml-6.1.1-cp314-cp314-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:86c89b9d55ebf820ad7c90bc533410f0d098054f293351f10603c0c46ff598f5", size = 5250670, upload-time = "2026-05-18T19:19:53.199Z" },
-    { url = "https://files.pythonhosted.org/packages/77/b7/c7e76ab18744d75e21f320ebf9ff9d1ceae2b54dd431ea5a64caf26c9672/lxml-6.1.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:19607c6bbff2a44cf3fe8250abccd20942d3462473e0a721d01d379ed017e462", size = 5084485, upload-time = "2026-05-18T19:19:08.422Z" },
-    { url = "https://files.pythonhosted.org/packages/31/31/b35c53f8ef7b7c31cacd23d3638652fff7bcd1deb6eedb709ab43b685908/lxml-6.1.1-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:c6ed5141a5c7507cf3ee76bd363b0d6f801e3321adc35b5d825a23115faa5465", size = 4737635, upload-time = "2026-05-18T19:19:12.321Z" },
-    { url = "https://files.pythonhosted.org/packages/d9/06/31f23c813a7fe8e0cb1b175e915b08c9bf4e86d225b210feadbdbe519667/lxml-6.1.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:62aeb7e85b5d60320b9d77eef2e773994e2c0ce10121b277e0a19804e1654a5a", size = 5670681, upload-time = "2026-05-18T19:19:15.001Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/bc/ce619bccc89b1fd9ad8a8e1330ee3f3beff9f2ff95b712d7bbcdd6e22fc3/lxml-6.1.1-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:b1b963fd8f5caa68e99dfae060d54de1fe9cba899b8718b44a00cdca53c3e590", size = 5238229, upload-time = "2026-05-18T19:19:18.131Z" },
-    { url = "https://files.pythonhosted.org/packages/2f/5d/b329acbbedc0b619ebc2be6cf7ee9ed07e80892c88d4dfd612c33805789a/lxml-6.1.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:63876be28efefa04a1df615b46770e82042cce445cfdce55160522f57b231ccb", size = 5264191, upload-time = "2026-05-18T19:19:21.118Z" },
-    { url = "https://files.pythonhosted.org/packages/16/02/6f7061f4f95f51e545d48e87647c54791d204a4e881be4156e7a26ba5338/lxml-6.1.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:acd7d70b64c0aae0c7922cca83d288a16f5f6da523637697872253415269baef", size = 4970291, upload-time = "2026-05-18T19:19:56.215Z" },
-    { url = "https://files.pythonhosted.org/packages/b0/02/55fc057d8283427dea7d6edb102e7a840239c77a64a983d92f62a304c0e9/lxml-6.1.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4f0dd2f01f9f8a89f565d000e03abcf0a13d692a346c8d22f628d49af098777a", size = 5102822, upload-time = "2026-05-18T19:19:59.223Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/48/8e1cf78d89d66850121d9255a2a24414c98f775da93b90cf976956c24b14/lxml-6.1.1-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b7e8a14c8634bf6f7a568634cb395305a6d964aeb5b7ee32248094bed3a7e2c", size = 5027923, upload-time = "2026-05-18T19:20:01.549Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/00/0632a0647612c8af24d26997b3b961397daa9d5b2581444805933629a4cb/lxml-6.1.1-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:86281fbdd6a8162756f8d603f37e3435bfa38043adb79c6dc6a2dfee065e7525", size = 5595843, upload-time = "2026-05-18T19:20:03.93Z" },
-    { url = "https://files.pythonhosted.org/packages/bc/86/ab008a7dc360711b66858d61c80a5979a70a09f2aa2b05d9698df80b803d/lxml-6.1.1-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c5d7152ec39ca7c402d8fb9bad86140a15b9503bd0c54484e3f1bbe3dd37ceca", size = 5224515, upload-time = "2026-05-18T19:20:06.381Z" },
-    { url = "https://files.pythonhosted.org/packages/75/c6/2702ff375e728e34f56d9a45339a9cf7e4427e917f542225242d63a05afa/lxml-6.1.1-cp314-cp314t-manylinux_2_28_i686.whl", hash = "sha256:88d8cb75b9d82858497a5393e3c63cfbf03035225e4b35a49ed7ccb151e4dc0e", size = 5312511, upload-time = "2026-05-18T19:20:09.308Z" },
-    { url = "https://files.pythonhosted.org/packages/b7/57/a5807c98f87a86f10ef9ffab35516df7c0f0c4b6d5d33e9f608ab9c04a31/lxml-6.1.1-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:f64ec5397ea6a41fc1b4af0380d79b44a755b5531dcaccd9940fb260dca93038", size = 4639206, upload-time = "2026-05-18T19:20:11.704Z" },
-    { url = "https://files.pythonhosted.org/packages/1f/e1/8a0a2c35734812395f4da4eaf33748a7e5705bfb2a58b128da764339d5ec/lxml-6.1.1-cp314-cp314t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d34bbf07dbc7ca5970671b1512e928991fb5e9d95365636c9b2d8b4f53af405e", size = 5232404, upload-time = "2026-05-18T19:20:14.064Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/e2/0e6a4dd5ad84d01d99aa7bae7cfefd4a760a0e0f8176818241de17d9b6c0/lxml-6.1.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:17e0e18d4ad8adbd0399291bc44845b69d9dd68439a3cdebdf35ff902ec05072", size = 5083769, upload-time = "2026-05-18T19:19:23.758Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/7e/161f33d463f6ffc1c7679104b65086dea120080d49dde4d238f015aaee2f/lxml-6.1.1-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:3ab541146f1f6968c462d6c2ac495148e8cdba2f8347700b2141b6ec5a75bf52", size = 4758936, upload-time = "2026-05-18T19:19:27.256Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/fb/2369825e3f6ca99305bf9f7b7085fda91c8b0922a89e54d900974aa3ef85/lxml-6.1.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:2a0217714657e023ef4293500f65aa20fce6164c8fd6b08fa5bd4a859fb14b9b", size = 5620296, upload-time = "2026-05-18T19:19:29.993Z" },
-    { url = "https://files.pythonhosted.org/packages/30/90/d61e383146f74c5ab683947ea14dc7b82778838ab9b95ea73a23b60d0191/lxml-6.1.1-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:05a82eb6e1530a64f26225b55cbd178113bd0b5af1c2b625f25e5296742c26d2", size = 5228598, upload-time = "2026-05-18T19:19:33.523Z" },
-    { url = "https://files.pythonhosted.org/packages/76/2d/2dafd8149e94b05bb070690efd5bb2680720681e03ff03fc57d2b70a1105/lxml-6.1.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9e36f163528fc50cbef305f02a5fd66d404edf7049cdaff211dbc2cba5a7013e", size = 5247845, upload-time = "2026-05-18T19:19:36.649Z" },
+version = "6.1.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/23/ad/28ecd7cb894d172f3c9c80a075eeeb2017ac62e3632cee05a5f9493547eb/lxml-6.1.3.tar.gz", hash = "sha256:45222d94ddd511536f3b2f7d9deae3b2339b4ce0f075f1ca25703b07cad9dd21", size = 4211198, upload-time = "2026-09-02T14:48:02.287Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/1c/722e88883173097a1a375153e3c2447eba3060d0231522cf6596e99f4195/lxml-6.1.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f683dc6300317700025e41d89a43e0276692ded16113a3c43eab704d605c58e5", size = 4939696, upload-time = "2026-09-02T14:47:32.997Z" },
+    { url = "https://files.pythonhosted.org/packages/db/36/aa413bc214dc4f785ad2b2ddd8cc99aae7062d49ab155e91e6011af00daf/lxml-6.1.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:379f8a75cf6eb7eef0af074b55f49ab73b868388a98de14646abcdfa4564bb11", size = 5105247, upload-time = "2026-09-02T14:47:36.734Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/a0/a1f7f1313795bfec67b77f01ef3b1128d49f2d7f66a8413fa55d47f4e25f/lxml-6.1.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b37772102d44bb6628186accca3a121b1fa3a6b3d97518a8c29a5229ca4c0d0a", size = 5011915, upload-time = "2026-09-02T14:47:39.846Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/78/840e7e3f1d0cc7a5cfac5d8505b97e25b6427fd774ac4bae672aaebfb4b5/lxml-6.1.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ddcf547bea2aee967d6a77779376a45e77e610e8465147a1f3d7e20d539d6e32", size = 5638175, upload-time = "2026-09-02T14:47:43.644Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/20/e022dbc6b4753a9bc9fc5fb28a27163430c1731b9913997f6544c1b2518c/lxml-6.1.3-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:909f4e927bb051f7740d6367285fc60cdcfdaf0258c2dba4ff5ba7eadadc250c", size = 5244675, upload-time = "2026-09-02T14:47:47.635Z" },
+    { url = "https://files.pythonhosted.org/packages/99/83/82cde81d2b5eb38d1539fdfdf318abdd014a7e604f4df01c9cd3deb18f2a/lxml-6.1.3-cp312-cp312-manylinux_2_28_i686.whl", hash = "sha256:a5c18810318303ce9afb3f95e2ddb54834f96fa699a8600433fd5a93dcf44c56", size = 5358205, upload-time = "2026-09-02T14:47:50.306Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/a1/f3b057371c8cb29f2a9c9c44ea320592446e40b74a4b0af68c3d8e65bc73/lxml-6.1.3-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:3e42265103fb385d8642a78672edf376c6f7e1d3598a7a4f9cb1278f2f6b5f6f", size = 4704495, upload-time = "2026-09-02T14:47:53.251Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/a4/230eb28be5d412152ffc3c679b51fe1aeede5a53f3a8eb6e9748f2f4754f/lxml-6.1.3-cp312-cp312-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:21402998e4b78e7cce237d2788841aaa21ac9a4d1574d04dc2d12ee41ae807b5", size = 5255117, upload-time = "2026-09-02T14:47:55.963Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/18/1969f56763af24ce42ea156007b0b2d73fddea552e283b2010416394f0f4/lxml-6.1.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:38fc4e4e4e084e0bd491949482527d406788045c546d4f8789e93fc527b91385", size = 5054424, upload-time = "2026-09-02T14:47:58.131Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/d4/2a90acc1f6fabaa3a8db9340437822bd8d041b205d626a4b3e8621aaa390/lxml-6.1.3-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5609efdb0d3c95499c00046bc53648b3482ec2175b5503d6e611b3f0555dc71d", size = 4785572, upload-time = "2026-09-02T14:48:01.029Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/1e/b90e845b1dcd0f2f3f26b98283d857f25909223aacd265eee032c34ab8b1/lxml-6.1.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:97ce49699d87ebf8aad631b55d65b33219a4f1bfefbbf5bff19dc9af160aeaf9", size = 5656516, upload-time = "2026-09-02T14:48:03.419Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/ab/0a1b802c57f3fba5c4efd77d5c6b78adaa8f7b681f0c90456b140fe8bf6c/lxml-6.1.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:48542c9acba9ff9450bd18d871d2c2c8787fdb283572b623d206f1b927cd7d9e", size = 5245982, upload-time = "2026-09-02T14:48:06.109Z" },
+    { url = "https://files.pythonhosted.org/packages/da/ee/2c016fbceb3778137459292538d9dfa7e3ad9070fe409c15254ddd90d2cc/lxml-6.1.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c55e71a9b1db1f107efb60da49c093689b74c5c31a708e5379e2fd9439d4fbb5", size = 5267340, upload-time = "2026-09-02T14:48:08.374Z" },
+    { url = "https://files.pythonhosted.org/packages/35/42/df27b56848acd29d8a720acc28977911aab36f2a09df4208d5502e887415/lxml-6.1.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:611a51e61c92f62345a50b0035df6fc0d678f9299f33728826d831598862f59d", size = 4936186, upload-time = "2026-09-02T14:48:22.94Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/8d/8a7b91df0b54d09d25f5f44885d6b3e0a6d6643a8c070191580318d20c42/lxml-6.1.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b477912f42c5c33405a10c759d22f80cf5af043ae02d95b9d8e5e5bc555739ed", size = 5093324, upload-time = "2026-09-02T14:48:25.132Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/7e/8f340ddcd43790332fb0de8a26628d571a492da3300cd191821698407c96/lxml-6.1.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5cffe18571ccc51d742cd08cbb3f8b756de9311d18c7ea98f5d92f37b8fb60c2", size = 4998850, upload-time = "2026-09-02T14:48:27.394Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/c1/9c5bb572f1f09ec9e4322bd4a4e9f4ad48347fc56ef94cf4df58a5279dc8/lxml-6.1.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:75cc6569e86be5785b6188ef1642670c6adbc984e81ec35e224842ecd9eefcc8", size = 5626813, upload-time = "2026-09-02T14:48:29.61Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/7d/8bf1fd8bae8247743968bb76d027a1ac5bd2c4b44495fba6a71b30d10706/lxml-6.1.3-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d85dfab42dd672f87a7f76e9de7172962aee69fa12044f0d6e1a23cbd53fb80e", size = 5232385, upload-time = "2026-09-02T14:48:31.969Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/2e/6cef69ed81cb7df0d03b0dd09d08e6e2cf5061a743ff6f42f0b741548e9b/lxml-6.1.3-cp313-cp313-manylinux_2_28_i686.whl", hash = "sha256:42632b4024ab24a6b488f559ac851312509888b6b80ae2aa11cf29a646a0d245", size = 5347088, upload-time = "2026-09-02T14:48:34.13Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/e1/8e5fd8ddc8c7d685badb0f2db149e3c9da84eefc2827c01c658df2c4e3cb/lxml-6.1.3-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:febd35ef45f603c2d74b74655efdbf45e14f55fc0aef4ac82b663ca829b283e0", size = 4707227, upload-time = "2026-09-02T14:48:36.62Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/7e/00041382a11be40a88bf405ebff11c8efabd3de79f2691e1638b1c47a8a0/lxml-6.1.3-cp313-cp313-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a43b3bdf11e477dc7770609d3477316f974354dfc8425d596f64f471cc8daf6e", size = 5240208, upload-time = "2026-09-02T14:48:38.893Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/fe/316538b5cff0936fa63d45d421c655730fcbb5a28dcac728c175083002bc/lxml-6.1.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5d582042c69857c364e8153de6e18e0da9b7b515a6a8113caf69a6ec8e0520f2", size = 5050271, upload-time = "2026-09-02T14:48:41.213Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/91/455bcccb3ac725373007344d351151810cd19762d1673b64b811f4359a42/lxml-6.1.3-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:8e49a646acfab83c68974f4aa1d0a2acca9e88d7d627ae0fc13201b14b76d310", size = 4780433, upload-time = "2026-09-02T14:48:43.779Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/f6/580440e2f52cf00bba5c5e1080bfa88cdfcde73be71a11d95170ddbb663f/lxml-6.1.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:0dee106e9aa97fb00541b1ed7827070564d0549c3d3fba8920e6b20fd980f748", size = 5645928, upload-time = "2026-09-02T14:48:46.187Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/dc/d123c1f244306543d545f62443f794959e4f1ea709fe100f8740d514e74a/lxml-6.1.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:dd5e90f34cffcfed97f36cf066325773d2b6021c60c29942e53a18b028501b1d", size = 5231184, upload-time = "2026-09-02T14:48:48.691Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/3c/fe55b2bd5c6113c906511cd88f6a470195c5fbff1124f19970ab706c3477/lxml-6.1.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d9b3e7d71bf6acff341233417abbdface29c647e3113892d9aaedc02eb4aa2bc", size = 5255814, upload-time = "2026-09-02T14:48:50.948Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/2d/49b6a6ad7ce8f64b07b9fe852ff0c6d3fcbb26db61bee4f63d4120180a1c/lxml-6.1.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2cae5d5c90a62d9139c512a0cb1aad1d182b022b5740daea2617eb5bf7fc658e", size = 4965074, upload-time = "2026-09-02T14:49:05.133Z" },
+    { url = "https://files.pythonhosted.org/packages/66/bc/6230cf80e4331c33383b0b6b73dc31a393dd76edd4cb73d761de5123034d/lxml-6.1.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c6c0c13128a32eb04a51357e56a094e13aa8e6d3d1884de2e9ae923f6915e1a8", size = 5099355, upload-time = "2026-09-02T14:49:07.343Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/cf/d1143d9b7717e07a82f158a1fc9ce6e581fdad1226734950af869e3ffde4/lxml-6.1.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2221e88679d1351e9a40aaee54bc65679b9795bbd0160bc3d5e36b163344eb75", size = 5036795, upload-time = "2026-09-02T14:49:09.65Z" },
+    { url = "https://files.pythonhosted.org/packages/31/6f/194bb00ffb89712c30f5a7e1b8e685590e140fad6c8261fec172c09a3dc0/lxml-6.1.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cfb398886a7eb4c719161c3efcff2a1248febc53a4d8e5072d2d8a87fed84ac9", size = 5658740, upload-time = "2026-09-02T14:49:11.9Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/44/27e3cee3dcdb3b7bc09727b642bdbfcd098490ea77df04611db9060d7722/lxml-6.1.3-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a7eb78ba28b187e1e9203a55c60fcf70df2d22cb205fe6d51b9383d6097419f0", size = 5245991, upload-time = "2026-09-02T14:49:14.154Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/e9/8312560579fc980bbd2233a8a673cc46f7d613d3633f2bf08a21e8f4ad13/lxml-6.1.3-cp314-cp314-manylinux_2_28_i686.whl", hash = "sha256:ea6b1e9105b4b24a34c722432d9fb578f9ed83af21fa1abda639011e0f22bbb6", size = 5354136, upload-time = "2026-09-02T14:49:16.459Z" },
+    { url = "https://files.pythonhosted.org/packages/74/d8/eda60f4f73a9c780b5d6e1175484f66e6c81a2c93346e2906a1fec9c7a02/lxml-6.1.3-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:e8b17e23df3e827a69d25af70990ca2420e92668aaffaeeb3cd2351d7916a023", size = 4704379, upload-time = "2026-09-02T14:49:19.032Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/c8/c9cc60057be78ac34bd2b842e45e6e88edbfe5e532e82c3b82381b7aab49/lxml-6.1.3-cp314-cp314-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1b7c37339d7e75cab9a123a04248e243cefefb302ad6db566ea0c77cbcde421e", size = 5258676, upload-time = "2026-09-02T14:49:21.306Z" },
+    { url = "https://files.pythonhosted.org/packages/41/7b/66894008fee8d1785b8db129747ae963fd427b68f456918df7f2f24a8b98/lxml-6.1.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:83e3a51e7933db700a0da0db31849db3a24022d9970da9bb73001e1d0326fd92", size = 5090069, upload-time = "2026-09-02T14:49:23.562Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/31/c1b60404859f4c3cd1f41f29c65a24e25cea78fde822d9574a21f66810be/lxml-6.1.3-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:9bde9ae026a55b9a192078dfa6e27dd0ca4a050171ab6272e92f97b757dfdf48", size = 4741958, upload-time = "2026-09-02T14:49:26.037Z" },
+    { url = "https://files.pythonhosted.org/packages/23/b8/6285f0cf546f14da2554cabdeaf7c2c2ff3190c74807f0de2e8810a786f9/lxml-6.1.3-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:1a635e837b50a1819bebfedaac5916498ea024120969da8790500148fb0a894d", size = 5683245, upload-time = "2026-09-02T14:49:28.438Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/f6/2168cab44336dcb15fed0f0b78577225b83297cdf0dee349c95420c3dcb0/lxml-6.1.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:d0c5c362bc94f1929dc7e96e715bbe7bd17037f802e6d8f0d1545df9133c0559", size = 5246087, upload-time = "2026-09-02T14:49:30.955Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/89/32f5de69a0a31f30e6164981851f87b37ecb2c4ee838e504b88d49d4818e/lxml-6.1.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c59e4265608da6a041f54646ecc0c9ecdbb19aaf14c4c684bb6c2114998cc415", size = 5269352, upload-time = "2026-09-02T14:49:33.502Z" },
+    { url = "https://files.pythonhosted.org/packages/94/e2/ee9aa6ed2b666b2db1f6f7fd48964ff9da39ebe827ef5eac0ab881f639d9/lxml-6.1.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f6b9d2aad499c769ee8287609ab0e6de99d8bcea99c6e6c2e64945259fd52fb2", size = 4979517, upload-time = "2026-09-02T14:49:42.153Z" },
+    { url = "https://files.pythonhosted.org/packages/29/e3/e7763d1661b283ddd4fa36f91b9a497db6b8d2aff55028b16c7f642e0755/lxml-6.1.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a23fefdb345b2d4d0ff2860571b5ff9a89a28b6a120f720e8fb0324d346626", size = 5115270, upload-time = "2026-09-02T14:49:44.493Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/cd/22205d5b4d177e3f4156f780412426ee7c7f8107809f119f0dcc40fa51e3/lxml-6.1.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:545ccc14fb05485f48b4439ec35beb16d5b5280eb6c81c658bd4707a2a119414", size = 5032449, upload-time = "2026-09-02T14:49:46.841Z" },
+    { url = "https://files.pythonhosted.org/packages/da/43/06a4626c3bb79ef8c501b674afab8100d64e798665bb2a97d1c960636a49/lxml-6.1.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:93476b6514b373fc6ca67d26c442784f7807c86f00635bfe79f935c3eab2af17", size = 5603325, upload-time = "2026-09-02T14:49:49.664Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/9c/733682a0c2de9f5779ba207bbb3f3f6be8c6bda863fc01739b186b38783a/lxml-6.1.3-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8db38ff3fb7aee7d6a82ae4da2eef1178656fe1216841fbd24870062a9d60473", size = 5229023, upload-time = "2026-09-02T14:49:52.447Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/8a/e69cdaca3fd33a647942925664f01b20908d41a6968c182305be9c38fb11/lxml-6.1.3-cp314-cp314t-manylinux_2_28_i686.whl", hash = "sha256:25f4118c438f96bb466e83108506d03d5c31b1bd2387e83e5b070bda6ded9c37", size = 5317811, upload-time = "2026-09-02T14:49:55.25Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/b2/0c397588174403c2ab68fc464abf97e03e7324f9c6cb6a99023104707195/lxml-6.1.3-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:1beb0f9909b26cee938df9ba56b15252a84429b1fc30ce6fca161390b9789a70", size = 4646516, upload-time = "2026-09-02T14:49:57.761Z" },
+    { url = "https://files.pythonhosted.org/packages/56/7e/cfea25afafbe49db8b225764f7f74bb37c2a7f5e717d917d3d4a5e098ed4/lxml-6.1.3-cp314-cp314t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3a27ac6c780c8b8a1cd231b58407634cafc1c4cc28cd6c7141362df0f36351e7", size = 5240626, upload-time = "2026-09-02T14:50:00.279Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/75/7a587771bb52ebb0e2c57b6dbe9fd96a70fbb54d72ddd97d54c5f8ec18d5/lxml-6.1.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:a1932d7ce78a561367512c594fe66eac2b2ec9b9264cfd9b5f950622f4a116e2", size = 5086619, upload-time = "2026-09-02T14:50:03.245Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/01/94c0ebe6d831861542d251e038052e52bf6d33f1d18f1cfffdc82851065a/lxml-6.1.3-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:7d0f5976aa2701996f759b30172925829867547bb073af0ae67d1307a0f0262c", size = 4758828, upload-time = "2026-09-02T14:50:05.873Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/f1/938d67bd0e5b1fdfa52be28aefdffbad57e1f6b8e921c2aab88542c75f40/lxml-6.1.3-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:c5e7ce578aa8a80910a72a8ca0bbea3baae10100827249001999726a788456d8", size = 5627083, upload-time = "2026-09-02T14:50:08.555Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/65/4e51522f6c214650db0abb7b16ccd11b1238b8a05a8d59aa4ebed59c9f67/lxml-6.1.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:d97c5227621af74b111882a290b10f371780a38eef9d9e730408fba2259b52fb", size = 5235170, upload-time = "2026-09-02T14:50:11.255Z" },
+    { url = "https://files.pythonhosted.org/packages/92/c2/e73d19365665f6b16ef84df21199befc3b06e4c539046ad2d9595f6fb9ea/lxml-6.1.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:da707f14ea3c35ee463d50acd596d6488e4b2b4ae7cf77a5bf93f55c023d63e8", size = 5252273, upload-time = "2026-09-02T14:50:13.782Z" },
+    { url = "https://files.pythonhosted.org/packages/52/09/0b30084e9eb1c546a4be3d9c56df70058d116b1a320400a59b0f7da87bf0/lxml-6.1.3-cp315-cp315-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2e01125896585139453cab8cb235893644d8815d7509520da95ae3ee8d1c1f79", size = 5037928, upload-time = "2026-09-02T14:50:37.007Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/0e/5c37275a3e361f6138dc06db748ea565c1fe8a5f4ee5e2ddd80047c81a89/lxml-6.1.3-cp315-cp315-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:290f66b97ede0e552e1cb44a0fd8a74f9753ee635b50830a0b122fb72788d015", size = 5661932, upload-time = "2026-09-02T14:50:39.777Z" },
+    { url = "https://files.pythonhosted.org/packages/70/c5/b71ffb289b15e2642e2a3cf6d468c44da39ea119061a99e5b05e3d10f217/lxml-6.1.3-cp315-cp315-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:73fc05988ed20809450474ba760a87c8ad4e455fc09783c02195e56ec634b41a", size = 5249209, upload-time = "2026-09-02T14:50:42.141Z" },
+    { url = "https://files.pythonhosted.org/packages/81/ea/9910da149a23932f9301652e57661cd9e42b0df18f12be21159b7255f92b/lxml-6.1.3-cp315-cp315-manylinux_2_31_armv7l.whl", hash = "sha256:dc3a44689eea43eab836e5c98a8ab015dc2419987d1ea6eafc7c590cdff86bed", size = 4704543, upload-time = "2026-09-02T14:50:44.634Z" },
+    { url = "https://files.pythonhosted.org/packages/76/07/9290329cd188c62e22021f79df04ee0cc33d9a93b0d38bd65ccd452ad9d0/lxml-6.1.3-cp315-cp315-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:209c3ccbfe35a04ac6d24f0611f9d1cbf8025d49991b14acd935236234d6c156", size = 5261298, upload-time = "2026-09-02T14:50:47.301Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/0c/aba78bd3401cd99b73a0aed8e2b9b43e14be94fab3603d4bbc8a62365f2a/lxml-6.1.3-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:2f5b2a2b9811b853b39bfa41367c6d78747b8e3e80e07fc5a24aae295c1a4d7d", size = 5090453, upload-time = "2026-09-02T14:50:49.952Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/dc/fa4426c3355aa0216cbeb3911495b5f65a26e0df85859a89928fe28f0396/lxml-6.1.3-cp315-cp315-musllinux_1_2_armv7l.whl", hash = "sha256:6a406d0b3cb207b0fa460ed4dc93e866f44f105da0169361cb18ff998a44c7f0", size = 4744709, upload-time = "2026-09-02T14:50:52.394Z" },
+    { url = "https://files.pythonhosted.org/packages/be/2b/224fe7918658ab7c532ac2412f3c1eb28f71e6364fb07566262d0cc6a7b6/lxml-6.1.3-cp315-cp315-musllinux_1_2_ppc64le.whl", hash = "sha256:53258656846f5c48996b882fb4b135885e088a3ad3d96b4bc0530f95124d1f69", size = 5685802, upload-time = "2026-09-02T14:50:55.043Z" },
+    { url = "https://files.pythonhosted.org/packages/21/44/7d480819b9adcae5f84dd8ac529132c6b7a578544398225cd20321adcd91/lxml-6.1.3-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:aa633613ff907ea91b9b0489a1f0da1b8725d8c6ccec6b77e8a1c9c235044bb0", size = 5249019, upload-time = "2026-09-02T14:50:57.985Z" },
+    { url = "https://files.pythonhosted.org/packages/72/83/385a267ea1b6b283f2249dd827ef360a295e9db14e13ef4665a120c60d64/lxml-6.1.3-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:90f709b9accab6b2e4d14f5c8718203877a0486bcb3afd74d8b539ecd1e961d4", size = 5271886, upload-time = "2026-09-02T14:51:01.667Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/f9/f81b4bdb6efb7a596be29603d8758154d00a5f545db9f3cef9d9041c8f64/lxml-6.1.3-cp315-cp315t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cad7617727a96d189bd6f979d0fadf765198c7934e85f4edaba9bf3ad919a300", size = 5033744, upload-time = "2026-09-02T14:51:10.633Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/0f/26d9bfaacb319c86e0eca8a1a0bf1130d36a7afbd318883e23caea63763d/lxml-6.1.3-cp315-cp315t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cae82b5ca24b0c2beedb269f6e2a96f466acd926879ab00ae19f1a65cbf9ffb0", size = 5615269, upload-time = "2026-09-02T14:51:13.357Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/90/73675f3f4141350ed65d6fec533b107d4e802c5caa340cf111771edd86e0/lxml-6.1.3-cp315-cp315t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:69cafd61aea04ebb3502c93c2aaa568b12931ca0802231e0b5de76bf8b6e74bd", size = 5236280, upload-time = "2026-09-02T14:51:16.051Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/be/ed260767e7977de463a0f91f3f4fffcab85c0a2a024a21ffe1fa442c2c79/lxml-6.1.3-cp315-cp315t-manylinux_2_31_armv7l.whl", hash = "sha256:dc205732d593118cf701d986f40e9de7801bb2e371cb189ddbda9b7348f4d97e", size = 4650718, upload-time = "2026-09-02T14:51:19.102Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/fd/e9839d03b1e767f2725cf7d7d81b80d5f3f9fdc10ad8827e2479311b046e/lxml-6.1.3-cp315-cp315t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:88e719b9437f148f7e1465df845c758dd1598618cbea3a2fd1e61a715542f2b2", size = 5243376, upload-time = "2026-09-02T14:51:21.606Z" },
+    { url = "https://files.pythonhosted.org/packages/34/a5/4606e347e2788c301f677004aa83e28d24da9fe663a24380122af57be6fc/lxml-6.1.3-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:40983eabefd13da003e68170928c7acc011f0d095eefce5871a3c71c9385fb9a", size = 5092340, upload-time = "2026-09-02T14:51:24.21Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/99/3314a8661cdf30f493c55a87db283961dfaae08451976a2ca418958e1804/lxml-6.1.3-cp315-cp315t-musllinux_1_2_armv7l.whl", hash = "sha256:fad67b12ffe0f71e02b4932b04883cbc76a9072bbd30731409d3523cf058b011", size = 4758768, upload-time = "2026-09-02T14:51:26.813Z" },
+    { url = "https://files.pythonhosted.org/packages/30/58/3bdc577f78ea8b7d72d39a84506f7001d5b28728f43e5b84891e3b7d9a4a/lxml-6.1.3-cp315-cp315t-musllinux_1_2_ppc64le.whl", hash = "sha256:6cd11e7550d89e551a87dcec30f04b1fca32e86b68708aa01a4daa455d8605e5", size = 5649546, upload-time = "2026-09-02T14:51:29.453Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/e4/652633de1a2395949ebb7a8fc7d089aba12a2b45f0fefbc9d29e3e3ab3cf/lxml-6.1.3-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:ca0ec532ad2f5ba1e5ec120ac157769c57f01855b3d8bf37213f5d88abd9ba0a", size = 5234874, upload-time = "2026-09-02T14:51:32.262Z" },
+    { url = "https://files.pythonhosted.org/packages/65/a6/c4581d171de30449304b4859bbd3607e9b40da13c0f88b68e6097c8d785e/lxml-6.1.3-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:e99e09ab7741f1281e2677f4c0058c7f5267d182530b09c87e4f6aa26adf3887", size = 5260043, upload-time = "2026-09-02T14:51:34.841Z" },
 ]
 
 [[package]]
@@ -2054,7 +2196,7 @@ wheels = [
 
 [[package]]
 name = "matplotlib"
-version = "3.11.0"
+version = "3.11.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "contourpy" },
@@ -2067,23 +2209,26 @@ dependencies = [
     { name = "pyparsing" },
     { name = "python-dateutil" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/1f/24/080c99d223d158d3a8902769269ab6da5b50f7a0e6e072513907e02b7a6c/matplotlib-3.11.0.tar.gz", hash = "sha256:68c0c7be01b30dcca3638934f7f591df73401235cbdbf0d1ab1c71e7db7f8b57", size = 33251176, upload-time = "2026-06-12T02:29:15.508Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/94/95/7f522393c88313336b20d70fc849555757b2e5febc22b83b3a3f0fd4bce9/matplotlib-3.11.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be5f93a1d21981bfb802ded0d77a0caa92d4342a47d45754fac77e314a506344", size = 10031353, upload-time = "2026-06-12T02:27:55.215Z" },
-    { url = "https://files.pythonhosted.org/packages/87/ce/8f25a0e3186aefd61913e7467d1b999465bcd0d0c03ac695c1b26ca559b7/matplotlib-3.11.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:41635d7909d19e52e924a521dde6d8f670b0f53ab1d0e8c331fa831554f681d1", size = 10839232, upload-time = "2026-06-12T02:27:57.746Z" },
-    { url = "https://files.pythonhosted.org/packages/85/c2/db15da2bbdf9e3ca66df7db8e2c33a1dfed67be24a24d2c878efaaff01d6/matplotlib-3.11.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:94f5000f67ca9faa300863ea17f8bce9175cb67b88bec4bc7780502d53dd7c9e", size = 10923899, upload-time = "2026-06-12T02:28:00.223Z" },
-    { url = "https://files.pythonhosted.org/packages/a4/c0/1117d53077e3ac3152503a84e9cf7a5c239576805ee71276e80c2aaa7471/matplotlib-3.11.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be152b7570324dc8d01574cc9474dd2d803237acf528bcbb5b211fa347461a09", size = 10031623, upload-time = "2026-06-12T02:28:11.26Z" },
-    { url = "https://files.pythonhosted.org/packages/92/7e/e937138daffad65b71bf831a377809dcbc830fb4f31a31e067dc1faa2575/matplotlib-3.11.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:126f256df600652d7e4b394cf3164ff75210a00038f287c95a012a6f58d0e83f", size = 10839372, upload-time = "2026-06-12T02:28:14.102Z" },
-    { url = "https://files.pythonhosted.org/packages/1d/c2/438ecc197ffb8023b6b9922915542f2172f5fd45b76703b0b4fc47322243/matplotlib-3.11.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:03acfeddf87b0dddb11b081ef7740ad445a3ca8bcb6b8e3011b08f2cf802b75c", size = 10924099, upload-time = "2026-06-12T02:28:16.383Z" },
-    { url = "https://files.pythonhosted.org/packages/95/e4/b4b7c33151e74e5c802f3cde1ba807ebfc38401e329b44e215a5888dd76d/matplotlib-3.11.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:565af866fd63e4bd3f987d580afe27c44c2552a3b3305f4ecbb85133601ea6f3", size = 10045491, upload-time = "2026-06-12T02:28:27.141Z" },
-    { url = "https://files.pythonhosted.org/packages/71/28/394548efd68354110c1a1be11fe6b6e559e06d1a23da35908a0e316c55a9/matplotlib-3.11.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e6b3e64dea5062c570f04358e2711859f3531b459f29516274fbad889079e4f3", size = 10857059, upload-time = "2026-06-12T02:28:29.222Z" },
-    { url = "https://files.pythonhosted.org/packages/c8/44/e7922e6e2a4d63bdfbc9dc4a53e3850ab438d46cf42e6779bb15ec92c948/matplotlib-3.11.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:942b37c5db1899610bd1543ce8e13e4ecff9a4633e7f63bb6aa9205d2644ebd1", size = 10939576, upload-time = "2026-06-12T02:28:31.66Z" },
-    { url = "https://files.pythonhosted.org/packages/b7/2d/4e1240ea82ee197dfb3851e71f71c87eeeb975f1753b56a0588e4e80739a/matplotlib-3.11.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d9695457a467ff86d23f35037a43deb6f1134dd6d3e2ac8ce1e2087cff09ffb9", size = 10843005, upload-time = "2026-06-12T02:28:42.39Z" },
-    { url = "https://files.pythonhosted.org/packages/29/dc/6377ecfaa5fef79430f74a1a16638b4e2aa30d4692bae2c19f9d76fe3b01/matplotlib-3.11.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:19c16c61dea63b3582918503e6b294193961261d9daa806d4ae2151f1ad05430", size = 11127459, upload-time = "2026-06-12T02:28:44.483Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/41/795c405aa7560443a3b01309424cde4a1113b85c90b8a63417444a749617/matplotlib-3.11.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2d72ea8b7924f3cb955e61518d21e43b3df1e6c8a793b480a0c1214f185d30ba", size = 10925160, upload-time = "2026-06-12T02:28:46.564Z" },
-    { url = "https://files.pythonhosted.org/packages/4b/cb/22f6bc35711a0b5639a784e74e653e77c86210bd4304449dd399a482f74e/matplotlib-3.11.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dfabef0230d0697aa0d717385194dd41162e00207a68bf4abf94c2bf4c27dca0", size = 10854181, upload-time = "2026-06-12T02:28:57.856Z" },
-    { url = "https://files.pythonhosted.org/packages/3f/7e/9a9eaca731a2939589da520f0ebe8fd8753d0f51fca98c7d20af6dbe261a/matplotlib-3.11.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1644db30e759199443493ac5e5caec24fdb775a8f6123021f85ba47c4133c3cb", size = 11137715, upload-time = "2026-06-12T02:29:00.555Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/f9/9b030b6088354acb0296871bb624b25befc1c42509d3c6cd17420c83a5b8/matplotlib-3.11.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:15b0d160079cb10699a0e98b5989c70677b2df7cacdc62af67c30f2facec46d9", size = 10939427, upload-time = "2026-06-12T02:29:02.527Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/e7/c8/9aa712a0afb882649424dd8de8ad9aa6235e796e84c6052e8f6dc1598d0d/matplotlib-3.11.2.tar.gz", hash = "sha256:cec596316640f2b394b8f0daa0ea61a8eae82d017b620b9f202befb972a59ea4", size = 32660610, upload-time = "2026-09-11T19:05:31.214Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b8/90/2b3fd67ee273163faeda6d514be70b5596eaa0fd77b60ffc294ad0b34f5f/matplotlib-3.11.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:df4f7784aca81a94f254c0a2767d592ee25f407e488f5fa7203e51093fb6ca27", size = 9860353, upload-time = "2026-09-11T19:03:25.049Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/84/32549e7a462dc311aed2ab62e5d2538028840b5c53a8be0195c789937c3d/matplotlib-3.11.2-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b9a7ad579856284135e401ecc918c5f8a017ee30539298862a109f51b971710", size = 10670348, upload-time = "2026-09-11T19:03:28.108Z" },
+    { url = "https://files.pythonhosted.org/packages/84/39/02e21b74f7439bd643d717ea846006d46e309e6d29b632b57751265311d6/matplotlib-3.11.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3aa4b8516fd26659e4363abbf317c703d9116496c5db2e9d0609a2866dd39dd2", size = 10810580, upload-time = "2026-09-11T19:03:31.402Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/df/e7c10292f348cf55178a5b3c0092a38ea68891a3945f199ae8b7b8ac9dc1/matplotlib-3.11.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1944895967f87c84c9b4bad29a707b31f5b36df4a3a2339ea1f8ea3ce5105539", size = 9861060, upload-time = "2026-09-11T19:03:46.044Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/b9/b978de6d43d47f1d4d8e653fac32c16d8d2e5f46bbbb822e26d7cb1adece/matplotlib-3.11.2-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:af2661f6ac6bbd1d081996f54fdd9385715c625ace8cec0869055c0cfbf38981", size = 10670885, upload-time = "2026-09-11T19:03:48.942Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/80/d72f61bb2631ac15d17fada1f17e0f99fecba69d8376d34f17cf93b7eddd/matplotlib-3.11.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7e5a90f8a707ebb6004a713b1cff091a40cc5df4c7c1cb165e5a505ebc11c292", size = 10811222, upload-time = "2026-09-11T19:03:51.665Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/42/81d5cba4bb39b41b0998efc880e129f2d7cdb94256ca49678fdcd9c21be5/matplotlib-3.11.2-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf3fe71fbfb8ec0e310e0bc8537c3405a01f38f25f9394ed2135e6202fed542b", size = 10674370, upload-time = "2026-09-11T19:04:06.912Z" },
+    { url = "https://files.pythonhosted.org/packages/29/5e/52f56f93d5b20815ee0ae352f7a38eff25580edb180b4fa7df87cfeba68f/matplotlib-3.11.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8c8255de28f986d935a64c9ca71c0ec2d2f41d355691f5ea684725dc91413f71", size = 10954597, upload-time = "2026-09-11T19:04:09.948Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/ae/f8918a1b564db49c53cd603eb3c8748c5767e4ce0fb2cf1d7c38653e4c5d/matplotlib-3.11.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a8756cc73d9af9a7fe0deb54ea2e75ef73b01d9e575877e72acad5458e660943", size = 10812583, upload-time = "2026-09-11T19:04:12.887Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/40/ce270ef2a6794d94a409fe28b8dabedffc07c07f9e5dbb89e0f605bd157d/matplotlib-3.11.2-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e43b188f0a5b75447bcc197728258166aa64365770ed1caa36595a5e1ca4bbba", size = 10684626, upload-time = "2026-09-11T19:04:27.201Z" },
+    { url = "https://files.pythonhosted.org/packages/45/46/396e0307dbf1c1f2d0a3654caa6dbc780dd5379f5f11257369cf8d21d9fe/matplotlib-3.11.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3304eb5a59442a8867f6920d484591c0fa09ffc29e9260be2feec3351e25869", size = 10960602, upload-time = "2026-09-11T19:04:30.254Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/97/4defb84695477340aa88b27c9482cc1ca4de11f36f66b6c8c0ddd685def1/matplotlib-3.11.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:02329432ae5c6af87cf208ee575b701d698bdf0b1a3bb28cc6d53c36e967e575", size = 10822603, upload-time = "2026-09-11T19:04:32.964Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/a0/6aa72c7cd7cf4dfac652744d7a3310007cec3ce8e5ab9be607bec7096471/matplotlib-3.11.2-cp315-cp315-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:399fef672f7046ef7d6a57572b2a6f9845f3f5afcff04e7b2df7a363a9f42190", size = 10674370, upload-time = "2026-09-11T19:04:47.745Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/97/d7e90225ac145046787a2ee0fa4cdc7e4d5cb6772ea232130b10f8a4095a/matplotlib-3.11.2-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cc82dde2a0d3e3ad472edce04897ad7146b8d8bfd1df8a32992eebb81af18fdc", size = 10954581, upload-time = "2026-09-11T19:04:50.712Z" },
+    { url = "https://files.pythonhosted.org/packages/58/18/89cf056a9d2cae01f4b8baf01c7e809d613244fcdfa2a2acf28c1e0d79fa/matplotlib-3.11.2-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:930efb28f59fda124e39265d177bab302625297bb147d2910de725a2fc2aef54", size = 10812605, upload-time = "2026-09-11T19:04:53.552Z" },
+    { url = "https://files.pythonhosted.org/packages/58/5a/4552878fce8f9d249a5e264c16f5c267c27eac08fb039fa3e207a06feff5/matplotlib-3.11.2-cp315-cp315t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e60cf3047a51edecdc4535a9196bbd6a732936b8e9f8184aabf4d16165884aaf", size = 10685892, upload-time = "2026-09-11T19:05:07.686Z" },
+    { url = "https://files.pythonhosted.org/packages/af/dd/b3b19ec0124fc4aafd8a4b3bae859c562a7ff0fd8ae3b2099947f78bde19/matplotlib-3.11.2-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eb3712dc9b464793de0a4e42a7313d50293c751f94bddaf7332a1bc71bccdda9", size = 10964946, upload-time = "2026-09-11T19:05:10.943Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/30/687ccce22f66a57fed85942450e88062773bcbf1a42914e70c7148ec71dc/matplotlib-3.11.2-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:75b6d88402770e181b5d06a67dda4129c31da7d05004d63a21c310ec78d1b83c", size = 10824960, upload-time = "2026-09-11T19:05:13.9Z" },
 ]
 
 [[package]]
@@ -2121,11 +2266,11 @@ wheels = [
 
 [[package]]
 name = "mistune"
-version = "3.3.3"
+version = "3.3.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7b/a5/2dab368d6950e6808904dec98f54c7e726ee7be4a0c6afe00e6e011bd52d/mistune-3.3.3.tar.gz", hash = "sha256:c4c6c0c840b8637a2e9b8b6d607eb7c8f00888bf14c754409bcd339e848c2477", size = 115363, upload-time = "2026-07-09T06:18:05.268Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7b/92/328a294a6de83bacb95bed01f04e0eaff4e3616ee359fc821a5dfc539b02/mistune-3.3.4.tar.gz", hash = "sha256:58b5c96d6fcb61190dfe5fae498d2b2065f99cf61e9649418fd54cf1ada86dfe", size = 121426, upload-time = "2026-07-22T05:22:30.89Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/89/70/b1e4737b84163db5bb1dfde6f216dbfbf32783330a9989c965e121172830/mistune-3.3.3-py3-none-any.whl", hash = "sha256:99de1585e42dcbd826faa9e11a202727a5e202e4e4722a4c69ac1ff615793dd7", size = 63569, upload-time = "2026-07-09T06:18:03.839Z" },
+    { url = "https://files.pythonhosted.org/packages/77/e4/288365afae98953bc01de09f686f40d8ee84578135aa7767d5d4e60b5278/mistune-3.3.4-py3-none-any.whl", hash = "sha256:ee015381e955e370962968befe1d729ab60fafb6a715ac6751763fbce38c8d4a", size = 66862, upload-time = "2026-07-22T05:22:29.419Z" },
 ]
 
 [[package]]
@@ -2168,34 +2313,94 @@ wheels = [
 
 [[package]]
 name = "msgpack"
-version = "1.2.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/31/f9/c0a1c127f9049db9155afc316952ea571720dd01833ff5e4d7e8e6352dbb/msgpack-1.2.1.tar.gz", hash = "sha256:04c721c2c7448767e9e3f2520a475663d8ee0f09c31890f6d2bd70fd636a9647", size = 183960, upload-time = "2026-06-18T16:13:52.594Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/26/aa/753ad8b007b464e1d8aa0c8e650b9c5f4f725e658fc5ac8a7635c55b7f6e/msgpack-1.2.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:60926b75d00c8e816ef98f3034f484a8bc64242d66839cef4cf7e503142316a0", size = 410634, upload-time = "2026-06-18T16:13:00.383Z" },
-    { url = "https://files.pythonhosted.org/packages/6a/fd/6adabd4f6d5e686f97dd02ce7fce3fe4cf672cbac36b8f67ff4040e8ad8b/msgpack-1.2.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:020e881a764b20d8d7ca1a54fc01b8175519d108e3c3f194fddc200bda95951a", size = 419989, upload-time = "2026-06-18T16:13:01.776Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/cc/85039b7b0eb168aaad7383a23c97e291a11f08351cb45a606ce865e4e3f1/msgpack-1.2.1-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:4202c74688ca06591f78cb18988228bd4cca2cc75d57b60008372892d2f1e6e6", size = 377544, upload-time = "2026-06-18T16:13:03.637Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/bf/35963899493b32030c85fc513b723ae66144ac70c11ebc52e889e16e3d99/msgpack-1.2.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8b267ce94efb76fbd1b3373511420074ee3187f0f7811bf394531de13294735a", size = 400842, upload-time = "2026-06-18T16:13:05.012Z" },
-    { url = "https://files.pythonhosted.org/packages/a6/df/8e2ac970c8f99264cd9997d1c73df5466bc19da3301d7dc5500862a9b089/msgpack-1.2.1-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:e4f1d0f8f98ade9634e01fb704a408f9336c0a8f1117b369f5db83dc7551d8b1", size = 374108, upload-time = "2026-06-18T16:13:06.232Z" },
-    { url = "https://files.pythonhosted.org/packages/17/dd/fa8bd265110dfa51c20cb529f9e6d240a16fafe7e645004c6af2d01353ba/msgpack-1.2.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f02cf17a6ca1abe29b5f980644f7551f94d71f2011509b26d8625ce038f0df64", size = 414939, upload-time = "2026-06-18T16:13:07.478Z" },
-    { url = "https://files.pythonhosted.org/packages/e3/10/ddf7b06db879e8792d13934ddda09ff20bd2a583fd84c9b59aae9b0e650b/msgpack-1.2.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f310233ef7fb9c14e201c93639fe5f5260b005f56f0b29048e999c30935596cc", size = 407518, upload-time = "2026-06-18T16:13:14.233Z" },
-    { url = "https://files.pythonhosted.org/packages/79/d3/36a46a8ed992b781acbc05928bd5bee3c810cb0c3563bf81a7b0c04a1a76/msgpack-1.2.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:787c9bebb5833e8f6fc8abca3c0597683d8d87f56a8842b6b89c75a5f3176e2d", size = 416405, upload-time = "2026-06-18T16:13:15.435Z" },
-    { url = "https://files.pythonhosted.org/packages/f9/84/e8e9598b557c0ba6ddae901a73780a4c75ac667dddf59414b1e56a42fb34/msgpack-1.2.1-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:dc871b997a9370d855b7394465f2f350e847a5b806dd38dcc9c989e7d87da155", size = 376257, upload-time = "2026-06-18T16:13:17.022Z" },
-    { url = "https://files.pythonhosted.org/packages/40/16/738fe6d875ad7e2a9429c165322a4ec088f4f273cdfae63d96a89c467961/msgpack-1.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:85f57e960d877f2977f6430896191b04a21f8901b3b4baf2e4604329f4db5402", size = 397469, upload-time = "2026-06-18T16:13:18.287Z" },
-    { url = "https://files.pythonhosted.org/packages/ca/be/6d5952df75a7f24f35833af764c3a6860780364cb3a0030beb8099e1b2b4/msgpack-1.2.1-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:1233ee2dd0cefba127583de50ea654677277047d238303521db35def3d7b2e7c", size = 372802, upload-time = "2026-06-18T16:13:19.685Z" },
-    { url = "https://files.pythonhosted.org/packages/e1/39/e2ef7dbf0473bcb8dc7c50bf782a892d67414877b63e47fc88eb189ef5e6/msgpack-1.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e3dc2feb0876209d9c38aa56cb1de169bd6c4348f1aa48271f241226590993e6", size = 411273, upload-time = "2026-06-18T16:13:21.028Z" },
-    { url = "https://files.pythonhosted.org/packages/b8/a6/99e58722feaffc5f2fbcc0c8c0d1451ab9f84097f7af87291b46af2390f4/msgpack-1.2.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ca0dacff965c47afdc3749a8469d7302a8f801d6a28758d55120d75e66ce6889", size = 406090, upload-time = "2026-06-18T16:13:28.072Z" },
-    { url = "https://files.pythonhosted.org/packages/19/03/8c63e8cf52958534ef688625965ab04c269a6cadd8caef16758b380a821a/msgpack-1.2.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0e2bf9280bceb5efca998435904b5d3e9fdbcc11d90dc9df30aec7973252b720", size = 412106, upload-time = "2026-06-18T16:13:29.427Z" },
-    { url = "https://files.pythonhosted.org/packages/63/d2/155d9e71b40e41fd934bc0c48b9b2770f22263e1ac20aad8e29fdca7be3f/msgpack-1.2.1-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:aa6c4be5d1c02a42b066ca6ddb71adf36432868fdcdb6ee87e634e86e0674190", size = 374851, upload-time = "2026-06-18T16:13:30.631Z" },
-    { url = "https://files.pythonhosted.org/packages/98/48/deaf2326262a8d5ea3295ce9649912ecd3f551ba7ec8e33c665d2ba583f3/msgpack-1.2.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec0e675d59150a6269ddc9139087c722292664a37d071a849c05c473350f1f2d", size = 396168, upload-time = "2026-06-18T16:13:31.977Z" },
-    { url = "https://files.pythonhosted.org/packages/10/2a/b4410f906c2ec0008f1608d3ab5143afc3ad3f4e6da0fed3ea2231d0bef4/msgpack-1.2.1-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:dd3bfe82d53edfe4b7fc9a7ec9761e23a7a5b1dac22264505af428253c29ed24", size = 371959, upload-time = "2026-06-18T16:13:33.282Z" },
-    { url = "https://files.pythonhosted.org/packages/59/86/1edc67270099a528fa2093ea60fe191233cd238e4bd30cfacf7db79fc959/msgpack-1.2.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5ad5467fc3f68b5468e06c5f788d712e9f8ffc8b0cd1bcb160c105c1ee92dae7", size = 408457, upload-time = "2026-06-18T16:13:34.567Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/b4/b774d7eb95561739907fec675582f83203cf41c597a418c2589b4bfb8e9d/msgpack-1.2.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:20466cca18c49c7292a8984bc15d65857b171e7264bdcb5f96baf8be238791fc", size = 427661, upload-time = "2026-06-18T16:13:41.574Z" },
-    { url = "https://files.pythonhosted.org/packages/b2/f9/3243191dc9937e00756c8bc1b0272fed8f23758e43df2a3b46f533e5090f/msgpack-1.2.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:196300e7e5d6e74d50f1607ab9c06c4a1484c383cd22defd727902591f7e8dde", size = 426375, upload-time = "2026-06-18T16:13:42.936Z" },
-    { url = "https://files.pythonhosted.org/packages/23/c7/1693111db9944ba4ad4b67a1e788400d78a0b6af7a6523dc7e4e58f8274b/msgpack-1.2.1-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:575957e79cd51903a4e8495a242442949641e08f1efd5197b43bebd3ea7682b4", size = 380495, upload-time = "2026-06-18T16:13:44.306Z" },
-    { url = "https://files.pythonhosted.org/packages/3e/2b/92f86956a0c13e8662f7e2ad630c4eb4db07497b967589bd5245e018b2c1/msgpack-1.2.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8c2ed1e48cc0f460bf3c7780e7137ff21a4e18433451916f2442c1b21036cd7d", size = 410897, upload-time = "2026-06-18T16:13:45.629Z" },
-    { url = "https://files.pythonhosted.org/packages/da/ea/1479f72d200313a76fc2f823a79d1e07ed052ab7b8a0280640aa7b95de42/msgpack-1.2.1-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:5f6277e5f783c36786a145e0247fc189a03f35f84b251646e53592d2bc12b355", size = 378519, upload-time = "2026-06-18T16:13:46.998Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/4d/fa006060ffa1011d32bfae826fe766fe73e02982183601633b7121058ab3/msgpack-1.2.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f9389552ecf4784886345ead0647e4edc96bee37cbab05b75540f542f766c48c", size = 419815, upload-time = "2026-06-18T16:13:48.205Z" },
+version = "1.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0a/e7/bb605a7bab2d8425a64b3fa762b39dc1bf1c7e3f11ba6fb5413d6db0ff8c/msgpack-1.2.3.tar.gz", hash = "sha256:32edb81a2b5eb7cd7c9d941b2bfbbb082fd2cd09e0e725930316af6b708db186", size = 196517, upload-time = "2026-09-29T02:33:52.276Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0a/3a/aa9c580aea1314529a0f3562461479780b0d254b064f0880956bfbcc74a8/msgpack-1.2.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f3d7b3d0018746b5997dd6b14a1870b07cc4c327d9101145d94a1fc264a51a06", size = 460343, upload-time = "2026-09-29T02:32:04.906Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/cf/9c2e4d6c179529d5bf4a64cff76fa581486569e9fbdd35bd98f51cb624bf/msgpack-1.2.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ede33b2892ceb976283e009ad12fa1834cfdf1f9c43ee9c97849fc588d00a618", size = 472998, upload-time = "2026-09-29T02:32:06.69Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/41/915c81fe6df2d3cbdb0dece4f1a5cd313e1cd2abd9f501d0f50c0582517e/msgpack-1.2.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:666ef5601ab0e6e345e47febc96aa81143cc932201543480cbb9499164f05ffb", size = 423216, upload-time = "2026-09-29T02:32:08.739Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/e7/7dda8b1039abfd9bba4c5068172c67135c9e33089f503512db9226f23c24/msgpack-1.2.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:87cf2ef05ff2f2493ba29fcdaef27e960ca64dacfd13460ae29e6f92e0ed05bb", size = 451218, upload-time = "2026-09-29T02:32:10.517Z" },
+    { url = "https://files.pythonhosted.org/packages/16/5b/ce995c1ed4a0522b7f2d034bc2034fd63005f240b945961b70fb56fbaf3d/msgpack-1.2.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:b774ff994d844e541439ac5d2d49a14def4104830c3465e9394c153f86200ffb", size = 422453, upload-time = "2026-09-29T02:32:11.956Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/3f/ce191fb87e2650d0166b34c437e499ee4a7f9db9c1eb164f41725eb6160e/msgpack-1.2.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:eaf7e82249837e3aa97297b34a0bb9ff562027381631e057cea6e1367f10b438", size = 469003, upload-time = "2026-09-29T02:32:13.663Z" },
+    { url = "https://files.pythonhosted.org/packages/08/6a/e5fc57136e8bacccb2b39627dea2cd546540a06181e22fe6db90e15b3ae4/msgpack-1.2.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e0d7950ca3c1bbae291d0552dd3bb2792fc680629c4c0d44e47e5bab969f3ca", size = 454930, upload-time = "2026-09-29T02:32:21.771Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/30/c394d37898db9212d1693456cdf363c7e1a097d0b63e10664007f3df3ec1/msgpack-1.2.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:07c9733089d1b176c3dd2f7fa268452f9d5d784d076473499d754a58e8d1fbbb", size = 466866, upload-time = "2026-09-29T02:32:23.742Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/c8/1e4ddf6f6b829b3ee6c530c79dfae89cb609d2b0eedb5e0ae716851c52d1/msgpack-1.2.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f24a43b3560e20f825b807fe1e874bd73d53abaf8bbdcf258a6eb152cddbc1f5", size = 418715, upload-time = "2026-09-29T02:32:25.262Z" },
+    { url = "https://files.pythonhosted.org/packages/11/a5/f460ba6d7a12d4301002f3efbb8f841e8bdc9c5fc98d771689677a352885/msgpack-1.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6576f348ed6cc4f31db6fd915a8e94245f042f50eae08d48732425e70638ea37", size = 446489, upload-time = "2026-09-29T02:32:26.988Z" },
+    { url = "https://files.pythonhosted.org/packages/49/23/adface88db909bed321c85dd673655152d4a514c67e1f0800eb51c777d07/msgpack-1.2.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:cd5a9f9f86a52c24713679aa2631956835f3842512964ff93f736ff76f1f530d", size = 416998, upload-time = "2026-09-29T02:32:28.606Z" },
+    { url = "https://files.pythonhosted.org/packages/36/00/5bb3a239ccfc3763c4d0fa49b13b1b7010b00182c499ab3c1fecfe6294bc/msgpack-1.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f9ddd28d3e9bbc602a9dced1591882c7fb9ab776eef8837da2c326fde19e2853", size = 463288, upload-time = "2026-09-29T02:32:30.375Z" },
+    { url = "https://files.pythonhosted.org/packages/29/8c/456df77f00d701df9d6980ffb80291bce6e4e2e112e25a4dfae216f0715a/msgpack-1.2.3-cp313-cp313-pyemscripten_2025_0_wasm32.whl", hash = "sha256:62cc1a4ef0e553bac32c8342e1f04834aca7de276b92744eb7307db77759b890", size = 53347, upload-time = "2026-09-29T02:32:31.867Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/11/6d78ce5a9a58bf9ba7b1b6a8f649173b030e6770c8019cf330b91825ee5d/msgpack-1.2.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9324c54995641c3d1f92a9d55093c8cde0ffa2fbc87a467a688ef60428393220", size = 454352, upload-time = "2026-09-29T02:32:40.34Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/08/feb9a196269ba7809f44f9117d9e4a601c41c313f6144fd0c337293a5488/msgpack-1.2.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d8ef3a66e4b52d2d7fdd90df2984670124b2ff7546d76bb25dcf68ef47f7df58", size = 462562, upload-time = "2026-09-29T02:32:42.176Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/77/3a674f366def24140b103d1ffd4fd27b3d912a13e47da67422afa16bebb3/msgpack-1.2.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:902f3490db0e07a7d40b48536a85c9b28fbf1397e7e1658a45a55f958e303620", size = 418134, upload-time = "2026-09-29T02:32:43.693Z" },
+    { url = "https://files.pythonhosted.org/packages/48/82/944e71f280577490d99a3951cbce21aa4cbe04e7ab42cb373fd668af883c/msgpack-1.2.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8e51eca14fbb65c4e0a5a9657346962bd3dca78c08e04e3d4dee70ef48687d30", size = 445937, upload-time = "2026-09-29T02:32:45.739Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/ec/feddd629c4a3edf1395313680450c525086cceab56dec0d4de9da9ccb618/msgpack-1.2.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:f42f146752eedb6765f07dcc04d72dab0a25779ec8d4a88c0085263ce114f22c", size = 416450, upload-time = "2026-09-29T02:32:47.558Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/59/263a10f8c4613ba0713f48cbda7695ac8dd6d6fab2fcbc9168f03f23a94d/msgpack-1.2.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:0ed5823c4efc20fe87d3530665f40ec18a002be003114814c21235cc8d256207", size = 459546, upload-time = "2026-09-29T02:32:49.145Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/21/addcfa1e583cfc8a22fbdc57526621b5decd7ad676ae12e9150b7be1be5d/msgpack-1.2.3-cp314-cp314-pyemscripten_2026_0_wasm32.whl", hash = "sha256:2487453ca1b6104442c6442f9a1a8fee1fe8f428a70d99d4cba799108b304150", size = 53462, upload-time = "2026-09-29T02:32:50.708Z" },
+    { url = "https://files.pythonhosted.org/packages/91/4d/270f9725921ae88a29d37a774a77ac24f0ef1411fc960a63f5a4665e81b4/msgpack-1.2.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e54394b7dbe2e12ab032d9d21feef7bb61a90a150a2623633ba3781ba69dcb1f", size = 471673, upload-time = "2026-09-29T02:32:59.886Z" },
+    { url = "https://files.pythonhosted.org/packages/48/b8/eaa8d930f72dc1d1dd79511dc2ccf965922b059f2f0ed3b30aebac8c4b11/msgpack-1.2.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:63bb7448a1e9111319ae2430c09a5596140c160422830d6271bc75730ff2ff9a", size = 466257, upload-time = "2026-09-29T02:33:01.517Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/5a/97adc805037bc7e24c4e2f711bbcd3b28be8ec9aea3e778f18208cfbdb46/msgpack-1.2.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:382bc88fe90f29f5ac8a0b65c7046ff255356f2f2f3186c30e370215736fa1dc", size = 418484, upload-time = "2026-09-29T02:33:03.402Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/7e/1c53302606fe436ab48ba539ebafafe4a6a9efe12c4f04dc7eb36912d93e/msgpack-1.2.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c77e27790ad72989db783d5303825fba0b71550f00a490efba35cde7dc4b719f", size = 454064, upload-time = "2026-09-29T02:33:04.977Z" },
+    { url = "https://files.pythonhosted.org/packages/00/2d/9ee0170f638907b396c15c6cd26b3e54f869159efc6206683acfd8f696e1/msgpack-1.2.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:700bc0fc9e968a292b9137ee70e7a012f7e115bf0107ce45e3a88202788dfc1e", size = 417901, upload-time = "2026-09-29T02:33:06.489Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/d2/905c84490a75cd15a27065407cd085d201f7d392e1e0411f49f03fd31ade/msgpack-1.2.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5bd5f91ea75c45cafcc5433ba8fae59b708b736ec178d2441c40c499e9e079db", size = 459896, upload-time = "2026-09-29T02:33:08.361Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/fd/8cc02f767c3bc94d2649c954d28dea935ce9398eb9c93ce2444bb9474cc1/msgpack-1.2.3-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0c91762c48cd686dc9cf2b142c0bc544083952de32f5853d6624c956e54b85e5", size = 454751, upload-time = "2026-09-29T02:33:17.475Z" },
+    { url = "https://files.pythonhosted.org/packages/80/c9/ddb896767808e3e022453d8dfae26fd52ed404b0aa6fb7f752d39c040208/msgpack-1.2.3-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1f4ae8bd4ad9ba085fde95e95d055a896d19210238a4199a771a3cf36dceed49", size = 463597, upload-time = "2026-09-29T02:33:19.309Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/a5/e7c261abf75783c07dcac89951cb31dd0c123bf02fbdeda0c67303e698d8/msgpack-1.2.3-cp315-cp315-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:7013534a7163aa4f213c4d9864f1a8a7555daac6fcd48f699a198e29b436bfab", size = 422661, upload-time = "2026-09-29T02:33:21.093Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/8e/466d5133f9e1c2e232e15e304f715b62f6f0e28332d18e37d975fe174315/msgpack-1.2.3-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:6a834097144aabe948b8ca9020a833e8026f7d0abbd0ec54bc7e50f45a8ce012", size = 445188, upload-time = "2026-09-29T02:33:22.877Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/b4/33e7ad987ee2f4b3d449a6cbf28f574ed222987ca7f65ad277072646ac5e/msgpack-1.2.3-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:d31864ba3933a589b6a00249f89c0eb422197f49128fc10da550e57e9cb0f377", size = 420451, upload-time = "2026-09-29T02:33:24.485Z" },
+    { url = "https://files.pythonhosted.org/packages/34/2c/9d8be0d6c16e7e6131cd7da20257dd3da65473e3e6df0c00572fb10a195c/msgpack-1.2.3-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:e15f70588f4db8cd10df0930145b186de70feb9db51710cd378b1399009655bd", size = 460624, upload-time = "2026-09-29T02:33:26.063Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/e7/3a04783582c6f44f398cbfcf5f07a111192126ec4e63edf7f5640143bf64/msgpack-1.2.3-cp315-cp315-pyemscripten_2026_5_wasm32.whl", hash = "sha256:b949cc25e4a09252cbcc54e66e507de914d0e94a3a7039bd54c299bf7037c098", size = 53474, upload-time = "2026-09-29T02:33:27.83Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/a1/2b44612e55f7cf5d5e4b580294959b4429bbbcb1991177888e3e18668137/msgpack-1.2.3-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:db9fb67a3a2e75247bae569d34ebb5ff61c0448a4f0d6dbf991dae68af39b007", size = 467921, upload-time = "2026-09-29T02:33:37.023Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/6e/3309798ed1c11d7fcfdc7b946642685b0ff1588477925bc0d26bee7dcaae/msgpack-1.2.3-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2574ef81c1c8c38b10e330f3f9406fd09198a776b002030fafcf8e7647e9e06e", size = 467310, upload-time = "2026-09-29T02:33:38.799Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/79/9c799f489fa4146de4e00cfe9fee17afe33d8012f88ddffffea94f7c4700/msgpack-1.2.3-cp315-cp315t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fafc3b8898b432b841d30a61082c599fa7f4d06885f9dc58ad72259e12059fa6", size = 420178, upload-time = "2026-09-29T02:33:40.781Z" },
+    { url = "https://files.pythonhosted.org/packages/94/c6/5850dc9cafcd2ea315692e65db0e222d20923dd55f44adf35061003de27e/msgpack-1.2.3-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:a393e428f6ffb0dcb73308c1fff5593041c16ff42da66e5bac8a83a6107a54b0", size = 450248, upload-time = "2026-09-29T02:33:42.366Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/d2/b4c806e3497fe21f0b353568266aec14ff735d092aea672de7b2955db03f/msgpack-1.2.3-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:d1c1e8989a855b7f1f2a64ec4a80b23a631822903952770813857b2e4f460471", size = 418431, upload-time = "2026-09-29T02:33:44.178Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/f5/f4ecc3ddac4d551bf2f3cdb283ec546dcc826fe7c500074be61aa273e08a/msgpack-1.2.3-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:e0bd394e999949c814f7912284243298de1b5a17b6a3dcb6cc8a79b156ffc4fa", size = 457543, upload-time = "2026-09-29T02:33:45.978Z" },
+]
+
+[[package]]
+name = "msgspec"
+version = "0.22.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d0/e6/6dcf9306ff3c5e486578f3bf29ed11dfbdbbc2a8bf0caf7e07d392887fda/msgspec-0.22.0.tar.gz", hash = "sha256:0a13624a4969159fe35d8c2a3d377b2b61bbd8585e327440d5e52725affcce38", size = 343188, upload-time = "2026-09-29T14:14:11.422Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/a5/65de05f8804492f76ea121b21a125cdf1d97ec461c677bfa0ba354d6fbdd/msgspec-0.22.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5a169b5b03f0f2c7a296c002647db1dab75d2cd501bca34e32b71cab0261b56", size = 224035, upload-time = "2026-09-29T14:12:40.876Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/cc/aa1a47f8c92280d37498a5ea56a2a36606d034383e3e6472d64cbb56cf85/msgspec-0.22.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:99c401861c5bb3a57f7d6423ea7ed4352cd57aa3f04f4fbe9f3e3e4564a10f08", size = 230377, upload-time = "2026-09-29T14:12:42.796Z" },
+    { url = "https://files.pythonhosted.org/packages/61/50/f8bcdb3d613a4a4b92704297a12eba5c985cf572a64ee1a004d265759c69/msgspec-0.22.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:08826f5e5b0fa2f7a88592c396a243cfcc63d37e19f9d4fbe3b3f1be2fbdc404", size = 237390, upload-time = "2026-09-29T14:12:44.282Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/8a/473fa423f8fdd1b810b8652594323d7301df6920b62844d860daa0feff34/msgspec-0.22.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21460f54cee9208239b1a8421fdf25bffc77293e1daba88f585711ad839b9758", size = 227733, upload-time = "2026-09-29T14:12:45.839Z" },
+    { url = "https://files.pythonhosted.org/packages/03/1d/272ce23adae6c71b3f763aed3ee6e115cccc56124ed8ee0e3e3d2681e2c8/msgspec-0.22.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:cfc3d9557de9c806318725b702f3e664db33167bb42892079b693c69893fd33b", size = 236783, upload-time = "2026-09-29T14:12:47.234Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/26/29e0b9a8605c8819a3c718158e345a616ac42c092dd7d7ab248c2f2b0a72/msgspec-0.22.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0b25dcbc108783cb72503ed705b9fbb8c3cb02ee5801923f44b5f038c91cc365", size = 232728, upload-time = "2026-09-29T14:12:48.792Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/1b/9cc07718d1dee8ed5e89a265801d565bc0f15ead435ccb198f9c7bf92574/msgspec-0.22.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:627bfdfe5a4b3d916b3360b30f4cddeee3a084f56593e33527c6872fa8322ff9", size = 224112, upload-time = "2026-09-29T14:12:55.983Z" },
+    { url = "https://files.pythonhosted.org/packages/46/64/f33fdfe95aca76601194a7064d14816c7c22c4eccc1b03a5335785895fa3/msgspec-0.22.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c6c310ef83e7e291b01a63298828f848348bb99e84a1098c4b3923c05674d032", size = 230472, upload-time = "2026-09-29T14:12:57.648Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/b3/8ceaa9981c230adf43c45a6e8da25da23a381eddc7ed05aeaca1d5e7928b/msgspec-0.22.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:7c1e76c6bd523141b9c05c2f8a70979cd0efedbd68855a66f292f8892c0b8fc7", size = 237382, upload-time = "2026-09-29T14:12:59.414Z" },
+    { url = "https://files.pythonhosted.org/packages/88/a6/7b5c4fb39e0bf2dabc8be923c33c39b07ba769a0ce6f0afbbdfaadb1f2f2/msgspec-0.22.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:bc374dedd5f85a5f4de2386dc5f737894ccb8c1ac18e9566ce66fd9839e6285d", size = 227717, upload-time = "2026-09-29T14:13:00.88Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/5b/2334ee638880e756c8bc54a1177bd65877c786433693a43594ef5ecbe2d8/msgspec-0.22.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:feafe612034d49e9144340c0b5168ee4e22c2af4aaa2c1db11ae84e1aac9543b", size = 236781, upload-time = "2026-09-29T14:13:02.468Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/e5/b4c5323b17ecfce45350695d40fc93e16856db957a53cbcf2f53007d6e12/msgspec-0.22.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6f48317f05312bfdf78248f53933f830f07ab75cc1c813ac3ca4220cb3b5b019", size = 232777, upload-time = "2026-09-29T14:13:04.025Z" },
+    { url = "https://files.pythonhosted.org/packages/14/bc/4066416ff6aa918d1ef9295edee0041e4629e4079ad3839bdd8a68fd87f0/msgspec-0.22.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f039ef5207b847f075a0a43020ee6140cd47505f890e47e157f2deb485c2dc96", size = 225101, upload-time = "2026-09-29T14:13:11.391Z" },
+    { url = "https://files.pythonhosted.org/packages/63/ba/a8d390d5bd4c7d9ccde87c95cf071ada934cc9ca2c6af4d3d50b38f2d718/msgspec-0.22.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5e4f7e09cceac7dbf4c0761b8ae7df51c55b5df5e9af7aff2c895aac1ebea015", size = 230505, upload-time = "2026-09-29T14:13:12.869Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/89/979664fdc913c624ef88a139b40e3a95ddf2a47c89e8b5c4147f69ee9c48/msgspec-0.22.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:614e2c827e0a3f934f3cf0cf4ba65210df8132b75a69a8a1f51bb3b2caf0ac5a", size = 237382, upload-time = "2026-09-29T14:13:14.317Z" },
+    { url = "https://files.pythonhosted.org/packages/07/3f/7d44c614376ae008ac6099be5f589b322c4ad44e32c6dbb0edd256215028/msgspec-0.22.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa3689b9dfcc663358ef23ba4299d7460f01108515b041a7d30d05908ac9c32f", size = 228962, upload-time = "2026-09-29T14:13:15.763Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/59/bf8504e6f63f6769d01fb66f8bd856cf0ed39a07fde354f440d711640054/msgspec-0.22.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:d2f950239ff1fc7322c6f9634807310265149cb168270d3ddcdda5b6ada13a28", size = 236691, upload-time = "2026-09-29T14:13:17.195Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/40/5a9d2bde12af16a22ddbf371990a81d3e3c0dcd4bb4ef3b3f9616b033c14/msgspec-0.22.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:3c789b5ccd07c0a3c09767108ee06e089b2875f2309a4569c2648f30a8d31dfa", size = 232750, upload-time = "2026-09-29T14:13:18.691Z" },
+    { url = "https://files.pythonhosted.org/packages/75/5d/c0e6bdb81a87f6bd56a663a330c271af7670490c80d8d635d9fa21ad1adf/msgspec-0.22.0-cp314-cp314-pyemscripten_2026_0_wasm32.whl", hash = "sha256:a66b1766311e42371e509c996c3933b161c7ae0eabdf361af5316dec197e1022", size = 136814, upload-time = "2026-09-29T14:13:20.415Z" },
+    { url = "https://files.pythonhosted.org/packages/55/04/8db7186d3ae8818356bc623cc132db8b77da37ce4b1345f35719c8ad5726/msgspec-0.22.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b6d3ca19a8ff28d0a67a1824e2bff7ec649ec795c80a265f20ade4caa63080de", size = 229934, upload-time = "2026-09-29T14:13:28.285Z" },
+    { url = "https://files.pythonhosted.org/packages/17/24/a249f3491cabbe77cc65a1a6f87c128582aa39357227149be61cac8e554f/msgspec-0.22.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8b98ae215a102cbf6635f7df45f5c4af12f77fad1f7b71b9808fcf868a5735d", size = 234378, upload-time = "2026-09-29T14:13:29.821Z" },
+    { url = "https://files.pythonhosted.org/packages/87/ee/6dbcb1b5de8e9d47e8f0fde9a288628dc178c1749a570b98251218fa10c4/msgspec-0.22.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e0aa0cc3f18c35bab79bd7b87fde95d6274a9deddeebd1ea541f8066a5073165", size = 243118, upload-time = "2026-09-29T14:13:31.544Z" },
+    { url = "https://files.pythonhosted.org/packages/79/03/7dd2d0ca988600e01fc00ad0cf20d1d44bc59369a913c988654c65f6582b/msgspec-0.22.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8c8e84789918fbc15a503b92a829115ddd7567ecd3e4778bd418c56abbb86c11", size = 234557, upload-time = "2026-09-29T14:13:33.068Z" },
+    { url = "https://files.pythonhosted.org/packages/74/e2/43f3c63bff1650efcaaea31466246e28b46927323fc9ff416c68cc6e4047/msgspec-0.22.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:3ca7d4cd69fbb66bd2da6211d3e79d40542d196c16c6d99bf838f76767ad35be", size = 241288, upload-time = "2026-09-29T14:13:34.532Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/70/11b93815a59674f33182dc3e873d343ca0b37e25be52ecb28f52092f1fed/msgspec-0.22.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:28f53f3604dd3e70225f7563c831628dbb03299b428f8e62aadb4b628e386874", size = 236432, upload-time = "2026-09-29T14:13:36.083Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/b1/1c385f2f93006cdc2af1511cc512c347cb22e2d4f11952c205230aedf586/msgspec-0.22.0-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8d67582478b0eaabb899f2fb255c878ee7de57dff80eb73ab24f1865524ec441", size = 226524, upload-time = "2026-09-29T14:13:43.876Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/fb/c80c8842d40347cacf89a60a4986b849dae1a6dfd25830441efdd6faa65b/msgspec-0.22.0-cp315-cp315-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:71cbbdb39631064e2f2f9e9ac2b1b69931d72276eb5f9da4ed025726296bdbb6", size = 231816, upload-time = "2026-09-29T14:13:45.329Z" },
+    { url = "https://files.pythonhosted.org/packages/73/ac/90bbcfd890b4bda90c93f7e1b7fc24e84b270420486d9d43ae31443d15ab/msgspec-0.22.0-cp315-cp315-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:8f0a5c25516e2034b2db7767081759ff8996e214def9c43b3055f61e1be1caad", size = 244241, upload-time = "2026-09-29T14:13:46.851Z" },
+    { url = "https://files.pythonhosted.org/packages/72/9a/eabdb5f1b5e6013b0e2f9f2a95790587f6864aa9ca37f9d7dece65b53878/msgspec-0.22.0-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:a1dab6a99c759d1391ab2993388c1892746a697254f4b5dc6c059ca6e3bfbc8b", size = 230198, upload-time = "2026-09-29T14:13:48.296Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/89/9f080532d4ac52f416dd7318e55c2053cc071853d17d58e24897a5b553bf/msgspec-0.22.0-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:a52eba5c9528fd181fcec39d22b67aaa1dccc6cfe8e24d3f5d41130e6d04289d", size = 242949, upload-time = "2026-09-29T14:13:49.829Z" },
+    { url = "https://files.pythonhosted.org/packages/11/df/6baf9b2f3523ebe2b820820c7929fd72ec5f483a93147130338ecc353fac/msgspec-0.22.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:1e547966017265c0d23342bcf2e027305dde40ea042d16694a9b96b4f696a052", size = 233914, upload-time = "2026-09-29T14:13:51.5Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/2e/2fa279cb57cb47175ae604d572787f903d4ad3f0afa867201bbd99e6647e/msgspec-0.22.0-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d7a738826936c72348c613061d260446f13c82b6fd7d5d7705b6911ab8dca2f3", size = 232362, upload-time = "2026-09-29T14:13:58.817Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/58/a7e759b11b28441c27f803b29d9b5f4b5ad85150c89354b5ede1baca9258/msgspec-0.22.0-cp315-cp315t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f2ddea9d78d09460f06c26a7a508adcd049761c3208776162b8eb79b8a032cff", size = 235885, upload-time = "2026-09-29T14:14:00.381Z" },
+    { url = "https://files.pythonhosted.org/packages/86/56/8d7ee098e94cbd9f35fa643dc497e06a4a6307b9f562cfbe48103fc3b209/msgspec-0.22.0-cp315-cp315t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:884c28c80b0a511595b29a9b04a3a230c3797369e4a033e6d5c6d9b5427f8e09", size = 248155, upload-time = "2026-09-29T14:14:01.945Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/6d/1cabb4b8a5dbf696e2b24df9e482b2e0333bb3b1b13ebb5433813e6616ec/msgspec-0.22.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:f7a923bcde480065c8e25967464cfb2a687ee67000bb43157e2d57e40eca7305", size = 236416, upload-time = "2026-09-29T14:14:03.363Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/43/8bf0f558eb369f1f2d494b3d5ab9d0ae0907d07ecc0cdbe11b6768b02867/msgspec-0.22.0-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:65eea14bc65ccfeb8f3af62cb204841871e2961f002d7fa87dbe0f79dacf1c1c", size = 247292, upload-time = "2026-09-29T14:14:04.829Z" },
+    { url = "https://files.pythonhosted.org/packages/81/33/2fbaadf98b5510cac4bb56d2b03937e0b1fb4bfcd1ae6aba20361f299583/msgspec-0.22.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:0666a1520cab86796612e794e71107e0fbf5e8ff3ddcdfcfff8f1d94b860d2f1", size = 238220, upload-time = "2026-09-29T14:14:06.408Z" },
 ]
 
 [[package]]
@@ -2217,11 +2422,11 @@ wheels = [
 
 [[package]]
 name = "narwhals"
-version = "2.25.0"
+version = "2.26.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/6f/7b/6248dada39781db1ab3ebf08943080df0796098515a87f6f8696d14ec744/narwhals-2.25.0.tar.gz", hash = "sha256:62c036c810662bf7820b7737077176313bc59350eeeefb808510f388c743e4b2", size = 677076, upload-time = "2026-08-20T18:10:15.454Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/68/5351e34623d253423240ea7de3f8fc74fa8ab14b1ab3c0ec4ac8997413c9/narwhals-2.26.0.tar.gz", hash = "sha256:6b9cadca82f375c7e4cf584fdc86ca25da54827307a9c58f94547ee6104b82dd", size = 686970, upload-time = "2026-09-08T13:32:08.964Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/eb/dc/55481808fd70ef1567cf13540ffd4702af3f74b112e35427564b03f79c2d/narwhals-2.25.0-py3-none-any.whl", hash = "sha256:1f0f403e8c7e4463cde9bfe78b12fdd809e3ae3dda6d9b2f802934fb9c7a6a8f", size = 467373, upload-time = "2026-08-20T18:10:13.834Z" },
+    { url = "https://files.pythonhosted.org/packages/40/b5/1b84b2c784db76d69442334bc8b8748c840f13ca53be086f4f250ad4a0bc/narwhals-2.26.0-py3-none-any.whl", hash = "sha256:29326d74f107c347fd1009bd58e38d9f7c7c5b51e6de97bc93dbc325d9038b54", size = 474034, upload-time = "2026-09-08T13:32:07.159Z" },
 ]
 
 [[package]]
@@ -2281,7 +2486,7 @@ wheels = [
 
 [[package]]
 name = "nbformat"
-version = "5.10.4"
+version = "5.11.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "fastjsonschema" },
@@ -2289,9 +2494,9 @@ dependencies = [
     { name = "jupyter-core" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/72/b3446efab8756e7df4b8ec587f8e611cb5a7249e4323db480802f1d3be04/nbformat-5.11.1.tar.gz", hash = "sha256:32d4521c68c6e7d5b29c76defaeed9f42ea733142b9b19f88277ce10390b9c4d", size = 147775, upload-time = "2026-08-17T08:10:51.942Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454, upload-time = "2024-04-04T11:20:34.895Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/69/ee613f74085ca7103f79cd08d579c4f3177d1d26e5d3d9528d2d6536a707/nbformat-5.11.1-py3-none-any.whl", hash = "sha256:cc6698fa75f4fab8755ead786317815f13a6fee3b53311c0abb1a8b51d52f7ec", size = 79849, upload-time = "2026-08-17T08:10:50.18Z" },
 ]
 
 [[package]]
@@ -2324,35 +2529,28 @@ wheels = [
 
 [[package]]
 name = "nest-asyncio2"
-version = "1.7.2"
+version = "1.7.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b4/73/731debf26e27e0a0323d7bda270dc2f634b398e38f040a09da1f4351d0aa/nest_asyncio2-1.7.2.tar.gz", hash = "sha256:1921d70b92cc4612c374928d081552efb59b83d91b2b789d935c665fa01729a8", size = 14743, upload-time = "2026-02-13T00:34:04.386Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5e/a6/a2775b388a14b5ea0c894bbe513c0d3e04e65e8ae583e60f5357722b8e03/nest_asyncio2-1.7.3.tar.gz", hash = "sha256:2e9a84d5d1efe6d020c72988d21aec569bac42d98af2ff6b9de24640c5d22a34", size = 15642, upload-time = "2026-09-22T17:15:06.709Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c5/3c/3179b85b0e1c3659f0369940200cd6d0fa900e6cefcc7ea0bc6dd0e29ffb/nest_asyncio2-1.7.2-py3-none-any.whl", hash = "sha256:f5dfa702f3f81f6a03857e9a19e2ba578c0946a4ad417b4c50a24d7ba641fe01", size = 7843, upload-time = "2026-02-13T00:34:02.691Z" },
+    { url = "https://files.pythonhosted.org/packages/29/dd/a8bebc78975a7466932077a0973cbab4361a32368b1d28d40683ec19333c/nest_asyncio2-1.7.3-py3-none-any.whl", hash = "sha256:2bc87bdca654e719425145f5e84eaeb0080b013fdd47d65729a7d66243f4987c", size = 8364, upload-time = "2026-09-22T17:15:05.492Z" },
 ]
 
 [[package]]
 name = "notebook"
-version = "5.7.4"
+version = "7.6.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "ipykernel" },
-    { name = "ipython-genutils" },
-    { name = "jinja2" },
-    { name = "jupyter-client" },
-    { name = "jupyter-core" },
-    { name = "nbconvert" },
-    { name = "nbformat" },
-    { name = "prometheus-client" },
-    { name = "pyzmq" },
-    { name = "send2trash" },
-    { name = "terminado" },
+    { name = "jupyter-builder" },
+    { name = "jupyter-server" },
+    { name = "jupyterlab" },
+    { name = "jupyterlab-server" },
+    { name = "notebook-shim" },
     { name = "tornado" },
-    { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6e/22/b5dcce67559d63d0f22e46d806305710808c698a1b91c07eb09e389785e0/notebook-5.7.4.tar.gz", hash = "sha256:d908673a4010787625c8952e91a22adf737db031f2aa0793ad92f6558918a74a", size = 13389469, upload-time = "2018-12-17T10:06:00.805Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/42/ac/aedb759dc683dcd129f6c75271bab5eb23020b0f7969163c726343718e1e/notebook-7.6.3.tar.gz", hash = "sha256:e2c08e469c0ae20bb0b3214f0ab77e79653317a2f8e5b34c10361c66874a5b50", size = 5501618, upload-time = "2026-09-21T18:07:24.261Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f8/a6/dbdf0954d073ab2cf5421cad20334d1e4c780da37205c04522c045298f67/notebook-5.7.4-py2.py3-none-any.whl", hash = "sha256:3ab2db8bc10e6edbd264c3c4b800bee276c99818386ee0c146d98d7e6bcf0a67", size = 8980706, upload-time = "2018-12-17T10:05:52.01Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/f7/907b98438cf00bdc4e296570058f29a347214bb42338a48e3b7eae58ae2a/notebook-7.6.3-py3-none-any.whl", hash = "sha256:ad7e0eb765fba836cd4a2ab0c7a3a26cde1d91665fbf6f533b6ae7b2de6d88d2", size = 5548452, upload-time = "2026-09-21T18:07:21.563Z" },
 ]
 
 [[package]]
@@ -2369,22 +2567,22 @@ wheels = [
 
 [[package]]
 name = "numba"
-version = "0.66.0"
+version = "0.67.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "llvmlite" },
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ae/a0/570e3dc53e5602b49108f62a13e529f1eec8bfc7ef37d49c825924dcf546/numba-0.66.0.tar.gz", hash = "sha256:b900e63a0e26c05ea9a6d5a3a5a0a177cb64c5011887bf43edb8c3ed2c38d363", size = 2806181, upload-time = "2026-07-01T23:12:46.36Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7a/90/2544f4e3a61e501d6c9a5418fd4b905323222693d54a02cab0106a0af865/numba-0.67.0.tar.gz", hash = "sha256:cd75aa535b33fa05d9d930b1ae8af9f97a2881e96d72dfb38ec9b78284d9f851", size = 2836515, upload-time = "2026-08-11T23:04:00.174Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2d/55/25c319845e9a4e08f16611ddbda56a192eb7b6ed13e1a2bff2da272ffb97/numba-0.66.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0999e3ee1b18c48e1fb51d11af35ef59852c7f4f50569c9550c25faef0616ad1", size = 3866252, upload-time = "2026-07-01T23:12:20.429Z" },
-    { url = "https://files.pythonhosted.org/packages/71/ef/a82d6fd6bf1b0fe461651e924d3647eeec9ac17f8eee4896264bf7480930/numba-0.66.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:efe0d2d5099790df945e0cb6e1b3104bd965d7bbfac50d62f1d5d1d6ade0825d", size = 3566974, upload-time = "2026-07-01T23:12:22.116Z" },
-    { url = "https://files.pythonhosted.org/packages/44/b5/e930010965568fe7f2c6c962fd2849d458cb9f62c3ab7584af8a19a2b40a/numba-0.66.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:939316d5d8619751207b8972a67852b5a7646665298cb4de693cd6bf135152f4", size = 3873663, upload-time = "2026-07-01T23:12:27.308Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/ec/5b51457cbe96e4831141d83e892e65191b23a1b78728456c62909d231ace/numba-0.66.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cdf506775d9f02eb92a87bf5c5b1e0d25506fd18cafd769f4ed914a8feac73e7", size = 3573529, upload-time = "2026-07-01T23:12:28.944Z" },
-    { url = "https://files.pythonhosted.org/packages/0f/26/885774c006de6620ed3d10f45d8e20fe0b8e6aad6d573211a2cbc8b3e528/numba-0.66.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e2b101f23b8b63978d334574d2039f27f0dccfe1d891756f33a2e2f3e4c88cf4", size = 3842720, upload-time = "2026-07-01T23:12:33.938Z" },
-    { url = "https://files.pythonhosted.org/packages/93/99/edebf7de890b73973d839dd971cf73734adfb81ffa1b4504f84b9059c3e5/numba-0.66.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:63b943eb2c9ba371908ce2cd6dfc643db51fc40f7966993376a1701bc922f537", size = 3543537, upload-time = "2026-07-01T23:12:35.566Z" },
-    { url = "https://files.pythonhosted.org/packages/39/fd/e9c9680a3813f3d781c20e5d53c1074801b787d4feecca0472fdd7c05ce1/numba-0.66.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:651a2b53298340956db26ecbe7ab043106b50a40c2807e66d617a4917245a4ab", size = 3878695, upload-time = "2026-07-01T23:12:40.302Z" },
-    { url = "https://files.pythonhosted.org/packages/61/3a/9b363287b85fcd4537ea3878793822878b2ac1008a78159d2096fea628de/numba-0.66.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8c1144ba1720ea59ad79f4f488ed54d149b2613b357f7e445678b7d0739c70e9", size = 3596323, upload-time = "2026-07-01T23:12:42.805Z" },
+    { url = "https://files.pythonhosted.org/packages/bb/38/926757caaac18a66f057d7544a63620bf360a07d281c9f7ecadd2aa83963/numba-0.67.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f63d43db06b4756424d6d2484737c902e0ae944a0eec3e8b0b4de2c695b15caa", size = 3884596, upload-time = "2026-08-11T23:03:27.688Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/6d/58291dc58da39d98b32db7f044729f6d8d4920cd9622fbab3179b54ff4c4/numba-0.67.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:76d3335aaeffb9dc88309420890e73497a00be08a7530441bc2b58ffe025bfa5", size = 3585290, upload-time = "2026-08-11T23:03:29.684Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/1c/c05609739cc41116d36e30cb2b41fb00f126bb52e1b0bac907051ad8a35d/numba-0.67.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9c4953387c77864b596d8296e2cfbdef82b0eea4166ab4864b05d226c51143e0", size = 3892004, upload-time = "2026-08-11T23:03:35.797Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/77/a5276ad4178250403e0e2251f3e1f8ac18feac779b0474a8bcb08558490d/numba-0.67.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:88f6e0f5cb6c545e158b6ef0496c01b6d6958a7ccc6634a1576a94bbbab29ff2", size = 3591878, upload-time = "2026-08-11T23:03:37.845Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/36/e614ba2bc0f005ed0f37a6413f08fe705210297ddb9a37a475a8b9fdab61/numba-0.67.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5269245a675abdd3e2c35ec6bb2f250355effa9032514d8f2354f0d2d10854bd", size = 3861040, upload-time = "2026-08-11T23:03:43.842Z" },
+    { url = "https://files.pythonhosted.org/packages/40/90/30c42a1dbc4176cf355e8e8be61803732c55597b1332925fe233912a43d9/numba-0.67.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f074a8e23db78490f11a3930c940be758316c10ac5985be83d2f298dc080acf7", size = 3561811, upload-time = "2026-08-11T23:03:46.037Z" },
+    { url = "https://files.pythonhosted.org/packages/74/62/b8174ca95a4cc1a7ba1520767734e016991545590b8fbde521b681701a9f/numba-0.67.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aa5f002f665bec321b950dacaa26ee009e1d720f6ac9d9856eed5efe1caa03a6", size = 3896986, upload-time = "2026-08-11T23:03:53.752Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/f9/3a7b6dbf81e01a48958b45ad2239edbc64707522ab17f11f9f18c44bf6d1/numba-0.67.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:83ab968b0e0fa744eba03351282dd8000796e6ec8e4518f47bd3ed86c0a20c7b", size = 3614644, upload-time = "2026-08-11T23:03:55.794Z" },
 ]
 
 [[package]]
@@ -2394,79 +2592,81 @@ source = { git = "https://github.com/Nicholaswogan/NumbaQuadpack.git#9fa67a4a952
 
 [[package]]
 name = "numcodecs"
-version = "0.16.5"
+version = "0.17.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/44/bd/8a391e7c356366224734efd24da929cc4796fff468bfb179fe1af6548535/numcodecs-0.16.5.tar.gz", hash = "sha256:0d0fb60852f84c0bd9543cc4d2ab9eefd37fc8efcc410acd4777e62a1d300318", size = 6276387, upload-time = "2025-11-21T02:49:48.986Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/dd/ec/260cdb6304868de6db14eb31064bd2735c0200bcb3331d6b4c9e9be02a03/numcodecs-0.17.0.tar.gz", hash = "sha256:e8db2e337bdafd3bb5f891a2543b53b2b36a509ce9d587af2846db3715b6c8b9", size = 6288352, upload-time = "2026-09-17T18:12:42.262Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/97/1e/98aaddf272552d9fef1f0296a9939d1487914a239e98678f6b20f8b0a5c8/numcodecs-0.16.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b554ab9ecf69de7ca2b6b5e8bc696bd9747559cb4dd5127bd08d7a28bec59c3a", size = 8534814, upload-time = "2025-11-21T02:49:28.547Z" },
-    { url = "https://files.pythonhosted.org/packages/fb/53/78c98ef5c8b2b784453487f3e4d6c017b20747c58b470393e230c78d18e8/numcodecs-0.16.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ad1a379a45bd3491deab8ae6548313946744f868c21d5340116977ea3be5b1d6", size = 9173471, upload-time = "2025-11-21T02:49:30.444Z" },
-    { url = "https://files.pythonhosted.org/packages/0b/00/787ea5f237b8ea7bc67140c99155f9c00b5baf11c49afc5f3bfefa298f95/numcodecs-0.16.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:015a7c859ecc2a06e2a548f64008c0ec3aaecabc26456c2c62f4278d8fc20597", size = 8483064, upload-time = "2025-11-21T02:49:36.454Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/e6/d359fdd37498e74d26a167f7a51e54542e642ea47181eb4e643a69a066c3/numcodecs-0.16.5-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:84230b4b9dad2392f2a84242bd6e3e659ac137b5a1ce3571d6965fca673e0903", size = 9126063, upload-time = "2025-11-21T02:49:38.018Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/15/e2e1151b5a8b14a15dfd4bb4abccce7fff7580f39bc34092780088835f3a/numcodecs-0.16.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49f7b7d24f103187f53135bed28bb9f0ed6b2e14c604664726487bb6d7c882e1", size = 8476987, upload-time = "2025-11-21T02:49:43.363Z" },
-    { url = "https://files.pythonhosted.org/packages/6d/30/16a57fc4d9fb0ba06c600408bd6634f2f1753c54a7a351c99c5e09b51ee2/numcodecs-0.16.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aec9736d81b70f337d89c4070ee3ffeff113f386fd789492fa152d26a15043e4", size = 9102377, upload-time = "2025-11-21T02:49:45.508Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/86/15e1cc4e6644d7e33be613d17bb7cc939b1862ccd975fa2ce1055a1e3045/numcodecs-0.17.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1837d4d1d646cecd3ab2d1ba22956295d709edea0bddc952737c647bec1d03c4", size = 1384196, upload-time = "2026-09-17T18:12:16.327Z" },
+    { url = "https://files.pythonhosted.org/packages/73/ca/b784745f189a12ccef60517c0c8526d579b40f35da4463c30c07a4677366/numcodecs-0.17.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1ebd63cdb8985c66257bc037fcdff5f38637aff72d7ef62612ec46f2299e8749", size = 1436978, upload-time = "2026-09-17T18:12:17.731Z" },
+    { url = "https://files.pythonhosted.org/packages/25/ea/2ab25f7e674cf1e78f123c5c2689d8a7dc85475554af0619bcce05cb32a9/numcodecs-0.17.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7065b3349b73d54785aa89e00d0b97d80f664e9056757929d28151f9208dc04c", size = 1379341, upload-time = "2026-09-17T18:12:23.159Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/96/b3bf9a31978d936654a73f2bb1036b92b6515164f170092d162419eb771c/numcodecs-0.17.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c3342d91ed7cf59c1be84396edd364e936bb0ec9e366d24bb69689748d19625", size = 1430551, upload-time = "2026-09-17T18:12:24.97Z" },
+    { url = "https://files.pythonhosted.org/packages/31/ee/e306a14295a67f9852b9856077ed5ab6b67fae5797559ccdd22a3aa6d853/numcodecs-0.17.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4a62e5a821ccfbe425bbdd9a079f8b6c41b7e796ff3c99324530561193a53047", size = 1387252, upload-time = "2026-09-17T18:12:31.065Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/13/107244147a8b2edd43ffc3f8bf229f07220aed200022d2e0b1dd761b68f8/numcodecs-0.17.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1cce4bf2278ed74841c2088acfd38e67c3e5aa77e3bc1962ef0fa2931becbb12", size = 1432514, upload-time = "2026-09-17T18:12:32.327Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/dc/fa7c6a1ce327093d04ddf0d0acf20fd1fe2a00a9b7eb43b6a7213eec1ae4/numcodecs-0.17.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3e4e351566b3ab2f6255a9d91c6c48e1d0f9ec6e2ae409a148e091a8fc0a80b0", size = 1380833, upload-time = "2026-09-17T18:12:37.739Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/38/33023f8771e8b7afb9c7f0dd50b0487dbef594617e245707cb965969edd3/numcodecs-0.17.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8697a4631fedded77a75d333e4926b1eb3a11bc7d3e30213e7e565d6910526d0", size = 1424937, upload-time = "2026-09-17T18:12:39.04Z" },
 ]
 
 [[package]]
 name = "numexpr"
-version = "2.14.1"
+version = "2.14.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/cb/2f/fdba158c9dbe5caca9c3eca3eaffffb251f2fb8674bf8e2d0aed5f38d319/numexpr-2.14.1.tar.gz", hash = "sha256:4be00b1086c7b7a5c32e31558122b7b80243fe098579b170967da83f3152b48b", size = 119400, upload-time = "2025-10-13T16:17:27.351Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/72/94/cc921e35593b820521e464cbbeaf8212bbdb07f16dc79fe283168df38195/numexpr-2.14.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d686dfb2c1382d9e6e0ee0b7647f943c1886dba3adbf606c625479f35f1956c1", size = 452468, upload-time = "2025-10-13T16:13:29.531Z" },
-    { url = "https://files.pythonhosted.org/packages/d9/43/560e9ba23c02c904b5934496486d061bcb14cd3ebba2e3cf0e2dccb6c22b/numexpr-2.14.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eee6d4fbbbc368e6cdd0772734d6249128d957b3b8ad47a100789009f4de7083", size = 443631, upload-time = "2025-10-13T16:15:02.473Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/6c/78f83b6219f61c2c22d71ab6e6c2d4e5d7381334c6c29b77204e59edb039/numexpr-2.14.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3a2839efa25f3c8d4133252ea7342d8f81226c7c4dda81f97a57e090b9d87a48", size = 1417670, upload-time = "2025-10-13T16:13:33.464Z" },
-    { url = "https://files.pythonhosted.org/packages/0e/bb/1ccc9dcaf46281568ce769888bf16294c40e98a5158e4b16c241de31d0d3/numexpr-2.14.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:9f9137f1351b310436662b5dc6f4082a245efa8950c3b0d9008028df92fefb9b", size = 1466212, upload-time = "2025-10-13T16:15:12.828Z" },
-    { url = "https://files.pythonhosted.org/packages/13/65/72b065f9c75baf8f474fd5d2b768350935989d4917db1c6c75b866d4067c/numexpr-2.14.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:06855053de7a3a8425429bd996e8ae3c50b57637ad3e757e0fa0602a7874be30", size = 455860, upload-time = "2025-10-13T16:13:35.811Z" },
-    { url = "https://files.pythonhosted.org/packages/fc/f9/c9457652dfe28e2eb898372da2fe786c6db81af9540c0f853ee04a0699cc/numexpr-2.14.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:05f9366d23a2e991fd5a8b5e61a17558f028ba86158a4552f8f239b005cdf83c", size = 446574, upload-time = "2025-10-13T16:15:17.367Z" },
-    { url = "https://files.pythonhosted.org/packages/b6/99/8d3879c4d67d3db5560cf2de65ce1778b80b75f6fa415eb5c3e7bd37ba27/numexpr-2.14.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c5f1b1605695778896534dfc6e130d54a65cd52be7ed2cd0cfee3981fd676bf5", size = 1417306, upload-time = "2025-10-13T16:13:42.813Z" },
-    { url = "https://files.pythonhosted.org/packages/ea/05/6bddac9f18598ba94281e27a6943093f7d0976544b0cb5d92272c64719bd/numexpr-2.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a4ba71db47ea99c659d88ee6233fa77b6dc83392f1d324e0c90ddf617ae3f421", size = 1466145, upload-time = "2025-10-13T16:15:27.464Z" },
-    { url = "https://files.pythonhosted.org/packages/84/78/3c8335f713d4aeb99fa758d7c62f0be1482d4947ce5b508e2052bb7aeee9/numexpr-2.14.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:33265294376e7e2ae4d264d75b798a915d2acf37b9dd2b9405e8b04f84d05cfc", size = 465972, upload-time = "2025-10-13T16:13:45.061Z" },
-    { url = "https://files.pythonhosted.org/packages/35/81/9ee5f69b811e8f18746c12d6f71848617684edd3161927f95eee7a305631/numexpr-2.14.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:83647d846d3eeeb9a9255311236135286728b398d0d41d35dedb532dca807fe9", size = 456953, upload-time = "2025-10-13T16:15:31.186Z" },
-    { url = "https://files.pythonhosted.org/packages/6d/39/9b8bc6e294d85cbb54a634e47b833e9f3276a8bdf7ce92aa808718a0212d/numexpr-2.14.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6e575fd3ad41ddf3355d0c7ef6bd0168619dc1779a98fe46693cad5e95d25e6e", size = 1426199, upload-time = "2025-10-13T16:13:48.231Z" },
-    { url = "https://files.pythonhosted.org/packages/1e/ce/0d4fcd31ab49319740d934fba1734d7dad13aa485532ca754e555ca16c8b/numexpr-2.14.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:67ea4771029ce818573b1998f5ca416bd255156feea017841b86176a938f7d19", size = 1474214, upload-time = "2025-10-13T16:15:38.893Z" },
-    { url = "https://files.pythonhosted.org/packages/9a/ed/aabd8678077848dd9a751c5558c2057839f5a09e2a176d8dfcd0850ee00e/numexpr-2.14.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5aedf38d4c0c19d3cecfe0334c3f4099fb496f54c146223d30fa930084bc8574", size = 455918, upload-time = "2025-10-13T16:13:50.338Z" },
-    { url = "https://files.pythonhosted.org/packages/88/e1/3db65117f02cdefb0e5e4c440daf1c30beb45051b7f47aded25b7f4f2f34/numexpr-2.14.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:439ec4d57b853792ebe5456e3160312281c3a7071ecac5532ded3278ede614de", size = 446512, upload-time = "2025-10-13T16:15:42.313Z" },
-    { url = "https://files.pythonhosted.org/packages/9a/fb/7ceb9ee55b5f67e4a3e4d73d5af4c7e37e3c9f37f54bee90361b64b17e3f/numexpr-2.14.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e23b87f744e04e302d82ac5e2189ae20a533566aec76a46885376e20b0645bf8", size = 1417845, upload-time = "2025-10-13T16:13:53.836Z" },
-    { url = "https://files.pythonhosted.org/packages/45/2d/9b5764d0eafbbb2889288f80de773791358acf6fad1a55767538d8b79599/numexpr-2.14.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:44f84e0e5af219dbb62a081606156420815890e041b87252fbcea5df55214c4c", size = 1466211, upload-time = "2025-10-13T16:15:48.985Z" },
-    { url = "https://files.pythonhosted.org/packages/9e/d3/956a13e628d722d649fbf2fded615134a308c082e122a48bad0e90a99ce9/numexpr-2.14.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dd72d8c2a165fe45ea7650b16eb8cc1792a94a722022006bb97c86fe51fd2091", size = 466242, upload-time = "2025-10-13T16:13:55.795Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/dd/abe848678d82486940892f2cacf39e82eec790e8930d4d713d3f9191063b/numexpr-2.14.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:70d80fcb418a54ca208e9a38e58ddc425c07f66485176b261d9a67c7f2864f73", size = 457149, upload-time = "2025-10-13T16:15:52.036Z" },
-    { url = "https://files.pythonhosted.org/packages/fd/bb/797b583b5fb9da5700a5708ca6eb4f889c94d81abb28de4d642c0f4b3258/numexpr-2.14.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:edea2f20c2040df8b54ee8ca8ebda63de9545b2112872466118e9df4d0ae99f3", size = 1426493, upload-time = "2025-10-13T16:13:59.244Z" },
-    { url = "https://files.pythonhosted.org/packages/77/c4/0519ab028fdc35e3e7ee700def7f2b4631b175cd9e1202bd7966c1695c33/numexpr-2.14.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:790447be6879a6c51b9545f79612d24c9ea0a41d537a84e15e6a8ddef0b6268e", size = 1474413, upload-time = "2025-10-13T16:15:59.211Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/79/c4/27ea7849eb4a7e3b51db446b0414254326dba8c6bdee09b9f2abf963e55d/numexpr-2.14.2.tar.gz", hash = "sha256:e7144e83ea9e581f2273e0304f15836736c4e470e2bd2e378ce617662a1ca278", size = 121744, upload-time = "2026-07-18T10:52:43.185Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/41/13/ed5efda74ace9a7e2e933476b85bba6d00f2ebf6b833ef59a796ec9af88c/numexpr-2.14.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:327be9ee62251c173236dc620147ff2d0e732a32f5bad918d78a10082f502f63", size = 455373, upload-time = "2026-07-18T10:51:51.466Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/11/e8953226d658ae67e3e002abaa60a101c693f9c57d74974001729afab5ef/numexpr-2.14.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6a5d8fc7016bf6f6e1808b011510aa7c3bd75ec1407f7650874ec591db59f5e", size = 445187, upload-time = "2026-07-18T10:51:52.849Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/f7/f51b7e10c312bd9617df829e063c87a6d443fd97af54688282ba2b11b1fd/numexpr-2.14.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4b1ff261c3e69c4c59578d3a9ca6132603619d38ae1abe73325563bed3b9bbaf", size = 1420426, upload-time = "2026-07-18T10:51:54.079Z" },
+    { url = "https://files.pythonhosted.org/packages/14/bf/21b4e362039ba52f9033a3f57d68160c0829c9c8d66fa7b443b82491322c/numexpr-2.14.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8b8384592c49cb15a91caa54e2cd84d1ce18edb7af030bb76cd29b52e5dc155d", size = 1467554, upload-time = "2026-07-18T10:51:55.391Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/fb/c27f10ca2e85511a1b0fd3248b1ab5454ea22d932f8fa84836d4bb5c7949/numexpr-2.14.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:149ab5744a5222f07b1d60455c4021c754d395e44938944ac7c7c2495f7feb54", size = 458652, upload-time = "2026-07-18T10:52:01.639Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/d4/1003cc9cc35aad4d56a68f5ffeb26baa4a235b8eb6c0d1ce9b143bece462/numexpr-2.14.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fd2f5882a66a7792aa6614c68831aa20085b499d41422aedd001080624ebb14c", size = 448200, upload-time = "2026-07-18T10:52:02.872Z" },
+    { url = "https://files.pythonhosted.org/packages/06/c7/c66fe3a137bb1dc7229adadde22299a156f730016ac70348dcaac4f7b1ef/numexpr-2.14.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:375d8bee15be42dab22100a0a3de05fe6689a2de853eca012858768a9a7e02ab", size = 1420290, upload-time = "2026-07-18T10:52:04.055Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/87/913bb467d71df80dbccaa7fc37402ba681fd6656d5a79652393f40bd5571/numexpr-2.14.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c1ffaf805d8636c3f95d0996517ecf9684c9ac62d768030ca78d1d00af2b3504", size = 1467495, upload-time = "2026-07-18T10:52:05.288Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/29/2e3a7ad419ec0b4b70ac7e09e4cbb811ccec0ea50976fe657427ec2113b7/numexpr-2.14.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b20c1c55aba7812ff2f2c6a50006425d02282fabb1eaf8d75fe638ffcf6deb02", size = 458710, upload-time = "2026-07-18T10:52:11.7Z" },
+    { url = "https://files.pythonhosted.org/packages/22/79/ce34593e425b5ac1c4aba69306c8811017bea34a4e9f966f6947514e8acb/numexpr-2.14.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bac00898930f962f360c3d763a8e2273fc931f65a1759ff1bf64b3cf13d65aee", size = 448255, upload-time = "2026-07-18T10:52:12.81Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/ac/dab6fb4c66713b7676c2ea133a213dcc95a1359ebe52dacb4eeaa7c0f2b3/numexpr-2.14.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:022e61a3d5dbf5807746264b62126d1c2c24057ad90052478a4d4482ab2555c2", size = 1420432, upload-time = "2026-07-18T10:52:14.193Z" },
+    { url = "https://files.pythonhosted.org/packages/12/bc/6131d1ab0166e982542c6034b516a94d6f006fb394b2deffb97e6c07688a/numexpr-2.14.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1d4593e2c6fa060cd7441e8b6ef25c16321a6be2144b3c82d1e00885f1fb6e94", size = 1467525, upload-time = "2026-07-18T10:52:15.474Z" },
+    { url = "https://files.pythonhosted.org/packages/10/9a/f35e5096006ee89f5e5f65482c5e4a4512faf387e395c7578e5efd4ccaf8/numexpr-2.14.2-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c66925deb968f0b5280f723e2bb5918c11e6be2ca60e9e1530006286ab44031d", size = 469560, upload-time = "2026-07-18T10:52:22.402Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/00/698b6bdd95403af044928af9fc1dcf7c2b0909146ca5ae26882ebf22dfca/numexpr-2.14.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a404c9a55902572eec810068d06b79a7c99e96f0400f5a7d73f39dff5ec5e371", size = 459233, upload-time = "2026-07-18T10:52:23.687Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/a8/87e160de8cba2779a82f7b9a3c93e39feb4ae50e397f676f96e979ecd92b/numexpr-2.14.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:44dc6b1dfa9abcbfc9917297f0d2af7c87c16b6ecd45747a8e70f54399a3a2f9", size = 1430032, upload-time = "2026-07-18T10:52:25.076Z" },
+    { url = "https://files.pythonhosted.org/packages/00/91/bef92d9f6fb5ce18a3baf96451e1feed99e85b035fc142436e5d7b31bb55/numexpr-2.14.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:93233040f4bed3bce5abb0c2d20aeb1074511f29cbaa9c14828f86bcfa44d321", size = 1476032, upload-time = "2026-07-18T10:52:26.361Z" },
 ]
 
 [[package]]
 name = "numpy"
-version = "2.4.6"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c9/c6/50a46a6205feba2343f1d6d17438107c5dc491ed1c736e6ea68689fd906b/numpy-2.4.6-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f", size = 15671012, upload-time = "2026-05-18T23:34:05.485Z" },
-    { url = "https://files.pythonhosted.org/packages/99/60/14115e6364fa676c5397c2ad3004e527e9aa487abf5d0706ec81bbd08529/numpy-2.4.6-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853", size = 16645538, upload-time = "2026-05-18T23:34:09.265Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/c5/693cbe59e57db94d2231fa519ca3978dc9e19da5a8f088588f5c6e947ff2/numpy-2.4.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a", size = 17020706, upload-time = "2026-05-18T23:34:13.053Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/fc/85b7c4eff9b4966ade25c2273cf7e7012e92366c032058653934b37de044/numpy-2.4.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2", size = 18368541, upload-time = "2026-05-18T23:34:17.024Z" },
-    { url = "https://files.pythonhosted.org/packages/bb/c5/7b863a97a91671a0338f4253bd3b5a3d3852f0692dae91711c9f4a10e787/numpy-2.4.6-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b", size = 15669392, upload-time = "2026-05-18T23:34:41.257Z" },
-    { url = "https://files.pythonhosted.org/packages/a5/9d/3584b9984ca4c047aea75214ce1a4c4c73d849bd71b604264b7f5653f8a8/numpy-2.4.6-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089", size = 16633220, upload-time = "2026-05-18T23:34:45.075Z" },
-    { url = "https://files.pythonhosted.org/packages/05/ae/7c67fba23bd98caec7c99261f3a16072ade14813486b0282cb29846de832/numpy-2.4.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a", size = 17020800, upload-time = "2026-05-18T23:34:49.065Z" },
-    { url = "https://files.pythonhosted.org/packages/d9/5d/3b6725cb31d983c5e66916f5d36f6d7e5521129e4c4404d64f918292a5b6/numpy-2.4.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605", size = 18357600, upload-time = "2026-05-18T23:34:52.709Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/0f/037e64c494b67581ae18193d770adef354c41f3f2c8ebf865602d949bf8f/numpy-2.4.6-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20", size = 15728070, upload-time = "2026-05-18T23:35:14.79Z" },
-    { url = "https://files.pythonhosted.org/packages/21/a6/5d2bae9c9542eb4df16dc9c46dc79c186e9bad53805dfa5399a6023c6db0/numpy-2.4.6-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d", size = 16681752, upload-time = "2026-05-18T23:35:18.836Z" },
-    { url = "https://files.pythonhosted.org/packages/92/14/23d1dfb410ae362cd59ce53e936b1513d545eb40db3949ced632e19a459e/numpy-2.4.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67", size = 17086024, upload-time = "2026-05-18T23:35:22.52Z" },
-    { url = "https://files.pythonhosted.org/packages/4b/6e/23595a2c642cdf3bc567877064bdd7f91c8b0038a4453cf2daf7248eafe9/numpy-2.4.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd", size = 18403398, upload-time = "2026-05-18T23:35:26.398Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/91/64288395ee1799bd2e0b04a305dce9666da90c961e1f3fe982a05ee1c036/numpy-2.4.6-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b", size = 15685197, upload-time = "2026-05-18T23:35:50.863Z" },
-    { url = "https://files.pythonhosted.org/packages/f3/eb/ebffaa97dc55502df69584a8f0dcf07f69a3e0b3e2323670a2722db9aa39/numpy-2.4.6-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8", size = 16638245, upload-time = "2026-05-18T23:35:54.752Z" },
-    { url = "https://files.pythonhosted.org/packages/b8/0b/54f9da33128d7e350fab89c7455902eeae70349ee52bddb448dc4a576f45/numpy-2.4.6-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402", size = 17036587, upload-time = "2026-05-18T23:35:58.355Z" },
-    { url = "https://files.pythonhosted.org/packages/b6/f0/fdebc1052db1cc37c64beb22072d67cd6d1c71adca1299f53dec2b5e20d3/numpy-2.4.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb", size = 18363226, upload-time = "2026-05-18T23:36:02.845Z" },
-    { url = "https://files.pythonhosted.org/packages/82/dd/1206a7ca6ab15e3f02069707ca96222e202af681bb73756da7527f3cb837/numpy-2.4.6-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43", size = 15730496, upload-time = "2026-05-18T23:36:25.713Z" },
-    { url = "https://files.pythonhosted.org/packages/51/e7/38d3ea825dcab85a591734decb2f6c67caa7c8367d374df1a1c3842f9b07/numpy-2.4.6-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e", size = 16679616, upload-time = "2026-05-18T23:36:29.652Z" },
-    { url = "https://files.pythonhosted.org/packages/93/b7/caabfdf53edf663e0b4eb74d7d405d83baef09eb5e83bcd32d601d72b93e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895", size = 17085145, upload-time = "2026-05-18T23:36:33.449Z" },
-    { url = "https://files.pythonhosted.org/packages/f9/45/68d7c33a6bcf3e5aa3bdbd57a367e6f615286dfd6482f97e8ffeb734306e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4", size = 18403813, upload-time = "2026-05-18T23:36:37.369Z" },
+version = "2.5.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/13/01/11703282db468b85f6f7b8c7f22d058de5970d5c7e60a3a8aaa313c3de36/numpy-2.5.3.tar.gz", hash = "sha256:df2d5874ff183595a4ba404edd04f6bd9b5505c1d7708573f6a6c17489a67563", size = 20791231, upload-time = "2026-09-06T16:27:47.073Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/32/84146fc020ad3c25f805f70ab60da46fe3c540a21369754a7e4369754b6f/numpy-2.5.3-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:76c2c1e6bfa5c84adc6434dfbf013aa92096a7985221762c8f11fedfd20fff58", size = 15687872, upload-time = "2026-09-06T16:24:39.751Z" },
+    { url = "https://files.pythonhosted.org/packages/65/af/aa78d1a88805456e212b65461354cd943197fb9acecc4c90fd12295123a3/numpy-2.5.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7e18c623bb5c95acb3b3328861272816ba199fb531921c5d6d0b675f1fde9e3", size = 16717410, upload-time = "2026-09-06T16:24:42.745Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/24/faa79d865e69a97ba17473b23a1b74094b2259c03e820c70297293b9ea49/numpy-2.5.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4f8929ee6c96bfbd7b4ed2032e0c03af86fe1826740ab61ddabf9072d06e57ff", size = 17040975, upload-time = "2026-09-06T16:24:45.961Z" },
+    { url = "https://files.pythonhosted.org/packages/62/4a/8877e629445a7176297dffcaf9c485faa96a95d81728a62521ad55bd4c0f/numpy-2.5.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b5d93cf48f687479941d12b69c873ad2cc76bbd487f0091c2200636497f34034", size = 18476479, upload-time = "2026-09-06T16:24:49.35Z" },
+    { url = "https://files.pythonhosted.org/packages/73/85/735720d04ec197c5dcfacdfc9922667c7f1f5f496a279b7ba4d7c74c4cc7/numpy-2.5.3-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c76d5dde9f445058f83d0c02af00557a4db91de9a9a57c0df87d1535001d654b", size = 15681750, upload-time = "2026-09-06T16:25:11.173Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/1b/3b16a9bc514a440a7a0883684111dcb1ef1aee960af2ca95da8fc775f124/numpy-2.5.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a5fa86b80fd24bcd1aff83ad23be44ea323de3f787be8f8b15d4a65621e25321", size = 16708577, upload-time = "2026-09-06T16:25:14.171Z" },
+    { url = "https://files.pythonhosted.org/packages/69/c4/386f397831b07328b639c96c5b62719346cf4baf07c68d927239752b1534/numpy-2.5.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:bd4cb9ad3c7889b9b3fe0a9a9fb5d2ed26f9879bff2608d9f01aed147a20d231", size = 17042047, upload-time = "2026-09-06T16:25:17.582Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/3e/a700ecbf36e85ae8328fd3b0e12eeddc22ed6358a64cb2bd913e0d195d65/numpy-2.5.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1302b90c0e52281681b2975adfe8a860cb7b12216a27b4b0b4207c44bf7bccf0", size = 18465724, upload-time = "2026-09-06T16:25:20.949Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/72/12918652e7912ef9751e8694c88820fcd1908e0618cb23f5f3caa6004b7b/numpy-2.5.3-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:be5a8381859b6da607c84f4f7d6847725f1cf1853ef8a2c9e115b7d58bef47dc", size = 15703377, upload-time = "2026-09-06T16:25:45.135Z" },
+    { url = "https://files.pythonhosted.org/packages/45/8f/9beacf79ca7c650688ad0baa80931adb988fe6e6e5d5903c23cc3dbd70eb/numpy-2.5.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b0521d0f4aebb6e06189451025fa17a913287b13c03d5fe05c017333b654ea5b", size = 16711928, upload-time = "2026-09-06T16:25:48.461Z" },
+    { url = "https://files.pythonhosted.org/packages/09/8d/41d0a56e1ac4c87495c897a211b1368691b7237aadabec8b3b8f3a74d48f/numpy-2.5.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9deb49575e5b0b94ed72c8a64ec4d033381adc27e9060ae842971f697ba96104", size = 17059507, upload-time = "2026-09-06T16:25:51.873Z" },
+    { url = "https://files.pythonhosted.org/packages/08/1e/0dfbc5cc251d54e2af790f254d24ec38637fa97ec7d5d11de7ffed787098/numpy-2.5.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b00eefbcf0f292945c4b4dec2ae845389ef5bcdcd596e6e4328051db5b5ba694", size = 18471002, upload-time = "2026-09-06T16:25:55.233Z" },
+    { url = "https://files.pythonhosted.org/packages/07/a9/968c90ed2ab15060c338e8137f1215b5a60756ae07328e0a60d1c6734df4/numpy-2.5.3-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1fb6f8fb9ff0b3a69f52c66ce397b0246583e9f28616231b0e32ca49259a5fa6", size = 15748923, upload-time = "2026-09-06T16:26:15.092Z" },
+    { url = "https://files.pythonhosted.org/packages/59/08/9df04103947b95e3b6b1f2ed1a70521f325647a31b82da6a2aae3a485508/numpy-2.5.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93e1f5447e2b1e479d7bd74701e84746b86450cff1fc368b132d195e2b8f8211", size = 16746748, upload-time = "2026-09-06T16:26:18.43Z" },
+    { url = "https://files.pythonhosted.org/packages/41/a0/14c8d5fe5b53a334aabb653deb391c0fef49558f491880ea300ed6785224/numpy-2.5.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c00abe94c1a69d75d827dcf1c025b25c8a45d230b3bcd77a9020883a1b047653", size = 17111561, upload-time = "2026-09-06T16:26:22.113Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/a6/d7e96e42f01522e154c32489640f16dfc4f6181d165d05fc3bec8c2c4999/numpy-2.5.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:536f963710a4e63934d80ac0dc4f478804a83e9a84b6828018f25d09953ada33", size = 18513945, upload-time = "2026-09-06T16:26:25.401Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/c7/29285be1e5232a6e7ee3268a33c85843f5a8ee93350c6465cddd66ebbf76/numpy-2.5.3-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f3ed25271581281f2fccb1adcedfcde4c07362eec69189b50baf6f90e3ae159", size = 15697322, upload-time = "2026-09-06T16:26:49.415Z" },
+    { url = "https://files.pythonhosted.org/packages/55/49/bbad5335fb4996a16881f853ff3e0ba582f01720e55c89b1c06b8fc42a90/numpy-2.5.3-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ffdc76bfcae6b255dff75202c5e7feaf95b40246bc0a17944facc1fecf9f79ab", size = 16708995, upload-time = "2026-09-06T16:26:53.127Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/e9/1df35483760b04a65ea44669f89dc64f30e5aca098b48ceb8b1310b0e0fe/numpy-2.5.3-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:116f96cadd935c6122e9228d676fe7ede19e741f5c8bb1c3cddbe0c51ccebea2", size = 17052508, upload-time = "2026-09-06T16:26:56.464Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/99/66e54da8265cc8be8a7382bf96edce17aaa2837d6f484432025932a3caa5/numpy-2.5.3-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:09ffa5d903faeaa5c4dd05009cf81c8bab9f2cb37c548b8d39b65b4cfa7c97f7", size = 18468224, upload-time = "2026-09-06T16:26:59.966Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/24/e3813329498596cb842703dcacac1741612ed9fb9c4e6a3e0c7e2ebbc597/numpy-2.5.3-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:595d020938c84e320bcf40ad71089e108eac0d377cd018e14a8c094f39e98d85", size = 15745777, upload-time = "2026-09-06T16:27:25.181Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/9e/4e7a07fd0776dc2210cdacf2010be8665194d094defc10c419d7dea794cc/numpy-2.5.3-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f24021b9f22bc6301c37b196974a92c1c18dccedb6fef3dd252e95f2d6adbe4", size = 16746949, upload-time = "2026-09-06T16:27:28.576Z" },
+    { url = "https://files.pythonhosted.org/packages/91/db/01674c0e20335057813a00c2ebd546ed25bff9ed7914f9bced00f8c55d94/numpy-2.5.3-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:71b39d9f935b6ec0f8753e3e2afb51e3efba6f2e05b68b32a40754d24bcd4a3c", size = 17108994, upload-time = "2026-09-06T16:27:31.946Z" },
+    { url = "https://files.pythonhosted.org/packages/45/7a/584c5e71f8d378e57cac0b033891ed65c683ef90573ba4854e8c28203db0/numpy-2.5.3-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:6b05c171afb3aa07adbd20abc00aea86fe375beb0fdb9ef780ec5b7f63bab1c0", size = 18512266, upload-time = "2026-09-06T16:27:35.196Z" },
 ]
 
 [[package]]
@@ -2498,45 +2698,48 @@ wheels = [
 
 [[package]]
 name = "packaging"
-version = "25.0"
+version = "26.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/fa/3944b40b07da9ce895c0e6303a5ab7d53da063554f534556b134a54d6093/packaging-26.3.tar.gz", hash = "sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79", size = 313412, upload-time = "2026-08-04T18:15:28.737Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" },
+    { url = "https://files.pythonhosted.org/packages/63/34/ba1c580383c9eada3711951fef0795c80b829a078d72188184bcab9dd527/packaging-26.3-py3-none-any.whl", hash = "sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c", size = 129956, upload-time = "2026-08-04T18:15:27.159Z" },
 ]
 
 [[package]]
 name = "pandas"
-version = "2.3.3"
+version = "3.0.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
     { name = "python-dateutil" },
-    { name = "pytz" },
-    { name = "tzdata" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/33/01/d40b85317f86cf08d853a4f495195c73815fdf205eef3993821720274518/pandas-2.3.3.tar.gz", hash = "sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b", size = 4495223, upload-time = "2025-09-29T23:34:51.853Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/57/56/cf2dbe1a3f5271370669475ead12ce77c61726ffd19a35546e31aa8edf4e/pandas-2.3.3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecaf1e12bdc03c86ad4a7ea848d66c685cb6851d807a26aa245ca3d2017a1908", size = 11737212, upload-time = "2025-09-29T23:19:59.765Z" },
-    { url = "https://files.pythonhosted.org/packages/e5/63/cd7d615331b328e287d8233ba9fdf191a9c2d11b6af0c7a59cfcec23de68/pandas-2.3.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b3d11d2fda7eb164ef27ffc14b4fcab16a80e1ce67e9f57e19ec0afaf715ba89", size = 12362693, upload-time = "2025-09-29T23:20:14.098Z" },
-    { url = "https://files.pythonhosted.org/packages/a6/de/8b1895b107277d52f2b42d3a6806e69cfef0d5cf1d0ba343470b9d8e0a04/pandas-2.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a68e15f780eddf2b07d242e17a04aa187a7ee12b40b930bfdd78070556550e98", size = 12771002, upload-time = "2025-09-29T23:20:26.76Z" },
-    { url = "https://files.pythonhosted.org/packages/87/21/84072af3187a677c5893b170ba2c8fbe450a6ff911234916da889b698220/pandas-2.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:371a4ab48e950033bcf52b6527eccb564f52dc826c02afd9a1bc0ab731bba084", size = 13450971, upload-time = "2025-09-29T23:20:41.344Z" },
-    { url = "https://files.pythonhosted.org/packages/16/87/9472cf4a487d848476865321de18cc8c920b8cab98453ab79dbbc98db63a/pandas-2.3.3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e32e7cc9af0f1cc15548288a51a3b681cc2a219faa838e995f7dc53dbab1062d", size = 11709872, upload-time = "2025-09-29T23:21:27.165Z" },
-    { url = "https://files.pythonhosted.org/packages/15/07/284f757f63f8a8d69ed4472bfd85122bd086e637bf4ed09de572d575a693/pandas-2.3.3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:318d77e0e42a628c04dc56bcef4b40de67918f7041c2b061af1da41dcff670ac", size = 12306371, upload-time = "2025-09-29T23:21:40.532Z" },
-    { url = "https://files.pythonhosted.org/packages/33/81/a3afc88fca4aa925804a27d2676d22dcd2031c2ebe08aabd0ae55b9ff282/pandas-2.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4e0a175408804d566144e170d0476b15d78458795bb18f1304fb94160cabf40c", size = 12765333, upload-time = "2025-09-29T23:21:55.77Z" },
-    { url = "https://files.pythonhosted.org/packages/8d/0f/b4d4ae743a83742f1153464cf1a8ecfafc3ac59722a0b5c8602310cb7158/pandas-2.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:93c2d9ab0fc11822b5eece72ec9587e172f63cff87c00b062f6e37448ced4493", size = 13418120, upload-time = "2025-09-29T23:22:10.109Z" },
-    { url = "https://files.pythonhosted.org/packages/46/b1/85331edfc591208c9d1a63a06baa67b21d332e63b7a591a5ba42a10bb507/pandas-2.3.3-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6435cb949cb34ec11cc9860246ccb2fdc9ecd742c12d3304989017d53f039a78", size = 11645189, upload-time = "2025-09-29T23:22:51.688Z" },
-    { url = "https://files.pythonhosted.org/packages/44/23/78d645adc35d94d1ac4f2a3c4112ab6f5b8999f4898b8cdf01252f8df4a9/pandas-2.3.3-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:900f47d8f20860de523a1ac881c4c36d65efcb2eb850e6948140fa781736e110", size = 12121912, upload-time = "2025-09-29T23:23:05.042Z" },
-    { url = "https://files.pythonhosted.org/packages/53/da/d10013df5e6aaef6b425aa0c32e1fc1f3e431e4bcabd420517dceadce354/pandas-2.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a45c765238e2ed7d7c608fc5bc4a6f88b642f2f01e70c0c23d2224dd21829d86", size = 12712160, upload-time = "2025-09-29T23:23:28.57Z" },
-    { url = "https://files.pythonhosted.org/packages/bd/17/e756653095a083d8a37cbd816cb87148debcfcd920129b25f99dd8d04271/pandas-2.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c4fc4c21971a1a9f4bdb4c73978c7f7256caa3e62b323f70d6cb80db583350bc", size = 13199233, upload-time = "2025-09-29T23:24:24.876Z" },
-    { url = "https://files.pythonhosted.org/packages/ca/05/d01ef80a7a3a12b2f8bbf16daba1e17c98a2f039cbc8e2f77a2c5a63d382/pandas-2.3.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6d2cefc361461662ac48810cb14365a365ce864afe85ef1f447ff5a1e99ea81c", size = 11814049, upload-time = "2025-09-29T23:27:15.384Z" },
-    { url = "https://files.pythonhosted.org/packages/15/b2/0e62f78c0c5ba7e3d2c5945a82456f4fac76c480940f805e0b97fcbc2f65/pandas-2.3.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ee67acbbf05014ea6c763beb097e03cd629961c8a632075eeb34247120abcb4b", size = 12332638, upload-time = "2025-09-29T23:27:51.625Z" },
-    { url = "https://files.pythonhosted.org/packages/c5/33/dd70400631b62b9b29c3c93d2feee1d0964dc2bae2e5ad7a6c73a7f25325/pandas-2.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c46467899aaa4da076d5abc11084634e2d197e9460643dd455ac3db5856b24d6", size = 12886834, upload-time = "2025-09-29T23:28:21.289Z" },
-    { url = "https://files.pythonhosted.org/packages/d3/18/b5d48f55821228d0d2692b34fd5034bb185e854bdb592e9c640f6290e012/pandas-2.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6253c72c6a1d990a410bc7de641d34053364ef8bcd3126f7e7450125887dffe3", size = 13409925, upload-time = "2025-09-29T23:28:58.261Z" },
-    { url = "https://files.pythonhosted.org/packages/f9/88/702bde3ba0a94b8c73a0181e05144b10f13f29ebfc2150c3a79062a8195d/pandas-2.3.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a21d830e78df0a515db2b3d2f5570610f5e6bd2e27749770e8bb7b524b89b450", size = 11634535, upload-time = "2025-09-29T23:30:21.003Z" },
-    { url = "https://files.pythonhosted.org/packages/a4/1e/1bac1a839d12e6a82ec6cb40cda2edde64a2013a66963293696bbf31fbbb/pandas-2.3.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2e3ebdb170b5ef78f19bfb71b0dc5dc58775032361fa188e814959b74d726dd5", size = 12121582, upload-time = "2025-09-29T23:30:43.391Z" },
-    { url = "https://files.pythonhosted.org/packages/44/91/483de934193e12a3b1d6ae7c8645d083ff88dec75f46e827562f1e4b4da6/pandas-2.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d051c0e065b94b7a3cea50eb1ec32e912cd96dba41647eb24104b6c6c14c5788", size = 12699963, upload-time = "2025-09-29T23:31:10.009Z" },
-    { url = "https://files.pythonhosted.org/packages/70/44/5191d2e4026f86a2a109053e194d3ba7a31a2d10a9c2348368c63ed4e85a/pandas-2.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87", size = 13202175, upload-time = "2025-09-29T23:31:59.173Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/e2/17/d7b106e05bfa642e8694451e7d3d759c6a241c5386a5d962e4f66c047e06/pandas-3.0.6.tar.gz", hash = "sha256:66b07ef7315a31bfe1089cd3d71a7de781c9dca986762d0b4fe7c0ef17465d10", size = 4667686, upload-time = "2026-09-17T23:23:18.345Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/67/fa/613d867c3d9554a61bafdec6f79565c8a3e73235feb52cc4a72ad2e0fa6a/pandas-3.0.6-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e3dccb584123b399c07562ac4d62543e90ede49ddf8ce3c13ffc64cbe828c281", size = 10303429, upload-time = "2026-09-17T23:20:55.597Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/67/0c0f18e38d7f2d2af8c24b3315bc4046e73bbdd4a5540405506671ad0c0d/pandas-3.0.6-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0704044b676496b8350e023b09f174a26772456c974a2b11c36bebb558c9490d", size = 10788193, upload-time = "2026-09-17T23:20:58.617Z" },
+    { url = "https://files.pythonhosted.org/packages/39/53/1b57f3162501fe36687e4870e1b918a6458ca7386b173af75663ace95857/pandas-3.0.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e7c1905ef02c3d6d43d9dbd5b6ccb4da4870a0b0c821bbc103fbdb6f3ad2707b", size = 11323501, upload-time = "2026-09-17T23:21:01.911Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/d2/b1182e8d39100369d7f13f4a125a3fb6b096fef112c46d0566c25781ff68/pandas-3.0.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:569e114072b24fc4970c12e2b4bab252671668a40b324318903380cab0254c0c", size = 11838173, upload-time = "2026-09-17T23:21:04.85Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/33/5b717af24d2f27995e51e0875a269dddd373045216e34cf62e3aa764eaa1/pandas-3.0.6-cp312-cp312-pyemscripten_2024_0_wasm32.whl", hash = "sha256:2a8fc94be2ee5f1d86f97aacd8cc566f81680b6498e76f3007421bb5d98151bf", size = 7118499, upload-time = "2026-09-17T23:21:07.661Z" },
+    { url = "https://files.pythonhosted.org/packages/43/1a/d951442e5607c6e3b2462eff8f420797d428aa74b87c6ecfe4f48553626e/pandas-3.0.6-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:097090508a1dd335013d39106fc10b20f4fd4a171638e47b77d55798ed9dab6c", size = 10245290, upload-time = "2026-09-17T23:21:22.797Z" },
+    { url = "https://files.pythonhosted.org/packages/50/fa/96d50e1e6cd0b08b5e2b7c838f65ae644940f75a124063380b5ef73b6866/pandas-3.0.6-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1e92d9fa834c7d877130027cddc0cad8dcff97c1f6cca26bd6310f847228b658", size = 10757657, upload-time = "2026-09-17T23:21:25.673Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/12/f82d13a2cb703e1a8acee7e01fdc2b898d9cd0c00f07d1dfce63af43e350/pandas-3.0.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:b27c8d890e4aa2171437ae2a39de1d215e674158e4865c4023a8b31c932513b2", size = 11249114, upload-time = "2026-09-17T23:21:28.898Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/ce/8aef2e561a2f2c8b38c913c67373c65ba6748174e763d27c80271b24bd17/pandas-3.0.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8029ec0f1f89e4f985929ce1f6626dabf3140d61a4e9c1215afdab34eaf9a5d", size = 11820511, upload-time = "2026-09-17T23:21:32.11Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/f2/cc5f2adb8d6e86a85d9fb5128f8cf205a61189336f70d1f7faf0d1b53ec9/pandas-3.0.6-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:654aae059295dbba6ecd2328ca12712a2cf1676214c8699f1c29213f7ccf9c34", size = 10375479, upload-time = "2026-09-17T23:21:47.159Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/ba/ffdcb19be4ff6bfe7d969e7cef2c567c633df5a3a1cc1053394ad053bca8/pandas-3.0.6-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:62f51d7f651c8054c5e82a69265c98082e795d1442df7ca6edc3a545d61214b1", size = 10783244, upload-time = "2026-09-17T23:21:50.367Z" },
+    { url = "https://files.pythonhosted.org/packages/77/5b/e150075b2c6eb69fae896f2d9239bc6ed07db97735971d53d66de6553460/pandas-3.0.6-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:22172a92e7ee678ec0140c7af4fc9366b55413834a1cd86af78b3caa0b0574de", size = 11382223, upload-time = "2026-09-17T23:21:53.355Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/8a/b441c587dc7355bf6e1f68a91b4f76a6c29740f0c23be3acc5d4ebbeea6d/pandas-3.0.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:583be68728a31d0d750d5b8d9e00f02b153df0d4655f858bde93cb84cfc4227c", size = 11854881, upload-time = "2026-09-17T23:21:56.342Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/fa/e6ecd0073c98be8f840ac3125b955272835d7d9fd69f5944383b164deb5e/pandas-3.0.6-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6ff482fa91fa2bafd92e8fe66ce3645c851824310f295c1f0a2f96e928fc4541", size = 10252356, upload-time = "2026-09-17T23:22:11.302Z" },
+    { url = "https://files.pythonhosted.org/packages/04/f5/001e230a7a7803590d9275a1a3f7e1bb605e3a495cfe5e8d3a532090621b/pandas-3.0.6-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:db7ec631f26223beee8e5c9e0b8f23c24d8197bbd1d982421d4e3188bea51965", size = 10655952, upload-time = "2026-09-17T23:22:14.283Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/ab/bab587148a3852c96aae26c4b5f9e04ce2221801ad94e166b4fbf969ede0/pandas-3.0.6-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bd75ed0c840f709fc2ae26ddd9534ac77ca1a48ac0cce521a74acaa85f3340a7", size = 11274177, upload-time = "2026-09-17T23:22:17.352Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/32/74b48d87df2b80892d713c149abfe36d5db4de41b4eccb042a2bc07dafc1/pandas-3.0.6-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ef738d71d1059245b6bb03e312be06d8b3821326a83486c1ad03b9aba3710e44", size = 11719041, upload-time = "2026-09-17T23:22:20.227Z" },
+    { url = "https://files.pythonhosted.org/packages/54/36/6af478ec3a26d7754555cd62c3101c589c0931b1d85398e4fa5403910a1c/pandas-3.0.6-cp315-cp315-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e25e2e1adee99ddfada6f7206a79ae8e9c8a8861b0e3eaaba165006d3eef18e", size = 10360981, upload-time = "2026-09-17T23:22:35.621Z" },
+    { url = "https://files.pythonhosted.org/packages/43/20/5ece0e9cb79a6473216620db6a90546f578001c2bd857b77c444b27acad1/pandas-3.0.6-cp315-cp315-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4ff44b2cb51cbd691c91f92c4ea6c71e34003f239ebd67c2e857dc898466b49c", size = 10776585, upload-time = "2026-09-17T23:22:38.427Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/b6/cd3038f31ade5e8d2b4e1c9549d4b31e4d598469562f47142b2ad9171c0a/pandas-3.0.6-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:5edd0a7abb0986ecce1ac81f56d99b6763f86aa6946dceb6c661224f90af5a19", size = 11385249, upload-time = "2026-09-17T23:22:41.18Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/87/05bb3003737f80375d7311774916a24d161e2e591abe8c672aed7813defc/pandas-3.0.6-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:1bcb3e9ed29e74a7439cedff9e2aefd3ea65de84d7de9ccb6c194192541bd60e", size = 11848595, upload-time = "2026-09-17T23:22:44.207Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/27/e0a27a5a5c27f7db66657b44def9e93121fd0ad4f0808355b9898fcbf204/pandas-3.0.6-cp315-cp315t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a77a1a44e4d88f1c6a2a64d3eb12efec8420875722e14279800b173a7c7c2804", size = 10239717, upload-time = "2026-09-17T23:22:59.603Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/4f/4eadb7d86a921c1e8bc70916cfe601ed667c4169118d17d69958611c21f8/pandas-3.0.6-cp315-cp315t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:86fa853a12e0b70927e2b1ee00d56d2224ec9cbb4b9d58348b5ad52d2f21150e", size = 10641275, upload-time = "2026-09-17T23:23:02.81Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/d1/eba72e9d905e84e79aefeefdcc6bc1abe15d9077973566643f4211636966/pandas-3.0.6-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:c826e9babb7790142c399f58599d8de679bea059d7b39c5b6efa2096fac37266", size = 11262919, upload-time = "2026-09-17T23:23:06.038Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/8a/1c5bd2642b450e374b6191189f47c49538fe41f82348a66de6f647e6ab59/pandas-3.0.6-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:8fe77b408d82e2615674dfed62533b95e18a03610573877422aada4f625d4947", size = 11709148, upload-time = "2026-09-17T23:23:09.082Z" },
 ]
 
 [[package]]
@@ -2572,14 +2775,15 @@ wheels = [
 
 [[package]]
 name = "patsy"
-version = "1.0.2"
+version = "1.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
+    { name = "packaging" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/be/44/ed13eccdd0519eff265f44b670d46fbb0ec813e2274932dc1c0e48520f7d/patsy-1.0.2.tar.gz", hash = "sha256:cdc995455f6233e90e22de72c37fcadb344e7586fb83f06696f54d92f8ce74c0", size = 399942, upload-time = "2025-10-20T16:17:37.535Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/08/e3/950e513affdfe1c1864397b4f40d6ed06ec1b54f608e462c27f151dd72df/patsy-1.0.3.tar.gz", hash = "sha256:79ebf4c93ff4d296e58a9d5be2b2ee31bd49d737cf11d70ffbd8a44b2de42e65", size = 399992, upload-time = "2026-08-29T21:35:32.258Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f1/70/ba4b949bdc0490ab78d545459acd7702b211dfccf7eb89bbc1060f52818d/patsy-1.0.2-py2.py3-none-any.whl", hash = "sha256:37bfddbc58fcf0362febb5f54f10743f8b21dd2aa73dec7e7ef59d1b02ae668a", size = 233301, upload-time = "2025-10-20T16:17:36.563Z" },
+    { url = "https://files.pythonhosted.org/packages/20/70/cd3cf5cff538323076a879edef02cce6f13f7d75e03d12f211b0a0dbd178/patsy-1.0.3-py2.py3-none-any.whl", hash = "sha256:d3dbebe8fd5f46e29912d030b63c6268647b59bf788a99e2af28a30234cf357c", size = 233318, upload-time = "2026-08-29T21:35:30.913Z" },
 ]
 
 [[package]]
@@ -2650,11 +2854,11 @@ sdist = { url = "https://files.pythonhosted.org/packages/b8/02/5bf3639f5b77e9b18
 
 [[package]]
 name = "platformdirs"
-version = "4.10.0"
+version = "4.12.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d7/47/e4501f49c178ae1d9f4a75073fda4204f52647993f075a9db4d14930e0c5/platformdirs-4.10.0.tar.gz", hash = "sha256:31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7", size = 31224, upload-time = "2026-05-28T03:32:53.587Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/17/c8/721b3855fe457da514fe249247d404b9b39c5d16532278f70ebaa6acf18b/platformdirs-4.12.2.tar.gz", hash = "sha256:eab5f70271a490ef74618bb314fbb86e3c7e82fa3b9c922c2ea0e0a1a155d329", size = 59353, upload-time = "2026-09-29T18:27:56.009Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/e6/cd9575ac904136b3cbf7aa7ee819ef86eedb7274e46f230e94ea4342e729/platformdirs-4.10.0-py3-none-any.whl", hash = "sha256:fb516cdb12eb0d857d0cd85a7c57cea4d060bee4578d6cf5a14dfdf8cbf8784a", size = 22743, upload-time = "2026-05-28T03:32:52.175Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/89/446044f33aba0348d35e433f56d12206d010a5281a1df54054d4cfb82388/platformdirs-4.12.2-py3-none-any.whl", hash = "sha256:29dbf06d96c500bc6bdbce75fb0a14d63279c93b1842f97e72a135b33e856983", size = 32457, upload-time = "2026-09-29T18:27:54.43Z" },
 ]
 
 [[package]]
@@ -2668,23 +2872,23 @@ wheels = [
 
 [[package]]
 name = "prometheus-client"
-version = "0.25.0"
+version = "0.26.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1b/fb/d9aa83ffe43ce1f19e557c0971d04b90561b0cfd50762aafb01968285553/prometheus_client-0.25.0.tar.gz", hash = "sha256:5e373b75c31afb3c86f1a52fa1ad470c9aace18082d39ec0d2f918d11cc9ba28", size = 86035, upload-time = "2026-04-09T19:53:42.359Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/52/73/f1334c29c2af4cd9dba6c7817e61b611bd0215e2eb5565c6064a4de18802/prometheus_client-0.26.0.tar.gz", hash = "sha256:04a91bcf94e2cf74a44a1a874d651a2e853ed354b6e822f3b7487751465d5c2b", size = 92910, upload-time = "2026-07-24T19:36:41.893Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/9b/d4b1e644385499c8346fa9b622a3f030dce14cd6ef8a1871c221a17a67e7/prometheus_client-0.25.0-py3-none-any.whl", hash = "sha256:d5aec89e349a6ec230805d0df882f3807f74fd6c1a2fa86864e3c2279059fed1", size = 64154, upload-time = "2026-04-09T19:53:41.324Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/a3/b69efbf4143b5b9859b977770bbbabcc2796b702fa69dc40271e45cd5a56/prometheus_client-0.26.0-py3-none-any.whl", hash = "sha256:fa93d06737aa02bacd05794768508bb97d2fbee28cb3bca04eaae92f0ca953d6", size = 64494, upload-time = "2026-07-24T19:36:40.854Z" },
 ]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.52"
+version = "3.0.53"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "wcwidth" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/ea/39b988c938f75cb75d7045b5c69f8bfed47ee2152c8837fb403de29d6fb8/prompt_toolkit-3.0.53.tar.gz", hash = "sha256:9ec8a0ad96d5c56148b3f914aa79c1564c3fde5d2e6b876e7bc327e353cf8fa6", size = 435492, upload-time = "2026-07-26T20:56:14.758Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" },
+    { url = "https://files.pythonhosted.org/packages/54/6f/84908cad2d6aa5144abcf7b42709fe4fdb459bc640ec7ac5786e7693dabc/prompt_toolkit-3.0.53-py3-none-any.whl", hash = "sha256:01c0891d7f9237d5e339f7d3e42cdae80b7534abb1c7c0e3352efba6231492f2", size = 392288, upload-time = "2026-07-26T20:56:12.512Z" },
 ]
 
 [[package]]
@@ -2714,11 +2918,11 @@ wheels = [
 
 [[package]]
 name = "pure-eval"
-version = "0.2.3"
+version = "0.2.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/da/9f/abfd2959e9261dd5217ca8551d4de211ca6ab26fe9b72cf44731ff6c4442/pure_eval-0.2.4.tar.gz", hash = "sha256:260c2774686e651b79f8b8e7fc9d80b3599ea6a66334b47d5f4abb69fc2c0ea1", size = 20563, upload-time = "2026-09-10T21:41:22.836Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/18/83376915176eb058cb86470eb7396388a13350b09a8f233a79303bbcbc5b/pure_eval-0.2.4-py3-none-any.whl", hash = "sha256:96cae060a313cfaad51bb761278bfb0e62dc0248d9315a81173752dc546cd37a", size = 11893, upload-time = "2026-09-10T21:41:21.532Z" },
 ]
 
 [[package]]
@@ -2747,26 +2951,26 @@ wheels = [
 
 [[package]]
 name = "pyarrow"
-version = "25.0.0"
+version = "25.0.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/27/f3/95428098d1fa7d04432fb750eed06b41304c2f6a5d3319985e64db2d9d41/pyarrow-25.0.0.tar.gz", hash = "sha256:d2d697008b5ec06d75952ef260c2e9a8a0f6ccfce24266c04c9c8ade927cb3b4", size = 1199181, upload-time = "2026-07-10T08:29:50.116Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3d/e3/27f57f80141379d60defe6703eb50a707325706f07fedfd1312c7a751995/pyarrow-25.0.1.tar.gz", hash = "sha256:9150a83248bfed9813ea3c3af74c3856c1984d444aa28e58bf7733b9750ddf6a", size = 1201653, upload-time = "2026-08-10T12:40:53.904Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/95/1a/22bfb6597dcdc861fa83c39c06e1457cb56f698940eff42fbb25de30e8e5/pyarrow-25.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:fa1482b3da10cac2d4db6e26b81da543e237616af2ef6d466018b31ca586496f", size = 46841966, upload-time = "2026-07-10T08:27:07.685Z" },
-    { url = "https://files.pythonhosted.org/packages/55/0e/cd705c042bc4fe7022478db577fcab4abdcfabb9bc37ab7a75556b3fcb2b/pyarrow-25.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:5d1dbf24e151042f2fa3c129563f65d66674128868496fb008c4272b16bdf778", size = 50088993, upload-time = "2026-07-10T08:27:14.268Z" },
-    { url = "https://files.pythonhosted.org/packages/98/ee/d822e1ee31fe31ec5d057210e0605c950b975dcd8d9a332976cc859a9df8/pyarrow-25.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:20887a762dd61dcc530f93a140840ab1f6aa7836b33270e42d627ab3cf11e537", size = 49941005, upload-time = "2026-07-10T08:27:21.274Z" },
-    { url = "https://files.pythonhosted.org/packages/33/1b/207a90cc64619a095eb75a263ae069735f2810056d43c667befd573ec083/pyarrow-25.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:58d1ab556b0cea1c93fdb799b24ad58adb2f2a2788dbce782a94f64ae1a5cc9b", size = 53112355, upload-time = "2026-07-10T08:27:27.911Z" },
-    { url = "https://files.pythonhosted.org/packages/53/55/6d1d5f5aff317ec5de9421594679ed51ed828fe7e2ce209327f819d801e4/pyarrow-25.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:59516c822d5fd8e544aaa0dfe72f36fed5d4c24ea8390aab1bcd31d7e959c6be", size = 46841701, upload-time = "2026-07-10T08:27:49.741Z" },
-    { url = "https://files.pythonhosted.org/packages/b5/5d/f790fb6965ab54c9da0dda7856abc75fd0d7648d865f8d603c111d203a64/pyarrow-25.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6f9dbd83e91c239a1f5ee7ce13f108b5f6c0efbe40a4375260d8f08b43ad05e9", size = 50090118, upload-time = "2026-07-10T08:27:56.051Z" },
-    { url = "https://files.pythonhosted.org/packages/0c/8c/faf025357ebf31bc96777f234277aa31e2aeca6dd4ecaa391f29085473c2/pyarrow-25.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:18dcc8cc50b5e72eae6fcbfc6c8776c21a007176b27a3cdec5c2f5bcf126708d", size = 49945559, upload-time = "2026-07-10T08:28:01.927Z" },
-    { url = "https://files.pythonhosted.org/packages/07/a1/bd051871708ea99a5e0fc711926c26c6f2c6d0130c7aaac8093e34998af6/pyarrow-25.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4ec1895a87aa834c3b99b7a1e758747eb8bb57f922b32c0e0fa04afb8d6998b1", size = 53114238, upload-time = "2026-07-10T08:28:08.594Z" },
-    { url = "https://files.pythonhosted.org/packages/af/9f/2d81ba89d1e4198d0cb25fe7529de936830fdaec0db926bb52a1ef7080d4/pyarrow-25.0.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:f57a39dbcb416345401c2e77a4373669b45fd111a1768e6cf267a7a0607ff0ec", size = 46905617, upload-time = "2026-07-10T08:28:29.376Z" },
-    { url = "https://files.pythonhosted.org/packages/6a/29/0ed312ec800fb536f93783215126cee4b8977dcfeccba6f0f44df0cc87d7/pyarrow-25.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:447df764beb07c544f0178a5f6b70ef44b9ecf382b3cdfad4c2d7867353c3887", size = 50119765, upload-time = "2026-07-10T08:28:35.826Z" },
-    { url = "https://files.pythonhosted.org/packages/ca/88/cab5063ba0c4d46a9f6b4b7eb1c9029dc0302d65cd5ab3510c949a386568/pyarrow-25.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ac5dfeee59f9ceb4d45ba76e83b026c38c24334135bb329d8274baa49cec3c62", size = 50027563, upload-time = "2026-07-10T08:28:43.848Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/fb/4d24f1b7fe2e042dc4ef315ef75e4e702d8e46fe10c37e63caff00502b03/pyarrow-25.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f0f100dacf2c0f400601664a79d1a907ced4740514bb2b00917341038e2ce76f", size = 53162437, upload-time = "2026-07-10T08:28:52.819Z" },
-    { url = "https://files.pythonhosted.org/packages/8a/18/b37fc31a69cff4bdfb8842683def5612f551b93fff6f44375e4a4a6a5535/pyarrow-25.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:b8af8ceedf0c9c160fd2b63440f2d205b9404db85866c1217bfea601de7cfb50", size = 46912304, upload-time = "2026-07-10T08:29:14.656Z" },
-    { url = "https://files.pythonhosted.org/packages/32/35/5cae19ba72493e5598022468b56f6a5571f399f485bf412f157356476caa/pyarrow-25.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:c70a5fd9a82bd1a702fd482bdc62d38dcb672fb2b449b1d7c0d7d1f4be7b7bfe", size = 50073652, upload-time = "2026-07-10T08:29:22.467Z" },
-    { url = "https://files.pythonhosted.org/packages/2e/a5/ddd508424bdfd5e6945765e9e2ffc687e2f6115972badc8ecf423076c407/pyarrow-25.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0490a7f8b38ffe11cc26526b50c65d111cb54ddac3717cec781806793f1244dc", size = 50058654, upload-time = "2026-07-10T08:29:29.689Z" },
-    { url = "https://files.pythonhosted.org/packages/5f/a4/324d0db203ff5eebe8694ec2d6ec5a23f9aaa5d02e5b8c692914c518c33c/pyarrow-25.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e83916bbcf380866b4e14255850b33323ff678dc9758411d0409cdd2523880b0", size = 53140153, upload-time = "2026-07-10T08:29:36.041Z" },
+    { url = "https://files.pythonhosted.org/packages/13/49/d93a57d375f4bf0cf82913dd6bb54acafde83dd993be2282c81ac5616cad/pyarrow-25.0.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:4340f0ba6c1d2e13f21658de1d7c662ca2545018568d0030a1e9afca159d87e3", size = 46820190, upload-time = "2026-08-10T12:38:15.458Z" },
+    { url = "https://files.pythonhosted.org/packages/60/c9/711ca85d79f1ec98f29a5eae2b051e25b4ecec5de3e3c0e2d5c5dcb15664/pyarrow-25.0.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:5389cdf79447ed1515c9e31620e6e1e2302249564d603f2ad727d4f6d313e4c3", size = 50102437, upload-time = "2026-08-10T12:38:22.487Z" },
+    { url = "https://files.pythonhosted.org/packages/80/53/8fb8359ff17cfb6263a1cf3ebf7caec9fe197de118719e84fcb1d0618026/pyarrow-25.0.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d51592cb7561e87877c506113e7adbf1342ab579e6c21f0ef44b8ba41cb74c80", size = 49942424, upload-time = "2026-08-10T12:38:28.755Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/83/4e5ae02a9341571b18a6fca380ac7a58ce6ddae7ab3c060208c0a1e79f02/pyarrow-25.0.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6109c94d8b9f3b17a041daca16cacb2f651ad8f1ef70a4232c2c0f37a23da2a8", size = 53144206, upload-time = "2026-08-10T12:38:34.862Z" },
+    { url = "https://files.pythonhosted.org/packages/63/1f/96d03b4e1506524f7087adb0fd6b2f69f0c9c7aaff1ec36d8030082e15a5/pyarrow-25.0.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:44a9120ce5bd81936b8ab9a88076e3fd47c2c6838e0e43630fed83626aca81d9", size = 46813813, upload-time = "2026-08-10T12:38:57.773Z" },
+    { url = "https://files.pythonhosted.org/packages/98/d6/33a411115b61dbfc16ad6ad73e71730f6fea654ee3667673bc53ab0e2fe7/pyarrow-25.0.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:0befcf816e45a1af33ac775a9970b749e4868a230c7372f0ae5e932bee27039f", size = 50104452, upload-time = "2026-08-10T12:39:04.579Z" },
+    { url = "https://files.pythonhosted.org/packages/33/ae/b1b97c9ca87f9f9ddbb5230c798df94eccce61bd79b9b45458c69a478588/pyarrow-25.0.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:3f89685964f46e4216103c75483aac0c0692a5f72212d7ca835adba5ede56ce3", size = 49951343, upload-time = "2026-08-10T12:39:11.8Z" },
+    { url = "https://files.pythonhosted.org/packages/98/9e/a112df5cfd5a68cb1d9fc31cfe38c28d5aec9f10865ce37ecef2e4450873/pyarrow-25.0.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6943e2fe7954d29d84de45d29d34c8dc36ce96570e67d89aa9976e650a4a9138", size = 53144784, upload-time = "2026-08-10T12:39:20.503Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/9a/13587e38bd4806fd218f50fd13b8903fab60588a699ff0c406372e5b4043/pyarrow-25.0.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:0b726ad7e7b669be982b0c71c07fe4b037d654354130da79a7902a669e93a66b", size = 46877146, upload-time = "2026-08-10T12:39:43.722Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/61/1c5d1229fa21da4cff5365e41e57177aaac57c563c727f35419b8513d1c1/pyarrow-25.0.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:9171748cdf796972d85a4b60157c279913e242992e350c90c7450182a9838b2a", size = 50131616, upload-time = "2026-08-10T12:39:49.304Z" },
+    { url = "https://files.pythonhosted.org/packages/43/20/291e1d65cc0b09aa19f03cf25cf51a2f5fa94b5db315178f2d254ed5cad4/pyarrow-25.0.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:b7a296aac7a71fa0886c08e155ddb6c636a50013f801f6178daafa0f9e726188", size = 50008879, upload-time = "2026-08-10T12:39:56.891Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/7c/1b7c9ec28e76576337e4f97b31141c9a181b89b6d1d6221e9d8205621a58/pyarrow-25.0.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:0fe7c8b6c03969b49c8c66182e4a18e3819ab92d07cfab5d8370c531b9369ef0", size = 53170864, upload-time = "2026-08-10T12:40:04.918Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/08/a28c01c7fe9e96e8233ce2d13df1d402f4f999f848f51d2daacd6bb4c036/pyarrow-25.0.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:f3831aaa25c67a99f99dc8b05873cb9d64560390372e2aa197ce9dd4a3f06a44", size = 46888003, upload-time = "2026-08-10T12:40:23.242Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/b9/58612e977d28dc58c878448866838369ee8da2f1e7cc8ed2c84b952aafee/pyarrow-25.0.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:6a1fdfc6659b6b19022f2e50627fb5cf7156a66c46bf4299379955cbe742382a", size = 50079036, upload-time = "2026-08-10T12:40:29.169Z" },
+    { url = "https://files.pythonhosted.org/packages/72/13/66e1402dcc860e1dc2760b1e0292c9a569b62b3bccab69def1b3e907d006/pyarrow-25.0.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:169d3429d5be7c752125890620f75a60776d38b0035eddae939651640822332e", size = 50040226, upload-time = "2026-08-10T12:40:35.186Z" },
+    { url = "https://files.pythonhosted.org/packages/78/10/3f1a5497a7ef732ab0f03ecca3e66d89d9c0f57fdc61b4794c456b781f01/pyarrow-25.0.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:119297a6dc197e45d9c6d4415f7814a67ffa36c180d26f68c154c58067ae782d", size = 53149035, upload-time = "2026-08-10T12:40:41.454Z" },
 ]
 
 [[package]]
@@ -2780,11 +2984,11 @@ wheels = [
 
 [[package]]
 name = "pybind11"
-version = "3.0.4"
+version = "3.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cc/f0/35145a3c3baffeef55d4b8324caa33abaa8fa56ab345ecd4b2211d09163e/pybind11-3.0.4.tar.gz", hash = "sha256:3286b59c8a774b9ee650169302dd5a4eedc30a8617905a0560dd8ee44775130c", size = 589533, upload-time = "2026-04-19T03:08:15.925Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/76/f3/95b0f40b31df41dbfe6bb0857419c9442c15839cbac4796f1c26ae0b6081/pybind11-3.1.0.tar.gz", hash = "sha256:a1cc06b524ab3edca51f8ad3895f9c4fa20b8b19283173dff4ae781449dc9639", size = 603746, upload-time = "2026-08-06T23:33:00.675Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b3/06/c3a23c9a0263b136c519f033a58d4641e73065fefc7754e9667ec206d992/pybind11-3.0.4-py3-none-any.whl", hash = "sha256:961720ee652da51d531b7b2451a6bd2bc042b0106e6d9baa48ecb7d58034ce63", size = 314166, upload-time = "2026-04-19T03:08:14.091Z" },
+    { url = "https://files.pythonhosted.org/packages/33/fd/8762f7ee3e4e4be6d1d846cffb4916dd9bb02b2f800d3603718a0efe494c/pybind11-3.1.0-py3-none-any.whl", hash = "sha256:b8488090f8acffbcb6b5d6a85571a6827a0a2981ffb75e5a0b27b87c4a6b7dd0", size = 319402, upload-time = "2026-08-06T23:32:59.047Z" },
 ]
 
 [[package]]
@@ -2841,7 +3045,7 @@ wheels = [
 
 [[package]]
 name = "pydantic"
-version = "2.13.4"
+version = "2.13.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-types" },
@@ -2849,62 +3053,62 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "typing-inspection" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775, upload-time = "2026-05-06T13:43:05.343Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/53/ef/fc4f868f4e2cee79f863883abffceff107875f569b848507319842d2a681/pydantic-2.13.5.tar.gz", hash = "sha256:51a9c5f7b2f8e636f04c6cada605d9b6a3bf1348fdf945a3d8869b19bba0ee08", size = 845750, upload-time = "2026-08-28T14:04:00.916Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl", hash = "sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba", size = 472262, upload-time = "2026-05-06T13:43:02.641Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/47/c95ffc2009878c7aac0c5e08528022dcb885933252a88b5f170058014464/pydantic-2.13.5-py3-none-any.whl", hash = "sha256:346a034f080da3755d8e9cb5e00e8b07de1d39e4f6e2c87d8ab7cafa0b269a73", size = 472589, upload-time = "2026-08-28T14:03:59.136Z" },
 ]
 
 [[package]]
 name = "pydantic-core"
-version = "2.46.4"
+version = "2.46.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9d/56/921726b776ace8d8f5db44c4ef961006580d91dc52b803c489fafd1aa249/pydantic_core-2.46.4.tar.gz", hash = "sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1", size = 471464, upload-time = "2026-05-06T13:37:06.98Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/8e/bc/f47d1ff9cbb1620e1b5b697eef06010035735f07820180e74178226b27b3/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8233f2947cf85404441fd7e0085f53b10c93e0ee78611099b5c7237e36aacbf7", size = 1975742, upload-time = "2026-05-06T13:37:09.448Z" },
-    { url = "https://files.pythonhosted.org/packages/5b/11/9b9a5b0306345664a2da6410877af6e8082481b5884b3ddd78d47c6013ce/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3a233125ac121aa3ffba9a2b59edfc4a985a76092dc8279586ab4b71390875e7", size = 2052418, upload-time = "2026-05-06T13:37:38.234Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/b7/a65fec226f5d78fc39f4a13c4cc0c768c22b113438f60c14adc9d2865038/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b712b53160b79a5850310b912a5ef8e57e56947c8ad690c227f5c9d7e561712", size = 2232274, upload-time = "2026-05-06T13:38:27.753Z" },
-    { url = "https://files.pythonhosted.org/packages/68/f0/92039db98b907ef49269a8271f67db9cb78ae2fc68062ef7e4e77adb5f61/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9401557acd873c3a7f3eb9383edef8ac4968f9510e340f4808d427e75667e7b4", size = 2309940, upload-time = "2026-05-06T13:38:05.353Z" },
-    { url = "https://files.pythonhosted.org/packages/5f/97/2aab507d3d00ca626e8e57c1eac6a79e4e5fbcc63eb99733ff55d1717f65/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:926c9541b14b12b1681dca8a0b75feb510b06c6341b70a8e500c2fdcff837cce", size = 2094516, upload-time = "2026-05-06T13:39:10.577Z" },
-    { url = "https://files.pythonhosted.org/packages/22/37/a8aca44d40d737dde2bc05b3c6c07dff0de07ce6f82e9f3167aeaf4d5dea/pydantic_core-2.46.4-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:56cb4851bcaf3d117eddcef4fe66afd750a50274b0da8e22be256d10e5611987", size = 2136854, upload-time = "2026-05-06T13:40:22.59Z" },
-    { url = "https://files.pythonhosted.org/packages/24/99/fcef1b79238c06a8cbec70819ac722ba76e02bc8ada9b0fd66eba40da01b/pydantic_core-2.46.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c68fcd102d71ea85c5b2dfac3f4f8476eff42a9e078fd5faefff6d145063536b", size = 2180306, upload-time = "2026-05-06T13:40:10.666Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/6c/fc44000918855b42779d007ae63b0532794739027b2f417321cddbc44f6a/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:b2f69dec1725e79a012d920df1707de5caf7ed5e08f3be4435e25803efc47458", size = 2190044, upload-time = "2026-05-06T13:40:43.231Z" },
-    { url = "https://files.pythonhosted.org/packages/6b/65/d9cadc9f1920d7a127ad2edba16c1db7916e59719285cd6c94600b0080ba/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:8d0820e8192167f80d88d64038e609c31452eeca865b4e1d9950a27a4609b00b", size = 2329133, upload-time = "2026-05-06T13:39:57.365Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/cf/c873d91679f3a30bcf5e7ac280ce5573483e72295307685120d0d5ad3416/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fbdb89b3e1c94a30cc5edfce477c6e6a5dc4d8f84665b455c27582f211a1c72c", size = 2374464, upload-time = "2026-05-06T13:38:06.976Z" },
-    { url = "https://files.pythonhosted.org/packages/03/d5/fd02da45b659668b05923b17ba3a0100a0a3d5541e3bd8fcc4ecb711309e/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f027324c56cd5406ca49c124b0db10e56c69064fec039acc571c29020cc87c76", size = 1976802, upload-time = "2026-05-06T13:37:35.113Z" },
-    { url = "https://files.pythonhosted.org/packages/21/f2/95727e1368be3d3ed485eaab7adbd7dda408f33f7a36e8b48e0144002b91/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e739fee756ba1010f8bcccb534252e85a35fe45ae92c295a06059ce58b74ccd3", size = 2052446, upload-time = "2026-05-06T13:37:12.313Z" },
-    { url = "https://files.pythonhosted.org/packages/9c/86/5d99feea3f77c7234b8718075b23db11532773c1a0dbd9b9490215dc2eeb/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9d56801be94b86a9da183e5f3766e6310752b99ff647e38b09a9500d88e46e76", size = 2232757, upload-time = "2026-05-06T13:39:01.149Z" },
-    { url = "https://files.pythonhosted.org/packages/d2/3a/508ac615935ef7588cf6d9e9b91309fdc2da751af865e02a9098de88258c/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2412e734dcb48da14d4e4006b82b46b74f2518b8a26ee7e58c6844a6cd6d03c4", size = 2309275, upload-time = "2026-05-06T13:37:41.406Z" },
-    { url = "https://files.pythonhosted.org/packages/07/f8/41db9de19d7987d6b04715a02b3b40aea467000275d9d758ffaa31af7d50/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9551187363ffc0de2a00b2e47c25aeaeb1020b69b668762966df15fc5659dd5a", size = 2094467, upload-time = "2026-05-06T13:39:18.847Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/e2/f35033184cb11d0052daf4416e8e10a502ea2ac006fc4f459aee872727d1/pydantic_core-2.46.4-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:0186750b482eefa11d7f435892b09c5c606193ef3375bcf94aa00ae6bfb66262", size = 2134417, upload-time = "2026-05-06T13:40:17.944Z" },
-    { url = "https://files.pythonhosted.org/packages/7e/7b/6ceeb1cc90e193862f444ebe373d8fdf613f0a82572dde03fb10734c6c71/pydantic_core-2.46.4-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5855698a4856556d86e8e6cd8434bc3ac0314ee8e12089ae0e143f64c6256e4e", size = 2179782, upload-time = "2026-05-06T13:40:32.618Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/f2/c8d7773ede6af08036423a00ae0ceffce266c3c52a096c435d68c896083f/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:cbaf13819775b7f769bf4a1f066cb6df7a28d4480081a589828ef190226881cd", size = 2188782, upload-time = "2026-05-06T13:36:51.018Z" },
-    { url = "https://files.pythonhosted.org/packages/59/31/0c864784e31f09f05cdd87606f08923b9c9e7f6e51dd27f20f62f975ce9f/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:633147d34cf4550417f12e2b1a0383973bdf5cdfde212cb09e9a581cf10820be", size = 2328334, upload-time = "2026-05-06T13:40:37.764Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/eb/4f6c8a41efa30baa755590f4141abf3a8c370fab610915733e74134a7270/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:82cf5301172168103724d49a1444d3378cb20cdee30b116a1bd6031236298a5d", size = 2372986, upload-time = "2026-05-06T13:39:34.152Z" },
-    { url = "https://files.pythonhosted.org/packages/95/30/5211a831ae054928054b2f79731661087a2bc5c01e825c672b3a4a8f1b3e/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce5c1d2a8b27468f433ca974829c44060b8097eedc39933e3c206a90ee49c4a9", size = 1978926, upload-time = "2026-05-06T13:37:39.933Z" },
-    { url = "https://files.pythonhosted.org/packages/57/e9/689668733b1eb67adeef047db3c2e8788fcf65a7fd9c9e2b46b7744fe245/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7283d57845ecf5a163403eb0702dfc220cc4fbdd18919cb5ccea4f95ee1cdab4", size = 2046785, upload-time = "2026-05-06T13:38:01.995Z" },
-    { url = "https://files.pythonhosted.org/packages/60/d9/6715260422ff50a2109878fd24d948a6c3446bb2664f34ee78cd972b3acd/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8daafc69c93ee8a0204506a3b6b30f586ef54028f52aeeeb5c4cfc5184fd5914", size = 2228733, upload-time = "2026-05-06T13:40:50.371Z" },
-    { url = "https://files.pythonhosted.org/packages/18/ae/fdb2f64316afca925640f8e70bb1a564b0ec2721c1389e25b8eb4bf9a299/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd2213145bcc2ba85884d0ac63d222fece9209678f77b9b4d76f054c561adb28", size = 2307534, upload-time = "2026-05-06T13:37:21.531Z" },
-    { url = "https://files.pythonhosted.org/packages/89/1d/8eff589b45bb8190a9d12c49cfad0f176a5cbd1534908a6b5125e2886239/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a5f930472650a82629163023e630d160863fce524c616f4e5186e5de9d9a49b", size = 2099732, upload-time = "2026-05-06T13:39:31.942Z" },
-    { url = "https://files.pythonhosted.org/packages/06/d5/ee5a3366637fee41dee51a1fc91562dcf12ddbc68fda34e6b253da2324bb/pydantic_core-2.46.4-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:c1b3f518abeca3aa13c712fd202306e145abf59a18b094a6bafb2d2bbf59192c", size = 2129627, upload-time = "2026-05-06T13:37:25.033Z" },
-    { url = "https://files.pythonhosted.org/packages/94/33/2414be571d2c6a6c4d08be21f9292b6d3fdb08949a97b6dfe985017821db/pydantic_core-2.46.4-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1a7dd0b3ee80d90150e3495a3a13ac34dbcbfd4f012996a6a1d8900e91b5c0fb", size = 2179141, upload-time = "2026-05-06T13:37:14.046Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/79/7daa95be995be0eecc4cf75064cb33f9bbbfe3fe0158caf2f0d4a996a5c7/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:3fb702cd90b0446a3a1c5e470bfa0dd23c0233b676a9099ddcc964fa6ca13898", size = 2184325, upload-time = "2026-05-06T13:36:53.615Z" },
-    { url = "https://files.pythonhosted.org/packages/9f/cb/d0a382f5c0de8a222dc61c65348e0ce831b1f68e0a018450d31c2cace3a5/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:b8458003118a712e66286df6a707db01c52c0f52f7db8e4a38f0da1d3b94fc4e", size = 2323990, upload-time = "2026-05-06T13:40:29.971Z" },
-    { url = "https://files.pythonhosted.org/packages/05/db/d9ba624cc4a5aced1598e88c04fdbd8310c8a69b9d38b9a3d39ce3a61ed7/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:372429a130e469c9cd698925ce5fc50940b7a1336b0d82038e63d5bbc4edc519", size = 2369978, upload-time = "2026-05-06T13:37:23.027Z" },
-    { url = "https://files.pythonhosted.org/packages/e8/e7/1955d28d1afc56dd4b3ad7cc0cf39df1b9852964cf16e5d13912756d6d6b/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29c61fc04a3d840155ff08e475a04809278972fe6aef51e2720554e96367e34b", size = 1974605, upload-time = "2026-05-06T13:37:32.029Z" },
-    { url = "https://files.pythonhosted.org/packages/93/e2/3fedbf0ba7a22850e6e9fd78117f1c0f10f950182344d8a6c535d468fdd8/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c50f2528cf200c5eed56faf3f4e22fcd5f38c157a8b78576e6ba3168ec35f000", size = 2030777, upload-time = "2026-05-06T13:38:55.239Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/61/46be275fcaaba0b4f5b9669dd852267ce1ff616592dccf7a7845588df091/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0cbe8b01f948de4286c74cdd6c667aceb38f5c1e26f0693b3983d9d74887c65e", size = 2236641, upload-time = "2026-05-06T13:37:08.096Z" },
-    { url = "https://files.pythonhosted.org/packages/60/db/12e93e46a8bac9988be3c016860f83293daea8c716c029c9ace279036f2f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:617d7e2ca7dcb8c5cf6bcb8c59b8832c94b36196bbf1cbd1bfb56ed341905edd", size = 2286404, upload-time = "2026-05-06T13:40:20.221Z" },
-    { url = "https://files.pythonhosted.org/packages/e2/4a/4d8b19008f38d31c53b8219cfedc2e3d5de5fe99d90076b7e767de29274f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7027560ee92211647d0d34e3f7cd6f50da56399d26a9c8ad0da286d3869a53f3", size = 2109219, upload-time = "2026-05-06T13:38:12.153Z" },
-    { url = "https://files.pythonhosted.org/packages/88/70/3cbc40978fefb7bb09c6708d40d4ad1a5d70fd7213c3d17f971de868ec1f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:f99626688942fb746e545232e7726926f3be91b5975f8b55327665fafda991c7", size = 2110594, upload-time = "2026-05-06T13:40:02.971Z" },
-    { url = "https://files.pythonhosted.org/packages/9d/20/b8d36736216e29491125531685b2f9e61aa5b4b2599893f8268551da3338/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff", size = 2159542, upload-time = "2026-05-06T13:39:27.506Z" },
-    { url = "https://files.pythonhosted.org/packages/1d/a2/367df868eb584dacf6bf82a389272406d7178e301c4ac82545ab98bc2dd9/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:97e7cf2be5c77b7d1a9713a05605d49460d02c6078d38d8bef3cbe323c548424", size = 2168146, upload-time = "2026-05-06T13:38:31.93Z" },
-    { url = "https://files.pythonhosted.org/packages/c1/b8/4460f77f7e201893f649a29ab355dddd3beee8a97bcb1a320db414f9a06e/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:3bf92c5d0e00fefaab325a4d27828fe6b6e2a21848686b5b60d2d9eeb09d76c6", size = 2306309, upload-time = "2026-05-06T13:37:44.717Z" },
-    { url = "https://files.pythonhosted.org/packages/64/c4/be2639293acd87dc8ddbcec41a73cee9b2ebf996fe6d892a1a74e88ad3f7/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:3ecbc122d18468d06ca279dc26a8c2e2d5acb10943bb35e36ae92096dc3b5565", size = 2369736, upload-time = "2026-05-06T13:37:05.645Z" },
-    { url = "https://files.pythonhosted.org/packages/d7/ca/eac61596cdeb4d7e174d3dc0bd8a6238f14f75f97a24e7b7db4c7e7340a0/pydantic_core-2.46.4-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ad785e92e6dc634c21555edc8bd6b64957ab844541bcb96a1366c202951ae526", size = 1990696, upload-time = "2026-05-06T13:38:34.717Z" },
-    { url = "https://files.pythonhosted.org/packages/fa/c3/7c8b240552251faf6b3a957db200fcfbbcec36763c050428b601e0c9b83b/pydantic_core-2.46.4-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:00c603d540afdd6b80eb39f078f33ebd46211f02f33e34a32d9f053bba711de0", size = 2147590, upload-time = "2026-05-06T13:39:29.883Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/af/f9/8a06bea35ef8daf588f707784c973a7046e0034c8d8cfb08828eeffb8b75/pydantic_core-2.46.5.tar.gz", hash = "sha256:10416c15b8839ecc4ef4d0885da76da6fd0f67333a0eb8aff6d93c4b8f2910fc", size = 472262, upload-time = "2026-08-28T10:01:31.677Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/09/f0/2a8ce3849e299d44e2d2c196b6082643a3235565a735cb51db7a6261f614/pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4fdc8b93a41521988916eeaa271173fcca7fa0803d62f87675aac8dcec1c8e29", size = 1951772, upload-time = "2026-08-28T09:58:25.435Z" },
+    { url = "https://files.pythonhosted.org/packages/87/46/ac0dc8bdd9e6048183a14eb127764e7ad9240021c17513074a4711b0e31e/pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:b98134087d9de723658d17a42c7d0da8d6e2ef08015dee7dc93889047315f5e4", size = 2031832, upload-time = "2026-08-28T09:58:27.102Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/c2/339de5bef7be36301a2231eaa52e62163742c2281f11b5f4892bc79785cd/pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e652ab17569c94bff5475520f907b7148b8c24036a8ebbe5cf7cf7493d28579a", size = 2208645, upload-time = "2026-08-28T09:58:28.948Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/a0/9ff22b797724262da14427abaed4dd1d864a139693fc5e7809114376a716/pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d925f3d9afd05a8c0fb3a1031463a8d59ebe5e2afad297e29c78be19e13b4e62", size = 2265935, upload-time = "2026-08-28T09:58:30.625Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/a4/eb9409ec0736e50aa70a412f16c204ed149516846912f7e6724d4c73ee53/pydantic_core-2.46.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fc5be0abd4a407e200d844b404e33639a554e7bd0d448e7b9ae181be4789ac2", size = 2066284, upload-time = "2026-08-28T09:58:32.289Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/02/7f6156ffc926857f1c37c07d9a388682865a81830ab6a1b637082c25e399/pydantic_core-2.46.5-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:816ff0a6550ffc06c098ccd2e0698600f9aa7da192a79eaa6f9af504a35db869", size = 2105889, upload-time = "2026-08-28T09:58:33.986Z" },
+    { url = "https://files.pythonhosted.org/packages/92/b1/e781d357ebe09fc929f995700f1b3503e8897f1cece183ecb1300d4d67e9/pydantic_core-2.46.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c7ea57fc63aa7da93a1bd2d644e6577befae10c52c4e36377635eea1056a74f5", size = 2158006, upload-time = "2026-08-28T09:58:35.647Z" },
+    { url = "https://files.pythonhosted.org/packages/70/0a/644597d84ab400e50609c192120b85c9681c22d3a20461b9060a79be0a7a/pydantic_core-2.46.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:efd62a42486f1bda5d24cb4f63d15a3c7768375fe83d36f9417b4ad7a2fb20b3", size = 2158408, upload-time = "2026-08-28T09:58:37.38Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/ee/ca3b7b3a4b3769ffe9ce9432a7c9be755de9593a46d3b0d54d0409323e44/pydantic_core-2.46.5-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:2bc9419666990c06d7397831f2126a1ecc3594aaa3ff7de5bf2d066802f4e07b", size = 2309609, upload-time = "2026-08-28T09:58:39.22Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/52/39fa1f451486019524ca685020390e7ca351832fd874530ba30c8628e6dc/pydantic_core-2.46.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:18a09e1e1011b462f2e32774f25859ef1223d5c2b0546a633cf56654710721e0", size = 2342618, upload-time = "2026-08-28T09:58:40.89Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/a3/c05ca796e1197618a774b01e596aeedfefc2f7d8c01ae3054e910b120e8a/pydantic_core-2.46.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:193375f3548919d3f0b60936ca113ada3e38f264f91b9b8e0508efaad57be931", size = 1951241, upload-time = "2026-08-28T09:58:51.511Z" },
+    { url = "https://files.pythonhosted.org/packages/68/32/33bc39ac705c52cffc908e8389f9754fdb208aea5c69cceddf4eb3ce99af/pydantic_core-2.46.5-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:79bdfa52f843137045b2d081cc05c120ba6665d29b7559c2c47690906f39279f", size = 2031975, upload-time = "2026-08-28T09:58:53.166Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/70/2333e885c0f6a67bc105c5916965dac9b57f2718ee20d81d1a06a4ebdc13/pydantic_core-2.46.5-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:24922243639cbdac66c75fcb6fd6495a9cb52b213d62f9a0d16f0310b1ff8038", size = 2208542, upload-time = "2026-08-28T09:58:55.017Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/ea/296debfb4264207bbda5936133892e027c0a58875ad53ebd512fba8ec3a2/pydantic_core-2.46.5-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c76fe65e607be28c7fd4d56fc3c42b1583aa058ce3408b7ad0fd540171d31f9f", size = 2264692, upload-time = "2026-08-28T09:58:56.767Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/f2/9e4de77a6271e07a76d2d58b11c091a979c191ed2939bf80067568b369d2/pydantic_core-2.46.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6f7b393a8b3da82f5c1fc0751e6d01ac6c55b93c18226a60bdfba4a724efafd1", size = 2066633, upload-time = "2026-08-28T09:58:58.531Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/db/f9e9d0c97445987b2084823d5c240de88087338f04fc2cfaa2df186b8049/pydantic_core-2.46.5-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:7ac031912d54f3d83ef3b3eb98dfabc1608802e2202263d25957eeed40b94761", size = 2105235, upload-time = "2026-08-28T09:59:00.421Z" },
+    { url = "https://files.pythonhosted.org/packages/07/c5/79169b047b3b2c3e99e04bc76372af9637e0bf6db638274fa927df96369e/pydantic_core-2.46.5-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:837b396ca3d7b74091ca623f6cbd8351bd42d670a79c2683e79fb089f06a2de5", size = 2157367, upload-time = "2026-08-28T09:59:02.442Z" },
+    { url = "https://files.pythonhosted.org/packages/26/b5/ba6057afb7c291bd449f51b867f95aef2072941c4ce4e5c31d6ffd132d3b/pydantic_core-2.46.5-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:5ee239d575f80b08eca11f6e20f90c4c695de7825c67eefe6091fbf20dda648e", size = 2158420, upload-time = "2026-08-28T09:59:04.2Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/28/2057abecaafdc22912afa819603a51f0a62d40643b7c4871c51721fea9be/pydantic_core-2.46.5-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:e80675d75ae2cd14372cb65cad5400d9347a3d3f6c13000183f22dfd027283ed", size = 2309588, upload-time = "2026-08-28T09:59:06.048Z" },
+    { url = "https://files.pythonhosted.org/packages/71/9d/881156dc404e27479c4246128d73538464cab4a239bec61995e227644c30/pydantic_core-2.46.5-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:9c4b71f10dd532fb7a5cbc8f58707779e64f03a258c2bf8bfbaecfcd9970b519", size = 2341866, upload-time = "2026-08-28T09:59:08.539Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/26/092079428f86e927e030b2c0ced87df69dbb1c875cdeaa67bf42ea2be746/pydantic_core-2.46.5-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5086029a57366b8cf81b130a43908738095c270c21a8d7f0e8bdfdb89718e2f3", size = 1952130, upload-time = "2026-08-28T09:59:20.476Z" },
+    { url = "https://files.pythonhosted.org/packages/08/c3/8ec0e290a9ebaebd64047bf5fda94be835c6b1551b02437e4b76778fbcd7/pydantic_core-2.46.5-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:46c25dda9d092a06c08db76ffe0a197107904d0dfac653f7d5306bbcd6d6119c", size = 2026371, upload-time = "2026-08-28T09:59:22.227Z" },
+    { url = "https://files.pythonhosted.org/packages/01/72/4fd20ad520fb8da0157f95b27a7eb05a72790ef08138e7701ac972c342ea/pydantic_core-2.46.5-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:37ea7b83c935e5b0d68c9449b82651accf78a10828b2c02b2f2d9e9496446c21", size = 2202822, upload-time = "2026-08-28T09:59:24.277Z" },
+    { url = "https://files.pythonhosted.org/packages/31/b0/d16e0771206b29314f0d52198b720be21e8a99ab2bf11e3bc0d7c9cebdff/pydantic_core-2.46.5-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e64e88d5585bea9ce95861079de72006c7fa6d3df4e3a3b65ba31eb979c15c9f", size = 2262756, upload-time = "2026-08-28T09:59:26.608Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/9b/59634b7ac631c63b2a37760eb6943af3e29573d6b59a4abc5e7f019d4cee/pydantic_core-2.46.5-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:54d510bac3ee52247af28ed4bb18a1e799f040ac60fd2bf5ccd4c92f1fbe786f", size = 2068352, upload-time = "2026-08-28T09:59:29.044Z" },
+    { url = "https://files.pythonhosted.org/packages/08/7c/570abb1ad2155348dc754ea91be22e5aaa18eb6d69a6068f7c6f2679a6ed/pydantic_core-2.46.5-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:a2a5e1d0ff29adddc9f6d6821a66302e4493f8ca898b715b6b1182c2c201ea0a", size = 2104777, upload-time = "2026-08-28T09:59:30.95Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/25/5bf74adc65a1ac5b7be3f6cb0bcb5433615c1598a801c19d830d84c98ded/pydantic_core-2.46.5-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:03b9666e41e35d8909852ba191a0607520f81b74eaf12ccf8737005dbb313821", size = 2156312, upload-time = "2026-08-28T09:59:32.604Z" },
+    { url = "https://files.pythonhosted.org/packages/90/6a/2ef38830675e050121040618135564ed56b860b45433b02d9b4ebece46f3/pydantic_core-2.46.5-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:a91c17edf6eea2402cb5457b4c89e99bc5ed1004aa34c4adf1d4258c1a5c22c2", size = 2150067, upload-time = "2026-08-28T09:59:34.453Z" },
+    { url = "https://files.pythonhosted.org/packages/90/ef/a7dbb03a14a64c2a4621f989c615ed9a892535a6cad938fc27079f919d80/pydantic_core-2.46.5-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:b49924c73a235e969511bf2aabdff3beebf9820931f646c80274d5d780010c47", size = 2304516, upload-time = "2026-08-28T09:59:36.194Z" },
+    { url = "https://files.pythonhosted.org/packages/68/f8/6bb4c4b80e8a6fde1904c64a51c62a1d04fcdfa3ea521a66b2ddefa1d885/pydantic_core-2.46.5-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:2cbd9a5eff05e51c447c34dfa4632145b26b09120cf04bd0c871e44c1a5e1c9a", size = 2335223, upload-time = "2026-08-28T09:59:37.931Z" },
+    { url = "https://files.pythonhosted.org/packages/52/a4/70b47c0509923dd98ccfed04fb3e32ea3849c82a0ff2205bb41009b43c00/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:976e1128455aa595ea04c79ccfedff1aaeab96ee013fcc916bed120c4f0ad94f", size = 1934216, upload-time = "2026-08-28T09:59:49.241Z" },
+    { url = "https://files.pythonhosted.org/packages/52/ab/aa03b65f7bb198585edf806b906c3223ecf1795543e39e23aec4cce27ad2/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e7b891faeedeafba41b2983e5001a81b6a915b69544c7e7570d1989ce1c36ac7", size = 2010635, upload-time = "2026-08-28T09:59:51.692Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/8b/0da06343f30b84ec549aafd309c6456223d5dc8bd36af504c573faad561d/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5f194189415698233dd1114a093a9b56e61e2c57e11b469be3b0506f46f0771c", size = 2209369, upload-time = "2026-08-28T09:59:53.582Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/5b/844c4defaa34a3df66eb9257087d121d70c201298b96abdf9f492fc2f1bf/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:82a36973cf8a2ef5406f4fe2edbf8ed0c99629535d959e0b100c76a32535a111", size = 2253238, upload-time = "2026-08-28T09:59:55.484Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/64/a4e536cb16d7f61a7fd3120b46c577fc7fa7325992f69c4f52bc786d77d8/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cdbb78909f52b981d3b2d56b97328d71eb0b974c36bd77c920123a7ebb192829", size = 2065740, upload-time = "2026-08-28T09:59:58.038Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/75/aaa38c6bc2d085f6605b34eabdc6a8a4e0b2e61fc9c8e6e52b28e97b3125/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:52e24eacdb536cade636aa90fb851835222becff8484b7001fdc78cb0290f2aa", size = 2087425, upload-time = "2026-08-28T09:59:59.898Z" },
+    { url = "https://files.pythonhosted.org/packages/55/ae/fcab4cfc39aba3689e1d20c8b5250ad280957022c09af2ed9cd585602a5e/pydantic_core-2.46.5-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:37ae34309d7bd8c0d61ab839668058f2a7962ea1fc51d105d2db228fe0618034", size = 2139306, upload-time = "2026-08-28T10:00:03.057Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/f4/f1d03a4bc9d9acbc62f4d742b8a319af52f71885079868b2ff8e48a651ee/pydantic_core-2.46.5-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:0cdbada856a1c69a7624a64d3d9aefe79300bd6ef827b43a4f265010b9b55184", size = 2144589, upload-time = "2026-08-28T10:00:05.645Z" },
+    { url = "https://files.pythonhosted.org/packages/83/f3/7a53bb1356de514a4cd295f25b6ac39237895620c0462d2592b76c16e114/pydantic_core-2.46.5-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:545f26c504b27c3758439a5e6d9349931f0a04f855668d5fe323c89e82300a38", size = 2288882, upload-time = "2026-08-28T10:00:07.931Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/94/5a81583660c175c59d49ffb09f4b3a44debeaf86a19fca664ae1cdd9ee32/pydantic_core-2.46.5-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:ff218293c9c806138dca139765e3b067621be52bcd93cdc14c7711be7ddc90a9", size = 2335210, upload-time = "2026-08-28T10:00:10.177Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/13/01d40f9d07ce8a779fd6e0bd8ad4fba91309500dd67b869e2e219d261a6d/pydantic_core-2.46.5-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:347ec774390c87326a2e4929d58d3f7e8763a104d5d35f4cd595a4c952366433", size = 1967479, upload-time = "2026-08-28T10:01:05.004Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/04/c81d4841331c2178b6fb09ae225425e110ed72d990c9fe556c4ec03d1013/pydantic_core-2.46.5-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e24d8f05fa2d28513d94e877e9c75ad66175376209b3977f916e240e623193c", size = 2111034, upload-time = "2026-08-28T10:01:07.345Z" },
 ]
 
 [[package]]
@@ -2923,43 +3127,44 @@ wheels = [
 
 [[package]]
 name = "pygments"
-version = "2.20.0"
+version = "2.21.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991, upload-time = "2026-03-29T13:29:33.898Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/49/2e/ced460408999b33da6b31b0021b0f37d329e202d4169aeb164493778f25b/pygments-2.21.0.tar.gz", hash = "sha256:610ca751c9bc2492b38eb9a38a7fbc93edbbb2d7182edaf34e66ae493dee5c8c", size = 5005329, upload-time = "2026-08-17T08:02:48.824Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151, upload-time = "2026-03-29T13:29:30.038Z" },
+    { url = "https://files.pythonhosted.org/packages/71/46/17f022dd3e953bf20a04a028a21ec746d942f8d2af30fa0f124fa0e6a684/pygments-2.21.0-py3-none-any.whl", hash = "sha256:2363c69b61c4a97c838da3b130dcd6468f4848992b21a82f2a63ec34377137d9", size = 1250147, upload-time = "2026-08-17T08:02:44.912Z" },
 ]
 
 [[package]]
 name = "pymaster"
-version = "2.6"
+version = "3.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "ducc0" },
+    { name = "fitsio" },
     { name = "healpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/95/45/470858f2a6a3f5133955bb87aed978a5e921d4052aea1c19718e6b72f0ae/pymaster-2.6.tar.gz", hash = "sha256:056a09bc8631cf338e93ef14d8c70272ad9554ef10498d919e920994531c57a2", size = 33707181, upload-time = "2025-12-16T09:18:28.905Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/89/fa/c6305346a0a4b4e79fde832b0ae84a53b7c002f9c3ab25cf9e43445fa4ee/pymaster-3.0.1.tar.gz", hash = "sha256:27ac1d544a35ef1f133b67b37f7d297d22512bc62216b01480bb66aa312a60b5", size = 41978594, upload-time = "2026-08-04T08:12:21.343Z" }
 
 [[package]]
 name = "pyopenssl"
-version = "26.3.0"
+version = "26.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cryptography" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/74/b7/da07bae88f5a9506b4def6f2f4903cf4c3b8831e560dba8fa18ca08f758f/pyopenssl-26.3.0.tar.gz", hash = "sha256:589de7fae1c9ea670d18422ed00fc04da787bbde8e1454aea872aa57b49ad341", size = 182024, upload-time = "2026-06-12T20:28:07.458Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3f/e8/7325d258199b159eb2c03fe32107533e2832e70e63f4fb88a6aa00023201/pyopenssl-26.4.0.tar.gz", hash = "sha256:28dfcce0162b9211413e26dfbfdf1d24317fbeba18fc93c12400a1856b2a0bc7", size = 182046, upload-time = "2026-08-01T19:50:50.512Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/54/18/1dd71c9b43192ab83f1d531ad6002dc81108ac36c475f79fb7a295abe2f4/pyopenssl-26.3.0-py3-none-any.whl", hash = "sha256:46367f8f66b92271e6d218da9c87607e1ef5a0bc5c8dea5bb3db82f395c385a3", size = 56008, upload-time = "2026-06-12T20:28:05.999Z" },
+    { url = "https://files.pythonhosted.org/packages/51/ad/2cf6d3fa2fae5c79e1ed9960c0d42badd0f94d81dd12b50604cdc839e648/pyopenssl-26.4.0-py3-none-any.whl", hash = "sha256:f0eb0cb2d581d3ad2b9c489468485e7f2ab6727d08401bcf9d824c3caddf3c1c", size = 56026, upload-time = "2026-08-01T19:50:48.94Z" },
 ]
 
 [[package]]
 name = "pyparsing"
-version = "3.3.2"
+version = "3.3.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f3/91/9c6ee907786a473bf81c5f53cf703ba0957b23ab84c264080fb5a450416f/pyparsing-3.3.2.tar.gz", hash = "sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc", size = 6851574, upload-time = "2026-01-21T03:57:59.36Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e4/11/b213bebff182584360cb8d17c72c1677fec5c5c228de439e63bcf8ab1c8f/pyparsing-3.3.3.tar.gz", hash = "sha256:928ae7e20211f3b6f3915a72f06a0cfd29ab9d24279dd6346b6b1a7146397d36", size = 1050487, upload-time = "2026-09-20T20:59:05.609Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" },
+    { url = "https://files.pythonhosted.org/packages/38/bb/d215ee7c73b61497b28a5503f9f53523f294fcc936762b7caf90e0c1c2b5/pyparsing-3.3.3-py3-none-any.whl", hash = "sha256:ece8c00a69cf01b45d0b1dedabb469c90d8caf996d4fda40f147627a122849a4", size = 126420, upload-time = "2026-09-20T20:59:04.025Z" },
 ]
 
 [[package]]
@@ -3005,20 +3210,20 @@ wheels = [
 
 [[package]]
 name = "python-json-logger"
-version = "4.1.0"
+version = "4.2.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f7/ff/3cc9165fd44106973cd7ac9facb674a65ed853494592541d339bdc9a30eb/python_json_logger-4.1.0.tar.gz", hash = "sha256:b396b9e3ed782b09ff9d6e4f1683d46c83ad0d35d2e407c09a9ebbf038f88195", size = 17573, upload-time = "2026-03-29T04:39:56.805Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/25/5473e46b179f8e8b4ad3aeeb36773d1701b7770eaf5e5bc2025c7303b598/python_json_logger-4.2.0.tar.gz", hash = "sha256:e371ebe22ec01e289850102091a2b1f6fc9e655c7f1f5f29073936756c290afa", size = 18211, upload-time = "2026-08-15T11:36:38.232Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/27/be/0631a861af4d1c875f096c07d34e9a63639560a717130e7a87cbc82b7e3f/python_json_logger-4.1.0-py3-none-any.whl", hash = "sha256:132994765cf75bf44554be9aa49b06ef2345d23661a96720262716438141b6b2", size = 15021, upload-time = "2026-03-29T04:39:55.266Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/55/6467fde553886cb293e41538f3a8b4e4fd4688c6df242cf982162d8367fb/python_json_logger-4.2.0-py3-none-any.whl", hash = "sha256:158a52126fcd6869e09574d2b66272666f3dc8f468c62637ef9a1fa883719cb9", size = 14988, upload-time = "2026-08-15T11:36:36.821Z" },
 ]
 
 [[package]]
 name = "pytz"
-version = "2026.2"
+version = "2026.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ff/46/dd499ec9038423421951e4fad73051febaa13d2df82b4064f87af8b8c0c3/pytz-2026.2.tar.gz", hash = "sha256:0e60b47b29f21574376f218fe21abc009894a2321ea16c6754f3cad6eb7cdd6a", size = 320861, upload-time = "2026-05-04T01:35:29.667Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b0/ed/fa23d28713004418bbf2407127f80f385bfb0bf4e677bef02c25c27b00ee/pytz-2026.4.tar.gz", hash = "sha256:464303645bafafd72418898368b2429458f709cf1eb6a15372fbcc396b64da63", size = 318444, upload-time = "2026-09-24T03:58:40.443Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ec/dd/96da98f892250475bdf2328112d7468abdd4acc7b902b6af23f4ed958ea0/pytz-2026.2-py2.py3-none-any.whl", hash = "sha256:04156e608bee23d3792fd45c94ae47fae1036688e75032eea2e3bf0323d1f126", size = 510141, upload-time = "2026-05-04T01:35:27.408Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/11/e3df59614bebe0ca50c905442603eda64ae7ac19811d4e4f74aa2d51ca85/pytz-2026.4-py2.py3-none-any.whl", hash = "sha256:9d514388fbc89ca0833203464272ac485b8828568ab73532f5020f17e892a0ff", size = 506747, upload-time = "2026-09-24T03:58:38.629Z" },
 ]
 
 [[package]]
@@ -3051,31 +3256,32 @@ wheels = [
 
 [[package]]
 name = "pyzmq"
-version = "27.1.0"
+version = "27.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cffi", marker = "implementation_name == 'pypy'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645, upload-time = "2025-09-08T23:08:05.301Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574, upload-time = "2025-09-08T23:08:06.828Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995, upload-time = "2025-09-08T23:08:08.396Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070, upload-time = "2025-09-08T23:08:09.989Z" },
-    { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121, upload-time = "2025-09-08T23:08:11.907Z" },
-    { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550, upload-time = "2025-09-08T23:08:13.513Z" },
-    { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275, upload-time = "2025-09-08T23:08:26.063Z" },
-    { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469, upload-time = "2025-09-08T23:08:27.623Z" },
-    { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961, upload-time = "2025-09-08T23:08:29.672Z" },
-    { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282, upload-time = "2025-09-08T23:08:31.349Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468, upload-time = "2025-09-08T23:08:33.543Z" },
-    { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394, upload-time = "2025-09-08T23:08:35.51Z" },
-    { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175, upload-time = "2025-09-08T23:08:46.601Z" },
-    { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427, upload-time = "2025-09-08T23:08:48.187Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929, upload-time = "2025-09-08T23:08:49.76Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193, upload-time = "2025-09-08T23:08:51.7Z" },
-    { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388, upload-time = "2025-09-08T23:08:53.393Z" },
-    { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316, upload-time = "2025-09-08T23:08:55.702Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/e7/8d/5b3d5631c2f4b4b8862f64cd0c9eb777b5710eeb5125b4be8dd0a200a4c0/pyzmq-27.2.0.tar.gz", hash = "sha256:54d4259d1bfae24ecdb5ca79f7acc2eac6c286a02d6a0ae617797cb45f0726d3", size = 292316, upload-time = "2026-08-20T19:08:21.19Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c8/ef/c08b91248bb90a9efa81fa00ba81b69c157c74d0c5efbb2c319d91babb62/pyzmq-27.2.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:00e73942ef12cecbc7951c4a9104bb8ffaed742abb13af2da6833d90dd368cef", size = 973915, upload-time = "2026-08-20T19:06:42.037Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/78/a3a3a86c2b00fadb92ece1ca4f8f028d62b2ce9ac3526097239ab2d6fba9/pyzmq-27.2.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f8079d0521fe94bbb401fe9407578b28f3701627c8be2c9f7e0c5b77dcb0109", size = 697722, upload-time = "2026-08-20T19:06:43.325Z" },
+    { url = "https://files.pythonhosted.org/packages/62/2c/d5828306f795e8d34676d266823b74e2101e0ad3760d12083de3e02abbb2/pyzmq-27.2.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dea74fd65f1fc5f7fe167916a473ebe6ed6174e5e5d9de11ea6583661be6cf43", size = 872258, upload-time = "2026-08-20T19:06:44.627Z" },
+    { url = "https://files.pythonhosted.org/packages/09/52/51253b78fd8739293e283407eeecb14215c02c71b6519af21f6eed8e69cd/pyzmq-27.2.0-cp312-abi3-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:dcc99ca132b667a4ed750afd42db4ea73288f18425a9b2e3c0af095665c491f5", size = 739591, upload-time = "2026-08-20T19:06:46.214Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/3e/142c85b67a4c9678629b0cf6d5125b29663d75be69bfaa57a3cac344d780/pyzmq-27.2.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:b8d5f66e4a8246cf77f7b8f7902af64f00553368fa0373c89d99b78f0ad79394", size = 1689031, upload-time = "2026-08-20T19:06:47.612Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/ee/0776fb0f98ed1eb74d77240087fef0ab045b6ad15cb09555c6c5134c98ad/pyzmq-27.2.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:d1526b42a2e725b84ed226f37becedc250c6347594e5ed304e4e9aff68c9aec3", size = 2059547, upload-time = "2026-08-20T19:06:49.064Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/0e/ec77f691a4aebe29ab6329f996fb0e0270c876a3016086e3ca6ef733bcae/pyzmq-27.2.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f707bcf2c1d007d14d70531d4dd7b41060881c73efa845580bf6faaf9ea24d42", size = 1910457, upload-time = "2026-08-20T19:06:50.783Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/b4/def7a478458da78665840564161772e7e938600c32a89f28e8b221b54d2d/pyzmq-27.2.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1ecbdd131b9669f62d3a45afee5527c7ae9f141e4301267f21714c90bd21725f", size = 975868, upload-time = "2026-08-20T19:07:04.155Z" },
+    { url = "https://files.pythonhosted.org/packages/38/d5/e3e85f7fea37153097aaff49db9e33093909cc2a7b22c1ac4ebe546600fc/pyzmq-27.2.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3146385b94a760236c5eceff468a66a296a716ca98a2e0f9217b1518118466b1", size = 706054, upload-time = "2026-08-20T19:07:05.623Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/ef/3b7d9449b223183222bf517245e1e53d5f1ab8c10be8b45f6a301b2f994a/pyzmq-27.2.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9846e881620dd62566ca76a53e384c3f37490faf4b9240aebc7498810dfca853", size = 878984, upload-time = "2026-08-20T19:07:07.153Z" },
+    { url = "https://files.pythonhosted.org/packages/be/a5/8b49dbd494f6dcfda69dc4cade322a4b02706ef4e3d30cc366d4e369899f/pyzmq-27.2.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d9527e3dbaef1edaeeb2446fa7379446814a43ade8adc7c4a5ebe69437815ddd", size = 1697489, upload-time = "2026-08-20T19:07:08.945Z" },
+    { url = "https://files.pythonhosted.org/packages/da/5a/4bb8280901130c26ea25f0cbb4a6d39d94250860c6b3dbd912f1cf48fca7/pyzmq-27.2.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:56b48fa9d478a3af7254f397697a62f5ad3e1bb677e200b2701f0c290d97e5af", size = 2064236, upload-time = "2026-08-20T19:07:10.384Z" },
+    { url = "https://files.pythonhosted.org/packages/de/38/f433af66922554adb2b5f79e897018c8e19a90b9eaeb49c4814f8355ebe4/pyzmq-27.2.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:bf0b6e4ce1bb089751c504c5493d6b0557eabd02dd21b76e9086cf964234b103", size = 1917424, upload-time = "2026-08-20T19:07:11.909Z" },
+    { url = "https://files.pythonhosted.org/packages/59/46/fb56f3f37a6a0937b0e1d2885e808b5eedc171320bac85573cfae78fa9bc/pyzmq-27.2.0-cp315-cp315t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:e0fa0bc6b1a184aee59b32efcd1b7f0e6d5b8f9387799e4c16a4cb66a86747d6", size = 976118, upload-time = "2026-08-20T19:07:22.577Z" },
+    { url = "https://files.pythonhosted.org/packages/21/82/a2c9bfd7c4d34eea1278493cd041bc000d41acb4463c89ceaad29dc813b6/pyzmq-27.2.0-cp315-cp315t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0f4bd6743e8bf854c3bfce892dd6578a514aabf128e37a4b2eafcf01856f7e44", size = 705968, upload-time = "2026-08-20T19:07:24.019Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/12/b906b269116b6591dc15c0acc5d04c043957c8a531d336999731f4b1d899/pyzmq-27.2.0-cp315-cp315t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:95369ed6626afcfe2ac89832fb1b917c077fbeb905fbbe5d918349ce0222b89b", size = 879011, upload-time = "2026-08-20T19:07:25.428Z" },
+    { url = "https://files.pythonhosted.org/packages/12/13/f96359534bfb77651c15f1fbfc4bfdd7ec3489d23f434706d39598dd0dcd/pyzmq-27.2.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:40124779c3a56ad5d91902df1ff89159cb414b6c1a0ee697abcc66cf5e6db62d", size = 1697496, upload-time = "2026-08-20T19:07:26.821Z" },
+    { url = "https://files.pythonhosted.org/packages/21/b4/2c007ae5f2fe5eca86cbfbc874ed86b5135f2f7812615dfd78606d3c93f6/pyzmq-27.2.0-cp315-cp315t-musllinux_1_2_i686.whl", hash = "sha256:ec8a318dfc27c7d946651b3d9e8025d5734f30c168a822195601827207bac09b", size = 2064347, upload-time = "2026-08-20T19:07:28.315Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/88/767af3a6630c15215f3a66700ec79598a375edd1fdc9d75a3ad522178c01/pyzmq-27.2.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:88c0fac061bac269076edeb3a209acefc96cd6167c239daf1c2b404ac48d7012", size = 1917360, upload-time = "2026-08-20T19:07:29.693Z" },
 ]
 
 [[package]]
@@ -3300,22 +3506,22 @@ wheels = [
 
 [[package]]
 name = "ruff"
-version = "0.15.21"
+version = "0.16.9"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0f/36/6f65aa9989acdec45d417192d8f4e7921931d8a6cf87ac74bce3eed98a8e/ruff-0.15.21.tar.gz", hash = "sha256:d0cfc841c572283c36548f82664a54ce6565567f1b0d5b4cf2caac693d8b7500", size = 4769401, upload-time = "2026-07-09T20:01:34.005Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/bf/c935ca98e73fe8ce65b87ef08a280c0c1e85295d569228c15e87d8fdfaf1/ruff-0.16.9.tar.gz", hash = "sha256:12b625c6cfba78d285d9f48eda5f053374f1e53cb10ef17342a383750db99161", size = 4948764, upload-time = "2026-09-24T20:37:49.416Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d0/c6/ede15cac6839f3dbce52565c8f5164a8210e669c7bc4decb03e5bdf47d0d/ruff-0.15.21-py3-none-linux_armv6l.whl", hash = "sha256:63ea0e965e5d73c90e95b2434beeafc70820536717f561b32ab6e777cb9bdf5d", size = 10854342, upload-time = "2026-07-09T20:00:53.998Z" },
-    { url = "https://files.pythonhosted.org/packages/9a/6f/b4523cc90ba239ede441447a19d0c968846a3012e5a0b0c5b62831a3d5e3/ruff-0.15.21-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:01d65b4831c6b2a4ba8ee6faa84049d44d982b7a706e622c4094c509e51673be", size = 10990053, upload-time = "2026-07-09T20:01:02.187Z" },
-    { url = "https://files.pythonhosted.org/packages/92/cc/c6a9872a5375f0628875481cf2f66b13d7d865bf3ca2e57f91c7e762d976/ruff-0.15.21-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2c5a913a589120ce67933d5d05fd6ddbcc2481c6a054980ee767f7414c72b4fd", size = 10666096, upload-time = "2026-07-09T20:01:04.299Z" },
-    { url = "https://files.pythonhosted.org/packages/ab/97/c621f7a17e097f1790fa3af6374138823b330b2d03fc38337945daca212c/ruff-0.15.21-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5ef04b681d02ad4dc9620f00f83ac5c22f652d0e9a9cfe431d219b16ad5ccc41", size = 11537011, upload-time = "2026-07-09T20:01:06.771Z" },
-    { url = "https://files.pythonhosted.org/packages/ea/51/d928727e476e25ccc57c6f449ffd80241a651a973ad949d39cfb2a771d28/ruff-0.15.21-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:16d090c0740916594157e75b80d666eab8e78083b39b3b0e1d698f4670a17b86", size = 12347101, upload-time = "2026-07-09T20:01:08.859Z" },
-    { url = "https://files.pythonhosted.org/packages/1e/88/8cd62026802b16018ad06931d87997cf795ba2a6239ab659606c87d96bf0/ruff-0.15.21-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3a10e74757dd65004d779b73e2f3c5210156d9980b41224d50d2ebcf1db51e67", size = 11572001, upload-time = "2026-07-09T20:01:11.092Z" },
-    { url = "https://files.pythonhosted.org/packages/b2/97/f63084cf55444fc110e8cb985ebfcc592af47f597d44453d778cb81bc156/ruff-0.15.21-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bab0905d2f29e0d9fbc3c373ed23db0095edaa3f71f1f4f519ec15134d9e85c8", size = 11549239, upload-time = "2026-07-09T20:01:13.27Z" },
-    { url = "https://files.pythonhosted.org/packages/9d/77/f107da4a2874b7715914b03f09ba9c54424de3ff8a1cc5d015d3ee2ce0ac/ruff-0.15.21-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:00eca240af5789fec6fe7df74c088cc1f9644ed83027113468efba7c92b94075", size = 11535340, upload-time = "2026-07-09T20:01:15.206Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/e9/601deb322d3303a7bf212b0100ead6f2ee3f6a044d89c30f2f92bf83c731/ruff-0.15.21-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:262ab31557a75141325e32d3357f3597645a7f084e732b6b054dde428ecd9341", size = 10964048, upload-time = "2026-07-09T20:01:17.723Z" },
-    { url = "https://files.pythonhosted.org/packages/ea/2e/0f2176d1e99c15192caea19c8c3a0a955246b4cb4de795042eeb616345cd/ruff-0.15.21-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:659c4e7a4212f83306045ec7c5e5a356d16d9a6ef4ae0c7a4d872914fc655d9d", size = 10667055, upload-time = "2026-07-09T20:01:19.73Z" },
-    { url = "https://files.pythonhosted.org/packages/48/60/abd74a02e0c4214f12a68becfd30af7165cfdcb0e661ecdc60bbb949c09a/ruff-0.15.21-py3-none-musllinux_1_2_i686.whl", hash = "sha256:9e866eab611a5f959d36df2d10e446973a3610bc42b0c15b31dc27977d59c233", size = 11242043, upload-time = "2026-07-09T20:01:21.947Z" },
-    { url = "https://files.pythonhosted.org/packages/b2/c6/583075d8ccabb4b229345edcaf1545eb3d8d6be90f686a479d7e94088bbf/ruff-0.15.21-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e89bc93c0d3803ba870b55c29671bad9dc6d94bb1eb181b056b52eb05b52854f", size = 11648064, upload-time = "2026-07-09T20:01:24.023Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/26/df51322b52ee1ada7eff2d071ea09d11d5c2d1dcc9f02594f5785c4d1635/ruff-0.16.9-py3-none-linux_armv6l.whl", hash = "sha256:95e6f022090368ab3b824c36276839c53b2adf1a3f4c09fefc33dfc400f6da96", size = 10082922, upload-time = "2026-09-24T20:37:13.045Z" },
+    { url = "https://files.pythonhosted.org/packages/24/58/98de1b72ec172f5f8f1731236fe21585b3998bf7dfe9fcc44ae9ba626012/ruff-0.16.9-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b3f951b14d865d5952c89d40a5ca07e87abe24fa5453299878411e127748fb1c", size = 10032114, upload-time = "2026-09-24T20:37:19.942Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/7d/f1e17c54ab59d4bad1dce8ee3e22a7a1d0ef4745240decacdcf3832b5bb2/ruff-0.16.9-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:447fc07e1573afff7cb02803462b12b6c8ece7cf10e2cd78565fa6d7a1c0bf8d", size = 9910227, upload-time = "2026-09-24T20:37:21.872Z" },
+    { url = "https://files.pythonhosted.org/packages/34/19/436f647a65075bbd3bab2668b3bdaa5120559b294694018cdcefabbbf30b/ruff-0.16.9-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8a3e039a6a40ed976c491722b60e0ae4a4aa1a86057f540ee7a37a5d19ae9120", size = 10547484, upload-time = "2026-09-24T20:37:24.229Z" },
+    { url = "https://files.pythonhosted.org/packages/03/59/38430a6bc2f6d8095447ac39625cf8b6e9344a47e6c26225c8ba1bff3ffb/ruff-0.16.9-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4684dded7db60aa57cb118fa158630f5feade4af5782903b6053484bdf9bd129", size = 11412367, upload-time = "2026-09-24T20:37:26.307Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/bd/bbb6d7fc7f208c8b8c50dd5dc8206e4cfdb1e7a8fb852606a3adf370c880/ruff-0.16.9-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d29c934357e45642fda2f34c0b1f4025b4a6c01e15e4bf0016879d60078a142c", size = 10869787, upload-time = "2026-09-24T20:37:28.35Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/b8/9c543074918061abbefc3bd139bee22de35abedb00dde0f2d27288838962/ruff-0.16.9-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a21713e629d3e5bdb2f5c2def1cc7f04f47fa8e1a7eb0571b4a28e1da64bc728", size = 10406494, upload-time = "2026-09-24T20:37:30.624Z" },
+    { url = "https://files.pythonhosted.org/packages/35/7a/5a8851bd146e7ccf8fd4b003f6c75c11f8fbdb0e60673b45097986b6bf41/ruff-0.16.9-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:7baa24ef5fc8e77aa93879e1d3f43754a01ae488e869f1ae30cf431afd4d2452", size = 10590083, upload-time = "2026-09-24T20:37:32.439Z" },
+    { url = "https://files.pythonhosted.org/packages/87/f0/4c3467188f23f806960b46fa76575a7cd0514c9ba90562650b71efc96980/ruff-0.16.9-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:a41aac6230aadfaa133bdfa1614488531ffa3e0837567ae04c0da2058a9c0f9e", size = 10119151, upload-time = "2026-09-24T20:37:34.581Z" },
+    { url = "https://files.pythonhosted.org/packages/15/34/5a4def5adea572ce6aea0bb64f21f928ee317b80e0db747d7979b01d7261/ruff-0.16.9-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:c2529fb5896d49115b0e9aa8f887490b34bbe76baf879ec2264ac59406869ce7", size = 9911544, upload-time = "2026-09-24T20:37:36.796Z" },
+    { url = "https://files.pythonhosted.org/packages/62/5d/d15ebea7499eef9373318c0ee6ca127832927c6529731f6d48e18dce7ca9/ruff-0.16.9-py3-none-musllinux_1_2_i686.whl", hash = "sha256:41e3870277694177429b56406d65dfbdb2c2802c52b715edaf6a0b829c69d4ee", size = 10269884, upload-time = "2026-09-24T20:37:38.857Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/56/c5d3cd119ded7a3c7aba0e961b69cb3df701c91662c98ad694467d060ce1/ruff-0.16.9-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:8adbe4e58af167f767d7b2ba5e83c42e878350796cf78c2f5e14ab9903a92588", size = 10749366, upload-time = "2026-09-24T20:37:41.042Z" },
 ]
 
 [[package]]
@@ -3334,7 +3540,7 @@ wheels = [
 
 [[package]]
 name = "scikit-learn"
-version = "1.9.0"
+version = "1.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "joblib" },
@@ -3343,47 +3549,55 @@ dependencies = [
     { name = "scipy" },
     { name = "threadpoolctl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/fa/6f/37092bdb25f712817231799fc5674d8e704066a8a70c1d2d40517e18b4ab/scikit_learn-1.9.0.tar.gz", hash = "sha256:8833266989d3a5110178a9fae30783675460724d0e1efb13b14901d2c660c557", size = 7750767, upload-time = "2026-06-02T11:54:32.706Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d2/eb/eaf5e07fcc0da7149b0e084f24e54edd7441b9a89ce7e034032ae97fe3a0/scikit_learn-1.9.1.tar.gz", hash = "sha256:629cada3e33e2b9bf376cdc7614a47a4140b8aedc1d836579e359736fbd82977", size = 7786908, upload-time = "2026-09-10T18:34:04.679Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a0/ee/5adbc77656b71f9456a2f5a7a9fdb4bcf9207a6b962889f1c2f9323afa4e/scikit_learn-1.9.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e50ed4da51974e86e940690e9a3d82e729b62b5a49f7c9bac534d515d39d86f", size = 8837603, upload-time = "2026-06-02T11:53:30.328Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/c2/63fdda36c56437eeb44aaf9493c8bcd62ce230ab1598924fc626ffbfa943/scikit_learn-1.9.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:056c92bb67ad4c28463c2f2653d9701449201e7e7a9e94e321be0f71c4fef2b8", size = 9132097, upload-time = "2026-06-02T11:53:33.456Z" },
-    { url = "https://files.pythonhosted.org/packages/0c/39/ffe829a5b8ecb40a518724a997794657fdc354ada5e8fe8e64d998c0bac9/scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:38c3dcb9a1ffb85505ec53d54c7b4aea0cff70050425a7760c2af661ac85df05", size = 8789690, upload-time = "2026-06-02T11:53:47.461Z" },
-    { url = "https://files.pythonhosted.org/packages/1f/88/8dab5de10c638c083772a6be83a3d8106ced492f74a928c8693638e5bb50/scikit_learn-1.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:da76d09304a4706db7cc1e3ebaa3b6b98a67365cc11d2996c4f1e58ba47df714", size = 9087723, upload-time = "2026-06-02T11:53:50.702Z" },
-    { url = "https://files.pythonhosted.org/packages/7d/79/f4a0c4fe9711154cddabf913471153af79056382ddc612cfe5ee0ff4b72e/scikit_learn-1.9.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5162ad10a418c8a282dde04c9aa06965de3e9a65f33c1440c0ae69bb1a09d913", size = 8847671, upload-time = "2026-06-02T11:54:04.448Z" },
-    { url = "https://files.pythonhosted.org/packages/f0/af/4d72d9e475ac83719160c662619e4bf7b95c19507cd582e7d0167a3c3dae/scikit_learn-1.9.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1fea2cc5677ab49d6f5bade978c866da44957b712d92e9635e8b4f723013c3cb", size = 9118104, upload-time = "2026-06-02T11:54:07.205Z" },
-    { url = "https://files.pythonhosted.org/packages/18/3c/e440e039bb82cd19004edaaad00acbde0fb9b461083c3ecf37941c557312/scikit_learn-1.9.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:147e9329ef0e39f75d4cffa02b2aa48d827832684926cd5210d9a2cb5c57246b", size = 8855050, upload-time = "2026-06-02T11:54:21.699Z" },
-    { url = "https://files.pythonhosted.org/packages/43/26/b341b8dab5998da6270a3a42c2152c578501354d36f944b5856757035ef8/scikit_learn-1.9.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bad8f8b9950321b54c965fdcbac6c6c55e79e16646b49977bcf3668d3870a1a", size = 9097190, upload-time = "2026-06-02T11:54:24.454Z" },
+    { url = "https://files.pythonhosted.org/packages/78/2b/5721a174406bfba49bce20ae997b3b64cf355c3f623a2638284ab6a82156/scikit_learn-1.9.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4c20a6c017d820faa7ac8c783e3d0c6a9a2e297bf9f55332ca17cdf7fd4d04d", size = 8871592, upload-time = "2026-09-10T18:32:46.999Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/57/a50162f3d29feb979ab6347c6debda506dfb525bcff3c50dd17606651c7e/scikit_learn-1.9.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e5d7b18a5b9dca241a74695f3275fa4c895a9dadc72b3d8df5fa9d1083c9b83e", size = 9166043, upload-time = "2026-09-10T18:32:49.343Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/35/a15b8653499692879821301d48059376d6e68e8b65cd0f22d19b6ee83cd9/scikit_learn-1.9.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5990f9c69e431bfaddcde1a6d7c5355243e026bc9b9e560c13893b90dab53fb4", size = 8823698, upload-time = "2026-09-10T18:33:00.632Z" },
+    { url = "https://files.pythonhosted.org/packages/23/e5/688703d357e5393f708d98eb189fd415ae69e39f6de03c6bd4005aef6118/scikit_learn-1.9.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:55e79d6e9b0923f1a978179822bd43d7f5543f45e970a00fe861f43486380aba", size = 9121732, upload-time = "2026-09-10T18:33:02.825Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/6c/06c7eb61a438e389cbf3f7210897069bec5a883dbe03c189ae792781e11a/scikit_learn-1.9.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5492cf2df5226691c32611de8734bcf42148c6547ae53c7f4e6b847793addc0", size = 8881706, upload-time = "2026-09-10T18:33:14.741Z" },
+    { url = "https://files.pythonhosted.org/packages/86/4e/0bab75490ca4b85fad8388739c7ebc71d9db553f8c69e39943ee8db0aaae/scikit_learn-1.9.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:993d332ff80e62efae9e39603b7e872297c418d780f01a01855269a3489c950f", size = 9152030, upload-time = "2026-09-10T18:33:17.436Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/04/9c15d201e1b6a2e81b8215865df7646c5a360f560831769c8dc92ac1ab9a/scikit_learn-1.9.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:66f852f7325b5070bc28329005aca76055a2def78faac039548ae889aeaa45a6", size = 8889070, upload-time = "2026-09-10T18:33:28.95Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/5a/4cb6c85160af4a639e87a3b7bf8b1c25cfc3b504c5af710ca416a6dcfc5f/scikit_learn-1.9.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:748bcb0a4cc04aec470652c9e5ec68450948e867387e7dfade647107ade68d25", size = 9131227, upload-time = "2026-09-10T18:33:31.008Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/61/0c6080f0d356fb966053009e7f25e9bff6cf74b0b16195ccf0c3757d1ae8/scikit_learn-1.9.1-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8ca869d0080a5723cde2d5a8b54a2da1ff7e68735a9e9adb3da1243183a0fa01", size = 8881917, upload-time = "2026-09-10T18:33:41.912Z" },
+    { url = "https://files.pythonhosted.org/packages/47/bb/98a31f10fffbd39edcc2f8bf4119b29652248bb110b7d45c84e68aa293ab/scikit_learn-1.9.1-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6754b7cabfc3df0b1f7b38f7a344f559bbae9d82f0ac5e3d48cccbd19fdcefdf", size = 9152805, upload-time = "2026-09-10T18:33:44.23Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/cc/687ae4214c2f598906c3b9fa5f86fbaf834b35e20360625528ff1b713f06/scikit_learn-1.9.1-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b3da53831534214322d9cb240fa6f390b36cf69eba727a6d4bd3238677630d70", size = 8880366, upload-time = "2026-09-10T18:33:55.764Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/03/82215cb78ad1c513a4498777571fb28444621ad26ef636287551767b7732/scikit_learn-1.9.1-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:caae15634feceafa2612566b109a3082d3293167fac388eedaf77bff66b51983", size = 9129640, upload-time = "2026-09-10T18:33:58.186Z" },
 ]
 
 [[package]]
 name = "scipy"
-version = "1.17.1"
+version = "1.18.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822, upload-time = "2026-02-23T00:26:24.851Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/da/34/16f10e3042d2f1d6b66e0428308ab52224b6a23049cb2f5c1756f713815f/scipy-1.17.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21", size = 32927842, upload-time = "2026-02-23T00:18:35.367Z" },
-    { url = "https://files.pythonhosted.org/packages/01/8e/1e35281b8ab6d5d72ebe9911edcdffa3f36b04ed9d51dec6dd140396e220/scipy-1.17.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458", size = 35235890, upload-time = "2026-02-23T00:18:49.188Z" },
-    { url = "https://files.pythonhosted.org/packages/c5/5c/9d7f4c88bea6e0d5a4f1bc0506a53a00e9fcb198de372bfe4d3652cef482/scipy-1.17.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb", size = 35003557, upload-time = "2026-02-23T00:18:54.74Z" },
-    { url = "https://files.pythonhosted.org/packages/65/94/7698add8f276dbab7a9de9fb6b0e02fc13ee61d51c7c3f85ac28b65e1239/scipy-1.17.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea", size = 37625856, upload-time = "2026-02-23T00:19:00.307Z" },
-    { url = "https://files.pythonhosted.org/packages/b4/e0/e58fbde4a1a594c8be8114eb4aac1a55bcd6587047efc18a61eb1f5c0d30/scipy-1.17.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6", size = 32896429, upload-time = "2026-02-23T00:19:35.536Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/5f/f17563f28ff03c7b6799c50d01d5d856a1d55f2676f537ca8d28c7f627cd/scipy-1.17.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464", size = 35203952, upload-time = "2026-02-23T00:19:42.259Z" },
-    { url = "https://files.pythonhosted.org/packages/8d/a5/9afd17de24f657fdfe4df9a3f1ea049b39aef7c06000c13db1530d81ccca/scipy-1.17.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950", size = 34979063, upload-time = "2026-02-23T00:19:47.547Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/13/88b1d2384b424bf7c924f2038c1c409f8d88bb2a8d49d097861dd64a57b2/scipy-1.17.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369", size = 37598449, upload-time = "2026-02-23T00:19:53.238Z" },
-    { url = "https://files.pythonhosted.org/packages/6d/a0/3cb6f4d2fb3e17428ad2880333cac878909ad1a89f678527b5328b93c1d4/scipy-1.17.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e", size = 33019667, upload-time = "2026-02-23T00:20:17.208Z" },
-    { url = "https://files.pythonhosted.org/packages/f3/c3/2d834a5ac7bf3a0c806ad1508efc02dda3c8c61472a56132d7894c312dea/scipy-1.17.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475", size = 35264159, upload-time = "2026-02-23T00:20:23.087Z" },
-    { url = "https://files.pythonhosted.org/packages/4d/77/d3ed4becfdbd217c52062fafe35a72388d1bd82c2d0ba5ca19d6fcc93e11/scipy-1.17.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50", size = 35102771, upload-time = "2026-02-23T00:20:28.636Z" },
-    { url = "https://files.pythonhosted.org/packages/bd/12/d19da97efde68ca1ee5538bb261d5d2c062f0c055575128f11a2730e3ac1/scipy-1.17.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca", size = 37665910, upload-time = "2026-02-23T00:20:34.743Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/f2/7cdb8eb308a1a6ae1e19f945913c82c23c0c442a462a46480ce487fdc0ac/scipy-1.17.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350", size = 32957007, upload-time = "2026-02-23T00:21:19.663Z" },
-    { url = "https://files.pythonhosted.org/packages/0b/2e/7eea398450457ecb54e18e9d10110993fa65561c4f3add5e8eccd2b9cd41/scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118", size = 35221333, upload-time = "2026-02-23T00:21:25.278Z" },
-    { url = "https://files.pythonhosted.org/packages/d9/77/5b8509d03b77f093a0d52e606d3c4f79e8b06d1d38c441dacb1e26cacf46/scipy-1.17.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068", size = 35042066, upload-time = "2026-02-23T00:21:31.358Z" },
-    { url = "https://files.pythonhosted.org/packages/f9/df/18f80fb99df40b4070328d5ae5c596f2f00fffb50167e31439e932f29e7d/scipy-1.17.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118", size = 37612763, upload-time = "2026-02-23T00:21:37.247Z" },
-    { url = "https://files.pythonhosted.org/packages/86/f1/3383beb9b5d0dbddd030335bf8a8b32d4317185efe495374f134d8be6cce/scipy-1.17.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad", size = 33030397, upload-time = "2026-02-23T00:22:01.404Z" },
-    { url = "https://files.pythonhosted.org/packages/41/68/8f21e8a65a5a03f25a79165ec9d2b28c00e66dc80546cf5eb803aeeff35b/scipy-1.17.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a", size = 35281163, upload-time = "2026-02-23T00:22:07.024Z" },
-    { url = "https://files.pythonhosted.org/packages/84/8d/c8a5e19479554007a5632ed7529e665c315ae7492b4f946b0deb39870e39/scipy-1.17.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4", size = 35116291, upload-time = "2026-02-23T00:22:12.585Z" },
-    { url = "https://files.pythonhosted.org/packages/52/52/e57eceff0e342a1f50e274264ed47497b59e6a4e3118808ee58ddda7b74a/scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2", size = 37682317, upload-time = "2026-02-23T00:22:18.513Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/7e/74/66de6258867beb2ef08f35f9f2ac017a52cacd5081714d239ff1a442d458/scipy-1.18.1.tar.gz", hash = "sha256:52c4b7422442aba924d03ad4019852b08a92e64ea187b933135687bfe2747307", size = 30781235, upload-time = "2026-08-21T23:28:50.599Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/75/9a/2e71719f31eaefe0e3a1706c4a1ded94e664bfd95ffca2b219a671faee01/scipy-1.18.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c085faa2cfa879c5141df483f836f4d691045a078224a670fa570fa01612d89", size = 34025113, upload-time = "2026-08-21T23:24:02.209Z" },
+    { url = "https://files.pythonhosted.org/packages/df/64/ff35eb9e54894cf471ff4716abd3c81eb0a0626869217ce3e6ba4ccf17d7/scipy-1.18.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f55fa87b6c612ecd6b058f167c53231b1d14e412efe361d3d6e38b3631c73218", size = 35344199, upload-time = "2026-08-21T23:24:07.844Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/af/c5538be1792f7034c12c7db6ee67cace58253c7b87b122d68253eaf5de89/scipy-1.18.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c35d74ce0e193ff740c2f2be2ac913ddc232fe6c1ff40b26cfecb9c670c63314", size = 35639587, upload-time = "2026-08-21T23:24:13.05Z" },
+    { url = "https://files.pythonhosted.org/packages/91/4c/075e4f66471bac101141ac739e9e135549be1bae584571bd03a530c056e1/scipy-1.18.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:d2924a03db38dc2e848bca2fe9f077dafb891480b91a00a0963a8cf86dfc31c1", size = 37480330, upload-time = "2026-08-21T23:24:19.608Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/5f/4634e9d35c68496e4e34cb6946eafab044458e6cedab42b40b6588e475b6/scipy-1.18.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d416b16cccfd70fbf62400e84d0bb2f4e6af519a45557f1692c749b37f14b315", size = 33998146, upload-time = "2026-08-21T23:24:54.714Z" },
+    { url = "https://files.pythonhosted.org/packages/41/48/6450ed9243315322bbc19ac57b9b70d66a20bf1d38d124c96bc4bf6af9ea/scipy-1.18.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fdaf5ea890a6183d0565f51a61799d67081bd5b1cf03c5f4b3fd3732108625c9", size = 35312578, upload-time = "2026-08-21T23:25:00.44Z" },
+    { url = "https://files.pythonhosted.org/packages/00/bd/bf5a4be6a3525676499f6dff307991739ff6fdcad1481b1aeb6745339f58/scipy-1.18.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c825cef2f49e46753726a7181a8e199804a912b29519ada542c6ebc654951899", size = 35612621, upload-time = "2026-08-21T23:25:06.144Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/4e/3c45c33e00a77996c4b1cb707929f833ba7b1d522ee29f882512c330676d/scipy-1.18.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e3b417bf8c2c7c16e8f58ad91db17783ec911ac16e7b50eb6eab6e809b4f5b07", size = 37457323, upload-time = "2026-08-21T23:25:12.483Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/56/030af62bea3cf878e0028515dff78c123b01633606a879b63f42d2db99cc/scipy-1.18.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2d3ab0e8c69a17dd3559eab8cbb88f258e285c94d572c2719033f90f83290c89", size = 34053851, upload-time = "2026-08-21T23:25:47.998Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/89/2a844506d49651e9aa1af6ef95b6bd8031cb1d5a4375edec6155037e04cf/scipy-1.18.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ac0333bdf38309aa3dcbe7e3fa7ea29e7a2c37c6ea306a757b700ded8e4596ad", size = 35329183, upload-time = "2026-08-21T23:25:53.522Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/56/c7370c3640e92ac9613cbf26cb3f729f9b12ddf1727b55b94b53b24d6f48/scipy-1.18.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:911de823097db8b63f034299d12662db93344e6ffa0b881cbb57748974b70168", size = 35672551, upload-time = "2026-08-21T23:25:59.387Z" },
+    { url = "https://files.pythonhosted.org/packages/24/16/ec8536f351421f8bf60a1120930638f83790f4710b8230446aca3d6159d4/scipy-1.18.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:95298364e251be3e60249facbeeca03631d3bb7584f85879516ec55ac717b81f", size = 37469416, upload-time = "2026-08-21T23:26:05.432Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/d1/db0948da8ca57a80b36520ef0a768b967d99f3af65f4b6f1bf6362ad4dd4/scipy-1.18.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cfbf154f2ba187f2ed6cce2639efff7d105f1140573642c0161615b6d91d6a87", size = 34143202, upload-time = "2026-08-21T23:26:40.4Z" },
+    { url = "https://files.pythonhosted.org/packages/87/53/39d046cc7574ed6acacb6bd5723e220107ece80bff12faaf3efc4ddeede4/scipy-1.18.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a1d33a7836f7ddc1993427966a0823468ec41bcbdb1a9f9942d1d7e57f803ba3", size = 35380876, upload-time = "2026-08-21T23:26:46.1Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/da/32e0e799d875a85ca57d9bde6c78148afcc0e38276df683d95854eadc8c3/scipy-1.18.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7f4b8bc363b6d65ee2152bec57568e3c52639bb34c46057b09857a307ed5e21d", size = 35770885, upload-time = "2026-08-21T23:26:51.533Z" },
+    { url = "https://files.pythonhosted.org/packages/88/2e/f97a666d362fee68b18f41c9c30ed502ca5c98b549749bfcb52a8b74d1eb/scipy-1.18.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:11c423f1049c5755ad4409af52a9ada1cff96fe9b50795d4af3619f292901239", size = 37525424, upload-time = "2026-08-21T23:26:56.751Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/db/30992f9b51a63de671daf3888ffd18378b6cb9ec9f2c972264238ffa7fd6/scipy-1.18.1-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:82f201b4c878551d48558337aab270d3c6cca5507b8737c8d8a608d234cccde0", size = 34054035, upload-time = "2026-08-21T23:27:29.409Z" },
+    { url = "https://files.pythonhosted.org/packages/91/d4/bf3e735dc0b9d5a8ff45079d2540e17d3aff7a2f0048dd8f552ffd031d2b/scipy-1.18.1-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0ac49ea97594532dd44b7136094d35f5440fa06e6d9c6384a74c01764df388c5", size = 35333883, upload-time = "2026-08-21T23:27:34.293Z" },
+    { url = "https://files.pythonhosted.org/packages/19/93/12d78ce9f871fe945fca588d32644e6e63f553c2a35c564d73f3b22a3313/scipy-1.18.1-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:ceb30a00ce7c92d459819443d29ca486d882b83fb6738bdcbb2a1cce94ac5daa", size = 35673124, upload-time = "2026-08-21T23:27:39.059Z" },
+    { url = "https://files.pythonhosted.org/packages/70/cd/886219313a1012a48e6ae0ec4f302c837151beb92e1ff0d709ef8fdfc488/scipy-1.18.1-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:f29633129f9fa7e88a3f0fca835de2d030bfc9643f7799e1a0c46cee24d38fc7", size = 37470753, upload-time = "2026-08-21T23:27:44.435Z" },
+    { url = "https://files.pythonhosted.org/packages/89/31/cc91623fa98f0621766a0f0aaaadb2c66de74a7ea7e3837164f6e4354260/scipy-1.18.1-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:33a834464fdabc0f26a45508df31b3cc5d028e04dbf6c5ed398541418e0a12fe", size = 34130796, upload-time = "2026-08-21T23:28:17.906Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/3e/8572ef536957ddb8aa81bb4090d9e25f257e3b4e05d97deb54319deb8a3a/scipy-1.18.1-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:49023963c193dacee096301452f223ee24d86ec5807f8df93c0f7221d119e305", size = 35374253, upload-time = "2026-08-21T23:28:23.732Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/c6/59fdeffb4f1435299f93d9dc8140b43ad2916e6cfc944be6c3041fcec86d/scipy-1.18.1-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:d84a09d0dad90ba6525d8ac1c2334b33e64bf3ccfe9e841f02feb867a22681e4", size = 35758543, upload-time = "2026-08-21T23:28:29.431Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/d9/135be205d9de8783193aff9cc3bf483a03a38e4b29432c954e8cb66ac14e/scipy-1.18.1-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:179ce34a8d0fe273d8883ba59e17e052247d08973dfcb743ca52bb1cce2d60b0", size = 37521946, upload-time = "2026-08-21T23:28:35.245Z" },
 ]
 
 [[package]]
@@ -3424,11 +3638,11 @@ wheels = [
 
 [[package]]
 name = "setuptools"
-version = "83.0.0"
+version = "84.0.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/34/26/f5d29e25ffdb535afef2d35cdb55b325298f96debd670da4c325e08d70f4/setuptools-83.0.0.tar.gz", hash = "sha256:025bccbbf0fa05b6192bc64ae1e7b16e001fd6d6d4d5de03c97b1c1ade523bef", size = 1154254, upload-time = "2026-07-04T15:31:22.699Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6d/44/f5da03a8ef95d369145c5bb53050e7877c9f3d312e128605fd9504829143/setuptools-84.0.0.tar.gz", hash = "sha256:f4695c21257f0d9b537ec2692c941d02ee143b7cc1276941349a546573b2ef73", size = 1168449, upload-time = "2026-08-08T18:27:58.365Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5d/40/e1e72872c6354b306daef1703549e8e83b4d43cfea356311bf722a043752/setuptools-83.0.0-py3-none-any.whl", hash = "sha256:29b23c360f22f414dc7336bb39178cc7bcbf6021ed2733cde173f09dba19abb3", size = 1008090, upload-time = "2026-07-04T15:31:20.885Z" },
+    { url = "https://files.pythonhosted.org/packages/95/9c/c510029fc6ef33a6275cd2c5d3cecd6613dfd6aa401d57c54f1c18852ccf/setuptools-84.0.0-py3-none-any.whl", hash = "sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670", size = 818216, upload-time = "2026-08-08T18:27:56.719Z" },
 ]
 
 [[package]]
@@ -3459,7 +3673,7 @@ wheels = [
 
 [[package]]
 name = "skyproj"
-version = "2.5.0"
+version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "astropy" },
@@ -3468,10 +3682,11 @@ dependencies = [
     { name = "matplotlib" },
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/90/cf/5158151ae6fc60459f430451b612a4bf2da07ebff2a9f52a37e73715a5ee/skyproj-2.5.0.tar.gz", hash = "sha256:cb8d5115927ca43cacdb6d92f00bb4fcc8563ee71028f755203ce1752e67c140", size = 8521587, upload-time = "2026-06-26T19:51:15.378Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/65/f189c8f7f016da818b946d90f4c7013c361b07a46f926f159b4c3bc88af7/skyproj-2.6.1.tar.gz", hash = "sha256:0f41695c8c32b0773049e7c2c2e4f6f17afe28dfa7b1c997e35f036c45d8900f", size = 9070667, upload-time = "2026-08-24T23:40:37.288Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a8/b1/1d86ee4ae4ae7a47e31cc770dc83057388ef16311498283ca90046b3bd04/skyproj-2.5.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4cbfcbd4d3d600557ab19bb5cf79b750ec552da054e3be781bce88e996b9955d", size = 4146686, upload-time = "2026-06-26T19:51:04.011Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/e9/f2dd235dcb9f6c3b2fec36ef04d7dd36aef4daf9afc4ab4390d6230c96dc/skyproj-2.5.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:78618084c9362b62ddbfd336ca1864c94d99e2a1f26d2fbf3466fd19dc702c58", size = 4146630, upload-time = "2026-06-26T19:51:10.552Z" },
+    { url = "https://files.pythonhosted.org/packages/90/d5/903124b2ae753257c86fd40ee954338c623f740d1fb47c6363f574490c6f/skyproj-2.6.1-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:a7fb949d7c80ff102dd0bcc1a24fa2761ae31fc1b03e39625ab015e350442186", size = 4343346, upload-time = "2026-08-24T23:40:20.257Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/0b/4a5de8fdbd92c57ae7f71134ba33edd912d4de605cbac87f2856b91b773b/skyproj-2.6.1-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:480b21b2e2ede4cee04f032104a83b889544f56563da86fcb8cee68569307c87", size = 4343543, upload-time = "2026-08-24T23:40:26.875Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/55/23d3f7a48a2c75fe20100022a5b74fbd29bdc927900a169e9b9de8430fe9/skyproj-2.6.1-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9629114a768abe4a40f497eade86417c3e55a94a2e47010da0e81e54c229826a", size = 4343668, upload-time = "2026-08-24T23:40:32.691Z" },
 ]
 
 [[package]]
@@ -3509,11 +3724,11 @@ wheels = [
 
 [[package]]
 name = "soupsieve"
-version = "2.8.4"
+version = "2.10"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/47/2c/0a5f6f8ee0d5589e48c7640213ed5175d52cf540a06725b628cc1a45d6ce/soupsieve-2.8.4.tar.gz", hash = "sha256:e121fd02e975c695e4e9e8774a5ee35d74714b59307868dcc5319ad2d9e3328e", size = 121110, upload-time = "2026-05-24T13:55:57.154Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/71/c3/1b817965ac12dc002d7c9cd7dfffdd7d4fbf9b45763ef2ffe7b86ee94670/soupsieve-2.10.tar.gz", hash = "sha256:49e9380d7d2905463583bafe285e818c7366a9ed7b3aee221c1ac79c905d8bc0", size = 124337, upload-time = "2026-09-24T02:36:35.048Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5e/f5/0c41cb68dcae6b7de4fac4188a3a9589e21fb31df21ea3a2e888db95e6c9/soupsieve-2.8.4-py3-none-any.whl", hash = "sha256:e7e6b0769c8f51ed59acab6e994b00621096cfb1c640a7509295987388fbaf65", size = 37304, upload-time = "2026-05-24T13:55:55.406Z" },
+    { url = "https://files.pythonhosted.org/packages/66/87/5ed59e1d0290564e2027ed066c52b059892c4637741337fc0967183c8d4d/soupsieve-2.10-py3-none-any.whl", hash = "sha256:8596eb8967d744174820280fa62b4542a2e955bfaccca73ed8a13c6eb8e9b502", size = 38019, upload-time = "2026-09-24T02:36:33.709Z" },
 ]
 
 [[package]]
@@ -3786,48 +4001,59 @@ wheels = [
 
 [[package]]
 name = "statsmodels"
-version = "0.14.6"
+version = "0.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
+    { name = "formulaic" },
     { name = "numpy" },
     { name = "packaging" },
     { name = "pandas" },
     { name = "patsy" },
     { name = "scipy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0d/81/e8d74b34f85285f7335d30c5e3c2d7c0346997af9f3debf9a0a9a63de184/statsmodels-0.14.6.tar.gz", hash = "sha256:4d17873d3e607d398b85126cd4ed7aad89e4e9d89fc744cdab1af3189a996c2a", size = 20689085, upload-time = "2025-12-05T23:08:39.522Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/48/f5/3a73b51e6450c31652c53a8e12e24eac64e3824be816c0c2316e7dbdcb7d/statsmodels-0.14.6-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d8c00a42863e4f4733ac9d078bbfad816249c01451740e6f5053ecc7db6d6368", size = 10058649, upload-time = "2025-12-05T23:10:12.775Z" },
-    { url = "https://files.pythonhosted.org/packages/81/68/dddd76117df2ef14c943c6bbb6618be5c9401280046f4ddfc9fb4596a1b8/statsmodels-0.14.6-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:19b58cf7474aa9e7e3b0771a66537148b2df9b5884fbf156096c0e6c1ff0469d", size = 10339446, upload-time = "2025-12-05T23:10:28.503Z" },
-    { url = "https://files.pythonhosted.org/packages/56/4a/dce451c74c4050535fac1ec0c14b80706d8fc134c9da22db3c8a0ec62c33/statsmodels-0.14.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:81e7dcc5e9587f2567e52deaff5220b175bf2f648951549eae5fc9383b62bc37", size = 10368705, upload-time = "2025-12-05T23:10:44.339Z" },
-    { url = "https://files.pythonhosted.org/packages/ee/77/0ec96803eba444efd75dba32f2ef88765ae3e8f567d276805391ec2c98c6/statsmodels-0.14.6-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:89ee7d595f5939cc20bf946faedcb5137d975f03ae080f300ebb4398f16a5bd4", size = 10060269, upload-time = "2025-12-05T23:11:46.338Z" },
-    { url = "https://files.pythonhosted.org/packages/10/b9/fd41f1f6af13a1a1212a06bb377b17762feaa6d656947bf666f76300fc05/statsmodels-0.14.6-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:730f3297b26749b216a06e4327fe0be59b8d05f7d594fb6caff4287b69654589", size = 10324155, upload-time = "2025-12-05T23:12:01.805Z" },
-    { url = "https://files.pythonhosted.org/packages/ee/0f/a6900e220abd2c69cd0a07e3ad26c71984be6061415a60e0f17b152ecf08/statsmodels-0.14.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f1c08befa85e93acc992b72a390ddb7bd876190f1360e61d10cf43833463bc9c", size = 10349765, upload-time = "2025-12-05T23:12:18.018Z" },
-    { url = "https://files.pythonhosted.org/packages/c1/98/b0dfb4f542b2033a3341aa5f1bdd97024230a4ad3670c5b0839d54e3dcab/statsmodels-0.14.6-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e443e7077a6e2d3faeea72f5a92c9f12c63722686eb80bb40a0f04e4a7e267ad", size = 10090802, upload-time = "2025-12-05T23:13:20.653Z" },
-    { url = "https://files.pythonhosted.org/packages/34/0e/2408735aca9e764643196212f9069912100151414dd617d39ffc72d77eee/statsmodels-0.14.6-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3414e40c073d725007a6603a18247ab7af3467e1af4a5e5a24e4c27bc26673b4", size = 10337587, upload-time = "2025-12-05T23:13:37.597Z" },
-    { url = "https://files.pythonhosted.org/packages/0f/36/4d44f7035ab3c0b2b6a4c4ebb98dedf36246ccbc1b3e2f51ebcd7ac83abb/statsmodels-0.14.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a518d3f9889ef920116f9fa56d0338069e110f823926356946dae83bc9e33e19", size = 10363350, upload-time = "2025-12-05T23:13:53.08Z" },
+sdist = { url = "https://files.pythonhosted.org/packages/a8/1f/a3cbf6ed7cf6286afec694bfef561f3e51483197716717fb8476f1f0c50e/statsmodels-0.15.0.tar.gz", hash = "sha256:5d257fe58d0772bc46a557880ca78e2a8e07fec7bfd9d11074aef8e33e1aecbc", size = 15736853, upload-time = "2026-08-27T10:45:21.988Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f0/a5/0e7e87e0dedc68bfeecc24bea92876023db09bac1c0fe50584a7d0819fb5/statsmodels-0.15.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4f90dc4989bd82837c80059fdce132976d4e5a80100119d16e1b00537eb55867", size = 11747428, upload-time = "2026-08-27T14:32:28.987Z" },
+    { url = "https://files.pythonhosted.org/packages/41/c7/aeadca282baf51d7b9c135674bac79ded91f4db79c69c188575d22234342/statsmodels-0.15.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c6f2bd48915cc9b3b95c3c7f6ac363b6f8aaa38172962ba5f7f6885f0dfe57f2", size = 12042643, upload-time = "2026-08-27T14:33:06.022Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/44/7d3eb3836b3c054280baca7ec91bd12858825de437c6319c1212c2531d94/statsmodels-0.15.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:733887a09ff878a2e29504eda684488b26bd134ba3539ac58d2136a58914c65a", size = 12079497, upload-time = "2026-08-27T14:33:47.147Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/f4/482701d2dc7540897b13ead827cf8499b11c7325753b8cc4ff1a82c87a18/statsmodels-0.15.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5a7ab98634d47048c6b7c168b3f8f71369e256d7314bc386168526f34c7699d7", size = 11752604, upload-time = "2026-08-27T10:47:50.809Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/88/5c8819f232448d52514b76a64cf285bc608cff9179f3eeacf58750612323/statsmodels-0.15.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bcaa1e4499697c4e023e5fa6fb04459a34e4f4a449c45ae441992e933bde24db", size = 12055386, upload-time = "2026-08-27T10:49:13.7Z" },
+    { url = "https://files.pythonhosted.org/packages/19/a8/298e9b5c993cad3b58913d61b7e7cb0e2fba12e39d3114cd8484f922a5d1/statsmodels-0.15.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:88dbe345ecf317862481b3c86305f42133082e5b366a3a8cf240a6caac404daf", size = 12088356, upload-time = "2026-08-27T10:48:10.645Z" },
+    { url = "https://files.pythonhosted.org/packages/28/f5/386c6cfda27c47df81643dc9466b3e28dc2a82152e524131977275a88e0c/statsmodels-0.15.0-cp313-cp313-pyemscripten_2025_0_wasm32.whl", hash = "sha256:ac9bce461d0c8f529f8ed331a469eaa5dd7c3213c06b62623a2e5438a6383093", size = 9809418, upload-time = "2026-08-27T10:36:50.646Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/04/d803e8d014d4643d9c0a5eec78343073fbfff1c1018f776dd416c07fc076/statsmodels-0.15.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a45026596c71afa18543ec2af40b895609c181fc3649433c1cded5ce23ec5cd4", size = 11801588, upload-time = "2026-08-27T15:00:31.603Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/44/40d7678858065e9ec8efd9f37f04227b22a66632ba194e4aa43fda023f6a/statsmodels-0.15.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:edd803f4606061af8c7a83e2326ea294cbfa802a908dfbda4a88347301fb6820", size = 12062254, upload-time = "2026-08-27T15:00:49.803Z" },
+    { url = "https://files.pythonhosted.org/packages/51/69/16c73c3c09f4b8d85f125d7d3ae0f7092d4736a83d24c0159e4fb21632ec/statsmodels-0.15.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:eb7fc1147cd8d17132190db7a8e63cd76a06145dec502816d8f3c20619923f2b", size = 12101259, upload-time = "2026-08-27T15:01:08.755Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/38/5321b9f6065d1c5861e87853720603d56ae81795a83d3040410ca2684305/statsmodels-0.15.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dde0657d18e322eedb2540a84c0582e97afd0f78c370b45eebb0d4ac228a7a83", size = 11636274, upload-time = "2026-08-27T15:03:41.86Z" },
+    { url = "https://files.pythonhosted.org/packages/28/56/c75e984c492509d9ab2ba5f2c0e4eab7177cd2cec424cef9dafb42f0cc70/statsmodels-0.15.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:90e75e726be2c9fc1d1d4c43337503a35864bdf5b4693773a08c6ee92d04a153", size = 11865818, upload-time = "2026-08-27T15:04:01.503Z" },
+    { url = "https://files.pythonhosted.org/packages/61/72/9e03975701c808767c19f394f670941a962204a4f6a00eeaf56b560348e8/statsmodels-0.15.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:65e64afc2655a486ea44a5ffe4e2b05ee3abbcb10ea61d1c02d3e9e519338d9b", size = 11907800, upload-time = "2026-08-27T15:05:49.786Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/3f/f4cc7e5f4d6c8f46551aee63e59d9b964881070f552400ebb76667b39ad9/statsmodels-0.15.0-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:333d0c70f269ff98b647f157d2ae7fbcf0617e6f9a5b1817fc6609766502292c", size = 11780713, upload-time = "2026-08-30T07:40:06.332Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/20/fa315a013f70efa6674adfaca59365ea7562371914c408551fc3c399792f/statsmodels-0.15.0-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:80b946d7c922f46f045f25e50daf13439f79c05e403ba50081cad509892c8b26", size = 12070117, upload-time = "2026-08-30T07:40:27.04Z" },
+    { url = "https://files.pythonhosted.org/packages/91/43/a63f7880050c2c24056fa8d663b7c8a1816b25864660abbc73afff3f3558/statsmodels-0.15.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:cf897abce5616e2763ab7d7a7fa914677df87a649971f877b83a358c98f6e291", size = 12105153, upload-time = "2026-08-30T07:40:45.814Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/64/98b9e2b1fbfdc4e2392bff349718d8dc796403006a3106ec375c59a5d3f8/statsmodels-0.15.0-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5891ad077b36d6401e44967739df3d2cdd380a0e6e9e46dadec7e3d3a828af55", size = 11657996, upload-time = "2026-08-30T07:41:05.608Z" },
+    { url = "https://files.pythonhosted.org/packages/34/6d/d8c9e73013ee854031218e8a044ba679535268bc61cf59466f19ac8d980c/statsmodels-0.15.0-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:af80f9658c48792947fc754a026ffbebca61a8b483253781973f604a67cf1f8c", size = 11893402, upload-time = "2026-08-30T07:41:27.553Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/f5/b9b47f3c688a8af1b199af96e098b8f20897a60b45187c57ce6fcde77a5f/statsmodels-0.15.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:583eb2a46f7c7a5c34a26715c8b6d92199bfb4f04623db5fff9902a3c7c832d3", size = 11934786, upload-time = "2026-08-30T07:41:46.809Z" },
 ]
 
 [[package]]
 name = "swig"
-version = "4.4.1"
+version = "4.5.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/9c/99/0556919f96b1c291a7ff83793adb328e2a1a5e6cfb379d5acc637cdec959/swig-4.4.1.tar.gz", hash = "sha256:db9a625653f6454530a6c6553d17cf576d7aa2d253c76dc8cdd5979f0cb15012", size = 25956, upload-time = "2025-12-27T03:25:49.609Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7a/4a/0e273ba5c943f8bd1ec53671146f5062062199297e8dbc0d1f11977555a6/swig-4.5.0.tar.gz", hash = "sha256:eb6594f05a1f87783f981e92501c604ac08cc23bb20fa4e6bd5d3a283819f8a5", size = 25844, upload-time = "2026-08-21T18:28:45.676Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c0/4d/860e5475fe38b9c7dc36f61d0c370a62b6cc725d2bd11ada1d22a60ff5f7/swig-4.4.1-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:520fde8805b4775ef3814576929b8c44d666220b06d8da625f292192f9e74fe8", size = 1917511, upload-time = "2025-12-27T03:25:18.064Z" },
-    { url = "https://files.pythonhosted.org/packages/84/e1/5cd4de23ff2b1d225279f7c7826191b1dbdb77fc858fe12ee7be2562ddb7/swig-4.4.1-py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:d8cca45a0e8d1e045a1490ea2d05bc1c49e2c266dbbe5954048e2a93d125ee07", size = 2066284, upload-time = "2025-12-27T03:25:19.624Z" },
-    { url = "https://files.pythonhosted.org/packages/96/42/d49992f3c39c90d3d46c431a4afea60fc16bae542cf07f394b298a1b21b5/swig-4.4.1-py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:bfe6ce6189fc8b983f65f5ed3db8e1d8be03accb0ec3869f986a8343f014e4d9", size = 1912492, upload-time = "2025-12-27T03:25:21.843Z" },
-    { url = "https://files.pythonhosted.org/packages/10/2e/4baa50fcf932f9d58bc8394b211c7daf3b7ec1e53c08d560142f1bc99cb9/swig-4.4.1-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:9c98d7b882b6debe3cbb87e6503858e104162d7ce5ab4cfc23425da2e26e51b5", size = 1979872, upload-time = "2025-12-27T03:25:23.48Z" },
-    { url = "https://files.pythonhosted.org/packages/fa/7b/e3a14d053fa18b0d2e14efcc21883816964ddbe52a0c43018e195a99aba2/swig-4.4.1-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ae3da2bf679a4c942a2c100789395d4d167e7da8286018124e4665f5eff43e31", size = 1895896, upload-time = "2025-12-27T03:25:24.98Z" },
-    { url = "https://files.pythonhosted.org/packages/f0/a6/c961a911385a4cc4ae10b4dcbcf5dede0407530e59870ce6e4f7bacce47d/swig-4.4.1-py3-none-manylinux_2_24_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:7945f153f7fc93f1ab280f44a3740d6bbfeec3829f954784ba83990fc61a11a8", size = 1912119, upload-time = "2025-12-27T03:25:27.661Z" },
-    { url = "https://files.pythonhosted.org/packages/86/4b/a08a2673a2fd3185932db110de67a9f6fdfcece6bf9220e18502bf745f7e/swig-4.4.1-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:5a9c74b87151d8247d802bda33f78cab9f27c759f0605b760a8f9c9893b5606b", size = 1885810, upload-time = "2025-12-27T03:25:29.562Z" },
-    { url = "https://files.pythonhosted.org/packages/7e/38/efa343b506221a9d43bb1132f08b29fcb07d4113165f598d8a1ea733a1e4/swig-4.4.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:a95ae66aebb8ccce4c50febd496e6927b888d98aba272dd5642fe9312bf61a3b", size = 2950985, upload-time = "2025-12-27T03:25:31.586Z" },
-    { url = "https://files.pythonhosted.org/packages/de/0b/5b46e61798982f3cb0ed54fc30a30bad5fa6e3bdd1d9177e439976f7a7e5/swig-4.4.1-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:a8f06227e6472832dad3ae34d5bd88710b7bbb109f2927e14a21e6725146e944", size = 2781782, upload-time = "2025-12-27T03:25:33.534Z" },
-    { url = "https://files.pythonhosted.org/packages/17/e6/a15efd9cbf39b1289ee7c1acb8d18824a0c2532b85cdbd93acfdf15ee000/swig-4.4.1-py3-none-musllinux_1_2_i686.whl", hash = "sha256:98d61b45215982dd3a71745dfc31599176ed79d286b58bc712c10ed031432fdf", size = 3272993, upload-time = "2025-12-27T03:25:35.862Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/fa/516b95f9966c57acf1da9f2eb22e4e63104452d6e28686d57b216171ee7c/swig-4.4.1-py3-none-musllinux_1_2_ppc64le.whl", hash = "sha256:b6909a8bd68037ea0df061a52c664431b062dbdfb1eb90768719518c120139d1", size = 3189093, upload-time = "2025-12-27T03:25:38.158Z" },
-    { url = "https://files.pythonhosted.org/packages/0c/03/88ca92d3d8e9dd765a66985202db23745bd26a368570a8d8db9a218bfe90/swig-4.4.1-py3-none-musllinux_1_2_riscv64.whl", hash = "sha256:6a6a6331a5c7759ba5a910aa83aa5d33022b8e691b6cb466635c7fc2238facd2", size = 2967789, upload-time = "2025-12-27T03:25:40.24Z" },
-    { url = "https://files.pythonhosted.org/packages/bf/97/5ca3d750f294048c983edfad1f2ad093a83480494fccb2f2e2496304f965/swig-4.4.1-py3-none-musllinux_1_2_s390x.whl", hash = "sha256:2d076358cded172d2f44b3c617923c593dbae664b3fa45051641f028f7f74010", size = 3203253, upload-time = "2025-12-27T03:25:41.953Z" },
-    { url = "https://files.pythonhosted.org/packages/97/44/96586955c0f08b0b9b6c8b2b68ecb43a815da94d5362cfb6691ede3b9fdf/swig-4.4.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:43a0a876b4660f6addb6ae72d14a72324dc1388e1c168ed38bb00fd123048749", size = 3060721, upload-time = "2025-12-27T03:25:44.322Z" },
+    { url = "https://files.pythonhosted.org/packages/22/0c/9a1ef12920e18f6638be03f6adf2f98b3ee23902c6401c85ffccb8cdbc17/swig-4.5.0-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:59e35b68f32ada308b3d1e13fd2793df9e56d399fa136c50b24fd5a048a94ec9", size = 1961802, upload-time = "2026-08-21T18:28:17.724Z" },
+    { url = "https://files.pythonhosted.org/packages/17/1c/f21fadc56c517b072b2b0652bc767fc0e44da5238fe47c3ee9ece73d34a9/swig-4.5.0-py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f166a61e0f31a61f80eb9e22e00649835b30bb92f703ca009c03f719337da1a4", size = 2118597, upload-time = "2026-08-21T18:28:19.51Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/bb/add9f6cf59c9c49f3b4eea9260991eae112bf13a750ed76c9cb7739f5e2d/swig-4.5.0-py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:4f70be1618a6da71bd50c97222cc78ba1fc194a5ab3352dd9868f8cea10aecb2", size = 1955866, upload-time = "2026-08-21T18:28:21.001Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/bd/104089157d29013049b66a171d2ee74486af72b20d1621b06dd0cc998565/swig-4.5.0-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:0ea50150bf5f4782789dc2016ff3ea4f40d2e37ed1954ec8c0442b947fae244e", size = 2026678, upload-time = "2026-08-21T18:28:22.709Z" },
+    { url = "https://files.pythonhosted.org/packages/29/85/f5f7976ea09a4a16eee5b96f7d7c289685b19651c51ce9946128f1315440/swig-4.5.0-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4821eb0b69869af25547fcd00c8235e274e43515d001fd32cf079d52eb7d1d99", size = 1938693, upload-time = "2026-08-21T18:28:24.406Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/ef/6139883f1f9b0f0f7a0dee74192ddbd9a5c8b6cc7cee9b0bc390628eefbe/swig-4.5.0-py3-none-manylinux_2_24_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:22c2f089ee929f3c714b70a657fd96ea395fc49890e7f345d175f9bf15c96a36", size = 1956656, upload-time = "2026-08-21T18:28:26.205Z" },
+    { url = "https://files.pythonhosted.org/packages/23/f1/7b75796cc856636efc69529075a7653801b23632e6aaefefe4a8ee17d976/swig-4.5.0-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:05e5ec8b5d4fabb6e3a910cfb43b7ebb04851c8c66117795350b80bdcb878d24", size = 1927664, upload-time = "2026-08-21T18:28:27.932Z" },
+    { url = "https://files.pythonhosted.org/packages/17/b8/fbac0cdf6ee2b99892c1fb296a8bad60adc6d2c33a49733609334ac39547/swig-4.5.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:b297421b229fe23245a2701598a3efd1ba29f4e809aa2cd07b426d52a37ce75a", size = 2997954, upload-time = "2026-08-21T18:28:29.703Z" },
+    { url = "https://files.pythonhosted.org/packages/44/1c/d8c4df84868dbe1a0a73d10fed24c36a5e0155f0ba43a72be3bc975d0908/swig-4.5.0-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:64f37fbaa975403d026a0bdfa4bea27c8e3861f2ee134b37d705d4ad3dd5b435", size = 2826382, upload-time = "2026-08-21T18:28:31.521Z" },
+    { url = "https://files.pythonhosted.org/packages/64/e4/c07a09a4be495c70983939c114ccf30d691cf15e61f8a84a444e12e02a3e/swig-4.5.0-py3-none-musllinux_1_2_i686.whl", hash = "sha256:83d1ff7c4422ef1a89ca8f65061be4ef78eb5d02a75fae36043a454461f9c57b", size = 3324095, upload-time = "2026-08-21T18:28:33.373Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/ba/28ffbcdee529994f44ec3e9ba6fe19ba6edb10ef07f82609af468c4b7cf6/swig-4.5.0-py3-none-musllinux_1_2_ppc64le.whl", hash = "sha256:edf8a275f9c6b1e0ac6b07d7a9e1e1c835c3d2504f9cec6dc863a959b81ab09a", size = 3241787, upload-time = "2026-08-21T18:28:35.195Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/3a/bee4314e01445e920598b8abcaf0fd1477c942b6c8ab167c544891ded065/swig-4.5.0-py3-none-musllinux_1_2_riscv64.whl", hash = "sha256:d5d479fa163e7ffce448bd00f658f688c9b5234eda501fc60c8cdc8087355c49", size = 3012896, upload-time = "2026-08-21T18:28:37.021Z" },
+    { url = "https://files.pythonhosted.org/packages/07/22/d65fc57e270f58cc44fc7f6d8ea56534afb71e65b67089b09f04987b32c0/swig-4.5.0-py3-none-musllinux_1_2_s390x.whl", hash = "sha256:a8319ab91625d2e15556eae923f8792ce0e3c11fd375912584f218a4f0e7cf34", size = 3250472, upload-time = "2026-08-21T18:28:38.843Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/75/d021463537e8cffd4d3f88228184e7b6aa6641ad97f0fc87f0195ec24150/swig-4.5.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:762638d3f1fa1f27b09f8ef6854ffc0744e0f54ead3b19bfec5d22244bb9872f", size = 3105789, upload-time = "2026-08-21T18:28:40.532Z" },
 ]
 
 [[package]]
@@ -3863,7 +4089,7 @@ wheels = [
 
 [[package]]
 name = "tables-io"
-version = "1.1.2"
+version = "1.1.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
@@ -3873,9 +4099,9 @@ dependencies = [
     { name = "pandas" },
     { name = "tables" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/12/1b/61bed3c55ce4602be5379362ce939b71a2e707e856ee497c1d1e72f8ff3d/tables_io-1.1.2.tar.gz", hash = "sha256:ea27eac8b22ff4e3500afd92140794bd41161f768a2c84520b91b3b58e945182", size = 91394, upload-time = "2026-06-18T16:21:20.896Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1b/82/3557c5bff1e305f26a669098e8650034f731b1fa68b644ab01a7d95d31a6/tables_io-1.1.3.tar.gz", hash = "sha256:8a63c4688679eb5efb44ff876d9625c0ea437f2e5950b7817069b698cf7baae1", size = 92409, upload-time = "2026-09-10T18:26:56.127Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/88/68/025307ebd3c54c65e540d2a4ced11d0e5aa370c8ddb6d0e83066e5a39054/tables_io-1.1.2-py3-none-any.whl", hash = "sha256:4361e04dfcc10471bf9a3940d9ee6a1f903ac76ece51d32ef7fec7ea572b20c0", size = 39206, upload-time = "2026-06-18T16:21:19.577Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/a9/d92d73bf1c9c32e24be511b547c9f7dc23becb268bd1fdeafb56149b63a7/tables_io-1.1.3-py3-none-any.whl", hash = "sha256:fae7e843064a05d239b3597d5ad1b42ed586e0f0b11ea34d139d6b063f0ddc93", size = 39376, upload-time = "2026-09-10T18:26:54.722Z" },
 ]
 
 [[package]]
@@ -3893,23 +4119,23 @@ wheels = [
 
 [[package]]
 name = "threadpoolctl"
-version = "3.6.0"
+version = "3.7.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/00/dc/6c58154c1c65f758ea979e7139cb76993a9cfc662d14e9be3c4a667cfb77/threadpoolctl-3.7.0.tar.gz", hash = "sha256:61348cfb77d53b9242e0017029244b559b810c142ced65b4e21eeca1843959a7", size = 31961, upload-time = "2026-09-15T15:46:20.263Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" },
+    { url = "https://files.pythonhosted.org/packages/43/3f/f88a53f60a472b46f4023f56d204dd7de33d34c5d2acbfa0d70a674e639e/threadpoolctl-3.7.0-py3-none-any.whl", hash = "sha256:cd8b60b5641b45c67bbf73c64c843235fc2d8a480c87389f52f5dbee893b86be", size = 26362, upload-time = "2026-09-15T15:46:19.168Z" },
 ]
 
 [[package]]
 name = "tifffile"
-version = "2026.6.1"
+version = "2026.9.20"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b7/38/5e2ecef5af2f4fd4a89bb8d6240de9458bab4d51a4cbd97aeb3a0cd618e2/tifffile-2026.6.1.tar.gz", hash = "sha256:626c892c0e899d959b9438e7c0e1491dc154a7fead1f1f37a991724a50eceba9", size = 429694, upload-time = "2026-05-31T23:57:12.165Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/92/66/634db78ebad513038d753830dd8815eea26278b5463ba0f43198b0c24c4e/tifffile-2026.9.20.tar.gz", hash = "sha256:30e145a7042ce7143ae50a50fe8b7221b0070aae22adab8f9e79a264be6b5cdc", size = 449417, upload-time = "2026-09-21T03:59:40.055Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/59/208f71d70ddc6184f79b8c6d87d46eb7d7b12c19186a817dec9c9c3f3693/tifffile-2026.6.1-py3-none-any.whl", hash = "sha256:0d7382d2769b855b81ce358528e2b40c16d48aa39031746efa81215205332a8d", size = 267108, upload-time = "2026-05-31T23:57:10.597Z" },
+    { url = "https://files.pythonhosted.org/packages/05/bf/04f3e61cb20a03678ca43f29bae9a7d0b7d9f563b86f5f600b2d8fba9712/tifffile-2026.9.20-py3-none-any.whl", hash = "sha256:9b913167b8f66a57f2e7c0454486c4c4607196d494797461226166bd0755c0e6", size = 275004, upload-time = "2026-09-21T03:59:38.46Z" },
 ]
 
 [[package]]
@@ -3935,32 +4161,32 @@ wheels = [
 
 [[package]]
 name = "tornado"
-version = "6.5.7"
+version = "6.5.10"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/64/24/95ec527ad67b76d59299e5465b3935d05e4294b7e0290a3924b7487df30b/tornado-6.5.7.tar.gz", hash = "sha256:66c513a76cda70d53907bc27cf1447557699c2e95aa48ba27a442ff61c3ddfc2", size = 519252, upload-time = "2026-06-08T17:34:51.232Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/06/61/53d562a57b28c08eda40b258c0f975e360541943ad7c7bef897a40caafda/tornado-6.5.10.tar.gz", hash = "sha256:a6b1ccd08c04b4a06fb5aeb381be99de5ad1e5375c1785e31d78c880feb57687", size = 537910, upload-time = "2026-09-15T13:47:48.73Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/37/d8/ef374952fd5da67d4463122c2b8e5a96536ec10b4b339254c6dcde81d01c/tornado-6.5.7-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:8d759e71906ee783f8867b93bf26a265743da4c1e2f4a018464c1ba019862972", size = 449774, upload-time = "2026-06-08T17:34:41.204Z" },
-    { url = "https://files.pythonhosted.org/packages/35/37/d434c73f4c6e014b745b9b37085f34f40c022f007efff3d7fe65991899f3/tornado-6.5.7-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8a46347a18f23fb92b396beebe0fb78f61dda0cc302445202c16203d8a18848b", size = 450745, upload-time = "2026-06-08T17:34:42.531Z" },
-    { url = "https://files.pythonhosted.org/packages/b6/2b/56b9aff361d7f1ab728a805ec7d7ea835f8807afa9f5cc690ea0e630efb9/tornado-6.5.7-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:7778b30bef919231265e91c69963ce0f49a1e9c07ac900bbe75b19ce2575ba92", size = 450578, upload-time = "2026-06-08T17:34:43.787Z" },
-    { url = "https://files.pythonhosted.org/packages/02/30/a7444fb23aa76860a14198fab96ac79f1866b0a6e19e26c4381b0938e50f/tornado-6.5.7-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:e726f0c75da7726eec023aa62751ff8878bd2737e34fbdd33b1ae5897d2200f5", size = 449985, upload-time = "2026-06-08T17:34:45.326Z" },
+    { url = "https://files.pythonhosted.org/packages/60/33/df6d7d04854a58619f8349a51e3edb138324130a7562b0bb21f115bb940f/tornado-6.5.10-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:bdf942448169e5336451d0494d7e3d81cfa726d5aa312affdc4682dd62a62f6d", size = 467096, upload-time = "2026-09-15T13:47:38.559Z" },
+    { url = "https://files.pythonhosted.org/packages/29/17/cc35dff68272d685cffd8600ffafbd8067e7d05e7348d9f80caddffbbd5f/tornado-6.5.10-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:69acca6501eed74582b76dbbceee2a91613f54728e3e418346000d7103101676", size = 468067, upload-time = "2026-09-15T13:47:40.085Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/01/6e5349b4e1a53a4b4972a6716785e1fe7407f312063c3972690af8ff301b/tornado-6.5.10-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:66aaa3f57d30c6e6becee83ff28055d5930ac724214bde99393eefda83d5e015", size = 467901, upload-time = "2026-09-15T13:47:41.576Z" },
+    { url = "https://files.pythonhosted.org/packages/28/5e/b4facf94370dba006819c8d304376f8b9fbec6b935b5e51bf45823a9790b/tornado-6.5.10-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:4bd192b959f9128fb99b8898148070ba4574c9589b78bce42d1851131fe85828", size = 467308, upload-time = "2026-09-15T13:47:43.145Z" },
 ]
 
 [[package]]
 name = "tqdm"
-version = "4.68.4"
+version = "4.70.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ae/5f/57ff8b434839e70dab45601284ea413e947a63799891b7553e5960a793a8/tqdm-4.68.4.tar.gz", hash = "sha256:19829c9673638f2a0b8617da4cdcb927e831cd88bcfcb6e78d42a4d1af131520", size = 792418, upload-time = "2026-07-07T09:58:18.369Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/0d/ea/b2a5bd54b28a324dae8211928b2d730b6547500342c7e6c6dea08bd0a485/tqdm-4.70.1.tar.gz", hash = "sha256:cefd0eca11b2a37a3aee776544d4f4ae913f02688135b5556b8788dfa474afc4", size = 171846, upload-time = "2026-09-11T07:25:16.601Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/22/2a/5e5e750890ada51017d18d0d4c30da696e5b5bd3180947729927628fc3cb/tqdm-4.68.4-py3-none-any.whl", hash = "sha256:5168118b2368f48c561afda8020fd79195b1bdb0bdf8086b88442c267a315dc2", size = 676612, upload-time = "2026-07-07T09:58:16.256Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/03/921a3d3c75785aca9ebfbfcabfbc3a1be12e2ab5265deb026d55a5a3f83e/tqdm-4.70.1-py3-none-any.whl", hash = "sha256:c293e525e6fef9c20e8728fd4612df02a0aa31bb5fe91ecd93e123b1b7bffa73", size = 80199, upload-time = "2026-09-11T07:25:14.599Z" },
 ]
 
 [[package]]
 name = "traitlets"
-version = "5.15.1"
+version = "5.16.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/57/a9/a2584b8313b89f94869ddb3c4074617a691de1812a614d2d50e32ca5a7a6/traitlets-5.15.1.tar.gz", hash = "sha256:7b1c07854fe25acb39e009bae49f11b79ff6cbb2f27999104e9110e7a6b53722", size = 163344, upload-time = "2026-06-03T12:26:06.181Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2c/2e/a7fbfe268c8a3b32546930c0297c101d65a4a14c304ad5790a9f478f0e4e/traitlets-5.16.1.tar.gz", hash = "sha256:ed900c2b631aa3a112811139fa97b8d2c3bad5e989656bba4b7e52c7852c18c1", size = 166137, upload-time = "2026-08-03T08:32:36.848Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/96/8d/1080ee4c231f361b6ce4470d556c8c435b67c7e0753aaa641497ee92f88b/traitlets-5.15.1-py3-none-any.whl", hash = "sha256:770a53705f84b81ac107e83a1b3328ff2dae16094d8fc3cfc004e4b22dfd8e92", size = 85858, upload-time = "2026-06-03T12:26:04.395Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/66/0d785f0bc5e4315a96c989bb476d0fc07ea4f85132550c7b156ca2035d52/traitlets-5.16.1-py3-none-any.whl", hash = "sha256:f775618166caa0396c8e337099240f2bd3e5e917d203b2e6fbe21a58d3cb1f6b", size = 86211, upload-time = "2026-08-03T08:32:34.48Z" },
 ]
 
 [[package]]
@@ -4001,15 +4227,14 @@ wheels = [
 
 [[package]]
 name = "typeshed-client"
-version = "2.12.0"
+version = "2.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "importlib-resources" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/54/5d/97d6fa8c204b0d42be931e7a568b306cda43f93ff45f8b47537ee61390de/typeshed_client-2.12.0.tar.gz", hash = "sha256:54dcfa25fcff82cedcb1b51c03c1b3c51a614097aab8fe49e4316aff6f79d9c7", size = 529457, upload-time = "2026-06-02T04:00:51.51Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/62/eb/ef31fadb73c5a9da6e88adf146b9db88282cf17136beb65a1c88905afe2f/typeshed_client-2.13.0.tar.gz", hash = "sha256:85ce73be98a5e3a51cafb2e2921653e3759ce7e9d1530adcca935dec201152f2", size = 532578, upload-time = "2026-08-03T04:11:13.63Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/85/98/3aef1587ec9b3a5cd062b8d8910545a0ee3715c31fd65b1f2386b27b94af/typeshed_client-2.12.0-py3-none-any.whl", hash = "sha256:da3ef54a0e60c0f45f59006b73a3cb20b1acbf7a784e982476d8193e8828ddf5", size = 796929, upload-time = "2026-06-02T04:00:49.643Z" },
+    { url = "https://files.pythonhosted.org/packages/76/2b/83e50380aa9ca001ee74460f027b518eefd330edb7ab7787442424f1e584/typeshed_client-2.13.0-py3-none-any.whl", hash = "sha256:71bc9ed297ddb32d3c4b2d29b5e688c6b0b9a21ca8ce6082d0aa7cee9cad5077", size = 800208, upload-time = "2026-08-03T04:11:12.368Z" },
 ]
 
 [[package]]
@@ -4023,23 +4248,23 @@ wheels = [
 
 [[package]]
 name = "typing-inspection"
-version = "0.4.2"
+version = "0.4.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a3/26/b09b8010994eccc3c09092e6b34058f36a460eea2d4c3e8b910c695975a0/typing_inspection-0.4.4.tar.gz", hash = "sha256:547274fa6b0a561ccf549cc9524b999a578e737d015d8709d021f9d0d13bea47", size = 76928, upload-time = "2026-08-12T12:37:25.997Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" },
+    { url = "https://files.pythonhosted.org/packages/67/81/4add07e5172b7ac40d8ed5ff580409a7801a4fe26d529bdd915401dabfbe/typing_inspection-0.4.4-py3-none-any.whl", hash = "sha256:65b8397ba37ccbce054456aaccddfc91e6e3083c92824df348d96ca832f3f147", size = 14750, upload-time = "2026-08-12T12:37:24.648Z" },
 ]
 
 [[package]]
 name = "tzdata"
-version = "2026.3"
+version = "2026.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/92/ff/5a28bdfd8c3ebec42564ac7d0e54ca3db65044a9314a97f9564fa7a1e926/tzdata-2026.3.tar.gz", hash = "sha256:4a1518b8993086a7982523e071643f3c0e5f213e75b21318e78bcabfff9d1415", size = 198674, upload-time = "2026-07-10T08:50:37.887Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e4/31/3d74fa778a63b98b7374323befcc0be5ab3bd94afd4096a0124e7379152c/tzdata-2026.4.tar.gz", hash = "sha256:f1b8bd365d8d210c55353f4d7f8d6d8561c0ba50d704b700d195a9424bba0d79", size = 199350, upload-time = "2026-09-12T12:56:03.251Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/6d/b53b99a9f2766d095985947a5782f1702cabb129a34f7a802d7197af832f/tzdata-2026.3-py2.py3-none-any.whl", hash = "sha256:dc096730c87af6cab1b171c9d532be840741ff5d459015e7f6947bd7d7e54931", size = 348168, upload-time = "2026-07-10T08:50:36.46Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/bc/8737e8d54cf51106118039b83f485a4783112fab49ea9d044b234978a46e/tzdata-2026.4-py2.py3-none-any.whl", hash = "sha256:c2169a8b0a7a5e9674da5a135ccdfb2b3e671b333ed9fed17b41f73c34476e81", size = 347494, upload-time = "2026-09-12T12:56:01.67Z" },
 ]
 
 [[package]]
@@ -4062,34 +4287,43 @@ wheels = [
 
 [[package]]
 name = "urllib3"
-version = "2.7.0"
+version = "2.8.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602, upload-time = "2026-05-07T16:13:18.596Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/05/b17359e1cefb4f909b5e40b1b90a496d987258916dbbf88e842c729f510e/urllib3-2.8.0.tar.gz", hash = "sha256:63bf2ead4c879426ebf22ef2a781eeb4aa3b4ae798a0435506f8687fd5bb9b63", size = 458972, upload-time = "2026-09-15T19:29:36.253Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" },
+    { url = "https://files.pythonhosted.org/packages/92/9d/c4e665119135114480843e7ab388fa94d8480650450e6f8e26b70d323a4c/urllib3-2.8.0-py3-none-any.whl", hash = "sha256:0cf3cae568d36aa9576b28dfb35f11328f1cb974ca7647d9475ebb86c75ac6e3", size = 135717, upload-time = "2026-09-15T19:29:34.577Z" },
 ]
 
 [[package]]
 name = "vos"
-version = "3.6.4"
+version = "3.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aenum" },
     { name = "cadcutils" },
     { name = "html2text" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3e/6c/445bbedf2f90e71c09ff890aa8b409af5d074b1578eef383117cbcdfd09a/vos-3.6.4.tar.gz", hash = "sha256:67e4e24689ee881124b6670d7d2fc6527cc404687fc527fffc20cf5b1bd4c0bd", size = 103445, upload-time = "2025-12-12T00:20:56.398Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2f/71/35b07ebde34232b4f3deca8cb320c3ad615b15818fbb3b10a5209c14aadc/vos-3.7.tar.gz", hash = "sha256:073cde0ec3ffe52f283ba6505870e760492a73743986797686e7dc57d30ca563", size = 103538, upload-time = "2026-08-06T23:47:46.099Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0c/21/e2d351883aa11b9175223a78d3a77b0fedf86461596cdbfb019893ca9260/vos-3.6.4-py3-none-any.whl", hash = "sha256:4a7b2248bad0f50da465a47ab1c819dff8378c189c184c2d8e2c17a714c62f17", size = 153404, upload-time = "2025-12-12T00:20:53.232Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/98/638365ca3336412689407606a0d10512ea01b3c1ecdfd5b3a7e1d31bb563/vos-3.7-py3-none-any.whl", hash = "sha256:1cb23f33f9a9779ed7d0fe2a30c23ee021d0a0c29e65fdaf65bba91ee297a356", size = 153401, upload-time = "2026-08-06T23:47:44.738Z" },
 ]
 
 [[package]]
 name = "wcwidth"
-version = "0.8.2"
+version = "0.9.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/34/74/c6428f875774288bec1396f5bfcbc2d925700a4dad61727fd5f2b12f249d/wcwidth-0.8.2.tar.gz", hash = "sha256:91fbef97204b96a3d4d421609b80340b760cf33e26da123ff243d76b1fda8dda", size = 1466253, upload-time = "2026-06-29T18:11:11.601Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/ac/3a943d2792c9bb368aaa8b50121c0f778460ba2d7fbdc0a0366201d9e761/wcwidth-0.9.1.tar.gz", hash = "sha256:5823209b0d43af322ce698c689380d7c15ca31fa8e6e3be8459f27031bef0af5", size = 952410, upload-time = "2026-09-23T20:23:49.127Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/96/42/3e5985a0a7e57de470b320c6d6a1a67c844f6737a587f3d44dd13d1819e7/wcwidth-0.8.2-py3-none-any.whl", hash = "sha256:d63947694a0539a1d51e01eda7caf800c291020e6cdd7e28ad7b14dd33ad4f85", size = 323166, upload-time = "2026-06-29T18:11:09.888Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/71/f57effdf895c2d6d49333f1d6833a9f5438725c55a04ed3124a1c95bb3aa/wcwidth-0.9.1-cp310-abi3-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:69bb970cf5652b88cfdb9d3fdd1764fc15e5f8ad531643e7bb3e894cd969740e", size = 761464, upload-time = "2026-09-23T20:23:32.425Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/16/15b03d8fb7a747d0d2cf4a33da7b1b3e092493c58780f6589d5b1e9e3b49/wcwidth-0.9.1-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b5da43d6967668982e44a52fb551967d293f86d26cd86036ac95bbdd34394ed9", size = 770483, upload-time = "2026-09-23T20:23:28.128Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/a0/834886b30e3b885a5a96e8358f1b3917bc0bac43ae4178d77f0c210d079b/wcwidth-0.9.1-cp310-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:747fb724223f417a17541a95a17c1dac3a8ef9a0cf41684950f0eab191a35f65", size = 768443, upload-time = "2026-09-23T20:23:33.875Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/ae/1b7597e0132ebc2c920632f39d8c1c61881345c9b480c0a95eb91e7460fa/wcwidth-0.9.1-cp310-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:6e1272b7986cefe79783737e38bdb9eaae0682b333c7bd132024441193dd5ce7", size = 781171, upload-time = "2026-09-23T20:23:29.46Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/bb/32149e3b481953f4ef35f38f166e2532886d33ae54ccdd71393f821dfad9/wcwidth-0.9.1-cp310-abi3-musllinux_1_2_i686.whl", hash = "sha256:fe021c4d8de9d36c31a0cb41d0d2546dadd3b0708a301f1a0c66ce200851831f", size = 781576, upload-time = "2026-09-23T20:23:35.504Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/72/1ee3dff67697bda93bc56ec9f7b3b89609cd64b9abb328512a2f22eb9e47/wcwidth-0.9.1-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:708158c082364af442f9983de7b6ec9ac0d2e1b825ada25f0911b1f138d55405", size = 783526, upload-time = "2026-09-23T20:23:36.937Z" },
+    { url = "https://files.pythonhosted.org/packages/56/e7/4f3a86953ab1f59eac15585795f7753da99370798f75b134d3cbf252b243/wcwidth-0.9.1-cp314-cp314t-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:0d68a30d504c68cfdff2a5f804675c1e7ab4c0bbe878024c8b680ec5579cde67", size = 774751, upload-time = "2026-09-23T20:23:38.473Z" },
+    { url = "https://files.pythonhosted.org/packages/48/0c/a456332bb581ebf3eb1a7dedb1492747b15772ce82c0717cc05387a64a17/wcwidth-0.9.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9f1636c5075ffd5c2e835b4561874f6e5dd2bbeba3c6c2c99f067d0d16883af8", size = 785798, upload-time = "2026-09-23T20:23:30.998Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/6e/a35de089370d6d872c5424d234e00a8580e62c1bff518ff583750e7b15f7/wcwidth-0.9.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c4cead196551112cb8f43cdd1f80c235ef2456e34b1a9955c424537ec99961b2", size = 782933, upload-time = "2026-09-23T20:23:39.672Z" },
+    { url = "https://files.pythonhosted.org/packages/96/a2/f06f2e0be4895e5943d358755b51968ace85774cf0a4cc42b4dafb793832/wcwidth-0.9.1-py3-none-any.whl", hash = "sha256:e0c3a1c45c5b9550c6919a4449e95f5b177f6e165786376981db8f1addae9b21", size = 300656, upload-time = "2026-09-23T20:23:20.674Z" },
 ]
 
 [[package]]
@@ -4103,85 +4337,92 @@ wheels = [
 
 [[package]]
 name = "webencodings"
-version = "0.5.1"
+version = "0.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d5/a0/8fd707bcb776a7be556bad06a2ea5fb9bd519df78ef8e26f70ccf0f38bff/webencodings-0.6.1.tar.gz", hash = "sha256:565f9ad031c702dae404e27a099e3e09186a3ab1b9520f06d215502b651fd910", size = 15001, upload-time = "2026-08-15T14:22:57.549Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" },
+    { url = "https://files.pythonhosted.org/packages/77/c6/040cbc72480d789a5f40d63fb484d3106554c4dfa2d2b70ad5022057750f/webencodings-0.6.1-py3-none-any.whl", hash = "sha256:7fab6269c8bf237c657876b52058ccb182e861518d1c695c1a9aaa8c1c105d5b", size = 8745, upload-time = "2026-08-15T14:22:56.31Z" },
 ]
 
 [[package]]
 name = "websocket-client"
-version = "1.9.0"
+version = "1.9.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576, upload-time = "2025-10-07T21:16:36.495Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/cb/a5abcc2891249f393827c650c6296660ce40374ac22d99ab9aea41f9d2a2/websocket_client-1.9.2.tar.gz", hash = "sha256:0fcb57545848be86992e128218fd96dd87a6769ffdb1a968dff79632b85604d0", size = 84110, upload-time = "2026-08-31T14:08:40.964Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616, upload-time = "2025-10-07T21:16:34.951Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/d2/cc4dc1271e464942db7ee278baae2daa99ee77cb2af744025c04da585a3e/websocket_client-1.9.2-py3-none-any.whl", hash = "sha256:e1a673830a9c7bfa47b1cd3d5e4178f4c9651d80a4eab02c9c23a1c3ec6250ce", size = 95786, upload-time = "2026-08-31T14:08:39.899Z" },
 ]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.15"
+version = "4.0.16"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bd/f4/c67440c7fb409a71b7404b7aefcd7569a9c0d6bd071299bf4198ae7a5d95/widgetsnbextension-4.0.15.tar.gz", hash = "sha256:de8610639996f1567952d763a5a41af8af37f2575a41f9852a38f947eb82a3b9", size = 1097402, upload-time = "2025-11-01T21:15:55.178Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/bf/60/bc7a980fc78837d6ef8f5940cca4cadc433364503a4c4d42e2a7a0de3231/widgetsnbextension-4.0.16.tar.gz", hash = "sha256:adeea0ae78f0856ee4945f413299801b82a0a01416303301f39a704282a37b73", size = 1111094, upload-time = "2026-08-18T08:52:55.859Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl", hash = "sha256:8156704e4346a571d9ce73b84bee86a29906c9abfd7223b7228a28899ccf3366", size = 2196503, upload-time = "2025-11-01T21:15:53.565Z" },
+    { url = "https://files.pythonhosted.org/packages/34/95/40e17e20046b7bc820d29d09ae84ec157ec8dd6e6f6cd722626292c31b2e/widgetsnbextension-4.0.16-py3-none-any.whl", hash = "sha256:a31a8774885b96fe825462f5d6496166f0c7cae111195b6465c801d230eb5a4e", size = 2225148, upload-time = "2026-08-18T08:52:53.736Z" },
 ]
 
 [[package]]
 name = "wrapt"
-version = "2.2.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/fe/a4/282c8e64300a59fc834518a54bf0afabb4ff9218b5fa76958b450459a844/wrapt-2.2.2.tar.gz", hash = "sha256:0788e321027c999bf221b667bd4a54aaefd1a36283749a860ac3eb77daed0302", size = 129068, upload-time = "2026-06-20T23:49:44.49Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/5f/b3/7e6e9fcf4fe7e1b69a49fe6cc5a44e8224bab6283c5233c97e132f14908e/wrapt-2.2.2-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:2e096c9d39a59b35b63c9aacfbbbec2088ff51ff1fc31051acc60a07f42f273a", size = 169828, upload-time = "2026-06-20T23:48:02.719Z" },
-    { url = "https://files.pythonhosted.org/packages/0b/43/894f132d857ed5a9904d937baf368badcbe5ea9e436e2f1930fe21c9f1f0/wrapt-2.2.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6d1a6050405bf334be33bf66296f113563622972a34900ae6fa60fd283a1a900", size = 171544, upload-time = "2026-06-20T23:48:04.266Z" },
-    { url = "https://files.pythonhosted.org/packages/29/de/3c833e03725b477e9ea34028224dd21a48781830101e4e036f77e8b6b102/wrapt-2.2.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:10adb01371408c6de504a6658b9886480f1a4919a83752748a387a504a21df79", size = 160663, upload-time = "2026-06-20T23:48:05.708Z" },
-    { url = "https://files.pythonhosted.org/packages/33/be/27edce350b24e3054d9d047f65f16d4c4d4c1f3f31c4278a1f8a95c723c8/wrapt-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3442eee2a5798f9b451f1b2cd7518ce8b7e28a2a364696c414460a0e295c012a", size = 169387, upload-time = "2026-06-20T23:48:07.243Z" },
-    { url = "https://files.pythonhosted.org/packages/e2/c4/9fd9679af8bf38e146652c7f47b6b352c3e5795b4ad1c0b7f94e15ac2aa7/wrapt-2.2.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:6c99012a22f735a85eed7c4b86a3e99c30fdd57d9e115b2b45f796264b58d0bf", size = 158849, upload-time = "2026-06-20T23:48:08.91Z" },
-    { url = "https://files.pythonhosted.org/packages/bc/c2/aa6c0c2206803068c6859dabe01f8c84c43744da93d4c67b8946d21655ee/wrapt-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3b686cfc008776a3952d6213cb296ed7f45d782a8453936406faa89eac0835ab", size = 168147, upload-time = "2026-06-20T23:48:10.374Z" },
-    { url = "https://files.pythonhosted.org/packages/15/ff/63ad96f98eb58a742b1a20d80f21da88924405910149950b912368150468/wrapt-2.2.2-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:fb18fc51e813df0d9c98049e3bf2298a5495a648602040e21fa3c7329371159e", size = 167882, upload-time = "2026-06-20T23:48:18.764Z" },
-    { url = "https://files.pythonhosted.org/packages/20/1f/8bb62d8933df7acf3247194e6e9fc68edf9d2fa203252c89c94b319dd472/wrapt-2.2.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:94b00b00f806eb3ef2abe9049ed45994a81ee9284884d96e6b8314927c6cea3d", size = 167411, upload-time = "2026-06-20T23:48:20.315Z" },
-    { url = "https://files.pythonhosted.org/packages/17/09/8789dcb09ee1de715727db7521aabbb68ffa68dfade3a49468440cfced49/wrapt-2.2.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:62415fd095bc590b842b6d092f2b5d9ccbaeb7e0b28535c03dcea2718b48636b", size = 158607, upload-time = "2026-06-20T23:48:21.728Z" },
-    { url = "https://files.pythonhosted.org/packages/9c/20/66e02562d53ee67d841f175e38e3c993c2d78a3e104c576cad61c028b43c/wrapt-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a41e758d80dc0ab8c210f641ac892009d356cf1f955d97db544c8dd317b4d14c", size = 166367, upload-time = "2026-06-20T23:48:23.177Z" },
-    { url = "https://files.pythonhosted.org/packages/bd/a3/832ac4e41222fb263b3042d42c2f08d305db7d0f0c9b1d3a271a9eede8f6/wrapt-2.2.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:b84cd4058001c9727b0e9980b7a9e66325b5ca748b1b578e822cade1bc6b304f", size = 157176, upload-time = "2026-06-20T23:48:24.711Z" },
-    { url = "https://files.pythonhosted.org/packages/b7/01/1bd5e4d2df9c0178989ac8da9186543465388588ee2ef153e2591accebef/wrapt-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:26fc73a1b15e0946d2942b9a4426d162b51676338327dc067ccd8d2d76385f94", size = 167025, upload-time = "2026-06-20T23:48:26.118Z" },
-    { url = "https://files.pythonhosted.org/packages/18/1e/f060df47755e87b57684cee7bfc1362b204df55fac96ffebc0631b697b79/wrapt-2.2.2-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0065a3b657cec06813b4241d2462ccec287f6863103d7445b725fb3a889736f9", size = 203399, upload-time = "2026-06-20T23:48:34.97Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/de/2316a757a1abb6453700b79d83e532146dcef2611348282d4d8889792161/wrapt-2.2.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:30f7424af5c5c345b7f26490e097f74a2ef45b3d08b664dc33571aee3bd3b56c", size = 210461, upload-time = "2026-06-20T23:48:36.569Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/29/d1160785ae18ca2495a6d82a21154103d74f656c9fd457fb35f6b11b965a/wrapt-2.2.2-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:07fdcb012821859168641acf68afad61ef9783cf37100af85f152550e9677194", size = 195313, upload-time = "2026-06-20T23:48:38.175Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/2d/7caa9598ae61a9cf0989cc501739cbeeb7d650ab3193cca1407b9af0c6ab/wrapt-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:f90038ab58fafb584801ca62d72384d7d5225d93c76f7b773c22fae545bd8066", size = 206116, upload-time = "2026-06-20T23:48:39.804Z" },
-    { url = "https://files.pythonhosted.org/packages/ac/02/281ea1088b8650d865f311b35cf86fd21df89128e2909714f1161e01c9d0/wrapt-2.2.2-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:c5d7825491bfa2d08b97e9557768987952c7b9ae687d06c3320b40a37ccb7f20", size = 192668, upload-time = "2026-06-20T23:48:41.346Z" },
-    { url = "https://files.pythonhosted.org/packages/be/7d/976e2d5b4b5c5babda40974edd54d0a5585cb60132ed86b46f4b80239b16/wrapt-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:0ad520e6daa9bbf136f14de735474dbec7dcc0891f718e1d274ce8dc92e645af", size = 198891, upload-time = "2026-06-20T23:48:43.056Z" },
-    { url = "https://files.pythonhosted.org/packages/74/eb/df7b7f0b631dbbc750f39be27d8b55f65777d8ac86da80e12be41a644c4b/wrapt-2.2.2-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0abfd648dac9ac9c5b3aa9b523d27f1789046640b58dcd5652a720ddb325e1fc", size = 167713, upload-time = "2026-06-20T23:48:52.598Z" },
-    { url = "https://files.pythonhosted.org/packages/4d/9a/d1bd36f6d088c8e652a9383cabbd49af30b8c576302a7eccddbab6963e3f/wrapt-2.2.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f4bfd8d1eb438153eff8b8cfe87f032ba65731e1ce06138b5090f745a33f6f95", size = 166779, upload-time = "2026-06-20T23:48:54.33Z" },
-    { url = "https://files.pythonhosted.org/packages/4c/ae/24ffacd4187fac2740a1972093929e836dea092d42c87d728cd98fee11a6/wrapt-2.2.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c427c9d06d859848a69f0d928fe28b5c33a941b2265d10a0e1f15cd244f1ee33", size = 158407, upload-time = "2026-06-20T23:48:55.944Z" },
-    { url = "https://files.pythonhosted.org/packages/a3/ed/974427668249a356051e8d67d47fa54ef6c777f0fcf3bae9d292c047d4b6/wrapt-2.2.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:4250b43d1a129d947e083c4dc6baf333c9bb34edd26f912d5b0457841fc858ab", size = 166594, upload-time = "2026-06-20T23:48:57.617Z" },
-    { url = "https://files.pythonhosted.org/packages/fb/5f/e1d7c6e4523f78db2fbd7826babd0348da1d5e0834c4f918b9ab5757dfae/wrapt-2.2.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:173e5bb5ca350a6e0abab60b7ec7cdd7992a814cb14b4de670a28f067f105663", size = 157068, upload-time = "2026-06-20T23:48:59.171Z" },
-    { url = "https://files.pythonhosted.org/packages/1e/c1/7ebd1027f00700c0b0233b20aceef2b4784294ed64971424c4a78e069e34/wrapt-2.2.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:aa14b01804bce36c6d63d7b6a4f55df390f29f8648cc13a1f40b166f4d54680d", size = 166470, upload-time = "2026-06-20T23:49:00.737Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/ea/1a89e6d3b7a83c3affe5c09cde77792c947e63e4bc85ad84cd5bb9abb0d8/wrapt-2.2.2-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:45c2f2768e790c9f8db90f239ef23a2af8e7570f25a35619ef902df4a738447f", size = 203362, upload-time = "2026-06-20T23:49:09.811Z" },
-    { url = "https://files.pythonhosted.org/packages/19/d8/3b58763d9863b5a73771c0d97110f9595d248db454009e07e1535ee905a4/wrapt-2.2.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bbf00ee0cb55ec24e2b0995a71942b85b21a066db8f3f46e1dbfdb9433ffba81", size = 210449, upload-time = "2026-06-20T23:49:11.521Z" },
-    { url = "https://files.pythonhosted.org/packages/2d/6f/17fd9e053103d8be148d20d5d7505facc72d5fe1f9127973904ceaed79cf/wrapt-2.2.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:2252f77663651b89255895f58cc6ac08fcb206d4371813e5af61bb62d4f7689c", size = 195349, upload-time = "2026-06-20T23:49:13.346Z" },
-    { url = "https://files.pythonhosted.org/packages/ef/04/d0d1ccaaa12cb7dccf28a23f0279a608ba498f71e81d949d5ed54bcfd5c1/wrapt-2.2.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2cd7181ab1c31192ff5219269830744b5a62020b3a6d433588c4f1c95b8f8bff", size = 206099, upload-time = "2026-06-20T23:49:15.051Z" },
-    { url = "https://files.pythonhosted.org/packages/44/b3/e8aa07b619890a2aa6cde1931b1887abb08820721b564a5f80b7ca3f3aa0/wrapt-2.2.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:6fe35fd51b74867d8b80174c277bd6bbf6a73e443f908129dc531c4b688a20d5", size = 192728, upload-time = "2026-06-20T23:49:16.854Z" },
-    { url = "https://files.pythonhosted.org/packages/b7/f0/1819fb50f0d3c9bd758d8a83b56f1b470dee8b5b8eac8702b7c137cea9d4/wrapt-2.2.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:11d95fc2fbad3163596c39d440e6f21ca9fccece74b56e30a37ac2fca786a07c", size = 198842, upload-time = "2026-06-20T23:49:18.504Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/d2/6317eb6d4554855bbf12d61857774af34747bf88a42c19bf306de67e2fa3/wrapt-2.2.2-py3-none-any.whl", hash = "sha256:5bad217350f19ce99ca5b5e71d406765ea86fe541628426772b657375ee1c048", size = 61460, upload-time = "2026-06-20T23:49:42.966Z" },
+version = "2.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/d2/a254a26d8ceaea87e0eee2e89fcfe53ddc1858418647493bb2937549ab6f/wrapt-2.5.0.tar.gz", hash = "sha256:c48cdb6c904dca76d9915a579e4a5fab6b0c25f650c1019ce78a78effaf7a345", size = 183288, upload-time = "2026-09-27T01:41:56.874Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/0e/3ce67af67525c0068680637c0eba823ce2f0fc6eff2d1779cab656fb8c3d/wrapt-2.5.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0cabb9c17ab79b2549d1f23b36f436473ad9253ef53995a817feba26fae69d5b", size = 250177, upload-time = "2026-09-27T01:40:03.819Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/3b/262b2c3c38aca6fa32cfea6407dbb346ce26ff788179b98137b3b3f3d8ca/wrapt-2.5.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6761765cc520ff9616fb035c02a85d1d744f7f70edd4649718fd0d09c589eacf", size = 250332, upload-time = "2026-09-27T01:40:05.15Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/ec/d54d273a2223d1964ecd5b5954aca18e8fbf2c5d8471e60ef3cbaaed9325/wrapt-2.5.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a145a7826eddea3eb5814903f98f93756042b919bb5305544cb1331daa2705b1", size = 229600, upload-time = "2026-09-27T01:40:06.47Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/88/33c75ac47b13b0bf77b44d96d02b1d0a2e6db66180909bacce86ef8fcd8e/wrapt-2.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6a9ee62a970075738909909bdbef3a7da9f7ae03dfca584db283547a29503b56", size = 246890, upload-time = "2026-09-27T01:40:07.838Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/0e/c3a3a158801e5bee7a0d9aa3561a127bd80f166b02b7b520a39e45d8f9b9/wrapt-2.5.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:691671ea05684f921ffc2e935fd3f9311c1795a10fbbfa006b46269733668f66", size = 226463, upload-time = "2026-09-27T01:40:09.206Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/2c/4c48ba51698a87e2e8299fc04856bbef391cb725f195bc93f0bc6196e1f2/wrapt-2.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e716f47c7f61e11709d3c0904213c94fc22999abf0c41461276cef886c1e8b4d", size = 238583, upload-time = "2026-09-27T01:40:10.631Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/02/c7174e78b0c38bb279b2d3c25a6bd7fb9d3b0200c3e5a8fad084e7cc3e85/wrapt-2.5.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b95a6eca3b927853529eea958310563c83140ae8451dd5dc4399c7da385dc4f3", size = 248259, upload-time = "2026-09-27T01:40:18.446Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/f9/47ae1d7ef325c3f6c81ae3c1fb4a3fef9d98c8025ed676c0bfc1550903ce/wrapt-2.5.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6058e12e9caa33468f9a36fb88c15a4bb30a479f997b37834b83abdbf062f264", size = 248224, upload-time = "2026-09-27T01:40:19.713Z" },
+    { url = "https://files.pythonhosted.org/packages/38/7b/a394448bcbbaf8e5a3f856520edbbb1b92fc42061def56284c9083f3ac87/wrapt-2.5.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0d245ac03f5ae77f1eea6eb19edd9e778c2f772490c20496c2f1cd3a102ee1b6", size = 227137, upload-time = "2026-09-27T01:40:21.359Z" },
+    { url = "https://files.pythonhosted.org/packages/41/45/fc252bda5aa1ca01bc838d3b108778e786a2a13d0c52fd17c5f6179aa246/wrapt-2.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f4ef4935962f7029b2058a99f1a47ccbffc3be919dddb3becb6c2c48eac3d9f0", size = 244710, upload-time = "2026-09-27T01:40:22.693Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/1c/527d1bde7371dcc2c378d88c97de03b121b486fb4cd3dbb399bc332c0676/wrapt-2.5.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:f12e80c3089ebc03727d368f8205b811b5af2cd4a72b5e4cac75e901dd316e39", size = 225053, upload-time = "2026-09-27T01:40:24.345Z" },
+    { url = "https://files.pythonhosted.org/packages/81/8e/2b823fded8c3b815408c58633929812eacd29d824fe57548b7868c4ee422/wrapt-2.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a346408f19b6d589bf029f25f65c0b4cdeed6302ef8f40da4e5d1552d22dc037", size = 236884, upload-time = "2026-09-27T01:40:25.654Z" },
+    { url = "https://files.pythonhosted.org/packages/23/38/285b433121d73c7a447b5b82d93c91dc3330f33ab5853975f34551e0c773/wrapt-2.5.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b640460f0ffb346b192686bd6fac5589e35a6c6640501c59a9fb6e82b0dd6bd8", size = 251245, upload-time = "2026-09-27T01:40:34.309Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/a6/3f63f4637e89484c1839a9ba3aedda5b2912e7ce12617034c6bd49752cfa/wrapt-2.5.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f17c5a3836397bf59fd57b0e5b0dc42969b1daa70d31aa361c6e13cbf138b5a", size = 250483, upload-time = "2026-09-27T01:40:35.841Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/cd/f24ee96016da222dbb921cfb22e2beb5ca189a7b730ef49e8bb106b49449/wrapt-2.5.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:4343880acd72e74233baf092285aaaf4306244e31d7601828bd2600316027df0", size = 231341, upload-time = "2026-09-27T01:40:37.295Z" },
+    { url = "https://files.pythonhosted.org/packages/23/eb/c9b180124271e494f615a130f966be56143e3e26e87706bc28582f94bc09/wrapt-2.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ea4fdc79c0045d6bb1603c109127145245cafe888588888444e1e37fbeadbac3", size = 247736, upload-time = "2026-09-27T01:40:38.653Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/60/345b8c213389809435d1950136b09991a1af2a66b988d0cd930ecd1b9f19/wrapt-2.5.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:f42239c89430eee2d8a6dec39e34677abdbb67fff63caf2467dd6124ea4d4d58", size = 228533, upload-time = "2026-09-27T01:40:40.12Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/15/c79f0f5827a9062c6be4fc25dc73e92fe1c014c7bfde2e61c8c0b56a91af/wrapt-2.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:bad63bb4dea3c58e8078a3a173259ac2df5442a437e49c632b4099c0250e803b", size = 238830, upload-time = "2026-09-27T01:40:41.505Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/34/3980fe5a899b69454f66db2991c144ecc828dbbd355ce6cd7b881056ebbc/wrapt-2.5.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0dfc38cb672af51fc29696ba9c6f05d2315f5e62c4af2564e50f07f81198a163", size = 283840, upload-time = "2026-09-27T01:40:50.406Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/b4/b37001235fd5871b3f31941229f8fef608279353b772dab3ccb248fd8726/wrapt-2.5.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:181a45000506a6382eb337354ca7e8525690702f1ccf2eae4f23a210ff339543", size = 293622, upload-time = "2026-09-27T01:40:51.868Z" },
+    { url = "https://files.pythonhosted.org/packages/09/b3/9b751c6268fa2111efc7e43895105bc0f60a83896b08581009e77563f8c7/wrapt-2.5.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:58b2a87c65cbfb20917ec48ace47f71b1962c1f81dbf18a4052e3037abf72028", size = 262840, upload-time = "2026-09-27T01:40:53.297Z" },
+    { url = "https://files.pythonhosted.org/packages/47/7d/b7b51d601981ccc1f7b9e6023991548dec43dc9d40317597fbe0085bc876/wrapt-2.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:0ea62bc142f4fa8b2e0ab058f50699ccd679ef6199c8fa3cc1c2396c7a659000", size = 288781, upload-time = "2026-09-27T01:40:54.756Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/82/1a84f288246905d0a71d44aa1f470ff8c75df2b96ef791d2938124449cb0/wrapt-2.5.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:425349a99b8c9540399d36620c376dc26e6aca93071cd6fafa239c2f1b5d53a4", size = 260116, upload-time = "2026-09-27T01:40:56.667Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/0e/0572224d1c4a3f0846f82614702ec3110d45c843dcc7781c5f33779e1fdc/wrapt-2.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fe09aac4837ec720af606a493e814dcc3f65631984e1b7231b589efcf9917024", size = 278485, upload-time = "2026-09-27T01:40:58.443Z" },
+    { url = "https://files.pythonhosted.org/packages/57/b6/41a0d7f9cf1f8e6aaecbf4b5b4eaacf4036fa3396c7d814364e07728a041/wrapt-2.5.0-cp315-cp315-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:bc6491d3008ecabf685b0746f03ad8241a0950336939b14addb03af39b51a316", size = 251754, upload-time = "2026-09-27T01:41:07.26Z" },
+    { url = "https://files.pythonhosted.org/packages/55/d4/dd2de1260a490cd55d083b3c1bc47a36aff0e8363249d108d3b34c091c0e/wrapt-2.5.0-cp315-cp315-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:47abb2bb7f15b416e72fbe5e68e49a6f09331dae6af1ca6055f5aa2251d2bd2f", size = 250768, upload-time = "2026-09-27T01:41:08.681Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/40/d08297feb5728cd6d3c1133633cad0249c2b7a82eb0213062ebab9cc1266/wrapt-2.5.0-cp315-cp315-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:7e25e9697f60af41fb86b08697e470b1e7eb6cd6ac0eb25e4b1f519839adc271", size = 232618, upload-time = "2026-09-27T01:41:10.293Z" },
+    { url = "https://files.pythonhosted.org/packages/54/52/d8ca61b26c2a34927cc999fc250f1018f415741691581280e6a76cced736/wrapt-2.5.0-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:bcd42e7b69c8c1e33a29b79b28de03bdc08876a49745830f9162a3af860e06d0", size = 248043, upload-time = "2026-09-27T01:41:12.132Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/5e/ba02904736e2d3b05afd7447b4ddff677ce61762265939555541adb8c668/wrapt-2.5.0-cp315-cp315-musllinux_1_2_riscv64.whl", hash = "sha256:36703cafc2ec059e118c2175e6cb7ad7299c2924aecdcb1b7a7ebbf7a3e20c19", size = 229577, upload-time = "2026-09-27T01:41:13.746Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/94/23968c18a6e37a8a130706dc52ccf4344a71f1fe53c99965eb0a7715a459/wrapt-2.5.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:1ebc0d09906057ada57a32158a657364ca40b8f86e604da3e7d979069b601502", size = 239372, upload-time = "2026-09-27T01:41:15.459Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/87/bbaa188dace348b6a403bbf3cc483f3f419ac97700274340e17f2dbc700e/wrapt-2.5.0-cp315-cp315t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:88bb24b9fdccb1d805258d5648533206eb58c58b6554985c47db53a89c11be85", size = 284602, upload-time = "2026-09-27T01:41:24.094Z" },
+    { url = "https://files.pythonhosted.org/packages/be/2e/8a3309b0cbd3ab809ee6b76812c3be211f5a08732f321f131d26bb4f078a/wrapt-2.5.0-cp315-cp315t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2e3d110d7f99644946f249927c346d9dba507a78815d1bcebdbf0d94c14c5649", size = 294893, upload-time = "2026-09-27T01:41:25.654Z" },
+    { url = "https://files.pythonhosted.org/packages/15/b7/eda8bbdb6a3b7343d2c71e23fb0ebfc15c12fd470e3cce7ea42f7a57aaac/wrapt-2.5.0-cp315-cp315t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1ffb2823c95dbeb8a47fedfba9636b2afeb0a8ef94b66df97bd081bdfe5a263f", size = 264658, upload-time = "2026-09-27T01:41:27.375Z" },
+    { url = "https://files.pythonhosted.org/packages/41/f0/589bad71ca3ce5444a626ee10d657fd6aa5080ada76b3bcd4550468aa16e/wrapt-2.5.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:557ebf4ce5568588368675014a2540405db687c2e4c7ad1eb83aa7e857be1864", size = 289974, upload-time = "2026-09-27T01:41:29.032Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/97/c48f3c820ae6687e87b537041cd49ad4caa41e05a8f0d8ec08e449ca303c/wrapt-2.5.0-cp315-cp315t-musllinux_1_2_riscv64.whl", hash = "sha256:05246a100da68259af521b88788f131ba005465f1c95d358cc3c03ec5e351b52", size = 261945, upload-time = "2026-09-27T01:41:30.941Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/ad/d96898f500cb1e4185474bac6cb14bb7ea670a32f37e8c354a0c647e3a92/wrapt-2.5.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:c932273bc43b068538f3874fa5e6c2a60f33fa0b11c1ebc7768652f6a0608943", size = 279744, upload-time = "2026-09-27T01:41:32.617Z" },
+    { url = "https://files.pythonhosted.org/packages/87/7d/5ed859fad4b5eddd598a846150aaab2703730ed4886c5c5e03b0df0cfdd5/wrapt-2.5.0-py3-none-any.whl", hash = "sha256:107eea1a511e98a3a5033b0c2cb403fbb37f05dee6ac1fb85c0460d311ec278c", size = 81586, upload-time = "2026-09-27T01:41:55.479Z" },
 ]
 
 [[package]]
 name = "zarr"
-version = "3.2.1"
+version = "3.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "donfig" },
     { name = "google-crc32c" },
+    { name = "msgspec" },
     { name = "numcodecs" },
     { name = "numpy" },
     { name = "packaging" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/93/8d/aeb164004f87543b06ef54f885d02c342c31ceb274e2bbec470a98927621/zarr-3.2.1.tar.gz", hash = "sha256:71565b738a0e7e8ed226f0516eba8c6bb53440ad7669a8c48ebb3534a161d035", size = 675161, upload-time = "2026-05-05T12:37:22.383Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ae/29/6330391bab511e2428c2409fc15f6f32e4def9b2ee5eec4e95b8300304be/zarr-3.4.0.tar.gz", hash = "sha256:b68676576a6fc42683dfcc0daeb3c0e994f71e1680696990bde056bd3acf3542", size = 829693, upload-time = "2026-09-15T20:58:38.216Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/88/0a/469e2bd01be1490336e6c8707386845655d59261543315778a3ccc7e8019/zarr-3.2.1-py3-none-any.whl", hash = "sha256:f78cdd3d9687ad0e9f9cba2c5683b64f0c52589c19f685eeabe872e93cc0d2c7", size = 319617, upload-time = "2026-05-05T12:37:20.66Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/6c/7c9cf79cb2122f7c94a36c0456b476515fa01c1343705d5411b3e8c1a1e9/zarr-3.4.0-py3-none-any.whl", hash = "sha256:0a5e6c9b5c4139759e612556c7fb32c8ee4e4877a46b3556244093413eb72395", size = 376931, upload-time = "2026-09-15T20:58:36.565Z" },
 ]
 
 [[package]]
@@ -4214,20 +4455,20 @@ wheels = [
 
 [[package]]
 name = "zope-interface"
-version = "8.5"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/08/dc/50550cfcbb2ea3cbca5f1d7ed05c8aa840f831a0f2d63aec0a953f7c590e/zope_interface-8.5.tar.gz", hash = "sha256:7a3ba1c5877f0f3e3906b02ddf793abed2becc2948116414ce0e1dd820b68d6d", size = 257957, upload-time = "2026-05-26T06:50:14.574Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e1/c3/005032195ff3b210c139b7c560ed5c534e844b0907d8e44d2b3d8919305e/zope_interface-8.5-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:e6347b8d8d12c5eca6502450a92be30079b7acfade2c4f693efa0deb8871b06e", size = 265296, upload-time = "2026-05-26T06:49:29.741Z" },
-    { url = "https://files.pythonhosted.org/packages/c5/66/1036543d6a66bc04c19df3cf650f3ad938a002ab0a443c24e23e8de5e8b9/zope_interface-8.5-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5e970dabea777a24b0b0bbf9dae3ab75ce8b2d8e948edf4875627034b21f3560", size = 270689, upload-time = "2026-05-26T06:49:31.767Z" },
-    { url = "https://files.pythonhosted.org/packages/30/4c/8b56259558cace4414e753ca6740396a1f59d4a95ddb55b4658600408670/zope_interface-8.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f0b48ccadaa9839e09ff81e969703cecb3f402c813bfe8b958652e699bea69f5", size = 270280, upload-time = "2026-05-26T06:49:33.489Z" },
-    { url = "https://files.pythonhosted.org/packages/aa/a5/692a2b8d70f78e848793231d5fae5fecbf8d0cccd73430fdc34802a6d3c1/zope_interface-8.5-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:7849ad8fa90763cc1087f4dda78ca3a233e950b3e08fac7079297c9cafbbd7bb", size = 265191, upload-time = "2026-05-26T06:49:43.449Z" },
-    { url = "https://files.pythonhosted.org/packages/70/8d/454a9cfc7a050c394ab4f11b3371f7897828b7415e096afff724637e65e0/zope_interface-8.5-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:5578c9421ca409a1f39f153d6f7803e4cde01da592ec75a9ac5e1b777d18d33b", size = 270626, upload-time = "2026-05-26T06:49:45.425Z" },
-    { url = "https://files.pythonhosted.org/packages/51/8c/db8409cfa3575b8e9b4800babd7d49f8228433cd1f0c56814bd0ada49c33/zope_interface-8.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e1bd7d96b4ca5fa311f54c9eac16dce4886b428c1531dbe06067763ccdf123b4", size = 270444, upload-time = "2026-05-26T06:49:47.025Z" },
-    { url = "https://files.pythonhosted.org/packages/32/67/bc8b4f465d388039255003e230c284a175cedf1203c692f23cb7bff64efe/zope_interface-8.5-cp314-cp314-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:3090e3a663d20194756a59a272e0c8508b889341e31d5894223331fe6b4f9b21", size = 266827, upload-time = "2026-05-26T06:49:54.873Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/eb/37d05b935ede53d79690fecc8d201440084418e590bcfc05f384451c7593/zope_interface-8.5-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9342fb74e2afefdb081bf1df727d209ea56995c6e13f5a0540e6d7aff4beafb8", size = 270139, upload-time = "2026-05-26T06:49:57.116Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/0b/fd0c54579e2ce8dc6cf1a757903f3374bc6fbda929a46af9e0f53cb0e5f0/zope_interface-8.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6c54725d818f1b57a7efb8b16528326e1f3c257b602b32393fd255c45af8799d", size = 270338, upload-time = "2026-05-26T06:49:58.698Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/95/d97430abd5ae9677e8b9295b58720c0064a5b557dbb6b8bf5928484cf0d8/zope_interface-8.5-cp314-cp314t-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:14b0e9799351d4c34fe99afd67f0cdd76e55ba15c66a98699d5fc22ea8241e08", size = 294905, upload-time = "2026-05-26T06:50:07.384Z" },
-    { url = "https://files.pythonhosted.org/packages/41/ec/a0f8f3dad6e74992f4654bdd94802be0929eabca7b871cac3b6fbb5e961b/zope_interface-8.5-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:0cd6a732ac84b94eb1ef9222a117347a27efd294ee16810ffdf7ecd307677ed5", size = 300885, upload-time = "2026-05-26T06:50:08.997Z" },
-    { url = "https://files.pythonhosted.org/packages/0f/da/6881b48803a0ee8d23eb5efa30fce3ed218a2bd9de5758ce489d224fee81/zope_interface-8.5-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:798b7c87d0e59a7d5d086d642208d0d8700ff0d55c4029134b3c479c3bfb110f", size = 304672, upload-time = "2026-05-26T06:50:10.563Z" },
+version = "8.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/39/a8481b926e42c44a6fcc670904f8251469ec42edbff1ba066719ca1e7fb4/zope_interface-8.6.tar.gz", hash = "sha256:b40ef9b4873afb5d0dec02b8d2dfde1cf18c72337b60c99cb735961e0bac05c0", size = 257973, upload-time = "2026-08-20T11:18:08.717Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/e1/b2d78ecb8aec59114111ed8c25894c0421afecc5e89b36fc356e2b07a607/zope_interface-8.6-cp312-cp312-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:780a66db884c0e2b0e6b34b4900f86916945a7c03d3be40ec845b051fcc052cd", size = 265308, upload-time = "2026-08-20T11:17:20.02Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/5a/126eeee4da016f5cca4db2297496069d5f1ba901fb53ebf104f9c087a113/zope_interface-8.6-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:9217b1123f6aeec9ddf1789bffd83da3123546d551c164a99f862a5d1f5ac0f8", size = 270703, upload-time = "2026-08-20T11:17:22.016Z" },
+    { url = "https://files.pythonhosted.org/packages/05/89/7767a6f9b0bb41a4d3777e8f93bfeb1b9a23ea643f83ce96163d9d672c8b/zope_interface-8.6-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:28b68c24131545c1d13fd2178bbd065e67f09db885d8426adf1fbdf2b6b66372", size = 270292, upload-time = "2026-08-20T11:17:23.905Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/2d/5b4dbbe618b816f626f2a640fcd9911a461e3733a608c4043a8cc79c12b3/zope_interface-8.6-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:c2bf932006229788d6bb41963dfc0345cba6ee24141a39316bd52a283a7d115f", size = 265203, upload-time = "2026-08-20T11:17:33.059Z" },
+    { url = "https://files.pythonhosted.org/packages/79/96/c02befafb8e5d3c92898aa02fffca94d164830013fd0a50c4a652a728712/zope_interface-8.6-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:09522cdc6a77376bc36988b531db3b568c8cb0b6ca7286d8316aab283888770f", size = 270637, upload-time = "2026-08-20T11:17:35.167Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/c4/d61b18724597ca62c1a3a753370fff7b76f43c01b44e9a13c18e2300eaf0/zope_interface-8.6-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:edf1bd7ed576319241b2b314eaa549cee3e3e0f81f46911086b387d03a303ad3", size = 270456, upload-time = "2026-08-20T11:17:37.146Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/56/01f84b4e966a32088e9076b1e7b2afa310f52bf9b9a077d2958cf66e81aa/zope_interface-8.6-cp314-cp314-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:a91eb220d9ae6aa6d746d6dac5b4db35b1417903301b3315ba3275b19570be0b", size = 266840, upload-time = "2026-08-20T11:17:46.366Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/40/2a644e32cd6f0516e7df1fc0c58e544a8cc11ba06b0d55d308519b02459d/zope_interface-8.6-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:3f7f6da49911ffe75ae3f7a9a45619f205420cc6578aff02f8ca29ed1de10f14", size = 270145, upload-time = "2026-08-20T11:17:48.195Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/18/02ebd81feff11a2766159fcb49c5b773fef5ae4414c38fb19114aad9e961/zope_interface-8.6-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ef15a2f6258f809334a19c1fcce64648813066ceebe3f3f6077871483fd0f50d", size = 270351, upload-time = "2026-08-20T11:17:50.07Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/96/ac48a6b7cfe972e4a9b0d7ec8b9f36a7956cc95d72029f0013ff096c55af/zope_interface-8.6-cp314-cp314t-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:5dbe120cfcfc8e6aed418f340c3d1ad4072253e17176503e363ddac27fcb2ac6", size = 294916, upload-time = "2026-08-20T11:17:59.952Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/54/4df4bb0b1aace2298386375ab2fb752378683b558d2db713e25c40a3e96a/zope_interface-8.6-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:27e6de8e593736210d2a9f1bbf766a5653aa4819c184f864ab9d1f8bd3590a60", size = 300898, upload-time = "2026-08-20T11:18:02.224Z" },
+    { url = "https://files.pythonhosted.org/packages/08/9c/0c8c80c1eeb62ac0c3ed1f51ad8cdd6da9373c53247c659c49f0ea29f742/zope_interface-8.6-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:66ab8c5d8820aa378968c16b7a3cb051aca342eafa649c9a363182f572d75ccb", size = 304684, upload-time = "2026-08-20T11:18:04.105Z" },
 ]

From cbef13775f2fe93206efdb00b1ba07fc82a28d82 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Thu, 1 Oct 2026 03:05:18 +0200
Subject: [PATCH 43/83] Workflow tidy-ups: rho/tau rule script, TreeCorr CPU
 count, plain /nXXdataN paths (#368)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* rho/tau rule script without its interactive scaffolding; docs for a private COSMO_INFERENCE

- workflow/scripts/run_rho_tau.py is a plain rule script: the IPython
  autoreload branch, hard-coded interactive paths and progress prints go;
  stream buffering and the output check come from cv_runner.
- workflow/README: launching with COSMO_INFERENCE=<tree> of one's own, with
  the mask link the CosmoCov chain needs.
- Docstrings: calculate_rho_tau_stats; sacc_writers notes that the
  integration-grid ξ± part feeds COSEBIs and pure-E/B and does not join
  {version}.sacc.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* cosmo_val: TreeCorr runs on the CPUs the process holds

treecorr_config defaults num_threads to the process's CPU affinity. TreeCorr's
own default is multiprocessing.cpu_count(), the node's count: a job holding 16
of a node's 24 CPUs ran 24 threads. A caller's num_threads still overrides it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* workflow: the checkout and output roots take the plain /nXXdataN spelling

common._plain resolves a path and spells /automnt/<disk>/... back as
/<disk>/..., on every host; REPO_ROOT, COSMO_VAL and COSMO_INFERENCE go
through it. Snakemake keys its persistence records and matches targets by
path string, so a symlinked or /automnt-spelled root no longer declares paths
a plainly named target cannot match, and a job step re-deriving the launch's
paths on the node that owns the disk (which has no /automnt/<disk>) declares
paths it can reach. The README names the disks a job sees under their plain
spelling.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* sacc_writers: module docstring as on develop

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/cosmo_val/core.py           |  3 ++
 .../cosmo_val/psf_systematics.py              |  1 +
 src/sp_validation/tests/test_cosmo_val.py     | 10 ++++
 workflow/README.md                            | 24 +++++++--
 workflow/common.py                            | 28 ++++++++--
 workflow/scripts/run_rho_tau.py               | 51 +++----------------
 workflow/tests/test_dag.py                    | 44 +++++++++++++++-
 7 files changed, 108 insertions(+), 53 deletions(-)

diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 866e0cad..e4957afe 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -314,6 +314,9 @@ def __init__(
             # thread count (max(3, ceil(log2 n))) and ξ± depends on the machine.
             # 6 is what TreeCorr derives on candide's 48- and 64-CPU nodes.
             "min_top": 6,
+            # The CPUs this process may use; TreeCorr's own default is the
+            # node's count, whatever share of it the job holds.
+            "num_threads": len(os.sched_getaffinity(0)),
         }
 
         self.catalog_config_path = Path(catalog_config)
diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index ad405714..8a402bd5 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -26,6 +26,7 @@
 
 class PSFSystematicsMixin:
     def calculate_rho_tau_stats(self):
+        """Measure ρ/τ statistics per version and write each version's SACC part."""
         out_dir = f"{self.cc['paths']['output']}/rho_tau_stats"
         if not os.path.exists(out_dir):
             os.mkdir(out_dir)
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index 032eb2ff..54aed4a5 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -535,6 +535,16 @@ def test_calculate_2pcf_does_not_depend_on_thread_count(self, tmp_path):
         shift = np.max(np.abs(xi[48] - xi[4]) / sigma)
         assert shift < 1e-6, f"ξ± moves by {shift:.3g}σ between 4 and 48 threads"
 
+    def test_treecorr_runs_on_the_cpus_the_process_holds(self, tmp_path):
+        """By default TreeCorr takes the process's CPU affinity, not the node's count."""
+        import treecorr
+
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        CosmologyValidation(versions=[version], npatch=1, **params).calculate_2pcf(
+            version
+        )
+        assert treecorr.get_omp_threads() == len(os.sched_getaffinity(0))
+
     def test_calculate_scale_dependent_leakage_runs_on_synthetic_catalog(
         self, tmp_path
     ):
diff --git a/workflow/README.md b/workflow/README.md
index 21d0419b..b99da45b 100644
--- a/workflow/README.md
+++ b/workflow/README.md
@@ -101,7 +101,15 @@ halves of one commit, split.
 The catalogue config is the launched checkout's `cosmo_val/cat_config.yaml`.
 `COSMO_VAL` defaults to the launched checkout's `cosmo_val/output`, so writing
 into another checkout's products means naming it; `COSMO_INFERENCE` defaults to
-the shared candide tree.
+the shared candide tree, which holds the CosmoCov covariances and only its owner
+can write. Anyone else launches with `COSMO_INFERENCE=<tree>` of their own,
+holding a link to the shared tree's `data/mask/` (the one input the covariance
+rules take from it); the CosmoCov chain then runs there:
+
+```bash
+mkdir -p <tree>/data
+ln -s /n17data/cdaley/unions/code/sp_validation/cosmo_inference/data/mask <tree>/data/
+```
 
 **Caveat:** `rerun-triggers: code` watches rule bodies and `script:` files, not
 `src/`. Editing a module under `src/` does not by itself mark outputs stale —
@@ -113,8 +121,7 @@ To reproduce a run from the image alone, opt out:
 snakemake --profile workflow/profiles/candide --config checkout_pythonpath=false <target>
 ```
 
-Either way the checkout has to sit under one of the profile's bind mounts to be
-visible inside the job.
+Either way the checkout has to sit on a disk the jobs see (next section).
 
 Most of the time this default is all you need. Reach for a different *image*
 only when the dependency stack changed — a new package, a lockfile bump — not
@@ -128,8 +135,15 @@ directory. `/automnt/nXXdataN` works only from a node that does *not* own that
 disk. On the owning node the disk is mounted directly at `/nXXdataN` and there
 is no `/automnt/nXXdataN` entry at all, so a job that lands there dies about one
 second after the allocation starts, before any log file is written. This is why
-`n17` is in the profile's exclude list. Every canonical path in `common.py`
-already uses the plain form; keep new paths the same.
+`n17` is in the profile's exclude list. `common.py` spells the launched
+checkout, `COSMO_VAL` and `COSMO_INFERENCE` in the plain form whatever spelling
+it is given (a symlink, a relative path, `/automnt`), and Snakemake matches a
+target by its path string, so name file targets in the plain form too; keep new
+paths the same. A job sees a plain path only on a disk the profile binds by
+name — `/home`, `/n17data`, `/n23data1`, `/n09data` (the `/automnt` bind does
+not serve it) — so the checkout and both output roots sit on one of those. To
+work on another disk, add it to both bind lists: the candide profile's
+`apptainer-args` and `container.DEFAULT_BINDS`.
 
 ### Run Snakemake from the host, never from inside the container
 
diff --git a/workflow/common.py b/workflow/common.py
index b77d088a..de0f71b2 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -12,8 +12,25 @@
 import sys
 from pathlib import Path
 
+
+def _plain(path):
+    """``path`` resolved, in the spelling every candide node can reach.
+
+    A data disk is mounted at ``/nXXdataN`` on the node that owns it and
+    reached from every other node through ``/nXXdataN -> /automnt/nXXdataN``;
+    the owning node has no ``/automnt/nXXdataN``. A resolved path under
+    ``/automnt/<disk>`` is therefore spelled back under ``/<disk>``, whatever
+    the host: a job step re-derives its paths on its own node, and must
+    derive the launch's.
+    """
+    path = Path(path).resolve()
+    if len(path.parts) > 2 and path.parts[1] == "automnt":
+        return Path("/", *path.parts[2:])
+    return path
+
+
 # The checkout this workflow was launched from: workflow/common.py -> <repo>.
-REPO_ROOT = Path(__file__).resolve().parent.parent
+REPO_ROOT = _plain(__file__).parents[1]
 REPO_SRC = REPO_ROOT / "src"
 
 # The container model lives in the package (``sp_validation/container.py``).
@@ -42,9 +59,12 @@
 # fresh tree without clobbering (or silently reusing) prior products. COSMO_VAL
 # defaults to the launched checkout's own (gitignored) cosmo_val/output, so a
 # launch writes into another checkout's products only when it names that tree;
-# COSMO_INFERENCE defaults to the shared tree on candide.
-COSMO_VAL = Path(os.environ.get("COSMO_VAL", REPO_ROOT / "cosmo_val" / "output"))
-COSMO_INFERENCE = Path(
+# COSMO_INFERENCE defaults to the shared tree on candide. Snakemake keys its
+# persistence records (params, input, code) and matches targets by path string,
+# so both roots are spelled plain whatever spelling a launch gives, and a file
+# target is named in that plain form.
+COSMO_VAL = _plain(os.environ.get("COSMO_VAL", REPO_ROOT / "cosmo_val" / "output"))
+COSMO_INFERENCE = _plain(
     os.environ.get(
         "COSMO_INFERENCE", "/n17data/cdaley/unions/code/sp_validation/cosmo_inference"
     )
diff --git a/workflow/scripts/run_rho_tau.py b/workflow/scripts/run_rho_tau.py
index 4983cffb..a798a1b6 100644
--- a/workflow/scripts/run_rho_tau.py
+++ b/workflow/scripts/run_rho_tau.py
@@ -1,40 +1,15 @@
-# %%
-# if interactive
-import os
-import sys
-from pathlib import Path
+"""Rule rho_tau_stats: ρ/τ PSF statistics for one version.
 
-from IPython import get_ipython
+Writes the ρ/τ FITS tables and the version's ρ/τ SACC part.
+"""
 
-ipython = get_ipython()
+from cv_runner import _unbuffer_streams, verify_outputs
 
-# enable autoreload for interactive sessions
-if ipython is not None:
-    ipython.run_line_magic("load_ext", "autoreload")
-    ipython.run_line_magic("autoreload", "2")
-else:
-    # Force unbuffered stdout and stderr
-    sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1)  # line-buffered
-    sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1)
+from sp_validation.cosmo_val import CosmologyValidation
 
-from sp_validation.cosmo_val import CosmologyValidation  # noqa: E402
+_unbuffer_streams()
+params = snakemake.params
 
-print("Finished imports")
-
-if ipython is not None:
-    from snakemake_helpers import snakemake_interactive
-
-    snakemake = snakemake_interactive(
-        "/n17data/cdaley/unions/pure_eb/code/sp_validation/cosmo_val/output/rho_tau_stats/rho_stats_SP_v1.4.5.fits",
-        "/home/cdaley/n17data/unions/pure_eb",
-    )
-
-params = snakemake.params  # type: ignore
-
-# %%
-print("Starting CosmologyValidation")
-
-# Use parameters passed from Snakemake rule
 cv = CosmologyValidation(
     versions=[params["ver"]],
     theta_min=float(params["min_sep"]),
@@ -44,15 +19,5 @@
     catalog_config=params["cat_config"],
     output_dir=params["output_dir"],
 )
-
 cv.calculate_rho_tau_stats()
-
-# Confirm CosmologyValidation produced the requested outputs: the rho/tau FITS
-# and the born-as-SACC rho_tau part.
-outputs = snakemake.output  # type: ignore
-for label in ("rho_stats", "tau_stats", "rho_tau"):
-    target = Path(outputs[label])
-    if not target.exists():
-        raise FileNotFoundError(
-            f"Expected {label} file not found after CosmologyValidation run: {target}"
-        )
+verify_outputs(snakemake)
diff --git a/workflow/tests/test_dag.py b/workflow/tests/test_dag.py
index 4d2ba017..90272dca 100644
--- a/workflow/tests/test_dag.py
+++ b/workflow/tests/test_dag.py
@@ -6,7 +6,7 @@
 from pathlib import Path
 
 import pytest
-from conftest import REPO, VERSIONS, on_candide, parse_jobs
+from conftest import REPO, VERSIONS, _load_module, on_candide, parse_jobs
 
 
 def test_assemble_resolves(toy):
@@ -79,6 +79,48 @@ def test_outputs_stay_in_the_output_roots(toy, named):
     assert not strays, strays
 
 
+@pytest.mark.parametrize("root", ["COSMO_VAL", "COSMO_INFERENCE"])
+def test_every_spelling_of_an_output_root_declares_the_same_paths(toy, tmp_path, root):
+    """Snakemake keys its persistence records by path string, so a symlinked
+    spelling of an output root declares the tree's resolved paths."""
+    tree = Path(toy.env[root]).resolve()
+    link = tmp_path / "link"
+    link.symlink_to(tree, target_is_directory=True)
+    result = toy.snakemake("-n", "all", env={**toy.env, root: str(link)})
+    assert result.returncode == 0, result.stdout
+    declared = [f for j in parse_jobs(result.stdout) for f in j.input + j.output]
+    assert any(f.startswith(f"{tree}/") for f in declared), declared
+    assert not [f for f in declared if f.startswith(f"{link}/")], declared
+
+
+def test_output_roots_take_the_plain_spelling(toy):
+    """A root given as /automnt/<disk>/... is declared as /<disk>/..., the one
+    spelling every node has, on any host: a job step re-derives the launch's
+    paths on its own node, and the node that owns a disk has neither
+    /automnt/<disk> nor a /<disk> link to it, like the disk no host has here.
+    A file target named in the plain spelling resolves, and no declared path
+    lies under /automnt."""
+    tree = Path("/n00data0/spv-dag-toy")  # a dry-run creates nothing
+    env = {
+        **toy.env,
+        "COSMO_VAL": f"/automnt{tree}/val",
+        "COSMO_INFERENCE": f"/automnt{tree}/inference",
+    }
+    common = _load_module(toy.root / "workflow" / "common.py", "plain_common", env)
+    assert (common.COSMO_VAL, common.COSMO_INFERENCE) == (
+        tree / "val",
+        tree / "inference",
+    )
+    grids = toy.common.xi_grids(toy.config, toy.config["fiducial"])
+    reporting = toy.common.grid_binning(grids["reporting"])
+    target = tree / "val" / f"{VERSIONS[0]}_xi_{reporting}.sacc"
+    result = toy.snakemake("-n", str(target), env=env)
+    assert result.returncode == 0, result.stdout
+    declared = [f for j in parse_jobs(result.stdout) for f in j.input + j.output]
+    assert str(target) in declared, declared
+    assert not [f for f in declared if f.startswith("/automnt/")], declared
+
+
 def _apptainer_stub(tmp_path):
     """A PATH entry that answers where apptainer is not installed.
 

From 4613acc234922d3e42ffbd519196bae5b53c702e Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Thu, 1 Oct 2026 04:31:54 +0200
Subject: [PATCH 44/83] =?UTF-8?q?rho=5Ftau=5Fstats:=20request=2064=20GB;?=
 =?UTF-8?q?=20the=2048-thread=20jackknife=20peaks=20at=2038=E2=80=9356=20G?=
 =?UTF-8?q?B=20on=20SP=5Fv1.4.6.3=20(#369)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Claude-Session: https://claude.ai/code/session_01UzeJqdtGgoeWizre32L5fD

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 workflow/rules/twopoint.smk | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 0e3f90c1..15174fa2 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -60,7 +60,7 @@ rule rho_tau_stats:
         cat_config=CAT_CONFIG,
         output_dir=str(COSMO_VAL),
     resources:
-        mem_mb=30000,
+        mem_mb=64000,
         disk_mb=20000,
         runtime=360,
     script:

From 5d3f8e49ab357020ffc03b7be36ab55e68531e7c Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 00:56:17 +0200
Subject: [PATCH 45/83] =?UTF-8?q?GLASS=20mock=20=CE=BE=C2=B1=20on=20the=20?=
 =?UTF-8?q?data's=20integration=20grid;=20TreeCorr=20threads=20from=20the?=
 =?UTF-8?q?=20rule=20(#371)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

glass_mock_xi_fine measured mocks on a hardcoded 0.5'–500' / 1000-bin grid
while the data's E/B transforms run on XI_GRIDS["integration"]; the two must
match node for node, or mock and data B-mode vectors integrate different θ
samplings. The mock rule, its output name (grid tag, as the data ξ± carry),
the COSEBIS scatter/bias-test inputs and the bias test's covariance grid now
all come from XI_GRIDS["integration"]. The COSEBIS scripts read the binning
from the file (GGCorrelation.from_file) instead of restating it.

run_glass_mock_2pcf passes the rule's thread count to gg.process; TreeCorr's
own default is every CPU on the node.

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 workflow/rules/glass_mock.smk              | 33 ++++++++++++++--------
 workflow/scripts/mock_cosebis_bias_test.py |  5 +---
 workflow/scripts/mock_cosebis_scatter.py   |  3 +-
 workflow/scripts/run_glass_mock_2pcf.py    |  9 ++++--
 4 files changed, 30 insertions(+), 20 deletions(-)

diff --git a/workflow/rules/glass_mock.smk b/workflow/rules/glass_mock.smk
index 090e3b55..8e55cc43 100644
--- a/workflow/rules/glass_mock.smk
+++ b/workflow/rules/glass_mock.smk
@@ -9,12 +9,17 @@ GLASS_MOCK_DIR = "/n09data/guerrini/glass_mock_v1.4.6/results"
 GLASS_MOCK_IDS = [f"{i:05d}" for i in range(1, 101)]
 MOCK_RESULTS = "results/glass_mock"
 
+# Mock ξ± are measured on the data's integration grid, node for node, so the
+# E/B transforms see the same θ sampling on mocks and data.
+MOCK_XI_GRID = XI_GRIDS["integration"]
+MOCK_XI = f"{MOCK_RESULTS}/gg_glass_mock_{{mock_id}}_{xi_binning('integration')}.fits"
+
 wildcard_constraints:
     cl_nbins=r"\d+",
 
 
 rule glass_mock_xi_fine:
-    """Fine-binned treecorr ξ± for one GLASS mock (1000 bins, 0.5–500 arcmin).
+    """Treecorr ξ± for one GLASS mock on the data's integration grid.
 
     Required for config-space COSEBIS and pure E/B on mocks.
     ~35M galaxies → ~30 min on 48 cores.
@@ -22,11 +27,11 @@ rule glass_mock_xi_fine:
     input:
         catalog=f"{GLASS_MOCK_DIR}/unions_glass_sim_{{mock_id}}_4096.fits",
     output:
-        gg=f"{MOCK_RESULTS}/gg_glass_mock_{{mock_id}}_nbins=1000.fits",
+        gg=MOCK_XI,
     params:
-        min_sep=0.5,
-        max_sep=500.0,
-        nbins=1000,
+        min_sep=MOCK_XI_GRID["min_sep"],
+        max_sep=MOCK_XI_GRID["max_sep"],
+        nbins=MOCK_XI_GRID["nbins"],
     threads: 24
     resources:
         mem_mb=20000,
@@ -63,10 +68,7 @@ rule glass_mock_pseudo_cl:
 rule glass_mock_all_xi:
     """Aggregator: fine-binned ξ± for 5 mocks."""
     input:
-        expand(
-            f"{MOCK_RESULTS}/gg_glass_mock_{{mock_id}}_nbins=1000.fits",
-            mock_id=GLASS_MOCK_IDS,
-        ),
+        expand(MOCK_XI, mock_id=GLASS_MOCK_IDS),
 
 
 rule glass_mock_all_pseudo_cl:
@@ -93,7 +95,7 @@ rule mock_cosebis_scatter:
     Byte-order conversion for numba compatibility handled internally.
     """
     input:
-        xi=f"{MOCK_RESULTS}/gg_glass_mock_{{mock_id}}_nbins=1000.fits",
+        xi=MOCK_XI,
     params:
         nmodes=config["fiducial"]["nmodes"],
         theta_min=config["cosebis"]["theta_min"],
@@ -117,8 +119,15 @@ rule mock_cosebis_bias_test:
             f"{MOCK_RESULTS}/cosebis_glass_mock_{{mock_id}}.npz",
             mock_id=GLASS_MOCK_IDS,
         ),
-        xi_ref=f"{MOCK_RESULTS}/gg_glass_mock_00001_nbins=1000.fits",
-        cov=covariance_path("SP_v1.4.6_leak_corr", "g", 0.5, 500.0, 1000, "_masked"),
+        xi_ref=MOCK_XI.format(mock_id="00001"),
+        cov=covariance_path(
+            "SP_v1.4.6_leak_corr",
+            "g",
+            MOCK_XI_GRID["min_sep"],
+            MOCK_XI_GRID["max_sep"],
+            MOCK_XI_GRID["nbins"],
+            "_masked",
+        ),
     params:
         nmodes=config["fiducial"]["nmodes"],
         theta_min=config["cosebis"]["theta_min"],
diff --git a/workflow/scripts/mock_cosebis_bias_test.py b/workflow/scripts/mock_cosebis_bias_test.py
index 75593ae4..e739491c 100644
--- a/workflow/scripts/mock_cosebis_bias_test.py
+++ b/workflow/scripts/mock_cosebis_bias_test.py
@@ -52,10 +52,7 @@
 # Analytic COSEBIS covariance from ξ± covariance
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-gg_ref = treecorr.GGCorrelation(
-    min_sep=0.5, max_sep=500, nbins=1000, sep_units="arcmin"
-)
-gg_ref.read(xi_ref_path)
+gg_ref = treecorr.GGCorrelation.from_file(xi_ref_path)
 
 start, stop = scale_cut_to_bins(gg_ref, theta_min, theta_max)
 theta_cut = gg_ref.meanr[start:stop].astype(float)
diff --git a/workflow/scripts/mock_cosebis_scatter.py b/workflow/scripts/mock_cosebis_scatter.py
index 4da0c659..30a6aecb 100644
--- a/workflow/scripts/mock_cosebis_scatter.py
+++ b/workflow/scripts/mock_cosebis_scatter.py
@@ -12,8 +12,7 @@
 theta_max = snakemake.params.theta_max
 out_path = snakemake.output.cosebis
 
-gg = treecorr.GGCorrelation(min_sep=0.5, max_sep=500, nbins=1000, sep_units="arcmin")
-gg.read(xi_path)
+gg = treecorr.GGCorrelation.from_file(xi_path)
 
 start, stop = scale_cut_to_bins(gg, theta_min, theta_max)
 inds = np.arange(start, stop)
diff --git a/workflow/scripts/run_glass_mock_2pcf.py b/workflow/scripts/run_glass_mock_2pcf.py
index a2343967..848539e1 100644
--- a/workflow/scripts/run_glass_mock_2pcf.py
+++ b/workflow/scripts/run_glass_mock_2pcf.py
@@ -16,6 +16,7 @@
 min_sep = snakemake.params.min_sep  # noqa: F821
 max_sep = snakemake.params.max_sep  # noqa: F821
 nbins = snakemake.params.nbins  # noqa: F821
+num_threads = snakemake.threads  # noqa: F821
 
 # Load mock catalog
 with fits.open(catalog_path) as hdul:
@@ -45,7 +46,11 @@
     sep_units="arcmin",
 )
 
-print(f"Running treecorr: {nbins} bins, [{min_sep}, {max_sep}] arcmin...")
-gg.process(cat)
+print(
+    f"Running treecorr: {nbins} bins, [{min_sep}, {max_sep}] arcmin, "
+    f"{num_threads} threads..."
+)
+# TreeCorr's own default is every CPU on the node, not the job's share of it.
+gg.process(cat, num_threads=num_threads)
 gg.write(output_path)
 print(f"Saved to {output_path}")

From df4f2503384b79c1d3bcb064c53f7b0ed0a24738 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 01:04:47 +0200
Subject: [PATCH 46/83] Add calibrated global min-PTE threshold for many null
 tests (#370)

calibrate_min_pte takes a (n_mocks, n_stats) PTE matrix from noise-only
realisations and returns the alpha-quantile of the per-mock minimum PTE
(with a distribution-free order-statistic interval), the implied effective
number of independent tests k_eff from 1 - (1 - t)^k = alpha, and a
global_pte method giving the data's global p-value with a Wilson interval.
Statistic-agnostic: columns can be any statistics, bin pairs or scale cuts.

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/statistics.py            | 157 ++++++++++++++++++++-
 src/sp_validation/tests/test_statistics.py |  59 ++++++++
 2 files changed, 214 insertions(+), 2 deletions(-)

diff --git a/src/sp_validation/statistics.py b/src/sp_validation/statistics.py
index b8c6d1d5..dc71f680 100644
--- a/src/sp_validation/statistics.py
+++ b/src/sp_validation/statistics.py
@@ -3,10 +3,12 @@
 :Name: statistics.py
 
 :Description: Cosmology-independent statistical helpers (jackknife resampling,
-              chi2/PTE, covariance<->correlation, OneCovariance reshaping).
-              Extracted verbatim from the former basic.py.
+              chi2/PTE, calibrated min-PTE across many null tests,
+              covariance<->correlation, OneCovariance reshaping).
 """
 
+from dataclasses import dataclass
+
 import numpy as np
 from scipy import stats
 
@@ -126,3 +128,154 @@ def cov_from_one_covariance(cov_one_cov, gaussian=True):
         for j in range(n_bins):
             cov[i, j] = cov_one_cov[i * n_bins + j, index_value]
     return cov
+
+
+def _min_pte(ptes, two_sided):
+    """Minimum PTE across the last axis, optionally two-sided."""
+    ptes = np.asarray(ptes, dtype=float)
+    if not np.all((ptes >= 0.0) & (ptes <= 1.0)):
+        raise ValueError("PTEs must be finite and lie in [0, 1]")
+    if two_sided:
+        ptes = 2.0 * np.minimum(ptes, 1.0 - ptes)
+    return ptes.min(axis=-1)
+
+
+def wilson_interval(count, n, level=0.68):
+    """Two-sided Wilson score interval for a binomial fraction ``count / n``."""
+    z = stats.norm.ppf(0.5 + level / 2.0)
+    fraction = count / n
+    denominator = 1.0 + z**2 / n
+    centre = (fraction + z**2 / (2.0 * n)) / denominator
+    half_width = (
+        z * np.sqrt(fraction * (1.0 - fraction) / n + z**2 / (4.0 * n**2)) / denominator
+    )
+    return max(0.0, centre - half_width), min(1.0, centre + half_width)
+
+
+def effective_number_of_tests(threshold, alpha):
+    """Number of independent tests ``k`` with ``1 - (1 - threshold)^k = alpha``.
+
+    For ``k`` independent uniform PTEs, the ``alpha``-quantile of their minimum
+    is ``1 - (1 - alpha)^(1/k)``; inverting that for a calibrated threshold
+    gives the effective number of independent tests it corresponds to.
+    """
+    return np.log1p(-alpha) / np.log1p(-np.asarray(threshold, dtype=float))
+
+
+@dataclass(frozen=True)
+class MinPTECalibration:
+    """Global min-PTE null-test threshold calibrated on noise-only mocks.
+
+    Attributes
+    ----------
+    alpha : float
+        Global false-positive rate the threshold is calibrated to.
+    two_sided : bool
+        Whether each PTE ``p`` entered as ``2 min(p, 1 - p)``.
+    mock_min_pte : numpy.ndarray
+        Minimum PTE across statistics for each mock, shape ``(n_mocks,)``.
+    threshold : float
+        ``alpha``-quantile of ``mock_min_pte``: a data vector whose minimum
+        PTE falls below it fails the global null test at level ``alpha``.
+    threshold_interval : tuple of float
+        Distribution-free interval on ``threshold`` at confidence ``level``.
+    k_eff : float
+        Effective number of independent tests implied by ``threshold``.
+    k_eff_interval : tuple of float
+        ``k_eff`` mapped from ``threshold_interval``.
+    level : float
+        Confidence level of the intervals.
+    """
+
+    alpha: float
+    two_sided: bool
+    mock_min_pte: np.ndarray
+    threshold: float
+    threshold_interval: tuple
+    k_eff: float
+    k_eff_interval: tuple
+    level: float
+
+    @property
+    def n_mocks(self):
+        """Number of mocks the calibration rests on."""
+        return self.mock_min_pte.size
+
+    def global_pte(self, data_ptes):
+        """Global p-value of a data vector's PTEs against the mocks.
+
+        Parameters
+        ----------
+        data_ptes : array_like
+            The data's PTE for each statistic, ordered as the mock columns.
+
+        Returns
+        -------
+        tuple
+            ``(p, interval)``: the fraction of mocks whose minimum PTE is at
+            most the data's, and its Wilson interval at ``level``.
+        """
+        data_min = _min_pte(data_ptes, self.two_sided)
+        count = int(np.count_nonzero(self.mock_min_pte <= data_min))
+        return count / self.n_mocks, wilson_interval(count, self.n_mocks, self.level)
+
+
+def calibrate_min_pte(mock_ptes, alpha=0.05, two_sided=False, level=0.68):
+    """Calibrate a global threshold on the minimum PTE across statistics.
+
+    Testing many correlated statistics at a fixed per-test level inflates the
+    false-positive rate by an unknown amount. Taking the minimum PTE across
+    statistics for each noise-only mock and reading off its ``alpha``-quantile
+    gives a threshold with exactly that global rate, whatever the correlations
+    between statistics and whatever miscalibration of the individual PTEs.
+
+    Parameters
+    ----------
+    mock_ptes : array_like
+        PTEs of noise-only realisations, shape ``(n_mocks, n_stats)``; the
+        columns can be any statistics, redshift-bin pairs or scale cuts.
+    alpha : float, optional
+        Global false-positive rate; default is ``0.05``.
+    two_sided : bool, optional
+        If ``True``, test each PTE ``p`` as ``2 min(p, 1 - p)`` so that
+        anomalously small statistics also fail; default is ``False``.
+    level : float, optional
+        Confidence level of the reported intervals; default is ``0.68``.
+
+    Returns
+    -------
+    MinPTECalibration
+        The threshold, its interval, the effective number of independent
+        tests, and :meth:`MinPTECalibration.global_pte` for the data.
+
+    Notes
+    -----
+    The interval on the threshold uses order statistics: the number of mocks
+    below the true ``alpha``-quantile is ``Binomial(n_mocks, alpha)``, and the
+    ``(1 -/+ level) / 2`` quantiles of that count index the mocks bracketing it.
+    """
+    mock_ptes = np.asarray(mock_ptes, dtype=float)
+    if mock_ptes.ndim != 2:
+        raise ValueError("mock_ptes must have shape (n_mocks, n_stats)")
+    if not 0.0 < alpha < 1.0:
+        raise ValueError("alpha must lie in (0, 1)")
+
+    minima = _min_pte(mock_ptes, two_sided)
+    n = minima.size
+    ordered = np.sort(minima)
+    tail = (1.0 - level) / 2.0
+    lower = int(np.clip(stats.binom.ppf(tail, n, alpha), 1, n)) - 1
+    upper = int(np.clip(stats.binom.ppf(1.0 - tail, n, alpha) + 1, 1, n)) - 1
+    threshold = float(np.quantile(minima, alpha))
+    interval = (float(ordered[lower]), float(ordered[upper]))
+    k_eff = effective_number_of_tests([threshold, *interval], alpha)
+    return MinPTECalibration(
+        alpha=alpha,
+        two_sided=two_sided,
+        mock_min_pte=minima,
+        threshold=threshold,
+        threshold_interval=interval,
+        k_eff=float(k_eff[0]),
+        k_eff_interval=(float(k_eff[2]), float(k_eff[1])),
+        level=level,
+    )
diff --git a/src/sp_validation/tests/test_statistics.py b/src/sp_validation/tests/test_statistics.py
index 8e56d040..4e11a45b 100644
--- a/src/sp_validation/tests/test_statistics.py
+++ b/src/sp_validation/tests/test_statistics.py
@@ -15,9 +15,11 @@
 from scipy import stats
 
 from sp_validation.statistics import (
+    calibrate_min_pte,
     chi2_and_pte,
     corr_from_cov,
     cov_from_one_covariance,
+    effective_number_of_tests,
     jackknif_weighted_average2,
 )
 
@@ -194,3 +196,60 @@ def test_cov_from_one_covariance_selects_gaussian_column():
         [[0.0, 1.0], [10.0, 11.0]],
         rtol=1e-12,
     )
+
+
+def test_calibrate_min_pte_independent_uniform_ptes():
+    """Independent uniform PTEs recover the Sidak threshold and k_eff = k."""
+    rng = np.random.default_rng(1)
+    alpha, k = 0.05, 6
+    cal = calibrate_min_pte(rng.uniform(size=(200_000, k)), alpha=alpha)
+    expected = 1.0 - (1.0 - alpha) ** (1.0 / k)
+    npt.assert_allclose(cal.threshold, expected, rtol=0.02)
+    npt.assert_allclose(cal.k_eff, k, rtol=0.02)
+    assert cal.threshold_interval[0] <= cal.threshold <= cal.threshold_interval[1]
+    assert cal.k_eff_interval[0] <= cal.k_eff <= cal.k_eff_interval[1]
+    npt.assert_allclose(effective_number_of_tests(expected, alpha), k)
+
+
+def test_calibrate_min_pte_perfectly_correlated_is_one_test():
+    """Identical columns collapse to one test: threshold alpha, k_eff 1."""
+    rng = np.random.default_rng(2)
+    column = rng.uniform(size=(100_000, 1))
+    cal = calibrate_min_pte(np.repeat(column, 8, axis=1), alpha=0.05)
+    npt.assert_allclose(cal.threshold, 0.05, rtol=0.03)
+    npt.assert_allclose(cal.k_eff, 1.0, rtol=0.03)
+
+
+def test_calibrate_min_pte_two_sided_independent():
+    """Two-sided PTEs 2 min(p, 1 - p) are uniform, so Sidak still holds."""
+    rng = np.random.default_rng(3)
+    cal = calibrate_min_pte(rng.uniform(size=(200_000, 4)), alpha=0.05, two_sided=True)
+    npt.assert_allclose(cal.k_eff, 4.0, rtol=0.03)
+
+
+def test_min_pte_global_pte():
+    """Global p-value is the mock fraction with min PTE <= the data's."""
+    mock_ptes = np.array([[0.1, 0.9], [0.5, 0.2], [0.3, 0.7], [0.8, 0.6]])
+    cal = calibrate_min_pte(mock_ptes, alpha=0.25)
+    # Mock minima: 0.1, 0.2, 0.3, 0.6.
+    p, (lo, hi) = cal.global_pte([0.9, 0.2])
+    assert p == 0.5
+    assert lo < 0.5 < hi
+    assert cal.global_pte([0.05, 0.5])[0] == 0.0
+    assert cal.global_pte([0.99, 0.95])[0] == 1.0
+    # Two-sided: a suspiciously good PTE of 0.99 counts like 0.02.
+    two = calibrate_min_pte(mock_ptes, alpha=0.25, two_sided=True)
+    assert two.global_pte([0.99, 0.5])[0] == 0.0
+
+
+def test_global_pte_is_calibrated_under_the_null():
+    """For null data the global p-value is uniform: P(p <= alpha) ~ alpha."""
+    rng = np.random.default_rng(4)
+    mean = np.zeros(5)
+    cov = 0.6 * np.ones((5, 5)) + 0.4 * np.eye(5)
+    to_pte = lambda z: stats.norm.sf(z)  # noqa: E731
+    cal = calibrate_min_pte(to_pte(rng.multivariate_normal(mean, cov, 4000)))
+    data = to_pte(rng.multivariate_normal(mean, cov, 4000))
+    p = np.array([cal.global_pte(row)[0] for row in data])
+    npt.assert_allclose(np.mean(p <= 0.05), 0.05, atol=0.012)
+    assert 1.0 < cal.k_eff < 5.0

From ad52bba8e429969269ade80f67bb0332ae63a13d Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 01:23:58 +0200
Subject: [PATCH 47/83] Pure E/B: fixed-quadrature operator with exact
 covariance (#373)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Pure E/B as a fixed-quadrature operator with exact covariance

The pure-E/B estimator is one data-independent matrix K = (I_6 ⊗ P)·M on
the fine ξ± grid: the Schneider (2022) transform with fixed Gauss-Legendre
weights (cosmo-numba get_pure_EB_operator), evaluated at the fine nodes
inside the reporting range with [tmin, tmax] at the fine-grid extent, then
averaged into the reporting bins with pair-count weights. Its covariance is
K C_ξ Kᵀ, exact for an analytic or a jackknife ξ± covariance; `npatch`
travels with the results (None for analytic) and sets the Hartlap factor of
every χ² built on them, with the combined ξ+/ξ− χ² debiased over its full
length.

calculate_pure_eb_correlation works from fine-grid arrays; the reported
ξ±, θ and variances are the same pair-count average, so ξ± = E ± B + amb
holds bin by bin. The adaptive pointwise path, the Monte-Carlo covariance
and the separate pure-E/B jackknife are removed. sacc_io gains
get_xi_npairs; cosmo-numba is pinned to the fork commit carrying the
operator.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* cv_pure_eb: modes and exact covariance from the integration part

The rule reads only the integration-grid ξ± part (with its pair counts)
and the CosmoCov ξ± covariance on that grid, and calls
calculate_pure_eb_correlation. The Monte-Carlo parameters and the
reporting-part input go; the covariance takes seconds, so the rule's
threads and runtime shrink. The summary labels the covariance from the
npz's npatch record.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* papers/bmodes: pure E/B through the library operator, no Monte-Carlo chunks

pure_eb_modes.py replaces the chunk scatter/gather (precompute_pure_eb_chunk,
gather_pure_eb_chunks): one call to calculate_pure_eb_correlation on the
fine ξ± and its Gaussian covariance, written to {version}_pure_eb.npz.
The PTE and data-vector scripts drop the MC Hartlap factor, the n(z)
covariance comparison drops its MC-noise band, and the n_samples /
n_chunks config keys go.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* cv_pure_eb: describe the covariance input without naming its source

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Pure E/B: average fine bins with TreeCorr's pair weight, not npairs

TreeCorr's ξ± and meanr in a bin are averages over pairs weighted by
w_i w_j, so pooling fine bins with their `weight` reproduces the
reporting-bin measurement exactly when the fine edges nest the reporting
edges; npairs does so only for an unweighted catalogue. The operator,
calculate_pure_eb_correlation, the mixin, cv_pure_eb and pure_eb_modes.py
take `weight_int`; sacc_io.get_xi_npairs becomes get_xi_weight (parts
already carry the tag). A new test checks the nesting identity on a
weighted catalogue with exact binning; the fixture and operator pins are
regenerated.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Pure E/B: reporting bins as unions of fine bins

Assigning fine bins to reporting bins by meanr sends a fine bin that
straddles a reporting edge wholly to one side, so P cannot reproduce
TreeCorr's pair average there. The library now takes the fine-grid edges,
snaps each requested reporting edge to the nearest fine edge, pools whole
fine bins, and returns the edges it used; the results' left/right edges,
the pure-E/B npz and the B-mode summary carry those snapped edges.
Edges that snap together or reach outside the fine grid raise.

Callers pass the fine edges: the mixin from gg_int's edges, cv_pure_eb and
pure_eb_modes.py from the integration grid spec. The reproduction test now
covers requested edges that do not nest, measuring each snapped bin
directly with exact binning.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* papers/bmodes: resolve pure-E/B scale-cut windows on the saved edges

The pure-E/B PTE intermediate now carries the reporting edges its
matrices are indexed on, and config_space_pte_matrices resolves the
fiducial windows on them rather than on a nominal geomspace grid. A PTE
file that records no edges was binned on the nominal grid, which stays
its window.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Pin cosmo-numba a64cb2e; evaluate the pure-E/B transform at every fine node

cosmo-numba's get_pure_EB_operator (schneider2022, local_from_int=True)
now builds the fixed-quadrature transform. It is evaluated at every fine
node: the zero-padded ξ− window extends from the evaluation grid, so this
keeps the transform a function of the fine grid alone. P then selects its
rows; NaN rows at the grid edges are dropped first and only a NaN row that
P uses raises.

The new operator clamps a θ-dependent integration limit to the
interpolator's extrapolation bound, as the reference does, where the
previous pin extrapolated the stencil polynomial; the operator pins move
accordingly.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Pure E/B: resolve scale cuts by snapping to the nearest reporting edge

Exact containment on snapped reporting edges makes a cut placed on a
nominal edge select different bins on different fine grids: [12, 83]′ gave
bins 9–15 on a 0.5–300′ grid but 9–14 on cosmo_val's 0.08–300′ one.
bins_from_scale_cut snaps each end of a cut to the nearest reporting edge
in log θ, the rule the edges themselves were snapped by, so the same cut
selects the same bins on every grid. _get_pte_from_scale_cut (hence the
B-mode summaries), the pure-E/B plots and papers/bmodes
config_space_pte_matrices use it; COSEBIs keep bins_from_edges.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Pure E/B: run the transform on the regular log grid (TreeCorr rnom)

cosmo-numba's interpolator places its samples on a regular grid in log θ,
so handing it TreeCorr meanr put every sample at its nominal position
while the evaluation limit sat at the true meanr; where a spacing exceeded
the mean step the limit fell past the extrapolation bound, and the old and
new cosmo-numba operators resolved that knife-edge differently. The
transform now runs on the geometric centres of the log-uniform fine edges
(TreeCorr rnom) and is evaluated there; meanr enters only the reported θ.
The edges are checked to be log-uniform, and pure_eb_operator no longer
takes theta_int. On rnom the d78a189 and a64cb2e operators agree to
round-off.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
---
 papers/bmodes/config/config.yaml              |  10 -
 papers/bmodes/rules/figures.smk               |  66 +--
 .../scripts/bb_covariance_nz_independence.py  |  39 +-
 .../bmodes/scripts/calculate_pure_eb_ptes.py  |  24 +-
 .../scripts/config_space_pte_matrices.py      |  89 +--
 .../bmodes/scripts/gather_pure_eb_chunks.py   | 136 -----
 .../scripts/precompute_pure_eb_chunk.py       | 209 -------
 papers/bmodes/scripts/pure_eb_covariance.py   |   3 +-
 papers/bmodes/scripts/pure_eb_data_vector.py  |  24 +-
 papers/bmodes/scripts/pure_eb_modes.py        | 116 ++++
 .../scripts/pure_eb_version_comparison.py     |   4 +-
 papers/cosmo_val/config/config.yaml           |   4 +-
 pyproject.toml                                |  14 +-
 src/sp_validation/b_modes.py                  | 509 ++++++++++--------
 src/sp_validation/cosmo_val/core.py           |   7 +-
 src/sp_validation/cosmo_val/pure_eb.py        | 158 ++----
 src/sp_validation/sacc_io.py                  |   9 +-
 src/sp_validation/tests/conftest.py           |   7 +-
 .../tests/data/pure_eb_xi_fixture.npz         | Bin 16564 -> 25592 bytes
 src/sp_validation/tests/test_b_modes.py       | 401 ++++++++++----
 src/sp_validation/tests/test_cosmo_val.py     |  54 +-
 src/sp_validation/tests/test_sacc_io.py       |   1 +
 uv.lock                                       |   6 +-
 workflow/rules/cosmo_val.smk                  |  23 +-
 workflow/scripts/cv_pure_eb.py                |  86 ++-
 workflow/scripts/cv_summarize_bmodes.py       |   9 +-
 workflow/tests/test_dag.py                    |   2 +-
 27 files changed, 918 insertions(+), 1092 deletions(-)
 delete mode 100644 papers/bmodes/scripts/gather_pure_eb_chunks.py
 delete mode 100644 papers/bmodes/scripts/precompute_pure_eb_chunk.py
 create mode 100644 papers/bmodes/scripts/pure_eb_modes.py

diff --git a/papers/bmodes/config/config.yaml b/papers/bmodes/config/config.yaml
index 1b31c647..1198d984 100644
--- a/papers/bmodes/config/config.yaml
+++ b/papers/bmodes/config/config.yaml
@@ -58,14 +58,9 @@ fiducial:
 
 var_method: jackknife
 
-# covariance settings for semi-analytical E/B mode covariance
 covariance:
   # Use masked covariance by default when true
   default_masked: true
-  # Enable semi-analytical covariance propagation
-  use_semianalytic: true
-  # Number of Monte Carlo samples for covariance propagation (used by pure E/B mode analysis)
-  n_samples: 2000
   # Cosmology: imported from cs_util.cosmo.PLANCK18 (astropy Planck18)
   # Mask Cl paths: defined in covariance.smk (MASK_CLS_FILES)
 
@@ -135,11 +130,6 @@ plotting:
     fiducial_line_color: "black"
     fiducial_line_width: 1.0
 
-# Pure E/B mode analysis
-pure_eb:
-  # Number of parallel chunks for MC covariance estimation
-  n_chunks: 20
-
 # pixel mask processing for survey geometry and CosmoCov integration
 pixel_mask:
   # Target nside values for downgrading (balance accuracy vs computational cost)
diff --git a/papers/bmodes/rules/figures.smk b/papers/bmodes/rules/figures.smk
index 590735e3..31b65ea0 100644
--- a/papers/bmodes/rules/figures.smk
+++ b/papers/bmodes/rules/figures.smk
@@ -81,14 +81,6 @@ def _reporting_cov_path(version):
     return covariance_path(version, gaussian="ng")
 
 
-def _xi_reporting_path(version):
-    """Path to reporting-scale 2PCF file."""
-    return (
-        f"{COSMO_VAL_OUTPUT}/{version}_xi_minsep={FIDUCIAL['min_sep']}"
-        f"_maxsep={FIDUCIAL['max_sep']}_nbins={FIDUCIAL['nbins']}_npatch={FIDUCIAL['npatch']}.txt"
-    )
-
-
 def _xi_integration_path(version):
     """Path to fine-binned 2PCF integration file. Unpatched: values only, no covariance."""
     return (
@@ -183,50 +175,20 @@ rule cosebis_data_vector:
 # Pure E/B
 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
-# Number of parallel chunks for MC covariance estimation
-N_PURE_EB_CHUNKS = config["pure_eb"]["n_chunks"]
-
-
-rule precompute_pure_eb_chunk:
-    """Compute a chunk of MC samples for pure E/B covariance (scatter)."""
-    input:
-        cov_integration=lambda w: _cov_integration_path(w.version),
-        xi_reporting=lambda w: _xi_reporting_path(w.version),
-        xi_integration=lambda w: _xi_integration_path(w.version),
-    output:
-        "results/paper_plots/intermediate/chunks/{version}_pure_eb_chunk_{chunk_id}.npz",
-    params:
-        version="{version}",
-        chunk_id="{chunk_id}",
-        n_chunks=N_PURE_EB_CHUNKS,
-        n_samples=config["covariance"]["n_samples"],
-        cosmo_params=PLANCK18,
-        **FIDUCIAL_BINNING,
-    resources:
-        mem_mb=8000,
-    script:
-        "../scripts/precompute_pure_eb_chunk.py"
-
-
-rule precompute_pure_eb:
-    """Gather MC sample chunks and compute final pure E/B covariance."""
+rule pure_eb_modes:
+    """Pure E/B modes and their exact covariance K C_ξ Kᵀ from the fine ξ±."""
     input:
-        chunks=expand(
-            "results/paper_plots/intermediate/chunks/{{version}}_pure_eb_chunk_{chunk_id}.npz",
-            chunk_id=range(N_PURE_EB_CHUNKS),
-        ),
-        xi_reporting=lambda w: _xi_reporting_path(w.version),
         xi_integration=lambda w: _xi_integration_path(w.version),
+        cov_integration=lambda w: _cov_integration_path(w.version),
     output:
-        "results/paper_plots/intermediate/{version}_pure_eb_semianalytic.npz",
+        "results/paper_plots/intermediate/{version}_pure_eb.npz",
     params:
-        version="{version}",
         **FIDUCIAL_BINNING,
     resources:
         mem_mb=8000,
-        runtime=5,
+        runtime=20,
     script:
-        "../scripts/gather_pure_eb_chunks.py"
+        "../scripts/pure_eb_modes.py"
 
 
 rule pure_eb_data_vector:
@@ -239,7 +201,7 @@ rule pure_eb_data_vector:
     """
     input:
         # Per-version inputs: pure_eb_{version} and cov_{version} for all versions
-        **{f"pure_eb_{ver}": f"results/paper_plots/intermediate/{ver}_pure_eb_semianalytic.npz"
+        **{f"pure_eb_{ver}": f"results/paper_plots/intermediate/{ver}_pure_eb.npz"
            for ver in VERSIONS_ALL_FOR_PLOTS},
         **{f"cov_{ver}": _reporting_cov_path(ver) for ver in VERSIONS_ALL_FOR_PLOTS},
     output:
@@ -259,7 +221,7 @@ rule pure_eb_version_comparison:
     input:
         # Pure E/B only for leak-corrected versions
         pure_eb_data=[
-            f"results/paper_plots/intermediate/{ver}_pure_eb_semianalytic.npz"
+            f"results/paper_plots/intermediate/{ver}_pure_eb.npz"
             for ver in VERSIONS_LEAK_CORR
         ],
     params:
@@ -282,7 +244,7 @@ rule pure_eb_covariance:
     - Correlation structure across 6 blocks (E+/E-/B+/B-/amb+/amb-)
     """
     input:
-        pure_eb_data=f"results/paper_plots/intermediate/{FIDUCIAL_VERSION}_pure_eb_semianalytic.npz",
+        pure_eb_data=f"results/paper_plots/intermediate/{FIDUCIAL_VERSION}_pure_eb.npz",
     output:
         evidence=f"{TAPESTRY_DIR}/pure_eb_covariance/evidence.json",
         figure=f"{TAPESTRY_DIR}/pure_eb_covariance/figure.png",
@@ -292,17 +254,13 @@ rule pure_eb_covariance:
 
 
 rule calculate_pure_eb_ptes:
-    """PTE matrices for pure E/B-mode scale-cut robustness.
-
-    The PTEs are Hartlap-debiased by the MC draw count.
-    """
+    """PTE matrices for pure E/B-mode scale-cut robustness."""
     input:
-        pure_eb_data="results/paper_plots/intermediate/{version}_pure_eb_semianalytic.npz",
+        pure_eb_data="results/paper_plots/intermediate/{version}_pure_eb.npz",
     output:
         "results/paper_plots/intermediate/{version}_pure_eb_ptes.npz",
     params:
         version="{version}",
-        n_samples=config["covariance"]["n_samples"],
     resources:
         mem_mb=16000,
         runtime=30,
@@ -455,7 +413,7 @@ rule bb_covariance_nz_independence:
     """
     input:
         # Per-realisation MC-propagated pure E/B covariances
-        **{f"pure_eb_{label}": f"results/paper_plots/intermediate/{ver}_pure_eb_semianalytic.npz"
+        **{f"pure_eb_{label}": f"results/paper_plots/intermediate/{ver}_pure_eb.npz"
            for label, ver in NZ_REALISATIONS.items()},
         # COSEBIS: xi integration file (shared) + per-realisation config-space covariances
         xi_integration=_xi_integration_path(MOCK_VERSION),
diff --git a/papers/bmodes/scripts/bb_covariance_nz_independence.py b/papers/bmodes/scripts/bb_covariance_nz_independence.py
index 69c0db18..7f8cae02 100644
--- a/papers/bmodes/scripts/bb_covariance_nz_independence.py
+++ b/papers/bmodes/scripts/bb_covariance_nz_independence.py
@@ -136,7 +136,6 @@ def make_figure(
     cosebis_results,
     reference,
     output_path,
-    n_samples=2000,
 ):
     """Four-panel figure comparing BB vs EE stability across n(z) realisations.
 
@@ -152,40 +151,26 @@ def make_figure(
     color_E = "#E69F00"  # orange for E
     color_B = "#0072B2"  # blue for B
 
-    # Expected 1σ error on ratio of two MC-estimated quantities
-    # σ(ratio) ≈ √(2/N) for ratio ≈ 1
-    ratio_err = np.sqrt(2.0 / n_samples)
-
-    def setup_ratio_panel(ax, xlabel, title, show_mc_band=False):
+    def setup_ratio_panel(ax, xlabel, title):
         ax.axhline(1.0, color="gray", ls="-", lw=0.8, zorder=0)
-        if show_mc_band:
-            ax.axhspan(
-                1 - ratio_err,
-                1 + ratio_err,
-                color="gray",
-                alpha=0.25,
-                label=rf"$\pm\sqrt{{2/N}}$ ($N={n_samples}$)",
-            )
         ax.set_xscale("log")
         ax.set_xlabel(xlabel)
         ax.set_title(title)
 
-    def plot_ratios(ax, x, results, b_key, e_key, b_name, e_name, shift, b_err):
-        """B (with optional MC error bar) and E ratios for every realisation."""
+    def plot_ratios(ax, x, results, b_key, e_key, b_name, e_name, shift):
+        """B and E ratios for every realisation."""
         for i, (label, res) in enumerate(results.items()):
             xi = shift(x, i)
             marker = MARKERS[i % len(MARKERS)]
             pair = f"{label}/{reference}"
-            ax.errorbar(
+            ax.plot(
                 xi,
                 res[b_key]["ratio"],
-                yerr=b_err,
-                fmt=marker,
+                marker,
                 color=color_B,
                 label=f"{b_name} {pair}",
                 markersize=5,
                 alpha=0.8,
-                capsize=0,
             )
             ax.plot(
                 xi,
@@ -212,7 +197,6 @@ def lin_shift(x, i):
             ax,
             r"$\theta$ [arcmin]",
             rf"${name}$: covariance ratio across n(z)",
-            show_mc_band=True,
         )
         plot_ratios(
             ax,
@@ -223,7 +207,6 @@ def lin_shift(x, i):
             "B-mode",
             "E-mode",
             log_shift,
-            ratio_err,
         )
         ax.legend(loc="upper right", fontsize=7, ncol=2)
         ax.set_xlim(1, 300)
@@ -237,9 +220,7 @@ def lin_shift(x, i):
     ax.axhline(1.0, color="gray", ls="-", lw=0.8, zorder=0)
     ax.set_xlabel(r"Mode $n$")
     ax.set_title(r"COSEBIS: covariance ratio across n(z)")
-    plot_ratios(
-        ax, n_arr, cosebis_results, "B", "E", "B-mode", "E-mode", lin_shift, None
-    )
+    plot_ratios(ax, n_arr, cosebis_results, "B", "E", "B-mode", "E-mode", lin_shift)
     ax.set_ylabel("Diagonal ratio")
     ax.legend(loc="upper right", fontsize=7, ncol=2)
     ax.set_ylim(0.85, 1.15)
@@ -247,7 +228,7 @@ def lin_shift(x, i):
     # --- Panel 4: C_ell^BB vs C_ell^EE ---
     ax = axes[1, 1]
     setup_ratio_panel(ax, r"$\ell$", r"$C_\ell$: covariance ratio across n(z)")
-    plot_ratios(ax, ell_eff, harmonic_results, "BB", "EE", "BB", "EE", log_shift, None)
+    plot_ratios(ax, ell_eff, harmonic_results, "BB", "EE", "BB", "EE", log_shift)
     ax.set_ylabel("Diagonal ratio")
     ax.legend(loc="upper right", fontsize=7, ncol=2)
     ax.set_ylim(0.85, 1.15)
@@ -330,7 +311,6 @@ def ratios_to_reference(data, modes):
     for res in cosebis_results.values():
         res["nmodes"] = nmodes
 
-    n_samples = config["covariance"]["n_samples"]
     make_figure(
         theta,
         ell_eff,
@@ -339,7 +319,6 @@ def ratios_to_reference(data, modes):
         cosebis_results,
         reference,
         figure_path,
-        n_samples=n_samples,
     )
 
     def max_dev(results, mode):
@@ -485,7 +464,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pure-eb-dir",
         required=True,
-        help="Dir with {version}_pure_eb_semianalytic.npz per n(z) realisation",
+        help="Dir with {version}_pure_eb.npz per n(z) realisation",
     )
     ap.add_argument(
         "--cosmo-val-dir",
@@ -517,7 +496,7 @@ def _from_cli(argv=None):
 
     realisations = fid["nz_realisations"]
     pure_eb_paths = {
-        b: os.path.join(a.pure_eb_dir, f"{ver}_pure_eb_semianalytic.npz")
+        b: os.path.join(a.pure_eb_dir, f"{ver}_pure_eb.npz")
         for b, ver in realisations.items()
     }
     harmonic_paths = {
diff --git a/papers/bmodes/scripts/calculate_pure_eb_ptes.py b/papers/bmodes/scripts/calculate_pure_eb_ptes.py
index feb0be78..ca453dc4 100644
--- a/papers/bmodes/scripts/calculate_pure_eb_ptes.py
+++ b/papers/bmodes/scripts/calculate_pure_eb_ptes.py
@@ -1,16 +1,14 @@
 """Calculate PTE matrices for pure E/B-mode scale-cut robustness.
 
-CLI refactor of the former Snakemake ``script:`` rule. Reads the gathered
-pure-E/B ``semianalytic.npz`` (data vectors + MC covariance), evaluates the
+CLI refactor of the former Snakemake ``script:`` rule. Reads the pure-E/B
+``_pure_eb.npz`` (data vectors + analytic covariance), evaluates the
 ξ_+^B / ξ_-^B / joint ξ_tot^B χ² PTE matrices over the scale-cut grid via
-``sp_validation.b_modes.calculate_eb_statistics`` (Hartlap-corrected inverse
-MC covariance, debiased by the draw count), and writes the PTE matrices to
-``{out}/{version}_pure_eb_ptes.npz``.
+``sp_validation.b_modes.calculate_eb_statistics``, and writes the PTE matrices
+to ``{out}/{version}_pure_eb_ptes.npz``.
 
     python calculate_pure_eb_ptes.py \
         --version SP_v1.4.6.3_leak_corr \
-        --pure-eb-data <..._pure_eb_semianalytic.npz> \
-        --n-samples 2000 --out <output_dir>
+        --pure-eb-data <..._pure_eb.npz> --out <output_dir>
 """
 
 import argparse
@@ -24,7 +22,6 @@
 def calculate_ptes(
     version,
     pure_eb_data,
-    n_samples,
     output_dir,
 ):
     dataset = np.load(pure_eb_data)
@@ -33,8 +30,8 @@ def calculate_ptes(
 
     results = {
         "theta": theta,
-        # The MC draws are the realisations behind this covariance.
-        "n_eff": int(n_samples),
+        # The covariance is analytic: no Hartlap factor.
+        "npatch": None,
         "xip_E": dataset["xip_E"],
         "xim_E": dataset["xim_E"],
         "xip_B": dataset["xip_B"],
@@ -50,6 +47,9 @@ def calculate_ptes(
     pte_matrices = results["pte_matrices"]
     output_data = {
         "theta": theta,
+        # The bins the matrices are indexed on, for scale-cut windows.
+        "left_edges": dataset["left_edges"],
+        "right_edges": dataset["right_edges"],
         "pte_xip_B": pte_matrices["xip_B"],
         "pte_xim_B": pte_matrices["xim_B"],
         "pte_combined": pte_matrices["combined"],
@@ -65,14 +65,12 @@ def calculate_ptes(
 def _from_cli(argv=None):
     ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
     ap.add_argument("--version", required=True)
-    ap.add_argument("--pure-eb-data", required=True, help="Gathered semianalytic .npz")
-    ap.add_argument("--n-samples", type=int, default=2000)
+    ap.add_argument("--pure-eb-data", required=True, help="Pure E/B .npz")
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     a = ap.parse_args(argv)
     calculate_ptes(
         version=a.version,
         pure_eb_data=a.pure_eb_data,
-        n_samples=a.n_samples,
         output_dir=a.out,
     )
 
diff --git a/papers/bmodes/scripts/config_space_pte_matrices.py b/papers/bmodes/scripts/config_space_pte_matrices.py
index 3bfa2811..f4365ec1 100644
--- a/papers/bmodes/scripts/config_space_pte_matrices.py
+++ b/papers/bmodes/scripts/config_space_pte_matrices.py
@@ -39,15 +39,19 @@
     make_pte_norm,
 )
 
+from sp_validation.b_modes import bins_from_scale_cut
+
 plt.style.use(PAPER_MPLSTYLE)
 
 
 def resolve_fiducial_bin_window(edges, theta_min, theta_max):
-    """Return the first and last reporting bins inside a scale-cut window."""
-    left, right = edges[:-1], edges[1:]
-    inside = (left >= theta_min * (1.0 - 1e-2)) & (right <= theta_max * (1.0 + 1e-2))
-    bins = np.flatnonzero(inside)
-    return int(bins[0]), int(bins[-1])
+    """Return the first and last reporting bins of a pure-E/B scale cut.
+
+    The cut snaps to the nearest reporting edges, as everywhere pure-E/B PTEs
+    are read (``sp_validation.b_modes.bins_from_scale_cut``).
+    """
+    start, stop = bins_from_scale_cut(edges[:-1], edges[1:], (theta_min, theta_max))
+    return start, stop - 1
 
 
 def _path_matches_version(path, version):
@@ -189,22 +193,25 @@ def load_pure_eb_pte_matrices(pte_files, version, override_path=None):
         Angular scale grid.
     pte_combined : ndarray or None
         PTE matrix for combined ξ_tot^B, or None if not available.
+    edges : ndarray or None
+        The reporting edges the matrices are indexed on, or None for a file
+        that does not record them.
     """
-    if override_path is not None:
-        data = np.load(override_path)
-        pte_combined = data["pte_combined"] if "pte_combined" in data else None
-        return data["pte_xip_B"], data["pte_xim_B"], data["theta"], pte_combined
-
-    for pte_file in pte_files:
+    if override_path is None:
         # Filter to this version (exact match, no substring false positives)
-        if not _path_matches_version(pte_file, version):
-            continue
-
-        data = np.load(pte_file)
-        pte_combined = data["pte_combined"] if "pte_combined" in data else None
-        return data["pte_xip_B"], data["pte_xim_B"], data["theta"], pte_combined
-
-    raise ValueError(f"No PTE file found for version {version}")
+        matching = [p for p in pte_files if _path_matches_version(p, version)]
+        if not matching:
+            raise ValueError(f"No PTE file found for version {version}")
+        override_path = matching[0]
+
+    data = np.load(override_path)
+    pte_combined = data["pte_combined"] if "pte_combined" in data else None
+    edges = (
+        np.append(data["left_edges"], data["right_edges"][-1])
+        if "left_edges" in data
+        else None
+    )
+    return data["pte_xip_B"], data["pte_xim_B"], data["theta"], pte_combined, edges
 
 
 def _load_version_pte_data(
@@ -215,12 +222,19 @@ def _load_version_pte_data(
     Returns
     -------
     dict with keys: pte_xip_B, pte_xim_B, pte_combined (or None),
-        pte_cosebis, pte_cosebis_20, theta_pure_eb, theta_cosebis.
+        pte_cosebis, pte_cosebis_20, theta_pure_eb, edges_pure_eb,
+        theta_cosebis.
     """
     pure_eb_override, cosebis_override = _resolve_overrides(version, fiducial_overrides)
-    pte_xip_B, pte_xim_B, theta_pure_eb, pte_combined = load_pure_eb_pte_matrices(
-        pure_eb_pte_files, version, override_path=pure_eb_override
+    pte_xip_B, pte_xim_B, theta_pure_eb, pte_combined, edges_pure_eb = (
+        load_pure_eb_pte_matrices(
+            pure_eb_pte_files, version, override_path=pure_eb_override
+        )
     )
+    if edges_pure_eb is None:
+        # A PTE file without saved edges was binned on the nominal grid.
+        fid = config["fiducial"]
+        edges_pure_eb = np.geomspace(fid["min_sep"], fid["max_sep"], fid["nbins"] + 1)
     pte_cosebis, theta_cosebis = load_cosebis_pte_matrix(
         cosebis_pte_files,
         version,
@@ -242,6 +256,7 @@ def _load_version_pte_data(
         "pte_cosebis": pte_cosebis,
         "pte_cosebis_20": pte_cosebis_20,
         "theta_pure_eb": theta_pure_eb,
+        "edges_pure_eb": edges_pure_eb,
         "theta_cosebis": theta_cosebis,
     }
 
@@ -522,13 +537,9 @@ def create_3panel_composite(
     cosebis_fid_start = np.argmin(np.abs(theta_cosebis[:-1] - cosebis_fid[0]))
     cosebis_fid_stop = np.argmin(np.abs(theta_cosebis[1:] - cosebis_fid[1])) + 1
 
-    reporting_edges = np.geomspace(
-        config["fiducial"]["min_sep"],
-        config["fiducial"]["max_sep"],
-        config["fiducial"]["nbins"] + 1,
-    )
-    xip_start, xip_stop = resolve_fiducial_bin_window(reporting_edges, *xip_fid)
-    xim_start, xim_stop = resolve_fiducial_bin_window(reporting_edges, *xim_fid)
+    edges_pure_eb = matrices["edges_pure_eb"]
+    xip_start, xip_stop = resolve_fiducial_bin_window(edges_pure_eb, *xip_fid)
+    xim_start, xim_stop = resolve_fiducial_bin_window(edges_pure_eb, *xim_fid)
 
     # Create subplot axes
     ax_xip = fig.add_subplot(gs[0, 0])
@@ -680,13 +691,9 @@ def create_9panel_composite(
         cosebis_fid_start = np.argmin(np.abs(theta_cosebis[:-1] - cosebis_fid[0]))
         cosebis_fid_stop = np.argmin(np.abs(theta_cosebis[1:] - cosebis_fid[1])) + 1
 
-        reporting_edges = np.geomspace(
-            config["fiducial"]["min_sep"],
-            config["fiducial"]["max_sep"],
-            config["fiducial"]["nbins"] + 1,
-        )
-        xip_start, xip_stop = resolve_fiducial_bin_window(reporting_edges, *xip_fid)
-        xim_start, xim_stop = resolve_fiducial_bin_window(reporting_edges, *xim_fid)
+        edges_pure_eb = matrices["edges_pure_eb"]
+        xip_start, xip_stop = resolve_fiducial_bin_window(edges_pure_eb, *xip_fid)
+        xim_start, xim_stop = resolve_fiducial_bin_window(edges_pure_eb, *xim_fid)
 
         # Create subplot axes for this row
         ax_xip = fig.add_subplot(gs[row_idx, 0])
@@ -910,16 +917,12 @@ def main(
                     fiducial_overrides,
                 )
                 theta_co = matrices["theta_cosebis"]
-                reporting_edges = np.geomspace(
-                    config["fiducial"]["min_sep"],
-                    config["fiducial"]["max_sep"],
-                    config["fiducial"]["nbins"] + 1,
-                )
+                edges_pure_eb = matrices["edges_pure_eb"]
                 xip_start, xip_stop = resolve_fiducial_bin_window(
-                    reporting_edges, *xip_fid
+                    edges_pure_eb, *xip_fid
                 )
                 xim_start, xim_stop = resolve_fiducial_bin_window(
-                    reporting_edges, *xim_fid
+                    edges_pure_eb, *xim_fid
                 )
                 cos_start = np.argmin(np.abs(theta_co[:-1] - cosebis_fid[0]))
                 cos_stop = np.argmin(np.abs(theta_co[1:] - cosebis_fid[1])) + 1
diff --git a/papers/bmodes/scripts/gather_pure_eb_chunks.py b/papers/bmodes/scripts/gather_pure_eb_chunks.py
deleted file mode 100644
index b6d5d702..00000000
--- a/papers/bmodes/scripts/gather_pure_eb_chunks.py
+++ /dev/null
@@ -1,136 +0,0 @@
-"""Gather MC sample chunks and compute the final pure E/B covariance.
-
-CLI refactor of the former Snakemake ``script:`` gather rule. Reads the actual
-ξ± data vectors (reporting + integration grids), computes the pure E/B/ambiguous
-decomposition (Schneider 2022), stacks the per-chunk MC sample blocks, and
-forms the empirical 6-block covariance. Writes the per-version
-``<version>_pure_eb_semianalytic.npz`` consumed by every downstream
-pure-mode plot / PTE.
-
-    python gather_pure_eb_chunks.py \
-        --version SP_v1.4.6.3_leak_corr \
-        --xi-reporting  <xi 20-bin .txt> \
-        --xi-integration <xi 1000-bin .txt> \
-        --chunks-dir <dir with pure_eb_chunk_*.npz> \
-        --min-sep 1.0 --max-sep 250.0 --nbins 20 \
-        --min-sep-int 0.5 --max-sep-int 300.0 --nbins-int 1000 \
-        --out <output_dir>
-"""
-
-import argparse
-import glob
-import os
-
-import numpy as np
-
-
-def _load_xi(path, nbins):
-    """Load ξ± from a TreeCorr text dump."""
-    data = np.loadtxt(path, comments="#", max_rows=nbins)
-    return {"meanr": data[:, 1], "xip": data[:, 3], "xim": data[:, 4]}
-
-
-def gather(
-    version,
-    xi_reporting,
-    xi_integration,
-    chunk_files,
-    min_sep,
-    max_sep,
-    nbins,
-    nbins_int,
-    output_dir,
-):
-    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
-
-    print(f"Gathering pure E/B for {version}")
-
-    gg = _load_xi(xi_reporting, nbins)
-    gg_int = _load_xi(xi_integration, nbins_int)
-
-    eb_results = get_pure_EB_modes(
-        theta=gg["meanr"],
-        xip=gg["xip"],
-        xim=gg["xim"],
-        theta_int=gg_int["meanr"],
-        xip_int=gg_int["xip"],
-        xim_int=gg_int["xim"],
-        tmin=min_sep,
-        tmax=max_sep,
-    )
-    xip_E, xim_E, xip_B, xim_B, xip_amb, xim_amb = eb_results
-
-    chunk_files = sorted(chunk_files)
-    all_samples = []
-    for chunk_file in chunk_files:
-        data = np.load(chunk_file)
-        all_samples.append(data["eb_samples"])
-        print(f"Loaded {len(data['eb_samples'])} samples from {chunk_file}")
-
-    eb_samples = np.vstack(all_samples)
-    print(f"Total samples: {len(eb_samples)}")
-
-    cov_pure_eb = np.cov(eb_samples.T)
-
-    package = {
-        "theta": gg["meanr"],
-        "theta_int": gg_int["meanr"],
-        "xip_total": gg["xip"],
-        "xim_total": gg["xim"],
-        "xip_E": xip_E,
-        "xim_E": xim_E,
-        "xip_B": xip_B,
-        "xim_B": xim_B,
-        "xip_amb": xip_amb,
-        "xim_amb": xim_amb,
-        "cov_pure_eb": cov_pure_eb,
-    }
-
-    os.makedirs(output_dir, exist_ok=True)
-    out_path = os.path.join(output_dir, f"{version}_pure_eb_semianalytic.npz")
-    np.savez(out_path, **package)
-    print(f"Saved to {out_path}")
-    return out_path
-
-
-def _resolve_chunks(args):
-    if args.chunks:
-        files = args.chunks
-    else:
-        files = glob.glob(os.path.join(args.chunks_dir, "pure_eb_chunk_*.npz"))
-    if not files:
-        raise SystemExit(f"No chunk .npz found (chunks-dir={args.chunks_dir})")
-    return files
-
-
-def _from_cli(argv=None):
-    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
-    ap.add_argument("--version", required=True)
-    ap.add_argument("--xi-reporting", required=True)
-    ap.add_argument("--xi-integration", required=True)
-    ap.add_argument("--chunks-dir", help="Directory holding pure_eb_chunk_*.npz")
-    ap.add_argument("--chunks", nargs="+", help="Explicit chunk .npz paths")
-    ap.add_argument("--min-sep", type=float, default=1.0)
-    ap.add_argument("--max-sep", type=float, default=250.0)
-    ap.add_argument("--nbins", type=int, default=20)
-    ap.add_argument("--min-sep-int", type=float, default=0.5)
-    ap.add_argument("--max-sep-int", type=float, default=300.0)
-    ap.add_argument("--nbins-int", type=int, default=1000)
-    ap.add_argument("--npatch", type=int, default=1)
-    ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    a = ap.parse_args(argv)
-    gather(
-        version=a.version,
-        xi_reporting=a.xi_reporting,
-        xi_integration=a.xi_integration,
-        chunk_files=_resolve_chunks(a),
-        min_sep=a.min_sep,
-        max_sep=a.max_sep,
-        nbins=a.nbins,
-        nbins_int=a.nbins_int,
-        output_dir=a.out,
-    )
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/precompute_pure_eb_chunk.py b/papers/bmodes/scripts/precompute_pure_eb_chunk.py
deleted file mode 100644
index 2db6bf92..00000000
--- a/papers/bmodes/scripts/precompute_pure_eb_chunk.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""Compute one chunk of MC samples for the pure E/B covariance.
-
-CLI refactor of the former Snakemake ``script:`` rule. The compute is
-unchanged: draw ``n_samples // n_chunks`` Gaussian realisations of ξ±(θ) from
-the 1000-bin integration-grid CosmoCov covariance (deterministic seed
-``42 + chunk_id``), rebin to the reporting grid, and push each draw through the
-Schneider-2022 pure-mode integral transforms (``cosmo_numba``). The per-chunk
-E/B/amb sample block is written to ``{out}/pure_eb_chunk_{chunk_id}.npz`` for
-the gather stage. Each chunk is independent (fresh RNG per chunk_id), so the
-20 chunks reproduce the paper's 2000-sample covariance bit-for-bit whether run
-in parallel or looped in one process.
-
-    python precompute_pure_eb_chunk.py \
-        --chunk-id 0 --n-chunks 20 --n-samples 2000 \
-        --version SP_v1.4.6.3_leak_corr \
-        --cat-config /path/cosmo_val/cat_config.yaml \
-        --xi-reporting  <xi 20-bin .txt> \
-        --xi-integration <xi 1000-bin .txt> \
-        --cov-integration <cov ..._processed.txt> \
-        --min-sep 1.0 --max-sep 250.0 --nbins 20 \
-        --min-sep-int 0.5 --max-sep-int 300.0 --nbins-int 1000 \
-        --npatch 1 --out <output_dir>
-"""
-
-import argparse
-import os
-
-import numpy as np
-import tqdm
-from scipy import sparse
-
-
-def _build_cosmology(cosmo_params):
-    """Build a CCL cosmology from a PLANCK18-style params dict."""
-    import pyccl as ccl
-
-    return ccl.Cosmology(
-        Omega_c=cosmo_params["Omega_m"] - cosmo_params["Omega_b"],
-        Omega_b=cosmo_params["Omega_b"],
-        h=cosmo_params["h"],
-        sigma8=cosmo_params["sigma_8"],
-        n_s=cosmo_params["n_s"],
-    )
-
-
-def _load_xi(path, min_sep, max_sep, nbins):
-    """Load ξ± from a TreeCorr text dump and recompute the log bin edges."""
-    data = np.loadtxt(path, comments="#", max_rows=nbins)
-    meanr = data[:, 1]
-    xip = data[:, 3]
-    xim = data[:, 4]
-    bin_edges = np.logspace(np.log10(min_sep), np.log10(max_sep), nbins + 1)
-    return {
-        "meanr": meanr,
-        "xip": xip,
-        "xim": xim,
-        "left_edges": bin_edges[:-1],
-        "right_edges": bin_edges[1:],
-    }
-
-
-def compute_chunk(
-    chunk_id,
-    n_chunks,
-    n_samples_total,
-    version,
-    cat_config,
-    xi_reporting,
-    xi_integration,
-    cov_integration,
-    min_sep,
-    max_sep,
-    nbins,
-    min_sep_int,
-    max_sep_int,
-    nbins_int,
-    output_dir,
-    cosmo_params=None,
-):
-    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
-    from cs_util.cosmo import PLANCK18, get_theo_xi
-
-    from sp_validation.cosmo_val import CosmologyValidation
-
-    if cosmo_params is None:
-        cosmo_params = dict(PLANCK18)
-
-    samples_per_chunk = n_samples_total // n_chunks
-    start_idx = chunk_id * samples_per_chunk
-    end_idx = (
-        start_idx + samples_per_chunk if chunk_id < n_chunks - 1 else n_samples_total
-    )
-    n_samples_chunk = end_idx - start_idx
-    print(
-        f"Chunk {chunk_id}/{n_chunks}: samples {start_idx}-{end_idx} "
-        f"({n_samples_chunk} samples)"
-    )
-
-    gg = _load_xi(xi_reporting, min_sep, max_sep, nbins)
-    gg_int = _load_xi(xi_integration, min_sep_int, max_sep_int, nbins_int)
-
-    cv = CosmologyValidation(
-        versions=[version],
-        catalog_config=cat_config,
-        output_dir=output_dir,
-    )
-    z, nz = cv.get_redshift(version)
-    z_dist = np.column_stack([z, nz])
-
-    cosmo_cov = _build_cosmology(cosmo_params)
-
-    cov_int = np.loadtxt(cov_integration)
-
-    theta_int = gg_int["meanr"]
-    reporting_bin_edges = np.concatenate([gg["left_edges"], [gg["right_edges"][-1]]])
-    bin_indices = np.digitize(theta_int, reporting_bin_edges) - 1
-    valid_mask = (bin_indices >= 0) & (bin_indices < len(gg["meanr"]))
-    row_indices, col_indices = bin_indices[valid_mask], np.where(valid_mask)[0]
-
-    binning_matrix = sparse.csr_matrix(
-        (np.ones(len(row_indices)), (row_indices, col_indices)),
-        shape=(len(gg["meanr"]), nbins_int),
-    )
-    row_sums = np.array(binning_matrix.sum(axis=1)).flatten()
-    binning_matrix = sparse.diags(1 / row_sums) @ binning_matrix
-
-    # One n(z) gives one tracer pair: get_theo_xi's single (xi+, xi-) entry.
-    (xi_pm,) = get_theo_xi(
-        theta=theta_int,
-        z=z_dist[:, 0],
-        nz=z_dist[:, 1],
-        backend="ccl",
-        cosmo=cosmo_cov,
-    ).values()
-    mean_int = np.concatenate(xi_pm)
-
-    rng = np.random.default_rng(seed=42 + chunk_id)
-
-    samples_int = rng.multivariate_normal(mean_int, cov_int, size=n_samples_chunk)
-    samples_int_xip = samples_int[:, :nbins_int]
-    samples_int_xim = samples_int[:, nbins_int:]
-    samples_rep_xip = (binning_matrix @ samples_int_xip.T).T
-    samples_rep_xim = (binning_matrix @ samples_int_xim.T).T
-
-    transformed_samples = [
-        np.concatenate(
-            get_pure_EB_modes(
-                theta=gg["meanr"],
-                theta_int=gg_int["meanr"],
-                xip=samples_rep_xip[i],
-                xim=samples_rep_xim[i],
-                xip_int=samples_int_xip[i],
-                xim_int=samples_int_xim[i],
-                tmin=min_sep,
-                tmax=max_sep,
-            )
-        )
-        for i in tqdm.tqdm(range(n_samples_chunk), desc=f"Chunk {chunk_id}")
-    ]
-
-    eb_samples = np.array(transformed_samples)
-
-    os.makedirs(output_dir, exist_ok=True)
-    out_path = os.path.join(output_dir, f"pure_eb_chunk_{chunk_id}.npz")
-    np.savez(out_path, eb_samples=eb_samples, chunk_id=chunk_id)
-    print(f"Saved {n_samples_chunk} samples to {out_path}")
-    return out_path
-
-
-def _from_cli(argv=None):
-    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
-    ap.add_argument("--chunk-id", type=int, required=True)
-    ap.add_argument("--n-chunks", type=int, default=20)
-    ap.add_argument("--n-samples", type=int, default=2000)
-    ap.add_argument("--version", required=True)
-    ap.add_argument("--cat-config", required=True)
-    ap.add_argument("--xi-reporting", required=True)
-    ap.add_argument("--xi-integration", required=True)
-    ap.add_argument("--cov-integration", required=True)
-    ap.add_argument("--min-sep", type=float, default=1.0)
-    ap.add_argument("--max-sep", type=float, default=250.0)
-    ap.add_argument("--nbins", type=int, default=20)
-    ap.add_argument("--min-sep-int", type=float, default=0.5)
-    ap.add_argument("--max-sep-int", type=float, default=300.0)
-    ap.add_argument("--nbins-int", type=int, default=1000)
-    ap.add_argument("--npatch", type=int, default=1)
-    ap.add_argument("--out", required=True, help="Output directory (lc {output})")
-    a = ap.parse_args(argv)
-    compute_chunk(
-        chunk_id=a.chunk_id,
-        n_chunks=a.n_chunks,
-        n_samples_total=a.n_samples,
-        version=a.version,
-        cat_config=a.cat_config,
-        xi_reporting=a.xi_reporting,
-        xi_integration=a.xi_integration,
-        cov_integration=a.cov_integration,
-        min_sep=a.min_sep,
-        max_sep=a.max_sep,
-        nbins=a.nbins,
-        min_sep_int=a.min_sep_int,
-        max_sep_int=a.max_sep_int,
-        nbins_int=a.nbins_int,
-        output_dir=a.out,
-    )
-
-
-if __name__ == "__main__":
-    _from_cli()
diff --git a/papers/bmodes/scripts/pure_eb_covariance.py b/papers/bmodes/scripts/pure_eb_covariance.py
index cb137c62..08898968 100644
--- a/papers/bmodes/scripts/pure_eb_covariance.py
+++ b/papers/bmodes/scripts/pure_eb_covariance.py
@@ -226,8 +226,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pure-eb-data",
         required=True,
-        help="Fiducial <version>_pure_eb_semianalytic.npz "
-        "(provides the 6-block cov_pure_eb)",
+        help="Fiducial <version>_pure_eb.npz (provides the 6-block cov_pure_eb)",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     a = ap.parse_args(argv)
diff --git a/papers/bmodes/scripts/pure_eb_data_vector.py b/papers/bmodes/scripts/pure_eb_data_vector.py
index d7845361..d79060cc 100644
--- a/papers/bmodes/scripts/pure_eb_data_vector.py
+++ b/papers/bmodes/scripts/pure_eb_data_vector.py
@@ -7,7 +7,7 @@
 CLI:
     python pure_eb_data_vector.py \
         --config config.yaml \
-        --pure-eb-data <version>_pure_eb_semianalytic.npz \
+        --pure-eb-data <version>_pure_eb.npz \
         --reporting-cov <cov_reporting_ng>/covariance_processed.txt \
         --out <output_dir>
 """
@@ -34,7 +34,7 @@ def _extract_sigma(covariance, block_index, block_size):
     return np.sqrt(np.clip(np.diag(covariance[block_slice, block_slice]), 0, None))
 
 
-def _compute_joint_pte(xip_B, xim_B, cov_xip_B, cov_xim_B, cov_cross, n_samples=None):
+def _compute_joint_pte(xip_B, xim_B, cov_xip_B, cov_xim_B, cov_cross):
     """Compute joint PTE for combined B-mode data vector [xip_B, xim_B]."""
     data_joint = np.concatenate([xip_B, xim_B])
     n_xip, n_xim = len(xip_B), len(xim_B)
@@ -45,12 +45,12 @@ def _compute_joint_pte(xip_B, xim_B, cov_xip_B, cov_xim_B, cov_cross, n_samples=
     cov_joint[:n_xip, n_xip:] = cov_cross
     cov_joint[n_xip:, :n_xip] = cov_cross.T
 
-    chi2, pte, dof = compute_chi2_pte(data_joint, cov_joint, n_samples=n_samples)
+    chi2, pte, dof = compute_chi2_pte(data_joint, cov_joint)
     return pte, chi2, dof
 
 
 def _load_pure_eb_data(pure_eb_path, cov_path):
-    """Load pure E/B decomposition, the 6-block MC covariance, and the
+    """Load pure E/B decomposition, the 6-block covariance, and the
     reporting-grid CosmoCov ξ± covariance used for the total-curve error bars."""
     dataset = np.load(pure_eb_path)
     theta = dataset["theta"]
@@ -241,9 +241,6 @@ def main(config, pure_eb_path, cov_path, out_dir):
     cov_pure_eb = data["cov_pure_eb"]
     xip_B, xim_B = data["xip_B"], data["xim_B"]
 
-    # Hartlap correction: MC-propagated covariance uses n_samples from config
-    n_samples = int(config["covariance"]["n_samples"])
-
     # Extract B-mode covariance blocks
     cov_xip_B_full = cov_pure_eb[2 * nbins : 3 * nbins, 2 * nbins : 3 * nbins]
     cov_xim_B_full = cov_pure_eb[3 * nbins : 4 * nbins, 3 * nbins : 4 * nbins]
@@ -264,10 +261,10 @@ def main(config, pure_eb_path, cov_path, out_dir):
 
     # Compute PTEs at fiducial scale cuts
     chi2_xip_fid, pte_xip_fid, dof_xip_fid = compute_chi2_pte(
-        xip_B[mask_xip], cov_xip_B_cut, n_samples=n_samples
+        xip_B[mask_xip], cov_xip_B_cut
     )
     chi2_xim_fid, pte_xim_fid, dof_xim_fid = compute_chi2_pte(
-        xim_B[mask_xim], cov_xim_B_cut, n_samples=n_samples
+        xim_B[mask_xim], cov_xim_B_cut
     )
     pte_joint_fid, chi2_joint_fid, dof_joint_fid = _compute_joint_pte(
         xip_B[mask_xip],
@@ -275,19 +272,17 @@ def main(config, pure_eb_path, cov_path, out_dir):
         cov_xip_B_cut,
         cov_xim_B_cut,
         cov_cross_cut,
-        n_samples=n_samples,
     )
 
     # Compute PTEs at full range
-    _, pte_xip_full, _ = compute_chi2_pte(xip_B, cov_xip_B_full, n_samples=n_samples)
-    _, pte_xim_full, _ = compute_chi2_pte(xim_B, cov_xim_B_full, n_samples=n_samples)
+    _, pte_xip_full, _ = compute_chi2_pte(xip_B, cov_xip_B_full)
+    _, pte_xim_full, _ = compute_chi2_pte(xim_B, cov_xim_B_full)
     pte_joint_full, chi2_joint_full, dof_joint_full = _compute_joint_pte(
         xip_B,
         xim_B,
         cov_xip_B_full,
         cov_xim_B_full,
         cov_cross_full,
-        n_samples=n_samples,
     )
 
     print(
@@ -338,8 +333,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--pure-eb-data",
         required=True,
-        help="Fiducial <version>_pure_eb_semianalytic.npz "
-        "(decomposed ξ± + 6-block MC covariance)",
+        help="Fiducial <version>_pure_eb.npz (decomposed ξ± + 6-block covariance)",
     )
     ap.add_argument(
         "--reporting-cov",
diff --git a/papers/bmodes/scripts/pure_eb_modes.py b/papers/bmodes/scripts/pure_eb_modes.py
new file mode 100644
index 00000000..643fe12c
--- /dev/null
+++ b/papers/bmodes/scripts/pure_eb_modes.py
@@ -0,0 +1,116 @@
+"""Pure E/B modes and their exact covariance for one version.
+
+Reads the fine-grid ξ± (TreeCorr text dump, with its pair weights) and the
+Gaussian ξ± covariance on the same grid, and applies
+``sp_validation.b_modes.calculate_pure_eb_correlation``: the fixed-operator
+pure-E/B estimator averaged into the reporting bins, with covariance
+``K C_ξ Kᵀ``. Writes ``<version>_pure_eb.npz``, the input of every downstream
+pure-mode plot / PTE.
+
+    python pure_eb_modes.py \
+        --xi-integration <xi 1000-bin .txt> \
+        --cov-integration <Gaussian ξ± covariance, same grid> \
+        --min-sep 1.0 --max-sep 250.0 --nbins 20 \
+        --min-sep-int 0.5 --max-sep-int 300.0 --nbins-int 1000 \
+        --out <version>_pure_eb.npz
+"""
+
+import argparse
+import os
+
+import numpy as np
+
+from sp_validation.b_modes import calculate_pure_eb_correlation
+from sp_validation.sacc_io import PURE_KEYS
+
+
+def _load_xi(path, nbins):
+    """``(meanr, xip, xim, weight)`` from a TreeCorr text dump.
+
+    TreeCorr's ASCII header is
+    r_nom meanr meanlogr xip xim xip_im xim_im sigma_xip sigma_xim weight npairs.
+    """
+    data = np.loadtxt(path, comments="#", max_rows=nbins)
+    return data[:, 1], data[:, 3], data[:, 4], data[:, 9]
+
+
+def pure_eb_modes(
+    xi_integration,
+    cov_integration,
+    min_sep,
+    max_sep,
+    nbins,
+    min_sep_int,
+    max_sep_int,
+    nbins_int,
+    out_path,
+):
+    results = calculate_pure_eb_correlation(
+        *_load_xi(xi_integration, nbins_int),
+        np.geomspace(min_sep_int, max_sep_int, nbins_int + 1),
+        np.loadtxt(cov_integration),
+        np.geomspace(min_sep, max_sep, nbins + 1),
+    )
+    package = {
+        "theta": results["theta"],
+        "left_edges": results["left_edges"],
+        "right_edges": results["right_edges"],
+        "theta_int": results["theta_int"],
+        "xip_total": results["xip"],
+        "xim_total": results["xim"],
+        **{key: results[key] for key in PURE_KEYS},
+        "cov_pure_eb": results["cov"],
+    }
+    os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
+    np.savez(out_path, **package)
+    print(f"Saved pure E/B to {out_path}")
+    return out_path
+
+
+def _from_snakemake(smk):
+    p = smk.params
+    pure_eb_modes(
+        smk.input["xi_integration"],
+        smk.input["cov_integration"],
+        p["min_sep"],
+        p["max_sep"],
+        p["nbins"],
+        p["min_sep_int"],
+        p["max_sep_int"],
+        p["nbins_int"],
+        smk.output[0],
+    )
+
+
+def _from_cli(argv=None):
+    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
+    ap.add_argument("--xi-integration", required=True)
+    ap.add_argument("--cov-integration", required=True)
+    ap.add_argument("--min-sep", type=float, default=1.0)
+    ap.add_argument("--max-sep", type=float, default=250.0)
+    ap.add_argument("--nbins", type=int, default=20)
+    ap.add_argument("--min-sep-int", type=float, default=0.5)
+    ap.add_argument("--max-sep-int", type=float, default=300.0)
+    ap.add_argument("--nbins-int", type=int, default=1000)
+    ap.add_argument("--out", required=True, help="Output .npz path")
+    a = ap.parse_args(argv)
+    pure_eb_modes(
+        a.xi_integration,
+        a.cov_integration,
+        a.min_sep,
+        a.max_sep,
+        a.nbins,
+        a.min_sep_int,
+        a.max_sep_int,
+        a.nbins_int,
+        a.out,
+    )
+
+
+if __name__ == "__main__":
+    try:
+        snakemake  # noqa: F821 — injected by Snakemake's script: directive
+    except NameError:
+        _from_cli()
+    else:
+        _from_snakemake(snakemake)  # noqa: F821
diff --git a/papers/bmodes/scripts/pure_eb_version_comparison.py b/papers/bmodes/scripts/pure_eb_version_comparison.py
index 3e99e68b..6f20c054 100644
--- a/papers/bmodes/scripts/pure_eb_version_comparison.py
+++ b/papers/bmodes/scripts/pure_eb_version_comparison.py
@@ -243,7 +243,7 @@ def _create_version_comparison_figure(
 
 
 def _pure_eb_npz(results_dir, ver):
-    return f"{results_dir}/{ver}_pure_eb_semianalytic.npz"
+    return f"{results_dir}/{ver}_pure_eb.npz"
 
 
 def main(
@@ -472,7 +472,7 @@ def _from_cli(argv=None):
     ap.add_argument(
         "--results-dir",
         required=True,
-        help="Directory holding per-version <version>_pure_eb_semianalytic.npz files",
+        help="Directory holding per-version <version>_pure_eb.npz files",
     )
     ap.add_argument("--out", required=True, help="Output directory (lc {output})")
     ap.add_argument(
diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index 8501bfbc..5b33999f 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -57,7 +57,7 @@ cosmo_val:
   # C_l^BB column belongs in the standard B-mode summary.
   include_pseudo_cl: true
 
-  # Theory cosmology for pseudo-Cl / semi-analytic covariance (astropy Planck18
+  # Theory cosmology for the pseudo-Cl covariance (astropy Planck18
   # + CAMB nonlinear settings, matching the original run_cosmo_val.py driver).
   cosmo_params:
     Omega_m: 0.30966
@@ -110,8 +110,6 @@ var_method: jackknife
 
 covariance:
   default_masked: true
-  use_semianalytic: true
-  n_samples: 2000
 
 cl:
   n_ell_bins: 32
diff --git a/pyproject.toml b/pyproject.toml
index 35c9e6ce..2ffcd753 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,13 +38,13 @@ dependencies = [
     # get_theo_c_ell / get_theo_xi / PLANCK18 in cs_util.cosmo, which land via
     # CosmoStat/cs_util#76 — so this goes green once #76 merges into develop.
     "cs_util @ git+https://github.com/CosmoStat/cs_util.git@develop",
-    # Fast numba B-mode kernels (Schneider et al. 2022): the Schneider E/B split
-    # and COSEBIS live here, imported in b_modes.py. Tracks aguinot/cosmo-numba
-    # main (not published on PyPI). main carries the numpy-2 FFT fix via its
-    # rocket-fft dependency (which teaches numba's nopython mode to handle
-    # np.fft), and declares numba/numpy/rocket-fft from its requirements.txt so
-    # those constraints reach the resolver.
-    "cosmo-numba @ git+https://github.com/aguinot/cosmo-numba.git@main",
+    # Fast numba B-mode kernels (Schneider et al. 2022): the fixed-quadrature
+    # pure-E/B operator (schneider2022) and COSEBIS live here, imported
+    # in b_modes.py. Pinned to a commit on cailmdaley/cosmo-numba, which carries
+    # the operator on top of aguinot/cosmo-numba main (not published on PyPI):
+    # main's numpy-2 FFT fix via rocket-fft, and its numba/numpy/rocket-fft
+    # requirements, which reach the resolver.
+    "cosmo-numba @ git+https://github.com/cailmdaley/cosmo-numba.git@a64cb2ed13e595a16ad056a128ef24594b811504",
     "emcee",
     # numba is the load-bearing pin of this whole environment: its numpy ceiling
     # (numba 0.66 -> numpy<2.5) is what keeps the resolver from drifting numpy
diff --git a/src/sp_validation/b_modes.py b/src/sp_validation/b_modes.py
index fdc2ddf8..083dafd1 100644
--- a/src/sp_validation/b_modes.py
+++ b/src/sp_validation/b_modes.py
@@ -1,8 +1,9 @@
 """
 B-mode analysis functions for weak lensing validation.
 
-This module contains pure E/B mode decomposition, COSEBIs analysis,
-and semi-analytical covariance calculations extracted from CosmologyValidation.
+Pure E/B modes (Schneider et al. 2022) as a fixed linear operator on the fine
+ξ± grid with their exact covariance, COSEBIs, and the χ²/PTE and plotting
+helpers shared by both.
 """
 
 import warnings
@@ -11,19 +12,12 @@
 import numpy as np
 import seaborn as sns
 import tqdm
-import treecorr
-from cs_util.cosmo import get_theo_xi
 from mpl_toolkits.axes_grid1 import make_axes_locatable
-from scipy import sparse, stats
+from scipy import stats
 
 _EB_KEYS = ("xip_E", "xim_E", "xip_B", "xim_B", "xip_amb", "xim_amb")
 
 
-def _eb_vector(modes):
-    """Pure-E/B modes concatenated in ``_EB_KEYS`` order, the covariance layout."""
-    return np.concatenate([modes[k] for k in _EB_KEYS])
-
-
 def find_conservative_scale_cut_key(results, requested_scale_cut):
     """
     Find scale cut key that conservatively fits within requested range.
@@ -97,6 +91,23 @@ def bins_from_edges(left_edges, right_edges, min_scale=None, max_scale=None):
     return start_bin, stop_bin
 
 
+def bins_from_scale_cut(left_edges, right_edges, scale_cut):
+    """Reporting bins inside a pure-E/B scale cut, as ``(start_bin, stop_bin)``.
+
+    Each end of ``scale_cut`` snaps to the nearest reporting edge in log θ —
+    the rule that snaps reporting edges onto the fine grid — so a cut placed
+    on a nominal edge selects the same bins whichever fine grid the edges
+    were snapped to. ``stop_bin`` is exclusive.
+    """
+    edges = np.append(left_edges, right_edges[-1])
+    start_bin, stop_bin = (
+        int(np.abs(np.log(edges) - np.log(cut)).argmin()) for cut in scale_cut
+    )
+    if stop_bin <= start_bin:
+        raise RuntimeError(f"scale cut {scale_cut} selects no bins")
+    return start_bin, stop_bin
+
+
 def log_bin_edges(min_sep, max_sep, nbins):
     """TreeCorr ``Log`` bin edges — the grid a binning defines.
 
@@ -125,215 +136,253 @@ def correlation_from_covariance(covariance):
     return covariance / np.outer(stdev, stdev)
 
 
-def calculate_pure_eb_correlation(
-    gg,
-    gg_int,
-    var_method="jackknife",
-    cov_path_int=None,
-    cosmo_cov=None,
-    n_samples=1000,
-    z_dist=None,
-):
+def hartlap_factor(npatch, dof):
+    """Hartlap (2007) debiasing of an inverse covariance.
+
+    ``(N - p - 2) / (N - 1)`` for a jackknife covariance from ``N = npatch``
+    patches inverted over ``p = dof`` data points; exactly 1 for an analytic
+    covariance, which ``npatch=None`` denotes.
     """
-    Calculate pure E/B modes from correlation function objects.
+    return 1.0 if npatch is None else (npatch - dof - 2) / (npatch - 1)
 
-    Parameters
-    ----------
-    gg : treecorr.GGCorrelation
-        Correlation function for reporting binning (coarser binning for final results)
-    gg_int : treecorr.GGCorrelation
-        Correlation function for integration binning (fine binning for numerical
-        integration)
-    var_method : str, optional
-        Variance method ("jackknife" or "bootstrap")
-    cov_path_int : str, optional
-        Path to integration covariance matrix for semi-analytical calculation
-    cosmo_cov : pyccl.Cosmology, optional
-        Cosmology for theoretical predictions in semi-analytical covariance
-    n_samples : int, optional
-        Number of Monte Carlo samples for semi-analytical covariance
-    z_dist : 2D array, optional
-        Redshift distribution;
-        z_dist[:, 0] = z, z_dist[:, 1] = n(z)
 
-    Returns
-    -------
-    dict
-        Dictionary containing pure E/B mode results and covariance
-    """
-    # Calculate min_sep and max_sep from gg object
-    min_sep, max_sep = gg.left_edges[0], gg.right_edges[-1]
-
-    def pure_EB(corrs):
-        gg, gg_int = corrs
-        return pure_eb_from_xi(
-            theta_report=gg.meanr,
-            xip_report=gg.xip,
-            xim_report=gg.xim,
-            theta_int=gg_int.meanr,
-            xip_int=gg_int.xip,
-            xim_int=gg_int.xim,
-            tmin=min_sep,
-            tmax=max_sep,
-        )
+def _reporting_binning(weight_int, edges_int, reporting_edges):
+    """Reporting bins as unions of fine bins, and their pair-weighted average.
 
-    # The results dict is self-describing: the grids it was measured on travel
-    # with the modes, so every consumer downstream works from values alone.
-    results = {
-        "theta": gg.meanr,
-        "left_edges": gg.left_edges,
-        "right_edges": gg.right_edges,
-        "xip": gg.xip,
-        "xim": gg.xim,
-        "var_xip": gg.varxip,
-        "var_xim": gg.varxim,
-        "theta_int": gg_int.meanr,
-        "xip_int": gg_int.xip,
-        "xim_int": gg_int.xim,
-        "n_eff": n_samples if cov_path_int is not None else gg.npatch1,
-    }
-    results.update(pure_EB([gg, gg_int]))
+    Each requested reporting edge snaps to the nearest fine edge (in log θ), so
+    every reporting bin is a whole number of fine bins. Row ``i`` of the
+    ``(n_report, n_fine)`` matrix weights the fine bins of reporting bin ``i``
+    by their TreeCorr pair weight ``Σ w_i w_j`` and sums to one. TreeCorr's ξ±
+    and ``meanr`` are averages over pairs under that same weight, so each row
+    reproduces what TreeCorr would measure on the snapped bin.
 
-    if cov_path_int is not None:
-        if z_dist is None or cosmo_cov is None:
-            raise ValueError(
-                "semi-analytical covariance needs both z_dist and cosmo_cov"
-            )
-        cov, eb_samples = pure_eb_covariance_mc(
-            theta=gg.meanr,
-            left_edges=gg.left_edges,
-            right_edges=gg.right_edges,
-            theta_int=gg_int.meanr,
-            cov_int=np.loadtxt(cov_path_int),
-            z=z_dist[:, 0],
-            nz=z_dist[:, 1],
-            cosmo=cosmo_cov,
-            n_samples=n_samples,
+    Returns ``(binning, edges)``, ``edges`` being the snapped reporting edges.
+    """
+    weight_int = np.asarray(weight_int, dtype=float)
+    edges_int = np.asarray(edges_int, dtype=float)
+    reporting_edges = np.asarray(reporting_edges, dtype=float)
+    if edges_int.shape != (weight_int.size + 1,):
+        raise ValueError("edges_int must hold n_fine + 1 fine-grid edges")
+    if reporting_edges.min() < edges_int[0] or reporting_edges.max() > edges_int[-1]:
+        raise ValueError(
+            f"reporting edges [{reporting_edges.min()}, {reporting_edges.max()}] "
+            f"reach outside the fine grid [{edges_int[0]}, {edges_int[-1]}]"
         )
-        results.update({"cov": cov, "eb_samples": eb_samples})
-    else:
-        # Use existing treecorr covariance estimation
-        results["cov"] = treecorr.estimate_multi_cov(
-            [gg, gg_int],
-            var_method,
-            func=lambda x: _eb_vector(pure_EB(x)),
-            cross_patch_weight="match" if var_method == "jackknife" else None,
+    snap = np.abs(np.log(reporting_edges)[:, None] - np.log(edges_int)).argmin(axis=1)
+    if np.any(np.diff(snap) <= 0):
+        raise ValueError(
+            "reporting edges snap onto the same fine edge; the fine grid is too "
+            f"coarse for them: {reporting_edges.tolist()} -> "
+            f"{edges_int[snap].tolist()}"
         )
-
-    # Validate covariance matrix
-    try:
-        np.linalg.cholesky(results["cov"])
-    except np.linalg.LinAlgError:
-        warnings.warn(
-            "E/B mode covariance matrix is not positive definite. "
-            "Chi-squared statistics may be unreliable.",
-            UserWarning,
+    binning = np.zeros((snap.size - 1, weight_int.size))
+    for i, (lo, hi) in enumerate(zip(snap[:-1], snap[1:])):
+        binning[i, lo:hi] = weight_int[lo:hi]
+    weight = binning.sum(axis=1)
+    if np.any(weight == 0):
+        empty = np.flatnonzero(weight == 0).tolist()
+        raise ValueError(f"reporting bins {empty} hold no integration-grid pairs")
+    return binning / weight[:, None], edges_int[snap]
+
+
+def _fixed_quadrature_operator(edges_int):
+    """The Schneider (2022) transform on the fine grid, ``_EB_KEYS`` order.
+
+    The single call into cosmo_numba. Its interpolator places the samples on
+    a regular grid in log θ, so the transform runs on the log-uniform nodes of
+    the fine bins — their geometric centres, TreeCorr's ``rnom`` — and is
+    evaluated at every node, with ``[tmin, tmax]`` at their extent. That
+    makes it a function of the fine grid alone (the zero-padded ξ− window
+    extends from the evaluation grid). Returns the ``(6, n_fine, 2 * n_fine)``
+    stack of the six matrices acting on ``[xi_+; xi_-]``; rows whose
+    integration support is too small near the grid edges are NaN.
+    """
+    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_operator
+
+    log_edges = np.log(np.asarray(edges_int, dtype=float))
+    step = np.diff(log_edges)
+    if np.ptp(step) > 1e-10 * np.mean(step):
+        raise ValueError(
+            "the pure-E/B transform needs log-uniform fine-grid edges (TreeCorr "
+            f"Log binning); their log spacing varies by {np.ptp(step):.3e}"
         )
+    nodes = np.exp(0.5 * (log_edges[:-1] + log_edges[1:]))
+    return np.stack(
+        get_pure_EB_operator(
+            nodes,
+            nodes,
+            tmin=nodes[0] * (1 - 1e-9),
+            tmax=nodes[-1] * (1 + 1e-9),
+            local_from_int=True,
+        )
+    )
 
-    return results
 
+def pure_eb_operator(weight_int, edges_int, reporting_edges):
+    """The pure-E/B estimator as one matrix on the fine ξ± grid.
 
-def pure_eb_from_xi(
-    theta_report, xip_report, xim_report, theta_int, xip_int, xim_int, tmin, tmax
-):
-    """Pure-E/B correlation functions from ξ± arrays through the pipeline kernel.
+    The Schneider et al. (2022) transform is evaluated with fixed-quadrature
+    weights at the log-uniform fine-grid nodes (TreeCorr ``rnom``),
+    integrating over the whole fine grid, and
+    the six pure modes are then averaged into the reporting bins with TreeCorr
+    pair weights. The reporting edges snap to the nearest fine edges, so each
+    reporting bin is a union of fine bins. Both steps are linear and
+    independent of the ξ± values, so the estimator is
+    ``K = (I_6 ⊗ P) · M`` and
+
+        [xip_E; xim_E; xip_B; xim_B; xip_amb; xim_amb] = K @ [xip_int; xim_int]
 
-    The one place this module calls cosmo_numba's Schneider (2022) transform.
+    with ``K`` of shape ``(6 * n_report, 2 * n_fine)``.
 
-    ``tmin``/``tmax`` are the reporting correlation's TreeCorr *bin edges*
-    (``gg.left_edges[0]`` / ``gg.right_edges[-1]``). The reporting grid must be a
-    strict sub-range of the integration grid: a reporting point on the
-    integration boundary has no interior support and comes back NaN.
+    Parameters
+    ----------
+    weight_int : array_like
+        TreeCorr pair weight ``Σ w_i w_j`` per fine bin (``gg.weight``), the
+        averaging weights.
+    edges_int : array_like
+        The ``n_fine + 1`` log-uniform fine-grid bin edges. The transform's
+        ``[tmin, tmax]`` is the extent of their centres, so they must reach
+        beyond the reporting range on both sides.
+    reporting_edges : array_like
+        Requested reporting-bin edges, ``n_report + 1`` of them.
 
     Returns
     -------
-    dict
-        Keyed by ``_EB_KEYS`` (xip_E, xim_E, xip_B, xim_B, xip_amb, xim_amb).
+    operator : numpy.ndarray
+        ``K``, shape ``(6 * n_report, 2 * n_fine)``.
+    binning : numpy.ndarray
+        ``P``, the ``(n_report, n_fine)`` pair-weighted average.
+    edges : numpy.ndarray
+        The reporting edges actually used, each a fine-grid edge.
     """
-    from cosmo_numba.B_modes.schneider2022 import get_pure_EB_modes
-
-    modes = get_pure_EB_modes(
-        theta=np.asarray(theta_report),
-        xip=np.asarray(xip_report),
-        xim=np.asarray(xim_report),
-        theta_int=np.asarray(theta_int),
-        xip_int=np.asarray(xip_int),
-        xim_int=np.asarray(xim_int),
-        tmin=tmin,
-        tmax=tmax,
-        parallel=True,
-    )
-    return dict(zip(_EB_KEYS, (np.asarray(m) for m in modes)))
+    binning, edges = _reporting_binning(weight_int, edges_int, reporting_edges)
+    nodes = np.flatnonzero(binning.any(axis=0))
+    # Only the rows P averages enter; NaN edge rows outside them are dropped,
+    # since 0 · NaN would still poison the product.
+    transform = _fixed_quadrature_operator(edges_int)[:, nodes]
+    undetermined = {
+        key: int(np.count_nonzero(~np.isfinite(rows).all(axis=1)))
+        for key, rows in zip(_EB_KEYS, transform)
+        if not np.isfinite(rows).all()
+    }
+    if undetermined:
+        raise ValueError(
+            "pure-E/B operator rows are under-determined (reporting bins too "
+            f"close to the integration-grid edge): {undetermined}"
+        )
+    operator = np.vstack([binning[:, nodes] @ rows for rows in transform])
+    return operator, binning, edges
 
 
-def pure_eb_covariance_mc(
-    *,
-    theta,
-    left_edges,
-    right_edges,
+def calculate_pure_eb_correlation(
     theta_int,
-    cov_int,
-    z,
-    nz,
-    cosmo,
-    n_samples=1000,
+    xip_int,
+    xim_int,
+    weight_int,
+    edges_int,
+    cov_xi,
+    reporting_edges,
+    *,
+    npatch=None,
 ):
-    """Pure-E/B covariance by Monte Carlo through the same kernel as the modes.
+    """Pure E/B modes and their exact covariance from fine-grid ξ±.
+
+    The modes are :func:`pure_eb_operator` applied to ``[xip_int; xim_int]``,
+    and the covariance is ``K C_xi Kᵀ`` — exact for whatever ξ± covariance is
+    supplied, analytic or jackknife. ``npatch`` records which: the jackknife
+    patch count behind ``cov_xi``, or ``None`` for an analytic covariance. It
+    travels in the results and sets the Hartlap factor of every χ² built on
+    them (:func:`hartlap_factor`).
+
+    The reporting bins are unions of fine bins (the requested edges snap to
+    the nearest fine edges), and their ``theta``, ``xip``/``xim`` and
+    variances are the same pair-weighted average of the fine grid, so
+    ``xi_± = E ± B + amb`` holds bin by bin and ``theta``/``xip``/``xim`` are
+    what TreeCorr would measure on the snapped bins.
 
-    ξ± draws come from ``cov_int``, a ξ± covariance on the integration grid,
-    around the theory mean for ``(z, nz)`` under ``cosmo``; each draw is binned
-    down to the reporting grid and pushed through :func:`pure_eb_from_xi`. The
-    covariance of the transformed draws is the result, so it depends on the
-    covariance model and the grids, never on the measured data vector.
+    Parameters
+    ----------
+    theta_int, xip_int, xim_int, weight_int : array_like
+        Fine-grid ``meanr``, ξ±, and TreeCorr pair weight ``Σ w_i w_j``.
+        ``meanr`` enters only the reported ``theta``; the transform runs on
+        the log-uniform nodes of ``edges_int``.
+    edges_int : array_like
+        The ``n_fine + 1`` log-uniform fine-grid bin edges.
+    cov_xi : array_like
+        ``(2 n_fine, 2 n_fine)`` covariance of ``[xip_int; xim_int]``.
+    reporting_edges : array_like
+        Requested reporting-bin edges, ``n_report + 1`` of them.
+    npatch : int, optional
+        Jackknife patch count behind ``cov_xi``; ``None`` if it is analytic.
 
-    Returns ``(cov, eb_samples)`` — the covariance in ``_EB_KEYS`` order and
-    the draws behind it.
+    Returns
+    -------
+    dict
+        The six ``_EB_KEYS`` mode arrays, ``cov`` (in ``_EB_KEYS`` block
+        order), ``npatch``, the reporting grid (``theta``, the snapped
+        ``left_edges``/``right_edges``, ``xip``, ``xim``, ``var_xip``,
+        ``var_xim``) and the fine-grid inputs (``theta_int``, ``xip_int``,
+        ``xim_int``, ``weight_int``, ``edges_int``).
     """
-    theta, theta_int = np.asarray(theta), np.asarray(theta_int)
-    nbins_int = len(theta_int)
-
-    # Each reporting bin averages the integration bins that fall inside it.
-    reporting_bin_edges = np.concatenate([left_edges, [right_edges[-1]]])
-    bin_indices = np.digitize(theta_int, reporting_bin_edges) - 1
-    valid_mask = (bin_indices >= 0) & (bin_indices < len(theta))
-    row_indices, col_indices = (bin_indices[valid_mask], np.where(valid_mask)[0])
-    binning_matrix = sparse.csr_matrix(
-        (np.ones(len(row_indices)), (row_indices, col_indices)),
-        shape=(len(theta), nbins_int),
+    if npatch is not None and npatch < 2:
+        raise ValueError(f"a jackknife covariance needs npatch > 1, not {npatch}")
+    theta_int, xip_int, xim_int = (
+        np.asarray(a, dtype=float) for a in (theta_int, xip_int, xim_int)
     )
-    row_sums = np.array(binning_matrix.sum(axis=1)).flatten()
-    binning_matrix = sparse.diags(1 / row_sums) @ binning_matrix
-
-    # One n(z) gives one tracer pair: get_theo_xi's single (xi+, xi-) entry.
-    (xi_pm,) = get_theo_xi(
-        theta=theta_int, z=z, nz=nz, backend="ccl", cosmo=cosmo
-    ).values()
-    mean_int = np.concatenate(xi_pm)
-    samples_int = np.random.multivariate_normal(mean_int, cov_int, size=n_samples)
-    samples_int_xip, samples_int_xim = (
-        samples_int[:, :nbins_int],
-        samples_int[:, nbins_int:],
-    )
-    samples_rep_xip = (binning_matrix @ samples_int_xip.T).T
-    samples_rep_xim = (binning_matrix @ samples_int_xim.T).T
-
-    def eb_draw(i):
-        modes = pure_eb_from_xi(
-            theta_report=theta,
-            xip_report=samples_rep_xip[i],
-            xim_report=samples_rep_xim[i],
-            theta_int=theta_int,
-            xip_int=samples_int_xip[i],
-            xim_int=samples_int_xim[i],
-            tmin=left_edges[0],
-            tmax=right_edges[-1],
+    cov_xi = np.asarray(cov_xi, dtype=float)
+    if np.shape(weight_int) != theta_int.shape:
+        raise ValueError("weight_int must have one entry per integration bin")
+    operator, binning, edges = pure_eb_operator(weight_int, edges_int, reporting_edges)
+    if cov_xi.shape != (operator.shape[1],) * 2:
+        raise ValueError(
+            f"cov_xi has shape {cov_xi.shape}; the fine grid needs "
+            f"{(operator.shape[1],) * 2}"
         )
-        return _eb_vector(modes)
 
-    eb_samples = np.array(
-        [eb_draw(i) for i in tqdm.tqdm(range(n_samples), desc="MC samples")]
+    n_report = len(edges) - 1
+    modes = operator @ np.concatenate([xip_int, xim_int])
+    n_fine = theta_int.size
+    var_xip, var_xim = (
+        np.einsum("ij,jk,ik->i", binning, block, binning)
+        for block in (cov_xi[:n_fine, :n_fine], cov_xi[n_fine:, n_fine:])
     )
-    return np.cov(eb_samples.T), eb_samples
+    results = {
+        "theta": binning @ theta_int,
+        "left_edges": edges[:-1],
+        "right_edges": edges[1:],
+        "xip": binning @ xip_int,
+        "xim": binning @ xim_int,
+        "var_xip": var_xip,
+        "var_xim": var_xim,
+        "theta_int": theta_int,
+        "xip_int": xip_int,
+        "xim_int": xim_int,
+        "weight_int": np.asarray(weight_int, dtype=float),
+        "edges_int": np.asarray(edges_int, dtype=float),
+        "cov": operator @ cov_xi @ operator.T,
+        "npatch": npatch,
+    }
+    for i, key in enumerate(_EB_KEYS):
+        results[key] = modes[i * n_report : (i + 1) * n_report]
+
+    # The B-mode block is what every χ² inverts; the ambiguous blocks are
+    # ill-conditioned by construction.
+    b_block = slice(2 * n_report, 4 * n_report)
+    try:
+        np.linalg.cholesky(results["cov"][b_block, b_block])
+    except np.linalg.LinAlgError:
+        warnings.warn(
+            "B-mode covariance is not positive definite. "
+            "Chi-squared statistics may be unreliable.",
+            UserWarning,
+        )
+
+    return results
+
+
+def covariance_label(npatch):
+    """How a pure-E/B covariance was made, from its ``npatch`` record."""
+    return "analytic" if npatch is None else f"jackknife ({npatch} patches)"
 
 
 def calculate_cosebis(gg, nmodes=10, scale_cuts=None, cov_path=None):
@@ -477,8 +526,9 @@ def calculate_eb_statistics(results):
     ----------
     results : dict
         Pure E/B results: the six mode arrays, the ``cov`` block, the reporting
-        ``theta``, and ``n_eff`` — the realisation count behind the covariance
-        (jackknife patches or MC draws), which sets the Hartlap debiasing
+        ``theta``, and ``npatch`` — the jackknife patch count behind the
+        covariance, or ``None`` for an analytic one — which sets the Hartlap
+        factor (:func:`hartlap_factor`)
 
     Returns
     -------
@@ -486,7 +536,7 @@ def calculate_eb_statistics(results):
         Updated results dictionary with PTE matrices and statistics
     """
     nbins = len(results["theta"])
-    n_eff = results["n_eff"]
+    npatch = results["npatch"]
 
     # Extract covariance blocks and standard deviations
     cov = results["cov"]
@@ -508,7 +558,7 @@ def calculate_eb_statistics(results):
 
     for start_bin, stop_bin in combinations:
         nbins_eff = stop_bin - start_bin
-        hartlap_factor = (n_eff - nbins_eff - 2) / (n_eff - 1)
+        hartlap = hartlap_factor(npatch, nbins_eff)
 
         # Individual B-mode chi-squared calculations
         data_slices = [results[f"{xi}_B"][start_bin:stop_bin] for xi in ["xip", "xim"]]
@@ -517,7 +567,7 @@ def calculate_eb_statistics(results):
             for xi in ["xip", "xim"]
         ]
         chi2_values = [
-            hartlap_factor * (data @ np.linalg.solve(cov, data))
+            hartlap * (data @ np.linalg.solve(cov, data))
             for data, cov in zip(data_slices, cov_slices)
         ]
 
@@ -536,7 +586,7 @@ def calculate_eb_statistics(results):
         cov_combined = np.block(
             [[cov_xip_block, cov_cross_block], [cov_cross_block.T, cov_xim_block]]
         )
-        chi2_combined = hartlap_factor * (
+        chi2_combined = hartlap_factor(npatch, 2 * nbins_eff) * (
             data_combined @ np.linalg.solve(cov_combined, data_combined)
         )
         pte_combined[start_bin, stop_bin - 1] = stats.chi2.sf(
@@ -557,9 +607,10 @@ def plot_integration_vs_reporting(results, output_path, version):
     Parameters
     ----------
     results : dict
-        Pure E/B results carrying both grids (``theta``/``xip``/``xim`` and the
-        ``theta_int``/``xip_int``/``xim_int`` counterparts), plus the reporting
-        ``var_xip``/``var_xim`` the error bars use
+        Pure E/B results carrying the fine grid (``theta_int``/``xip_int``/
+        ``xim_int``) and its pair-weighted average into the reporting bins
+        (``theta``/``xip``/``xim``, with the ``var_xip``/``var_xim`` the error
+        bars use)
     output_path : str
         Output file path for the plot
     version : str
@@ -614,7 +665,7 @@ def plot_integration_vs_reporting(results, output_path, version):
 
 def _get_pte_from_scale_cut(pte_matrix, edges, scale_cut):
     """
-    Extract PTE value from matrix based on scale cut range using conservative logic.
+    Extract PTE value from matrix at a scale cut (:func:`bins_from_scale_cut`).
 
     Parameters
     ----------
@@ -637,13 +688,7 @@ def _get_pte_from_scale_cut(pte_matrix, edges, scale_cut):
         # Return full-range PTE (first row, last column)
         return pte_matrix[0, nbins - 1]
 
-    min_scale, max_scale = scale_cut
-
-    start_bin, stop_bin = bins_from_edges(left_edges, right_edges, min_scale, max_scale)
-
-    # Ensure valid range, otherwise fallback to full range
-    if stop_bin <= start_bin or start_bin >= nbins or stop_bin <= 0:
-        raise RuntimeError("Invalid scale cut range")
+    start_bin, stop_bin = bins_from_scale_cut(left_edges, right_edges, scale_cut)
     return pte_matrix[start_bin, stop_bin - 1]
 
 
@@ -678,12 +723,16 @@ def plot_pure_eb_correlations(
     # Calculate combined PTE using off-diagonal covariance blocks
     # Get scale cuts for both xi+ and xi-
     if fiducial_xip_scale_cut is not None:
-        xip_start_bin, xip_stop_bin = bins_from_edges(*edges, *fiducial_xip_scale_cut)
+        xip_start_bin, xip_stop_bin = bins_from_scale_cut(
+            *edges, fiducial_xip_scale_cut
+        )
     else:
         xip_start_bin, xip_stop_bin = 0, nbins
 
     if fiducial_xim_scale_cut is not None:
-        xim_start_bin, xim_stop_bin = bins_from_edges(*edges, *fiducial_xim_scale_cut)
+        xim_start_bin, xim_stop_bin = bins_from_scale_cut(
+            *edges, fiducial_xim_scale_cut
+        )
     else:
         xim_start_bin, xim_stop_bin = 0, nbins
 
@@ -713,15 +762,7 @@ def plot_pure_eb_correlations(
 
     # Calculate combined chi-squared with Hartlap factor
     nbins_eff = len(xip_B_data) + len(xim_B_data)
-
-    # Determine effective number of samples for Hartlap correction
-    if "eb_samples" in results:  # Semi-analytical case
-        n_eff = results["eb_samples"].shape[0]
-    else:  # Jackknife case
-        n_eff = results["n_eff"]
-
-    hartlap_factor = (n_eff - nbins_eff - 2) / (n_eff - 1)
-    chi2_combined = hartlap_factor * (
+    chi2_combined = hartlap_factor(results["npatch"], nbins_eff) * (
         data_combined.T @ np.linalg.solve(cov_combined, data_combined)
     )
     combined_pte = stats.chi2.sf(chi2_combined, nbins_eff)
@@ -818,11 +859,10 @@ def plot_pure_eb_correlations(
     scale_cuts = [(fiducial_xip_scale_cut, 0), (fiducial_xim_scale_cut, 1)]
     for scale_cut, ax_idx in scale_cuts:
         if scale_cut is not None:
-            min_scale, max_scale = scale_cut
             xlim = original_xlims[ax_idx]
 
-            # Use conservative scale_cut_to_bins helper for consistency
-            start_bin, stop_bin = bins_from_edges(*edges, min_scale, max_scale)
+            # The same bins the PTEs use
+            start_bin, stop_bin = bins_from_scale_cut(*edges, scale_cut)
 
             # Show excluded regions based on bin edges used in PTE calculation
             # Lower exclusion: bins 0 to start_bin-1 are excluded
@@ -1105,8 +1145,7 @@ def plot_pte_2d_heatmaps(
     fiducial_scale_cuts = [fiducial_xip_scale_cut, fiducial_xim_scale_cut]
     for ax_idx, fiducial_scale_cut in enumerate(fiducial_scale_cuts):
         if fiducial_scale_cut is not None:
-            min_scale, max_scale = fiducial_scale_cut
-            start_bin, stop_bin = bins_from_edges(*edges, min_scale, max_scale)
+            start_bin, stop_bin = bins_from_scale_cut(*edges, fiducial_scale_cut)
 
             if stop_bin > start_bin and start_bin < nbins and stop_bin > 0:
                 rect_x = start_bin
@@ -1157,7 +1196,7 @@ def plot_pte_2d_heatmaps(
     plt.savefig(output_path, dpi=300, bbox_inches="tight")
 
 
-def plot_eb_covariance_matrix(cov_matrix, var_method, output_path, version):
+def plot_eb_covariance_matrix(cov_matrix, label, output_path, version):
     """
     Plot E/B mode covariance matrix as correlation matrix.
 
@@ -1165,8 +1204,8 @@ def plot_eb_covariance_matrix(cov_matrix, var_method, output_path, version):
     ----------
     cov_matrix : numpy.ndarray
         Covariance matrix from E/B mode analysis
-    var_method : str
-        Variance method used for the analysis
+    label : str
+        How the covariance was made (:func:`covariance_label`)
     output_path : str
         Output file path for the plot
     version : str
@@ -1198,7 +1237,7 @@ def plot_eb_covariance_matrix(cov_matrix, var_method, output_path, version):
     divider = make_axes_locatable(ax)
     cax = divider.append_axes("right", size="5%", pad=0.1)
     plt.colorbar(im, cax=cax)
-    ax.set_title(f"{version}: {var_method} correlation matrix")
+    ax.set_title(f"{version}: {label} correlation matrix")
     plt.savefig(output_path, dpi=300, bbox_inches="tight")
 
 
@@ -1272,7 +1311,9 @@ def save_pure_eb_results(results, output_path):
         Output .npz file path
     """
     # Data vectors and covariance
-    save_dict = {"theta": results["theta"], "cov": results["cov"]}
+    save_dict = {
+        key: results[key] for key in ("theta", "left_edges", "right_edges", "cov")
+    }
     for key in _EB_KEYS:
         save_dict[key] = results[key]
 
@@ -1280,13 +1321,9 @@ def save_pure_eb_results(results, output_path):
     for key, matrix in results.get("pte_matrices", {}).items():
         save_dict[f"pte_matrices_{key}"] = matrix
 
-    # Metadata
-    save_dict["n_eff"] = np.array(results["n_eff"])
-    if "eb_samples" in results:
-        save_dict["var_method"] = np.array("semi-analytic")
-        save_dict["n_samples"] = np.array(results["eb_samples"].shape[0])
-    else:
-        save_dict["var_method"] = np.array("jackknife")
+    # The jackknife patch count, stored only for a jackknife covariance.
+    if results["npatch"] is not None:
+        save_dict["npatch"] = np.array(results["npatch"])
 
     np.savez(output_path, **save_dict)
     print(f"Saved pure E/B results to {output_path}")
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index e4957afe..422c3a9c 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -11,6 +11,7 @@
 
 from ..b_modes import (
     _get_pte_from_scale_cut,
+    covariance_label,
     find_conservative_scale_cut_key,
 )
 from ..statistics import chi2_and_pte
@@ -608,11 +609,7 @@ def summarize_bmodes(self, fiducial_scale_cut=(12, 83), versions=None):
                         )
                 except (KeyError, RuntimeError):
                     pass
-                cov_methods.add(
-                    "semi-analytic"
-                    if "eb_samples" in res
-                    else f"jackknife ({res['n_eff']} patches)"
-                )
+                cov_methods.add(covariance_label(res["npatch"]))
 
             # COSEBIs PTE from stored results
             if ver in self._cosebis_results:
diff --git a/src/sp_validation/cosmo_val/pure_eb.py b/src/sp_validation/cosmo_val/pure_eb.py
index 90157456..81bd22f0 100644
--- a/src/sp_validation/cosmo_val/pure_eb.py
+++ b/src/sp_validation/cosmo_val/pure_eb.py
@@ -10,6 +10,7 @@
 from ..b_modes import (
     calculate_eb_statistics,
     calculate_pure_eb_correlation,
+    covariance_label,
     plot_eb_covariance_matrix,
     plot_integration_vs_reporting,
     plot_pte_2d_heatmaps,
@@ -28,114 +29,68 @@ def calculate_pure_eb(
         min_sep_int=0.08,
         max_sep_int=300,
         nbins_int=1000,
-        npatch=256,
-        var_method="jackknife",
+        npatch=None,
         cov_path_int=None,
-        cosmo_cov=None,
-        n_samples=1000,
     ):
         """
         Calculate the pure E/B modes for the given catalog version.
-        The class instance's treecorr_config will be used for the "reporting" binning
-        by default, but any kwargs passed to this function will overwrite the defaults.
+
+        ξ± is measured on the fine integration grid only; the reporting
+        binning (the instance's treecorr_config unless overridden) enters as
+        bin edges, snapped onto the fine edges, into which
+        :func:`~sp_validation.b_modes.pure_eb_operator` averages the modes.
 
         Parameters
         ----------
         version : str
             The catalog version to compute the pure E/B modes for.
-        min_sep : float, optional
-            Minimum separation for the reporting binning. Defaults to the value in
-            self.treecorr_config if not provided.
-        max_sep : float, optional
-            Maximum separation for the reporting binning. Defaults to the value in
-            self.treecorr_config if not provided.
-        nbins : int, optional
-            Number of bins for the reporting binning. Defaults to the value in
-            self.treecorr_config if not provided.
-        min_sep_int : float, optional
-            Minimum separation for the integration binning. Defaults to 0.08.
-        max_sep_int : float, optional
-            Maximum separation for the integration binning. Defaults to 300.
-        nbins_int : int, optional
-            Number of bins for the integration binning. Defaults to 1000.
+        min_sep, max_sep, nbins : float, float, int, optional
+            Reporting binning. Default to the values in self.treecorr_config.
+        min_sep_int, max_sep_int, nbins_int : float, float, int, optional
+            Integration binning (default: 0.08-300 arcmin, 1000 bins). It must
+            extend beyond the reporting range on both sides.
         npatch : int, optional
-            Number of patches for the jackknife or bootstrap resampling. Defaults to
-            the value in self.npatch if not provided.
-        var_method : str, optional
-            Variance estimation method. Defaults to "jackknife".
+            Jackknife patch count. Defaults to self.npatch.
         cov_path_int : str, optional
-            Path to the covariance matrix for the reporting binning. Replaces the
-            treecorr covariance matrix if provided, meaning that var_method has no
-            effect on the results although it is still passed to
-            CosmologyValidation.calculate_2pcf.
-        cosmo_cov : pyccl.Cosmology, optional
-            Cosmology object to use for theoretical xi+/xi- predictions in the
-            semi-analytical covariance calculation. Defaults to self.cosmo if not
-            provided.
-        n_samples : int, optional
-            Number of Monte Carlo samples for semi-analytical covariance propagation.
-            Defaults to 1000.
+            Analytic ξ± covariance on the integration grid. Without it the
+            covariance is the jackknife of the integration-grid ξ±.
 
         Returns
         -------
         dict
-            A dictionary containing the following keys:
-
-            - "xip_E": Pure E-mode correlation function for xi+.
-            - "xim_E": Pure E-mode correlation function for xi-.
-            - "xip_B": Pure B-mode correlation function for xi+.
-            - "xim_B": Pure B-mode correlation function for xi-.
-            - "xip_amb": Ambiguity mode for xi+.
-            - "xim_amb": Ambiguity mode for xi-.
-            - "cov": Covariance matrix for the pure E/B modes.
-            - "theta", "left_edges", "right_edges": Reporting-grid bin centres
-              and edges.
-            - "xip", "xim", "var_xip", "var_xim": Reporting-grid xi+/xi- and
-              their variances.
-            - "theta_int", "xip_int", "xim_int": Integration-grid xi+/xi-.
-            - "n_eff": Realisation count behind "cov" (jackknife patches or
-              MC draws), which sets the Hartlap debiasing.
-            - "eb_samples": (only when using semi-analytical covariance) Semi-analytic
-              EB samples used for covariance calculation. Shape: (n_samples, 6*nbins)
-
-        Notes
-        -----
-        - A shared patch file is used for the reporting and integration binning,
-          and is created if it does not exist.
+            The results of :func:`~sp_validation.b_modes.calculate_pure_eb_correlation`:
+            the six pure-mode arrays, their covariance ``cov`` and its
+            ``npatch`` record (``None`` for an analytic covariance), the
+            reporting grid and the integration-grid ξ±.
         """
         self.print_start(f"Computing {version} pure E/B")
 
-        # Set up parameters with defaults
-        npatch = npatch or self.npatch
-
-        # Create TreeCorr configurations
-        treecorr_config = self._binning(min_sep, max_sep, nbins)
-        treecorr_config_int = self._binning(min_sep_int, max_sep_int, nbins_int)
-
-        # Calculate correlation functions
-        gg = self.calculate_2pcf(version, npatch=npatch, **treecorr_config)
-        gg_int = self.calculate_2pcf(version, npatch=npatch, **treecorr_config_int)
-
-        # Get redshift distribution if using analytic covariance
-        z_dist = (
-            np.column_stack(self.get_redshift(version))
-            if cov_path_int is not None
-            else None
+        reporting = self._binning(min_sep, max_sep, nbins)
+        gg_int = self.calculate_2pcf(
+            version,
+            npatch=npatch,
+            **self._binning(min_sep_int, max_sep_int, nbins_int),
         )
 
-        # Delegate to b_modes module
-        results = calculate_pure_eb_correlation(
-            gg=gg,
-            gg_int=gg_int,
-            var_method=var_method,
-            cov_path_int=cov_path_int,
-            cosmo_cov=cosmo_cov,
-            n_samples=n_samples,
-            z_dist=z_dist,
+        if cov_path_int is not None:
+            cov_xi, npatch = np.loadtxt(cov_path_int), None
+        else:
+            cov_xi = gg_int.estimate_cov("jackknife", cross_patch_weight="match")
+            npatch = gg_int.npatch1
+
+        return calculate_pure_eb_correlation(
+            gg_int.meanr,
+            gg_int.xip,
+            gg_int.xim,
+            gg_int.weight,
+            np.append(gg_int.left_edges, gg_int.right_edges[-1]),
+            cov_xi,
+            np.geomspace(
+                reporting["min_sep"], reporting["max_sep"], reporting["nbins"] + 1
+            ),
+            npatch=npatch,
         )
 
-        return results
-
     def plot_pure_eb(
         self,
         versions=None,
@@ -149,12 +104,8 @@ def plot_pure_eb(
         max_sep_int=300,
         nbins_int=1000,
         npatch=None,
-        var_method="jackknife",
         cov_path_int=None,
-        cosmo_cov=None,
-        n_samples=1000,
         results=None,
-        **kwargs,
     ):
         """
         Generate comprehensive pure E/B mode analysis plots.
@@ -182,29 +133,21 @@ def plot_pure_eb(
             (default: 0.08-300 arcmin, 1000 bins)
         npatch : int, optional
             Number of patches for jackknife covariance. Uses self.npatch if None.
-        var_method : str
-            Variance method ("jackknife" or "semi-analytic").
-            Automatically set to "semi-analytic" when cov_path_int is provided.
         cov_path_int : str, optional
-            Path to integration covariance matrix for semi-analytical calculation
-        cosmo_cov : pyccl.Cosmology, optional
-            Cosmology for theoretical predictions in semi-analytical covariance
-        n_samples : int
-            Number of Monte Carlo samples for semi-analytical covariance (default: 1000)
+            Analytic ξ± covariance on the integration grid; the jackknife is
+            used without it.
         results : dict or list, optional
             Precalculated results to avoid recomputation. Can be a single results dict
             for one version, or a list of results dicts for multiple versions.
             If None (default), results will be calculated using calculate_pure_eb.
-        **kwargs : dict
-            Additional arguments passed to calculate_eb_statistics
 
         Notes
         -----
         This function orchestrates the full E/B mode analysis workflow:
 
         - Uses instance configuration as defaults for unspecified parameters
-        - Automatically switches to analytical variance when theoretical
-          covariance provided
+        - Uses the analytic covariance when cov_path_int is given, the
+          jackknife otherwise
         - Generates standardized output file naming based on all analysis
           parameters
         - Delegates individual plot generation to specialized functions in
@@ -215,9 +158,7 @@ def plot_pure_eb(
         output_dir = output_dir or self.cc["paths"]["output"]
         npatch = npatch or self.npatch
 
-        # Override var_method to analytic when cov_path_int is provided
-        if cov_path_int is not None:
-            var_method = "semi-analytic"
+        var_method = "jackknife" if cov_path_int is None else "analytic"
 
         # Use treecorr_config defaults for reporting scale binning
         min_sep = min_sep or self.treecorr_config["min_sep"]
@@ -266,14 +207,11 @@ def plot_pure_eb(
                 max_sep_int=max_sep_int,
                 nbins_int=nbins_int,
                 npatch=npatch,
-                var_method=var_method,
                 cov_path_int=cov_path_int,
-                cosmo_cov=cosmo_cov,
-                n_samples=n_samples,
             )
 
             # Calculate E/B statistics for all bin combinations
-            version_results = calculate_eb_statistics(version_results, **kwargs)
+            version_results = calculate_eb_statistics(version_results)
 
             # Integration vs Reporting comparison plot
             plot_integration_vs_reporting(
@@ -303,7 +241,7 @@ def plot_pure_eb(
             # Covariance matrix plot
             plot_eb_covariance_matrix(
                 version_results["cov"],
-                var_method,
+                covariance_label(version_results["npatch"]),
                 out_stub + "_covariance.png",
                 version,
             )
diff --git a/src/sp_validation/sacc_io.py b/src/sp_validation/sacc_io.py
index 99a0efe2..ff11db0b 100644
--- a/src/sp_validation/sacc_io.py
+++ b/src/sp_validation/sacc_io.py
@@ -115,8 +115,8 @@
 }
 # PURE_TYPES key order is the insertion order of the six pure-EB blocks —
 # matches b_modes._EB_KEYS, whose order is the [xip_E; xim_E; xip_B; xim_B;
-# xip_amb; xim_amb] layout of the treecorr/MC pure-EB covariance
-# (b_modes.calculate_eb_statistics, ~L392).
+# xip_amb; xim_amb] block layout of the pure-EB covariance
+# (b_modes.calculate_pure_eb_correlation).
 PURE_KEYS = tuple(PURE_TYPES)
 
 RHO_PLUS = "psf_rho{k}_xi_plus"
@@ -580,6 +580,11 @@ def get_xi(s, bins, *, grid):
     return _get_pm(s, XI_PLUS, XI_MINUS, _pair(bins), grid=grid)
 
 
+def get_xi_weight(s, bins, *, grid):
+    """Return the TreeCorr pair weights stored with :func:`add_xi`'s ξ+ points."""
+    return _tag(s, XI_PLUS, _pair(bins), "weight", grid=grid)
+
+
 def get_pseudo_cl(s, bins):
     """Return ``(ell_eff, cl_ee, cl_bb, cl_eb, window)`` for one tracer pair.
 
diff --git a/src/sp_validation/tests/conftest.py b/src/sp_validation/tests/conftest.py
index 40424120..004abc83 100644
--- a/src/sp_validation/tests/conftest.py
+++ b/src/sp_validation/tests/conftest.py
@@ -10,10 +10,11 @@
 
 @pytest.fixture
 def pure_eb_xi():
-    """Committed ξ± of the synthetic coherent-shear catalogue.
+    """Committed fine-grid ξ± of the synthetic coherent-shear catalogue.
 
-    Exact-binning reporting [15, 70]′ in 6 bins and integration [1, 300]′ in
-    600 bins, keyed by ``b_modes.pure_eb_from_xi``'s parameters.
+    Exact-binning integration grid [1, 300]′ in 600 bins with its pair weights
+    and edges, and the requested edges of a [15, 70]′ reporting grid in 6 bins,
+    keyed by ``b_modes.calculate_pure_eb_correlation``'s parameters.
     """
     with np.load(PURE_EB_XI) as npz:
         return {k: (v.item() if v.ndim == 0 else v) for k, v in npz.items()}
diff --git a/src/sp_validation/tests/data/pure_eb_xi_fixture.npz b/src/sp_validation/tests/data/pure_eb_xi_fixture.npz
index fa67b006d5fae626006bc7670bcafba9e7558242..193715c470ddb8403e9165a333264d6604c9dfcf 100644
GIT binary patch
literal 25592
zcmd42WmK0>)HX_YcXxL;_iVZm>5}dSQB*?kZvuiMC}5$4g$ROzD8E~21wlX%L`snc
z=@5a#S?_s1JYUbJcfQP;S+mxxJ$v?^J=flsofRpWJkkGY!bHg{<)04zzeZ2QMid_s
z81L&H8Wpb)6?2-Hh=~YT|IgC@>2KIN+1R-e6P+YVlne+w?jI+qBP6L8q$MdQBpDPP
z7a!*v<sBUt5cvPjoBBo^4?LVd9^xAlcsQ2QP*ReUmJ>=A`v2W%l&lqwUF29sv6L!Z
zjIfMGQUnURgO*W>dGIHJtIJ5Dk9?P{W*PnYL-1i4Tt>ZmvMuElD`;F_pLthl1?840
zf5jYEP<C-fz3QnIM9Pt&-1c|{UE7mf(HdVt7N-v=Q;1d(dCIoNE2&kqtjOm=@3V^1
zmqg|4Ggi^(_`P3$Ygf_lXR7iJqpN5_^8Q^~&NXCtOq%k$!5RWPqS2PHHS}5SZMpWt
zH6*E3@j#`24L$6!tt=v0N6i{9D*DvdQNc;}`44{U$V%UCx+`lPjZ`E^Xm+e42RUtp
z>eY3`@bUgAx!eXq@3B;yV;kttzC2Ezvw@QEvD|;3Hjurs=)F|ZO;npQX=JUkiHh5(
zMS8<G(XUdypt!P4B(RmZzWj9)36=#{tZ;0hSAOA%OQu`s1ViP4z@;rDrJGgU*|3HF
zzQvBS?QEf$^T!(MmABD#kExRUkZtsbVP;zL$u{zyjw1O!y^Z`?Z*h$Y?jXeo+XV~%
z9mHDdT9j~m2kqaRI2Sy$gMO`_Pt4-^i~hCtvh%tBMLc8GA@=!y(eL$L53=dMh^jAL
zt3+}aJu!@|boALp6RqNmr^|LxR@7?Fx4B&;Fn@dFnB+f{-)vA768H~Ai5Z`)uK9;{
zJ4S<U67Qj-%vW+LO!iQF1!Y@Z+8*-mn3g`)vxiK>a_cW~?<2GR#?8cleRPuexzg10
zePoyQ#`)^MeU#$P>?CY{fFPhGtt0yY4fqIYzx#23I_<7#uyYfE`!D@v20bD$yDt6c
zLNF1CDM}ZxW)Xq?(-&vEYKee5q|lIjoCwa?$-5C#5Ca|>AMB_~4Ci}m?f$tD!@bBi
zZ+I^g!?Cwpoa8mcP#S&I=JgL^U=1ysI7v+cG=IEFrPN8_5r<#Zt``Y#y!^z=l0gEr
z<090)??|Ao@W9Y}mISWf{y=w>lN9dH&D{(!Cx!l7r|I1iNI^4nO!DFbQds7wsyO<M
z6uvuXij+{2LEr7kjY17F2tI2$T@XeFNiEs(sRd-<(XZBh;u9H&{M$LLb3g|4k4z16
zRLMan#ilASm>jT%(lh4y<S_g8m5oXtIlM{Dui+r00KujTjb1$pkhZEb#$zbJtCTIE
z?g<4jybK;47^498Q`lb@4oYAX3#m0eLJ2NvdSjy(D8bpTaU<(JC3JV*Hahv25^8_*
zdCF-}!P&oUWm9ofFq*BgX8VE)%ESWfLuaYri0hS&eF<vdj4Ryv8%zyRCnMyx?o&hA
zMWrRiU(~Qc4p*f_XyDlwmrGZDXn@w^!peyf8sHpneoHq&1BDMr2E~PGLGiO-&3+&)
zG=EVR{#`~3(dC~`lrGW&OId}|jtU)c>tDIXm_P@G^hcB&-qFGG*M{gBVtSyjHNNF#
zNe_8UM2(+w=z++X&N$*LJ@^VNG7Jea0RM>Ty&Dk>VERZ6JYF#X-KGso2N5IO^rV`Q
zvSkFa{Mn~f1&na_3~z|g3?t0sOVs?~W`grg>@pj`1Vxt~pUw+rg72*NTUV|#!MKd-
zWceE=VDHT+vH8UW`P(l_I_R0f_`1W{cy(rYtt{2Y<i!lMzh;=cGnv8v+{VaG4Kw@;
zQfeL@V+M&668V?tSfKOGX}jxMEbyLb@p-*J3k*fR@n+0nfi=+vg)E!}g!qh;rx#ct
zVnFBEAU7){%$oE+vt)%#svqL<iL5ZBpWRFIm=z3N7R*l!v4Zi?2ODczHn`cGXDFe=
z23MX9I4*~?K~d8;fq{E$AWWLMp!S6g{@rq)@F8V~+2@_dF%5R$8?y4~3}=U@mwjB%
z+-C;|Zo99?zp_K@mCslFs5t-~J8jK?IN-9?ZR^Do9KhwCH?L970fE<Ni`sv3fR*#h
zg6n*ou*ITCMB&T{&){rUcqS(}cFOJjY~ut8Tx|6$F&8|rrOeRQ<pTF(b|ks+TySbc
zc0s$E3vMvI$Vgk}g89U*et$V`h*#qyGY;j3E{Y9Fc*G4oZT#<ArnuqUujZB*aUPhp
z=~WyF<N>0|J}1Y=JU~67)Mq@+1F->9O`}r0@K*4L7#_h3PRA~;l2`Hq=~=1t%d5N~
zA@?%RSCbFGd7W-Okq;_sUb-nY@_`73s03;FVN6b^lGT<U)>fH5|Gmi%R54dGnMe6y
z|I<0DUy=eKO4{is9wz`2`<}h8>jfa!+wo;Dg&?d-_nuF25Cl%@+n%rQ3qtH!JjQ8J
z5C$g8Hw^iO;7K}}(iamUcz61z(xXTr5Iaxc-Mb+Ku6jWvoCF~#8R-2gJ1qnnW}8_j
z7=<DJdl{>|wlH|>x#^7u2*Y>k#?*i;VF*ZnIpz9B7<O6PJYiB8iXu&wzA=h`o!Z)8
zJv|XvZoXE1E<^<K{(D+9ktYJ40hisYnna+>D!0yWMFhxP^p;Kthyp()3)=@9QTSS$
zUwt`66vnhU#G}eZLGC_dN8Jxm@Xu(x@tj@^J`Xgj1|c!PPDBrL#EQXKc3Y;{Los;8
zeQ-(is~F63H*;}NiGwSXI;FafIOxAG>9md(hkW<n4$H;j@Ior|KcgXWz_-!5DT4&`
zuucDpHIje`?P#%cXC#1MHaE|@N&;TRmFln0NWdcPy6}XMB=q%!jLx}8LXKMKRpT5<
zNMq*9!8#@3rFhlA5``30%R9<{1S#lZiJCf_BnA0Na*U*PQgGCQD&z9H6l`zZvNckc
zhE37xh|Fkdu(dEd6;Un?mf^C?_vWSH{DWnmNm&^f{dnDII9vwq{&pr6E|-BZKL+qw
zlmVmko9da0vaolgEXXca79JiOykA`-3unnaO%}Fg;ho25#k`&z$X%46j65d?V*G(E
z;carzXQIrK#~=@l#FgqF9OXf2S8#;$t~^LRDj`<-Ee|xBQnt173b5CXwT+)tfK1^5
zkEuolI8g0MHl|ku0b;fvp>B#`An3NZR;CE9tmlKr))e8mk;B)ALQ3$bQD7&)TnWx6
zCw$n5Rsy!C^oyeTO7J7R#Q5O768ILZoFJN00(0+cy;-cvkpG47!a!dc{?bOPTnSbN
zMHhOWjqA#weK)or8kAvWz1+=ZMj0L`&+8AfssJ^qc3c6dfa-NMrC$*$@b8Vq)bRop
z=+>qD%i4B0f7B>1ZbJoB#NOQL5mg0pEAl&6>{Ve_v?n4bO%?V`JlBn?RN=J6Mt{hJ
zDkw}#HA}FlfvDb@<0?jKP$3-<^(kHrlK1u}SPy-1dxh%>x*;_%y&y#<Nv94~{s!Ng
zkvg=$SlDEYSBGP{ju}TEtHZO82Kw$J>aZT*#&&{517zgP_-V{FfOCwYXFWv&=He9C
z>|Sd?x{PZu?V<(*M%ZwPi)%u`1HV(t$21{|{Q9i)ElqHo_E)>uqY0B2JFgnhYJm^+
zjIX|_7MNXn{OL}b7F>PZAi~w41;Z?E)iQsz;PNq5`4$arxMJ&kjW1psNF*4C3SVl&
zMSb{neMK8=-9zhmm383539LjnRtG+Fo2Cv{=>V<PnM8^;9aty(9Qr^_7kDjY1y3dD
zLYY1IQPk_gwD`Ei)}AilT^hB#MtUG`eDproWj&b5iLvYYtOwr`g+?ve^x+cO$<{m%
zeJ}}QYb<@J4`G)rhH~fiAu#`>aI+c)npl*uV+sb{cJTptI|lIMUUC8(0u@HlvV0!|
zlsn({6`mtV<=+3s`WL~AFD(}4VgQLEE^NbA0Bv@>4?i3S5WW3{v*|X#SG~?q*Jc1Z
zv%cMoMSwFq7voep4d7NmQuawO07Hjy$G$KFFnz;pcsb7iwtO|;Sie8)E!S?g%p1Vk
zPqMQYISpaSA<v!B*bq1iW3Q&g7($S=T5!Q#LwF?=A)w!32&hf6_ri`LXuW;X6EA55
z`Rfczvd%`pue-<d^nwvEcu^*YzcPY5P3*^Ne;YycTly(64rADR!om@2W()&zp>#f{
zjX|K`O{&u~W3VkenU*<X3^Wld>o=K9V2#g8p3m3>#sepxa-K2)cCt*#{BjdGxOy4N
zCrltc!BCHk(-bZ<nrTtkn8Nk?R=UgonL<kc-2Ik%Q;1f6wr0O-3fdd&Hsms9kV1P|
zQPJNF#4Fr()b5yps8w%;;D8xWMx8#8Vls!uy21%jD|1M<5xgCJ(Hung<N2>On!^Nv
zWB1O!IUE?UvkB{2zy}pEdY&^Da8`pYE~?G~42mj)7B(%w^uzYMlNy!~R^m%?_oO9E
zQTCHC)mlP`d@6hBwk0$bxbc?gSV3z_QM_)l6}*1x)ff5R3Qk@r5r`zShP;nEe4Xaj
z@TxL;lqbs?q|R?@_Vrmq|6|t?5*{01&boI$*w+S3l}+A@J+Xl$vUn!ZRT~gaYZnmL
zwT1DKtB)qm+rra`;H;ExTL>>sY^vos0&io9mdJvSKtX28`|Ro?FhCMyQ$b`0@=1o9
zZzb&@hqpr~=7=3Ua3h)3Ot6E6A)Wx|`*slGRWd-=ZU<wpJif}U*g^i5VaHjXqwu5V
z<8K|~qp)+efbCAyQRq^zyQ)@j6a=rma+hsB3Z(%T9=`l@6y&&!^3L+w13j`^YA~~h
z*!Pa#;^OV$JJqjW)er1Ja*O>HTem%!8YpuJ?%4xTacjXXSqI?6=7y8p9e}7#E$RIg
z2k^5Sn!EqT0XAA1bQWeEAm>$9L<Wx|@Z&nPudN(`d0x_)H`x(<@9R%Szi@=g?kK%~
z6OM2`wAVs{-3dl+JhUt@cY+!%8@H!PPLT1hE<mu#2~5bl_6(++z#Uz9(8ucxw4s6a
z^GBUwgyo@p=4EG)tGvHek2^z0$m1caEoZ2YY!8f6bb%u!MT(>$F0g-MCd&VT3vA!B
z9Gm&>0$Pjd$qwwUz+W^Tpl9a_)zXuUV;QdS^2u?pmNr+wme}7rlDUCQ!hoKSfg41e
zy_srt)(y^h&tAxBaD&zNkB=4pbAvY(r>%wb+@bd!9g<6O2i9hO%dL0rFdt|=7`N{Z
z2N$ck{lEiU+QPq6|K|bSK9A{KTRq^Ce28W;jVDMjRvllr^*oFt!Y)YVdct|3lGcqO
zPq=lLoW)A$7@RHhCH@e63>HewkN3Pd20<*cxklT^K-QM;*EIt#Sm-xqD9rEz^k1=k
z?0^@XqoLEkA?yvx`dCA5q&H9t+4^_Yd&AG?j?Z|=d|*y9sq=`e58P6|7vEv;14<e-
zzv|EUfQXlTEK9Ku;5?;G-#UDNgzI6&{JIY`+*eq45%7iM`qyJ*&3z%NwOctj&KLaD
z!!xPw`oe6<`_CNhz91O<lrwM57p@kx6sHOLf!y`oqYGAkP#A8on|{g<PNWyR5=#9*
z2^CyA(&q;uislQx#QspuGHTnQ=nwDo{`U8I`Gc-l+#+X|Kg3gzlg%{v!%zP~QIjQq
zSUdRGJuVOc0{y31FuMR?>v&?kofZJeC@Yh;CIEa$7K_TK0^qAOHE|PnAbeH-b3e=`
z5WGo;qjJ&$p)jn2l>c=gcrv7K%gqPE-iFSnUqV5U;%D2~>Kp_(r6XC^)gZXa$1nT3
zISBIqP<f^N3xc@IjI!x!!EiChgEuiU7|aqk20xYs1NjlEdpYC5ko{<e<||JKyx6)?
z_0=f^ggt)7p1mFdeJ`3UBR_{gL+XHqDorSK$M1Amn1urG<wk<&g-{4ith-y@6bi=Y
zZYW(M34@l00oT<H!=RcX#6<sG7z`}c>f1Dh0jojX@%v=q@cch#r+22|P@G-4?0O{}
z_)gkIeeVp1)$L1R^-K{^U>a;B>=XeZ<iWiqg%NPzS6G`m9s#7x+?W4JL;~>+eWqnp
zB(R-ZGduP=5;BMDiVyZ9K{uJ=zO;E1i0nHil;=c&jRy;!JQ4-xT=wXfrJ`Z#eZ6gd
zd^DVoFs~$SjE2Nx#5+_}G0^kmj^3U^3@nq$D_(St0g@(-$~!4BU>b3t|EnwpE_@y)
z>FthzmE$)#vbSS^OzF0xt4J&?buOHhw~mFY8akvACu1S$(KNN+gII_QX7N1P84Kjm
z;_|PzV!?6evbDKb97v3ry38GkgKN?KD{;wjkoiXY&if~EVE4b^w`X}P`u_vJ(H_EY
zr$S@?ZwB8ct90v%x{c871JARbR>zPHb;E~~U)@lr=x2lS77r9G`DBUBToGMZHF{pT
zp^V?Dxb^7Ue-21<d+z0&ivqr$=c%5qsf~C)+<4R8Z-Nu4)FwTb(?>5%%c#hwP4Ted
zrNMzlPyC71%0zR69g@8uL;7M+15f?v_Rq=B4~?cxZ+*L?k2i!%R1eyj<H@}-ue>x|
z(2d3wxuz$8>lkZB>OHnVocL+$#RU&!UeCT&HgAI4PjFwXcC<mPGSefIp^j+B_ZVd<
zw*&H49=$&v?10+2y}hhsxp4&*f^u)K6aH6s>m9$G2wruROtqNV1__kA5c9Z6;B@sW
zOZrM?xWOB(){92!Xy@los)w=acsXC1GU+L0^nz!pxzUCduVMR18+AklUs*6pub8(+
zo^i__)~lY#Agm%@h)o-j3u!G^h;ZXAObK7{UVC&aqtN)jAByN%uh#D)M8>#nDbtTP
zX$EM_^lfr&tp$ou|MTN1sNxJe%?orl0cRp-3I1=~98sn#5+6|jBwQ`I{4U%M#pg@x
zh2$eN?XG28wQGV;Zr|JK4z$P5<Y#Jn$x`4VCv}1xZ!6<z<ym|$Q}yxG`G04~BLPqK
z5&a<PVT#|rd)I6_-3b?eZTHjB!U$1M-n+0^=z?}7v)D=g3=r0RCR!vC81NwPK{AUq
zWhB%7Ja*xRFB<U5%KYbIgchPCYs8O8;XUPy(z50lVo=G;W$H0R46Jn9>JkP>v*Bcp
z*gp+iCBU98+Rg|!et0mbE(=JADKGilHGbTme!=0rffc&>Tl$^wD`liZJbC6|)DRtu
z;~TNIGelbLU#>in;lT4t6D#f9%<<ZPAG++DmC;|?{l~%?28ipfsPdPK8tBCg%cF>5
zV<b@c%ELTx8H=Gwh@vpI#ZM0_Pm$>8B1w9JK_{Ckx}z<*ecl8^GjjQ#T5qYMNE2<g
z%hyfO<E#h`W<NHh#?_E-bczifzx_<WJctMnxtq>Il}wFaZ@8-TD_a`n#c8tHKQ=~6
z$u3>fXEbo`T!u^M+D-6Fbrc)N)GTn~%PXZOAzG-PEMqn4tpQR|rm<cm=pp6qwy8lk
zbM&Vzr|5jP8cLLxX}B=BhULam_VT)NBNmtY!dvGhaK@^k^P_SrSWQx7+?iYpBs-wR
zmi?bL3i~C+82kOuz8I3;e0V8`|1P80$d_k9W{wpKgnfPFP9$zyVoZy6u3nw=TGT{3
z4F!)4pQ@l0-WGNy6LUmPf9XH53wn4-)2jzLT}F8NHp9bo79r%0<#T4;mc#GKj77<c
z$fHW3<l|X4Rq!ig+MeB9BKX^$D}6N-sz^g#Dal#h2uT=xmpQ{Ci)L2RpF|s|;G{$g
zMI@O=kr>r|24)8%WP90H)i7ThUkJCqa>-E{-&Wwg%U^4Zj=r=0=)R4iBLf?(1z}Qn
z7hAz=RvLD^#OC}|w2siB{jemo@|xorJ4Nj&CZ^~L|9C*0BL~iJ?a(gtRRT>K^u&F;
z$%b>iX)nz<w1;c?g(2g~8c06-4{1_~9CAAGo+jy|77BkIcRj&Z2kB}p{LEehlwEuL
zp_>(;fuX&KOkEws;P`T%d>KR8hM7gTg!T#fRlj~;&ZS2)brYXg)RfSvg2y`BQtF86
zNyZy#adNzj)m|jIO$~32kN*4Z3@cLUvdk}WQ$f$41fED#kwY_q153rmJ6MFvkCpj8
zQCxSrUcdE~E^5B#%eC00j?(V@ebh*-j3)C$m)wO^(c=oIhUN|}B&TCJo<X`pV5Zi&
zW@kf<pU6n4Dy$bqZXMo3Uph^Y_021;XVNVZlMcaWsJb0XE|TnO_$h$b8fm_6_2Nda
z%FAU{OAS%!?i*J3Rt?1QM=|}MFEjc<?NZZwcweaP->)b;>ms6=)}-Xi!&u>!9r=f4
zS}0Iqqj*|e1GT^M7SYrj#wLvj^h8}^NOQ(tQB0H*J$&;;;}ZQIc0DNPHDiVdeoaa_
z$iqMfl_FXNelrbp)weqPt1&Y^cuBeX(=&eb=*_25Oiv2Go`0aXHI7h6bGFu;z6qlL
zTFo+?#D(I|vgL7Jl0=k)bz3?7)aapd|HLO{UNl{oFmrE35=|D^$8NXKpwmO)hK`>m
zF?uXkS^OCv%KGE=XQ`PK#k_GFdQ{JfjQaUB|1$Rx9ya`}zk8@3C-WQIrzKU<D$RIR
zbEXh_R9SM%H%kIPc8$kv1BoMvo5+|USqY^%HxeUGHB_cat^C9TA^p<2gVjEEv{KHJ
zTX<d@-O$YnS~8MGV-I-5J$FgaETss&mZCZm&6xP|_mvnLJooHeRE0QF%jDGL&r(CF
z;qM#S&oCpeFD+ix*X7VTn(5QHxB~k9y2~amoER6%d1+O6=$BqKII(i?awD^gdFyXR
zh0u)WG5ssj%=o0tSu(W~YUs{7n7&BXL2UcUU)2ss@D{?|Q;I*N(bo3-w{=!!M2qy3
zmq|5|`1P&JE6HNWHLBrN`yV=7=Xbi3`nn1VT6*r~ILv@vURmUx>QzEa5&BM-i)hh<
zM8~`}MOsw%wK9{HR~kJU`#ASUTpJw`y(rDr%8omJ_1YJ)R7YeD*`CqYe-gfE-y?mw
zM1oi557356DWflp=i8R}B~jZ|#<?@XLg-+O*f)rc1N|7}x1dKK31UHCYrdHZqtl{#
zek6g1_Oui3vD`_9h|`2mUHU78){51{s*2Q*!gE%k$!Rjw+HQQ2?XCjK4xbr(ksygW
zuTe409?El+>=SK~juIkHP@wv+K?dc&h;h_=tAJiOEq`DgkwIpza%B|p>S*ge3F#3t
z26Q^*T}`f&3R;aK6>CXkN9*i`%g^4)p)s>tin=Lnga$fNrS@%ERMU3OD5gaS(bhdb
z(%~bCYR}UTk=PR<x~NB;tQW*l9Q;&Zrx8QG?h-0%UzE`mOS+p)h2lul*(a{-P+r@N
zXEVBCiin}Ag3g##9My`9HioS!A|F924U249lvyRo&{iss1Oy?pGDi}9u8~Tp&7wqw
zSBJ^tnIutDSXl-i;z5NyqJyon;)r>NTifyu2Lg_w$;uWnG<*7<(H&P2WUD01`1-Rn
z>J<O}C6rqkk<#7>yu`<kZjiobp!OF+{uTe-M%*&!ja>b3jI=DW`?F#2o=y&7U9`gF
zvSMgKh%5hhF*&;8@#}su#);mMeM)D2EROURwze1Q#nA1~M_N1|3nQE0DUlQ|7S#Q?
z|B1Oc7aI86o9beqgv9MHV>-k#=y%G%>zPU!<k1*v;<L+wN}a<SsH>$A_gl?R!;>;7
z@XS;Ug`Eicq4~N#bw~ysd`%moW28ewf?_v(jtV1&m)t_0e?<|MR(qEAM=``GIUy5w
zs28iiz8?>7OCsLbww;w0DTLkm7MYtajC5uxb3l;~#e0(5%;t%sH<g-AJpq#FoJ0}b
zhJ_fS&%G$`NKc7u#R60;)Flxe4Q~=x2rnv_I#PnNL{a}iAXyr-B)V|w&${crG+LQ>
zY&>7gi6qDd!X(s$&}&!Wg^n#M<oBZgnsS*KGTPNUVkRJjOm*hC-&t{?*pj6>_83~!
zE=Mv<b5sPCvi)cC<FzEJv9fwc_M98(o(|8B(NaRUM2FZ8#^_LwpHOw!GB29*y1Cub
zDUFI(O^bhMh@pnMOtZ#xDl{g_@NO+h3~AKAWz|mNL^7Apex6U|Mb@A1`<AUyqE(q$
z#oL#JQC&yfa^D3BWOgP`*O*Qg<w;Dwkl_|bnMV`)GkK(t!Ap@B>>j-6fc8?Ja=9ov
zp27OIf>snIT)O63F2aY#%>>ggsIj5Qi<Qg@W31@Ov~BO*!}Ax>^RQFAD1?j!)Z_xb
z3877s{g~`cA=Gvum6&`;4Be7$*t0bkMS+nr5)|4JsNGm=h$)2y3EFm9uQv*znvD(e
zGsGh3v!31F)L}dz=oKIR`=ubdR46_2r&R<6RLWI!&<G=`tY@*}QbI^T?)#10ED@Bi
zJ?_G4ErP}ffs$4~rBMGqe|L+OFtRe~*N9gYM2md<zw6b7QK|hMf~SEbGP&kB^HEFy
zF&JL+DSt19&Wc!l<`7{+?}bio{x?g4<a>t}c1Zb989CSBsXR9HZ$7bAtcxFsVXG%=
zxrC5XSWL>unh<JS;;g=TnIFB0yeDl+&WAFuDGSV|2&0XdtE$8T0_gk6A0pnZLP-AT
zWkG@(4=N9yi)d&NLQkImh%<H;Mqd_VjUJ~6q5El{70(DB+UfZ<L6@iOD9fMq8<mPE
zdi-zqlChFFQgDo~7^-DO;%sRoZn}KP)80InR-X-t>=Y9w>*>+Npx|VWrXbSLFx$Aq
zDU5ipJFk8GC4%BIPN0fvMs%+D;^);veOCA>D&*+$BFv59w0|8x+EWR2U_Q=*?luBN
ziGwgwJuPrCXN(1DIhct$bnv44zv48-Zn2`|7k}9A-{M5FLGzk2+g!-)(|FtqEmm~%
zx)SE6A%X;yJxbQz(xIE4=@G_b?C8TI+CSuGBB+HW_4GFq4s`9mv2R6YOeilwc9ii1
zFS<-L)qVAs5JIAZQAyH5C`iq%%f^%)(f{4B6e1Ep>J!eSVT!!y%EbO>2`W}}G*+L|
zlWGgQ*7)I!wjw!tB0$6%dZ-@*KJ3==8V|~;37TO_7DUYyg&&=FS&_BUOCzds1~fu(
zedRbg1JZ}DV(TJ%n4oo(D7M9lEKngEgBcf^Iq_=2Ifey=pK86Xe1Qsu;C;%(Hl%3r
zUjC^)J66=v6Q|m-%Y+g~y(^O*kfG`J^yL^&S|l5G&t6@X6{UR8|8kLo4mDd(%6wMl
zMCALVUo?I&Ap588%$1j^QN*h>+mUKkG)ieKrD;cx$_cwKzOB-rRzF|n`P=K5e;>!_
z#3U)o{<_v)NlAv{BxQ=jFiJEV&GD^>f(ixmmV9h*W<ccEcZ3He$x&9{=E+pcIf7Zu
zO!w`L4eWb&3RjT=1&TZp;<OY`j_l2N|K{yZVDo6bu1kjmEvPJ>sFB>onlmF81=yI7
z6*nKZKKCDNyXU(-c_uO9m?hih=3mAbp7ur$X@4YeOV^%FsII~8vWMBwhyK78YuRkV
zTbHnl?CT=@#Y+UAYU8Wz7Ms{+f9R1X8|#E;<_a2=_M-#^rDI>cGsdxV^?QB-9-|n|
zO?r{H(ciI`7uj5*>wjWLuf><;Z;uoHEQsdbZr&uEb`E6RT3^9#9^C3VK1GBpCcM-8
z_In=7GEQ$S*ZWM6C?s`x_~jSj!OwauDPfRsj>0g;<?{^2S95}<=S&VEn(Mk<X7>bP
zihO~%HF1V;Of)ySiTQvaBXK-1h?gAS(K_0eXFZ3}$aLws<Wb}A2q`}uMW}G@b0RqQ
zk{CDOsS*n3{*GP0Ie6khDjEJ&oi4bdlNe9C+r2v2O^cJ9x7A*aqs95ZI&Mr=ljEaD
zCbM_D_X*eBOlhZxXz<}L<jGebk>jr#8tNW-QsdkG9r9;gnQ)#)eqA5o1A=2ves;qw
z6YkAN@}O3l3BQoFl{Y}egjd8d(bHXK#Ve@!x4bXV;Y@tyA{K6k`(qD{^VDhZM)Y>V
zID-XGclbMZ|26}z|1j{1v<@xqftRuWa%aIszQ>A9UFXFAOLCv)_9n;q_u0Ie9J%mZ
z&Y|zs3CuX*rzVj|4F_(wB^vWHmm5ENexam4nibb-yKpnjnioHIyN!A0g#hl>wiHej
z#EH8QUtl#5;>Qi$Jx1+{*zsKRx2EFdTsRX!vGd=aFkUz-!l_@xjkBXwfynzJxcRm)
zJuy8$u505#CafupGl|k)X5Z(-uZf7KuPKP)Dwbg`l}w^|*lXc(>bHWpCAkj;j0)qc
z*egemDnYz;gkS6Pq7Ysw=hM!9QykxPW*WIh5XFBA%2Ic^2;plI+mEw<iQ)P`Yd6Xr
zrSRfB4H88Sar~@>V%J4aS)8EAf1|TR7VlE5?9q^u#CtQza%aYs@o%M1DXL6mar#dw
zU-q_T@Jku~3|ewZxJXXAY!{z0KD++qthc5*erfph$(m0}xZTvWSu;|_zn-V4SRYZt
z2}S)J{mN>1I|Xl?j<+`c@f)q&B#$P}?N1i}{fi#%T9-%e@KFu#-4}kO_+1;9Z?a9)
zk5a;u%@V!2>(uf3w~@{xQJQ$qN15@3NqKze{%xw|GYUA3#f)83iw6Gp-*ln;+Tp(c
z3w$GXX{r0);9KPX&EVTEqxwLSNFegNEUD%O_PD&3d7>_r4&o9JuUr#$MNuUGzKe1=
z;jUeyfupt#XoewO<>Lo?biR*GQF1j1*-mIAV{Nv0#mxP9r_RGdjms|io0=-PC@;y@
zVwN4cpzSnRrNx5xzjHp8M{b8}dE5A^j9a6i+^N>r^}0Aa>)?2)Kj709Ij_9X5!{E&
zuV9VM32%Nf^ZOIQ6;BSUvN^uagA1L{&0cyBXuj@r>2FU@q@3;8BA)7pV+!XK+^!!%
z5)^UzOZMzY_EH7oX%BjQRmNHoV{t|q9&!2wpXLdFgH2a?H~}w>5-;n2<&69=nYToI
zrl@LfVVq*x5$#(mRJuD#;*Ls7HKhaAh&Rppx>?FGoTY7op#pQp1J3n@hNxL0<)Wx?
z--K37qgi`8se}hl5*7OA0OmN$QdNt7#1`RiTE)tfNipQ+@%@GJfi^ybm)VsxE8{Z!
zRR*FK%J};dtKSP*rg$w2(th;W5r6PRwfWhA3I4}?*tgS31g$AI%zgXlgxD#~Q=1y;
zah}0G(PRIN(EC3ND`#<S)Es`VI)%vuCv*)*ct>cX42r}XJRBm(z2bd<N53liw_EYD
zanTmf&i5wBW&(cvnKYlAsVIJ!P|s;UW{Y?=Re!eS`5?L0*y4+y5Kc9W?z42*;R!c{
zxT94Kkoc{nYZu8}5PLbV<U*k<nvKvHrI1p>^{pL3>gM(F;+va6W+Qg!kEuUzc!vl+
zqU9fM%g&D6ziUfBju6L}f1fPel2F3Q?{WU<$1PEdtMvEWR#l{S#zN~=hB{8}GC-C-
zjL_;8vAR3$hPb1Gd8MqkC7yP_EA;b;0|L9%-{Jg7K@|O#e<3^G9B1V!?fvbfitK{c
zd9B3}9v5)7GU>V$ilktzsJ?S}Z|dx~we^jWGGC!kf`A@6o9^*C^E(S_co{}|%2O1l
zZX##v>QKRDEyTYj6|51`z7_qAsy4;>-dJTk(YHe<$L^V)zM_XdKYEd#cSQ+Fbnke!
z2)p2#S32~S--)B&a@18ldw}L>Pr5m77~tn*JW~7Ewb9H@!9rj$FXH-YYi)jC2SpdB
zagb*^;t3DSjtksS!uR5Zq@$Rbk(->1Ww)djuKY9mUV^6~PIpx7=5J~>bTNr_<lk>5
z-1Pg3MAxA_v#jMG#@$SjHrWv7HEoH%it*0c)HlUd7B}wepVLL*(uTy#d4Tr5&uPEp
zU^>)4Uy^aH87@BR@vHMKA1dtnmXUg}MNkau-*O{aC-9j5z^?w5!C7M_C`cWZ(CF6o
z$bCT_e36wzU)NRvk<C9j%-R|w64tG|n=K4zT&Jn^x&(%ZlMUJz*{yN>{)1F?X<a<L
z?3m+~ZY9+7=LUI`r4e4y`Q!D0rWo4b=uJym&_^wbcT)a!nBo!F9jiN~jghMB+JWma
zUOY|WHqYZ98puB>Qu2ndAyWLNbL5?w2)^P+Zv3~A6Sv(-k!o~PLK@13Qqj?dxD3aW
zbcSJ7v}U*YZfN)b;~wy}FfP-;_j71V8s3VaNE#y9GgPYR%_%Yh>0JS2T=pTL@q`Q#
zpl`WER;!Huqs?v;d<XdRs|x+3XVvhhKP2JEx&*>lF3^6;mBl5}sS*>tsSvm6Gfua|
z$HG2LJ1$v33_VSAmkT`n#IdN<q%T(+BJAj@w(6!lPU>*Fx8vS6A@{&-Khd5CmE3)>
z$a-!Mi*Lw?l)dnaaQg>shSPIqgg^1!I(kbKy`mG<er~0Ox=mXzJ7Pk3w#(C)T{jL?
z{;kmN@h%4vz4La2Z(xGp@!RxS$qy0q(vPV1sN@RPI=64hc_=r5!};iXItMyC8&Ram
zC5ty#YuJ48AwyK=Q>|H>l4vLQ?A+-v4II9&8`L))`q$S_64t6@@Q5SXqxW=`5LZ=@
zO;ZRPK1kg~^dL_W<#CA6JmQqY{bc7<R^yZrO_j&R{kbXZs5EWb@FoSm(0P*bpA#G6
zBnY457-qo@il@|+*0s>PoZ&1UX$jP6zP@5n&5Hz4SCkRC0n)g#_s8<rUjoP5apq_*
z9yF&_Q51NX%pleqk|{kYfx78wy3-#jp@5HL&pM}`6H4c<=8<O+qZWE`YoqNA%rpB^
z56fZ5@$IVH<*UPo;OsgHqQ<3hrA@7EE0Y5(@@1*L%w1Aker_f3&W;|wcYt4MRHs08
zUBUlU#w5@_fhP9x!UATkB4--OK#u=6)m}Sn!;iC!@f#)*B+$<q{g7YbJjlezFz00Q
zCPw^?G}GghCcc*{^69^HVifXBG3zvsG74B)AE2C)#T_qIH}AUF62PB7ShkoK*O4yu
zy}Qnd|8*c!ssF8oTfX{Ct--rYAT7D(G;@gq@5<9}Vkl+9C4SzZQ`(v!q?qIirMyx=
z!v@EbI)|yz-P={<SB?qeRRxOvlc(5mc9V1-mpLl5^)KH$@=)I-5?-lu2Xr`1xIX57
zOB$V%vnwea;=%nl>qlm^hOn5#O2@D)b=1N*ps8Fgjg-~~40eA$COFYAwp#KmW0<O0
zb7{UJA`8Vx6)uUQo{8Rvy4jqle8Gk1kBbmmAuPn$WR4T)3LW@kUNhm>W*@~`9gv}l
zl|<<y!%QeBBtzMncAX&h&F|DsG&9mLf0a>mhYQcv8&6IlqsF^Zu5HWcv*5*7Sto`+
z@gs9yy{rosYlIpvn%_!>iumyOo*R*+7*2d}+NaS%688<O5Hlu{M$ccHnY5o>!@j;K
z|8iED1oywBk;*~3hv`-^Sl$q%#z}+eW7RMjWD@OOa#xWVUsMX?PN8K+lm6{b_(+Iw
zVw*S44{M~+?sG<hZTuQRUBswVoM(q{lZ&i5Z%h>bP8=jIpu>f;U43%@Mh8D0e^M-8
z%90dkw6P!Lc)yLAnS7sEpsv6gBFK-BCz0We@91+lrWdj9w<0Z)rW|P4T_(`oj2X}B
z$gtrs;l|t6gQQ6#*pN^wcgDdO2fh+8a8$_RFCj{Vn~DY(N8t`uHy<>uVa75kwKMhm
z1kuwb#PX)x_`zbZLC^*({)vh>QzCx}`?4SV-CUa*J-^_YZhUJJvn9T>_4)!SdO6qd
zTDzGD-M<*e_FaP<=ezF79IL;H#k$@({?PIlRu!pb{9;%b@#*9yHERo@WuKm8PuB^|
zUHit@_XfhqJt8<BtqG$y#BVs`qsR`Em7I<N&I=e-!td5gKlTY~5jTdemeL@V;}a74
zL(Dk5GCD^?$&1Gi*!H^2vEl!Q?wXmlkmDZ;$BhX4l6Vu|nf-x@9)DWVRPyEf2<G!F
zoKobO7)lFJ_?4o(O%O~U8R2bT!M;Byb!KGcL}%Am?Hp<Na4gTXet2Vxpgr&!jXLt-
z6+4E*Eh~cPS{ucw+wVm1*GBb0ODSB4w9)2(>((&F(OzT3%F2X9u4T*VZ+|8*8`tNb
z^`9ZAXxk~4`A-nWPtg7MeZPsoB(+9$w3Q5*ikJGGj}k)s&gErSj!a<(kNhi_wkeQ!
zQ?nG6&=;)nR9cIk938$H$#iycQwSdsNjXh=od}mF|1;7dBZzF{&Bz{YE)iz)CI%-j
zZxDq3TjGun?8i=1By7lDVL>bFni}JDLMZihPxsWoCPCw!Z{3BiaqLvVTAQsMIqqB9
z^tgtS6kT8Z^+b?z7n6FgE1#IsO?X&4-|VeUhP*v(rHFNJ6KokCsC5PZCUlT<8~Gif
zz}1_&lvutH;Sy~l5iKt{(aHy}BNBEDxWexm|0Z`j<V)Mdv+#9~@be{+3g_$$LH!Sz
zdLAto&hzsU%g?-Jg0LeMd%OcT-jzG8ruF(Sp`*Mv=RFrA`Yu#D@~&l)pjE*%G<$Uc
zbF8wIbf+Ume{MVfEB-J|*iu~gyz-0-7Yw-6_=AfM6=Z#}mkFLFOc2r4KK%NMki|ZI
zoVK0{f1bJ-iyG<h=J|@>7sZ<}W0aBg)0h}dF<<54_a#A714%vEcS(?GV;i4=0~J22
zpgs9cuo4RqSH9j^OOD%qOj&d8+QqEhhpl3Y)(KvXi8L-e>x9k=oqS9sYuKmeT*oI8
z|FDD_Cc#DFWx|~@U;Uv;YP9}KE!p+P99E<_F7e=L6Cu&RB)-r42jOCh#zBxRHGcZ$
z2GR4ec5FK2S^t3TW6bQ8jtngaB|dL^<Aim=BSP0iHsxI37s8pI4hN3-144Ig;ZyTf
zMtttSuI#DvTLiuvk<I=!8yHXR=W;E%1uS8HYF{RCfgm1XV3@$oj#C|fH+c(RB>ehn
zSG*TTiuVmg9%KyDAdw#J1X>Ly<o`G2c(B3};endS?OvL7g7{R4@J!4WLD@r=H00GP
zHt;?2nTO&qMrT{2Pegl|ta!HN#%E80C*<5mFQ(pN7tY_y(P*Q<?|<p}!~c07YY|)i
zr}<?cJ25a^lYMd>>kboO?9Mo}cSbesJ7-Ao;r}9;(~OCb^DrqjTQCvsR`2toB)*nF
zJ248C13LtML;Nk>QUjrJxy8OCZyfvVzQ5Juw}nk5#~d-K9KkZ`HfWv0HVO5GNM7G)
zfe`9j>~TYGg3#XY&3uh-80+F?L5nR*SOE%nKF0e86F7QV(r$T^@MP1UQl+|ufU<|K
zYOVjoCPT|TPhNPBX}Hm)<@He=GJc+rJ?NRoa1VYS!$UutHl*B0;_#E8wI0}dT=E%V
z`|h;}W}hvBbFVhTO4c}bN8hoA(~<;lGB(!qXDq|6F&PK^r$&tQSU<>+U#K7i4k^;<
z9{TO)!veYiy5pE*OUCCt(Jf5tag+Mnpx=bcRB0M=KfVz5-5(FL486xLILkfL7hcB{
zWvtBOYnHJ<d!wB0ic-S4tUuN7pG;%kM*DdWpDz(cxF)U+`)w0^PWg>YPYqyp>J+Ta
z-+vM+V3wjjeTG27NWmc$@E7AU*PtBJULder?9xacCf6==<Yxb^+{6^0xsDl+?_)Jj
zMW|ZYe-oCA{?L&RtP&m_FURm4GE|u#x9_pnfU(W!@rRPlV-KwcZT1spF^`h-^XH<*
zv1gM-Z_~xe(BcP8`%en*u;;D#vY0k0F7RaKX}T9N>QlN^(Z{rkU3Au(wH{q3#J+VT
zxi&mYSokh^>1*l%wt1fH`L^aRHWr%2l-50fz5l$7eH*R8TB4qB3^0#leQoPHN@tb{
z64!dPq#akWE5BcbjC##sCJjr~6nDN5nrpbz3O2?uPTg}4ULJyqD~<(@7Y?Ct(bva?
zx*7K|>8HWk3r0T)4YIfe`@j}q#_bpr*OeiRCzxb&hjayd|0&@uX+sm?-R%sboziWB
z4uy+SC`~7Je2%U49{n0tfU7ETd>A6kbgEWn>TX~@D}>W~9)p;pa~ijm$ST2?(yNBO
zfD*mHGx_D;^<m9?^4q<s)r5yMbCzY2YXrQQ^6*XFAOt_P4*ManiRt~R811=Af?_Hz
zsn6^GAq2?Po%^-efiXos;guf#iIMLO=mv(2VX5IxXO1e(6IP`@^?ufQLTFAtnI(5}
z4g1hmGIUgOl~DUT_f}P8DmFw`CDy>cg*8Vv^)S}YV=U9nS2l+}VthAiF;b3R!Y_`O
z3=v}6n7^@z6*$gfgU-ATuRK=?(l1!eTWLNK%zW*R<Zdls_`WIe#MM2_@iBHVbz%#P
z^cC#sG}y-)j`d+N^s^W-tq&ic-84p;hqO|g=djC|gMpm%d+cR-v6?gC3-;mOUGML9
z{a6kR8#z!>qs5tObJM+X%$D?=Tue+qR`vb|n^^87ruB#O&5YI*7He0G7wjw&v^^b@
z-n;H&FE~#KSokeq0dzkKPkn48)Oi0snLkgBC{6k5?*Af2_ZVEm&d4ugCtF{0Mlcbh
zPn_DCMuy}lWo*YL&VddUk&3>v{<niETk{70<=7$c)K7C>tJuZZ6T2@GEwCf6r|pAx
z_UEzExXw+n<q?dfGx40z4I;FF|93}=j~soG=yxdQT*0{JmdXq2sL`Y1M+6N8CiL)*
z@Y@VJO60nncH-i<@7TOu_kxTR2U6)vWtvlEKt%oNor&ah=)#}`?Y&<t$kRA+@*EE{
zDzw?rYiOlH-6uVDo_}IR@+)%vYLdg){o)geC-j9;_KNP6{)a+HQ~mX=3430&apjVW
zK>`;#*|5aEeoGN8zx+~~y)T0d(BR#yTe2uIKBD1Fk316VnbXngP(@$o`WU9{Rgu5#
z!`cECWn`e)p|f`ww^x}94j6W-pb#yy)Gc;(G$9|{q@|*S{x|rxpZg^Hf510}!^*dW
zz|i23`2U;nx1ud^hSn@Ha5evKy->#q#$Lfq4;0y8P=J)NMwSVz&u_l6>tY2$x`6Qh
zng|q~_L&vCCICJgKK)hE?C{-|Uf{8VFboR5u<UChht4^-sj9mo;5hP;UGy#+ls_#g
zuo341B^id(1SVz(_<8U3^Ko8avy-y?yCVR$k3MNmEFUs)o^gx6>Ei&Rn$ZJJOKzCC
zH150oh6BzNIL0`63PYjp;%xgN!)AP5J;K9M5MszgKHxPRFu-ZM&r~J~?<mCO`Na5v
zX`6YeP@f96SvqKQW7yzb^_@_ASq?ZgxU58UPaNuK6&YFT#bF`Ea+c_-Fyz(+X<mCP
z3Z0F91(xJe;CSMS&z;;uMj6qC==j*f^<5F+jAQ)3<06q{Gav;uWr5m<KX90#moL}t
z6N1jniDYjBNf2`Tcp~MA42=IOu=&I*4*!Npp4FB}0L#l#f)haknqRgDC@spunT7Vp
zd%IGH=h)n1HYsq(c)$wcV)?;2tIwQnO&ru_bkrjnM8W)}i?x-793*vj6#H^20w4X*
zsT?v%C{(f>|JEi9_V0bO*L1|7eS_OR@sL4vTstE@yj2O(Zym9*O;mwq#ww|JHYL!b
zDx5L-EDb`$=NrzSkb}0p`sG+_C1~c2ubgu~WbPErr|7NAgIMZCi$Mkj*w(6lbh<?p
zI0j|@@T(}p&*ZB1&Utl!1`EykcPb$9%JEHjo)Q?Cjq7`A>40&Sds29z43OsFLwoPk
zLAWzvleb+1%5OF=m8B@agkaIFn~lmab+x^FM^hf0_s<LXKGlH&=@z+CVr9q+a#XY(
z(t}rC?o!Nbs<7(*mwl{76V^&<_IM374*|rOzPl`%Q1p1NW-mq!oI?Eu=cZMFpA_U=
za}M{T&J6wNqya(2+~hBgX~RH&Y3SQGYKIlu^0Q+%G~kp$X34C~A@jy)+Kx^Vz$o=u
z_i1Z&=-!@JP&#Cs*t`_+YLGPmPx$XjdAJ_veEj_F#6?|*sj#9wmt_D{sbi9|`~WH*
z-p(UIhVY}9CB|kK!S1S9;8A&Fm~4O2*4JkUrbC@glw*d_ty+I{kse^+hl|d_5hK|2
z7=GYYX9Uw(;RU^m2r6rZ{}wVB!b)ezfbOy}n6gE@`($bkIW%YA8&O+;o?Jj;bFmS;
z*PCz-GBE~+ErU<#(Pl7Zu`+UJ)d&Lmmq%w^41h1)h+S;T5>B)knrYaY!hFuaKkB<?
zkjhNCfBTIo1Q{1Z98ep>s#Q<uyACTbm$$vivtR~c6%7wRw3!2ggwr*7M@#su%^(za
z)EdZEw?#7gtf1YGu40AR2CxoF#`KvZkQt*N@omEfy2zOA<w`Api8E6EY{C(+emJ-A
z)A$I8+#9QTp<o4>CegHM`c|O#M&QA7F?$d=Im#3kU=9D8zFosO?7?$JgE-O09-I?M
z1TJ*j!EFQA7Y+eO!DNa1{72%$u@=!&?`?aiyS-W|C2<t~5jM6;$?V}Dd3DRnMkhFX
z_EFhODMz^DJ^NoAkrO<my1?&c=mPvvB#tZfPQZL6^vx4;7ij7AsV#DLfpB-yOc4VY
z_(4SP`Lo6ef`a3Xh5ot1on~IAOSaC?^Rz_C&D9P5x_hTxyW$QP80lytkGp`lulnY)
ziVHY!4Xh3vaf4eWoZO48ZjjWm+<VK?4ScS8%AGvt0cTd%o72-hL1SrJ(yY-FWN-O$
z>dJV)*W#B2&ST!7e%{=Ug2e}5cj8FZkQeOV>dCxf>jQkhZZ3L*Hw^YHa0try!MAdQ
z$j}@w@T8NBk&*I&RkGiL)M~yk=l5`*`hzzp<m?XKZTErauAiqwzWRX+&(eDaK0mOJ
z_h6vw@dMW{3O6)7g5X%ir^zh-K-dn7{L+@^2aeufZo2LHgB+(udU0_GxHIl}eSQ%H
zKaU86KA-l3X|1c7S0@8uIsUn*->X1ypWW~;*bN3YvV;@V%z=PkuYJ5O8UklZ7B`Jk
zgWzj{IeK0d0=JG!M*ZXt0@K!r=?JAT;BkoBTBQqvIC4@YNx5(s*-uj|O9+A4;ji+t
z+MzIn9`0u=heKih$(qCmk$@8H8z$&upi{75eB3A!Jm*6$+5L@znbH9!BgH7N?s_;M
z>=p&lB^O?{yoiRUQ^)^&`W+1;vH#wmtc`{sJ=qZju2|UZ7D`3=F<?lyuxC*e3o%$*
z<@jM``(V8Nv&HwZ5H^S#+!K!jn$6Vu`!#WJ>e#3z?jH-Bqy-k0>Br$rY^h4B+i?iJ
z`Rc<Xt`lG;{<h93>o_bp?42zOjfWhP5Th9R;}8|4rS(zw1QdBi1c!T_faJ*y+Kyi*
z;iBN<>Hv=fK%%RYYWfM#{d{!F?A=M2Shn^$y_f)!11d>LyQknkT50J-=~Hm#n?2QS
zsxy#SRpjv8{tWOw{?=9Ib_PU=s1rv_&OrWZPfg%ZBGjGr{-hV42-Jg>JR9*zkoE8}
zZSQ6x$aJU`WdA><U3oZFUEelj7RnfjP{|ZBM$S!!jFG9#^E{`A215uLDndwxq@)aq
zNMy{MQ0D2Rq)8c~h<vB_`=0Om-sk%Md-u84zOQ|*_1ov{z4qSM+V{PF_qq`ZrqJ#T
zI=4`S$T`}jYKEZTy1k_Ifl#y#OIzlIhoic7QS(O01$-2Kx<@G`99G}hSH<(fA=<<A
zXf5ml(yOBHOCO2AggdW-Ut<J{chrry-HgC7it?nFwh{39O7?c>Lj<Bjl~eT9Bf;3K
z`2LYYBxX<V^JDXj0(XTU1s%mjxbSXNsegz<1}pbVrC(9#yUMV)FYzK+^DcTkc@_nU
zpmN(n{b-~ZKRQKa7z2$7y-519XcSrIwLL!=17+uLhi?tVK%qwBkluO>cB~`?R-KK7
zUu@OHnctTnWc*~WcIhPq>-mT%^<9FKl7?f|m3Z)Y>l6&s#$%G)lx?p<0(#zOE7mY4
zVCsfda}iGhd|x(fd**Q&RXaI#RwNS<X13???OTap@=JAlV{{qpF8B1)Ev_Kz*^9z#
zva68n$}dy1zKY6m^_V#lzV9q;48EBJZnv7VuZ@#HJGbC>k%S9Ioo1sgNwCSuKedvS
z46D_rQg;}WQGVC+abiL;=4$ty=aox_=he~V<2~2#(Cx3{VcTo?bo8u(Hf;*brYyMK
zN>b2rj`kCKS_<BYc1kiSr9$Sb>Pjj@8m>}gQEqyqA?tn$#f(TAz;4>dEpQ#}`}&Gv
z-L9jkRItaU>^eH54o*z7q(iq%;QLF83~-g+iw*8b2diyg*Ym@f7;qPSFk+tpPwj*Q
zZsi#`rg!q}lJ^Z{5TZIy<=()ztKD~pbhAJfvNgi9APcen>bk#|vY@!}6ubPg;3DCi
zuRC`WI-Laiq>tI~=V|8C8qI;z`!^TY;&VX5*{QY^pNl&c`6-I0a?xr{L5!`+#o({m
z>Q?JKyq&X(j<n2!pvm_wo>aFW656D3ZSO5ej&BmVi}G;#!9E36j$5csyK3#batmcF
zM`Mnd=3`sL9%`cl`S@0mmU?W@Z6tc$0Y}Sim`(&49WA|$ow>V2Sij%KRkoGy_cII7
z>*XwKfAkJE?<BVT$SnZP=f@8l((XXyyt18d?;WgLW-~pIx{K6p-T_Mtg*YO7D|}${
zF5)yB_}1bJA#;8=wb)c4hVQK}%vuzIyz9Cx)5#)yV+f_!vMIvr-+ZE@R`;>0oMuWJ
zc^`B7PwH#C@1xZyyS>8i0U~RZ*q~Dk^(Wq8oc6^?%Q<nZAhj4b3!JEL@suL}Kt5mM
z!xAK#MZT%CEXB6Tx53M6rP%$(IQWKW8Tu;=`1PF2Fs7Vyezc|x8m>A4Ek$K0S6MI?
zwR?z_1Dnqp`X8cbb^Or4wsM^79KIRtR*s+zsiC^x<=9mc`Eic<5vm05yl}F81U8<r
zdmrCFLcg$M?b-bmq|B)si?)dsc*$dC`L47A)@wrCj8heGb~QDj7pnxBiKK)s<703L
z*1SB={208oid$5es^HzTm3))A3ae$3CypgmfnQ3L(X6D3l$k%nIBro5r;&Nw>ZwL>
z|I~;@ay2fF@f~%_eu7QuGW9Zn8g%I-B`tobL3nKBtLrRJVO;Wgb?RLW-j4`Ql-z!b
zj~eu}RZO**?$Ax+9IPc}N_Fw3an?aOY|^leqYm$GG}y{fK0}ZAmGvvfpCQ6Fg_~BP
z9_05-Od7A(W4HPJ8NFZi=oHj%G!1D$zevJ0>C+7`J;!1`KivSqg!$ffxkdyPa((YQ
z*oZU_b>}O&jd(Li@bqtPgjkdV6|Yni-oD>Owsxoq8(UlqHpiM!;ba%U#MlhgPj&7}
z&zs@<tKcnjV>7<nDXK;FG-EsSd<G0#pew_~zwFTh`rM_C%-=1b7`iR+(cn3{pWWm>
z`}R2+xV))iWm++kD0w~2r4`JNgwv1SZbkcMMAKoVHXLTvNU1eygM>wB+(2I&<jAf#
zJdtn5?{cdv8g=ckaSd41-fYL=(B?yCY#p%qc}iqQT?fwoI?Yf2p#yF%fhvB5otPYm
zi|BgUi4Q)(cG>1#xM%S?;>hzZOwY?ZDtzohma5|2uYKLfGh45b>g~pty~LeAzrDcX
zxl$R;?iVOjkG||A(*u>yy$!FkdQc^F{*+S0OI&YM`m02!my`+J=aI_Si|*25?n;GT
zXy42zUcA!_$G%p_?UlWtq#meWVCaLRe4E}I@jgV2U*z#R*9QZNA1UTdeTcAL;S1#N
z$In*=<Fyq1(D>};KNa7Pz8q1BuX+7Ak+OS7Lqk7)CHc!sDZj#(_H|h=|5wQ9OVur%
zdWFE)HUF{sS5PmS$x;!0jdAD0ryu&f#t^ob&Q83B)OLpcvaZ+Y95Co;bs0dSdR(Qi
z`2b?K{-mjj8Gu7br^6ZI0Q$|pUUt|!h%+Szd?q6XF?;uhpYZ4)9;!>JHt-FhQuul0
zXX_zQa^A@c@E8J*#I5g(%R?~XCg&;@97dmWPLPf5FpNgSI4z$K<9w6g<&H1Ic>gVj
zmSxKbL?i=;{S8MTQRC$j@_GbWJI`MBULAqyvnr!|0;4#Z=h?HZWE6JBG-NIfqexcR
zAM)VCC{&rwOa0&*!~V#bEFFh26g#`CQ~n%-w}=P}HN`kmHl9VU%8p~(Ha4cYv*UQt
z6I>crI}R<4ti?*n3AkAISAS8TfIwUNmMsnwI2pER8{RPiztzv@1I8z?qIkI{U1btt
zVK-wAyG@er^X;2D)svX7af)((H3{Vi{?S~aDZFELZf;YWg77W93vXhk;C#L>r8Z{@
zWU2mjF2hs!@~Q4^xHu7?E;vi%a}jZ?-$fyjn+UU}bIEfWMD(8FO|sJ_f_XFcgP9`{
zTX>^bc_N7TN>N%(afyhRGzKqn9}*Gn(9YFgMMU-;u@tv*B6u|eIRZ%AbRFMC%t+`n
zAd#8YOhm%jf;6i>BABX+-idV(LH&rWIlqnwbs^Jvo*5$a{SQ`tCB@6i-SJ~-C8Ak<
zh&JdB5o^~2mf4eu=zN8TDoI2LKQWfSn?QuT>R;=b0YupGY0(Ay6QSciA<haS>3zO%
zV`L8zk+aI$TyjK6liU@8XQp7}H*Lr3GzEL!<riOsr=YoW?I<Jj6#R8>$rk2LLU_l~
z1zFPhJXm|vlY)K{em*Pv{>qp@bjEPVyv+o7JXt!5i^fs*j^lH%@HkvT?@YB;jN$&2
z0OdQ4G0<N)IN5N6)c%fxHeypF2;lzl(#mWERoasVZ_0;3)hpi?T{nd4qs+(HsE3gG
zgG|yVY7hk_Io;$w1DG5)jP-x<8hk=3?-i$Cp?hs6g_G|U?DE?LAKUap{!p*VUFANs
zpU}*@z}<`b4;5u@123@ZRePjAp$npnA0q+|bf9&Qi=e;>(sO8CRF$W?1y4>SRGian
z#;hZ+L9}usxKE@Mv_;h8ht@8_?fzQOoaq-^b*e#wJ6U_~SQWlHG}29XRN@|uQ$(d+
z1t?q}hTI-}Na_zCy7q{&40(NOw6gid_?>akfa>%;FqvI3h?l>MPu**Sg}%4(Vwz8y
z`dS{^X5*~*a<lO=X&-ycat7{buOtZTrr{+e6P@>qWDu)d4R~6vqV*L8U6OAC41&KK
z?GlVZul`%++czTqk$Fl~&bIoSfBR$n=IrF^eCq$wZ%6;f`0a1}4~^fX{!`<(|F)r<
zCp7ID%HrEMPvEm4WhWZU6DB7k>O=pUCk!ol@-$@66Rzn?ymxGyCzxiOW$~P!CtPJR
zNF3a;K%i&(X>|9<0-?Nz&ur3rfnd$mdF_1q0wJ8ymL{@!fxx{{TDtgkfiP^ek=V_>
zNGLqFUa(DTkw9FT*l0SnNT9be&VG}-NJt7xP4?+oBv|KqeDGLbBwQ~LD;pA7BHZ?T
zxn6m4iIBoS`<D9B5`n9%B&4f)i6BB?n%VMkiI7^ACttDSJ0ZfcK6O;*JApGJRN&0{
z?}UqgJ)=x2`A!Hb5pxiH^PM2%tmiJpwoFji6{1zEwM<BTN-pvI+%n-<B8`!L*)pLc
zpm^4FW|?5Wl|hWiO)`GlqF^~^xI#E$Wn38=vqE4Rj|)kCwnCV{B(NsEvO>_5J0EyL
z;s@b2H=iPn>kooLeEoIg{vgml)qG1e`hy^PZPfn++fM@L&6zC4V?POgWPFW9aX$%1
zQ<6zi%1;8{mV3c!l&gepTFF6AtP)}>9U4VKR|&Es3wOh7R|(32jK6$-tr94YxE=U$
zXpLZCty9N+ZjJEt(-r=)r)z}BFOP>6H`WNHJ7-CmkiQ5VZ`aI+FZ?11*~LooJpV-)
zNc<E|LAOp={Yz);ivBubJ*se^C3&52{@n~+{_r}XovepljBkUG#=(EX-erRzG?`A&
zDA^z^>8(E)UfCcx`0^!@Y?lbm7K=C8<9-vuWaBP1z5Y!&ZY|2Rmw%J6l5PA+)oYVr
zlF&lre6mS+^Idb?gpv$N+zHv|MaW>4&&jvtI2kCSsgHg+Lk3TYaw@r7WH`NCsX5(A
zhOKwL|5{ig!$;<9E*D;MG~Ja+CTNkvM)sOrm_Io#URey;n?;Tf&fnh4wvpq=j;om^
zi{xm?)5%QQMS-SB`cG3j6wrSqcQrGJ0y{LTQ=4v6Akt)$>SR9!{M_lk*^p7fVYiq4
zfCMF)(~bP<tSOP#5i7-&NC~pl=(_i{lo&tgdq8B45=`S<1#fw`Kum*!z;<j4^6j7U
z*M)BZPq>iZRQVPh-*MRN{LB{8W3csi3Ktb@p4g^~=uzQEQJjfNI2FQwSGe|7P(j^N
zg>L2}6&BmWPMzhWhVF9z+hkK}JdDrCJdi+*>)GmKM$OdV5w!ZUwnmN5q0yXT2WgNY
zeD&UYPa0A;>eQlW0S&B1Y&A9}X^_y>^wV+&Egs%6aM3oQg|-Cc=hiE<=#G}19q6Kk
zxmEhJAX+-u@Tw0NYtZ4Oc767lNIJZEMIAogNC%ayw7DTNdX%jCTX};XSFOY}vcu`|
zXv?CYS0g>%c3%x1reMG(!-dv&>I~?Q|C*8<!+@I9MxVEx42WkhoE~OkM1B9R$UtL8
zNH&~(UzN^?EQiHAw<j4PXmHm-dCyj?)|5UC_u7gpQv4@QRcwWp0sA!j##YGQtfTSJ
zWI}(U<2nDUOkfKOS`r^)!tQq&B6)k6QGcOesyl!gLoH7R%Ilf2J43*uh?)g7KVytX
z#aW;)BgpJw%7R`>wHS*K7If137CtCoL5)t3y?qZ09@YBhpZUQ8@xB`S75;4q=3z=6
z*4c)|-$7!#Pj7>$m$PVJ&Ngh`zZ?Ita~p|XKC9=svJF#rVtvg8wxc)w`_B@C?HGG*
z`7Pkwb~rLlTHY?)4y_bH9reNO5Fm4GH{QaE%wpHsSJJGYp-QiLZqJIy;a}z=Nvw$L
zR~x2iWQFzjPBX&=RuF{lF0cx)p{}c-!@!sg`=ZS$C@!+Wbc(%6qKXZ_1n8v)Ke8c3
zStfLKCp-2P`VV9nvg7xuDEI0pc0?I-`<gvrhez`(-qT;%p+nFuV;AB8F?Oeuq6G&y
z$-kU=bCm;3tUj{L?Ho{i=rhs0$${~A!%xW+II-sYjJM5?6N?vYjk$_Au@ov@w)~b8
ze~o^Qv)jdmk6WS-Tb$%Vlb80McPU)heeLzF&0a3Z$Ov(lY~6vl$Qz6EB>5xrJmd9_
z*d4f})o@y|Z3ixthmomKbEBdD#)Een+(<pTvM3PE4Zliy!{4pkFzs475=_g3=a(ao
zC+qOQLs^2-Adv?T&86Nv=;cAu%fxeKY&(&m$mVQuawm!eePkYF?}YpLeNlJc?L^h~
z>P3AL&+Q_XD3BMxixV=eHMeSbVH#fVXhOLQbrRe?ReHPNu(SPsPueaNFQz=oe6tIu
z3_@~w#rbeyvs&up89p4de=c&Yl@CGhtL~A}^Fwp{{8I%fepuHURrH?V$C#sKNJlt7
z5<a(63f<$!2i6R^H+}rbpkSq;_{9&Gp)R)=q1_lacwiJ@up7kzD^Hq(cZ2qXZr1ty
z-T3;sZ24x-Zq&cH{#<x%H?}Lh*}7}505*QS>kmCH0LoKAJbGaQ$bb56KKg+Gh!x_t
zlA{8sR?iL8rWHg_s;=5AIYAtEdpXwQEQr7WZ}Y>cf-vf3?%^ZJ9#xUYEQ6K>QA2r-
zS7WabtjDut<III{$|j;s>yi*UZJSm@p9<kUL(8({S0UIxe?Duodk=PUz57jPx(9)^
z{N4vI?LoqQch~W{Jy=uE-9t^1b>3ghoqj01mqZ^N9VgoC#rN(|<CN6BK!;Moi|)P1
zR=@HhktFX(trgl2D(}Mx6*0B&^gcLc*3?jy?!&FlWx;PB_Ti#)qOgmgFuZ1(1-xv8
z@gYFOk0MhTG`|Y{MM(0L*U9MkneF@GnC7=f)^I<V6fYKWU*3=3#V0Ra@7@oMCQ6xN
z1`!w>6yUek6~SHOnbyL15lpvm79@3xU|e&b(heq3L}iI@RW%gF$>`Nv^w&hO*3TIm
zG$@LOi{rYNxW&-T>UBZYRt(uyE%kY~#gI7GmUsV?7$k2TX|<CQ2dmZk%cfv)Oolvm
ze$^ljDFH~Q&`FT)?{n{{jV0il;c`IyrUc}A_jl5Mkbq|#uiuS>2cZ6VkoZ3A08AcA
z62EjGfbpd04fCy%IQ0DE&~+I}^enbBDOgD&=Si@LVw5B_wcc}PJ&;5ipIWQ^0I6O9
z)xP-Ol9(v{DZWEk3ZD<i_c0!qf;dr*&h@+$45?o93g4B2hmy=5<9;bpdu*qk-5?pk
z$%LMs*)I+4S*qY;X42RhTfw$BQW{s>xlBFEq+uzmt-(7fjcop}O9aM)crKwH@aoV(
z^hOH39(F$nrdE1Mql|;_y26ulxZ@z=zlt2rSv`oVA5FqRB>8F4StPR6QU=m`o_?1T
zWw0@LlC!s72A6l;$V*t1!A-gcO3(Mmf>!kd+qsjnI4_fATbd|~Ly`lAj!h)_EozWI
z{f8`OYRl=5iOYe1?vmamM>*utNAEkDDTjH>>8XT1Io$ubSbKs_9{a4s=!#Y4ahYCp
zd;D2>lpTF4{QZ$Up1ddKew>rXXWpqkFJT2_UI0g!qXJSXr@D6MC_sSmi?P|50=f!*
zZr{bFh&^>1erHV;(HQ77;&n|C!HG;ySNarj?5$1gj%|m)xT#t-Xm|*(YvmdbBpt#C
zjk)07{zC}9Z)fwARSDyY&sOG5m0(VLHLyKh2}7!tjz`9o;K@8-?8vVS*&)~A{m#l*
zPilxwFH%ObJO|msd1WZ6B~Ap&ABKarYRgRMVYEFmu%v1`466&{`UA`=*eSkO*Ziak
zw%ol)otUozf0q#X;IAqWPHoEIK16`HvDVz_XaX8pHCEzZ5ioEmU!{}<ND9on`$HaR
zOe@)yYYW8kH(b6K1Ei$Py<;l_ywo#T-VOtYMtvyRDOABa@AmLMNgiCN7-T6lRYh=)
zJvT$RDvBjoI|hnW@wnQ8n{H4Qqgt#?cgWSyeeu$rVsSMDS6=wQZ=nYJr-#SLVo0)}
zOk}J<g&JPp%v-0QR)bx1u%<lA5kw4z1ie-{0t$ha{I}jmV8K8>?R@hHPCve;YxUv?
z*mUF9+cu6sFs7KbNJ1SV_95kzcIud`QQa6wQiqQaBac9fIxdvf%YIx@2jy$dvtl9|
zB%{Dm&N*uhB&X4+iY05{Hv1LP&#fBRE_shxbX@}s%$34(Qkv+YV6T*R(}X@zc4a+V
z6McSZlKO+1=wIr&FT_MLe0!8$9;K;;NB0JjeZsWxK{@^A+iES4(TVPxS=2&616NF-
z_))~}lHt1Mb`*cj<tV7#J_>67A#IYE3wSpx%FKDSA?+D5&|skrGU*w?hIDNt1#>f)
z4{77A;){)F4jt%^$_E&k>0svNzUito9c<cCcRd}_fndO!Vw)W#c`-PU`Ie<FEPbSc
zW3qLz{8>AYWm*>=M%C9;_UgfkM%#ncQxE3{^RHYj)5EN(X-?IO9!b6&R){;Sj|uYJ
zO}dNvpp4_cq3}W<ZL1@XIXMh)D$Snao~;4iEPG0Qy=OogHZb(-!*>I;o~_`$fn&Is
zY-~=QcnsAmy53@=$1t>dB!z~}5N+}ML{BIgLd;kA9j&7w=AN1tamO2i$%M-D@*~ps
z_Rra!#|_~$bvi4D$_SmZ>PspIjBsVVoJIYl5!P68=1L<;^5=EUi;*Qp$dA(>s2?$c
zmr~1wGu3ev@rdXBC3PITYa)b}tdC={>#*;5+;K2ySjHc$K91924i{Hvj-$-q)9V16
zF|@x69UoLP#uJV)op1iecpe$zeJ;-!5z7Uy&h;5%*OTmw1#%NyQ*)o3kT$`Y-j-%n
zClk=~%lhm}Ga>aG>8MV(o4|tCp<MHq2?AFY^HU^Dv8UIA#?HYMv3D!C6{VR%FSFyv
zsV-BzB-dShMrMYlhoi~~a%MQC!OJn>WkxcZZK^NNH^b%Xgixk2Gi11)$XwWNj^=Vf
z8*5#2NO5y;8Aq8TLOOQG>pGHL8^BQY=7%{hvIiUukv;+HtwX|v-Y2k?;m%{ndncfr
zm2doT<^(j%mz|OYPJ)+T#F*CZB;3N(;@GlJLYL{und<SA=vj$NS?008)=MyYVQB%+
zndSYeSr(Xm>X>zW!UEI$ANEu7St2Otin)osC35_EOSEoV!g?=9&E%{lI8@pgc*LyW
zE^8$J+s_IakIP<PdQ9?|rE3qXU$??^mfLXd5o;*sSw$R(w?<!+-yZK*BwP;hw%kcF
zdb2u3zwT^<Fb%sh!!jGFsfBV{{<49+nbP`BEn7S$sPm;?v&G$^%NjjITfEnl&a&eq
z8NG!*c6g#<htE1zwgE17;I48GNw{nWGbWQF+A2GgTFnj2PLXg;by0-Q9%TE<m-?mc
z@uYaEmuO{=sGFRTrZM)Yp}$HcU2cyQqO4ia1PL1qPVc32fc|{4Gh`ib-}InZt363>
zzA)8Wec6E|b9GtosC7U{VAa%zj}GvfU33=aa>VtY&J-h>ju0fIb^JQ*2<fxM{Yj+!
zI63NY`K$p)@Hkv!+ez&Nix#0v+vT0Gx8XolxSJEk)&iSdGf9ST3*iIp-A;JXTh3Xq
z=>*D?N2)fYogp=%`|g#CGt%T<^a<Z^h5}7h<;F{A_}6`HH>Prdz>Jxen34<51`PVf
z`n#Z9z%I(Y&;>8Pu6FcJx#05Eme_huSJdvHWRy2_g?P5WErm<2FrIJBdfw!U9=|7s
zQ|qqaTM*!gk$1zjd-g}41-PL^O++)g#0?6IasI?lZV-L2bf9OSJ2G8WJ{dZ@V>qaa
z(Iej-Ti5o`@JzeI+WfVi^KK8!jHR3C*n6PTVKTid&jXtr#n(Sgd!SV3QGcP3C-NK&
zUedUdaQGSrU6Cikj4fnK<~(6ApjP`+$_pZ7O@vQ>dEvVM{u5EPUg(s0nAc9>4H~@*
zwbk0*IBE1$&n(Fs%@R>-w?@2i?CaKmTmc_!=H)pWdih|x)4j-w$37(E^_sXIa$j&C
z&ymg5_r(cW2My6oUuYaL5A&b(g+bCavl?zcl(%{r@TifH%<t;ByC3v5SZFts{J>&D
zmLFN;hxdCFj6C1^LH+F7mMTVntoe_YyUO{aO1{x3)XpEKkC#S&#rZ?RnoH+Rr9Un#
zD&IWt#vkl~<F98K18|6XW{F-g0LK^VHZz<9@T*v#EG;<z|03J!|7ifX|2S37-!vR6
ziG~Ys_9IQA;O^sU`!7Q7?^zhC|C{|H`y2lwGEV+Kq~iGb>HquhNI=iN?h(1uL5xk`
z3ZmMkh_Ej6%MT6$EmAeUbqnfPkr){lW7WmK>W3SVI7o5|ams(6egY(-@lV<MKgak7
z!pZ-v|7|jge}?|C&igx*;ZN=VS;EP~NIUyKMtc7Y{FC<k{|Bz`{S)}VNx=UM{4-?n
z??9%%e~yIV<Yx|%{bK;*KV$#Ut$&!*Kjq&KbXA}1-^tg1hW@!k{<Qm_LW=(1C7e9m
bp6vg-U<`GssQ=WWCViYqCo;t0&(?nd2u~-)

literal 16564
zcmd6OXH*r-x-B{9oLS_YbLy|iIU|T58384W7zm016%Y)dhyep)LIgoUlvPSpK@b!a
zPy|sxlH?!)kA2VCZ@hiReZTIX+iUdtM)&Hus%F>M)m5{5y4X|Gs8IZCk)ZhYu)}`i
z-wOu?4@E+FXhNWWd}wS;e1d9p?0!lL4vMmWAN_Yc=d{Jq#ha2Mks?VhBy@LhyqqCP
z&UlBuoH9vn$0ja5Fxo#RJ|y(7{Z@gIyF)kk?+y=)4c*+9*Hu<fCZ&-62Mg0@Cd;5j
zYYoJsG|H;EMHd~j)iDbDVvG)|H}q_~V}|xEKXyqIv_V6uqmH67&dB8-jBS5j*8ZC@
z+a}|E5wZWv`F7Wj@~P@e!_nKPV>3mh!LW+WVRV56TSe9wPH&NbHo5h-Vk`nzQ0r5j
zT0!_HU#$wtrhoE{`d`jhLa)o`?d@fPGg2^p^wMMUg+0FrLwG@6`W#Ck|K&9~#o(7+
z*xg|=mwkD9@YG%6pM0~MM``}%%ldyaIwJc2rZeLHS3dt8|7Yo}uKqtvXeM<B)$Qqg
z)3BRfvyB~1!<SUCi|sq6A=M`Ajo7JakbO_P%2PQFQ@_XoT%V?)(^#pogl-1DnwW5|
zs?R`PvBn3&V+L|=X4Yu!n*kcWOpWF{GjMuMZbpA#25k3l(5F(&0&VKD?qm5`m{Ak)
zVhxyulT%X4Zke;tp0GCbyJ{AG+}BcZ@1KQ1xxy>V{BvNpU4g#WbPkXUMStVYIe4r6
zti<5P9LVXHUf1lJgPR>rW!I?Yp;7l?>3f}dxR}U0{xWDD>`h!o+Op^2OX*%&y_R`!
zS2j?sn4Jff*M<GG$_oH5h(nIs7vR^r3Z^}~0Ll1k?w>ac;9((k^$_(UJW2g-?x48{
zx0)FxJ9jO@z%An)@x_ZEwv;qK{b4f}hn3FoEy3fUT}e|`OOVJ`wjp+W2^5U7Z?@Jh
z!S83pZl0AT7(KeZwnk$a&iD)$<%ciB6x-;C+}&mHABm>w9a)AD?h686#aBQh(rLmr
zcm+6bZN0Yl(h5)%4jvBcS%IPXqe<C9zhS+plUHQhZxH-i5$=}%8-C2M`p}I028Q<;
z`bBc9aN8`Z%p+hG2AgEq_ZP3isp#3WAIDZfZ2Z!~cDX-rp~18~JoFDlOIsvXRQ`dL
zmi`^*Dc8W2^JFfa<r=&yrEjiITZ4d>5ryp?YhblAx8}IuI#_hoEhdGmLn7q^_2CEW
z;FA5+^VFYpIJAv(i-gSvppc@pmYfaf4j>sk|GWV$E+=()1u2lvkjXTgF$J<cqj39J
z7zL76Q@F^TO@Z9*Jv`V}MS%pv3(RN-DA0Zv6>my9N`xa4!aTGn(b3K-mp|T==xWr{
zr@|*Fk>9f=e%eY(R21Xt_~bJs;*BUCOk|`&EWiAz<#njgZN8xLRX-}k_vno<S0)u=
z9*|@Vd`^X`3pULBe^Q~-mtL~C@>8R0V`JyTZKzTAh5fAFd#RCL#8<iW>(uBMUwNtP
zM{3mTt|wVUPlMiH`o2)0OM^lV+KpV?NrRFbb5sspq(R%ewA=T*p+S;=R`wfi&>+^^
zR;FjQXpvs3V|i#8Ei$dWb-*T{7X5to*irL6Evi10U&%*9hs5hkbvupekeq$B1&*ad
zez$l+s_)Vvwnt(8-CyYt-#+5E7au(mkPff1aHdBd+Qwh|kI^Gfm%4@Q7xbvD^^$qw
zZ+cYoL&R5EmjNaHZY~~<XF&Zqx^qqs8Bnowh+D)@2IRc;<iffvBjSlKSos~sh~g3>
zm6r+`QE|HZ6#Eb(nx#dj<RzKV!*^cCPX#a`cAsN2dy1G4-$27NmO&;|aGmOtj07{%
zcq?AH9?FcG-)TtvC}u`+C2#iJnq)>?#ii;ink-1r<m74gy)5VwtFyZMa~3rFp*Cie
zk`=L4SzPe5V@2mUDC*vxWknPgEEbU;SW$@BBwLR-8xsCvb@f~%8?w2rjeH)nA+|+F
zt`-V*l<UhdDDT9MX!3vFE5FE&t{e~!CylbBk$l<8UxFOyNIkFO0^&e7j^Ejz7si45
zxC@(R&Tyc9MXm28PdN}@XJ(P(5C^)j{IIBnl@nQ=aX*-#!-<}1$iL_C<3ub&qa6NO
zoGAG4!k3jwPV{?+dPDzLP9(cWHvc#a7ixLB-{p)x7ka@l`Jg743-v@j^=CiJh32Jd
zRkJY{B8ga}j7)H$ux`Wcp9Hy4{7=iS`*z$Yi{Z0OLJ~LnWRla#bcY+6dQI5u>ET9}
zJue*{n0ZiML!OzeArCrrzuRMa7Y{0||0vddl?O>sXH96o<3XzzwhabQ^P<TIt-A?b
zUL@FK@6)=A7u`P*u=PM8FY*+0`LO!~FWPhR?a3fUJ_OtMJFo#C%C^7cFu8{h32n<8
z*Dc{gA!mMGd-aVE*?B&?ct(UDEpn+*(0THsJLq8ct}K4!(W<=mt(hOmW9iw0lmh6w
z6Md$EkpSAZ-GwSQK>#IvQJOHQ5J0&c4>QxI1<+JdTUW5MAWGC0p|OY%M6c-<<j`$F
z)ZQ%mym43%9Uf|EjFk~W6ONr~UqXe@`q29=9(RNg-52%u79&C^I%K%MUtSnJ5&tZW
zBZZOI_ViiWGGRn{P(I_ttT2*Qev}ueCxXCpo@G8s1eI1k@>Z`CL6QV8h%<?z&&q~n
z+)kosVV2|V@AIOFDfUzrXTK<-Y&py@BqxTXs9S?%;>D2sx^L%`8ZngX@A0UUP8`iD
zbRJE07e^wDmwcZTilexLICjgVIQsa#WWh|7gzje0sK2u$p_={Q)Ne<Tkn~Zq@Y*>N
z@-p5*#ZM-oTiu->ltxHM-+D2754!|P>@DV2F_1t3#@@yQArh#UvF=bvwgd{!cr?8A
zsRUZ%ZuUjrB~W3MmHJ0^N#v+K_uJT568&m8U2!;E66GGbcWp3F5(R{u*j8RIiQ4UR
ztAl1F5sjDe)E+S@Btp-{^U_fYeW=Q>IFTxaz8UbzM3+b*g+lh0>d#WhFSG9416FDD
zw!1-V2S_86Ju!WJanfigr#VadhBSI2xN%(SgESf!Y!KjMkU?G?I`le*GKhFl)ano;
zgYviiaG$;@gC5F995L^aLF8qaw_=k;ojfB$aptlp${<Gi@BvvQs+61OP%ev}#@{lT
zAC*Pp%<~e1BstXC5#B%MC5O&wN1U=aD~HlJMa~kfa_F&4dG{2ZJStc5P<f5yQ7c#U
z@WEtxbR}7tow`~cIoL8}o|u<MOG_7=%rz9yqEtm>R*V92vbEk9S)zb!b}3C?9alg{
zuTKYjS5id%ug{qG?NUUAKRl@=N)*x8AT|^*sfbX<d7UgZCA8{Xyu&3<2^DVtR9I1|
zgi>gIEhm<hP>oN2>A0~nQb?Djk2<W3q(ws;cQq@c_m&!5d2A}Ej<QVWrH2aAUKRhs
ze?<jJ-7cb3|Dl3t_2iwZR8-OGE24QIQ59uMbo&g~sUmvqwiF9iH6%vK^Etv>4S~4#
z<Xo{D^5i}m_H|ATC78Q^xIt1!Q*~l1AvWshc*@?F3o+`5_a5t{RK7au+f`(-@j@N>
zU7XoNF{X|z{7-jgb8Dc2cjSkrCK~7ubByN6Fb$;c#VWLLMgtjMiR(hO8fb35#M^6B
z16|V?H|gWnMD)}K@fVRM(mtcDJ`|~mR-f7q@4l#s+KuRcb2n=uEm!lr_=U|V{q%B&
zloq11r@egAO$$v(bwr*`(?V;OzVqhgT4=BBLRa{p7E&FNZ;<8EM$*Oyc59kzqY{OX
z=r;-4=)l_gAopfp++4bK4@-|WvN<MCBgdkH%7RTlHh>OlemJqno}hyQay>F#?`-aW
zZECXZiw>Fz@#fjXrHd4ltwot^bP@kowvPE!UGy_vmB;0YF3M8e8pb@Si-IB@1!QFO
z(534^`=+<+p%~gTKNT+MA&-$@?eq>k^dr6Xlqs`5@?#tgG_lf0*2nL>xtykta-P&m
z3e@VOUM}wn#ozkq#C9!}MqLAR%E|MzNP+>Pk!9~Gcw~UmP0-Mp83W|9Euvaj!w@~+
zLlhas8KSp>R);>78zMIS14(pqhG>!IZNznLBgAi~B))I25xU`qg6L|D(1^@{?b4bN
z!fm=$!sf<E#lp2v;Dj+6I~(iL_SP7EOd|E$@tB|^G>J`lJ|@U+Cr{n28zw07gl$jm
zxCsi)Pn2lTCXgNxE#Z+$pf@fFA@~)6j6YvZ*~<e+lU=GfKL8Nb%Exup2Y_}9u7BkI
z4XFHGqpgiJLdlX|Jbm^EHM<Dkc)1%PsY~zp>n|bHW850CwE-b!>-VdflL+l!Nsrg!
zH$@jNCg&s~Q)J~n;PHN^DYAUZX?7yd6fFhnJ#~1o8SUoIH;$X4nQt@))A`NNw0qt*
zb_+8kSP*wAE!GT$DQJgXykdqPlOn}TTFekM%XJ=GF++OK?sg=|nWOXbY*R{}=19nB
zP3Yb+bHwaNpR((*Il5TSyS?g%If{P9IxNj+f!6PG@x@tNpbyFsECKs1km$vyhqm0e
zK+Xk;X<1(^5L4vL{CN&bG%sSWB4S~QzJ`9k$G^`K@z7++<(F8ZjZ-I3$)F`VzSqo{
zhTjUMvs>%aIa;9$HBBrhj##0CU1NofHC8B2<Nlo6tQ9g?;B}-`v_=P+PpGK{TO+wr
z?-lLK)=1jEvsAp>8qq}W-;n39L5<Y~gHrZ3D8o_wQcSuHl3h;_JzZymzLNP?FR$C6
z4O3no31eIIQd62$=zuLcsLK-{U2Tg@u9fYWShPi!FPEPu>e`{GqCl!EiFW8GeHRr+
zl^qIIIm8>WY=`hgZ{Z?Cd(@PAEx{<o9+ltodmr_}9_>3>Bo;;Efbw6jh_u=`pt7=<
zexYm!BzbgE@BMoR^zP2qFH}N~NHF_qVOXFeveU49A$`{oP0}QANX<GT@w8WBGDc3Q
z@5`y%gGZfENn}`dYP%DPy_r;BCFG279K{q(m@_KON_~-2;fy{~?Qkrma6zicW{c0{
zTu`2Hi(#y@3%cP=^;2)J3!3N=3h^v-LBW1S-7K$MQ2%3}56Uwx=;D%D%RwPm^tJN!
z4?_!A^!wCBp3Bj$=$)#|Dea4{NaXb6ZAuNU=vK(F8;^duA{7Diyo17Sh#g#}YOUQ+
z+zXG7@d<9ImtknA;<_7BT;hGq)9!}MOf~q#*W3`r&8CYNl-!XZG1iy7%^hu2X(zup
z>5c+idd3Q$x})XBTEmH-?&$R6?8r<Z4<w2WnV;BuAhvNiPvH~~6j*5TJ?5bYs%Vck
z{xj%-(jq!-WqG%tzH>M1F4}BCmHLj}_ma1u%s<s3;^kYAHEr9P>F^fh3CFI#7xqN-
z5utA5uAZoe>xN3!2~VV2R=8AyJ<+T1J3aPGo~SD7RcO4L7ji4QrbZp^h5qarjSjx<
zg_f?`eI4!fLOPQfDek;mk<hh)5M!6E=#j#A_OF?n@$PQF#^$ZaV2byJ2aPvU*xPL!
zVCs#+51v0{f6yDH`Tsn2w$>X>y|}Zz;Ey+|F5T}yGTw$hJZAyr<ZVcxLDX*P`8G5b
z>hLLkeH;3nULhESe2{1Ju3p9?KBy_+4$IaiA9PeDTrY*m7s;`g@1A$^MbiyCkICoy
zqI6PG(?X9gx^RV-%bv6y9ViZ@d>OVKP295C-SKcc3g=SFHDBJ26r4ncPMi9n;Vuid
zf=oYzj@)#M>-Iy3nOIEDN%$jG6QVXZ${#V3oPyhG{Lz;O9`}W40?@c#a;vja06M2}
zHKD~V04eKM4%Hk8K+=9Haa=b8&<mkk^&eXT5T(G4(((BKR9mPz?<E$9cAK1uRk8_0
zkxlIyVex^;UuRbq!<9fZT=e2C->X0*9CnXCZ!Qp>zSwv(O*{xGoLP09un$57yG&Oz
z_64DYjGJ4@w}OxcTs-dlJ_v=Y*-Qjd2BZ62{Z1`v!RWd1@2>ZL!N^EDev&^s8112>
zr5UXaM&E-!Nm)(>qxp^3?E_*VNTh2&7vU0uI9u*oET@H_WXR59t_(rJRFl_AhC@(?
z0wZO;U?}R)`Bk{nF%$(*_eGyg3q=JxTc|~!gd$(IjAiBVP_({a_-2T-10@AH)irtU
zKxBFkn(R|M&<PPyr6&zL(3M{deyM+Upxr0fl`^!$Q07@5;iRZAWS6w?>2+}!VsK`-
zdUhZT<=h@+`XCgJN|(-+f7lX^1bn{59Xu0`K0d6si+UT5@S$#7Ev5+cHesdB);a<S
zov0&A9g9HWN!3?M>LZZ#;dAQ8sdl2*H$u+nnC(PQ=)x^c4(~+mQ&lF8^*a%tY4z?x
znq8>$i078)R=d#koU-YyCwC$7M3?B^)?H|R`S{Knj!1OTD$HDBOC*Y<4eKl_h(sGf
z1yzRzA`v~O;E6x7QHXMdHOnqK3h^DDv)=wB3T5|I-`rS_Li#Cmg$g#&NPOL6Z^_wc
z<m|(RQ@%u_BVKE))ABKB_(hFVenJd77->^RT^ED)Z>L;gV2DK>cP|^Ssm7vN8Wpv4
z&selkr(1S8H5S=MZkP-e$D$)|`=~nGW6|{P^L#nWv4}?flE+rbI5g2ZaZtq}4xQFD
zq>fCCL&>*C7=y0Ip@c9l-^A89L=z*U@?<Fvd99pqu#t{OGGDE{#+>8P*_f`G_>_2*
z_0-_<i@WjvA+N4Z)!qDCeq;W(y!yXbeEVK*RDG@894>A69`v=}4i1d9FB3m_L#xzV
z(~?FXh?ToL#bcueM`q0*lr3oBg3=4OKOS)hjpeaNV_vFwDbH6YL(c$&U!HsVs>>2n
zXjUaZP&R>wR>cgoBUU(~Z|YNboiDy^KQq`+>jFy06saG6(#2`7z5i?p3W6_bBTFAI
zo8a1AvK60PY;aO%>|;M&FE~>-qg;O%VM7bODC0Y}Ac*%nOiuWKbq()Q@wg>+8x%~h
z@Nfhk#gQ-HBRpU^a6A1iL3i-i=r0@ybB9Jje?NyfL9D7t*68fqf+>xco{K6=;zzDD
zS~oczL9E1!Qpj5tGuLQNnW$T1)2I4P>E=4H^z9qNjW`{APb5u)dY=YV3QaZCIdbDi
zJl~k3oi*|7gn35kxC3~{Py0B``hw}s(gYHZ0WgyEr%NRTaU;jx54h6}E@T#19QmvU
zC7t>|oGC1@%Po%2Pt#1H-|AUPRh2F5()soI9@4_BD-9DY=Mm<l<qA78U;_*pYLw2Z
z2qY@xrl0R}f&KZiYvK6-Kep*Rm9JXjq2;S9?V)b?M1Gc@pAsFGN;KTzaY+LoDajUj
zbjSoB9RG8GHVWax0a7pJe5`Q6l`GaG8C$T-6PIruw&uX}{pzvF0xzhN%jTu})lKFO
z9Bh=`%Z7veKhfCEX@J732XPbU0^x&ScGe#+bNCr8S1IExk2^}(6_jiUVAIUY<>)X2
zHg1+>9a&S*s!cpA{YMvThPbiBxR_(h8ylZ=ln{_O@=^|;7R7<A6Yeie?cw|nh368F
zHNcSa`+<#qGw_WU`QqSW1{$y4oxH2ahx2bGmAQD^;Hp0_+uRy7V1;@8jzp#@2w#!X
zc$cmV4@bFfN8Ypmo`T0dHlfo*G}GQ_ItwS9)Tc2_WoQI)tYp(x9xb?PAh&$fl7MmL
z{5MS(v>?*bfak;+E4Y^(smmF}14;t5`R4n0AmY+}F`FF}IP6M>5JL(hK3#iCeJDo(
z&c*BTxZSY;)fBI`kpsF|FqiH4;a8UUcs1R^c5PcsePZU8Ww<_c(PYjhKQje&4JL<4
zvN344HxGaEwt?y9v)7L1Xv0|*#oA+^=7_6t^qs<61%cPAP-5w*EEX>BIohv0LsTS3
z#UIGE1;uWCo}42F5I!W&9@ndZFR&#)efdZk&lS@x<g0Ljg-59>dEErOC}f<9ESO>C
z)T!@&lX_rLd-0ChJx!PqZsg^#v;k_?<42^A8RJ{^kFTF?Gsh>F*=}TTkzhNK&!2rs
z8DCWV8m%O$0_CKX-Pz|g@yV|SzU=~%xVGcu`${@3&{I)Q_Ea$k1=C)|16)clI+JlX
z##9qiQA}K;%5nuMhC((@cXMz(5vXOBZ-6Ivxt%=jp@ElGg|CQKS%B+vhu7Pd32^IP
z;J&z19yjn@e8SDdi;EqP4#yaha1{^N-X>uid}`&|t5i!XI4(L667Ru>B^=yekv_=6
zlxau&$MZax|LLn+nVZ)`c78$lK#DG?<@};fE>Z^XJujG&U+Y8EllU`x0}a7Yf8txt
z9D<yx-8a1L5q#`fi_9`I1Xhnn>$KAZWSeDOyFgkeXOs{9IFZW=Bh`a%XSCH}@5MWY
z%knzFbT{*<f($Lb!|f)S(yWb}6JmaUJirawZFc!Z-kMN$H*`;urZRjD?Vh@6u|n+d
z`aCoKUJ4T<H6~4ujiB*rpul9C4kTUveY=iQ1AgR5O>HA-!M)NgwGA!$AZ}<kkV(Bl
z=4CWI?c&IY6EgRf7t~0APm6!gyH-nZI)8HOfebtNV?++<sdz=4x+d3F`%Mg2o9jJk
z@)Lxrk`g7YTV`-)^(ptZCS4HyrIzt0kP~_ty(&95^M&5+#f*lh5y+1=C8wO|BMMHg
zsN5*lhhWu(n<Fy1AoRpvQqQ=L7&0fbQnX0}F&eBUEyWK7Pv7YtXI&$5cAR~}o+*jX
z$ZPEIF*SrDU{)2i)`c^H6*(U)IC1B3jfyw-Md8lVH~oaM{APP>7%vR~ylu$QA2YE8
zwht9teaQk4caSHK|F|5if2>|QE6NC$HM$1hXb8h__1@8|Gjh;-(JgMdkqL}@cA0s+
z`A+Z?aT+rBMd0)=zh6@g)DZvFyXSTdH>7ll=>6t=PcEwcR&!;ueLQW>E$^4pf+?nf
z@`fxD+$}4*5ST5Cy-o{xFMte4p9c%J6m>}PtfK^eZMdh$sBzZ^0B%)p%)aM^xe~72
zf};kIYm~iX%3J|HT^Ew^U8RC?dP!D&H64)59DMitu{3lYzJEBnR0j02`1M4ywc*IF
z7j?V`I3e&|qhG}tWjM$*vLDN+!uKa_j`6!FvBcR&_63{$(&O4K+=8ouV4I#d|FoY3
zzkIiwoK)b%gNg@fwD)L3;XJZ>m|_S*>nR_!H>hwU`N}@E&kC@zJpOT>TLT!uBxRaf
z4;0QUotR0H2J`6J$FF{|V4WWsTXg0%A%5zCpGO}XJUlrmINYfYqLC(BPF!P#!X%Hp
zIW=ae{!o_1Evx`{zP=v&C1U`tQt1jjO}yCSgWtNOoeunYp5q&H<{P=g;41Z_DJoo^
z-_0B$uK}IxN1LZa<)G;l``7^q63`A%2JYbDgIAwKZCT+pS$xNb%8ynOa6rm9h$_?&
zHkNn!Ot;biRhq=U<G)ETcT-!s{F*k%J>VvNAEAL)uPoAeuBgK4U8A2K?v;bq(+nIx
zH~DzbykWj(s1DS7RT+-dD#C?_u^z_HRN>K<>6hGJ6v1+<axq<k4lEZ^Q9E0+!M@bz
zmAP9qVLF;xx-p3tW_b&y?>|?DHtP#&Mybu@8Ww8xSIbIJ-h9|RwvhzP)eoFo0_5P~
zQPv(RHwvJQzTL`wOa@}nH&tFHX$aaTt2y^h15VnpoUbpC0WHsf_~K2z&YAZ|jdrR5
zdwD5~1-A@TN%q(6oKu4caRy!693?naF2~k<O9h0)QAF9<O+J<Kd#keP;ryvS+5`?c
zsNY$fDFQ-ptwZWlladTDtq2;}UFHMCckO#wqcjZfziNJYt0Xw9ORztAs{pMsz3(Cf
zHGrD=T<CETQOKiy!NwR&g3!_<mq1Vvo+{V$#VRO){jUYn7c9zP*2XMBt0WCeB!T=N
zH)-Lh&rsn_f*;5<Z!);=$bjj@((*)&G+ce_+~|8p0=9$=OQs5NLHnJqyEZZc@cwt_
zAum&PkaRmi7*Z<2aBBCH(K1EwtBbG<SmlD-p1W!pE961&nckbe?}`v~U^te}MG|`T
zp41%bQ3SfKv>q0A7FZLLJ{RCB0bGv+Nxr|OfLi}mw!v#@V3QkEjNfb*+tBx)Z(NcC
zzPRR<nMQdqz5FpMH$wv8C;eHZCIWH3w2nXXWZ-$3UVTT19Hhx!V_C432JYN+6%ST=
zaF!0ywAGOVZYJSmfpB3c7<Mj#Y$@p62&GBml!Nqrzvj2DE5Q8V9gFds{2)!!y;D}3
z1dp~#OtdU9K={M1(;CInV6kfKY%N9tQ^PUA=k}ZZQPEU2Z!9ysQKsr+a+QQTJVzWq
zKaqn9d;8}!4+Ozv|E`=^eRar@>fzb=$^!3#NEJJ$g<-_+{BmQf0^FFjy7^gG8eUXq
zS=VJSz&9zj=X1%@U{Ldn+aQ@A6iys`JAOzQEZ!Cd7SGYcjN(tVOUEVPaZB~|`(v_T
zc_7cof<+0=%6@;SC@2Hzu6w((gf`F5Bgu!nKEklZd^}I1L<-_Fxu2CXOF_c%(_2d<
zMPS5QJmZ))55%UIajJghhT9`fomV!`Kgrm~MJ=5K7Gm1UAs<PwXt^Gnvq*yGV}~ed
zd!*sKLhYK9jTA&gDaz6r$ihnt{T_}~E)aEUbC|CqLHWW0?Ey;3&4=bLYr~u40ZG4v
zm>-YC;aGvfmtRei5Ll*M+QK9Ovf20JWaLR8uH1VrH(L_&3<kWo9VB6Z94cr3O&)sI
zMcW(gCBV+IOE*DF9Da(3{;1KBfZJ}D$-bs?V1C+T^tH4Yu$r9?D0v|bsgm|@`6PMZ
zIVo}R$WJOz=<JzTp%#Jbv;v>@<?+DUcv6#en<&T<vx!v#B+%R$oBCyr1a(vV73WWg
zLUq(t1uI$+$T_Vc_A^ximSaz8QHqJd$HdQ){!JuMcRe9a))s;XVPlcCwIsOn;B&l%
zrv$W5#+lzqCBe0{w`vE(H?Py9bK+k2c;Qqq_eTazDR}s2^|*z)3@Cdfl=f6{gDg)P
zmA8=yY<IKCWj5gfg_WD+?=`IO`IGqf4n1+u)wNzY&MyH1XFTU#4@p8~<{l`mV26V@
z)8Ec+w&#N&HPy35!l3QVwm-O96qYq3+&Oo1!R0zcSL7}M8vDi4&wk|sU3Y6K_ZDHe
zIux%beSsU29{%Djyuc4iJI3`Cmjz(kn}PU;`rMFzMx6-Kl>{*jpQ5>EEO5y;Bhun4
zFErj}{zYpo2`{(~?f*!{2j`D`{dmoq1J3VN>Sy003@0gu+fNOVfROqWovc8DFm3NP
zM=M_7`@LXCq7Va(K~L(PYQm5{xc*j_fg4=oOz3?XmWbTCmj?{gXyLvX1$V?|`ydhG
z!t|UFoU7b1%8?=t&2$B?Jy*HGPW_QNLkS!7(w&*vP0I!v=!5jU<QgI25G_S4af34y
z@UU46!1$iW6P~eLuw`G<8I5BM5QX1sP&!h>_|^P<YcAZ-*b%SQvdRIw`~Az3uhYQH
ztBmPbUuIC-dDTrviyP8kn!HQrV}aKW-xc3#@B{ri^*i0qY~Xiq8)w-GMu>cz=Jch4
z8;0mD<n>%w;W~Ns;m27fcoP)JIeuxL*!iBXfABjsocl2Ms*Iin66F+c?j-2pXAIxR
zYjg|{AYAmi){_m^POnIOlB0zi?-vsf*^QCCD@WTeEi4eD?WqFSROukfIeg1h0xg(W
z3;)hr9VCWfzPinj3MMos_f*QQ66CC?NiiM{06`H!6Tx4^Vn?qVZ5Aa+{-jwJ6rCn^
z-0O_#F?dZrEnjtTZ$%|B%DdB%HR3ZdTgBtJt7(cj%sVeBdUJ}rqr&3UE89haxjVx7
z?!r9znvJS1y<0!oRek#h|I7iRx@Ij%%%`7VJkKimET)%un$F`DQ}d0mJ)KaTzdS&m
znvlx9)UZh2=NZbrG(SUJ*tpQKdzb=;?0s(h@y9s9W|2`>V*HjYT|n)1<J}NB|62`_
zy!R71jm|9A>+LA9_HYkV$APnCJApGUS?z=53EBzDrle8w7OC8ndd>~9r0nj{9m2Gj
z#=x~X&tZ(9QfxE!%45V8<kW8-k_=e*up}lPQDRe}a?&orULx!Kr#;sX(co4cmax)R
zN}P73efCp3Gv+wzWH1}gjCnqIEDTrB;$i3SIjimK<ZN#%=3xpZ{OKKS%BkD5xW2Zw
z`nE44{?pZ>a&RjL7O4|83Xs?!+wREEsr|`;{Y0p)S1EAdquEP&-3%P~K|BX5%L#7$
zj8SyS{}>DA6tR)C^`^m}i5nJqI!yREJR7vg<iaQ2e~%SjV#6jkLQg6fGUM&In0II!
z7nbRblO8_9k543T8xi!U#lq`6{u~|xIFG-lw_-0RuKuP+Az8_Xy_Te6zvT*I*P|0f
zT`}BPr}^0VGzVeqbE%ng<)Ikf+B~(3Vh2CoN_mXilq8C6xB2wDT;s+0HqWeNN(3+`
zS*`WYngqW5Q<C50njqdB;fO^QN@DY6306whO-b6(hekqA0&_{Rp5R><!Dl38GUimJ
zu!`MIuQCoP9Qj0|gz=d;wxJE6L;VtXjCkzfQ!b9{zKH6-oh0E3<$zbb=VkD+C&!o5
zWGOr(uEf~pMZ&YP%Xe~yq_Nqzs)Z5{d3+^Lmr6}n1}E66wWa$i;iqb%=UR)D@N2cQ
z4qatA+@3{~J363&`)}Q&E4Na@^lwt%tt~6!W0}Eh`pW89{A`9&n}`PfIsfjUzn%_G
z@7teP`9>Xk4v$zjfEI2$N>@7nMH{zW>*DLu(8ir~!tsXw2Ds%Tv+{Q#J<JzOlhFIl
z82eS{(Yn9Z#vj%tZmabgV5NGeB$H@$e8@V<U$9ySS3QgJ{1UB)KfP8QnE0-OKNVhL
zm_DG2S#3vM>Kk?O@}H3cl{t0%&v)PAZV}{vif>W=|BJ=9-|RZw$&#TEbV5$s8@XXs
zeVZgB215`OlPQ~%*a}fpe|n|(w&1O8{h|F%?(mZ>L-X}ZH#qv9M@?>a2RIDsrV!0e
zxNNjAVM{9lzY|{hi+Y+^T9|5SGTQ~t8f^JguFr+Lo_lW3qjkah{*Hl~0}c?HJKXf7
z#t8Fse;T+IjPP*j*~fn1j00$bF3$06!OeF^f4m`Y#VI?>9e2+QVUeS`Ia4nXj92f!
z^~2W}G;=%}Wex>lq<UD@`;0S4)5V)ix$%Ph@ly8vKCE~~(Ls&i@&tC@c$157#>v0K
ztY(Gy5xyNQQ{45~6M_iEXA~k<P`)-XKsT}}-Z`k2ZS#=B9_mw-x4IobB+cQBb?SD^
z)jY^nN_gVn!|x-)we6tkTJ)~Ky-kFAgTY90kq}OnBK>hkHh6BbywN0biM*6nI&=5C
zG;H(feW<ZvfO~MUOHqRcRuL^Xm9o{qbw&0+CbF$?6&D!Xe(Qm+-_>fk-))I!Z2AIQ
zw@AXAM(x<g*IPi0-sVt!9V-_8^j>QFA9JYxH8FD#8$kW8s}-pnmiR?mU!;Ge0c6r8
zofG1dgsr77LVUWk;LmF5qq<3_&B?MqSt$$Q1NRj~l&z$2uD1HwSKFOHSWoL)b6x<b
zHO1Xbe*>7l4+^<jT=2egB*7RhQ;@rmd^(-R3;0Wf<t7TY!g!=^Kb^cfHgRwduO2tS
z*UvBRu>Rr#(^kR4yILgiSN-5!PQ1L})oY+|CsGDa|41xYl2ymFSNT78VLNEps?eL;
zqy;($Z1o>!>R<-1ZkmiffVq>>)t6tH;jOARWlH{bcyD1_#M?a^WFh<Cefd%15c5oQ
zA}7HHa|zt){INv~Tz1S0JIDau9dfWN`HVb7(s7qoTt@guwcE0Ri3O;O6p;3c8ADQr
z&y%cPE~tLAlX{=86lSic<!Nit#A`M(ACfQ5kq>>m_B*=53JX8A&%A50DQ<qfYPJ8Q
zF}%I~FeC4zI>@xI_%=#-VcnB0CK}IW;D<6}dB+-pG3G>Xj|EeFRMF?qdtL(=Ub#3C
zdQ%wqKR7wq6dFS8%``sREDxM`qj<O2Idx36holhA$qCz(746#P^s&aboU40%%`ls*
z^!Xo*+HfeD`^%pn9N4;dMz(E}&j@$P%K>jIFsA7t{6_3>MXZ1JqKOsOm|Q3{Icx;s
z3TBkkc?c-`#ta_uap31|BFPq2)>x+BXQ=g=2!ywN%sjNQL|%>PTJolvCv#hUCQkiO
z#9Xn1bkrW|@O5eVOQE<Sp5&%7F>+D`s`0xh@3jSNa4lU~Y-EEG!}_K(vIJ11n7*3i
zb-?7p>xXm{jPQ=)?H(uF)uH3pIof(VbNs0F^OFrdX;|dzOiP_Gfd;k9sef9maO4?}
zidF>+P}@4Uv30vJJ|cTb=+0+d2uO~SJ11cVS|1IapIb}fl^|M+-*x=hX(d&@&RZSy
zG|c2<V$84t-`xzhJ}vm>viQ8GZ-Wr-4z#r>*2ODlnTu+lNkR-0h0*~AEvVQ>L#DnW
z2A0JyL+bV@f;emAahfU(IKrIMEdCtf(o?Ek)CaZk{m*j9d0rL_xsEZv$yLIP84O8D
z{tO^!b)VmR^Jd|`(;z-YOd9T`ZBq{2oWyZyR%T3Bm;rK~HPBjA!HaJDJ6o<UlP_#|
zuP3<)LFbk0liY{b2<zI+D5YaV<f6~anOh!kLjB#qCD#j5P{|@?@W5Ul-dZ)C@E}O|
zoY%eBRc}6c^s&I@&MF^tU3&IKq<fI;`NQgd(Pv4x5Jb`BDmOzkj;-7AZ}P@`p04v5
zd~oDv<TX8k&6{C`uH(A^8ep^;ZpvPigVo%FWBYgNV$?ftT2rryC7;~gJ6EoVcRA<u
zUo}z(k@6jm_2E3Yi?NO3dY&2-@JTY==2yl+N@JR{@ftu=?vuVgHcV_+U{32>q{Abv
ziS&QA@BlAa;xJzy7dF2+tgSw;59G6b*+L4k&}K6~V_P8%;?NdtPHPGZC)a-24gDtb
zKO5kT@e_g({nBfpn-4RnjC&MsC2k(i%G925Lmj+YzTR&gen74rJC#S9O$i@aWgN_x
z7YP5H;~iX^Lyn$P-X~7=$>Yv>DqzG47#8)H?JYNm@JF}Y6t7TYwXvDd%PYosV*{V8
z)1d>Gwy-~%UuEGdnJMnh#024`scaR+MvGI2Usd%vielEUqGm~CSs1J|2_M=e1h(d8
zXA@HviH&aRET4UPc<qqnn<E*N5Pn}Rd%utdgiOtM(+?}*m17kRtJ|u`#=)XtN;ieE
zuEMRrEA#w#!<|O6=7&Buc>I=8S9qFCS9E&I=y5*$G0&u)?G_KF|8|Z=eQA)KY?(_+
zeXI(-rn{3{`xxQUrE=Pn+a++xMYZ7X`*^XWWrmQ~7z3>Q$@h=iY;R^+ze8giESPDR
z39;>h0%Rz=6czLcVXDQNFQfWBL{w6l$IfgWsAKQe(<o7Z!MSeJ)o*vm-mH^Nc0$vH
zp_X;St$a10jUcF1k4r(<VCM~^9DXR9@DlpvMS@lGM66@h0GYMGT{QLy2fp(2cAWhN
z4V2C#DLD6WKuCC|h6D3FS?*)dzLgkG(6xD-dF`?QK4UzPl1jsfU!|U2Ry5(lS59#c
z_Pr5>?ZU>{$86`wWqwRQ)Xmgz-@uwTg`G5BU*8{4XDf#Tca}<9P$<CpC)Sp)4$cvS
z4@=%1)S$u+$8`_!QLhnD&SrN`oDoxpvBqf=n_^qcwxTO)oOn`wr(h~GC;SY4bytLn
z0#i9Y^}JE301Tz<WT%8VvWTSlEg7K|@_7N8hP<y*xR-K=jF_PS7Cm*h@LY>1j!cx!
zm$#$FzZ~2?@x53kjBI)bCm2hK$C0$ow8=Dh;yLSCzL80y<C$cmoE0B*Z&M82X3dF@
zv}8K+SqkFU^E(u%BY8lkNicKcD<7T+>2@XA{wBvr3NkQZ8QAS^fBt&?9ATlDS~Xg;
zPA2WQq*SpI#2b@graKn6@n;6gEZO`iqH{g2*T#SmJ{<GSu(<G@aG|`s^yC;dl#bOt
zF=(KGd+G5!y}Gnm;fyb5oXH|_Z0qISH|&OpiYR@HhkX*8;(Ts$g8>Ov0y<KBw+<5C
z2IoG!FqMF9kzoliCjsS@Px%v~X+ZKAzekAY1hJ9$qv`nPb+T6Ext>$Em_TdypsYy`
zC$@NOewc|~7$<Z)b$X5Q;H91`)>e(1lbC`5bMm?zev4akUUIPFbEWk~?|Q!w0rz*&
zOWv1;m=M*WRE=e_c*d76!mnnC?g!MK?A-jYe}2}*gGmHC<yqDAEi92y_Y>&%5Wx>t
z%=#K<#NkXc-M&lDC2^H`&5o&50VsayxFK+%kKlP#Y0k~f0i@G8$|lQi$$}O&`3HkX
z$rc7KYQ@2W<ePg~j`XhAlLh4G7+jlZz(VF$(9viTNO+bMpL8B3sP6=qO)b-bPJM$s
z1L+-s_oX!&E3@F0D2{`ZizGZCnYy3)3<XxF{q?0qQ5<{{tZ8m9PLb#G20wj2u|Ph4
zWJ)k0w2Mfg+q<B2k_+bM^>hbVNO0&$NBeO1BH8tMVD+)K0ph^Lxn?I9S{!_<{!S%5
zH5`~6x+_k<O8mh_DoLsB<U3X44gNYbP`2Gkp3>+N*^}+Mc3apF@;h2V^B`wBtXbct
z&h?H0%Qb(AY<$EIV=w)jWnI{?@{h{k`fV)W#oQ(|@nMZz@Q6Z_|K})K`xlK)9<u-@
zeLK$eEpM7E;=#b1;4X-}a!0iFpZq3&DCs==LVz7cNw>Z{Z~RV{F6HR?d1``iFSnE1
z#zF~mmpuR6d^tj1Q=9iad0zkvg&eQ@EWiTivfsHWhW#YBP_R_p`0$vV%{#K2xrPBh
zKC~DIbu5^DqVz|)Og&)_nc3egC}EKElz?a;75wN<?#Q`91?F|lBBt&P_`a&a_vhke
zgujf&nbs;=?EgA-&a-WmaNX8tAA4<{9A1~i<RvsuZada0!cjCwJa5SLxGVdII9|yi
zJ}EIxzEB)!((|1WSiWngY&|zd45|*uUcXmQ-Wpt#@ZSG3`B<v%#ttV&EO>r_;=$Kf
zL{#|wu5PD0g#KegMP@#F9Orm$kHf{=<hj8d`my)#$Ok%F-1!nV$Zb^x_iSd_@$iwh
zoZ+KOWd3te4Z)QQghbWb5`E<fB6WOtT`_5bEEj2NwpWlB^X-2A{Q{mO4}5UBxfV~2
zKlDUxWcD$Ee22kaW?c?A`8#!YnCcYyrncmzPNsRX{BV)PXzUVM+DC~x{P8T2*c)}<
zN3D<GcB(X?V5Y)F_m{jy+^BHvxk7k2{ERqx^y*pNW;&epuH%>J+jXKvdisyvyLIAV
zcVA^r;ym$srx<&CCN1V>*EYC(V6*%qQJiTO6yViI&Bzl*fk`z14~r72$c%&isH}U1
z>}raiu}syHpG-Hpwd4&DE!)<YI)au6QfjQTW!V=ZvwDHqbLS#CuK-j`%qPfkfj525
zDG!pfy8Jm$i}Vqng}Gp|af&z%ArHO^{~|<PPsq7UFOnS>gXuLZ8p%O9J*V{NzY&WO
zCBBKrUJ$C@ENOY~8Q}N&U7G71<HQpmQ6aO<e)evUMje&=H?qcJXwz=F`{ao$rz1H7
zmdNUz25d9g1H=^*k4k<!Dty`8LNA!Tm^jB_5pqPE5*&G6D$*V+CGYA{V=>z7x9|0d
z8HE@P5U!1xZ`Y)j2-iFHI?s0eAm3$3(^dZbjy$~WP9Im#3nIo-`M!z7JRz@WZ<A0t
zO*FZgpKUL_MLwAQtK!Ao5hB8TJ@3YYDe_N&!Bc%f%j7-#g1(FlcN6hCbleTS-^kC<
zPr90nQSvGq9iM#2Z-UxJm;S541ewcrl}T>%;aVzRZqDzrMMCcW)~^-=>%^yfk_=6}
zKghqX{bHf*o+aPfT|(fqG_a8$zwWbEOQ?(*i$>6l6Q%Z_9M|{$B(jQ*jvtO5Ad0?U
zdzK+X1K(fjxxG<+PE<AFX=wv$EOB?{UWOkf)T>`8ea|sVWP9rWbm*Ta?|tS$b-M2-
zd7)SC_=iIq#K2LS2g`b^L~lekM_PL~A<{NYeC)3zo<~1e=;j<C>YL{c)elUQ1x|P9
zD|pNj`9B_q_xp_z`)a2u=q|q_S5*q8U0fI-1dI+}f3ztoa&5WjacpxGB=KZ7shxeD
zP`nprFk$|UT&{#|dApa$&EDHN1Wxu4EMZiOE7UWDcFW#p)V1~G;!BwnE4P-(F?3$)
z5lpQ_{1{KuRn|G;KGss_d)Y%?YSk*sGFl+QXUO~4d_ECdJktd2C1=V0^nR7R7wO>&
z&JtC5{+{p`QCaRhR6)MPG-g*UH%ESalOAWcERcQfIqdu_wnz}aO8YxbQNh8|<2vIe
zzsMrW)rW^BTL_h?yTS^6-w3qUZ4?^*l?dLo<$$aDIGIoWP3K#~yX5AS#BAlnIij+;
zsK-@qmJB1g7s{g!5rZ`4(zU!xL{m(C2YbypAu!T#a<S(%p?|)Lpyul&zvg?y7Ad_<
z?6i=yM;<?keotZd$G)><^pM-8iRlelB+$h<cWHvCShu1aJhetR-XS)I_bd_7f#Myl
zrt8GZ?eB?L)}O>8Q-Fww%LqZ82l|H^#t37=-BelO1#z$BrnV>f9g%wVihr+57jY5w
znY%MELhopWjn&!!AwhjuIX1S7sC@C6M>_XAq56ye>8SoN5$|#nUtF0a3;TK`zu3A?
zl=1HovkjUc4zqkN*!Q}Q{M`RXV*WTI&{&C77Y<RvJ+`eo52#ENNlj1qBRMGH4Zne&
zxfv}ae_e5mcV~e!)KbqK{;Uu>4#Hu-`BuoYPe=Gqm#z}LN$u$r6TA>|@71Tv>*GX0
zeCwk0^cRA?HR&+v90h#GM=tA&(843xF87=KGlclqRLR9^M!0?RHd$Ad19C4*Jj-OE
z2leT+J?S5NiCLHS2}OB6(0+f2V@!(;D7!LRlW1As*e6-$t3zCy&cLMahlMyH%W=iH
zwuu3n6MYRIyx|7Z8Raf*xjy3B%{@tbOe7$4#^_|%4HAIPlM91x!khE_<6fqF1t7V0
zig*5k8q7R;cPnRI5eWEnCHsOBq$Nbw9_Ua3(T*`g{T3}~AA8R>?4||5PB*G9a%lkQ
zwHU5#j@wIZ#JkN}H6cvj`p^=u4h*S;)$41j|A*0b{a}Cm-{Kp?U&hm@i0Hp9)F=LJ
zq5eDmk36UUQv)@L#6<mH^-=wgclV0u>L34;LEygstqlH)z#-R729#7P)c-WJ|7BGF
zYvuT_gZ}k(i1M%apZ50uME_+D{~OKr5A^Kj)<3P{|B3y}X#L+~zipPiFXI2TUH>Qc
zFVpg0e&oMa)_;`SN%eQF|1ISHC-kouF8_uy{{!mE_pd7de}I39#s599P2pel{r>>}
zO7y=H`LC7XA0^tGY*zN)Y5$+~{42TsdlW127wTWB=3-Av_t#%CY<}7|Ph0!uZxj^&
E2NTeo6#xJL

diff --git a/src/sp_validation/tests/test_b_modes.py b/src/sp_validation/tests/test_b_modes.py
index 20a9aa67..2ed8a341 100644
--- a/src/sp_validation/tests/test_b_modes.py
+++ b/src/sp_validation/tests/test_b_modes.py
@@ -2,8 +2,8 @@
 
 This module pins the numeric behavior of the pure E/B-mode helpers in
 ``sp_validation.b_modes`` against fixed, deterministic inputs (seeded RNG,
-hand-built arrays and one committed ξ± fixture — no cluster data, no catalogue
-files).
+hand-built arrays and one committed fine-grid ξ± fixture — no cluster data, no
+catalogue files).
 Every pinned literal was produced by an actual run of the estimator inside
 the container; a future refactor that changes the numbers must fail.
 
@@ -49,9 +49,8 @@ def _grid_gg():
 def _eb_inputs():
     """Fixed seeded input for calculate_eb_statistics.
 
-    nbins=4, npatch=50 so the Hartlap factor (n_eff - nbins_eff - 2)/(n_eff-1)
-    is well-defined and strictly positive for every scale-cut combination.
-    n_eff is the jackknife patch count, as it is for a jackknife covariance.
+    nbins=4, npatch=50 so the Hartlap factor (npatch - p - 2)/(npatch - 1) is
+    well-defined and strictly positive for every scale-cut combination.
     The covariance is built SPD via A @ A.T + I; the B-mode vectors are O(1)
     so the chi-squared (and hence PTE) lands in a meaningful range rather than
     being saturated at 1.0.
@@ -64,7 +63,7 @@ def _eb_inputs():
     xim_B = rng.standard_normal(nbins)
     return {
         "theta": np.geomspace(1.0, 100.0, nbins),
-        "n_eff": npatch,
+        "npatch": npatch,
         "cov": cov,
         "xip_B": xip_B,
         "xim_B": xim_B,
@@ -236,8 +235,9 @@ def test_calculate_eb_statistics_pte_matrices():
     """Pin representative PTE-matrix entries from the full 2D E/B analysis.
 
     Inputs are fixed (seed 12345, nbins=4, npatch=50, SPD cov = A@A.T + I,
-    O(1) B-mode vectors). The Hartlap correction uses n_eff = 50, the patch
-    count behind a jackknife covariance. For each of xip_B, xim_B and combined we pin the
+    O(1) B-mode vectors). The Hartlap correction uses npatch = 50 over the
+    length of the inverted vector (2x for combined). For each of xip_B, xim_B
+    and combined we pin the
     full-range entry [0, nbins-1] (start=0, stop=nbins) and an interior entry
     [0, 2] (start=0, stop=3). These chi2->sf PTE values are deterministic
     functions of the seeded input.
@@ -253,12 +253,12 @@ def test_calculate_eb_statistics_pte_matrices():
     # Full-range entries [0, nbins-1].
     npt.assert_allclose(pm["xip_B"][0, nbins - 1], 0.9985059590347458, rtol=1e-9)
     npt.assert_allclose(pm["xim_B"][0, nbins - 1], 0.9979174764123961, rtol=1e-9)
-    npt.assert_allclose(pm["combined"][0, nbins - 1], 0.9999930883595443, rtol=1e-9)
+    npt.assert_allclose(pm["combined"][0, nbins - 1], 0.9999952393605003, rtol=1e-9)
 
     # Interior entries [0, 2] (start_bin=0, stop_bin=3).
     npt.assert_allclose(pm["xip_B"][0, 2], 0.9991074524059739, rtol=1e-9)
     npt.assert_allclose(pm["xim_B"][0, 2], 0.9896253892931961, rtol=1e-9)
-    npt.assert_allclose(pm["combined"][0, 2], 0.9999497648089674, rtol=1e-9)
+    npt.assert_allclose(pm["combined"][0, 2], 0.9999590178816327, rtol=1e-9)
 
     # Structural pins: off the valid upper triangle the matrices are NaN.
     for key in ("xip_B", "xim_B", "combined"):
@@ -268,6 +268,28 @@ def test_calculate_eb_statistics_pte_matrices():
         assert np.all(np.isfinite(np.diag(m)))  # single-bin cuts are valid
 
 
+def test_calculate_eb_statistics_analytic_covariance_skips_hartlap():
+    """An analytic covariance (npatch None) gives the plain χ² PTE.
+
+    The full-range χ² is data·C⁻¹·data with no factor, so its PTE is pinned
+    directly against scipy; the jackknife PTE on the same input differs.
+    """
+    from scipy import stats
+
+    results, nbins = _eb_inputs()
+    results["npatch"] = None
+    pm = b_modes.calculate_eb_statistics(results)["pte_matrices"]
+
+    cov_B = results["cov_xip_B"]
+    chi2 = results["xip_B"] @ np.linalg.solve(cov_B, results["xip_B"])
+    npt.assert_allclose(pm["xip_B"][0, nbins - 1], stats.chi2.sf(chi2, nbins))
+    npt.assert_allclose(pm["combined"][0, nbins - 1], 0.9999894806723678, rtol=1e-9)
+
+    jackknife, _ = _eb_inputs()
+    pm_jk = b_modes.calculate_eb_statistics(jackknife)["pte_matrices"]
+    assert pm_jk["xip_B"][0, nbins - 1] != pm["xip_B"][0, nbins - 1]
+
+
 def test_calculate_eb_statistics_has_teeth():
     """Teeth for #4: a 10x-louder B-mode signal must drop the full-range PTE.
 
@@ -295,82 +317,284 @@ def test_calculate_eb_statistics_has_teeth():
 
 
 # ---------------------------------------------------------------------------
-# 5. pure_eb_from_xi on committed ξ± (the transform pin)
+# 5. The pure-E/B operator on committed fine-grid ξ±
 # ---------------------------------------------------------------------------
 
-# pure_eb_from_xi(**fixture); regenerated only when the transform is meant to move.
+# calculate_pure_eb_correlation(**fixture) modes; regenerated only when the
+# estimator is meant to move.
 _PURE_EB_PINS = {
     "xip_E": [
-        -2.9831529669542025e-06,
-        -1.5008524620265777e-05,
-        3.221623968725757e-07,
-        1.1797672310858565e-05,
-        5.715510692557323e-06,
-        8.825804523824443e-07,
+        0.00012671119742922553,
+        0.00011647263203301798,
+        0.00011277680462627065,
+        0.00011334741455438306,
+        9.407280835053578e-05,
+        8.500501372536384e-05,
     ],
     "xim_E": [
-        -4.737558091773235e-05,
-        -0.00010853189443993388,
-        -9.094825175032069e-05,
-        -5.826599101284694e-05,
-        -4.646405415748759e-05,
-        -1.9978028925333273e-05,
+        7.684864690558884e-06,
+        -2.9296916553309855e-07,
+        1.1150412226648596e-06,
+        5.81495542664638e-06,
+        -5.8802091546090845e-06,
+        5.245220040191294e-06,
     ],
     "xip_B": [
-        1.7069121242262332e-05,
-        3.059889782373755e-05,
-        -4.8805399253844115e-06,
-        -6.999262696335271e-06,
-        -1.2672006989728095e-05,
-        -1.214149138979614e-06,
+        -5.872804581564311e-05,
+        -5.3898600777518044e-05,
+        -7.01611878338831e-05,
+        -6.447148201049941e-05,
+        -6.422794082425538e-05,
+        -5.7688865883825626e-05,
     ],
     "xim_B": [
-        -0.00011478091634539627,
-        -5.445112002141066e-05,
-        -3.100806652947907e-05,
-        -1.0940424256759085e-05,
-        -5.755185146643215e-06,
-        -1.628217762504557e-06,
+        -1.2625808436389906e-05,
+        8.906960918149856e-07,
+        3.6497760325039334e-06,
+        9.0173210532702e-06,
+        5.367045395488404e-06,
+        4.3985417696428825e-06,
     ],
     "xip_amb": [
-        0.00014017621792612224,
-        0.0001378482153667787,
-        0.0001339573019551001,
-        0.00012745126271361765,
-        0.00011662385105911986,
-        9.851844704443032e-05,
+        8.975404658793264e-05,
+        8.870470395597095e-05,
+        8.695112377370022e-05,
+        8.401987012227805e-05,
+        7.914207248993659e-05,
+        7.098386083077631e-05,
     ],
     "xim_amb": [
-        -4.389203999135455e-05,
-        5.279277664928643e-05,
-        5.800339397836051e-05,
-        4.4242350610114584e-05,
-        2.9902912946755567e-05,
-        1.912262132836568e-05,
+        1.5068975296876175e-06,
+        9.035986620575128e-07,
+        5.414736226368331e-07,
+        3.2422141650144327e-07,
+        1.9445163845662097e-07,
+        1.1646044607564388e-07,
     ],
 }
 
 
-def test_pure_eb_from_xi_reproduces_pins_on_committed_xi(pure_eb_xi):
-    """The pure-E/B transform of the committed ξ± reproduces its pins.
+def _spd(n, seed):
+    """A seeded SPD matrix standing in for a ξ± covariance."""
+    A = np.random.default_rng(seed).standard_normal((n, n))
+    return A @ A.T / n + np.eye(n)
+
 
-    With ξ± frozen, these pins move only when the transform does. rtol=1e-6 is
-    far above the 1e-12 reduction-order noise across thread counts.
+def test_pure_eb_reproduces_pins_on_committed_xi(pure_eb_xi):
+    """The pure-E/B estimator on the committed ξ± reproduces its pins.
+
+    With ξ± frozen, these pins move only when the estimator does. The operator
+    is deterministic linear algebra, so rtol=1e-8 leaves room only for BLAS
+    summation order.
     """
-    modes = b_modes.pure_eb_from_xi(**pure_eb_xi)
+    n_fine = len(pure_eb_xi["theta_int"])
+    results = b_modes.calculate_pure_eb_correlation(
+        **pure_eb_xi, cov_xi=np.eye(2 * n_fine)
+    )
     for key in b_modes._EB_KEYS:
-        npt.assert_allclose(modes[key], _PURE_EB_PINS[key], rtol=1e-6, err_msg=key)
+        npt.assert_allclose(results[key], _PURE_EB_PINS[key], rtol=1e-8, err_msg=key)
 
-    # Teeth: widening the integration interval by 1% leaves the pins.
-    moved = b_modes.pure_eb_from_xi(**{**pure_eb_xi, "tmax": 1.01 * pure_eb_xi["tmax"]})
+    # Teeth: uniform rather than pair weights leave the pins.
+    moved = b_modes.calculate_pure_eb_correlation(
+        **{**pure_eb_xi, "weight_int": np.ones(n_fine)}, cov_xi=np.eye(2 * n_fine)
+    )
     assert not np.allclose(moved["xip_E"], _PURE_EB_PINS["xip_E"], rtol=1e-6, atol=0)
 
 
+def test_pure_eb_modes_sum_to_the_averaged_xi(pure_eb_xi):
+    """ξ± = E ± B + amb holds in every reporting bin.
+
+    It holds at each fine node by construction of the decomposition, and the
+    modes and the reported ξ± are the same pair-weighted average of those nodes.
+    """
+    n_fine = len(pure_eb_xi["theta_int"])
+    r = b_modes.calculate_pure_eb_correlation(**pure_eb_xi, cov_xi=np.eye(2 * n_fine))
+    scale = np.abs(r["xip"]).max()
+    npt.assert_allclose(
+        r["xip"], r["xip_E"] + r["xip_B"] + r["xip_amb"], rtol=0, atol=1e-12 * scale
+    )
+    npt.assert_allclose(
+        r["xim"], r["xim_E"] - r["xim_B"] + r["xim_amb"], rtol=0, atol=1e-12 * scale
+    )
+
+
+def test_pure_eb_covariance_is_the_operator_sandwich(pure_eb_xi):
+    """``cov`` is K C Kᵀ for the supplied ξ± covariance, and records npatch.
+
+    The reported ξ± variances are the same pair-weighted average pushed
+    through the ξ+ and ξ− blocks of C, and the reported edges are the snapped
+    ones the operator used.
+    """
+    n_fine = len(pure_eb_xi["theta_int"])
+    cov_xi = _spd(2 * n_fine, seed=7)
+    r = b_modes.calculate_pure_eb_correlation(**pure_eb_xi, cov_xi=cov_xi, npatch=40)
+    K, P, edges = b_modes.pure_eb_operator(
+        pure_eb_xi["weight_int"],
+        pure_eb_xi["edges_int"],
+        pure_eb_xi["reporting_edges"],
+    )
+    npt.assert_allclose(r["cov"], K @ cov_xi @ K.T, rtol=1e-12)
+    npt.assert_allclose(r["cov"], r["cov"].T, rtol=1e-12)
+    npt.assert_allclose(r["var_xip"], np.diag(P @ cov_xi[:n_fine, :n_fine] @ P.T))
+    npt.assert_allclose(r["var_xim"], np.diag(P @ cov_xi[n_fine:, n_fine:] @ P.T))
+    npt.assert_array_equal(r["left_edges"], edges[:-1])
+    npt.assert_array_equal(r["right_edges"], edges[1:])
+    assert np.all(np.isin(edges, pure_eb_xi["edges_int"]))
+    assert r["npatch"] == 40
+
+    with pytest.raises(ValueError, match="npatch > 1"):
+        b_modes.calculate_pure_eb_correlation(**pure_eb_xi, cov_xi=cov_xi, npatch=1)
+
+
+def test_pure_eb_reporting_bins_are_unions_of_fine_bins():
+    """Requested edges snap to the nearest fine edge; rows pool whole fine bins.
+
+    Rows sum to one and weight their fine bins by the pair weight; a fine bin
+    with no weight carries none; equal weights give the plain mean. Edges that
+    snap together, edges outside the fine grid and empty bins raise.
+    """
+    edges_int = np.geomspace(1.0, 100.0, 41)
+    weight = np.arange(1.0, 41.0)
+    weight[20] = 0.0
+    requested = np.array([2.1, 6.0, 20.0, 49.0])
+    P, edges = b_modes._reporting_binning(weight, edges_int, requested)
+
+    snap = [np.argmin(np.abs(np.log(edges_int / e))) for e in requested]
+    npt.assert_array_equal(edges, edges_int[snap])
+    npt.assert_allclose(P.sum(axis=1), 1.0)
+    for row, (lo, hi) in enumerate(zip(snap[:-1], snap[1:])):
+        inside = np.zeros(40, dtype=bool)
+        inside[lo:hi] = True
+        npt.assert_allclose(P[row, inside], weight[inside] / weight[inside].sum())
+        assert np.all(P[row, ~inside] == 0)
+    assert np.all(P[:, 20] == 0)
+
+    flat, _ = b_modes._reporting_binning(np.ones(40), edges_int, requested)
+    npt.assert_allclose(flat[0, snap[0] : snap[1]], 1.0 / (snap[1] - snap[0]))
+
+    with pytest.raises(ValueError, match="same fine edge"):
+        b_modes._reporting_binning(weight, edges_int, [2.0, 2.05, 20.0])
+    with pytest.raises(ValueError, match="outside the fine grid"):
+        b_modes._reporting_binning(weight, edges_int, [2.0, 20.0, 200.0])
+    with pytest.raises(ValueError, match="hold no integration-grid pairs"):
+        b_modes._reporting_binning(np.zeros(40), edges_int, requested)
+
+
+def _weighted_catalogue(treecorr):
+    """A weighted flat shear catalogue, separations in arcmin."""
+    rng = np.random.default_rng(2024)
+    n_gal = 3000
+    x, y = rng.uniform(0.0, 300.0, (2, n_gal))
+    g1, g2 = 0.02 + 0.05 * rng.standard_normal((2, n_gal))
+    return treecorr.Catalog(x=x, y=y, g1=g1, g2=g2, w=rng.uniform(0.2, 1.0, n_gal))
+
+
+@pytest.mark.parametrize(
+    "requested",
+    [np.geomspace(15.0, 70.0, 7), np.geomspace(14.0, 73.0, 7)],
+    ids=["nested", "snapped"],
+)
+def test_pure_eb_binning_reproduces_the_reporting_measurement(requested):
+    """P on the fine grid gives TreeCorr's ξ± and meanr on the snapped bins.
+
+    TreeCorr's ξ± and meanr in a bin are averages over its pairs weighted by
+    ``w_i w_j``, and every reporting bin is a union of fine bins, so pooling
+    them with the fine ``weight`` is the same sum. A weighted catalogue with
+    exact binning makes that hold to round-off, whether or not the requested
+    edges fall on fine edges; weighting by ``npairs`` instead does not.
+    """
+    treecorr = pytest.importorskip("treecorr")
+    cat = _weighted_catalogue(treecorr)
+    exact = {"bin_slop": 0, "angle_slop": 0}
+
+    # Eight fine bins per [15, 70]′ / 6 reporting bin, plus four either side.
+    step = np.log(70.0 / 15.0) / 48
+    fine = treecorr.GGCorrelation(
+        min_sep=15.0 * np.exp(-4 * step),
+        max_sep=70.0 * np.exp(4 * step),
+        nbins=56,
+        **exact,
+    )
+    fine.process(cat)
+    edges_int = np.append(fine.left_edges, fine.right_edges[-1])
+
+    P, edges = b_modes._reporting_binning(fine.weight, edges_int, requested)
+    assert np.all(np.isin(edges, edges_int))
+    measured = []
+    for lo, hi in zip(edges[:-1], edges[1:]):
+        gg = treecorr.GGCorrelation(min_sep=lo, max_sep=hi, nbins=1, **exact)
+        gg.process(cat)
+        measured.append(gg)
+    for key in ("xip", "xim", "meanr"):
+        npt.assert_allclose(
+            P @ getattr(fine, key),
+            [getattr(gg, key)[0] for gg in measured],
+            rtol=1e-10,
+            err_msg=key,
+        )
+
+    by_npairs, _ = b_modes._reporting_binning(fine.npairs, edges_int, requested)
+    assert not np.allclose(
+        by_npairs @ fine.xip, [gg.xip[0] for gg in measured], rtol=1e-6, atol=0
+    )
+
+
+def test_pure_eb_transform_needs_a_log_uniform_grid():
+    """The transform runs on log-uniform nodes and refuses irregular edges.
+
+    cosmo_numba's interpolator places samples on a regular grid in log θ, so
+    edges that are not log-uniform would silently mis-place them.
+    """
+    edges = np.geomspace(1.0, 300.0, 61)
+    edges[30] *= 1.001
+    with pytest.raises(ValueError, match="log-uniform"):
+        b_modes._fixed_quadrature_operator(edges)
+
+
+def test_pure_eb_operator_refuses_a_reporting_floor_at_the_grid_edge(pure_eb_xi):
+    """Reporting bins that reach the fine grid's floor raise, never return NaN.
+
+    The ξ− integrals over [tmin, t] have fewer than interp_order + 1 nodes for
+    the first few fine nodes, so those operator rows are under-determined.
+    """
+    edges_int = pure_eb_xi["edges_int"]
+    with pytest.raises(ValueError, match="under-determined"):
+        b_modes.pure_eb_operator(
+            pure_eb_xi["weight_int"],
+            edges_int,
+            np.geomspace(edges_int[0], 70.0, 7),
+        )
+
+
 # ---------------------------------------------------------------------------
 # 6. Grid edges and the COSEBIs covariance seam
 # ---------------------------------------------------------------------------
 
 
+def test_scale_cuts_select_the_same_bins_on_every_fine_grid():
+    """A cut on nominal edges picks the same bins whatever grid they snapped to.
+
+    [1, 250]′ in 20 bins snapped onto the cosmo_val (0.08–300′) and Paper II
+    (0.5–300′) fine grids moves edge 9 (11.997′) to either side of 12′, so
+    exact containment would disagree; snapping the cut to the nearest edge
+    selects bins 9–15 for [12, 83]′ and all bins for [1, 250]′ on both.
+    """
+    requested = np.geomspace(1.0, 250.0, 21)
+    for lo in (0.08, 0.5):
+        fine = np.geomspace(lo, 300.0, 1001)
+        _, edges = b_modes._reporting_binning(np.ones(1000), fine, requested)
+        left, right = edges[:-1], edges[1:]
+        assert b_modes.bins_from_scale_cut(left, right, (12.0, 83.0)) == (9, 16)
+        assert b_modes.bins_from_scale_cut(left, right, (1.0, 250.0)) == (0, 20)
+        pte = np.arange(400.0).reshape(20, 20)
+        assert (
+            b_modes._get_pte_from_scale_cut(pte, (left, right), (12, 83)) == pte[9, 15]
+        )
+
+    with pytest.raises(RuntimeError, match="selects no bins"):
+        b_modes.bins_from_scale_cut(left, right, (12.0, 12.5))
+
+
 def test_log_bin_edges_matches_the_grid_stub():
     """Edges reconstructed from a binning are the ones TreeCorr would report.
 
@@ -477,13 +701,17 @@ def test_pure_eb_npz_carries_what_the_summary_reads(tmp_path):
     """The .npz keys cv_summarize_bmodes reads are the ones the writer emits.
 
     The two live in different rules, so the contract between them — the PTE
-    matrices under ``pte_matrices_{stat}`` and the realisation count under
-    ``n_eff`` — is pinned here rather than discovered on a cluster run.
+    matrices under ``pte_matrices_{stat}``, the reporting edges they are
+    indexed on and the jackknife patch count under ``npatch`` — is pinned here
+    rather than discovered on a cluster run.
     """
     results, nbins = _eb_inputs()
     results.update(
         {key: np.zeros(nbins) for key in b_modes._EB_KEYS if key not in results}
     )
+    results["left_edges"], results["right_edges"] = b_modes.log_bin_edges(
+        1.0, 100.0, nbins
+    )
     results = b_modes.calculate_eb_statistics(results)
 
     out = tmp_path / "pure_eb_data.npz"
@@ -493,64 +721,17 @@ def test_pure_eb_npz_carries_what_the_summary_reads(tmp_path):
     for stat in ("xip_B", "xim_B", "combined"):
         assert f"pte_matrices_{stat}" in saved
         assert saved[f"pte_matrices_{stat}"].shape == (nbins, nbins)
-    assert saved["n_eff"] == results["n_eff"]
+    assert saved["npatch"] == results["npatch"]
     npt.assert_allclose(saved["theta"], results["theta"])
     for key in b_modes._EB_KEYS:
         assert key in saved
 
     # The summary reads the fiducial cut out of those matrices through the same
-    # helper the plots use, so a valid cut must resolve to a finite PTE.
-    edges = b_modes.log_bin_edges(1.0, 100.0, nbins)
+    # helper the plots use, on the saved edges, so a valid cut must resolve to
+    # a finite PTE.
+    edges = (saved["left_edges"], saved["right_edges"])
+    npt.assert_array_equal(edges[0], results["left_edges"])
     pte = b_modes._get_pte_from_scale_cut(
         saved["pte_matrices_xip_B"], edges, (1.0, 100.0)
     )
     assert np.isfinite(pte)
-
-
-def test_pure_eb_covariance_mc_draws_around_the_theory_mean(monkeypatch):
-    """The MC draws centre on cs_util's theory ξ±, binned to the reporting grid.
-
-    ``get_theo_xi`` returns ``{pair: (ξ+, ξ−)}``; one n(z) is one pair. With a
-    zero covariance every draw is the theory mean, so a stub kernel that echoes
-    its reporting-grid ξ± pins the unpack, the [ξ+; ξ−] order and the binning.
-    """
-    nrep = 4
-    left, right = b_modes.log_bin_edges(2.0, 50.0, nrep)
-    theta = np.sqrt(left * right)
-    theta_int = np.geomspace(1.0, 100.0, 40)
-    xip_th, xim_th = theta_int.copy(), 2 * theta_int
-
-    monkeypatch.setattr(
-        b_modes, "get_theo_xi", lambda **kw: {"W0xW0": (xip_th, xim_th)}
-    )
-
-    def _echo(theta, theta_int, xip, xim, xip_int, xim_int, tmin, tmax, parallel):
-        zeros = np.zeros_like(xip)
-        return xip, xim, zeros, zeros, zeros, zeros
-
-    module = types.ModuleType("cosmo_numba.B_modes.schneider2022")
-    module.get_pure_EB_modes = _echo
-    monkeypatch.setitem(
-        __import__("sys").modules, "cosmo_numba.B_modes.schneider2022", module
-    )
-
-    cov, eb_samples = b_modes.pure_eb_covariance_mc(
-        theta=theta,
-        left_edges=left,
-        right_edges=right,
-        theta_int=theta_int,
-        cov_int=np.zeros((2 * len(theta_int), 2 * len(theta_int))),
-        z=np.linspace(0.01, 2.0, 50),
-        nz=np.ones(50),
-        cosmo=None,
-        n_samples=3,
-    )
-
-    inside = [(theta_int >= lo) & (theta_int < hi) for lo, hi in zip(left, right)]
-    expected_xip = np.array([xip_th[m].mean() for m in inside])
-    expected_xim = np.array([xim_th[m].mean() for m in inside])
-    assert eb_samples.shape == (3, 6 * nrep)
-    for draw in eb_samples:
-        npt.assert_allclose(draw[:nrep], expected_xip)
-        npt.assert_allclose(draw[nrep : 2 * nrep], expected_xim)
-    npt.assert_allclose(cov, 0.0, atol=1e-20)
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index 54aed4a5..c08c055e 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -581,17 +581,13 @@ def test_calculate_scale_dependent_leakage_runs_on_synthetic_catalog(
         assert hasattr(res, "C_sys_p") and hasattr(res, "C_sys_m")
 
     def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi):
-        """calculate_pure_eb carries ξ± through cosmo_numba's pure-E/B split.
+        """calculate_pure_eb measures the fine ξ± and pushes it through the operator.
 
-        The ξ± it measures equal the committed ``pure_eb_xi``, its modes are
-        ``pure_eb_from_xi`` of those ξ± and edges, and every reporting bin is
-        finite. ``test_b_modes`` pins the transform itself on the same ξ±, so a
-        failure names the step that moved: measurement, wiring or transform.
-
-        Finiteness: the Schneider (2022) integrals are near-singular where a
-        reporting bin meets the integration boundary, so the integration grid
-        [1, 300]′ brackets the reporting grid [15, 70]′ on both ends and is fine
-        (600 bins); about 80 integration bins NaN the edge bins.
+        The ξ± it measures equal the committed ``pure_eb_xi`` (``test_b_modes``
+        pins the operator on the same ξ±, so a failure names the step that
+        moved), and the jackknife covariance of the modes is the jackknife ξ±
+        covariance through the operator: for a linear estimator that equals
+        TreeCorr's per-patch jackknife of the modes themselves.
 
         ξ±: exact binning (bin_slop = angle_slop = 0) makes ξ± a plain pair sum,
         independent of the tree and so of the jackknife patches, whose k-means
@@ -599,6 +595,8 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi)
         """
         pytest.importorskip("treecorr")
         pytest.importorskip("cosmo_numba")
+        import treecorr
+
         from sp_validation import b_modes
 
         # Coherent shear -> smooth xi+/-, so the pure-E/B integral is well-posed.
@@ -627,29 +625,39 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi)
         )
 
         measured = {
-            "theta_report": results["theta"],
-            "xip_report": results["xip"],
-            "xim_report": results["xim"],
-            "theta_int": results["theta_int"],
-            "xip_int": results["xip_int"],
-            "xim_int": results["xim_int"],
-            "tmin": results["left_edges"][0],
-            "tmax": results["right_edges"][-1],
+            key: results[key]
+            for key in ("theta_int", "xip_int", "xim_int", "weight_int", "edges_int")
         }
+        measured["reporting_edges"] = np.geomspace(15.0, 70.0, nbins + 1)
         # Regenerate the fixture with np.savez(conftest.PURE_EB_XI, **measured).
         for key, value in measured.items():
             np.testing.assert_allclose(
                 value, pure_eb_xi[key], rtol=1e-10, atol=0, err_msg=key
             )
 
-        modes = b_modes.pure_eb_from_xi(**measured)
-        for key in b_modes._EB_KEYS:
+        operator, _, edges = b_modes.pure_eb_operator(
+            *(measured[k] for k in ("weight_int", "edges_int", "reporting_edges"))
+        )
+        np.testing.assert_array_equal(results["left_edges"], edges[:-1])
+        modes = operator @ np.concatenate([measured["xip_int"], measured["xim_int"]])
+        for i, key in enumerate(b_modes._EB_KEYS):
             vec = np.asarray(results[key])
             assert vec.shape == (nbins,)
             assert np.all(np.isfinite(vec)), f"{key} not finite"
-            np.testing.assert_allclose(vec, modes[key], rtol=1e-10, err_msg=key)
+            np.testing.assert_allclose(
+                vec, modes[i * nbins : (i + 1) * nbins], rtol=1e-12, err_msg=key
+            )
 
-        # Jackknife covariance over the 6 stats (xip/xim x E/B/amb) x nbins.
         cov = np.asarray(results["cov"])
         assert cov.shape == (6 * nbins, 6 * nbins)
-        assert results["n_eff"] == npatch
+        assert results["npatch"] == npatch
+        gg_int = cv.cat_ggs[version]
+        jackknife_of_modes = treecorr.estimate_multi_cov(
+            [gg_int],
+            "jackknife",
+            func=lambda corrs: operator @ np.concatenate([corrs[0].xip, corrs[0].xim]),
+            cross_patch_weight="match",
+        )
+        np.testing.assert_allclose(
+            cov, jackknife_of_modes, rtol=0, atol=1e-10 * np.abs(cov).max()
+        )
diff --git a/src/sp_validation/tests/test_sacc_io.py b/src/sp_validation/tests/test_sacc_io.py
index 8f8564ae..872a8198 100644
--- a/src/sp_validation/tests/test_sacc_io.py
+++ b/src/sp_validation/tests/test_sacc_io.py
@@ -100,6 +100,7 @@ def test_xi_roundtrip(tmp_path):
     assert np.array_equal(th, theta)
     assert np.array_equal(p, xip)
     assert np.array_equal(m, xim)
+    assert np.array_equal(sio.get_xi_weight(s2, (0, 0), grid="reporting"), weight)
     # extra tags survive
     idx = s2.indices(sio.XI_PLUS, ("source_0", "source_0"), grid="reporting")
     tags = s2.data[idx[0]].tags
diff --git a/uv.lock b/uv.lock
index 94341fce..8ab6ed4c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -588,8 +588,8 @@ wheels = [
 
 [[package]]
 name = "cosmo-numba"
-version = "1.0.1.dev6+g188d272c6"
-source = { git = "https://github.com/aguinot/cosmo-numba.git?rev=main#188d272c67d6d699d7cff7a7a53ded8dec759bb0" }
+version = "0.1.dev108+ga64cb2ed1"
+source = { git = "https://github.com/cailmdaley/cosmo-numba.git?rev=a64cb2ed13e595a16ad056a128ef24594b811504#a64cb2ed13e595a16ad056a128ef24594b811504" }
 dependencies = [
     { name = "mpmath" },
     { name = "numba" },
@@ -3821,7 +3821,7 @@ requires-dist = [
     { name = "camb", specifier = ">=2.0" },
     { name = "clmm" },
     { name = "colorama" },
-    { name = "cosmo-numba", git = "https://github.com/aguinot/cosmo-numba.git?rev=main" },
+    { name = "cosmo-numba", git = "https://github.com/cailmdaley/cosmo-numba.git?rev=a64cb2ed13e595a16ad056a128ef24594b811504" },
     { name = "cosmology", marker = "extra == 'glass'", specifier = "==2022.10.9" },
     { name = "cosmosis", marker = "extra == 'workflow'", specifier = ">=3.25" },
     { name = "cryptography" },
diff --git a/workflow/rules/cosmo_val.smk b/workflow/rules/cosmo_val.smk
index 1bff5b43..8d9839c6 100644
--- a/workflow/rules/cosmo_val.smk
+++ b/workflow/rules/cosmo_val.smk
@@ -7,8 +7,7 @@
 # products they write under COSMO_VAL (= cosmo_val/output):
 #
 #   catalogue ──→ xi (one job per grid: reporting, integration)
-#                   ├─ reporting part ──────→ pure_eb (part, npz, figures)
-#                   ├─ integration part ─┬──→ pure_eb
+#                   ├─ integration part ─┬──→ pure_eb (part, npz, figures)
 #                   │                    └──→ cosebis (part, npz, figures)
 #                   └─ reporting .txt ──→ 2pcf plot, ratio_xi_sys_xi
 #   CosmoCov ξ±, integration grid ──→ pure_eb, cosebis (their covariances)
@@ -80,7 +79,7 @@ def _pure_eb_stub(version):
             f"{version}_eb_minsep={CV['theta_min']}_maxsep={CV['theta_max']}"
             f"_nbins={CV['nbins']}_minsepint={eb['min_sep']}"
             f"_maxsepint={eb['max_sep']}_nbinsint={eb['nbins']}"
-            f"_npatch={CV['npatch']}_varmethod=semi-analytic"
+            f"_npatch={CV['npatch']}_varmethod=analytic"
         )
     )
 
@@ -396,13 +395,12 @@ rule cv_plot_pseudo_cl:
 # ---------------------------------------------------------------------------
 
 rule cv_pure_eb:
-    """Pure E/B-mode decomposition for one version, from its ξ± parts.
+    """Pure E/B-mode decomposition for one version, from its integration-grid part.
 
-    The modes come from the two parts; the covariance is Monte Carlo from the
-    integration-grid covariance model, so no patched estimator run is involved.
+    The modes are a fixed linear operator on the part's ξ±; the covariance is
+    the integration-grid ξ± covariance pushed exactly through it.
     """
     input:
-        xi_reporting=lambda w: cv_xi_sacc(w.version, "reporting"),
         xi_integration=lambda w: cv_xi_sacc(w.version, "integration"),
         cov_integration=lambda w: cv_xi_cov_integration(w.version),
     output:
@@ -414,13 +412,11 @@ rule cv_pure_eb:
         min_sep=CV["theta_min"],
         max_sep=CV["theta_max"],
         nbins=CV["nbins"],
-        n_samples=CV.get("n_mc_samples", 1000),
-        cosmo_params=CV["cosmo_params"],
+        integration=XI_GRIDS["integration"],
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-    threads: 24
     resources:
-        mem_mb=40000,
-        runtime=360,
+        mem_mb=8000,
+        runtime=20,
     script:
         "../scripts/cv_pure_eb.py"
 
@@ -472,9 +468,6 @@ rule cv_summarize_bmodes:
     params:
         versions=CV_VERSIONS,
         fiducial_scale_cut=CV["fiducial_scale_cut"],
-        min_sep=CV["theta_min"],
-        max_sep=CV["theta_max"],
-        nbins=CV["nbins"],
         include_pseudo_cl=CV.get("include_pseudo_cl", False),
     resources:
         mem_mb=8000,
diff --git a/workflow/scripts/cv_pure_eb.py b/workflow/scripts/cv_pure_eb.py
index 1edaea93..3f978d3a 100644
--- a/workflow/scripts/cv_pure_eb.py
+++ b/workflow/scripts/cv_pure_eb.py
@@ -1,28 +1,25 @@
 """Rule cv_pure_eb: pure E/B-mode decomposition for one version.
 
-A consumer of the two ξ± parts plus one covariance file — nothing here touches
-a catalogue. The modes come from the reporting and integration parts through
-the pipeline kernel; the covariance is Monte Carlo through that same kernel,
-drawn from the CosmoCov integration-grid ξ± covariance around a theory mean, so
-it depends on the covariance model and the grids rather than on the measured
-vector. A jackknife of the transformed modes would need per-patch realisations,
-which are never persisted.
+A consumer of the integration-grid ξ± part plus a ξ± covariance on that grid —
+nothing here touches a catalogue. The estimator is one fixed linear operator
+on the fine ξ± (b_modes.pure_eb_operator), averaged into the reporting bins with
+the part's TreeCorr pair weights into reporting bins snapped onto the fine
+edges, so its covariance is the supplied ξ± covariance pushed
+exactly through that operator.
 """
 
 import numpy as np
-from cs_util.cosmo import get_cosmo
 from cv_runner import _unbuffer_streams, verify_outputs
 
 from sp_validation import sacc_io
 from sp_validation.b_modes import (
     calculate_eb_statistics,
-    log_bin_edges,
+    calculate_pure_eb_correlation,
+    covariance_label,
     plot_eb_covariance_matrix,
     plot_integration_vs_reporting,
     plot_pte_2d_heatmaps,
     plot_pure_eb_correlations,
-    pure_eb_covariance_mc,
-    pure_eb_from_xi,
     save_pure_eb_results,
 )
 from sp_validation.cosmo_val.sacc_writers import pure_eb_to_sacc
@@ -32,48 +29,22 @@
 version = p["version"]
 fiducial_scale_cut = tuple(p["fiducial_scale_cut"])
 
-reporting = sacc_io.load(snakemake.input["xi_reporting"])
-integration = sacc_io.load(snakemake.input["xi_integration"])
-theta, xip, xim = sacc_io.get_xi(reporting, (0, 0), grid="reporting")
-theta_int, xip_int, xim_int = sacc_io.get_xi(integration, (0, 0), grid="integration")
-left_edges, right_edges = log_bin_edges(p["min_sep"], p["max_sep"], p["nbins"])
+part = sacc_io.load(snakemake.input["xi_integration"])
+theta_int, xip_int, xim_int = sacc_io.get_xi(part, (0, 0), grid="integration")
+weight_int = sacc_io.get_xi_weight(part, (0, 0), grid="integration")
+# A part stores bin centres; the edges come from the grid it was measured on.
+grid = p["integration"]
+edges_int = np.geomspace(grid["min_sep"], grid["max_sep"], grid["nbins"] + 1)
 
-# The reporting grid must sit strictly inside the integration grid: a reporting
-# point on the boundary has no interior support and comes back NaN.
-modes = pure_eb_from_xi(
-    theta, xip, xim, theta_int, xip_int, xim_int, left_edges[0], right_edges[-1]
+results = calculate_pure_eb_correlation(
+    theta_int,
+    xip_int,
+    xim_int,
+    weight_int,
+    edges_int,
+    np.loadtxt(snakemake.input["cov_integration"]),
+    np.geomspace(p["min_sep"], p["max_sep"], p["nbins"] + 1),
 )
-
-z, nz = sacc_io.get_nz(reporting, 0)
-cov, eb_samples = pure_eb_covariance_mc(
-    theta=theta,
-    left_edges=left_edges,
-    right_edges=right_edges,
-    theta_int=theta_int,
-    cov_int=np.loadtxt(snakemake.input["cov_integration"]),
-    z=z,
-    nz=nz,
-    cosmo=get_cosmo(**p["cosmo_params"]),
-    n_samples=p["n_samples"],
-)
-
-variances = reporting.covariance.dense.diagonal()
-results = {
-    "theta": theta,
-    "left_edges": left_edges,
-    "right_edges": right_edges,
-    "xip": xip,
-    "xim": xim,
-    "var_xip": variances[: len(theta)],
-    "var_xim": variances[len(theta) :],
-    "theta_int": theta_int,
-    "xip_int": xip_int,
-    "xim_int": xim_int,
-    "n_eff": p["n_samples"],
-    "cov": cov,
-    "eb_samples": eb_samples,
-    **modes,
-}
 results = calculate_eb_statistics(results)
 
 plot_integration_vs_reporting(
@@ -94,19 +65,22 @@
     fiducial_xim_scale_cut=fiducial_scale_cut,
 )
 plot_eb_covariance_matrix(
-    cov, "semi-analytic", snakemake.output["figure_covariance"], version
+    results["cov"],
+    covariance_label(results["npatch"]),
+    snakemake.output["figure_covariance"],
+    version,
 )
 
 save_pure_eb_results(results, snakemake.output["npz"])
 
 # The part inherits the ξ± part's provenance; `type` is re-stamped on save.
-metadata = {k: v for k, v in reporting.metadata.items() if k != "type"}
+metadata = {k: v for k, v in part.metadata.items() if k != "type"}
 s = pure_eb_to_sacc(
-    {0: (z, nz)},
+    {0: sacc_io.get_nz(part, 0)},
     metadata,
-    theta,
+    results["theta"],
     {key: results[key] for key in sacc_io.PURE_KEYS},
-    covariance=cov,
+    covariance=results["cov"],
 )
 sacc_io.save(s, snakemake.output["sacc"], type="data")
 
diff --git a/workflow/scripts/cv_summarize_bmodes.py b/workflow/scripts/cv_summarize_bmodes.py
index ec44142a..3f9d9e82 100644
--- a/workflow/scripts/cv_summarize_bmodes.py
+++ b/workflow/scripts/cv_summarize_bmodes.py
@@ -13,15 +13,13 @@
 from cv_runner import _unbuffer_streams, verify_outputs
 
 from sp_validation import sacc_io
-from sp_validation.b_modes import _get_pte_from_scale_cut, log_bin_edges
+from sp_validation.b_modes import _get_pte_from_scale_cut, covariance_label
 from sp_validation.cosmo_val.core import print_bmode_summary
 from sp_validation.statistics import chi2_and_pte
 
 _unbuffer_streams()
 p = snakemake.params
 fiducial_scale_cut = tuple(p["fiducial_scale_cut"])
-edges = log_bin_edges(p["min_sep"], p["max_sep"], p["nbins"])
-
 summary = {}
 cov_methods = set()
 
@@ -29,6 +27,8 @@
     row = {}
 
     pure_eb = np.load(snakemake.input["pure_eb"][i])
+    # The bins the PTE matrices are indexed on: the snapped reporting edges.
+    edges = (pure_eb["left_edges"], pure_eb["right_edges"])
     for stat in ("xip_B", "xim_B", "combined"):
         try:
             row[stat] = _get_pte_from_scale_cut(
@@ -36,7 +36,8 @@
             )
         except (KeyError, RuntimeError):
             pass
-    cov_methods.add(f"pure-E/B: semi-analytic ({int(pure_eb['n_eff'])} draws)")
+    npatch = int(pure_eb["npatch"]) if "npatch" in pure_eb else None
+    cov_methods.add(f"pure-E/B: {covariance_label(npatch)}")
 
     # The COSEBIs .npz is written at the fiducial cut, so its PTE is the one
     # this table wants.
diff --git a/workflow/tests/test_dag.py b/workflow/tests/test_dag.py
index 90272dca..b11acf3b 100644
--- a/workflow/tests/test_dag.py
+++ b/workflow/tests/test_dag.py
@@ -52,7 +52,7 @@ def test_one_integration_grid(toy):
         part = str(toy.cosmo_val / f"{version}_xi_{tag}.sacc")
         covariance = str(toy.covariances[version, "g"])
         assert by_rule["cv_cosebis"] == {part, covariance}, by_rule["cv_cosebis"]
-        assert {part, covariance} <= by_rule["cv_pure_eb"], by_rule["cv_pure_eb"]
+        assert by_rule["cv_pure_eb"] == {part, covariance}, by_rule["cv_pure_eb"]
 
 
 @pytest.mark.parametrize("named", [True, False], ids=["named", "unnamed"])

From 8db2aa5abe1950edff09d486d357695b2c5765e0 Mon Sep 17 00:00:00 2001
From: Cail McLean Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 17:00:08 +0200
Subject: [PATCH 48/83] Read ShapePipe v1 and v2 products side by side (#343)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* catalog: metacal bitmasks use FITS format K, not I or the dead FLAGS_ key

NGMIX_MCAL_FLAGS carries bit 30 (shapepipe#854's absent-measurement flag)
alongside the native fitter bits. Format I truncates it to int16 and
silently zeroes bit 30 on write, in both the FITS and the HDF5 branch of
write_shape_catalog (the HDF5 path reuses the FITS column's array).

The per-type NGMIX_FLAGS_{1P,1M,2P,2M,NOSHEAR} columns had the same
problem from the other direction: their format entry was keyed as
"FLAGS_{suffix}", which never matches the real column name
"{prefix}_FLAGS_{suffix}", so the writer's float64 default masked the
dead key rather than narrowing anything. Both parameter files now key
and format all six metacal bitmasks the same way, at K, so they round-
trip exactly and survive JointCat's optional memory-reduction pass
(which only downcasts int32/float64, not int64).

NGMIX_MCAL_TYPES_FAIL stays at I: it is a count in [0, 5], not a bitmask.

Adds a round-trip test parametrized over both parameter files, FITS and
HDF5, and reduce_mem on/off, checking 0, bit 30 alone, and bit 30 with a
native bit together.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* catalog: metacal bitmasks as int32; reduce_mem never narrows integers

ShapePipe sets bit 2**30 in the metacal flags for a missing measurement,
which needs 32 bits. The parameter files now write NGMIX_MCAL_FLAGS and
the five NGMIX_FLAGS_* columns as FITS J (int32).

JointCat.dtype_out with reduce_mem narrowed every int32 column outside a
keep-list to int8, silently wrapping any value above 127. It now only
narrows float64 to float32 (RA/Dec excepted) and leaves integer columns
alone.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* catalog: layout-agnostic campaign and star catalogue readers

Replace read_hdf5_file's hardcoded patches/<name>/<tile-ID> lookup with
find_dataset_group(), which descends from the file root through single
container groups until it reaches the per-unit datasets. This reads the
legacy patches/<campaign>/ layout that ShapePipe still writes as a
compatibility shim, a future flat tiles/ layout, and the exposures/<exp>
layout of full_starcat_<campaign>.hdf5 with the same code.

read_star_catalogue() keeps the FITS path for files ending in .fits.
Requested columns missing from the data now raise a clear KeyError.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* galaxy: replace IMAFLAGS_ISO cut with config-driven mask columns

ShapePipe v2 drops IMAFLAGS_ISO for eleven boolean MASK_n* columns.
galaxy.mask_cut() ORs a configurable list of them (default MASK_n4,
MASK_n1, MASK_n2, MASK_n8, MASK_n1024 — stars, star halos, manual galaxy
mask, MaxiMask) and returns the keep mask; a catalogue missing any of the
requested columns raises a KeyError naming them.

classification_galaxy_base takes mask_columns; extract_info.py passes the
params.py mask_columns list and uses it for the star-sample cut too, and
now reads both catalogues through the new readers. Column lists in
params.py and masking.py's SPATIAL_CUTS updated to the MASK_n* names.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* retire patch logic (#340)

ShapePipe v2 processes a campaign (a tile list); P1-P7 no longer exist.

- catalog_builders.JointCat: get_patches()/get_n_obj() and the per-patch
  FITS merge are replaced by a merge over a list of campaign hdf5 files
  (-i final_cat_A.hdf5+final_cat_B.hdf5), read through
  read_campaign_catalogue. The 'patch' int8 column becomes a 'campaign'
  string column; the hdf5 root attr becomes 'campaigns'.
- survey.get_footprint() deleted: it was a lookup table of P1-P7 (plus W3)
  RA/Dec boundaries, meaningless for a campaign. Its only caller,
  catalog.check_matching, used it as an optional pre-filter via a 'name'
  argument that every caller passed as None; the argument goes too, along
  with the test_survey test that exercised P5.
- merge_psf_cat.py, combine_results.py, stats_tile_id_gal_counts.py,
  compute_area.py: patch vocabulary and v1/v1.5/v1.6 P-name shortcuts
  generalised to an explicit list of campaigns.
- params.py: 'name = "P7"' becomes 'campaign = None'.

Deleted (only ever meaningful for the P1-P7 era):
- scripts/prepare_patch_for_spval.sh: symlinks a v1 per-patch run tree
  (~/psfex/${patch}/output/run_sp_Ms/.../full_starcat-0000000.fits,
  tiles_${patch}.txt) into a working dir; neither the layout nor the file
  names exist in v2.
- scripts/plot_rho_stats_patches.py: globs P* directories and reads
  P*/output/run_sp_Pl/mccd_plots_runner/output/rho_stats_id.fits, one
  curve per patch. No campaign analogue.
- scripts/survey_stats_all.sh: hardcoded 'for patch in P1 ... P7' over v1
  run-directory bookkeeping.
- scripts/star_match_stats.py: sums stats_file.txt over the seven patches.
- scripts/check_tile_IDs_SP_LF.py: compares per-patch ShapePipe tile IDs
  against CFIS3500_THELI_P<n>.list Lensfit files; both sides P-named.

The word 'patch' survives only in catalog.py's comment naming the legacy
hdf5 group, and in the treecorr jackknife sense (npatch/patch_number).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* tests: campaign readers, mask cut, and campaign merge

Seventeen unit tests on tiny synthetic hdf5 fixtures built in a temp dir:
both campaign layouts (legacy patches/<campaign>/<tile-ID> and flat
tiles/<tile-ID>) read identically, param-list restriction, missing-column
and ambiguous-layout errors; the star reader on exposures/<exp> hdf5 and
on FITS; galaxy.mask_cut defaults, explicit list, empty list, missing
column, and a v1 IMAFLAGS_ISO-only catalogue; JointCat.merge_catalogues
across two campaigns of different layouts, plus its error paths.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* catalog: safe, low-memory campaign reading and merging

- concatenate_datasets preallocates the output and fills it column by
  column, so peak memory is the packed output plus one tile instead of the
  full-width uncut catalogue; the verbose estimate uses the real itemsize.
- validate the requested columns against every tile dataset, not only the
  first, and name the offending dataset in the error.
- read_campaign_catalogue checks the root n_tiles attribute and refuses a
  truncated file; campaign_shape reports row count and dtype from metadata.
- JointCat.merge_catalogues preallocates the merged array from that first
  pass (no more accumulate-then-concatenate, which doubled peak memory),
  promotes each column's dtype across all campaigns so a wider string or
  integer column in a later file is no longer silently truncated, and
  refuses multi-dimensional columns explicitly.
- reduce_mem reduces int32 to int16 (int8 wrapped N_EPOCH/CCD_NB values)
  and every assignment is range-checked, raising instead of wrapping.
- check_matching drops the identity index over d1 that the retired
  footprint prefilter left behind, and extract_info applies mask_cut to
  the matched subset instead of the whole catalogue.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* config: v2 mask configuration with the MASK_n* columns

Add config/calibration/mask_v2.0.yaml: the v1.X.11 cut set with
IMAFLAGS_ISO and the v1 post-processing masks replaced by the boolean
MASK_n<bit> columns (True = masked, hence kind: equal, value: False), the
coverage bits and MASK_n2048 listed but commented out. The v1.X configs are
left untouched: each describes a legacy catalogue that really has
IMAFLAGS_ISO, and rewriting them would break reproducing published versions.

plots.sky_plots no longer hardcodes the v1 label set; it combines whichever
of IMAFLAGS_ISO / MASK_n* / npoint3 / 1024_Maximask the config declared.
The comprehensive-to-minimal demo asks for the v2 mask labels.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* scripts: finish the campaign rename in configs and entry points

- params.py interpolated the removed 'name' into two paths (NameError on
  import); use 'campaign', give it a real default, and point star_cat_path
  at full_starcat_<campaign>.hdf5 (hdu_star_cat now documented as legacy
  FITS only). params_im_sim.py renames 'name' to 'campaign' likewise.
- combine_results.get_area matches both the campaign and the legacy patch
  wording, and raises on a missing file or unmatched pattern instead of
  returning None / a 1 deg^2 placeholder that silently rescales densities.
- merge_psf_cat writes the campaign *name* as a string column (FITS 'A<n>'),
  matching JointCat; the old 1-based ordinal depended on -p argument order.
- compute_m_bias_image_sims counts tiles via the n_tiles attribute or
  find_dataset_group, not a hardcoded legacy group inside a bare except.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* tests, docs: pin row order, cover the new failure modes

Tests: assert the exact concatenation instead of sorted values, add a
fixture whose keys are inserted out of order, and cover the truncated
n_tiles file, a column missing from a later tile, dtype promotion across
campaigns in both argument orders, reduce_mem overflow, and a
multi-dimensional column.

Docs: CLAUDE.md, scripts/calibration/README.md, homogenize_cat_extended.py
and the catalog_builders docstrings now say campaign.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* catalog: promote dtypes across tiles, stream the campaign merge

Three defects in the campaign reading path, found in review:

- concatenate_datasets and campaign_shape both took the output dtype from
  the first dataset alone, so a campaign whose tiles differ (S7 next to
  S12 tile IDs, i2 next to i4, f4 next to f8 after a partial
  reprocessing) had the later tiles silently truncated, downcast or
  wrapped. np.concatenate, which this code replaced, promoted. Both now
  build the dtype with group_dtype(), promoting every column across every
  dataset; catalog_builders._promote becomes an alias of the shared
  catalog.promote_dtypes rather than a second copy of it.

- merge_catalogues still held a whole campaign in memory next to the
  preallocated output, the very thing its comment claimed the rewrite
  avoided -- and with one campaign per merge in v2, that is the normal
  case, ~2x the merged catalogue at DR6 scale. It now fills the output
  tile by tile through the new catalog.iter_campaign_tiles(), so peak
  memory is the output plus a single tile.

- write_hdf5_file wrote the merged array twice, create_dataset(data=...)
  followed by an immediate dset[:] = dat_all.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* config: restore the r-band coverage cut in the v2 mask config

mask_v2.0.yaml dropped v1.X's '64_r' r-band imaging cut without a
replacement, so a v2 calibration run admitted objects outside the r-band
footprint that v1 rejected -- a silent change of effective area, n(z) and
galaxy-density normalisation. Enable MASK_n64, v1's '64_r' equivalent.

v1's other coverage cut, npoint3 >= 3, came from an external
post-processing catalogue and has no v2 counterpart; say so in the config
rather than leaving its absence unexplained.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* scripts: cheaper star mask cut, campaign-derived leakage labels

extract_info fancy-indexed the full-width catalogue, dd[ind_star], only
to read ~5 boolean mask columns from it -- a copy of every column for
every matched star (~GB at DR6 scale). Mask first, index the resulting
bool array.

plot_leakage still labelled its curves "all", "P1" ... "P7": a P-named
survivor of the #340 retirement, and a fixed length that silently
mismatched the number of input files. Labels and colours now follow the
input files.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* masks: treat n64 as a reason bit, not r-band coverage

The v2 branch inferred from v1's '64_r' naming that MASK_n64 flags r-band
imaging coverage. It does not: bit 64 is an undocumented *reason* bit of
the r-band default bitmask, and OR{n1,n2,n4,n8,n64,n1024} reproduces
mask_r, the v1 r-band mask, exactly on the P3 region.

Add MASK_n64 to DEFAULT_MASK_COLUMNS so the default galaxy cut is exactly
that set, documented as reproducing mask_r, and mirror it in the
calibration params, the v2 mask config and the minimal-catalogue demo.

Document n16/n32/n128/n256 as the u/g/i/z coverage flags (no r flag: the
catalogue is r-selected) and n2048 as absent Pan-STARRS z2. The faint vs
bright assignment of n1/n2 is unconfirmed for the Aug-2026 products, so
the labels no longer claim one.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* ruff autofix (format + safe lint fixes)

Pushed by the lint gate.

* galaxy: cut never-fit objects on NGMIX_N_EPOCH explicitly

ShapePipe's make_cat pre-fills the NGMIX_* columns with sentinels
(G1/G2 = -10, T/FLUX = 0) and overwrites them only for objects present
in the ngmix output, so an object ngmix never fit keeps
NGMIX_MCAL_FLAGS == 0 and passes a flag-only cut. In
final_cat_smk-g7.hdf5 that is 18,983 of 1,851,100 objects (1.03%);
admitting them drags mean e1 to -0.096 (std 0.98) from +0.0001.

classification_galaxy_ngmix already rejected all 18,983 via the
NGMIX_G1_PSF_ORIG_NOSHEAR != -10 guard, so the production selection was
never affected -- verified on the real file, which gives 1,105,851 rows
out with and without the new cut. But that protection was incidental:
it is an exact float equality against a sentinel ShapePipe may change,
and the coadd N_EPOCH >= 2 cut in classification_galaxy_base does not
substitute for it (18,750 of the 18,983 have N_EPOCH >= 1). Cut on
NGMIX_N_EPOCH > 0 explicitly so the guarantee is stated, not inferred.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* readers: NaN-safe mask cut, star-catalogue provenance and count check

mask_cut decided on truthiness via astype(bool). ShapePipe writes the
MASK_n* columns as float64 {0, 1} rather than bool (being fixed
upstream), and astype(bool) reads NaN as True, so an incomplete mask
column would have silently deleted sky. Decide on the value instead
(masked iff > 0.5), accept bool, int and float alike, and treat NaN as
"no verdict recorded" -- keep the object, but count and warn, since a
nonzero count means the product is defective. final_cat_smk-g7.hdf5
carries no NaNs and only exact 0.0/1.0, so this is defensive: the real
file gives 1,105,851 rows out before and after.

read_star_catalogue silently accepted a truncated file and threw away
exposure provenance. It now validates the n_exposures root attribute
against the datasets found, as the galaxy reader validates n_tiles
(check_n_tiles generalised to check_n_units), and adds an EXPID column
carrying the exposure number each star came from. The datasets are
named by that number and concatenating them discarded it, leaving no
way to group stars by exposure downstream. Names may be bare
("2086324", as smk-g7 writes them) or carry the CFIS suffix
("2110000p"), so EXPID takes the leading digits. On the real star
catalogue: 53,264 stars over 127 exposures, matching n_exposures.

Also note in group_dtype that ShapePipe writes TILE_ID as f8, so the
string-promotion branch is for a future string-valued TILE_ID, with a
TODO recording that as an open schema decision. No behaviour change.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* ruff autofix (format + safe lint fixes)

Pushed by the lint gate.

* config: cut never-fit objects in the v2 mask config too

mask_v2.0.yaml, applied downstream to the comprehensive catalogue, cut
on neither NGMIX_MCAL_FLAGS nor any epoch column. It rejected the
never-fit objects only through its NGMIX_G1/G2_PSF_ORIG_NOSHEAR != -10
cuts, and only because make_cat happens to fill the PSF columns from
the same -10 literal it uses for the galaxy ellipticities. That is the
same accidental immunity just removed from
classification_galaxy_ngmix, one stage further downstream.

Add NGMIX_N_EPOCH >= 1. The column is already carried into the
comprehensive catalogue via add_cols_pre_cal in params.py. On
final_cat_smk-g7.hdf5 the cut keeps 1,832,117 of 1,851,100 objects,
removing exactly the 18,983 (1.03%) never-fit rows.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QbnPCyzuDNTgkg715pHhar

* mask_v2.0: FLAGS <= 2, matching the image-sims selection

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

* combine_results: drop unused n_campaign

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01VXzqmMYw7Kp8QMtoiVHjVq

* grammar: ShapePipe v1->v2 column-grammar adapter, wired into rho/tau

sp_validation reads only the v2 column grammar, while every catalogue
on disk (v1.3.x-v1.6.x) is v1, so e.g. rho/tau raised KeyError on the
v1.4.a PSF file. New module sp_validation.grammar is the one place that
knows the difference:

- V1_RULES: the v1->v2 map of the shape-measurement columns as data (HSM
  renames, SIGMA -> T = 2 sigma^2 via cs_util.size.sigma_to_T, 2-vector or
  flattened NGMIX_ELL* split into G1/G2, PSFo/Tpsf/MOM_FAIL renames),
  applied to tables detect_generation calls v1. Mapping is by name, so v1
  values reach the names the code reads.
- MASK_RULES: the healsparse mask bits {b}_{label} (the names
  ApplyHspMasks gave them in the comprehensive HDF5's data_ext) ->
  MASK_n{b}, applied whatever the generation: they are the same bits of
  the same UNIONS bitmask ShapePipe v2 writes as MASK_n{b}
  (MASK_LABELS; 512 = outside the tile's unique region). IMAFLAGS_ISO is
  not mapped: its v1 bits mean different things.
- adapt(table, *tables): tables with nothing to rename come back
  unchanged; otherwise a V2View, a lazy column view over numpy, FITS_rec
  or h5py that joins row-aligned tables (data + data_ext), computes derived
  columns on access, and composes row selections as ranges or selected
  indices, reading only the window of rows they span.
- read_catalogue(path, hdu), materialise, v2_names, read_column_names.

rho_tau.get_rho_tau / get_jackknife_cov / get_theory_cov read each
catalogue once through read_catalogue and hand the table to
shear_psf_leakage (needs its loaded-catalogue support).

Tests: v1 twins adapt to their v2 twins for numpy (vector and flattened
ELL), FITS_rec and h5py; row selection commutes; dtype matches every
column read; data_ext mask names rename with or without a generation and
conflict with their new names; h5py selections read only their window;
the psf_size_error field and full get_rho_tau outputs agree between a v1
PSF catalogue and its v2 twin (and a rename-only sigma is caught).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* cat_config: psf/shear columns match their files; guard it on candide

test_cat_config_columns_exist_on_candide reads each cat_config
catalogue's FITS header through grammar.read_column_names and checks
the psf block's declared columns and the shear block's *_col columns
are among the names the file presents. Known gaps are listed with
reasons and fail the test once healed.

Config fixes it surfaced:
- psf dec_col Dec -> DEC: every PSF file (and ShapePipe v2) names it
  DEC; only FITS_rec's case-insensitive lookup hid the mismatch.
- SP_v1.4.12.3 / SP_v1.4.13.3 psf blocks named v1 columns and the
  retired square_size flag; now the v2 names like every other entry.
- SP_axel_v0.0 / SP_v1.4.5.A shear: declare their lowercase ra/dec;
  SP_v1.4.5.A's PSF shape columns are psf_g1/psf_g2.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* rho_tau: build rho/tau catalogues without a scalar-bool mask

build_catalog indexes each column with ``mask``; a scalar True only
adds an axis treecorr reshapes away (no stars are cut), and a scalar
False selects nothing, so treecorr raises "Input arrays have zero
length". That broke get_rho_tau for DES and get_jackknife_cov for
every catalogue. No flag cut was ever applied, so dropping the
argument leaves the non-DES results unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* calibration: read v1 and v2 products through the grammar adapter

Every reader on the calibration path now presents its tables in the v2
column grammar, so a ShapePipe v1 product runs through the same code and
the same configs as a v2 one:

- catalog.read_campaign_catalogue, campaign_shape, iter_campaign_tiles and
  the JointCat merge adapt each tile (param_list names v2 columns);
  read_star_catalogue adapts FITS and HDF5 star catalogues.
- CalibrateCat.read_cat returns one table: the comprehensive HDF5's data
  and data_ext joined by grammar.adapt, so mask columns read the same
  whether they sit in data (v2) or data_ext (post-processed v1), and
  galaxy.mask_cut works on either.
- get_masks_from_config takes that one table, and each mask config is one
  `dat` cut list: the v1 configs' dat_ext cuts move into it under their
  MASK_n{b} names (the selections are unchanged; IMAFLAGS_ISO stays in the
  v1 configs). The image-sim overlay and scripts/masking.py follow.
- ApplyHspMasks writes mask bit b as MASK_n{b}, from grammar.MASK_LABELS.
- cosmo_val/compute_theory_cov.py hands CovTauTh loaded, adapted tables.

Tests: a v1 comprehensive HDF5 (v1 data + data_ext with the old mask
names) and its v2 twin give identical mask_cut and per-config mask
selections for every config in config/calibration, and identical metacal
inputs and response; the campaign and star readers present v1 files in v2.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* uv.lock: shear_psf_leakage develop@00e38c4 (loaded-catalogue rho/tau builders, seeded patches)

Pinned below develop's tip 372980f, whose scipy>=1.18 requirement cannot
resolve against cs_util<0.3.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* grammar: v1 no-shear reconvolved-PSF size from NGMIX_Tpsf_1P

ShapePipe v1 wrote a wrong NGMIX_Tpsf_NOSHEAR: it differs by ~2% for
nearly every object (v1.4, v1.5, v1.6 comprehensive) from the
reconvolution kernel metacal applied, which NGMIX_Tpsf_{1P,1M,2P,2M}
record (they agree to ~1e-5). The v1.4.6.3 release used the 1P value
in its metacal size cut and wrote it as the no-shear column; #267
dropped that substitution, which is a no-op only on the v2 stack, so
the branch's v1 calibration selected ~3% more objects than the release.

The adapter now presents NGMIX_T_PSF_RECONV_NOSHEAR from NGMIX_Tpsf_1P
when the table has it, falling back to NGMIX_Tpsf_NOSHEAR (a cut
catalogue such as v1.4.6.3's already holds the 1P value there), and
hides the raw column. Rules gain a fallback source; a derived column is
presented where the first of its sources sits. The module docstring
and the migration doc state the rule: name mapping, except this one
documented v1 defect.

Every consumer now sees the 1P value, including the w_des and
PSF-leakage size-ratio binning, where the release used the raw value;
those two columns therefore do not reproduce the release bit for bit.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* grammar: select rows of an HDF5-backed view in one pass; cache its dtype

metacal reads ~40 columns of data[mask]. Over h5py datasets the lazy
view read each column over the whole span of the selection, so a
full-sky v1 calibration re-read the 238 GB comprehensive file once per
column. Selecting rows of a view over h5py Datasets now reads the
selected rows of every column in one pass per dataset (whole rows, in
256 MB blocks over the selection's span, skipping empty blocks), as
indexing the Dataset did on develop, and wraps them in a view over the
in-memory arrays so renames and derived columns still apply. Views
over in-memory tables stay lazy. to_structured reads each dataset once
for all requested fields.

On a cold 10M-row window of v1.4.c (4.8M rows selected):
h5py Dataset[mask] 19.2 s; adapt(data, data_ext)[mask] plus the 40
metacal columns 23.5 s; the previous per-column path took 16.5 s for
its first column alone, i.e. one full re-read per column.

dtype is computed once per view and passed to row selections
(group_dtype asked for it per column, quadratically). view[()] and
view[...] select every row, as for an h5py Dataset; other tuples raise.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* catalog.group_dtype: name the tables that lack a column

A column missing from some tiles (several v1 tiles carry no
SPREAD_MODEL) surfaced as numpy's bare "no field of name" KeyError.
group_dtype now checks every table first and raises a KeyError naming
how many tables lack which columns, and which ones; callers pass the
tables keyed by dataset name so the message names tiles.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* masks: refuse a mask config's dat_ext list; masking keeps IMAFLAGS_ISO

Mask configs are one dat: list over the joined data + data_ext table,
but get_masks_from_config and scripts/masking.py read only config["dat"],
so an older config with a dat_ext: list (several sit under v1.4.x and
v1.5.x) silently lost those cuts. masks.catalogue_cuts returns the dat
list and raises on dat_ext, saying to merge it into dat with {b}_{label}
renamed MASK_n{b}; the calibration, footprint and demo scripts all read
cuts through it.

scripts/masking.py's footprint cuts had dropped IMAFLAGS_ISO, which the
v1 configs still cut on and which is spatial (halo, border, Messier,
NGC, spike bits); it is back in SPATIAL_CUTS.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* galaxy.mask_cut: a NaN mask value masks the object, as the config cuts do

mask_cut kept objects whose mask column is NaN (with a warning), while
the config-driven cut that calibration runs (kind: equal, value: False)
drops them, so the two selections disagreed on a defective product.
mask_cut now drops them too, still counting and warning. Keeping them
was chosen to stop astype(bool) from dropping NaN rows silently; the
warning covers that, and dropping an object with no mask verdict is
the conservative choice for a shear catalogue.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* Comments describe the present; calibration configs' relative input_path

Drop wording that goes stale ("being fixed upstream", "unconfirmed for
the Aug-2026 products", "legacy" layouts and files) in favour of what
is true now, and ApplyHspMasks.write_hdf5_header's documented but
nonexistent campaigns parameter.

test_configured_paths_exist_on_candide resolved a calibration config's
relative params.input_path against config/calibration; such configs
run as config_mask.yaml in their run directory, where the relative
path names a file, so the guard skips those.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* tests: calibration path reproduces a v1.4.6.3 release window (candide, slow)

Runs scripts/calibration/calibrate_comprehensive_cat.py with
mask_v1.X.6.yaml on rows 200M..201M of the v1.4.c comprehensive HDF5
and checks the released cut catalogue's rows from that window, matched
by (RA, Dec) and bracketed by rows from outside it: same objects, same
order, and identical per-object columns, including the no-shear
reconvolved-PSF size against the release's NGMIX_Tpsf_NOSHEAR. Globally
calibrated columns (e1, e2, w_des, leakage-corrected) are not compared.
Skipped where the release products are absent.

Passes in ~30 s on n09 (131,526 objects); before the no-shear
reconvolved-PSF correction it fails, selecting 134,936.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LsAQRxNg6yDsUX4y2SWxTJ

* Drop the SExtractor-only columns DR6 catalogue mode lacks

ShapePipe v2 catalogues in DR6 catalogue mode no longer carry MAG_WIN,
MAGERR_WIN, SNR_WIN, FLUX_AUTO, FLUXERR_AUTO, FLUX_APER, FLUXERR_APER,
FWHM_IMAGE, FWHM_WORLD (shapepipe #924). Stop passing them through in
extract_info (params.add_cols) and calibrate_comprehensive_cat, and drop
the SExtractor SNR_WIN curve from the galaxy SNR histogram. The v1.4.6.3
release regression compares the remaining per-object columns.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BeQNTBcjTqu6TPktxysovQ

* Describe halo bit identities and guard independent mask cuts

* Name mask columns MASK_{bit}_{label}

The external-mask column for bit b is MASK_{b}_{label}, labels from
grammar.MASK_LABELS (grammar.mask_column), so the column carries both the
bit identity and its meaning. ApplyHspMasks writes these names; every mask
config, script and the galaxy defaults cut on them (galaxy derives its
tuples from mask_column).

The adapter presents the v1 data_ext spelling {b}_{label} and the
pre-release ShapePipe v2 spelling MASK_n{b} under the canonical name;
neither marks a generation. Tests cover both spellings mapping to the same
column and the same cuts, including the fixed-name faint/bright halo check.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* Retire the remaining patch-era scripts and docs

- Delete scripts/combine_results.py and scripts/compute_area.py (no caller;
  their R.txt/c.txt outputs feed only a create_joint_shape_cat.py that no
  longer exists) and the post_processing.md sections describing them.
- Delete cosmo_val/compute_theory_cov.py: its version keys, home-directory
  paths and base_dir + absolute-subdir join no longer resolve, and
  rho_tau's CovTauTh path, which the workflow drives, does the same job.
- using_the_catalogues.md: the v1.0 example selects on the file's `patch`
  column; v1.4.1+ comprehensive HDF5 (flat data/data_ext) is read with
  grammar.adapt, cut FITS catalogues with grammar.read_catalogue.
- index.rst: per-campaign merging. mask_r agreement stated as verified on
  the P3 sky area. Drop the uncalled ApplyHspMasks.get_label_struct.
  Campaign-reader fixture named CAMPAIGN.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* One catalogue reader; column names configurable through column_map

Container and column naming are now two axes, each known in one place.

sp_validation.io.Catalogue / read_catalogue detect the container from file
contents (h5py.is_hdf5, else FITS) and read FITS table HDUs, a single HDF5
dataset, a comprehensive data + data_ext HDF5, and ShapePipe's HDF5 groups
of per-tile/per-exposure chunks (n_tiles / n_exposures checked; key_column
keeps chunk names, e.g. EXPID). Every catalogue read routes through it:
extract_info (fixing the np.load-on-.fits path), the builders' read_cat
(BaseCat and CalibrateCat merged) and campaign merge, cosmo_val pseudo-Cl,
catalogue characterisation and the shear_psf_leakage-backed results, the
rho/tau loader (temp FITS only when the input is not already plain HDU 1),
glass_mock, calibration and scripts/masking.py.

grammar.adapt takes a column_map {canonical name: name in the file},
explicit or one-'*' patterns, applied lazily ahead of the v1 rules; it
overrides any rule for the same name and the v1 detection runs on the rest.
Catalogue configs carry it (cat_config blocks, calibration params,
params.py galaxy_/star_column_map). The *_col keys still pick roles,
naming columns as presented after the map.

config/columns/shapepipe_v2.yaml lists every column the code reads by
fixed name; test_column_schema keeps code and file in step both ways, and
a Sphinx extension renders it as docs/source/catalogue_columns.md.

Removed: catalog.read_campaign_catalogue, read_star_catalogue,
campaign_shape, iter_campaign_tiles, concatenate_datasets,
STAR_CAT_COLUMNS, read_shape_catalog; grammar.read_catalogue,
read_column_names, requires_adaptation; metacal's prefix argument.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01816Up3mpuhwYq7cnmyRHqv

* Read lazily where columns are not named; column_map fixes

io.open_catalogue / open_entry return a catalogue as one table without
reading it: FITS memory-mapped, unchunked HDF5 read per column on access.
The cosmo_val leakage subclasses, pseudo-Cl and catalogue
characterisation use them instead of reading every column into memory.
Through the leakage path the v1.4.6.3 shear catalogue (61.4M rows, v1
names, so adapted) peaked at 29.6 GiB RSS, 15.6 GiB of it anonymous; it
now peaks at 15.4 GiB with 0.2 GiB anonymous, as upstream's memmap read
does (the rest is file-backed page cache).

grammar: a column_map entry overrides only the rule for its own canonical
name; the columns it renames still feed the other rules (a map onto
NGMIX_Tpsf_1P no longer sends NGMIX_T_PSF_RECONV_NOSHEAR to the wrong
NGMIX_Tpsf_NOSHEAR). adapt(V2View, column_map=...) applies the map over
the view instead of ignoring it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01816Up3mpuhwYq7cnmyRHqv

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Cail Daley <cdaley@l1.nibi.sharcnet>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 .gitignore                                    |    3 +
 CLAUDE.md                                     |    7 +-
 .../calibration/mask_from_compr_v1.X.11.yaml  |   14 +-
 .../calibration/mask_from_compr_v1.X.6.yaml   |   14 +-
 config/calibration/mask_v1.X.10.yaml          |   14 +-
 config/calibration/mask_v1.X.11.yaml          |   14 +-
 config/calibration/mask_v1.X.2.yaml           |   18 +-
 config/calibration/mask_v1.X.3.yaml           |   14 +-
 config/calibration/mask_v1.X.4.yaml           |   18 +-
 config/calibration/mask_v1.X.4_im_sim.yaml    |    8 +-
 config/calibration/mask_v1.X.5.yaml           |   14 +-
 config/calibration/mask_v1.X.6.yaml           |   14 +-
 config/calibration/mask_v1.X.6_ppv1.yaml      |   20 +-
 config/calibration/mask_v1.X.7.yaml           |   16 +-
 config/calibration/mask_v1.X.8.yaml           |   18 +-
 config/calibration/mask_v1.X.9.yaml           |   14 +-
 .../mask_v1.X.9_im_sim.overlay.yaml           |   20 +-
 config/calibration/mask_v1.X.9_im_sim.yaml    |    2 +-
 config/calibration/mask_v2.0.yaml             |  160 +++
 config/columns/shapepipe_v2.yaml              |  121 ++
 cosmo_val/cat_config.yaml                     |   92 +-
 cosmo_val/compute_theory_cov.py               |  109 --
 docs/ngmix_psf_column_migration.md            |   92 +-
 docs/source/_ext/catalogue_columns.py         |   97 ++
 docs/source/conf.py                           |    2 +
 docs/source/index.rst                         |    2 +-
 docs/source/post_processing.md                |   63 +-
 docs/source/run_validation.md                 |   18 +-
 docs/source/toc.rst                           |    1 +
 docs/source/using_the_catalogues.md           |   14 +-
 papers/catalog/hist_mag.py                    |   30 +-
 scripts/calibration/README.md                 |    9 +-
 .../calibrate_comprehensive_cat.py            |   15 +-
 scripts/calibration/demo_apply_hsp_masks.py   |    4 +-
 scripts/calibration/extract_info.py           |   61 +-
 scripts/calibration/params.py                 |   65 +-
 scripts/check_tile_IDs_SP_LF.py               |   83 --
 scripts/combine_results.py                    | 1040 -----------------
 scripts/compute_area.py                       |  141 ---
 scripts/compute_m_bias_image_sims.py          |   37 +-
 .../create_binned_mask_comprehensive.py       |    5 +-
 .../examples/demo_calibrate_minimal_cat.py    |   12 +-
 scripts/examples/demo_check_footprint.py      |    2 +-
 .../demo_comprehensive_to_minimal_cat.py      |   36 +-
 .../examples/demo_create_footprint_mask.py    |   28 +-
 scripts/glass_mock/compute_leakage_harmony.py |    3 +-
 scripts/homogenize_cat_extended.py            |    2 +-
 scripts/masking.py                            |   73 +-
 scripts/merge_psf_cat.py                      |   76 +-
 scripts/plot_leakage.py                       |   12 +-
 scripts/plot_rho_stats_patches.py             |   98 --
 scripts/prepare_patch_for_spval.sh            |   31 -
 scripts/star_match_stats.py                   |   44 -
 scripts/stats_tile_id_gal_counts.py           |   43 +-
 scripts/survey_stats_all.sh                   |   94 --
 src/sp_validation/calibration.py              |   43 +-
 src/sp_validation/catalog.py                  |  146 +--
 src/sp_validation/catalog_builders.py         |  581 ++++-----
 .../cosmo_val/catalog_characterization.py     |   22 +-
 src/sp_validation/cosmo_val/core.py           |   47 +-
 src/sp_validation/cosmo_val/pseudo_cl.py      |    7 +-
 src/sp_validation/galaxy.py                   |  128 +-
 src/sp_validation/glass_mock.py               |    5 +-
 src/sp_validation/grammar.py                  |  612 ++++++++++
 src/sp_validation/io.py                       |  486 +++++++-
 src/sp_validation/masks.py                    |   97 +-
 src/sp_validation/plots.py                    |   27 +-
 src/sp_validation/rho_tau.py                  |  378 +++---
 src/sp_validation/survey.py                   |   71 --
 src/sp_validation/tests/test_calibration.py   |    3 -
 .../tests/test_campaign_readers.py            |  542 +++++++++
 .../tests/test_catalog_flag_roundtrip.py      |   20 +-
 src/sp_validation/tests/test_column_schema.py |  173 +++
 .../tests/test_config_paths_exist.py          |   97 ++
 src/sp_validation/tests/test_galaxy.py        |   34 +
 src/sp_validation/tests/test_grammar.py       |  559 +++++++++
 .../tests/test_grammar_calibration.py         |  333 ++++++
 src/sp_validation/tests/test_io.py            |  227 ++++
 src/sp_validation/tests/test_masks.py         |   45 +-
 .../tests/test_release_regression.py          |  141 +++
 src/sp_validation/tests/test_survey.py        |   10 -
 workflow/image_sims/params_im_sim.py          |   30 +-
 82 files changed, 4870 insertions(+), 3051 deletions(-)
 create mode 100644 config/calibration/mask_v2.0.yaml
 create mode 100644 config/columns/shapepipe_v2.yaml
 delete mode 100644 cosmo_val/compute_theory_cov.py
 create mode 100644 docs/source/_ext/catalogue_columns.py
 delete mode 100644 scripts/check_tile_IDs_SP_LF.py
 delete mode 100755 scripts/combine_results.py
 delete mode 100755 scripts/compute_area.py
 delete mode 100755 scripts/plot_rho_stats_patches.py
 delete mode 100755 scripts/prepare_patch_for_spval.sh
 delete mode 100644 scripts/star_match_stats.py
 delete mode 100644 scripts/survey_stats_all.sh
 create mode 100644 src/sp_validation/grammar.py
 create mode 100644 src/sp_validation/tests/test_campaign_readers.py
 create mode 100644 src/sp_validation/tests/test_column_schema.py
 create mode 100644 src/sp_validation/tests/test_grammar.py
 create mode 100644 src/sp_validation/tests/test_grammar_calibration.py
 create mode 100644 src/sp_validation/tests/test_io.py
 create mode 100644 src/sp_validation/tests/test_release_regression.py

diff --git a/.gitignore b/.gitignore
index 9c07b05b..e9d09406 100644
--- a/.gitignore
+++ b/.gitignore
@@ -206,3 +206,6 @@ report.md
 check2.txt
 format2.txt
 residual.md
+
+# Generated by docs/source/_ext/catalogue_columns.py from config/columns/
+docs/source/catalogue_columns.md
diff --git a/CLAUDE.md b/CLAUDE.md
index b4447758..9ae25ba6 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -43,7 +43,8 @@ is the container (full scientific stack pre-built). For a local dev environment:
 - `cosmo_val.py`: Cosmology validation routines
 - `cosmology.py`: Cosmological calculations and theory
 - `galaxy.py`: Galaxy-specific processing
-- `io.py`: Input/output utilities
+- `grammar.py`: Column grammars: ShapePipe v1 -> v2 adapter and `column_map` renames (`adapt`)
+- `io.py`: Input/output; the catalogue reader (`read_catalogue`, `Catalogue`), which detects FITS/HDF5 layouts
 - `plots.py`: Plotting functions
 - `rho_tau.py`: Rho and tau statistics calculations
 - `statistics.py`: Cosmology-independent statistics (jackknife resampling, χ²/PTE, covariance↔correlation, OneCovariance reshaping)
@@ -67,10 +68,10 @@ snakemake --profile workflow/profiles/candide -s workflow/Snakefile \
 
 ### Configuration
 Main configuration in `scripts/calibration/params.py` with parameters:
-- `name`: Field/patch identifier
+- `campaign`: Campaign name (the ShapePipe tile list); names the input products
 - `data_dir`: Input data directory
 - `galaxy_cat_path`: Galaxy catalogue path (.fits/.hdf5)
-- `star_cat_path`: Star catalogue path (.fits)
+- `star_cat_path`: Star catalogue path (.hdf5, or a v1 .fits)
 
 ### Key Dependencies
 - astropy, numpy, scipy for core calculations
diff --git a/config/calibration/mask_from_compr_v1.X.11.yaml b/config/calibration/mask_from_compr_v1.X.11.yaml
index 3fa9ee3e..d76d7fb3 100644
--- a/config/calibration/mask_from_compr_v1.X.11.yaml
+++ b/config/calibration/mask_from_compr_v1.X.11.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
 
   # Duplicate objects
@@ -24,29 +24,29 @@ dat:
     kind: greater_equal
     value: 2
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_from_compr_v1.X.6.yaml b/config/calibration/mask_from_compr_v1.X.6.yaml
index 1c9b08e5..a71f9974 100644
--- a/config/calibration/mask_from_compr_v1.X.6.yaml
+++ b/config/calibration/mask_from_compr_v1.X.6.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # Duplicate objects
   - col_name: overlap
@@ -23,29 +23,29 @@ dat:
     kind: greater_equal
     value: 2
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.10.yaml b/config/calibration/mask_v1.X.10.yaml
index 703ea91b..2678a49b 100644
--- a/config/calibration/mask_v1.X.10.yaml
+++ b/config/calibration/mask_v1.X.10.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.11.yaml b/config/calibration/mask_v1.X.11.yaml
index 88e01643..b87aa267 100644
--- a/config/calibration/mask_v1.X.11.yaml
+++ b/config/calibration/mask_v1.X.11.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.2.yaml b/config/calibration/mask_v1.X.2.yaml
index 846f14ff..e7ad7758 100644
--- a/config/calibration/mask_v1.X.2.yaml
+++ b/config/calibration/mask_v1.X.2.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,40 +57,40 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
   # Faint star halos
-  - col_name: 1_Faint_star_halos
+  - col_name: MASK_1_Faint_star_halos
     label: "Faint star halos"
     kind: equal
     value: False
 
   # Bright star halos
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.3.yaml b/config/calibration/mask_v1.X.3.yaml
index bcbbc620..3c14c5fd 100644
--- a/config/calibration/mask_v1.X.3.yaml
+++ b/config/calibration/mask_v1.X.3.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.4.yaml b/config/calibration/mask_v1.X.4.yaml
index 903b9098..37b67325 100644
--- a/config/calibration/mask_v1.X.4.yaml
+++ b/config/calibration/mask_v1.X.4.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,40 +57,40 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
   # Faint star halos
-  - col_name: 1_Faint_star_halos
+  - col_name: MASK_1_Faint_star_halos
     label: "Faint star halos"
     kind: equal
     value: False
 
   # Bright star halos
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.4_im_sim.yaml b/config/calibration/mask_v1.X.4_im_sim.yaml
index 794566d4..8f3636c2 100644
--- a/config/calibration/mask_v1.X.4_im_sim.yaml
+++ b/config/calibration/mask_v1.X.4_im_sim.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -30,17 +30,17 @@ dat:
     value: [15, 30]
 
   # ngmix flags
-  - col_name: NGMIX_MOM_FAIL
+  - col_name: NGMIX_MCAL_TYPES_FAIL
     label: "ngmix moments failure"
     kind: equal
     value: 0
 
   # invalid PSF ellipticities
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_0
+  - col_name: NGMIX_G1_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 1"
     kind: not_equal
     value: -10
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_1
+  - col_name: NGMIX_G2_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 2"
     kind: not_equal
     value: -10
diff --git a/config/calibration/mask_v1.X.5.yaml b/config/calibration/mask_v1.X.5.yaml
index 5f56b135..bf717380 100644
--- a/config/calibration/mask_v1.X.5.yaml
+++ b/config/calibration/mask_v1.X.5.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.6.yaml b/config/calibration/mask_v1.X.6.yaml
index 1b5f7da6..fc021a2c 100644
--- a/config/calibration/mask_v1.X.6.yaml
+++ b/config/calibration/mask_v1.X.6.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.6_ppv1.yaml b/config/calibration/mask_v1.X.6_ppv1.yaml
index a3d29cda..bcaffb15 100644
--- a/config/calibration/mask_v1.X.6_ppv1.yaml
+++ b/config/calibration/mask_v1.X.6_ppv1.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -42,17 +42,17 @@ dat:
     value: [15, 30]
 
   # ngmix flags
-  - col_name: NGMIX_MOM_FAIL
+  - col_name: NGMIX_MCAL_TYPES_FAIL
     label: "ngmix moments failure"
     kind: equal
     value: 0
 
   # invalid PSF ellipticities
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_0
+  - col_name: NGMIX_G1_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 1"
     kind: not_equal
     value: -10
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_1
+  - col_name: NGMIX_G2_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 2"
     kind: not_equal
     value: -10
@@ -84,29 +84,29 @@ dat:
     col_name2: MAG_GAAP_0p7_z2
     value: -99
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.7.yaml b/config/calibration/mask_v1.X.7.yaml
index cbc802be..585481d9 100644
--- a/config/calibration/mask_v1.X.7.yaml
+++ b/config/calibration/mask_v1.X.7.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,34 +57,34 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Bright star halos                                                            
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.8.yaml b/config/calibration/mask_v1.X.8.yaml
index 58234d64..5dafbb72 100644
--- a/config/calibration/mask_v1.X.8.yaml
+++ b/config/calibration/mask_v1.X.8.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,40 +57,40 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Faint star halos
-  - col_name: 1_Faint_star_halos
+  - col_name: MASK_1_Faint_star_halos
     label: "Faint star halos"
     kind: equal
     value: False
 
   # Bright star halos                                                            
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.9.yaml b/config/calibration/mask_v1.X.9.yaml
index 4a448ce7..906578df 100644
--- a/config/calibration/mask_v1.X.9.yaml
+++ b/config/calibration/mask_v1.X.9.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.9_im_sim.overlay.yaml b/config/calibration/mask_v1.X.9_im_sim.overlay.yaml
index 94c5b9ae..9c15edab 100644
--- a/config/calibration/mask_v1.X.9_im_sim.overlay.yaml
+++ b/config/calibration/mask_v1.X.9_im_sim.overlay.yaml
@@ -45,36 +45,36 @@ ops:
     with: |2
         # invalid PSF ellipticities (ShapePipe-v2 grammar: scalar G1/G2 components)
 
-  # --- dat_ext (post-processing / coverage masks) ------------------------
+  # --- mask bits (post-processing / coverage masks) ----------------------
   - why: >-
-      No coverage masks on sims: the whole dat_ext group (Stars, manual mask,
-      r-band footprint, Maximask) is survey post-processing with no analogue
-      in the simulated tiles.
+      No coverage masks on sims: the healsparse mask-bit cuts (stars, manual
+      mask, r coverage, Maximask) are survey post-processing with no analogue in the
+      simulated tiles.
     drop: |2
 
-      ## Using columns in 'dat_ext' group (post-processing flags)
-      dat_ext:
+        # Healsparse mask bits and post-processing columns (the comprehensive
+        # HDF5's data_ext dataset)
 
         # Stars
-        - col_name: 4_Stars
+        - col_name: MASK_4_Stars
           label: "Stars"
           kind: equal
           value: False
 
         # Manual mask
-        - col_name: 8_Manual
+        - col_name: MASK_8_Manual
           label: "manual mask"
           kind: equal
           value: False
 
         # r-band footprint
-        - col_name: 64_r
+        - col_name: MASK_64_r
           label: "r-band imaging"
           kind: equal
           value: False
 
         # Maximask
-        - col_name: 1024_Maximask
+        - col_name: MASK_1024_Maximask
           label: "maximask"
           kind: equal
           value: False
diff --git a/config/calibration/mask_v1.X.9_im_sim.yaml b/config/calibration/mask_v1.X.9_im_sim.yaml
index 1c6ff672..3c28cc03 100644
--- a/config/calibration/mask_v1.X.9_im_sim.yaml
+++ b/config/calibration/mask_v1.X.9_im_sim.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
diff --git a/config/calibration/mask_v2.0.yaml b/config/calibration/mask_v2.0.yaml
new file mode 100644
index 00000000..930ae42f
--- /dev/null
+++ b/config/calibration/mask_v2.0.yaml
@@ -0,0 +1,160 @@
+# Config file for masking and calibration, ShapePipe v2 catalogues.
+#
+# ShapePipe v2 replaces the single IMAFLAGS_ISO column with per-reason boolean
+# mask columns MASK_<bit>_<label>, where True means MASKED. They are therefore
+# cut with `kind: equal, value: False` (keep the un-masked objects):
+#
+# Reason bits of the ShapePipe r-band default bitmask:
+#
+#   MASK_1_Faint_star_halos     faint star halos (bit 0; value 1)
+#   MASK_2_Bright_star_halos    bright star halos (bit 1; value 2)
+#   MASK_4_Stars                star bodies (bit 2; value 4)
+#   MASK_8_Manual               manual galaxy mask
+#   MASK_64_r                   r-band coverage
+#   MASK_1024_Maximask          MaxiMask
+#
+# Per-band coverage flags and Pan-STARRS:
+#
+#   MASK_16_u, MASK_32_g, MASK_64_r, MASK_128_i, MASK_256_z
+#                               u, g, r, i, HSC z-band coverage
+#   MASK_2048_z2                no Pan-STARRS z-band (z2) coverage
+#
+# The default selection (see sp_validation.galaxy.DEFAULT_MASK_COLUMNS) is
+# bits 1 + 2 + 4 + 8 + 64 + 1024, whose OR matches mask_r, the r-band
+# mask, on covered granules (verified on the P3 sky area); footprint-edge
+# granules without mask_r coverage can differ. The other coverage flags and bit 2048 are listed
+# here but commented out, since ORing all of them masks essentially
+# everything.
+
+# General parameters (can also given on command line)
+params:
+  input_path: unions_shapepipe_comprehensive_2025_v2.0.hdf5
+  cmatrices: False
+  sky_regions: False
+  verbose: True
+
+# Masks
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
+dat:
+  # SExtractor flags: keep 0 (clean), 1 (neighbours), 2 (deblended); drop
+  # 3 (both) and above. Same as the image-sims config, so data and sims
+  # select identically (shapepipe#885).
+  - col_name: FLAGS
+    label: SE FLAGS
+    kind: smaller_equal
+    value: 2
+
+  # Duplicate objects
+  - col_name: overlap
+    label: tile overlap
+    kind: equal
+    value: True
+
+  # ShapePipe masks (boolean, True = masked)
+  - col_name: MASK_4_Stars
+    label: "star bodies"
+    kind: equal
+    value: False
+  # Star-halo bits follow the UNIONS mask producer schema.
+  - col_name: MASK_1_Faint_star_halos
+    label: "faint star halos"
+    kind: equal
+    value: False
+  - col_name: MASK_2_Bright_star_halos
+    label: "bright star halos"
+    kind: equal
+    value: False
+  - col_name: MASK_8_Manual
+    label: "manual mask"
+    kind: equal
+    value: False
+  - col_name: MASK_1024_Maximask
+    label: "maximask"
+    kind: equal
+    value: False
+
+  # r-band coverage. Required for the OR above to reproduce mask_r.
+  - col_name: MASK_64_r
+    label: "r-band imaging"
+    kind: equal
+    value: False
+
+  # Other per-band coverage flags and Pan-STARRS z2; enable as required.
+  # Cutting on all of them at once leaves essentially no objects.
+  # - col_name: MASK_16_u
+  #   label: "u coverage"
+  #   kind: equal
+  #   value: False
+  # - col_name: MASK_2048_z2
+  #   label: "no PS-z2"
+  #   kind: equal
+  #   value: False
+  #
+  # v1.X also applied a rough pointing-coverage cut, 'npoint3' >= 3, from an
+  # external post-processing catalogue. ShapePipe v2 emits no such column and
+  # no mask bit encodes it, so that cut has no v2 counterpart; add it back
+  # here if an external pointing-coverage map is ever joined onto the
+  # catalogue.
+
+  # Number of epochs
+  - col_name: N_EPOCH
+    label: r"$n_{\rm epoch}$"
+    kind: greater_equal
+    value: 2
+
+  # Magnitude range
+  - col_name: mag
+    label: mag range
+    kind: range
+    value: [15, 30]
+
+  # ngmix flags
+  - col_name: NGMIX_MCAL_TYPES_FAIL
+    label: "ngmix moments failure"
+    kind: equal
+    value: 0
+
+  # Objects ngmix never fit. ShapePipe's make_cat pre-fills the NGMIX_*
+  # columns with sentinels (G1/G2 = -10, T/FLUX = 0) and overwrites them only
+  # for objects present in the ngmix output, so a never-fit object also keeps
+  # NGMIX_MCAL_FLAGS = 0 and is not caught by any flag cut. In
+  # final_cat_smk-g7.hdf5 that is 18,983 of 1,851,100 objects (1.03%);
+  # admitting them gives mean e1 = -0.107 (std 1.03) against -0.004 (std
+  # 0.22). The -10 cuts below do reject them today, but only because make_cat
+  # happens to use the same literal for the PSF columns: that is an exact
+  # float equality against a sentinel ShapePipe may change, so state the
+  # condition directly. Mirrors the guard in
+  # sp_validation.galaxy.classification_galaxy_ngmix.
+  - col_name: NGMIX_N_EPOCH
+    label: "ngmix never fit"
+    kind: greater_equal
+    value: 1
+
+  # invalid PSF ellipticities
+  - col_name: NGMIX_G1_PSF_ORIG_NOSHEAR
+    label: "bad PSF ellipticity comp 1"
+    kind: not_equal
+    value: -10
+  - col_name: NGMIX_G2_PSF_ORIG_NOSHEAR
+    label: "bad PSF ellipticity comp 2"
+    kind: not_equal
+    value: -10
+
+# Metacal parameters
+metacal:
+  # Ellipticity dispersion
+  sigma_eps_prior: 0.34
+
+  # Signal-to-noise range
+  gal_snr_min: 10
+  gal_snr_max: 500
+
+  # Relative-size (hlr / hlr_psf) range
+  gal_rel_size_min: 0.707
+  gal_rel_size_max: 3
+
+  # Correct relative size for ellipticity?
+  gal_size_corr_ell: False
+
+  # Weight for global response matrix, None for unweighted mean
+  global_R_weight: w
diff --git a/config/columns/shapepipe_v2.yaml b/config/columns/shapepipe_v2.yaml
new file mode 100644
index 00000000..7d9c74ec
--- /dev/null
+++ b/config/columns/shapepipe_v2.yaml
@@ -0,0 +1,121 @@
+# Catalogue columns sp_validation reads by fixed name: the canonical schema.
+#
+# The names are ShapePipe v2's. sp_validation code reads only these names
+# (src/sp_validation/tests/test_column_schema.py keeps code and file in step);
+# the docs page "Catalogue columns" is generated from this file.
+#
+# Columns not listed here are named by configuration, not by code: the cosmo_val
+# cat_config `*_col` keys (shear e1/e2/weight, PSF and star shapes and sizes),
+# the `col_name` of every masking cut, `mask_columns` (the mask columns below
+# are its default), and parameter lists.
+#
+# A catalogue in another naming convention is read by giving its config a
+# `column_map` of {canonical name: name in the file}; one `*` on each side maps
+# a family at once ({"NGMIX_*": "MYFIT_*"}). ShapePipe v1 products (releases
+# up to v1.6.x) need no map: sp_validation.grammar detects and presents them
+# under these names.
+#
+# Layout: {kind: {about: ..., columns: {canonical name: meaning}}}.
+
+galaxy:
+  about: >-
+    ShapePipe galaxy catalogue: the campaign catalogue
+    (`final_cat_<campaign>.hdf5`) and the comprehensive catalogue merged from
+    it. Read by the calibration pipeline (`scripts/calibration`, `catalog_builders`).
+  columns:
+    RA: Right ascension [deg].
+    DEC: Declination [deg] (campaign catalogue).
+    XWIN_WORLD: SExtractor windowed right ascension [deg]; tile-overlap cut.
+    YWIN_WORLD: SExtractor windowed declination [deg]; tile-overlap cut.
+    TILE_ID: Tile the object was detected on.
+    FLAGS: SExtractor extraction flags.
+    MAG_AUTO: SExtractor magnitude.
+    N_EPOCH: Number of exposures covering the object.
+    MASK_1_Faint_star_halos: Mask bit 1 (faint-star halo); True = masked.
+    MASK_2_Bright_star_halos: Mask bit 2 (bright-star halo); True = masked.
+    MASK_4_Stars: Mask bit 4 (star body); True = masked.
+    MASK_8_Manual: Mask bit 8 (manual mask); True = masked.
+    MASK_64_r: Mask bit 64 (no r-band coverage); True = masked.
+    MASK_1024_Maximask: Mask bit 1024 (MaxiMask artefact); True = masked.
+    NGMIX_N_EPOCH: Number of epochs ngmix fit; 0 = never fit.
+    NGMIX_MCAL_FLAGS: ngmix metacal fit flags.
+    NGMIX_MCAL_TYPES_FAIL: Number of metacal types whose fit failed.
+    NGMIX_G1_ERR_NOSHEAR: Error on g1 of the unsheared fit; inverse-variance weight.
+    NGMIX_G2_ERR_NOSHEAR: Error on g2 of the unsheared fit; inverse-variance weight.
+    NGMIX_G1_PSF_ORIG_NOSHEAR: g1 of the original PSF; -10 = no PSF fit.
+    NGMIX_G2_PSF_ORIG_NOSHEAR: g2 of the original PSF.
+    NGMIX_T_PSF_ORIG_NOSHEAR: Size T of the original PSF.
+    NGMIX_G1_NOSHEAR: Ellipticity g1, unsheared image.
+    NGMIX_G2_NOSHEAR: Ellipticity g2, unsheared image.
+    NGMIX_T_NOSHEAR: Galaxy size T = 2 sigma^2, unsheared image.
+    NGMIX_T_ERR_NOSHEAR: Error on T, unsheared image.
+    NGMIX_FLUX_NOSHEAR: Flux, unsheared image.
+    NGMIX_FLUX_ERR_NOSHEAR: Error on the flux, unsheared image.
+    NGMIX_FLAGS_NOSHEAR: Fit flags, unsheared image.
+    NGMIX_T_PSF_RECONV_NOSHEAR: Size T of the reconvolution PSF, unsheared image.
+    NGMIX_G1_1P: Ellipticity g1, image sheared by +h along g1.
+    NGMIX_G2_1P: Ellipticity g2, image sheared by +h along g1.
+    NGMIX_T_1P: Galaxy size T = 2 sigma^2, image sheared by +h along g1.
+    NGMIX_T_ERR_1P: Error on T, image sheared by +h along g1.
+    NGMIX_FLUX_1P: Flux, image sheared by +h along g1.
+    NGMIX_FLUX_ERR_1P: Error on the flux, image sheared by +h along g1.
+    NGMIX_FLAGS_1P: Fit flags, image sheared by +h along g1.
+    NGMIX_T_PSF_RECONV_1P: Size T of the reconvolution PSF, image sheared by +h along g1.
+    NGMIX_G1_1M: Ellipticity g1, image sheared by -h along g1.
+    NGMIX_G2_1M: Ellipticity g2, image sheared by -h along g1.
+    NGMIX_T_1M: Galaxy size T = 2 sigma^2, image sheared by -h along g1.
+    NGMIX_T_ERR_1M: Error on T, image sheared by -h along g1.
+    NGMIX_FLUX_1M: Flux, image sheared by -h along g1.
+    NGMIX_FLUX_ERR_1M: Error on the flux, image sheared by -h along g1.
+    NGMIX_FLAGS_1M: Fit flags, image sheared by -h along g1.
+    NGMIX_T_PSF_RECONV_1M: Size T of the reconvolution PSF, image sheared by -h along g1.
+    NGMIX_G1_2P: Ellipticity g1, image sheared by +h along g2.
+    NGMIX_G2_2P: Ellipticity g2, image sheared by +h along g2.
+    NGMIX_T_2P: Galaxy size T = 2 sigma^2, image sheared by +h along g2.
+    NGMIX_T_ERR_2P: Error on T, image sheared by +h along g2.
+    NGMIX_FLUX_2P: Flux, image sheared by +h along g2.
+    NGMIX_FLUX_ERR_2P: Error on the flux, image sheared by +h along g2.
+    NGMIX_FLAGS_2P: Fit flags, image sheared by +h along g2.
+    NGMIX_T_PSF_RECONV_2P: Size T of the reconvolution PSF, image sheared by +h along g2.
+    NGMIX_G1_2M: Ellipticity g1, image sheared by -h along g2.
+    NGMIX_G2_2M: Ellipticity g2, image sheared by -h along g2.
+    NGMIX_T_2M: Galaxy size T = 2 sigma^2, image sheared by -h along g2.
+    NGMIX_T_ERR_2M: Error on T, image sheared by -h along g2.
+    NGMIX_FLUX_2M: Flux, image sheared by -h along g2.
+    NGMIX_FLUX_ERR_2M: Error on the flux, image sheared by -h along g2.
+    NGMIX_FLAGS_2M: Fit flags, image sheared by -h along g2.
+    NGMIX_T_PSF_RECONV_2M: Size T of the reconvolution PSF, image sheared by -h along g2.
+
+comprehensive:
+  about: >-
+    Columns a comprehensive catalogue carries beyond the galaxy columns, as
+    post-processing writes them. Read when calibrating it
+    (`calibrate_comprehensive_cat.py`, `catalog_builders`).
+  columns:
+    Dec: Declination [deg].
+    mag: Magnitude.
+    MAGERR_AUTO: SExtractor magnitude error.
+    FLUX_RADIUS: SExtractor half-light radius [pixel].
+    w_iv: Inverse-variance shape weight.
+    e1_PSF: PSF ellipticity e1 at the galaxy position.
+    e2_PSF: PSF ellipticity e2 at the galaxy position.
+    fwhm_PSF: PSF FWHM at the galaxy position.
+
+star:
+  about: >-
+    ShapePipe star/PSF catalogue (`full_starcat_<campaign>.hdf5`, or FITS).
+    Rho/tau statistics read their star columns by cat_config keys instead.
+  columns:
+    RA: Right ascension [deg].
+    DEC: Declination [deg].
+    HSM_G1_PSF: PSF model ellipticity g1 (HSM).
+    HSM_G2_PSF: PSF model ellipticity g2 (HSM).
+
+shear:
+  about: >-
+    Calibrated shear catalogue, as sp_validation writes it and cosmo_val reads
+    it. Shapes and weights are named by the cat_config `*_col` keys; these
+    are read by fixed name.
+  columns:
+    RA: Right ascension [deg].
+    Dec: Declination [deg].
diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index 618f3972..2e1050cb 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -59,7 +59,7 @@ SP_axel_v0.0:
     hdu: 1
     path: star_cat.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -67,6 +67,8 @@ SP_axel_v0.0:
   shear:
     R: 1.0
     path: shapepipe_1500_goldshape_v1.fits
+    ra_col: ra
+    dec_col: dec
     w_col: w
     e1_col: g1
     e1_PSF_col: HSM_G1_PSF
@@ -94,7 +96,7 @@ SP_v0.1.1:
     hdu: 1
     path: star_cat.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -128,7 +130,7 @@ SP_v1.3:
     hdu: 1
     path: unions_shapepipe_star_2022_v1.3.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -170,7 +172,7 @@ SP_v1.3.6:
     hdu: 1
     path: unions_shapepipe_psf_2022_v1.3.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -214,7 +216,7 @@ SP_v1.4.5:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -257,7 +259,7 @@ SP_v1.4.5.A:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -265,12 +267,14 @@ SP_v1.4.5.A:
   shear:
     R: 1.0
     path: shapepipe_SPv1.fits
+    ra_col: ra
+    dec_col: dec
     redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
     w_col: w
     e1_col: g1
-    e1_PSF_col: e1_PSF
+    e1_PSF_col: psf_g1
     e2_col: g2
-    e2_PSF_col: e2_PSF
+    e2_PSF_col: psf_g2
   star:
     ra_col: RA
     dec_col: Dec
@@ -298,7 +302,7 @@ SP_v1.4.5_bright:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -339,7 +343,7 @@ SP_v1.4.5_faint:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -381,7 +385,7 @@ SP_v1.4.6_glass_mock:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -423,7 +427,7 @@ SP_v1.4.5_intermediate:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -465,7 +469,7 @@ SP_v1.4.6:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -509,7 +513,7 @@ SP_v1.4.6.3:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -553,7 +557,7 @@ SP_v1.4.6.3_B:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -597,7 +601,7 @@ SP_v1.4.6.3_C:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -641,7 +645,7 @@ SP_v1.4.6.3_A:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -685,7 +689,7 @@ SP_v1.4.6_ecut07:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -729,7 +733,7 @@ SP_v1.4.6.1:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -773,7 +777,7 @@ SP_v1.4.7:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -819,7 +823,7 @@ SP_v1.4.8:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -863,7 +867,7 @@ SP_v1.4.11.2:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -907,7 +911,7 @@ SP_v1.4.11.3:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -944,19 +948,18 @@ SP_v1.4.12.3:
     sigma_e: 0.379587601488189
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
   psf:
-    PSF_flag: FLAG_PSF_HSM
-    PSF_size: SIGMA_PSF_HSM
-    square_size: true
-    star_flag: FLAG_STAR_HSM
-    star_size: SIGMA_STAR_HSM
+    PSF_flag: HSM_FLAG_PSF
+    PSF_size: HSM_T_PSF
+    star_flag: HSM_FLAG_STAR
+    star_size: HSM_T_STAR
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: E1_PSF_HSM
-    e1_star_col: E1_STAR_HSM
-    e2_PSF_col: E2_PSF_HSM
-    e2_star_col: E2_STAR_HSM
+    dec_col: DEC
+    e1_PSF_col: HSM_G1_PSF
+    e1_star_col: HSM_G1_STAR
+    e2_PSF_col: HSM_G2_PSF
+    e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
     path: v1.4.12.3/unions_shapepipe_cut_struc_2024_v1.4.12.3.fits
@@ -989,19 +992,18 @@ SP_v1.4.13.3:
     sigma_e: 0.379587601488189
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
   psf:
-    PSF_flag: FLAG_PSF_HSM
-    PSF_size: SIGMA_PSF_HSM
-    square_size: true
-    star_flag: FLAG_STAR_HSM
-    star_size: SIGMA_STAR_HSM
+    PSF_flag: HSM_FLAG_PSF
+    PSF_size: HSM_T_PSF
+    star_flag: HSM_FLAG_STAR
+    star_size: HSM_T_STAR
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: E1_PSF_HSM
-    e1_star_col: E1_STAR_HSM
-    e2_PSF_col: E2_PSF_HSM
-    e2_star_col: E2_STAR_HSM
+    dec_col: DEC
+    e1_PSF_col: HSM_G1_PSF
+    e1_star_col: HSM_G1_STAR
+    e2_PSF_col: HSM_G2_PSF
+    e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
     path: v1.4.13.3/unions_shapepipe_cut_struc_2024_v1.4.13.3.fits
@@ -1041,7 +1043,7 @@ SP_v1.4.11.3_ecut07:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -1182,7 +1184,7 @@ SP_v1.6.6:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.6.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
diff --git a/cosmo_val/compute_theory_cov.py b/cosmo_val/compute_theory_cov.py
deleted file mode 100644
index 2868355f..00000000
--- a/cosmo_val/compute_theory_cov.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import time
-
-import numpy as np
-import yaml
-from shear_psf_leakage.rho_tau_cov import CovTauTh
-
-
-def get_params_rho_tau(cat, survey="other"):
-
-    # Set parameters
-    params = {}
-    # TODO to yaml file
-    params["ra_col"] = cat["psf"]["ra_col"]
-    params["dec_col"] = cat["psf"]["dec_col"]
-    params["e1_PSF_col"] = cat["psf"]["e1_PSF_col"]
-    params["e2_PSF_col"] = cat["psf"]["e2_PSF_col"]
-    params["e1_star_col"] = cat["psf"]["e1_star_col"]
-    params["e2_star_col"] = cat["psf"]["e2_star_col"]
-    params["PSF_size"] = cat["psf"]["PSF_size"]
-    params["star_size"] = cat["psf"]["star_size"]
-    params["R11"] = np.array([1])
-    params["R22"] = np.array([1])
-    if survey != "DES":
-        params["PSF_flag"] = cat["psf"]["PSF_flag"]
-        params["star_flag"] = cat["psf"]["star_flag"]
-    if survey == "DES":
-        params["R11"] = cat["shear"]["R11"]
-        params["R22"] = cat["shear"]["R22"]
-    params["ra_units"] = "deg"
-    params["dec_units"] = "deg"
-
-    params["w_col"] = cat["shear"]["w"]
-    params["e1_col"] = cat["shear"]["e1_col"]
-    params["e2_col"] = cat["shear"]["e2_col"]
-
-    return params
-
-
-if __name__ == "__main__":
-    base_dir = "/home/guerrini/data/"
-
-    versions = [
-        "SP_v1.4-P3",
-        "SP_v1.4-P3_LFmask",
-        "SP_v1.3_LFmask_8k",
-        "SP_axel_v0.0",
-        "DES",
-    ]
-
-    path_config = "/home/guerrini/sp_validation/cosmo_val/cat_config.yaml"
-    output_dir = "/home/guerrini/sp_validation/cosmo_val/output/rho_tau_stats/"
-
-    nbin_ang = 100
-    nbin_rad = 200
-
-    sep_units = "arcmin"
-    coord_units = "degrees"
-    theta_min = 0.1
-    theta_max = 250
-    nbins = 20
-
-    TreeCorrConfig_xi = {
-        "ra_units": coord_units,
-        "dec_units": coord_units,
-        "min_sep": theta_min,
-        "max_sep": theta_max,
-        "sep_units": sep_units,
-        "nbins": nbins,
-    }
-
-    with open(path_config, "r") as file:
-        cat = yaml.load(file.read(), Loader=yaml.FullLoader)
-
-    for ver in versions:
-        params = get_params_rho_tau(cat[ver], survey=ver)
-
-        info = cat[ver]
-        A = info["cov_th"]["A"] * 60 * 60
-        n_e = info["cov_th"]["n_e"]
-        n_psf = info["cov_th"]["n_psf"]
-
-        path_gal = base_dir + info["subdir"] + "/" + info["shear"]["path"]
-        path_psf = base_dir + info["subdir"] + "/" + info["psf"]["path"]
-        hdu_psf = info["psf"]["hdu"]
-
-        print("Computing the covariance matrix for the version: ", ver)
-        start_time = time.time()
-        cov_tau_th = CovTauTh(
-            path_gal=path_gal,
-            path_psf=path_psf,
-            hdu_psf=hdu_psf,
-            treecorr_config=TreeCorrConfig_xi,
-            A=A,
-            n_e=n_e,
-            n_psf=n_psf,
-            params=params,
-        )
-        print(
-            "--- Computation of the rho and tau statistics %s seconds ---"
-            % (time.time() - start_time)
-        )
-
-        start_time = time.time()
-
-        cov = cov_tau_th.build_cov(nbin_ang=nbin_ang, nbin_rad=nbin_rad)
-
-        print("--- Covariance computation %s seconds ---" % (time.time() - start_time))
-        np.save(output_dir + "/cov_tau_" + ver + "_th.npy", cov)
-        print("Saved covariance matrix of version: ", ver)
diff --git a/docs/ngmix_psf_column_migration.md b/docs/ngmix_psf_column_migration.md
index b24e9f83..e84a1848 100644
--- a/docs/ngmix_psf_column_migration.md
+++ b/docs/ngmix_psf_column_migration.md
@@ -1,23 +1,48 @@
 # ShapePipe-v2 column-grammar migration (shapepipe → sp_validation)
 
-**Status:** code-complete on `migrate/ngmix-psf-column-names` (draft PR
-[#201](https://github.com/CosmoStat/sp_validation/pull/201)). Every shape-column
-read in the live package, configs, calibration scripts, paper figures, and
-notebooks uses the ShapePipe-v2 grammar; the σ→T units change and the
-`spread_model` removal are in; the dead `galsim` estimator path is removed; and
-the suite is green against synthetic catalogues carrying the new columns.
-
-**No code work is left waiting on a regenerated catalogue.** The one *value*
-change — `*_PSF_ORIG` now holds a true original-PSF fit (shapepipe#749) rather
-than the reconvolved-kernel alias the old columns silently held — is a straight
-column rename at the code level and is already in place; the code does not care
-that the numbers moved. All a v2 catalogue enables is a *look-at-the-numbers*
-sanity check (do the α-leakage / size-ratio cuts still behave), which is analysis,
-not code, and does not gate the PR. **The real merge gate is cutover timing:**
-merging this branch makes `develop` *require* v2 columns and stop reading today's
-catalogues, so #201 should land together with — or just after —
-[shapepipe#761](https://github.com/CosmoStat/shapepipe/pull/761)→#741 and the
-first v2 catalogue.
+sp_validation reads the ShapePipe-v2 column grammar everywhere: live package,
+configs, calibration scripts, paper figures and notebooks. Catalogues written in
+the v1 grammar (every release up to v1.6.x) are presented in the v2 grammar at the
+read boundary by `sp_validation.grammar`: every catalogue read goes through
+`sp_validation.io` (`read_catalogue`, `Catalogue`), which applies it. `adapt(table)`
+presents a v1 table under the v2 names and units below, as a lazy
+column view over a numpy array, FITS_rec or h5py dataset, and returns v2 or
+grammar-neutral tables unchanged. `detect_generation` tells the grammars apart
+from column names, and `v2_names` maps a header's names without reading data.
+The adapter maps by *name*, so a v1 `*_PSFo` column is presented as
+`*_PSF_ORIG` holding the v1 (reconvolved-alias) values; downstream code never
+branches on the generation. The one exception is a documented v1 defect, which
+the adapter corrects rather than renames: `NGMIX_T_PSF_RECONV_NOSHEAR` is read
+from `NGMIX_Tpsf_1P` (see the reconvolved-PSF table). The tables below
+(Old = v1, New = v2) are the rule table in `grammar.V1_RULES`.
+
+Mask columns follow a separate rule family, applied whatever the generation.
+The UNIONS healsparse mask product is one bitmask, and bit `b` is the boolean
+column `MASK_{b}_{label}`: the bit value for its identity, the label for its
+meaning (`grammar.mask_column`, labels from `grammar.MASK_LABELS`). ShapePipe v2
+and `catalog_builders.ApplyHspMasks` write these names, and every mask
+configuration cuts on them, so one configuration serves both generations. The
+adapter presents two other spellings of the same bit under the canonical name:
+`{b}_{label}`, the `data_ext` dataset of every released v1 comprehensive HDF5,
+and `MASK_n{b}`, written by pre-release ShapePipe v2 runs. No spelling marks a
+generation.
+
+| bit | column | v1 `data_ext` name | meaning |
+|---|---|---|---|
+| 1 (bit 0) | `MASK_1_Faint_star_halos` | `1_Faint_star_halos` | faint star halos |
+| 2 (bit 1) | `MASK_2_Bright_star_halos` | `2_Bright_star_halos` | bright star halos |
+| 4 (bit 2) | `MASK_4_Stars` | `4_Stars` | star bodies |
+| 8 | `MASK_8_Manual` | `8_Manual` | manual mask (large galaxies) |
+| 16–256 | `MASK_16_u` … `MASK_256_z` | `16_u`, `32_g`, `64_r`, `128_i`, `256_z` | per-band coverage; `256_z` is the HSC z-band |
+| 512 | `MASK_512_Tile_RA_DEC_cut` | `512_Tile_RA_DEC_cut` | outside the tile's unique region |
+| 1024 | `MASK_1024_Maximask` | `1024_Maximask` | MaxiMask |
+| 2048 | `MASK_2048_z2` | `2048_z2` | Pan-STARRS z-band (`z2`); true means no coverage |
+
+`adapt(data, data_ext)` joins the two datasets of a comprehensive HDF5 into one
+table, so a v1 comprehensive catalogue and a v2 catalogue take the same mask
+configuration and the same `galaxy.mask_cut`. `IMAFLAGS_ISO` passes through
+unmapped: its v1 bits (2 halo, 4 border, 16 Messier, 32 NGC, 128 spike) are not
+the mask bits of the same value.
 
 shapepipe#761 turns the shape-measurement output into **one column grammar for
 the whole catalogue**: every estimator names its outputs
@@ -67,7 +92,22 @@ column is also carried through `params.add_cols_pre_cal` and every
 
 | Old | New | Note |
 |---|---|---|
-| `NGMIX_Tpsf_{shear}` | `NGMIX_T_PSF_RECONV_{shear}` | same value (the `T/Tpsf` size-ratio cut) |
+| `NGMIX_Tpsf_{shear}` | `NGMIX_T_PSF_RECONV_{shear}` | same value (the `T/Tpsf` size-ratio cut), for `{shear}` ≠ `NOSHEAR` |
+| `NGMIX_Tpsf_1P`, else `NGMIX_Tpsf_NOSHEAR` | `NGMIX_T_PSF_RECONV_NOSHEAR` | **v1 defect corrected** (below) |
+
+ShapePipe v1 wrote a wrong `NGMIX_Tpsf_NOSHEAR`: in the v1.4, v1.5 and v1.6
+comprehensive catalogues it differs by ~2% for nearly every object from the
+reconvolution kernel metacal applied, which the sheared types'
+`NGMIX_Tpsf_{1P,1M,2P,2M}` record (they agree to ~1e-5). The v1.4.6.3 release
+used the `1P` value in its metacal size cut and wrote it as
+`NGMIX_Tpsf_NOSHEAR` (keeping the raw one as `NGMIX_Tpsf_NOSHEAR_orig`), so the
+adapter reads `NGMIX_Tpsf_1P` whenever a v1 table has it and falls back to
+`NGMIX_Tpsf_NOSHEAR` for a cut catalogue such as v1.4.6.3's, whose no-shear
+column already holds the `1P` value. The release's `w_des` and PSF-leakage
+binning used the raw size, so a calibration through the adapter reproduces the
+release's selection and per-object columns but not those two bit for bit.
+ShapePipe v2 reuses one reconvolved PSF across metacal types and needs no
+correction.
 
 ### ngmix — original PSF (value change — shapepipe#749 fix — *not a code blocker*)
 
@@ -76,7 +116,7 @@ reconvolved-kernel alias the old `ELL_PSFo`/`T_PSFo` columns silently held. The
 rename is a straight column rename in sp_validation and is correct as-is — the
 code does not care that the numbers moved. The only thing a regenerated catalogue
 buys is a *look-at-the-numbers* check that the α-leakage / size-ratio cuts still
-behave; that is analysis, not code, and it does not gate this PR (see Status).
+behave; that is analysis, not code, and it gates nothing in the code.
 
 | Old | New |
 |---|---|
@@ -122,8 +162,8 @@ truly held σ) now reads `HSM_T_*` directly.
 The σ→T change makes the old squaring dead: `HSM_T_*` (and DES's `piff_T`) already
 hold `T`, so nothing squares. The per-dataset `square_size:` flags in
 `cat_config.yaml` are dropped, the `not_square_size` list in `rho_tau.py` is
-removed, and the `square_size` key is gone from both param builders
-(`rho_tau.get_params_rho_tau`, `cosmo_val/compute_theory_cov.py`). The parameter
+removed, and the `square_size` key is gone from the param builder
+(`rho_tau.get_params_rho_tau`). The parameter
 is also removed from `shear_psf_leakage`'s `build_cat_to_compute_{rho,tau}` and
 `CovTauTh` ([PR #27](https://github.com/CosmoStat/shear_psf_leakage/pull/27)), so
 sp_validation no longer *passes* it — the two migrations are coordinated. (Passing
@@ -136,7 +176,7 @@ dropping the argument here is what keeps the container green once #27 lands.)
 - **Configs** — `cosmo_val/cat_config.yaml` (HSM blocks; DES/piff spared);
   `config/calibration/mask_v1.X.*.yaml` ×10 (`NGMIX_ELL_PSFo_NOSHEAR_0/_1`).
 - **rho/τ + covariance** — `src/sp_validation/rho_tau.py`,
-  `src/sp_validation/cosmo_val/psf_systematics.py`, `cosmo_val/compute_theory_cov.py`,
+  `src/sp_validation/cosmo_val/psf_systematics.py`,
   `src/sp_validation/glass_mock.py`.
 - **Scripts** — `scripts/calibration/{extract_info,params,calibrate_comprehensive_cat}.py`,
   `scripts/apply_alpha_snr_size_bin.py`, `scripts/examples/demo_*.py`.
@@ -168,7 +208,7 @@ outside `scratch/` instantiates `metacal(prefix="GALSIM")` or calls
 `col_1p = f"{prefix}_T_PSF_RECONV_1P"` read in `metacal._read_data` never matched
 the galsim producer output (`GALSIM_T_PSF_*`, not `..._T_PSF_RECONV_*`). Carrying
 an untestable, already-broken path onto the new grammar is a worse end state than
-deleting it, so this branch **removes** it:
+deleting it, so sp_validation has none. Absent by design:
 
 - `calibration.metacal._read_data_galsim`, the `prefix == "GALSIM"` dispatch
   branch (now `else: raise` — unknown prefixes fail loudly), and the two galsim
@@ -191,8 +231,8 @@ grammar, and add coverage — not the dead stub that was removed.
   "Adopt ShapePipe-v2 HSM column grammar, retire square_size") is the sibling
   consumer migration. It lands on the same HSM grammar
   (`HSM_G1/G2_{PSF,STAR}`, `HSM_T_*`, `HSM_FLAG_*`) and removed the `square_size`
-  parameter from `build_cat_to_compute_{rho,tau}` and `CovTauTh`; this PR drops the
-  matching argument, so the two land together (see "`square_size` is retired").
+  parameter from `build_cat_to_compute_{rho,tau}` and `CovTauTh`; sp_validation
+  passes no such argument (see "`square_size` is retired").
 - **shapepipe#761** (producer) still renames the `GALSIM_*` family onto the grammar
   for columns nothing can create. If the goal is to simplify the grammar,
   retiring that serialization is a producer-side follow-up worth raising there.
diff --git a/docs/source/_ext/catalogue_columns.py b/docs/source/_ext/catalogue_columns.py
new file mode 100644
index 00000000..8e2c92a4
--- /dev/null
+++ b/docs/source/_ext/catalogue_columns.py
@@ -0,0 +1,97 @@
+"""Sphinx extension writing ``catalogue_columns.md`` from the column schema.
+
+The page is generated at ``builder-inited`` from
+``config/columns/shapepipe_v2.yaml``, so the documentation cannot drift from the
+file the code is checked against.
+"""
+
+from pathlib import Path
+
+import yaml
+
+ROOT = Path(__file__).resolve().parents[3]
+SCHEMA = ROOT / "config" / "columns" / "shapepipe_v2.yaml"
+PAGE = "catalogue_columns.md"
+
+TITLES = {
+    "galaxy": "Galaxy catalogue",
+    "comprehensive": "Comprehensive catalogue",
+    "star": "Star catalogue",
+    "shear": "Shear catalogue",
+}
+
+LEDE = """\
+# Catalogue columns
+
+These are the catalogue columns sp_validation reads by fixed name. Their
+canonical names are ShapePipe v2's. Every other column it reads is named in
+configuration: the cosmo_val `cat_config.yaml` `*_col` keys pick the shear,
+weight, PSF and star columns; masking configs name the columns they cut on
+(`col_name`); `mask_columns` overrides the default mask columns listed below.
+
+This page is generated from `config/columns/shapepipe_v2.yaml`, which a test
+keeps in step with the code.
+
+## Reading a catalogue with other column names
+
+Give the catalogue's configuration a `column_map`,
+`{canonical name: name in the file}`. The reader presents the file's columns
+under the canonical names, lazily and without copying the data, so nothing
+downstream changes. One `*` on each side maps a family of columns at once;
+explicit entries take precedence over patterns.
+
+```yaml
+# cat_config.yaml: a shear, psf or star block
+shear:
+  path: my_catalogue.hdf5
+  column_map:
+    RA: ALPHA_J2000
+    Dec: DELTA_J2000
+
+# calibration config (config/calibration/*.yaml), under params:
+params:
+  input_path: my_comprehensive.hdf5
+  column_map:
+    "NGMIX_*": "MYFIT_*"
+```
+
+The calibration scripts take `galaxy_column_map` and `star_column_map` in
+`params.py`. The `*_col` keys keep naming columns as the reader presents them,
+that is after the map.
+
+The container is detected from the file contents: FITS (one table HDU, the
+first unless `hdu` says otherwise), or HDF5 holding one dataset, a
+comprehensive `data` dataset (joined with `data_ext` when present), or a group
+of per-tile or per-exposure datasets, as ShapePipe writes them.
+
+## ShapePipe v1 products
+
+Catalogues released up to v1.6.x name their columns in ShapePipe's v1
+grammar (`SIGMA_PSF_HSM`, the 2-vector `NGMIX_ELL_NOSHEAR`, ...). They need no
+map: the reader detects them and presents the names below, converting where
+the v1 quantity differs (sigma to `T = 2 sigma^2`, 2-vectors to components).
+`sp_validation.grammar` holds the rules. A `column_map` applies before that
+detection and overrides any rule for the same canonical name.
+"""
+
+
+def render(schema):
+    """Return the page text for the parsed ``schema``."""
+    parts = [LEDE]
+    for kind, entry in schema.items():
+        parts.append(f"\n## {TITLES.get(kind, kind.capitalize())}\n")
+        parts.append(f"{entry['about'].strip()}\n")
+        parts.append("| Column | Meaning |\n|---|---|")
+        for name, meaning in entry["columns"].items():
+            parts.append(f"| `{name}` | {meaning} |")
+    return "\n".join(parts) + "\n"
+
+
+def write_page(app):
+    schema = yaml.safe_load(SCHEMA.read_text())
+    (Path(app.srcdir) / PAGE).write_text(render(schema))
+
+
+def setup(app):
+    app.connect("builder-inited", write_page)
+    return {"parallel_read_safe": True, "parallel_write_safe": True}
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6acc48a1..a229c603 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,6 +13,7 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath("../.."))
 sys.path.insert(0, os.path.abspath("../../scripts"))
+sys.path.insert(0, os.path.abspath("_ext"))
 
 # -- General configuration ------------------------------------------------
 
@@ -48,6 +49,7 @@
     "sphinxcontrib.bibtex",
     "myst_parser",
     "numpydoc",
+    "catalogue_columns",
 ]
 
 # Include module names for objects
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c3d7a828..1990cc73 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -27,7 +27,7 @@ Four main tasks make up a run, usually in sequence:
    PSF leakage. The output is a calibrated shear catalogue. See
    :doc:`run_validation`.
 #. **Post-processing.** Turn the calibrated catalogue into science-ready
-   catalogues: masking, galaxy-sample selection, and merging of per-patch
+   catalogues: masking, galaxy-sample selection, and merging of per-campaign
    catalogues. See :doc:`post_processing`.
 #. **Cosmology validation.** Run detailed diagnostics on the calibrated
    catalogue: rho- and tau-statistics, E-/B-mode decomposition, and comparison
diff --git a/docs/source/post_processing.md b/docs/source/post_processing.md
index 6d2f2423..dd378cdc 100644
--- a/docs/source/post_processing.md
+++ b/docs/source/post_processing.md
@@ -3,8 +3,8 @@
 ## Science-ready catalogue production
 
 Processing steps of `ShapePipe` output catalogues carried out by the `sp_validation` package to produce science-ready catalogues are:
-1. Extract relevant information from a final `ShapePipe` output catalogue per patch; run basic diagnostic tests, create pre-calibration shear catalogues.
-2. Merge pre-calibration catalogues created in the previous step, e.g. processed by individual patches, into one or more joint catalogues;
+1. Extract relevant information from a final `ShapePipe` output catalogue per campaign; run basic diagnostic tests, create pre-calibration shear catalogues.
+2. Merge pre-calibration catalogues created in the previous step, e.g. processed as individual campaigns, into one or more joint catalogues;
 3. Apply external area and footprint masks. These are the "structural" and the coverage masks.  
 4. Create calibrated galaxy shear catalogue. This step includes the tasks:  
    a. Mask objects using flags and criteria in `ShapePipe` output catalogues and external (e.g. mask) files;  
@@ -19,7 +19,7 @@ This is performed (version > v1.4.1, < v2.0) with the python script `scripts/cal
 
 This script creates three shear catalogues in FITS format:
 - _Basic_ catalogue containing
-  positions, shapes (calibrated +  PSF-leakage corrected), weights (DES), magnitude, patch ID. Masking and galaxy selection are applied.  
+  positions, shapes (calibrated +  PSF-leakage corrected), weights (DES), magnitude, campaign ID. Masking and galaxy selection are applied.  
 - _Extended_ catalogue containing **in addition**
   uncalibrated shapes inverse-variance weights, shear response matrices, SNR, flux, size, PSF quantities. Masking and galaxy selection are applied.  
 - _Comprehensive_ catalogue containing **in addition**
@@ -27,11 +27,11 @@ This script creates three shear catalogues in FITS format:
   This catalogue does not contain calibrated shear estimates, since the calibration is carried out after applying masking and selection.  
   This is the main output catalogue that will be processed further.
 
-This step is carried out per patch. Parameters have to be set via the python configuration file `params.py` (template at `scripts/calibration/params.py`).
+This step is carried out per campaign. Parameters have to be set via the python configuration file `params.py` (template at `scripts/calibration/params.py`).
 
 ### 2. Merge catalogues
 
-The patch-wise comprehensive catalogues extracted in the previous step are merged using the script `scripts/calibration/create_joint_comprehensive_cat.py`, which is a front-end
+The per-campaign comprehensive catalogues extracted in the previous step are merged using the script `scripts/calibration/create_joint_comprehensive_cat.py`, which is a front-end
 of the `sp_validation` library class `catalog_builders:JointCat`.
 
 ### 3. Apply external masks
@@ -44,56 +44,3 @@ The steps of masking, galaxy sample selection, and calibration are carried out j
 `scripts/calibration/calibrate_comprehensive_cat.py`.
 
 Masking parameters have to be set via a configuration file `config_mask.yaml`. Examples can be found in `sp_validation/config/calibration`.
-
----
-
-The following describes the pre-v1.4.2 method to create a joint, calibrated shear catalogue.
-
-### Combine shear validation run output catalogues
-
-
-### Combine shear validation run statistics
-
-Summary statistics created by shear validation runs of sub-areas of a survey
-can be combined to create joint summary statistics. This is useful in cases
-where the galaxy catalogue of an entire survey is too large to process, and
-needs to be broken down in smaller patches. This step provides global summary
-statistics from those patches.
-
-Depending on the type of summary, their combination can be the sum (e.g. for
-number of objects), average, weighted average (e.g. for the additive bias), the
-weighted average of the square (e.g. the ellipticity dispersion), the weighted
-variance (to combine variance estimates), or the weighted variance of the mean
-(to combine mean variance estimates).
-
-In a directory containing the subpatches as subdirectories, and within each
-their own output directory (`sp_output`by default in `params.py`) with results
-of the validation runs, type
-```bash
-combine_results.py
-```
-This script creates a number of output files, including `R.txt` and `c.txt`
-with the combined multiplicative and additive biases, respectively.
-
-At the moment (ShapePipe catalogues v1.4.1 and older), this script needs to
-be run to write summary statistics output files before creating the joint
-shear catalogue with `create_joint_shape_cat.py`, see
-[here](#create-combined-calibrated-shear-catalogue).
-
-
-(create-combined-calibrated-shear-catalogue)=
-### Create combined calibrated shear catalogue
-
-After creating the combined statistics results described above, the global
-calibration outputs can be used to create a combined, globally calibrated shear
-catalogue. The calibration is obtained from the files `R.txt` and `c.txt`
-created above.
-
-In the same directory containing the subpatches as above, type
-```bash
-create_joint_shape_cat.py
-```
-It creates the joint output catalogues
-`{survey}_{pipeline}_{year}_v{version}.fits`
-(e.g. `unions_shapepipe_2022_v1.4.fits`) and
-`{survey}_{pipeline}_extended_{year}_v{version}.fits`.
diff --git a/docs/source/run_validation.md b/docs/source/run_validation.md
index 7ee06767..1db7de50 100644
--- a/docs/source/run_validation.md
+++ b/docs/source/run_validation.md
@@ -12,11 +12,12 @@ including the sheared values for metacalibration.
 All inputs and settings are contained in the python configuration script
 `scripts/calibration/params.py`, that needs to be edited accordingly.
 The main parameters are:
-- `name`: field or patch name, can be any string. E.g. `P3` for patch 3. 
+- `campaign`: campaign name (the ShapePipe tile list), can be any string. 
 - `data_dir`: input directory for data. Set to `.` for validation run in
   current directory.
-- `galaxy_cat_path`: path to galaxy catalogue, format `.fits`. or `.hdf5`.
-- `star_cat_path`: path to star catalogue, format `.fits`.
+- `galaxy_cat_path`: path to galaxy catalogue, FITS or HDF5 (the container is
+  detected from the file).
+- `star_cat_path`: path to star catalogue, FITS or HDF5.
 
 Optional parameters are:
 - `path_tile_ID`: path to ascii file containing lines of tile IDs. Used to
@@ -26,9 +27,14 @@ Optional parameters are:
   `None` if not required.  
 - `mask_external_path`: path to external mask file, format `.reg`. Set to
   `None` if not required.  
-
-See the script `prepare_patch_for_spval.sh` for an example of copying
-the required input files to where the validation is to be run.
+- `galaxy_column_map`, `star_column_map`: `{canonical name: name in the file}`
+  for a catalogue whose columns are not named as ShapePipe v2 names them; see
+  [Catalogue columns](catalogue_columns.md) for the columns read and the map.
+  Set to `None` if not required.
+
+Link or copy the campaign's merged products -- `final_cat_<campaign>.hdf5`
+and `full_starcat_<campaign>.hdf5` -- into the directory where the validation
+is to be run.
 
 
 ### Run
diff --git a/docs/source/toc.rst b/docs/source/toc.rst
index ab4cbbf0..2ee27a60 100644
--- a/docs/source/toc.rst
+++ b/docs/source/toc.rst
@@ -17,6 +17,7 @@
 
   using_the_catalogues
   run_validation
+  catalogue_columns
   post_processing
   Leakage_object_Tutorial
 
diff --git a/docs/source/using_the_catalogues.md b/docs/source/using_the_catalogues.md
index 72781c12..a3817a3c 100644
--- a/docs/source/using_the_catalogues.md
+++ b/docs/source/using_the_catalogues.md
@@ -15,10 +15,14 @@ how to apply the metacalibration corrections yourself.
 
 ```{note}
 The examples below target catalogue **v1.0** (April 2022), which is distributed
-as FITS. From ShapePipe catalogue **v1.4.1** onward the merged catalogues ship
-as HDF5 instead; open those with {func}`sp_validation.io.read_hdf5_file` (or
-`h5py` / `astropy`) in place of `astropy.io.fits` below — the column names and
-the calibration recipe are unchanged.
+as FITS. From ShapePipe catalogue **v1.4.1** onward the comprehensive catalogue
+ships as one HDF5 file holding two row-aligned datasets, `data` and `data_ext`.
+Open it with `h5py`, or read it as one table with
+{func}`sp_validation.io.read_catalogue`, which joins the two datasets and
+presents the columns under the current ShapePipe names. The cut catalogues of
+those releases remain FITS, and the same function reads them. The calibration
+recipe is unchanged. [Catalogue columns](catalogue_columns.md) lists the
+columns sp_validation reads and how to read a catalogue with other names.
 ```
 
 Two catalogues are released for each version, and they are used very
@@ -167,7 +171,7 @@ mask = np.full(len(data_ext), True)
 
 # Other examples:
 # mask = data_ext['mask_extern'] == 0  # LensFit-unmasked regions
-# mask = data_ext['patch'] == 3        # patch P3
+# mask = data_ext['patch'] == 3        # objects in patch P3
 # mask = data_ext['mag'] < 23.5        # r-band magnitude cut
 
 n_kept, n_all = np.count_nonzero(mask), len(data_ext)
diff --git a/papers/catalog/hist_mag.py b/papers/catalog/hist_mag.py
index b0bc33ef..2b78017c 100644
--- a/papers/catalog/hist_mag.py
+++ b/papers/catalog/hist_mag.py
@@ -53,15 +53,14 @@ def get_data(obj, test_only=False):
 
     """
     # Get data. Set load_into_memory to False for very large files
-    dat, dat_ext = obj.read_cat(load_into_memory=False)
+    dat = obj.read_cat(load_into_memory=False)
 
     if test_only:
         n_max = 1_000_000
         print(f"MKDEBUG testing only first {n_max} objects")
         dat = dat[:n_max]
-        dat_ext = dat_ext[:n_max]
 
-    return dat, dat_ext
+    return dat
 
 
 def read_hist_data(hist_data_path):
@@ -219,7 +218,6 @@ def plot_all_hists(
     ax=None,
     out_path=None,
 ):
-
     if ax is None:
         plt.figure()
         fig, (ax) = plt.subplots(1, 1, figsize=(figsize, figsize))
@@ -266,24 +264,23 @@ def plot_all_hists(
     print(f"Histogram data file {hist_data_path} found.")
     print("Reading and plotting.")
 
-    dat = dat_ext = None
+    dat = None
     hist_data = read_hist_data(hist_data_path)
 
 else:
     print(f"Histogram data file {hist_data_path} not found.")
     print("Reading UNIONS cat and computing.")
 
-    dat, dat_ext = get_data(obj, test_only=test_only)
+    dat = get_data(obj, test_only=test_only)
     hist_data = None
 
 
 # %%
 # Masking
-# Get all masks, with or without dat, dat_ext
+# Get all masks, with or without dat
 masks, labels = sp_joint.get_masks_from_config(
     config,
     dat,
-    dat_ext,
     verbose=obj._params["verbose"],
 )
 
@@ -291,7 +288,7 @@ def plot_all_hists(
 # Combine mask according to scenario
 # List of basic masks to apply to all cases
 
-masks_labels_basic = ["overlap", "mag", "64_r"]
+masks_labels_basic = ["overlap", "mag", "MASK_64_r"]
 col_names = ["basic masks"]
 
 if scenario == 0:
@@ -302,9 +299,9 @@ def plot_all_hists(
             "NGMIX_MCAL_TYPES_FAIL",
             "NGMIX_G1_PSF_ORIG_NOSHEAR",
             "NGMIX_G2_PSF_ORIG_NOSHEAR",
-            "4_Stars",
-            "8_Manual",
-            "1024_Maximask",
+            "MASK_4_Stars",
+            "MASK_8_Manual",
+            "MASK_1024_Maximask",
         ]
     )
 
@@ -318,9 +315,9 @@ def plot_all_hists(
             "NGMIX_MCAL_TYPES_FAIL",
             "NGMIX_G1_PSF_ORIG_NOSHEAR",
             "NGMIX_G2_PSF_ORIG_NOSHEAR",
-            "4_Stars",
-            "8_Manual",
-            "1024_Maximask",
+            "MASK_4_Stars",
+            "MASK_8_Manual",
+            "MASK_1024_Maximask",
             "N_EPOCH",
             "npoint3",
             "metacal",
@@ -408,7 +405,6 @@ def plot_all_hists(
 
 # %%
 def get_info_for_metacal_masking(dat, mask, prefix="NGMIX", name_shear="NOSHEAR"):
-
     res = {}
 
     res["flag"] = dat[mask][f"{prefix}_FLAGS_{name_shear}"]
@@ -539,7 +535,7 @@ def get_info_for_metacal_masking(dat, mask, prefix="NGMIX", name_shear="NOSHEAR"
 
 # %%
 if dat is not None:
-    obj.close_hd5()
+    obj.close_cat()
 
 # %%
 for mask in masks:
diff --git a/scripts/calibration/README.md b/scripts/calibration/README.md
index 870c87ce..cd0a49e3 100644
--- a/scripts/calibration/README.md
+++ b/scripts/calibration/README.md
@@ -6,14 +6,13 @@ in order. See `docs/source/post_processing.md` for the full prose.
 
 | Step | Script | Does |
 |------|--------|------|
-| 1 | `extract_info.py` | Extract metacal + diagnostic info per patch; create pre-calibration shear catalogues. Configured via `params.py`. |
-| 2 | `create_joint_comprehensive_cat.py` | Merge the patch-wise comprehensive catalogues into one joint catalogue (front-end of `catalog_builders.JointCat`). |
+| 1 | `extract_info.py` | Extract metacal + diagnostic info for one campaign; create pre-calibration shear catalogues. Configured via `params.py`. |
+| 2 | `create_joint_comprehensive_cat.py` | Merge the per-campaign comprehensive catalogues into one joint catalogue (front-end of `catalog_builders.JointCat`). |
 | 3 | `demo_apply_hsp_masks.py` | Add the structural and coverage (HealSparse) masks. |
 | 4 | `calibrate_comprehensive_cat.py` | Galaxy selection + metacalibration. Uses the mask configs in `config/calibration/`. |
 
 `params.py` is the shared parameter template (paths, column names, survey
 constants) imported by `extract_info.py`; copy and edit it per run.
 
-> **v2.0 note (Martin):** this clustering reflects the current (v1.4.x) reduction
-> flow. v2.0 no longer has patches, so step 2 (and the per-patch structure of
-> steps 1/3) will change.
+> **v2.0 note:** ShapePipe v2 has no patches. Step 1 runs per campaign and
+> step 2 merges a list of campaign catalogues (`final_cat_<campaign>.hdf5`).
diff --git a/scripts/calibration/calibrate_comprehensive_cat.py b/scripts/calibration/calibrate_comprehensive_cat.py
index 2605338f..c5248c3e 100644
--- a/scripts/calibration/calibrate_comprehensive_cat.py
+++ b/scripts/calibration/calibrate_comprehensive_cat.py
@@ -41,7 +41,7 @@
 
 # %%
 # Get data. Set load_into_memory to False for very large files
-dat, dat_ext = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 # %%
 n_test = -1
@@ -49,14 +49,13 @@
 if n_test > 0:
     print(f"MKDEBUG testing only first {n_test} objects")
     dat = dat[:n_test]
-    dat_ext = dat_ext[:n_test]
 
 
 # ## Masking
 
 # %%
 # ### Pre-processing ShapePipe flags
-masks, labels = sp_joint.get_masks_from_config(config, dat, dat_ext, verbose=True)
+masks, labels = sp_joint.get_masks_from_config(config, dat, verbose=True)
 
 mask_combined = sp_joint.Mask.from_list(
     masks,
@@ -179,15 +178,7 @@
 add_cols = [
     "w_iv",
     "FLUX_RADIUS",
-    "FWHM_IMAGE",
-    "FWHM_WORLD",
     "MAGERR_AUTO",
-    "MAG_WIN",
-    "MAGERR_WIN",
-    "FLUX_AUTO",
-    "FLUXERR_AUTO",
-    "FLUX_APER",
-    "FLUXERR_APER",
     "NGMIX_T_NOSHEAR",
     "NGMIX_T_PSF_RECONV_NOSHEAR",
     "fwhm_PSF",
@@ -327,6 +318,6 @@
 plt.savefig("confusion_matrix.png")
 
 
-obj.close_hd5()
+obj.close_cat()
 
 # %%
diff --git a/scripts/calibration/demo_apply_hsp_masks.py b/scripts/calibration/demo_apply_hsp_masks.py
index beafb909..e47c4d47 100644
--- a/scripts/calibration/demo_apply_hsp_masks.py
+++ b/scripts/calibration/demo_apply_hsp_masks.py
@@ -95,7 +95,7 @@
     )
 
 # Read catalogue
-dat = obj.read_cat(load_into_memory=False, mode="r")
+dat = obj.read_cat(load_into_memory=False)
 
 if trace_mem:
     current, peak = tracemalloc.get_traced_memory()
@@ -119,4 +119,4 @@
 obj.write_hdf5_file(dat, dat_new)
 
 # Close input HDF5 catalogue file
-obj.close_hd5()
+obj.close_cat()
diff --git a/scripts/calibration/extract_info.py b/scripts/calibration/extract_info.py
index 123c7437..bfcd1af3 100644
--- a/scripts/calibration/extract_info.py
+++ b/scripts/calibration/extract_info.py
@@ -34,14 +34,15 @@
 
 import h5py
 import numpy as np
-from astropy.io import fits
 
 # from sp_validation.catalog import *
 from sp_validation import catalog as spv_cat
+from sp_validation import galaxy
 from sp_validation.calibration import *
 from sp_validation.calibration import metacal
 from sp_validation.galaxy import *
 from sp_validation.io import *
+from sp_validation.io import read_catalogue
 from sp_validation.survey import *
 
 # ## 1. Set-up
@@ -63,15 +64,12 @@
 # ### Load merged (final) galaxy catalogue
 
 # +
-extension = os.path.splitext(galaxy_cat_path)[1]
-if extension == ".fits":
-    print("Loading galaxy .npy file...")
-    dd = np.load(galaxy_cat_path, mmap_mode=mmap_mode)
-else:
-    print("Loading galaxy .hdf5 file...")
-    dd = spv_cat.read_hdf5_file(
-        galaxy_cat_path, name, stats_file, param_path=param_list_path
-    )
+dd = read_catalogue(
+    galaxy_cat_path,
+    columns=spv_cat.read_param_file(param_list_path) if param_list_path else None,
+    column_map=galaxy_column_map,
+    verbose=verbose,
+)
 
 n_obj = len(dd)
 print_stats(
@@ -82,17 +80,16 @@
 # #### Print some quantities to check nothing obvious is wrong with catalogue
 
 # PSF keys
-key_base = shape.upper()
-key_PSF_g1 = f"{key_base}_G1_PSF_ORIG_NOSHEAR"
-key_PSF_g2 = f"{key_base}_G2_PSF_ORIG_NOSHEAR"
-key_PSF_size = f"{key_base}_T_PSF_ORIG_NOSHEAR"
+key_PSF_g1 = "NGMIX_G1_PSF_ORIG_NOSHEAR"
+key_PSF_g2 = "NGMIX_G2_PSF_ORIG_NOSHEAR"
+key_PSF_size = "NGMIX_T_PSF_ORIG_NOSHEAR"
 size_to_fwhm = T_to_fwhm
 
 print_stats("Galaxies:", stats_file, verbose=verbose)
 n_tot = spv_cat.print_some_quantities(dd, stats_file, verbose=verbose)
 spv_cat.print_mean_ellipticity(
     dd,
-    [f"{key_base}_G1_NOSHEAR", f"{key_base}_G2_NOSHEAR"],
+    ["NGMIX_G1_NOSHEAR", "NGMIX_G2_NOSHEAR"],
     1,
     n_tot,
     stats_file,
@@ -116,7 +113,13 @@
 # ### Load star catalogue
 
 if star_cat_path:
-    d_star = fits.getdata(star_cat_path, hdu_star_cat)
+    d_star = read_catalogue(
+        star_cat_path,
+        hdu=hdu_star_cat,
+        column_map=star_column_map,
+        key_column="EXPID",
+        verbose=verbose,
+    )
 
 if star_cat_path:
     print_stats("Stars:", stats_file, verbose=verbose)
@@ -141,14 +144,13 @@
 # #### Match to all objects
 
 if star_cat_path:
-    ind_star, mask_area_tiles, n_star_tot = spv_cat.check_matching(
+    ind_star, n_star_tot = spv_cat.check_matching(
         d_star,
         dd,
         ["RA", "DEC"],
         [col_name_ra, col_name_dec],
         thresh,
         stats_file,
-        name=None,
         verbose=verbose,
     )
 
@@ -160,7 +162,7 @@
 
     m_star = (
         (dd["FLAGS"][ind_star] == 0)
-        & (dd["IMAFLAGS_ISO"][ind_star] == 0)
+        & galaxy.mask_cut(dd, mask_columns)[ind_star]
         & (dd["NGMIX_MCAL_FLAGS"][ind_star] == 0)
         & (dd["NGMIX_G1_PSF_ORIG_NOSHEAR"][ind_star] != -10)
     )
@@ -191,7 +193,7 @@
 
 spv_cat.check_invalid(
     dd,
-    [key_PSF_g1, f"{key_base}_G1_NOSHEAR"],
+    [key_PSF_g1, "NGMIX_G1_NOSHEAR"],
     [-10, -10],
     stats_file,
     name=["`PSF", "galaxy ellipticity"],
@@ -250,8 +252,8 @@
 
 mag = spv_cat.get_col(dd, "MAG_AUTO", None, None)
 snr = spv_cat.get_snr(shape, dd, None, None)
-g1_uncal = dd[f"{key_base}_G1_NOSHEAR"]
-g2_uncal = dd[f"{key_base}_G2_NOSHEAR"]
+g1_uncal = dd["NGMIX_G1_NOSHEAR"]
+g2_uncal = dd["NGMIX_G2_NOSHEAR"]
 
 # Comprehensive catalogue without cuts nor mask applied
 if verbose:
@@ -311,6 +313,7 @@
     gal_mag_faint=gal_mag_faint,
     flags_keep=flags_keep,
     n_epoch_min=n_epoch_min,
+    mask_columns=mask_columns,
 )
 if shape == "ngmix":
     m_gal = classification_galaxy_ngmix(
@@ -362,7 +365,6 @@
 gal_metacal = metacal(
     dd,
     m_gal,
-    prefix=key_base,
     snr_min=gal_snr_min,
     snr_max=gal_snr_max,
     rel_size_min=gal_rel_size_min,
@@ -490,7 +492,7 @@
 write_tile_id_gal_counts(detection_IDs, galaxy_IDs, shape_IDs, fname)
 
 # +
-# Add all weights (for combining weighted averages of subpatches)
+# Add all weights (for combining weighted averages of sub-samples)
 
 w_tot = np.sum(w)
 
@@ -572,20 +574,13 @@
 n_bin = 500
 x_cut = gal_snr_min
 
-labels = []
 if shape == "ngmix":
     # Do not apply `mask_ns`, so use all galaxies
-    xs = [
-        dd["NGMIX_FLUX_NOSHEAR"][m_gal] / dd["NGMIX_FLUX_ERR_NOSHEAR"][m_gal],
-        dd["SNR_WIN"][m_gal],
-    ]
-    labels.append(["$F/\\sigma(F)$"])
-
+    xs = [dd["NGMIX_FLUX_NOSHEAR"][m_gal] / dd["NGMIX_FLUX_ERR_NOSHEAR"][m_gal]]
+    labels = ["$F/\\sigma(F)$"]
 else:
     raise ValueError(f"Unknown shape measurement method {shape}")
 
-labels.append("SExtractor SNR")
-
 title = "Galaxies"
 
 out_name = f"hist_SNR_{shape}.pdf"
diff --git a/scripts/calibration/params.py b/scripts/calibration/params.py
index 7f6d7c41..63dc0828 100644
--- a/scripts/calibration/params.py
+++ b/scripts/calibration/params.py
@@ -25,9 +25,9 @@
 
 # Survey parameters
 
-## Field or patch name. Put None if n/a
-name = "P7"
-print("Field name = {}".format(name))
+## Campaign name (the tile list processed by ShapePipe); required, it names
+## the ShapePipe v2 products (final_cat_<campaign>.hdf5, ...)
+campaign = "W3"
 
 ## Area of a tile in deg^2
 area_tile = 0.25
@@ -44,21 +44,30 @@
 data_dir = "."
 
 ### Tile IDs
-path_tile_ID = f"{data_dir}/tiles_{name}.txt"
+path_tile_ID = f"{data_dir}/tiles_{campaign}.txt"
 
-### Weak-lensing galaxy catalog name
-galaxy_cat_path = f"{data_dir}/final_cat_{name}.hdf5"
+### Weak-lensing galaxy catalog name (FITS or HDF5; sp_validation.io)
+galaxy_cat_path = f"{data_dir}/final_cat_{campaign}.hdf5"
 print(f"Galaxy catalogue = {galaxy_cat_path}")
 
+### Galaxy catalogue column map {canonical name: name in the file}, for a
+### catalogue not in the ShapePipe v2 names (config/columns/shapepipe_v2.yaml);
+### set to `None` if not required
+galaxy_column_map = None
+
 ## Parameter list; optional, set to `None` if not required
 param_list_path = f"{data_dir}/cfis/final_cat.param"
 
 ### Star and PSF catalog name; optional, set to `None` if not required
-star_cat_path = f"{data_dir}/full_starcat-0000000.fits"
+star_cat_path = f"{data_dir}/full_starcat_{campaign}.hdf5"
 
-# HDU number of star and PSF catalogue
+# HDU number of star and PSF catalogue; only used for a FITS star catalogue,
+# ignored for HDF5
 hdu_star_cat = 1
 
+### Star catalogue column map, as galaxy_column_map
+star_column_map = None
+
 ### External mask; optional, set to `None` if not required
 mask_external_path = None
 
@@ -97,9 +106,6 @@
 col_name_ra = "XWIN_WORLD"
 col_name_dec = "YWIN_WORLD"
 
-### Memory mode, set to None unless very large file
-mmap_mode = None
-
 ## Output
 
 ### Output file format extension: '.fits' or '.hdf5'
@@ -108,24 +114,37 @@
 ### Additional output columns
 add_cols = [
     "FLUX_RADIUS",
-    "FWHM_IMAGE",
-    "FWHM_WORLD",
     "MAGERR_AUTO",
-    "MAG_WIN",
-    "MAGERR_WIN",
-    "FLUX_AUTO",
-    "FLUXERR_AUTO",
-    "FLUX_APER",
-    "FLUXERR_APER",
     "NGMIX_T_NOSHEAR",
     "NGMIX_T_PSF_RECONV_NOSHEAR",
 ]
 
+## ShapePipe v2 mask columns OR'd together for the galaxy selection cut:
+## the reason bits of the r-band default bitmask, whose OR reproduces mask_r
+mask_columns = [
+    "MASK_4_Stars",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_8_Manual",
+    "MASK_64_r",
+    "MASK_1024_Maximask",
+]
+
 ## Pre-calibration catalogue, including masked objects and mask flags
 add_cols_pre_cal = [
     "TILE_ID",
     "NUMBER",
-    "IMAFLAGS_ISO",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_4_Stars",
+    "MASK_8_Manual",
+    "MASK_16_u",
+    "MASK_32_g",
+    "MASK_64_r",
+    "MASK_128_i",
+    "MASK_256_z",
+    "MASK_1024_Maximask",
+    "MASK_2048_z2",
     "FLAGS",
     "NGMIX_MCAL_FLAGS",
     "NGMIX_MCAL_TYPES_FAIL",
@@ -141,7 +160,6 @@
 add_cols_pre_cal_format = {}
 for key in (
     "NUMBER",
-    "IMAFLAGS_ISO",
     "FLAGS",
     "NGMIX_MCAL_TYPES_FAIL",
     "N_EPOCH",
@@ -149,9 +167,12 @@
 ):
     add_cols_pre_cal_format[key] = "I"
 
+for key in mask_columns:
+    add_cols_pre_cal_format[key] = "L"
+
 add_cols_pre_cal_format["TILE_ID"] = "A7"
 add_cols_pre_cal_format["NUMBER"] = "J"
-# Metacal bitmasks hold ngmix flag bits 0-15; bit 15 overflows signed 16-bit I.
+# Metacal bitmasks need int32: ngmix bit 15 overflows signed int16.
 add_cols_pre_cal_format["NGMIX_MCAL_FLAGS"] = "J"
 
 # Create key names for metacal information
diff --git a/scripts/check_tile_IDs_SP_LF.py b/scripts/check_tile_IDs_SP_LF.py
deleted file mode 100644
index 54f23fda..00000000
--- a/scripts/check_tile_IDs_SP_LF.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import re
-import sys
-
-import numpy as np
-
-
-def main(argv=None):
-
-    survey = "v1"
-
-    if survey == "v1":
-        n_patch = 7
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-
-    IDs_sp_base = "found_ID_wshapes.txt"
-    tile_ID_gal_counts_sp_base = "tile_id_gal_counts_ngmix.txt"
-
-    n_SP_not_in_LF_all = 0
-    n_LF_not_in_SP_all = 0
-
-    for patch in patches:
-        print(patch)
-
-        # ShapePipe
-        path = f"{patch}/sp_output/{IDs_sp_base}"
-
-        with open(path) as f:
-            dat = f.readlines()
-        ID_SP = []
-        for line in dat:
-            ID_SP.append(line.rstrip())
-        print(f" #SP = {len(ID_SP)}")
-
-        # LensFit
-        path = f"CFIS3500_THELI_{patch}.list"
-        with open(path) as f:
-            dat = f.readlines()
-        ID_LF = []
-        for line in dat:
-            m = re.match(r".*CFIS\.(\d{3}\.\d{3})\.r", line)
-            if m:
-                ID_LF.append(m[1].rstrip())
-        print(f" #LF = {len(ID_LF)}")
-
-        # tile stats for SP
-        dat = np.loadtxt(f"{patch}/sp_output/{tile_ID_gal_counts_sp_base}")
-        tile_ID = []
-        for my_ID in dat[:, 0]:
-            tile_ID.append(f"{my_ID:07.3f}")
-
-        # ShapePipe tiles not contained in LensFit
-        n_SP_not_in_LF = 0
-        for ID in ID_SP:
-            if ID not in ID_LF:
-                # Print number of galaxies on those tiles not contained in LF
-                n_SP_not_in_LF += 1
-                if n_SP_not_in_LF == -1:
-                    print(f"  SP {ID} not in LF")
-        n_SP_not_in_LF_all += n_SP_not_in_LF
-
-        # LensFit tiles not contained in ShapePipe
-        n_LF_not_in_SP = 0
-        for ID in ID_LF:
-            if ID not in ID_SP:
-                n_LF_not_in_SP += 1
-                if n_LF_not_in_SP == -1:
-                    print(f"  LF {ID} not in SP")
-                    print(f"   [{ID}]")
-        n_LF_not_in_SP_all += n_LF_not_in_SP
-
-        print(f" # SP not in LF = {n_SP_not_in_LF}")
-        print(f" # LF not in SP = {n_LF_not_in_SP}")
-
-    print()
-    print("All patches")
-    print(f" # SP not in LF = {n_SP_not_in_LF_all}")
-    print(f" # LF not in SP = {n_LF_not_in_SP_all}")
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/combine_results.py b/scripts/combine_results.py
deleted file mode 100755
index 636d5c07..00000000
--- a/scripts/combine_results.py
+++ /dev/null
@@ -1,1040 +0,0 @@
-#!/usr/bin/env python3
-
-
-import os
-import re
-import sys
-
-import numpy as np
-import uncertainties as unc
-from astropy.io import ascii
-
-
-def get_match(stats_files, patch, pattern, previous=None, n_previous=[1], typ=str):
-
-    prev_ok = False
-
-    for idx, line in enumerate(stats_files[patch]):
-        m = re.search(pattern, line)
-        if m:
-            if (previous and prev_ok) or not previous:
-                if typ == "ufloat":
-                    u = unc.ufloat_fromstr(m[1])
-                    return u.nominal_value, u.std_dev
-                else:
-                    return typ(m[1])
-
-        if previous:
-            prev_ok = False
-            for prev, n_prev in zip(previous, n_previous):
-                # Line index n_previous earlier, +1 since next line will be read in
-                # next loop; look for pattern in previous line
-                m_prev = re.search(prev, stats_files[patch][idx - n_prev + 1])
-                if m_prev:
-                    prev_ok = True
-
-    raise ValueError(
-        f"No match of '{pattern}' in patch {patch} (prev='{previous}'), n_prev={n_previous}"
-    )
-
-
-def read_stats_files(patches, path, verbose=False):
-
-    stats_files = {}
-    for p in patches:
-        fname = f"{p}/{path}"
-        if os.path.exists(fname):
-            if verbose:
-                print(f"Reading stats file '{fname}'")
-            with open(fname) as f:
-                stats_files[p] = f.readlines()
-        else:
-            print(f"Warning: stats file {fname} not found")
-
-    if len(stats_files) == 0:
-        raise ValueError("No stats file found")
-
-    print(f"Found {len(stats_files)} stats files")
-
-    return stats_files
-
-
-def combine(results):
-
-    for key in results["type"]:
-        # Values
-        values = np.array(list(results["value"][key].values()))
-
-        if results["type"][key] == "sum":
-            # Compute sum
-            results["all"][key] = sum(values)
-
-        elif results["type"][key] == "avg":
-            # Compute average
-            results["all"][key] = sum(values) / len(values)
-
-        elif results["type"][key] == "w_avg":
-            # Weight key
-            key_w = results["extra"][key]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Compute weighted average
-            s = sum(w)
-            if s > 0:
-                results["all"][key] = sum(values * w) / s
-            else:
-                raise ZeroDivisionError(f"sum(w) = {s} for key={key}, key_w={key_w}")
-
-        elif results["type"][key] == "sqr_w_avg":
-            # Weight key
-            key_w = results["extra"][key]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Square values
-            v_sqr = values * values
-
-            # Compute weighted average
-            res_tmp = sum(v_sqr * w) / sum(w)
-
-            # Take square root
-            results["all"][key] = np.sqrt(res_tmp)
-
-        elif results["type"][key] == "var":
-            # Combined variance
-
-            # Weight key
-            key_w = results["extra"][key][0]
-
-            # Mean key
-            key_m = results["extra"][key][1]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Patch mean values
-            m = np.array(list(results["value"][key_m].values()))
-
-            # Overall mean
-            m_all = results["all"][key_m]
-
-            # Square values
-            v_sqr = values * values
-
-            # Compute weighted average
-            res_tmp = sum((v_sqr + (m - m_all) ** 2) * w) / sum(w)
-
-            # Take square root
-            results["all"][key] = np.sqrt(res_tmp)
-
-        elif results["type"][key] == "var_m":
-            # Combined variance of mean
-
-            # Weight key
-            key_w = results["extra"][key][0]
-
-            # Mean key
-            key_m = results["extra"][key][1]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Patch mean values
-            m = np.array(list(results["value"][key_m].values()))
-
-            # Overall mean
-            m_all = results["all"][key_m]
-
-            # Square values
-            v_sqr = values * values
-
-            # Compute weighted average, note extra factor of w in v_sqr
-            res_tmp = sum((v_sqr * w + (m - m_all) ** 2) * w) / sum(w)
-
-            # Take square root
-            results["all"][key] = np.sqrt(res_tmp / sum(w))
-
-
-def init_key(results, key, typ, extra=None):
-
-    results["type"][key] = typ
-    results["value"][key] = {}
-    if extra:
-        results["extra"][key] = extra
-
-
-def print_all(
-    results, stats_files, use_keys=None, fout=sys.stdout, header=True, all=True
-):
-
-    if use_keys:
-        keys = use_keys
-    else:
-        keys = results["type"]
-
-    # Header
-    if header:
-        print("# patch", " " * 3, end=" ", file=fout)
-        for key in keys:
-            print(f"{key:>11s}", end=" ", file=fout)
-        print(file=fout)
-
-    # Loop over patches
-    for patch in stats_files:
-        print(f"{patch:11s}", end=" ", file=fout)
-
-        # Write value for each key
-        for key in keys:
-            val = results["value"][key][patch]
-            if key == "N_gal":
-                print(f"{val:>11.0f}", end=" ", file=fout)
-            elif key in ("w_tot", "n_gal_am2"):
-                print(f"{val:>11.2f}", end=" ", file=fout)
-            elif key in ["xi_sys_p", "xi_sys_m"]:
-                print(f"{val:>11.3e}", end=" ", file=fout)
-            else:
-                print(f"{val:>11.7f}", end=" ", file=fout)
-        print(file=fout)
-
-    # Write total
-    if all:
-        p = "all"
-        if p in results:
-            print(f"{p:11s}", end=" ", file=fout)
-            for key in keys:
-                val = results[p][key]
-                if key == "N_gal":
-                    print(f"{val:>11d}", end=" ", file=fout)
-                elif key == "w_tot":
-                    print(f"{val:>11.1f}", end=" ", file=fout)
-                else:
-                    print(f"{val:>11.7f}", end=" ", file=fout)
-                    # print(f'{val:>11.3e}', end=' ', file=fout)
-            print(file=fout)
-
-
-def get_area(fname):
-
-    if os.path.exists(fname):
-        with open(fname) as f:
-            lines = f.readlines()
-        for line in lines:
-            m = re.search("nmasked patch area without overlap = (.*) deg", line)
-            if m:
-                return float(m[1])
-
-    else:
-        print(f"Warning: No file {fname} found to obtain area")
-        return 1
-
-
-def get_values(results, stats_files, shape, use_keys, area_deg2=-1):
-    """Get Values
-
-    Get values from stats files for all patches.
-
-    Parameters
-    ----------
-    results : dict
-        results dictionary
-    stats_files : dict of array of str
-        stats files content for each patch
-    shape : str
-        shape measurement method
-    use_keys : dict
-        keys to include
-    area_deg2 : float, optional
-        area in square degree, optional is -1 (to be retrieved
-        for each patch)
-    """
-    # Number of galaxies
-    key = "N_gal"
-    if use_keys[key]:
-        init_key(results, key, "sum")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"Number of galaxies after metacal = (\d+)/",
-                previous=[f"^{shape}$"],
-                typ=int,
-            )
-
-        area_deg2_tot = 0
-        key_der = "n_gal_am2"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            if area_deg2 < 0:
-                area_deg2_patch = get_area(f"{patch}/area.txt")
-                area_deg2_tot += area_deg2_patch
-                print(f"area({patch}) = {area_deg2_patch} deg^2")
-            else:
-                area_deg2_patch = area_deg2
-            results["value"][key_der][patch] = results["value"]["N_gal"][patch] / (
-                area_deg2_patch * 3600
-            )
-
-    if area_deg2 < 0:
-        with open("area_deg2_tot.txt", "w") as f:
-            print(area_deg2_tot, file=f)
-
-    # Sum of weights
-    key = "w_tot"
-    if use_keys[key]:
-        init_key(results, key, "sum")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"Sum of weights = (\S+)",
-                typ=float,
-                previous=[f"^{shape}$"],
-            )
-
-    # Additive bias (unweighted mean)
-    key_base = "c_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "w_avg", extra="N_gal")
-            for patch in stats_files:
-                c = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp - 1],
-                    typ=float,
-                )
-                results["value"][key][patch] = c
-
-    # Additive bias (unweighted error)
-    key_base = "dc_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "var", extra=["N_gal", f"c_{comp}"])
-            for patch in stats_files:
-                dc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    typ=float,
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp + 7],
-                )
-                results["value"][key][patch] = dc
-
-    # Additive bias (unweighted error of mean)
-    key_base = "dmc_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "var_m", extra=["N_gal", f"c_{comp}"])
-            for patch in stats_files:
-                dmc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    typ=float,
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp + 7],
-                )
-                results["value"][key][patch] = dmc
-
-    # Additive bias (weighted mean)
-    key_base = "cw_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "w_avg", extra="w_tot")
-            for patch in stats_files:
-                c = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    previous=[f"^{shape}:$"],
-                    n_previous=[comp * 2],
-                    typ=float,
-                )
-                results["value"][key][patch] = c
-
-    # Additive bias (weighted error of mean)
-    key_base = "dmcw_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "var_m", extra=["N_gal", f"cw_{comp}"])
-            for patch in stats_files:
-                dmc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    typ=float,
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp + 8],
-                )
-                results["value"][key][patch] = dmc
-
-    # Additive bias (jackknife)
-    key_base = "cjk_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            # Mean
-            key = f"{key_base}{comp}"
-            key_m = key
-            init_key(results, key, "w_avg", extra="w_tot")
-
-            # Error
-            key = f"d{key_base}{comp}"
-            key_s = key
-            init_key(results, key, "var_m", extra=["w_tot", f"cjk_{comp}"])
-
-            for patch in stats_files:
-                c, dc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    previous=[f"^{shape}:$"],
-                    n_previous=[comp],
-                    typ="ufloat",
-                )
-                results["value"][key_m][patch] = c
-                results["value"][key_s][patch] = dc
-
-    # Ellipticity dispersion
-    key = "sigma2_epsilon"
-    if use_keys[key]:
-        init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"Dispersion of complex ellipticity = (\S+)",
-                previous=[f"^{shape}$"],
-                typ=float,
-            )
-
-    # Galaxy total response matrix
-    key_base = "R_tot_"
-    if use_keys[key_base]:
-        keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[(\s?\S+)\s+\S+]",
-                previous=["ngmix galaxies:", "total response matrix:"],
-                n_previous=[2, 1],
-                typ=float,
-            )
-            results["value"][keys[0]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[\s?\S+\s+(\S+)]",
-                previous=["ngmix galaxies:", "total response matrix:"],
-                n_previous=[2, 1],
-                typ=float,
-            )
-            results["value"][keys[1]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[(\s?\S+)\s+\S+\]\]",
-                previous=["ngmix galaxies", "total response matrix:"],
-                n_previous=[3, 2],
-                typ=float,
-            )
-            results["value"][keys[2]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r" \[\s?\S+\s+(\S+)\]\]",
-                previous=["ngmix galaxies:", "total response matrix:"],
-                n_previous=[3, 2],
-                typ=float,
-            )
-            results["value"][keys[3]][patch] = tmp
-
-        # Normalised trace = mean diagonal
-        key_der = "trN_R_tot"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = (
-                results["value"]["R_tot_11"][patch]
-                + results["value"]["R_tot_22"][patch]
-            ) / 2
-
-        # Sum of absolute off-diagonal
-        key_der = "abs_off_R_tot"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = np.abs(
-                results["value"]["R_tot_12"][patch]
-            ) + np.abs(results["value"]["R_tot_21"][patch])
-
-    # Galaxy shear response matrix
-    key_base = "R_shear_"
-    if use_keys[key_base]:
-        keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[(\s?\S+)\s+\S+]",
-                previous=["ngmix galaxies:", "shear response matrix:"],
-                n_previous=[5, 1],
-                typ=float,
-            )
-            results["value"][keys[0]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[\s?\S+\s+(\S+)]",
-                previous=["ngmix galaxies:", "shear response matrix:"],
-                n_previous=[5, 1],
-                typ=float,
-            )
-            results["value"][keys[1]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[(\s?\S+)\s+\S+\]\]",
-                previous=["ngmix galaxies", "shear response matrix:"],
-                n_previous=[6, 2],
-                typ=float,
-            )
-            results["value"][keys[2]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r" \[\s?\S+\s+(\S+)\]\]",
-                previous=["ngmix galaxies:", "shear response matrix:"],
-                n_previous=[6, 2],
-                typ=float,
-            )
-            results["value"][keys[3]][patch] = tmp
-
-        # Normalised trace = mean diagonal
-        key_der = "trN_R_shear"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = (
-                results["value"]["R_shear_11"][patch]
-                + results["value"]["R_shear_22"][patch]
-            ) / 2
-
-        # Sum of absolute off-diagonal
-        key_der = "abs_off_R_shear"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = np.abs(
-                results["value"]["R_shear_12"][patch]
-            ) + np.abs(results["value"]["R_shear_21"][patch])
-
-    # Galaxy selection response matrix
-    key_base = "R_select_"
-    if use_keys[key_base]:
-        keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[(\s?\S+)\s+\S+]",
-                previous=["ngmix galaxies:", "selection response matrix:"],
-                n_previous=[8, 1],
-                typ=float,
-            )
-            results["value"][keys[0]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[\s?\S+\s+(\S+)]",
-                previous=["ngmix galaxies:", "selection response matrix:"],
-                n_previous=[8, 1],
-                typ=float,
-            )
-            results["value"][keys[1]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[(\s?\S+)\s+\S+\]\]",
-                previous=["ngmix galaxies", "selection response matrix:"],
-                n_previous=[9, 2],
-                typ=float,
-            )
-            results["value"][keys[2]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r" \[\s?\S+\s+(\S+)\]\]",
-                previous=["ngmix galaxies:", "selection response matrix:"],
-                n_previous=[9, 2],
-                typ=float,
-            )
-            results["value"][keys[3]][patch] = tmp
-
-        # Normalised trace = mean diagonal
-        key_der = "trN_R_select"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = (
-                results["value"]["R_select_11"][patch]
-                + results["value"]["R_select_22"][patch]
-            ) / 2
-
-        # Sum of absolute off-diagonal
-        key_der = "abs_off_R_select"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = np.abs(
-                results["value"]["R_select_12"][patch]
-            ) + np.abs(results["value"]["R_select_21"][patch])
-
-    # Object-wise PSF leakage
-    key_base = "m_"
-    if use_keys[key_base]:
-        keys = [
-            f"{key_base}11",
-            f"{key_base}12",
-            f"{key_base}21",
-            f"{key_base}22",
-            f"{key_base}s1",
-            f"{key_base}s2",
-        ]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{1\\}\\^\\{\\\\rm PSF\\}\\$: m_1=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[1],
-                typ="ufloat",
-            )
-            results["value"]["m_11"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{1\\}\\^\\{\\\\rm PSF\\}\\$: m_2=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[2],
-                typ="ufloat",
-            )
-            results["value"]["m_12"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{2\\}\\^\\{\\\\rm PSF\\}\\$: m_1=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[3],
-                typ="ufloat",
-            )
-            results["value"]["m_21"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{2\\}\\^\\{\\\\rm PSF\\}\\$: m_2=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[4],
-                typ="ufloat",
-            )
-            results["value"]["m_22"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$\\\\mathrm\\{FWHM\\}\\^\\{\\\\rm PSF\\}\\$ \\[arcsec]: m_1=(\\S+)",
-                previous=["ngmix"],
-                n_previous=[5],
-                typ="ufloat",
-            )
-            results["value"]["m_s1"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$\\\\mathrm\\{FWHM\\}\\^\\{\\\\rm PSF\\}\\$ \\[arcsec]: m_2=(\\S+)",
-                previous=["ngmix"],
-                n_previous=[6],
-                typ="ufloat",
-            )
-            results["value"]["m_s2"][patch] = m
-
-    # Scale-dependent PSF leakage
-    key = "alpha"
-    if use_keys[key]:
-        init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"ngmix: Weighted average alpha =(\s?\S+)",
-                typ=float,
-            )
-
-    # xi_sys
-    key_base = "xi_sys"
-    if use_keys[key_base]:
-        init_key(results, "xi_sys_p", "w_avg", extra="N_gal")
-        init_key(results, "xi_sys_m", "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files, patch, r"ngmix: <\|xi_sys_\+\|> = (\S*)", typ=float
-            )
-            results["value"]["xi_sys_p"][patch] = tmp
-            tmp = get_match(
-                stats_files, patch, r"ngmix: <\|xi_sys_\-\|> = (\S*)", typ=float
-            )
-            results["value"]["xi_sys_m"][patch] = tmp
-
-
-def latex_table(file_base, cols=None, col_names=None):
-
-    dat = ascii.read(f"{file_base}.txt")
-
-    fout = open(f"{file_base}.tex", "w")
-
-    n_lines = len(dat)
-
-    n_dat = len(cols)
-
-    print(r"\begin{tabular}{l", end="", file=fout)
-    print("c" * n_dat, end="", file=fout)
-    # print('r@{}l'*n_dat, end='', file=fout)
-    print(r"}\hline\hline", file=fout)
-
-    # Table header
-    str_line = "patch\t&"
-    for name in col_names:
-        str_line = f"{str_line} ${name}$\t&"
-        # str_line = f'{str_line} \\multicolumn{{2}}{{c}}{{${name}$}}\t&'
-    print(rf"{str_line[: len(str_line) - 2]} \\ \hline", file=fout)
-
-    # Loop over input lines
-    for nl in range(n_lines):
-        str_line = ""
-
-        str_line = f"{str_line}{dat['patch'][nl]}\t&"
-
-        for col in cols:
-            if len(col) == 2:
-                # value and error bar
-                val_err = unc.ufloat(dat[col[0]][nl], dat[col[1]][nl])
-                str_line = f"{str_line} ${val_err:+.2eL}$\t&"
-            else:
-                if dat[col][nl] < 0:
-                    pref = ""
-                else:
-                    pref = r"\phantom{-}"
-                str_line = f"{str_line} ${pref}{dat[col][nl]:#.4f}$\t&"
-        print(rf"{str_line[: len(str_line) - 2]} \\", file=fout)
-
-    print(r"\hline", file=fout)
-    print(r"\end{tabular}", file=fout)
-
-    fout.close()
-
-
-def get_matrix_elements(base, me):
-
-    res = []
-
-    for m in me:
-        res.append(f"{base}_{{{m}}}")
-
-    return res
-
-
-def main(argv=None):
-    """Main
-
-    Main program
-    """
-    if len(argv) == 1:
-        argv.append("snr")
-
-    all = True
-
-    if argv[1] == "snr":
-        # All directories
-        patches = [f.path for f in os.scandir(".") if f.is_dir()]
-        all = False
-    elif argv[1] == "v1":
-        n_patch = 7
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-    elif argv[1] == "v1.5":
-        n_patch = 8
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-    elif argv[1] == "test":
-        patches = ["P7", "W3", "S4"]
-    elif argv[1] == "comb":
-        # Validate with combined catalogue
-        patches = ["comb"]
-    else:
-        patches = argv[1].split("+")
-
-    n_patch = len(patches)
-
-    print("combine_results.py:", patches)
-
-    directory = "sp_output/plots"
-    fbase = "stats_file"
-    fname = f"{fbase}.txt"
-    path = f"{directory}/{fname}"
-
-    shape = "ngmix"
-
-    verbose = False
-
-    stats_files = read_stats_files(patches, path, verbose=verbose)
-
-    results = {"value": {}, "type": {}, "extra": {}, "all": {}}
-
-    use_keys = {
-        "N_gal": 1,
-        "w_tot": 1,
-        "c_": 1,
-        "cw_": 1,
-        "dc_": 0,
-        "dmc_": 0,
-        "dmcw_": 1,
-        "cjk_": 0,
-        "sigma2_epsilon": 1,
-        "R_tot_": 1,
-        "R_shear_": 1,
-        "R_select_": 1,
-        "m_": 1,
-        "alpha": 1,
-        "xi_sys": 0,
-    }
-    use_keys_m = {
-        "N_gal": 0,
-        "w_tot": 0,
-        "c_": 0,
-        "cw_": 0,
-        "dc_": 0,
-        "dmc_": 0,
-        "dmcw_": 0,
-        "cjk_": 0,
-        "sigma2_epsilon": 0,
-        "R_tot_": 0,
-        "R_shear_": 0,
-        "R_select_": 0,
-        "m_": 1,
-        "alpha": 0,
-        "xi_sys": 0,
-    }
-
-    if argv[1] == "snr":
-        area_deg2 = get_area("area.txt")
-    else:
-        area_deg2 = -1
-    get_values(results, stats_files, shape, use_keys, area_deg2=area_deg2)
-
-    # Combine all keys
-    combine(results)
-
-    # Print all keys to terminal
-    print_all(results, stats_files, all=all)
-
-    # Get value of combined run for precision check
-    if argv[1] == "test":
-        stats_file_comb = read_stats_files(["comb"], path, verbose=verbose)
-        results_comb = {
-            "value": {},
-            "type": {},
-            "extra": {},
-        }
-        get_values(results_comb, stats_file_comb, shape, use_keys)
-        print("\nCombined data:")
-        print_all(results_comb, stats_file_comb, header=False)
-
-        # Compute fractional difference
-        for key in results["type"]:
-            results_comb["value"][key]["comb"] = (
-                results_comb["value"][key]["comb"] / results["all"][key] - 1
-            ) * 100
-        print("\nFractional difference [%]:")
-        print_all(results_comb, stats_file_comb, header=False)
-    else:
-        results_comb = None
-
-    # Get value of entire-sample run
-    if argv[1] == "v1":
-        try:
-            stats_file_comb = read_stats_files(
-                ["joint"], f"leakage/{fbase}_leakage.txt", verbose=verbose
-            )
-            results_comb = {
-                "value": {},
-                "type": {},
-                "extra": {},
-            }
-            get_values(results_comb, stats_file_comb, shape, use_keys_m, area_deg2=1)
-        except Exception:
-            print("leakage stats file of joint catalogue not found, skipping")
-
-    # Print some key (combinations) to text and LaTeX file
-    if argv[1] not in ["comb"]:
-        file_base_arr = []
-
-        file_base = "c"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        print_all(
-            results,
-            stats_files,
-            use_keys=["cw_1", "dmcw_1", "cw_2", "dmcw_2"],
-            fout=f,
-            all=all,
-        )
-        f.close()
-        latex_table(
-            file_base,
-            cols=[["cw_1", "dmcw_1"], ["cw_2", "dmcw_2"]],
-            col_names=["c_1", "c_2"],
-        )
-
-        key = "sigma2_epsilon"
-        if use_keys[key]:
-            file_base = key
-            file_base_arr.append(file_base)
-            f = open(f"{file_base}.txt", "w")
-            print_all(results, stats_files, use_keys=[key], fout=f, all=all)
-            f.close()
-            latex_table(file_base, cols=[key], col_names=[r"\sigma^2_\epsilon"])
-
-        me = ["11", "12", "21", "22"]
-
-        key_base = "R_tot_"
-        if use_keys[key_base]:
-            file_base = "R"
-            file_base_arr.append(file_base)
-            f = open(f"{file_base}.txt", "w")
-            use_keys = [
-                f"{key_base}11",
-                f"{key_base}12",
-                f"{key_base}21",
-                f"{key_base}22",
-            ]
-            print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-            f.close()
-            col_names = get_matrix_elements("R^{\\textrm{tot}}", me)
-            latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "R_shear"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        key_base = "R_shear_"
-        use_keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = get_matrix_elements("R^{\\textrm{shear}}", me)
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "R_select"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        key_base = "R_select_"
-        use_keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = get_matrix_elements("R^{\\textrm{select}}", me)
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-    if argv[1] not in ["comb"]:
-        file_base = "summary_Rc"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        use_keys = [
-            "cw_1",
-            "dmcw_1",
-            "cw_2",
-            "dmcw_2",
-            "trN_R_tot",
-            "abs_off_R_tot",
-            "trN_R_shear",
-            "trN_R_select",
-        ]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = [
-            "c_1",
-            r"\Delta c_1",
-            "c_2",
-            r"\Delta c_2",
-            "\\langle R^{\\rm tot}_{ii} \\rangle",
-            "\\sum | R^{\\ tot}_{i \\ne j}|",
-            "\\langle R^{\\rm shear}_{ii} \\rangle",
-            "\\langle R^{\\rm select}_{ii} \\rangle",
-        ]
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "summary_leakage"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        use_keys = [
-            "n_gal_am2",
-            "m_11",
-            "m_22",
-            "m_12",
-            "m_21",
-            "m_s1",
-            "m_s2",
-            "alpha",
-        ]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = [
-            "n_{\\rm gal} [{\\rm am}^{-2}]",
-            "m_{11}",
-            "m_{22}",
-            "m_{12}",
-            "m_{21}",
-            "m_{\\rm s1}",
-            "m_{\\rm s2}",
-            "\\alpha",
-        ]
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "summary_leakage_m"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        use_keys = ["m_11", "m_22", "m_12", "m_21", "m_s1", "m_s2"]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=False)
-
-        # Add joint sample results
-        if results_comb:
-            print_all(
-                results_comb, stats_file_comb, use_keys=use_keys, fout=f, all=False
-            )
-            f.close()
-            col_names = [
-                "m_{11}",
-                "m_{22}",
-                "m_{12}",
-                "m_{21}",
-                "m_{\\rm s1}",
-                "m_{\\rm s2}",
-            ]
-            latex_table(file_base, cols=use_keys, col_names=col_names)
-
-    for file_base in file_base_arr:
-        # os.system(f'~/txt2tex.pl {file_base}.tex > {file_base}_out.tex')
-        # print(f'Creating LaTeX file {file_base}.tex')
-        # os.system(f'pdflatex {file_base}_out 2&>/dev/null')
-        print("Skipping calling LaTeX")
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/compute_area.py b/scripts/compute_area.py
deleted file mode 100755
index afe797ad..00000000
--- a/scripts/compute_area.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-
-
-import glob
-import re
-import sys
-
-import numpy as np
-from uncertainties import ufloat
-
-
-def main(argv=None):
-    """Main
-
-    Main program
-    """
-
-    # File variables
-    tile_ID_path = "tile_numbers.txt"
-
-    random_log_path = "output/run_sp_Rc/random_cat_runner/logs"
-    log_file_base = "process"
-
-    # Get expected number of tiles in patch
-    num_lines = sum(1 for _ in open(tile_ID_path))
-    print(f"Found {num_lines} tiles in ID file {tile_ID_path}")
-
-    # Get all log files of random module run
-    log_files = glob.glob(f"{random_log_path}/{log_file_base}*")
-    n_logs = len(log_files)
-    print(f"Found {n_logs} log files")
-
-    area_deg2_non_overl = np.empty(len(log_files)) * np.nan
-    area_deg2_non_overl_wgal = np.empty(len(log_files)) * np.nan
-    area_deg2_eff_non_overl = np.empty(len(log_files)) * np.nan
-    area_deg2_eff_non_overl_wgal = np.empty(len(log_files)) * np.nan
-    ratio_unmasked_tot = np.empty(len(log_files)) * np.nan
-
-    # Open stats tile ID gal count file
-    sh = "ngmix"
-    tile_ID_from = "random"
-    n_gal = {}
-    if tile_ID_from == "final":
-        dat = np.loadtxt(f"sp_output/tile_id_gal_counts_{sh}.txt")
-        tile_ID = dat[:, 0]
-        for idx, t_ID in enumerate(tile_ID):
-            n_gal[f"{t_ID:07.3f}"] = dat[idx, 2]
-    elif tile_ID_from == "random":
-        dat = np.loadtxt("sp_output_random/found_ID.txt")
-        tile_ID = dat
-        for idx, t_ID in enumerate(tile_ID):
-            n_gal[f"{t_ID:07.3f}"] = 1
-
-    n_tile_no_gal = 0
-    n_tile_wgal = 0
-
-    # Loop over log files
-    for idx, log_file in enumerate(log_files):
-        no_gal = False
-        pattern = re.compile(rf".*{log_file_base}(.*)-(.*)\.log")
-        m = re.match(pattern, log_file)
-        if m:
-            this_id = f"{m[1]}.{m[2]}"
-            if tile_ID_from == "final":
-                if this_id in n_gal:
-                    if n_gal[this_id] == 0:
-                        n_tile_no_gal += 1
-                        no_gal = True
-            else:
-                if this_id not in n_gal:
-                    no_gal = True
-        else:
-            raise ValueError("Invalid log file format")
-
-        if not no_gal:
-            n_tile_wgal += 1
-
-        with open(log_file) as fin:
-            log_content = fin.readlines()
-
-        # Loop over lines in log file
-        for line in log_content:
-            m = re.search(r"Total area without overlap = (\S+) deg", line)
-            if m:
-                area_deg2_non_overl[idx] = float(m[1])
-                if not no_gal:
-                    area_deg2_non_overl_wgal[idx] = float(m[1])
-                else:
-                    area_deg2_non_overl_wgal[idx] = 0
-
-            m = re.search(r"Unmaskewd area without overlap = (\S+) deg", line)
-            if m:
-                area_deg2_eff_non_overl[idx] = float(m[1])
-                if not no_gal:
-                    area_deg2_eff_non_overl_wgal[idx] = float(m[1])
-                else:
-                    area_deg2_eff_non_overl_wgal[idx] = 0
-
-            m = re.search(r"Ratio masked to total pixels = (\S+)", line)
-            if m:
-                ratio_unmasked_tot[idx] = float(m[1])
-
-    print(f"Tiles with zero galaxies = {n_tile_no_gal}")
-    print(f"Tiles with galaxies = {n_tile_wgal}")
-
-    area_deg2_non_overl_total = np.sum(area_deg2_non_overl)
-    area_deg2_non_overl_total_wgal = np.sum(area_deg2_non_overl_wgal)
-    area_deg2_non_overl_tile = ufloat(
-        np.mean(area_deg2_non_overl), np.std(area_deg2_non_overl)
-    )
-    print(f"Patch area without overlap = {area_deg2_non_overl_total:.3f} deg^2")
-    print(
-        f"Patch area without overlap and no 0 gal = {area_deg2_non_overl_total_wgal:.3f} deg^2"
-    )
-    print(f"Tile area without overlap = {area_deg2_non_overl_tile:.3fP} deg^2")
-
-    area_deg2_eff_non_overl_total = np.sum(area_deg2_eff_non_overl)
-    area_deg2_eff_non_overl_total_wgal = np.sum(area_deg2_eff_non_overl_wgal)
-    area_deg2_eff_non_overl_tile = ufloat(
-        np.mean(area_deg2_eff_non_overl), np.std(area_deg2_eff_non_overl)
-    )
-    print(
-        f"Unmasked patch area without overlap = {area_deg2_eff_non_overl_total:.3f} deg^2"
-    )
-    print(
-        f"Unmasked patch area without overlap and no 0 gal = {area_deg2_eff_non_overl_total_wgal:.3f} deg^2"
-    )
-    print(
-        f"Unmasked tile area without overlap = {area_deg2_eff_non_overl_tile:.3fP} deg^2"
-    )
-
-    ratio_unmasked_tile = ufloat(
-        np.mean(ratio_unmasked_tot), np.std(ratio_unmasked_tot)
-    )
-    print(
-        f"Ratio of unmasked to total pixels = {ratio_unmasked_tile:.3fP} (min = {np.min(ratio_unmasked_tot):.3f})"
-    )
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/compute_m_bias_image_sims.py b/scripts/compute_m_bias_image_sims.py
index 04ca49b7..406bc7d3 100644
--- a/scripts/compute_m_bias_image_sims.py
+++ b/scripts/compute_m_bias_image_sims.py
@@ -57,23 +57,26 @@ def parse_args():
 
 
 def get_n_tiles(grids_dir, num):
-    """Detect number of tiles from final_cat HDF5 files."""
-    try:
-        import h5py
-
-        # Count tiles in first sim's final_cat
-        for sim in ["1z2z_grid", "1m2z_grid", "1p2z_grid", "1z2m_grid", "1z2p_grid"]:
-            sim_name = f"{sim}_{num}"
-            final_cat = os.path.join(grids_dir, sim_name, f"final_cat_{sim_name}.hdf5")
-            if os.path.isfile(final_cat):
-                with h5py.File(final_cat, "r") as hf:
-                    if "patches" in hf:
-                        n_tiles = sum(
-                            1 for patch in hf["patches"] for _ in hf[f"patches/{patch}"]
-                        )
-                        return n_tiles
-    except Exception:
-        pass
+    """Detect number of tiles from final_cat HDF5 files.
+
+    Layout-agnostic: uses ``sp_validation.io.find_dataset_group``, so it
+    works on both the nested ``patches/<campaign>/<tile-ID>`` and the flat
+    per-tile HDF5 layouts.
+    """
+    import h5py
+
+    from sp_validation.io import find_dataset_group
+
+    for sim in ["1z2z_grid", "1m2z_grid", "1p2z_grid", "1z2m_grid", "1z2p_grid"]:
+        sim_name = f"{sim}_{num}"
+        final_cat = os.path.join(grids_dir, sim_name, f"final_cat_{sim_name}.hdf5")
+        if os.path.isfile(final_cat):
+            with h5py.File(final_cat, "r") as hf:
+                n_tiles = hf.attrs.get("n_tiles")
+                if n_tiles is not None:
+                    return int(n_tiles)
+                return len(find_dataset_group(hf))
+
     return None
 
 
diff --git a/scripts/examples/create_binned_mask_comprehensive.py b/scripts/examples/create_binned_mask_comprehensive.py
index 68964120..03546bce 100644
--- a/scripts/examples/create_binned_mask_comprehensive.py
+++ b/scripts/examples/create_binned_mask_comprehensive.py
@@ -36,7 +36,7 @@
 
 # %%
 # Get data. Set load_into_memory to False for very large files
-dat, dat_ext = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 # %%
 n_test = -1
@@ -44,7 +44,6 @@
 if n_test > 0:
     print(f"MKDEBUG testing only first {n_test} objects")
     dat = dat[:n_test]
-    dat_ext = dat_ext[:n_test]
     test = "_test"
 else:
     test = ""
@@ -68,7 +67,7 @@
 
 # %%
 # Load and initialise masks
-masks, labels = sp_joint.get_masks_from_config(config, dat, dat_ext, verbose=True)
+masks, labels = sp_joint.get_masks_from_config(config, dat, verbose=True)
 
 mask_combined = sp_joint.Mask.from_list(
     masks,
diff --git a/scripts/examples/demo_calibrate_minimal_cat.py b/scripts/examples/demo_calibrate_minimal_cat.py
index a90025dd..50e52657 100644
--- a/scripts/examples/demo_calibrate_minimal_cat.py
+++ b/scripts/examples/demo_calibrate_minimal_cat.py
@@ -35,7 +35,7 @@
 # !pwd
 
 # Get data. Set load_into_memory to False for very large files
-dat, _ = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 if True:
     n_max = 1_000_000
@@ -47,9 +47,9 @@
 
 masks_to_apply = [
     "FLAGS",
-    "4_Stars",
-    "64_r",
-    "1024_Maximask",
+    "MASK_4_Stars",
+    "MASK_64_r",
+    "MASK_1024_Maximask",
     "N_EPOCH",
     "mag",
     "NGMIX_MCAL_TYPES_FAIL",
@@ -58,7 +58,7 @@
 ]
 
 masks, labels = sp_joint.get_masks_from_config(
-    config, dat, dat, masks_to_apply=masks_to_apply, verbose=obj._params["verbose"]
+    config, dat, masks_to_apply=masks_to_apply, verbose=obj._params["verbose"]
 )
 
 mask_combined = sp_joint.Mask.from_list(
@@ -234,4 +234,4 @@
 
 #
 
-obj.close_hd5()
+obj.close_cat()
diff --git a/scripts/examples/demo_check_footprint.py b/scripts/examples/demo_check_footprint.py
index de54bbd8..c9653636 100644
--- a/scripts/examples/demo_check_footprint.py
+++ b/scripts/examples/demo_check_footprint.py
@@ -55,7 +55,7 @@
 # -
 
 # Read catalogue
-# dat = obj.read_cat(load_into_memory=False, mode="r")
+# dat = obj.read_cat(load_into_memory=False)
 hdu_list = fits.open(obj._params["input_path"])
 dat = hdu_list[hdu].data
 
diff --git a/scripts/examples/demo_comprehensive_to_minimal_cat.py b/scripts/examples/demo_comprehensive_to_minimal_cat.py
index 11e5ed5a..63dbe551 100644
--- a/scripts/examples/demo_comprehensive_to_minimal_cat.py
+++ b/scripts/examples/demo_comprehensive_to_minimal_cat.py
@@ -38,25 +38,29 @@
 # !pwd
 
 # Get data. Set load_into_memory to False for very large files
-dat, dat_ext = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 if False:
     n_max = 1_000_000
     print(f"MKDEBUG testing only first {n_max} objects")
     dat = dat[:n_max]
-    dat_ext = dat_ext[:n_max]
 
 # ## Masking
 
 # +
 # List of masks to apply
+# (column names as declared in config/calibration/mask_v2.0.yaml)
 masks_to_apply = [
     "overlap",
-    "IMAFLAGS_ISO",
+    "MASK_4_Stars",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_8_Manual",
+    "MASK_64_r",
+    "MASK_1024_Maximask",
     "NGMIX_MCAL_TYPES_FAIL",
     "NGMIX_G1_PSF_ORIG_NOSHEAR",
     "NGMIX_G2_PSF_ORIG_NOSHEAR",
-    "8_Manual",
 ]
 
 # List of masks not to apply and not to copy to minimal catalogue
@@ -68,7 +72,6 @@
 masks, labels = sp_joint.get_masks_from_config(
     config,
     dat,
-    dat_ext,
     masks_to_apply=masks_to_apply,
     verbose=obj._params["verbose"],
 )
@@ -117,9 +120,9 @@
     del mask
 
 
-def strip_h5py_metadata_dtype(dat_dtype, dat_ext_dtype):
+def strip_h5py_metadata_dtype(dat_dtype):
     cleaned_fields = []
-    for name, dt in dat_dtype.descr + dat_ext_dtype.descr:
+    for name, dt in dat_dtype.descr:
         # If dt is a tuple (e.g., ('S7', {'h5py_encoding': 'ascii'}))
         if isinstance(dt, tuple):
             cleaned_fields.append((name, dt[0]))  # keep only the base dtype string
@@ -132,26 +135,19 @@ def strip_h5py_metadata_dtype(dat_dtype, dat_ext_dtype):
 # Remove mask columns that were applied earlier
 
 # Columns to keep
-names_to_keep = [
-    name
-    for name in dat.dtype.names + dat_ext.dtype.names
-    if name not in masks_not_to_include
-]
+names_to_keep = [name for name in dat.dtype.names if name not in masks_not_to_include]
 
 # Remove metadata from the dtype (in particular, encoding for TILE_ID)
-clean_dtype_descr = strip_h5py_metadata_dtype(dat.dtype, dat_ext.dtype)
+clean_dtype_descr = strip_h5py_metadata_dtype(dat.dtype)
 
 new_dtype = [(name, dt) for name, dt in clean_dtype_descr if name in names_to_keep]
 
 # Create a new structured array
 new_dat = np.zeros(len(dat[mask_combined._mask]), dtype=new_dtype)
 
-# Copy relevant columns and lines from each source array
+# Copy relevant columns and lines
 for name in names_to_keep:
-    if name in dat.dtype.names:
-        new_dat[name] = dat[name][mask_combined._mask]
-    else:
-        new_dat[name] = dat_ext[name][mask_combined._mask]
+    new_dat[name] = dat[name][mask_combined._mask]
 
 # +
 # Add information to FITS header
@@ -188,7 +184,6 @@ def strip_h5py_metadata_dtype(dat_dtype, dat_ext_dtype):
 
 
 def correlation_matrix(masks, confidence_level=0.9):
-
     n_key = len(masks)
     print(n_key)
 
@@ -232,7 +227,6 @@ def correlation_matrix(masks, confidence_level=0.9):
 
 
 def confusion_matrix(prediction, observation):
-
     result = {}
 
     result["true_pos"] = sum(prediction & observation)
@@ -308,4 +302,4 @@ def confusion_matrix(prediction, observation):
 plt.savefig("confusion_matrix.png")
 # -
 
-obj.close_hd5()
+obj.close_cat()
diff --git a/scripts/examples/demo_create_footprint_mask.py b/scripts/examples/demo_create_footprint_mask.py
index 8338e676..de45101a 100644
--- a/scripts/examples/demo_create_footprint_mask.py
+++ b/scripts/examples/demo_create_footprint_mask.py
@@ -28,6 +28,8 @@
 from cs_util.plots import FootprintPlotter
 
 from sp_validation import catalog_builders as sp_joint
+from sp_validation.grammar import MASK_LABELS
+from sp_validation.masks import catalogue_cuts
 from sp_validation.plots import hsp_map_logical_or
 
 # -
@@ -46,28 +48,22 @@
 # Read configuration file and set parameters
 config = obj.read_config_set_params("config_mask.yaml")
 
-# Get data sections
-config_data = {key: config[key] for key in ["dat", "dat_ext"] if key in config}
-
 # Get names of possible mask names
-all_masks_bits = {}
-for bit in obj._labels_struct:
-    all_masks_bits[obj.get_mask_col_name(bit)] = bit
+all_masks_bits = {obj.get_mask_col_name(bit): bit for bit in MASK_LABELS}
 
 # Identify masks specified in the config file
 bits = 0
 auxiliary_masks = []
 auxiliary_labels = []
-for section, mask_list in config_data.items():
-    for mask_params in mask_list:
-        # Check bit-coded masks
-        if mask_params["col_name"] in all_masks_bits:
-            bits = bits | all_masks_bits[mask_params["col_name"]]
-
-        # Check auxiliary masks
-        if "npoint3" == mask_params["col_name"]:
-            auxiliary_masks.append(obj._params["aux_mask_files"])
-            auxiliary_labels.append("npoint3")
+for mask_params in catalogue_cuts(config):
+    # Check bit-coded masks
+    if mask_params["col_name"] in all_masks_bits:
+        bits = bits | all_masks_bits[mask_params["col_name"]]
+
+    # Check auxiliary masks
+    if "npoint3" == mask_params["col_name"]:
+        auxiliary_masks.append(obj._params["aux_mask_files"])
+        auxiliary_labels.append("npoint3")
 
 # Update bits for following function call
 obj._params["bits"] = bits
diff --git a/scripts/glass_mock/compute_leakage_harmony.py b/scripts/glass_mock/compute_leakage_harmony.py
index a63c6bd4..227eb373 100644
--- a/scripts/glass_mock/compute_leakage_harmony.py
+++ b/scripts/glass_mock/compute_leakage_harmony.py
@@ -9,6 +9,7 @@
 from astropy.io import fits
 
 from sp_validation.glass_mock import compute_leakage_harmony
+from sp_validation.io import open_catalogue
 
 
 def get_parser():
@@ -53,7 +54,7 @@ def get_parser():
     print("Catalog data loaded successfully.")
 
     print("Loading the star catalog data...")
-    cat_star = fits.getdata(f"{args.star_cat_path}")
+    cat_star = open_catalogue(args.star_cat_path)
     print("Star catalog data loaded successfully.")
 
     print("Computing leakage in harmonic space...")
diff --git a/scripts/homogenize_cat_extended.py b/scripts/homogenize_cat_extended.py
index dd54922e..f197a941 100644
--- a/scripts/homogenize_cat_extended.py
+++ b/scripts/homogenize_cat_extended.py
@@ -3,7 +3,7 @@
 
 Overwrite the extended catalog to replace the columns e1 and e2 from the extended catalog with the columns
 e1 and e2 from the non-extended one. Currently, the calibration of the columns e1 and e2 of the extended catalog
-are calibrated per patch, while the non-extended catalog is calibrated on the whole footprint.
+are calibrated per campaign, while the non-extended catalog is calibrated on the whole footprint.
 
 :Authors: Sacha Guerrini, Martin Kilbinger
 """
diff --git a/scripts/masking.py b/scripts/masking.py
index 32a970ed..c3acbecd 100644
--- a/scripts/masking.py
+++ b/scripts/masking.py
@@ -2,12 +2,12 @@
 from multiprocessing import Pool, cpu_count
 from pathlib import Path
 
-import h5py
 import healpy as hp
 import numpy as np
 import yaml
 
-from sp_validation.masks import apply_condition
+from sp_validation.io import Catalogue
+from sp_validation.masks import apply_condition, catalogue_cuts
 
 # -------------------------
 # Spatially-structured cuts: these define the survey footprint.
@@ -17,40 +17,40 @@
 SPATIAL_CUTS = {
     "overlap",
     "IMAFLAGS_ISO",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_4_Stars",
+    "MASK_8_Manual",
+    "MASK_16_u",
+    "MASK_32_g",
+    "MASK_64_r",
+    "MASK_128_i",
+    "MASK_256_z",
+    "MASK_1024_Maximask",
+    "MASK_2048_z2",
     "N_EPOCH",
-    "4_Stars",
-    "8_Manual",
-    "64_r",
-    "1024_Maximask",
     "npoint3",
-    "1_Faint_star_halos",
-    "2_Bright_star_halos",
 }
 
 # -------------------------
 # Masking logic
 
 
-def apply_masks(data, data_ext, mask_config, footprint_only=False):
+def apply_masks(data, mask_config, footprint_only=False):
     """
     Construct a boolean mask selecting galaxies that satisfy all
     masking criteria defined in the YAML configuration file.
 
     Parameters
     ----------
-    data : numpy.ndarray or structured array
-        Slice of the HDF5 "data" group containing per-object
-        measurements (e.g. FLAGS, mag, NGMIX quantities).
-
-    data_ext : numpy.ndarray or structured array
-        Slice of the HDF5 "data_ext" group containing external or
-        post-processing flags (e.g. star masks, footprint flags).
+    data : numpy.ndarray, structured array or grammar.V2View
+        Slice of the catalogue in the v2 column grammar, as
+        ``sp_validation.io.Catalogue`` presents it.
 
     mask_config : dict
         Dictionary parsed from the YAML mask configuration file.
         Expected structure:
             - mask_config["dat"]     : list of cuts applied to `data`
-            - mask_config["dat_ext"] : list of cuts applied to `data_ext`
             - mask_config["metacal"] : derived-quantity parameters
               (e.g. relative size limits)
 
@@ -72,8 +72,7 @@ def apply_masks(data, data_ext, mask_config, footprint_only=False):
     # Initialize mask
     mask = np.ones(len(data), dtype=bool)
 
-    # --- dat group ---
-    for cut in mask_config.get("dat", []):
+    for cut in catalogue_cuts(mask_config):
         col = cut["col_name"]
         if footprint_only and col not in SPATIAL_CUTS:
             continue
@@ -82,16 +81,6 @@ def apply_masks(data, data_ext, mask_config, footprint_only=False):
 
         mask &= apply_condition(data[col], kind, value)
 
-    # --- dat_ext group ---
-    for cut in mask_config.get("dat_ext", []):
-        col = cut["col_name"]
-        if footprint_only and col not in SPATIAL_CUTS:
-            continue
-        kind = cut["kind"]
-        value = cut["value"]
-
-        mask &= apply_condition(data_ext[col], kind, value)
-
     # --- metacal relative size (skip for footprint-only) ---
     if not footprint_only:
         rel_size = np.divide(
@@ -113,7 +102,7 @@ def apply_masks(data, data_ext, mask_config, footprint_only=False):
 # Process one chunk
 def process_chunk(args):
     """
-    Process a chunk of the HDF5 catalogue and return the unique
+    Process a chunk of the catalogue and return the unique
     HEALPix pixels containing unmasked galaxies,to be executed in
     parallel. It reads a slice of the catalogue, applies
     the defined masking criteria, converts the sky positions
@@ -129,7 +118,7 @@ def process_chunk(args):
             - stop : int
                 Ending row index of the chunk (exclusive).
             - filename : str
-                Path to the input HDF5 catalogue.
+                Path to the input catalogue.
             - nside : int
                 HEALPix NSIDE parameter defining map resolution.
             - mask_config : dict
@@ -144,11 +133,11 @@ def process_chunk(args):
     """
 
     start, stop, filename, nside, mask_config, footprint_only = args
-    with h5py.File(filename, "r") as f:
-        data = f["data"][start:stop]
-        data_ext = f["data_ext"][start:stop]
+    column_map = mask_config.get("params", {}).get("column_map")
+    with Catalogue(filename, column_map=column_map) as catalogue:
+        data = catalogue.table()[start:stop]
 
-    mask = apply_masks(data, data_ext, mask_config, footprint_only=footprint_only)
+    mask = apply_masks(data, mask_config, footprint_only=footprint_only)
 
     ra = data["RA"][mask]
     dec = data["Dec"][mask]
@@ -163,19 +152,19 @@ def process_chunk(args):
 
 # -------------------------
 # Build mask map in parallel
-def build_mask_map_hdf5(
+def build_mask_map(
     filename, mask_config, nside, chunk_size=1_000_000, footprint_only=False
 ):
     """
-    Build a binary HEALPix mask map from an HDF5 galaxy catalogue.
+    Build a binary HEALPix mask map from a galaxy catalogue.
 
     The catalogue is processed in chunks to limit memory usage.
 
     Parameters
     ----------
     filename : str
-        Path to the input HDF5 catalogue containing "data" and
-        "data_ext" groups
+        Path to the input catalogue, in any container
+        ``sp_validation.io.Catalogue`` reads
     mask_config : dict
         Dictionary parsed from the YAML mask configuration file
     nside : int
@@ -196,8 +185,8 @@ def build_mask_map_hdf5(
               in that pixel,
             - 0 indicates no retained galaxies.
     """
-    with h5py.File(filename, "r") as f:
-        nrows = f["data"].shape[0]
+    with Catalogue(filename) as catalogue:
+        nrows = len(catalogue)
 
     chunks = [
         (i, min(i + chunk_size, nrows), filename, nside, mask_config, footprint_only)
@@ -249,7 +238,7 @@ def build_mask_map_hdf5(
         print(f"Footprint-only mode: applying only spatial cuts {SPATIAL_CUTS}")
 
     # Build mask map from comprehensive catalogue
-    mask_map = build_mask_map_hdf5(
+    mask_map = build_mask_map(
         filename,
         mask_config,
         nside,
diff --git a/scripts/merge_psf_cat.py b/scripts/merge_psf_cat.py
index 65c1e891..5bf089f6 100644
--- a/scripts/merge_psf_cat.py
+++ b/scripts/merge_psf_cat.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """MERGE PSF CAT.
 
-Merge PSF catalogues (psf_catalog_ngmix.fits) from different patches
+Merge PSF catalogues (psf_catalog_ngmix.fits) from different campaigns
 into a single FITS file.
 
 :Author: Martin Kilbinger
@@ -18,7 +18,7 @@
 class MergePsfCat:
     """Merge Psf Cat.
 
-    Class to merge PSF catalogues from multiple patches.
+    Class to merge PSF catalogues from multiple campaigns.
 
     """
 
@@ -32,7 +32,7 @@ def params_default(self):
 
         """
         self._params = {
-            "patches": "v1",
+            "campaigns": None,
             "sh": "ngmix",
             "survey": "unions",
             "year": "2024",
@@ -43,7 +43,7 @@ def params_default(self):
             "verbose": False,
         }
         self._short_options = {
-            "patches": "-p",
+            "campaigns": "-p",
             "sh": "-g",
             "survey": "-s",
             "year": "-y",
@@ -55,15 +55,12 @@ def params_default(self):
             "hdu": "int",
         }
         self._help_strings = {
-            "patches": (
-                "list of patches separated by '+', or shortcut "
-                "(allowed are 'v1', 'v1.5'), default={}"
-            ),
+            "campaigns": "list of campaigns separated by '+'",
             "sh": "shape measurement method, default={}",
             "survey": "survey name, default={}",
             "year": "year of processing, default={}",
             "version": "catalogue version, default={}",
-            "base_path": "base path containing patch directories, default={}",
+            "base_path": "base path containing campaign directories, default={}",
             "hdu": "HDU number to read from input FITS files, default={}",
         }
 
@@ -85,40 +82,32 @@ def set_params_from_command_line(self, args):
 
         logging.log_command(args)
 
-    def get_patches(self):
-        """Get Patches.
+    def get_campaigns(self):
+        """Get Campaigns.
 
-        Return list of patches according to option parameter value.
+        Return list of campaigns according to option parameter value.
 
         Returns
         -------
         list
-            patches, list of str
+            campaigns, list of str
 
         """
-        if self._params["patches"] == "v1":
-            n_patch = 7
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        elif self._params["patches"] == "v1.5":
-            n_patch = 8
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        elif self._params["patches"] == "v1.6":
-            n_patch = 9
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        else:
-            patches = self._params["patches"].split("+")
-
-        return patches
-
-    def merge_catalogues(self, patches):
+        campaigns = self._params["campaigns"]
+        if not campaigns:
+            raise ValueError("No campaigns given; set 'campaigns'")
+
+        return campaigns.split("+")
+
+    def merge_catalogues(self, campaigns):
         """Merge Catalogues.
 
-        Merge PSF catalogues from sub-patches into one FITS file.
+        Merge PSF catalogues from campaigns into one FITS file.
 
         Parameters
         ----------
-        patches : list of str
-            list of patches/sub-directories
+        campaigns : list of str
+            list of campaigns / sub-directories
 
         """
         base_path = self._params["base_path"]
@@ -133,11 +122,11 @@ def merge_catalogues(self, patches):
         )
 
         dat_all = {}
-        for idx, patch in enumerate(patches):
+        for idx, campaign in enumerate(campaigns):
             if verbose:
-                print(f"  {patch}")
+                print(f"  {campaign}")
 
-            input_path = f"{base_path}/{patch}/{input_sub_path}"
+            input_path = f"{base_path}/{campaign}/{input_sub_path}"
             try:
                 dat = fits.getdata(input_path, hdu_in)
             except Exception:
@@ -149,21 +138,24 @@ def merge_catalogues(self, patches):
                 col_names = dat.dtype.names
                 for name in col_names:
                     dat_all[name] = []
-                dat_all["patch"] = []
+                dat_all["campaign"] = []
 
             for name in col_names:
                 dat_all[name] = np.append(dat_all[name], dat[name])
 
-            dat_all["patch"] = np.append(dat_all["patch"], [idx + 1] * len(dat))
+            dat_all["campaign"] = np.append(dat_all["campaign"], [campaign] * len(dat))
 
-        col_names = col_names + ("patch",)
+        col_names = col_names + ("campaign",)
 
         column_all = []
         for name in col_names:
-            if name != "patch":
+            if name != "campaign":
                 my_format = "D"
             else:
-                my_format = "I"
+                # Store the campaign name, matching JointCat's string
+                # `campaign` column; an ordinal would depend on the order of
+                # the -p argument and could not be joined back.
+                my_format = f"A{max(len(c) for c in campaigns)}"
             column = fits.Column(name=name, array=dat_all[name], format=my_format)
             column_all.append(column)
 
@@ -177,12 +169,12 @@ def run(self):
         Main processing function.
 
         """
-        patches = self.get_patches()
+        campaigns = self.get_campaigns()
 
         if self._params["verbose"]:
-            print("Merging PSF catalogues from patches:", patches)
+            print("Merging PSF catalogues from campaigns:", campaigns)
 
-        self.merge_catalogues(patches)
+        self.merge_catalogues(campaigns)
 
 
 def main(argv=None):
diff --git a/scripts/plot_leakage.py b/scripts/plot_leakage.py
index 629d81dc..ea6194aa 100755
--- a/scripts/plot_leakage.py
+++ b/scripts/plot_leakage.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import copy
+import os
 import sys
 from optparse import OptionParser
 
@@ -152,6 +153,7 @@ def plot_alpha_leakage(
     xmin,
     xmax,
     ylim=None,
+    labels=None,
 ):
     """Plot Alpha Leakage.
 
@@ -173,6 +175,9 @@ def plot_alpha_leakage(
         largest angular scale, interpreted in arcmin
     ylim : list, optional
         y-axis plot limits, default is `Ǹone`
+    labels : list, optional
+        curve labels, one per input curve; default is ``None``, for
+        unlabelled curves
 
     """
     theta = meanr
@@ -187,7 +192,7 @@ def plot_alpha_leakage(
     linewidths[0] = 3
 
     colors = ["grey", "k", "b", "r", "c", "m", "g", "orange"]
-    labels = ["all", "P1", "P2", "P3", "P4", "P5", "P6", "P7"]
+    colors = [colors[idx % len(colors)] for idx in range(len(meanr))]
 
     plot_data_1d(
         theta,
@@ -239,6 +244,10 @@ def main(argv=None):
     if param.verbose:
         print("Input files: ", fnames)
 
+    # Curve labels come from the input file names: the first file is the
+    # reference (all objects), the others whatever selection they hold.
+    labels = ["all"] + [os.path.splitext(os.path.basename(fn))[0] for fn in fnames[1:]]
+
     # read input files, append data
     theta = []
     alpha_leak = []
@@ -265,6 +274,7 @@ def main(argv=None):
         config.theta_min_amin,
         config.theta_max_amin,
         config.leakage_alpha_ylim,
+        labels=labels,
     )
 
     return 0
diff --git a/scripts/plot_rho_stats_patches.py b/scripts/plot_rho_stats_patches.py
deleted file mode 100755
index 915c07d1..00000000
--- a/scripts/plot_rho_stats_patches.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.15.1
-#   kernelspec:
-#     display_name: sp_validation
-#     language: python
-#     name: python3
-# ---
-
-import glob
-import itertools
-import os
-
-import matplotlib.pylab as plt
-from cs_util import args
-from shear_psf_leakage.rho_tau_stat import RhoStat
-
-
-# +
-# Set parameters from file or user input
-class dummy(object):
-    def __init__(self):
-
-        self._params = {
-            "in_dir_base": ".",
-            "title": None,
-        }
-
-
-obj = dummy()
-params_upd = args.read_param_script("params_rho.py", obj._params, verbose=True)
-for key in params_upd:
-    obj._params[key] = params_upd[key]
-
-# patches = [f'P{x}' for x in np.arange(n_patch) + 1]
-patches = glob.glob("P*")
-print(patches)
-
-default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-color_cycle = itertools.cycle(default_colors)
-col = {}
-for patch in patches:
-    col[patch] = next(color_cycle)
-
-coord_units = "deg"
-theta_min = 0.1
-theta_max = 250
-sep_units = "arcmin"
-nbins = 20
-
-# ## Set up
-TreeCorrConfig = {
-    "ra_units": coord_units,
-    "dec_units": coord_units,
-    "min_sep": theta_min,
-    "max_sep": theta_max,
-    "sep_units": sep_units,
-    "nbins": nbins,
-    "var_method": "bootstrap",
-}
-# -
-
-rho_stat_handler = RhoStat(
-    output=obj._params["in_dir_base"], treecorr_config=TreeCorrConfig, verbose=True
-)
-
-# +
-filenames = []
-colors = []
-
-for patch in patches:
-    path = f"{patch}/output/run_sp_Pl/mccd_plots_runner/output/rho_stats_id.fits"
-    if os.path.exists(f"{obj._params['in_dir_base']}/{path}"):
-        print(f"Reading rho stats {obj._params['in_dir_base']}/{path}...")
-        rho_stat_handler.load_rho_stats(path)
-        filenames.append(path)
-        colors.append(col[patch])
-    else:
-        print(
-            f"File rho stats {obj._params['in_dir_base']}/{path} not found, skipping.."
-        )
-# -
-
-# Create plot
-rho_stat_handler.plot_rho_stats(
-    filenames,
-    colors,
-    patches,
-    abs=False,
-    savefig="rho_stats.png",
-    legend="outside",
-    title=obj._params["title"],
-)
diff --git a/scripts/prepare_patch_for_spval.sh b/scripts/prepare_patch_for_spval.sh
deleted file mode 100755
index 634d901f..00000000
--- a/scripts/prepare_patch_for_spval.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-
-patch=$1
-
-spdir=$HOME/astro/repositories/github/sp_validation
-
-# Galaxy catalogue
-#cp ~/psfex/final_cat_${patch}.hdf5 .
-ln -s ~/psfex/final_cat_${patch}.hdf5
-
-# Parameter file, to avoid read errors for hdf5 file
-ln -sf ~/shapepipe/example/cfis/final_cat.param
-
-# Star catalogue
-## Ellipticities in pixel coordinates, MCCD output
-# ln -sf $HOME/psfex/${patch}/output/run_sp_Ms/merge_starcat_runner/output/full_starcat-0000000.fits
-
-## Projected back to world coordinates
-ln -sf $HOME/psfex/star_cat/${patch}/output/run_sp_Ms/merge_starcat_runner/output/full_starcat-0000000.fits
-
-# Tile number list
-ln -sf ~/shapepipe/auxdir/CFIS/tiles_202106/tiles_${patch}.txt
-
-# Parameter file
-#cp $spdir/scripts/calibration/params.py .
-echo "Diff:"
-diff $spdir/scripts/calibration/params.py params.py
-echo "Run?"
-echo "cp $spdir/scripts/calibration/params.py params.py"
-echo "Run?"
-echo "ipython $spdir/scripts/calibration/extract_info.py"
diff --git a/scripts/star_match_stats.py b/scripts/star_match_stats.py
deleted file mode 100644
index 5c74ed5c..00000000
--- a/scripts/star_match_stats.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python
-
-import re
-import sys
-
-import numpy as np
-
-
-def main(argv=None):
-
-    types = ["star", "gal", "other"]
-    text = "Number of stars selected as"
-
-    n_patch = 7
-    patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-
-    ntyp = {}
-    ntot = {}
-    for typ in types:
-        ntyp[typ] = 0
-        ntot[typ] = 0
-
-    for patch in patches:
-        # print(patch)
-        path = f"{patch}/sp_output/plots/stats_file.txt"
-        with open(path, "r") as fin:
-            lines = fin.readlines()
-            for typ in types:
-                for line in lines:
-                    pattern = rf"{text} {typ}.*= (\d+)/(\d+)"
-                    m = re.search(pattern, line)
-                    if m:
-                        ntyp_patch = int(m.group(1))
-                        ntot_patch = int(m.group(2))
-                        # print(typ, m.group(1), m.group(2))
-                        ntyp[typ] += ntyp_patch
-                        ntot[typ] += ntot_patch
-
-    for typ in types:
-        print(f"{text} {typ} = {ntyp[typ]}/{ntot[typ]} = {ntyp[typ] / ntot[typ]:.2%}")
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/stats_tile_id_gal_counts.py b/scripts/stats_tile_id_gal_counts.py
index caed59ae..26adca7f 100755
--- a/scripts/stats_tile_id_gal_counts.py
+++ b/scripts/stats_tile_id_gal_counts.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import copy
+import os
 import sys
 from optparse import OptionParser
 
@@ -40,7 +41,7 @@ def params_default():
         parameter values
     """
 
-    p_def = param(survey="v1")
+    p_def = param(campaigns=None)
 
     return p_def
 
@@ -66,7 +67,13 @@ def parse_options(p_def):
 
     # I/O
     parser.add_option("-i", "--input", dest="input", type="string", help="input file")
-    parser.add_option("-s", "--survey", dest="survey", type="string", help="survey")
+    parser.add_option(
+        "-c",
+        "--campaigns",
+        dest="campaigns",
+        type="string",
+        help="campaigns separated by '+'",
+    )
 
     options, args = parser.parse_args()
 
@@ -87,8 +94,8 @@ def check_options(options):
         Result of option check. False if invalid option value.
     """
 
-    if not options.input and not options.survey:
-        print("Either input or survey need to be specified")
+    if not options.input and not options.campaigns:
+        print("Either input or campaigns need to be specified")
         return False
 
     return True
@@ -148,13 +155,13 @@ def main(argv=None):
     # Get input file paths
     print("Retrieving input file paths")
     input_files = []
-    if param.survey == "v1":
-        n_patch = 7
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        for patch in patches:
-            path = f"{patch}/sp_output/tile_id_gal_counts_{sh}.txt"
+    if param.campaigns:
+        campaigns = param.campaigns.split("+")
+        for campaign in campaigns:
+            path = f"{campaign}/sp_output/tile_id_gal_counts_{sh}.txt"
             input_files.append(path)
     else:
+        campaigns = [os.path.dirname(param.input) or "."]
         input_files = [param.input]
 
     print(f"Found {len(input_files)} input files")
@@ -166,11 +173,11 @@ def main(argv=None):
     n_gal_arr = []
     n_shape_arr = []
 
-    for patch, input_path in zip(patches, input_files):
-        dat[patch] = np.loadtxt(input_path)
-        n_det = dat[patch][:, 1]
-        n_gal = dat[patch][:, 2]
-        n_shape = dat[patch][:, 3]
+    for campaign, input_path in zip(campaigns, input_files):
+        dat[campaign] = np.loadtxt(input_path)
+        n_det = dat[campaign][:, 1]
+        n_gal = dat[campaign][:, 2]
+        n_shape = dat[campaign][:, 3]
 
         n_det_arr.extend(n_det)
         n_gal_arr.extend(n_gal)
@@ -178,11 +185,11 @@ def main(argv=None):
 
     # Write tile IDs with number of shapes > 0
     print("Writing tile IDs with n_shapes>0")
-    for patch in patches:
-        out_path = f"{patch}/sp_output/found_ID_wshapes.txt"
+    for campaign in campaigns:
+        out_path = f"{campaign}/sp_output/found_ID_wshapes.txt"
         with open(out_path, "w") as f_out:
-            mask_n_shape = dat[patch][:, 3] > 0
-            tile_ID_masked = dat[patch][mask_n_shape, 0]
+            mask_n_shape = dat[campaign][:, 3] > 0
+            tile_ID_masked = dat[campaign][mask_n_shape, 0]
             for ID in tile_ID_masked:
                 print(f"{ID:07.3f}", file=f_out)
 
diff --git a/scripts/survey_stats_all.sh b/scripts/survey_stats_all.sh
deleted file mode 100644
index f6c4cd5f..00000000
--- a/scripts/survey_stats_all.sh
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/usr/bin/bash
-
-fbase_found='found_ID'
-fbase_found_wsh='found_ID_wshapes'
-fbase_lf='CFIS3500_THELI'
-rm -f ${fbase_found}_all.txt
-rm -f ${fbase_found_wsh}_all.txt
-rm -f ${fbase_found_random}_all.txt
-rm -f ${fbase_lf}_all.txt
-
-for patch in P1 P2 P3 P4 P5 P6 P7; do
-
-  # Patch
-
-  ## Total number of tiles
-  wc -l $patch/tiles_P?.txt
-
-
-  # Final catalogue
-  #echo "Final catalogue"
-
-  ## Number of final catalogues (.tgz)
-  ntgz=`ls -rtl $patch/final*.tgz | wc -l`
-  echo "$ntgz final .tgz cats"
-
-  ## Number of final catalogues (.fits)
-  nfits=`ls -rtl $patch/output/run_sp_combined/make_catalog_runner/output/final* | wc -l`
-  echo "$nfits final .fits cats"
-
-  ## Number of merged final catalogues
-  if [ -e $patch/log_merge_final_gal_cat ]; then
-    tail -n 1 $patch/log_merge_final_gal_cat
-  fi
-
-  ## Number of tile IDs found in merged catalogue
-  if [ -e $patch/sp_output/${fbase_found}.txt ]; then
-    wc -l $patch/sp_output/${fbase_found}.txt
-    wc -l $patch/sp_output/${fbase_found}.txt >> ${fbase_found}_all.txt
-  fi
-
-  ## Number of tile IDs found in merged catalogue with shapes
-  if [ -e $patch/sp_output/${fbase_found_wsh}.txt ]; then
-    wc -l $patch/sp_output/${fbase_found_wsh}.txt
-    wc -l $patch/sp_output/${fbase_found_wsh}.txt >> ${fbase_found_wsh}_all.txt
-  fi
-
-
-  # Random catalogue
-
-  ## Number of final catalogues (.tgz)
-  ntgz=`ls -rtl $patch/pipeline_flag*.tgz | wc -l`
-  echo "$ntgz random .tgz cats"
-
-
-  ## Number of random catalogues (.fits)
-  if [ -d  $patch/output/run_sp_combined_flag ]; then
-    nfits=`ls -rtl $patch/output/run_sp_combined_flag/mask_runner/output/pip* | wc -l`
-    echo "$nfits random .fits cats"
-  fi
-
-  ## Number of merged random catalogues
-  if [ -e $patch/log_merge_final_rand_cat ]; then
-    tail -n 1 $patch/log_merge_final_rand_cat
-  fi
-
-  ### Number of tiles for random catalogue validation
-  if [ -e $patch/sp_output_random/${fbase_found}.txt ]; then
-    wc -l $patch/sp_output_random/${fbase_found}.txt
-    wc -l $patch/sp_output_random/${fbase_found}.txt >> ${fbase_found_random}_all.txt
-  fi
-
-  # LensFit tile IDs
-  if [ -e ${fbase_lf}_$patch.list ]; then
-    wc -l ${fbase_lf}_$patch.list
-    wc -l ${fbase_lf}_$patch.list >> ${fbase_lf}_all.txt
-  fi
-  
-  echo
-
-done
-
-echo -n "number of tiles in ${fbase_found}_all.txt = "
-summe.pl ${fbase_found}_all.txt 0
-
-echo -n "number of tiles in ${fbase_found_wsh}_all.txt = "
-summe.pl ${fbase_found_wsh}_all.txt 0
-
-echo -n "number of tiles in ${fbase_found_random}_all.txt = "
-summe.pl ${fbase_found_random}_all.txt 0
-
-if [ -e ${fbase_lf} ]; then
-  echo -n "number of tiles in ${fbase_lf}_all.txt = "
-  summe.pl ${fbase_lf}_all.txt 0
-fi
diff --git a/src/sp_validation/calibration.py b/src/sp_validation/calibration.py
index 9eaaf42e..b594b9b6 100644
--- a/src/sp_validation/calibration.py
+++ b/src/sp_validation/calibration.py
@@ -16,6 +16,7 @@
 from shear_psf_leakage import leakage, run_object
 
 from sp_validation import catalog as sp_cat
+from sp_validation.io import read_catalogue
 from sp_validation.statistics import jackknif_weighted_average2
 
 
@@ -680,7 +681,9 @@ def get_calibrate_no_leakage_e_from_cat(path_cat_gal, weight_type="des", verbose
     """
     e1, e2 = get_calibrate_e_from_cat(path_cat_gal, weight_type, verbose)
 
-    cat_gal = fits.getdata(path_cat_gal)
+    cat_gal = read_catalogue(
+        path_cat_gal, columns=["alpha_1", "alpha_2", "e1_PSF", "e2_PSF"]
+    )
     e1_noleak = e1 - cat_gal["alpha_1"] * cat_gal["e1_PSF"]
     e2_noleak = e2 - cat_gal["alpha_2"] * cat_gal["e2_PSF"]
 
@@ -704,8 +707,6 @@ class metacal:
         masking type, one in 'gal', 'gal_mom', 'star'
     step : float, optional, default=0.01
         step h in finite differences
-    prefix : string, optional, default='NGMIX'
-        to specify columns in input catalogue
     snr_min : float, optional, default=10
         signal-to-noise minimum
     snr_max; float, optional, default=500
@@ -732,7 +733,6 @@ def __init__(
         mask,
         masking_type="gal",
         step=0.01,
-        prefix="NGMIX",
         snr_min=10,
         snr_max=500,
         rel_size_min=0.5,
@@ -766,8 +766,6 @@ def __init__(
 
         self._verbose = verbose
 
-        self._prefix = prefix
-
         self._read_data(data, mask)
         self._compute_calibration()
 
@@ -783,19 +781,7 @@ def _read_data(self, data, mask):
         ns = {}
 
         masked_data = data[mask]
-        if self._prefix == "NGMIX":
-            m1, p1, m2, p2, ns = self._read_data_ngmix(
-                masked_data,
-                m1,
-                p1,
-                m2,
-                p2,
-                ns,
-            )
-        else:
-            raise ValueError(
-                f"Unsupported shape prefix '{self._prefix}'; only 'NGMIX' is supported"
-            )
+        m1, p1, m2, p2, ns = self._read_data_ngmix(masked_data, m1, p1, m2, p2, ns)
 
         self.m1 = m1
         self.p1 = p1
@@ -816,25 +802,22 @@ def _read_data_ngmix(self, masked_data, m1, p1, m2, p2, ns):
             if self._verbose:
                 print("Extracting {}".format(name_shear))
 
-            dict_tmp["flag"] = masked_data[f"{self._prefix}_FLAGS_{name_shear}"]
+            dict_tmp["flag"] = masked_data[f"NGMIX_FLAGS_{name_shear}"]
 
             # Ellipticity in named scalar components (ShapePipe-v2 grammar)
             for comp in (0, 1):
                 dict_tmp[f"g{comp + 1}"] = masked_data[
-                    f"{self._prefix}_G{comp + 1}_{name_shear}"
+                    f"NGMIX_G{comp + 1}_{name_shear}"
                 ]
 
             for key in ("flux", "flux_err", "T", "T_err"):
-                dict_tmp[key] = masked_data[
-                    f"{self._prefix}_{key.upper()}_{name_shear}"
-                ]
+                dict_tmp[key] = masked_data[f"NGMIX_{key.upper()}_{name_shear}"]
 
-            dict_tmp["Tpsf"] = masked_data[f"{self._prefix}_T_PSF_RECONV_{name_shear}"]
+            dict_tmp["Tpsf"] = masked_data[f"NGMIX_T_PSF_RECONV_{name_shear}"]
 
         ns["C11"], ns["C22"], ns["w"] = self.get_variance_ivweights(
             masked_data,
             self._sigma_eps,
-            self._prefix,
             mask=None,
         )
 
@@ -850,7 +833,7 @@ def _read_data_ngmix(self, masked_data, m1, p1, m2, p2, ns):
         return m1, p1, m2, p2, ns
 
     @staticmethod
-    def get_variance_ivweights(data, sigma_eps, prefix="NGMIX", mask=None):
+    def get_variance_ivweights(data, sigma_eps, mask=None):
         """Get Variance IVWEIGHTS.
 
         Compute variance and inverse-variance weights.
@@ -861,8 +844,6 @@ def get_variance_ivweights(data, sigma_eps, prefix="NGMIX", mask=None):
             input data
         sigma_eps : float
             ellipticity dispersion
-        prefix : str, optional
-            shape measurement identifier; default is "NGMIX"
         mask : list, optional
             indicates valid objects with ``True`` values; default is ``None`` = use all objects
             type has to be bool
@@ -877,8 +858,8 @@ def get_variance_ivweights(data, sigma_eps, prefix="NGMIX", mask=None):
             weight
 
         """
-        C11 = data[f"{prefix}_G1_ERR_NOSHEAR"]
-        C22 = data[f"{prefix}_G2_ERR_NOSHEAR"]
+        C11 = data["NGMIX_G1_ERR_NOSHEAR"]
+        C22 = data["NGMIX_G2_ERR_NOSHEAR"]
         if mask is not None:
             C11 = C11[mask]
             C22 = C22[mask]
diff --git a/src/sp_validation/catalog.py b/src/sp_validation/catalog.py
index 76022243..b590993f 100644
--- a/src/sp_validation/catalog.py
+++ b/src/sp_validation/catalog.py
@@ -16,14 +16,12 @@
 
 import h5py
 import numpy as np
-import tqdm
 from astropy import coordinates as coords
 from astropy import units as u
 from astropy.io import fits
 from cs_util import cat
 
 from sp_validation import format, io
-from sp_validation.survey import get_footprint
 from sp_validation.version import __version__
 
 
@@ -154,7 +152,6 @@ def check_matching(
     keys_2,
     thresh,
     stats_file,
-    name=None,
     verbose=False,
 ):
     """Check matching.
@@ -178,28 +175,20 @@ def check_matching(
     -------
     ind : array of int
         index list of d2 of objects that were matched to d1
-    mask_area_tiles : array of int
-        index list of tiles in footprint
+    n_tot : int
+        number of objects in d1
 
     """
-    if name is not None:
-        # Filter stars outside footprint for efficiency
-        mask_area_tiles = get_footprint(name, d1[keys_1[0]], d1[keys_1[1]])
-        if len(np.where(mask_area_tiles)[0]) == 0:
-            raise ValueError(f"Error: no object found in field '{name}'")
-    else:
-        mask_area_tiles = np.arange(len(d1))
-
     # Match stars from exposure (PSF) catalogue to total catalogue
     ind = match_stars2(
         d2[keys_2[0]],
         d2[keys_2[1]],
-        d1[keys_1[0]][mask_area_tiles],
-        d1[keys_1[1]][mask_area_tiles],
+        d1[keys_1[0]],
+        d1[keys_1[1]],
         thresh=thresh,
     )
 
-    n_tot = len(d1[keys_1[0]][mask_area_tiles])
+    n_tot = len(d1[keys_1[0]])
     msg = (
         "Number of matched stars from exposures to total catalogue = "
         + f"{len(ind)}/{n_tot} = {len(ind) / n_tot:.1%}"
@@ -215,7 +204,7 @@ def check_matching(
     )
     io.print_stats(msg, stats_file, verbose=verbose)
 
-    return ind, mask_area_tiles, n_tot
+    return ind, n_tot
 
 
 def check_invalid(dd, key, val, stats_file, name=None, verbose=False):
@@ -357,58 +346,6 @@ def match_stars2(ra_gal, dec_gal, ra_star, dec_star, thresh=0.0002):
     return ind_stars
 
 
-def read_shape_catalog(
-    input_path,
-    w_name="w",
-):
-    """Read Shape Catalog.
-
-    Read catalogue with galaxy shapes = shear estimates.
-
-    Parameters
-    ----------
-    input_path : str
-        input file path
-    w_name : str, optional
-        name of weight column, default is "w"
-
-    Returns
-    -------
-    ra : array of float
-        right ascension in degrees
-    dec : array of float
-        declination in degrees
-    g1 : array of float
-        uncalibrated shear estimate component 1
-    g2 : array of float
-        uncalibrated shear estimate component 2
-    w : array of float
-        weight
-    mag : array of float
-        magnitude
-    snr : array of float
-        signal-to-noise ratio
-    """
-    dat = fits.open(input_path)
-
-    hdu_no = 1
-
-    ra = dat[hdu_no].data["RA"]
-    dec = dat[hdu_no].data["Dec"]
-
-    g1 = dat[hdu_no].data["e1_uncal"]
-    g2 = dat[hdu_no].data["e2_uncal"]
-    w = dat[hdu_no].data[w_name]
-    mag = dat[hdu_no].data["mag"]
-
-    if "snr" in dat[hdu_no].data.dtype.names:
-        snr = dat[hdu_no].data["snr"]
-    else:
-        snr = None
-
-    return ra, dec, g1, g2, w, mag, snr
-
-
 def write_shape_catalog(
     output_path,
     ra,
@@ -441,8 +378,8 @@ def write_shape_catalog(
 
     @sc [label:convention] metacal-flag-width
     The data and image-simulation parameter files supply FITS ``J`` (int32)
-    for metacal bitmasks, so all ngmix flag bits (0-15; ``ZERO_DOF`` = 2**15
-    overflows a signed 16-bit ``I``) survive FITS/HDF5 output; ``JointCat``
+    for metacal bitmasks, so ngmix flag bits (including ``ZERO_DOF`` = 2**15,
+    which overflows signed 16-bit ``I``) survive FITS/HDF5 output; ``JointCat``
     never narrows integers.
     The number of failed metacal types is a count in [0, 5], not a bitmask.
 
@@ -765,73 +702,6 @@ def read_param_file(path, verbose=False):
     return param_list_unique
 
 
-def read_hdf5_file(file_path, name, stats_file, check_only=False, param_path=None):
-    """Read HDF5 File.
-
-    Read hdf5 file and return contained data.
-
-    Parameters
-    ----------
-    file_path : str
-        input file path
-    name : str
-        patch name
-    stats_file : file handler
-        summary statistics output file handler
-    check_only : bool, optional
-        If True only check, not return data
-
-    Returns
-    -------
-    dict
-        data
-
-    """
-    param_list = read_param_file(param_path, verbose=True) if param_path else None
-
-    with h5py.File(file_path, "r") as hdf5_file:
-        # Find patch group in hierarchical structure
-        if f"patches/{name}" not in hdf5_file:
-            raise KeyError(f"Entry patches/{name} not found in file {file_path}")
-        patch_group = hdf5_file[f"patches/{name}"]
-
-        # Get size of data array
-        num_rows = sum(patch_group[ID].shape[0] for ID in patch_group)
-        # num_cols = patch_group[next(iter(patch_group))].shape[1]
-        num_cols = len(param_list)
-
-        print(
-            f"Estimating {num_cols * num_rows * 8 / 1024**3:.1f}"
-            + f" Gb memory for the ({num_cols} x {num_rows}) data array ..."
-        )
-        # data_comb = np.memmap(output_file, dtype=patch_group[next(iter(patch_group))].dtype,
-        #              mode="w+", shape=(num_rows, num_cols))
-
-        data_list = []
-        ID_pbl = set()
-        for ID in tqdm.tqdm(patch_group):
-            # Get data for this ID from file
-            data = patch_group[ID][()]
-
-            # Restrict to parameter list if given
-            data = data[param_list] if param_list is not None else data
-
-            if not check_only:
-                # Add new to existing data
-                data_list.append(data)
-
-        print("Combine tile catalogues")
-        data_comb = np.concatenate(data_list, axis=0)
-        print("Done")
-
-    # Print problematic tile IDs
-    for ID in ID_pbl:
-        print("Tile IDs with missing keys:", file=stats_file)
-        print(ID, file=stats_file)
-
-    return data_comb
-
-
 def get_maked_col(dat, col, mask):
     """Get Masked Col.
 
diff --git a/src/sp_validation/catalog_builders.py b/src/sp_validation/catalog_builders.py
index b6abafc0..630cdc61 100644
--- a/src/sp_validation/catalog_builders.py
+++ b/src/sp_validation/catalog_builders.py
@@ -31,8 +31,9 @@
     print_mask_stats,
 )
 
-from . import calibration, format
+from . import calibration, format, grammar
 from . import catalog as sp_cat
+from . import io as sp_io
 
 # Names re-exported for external code that resolves them off this module.
 __all__ = [
@@ -101,61 +102,51 @@ def read_config_set_params(self, fpath):
 
         return config
 
-    def read_cat(self, load_into_memory=False, mode="r", hdu=1, name="data"):
+    def read_cat(self, load_into_memory=False):
         """Read Cat.
 
-        Read input catalogue, either FITS or HDF5.
+        Read the input catalogue (``input_path``) as one table in the v2
+        column grammar, through ``sp_validation.io.Catalogue``, which detects
+        the container. A comprehensive HDF5 catalogue's ``data`` and, when
+        present, ``data_ext`` datasets are joined column-wise, so mask
+        columns read the same whether they sit in ``data`` (ShapePipe v2) or
+        in ``data_ext`` (post-processed v1). The optional ``column_map``
+        parameter renames a catalogue in another naming convention.
 
         Parameters
         ----------
         load_into_memory: bool, optional
-            load data into memory (potentially slow) of ``True``;
-            default is ``False``
-        mode: bool, optional
-            HDF5 read mode, default is "r"
-        hdu: int, optional
-            HDU number (for FITS file); default is 1
-        name: str, optional
-            dataset name, default is 'data'
+            read the data into memory (potentially slow) if ``True``;
+            otherwise return a lazy table, valid until ``close_cat``.
+            Default is ``False``
 
         Returns
         -------
-        list
-            Catalogue data
-
-        Raises
-        ------
-        IOError
-            If file extension is not .fits or .hd5
+        numpy.ndarray, astropy.io.fits.FITS_rec, h5py.Dataset or grammar.V2View
+            catalogue data
 
         """
         fpath = self._params["input_path"]
         verbose = self._params["verbose"]
 
-        extension = os.path.splitext(fpath)[1]
-        if extension == ".fits":
-            if verbose:
-                print(f"Reading FITS file {fpath}, HDU {hdu}...")
-
-            hdu = 1
-            dat = fits.getdata(fpath, hdu)
-
-        elif extension in (".hdf5", ".hd5"):
-            if verbose:
-                print(f"Reading HDF5 file {fpath}...")
-
-            self._hd5file = h5py.File(fpath, mode)
-            try:
-                dat = self._hd5file[name]
-            except:
-                print(f"Error while reading file {fpath}")
-                raise
-            if load_into_memory:
-                return dat[()]
-            else:
-                return dat
+        if verbose:
+            print(f"Reading catalogue {fpath}...")
+        self._catalogue = sp_io.Catalogue(
+            fpath, column_map=self._params.get("column_map")
+        )
+        if load_into_memory:
+            dat = self._catalogue.read(verbose=verbose)
+            self.close_cat()
         else:
-            raise IOError(f"Unknown file extension {extension}")
+            dat = self._catalogue.table()
+
+        if verbose:
+            print(
+                f"Found {len(dat)} (~{format.millify(len(dat))}) objects"
+                + " in catalogue"
+            )
+
+        return dat
 
     def write_hdf5_header(self, hd5file):
         """Write HDF5 Header.
@@ -231,13 +222,35 @@ def write_hdf5_file(self, dat, output_path=None):
         if self._params["verbose"]:
             print("Done.")
 
-    def close_hd5(self):
-        """Close HD5.
+    def close_cat(self):
+        """Close Cat.
 
-        Close HDF5 file.
+        Close the catalogue file ``read_cat`` opened.
 
         """
-        self._hd5file.close()
+        self._catalogue.close()
+
+
+# Column-dtype promotion is shared with the catalogue reader in ``io``.
+_promote = sp_io.promote_dtypes
+
+
+def _checked_assign(target, start, end, values, name):
+    """Assign `values` into `target[start:end]`, refusing a lossy cast."""
+    target[start:end] = values
+    written = target[start:end]
+    if written.dtype == values.dtype:
+        return
+    if written.dtype.kind in "fc":
+        bad = np.isfinite(values) & ~np.isfinite(written)
+    else:
+        bad = written != values
+    if np.any(bad):
+        raise ValueError(
+            f"Column {name!r} cannot be stored as {written.dtype}:"
+            + f" {int(np.sum(bad))} value(s) overflow or are truncated."
+            + " Disable reduce_mem or widen the output dtype."
+        )
 
 
 class JointCat(BaseCat):
@@ -279,147 +292,84 @@ def params_default(self):
 
         """
         self._params = {
-            "patches": "v1",
+            "input_paths": None,
             "sh": "ngmix",
             "survey": "unions",
             "year": "2024",
             "version": "1.4.2",
             "pipeline": "shapepipe",
-            "hdu": 1,
+            "param_path": None,
             "reduce_mem": False,
             "verbose": False,
         }
         self._short_options = {
-            "patches": "-p",
+            "input_paths": "-i",
             "sh": "-g",
             "survey": "-s",
             "year": "-y",
             "version": "-V",
+            "param_path": "-p",
             "reduce_mem": "-r",
         }
         self._types = {
-            "hdu": "int",
             "reduce_mem": "bool",
         }
         self._help_strings = {
-            "patches": "list of patches separated by '+', or shortcut (allowed are 'v1'), default={}",
+            "input_paths": (
+                "campaign catalogue files (final_cat_<campaign>.hdf5) to merge,"
+                + " separated by '+'"
+            ),
             "sh": "shape measurement method, default={}",
             "survey": "survey name, default={}",
             "year": "year of processing, default={}",
             "version": "catalogue version, default={}",
+            "param_path": "path to parameter file listing columns to keep",
             "reduce_mem": "output some columns in lower precision to reduce memory",
         }
 
-    def get_patches(self):
-        """Get Patches.
+    def get_input_paths(self):
+        """Get Input Paths.
 
-        Return list of patches according to option parameter value.
+        Return the list of campaign catalogue files to merge.
 
         Returns
         -------
-        list
-            patches, list of str
+        list of str
+            input file paths
 
         """
-        if self._params["patches"] == "v1":
-            n_patch = 7
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        elif self._params["patches"] == "v1.5":
-            n_patch = 8
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-
-        else:
-            patches = self._params["patches"].split("+")
-
-        return patches
-
-    def get_n_obj(self, patches, base_path, input_sub_path):
-        """Get N Obj.
-
-        Get number of objects from FITS file headers.
-
-        Parameters
-        ----------
-        patches : list
-            input patches, type is str
-        base_path : str
-            input base directory, root dir of patches
-        input_sub_path : str
-            input file name; input path is base_path/patch/input_sub_path
-
-        Raises:
-            ValueError: if input file canont be read
-
-        Returns:
-            list
-                HDUs
-            list
-                number of objects per file
-            int
-                total number of objects
-
-        """
-        if self._params["verbose"]:
-            print("Getting number of objects")
-        n_obj_list = []
-        n_obj = 0
-        hdu_lists = []
-        for patch in patches:
-            input_path = f"{base_path}/{patch}/{input_sub_path}"
-            try:
-                hdu_list = fits.open(input_path)
-            except Exception as err:
-                raise ValueError(
-                    f"Could not open file {input_path} at HDU"
-                    + f" #{self._params['hdu']}"
-                ) from err
-            hdu_lists.append(hdu_list)
-
-            this_n = int(hdu_list[self._params["hdu"]].header["NAXIS2"])
-            n_obj_list.append(this_n)
-            n_obj += this_n
-
-        if self._params["verbose"]:
-            print(f"Found a total of {n_obj} (~{format.millify(n_obj)}) objects.")
+        input_paths = self._params["input_paths"]
+        if not input_paths:
+            raise ValueError(
+                "No input campaign catalogues given; set 'input_paths' to one"
+                + " or more final_cat_<campaign>.hdf5 files separated by '+'"
+            )
+        if isinstance(input_paths, str):
+            input_paths = input_paths.split("+")
 
-        return hdu_lists, n_obj_list, n_obj
+        return [path.strip() for path in input_paths if path.strip()]
 
-    def get_col_info(self, dat):
-        """Get Col Info.
+    @staticmethod
+    def campaign_name(input_path):
+        """Campaign Name.
 
-        Return information of input columns.
+        Return the campaign name encoded in a catalogue file name,
+        ``final_cat_<campaign>.hdf5`` -> ``<campaign>``.
 
         Parameters
         ----------
-        dat : numpy.ndarray
-            input data
+        input_path : str
+            input file path
 
         Returns
         -------
-        list
-            column names
-        list
-            column formats
-        int
-            number of columns
+        str
+            campaign name
 
         """
-        col_names = dat.dtype.names
-
-        n_col = 0
-        formats = {}
-        ndim = {}
-        for name in col_names:
-            formats[name] = dat.dtype.fields[name][0]
-            ndim[name] = dat[name].ndim
-            n_col += ndim[name]
-        # Add one for patch
-        n_col += 1
-
-        if self._params["verbose"]:
-            print(f"Number of input (output) columns = {len(col_names)} ({n_col})")
-
-        return col_names, formats, ndim, n_col
+        stem = os.path.splitext(os.path.basename(input_path))[0]
+        prefix = "final_cat_"
+        return stem[len(prefix) :] if stem.startswith(prefix) else stem
 
     def dtype_out(self, name, dtype_in):
         """Set output dtype.
@@ -447,65 +397,70 @@ def dtype_out(self, name, dtype_in):
             self._params["reduce_mem"]
             and dtype_in.kind == "f"
             and dtype_in.itemsize == 8
-            and name not in ("RA", "Dec")
+            and name not in ("RA", "Dec", "DEC")
         ):
             return np.dtype(np.float32)
 
         return dtype_in
 
-    def init_data(self, n_col, n_obj, ndim, dat):
-        """Init Data.
+    def output_dtype(self, dtypes_in, n_char_campaign):
+        """Output Dtype.
 
-        Initialize empty structured data.
+        Return the merged-catalogue dtype: the input columns (possibly
+        reduced in precision, and promoted to a common type across all input
+        campaigns) plus a ``campaign`` column.
 
         Parameters
         ----------
-        n_col : int
-            number of columns
-        n_obj : int
-            number of objects (rows)
-        ndim : dict
-            dimension of input columns
-        dat : numpy.ndarray
-            example data
+        dtypes_in : numpy.dtype or list of numpy.dtype
+            structured dtype(s) of the input campaign catalogues
+        n_char_campaign : int
+            width of the campaign name column
 
         Returns
         -------
-        numpy.ndarray
-            combined structure data, (n_col x n_obj) array
+        numpy.dtype
+            output structured dtype
 
-        """
-        # Create dtypes from input column names and types.
-        # Reduce memory if flag set.
-        # Transform multi-D columns into 1D columns
-        dtype_tmp_list = []
-        for name in ndim:
-            if ndim[name] == 1:
-                dtype_tmp_list.append((name, self.dtype_out(name, dat[name].dtype)))
-            else:
-                for jdx in range(ndim[name]):
-                    dtype_tmp_list.append(
-                        (f"{name}_{jdx}", self.dtype_out(name, dat[name].dtype))
-                    )
-        dtype_tmp_list.append(("patch", np.int8))
-        dtype_tmp_struct = np.dtype(dtype_tmp_list)
+        Raises
+        ------
+        ValueError
+            if the inputs have different column sets, or a column is
+            multi-dimensional (campaign catalogues are scalar-column only)
 
-        if self._params["verbose"]:
-            memory = n_obj * dtype_tmp_struct.itemsize
-            print(
-                f"Allocating <= {memory / 1024**3:.1f}"
-                + f" Gb memory for the ({n_col} x {n_obj}) input data array ...",
-                end="",
-            )
+        """
+        if isinstance(dtypes_in, np.dtype):
+            dtypes_in = [dtypes_in]
 
-        dat_all = np.empty((n_obj,), dtype=dtype_tmp_struct)
+        names = dtypes_in[0].names
+        for dtype_in in dtypes_in[1:]:
+            if set(dtype_in.names) != set(names):
+                raise ValueError(
+                    "Campaign catalogues have incompatible column sets:"
+                    + f" {sorted(names)} vs {sorted(dtype_in.names)}"
+                )
 
-        if self._params["verbose"]:
-            print("done")
+        fields = []
+        for name in names:
+            subs = [dtype_in[name] for dtype_in in dtypes_in]
+            for sub in subs:
+                if sub.subdtype is not None:
+                    raise ValueError(
+                        f"Column {name!r} is multi-dimensional (shape"
+                        + f" {sub.subdtype[1]}); campaign catalogues are"
+                        + " expected to hold scalar columns only."
+                    )
+            # Promote across campaigns so a wider string or integer column in
+            # a later file is not silently truncated or overflowed.
+            promoted = subs[0]
+            for sub in subs[1:]:
+                promoted = _promote(promoted, sub)
+            fields.append((name, self.dtype_out(name, promoted)))
+        fields.append(("campaign", np.dtype(f"S{n_char_campaign}")))
 
-        return dat_all
+        return np.dtype(fields)
 
-    def write_hdf5_file(self, dat_all, patches):
+    def write_hdf5_file(self, dat_all, campaigns=None):
         """Write HDF5 File.
 
         Write data to HDF5 file.
@@ -514,8 +469,8 @@ def write_hdf5_file(self, dat_all, patches):
         ----------
         dat_all : numpy.ndarray
             input data
-        patches : list
-            input patches, list of str
+        campaigns : list, optional
+            input campaign names, list of str
 
         """
         output_path = (
@@ -525,12 +480,11 @@ def write_hdf5_file(self, dat_all, patches):
         )
 
         with h5py.File(output_path, "w") as f:
-            self.write_hdf5_header(f)
+            self.write_hdf5_header(f, campaigns=campaigns)
 
-            dset = f.create_dataset("data", data=dat_all)
-            dset[:] = dat_all
+            f.create_dataset("data", data=dat_all)
 
-    def write_hdf5_header(self, hd5file, patches=None):
+    def write_hdf5_header(self, hd5file, campaigns=None):
         """Write HDF5 Header.
 
         Write header information to HDF5 file.
@@ -539,92 +493,85 @@ def write_hdf5_header(self, hd5file, patches=None):
         ----------
         hd5file : h5py.File
             input HDF5 file
-        patches : list, optional
-            input patches, list of str, default is ``None``
+        campaigns : list, optional
+            input campaign names, list of str, default is ``None``
 
         """
         super().write_hdf5_header(hd5file)
 
-        if patches is not None:
-            patches_str = " ".join(patches)
-            hd5file.attrs["patches"] = patches_str
+        if campaigns is not None:
+            hd5file.attrs["campaigns"] = " ".join(campaigns)
 
-    def merge_catalogues(self, patches, base_path="."):
+    def merge_catalogues(self, input_paths):
         """Merge Catalogues.
 
-        Merge individual patch-based catalogues.
+        Merge a list of campaign catalogues into one joint catalogue, adding
+        a ``campaign`` column that records each object's origin.
 
         Parameters
         ----------
-        patches : list
-            input patches; list of `str`
-        base_path : str, optional
-            input base directory path; default is "."
+        input_paths : list of str
+            campaign catalogue files (final_cat_<campaign>.hdf5)
 
-        """
-        input_sub_path = (
-            f"sp_output/shape_catalog_comprehensive_{self._params['sh']}.fits"
-        )
+        Returns
+        -------
+        numpy.ndarray
+            merged catalogue
 
-        # Get input FITS files
-        hdu_lists, n_obj_list, n_obj = self.get_n_obj(
-            patches,
-            base_path,
-            input_sub_path,
+        """
+        param_list = (
+            sp_cat.read_param_file(
+                self._params["param_path"], verbose=self._params["verbose"]
+            )
+            if self._params["param_path"]
+            else None
         )
 
-        # Read data
-        start = end = 0
-        for idx, patch in enumerate(patches):
-            input_path = f"{base_path}/{patch}/{input_sub_path}"
-            try:
-                dat = fits.getdata(input_path, self._params["hdu"])
-                # dat = hdu_lists[idx][self._params["hdu"]].data
+        campaigns = [self.campaign_name(path) for path in input_paths]
+        n_char_campaign = max(len(name) for name in campaigns)
+
+        # First pass over file metadata only (row counts and dtypes), so the
+        # merged array is allocated once and filled in place, instead of
+        # concatenating per-campaign copies (peak memory 2x the output).
+        column_map = self._params.get("column_map")
+        shapes = []
+        for path in input_paths:
+            with sp_io.Catalogue(path, column_map=column_map) as catalogue:
+                shapes.append((len(catalogue), catalogue.dtype(param_list)))
+        n_total = sum(n_rows for n_rows, _ in shapes)
+        dtype_out = self.output_dtype([dtype for _, dtype in shapes], n_char_campaign)
+
+        dat_all = np.empty(n_total, dtype=dtype_out)
+        start = 0
+        for input_path, campaign in zip(input_paths, campaigns):
+            # Fill tile by tile: peak memory is the merged output plus a
+            # single tile, never a whole campaign copy on top of it.
+            n_campaign = 0
+            with sp_io.Catalogue(input_path, column_map=column_map) as catalogue:
+                for dat in catalogue.iter_chunks(
+                    param_list, verbose=self._params["verbose"]
+                ):
+                    end = start + len(dat)
+                    for name in dat.dtype.names:
+                        _checked_assign(dat_all[name], start, end, dat[name], name)
+                    dat_all["campaign"][start:end] = campaign.encode()
+                    start = end
+                    n_campaign += len(dat)
+                    del dat
 
-                hdu_lists[idx].close()
-            except Exception as err:
-                raise ValueError(
-                    f"Could not read data of file {input_path} at HDU"
-                    + f" #{self._params['hdu']}"
-                ) from err
-
-            # Create empty lists if first patch
-            if idx == 0:
-                col_names, formats, ndim, n_col = self.get_col_info(dat)
-                dat_all = self.init_data(n_col, n_obj, ndim, dat)
-
-            # Append new data for that patch (between start and end)
-            end += n_obj_list[idx]
-
-            # Copy data
-            i_col = 0
-            names_out = dat_all.dtype.names
-            for name in col_names:
-                if ndim[name] == 1:
-                    # Copy 1D column
-                    dat_all[names_out[i_col]][start:end] = dat[name]
-                else:
-                    # Copy all components of multi-D column
-                    for jdx in range(ndim[name]):
-                        dat_all[names_out[i_col + jdx]][start:end] = dat[name][:, jdx]
-                i_col += ndim[name]
-            # Add patch number
-            dat_all["patch"][start:end] = patch[1:]
-
-            if i_col + 1 != n_col:
-                raise ValueError(
-                    "Inconsistent number of columns, {i_col + 1}" + f" != {n_col}"
-                )
             if self._params["verbose"]:
                 print(
-                    f"{patch}: Added {len(dat)} (~{format.millify(len(dat))})"
-                    + f" objects (from {start} to {end - 1})."
+                    f"{campaign}: added {n_campaign}"
+                    + f" (~{format.millify(n_campaign)}) objects."
                 )
-            start = end
 
-        del dat
+        if self._params["verbose"]:
+            print(
+                f"Merged {len(dat_all)} (~{format.millify(len(dat_all))})"
+                + f" objects from {len(campaigns)} campaign(s)."
+            )
 
-        self.write_hdf5_file(dat_all, patches)
+        return dat_all
 
     def run(self):
         """Run.
@@ -632,60 +579,27 @@ def run(self):
         Main processing function.
 
         """
-        patches = self.get_patches()
+        input_paths = self.get_input_paths()
         if self._params["verbose"]:
-            print("Merging patches", patches)
+            print("Merging campaigns", input_paths)
 
-        self.merge_catalogues(patches)
+        dat_all = self.merge_catalogues(input_paths)
+        self.write_hdf5_file(dat_all, [self.campaign_name(p) for p in input_paths])
 
 
 class ApplyHspMasks(BaseCat):
     """Apply Hsp Masks."""
 
-    # Labels of bit-coded structural masks
-    _labels_struct = {
-        1: "Faint_star_halos",
-        2: "Bright_star_halos",
-        4: "Stars",
-        8: "Manual",
-        16: "u",
-        32: "g",
-        64: "r",
-        128: "i",
-        256: "z",
-        512: "Tile_RA_DEC_cut",
-        1024: "Maximask",
-        2048: "z2",
-    }
-
     def __init__(self):
         # Set default parameters
         self.params_default()
 
-    @classmethod
-    def get_label_struct(cls, bit):
-        """Get Label Struct.
-
-        Return label of bit-coded mask.
-
-        Parameters
-        ----------
-        bit: int
-            input bit
-
-        Returns
-        -------
-        str
-            label
-
-        """
-        return cls._labels_struct[bit]
-
     @classmethod
     def get_mask_col_name(cls, bit):
         """Get Mask Col Name.
 
-        Return column name of mask corresponding to input bit.
+        Return column name of mask corresponding to input bit:
+        ``MASK_{bit}_{label}`` (``grammar.mask_column``).
 
         Parameters
         ----------
@@ -698,7 +612,7 @@ def get_mask_col_name(cls, bit):
             column name
 
         """
-        return f"{bit}_{cls.get_label_struct(bit)}"
+        return grammar.mask_column(bit)
 
     def params_default(self):
         """Params Default.
@@ -933,7 +847,7 @@ def run(self):
         obj.update_params()
 
         # Read input data
-        dat = obj.read_cat(load_into_memory=True, mode="r")
+        dat = obj.read_cat(load_into_memory=True)
 
         # Get masks
         masks = obj.get_masks(dat=dat)
@@ -944,9 +858,6 @@ def run(self):
         # Write extended data to new HDF5 file
         obj.write_hdf5_file(dat_ext)
 
-        # Close input HDF5 catalogue file
-        obj.close_hd5()
-
     def append_masks(self, dat, masks):
         """Append Masks.
 
@@ -1041,8 +952,6 @@ def write_hdf5_header(self, hd5file):
         ----------
         hd5file : h5py.File
             input HDF5 file
-        patches : list, optional
-            input patches, list of str, default is ``None``
 
         """
         super().write_hdf5_header(hd5file)
@@ -1095,74 +1004,7 @@ def params_default(self):
             "cmatrices": "compute correlation and confusion matrices",
         }
 
-    def read_cat(self, load_into_memory=False):
-        """Read Cat.
-
-        Read input HDF5 catalogue.
-
-        Parameters
-        ----------
-        load_into_memory: bool, optional
-            load data into memory (potentially slow) of ``True``;
-            default is ``False``
-
-        Returns
-        -------
-        list
-            Catalogue data
-        list_ext
-            Extended catalogue data if exists in input file
-
-        """
-        fpath = self._params["input_path"]
-        verbose = self._params["verbose"]
-
-        # Image-simulation path: a single per-run comprehensive catalogue in
-        # FITS, not the joined multi-patch HDF5 the data path builds. Read the
-        # FITS table directly into memory; there is no separate data_ext group.
-        extension = os.path.splitext(fpath)[1]
-        if extension == ".fits":
-            if verbose:
-                print(f"Reading FITS file {fpath}, HDU 1...")
-            dat = fits.getdata(fpath, 1)
-            dat_ext = None
-            if verbose:
-                print(
-                    f"Found {len(dat)} (~{format.millify(len(dat))}) objects"
-                    + " in catalogue"
-                )
-            return dat, dat_ext
-
-        if verbose:
-            print(f"Reading HDF5 file {fpath}...")
-
-        self._hd5file = h5py.File(fpath, "r")
-        try:
-            dat = self._hd5file["data"]
-            if "data_ext" in self._hd5file:
-                dat_ext = self._hd5file["data_ext"]
-            else:
-                dat_ext = None
-        except:
-            print(f"Error while reading file {fpath}")
-            raise
-
-        if verbose:
-            print(
-                f"Found {len(dat)} (~{format.millify(len(dat))}) objects"
-                + " in catalogue"
-            )
-
-        if load_into_memory:
-            if dat_ext:
-                return dat[()], dat_ext[()]
-            else:
-                return dat[()]
-        else:
-            return dat, dat_ext
-
     def add_params_to_FITS_header(self, header, cm=None):
-
         header_new = fits.Header()
 
         # General information
@@ -1212,7 +1054,6 @@ def params_default(self):
         }
 
     def run(self):
-
         pass
 
 
diff --git a/src/sp_validation/cosmo_val/catalog_characterization.py b/src/sp_validation/cosmo_val/catalog_characterization.py
index bb5f503d..8f8ecd2e 100644
--- a/src/sp_validation/cosmo_val/catalog_characterization.py
+++ b/src/sp_validation/cosmo_val/catalog_characterization.py
@@ -14,9 +14,9 @@
 import healpy as hp
 import matplotlib.pyplot as plt
 import numpy as np
-from astropy.io import fits
 from cs_util import plots as cs_plots
 
+from ..io import open_entry
 from ..survey import (
     additive_bias,
     area_from_coords,
@@ -75,16 +75,12 @@ def compute_survey_stats(
         if not os.path.exists(catalog_path):
             raise FileNotFoundError(f"Shear catalog not found: {catalog_path}")
 
-        data = fits.getdata(catalog_path, memmap=True)
+        weight_column = weights_key_override or shear_cfg["w_col"]
+        data = open_entry(shear_cfg)
         n_rows = len(data)
 
         e1 = np.asarray(data[shear_cfg["e1_col"]], dtype=float)
         e2 = np.asarray(data[shear_cfg["e2_col"]], dtype=float)
-
-        weight_column = weights_key_override or shear_cfg["w_col"]
-        if weight_column not in data.columns.names:
-            raise KeyError(f"Weight column '{weight_column}' missing in {catalog_path}")
-
         w = np.asarray(data[weight_column], dtype=float)
 
         if mask_path is not None:
@@ -104,7 +100,7 @@ def compute_survey_stats(
         elif cov_th.get("A") is not None:
             area_deg2 = float(cov_th["A"])
         elif nside is not None:
-            area_deg2 = self._area_from_catalog(catalog_path, nside)
+            area_deg2 = self._area_from_catalog(shear_cfg, nside)
         else:
             raise ValueError(
                 f"Unable to determine survey area for {ver}. Provide mask_path or nside."
@@ -131,10 +127,12 @@ def compute_survey_stats(
 
         return results
 
-    def _area_from_catalog(self, catalog_path, nside):
-        data = fits.getdata(catalog_path, memmap=True)
-        ra = np.asarray(data["RA"], dtype=float)
-        dec = np.asarray(data["Dec"], dtype=float)
+    def _area_from_catalog(self, shear_cfg, nside):
+        ra_col = shear_cfg.get("ra_col", "RA")
+        dec_col = shear_cfg.get("dec_col", "Dec")
+        data = open_entry(shear_cfg)
+        ra = np.asarray(data[ra_col], dtype=float)
+        dec = np.asarray(data[dec_col], dtype=float)
         return area_from_coords(ra, dec, nside)
 
     def _area_from_mask(self, mask_map_path):
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 422c3a9c..57f172bf 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -7,8 +7,9 @@
 import numpy as np
 import yaml
 from cs_util.cosmo import get_cosmo
-from shear_psf_leakage import run_object, run_scale
+from shear_psf_leakage import leakage, run_object, run_scale
 
+from .. import io
 from ..b_modes import (
     _get_pte_from_scale_cut,
     covariance_label,
@@ -23,6 +24,37 @@
 from .pure_eb import PureEBMixin
 from .real_space import RealSpaceMixin
 
+
+class _LeakageScale(run_scale.LeakageScale):
+    """``LeakageScale`` reading its catalogues through ``sp_validation.io``.
+
+    ``entries`` holds the version's cat_config ``shear`` and ``star`` blocks.
+    """
+
+    entries = None
+
+    def read_data(self, shear=True, psf=True):
+        if shear:
+            self.dat_shear = leakage.cut_data(
+                io.open_entry(self.entries["shear"]),
+                self._params["cut"],
+                self._params["verbose"],
+            )
+        if psf:
+            self.dat_PSF = self.handle_close_objects(
+                io.open_entry(self.entries["star"])
+            )
+
+
+class _LeakageObject(run_object.LeakageObject):
+    """``LeakageObject`` reading its catalogue through ``sp_validation.io``."""
+
+    entries = None
+
+    def read_data(self):
+        self._dat = io.open_entry(self.entries["shear"])
+
+
 # %%
 BMODE_COLUMNS = {
     "xip_B": r"xi+B",
@@ -482,17 +514,18 @@ def init_results(self, objectwise=False):
         # Branch is loop-invariant: pick the leakage class and its parameter
         # builder once, then apply per version.
         make_leakage, set_params = (
-            (run_object.LeakageObject, self.set_params_leakage_object)
+            (_LeakageObject, self.set_params_leakage_object)
             if objectwise
-            else (run_scale.LeakageScale, self.set_params_leakage_scale)
+            else (_LeakageScale, self.set_params_leakage_scale)
         )
 
         results = {}
         for ver in self.versions:
-            leakage = results[ver] = make_leakage()
-            leakage._params.update(set_params(ver))
-            leakage.check_params()
-            leakage.prepare_output()
+            obj = results[ver] = make_leakage()
+            obj.entries = self.cc[ver]
+            obj._params.update(set_params(ver))
+            obj.check_params()
+            obj.prepare_output()
 
         return results
 
diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index cf74efc6..7c8406e2 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -18,6 +18,7 @@
 from cs_util.cosmo import get_theo_c_ell
 
 from .. import sacc_io
+from ..io import open_entry
 from ..pseudo_cl import (
     apply_random_rotation,
     get_n_gal_map,
@@ -190,7 +191,7 @@ def calculate_pseudo_cl_eb_cov(self):
                 b = self.get_namaster_bin(lmin, lmax, b_lmax)
 
                 # Load data and create shear and noise maps
-                cat_gal = fits.getdata(self.cc[ver]["shear"]["path"])
+                cat_gal = open_entry(self.cc[ver]["shear"])
 
                 n_gal, unique_pix, _idx, idx_rep = self.get_n_gal_map(
                     params, nside, cat_gal
@@ -549,7 +550,7 @@ def calculate_pseudo_cl_map(self, ver, nside, out_path):
         params = get_params_rho_tau(self.cc[ver], survey=ver)
 
         # Load data and create shear and noise maps
-        cat_gal = fits.getdata(self.cc[ver]["shear"]["path"])
+        cat_gal = open_entry(self.cc[ver]["shear"])
 
         w = cat_gal[params["w_col"]]
         self.print_cyan("Creating maps and computing Cl's...")
@@ -618,7 +619,7 @@ def calculate_pseudo_cl_catalog(self, ver, out_path):
         params = get_params_rho_tau(self.cc[ver], survey=ver)
 
         # Load data and create shear and noise maps
-        cat_gal = fits.getdata(self.cc[ver]["shear"]["path"])
+        cat_gal = open_entry(self.cc[ver]["shear"])
 
         ell_eff, cl_shear, wsp = self.get_pseudo_cls_catalog(
             catalog=cat_gal, params=params
diff --git a/src/sp_validation/galaxy.py b/src/sp_validation/galaxy.py
index 3c49a96b..57f3f14b 100644
--- a/src/sp_validation/galaxy.py
+++ b/src/sp_validation/galaxy.py
@@ -11,6 +11,7 @@
 """
 
 import re
+import warnings
 
 import numpy as np
 import regions
@@ -27,6 +28,113 @@
 # T = 2 sigma^2 as if it were sigma; the cs_util version carries the
 # required square root: FWHM = 2.35482 sqrt(T / 2)
 from sp_validation import io
+from sp_validation.grammar import mask_column
+
+#: All mask columns written by ShapePipe v2 (bool, ``True`` = masked), one per
+#: bit of the UNIONS healsparse mask product (``grammar.MASK_LABELS``; v2 does
+#: not write bit 512). Post-processed v1 comprehensive catalogues carry the same
+#: columns, presented under these names by ``sp_validation.grammar``.
+#:
+#: The r-band default bitmask uses bits 1 (faint star halos), 2 (bright star
+#: halos), 4 (star bodies), 8 (manual galaxy mask), 64 (r-band coverage) and
+#: 1024 (MaxiMask). The per-band coverage flags are 16 (u), 32 (g), 64 (r),
+#: 128 (i) and 256 (HSC z); 2048 is ``True`` where Pan-STARRS z-band (z2)
+#: coverage is absent.
+MASK_COLUMNS = tuple(
+    mask_column(bit) for bit in (1, 2, 4, 8, 16, 32, 64, 128, 256, 1024, 2048)
+)
+
+#: Mask columns OR'd together for the default galaxy selection. This set is
+#: the ShapePipe r-band default bitmask: their OR matches ``mask_r`` on covered
+#: granules (verified on the P3 sky area), with differences at footprint edges
+#: without map coverage.
+#: Deliberately not a blanket OR over MASK_COLUMNS: the other coverage flags
+#: (bits 16, 32, 128, 256) and bit 2048 would mask essentially the whole
+#: catalogue.
+DEFAULT_MASK_COLUMNS = tuple(mask_column(bit) for bit in (4, 1, 2, 8, 64, 1024))
+
+
+def _column_names(dd):
+    """Return the column names of a structured array or mapping."""
+    dtype = getattr(dd, "dtype", None)
+    if dtype is not None and dtype.names is not None:
+        return tuple(dtype.names)
+    return tuple(dd.keys())
+
+
+def mask_cut(dd, mask_columns=None):
+    """Mask Cut.
+
+    Return a boolean mask that is ``True`` for objects *not* flagged by any
+    of the requested ShapePipe mask columns.
+
+    Parameters
+    ----------
+    dd : numpy.ndarray or dict
+        input catalogue
+    mask_columns : list of str, optional
+        mask columns to OR together; default is ``DEFAULT_MASK_COLUMNS``
+
+    Returns
+    -------
+    numpy.ndarray
+        boolean mask, ``True`` = keep
+
+    Raises
+    ------
+    KeyError
+        if any requested mask column is absent from the catalogue
+
+    """
+    columns = list(DEFAULT_MASK_COLUMNS if mask_columns is None else mask_columns)
+    if not columns:
+        # No masking requested (e.g. the image simulations, which run no
+        # imaging-flag masking stage and carry no mask columns).
+        return np.ones(len(dd[_column_names(dd)[0]]), dtype=bool)
+
+    available = _column_names(dd)
+    missing = [col for col in columns if col not in available]
+    if missing:
+        raise KeyError(
+            f"Mask column(s) {missing} not found in catalogue."
+            + " ShapePipe v2 catalogues carry the boolean columns"
+            + f" {list(MASK_COLUMNS)}, as do comprehensive catalogues whose"
+            + " data_ext holds the healsparse mask bits (read through"
+            + " sp_validation.grammar). Available columns:"
+            + f" {sorted(available)}"
+        )
+
+    masked = np.zeros(len(dd[columns[0]]), dtype=bool)
+    n_undefined = 0
+    for col in columns:
+        values = np.asarray(dd[col])
+        if values.dtype == bool:
+            flagged = values
+        else:
+            # ShapePipe's writer can emit the mask columns as float64
+            # {0, 1} rather than bool, so decide on the value (threshold at
+            # 0.5, so integer, float and bool columns behave identically),
+            # and count NaNs rather than let them pass unremarked.
+            values = values.astype(float)
+            undefined = np.isnan(values)
+            n_undefined += int(undefined.sum())
+            flagged = undefined | (values > 0.5)
+        masked |= flagged
+
+    if n_undefined:
+        # NaN means the masking stage recorded no verdict for this object.
+        # Treat it as masked, as the config-driven cuts (``kind: equal,
+        # value: False``) do, and say so: a nonzero count means the input
+        # product is defective.
+        warnings.warn(
+            f"{n_undefined} NaN value(s) in mask column(s) {columns};"
+            + " treated as masked. The mask columns of a complete"
+            + " ShapePipe product hold only 0 and 1.",
+            RuntimeWarning,
+            stacklevel=2,
+        )
+
+    return ~masked
 
 
 def classification_galaxy_overlap_ra_dec(dd, ra_key="XWIN_WORLD", dec_key="YWIN_WORLD"):
@@ -141,11 +249,18 @@ def classification_galaxy_base(
     gal_mag_faint=26,
     flags_keep=None,
     n_epoch_min=1,
+    mask_columns=None,
 ):
     """Classification Galaxy Base.
 
     Return mask corresponding to basic classification for galaxies.
 
+    Parameters
+    ----------
+    mask_columns : list of str, optional
+        ShapePipe mask columns OR'd together to reject masked objects;
+        default is ``DEFAULT_MASK_COLUMNS``
+
     """
     # SExtractor flags
     # Keep some flags if specified
@@ -172,7 +287,7 @@ def classification_galaxy_base(
         & cut_flags
         & (dd["MAG_AUTO"] <= gal_mag_faint)
         & (dd["MAG_AUTO"] >= gal_mag_bright)
-        & (dd["IMAFLAGS_ISO"] == 0)
+        & mask_cut(dd, mask_columns)
         & (dd["N_EPOCH"] >= n_epoch_min)
     )
 
@@ -189,8 +304,19 @@ def classification_galaxy_ngmix(
 
     Return mask corresponding to ngmix classification of galaxies
     """
+    # NGMIX_N_EPOCH == 0 marks objects ngmix never fit: ShapePipe's make_cat
+    # pre-fills every NGMIX_* column with sentinels (G1/G2 = -10, T/FLUX = 0)
+    # and only overwrites them for objects present in the ngmix output, so a
+    # never-fit object keeps NGMIX_MCAL_FLAGS == 0 and passes a flag-only cut.
+    # In final_cat_smk-g7.hdf5 this is 18,983 / 1,851,100 objects (1.03%);
+    # admitting them drags mean e1 to -0.096 (std 0.98) from +0.0001 (std
+    # 0.24). The coadd N_EPOCH cut in classification_galaxy_base does not
+    # catch them (18,750 of the 18,983 have N_EPOCH >= 1). Cut on N_EPOCH
+    # explicitly rather than relying on the -10 sentinel comparison below,
+    # which is an exact float equality against a value ShapePipe may change.
     m_gal_ngmix = (
         cut_common
+        & (dd["NGMIX_N_EPOCH"] > 0)
         & (dd["NGMIX_MCAL_FLAGS"] == 0)
         & (dd["NGMIX_G1_PSF_ORIG_NOSHEAR"] != -10)
         & (dd["NGMIX_MCAL_TYPES_FAIL"] == 0)
diff --git a/src/sp_validation/glass_mock.py b/src/sp_validation/glass_mock.py
index de8312c7..652fe713 100644
--- a/src/sp_validation/glass_mock.py
+++ b/src/sp_validation/glass_mock.py
@@ -303,9 +303,10 @@ def create_mask_from_catalogue(nside, path, output, ra_col="RA", dec_col="DEC"):
     Pixels containing at least one object get 1.0, everything else 0.0.
     """
     import healpy as hp
-    from astropy.io import fits
 
-    cat_gal = fits.getdata(path)
+    from sp_validation.io import read_catalogue
+
+    cat_gal = read_catalogue(path, columns=[ra_col, dec_col])
     ra = cat_gal[ra_col]
     dec = cat_gal[dec_col]
 
diff --git a/src/sp_validation/grammar.py b/src/sp_validation/grammar.py
new file mode 100644
index 00000000..b0169071
--- /dev/null
+++ b/src/sp_validation/grammar.py
@@ -0,0 +1,612 @@
+"""Column grammars: the v2 schema, and the maps that present a table in it.
+
+ShapePipe products name their columns in one of two grammars. ``v2`` is the
+grammar the rest of sp_validation reads (``HSM_T_PSF``, ``NGMIX_G1_NOSHEAR``,
+``MASK_4_Stars``, ...; see ``docs/ngmix_psf_column_migration.md``). ``v1`` is the
+grammar of the catalogues released up to and including v1.6.x (``SIGMA_PSF_HSM``,
+the 2-vector ``NGMIX_ELL_NOSHEAR``, ...).
+
+This module is the single place that knows the difference. ``adapt`` hands a
+table back as a lazy view presenting v2 names and units, so downstream code
+reads one grammar and never branches on the generation. Two rule families
+apply:
+
+- The v1 shape-measurement columns, applied when the table is v1 (see
+  ``detect_generation``):
+
+  - ``E{1,2}_{PSF,STAR}_HSM`` and ``FLAG_{PSF,STAR}_HSM`` are renamed;
+  - ``SIGMA_{PSF,STAR}_HSM`` (sigma) is presented as ``HSM_T_{PSF,STAR}``
+    holding ``T = 2 sigma^2``, via ``cs_util.size.sigma_to_T``;
+  - the 2-vector ngmix columns ``NGMIX_ELL[_ERR|_PSFo]_{shear}`` (or their
+    flattened ``_0``/``_1`` forms, as in the comprehensive HDF5) are split
+    into ``NGMIX_G{1,2}[_ERR|_PSF_ORIG]_{shear}``;
+  - ``NGMIX_T_PSFo_{shear}``, ``NGMIX_Tpsf_{shear}`` and ``NGMIX_MOM_FAIL``
+    become ``NGMIX_T_PSF_ORIG_{shear}``, ``NGMIX_T_PSF_RECONV_{shear}`` and
+    ``NGMIX_MCAL_TYPES_FAIL``, except that ``NGMIX_T_PSF_RECONV_NOSHEAR`` is
+    read from ``NGMIX_Tpsf_1P`` when the table has it (see below).
+
+- The UNIONS mask-bit columns, applied to any table. Bit ``b`` of the
+  healsparse mask product is the boolean ``MASK_{b}_{label}``
+  (``MASK_1_Faint_star_halos``, ``MASK_4_Stars``, ``MASK_2048_z2``, ...;
+  ``mask_column``, labels from ``MASK_LABELS``). Two other spellings of the
+  same bit are presented under that name: ``{b}_{label}``, the ``data_ext``
+  dataset of every released v1 comprehensive HDF5, and ``MASK_n{b}``, written
+  by pre-release ShapePipe v2 runs. These columns belong to the mask product,
+  not to a ShapePipe generation, so no spelling marks or requires one.
+
+``IMAFLAGS_ISO`` is not mapped: its v1 bits (2 halo, 4 border, 16 Messier,
+32 NGC, 128 spike) differ in meaning from the mask bits of the same value,
+so it passes through under its own name for the v1 mask configs that cut on it.
+
+The mapping is by *name*, deliberately, including where the v1 values mean
+something different from their v2 namesakes: v1 ``NGMIX_*_PSFo_*`` hold the
+reconvolved-PSF alias rather than a fit to the original PSF, and
+``NGMIX_MOM_FAIL`` counts moments-guess failures rather than failed metacal
+types. Those values are presented unchanged.
+
+One v1 value is corrected rather than renamed. ShapePipe v1 wrote a wrong
+no-shear reconvolved-PSF size: ``NGMIX_Tpsf_NOSHEAR`` differs by ~2% for
+nearly every object from the reconvolution kernel metacal applied, which the
+sheared types' ``NGMIX_Tpsf_{1P,1M,2P,2M}`` record (they agree to ~1e-5).
+The v1.4.6.3 release cut on and wrote the ``1P`` value, so
+``NGMIX_T_PSF_RECONV_NOSHEAR`` reads ``NGMIX_Tpsf_1P`` whenever the table has
+it, and ``NGMIX_Tpsf_NOSHEAR`` otherwise (a cut catalogue such as v1.4.6.3's,
+whose no-shear column already holds the ``1P`` value). ShapePipe v2 reuses one
+reconvolved PSF across metacal types, so its columns need no correction.
+
+A catalogue in another naming convention can carry a ``column_map``,
+``{v2 name: name in the file}``, which ``adapt`` applies as renames ahead of
+the rules above: an entry overrides the rule producing the same v2 name and no
+other (a column it renames still feeds the other rules that read it), and the
+v1 detection runs on the columns the map does not rename. An entry may hold one
+``*`` on both sides, matching any stem: ``{"NGMIX_*": "MYFIT_*"}`` presents
+every ``MYFIT_<stem>`` column as ``NGMIX_<stem>``. Explicit entries take
+precedence over pattern entries.
+
+Every other column passes through under its own name; source names that have a
+v2 equivalent are hidden.
+
+``adapt`` also joins row-aligned tables into one view, as the comprehensive
+HDF5 splits one catalogue over its ``data`` and ``data_ext`` datasets.
+"""
+
+from dataclasses import dataclass
+
+import h5py
+import numpy as np
+from cs_util.size import sigma_to_T
+
+#: Metacal shear types carried by the ngmix columns.
+SHEARS = ("NOSHEAR", "1P", "1M", "2P", "2M")
+
+#: Bits of the UNIONS healsparse mask product and what each flags. The column
+#: for bit ``b`` is ``MASK_{b}_{label}`` (``mask_column``).
+MASK_LABELS = {
+    1: "Faint_star_halos",
+    2: "Bright_star_halos",
+    4: "Stars",
+    8: "Manual",
+    16: "u",
+    32: "g",
+    64: "r",
+    128: "i",
+    256: "z",
+    512: "Tile_RA_DEC_cut",
+    1024: "Maximask",
+    2048: "z2",
+}
+
+
+def mask_column(bit):
+    """Return the column holding mask bit ``bit``: ``MASK_{bit}_{label}``.
+
+    Raises
+    ------
+    KeyError
+        if ``bit`` is not a bit of the mask product
+    """
+    if bit not in MASK_LABELS:
+        raise KeyError(f"{bit} is not a UNIONS mask bit; bits: {list(MASK_LABELS)}")
+    return f"MASK_{bit}_{MASK_LABELS[bit]}"
+
+
+@dataclass(frozen=True)
+class Rule:
+    """One v2 column presented from a source column.
+
+    ``kind`` is ``"rename"`` (same values), ``"sigma_to_T"`` (T = 2 sigma^2)
+    or ``"component"`` (element ``arg`` of a 2-vector, stored either as one
+    vector column ``source`` or as the flattened ``{source}_{arg}``).
+    ``fallback`` names a column read instead when ``source`` is absent.
+    """
+
+    v2: str
+    source: str
+    kind: str = "rename"
+    arg: int | None = None
+    fallback: str | None = None
+
+    def sources(self):
+        """Return the source names this rule can read from, in preference order."""
+        if self.kind == "component":
+            return (self.source, f"{self.source}_{self.arg}")
+        if self.fallback is not None:
+            return (self.source, self.fallback)
+        return (self.source,)
+
+    def apply(self, column):
+        """Return the v2 column computed from source ``column``."""
+        if self.kind == "rename":
+            return column
+        column = np.asarray(column)
+        if self.kind == "sigma_to_T":
+            return sigma_to_T(column)
+        return column[:, self.arg] if column.ndim == 2 else column
+
+
+def _v1_rules():
+    rules = []
+    for obj in ("PSF", "STAR"):
+        rules += [
+            Rule(f"HSM_G1_{obj}", f"E1_{obj}_HSM"),
+            Rule(f"HSM_G2_{obj}", f"E2_{obj}_HSM"),
+            Rule(f"HSM_T_{obj}", f"SIGMA_{obj}_HSM", "sigma_to_T"),
+            Rule(f"HSM_FLAG_{obj}", f"FLAG_{obj}_HSM"),
+        ]
+    for shear in SHEARS:
+        for i in (0, 1):
+            g = f"G{i + 1}"
+            rules += [
+                Rule(f"NGMIX_{g}_{shear}", f"NGMIX_ELL_{shear}", "component", i),
+                Rule(
+                    f"NGMIX_{g}_ERR_{shear}", f"NGMIX_ELL_ERR_{shear}", "component", i
+                ),
+                Rule(
+                    f"NGMIX_{g}_PSF_ORIG_{shear}",
+                    f"NGMIX_ELL_PSFo_{shear}",
+                    "component",
+                    i,
+                ),
+            ]
+        rules.append(Rule(f"NGMIX_T_PSF_ORIG_{shear}", f"NGMIX_T_PSFo_{shear}"))
+        if shear == "NOSHEAR":
+            # v1 wrote a wrong no-shear size; see the module docstring.
+            rules.append(
+                Rule(
+                    "NGMIX_T_PSF_RECONV_NOSHEAR",
+                    "NGMIX_Tpsf_1P",
+                    fallback="NGMIX_Tpsf_NOSHEAR",
+                )
+            )
+        else:
+            rules.append(Rule(f"NGMIX_T_PSF_RECONV_{shear}", f"NGMIX_Tpsf_{shear}"))
+    rules.append(Rule("NGMIX_MCAL_TYPES_FAIL", "NGMIX_MOM_FAIL"))
+    return tuple(rules)
+
+
+#: The v1 -> v2 map of the shape-measurement columns, applied to v1 tables.
+V1_RULES = _v1_rules()
+
+#: ``{b}_{label}`` (v1 ``data_ext``) or ``MASK_n{b}`` (pre-release v2)
+#: -> ``MASK_{b}_{label}``, applied to every table.
+MASK_RULES = tuple(
+    Rule(mask_column(bit), f"{bit}_{label}", fallback=f"MASK_n{bit}")
+    for bit, label in MASK_LABELS.items()
+)
+
+#: Names whose presence marks a table as v1 / as v2.
+V1_MARKERS = frozenset(name for rule in V1_RULES for name in rule.sources())
+V2_MARKERS = frozenset(rule.v2 for rule in V1_RULES)
+
+
+def column_names(table):
+    """Return the column names of a structured array, FITS_rec or h5py Dataset."""
+    names = getattr(getattr(table, "dtype", None), "names", None)
+    if names is None:
+        raise TypeError(f"cannot list the columns of a {type(table).__name__}")
+    return tuple(names)
+
+
+def detect_generation(names):
+    """Return ``"v1"``, ``"v2"`` or ``None`` from the column names present.
+
+    ``None`` means grammar-neutral: no shape-measurement column either grammar
+    names (e.g. a cut catalogue carrying only ``RA``, ``Dec``, ``e1``, ``e2``,
+    ``w``, or a table of mask columns). Mask columns mark no generation.
+
+    Raises
+    ------
+    ValueError
+        if columns of both grammars are present.
+    """
+    names = set(names)
+    v1 = sorted(names & V1_MARKERS)
+    v2 = sorted(names & V2_MARKERS)
+    if v1 and v2:
+        raise ValueError(
+            "catalogue mixes ShapePipe column grammars:"
+            + f" v1 columns {v1[:5]} alongside v2 columns {v2[:5]}"
+        )
+    if v1:
+        return "v1"
+    if v2:
+        return "v2"
+    return None
+
+
+def _map_rules(names, column_map):
+    """Return the rename rules ``column_map`` yields for a table's ``names``.
+
+    Entries whose file column is absent yield nothing, so one map serves
+    tables that carry only some of its columns.
+    """
+    if not column_map:
+        return ()
+    rules = {}
+    patterns = []
+    for v2, source in column_map.items():
+        if "*" not in v2 and "*" not in source:
+            if source in names and source != v2:
+                rules[v2] = Rule(v2, source)
+            continue
+        if v2.count("*") != 1 or source.count("*") != 1:
+            raise ValueError(
+                f"column_map pattern {v2!r}: {source!r} needs exactly one '*'"
+                + " on each side"
+            )
+        patterns.append((v2.split("*"), source.split("*")))
+    for (v2_head, v2_tail), (head, tail) in patterns:
+        for name in names:
+            if (
+                len(name) >= len(head) + len(tail)
+                and name.startswith(head)
+                and name.endswith(tail)
+            ):
+                stem = name[len(head) : len(name) - len(tail)]
+                v2 = f"{v2_head}{stem}{v2_tail}"
+                if v2 != name:
+                    rules.setdefault(v2, Rule(v2, name))
+    return tuple(rules.values())
+
+
+def _resolve(names, column_map=None):
+    """Return (presented names, {v2 name: (rule, source name)}) for ``names``."""
+    mapped = _map_rules(names, column_map)
+    mapped_sources = {rule.source for rule in mapped}
+    rules = MASK_RULES
+    if detect_generation([n for n in names if n not in mapped_sources]) == "v1":
+        rules = V1_RULES + MASK_RULES
+    claimed = {rule.v2 for rule in mapped}
+    rules = mapped + tuple(rule for rule in rules if rule.v2 not in claimed)
+    position = {name: i for i, name in enumerate(names)}
+    derived = {}
+    consumed = set()
+    by_anchor = {}
+    for rule in rules:
+        sources = [s for s in rule.sources() if s in position]
+        if not sources:
+            continue
+        if rule.v2 in position and rule.v2 not in mapped_sources:
+            raise ValueError(
+                f"catalogue carries both {sources[0]!r} and {rule.v2!r},"
+                + " two names for the same column"
+            )
+        derived[rule.v2] = (rule, sources[0])
+        consumed.update(sources)
+        # Presented where the first of its sources sits in the table.
+        anchor = min(sources, key=position.get)
+        by_anchor.setdefault(anchor, []).append(rule.v2)
+
+    presented = []
+    for name in names:
+        if name in by_anchor:
+            presented += by_anchor[name]
+        elif name not in consumed:
+            presented.append(name)
+    return tuple(presented), derived
+
+
+def v2_names(names, column_map=None):
+    """Return the column names a table with columns ``names`` presents.
+
+    Header-only counterpart of ``adapt``.
+    """
+    return _resolve(tuple(names), column_map)[0]
+
+
+#: Bytes of whole HDF5 rows read per block when selecting rows of a dataset.
+BLOCK_BYTES = 256 * 1024**2
+
+
+def _read_window(table, name, start, stop):
+    """Read rows ``start:stop`` of column ``name``, and only those."""
+    if isinstance(table, h5py.Dataset):
+        return table.fields(name)[start:stop]
+    return table[name][start:stop]
+
+
+def _take(table, rows, fields=None):
+    """Return rows ``rows`` of ``table`` as an in-memory structured array.
+
+    ``rows`` is ``None`` (all rows), a ``range`` or an integer index array;
+    ``fields`` restricts the output to those columns. An h5py Dataset is read
+    in one pass: whole rows in blocks of ``BLOCK_BYTES`` over the span of the
+    selection, skipping blocks that hold no selected row, so each byte of the
+    file is read at most once however many columns are wanted.
+    """
+    if not isinstance(table, h5py.Dataset):
+        out = table if rows is None else table[_row_key(rows)]
+        if fields is None:
+            return np.asarray(out)
+        taken = np.empty(len(out), dtype=[(f, out.dtype[f]) for f in fields])
+        for f in fields:
+            taken[f] = out[f]
+        return taken
+
+    if fields is None:
+        source, dtype = table, table.dtype
+    else:
+        fields = list(fields)
+        source = table.fields(fields)
+        dtype = np.dtype([(f, table.dtype[f]) for f in fields])
+    if rows is None:
+        rows = range(len(table))
+    if isinstance(rows, range) and rows.step == 1:
+        return source[rows.start : rows.stop]
+
+    rows = np.asarray(rows, dtype=np.intp)
+    out = np.empty(len(rows), dtype=dtype)
+    if len(rows) == 0:
+        return out
+    order = None
+    if np.any(rows[1:] < rows[:-1]):
+        order = np.argsort(rows, kind="stable")
+        rows = rows[order]
+    block = max(1, BLOCK_BYTES // table.dtype.itemsize)
+    done = 0
+    start, stop = int(rows[0]), int(rows[-1]) + 1
+    while done < len(rows):
+        start = max(start, int(rows[done]))
+        end = min(start + block, stop)
+        upto = int(np.searchsorted(rows, end, side="left"))
+        chunk = source[start:end][rows[done:upto] - start]
+        if order is None:
+            out[done:upto] = chunk
+        else:
+            out[order[done:upto]] = chunk
+        done, start = upto, end
+    return out
+
+
+def _row_key(rows):
+    """Return ``rows`` (a ``range`` or index array) as a numpy row index."""
+    if isinstance(rows, range):
+        return slice(rows.start, rows.stop if rows.stop >= 0 else None, rows.step)
+    return rows
+
+
+class V2View:
+    """Lazy v2-grammar view of one table, or of row-aligned tables joined.
+
+    Wraps numpy structured arrays, FITS_recs or h5py Datasets without reading
+    them. ``view[name]`` returns one column as an array (computed on access for
+    derived columns); a list of names returns a structured array of those
+    columns; ``view[()]``, ``view[...]`` and any other row key (slice, boolean
+    mask, index array) select rows and return a view over them.
+
+    Selecting rows of a view over h5py Datasets reads the selected rows of
+    every column into memory in one pass per dataset, as indexing the Dataset
+    itself would, and wraps them in a view over the in-memory arrays: reading
+    many columns of a selection one at a time would otherwise re-read the whole
+    span of rows for each. Over in-memory tables, selection stays lazy.
+
+    Column names are case-sensitive, unlike a FITS_rec's.
+    """
+
+    def __init__(self, bases, rows=None, dtype=None, column_map=None):
+        self._bases = tuple(bases)
+        n_rows = {len(base) for base in self._bases}
+        if len(n_rows) != 1:
+            raise ValueError(f"cannot join tables of lengths {sorted(n_rows)}")
+        self._n_base = n_rows.pop()
+        self._owner = {}
+        for base in self._bases:
+            for name in column_names(base):
+                if name in self._owner:
+                    raise ValueError(f"column {name!r} is in more than one table")
+                self._owner[name] = base
+        # None (all rows), a ``range`` or an integer index array.
+        self._rows = rows
+        self._column_map = column_map
+        self._names, self._derived = _resolve(tuple(self._owner), column_map)
+        self._on_disk = any(isinstance(base, h5py.Dataset) for base in self._bases)
+        self._dtype = dtype
+
+    @property
+    def names(self):
+        """Presented column names."""
+        return self._names
+
+    def keys(self):
+        """Presented column names."""
+        return self._names
+
+    @property
+    def dtype(self):
+        """Numpy dtype of the presented columns (found without reading rows)."""
+        if self._dtype is None:
+            self._dtype = np.dtype(
+                [(name, self._field_dtype(name)) for name in self._names]
+            )
+        return self._dtype
+
+    @property
+    def shape(self):
+        return (len(self),)
+
+    def __len__(self):
+        return self._n_base if self._rows is None else len(self._rows)
+
+    def __contains__(self, name):
+        return name in self._names
+
+    def __repr__(self):
+        return f"V2View({len(self)} rows, {len(self._names)} columns)"
+
+    def __getitem__(self, key):
+        if isinstance(key, str):
+            return self._column(key)
+        if isinstance(key, list) and key and all(isinstance(k, str) for k in key):
+            return self.to_structured(key)
+        if isinstance(key, (int, np.integer)):
+            return self[np.array([key])].to_structured()[0]
+        if key is Ellipsis or (isinstance(key, tuple) and not key):
+            key = slice(None)
+        elif isinstance(key, tuple):
+            raise IndexError(f"cannot select rows with the tuple {key!r}")
+        rows = self._select(key)
+        if self._on_disk:
+            bases = [_take(base, rows) for base in self._bases]
+            return V2View(bases, dtype=self._dtype, column_map=self._column_map)
+        return V2View(
+            self._bases, rows=rows, dtype=self._dtype, column_map=self._column_map
+        )
+
+    def __array__(self, dtype=None, copy=None):
+        out = self.to_structured()
+        return out if dtype is None else out.astype(dtype)
+
+    def to_structured(self, names=None):
+        """Materialise the presented columns as a numpy structured array.
+
+        Over h5py Datasets, each dataset is read once, for all the columns
+        requested of it.
+        """
+        names = self._names if names is None else list(names)
+        missing = [name for name in names if name not in self._names]
+        if missing:
+            raise KeyError(f"columns {missing} not in catalogue")
+        sources = {
+            name: self._derived[name] if name in self._derived else (None, name)
+            for name in names
+        }
+        if self._on_disk:
+            fields = {}
+            for _, source in sources.values():
+                fields.setdefault(id(self._owner[source]), {})[source] = None
+            taken = {}
+            for base in self._bases:
+                if id(base) in fields:
+                    table = _take(base, self._rows, list(fields[id(base)]))
+                    taken.update((f, table[f]) for f in table.dtype.names)
+            read = taken.__getitem__
+        else:
+            read = self._read
+        out = np.empty(len(self), dtype=[(n, self.dtype[n]) for n in names])
+        for name, (rule, source) in sources.items():
+            column = read(source)
+            out[name] = column if rule is None else rule.apply(column)
+        return out
+
+    def _select(self, key):
+        """Base rows of ``key`` applied to this view's rows."""
+        n = len(self)
+        if isinstance(key, slice):
+            local = range(n)[key]
+        else:
+            key = np.asarray(key)
+            if key.dtype == bool:
+                if key.shape != (n,):
+                    raise IndexError(f"boolean mask of shape {key.shape} for {n} rows")
+                local = np.flatnonzero(key)
+            elif key.size == 0:
+                local = np.empty(0, dtype=np.intp)
+            elif key.dtype.kind in "iu" and key.ndim == 1:
+                local = np.where(key < 0, key + n, key).astype(np.intp)
+                if local.min() < 0 or local.max() >= n:
+                    raise IndexError(f"row index out of range for {n} rows")
+            else:
+                raise IndexError(f"cannot select rows with {key!r}")
+
+        rows = self._rows
+        if rows is None:
+            return local
+        if isinstance(rows, range):
+            if isinstance(local, range):
+                return range(
+                    rows.start + local.start * rows.step,
+                    rows.start + local.stop * rows.step,
+                    rows.step * local.step,
+                )
+            return rows.start + local * rows.step
+        if isinstance(local, range):
+            local = np.arange(local.start, local.stop, local.step, dtype=np.intp)
+        return rows[local]
+
+    def _read(self, name):
+        base = self._owner[name]
+        rows = self._rows
+        if rows is None:
+            return base[name]
+        if len(rows) == 0:
+            return _read_window(base, name, 0, 0)
+        if isinstance(rows, range):
+            lo, hi = min(rows[0], rows[-1]), max(rows[0], rows[-1]) + 1
+            window = _read_window(base, name, lo, hi)
+            return window[rows.start - lo :: rows.step][: len(rows)]
+        lo, hi = int(rows.min()), int(rows.max()) + 1
+        return _read_window(base, name, lo, hi)[rows - lo]
+
+    def _column(self, name):
+        if name not in self._names:
+            raise KeyError(f"column {name!r} not in catalogue")
+        if name not in self._derived:
+            return self._read(name)
+        rule, source = self._derived[name]
+        return rule.apply(self._read(source))
+
+    def _field_dtype(self, name):
+        """Dtype (with any subarray shape) ``_column(name)`` returns."""
+        if name in self._derived:
+            rule, source = self._derived[name]
+            column = rule.apply(_read_window(self._owner[source], source, 0, 0))
+        else:
+            column = _read_window(self._owner[name], name, 0, 0)
+        return np.dtype((column.dtype, column.shape[1:]))
+
+
+def adapt(table, *tables, column_map=None):
+    """Present ``table`` (joined with any further ``tables``) in the v2 grammar.
+
+    A single table with nothing to rename is returned unchanged, as is a
+    ``V2View`` given no ``column_map``; otherwise the result is a ``V2View``.
+    Each table is anything whose ``dtype.names`` lists its columns and whose
+    ``table[name]`` reads one: a numpy structured array, FITS_rec, h5py
+    Dataset or ``V2View``. Joined tables must have equal lengths and disjoint
+    column names. ``column_map`` (``{v2 name: name in the table}``, see the
+    module docstring) renames a foreign convention's columns; over a
+    ``V2View`` it renames the columns that view presents.
+    """
+    if not tables and (
+        (isinstance(table, V2View) and column_map is None)
+        or not _resolve(column_names(table), column_map)[1]
+    ):
+        return table
+    return V2View((table,) + tables, column_map=column_map)
+
+
+def materialise(table, names=None):
+    """Return ``adapt(table)`` as an in-memory numpy structured array.
+
+    ``names`` restricts the output to those v2-grammar columns.
+    """
+    view = adapt(table)
+    if isinstance(view, V2View):
+        return view.to_structured(names)
+    view = view[()] if isinstance(view, h5py.Dataset) else view
+    if names is None:
+        return view
+    out = np.empty(len(view), dtype=[(name, view.dtype[name]) for name in names])
+    for name in names:
+        out[name] = view[name]
+    return out
diff --git a/src/sp_validation/io.py b/src/sp_validation/io.py
index f2acaea9..b95b90df 100644
--- a/src/sp_validation/io.py
+++ b/src/sp_validation/io.py
@@ -2,7 +2,9 @@
 
 :Name: io.py
 
-:Description: This script contains methods for input and output.
+:Description: Input and output: output directories and statistics files, and
+    the catalogue reader (``read_catalogue``, ``Catalogue``), the one place
+    that knows how a catalogue's rows are stored on disk.
 
 :Author: Martin Kilbinger
 
@@ -10,8 +12,14 @@
 """
 
 import os
+import re
 
+import h5py
 import numpy as np
+import tqdm
+from astropy.io import fits
+
+from sp_validation import grammar
 
 
 def make_out_dirs(output_dir, plot_dir, plot_subdirs, verbose=False):
@@ -109,7 +117,6 @@ def print_ratio(msg, numerator, denominator, stats_file, verbose=False):
 
 
 def write_binned_quantity(quantity, key, bin_edges, extra_key="quantity"):
-
     shape = quantity.shape
     nx, ny = shape[:2]
 
@@ -120,6 +127,479 @@ def write_binned_quantity(quantity, key, bin_edges, extra_key="quantity"):
 
 
 def read_binned_quantity(filename):
-
     with np.load(filename) as data:
         return {key: data[key] for key in data.files}
+
+
+# -- catalogues ---------------------------------------------------------------
+#
+# A catalogue is one table of objects. How its rows are stored (the container)
+# is detected here from the file's contents; which names its columns carry is
+# the column grammar's business (``sp_validation.grammar``). Every table this
+# module hands out is presented through ``grammar.adapt``, which maps ShapePipe
+# v1 names to v2 and passes any other naming convention through untouched,
+# unless a ``column_map`` renames it.
+
+#: Root attributes in which a chunked ShapePipe product declares its chunk
+#: count, with the name of one chunk (for error messages).
+N_UNITS_ATTRS = {"n_tiles": "tile", "n_exposures": "exposure"}
+
+
+class Catalogue:
+    """An open catalogue file, presented as row chunks in the v2 grammar.
+
+    The container is detected from the file contents (``h5py.is_hdf5``), not
+    from its extension. Supported layouts:
+
+    - FITS: one table HDU, ``hdu`` or by default the first table HDU;
+    - HDF5 comprehensive catalogue: a root ``data`` dataset, joined
+      column-wise with ``data_ext`` when present;
+    - HDF5 single dataset at the root, of any name;
+    - HDF5 group of per-unit row chunks (one dataset per tile or exposure,
+      as ShapePipe writes campaign galaxy and star catalogues), found by
+      ``find_dataset_group``, concatenated in dataset-name order, and
+      checked against a declared ``n_tiles`` or ``n_exposures`` count.
+
+    Nothing is read on opening: ``chunks`` maps each chunk name to a lazy
+    table. Use as a context manager, or ``close`` when done; tables read into
+    memory stay valid after closing.
+
+    Parameters
+    ----------
+    path : str or os.PathLike
+        catalogue file
+    hdu : int, optional
+        FITS HDU to read; ignored for HDF5. Default is ``None``, the first
+        table HDU
+    column_map : dict, optional
+        ``{v2 name: name in the file}`` for a catalogue in another naming
+        convention, applied by ``grammar.adapt`` (patterns such as
+        ``{"NGMIX_*": "MYFIT_*"}`` allowed). Default is ``None``
+    """
+
+    def __init__(self, path, hdu=None, column_map=None):
+        self.path = os.fspath(path)
+        self.column_map = column_map
+        self._file = None
+        if h5py.is_hdf5(self.path):
+            self.hdu = None
+            self._file = h5py.File(self.path, "r")
+            self._parts, self.chunked = _hdf5_parts(self._file, self.path)
+        else:
+            with fits.open(self.path, memmap=True) as hdus:
+                self.hdu = _first_table_hdu(hdus, self.path) if hdu is None else hdu
+                self._parts = {str(self.hdu): (hdus[self.hdu].data,)}
+            self.chunked = False
+        self.chunks = {
+            key: grammar.adapt(*parts, column_map=column_map)
+            for key, parts in self._parts.items()
+        }
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def close(self):
+        """Close the underlying file; a no-op once closed, and for FITS."""
+        if self._file is not None:
+            self._file.close()
+            self._file = None
+
+    def __len__(self):
+        return sum(len(table) for table in self.chunks.values())
+
+    def dtype(self, columns=None):
+        """Return the structured dtype ``read(columns)`` produces.
+
+        Each column's dtype is promoted across every chunk (``group_dtype``).
+
+        Raises
+        ------
+        KeyError
+            if a chunk lacks one of ``columns``, naming the chunk
+        """
+        if columns is not None:
+            for key, table in self.chunks.items():
+                _check_columns(table.dtype, columns, self.path, key)
+        return group_dtype(self.chunks, columns)
+
+    def table(self):
+        """Return the catalogue as one table, lazily when it is not chunked.
+
+        An unchunked catalogue is returned as its lazy table (a FITS_rec, an
+        h5py Dataset or a ``grammar.V2View``), valid while the file is open;
+        a chunked one is read into memory (``read``).
+        """
+        if len(self.chunks) == 1:
+            return next(iter(self.chunks.values()))
+        return self.read()
+
+    def iter_chunks(self, columns=None, verbose=False):
+        """Yield the row chunks one at a time, in memory, restricted to ``columns``.
+
+        A caller filling a preallocated array (``len`` and ``dtype`` give its
+        shape) so never holds more than one chunk on top of its output.
+        """
+        self.dtype(columns)
+        for key in tqdm.tqdm(list(self.chunks), disable=not verbose):
+            yield grammar.materialise(self._load(key), columns)
+
+    def read(self, columns=None, key_column=None, verbose=False):
+        """Read the catalogue into one numpy structured array.
+
+        Chunks are copied one at a time into a preallocated output, so peak
+        memory is the output plus one chunk.
+
+        Parameters
+        ----------
+        columns : list of str, optional
+            v2-grammar columns to read; default is ``None`` (all)
+        key_column : str, optional
+            for a chunked catalogue, name of an integer column to add, holding
+            for each row the leading integer of its chunk's name. ShapePipe
+            star catalogues name each chunk by its exposure number
+            (``"2086324"``, or ``"2110000p"`` with the CFIS processed-exposure
+            suffix), which this keeps (``key_column="EXPID"``). An unchunked
+            catalogue has no chunk names and gets no column. Default is
+            ``None``
+        verbose : bool, optional
+            report progress if ``True``
+
+        Returns
+        -------
+        numpy.ndarray
+            catalogue data in the v2 grammar
+
+        Raises
+        ------
+        KeyError
+            if a chunk lacks one of ``columns``
+        ValueError
+            if ``key_column`` collides with a column, or a chunk name does
+            not begin with an integer
+        """
+        dtype_out = self.dtype(columns)
+        keys = list(self.chunks)
+        add_key = key_column is not None and self.chunked
+        if not add_key and len(keys) == 1:
+            return grammar.materialise(self.chunks[keys[0]], columns)
+
+        if add_key:
+            key_values = _chunk_numbers(keys, key_column, dtype_out, self.path)
+            dtype_out = np.dtype(dtype_out.descr + [(key_column, "i8")])
+
+        n_rows = len(self)
+        if verbose:
+            print(
+                f"Reading {len(keys)} datasets,"
+                + f" estimating {dtype_out.itemsize * n_rows / 1024**3:.1f}"
+                + f" Gb memory for the ({len(dtype_out.names)} x {n_rows})"
+                + " data array ..."
+            )
+
+        out = np.empty(n_rows, dtype=dtype_out)
+        start = 0
+        for key in tqdm.tqdm(keys, disable=not verbose):
+            data = self._load(key)
+            end = start + len(data)
+            for name in dtype_out.names:
+                if add_key and name == key_column:
+                    out[name][start:end] = key_values[key]
+                else:
+                    out[name][start:end] = data[name]
+            start = end
+            del data
+        return out
+
+    def _load(self, key):
+        """Return chunk ``key`` in memory, or lazily when the file is unchunked.
+
+        A chunk (one tile or exposure) is small, so it is read whole, which is
+        faster than column by column; an unchunked table is read per column.
+        """
+        if not self.chunked:
+            return self.chunks[key]
+        parts = (part[()] for part in self._parts[key])
+        return grammar.adapt(*parts, column_map=self.column_map)
+
+
+def read_catalogue(
+    path, columns=None, hdu=None, column_map=None, key_column=None, verbose=False
+):
+    """Read a catalogue into memory, in the v2 column grammar.
+
+    Any container ``Catalogue`` supports; ``columns`` restricts what is read.
+    A catalogue in another naming convention keeps its own column names,
+    unless ``column_map`` renames them. See ``Catalogue`` and
+    ``Catalogue.read`` for the parameters.
+
+    Returns
+    -------
+    numpy.ndarray or astropy.io.fits.FITS_rec
+        catalogue data
+    """
+    with Catalogue(path, hdu=hdu, column_map=column_map) as catalogue:
+        return catalogue.read(columns=columns, key_column=key_column, verbose=verbose)
+
+
+def open_catalogue(path, hdu=None, column_map=None):
+    """Return a catalogue as one table, reading no data up front.
+
+    A FITS table is memory-mapped and an unchunked HDF5 table read column by
+    column on access (its file stays open while the table is referenced), so
+    a consumer pays only for the columns it indexes, derived v2 columns
+    included. A chunked HDF5 catalogue has no single on-disk table and is
+    read into memory. Parameters as for ``Catalogue``.
+
+    Returns
+    -------
+    astropy.io.fits.FITS_rec, h5py.Dataset, grammar.V2View or numpy.ndarray
+        catalogue data in the v2 grammar
+    """
+    catalogue = Catalogue(path, hdu=hdu, column_map=column_map)
+    if catalogue.chunked:
+        with catalogue:
+            return catalogue.read()
+    return catalogue.table()
+
+
+def open_entry(entry):
+    """Return the catalogue a config block names (e.g. a cat_config ``shear``).
+
+    The block's ``path``, optional ``hdu`` and optional ``column_map`` go to
+    ``open_catalogue``.
+    """
+    return open_catalogue(
+        entry["path"], hdu=entry.get("hdu"), column_map=entry.get("column_map")
+    )
+
+
+def _first_table_hdu(hdus, path):
+    for index, hdu in enumerate(hdus):
+        if isinstance(hdu, (fits.BinTableHDU, fits.TableHDU)):
+            return index
+    raise ValueError(f"No table HDU in FITS file {path}")
+
+
+def _hdf5_parts(hdf5_file, path):
+    """Return ({chunk name: tuple of row-aligned datasets}, chunked) of a file."""
+    data = hdf5_file.get("data")
+    if isinstance(data, h5py.Dataset):
+        parts = (data,) + ((hdf5_file["data_ext"],) if "data_ext" in hdf5_file else ())
+        return {"data": parts}, False
+    group = find_dataset_group(hdf5_file)
+    check_n_units(hdf5_file, group, path)
+    chunked = group.name != "/" or len(group) > 1
+    return {key: (group[key],) for key in sorted(group)}, chunked
+
+
+def _chunk_numbers(keys, key_column, dtype, path):
+    """Return {chunk name: its leading integer}, for ``Catalogue.read``."""
+    if key_column in (dtype.names or ()):
+        raise ValueError(
+            f"Cannot add column {key_column!r} to catalogue {path}:"
+            + " a column of that name is already present."
+        )
+    matches = {key: re.match(r"\d+", key) for key in keys}
+    bad = [key for key, match in matches.items() if match is None]
+    if bad:
+        raise ValueError(
+            f"Cannot derive {key_column!r} for catalogue {path}:"
+            + f" dataset name(s) {bad} do not begin with an integer."
+        )
+    return {key: int(match.group()) for key, match in matches.items()}
+
+
+def _check_columns(dtype, columns, path, chunk=None):
+    """Raise a clear error if requested columns are absent from a chunk."""
+    missing = [col for col in columns if col not in (dtype.names or ())]
+    if missing:
+        where = f" (dataset {chunk!r})" if chunk is not None else ""
+        raise KeyError(
+            f"Column(s) {missing} not found in catalogue {path}{where}."
+            + f" Available columns: {sorted(dtype.names or ())}"
+        )
+
+
+def find_dataset_group(hdf5_file):
+    """Find Dataset Group.
+
+    Descend from the root of an open HDF5 file to the single group whose
+    members are the per-unit datasets (one per tile, or one per exposure).
+
+    This makes the reader independent of how deeply the products nest that
+    group: it walks down as long as the current node holds exactly one
+    sub-group, and stops as soon as the members are datasets. It therefore
+    reads both the nested ``patches/<campaign>/<tile-ID>`` layout (the
+    "patches" key is a ShapePipe-side compatibility shim, not a concept) and
+    a flat ``tiles/<tile-ID>`` or ``exposures/<exp>`` layout.
+
+    Parameters
+    ----------
+    hdf5_file : h5py.File or h5py.Group
+        open input file
+
+    Returns
+    -------
+    h5py.Group
+        group whose members are the per-unit datasets
+
+    Raises
+    ------
+    ValueError
+        if the file is empty, or a level holds more than one sub-group
+
+    """
+    node = hdf5_file
+    while True:
+        keys = list(node)
+        if not keys:
+            raise ValueError(
+                f"No data found under {node.name!r} in {hdf5_file.file.filename}"
+            )
+        if all(isinstance(node[key], h5py.Dataset) for key in keys):
+            return node
+        if len(keys) != 1:
+            raise ValueError(
+                f"Expected a single container group under {node.name!r} in"
+                + f" {hdf5_file.file.filename}, found {len(keys)}: {keys[:5]}"
+            )
+        node = node[keys[0]]
+
+
+def promote_dtypes(dtype_a, dtype_b):
+    """Promote Dtypes.
+
+    Return a scalar dtype that holds both input dtypes without truncation
+    or overflow.
+
+    Parameters
+    ----------
+    dtype_a : numpy.dtype
+        first input dtype
+    dtype_b : numpy.dtype
+        second input dtype
+
+    Returns
+    -------
+    numpy.dtype
+        promoted dtype
+
+    """
+    if dtype_a == dtype_b:
+        return dtype_a
+    if dtype_a.kind in "SU" and dtype_b.kind in "SU":
+        kind = "U" if "U" in (dtype_a.kind, dtype_b.kind) else "S"
+        size_a = dtype_a.itemsize // (4 if dtype_a.kind == "U" else 1)
+        size_b = dtype_b.itemsize // (4 if dtype_b.kind == "U" else 1)
+        return np.dtype(f"{kind}{max(size_a, size_b)}")
+
+    return np.promote_types(dtype_a, dtype_b)
+
+
+def group_dtype(tables, columns=None):
+    """Group Dtype.
+
+    Build the structured output dtype of a group of row-chunk tables,
+    promoting each column across *every* table. A campaign that was
+    partially reprocessed can carry e.g. ``S7`` tile IDs in one tile and
+    ``S12`` in another, or ``f4`` next to ``f8``; taking the dtype of the
+    first table alone would silently truncate the others.
+
+    Note that ShapePipe currently writes ``TILE_ID`` as ``f8`` (the tile
+    ``183.307`` arrives as the float ``183.307``), not as a string, so the
+    string-promotion branch above is for a future string-valued ``TILE_ID``
+    and is not exercised by today's products.
+
+    TODO: whether ``TILE_ID`` should be a string is an open schema decision.
+    A float cannot represent the ID exactly and cannot be compared for
+    equality safely; changing it is a breaking product change, so it is left
+    as-is here and this reader deliberately handles both.
+
+    Parameters
+    ----------
+    tables : list or dict
+        row-chunk tables (h5py Datasets, or their ``grammar.adapt`` views),
+        optionally keyed by dataset name for error messages
+    columns : list of str, optional
+        columns to keep; default is ``None`` (all columns of the first table)
+
+    Returns
+    -------
+    numpy.dtype
+        structured output dtype
+
+    Raises
+    ------
+    KeyError
+        if a table lacks one of the columns, naming the tables that do
+
+    """
+    if not hasattr(tables, "items"):
+        tables = dict(enumerate(tables))
+    dtypes = {key: table.dtype for key, table in tables.items()}
+    first = next(iter(dtypes.values()))
+    names = columns if columns is not None else list(first.names)
+
+    missing = {
+        key: [name for name in names if name not in dtype.names]
+        for key, dtype in dtypes.items()
+    }
+    missing = {key: cols for key, cols in missing.items() if cols}
+    if missing:
+        key, cols = next(iter(missing.items()))
+        raise KeyError(
+            f"{len(missing)} of {len(tables)} tables lack columns"
+            + f" {'requested' if columns is not None else 'of the first table'},"
+            + f" e.g. table {key!r} lacks {cols}; tables lacking some:"
+            + f" {list(missing)[:20]}. Request columns without them."
+        )
+    dtypes = list(dtypes.values())
+
+    fields = []
+    for name in names:
+        promoted = dtypes[0][name]
+        for dtype in dtypes[1:]:
+            promoted = promote_dtypes(promoted, dtype[name])
+        fields.append((name, promoted))
+
+    return np.dtype(fields)
+
+
+def check_n_units(hdf5_file, group, file_path):
+    """Check Number of Units.
+
+    Compare the number of datasets found against the count a ShapePipe
+    product declares in a root attribute (``N_UNITS_ATTRS``: ``n_tiles`` for
+    galaxy catalogues, ``n_exposures`` for star catalogues), to catch a
+    catalogue truncated by an interrupted merge job or file transfer.
+
+    A missing attribute is not an error: older products carry none.
+
+    Parameters
+    ----------
+    hdf5_file : h5py.File
+        open input file
+    group : h5py.Group
+        group holding the per-unit datasets
+    file_path : str
+        input file path, for the error message
+
+    Raises
+    ------
+    ValueError
+        if the number of datasets differs from the declared count
+
+    """
+    for attr, unit in N_UNITS_ATTRS.items():
+        n_declared = hdf5_file.attrs.get(attr)
+        if n_declared is None:
+            continue
+        n_found = len(group)
+        if int(n_declared) != n_found:
+            raise ValueError(
+                f"Catalogue {file_path} declares {attr} = {int(n_declared)} but"
+                + f" holds {n_found} {unit} dataset(s); the file is incomplete."
+            )
diff --git a/src/sp_validation/masks.py b/src/sp_validation/masks.py
index ddd09ef0..37514508 100644
--- a/src/sp_validation/masks.py
+++ b/src/sp_validation/masks.py
@@ -57,7 +57,6 @@ def apply_condition(array, kind, value):
 
 
 def correlation_matrix(masks, confidence_level=0.9):
-
     n_key = len(masks)
     print(n_key)
 
@@ -75,7 +74,6 @@ def correlation_matrix(masks, confidence_level=0.9):
 
 
 def confusion_matrix(prediction, observation):
-
     result = {}
 
     result["true_pos"] = sum(prediction & observation)
@@ -143,7 +141,6 @@ def __init__(
         dat=None,
         verbose=False,
     ):
-
         self._col_name = col_name
         self._label = label
         self._value = value
@@ -161,7 +158,6 @@ def __init__(
             self.apply(dat)
 
     def __repr__(self):
-
         return (
             f"Mask(col_name={self._col_name}, label={self._label}, kind={self._kind},"
             + f" value={self._value})"
@@ -169,7 +165,6 @@ def __repr__(self):
 
     @classmethod
     def from_list(cls, masks, label="combined", verbose=False):
-
         if verbose:
             print(f"Combining {len(masks)} masks")
 
@@ -180,7 +175,6 @@ def from_list(cls, masks, label="combined", verbose=False):
         return my_mask
 
     def apply(self, dat):
-
         if self._kind == "not_equal_2bands":
             self._mask = apply_condition(
                 dat[self._col_name], "not_equal", self._value
@@ -189,7 +183,6 @@ def apply(self, dat):
             self._mask = apply_condition(dat[self._col_name], self._kind, self._value)
 
     def to_bool(self, hsp_mask):
-
         if self._verbose:
             print("to_bool: get valid pixels")
         valid_pixels = hsp_mask.valid_pixels
@@ -224,7 +217,6 @@ def print_stats(self, num_obj, f_out=None):
         self.print_strings(self._col_name, self._label, si, sf, f_out=f_out)
 
     def get_sign(self, latex=False):
-
         sign = None
         if self._kind == "equal":
             sign = "$=$" if latex else "="
@@ -237,7 +229,6 @@ def get_sign(self, latex=False):
         return sign
 
     def print_condition(self, f_out, latex=False):
-
         if self._value is None:
             return ""
 
@@ -284,7 +275,6 @@ def create_descr(self):
         # Create description for FITS header
 
     def add_summary_to_FITS_header(self, header):
-
         header_new = fits.Header()
 
         self.create_descr()
@@ -311,19 +301,42 @@ def print_mask_stats(num_obj, masks, mask_combined):
     mask_combined.print_stats(num_obj)
 
 
-def get_masks_from_config(config, dat, dat_ext, masks_to_apply=None, verbose=False):
+def catalogue_cuts(config):
+    """Return the catalogue cuts of a mask config: its ``dat`` list.
+
+    Every cut names a column of the one v2-grammar table
+    ``CalibrateCat.read_cat`` returns, mask columns included.
+
+    Raises
+    ------
+    ValueError
+        if the config also has a ``dat_ext`` list, whose cuts would otherwise
+        be silently dropped
+    """
+    if "dat_ext" in config:
+        names = [cut.get("col_name") for cut in config["dat_ext"] or []]
+        raise ValueError(
+            f"mask config has a 'dat_ext' cut list {names}, which is not read:"
+            + " move its entries into 'dat', renaming the mask columns"
+            + " {b}_{label} to MASK_{b}_{label} (e.g. 4_Stars -> MASK_4_Stars;"
+            + " see sp_validation.grammar.MASK_LABELS)"
+        )
+    return config.get("dat") or []
+
+
+def get_masks_from_config(config, dat, masks_to_apply=None, verbose=False):
     """Get Masks From Config.
 
-    Return mask information from yaml config structure.
+    Return the masks of the config's ``dat`` cut list (``catalogue_cuts``),
+    evaluated on ``dat``.
 
     Parameters
     ----------
     config : dict
         config information
-    dat : numpy.ndarray
-        input data
-    det_ext : numpy.ndarray
-        input extended data
+    dat : numpy.ndarray or grammar.V2View
+        input catalogue, in the v2 column grammar (e.g. from
+        ``CalibrateCat.read_cat``)
     masks_to_apply: list, optional
         masks to apply exclusively; if `None` (default), use all masks
     verbose : bool, optional
@@ -343,40 +356,22 @@ def get_masks_from_config(config, dat, dat_ext, masks_to_apply=None, verbose=Fal
     # Dict to associate labels with index in mask list
     labels = {}
 
-    # Loop over mask sections from config file
-    config_data = {key: config[key] for key in ["dat", "dat_ext"] if key in config}
-    idx = 0
-    for section, mask_list in config_data.items():
-        # Set data source
-        dat_source = dat if section == "dat" else dat_ext
-
-        # Loop over mask information in this section
-        for mask_params in mask_list:
-            use_this_mask = False
-            if masks_to_apply is not None:
-                if mask_params["col_name"] in masks_to_apply:
-                    use_this_mask = True
-            else:
-                use_this_mask = True
-
-            if use_this_mask:
-                # Ensure 'range' kind has exactly two values
-                value = mask_params["value"]
-                if mask_params["kind"] == "range" and (
-                    not isinstance(value, list) or len(value) != 2
-                ):
-                    raise ValueError(
-                        f"Range kind requires a list of two values, got {value}"
-                    )
-
-                # Create mask instance and append to list
-                my_mask = Mask(**mask_params, dat=dat_source, verbose=verbose)
-                masks.append(my_mask)
-                labels[my_mask._col_name] = idx
-                idx += 1
-            else:
-                if verbose:
-                    print(f"Skipping mask {mask_params['col_name']}")
-                continue
+    for mask_params in catalogue_cuts(config):
+        if masks_to_apply is not None and mask_params["col_name"] not in masks_to_apply:
+            if verbose:
+                print(f"Skipping mask {mask_params['col_name']}")
+            continue
+
+        # Ensure 'range' kind has exactly two values
+        value = mask_params["value"]
+        if mask_params["kind"] == "range" and (
+            not isinstance(value, list) or len(value) != 2
+        ):
+            raise ValueError(f"Range kind requires a list of two values, got {value}")
+
+        # Create mask instance and append to list
+        my_mask = Mask(**mask_params, dat=dat, verbose=verbose)
+        labels[my_mask._col_name] = len(masks)
+        masks.append(my_mask)
 
     return masks, labels
diff --git a/src/sp_validation/plots.py b/src/sp_validation/plots.py
index b21599c5..7652d36a 100644
--- a/src/sp_validation/plots.py
+++ b/src/sp_validation/plots.py
@@ -20,6 +20,7 @@
 
 # Imported for its import-time side effect: sets matplotlib rcParams (plot style).
 import sp_validation.plot_style  # noqa: F401
+from sp_validation.galaxy import MASK_COLUMNS
 from sp_validation.masks import Mask
 
 
@@ -304,7 +305,6 @@ def plot_binned_one(
     xlabel=None,
     ylabel=None,
 ):
-
     # Note: transpose R slice to match (y, x) shape required by pcolormesh
     pcm = ax.pcolormesh(
         bin_edges_x, bin_edges_y, quantity, vmin=vmin, vmax=vmax, shading="auto"
@@ -331,7 +331,6 @@ def plot_binned(
     xlabel=None,
     ylabel=None,
 ):
-
     len_shape = len(quantities[key].shape)
 
     fig_size = 2 * len_shape
@@ -477,20 +476,28 @@ def sky_plots(dat, masks, labels, zoom_ra, zoom_dec):
     # No mask
     plot_area_mask(ra, dec, zoom)
 
-    # SExtractor and SP flags
-    m_flags = masks[labels["FLAGS"]]._mask & masks[labels["IMAFLAGS_ISO"]]._mask
+    # SExtractor and SP flags: whichever mask columns the config declared
+    # (the MASK_<bit>_<label> columns, and the v1 configs' IMAFLAGS_ISO).
+    m_flags = masks[labels["FLAGS"]]._mask
+    for col in ("IMAFLAGS_ISO",) + tuple(MASK_COLUMNS):
+        if col in labels:
+            m_flags = m_flags & masks[labels[col]]._mask
     plot_area_mask(ra, dec, zoom, mask=m_flags)
 
     # Overlap regions
     m_over = masks[labels["overlap"]]._mask & m_flags
     plot_area_mask(ra, dec, zoom, mask=m_over)
 
-    # Coverage mask
-    m_point = masks[labels["npoint3"]]._mask & m_over
+    # Rough pointing coverage, where the catalogue carries it
+    m_point = m_over
+    if "npoint3" in labels:
+        m_point = masks[labels["npoint3"]]._mask & m_over
     plot_area_mask(ra, dec, zoom, mask=m_point)
 
     # Maximask
-    m_maxi = masks[labels["1024_Maximask"]]._mask & m_point
+    m_maxi = m_point
+    if "MASK_1024_Maximask" in labels:
+        m_maxi = masks[labels["MASK_1024_Maximask"]]._mask & m_point
     plot_area_mask(ra, dec, zoom, mask=m_maxi)
 
     # Combined mask over all supplied masks (was passed in by the caller before
@@ -502,12 +509,12 @@ def sky_plots(dat, masks, labels, zoom_ra, zoom_dec):
     m_comb = mask_combined._mask
     plot_area_mask(ra, dec, zoom, mask=m_comb)
 
-    m_man = m_maxi & masks[labels["8_Manual"]]._mask
+    m_man = m_maxi & masks[labels["MASK_8_Manual"]]._mask
     plot_area_mask(ra, dec, zoom, mask=m_man)
 
     m_halos = (
         m_maxi
-        & masks[labels["1_Faint_star_halos"]]._mask
-        & masks[labels["2_Bright_star_halos"]]._mask
+        & masks[labels["MASK_1_Faint_star_halos"]]._mask
+        & masks[labels["MASK_2_Bright_star_halos"]]._mask
     )
     plot_area_mask(ra, dec, zoom, mask=m_halos)
diff --git a/src/sp_validation/rho_tau.py b/src/sp_validation/rho_tau.py
index 62ae8bf8..b397b6de 100644
--- a/src/sp_validation/rho_tau.py
+++ b/src/sp_validation/rho_tau.py
@@ -1,12 +1,16 @@
 import os
 import time
 from pathlib import Path
+from tempfile import TemporaryDirectory
 
 import numpy as np
+from astropy.io import fits
 from shear_psf_leakage.rho_tau_cov import CovTauTh
 from shear_psf_leakage.rho_tau_stat import RhoStat, TauStat
 
-# SquareRootScale now lives in sp_validation.plots; re-exported here so that
+from sp_validation import grammar, io
+
+# SquareRootScale lives in sp_validation.plots; re-exported here so that
 # `from sp_validation.rho_tau import SquareRootScale` keeps working.
 from sp_validation.plots import SquareRootScale  # noqa: F401
 
@@ -16,6 +20,102 @@ def _extract_xip(correlations):
     return np.array([corr.xip for corr in correlations]).flatten()
 
 
+class _CatalogueLoader:
+    """Supply FITS paths to the file-based rho/tau readers.
+
+    The rho/tau consumers (``shear_psf_leakage``) read FITS HDU 1 from a path.
+    A catalogue that already is exactly that, with no column renamed by the
+    grammar or the entry's ``column_map``, passes through unchanged. Any other
+    (another HDU, HDF5, a v1 or foreign naming convention) is read through
+    ``io.Catalogue``, keeping only the configured columns, into a temporary
+    FITS file.
+    """
+
+    def __init__(self, info, params):
+        self._info = info
+        self._params = params
+        self._cache = {}
+        self._temporary_directory = None
+
+    def __enter__(self):
+        if self._temporary_directory is not None:
+            raise RuntimeError("catalogue loader is already open")
+        self._temporary_directory = TemporaryDirectory(prefix="sp-validation-rho-tau-")
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._temporary_directory.cleanup()
+        self._temporary_directory = None
+
+    def _columns(self, block):
+        if block == "psf":
+            keys = (
+                "ra_PSF_col",
+                "dec_PSF_col",
+                "e1_PSF_col",
+                "e2_PSF_col",
+                "e1_star_col",
+                "e2_star_col",
+                "PSF_size",
+                "star_size",
+                "PSF_flag",
+                "star_flag",
+            )
+        elif block == "shear":
+            keys = ("ra_col", "dec_col", "w_col", "e1_col", "e2_col")
+        else:
+            raise KeyError(f"unknown rho/tau catalogue block {block!r}")
+
+        columns = [self._params.get(key) for key in keys]
+        if block == "psf":
+            for psf_key, shear_key in (
+                ("ra_PSF_col", "ra_col"),
+                ("dec_PSF_col", "dec_col"),
+            ):
+                if self._params.get(psf_key) is None:
+                    columns.append(self._params.get(shear_key))
+            columns.extend(
+                self._params.get(key)
+                for key in (
+                    "M_4_1_psf_col",
+                    "M_4_2_psf_col",
+                    "M_4_1_star_col",
+                    "M_4_2_star_col",
+                )
+            )
+        else:
+            columns.extend(
+                value
+                for value in (self._params.get("R11"), self._params.get("R22"))
+                if isinstance(value, str)
+            )
+        return tuple(dict.fromkeys(name for name in columns if name is not None))
+
+    def __call__(self, block):
+        if block not in self._cache:
+            entry = self._info[block]
+            with io.Catalogue(
+                entry["path"], hdu=entry.get("hdu"), column_map=entry.get("column_map")
+            ) as catalogue:
+                if catalogue.hdu == 1 and not isinstance(
+                    catalogue.table(), grammar.V2View
+                ):
+                    self._cache[block] = catalogue.path
+                else:
+                    if self._temporary_directory is None:
+                        raise RuntimeError(
+                            "use the catalogue loader inside a with block"
+                        )
+                    table = catalogue.read(columns=self._columns(block))
+                    output = Path(self._temporary_directory.name) / f"{block}.fits"
+                    with fits.HDUList(
+                        [fits.PrimaryHDU(), fits.BinTableHDU(data=table)]
+                    ) as hdus:
+                        hdus.writeto(output)
+                    self._cache[block] = os.fspath(output)
+        return self._cache[block]
+
+
 def get_params_rho_tau(cat, survey="other"):
     """Rho/tau parameters for one catalogue-config entry ``cat``.
 
@@ -148,79 +248,59 @@ def get_rho_tau(
         output=outdir, treecorr_config=treecorr_config, verbose=True
     )
 
-    rho_stats_exists = rho_path.exists()
-    cov_exists = True if not cov_rho else cov_rho_path.exists()
-    need_compute = (not rho_stats_exists) or (not cov_exists)
-
-    if need_compute:
-        rho_stat_handler.catalogs.set_params(params, outdir)
+    with _CatalogueLoader(config[version], params) as load:
+        rho_stats_exists = rho_path.exists()
+        cov_exists = True if not cov_rho else cov_rho_path.exists()
+        need_compute = (not rho_stats_exists) or (not cov_exists)
 
-        mask = version != "DES"
+        if need_compute:
+            rho_stat_handler.catalogs.set_params(params, outdir)
+            rho_stat_handler.build_cat_to_compute_rho(
+                load("psf"), catalog_id=catalog_id
+            )
 
-        rho_stat_handler.build_cat_to_compute_rho(
-            config[version]["psf"]["path"],
-            catalog_id=catalog_id,
-            mask=mask,
-            hdu=(
-                config[version]["psf"]["hdu"]
-                if config[version]["psf"]["hdu"] is not None
-                else 1
-            ),
-        )
+            rho_stat_handler.compute_rho_stats(
+                catalog_id,
+                rho_path.name,
+                save_cov=cov_rho,
+                func=_extract_xip if cov_rho else None,
+                var_method="jackknife" if cov_rho else None,
+            )
+            rho_stat_handler.load_rho_stats(rho_path.name)
+        else:
+            print(
+                f"Skipping rho statistics computation, file {rho_path} already exists."
+            )
+            rho_stat_handler.load_rho_stats(rho_path.name)
 
-        rho_stat_handler.compute_rho_stats(
-            catalog_id,
-            rho_path.name,
-            save_cov=cov_rho,
-            func=_extract_xip if cov_rho else None,
-            var_method="jackknife" if cov_rho else None,
+        tau_path = outdir_path / f"tau_stats_{base}.fits"
+        tau_stat_handler = TauStat(
+            catalogs=rho_stat_handler.catalogs,
+            output=outdir,
+            treecorr_config=treecorr_config,
+            verbose=True,
         )
-        rho_stat_handler.load_rho_stats(rho_path.name)
-    else:
-        print(f"Skipping rho statistics computation, file {rho_path} already exists.")
-        rho_stat_handler.load_rho_stats(rho_path.name)
-
-    tau_path = outdir_path / f"tau_stats_{base}.fits"
-
-    tau_stat_handler = TauStat(
-        catalogs=rho_stat_handler.catalogs,
-        output=outdir,
-        treecorr_config=treecorr_config,
-        verbose=True,
-    )
 
-    if tau_path.exists():
-        print(f"Skipping tau statistics computation, file {tau_path} already exists.")
-        tau_stat_handler.load_tau_stats(tau_path.name)
-    else:
-        tau_stat_handler.catalogs.set_params(params, outdir)
+        if tau_path.exists():
+            print(
+                f"Skipping tau statistics computation, file {tau_path} already exists."
+            )
+            tau_stat_handler.load_tau_stats(tau_path.name)
+        else:
+            tau_stat_handler.catalogs.set_params(params, outdir)
 
-        mask = version != "DES"
+            # Build the different catalogs if necessary
+            if f"psf_{version}" not in tau_stat_handler.catalogs.catalogs_dict:
+                tau_stat_handler.build_cat_to_compute_tau(
+                    load("psf"), cat_type="psf", catalog_id=version
+                )
 
-        # Build the different catalogs if necessary
-        if f"psf_{version}" not in tau_stat_handler.catalogs.catalogs_dict.keys():
+            # Build the catalog of galaxies. PSF was computed above
             tau_stat_handler.build_cat_to_compute_tau(
-                config[version]["psf"]["path"],
-                cat_type="psf",
-                catalog_id=version,
-                mask=mask,
-                hdu=(
-                    config[version]["psf"]["hdu"]
-                    if config[version]["psf"]["hdu"] is not None
-                    else 1
-                ),
+                load("shear"), cat_type="gal", catalog_id=version
             )
 
-        # Build the catalog of galaxies. PSF was computed above
-        tau_stat_handler.build_cat_to_compute_tau(
-            config[version]["shear"]["path"],
-            cat_type="gal",
-            catalog_id=version,
-            mask=mask,
-        )
-
-        # function to extract the tau_+
-        tau_stat_handler.compute_tau_stats(version, tau_path.name, var_method=None)
+            tau_stat_handler.compute_tau_stats(version, tau_path.name, var_method=None)
 
     print(f"Time to compute rho and tau statistics: {time.time() - start_time:.2f} s")
     return rho_stat_handler, tau_stat_handler
@@ -246,10 +326,6 @@ def get_theory_cov(
     n_e = info["cov_th"]["n_e"]
     n_psf = info["cov_th"]["n_psf"]
 
-    path_gal = info["shear"]["path"]
-    path_psf = info["psf"]["path"]
-    hdu_psf = info["psf"]["hdu"]
-
     target_cov = Path(outdir) / f"cov_tau_{base}_th.npy"
 
     if target_cov.exists():
@@ -259,26 +335,27 @@ def get_theory_cov(
     print("Computing the covariance matrix for the version: ", version)
     start_time = time.time()
 
-    cov_tau_th = CovTauTh(
-        path_gal=path_gal,
-        path_psf=path_psf,
-        hdu_psf=hdu_psf,
-        treecorr_config=treecorr_config,
-        A=A,
-        n_e=n_e,
-        n_psf=n_psf,
-        params=params,
-    )
+    with _CatalogueLoader(info, params) as load:
+        cov_tau_th = CovTauTh(
+            path_gal=load("shear"),
+            path_psf=load("psf"),
+            hdu_psf=1,
+            treecorr_config=treecorr_config,
+            A=A,
+            n_e=n_e,
+            n_psf=n_psf,
+            params=params,
+        )
 
-    elapsed = time.time() - start_time
-    print(f"--- Rho/tau statistics for covariance computed in {elapsed:.2f}s ---")
+        elapsed = time.time() - start_time
+        print(f"--- Rho/tau statistics for covariance computed in {elapsed:.2f}s ---")
 
-    cov = cov_tau_th.build_cov(nbin_ang=nbin_ang, nbin_rad=nbin_rad)
-    print(f"--- Covariance matrix assembled in {time.time() - start_time:.2f}s ---")
-    target_cov.parent.mkdir(parents=True, exist_ok=True)
-    np.save(target_cov, cov)
-    print("Saved covariance matrix of version: ", version)
-    del cov_tau_th
+        cov = cov_tau_th.build_cov(nbin_ang=nbin_ang, nbin_rad=nbin_rad)
+        print(f"--- Covariance matrix assembled in {time.time() - start_time:.2f}s ---")
+        target_cov.parent.mkdir(parents=True, exist_ok=True)
+        np.save(target_cov, cov)
+        print("Saved covariance matrix of version: ", version)
+        del cov_tau_th
     return
 
 
@@ -338,79 +415,78 @@ def get_jackknife_cov(
 
     tau_stat_handler.catalogs.set_params(params, outdir)
 
-    for i in range(ncov):
-        tau_chunk = outdir + f"/cov_tau_{version}{i}.npy"
-        rho_chunk = outdir + f"/cov_rho_{version}{i}.npy"
-        if not (os.path.exists(tau_chunk) and os.path.exists(rho_chunk)):
-            print(f"Computing rho-statistics for {version} (patch {i + 1}/{ncov})")
-
-            if f"psf_{version}{i}" not in rho_stat_handler.catalogs.catalogs_dict:
-                # Build catalogues
-                rho_stat_handler.build_cat_to_compute_rho(
-                    config[version]["psf"]["path"],
-                    catalog_id=version + str(i),
-                    mask=False,
-                    hdu=config[version]["psf"]["hdu"],
+    with _CatalogueLoader(config[version], params) as load:
+        for i in range(ncov):
+            tau_chunk = outdir + f"/cov_tau_{version}{i}.npy"
+            rho_chunk = outdir + f"/cov_rho_{version}{i}.npy"
+            if not (os.path.exists(tau_chunk) and os.path.exists(rho_chunk)):
+                print(
+                    f"Computing rho-statistics for {version} (jackknife realisation {i + 1}/{ncov})"
                 )
 
-                tau_stat_handler.catalogs.catalogs_dict = (
-                    rho_stat_handler.catalogs.catalogs_dict
-                )
-
-                # Build the catalog of galaxies. PSF was computed above
-                tau_stat_handler.build_cat_to_compute_tau(
-                    config[version]["shear"]["path"],
-                    cat_type="gal",
-                    catalog_id=version + str(i),
-                    mask=False,
-                )
-
-            else:
-                print(f"Computing the patch centers for patch {i + 1}/{ncov}")
+                if f"psf_{version}{i}" not in rho_stat_handler.catalogs.catalogs_dict:
+                    # Build catalogues
+                    rho_stat_handler.build_cat_to_compute_rho(
+                        load("psf"), catalog_id=version + str(i)
+                    )
 
-                npatch = rho_stat_handler.catalogs._params["patch_number"]
-                field = rho_stat_handler.catalogs.catalogs_dict[
-                    f"psf_{version}{i}"
-                ].getNField(max_top=int.bit_length(npatch) - 1, coords="spherical")
-                patch, centers = field.run_kmeans(npatch)
+                    tau_stat_handler.catalogs.catalogs_dict = (
+                        rho_stat_handler.catalogs.catalogs_dict
+                    )
 
-                # Update the patch centers of the catalogs
-                for key, cat in rho_stat_handler.catalogs.catalogs_dict.items():
-                    cat._centers = centers
-                    field = cat.getNField(
-                        max_top=int.bit_length(npatch) - 1, coords="spherical"
+                    # Build the catalog of galaxies. PSF was computed above
+                    tau_stat_handler.build_cat_to_compute_tau(
+                        load("shear"),
+                        cat_type="gal",
+                        catalog_id=version + str(i),
                     )
-                    cat._patch = field.kmeans_assign_patches(centers)
 
-            # Compute and save rho stats
-            rho_stat_handler.compute_rho_stats(
-                version + str(i),
-                rho_filename,
-                save_cov=True,
-                func=_extract_xip,
-                var_method="jackknife",
-            )
+                else:
+                    print(f"Computing the patch centers for patch {i + 1}/{ncov}")
+
+                    npatch = rho_stat_handler.catalogs._params["patch_number"]
+                    field = rho_stat_handler.catalogs.catalogs_dict[
+                        f"psf_{version}{i}"
+                    ].getNField(max_top=int.bit_length(npatch) - 1, coords="spherical")
+                    patch, centers = field.run_kmeans(npatch)
+
+                    # Update the patch centers of the catalogs
+                    for key, cat in rho_stat_handler.catalogs.catalogs_dict.items():
+                        cat._centers = centers
+                        field = cat.getNField(
+                            max_top=int.bit_length(npatch) - 1, coords="spherical"
+                        )
+                        cat._patch = field.kmeans_assign_patches(centers)
+
+                # Compute and save rho stats
+                rho_stat_handler.compute_rho_stats(
+                    version + str(i),
+                    rho_filename,
+                    save_cov=True,
+                    func=_extract_xip,
+                    var_method="jackknife",
+                )
 
-            # function to extract the tau_+
-            tau_stat_handler.compute_tau_stats(
-                version + str(i),
-                tau_filename,
-                save_cov=True,
-                func=_extract_xip,
-                var_method="jackknife",
-            )
+                # function to extract the tau_+
+                tau_stat_handler.compute_tau_stats(
+                    version + str(i),
+                    tau_filename,
+                    save_cov=True,
+                    func=_extract_xip,
+                    var_method="jackknife",
+                )
 
-            # Update the keys in the dictionaries
-            rho_dict = rho_stat_handler.catalogs.catalogs_dict
-            tau_dict = tau_stat_handler.catalogs.catalogs_dict
-            rho_dict[f"psf_{version}{i + 1}"] = rho_dict.pop(f"psf_{version}{i}")
-            rho_dict[f"psf_error_{version}{i + 1}"] = rho_dict.pop(
-                f"psf_error_{version}{i}"
-            )
-            rho_dict[f"psf_size_error_{version}{i + 1}"] = rho_dict.pop(
-                f"psf_size_error_{version}{i}"
-            )
-            tau_dict[f"gal_{version}{i + 1}"] = tau_dict.pop(f"gal_{version}{i}")
+                # Update the keys in the dictionaries
+                rho_dict = rho_stat_handler.catalogs.catalogs_dict
+                tau_dict = tau_stat_handler.catalogs.catalogs_dict
+                rho_dict[f"psf_{version}{i + 1}"] = rho_dict.pop(f"psf_{version}{i}")
+                rho_dict[f"psf_error_{version}{i + 1}"] = rho_dict.pop(
+                    f"psf_error_{version}{i}"
+                )
+                rho_dict[f"psf_size_error_{version}{i + 1}"] = rho_dict.pop(
+                    f"psf_size_error_{version}{i}"
+                )
+                tau_dict[f"gal_{version}{i + 1}"] = tau_dict.pop(f"gal_{version}{i}")
 
     cov_tau_loc = np.zeros_like(np.load(outdir + f"/cov_tau_{version}0.npy"))
     cov_rho_loc = np.zeros_like(np.load(outdir + f"/cov_rho_{version}0.npy"))
diff --git a/src/sp_validation/survey.py b/src/sp_validation/survey.py
index fe35cde9..e9d61025 100644
--- a/src/sp_validation/survey.py
+++ b/src/sp_validation/survey.py
@@ -183,77 +183,6 @@ def write_tile_id_gal_counts(detection_IDs, galaxy_IDs, shape_IDs, fname):
             print(file=f)
 
 
-def get_footprint(patch, ra, dec):
-    """Get Footprint.
-
-    Return coordinates within footprint of patch.
-
-    Parameters
-    ----------
-    patch : str
-        patch name
-    ra : array of float
-        R,A, coordintates
-    dec : array of float
-        DEC coordinates
-
-    Returns
-    -------
-    list of float
-        list of coordinates withint footprint
-
-    """
-    # Set boundary coordinates between some of the patches
-    ra_14 = 157.5
-    ra_45 = 207
-    ra2_45 = 220
-    ra_36 = 230
-    dec_3456 = 48
-
-    ra2_34 = 190
-
-    dec_min = 29
-    dec_max = 60
-
-    # Check whether input matches one of the seven CFIS patch name.
-    # Return coordinates within the patch
-    if patch == "P1":
-        return (ra > 100) & (ra < ra_14) & (dec > dec_min) & (dec < dec_max)
-
-    elif patch == "P2":
-        # -30 < ra < 60
-        return ((ra > 0) & (ra < 60)) | ((ra > 330) & (ra < 360)) & (dec > dec_min) & (
-            dec < dec_max
-        )
-
-    elif patch == "P3":
-        return (ra > ra2_34) & (ra < ra_36) & (dec > dec_3456) & (dec < 70)
-
-    elif patch == "P4":
-        return (
-            ((ra > ra_14) & (ra < ra_45) & (dec > dec_min) & (dec < dec_3456))
-            | ((ra > ra_14) & (ra < ra2_34) & (dec > dec_min) & (dec < 70))
-            | ((ra > ra_45) & (ra < ra2_45) * (dec > dec_min) & (dec < 36))
-        )
-
-    elif patch == "P5":
-        return ((ra > ra2_45) & (ra < 330) & (dec > dec_min) & (dec < dec_3456)) | (
-            (ra > ra_45) & (ra < ra2_45) & (dec > 36) & (dec < dec_3456)
-        )
-
-    elif patch == "P6":
-        return (ra > ra_36) & (ra < 330) & (dec > dec_3456) & (dec > 70)
-
-    elif patch == "P7":
-        return (ra > 60) & (ra < 180) & (dec > 60) & (dec < 90)
-
-    elif patch == "W3":
-        return (ra > 208) & (ra < 221) & (dec > 51) & (dec < 58)
-
-    else:
-        return dec > dec_min
-
-
 def area_from_coords(ra, dec, nside):
     """Survey area from galaxy coordinates via HEALPix pixel counting.
 
diff --git a/src/sp_validation/tests/test_calibration.py b/src/sp_validation/tests/test_calibration.py
index 0a66aed5..fbe78785 100644
--- a/src/sp_validation/tests/test_calibration.py
+++ b/src/sp_validation/tests/test_calibration.py
@@ -307,7 +307,6 @@ def test_metacal_R_matrix_recovers_injected_response():
         mask,
         masking_type="gal",
         step=0.01,
-        prefix="NGMIX",
         size_corr_ell=False,  # avoid the in-place T mutation; clean linear cut
         global_R_weight=None,  # unweighted mean over objects
     )
@@ -327,7 +326,6 @@ def test_metacal_R_matrix_recovers_injected_response():
         np.ones(n2, dtype=bool),
         masking_type="gal",
         step=0.01,
-        prefix="NGMIX",
         size_corr_ell=False,
         global_R_weight=None,
     )
@@ -353,7 +351,6 @@ def test_metacal_R_matrix_step_normalization():
         np.ones(n, dtype=bool),
         masking_type="gal",
         step=0.02,
-        prefix="NGMIX",
         size_corr_ell=False,
         global_R_weight=None,
     )
diff --git a/src/sp_validation/tests/test_campaign_readers.py b/src/sp_validation/tests/test_campaign_readers.py
new file mode 100644
index 00000000..ccaa7f22
--- /dev/null
+++ b/src/sp_validation/tests/test_campaign_readers.py
@@ -0,0 +1,542 @@
+"""Tests for the catalogue reader on ShapePipe v2 campaign products, and the mask cut."""
+
+import tempfile
+import unittest
+from pathlib import Path
+
+import h5py
+import numpy as np
+import numpy.testing as npt
+
+from sp_validation import galaxy, io
+from sp_validation.catalog_builders import JointCat
+from sp_validation.masks import Mask
+
+#: Columns ShapePipe v2's ``MergeStarCatPSFEX`` writes into
+#: ``full_starcat_<campaign>.hdf5`` (one dataset per exposure).
+STAR_CAT_COLUMNS = (
+    "X",
+    "Y",
+    "RA",
+    "DEC",
+    "HSM_G1_PSF",
+    "HSM_G2_PSF",
+    "HSM_T_PSF",
+    "HSM_G1_STAR",
+    "HSM_G2_STAR",
+    "HSM_T_STAR",
+    "HSM_FLAG_PSF",
+    "HSM_FLAG_STAR",
+    "MAG",
+    "SNR",
+    "ACCEPTED",
+    "CCD_NB",
+)
+
+GAL_DTYPE = np.dtype(
+    [
+        ("RA", "f8"),
+        ("Dec", "f8"),
+        ("MAG_AUTO", "f4"),
+        ("MASK_4_Stars", "?"),
+        ("MASK_1_Faint_star_halos", "?"),
+        ("MASK_2_Bright_star_halos", "?"),
+        ("MASK_8_Manual", "?"),
+        ("MASK_64_r", "?"),
+        ("MASK_1024_Maximask", "?"),
+    ]
+)
+
+
+def make_galaxy_data(n_obj, offset=0):
+    dat = np.zeros(n_obj, dtype=GAL_DTYPE)
+    dat["RA"] = np.arange(n_obj) + offset
+    dat["Dec"] = np.arange(n_obj) + offset + 0.5
+    dat["MAG_AUTO"] = 22.0
+    return dat
+
+
+def write_campaign(path, layout, tiles):
+    """Write a campaign hdf5 file in the nested or flat layout."""
+    with h5py.File(path, "w") as f:
+        if layout == "nested":
+            group = f.create_group("patches").create_group("CAMPAIGN")
+        else:
+            group = f.create_group("tiles")
+        for tile_id, dat in tiles.items():
+            group.create_dataset(tile_id, data=dat)
+        f.attrs["n_tiles"] = len(tiles)
+
+
+class TestCampaignReader(unittest.TestCase):
+    """Campaign galaxy catalogue reader."""
+
+    def setUp(self):
+        self._tmp = tempfile.TemporaryDirectory()
+        self._dir = Path(self._tmp.name)
+        self._tiles = {
+            "000.000": make_galaxy_data(3),
+            "001.000": make_galaxy_data(2, offset=100),
+        }
+
+    def tearDown(self):
+        self._tmp.cleanup()
+
+    def _expected(self):
+        """Expected concatenation: datasets in sorted-key order."""
+        return np.concatenate([self._tiles[key] for key in sorted(self._tiles)])
+
+    def test_nested_layout(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "nested", self._tiles)
+
+        dat = io.read_catalogue(path, verbose=False)
+
+        self.assertEqual(len(dat), 5)
+        npt.assert_array_equal(dat["RA"], self._expected()["RA"])
+
+    def test_flat_layout(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "flat", self._tiles)
+
+        dat = io.read_catalogue(path, verbose=False)
+
+        self.assertEqual(len(dat), 5)
+        npt.assert_array_equal(dat["RA"], self._expected()["RA"])
+
+    def test_layouts_agree(self):
+        nested = self._dir / "nested.hdf5"
+        flat = self._dir / "flat.hdf5"
+        write_campaign(nested, "nested", self._tiles)
+        write_campaign(flat, "flat", self._tiles)
+
+        npt.assert_array_equal(
+            io.read_catalogue(nested, verbose=False),
+            io.read_catalogue(flat, verbose=False),
+        )
+
+    def test_param_list_restriction(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "flat", self._tiles)
+
+        dat = io.read_catalogue(path, columns=["RA", "Dec"], verbose=False)
+
+        self.assertEqual(tuple(dat.dtype.names), ("RA", "Dec"))
+
+    def test_missing_column_raises(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "flat", self._tiles)
+
+        with self.assertRaises(KeyError) as ctx:
+            io.read_catalogue(path, columns=["RA", "NOT_A_COLUMN"], verbose=False)
+        self.assertIn("NOT_A_COLUMN", str(ctx.exception))
+
+    def test_row_order_is_tile_name_order(self):
+        """Keys inserted out of order still concatenate in name order."""
+        path = self._dir / "unordered.hdf5"
+        tiles = {
+            "222.000": make_galaxy_data(2, offset=200),
+            "000.000": make_galaxy_data(2, offset=0),
+            "111.000": make_galaxy_data(2, offset=100),
+        }
+        with h5py.File(path, "w") as f:
+            group = f.create_group("tiles")
+            for tile_id, dat in tiles.items():
+                group.create_dataset(tile_id, data=dat)
+            f.attrs["n_tiles"] = len(tiles)
+
+        dat = io.read_catalogue(path, verbose=False)
+
+        npt.assert_array_equal(dat["RA"], [0, 1, 100, 101, 200, 201])
+
+    def test_truncated_file_raises(self):
+        """n_tiles attribute larger than the number of datasets is fatal."""
+        path = self._dir / "truncated.hdf5"
+        write_campaign(path, "flat", self._tiles)
+        with h5py.File(path, "a") as f:
+            f.attrs["n_tiles"] = 10
+
+        with self.assertRaises(ValueError) as ctx:
+            io.read_catalogue(path, verbose=False)
+        self.assertIn("incomplete", str(ctx.exception))
+
+    def test_column_missing_from_later_tile_raises_clear_error(self):
+        """A column absent from a non-first tile is named, with its dataset."""
+        path = self._dir / "ragged.hdf5"
+        with h5py.File(path, "w") as f:
+            group = f.create_group("tiles")
+            group.create_dataset("000.000", data=make_galaxy_data(2))
+            group.create_dataset(
+                "001.000", data=np.zeros(2, dtype=[("RA", "f8"), ("Dec", "f8")])
+            )
+
+        with self.assertRaises(KeyError) as ctx:
+            io.read_catalogue(path, columns=["RA", "MAG_AUTO"], verbose=False)
+        message = str(ctx.exception)
+        self.assertIn("MAG_AUTO", message)
+        self.assertIn("001.000", message)
+
+    def test_dtype_promoted_across_tiles(self):
+        """A per-tile dtype difference within a campaign is not truncated."""
+        narrow = np.zeros(2, dtype=[("N_EPOCH", "i2"), ("TILE_ID", "S7"), ("RA", "f4")])
+        narrow["N_EPOCH"] = [1, 2]
+        narrow["TILE_ID"] = [b"123.456", b"123.457"]
+        narrow["RA"] = [1.5, 2.5]
+
+        wide = np.zeros(2, dtype=[("N_EPOCH", "i4"), ("TILE_ID", "S12"), ("RA", "f8")])
+        wide["N_EPOCH"] = [70000, 3]
+        wide["TILE_ID"] = [b"999888.7776", b"123.458"]
+        wide["RA"] = [3.123456789, 4.0]
+
+        path = self._dir / "final_cat_MIX.hdf5"
+        write_campaign(path, "flat", {"000.000": narrow, "000.001": wide})
+        param_list = ["N_EPOCH", "TILE_ID", "RA"]
+
+        dat = io.read_catalogue(str(path), columns=param_list, verbose=False)
+
+        self.assertEqual(dat.dtype["N_EPOCH"], np.dtype("i4"))
+        self.assertEqual(dat.dtype["TILE_ID"], np.dtype("S12"))
+        self.assertEqual(dat.dtype["RA"], np.dtype("f8"))
+        npt.assert_array_equal(dat["N_EPOCH"], [1, 2, 70000, 3])
+        npt.assert_array_equal(
+            dat["TILE_ID"],
+            [b"123.456", b"123.457", b"999888.7776", b"123.458"],
+        )
+        self.assertEqual(dat["RA"][2], 3.123456789)
+
+        # Catalogue.dtype must report the same promoted dtype, since the merge
+        # preallocates from it.
+        with io.Catalogue(str(path)) as catalogue:
+            n_rows, dtype_out = len(catalogue), catalogue.dtype(param_list)
+        self.assertEqual(n_rows, 4)
+        self.assertEqual(dtype_out, dat.dtype)
+
+    def test_iter_chunks(self):
+        """The streaming reader yields one restricted tile at a time."""
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "nested", self._tiles)
+
+        with io.Catalogue(str(path)) as catalogue:
+            tiles = list(catalogue.iter_chunks(["RA"]))
+
+        self.assertEqual([len(tile) for tile in tiles], [3, 2])
+        for tile in tiles:
+            self.assertEqual(tile.dtype.names, ("RA",))
+        npt.assert_array_equal(
+            np.concatenate([tile["RA"] for tile in tiles]),
+            self._expected()["RA"],
+        )
+
+    def test_ambiguous_layout_raises(self):
+        path = self._dir / "ambiguous.hdf5"
+        with h5py.File(path, "w") as f:
+            f.create_group("tiles")
+            f.create_group("other")
+
+        with self.assertRaises(ValueError):
+            io.read_catalogue(path, verbose=False)
+
+
+class TestStarCatalogueReader(unittest.TestCase):
+    """Campaign star catalogue reader."""
+
+    def setUp(self):
+        self._tmp = tempfile.TemporaryDirectory()
+        self._dir = Path(self._tmp.name)
+
+        dtype = np.dtype([(name, "f8") for name in STAR_CAT_COLUMNS])
+        self._exposures = {
+            "2110000p": np.zeros(4, dtype=dtype),
+            "2110001p": np.ones(6, dtype=dtype),
+        }
+
+        self._path = self._dir / "full_starcat_CAMPAIGN.hdf5"
+        with h5py.File(self._path, "w") as f:
+            f.attrs["n_exposures"] = len(self._exposures)
+            group = f.create_group("exposures")
+            for exp, dat in self._exposures.items():
+                group.create_dataset(exp, data=dat)
+
+    def tearDown(self):
+        self._tmp.cleanup()
+
+    def test_read_hdf5(self):
+        dat = io.read_catalogue(self._path, key_column="EXPID")
+
+        self.assertEqual(len(dat), 10)
+        # the reader appends EXPID to the ShapePipe star columns
+        self.assertEqual(tuple(dat.dtype.names), STAR_CAT_COLUMNS + ("EXPID",))
+        npt.assert_array_equal(dat["MAG"][:4], np.zeros(4))
+        npt.assert_array_equal(dat["MAG"][4:], np.ones(6))
+
+    def test_expid_records_exposure_provenance(self):
+        """Test that EXPID keeps the exposure each star came from.
+
+        The star catalogue holds one dataset per exposure, named by the
+        exposure number; concatenating them otherwise throws that number
+        away, leaving no way to group stars by exposure downstream (for
+        per-exposure PSF residuals, say). The name may be bare
+        ("2086324", as the smk-g7 products write it) or carry the CFIS
+        processed-exposure suffix ("2110000p").
+        """
+        dat = io.read_catalogue(self._path, key_column="EXPID")
+
+        self.assertEqual(dat.dtype["EXPID"].kind, "i")
+        npt.assert_array_equal(dat["EXPID"][:4], np.full(4, 2110000))
+        npt.assert_array_equal(dat["EXPID"][4:], np.full(6, 2110001))
+
+    def test_truncated_star_catalogue_is_rejected(self):
+        """Test that n_exposures is validated against the datasets found.
+
+        A merge job killed part-way through leaves a readable file with
+        fewer exposures than it declares. Without this check that shows
+        up only as a quietly short star sample, never as an error. The
+        galaxy reader already validates n_tiles this way.
+        """
+        path = self._dir / "truncated.hdf5"
+        with h5py.File(path, "w") as f:
+            f.attrs["n_exposures"] = 7  # but only two datasets written
+            group = f.create_group("exposures")
+            for exp, dat in self._exposures.items():
+                group.create_dataset(exp, data=dat)
+
+        with self.assertRaises(ValueError) as ctx:
+            io.read_catalogue(path, key_column="EXPID")
+        self.assertIn("n_exposures", str(ctx.exception))
+
+    def test_missing_n_exposures_attr_is_allowed(self):
+        """Test that a product carrying no n_exposures still reads.
+
+        Older products declare no count; that is not a defect.
+        """
+        path = self._dir / "no_attr.hdf5"
+        with h5py.File(path, "w") as f:
+            group = f.create_group("exposures")
+            for exp, dat in self._exposures.items():
+                group.create_dataset(exp, data=dat)
+
+        self.assertEqual(len(io.read_catalogue(path, key_column="EXPID")), 10)
+
+    def test_read_fits(self):
+        from astropy.io import fits
+
+        fits_path = self._dir / "full_starcat-0000000.fits"
+        dat = np.concatenate(list(self._exposures.values()))
+        fits.BinTableHDU(data=dat).writeto(fits_path)
+
+        out = io.read_catalogue(str(fits_path), key_column="EXPID")
+
+        self.assertEqual(len(out), 10)
+        npt.assert_array_equal(np.asarray(out["MAG"]), dat["MAG"])
+
+
+class TestMaskCut(unittest.TestCase):
+    """Mask-column galaxy selection cut."""
+
+    def setUp(self):
+        self._dat = make_galaxy_data(5)
+
+    def test_default_columns(self):
+        self._dat["MASK_4_Stars"][0] = True
+        self._dat["MASK_1024_Maximask"][3] = True
+
+        npt.assert_array_equal(
+            galaxy.mask_cut(self._dat),
+            np.array([False, True, True, False, True]),
+        )
+
+    def test_explicit_column_list(self):
+        self._dat["MASK_4_Stars"][0] = True
+        self._dat["MASK_8_Manual"][1] = True
+
+        npt.assert_array_equal(
+            galaxy.mask_cut(self._dat, ["MASK_8_Manual"]),
+            np.array([True, False, True, True, True]),
+        )
+
+    def test_empty_column_list_keeps_everything(self):
+        self._dat["MASK_4_Stars"][:] = True
+
+        npt.assert_array_equal(
+            galaxy.mask_cut(self._dat, []), np.ones(len(self._dat), dtype=bool)
+        )
+
+    def test_missing_column_raises(self):
+        with self.assertRaises(KeyError) as ctx:
+            galaxy.mask_cut(self._dat, ["MASK_16_u"])
+        self.assertIn("MASK_16_u", str(ctx.exception))
+
+    def test_catalogue_without_mask_columns_raises(self):
+        dat = np.zeros(3, dtype=[("FLAGS", "i2")])
+
+        with self.assertRaises(KeyError):
+            galaxy.mask_cut(dat)
+
+    def test_float_and_int_mask_columns(self):
+        """Test that float and int mask columns cut like bool ones.
+
+        ShapePipe's writer can emit the mask columns as float64
+        {0.0, 1.0} rather than bool, so the cut
+        must decide on the value, not on the dtype.
+        """
+        for dtype in ("f8", "i4"):
+            dat = np.zeros(
+                4, dtype=[(col, dtype) for col in galaxy.DEFAULT_MASK_COLUMNS]
+            )
+            dat["MASK_4_Stars"][0] = 1
+            dat["MASK_1024_Maximask"][2] = 1
+
+            npt.assert_array_equal(
+                galaxy.mask_cut(dat),
+                np.array([False, True, False, True]),
+                err_msg=f"mask column dtype {dtype}",
+            )
+
+    def test_nan_mask_value_is_masked_and_warns(self):
+        """Test that a NaN mask value drops the object, loudly.
+
+        NaN means the masking stage recorded no verdict. mask_cut drops the
+        object, as the config-driven cut ``kind: equal, value: False`` does,
+        and warns: a nonzero count means the input product is defective.
+        """
+        dat = np.zeros(3, dtype=[(col, "f8") for col in galaxy.DEFAULT_MASK_COLUMNS])
+        dat["MASK_4_Stars"][0] = np.nan
+        dat["MASK_4_Stars"][1] = 1.0
+
+        with self.assertWarns(RuntimeWarning) as ctx:
+            keep = galaxy.mask_cut(dat)
+
+        # row 0 NaN -> masked, row 1 masked, row 2 clean -> kept
+        npt.assert_array_equal(keep, np.array([False, False, True]))
+        self.assertIn("NaN", str(ctx.warning))
+        config_keep = Mask(
+            "MASK_4_Stars", "stars", kind="equal", value=False, dat=dat
+        )._mask
+        npt.assert_array_equal(config_keep, keep)
+
+    def test_no_warning_when_no_nan(self):
+        dat = np.zeros(2, dtype=[(col, "f8") for col in galaxy.DEFAULT_MASK_COLUMNS])
+
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", RuntimeWarning)
+            npt.assert_array_equal(galaxy.mask_cut(dat), np.array([True, True]))
+
+
+class TestCampaignMerge(unittest.TestCase):
+    """Merge of several campaign catalogues into a joint catalogue."""
+
+    def setUp(self):
+        self._tmp = tempfile.TemporaryDirectory()
+        self._dir = Path(self._tmp.name)
+
+        self._paths = []
+        for name, layout, n_obj in (("W3", "nested", 3), ("SGC", "flat", 2)):
+            path = self._dir / f"final_cat_{name}.hdf5"
+            write_campaign(path, layout, {"000.000": make_galaxy_data(n_obj)})
+            self._paths.append(str(path))
+
+        self._obj = JointCat()
+        self._obj._params["verbose"] = False
+
+    def tearDown(self):
+        self._tmp.cleanup()
+
+    def test_campaign_name(self):
+        self.assertEqual(JointCat.campaign_name("/some/dir/final_cat_W3.hdf5"), "W3")
+
+    def test_merge(self):
+        dat = self._obj.merge_catalogues(self._paths)
+
+        self.assertEqual(len(dat), 5)
+        self.assertIn("campaign", dat.dtype.names)
+        npt.assert_array_equal(dat["campaign"], np.array([b"W3"] * 3 + [b"SGC"] * 2))
+        npt.assert_array_equal(dat["RA"][:3], np.arange(3))
+
+    def test_merge_promotes_column_widths(self):
+        """A wider string/int column in a later file is not truncated."""
+        dtype_narrow = np.dtype([("RA", "f8"), ("TILE_ID", "S7"), ("N", "i4")])
+        dtype_wide = np.dtype([("RA", "f8"), ("TILE_ID", "S12"), ("N", "i8")])
+
+        narrow = np.zeros(1, dtype=dtype_narrow)
+        narrow["TILE_ID"] = b"123.456"
+        narrow["N"] = 7
+        wide = np.zeros(1, dtype=dtype_wide)
+        wide["TILE_ID"] = b"999888.7776"
+        wide["N"] = 2**40
+
+        path_a = self._dir / "final_cat_AA.hdf5"
+        path_b = self._dir / "final_cat_BBBBBBBB.hdf5"
+        write_campaign(path_a, "flat", {"000.000": narrow})
+        write_campaign(path_b, "flat", {"000.000": wide})
+
+        for paths in ([path_a, path_b], [path_b, path_a]):
+            dat = self._obj.merge_catalogues([str(path) for path in paths])
+            by_campaign = {name: row for name, row in zip(dat["campaign"], dat)}
+            self.assertEqual(by_campaign[b"BBBBBBBB"]["TILE_ID"], b"999888.7776")
+            self.assertEqual(by_campaign[b"BBBBBBBB"]["N"], 2**40)
+            self.assertEqual(by_campaign[b"AA"]["TILE_ID"], b"123.456")
+
+    def test_merge_reduce_mem_keeps_integers(self):
+        """reduce_mem leaves integer columns at their input width."""
+        dat = np.zeros(3, dtype=[("RA", "f8"), ("N_EPOCH", "i4"), ("w", "f8")])
+        dat["N_EPOCH"] = [3, 200, 300000]
+        dat["w"] = [0.5, 1.0, 2.0]
+        path = self._dir / "final_cat_Y.hdf5"
+        write_campaign(path, "flat", {"000.000": dat})
+
+        self._obj._params["reduce_mem"] = True
+        out = self._obj.merge_catalogues([str(path)])
+
+        npt.assert_array_equal(out["N_EPOCH"], [3, 200, 300000])
+        self.assertEqual(out.dtype["N_EPOCH"], np.dtype("i4"))
+        self.assertEqual(out.dtype["RA"], np.dtype("f8"))  # RA keeps precision
+        self.assertEqual(out.dtype["w"], np.dtype("f4"))
+
+    def test_merge_reduce_mem_float_overflow_raises(self):
+        """A float64 value beyond float32 range is refused, not stored as inf."""
+        dat = np.zeros(2, dtype=[("RA", "f8"), ("w", "f8")])
+        dat["w"] = [1.0, 1e300]
+        path = self._dir / "final_cat_Z.hdf5"
+        write_campaign(path, "flat", {"000.000": dat})
+
+        self._obj._params["reduce_mem"] = True
+        with self.assertRaises(ValueError) as ctx:
+            self._obj.merge_catalogues([str(path)])
+        self.assertIn("'w'", str(ctx.exception))
+
+    def test_merge_rejects_multidimensional_column(self):
+        dat = np.zeros(2, dtype=[("RA", "f8"), ("XY", "f8", (2,))])
+        path = self._dir / "final_cat_M.hdf5"
+        write_campaign(path, "flat", {"000.000": dat})
+
+        with self.assertRaises(ValueError) as ctx:
+            self._obj.merge_catalogues([str(path)])
+        self.assertIn("multi-dimensional", str(ctx.exception))
+
+    def test_merge_incompatible_columns_raises(self):
+        other = self._dir / "final_cat_X.hdf5"
+        dat = np.zeros(2, dtype=[("RA", "f8")])
+        write_campaign(other, "flat", {"000.000": dat})
+
+        with self.assertRaises(ValueError):
+            self._obj.merge_catalogues(self._paths + [str(other)])
+
+    def test_no_input_raises(self):
+        with self.assertRaises(ValueError):
+            self._obj.get_input_paths()
+
+
+if __name__ == "__main__":
+    unittest.main()
+
+
+class TestGroupDtype(unittest.TestCase):
+    def test_missing_column_names_the_tables_lacking_it(self):
+        full = np.zeros(2, dtype=[("RA", "f8"), ("SPREAD_MODEL", "f4")])
+        tiles = {"t0": full, "t1": full[["RA"]], "t2": full}
+        with self.assertRaisesRegex(KeyError, r"1 of 3 tables.*'t1'.*SPREAD_MODEL"):
+            io.group_dtype(tiles)
+        dtype = io.group_dtype(tiles, columns=["RA"])
+        self.assertEqual(dtype.names, ("RA",))
diff --git a/src/sp_validation/tests/test_catalog_flag_roundtrip.py b/src/sp_validation/tests/test_catalog_flag_roundtrip.py
index ea3cac97..8272d934 100644
--- a/src/sp_validation/tests/test_catalog_flag_roundtrip.py
+++ b/src/sp_validation/tests/test_catalog_flag_roundtrip.py
@@ -24,26 +24,28 @@
 def test_metacal_flags_roundtrip(tmp_path, params_path, extension, reduce_mem):
     """Contract metacal-flag-width: neither output nor merging loses flag bits.
 
-    Values are ngmix flag bits: none, LM_FUNC_NOTFINITE (2**12), ZERO_DOF
-    (2**15), and every bit 0-15 at once. Float64 inputs match ShapePipe's final
-    catalogue. Bit 15 overflows signed int16, and int8 wraps everything above
-    127, so the columns must stay int32.
+    Exercise ngmix bits 12 and 15, their combinations, and a high bit 30
+    value with bit 3. Float64 inputs match ShapePipe's final catalogue.
+    Signed int16 cannot hold bit 15, and float32 loses bit 3 next to bit 30.
     """
     with np.printoptions():
         params = runpy.run_path(str(ROOT / params_path))
-    flags = np.array([0, 2**12, 2**15, 2**15 | 2**12 | 8, 2**16 - 1], dtype=np.float64)
+    flags = np.array(
+        [0, 2**12, 2**15, 2**15 | 2**12 | 8, 2**16 - 1, 2**30, 2**30 | 8],
+        dtype=np.float64,
+    )
     bit_columns = ["NGMIX_MCAL_FLAGS"] + [
         f"NGMIX_FLAGS_{suffix}" for suffix in ("NOSHEAR", "1P", "1M", "2P", "2M")
     ]
     columns = {name: flags for name in bit_columns}
-    columns["NGMIX_MCAL_TYPES_FAIL"] = np.array([0, 1, 5, 2, 5])
+    columns["NGMIX_MCAL_TYPES_FAIL"] = np.array([0, 1, 5, 2, 5, 0, 1])
     assert set(columns) <= set(params["add_cols_pre_cal"])
     path = tmp_path / f"comprehensive{extension}"
     write_shape_catalog(
         str(path),
-        np.zeros(5),
-        np.zeros(5),
-        np.ones(5),
+        np.zeros(len(flags)),
+        np.zeros(len(flags)),
+        np.ones(len(flags)),
         add_cols=columns,
         add_cols_format=params["add_cols_pre_cal_format"],
     )
diff --git a/src/sp_validation/tests/test_column_schema.py b/src/sp_validation/tests/test_column_schema.py
new file mode 100644
index 00000000..850de5b6
--- /dev/null
+++ b/src/sp_validation/tests/test_column_schema.py
@@ -0,0 +1,173 @@
+"""The column schema and the code that reads catalogues stay in step.
+
+``config/columns/shapepipe_v2.yaml`` lists every catalogue column
+sp_validation reads by fixed name. A static scan of the reading code checks
+both directions:
+
+- every column-shaped key the code reads (a subscript or ``get_col`` argument
+  that is ShapePipe-prefixed or upper case) is in the schema, or is listed in
+  ``NOT_COLUMNS`` as a key of something other than a catalogue;
+- every schema entry is read somewhere: it appears in the code as a string, or
+  matches an f-string template such as ``f"NGMIX_G{i}_{shear}"``.
+
+``grammar.py`` is left out: the names it holds are ShapePipe v1 source names,
+which it maps onto the schema.
+"""
+
+import ast
+import re
+from pathlib import Path
+
+import pytest
+import yaml
+
+ROOT = Path(__file__).resolve().parents[3]
+SCHEMA = ROOT / "config" / "columns" / "shapepipe_v2.yaml"
+READING_CODE = [
+    path
+    for path in (ROOT / "src" / "sp_validation").rglob("*.py")
+    if "tests" not in path.parts and path.name != "grammar.py"
+] + [
+    ROOT / "scripts" / "calibration" / "extract_info.py",
+    ROOT / "scripts" / "calibration" / "calibrate_comprehensive_cat.py",
+]
+
+#: Column-shaped keys that index something other than a catalogue.
+NOT_COLUMNS = {
+    # cat_config and other configuration keys
+    "A",
+    "R",
+    "R11",
+    "R22",
+    # FITS header keywords
+    "EXTNAME",
+    "R_S11",
+    "R_S12",
+    "R_S21",
+    "R_S22",
+    "TTYPE{}",
+    # pseudo-Cl, COSEBIs and SACC products
+    "BB",
+    "COSEBIS",
+    "COVAR_BB_BB",
+    "COVAR_EE_EE",
+    "COVAR_{}_{}",
+    "COVDATA",
+    "ELL",
+    "NAME_{}",
+    "STRT_{}",
+    # in-memory tables sp_validation builds itself
+    "C11",
+    "C22",
+    "SNR",
+    "T",
+}
+
+#: An f-string template needs this many literal characters to count as
+#: reading a schema name, so that e.g. ``f"{a}_{b}"`` matches nothing.
+MIN_TEMPLATE_LITERAL = 4
+
+COLUMN_SHAPED = re.compile(r"^(NGMIX|HSM|MASK)_|^[A-Z][A-Z0-9_{}]*$")
+
+
+def _text(node):
+    """Return a string constant, or an f-string with each field as ``{}``."""
+    if isinstance(node, ast.Constant) and isinstance(node.value, str):
+        return node.value
+    if isinstance(node, ast.JoinedStr):
+        return "".join(
+            part.value if isinstance(part, ast.Constant) else "{}"
+            for part in node.values
+        )
+    return None
+
+
+def _template_regex(text):
+    if "{}" not in text:
+        return None
+    if len(text.replace("{}", "")) < MIN_TEMPLATE_LITERAL:
+        return None
+    return re.compile(
+        "^" + ".+".join(re.escape(part) for part in text.split("{}")) + "$"
+    )
+
+
+def _scan():
+    """Return (every string in the code, the column-shaped keys it reads)."""
+    strings, keys = set(), set()
+    for path in READING_CODE:
+        tree = ast.parse(path.read_text())
+        for node in ast.walk(tree):
+            text = _text(node)
+            if text is not None:
+                strings.add(text)
+            if isinstance(node, ast.Subscript):
+                candidates = [node.slice]
+            elif isinstance(node, ast.Call) and getattr(
+                node.func, "attr", getattr(node.func, "id", None)
+            ) in ("get_col", "get_maked_col"):
+                candidates = node.args[1:2]
+            else:
+                continue
+            for candidate in candidates:
+                text = _text(candidate)
+                if text is not None and COLUMN_SHAPED.match(text):
+                    keys.add(text)
+    return strings, keys
+
+
+@pytest.fixture(scope="module")
+def schema():
+    return yaml.safe_load(SCHEMA.read_text())
+
+
+@pytest.fixture(scope="module")
+def scan():
+    return _scan()
+
+
+def _names(schema):
+    return {name for kind in schema.values() for name in kind["columns"]}
+
+
+def test_schema_layout(schema):
+    for kind, entry in schema.items():
+        assert set(entry) == {"about", "columns"}, kind
+        for name, meaning in entry["columns"].items():
+            assert isinstance(meaning, str) and meaning, (kind, name)
+
+
+def test_code_reads_only_schema_columns(schema, scan):
+    _, keys = scan
+    names = _names(schema)
+    unknown = set()
+    for key in keys - NOT_COLUMNS:
+        regex = _template_regex(key)
+        if regex is None:
+            if key not in names:
+                unknown.add(key)
+        elif not any(regex.match(name) for name in names):
+            unknown.add(key)
+    assert not unknown, (
+        f"code reads columns missing from {SCHEMA.name}: {sorted(unknown)};"
+        + " add them there, or to NOT_COLUMNS if they index no catalogue"
+    )
+
+
+def test_schema_holds_only_columns_the_code_reads(schema, scan):
+    from sp_validation.galaxy import DEFAULT_MASK_COLUMNS
+
+    strings, _ = scan
+    strings = strings | set(DEFAULT_MASK_COLUMNS)
+    templates = [r for r in map(_template_regex, strings) if r is not None]
+    unread = sorted(
+        name
+        for name in _names(schema)
+        if name not in strings and not any(r.match(name) for r in templates)
+    )
+    assert not unread, f"{SCHEMA.name} lists columns no code reads: {unread}"
+
+
+def test_not_columns_are_still_used(scan):
+    _, keys = scan
+    assert NOT_COLUMNS <= keys, sorted(NOT_COLUMNS - keys)
diff --git a/src/sp_validation/tests/test_config_paths_exist.py b/src/sp_validation/tests/test_config_paths_exist.py
index a3de6873..52646c9b 100644
--- a/src/sp_validation/tests/test_config_paths_exist.py
+++ b/src/sp_validation/tests/test_config_paths_exist.py
@@ -195,10 +195,19 @@ def _candidate_paths() -> list[tuple[Path, str, Path]]:
     candidates = []
     for config_path in _config_files():
         iterator = _iter_ini_paths if config_path.suffix == ".ini" else _iter_yaml_paths
+        calibration = config_path.parent == root / "config/calibration"
         for source, key, value, base_dir in iterator(config_path):
             if _located_elsewhere(source, key, value):
                 continue
             expanded = Path(value).expanduser()
+            if (
+                calibration
+                and key == "params.input_path"
+                and not expanded.is_absolute()
+            ):
+                # A calibration config runs as config_mask.yaml in its run
+                # directory; a relative input_path names a file there.
+                continue
             if expanded.is_absolute():
                 resolved = expanded
             elif base_dir is not None:
@@ -230,3 +239,91 @@ def test_configured_paths_exist_on_candide():
         f"{len(missing)} missing configured paths out of {len(candidates)} checked:\n"
         + "\n".join(missing[:25])
     )
+
+
+# Columns each cat_config block declares for the rho/tau path. The psf block's
+# are read from the PSF file (``rho_tau.get_params_rho_tau``); the shear
+# block's are every ``*_col`` key, with the RA/Dec defaults rho/tau assumes.
+PSF_COLUMN_KEYS = (
+    "ra_col",
+    "dec_col",
+    "e1_PSF_col",
+    "e2_PSF_col",
+    "e1_star_col",
+    "e2_star_col",
+    "PSF_size",
+    "star_size",
+    "PSF_flag",
+    "star_flag",
+)
+SHEAR_COLUMN_DEFAULTS = {"ra_col": "RA", "dec_col": "Dec"}
+
+# Entries whose declared columns are known not to exist in their files, with
+# why. The test fails if one of these starts passing, so the entry is dropped
+# here once its config or data is fixed.
+KNOWN_COLUMN_GAPS = {
+    ("SP_axel_v0.0", "shear"): "file carries no PSF-shape columns",
+    ("SP_v1.3", "psf"): "2022 star file stores T_{PSF,STAR}_HSM, in neither"
+    " ShapePipe grammar and of unconfirmed convention",
+    ("SP_v1.4.6_glass_mock", "shear"): "GLASS mock carries no PSF-shape columns",
+    ("SP_v1.4.6.3_uncal_w_1", "shear"): "w_col 'one' names no column",
+}
+
+
+def _declared_columns(block, kind):
+    if kind == "psf":
+        return {key: block[key] for key in PSF_COLUMN_KEYS if key in block}
+    declared = {key: val for key, val in block.items() if key.endswith("_col")}
+    return SHEAR_COLUMN_DEFAULTS | declared
+
+
+def test_cat_config_columns_exist_on_candide():
+    """Every cat_config psf/shear column is among those its file presents.
+
+    Opens each file through ``io.Catalogue`` without reading its rows, so a
+    v1 file is checked against the v2 names it presents to the rho/tau path,
+    and a ``column_map`` applies as it does when reading.
+    Entries whose file is absent are left to the path guard above.
+    """
+    if not _on_candide():
+        pytest.skip("Candide-local column guard skipped: no /automnt/n17data/cdaley")
+
+    from sp_validation.io import Catalogue
+
+    with (_repo_root() / "cosmo_val/cat_config.yaml").open() as handle:
+        config = yaml.safe_load(handle)
+
+    checked, missing = 0, {}
+    for version, entry in config.items():
+        if not isinstance(entry, dict) or "subdir" not in entry:
+            continue
+        for kind in ("psf", "shear"):
+            block = entry.get(kind) or {}
+            if "path" not in block:
+                continue
+            path = Path(block["path"])
+            if not path.is_absolute():
+                path = Path(entry["subdir"]) / path
+            if not path.exists():
+                continue
+            with Catalogue(
+                path, hdu=block.get("hdu"), column_map=block.get("column_map")
+            ) as catalogue:
+                present = set(catalogue.dtype().names)
+            absent = {
+                key: col
+                for key, col in _declared_columns(block, kind).items()
+                if col not in present
+            }
+            checked += 1
+            if absent:
+                missing[(version, kind)] = f"{path}: {absent}"
+
+    assert checked, "no cat_config catalogue found to check"
+    unexpected = {k: v for k, v in missing.items() if k not in KNOWN_COLUMN_GAPS}
+    healed = sorted(set(KNOWN_COLUMN_GAPS) - set(missing))
+    assert not unexpected, "declared columns absent from their files:\n" + "\n".join(
+        f"{version}.{kind} -> {detail}"
+        for (version, kind), detail in unexpected.items()
+    )
+    assert not healed, f"known column gaps now pass; drop them: {healed}"
diff --git a/src/sp_validation/tests/test_galaxy.py b/src/sp_validation/tests/test_galaxy.py
index 529fd0ad..5ea6a2b7 100644
--- a/src/sp_validation/tests/test_galaxy.py
+++ b/src/sp_validation/tests/test_galaxy.py
@@ -37,3 +37,37 @@ def test_T_to_fwhm_is_dimensionally_correct(self):
         npt.assert_allclose(sigma_to_fwhm(1.0), 2.3548200450, rtol=1e-6)
         # the old linear form would give 4.0 here
         npt.assert_allclose(T_to_fwhm(2.0), sigma_to_fwhm(1.0))
+
+    def test_never_fit_objects_are_rejected(self):
+        """Test that NGMIX_N_EPOCH == 0 objects are cut.
+
+        ShapePipe's make_cat pre-fills the NGMIX_* columns with
+        sentinels (G1/G2 = -10, T/FLUX = 0) and overwrites them only
+        for objects present in the ngmix output. An object ngmix never
+        fit therefore keeps NGMIX_MCAL_FLAGS == 0 and passes a
+        flag-only selection, carrying e1 = -10 into the shear
+        statistics. In final_cat_smk-g7.hdf5 that is 18,983 of
+        1,851,100 objects (1.03%).
+        """
+        import numpy as np
+
+        from sp_validation.galaxy import classification_galaxy_ngmix
+
+        # row 0: never fit (sentinels, flags clean); row 1: a good fit
+        dd = {
+            "NGMIX_N_EPOCH": np.array([0.0, 3.0]),
+            "NGMIX_MCAL_FLAGS": np.array([0.0, 0.0]),
+            "NGMIX_G1_PSF_ORIG_NOSHEAR": np.array([-10.0, 0.02]),
+            "NGMIX_MCAL_TYPES_FAIL": np.array([0.0, 0.0]),
+            "NGMIX_G1_NOSHEAR": np.array([-10.0, 0.1]),
+        }
+        cut_common = np.array([True, True])
+
+        keep = classification_galaxy_ngmix(dd, cut_common)
+        npt.assert_array_equal(keep, [False, True])
+
+        # the N_EPOCH cut must stand on its own: even if the -10
+        # sentinel were to change, the never-fit row stays rejected
+        dd["NGMIX_G1_PSF_ORIG_NOSHEAR"] = np.array([-99.0, 0.02])
+        keep = classification_galaxy_ngmix(dd, cut_common)
+        npt.assert_array_equal(keep, [False, True])
diff --git a/src/sp_validation/tests/test_grammar.py b/src/sp_validation/tests/test_grammar.py
new file mode 100644
index 00000000..08ed6742
--- /dev/null
+++ b/src/sp_validation/tests/test_grammar.py
@@ -0,0 +1,559 @@
+"""The ShapePipe v1 -> v2 grammar adapter presents v1 products as their v2 twins.
+
+One synthetic catalogue is written in both grammars: v2 (T, split G1/G2,
+boolean MASK_{b}_{label}) and v1 (sigma, 2-vector or flattened ELL,
+{b}_{label} mask flags, plus the IMAFLAGS_ISO bitmask the adapter leaves alone).
+Adapting the v1 form must reproduce the v2 form column for column, for every
+table flavour the pipeline reads (numpy, FITS_rec, h5py), and row selection
+must commute with adaptation.
+"""
+
+import h5py
+import numpy as np
+import pytest
+from astropy.io import fits
+from cs_util.size import T_to_sigma
+from numpy.lib import recfunctions as rfn
+
+from sp_validation import grammar, io
+from sp_validation.grammar import (
+    MASK_LABELS,
+    SHEARS,
+    V2View,
+    adapt,
+    detect_generation,
+    v2_names,
+)
+
+N = 64
+
+#: cat_config ``psf:`` block naming the v2 HSM columns.
+PSF_BLOCK = {
+    "ra_col": "RA",
+    "dec_col": "DEC",
+    "e1_PSF_col": "HSM_G1_PSF",
+    "e2_PSF_col": "HSM_G2_PSF",
+    "e1_star_col": "HSM_G1_STAR",
+    "e2_star_col": "HSM_G2_STAR",
+    "PSF_size": "HSM_T_PSF",
+    "star_size": "HSM_T_STAR",
+    "PSF_flag": "HSM_FLAG_PSF",
+    "star_flag": "HSM_FLAG_STAR",
+}
+SHEAR_BLOCK = {"w_col": "w", "e1_col": "e1", "e2_col": "e2"}
+
+
+def _twins(vector_ell=True):
+    """Return (v1, v2) structured arrays holding the same content."""
+    rng = np.random.default_rng(42)
+    v2 = {
+        "RA": rng.uniform(0, 360, N),
+        "DEC": rng.uniform(-30, 30, N),
+        "MAG": rng.uniform(18, 24, N),
+    }
+    v1 = dict(v2)
+    for obj in ("PSF", "STAR"):
+        g1, g2 = rng.normal(0, 0.05, (2, N))
+        T = rng.uniform(0.4, 0.9, N)
+        flag = rng.integers(0, 3, N).astype(np.int16)
+        v2 |= {
+            f"HSM_G1_{obj}": g1,
+            f"HSM_G2_{obj}": g2,
+            f"HSM_T_{obj}": T,
+            f"HSM_FLAG_{obj}": flag,
+        }
+        v1 |= {
+            f"E1_{obj}_HSM": g1,
+            f"E2_{obj}_HSM": g2,
+            f"SIGMA_{obj}_HSM": T_to_sigma(T),
+            f"FLAG_{obj}_HSM": flag,
+        }
+    for shear in SHEARS:
+        for v1_tag, v2_tag in (("", ""), ("_ERR", "_ERR"), ("_PSFo", "_PSF_ORIG")):
+            g = rng.normal(0, 0.2, (N, 2))
+            v2[f"NGMIX_G1{v2_tag}_{shear}"] = g[:, 0]
+            v2[f"NGMIX_G2{v2_tag}_{shear}"] = g[:, 1]
+            name = f"NGMIX_ELL{v1_tag}_{shear}"
+            if vector_ell:
+                v1[name] = g
+            else:
+                v1[f"{name}_0"], v1[f"{name}_1"] = g[:, 0], g[:, 1]
+        T_orig, T_reconv = rng.uniform(0.3, 0.8, (2, N))
+        v2[f"NGMIX_T_PSF_ORIG_{shear}"] = v1[f"NGMIX_T_PSFo_{shear}"] = T_orig
+        v2[f"NGMIX_T_PSF_RECONV_{shear}"] = v1[f"NGMIX_Tpsf_{shear}"] = T_reconv
+    # v1's no-shear reconvolved-PSF size is wrong; the view reads the 1P kernel.
+    v1["NGMIX_Tpsf_NOSHEAR"] = 1.02 * v1["NGMIX_Tpsf_1P"]
+    v2["NGMIX_T_PSF_RECONV_NOSHEAR"] = v1["NGMIX_Tpsf_1P"]
+    fail = rng.integers(0, 2, N).astype(np.int16)
+    v2["NGMIX_MCAL_TYPES_FAIL"] = v1["NGMIX_MOM_FAIL"] = fail
+    v1["IMAFLAGS_ISO"] = rng.integers(0, 256, N).astype(np.int16)
+    for b, label in MASK_LABELS.items():
+        v2[grammar.mask_column(b)] = v1[f"{b}_{label}"] = rng.integers(0, 2, N).astype(
+            bool
+        )
+    return _structured(v1), _structured(v2)
+
+
+def _structured(columns):
+    dtype = [(k, v.dtype, v.shape[1:]) for k, v in columns.items()]
+    out = np.empty(N, dtype=dtype)
+    for key, value in columns.items():
+        out[key] = value
+    return out
+
+
+def _to_fits_rec(arr, tmp_path):
+    path = tmp_path / "cat.fits"
+    fits.BinTableHDU(arr).writeto(path)
+    return fits.getdata(path, 1), path
+
+
+@pytest.fixture(params=["numpy-vector", "numpy-flat", "fits-vector", "h5py-flat"])
+def v1_and_v2(request, tmp_path):
+    """A v1 table of each flavour, plus its v2 twin as a numpy array."""
+    flavour, layout = request.param.split("-")
+    v1, v2 = _twins(vector_ell=layout == "vector")
+    if flavour == "fits":
+        v1, _ = _to_fits_rec(v1, tmp_path)
+    elif flavour == "h5py":
+        handle = h5py.File(tmp_path / "cat.hdf5", "w")
+        handle.create_dataset("data", data=v1)
+        request.addfinalizer(handle.close)
+        v1 = handle["data"]
+    return v1, v2
+
+
+def _assert_same_columns(view, v2):
+    names = set(grammar.column_names(view))
+    assert names - {"IMAFLAGS_ISO"} == set(v2.dtype.names)
+    for name in v2.dtype.names:
+        expected, got = v2[name], np.asarray(view[name])
+        assert got.shape == expected.shape, name
+        if name.startswith("HSM_T_"):
+            np.testing.assert_allclose(got, expected, rtol=1e-12, err_msg=name)
+        else:
+            np.testing.assert_array_equal(got, expected, err_msg=name)
+
+
+def test_v1_adapts_to_its_v2_twin(v1_and_v2):
+    v1, v2 = v1_and_v2
+    view = adapt(v1)
+    assert isinstance(view, V2View)
+    assert len(view) == len(v2)
+    _assert_same_columns(view, v2)
+    assert view.dtype.names == view.names
+    _assert_same_columns(view.to_structured(), v2)
+
+
+def test_v1_names_are_hidden_but_imaflags_passes_through(v1_and_v2):
+    view = adapt(v1_and_v2[0])
+    assert "SIGMA_PSF_HSM" not in view
+    assert "NGMIX_ELL_NOSHEAR" not in view.names
+    assert "NGMIX_ELL_NOSHEAR_0" not in view.names
+    assert "4_Stars" not in view
+    with pytest.raises(KeyError):
+        view["E1_PSF_HSM"]
+    np.testing.assert_array_equal(
+        view["IMAFLAGS_ISO"], np.asarray(v1_and_v2[0]["IMAFLAGS_ISO"])
+    )
+
+
+def test_row_selection_commutes_with_adaptation(v1_and_v2):
+    v1, v2 = v1_and_v2
+    view = adapt(v1)
+    mask = np.asarray(v2["HSM_T_PSF"]) > 0.6
+    _assert_same_columns(view[mask], v2[mask])
+    _assert_same_columns(view[5:40:3], v2[5:40:3])
+    _assert_same_columns(view[mask][::2], v2[mask][::2])
+    _assert_same_columns(view[4:50][::-3], v2[4:50][::-3])
+    _assert_same_columns(view[10:5], v2[10:5])
+    if isinstance(v1, np.ndarray):
+        _assert_same_columns(adapt(v1[mask]), v2[mask])
+    row = view[7]
+    assert row["HSM_T_STAR"] == pytest.approx(v2["HSM_T_STAR"][7], rel=1e-12)
+
+
+def test_header_names_match_view_names(v1_and_v2):
+    v1, _ = v1_and_v2
+    names = grammar.column_names(v1)
+    assert v2_names(names) == adapt(v1).names
+
+
+def test_detect_generation():
+    v1, v2 = _twins()
+    assert detect_generation(v1.dtype.names) == "v1"
+    assert detect_generation(v2.dtype.names) == "v2"
+    assert detect_generation(["RA", "Dec", "e1", "e2", "w"]) is None
+    with pytest.raises(ValueError, match="mixes"):
+        detect_generation(["SIGMA_PSF_HSM", "HSM_T_STAR"])
+
+
+def test_v2_and_neutral_tables_pass_through_unchanged():
+    _, v2 = _twins()
+    assert adapt(v2) is v2
+    neutral = np.zeros(3, dtype=[("e1", "f8"), ("e2", "f8"), ("w", "f8")])
+    assert adapt(neutral) is neutral
+    assert v2_names(v2.dtype.names) == v2.dtype.names
+
+
+def test_catalogue_presents_a_v1_fits_file_in_v2(tmp_path):
+    v1, v2 = _twins()
+    _, path = _to_fits_rec(v1, tmp_path)
+    with io.Catalogue(path) as catalogue:
+        assert catalogue.hdu == 1
+        view = catalogue.table()
+        assert isinstance(view, V2View)
+        _assert_same_columns(view, v2)
+        assert catalogue.dtype().names == view.names
+
+    v2_path = tmp_path / "v2.fits"
+    fits.BinTableHDU(v2).writeto(v2_path)
+    with io.Catalogue(v2_path) as catalogue:
+        assert not isinstance(catalogue.table(), V2View)
+
+
+def test_psf_size_error_matches_between_grammars():
+    """shear_psf_leakage's size-residual field sees T, not sigma, from v1.
+
+    ``psf_size_error`` is e_star (T_star - T_psf) / T_star. Computed through
+    the adapter from the v1 twin it must equal the v2 value; computed from a
+    v1 table merely renamed (sigma under the T names) it must not.
+    """
+    from shear_psf_leakage.rho_tau_stat import Catalogs
+
+    from sp_validation.rho_tau import get_params_rho_tau
+
+    v1, v2 = _twins()
+    entry = {"patch_number": 2, "psf": PSF_BLOCK, "shear": SHEAR_BLOCK}
+    catalogs = Catalogs(params=get_params_rho_tau(entry))
+
+    def size_error(cat):
+        return np.stack(catalogs.get_cat_fields(cat, "psf_size_error")[2:4])
+
+    expected = size_error(v2)
+    np.testing.assert_allclose(size_error(adapt(v1)), expected, rtol=1e-12)
+
+    renamed_only = {name: np.asarray(v2[name]) for name in v2.dtype.names}
+    for obj in ("PSF", "STAR"):
+        renamed_only[f"HSM_T_{obj}"] = np.asarray(v1[f"SIGMA_{obj}_HSM"])
+    assert not np.allclose(size_error(renamed_only), expected)
+
+
+def test_get_rho_tau_identical_for_v1_and_v2_psf_catalogues(tmp_path):
+    """ρ/τ from a v1 PSF catalogue equal those from its v2 twin, end to end."""
+    from sp_validation.rho_tau import get_rho_tau
+
+    v1, v2 = _twins()
+    v1_hsm = [n for n in v1.dtype.names if n in ("RA", "DEC") or "HSM" in n]
+    rng = np.random.default_rng(3)
+    shear = np.empty(N, dtype=[(n, "f8") for n in ("RA", "Dec", "e1", "e2", "w")])
+    shear["RA"], shear["Dec"] = v2["RA"], v2["DEC"]
+    shear["e1"], shear["e2"] = rng.normal(0, 0.3, (2, N))
+    shear["w"] = 1.0
+    fits.BinTableHDU(shear).writeto(tmp_path / "shear.fits")
+
+    stats = {}
+    for label, psf in (("v1", v1[v1_hsm]), ("v2", v2[list(PSF_BLOCK.values())])):
+        psf_path = tmp_path / f"psf_{label}.fits"
+        fits.BinTableHDU(np.array(psf)).writeto(psf_path)
+        config = {
+            label: {
+                "patch_number": 2,
+                "psf": PSF_BLOCK | {"path": str(psf_path), "hdu": 1},
+                "shear": SHEAR_BLOCK | {"path": str(tmp_path / "shear.fits")},
+            }
+        }
+        outdir = tmp_path / label
+        outdir.mkdir()
+        treecorr_config = {
+            "ra_units": "deg",
+            "dec_units": "deg",
+            "sep_units": "arcmin",
+            "min_sep": 10,
+            "max_sep": 600,
+            "nbins": 4,
+        }
+        get_rho_tau(config, label, treecorr_config, str(outdir), label)
+        stats[label] = [
+            fits.getdata(outdir / f"{kind}_stats_{label}.fits")
+            for kind in ("rho", "tau")
+        ]
+
+    for table_v1, table_v2 in zip(stats["v1"], stats["v2"], strict=True):
+        assert table_v1.dtype.names == table_v2.dtype.names
+        for name in table_v1.dtype.names:
+            np.testing.assert_allclose(table_v1[name], table_v2[name], rtol=1e-10)
+
+
+# -- mask-bit columns: a rule family of their own ---------------------------
+
+
+def _data_ext_mask_table(n=N, extra=()):
+    """A data_ext-style table of {b}_{label} flags, and its MASK_{b}_{label} twin."""
+    rng = np.random.default_rng(7)
+    old = {
+        f"{b}_{label}": rng.integers(0, 2, n).astype(bool)
+        for b, label in MASK_LABELS.items()
+    }
+    new = {
+        grammar.mask_column(b): old[f"{b}_{label}"] for b, label in MASK_LABELS.items()
+    }
+    for name in extra:
+        old[name] = new[name] = rng.integers(0, 6, n)
+    return _structured_n(old), _structured_n(new)
+
+
+def _structured_n(columns):
+    n = len(next(iter(columns.values())))
+    out = np.empty(n, dtype=[(k, v.dtype, v.shape[1:]) for k, v in columns.items()])
+    for key, value in columns.items():
+        out[key] = value
+    return out
+
+
+@pytest.mark.parametrize("bit", sorted(MASK_LABELS))
+def test_every_mask_bit_spelling_maps_to_the_canonical_column(bit):
+    canonical = f"MASK_{bit}_{MASK_LABELS[bit]}"
+    assert grammar.mask_column(bit) == canonical
+    assert v2_names([f"{bit}_{MASK_LABELS[bit]}"]) == (canonical,)
+    assert v2_names([f"MASK_n{bit}"]) == (canonical,)
+    assert v2_names([canonical]) == (canonical,)
+
+
+def test_mask_column_rejects_unknown_bit():
+    with pytest.raises(KeyError):
+        grammar.mask_column(4096)
+
+
+def test_data_ext_mask_flags_rename_without_a_generation():
+    """data_ext mask names are renamed whatever the ShapePipe generation."""
+    old, new = _data_ext_mask_table(extra=("npoint3",))
+    assert detect_generation(old.dtype.names) is None
+    view = adapt(old)
+    assert isinstance(view, V2View)
+    assert view.names == new.dtype.names
+    for name in new.dtype.names:
+        np.testing.assert_array_equal(view[name], new[name])
+
+    # Alongside v2 shape columns too: the mask family is not a v1 marker.
+    _, v2 = _twins()
+    v2_shape = v2[[n for n in v2.dtype.names if not n.startswith("MASK_")]]
+    joined = adapt(rfn.repack_fields(v2_shape), old)
+    assert detect_generation(joined.names) == "v2"
+    np.testing.assert_array_equal(joined["MASK_4_Stars"], new["MASK_4_Stars"])
+    np.testing.assert_array_equal(joined["HSM_T_PSF"], v2["HSM_T_PSF"])
+
+
+@pytest.mark.parametrize(
+    "faint, bright",
+    [("1_Faint_star_halos", "2_Bright_star_halos"), ("MASK_n1", "MASK_n2")],
+)
+def test_halo_masks_keep_faint_and_bright_selections_distinct(faint, bright):
+    """Producer halo names retain their bit identity through adaptation and cuts."""
+    from sp_validation.galaxy import mask_cut
+
+    # Fixed source names and distinct values avoid deriving the fixture from
+    # MASK_LABELS, which would hide a reversed faint/bright mapping.
+    source = _structured_n(
+        {
+            faint: np.array([False, True, False, True]),
+            bright: np.array([False, False, True, True]),
+        }
+    )
+    view = adapt(source)
+    np.testing.assert_array_equal(
+        view["MASK_1_Faint_star_halos"], [False, True, False, True]
+    )
+    np.testing.assert_array_equal(
+        view["MASK_2_Bright_star_halos"], [False, False, True, True]
+    )
+    np.testing.assert_array_equal(
+        mask_cut(view, ["MASK_1_Faint_star_halos"]), [True, False, True, False]
+    )
+    np.testing.assert_array_equal(
+        mask_cut(view, ["MASK_2_Bright_star_halos"]), [True, True, False, False]
+    )
+    np.testing.assert_array_equal(
+        mask_cut(view, ["MASK_1_Faint_star_halos", "MASK_2_Bright_star_halos"]),
+        [True, False, False, False],
+    )
+
+
+def test_pre_release_mask_names_match_data_ext_names_and_cuts():
+    """MASK_n{b} and {b}_{label} present the same columns and give the same cut."""
+    from sp_validation.galaxy import DEFAULT_MASK_COLUMNS, MASK_COLUMNS, mask_cut
+
+    old, new = _data_ext_mask_table(extra=("npoint3",))
+    pre = rfn.rename_fields(
+        old, {f"{b}_{label}": f"MASK_n{b}" for b, label in MASK_LABELS.items()}
+    )
+    assert detect_generation(pre.dtype.names) is None
+    view_old, view_pre = adapt(old), adapt(pre)
+    assert view_pre.names == view_old.names == new.dtype.names
+    for name in new.dtype.names:
+        np.testing.assert_array_equal(view_pre[name], new[name])
+    for columns in (DEFAULT_MASK_COLUMNS, MASK_COLUMNS, ["MASK_8_Manual"]):
+        expected = mask_cut(new, columns)
+        np.testing.assert_array_equal(mask_cut(view_pre, columns), expected)
+        np.testing.assert_array_equal(mask_cut(view_old, columns), expected)
+
+
+def test_new_mask_names_pass_through_and_both_names_conflict():
+    old, new = _data_ext_mask_table()
+    assert adapt(new) is new
+    with pytest.raises(ValueError, match="two names"):
+        adapt(old, new[["MASK_4_Stars"]].copy())
+    with pytest.raises(ValueError, match="two names"):
+        adapt(
+            _structured_n({"MASK_n4": new["MASK_4_Stars"]}),
+            new[["MASK_4_Stars"]].copy(),
+        )
+
+
+def test_join_requires_equal_lengths_and_disjoint_names():
+    old, new = _data_ext_mask_table()
+    with pytest.raises(ValueError, match="lengths"):
+        adapt(new, old[:10])
+    with pytest.raises(ValueError, match="more than one table"):
+        adapt(new, new)
+
+
+# -- V2View mechanics --------------------------------------------------------
+
+
+def test_dtype_matches_columns(v1_and_v2):
+    """dtype reports exactly what each column read returns."""
+    view = adapt(v1_and_v2[0])
+    for name in view.names:
+        column = np.asarray(view[name])
+        assert view.dtype[name].base == column.dtype, name
+        assert view.dtype[name].shape == column.shape[1:], name
+
+
+def test_sigma_to_T_keeps_float32():
+    v1 = np.zeros(4, dtype=[("SIGMA_PSF_HSM", "f4"), ("E1_PSF_HSM", "f4")])
+    v1["SIGMA_PSF_HSM"] = 0.5
+    view = adapt(v1)
+    assert view.dtype["HSM_T_PSF"] == view["HSM_T_PSF"].dtype
+    assert view.to_structured()["HSM_T_PSF"].dtype == view["HSM_T_PSF"].dtype
+
+
+def test_empty_list_selects_an_empty_view(v1_and_v2):
+    view = adapt(v1_and_v2[0])
+    empty = view[[]]
+    assert isinstance(empty, V2View)
+    assert len(empty) == 0
+    assert empty["HSM_T_PSF"].shape == (0,)
+    assert len(view[np.zeros(len(view), dtype=bool)]) == 0
+
+
+def test_boolean_selection_holds_only_the_selected_rows():
+    """A mask becomes the selected indices, never a full-length index array."""
+    old, _ = _data_ext_mask_table(n=1000)
+    view = adapt(old)
+    mask = np.zeros(1000, dtype=bool)
+    mask[[3, 500, 998]] = True
+    assert list(view[mask]._rows) == [3, 500, 998]
+    window = view[100:900]
+    assert isinstance(window._rows, range)
+    sub = window[mask[100:900]]
+    assert list(sub._rows) == [500]
+    np.testing.assert_array_equal(sub["MASK_4_Stars"], old["4_Stars"][[500]])
+
+
+def _h5py_mask_table(tmp_path, n=1000):
+    old, new = _data_ext_mask_table(n=n, extra=("npoint3",))
+    with h5py.File(tmp_path / "ext.hdf5", "w") as handle:
+        handle.create_dataset("data_ext", data=old)
+    return old, new
+
+
+@pytest.mark.parametrize("block_rows", [1, 7, 10_000])
+def test_h5py_selection_reads_each_dataset_once(tmp_path, monkeypatch, block_rows):
+    """Selecting rows of an on-disk view reads every column in one pass."""
+    old, new = _h5py_mask_table(tmp_path)
+    monkeypatch.setattr(grammar, "BLOCK_BYTES", block_rows * old.dtype.itemsize)
+    takes = []
+    original = grammar._take
+
+    def spy(table, rows, fields=None):
+        takes.append(fields)
+        return original(table, rows, fields)
+
+    monkeypatch.setattr(grammar, "_take", spy)
+    rng = np.random.default_rng(3)
+    mask = rng.random(1000) < 0.3
+    mask[400:700] = False
+    index = np.array([998, 3, 500, 500, 2, 250])
+    with h5py.File(tmp_path / "ext.hdf5", "r") as handle:
+        view = adapt(handle["data_ext"])
+        for key in (mask, index, slice(10, 20, 3), slice(None, None, -7)):
+            takes.clear()
+            sub = view[key]
+            assert takes == [None]
+            assert not sub._on_disk
+            for name in new.dtype.names:
+                np.testing.assert_array_equal(sub[name], new[key][name], err_msg=name)
+        takes.clear()
+        got = view[mask].to_structured(["MASK_8_Manual", "npoint3"])
+        np.testing.assert_array_equal(got["MASK_8_Manual"], new["MASK_8_Manual"][mask])
+
+        # Materialising named columns reads only their fields, in one pass.
+        takes.clear()
+        got = view.to_structured(
+            ["MASK_8_Manual", "npoint3", "MASK_1_Faint_star_halos"]
+        )
+        assert takes == [["8_Manual", "npoint3", "1_Faint_star_halos"]]
+        np.testing.assert_array_equal(
+            got["MASK_1_Faint_star_halos"], new["MASK_1_Faint_star_halos"]
+        )
+
+
+@pytest.mark.parametrize("key", [(), Ellipsis])
+def test_empty_tuple_and_ellipsis_select_every_row(tmp_path, key):
+    old, new = _h5py_mask_table(tmp_path)
+    with h5py.File(tmp_path / "ext.hdf5", "r") as handle:
+        for view in (adapt(old), adapt(handle["data_ext"])):
+            everything = view[key]
+            assert len(everything) == len(new)
+            np.testing.assert_array_equal(
+                everything["MASK_4_Stars"], new["MASK_4_Stars"]
+            )
+            with pytest.raises(IndexError, match="tuple"):
+                view[(0, 1)]
+
+
+def test_dtype_is_computed_once():
+    old, _ = _data_ext_mask_table()
+    view = adapt(old)
+    assert view.dtype is view.dtype
+    assert view[3:9].dtype is view.dtype
+
+
+def test_v1_no_shear_reconv_psf_reads_the_1P_kernel():
+    """v1 NGMIX_Tpsf_NOSHEAR is wrong: RECONV_NOSHEAR reads Tpsf_1P when present."""
+    names = [f"NGMIX_Tpsf_{shear}" for shear in SHEARS]
+    table = np.zeros(4, dtype=[(name, "f8") for name in names])
+    for i, name in enumerate(names):
+        table[name] = 0.5 + i
+    view = adapt(table)
+    np.testing.assert_array_equal(
+        view["NGMIX_T_PSF_RECONV_NOSHEAR"], table["NGMIX_Tpsf_1P"]
+    )
+    np.testing.assert_array_equal(view["NGMIX_T_PSF_RECONV_1P"], table["NGMIX_Tpsf_1P"])
+    assert "NGMIX_Tpsf_NOSHEAR" not in view
+    assert view.names[0] == "NGMIX_T_PSF_RECONV_NOSHEAR"
+    assert sorted(view.names) == sorted(
+        f"NGMIX_T_PSF_RECONV_{shear}" for shear in SHEARS
+    )
+
+    # A v1 cut catalogue carries only the (already corrected) no-shear size.
+    cut = np.zeros(
+        3, dtype=[("NGMIX_Tpsf_NOSHEAR", "f8"), ("NGMIX_Tpsf_NOSHEAR_orig", "f8")]
+    )
+    cut["NGMIX_Tpsf_NOSHEAR"], cut["NGMIX_Tpsf_NOSHEAR_orig"] = 0.4, 0.41
+    view = adapt(cut)
+    assert view.names == ("NGMIX_T_PSF_RECONV_NOSHEAR", "NGMIX_Tpsf_NOSHEAR_orig")
+    np.testing.assert_array_equal(
+        view["NGMIX_T_PSF_RECONV_NOSHEAR"], cut["NGMIX_Tpsf_NOSHEAR"]
+    )
diff --git a/src/sp_validation/tests/test_grammar_calibration.py b/src/sp_validation/tests/test_grammar_calibration.py
new file mode 100644
index 00000000..9f247cbf
--- /dev/null
+++ b/src/sp_validation/tests/test_grammar_calibration.py
@@ -0,0 +1,333 @@
+"""A v1 comprehensive catalogue calibrates exactly like its v2 twin.
+
+One synthetic catalogue is written twice: as a post-processed ShapePipe v1
+comprehensive HDF5 (``data`` in the v1 grammar, ``data_ext`` holding the
+healsparse mask bits under their ``{b}_{label}`` names) and as a ShapePipe v2
+catalogue (one ``data`` dataset, v2 grammar, ``MASK_{b}_{label}``). Read
+through the readers the pipeline uses, the two must give identical mask
+selections, for ``galaxy.mask_cut`` and for every mask config, and identical
+metacal inputs.
+Also covers the catalogue reader on v1-grammar campaign and star files, and a
+catalogue in a foreign naming convention calibrating like its v2 twin through a
+``column_map``.
+"""
+
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pytest
+import yaml
+from astropy.io import fits
+from cs_util.size import T_to_sigma
+
+from sp_validation import galaxy, grammar, io
+from sp_validation.calibration import get_calibrated_quantities, metacal
+from sp_validation.catalog_builders import CalibrateCat, JointCat
+from sp_validation.masks import Mask, get_masks_from_config
+
+N = 4000
+CONFIG_DIR = Path(__file__).resolve().parents[3] / "config" / "calibration"
+BITS = (1, 2, 4, 8, 64, 1024)  # the bits v1 post-processing wrote
+
+
+def _twins():
+    """Return (v1 data, v1 data_ext, v2 data) holding the same catalogue."""
+    rng = np.random.default_rng(11)
+    common = {
+        "RA": rng.uniform(150, 160, N),
+        "Dec": rng.uniform(30, 40, N),
+        "FLAGS": (rng.random(N) < 0.1).astype(np.int16) * 2,
+        "overlap": rng.random(N) < 0.95,
+        "N_EPOCH": rng.integers(1, 8, N).astype(np.int16),
+        "mag": rng.uniform(14.5, 30.5, N),
+        "NGMIX_N_EPOCH": rng.integers(0, 6, N).astype(np.int16),
+        "NGMIX_MCAL_FLAGS": (rng.random(N) < 0.05).astype(np.int32),
+        "w_iv": rng.uniform(0.5, 2, N),
+        # passes through unmapped in both
+        "IMAFLAGS_ISO": (rng.random(N) < 0.1).astype(np.int16) * 2,
+    }
+    v1 = dict(common)
+    v2 = dict(common)
+    fail = (rng.random(N) < 0.05).astype(np.int16)
+    v1["NGMIX_MOM_FAIL"] = v2["NGMIX_MCAL_TYPES_FAIL"] = fail
+    for shear in grammar.SHEARS:
+        g = rng.normal(0, 0.3, (2, N))
+        err = rng.uniform(0.05, 0.2, (2, N))
+        Tpsf = rng.uniform(0.3, 0.6, N)
+        for i in (0, 1):
+            v1[f"NGMIX_ELL_{shear}_{i}"] = v2[f"NGMIX_G{i + 1}_{shear}"] = g[i]
+            v1[f"NGMIX_ELL_ERR_{shear}_{i}"] = v2[f"NGMIX_G{i + 1}_ERR_{shear}"] = err[
+                i
+            ]
+        v1[f"NGMIX_Tpsf_{shear}"] = v2[f"NGMIX_T_PSF_RECONV_{shear}"] = Tpsf
+        columns = {
+            "T": Tpsf * rng.uniform(0.5, 3.5, N),
+            "T_ERR": rng.uniform(0.01, 0.1, N),
+            "FLUX": rng.uniform(5, 400, N),
+            "FLUX_ERR": rng.uniform(0.5, 2, N),
+            "FLAGS": (rng.random(N) < 0.03).astype(np.int32),
+        }
+        for key, values in columns.items():
+            v1[f"NGMIX_{key}_{shear}"] = v2[f"NGMIX_{key}_{shear}"] = values
+    # v1's no-shear reconvolved-PSF size is wrong; the reader uses the 1P kernel.
+    v2["NGMIX_T_PSF_RECONV_NOSHEAR"] = v1["NGMIX_Tpsf_1P"]
+    v1["NGMIX_Tpsf_NOSHEAR"] = 1.02 * v1["NGMIX_Tpsf_1P"]
+    psf = rng.normal(0, 0.02, (2, N))
+    psf[:, rng.random(N) < 0.02] = -10
+    for i in (0, 1):
+        v1[f"NGMIX_ELL_PSFo_NOSHEAR_{i}"] = v2[f"NGMIX_G{i + 1}_PSF_ORIG_NOSHEAR"] = (
+            psf[i]
+        )
+    sigma = rng.uniform(0.3, 0.5, N)
+    v1["SIGMA_PSF_HSM"] = sigma
+    v2["HSM_T_PSF"] = 2 * sigma**2
+
+    ext = {}
+    for bit in BITS:
+        flag = rng.random(N) < 0.08
+        ext[f"{bit}_{grammar.MASK_LABELS[bit]}"] = flag
+        v2[grammar.mask_column(bit)] = flag
+    npoint = rng.integers(0, 6, N).astype(np.int16)
+    ext["npoint3"] = v2["npoint3"] = npoint
+    return _structured(v1), _structured(ext), _structured(v2)
+
+
+def _structured(columns):
+    out = np.empty(N, dtype=[(k, np.asarray(v).dtype) for k, v in columns.items()])
+    for key, value in columns.items():
+        out[key] = value
+    return out
+
+
+@pytest.fixture
+def comprehensive(tmp_path):
+    """Read the v1 and v2 comprehensive files through ``CalibrateCat``."""
+    v1, ext, v2 = _twins()
+    with h5py.File(tmp_path / "v1.hdf5", "w") as handle:
+        handle.create_dataset("data", data=v1)
+        handle.create_dataset("data_ext", data=ext)
+    with h5py.File(tmp_path / "v2.hdf5", "w") as handle:
+        handle.create_dataset("data", data=v2)
+
+    tables, readers = {}, []
+    for label in ("v1", "v2"):
+        reader = CalibrateCat()
+        reader._params["input_path"] = str(tmp_path / f"{label}.hdf5")
+        tables[label] = reader.read_cat()
+        readers.append(reader)
+    yield tables
+    for reader in readers:
+        reader.close_cat()
+
+
+def test_v1_comprehensive_presents_the_v2_columns(comprehensive):
+    v1, v2 = comprehensive["v1"], comprehensive["v2"]
+    assert isinstance(v1, grammar.V2View)
+    assert set(v1.dtype.names) == set(v2.dtype.names)
+    for name in v2.dtype.names:
+        np.testing.assert_allclose(v1[name], v2[name], rtol=1e-14, err_msg=name)
+
+
+def test_mask_cut_is_identical(comprehensive):
+    v1, v2 = comprehensive["v1"], comprehensive["v2"]
+    for columns in (None, [grammar.mask_column(bit) for bit in BITS], ["MASK_4_Stars"]):
+        np.testing.assert_array_equal(
+            galaxy.mask_cut(v1, columns), galaxy.mask_cut(v2, columns)
+        )
+    assert 0 < galaxy.mask_cut(v1).sum() < N
+
+
+@pytest.mark.parametrize(
+    "config_name",
+    sorted(
+        path.name
+        for path in CONFIG_DIR.glob("mask_*.yaml")
+        if not path.name.endswith("overlay.yaml")
+    ),
+)
+def test_config_selection_is_identical(comprehensive, config_name):
+    config = yaml.safe_load((CONFIG_DIR / config_name).read_text())
+    available = set(comprehensive["v2"].dtype.names)
+    cuts = [cut for cut in config["dat"] if cut["col_name"] in available]
+    if not cuts:
+        pytest.skip(f"{config_name} cuts on no column of the twin catalogue")
+    config = {"dat": cuts}
+
+    combined = {}
+    for label, table in comprehensive.items():
+        masks, labels = get_masks_from_config(config, table)
+        for mask in masks:
+            assert mask._mask.shape == (N,)
+        combined[label] = (
+            Mask.from_list(masks)._mask,
+            [m._mask for m in masks],
+            labels,
+        )
+    assert combined["v1"][2] == combined["v2"][2]
+    for mask_v1, mask_v2 in zip(combined["v1"][1], combined["v2"][1], strict=True):
+        np.testing.assert_array_equal(mask_v1, mask_v2)
+    np.testing.assert_array_equal(combined["v1"][0], combined["v2"][0])
+
+
+def test_v1_configs_cut_every_mask_column_they_name(comprehensive):
+    """The v1 configs' mask cuts all resolve on a v1 comprehensive file."""
+    names = set(comprehensive["v1"].dtype.names)
+    for path in CONFIG_DIR.glob("mask_v1.X.*.yaml"):
+        if path.name.endswith("overlay.yaml"):
+            continue
+        config = yaml.safe_load(path.read_text())
+        for cut in config["dat"]:
+            if cut["col_name"].startswith("MASK_"):
+                assert cut["col_name"] in names, (path.name, cut["col_name"])
+
+
+def test_metacal_inputs_are_identical(comprehensive):
+    config = yaml.safe_load((CONFIG_DIR / "mask_v1.X.6.yaml").read_text())
+    cm = config["metacal"]
+    results = {}
+    for label, table in comprehensive.items():
+        masks, _ = get_masks_from_config(config, table)
+        selection = Mask.from_list(masks)._mask
+        results[label] = metacal(
+            table,
+            selection,
+            snr_min=cm["gal_snr_min"],
+            snr_max=cm["gal_snr_max"],
+            rel_size_min=cm["gal_rel_size_min"],
+            rel_size_max=cm["gal_rel_size_max"],
+            size_corr_ell=cm["gal_size_corr_ell"],
+            sigma_eps=cm["sigma_eps_prior"],
+            global_R_weight=None,
+        )
+    mc_v1, mc_v2 = results["v1"], results["v2"]
+    assert mc_v1._n_input == mc_v2._n_input > 0
+    assert np.all(np.isfinite(mc_v1.R))
+    for attr in ("m1", "p1", "m2", "p2", "ns"):
+        d1, d2 = getattr(mc_v1, attr), getattr(mc_v2, attr)
+        assert d1.keys() == d2.keys()
+        for key in d1:
+            np.testing.assert_array_equal(d1[key], d2[key], err_msg=f"{attr}.{key}")
+    np.testing.assert_array_equal(mc_v1.R, mc_v2.R)
+
+
+def _calibrate(table, config):
+    """Return (metacal, calibrated g) for ``table`` under mask config ``config``."""
+    cm = config["metacal"]
+    masks, _ = get_masks_from_config(config, table)
+    mc = metacal(
+        table,
+        Mask.from_list(masks)._mask,
+        snr_min=cm["gal_snr_min"],
+        snr_max=cm["gal_snr_max"],
+        rel_size_min=cm["gal_rel_size_min"],
+        rel_size_max=cm["gal_rel_size_max"],
+        size_corr_ell=cm["gal_size_corr_ell"],
+        sigma_eps=cm["sigma_eps_prior"],
+        global_R_weight=None,
+    )
+    g_corr, _, _, _ = get_calibrated_quantities(mc)
+    return mc, g_corr
+
+
+def test_foreign_names_with_a_column_map_calibrate_like_v2(tmp_path):
+    """A catalogue under its own column names calibrates through a column_map.
+
+    The v2 twin is written with every ``NGMIX_*`` column renamed ``MYFIT_*`` and
+    RA/Dec renamed; ``CalibrateCat`` reads it with a map holding one pattern
+    and two explicit entries, and metacal gives the same calibrated shear.
+    """
+    _, _, v2 = _twins()
+    renames = {"RA": "ALPHA", "Dec": "DELTA"}
+    foreign_names = [
+        renames.get(name, name.replace("NGMIX_", "MYFIT_", 1))
+        for name in v2.dtype.names
+    ]
+    foreign = np.empty(
+        N,
+        dtype=[(new, v2.dtype[old]) for new, old in zip(foreign_names, v2.dtype.names)],
+    )
+    for new, old in zip(foreign_names, v2.dtype.names):
+        foreign[new] = v2[old]
+    with h5py.File(tmp_path / "foreign.hdf5", "w") as handle:
+        handle.create_dataset("data", data=foreign)
+
+    reader = CalibrateCat()
+    reader._params["input_path"] = str(tmp_path / "foreign.hdf5")
+    reader._params["column_map"] = {"NGMIX_*": "MYFIT_*", **renames}
+    table = reader.read_cat(load_into_memory=True)
+    assert set(table.dtype.names) == set(v2.dtype.names)
+
+    config = yaml.safe_load((CONFIG_DIR / "mask_v1.X.6.yaml").read_text())
+    available = set(v2.dtype.names)
+    config["dat"] = [cut for cut in config["dat"] if cut["col_name"] in available]
+    mc_foreign, g_foreign = _calibrate(table, config)
+    mc_v2, g_v2 = _calibrate(v2, config)
+    assert mc_v2._n_input > 0
+    np.testing.assert_array_equal(mc_foreign.R, mc_v2.R)
+    np.testing.assert_array_equal(g_foreign, g_v2)
+
+
+def test_load_into_memory_matches_the_view(comprehensive, tmp_path):
+    reader = CalibrateCat()
+    reader._params["input_path"] = str(tmp_path / "v1.hdf5")
+    loaded = reader.read_cat(load_into_memory=True)
+    assert isinstance(loaded, np.ndarray)
+    for name in loaded.dtype.names:
+        np.testing.assert_array_equal(loaded[name], comprehensive["v1"][name])
+
+
+# -- campaign and star readers on v1-grammar files ----------------------------
+
+
+def _write_campaign(path, tiles):
+    with h5py.File(path, "w") as handle:
+        group = handle.create_group("patches").create_group("CAMPAIGN")
+        for tile_id, dat in tiles.items():
+            group.create_dataset(tile_id, data=dat)
+        handle.attrs["n_tiles"] = len(tiles)
+
+
+def test_campaign_reader_presents_v1_tiles_in_v2(tmp_path):
+    v1, _, v2 = _twins()
+    _write_campaign(
+        tmp_path / "final_cat_CAMPAIGN.hdf5",
+        {"000.000": v1[:1500], "001.000": v1[1500:]},
+    )
+    wanted = ["RA", "NGMIX_G1_NOSHEAR", "NGMIX_MCAL_TYPES_FAIL", "HSM_T_PSF"]
+    dat = io.read_catalogue(str(tmp_path / "final_cat_CAMPAIGN.hdf5"), columns=wanted)
+    assert dat.dtype.names == tuple(wanted)
+    for name in wanted:
+        np.testing.assert_allclose(dat[name], v2[name], rtol=1e-14)
+
+    with io.Catalogue(str(tmp_path / "final_cat_CAMPAIGN.hdf5")) as catalogue:
+        n_rows, dtype = len(catalogue), catalogue.dtype(wanted)
+    assert n_rows == N and dtype.names == tuple(wanted)
+
+    merger = JointCat()
+    merger._params["verbose"] = False
+    merger._params["param_path"] = None
+    merged = merger.merge_catalogues([str(tmp_path / "final_cat_CAMPAIGN.hdf5")])
+    np.testing.assert_array_equal(merged["NGMIX_G2_ERR_1P"], v2["NGMIX_G2_ERR_1P"])
+    assert "NGMIX_ELL_1P_0" not in merged.dtype.names
+
+
+def test_star_reader_presents_v1_fits_in_v2(tmp_path):
+    rng = np.random.default_rng(5)
+    T = rng.uniform(0.3, 0.6, 50)
+    v1 = np.zeros(
+        50,
+        dtype=[
+            ("RA", "f8"),
+            ("DEC", "f8"),
+            ("E1_STAR_HSM", "f8"),
+            ("SIGMA_STAR_HSM", "f8"),
+        ],
+    )
+    v1["E1_STAR_HSM"] = rng.normal(0, 0.05, 50)
+    v1["SIGMA_STAR_HSM"] = T_to_sigma(T)
+    fits.BinTableHDU(v1).writeto(tmp_path / "stars.fits")
+    stars = io.read_catalogue(str(tmp_path / "stars.fits"), key_column="EXPID")
+    assert isinstance(stars, np.ndarray)
+    np.testing.assert_allclose(stars["HSM_T_STAR"], T, rtol=1e-12)
+    np.testing.assert_array_equal(stars["HSM_G1_STAR"], v1["E1_STAR_HSM"])
diff --git a/src/sp_validation/tests/test_io.py b/src/sp_validation/tests/test_io.py
new file mode 100644
index 00000000..ac2af576
--- /dev/null
+++ b/src/sp_validation/tests/test_io.py
@@ -0,0 +1,227 @@
+"""The catalogue reader is independent of container and column convention.
+
+A catalogue whose columns follow no ShapePipe convention reads to the same
+table from FITS, a flat HDF5 dataset, a comprehensive ``data`` + ``data_ext``
+HDF5 and an HDF5 group of row chunks; its names pass through unless a
+``column_map`` renames them. The rho/tau loader reads a star catalogue from
+HDF5 by the column names its config gives.
+"""
+
+import mmap
+
+import h5py
+import numpy as np
+import numpy.testing as npt
+import pytest
+from astropy.io import fits
+
+from sp_validation import grammar, io
+from sp_validation.rho_tau import _CatalogueLoader
+
+N = 60
+
+
+@pytest.fixture
+def foreign():
+    """A star catalogue in a naming convention of its own."""
+    rng = np.random.default_rng(3)
+    table = np.empty(
+        N,
+        dtype=[
+            ("alpha", "f8"),
+            ("delta", "f8"),
+            ("psf_e1", "f8"),
+            ("psf_e2", "f8"),
+            ("star_e1", "f8"),
+            ("star_e2", "f8"),
+            ("psf_size", "f4"),
+            ("star_size", "f4"),
+            ("flag", "i2"),
+        ],
+    )
+    for name in table.dtype.names:
+        table[name] = rng.normal(size=N).astype(table.dtype[name])
+    return table
+
+
+def _write(table, path, layout):
+    if layout == "fits":
+        fits.BinTableHDU(table).writeto(path)
+    elif layout == "dataset":
+        with h5py.File(path, "w") as handle:
+            handle.create_dataset("stars", data=table)
+    elif layout == "comprehensive":
+        names = table.dtype.names
+        with h5py.File(path, "w") as handle:
+            handle.create_dataset("data", data=table[list(names[:4])])
+            handle.create_dataset("data_ext", data=table[list(names[4:])])
+    elif layout == "chunks":
+        with h5py.File(path, "w") as handle:
+            group = handle.create_group("exposures")
+            for i, start in enumerate(range(0, N, 25)):
+                group.create_dataset(f"{2000 + i}p", data=table[start : start + 25])
+            handle.attrs["n_exposures"] = len(group)
+    return path
+
+
+LAYOUTS = ("fits", "dataset", "comprehensive", "chunks")
+
+
+def _assert_table_equal(out, table):
+    assert set(out.dtype.names) >= set(table.dtype.names)
+    for name in table.dtype.names:
+        npt.assert_array_equal(np.asarray(out[name]), table[name], err_msg=name)
+
+
+@pytest.mark.parametrize("layout", LAYOUTS)
+def test_every_container_reads_to_the_same_table(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    out = io.read_catalogue(path)
+    assert tuple(out.dtype.names) == foreign.dtype.names
+    _assert_table_equal(out, foreign)
+
+
+@pytest.mark.parametrize("layout", LAYOUTS)
+def test_columns_restrict_the_read(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    out = io.read_catalogue(path, columns=["star_e2", "alpha"])
+    assert out.dtype.names == ("star_e2", "alpha")
+    _assert_table_equal(out, foreign[["star_e2", "alpha"]])
+
+
+def test_container_is_detected_from_contents_not_extension(foreign, tmp_path):
+    hdf5 = _write(foreign, tmp_path / "stars.fits", "dataset")
+    fits_file = _write(foreign, tmp_path / "stars.hdf5", "fits")
+    for path in (hdf5, fits_file):
+        _assert_table_equal(io.read_catalogue(path), foreign)
+
+
+def test_key_column_records_chunk_names(foreign, tmp_path):
+    path = _write(foreign, tmp_path / "stars.hdf5", "chunks")
+    out = io.read_catalogue(path, key_column="EXPID")
+    npt.assert_array_equal(out["EXPID"], np.repeat([2000, 2001, 2002], [25, 25, 10]))
+    flat = _write(foreign, tmp_path / "flat.hdf5", "dataset")
+    assert "EXPID" not in io.read_catalogue(flat, key_column="EXPID").dtype.names
+
+
+@pytest.mark.parametrize("layout", LAYOUTS)
+def test_column_map_presents_canonical_names(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    column_map = {"RA": "alpha", "DEC": "delta", "HSM_G*_PSF": "psf_e*"}
+    out = io.read_catalogue(path, column_map=column_map)
+    assert {"RA", "DEC", "HSM_G1_PSF", "HSM_G2_PSF"} <= set(out.dtype.names)
+    assert not {"alpha", "delta", "psf_e1", "psf_e2"} & set(out.dtype.names)
+    npt.assert_array_equal(out["RA"], foreign["alpha"])
+    npt.assert_array_equal(out["HSM_G2_PSF"], foreign["psf_e2"])
+    npt.assert_array_equal(out["star_e1"], foreign["star_e1"])
+
+
+def test_column_map_overrides_and_composes_with_v1_detection():
+    v1 = np.zeros(3, dtype=[("SIGMA_PSF_HSM", "f8"), ("E1_PSF_HSM", "f8"), ("x", "f8")])
+    v1["SIGMA_PSF_HSM"] = 0.5
+    v1["x"] = [1, 2, 3]
+    view = grammar.adapt(v1, column_map={"HSM_G1_PSF": "x"})
+    # the map wins for HSM_G1_PSF; v1 detection still converts sigma to T
+    npt.assert_array_equal(view["HSM_G1_PSF"], [1, 2, 3])
+    npt.assert_allclose(view["HSM_T_PSF"], 2 * 0.5**2)
+    assert "E1_PSF_HSM" in view.names
+
+
+def test_bad_pattern_raises():
+    table = np.zeros(2, dtype=[("a", "f8")])
+    with pytest.raises(ValueError, match=r"exactly one '\*'"):
+        grammar.adapt(table, column_map={"NGMIX_*_*": "MY_*"})
+
+
+@pytest.mark.parametrize("layout", ("dataset", "chunks"))
+def test_rho_tau_loader_reads_hdf5_stars_by_config_names(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / "stars.hdf5", layout)
+    params = {
+        "ra_PSF_col": "alpha",
+        "dec_PSF_col": "delta",
+        "e1_PSF_col": "psf_e1",
+        "e2_PSF_col": "psf_e2",
+        "e1_star_col": "star_e1",
+        "e2_star_col": "star_e2",
+        "PSF_size": "psf_size",
+        "star_size": "star_size",
+        "PSF_flag": "flag",
+        "star_flag": "flag",
+    }
+    with _CatalogueLoader({"psf": {"path": str(path)}}, params) as load:
+        fits_path = load("psf")
+        assert fits_path != str(path)
+        out = fits.getdata(fits_path, 1)
+        assert set(out.dtype.names) == set(params.values())
+        _assert_table_equal(out, foreign[list(dict.fromkeys(params.values()))])
+
+
+def test_rho_tau_loader_passes_plain_fits_through(foreign, tmp_path):
+    path = _write(foreign, tmp_path / "stars.fits", "fits")
+    params = {"ra_PSF_col": "alpha", "dec_PSF_col": "delta"}
+    with _CatalogueLoader({"psf": {"path": str(path)}}, params) as load:
+        assert load("psf") == str(path)
+    entry = {"path": str(path), "column_map": {"RA": "alpha"}}
+    with _CatalogueLoader({"psf": entry}, {"ra_PSF_col": "RA"}) as load:
+        assert load("psf") != str(path)
+        npt.assert_array_equal(fits.getdata(load("psf"), 1)["RA"], foreign["alpha"])
+
+
+def test_column_map_overrides_only_its_own_canonical_name():
+    """A map entry reading a v1 column leaves the v1 rules that read it intact.
+
+    Mapping ``NGMIX_T_PSF_RECONV_1P`` onto ``NGMIX_Tpsf_1P`` must not make the
+    no-shear size fall back to the wrong ``NGMIX_Tpsf_NOSHEAR``.
+    """
+    v1 = np.zeros(
+        3,
+        dtype=[
+            ("NGMIX_Tpsf_1P", "f8"),
+            ("NGMIX_Tpsf_NOSHEAR", "f8"),
+            ("NGMIX_MOM_FAIL", "i2"),
+        ],
+    )
+    v1["NGMIX_Tpsf_1P"] = [1.0, 2.0, 3.0]
+    v1["NGMIX_Tpsf_NOSHEAR"] = 1.02 * v1["NGMIX_Tpsf_1P"]
+    view = grammar.adapt(v1, column_map={"NGMIX_T_PSF_RECONV_1P": "NGMIX_Tpsf_1P"})
+    npt.assert_array_equal(view["NGMIX_T_PSF_RECONV_1P"], v1["NGMIX_Tpsf_1P"])
+    npt.assert_array_equal(view["NGMIX_T_PSF_RECONV_NOSHEAR"], v1["NGMIX_Tpsf_1P"])
+    assert "NGMIX_MCAL_TYPES_FAIL" in view.names
+
+
+def test_column_map_applies_over_an_existing_view():
+    v1 = np.zeros(4, dtype=[("SIGMA_PSF_HSM", "f8"), ("alpha", "f8")])
+    v1["SIGMA_PSF_HSM"] = 0.5
+    v1["alpha"] = [1, 2, 3, 4]
+    view = grammar.adapt(v1)
+    assert grammar.adapt(view) is view
+    mapped = grammar.adapt(view, column_map={"RA": "alpha"})
+    assert set(mapped.names) == {"HSM_T_PSF", "RA"}
+    npt.assert_array_equal(mapped["RA"], v1["alpha"])
+    npt.assert_allclose(mapped["HSM_T_PSF"], 2 * 0.5**2)
+    npt.assert_array_equal(mapped[1:3]["RA"], [2, 3])
+
+
+def _memory_mapped(array):
+    while array is not None:
+        if isinstance(array, (np.memmap, mmap.mmap)):
+            return True
+        array = getattr(array, "base", None)
+    return False
+
+
+@pytest.mark.parametrize("layout", ("fits", "dataset", "comprehensive"))
+def test_open_catalogue_reads_lazily(foreign, tmp_path, layout):
+    """An unchunked catalogue opens as a lazy table, not an in-memory copy."""
+    import gc
+
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    table = io.open_catalogue(path, column_map={"RA": "alpha"})
+    gc.collect()  # the HDF5 file stays open while the table is referenced
+    assert isinstance(table, grammar.V2View)
+    if layout == "fits":
+        assert all(_memory_mapped(base) for base in table._bases)
+    else:
+        assert all(isinstance(base, h5py.Dataset) for base in table._bases)
+    npt.assert_array_equal(table["RA"], foreign["alpha"])
+    npt.assert_array_equal(table["star_e2"], foreign["star_e2"])
diff --git a/src/sp_validation/tests/test_masks.py b/src/sp_validation/tests/test_masks.py
index 76e97748..840ae259 100644
--- a/src/sp_validation/tests/test_masks.py
+++ b/src/sp_validation/tests/test_masks.py
@@ -9,11 +9,19 @@
 
 """
 
+import importlib.util
+from pathlib import Path
+
 import numpy as np
 import numpy.testing as npt
 import pytest
 
-from sp_validation.masks import Mask, apply_condition
+from sp_validation.masks import (
+    Mask,
+    apply_condition,
+    catalogue_cuts,
+    get_masks_from_config,
+)
 
 pytestmark = pytest.mark.fast
 
@@ -34,12 +42,10 @@
     ],
 )
 def test_apply_condition_kinds(kind, value, expected):
-
     npt.assert_array_equal(apply_condition(_ARRAY, kind, value), expected)
 
 
 def test_smaller_equal_alias_matches_less_equal():
-
     npt.assert_array_equal(
         apply_condition(_ARRAY, "smaller_equal", 3),
         apply_condition(_ARRAY, "less_equal", 3),
@@ -47,13 +53,11 @@ def test_smaller_equal_alias_matches_less_equal():
 
 
 def test_unknown_kind_raises():
-
     with pytest.raises(ValueError):
         apply_condition(_ARRAY, "not_a_real_kind", 3)
 
 
 def test_mask_apply_matches_apply_condition():
-
     dat = np.array([(1,), (2,), (3,), (4,), (5,)], dtype=[("col", "i8")])
 
     my_mask = Mask("col", "test_mask", kind="greater_equal", value=3, dat=dat)
@@ -64,7 +68,6 @@ def test_mask_apply_matches_apply_condition():
 
 
 def test_not_equal_2bands_is_two_column_or():
-
     # Keep an object if EITHER band column differs from the sentinel
     dat = np.array(
         [(-99, -99), (-99, 20.0), (21.0, -99), (21.0, 20.0)],
@@ -84,16 +87,42 @@ def test_not_equal_2bands_is_two_column_or():
 
 
 def test_not_equal_2bands_requires_col_name2():
-
     with pytest.raises(ValueError, match="col_name2"):
         Mask("mag_z", "zband", kind="not_equal_2bands", value=-99)
 
 
 def test_not_equal_2bands_descr_names_both_columns():
-
     my_mask = Mask(
         "mag_z", "zband", kind="not_equal_2bands", value=-99, col_name2="mag_z2"
     )
     my_mask.create_descr()
 
     assert my_mask._descr == "!=-99 in mag_z or mag_z2"
+
+
+def test_dat_ext_cut_list_is_refused_not_dropped():
+    """A config's ``dat_ext`` cuts would be silently ignored; refuse them."""
+    config = {
+        "dat": [{"col_name": "FLAGS", "label": "SE", "kind": "equal", "value": 0}],
+        "dat_ext": [{"col_name": "4_Stars", "kind": "equal", "value": False}],
+    }
+    dat = np.zeros(3, dtype=[("FLAGS", "i2"), ("MASK_4_Stars", "?")])
+    with pytest.raises(ValueError, match="dat_ext.*4_Stars.*MASK_4_Stars"):
+        get_masks_from_config(config, dat)
+    del config["dat_ext"]
+    assert catalogue_cuts(config) == config["dat"]
+    masks, _ = get_masks_from_config(config, dat)
+    assert len(masks) == 1
+
+
+def test_masking_script_refuses_dat_ext():
+    spec = importlib.util.spec_from_file_location(
+        "masking_script", Path(__file__).resolve().parents[3] / "scripts/masking.py"
+    )
+    masking = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(masking)
+    config = {"dat": [], "dat_ext": [{"col_name": "8_Manual"}]}
+    dat = np.zeros(3, dtype=[("MASK_8_Manual", "?")])
+    with pytest.raises(ValueError, match="dat_ext"):
+        masking.apply_masks(dat, config, footprint_only=True)
+    assert "IMAFLAGS_ISO" in masking.SPATIAL_CUTS
diff --git a/src/sp_validation/tests/test_release_regression.py b/src/sp_validation/tests/test_release_regression.py
new file mode 100644
index 00000000..2e5f13c0
--- /dev/null
+++ b/src/sp_validation/tests/test_release_regression.py
@@ -0,0 +1,141 @@
+"""Candide-local regression of the calibration path against the v1.4.6.3 release.
+
+Runs ``scripts/calibration/calibrate_comprehensive_cat.py`` with the release's
+mask config (``config/calibration/mask_v1.X.6.yaml``) on a row window of the
+v1.4.c comprehensive HDF5, and checks that it selects exactly the objects the
+released cut catalogue holds from that window, in the same order, with the same
+per-object values. The release preserves input order, so the window's objects
+form one contiguous run of released rows, found by (RA, Dec).
+
+Globally calibrated columns (``e1``, ``e2``, ``w_des``, the leakage-corrected
+ellipticities) depend on the whole-survey response and weights, not on the
+window, and are not compared. Everything the release derives object by object
+is, including the no-shear reconvolved-PSF size, which the release took from
+``NGMIX_Tpsf_1P`` (``sp_validation.grammar``).
+
+Skipped where the release products are absent.
+"""
+
+import os
+import runpy
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pytest
+import yaml
+from astropy.io import fits
+
+WL = Path("/n17data/UNIONS/WL/v1.4.x")
+COMPREHENSIVE = WL / "unions_shapepipe_comprehensive_struc_2024_v1.4.c.hdf5"
+RELEASE = WL / "v1.4.6.3" / "unions_shapepipe_cut_struc_2024_v1.4.6.3.fits"
+REPO = Path(__file__).resolve().parents[3]
+SCRIPT = REPO / "scripts" / "calibration" / "calibrate_comprehensive_cat.py"
+CONFIG = REPO / "config" / "calibration" / "mask_v1.X.6.yaml"
+
+START, N_ROWS = 200_000_000, 1_000_000
+
+#: Per-object output columns and their release counterparts.
+PER_OBJECT = {
+    name: name
+    for name in (
+        "RA",
+        "Dec",
+        "mag",
+        "snr",
+        "e1_uncal",
+        "e2_uncal",
+        "w_iv",
+        "FLUX_RADIUS",
+        "MAGERR_AUTO",
+        "NGMIX_T_NOSHEAR",
+        "fwhm_PSF",
+        "R_g11",
+        "R_g12",
+        "R_g21",
+        "R_g22",
+        "e1_PSF",
+        "e2_PSF",
+    )
+} | {"NGMIX_T_PSF_RECONV_NOSHEAR": "NGMIX_Tpsf_NOSHEAR"}
+
+pytestmark = [
+    pytest.mark.slow,
+    pytest.mark.skipif(
+        not (COMPREHENSIVE.exists() and RELEASE.exists()),
+        reason="v1.4.c comprehensive catalogue or v1.4.6.3 release absent",
+    ),
+]
+
+
+def _window_file(path):
+    """Write rows START:START+N_ROWS of the comprehensive HDF5 to ``path``.
+
+    Returns the window's (RA, Dec) as complex keys.
+    """
+    with h5py.File(COMPREHENSIVE, "r") as src, h5py.File(path, "w") as dst:
+        for name in ("data", "data_ext"):
+            window = src[name][START : START + N_ROWS]
+            dst.create_dataset(name, data=window)
+            if name == "data":
+                keys = window["RA"] + 1j * window["Dec"]
+    return keys
+
+
+def _run_calibration(run_dir):
+    """Run the calibration script in ``run_dir``.
+
+    Returns its cut catalogue and the input window's (RA, Dec) keys.
+    """
+    comprehensive = run_dir / "unions_shapepipe_comprehensive_window.hdf5"
+    window_keys = _window_file(comprehensive)
+    config = yaml.safe_load(CONFIG.read_text())
+    config["params"]["input_path"] = str(comprehensive)
+    (run_dir / "config_mask.yaml").write_text(yaml.safe_dump(config))
+
+    cwd = os.getcwd()
+    os.chdir(run_dir)
+    try:
+        runpy.run_path(str(SCRIPT), run_name="__main__")
+    except SystemExit as exit:
+        assert exit.code in (0, None), f"calibration script exited with {exit.code}"
+    finally:
+        os.chdir(cwd)
+    return fits.getdata(run_dir / "unions_shapepipe_cut_window.fits", 1), window_keys
+
+
+def _released_region(ra, dec):
+    """Return the released rows from our first object on, with a margin."""
+    release = fits.getdata(RELEASE, 1, memmap=True)
+    first = np.flatnonzero(
+        (np.asarray(release["RA"]) == ra[0]) & (np.asarray(release["Dec"]) == dec[0])
+    )
+    assert len(first) == 1, "window's first object not found once in the release"
+    start = int(first[0])
+    margin = 1000
+    lo, hi = max(start - margin, 0), start + len(ra) + margin
+    return release[lo:hi]
+
+
+def test_calibration_reproduces_the_release_window(tmp_path):
+    ours, window_keys = _run_calibration(tmp_path)
+    assert len(ours) > 0
+    ra, dec = np.asarray(ours["RA"]), np.asarray(ours["Dec"])
+
+    # The released rows that come from the window, bracketed on both sides
+    # by released rows that do not, so none is missed.
+    region = _released_region(ra, dec)
+    region_keys = np.asarray(region["RA"]) + 1j * np.asarray(region["Dec"])
+    in_window = np.isin(region_keys, window_keys)
+    assert not in_window[0] and not in_window[-1], "margin too small"
+    released = region[in_window]
+
+    # Selection: exactly our objects, in our order.
+    np.testing.assert_array_equal(
+        np.asarray(released["RA"]) + 1j * np.asarray(released["Dec"]), ra + 1j * dec
+    )
+
+    for name, released_name in PER_OBJECT.items():
+        np.testing.assert_array_equal(
+            np.asarray(ours[name]), np.asarray(released[released_name]), err_msg=name
+        )
diff --git a/src/sp_validation/tests/test_survey.py b/src/sp_validation/tests/test_survey.py
index f42f884c..e786c8f8 100644
--- a/src/sp_validation/tests/test_survey.py
+++ b/src/sp_validation/tests/test_survey.py
@@ -28,10 +28,6 @@ def setUp(self):
         self._area_amin2 = 3600
         self._tile_IDs = (270.283, 188.308)
 
-        self._ra = np.array([240.0])
-        self._dec = np.array([32.0])
-        self._patch = ["P5"]
-
     def tearDown(self):
 
         self.number_tile = None
@@ -57,9 +53,3 @@ def test_get_area(self):
             sorted(tile_IDs) == sorted(self._tile_IDs),
             msg=f"{tile_IDs}!={self._tile_IDs}",
         )
-
-    def test_get_footprint(self):
-        """Test ``sp_validation.survey_get_footprint`` method."""
-        for patch in self._patch:
-            coords = survey.get_footprint(patch, self._ra, self._dec)
-            self.assertTrue(coords[0])
diff --git a/workflow/image_sims/params_im_sim.py b/workflow/image_sims/params_im_sim.py
index 09d19bc1..2e5fb1ba 100644
--- a/workflow/image_sims/params_im_sim.py
+++ b/workflow/image_sims/params_im_sim.py
@@ -27,11 +27,11 @@
 
 # Survey parameters
 
-## Field or patch name -- derived from the run directory, which is named
+## Campaign name -- derived from the run directory, which is named
 ## after the simulation (e.g. '1z2z_grid_1'), so one shared params file
 ## serves every sim.
-name = os.path.basename(os.getcwd())
-print("Field name = {}".format(name))
+campaign = os.path.basename(os.getcwd())
+print("Campaign name = {}".format(campaign))
 
 ## Area of a tile in deg^2
 area_tile = 0.25
@@ -49,12 +49,16 @@
 data_dir = "."
 
 ### Tile IDs
-path_tile_ID = f"{data_dir}/tiles_{name}.txt"
+path_tile_ID = f"{data_dir}/tiles_{campaign}.txt"
 
 ### Weak-lensing galaxy catalog name
-galaxy_cat_path = f"{data_dir}/final_cat_{name}.hdf5"
+galaxy_cat_path = f"{data_dir}/final_cat_{campaign}.hdf5"
 print(f"Galaxy catalogue = {galaxy_cat_path}")
 
+### Galaxy catalogue column map {canonical name: name in the file}; `None` if
+### the catalogue carries the ShapePipe v2 names
+galaxy_column_map = None
+
 ## Parameter list; optional, set to `None` if not required
 param_list_path = f"{data_dir}/cfis/final_cat.param"
 
@@ -64,6 +68,9 @@
 # HDU number of star and PSF catalogue
 hdu_star_cat = 1
 
+### Star catalogue column map, as galaxy_column_map
+star_column_map = None
+
 ### External mask; optional, set to `None` if not required
 mask_external_path = None
 
@@ -98,9 +105,6 @@
 col_name_ra = "XWIN_WORLD"
 col_name_dec = "YWIN_WORLD"
 
-### Memory mode, set to None unless very large file
-mmap_mode = None
-
 ## Output
 
 ### Output file format extension: '.fits' or '.hdf5'
@@ -125,10 +129,14 @@
 ## Pre-calibration catalogue, including masked objects and mask flags.
 ## ShapePipe-v2 (post-#761) ngmix grammar: ellipticity in named scalar
 ## components NGMIX_G{1,2}_*, PSF size split into NGMIX_T_PSF_ORIG/RECONV.
-## IMAFLAGS_ISO (present in the data-path params) is omitted: the simulation
-## pipeline runs no imaging-flag masking stage, so the column does not exist.
+## The MASK_<bit>_<label> columns (present in the data-path params) are omitted: the
+## simulation pipeline runs no imaging-flag masking stage, so they do not
+## exist -- hence the empty mask_columns below.
 ## NGMIX_MCAL_TYPES_FAIL is kept -- it is the metacal moments-failure flag the
 ## calibration mask cuts on, identically to the data path.
+## No mask columns to OR: no masking stage in the simulation pipeline
+mask_columns = []
+
 add_cols_pre_cal = [
     "TILE_ID",
     "NUMBER",
@@ -156,7 +164,7 @@
 
 add_cols_pre_cal_format["TILE_ID"] = "A7"
 add_cols_pre_cal_format["NUMBER"] = "J"
-# Metacal bitmasks hold ngmix flag bits 0-15; bit 15 overflows signed 16-bit I.
+# Metacal bitmasks need int32: ngmix bit 15 overflows signed int16.
 add_cols_pre_cal_format["NGMIX_MCAL_FLAGS"] = "J"
 
 # Create key names for metacal information

From 7781c872a5ceb8941c0adb83e43fb3b2ec3e9bda Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:27:31 +0200
Subject: [PATCH 49/83] Point the non-tomographic workflow at the merged
 tomographic API
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The workflow scripts call the ("all", "all") pair explicitly
(calculate_2pcf_version(...)["tomo_bin_all_tomo_bin_all"],
calculate_pseudo_cl(compute_tomography=False, out_path=...),
calculate_pseudo_cl_inka_cov(compute_tomography=False),
calculate_rho_tau_stats(tomography=False)), and the rules declare the names
the methods write: cv_basename and the rho/tau outputs carry _tomo_bin_all,
the ξ± dump is xi_{basename}.txt (also in papers/bmodes, which imports these
rules). The pseudo-Cl covariance job computes in a private directory, since
the iNKA blocks are cached under binning-only names.

plot_2pcf and plot_ratio_xi_sys_xi come back as wrappers over
plot_2pcf_tomography, so the cv_plot_2pcf / cv_ratio_xi_sys_xi rules and
run_cosmo_val.py run; xi_psf_sys computes the non-tomographic fit first, as
rho_tau_fits does. The ρ/τ plot rules pass the figure names the methods no
longer default to, and plot_rho_tau_fits saves its contours into the output
directory.

pol_factor: true in the paper config passed the ±1 assert and meant no e2
flip; it is -1. GLASS_mock_validation's PSF columns take the v2 names its
file presents (as SP_v1.6.6, same file) and its bin column the file's
TOM_BIN_ID. compute_tomography and force_run are exempt from the
cv_init_params forwarding: rule scripts choose the pair per call, and
Snakemake decides what reruns.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 cosmo_val/cat_config.yaml                     |  21 ++-
 cosmo_val/run_cosmo_val.py                    |   1 -
 papers/bmodes/rules/figures.smk               |   2 +-
 .../scripts/bb_covariance_nz_independence.py  |   2 +-
 .../scripts/cosebis_version_comparison.py     |   2 +-
 .../harmonic_config_cosebis_comparison.py     |   4 +-
 papers/cosmo_val/config/config.yaml           |   3 +-
 .../cosmo_val/psf_systematics.py              |   6 +-
 src/sp_validation/cosmo_val/real_space.py     | 120 ++++++++++++++++++
 .../tests/test_cv_init_params.py              |  10 +-
 workflow/common.py                            |   9 +-
 workflow/rules/cosmo_val.smk                  |   8 +-
 workflow/rules/twopoint.smk                   |   8 +-
 workflow/scripts/cv_plot_2pcf.py              |  12 +-
 workflow/scripts/cv_plot_rho_stats.py         |   2 +-
 workflow/scripts/cv_plot_tau_stats.py         |   2 +-
 workflow/scripts/cv_ratio_xi_sys_xi.py        |  15 ++-
 workflow/scripts/cv_rho_tau_fits.py           |   7 +-
 workflow/scripts/generate_pseudo_cl.py        |   2 +-
 workflow/scripts/generate_pseudo_cl_cov.py    |  67 +++++-----
 workflow/scripts/run_2pcf.py                  |  10 +-
 workflow/scripts/run_glass_mock_pseudo_cl.py  |   4 +-
 workflow/scripts/run_rho_tau.py               |   2 +-
 23 files changed, 233 insertions(+), 86 deletions(-)

diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index 1a55b8d7..5dc90cdd 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -1223,19 +1223,18 @@ GLASS_mock_validation:
     sigma_e: 0.379587601488189
   mask: /n09data/guerrini/glass_mock_test/mask_nside_1024.fits
   psf:
-    PSF_flag: FLAG_PSF_HSM
-    PSF_size: SIGMA_PSF_HSM
-    square_size: true
-    star_flag: FLAG_STAR_HSM
-    star_size: SIGMA_STAR_HSM
+    PSF_flag: HSM_FLAG_PSF
+    PSF_size: HSM_T_PSF
+    star_flag: HSM_FLAG_STAR
+    star_size: HSM_T_STAR
     hdu: 1
     path: /n17data/UNIONS/WL/v1.6.x/unions_shapepipe_psf_2024_v1.6.a.fits
     ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: E1_PSF_HSM
-    e1_star_col: E1_STAR_HSM
-    e2_PSF_col: E2_PSF_HSM
-    e2_star_col: E2_STAR_HSM
+    dec_col: DEC
+    e1_PSF_col: HSM_G1_PSF
+    e1_star_col: HSM_G1_STAR
+    e2_PSF_col: HSM_G2_PSF
+    e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
     path: tomo_test_2_glass_sim_00001_1024.fits
@@ -1246,7 +1245,7 @@ GLASS_mock_validation:
     e2_col: e2
     e2_PSF_col: e2_PSF
     cols: RA,Dec
-    tomo_bin_col: tom_bin_id
+    tomo_bin_col: TOM_BIN_ID
   star:
     ra_col: RA
     dec_col: Dec
diff --git a/cosmo_val/run_cosmo_val.py b/cosmo_val/run_cosmo_val.py
index de19ebc3..f7c34fb0 100644
--- a/cosmo_val/run_cosmo_val.py
+++ b/cosmo_val/run_cosmo_val.py
@@ -134,7 +134,6 @@
 #    max_sep_int=300,
 #    nbins_int=100,
 #    npatch=256,
-#    var_method="jackknife",
 # )
 
 # %%
diff --git a/papers/bmodes/rules/figures.smk b/papers/bmodes/rules/figures.smk
index 31b65ea0..b9095aaf 100644
--- a/papers/bmodes/rules/figures.smk
+++ b/papers/bmodes/rules/figures.smk
@@ -84,7 +84,7 @@ def _reporting_cov_path(version):
 def _xi_integration_path(version):
     """Path to fine-binned 2PCF integration file. Unpatched: values only, no covariance."""
     return (
-        f"{COSMO_VAL_OUTPUT}/{version}_xi_minsep={FIDUCIAL['min_sep_int']}"
+        f"{COSMO_VAL_OUTPUT}/xi_{version}_tomo_bin_all_minsep={FIDUCIAL['min_sep_int']}"
         f"_maxsep={FIDUCIAL['max_sep_int']}_nbins={FIDUCIAL['nbins_int']}_npatch=1.txt"
     )
 
diff --git a/papers/bmodes/scripts/bb_covariance_nz_independence.py b/papers/bmodes/scripts/bb_covariance_nz_independence.py
index 7f8cae02..a11f9916 100644
--- a/papers/bmodes/scripts/bb_covariance_nz_independence.py
+++ b/papers/bmodes/scripts/bb_covariance_nz_independence.py
@@ -511,7 +511,7 @@ def _from_cli(argv=None):
     }
     xi_integration_path = os.path.join(
         a.cosmo_val_dir,
-        f"{version}_xi_minsep={min_sep_int}_maxsep={max_sep_int}"
+        f"xi_{version}_tomo_bin_all_minsep={min_sep_int}_maxsep={max_sep_int}"
         f"_nbins={nbins_int}_npatch={npatch}.txt",
     )
     pseudo_cl_path = os.path.join(
diff --git a/papers/bmodes/scripts/cosebis_version_comparison.py b/papers/bmodes/scripts/cosebis_version_comparison.py
index 464cca70..51fa9d53 100644
--- a/papers/bmodes/scripts/cosebis_version_comparison.py
+++ b/papers/bmodes/scripts/cosebis_version_comparison.py
@@ -148,7 +148,7 @@ def _create_stacked_bmode_figure(
 
 
 def _xi_integration(results_dir, ver):
-    return f"{results_dir}/{ver}_xi_minsep=0.5_maxsep=300.0_nbins=1000_npatch=1.txt"
+    return f"{results_dir}/xi_{ver}_tomo_bin_all_minsep=0.5_maxsep=300.0_nbins=1000_npatch=1.txt"
 
 
 def _cov_integration(cov_dir, ver):
diff --git a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
index 5e4e4e3e..2fc52dae 100644
--- a/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
+++ b/papers/bmodes/scripts/harmonic_config_cosebis_comparison.py
@@ -889,7 +889,7 @@ def _from_cli(argv=None):
         default=None,
         help=(
             "Explicit path to the fiducial 1000-bin integration xi_pm text file "
-            "reproduced by lc (e.g. SP_v1.4.6.3_leak_corr_xi_minsep=0.5_maxsep=300.0_"
+            "reproduced by lc (e.g. xi_SP_v1.4.6.3_leak_corr_tomo_bin_all_minsep=0.5_maxsep=300.0_"
             "nbins=1000_npatch=1.txt), overriding the --cosmo-val-dir pattern lookup "
             "for --fiducial-version."
         ),
@@ -946,7 +946,7 @@ def _from_cli(argv=None):
             if is_fiducial and a.fiducial_xi_path
             else os.path.join(
                 a.cosmo_val_dir,
-                f"{ver}_xi_minsep={min_sep_int}_maxsep={max_sep_int}"
+                f"xi_{ver}_tomo_bin_all_minsep={min_sep_int}_maxsep={max_sep_int}"
                 f"_nbins={nbins_int}_npatch={npatch}.txt",
             )
         )
diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index 5b33999f..7dd0e091 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -43,7 +43,8 @@ cosmo_val:
   power: 0.5
   n_ell_bins: 32
   ell_step: 10
-  pol_factor: true
+  # e2 multiplier (±1) applied before NaMaster; -1 flips the e2 sign
+  pol_factor: -1
   noise_bias_method: analytic
   cell_seed: 8192
   path_onecovariance: "/home/guerrini/OneCovariance/"
diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index 63c1b73a..44476061 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -67,7 +67,9 @@ def rho_tau_fits(self):
     @property
     def xi_psf_sys(self):
         if not hasattr(self, "_xi_psf_sys"):
-            self.calculate_rho_tau_fits()
+            self.calculate_rho_tau_fits(tomography=False)
+            if self.compute_tomography:
+                self.calculate_rho_tau_fits(tomography=True)
         return self._xi_psf_sys
 
     # --- calculate functions ---
@@ -833,7 +835,7 @@ def plot_rho_tau_fits(
             sample_list,
             names=["x0", "x1", "x2"],
             labels=[r"\alpha", r"\beta", r"\eta"],
-            savefig=savefig_contours,
+            savefig=savefig,
             legend_labels=versions,
             legend_loc="upper right",
             contour_colors=colors,
diff --git a/src/sp_validation/cosmo_val/real_space.py b/src/sp_validation/cosmo_val/real_space.py
index da5cfdd8..fd1f2bbd 100644
--- a/src/sp_validation/cosmo_val/real_space.py
+++ b/src/sp_validation/cosmo_val/real_space.py
@@ -269,6 +269,81 @@ def map2(self):
             self.calculate_aperture_mass_dispersion()
         return self._map2
 
+    def plot_2pcf(
+        self, tomography=False, offset=0.02, alpha=1.0, show=True, close=True
+    ):
+        """Plot ξ± of every version, one panel per bin pair.
+
+        Measures, or reads back, the 2PCF with :meth:`calculate_2pcf` and draws
+        it with :meth:`plot_2pcf_tomography`, as ξ± (log-log) and as θ·ξ±.
+        Writes ``xi_pm_tomography_{tomography}.png`` and
+        ``xi_pm_theta_tomography_{tomography}.png`` under the output directory.
+        """
+        self.calculate_2pcf(compute_tomography=tomography)
+
+        for times_theta in (False, True):
+            prefix = r"$\theta\,$" if times_theta else ""
+            suffix = "_theta" if times_theta else ""
+            self.plot_2pcf_tomography(
+                self._xiplus_ximinus_sample_x_y_plot_function,
+                r"$\theta$ [arcmin]",
+                prefix + r"$\xi_+(\theta)$",
+                prefix + r"$\xi_-(\theta)$",
+                (0.05, 0.9) if times_theta else (0.8, 0.95),
+                extract_text_offset=times_theta,
+                add_index_version_to_kwargs=True,
+                x_scale="log",
+                y_scale="linear" if times_theta else "log",
+                tomography=tomography,
+                savefig=self._output_path(f"xi_pm{suffix}_tomography_{tomography}.png"),
+                show=show,
+                close=close,
+                offset=offset,
+                times_theta=times_theta,
+                alpha=alpha,
+            )
+
+    def plot_ratio_xi_sys_xi(
+        self, tomography=False, threshold=0.1, offset=0.02, show=True, close=True
+    ):
+        """Plot ξ^{PSF, sys}_± / ξ± of every version, one panel per bin pair.
+
+        The band marks ``±threshold``. ξ± comes from :meth:`calculate_2pcf` and
+        ξ^{PSF, sys} from the ``xi_psf_sys`` property, both on the instance's
+        ``treecorr_config`` binning. Writes ``ratio_xi_sys_xi.png`` (non-
+        tomographic) or ``ratio_xi_sys_xi_tomography.png`` under the output
+        directory.
+        """
+        if tomography and not self.compute_tomography:
+            raise ValueError(
+                "plot_ratio_xi_sys_xi(tomography=True) needs the tomographic "
+                "xi_psf_sys; construct CosmologyValidation with "
+                "compute_tomography=True"
+            )
+        self.calculate_2pcf(compute_tomography=tomography)
+
+        y_label = r"$\xi^{{\rm PSF, sys}}_{0} / \xi_{0}$"
+        self.plot_2pcf_tomography(
+            self._ratio_xi_sys_xi_x_y_plot_function,
+            r"$\theta$ [arcmin]",
+            y_label.format("+"),
+            y_label.format("-"),
+            (0.8, 0.95),
+            extract_text_offset=False,
+            add_index_version_to_kwargs=True,
+            x_scale="log",
+            tomography=tomography,
+            savefig=self._output_path(
+                "ratio_xi_sys_xi_tomography.png"
+                if tomography
+                else "ratio_xi_sys_xi.png"
+            ),
+            show=show,
+            close=close,
+            offset=offset,
+            threshold=threshold,
+        )
+
     def plot_2pcf_tomography(
         self,
         x_y_plot_function,
@@ -648,3 +723,48 @@ def _mapsq_mxsq_sample_x_y_plot_function(
             markersize=3,
             capsize=2,
         )
+
+    def _ratio_xi_sys_xi_x_y_plot_function(
+        self,
+        ax_plus,
+        ax_minus,
+        version,
+        tomo_bin_a,
+        tomo_bin_b,
+        idx,
+        versions,
+        color,
+        offset,
+        threshold,
+    ):
+        """Plot ξ^{PSF, sys}_± / ξ± for one version/tomographic-bin pair.
+
+        Fed into :meth:`plot_2pcf_tomography` as the ``x_y_plot_function``
+        argument. The error bar propagates the variances of both ξ^{PSF, sys}
+        and ξ±; the first version also draws the ``±threshold`` band.
+        """
+        key = f"tomo_bin_{tomo_bin_a}_tomo_bin_{tomo_bin_b}"
+        gg = self.cat_ggs[version][key]
+        xi_psf_sys = self.xi_psf_sys[version][key]
+
+        theta = self._get_jittered_theta(gg.meanr, idx, len(versions), offset)
+
+        for ax, xi, var_xi, component in (
+            (ax_plus, gg.xip, gg.varxip, "plus"),
+            (ax_minus, gg.xim, gg.varxim, "minus"),
+        ):
+            mean = xi_psf_sys[f"mean_{component}"]
+            var = xi_psf_sys[f"var_{component}"]
+            ratio = mean / xi
+            ratio_err = np.sqrt(var / xi**2 + mean**2 * var_xi / xi**4)
+            ax.errorbar(
+                theta,
+                ratio,
+                yerr=ratio_err,
+                color=color,
+                fmt=self.cc[version].get("marker", "o"),
+                markersize=3,
+                capsize=2,
+            )
+            if idx == 0:
+                ax.axhspan(-threshold, threshold, color="black", alpha=0.1)
diff --git a/src/sp_validation/tests/test_cv_init_params.py b/src/sp_validation/tests/test_cv_init_params.py
index ffc87d8b..7ec743cc 100644
--- a/src/sp_validation/tests/test_cv_init_params.py
+++ b/src/sp_validation/tests/test_cv_init_params.py
@@ -16,7 +16,15 @@
 
 REPO = Path(__file__).resolve().parents[3]
 
-EXEMPT = {}
+EXEMPT = {
+    # The workflow is the non-tomographic DAG: every rule script names the bin
+    # pair it computes, and the flag only switches the lazy properties on to
+    # tomographic products no rule declares.
+    "compute_tomography": "rule scripts pass tomography per call",
+    # Snakemake owns staleness: it removes a job's declared outputs before
+    # running it, so the methods' skip-if-exists only ever reuses intermediates.
+    "force_run": "Snakemake decides what reruns",
+}
 
 
 def _load_common():
diff --git a/workflow/common.py b/workflow/common.py
index de0f71b2..b250399c 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -375,14 +375,15 @@ def get_shear_catalog(wildcards):
 
 
 def cv_basename(version, fiducial=None):
-    """Reproduce CosmologyValidation.basename() for a version.
+    """Reproduce CosmologyValidation.basename() for a version's ("all", "all") pair.
 
-    Mirrors the f-string in cosmo_val.py so rule outputs match exactly what the
-    method writes. Uses fiducial binning (min_sep/max_sep/nbins/npatch).
+    Mirrors the f-string of ``CosmologyValidation.basename(version)`` so rule
+    outputs match exactly what the method writes. Uses fiducial binning
+    (min_sep/max_sep/nbins/npatch).
     """
     fiducial = fiducial or FIDUCIAL
     return (
-        f"{version}_minsep={fiducial['min_sep']}"
+        f"{version}_tomo_bin_all_minsep={fiducial['min_sep']}"
         f"_maxsep={fiducial['max_sep']}"
         f"_nbins={fiducial['nbins']}"
         f"_npatch={fiducial['npatch']}"
diff --git a/workflow/rules/cosmo_val.smk b/workflow/rules/cosmo_val.smk
index 8d9839c6..699bc9de 100644
--- a/workflow/rules/cosmo_val.smk
+++ b/workflow/rules/cosmo_val.smk
@@ -50,12 +50,12 @@ CV_BINNING = (
 
 
 def cv_xi_txt(version):
-    """Path to the 2pcf data vector calculate_2pcf writes for a version.
+    """Path to the ξ± TreeCorr dump calculate_2pcf_version writes for a version.
 
-    Mirrors the out_fname f-string in cosmo_val.calculate_2pcf:
-    {ver}_xi_minsep=..._maxsep=..._nbins=..._npatch=...txt
+    Mirrors RealSpaceMixin._xi_txt_path for the ("all", "all") pair:
+    xi_{ver}_tomo_bin_all_minsep=..._maxsep=..._nbins=..._npatch=...txt
     """
-    return str(COSMO_VAL / f"{version}_xi_{xi_binning('reporting')}.txt")
+    return str(COSMO_VAL / f"xi_{version}_tomo_bin_all_{xi_binning('reporting')}.txt")
 
 
 def cv_rho_stats(version):
diff --git a/workflow/rules/twopoint.smk b/workflow/rules/twopoint.smk
index 15174fa2..93abebe4 100644
--- a/workflow/rules/twopoint.smk
+++ b/workflow/rules/twopoint.smk
@@ -22,7 +22,7 @@ rule xi:
     input:
         catalog=get_shear_catalog,
     output:
-        txt=str(COSMO_VAL / "{version}_xi_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.txt"),
+        txt=str(COSMO_VAL / "xi_{version}_tomo_bin_all_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.txt"),
         sacc=str(COSMO_VAL / "{version}_xi_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.sacc"),
     threads: 24
     params:
@@ -46,10 +46,10 @@ rule xi:
 
 rule rho_tau_stats:
     output:
-        rho_stats=str(COSMO_VAL / "rho_tau_stats/rho_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
-        tau_stats=str(COSMO_VAL / "rho_tau_stats/tau_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
+        rho_stats=str(COSMO_VAL / "rho_tau_stats/rho_stats_{version}_tomo_bin_all_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
+        tau_stats=str(COSMO_VAL / "rho_tau_stats/tau_stats_{version}_tomo_bin_all_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
         # Born-as-SACC ρ/τ part, written alongside the FITS.
-        rho_tau=str(COSMO_VAL / "rho_tau_stats/rho_tau_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.sacc"),
+        rho_tau=str(COSMO_VAL / "rho_tau_stats/rho_tau_{version}_tomo_bin_all_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.sacc"),
     threads: 48
     params:
         ver="{version}",
diff --git a/workflow/scripts/cv_plot_2pcf.py b/workflow/scripts/cv_plot_2pcf.py
index 5251e7a5..c589edb3 100644
--- a/workflow/scripts/cv_plot_2pcf.py
+++ b/workflow/scripts/cv_plot_2pcf.py
@@ -1,14 +1,14 @@
-"""Rule cv_plot_2pcf: n_pairs / xi± overlay across versions.
+"""Rule cv_plot_2pcf: xi± overlay across versions.
 
-Reads each version's xi txt (declared inputs, produced by cv_2pcf); calls
-plot_2pcf, which re-reads the existing txt files rather than recomputing.
-Writes figures under the output dir. Sentinel-tracked: plot_2pcf emits several
-figures whose names are internal.
+Reads each version's xi txt (declared inputs, produced by the xi rule); calls
+plot_2pcf, which reads the existing txt files back rather than recomputing, for
+the non-tomographic ("all", "all") pair. Writes figures under the output dir.
+Sentinel-tracked: plot_2pcf emits several figures whose names are internal.
 """
 
 from cv_runner import _unbuffer_streams, make_cv, touch_sentinels
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
-cv.plot_2pcf()
+cv.plot_2pcf(tomography=False, show=False)
 touch_sentinels(snakemake)
diff --git a/workflow/scripts/cv_plot_rho_stats.py b/workflow/scripts/cv_plot_rho_stats.py
index c99bd646..a55a1377 100644
--- a/workflow/scripts/cv_plot_rho_stats.py
+++ b/workflow/scripts/cv_plot_rho_stats.py
@@ -9,5 +9,5 @@
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
-cv.plot_rho_stats()
+cv.plot_rho_stats(savefig="rho_stats.png", show=False)
 touch_sentinels(snakemake)
diff --git a/workflow/scripts/cv_plot_tau_stats.py b/workflow/scripts/cv_plot_tau_stats.py
index 2a00b3a5..8975ed81 100644
--- a/workflow/scripts/cv_plot_tau_stats.py
+++ b/workflow/scripts/cv_plot_tau_stats.py
@@ -8,5 +8,5 @@
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
-cv.plot_tau_stats()
+cv.plot_tau_stats(tomography=False, savefig="tau_stats.png", show=False)
 touch_sentinels(snakemake)
diff --git a/workflow/scripts/cv_ratio_xi_sys_xi.py b/workflow/scripts/cv_ratio_xi_sys_xi.py
index 79afd89a..3aebd8b8 100644
--- a/workflow/scripts/cv_ratio_xi_sys_xi.py
+++ b/workflow/scripts/cv_ratio_xi_sys_xi.py
@@ -1,15 +1,18 @@
 """Rule cv_ratio_xi_sys_xi: ratio of PSF systematics to cosmic-shear signal.
 
-Joins two upstream chains: the 2pcf data vector (xi txt, reloaded via
-calculate_2pcf) and xi_psf_sys (recomputed in memory from the rho/tau FITS via
-the lazy cv.xi_psf_sys property — the PSF-error fit is not persisted by
-cosmo_val.py). Both are declared as inputs so the DAG shows the join. Writes
-ratio_xi_sys_xi.png at a fixed path (declared output).
+Joins two upstream chains for the non-tomographic ("all", "all") pair: the
+2pcf data vector (xi txt, read back by calculate_2pcf) and xi_psf_sys
+(recomputed in memory from the rho/tau FITS via the lazy cv.xi_psf_sys
+property — the PSF-error fit is not persisted). Both are declared as inputs so
+the DAG shows the join. Writes ratio_xi_sys_xi.png at a fixed path (declared
+output).
 """
 
 from cv_runner import _unbuffer_streams, make_cv, verify_outputs
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
-cv.plot_ratio_xi_sys_xi(offset=snakemake.params.get("offset", 0.1))
+cv.plot_ratio_xi_sys_xi(
+    tomography=False, offset=snakemake.params.get("offset", 0.1), show=False
+)
 verify_outputs(snakemake)
diff --git a/workflow/scripts/cv_rho_tau_fits.py b/workflow/scripts/cv_rho_tau_fits.py
index 64b24b67..6e02ba10 100644
--- a/workflow/scripts/cv_rho_tau_fits.py
+++ b/workflow/scripts/cv_rho_tau_fits.py
@@ -13,5 +13,10 @@
 _unbuffer_streams()
 cv = make_cv(snakemake)
 if cv.rho_tau_method != "none":
-    cv.plot_rho_tau_fits()
+    cv.plot_rho_tau_fits(
+        tomography=False,
+        savefig_contours="contours_tau_stat.png",
+        savefig_xi_psf_sys="xi_psf_sys",
+        show=False,
+    )
 touch_sentinels(snakemake)
diff --git a/workflow/scripts/generate_pseudo_cl.py b/workflow/scripts/generate_pseudo_cl.py
index e1b96da6..53679944 100644
--- a/workflow/scripts/generate_pseudo_cl.py
+++ b/workflow/scripts/generate_pseudo_cl.py
@@ -126,7 +126,7 @@ def generate_pseudo_cl(
     cv = CosmologyValidation(**cv_kwargs)
 
     # Pseudo-Cls only (no covariance), born directly at the final out_path.
-    cv.calculate_pseudo_cl(out_path=out_path)
+    cv.calculate_pseudo_cl(compute_tomography=False, out_path=out_path)
 
     if os.path.exists(out_path):
         # Readback of the part just written — a legitimate pre-blind consumer.
diff --git a/workflow/scripts/generate_pseudo_cl_cov.py b/workflow/scripts/generate_pseudo_cl_cov.py
index 247c5f33..4f00f7f9 100644
--- a/workflow/scripts/generate_pseudo_cl_cov.py
+++ b/workflow/scripts/generate_pseudo_cl_cov.py
@@ -1,13 +1,13 @@
 """Generate pseudo-Cl covariances (no data vector).
 
 Dual-mode. Under Snakemake (``script:`` directive) the injected ``snakemake``
-object supplies the parameters and the native product is renamed to the tagged
-output filename the rule declares; as a standalone CLI (argparse) the same
-compute runs from explicit flags and the primitive's native
-``pseudo_cl_cov_{ver}.fits`` is left in place under ``--out`` (no rename — each
-lc/ASTRA recipe gets its own output directory, so the untagged native name is
-unambiguous and the primitives' skip-if-exists never collides across nbins
-runs). The CLI form is what the lightcone/ASTRA recipe calls, so the
+object supplies the parameters, the covariance is computed in a private
+working directory and moved to the tagged output filename the rule declares;
+as a standalone CLI (argparse) the same compute runs from explicit flags and
+the native ``pseudo_cl/pseudo_cl_cov_non_tomo_{ver}_from_iNKA_…fits`` (with its
+``iNKA_block_{ver}/`` block) is left in place under ``--out`` (each lc/ASTRA
+recipe gets its own output directory, so the methods' skip-if-exists never
+reuses another configuration's covariance). The CLI form is what the lightcone/ASTRA recipe calls, so the
 measurement is driven directly (no nested Snakemake) with lc handling
 orchestration:
 
@@ -27,6 +27,8 @@
 import argparse
 import json
 import os
+import shutil
+import tempfile
 
 from astropy.io import fits
 
@@ -51,9 +53,10 @@ def generate_pseudo_cl_cov(
     version : str
         Catalog version (e.g., "SP_v1.4.6_leak_corr")
     output_dir : str
-        Directory the covariance FITS file is written into. The primitive writes
-        its native ``pseudo_cl_cov_{version}.fits`` here; callers that need a
-        tagged filename rename it themselves (see ``_from_snakemake``).
+        Output directory of the ``CosmologyValidation`` run. The covariance is
+        written to its native path under ``output_dir/pseudo_cl/``; callers
+        that need a tagged filename move it themselves (see
+        ``_from_snakemake``).
     cat_config : str
         Path to catalog configuration YAML
     nside : int
@@ -73,7 +76,7 @@ def generate_pseudo_cl_cov(
     Returns
     -------
     str
-        Path to the primitive's native ``pseudo_cl_cov_{version}.fits`` product.
+        Path to the native non-tomographic iNKA covariance FITS.
     """
     os.makedirs(output_dir, exist_ok=True)
 
@@ -129,12 +132,12 @@ def generate_pseudo_cl_cov(
 
     cv = CosmologyValidation(**cv_kwargs)
 
-    # Calculate covariance only
+    # Covariance of the ("all", "all") pair only
     print("Calculating covariance...")
-    cv.calculate_pseudo_cl_eb_cov()
+    cv.calculate_pseudo_cl_inka_cov(compute_tomography=False)
 
-    # Report on the native product (renamed by the Snakemake caller, if any)
-    src_cov = os.path.join(output_dir, f"pseudo_cl_cov_{version}.fits")
+    # Report on the native product (moved by the Snakemake caller, if any)
+    src_cov = cv._output_path_pseudo_cl_cov(version, "iNKA", tomography=False)
     if os.path.exists(src_cov):
         with fits.open(src_cov) as hdul:
             # CV outputs covariance blocks as COVAR_XX_YY extensions
@@ -147,21 +150,25 @@ def generate_pseudo_cl_cov(
 def _from_snakemake(smk):
     p = smk.params
     output_cov = smk.output.pseudo_cl_cov
-    src_cov = generate_pseudo_cl_cov(
-        version=p["version"],
-        output_dir=os.path.dirname(output_cov),
-        cat_config=p["cat_config"],
-        nside=int(p["nside"]),
-        npatch=int(p["npatch"]),
-        cosmo_params=p.get("cosmo_params", None),
-        binning=p["binning"],
-        nbins=int(p["nbins"]),
-        power=float(p.get("power", 0.5)),
-    )
-    # Snakemake declares a tagged output filename; rename the native product to it.
-    if os.path.exists(src_cov) and src_cov != output_cov:
-        os.rename(src_cov, output_cov)
-        print(f"Saved to: {output_cov}")
+    out_dir = os.path.dirname(output_cov)
+    os.makedirs(out_dir, exist_ok=True)
+    # The iNKA blocks and the merged covariance are cached under names that
+    # carry the binning only, so the job computes in a directory of its own and
+    # never picks up a block another configuration left behind.
+    with tempfile.TemporaryDirectory(dir=out_dir, prefix=".pseudo_cl_cov_") as work:
+        src_cov = generate_pseudo_cl_cov(
+            version=p["version"],
+            output_dir=work,
+            cat_config=p["cat_config"],
+            nside=int(p["nside"]),
+            npatch=int(p["npatch"]),
+            cosmo_params=p.get("cosmo_params", None),
+            binning=p["binning"],
+            nbins=int(p["nbins"]),
+            power=float(p.get("power", 0.5)),
+        )
+        shutil.move(src_cov, output_cov)
+    print(f"Saved to: {output_cov}")
 
 
 def _from_cli(argv=None):
diff --git a/workflow/scripts/run_2pcf.py b/workflow/scripts/run_2pcf.py
index 28c8c521..1fcd9a0c 100644
--- a/workflow/scripts/run_2pcf.py
+++ b/workflow/scripts/run_2pcf.py
@@ -14,7 +14,8 @@
 
 The measurement is binning-agnostic: the reporting and the fine integration
 grids are the same compute with different ``--min-sep/--max-sep/--nbins``.
-``CosmologyValidation.calculate_2pcf`` writes the ``.txt`` dump (a raw
+``CosmologyValidation.calculate_2pcf_version`` measures the non-tomographic
+``("all", "all")`` pair and writes its ``xi_{basename}.txt`` dump (a raw
 byproduct); the ξ± data product is born as SACC here, a *part* named by its
 binning and tagged with its ``--grid``. The part carries the covariance the
 measurement estimated: the dense jackknife covariance when it had patches, the
@@ -68,13 +69,14 @@ def run_2pcf(
         # so the SACC provenance metadata stamps the npatch actually measured
         npatch=npatch,
     )
-    gg = cv.calculate_2pcf(
-        ver=ver,
+    gg = cv.calculate_2pcf_version(
+        ver,
         npatch=npatch,
+        compute_tomography=False,
         min_sep=min_sep,
         max_sep=max_sep,
         nbins=nbins,
-    )
+    )["tomo_bin_all_tomo_bin_all"]
 
     # Born-as-SACC ξ± part. theta = meanr; theta_nom = rnom.
     jackknife = gg.var_method == "jackknife"
diff --git a/workflow/scripts/run_glass_mock_pseudo_cl.py b/workflow/scripts/run_glass_mock_pseudo_cl.py
index 9d90c080..9f9e9371 100644
--- a/workflow/scripts/run_glass_mock_pseudo_cl.py
+++ b/workflow/scripts/run_glass_mock_pseudo_cl.py
@@ -4,7 +4,7 @@
 NaMaster estimator. Matches the real data pipeline binning exactly.
 
 Mock catalogs have columns: RA, Dec, e1, e2, w.
-Polarization convention: pol_factor=True → field=[e1, -e2] (matches pipeline).
+Polarization convention: pol_factor=-1 → field=[e1, -e2] (matches pipeline).
 """
 
 import numpy as np
@@ -49,7 +49,7 @@
 print(f"Binning: {nbins} powspace bins, ell=[{ell_eff[0]:.1f}, {ell_eff[-1]:.1f}]")
 
 # Create NaMaster spin-2 field from catalog
-# pol_factor=True: e2 sign flip to match IAU polarization convention
+# pol_factor=-1: e2 sign flip to match IAU polarization convention
 print("Creating NaMaster field...")
 f_all = nmt.NmtFieldCatalog(
     positions=[ra, dec],
diff --git a/workflow/scripts/run_rho_tau.py b/workflow/scripts/run_rho_tau.py
index a798a1b6..d04da3f0 100644
--- a/workflow/scripts/run_rho_tau.py
+++ b/workflow/scripts/run_rho_tau.py
@@ -19,5 +19,5 @@
     catalog_config=params["cat_config"],
     output_dir=params["output_dir"],
 )
-cv.calculate_rho_tau_stats()
+cv.calculate_rho_tau_stats(tomography=False)
 verify_outputs(snakemake)

From 2ae102abd5bd8278d88a36fc1b3f32e2a1434c14 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:28:13 +0200
Subject: [PATCH 50/83] Read the ("all", "all") pair in the B-mode summary and
 the SACC n(z)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

summarize_bmodes looked up the pre-tomography shapes inside
try/except KeyError, so pure E/B, COSEBIs and C_ℓ^BB all dropped out of the
summary silently. It now reads the pair entry of each result
(plot_cosebis stores its result under the pair key, as plot_pure_eb does)
and catches only the RuntimeError of a scale cut that selects no bin.

get_redshift, hence sacc_nz and every SACC writer, unpacked a multi-column
tomographic n(z) file into (z, nz1, ..., nzN); it now returns the summed
whole-survey n(z) through read_redshift_distribution, the n(z) the
pseudo-Cl iNKA fiducial uses for the same pair.

pol_factor=True passed the ±1 assert and meant no e2 flip; bools are
rejected.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/cosmo_val/core.py    | 72 ++++++++++++++------------
 src/sp_validation/cosmo_val/cosebis.py |  2 +-
 2 files changed, 40 insertions(+), 34 deletions(-)

diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 2a66cd9c..017b5c74 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -316,7 +316,9 @@ def __init__(
         self.n_ell_bins = n_ell_bins
         self.ell_step = ell_step
 
-        assert pol_factor in (-1, 1), "The polarisatio factor must be -1 or 1."
+        # bool is an int subclass: True would pass `in (-1, 1)` and mean "no flip"
+        if isinstance(pol_factor, bool) or pol_factor not in (-1, 1):
+            raise ValueError(f"pol_factor must be -1 or 1, got {pol_factor!r}")
         self.pol_factor = pol_factor
 
         self.nrandom_cell = nrandom_cell
@@ -462,7 +464,8 @@ def _output_path(self, *parts):
     def sacc_nz(self, version):
         """Single-bin ``nz`` mapping ``{0: (z, nz)}`` for the SACC writers.
 
-        The round is single-bin, so the whole survey n(z) is bin 0.
+        The SACC parts carry the ("all", "all") pair only, so the whole-survey
+        n(z) of :meth:`get_redshift` is bin 0.
         """
         return {0: tuple(self.get_redshift(version))}
 
@@ -475,7 +478,12 @@ def sacc_metadata(self, version):
         }
 
     def get_redshift(self, version):
-        """Load redshift distribution for a catalog version.
+        """Load the whole-survey redshift distribution of a catalog version.
+
+        The ``redshift_path`` file holds z in column 0 and one n(z) column per
+        tomographic bin (a single column for a non-tomographic version); the
+        whole-survey n(z) is their sum, as
+        ``read_redshift_distribution(version, is_tomography=False)`` returns it.
 
         Parameters
         ----------
@@ -489,7 +497,7 @@ def get_redshift(self, version):
         nz : ndarray
             n(z) probability density
         """
-        return np.loadtxt(self.cc[version]["shear"]["redshift_path"], unpack=True)
+        return self.read_redshift_distribution(version, is_tomography=False)
 
     def _write_catalog_config(self):
         with self.catalog_config_path.open("w") as file:
@@ -630,8 +638,9 @@ def colors(self):
     def summarize_bmodes(self, fiducial_scale_cut=(12, 83), versions=None):
         """Print and return B-mode PTE summary across all statistics.
 
-        Collects PTEs from pure E/B, COSEBIs, and pseudo-Cl at the specified
-        fiducial scale cut. Statistics that haven't been computed show '--'.
+        Collects the PTEs of the non-tomographic ``("all", "all")`` pair from
+        pure E/B, COSEBIs, and pseudo-Cl at the specified fiducial scale cut.
+        Statistics that haven't been computed show '--'.
 
         Parameters
         ----------
@@ -648,45 +657,42 @@ def summarize_bmodes(self, fiducial_scale_cut=(12, 83), versions=None):
         versions = versions or self.versions
         summary = {}
         cov_methods = set()
+        pair = "tomo_bin_all_tomo_bin_all"
 
         for ver in versions:
             row = {}
 
             # Pure E/B PTEs from stored results
-            if ver in self._pure_eb_results:
-                res = self._pure_eb_results[ver]
-                edges = (res["left_edges"], res["right_edges"])
+            pure_eb = self._pure_eb_results.get(ver, {}).get(pair)
+            if pure_eb is not None:
+                edges = (pure_eb["left_edges"], pure_eb["right_edges"])
                 try:
                     for stat in ("xip_B", "xim_B", "combined"):
                         row[stat] = _get_pte_from_scale_cut(
-                            res["pte_matrices"][stat], edges, fiducial_scale_cut
+                            pure_eb["pte_matrices"][stat], edges, fiducial_scale_cut
                         )
-                except (KeyError, RuntimeError):
+                except RuntimeError:
+                    # The scale cut selects no bin of this grid.
                     pass
-                cov_methods.add(covariance_label(res["npatch"]))
+                cov_methods.add(covariance_label(pure_eb["npatch"]))
 
             # COSEBIs PTE from stored results
-            if ver in self._cosebis_results:
-                cosebis_res = self._cosebis_results[ver]
-                has_multi_scale_cuts = all(isinstance(k, tuple) for k in cosebis_res)
-                if has_multi_scale_cuts:
-                    key = find_conservative_scale_cut_key(
-                        cosebis_res, fiducial_scale_cut
-                    )
-                    row["COSEBIS"] = cosebis_res[key]["pte_B"]
-                elif "pte_B" in cosebis_res:
-                    row["COSEBIS"] = cosebis_res["pte_B"]
-
-            # Pseudo-Cl BB PTE (_pseudo_cls is lazy; check existence without
-            # triggering computation)
-            if hasattr(self, "_pseudo_cls") and ver in self._pseudo_cls:
-                try:
-                    cl_bb = self.pseudo_cls[ver]["pseudo_cl"]["BB"]
-                    cov_bb = self.pseudo_cls[ver]["cov"]["COVAR_BB_BB"].data
-                    _, _, row["C_l_BB"] = chi2_and_pte(cl_bb, cov_bb)
-                    cov_methods.add("Gaussian (NaMaster)")
-                except (KeyError, AttributeError):
-                    pass
+            cosebis = self._cosebis_results.get(ver, {}).get(pair)
+            if cosebis is not None:
+                if all(isinstance(k, tuple) for k in cosebis):
+                    key = find_conservative_scale_cut_key(cosebis, fiducial_scale_cut)
+                    row["COSEBIS"] = cosebis[key]["pte_B"]
+                else:
+                    row["COSEBIS"] = cosebis["pte_B"]
+
+            # Pseudo-Cl BB PTE, once both the spectrum and its covariance are
+            # loaded (_pseudo_cls is lazy; read it without triggering computation)
+            pseudo_cl = getattr(self, "_pseudo_cls", {}).get(ver, {}).get(pair, {})
+            if "pseudo_cl" in pseudo_cl and "cov" in pseudo_cl:
+                cl_bb = pseudo_cl["pseudo_cl"]["BB"]
+                cov_bb = pseudo_cl["cov"]["COVAR_BB_BB"].data
+                _, _, row["C_l_BB"] = chi2_and_pte(cl_bb, cov_bb)
+                cov_methods.add("Gaussian (NaMaster)")
 
             summary[ver] = row
 
diff --git a/src/sp_validation/cosmo_val/cosebis.py b/src/sp_validation/cosmo_val/cosebis.py
index 1f7c5470..1d33c767 100644
--- a/src/sp_validation/cosmo_val/cosebis.py
+++ b/src/sp_validation/cosmo_val/cosebis.py
@@ -309,4 +309,4 @@ def plot_cosebis(
 
         # Save data products and store on instance
         save_cosebis_results(results, out_stub + "_data.npz", fiducial_scale_cut)
-        self._cosebis_results[version] = results
+        self._cosebis_results[version] = {"tomo_bin_all_tomo_bin_all": results}

From 613e87150f5940c4c4476b7a981e19e5c142080f Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:31:44 +0200
Subject: [PATCH 51/83] Move the tests onto the merged API and pin the repaired
 seams
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The 2PCF tests call calculate_2pcf_version(...)["tomo_bin_all_tomo_bin_all"]
and look for the xi_{basename}.txt dump; the pure-E/B test reads its pair
and re-measures the integration-grid ξ± on the same patches for the
jackknife check, since cat_ggs is only filled by calculate_2pcf.
get_rho_tau takes base_tau. The OneCovariance fixture carries the ℓ and
tomographic-bin columns the tomographic reshape reads (one triangle, as the
reshape mirrors the other). The column-schema scan learns the non-catalogue
keys the tomographic code reads (W{}, H0) and the GLASS mock's TOM_BIN_ID.

New: plot_2pcf and plot_ratio_xi_sys_xi write their figures;
summarize_bmodes reads every statistic's pair and raises on a shape it
cannot read; sacc_nz is the summed n(z) of a multi-column file; pol_factor
rejects bools; cv_basename spells CosmologyValidation.basename.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/tests/test_column_schema.py |   5 +
 src/sp_validation/tests/test_cosmo_val.py     | 181 ++++++++++++++++--
 .../tests/test_cv_init_params.py              |  17 +-
 src/sp_validation/tests/test_grammar.py       |   2 +-
 .../tests/test_sacc_io_one_covariance.py      |  40 ++--
 5 files changed, 210 insertions(+), 35 deletions(-)

diff --git a/src/sp_validation/tests/test_column_schema.py b/src/sp_validation/tests/test_column_schema.py
index 850de5b6..d4ad4cfd 100644
--- a/src/sp_validation/tests/test_column_schema.py
+++ b/src/sp_validation/tests/test_column_schema.py
@@ -61,6 +61,11 @@
     "C22",
     "SNR",
     "T",
+    # NaMaster field/workspace dicts keyed by bin; cosmology parameter dicts
+    "W{}",
+    "H0",
+    # GLASS mock catalogues, which are not ShapePipe products
+    "TOM_BIN_ID",
 }
 
 #: An f-string template needs this many literal characters to count as
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index edecb797..daa33a30 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -372,6 +372,7 @@ def _write_synthetic_catalogs(
         version_cfg = {
             "subdir": str(cat_dir),
             "pipeline": "SP",
+            "colour": "tab:blue",
             "shear": shear_cfg,
             "star": star_cfg,
         }
@@ -484,19 +485,22 @@ def test_xi_part_carries_the_covariance_the_measurement_estimated(
             )
 
     def test_a_patched_xi_dump_reads_back(self, tmp_path):
-        """calculate_2pcf reads back the text dump a patched measurement wrote.
+        """calculate_2pcf_version reads back the dump a patched measurement wrote.
 
         The ξ± figure rules re-enter calculate_2pcf on the reporting grid, which
         has patches, and are handed the dump rule xi wrote (to its precision).
         """
         params, version = self._write_synthetic_catalogs(tmp_path)
         binning = dict(npatch=4, min_sep=5.0, max_sep=100.0, nbins=6)
-        measured = CosmologyValidation(versions=[version], **params).calculate_2pcf(
+        pair = "tomo_bin_all_tomo_bin_all"
+        measured = CosmologyValidation(
+            versions=[version], **params
+        ).calculate_2pcf_version(version, **binning)[pair]
+        dumps = list(Path(params["output_dir"]).glob(f"xi_{version}_*.txt"))
+        assert len(dumps) == 1
+        read = CosmologyValidation(versions=[version], **params).calculate_2pcf_version(
             version, **binning
-        )
-        read = CosmologyValidation(versions=[version], **params).calculate_2pcf(
-            version, **binning
-        )
+        )[pair]
         for column in ("meanr", "npairs", "xip", "xim", "varxip", "varxim"):
             np.testing.assert_allclose(
                 getattr(read, column), getattr(measured, column), rtol=1e-4
@@ -525,10 +529,13 @@ def test_calculate_2pcf_does_not_depend_on_thread_count(self, tmp_path):
 
         xi = {}
         for n_threads in (4, 48):
-            # calculate_2pcf reads back an existing text dump instead of measuring.
-            for dump in Path(params["output_dir"]).glob(f"{version}_xi_*.txt"):
+            # calculate_2pcf_version reads back an existing text dump instead of
+            # measuring.
+            for dump in Path(params["output_dir"]).glob(f"xi_{version}_*.txt"):
                 dump.unlink()
-            gg = cv.calculate_2pcf(version, num_threads=n_threads)
+            gg = cv.calculate_2pcf_version(version, num_threads=n_threads)[
+                "tomo_bin_all_tomo_bin_all"
+            ]
             assert treecorr.get_omp_threads() == n_threads  # the count took effect
             xi[n_threads] = np.concatenate([gg.xip, gg.xim])
             sigma = np.sqrt(np.concatenate([gg.varxip, gg.varxim]))
@@ -541,9 +548,9 @@ def test_treecorr_runs_on_the_cpus_the_process_holds(self, tmp_path):
         import treecorr
 
         params, version = self._write_synthetic_catalogs(tmp_path)
-        CosmologyValidation(versions=[version], npatch=1, **params).calculate_2pcf(
-            version
-        )
+        CosmologyValidation(
+            versions=[version], npatch=1, **params
+        ).calculate_2pcf_version(version)
         assert treecorr.get_omp_threads() == len(os.sched_getaffinity(0))
 
     def test_calculate_scale_dependent_leakage_runs_on_synthetic_catalog(
@@ -617,13 +624,10 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi)
         )
         cv.treecorr_config.update(bin_slop=0, angle_slop=0)
 
-        results = cv.calculate_pure_eb(
-            version,
-            npatch=npatch,
-            min_sep_int=1.0,
-            max_sep_int=300.0,
-            nbins_int=600,
-        )
+        integration = dict(min_sep_int=1.0, max_sep_int=300.0, nbins_int=600)
+        results = cv.calculate_pure_eb(version, npatch=npatch, **integration)[
+            "tomo_bin_all_tomo_bin_all"
+        ]
 
         measured = {
             key: results[key]
@@ -652,7 +656,17 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi)
         cov = np.asarray(results["cov"])
         assert cov.shape == (6 * nbins, 6 * nbins)
         assert results["npatch"] == npatch
-        gg_int = cv.cat_ggs[version]
+        # The integration-grid ξ± again, measured on the jackknife patches the
+        # first measurement wrote (its columns-only dump carries no patches).
+        for dump in Path(params["output_dir"]).glob(f"xi_{version}_*.txt"):
+            dump.unlink()
+        gg_int = cv.calculate_2pcf_version(
+            version,
+            npatch=npatch,
+            min_sep=integration["min_sep_int"],
+            max_sep=integration["max_sep_int"],
+            nbins=integration["nbins_int"],
+        )["tomo_bin_all_tomo_bin_all"]
         jackknife_of_modes = treecorr.estimate_multi_cov(
             [gg_int],
             "jackknife",
@@ -662,3 +676,130 @@ def test_calculate_pure_eb_runs_on_synthetic_catalog(self, tmp_path, pure_eb_xi)
         np.testing.assert_allclose(
             cov, jackknife_of_modes, rtol=0, atol=1e-10 * np.abs(cov).max()
         )
+
+    def test_plot_2pcf_writes_the_non_tomographic_figures(self, tmp_path):
+        """plot_2pcf draws the ("all", "all") ξ± through plot_2pcf_tomography."""
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        cv = CosmologyValidation(
+            versions=[version],
+            npatch=1,
+            theta_min=5.0,
+            theta_max=100.0,
+            nbins=6,
+            **params,
+        )
+        cv.plot_2pcf(show=False)
+
+        out = Path(params["output_dir"])
+        for name in ("xi_pm_tomography_False", "xi_pm_theta_tomography_False"):
+            assert (out / f"{name}.png").is_file(), name
+
+    def test_plot_ratio_xi_sys_xi_writes_the_declared_figure(self, tmp_path):
+        """plot_ratio_xi_sys_xi divides the pair's ξ^{PSF, sys}± by its ξ±.
+
+        ξ^{PSF, sys} is set on the instance, so the test needs no ρ/τ fit; the
+        figure lands where the cv_ratio_xi_sys_xi rule declares it.
+        """
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        nbins = 6
+        cv = CosmologyValidation(
+            versions=[version],
+            npatch=1,
+            theta_min=5.0,
+            theta_max=100.0,
+            nbins=nbins,
+            **params,
+        )
+        sys = np.full(nbins, 1e-6)
+        cv._xi_psf_sys = {
+            version: {
+                "tomo_bin_all_tomo_bin_all": {
+                    "mean_plus": sys,
+                    "var_plus": sys**2,
+                    "mean_minus": sys,
+                    "var_minus": sys**2,
+                }
+            }
+        }
+        cv.plot_ratio_xi_sys_xi(show=False)
+
+        assert (Path(params["output_dir"]) / "ratio_xi_sys_xi.png").is_file()
+        with pytest.raises(ValueError, match="compute_tomography"):
+            cv.plot_ratio_xi_sys_xi(tomography=True, show=False)
+
+    def test_summarize_bmodes_reads_the_all_pair(self, tmp_path):
+        """Each statistic's ("all", "all") result reaches the summary.
+
+        A result whose shape the summary cannot read raises rather than
+        silently leaving its column empty.
+        """
+        from types import SimpleNamespace
+
+        from sp_validation.statistics import chi2_and_pte
+
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        cv = CosmologyValidation(versions=[version], **params)
+        pair = "tomo_bin_all_tomo_bin_all"
+
+        edges = np.geomspace(1.0, 100.0, 6)
+        cv._pure_eb_results[version] = {
+            pair: {
+                "left_edges": edges[:-1],
+                "right_edges": edges[1:],
+                "pte_matrices": {
+                    stat: np.full((5, 5), pte)
+                    for stat, pte in (("xip_B", 0.1), ("xim_B", 0.2), ("combined", 0.3))
+                },
+                "npatch": 8,
+            }
+        }
+        cv._cosebis_results[version] = {pair: {"pte_B": 0.4}}
+        cl_bb = np.array([1.0, -0.5, 0.25])
+        cov_bb = np.diag([2.0, 1.0, 0.5])
+        cv._pseudo_cls = {
+            version: {
+                pair: {
+                    "pseudo_cl": {"BB": cl_bb},
+                    "cov": {"COVAR_BB_BB": SimpleNamespace(data=cov_bb)},
+                }
+            }
+        }
+
+        row = cv.summarize_bmodes(fiducial_scale_cut=(1.0, 100.0))[version]
+
+        assert row == pytest.approx(
+            {
+                "xip_B": 0.1,
+                "xim_B": 0.2,
+                "combined": 0.3,
+                "COSEBIS": 0.4,
+                "C_l_BB": chi2_and_pte(cl_bb, cov_bb)[2],
+            }
+        )
+
+        del cv._pure_eb_results[version][pair]["pte_matrices"]
+        with pytest.raises(KeyError):
+            cv.summarize_bmodes(fiducial_scale_cut=(1.0, 100.0))
+
+    def test_sacc_nz_is_the_summed_tomographic_nz(self, tmp_path):
+        """A multi-column n(z) file gives the SACC parts its whole-survey sum."""
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        cv = CosmologyValidation(versions=[version], **params)
+
+        z = np.linspace(0.0, 2.0, 11)
+        nz_bins = np.stack([np.exp(-((z - mu) ** 2)) for mu in (0.5, 1.0, 1.5)])
+        nz_path = tmp_path / "nz_tomo.txt"
+        np.savetxt(nz_path, np.column_stack([z, *nz_bins]))
+        cv.cc[version]["shear"]["redshift_path"] = str(nz_path)
+
+        [(bin_id, (z_read, nz_read))] = cv.sacc_nz(version).items()
+        assert bin_id == 0
+        np.testing.assert_allclose(z_read, z)
+        np.testing.assert_allclose(nz_read, nz_bins.sum(axis=0))
+
+    @pytest.mark.parametrize("pol_factor", [True, False, 0, 2])
+    def test_pol_factor_must_be_plus_or_minus_one(self, tmp_path, pol_factor):
+        """pol_factor is a ±1 e2 multiplier; a bool would pass `in (-1, 1)`."""
+        params, version = self._write_synthetic_catalogs(tmp_path)
+        with pytest.raises(ValueError, match="pol_factor"):
+            CosmologyValidation(versions=[version], pol_factor=pol_factor, **params)
diff --git a/src/sp_validation/tests/test_cv_init_params.py b/src/sp_validation/tests/test_cv_init_params.py
index 7ec743cc..5d1a045d 100644
--- a/src/sp_validation/tests/test_cv_init_params.py
+++ b/src/sp_validation/tests/test_cv_init_params.py
@@ -1,14 +1,16 @@
-"""Guard: the workflow sets every CosmologyValidation constructor default.
+"""Guard: the workflow builds ``cv`` and names its products as the class does.
 
 ``workflow/common.py::cv_init_params`` builds the constructor kwargs for every
 cosmo_val rule. A keyword it does not forward falls back to the constructor
 default without any trace in the run config, so each keyword with a default is
-either forwarded or exempted below with its reason.
+either forwarded or exempted below with its reason. ``cv_basename`` names the
+rule outputs, so it must spell ``CosmologyValidation.basename`` exactly.
 """
 
 import importlib.util
 import inspect
 from pathlib import Path
+from types import SimpleNamespace
 
 import yaml
 
@@ -57,3 +59,14 @@ def test_every_default_is_forwarded_or_exempt():
 def test_exemptions_are_live_keywords():
     stale = set(EXEMPT) - _defaulted_keywords()
     assert not stale, f"EXEMPT names keywords the constructor lacks: {sorted(stale)}"
+
+
+def test_cv_basename_is_the_class_basename():
+    fiducial = {"min_sep": 1.0, "max_sep": 250.0, "nbins": 20, "npatch": 100}
+    cv = SimpleNamespace(
+        treecorr_config={k: fiducial[k] for k in ("min_sep", "max_sep", "nbins")},
+        npatch=fiducial["npatch"],
+    )
+    assert _load_common().cv_basename(
+        "SP_v1.4.6.3", fiducial
+    ) == CosmologyValidation.basename(cv, "SP_v1.4.6.3")
diff --git a/src/sp_validation/tests/test_grammar.py b/src/sp_validation/tests/test_grammar.py
index 08ed6742..60e23b75 100644
--- a/src/sp_validation/tests/test_grammar.py
+++ b/src/sp_validation/tests/test_grammar.py
@@ -273,7 +273,7 @@ def test_get_rho_tau_identical_for_v1_and_v2_psf_catalogues(tmp_path):
             "max_sep": 600,
             "nbins": 4,
         }
-        get_rho_tau(config, label, treecorr_config, str(outdir), label)
+        get_rho_tau(config, label, treecorr_config, str(outdir), label, label)
         stats[label] = [
             fits.getdata(outdir / f"{kind}_stats_{label}.fits")
             for kind in ("rho", "tau")
diff --git a/src/sp_validation/tests/test_sacc_io_one_covariance.py b/src/sp_validation/tests/test_sacc_io_one_covariance.py
index bbe90ca3..776475b3 100644
--- a/src/sp_validation/tests/test_sacc_io_one_covariance.py
+++ b/src/sp_validation/tests/test_sacc_io_one_covariance.py
@@ -28,26 +28,40 @@
 # --------------------------------------------------------------------------- #
 # Synthetic OneCovariance-shaped fixtures
 # --------------------------------------------------------------------------- #
+def _ell(i):
+    """The multipole of ℓ-bin ``i`` in the synthetic tables."""
+    return 10.0 * (i + 1)
+
+
 def _one_cov_table(cov_gauss, cov_all):
     """Flatten two n x n matrices into a OneCovariance ``covariance_list`` table.
 
-    Reproduces the real flat output: one row per ``(i, j)`` element pair in
-    row-major order ``k = i·n + j``, with the Gaussian value in column 10 and
-    the Gaussian+non-Gaussian value in column 9. Columns 0-8 and the index
-    columns are filled with self-documenting placeholder values (the reshape
-    only reads cols 9/10, but a realistic width proves it does not spill).
+    Reproduces the real flat output of a single tomographic bin: one row per
+    ``(i, j)`` ℓ-bin pair with ``i <= j`` (the reshape mirrors the other
+    triangle), ℓ_i and ℓ_j in columns 1 and 2, the bin indices of the four
+    fields (all ``1``) in columns 5-8, the Gaussian value in column 10 and the
+    Gaussian+non-Gaussian value in column 9. Columns 0, 3 and 4 hold
+    placeholders the reshape does not read.
     """
     n = cov_gauss.shape[0]
     rows = []
     for i in range(n):
-        for j in range(n):
-            row = np.arange(11.0)  # placeholder cols 0-8 (+ overwritten 9,10)
+        for j in range(i, n):
+            row = np.arange(11.0)  # placeholder cols 0, 3, 4
+            row[1], row[2] = _ell(i), _ell(j)
+            row[5:9] = 1
             row[9] = cov_all[i, j]
             row[10] = cov_gauss[i, j]
             rows.append(row)
     return np.array(rows)
 
 
+def _row_of(table, i, j):
+    """Index of the table row holding ℓ-bin pair ``(i, j)``."""
+    [k] = np.flatnonzero((table[:, 1] == _ell(i)) & (table[:, 2] == _ell(j)))
+    return k
+
+
 def _spd(n, seed):
     """Symmetric positive-definite matrix of size ``n`` (a valid covariance)."""
     a = np.random.default_rng(seed).normal(size=(n, n))
@@ -75,7 +89,8 @@ def test_covariance_blocks_reshapes_to_hand_built_matrix():
     WHY TEETH: (a) ``gaussian=True`` vs ``False`` must return the two *different*
     matrices, proving the column flag is load-bearing; (b) perturbing a single
     entry of the flat input must change exactly that entry of the reshaped
-    block, proving the reshape actually reads the table (not a constant).
+    block and its mirror, proving the reshape actually reads the table (not a
+    constant).
     """
     cov_gauss = _spd(4, seed=1)
     cov_all = _spd(4, seed=2)
@@ -94,12 +109,13 @@ def test_covariance_blocks_reshapes_to_hand_built_matrix():
     assert not np.allclose(block_g, block_a)
 
     # TEETH: a perturbation of one flat-table entry moves exactly that block
-    # entry (row k = i·n + j, col 10 for gaussian).
+    # entry and its mirror (col 10 for gaussian).
     perturbed = table.copy()
-    perturbed[2 * 4 + 1, 10] += 5.0  # element (i=2, j=1)
+    perturbed[_row_of(table, 1, 2), 10] += 5.0
     [(_, block_p)] = sio.covariance_blocks(perturbed, selector, gaussian=True)
-    npt.assert_allclose(block_p[2, 1] - block_g[2, 1], 5.0, rtol=1e-12)
-    block_p[2, 1] = block_g[2, 1]
+    for i, j in ((1, 2), (2, 1)):
+        npt.assert_allclose(block_p[i, j] - block_g[i, j], 5.0, rtol=1e-12)
+        block_p[i, j] = block_g[i, j]
     npt.assert_allclose(block_p, block_g, rtol=1e-12)  # nothing else moved
 
 

From b9a9e9bdc95d9340368dd7f4c61ea46a6e7d603c Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:33:22 +0200
Subject: [PATCH 52/83] Repair the tomography branch's own callers of reshaped
 helpers
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

glass_mock.compute_two_point_cl_map unpacked four values from
get_n_gal_map, which returns the count map only; it takes the pixel
bookkeeping from get_pixels and passes it on. Both mock pseudo-Cℓ helpers
label the coupled spectrum ℓ = 0..lmax-1, the range compute_coupled_cell
returns (checked at nside 32: 64 entries, labels 0..63).

The test-reference generator and the namaster covariance paper script call
get_params_rho_tau without survey=, the generator reads its catalogue
through io.open_entry and carries the mandatory patch_number, and the paper
script takes the pixel bookkeeping from get_pixels.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../2025_09_11_namaster_covariance.py         |  7 +++--
 src/sp_validation/glass_mock.py               | 26 +++++++++++++------
 .../generate_test_cl_catalog_reference.py     |  9 ++++---
 3 files changed, 28 insertions(+), 14 deletions(-)

diff --git a/papers/harmonic/2025_09_11_namaster_covariance.py b/papers/harmonic/2025_09_11_namaster_covariance.py
index aea3fc7e..47a77a92 100644
--- a/papers/harmonic/2025_09_11_namaster_covariance.py
+++ b/papers/harmonic/2025_09_11_namaster_covariance.py
@@ -112,11 +112,14 @@
     catalog_config="/home/guerrini/sp_validation/cosmo_val/cat_config.yaml",
 )
 # %%
-params = get_params_rho_tau(cv.cc["SP_v1.4.5_leak_corr"], survey="SP_v1.4.5_leak_corr")
+params = get_params_rho_tau(cv.cc["SP_v1.4.5_leak_corr"])
 
 # %%
 print("Collecting maps...")
-n_gal, unique_pix, idx, idx_rep = cv.get_n_gal_map(params, nside, cat_gal)
+unique_pix, idx, idx_rep = cv.get_pixels(params, nside, cat_gal)
+n_gal = cv.get_n_gal_map(
+    params, nside, cat_gal, unique_pix=unique_pix, idx=idx, idx_rep=idx_rep
+)
 mask = n_gal > 0
 
 print("Computing noise...")
diff --git a/src/sp_validation/glass_mock.py b/src/sp_validation/glass_mock.py
index d09ffc55..b6347aed 100644
--- a/src/sp_validation/glass_mock.py
+++ b/src/sp_validation/glass_mock.py
@@ -475,16 +475,19 @@ def compute_two_point_xi(cat, config=None):
     return gg
 
 
-def get_n_gal_map(nside, ra, dec):
-    """Galaxy-count HEALPix map plus the unique-pixel bookkeeping arrays.
+def get_n_gal_map(nside, ra, dec, unique_pix=None, idx=None, idx_rep=None):
+    """Galaxy-count HEALPix map, shape ``(npix,)``.
 
     The unweighted twin of the pseudo-Cl ``get_n_gal_map`` primitive: delegates
-    to it with ``weights=None`` (counts). Imported lazily so this module keeps
-    resolving in CAMB-only environments without the harmonic stack.
+    to it with ``weights=None`` (counts), reusing the ``get_pixels`` bookkeeping
+    when given. Imported lazily so this module keeps resolving in CAMB-only
+    environments without the harmonic stack.
     """
     from sp_validation.pseudo_cl import get_n_gal_map as _get_n_gal_map
 
-    return _get_n_gal_map(nside, ra, dec)
+    return _get_n_gal_map(
+        nside, ra, dec, unique_pix=unique_pix, idx=idx, idx_rep=idx_rep
+    )
 
 
 def compute_two_point_cl(cat, nside=1024, lmin=8, n_bins=32):
@@ -527,7 +530,8 @@ def compute_two_point_cl(cat, nside=1024, lmin=8, n_bins=32):
     cl_coupled = nmt.compute_coupled_cell(f_all, f_all)
     cl_all = wsp.decouple_cell(cl_coupled)
 
-    cl_coupled = np.concatenate([np.arange(1, lmax + 1)[np.newaxis, :], cl_coupled])
+    # compute_coupled_cell runs over ell = 0..b_lmax, index = ell
+    cl_coupled = np.concatenate([np.arange(lmax)[np.newaxis, :], cl_coupled])
     cl_all = np.concatenate([ell_eff[np.newaxis, ...], cl_all])
     return cl_coupled, cl_all
 
@@ -551,6 +555,8 @@ def compute_two_point_cl_map(cat, nside=1024, lmin=8, n_bins=32):
     import healpy as hp
     import pymaster as nmt
 
+    from sp_validation.pseudo_cl import get_pixels
+
     e1 = cat["e1"]
     e2 = cat["e2"]
     ra = cat["ra"]
@@ -562,7 +568,10 @@ def compute_two_point_cl_map(cat, nside=1024, lmin=8, n_bins=32):
     b, ell_eff, lmax, b_lmax = _mock_powspace_bin(nside, lmin, n_bins)
 
     factor = -1
-    n_gal_map, unique_pix, _idx, idx_rep = get_n_gal_map(nside, ra, dec)
+    unique_pix, idx, idx_rep = get_pixels(ra, dec, nside)
+    n_gal_map = get_n_gal_map(
+        nside, ra, dec, unique_pix=unique_pix, idx=idx, idx_rep=idx_rep
+    )
     mask = n_gal_map > 0
 
     shear_map_e1 = np.zeros(hp.nside2npix(nside))
@@ -582,7 +591,8 @@ def compute_two_point_cl_map(cat, nside=1024, lmin=8, n_bins=32):
     cl_coupled = nmt.compute_coupled_cell(f_all, f_all)
     cl_all = wsp.decouple_cell(cl_coupled)
 
-    cl_coupled = np.concatenate([np.arange(1, lmax + 1)[np.newaxis, :], cl_coupled])
+    # compute_coupled_cell runs over ell = 0..b_lmax, index = ell
+    cl_coupled = np.concatenate([np.arange(lmax)[np.newaxis, :], cl_coupled])
     cl_all = np.concatenate([ell_eff[np.newaxis, ...], cl_all])
     return cl_coupled, cl_all
 
diff --git a/src/sp_validation/tests/data/generate_test_cl_catalog_reference.py b/src/sp_validation/tests/data/generate_test_cl_catalog_reference.py
index 7c41d564..dbed4a60 100644
--- a/src/sp_validation/tests/data/generate_test_cl_catalog_reference.py
+++ b/src/sp_validation/tests/data/generate_test_cl_catalog_reference.py
@@ -4,10 +4,10 @@
 import IPython
 import numpy as np
 import yaml
-from astropy.io import fits
 from astropy.table import Table
 
 from sp_validation.cosmo_val import CosmologyValidation
+from sp_validation.io import open_entry
 from sp_validation.rho_tau import get_params_rho_tau
 
 ipython = IPython.get_ipython()
@@ -65,7 +65,7 @@
     "star_flag": "w",
 }
 config_data = {
-    "nz": {"subdir": str(nz_dir), "dndz": {"blind": "A", "path": "dndz"}},
+    "nz": {"subdir": str(nz_dir), "dndz": {"path": "dndz_{pipeline}_A.txt"}},
     "paths": {"output": str(output_dir)},
     version: {
         "subdir": str(cat_dir),
@@ -73,6 +73,7 @@
         "shear": shear_cfg,
         "star": {**psf_cfg},
         "psf": psf_cfg,
+        "patch_number": 150,
     },
 }
 
@@ -93,8 +94,8 @@
 cv._test_version = version
 
 ver = cv._test_version
-params = get_params_rho_tau(cv.cc[ver], survey=ver)
-cat_gal = fits.getdata(cv.cc[ver]["shear"]["path"])
+params = get_params_rho_tau(cv.cc[ver])
+cat_gal = open_entry(cv.cc[ver]["shear"])
 
 cat_gal_tomo_bin_1 = cat_gal[cat_gal[params["tomo_bin_col"]] == 1]
 cat_gal_tomo_bin_2 = cat_gal[cat_gal[params["tomo_bin_col"]] == 2]

From 45c51e597c795ab99dc948cbe421bb3416331094 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:34:56 +0200
Subject: [PATCH 53/83] Apply pixel windows only to map-based pseudo-Cl spectra

Catalogue spectra are not pixelized.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/cosmo_val/pseudo_cl.py     | 55 +++++++++---
 src/sp_validation/cosmo_val/sacc_writers.py  | 10 ++-
 src/sp_validation/pseudo_cl.py               | 12 ++-
 src/sp_validation/tests/test_pseudo_cl.py    | 92 ++++++++++++++++++--
 src/sp_validation/tests/test_sacc_writers.py | 18 +++-
 5 files changed, 160 insertions(+), 27 deletions(-)

diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index ce6676bc..78eab1ba 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -28,6 +28,23 @@
 from .sacc_writers import pseudo_cl_to_sacc
 
 
+def _apply_pixel_window_to_fiducial_cl(fiducial_cl, nside, cell_method):
+    """Apply ``pw²(ℓ)`` to fiducial spectra only for HEALPix-map measurements.
+
+    Each spectrum array uses index ``ell``, so the window spans the matching
+    ``ell = 0..len(spectrum)-1`` grid. Catalogue spectra are point-sampled and
+    must remain unchanged.
+    """
+    if cell_method != "map" or not fiducial_cl:
+        return fiducial_cl
+
+    lmax = len(next(iter(fiducial_cl.values()))) - 1
+    pixwin = hp.pixwin(nside, lmax=lmax)
+    return {
+        key: np.asarray(cl, dtype=float) * pixwin**2 for key, cl in fiducial_cl.items()
+    }
+
+
 def plot_pseudo_cl_spectrum(datasets, spectrum, output_path):
     """Two-panel ℓC_ℓ / C_ℓ figure for one spectrum across catalogue versions.
 
@@ -272,7 +289,9 @@ def calculate_pseudo_cl_map(self, ver, nside, out_path, tomo_bin_a, tomo_bin_b):
 
         self.print_cyan("Saving pseudo-Cl's...")
         tomo_bin_pair = (tomo_bin_a, tomo_bin_b)
-        self._save_pseudo_cl(ver, out_path, tomo_bin_pair, ell_eff, cl_shear, wsp)
+        self._save_pseudo_cl(
+            ver, out_path, tomo_bin_pair, ell_eff, cl_shear, wsp, nside=nside
+        )
 
         self._pseudo_cls[ver][f"tomo_bin_{tomo_bin_a}_tomo_bin_{tomo_bin_b}"][
             "pseudo_cl"
@@ -304,8 +323,11 @@ def calculate_pseudo_cl_catalog(self, ver, out_path, tomo_bin_a, tomo_bin_b):
     def calculate_pseudo_cl_inka_cov(
         self, compute_tomography=True, load_all_block=False
     ):
-        """
-        Compute a theoretical Gaussian covariance of the Pseudo-Cl for EE, EB and BB.
+        """Compute the Gaussian iNKA covariance for EE, EB, and BB.
+
+        Apply the HEALPix pixel window ``pw²(ℓ)`` to each fiducial spectrum only
+        when ``cell_method == "map"``. Catalogue-based spectra have no pixel
+        window and use the unmodified fiducial.
         """
         self.print_start("Computing Pseudo-Cl covariance")
 
@@ -399,6 +421,9 @@ def calculate_pseudo_cl_inka_cov(
                 key: np.concatenate(([0.0], np.asarray(value[:-1], dtype=float)))
                 for key, value in fiducial_cl.items()
             }
+            fiducial_cl = _apply_pixel_window_to_fiducial_cl(
+                fiducial_cl, nside, self.cell_method
+            )
 
             self.print_cyan(
                 "Estimating and adding the noise bias to the fiducial power spectra"
@@ -1180,10 +1205,14 @@ def _output_path_iNKA_block_cov(self, ver, tomo_bin_quad):
             f"pseudo_cl_cov_from_iNKA_tomo_bin_{bin_key_a1}_tomo_bin_{bin_key_a2}_tomo_bin_{bin_key_b1}_tomo_bin_{bin_key_b2}_{ver}_binning_{self.binning}_nbins_{self.n_ell_bins}.fits",
         )
 
-    def _save_pseudo_cl(self, ver, out_path, tomo_bin_pair, ell_eff, cl_all, wsp):
-        """Write one bin pair's pseudo-Cl: SACC for ``("all", "all")``, else FITS."""
+    def _save_pseudo_cl(
+        self, ver, out_path, tomo_bin_pair, ell_eff, cl_all, wsp, nside=None
+    ):
+        """Write one pair as SACC or FITS, forwarding ``nside`` for map spectra."""
         if tuple(tomo_bin_pair) == ("all", "all"):
-            self.pseudo_cl_to_sacc_part(ver, out_path, ell_eff, cl_all, wsp)
+            self.pseudo_cl_to_sacc_part(
+                ver, out_path, ell_eff, cl_all, wsp, nside=nside
+            )
         else:
             self.save_pseudo_cl(ell_eff, cl_all, out_path)
 
@@ -1202,12 +1231,15 @@ def _load_pseudo_cl_sacc(out_path):
         ell, ee, bb, eb, _window = sacc_io.get_pseudo_cl(s, SACC_BIN)
         return {"ELL": ell, "EE": ee, "EB": eb, "BB": bb}
 
-    def pseudo_cl_to_sacc_part(self, version, out_path, ell_eff, cl_all, wsp):
-        """Write the pseudo-Cl SACC part (EE/BB/EB + shared bandpower window).
+    def pseudo_cl_to_sacc_part(
+        self, version, out_path, ell_eff, cl_all, wsp, nside=None
+    ):
+        """Write the pseudo-Cl SACC part with its shared bandpower window.
 
-        ``cl_all`` is NaMaster's decoupled ``(4, nbp)`` array (EE, EB, BE, BB);
-        the writer takes the shared bandpower window from ``wsp``. No covariance
-        is attached here.
+        ``cl_all`` is NaMaster's decoupled ``(4, nbp)`` array (EE, EB, BE, BB).
+        Map-based callers pass ``nside`` so the window includes ``pw²(ℓ)``;
+        catalogue-based callers leave it unset because their spectra have no
+        HEALPix pixel window. No covariance is attached here.
         """
         s = pseudo_cl_to_sacc(
             self.sacc_nz(version),
@@ -1215,6 +1247,7 @@ def pseudo_cl_to_sacc_part(self, version, out_path, ell_eff, cl_all, wsp):
             ell_eff,
             cl_all,
             wsp,
+            nside=nside,
         )
         sacc_io.save(s, out_path, type="data")
 
diff --git a/src/sp_validation/cosmo_val/sacc_writers.py b/src/sp_validation/cosmo_val/sacc_writers.py
index 8b7f57b7..b10bfe81 100644
--- a/src/sp_validation/cosmo_val/sacc_writers.py
+++ b/src/sp_validation/cosmo_val/sacc_writers.py
@@ -68,14 +68,16 @@ def xi_to_sacc(
     return s
 
 
-def pseudo_cl_to_sacc(nz, metadata, ell_eff, cl_all, wsp, covariance=None):
+def pseudo_cl_to_sacc(nz, metadata, ell_eff, cl_all, wsp, covariance=None, nside=None):
     """One pseudo-Cℓ part: EE/BB/EB with the shared bandpower window.
 
     ``cl_all`` is NaMaster's decoupled ``(4, nbp)`` array (EE, EB, BE, BB); the
-    window comes from :func:`bandpower_window_from_workspace`. ``covariance``,
-    when given, is the dense ``[EE; BB; EB]``-ordered block matching insertion.
+    window comes from :func:`bandpower_window_from_workspace`. Supply ``nside``
+    only for HEALPix-map spectra, whose measured spectrum includes ``pw²(ℓ)``;
+    catalogue-based spectra have no pixel window. ``covariance``, when given,
+    is the dense ``[EE; BB; EB]``-ordered block matching insertion.
     """
-    window_ells, window_weights = bandpower_window_from_workspace(wsp)
+    window_ells, window_weights = bandpower_window_from_workspace(wsp, nside)
     s = sio.new_sacc(nz, metadata)
     sio.add_pseudo_cl(
         s,
diff --git a/src/sp_validation/pseudo_cl.py b/src/sp_validation/pseudo_cl.py
index 052ef57e..f52ab1d1 100644
--- a/src/sp_validation/pseudo_cl.py
+++ b/src/sp_validation/pseudo_cl.py
@@ -1054,9 +1054,13 @@ def get_pseudo_cl_iNKA_covariance(
 _NMT_EE = 0
 
 
-def bandpower_window_from_workspace(wsp):
+def bandpower_window_from_workspace(wsp, nside=None):
     """Extract the bandpower window matrix ``W`` for a spin-2×spin-2 workspace.
 
+    For HEALPix-map spectra, supply ``nside`` to include the pixel window
+    ``pw²(ℓ)`` in the measured spectrum. Catalogue-based spectra have no pixel
+    window and must leave ``nside`` unset.
+
     NaMaster's ``get_bandpower_windows()`` returns a four-index array
     ``(n_cl_out, n_bpw, n_cl_in, n_ell)`` describing how each output bandpower
     is built from the input multipoles across the EE/EB/BE/BB spectra. SACC's
@@ -1071,9 +1075,13 @@ def bandpower_window_from_workspace(wsp):
         ``compute_coupled_cell``.
     window_weights : np.ndarray
         ``W`` of shape ``(n_ell, n_bpw)`` — one column per bandpower, the layout
-        :func:`sp_validation.sacc_io.add_pseudo_cl` expects.
+        :func:`sp_validation.sacc_io.add_pseudo_cl` expects. If ``nside`` is
+        given, this includes ``pw²(ℓ)`` on the window's multipole grid.
     """
     bpw = wsp.get_bandpower_windows()  # (n_cl_out, n_bpw, n_cl_in, n_ell)
     diagonal = bpw[_NMT_EE, :, _NMT_EE, :]  # (n_bpw, n_ell)
     window_ells = np.arange(diagonal.shape[1], dtype=float)
+    if nside is not None:
+        pixwin = hp.pixwin(nside, lmax=diagonal.shape[1] - 1)
+        diagonal = diagonal * pixwin**2
     return window_ells, diagonal.T
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index e9fdab14..80f4d90d 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -60,8 +60,12 @@
 
 from sp_validation import sacc_io
 from sp_validation.cosmo_val import CosmologyValidation
+from sp_validation.cosmo_val.pseudo_cl import _apply_pixel_window_to_fiducial_cl
 from sp_validation.cosmo_val.sacc_writers import BIN as SACC_BIN
-from sp_validation.pseudo_cl import apply_random_rotation
+from sp_validation.pseudo_cl import (
+    apply_random_rotation,
+    bandpower_window_from_workspace,
+)
 from sp_validation.rho_tau import get_params_rho_tau
 
 # These tests need the full harmonic-space stack (pymaster/NaMaster + healpy),
@@ -658,14 +662,24 @@ def test_apply_random_rotation_reproducible_with_seed(cv, cat_and_params):
 # ===========================================================================
 # calculate_pseudo_cl_catalog -- deterministic end-to-end catalog path
 # ===========================================================================
-def test_calculate_pseudo_cl_catalog_end_to_end(cv, tmp_path):
-    """End-to-end catalog path: SACC round-trip of ell + EE/EB/BB.
-
-    The catalog method has no random noise debiasing, so it is reproducible to
-    the same ~2e-12 catalog-path float noise; we pin the round-tripped spectra.
-    """
+def test_calculate_pseudo_cl_catalog_end_to_end(cv, tmp_path, monkeypatch):
+    """Catalog SACC output retains the unwindowed workspace bandpower window."""
     ver = cv._test_version
+    cv.cell_method = "catalog"
     cv._pseudo_cls = {ver: {"tomo_bin_all_tomo_bin_all": {}}}
+    saved = {}
+    save_pseudo_cl = cv._save_pseudo_cl
+
+    def capture_workspace(
+        ver, out_path, tomo_bin_pair, ell_eff, cl_all, wsp, nside=None
+    ):
+        saved["workspace"] = wsp
+        saved["nside"] = nside
+        return save_pseudo_cl(
+            ver, out_path, tomo_bin_pair, ell_eff, cl_all, wsp, nside=nside
+        )
+
+    monkeypatch.setattr(cv, "_save_pseudo_cl", capture_workspace)
     out_path = cv._output_path(f"pseudo_cl_{ver}.sacc")
     cv.calculate_pseudo_cl_catalog(ver, out_path, tomo_bin_a="all", tomo_bin_b="all")
 
@@ -673,6 +687,12 @@ def test_calculate_pseudo_cl_catalog_end_to_end(cv, tmp_path):
     s = sacc_io.load(out_path, allow_unblinded=True)
     ell, ee, bb, eb, window = sacc_io.get_pseudo_cl(s, SACC_BIN)
     assert window is not None  # the shared BandpowerWindow rides the part
+    assert saved["nside"] is None
+    window_ells, unwindowed_weights = bandpower_window_from_workspace(
+        saved["workspace"]
+    )
+    assert len(window_ells) == window.weight.shape[0]
+    npt.assert_allclose(window.weight, unwindowed_weights, rtol=1e-12, atol=1e-15)
 
     npt.assert_allclose(
         ell,
@@ -741,6 +761,64 @@ def test_calculate_pseudo_cl_catalog_end_to_end(cv, tmp_path):
     npt.assert_allclose(ee, cl_prim[0], rtol=RTOL_CAT, atol=ATOL_CAT)
 
 
+def test_calculate_pseudo_cl_map_sacc_pixel_window(cv, monkeypatch):
+    """Map SACC output multiplies its workspace window by pw²(ℓ)."""
+    ver = cv._test_version
+    cv.cell_method = "map"
+    cv.noise_bias_method = "analytic"
+    cv._pseudo_cls = {ver: {"tomo_bin_all_tomo_bin_all": {}}}
+    saved = {}
+    save_pseudo_cl = cv._save_pseudo_cl
+
+    def capture_workspace(
+        ver, out_path, tomo_bin_pair, ell_eff, cl_all, wsp, nside=None
+    ):
+        saved["workspace"] = wsp
+        saved["nside"] = nside
+        return save_pseudo_cl(
+            ver, out_path, tomo_bin_pair, ell_eff, cl_all, wsp, nside=nside
+        )
+
+    monkeypatch.setattr(cv, "_save_pseudo_cl", capture_workspace)
+    out_path = cv._output_path(f"pseudo_cl_map_{ver}.sacc")
+    cv.calculate_pseudo_cl_map(ver, NSIDE, out_path, "all", "all")
+
+    s = sacc_io.load(out_path, allow_unblinded=True)
+    _ell, _ee, _bb, _eb, window = sacc_io.get_pseudo_cl(s, SACC_BIN)
+    assert saved["nside"] == NSIDE
+    window_ells, unwindowed_weights = bandpower_window_from_workspace(
+        saved["workspace"]
+    )
+    assert len(window_ells) == window.weight.shape[0]
+    pw2 = healpy.pixwin(NSIDE, lmax=len(window_ells) - 1) ** 2
+    nonzero = np.abs(unwindowed_weights) > 1e-20
+    npt.assert_allclose(
+        window.weight[nonzero] / unwindowed_weights[nonzero],
+        np.broadcast_to(pw2[:, None], unwindowed_weights.shape)[nonzero],
+        rtol=1e-12,
+    )
+
+
+def test_fiducial_pixel_window_applies_only_to_map():
+    """iNKA fiducials carry pw² on maps and remain unchanged for catalogues."""
+    nside = 4
+    ell_grid = np.arange(12)
+    fiducial = {
+        "W1xW1": 1.0 + ell_grid.astype(float),
+        "W1xW2": 2.0 + 2 * ell_grid.astype(float),
+    }
+    original = {key: value.copy() for key, value in fiducial.items()}
+
+    catalog_fiducial = _apply_pixel_window_to_fiducial_cl(fiducial, nside, "catalog")
+    map_fiducial = _apply_pixel_window_to_fiducial_cl(fiducial, nside, "map")
+    pw2 = healpy.pixwin(nside, lmax=len(ell_grid) - 1) ** 2
+
+    for key, cl in original.items():
+        npt.assert_array_equal(catalog_fiducial[key], cl)
+        npt.assert_allclose(map_fiducial[key], cl * pw2, rtol=1e-14, atol=0.0)
+        npt.assert_array_equal(fiducial[key], cl)
+
+
 def test_calculate_pseudo_cl_catalog_end_to_end_tomo(cv, tmp_path):
     """End-to-end catalog path: FITS round-trip of ell + EE/EB/BB.
 
diff --git a/src/sp_validation/tests/test_sacc_writers.py b/src/sp_validation/tests/test_sacc_writers.py
index c4963b33..74181190 100644
--- a/src/sp_validation/tests/test_sacc_writers.py
+++ b/src/sp_validation/tests/test_sacc_writers.py
@@ -152,9 +152,11 @@ def get_bandpower_windows(self):
     assert np.array_equal(data["BB"], cl_all[3])
 
 
-def test_pseudo_cl_to_sacc_real_namaster(tmp_path):
-    """A real small-nside NaMaster workspace's window survives the writer."""
+def test_pseudo_cl_to_sacc_real_namaster_pixel_window(tmp_path):
+    """The SACC window gains pw²(ℓ) only when a map nside is supplied."""
     pytest.importorskip("pymaster")
+    import healpy as hp
+
     from sp_validation.pseudo_cl import get_pseudo_cls_map
 
     nside = 32
@@ -169,9 +171,19 @@ def test_pseudo_cl_to_sacc_real_namaster(tmp_path):
     ell_r, ee, bb, eb, window = sio.get_pseudo_cl(s2, (0, 0))
     assert np.array_equal(ell_r, ell_eff)
     assert np.array_equal(ee, cl_all[0]) and np.array_equal(bb, cl_all[3])
-    # window columns correspond to the bandpowers, one per ell_eff
+    # With nside unset, the workspace window carries no pixel window.
     assert window.weight.shape[1] == len(ell_eff)
 
+    mapped = sw.pseudo_cl_to_sacc({0: _nz()}, META, ell_eff, cl_all, wsp, nside=nside)
+    mapped_window = mapped.get_bandpower_windows(mapped.indices(sio.CL_EE))
+    pw2 = hp.pixwin(nside, lmax=window.weight.shape[0] - 1) ** 2
+    nonzero = np.abs(window.weight) > 1e-20
+    np.testing.assert_allclose(
+        mapped_window.weight[nonzero] / window.weight[nonzero],
+        np.broadcast_to(pw2[:, None], window.weight.shape)[nonzero],
+        rtol=1e-12,
+    )
+
 
 def test_cosebis_to_sacc(tmp_path):
     En, Bn = np.arange(1, 11) * 1e-6, np.arange(1, 11) * 1e-7

From 497d7b8a87fc2b735ac420c1d52e74f9a72ffa15 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:37:14 +0200
Subject: [PATCH 54/83] Pin the OneCovariance fixture to the real table; drop
 the GLASS xfail
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The OneCovariance fixture is the full row-major n×n table of one bin, as
the file is written, now with the ℓ (cols 1-2) and bin (cols 5-8) columns
the tomographic reshape reads; the perturbation teeth keep the table
symmetric. The tomographic reshape stays: PseudoClMixin's OneCovariance
covariance needs it, and on a single-bin table it returns the matrix the
row-major reshape did.

test_matter_maps_are_seed_deterministic passes (XPASS) with the merged
pins (glass 2026.2, glass.ext.camb 2023.6, cosmology.compat.camb 0.2.0)
overlaid on the current image, so its xfail goes. It fails on an image
built from the old lock (no cosmology.compat) until uv.lock is re-locked.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/tests/test_glass_mock.py    | 13 -----------
 .../tests/test_sacc_io_one_covariance.py      | 22 +++++++++----------
 2 files changed, 11 insertions(+), 24 deletions(-)

diff --git a/src/sp_validation/tests/test_glass_mock.py b/src/sp_validation/tests/test_glass_mock.py
index 233d8aa1..a559658a 100644
--- a/src/sp_validation/tests/test_glass_mock.py
+++ b/src/sp_validation/tests/test_glass_mock.py
@@ -131,19 +131,6 @@ def test_config_change_breaks_reference():
 
 
 @pytest.mark.skipif(not HAVE_GLASS, reason="GLASS not installed in this image")
-@pytest.mark.xfail(
-    reason=(
-        "glass_mock map path is incompatible with the installed glass/cosmology "
-        "API: cosmology.Cosmology.from_camb returns a CambCosmology lacking "
-        "comoving_distance, which glass.distance_grid / MultiPlaneConvergence "
-        "require. The map path was never exercised before GLASS was added to the "
-        "image. Fix = pin a compatible glass+cosmology pair (or adapt the API "
-        "calls) and verify in the fresh image; then drop this xfail. "
-        "See fiber shapepipe/sp_validation glass-cosmology-api-pin."
-    ),
-    strict=False,
-    raises=AttributeError,
-)
 def test_matter_maps_are_seed_deterministic():
     """Same config + seed → bit-identical matter/lensing maps.
 
diff --git a/src/sp_validation/tests/test_sacc_io_one_covariance.py b/src/sp_validation/tests/test_sacc_io_one_covariance.py
index 776475b3..1454bc77 100644
--- a/src/sp_validation/tests/test_sacc_io_one_covariance.py
+++ b/src/sp_validation/tests/test_sacc_io_one_covariance.py
@@ -37,16 +37,15 @@ def _one_cov_table(cov_gauss, cov_all):
     """Flatten two n x n matrices into a OneCovariance ``covariance_list`` table.
 
     Reproduces the real flat output of a single tomographic bin: one row per
-    ``(i, j)`` ℓ-bin pair with ``i <= j`` (the reshape mirrors the other
-    triangle), ℓ_i and ℓ_j in columns 1 and 2, the bin indices of the four
-    fields (all ``1``) in columns 5-8, the Gaussian value in column 10 and the
-    Gaussian+non-Gaussian value in column 9. Columns 0, 3 and 4 hold
-    placeholders the reshape does not read.
+    ``(i, j)`` ℓ-bin pair in row-major order ``k = i·n + j``, ℓ_i and ℓ_j in
+    columns 1 and 2, the bin indices of the four fields (all ``1``) in columns
+    5-8, the Gaussian value in column 10 and the Gaussian+non-Gaussian value in
+    column 9. Columns 0, 3 and 4 hold placeholders the reshape does not read.
     """
     n = cov_gauss.shape[0]
     rows = []
     for i in range(n):
-        for j in range(i, n):
+        for j in range(n):
             row = np.arange(11.0)  # placeholder cols 0, 3, 4
             row[1], row[2] = _ell(i), _ell(j)
             row[5:9] = 1
@@ -89,8 +88,9 @@ def test_covariance_blocks_reshapes_to_hand_built_matrix():
     WHY TEETH: (a) ``gaussian=True`` vs ``False`` must return the two *different*
     matrices, proving the column flag is load-bearing; (b) perturbing a single
     entry of the flat input must change exactly that entry of the reshaped
-    block and its mirror, proving the reshape actually reads the table (not a
-    constant).
+    block, proving the reshape actually reads the table (not a constant). The
+    table stays symmetric, as a covariance is, so the entry is perturbed with
+    its mirror.
     """
     cov_gauss = _spd(4, seed=1)
     cov_all = _spd(4, seed=2)
@@ -108,10 +108,10 @@ def test_covariance_blocks_reshapes_to_hand_built_matrix():
     # TEETH: gaussian and gauss+ng select different columns -> different blocks.
     assert not np.allclose(block_g, block_a)
 
-    # TEETH: a perturbation of one flat-table entry moves exactly that block
-    # entry and its mirror (col 10 for gaussian).
+    # TEETH: a perturbation of one flat-table entry (and its mirror) moves
+    # exactly that block entry (and its mirror), col 10 for gaussian.
     perturbed = table.copy()
-    perturbed[_row_of(table, 1, 2), 10] += 5.0
+    perturbed[[_row_of(table, 1, 2), _row_of(table, 2, 1)], 10] += 5.0
     [(_, block_p)] = sio.covariance_blocks(perturbed, selector, gaussian=True)
     for i, j in ((1, 2), (2, 1)):
         npt.assert_allclose(block_p[i, j] - block_g[i, j], 5.0, rtol=1e-12)

From 9010a8cdf163c7f2149bba20a22e5d3e76e4c3ec Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:40:53 +0200
Subject: [PATCH 55/83] Keep the GLASS xfail until the re-locked image; cite
 the OneCovariance writer
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

test_matter_maps_are_seed_deterministic passes with the locked glass
2026.2 / cosmology-compat-camb 0.2.0 on the path, but the image is still
built from the old lock, so the xfail stays (raises=ModuleNotFoundError)
until it XPASSes in a rebuilt image.

The OneCovariance fixture's full row-major table is what
cov_output.__write_cov_list writes: every (ℓ1, ℓ2) pair for every ordered
pair of spectra.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/tests/test_glass_mock.py           | 10 ++++++++++
 .../tests/test_sacc_io_one_covariance.py             | 12 +++++++-----
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/src/sp_validation/tests/test_glass_mock.py b/src/sp_validation/tests/test_glass_mock.py
index a559658a..54014403 100644
--- a/src/sp_validation/tests/test_glass_mock.py
+++ b/src/sp_validation/tests/test_glass_mock.py
@@ -131,6 +131,16 @@ def test_config_change_breaks_reference():
 
 
 @pytest.mark.skipif(not HAVE_GLASS, reason="GLASS not installed in this image")
+@pytest.mark.xfail(
+    reason=(
+        "glass_mock builds its cosmology with cosmology.compat.camb, which an image "
+        "built before the lock carried glass 2026.2 + cosmology-compat-camb 0.2.0 "
+        "lacks. With those locked pins on the path the test passes; drop this "
+        "xfail once it XPASSes in the image built from the current uv.lock."
+    ),
+    strict=False,
+    raises=ModuleNotFoundError,
+)
 def test_matter_maps_are_seed_deterministic():
     """Same config + seed → bit-identical matter/lensing maps.
 
diff --git a/src/sp_validation/tests/test_sacc_io_one_covariance.py b/src/sp_validation/tests/test_sacc_io_one_covariance.py
index 1454bc77..d69fb64c 100644
--- a/src/sp_validation/tests/test_sacc_io_one_covariance.py
+++ b/src/sp_validation/tests/test_sacc_io_one_covariance.py
@@ -36,11 +36,13 @@ def _ell(i):
 def _one_cov_table(cov_gauss, cov_all):
     """Flatten two n x n matrices into a OneCovariance ``covariance_list`` table.
 
-    Reproduces the real flat output of a single tomographic bin: one row per
-    ``(i, j)`` ℓ-bin pair in row-major order ``k = i·n + j``, ℓ_i and ℓ_j in
-    columns 1 and 2, the bin indices of the four fields (all ``1``) in columns
-    5-8, the Gaussian value in column 10 and the Gaussian+non-Gaussian value in
-    column 9. Columns 0, 3 and 4 hold placeholders the reshape does not read.
+    Reproduces the real flat output of a single tomographic bin, as
+    OneCovariance's ``__write_cov_list`` writes it (every ℓ pair, both
+    orders): one row per ``(i, j)`` ℓ-bin pair in row-major order
+    ``k = i·n + j``, ℓ_i and ℓ_j in columns 1 and 2, the bin indices of the
+    four fields (all ``1``) in columns 5-8, the Gaussian value in column 10 and
+    the total (Gaussian + non-Gaussian) value in column 9. Columns 0, 3 and 4
+    hold placeholders the reshape does not read.
     """
     n = cov_gauss.shape[0]
     rows = []

From f7f2abb835924f353f4512f7504b458c795539ab Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:41:07 +0200
Subject: [PATCH 56/83] Name the jackknife rho/tau draws by the id the library
 writes them under

shear_psf_leakage saves each jackknife draw as cov_{rho,tau}_{catalog_id}.npy.
#295 renamed the files get_jackknife_cov reads to cov_{rho,tau}_{base}{i}.npy
but kept catalog_id = version + i, so a fresh cov_estimate_method="jk" run
computed every draw and then raised FileNotFoundError. The draws now take
catalog_id = base_tau + i, which keeps the bins of a tomographic run apart,
and the reader looks for exactly those files.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/rho_tau.py            | 56 +++++++++++++++----------
 src/sp_validation/tests/test_grammar.py | 46 ++++++++++++++++++++
 2 files changed, 80 insertions(+), 22 deletions(-)

diff --git a/src/sp_validation/rho_tau.py b/src/sp_validation/rho_tau.py
index 9b2ebf54..614f1589 100644
--- a/src/sp_validation/rho_tau.py
+++ b/src/sp_validation/rho_tau.py
@@ -462,17 +462,30 @@ def get_jackknife_cov(
 
     tau_stat_handler.catalogs.set_params(params, outdir)
 
+    # shear_psf_leakage keys its catalogues by catalog_id and writes each
+    # draw's covariance to cov_{rho,tau}_{catalog_id}.npy; base_tau names the
+    # draws so that the bins of a tomographic run never share them.
+    def catalog_id(i):
+        return f"{base_tau}{i}"
+
+    def chunks(i):
+        return [
+            os.path.join(outdir, f"cov_{kind}_{catalog_id(i)}.npy")
+            for kind in ("rho", "tau")
+        ]
+
     with _CatalogueLoader(config[version], params) as load:
         for i in range(ncov):
-            tau_chunk = outdir + f"/cov_tau_{base_tau}{i}.npy"
-            rho_chunk = outdir + f"/cov_rho_{base_rho}{i}.npy"
-            if not (os.path.exists(tau_chunk) and os.path.exists(rho_chunk)):
+            if not all(os.path.exists(chunk) for chunk in chunks(i)):
                 print(f"Computing rho-statistics for {version} (patch {i + 1}/{ncov})")
 
-                if f"psf_{version}{i}" not in rho_stat_handler.catalogs.catalogs_dict:
+                if (
+                    f"psf_{catalog_id(i)}"
+                    not in rho_stat_handler.catalogs.catalogs_dict
+                ):
                     # Build catalogues
                     rho_stat_handler.build_cat_to_compute_rho(
-                        load("psf"), catalog_id=version + str(i), mask=mask_star
+                        load("psf"), catalog_id=catalog_id(i), mask=mask_star
                     )
 
                     tau_stat_handler.catalogs.catalogs_dict = (
@@ -483,7 +496,7 @@ def get_jackknife_cov(
                     tau_stat_handler.build_cat_to_compute_tau(
                         load("shear"),
                         cat_type="gal",
-                        catalog_id=version + str(i),
+                        catalog_id=catalog_id(i),
                         mask=mask_gal,
                     )
 
@@ -492,7 +505,7 @@ def get_jackknife_cov(
 
                     npatch = rho_stat_handler.catalogs._params["patch_number"]
                     field = rho_stat_handler.catalogs.catalogs_dict[
-                        f"psf_{version}{i}"
+                        f"psf_{catalog_id(i)}"
                     ].getNField(max_top=int.bit_length(npatch) - 1, coords="spherical")
                     patch, centers = field.run_kmeans(npatch)
 
@@ -506,7 +519,7 @@ def get_jackknife_cov(
 
                 # Compute and save rho stats
                 rho_stat_handler.compute_rho_stats(
-                    version + str(i),
+                    catalog_id(i),
                     rho_filename,
                     save_cov=True,
                     func=_extract_xip,
@@ -515,7 +528,7 @@ def get_jackknife_cov(
 
                 # function to extract the tau_+
                 tau_stat_handler.compute_tau_stats(
-                    version + str(i),
+                    catalog_id(i),
                     tau_filename,
                     save_cov=True,
                     func=_extract_xip,
@@ -525,22 +538,21 @@ def get_jackknife_cov(
                 # Update the keys in the dictionaries
                 rho_dict = rho_stat_handler.catalogs.catalogs_dict
                 tau_dict = tau_stat_handler.catalogs.catalogs_dict
-                rho_dict[f"psf_{version}{i + 1}"] = rho_dict.pop(f"psf_{version}{i}")
-                rho_dict[f"psf_error_{version}{i + 1}"] = rho_dict.pop(
-                    f"psf_error_{version}{i}"
-                )
-                rho_dict[f"psf_size_error_{version}{i + 1}"] = rho_dict.pop(
-                    f"psf_size_error_{version}{i}"
+                for prefix in ("psf", "psf_error", "psf_size_error"):
+                    rho_dict[f"{prefix}_{catalog_id(i + 1)}"] = rho_dict.pop(
+                        f"{prefix}_{catalog_id(i)}"
+                    )
+                tau_dict[f"gal_{catalog_id(i + 1)}"] = tau_dict.pop(
+                    f"gal_{catalog_id(i)}"
                 )
-                tau_dict[f"gal_{version}{i + 1}"] = tau_dict.pop(f"gal_{version}{i}")
 
-    cov_tau_loc = np.zeros_like(np.load(outdir + f"/cov_tau_{base_tau}0.npy"))
-    cov_rho_loc = np.zeros_like(np.load(outdir + f"/cov_rho_{base_rho}0.npy"))
+    cov_rho_loc, cov_tau_loc = (np.zeros_like(np.load(c)) for c in chunks(0))
     for i in range(ncov):
-        cov_tau_loc += np.load(outdir + f"/cov_tau_{base_tau}{i}.npy")
-        cov_rho_loc += np.load(outdir + f"/cov_rho_{base_rho}{i}.npy")
-        os.remove(outdir + f"/cov_tau_{base_tau}{i}.npy")
-        os.remove(outdir + f"/cov_rho_{base_rho}{i}.npy")
+        rho_chunk, tau_chunk = chunks(i)
+        cov_rho_loc += np.load(rho_chunk)
+        cov_tau_loc += np.load(tau_chunk)
+        os.remove(rho_chunk)
+        os.remove(tau_chunk)
 
     cov_tau = cov_tau_loc / ncov
     cov_rho = cov_rho_loc / ncov
diff --git a/src/sp_validation/tests/test_grammar.py b/src/sp_validation/tests/test_grammar.py
index 60e23b75..a855e73b 100644
--- a/src/sp_validation/tests/test_grammar.py
+++ b/src/sp_validation/tests/test_grammar.py
@@ -285,6 +285,52 @@ def test_get_rho_tau_identical_for_v1_and_v2_psf_catalogues(tmp_path):
             np.testing.assert_allclose(table_v1[name], table_v2[name], rtol=1e-10)
 
 
+def test_jackknife_cov_reads_the_draws_it_writes(tmp_path):
+    """The jackknife ρ/τ covariance averages the per-draw files the library saves.
+
+    shear_psf_leakage writes each draw to cov_{rho,tau}_{catalog_id}.npy; the
+    reader must look for those names, and leave only the averaged products.
+    """
+    from sp_validation.rho_tau import get_jackknife_cov
+
+    _, v2 = _twins()
+    rng = np.random.default_rng(5)
+    shear = np.empty(N, dtype=[(n, "f8") for n in ("RA", "Dec", "e1", "e2", "w")])
+    shear["RA"], shear["Dec"] = v2["RA"], v2["DEC"]
+    shear["e1"], shear["e2"] = rng.normal(0, 0.3, (2, N))
+    shear["w"] = 1.0
+    fits.BinTableHDU(shear).writeto(tmp_path / "shear.fits")
+    fits.BinTableHDU(np.array(v2[list(PSF_BLOCK.values())])).writeto(
+        tmp_path / "psf.fits"
+    )
+    config = {
+        "v": {
+            "patch_number": 2,
+            "psf": PSF_BLOCK | {"path": str(tmp_path / "psf.fits"), "hdu": 1},
+            "shear": SHEAR_BLOCK | {"path": str(tmp_path / "shear.fits")},
+        }
+    }
+    treecorr_config = {
+        "ra_units": "deg",
+        "dec_units": "deg",
+        "sep_units": "arcmin",
+        "min_sep": 10,
+        "max_sep": 600,
+        "nbins": 4,
+    }
+    outdir = tmp_path / "out"
+    outdir.mkdir()
+    get_jackknife_cov(
+        config, "v", treecorr_config, str(outdir), "v", "v_bin_1", npatch=2, ncov=2
+    )
+
+    cov_tau = np.load(outdir / "cov_tau_v_bin_1_jk.npy")
+    cov_rho = np.load(outdir / "cov_rho_v_jk.npy")
+    assert cov_tau.shape == (3 * 4, 3 * 4)
+    assert cov_rho.shape == (6 * 4, 6 * 4)
+    assert not list(outdir.glob("cov_*[0-9].npy"))
+
+
 # -- mask-bit columns: a rule family of their own ---------------------------
 
 

From d603c5c1a2521c9c7fb929753d177ca5806dbf5b Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:43:40 +0200
Subject: [PATCH 57/83] Compute the iNKA fiducial C_ell with CCL, from the
 configured cosmology

The pseudo-C_ell covariance's fiducial spectrum is built from self.cosmo,
a CCL Cosmology. The tomographic pseudo-C_ell work (#221, reapplied as #240;
a941f542, 753abc13) evaluated it with
cs_util's CAMB backend, which rebuilds a CAMB cosmology from H0, ombh2,
omch2, ns, As/sigma8 and w/wa only: the neutrino mass is dropped (CAMB's
0.06 eV default stands in) and CAMB's mead2020 non-linear model replaces
the object's halofit. Against CCL that is -4% at ell=1000 for Planck 2018
and -8.5% with mnu=0.3. develop passed backend="ccl"; the default is CCL
again.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/pseudo_cl.py            |  8 ++++++--
 src/sp_validation/tests/test_pseudo_cl.py | 24 +++++++++++++++++++++++
 2 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/src/sp_validation/pseudo_cl.py b/src/sp_validation/pseudo_cl.py
index f52ab1d1..5bb1b392 100644
--- a/src/sp_validation/pseudo_cl.py
+++ b/src/sp_validation/pseudo_cl.py
@@ -969,10 +969,14 @@ def get_pseudo_cls_catalog(
 
 
 # ---------------------- Covariance computation functions ----------------------
-def get_fiducial_cl(z, dndz, lmax, cosmo, backend="camb"):
+def get_fiducial_cl(z, dndz, lmax, cosmo, backend="ccl"):
     """
     Get the fiducial Cl's using the redshift distribution.
-    Cosmology is determined by the input cosmo object.
+
+    Cosmology is determined by the input CCL cosmo object. The default CCL
+    backend evaluates that object as configured; cs_util's CAMB backend
+    rebuilds a CAMB cosmology from it, dropping the neutrino mass and using
+    CAMB's own non-linear model.
     """
     ell = np.arange(1, lmax + 1)
 
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index 80f4d90d..11bcb720 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -819,6 +819,30 @@ def test_fiducial_pixel_window_applies_only_to_map():
         npt.assert_array_equal(fiducial[key], cl)
 
 
+def test_fiducial_cl_follows_the_configured_cosmology():
+    """The iNKA fiducial is CCL's prediction for the configured cosmology.
+
+    A non-default neutrino mass makes the check bite: cs_util's CAMB backend
+    drops it.
+    """
+    import pyccl as ccl
+    from cs_util.cosmo import get_cosmo
+
+    from sp_validation.pseudo_cl import get_fiducial_cl
+
+    cosmo = get_cosmo(mnu=0.3)
+    z = np.linspace(0.01, 3.0, 200)
+    dndz = np.exp(-(((z - 0.7) / 0.3) ** 2))
+    lmax = 128
+    fiducial = get_fiducial_cl(z, dndz, lmax, cosmo)["W1xW1"]
+
+    ell = np.arange(1, lmax + 1)
+    tracer = ccl.WeakLensingTracer(cosmo, dndz=(z, dndz))
+    npt.assert_allclose(
+        fiducial, ccl.angular_cl(cosmo, tracer, tracer, ell), rtol=1e-10
+    )
+
+
 def test_calculate_pseudo_cl_catalog_end_to_end_tomo(cv, tmp_path):
     """End-to-end catalog path: FITS round-trip of ell + EE/EB/BB.
 

From 551f5c9c3e8ba82b2ab1b3c4c0ef30c3b8a71ae7 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:45:58 +0200
Subject: [PATCH 58/83] Default the catalogue pseudo-Cl wrapper to the ("all",
 "all") pair

PseudoClMixin.get_pseudo_cls_catalog forwarded tomo_bin_a=tomo_bin_b=None
over the primitive's "all" defaults, which the primitive reads as a
tomographic selection: a call without bins selected an empty catalogue
(NaMaster then fails inside ducc0) or tripped the tomography assertion.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/cosmo_val/pseudo_cl.py  |  2 +-
 src/sp_validation/tests/test_pseudo_cl.py | 10 ++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index 78eab1ba..aa25cbdf 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -974,7 +974,7 @@ def get_pseudo_cls_map(
         )
 
     def get_pseudo_cls_catalog(
-        self, catalog, params, wsp=None, tomo_bin_a=None, tomo_bin_b=None
+        self, catalog, params, wsp=None, tomo_bin_a="all", tomo_bin_b="all"
     ):
         """Catalog-based pseudo-cl (thin wrapper, state -> primitive)."""
         return spv_pseudo_cl.get_pseudo_cls_catalog(
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index 11bcb720..9f99a67c 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -613,6 +613,16 @@ def test_get_pseudo_cls_catalog(cv, cat_and_params):
     npt.assert_allclose(cl_all[2], cl_all[1], rtol=RTOL_CAT, atol=ATOL_CAT)
 
 
+def test_get_pseudo_cls_catalog_defaults_to_the_whole_catalogue(cv, cat_and_params):
+    """Called without bins, the wrapper measures the ("all", "all") pair."""
+    cat_gal, params = cat_and_params
+    _, cl_default, _ = cv.get_pseudo_cls_catalog(catalog=cat_gal, params=params)
+    _, cl_all, _ = cv.get_pseudo_cls_catalog(
+        catalog=cat_gal, params=params, tomo_bin_a="all", tomo_bin_b="all"
+    )
+    npt.assert_allclose(cl_default, cl_all, rtol=RTOL_CAT, atol=ATOL_CAT)
+
+
 # ===========================================================================
 # apply_random_rotation -- invariant + reproducibility
 # ===========================================================================

From 465ae282c26ab992a12ab3f995c209c6327af751 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Fri, 2 Oct 2026 23:51:40 +0200
Subject: [PATCH 59/83] Let the object-wise leakage reader take a row selection

shear_psf_leakage f1a2c071 (#48) has temporarily_read_data(selection=...)
call self.read_data(selection=selection). _LeakageObject overrode
read_data without that argument, so every tomographic bin of the
object-wise leakage raised TypeError. The override now keeps the rows a
boolean selection marks, over the rows io.open_entry returns, which are
the rows _get_galaxy_mask builds its masks on.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/cosmo_val/core.py         | 14 ++++++++++++--
 src/sp_validation/tests/test_psf_leakage.py | 20 ++++++++++++++++++++
 2 files changed, 32 insertions(+), 2 deletions(-)

diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index 017b5c74..c6ed7b7b 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -52,8 +52,18 @@ class _LeakageObject(run_object.LeakageObject):
 
     entries = None
 
-    def read_data(self):
-        self._dat = io.open_entry(self.entries["shear"])
+    def read_data(self, selection=None):
+        """Read the shear entry, keeping the rows ``selection`` marks.
+
+        ``selection`` is an optional boolean array over the rows of
+        ``io.open_entry(entries["shear"])``, in that order.
+        """
+        dat = io.open_entry(self.entries["shear"])
+        if selection is not None:
+            if len(selection) != len(dat):
+                raise ValueError("Selection array has different length than catalogue")
+            dat = dat[selection]
+        self._dat = dat
 
 
 # %%
diff --git a/src/sp_validation/tests/test_psf_leakage.py b/src/sp_validation/tests/test_psf_leakage.py
index bef99e32..0290ad4e 100644
--- a/src/sp_validation/tests/test_psf_leakage.py
+++ b/src/sp_validation/tests/test_psf_leakage.py
@@ -206,6 +206,26 @@ def test_objectwise_leakage_and_plot_smoke(tmp_path, monkeypatch):
     assert (tmp_path / "output" / "leakage_coefficients_tomo.png").exists()
 
 
+def test_leakage_object_reads_the_selected_rows(tmp_path):
+    """The object-wise reader keeps the rows a galaxy mask selects."""
+    pytest.importorskip("shear_psf_leakage")
+    from sp_validation.cosmo_val import CosmologyValidation
+    from sp_validation.cosmo_val.core import _LeakageObject
+
+    version, params = _objectwise_config(tmp_path)
+    cv = CosmologyValidation(versions=[version], npatch=1, **params)
+    obj = object.__new__(_LeakageObject)
+    obj.entries = cv.cc[version]
+
+    mask = cv._get_galaxy_mask(version, 2)
+    obj.read_data(selection=mask)
+    assert len(obj._dat) == mask.sum()
+    assert np.all(obj._dat["tomo_bin"] == 2)
+
+    with pytest.raises(ValueError, match="different length"):
+        obj.read_data(selection=mask[1:])
+
+
 class _ObjectwiseControlFlow(PSFSystematicsMixin):
     """Two versions, one of which lacks a catalogue column."""
 

From e3bd484774c8dcde387d89dd3d4eccb99fe64f6c Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 00:00:58 +0200
Subject: [PATCH 60/83] Restore Hartlap debiasing of simulation rho/tau
 covariances

The inverse of a covariance estimated from N realisations is biased high;
the PSF-leakage fits correct it with the realisation count. develop passed
300 for cov_estimate_method="sim"; #295 (ee75f539) kept only the jackknife
patch count, so simulation covariances went undebiased and the fitted
leakage parameters came out too tight (about 12% for 60 tau points).

Nothing records the simulation count: no code in this repository's
history writes cov_tau_*_sim.npy, and the file holds only the matrix.
The count is therefore the constructor parameter n_sim_cov, default 300
as on develop, forwarded by the workflow from config["cosmo_val"].

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 papers/cosmo_val/config/config.yaml           |  2 ++
 src/sp_validation/cosmo_val/core.py           | 11 +++++--
 .../cosmo_val/psf_systematics.py              |  8 +++--
 src/sp_validation/tests/test_psf_leakage.py   | 33 +++++++++++++++++++
 workflow/common.py                            |  1 +
 5 files changed, 51 insertions(+), 4 deletions(-)

diff --git a/papers/cosmo_val/config/config.yaml b/papers/cosmo_val/config/config.yaml
index 7dd0e091..f469cc44 100644
--- a/papers/cosmo_val/config/config.yaml
+++ b/papers/cosmo_val/config/config.yaml
@@ -33,6 +33,8 @@ cosmo_val:
   cov_estimate_method: th
   compute_cov_rho: true
   n_cov: 100
+  # simulations behind a cov_estimate_method: sim rho/tau covariance (Hartlap)
+  n_sim_cov: 300
   var_method: jackknife
   quantile: 0.1587
   ylim_xi_sys_ratio: [-0.02, 0.5]
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index c6ed7b7b..4fbd2a89 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -129,8 +129,13 @@ class CosmologyValidation(
         Output directory. If None, the catalog config's paths.output.
     rho_tau_method : {'lsq', 'mcmc'}, default 'lsq'
         Fitting method for PSF leakage systematics parameters.
-    cov_estimate_method : {'th', 'jk'}, default 'th'
-        Covariance estimation: 'th' for semi-analytic theory, 'jk' for jackknife.
+    cov_estimate_method : {'th', 'jk', 'sim'}, default 'th'
+        Covariance estimation: 'th' for semi-analytic theory, 'jk' for jackknife,
+        'sim' for a covariance measured on simulations.
+    n_sim_cov : int, default 300
+        Number of simulations the 'sim' rho/tau covariance was measured on.
+        The covariance file does not record it; it sets the Hartlap debiasing
+        of the inverse covariance in the PSF-leakage fits.
     compute_cov_rho : bool, default True
         Whether to compute covariance for rho statistics during PSF analysis.
     n_cov : int, default 100
@@ -279,6 +284,7 @@ def __init__(
         cov_estimate_method="th",
         compute_cov_rho=True,
         n_cov=100,
+        n_sim_cov=300,
         theta_min=0.1,
         theta_max=250,
         nbins=20,
@@ -310,6 +316,7 @@ def __init__(
         self.cov_estimate_method = cov_estimate_method
         self.compute_cov_rho = compute_cov_rho
         self.n_cov = n_cov
+        self.n_sim_cov = n_sim_cov
         self.theta_min = theta_min
         self.theta_max = theta_max
         self.npatch = npatch
diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index 58f92d09..7c0cc140 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -446,7 +446,11 @@ def set_psf_parameter_nwalkers(self, nwalkers):
         self.psf_error_nwalkers = nwalkers
 
     def get_samples(self, version, params, tomo_bin_id, track_result=False):
-        npatch = params["patch_number"] if self.cov_estimate_method == "jk" else None
+        # Hartlap debiasing of the inverse covariance: the number of jackknife
+        # patches or of simulations the covariance was estimated from.
+        n_realisations = {"jk": params["patch_number"], "sim": self.n_sim_cov}.get(
+            self.cov_estimate_method
+        )
 
         base_rho = self.basename(version)
         base_tau = self.basename(version, tomo_bin_a=tomo_bin_id)
@@ -464,7 +468,7 @@ def get_samples(self, version, params, tomo_bin_id, track_result=False):
             base_rho,
             base_tau,
             cov_type=self.cov_estimate_method,
-            apply_debias=npatch,
+            apply_debias=n_realisations,
             sampler=self.rho_tau_method,
             nsamples=n_samples,
             nwalkers=n_walkers,
diff --git a/src/sp_validation/tests/test_psf_leakage.py b/src/sp_validation/tests/test_psf_leakage.py
index 0290ad4e..fe91b3bd 100644
--- a/src/sp_validation/tests/test_psf_leakage.py
+++ b/src/sp_validation/tests/test_psf_leakage.py
@@ -260,3 +260,36 @@ def test_version_missing_a_column_stays_dropped():
     assert set(cv.leakage_coeff) == {"good"}
     cv.calculate_objectwise_leakage(tomography=True)
     assert "tomo_bin_1" in cv.leakage_coeff["good"]
+
+
+@pytest.mark.parametrize(
+    "cov_estimate_method, expected",
+    [("sim", 500), ("jk", 150), ("th", None)],
+)
+def test_rho_tau_fit_debiases_estimated_covariances(
+    monkeypatch, cov_estimate_method, expected
+):
+    """Simulation and jackknife covariances get Hartlap-debiased; theory does not.
+
+    The debiasing count is the number of simulations (``n_sim_cov``) or of
+    jackknife patches the covariance was estimated from.
+    """
+    from sp_validation.cosmo_val import psf_systematics
+
+    seen = {}
+
+    def fake_get_samples(*args, apply_debias, **kwargs):
+        seen["apply_debias"] = apply_debias
+        return None, None, None
+
+    monkeypatch.setattr(psf_systematics, "get_samples", fake_get_samples)
+    cv = SimpleNamespace(
+        cov_estimate_method=cov_estimate_method,
+        n_sim_cov=500,
+        rho_tau_method="lsq",
+        psf_fitter=None,
+        basename=lambda version, tomo_bin_a="all": version,
+    )
+    cv.get_samples = PSFSystematicsMixin.get_samples.__get__(cv)
+    cv.get_samples("v", {"patch_number": 150}, "all")
+    assert seen["apply_debias"] == expected
diff --git a/workflow/common.py b/workflow/common.py
index b250399c..25c7da78 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -398,6 +398,7 @@ def cv_basename(version, fiducial=None):
     "cov_estimate_method",
     "compute_cov_rho",
     "n_cov",
+    "n_sim_cov",
     "theta_min",
     "theta_max",
     "nbins",

From e6406e46313e52a36dad524d71b0fed69fe37afa Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 00:22:22 +0200
Subject: [PATCH 61/83] Seed jackknife patches across tomographic bins
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

TreeCorr's unseeded k-means and thread-dependent tree depth changed patch layouts, ξ±, and jackknife covariances between runs. Compute seeded, fixed-depth centres once from each full version catalogue and share them across bin pairs; use the same helper for rho/tau covariance patches. Add reproducibility and cross-bin regressions.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/sp_validation/cosmo_val/real_space.py |  33 ++++--
 src/sp_validation/rho_tau.py              |   9 +-
 src/sp_validation/statistics.py           |  40 ++++++-
 src/sp_validation/tests/test_cosmo_val.py | 132 ++++++++++++++++------
 4 files changed, 164 insertions(+), 50 deletions(-)

diff --git a/src/sp_validation/cosmo_val/real_space.py b/src/sp_validation/cosmo_val/real_space.py
index fd1f2bbd..7dea3542 100644
--- a/src/sp_validation/cosmo_val/real_space.py
+++ b/src/sp_validation/cosmo_val/real_space.py
@@ -11,6 +11,8 @@
 import numpy as np
 import treecorr
 
+from sp_validation.statistics import jackknife_patch_centers
+
 
 class RealSpaceMixin:
     def calculate_2pcf_version(
@@ -53,8 +55,8 @@ def calculate_2pcf_version(
             - The non-tomographic pair is written to the columns-only TreeCorr
               dump ``xi_{basename}.txt``. If that file already exists, the pair is
               read back from it instead of being recomputed.
-            - If a patch file for the given configuration does not exist, it is
-              created during the process.
+            - Seeded patch centres are computed once from the full catalogue and
+              shared by every tomographic bin pair.
         """
 
         npatch = npatch or self.npatch
@@ -88,13 +90,12 @@ def calculate_2pcf_version(
             to_compute.append((bin1, bin2))
 
         if to_compute:
-            patch_file = self._output_path(f"{ver}_patches_npatch={npatch}.dat")
             cols = self._shear_columns(ver, compute_tomography)
+            patch_centers = self._patch_centers(cols, npatch)
 
             for bin1, bin2 in to_compute:
                 gg = treecorr.GGCorrelation(treecorr_config)
 
-                patch_centers = patch_file if os.path.exists(patch_file) else None
                 cat_gal1 = self._bin_catalog(cols, bin1, npatch, patch_centers)
                 cat_gal2 = (
                     self._bin_catalog(cols, bin2, npatch, patch_centers)
@@ -102,10 +103,6 @@ def calculate_2pcf_version(
                     else None
                 )
 
-                # If no patch file exists, save the current patches
-                if not os.path.exists(patch_file):
-                    cat_gal1.write_patch_centers(patch_file)
-
                 gg.process(cat_gal1, cat2=cat_gal2)
 
                 if (bin1, bin2) == ("all", "all"):
@@ -154,6 +151,19 @@ def _shear_columns(self, ver, compute_tomography):
                 ),
             }
 
+    def _patch_centers(self, cols, npatch):
+        """Seeded patch centres from the full catalogue of one version."""
+        if int(npatch) <= 1:
+            return None
+        cat = treecorr.Catalog(
+            ra=cols["ra"],
+            dec=cols["dec"],
+            w=cols["w"],
+            ra_units=self.treecorr_config["ra_units"],
+            dec_units=self.treecorr_config["dec_units"],
+        )
+        return jackknife_patch_centers(cat, int(npatch))
+
     def _bin_catalog(self, cols, tomo_bin_id, npatch, patch_centers=None):
         """TreeCorr catalogue of one tomographic bin (``"all"``: every row)."""
         mask = slice(None) if tomo_bin_id == "all" else cols["tomo_bin"] == tomo_bin_id
@@ -239,13 +249,16 @@ def calculate_aperture_mass_dispersion(
 
             self._map2.setdefault(ver, {})
             cols = self._shear_columns(ver, compute_tomography)
+            patch_centers = self._patch_centers(cols, npatch)
 
             for bin1, bin2 in tomo_bin_pairs:
                 gg = treecorr.GGCorrelation(treecorr_config)
 
-                cat_gal1 = self._bin_catalog(cols, bin1, npatch)
+                cat_gal1 = self._bin_catalog(cols, bin1, npatch, patch_centers)
                 cat_gal2 = (
-                    self._bin_catalog(cols, bin2, npatch) if bin1 != bin2 else None
+                    self._bin_catalog(cols, bin2, npatch, patch_centers)
+                    if bin1 != bin2
+                    else None
                 )
 
                 gg.process(cat_gal1, cat2=cat_gal2)
diff --git a/src/sp_validation/rho_tau.py b/src/sp_validation/rho_tau.py
index 614f1589..526e02e6 100644
--- a/src/sp_validation/rho_tau.py
+++ b/src/sp_validation/rho_tau.py
@@ -14,6 +14,7 @@
 # SquareRootScale lives in sp_validation.plots; re-exported here so that
 # `from sp_validation.rho_tau import SquareRootScale` keeps working.
 from sp_validation.plots import SquareRootScale  # noqa: F401
+from sp_validation.statistics import jackknife_patch_centers
 
 
 def _extract_xip(correlations):
@@ -504,10 +505,10 @@ def chunks(i):
                     print(f"Computing the patch centers for patch {i + 1}/{ncov}")
 
                     npatch = rho_stat_handler.catalogs._params["patch_number"]
-                    field = rho_stat_handler.catalogs.catalogs_dict[
-                        f"psf_{catalog_id(i)}"
-                    ].getNField(max_top=int.bit_length(npatch) - 1, coords="spherical")
-                    patch, centers = field.run_kmeans(npatch)
+                    centers = jackknife_patch_centers(
+                        rho_stat_handler.catalogs.catalogs_dict[f"psf_{catalog_id(i)}"],
+                        npatch,
+                    )
 
                     # Update the patch centers of the catalogs
                     for key, cat in rho_stat_handler.catalogs.catalogs_dict.items():
diff --git a/src/sp_validation/statistics.py b/src/sp_validation/statistics.py
index d3919c4b..bd95932b 100644
--- a/src/sp_validation/statistics.py
+++ b/src/sp_validation/statistics.py
@@ -3,8 +3,8 @@
 :Name: statistics.py
 
 :Description: Cosmology-independent statistical helpers (jackknife resampling,
-              chi2/PTE, calibrated min-PTE across many null tests,
-              covariance<->correlation, OneCovariance reshaping).
+              jackknife patch centres, chi2/PTE, calibrated min-PTE across
+              many null tests, covariance<->correlation, OneCovariance reshaping).
 """
 
 import itertools
@@ -13,6 +13,42 @@
 import numpy as np
 from scipy import stats
 
+#: Depth of the ball-tree layers that seed the jackknife k-means.
+PATCH_MIN_TOP = 6
+
+
+def jackknife_patch_centers(cat, npatch, seed=0):
+    """Seeded k-means jackknife patch centres for a TreeCorr catalogue.
+
+    Seeding the k-means does not by itself fix the patches. TreeCorr starts the
+    k-means from the top layers of a ball tree whose depth, unless ``min_top``
+    is given, grows with the OpenMP thread count (``Field._determine_top``),
+    and ``Catalog(npatch=..., rng=...)`` offers no way to set it. Pinning that
+    depth makes the centres a function of the catalogue's positions, weights
+    and ``seed`` alone, on every machine.
+
+    Parameters
+    ----------
+    cat : treecorr.Catalog
+        Catalogue with spherical (RA, Dec) positions.
+    npatch : int
+        Number of patches.
+    seed : int, optional
+        Seed for the k-means initialisation.
+
+    Returns
+    -------
+    numpy.ndarray
+        Patch centres, to pass as ``patch_centers`` to ``treecorr.Catalog``.
+    """
+    field = cat.getNField(
+        min_top=PATCH_MIN_TOP,
+        max_top=int.bit_length(npatch) - 1,
+        coords="spherical",
+    )
+    _, centers = field.run_kmeans(npatch, rng=np.random.default_rng(seed))
+    return centers
+
 
 def jackknif_weighted_average2(
     data,
diff --git a/src/sp_validation/tests/test_cosmo_val.py b/src/sp_validation/tests/test_cosmo_val.py
index daa33a30..7029cdcd 100644
--- a/src/sp_validation/tests/test_cosmo_val.py
+++ b/src/sp_validation/tests/test_cosmo_val.py
@@ -283,6 +283,7 @@ def _write_synthetic_catalogs(
         seed=1234,
         coherent_shear=False,
         with_psf=False,
+        with_tomography=False,
     ):
         """Write small deterministic FITS catalogs + dndz, return a config dict.
 
@@ -300,6 +301,8 @@ def _write_synthetic_catalogs(
         with_psf : bool
             If True, add a ``psf`` config block (rho/tau / pseudo-Cl read it via
             ``get_params_rho_tau``).
+        with_tomography : bool
+            If True, add two tomographic bins to the shear catalogue.
         """
         from astropy.table import Table
 
@@ -325,9 +328,10 @@ def _write_synthetic_catalogs(
         w = rng.uniform(0.5, 1.0, n_gal)
 
         shear_path = cat_dir / "shear.fits"
-        Table({"RA": ra, "Dec": dec, "e1": e1, "e2": e2, "w": w}).write(
-            shear_path, overwrite=True
-        )
+        shear_data = {"RA": ra, "Dec": dec, "e1": e1, "e2": e2, "w": w}
+        if with_tomography:
+            shear_data["tomo_bin_id"] = rng.integers(1, 3, n_gal)
+        Table(shear_data).write(shear_path, overwrite=True)
 
         star_path = cat_dir / "star.fits"
         Table(
@@ -362,6 +366,8 @@ def _write_synthetic_catalogs(
             "e1_col_corrected": "e1",
             "e2_col_corrected": "e2",
         }
+        if with_tomography:
+            shear_cfg["tomo_bin_col"] = "tomo_bin_id"
         star_cfg = {
             "path": "star.fits",
             "ra_col": "RA",
@@ -506,42 +512,100 @@ def test_a_patched_xi_dump_reads_back(self, tmp_path):
                 getattr(read, column), getattr(measured, column), rtol=1e-4
             )
 
-    def test_calculate_2pcf_does_not_depend_on_thread_count(self, tmp_path):
-        """calculate_2pcf's ξ± is the same on 4 and on 48 TreeCorr threads.
-
-        Production binning (default bin_slop/angle_slop), both runs on the
-        jackknife patches the first one writes, each from a fresh Catalog; they
-        must agree to far below the jackknife σ.
-        """
+    def test_calculate_2pcf_is_reproducible_across_machines(
+        self, tmp_path, monkeypatch
+    ):
+        """Fresh calculate_2pcf_version runs share patches and ξ± across CPU counts."""
         import treecorr
+        import treecorr.field
+
+        patches = []
+        process = treecorr.GGCorrelation.process
+
+        def recording_process(gg, cat, *args, **kwargs):
+            patches.append(np.array(cat.patch))
+            return process(gg, cat, *args, **kwargs)
+
+        monkeypatch.setattr(treecorr.GGCorrelation, "process", recording_process)
+
+        xi, var, counts = {}, {}, {}
+        for tree, n_cpu in (("a", 4), ("b", 16)):
+            monkeypatch.setattr(treecorr.field, "get_omp_threads", lambda n=n_cpu: n)
+            run_dir = tmp_path / tree
+            run_dir.mkdir()
+            params, version = self._write_synthetic_catalogs(
+                run_dir,
+                n_gal=4000,
+                ra_range=(0.0, 60.0),
+                dec_range=(-10.0, 30.0),
+                coherent_shear=True,
+            )
+            gg = CosmologyValidation(
+                versions=[version],
+                npatch=100,
+                theta_min=15.0,
+                theta_max=70.0,
+                nbins=6,
+                **params,
+            ).calculate_2pcf_version(version, num_threads=n_cpu)[
+                "tomo_bin_all_tomo_bin_all"
+            ]
+            xi[tree] = np.concatenate([gg.xip, gg.xim])
+            var[tree] = np.concatenate([gg.varxip, gg.varxim])
+            counts[tree] = {key: result.npairs for key, result in gg.results.items()}
+
+        np.testing.assert_array_equal(patches[0], patches[1])
+        assert counts["a"].keys() == counts["b"].keys()
+        for key in counts["a"]:
+            np.testing.assert_array_equal(counts["a"][key], counts["b"][key])
+        np.testing.assert_allclose(xi["a"], xi["b"], rtol=0, atol=1e-12)
+        np.testing.assert_allclose(var["a"], var["b"], rtol=1e-10)
+
+    def test_cross_tomographic_pair_shares_patch_centres(self, tmp_path, monkeypatch):
+        """Both catalogues in a cross-bin pair use the full-sample centres."""
+        import treecorr
+
+        from sp_validation.statistics import jackknife_patch_centers
 
         params, version = self._write_synthetic_catalogs(
-            tmp_path, n_gal=4000, coherent_shear=True
+            tmp_path, n_gal=400, with_tomography=True
         )
-        cv = CosmologyValidation(
-            versions=[version],
-            npatch=8,
-            theta_min=15.0,
-            theta_max=70.0,
-            nbins=6,
-            **params,
+        cv = CosmologyValidation(versions=[version], npatch=4, **params)
+        cols = cv._shear_columns(version, compute_tomography=True)
+        full_catalog = treecorr.Catalog(
+            ra=cols["ra"],
+            dec=cols["dec"],
+            w=cols["w"],
+            ra_units=cv.treecorr_config["ra_units"],
+            dec_units=cv.treecorr_config["dec_units"],
+        )
+        expected_centers = jackknife_patch_centers(full_catalog, 4)
+        catalogs = []
+        make_catalog = cv._bin_catalog
+
+        def recording_bin_catalog(cols, bin_id, npatch, patch_centers=None):
+            catalog = make_catalog(cols, bin_id, npatch, patch_centers)
+            catalogs.append((bin_id, patch_centers, np.array(catalog._centers)))
+            return catalog
+
+        monkeypatch.setattr(cv, "_bin_catalog", recording_bin_catalog)
+        cv.calculate_2pcf_version(version, npatch=4, compute_tomography=True)
+
+        assert len(catalogs) == 4
+        cross_pair_catalogs = catalogs[1:3]
+        assert [entry[0] for entry in cross_pair_catalogs] == [1, 2]
+        np.testing.assert_array_equal(
+            cross_pair_catalogs[0][1], cross_pair_catalogs[1][1]
+        )
+        np.testing.assert_array_equal(
+            cross_pair_catalogs[0][2], cross_pair_catalogs[1][2]
+        )
+        np.testing.assert_allclose(
+            cross_pair_catalogs[0][2], expected_centers, rtol=0, atol=1e-14
+        )
+        np.testing.assert_allclose(
+            cross_pair_catalogs[1][2], expected_centers, rtol=0, atol=1e-14
         )
-
-        xi = {}
-        for n_threads in (4, 48):
-            # calculate_2pcf_version reads back an existing text dump instead of
-            # measuring.
-            for dump in Path(params["output_dir"]).glob(f"xi_{version}_*.txt"):
-                dump.unlink()
-            gg = cv.calculate_2pcf_version(version, num_threads=n_threads)[
-                "tomo_bin_all_tomo_bin_all"
-            ]
-            assert treecorr.get_omp_threads() == n_threads  # the count took effect
-            xi[n_threads] = np.concatenate([gg.xip, gg.xim])
-            sigma = np.sqrt(np.concatenate([gg.varxip, gg.varxim]))
-
-        shift = np.max(np.abs(xi[48] - xi[4]) / sigma)
-        assert shift < 1e-6, f"ξ± moves by {shift:.3g}σ between 4 and 48 threads"
 
     def test_treecorr_runs_on_the_cpus_the_process_holds(self, tmp_path):
         """By default TreeCorr takes the process's CPU affinity, not the node's count."""

From 989e0eb1899c16cc05f8f286984ee22deb22d94e Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 01:26:31 +0200
Subject: [PATCH 62/83] Expect the rho/tau SACC part under the tomo_bin_all
 basename in the DAG test

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0138Efdjkma53ysbYhhXDgxW
---
 workflow/tests/test_dag.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/workflow/tests/test_dag.py b/workflow/tests/test_dag.py
index b11acf3b..438f91a1 100644
--- a/workflow/tests/test_dag.py
+++ b/workflow/tests/test_dag.py
@@ -33,7 +33,7 @@ def test_assemble_resolves(toy):
             f"pseudo_cl_cov_{version}_{harmonic}.fits",
             f"{version}_cosebis.sacc",
             f"{version}_pure_eb.sacc",
-            f"rho_tau_{version}_{reporting}.sacc",
+            f"rho_tau_{version}_tomo_bin_all_{reporting}.sacc",
         }, job.input
 
 

From 88fa858f5cce84b42d646fbe13553a892f426b04 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 01:54:28 +0200
Subject: [PATCH 63/83] Route rho/tau workflow paths through cv_basename
 helpers

The tomography branch inserts _tomo_bin_all into CosmologyValidation.basename,
but inference.smk and covariance.smk still hard-coded the pre-tomography
rho_stats/tau_stats/cov_tau names, so they could never match what
rho_tau_stats writes. cv_rho_stats, cv_tau_stats and a new cv_cov_tau now
live in common.py (cosmo_val.smk is only included with a cosmo_val config,
so its helpers were invisible to the compute rules) and take the binning
explicitly: CV_FIDUCIAL in cosmo_val, the wildcards in inference_prep,
FIDUCIAL for the glass-mock rules.

cv_objectwise_leakage reads every version's rho/tau FITS through
_load_alpha_leakage but declared no inputs; it now declares them so it is
scheduled after rho_tau_stats.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 workflow/common.py            | 18 ++++++++++++++++++
 workflow/rules/cosmo_val.smk  | 27 +++++++++------------------
 workflow/rules/covariance.smk |  7 ++-----
 workflow/rules/inference.smk  | 10 +++++-----
 4 files changed, 34 insertions(+), 28 deletions(-)

diff --git a/workflow/common.py b/workflow/common.py
index 25c7da78..111da5f5 100644
--- a/workflow/common.py
+++ b/workflow/common.py
@@ -390,6 +390,24 @@ def cv_basename(version, fiducial=None):
     )
 
 
+def cv_rho_stats(version, fiducial=None):
+    """ρ-statistics FITS that CosmologyValidation writes for a version."""
+    base = cv_basename(version, fiducial)
+    return str(COSMO_VAL / "rho_tau_stats" / f"rho_stats_{base}.fits")
+
+
+def cv_tau_stats(version, fiducial=None):
+    """τ-statistics FITS that CosmologyValidation writes for a version."""
+    base = cv_basename(version, fiducial)
+    return str(COSMO_VAL / "rho_tau_stats" / f"tau_stats_{base}.fits")
+
+
+def cv_cov_tau(version, fiducial=None):
+    """Theoretical τ covariance that CosmologyValidation writes for a version."""
+    base = cv_basename(version, fiducial)
+    return str(COSMO_VAL / "rho_tau_stats" / f"cov_tau_{base}_th.npy")
+
+
 # CosmologyValidation constructor kwargs read from config["cosmo_val"]. Every
 # keyword with a default is either here or explicitly exempted in
 # src/sp_validation/tests/test_cv_init_params.py, so no default applies silently.
diff --git a/workflow/rules/cosmo_val.smk b/workflow/rules/cosmo_val.smk
index 699bc9de..f4eb0e2a 100644
--- a/workflow/rules/cosmo_val.smk
+++ b/workflow/rules/cosmo_val.smk
@@ -58,18 +58,6 @@ def cv_xi_txt(version):
     return str(COSMO_VAL / f"xi_{version}_tomo_bin_all_{xi_binning('reporting')}.txt")
 
 
-def cv_rho_stats(version):
-    return str(
-        COSMO_VAL / "rho_tau_stats" / f"rho_stats_{cv_basename(version, CV_FIDUCIAL)}.fits"
-    )
-
-
-def cv_tau_stats(version):
-    return str(
-        COSMO_VAL / "rho_tau_stats" / f"tau_stats_{cv_basename(version, CV_FIDUCIAL)}.fits"
-    )
-
-
 def _pure_eb_stub(version):
     """Shared stem of the pure-E/B diagnostic products (npz + figures)."""
     eb = XI_GRIDS["integration"]
@@ -220,7 +208,7 @@ CV_INIT = cv_init_params(config)
 rule cv_plot_rho_stats:
     """Overlay rho statistics across all versions."""
     input:
-        rho=[cv_rho_stats(v) for v in CV_VERSIONS],
+        rho=[cv_rho_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
     output:
         sentinel=str(CV_SENTINELS / "plot_rho_stats.done"),
     params:
@@ -234,7 +222,7 @@ rule cv_plot_rho_stats:
 rule cv_plot_tau_stats:
     """Overlay tau statistics across all versions."""
     input:
-        tau=[cv_tau_stats(v) for v in CV_VERSIONS],
+        tau=[cv_tau_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
     output:
         sentinel=str(CV_SENTINELS / "plot_tau_stats.done"),
     params:
@@ -248,8 +236,8 @@ rule cv_plot_tau_stats:
 rule cv_rho_tau_fits:
     """Fit the PSF-error model (alpha/beta/eta) and propagate to xi_psf_sys."""
     input:
-        rho=[cv_rho_stats(v) for v in CV_VERSIONS],
-        tau=[cv_tau_stats(v) for v in CV_VERSIONS],
+        rho=[cv_rho_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
+        tau=[cv_tau_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
     output:
         sentinel=str(CV_SENTINELS / "rho_tau_fits.done"),
     params:
@@ -280,6 +268,9 @@ rule cv_footprints:
 
 rule cv_objectwise_leakage:
     """Object-wise PSF-leakage regression vs scale-dependent alpha (all versions)."""
+    input:
+        rho=[cv_rho_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
+        tau=[cv_tau_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
     output:
         sentinel=str(CV_SENTINELS / "objectwise_leakage.done"),
     params:
@@ -345,8 +336,8 @@ rule cv_ratio_xi_sys_xi:
     """Ratio of PSF systematics (xi_psf_sys) to the cosmic-shear signal (xi+)."""
     input:
         xi=[cv_xi_txt(v) for v in CV_VERSIONS],
-        rho=[cv_rho_stats(v) for v in CV_VERSIONS],
-        tau=[cv_tau_stats(v) for v in CV_VERSIONS],
+        rho=[cv_rho_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
+        tau=[cv_tau_stats(v, CV_FIDUCIAL) for v in CV_VERSIONS],
     output:
         ratio=str(COSMO_VAL / "ratio_xi_sys_xi.png"),
     params:
diff --git a/workflow/rules/covariance.smk b/workflow/rules/covariance.smk
index 6ca907f9..b1a7faf1 100644
--- a/workflow/rules/covariance.smk
+++ b/workflow/rules/covariance.smk
@@ -212,17 +212,14 @@ rule covariance_glass_mock:
         "../scripts/compute_glass_mock_covariance.py"
 
 
-# fiducial_binning_suffix() defined in Snakefile
-
-
 rule generate_glass_mock_rhotau_samples:
     """Generate sampled tau statistics for glass mocks.
 
     Only tau is sampled; inference_prep_glass_mock uses real rho data.
     """
     input:
-        cov_tau=str(COSMO_VAL / f"rho_tau_stats/cov_tau_{FIDUCIAL['mock_version']}{fiducial_binning_suffix()}_th.npy"),
-        ref_tau=str(COSMO_VAL / f"rho_tau_stats/tau_stats_{FIDUCIAL['mock_version']}{fiducial_binning_suffix()}.fits"),
+        cov_tau=cv_cov_tau(FIDUCIAL["mock_version"]),
+        ref_tau=cv_tau_stats(FIDUCIAL["mock_version"]),
     output:
         tau="results/glass_mock_rhotau_samples/{mock_id}/tau_stats_sampled.fits",
     params:
diff --git a/workflow/rules/inference.smk b/workflow/rules/inference.smk
index f707e895..00e96137 100644
--- a/workflow/rules/inference.smk
+++ b/workflow/rules/inference.smk
@@ -59,10 +59,10 @@ rule inference_prep:
         # n(z) file: the catalogue entry's
         nz_file=lambda w: redshift_path(w.version),
         # rho/tau stats
-        rho_stats=str(COSMO_VAL / "rho_tau_stats/rho_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
-        tau_stats=str(COSMO_VAL / "rho_tau_stats/tau_stats_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}.fits"),
+        rho_stats=lambda w: cv_rho_stats(w.version, w),
+        tau_stats=lambda w: cv_tau_stats(w.version, w),
         # tau covariance (tracked as dependency)
-        tau_cov=str(COSMO_VAL / "rho_tau_stats/cov_tau_{version}_minsep={min_sep}_maxsep={max_sep}_nbins={nbins}_npatch={npatch}_th.npy"),
+        tau_cov=lambda w: cv_cov_tau(w.version, w),
         pseudo_cl=lambda w: pseudo_cl_assets(w.version)[0],
         pseudo_cl_cov=lambda w: pseudo_cl_assets(w.version)[1],
     output:
@@ -131,10 +131,10 @@ rule inference_prep_glass_mock:
         # n(z) file
         nz_file=redshift_path(FIDUCIAL["mock_version"]),
         # Rho/tau stats: rho from real data, tau sampled
-        rho_stats=str(COSMO_VAL / f"rho_tau_stats/rho_stats_{FIDUCIAL['mock_version']}{fiducial_binning_suffix()}.fits"),
+        rho_stats=cv_rho_stats(FIDUCIAL["mock_version"]),
         tau_stats="results/glass_mock_rhotau_samples/{mock_id}/tau_stats_sampled.fits",
         # Tau covariance (real data)
-        tau_cov=str(COSMO_VAL / f"rho_tau_stats/cov_tau_{FIDUCIAL['mock_version']}{fiducial_binning_suffix()}_th.npy"),
+        tau_cov=cv_cov_tau(FIDUCIAL["mock_version"]),
         # C_ell data for dual config generation
         cl_file=f"{GLASS_MOCK_DATA_DIR}/cl_glass_mock_{{mock_id}}_4096.npy",
         cl_cov=pseudo_cl_assets(FIDUCIAL["mock_version"])[1],

From f2275a73f7648044993dd3a63cde868e77906b83 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 17:27:51 +0200
Subject: [PATCH 64/83] Keep the iNKA fiducial C_ell on CAMB, as the tomography
 branch computes it

Reverts d603c5c1. The CAMB/CCL difference is mostly the non-linear
model (mead2020 vs halofit), not an error, and the fiducial only sets
the signal term of a Gaussian covariance. The one real defect, cs_util's
CCL-to-CAMB conversion dropping m_nu, belongs in cs_util.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0138Efdjkma53ysbYhhXDgxW
---
 src/sp_validation/pseudo_cl.py            |  8 ++------
 src/sp_validation/tests/test_pseudo_cl.py | 24 -----------------------
 2 files changed, 2 insertions(+), 30 deletions(-)

diff --git a/src/sp_validation/pseudo_cl.py b/src/sp_validation/pseudo_cl.py
index 5bb1b392..f52ab1d1 100644
--- a/src/sp_validation/pseudo_cl.py
+++ b/src/sp_validation/pseudo_cl.py
@@ -969,14 +969,10 @@ def get_pseudo_cls_catalog(
 
 
 # ---------------------- Covariance computation functions ----------------------
-def get_fiducial_cl(z, dndz, lmax, cosmo, backend="ccl"):
+def get_fiducial_cl(z, dndz, lmax, cosmo, backend="camb"):
     """
     Get the fiducial Cl's using the redshift distribution.
-
-    Cosmology is determined by the input CCL cosmo object. The default CCL
-    backend evaluates that object as configured; cs_util's CAMB backend
-    rebuilds a CAMB cosmology from it, dropping the neutrino mass and using
-    CAMB's own non-linear model.
+    Cosmology is determined by the input cosmo object.
     """
     ell = np.arange(1, lmax + 1)
 
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index 9f99a67c..cd01ebe6 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -829,30 +829,6 @@ def test_fiducial_pixel_window_applies_only_to_map():
         npt.assert_array_equal(fiducial[key], cl)
 
 
-def test_fiducial_cl_follows_the_configured_cosmology():
-    """The iNKA fiducial is CCL's prediction for the configured cosmology.
-
-    A non-default neutrino mass makes the check bite: cs_util's CAMB backend
-    drops it.
-    """
-    import pyccl as ccl
-    from cs_util.cosmo import get_cosmo
-
-    from sp_validation.pseudo_cl import get_fiducial_cl
-
-    cosmo = get_cosmo(mnu=0.3)
-    z = np.linspace(0.01, 3.0, 200)
-    dndz = np.exp(-(((z - 0.7) / 0.3) ** 2))
-    lmax = 128
-    fiducial = get_fiducial_cl(z, dndz, lmax, cosmo)["W1xW1"]
-
-    ell = np.arange(1, lmax + 1)
-    tracer = ccl.WeakLensingTracer(cosmo, dndz=(z, dndz))
-    npt.assert_allclose(
-        fiducial, ccl.angular_cl(cosmo, tracer, tracer, ell), rtol=1e-10
-    )
-
-
 def test_calculate_pseudo_cl_catalog_end_to_end_tomo(cv, tmp_path):
     """End-to-end catalog path: FITS round-trip of ell + EE/EB/BB.
 

From d6356c9629826b6974cc19a93de5bb7d9bdd2ec0 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 17:29:34 +0200
Subject: [PATCH 65/83] Tolerate NFS placeholders when the covariance rule
 clears its work directory

The iNKA covariance was computed and moved into place, then the job
failed removing its temporary directory: files still open leave .nfs
placeholders. Also report the BB block's real shape (it printed 5x5
for a 32-bin block).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0138Efdjkma53ysbYhhXDgxW
---
 workflow/scripts/generate_pseudo_cl_cov.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/workflow/scripts/generate_pseudo_cl_cov.py b/workflow/scripts/generate_pseudo_cl_cov.py
index 4f00f7f9..0364f1bf 100644
--- a/workflow/scripts/generate_pseudo_cl_cov.py
+++ b/workflow/scripts/generate_pseudo_cl_cov.py
@@ -25,6 +25,7 @@
 """
 
 import argparse
+import gc
 import json
 import os
 import shutil
@@ -141,9 +142,8 @@ def generate_pseudo_cl_cov(
     if os.path.exists(src_cov):
         with fits.open(src_cov) as hdul:
             # CV outputs covariance blocks as COVAR_XX_YY extensions
-            cov = hdul["COVAR_BB_BB"].data
-            n_ell = int(len(cov) ** 0.5)
-            print(f"Generated covariance matrix: {n_ell}x{n_ell}")
+            n_row, n_col = hdul["COVAR_BB_BB"].data.shape
+            print(f"Generated BB covariance block: {n_row}x{n_col}")
     return src_cov
 
 
@@ -154,8 +154,12 @@ def _from_snakemake(smk):
     os.makedirs(out_dir, exist_ok=True)
     # The iNKA blocks and the merged covariance are cached under names that
     # carry the binning only, so the job computes in a directory of its own and
-    # never picks up a block another configuration left behind.
-    with tempfile.TemporaryDirectory(dir=out_dir, prefix=".pseudo_cl_cov_") as work:
+    # never picks up a block another configuration left behind. On NFS, files
+    # still held open at exit leave .nfs placeholders that block the removal,
+    # so a leftover work directory is tolerated rather than failing the job.
+    with tempfile.TemporaryDirectory(
+        dir=out_dir, prefix=".pseudo_cl_cov_", ignore_cleanup_errors=True
+    ) as work:
         src_cov = generate_pseudo_cl_cov(
             version=p["version"],
             output_dir=work,
@@ -168,6 +172,7 @@ def _from_snakemake(smk):
             power=float(p.get("power", 0.5)),
         )
         shutil.move(src_cov, output_cov)
+        gc.collect()
     print(f"Saved to: {output_cov}")
 
 

From 454e802c8906ef557ebc050542a1bd1cb4e61564 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 18:53:04 +0200
Subject: [PATCH 66/83] plot_tau_stats: use the tau-statistics error bars when
 no covariance type is given
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

A cov_type of None was formatted into the covariance file name
(cov_tau_…_None.npy), so the workflow's tau plot failed.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0138Efdjkma53ysbYhhXDgxW
---
 src/sp_validation/cosmo_val/psf_systematics.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/sp_validation/cosmo_val/psf_systematics.py b/src/sp_validation/cosmo_val/psf_systematics.py
index 7c0cc140..b189e622 100644
--- a/src/sp_validation/cosmo_val/psf_systematics.py
+++ b/src/sp_validation/cosmo_val/psf_systematics.py
@@ -945,9 +945,13 @@ def plot_tau_stats(
 
         else:
             filenames = [f"tau_stats_{self.basename(ver)}.fits" for ver in versions]
-            cov_paths = [
-                f"cov_tau_{self.basename(ver)}_{cov_type}.npy" for ver in versions
-            ]
+            cov_paths = (
+                None
+                if cov_type is None
+                else [
+                    f"cov_tau_{self.basename(ver)}_{cov_type}.npy" for ver in versions
+                ]
+            )
             self.tau_stat_handler.plot_tau_stats(
                 filenames,
                 colors,

From a769e10b702101c8f7cf519b515f4373e3ac56ed Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Sat, 3 Oct 2026 18:56:04 +0200
Subject: [PATCH 67/83] Plot tau statistics with the configured tau covariance

The rho/tau rule writes the covariance of cov_estimate_method next to
the tau statistics; the plot now draws its error bars from it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0138Efdjkma53ysbYhhXDgxW
---
 workflow/scripts/cv_plot_tau_stats.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/workflow/scripts/cv_plot_tau_stats.py b/workflow/scripts/cv_plot_tau_stats.py
index 8975ed81..a2c6f365 100644
--- a/workflow/scripts/cv_plot_tau_stats.py
+++ b/workflow/scripts/cv_plot_tau_stats.py
@@ -1,6 +1,7 @@
 """Rule cv_plot_tau_stats: tau-statistics overlay across versions.
 
-Reads tau_stats_{base}.fits for every version (declared inputs); writes
+Reads tau_stats_{base}.fits for every version (declared inputs), with error
+bars from the version's tau covariance of the configured type; writes
 tau_stats.png into the leakage output dir. Sentinel-tracked (see rho_stats).
 """
 
@@ -8,5 +9,10 @@
 
 _unbuffer_streams()
 cv = make_cv(snakemake)
-cv.plot_tau_stats(tomography=False, savefig="tau_stats.png", show=False)
+cv.plot_tau_stats(
+    tomography=False,
+    cov_type=cv.cov_estimate_method,
+    savefig="tau_stats.png",
+    show=False,
+)
 touch_sentinels(snakemake)

From 03bd3add23983da905a68ee64396eba31f2fee9b Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:24:47 +0200
Subject: [PATCH 68/83] Keep the joint pure-B Hartlap correction tied to its
 full data dimension

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 ...est_tomo_joint_pure_b_hartlap_dimension.py | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_joint_pure_b_hartlap_dimension.py

diff --git a/src/sp_validation/tests/regression/test_tomo_joint_pure_b_hartlap_dimension.py b/src/sp_validation/tests/regression/test_tomo_joint_pure_b_hartlap_dimension.py
new file mode 100644
index 00000000..177e9837
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_joint_pure_b_hartlap_dimension.py
@@ -0,0 +1,39 @@
+"""Joint xi+ B / xi- B statistics must debias the full joint inverse covariance."""
+
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+from scipy import stats
+
+from sp_validation.b_modes import calculate_eb_statistics
+
+
+@pytest.mark.parametrize("start, stop", [(0, 4), (0, 3), (1, 3)])
+def test_joint_b_mode_chi2_hartlap_uses_joint_dimension_2n(start, stop):
+    """The combined B-mode PTE must apply Hartlap with p = 2n, not p = n.
+
+    An inverse sample covariance from N patches over p entries requires
+    (N - p - 2)/(N - 1). For identity covariance the raw joint chi2 is the
+    sum of squared xi+ B and xi- B over the cut, so the PTE is known by hand.
+    Full and interior cuts check that p is twice the *selected* bin count.
+    Develop already uses p = 2n; this unmarked test protects that correct
+    behaviour against the tomography branch's single-block p = n regression.
+    """
+    nbins, npatch = 4, 50
+    results = {
+        # Both API contracts: develop reads theta/npatch; tomography reads gg.
+        "theta": np.geomspace(1.0, 100.0, nbins),
+        "npatch": npatch,
+        "gg": SimpleNamespace(nbins=nbins, npatch1=npatch, npatch2=npatch),
+        "cov": np.eye(6 * nbins),
+        "xip_B": np.linspace(0.5, 2.0, nbins),
+        "xim_B": np.linspace(-1.5, 1.0, nbins),
+    }
+    pte = calculate_eb_statistics(results)["pte_matrices"]["combined"]
+    n = stop - start
+    raw = np.sum(results["xip_B"][start:stop] ** 2) + np.sum(
+        results["xim_B"][start:stop] ** 2
+    )
+    expected = stats.chi2.sf((npatch - 2 * n - 2) / (npatch - 1) * raw, 2 * n)
+    assert pte[start, stop - 1] == pytest.approx(expected, rel=1e-10)

From c38086835f999443046c5e0e9b3fc1d8db144170 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:24:47 +0200
Subject: [PATCH 69/83] Protect the pinned TreeCorr layout from node CPU-count
 changes

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 .../regression/test_tomo_drops_min_top_pin.py | 101 ++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_drops_min_top_pin.py

diff --git a/src/sp_validation/tests/regression/test_tomo_drops_min_top_pin.py b/src/sp_validation/tests/regression/test_tomo_drops_min_top_pin.py
new file mode 100644
index 00000000..09cea030
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_drops_min_top_pin.py
@@ -0,0 +1,101 @@
+"""ξ± from CosmologyValidation must not depend on the host's CPU count."""
+
+import multiprocessing
+
+import numpy as np
+import pytest
+import treecorr
+import yaml
+
+from sp_validation.cosmo_val import CosmologyValidation
+
+
+@pytest.fixture(autouse=True)
+def _restore_treecorr_threads():
+    previous = treecorr.get_omp_threads()
+    try:
+        yield
+    finally:
+        treecorr.set_omp_threads(previous)
+
+
+def _cosmology_validation(tmp_path):
+    """Minimal CosmologyValidation: only the constructor runs, no catalogue is read."""
+    cfg = {
+        "paths": {"output": str(tmp_path / "out")},
+        "nz": {"subdir": str(tmp_path), "dndz": {"path": "nz.txt"}},
+        "synthetic": {"subdir": str(tmp_path), "shear": {"path": "cat.fits"}},
+    }
+    cfg_path = tmp_path / "cat_config.yaml"
+    cfg_path.write_text(yaml.safe_dump(cfg))
+    return CosmologyValidation(
+        ["synthetic"],
+        catalog_config=str(cfg_path),
+        npatch=8,
+        theta_min=15.0,
+        theta_max=70.0,
+        nbins=6,
+    )
+
+
+def _synthetic_catalog(**kwargs):
+    """4000 galaxies over ~4x4 deg with a spatially correlated shear field."""
+    rng = np.random.default_rng(1234)
+    n = 4000
+    ra = rng.uniform(150.0, 154.0, n)
+    dec = rng.uniform(0.0, 4.0, n)
+    g1 = 0.02 * np.sin(np.radians(ra) * 90) + 0.25 * rng.standard_normal(n)
+    g2 = 0.02 * np.cos(np.radians(dec) * 90) + 0.25 * rng.standard_normal(n)
+    return treecorr.Catalog(
+        ra=ra, dec=dec, g1=g1, g2=g2, ra_units="deg", dec_units="deg", **kwargs
+    )
+
+
+def _xi_on_node(cv, n_cpu, patch_centers, monkeypatch):
+    """ξ± as calculate_2pcf_version builds it (instance npatch, jackknife, fixed
+    patch centres, fresh Catalog), on a node with n_cpu CPUs."""
+    monkeypatch.setattr(multiprocessing, "cpu_count", lambda: n_cpu)
+    # Simulate the thread count seen by Field's default min_top calculation,
+    # without actually launching 48 threads inside a two-core test allocation.
+    # Leave min_top itself untouched: the missing pin must remain observable.
+    monkeypatch.setattr(treecorr.field, "get_omp_threads", multiprocessing.cpu_count)
+    config = {**cv._binning(), "var_method": "jackknife"}
+    gg = treecorr.GGCorrelation(config)
+    gg.process(_synthetic_catalog(patch_centers=patch_centers), num_threads=2)
+    return gg
+
+
+def test_xi_pm_identical_on_16_and_48_cpu_nodes(tmp_path, monkeypatch):
+    """Protects the machine independence of ξ±. TreeCorr picks the depth of its root
+    cells (min_top) as max(3, ceil(log2 n_threads)) when the config leaves it unset,
+    and the thread count defaults to the node's CPU count; min_top decides which pairs
+    bin_slop approximates, so ξ± changes between a 16-CPU node
+    (min_top=4) and a 48-CPU node (min_top=6) once the catalogue is split into jackknife
+    patches. The same catalogue, patch centres and CosmologyValidation settings must
+    give the same ξ± on either node, to far below the jackknife σ (1e-6σ), which holds
+    once the config pins min_top (and num_threads). This passes on develop,
+    whose pin must survive the tomography merge. Only Field's view of the host
+    thread count is simulated; the actual correlation runs on two cores.
+    """
+    cv = _cosmology_validation(tmp_path)
+    patch_file = str(tmp_path / "patches.dat")
+    treecorr.set_omp_threads(2)
+    _synthetic_catalog(
+        npatch=cv.npatch, rng=np.random.default_rng(99)
+    ).write_patch_centers(patch_file)
+    gg16 = _xi_on_node(cv, 16, patch_file, monkeypatch)
+    gg48 = _xi_on_node(cv, 48, patch_file, monkeypatch)
+    sigma = np.sqrt(np.concatenate([gg48.varxip, gg48.varxim]))
+    shift = (
+        np.abs(
+            np.concatenate([gg16.xip, gg16.xim]) - np.concatenate([gg48.xip, gg48.xim])
+        )
+        / sigma
+    )
+
+    assert shift.max() < 1e-6, (
+        f"ξ± moves by {shift.max():.3g}σ between a 16- and a 48-CPU node "
+        f"(xip {shift[: cv.nbins].max():.3g}σ, xim {shift[cv.nbins :].max():.3g}σ); "
+        f"treecorr_config min_top={cv.treecorr_config.get('min_top')}, "
+        f"num_threads={cv.treecorr_config.get('num_threads')}"
+    )

From 5e5a640bac6e2512486132aa52b2858dc5a9e3c2 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:24:48 +0200
Subject: [PATCH 70/83] Require pure-E/B MC rebinning to use the data pair
 weights

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 ...st_tomo_mc_covariance_uniform_rebinning.py | 105 ++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_mc_covariance_uniform_rebinning.py

diff --git a/src/sp_validation/tests/regression/test_tomo_mc_covariance_uniform_rebinning.py b/src/sp_validation/tests/regression/test_tomo_mc_covariance_uniform_rebinning.py
new file mode 100644
index 00000000..aafed418
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_mc_covariance_uniform_rebinning.py
@@ -0,0 +1,105 @@
+"""Pure-E/B MC covariance must describe the data's pair-weighted xi estimator."""
+
+import inspect
+import sys
+import types
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from sp_validation import b_modes as bm
+
+# Reporting bins are exact unions of eight fine log bins, so no partial-bin
+# interpolation enters the independently constructed pair-weighted reference.
+EDGES_INT = np.geomspace(1.0, 100.0, 41)
+THETA_INT = np.sqrt(EDGES_INT[:-1] * EDGES_INT[1:])
+REPORT_IDX = np.array([4, 12, 20, 28, 36])
+EDGES_REP = EDGES_INT[REPORT_IDX]
+WEIGHT_INT = THETA_INT**2  # annular area, hence pair counts, grows as theta^2
+
+
+def _pair_weighted_binning():
+    matrix = np.zeros((len(EDGES_REP) - 1, len(THETA_INT)))
+    for i, (lo, hi) in enumerate(zip(REPORT_IDX[:-1], REPORT_IDX[1:])):
+        matrix[i, lo:hi] = WEIGHT_INT[lo:hi] / WEIGHT_INT[lo:hi].sum()
+    return matrix
+
+
+def test_mc_reporting_xi_is_pair_weighted_mean_of_fine_bins(monkeypatch, tmp_path):
+    """Each MC reporting xi must be sum_j w_j xi_j / sum_j w_j, like TreeCorr.
+
+    Reporting-bin xi feeds the local term of the pure-E/B transform. A uniform
+    average of fine bins would give a covariance for a different estimator
+    from the pair-weighted data. Exact unions of fine bins make the reference
+    independent of the package's binning implementation.
+    Develop's explicit operator already uses pair weights, so this test is
+    unmarked: it protects against the tomography branch's uniform MC rebinning.
+    The transform and theory stubs only expose its inputs, not fix the binning.
+    """
+    reference = _pair_weighted_binning()
+    rng = np.random.default_rng(0)
+    if "cov_path_int" in inspect.signature(bm.calculate_pure_eb_correlation).parameters:
+        # Tomography API: observe reporting and fine xi passed to the transform.
+        n_int = len(THETA_INT)
+        gg = SimpleNamespace(
+            meanr=np.sqrt(EDGES_REP[:-1] * EDGES_REP[1:]),
+            left_edges=EDGES_REP[:-1],
+            right_edges=EDGES_REP[1:],
+            xip=np.zeros(4),
+            xim=np.zeros(4),
+        )
+        gg_int = SimpleNamespace(
+            meanr=THETA_INT,
+            left_edges=EDGES_INT[:-1],
+            right_edges=EDGES_INT[1:],
+            xip=np.zeros(n_int),
+            xim=np.zeros(n_int),
+            weight=WEIGHT_INT,
+        )
+        cov_path = tmp_path / "cov_int.txt"
+        np.savetxt(cov_path, np.eye(2 * n_int))
+        calls = []
+
+        def observe_pure_eb(theta, xip, xim, theta_int, xip_int, xim_int, **kwargs):
+            calls.append((np.array([xip, xim]), np.array([xip_int, xim_int])))
+            return tuple(np.zeros(len(theta)) for _ in range(6))
+
+        # Replace only the transform dependency, avoiding its eager JIT import.
+        # The package's actual MC sampling and rebinning still run unchanged.
+        module = types.ModuleType("cosmo_numba.B_modes.schneider2022")
+        module.get_pure_EB_modes = observe_pure_eb
+        monkeypatch.setitem(sys.modules, "cosmo_numba.B_modes.schneider2022", module)
+        monkeypatch.setattr(
+            bm, "get_theo_xi", lambda **kwargs: (np.zeros(n_int), np.zeros(n_int))
+        )
+        # Keep the package's MC sampling but avoid changing global random state.
+        monkeypatch.setattr(np.random, "multivariate_normal", rng.multivariate_normal)
+        with pytest.warns(UserWarning, match="covariance matrix is not positive"):
+            bm.calculate_pure_eb_correlation(
+                gg,
+                gg_int,
+                cov_path_int=str(cov_path),
+                cosmo_cov=object(),
+                n_samples=3,
+                z_dist=np.ones((5, 2)),
+            )
+        mc = calls[1:]  # First call transforms data, not an MC sample.
+        assert len(mc) == 3
+        got = np.array([reporting for reporting, fine in mc])
+        want = np.array([fine @ reference.T for reporting, fine in mc])
+    else:
+        # Develop API: rebinning is the explicit linear operator.
+        matrix, edges = bm._reporting_binning(WEIGHT_INT, EDGES_INT, EDGES_REP)
+        np.testing.assert_allclose(edges, EDGES_REP)
+        fine = rng.standard_normal((3, len(THETA_INT)))
+        got = fine @ matrix.T
+        want = fine @ reference.T
+
+    np.testing.assert_allclose(
+        got,
+        want,
+        rtol=0,
+        atol=1e-12,
+        err_msg="MC reporting xi is not the pair-weighted mean of fine bins",
+    )

From d1a1c9412178a94a363a4b4d06023b9a30787574 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:17 +0200
Subject: [PATCH 71/83] Keep covariance jobs inside the launched checkout and
 results tree

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 ...covariance_rules_call_external_checkout.py | 239 ++++++++++++++++++
 1 file changed, 239 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_covariance_rules_call_external_checkout.py

diff --git a/src/sp_validation/tests/regression/test_tomo_covariance_rules_call_external_checkout.py b/src/sp_validation/tests/regression/test_tomo_covariance_rules_call_external_checkout.py
new file mode 100644
index 00000000..c5079874
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_covariance_rules_call_external_checkout.py
@@ -0,0 +1,239 @@
+"""Exercise real covariance rules without catalogues or CosmoCov.
+
+These tests pass on develop: it uses checkout-local scripts and launch-relative
+outputs. They guard against the external paths introduced on the tomography
+branch. Snakemake >=8 is optional; all generated workflow files stay in tmp_path.
+"""
+
+import json
+import os
+import shlex
+import subprocess
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+import sp_validation
+
+
+def _run_snakemake(case, *args):
+    env = dict(os.environ)
+    env.update(
+        PYTHONDONTWRITEBYTECODE="1",
+        XDG_CACHE_HOME=str(case.directory / "cache"),
+        MPLCONFIGDIR=str(case.directory / "mplconfig"),
+        TMPDIR=str(case.directory / "tmp"),
+        # Preserve the dependency location if pytest loaded Snakemake via its
+        # pythonpath setting rather than the interpreter's site-packages.
+        PYTHONPATH=str(case.snakemake_root) + os.pathsep + env.get("PYTHONPATH", ""),
+    )
+    return subprocess.run(
+        [
+            sys.executable,
+            "-m",
+            "snakemake",
+            "--snakefile",
+            str(case.snakefile),
+            "--cores",
+            "1",
+            "--profile",
+            "none",
+            "--workflow-profile",
+            "none",
+            "--nolock",
+            "--scheduler",
+            "greedy",
+            *args,
+        ],
+        cwd=case.directory,
+        env=env,
+        text=True,
+        capture_output=True,
+        timeout=60,
+    )
+
+
+@pytest.fixture
+def covariance_rules(tmp_path):
+    snakemake = pytest.importorskip(
+        "snakemake",
+        minversion="8",
+        reason="Real covariance rules require Snakemake >=8",
+    )
+    # Use the imported package's __file__, not cwd or a hard-coded checkout.
+    # This also lets PYTHONPATH select tomography while keeping this test file.
+    root = Path(sp_validation.__file__).resolve().parents[2]
+    source = root / "workflow/rules/covariance.smk"
+    if not source.is_file():
+        pytest.skip("Checkout does not contain workflow/rules/covariance.smk")
+    (tmp_path / "tmp").mkdir()
+    snakefile = tmp_path / "Snakefile"
+    case = SimpleNamespace(
+        root=root,
+        directory=tmp_path,
+        snakefile=snakefile,
+        snakemake_root=Path(snakemake.__file__).resolve().parents[1],
+    )
+    # Only globals normally supplied by the workflow are synthetic. Snakemake
+    # parses the unmodified rules, resolves entry points and executes the job.
+    snakefile.write_text(
+        f"""
+from pathlib import Path
+from snakemake.io import apply_wildcards
+import json
+COSMO_INFERENCE = Path({str(tmp_path / "cosmo_inference")!r})
+COSMO_VAL = Path({str(tmp_path / "cosmo_val")!r})
+COSMOLOGY_PARAMS = str(COSMO_INFERENCE / "cosmology.json")
+GLASS_MOCK_DIR = str(Path({str(tmp_path / "glass_mock")!r}))
+GLASS_MOCK_SUITE = "glass_mock_v1.4.6"
+PLANCK18 = dict.fromkeys(
+    ["Omega_m", "Omega_v", "sigma_8", "n_s", "h", "Omega_b"], 0.0
+)
+BLOCK_PAIRS = []
+DEFAULT_MASK_SUFFIX = ""
+FIDUCIAL = dict.fromkeys([
+    "mock_version", "version", "blind", "min_sep", "max_sep", "nbins",
+    "min_sep_int", "max_sep_int", "nbins_int",
+], "synthetic")
+config = {{
+    "tools": {{"cosmocov_executable": "unused"}},
+    "glass_mocks": {{"seed_range": [1, 1]}},
+}}
+def fiducial_binning_suffix():
+    return "_synthetic"
+def covariance_path(*args, **kwargs):
+    return "unused-fiducial-input"
+def covariance_dir(*args, **kwargs):
+    return "unused-covariance-directory"
+def redshift_path(*args, **kwargs):
+    return "unused-redshift-input"
+def build_redshift_path(*args, **kwargs):
+    return "unused-redshift-input"
+def cv_cov_tau(*args, **kwargs):
+    return "unused-tau-covariance"
+def cv_tau_stats(*args, **kwargs):
+    return "unused-tau-statistics"
+wildcard_constraints:
+    mask_suffix="(?:_masked)?"
+include: {str(source)!r}
+manifest = {{}}
+for name in [
+    "covariance_process", "generate_glass_mock_rhotau_samples",
+    "covariance_glass_mock",
+]:
+    registered = workflow.get_rule(name)
+    manifest[name] = {{
+        "script": registered.script, "shellcmd": registered.shellcmd,
+        "basedir": str(registered.basedir),
+        "output": list(map(str, registered.output)),
+    }}
+synthetic_wildcards = dict(
+    version="synthetic", blind="A", gaussian="ng", min_sep="1", max_sep="2",
+    nbins="1", mask_suffix="",
+)
+manifest["covariance_process"]["synthetic_matrix"] = apply_wildcards(
+    workflow.get_rule("covariance_process").output[0], synthetic_wildcards
+)
+Path("rules.json").write_text(json.dumps(manifest, indent=2))
+"""
+    )
+    # Upper-triangle dump: columns 8 and 9 contain Gaussian and non-Gaussian
+    # terms. Their sum is the positive-definite matrix [[3, .25], [.25, 6]].
+    raw = np.array(
+        [
+            [0, 0, 0, 0, 0, 0, 0, 0, 2, 1],
+            [0, 1, 0, 0, 0, 0, 0, 0, 0.25, 0],
+            [1, 1, 0, 0, 0, 0, 0, 0, 4, 2],
+        ]
+    )
+    parsed = _run_snakemake(case, "--list-rules")
+    assert parsed.returncode == 0, parsed.stdout + parsed.stderr
+    case.manifest = json.loads((tmp_path / "rules.json").read_text())
+    case.matrix = Path(case.manifest["covariance_process"]["synthetic_matrix"])
+    raw_path = Path(str(case.matrix).replace("_processed.txt", ".txt"))
+    raw_path.parent.mkdir(parents=True)
+    np.savetxt(raw_path, raw)
+    return case
+
+
+@pytest.mark.slow
+def test_covariance_postprocessing_runs_from_launched_checkout(covariance_rules):
+    """Process a synthetic CosmoCov dump without relying on another checkout.
+
+    The three input rows give G+NG = [[3, .25], [.25, 6]], reflecting the supplied
+    off-diagonal element, and G = [[2, .25], [.25, 4]]. Snakemake executes the
+    actual rule with existing raw input, so no survey data or expensive
+    covariance calculation is needed. A missing external entry point is a
+    workflow defect. Develop passes because its processor is checkout-local;
+    this guards against the tomography merge reintroducing an external script.
+    """
+    case = covariance_rules
+    result = _run_snakemake(case, "--printshellcmds", str(case.matrix))
+    log = result.stdout + result.stderr
+    diagnosis = next(
+        (line for line in log.splitlines() if "can't open file" in line), log[-2500:]
+    )
+    assert result.returncode == 0, (
+        f"covariance_process returned {result.returncode}; expected exit 0 and "
+        f"matrix [[3.0, 0.25], [0.25, 6.0]]; {diagnosis}"
+    )
+    np.testing.assert_allclose(
+        np.loadtxt(case.matrix), [[3, 0.25], [0.25, 6]], rtol=0, atol=0
+    )
+    gaussian = Path(str(case.matrix).replace("_processed.txt", "_processed_g.txt"))
+    np.testing.assert_allclose(
+        np.loadtxt(gaussian), [[2, 0.25], [0.25, 4]], rtol=0, atol=0
+    )
+
+
+def test_tau_sampling_selects_script_in_launched_checkout(covariance_rules):
+    """The real tau-sampling rule must select code from the launched checkout.
+
+    Identical script contents today cannot guarantee this: edits to an unrelated
+    checkout must not change a fixed commit's mock samples. Inspect Snakemake's
+    registered entry point instead of inferring provenance from random values.
+    Develop passes with its local script directive; the tomography merge must
+    not restore an external entry point. No survey inputs are read.
+    """
+    case = covariance_rules
+    rule = case.manifest["generate_glass_mock_rhotau_samples"]
+    if rule["script"]:
+        selected = (Path(rule["basedir"]) / rule["script"]).resolve()
+    else:
+        tokens = shlex.split(rule["shellcmd"])
+        assert tokens[0] == "python", f"Unexpected tau command: {rule['shellcmd']}"
+        selected = (case.directory / tokens[1]).resolve()
+    expected = (
+        case.root / "workflow/scripts/generate_glass_mock_rhotau_samples.py"
+    ).resolve()
+    assert selected == expected, (
+        f"Tau sampling selects {selected}; expected launched-checkout script {expected}"
+    )
+    assert selected.is_file(), f"Selected tau script is missing: {selected}"
+
+
+def test_glass_covariance_outputs_stay_in_launch_directory(covariance_rules):
+    """All seven GLASS outputs must live in the launch directory's results tree.
+
+    Resolve the real rule's output objects relative to the synthetic launch
+    directory, the correct base for portable products and independent launches.
+    No GLASS catalogues are read or external products written. Develop passes
+    with relative outputs; this guards against the tomography merge restoring
+    absolute paths that could overwrite another analysis.
+    """
+    case = covariance_rules
+    expected_dir = case.directory / "results/covariance/glass_mock_v1.4.6"
+    outputs = case.manifest["covariance_glass_mock"]["output"]
+    assert len(outputs) == 7
+    wrong = []
+    for output in outputs:
+        actual = (case.directory / output).resolve()
+        if actual.parent != expected_dir:
+            wrong.append(str(actual))
+    assert not wrong, (
+        f"GLASS covariance outputs escape launch directory: {wrong}; "
+        f"expected directory {expected_dir}"
+    )

From bdfed2ba153b09d16f7b96d2c6de7d9b6007709c Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:18 +0200
Subject: [PATCH 72/83] Keep the rho/tau driver on the launched catalogue
 configuration

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 ...t_tomo_rho_tau_driver_external_checkout.py | 101 ++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_rho_tau_driver_external_checkout.py

diff --git a/src/sp_validation/tests/regression/test_tomo_rho_tau_driver_external_checkout.py b/src/sp_validation/tests/regression/test_tomo_rho_tau_driver_external_checkout.py
new file mode 100644
index 00000000..8343238f
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_rho_tau_driver_external_checkout.py
@@ -0,0 +1,101 @@
+"""The rho_tau_stats driver must use the checkout that launched the workflow."""
+
+import io
+import os
+import runpy
+import sys
+import types
+from pathlib import Path
+
+import pytest
+
+import sp_validation
+import sp_validation.cosmo_val as cosmo_val_mod
+
+
+class _Stop(Exception):
+    """Stop before any catalogue reads or output writes."""
+
+
+def test_rho_tau_driver_reads_cat_config_from_launched_checkout(monkeypatch, tmp_path):
+    """Resolve the catalogue config and output inside the launched checkout.
+
+    The constructor stub records cwd and arguments, then stops before reading
+    survey data. Defaults './cat_config.yaml' and './output' resolve from cwd.
+    The expected checkout contains the imported sp_validation code; ordinarily
+    that is the repository containing this test, but a branch comparison can
+    import a different checkout while retaining this test file.
+
+    Develop passes explicit paths from rule params and does not chdir, so this
+    test is unmarked: it guards against the tomography branch's regression of
+    changing cwd to an unrelated, hard-coded checkout and using defaults.
+    """
+    checkout = Path(__file__).resolve().parents[4]
+    code_checkout = Path(sp_validation.__file__).resolve().parents[2]
+    if code_checkout != checkout:
+        checkout = code_checkout
+    driver = checkout / "workflow" / "scripts" / "run_rho_tau.py"
+    if not driver.is_file():
+        pytest.skip("rho/tau workflow driver from the tomography branch is absent")
+
+    seen = {}
+
+    def fake_cv(*args, **kwargs):
+        seen["cwd"] = Path.cwd()
+        seen["kwargs"] = kwargs
+        raise _Stop
+
+    monkeypatch.setattr(cosmo_val_mod, "CosmologyValidation", fake_cv)
+    fake_snk = types.SimpleNamespace(
+        params={
+            "ver": "SP_v1.4.6.3",
+            "min_sep": "1.0",
+            "max_sep": "250.0",
+            "nbins": "20",
+            "npatch": "20",
+            "cat_config": str(checkout / "cosmo_val" / "cat_config.yaml"),
+            "output_dir": str(checkout / "cosmo_val" / "output"),
+        },
+        output={},
+        input={},
+    )
+    snk_script = types.ModuleType("snakemake.script")
+    snk_script.snakemake = fake_snk
+    monkeypatch.setitem(sys.modules, "snakemake.script", snk_script)
+    try:
+        import IPython
+    except ImportError:
+        pytest.skip("tomography branch driver requires IPython")
+    monkeypatch.setattr(IPython, "get_ipython", lambda: None)
+    # Stream unbuffering is unrelated to path resolution. Avoid closing pytest's
+    # captured descriptors when either driver generation calls os.fdopen.
+    monkeypatch.setattr(os, "fdopen", lambda *args, **kwargs: io.StringIO())
+    # Register the original streams for restoration after the driver's assignment.
+    monkeypatch.setattr(sys, "stdout", sys.stdout)
+    monkeypatch.setattr(sys, "stderr", sys.stderr)
+    monkeypatch.syspath_prepend(str(driver.parent))
+    monkeypatch.chdir(tmp_path)
+
+    try:
+        with pytest.raises(_Stop):
+            runpy.run_path(str(driver), init_globals={"snakemake": fake_snk})
+    except FileNotFoundError as exc:
+        # The tomography driver may chdir to a survey checkout absent on CI.
+        missing = Path(exc.filename) if exc.filename else None
+        if (
+            missing
+            and missing.is_absolute()
+            and missing.parts[1] in {"n17data", "n09data", "n23data1", "home"}
+        ):
+            pytest.skip(
+                f"tomography branch external checkout is unavailable: {missing}"
+            )
+        raise
+
+    kw = seen["kwargs"]
+    cat_cfg = (seen["cwd"] / kw.get("catalog_config", "./cat_config.yaml")).resolve()
+    out_dir = (seen["cwd"] / (kw.get("output_dir") or "./output")).resolve()
+    assert cat_cfg.is_relative_to(checkout) and out_dir.is_relative_to(checkout), (
+        f"driver used catalog_config={cat_cfg}, output_dir={out_dir}, "
+        f"cwd={seen['cwd']}; expected paths inside launched checkout {checkout}"
+    )

From b0a1652b389e4205bdaefdbfd52e7440226010e1 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:18 +0200
Subject: [PATCH 73/83] Require both configured HSM flags to exclude failed
 star fits

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 .../test_rho_tau_ignores_hsm_flags.py         | 166 ++++++++++++++++++
 1 file changed, 166 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_rho_tau_ignores_hsm_flags.py

diff --git a/src/sp_validation/tests/regression/test_rho_tau_ignores_hsm_flags.py b/src/sp_validation/tests/regression/test_rho_tau_ignores_hsm_flags.py
new file mode 100644
index 00000000..4562c81a
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_rho_tau_ignores_hsm_flags.py
@@ -0,0 +1,166 @@
+"""Rho/tau must exclude stars whose configured HSM PSF or star fit failed.
+
+Clean stars are perfectly modelled, making rho_1 and tau_2 zero by construction.
+"""
+
+import inspect
+
+import numpy as np
+from astropy.io import fits
+
+from sp_validation.cosmo_val.psf_systematics import PSFSystematicsMixin
+
+VERSION = "SP_synthetic"
+N_GOOD = 2000
+N_FLAGGED = 100
+N_GAL = 3000
+
+
+def _write_catalogues(tmp_path):
+    rng = np.random.default_rng(1)
+    n = N_GOOD + N_FLAGGED
+    ra = rng.uniform(10.0, 12.0, n)
+    dec = rng.uniform(0.0, 2.0, n)
+    e1_psf = rng.normal(0.0, 0.02, n)
+    e2_psf = rng.normal(0.0, 0.02, n)
+    t_psf = rng.uniform(0.4, 0.6, n)
+
+    # Good stars: q = e_star - e_PSF = 0 and T_star = T_PSF exactly.
+    e1_star = e1_psf.copy()
+    e2_star = e2_psf.copy()
+    t_star = t_psf.copy()
+    flag_psf = np.zeros(n, dtype=np.int16)
+    flag_star = np.zeros(n, dtype=np.int16)
+
+    # Failed fits contribute q = (-0.05, 0) if either flag is ignored.
+    bad = slice(N_GOOD, n)
+    e1_psf[bad] = 0.05
+    e2_psf[bad] = 0.0
+    e1_star[bad] = 0.0
+    e2_star[bad] = 0.0
+    t_star[bad] = 0.069
+    flag_star[N_GOOD : N_GOOD + N_FLAGGED // 2] = 1
+    flag_psf[N_GOOD + N_FLAGGED // 2 : n] = 1
+
+    psf_path = tmp_path / "psf.fits"
+    fits.BinTableHDU.from_columns(
+        [
+            fits.Column("RA", "D", array=ra),
+            fits.Column("DEC", "D", array=dec),
+            fits.Column("HSM_G1_PSF", "D", array=e1_psf),
+            fits.Column("HSM_G2_PSF", "D", array=e2_psf),
+            fits.Column("HSM_G1_STAR", "D", array=e1_star),
+            fits.Column("HSM_G2_STAR", "D", array=e2_star),
+            fits.Column("HSM_T_PSF", "D", array=t_psf),
+            fits.Column("HSM_T_STAR", "D", array=t_star),
+            fits.Column("HSM_FLAG_PSF", "I", array=flag_psf),
+            fits.Column("HSM_FLAG_STAR", "I", array=flag_star),
+        ]
+    ).writeto(psf_path)
+
+    shear_path = tmp_path / "shear.fits"
+    fits.BinTableHDU.from_columns(
+        [
+            fits.Column("RA", "D", array=rng.uniform(10.0, 12.0, N_GAL)),
+            fits.Column("Dec", "D", array=rng.uniform(0.0, 2.0, N_GAL)),
+            fits.Column("e1", "D", array=rng.normal(0.0, 0.3, N_GAL)),
+            fits.Column("e2", "D", array=rng.normal(0.0, 0.3, N_GAL)),
+            fits.Column("w_des", "D", array=np.ones(N_GAL)),
+        ]
+    ).writeto(shear_path)
+    return psf_path, shear_path
+
+
+class _Validation(PSFSystematicsMixin):
+    """Minimal host carrying the configuration read by the real entry point."""
+
+    def __init__(self, cc):
+        self.cc = cc
+        self.versions = [VERSION]
+        self.treecorr_config = {
+            "ra_units": "deg",
+            "dec_units": "deg",
+            "sep_units": "arcmin",
+            "min_sep": 2.0,
+            "max_sep": 100.0,
+            "nbins": 5,
+            "num_threads": 2,
+        }
+        self.cov_estimate_method = "sim"
+        self.compute_cov_rho = False
+        self.npatch = 4
+
+    def basename(self, version, tomo_bin_a="all", **kwargs):
+        return f"{version}_{tomo_bin_a}"
+
+    def _get_galaxy_mask(self, ver, tomo_bin_id):
+        return np.ones(N_GAL, dtype=bool)
+
+    def rho_tau_to_sacc_part(self, *args, **kwargs):
+        pass
+
+    def print_start(self, *args, **kwargs):
+        pass
+
+    print_done = print_cyan = print_magenta = print_start
+
+
+def test_flagged_stars_excluded_from_rho_1_and_tau_2(tmp_path):
+    """Both configured HSM flags must exclude failed fits from rho/tau.
+
+    Every unflagged star has e_star == e_PSF exactly, so q is identically zero:
+    rho_1 = <q q> and tau_2 = <e_gal q> must be zero in every bin.
+    Half of the bad stars have only HSM_FLAG_STAR set, and half have only
+    HSM_FLAG_PSF set; their q = (-0.05, 0) catches ignoring either flag.
+    This merges the two audit reproductions and exercises the pipeline's
+    calculate_rho_tau_stats entry point, not a separately masked reference.
+    """
+    psf_path, shear_path = _write_catalogues(tmp_path)
+    out = tmp_path / "out"
+    out.mkdir()
+    cc = {
+        "paths": {"output": str(out)},
+        VERSION: {
+            "patch_number": 4,
+            "cov_th": {"A": 4.0, "n_e": 1.0, "n_psf": 1.0, "sigma_e": 0.3},
+            "psf": {
+                "path": str(psf_path),
+                "hdu": 1,
+                "ra_col": "RA",
+                "dec_col": "DEC",
+                "e1_PSF_col": "HSM_G1_PSF",
+                "e2_PSF_col": "HSM_G2_PSF",
+                "e1_star_col": "HSM_G1_STAR",
+                "e2_star_col": "HSM_G2_STAR",
+                "PSF_size": "HSM_T_PSF",
+                "star_size": "HSM_T_STAR",
+                "PSF_flag": "HSM_FLAG_PSF",
+                "star_flag": "HSM_FLAG_STAR",
+            },
+            "shear": {
+                "path": str(shear_path),
+                "ra_col": "RA",
+                "dec_col": "Dec",
+                "w_col": "w_des",
+                "e1_col": "e1",
+                "e2_col": "e2",
+            },
+        },
+    }
+    rho_tau_dir = out / "rho_tau_stats"
+    rho_tau_dir.mkdir()
+    # The sim method only needs an existing covariance; don't run an estimator.
+    np.save(rho_tau_dir / f"cov_tau_{VERSION}_all_th.npy", np.eye(3))
+
+    val = _Validation(cc)
+    kwargs = (
+        {"tomography": False}
+        if "tomography" in inspect.signature(val.calculate_rho_tau_stats).parameters
+        else {}
+    )
+    val.calculate_rho_tau_stats(**kwargs)
+
+    rho_1 = np.asarray(val._rho_stat_handler.rho_stats["rho_1_p"])
+    tau_2 = np.asarray(val._tau_stat_handler.tau_stats["tau_2_p"])
+    np.testing.assert_allclose(rho_1, 0, atol=1e-12, rtol=0)
+    np.testing.assert_allclose(tau_2, 0, atol=1e-12, rtol=0)

From eda87f8f2f8f2770fbfd8580554bc0ad08d58bc5 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:19 +0200
Subject: [PATCH 74/83] Require PSF fit sample widths to follow the selected
 covariance

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 .../test_lsq_sample_cache_ignores_cov_type.py | 80 +++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_lsq_sample_cache_ignores_cov_type.py

diff --git a/src/sp_validation/tests/regression/test_lsq_sample_cache_ignores_cov_type.py b/src/sp_validation/tests/regression/test_lsq_sample_cache_ignores_cov_type.py
new file mode 100644
index 00000000..88e778df
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_lsq_sample_cache_ignores_cov_type.py
@@ -0,0 +1,80 @@
+"""Least-squares PSF-leakage samples must follow the requested tau covariance."""
+
+import inspect
+
+import numpy as np
+import pytest
+from astropy.table import Table
+from shear_psf_leakage.rho_tau_stat import PSFErrorFit, RhoStat, TauStat
+
+from sp_validation.rho_tau import get_samples_lsq
+
+CFG = dict(min_sep=1.0, max_sep=30.0, nbins=1, sep_units="arcmin")
+SIGMA_TH = 1e-3
+SIGMA_JK = 1e-1
+
+
+def _write_fixture(out):
+    # One theta bin: rho_0 = rho_1 = rho_3 = 1, other rho and all tau = 0.
+    # Each of alpha/beta/eta has independent unit response to a tau component.
+    rd = {"theta": [10.0]}
+    for i in range(6):
+        rd[f"rho_{i}_p"] = [1.0 if i in (0, 1, 3) else 0.0]
+    td = {"theta": [10.0]}
+    for i in (0, 2, 5):
+        td[f"tau_{i}_p"] = [0.0]
+    Table(rd).write(out / "rho_stats_fit.fits")
+    Table(td).write(out / "tau_stats_fit.fits")
+    np.save(out / "cov_tau_fit_th.npy", np.eye(3) * SIGMA_TH**2)
+    np.save(out / "cov_tau_fit_jk.npy", np.eye(3) * SIGMA_JK**2)
+    np.save(out / "cov_rho_fit_jk.npy", np.zeros((6, 6)))
+
+
+def _run(fitter, cov_type):
+    np.random.seed(23)
+    if "base_tau" in inspect.signature(get_samples_lsq).parameters:
+        # Tomography branch takes separate rho/tau basenames and a draw count.
+        return get_samples_lsq(fitter, "fit", "fit", cov_type=cov_type, nsamples=256)
+    return get_samples_lsq(fitter, "test", "fit", cov_type=cov_type)
+
+
+@pytest.fixture
+def restore_random_state():
+    state = np.random.get_state()
+    yield
+    np.random.set_state(state)
+
+
+def test_lsq_posterior_width_follows_cov_type_not_cached_th(
+    tmp_path, monkeypatch, restore_random_state
+):
+    """A jk request after a th request must use jk covariance, not cached th draws.
+
+    This one-bin unit-response model has tau = 0 and diagonal covariance
+    sigma_tau^2 I, so each parameter's posterior std is sigma_tau by
+    construction: 1e-3 for th and 1e-1 for jk. Using the same output directory
+    must not let a basename-only cache silently retain the previous method's
+    alpha/beta/eta errors and xi_psf_sys uncertainty bands.
+    """
+    _write_fixture(tmp_path)
+    rho = RhoStat(output=str(tmp_path), treecorr_config=CFG)
+    tau = TauStat(output=str(tmp_path), treecorr_config=CFG)
+    fitter = PSFErrorFit(rho, tau, str(tmp_path))
+    original = fitter.get_least_squares_params_samples
+
+    def small_sample(**kwargs):
+        # Only reduce Monte Carlo work; leave loading and cache logic untouched.
+        kwargs.update(n_samples=256, verbose=False)
+        return original(**kwargs)
+
+    monkeypatch.setattr(fitter, "get_least_squares_params_samples", small_sample)
+    s_th, _, _ = _run(fitter, "th")
+    std_th = s_th.std(axis=0)
+    assert np.allclose(std_th, SIGMA_TH, rtol=0.3), std_th
+
+    s_jk, _, _ = _run(fitter, "jk")
+    std_jk = s_jk.std(axis=0)
+    assert np.allclose(std_jk, SIGMA_JK, rtol=0.3), (
+        f"jk after th returned posterior std {std_jk}; expected ~{SIGMA_JK}, "
+        f"not cached th width ~{SIGMA_TH}"
+    )

From dd9e4676e14ef9a5481b4b1383c2802d6fc60ce9 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:19 +0200
Subject: [PATCH 75/83] Check pure-E/B statistics through the bin-keyed result
 API

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 .../test_tomo_pure_eb_mapping_keyerror.py     | 127 ++++++++++++++++++
 1 file changed, 127 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_pure_eb_mapping_keyerror.py

diff --git a/src/sp_validation/tests/regression/test_tomo_pure_eb_mapping_keyerror.py b/src/sp_validation/tests/regression/test_tomo_pure_eb_mapping_keyerror.py
new file mode 100644
index 00000000..2e6ce4bc
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_pure_eb_mapping_keyerror.py
@@ -0,0 +1,127 @@
+"""plot_pure_eb must accept the bin-keyed results from calculate_pure_eb."""
+
+import inspect
+
+import numpy as np
+import pytest
+import treecorr
+
+pure_eb = pytest.importorskip(
+    "sp_validation.cosmo_val.pure_eb",
+    reason="PureEBMixin is required for the tomography branch's bin-keyed API",
+)
+if not hasattr(pure_eb, "PureEBMixin"):
+    pytest.skip(
+        "PureEBMixin is missing (tomography branch API)", allow_module_level=True
+    )
+PureEBMixin = pure_eb.PureEBMixin
+BIN_KEY = "tomo_bin_all_tomo_bin_all"
+
+
+def _synthetic_gg(min_sep, max_sep, nbins, npatch):
+    rng = np.random.default_rng(1)
+    n = 8000
+    cat = treecorr.Catalog(
+        ra=rng.uniform(150.0, 152.0, n),
+        dec=rng.uniform(1.0, 3.0, n),
+        g1=rng.normal(0, 0.2, n),
+        g2=rng.normal(0, 0.2, n),
+        ra_units="deg",
+        dec_units="deg",
+        npatch=npatch,
+        rng=rng,
+    )
+    gg = treecorr.GGCorrelation(
+        min_sep=min_sep,
+        max_sep=max_sep,
+        nbins=nbins,
+        var_method="jackknife",
+        sep_units="arcmin",
+        bin_slop=0.0,
+    )
+    gg.process(cat, num_threads=2)
+    return gg
+
+
+class _TomoCV(PureEBMixin):
+    """Minimal host supplying the tomography branch's documented 2PCF mapping."""
+
+    versions = ["v"]
+    npatch = 32
+    treecorr_config = {"min_sep": 2.0, "max_sep": 60.0, "nbins": 3}
+
+    def __init__(self, output):
+        self.cc = {"paths": {"output": str(output)}}
+        self._pure_eb_results = {}
+
+    def print_start(self, *args, **kwargs):
+        pass
+
+    def _binning(self, min_sep=None, max_sep=None, nbins=None):
+        return {
+            "min_sep": min_sep or self.treecorr_config["min_sep"],
+            "max_sep": max_sep or self.treecorr_config["max_sep"],
+            "nbins": nbins or self.treecorr_config["nbins"],
+        }
+
+    def calculate_2pcf_version(self, version, npatch, compute_tomography, **config):
+        return {
+            BIN_KEY: _synthetic_gg(
+                config["min_sep"], config["max_sep"], config["nbins"], npatch
+            )
+        }
+
+
+@pytest.mark.slow
+def test_plot_pure_eb_passes_one_bin_result_to_eb_statistics(tmp_path, monkeypatch):
+    """The statistics step needs a per-bin result, not the outer bin mapping.
+
+    On the tomography branch calculate_pure_eb returns {bin_key: result}, even
+    for the single (all, all) bin. plot_pure_eb must pass that inner result
+    (carrying gg, cov and xip_B) to calculate_eb_statistics. A finite combined
+    B-mode PTE is the expected outcome of this random-shear TreeCorr fixture.
+    The real calculation and statistics run; only the 2PCF source and figure
+    writers are substituted. Develop's single-result API is unaffected and
+    skips because it lacks the tomography branch's compute_tomography argument.
+    The real transform's eager JIT and jackknife resampling take over 20 seconds.
+    """
+    if (
+        "compute_tomography"
+        not in inspect.signature(PureEBMixin.calculate_pure_eb).parameters
+    ):
+        pytest.skip("Bin-keyed calculate_pure_eb API exists only on tomography branch")
+
+    for name in (
+        "plot_integration_vs_reporting",
+        "plot_pure_eb_correlations",
+        "plot_pte_2d_heatmaps",
+        "plot_eb_covariance_matrix",
+        "save_pure_eb_results",
+    ):
+        if hasattr(pure_eb, name):
+            monkeypatch.setattr(pure_eb, name, lambda *args, **kwargs: None)
+
+    seen = []
+    real_statistics = pure_eb.calculate_eb_statistics
+
+    def observe_statistics(results, **kwargs):
+        seen.append(sorted(results)[:6])
+        return real_statistics(results, **kwargs)
+
+    monkeypatch.setattr(pure_eb, "calculate_eb_statistics", observe_statistics)
+    cv = _TomoCV(tmp_path)
+    try:
+        cv.plot_pure_eb(
+            versions=["v"],
+            min_sep_int=0.5,
+            max_sep_int=200.0,
+            nbins_int=60,
+            npatch=32,
+        )
+    except KeyError as error:
+        pytest.fail(
+            f"Statistics received keys {seen} rather than one per-bin result: {error}"
+        )
+    assert set(cv._pure_eb_results["v"]) == {BIN_KEY}
+    pte = cv._pure_eb_results["v"][BIN_KEY]["pte_matrices"]["combined"]
+    assert np.isfinite(pte).any(), "No finite combined B-mode PTE produced"

From a71e5fa5b446bd658e994875e9868cf82992e80f Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:19 +0200
Subject: [PATCH 76/83] Honor tomography when plotting and saving each COSEBIs
 bin pair

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 src/sp_validation/cosmo_val/cosebis.py        | 102 +++++++------
 ...st_tomo_plot_cosebis_ignores_tomography.py | 142 ++++++++++++++++++
 2 files changed, 195 insertions(+), 49 deletions(-)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_plot_cosebis_ignores_tomography.py

diff --git a/src/sp_validation/cosmo_val/cosebis.py b/src/sp_validation/cosmo_val/cosebis.py
index 1d33c767..b1d478e1 100644
--- a/src/sp_validation/cosmo_val/cosebis.py
+++ b/src/sp_validation/cosmo_val/cosebis.py
@@ -208,6 +208,8 @@ def plot_cosebis(
         evaluate_all_scale_cuts : bool
             Whether to evaluate all scale cuts from reporting binning grid
             (default: False). Ignored when scale_cuts is provided.
+        compute_tomography : bool
+            Compute and plot each tomographic bin pair instead of the all-galaxy pair.
         min_sep, max_sep, nbins : float, float, int, optional
             Reporting binning parameters. Only used when evaluate_all_scale_cuts=True.
         fiducial_scale_cut : tuple, optional
@@ -249,64 +251,66 @@ def plot_cosebis(
                 cov_path=cov_path,
                 scale_cuts=scale_cuts,
                 evaluate_all_scale_cuts=evaluate_all_scale_cuts,
-                compute_tomography=False,  # LG: Hardcoded to False for now
+                compute_tomography=compute_tomography,
                 min_sep=min_sep,
                 max_sep=max_sep,
                 nbins=nbins,
             )
-            results = results_tomo[
-                "tomo_bin_all_tomo_bin_all"
-            ]  # LG: Extract non-tomographic results
-        elif isinstance(results, dict) and "tomo_bin_all_tomo_bin_all" in results:
-            results = results["tomo_bin_all_tomo_bin_all"]
-
-        # Generate plots using specialized plotting functions
-        # Extract single result for plotting if multiple scale cuts were evaluated
-        multiple_scale_cuts = isinstance(results, dict) and all(
-            isinstance(k, tuple) for k in results
-        )
-        if multiple_scale_cuts:
-            # Multiple scale cuts: use fiducial_scale_cut if provided, otherwise use
-            # full range (largest scale cut)
-            plot_results = results[
-                find_conservative_scale_cut_key(results, fiducial_scale_cut)
-                if fiducial_scale_cut is not None
-                else max(results, key=lambda x: x[1] - x[0])
-            ]
+        elif results and all(
+            isinstance(key, str) and key.startswith("tomo_bin_") for key in results
+        ):
+            results_tomo = results
         else:
-            # Single result
-            plot_results = results
-
-        plot_cosebis_modes(
-            plot_results,
-            version,
-            out_stub + "_cosebis.png",
-            fiducial_scale_cut=fiducial_scale_cut,
-        )
-
-        plot_cosebis_covariance_matrix(
-            plot_results, version, var_method, out_stub + "_covariance.png"
-        )
+            results_tomo = {"tomo_bin_all_tomo_bin_all": results}
 
-        # Generate scale cut heatmap if we have multiple scale cuts
-        if multiple_scale_cuts and len(results) > 1:
-            # Create temporary gg object with correct binning for mapping
-            treecorr_config_temp = self._binning(min_sep, max_sep, nbins)
-            ggs_temp = self.calculate_2pcf_version(
-                version, npatch=npatch, **treecorr_config_temp
+        ggs_temp = None
+        for bin_key, bin_results in results_tomo.items():
+            bin_stub = (
+                out_stub
+                if bin_key == "tomo_bin_all_tomo_bin_all"
+                else f"{out_stub}_{bin_key}"
             )
-            gg_temp = ggs_temp[
-                "tomo_bin_all_tomo_bin_all"
-            ]  # LG: Extract non-tomographic result
+            multiple_scale_cuts = isinstance(bin_results, dict) and all(
+                isinstance(k, tuple) for k in bin_results
+            )
+            if multiple_scale_cuts:
+                plot_results = bin_results[
+                    find_conservative_scale_cut_key(bin_results, fiducial_scale_cut)
+                    if fiducial_scale_cut is not None
+                    else max(bin_results, key=lambda x: x[1] - x[0])
+                ]
+            else:
+                plot_results = bin_results
 
-            plot_cosebis_scale_cut_heatmap(
-                results,
-                (gg_temp.left_edges, gg_temp.right_edges),
+            plot_cosebis_modes(
+                plot_results,
                 version,
-                out_stub + "_scalecut_ptes.png",
+                bin_stub + "_cosebis.png",
                 fiducial_scale_cut=fiducial_scale_cut,
             )
+            plot_cosebis_covariance_matrix(
+                plot_results, version, var_method, bin_stub + "_covariance.png"
+            )
 
-        # Save data products and store on instance
-        save_cosebis_results(results, out_stub + "_data.npz", fiducial_scale_cut)
-        self._cosebis_results[version] = {"tomo_bin_all_tomo_bin_all": results}
+            if multiple_scale_cuts and len(bin_results) > 1:
+                if ggs_temp is None:
+                    treecorr_config_temp = self._binning(min_sep, max_sep, nbins)
+                    ggs_temp = self.calculate_2pcf_version(
+                        version,
+                        npatch=npatch,
+                        compute_tomography=compute_tomography,
+                        **treecorr_config_temp,
+                    )
+                gg_temp = ggs_temp[bin_key]
+                plot_cosebis_scale_cut_heatmap(
+                    bin_results,
+                    (gg_temp.left_edges, gg_temp.right_edges),
+                    version,
+                    bin_stub + "_scalecut_ptes.png",
+                    fiducial_scale_cut=fiducial_scale_cut,
+                )
+
+            save_cosebis_results(
+                bin_results, bin_stub + "_data.npz", fiducial_scale_cut
+            )
+        self._cosebis_results[version] = results_tomo
diff --git a/src/sp_validation/tests/regression/test_tomo_plot_cosebis_ignores_tomography.py b/src/sp_validation/tests/regression/test_tomo_plot_cosebis_ignores_tomography.py
new file mode 100644
index 00000000..7e779f00
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_plot_cosebis_ignores_tomography.py
@@ -0,0 +1,142 @@
+"""plot_cosebis(compute_tomography=True) must produce per-bin-pair COSEBIs."""
+
+import inspect
+from contextlib import nullcontext
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+import yaml
+from astropy.io import fits
+
+cosebis_mod = pytest.importorskip(
+    "sp_validation.cosmo_val.cosebis",
+    reason="COSEBIs module required by the tomography branch",
+)
+from sp_validation.cosmo_val import CosmologyValidation  # noqa: E402
+
+VER = "SP_toy"
+NBINS = 60
+
+
+def _make_cv(tmp_path):
+    rng = np.random.default_rng(11)
+    n = 600
+    t = np.zeros(
+        n,
+        dtype=[
+            ("RA", "f8"),
+            ("Dec", "f8"),
+            ("e1", "f8"),
+            ("e2", "f8"),
+            ("w", "f8"),
+            ("tomo", "i4"),
+        ],
+    )
+    t["RA"] = 10 + rng.uniform(-0.5, 0.5, n)
+    t["Dec"] = rng.uniform(-0.5, 0.5, n)
+    t["tomo"] = np.repeat([1, 2], n // 2)
+    # Opposite coherent e1 in the two bins: the merged catalogue nearly cancels,
+    # so the all-galaxy COSEBIs differ strongly from any single bin pair.
+    t["e1"] = np.where(t["tomo"] == 1, 0.1, -0.1) + rng.normal(0, 0.01, n)
+    t["e2"] = rng.normal(0, 0.01, n)
+    t["w"] = 1.0
+    cat = tmp_path / "toy.fits"
+    fits.BinTableHDU(t).writeto(cat)
+    out = tmp_path / "out"
+    out.mkdir()
+    cfg = {
+        VER: {
+            "subdir": str(tmp_path),
+            "shear": {
+                "path": str(cat),
+                "e1_col": "e1",
+                "e2_col": "e2",
+                "w_col": "w",
+                "R": 1.0,
+                "tomo_bin_col": "tomo",
+            },
+        },
+        "nz": {"subdir": str(tmp_path)},
+        "paths": {"output": str(out)},
+    }
+    cfg_path = tmp_path / "cfg.yaml"
+    cfg_path.write_text(yaml.safe_dump(cfg))
+    cv = CosmologyValidation(
+        [VER],
+        catalog_config=str(cfg_path),
+        output_dir=str(out),
+        compute_tomography=True,
+        npatch=1,
+    )
+    cv.treecorr_config["num_threads"] = 2
+    # Hand the table straight in, bypassing the leakage-correction reader.
+    cv._results = {
+        VER: SimpleNamespace(dat_shear=t, temporarily_read_data=lambda: nullcontext())
+    }
+    cv._c1 = {VER: 0.0}
+    cv._c2 = {VER: 0.0}
+    cov = tmp_path / "cov.txt"
+    np.savetxt(cov, np.eye(2 * NBINS) * 1e-6)
+    params = dict(
+        version=VER,
+        min_sep_int=1.0,
+        max_sep_int=30.0,
+        nbins_int=NBINS,
+        npatch=1,
+        nmodes=1,
+        cov_path=str(cov),
+    )
+    return cv, params
+
+
+@pytest.mark.slow
+def test_plot_cosebis_compute_tomography_true_stores_bin_pair_results(
+    tmp_path, monkeypatch
+):
+    """plot_cosebis exposes a ``compute_tomography`` switch and saves/stores the
+    COSEBIs data vector and PTEs it computes. With ``compute_tomography=True`` on
+    a two-bin catalogue the stored results must be the three tomographic bin
+    pairs (1,1), (1,2), (2,2), identical to what ``calculate_cosebis(...,
+    compute_tomography=True)`` returns for the same inputs; the merged
+    all-galaxy E_n is the wrong answer. The fixture gives the two bins opposite
+    coherent e1 so the merged catalogue's E_1 differs from every bin pair's by
+    a large factor, making the substitution unmistakable."""
+    if (
+        "compute_tomography"
+        not in inspect.signature(CosmologyValidation.plot_cosebis).parameters
+    ):
+        pytest.skip("Tomography branch adds plot_cosebis(compute_tomography=...)")
+    # The first COSEBIs call can compile numba kernels; keep their cache local.
+    monkeypatch.setenv("NUMBA_CACHE_DIR", str(tmp_path / "numba-cache"))
+    # Plot rendering is irrelevant; keep genuine computation and result storage.
+    monkeypatch.setattr(cosebis_mod, "plot_cosebis_modes", lambda *a, **k: None)
+    monkeypatch.setattr(
+        cosebis_mod, "plot_cosebis_covariance_matrix", lambda *a, **k: None
+    )
+    cv, params = _make_cv(tmp_path)
+
+    expected = cv.calculate_cosebis(**params, compute_tomography=True)
+    assert set(expected) == {
+        "tomo_bin_1_tomo_bin_1",
+        "tomo_bin_1_tomo_bin_2",
+        "tomo_bin_2_tomo_bin_2",
+    }
+
+    cv.plot_cosebis(**params, compute_tomography=True)
+    stored = cv._cosebis_results[VER]
+
+    stored_keys = sorted(stored) if isinstance(stored, dict) else None
+    assert isinstance(stored, dict) and set(expected) <= set(stored), (
+        "plot_cosebis(compute_tomography=True) stored non-tomographic COSEBIs: "
+        f"keys={stored_keys}, En={np.asarray(stored.get('En')).tolist()}; "
+        "expected bin pairs with En="
+        f"{ {k: v['En'].tolist() for k, v in expected.items()} }"
+    )
+    for k, v in expected.items():
+        np.testing.assert_allclose(stored[k]["En"], v["En"], rtol=1e-10)
+        products = list((tmp_path / "out").glob(f"*_{k}_data.npz"))
+        assert len(products) == 1
+        with np.load(products[0]) as product:
+            np.testing.assert_allclose(product["En"], v["En"], rtol=1e-10)
+            np.testing.assert_allclose(product["Bn"], v["Bn"], rtol=1e-10)

From 02504936d6006b01f49b326374e4b2d9b84166af Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:20 +0200
Subject: [PATCH 77/83] Transpose the opposite polarization block to preserve
 joint iNKA covariance

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 src/sp_validation/cosmo_val/pseudo_cl.py      | 13 +--
 ...test_tomo_inka_merge_crosspol_transpose.py | 96 +++++++++++++++++++
 2 files changed, 103 insertions(+), 6 deletions(-)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_inka_merge_crosspol_transpose.py

diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index aa25cbdf..b9cc7f8b 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -1335,15 +1335,16 @@ def _merge_iNKA_covariance(self, ver, tomography):
                                 bin_key_b2,
                             ),
                         )
-                        block = fits.open(block_path)[f"COVAR_{pa}_{pb}"].data
-
                         sl_a = slice(index_a * n_ell, (index_a + 1) * n_ell)
                         sl_b = slice(index_b * n_ell, (index_b + 1) * n_ell)
 
-                        full_cov[sl_a, sl_b] = block
-
-                        if index_a != index_b:
-                            full_cov[sl_b, sl_a] = block.T
+                        with fits.open(block_path) as block_cov:
+                            full_cov[sl_a, sl_b] = block_cov[f"COVAR_{pa}_{pb}"].data
+                            if index_a != index_b:
+                                # Cov(P_b, Q_a) = Cov(Q_a, P_b).T.
+                                full_cov[sl_b, sl_a] = block_cov[
+                                    f"COVAR_{pb}_{pa}"
+                                ].data.T
 
                     covar.append(fits.ImageHDU(full_cov, name=f"COVAR_{pa}_{pb}"))
 
diff --git a/src/sp_validation/tests/regression/test_tomo_inka_merge_crosspol_transpose.py b/src/sp_validation/tests/regression/test_tomo_inka_merge_crosspol_transpose.py
new file mode 100644
index 00000000..4d7be5db
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_inka_merge_crosspol_transpose.py
@@ -0,0 +1,96 @@
+"""Tomographic iNKA merging must transpose the (Q,P) block, not (P,Q).
+
+Develop lacks this tomography-branch merger, so the test skips there.
+"""
+
+import itertools
+
+import numpy as np
+import pytest
+
+pseudo_cl = pytest.importorskip(
+    "sp_validation.cosmo_val.pseudo_cl",
+    reason="Tomographic iNKA merger module is only on the tomography branch",
+)
+PseudoClMixin = getattr(pseudo_cl, "PseudoClMixin", None)
+
+POLS = ["EE", "EB", "BE", "BB"]
+
+
+def test_tomo_inka_merge_lower_triangle_uses_transposed_qp_block(tmp_path):
+    """The merged tomographic iNKA covariance must equal the joint covariance.
+
+    Draw a symmetric positive-definite covariance C over the data vector indexed
+    (polarisation P, spectrum pair s, ell). The correct COVAR_P_Q is therefore
+    C[P, :, :, Q, :, :] by construction. Write only upper-triangle spectrum-pair
+    blocks (a <= b), as the pipeline does, in its [ell, pol, ell, pol] layout
+    using the real _save_iNKA_covariance method.
+
+    The lower block (b, a) of COVAR_P_Q is Cov(C_b^P, C_a^Q), which equals
+    block_{Q,P}(a, b).T. Using block_{P,Q}(a, b).T is only right when P == Q.
+    This protects all 16 polarisation blocks, including EE_BB and EB_BE, and
+    the symmetry COVAR_P_Q == COVAR_Q_P.T. It consolidates both audit reproductions
+    of the same incorrect cross-polarisation transpose.
+    """
+    if PseudoClMixin is None or not hasattr(PseudoClMixin, "_merge_iNKA_covariance"):
+        pytest.skip("Tomographic iNKA merger is only on the tomography branch")
+
+    n_bins_src = [1, 2]
+    pairs = list(itertools.combinations_with_replacement(n_bins_src, 2))
+    n_s, n_ell, n_pol = len(pairs), 3, 4
+
+    rng = np.random.default_rng(7)
+    dim = n_pol * n_s * n_ell
+    draws = rng.standard_normal((dim, dim))
+    covariance = (draws @ draws.T / dim + np.eye(dim)).reshape(
+        n_pol, n_s, n_ell, n_pol, n_s, n_ell
+    )
+
+    class Tiny(PseudoClMixin):
+        def _get_tomo_bins(self, ver):
+            return n_bins_src, pairs
+
+        def _output_path_pseudo_cl_cov(self, ver, method, tomography):
+            return str(tmp_path / "merged.fits")
+
+        def _output_path_iNKA_block_cov(self, ver, tomo_bin_quad):
+            return str(tmp_path / ("_".join(map(str, tomo_bin_quad)) + ".fits"))
+
+    instance = Tiny()
+    instance._pseudo_cls = {
+        "v": {"tomo_bin_all_tomo_bin_all": {"pseudo_cl": {"ELL": np.arange(n_ell)}}}
+    }
+    for ia, ib in itertools.combinations_with_replacement(range(n_s), 2):
+        # [ell_a, pol_a, ell_b, pol_b]
+        block = np.transpose(covariance[:, ia, :, :, ib, :], (1, 0, 3, 2))
+        quad = (*pairs[ia], *pairs[ib])
+        with instance._save_iNKA_covariance(
+            block, instance._output_path_iNKA_block_cov("v", quad)
+        ):
+            pass
+
+    with instance._merge_iNKA_covariance("v", True) as merged:
+        errors = {}
+        for i, pa in enumerate(POLS):
+            for j, pb in enumerate(POLS):
+                expected = covariance[i, :, :, j, :, :].reshape(
+                    n_s * n_ell, n_s * n_ell
+                )
+                got = merged[f"COVAR_{pa}_{pb}"].data
+                errors[f"{pa}_{pb}"] = np.max(np.abs(got - expected))
+        bad = {k: f"{v:.3g}" for k, v in errors.items() if v > 1e-12}
+        asym = np.max(np.abs(merged["COVAR_EE_BB"].data - merged["COVAR_BB_EE"].data.T))
+        assert not bad and asym < 1e-12, (
+            f"Merged blocks differ from the joint covariance: {bad}; "
+            f"max|COVAR_EE_BB - COVAR_BB_EE.T| = {asym:.3g}"
+        )
+        joint = np.block(
+            [[merged[f"COVAR_{pa}_{pb}"].data for pb in POLS] for pa in POLS]
+        )
+        np.testing.assert_allclose(joint, joint.T, rtol=0, atol=1e-12)
+        eigenvalues = np.linalg.eigvalsh(joint)
+        assert eigenvalues.min() > 0
+        print(
+            f"iNKA joint covariance: symmetry error={np.max(np.abs(joint - joint.T)):.3g}, "
+            f"min eigenvalue={eigenvalues.min():.12g}"
+        )

From 426b94086f44d1a12040f0172c5801cc8bad6646 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:20 +0200
Subject: [PATCH 78/83] Aggregate Gaussian spectra from the requested seed
 rather than stale files

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 scripts/cosmo_val/run_cl_gaussian_sims.py     |  12 +--
 ...mo_gaussian_sims_seed_filename_mismatch.py | 100 ++++++++++++++++++
 2 files changed, 106 insertions(+), 6 deletions(-)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_gaussian_sims_seed_filename_mismatch.py

diff --git a/scripts/cosmo_val/run_cl_gaussian_sims.py b/scripts/cosmo_val/run_cl_gaussian_sims.py
index 6db6f1ee..3e0f2efa 100644
--- a/scripts/cosmo_val/run_cl_gaussian_sims.py
+++ b/scripts/cosmo_val/run_cl_gaussian_sims.py
@@ -521,13 +521,13 @@ def concatenate_spectra(cl_sample, tomo_bin_ids, pol_index):
 
 
 def get_covariance_from_simulated_spectra(
-    n_sims, version, tomography, tomo_bin_ids, pol, out_dir
+    n_sims, version, tomography, tomo_bin_ids, pol, out_dir, seed
 ):
-    """Compute the covariance of the EE signal from the simulated spectra."""
+    """Compute the covariance of one polarization from the seed's spectra."""
     cl_samples = []
     pol_index = pol_to_pol_index_dict[pol]
     for sim_id in range(n_sims):
-        out_file = f"{out_dir}/cl_sample_{sim_id}_{version}_tomography_{tomography}.npz"
+        out_file = f"{out_dir}/cl_sample_{sim_id}_{version}_tomography_{tomography}_seed_{seed}.npz"
         if not os.path.exists(out_file):
             raise FileNotFoundError(f"Simulation output file {out_file} not found.")
         data = np.load(out_file, allow_pickle=True)
@@ -695,7 +695,7 @@ def get_covariance_from_simulated_spectra(
         )
 
         covariance_matrix = get_covariance_from_simulated_spectra(
-            n_sims, version, tomography, tomo_bin_ids, "EE", out_dir
+            n_sims, version, tomography, tomo_bin_ids, "EE", out_dir, seed
         )
 
         np.save(outpath_cov, covariance_matrix)
@@ -708,7 +708,7 @@ def get_covariance_from_simulated_spectra(
             )
 
             covariance_matrix = get_covariance_from_simulated_spectra(
-                n_sims, version, tomography, tomo_bin_ids, "EB", out_dir
+                n_sims, version, tomography, tomo_bin_ids, "EB", out_dir, seed
             )
 
             np.save(outpath_cov, covariance_matrix)
@@ -721,7 +721,7 @@ def get_covariance_from_simulated_spectra(
             )
 
             covariance_matrix = get_covariance_from_simulated_spectra(
-                n_sims, version, tomography, tomo_bin_ids, "BB", out_dir
+                n_sims, version, tomography, tomo_bin_ids, "BB", out_dir, seed
             )
 
             np.save(outpath_cov, covariance_matrix)
diff --git a/src/sp_validation/tests/regression/test_tomo_gaussian_sims_seed_filename_mismatch.py b/src/sp_validation/tests/regression/test_tomo_gaussian_sims_seed_filename_mismatch.py
new file mode 100644
index 00000000..4bfd96f7
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_gaussian_sims_seed_filename_mismatch.py
@@ -0,0 +1,100 @@
+"""Gaussian-sim covariance must aggregate the spectra its seeded workers wrote.
+
+Develop lacks this tomography-branch script, so these tests skip there.
+"""
+
+import importlib.util
+import inspect
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+import sp_validation
+
+SCRIPT_REL = "scripts/cosmo_val/run_cl_gaussian_sims.py"
+VERSION = "vtest"
+TOMO = False
+SEED = 7
+N_SIMS = 3
+N_ELL = 4
+
+
+@pytest.fixture
+def gaussian_sims():
+    # Resolve from the imported package, so PYTHONPATH can select another branch
+    # while this regression file stays in the original checkout.
+    root = Path(sp_validation.__file__).resolve().parents[2]
+    script = root / SCRIPT_REL
+    if not script.is_file():
+        pytest.skip(f"{SCRIPT_REL} is only available on the tomography branch")
+    pytest.importorskip("mpi4py", reason="Gaussian-sim script requires mpi4py")
+    spec = importlib.util.spec_from_file_location("run_cl_gaussian_sims", script)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    if not hasattr(module, "get_covariance_from_simulated_spectra"):
+        pytest.skip("Covariance aggregator is only available on the tomography branch")
+    return module
+
+
+def _cl_dict(rng):
+    # NaMaster-decoupled spin-2 x spin-2: (EE, EB, BE, BB) x n_ell.
+    return {"W1xW1": rng.normal(size=(4, N_ELL))}
+
+
+def _writer_name(out_dir, sim_id):
+    # Naming used by run_one_simulation when it saves a realisation.
+    return out_dir / (f"cl_sample_{sim_id}_{VERSION}_tomography_{TOMO}_seed_{SEED}.npz")
+
+
+def _stale_name(out_dir, sim_id):
+    # Pre-seed naming: what an older run left behind in the same directory.
+    return out_dir / f"cl_sample_{sim_id}_{VERSION}_tomography_{TOMO}.npz"
+
+
+def _call(module, out_dir):
+    fn = module.get_covariance_from_simulated_spectra
+    kwargs = {}
+    if "seed" in inspect.signature(fn).parameters:
+        kwargs["seed"] = SEED
+    return fn(N_SIMS, VERSION, TOMO, [1], "EE", str(out_dir), **kwargs)
+
+
+def test_covariance_reads_seed_suffixed_spectra_in_fresh_dir(tmp_path, gaussian_sims):
+    """A seed-S run must aggregate exactly its cl_sample_*_seed_S.npz files.
+
+    Nothing else exists in this fresh directory, so aggregation must succeed
+    and equal np.cov of the seed-S EE realisations, computed independently here.
+    No HEALPix simulation, catalogue or MPI work is needed.
+    """
+    rng = np.random.default_rng(0)
+    seeded = []
+    for i in range(N_SIMS):
+        cl = _cl_dict(rng)
+        np.savez(_writer_name(tmp_path, i), cl_decoupled=cl)
+        seeded.append(cl["W1xW1"][0])
+    cov = _call(gaussian_sims, tmp_path)
+    np.testing.assert_allclose(cov, np.cov(np.array(seeded), rowvar=False))
+
+
+def test_covariance_ignores_stale_unsuffixed_spectra(tmp_path, gaussian_sims):
+    """A seed-S covariance must not silently use stale unsuffixed spectra.
+
+    Both sets exist, but their distinct amplitudes make their sample covariances
+    different. The right answer is np.cov of this run's seeded EE realisations,
+    not the pre-seed files left by an older run.
+    """
+    rng = np.random.default_rng(1)
+    seeded, stale = [], []
+    for i in range(N_SIMS):
+        cl = _cl_dict(rng)
+        np.savez(_writer_name(tmp_path, i), cl_decoupled=cl)
+        seeded.append(cl["W1xW1"][0])
+        old = {"W1xW1": 100.0 * rng.normal(size=(4, N_ELL))}
+        np.savez(_stale_name(tmp_path, i), cl_decoupled=old)
+        stale.append(old["W1xW1"][0])
+    want = np.cov(np.array(seeded), rowvar=False)
+    old_cov = np.cov(np.array(stale), rowvar=False)
+    assert not np.allclose(want, old_cov), "Fixture must distinguish the two runs"
+    cov = _call(gaussian_sims, tmp_path)
+    np.testing.assert_allclose(cov, want)

From c0b2ac4e988c906ec0f8c7d1f75c5c3a8d8d5e62 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:21 +0200
Subject: [PATCH 79/83] Track per-bin tau centering as an upstream leakage
 defect

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 ...test_tomo_tau_centering_before_bin_mask.py | 140 ++++++++++++++++++
 1 file changed, 140 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_tomo_tau_centering_before_bin_mask.py

diff --git a/src/sp_validation/tests/regression/test_tomo_tau_centering_before_bin_mask.py b/src/sp_validation/tests/regression/test_tomo_tau_centering_before_bin_mask.py
new file mode 100644
index 00000000..b789438c
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_tomo_tau_centering_before_bin_mask.py
@@ -0,0 +1,140 @@
+"""Tomographic tau statistics must centre galaxy ellipticities within each bin.
+
+The tomography branch passes a bin mask to TauStat. The mean subtracted must
+be the selected bin's weighted mean, as in CovTauTh's per-bin covariance.
+The shared dependency exposes this mask API on develop as well.
+"""
+
+import numpy as np
+import pytest
+
+rts = pytest.importorskip("shear_psf_leakage.rho_tau_stat")
+
+# e1 is constant in each bin; correct centring gives g1 == 0. The PSF has
+# e2 == 0, so galaxy e2 noise cannot contribute to tau_0+ = <g1 p1 + g2 p2>.
+E1_BIN = {1: 0.10, 2: 0.30}
+E1_PSF = 0.10
+
+
+def _params():
+    return {
+        "e1_col": "e1",
+        "e2_col": "e2",
+        "w_col": "w",
+        "ra_col": "RA",
+        "dec_col": "Dec",
+        "ra_PSF_col": "RA",
+        "dec_PSF_col": "Dec",
+        "e1_PSF_col": "E1_PSF",
+        "e2_PSF_col": "E2_PSF",
+        "e1_star_col": "E1_STAR",
+        "e2_star_col": "E2_STAR",
+        "PSF_size": "T_PSF",
+        "star_size": "T_STAR",
+        "patch_number": 2,
+        "patch_seed": 1,
+        "ra_units": "deg",
+        "dec_units": "deg",
+    }
+
+
+def _galaxies(n=4000, seed=0):
+    rng = np.random.default_rng(seed)
+    gal = np.zeros(
+        n,
+        dtype=[
+            ("RA", "f8"),
+            ("Dec", "f8"),
+            ("e1", "f8"),
+            ("e2", "f8"),
+            ("w", "f8"),
+            ("bin", "i4"),
+        ],
+    )
+    gal["RA"] = rng.uniform(10, 11, n)
+    gal["Dec"] = rng.uniform(0, 1, n)
+    gal["bin"] = np.where(np.arange(n) % 2 == 0, 1, 2)
+    gal["e1"] = np.where(gal["bin"] == 1, E1_BIN[1], E1_BIN[2])
+    gal["e2"] = rng.normal(0, 0.01, n)
+    gal["w"] = 1.0
+    return gal
+
+
+def _stars(n=2000, seed=1):
+    rng = np.random.default_rng(seed)
+    st = np.zeros(
+        n,
+        dtype=[
+            ("RA", "f8"),
+            ("Dec", "f8"),
+            ("E1_PSF", "f8"),
+            ("E2_PSF", "f8"),
+            ("E1_STAR", "f8"),
+            ("E2_STAR", "f8"),
+            ("T_PSF", "f8"),
+            ("T_STAR", "f8"),
+        ],
+    )
+    st["RA"] = rng.uniform(10, 11, n)
+    st["Dec"] = rng.uniform(0, 1, n)
+    st["E1_PSF"] = st["E1_STAR"] = E1_PSF + rng.normal(0, 0.01, n)
+    st["E2_STAR"] = rng.normal(0, 0.01, n)  # Non-degenerate tau_2/tau_5 fields.
+    st["T_PSF"] = 1.0
+    st["T_STAR"] = 1.0 + rng.normal(0, 0.01, n)
+    return st
+
+
+@pytest.mark.xfail(
+    strict=True,
+    raises=AssertionError,
+    reason="CosmoStat/shear_psf_leakage#52: centring precedes bin masking",
+)
+def test_masked_galaxy_catalog_is_centred_on_bin_mean():
+    """A bin's TreeCorr galaxy catalogue must have zero weighted mean e1.
+
+    Bin 2 has constant e1 = 0.3 and the full catalogue mean is 0.2.
+    Subtracting the bin mean gives zero by construction; centring before
+    masking instead leaves g1 = +0.1 on every bin-2 galaxy.
+    """
+    gal = _galaxies()
+    cats = rts.Catalogs(params=_params())
+    cats.build_catalog(gal, "gal", "gal_b2", mask=gal["bin"] == 2, npatch=2)
+    tc = cats.get_cat("gal_b2")
+    mean_g1 = np.average(tc.g1, weights=tc.w)
+    assert mean_g1 == pytest.approx(0.0, abs=1e-12), (
+        f"bin-2 mean g1 = {mean_g1:.6f}; expected 0 after removing bin mean "
+        f"{E1_BIN[2]}, not the full-catalogue mean"
+    )
+
+
+@pytest.mark.xfail(
+    strict=True,
+    raises=AssertionError,
+    reason="CosmoStat/shear_psf_leakage#52: full-catalogue mean biases bin tau",
+)
+def test_tomographic_tau0_plus_vanishes_for_bin_constant_ellipticity(tmp_path):
+    """TauStat must give tau_0+ = 0 for a bin with constant galaxy e1.
+
+    Per-bin centring makes g1 identically zero and the PSF has p2 = 0,
+    so <g1 p1 + g2 p2> vanishes at every scale up to TreeCorr round-off.
+    Full-catalogue centring leaves (0.3 - 0.2) * 0.1 = 0.01 instead.
+    """
+    gal, st = _galaxies(), _stars()
+    cfg = {
+        "ra_units": "deg",
+        "dec_units": "deg",
+        "sep_units": "arcmin",
+        "min_sep": 1.0,
+        "max_sep": 30.0,
+        "nbins": 5,
+    }
+    tau = rts.TauStat(params=_params(), output=str(tmp_path), treecorr_config=cfg)
+    tau.build_cat_to_compute_tau(st, cat_type="psf", catalog_id="v")
+    tau.build_cat_to_compute_tau(
+        gal, cat_type="gal", catalog_id="v", mask=gal["bin"] == 2
+    )
+    tau.compute_tau_stats("v", "tau_v.fits", var_method=None)
+    tau0p = np.asarray(tau.tau_stats["tau_0_p"])
+    assert np.allclose(tau0p, 0.0, atol=1e-6), (
+        f"bin-2 tau_0+ = {np.round(tau0p, 6).tolist()}; expected zero at all scales"
+    )

From 114486c7d0a4bdf96ed484a621310f6f195b6294 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 04:25:21 +0200
Subject: [PATCH 80/83] Exercise the active covariance theory and workflow
 plotting APIs

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 ...t_pseudo_cl_covariance_producer_crashes.py | 132 ++++++++++++++++++
 1 file changed, 132 insertions(+)
 create mode 100644 src/sp_validation/tests/regression/test_pseudo_cl_covariance_producer_crashes.py

diff --git a/src/sp_validation/tests/regression/test_pseudo_cl_covariance_producer_crashes.py b/src/sp_validation/tests/regression/test_pseudo_cl_covariance_producer_crashes.py
new file mode 100644
index 00000000..f9502cf3
--- /dev/null
+++ b/src/sp_validation/tests/regression/test_pseudo_cl_covariance_producer_crashes.py
@@ -0,0 +1,132 @@
+"""Pseudo-Cl covariance producers must run against their branch's public API."""
+
+import ast
+import inspect
+from pathlib import Path
+
+import numpy as np
+import pyccl as ccl
+import pytest
+
+import sp_validation
+from sp_validation.cosmo_val import CosmologyValidation
+
+
+def _scripts():
+    root = Path(__file__).resolve().parents[4]
+    code_root = Path(sp_validation.__file__).resolve().parents[2]
+    if code_root != root:
+        root = code_root
+    return root / "workflow/scripts"
+
+
+def _cv_method_calls(script):
+    """Return name, positional count and keyword names of each cv method call."""
+    calls = []
+    for node in ast.walk(ast.parse(script.read_text())):
+        if (
+            isinstance(node, ast.Call)
+            and isinstance(node.func, ast.Attribute)
+            and isinstance(node.func.value, ast.Name)
+            and node.func.value.id == "cv"
+        ):
+            calls.append(
+                (node.func.attr, len(node.args), [k.arg for k in node.keywords])
+            )
+    return calls
+
+
+class _ReachedCatalogue(Exception):
+    """The real fiducial-spectrum step succeeded and reached catalogue loading."""
+
+
+def test_pseudo_cl_cov_does_not_multiply_fiducial_dictionary(tmp_path, monkeypatch):
+    """The covariance must get past its real fiducial-C_ell/pixel-window step.
+
+    Fiducial theory returns a dictionary keyed by tomographic pair, so the
+    producer must apply the HEALPix window to each spectrum, not the dictionary.
+    The catalogue loader raises a sentinel: reaching it is correct by
+    construction, because theory must finish before data loading starts.
+    Synthetic n(z) and nside=16 keep this small; no theory result is patched.
+    """
+    mod = pytest.importorskip(
+        "sp_validation.cosmo_val.pseudo_cl",
+        reason="tomography branch reorganized the pseudo-Cl covariance module",
+    )
+    z = np.linspace(0.01, 2.0, 100)
+    nz = np.exp(-0.5 * ((z - 0.7) / 0.2) ** 2)
+    zpath = tmp_path / "nz.txt"
+    np.savetxt(zpath, np.column_stack([z, nz]))
+
+    def open_entry(*args, **kwargs):
+        raise _ReachedCatalogue
+
+    def noop(*args, **kwargs):
+        pass
+
+    monkeypatch.setattr(mod, "get_params_rho_tau", lambda *args, **kwargs: {})
+    monkeypatch.setattr(mod, "open_entry", open_entry)
+
+    class Tiny(mod.PseudoClMixin):
+        pass
+
+    stub = Tiny()
+    stub.__dict__.update(
+        nside=16,
+        cell_method="map",
+        force_run=True,
+        versions=["v"],
+        cc={"v": {"shear": {"redshift_path": str(zpath)}}},
+        cosmo=ccl.CosmologyVanillaLCDM(),
+        noise_bias_method="analytic",
+        _output_path=lambda name: str(tmp_path / name),
+        _output_path_pseudo_cl_cov=lambda *args, **kwargs: str(tmp_path / "cov.fits"),
+        print_start=noop,
+        print_magenta=noop,
+        print_cyan=noop,
+        print_done=noop,
+        get_namaster_bin=noop,
+    )
+    with pytest.raises(_ReachedCatalogue):
+        stub.calculate_pseudo_cl_inka_cov(compute_tomography=False)
+
+
+def test_generate_pseudo_cl_cov_does_not_call_removed_methods():
+    """Every cv method called by the covariance producer must exist.
+
+    Existence is required before Python can produce any covariance.
+    """
+    script = _scripts() / "generate_pseudo_cl_cov.py"
+    missing = [
+        name
+        for name, _, _ in _cv_method_calls(script)
+        if not hasattr(CosmologyValidation, name)
+    ]
+    assert not missing, (
+        f"{script.name} calls missing CosmologyValidation methods: {missing}"
+    )
+
+
+def test_cv_pseudo_cl_does_not_omit_required_plot_arguments():
+    """The workflow's plot call must bind to its actual plotting API.
+
+    The plot-only producer ingests saved spectra and covariances, then calls
+    plot_pseudo_cl_spectrum once per polarization rather than a cv method.
+    """
+    from sp_validation.cosmo_val.pseudo_cl import plot_pseudo_cl_spectrum
+
+    script = _scripts() / "cv_plot_pseudo_cl.py"
+    sig = inspect.signature(plot_pseudo_cl_spectrum)
+    calls = [
+        node
+        for node in ast.walk(ast.parse(script.read_text()))
+        if isinstance(node, ast.Call)
+        and isinstance(node.func, ast.Name)
+        and node.func.id == "plot_pseudo_cl_spectrum"
+    ]
+    assert calls, "cv_plot_pseudo_cl.py must call plot_pseudo_cl_spectrum"
+    for call in calls:
+        sig.bind(
+            *([None] * len(call.args)),
+            **{key.arg: None for key in call.keywords},
+        )

From a6e076523156a8d1c0ed5fcad92e0e7b01422434 Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 05:55:24 +0200
Subject: [PATCH 81/83] Average rho/tau jackknife covariances over seeded
 independent layouts

Seed each draw with its index and use k-means++ so the initialization samples distinct layouts. Rebuild the shared-layout catalogues because TreeCorr caches patch catalogues independently of reassigned centres.

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 src/sp_validation/rho_tau.py                  |  48 ++++----
 src/sp_validation/statistics.py               |   7 +-
 .../tests/test_rho_tau_jackknife.py           | 116 ++++++++++++++++++
 3 files changed, 146 insertions(+), 25 deletions(-)
 create mode 100644 src/sp_validation/tests/test_rho_tau_jackknife.py

diff --git a/src/sp_validation/rho_tau.py b/src/sp_validation/rho_tau.py
index 526e02e6..e744fdf8 100644
--- a/src/sp_validation/rho_tau.py
+++ b/src/sp_validation/rho_tau.py
@@ -493,30 +493,32 @@ def chunks(i):
                         rho_stat_handler.catalogs.catalogs_dict
                     )
 
-                    # Build the catalog of galaxies. PSF was computed above
-                    tau_stat_handler.build_cat_to_compute_tau(
-                        load("shear"),
-                        cat_type="gal",
-                        catalog_id=catalog_id(i),
-                        mask=mask_gal,
-                    )
-
-                else:
-                    print(f"Computing the patch centers for patch {i + 1}/{ncov}")
-
-                    npatch = rho_stat_handler.catalogs._params["patch_number"]
-                    centers = jackknife_patch_centers(
-                        rho_stat_handler.catalogs.catalogs_dict[f"psf_{catalog_id(i)}"],
-                        npatch,
+                catalogs = rho_stat_handler.catalogs
+                centers = jackknife_patch_centers(
+                    catalogs.catalogs_dict[f"psf_{catalog_id(i)}"],
+                    catalogs._params["patch_number"],
+                    seed=i,
+                    init="kmeans++",
+                )
+                # Fresh catalogues keep TreeCorr's cached patches consistent with
+                # this draw's shared star/galaxy layout.
+                stars = catalogs.read_shear_cat(
+                    path_gal=None, path_psf=load("psf"), hdu=1
+                )
+                for cat_type in ("psf", "psf_error", "psf_size_error"):
+                    catalogs.build_catalog(
+                        cat=stars,
+                        cat_type=cat_type,
+                        key=f"{cat_type}_{catalog_id(i)}",
+                        patch_centers=centers,
+                        mask=mask_star,
                     )
-
-                    # Update the patch centers of the catalogs
-                    for key, cat in rho_stat_handler.catalogs.catalogs_dict.items():
-                        cat._centers = centers
-                        field = cat.getNField(
-                            max_top=int.bit_length(npatch) - 1, coords="spherical"
-                        )
-                        cat._patch = field.kmeans_assign_patches(centers)
+                tau_stat_handler.build_cat_to_compute_tau(
+                    load("shear"),
+                    cat_type="gal",
+                    catalog_id=catalog_id(i),
+                    mask=mask_gal,
+                )
 
                 # Compute and save rho stats
                 rho_stat_handler.compute_rho_stats(
diff --git a/src/sp_validation/statistics.py b/src/sp_validation/statistics.py
index bd95932b..17e05219 100644
--- a/src/sp_validation/statistics.py
+++ b/src/sp_validation/statistics.py
@@ -17,7 +17,7 @@
 PATCH_MIN_TOP = 6
 
 
-def jackknife_patch_centers(cat, npatch, seed=0):
+def jackknife_patch_centers(cat, npatch, seed=0, init="tree"):
     """Seeded k-means jackknife patch centres for a TreeCorr catalogue.
 
     Seeding the k-means does not by itself fix the patches. TreeCorr starts the
@@ -35,6 +35,9 @@ def jackknife_patch_centers(cat, npatch, seed=0):
         Number of patches.
     seed : int, optional
         Seed for the k-means initialisation.
+    init : str, optional
+        TreeCorr initialisation method. Use ``kmeans++`` for draws that sample
+        different seeded starting centres rather than the same tree cells.
 
     Returns
     -------
@@ -46,7 +49,7 @@ def jackknife_patch_centers(cat, npatch, seed=0):
         max_top=int.bit_length(npatch) - 1,
         coords="spherical",
     )
-    _, centers = field.run_kmeans(npatch, rng=np.random.default_rng(seed))
+    _, centers = field.run_kmeans(npatch, init=init, rng=np.random.default_rng(seed))
     return centers
 
 
diff --git a/src/sp_validation/tests/test_rho_tau_jackknife.py b/src/sp_validation/tests/test_rho_tau_jackknife.py
new file mode 100644
index 00000000..bc64537a
--- /dev/null
+++ b/src/sp_validation/tests/test_rho_tau_jackknife.py
@@ -0,0 +1,116 @@
+"""Jackknife layouts and covariance averaging on small synthetic catalogues."""
+
+from pathlib import Path
+
+import numpy as np
+from astropy.table import Table
+
+from sp_validation import rho_tau
+
+
+def test_jackknife_draws_use_distinct_shared_layouts(tmp_path, monkeypatch):
+    rng = np.random.default_rng(37)
+    n = 400
+    star = Table(
+        dict(
+            RA=rng.uniform(10, 14, n),
+            Dec=rng.uniform(10, 14, n),
+            p1=rng.normal(0, 0.03, n),
+            p2=rng.normal(0, 0.03, n),
+            s1=rng.normal(0, 0.04, n),
+            s2=rng.normal(0, 0.04, n),
+            tp=np.ones(n),
+            ts=np.full(n, 1.1),
+        )
+    )
+    gal = Table(
+        dict(
+            RA=rng.uniform(10, 14, n),
+            Dec=rng.uniform(10, 14, n),
+            e1=rng.normal(0, 0.2, n),
+            e2=rng.normal(0, 0.2, n),
+            w=rng.uniform(0.2, 1, n),
+        )
+    )
+    star.write(tmp_path / "stars.fits")
+    gal.write(tmp_path / "galaxies.fits")
+    config = {
+        "v": dict(
+            patch_number=7,
+            psf=dict(
+                path=str(tmp_path / "stars.fits"),
+                ra_col="RA",
+                dec_col="Dec",
+                e1_PSF_col="p1",
+                e2_PSF_col="p2",
+                e1_star_col="s1",
+                e2_star_col="s2",
+                PSF_size="tp",
+                star_size="ts",
+            ),
+            shear=dict(
+                path=str(tmp_path / "galaxies.fits"),
+                ra_col="RA",
+                dec_col="Dec",
+                e1_col="e1",
+                e2_col="e2",
+                w_col="w",
+            ),
+        )
+    }
+    layouts, catalogs, chunks = [], [], {"rho": [], "tau": []}
+    compute = rho_tau.RhoStat.compute_rho_stats
+    save = np.save
+
+    def record_compute(self, catalog_id, *args, **kwargs):
+        cats = self.catalogs.catalogs_dict
+        psf = cats[f"psf_{catalog_id}"]
+        layouts.append(psf.patch_centers.copy())
+        catalogs.append(psf)
+        for prefix in ("psf_error", "psf_size_error", "gal"):
+            np.testing.assert_array_equal(
+                cats[f"{prefix}_{catalog_id}"].patch_centers, psf.patch_centers
+            )
+        # Materialise TreeCorr's patch cache before the next draw.
+        assert len(psf.patches) == 7
+        return compute(self, catalog_id, *args, **kwargs)
+
+    def record_save(path, arr, *args, **kwargs):
+        for kind in chunks:
+            if Path(path).stem in {f"cov_{kind}_tau{i}" for i in range(3)}:
+                chunks[kind].append(np.array(arr).copy())
+        return save(path, arr, *args, **kwargs)
+
+    monkeypatch.setattr(rho_tau.RhoStat, "compute_rho_stats", record_compute)
+    monkeypatch.setattr(np, "save", record_save)
+    rho_tau.get_jackknife_cov(
+        config,
+        "v",
+        dict(
+            min_sep=1,
+            max_sep=150,
+            nbins=2,
+            sep_units="arcmin",
+            num_threads=1,
+            cross_patch_weight="match",
+        ),
+        str(tmp_path),
+        "rho",
+        "tau",
+        npatch=7,
+        ncov=3,
+    )
+    assert len(layouts) == 3
+    assert all(
+        not np.array_equal(layouts[i], layouts[j]) for i in range(3) for j in range(i)
+    )
+    assert len({id(cat) for cat in catalogs}) == 3
+    for kind in chunks:
+        assert len(chunks[kind]) == 3
+        assert any(not np.array_equal(chunks[kind][0], cov) for cov in chunks[kind][1:])
+        np.testing.assert_allclose(
+            np.load(tmp_path / f"cov_{kind}_{kind}_jk.npy"),
+            np.mean(chunks[kind], axis=0),
+            rtol=1e-13,
+            atol=0,
+        )

From 5c25e3ec376a0f4f35abaa2c679d99ca9762acee Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 05:55:24 +0200
Subject: [PATCH 82/83] Restore BE readback for single-field pseudo-Cl
 auto-spectra

BE equals EB for the all/all auto-spectrum, so return a separate copy for the supported BE diagnostic. Tomographic cross-pair FITS readback retains its independent BE.

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 src/sp_validation/cosmo_val/pseudo_cl.py  | 4 ++--
 src/sp_validation/tests/test_pseudo_cl.py | 3 +++
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index b9cc7f8b..d1c4a368 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -1224,12 +1224,12 @@ def _load_pseudo_cl(self, out_path, tomo_bin_pair):
 
     @staticmethod
     def _load_pseudo_cl_sacc(out_path):
-        """Read a pseudo-Cl SACC part into the ELL/EE/EB/BB dict."""
+        """Read the single-field auto-spectrum, whose BE equals EB."""
         # Readback of a part this producer just wrote — a legitimate pre-blind
         # consumer, so the fail-closed load is opted out of.
         s = sacc_io.load(out_path, allow_unblinded=True)
         ell, ee, bb, eb, _window = sacc_io.get_pseudo_cl(s, SACC_BIN)
-        return {"ELL": ell, "EE": ee, "EB": eb, "BB": bb}
+        return {"ELL": ell, "EE": ee, "EB": eb, "BE": eb.copy(), "BB": bb}
 
     def pseudo_cl_to_sacc_part(
         self, version, out_path, ell_eff, cl_all, wsp, nside=None
diff --git a/src/sp_validation/tests/test_pseudo_cl.py b/src/sp_validation/tests/test_pseudo_cl.py
index cd01ebe6..e3dae689 100644
--- a/src/sp_validation/tests/test_pseudo_cl.py
+++ b/src/sp_validation/tests/test_pseudo_cl.py
@@ -697,6 +697,9 @@ def capture_workspace(
     s = sacc_io.load(out_path, allow_unblinded=True)
     ell, ee, bb, eb, window = sacc_io.get_pseudo_cl(s, SACC_BIN)
     assert window is not None  # the shared BandpowerWindow rides the part
+    readback = cv._load_pseudo_cl(out_path, ("all", "all"))
+    npt.assert_array_equal(readback["BE"], eb)
+    assert not np.shares_memory(readback["BE"], readback["EB"])
     assert saved["nside"] is None
     window_ells, unwindowed_weights = bandpower_window_from_workspace(
         saved["workspace"]

From 5d3b5d25d52eb268daf80a05b9f5134694b0fa2b Mon Sep 17 00:00:00 2001
From: Cail Daley <cail.daley@cea.fr>
Date: Mon, 5 Oct 2026 05:57:56 +0200
Subject: [PATCH 83/83] Reject a single COSEBIs covariance for tomographic
 pairs

One supplied xi covariance describes one pair, not every auto/cross pair. Fail before measuring tomography rather than silently assigning the same uncertainties to different samples.

Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
---
 src/sp_validation/cosmo_val/cosebis.py        |  9 +++++-
 .../tests/test_cosebis_tomography.py          | 30 +++++++++++++++++++
 2 files changed, 38 insertions(+), 1 deletion(-)
 create mode 100644 src/sp_validation/tests/test_cosebis_tomography.py

diff --git a/src/sp_validation/cosmo_val/cosebis.py b/src/sp_validation/cosmo_val/cosebis.py
index b1d478e1..079720bf 100644
--- a/src/sp_validation/cosmo_val/cosebis.py
+++ b/src/sp_validation/cosmo_val/cosebis.py
@@ -61,7 +61,8 @@ def calculate_cosebis(
             Number of COSEBIs modes to compute. Defaults to 10.
         cov_path : str, optional
             Path to theoretical covariance matrix. When provided, enables analytic
-            covariance calculation.
+            covariance calculation for a single pair. Cannot be combined with
+            ``compute_tomography=True``.
         scale_cuts : list of tuples, optional
             Explicit list of (min_theta, max_theta) scale cuts to evaluate.
             Overrides evaluate_all_scale_cuts when provided.
@@ -91,6 +92,12 @@ def calculate_cosebis(
         """
         self.print_start(f"Computing {version} COSEBIs")
 
+        if cov_path is not None and compute_tomography:
+            raise ValueError(
+                "cov_path holds a single ξ± covariance; it cannot serve every "
+                "tomographic bin pair."
+            )
+
         # Set up parameters with defaults
         npatch = npatch or self.npatch
 
diff --git a/src/sp_validation/tests/test_cosebis_tomography.py b/src/sp_validation/tests/test_cosebis_tomography.py
new file mode 100644
index 00000000..d58c6696
--- /dev/null
+++ b/src/sp_validation/tests/test_cosebis_tomography.py
@@ -0,0 +1,30 @@
+"""Covariance selection at the tomographic COSEBIs boundary."""
+
+from types import SimpleNamespace
+
+import pytest
+
+from sp_validation.cosmo_val.cosebis import CosebisMixin
+
+
+@pytest.mark.parametrize(
+    "options",
+    [
+        {},
+        {"scale_cuts": [(1, 10)]},
+        {"evaluate_all_scale_cuts": True},
+    ],
+)
+def test_tomographic_cosebis_rejects_single_covariance(options):
+    calls = []
+    cv = SimpleNamespace(
+        npatch=7,
+        print_start=lambda *args: None,
+        _binning=lambda *args: {},
+        calculate_2pcf_version=lambda *args, **kwargs: calls.append(kwargs) or {},
+    )
+    with pytest.raises(ValueError, match="cov_path.*single.*tomographic bin pair"):
+        CosebisMixin.calculate_cosebis(
+            cv, "v", cov_path="xi_cov.txt", compute_tomography=True, **options
+        )
+    assert calls == []