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NatLeung96 merged 6 commits into from
Jun 18, 2026
Merged

add interactive 3D volume plotting to msmapper #39

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fa2afe0
add interactive 3D volume plotting to msmapper
NatLeung96 Jun 17, 2026
7c71f8a
update dependencies
NatLeung96 Jun 17, 2026
ab4b287
Merge branch 'main' into nl/add-plotly-volume
NatLeung96 Jun 17, 2026
0952b8d
update msmapper
NatLeung96 Jun 18, 2026
90fecb4
update msmapper imports
NatLeung96 Jun 18, 2026
9ad50d9
remove return type annotation
NatLeung96 Jun 18, 2026
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185 changes: 165 additions & 20 deletions mmg_toolbox/diffraction/msmapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,9 @@
from mmg_toolbox import version_info
from mmg_toolbox.nexus import nexus_writer as nw
from mmg_toolbox.fitting import FitResults
from .plotly_colourmaps import PLOTLY_CMAPS

from collections.abc import Callable


MSMAPPER_VERSION = '1.9'
Expand Down Expand Up @@ -57,17 +60,20 @@ def create_bean(input_files, output_file, start=None, shape=None, step=None,
"inputs": input_files, # Filename of scan file
"output": output_file,
# Output filename - must be in processing directory, or somewhere you can write to
"splitterName": "gaussian", # one of the following strings "nearest", "gaussian", "negexp", "inverse"
# one of the following strings "nearest", "gaussian", "negexp", "inverse"
"splitterName": "gaussian",
"splitterParameter": 2.0,
# splitter's parameter is distance to half-height of the weight function.
# If you use None or "" then it is treated as "nearest"
"scaleFactor": 2.0,
# the oversampling factor for each image; to ensure that are no gaps in between pixels in mapping
"step": step,
# a single value or list if 3 values and determines the lengths of each side of the voxels in the volume
"start": start, # location in HKL space of the bottom corner of the array.
# location in HKL space of the bottom corner of the array.
"start": start,
"shape": shape, # size of the array to create for reciprocal space volume
"reduceToNonZero": reduce_box # True/False, if True, attempts to reduce the volume output
# True/False, if True, attempts to reduce the volume output
"reduceToNonZero": reduce_box
}
if output_mode:
bean['outputMode'] = output_mode
Expand All @@ -86,12 +92,13 @@ def create_bean(input_files, output_file, start=None, shape=None, step=None,


def update_msmapper_nexus(filename: str, hkl_slice: tuple[np.ndarray, np.ndarray, np.ndarray],
orthogonal_axes: tuple[np.ndarray, np.ndarray, np.ndarray] | None = None,
average_axes: tuple[np.ndarray, np.ndarray, np.ndarray] | None = None,
orthogonal_axes: tuple[np.ndarray,
np.ndarray, np.ndarray] | None = None,
average_axes: tuple[np.ndarray,
np.ndarray, np.ndarray] | None = None,
fit_options: dict | None = None, h_result: FitResults | None = None,
k_result: FitResults | None = None, l_result: FitResults | None = None,
q_result: FitResults | None = None, tth_result: FitResults | None = None):

"""
Update the NeXus file generated by msmapper with additional analysis data.

Expand Down Expand Up @@ -191,7 +198,8 @@ def update_msmapper_nexus(filename: str, hkl_slice: tuple[np.ndarray, np.ndarray

with h5py.File(filename, 'a') as hdf:
entry = nw.add_nxentry(hdf, 'analysis', definition=None, default=True)
nw.add_nxprocess(entry, 'process', program='mmg_toolbox', version=version_info())
nw.add_nxprocess(entry, 'process',
program='mmg_toolbox', version=version_info())

# hkl
h = hdf['/processed/reciprocal_space/h-axis'][:]
Expand All @@ -205,7 +213,8 @@ def update_msmapper_nexus(filename: str, hkl_slice: tuple[np.ndarray, np.ndarray
k_axis['k'] = h5py.SoftLink('/processed/reciprocal_space/k-axis')
nw.add_nxfield(k_axis, 'intensity', k_slice)

l_axis = nw.add_nxdata(entry, 'l_axis', ['l'], 'intensity', default=True)
l_axis = nw.add_nxdata(
entry, 'l_axis', ['l'], 'intensity', default=True)
l_axis['l'] = h5py.SoftLink('/processed/reciprocal_space/l-axis')
nw.add_nxfield(l_axis, 'intensity', l_slice)

Expand All @@ -216,7 +225,8 @@ def update_msmapper_nexus(filename: str, hkl_slice: tuple[np.ndarray, np.ndarray
nw.add_nxfield(orthog, 'Qz', qz, units='1/angstrom')
orthog['volume'] = h5py.SoftLink('/processed/reciprocal_space/volume')
orthog['weight'] = h5py.SoftLink('/processed/reciprocal_space/weight')
nw.add_attr(orthog, Qx_indices=1, Qy_indices=2, Qz_indices=0, default_slice=len(l) // 2)
nw.add_attr(orthog, Qx_indices=1, Qy_indices=2,
Qz_indices=0, default_slice=len(l) // 2)

# volume averaged at each magnitude
qvsi = nw.add_nxdata(entry, 'wavevector', ['q'], 'intensity')
Expand All @@ -231,20 +241,155 @@ def update_msmapper_nexus(filename: str, hkl_slice: tuple[np.ndarray, np.ndarray
fit_proc = nw.add_nxprocess(entry, 'peak_fit',
program='mmg_toolbox.utils.fitting.multipeakfit',
**fit_options)
nw.add_nxnote(fit_proc, 'fit_results_h', 'fit results for h-axis', data=str(h_result))
nw.add_nxnote(fit_proc, 'fit_results_h',
'fit results for h-axis', data=str(h_result))
nw.add_nxparameters(fit_proc, 'fit_data_h', **h_result.results())
nw.add_nxnote(fit_proc, 'fit_results_k', 'fit results for k-axis', data=str(k_result))
nw.add_nxnote(fit_proc, 'fit_results_k',
'fit results for k-axis', data=str(k_result))
nw.add_nxparameters(fit_proc, 'fit_data_k', **k_result.results())
nw.add_nxnote(fit_proc, 'fit_results_l', 'fit results for l-axis', data=str(l_result))
nw.add_nxnote(fit_proc, 'fit_results_l',
'fit results for l-axis', data=str(l_result))
nw.add_nxparameters(fit_proc, 'fit_data_l', **l_result.results())
nw.add_nxnote(fit_proc, 'fit_results_q', 'fit results for wavevector magnitude |Q|', data=str(q_result))
nw.add_nxnote(fit_proc, 'fit_results_q',
'fit results for wavevector magnitude |Q|', data=str(q_result))
nw.add_nxparameters(fit_proc, 'fit_data_q', **q_result.results())
nw.add_nxnote(fit_proc, 'fit_results_tth', 'fit results for remapped two-theta', data=str(tth_result))
nw.add_nxparameters(fit_proc, 'fit_data_tth', **tth_result.results())
nw.add_nxnote(fit_proc, 'fit_results_tth',
'fit results for remapped two-theta', data=str(tth_result))
nw.add_nxparameters(fit_proc, 'fit_data_tth',
**tth_result.results())

nw.add_nxfield(h_axis, 'fit', h_result.fit_data(
h, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(k_axis, 'fit', k_result.fit_data(
k, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(l_axis, 'fit', l_result.fit_data(
l, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(qvsi, 'fit', q_result.fit_data(
wavevector, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(tthvsi, 'fit', tth_result.fit_data(
tth, ntimes=1)[1], add_to_signal=True)


def plot_voxel_image(
h: np.ndarray,
k: np.ndarray,
l: np.ndarray,
values: np.ndarray,
title: str | None = None,
cmap: str = "inferno",
figsize: tuple[int, int] = (9, 6),
isomin: float = 0.001,
isomax: float = 1.0,
):
"""
Plots an interactive 3D volume using Plotly and ipywidgets.

:param h: 3D array containing h coordinates.
:type h: np.ndarray

:param k: 3D array containing k coordinates.
:type k: np.ndarray

:param l: 3D array containing l coordinates.
:type l: np.ndarray

:param values: 3D array containing the voxel values.
:type values: np.ndarray

nw.add_nxfield(h_axis, 'fit', h_result.fit_data(h, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(k_axis, 'fit', k_result.fit_data(k, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(l_axis, 'fit', l_result.fit_data(l, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(qvsi, 'fit', q_result.fit_data(wavevector, ntimes=1)[1], add_to_signal=True)
nw.add_nxfield(tthvsi, 'fit', tth_result.fit_data(tth, ntimes=1)[1], add_to_signal=True)
:param title: Title to be displayed on the plot.
:type title: str | None

:param cmap: Name of the colourmap to be used in plot. Defaults to "inferno".
:type cmap: str

:param figsize: Tuple containing the width and height of the plot in inches.
:type figsize: tuple[int,int] | None

:param isomin: Float that sets the minimum boudary for the iso-surface plot.
:type isomin: float

:param isomax: Float that sets the maximum boundary for the iso-surface plot.
:type isomax: float

:returns: VBox. ipywidgets VBox containing controls and the figure widget.
:rtype ipywidgets.Box:

:returns: fig.write_image. A function to save the figure to a given filepath
:rtype fig.write_image: function (str) -> void
"""

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import plotly.graph_objects as go
from ipywidgets import VBox, HBox, Dropdown, FloatText

fig = go.FigureWidget(
data=go.Volume(
x=h.flatten(),
y=k.flatten(),
z=l.flatten(),
value=values,
colorscale=cmap,
isomin=isomin,
isomax=isomax,
opacity=0.1,
surface_count=10,
showscale=False,
),
layout={
'title': title,
'scene': {
'xaxis': {'title': 'H (r.l.u.)'},
'yaxis': {'title': 'K (r.l.u.)'},
'zaxis': {'title': 'L (r.l.u.)'},
'aspectmode': 'data',
'camera': {
'eye': {
'x': 1.5,
'y': 1.5,
'z': 1.5,
}
}
},
'width': figsize[0] * 96,
'height': figsize[1] * 96,
},
)

colourmap_dropdown = Dropdown(
options=PLOTLY_CMAPS,
value=cmap,
description='Colourmap:',
style={'description_width': 'initial'}
)

isomin_input = FloatText(
value=isomin,
description="isomin:",
style={"description_width": "50px"},
layout={"width": "150px"},
)

isomax_input = FloatText(
value=isomax,
description="isomax:",
style={"description_width": "50px"},
layout={"width": "150px"},
)

def update_plot(_) -> None:
cmap = colourmap_dropdown.value
vmin = isomin_input.value
vmax = isomax_input.value

with fig.batch_update():
fig.data[0].colorscale = cmap
fig.data[0].isomin = vmin
fig.data[0].isomax = vmax

colourmap_dropdown.observe(update_plot, names='value')
isomin_input.observe(update_plot, names='value')
isomax_input.observe(update_plot, names='value')

iso_controls = HBox([isomin_input, isomax_input])

widget = VBox([colourmap_dropdown, iso_controls, fig])
return widget, fig.write_image
96 changes: 96 additions & 0 deletions mmg_toolbox/diffraction/plotly_colourmaps.py
View file
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Original file line number Diff line number Diff line change
@@ -0,0 +1,96 @@
PLOTLY_CMAPS = [
'aggrnyl',
'agsunset',
'blackbody',
'bluered',
'blues',
'blugrn',
'bluyl',
'brwnyl',
'bugn',
'bupu',
'burg',
'burgyl',
'cividis',
'darkmint',
'electric',
'emrld',
'gnbu',
'greens',
'greys',
'hot',
'inferno',
'jet',
'magenta',
'magma',
'mint',
'orrd',
'oranges',
'oryel',
'peach',
'pinkyl',
'plasma',
'plotly3',
'pubu',
'pubugn',
'purd',
'purp',
'purples',
'purpor',
'rainbow',
'rdbu',
'rdpu',
'redor',
'reds',
'sunset',
'sunsetdark',
'teal',
'tealgrn',
'turbo',
'viridis',
'ylgn',
'ylgnbu',
'ylorbr',
'ylorrd',
'algae',
'amp',
'deep',
'dense',
'gray',
'haline',
'ice',
'matter',
'solar',
'speed',
'tempo',
'thermal',
'turbid',
'armyrose',
'brbg',
'earth',
'fall',
'geyser',
'prgn',
'piyg',
'picnic',
'portland',
'puor',
'rdgy',
'rdylbu',
'rdylgn',
'spectral',
'tealrose',
'temps',
'tropic',
'balance',
'curl',
'delta',
'oxy',
'edge',
'hsv',
'icefire',
'phase',
'twilight',
'mrybm',
'mygbm',
]
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