From 80159880e3c742090e749085033cc37dad9a8f37 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Thu, 13 Jun 2024 17:07:02 +0200 Subject: [PATCH 01/62] added first version of conditioned random field generator new file: random_fields/generate_conditioned_field.py --- random_fields/generate_conditioned_field.py | 186 ++++++++++++++++++++ 1 file changed, 186 insertions(+) create mode 100644 random_fields/generate_conditioned_field.py diff --git a/random_fields/generate_conditioned_field.py b/random_fields/generate_conditioned_field.py new file mode 100644 index 0000000..c3cc59d --- /dev/null +++ b/random_fields/generate_conditioned_field.py @@ -0,0 +1,186 @@ +from enum import Enum +from typing import List +import numpy as np +import gstools as gs + +from sklearn.gaussian_process import GaussianProcessRegressor as GPR +from sklearn.gaussian_process.kernels import RBF, \ + ConstantKernel as C, \ + WhiteKernel as W, Matern + + +class ModelName(Enum): + """ + Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" + """ + Gaussian = gs.Gaussian + Exponential = gs.Exponential + Matern = gs.Matern + Linear = gs.Linear + + +class ConditionedRandomFields: + """ + Generates standard normal conditioned random fields + """ + + def __init__(self, model_name: ModelName, n_dim: int, + mean: float, variance: float, + v_scale_fluctuation: float, anisotropy: List[float], angle: List[float], + seed: int = 14, v_dim: int = 1) -> None: + """ + Initialise generation of random fields + + Parameters: + ----------- + model_name: str + Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" + n_dim: int + The dimension of the random field + mean: float + The mean of the random field + variance: float + The variance of the random field + v_scale_fluctuation: float + The vertical scale of fluctuation of the random field + anisotropy: list + The anisotropy of the random field (per dimension) + angle: list + The angle of the random field (per dimension) + seed: int + The seed number for the random number generator + v_dim: int + The dimension of the vertical scale of fluctuation + """ + # initialise model + if model_name.name not in ModelName.__members__: + raise ValueError(f'Model name: {model_name} is not supported') + + if n_dim not in [1, 2, 3]: + raise ValueError('Only 1, 2, 3 dimensions are supported') + + aux_keys = [anisotropy, angle] + for key in aux_keys: + if len(key) != n_dim - 1: + raise ValueError(f'Number of dimensions: {n_dim} does not match number of\ + random field properties: {len(variance)}. It should be {n_dim - 1}.') + + self.random_field_model_name = model_name.name + self.random_field_model = model_name.value + self.n_dim = n_dim + self.seed = seed + self.mean = mean + self.variance = variance + self.vertical_scale_fluctuation = v_scale_fluctuation + self.anisotropy = anisotropy + self.angle = angle + self.v_dim = v_dim + self.random_field = None + self.kriged_field = None + + + + def set_conditioning_points(self, + points: np.ndarray, + values: np.ndarray, + kernel = None ) -> None: + """ + Initiates the conditioning points and inverts the covariance matrix + + Parameters: + ----------- + points: array-like + The contitioning point coordinates + values: array-like + The conditioning point values + kernel: sklearn kernel + The (calibrated) correlation kernel from sklearn + """ + + if max(points.shape) > 2000: + print('too many conditioning points!') + + if kernel == None: + kernel = W(0.01) + RBF(length_scale=[1.0]*self.n_dim) + + self.kernel_ = kernel + self.GaussianProcess = GPR(kernel = kernel) + self.GaussianProcess.optimizer = None + self.GaussianProcess.fit(points,values) + self.conditioning_points = points + self.conditioning_values = values + + + self.kriged_field == None + + + def generate(self, nodes: np.ndarray) -> None: + """ + Generate conditioned random field + + Parameters: + ------------ + nodes: list + The nodes of the random field + """ + # check dimensions of nodes agrees with dimensions of model + if nodes.shape[1] != self.n_dim: + raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') + + # scale of fluctuation + + LS_sklearn = np.array(self.GaussianProcess.kernel_.get_params()['k2__length_scale']) + + # + # create kriged field + if self.kriged_field == None: + self.kriged_field = self.GaussianProcess.predict(nodes) + + + + + + if self.random_field_model_name == 'Gaussian': + f_correct = np.sqrt(2 / np.pi) + elif self.random_field_model_name == 'Exponential': + f_correct = np.sqrt(1 / 2) + elif self.random_field_model_name == 'Matern': + f_correct = np.sqrt(1 / 2) + else: + print('!! random field model',self.random_field_model_name,'not implemented... !!') + + + LS_gstools = LS_sklearn * f_correct + + # apply the anisotropy to the other dimensions + # mask = np.arange(len(scale_fluctuation)) != self.v_dim + # scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy + + model = self.random_field_model(dim=self.n_dim, var=1., len_scale=LS_gstools, angles=self.angle) + self.random_field = gs.SRF(model, mean=0., seed=self.seed) + + + + nodes_cpoints = np.vstack([nodes,self.conditioning_points]) + + self.random_field(nodes_cpoints.T) + + Z_rf = self.random_field.field[:nodes.shape[0]] + cvalues_rf = self.random_field.field[nodes.shape[0]:] + + + + GPrf = GPR(kernel = self.GaussianProcess.kernel_) + GPrf.optimizer = None + GPrf.fit(self.conditioning_points,cvalues_rf) + + Z_kriged_rf = GPrf.predict(nodes) + + Z_kriged = self.GaussianProcess.predict(nodes) + + Z_crf = Z_kriged + Z_rf - Z_kriged_rf + + self.conditioned_random_field = self.mean + np.sqrt(self.variance) * Z_crf + + + From dfe882688fbc0f81030cd5944a29fd82ddf5776b Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 14 Jun 2024 09:55:08 +0200 Subject: [PATCH 02/62] corrections, clean-up, efficiency and commenting modified: random_fields/generate_conditioned_field.py --- random_fields/generate_conditioned_field.py | 65 ++++++++++----------- 1 file changed, 30 insertions(+), 35 deletions(-) diff --git a/random_fields/generate_conditioned_field.py b/random_fields/generate_conditioned_field.py index c3cc59d..893fffa 100644 --- a/random_fields/generate_conditioned_field.py +++ b/random_fields/generate_conditioned_field.py @@ -76,7 +76,7 @@ def __init__(self, model_name: ModelName, n_dim: int, self.angle = angle self.v_dim = v_dim self.random_field = None - self.kriged_field = None + self.Z_kriged_field = None @@ -110,8 +110,7 @@ def set_conditioning_points(self, self.conditioning_points = points self.conditioning_values = values - - self.kriged_field == None + self.Z_kriged_field == None def generate(self, nodes: np.ndarray) -> None: @@ -132,55 +131,51 @@ def generate(self, nodes: np.ndarray) -> None: LS_sklearn = np.array(self.GaussianProcess.kernel_.get_params()['k2__length_scale']) # - # create kriged field - if self.kriged_field == None: - self.kriged_field = self.GaussianProcess.predict(nodes) - + # create kriged mean field + if not isinstance(self.Z_kriged_field, np.ndarray) : + print('kriging field for first time') + self.Z_kriged_field = self.GaussianProcess.predict(nodes) + f_correct = {'Gaussian':np.sqrt(2 / np.pi), + 'Exponential':np.sqrt(1 / 2), + 'Matern':np.sqrt(1/ 2 ) } - if self.random_field_model_name == 'Gaussian': - f_correct = np.sqrt(2 / np.pi) - elif self.random_field_model_name == 'Exponential': - f_correct = np.sqrt(1 / 2) - elif self.random_field_model_name == 'Matern': - f_correct = np.sqrt(1 / 2) - else: + if not self.random_field_model_name in f_correct.keys(): print('!! random field model',self.random_field_model_name,'not implemented... !!') - - LS_gstools = LS_sklearn * f_correct - - # apply the anisotropy to the other dimensions - # mask = np.arange(len(scale_fluctuation)) != self.v_dim - # scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy + # + # correct the length scales between libraries + LS_gstools = LS_sklearn * f_correct[self.random_field_model_name] model = self.random_field_model(dim=self.n_dim, var=1., len_scale=LS_gstools, angles=self.angle) self.random_field = gs.SRF(model, mean=0., seed=self.seed) - - nodes_cpoints = np.vstack([nodes,self.conditioning_points]) + # + # create single random field at nodes and conditioning points self.random_field(nodes_cpoints.T) + Z_crf_nodes = self.random_field.field[:nodes.shape[0]] + Z_rf_cpoints = self.random_field.field[nodes.shape[0]:] - Z_rf = self.random_field.field[:nodes.shape[0]] - cvalues_rf = self.random_field.field[nodes.shape[0]:] - - - + # + # create kriged mean field of random field GPrf = GPR(kernel = self.GaussianProcess.kernel_) GPrf.optimizer = None - GPrf.fit(self.conditioning_points,cvalues_rf) - - Z_kriged_rf = GPrf.predict(nodes) + GPrf.fit(self.conditioning_points,Z_rf_cpoints) + GPrf.L_ = self.GaussianProcess.L_ + Z_kriged_nodes_rf = GPrf.predict(nodes) - Z_kriged = self.GaussianProcess.predict(nodes) - - Z_crf = Z_kriged + Z_rf - Z_kriged_rf - - self.conditioned_random_field = self.mean + np.sqrt(self.variance) * Z_crf + # + # replace kriged mean field (the kriged parts) to create conditioned random field + Z_crf_nodes += self.Z_kriged_field + Z_crf_nodes -= Z_kriged_nodes_rf + + # + # scale conditioned random field + self.conditioned_random_field = self.mean + np.sqrt(self.variance) * Z_crf_nodes From 7e2ddfae9d7b74e57df3f9a4e5432da1edae878f Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 28 Jun 2024 15:10:33 +0200 Subject: [PATCH 03/62] Added tutorial: Changes to be committed: modified: ../README.md new file: Makefile renamed: static/random_field.png -> _static/random_field.png new file: _static/random_field_1.png new file: _static/random_field_2.png new file: _static/random_field_3.png renamed: static/random_field_3D.png -> _static/random_field_3D.png new file: authors.rst new file: conf.py new file: contributions.rst new file: index.rst new file: installation.rst new file: make.bat new file: requirements.txt new file: theory.rst new file: tutorials.rst modified: ../random_fields/generate_conditioned_field.py modified: ../random_fields/generate_field.py modified: ../random_fields/utils.py --- README.md | 4 +- docs/Makefile | 20 ++++ docs/{static => _static}/random_field.png | Bin docs/_static/random_field_1.png | Bin 0 -> 29598 bytes docs/_static/random_field_2.png | Bin 0 -> 98428 bytes docs/_static/random_field_3.png | Bin 0 -> 120058 bytes docs/{static => _static}/random_field_3D.png | Bin docs/authors.rst | 19 ++++ docs/conf.py | 62 ++++++++++ docs/contributions.rst | 99 ++++++++++++++++ docs/index.rst | 59 ++++++++++ docs/installation.rst | 10 ++ docs/make.bat | 35 ++++++ docs/requirements.txt | 2 + docs/theory.rst | 6 + docs/tutorials.rst | 110 ++++++++++++++++++ random_fields/generate_conditioned_field.py | 96 ---------------- random_fields/generate_field.py | 113 +++++++++++++++++++ random_fields/utils.py | 51 ++++++++- 19 files changed, 587 insertions(+), 99 deletions(-) create mode 100644 docs/Makefile rename docs/{static => _static}/random_field.png (100%) create mode 100644 docs/_static/random_field_1.png create mode 100644 docs/_static/random_field_2.png create mode 100644 docs/_static/random_field_3.png rename docs/{static => _static}/random_field_3D.png (100%) create mode 100644 docs/authors.rst create mode 100644 docs/conf.py create mode 100644 docs/contributions.rst create mode 100644 docs/index.rst create mode 100644 docs/installation.rst create mode 100644 docs/make.bat create mode 100644 docs/requirements.txt create mode 100644 docs/theory.rst create mode 100644 docs/tutorials.rst diff --git a/README.md b/README.md index 0309bbf..485f0bb 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,7 @@ plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.random_field], title="Random Fi The result is the following random field: -![Random field 2D](./docs/static/random_field.png) +![Random field 2D](./docs/_static/random_field.png) ## Example in 3D @@ -127,4 +127,4 @@ plot3D([np.array([x.ravel(), y.ravel(), z.ravel()]).T], [rf.random_field], title ``` The result is the following random field: -![Random field 3D](./docs/static/random_field_3D.png) +![Random field 3D](./docs/_static/random_field_3D.png) diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/static/random_field.png b/docs/_static/random_field.png similarity index 100% rename from docs/static/random_field.png rename to docs/_static/random_field.png diff --git a/docs/_static/random_field_1.png b/docs/_static/random_field_1.png new file mode 100644 index 0000000000000000000000000000000000000000..0538fc64ee0628fc6355a1a0235d825317cbd36a GIT binary patch literal 29598 zcmbSzcRbeZ+y4jIdrM>{AyO(N#Dz;qXizD$p+yq1DO|{mriRhbq%>qiDy1PBC>2R4 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+STEM team +========= + +Maintainers +----------- +- Bruno Zuada Coelho `@brunozc `_ +- Aron Noordam `@aronnoordam `_ + +Developers +---------- +- Marjan Fathian `@indigocoral `_ +- Jonathan Nuttall `@mcgicjn2 `_ +- Davide Moretti `@morettid `_ +- Jelme Pennings `@jkpennings `_ +- Bram van den Eijnden `@bramvandeneijnden `_ + + diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..60f76a3 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,62 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import random_fields + + +project = 'RandomFields' +copyright = '2024, STEM team' +author = 'STEM team' +version = random_fields.__version__ + + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = ['sphinx.ext.autodoc', 'sphinx_rtd_theme', 'sphinx.ext.intersphinx'] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# to add __init__ documentation to the build +autoclass_content = 'both' + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'sphinx_rtd_theme' +html_static_path = ['_static'] + +# # Configure the RTD theme options +html_theme_options = { + 'logo_only': False, + 'display_version': True, + 'prev_next_buttons_location': 'bottom', + 'style_external_links': True, # Toc options + 'collapse_navigation': False, + 'sticky_navigation': True, + 'navigation_depth': 2, + 'includehidden': True, + 'titles_only': True, +} + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +html_show_sphinx = False +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +html_show_copyright = True +# If true shows the source link to the rst code +html_show_sourcelink = False + +# Link to the github +html_context = { + "display_github": True, + "github_user": "stemVibrations", + "github_repo": "RanndomFields", + "github_version": "main", + "conf_py_path": "/docs/", +} diff --git a/docs/contributions.rst b/docs/contributions.rst new file mode 100644 index 0000000..330a183 --- /dev/null +++ b/docs/contributions.rst @@ -0,0 +1,99 @@ +Code contribution +================= + +Steps for submitting your code +------------------------------ + +When contributing code follow this checklist: + + #. Fork the repository on GitHub. + #. Create an issue with the desired feature or bug fix. + #. Make your modifications or additions in a feature branch. + #. Make changes and commit your changes using a descriptive commit message. + #. Provide tests for your changes, and ensure they all pass. + #. Provide documentation for your changes, in accordance with the style of the rest of the project (see :ref:`style_guide`). + #. Create a pull request to STEM main branch. The STEM team will review and discuss your Pull Request with you. + +For any questions, please get in contact with one of the members of :doc:`authors`. + + +.. _style_guide: + +Code style guide +---------------- +The additional features should follow the style of the STEM project. + +We follow the PEP 8 style guide for Python code, with our custom modifications as defined in the +`Yapf file <../../.style.yapf>`_ and the `flake8 file <../../.flake8>`_. These files can be ran manually by using the +following command from the root directory of the project: + +.. code-block:: + + pre-commit run --all-files + + +The class or function name should be clear and descriptive of the functionality it provides. + +There should be a docstring at the beginning of the class or function describing its purpose and usage. +The docstring should be in the form of a triple-quoted string. + +The class or function must have a type annotation. +The class should specify the attributes and inheritance. +The function should specify the arguments, exceptions and returns (in this order). +The return type annotation should be in the form of a comment after the closing parenthesis of the arguments. + +Please, avoid inheritance, and favour composition when writing your code. + +An example of a class: + +.. code-block:: + + class ResidualConvergenceCriteria(ConvergenceCriteriaABC): + """ + Class containing information about the residual convergence criteria + + Inheritance: + - :class:`ConvergenceCriteriaABC` + + Attributes: + - residual_relative_tolerance (float): The relative tolerance for the residual. Default value is 1e-4. + - residual_absolute_tolerance (float): The absolute tolerance for the residual. Default value is 1e-9. + """ + def __init__(self): + """ + Constructor of the ResidualConvergenceCriteria class. + """ + self.residual_relative_tolerance: float = 1e-4 + self.residual_absolute_tolerance: float = 1e-9 + + +An example of a function: + +.. code-block:: + + def create_solver_settings_dictionary(self, model: Model, mesh_file_name: str, materials_file_name: str) -> Dict[str, Any]: + """ + Creates a dictionary containing the solver settings. + + Args: + - model (:class:`random_fields.model.Model`): The model object containing the solver data and model parts. + - mesh_file_name (str): The name of the mesh file. + - materials_file_name (str): The name of the materials parameters json file. + + Raises: + - ValueError: if solver_settings in model are not initialised. + + Returns: + - Dict[str, Any]: dictionary containing the part of the project parameters + dictionary related to problem data and solver settings. + """ + + if model.project_parameters is None: + raise ValueError("Solver settings are not initialised in model.") + + return self.solver_io.create_settings_dictionary( + model.project_parameters, + Path(mesh_file_name).stem, + materials_file_name, + model.get_all_model_parts(), + ) diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..2b4c905 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,59 @@ +RandomFields +==== +Python package to generate random fields in 2D or 3D on unstructured grids. + + +User guide +========== + +Background information +...................... +The random fields are generated using [gstools](https://geostat-framework.readthedocs.io/en/latest/). + +Currently, the following covariance models are available: +- Gaussian +- Exponential +- Matern +- Linear + + +RandomFields Installation +................. +To install RandomFields you need to install the following items: + +* :ref:`python_stem` + +Tutorials +......... + +* :ref:`tutorial1` + +* :ref:`tutorial2` + +* :ref:`tutorial3` + +Theory +...... + +under :ref:`theory` + +How to contribute +================= +If you want contribute to RandomFields please follow the steps defined in :doc:`contributions`. + + +STEM team +========= +STEM is a research programme that results from a collaboration between the following partners: + +* `ProRail `_ +* `Deltares `_ +* `TNO `_ +* `TU Delft `_ + +The :doc:`authors`. + +Package documentation +===================== + +The :doc:`randomfields` documentation. diff --git a/docs/installation.rst b/docs/installation.rst new file mode 100644 index 0000000..6a59805 --- /dev/null +++ b/docs/installation.rst @@ -0,0 +1,10 @@ +RandomFields installation +================= + +.. _python_stem: + +RandomFields Python package +------------------- + + +To be copied from STEM \ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..954237b --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..2540854 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,2 @@ +Sphinx==6.2.1 +sphinx-rtd-theme==1.2.2 diff --git a/docs/theory.rst b/docs/theory.rst new file mode 100644 index 0000000..7dc7389 --- /dev/null +++ b/docs/theory.rst @@ -0,0 +1,6 @@ + +.. _theory: + +Theory +====== + diff --git a/docs/tutorials.rst b/docs/tutorials.rst new file mode 100644 index 0000000..8092f83 --- /dev/null +++ b/docs/tutorials.rst @@ -0,0 +1,110 @@ +RandomFields tutorials +====================== + +.. _tutorial1: + +Example in 1D +----------------------------- +This tutorial shows step by step guide on how to set up the random field generator and visualise the resulting random fields. + +First the necessary packages are imported and paths are defined. + +.. code-block:: python + + import numpy as np + from random_fields.generate_field import RandomFields, ModelName + from random_fields.utils import plot2D + +Then, you need to define the 1D grid: + +.. code-block:: python + + x = np.linspace(0, 100, 501).reshape(-1,1) + +In this example, we create a random field over a line of length 100, divided into steps of 0.2 + +Then we define the random field properties: + +.. code-block:: python + + nb_dimensions = 1 + mean = 10 + variance = 2 + vertical_scale_fluctuation = 10 + anisotropy = [] + angle = [] + model_rf = ModelName.Gaussian + +In this example, we use a a Gaussian covariance model. + +Then we create the random field, in which the index of the 'vertical' dimension needs to be set to `0`: + +.. code-block:: python + + rf = RandomFields(model_rf, nb_dimensions, mean, variance, + vertical_scale_fluctuation, anisotropy, angle, seed=1) + rf.v_dim = 0 + rf.generate(x.reshape(-1,1)) + +To visualise the results, you can run: + +.. code-block:: python + + plot1D([x],[rf.random_field], + title = 'Random Field', + output_folder="./", + output_name="random_field_1.png") + +.. image:: _static/random_field_1.png + + +Conditioning data can be added to the random field. The noise level represents the uncertainty in the individual conditioning points and is formulated as the normalised variance: the variance of the uncertainty in a single data point divided by the variance of the random field. ``noise_level`` can be a scalar, or an array of the same dimensions as ``conditioning _values``. + +.. code-block:: python + + conditioning_points = np.array([[30,50,70]]).T + conditioning_values = np.array([-2.5,0,2.5]) + + rf.set_conditioning_points(conditioning_points,conditioning_values, noise_level = 0.04) + + +Ten conditional random fields can be sampled and visualised as follows: + +.. code-block:: python + + for i in range(10): + rf.seed = i + rf.generate_conditioned(x) + rf_conditioned[i] = rf.conditioned_random_field + + plot1D([x]*10,rf_conditioned, + title = 'Conditioned Random Field', + output_folder="./", + output_name="random_field_2.png") + + +.. image:: _static/random_field_2.png + + +As a by-product of the conditioned random field generator, the kriging mean and variance are provided: + +.. code-block:: python + + mean = rf.kriging_mean + std = rf.kriging_std + + plt.scatter(conditioning_points, conditioning_values, label = 'conditioning points') + plt.plot(x, mean, label = 'kriged field') + plt.fill_between(x.ravel(), mean - 1.65*std, mean + 1.65*std, label = '90% uncertainty bound') + + +.. image:: _static/random_field_3.png + + +Note that only simple kriging is applied for conditioning the random fields. See :ref:`theory` for more details. + +.. _tutorial2: + + + +.. _tutorial3: diff --git a/random_fields/generate_conditioned_field.py b/random_fields/generate_conditioned_field.py index 893fffa..f4f9a02 100644 --- a/random_fields/generate_conditioned_field.py +++ b/random_fields/generate_conditioned_field.py @@ -76,106 +76,10 @@ def __init__(self, model_name: ModelName, n_dim: int, self.angle = angle self.v_dim = v_dim self.random_field = None - self.Z_kriged_field = None - def set_conditioning_points(self, - points: np.ndarray, - values: np.ndarray, - kernel = None ) -> None: - """ - Initiates the conditioning points and inverts the covariance matrix - - Parameters: - ----------- - points: array-like - The contitioning point coordinates - values: array-like - The conditioning point values - kernel: sklearn kernel - The (calibrated) correlation kernel from sklearn - """ - - if max(points.shape) > 2000: - print('too many conditioning points!') - - if kernel == None: - kernel = W(0.01) + RBF(length_scale=[1.0]*self.n_dim) - self.kernel_ = kernel - self.GaussianProcess = GPR(kernel = kernel) - self.GaussianProcess.optimizer = None - self.GaussianProcess.fit(points,values) - self.conditioning_points = points - self.conditioning_values = values - - self.Z_kriged_field == None - - - def generate(self, nodes: np.ndarray) -> None: - """ - Generate conditioned random field - - Parameters: - ------------ - nodes: list - The nodes of the random field - """ - # check dimensions of nodes agrees with dimensions of model - if nodes.shape[1] != self.n_dim: - raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') - - # scale of fluctuation - - LS_sklearn = np.array(self.GaussianProcess.kernel_.get_params()['k2__length_scale']) - - # - # create kriged mean field - if not isinstance(self.Z_kriged_field, np.ndarray) : - print('kriging field for first time') - self.Z_kriged_field = self.GaussianProcess.predict(nodes) - - - f_correct = {'Gaussian':np.sqrt(2 / np.pi), - 'Exponential':np.sqrt(1 / 2), - 'Matern':np.sqrt(1/ 2 ) } - - - if not self.random_field_model_name in f_correct.keys(): - print('!! random field model',self.random_field_model_name,'not implemented... !!') - - # - # correct the length scales between libraries - LS_gstools = LS_sklearn * f_correct[self.random_field_model_name] - - model = self.random_field_model(dim=self.n_dim, var=1., len_scale=LS_gstools, angles=self.angle) - self.random_field = gs.SRF(model, mean=0., seed=self.seed) - - nodes_cpoints = np.vstack([nodes,self.conditioning_points]) - - # - # create single random field at nodes and conditioning points - self.random_field(nodes_cpoints.T) - Z_crf_nodes = self.random_field.field[:nodes.shape[0]] - Z_rf_cpoints = self.random_field.field[nodes.shape[0]:] - - # - # create kriged mean field of random field - GPrf = GPR(kernel = self.GaussianProcess.kernel_) - GPrf.optimizer = None - GPrf.fit(self.conditioning_points,Z_rf_cpoints) - GPrf.L_ = self.GaussianProcess.L_ - Z_kriged_nodes_rf = GPrf.predict(nodes) - - # - # replace kriged mean field (the kriged parts) to create conditioned random field - Z_crf_nodes += self.Z_kriged_field - Z_crf_nodes -= Z_kriged_nodes_rf - - # - # scale conditioned random field - self.conditioned_random_field = self.mean + np.sqrt(self.variance) * Z_crf_nodes diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index 3ec494e..f1b55ce 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -4,6 +4,12 @@ import gstools as gs +from sklearn.gaussian_process import GaussianProcessRegressor as GPR +from sklearn.gaussian_process.kernels import RBF, \ + ConstantKernel as C, \ + WhiteKernel as W, Matern + + class ModelName(Enum): """ Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" @@ -60,6 +66,7 @@ def __init__(self, model_name: ModelName, n_dim: int, raise ValueError(f'Number of dimensions: {n_dim} does not match number of\ random field properties: {len(variance)}. It should be {n_dim - 1}.') + self.random_field_model_name = model_name.name self.random_field_model = model_name.value self.n_dim = n_dim self.seed = seed @@ -70,6 +77,7 @@ def __init__(self, model_name: ModelName, n_dim: int, self.angle = angle self.v_dim = v_dim self.random_field = None + self.z_kriged_field = None def generate(self, nodes: np.ndarray) -> None: """ @@ -94,3 +102,108 @@ def generate(self, nodes: np.ndarray) -> None: model = self.random_field_model(dim=self.n_dim, var=self.variance, len_scale=scale_fluctuation, angles=self.angle) self.random_field = gs.SRF(model, mean=self.mean, seed=self.seed) self.random_field(nodes.T) + + + def set_conditioning_points(self, + points: np.ndarray, + values: np.ndarray, + noise_level = 0.0001 ) -> None: + """ + Initiates the conditioning points and inverts the covariance matrix + + Parameters: + ----------- + points: array-like + The contitioning point coordinates + values: array-like + The conditioning point values + kernel: sklearn kernel + The (calibrated) correlation kernel from sklearn (default = None) + noise_level: float or array-like + normalised variance of the observed data, as a single float or as + an array of the same dimensions as `values` (default = 0.0001) + + """ + self.noise_level = noise_level + + if max(points.shape) > 2000: + print('too many conditioning points!') + + + # scale of fluctuation + scale_fluctuation = np.ones(self.n_dim) * self.vertical_scale_fluctuation + + # apply the anisotropy to the other dimensions + mask = np.arange(len(scale_fluctuation)) != self.v_dim + scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy + + # + # correct the length scales between libraries + if self.random_field_model_name == 'Gaussian': + ls_sklearn = scale_fluctuation * 2/np.pi + self.KrigingKernel = W(noise_level) + RBF(length_scale = ls_sklearn) + elif self.random_field_model_name == 'Exponential': + ls_sklearn = scale_fluctuation / np.sqrt(1/2) + self.KrigingKernel = W(noise_level) + Matern(length_scale = ls_sklearn, nu = 0.5) + elif self.random_field_model_name == 'Matern': + ls_sklearn = scale_fluctuation / np.sqrt(1/2) + self.KrigingKernel = W(noise_level) + Matern(length_scale = ls_sklearn) + + self.GaussianProcess = GPR(kernel = self.KrigingKernel) + self.GaussianProcess.optimizer = None + self.GaussianProcess.fit(points,(values - self.mean) / np.sqrt(self.variance) ) + self.conditioning_points = points + self.conditioning_values = values + + self.kriging_mean = None + self.kriging_std = None + + + def generate_conditioned(self, nodes: np.ndarray) -> None: + """ + Generate conditioned random field + + Parameters: + ------------ + nodes: list + The nodes of the random field + + """ + # check dimensions of nodes agrees with dimensions of model + if nodes.shape[1] != self.n_dim: + raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') + # + # create kriged mean field + z_kriged_field,std_kriged_field = self.GaussianProcess.predict(nodes,return_std = True) + + std_kriged_field = np.sqrt(std_kriged_field**2 - self.noise_level) + + std_kriged_field *= np.sqrt(self.variance) + self.kriging_mean = self.mean + np.sqrt(self.variance) * z_kriged_field + # + # create single random field at nodes and conditioning points + nodes_cpoints = np.vstack([nodes,self.conditioning_points]) + self.generate(nodes_cpoints) + + z_crf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) + z_rf_cpoints = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) + + np.random.seed(self.seed) + z_rf_cpoints += np.sqrt(self.noise_level) * np.random.normal(size = z_rf_cpoints.shape) + + # + # create kriged mean field of random field + GPrf = GPR(kernel = self.GaussianProcess.kernel_) + GPrf.optimizer = None + GPrf.fit(self.conditioning_points,z_rf_cpoints) + GPrf.L_ = self.GaussianProcess.L_ + z_kriged_nodes_rf = GPrf.predict(nodes) + # + # replace kriged mean field (the kriged parts) to create conditioned random field + z_crf_nodes -= z_kriged_nodes_rf + z_crf_nodes += z_kriged_field + + # + # scale conditioned random field + self.kriging_std = np.sqrt(self.variance) * std_kriged_field + self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_crf_nodes \ No newline at end of file diff --git a/random_fields/utils.py b/random_fields/utils.py index 3e804ae..7a039d0 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -94,4 +94,53 @@ def plot2D(coordinates: list, random_field: list, title: str = "Random Field", fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax) fig.suptitle(title) plt.savefig(os.path.join(output_folder, output_name)) - plt.close() \ No newline at end of file + plt.close() + + +def plot1D(coordinates: list, random_field: list, title: str = "Random Field", + output_folder = "./", output_name: str = "random_field.png"): + """ + Plot 1D random field + + Parameters + ---------- + coordinates : list + List of coordinates of the random field + random_field : list + List of random field values + title : str + Title of the plot + output_folder : str + Output folder + output_name : str + Output fine name + """ + + # create output folder + if not os.path.exists(output_folder): + os.makedirs(output_folder) + + # make plot + fig, ax = plt.subplots(figsize=(8, 4)) + ax.set_position([0.1, 0.1, 0.7, 0.8]) + + vmin = np.min([np.min(aux) for aux in random_field]) + vmax = np.max([np.max(aux) for aux in random_field]) + + color = 'black' + if len(coordinates) > 1: + color = 'gray' + + for i, coord in enumerate(coordinates): + x = coord[:, 0] + y = np.array(random_field[i]).ravel() + ax.plot(x, y, color = color) + + ax.set_xlabel('x coordinate') + ax.set_ylabel('random field value') + + plt.tight_layout() + + fig.suptitle(title) + plt.savefig(os.path.join(output_folder, output_name)) + plt.close() \ No newline at end of file From b9324d8e33e2f3bec644cd391b001eab79a1b675 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 28 Jun 2024 16:56:41 +0200 Subject: [PATCH 04/62] Extension of tutorial to 3D conditioned random fields Changes committed: new file: _static/conditioned_random_field_3D.png new file: _static/kriging_mean_2D.png new file: _static/kriging_mean_3D.png new file: _static/kriging_std_2D.png new file: _static/kriging_std_3D.png new file: _static/random_field_4.png new file: _static/random_field_5.png modified: index.rst modified: tutorials.rst modified: ../random_fields/utils.py --- docs/_static/conditioned_random_field_3D.png | Bin 0 -> 93281 bytes docs/_static/kriging_mean_2D.png | Bin 0 -> 23991 bytes docs/_static/kriging_mean_3D.png | Bin 0 -> 90389 bytes docs/_static/kriging_std_2D.png | Bin 0 -> 27517 bytes docs/_static/kriging_std_3D.png | Bin 0 -> 82441 bytes docs/_static/random_field_4.png | Bin 0 -> 49390 bytes docs/_static/random_field_5.png | Bin 0 -> 51402 bytes docs/index.rst 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0000000..34321dd --- /dev/null +++ b/tests/test_conditional_rf.py @@ -0,0 +1,92 @@ +import os +import pickle +from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np +import pytest + +from random_fields.geostatistical_cpt_interpretation import CPT_data, MarginalTransformation, GeostatisticalModel +from random_fields.generate_field import RandomFields, ModelName + +SHOW_RESULTS = False + + + +@pytest.mark.parametrize("cpt_folder_path, test_file", [ + ("./tests/cpts/gef", "./tests/data/2d_rf_gef.pickle"), + ("./tests/cpts/xml", "./tests/data/2d_rf_xml.pickle") +]) +def test_2d_random_field(cpt_folder_path, test_file): + """ + Test the generation of a conditional 2D random field with GEF & XML files. + + Args: + - cpt_folder_path (str): path to the folder containing the CPT files. + - test_file (str): path to the file containing the expected results. + """ + + cpt_folder = Path(cpt_folder_path) + cpt_data = CPT_data(cpt_directory=cpt_folder) + cpt_data.read_cpt_data() + cpt_data.interpret_cpt_data() + + cpt_data.plot_coordinates(show=False) + assert os.path.exists("coordinates.png") + os.remove("coordinates.png") + + # transform the data to a modelling domain + cpt_data.data_coordinate_change(orientation_x_axis=73, based_on_midpoint=True) + + marginal_transformator = MarginalTransformation(cpt_data.vs, min_value=50) + marginal_transformator.plot(x_label='$u$ : standard-normal variable', y_label='$v$ : shear wave velocity [m/s]') + + np.random.seed(14) + index_selection = np.random.choice(len(cpt_data.vs), size=2000, replace=False) + coords = cpt_data.data_coords[index_selection] + z_data = marginal_transformator.x_to_z(x=cpt_data.vs[index_selection]) + + geo_model = GeostatisticalModel(nb_dimensions=2, v_dim=1) + geo_model.calibrate(coords=coords[:, [2, 1]], values=z_data) + + random_field_generator = RandomFields(model_name=ModelName.Gaussian, + n_dim=2, + mean=0, + variance=1, + v_scale_fluctuation=geo_model.vertical_scale_fluctuation, + anisotropy=geo_model.anisotropy, + angle=[0], + seed=14) + + I = np.random.choice(len(cpt_data.vs), size=500, replace=False) + + coords = cpt_data.data_coords[I] + values = cpt_data.vs[I] + random_field_generator.set_conditioning_points(points=coords[:, [2, 1]], + values=marginal_transformator.x_to_z(x=values), + noise_level=geo_model.noise_level) + + # create grit of points on the domnain (-220,220) by (-24,-1) to generate a field for. + x = np.linspace(-220, 220, 250) + z = np.linspace(-24, -1, 250) + X, Z = np.meshgrid(x, z) + + # generate a conditioned random field + sample_coords = np.array([X.ravel(), Z.ravel()]).T + random_field_generator.generate_conditioned(nodes=sample_coords) + + # Transform the generated standard-normal field to the distribution of the shear wave velocity + z_map = random_field_generator.conditioned_random_field + vs_map = marginal_transformator.z_to_x(z_map[:250 * 250].reshape([250, 250])) + + # check the results + with open(test_file, "rb") as f: + vs_test = pickle.load(f) + + assert np.allclose(vs_map, vs_test) + + if SHOW_RESULTS: + fig, ax = plt.subplots(figsize=(10, 5)) + plt.contourf(X, Z, vs_map, vmin=50, vmax=400) + scatter = plt.scatter(cpt_data.data_coords[:, 2], cpt_data.data_coords[:, 1], c=cpt_data.vs, vmin=50, vmax=400) + plt.colorbar(scatter, ax=ax, label="Vs [m/s]") + plt.show() From cc45031c4c7c5d57e551c2be7dca0d55f9a90a5a Mon Sep 17 00:00:00 2001 From: brunozc Date: Mon, 13 Jan 2025 19:48:28 +0100 Subject: [PATCH 36/62] Downgrade scipy version to 1.13.1 in requirements files --- requirements.txt | 2 +- requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index aaea346..9f8cff9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ numpy==2.0.2 matplotlib==3.9.3 -scipy==1.15.0 +scipy==1.13.1 gstools==1.6.0 scikit-learn==1.6.0 d-geolib-plus==0.4.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index 4b911b8..bf98b9a 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,6 +1,6 @@ numpy==2.0.2 matplotlib==3.9.3 -scipy==1.15.0 +scipy==1.13.1 gstools==1.6.0 scikit-learn==1.6.0 mypy==1.13.0 From f19560f9004707fece9d9312db3fdb3abc2521e9 Mon Sep 17 00:00:00 2001 From: brunozc Date: Mon, 13 Jan 2025 20:57:28 +0100 Subject: [PATCH 37/62] Refactor code for improved readability and maintainability in random field generation functions --- random_fields/generate_field.py | 18 +-- .../geostatistical_cpt_interpretation.py | 149 ++++++++++-------- random_fields/utils.py | 17 +- tests/test_conditional_rf.py | 25 ++- 4 files changed, 112 insertions(+), 97 deletions(-) diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index 4c939bf..520df08 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -143,7 +143,7 @@ def generate(self, nodes: npt.NDArray[np.float64]) -> None: def set_conditioning_points(self, points: npt.NDArray[np.float64], values: npt.NDArray[np.float64], - noise_level: Union[float, npt.NDArray[np.float64]]=0.0001) -> None: + noise_level: Union[float, npt.NDArray[np.float64]] = 0.0001) -> None: """ Initiates the conditioning points and inverts the covariance matrix @@ -167,12 +167,10 @@ def set_conditioning_points(self, # check the maximum number of conditioning points to keep the computation and memory cost reasonable if points.shape[0] > self.max_conditioning_points: - raise Exception( - f'Too many conditioning points!\n' - 'There are {points.shape[0]} points, while the maximum allowed amount' - f'is {self.max_conditioning_points}.\n' - 'Consider increasing `max_conditioning_points` or use fewer conditioning points.' - ) + raise Exception(f'Too many conditioning points!\n' + 'There are {points.shape[0]} points, while the maximum allowed amount' + f'is {self.max_conditioning_points}.\n' + 'Consider increasing `max_conditioning_points` or use fewer conditioning points.') # check dimensions of conditioning points agrees with dimensions of model if points.shape[1] != self.n_dim: @@ -231,8 +229,10 @@ def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: # Split the generated field into the nodal coordinates and the conditioning points # Standardize the distribution to marginal ~N(0,1) - z_cond_rf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) # type: ignore - z_rf_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) # type: ignore + z_cond_rf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt( # type: ignore + self.variance) # type: ignore + z_rf_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt( # type: ignore + self.variance) # type: ignore # add the noise to the conditioning points z_rf_cond_points += np.sqrt(self.noise_level) * np.random.normal(size=z_rf_cond_points.shape) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 0425ea5..a52f526 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -74,13 +74,13 @@ def __init__(self, self._interp_log_data = np.interp(self._interp_z, z_points, x_points_ln) def plot(self, - title: str="Marginal distribution transformation", - output_folder: Path=Path("./"), - output_name: str="marginal_distribution.png", - x_label: str=r'$Z\sim N(0, 1)$', - y_label: str=r'$X$', - figsize: Tuple[float, float]=(6, 5), - show: bool=False) -> None: + title: str = "Marginal distribution transformation", + output_folder: Path = Path("./"), + output_name: str = "marginal_distribution.png", + x_label: str = r'$Z\sim N(0, 1)$', + y_label: str = r'$X$', + figsize: Tuple[float, float] = (6, 5), + show: bool = False) -> None: r"""Plots the transformation model for the marginal distribution. Args: @@ -183,7 +183,7 @@ def __init__(self, cpt_directory: Path) -> None: self.young_modulus = np.empty(0) self.poisson = 0.495 # ToDo: add Poisson ratio see #6 - def read_cpt_data(self, addition: bool=False) -> None: + def read_cpt_data(self, addition: bool = False) -> None: """read_cpt_data Reads in and pre-processes the CPT data Args: @@ -231,7 +231,7 @@ def __find_duplicates(self) -> List[int]: seen.add(value.name) return unique_idx - def interpret_cpt_data(self, interpreter: Optional[RobertsonCptInterpretation]=None, v_dim: int=1) -> None: + def interpret_cpt_data(self, interpreter: Optional[RobertsonCptInterpretation] = None, v_dim: int = 1) -> None: """ Interpret_cpt_data interprets the CPT data based on the provided interpreter settings @@ -319,7 +319,7 @@ def data_coordinate_change(self, x_ref: float = 0., y_ref: float = 0., orientation_x_axis: float = 0., - based_on_midpoint: bool=False) -> None: + based_on_midpoint: bool = False) -> None: """ Coordinate system transformation, relative to a reference point (x_ref, y_ref) @@ -344,18 +344,17 @@ def data_coordinate_change(self, # rotate counterclock-wise back to N000 minus 90 degrees: theta_rad = orientation_x_axis / 180 * np.pi - np.pi / 2 - rotation = np.array([[np.cos(theta_rad), 0, -np.sin(theta_rad)], - [0., 1., 0.], + rotation = np.array([[np.cos(theta_rad), 0, -np.sin(theta_rad)], [0., 1., 0.], [np.sin(theta_rad), 0, np.cos(theta_rad)]]) self.data_coords = self.data_coords @ rotation.T self.cpt_locations = self.cpt_locations @ rotation[::2, ::2].T - - def plot_coordinates(self, output_folder: Path=Path("./"), - output_name: str="coordinates.png", - original_coordinates: bool=False, - show: bool=False) -> None: + def plot_coordinates(self, + output_folder: Path = Path("./"), + output_name: str = "coordinates.png", + original_coordinates: bool = False, + show: bool = False) -> None: """ Plots the coordinates of the CPTs @@ -380,11 +379,12 @@ def plot_coordinates(self, output_folder: Path=Path("./"), plt.show() plt.close() + class GeostatisticalModel(): """ Class for the characterisation of spatial variability by means of a geostatistical model - Attr: + Attributes: - nb_dimensions (int): number of physical dimensions (1, 2 or 3) - v_dim (int): index corresponding to vertical - gpr (:class:`sklearn.gaussian_process.GaussianProcessRegressor`). Gaussian process regressor. @@ -394,11 +394,13 @@ class GeostatisticalModel(): geostatistical model. - noise_level (Optional[float]): noise level b - vertical_scale_fluctuation (Optional[float]): vertical scale of fluctuation - - anisotropy (Optional[List[np.float64]]): anisotropy in the scale of fluctuation, relative to the vertical scale of fluctuation. + - anisotropy (Optional[List[np.float64]]): anisotropy in the scale of fluctuation, relative to the vertical + scale of fluctuation. """ - def __init__(self, nb_dimensions: int, + def __init__(self, + nb_dimensions: int, v_dim: int, length_scale: float = 100., length_scale_bounds: List[float] = [1.0, 1000.], @@ -424,9 +426,9 @@ def __init__(self, nb_dimensions: int, self.data_coords = np.empty(0) self.data_values = np.empty(0) - self.noise_level = None - self.vertical_scale_fluctuation = None - self.anisotropy = None + self.noise_level = np.empty(0) + self.vertical_scale_fluctuation = np.empty(0) + self.anisotropy = np.empty(0) # check if `kernel` has the correct parent class if custom_kernel: @@ -485,10 +487,11 @@ def calibrate(self, coords: npt.NDArray[np.float64], values: npt.NDArray[np.floa mask = np.arange(len(gpr_length_scales)) != self.v_dim self.anisotropy = gpr_length_scales[mask] / gpr_length_scales[self.v_dim] + class ElasticityFieldsFromCpt: """Class for the automatic generation of fields of elastic parameters (vs, rho, G0 and E) - Attr: + Attributes: - poisson_ratio (float): poisson ratio. - cpt_file_folder (str): path of the folder containing the CPT files (.gef) - x_ref (float): physical x-coordinate of the origin of the model @@ -500,7 +503,6 @@ class ElasticityFieldsFromCpt: geostatistical model. - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `G0` - - conditioning_data (class:`CPT_data`): instance of the `class:CPT_data` class. - coord_calibration (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the calibration of the geostatistical model. @@ -516,34 +518,36 @@ class ElasticityFieldsFromCpt: - vs (npt.NDArray[np.float64]): generated conditioned random field of shear wave velocity `vs` - rho (npt.NDArray[np.float64]): generated conditioned random field of density `rho` - - g0 (npt.NDArray[np.float64]): generated conditioned random field of shear modulus `g0 = rho * vs**2` + - G0 (npt.NDArray[np.float64]): generated conditioned random field of shear modulus `G0 = rho * vs**2` - young_modulus (npt.NDArray[np.float64]): generated conditioned random field of elastic modulus - `young_modulus = 2 * g0 * (1. + poisson_ratio)` + `young_modulus = 2 * G0 * (1. + poisson_ratio)` - generated_field (list): list of randopm field values corresponding to parameter `return_property` """ def __init__(self, - cpt_file_folder: str='./', - x_ref: float=0., - y_ref: float=0., - orientation_x_axis: float=0., - based_on_midpoint: bool=False, - max_conditioning_points: int=2000, - return_property: str='young_modulus') -> None: + cpt_file_folder: str = './', + x_ref: float = 0., + y_ref: float = 0., + orientation_x_axis: float = 0., + based_on_midpoint: bool = False, + max_conditioning_points: int = 2000, + seed: int = 14, + return_property: str = 'young_modulus') -> None: """ - initiation of the class + Initiation of the class Args: - - cpt_file_folder (str, optional): path to tyhe folder containing the CPT files (.gef). - - x_ref (float, optional): physical x-coordinate of the origin of the model. Defaults to 0.. - - y_ref (float, optional): physical y-coordinate of the origin of the model. Defaults to 0.. - - orientation_x_axis (float, optional): orientation of the x-axis of the model - (azimuth, clockwise g=from the North). Defaults to 0.. - - based_on_midpoint (bool, optional): If True, ignores x_ref and y_ref and places origin of the model + - cpt_file_folder (str): path to tyhe folder containing the CPT files (.gef). Defaults to './'. + - x_ref (float): physical x-coordinate of the origin of the model. Defaults to 0. + - y_ref (float): physical y-coordinate of the origin of the model. Defaults to 0. + - orientation_x_axis (float): orientation of the x-axis of the model + (azimuth, clockwise g=from the North). Defaults to 0. + - based_on_midpoint (bool): If True, ignores x_ref and y_ref and places origin of the model in the center of the set of CPT locations. Defaults to False. - - max_conditioning_points (int, optional): Maximum number of data points to use in the calibration + - max_conditioning_points (int): Maximum number of data points to use in the calibration of the geostatistical model. Defaults to 2000. - - return_property (str, optional): property to return through attribute + - seed (int): Seed for the `:class:RandomFields`. Defaults to 14. + - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `G0`. Defaults to `young_modulus`. """ @@ -556,7 +560,10 @@ def __init__(self, self.max_conditioning_points = max_conditioning_points self.return_property = return_property - self.conditioning_data = CPT_data(cpt_directory=self.cpt_file_folder) + self.seed = seed + np.random.seed(seed) + + self.conditioning_data = CPT_data(cpt_directory=Path(self.cpt_file_folder)) self.conditioning_data.read_cpt_data() self.conditioning_data.interpret_cpt_data() self.conditioning_data.data_coordinate_change(orientation_x_axis=self.orientation_x_axis, @@ -568,25 +575,29 @@ def __init__(self, self.trans_model_rho = MarginalTransformation(self.conditioning_data.rho) self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs) - self.geostat_model = None - self.random_fields_vs = None - self.random_fields_rho = None - self.vs = None - self.rho = None - self.GO = None - self.young_modulus = None - self.generated_field = None - - def calibrate_geostat_model(self, v_dim: int=1, calibration_indices: tuple=(0, 1), seed: int=14) -> None: + # self.geostat_model = GeostatisticalModel(nb_dimensions=3, v_dim=1) + # self.random_fields_vs = RandomFields(model_name=ModelName.Gaussian, n_dim=3, mean=0, variance=1, + # v_scale_fluctuation=1.0, anisotropy=[1., 1.], angle=[0., 0.], + # ) + self.geostat_model: Optional[GeostatisticalModel] = None + self.random_fields_vs: Optional[RandomFields] = None + + self.random_fields_rho: Optional[RandomFields] = None + self.vs = np.empty(0) + self.rho = np.empty(0) + self.GO = np.empty(0) + self.young_modulus = np.empty(0) + self.generated_field: List[npt.NDArray[np.float64]] = [np.empty(0)] + + def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int, int] = (0, 1)) -> None: """calibrates the geostatistical model Args: - - v_dim (int, optional): vertical dimension. Defaults to 1. - - calibration_indices (Tuple, optional): _description_. Defaults to (0,1). - - seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 14. + - v_dim (int): vertical dimension. Defaults to 1. + - calibration_indices (Tuple): _description_. Defaults to (0, 1). """ - np.random.seed(seed) + np.random.seed(self.seed) ndim_calibrate = len(calibration_indices) self.coord_calibration = np.zeros([self.max_conditioning_points, 3]) @@ -603,10 +614,10 @@ def calibrate_geostat_model(self, v_dim: int=1, calibration_indices: tuple=(0, 1 n_dim=3, mean=0, variance=1, - v_scale_fluctuation=self.geostat_model.vertical_scale_fluctuation, - anisotropy=(self.geostat_model.anisotropy * 2)[:2], - angle=[0] * 2, - seed=seed, + v_scale_fluctuation=float(self.geostat_model.vertical_scale_fluctuation), + anisotropy=([float(self.geostat_model.anisotropy)] * 2)[:2], + angle=[0.] * 2, + seed=self.seed, max_conditioning_points=self.max_conditioning_points) self.random_fields_vs.set_conditioning_points(points=self.coord_calibration, values=self.trans_model_vs.x_to_z( @@ -616,10 +627,10 @@ def calibrate_geostat_model(self, v_dim: int=1, calibration_indices: tuple=(0, 1 n_dim=3, mean=0, variance=1, - v_scale_fluctuation=self.geostat_model.vertical_scale_fluctuation, - anisotropy=(self.geostat_model.anisotropy * 2)[:2], + v_scale_fluctuation=float(self.geostat_model.vertical_scale_fluctuation), + anisotropy=([float(self.geostat_model.anisotropy)] * 2)[:2], angle=[0] * 2, - seed=seed + 1, + seed=self.seed + 1, max_conditioning_points=self.max_conditioning_points) self.random_fields_rho.set_conditioning_points(points=self.coord_calibration, values=self.trans_model_rho.x_to_z( @@ -634,12 +645,14 @@ def generate(self, coordinates: npt.NDArray[np.float64]) -> None: """ # generate conditioned standard-normal equivalent + assert self.random_fields_vs is not None, "RandomFields instance not initialized" self.random_fields_vs.generate_conditioned(nodes=coordinates) + assert self.random_fields_rho is not None, "RandomFields instance not initialized" self.random_fields_rho.generate_conditioned(nodes=coordinates) # transform standard-normal fields to physical (marginal) distributions: - self.vs = self.trans_model_vs.z_to_x(self.random_fields_vs.conditioned_random_field) - self.rho = self.trans_model_rho.z_to_x(self.random_fields_rho.conditioned_random_field) + self.vs = self.trans_model_vs.z_to_x(np.array(self.random_fields_vs.conditioned_random_field)) + self.rho = self.trans_model_rho.z_to_x(np.array(self.random_fields_rho.conditioned_random_field)) # derive dependent fields self.GO = self.rho * self.vs**2 @@ -650,7 +663,7 @@ def generate(self, coordinates: npt.NDArray[np.float64]) -> None: self.generated_field = list(self.young_modulus) elif self.return_property.lower() == 'rho': self.generated_field = list(self.rho) - elif self.return_property.lower() == 'g0': + elif self.return_property.lower() == 'G0': self.generated_field = list(self.GO) elif self.return_property.lower() == 'vs': self.generated_field = list(self.vs) diff --git a/random_fields/utils.py b/random_fields/utils.py index a7d91a8..d43058f 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -8,6 +8,7 @@ import gstools as gs + def plot3D(coordinates: List[npt.NDArray[np.float64]], random_field: List[gs.field.srf.SRF], title: str = "Random Field", @@ -79,8 +80,10 @@ def plot2D(coordinates: List[npt.NDArray[np.float64]], - output_folder (str): Output folder - output_name (str): Output fine name - colorbar_label (str): Label for the colorbar (optional, deafult = '') - - conditioning_coordinates (List[npt.NDArray[np.float64]]): List of coordinates if the conditioning points (optional, default = []) - - conditioning_values (List[npt.NDArray[np.float64]]): List of the values at the conditioning points (optional, default = []) + - conditioning_coordinates (List[npt.NDArray[np.float64]]): List of coordinates if the conditioning points + (optional, default = []) + - conditioning_values (List[npt.NDArray[np.float64]]): List of the values at the conditioning points + (optional, default = []) - figsize (Tuple[float, float]): Figure size (optional, default = (6, 5)) - show (bool): Show the plot (optional, default = False) """ @@ -166,10 +169,12 @@ def plot1D(coordinates: List[npt.NDArray[np.float64]], if conditional_points: plt.plot(x, conditional_points[2], label='kriged field', color='k') - plt.fill_between(x.ravel(), conditional_points[2] - 1.65 * conditional_points[3], - conditional_points[2] + 1.65 * conditional_points[3], - label='90% uncertainty bound', - color='gray', alpha=0.5) + plt.fill_between(x.ravel(), + conditional_points[2] - 1.65 * conditional_points[3], + conditional_points[2] + 1.65 * conditional_points[3], + label='90% uncertainty bound', + color='gray', + alpha=0.5) plt.scatter(conditional_points[0], conditional_points[1], label='conditioning points', color='k') plt.legend() diff --git a/tests/test_conditional_rf.py b/tests/test_conditional_rf.py index 34321dd..04d593b 100644 --- a/tests/test_conditional_rf.py +++ b/tests/test_conditional_rf.py @@ -11,11 +11,8 @@ SHOW_RESULTS = False - -@pytest.mark.parametrize("cpt_folder_path, test_file", [ - ("./tests/cpts/gef", "./tests/data/2d_rf_gef.pickle"), - ("./tests/cpts/xml", "./tests/data/2d_rf_xml.pickle") -]) +@pytest.mark.parametrize("cpt_folder_path, test_file", [("./tests/cpts/gef", "./tests/data/2d_rf_gef.pickle"), + ("./tests/cpts/xml", "./tests/data/2d_rf_xml.pickle")]) def test_2d_random_field(cpt_folder_path, test_file): """ Test the generation of a conditional 2D random field with GEF & XML files. @@ -49,21 +46,21 @@ def test_2d_random_field(cpt_folder_path, test_file): geo_model.calibrate(coords=coords[:, [2, 1]], values=z_data) random_field_generator = RandomFields(model_name=ModelName.Gaussian, - n_dim=2, - mean=0, - variance=1, - v_scale_fluctuation=geo_model.vertical_scale_fluctuation, - anisotropy=geo_model.anisotropy, - angle=[0], - seed=14) + n_dim=2, + mean=0, + variance=1, + v_scale_fluctuation=geo_model.vertical_scale_fluctuation, + anisotropy=geo_model.anisotropy, + angle=[0], + seed=14) I = np.random.choice(len(cpt_data.vs), size=500, replace=False) coords = cpt_data.data_coords[I] values = cpt_data.vs[I] random_field_generator.set_conditioning_points(points=coords[:, [2, 1]], - values=marginal_transformator.x_to_z(x=values), - noise_level=geo_model.noise_level) + values=marginal_transformator.x_to_z(x=values), + noise_level=geo_model.noise_level) # create grit of points on the domnain (-220,220) by (-24,-1) to generate a field for. x = np.linspace(-220, 220, 250) From c94a6d32df3a9c2e736ebc38f998b39763a32475 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 09:58:19 +0100 Subject: [PATCH 38/62] fixed test for linux --- tests/data/random_field_3D_linux.eps | 28744 +++++++++++++++++++++++++ tests/test_random_field_utils.py | 12 +- 2 files changed, 28754 insertions(+), 2 deletions(-) create mode 100644 tests/data/random_field_3D_linux.eps diff --git a/tests/data/random_field_3D_linux.eps b/tests/data/random_field_3D_linux.eps new file mode 100644 index 0000000..1406839 --- /dev/null +++ b/tests/data/random_field_3D_linux.eps @@ 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GitHub Actions") +# @pytest.mark.skip(reason="3D plot not working on GitHub Actions") def test_distribution_RF_struc_3D(cleanup_generated_files): """test distribution of 3D random field with structured mesh""" @@ -59,7 +60,14 @@ def test_distribution_RF_struc_3D(cleanup_generated_files): output_folder="./", output_name="random_field.eps") - with open("./tests/data/random_field_3D.eps", "r") as fi: + if sys.platform == "win32": + file_test = "./tests/data/random_field_3D.eps" + elif sys.platform == "linux": + file_test = "./tests/data/random_field_3D_linux.eps" + else: + raise Exception("Platform not supported") + + with open(file_test, "r") as fi: data_org = fi.read().splitlines() with open("./random_field.eps", "r") as fi: From ea2eb6ad84a42aa8c65e7fa447a7ca6dce1884c9 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 10:11:05 +0100 Subject: [PATCH 39/62] Fix file path for 3D random field test on Windows and remove skip marker --- ...eld_3D.eps => 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-460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e46 +60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60 +470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60470d60460b5e460b5e460b5e460b5e460b5e46 0b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460b5e460a5d460a5d460a5d460a5d460a5d460a5d460a 5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d 460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d460a5d46085c46085c46085c46085c46085c46085c46085c46085c46085c46 diff --git a/tests/test_random_field_utils.py b/tests/test_random_field_utils.py index 2280584..0e00e8d 100644 --- a/tests/test_random_field_utils.py +++ b/tests/test_random_field_utils.py @@ -41,8 +41,6 @@ def test_distribution_RF_struc(cleanup_generated_files): data = [val == data_new[header + i] for i, val in enumerate(data_org[header:idx_end])] assert all(data) - -# @pytest.mark.skip(reason="3D plot not working on GitHub Actions") def test_distribution_RF_struc_3D(cleanup_generated_files): """test distribution of 3D random field with structured mesh""" @@ -61,7 +59,7 @@ def test_distribution_RF_struc_3D(cleanup_generated_files): output_name="random_field.eps") if sys.platform == "win32": - file_test = "./tests/data/random_field_3D.eps" + file_test = "./tests/data/random_field_3D_windows.eps" elif sys.platform == "linux": file_test = "./tests/data/random_field_3D_linux.eps" else: From 6d045694703731c3bfc519c9db7e239ca3c6cc4d Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 13:34:37 +0100 Subject: [PATCH 40/62] Add Read the Docs configuration file for documentation build --- .readthedocs.yaml | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 .readthedocs.yaml diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..51e80ed --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,28 @@ +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version, and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.11" + jobs: + pre_build: + - sphinx-apidoc -o docs ./random_fields + - pip install . + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Optionally, but recommended, +# declare the Python requirements required to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt + + From 6439e69a7577f3b278acdca44c1ff53a446ac7af Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 13:35:00 +0100 Subject: [PATCH 41/62] 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List[RandomFieldProperties] = [RandomFieldProperties.YOUNG_MODULUS]) -> None: """ Initiation of the class @@ -547,9 +560,9 @@ def __init__(self, - max_conditioning_points (int): Maximum number of data points to use in the calibration of the geostatistical model. Defaults to 2000. - seed (int): Seed for the `:class:RandomFields`. Defaults to 14. - - return_property (str): property to return through attribute - `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `G0`. - Defaults to `young_modulus`. + - porosity (float): porosity of the soil. Defaults to 0.3. + - water_density (float): density of the water. Defaults to 1000. + - return_property (List[RandomFieldProperties]): property to return """ self.poisson_ratio = 0.495 self.cpt_file_folder = cpt_file_folder @@ -558,6 +571,8 @@ def __init__(self, self.orientation_x_axis = orientation_x_axis self.based_on_midpoint = based_on_midpoint self.max_conditioning_points = max_conditioning_points + self.porosity = porosity + self.water_density = water_density self.return_property = return_property self.seed = seed @@ -569,25 +584,20 @@ def __init__(self, self.conditioning_data.data_coordinate_change(orientation_x_axis=self.orientation_x_axis, based_on_midpoint=self.based_on_midpoint) - self.thinning_sample_index = np.random.choice(self.conditioning_data.data_coords.shape[0], - size=self.max_conditioning_points, - replace=False) + self.conditional_sample_index = np.random.choice(self.conditioning_data.data_coords.shape[0], + size=self.max_conditioning_points, + replace=False) self.trans_model_rho = MarginalTransformation(self.conditioning_data.rho) self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs) - # self.geostat_model = GeostatisticalModel(nb_dimensions=3, v_dim=1) - # self.random_fields_vs = RandomFields(model_name=ModelName.Gaussian, n_dim=3, mean=0, variance=1, - # v_scale_fluctuation=1.0, anisotropy=[1., 1.], angle=[0., 0.], - # ) self.geostat_model: Optional[GeostatisticalModel] = None self.random_fields_vs: Optional[RandomFields] = None self.random_fields_rho: Optional[RandomFields] = None - self.vs = np.empty(0) - self.rho = np.empty(0) - self.GO = np.empty(0) + self.solid_density = np.empty(0) self.young_modulus = np.empty(0) - self.generated_field: List[npt.NDArray[np.float64]] = [np.empty(0)] + self.generated_field: List[npt.NDArray[np.float64]] = [] + self.conditioning_sampled_data: List[npt.NDArray[np.float64]] = [] def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int, int] = (0, 1)) -> None: """calibrates the geostatistical model @@ -600,14 +610,15 @@ def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int np.random.seed(self.seed) ndim_calibrate = len(calibration_indices) - self.coord_calibration = np.zeros([self.max_conditioning_points, 3]) + self.coordinates_sampled_conditioning = np.zeros([self.max_conditioning_points, 3]) for i in calibration_indices: - self.coord_calibration[:, i] = self.conditioning_data.data_coords[self.thinning_sample_index, i] + self.coordinates_sampled_conditioning[:, i] = self.conditioning_data.data_coords[self.conditional_sample_index, i] # calibrate geostatistical model based on standard-normal equivalent values of the shear wave velocity data self.geostat_model = GeostatisticalModel(nb_dimensions=ndim_calibrate, v_dim=v_dim) - self.geostat_model.calibrate(self.coord_calibration[:, calibration_indices], - self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.thinning_sample_index])) + self.geostat_model.calibrate( + self.coordinates_sampled_conditioning[:, calibration_indices], + self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.conditional_sample_index])) # initiate two independent random field generators for shear wave velocity `vs` and density `rho` self.random_fields_vs = RandomFields(model_name=ModelName.Gaussian, @@ -619,9 +630,9 @@ def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int angle=[0.] * 2, seed=self.seed, max_conditioning_points=self.max_conditioning_points) - self.random_fields_vs.set_conditioning_points(points=self.coord_calibration, + self.random_fields_vs.set_conditioning_points(points=self.coordinates_sampled_conditioning, values=self.trans_model_vs.x_to_z( - self.conditioning_data.vs[self.thinning_sample_index]), + self.conditioning_data.vs[self.conditional_sample_index]), noise_level=self.geostat_model.noise_level) self.random_fields_rho = RandomFields(model_name=ModelName.Gaussian, n_dim=3, @@ -632,13 +643,14 @@ def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int angle=[0] * 2, seed=self.seed + 1, max_conditioning_points=self.max_conditioning_points) - self.random_fields_rho.set_conditioning_points(points=self.coord_calibration, + self.random_fields_rho.set_conditioning_points(points=self.coordinates_sampled_conditioning, values=self.trans_model_rho.x_to_z( - self.conditioning_data.rho[self.thinning_sample_index]), + self.conditioning_data.rho[self.conditional_sample_index]), noise_level=self.geostat_model.noise_level) def generate(self, coordinates: npt.NDArray[np.float64]) -> None: - """generate random fields for `rho`, `vs` and derive fields `G0` and `young_modulus`. + """ + Generate random fields for `rho`, `vs` and derive fields `young_modulus` and `solid density`. Args: - coordinates (npt.NDArray[np.float64]): 3D coordinated of points to generate random field values for @@ -651,19 +663,21 @@ def generate(self, coordinates: npt.NDArray[np.float64]) -> None: self.random_fields_rho.generate_conditioned(nodes=coordinates) # transform standard-normal fields to physical (marginal) distributions: - self.vs = self.trans_model_vs.z_to_x(np.array(self.random_fields_vs.conditioned_random_field)) - self.rho = self.trans_model_rho.z_to_x(np.array(self.random_fields_rho.conditioned_random_field)) + vs = self.trans_model_vs.z_to_x(np.array(self.random_fields_vs.conditioned_random_field)) + rho = self.trans_model_rho.z_to_x(np.array(self.random_fields_rho.conditioned_random_field)) # derive dependent fields - self.GO = self.rho * self.vs**2 - self.young_modulus = 2 * self.GO * (1. + self.poisson_ratio) - - # only one parameter is communicated back as the generated field to STEM - if self.return_property.lower() == 'young_modulus': - self.generated_field = list(self.young_modulus) - elif self.return_property.lower() == 'rho': - self.generated_field = list(self.rho) - elif self.return_property.lower() == 'G0': - self.generated_field = list(self.GO) - elif self.return_property.lower() == 'vs': - self.generated_field = list(self.vs) + GO = rho * vs**2 + self.young_modulus = 2 * GO * (1. + self.poisson_ratio) + self.solid_density = rho * (1. - self.porosity) + self.porosity * self.water_density + + for value in self.return_property: + if value.name == "YOUNG_MODULUS": + self.conditioning_sampled_data.append( + self.conditioning_data.young_modulus[self.conditional_sample_index]) + self.generated_field.append(self.young_modulus) + elif value.name == "DENSITY_SOLID": + rho_solid = self.conditioning_data.rho[self.conditional_sample_index] * ( + 1. - self.porosity) + self.porosity * self.water_density + self.conditioning_sampled_data.append(rho_solid) + self.generated_field.append(self.solid_density) diff --git a/random_fields/utils.py b/random_fields/utils.py index d43058f..fda30a1 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -14,6 +14,8 @@ def plot3D(coordinates: List[npt.NDArray[np.float64]], title: str = "Random Field", output_folder="./", output_name: str = "random_field.png", + figsize: Tuple[float, float] = (10, 10), + conditional_points: List[npt.NDArray[np.float64]] = [], show: bool = False): """ Plots and saves 3D random field @@ -24,6 +26,9 @@ def plot3D(coordinates: List[npt.NDArray[np.float64]], - title (str): Title of the plot - output_folder (str): Output folder - output_name (str): Output fine name + - figsize: Tuple[float, float] = (10, 10), + - conditional_points (List[npt.NDArray[np.float64]]): List of coordinates if the conditioning points + (optional, default = []) - show (bool): Show the plot (optional, default = False) """ # create output folder @@ -31,7 +36,7 @@ def plot3D(coordinates: List[npt.NDArray[np.float64]], os.makedirs(output_folder) # make plot - fig = plt.figure(1, figsize=(6, 5)) + fig = plt.figure(1, figsize=figsize) ax: Axes3D = fig.add_subplot(projection='3d') ax.set_position([0.1, 0.1, 0.8, 0.8]) # rotate the axes so that y is vertical @@ -43,12 +48,17 @@ def plot3D(coordinates: List[npt.NDArray[np.float64]], for i, coord in enumerate(coordinates): x, y, z = coord[:, 0], coord[:, 1], coord[:, 2] - ax.scatter(x, y, z, c=rfield[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s") + ax.scatter(x, y, z, c=rfield[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s", s=20) ax.set_xlabel('x coordinate') ax.set_ylabel('y coordinate') ax.set_zlabel('z coordinate') + if conditional_points: + ax.scatter(conditional_points[0][:, 0], + conditional_points[0][:, 1], + conditional_points[0][:, 2], c=conditional_points[1], vmin=vmin, vmax=vmax, cmap="viridis") + cax = ax.inset_axes((1.1, 0., 0.05, 1)) norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax) @@ -69,6 +79,7 @@ def plot2D(coordinates: List[npt.NDArray[np.float64]], conditioning_coordinates: List[npt.NDArray[np.float64]] = [], conditioning_values: List[npt.NDArray[np.float64]] = [], figsize: Tuple[float, float] = (6, 5), + conditional_points: Optional[List[npt.NDArray[np.float64]]] = None, show: bool = False): """ Plots and saves 2D random field @@ -81,10 +92,12 @@ def plot2D(coordinates: List[npt.NDArray[np.float64]], - output_name (str): Output fine name - colorbar_label (str): Label for the colorbar (optional, deafult = '') - conditioning_coordinates (List[npt.NDArray[np.float64]]): List of coordinates if the conditioning points - (optional, default = []) + (optional, default = []) - conditioning_values (List[npt.NDArray[np.float64]]): List of the values at the conditioning points (optional, default = []) - figsize (Tuple[float, float]): Figure size (optional, default = (6, 5)) + - conditional_points (List[npt.NDArray[np.float64]]): List of the values at the conditioning points + (optional, default = None) - show (bool): Show the plot (optional, default = False) """ @@ -104,6 +117,7 @@ def plot2D(coordinates: List[npt.NDArray[np.float64]], x, y = coord[:, 0], coord[:, 1] ax.scatter(x, y, c=rfield[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s") + ax.set_aspect('auto') ax.set_xlabel('x coordinate') ax.set_ylabel('y coordinate') @@ -118,6 +132,16 @@ def plot2D(coordinates: List[npt.NDArray[np.float64]], edgecolors='red', linewidth=0.25) + if conditional_points: + ax.scatter( + conditional_points[0][:, 0], + conditional_points[0][:, 1], + c=conditional_points[1], + vmin=vmin, + vmax=vmax, + cmap="viridis", + ) + cax = ax.inset_axes((1.1, 0., 0.05, 1)) norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax, label=colorbar_label) diff --git a/tests/test_random_field_utils.py b/tests/test_random_field_utils.py index 0e00e8d..c44b034 100644 --- a/tests/test_random_field_utils.py +++ b/tests/test_random_field_utils.py @@ -41,6 +41,7 @@ def test_distribution_RF_struc(cleanup_generated_files): data = [val == data_new[header + i] for i, val in enumerate(data_org[header:idx_end])] assert all(data) + def test_distribution_RF_struc_3D(cleanup_generated_files): """test distribution of 3D random field with structured mesh""" From 268d135ed4b7e717bee6eb347d36f5114fcaee49 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 13:37:18 +0100 Subject: [PATCH 42/62] fixed style --- random_fields/geostatistical_cpt_interpretation.py | 4 +++- random_fields/utils.py | 6 +++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 0c3298e..8ef0f54 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -612,7 +612,9 @@ def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int ndim_calibrate = len(calibration_indices) self.coordinates_sampled_conditioning = np.zeros([self.max_conditioning_points, 3]) for i in calibration_indices: - self.coordinates_sampled_conditioning[:, i] = self.conditioning_data.data_coords[self.conditional_sample_index, i] + self.coordinates_sampled_conditioning[:, + i] = self.conditioning_data.data_coords[self.conditional_sample_index, + i] # calibrate geostatistical model based on standard-normal equivalent values of the shear wave velocity data self.geostat_model = GeostatisticalModel(nb_dimensions=ndim_calibrate, v_dim=v_dim) diff --git a/random_fields/utils.py b/random_fields/utils.py index fda30a1..0007ed8 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -57,7 +57,11 @@ def plot3D(coordinates: List[npt.NDArray[np.float64]], if conditional_points: ax.scatter(conditional_points[0][:, 0], conditional_points[0][:, 1], - conditional_points[0][:, 2], c=conditional_points[1], vmin=vmin, vmax=vmax, cmap="viridis") + conditional_points[0][:, 2], + c=conditional_points[1], + vmin=vmin, + vmax=vmax, + cmap="viridis") cax = ax.inset_axes((1.1, 0., 0.05, 1)) norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) From e224365746b3cf7b9ec4501efe9d0f2203a8cc48 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 13:52:14 +0100 Subject: [PATCH 43/62] Update 3D random field test to use pickle format and change output file to PNG --- tests/data/random_field_3D_linux.eps | 28744 -------------------- tests/data/random_field_3D_linux.pickle | Bin 0 -> 24152 bytes tests/data/random_field_3D_windows.eps | 28744 -------------------- tests/data/random_field_3D_windows.pickle | Bin 0 -> 24152 bytes tests/test_random_field_utils.py | 21 +- 5 files changed, 9 insertions(+), 57500 deletions(-) delete mode 100644 tests/data/random_field_3D_linux.eps create mode 100644 tests/data/random_field_3D_linux.pickle delete mode 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Tutorials @@ -41,9 +41,10 @@ How to contribute If you want to contribute to RandomFields please follow the steps defined in :doc:`contributions`. -STEM team -========= -STEM is a research programme that results from a collaboration between the following partners: +Random Fields team +================== +The Random Field package is part of the STEM is a research programme that results from a +collaboration between the following partners: * `ProRail `_ * `Deltares `_ From 60b78371acefad71d1d83fd4c9b421b145d165dd Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 13:58:07 +0100 Subject: [PATCH 45/62] Update tutorial to reflect 2D/3D conditioned Random Field on CPT data --- docs/tutorial_4.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst index 73869d2..aff8beb 100644 --- a/docs/tutorial_4.rst +++ b/docs/tutorial_4.rst @@ -3,7 +3,7 @@ RandomFields tutorials .. _tutorial4: -2D conditioned Random Field on CPT data +2D/3D conditioned Random Field on CPT data --------------------------------------- This tutorial shows how a subsurface model can be created directly from CPT data. First, the concept of the CPT-based random field generation for subsurface models is presented. @@ -235,4 +235,3 @@ To visualise the results we can make use of the `plot3D` function. show=True) .. image:: _static/random_field_3d_young.png - \ No newline at end of file From ecc4d98a7c9442a3f37c1dfd38b20aa4f93a9a1d Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 14:06:35 +0100 Subject: [PATCH 46/62] Update tutorial heading to use "and" for clarity in 2D and 3D conditioned Random Field on CPT data --- docs/tutorial_4.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst index aff8beb..bd02f6c 100644 --- a/docs/tutorial_4.rst +++ b/docs/tutorial_4.rst @@ -3,8 +3,8 @@ RandomFields tutorials .. _tutorial4: -2D/3D conditioned Random Field on CPT data ---------------------------------------- +2D and 3D conditioned Random Field on CPT data +---------------------------------------------- This tutorial shows how a subsurface model can be created directly from CPT data. First, the concept of the CPT-based random field generation for subsurface models is presented. Second, the implementation of the CPT-based random field generation is described. From 2833dbc6e8f0ab446e83976a664ebaa606f2dfca Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 14:12:25 +0100 Subject: [PATCH 47/62] fixed bug in test --- tests/test_random_field_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_random_field_utils.py b/tests/test_random_field_utils.py index 64b4e3e..a911ec1 100644 --- a/tests/test_random_field_utils.py +++ b/tests/test_random_field_utils.py @@ -71,5 +71,4 @@ def test_distribution_RF_struc_3D(cleanup_generated_files): data_test = pickle.load(fi) assert os.path.isfile("random_field.png") - os.remove("random_field.png") assert all(rf.random_field.field == data_test) \ No newline at end of file From c4a686f4d6f1a10d7daeeeeacfb5a7a081b6ea09 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 14:16:19 +0100 Subject: [PATCH 48/62] Update output file format in 3D random field test from PNG to EPS --- tests/test_random_field_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_random_field_utils.py b/tests/test_random_field_utils.py index a911ec1..766b710 100644 --- a/tests/test_random_field_utils.py +++ b/tests/test_random_field_utils.py @@ -58,7 +58,7 @@ def test_distribution_RF_struc_3D(cleanup_generated_files): plot3D([np.array([x.ravel(), y.ravel(), z.ravel()]).T], [rf.random_field], title="Random Field", output_folder="./", - output_name="random_field.png") + output_name="random_field.eps") if sys.platform == "win32": file_test = "./tests/data/random_field_3D_windows.pickle" @@ -70,5 +70,5 @@ def test_distribution_RF_struc_3D(cleanup_generated_files): with open(file_test, "rb") as fi: data_test = pickle.load(fi) - assert os.path.isfile("random_field.png") + assert os.path.isfile("random_field.eps") assert all(rf.random_field.field == data_test) \ No newline at end of file From 50b54caa395e3e91b9911c9cb2054cd9cc24dc60 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 14:56:57 +0100 Subject: [PATCH 49/62] Add tests for 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ElasticityFieldsFromCpt, RandomFieldProperties +from random_fields.utils import plot3D def test_model_names(): @@ -144,3 +147,59 @@ def test_conditioned_RF_3D_mean_variance(): np.testing.assert_array_almost_equal(kriging_mean_ref, rf.kriging_mean, decimal=4) np.testing.assert_array_almost_equal(kriging_std_ref, rf.kriging_std, decimal=4) np.testing.assert_array_almost_equal(kriging_field_ref, rf.conditioned_random_field, decimal=4) + + +def test_conditioned_RF_helper_3D(): + """ + Test the ElasticityFieldsFromCpt class + """ + + orientation_x_axis = 72. + cpt_folder = "./tests/cpts/gef" + elastic_field_generator_cpt = ElasticityFieldsFromCpt(cpt_file_folder=cpt_folder, + based_on_midpoint = True, + max_conditioning_points = 1000, + orientation_x_axis = orientation_x_axis, + porosity = 0.3, + water_density = 1000, + return_property = [RandomFieldProperties.YOUNG_MODULUS, RandomFieldProperties.DENSITY_SOLID], + ) + elastic_field_generator_cpt.calibrate_geostat_model() + + # create grit of points on the domnain (-220,220) by (-24,-1) to generate a field for. + import numpy as np + x = np.linspace(-5, 5, 21) + y = np.linspace(-25, 0, 26) + z = np.linspace(0, 30, 31) + X, Y, Z = np.meshgrid(x, y, z) + + elastic_field_generator_cpt.generate(np.array([X.ravel(), Y.ravel(), Z.ravel()]).T) + + plot3D([np.array([X.ravel(), Y.ravel(), Z.ravel()]).T], [elastic_field_generator_cpt.generated_field[0]], + title="Random Field", + output_folder="./", + output_name="random_field_3D.png", + figsize=(10, 10), + conditional_points=[elastic_field_generator_cpt.coordinates_sampled_conditioning, + elastic_field_generator_cpt.conditioning_sampled_data[0]], + ) + + with open("./tests/data/conditional_random_field_3D.pickle", "rb") as fi: + data_org = pickle.load(fi) + + assert os.path.isfile("random_field_3D.png") + os.remove("random_field_3D.png") + assert all(data_org[0] == elastic_field_generator_cpt.generated_field[0]) + assert all(data_org[1] == elastic_field_generator_cpt.generated_field[1]) + +def test_RF_properties(): + """ + Test the RandomFieldProperties enum + """ + # Check that the enum members are instances of RandomFieldProperties + assert isinstance(RandomFieldProperties.YOUNG_MODULUS, RandomFieldProperties) + assert isinstance(RandomFieldProperties.DENSITY_SOLID, RandomFieldProperties) + + # Check that the names match correctly + assert RandomFieldProperties.YOUNG_MODULUS.name == "YOUNG_MODULUS" + assert RandomFieldProperties.DENSITY_SOLID.name == "DENSITY_SOLID" From 963cf01125ce60b709d41ea2720ab6260c2f12c8 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 15:04:24 +0100 Subject: [PATCH 50/62] Refactor assertions in 3D conditioned random field test to use np.testing.assert_allclose for better precision --- tests/test_random_field.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_random_field.py b/tests/test_random_field.py index 5191fd7..1aed1c5 100644 --- a/tests/test_random_field.py +++ b/tests/test_random_field.py @@ -189,8 +189,8 @@ def test_conditioned_RF_helper_3D(): assert os.path.isfile("random_field_3D.png") os.remove("random_field_3D.png") - assert all(data_org[0] == elastic_field_generator_cpt.generated_field[0]) - assert all(data_org[1] == elastic_field_generator_cpt.generated_field[1]) + np.testing.assert_allclose(data_org[0], elastic_field_generator_cpt.generated_field[0]) + np.testing.assert_allclose(data_org[1], elastic_field_generator_cpt.generated_field[1]) def test_RF_properties(): """ From e7247bebf7a6019b76d19f07b93a7c325ff2feb0 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 15:19:52 +0100 Subject: [PATCH 51/62] Increase precision of assertions in 3D conditioned random field test by adding relative tolerance --- tests/test_random_field.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_random_field.py b/tests/test_random_field.py index 1aed1c5..b6f4ba3 100644 --- a/tests/test_random_field.py +++ b/tests/test_random_field.py @@ -189,8 +189,8 @@ def test_conditioned_RF_helper_3D(): assert os.path.isfile("random_field_3D.png") os.remove("random_field_3D.png") - np.testing.assert_allclose(data_org[0], elastic_field_generator_cpt.generated_field[0]) - np.testing.assert_allclose(data_org[1], elastic_field_generator_cpt.generated_field[1]) + np.testing.assert_allclose(data_org[0], elastic_field_generator_cpt.generated_field[0], rtol=1e-3) + np.testing.assert_allclose(data_org[1], elastic_field_generator_cpt.generated_field[1], rtol=1e-3) def test_RF_properties(): """ From caa7cd60bba84a6d69fa50467a480cb5776136f2 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 15:29:06 +0100 Subject: [PATCH 52/62] Update GitHub Actions workflow to allow manual triggering and broaden pull request branch coverage --- .github/workflows/tests.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1e384af..2299602 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -2,10 +2,11 @@ name: Tests # Controls when the workflow will run on: - push: - branches: [ "**" ] pull_request: - branches: [ "main" ] + branches: [ "**" ] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: jobs: test: From 8d5e1d39d85608ccbca4160b21c0d16f08ee80bb Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 16:59:01 +0100 Subject: [PATCH 53/62] Fix formatting in tutorial_4.rst by adding missing newlines for better readability --- docs/tutorial_4.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst index bd02f6c..f31494f 100644 --- a/docs/tutorial_4.rst +++ b/docs/tutorial_4.rst @@ -215,6 +215,7 @@ Now we create a list of the points where we want to generate the random field. I This grid is typically the mesh of a finite element calculation. .. code-block:: python + x = np.linspace(-5, 5, 21) y = np.linspace(-25, 0, 26) z = np.linspace(0, 30, 31) @@ -225,6 +226,7 @@ This grid is typically the mesh of a finite element calculation. To visualise the results we can make use of the `plot3D` function. .. code=block:: python + plot3D([np.array([X.ravel(), Y.ravel(), Z.ravel()]).T], [elastic_field_generator_cpt.generated_field[0]], title="Random Field", output_folder="./", From 69fe1a3336dbff57b090dde464403bbc0dce002d Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 17:05:26 +0100 Subject: [PATCH 54/62] Improve mathematical notation in tutorial_4.rst for clarity and consistency --- docs/tutorial_4.rst | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst index f31494f..03cee89 100644 --- a/docs/tutorial_4.rst +++ b/docs/tutorial_4.rst @@ -14,21 +14,21 @@ Theory ...... The processing of the CPT data is based on the following steps: -* CPT data is read and interpreted into a set of point with coordinates `(x, y, z)` and corresponding interpreted - properties $z_{data}$ (e.g. shear wave velocity `vs` based on tip resistance and sleeve friction). - The the distribution of the dataset $x_{data}$ is considered to be representative for the distribution - of the site $x\inX$. +* CPT data is read and interpreted into a set of point with coordinates :math:`(x, y, z)` and corresponding interpreted + properties :math:`z_{data}` (e.g. shear wave velocity :math:`v_{s}` based on tip resistance and sleeve friction). + The the distribution of the dataset :math:`x_{data}` is considered to be representative for the distribution + of the site. * A transfomration model is created based on the CPT data. - This transfomration model $X=T(Z)$ characterises the distribution of the physical stochastic parameter $X$ - as a function of the standard-normal stochastic variable $Z \\sim N(0,1)$. - This model is used to create the standard-normal equivalent data $z_{data} = T^{-1}(x_{data})$ + This transfomration model :math:`X=T(Z)` characterises the distribution of the physical stochastic parameter :math:`X` + as a function of the standard-normal stochastic variable :math:`Z \sim N(0,1)`. + This model is used to create the standard-normal equivalent data :math:`z_{data} = T^{-1}(x_{data})` * The standard-normal data is used to calibrate a Gaussian regression model. Sampling from this model provides the conditioned. Internally, the sampoling is performed in the traditional Kriging formulation, in which an unconditioned random field is conditioned to the conditioning data. - This takes place in the standard-normal space, resultin gin standar-normal fields $u_{field}$. + This takes place in the standard-normal space, resultin gin standar-normal fields :math:`u_{field}`. * The generated conditioned random fields are transformed to the parameter space using the - transformation model: $v_{field} = T(u_{field})$. + transformation model: :math:`v_{field} = T(u_{field})` Example @@ -226,7 +226,7 @@ This grid is typically the mesh of a finite element calculation. To visualise the results we can make use of the `plot3D` function. .. code=block:: python - + plot3D([np.array([X.ravel(), Y.ravel(), Z.ravel()]).T], [elastic_field_generator_cpt.generated_field[0]], title="Random Field", output_folder="./", From 1f6152a9313902381e36b0da38cebe8b6955be30 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 14 Jan 2025 17:11:25 +0100 Subject: [PATCH 55/62] Refactor ElasticityFieldsFromCpt class to improve variable naming and code readability --- .../geostatistical_cpt_interpretation.py | 71 +++++++++---------- tests/test_random_field.py | 38 +++++----- 2 files changed, 56 insertions(+), 53 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 8ef0f54..cc2906d 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -405,7 +405,6 @@ class GeostatisticalModel(): - vertical_scale_fluctuation (Optional[float]): vertical scale of fluctuation - anisotropy (Optional[List[np.float64]]): anisotropy in the scale of fluctuation, relative to the vertical scale of fluctuation. - """ def __init__(self, @@ -510,14 +509,14 @@ class ElasticityFieldsFromCpt: the center of the set of CPT locations. - max_conditioning_points (int): Maximum number of data points to use in the calibration of the geostatistical model. - - seed (int): Seed for the `:class:RandomFields` - porosity (float): porosity of the soil - water_density (float): density of the water - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `G0` + - seed (int): Seed for the `:class:RandomFields` + - coordinates_sampled_conditioning (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the + calibration of the geostatistical model. - conditioning_data (class:`CPT_data`): instance of the `class:CPT_data` class. - - coord_calibration (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the calibration - of the geostatistical model. - conditional_sample_index (npt.NDArray[int]): Indices of the sample from the conditioning data, selected for the calibration. - trans_model_rho (class:`MarginalTransformation`): Transformation model of the marginal distribution @@ -525,14 +524,12 @@ class ElasticityFieldsFromCpt: - trans_model_vs (class:`MarginalTransformation`): Transformation model of the marginal distribution of shear wave velocity vs. - geostat_model (class:`GeostatisticalModel`): Geostatistical model - - random_fields_vs (class:`RandomFields`): Random field generator for shear wave velocity `vs` - - random_fields_rho (class:`RandomFields`): Random field generator for shear wave velocity `rho` - - vs (npt.NDArray[np.float64]): generated conditioned random field of shear wave velocity `vs` - - rho (npt.NDArray[np.float64]): generated conditioned random field of density `rho` - - G0 (npt.NDArray[np.float64]): generated conditioned random field of shear modulus `G0 = rho * vs**2` - young_modulus (npt.NDArray[np.float64]): generated conditioned random field of elastic modulus `young_modulus = 2 * G0 * (1. + poisson_ratio)` + - solid_density (npt.NDArray[np.float64]): generated conditioned random field of solid density - generated_field (list): list of randopm field values corresponding to parameter `return_property` + - _random_fields_vs (class:`RandomFields`): Random field generator for shear wave velocity `vs` + - _random_fields_rho (class:`RandomFields`): Random field generator for shear wave velocity `rho` """ def __init__(self, @@ -578,6 +575,7 @@ def __init__(self, self.seed = seed np.random.seed(seed) + self.coordinates_sampled_conditioning = np.empty(0) self.conditioning_data = CPT_data(cpt_directory=Path(self.cpt_file_folder)) self.conditioning_data.read_cpt_data() self.conditioning_data.interpret_cpt_data() @@ -591,9 +589,8 @@ def __init__(self, self.trans_model_rho = MarginalTransformation(self.conditioning_data.rho) self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs) self.geostat_model: Optional[GeostatisticalModel] = None - self.random_fields_vs: Optional[RandomFields] = None - - self.random_fields_rho: Optional[RandomFields] = None + self._random_fields_vs: Optional[RandomFields] = None + self._random_fields_rho: Optional[RandomFields] = None self.solid_density = np.empty(0) self.young_modulus = np.empty(0) self.generated_field: List[npt.NDArray[np.float64]] = [] @@ -623,32 +620,32 @@ def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.conditional_sample_index])) # initiate two independent random field generators for shear wave velocity `vs` and density `rho` - self.random_fields_vs = RandomFields(model_name=ModelName.Gaussian, - n_dim=3, - mean=0, - variance=1, - v_scale_fluctuation=float(self.geostat_model.vertical_scale_fluctuation), - anisotropy=([float(self.geostat_model.anisotropy)] * 2)[:2], - angle=[0.] * 2, - seed=self.seed, - max_conditioning_points=self.max_conditioning_points) - self.random_fields_vs.set_conditioning_points(points=self.coordinates_sampled_conditioning, - values=self.trans_model_vs.x_to_z( - self.conditioning_data.vs[self.conditional_sample_index]), - noise_level=self.geostat_model.noise_level) - self.random_fields_rho = RandomFields(model_name=ModelName.Gaussian, + self._random_fields_vs = RandomFields(model_name=ModelName.Gaussian, n_dim=3, mean=0, variance=1, v_scale_fluctuation=float(self.geostat_model.vertical_scale_fluctuation), anisotropy=([float(self.geostat_model.anisotropy)] * 2)[:2], - angle=[0] * 2, - seed=self.seed + 1, + angle=[0.] * 2, + seed=self.seed, max_conditioning_points=self.max_conditioning_points) - self.random_fields_rho.set_conditioning_points(points=self.coordinates_sampled_conditioning, - values=self.trans_model_rho.x_to_z( - self.conditioning_data.rho[self.conditional_sample_index]), + self._random_fields_vs.set_conditioning_points(points=self.coordinates_sampled_conditioning, + values=self.trans_model_vs.x_to_z( + self.conditioning_data.vs[self.conditional_sample_index]), noise_level=self.geostat_model.noise_level) + self._random_fields_rho = RandomFields(model_name=ModelName.Gaussian, + n_dim=3, + mean=0, + variance=1, + v_scale_fluctuation=float(self.geostat_model.vertical_scale_fluctuation), + anisotropy=([float(self.geostat_model.anisotropy)] * 2)[:2], + angle=[0] * 2, + seed=self.seed + 1, + max_conditioning_points=self.max_conditioning_points) + self._random_fields_rho.set_conditioning_points(points=self.coordinates_sampled_conditioning, + values=self.trans_model_rho.x_to_z( + self.conditioning_data.rho[self.conditional_sample_index]), + noise_level=self.geostat_model.noise_level) def generate(self, coordinates: npt.NDArray[np.float64]) -> None: """ @@ -659,14 +656,14 @@ def generate(self, coordinates: npt.NDArray[np.float64]) -> None: """ # generate conditioned standard-normal equivalent - assert self.random_fields_vs is not None, "RandomFields instance not initialized" - self.random_fields_vs.generate_conditioned(nodes=coordinates) - assert self.random_fields_rho is not None, "RandomFields instance not initialized" - self.random_fields_rho.generate_conditioned(nodes=coordinates) + assert self._random_fields_vs is not None, "RandomFields instance not initialized" + self._random_fields_vs.generate_conditioned(nodes=coordinates) + assert self._random_fields_rho is not None, "RandomFields instance not initialized" + self._random_fields_rho.generate_conditioned(nodes=coordinates) # transform standard-normal fields to physical (marginal) distributions: - vs = self.trans_model_vs.z_to_x(np.array(self.random_fields_vs.conditioned_random_field)) - rho = self.trans_model_rho.z_to_x(np.array(self.random_fields_rho.conditioned_random_field)) + vs = self.trans_model_vs.z_to_x(np.array(self._random_fields_vs.conditioned_random_field)) + rho = self.trans_model_rho.z_to_x(np.array(self._random_fields_rho.conditioned_random_field)) # derive dependent fields GO = rho * vs**2 diff --git a/tests/test_random_field.py b/tests/test_random_field.py index b6f4ba3..df80d75 100644 --- a/tests/test_random_field.py +++ b/tests/test_random_field.py @@ -156,14 +156,15 @@ def test_conditioned_RF_helper_3D(): orientation_x_axis = 72. cpt_folder = "./tests/cpts/gef" - elastic_field_generator_cpt = ElasticityFieldsFromCpt(cpt_file_folder=cpt_folder, - based_on_midpoint = True, - max_conditioning_points = 1000, - orientation_x_axis = orientation_x_axis, - porosity = 0.3, - water_density = 1000, - return_property = [RandomFieldProperties.YOUNG_MODULUS, RandomFieldProperties.DENSITY_SOLID], - ) + elastic_field_generator_cpt = ElasticityFieldsFromCpt( + cpt_file_folder=cpt_folder, + based_on_midpoint=True, + max_conditioning_points=1000, + orientation_x_axis=orientation_x_axis, + porosity=0.3, + water_density=1000, + return_property=[RandomFieldProperties.YOUNG_MODULUS, RandomFieldProperties.DENSITY_SOLID], + ) elastic_field_generator_cpt.calibrate_geostat_model() # create grit of points on the domnain (-220,220) by (-24,-1) to generate a field for. @@ -175,14 +176,18 @@ def test_conditioned_RF_helper_3D(): elastic_field_generator_cpt.generate(np.array([X.ravel(), Y.ravel(), Z.ravel()]).T) - plot3D([np.array([X.ravel(), Y.ravel(), Z.ravel()]).T], [elastic_field_generator_cpt.generated_field[0]], - title="Random Field", - output_folder="./", - output_name="random_field_3D.png", - figsize=(10, 10), - conditional_points=[elastic_field_generator_cpt.coordinates_sampled_conditioning, - elastic_field_generator_cpt.conditioning_sampled_data[0]], - ) + plot3D( + [np.array([X.ravel(), Y.ravel(), Z.ravel()]).T], + [elastic_field_generator_cpt.generated_field[0]], + title="Random Field", + output_folder="./", + output_name="random_field_3D.png", + figsize=(10, 10), + conditional_points=[ + elastic_field_generator_cpt.coordinates_sampled_conditioning, + elastic_field_generator_cpt.conditioning_sampled_data[0] + ], + ) with open("./tests/data/conditional_random_field_3D.pickle", "rb") as fi: data_org = pickle.load(fi) @@ -192,6 +197,7 @@ def test_conditioned_RF_helper_3D(): np.testing.assert_allclose(data_org[0], elastic_field_generator_cpt.generated_field[0], rtol=1e-3) np.testing.assert_allclose(data_org[1], elastic_field_generator_cpt.generated_field[1], rtol=1e-3) + def test_RF_properties(): """ Test the RandomFieldProperties enum From 580569bf27bb9db78b3aa93756d9c62304874760 Mon Sep 17 00:00:00 2001 From: brunozc Date: Wed, 15 Jan 2025 21:02:20 +0100 Subject: [PATCH 56/62] Update docs/tutorial_4.rst Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- docs/tutorial_4.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst index 03cee89..8a6258a 100644 --- a/docs/tutorial_4.rst +++ b/docs/tutorial_4.rst @@ -18,7 +18,7 @@ The processing of the CPT data is based on the following steps: properties :math:`z_{data}` (e.g. shear wave velocity :math:`v_{s}` based on tip resistance and sleeve friction). The the distribution of the dataset :math:`x_{data}` is considered to be representative for the distribution of the site. -* A transfomration model is created based on the CPT data. +* A transformation model is created based on the CPT data. This transfomration model :math:`X=T(Z)` characterises the distribution of the physical stochastic parameter :math:`X` as a function of the standard-normal stochastic variable :math:`Z \sim N(0,1)`. This model is used to create the standard-normal equivalent data :math:`z_{data} = T^{-1}(x_{data})` From 1753666077ba7a1c7e5deb889a26eab8ff274f5e Mon Sep 17 00:00:00 2001 From: brunozc Date: Wed, 15 Jan 2025 21:02:40 +0100 Subject: [PATCH 57/62] Update docs/conf.py Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 9513ab7..95a8e60 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -54,7 +54,7 @@ html_context = { "display_github": True, "github_user": "StemVibrations", - "github_repo": "RanndomFields", + "github_repo": "RandomFields", "github_version": "main", "conf_py_path": "/docs/", } From 829600e5b3e6b3c0555d5f08c84f69593e1da3cf Mon Sep 17 00:00:00 2001 From: brunozc Date: Wed, 15 Jan 2025 21:03:42 +0100 Subject: [PATCH 58/62] Update docs/tutorial_4.rst Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- docs/tutorial_4.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst index 8a6258a..8355e84 100644 --- a/docs/tutorial_4.rst +++ b/docs/tutorial_4.rst @@ -19,7 +19,7 @@ The processing of the CPT data is based on the following steps: The the distribution of the dataset :math:`x_{data}` is considered to be representative for the distribution of the site. * A transformation model is created based on the CPT data. - This transfomration model :math:`X=T(Z)` characterises the distribution of the physical stochastic parameter :math:`X` + This transformation model :math:`X=T(Z)` characterises the distribution of the physical stochastic parameter :math:`X` as a function of the standard-normal stochastic variable :math:`Z \sim N(0,1)`. This model is used to create the standard-normal equivalent data :math:`z_{data} = T^{-1}(x_{data})` * The standard-normal data is used to calibrate a Gaussian regression model. From 146171560012a719a5228480469d2e43d0b69821 Mon Sep 17 00:00:00 2001 From: brunozc Date: Wed, 15 Jan 2025 21:04:25 +0100 Subject: [PATCH 59/62] Update random_fields/generate_field.py Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- random_fields/generate_field.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index 520df08..b4f09a6 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -26,7 +26,7 @@ class RandomFields: - max_conditioning_points (int): maximum number of - random_field_model_name (str): - random_field_model (int): - - n_dim (int): number of physical dimesions (1,2 or 3) + - n_dim (int): number of physical dimensions (1,2 or 3) - seed (int): seed for the random number generator - mean (float): unconditioned mean of the random field - variance (float): unconditioned variance of the random field From cc0faad7c2d16479200d5eedfab162e4afecf3e8 Mon Sep 17 00:00:00 2001 From: brunozc Date: Wed, 15 Jan 2025 21:04:40 +0100 Subject: [PATCH 60/62] Update random_fields/generate_field.py Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- random_fields/generate_field.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index b4f09a6..1cd7419 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -34,7 +34,7 @@ class RandomFields: - anisotropy (list): ratio between horizontal scales and vertical scale of fluctuation - angle (list): rotation angles of the principal directions of the scales of fluctuation relative to the vertical - - v_dim (int): dimesion number corresponding to the vertical scale of fluctuation + - v_dim (int): dimension number corresponding to the vertical scale of fluctuation - random_field (:class:gstools.SRF): random field generator - z_kriged_field (array): standard-normal kriged random field - gaussian_process (:class:sklearn.gaussian_process.GaussianProcessRegressor): Gaussian process regressor From 9d1dc3df208b678249f3bfb2e8ba4da021cc38b7 Mon Sep 17 00:00:00 2001 From: brunozc Date: Thu, 16 Jan 2025 09:38:47 +0100 Subject: [PATCH 61/62] Enhance calibration method to accept three boolean indices and add validation checks --- .../geostatistical_cpt_interpretation.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index cc2906d..cdc844e 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -596,16 +596,30 @@ def __init__(self, self.generated_field: List[npt.NDArray[np.float64]] = [] self.conditioning_sampled_data: List[npt.NDArray[np.float64]] = [] - def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: Tuple[int, int] = (0, 1)) -> None: + def calibrate_geostat_model(self, + v_dim: int = 1, + calibration_indices: Tuple[bool, bool, bool] = (True, True, True)) -> None: """calibrates the geostatistical model Args: - v_dim (int): vertical dimension. Defaults to 1. - - calibration_indices (Tuple): _description_. Defaults to (0, 1). + - calibration_indices (Tuple[bool, bool, bool]): Calibration indices. Default (True, True, True). + + Raises: + - ValueError: if calibration indices are not a tuple of three boolean values + - ValueError: if at least two calibration indices are not provided """ np.random.seed(self.seed) + # Check that calibration indices are a tuple of three boolean values + if len(calibration_indices) != 3: + raise ValueError("Calibration indices should be a tuple of three boolean values") + + # Check that at least two calibration indices are provided + if sum(calibration_indices) < 2: + raise ValueError("At least two calibration indices are required") + ndim_calibrate = len(calibration_indices) self.coordinates_sampled_conditioning = np.zeros([self.max_conditioning_points, 3]) for i in calibration_indices: From 33b281519748c1adadef59e47e2ffa22b2387e13 Mon Sep 17 00:00:00 2001 From: brunozc Date: Thu, 20 Feb 2025 14:27:40 +0100 Subject: [PATCH 62/62] update: refine dependencies and remove redundant entries in setup.cfg --- setup.cfg | 7 ------- 1 file changed, 7 deletions(-) diff --git a/setup.cfg b/setup.cfg index 29faa94..e00c48b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -21,19 +21,12 @@ install_requires = gstools>=1.6.0 scikit-learn>=1.6.0 d-geolib-plus==0.4.1 -python_requires = >=3.9 - numpy>=2.0.2 - matplotlib>=3.9.3 - gstools>=1.6.0 -python_requires = >=3.8 [options.extras_require] testing = pytest>=7.4.2 pytest-cov>=4.1.0 tox>=4.4 - pytest-cov>=6.0 - tox>=4.23 mypy>=1.13 pre-commit==3.6.2 yapf==0.43.0

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zo+8`u035Da_=Vuyri8;x%Qai3rvn7T$O4llaMPGLHwOY(qS2s89!3~XDQ3)+aONOq zkq87LLM#XxyC9Nfb*Db63MXfc2Fqgvh=NC;ZM!g)Fb|ZYZ4hE15Ex%>;L_O*NbbtP1B79a=stLyL+2ho{uKB1!Jgt$N@ z?gQv4Cf)W0-l^uE+{mGDR)Oa>^T71i2G(%5li88|$`uO8*?{ceJuoB%fw#0>***3> z0wx+QEv-P12s%t80^tK01ZIf(?~NNfAnmk(>&pTs7zBoj2y)_1T74rdcCV=KaPabA zH=W0=)AKPm0g&WCov~q{-zFp!@Y{D}WMno;O%QGDITgmMQ^+}q4;-|L@E<<61_Y3@ z$uK}B(;h}|!XVa9ttk_Pkuz|&iKibxj>BLe-VB2JddN~fH+ec@Hq*2ZBPl6aHE$_# zPNlMggOW `@bramvandeneijnden `_ - Davide Moretti `@morettid `_ - - diff --git a/docs/conf.py b/docs/conf.py index 5aeb1f3..9513ab7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -8,13 +8,11 @@ import random_fields - project = 'RandomFields' copyright = '2024, STEM team' author = 'STEM team' version = random_fields.__version__ - # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/docs/contributions.rst b/docs/contributions.rst index 90b69c2..72bab45 100644 --- a/docs/contributions.rst +++ b/docs/contributions.rst @@ -12,7 +12,7 @@ When contributing code follow this checklist: #. Make changes and commit your changes using a descriptive commit message. #. Provide tests for your changes, and ensure they all pass. #. Provide documentation for your changes, in accordance with the style of the rest of the project (see :ref:`style_guide`). - #. Create a pull request to STEM main branch. The STEM team will review and discuss your Pull Request with you. + #. Create a pull request to RandomFields main branch. The STEM team will review and discuss your Pull Request with you. For any questions, please get in contact with one of the members of :doc:`authors`. @@ -21,7 +21,7 @@ For any questions, please get in contact with one of the members of :doc:`author Code style guide ---------------- -The additional features should follow the style of the STEM project. +The additional features should follow the style of the RandomFields project. We follow the PEP 8 style guide for Python code, with our custom modifications as defined in the `Yapf file <../../.style.yapf>`_ and the `flake8 file <../../.flake8>`_. These files can be ran manually by using the @@ -50,21 +50,21 @@ An example of a class: class ClassName(object): """ - Work in progress: docstring style to be specified + Work in progress: docstring style to be specified Inheritance: -:class:`object` Attributes: - - (): ,shape (n,m) - - (): ,shape (m,) + - (): ,shape (n,m) + - (): ,shape (m,) """ def __init__(self,,...): """ Constructor of the RandomFields class - + Args: - : () , shape (n,m,), default """ @@ -82,7 +82,7 @@ An example of a function: Args: - nodes (ndarray): The nodes of the random field. shape (:,`self.n_dim`) - + Raises: - ValueError: if dimensions of `nodes` do not match dimensions of the model @@ -101,11 +101,11 @@ An example of a function: mask = np.arange(len(scale_fluctuation)) != self.v_dim scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy - model = self.random_field_model(dim = self.n_dim, - var = self.variance, - len_scale = scale_fluctuation, + model = self.random_field_model(dim = self.n_dim, + var = self.variance, + len_scale = scale_fluctuation, angles = self.angle) - self.random_field = gs.SRF(model, - mean = self.mean, + self.random_field = gs.SRF(model, + mean = self.mean, seed = self.seed) self.random_field(nodes.T) diff --git a/docs/index.rst b/docs/index.rst index 142a41a..8efa79e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -8,7 +8,7 @@ User guide Background information ...................... -The random fields are generated using [gstools](https://geostat-framework.readthedocs.io/en/latest/). +The random fields are generated using `gstools `_. Currently, the following covariance models are available: @@ -17,7 +17,7 @@ Currently, the following covariance models are available: * Matern * Linear (not for conditioned random fields) -Additional functoinalities are used to generate conditioned random fields. See :doc:`theory` for details. +Additional functoinalities are used to generate conditioned random fields. Tutorials @@ -27,10 +27,14 @@ Tutorials 1D unconditioned and conditioned random field generation. * :ref:`tutorial2` - 2D and 3D unconditioned and conditioned random field simulation + 2D unconditioned and conditioned random field simulation. * :ref:`tutorial3` - Non-normal Gaussian fields and conditioning based on CPT data + 3D unconditioned and conditioned random field simulation. + +* :ref:`tutorial4` + Non-normal Gaussian fields and conditioning based on CPT data. + How to contribute ================= @@ -48,6 +52,7 @@ STEM is a research programme that results from a collaboration between the follo The :doc:`authors`. + Package documentation ===================== diff --git a/docs/make.bat b/docs/make.bat index 954237b..32bb245 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -1,35 +1,35 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/theory.rst b/docs/theory.rst deleted file mode 100644 index 7dc7389..0000000 --- a/docs/theory.rst +++ /dev/null @@ -1,6 +0,0 @@ - -.. _theory: - -Theory -====== - diff --git a/docs/tutorial_1.rst b/docs/tutorial_1.rst new file mode 100644 index 0000000..c90d0bc --- /dev/null +++ b/docs/tutorial_1.rst @@ -0,0 +1,116 @@ +RandomFields tutorials +====================== + +.. _tutorial1: + +Example in 1D +------------- +This tutorial shows step by step guide on how to set up the random field generator and visualise the resulting +random fields. + +First the necessary packages are imported. + +.. code-block:: python + + import numpy as np + from random_fields.generate_field import RandomFields, ModelName + from random_fields.utils import plot1D + +Then, you need to define the 1D grid: + +.. code-block:: python + + x = np.linspace(0, 100, 501).reshape(-1, 1) + +In this example, we create a random field over a line of length 100, divided into steps of 0.2. + +Then we define the random field properties: + +.. code-block:: python + + nb_dimensions = 1 + mean = 10 + variance = 2 + vertical_scale_fluctuation = 10 + anisotropy = [] + angle = [] + model_rf = ModelName.Gaussian + +In this example, we use a a Gaussian covariance model. + +Then we create the random field, in which the index of the 'vertical' dimension needs to be set to `0`: + +.. code-block:: python + + rf = RandomFields(model_rf, nb_dimensions, mean, variance, + vertical_scale_fluctuation, anisotropy, angle, seed=1) + rf.v_dim = 0 + rf.generate(x) + +To visualise the results: + +.. code-block:: python + + plot1D([x],[rf.random_field], + title = 'Random Field', + output_folder="./", + output_name="random_field_1.png", + show=True) + +.. image:: _static/random_field_1.png + + +Conditioning data can be added to the random field. +The noise level represents the uncertainty in the individual conditioning points and is formulated as +the normalised variance: the variance of the uncertainty in a single data point divided by the variance of the +random field. ``noise_level`` can be a scalar, or an array of the same dimensions as ``conditioning _values``. + +.. code-block:: python + + conditioning_points = np.array([[30, 50, 70]]).T + conditioning_values = np.array([-2.5, 0, 2.5]) + + rf.set_conditioning_points(conditioning_points, conditioning_values, noise_level=0.04) + +Ten conditional random fields can be sampled and visualised as follows: + +.. code-block:: python + + nb_random_fields = 10 + + rf_conditioned = np.empty((nb_random_fields, x.shape[0])) + + for i in range(nb_random_fields): + rf.seed = i + rf.generate_conditioned(x) + rf_conditioned[i] = rf.conditioned_random_field + + plot1D([x] * nb_random_fields, rf_conditioned, + title = 'Conditioned Random Field', + output_folder="./", + output_name="random_field_2.png", + show=True) + + +.. image:: _static/random_field_2.png + + +As a by-product of the conditioned random field generator, the kriging mean and variance are provided. +These can be visualised as follows: + +.. code-block:: python + + mean = rf.kriging_mean + std = rf.kriging_std + + plot1D([x] * nb_random_fields, rf_conditioned, + title = 'Conditioned Random Field', + output_folder="./", + output_name="random_field_3.png", + conditional_points=[conditioning_points, conditioning_values, rf.kriging_mean, rf.kriging_std], + show=True) + +.. image:: _static/random_field_3.png + + +Note that only simple kriging is applied for conditioning the random fields. diff --git a/docs/tutorial_2.rst b/docs/tutorial_2.rst new file mode 100644 index 0000000..55afb45 --- /dev/null +++ b/docs/tutorial_2.rst @@ -0,0 +1,92 @@ +RandomFields tutorials +====================== + +.. _tutorial2: + +Example in 2D +------------- + +This tutorial shows step by step guide on how to set up the random field generator in 2D +and visualise the resulting random fields. + +First the necessary packages are imported, the 2D grid is defined and the random field properties are set: + +.. code-block:: python + + import numpy as np + from random_fields.generate_field import RandomFields, ModelName + from random_fields.utils import plot2D + + + x = np.linspace(0, 100, 51) + y = np.linspace(0, 50, 51) + x, y = np.meshgrid(x, y) + + nb_dimensions = 2 + mean = 10 + variance = 2 + vertical_scale_fluctuation = 10 + anisotropy = [1] + angle = [0] + model_rf = ModelName.Gaussian + +An unconditioned random field is constructed as: + +.. code-block:: python + + rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, anisotropy, angle, seed=14) + rf.generate(np.array([x.ravel(), y.ravel()]).T) + + plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.random_field], + title="Random Field", + output_folder="./", + output_name="random_field_4.png", + show=True) + +.. image:: _static/random_field_4.png + +Conditioning is similar to :ref:`tutorial1`. +First we define the conditioning points and values, and then we generate the conditioned random field. +In this case we define a conditional random field where we know the points that are located at x=50 and all y values. +(with a value of 15). + +.. code-block:: python + + xc = np.array([50.] * 51) + yc = np.linspace(0, 50, 51) + + vc = np.array([15] * 51) + + rf.set_conditioning_points(np.array([xc,yc]).T, vc, noise_level=0.0001) + rf.generate_conditioned(np.array([x.ravel(), y.ravel()]).T) + +The 2D conditioned random field can be visualised as: + +.. code-block:: python + + plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.conditioned_random_field], + title="Conditioned Random Field", + output_folder="./", + output_name="random_field_5.png", + show=True) + +.. image:: _static/random_field_5.png + +The kriging mean and variance can be visualised as: + +.. code-block:: python + + plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.kriging_mean], + title="kriging mean", + output_folder="./", + output_name="kriging_mean_2D.png", + show=True) + + plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.kriging_std], + title="kriging std", + output_folder="./", + output_name="kriging_std_2D.png", + show=True) + +.. image:: _static/kriging_mean_2D.png +.. image:: _static/kriging_std_2D.png diff --git a/docs/tutorial_3.rst b/docs/tutorial_3.rst new file mode 100644 index 0000000..ce370b6 --- /dev/null +++ b/docs/tutorial_3.rst @@ -0,0 +1,86 @@ +RandomFields tutorials +====================== + +.. _tutorial3: + +Example in 3D +------------- + +This tutorial shows step by step guide on how to set up the random field generator in 3D +and visualise the resulting random fields. + +First the necessary packages are imported, the 3D grid is defined and the random field properties are set: + +.. code-block:: python + + import numpy as np + from random_fields.generate_field import RandomFields, ModelName + from random_fields.utils import plot3D + + # mesh coordinates + x = np.linspace(0, 100, 51) + y = np.linspace(0, 50, 51) + z = np.linspace(0, 25, 51) + x, y, z = [i.ravel() for i in np.meshgrid(x, y, z)] + + # random field properties + nb_dimensions = 3 + mean = 10 + variance = 2 + vertical_scale_fluctuation = 10 + anisotropy = [2.5, 2.5] + angle = [0, 0] + model_rf = ModelName.Gaussian + + # generate and plot random field + rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, + anisotropy, angle, seed=14) + rf.generate(np.array([x, y, z]).T) + plot3D([np.array([x, y, z]).T], [rf.random_field], + title="Random Field", + output_folder="./", + output_name="random_field_3D.png", + show=True) + +.. image:: _static/random_field_3D.png + +Conditioning is similar to :ref:`tutorial1`. +First we define the conditioning points and values, and then we generate the conditioned random field. +In this case we define a conditional random field where we know the points that are located at x=50 and z=25 +and all y values (with a value of 15). + +.. code-block:: python + + # declare conditioning points + xc = np.array([50.] * 51) + yc = np.linspace(0, 50, 51) + zc = np.array([25] * 51) + vc = np.array([15] * 51) + rf.set_conditioning_points(np.array([xc, yc, zc]).T, vc, noise_level=0.0001) + + # generate conditioned random field model + rf.generate_conditioned(np.array([x, y, z]).T) + +To visualise the results we can plot the conditioned random field, kriging mean and kriging standard deviation: + +.. code-block:: python + + plot3D([np.array([x, y, z]).T], [rf.conditioned_random_field], + title="Conditioned Random Field", + output_folder="./", + output_name="conditioned_random_field_3D.png") + + plot3D([np.array([x, y, z]).T], [rf.kriging_mean], + title="Kriging mean 3D", + output_folder="./", + output_name="kriging_mean_3D.png") + + plot3D([np.array([x, y, z]).T], [rf.kriging_std], + title="Kriging std 3D", + output_folder="./", + output_name="kriging_std_3D.png") + + +.. image:: _static/conditioned_random_field_3D.png +.. image:: _static/kriging_mean_3D.png +.. image:: _static/kriging_std_3D.png diff --git a/docs/tutorial_4.rst b/docs/tutorial_4.rst new file mode 100644 index 0000000..df54e1b --- /dev/null +++ b/docs/tutorial_4.rst @@ -0,0 +1,8 @@ +RandomFields tutorials +====================== + +.. _tutorial4: + +Conditioned random fields on CPT data +------------------------------------- + diff --git a/docs/tutorials.rst b/docs/tutorials.rst deleted file mode 100644 index 23faeae..0000000 --- a/docs/tutorials.rst +++ /dev/null @@ -1,252 +0,0 @@ -RandomFields tutorials -====================== - -.. _tutorial1: - -Example in 1D -------------- -This tutorial shows step by step guide on how to set up the random field generator and visualise the resulting random fields. - -First the necessary packages are imported and paths are defined. - -.. code-block:: python - - import numpy as np - from random_fields.generate_field import RandomFields, ModelName - from random_fields.utils import plot2D - -Then, you need to define the 1D grid: - -.. code-block:: python - - x = np.linspace(0, 100, 501).reshape(-1,1) - -In this example, we create a random field over a line of length 100, divided into steps of 0.2 - -Then we define the random field properties: - -.. code-block:: python - - nb_dimensions = 1 - mean = 10 - variance = 2 - vertical_scale_fluctuation = 10 - anisotropy = [] - angle = [] - model_rf = ModelName.Gaussian - -In this example, we use a a Gaussian covariance model. - -Then we create the random field, in which the index of the 'vertical' dimension needs to be set to `0`: - -.. code-block:: python - - rf = RandomFields(model_rf, nb_dimensions, mean, variance, - vertical_scale_fluctuation, anisotropy, angle, seed=1) - rf.v_dim = 0 - rf.generate(x.reshape(-1,1)) - -To visualise the results, you can run: - -.. code-block:: python - - plot1D([x],[rf.random_field], - title = 'Random Field', - output_folder="./", - output_name="random_field_1.png") - -.. image:: _static/random_field_1.png - - -Conditioning data can be added to the random field. The noise level represents the uncertainty in the individual conditioning points and is formulated as the normalised variance: the variance of the uncertainty in a single data point divided by the variance of the random field. ``noise_level`` can be a scalar, or an array of the same dimensions as ``conditioning _values``. - -.. code-block:: python - - conditioning_points = np.array([[30,50,70]]).T - conditioning_values = np.array([-2.5,0,2.5]) - - rf.set_conditioning_points(conditioning_points,conditioning_values, noise_level = 0.04) - - -Ten conditional random fields can be sampled and visualised as follows: - -.. code-block:: python - - for i in range(10): - rf.seed = i - rf.generate_conditioned(x) - rf_conditioned[i] = rf.conditioned_random_field - - plot1D([x]*10,rf_conditioned, - title = 'Conditioned Random Field', - output_folder="./", - output_name="random_field_2.png") - - -.. image:: _static/random_field_2.png - - -As a by-product of the conditioned random field generator, the kriging mean and variance are provided: - -.. code-block:: python - - mean = rf.kriging_mean - std = rf.kriging_std - - plt.scatter(conditioning_points, conditioning_values, label = 'conditioning points') - plt.plot(x, mean, label = 'kriged field') - plt.fill_between(x.ravel(), mean - 1.65*std, mean + 1.65*std, label = '90% uncertainty bound') - - -.. image:: _static/random_field_3.png - - -Note that only simple kriging is applied for conditioning the random fields. See :ref:`theory` for more details. - - - -.. _tutorial2: - -Example in 2D -------------- - -A (conditioned) 2D random field is constructed as follows: - -.. code-block:: python - - import numpy as np - from random_fields.generate_field import RandomFields, ModelName - from random_fields.utils import plot2D - - - x = np.linspace(0, 100, 51) - y = np.linspace(0, 50, 51) - x, y = np.meshgrid(x, y) - - nb_dimensions = 2 - mean = 10 - variance = 2 - vertical_scale_fluctuation = 10 - anisotropy = [1] - angle = [0] - model_rf = ModelName.Gaussian - -An unconditioned random field is constructed as: - -.. code-block:: python - - rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, anisotropy, angle, seed=14) - rf.generate(np.array([x.ravel(), y.ravel()]).T) - - plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.random_field], title="Random Field", output_folder="./", output_name="random_field_4.png") - - -.. image:: _static/random_field_4.png - -Conditioning is consistent with the 1D example: - -.. code-block:: python - - xc = np.array([50.]*51) - yc = np.linspace(0,50,51) - - vc = np.array([15]*51) - - rf.set_conditioning_points(np.array([xc,yc]).T,vc,noise_level = 0.0001) - rf.generate_conditioned(np.array([x.ravel(), y.ravel()]).T) - - plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.conditioned_random_field], - title="Conditioned Random Field", - output_folder="./", - output_name="random_field_5.png") - - -.. image:: _static/random_field_5.png - -The kriging mean and variance can be visualised as: - -.. code-block:: python - - plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.kriging_mean], - title="kriging mean", - output_folder="./", - output_name="kriging_mean_2D.png") - - plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.kriging_std], - title="kriging std", - output_folder="./", - output_name="kriging_std_2D.png") - -.. image:: _static/kriging_mean_2D.png - :width: 49% -.. image:: _static/kriging_std_2D.png - :width: 49% - -In the same way, a 3D conditional random field can be created: - - -.. code-block:: python - - # mesh coordinates - x = np.linspace(0, 100, 51) - y = np.linspace(0, 50, 51) - z = np.linspace(0, 25, 51) - x, y, z = [i.ravel() for i in np.meshgrid(x, y, z)] - - # random field properties - nb_dimensions = 3 - mean = 10 - variance = 2 - vertical_scale_fluctuation = 10 - anisotropy = [2.5, 2.5] - angle = [0, 0] - model_rf = ModelName.Gaussian - - # generate and plot random field - rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, - anisotropy, angle, seed=14) - rf.generate(np.array([x, y, z]).T) - plot3D([np.array([x, y, z]).T], [rf.random_field], - title="Random Field", - output_folder="./", - output_name="random_field_3D.png") - - # declae conditioning points - xc = np.array([50.]*51) - yc = np.linspace(0,50,51) - zc = np.array([25]*51) - vc = np.array([15]*51) - rf.set_conditioning_points(np.array([xc,yc,zc]).T,vc,noise_level = 0.0001) - - # generate and plot conditioned random field model - rf.generate_conditioned(np.array([x, y,z]).T) - plot3D([np.array([x, y, z]).T], [rf.conditioned_random_field], - title="Conditioned Random Field", - output_folder="./", - output_name="conditioned_random_field_3D.png") - plot3D([np.array([x, y, z]).T], [rf.kriging_mean], - title="Kriging mean 3D", - output_folder="./", - output_name="kriging_mean_3D.png") - plot3D([np.array([x, y, z]).T], [rf.kriging_std], - title="Kriging std 3D", - output_folder="./", - output_name="kriging_std_3D.png") - - -.. image:: _static/conditioned_random_field_3D.png - :width: 49% -.. image:: _static/kriging_mean_3D.png - :width: 49% -.. image:: _static/kriging_std_3D.png - :width: 49% - - - - -.. _tutorial3: - -Tutorial RandomFields in STEM ------------------------------ - -TODO \ No newline at end of file diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index df0a657..0d4ec37 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -1,13 +1,11 @@ from enum import Enum -from typing import List +from typing import List, Union import numpy as np import numpy.typing as npt import gstools as gs - from sklearn.gaussian_process import GaussianProcessRegressor as GPR -from sklearn.gaussian_process.kernels import RBF, ConstantKernel , \ - WhiteKernel , Matern +from sklearn.gaussian_process.kernels import RBF, WhiteKernel, Matern class ModelName(Enum): @@ -28,44 +26,45 @@ class RandomFields(): - None Attributes: - - max_conditioning_points (int): maximum number of - - random_field_model_name (str): + - max_conditioning_points (int): maximum number of + - random_field_model_name (str): - random_field_model (int): - - n_dim (int): number of physical dimesions (1,2 or 3) + - n_dim (int): number of physical dimesions (1,2 or 3) - seed (int): seed for the random number generator - - mean (float): unconditioned mean of the random field + - mean (float): unconditioned mean of the random field - variance (float): unconditioned variance of the random field - - vertical_scale_fluctuation (float): - - anisotropy (list): ratio between horizontal scales and vertical scale of fluctuation, shape (`self.n_dims` - 1) - - angle (list): rotation angles of the principal directions of the scales of fluctuation relative to the vertical, shape (`self.n_dims` - 1) + - vertical_scale_fluctuation (float): vertical scale of fluctuation of the random field + - anisotropy (list): ratio between horizontal scales and vertical scale of fluctuation + - angle (list): rotation angles of the principal directions of the scales of fluctuation relative to the + vertical - v_dim (int): dimesion number corresponding to the vertical scale of fluctuation - - random_field (:class:gstools.SRF) random field generator + - random_field (:class:gstools.SRF): random field generator - z_kriged_field (array): standard-normal kriged random field - - gaussian_process (:class:sklearn.gaussian_process.GaussianProcessRegressor) - - conditioning_points (array): coordinates of the conditioning points, shape (:,`self.n_dims`) - - conditioning_values (array): values of the conditioning points, shape (:,) - - kriging_mean (array): mean of the conditioned random field, shape (:,) - - kriging_std = (array): standard deviation of the conditioned random field, shape (:,) - - conditioned_random_field (array): conditioned random field, shape (:,) - + - gaussian_process (:class:sklearn.gaussian_process.GaussianProcessRegressor): Gaussian process regressor + - conditioning_points (array): coordinates of the conditioning points + - conditioning_values (array): values of the conditioning points + - kriging_mean (array): mean of the conditioned random field + - kriging_std = (array): standard deviation of the conditioned random field + - conditioned_random_field (array): conditioned random field """ - def __init__(self, - model_name: ModelName, + def __init__(self, + model_name: ModelName, n_dim: int, - mean: float, + mean: float, variance: float, - v_scale_fluctuation: float, - anisotropy: List[float], + v_scale_fluctuation: float, + anisotropy: List[float], angle: List[float], - seed: int = 14, + seed: int = 14, v_dim: int = 1, - max_conditioning_points:int = 2000) -> None: + max_conditioning_points: int = 2000) -> None: """ Initialise generation of random fields Args: - - model_name (str): Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" + - model_name (ModelName): Name of the model to be used. Options are: + "Gaussian", "Exponential", "Matern", "Linear" - n_dim (int): The dimension of the random field - mean (float): The mean of the random field - variance (float): The variance of the random field @@ -74,32 +73,23 @@ def __init__(self, - angle (list): The angle of the random field (per dimension) - seed (int): The seed number for the random number generator - v_dim (int): The dimension of the vertical scale of fluctuation - - max_conditioning_points (int): Maximum number of points to be used as conditioning points: increase leads to - longer computation times and high memory use due to factorisation of dense matrices + - max_conditioning_points (int): Maximum number of points to be used as conditioning points: increase leads + to longer computation times and high memory use due to factorisation of dense matrices Raises: - - ValueError: if model_name.name if not a member of class ModelName - - ValueError: if the number of dimensions is not in [1,2,3] + - ValueError: if model name is not a member of class ModelName - ValueError: if the number of dimensions is nt consistent with the number of random field properties - - Returns: - - self - """ # initialise model if model_name.name not in ModelName.__members__: raise ValueError(f'Model name: {model_name} is not supported') - if n_dim not in [1, 2, 3]: - raise ValueError('Only 1, 2, 3 dimensions are supported') - aux_keys = [anisotropy, angle] for key in aux_keys: if len(key) != n_dim - 1: - raise ValueError(f'Number of dimensions: {n_dim} does not match number of ' + + raise ValueError(f'Number of dimensions: {n_dim} does not match number of ' + f'random field properties: {len(key)}. It should be {n_dim - 1}.') - self.max_conditioning_points = max_conditioning_points self.random_field_model_name = model_name.name self.random_field_model = model_name.value @@ -111,29 +101,28 @@ def __init__(self, self.anisotropy = anisotropy self.angle = angle self.v_dim = v_dim - self.random_field = None + self.random_field: gs.SRF = gs.SRF(model_name.value(), mean=0, seed=seed) self.z_kriged_field = None self.gaussian_process = GPR() - self.conditioning_points = None - self.conditioning_values = None + self.conditioning_points: npt.NDArray[np.float64] = np.empty((0, n_dim)) + self.conditioning_values: npt.NDArray[np.float64] = np.empty((0, n_dim)) self.kriging_mean = None self.kriging_std = None self.conditioned_random_field = None - + # fix seed for numpy random number generator + np.random.seed(self.seed) def generate(self, nodes: npt.NDArray[np.float64]) -> None: """ Generate random field Args: - - nodes (ndarray): The nodes of the random field. shape (:,`self.n_dim`) - + - nodes (ndarray): The nodes of the random field + Raises: - ValueError: if dimensions of `nodes` do not match dimensions of the model - - Returns: - """ + # check dimensions of nodes agrees with dimensions of model if nodes.shape[1] != self.n_dim: raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') @@ -145,54 +134,52 @@ def generate(self, nodes: npt.NDArray[np.float64]) -> None: mask = np.arange(len(scale_fluctuation)) != self.v_dim scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy - model = self.random_field_model(dim = self.n_dim, - var = self.variance, - len_scale = scale_fluctuation, - angles = self.angle) - self.random_field = gs.SRF(model, - mean = self.mean, - seed = self.seed) - self.random_field(nodes.T) + model = self.random_field_model(dim=self.n_dim, + var=self.variance, + len_scale=scale_fluctuation, + angles=self.angle) + self.random_field = gs.SRF(model, mean=self.mean, seed=self.seed) + self.random_field(nodes.T) def set_conditioning_points(self, points: npt.NDArray[np.float64], values: npt.NDArray[np.float64], - noise_level = 0.0001 ) -> None: + noise_level: Union[float, npt.NDArray[np.float64]]=0.0001) -> None: """ Initiates the conditioning points and inverts the covariance matrix - + Args: - - points (array): The contitioning point coordinates. Dimensions `(:,self.n_dims)` - - values: array-like: The conditioning point values. - - kernel: (:class: sklearn.gaussian_process.kernels.Kernel): The (calibrated) correlation - kernel from sklearn (default = None). - - noise_level (float or array): normalised variance of the observed data, as a single float - or as an array of the same dimensions as `values` (default = 0.0001). + - points (npt.NDArray[np.float64]): The contitioning point coordinates. + - values: (npt.NDArray[np.float64]): The conditioning point values. + - kernel: (:class: sklearn.gaussian_process.kernels.Kernel): The (calibrated) correlation + kernel from sklearn (default=None). + - noise_level (Union[float, npt.NDArray[np.float64]]): normalised variance of the observed data, + as a single float or as an array of the same dimensions as `values` (default=0.0001). Raises: - - Exception: if more than `self.max_conditioning_points` conditioning points are specified, - to prevent excessive computation and memory usage. + - Exception: if more than `max_conditioning_points` conditioning points are specified, + to prevent excessive computation and memory usage. - ValueError: if dimensions of `points` do not match dimensions of the model - - Returns: - """ self.noise_level = noise_level self.conditioning_points = points self.conditioning_values = values - # check the maximum number of conditioning points to keep the computation and memory cost reasonable + # check the maximum number of conditioning points to keep the computation and memory cost reasonable if points.shape[0] > self.max_conditioning_points: - raise Exception(f'Too many conditioning points! There are {points.shape[0]} ' + - f'points, while the maximum allowed amount is {self.max_conditioning_points}.' + - f'\n Consider increasing `max_conditioning_points` or use fewer conditioning points.' ) + raise Exception( + f'Too many conditioning points!\n' + 'There are {points.shape[0]} points, while the maximum allowed amount' + f'is {self.max_conditioning_points}.\n' + 'Consider increasing `max_conditioning_points` or use fewer conditioning points.' + ) # check dimensions of conditioning points agrees with dimensions of model if points.shape[1] != self.n_dim: - raise ValueError(f'Dimensions of conditioning points: {points.shape[1]} do not match dimensions of model: {self.n_dim}') - + raise ValueError( + f'Dimensions of conditioning points: {points.shape[1]} do not match dimensions of model: {self.n_dim}') # scale of fluctuation scale_fluctuation = np.ones(self.n_dim) * self.vertical_scale_fluctuation @@ -201,78 +188,72 @@ def set_conditioning_points(self, mask = np.arange(len(scale_fluctuation)) != self.v_dim scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy - # # correct the length scales between libraries if self.random_field_model_name == 'Gaussian': - ls_sklearn = scale_fluctuation * 2/np.pi - self.kriging_kernel = WhiteKernel(noise_level) + RBF(length_scale = ls_sklearn) + ls_sklearn = scale_fluctuation * 2 / np.pi + self.kriging_kernel = WhiteKernel(noise_level) + RBF(length_scale=ls_sklearn) elif self.random_field_model_name == 'Exponential': - ls_sklearn = scale_fluctuation / np.sqrt(1/2) - self.kriging_kernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn, nu = 0.5) + ls_sklearn = scale_fluctuation / np.sqrt(1 / 2) + self.kriging_kernel = WhiteKernel(noise_level) + Matern(length_scale=ls_sklearn, nu=0.5) elif self.random_field_model_name == 'Matern': - ls_sklearn = scale_fluctuation / np.sqrt(1/2) - self.kriging_kernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn) + ls_sklearn = scale_fluctuation / np.sqrt(1 / 2) + self.kriging_kernel = WhiteKernel(noise_level) + Matern(length_scale=ls_sklearn) - # fit a GP against conditioning points standardised by simulation field statistics: NOT by conditioning point statistics - self.gaussian_process = GPR(kernel = self.kriging_kernel) + # fit a GP against conditioning points standardised by simulation field statistics: + # NOT by conditioning point statistics + self.gaussian_process = GPR(kernel=self.kriging_kernel) self.gaussian_process.optimizer = None - self.gaussian_process.fit(points,(values - self.mean) / np.sqrt(self.variance) ) - - # reset variables - self.kriging_mean = np.array([]) - self.kriging_std = np.array([]) - self.conditioned_random_field = np.array([]) + self.gaussian_process.fit(points, (values - self.mean) / np.sqrt(self.variance)) + + # # reset variables + # self.kriging_mean = np.array([]) + # self.kriging_std = np.array([]) + # self.conditioned_random_field = np.array([]) def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: """ Generate conditioned random field Args: - - nodes (array): The nodes of the random field, shape (:,`self.n_dims`) - + - nodes (npt.NDArray[np.float64]): The nodes of the random field + Raises: - ValueError: if dimensions of nodes do not match the dimensions of the model - - Returns: - - """ + # check dimensions of nodes agrees with dimensions of model if nodes.shape[1] != self.n_dim: raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') - # + # create kriged mean field - z_kriged_field,std_kriged_field = self.gaussian_process.predict(nodes,return_std = True) + z_kriged_field, std_kriged_field = self.gaussian_process.predict(nodes, return_std=True) std_kriged_field = np.sqrt(std_kriged_field**2 - self.noise_level) std_kriged_field *= np.sqrt(self.variance) self.kriging_mean = self.mean + np.sqrt(self.variance) * z_kriged_field - # + # create single random field at nodes and conditioning points - self.generate(np.vstack([nodes,self.conditioning_points])) + self.generate(np.vstack([nodes, self.conditioning_points])) # Split the generated field into the nodal coordinates and the conditioning points - # Standardize the distribution to marginal ~N(0,1) - z_cond_rf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) - z_rf_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) - + # Standardize the distribution to marginal ~N(0,1) + z_cond_rf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) # type: ignore + z_rf_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) # type: ignore + # add the noise to the conditioning points - np.random.seed(self.seed) - z_rf_cond_points += np.sqrt(self.noise_level) * np.random.normal(size = z_rf_cond_points.shape) - - # - # create kriged mean field of random field - gp_rf = GPR(kernel = self.gaussian_process.kernel_) + z_rf_cond_points += np.sqrt(self.noise_level) * np.random.normal(size=z_rf_cond_points.shape) + + # create kriged mean field of random field + gp_rf = GPR(kernel=self.gaussian_process.kernel_) gp_rf.optimizer = None - gp_rf.fit(self.conditioning_points,z_rf_cond_points) + gp_rf.fit(self.conditioning_points, z_rf_cond_points) gp_rf.L_ = self.gaussian_process.L_ - # + # replace kriged mean field (the kriged parts) to create conditioned random field z_cond_rf_nodes -= gp_rf.predict(nodes) - z_cond_rf_nodes += z_kriged_field - - # + z_cond_rf_nodes += z_kriged_field + # scale conditioned random field self.kriging_std = np.sqrt(self.variance) * std_kriged_field - self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_cond_rf_nodes \ No newline at end of file + self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_cond_rf_nodes From 860e7ee7ec641eef91017d0930fe9ccd6abc1ef1 Mon Sep 17 00:00:00 2001 From: brunozc Date: Fri, 10 Jan 2025 10:05:53 +0100 Subject: [PATCH 30/62] style fixed --- random_fields/utils.py | 127 +++++++++++++++++++++---------- tests/test_random_field.py | 19 +++-- tests/test_random_field_utils.py | 11 ++- 3 files changed, 105 insertions(+), 52 deletions(-) diff --git a/random_fields/utils.py b/random_fields/utils.py index 3caca1a..a7d91a8 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -1,20 +1,29 @@ import os +from typing import List, Tuple, Optional import numpy as np +import numpy.typing as npt import matplotlib.pylab as plt import matplotlib as mpl +from mpl_toolkits.mplot3d import Axes3D +import gstools as gs -def plot3D(coordinates: list, random_field: list, title: str = "Random Field", - output_folder = "./", output_name: str = "random_field.png"): +def plot3D(coordinates: List[npt.NDArray[np.float64]], + random_field: List[gs.field.srf.SRF], + title: str = "Random Field", + output_folder="./", + output_name: str = "random_field.png", + show: bool = False): """ - Plots and saves 3D random field + Plots and saves 3D random field Args: - - coordinates (list): List of coordinates of the random field - - random_field (list): List of random field values + - coordinates (List[npt.NDArray[np.float64]]): List of coordinates of the random field + - random_field (List[gs.field.srf.SRF]): List of random field values - title (str): Title of the plot - output_folder (str): Output folder - output_name (str): Output fine name + - show (bool): Show the plot (optional, default = False) """ # create output folder if not os.path.exists(output_folder): @@ -22,46 +31,58 @@ def plot3D(coordinates: list, random_field: list, title: str = "Random Field", # make plot fig = plt.figure(1, figsize=(6, 5)) - ax = fig.add_subplot(projection='3d') + ax: Axes3D = fig.add_subplot(projection='3d') ax.set_position([0.1, 0.1, 0.8, 0.8]) # rotate the axes so that y is vertical ax.view_init(125, -90, 0) - vmin = np.min([np.min(aux) for aux in random_field]) - vmax = np.max([np.max(aux) for aux in random_field]) + rfield = np.array(random_field) + vmax = np.max(rfield) + vmin = np.min(rfield) for i, coord in enumerate(coordinates): x, y, z = coord[:, 0], coord[:, 1], coord[:, 2] - ax.scatter(x, y, z, c=random_field[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s") + ax.scatter(x, y, z, c=rfield[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s") ax.set_xlabel('x coordinate') ax.set_ylabel('y coordinate') ax.set_zlabel('z coordinate') - cax = ax.inset_axes([1.1, 0., 0.05, 1]) + cax = ax.inset_axes((1.1, 0., 0.05, 1)) norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax) fig.suptitle(title) plt.savefig(os.path.join(output_folder, output_name)) + + if show: + plt.show() plt.close() -def plot2D(coordinates: list, random_field: list, title: str = "Random Field", - output_folder = "./", output_name: str = "random_field.png", - colorbar_label: str = '',conditioning_coordinates: list = [], - conditioning_values:list = [],figsize: tuple = (6,5)): +def plot2D(coordinates: List[npt.NDArray[np.float64]], + random_field: List['gs.field.srf.SRF'], + title: str = "Random Field", + output_folder="./", + output_name: str = "random_field.png", + colorbar_label: str = '', + conditioning_coordinates: List[npt.NDArray[np.float64]] = [], + conditioning_values: List[npt.NDArray[np.float64]] = [], + figsize: Tuple[float, float] = (6, 5), + show: bool = False): """ Plots and saves 2D random field Args: - - coordinates (list): List of coordinates of the random field - - random_field (list): List of random field values + - coordinates (List[npt.NDArray[np.float64]]): List of coordinates of the random field + - random_field (List[gs.field.srf.SRF]): List of random field values - title (str): Title of the plot - output_folder (str): Output folder - output_name (str): Output fine name - colorbar_label (str): Label for the colorbar (optional, deafult = '') - - conditioning_coordinates (list): List of coordinates if the conditioning points (optional, default = []) - - conditioning_values (list): List of the values at the conditioning points (optional, default = []) + - conditioning_coordinates (List[npt.NDArray[np.float64]]): List of coordinates if the conditioning points (optional, default = []) + - conditioning_values (List[npt.NDArray[np.float64]]): List of the values at the conditioning points (optional, default = []) + - figsize (Tuple[float, float]): Figure size (optional, default = (6, 5)) + - show (bool): Show the plot (optional, default = False) """ # create output folder @@ -70,43 +91,60 @@ def plot2D(coordinates: list, random_field: list, title: str = "Random Field", # make plot fig, ax = plt.subplots(figsize=figsize) - ax.set_position([0.1, 0.1, 0.7, 0.8]) + ax.set_position((0.1, 0.1, 0.7, 0.8)) - vmin = np.min([np.min(aux) for aux in random_field]) - vmax = np.max([np.max(aux) for aux in random_field]) + rfield = np.array(random_field) + vmax = np.max(rfield) + vmin = np.min(rfield) for i, coord in enumerate(coordinates): x, y = coord[:, 0], coord[:, 1] - ax.scatter(x, y, c=random_field[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s") + ax.scatter(x, y, c=rfield[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors=None, marker="s") ax.set_xlabel('x coordinate') ax.set_ylabel('y coordinate') - for i,coord in enumerate(conditioning_coordinates): + for i, coord in enumerate(conditioning_coordinates): x, y = coord[:, 0], coord[:, 1] - ax.scatter(x, y, c=conditioning_values[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors='red', linewidth = 0.25 ) - - cax = ax.inset_axes([1.1, 0., 0.05, 1]) + ax.scatter(x, + y, + c=conditioning_values[i], + vmin=vmin, + vmax=vmax, + cmap="viridis", + edgecolors='red', + linewidth=0.25) + + cax = ax.inset_axes((1.1, 0., 0.05, 1)) norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) - fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax,label = colorbar_label) + fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax, label=colorbar_label) fig.suptitle(title) plt.savefig(os.path.join(output_folder, output_name)) + if show: + plt.show() plt.close() -def plot1D(coordinates: list, random_field: list, title: str = "Random Field", - output_folder = "./", output_name: str = "random_field.png"): +def plot1D(coordinates: List[npt.NDArray[np.float64]], + random_field: List[gs.field.srf.SRF], + title: str = "Random Field", + output_folder="./", + output_name: str = "random_field.png", + conditional_points: Optional[List[npt.NDArray[np.float64]]] = None, + show: bool = False): """ Plots and saves 1D random field Args: - - coordinates (list): List of coordinates of the random field - - random_field (list): List of random field values + - coordinates (List[npt.NDArray[np.float64]]): List of coordinates of the random field + - random_field (List[gs.field.srf.SRF]): List of random field values - title (str): Title of the plot - output_folder (str): Output folder - output_name (str): Output fine name - + - conditional_points (List[npt.NDArray[np.float64]]): List of the values at the conditioning points, and + kriging mean and standard deviation (optional, default = None) + - show (bool): Show the plot (optional, default = False) """ # create output folder @@ -115,20 +153,26 @@ def plot1D(coordinates: list, random_field: list, title: str = "Random Field", # make plot fig, ax = plt.subplots(figsize=(8, 4)) - ax.set_position([0.1, 0.1, 0.7, 0.8]) - - vmin = np.min([np.min(aux) for aux in random_field]) - vmax = np.max([np.max(aux) for aux in random_field]) + ax.set_position((0.1, 0.1, 0.7, 0.8)) color = 'black' if len(coordinates) > 1: - color = 'gray' + color = 'gray' for i, coord in enumerate(coordinates): x = coord[:, 0] y = np.array(random_field[i]).ravel() - ax.plot(x, y, color = color) - + ax.plot(x, y, color=color) + + if conditional_points: + plt.plot(x, conditional_points[2], label='kriged field', color='k') + plt.fill_between(x.ravel(), conditional_points[2] - 1.65 * conditional_points[3], + conditional_points[2] + 1.65 * conditional_points[3], + label='90% uncertainty bound', + color='gray', alpha=0.5) + plt.scatter(conditional_points[0], conditional_points[1], label='conditioning points', color='k') + plt.legend() + ax.set_xlabel('x coordinate') ax.set_ylabel('random field value') @@ -136,4 +180,7 @@ def plot1D(coordinates: list, random_field: list, title: str = "Random Field", fig.suptitle(title) plt.savefig(os.path.join(output_folder, output_name)) - plt.close() \ No newline at end of file + + if show: + plt.show() + plt.close() diff --git a/tests/test_random_field.py b/tests/test_random_field.py index 404a8c2..dd975ab 100644 --- a/tests/test_random_field.py +++ b/tests/test_random_field.py @@ -19,11 +19,13 @@ def test_model_names(): rf = RandomFields(ModelName.Linear, 2, 2, 2, 2, [2], [2]) assert rf.random_field_model.__name__ == 'Linear' + def test_failure_ndim(): "test failure with number of dimensions" with pytest.raises(ValueError): RandomFields(ModelName.Gaussian, 4, 2, 2, 2, [2], [2]) + def test_model_name_failure(): """test failure with wrong model name""" with pytest.raises(AttributeError): @@ -118,20 +120,19 @@ def test_conditioned_RF_3D_mean_variance(): model_rf = ModelName.Gaussian # generate and plot random field - rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, - anisotropy, angle, seed=14) + rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, anisotropy, angle, seed=14) rf.generate(np.array([x, y, z]).T) # declare conditioning points - xc = np.array([50.]*4) - yc = np.linspace(0,50,4) - zc = np.array([25]*4) - vc = np.array([15]*4) + xc = np.array([50.] * 4) + yc = np.linspace(0, 50, 4) + zc = np.array([25] * 4) + vc = np.array([15] * 4) - rf.set_conditioning_points(np.array([xc,yc,zc]).T,vc,noise_level = 0.01) + rf.set_conditioning_points(np.array([xc, yc, zc]).T, vc, noise_level=0.01) # generate conditioned random field model - rf.generate_conditioned(np.array([x, y,z]).T) + rf.generate_conditioned(np.array([x, y, z]).T) # load reference solution data_ref = np.loadtxt('./tests/data/conditioned_rf_3D.txt') @@ -143,5 +144,3 @@ def test_conditioned_RF_3D_mean_variance(): np.testing.assert_array_almost_equal(kriging_mean_ref, rf.kriging_mean, decimal=4) np.testing.assert_array_almost_equal(kriging_std_ref, rf.kriging_std, decimal=4) np.testing.assert_array_almost_equal(kriging_field_ref, rf.conditioned_random_field, decimal=4) - - diff --git a/tests/test_random_field_utils.py b/tests/test_random_field_utils.py index dfb6b3e..4fc4db3 100644 --- a/tests/test_random_field_utils.py +++ b/tests/test_random_field_utils.py @@ -24,7 +24,10 @@ def test_distribution_RF_struc(cleanup_generated_files): rf = RandomFields(ModelName.Gaussian, 2, 10, 2, 1, [1], [1], seed=14) rf.generate(np.array([x.ravel(), y.ravel()]).T) - plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.random_field], title="Random Field", output_folder="./", output_name="random_field.eps") + plot2D([np.array([x.ravel(), y.ravel()]).T], [rf.random_field], + title="Random Field", + output_folder="./", + output_name="random_field.eps") with open("./tests/data/random_field.eps", "r") as fi: data_org = fi.read().splitlines() @@ -37,6 +40,7 @@ def test_distribution_RF_struc(cleanup_generated_files): data = [val == data_new[header + i] for i, val in enumerate(data_org[header:idx_end])] assert all(data) + @pytest.mark.skip(reason="3D plot not working on GitHub Actions") def test_distribution_RF_struc_3D(cleanup_generated_files): """test distribution of 3D random field with structured mesh""" @@ -50,7 +54,10 @@ def test_distribution_RF_struc_3D(cleanup_generated_files): rf = RandomFields(ModelName.Gaussian, 3, 10, 2, 1, [1, 0.5], [1, 1], seed=14) rf.generate(np.array([x.ravel(), y.ravel(), z.ravel()]).T) - plot3D([np.array([x.ravel(), y.ravel(), z.ravel()]).T], [rf.random_field], title="Random Field", output_folder="./", output_name="random_field.eps") + plot3D([np.array([x.ravel(), y.ravel(), z.ravel()]).T], [rf.random_field], + title="Random Field", + output_folder="./", + output_name="random_field.eps") with open("./tests/data/random_field_3D.eps", "r") as fi: data_org = fi.read().splitlines() From 98f6940eedbd75f11af27885c2889f224073ea60 Mon Sep 17 00:00:00 2001 From: brunozc Date: Fri, 10 Jan 2025 17:20:01 +0100 Subject: [PATCH 31/62] WIP --- .../gef}/CPT000000217393_IMBRO.gef | 0 .../gef}/CPT000000217402_IMBRO.gef | 0 .../gef}/CPT000000217406_IMBRO.gef | 0 .../gef}/CPT000000217408_IMBRO.gef | 0 .../gef}/CPT000000217409_IMBRO.gef | 0 cpts/xml/CPT000000217393.xml | 1 + cpts/xml/CPT000000217402.xml | 1 + cpts/xml/CPT000000217406.xml | 1 + cpts/xml/CPT000000217408.xml | 1 + cpts/xml/CPT000000217409.xml | 1 + random_fields/generate_field.py | 6 +- .../geostatistical_cpt_interpretation.py | 646 ++++++++++-------- .../DelftSchiedam_cpts/locatie_levering.kml | 117 ---- 13 files changed, 350 insertions(+), 424 deletions(-) rename {tests/data/DelftSchiedam_cpts => cpts/gef}/CPT000000217393_IMBRO.gef (100%) rename {tests/data/DelftSchiedam_cpts => cpts/gef}/CPT000000217402_IMBRO.gef (100%) rename {tests/data/DelftSchiedam_cpts => cpts/gef}/CPT000000217406_IMBRO.gef (100%) rename {tests/data/DelftSchiedam_cpts => cpts/gef}/CPT000000217408_IMBRO.gef (100%) rename {tests/data/DelftSchiedam_cpts => cpts/gef}/CPT000000217409_IMBRO.gef (100%) create mode 100644 cpts/xml/CPT000000217393.xml create mode 100644 cpts/xml/CPT000000217402.xml create mode 100644 cpts/xml/CPT000000217406.xml create mode 100644 cpts/xml/CPT000000217408.xml create mode 100644 cpts/xml/CPT000000217409.xml delete mode 100644 tests/data/DelftSchiedam_cpts/locatie_levering.kml diff --git a/tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef b/cpts/gef/CPT000000217393_IMBRO.gef similarity index 100% rename from tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef rename to cpts/gef/CPT000000217393_IMBRO.gef diff --git a/tests/data/DelftSchiedam_cpts/CPT000000217402_IMBRO.gef b/cpts/gef/CPT000000217402_IMBRO.gef similarity index 100% rename from tests/data/DelftSchiedam_cpts/CPT000000217402_IMBRO.gef rename to cpts/gef/CPT000000217402_IMBRO.gef diff --git a/tests/data/DelftSchiedam_cpts/CPT000000217406_IMBRO.gef b/cpts/gef/CPT000000217406_IMBRO.gef similarity index 100% rename from tests/data/DelftSchiedam_cpts/CPT000000217406_IMBRO.gef rename to cpts/gef/CPT000000217406_IMBRO.gef diff --git a/tests/data/DelftSchiedam_cpts/CPT000000217408_IMBRO.gef b/cpts/gef/CPT000000217408_IMBRO.gef similarity index 100% rename from tests/data/DelftSchiedam_cpts/CPT000000217408_IMBRO.gef rename to cpts/gef/CPT000000217408_IMBRO.gef diff --git a/tests/data/DelftSchiedam_cpts/CPT000000217409_IMBRO.gef b/cpts/gef/CPT000000217409_IMBRO.gef similarity index 100% rename from tests/data/DelftSchiedam_cpts/CPT000000217409_IMBRO.gef rename to cpts/gef/CPT000000217409_IMBRO.gef diff --git a/cpts/xml/CPT000000217393.xml b/cpts/xml/CPT000000217393.xml new file mode 100644 index 0000000..d660701 --- /dev/null +++ b/cpts/xml/CPT000000217393.xml @@ -0,0 +1 @@ +dispatch-2025-01-10T17:08:11+01:00CPT00000021739330124359IMBROpubliekeTaakinfrastructuurLand2023-04-17ISO22476D1nee51.961881008 4.380341476RDNAPTRANS201885799.579 441974.0472022-12-12RTKGPS5tot10cmmaaiveld-0.824NAP2022-12-12RTKGPS4tot10cmnee2022-12-21elektrischContinuklasse2einddiepte0.0025.057Fugro Cone Penetrometer 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\ No newline at end of file diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index 0d4ec37..dca891a 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -95,6 +95,7 @@ def __init__(self, self.random_field_model = model_name.value self.n_dim = n_dim self.seed = seed + np.random.seed(seed) self.mean = mean self.variance = variance self.vertical_scale_fluctuation = v_scale_fluctuation @@ -205,11 +206,6 @@ def set_conditioning_points(self, self.gaussian_process.optimizer = None self.gaussian_process.fit(points, (values - self.mean) / np.sqrt(self.variance)) - # # reset variables - # self.kriging_mean = np.array([]) - # self.kriging_std = np.array([]) - # self.conditioned_random_field = np.array([]) - def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: """ Generate conditioned random field diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 6dfac92..46e91d2 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -1,250 +1,258 @@ -#%% - -from typing import Optional, Tuple - import os +from typing import Optional, List, Tuple +from pathlib import Path import numpy as np import numpy.typing as npt import matplotlib.pyplot as plt import scipy.stats as st -import logging - -from random_fields.generate_field import RandomFields, ModelName - from sklearn.gaussian_process import GaussianProcessRegressor as GPR -from sklearn.gaussian_process.kernels import RBF, WhiteKernel, Sum - +from sklearn.gaussian_process.kernels import RBF, WhiteKernel from geolib_plus.gef_cpt import GefCpt +from geolib_plus.bro_xml_cpt import BroXmlCpt from geolib_plus.robertson_cpt_interpretation import RobertsonCptInterpretation from geolib_plus.robertson_cpt_interpretation import UnitWeightMethod from geolib_plus.robertson_cpt_interpretation import OCRMethod from geolib_plus.robertson_cpt_interpretation import ShearWaveVelocityMethod +from random_fields.generate_field import RandomFields, ModelName + class MarginalTransformation(): - """Class for the transformation model between the distributions of the pysical values and the standard-normal distribution. + """ + Class for the transformation model between the distributions of the physical values and the standard-normal + distribution. Attributes: - - data (npt.NDArray[np.float64]): sampling data to construct transformation + - data (npt.NDArray[np.float64]): sampling data to construct transformation - n (int): number of samples: length of `data` - - lognormal (bool): If True, treats the data as lognormally distributed. Defaults to False. + - lognormal (bool): If True, treats the data as lognormally distributed. Defaults to False - _interp_z (npt.NDArray[np.float64]): internal interpolation data for transformation - _interp_data (npt.NDArray[np.float64]): internal interpolation data for transformation - _interp_log_data (npt.NDArray[np.float64]): internal interpolation data for transformation - """ + def __init__(self, data: npt.NDArray[np.float64], - min: Optional[float] = None, - max: Optional[float] = None, - lognormal: Optional[bool] = False): + min_value: Optional[float] = None, + max_value: Optional[float] = None, + lognormal: bool = False): """ Initiate class Args: - - data (array-like): sample data to construct transformation - - min (float, optional): minimum value of the population. Defaults to None. - - max (float, optional): maximum value of the population. Defaults to None. - - lognormal (bool, optional): If True, treats the data as lognormally distributed. Defaults to False. - """ + - data (npt.NDArray[np.float64]): sample data to construct transformation + - min_value (Optional[np.float64]): minimum value of the population. Defaults to None. + - max_value (Optional[np.float64]): maximum value of the population. Defaults to None. + - lognormal (bool): If True, treats the data as lognormally distributed. Defaults to False. + """ self.data = np.sort(data) self.n = len(data) self.lognormal = lognormal - # if `max` and `min` are not given: add a 1% margin on the data - if type(min) == type(None): - min = self.data.min() - (self.data.max() - self.data.min())/100 - min_ln = np.log(self.data.min()) - (np.log(self.data.max()) - np.log(self.data.min()))/100 + # if `max_value` and `min_value` are not given: add a 1% margin on the data + if not min_value: + min_value = self.data.min() - (self.data.max() - self.data.min()) / 100 + min_ln = np.log(self.data.min()) - (np.log(self.data.max()) - np.log(self.data.min())) / 100 else: - min_ln = np.log(min) - if type(max) == type(None): - max = self.data.max() + (self.data.max() - self.data.min())/100 - max_ln = np.log(self.data.max()) + (np.log(self.data.max()) - np.log(self.data.min()))/100 + min_ln = np.log(min_value) + if not max_value: + max_value = self.data.max() + (self.data.max() - self.data.min()) / 100 + max_ln = np.log(self.data.max()) + (np.log(self.data.max()) - np.log(self.data.min())) / 100 else: - max_ln = np.log(max) + max_ln = np.log(max_value) - self.z = st.norm.ppf(np.linspace(.5/self.n,1-.5/self.n,self.n)) + self.z = st.norm.ppf(np.linspace(.5 / self.n, 1 - .5 / self.n, self.n)) # adds minimum and maximum to data-set - x_points = np.hstack([min,self.data,max]) - x_points_ln = np.hstack([min_ln,np.log(self.data),max_ln]) - z_points = np.hstack([-8.,self.z,8.]) + x_points = np.hstack([np.float64(min_value), self.data, np.float64(max_value)]) + x_points_ln = np.hstack([min_ln, np.log(self.data), max_ln]) + z_points = np.hstack([-8., self.z, 8.]) # initiates internal points for interpolation - self._interp_z = np.linspace(-8,8,101) - self._interp_data = np.interp(self._interp_z,z_points,x_points) - self._interp_log_data = np.interp(self._interp_z,z_points,x_points_ln) + self._interp_z = np.linspace(-8, 8, 101) + self._interp_data = np.interp(self._interp_z, z_points, x_points) + self._interp_log_data = np.interp(self._interp_z, z_points, x_points_ln) def plot(self, - title: str = "Marginal distribution transformation ", - x_label: str = '$Z\sim N(0,1)$', - y_label: str = '$X $', - figsize: tuple = (6.,5.)): - """Plots the transformation model for the marginal distribution. + title: str="Marginal distribution transformation ", + x_label: str=r'$Z\sim N(0, 1)$', + y_label: str=r'$X$', + figsize: Tuple[float, float]=(6, 5)): + r"""Plots the transformation model for the marginal distribution. Args: - title (str, optional): plot title. Defaults to "Marginal distribution transformation ". - x_label (str, optional): horizontal axis label. Defaults to '\sim N(0,1)$'. - y_label (str, optional): vertical axis label. Defaults to ' $'. - figsize (Tuple(float), optional): figure size. Defaults to (6.,5.). + title (str): plot title. Defaults to "Marginal distribution transformation ". + x_label (str): horizontal axis label. Defaults to '\sim N(0,1)$'. + y_label (str): vertical axis label. Defaults to ' $'. + figsize (Tuple): figure size. Defaults to (6.,5.). """ - plt.figure(figsize = figsize) - plt.plot(self.z,self.data,'.',color = 'gray',alpha = 0.5,label = 'data') - plt.plot(self._interp_z,self._interp_data,'-',color = 'r',label = 'transformation model') + plt.figure(figsize=figsize) + plt.plot(self.z, self.data, '.', color='gray', alpha=0.5, label='data') + plt.plot(self._interp_z, self._interp_data, '-', color='r', label='transformation model') plt.xlabel(x_label) plt.ylabel(y_label) plt.legend() plt.title(title) plt.grid() - plt.show() + plt.savefig("aaa.png") + plt.close() - def x_to_z(self, - x: npt.NDArray[np.float64]): - """Transforms data from the distribution of $$X$$ to the standard normal - equivalent distribution $$Z\sim N(0,1)$$ + def x_to_z(self, x: npt.NDArray[np.float64]): + r""" + Transforms data from the distribution of $$X$$ to the standard normal + equivalent distribution $$Z\sim N(0, 1)$$ Args: - x (npt.NDArray[np.float64]): values $$x \in X$$ + - x (npt.NDArray[np.float64]): values $$x \in X$$ Returns: - z (npt.NDArray[np.float64]): standard-normal equivalent to `x` + - z (npt.NDArray[np.float64]): standard-normal equivalent to `x` """ if self.lognormal: - z = np.interp(np.log(x),self._interp_log_data,self._interp_z) + z = np.interp(np.log(x), self._interp_log_data, self._interp_z) else: - z = np.interp(x,self._interp_data,self._interp_z) + z = np.interp(x, self._interp_data, self._interp_z) return z - def z_to_x(self, - z:npt.NDArray[np.float64]): - """Transforms from a standard-normal values `z` to the corresponding values in `x`. + def z_to_x(self, z: npt.NDArray[np.float64]): + r""" + Transforms from a standard-normal values `z` to the corresponding values in `x`. Args: - z (npt.NDArray[np.float64]): standard normal values $z \in Z\sim N(0,1)$ + - z (npt.NDArray[np.float64]): standard normal values $z \in Z\sim N(0,1)$ Returns: - x (npt.NDArray[np.float64]): standard-normal equivalent to `x` + - x (npt.NDArray[np.float64]): standard-normal equivalent to `x` """ if self.lognormal: - x = np.exp(np.interp(z,self._interp_z,self._interp_log_data)) + x = np.exp(np.interp(z, self._interp_z, self._interp_log_data)) else: - x = np.interp(z,self._interp_z,self._interp_data) + x = np.interp(z, self._interp_z, self._interp_data) return x -class CPT_data(): - """CPT_data class for the handling and interpretation of a series of CPT files in .GEF data format. + +class CPT_data: + """ + CPT class for the handling the parsing and interpretation of CPT files in .GEF & xml file formats. Attributes: - gravity (float): gravity in meters per second squared - - cpt_list (List(class:`GefCPT`)): list of cpt instances + - cpt_list (List[class:`GefCPT`]): list of cpt instances - cpt_directory (str): path from which the .gef files must be read - - cpt_filepaths (list): list of interpreted cpt filepaths - - interpreter (class:`RobertsonCptInterpretation`): instance of D-GeoLib+ CPT interpretation class + - cpt_filepaths (List[str]): list of interpreted cpt filepaths + - interpreter (class:`RobertsonCptInterpretation`): instance of D-GeoLib+ CPT interpretation class - data_coords (npt.NDArray(np.float64)): XYZ coordinates of the data - cpt_locations (npt.NDArray(np.float64)): XZ coordinates of the data - rho (npt.NDArray(np.float64)): interpreted density at CPT coordinates in `data_coord` - vs (npt.NDArray(np.float64)): interpreted shear wave velocity at CPT coordinates in `data_coord` - - g0 (npt.NDArray(np.float64)): interpreted g0 at CPT coordinates in `data_coord` + - G0 (npt.NDArray(np.float64)): interpreted dynamic shear modulus G0 at CPT coordinates in `data_coord` - young_modulus (npt.NDArray(np.float64)): interpreted Young modulus at CPT coordinates in `data_coord` + - poisson (npt.NDArray(np.float64)): interpreted Poisson ratio at CPT coordinates in `data_coord` + """ - """ - - def __init__(self,cpt_directory = './'): - """Initiator of instances of the class. + def __init__(self, cpt_directory: Path) -> None: + """ + Initiator of instances of the class. Args: - cpt_directory (str, optional): Path to the folder containing the cpt files to be interpreted and used for the geostatistical model. Defaults to './'. - """ - self.gravity = 9.81 - self.cpt_list = [] - self.cpt_directory = cpt_directory - self.cpt_filepaths = [] + - cpt_directory (Path): Path to the folder containing the cpt files to be interpreted + """ + self.gravity = 9.81 + self.cpt_list: List[GefCpt] = [] + self.cpt_directory = Path(cpt_directory) + self.cpt_filepaths: List[Path] = [] self.interpreter = RobertsonCptInterpretation() self.interpreter.unitweightmethod = UnitWeightMethod.LENGKEEK self.interpreter.shearwavevelocitymethod = ShearWaveVelocityMethod.ZANG self.interpreter.ocrmethod = OCRMethod.MAYNE - def read_cpt_data(self,addition = False): + self.data_coords = np.empty((0, 3), dtype=np.float64) + self.cpt_locations = np.empty((0, 2), dtype=np.float64) + self.rho = np.empty(0) + self.vs = np.empty(0) + self.G0 = np.empty(0) + self.young_modulus = np.empty(0) + self.poisson = np.empty(0) + + def read_cpt_data(self, addition: bool=False) -> None: """read_cpt_data Reads in and pre-processes the CPT data Args: - addition (bool, optional): If True, reads in the CPT data in addition to the existing data. Defaults to False. + - addition (bool): If True, reads in the CPT data in addition to the existing data. + Defaults to False. + """ - Raises: - Warning: if .xml-files are encountered in directory - - """ if not addition: self.cpt_list = [] self.cpt_filepaths = [] - # temporarily diable warning logging - initial_logging_level = logging.getLogger().getEffectiveLevel() - logging.disable(logging.ERROR) + list_of_files = os.listdir(self.cpt_directory) + + files_type = {'.gef': GefCpt, '.xml': BroXmlCpt} - for gef_name in os.listdir(self.cpt_directory): - if gef_name.endswith('.gef'): - print(gef_name) + for file_ext, cpt_class in files_type.items(): + # collect gef files + gef_files = [f for f in list_of_files if f.endswith(file_ext)] - cpt = GefCpt() - cpt.read(self.cpt_directory + '/' + gef_name) + for gef_name in gef_files: + cpt = cpt_class() + cpt.read(self.cpt_directory / gef_name) cpt.pre_process_data() self.cpt_list.append(cpt) - self.cpt_filepaths.append(self.cpt_directory + '/' + gef_name) - - elif gef_name.endswith('.xml'): - try: + self.cpt_filepaths.append(self.cpt_directory / gef_name) - cpt = GefCpt() - cpt.read(self.cpt_directory + '/' + gef_name) - cpt.pre_process_data() - - self.cpt_list.append(cpt) - self.cpt_filepaths.append(self.cpt_directory + '/' + gef_name) - print(gef_name) - except: - raise Warning('Ignoring file {gef_name}. xml-files for CPT data may not be supported by geolib_plus?') - - # set logging level back to initial level - logging.disable(initial_logging_level) + # remove duplicates for the case that both the GEF and XML files are present + unique_idx = self.__find_duplicates() + self.cpt_list = [self.cpt_list[i] for i in unique_idx] + self.cpt_filepaths = [self.cpt_filepaths[i] for i in unique_idx] + def __find_duplicates(self) -> List[int]: + """ + Finds the duplicates from the CPT data for the case that XML and GEF files are present. - def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterpretation): - """interpret_cpt_data interprets the CPT data based on the provided interpreter settings + Returns: + - unique_idx (List[int]): list of unique indices + """ + unique_idx = [] + seen = set() + for i, value in enumerate(self.cpt_list): + if value.name not in seen: + unique_idx.append(i) + seen.add(value.name) + return unique_idx + + def interpret_cpt_data(self, interpreter: Optional[RobertsonCptInterpretation]=None, v_dim: int=1) -> None: + """ + Interpret_cpt_data interprets the CPT data based on the provided interpreter settings Args: - interpreter (:class:`geolib_plus.robertson_cpt_interpretation.RobertsonCptInterpretation`, optional): Interpretation methods. Defaults to :class:`RobertsonCptInterpretation`. - + - interpreter (:class:`geolib_plus.robertson_cpt_interpretation.RobertsonCptInterpretation`): + Interpretation methods. Defaults to :class:`RobertsonCptInterpretation`. + - v_dim (int): index corresponding to vertical. Defaults to 1. """ if interpreter: self.interpreter = interpreter - self.data_coords = np.array([[]]*3,dtype = np.float64).T - self.cpt_locations = np.array([[]]*2,dtype = np.float64).T - self.rho = np.array([]) - self.vs = np.array([]) - for cpt in self.cpt_list: cpt.interpret_cpt(self.interpreter) n_readings = len(cpt.depth_to_reference) - coords = np.array([[cpt.coordinates[0],0,cpt.coordinates[1]]]*n_readings) - coords[:,v_dim] = cpt.depth_to_reference + coords = np.array([[cpt.coordinates[0], 0, cpt.coordinates[1]]] * n_readings) + coords[:, v_dim] = cpt.depth_to_reference self.cpt_locations = np.vstack([self.cpt_locations, cpt.coordinates]) - self.data_coords = np.vstack([self.data_coords,coords]) - self.rho = np.hstack([self.rho,cpt.rho]) - self.vs = np.hstack([self.vs,cpt.vs]) + self.data_coords = np.vstack([self.data_coords, coords]) + self.rho = np.hstack([self.rho, cpt.rho]) + self.vs = np.hstack([self.vs, cpt.vs]) + self.poisson = 0.495 # ToDo: add Poisson ratio see #6 - self.g0 = self.rho*self.vs**2 - self.young_modulus = 2.*self.g0*(1.+0.2) + self.G0 = self.rho * self.vs**2 + self.young_modulus = 2. * self.G0 * (1. + self.poisson) - def save_conditioning_points_to_vtk(self, - filename = 'conditioning_data.vtk', - variable_names = ['vs'] ): + def save_conditioning_points_to_vtk(self, filename='conditioning_data.vtk', variable_names=['vs']): """Saves the conditioning data to a .vtk file for visualisation. Args: @@ -252,96 +260,103 @@ def save_conditioning_points_to_vtk(self, - variable_names (list[str]): list of variables to save. variables should be attributes. Raises: - ValueError: if type and dimensions of `coordinates` not correct + ValueError: if type and dimensions of `coordinates` not correct ValueError: if variables in `variable_names` are not an attribute - ValueError: if variable dimensions are not correct. + ValueError: if variable dimensions are not correct. """ coordinates = self.data_coords + # ToDo: replace this with vtk writer + if not isinstance(coordinates, np.ndarray) or coordinates.shape[1] != 3: raise ValueError("Coordinates should be a numpy array with shape (N, 3).") - n_points = len(coordinates) + n_points = len(coordinates) n_values = len(variable_names) scalars = [] for varname in variable_names: - if hasattr(self,varname): + if hasattr(self, varname): scalar = self.__getattribute__(varname) - if len(scalar) == n_points and isinstance(scalar,np.ndarray): + if len(scalar) == n_points and isinstance(scalar, np.ndarray): scalars.append(scalar) else: raise ValueError(f"`{varname}` should be a numpy array of length {n_points}.") else: raise ValueError(f"{varname} is not an attribute of `conditioning_data`") - - if not isinstance(scalars, list) or len(scalars) != len(variable_names) or len(scalars[0]) != coordinates.shape[0]: + + if not isinstance(scalars, list) or len(scalars) != len(variable_names) or len( + scalars[0]) != coordinates.shape[0]: raise ValueError("Scalars should be a numpy array with shape (N, 3), matching the number of coordinates.") - with open(filename,'w') as file: + with open(filename, 'w') as file: file.write('# vtk DataFile Version 5.1\n') file.write('vtk output\n') file.write('ASCII\n') file.write('DATASET POLYDATA\n') file.write(f'POINTS {n_points} float\n') for point in coordinates: - file.write('%10.2f %10.2f %10.2f\n'%(point[0],point[1],point[2])) + file.write('%10.2f %10.2f %10.2f\n' % (point[0], point[1], point[2])) file.write(f'POINT_DATA {n_points}\n') file.write(f'FIELD FieldData {n_values}\n') - for z,name in zip(scalars,variable_names): + for z, name in zip(scalars, variable_names): file.write(f'{name} 1 {n_points} float\n') for x in z: - file.write('%.3g '%(x)) + file.write('%.3g \n' % (x)) file.write('\n') print(f"File saved as {filename}") - def data_coordinate_change(self, - x_ref:float = 0., - y_ref:float = 0., - orientation_x_axis:float = 0., - based_on_midpoint = False): - """data_coordinate_change Coordinate system transformation, relative to a reference point (x_ref,y_ref) + x_ref: float = 0., + y_ref: float = 0., + orientation_x_axis: float = 0., + based_on_midpoint: bool=False) -> None: + """ + Coordinate system transformation, relative to a reference point (x_ref, y_ref) Args: - - x_ref (float, optional): _description_. Defaults to 0.. - - y_ref (float, optional): _description_. Defaults to 0.. - - orientation_x_axis (float, optional): orienation of the model in degrees, clock-wise from North: r_ref = 0 results - in a 90 degrees clock-wise rotation in the x-y plane . Defaults to 0.. - - based_on_midpoint (bool, optional): If True, translates the domain relative to the midpoint of the CPT locations - `self.cpt_locations`. Defaults to False. - """ - - # + - x_ref (float): Defaults to 0.. + - y_ref (float): Defaults to 0.. + - orientation_x_axis (float): orienation of the model in degrees, clock-wise from North: + r_ref=0 results in a 90 degrees clock-wise rotation in the x-y plane. Defaults to 0. + - based_on_midpoint (bool): If True, translates the domain relative to the midpoint of the + CPT locations. Defaults to False. + """ + # translate coordinates if based_on_midpoint: - x_ref = (self.cpt_locations[:,0].min() + self.cpt_locations[:,0].max())/2. - y_ref = (self.cpt_locations[:,1].min() + self.cpt_locations[:,1].max())/2. + x_ref = (self.cpt_locations[:, 0].min() + self.cpt_locations[:, 0].max()) / 2 + y_ref = (self.cpt_locations[:, 1].min() + self.cpt_locations[:, 1].max()) / 2 - self.data_coords[:,0] -= x_ref - self.data_coords[:,2] -= y_ref - self.cpt_locations[:,0] -= x_ref - self.cpt_locations[:,1] -= y_ref + self.data_coords[:, 0] -= x_ref + self.data_coords[:, 2] -= y_ref + self.cpt_locations[:, 0] -= x_ref + self.cpt_locations[:, 1] -= y_ref # rotate counterclock-wise back to N000 minus 90 degrees: - theta_rad = orientation_x_axis/180*np.pi - np.pi/2 + theta_rad = orientation_x_axis / 180 * np.pi - np.pi / 2 rotation = np.array([[np.cos(theta_rad), 0, -np.sin(theta_rad)], [0., 1., 0.], [np.sin(theta_rad), 0, np.cos(theta_rad)]]) - - self.data_coords = self.data_coords @ rotation.T - self.cpt_locations = self.cpt_locations @ rotation[::2,::2].T - self.save_conditioning_points_to_vtk(variable_names = ['vs','rho','young_modulus']) + self.data_coords = self.data_coords @ rotation.T + self.cpt_locations = self.cpt_locations @ rotation[::2, ::2].T - def plot_coordinates(self,original_coordinates = False): - """plot_coordinates Plots the coordinates of the CPTs + def plot_coordinates(self, output_folder: Path=Path("./"), + output_name: str="coordinates.png", + original_coordinates: bool=False, + show: bool=False) -> None: + """ + Plots the coordinates of the CPTs Args: - - original_coordinates (bool, optional): If True, plots in original coordinates. Defaults to False. + - output_folder (Path): output folder. Defaults to Path("./"). + - output_name (str): output file name. Defaults to "coordinates.png". + - original_coordinates (bool): If True, plots in original coordinates. Defaults to False. + - show (bool): If True, shows the plot. Defaults to False. """ if original_coordinates: @@ -349,157 +364,182 @@ def plot_coordinates(self,original_coordinates = False): else: locations = self.cpt_locations - plt.plot(locations[:,0],locations[:,1],'dk',label = 'CPT locations') + plt.plot(locations[:, 0], locations[:, 1], 'dk', label='CPT locations') plt.xlabel('x-coordinate [m]') plt.ylabel('y_coordinate [m]') plt.axis('equal') - plt.show() + plt.savefig(os.path.join(output_folder, output_name)) + if show: + plt.show() + plt.close() class GeostatisticalModel(): - """Class for the characterisation of spatial variability by means of a geostatistical model + """ + Class for the characterisation of spatial variability by means of a geostatistical model Attr: - nb_dimensions (int): number of physical dimensions (1, 2 or 3) - v_dim (int): index corresponding to vertical - - gpr (:class:`sklearn.gaussian_process.GaussianProcessRegressor`). Gaussian process regressor. - - data_coords (npt.NDArray[np.float64]): coordinates of the data used for the calibtration of the geostatistical model. - - data_values (npt.NDArray[np.float64]): values of the data used for the calibtration of the geostatistical model. - - noise_level (float): noise level b - - vertical_scale_fluctuation (float): vertical scale of fluctuation - - anisotropy (list): anisotropy in the scale of fluctuation, relative to the vertical scale of fluctuation. - - """ - - def __init__(self,nb_dimensions: int,v_dim: int, kernel = None): - """__init__ Initiates instance and sets kernel to WhiteKernel() + RBF() if kernel is not provided + - gpr (:class:`sklearn.gaussian_process.GaussianProcessRegressor`). Gaussian process regressor. + - data_coords (npt.NDArray[np.float64]): coordinates of the data used for the calibtration of the + geostatistical model. + - data_values (npt.NDArray[np.float64]): values of the data used for the calibtration of the + geostatistical model. + - noise_level (Optional[float]): noise level b + - vertical_scale_fluctuation (Optional[float]): vertical scale of fluctuation + - anisotropy (Optional[List[np.float64]]): anisotropy in the scale of fluctuation, relative to the vertical scale of fluctuation. + + """ + + def __init__(self, nb_dimensions: int, + v_dim: int, + length_scale: float = 100., + length_scale_bounds: List[float] = [1.0, 1000.], + lenght_scale_bounds_vertical: List[float] = [0.01, 10.], + custom_kernel=None) -> None: + """ + Initiates instance and sets kernel to WhiteKernel() + RBF() if kernel is not provided Args: - nb_dimensions (int): number of physical dimensions. - v_dim (int): Dimension index of the vertical scale of fluctuation - - kernel (:class:`sklearn.gaussian_process.kernels.Kernel`, optional): Spatial correlation kernel. Defaults to None. + - lenght_scale (float): length scale of the kernel. Defaults to 100 + - length_scale_bounds (List[float]): bounds of the length scale. Defaults to [1.0, 1000.] + - lenght_scale_bounds_vertical (List[float]): bounds of the vertical length scale. Defaults to [0.01, 10.] + - custom_kernel (:class:`sklearn.gaussian_process.kernels.Kernel`): Custom spatial correlation kernel. + Defaults to None. Raises: TypeError: if `kernel` is not a class from module `sklearn.gaussian_process.kernels` - - """ + """ self.nb_dimensions = nb_dimensions self.v_dim = v_dim - self.data_coords = np.array([],dtype = np.float64) - self.data_values = np.array([],dtype = np.float64) + self.data_coords = np.empty(0) + self.data_values = np.empty(0) self.noise_level = None + self.vertical_scale_fluctuation = None + self.anisotropy = None # check if `kernel` has the correct parent class - if kernel: - if kernel.__module__ != 'sklearn.gaussian_process.kernels': + if custom_kernel: + if custom_kernel.__module__ != 'sklearn.gaussian_process.kernels': raise TypeError("`kernel` should an instance of a class from module `sklearn.gaussian_process.kernels`") # if kernel does not exist, create a default - if not kernel: - length_scale = [100.]*nb_dimensions - length_scale_bounds = [[1.0,1000.]]*nb_dimensions - length_scale[v_dim] = self.v_dim - length_scale_bounds[v_dim] = [0.01,10.] - - kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale, - length_scale_bounds = length_scale_bounds) - self.gpr = GPR(kernel = kernel) + if not custom_kernel: + length_scale_kernel = length_scale * np.ones(nb_dimensions) + length_scale_bounds_kernel = [length_scale_bounds] * nb_dimensions + length_scale_kernel[v_dim] = self.v_dim + length_scale_bounds_kernel[v_dim] = lenght_scale_bounds_vertical + kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale_kernel, + length_scale_bounds=length_scale_bounds_kernel) + self.gpr = GPR(kernel=kernel) - def calibrate(self,coords: npt.NDArray[np.float64],values: npt.NDArray[np.float64]): - """calibrates the geosatistical model + def calibrate(self, coords: npt.NDArray[np.float64], values: npt.NDArray[np.float64]) -> None: + """ + Calibrates the geostatistical model Args: - coords (npt.NDArray[np.float64]): data spatial coordinates - values (npt.NDArray[np.float64]): data values + - coords (npt.NDArray[np.float64]): data spatial coordinates + - values (npt.NDArray[np.float64]): data values + + Raises: + - Warning: if `coords` is transposed to match dimensions (n_points,n_dims) + - Exception: if shape of argument 'coords' incompattible with dimensionality + + """ - """ if coords.shape[0] == self.nb_dimensions and coords.shape[1] != self.nb_dimensions: self.data_coords = coords.T - raise Warning('`coords` is transposed to match dimensions (n_points,n_dims)') + raise Warning('`coords` is transposed to match dimensions (n_points,n_dims)') elif coords.shape[1] == self.nb_dimensions: self.data_coords = coords else: - raise Exception("Shape of argument 'X' incompattible with dimensionality:",X.shape) + raise Exception("Shape of argument 'coords' incompattible with dimensionality:", coords.shape) self.data_values = values # calibrate - self.gpr.fit(self.data_coords,self.data_values) + self.gpr.fit(self.data_coords, self.data_values) # extract noise level - try: + if "k1__noise_level" in self.gpr.kernel_.get_params(): self.noise_level = self.gpr.kernel_.get_params()['k1__noise_level'] - except: - self.noise_level = None # extracts length scales and transforms to scales of fluctuation - try: + if "k2__length_scale" in self.gpr.kernel_.get_params(): gpr_length_scales = self.gpr.kernel_.get_params()['k2__length_scale'] if 'RBF' in str(self.gpr.kernel): self.vertical_scale_fluctuation = gpr_length_scales[self.v_dim] * np.pi / 2. elif 'Matern' in str(self.gpr.kernel): - self.vertical_scale_fluctuation = gpr_length_scales[self.v_dim] * np.sqrt(1/2) + self.vertical_scale_fluctuation = gpr_length_scales[self.v_dim] * np.sqrt(1 / 2) mask = np.arange(len(gpr_length_scales)) != self.v_dim - self.anisotropy = list(gpr_length_scales[mask] / gpr_length_scales[self.v_dim]) - except: - gpr_length_scales = None - self.vertical_scale_fluctuation = None - self.anisotropy = None + self.anisotropy = gpr_length_scales[mask] / gpr_length_scales[self.v_dim] class ElasticityFieldsFromCpt(): """Class for the automatic generation of fields of elastic parameters (vs, rho, g0 and e) Attr: - - poisson_ratio (float): poisson ratio. + - poisson_ratio (float): poisson ratio. - cpt_file_folder (str): path of the folder containing the CPT files (.gef) - x_ref (float): physical x-coordinate of the origin of the model - y_ref (float): physical y-coordinate of the origin of the model - orientation_x_axis (float): orientation of the x-axis of the model (azimuth, clockwise g=from the North) - - based_on_midpoint (str): If True, ignores x_ref and y_ref and places origin of the model in the center of the set of CPT locations. - - max_conditioning_points (int): Maximum number of data points to use in the calibration of the geostatistical model. - - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `g0` - - - conditioning_data (class:`CPT_data`): instance of the `class:CPT_data` class. - - coord_calibration (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the calibration of the geostatistical model. - - thinning_sample_index (npt.NDArray[int]): Indices of the sample from the conditioning data, selected for the calibration. - - trans_model_rho (class:`MarginalTransformation`): Transformation model of the marginal distribution of rho. - - trans_model_vs (class:`MarginalTransformation`): Transformation model of the marginal distribution of shear wave velocity vs. + - based_on_midpoint (str): If True, ignores x_ref and y_ref and places origin of the model in + the center of the set of CPT locations. + - max_conditioning_points (int): Maximum number of data points to use in the calibration of the + geostatistical model. + - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. + Options are `young_modulus`, `rho`, `vs`, `g0` + + - conditioning_data (class:`CPT_data`): instance of the `class:CPT_data` class. + - coord_calibration (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the calibration + of the geostatistical model. + - thinning_sample_index (npt.NDArray[int]): Indices of the sample from the conditioning data, selected + for the calibration. + - trans_model_rho (class:`MarginalTransformation`): Transformation model of the marginal distribution + of rho. + - trans_model_vs (class:`MarginalTransformation`): Transformation model of the marginal distribution of + shear wave velocity vs. - geostat_model (class:`GeostatisticalModel`): Geostatistical model - random_fields_vs (class:`RandomFields`): Random field generator for shear wave velocity `vs` - random_fields_rho (class:`RandomFields`): Random field generator for shear wave velocity `rho` - vs (npt.NDArray[np.float64]): generated conditioned random field of shear wave velocity `vs` - rho (npt.NDArray[np.float64]): generated conditioned random field of density `rho` - - g0 (npt.NDArray[np.float64]): generated conditioned random field of shear modulus `g0 = rho * vs**2` - - young_modulus (npt.NDArray[np.float64]): generated conditioned random field of elastic modulus `young_modulus = 2 * g0 * (1. + poisson_ratio)` + - g0 (npt.NDArray[np.float64]): generated conditioned random field of shear modulus `g0 = rho * vs**2` + - young_modulus (npt.NDArray[np.float64]): generated conditioned random field of elastic modulus + `young_modulus = 2 * g0 * (1. + poisson_ratio)` - generated_field (list): list of randopm field values corresponding to parameter `return_property` + """ - - """ - - - def __init__(self, - cpt_file_folder:str = './', - x_ref:float = 0., - y_ref:float = 0., - orientation_x_axis:float = 0., - based_on_midpoint:bool = False, - max_conditioning_points:int = 2000, - return_property:str = 'young_modulus'): + cpt_file_folder: str = './', + x_ref: float = 0., + y_ref: float = 0., + orientation_x_axis: float = 0., + based_on_midpoint: bool = False, + max_conditioning_points: int = 2000, + return_property: str = 'young_modulus'): """initiation Args: - - cpt_file_folder (str, optional): path to tyhe folder containing the CPT files (.gef) from which the . Defaults to './'. + - cpt_file_folder (str, optional): path to tyhe folder containing the CPT files (.gef). - x_ref (float, optional): physical x-coordinate of the origin of the model. Defaults to 0.. - y_ref (float, optional): physical y-coordinate of the origin of the model. Defaults to 0.. - - orientation_x_axis (float, optional): orientation of the x-axis of the model (azimuth, clockwise g=from the North). Defaults to 0.. - - based_on_midpoint (bool, optional): If True, ignores x_ref and y_ref and places origin of the model in the center of the set of CPT locations. Defaults to False. - - max_conditioning_points (int, optional): Maximum number of data points to use in the calibration of the geostatistical model. Defaults to 2000. - - return_property (str, optional): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `g0`. Defaults to `young_modulus`. - """ - self.poisson_ratio = 0.2 + - orientation_x_axis (float, optional): orientation of the x-axis of the model + (azimuth, clockwise g=from the North). Defaults to 0.. + - based_on_midpoint (bool, optional): If True, ignores x_ref and y_ref and places origin of the model + in the center of the set of CPT locations. Defaults to False. + - max_conditioning_points (int, optional): Maximum number of data points to use in the calibration + of the geostatistical model. Defaults to 2000. + - return_property (str, optional): property to return through attribute + `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `g0`. + Defaults to `young_modulus`. + """ + self.poisson_ratio = 0.495 self.cpt_file_folder = cpt_file_folder self.x_ref = x_ref self.y_ref = y_ref @@ -508,15 +548,18 @@ def __init__(self, self.max_conditioning_points = max_conditioning_points self.return_property = return_property - self.conditioning_data = CPT_data(cpt_directory = self.cpt_file_folder) + self.conditioning_data = CPT_data(cpt_directory=self.cpt_file_folder) self.conditioning_data.read_cpt_data() self.conditioning_data.interpret_cpt_data() - self.conditioning_data.data_coordinate_change(orientation_x_axis = self.orientation_x_axis,based_on_midpoint = self.based_on_midpoint) + self.conditioning_data.data_coordinate_change(orientation_x_axis=self.orientation_x_axis, + based_on_midpoint=self.based_on_midpoint) - self.thinning_sample_index = np.random.choice(self.conditioning_data.data_coords.shape[0],size = self.max_conditioning_points,replace = False) + self.thinning_sample_index = np.random.choice(self.conditioning_data.data_coords.shape[0], + size=self.max_conditioning_points, + replace=False) self.trans_model_rho = MarginalTransformation(self.conditioning_data.rho) - self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs,lognormal = True) + self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs) self.geostat_model = None self.random_fields_vs = None self.random_fields_rho = None @@ -526,73 +569,72 @@ def __init__(self, self.young_modulus = None self.generated_field = None - def calibrate_geostat_model(self, - v_dim:int = 1, - calibration_indices:tuple = (0,1), - seed:int = 13): + def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: tuple = (0, 1), seed: int = 13): """calibrates the geostatistical model - + Args: v_dim (int, optional): vertical dimension. Defaults to 1. calibration_indices (Tuple, optional): _description_. Defaults to (0,1). seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 13. """ - # + # ndim_calibrate = len(calibration_indices) - self.coord_calibration = np.zeros([self.max_conditioning_points,3]) + self.coord_calibration = np.zeros([self.max_conditioning_points, 3]) for i in calibration_indices: - self.coord_calibration[:,i] = self.conditioning_data.data_coords[self.thinning_sample_index,i] - - # calibrate geostatistical model based on standard-normal equivalent values of the shear wave velocity data - self.geostat_model = GeostatisticalModel(nb_dimensions = ndim_calibrate, v_dim = v_dim) - self.geostat_model.calibrate(self.coord_calibration[:,calibration_indices],self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.thinning_sample_index])) - - # initiate two independent random field generators for shear wave velocity `vs` and density `rho` - self.random_fields_vs = RandomFields(model_name = ModelName.Gaussian, - n_dim = 3, - mean = 0, - variance = 1, - v_scale_fluctuation = self.geostat_model.vertical_scale_fluctuation, - anisotropy = (self.geostat_model.anisotropy*2)[:2], - angle = [0]*2, - seed = seed, - max_conditioning_points=self.max_conditioning_points) - self.random_fields_vs.set_conditioning_points(points = self.coord_calibration, - values = self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.thinning_sample_index]), - noise_level = self.geostat_model.noise_level) - self.random_fields_rho = RandomFields(model_name = ModelName.Gaussian, - n_dim = 3, - mean = 0, - variance = 1, - v_scale_fluctuation = self.geostat_model.vertical_scale_fluctuation, - anisotropy = (self.geostat_model.anisotropy*2)[:2], - angle = [0]*2, - seed = seed+1, - max_conditioning_points=self.max_conditioning_points) - self.random_fields_rho.set_conditioning_points(points = self.coord_calibration, - values = self.trans_model_rho.x_to_z(self.conditioning_data.rho[self.thinning_sample_index]), - noise_level = self.geostat_model.noise_level) - - - def generate(self,coordinates): + self.coord_calibration[:, i] = self.conditioning_data.data_coords[self.thinning_sample_index, i] + + # calibrate geostatistical model based on standard-normal equivalent values of the shear wave velocity data + self.geostat_model = GeostatisticalModel(nb_dimensions=ndim_calibrate, v_dim=v_dim) + self.geostat_model.calibrate(self.coord_calibration[:, calibration_indices], + self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.thinning_sample_index])) + + # initiate two independent random field generators for shear wave velocity `vs` and density `rho` + self.random_fields_vs = RandomFields(model_name=ModelName.Gaussian, + n_dim=3, + mean=0, + variance=1, + v_scale_fluctuation=self.geostat_model.vertical_scale_fluctuation, + anisotropy=(self.geostat_model.anisotropy * 2)[:2], + angle=[0] * 2, + seed=seed, + max_conditioning_points=self.max_conditioning_points) + self.random_fields_vs.set_conditioning_points(points=self.coord_calibration, + values=self.trans_model_vs.x_to_z( + self.conditioning_data.vs[self.thinning_sample_index]), + noise_level=self.geostat_model.noise_level) + self.random_fields_rho = RandomFields(model_name=ModelName.Gaussian, + n_dim=3, + mean=0, + variance=1, + v_scale_fluctuation=self.geostat_model.vertical_scale_fluctuation, + anisotropy=(self.geostat_model.anisotropy * 2)[:2], + angle=[0] * 2, + seed=seed + 1, + max_conditioning_points=self.max_conditioning_points) + self.random_fields_rho.set_conditioning_points(points=self.coord_calibration, + values=self.trans_model_rho.x_to_z( + self.conditioning_data.rho[self.thinning_sample_index]), + noise_level=self.geostat_model.noise_level) + + def generate(self, coordinates): """generate random fields for `rho`, `vs` and derive fields `g0` and `young_modulus`. Args: coordinates (numpy.typing.NDArray[np.float64]): 3D coordinated of points to generate random field values for - """ + """ - # generate conditioned standard-normal equivalent - self.random_fields_vs.generate_conditioned(nodes = coordinates) - self.random_fields_rho.generate_conditioned(nodes = coordinates) + # generate conditioned standard-normal equivalent + self.random_fields_vs.generate_conditioned(nodes=coordinates) + self.random_fields_rho.generate_conditioned(nodes=coordinates) - # transform standard-normal fields to physical (marginal) distributions: + # transform standard-normal fields to physical (marginal) distributions: self.vs = self.trans_model_vs.z_to_x(self.random_fields_vs.conditioned_random_field) self.rho = self.trans_model_rho.z_to_x(self.random_fields_rho.conditioned_random_field) # derive dependent fields self.g0 = self.rho * self.vs**2 - self.young_modulus = 2*self.g0*(1. + self.poisson_ratio) + self.young_modulus = 2 * self.g0 * (1. + self.poisson_ratio) # only one parameter is communicated back as the generated field to STEM / KRATOS if self.return_property.lower() == 'young_modulus': @@ -600,6 +642,6 @@ def generate(self,coordinates): elif self.return_property.lower() == 'rho': self.generated_field = list(self.rho) elif self.return_property.lower() == 'g0': - self.generated_field = list(self.g0) + self.generated_field = list(self.g0) elif self.return_property.lower() == 'vs': self.generated_field = list(self.vs) diff --git a/tests/data/DelftSchiedam_cpts/locatie_levering.kml b/tests/data/DelftSchiedam_cpts/locatie_levering.kml deleted file mode 100644 index fdc81d5..0000000 --- a/tests/data/DelftSchiedam_cpts/locatie_levering.kml +++ /dev/null @@ -1,117 +0,0 @@ - - - - Bestelling - true - - - - BRO_GeotechnischSondeeronderzoek - true - - CPT000000217408.xml - #place_mark_circle_highlight - - true - relativeToGround - 4.379426638,51.963590388 - - - - CPT000000217408_IMBRO.gef - #place_mark_circle_highlight - - true - relativeToGround - 4.379426638,51.963590388 - - - - CPT000000217402.xml - #place_mark_circle_highlight - - true - relativeToGround - 4.379010355,51.964487234 - - - - CPT000000217402_IMBRO.gef - #place_mark_circle_highlight - - true - relativeToGround - 4.379010355,51.964487234 - - - - CPT000000217409.xml - #place_mark_circle_highlight - - true - relativeToGround - 4.37994462,51.962745153 - - - - CPT000000217409_IMBRO.gef - #place_mark_circle_highlight - - true - relativeToGround - 4.37994462,51.962745153 - - - - CPT000000217393.xml - #place_mark_circle_highlight - - true - relativeToGround - 4.380341466,51.961880998 - - - - CPT000000217393_IMBRO.gef - #place_mark_circle_highlight - - true - relativeToGround - 4.380341466,51.961880998 - - - - CPT000000217406.xml - #place_mark_circle_highlight - - true - relativeToGround - 4.380857047,51.961036002 - - - - CPT000000217406_IMBRO.gef - #place_mark_circle_highlight - - true - relativeToGround - 4.380857047,51.961036002 - - - - - From 21da9066f694e8a1d8cc746a07970e4a3b8d6dec Mon Sep 17 00:00:00 2001 From: brunozc Date: Mon, 13 Jan 2025 10:47:13 +0100 Subject: [PATCH 32/62] Sort file list in CPT directory and update default seed value for geostatistical model calibration --- random_fields/geostatistical_cpt_interpretation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 46e91d2..3f2c120 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -187,7 +187,7 @@ def read_cpt_data(self, addition: bool=False) -> None: self.cpt_list = [] self.cpt_filepaths = [] - list_of_files = os.listdir(self.cpt_directory) + list_of_files = sorted(os.listdir(self.cpt_directory)) files_type = {'.gef': GefCpt, '.xml': BroXmlCpt} @@ -569,13 +569,13 @@ def __init__(self, self.young_modulus = None self.generated_field = None - def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: tuple = (0, 1), seed: int = 13): + def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: tuple = (0, 1), seed: int = 14): """calibrates the geostatistical model Args: v_dim (int, optional): vertical dimension. Defaults to 1. calibration_indices (Tuple, optional): _description_. Defaults to (0,1). - seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 13. + seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 14. """ # From 47d5c2b017ba91b37d97eea2b3f73da1abbfcf31 Mon Sep 17 00:00:00 2001 From: brunozc Date: Mon, 13 Jan 2025 13:45:53 +0100 Subject: [PATCH 33/62] Update dependencies and enhance documentation for CPT-based random field generation --- docs/_static/2D_cpt_conditioned_field.png | Bin 0 -> 69151 bytes docs/_static/coordinates_1.png | Bin 0 -> 20963 bytes docs/_static/coordinates_2.png | Bin 0 -> 19608 bytes docs/_static/marginal_distribution.png | Bin 0 -> 36433 bytes docs/tutorial_1.rst | 4 +- docs/tutorial_2.rst | 4 +- docs/tutorial_3.rst | 4 +- docs/tutorial_4.rst | 169 +++++++++++++++++- random_fields/generate_field.py | 5 +- .../geostatistical_cpt_interpretation.py | 79 ++++---- requirements.txt | 2 +- requirements_dev.txt | 2 +- setup.cfg | 2 +- .../cpts}/gef/CPT000000217393_IMBRO.gef | 0 .../cpts}/gef/CPT000000217402_IMBRO.gef | 0 .../cpts}/gef/CPT000000217406_IMBRO.gef | 0 .../cpts}/gef/CPT000000217408_IMBRO.gef | 0 .../cpts}/gef/CPT000000217409_IMBRO.gef | 0 {cpts => tests/cpts}/xml/CPT000000217393.xml | 0 {cpts => tests/cpts}/xml/CPT000000217402.xml | 0 {cpts => tests/cpts}/xml/CPT000000217406.xml | 0 {cpts => tests/cpts}/xml/CPT000000217408.xml | 0 {cpts => tests/cpts}/xml/CPT000000217409.xml | 0 23 files changed, 222 insertions(+), 49 deletions(-) create mode 100644 docs/_static/2D_cpt_conditioned_field.png create mode 100644 docs/_static/coordinates_1.png create mode 100644 docs/_static/coordinates_2.png create mode 100644 docs/_static/marginal_distribution.png rename {cpts => tests/cpts}/gef/CPT000000217393_IMBRO.gef (100%) rename {cpts => tests/cpts}/gef/CPT000000217402_IMBRO.gef (100%) rename {cpts => tests/cpts}/gef/CPT000000217406_IMBRO.gef (100%) rename {cpts => tests/cpts}/gef/CPT000000217408_IMBRO.gef (100%) rename {cpts => tests/cpts}/gef/CPT000000217409_IMBRO.gef (100%) rename {cpts => tests/cpts}/xml/CPT000000217393.xml (100%) rename {cpts => tests/cpts}/xml/CPT000000217402.xml (100%) rename {cpts => 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z7ZtHjkwC$~U=~HYwr_{=_`dx9<;>V>W3x^`K!8H6%8QiZ&ub(PNi{;o$!FQ>t*fQQ z3L$X$ZhQNoTaJjfG_xvDM_N-|gH+vSO zgYdHWaE2|;vz;t?cHOFn`kb_aa$cYHZ(1_Pbu)c>G|Ve5Znd@+LEgZN*48T^*HquP zRcvTE6B^%8?Ca;p%&@SuK*Jr6xHBz&ZS~a5^7MZ&wA9p62%&Y{)Xg?VymAbJDeau3ujPXW)SuxGRe9#`UvCNC>Ya0}U@tTwUm^BrYjAJ`^_5?s@4_ZCl%b z-Da4KY~B==qNDo!u6z#Wa&!VdAYTKctGk;l(o@};;4<&+>wAQ%oW>-g8rYg#*8!;} zHKFCMs6@Dwl$8Sl1DTXTY01;4)AEkDUB>Fc zwOb=Q;xN=S;ov&be_knmo3ryK=s87QXYcNemO8GJ;KCw;<~Dz3}|;LQ`_ +The CPT data is read from a folder containing the CPT files. The CPT data can be in 'gef' or 'xml' format. +The CPTs used on this example are in 'gef' format, and can be found +`here `_. + +.. code-block:: python + + path_cpts = "./cpt_data" + cpt_folder = Path(path_cpts) + cpt_data = CPT_data(cpt_directory=cpt_folder) + cpt_data.read_cpt_data() + cpt_data.interpret_cpt_data() + +The location of the CPTs can be visualised and its coordinated can be changed/rotated using the following code: + +.. code-block:: python + + # visualise + plt.title('Original coordinates') + cpt_data.plot_coordinates(show=True) + + # transform the data to a modelling domain + cpt_data.data_coordinate_change(orientation_x_axis=73, based_on_midpoint=True) + + plt.title('Model coordinates') + cpt_data.plot_coordinates(show=True) + +.. image:: _static/coordinates_1.png + :scale: 50% +.. image:: _static/coordinates_2.png + :scale: 50% + +To create the transformation model, the marginal transformation is created. +The marginal transformation model can be generated and visualised as follows: + +.. code-block:: python + + marginal_transformator = MarginalTransformation(cpt_data.vs, min_value=50) + marginal_transformator.plot(x_label='$u$ : standard-normal variable', y_label='$v$ : shear wave velocity [m/s]', + show=True) + + +.. image:: _static/marginal_distribution.png + +Next, the data for the calibration of the geostatistical model can be selected. +To allow a faster calibration of the geostatistical model, only a selection of the data is used. +This selection of 2000 pooints is made randomly. +The data is transformed to standard-normal data using the transformator and only the `z` (horizontal) +and `y` (vertical) coordinates are selected. +Calibration is based on likelihood maximisation. + +.. code-block:: python + + index_selection = np.random.choice(len(cpt_data.vs), size=2000, replace=False) + coords = cpt_data.data_coords[index_selection] + z_data = marginal_transformator.x_to_z(x=cpt_data.vs[index_selection]) + + geo_model = GeostatisticalModel(nb_dimensions=2, v_dim=1) + geo_model.calibrate(coords=coords[:, [2, 1]], values=z_data) + + +Next, the random field properties are transferred to the random field generator. +This generator needs to be initiated with the same spatial correlation model as used for the calibration +(default=Gaussian). In addition, conditioning points are required to generate meaningful conditioned random fields. +These points can, but don't need to be, the same points as used for the calibration. +Also, conditional simulation can account for the noise in the calibration and conditioning data. +This noise is not included in the field itself, but instead allows for a small deviation of the generated random fields +from the conditioning points: + +.. code-block:: python + + random_field_generator = RandomFields(model_name=ModelName.Gaussian, + n_dim=2, + mean=0, + variance=1, + v_scale_fluctuation=geo_model.vertical_scale_fluctuation, + anisotropy=geo_model.anisotropy, + angle=[0], + seed=seed) + + I = np.random.choice(len(cpt_data.vs), size=500, replace=False) + + coords = cpt_data.data_coords[I] + values = cpt_data.vs[I] + random_field_generator.set_conditioning_points(points=coords[:, [2, 1]], + values=marginal_transformator.x_to_z(x=values), + noise_level=geo_model.noise_level) + +To generate the Random Field a coordinate grid needs to be specified. + +.. code-block:: python + + # create grid of points on the domnain (-220, 220) by (-24, -1) + x = np.linspace(-220, 220, 250) + z = np.linspace(-24, -1, 250) + X,Z = np.meshgrid(x, z) + + # generate a conditioned random field + sample_coords = np.array([X.ravel(), Z.ravel()]).T + random_field_generator.generate_conditioned(nodes=sample_coords) + +The conditioned random field that is generated contains values at the prediction points (the regular grid) as well +as on the conditioning point coordinates. In the generated array, the values generated at the conditioning point +coordinates are at the end. They are generally not needed as part of the generated random field and can be left out. +The generated standard-normal field needs to be transformed to the marginal distribution of the physical variable +(in this case the shear wave velocity) by the marginal transformation. + +.. code-block:: python + + # Transform the generated standard-normal field to the distribution of the shear wave velocity + z_map = random_field_generator.conditioned_random_field + vs_map = marginal_transformator.z_to_x(z_map[:250 * 250].reshape([250, 250])) + + fig, ax = plt.subplots(figsize=(10, 5)) + plt.contourf(X, Z, vs_map, vmin=50, vmax=400) + scatter = plt.scatter(cpt_data.data_coords[:, 2], cpt_data.data_coords[:, 1], c=cpt_data.vs, vmin=50, vmax=400) + plt.colorbar(scatter, ax=ax, label="Vs [m/s]") + plt.savefig(file_name) + plt.close() + +This results in a 2D cross section of the generated random field, on the shear wave velocity, +conditioned on the CPT data. + +.. image:: _static/2D_cpt_conditioned_field.png diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index dca891a..4c939bf 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -18,13 +18,10 @@ class ModelName(Enum): Linear = gs.Linear -class RandomFields(): +class RandomFields: """ Generates (conditioned) random fields - Inheritance: - - None - Attributes: - max_conditioning_points (int): maximum number of - random_field_model_name (str): diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 3f2c120..0425ea5 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -74,17 +74,23 @@ def __init__(self, self._interp_log_data = np.interp(self._interp_z, z_points, x_points_ln) def plot(self, - title: str="Marginal distribution transformation ", + title: str="Marginal distribution transformation", + output_folder: Path=Path("./"), + output_name: str="marginal_distribution.png", x_label: str=r'$Z\sim N(0, 1)$', y_label: str=r'$X$', - figsize: Tuple[float, float]=(6, 5)): + figsize: Tuple[float, float]=(6, 5), + show: bool=False) -> None: r"""Plots the transformation model for the marginal distribution. Args: - title (str): plot title. Defaults to "Marginal distribution transformation ". - x_label (str): horizontal axis label. Defaults to '\sim N(0,1)$'. - y_label (str): vertical axis label. Defaults to ' $'. - figsize (Tuple): figure size. Defaults to (6.,5.). + - title (str): plot title. Defaults to "Marginal distribution transformation ". + - output_folder (Path): output folder. Defaults to Path("./"). + - output_name (str): output file name. Defaults to "marginal_distribution.png". + - x_label (str): horizontal axis label. Defaults to '\sim N(0,1)$'. + - y_label (str): vertical axis label. Defaults to '$X$'. + - figsize (Tuple): figure size. Defaults to (6, 5). + - show (bool): If True, shows the plot. Defaults to False. """ plt.figure(figsize=figsize) plt.plot(self.z, self.data, '.', color='gray', alpha=0.5, label='data') @@ -94,7 +100,9 @@ def plot(self, plt.legend() plt.title(title) plt.grid() - plt.savefig("aaa.png") + plt.savefig(os.path.join(output_folder, output_name)) + if show: + plt.show() plt.close() def x_to_z(self, x: npt.NDArray[np.float64]): @@ -147,7 +155,7 @@ class CPT_data: - vs (npt.NDArray(np.float64)): interpreted shear wave velocity at CPT coordinates in `data_coord` - G0 (npt.NDArray(np.float64)): interpreted dynamic shear modulus G0 at CPT coordinates in `data_coord` - young_modulus (npt.NDArray(np.float64)): interpreted Young modulus at CPT coordinates in `data_coord` - - poisson (npt.NDArray(np.float64)): interpreted Poisson ratio at CPT coordinates in `data_coord` + - poisson (float): interpreted Poisson ratio at CPT coordinates in `data_coord` """ def __init__(self, cpt_directory: Path) -> None: @@ -173,7 +181,7 @@ def __init__(self, cpt_directory: Path) -> None: self.vs = np.empty(0) self.G0 = np.empty(0) self.young_modulus = np.empty(0) - self.poisson = np.empty(0) + self.poisson = 0.495 # ToDo: add Poisson ratio see #6 def read_cpt_data(self, addition: bool=False) -> None: """read_cpt_data Reads in and pre-processes the CPT data @@ -247,7 +255,6 @@ def interpret_cpt_data(self, interpreter: Optional[RobertsonCptInterpretation]=N self.data_coords = np.vstack([self.data_coords, coords]) self.rho = np.hstack([self.rho, cpt.rho]) self.vs = np.hstack([self.vs, cpt.vs]) - self.poisson = 0.495 # ToDo: add Poisson ratio see #6 self.G0 = self.rho * self.vs**2 self.young_modulus = 2. * self.G0 * (1. + self.poisson) @@ -478,8 +485,8 @@ def calibrate(self, coords: npt.NDArray[np.float64], values: npt.NDArray[np.floa mask = np.arange(len(gpr_length_scales)) != self.v_dim self.anisotropy = gpr_length_scales[mask] / gpr_length_scales[self.v_dim] -class ElasticityFieldsFromCpt(): - """Class for the automatic generation of fields of elastic parameters (vs, rho, g0 and e) +class ElasticityFieldsFromCpt: + """Class for the automatic generation of fields of elastic parameters (vs, rho, G0 and E) Attr: - poisson_ratio (float): poisson ratio. @@ -492,7 +499,7 @@ class ElasticityFieldsFromCpt(): - max_conditioning_points (int): Maximum number of data points to use in the calibration of the geostatistical model. - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. - Options are `young_modulus`, `rho`, `vs`, `g0` + Options are `young_modulus`, `rho`, `vs`, `G0` - conditioning_data (class:`CPT_data`): instance of the `class:CPT_data` class. - coord_calibration (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the calibration @@ -516,14 +523,15 @@ class ElasticityFieldsFromCpt(): """ def __init__(self, - cpt_file_folder: str = './', - x_ref: float = 0., - y_ref: float = 0., - orientation_x_axis: float = 0., - based_on_midpoint: bool = False, - max_conditioning_points: int = 2000, - return_property: str = 'young_modulus'): - """initiation + cpt_file_folder: str='./', + x_ref: float=0., + y_ref: float=0., + orientation_x_axis: float=0., + based_on_midpoint: bool=False, + max_conditioning_points: int=2000, + return_property: str='young_modulus') -> None: + """ + initiation of the class Args: - cpt_file_folder (str, optional): path to tyhe folder containing the CPT files (.gef). @@ -536,7 +544,7 @@ def __init__(self, - max_conditioning_points (int, optional): Maximum number of data points to use in the calibration of the geostatistical model. Defaults to 2000. - return_property (str, optional): property to return through attribute - `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `g0`. + `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `G0`. Defaults to `young_modulus`. """ self.poisson_ratio = 0.495 @@ -565,20 +573,21 @@ def __init__(self, self.random_fields_rho = None self.vs = None self.rho = None - self.g0 = None + self.GO = None self.young_modulus = None self.generated_field = None - def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: tuple = (0, 1), seed: int = 14): + def calibrate_geostat_model(self, v_dim: int=1, calibration_indices: tuple=(0, 1), seed: int=14) -> None: """calibrates the geostatistical model Args: - v_dim (int, optional): vertical dimension. Defaults to 1. - calibration_indices (Tuple, optional): _description_. Defaults to (0,1). - seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 14. + - v_dim (int, optional): vertical dimension. Defaults to 1. + - calibration_indices (Tuple, optional): _description_. Defaults to (0,1). + - seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 14. """ - # + np.random.seed(seed) + ndim_calibrate = len(calibration_indices) self.coord_calibration = np.zeros([self.max_conditioning_points, 3]) for i in calibration_indices: @@ -617,11 +626,11 @@ def calibrate_geostat_model(self, v_dim: int = 1, calibration_indices: tuple = ( self.conditioning_data.rho[self.thinning_sample_index]), noise_level=self.geostat_model.noise_level) - def generate(self, coordinates): - """generate random fields for `rho`, `vs` and derive fields `g0` and `young_modulus`. + def generate(self, coordinates: npt.NDArray[np.float64]) -> None: + """generate random fields for `rho`, `vs` and derive fields `G0` and `young_modulus`. Args: - coordinates (numpy.typing.NDArray[np.float64]): 3D coordinated of points to generate random field values for + - coordinates (npt.NDArray[np.float64]): 3D coordinated of points to generate random field values for """ # generate conditioned standard-normal equivalent @@ -633,15 +642,15 @@ def generate(self, coordinates): self.rho = self.trans_model_rho.z_to_x(self.random_fields_rho.conditioned_random_field) # derive dependent fields - self.g0 = self.rho * self.vs**2 - self.young_modulus = 2 * self.g0 * (1. + self.poisson_ratio) + self.GO = self.rho * self.vs**2 + self.young_modulus = 2 * self.GO * (1. + self.poisson_ratio) - # only one parameter is communicated back as the generated field to STEM / KRATOS + # only one parameter is communicated back as the generated field to STEM if self.return_property.lower() == 'young_modulus': self.generated_field = list(self.young_modulus) elif self.return_property.lower() == 'rho': self.generated_field = list(self.rho) elif self.return_property.lower() == 'g0': - self.generated_field = list(self.g0) + self.generated_field = list(self.GO) elif self.return_property.lower() == 'vs': self.generated_field = list(self.vs) diff --git a/requirements.txt b/requirements.txt index f1ba7e8..e8071f1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,4 @@ matplotlib==3.9.3 scipy==1.15.0 gstools==1.6.0 scikit-learn==1.6.0 -CPTtool==2.2.2 +d-geolib_plus=0.4.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index 4d5acc9..2193658 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -10,4 +10,4 @@ yapf==0.43.0 tox==4.23.2 flake8==7.0.0 pre-commit==3.6.2 -CPTtool==2.2.2 \ No newline at end of file +d-geolib_plus=0.4.1 diff --git a/setup.cfg b/setup.cfg index 67e0193..8ff02b2 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,7 +19,7 @@ install_requires = matplotlib>=3.9.3 gstools>=1.6.0 scikit-learn>=1.6.0 - CPTtool==2.2.2 + d-geolib_plus=0.4.1 python_requires = >=3.9 [options.extras_require] diff --git a/cpts/gef/CPT000000217393_IMBRO.gef b/tests/cpts/gef/CPT000000217393_IMBRO.gef similarity index 100% rename from cpts/gef/CPT000000217393_IMBRO.gef rename to tests/cpts/gef/CPT000000217393_IMBRO.gef diff --git a/cpts/gef/CPT000000217402_IMBRO.gef b/tests/cpts/gef/CPT000000217402_IMBRO.gef similarity index 100% rename from cpts/gef/CPT000000217402_IMBRO.gef rename to tests/cpts/gef/CPT000000217402_IMBRO.gef diff --git a/cpts/gef/CPT000000217406_IMBRO.gef b/tests/cpts/gef/CPT000000217406_IMBRO.gef similarity index 100% rename from cpts/gef/CPT000000217406_IMBRO.gef rename to tests/cpts/gef/CPT000000217406_IMBRO.gef diff --git a/cpts/gef/CPT000000217408_IMBRO.gef b/tests/cpts/gef/CPT000000217408_IMBRO.gef similarity index 100% rename from cpts/gef/CPT000000217408_IMBRO.gef rename to tests/cpts/gef/CPT000000217408_IMBRO.gef diff --git a/cpts/gef/CPT000000217409_IMBRO.gef b/tests/cpts/gef/CPT000000217409_IMBRO.gef similarity index 100% rename from cpts/gef/CPT000000217409_IMBRO.gef rename to tests/cpts/gef/CPT000000217409_IMBRO.gef diff --git a/cpts/xml/CPT000000217393.xml b/tests/cpts/xml/CPT000000217393.xml similarity index 100% rename from cpts/xml/CPT000000217393.xml rename to tests/cpts/xml/CPT000000217393.xml diff --git a/cpts/xml/CPT000000217402.xml b/tests/cpts/xml/CPT000000217402.xml similarity index 100% rename from cpts/xml/CPT000000217402.xml rename to tests/cpts/xml/CPT000000217402.xml diff --git a/cpts/xml/CPT000000217406.xml b/tests/cpts/xml/CPT000000217406.xml similarity index 100% rename from cpts/xml/CPT000000217406.xml rename to tests/cpts/xml/CPT000000217406.xml diff --git a/cpts/xml/CPT000000217408.xml b/tests/cpts/xml/CPT000000217408.xml similarity index 100% rename from cpts/xml/CPT000000217408.xml rename to tests/cpts/xml/CPT000000217408.xml diff --git a/cpts/xml/CPT000000217409.xml b/tests/cpts/xml/CPT000000217409.xml similarity index 100% rename from cpts/xml/CPT000000217409.xml rename to tests/cpts/xml/CPT000000217409.xml From da38c12856b6ccff87e676d4c9c8784471d10de8 Mon Sep 17 00:00:00 2001 From: brunozc Date: Mon, 13 Jan 2025 19:29:07 +0100 Subject: [PATCH 34/62] Fix package naming for d-geolib-plus in requirements files --- requirements.txt | 2 +- requirements_dev.txt | 2 +- setup.cfg | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index e8071f1..aaea346 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,4 @@ matplotlib==3.9.3 scipy==1.15.0 gstools==1.6.0 scikit-learn==1.6.0 -d-geolib_plus=0.4.1 +d-geolib-plus==0.4.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index 2193658..4b911b8 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -10,4 +10,4 @@ yapf==0.43.0 tox==4.23.2 flake8==7.0.0 pre-commit==3.6.2 -d-geolib_plus=0.4.1 +d-geolib-plus==0.4.1 diff --git a/setup.cfg b/setup.cfg index 8ff02b2..38bd419 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,7 +19,7 @@ install_requires = matplotlib>=3.9.3 gstools>=1.6.0 scikit-learn>=1.6.0 - d-geolib_plus=0.4.1 + d-geolib-plus==0.4.1 python_requires = >=3.9 [options.extras_require] From 93dd70c4c874d801d6f422982695cd0045cddf8b Mon Sep 17 00:00:00 2001 From: brunozc Date: Mon, 13 Jan 2025 19:47:04 +0100 Subject: [PATCH 35/62] Add unit tests for conditional 2D random field generation using GEF and XML files --- tests/data/2d_rf_gef.pickle | Bin 0 -> 500162 bytes tests/data/2d_rf_xml.pickle | Bin 0 -> 500162 bytes tests/test_conditional_rf.py | 92 +++++++++++++++++++++++++++++++++++ 3 files changed, 92 insertions(+) create mode 100644 tests/data/2d_rf_gef.pickle create mode 100644 tests/data/2d_rf_xml.pickle create mode 100644 tests/test_conditional_rf.py diff --git a/tests/data/2d_rf_gef.pickle b/tests/data/2d_rf_gef.pickle new file mode 100644 index 0000000000000000000000000000000000000000..4fcd3410ccddef1a1fd00016482d27155c45c4d5 GIT binary patch literal 500162 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zo+8`u035Da_=Vuyri8;x%Qai3rvn7T$O4llaMPGLHwOY(qS2s89!3~XDQ3)+aONOq zkq87LLM#XxyC9Nfb*Db63MXfc2Fqgvh=NC;ZM!g)Fb|ZYZ4hE15Ex%>;L_O*NbbtP1B79a=stLyL+2ho{uKB1!Jgt$N@ z?gQv4Cf)W0-l^uE+{mGDR)Oa>^T71i2G(%5li88|$`uO8*?{ceJuoB%fw#0>***3> z0wx+QEv-P12s%t80^tK01ZIf(?~NNfAnmk(>&pTs7zBoj2y)_1T74rdcCV=KaPabA zH=W0=)AKPm0g&WCov~q{-zFp!@Y{D}WMno;O%QGDITgmMQ^+}q4;-|L@E<<61_Y3@ z$uK}B(;h}|!XVa9ttk_Pkuz|&iKibxj>BLe-VB2JddN~fH+ec@Hq*2ZBPl6aHE$_# zPNlMggOW Date: Fri, 28 Jun 2024 17:02:59 +0200 Subject: [PATCH 05/62] added sklearn to the requirements Changes to be committed: modified: tutorials.rst modified: ../requirements.txt --- docs/tutorials.rst | 12 +++++------- requirements.txt | 1 + 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/docs/tutorials.rst b/docs/tutorials.rst index af885e8..23faeae 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -103,6 +103,10 @@ As a by-product of the conditioned random field generator, the kriging mean and Note that only simple kriging is applied for conditioning the random fields. See :ref:`theory` for more details. + + +.. _tutorial2: + Example in 2D ------------- @@ -239,16 +243,10 @@ In the same way, a 3D conditional random field can be created: -.. _tutorial2: - -Tutorial CPT interpretation -=========================== - -TODO .. _tutorial3: Tutorial RandomFields in STEM -============================= +----------------------------- TODO \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 28fa2ce..a879501 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ numpy==1.24.2 gstools==1.5.0 matplotlib>=3.7.0 +sklearn From 78643d3cc4e7748613b490746a7badecad9e6ddd Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 28 Jun 2024 17:09:41 +0200 Subject: [PATCH 06/62] deleted: random_fields/generate_conditioned_field.py --- random_fields/generate_conditioned_field.py | 85 --------------------- 1 file changed, 85 deletions(-) delete mode 100644 random_fields/generate_conditioned_field.py diff --git a/random_fields/generate_conditioned_field.py b/random_fields/generate_conditioned_field.py deleted file mode 100644 index f4f9a02..0000000 --- a/random_fields/generate_conditioned_field.py +++ /dev/null @@ -1,85 +0,0 @@ -from enum import Enum -from typing import List -import numpy as np -import gstools as gs - -from sklearn.gaussian_process import GaussianProcessRegressor as GPR -from sklearn.gaussian_process.kernels import RBF, \ - ConstantKernel as C, \ - WhiteKernel as W, Matern - - -class ModelName(Enum): - """ - Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" - """ - Gaussian = gs.Gaussian - Exponential = gs.Exponential - Matern = gs.Matern - Linear = gs.Linear - - -class ConditionedRandomFields: - """ - Generates standard normal conditioned random fields - """ - - def __init__(self, model_name: ModelName, n_dim: int, - mean: float, variance: float, - v_scale_fluctuation: float, anisotropy: List[float], angle: List[float], - seed: int = 14, v_dim: int = 1) -> None: - """ - Initialise generation of random fields - - Parameters: - ----------- - model_name: str - Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" - n_dim: int - The dimension of the random field - mean: float - The mean of the random field - variance: float - The variance of the random field - v_scale_fluctuation: float - The vertical scale of fluctuation of the random field - anisotropy: list - The anisotropy of the random field (per dimension) - angle: list - The angle of the random field (per dimension) - seed: int - The seed number for the random number generator - v_dim: int - The dimension of the vertical scale of fluctuation - """ - # initialise model - if model_name.name not in ModelName.__members__: - raise ValueError(f'Model name: {model_name} is not supported') - - if n_dim not in [1, 2, 3]: - raise ValueError('Only 1, 2, 3 dimensions are supported') - - aux_keys = [anisotropy, angle] - for key in aux_keys: - if len(key) != n_dim - 1: - raise ValueError(f'Number of dimensions: {n_dim} does not match number of\ - random field properties: {len(variance)}. It should be {n_dim - 1}.') - - self.random_field_model_name = model_name.name - self.random_field_model = model_name.value - self.n_dim = n_dim - self.seed = seed - self.mean = mean - self.variance = variance - self.vertical_scale_fluctuation = v_scale_fluctuation - self.anisotropy = anisotropy - self.angle = angle - self.v_dim = v_dim - self.random_field = None - - - - - - - From 5fadc51e5c75ef416565b336a1de4525a509b531 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 28 Jun 2024 17:13:07 +0200 Subject: [PATCH 07/62] modified: setup.cfg --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 4970b85..5de7191 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,6 +17,7 @@ install_requires = numpy>=1.24 gstools>=1.5.0 matplotlib>=3.7.1 + sklearn python_requires = >=3.8 [options.extras_require] From 04170dda7e8b6f033575c7e7af96ae7c106b874d Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 28 Jun 2024 17:16:20 +0200 Subject: [PATCH 08/62] modified: setup.cfg (sklearn --> scikit-learn) --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 5de7191..a0756ad 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,7 +17,7 @@ install_requires = numpy>=1.24 gstools>=1.5.0 matplotlib>=3.7.1 - sklearn + scikit-learn python_requires = >=3.8 [options.extras_require] From 3b9df07842a5f2a3167932e279bd37a4937b739f Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden <38314273+bramvandeneijnden@users.noreply.github.com> Date: Thu, 15 Aug 2024 16:32:23 +0200 Subject: [PATCH 09/62] Update docs/installation.rst Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- docs/installation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/installation.rst b/docs/installation.rst index 6a59805..93510c4 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -7,4 +7,4 @@ RandomFields Python package ------------------- -To be copied from STEM \ No newline at end of file +Work in progress \ No newline at end of file From 72e157b39ce99f963746c060d3a07c470ef6f079 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden <38314273+bramvandeneijnden@users.noreply.github.com> Date: Thu, 15 Aug 2024 16:33:52 +0200 Subject: [PATCH 10/62] Update random_fields/generate_field.py Co-authored-by: aronnoordam <51492202+aronnoordam@users.noreply.github.com> --- random_fields/generate_field.py | 1 + 1 file changed, 1 insertion(+) diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index f1b55ce..3b8fcbd 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -1,6 +1,7 @@ from enum import Enum from typing import List import numpy as np +import numpy.typing as npt import gstools as gs From 07128697927af9f083db5ac7693581a92d1e3571 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Tue, 20 Aug 2024 09:45:03 +0200 Subject: [PATCH 11/62] Changes for merge `Conditional random fields` modified: docs/contributions.rst modified: docs/installation.rst modified: random_fields/generate_field.py modified: random_fields/utils.py modified: requirements_dev.txt new file: tests/data/kriging_mean_3D.txt new file: tests/data/kriging_std_3D.txt modified: tests/test_random_field.py --- docs/contributions.rst | 20 +- docs/installation.rst | 4 +- random_fields/generate_field.py | 208 +++-- random_fields/utils.py | 65 +- requirements_dev.txt | 1 + tests/data/kriging_mean_3D.txt | 1331 +++++++++++++++++++++++++++++++ tests/data/kriging_std_3D.txt | 1331 +++++++++++++++++++++++++++++++ tests/test_random_field.py | 60 +- 8 files changed, 2900 insertions(+), 120 deletions(-) create mode 100644 tests/data/kriging_mean_3D.txt create mode 100644 tests/data/kriging_std_3D.txt diff --git a/docs/contributions.rst b/docs/contributions.rst index 330a183..c8d5f8d 100644 --- a/docs/contributions.rst +++ b/docs/contributions.rst @@ -48,23 +48,25 @@ An example of a class: .. code-block:: - class ResidualConvergenceCriteria(ConvergenceCriteriaABC): + class ClassName(object): """ - Class containing information about the residual convergence criteria + Work in progress: docstring style to be specified Inheritance: - - :class:`ConvergenceCriteriaABC` + -:class:`object` Attributes: - - residual_relative_tolerance (float): The relative tolerance for the residual. Default value is 1e-4. - - residual_absolute_tolerance (float): The absolute tolerance for the residual. Default value is 1e-9. + - (): + - (): + + """ - def __init__(self): + def __init__(self,,...): """ - Constructor of the ResidualConvergenceCriteria class. + Constructor of the RandomFields class """ - self.residual_relative_tolerance: float = 1e-4 - self.residual_absolute_tolerance: float = 1e-9 + self.attribute_name_1: = + self.attribute_name_2: = An example of a function: diff --git a/docs/installation.rst b/docs/installation.rst index 93510c4..c9dcb89 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -1,10 +1,10 @@ RandomFields installation ================= -.. _python_stem: +.. _python_random_fields: RandomFields Python package ------------------- -Work in progress \ No newline at end of file +To be copied from STEM \ No newline at end of file diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index 3b8fcbd..9f273a7 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -6,9 +6,8 @@ from sklearn.gaussian_process import GaussianProcessRegressor as GPR -from sklearn.gaussian_process.kernels import RBF, \ - ConstantKernel as C, \ - WhiteKernel as W, Matern +from sklearn.gaussian_process.kernels import RBF, ConstantKernel , \ + WhiteKernel , Matern class ModelName(Enum): @@ -21,38 +20,72 @@ class ModelName(Enum): Linear = gs.Linear -class RandomFields: +class RandomFields(): """ - Generate random fields + Generates (conditioned) random fields + + Inheritance: + - None + + Attributes: + - max_conditioning_points (int): + - random_field_model_name (str): + - random_field_model (int): + - n_dim (int):) + - seed (int): + - mean (float): + - variance (float): + - vertical_scale_fluctuation (list): + - anisotropy (list): + - angle (list): + - v_dim (int): + - random_field (:class:) + - z_kriged_field (array) + - GaussianProcess (:class:) + - conditioning_points (array): + - conditioning_values (array): + - kriging_mean (array): + - kriging_std = (array): + - conditioned_random_field (array): + + """ - def __init__(self, model_name: ModelName, n_dim: int, - mean: float, variance: float, - v_scale_fluctuation: float, anisotropy: List[float], angle: List[float], - seed: int = 14, v_dim: int = 1) -> None: + def __init__(self, + model_name: ModelName, + n_dim: int, + mean: float, + variance: float, + v_scale_fluctuation: float, + anisotropy: List[float], + angle: List[float], + seed: int = 14, + v_dim: int = 1, + max_conditioning_points:int = 2000) -> None: """ Initialise generation of random fields - Parameters: - ----------- - model_name: str - Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" - n_dim: int - The dimension of the random field - mean: float - The mean of the random field - variance: float - The variance of the random field - v_scale_fluctuation: float - The vertical scale of fluctuation of the random field - anisotropy: list - The anisotropy of the random field (per dimension) - angle: list - The angle of the random field (per dimension) - seed: int - The seed number for the random number generator - v_dim: int - The dimension of the vertical scale of fluctuation + Args: + - model_name (str): Name of the model to be used. Options are: "Gaussian", "Exponential", "Matern", "Linear" + - n_dim (int): The dimension of the random field + - mean (float): The mean of the random field + - variance (float): The variance of the random field + - v_scale_fluctuation (float): The vertical scale of fluctuation of the random field + - anisotropy (list): The anisotropy of the random field (per dimension) + - angle (list): The angle of the random field (per dimension) + - seed (int): The seed number for the random number generator + - v_dim (int): The dimension of the vertical scale of fluctuation + - max_conditioning_points (int): Maximum number of points to be used as conditioning points: increase leads to + longer computation times and high memory use due to factorisation of dense matrices + + Raises: + - ValueError: if model_name.name if not a member of class ModelName + - ValueError: if the number of dimensions is not in [1,2,3] + - ValueError: if the number of dimensions is nt consistent with the number of random field properties + + Returns: + - self + """ # initialise model if model_name.name not in ModelName.__members__: @@ -64,9 +97,11 @@ def __init__(self, model_name: ModelName, n_dim: int, aux_keys = [anisotropy, angle] for key in aux_keys: if len(key) != n_dim - 1: - raise ValueError(f'Number of dimensions: {n_dim} does not match number of\ - random field properties: {len(variance)}. It should be {n_dim - 1}.') + raise ValueError(f'Number of dimensions: {n_dim} does not match number of ' + + f'random field properties: {len(key)}. It should be {n_dim - 1}.') + + self.max_conditioning_points = max_conditioning_points self.random_field_model_name = model_name.name self.random_field_model = model_name.value self.n_dim = n_dim @@ -79,15 +114,25 @@ def __init__(self, model_name: ModelName, n_dim: int, self.v_dim = v_dim self.random_field = None self.z_kriged_field = None + self.GaussianProcess = GPR() + self.conditioning_points = None + self.conditioning_values = None + self.kriging_mean = None + self.kriging_std = None + self.conditioned_random_field = None + - def generate(self, nodes: np.ndarray) -> None: + def generate(self, nodes: npt.NDArray[np.float64]) -> None: """ Generate random field - Parameters: - ------------ - nodes: list - The nodes of the random field + Args: + - nodes (list): The nodes of the random field + + Raises: + + Returns: + """ # check dimensions of nodes agrees with dimensions of model if nodes.shape[1] != self.n_dim: @@ -100,35 +145,45 @@ def generate(self, nodes: np.ndarray) -> None: mask = np.arange(len(scale_fluctuation)) != self.v_dim scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy - model = self.random_field_model(dim=self.n_dim, var=self.variance, len_scale=scale_fluctuation, angles=self.angle) - self.random_field = gs.SRF(model, mean=self.mean, seed=self.seed) + model = self.random_field_model(dim = self.n_dim, + var = self.variance, + len_scale = scale_fluctuation, + angles = self.angle) + self.random_field = gs.SRF(model, + mean = self.mean, + seed = self.seed) self.random_field(nodes.T) def set_conditioning_points(self, - points: np.ndarray, - values: np.ndarray, + points: npt.NDArray[np.float64], + values: npt.NDArray[np.float64], noise_level = 0.0001 ) -> None: """ Initiates the conditioning points and inverts the covariance matrix - Parameters: - ----------- - points: array-like - The contitioning point coordinates - values: array-like - The conditioning point values - kernel: sklearn kernel - The (calibrated) correlation kernel from sklearn (default = None) - noise_level: float or array-like - normalised variance of the observed data, as a single float or as - an array of the same dimensions as `values` (default = 0.0001) + Args: + - points (array): The contitioning point coordinates + - values: array-like: The conditioning point values + - kernel: (:class: sklearn.gaussian_process.kernels.Kernel): The (calibrated) correlation + kernel from sklearn (default = None) + - noise_level (float or array): normalised variance of the observed data, as a single float + or as an array of the same dimensions as `values` (default = 0.0001) + Raises: + + Returns: + """ self.noise_level = noise_level - if max(points.shape) > 2000: - print('too many conditioning points!') + + + # check the maximum number of conditioning points to keep the computation and memory cost reasonable + if points.shape[0] > self.max_conditioning_points: + raise Exception(f'Too many conditioning points! There are {max(points.shape)} ' + + f'points, while the maximum allowed amount is {self.max_conditioning_points}.' + + f'\n Consider increasing `max_conditioning_points` or use fewer conditioning points.' ) # scale of fluctuation @@ -142,14 +197,15 @@ def set_conditioning_points(self, # correct the length scales between libraries if self.random_field_model_name == 'Gaussian': ls_sklearn = scale_fluctuation * 2/np.pi - self.KrigingKernel = W(noise_level) + RBF(length_scale = ls_sklearn) + self.KrigingKernel = WhiteKernel(noise_level) + RBF(length_scale = ls_sklearn) elif self.random_field_model_name == 'Exponential': ls_sklearn = scale_fluctuation / np.sqrt(1/2) - self.KrigingKernel = W(noise_level) + Matern(length_scale = ls_sklearn, nu = 0.5) + self.KrigingKernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn, nu = 0.5) elif self.random_field_model_name == 'Matern': ls_sklearn = scale_fluctuation / np.sqrt(1/2) - self.KrigingKernel = W(noise_level) + Matern(length_scale = ls_sklearn) + self.KrigingKernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn) + # fit a GP against conditioning points standardised by simulation field statistics: NOT by conditioning point statistics self.GaussianProcess = GPR(kernel = self.KrigingKernel) self.GaussianProcess.optimizer = None self.GaussianProcess.fit(points,(values - self.mean) / np.sqrt(self.variance) ) @@ -158,17 +214,20 @@ def set_conditioning_points(self, self.kriging_mean = None self.kriging_std = None + self.conditioned_random_field = None - - def generate_conditioned(self, nodes: np.ndarray) -> None: + def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: """ Generate conditioned random field - Parameters: - ------------ - nodes: list - The nodes of the random field + Args: + nodes (list): The nodes of the random field + Raises: + + Returns: + + """ # check dimensions of nodes agrees with dimensions of model if nodes.shape[1] != self.n_dim: @@ -186,25 +245,28 @@ def generate_conditioned(self, nodes: np.ndarray) -> None: nodes_cpoints = np.vstack([nodes,self.conditioning_points]) self.generate(nodes_cpoints) - z_crf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) - z_rf_cpoints = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) + # Split the generated field into the nodal coordinates and the conditioning points + # Standardize the distribution to marginal ~N(0,1) + z_cond_RF_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) + z_RF_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) + # add the noise to the conditioning points np.random.seed(self.seed) - z_rf_cpoints += np.sqrt(self.noise_level) * np.random.normal(size = z_rf_cpoints.shape) + z_RF_cond_points += np.sqrt(self.noise_level) * np.random.normal(size = z_RF_cond_points.shape) # # create kriged mean field of random field - GPrf = GPR(kernel = self.GaussianProcess.kernel_) - GPrf.optimizer = None - GPrf.fit(self.conditioning_points,z_rf_cpoints) - GPrf.L_ = self.GaussianProcess.L_ - z_kriged_nodes_rf = GPrf.predict(nodes) + GP_RF = GPR(kernel = self.GaussianProcess.kernel_) + GP_RF.optimizer = None + GP_RF.fit(self.conditioning_points,z_RF_cond_points) + GP_RF.L_ = self.GaussianProcess.L_ + z_kriged_nodes_rf = GP_RF.predict(nodes) # # replace kriged mean field (the kriged parts) to create conditioned random field - z_crf_nodes -= z_kriged_nodes_rf - z_crf_nodes += z_kriged_field + z_cond_RF_nodes -= z_kriged_nodes_rf + z_cond_RF_nodes += z_kriged_field # # scale conditioned random field self.kriging_std = np.sqrt(self.variance) * std_kriged_field - self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_crf_nodes \ No newline at end of file + self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_cond_RF_nodes \ No newline at end of file diff --git a/random_fields/utils.py b/random_fields/utils.py index c21c8a1..3cb4251 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -7,20 +7,14 @@ def plot3D(coordinates: list, random_field: list, title: str = "Random Field", output_folder = "./", output_name: str = "random_field.png"): """ - Plot 3D random field - - Parameters - ---------- - coordinates : list - List of coordinates of the random field - random_field : list - List of random field values - title : str - Title of the plot - output_folder : str - Output folder - output_name : str - Output fine name + Plots and saves 3D random field + + Args: + - coordinates (list): List of coordinates of the random field + - random_field (list): List of random field values + - title (str): Title of the plot + - output_folder (str): Output folder + - output_name (str): Output fine name """ # create output folder if not os.path.exists(output_folder): @@ -53,22 +47,21 @@ def plot3D(coordinates: list, random_field: list, title: str = "Random Field", def plot2D(coordinates: list, random_field: list, title: str = "Random Field", - output_folder = "./", output_name: str = "random_field.png"): + output_folder = "./", output_name: str = "random_field.png", + colorbar_label: str = '',conditioning_coordinates: list = [], + conditioning_values:list = [],figsize: tuple = (6,5)): """ - Plot 2D random field - - Parameters - ---------- - coordinates : list - List of coordinates of the random field - random_field : list - List of random field values - title : str - Title of the plot - output_folder : str - Output folder - output_name : str - Output fine name + Plots and saves 2D random field + + Args: + - coordinates (list): List of coordinates of the random field + - random_field (list): List of random field values + - title (str): Title of the plot + - output_folder (str): Output folder + - output_name (str): Output fine name + - colorbar_label (str): Label for the colorbar (optional, deafult = '') + - conditioning_coordinates (list): List of coordinates if the conditioning points (optional, default = []) + - conditioning_values (list): List of the values at the conditioning points (optional, default = []) """ # create output folder @@ -76,7 +69,7 @@ def plot2D(coordinates: list, random_field: list, title: str = "Random Field", os.makedirs(output_folder) # make plot - fig, ax = plt.subplots(figsize=(6, 5)) + fig, ax = plt.subplots(figsize=figsize) ax.set_position([0.1, 0.1, 0.7, 0.8]) vmin = np.min([np.min(aux) for aux in random_field]) @@ -89,21 +82,25 @@ def plot2D(coordinates: list, random_field: list, title: str = "Random Field", ax.set_xlabel('x coordinate') ax.set_ylabel('y coordinate') + for i,coord in enumerate(conditioning_coordinates): + x, y = coord[:, 0], coord[:, 1] + ax.scatter(x, y, c=conditioning_values[i], vmin=vmin, vmax=vmax, cmap="viridis", edgecolors='red', linewidth = 0.25 ) + cax = ax.inset_axes([1.1, 0., 0.05, 1]) norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) - fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax) + fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap="viridis"), ax=ax, cax=cax,label = colorbar_label) fig.suptitle(title) plt.savefig(os.path.join(output_folder, output_name)) - + plt.close() def plot1D(coordinates: list, random_field: list, title: str = "Random Field", output_folder = "./", output_name: str = "random_field.png"): """ - Plot 1D random field + Plots and saves 1D random field - Parameters + Args: ---------- coordinates : list List of coordinates of the random field diff --git a/requirements_dev.txt b/requirements_dev.txt index e1ee5ed..2c51ce0 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,5 +1,6 @@ numpy==1.24.2 gstools==1.5.0 matplotlib>=3.7.0 +scikit-learn>=1.2.2 pytest==7.4.2 pytest-cov==4.1.0 \ No newline at end of file diff --git a/tests/data/kriging_mean_3D.txt b/tests/data/kriging_mean_3D.txt new file mode 100644 index 0000000..707a9a0 --- /dev/null +++ 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+1.999995657032044516e+00 +1.999993059562356734e+00 +1.999989442632843284e+00 +1.999984714032172706e+00 +1.999978933189071606e+00 +1.999972364128423497e+00 +1.999965492301618131e+00 +1.999958986464591471e+00 +1.999953601202381970e+00 +1.999950036313591806e+00 +1.999948788158081525e+00 diff --git a/tests/test_random_field.py b/tests/test_random_field.py index d766764..9776654 100644 --- a/tests/test_random_field.py +++ b/tests/test_random_field.py @@ -86,14 +86,70 @@ def test_distribution_RF_unstruc(): x = np.array(nodes_fields)[:, 0] y = np.array(nodes_fields)[:, 1] - rf_emsamble = [] + rf_ensamble = [] param = [] for i in range(nb_runs): rf = RandomFields(ModelName.Gaussian, 2, 10, 2, 0.05, [1], [1], seed=i) rf.generate(np.array([x.ravel(), y.ravel()]).T) - rf_emsamble.append(rf.random_field.field) + rf_ensamble.append(rf.random_field.field) mu_un, std_un = norm.fit(rf.random_field.field) param.append([mu_un, std_un]) np.testing.assert_array_almost_equal(np.mean(np.array(param)[:, 0]), 10, decimal=2) np.testing.assert_array_almost_equal(np.mean(np.array(param)[:, 1])**2, 2, decimal=2) + + +def test_conditioned_RF_3D_mean_variance(): + """test the conditioned random field for correct mean and variance""" + + # mesh coordinates + x = np.linspace(0, 100, 11) + y = np.linspace(0, 50, 11) + z = np.linspace(0, 25, 11) + x, y, z = [i.ravel() for i in np.meshgrid(x, y, z)] + + # random field properties + nb_dimensions = 3 + mean = 10 + variance = 2 + vertical_scale_fluctuation = 10 + anisotropy = [2.5, 2.5] + angle = [0, 0] + model_rf = ModelName.Gaussian + + # generate and plot random field + rf = RandomFields(model_rf, nb_dimensions, mean, variance, vertical_scale_fluctuation, + anisotropy, angle, seed=14) + rf.generate(np.array([x, y, z]).T) + + # declare conditioning points + xc = np.array([50.]*5) + yc = np.linspace(0,50,5) + zc = np.array([25]*5) + vc = np.array([15]*5) + + rf.set_conditioning_points(np.array([xc,yc,zc]).T,vc,noise_level = 0.01) + + # generate and plot conditioned random field model + rf.generate_conditioned(np.array([x, y,z]).T) + + rf.conditioned_random_field + rf.kriging_mean + rf.kriging_std + + ## to regenerate new reference solutions + # np.savetxt('./data/kriging_mean_3D.txt',rf.kriging_mean) + # np.savetxt('./data/kriging_std_3D.txt',rf.kriging_std) + + kriging_mean_ref = np.loadtxt('./tests/data/kriging_mean_3D.txt') + kriging_std_ref = np.loadtxt('./tests/data/kriging_std_3D.txt') + + # evaluate and test maximum difference + max_error_mean = np.max(np.abs(kriging_mean_ref - rf.kriging_mean)) + max_error_std = np.max(np.abs(kriging_std_ref - rf.kriging_std)) + + np.testing.assert_array_almost_equal(max_error_mean, 0., decimal=4) + np.testing.assert_array_almost_equal(max_error_std, 0., decimal=4) + + +test_conditioned_RF_3D_mean_variance() \ No newline at end of file From b60343d6e15d3f118ac79eb6b1be8056cd01d571 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 23 Aug 2024 11:15:28 +0200 Subject: [PATCH 12/62] changes accounting for review comments: - style changes - update docstrings - extended test modified: docs/contributions.rst modified: random_fields/generate_field.py modified: random_fields/utils.py modified: requirements.txt modified: requirements_dev.txt new file: tests/data/conditioned_rf_3D.txt deleted: tests/data/kriging_mean_3D.txt deleted: tests/data/kriging_std_3D.txt modified: tests/test_random_field.py --- docs/contributions.rst | 50 +- random_fields/generate_field.py | 114 +-- random_fields/utils.py | 17 +- requirements.txt | 4 +- requirements_dev.txt | 4 +- tests/data/conditioned_rf_3D.txt | 3 + tests/data/kriging_mean_3D.txt | 1331 ------------------------------ tests/data/kriging_std_3D.txt | 1331 ------------------------------ tests/test_random_field.py | 42 +- 9 files changed, 120 insertions(+), 2776 deletions(-) create mode 100644 tests/data/conditioned_rf_3D.txt delete mode 100644 tests/data/kriging_mean_3D.txt delete mode 100644 tests/data/kriging_std_3D.txt diff --git a/docs/contributions.rst b/docs/contributions.rst index c8d5f8d..90b69c2 100644 --- a/docs/contributions.rst +++ b/docs/contributions.rst @@ -56,14 +56,17 @@ An example of a class: -:class:`object` Attributes: - - (): - - (): + - (): ,shape (n,m) + - (): ,shape (m,) """ def __init__(self,,...): """ Constructor of the RandomFields class + + Args: + - : () , shape (n,m,), default """ self.attribute_name_1: = self.attribute_name_2: = @@ -73,29 +76,36 @@ An example of a function: .. code-block:: - def create_solver_settings_dictionary(self, model: Model, mesh_file_name: str, materials_file_name: str) -> Dict[str, Any]: + def generate(self, nodes: npt.NDArray[np.float64]) -> None: """ - Creates a dictionary containing the solver settings. + Generate random field Args: - - model (:class:`random_fields.model.Model`): The model object containing the solver data and model parts. - - mesh_file_name (str): The name of the mesh file. - - materials_file_name (str): The name of the materials parameters json file. - + - nodes (ndarray): The nodes of the random field. shape (:,`self.n_dim`) + Raises: - - ValueError: if solver_settings in model are not initialised. + - ValueError: if dimensions of `nodes` do not match dimensions of the model Returns: - - Dict[str, Any]: dictionary containing the part of the project parameters - dictionary related to problem data and solver settings. - """ - if model.project_parameters is None: - raise ValueError("Solver settings are not initialised in model.") + """ - return self.solver_io.create_settings_dictionary( - model.project_parameters, - Path(mesh_file_name).stem, - materials_file_name, - model.get_all_model_parts(), - ) + # check dimensions of nodes agrees with dimensions of model + if nodes.shape[1] != self.n_dim: + raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') + + # scale of fluctuation + scale_fluctuation = np.ones(self.n_dim) * self.vertical_scale_fluctuation + + # apply the anisotropy to the other dimensions + mask = np.arange(len(scale_fluctuation)) != self.v_dim + scale_fluctuation[mask] = scale_fluctuation[mask] * self.anisotropy + + model = self.random_field_model(dim = self.n_dim, + var = self.variance, + len_scale = scale_fluctuation, + angles = self.angle) + self.random_field = gs.SRF(model, + mean = self.mean, + seed = self.seed) + self.random_field(nodes.T) diff --git a/random_fields/generate_field.py b/random_fields/generate_field.py index 9f273a7..df0a657 100644 --- a/random_fields/generate_field.py +++ b/random_fields/generate_field.py @@ -28,26 +28,25 @@ class RandomFields(): - None Attributes: - - max_conditioning_points (int): - - random_field_model_name (str): + - max_conditioning_points (int): maximum number of + - random_field_model_name (str): - random_field_model (int): - - n_dim (int):) - - seed (int): - - mean (float): - - variance (float): - - vertical_scale_fluctuation (list): - - anisotropy (list): - - angle (list): - - v_dim (int): - - random_field (:class:) - - z_kriged_field (array) - - GaussianProcess (:class:) - - conditioning_points (array): - - conditioning_values (array): - - kriging_mean (array): - - kriging_std = (array): - - conditioned_random_field (array): - + - n_dim (int): number of physical dimesions (1,2 or 3) + - seed (int): seed for the random number generator + - mean (float): unconditioned mean of the random field + - variance (float): unconditioned variance of the random field + - vertical_scale_fluctuation (float): + - anisotropy (list): ratio between horizontal scales and vertical scale of fluctuation, shape (`self.n_dims` - 1) + - angle (list): rotation angles of the principal directions of the scales of fluctuation relative to the vertical, shape (`self.n_dims` - 1) + - v_dim (int): dimesion number corresponding to the vertical scale of fluctuation + - random_field (:class:gstools.SRF) random field generator + - z_kriged_field (array): standard-normal kriged random field + - gaussian_process (:class:sklearn.gaussian_process.GaussianProcessRegressor) + - conditioning_points (array): coordinates of the conditioning points, shape (:,`self.n_dims`) + - conditioning_values (array): values of the conditioning points, shape (:,) + - kriging_mean (array): mean of the conditioned random field, shape (:,) + - kriging_std = (array): standard deviation of the conditioned random field, shape (:,) + - conditioned_random_field (array): conditioned random field, shape (:,) """ @@ -114,7 +113,7 @@ def __init__(self, self.v_dim = v_dim self.random_field = None self.z_kriged_field = None - self.GaussianProcess = GPR() + self.gaussian_process = GPR() self.conditioning_points = None self.conditioning_values = None self.kriging_mean = None @@ -127,9 +126,10 @@ def generate(self, nodes: npt.NDArray[np.float64]) -> None: Generate random field Args: - - nodes (list): The nodes of the random field + - nodes (ndarray): The nodes of the random field. shape (:,`self.n_dim`) Raises: + - ValueError: if dimensions of `nodes` do not match dimensions of the model Returns: @@ -163,28 +163,36 @@ def set_conditioning_points(self, Initiates the conditioning points and inverts the covariance matrix Args: - - points (array): The contitioning point coordinates - - values: array-like: The conditioning point values + - points (array): The contitioning point coordinates. Dimensions `(:,self.n_dims)` + - values: array-like: The conditioning point values. - kernel: (:class: sklearn.gaussian_process.kernels.Kernel): The (calibrated) correlation - kernel from sklearn (default = None) + kernel from sklearn (default = None). - noise_level (float or array): normalised variance of the observed data, as a single float - or as an array of the same dimensions as `values` (default = 0.0001) + or as an array of the same dimensions as `values` (default = 0.0001). Raises: + - Exception: if more than `self.max_conditioning_points` conditioning points are specified, + to prevent excessive computation and memory usage. + - ValueError: if dimensions of `points` do not match dimensions of the model Returns: """ - self.noise_level = noise_level - + self.noise_level = noise_level + self.conditioning_points = points + self.conditioning_values = values # check the maximum number of conditioning points to keep the computation and memory cost reasonable if points.shape[0] > self.max_conditioning_points: - raise Exception(f'Too many conditioning points! There are {max(points.shape)} ' + + raise Exception(f'Too many conditioning points! There are {points.shape[0]} ' + f'points, while the maximum allowed amount is {self.max_conditioning_points}.' + f'\n Consider increasing `max_conditioning_points` or use fewer conditioning points.' ) + # check dimensions of conditioning points agrees with dimensions of model + if points.shape[1] != self.n_dim: + raise ValueError(f'Dimensions of conditioning points: {points.shape[1]} do not match dimensions of model: {self.n_dim}') + # scale of fluctuation scale_fluctuation = np.ones(self.n_dim) * self.vertical_scale_fluctuation @@ -197,33 +205,33 @@ def set_conditioning_points(self, # correct the length scales between libraries if self.random_field_model_name == 'Gaussian': ls_sklearn = scale_fluctuation * 2/np.pi - self.KrigingKernel = WhiteKernel(noise_level) + RBF(length_scale = ls_sklearn) + self.kriging_kernel = WhiteKernel(noise_level) + RBF(length_scale = ls_sklearn) elif self.random_field_model_name == 'Exponential': ls_sklearn = scale_fluctuation / np.sqrt(1/2) - self.KrigingKernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn, nu = 0.5) + self.kriging_kernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn, nu = 0.5) elif self.random_field_model_name == 'Matern': ls_sklearn = scale_fluctuation / np.sqrt(1/2) - self.KrigingKernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn) + self.kriging_kernel = WhiteKernel(noise_level) + Matern(length_scale = ls_sklearn) # fit a GP against conditioning points standardised by simulation field statistics: NOT by conditioning point statistics - self.GaussianProcess = GPR(kernel = self.KrigingKernel) - self.GaussianProcess.optimizer = None - self.GaussianProcess.fit(points,(values - self.mean) / np.sqrt(self.variance) ) - self.conditioning_points = points - self.conditioning_values = values + self.gaussian_process = GPR(kernel = self.kriging_kernel) + self.gaussian_process.optimizer = None + self.gaussian_process.fit(points,(values - self.mean) / np.sqrt(self.variance) ) - self.kriging_mean = None - self.kriging_std = None - self.conditioned_random_field = None + # reset variables + self.kriging_mean = np.array([]) + self.kriging_std = np.array([]) + self.conditioned_random_field = np.array([]) def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: """ Generate conditioned random field Args: - nodes (list): The nodes of the random field + - nodes (array): The nodes of the random field, shape (:,`self.n_dims`) Raises: + - ValueError: if dimensions of nodes do not match the dimensions of the model Returns: @@ -234,7 +242,7 @@ def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: raise ValueError(f'Dimensions of nodes: {nodes.shape[1]} do not match dimensions of model: {self.n_dim}') # # create kriged mean field - z_kriged_field,std_kriged_field = self.GaussianProcess.predict(nodes,return_std = True) + z_kriged_field,std_kriged_field = self.gaussian_process.predict(nodes,return_std = True) std_kriged_field = np.sqrt(std_kriged_field**2 - self.noise_level) @@ -242,31 +250,29 @@ def generate_conditioned(self, nodes: npt.NDArray[np.float64]) -> None: self.kriging_mean = self.mean + np.sqrt(self.variance) * z_kriged_field # # create single random field at nodes and conditioning points - nodes_cpoints = np.vstack([nodes,self.conditioning_points]) - self.generate(nodes_cpoints) + self.generate(np.vstack([nodes,self.conditioning_points])) # Split the generated field into the nodal coordinates and the conditioning points # Standardize the distribution to marginal ~N(0,1) - z_cond_RF_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) - z_RF_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) + z_cond_rf_nodes = (self.random_field.field[:nodes.shape[0]] - self.mean) / np.sqrt(self.variance) + z_rf_cond_points = (self.random_field.field[nodes.shape[0]:] - self.mean) / np.sqrt(self.variance) # add the noise to the conditioning points np.random.seed(self.seed) - z_RF_cond_points += np.sqrt(self.noise_level) * np.random.normal(size = z_RF_cond_points.shape) + z_rf_cond_points += np.sqrt(self.noise_level) * np.random.normal(size = z_rf_cond_points.shape) # # create kriged mean field of random field - GP_RF = GPR(kernel = self.GaussianProcess.kernel_) - GP_RF.optimizer = None - GP_RF.fit(self.conditioning_points,z_RF_cond_points) - GP_RF.L_ = self.GaussianProcess.L_ - z_kriged_nodes_rf = GP_RF.predict(nodes) + gp_rf = GPR(kernel = self.gaussian_process.kernel_) + gp_rf.optimizer = None + gp_rf.fit(self.conditioning_points,z_rf_cond_points) + gp_rf.L_ = self.gaussian_process.L_ # # replace kriged mean field (the kriged parts) to create conditioned random field - z_cond_RF_nodes -= z_kriged_nodes_rf - z_cond_RF_nodes += z_kriged_field + z_cond_rf_nodes -= gp_rf.predict(nodes) + z_cond_rf_nodes += z_kriged_field # # scale conditioned random field self.kriging_std = np.sqrt(self.variance) * std_kriged_field - self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_cond_RF_nodes \ No newline at end of file + self.conditioned_random_field = self.mean + np.sqrt(self.variance) * z_cond_rf_nodes \ No newline at end of file diff --git a/random_fields/utils.py b/random_fields/utils.py index 3cb4251..3caca1a 100644 --- a/random_fields/utils.py +++ b/random_fields/utils.py @@ -101,17 +101,12 @@ def plot1D(coordinates: list, random_field: list, title: str = "Random Field", Plots and saves 1D random field Args: - ---------- - coordinates : list - List of coordinates of the random field - random_field : list - List of random field values - title : str - Title of the plot - output_folder : str - Output folder - output_name : str - Output fine name + - coordinates (list): List of coordinates of the random field + - random_field (list): List of random field values + - title (str): Title of the plot + - output_folder (str): Output folder + - output_name (str): Output fine name + """ # create output folder diff --git a/requirements.txt b/requirements.txt index a879501..39491e5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ numpy==1.24.2 gstools==1.5.0 -matplotlib>=3.7.0 -sklearn +matplotlib==3.7.4 +scikit-learn==1.5.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index 2c51ce0..d448391 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,6 +1,6 @@ numpy==1.24.2 gstools==1.5.0 -matplotlib>=3.7.0 -scikit-learn>=1.2.2 +matplotlib==3.7.4 +scikit-learn==1.5.1 pytest==7.4.2 pytest-cov==4.1.0 \ No newline at end of file diff --git a/tests/data/conditioned_rf_3D.txt b/tests/data/conditioned_rf_3D.txt new file mode 100644 index 0000000..d3e0088 --- /dev/null +++ b/tests/data/conditioned_rf_3D.txt @@ -0,0 +1,3 @@ +1.001037129583365726e+01 1.001779265915069317e+01 1.002616224620184759e+01 1.003297125891605290e+01 1.003561413196588425e+01 1.041995334622643554e+01 1.072045835625976373e+01 1.105935873525189805e+01 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11) + x = np.linspace(0, 100, 5) + y = np.linspace(0, 50, 5) + z = np.linspace(0, 25, 5) x, y, z = [i.ravel() for i in np.meshgrid(x, y, z)] # random field properties @@ -123,33 +123,25 @@ def test_conditioned_RF_3D_mean_variance(): rf.generate(np.array([x, y, z]).T) # declare conditioning points - xc = np.array([50.]*5) - yc = np.linspace(0,50,5) - zc = np.array([25]*5) - vc = np.array([15]*5) + xc = np.array([50.]*4) + yc = np.linspace(0,50,4) + zc = np.array([25]*4) + vc = np.array([15]*4) rf.set_conditioning_points(np.array([xc,yc,zc]).T,vc,noise_level = 0.01) - # generate and plot conditioned random field model + # generate conditioned random field model rf.generate_conditioned(np.array([x, y,z]).T) - rf.conditioned_random_field - rf.kriging_mean - rf.kriging_std + # load reference solution + data_ref = np.loadtxt('./tests/data/conditioned_rf_3D.txt') + kriging_mean_ref = data_ref[0] + kriging_std_ref = data_ref[1] + kriging_field_ref = data_ref[2] - ## to regenerate new reference solutions - # np.savetxt('./data/kriging_mean_3D.txt',rf.kriging_mean) - # np.savetxt('./data/kriging_std_3D.txt',rf.kriging_std) + # test difference in mean, std and single realisation + np.testing.assert_array_almost_equal(kriging_mean_ref, rf.kriging_mean, decimal=4) + np.testing.assert_array_almost_equal(kriging_std_ref, rf.kriging_std, decimal=4) + np.testing.assert_array_almost_equal(kriging_field_ref, rf.conditioned_random_field, decimal=4) - kriging_mean_ref = np.loadtxt('./tests/data/kriging_mean_3D.txt') - kriging_std_ref = np.loadtxt('./tests/data/kriging_std_3D.txt') - # evaluate and test maximum difference - max_error_mean = np.max(np.abs(kriging_mean_ref - rf.kriging_mean)) - max_error_std = np.max(np.abs(kriging_std_ref - rf.kriging_std)) - - np.testing.assert_array_almost_equal(max_error_mean, 0., decimal=4) - np.testing.assert_array_almost_equal(max_error_std, 0., decimal=4) - - -test_conditioned_RF_3D_mean_variance() \ No newline at end of file From f76e0f067a64df0d5dc2b549a55efb737383c08a Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 23 Aug 2024 11:51:54 +0200 Subject: [PATCH 13/62] modified: requirements.txt modified: requirements_dev.txt --- requirements.txt | 2 +- requirements_dev.txt | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 39491e5..87c6dbd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ numpy==1.24.2 gstools==1.5.0 -matplotlib==3.7.4 +matplotlib>=3.7.0 scikit-learn==1.5.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index d448391..877675e 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,6 +1,6 @@ numpy==1.24.2 gstools==1.5.0 -matplotlib==3.7.4 +matplotlib>=3.7.0 scikit-learn==1.5.1 pytest==7.4.2 -pytest-cov==4.1.0 \ No newline at end of file +pytest-cov==4.1.0 From fadddbad9516efe5f676ad274cc7b7d099103b63 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Thu, 24 Oct 2024 16:49:08 +0200 Subject: [PATCH 14/62] Introduction of geostatistical interpretation of CPTs new file: random_fields/geostatistical_cpt_interpretation.py modified: random_fields/geostatistical_cpt_interpretation.py --- .../geostatistical_cpt_interpretation.py | 502 ++++++++++++++++++ 1 file changed, 502 insertions(+) create mode 100644 random_fields/geostatistical_cpt_interpretation.py diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py new file mode 100644 index 0000000..b094554 --- /dev/null +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -0,0 +1,502 @@ +#%% + +from typing import Optional + +import os +import numpy as np +import numpy.typing as npt +import matplotlib.pyplot as plt +from geolib_plus.gef_cpt import GefCpt +import scipy.stats as st + +from generate_field import RandomFields, ModelName + +from sklearn.gaussian_process import GaussianProcessRegressor as GPR +from sklearn.gaussian_process.kernels import RBF, WhiteKernel, Sum + +from geolib_plus.gef_cpt import GefCpt +from geolib_plus.robertson_cpt_interpretation import RobertsonCptInterpretation +from geolib_plus.robertson_cpt_interpretation import UnitWeightMethod +from geolib_plus.robertson_cpt_interpretation import OCRMethod +from geolib_plus.robertson_cpt_interpretation import ShearWaveVelocityMethod + +class MarginalTransformation(): + """lass for back-and-forth transformation between marginal an standard-normal distributions + + Returns: + _type_: _description_ + """ + + def __init__(self, + data: npt.NDArray[np.float64], + min: float = None, + max: float = None, + lognormal: bool = False): + """ + Initiate class + + Args: + - data (array-like): sampling data to construct transformation + - min (float, optional): _description_. Defaults to None. + - max (float, optional): _description_. Defaults to None. + - lognormal (bool, optional): _description_. Defaults to False. + """ + + self.data = np.sort(data) + self.n = len(data) + self.lognormal = lognormal + + if type(min) == type(None): + self.min = self.data.min() - (self.data.max() - self.data.min())/100 + self.min_ln = np.log(self.data.min()) - (np.log(self.data.max()) - np.log(self.data.min()))/100 + else: + self.min = min + self.min_ln = np.log(min) + if type(max) == type(None): + self.max = self.data.max() + (self.data.max() - self.data.min())/100 + self.max_ln = np.log(self.data.max()) + (np.log(self.data.max()) - np.log(self.data.min()))/100 + else: + self.max = max + self.max_ln = np.log(max) + + self.z = st.norm.ppf(np.linspace(.5/self.n,1-.5/self.n,self.n)) + + x_points = np.hstack([self.min,self.data,self.max]) + x_points_ln = np.hstack([self.min_ln,np.log(self.data),self.max_ln]) + z_points = np.hstack([-8.,self.z,8.]) + + self._z = np.linspace(-8,8,101) + self._data = np.interp(self._z,z_points,x_points) + self._log_data = np.interp(self._z,z_points,x_points_ln) + + + def plot(self, + title: str = "Marginal distribution transformation ", + x_label: str = '$Z\sim N(0,1)$', + y_label: str = '$X $', + figsize: tuple = (6,5)): + """Plots the transformation model for the marginal distribution. + + Args: + title (str, optional): plot title. Defaults to "Marginal distribution transformation ". + x_label (str, optional): horizontal axis label. Defaults to '\sim N(0,1)$'. + y_label (str, optional): vertical axis label. Defaults to ' $'. + figsize (tuple, optional): figure size. Defaults to (6,5). + """ + plt.figure(figsize = figsize) + plt.plot(self.z,self.data,'.',color = 'gray',alpha = 0.5,label = 'data') + plt.plot(self._z,self._d,'-',color = 'r',label = 'transformation model') + plt.xlabel(x_label) + plt.ylabel(y_label) + plt.legend() + plt.title(title) + plt.grid() + plt.show() + + def XtoZ(self,x: npt.NDArray[np.float64]): + """XtoZ transforms data from the distribution of $$X$$ to the standard normal + equivalent distribution $$Z\sim N(0,1)$$ + + Args: + x (npt.NDArray[np.float64]): values $$x \in X$$ + + Returns: + z (npt.NDArray[np.float64]): standard-normal equivalent to `x` + """ + if self.lognormal: + z = np.interp(np.log(x),self._log_data,self._z) + else: + z = np.interp(x,self._data,self._z) + return z + + + def ZtoX(self,Z): + """ZtoX transforms from a standard-normal values `z` to the corresponding values in `x`. + + Args: + z (npt.NDArray[np.float64]): standard normal values $z \in Z\sim N(0,1)$ + + Returns: + x (npt.NDArray[np.float64]): standard-normal equivalent to `x` + """ + if self.lognormal: + x = np.exp(np.interp(Z,self._z,self._log_data)) + else: + x = np.interp(Z,self._z,self._data) + return x + + +class CPT_data(): + """CPT_data _summary_ + + + """ + + def __init__(self,cpt_directory = './'): + """__init__ _summary_ + + Args: + cpt_directory (str, optional): _description_. Defaults to './'. + """ + self.gravity = 9.81 + self.cpt_list = [] + self.cpt_directory = cpt_directory + self.cpt_filepaths = [] + + self.interpreter = RobertsonCptInterpretation() + self.interpreter.unitweightmethod = UnitWeightMethod.LENGKEEK + self.interpreter.shearwavevelocitymethod = ShearWaveVelocityMethod.ZANG + self.interpreter.ocrmethod = OCRMethod.MAYNE + + def read_cpt_data(self,addition = False): + """read_cpt_data Reads in and pre-processes the CPT data + + Args: + addition (bool, optional): If True, reads in the CPT data in addition to the existing data. Defaults to False. + + Raises: + Warning: if .xml-files are encountered in directory + + """ + if not addition: + self.cpt_list = [] + self.cpt_filepaths = [] + + + for gef_name in os.listdir(self.cpt_directory): + if gef_name.endswith('.gef'): + print(gef_name) + + cpt = GefCpt() + cpt.read(self.cpt_directory + '/' + gef_name) + cpt.pre_process_data() + + self.cpt_list.append(cpt) + self.cpt_filepaths.append(self.cpt_directory + '/' + gef_name) + + elif gef_name.endswith('.xml'): + try: + + cpt = GefCpt() + cpt.read(self.cpt_directory + '/' + gef_name) + cpt.pre_process_data() + + self.cpt_list.append(cpt) + self.cpt_filepaths.append(self.cpt_directory + '/' + gef_name) + print(gef_name) + except: + raise Warning('Ignoring file {gef_name}. xml-files for CPT data may not be supported by geolib_plus?') + + + + def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterpretation): + """interpret_cpt_data interprets the CPT data based on the provided interpreter settings + + Args: + interpreter (:class:`geolib_plus.robertson_cpt_interpretation.RobertsonCptInterpretation`, optional): Interpretation methods. Defaults to RobertsonCptInterpretation. + + """ + + if interpreter: + self.interpreter = interpreter + + self.data_coords = np.array([[]]*3,dtype = np.float64).T + self.cpt_locations = np.array([[]]*2,dtype = np.float64).T + self.rho = np.array([]) + self.vs = np.array([]) + + for cpt in self.cpt_list: + cpt.interpret_cpt(self.interpreter) + + n_readings = len(cpt.depth_to_reference) + coords = np.array([[cpt.coordinates[0],0,cpt.coordinates[1]]]*n_readings) + coords[:,v_dim] = cpt.depth_to_reference + + self.cpt_locations = np.vstack([self.cpt_locations, cpt.coordinates]) + self.data_coords = np.vstack([self.data_coords,coords]) + self.rho = np.hstack([self.rho,cpt.rho]) + self.vs = np.hstack([self.vs,cpt.vs]) + + self.g_0 = self.rho/self.gravity*self.vs**2 + + + def data_coordinate_change(self, + x_ref:float = 0., + y_ref:float = 0., + theta_ref:float = 0., + based_on_midpoint = False): + """data_coordinate_change Coordinate system transformation, relative to a reference point (x_ref,y_ref) + + Args: + - x_ref (float, optional): _description_. Defaults to 0.. + - y_ref (float, optional): _description_. Defaults to 0.. + - theta_ref (float, optional): orienation of the model in degrees, clock-wise from North: r_ref = 0 results + in a 90 degrees clock-wise rotation in the x-y plane . Defaults to 0.. + - based_on_midpoint (bool, optional): If True, translates the domain relative to the midpoint of the CPT locations + `self.cpt_locations`. Defaults to False. + """ + + # + # translate coordinates + if based_on_midpoint: + x_ref = (self.cpt_locations[:,0].min() + self.cpt_locations[:,0].max())/2. + y_ref = (self.cpt_locations[:,1].min() + self.cpt_locations[:,1].max())/2. + + self.data_coords[:,0] -= x_ref + self.data_coords[:,2] -= y_ref + self.cpt_locations[:,0] -= x_ref + self.cpt_locations[:,1] -= y_ref + + # rotate counterclock-wise back to N000 minus 90 degrees: + theta_rad = theta_ref/180*np.pi - np.pi/2 + rotation = np.array([[np.cos(theta_rad), 0, -np.sin(theta_rad)], + [0., 1., 0.], + [np.sin(theta_rad), 0, np.cos(theta_rad)]]) + + self.data_coords = self.data_coords @ rotation.T + self.cpt_locations = self.cpt_locations @ rotation[::2,::2].T + + def plot_coordinates(self,original_coordinates = False): + """plot_coordinates Plots the coordinates of the CPTs + + Args: + - original_coordinates (bool, optional): If True, plots in original coordinates. Defaults to False. + """ + + if original_coordinates: + locations = np.array([cpt.coordinates for cpt in self.cpt_list]) + else: + locations = self.cpt_locations + + plt.plot(locations[:,0],locations[:,1],'dk',label = 'CPT locations') + plt.xlabel('x-coordinate [m]') + plt.ylabel('y_coordinate [m]') + plt.axis('equal') + plt.show() + +class GeostatisticalModel(): + """GeostatisticalModel + + Attr: + - nb_dimensions (int): + - v_dim (int): + - gpr (:class:`sklearn.gaussian_process.GaussianProcessRegressor`). Gaussian process regressor. + - data_coords (npt.NDArray[np.float64]): + - data_values (npt.NDArray[np.float64]): + - noise_level (float): Defaults to None + + """ + + def __init__(self,nb_dimensions: int,v_dim: int, kernel = None): + """__init__ Initiates instance and sets kernel to WhiteKernel() + RBF() if kernel is not provided + + Args: + - nb_dimensions (int): number of physical dimensions. + - v_dim (int): Dimension index of the vertical scale of fluctuation + - kernel (:class:`sklearn.gaussian_process.kernels.Kernel`, optional): Spatial correlation kernel. Defaults to None. + + Raises: + TypeError: if `kernel` is not a class from module `sklearn.gaussian_process.kernels` + + """ + self.nb_dimensions = nb_dimensions + self.v_dim = v_dim + + self.data_coords = np.array([],dtype = np.float64) + self.data_values = np.array([],dtype = np.float64) + self.noise_level = None + + # check if `kernel` has the correct parent class + if kernel: + if kernel.__module__ != 'sklearn.gaussian_process.kernels': + raise TypeError("`kernel` should an instance of a class from module `sklearn.gaussian_process.kernels`") + + # if kernel does not exist, create a default + if not kernel: + length_scale = [100.]*nb_dimensions + length_scale[v_dim] = 1. + kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale) + + self.gpr = GPR(kernel = kernel) + + + def calibrate(self,coords: npt.NDArray[np.float64],values: npt.NDArray[np.float64]): + """calibrates the + + Args: + coords (npt.NDArray[np.float64]): data spatial coordinates + values (npt.NDArray[np.float64]): data values + + Raises: + Exception: + """ + if coords.shape[0] == self.nb_dimensions and coords.shape[1] != self.nb_dimensions: + self.data_coords = coords.T + raise Warning('`coords` is transposed to match dimensions (n_points,n_dims)') + elif coords.shape[1] == self.nb_dimensions: + self.data_coords = coords + else: + raise Exception("Shape of argument 'X' incompattible with dimensionality:",X.shape) + + self.data_values = values + + # calibrate + self.gpr.fit(self.data_coords,self.data_values) + + + # extract noise level + try: + self.noise_level = self.gpr.kernel_.get_params()['k1__noise_level'] + except: + self.noise_level = None + + # extracts length scales and transforms to scales of fluctuation + try: + gpr_length_scales = self.gpr.kernel_.get_params()['k2__length_scale'] + if 'RBF' in str(self.gpr.kernel): + self.vertical_scale_fluctuation = gpr_length_scales[self.v_dim] * np.pi / 2. + elif 'Matern' in str(self.gpr.kernel): + self.vertical_scale_fluctuation = gpr_length_scales[self.v_dim] * np.sqrt(1/2) + mask = np.arange(len(gpr_length_scales)) != self.v_dim + self.anisotropy = list(gpr_length_scales[mask] / gpr_length_scales[self.v_dim]) + except: + gpr_length_scales = None + self.vertical_scale_fluctuation = None + self.anisotropy = None + +class ElasticityFieldsFromCpt(): + """ElasticityFieldsFromCpt + + + Class for the automatic generation of fields of elastic parameters (vs and rho) + """ + + + def __init__(self, + cpt_file_folder:str = './', + x_ref:float = 0., + y_ref:float = 0., + theta_ref:float = 0., + based_on_midpoint:bool = False, + max_conditioning_points:int = 2000): + + self.cpt_file_folder = cpt_file_folder + self.x_ref = x_ref + self.y_ref = y_ref + self.theta_ref = theta_ref + self.based_on_midpoint = based_on_midpoint + self.max_conditioning_points = max_conditioning_points + + self.conditioning_data = CPT_data(cpt_directory = self.cpt_file_folder) + self.conditioning_data.read_cpt_data() + self.conditioning_data.interpret_cpt_data() + self.conditioning_data.data_coordinate_change(theta_ref = self.theta_ref,based_on_midpoint = self.based_on_midpoint) + + self.thinning_sample_index = np.random.choice(self.conditioning_data.data_coords.shape[0],size = self.max_conditioning_points,replace = False) + + self.trans_model_rho = MarginalTransformation(self.conditioning_data.rho) + self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs,lognormal = True) + + + def calibrate_geostat_model(self, + v_dim:int = 1, + calibration_indices:tuple = (0,1), + seed:int = 13): + """calibrates the geostatistical model + Args: + v_dim (int, optional): vertical dimension. Defaults to 1. + calibration_indices (tuple, optional): _description_. Defaults to (0,1). + seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 13. + """ + + ndim_calibrate = len(calibration_indices) + + length_scale_prior = [100.]*ndim_calibrate + length_scale_prior[v_dim] = 1. + + kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale_prior) + + self.coord_calibration = np.zeros([self.max_conditioning_points,3]) + for i in calibration_indices: + self.coord_calibration[:,i] = self.conditioning_data.data_coords[self.thinning_sample_index,i] + + + self.geostat_model = GeostatisticalModel(nb_dimensions = ndim_calibrate, v_dim = v_dim,kernel = kernel) + self.geostat_model.calibrate(self.coord_calibration[:,calibration_indices],self.trans_model_vs.XtoZ(self.conditioning_data.vs[self.thinning_sample_index])) + + + self.rf_vs = RandomFields(model_name = ModelName.Gaussian, + n_dim = 3, + mean = 0, + variance = 1, + v_scale_fluctuation = self.geostat_model.vertical_scale_fluctuation, + anisotropy = self.geostat_model.anisotropy*2, + angle = [0]*2, + seed = seed, + max_conditioning_points=self.max_conditioning_points) + self.rf_vs.set_conditioning_points(points = self.coord_calibration, + values = self.trans_model_vs.XtoZ(self.conditioning_data.vs[self.thinning_sample_index]), + noise_level = self.geostat_model.noise_level) + self.rf_rho = RandomFields(model_name = ModelName.Gaussian, + n_dim = 3, + mean = 0, + variance = 1, + v_scale_fluctuation = self.geostat_model.vertical_scale_fluctuation, + anisotropy = self.geostat_model.anisotropy*2, + angle = [0]*2, + seed = seed+1, + max_conditioning_points=self.max_conditioning_points) + self.rf_rho.set_conditioning_points(points = self.coord_calibration, + values = self.trans_model_rho.XtoZ(self.conditioning_data.rho[self.thinning_sample_index]), + noise_level = self.geostat_model.noise_level) + + + def generate(self,coordinates): + """generate _summary_ + + Args: + coordinates (numpy.typing.NDArray[np.float64]): 3D coordinated of points to generate random field values for + """ + self.rf_vs.generate_conditioned(nodes = coordinates) + self.rf_rho.generate_conditioned(nodes = coordinates) + + self.vs = self.trans_model_vs.ZtoX(self.rf_vs.conditioned_random_field) + self.rho = self.trans_model_rho.ZtoX(self.rf_rho.conditioned_random_field) + self.g0 = self.rho * self.vs**2 + +if __name__ == '__main__': + + folder = r'C:\_projects\2024\STEM\cpt_interpretation' + + x = np.linspace(-210, 210, 101) + y = np.linspace(-25, -5, 21) + z = np.linspace(-25, 25, 21) + x, y, z = np.meshgrid(x, y, z) + xy_mesh = np.array([x.ravel(), y.ravel(), z.ravel()]).T + + efc = ElasticityFieldsFromCpt(cpt_file_folder = folder, + based_on_midpoint = True, + max_conditioning_points = 1000) + efc.calibrate_geostat_model(calibration_indices=(0,1)) + + print(efc.geostat_model.gpr.kernel_) + + %matplotlib qt + + efc.generate(xy_mesh) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = xy_mesh[:,0] + y = xy_mesh[:,2] + z = xy_mesh[:,1] + xd = efc.conditioning_data.data_coords[:,0] + yd = 0*efc.conditioning_data.data_coords[:,2] + zd = efc.conditioning_data.data_coords[:,1] + ax.scatter(xd, yd, zd,c=efc.conditioning_data.rho) + ax.scatter(x, y, z,c=efc.rho, edgecolors=None, marker="s") + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + ax.set_zlim([-25,5]) + plt.axis('equal') \ No newline at end of file From ecfd3e4a46e46f909c0f63c5c13a05907a0da639 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Thu, 24 Oct 2024 16:59:30 +0200 Subject: [PATCH 15/62] TODO: update dependencies... modified: random_fields/geostatistical_cpt_interpretation.py --- .../geostatistical_cpt_interpretation.py | 68 ++++++++++--------- 1 file changed, 35 insertions(+), 33 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index b094554..5d329e0 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -21,17 +21,13 @@ from geolib_plus.robertson_cpt_interpretation import ShearWaveVelocityMethod class MarginalTransformation(): - """lass for back-and-forth transformation between marginal an standard-normal distributions - - Returns: - _type_: _description_ - """ - + """Class for the transformation model between the distributions of the pysical values and the standard-normal distribution + """ def __init__(self, data: npt.NDArray[np.float64], - min: float = None, - max: float = None, - lognormal: bool = False): + min: Optional[float] = None, + max: Optional[float] = None, + lognormal: Optional[bool] = False): """ Initiate class @@ -69,7 +65,6 @@ def __init__(self, self._data = np.interp(self._z,z_points,x_points) self._log_data = np.interp(self._z,z_points,x_points_ln) - def plot(self, title: str = "Marginal distribution transformation ", x_label: str = '$Z\sim N(0,1)$', @@ -109,7 +104,6 @@ def XtoZ(self,x: npt.NDArray[np.float64]): z = np.interp(x,self._data,self._z) return z - def ZtoX(self,Z): """ZtoX transforms from a standard-normal values `z` to the corresponding values in `x`. @@ -124,7 +118,6 @@ def ZtoX(self,Z): else: x = np.interp(Z,self._z,self._data) return x - class CPT_data(): """CPT_data _summary_ @@ -223,14 +216,14 @@ def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterp def data_coordinate_change(self, x_ref:float = 0., y_ref:float = 0., - theta_ref:float = 0., + orientation_x_axis:float = 0., based_on_midpoint = False): """data_coordinate_change Coordinate system transformation, relative to a reference point (x_ref,y_ref) Args: - x_ref (float, optional): _description_. Defaults to 0.. - y_ref (float, optional): _description_. Defaults to 0.. - - theta_ref (float, optional): orienation of the model in degrees, clock-wise from North: r_ref = 0 results + - orientation_x_axis (float, optional): orienation of the model in degrees, clock-wise from North: r_ref = 0 results in a 90 degrees clock-wise rotation in the x-y plane . Defaults to 0.. - based_on_midpoint (bool, optional): If True, translates the domain relative to the midpoint of the CPT locations `self.cpt_locations`. Defaults to False. @@ -248,7 +241,7 @@ def data_coordinate_change(self, self.cpt_locations[:,1] -= y_ref # rotate counterclock-wise back to N000 minus 90 degrees: - theta_rad = theta_ref/180*np.pi - np.pi/2 + theta_rad = orientation_x_axis/180*np.pi - np.pi/2 rotation = np.array([[np.cos(theta_rad), 0, -np.sin(theta_rad)], [0., 1., 0.], [np.sin(theta_rad), 0, np.cos(theta_rad)]]) @@ -314,14 +307,19 @@ def __init__(self,nb_dimensions: int,v_dim: int, kernel = None): # if kernel does not exist, create a default if not kernel: length_scale = [100.]*nb_dimensions + length_scale_bounds = [[1.0,1000.],[1.0,1000.],[1.0,1000.]] length_scale[v_dim] = 1. - kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale) + length_scale_bounds[v_dim] = [0.01,10.] + + + kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale, + length_scale_bounds = length_scale_bounds) self.gpr = GPR(kernel = kernel) def calibrate(self,coords: npt.NDArray[np.float64],values: npt.NDArray[np.float64]): - """calibrates the + """calibrates the geosatistical model Args: coords (npt.NDArray[np.float64]): data spatial coordinates @@ -376,21 +374,21 @@ def __init__(self, cpt_file_folder:str = './', x_ref:float = 0., y_ref:float = 0., - theta_ref:float = 0., + orientation_x_axis:float = 0., based_on_midpoint:bool = False, max_conditioning_points:int = 2000): self.cpt_file_folder = cpt_file_folder self.x_ref = x_ref self.y_ref = y_ref - self.theta_ref = theta_ref + self.orientation_x_axis = orientation_x_axis self.based_on_midpoint = based_on_midpoint self.max_conditioning_points = max_conditioning_points self.conditioning_data = CPT_data(cpt_directory = self.cpt_file_folder) self.conditioning_data.read_cpt_data() self.conditioning_data.interpret_cpt_data() - self.conditioning_data.data_coordinate_change(theta_ref = self.theta_ref,based_on_midpoint = self.based_on_midpoint) + self.conditioning_data.data_coordinate_change(orientation_x_axis = self.orientation_x_axis,based_on_midpoint = self.based_on_midpoint) self.thinning_sample_index = np.random.choice(self.conditioning_data.data_coords.shape[0],size = self.max_conditioning_points,replace = False) @@ -414,23 +412,22 @@ def calibrate_geostat_model(self, length_scale_prior = [100.]*ndim_calibrate length_scale_prior[v_dim] = 1. - kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale_prior) + #kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale_prior) self.coord_calibration = np.zeros([self.max_conditioning_points,3]) for i in calibration_indices: self.coord_calibration[:,i] = self.conditioning_data.data_coords[self.thinning_sample_index,i] - self.geostat_model = GeostatisticalModel(nb_dimensions = ndim_calibrate, v_dim = v_dim,kernel = kernel) + self.geostat_model = GeostatisticalModel(nb_dimensions = ndim_calibrate, v_dim = v_dim) self.geostat_model.calibrate(self.coord_calibration[:,calibration_indices],self.trans_model_vs.XtoZ(self.conditioning_data.vs[self.thinning_sample_index])) - self.rf_vs = RandomFields(model_name = ModelName.Gaussian, n_dim = 3, mean = 0, variance = 1, v_scale_fluctuation = self.geostat_model.vertical_scale_fluctuation, - anisotropy = self.geostat_model.anisotropy*2, + anisotropy = (self.geostat_model.anisotropy*2)[:2], angle = [0]*2, seed = seed, max_conditioning_points=self.max_conditioning_points) @@ -442,7 +439,7 @@ def calibrate_geostat_model(self, mean = 0, variance = 1, v_scale_fluctuation = self.geostat_model.vertical_scale_fluctuation, - anisotropy = self.geostat_model.anisotropy*2, + anisotropy = (self.geostat_model.anisotropy*2)[:2], angle = [0]*2, seed = seed+1, max_conditioning_points=self.max_conditioning_points) @@ -466,18 +463,19 @@ def generate(self,coordinates): if __name__ == '__main__': - folder = r'C:\_projects\2024\STEM\cpt_interpretation' + folder = r'C:\Users\eijnden\OneDrive - Stichting Deltares\Desktop\DS_cpt\100' - x = np.linspace(-210, 210, 101) - y = np.linspace(-25, -5, 21) - z = np.linspace(-25, 25, 21) + x = np.linspace( -50, 50, 41) + y = np.linspace(-10, -0, 41) + z = np.linspace(-20, 20, 41) x, y, z = np.meshgrid(x, y, z) xy_mesh = np.array([x.ravel(), y.ravel(), z.ravel()]).T efc = ElasticityFieldsFromCpt(cpt_file_folder = folder, based_on_midpoint = True, - max_conditioning_points = 1000) - efc.calibrate_geostat_model(calibration_indices=(0,1)) + max_conditioning_points = 1000, + orientation_x_axis = 162) + efc.calibrate_geostat_model(calibration_indices=(0,1,2)) print(efc.geostat_model.gpr.kernel_) @@ -491,10 +489,14 @@ def generate(self,coordinates): y = xy_mesh[:,2] z = xy_mesh[:,1] xd = efc.conditioning_data.data_coords[:,0] - yd = 0*efc.conditioning_data.data_coords[:,2] + yd = efc.conditioning_data.data_coords[:,2] zd = efc.conditioning_data.data_coords[:,1] - ax.scatter(xd, yd, zd,c=efc.conditioning_data.rho) + #%% + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') ax.scatter(x, y, z,c=efc.rho, edgecolors=None, marker="s") + ax.scatter(xd, yd, zd,c=efc.conditioning_data.rho) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') From 837f49b348c4d36ab4d567e36c0559ac597797c7 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 25 Oct 2024 13:35:25 +0200 Subject: [PATCH 16/62] added function to export conditioning data for visualisation modified: random_fields/geostatistical_cpt_interpretation.py --- .../geostatistical_cpt_interpretation.py | 63 ++++++++++++++++++- 1 file changed, 61 insertions(+), 2 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 5d329e0..6210076 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -6,7 +6,6 @@ import numpy as np import numpy.typing as npt import matplotlib.pyplot as plt -from geolib_plus.gef_cpt import GefCpt import scipy.stats as st from generate_field import RandomFields, ModelName @@ -20,6 +19,7 @@ from geolib_plus.robertson_cpt_interpretation import OCRMethod from geolib_plus.robertson_cpt_interpretation import ShearWaveVelocityMethod + class MarginalTransformation(): """Class for the transformation model between the distributions of the pysical values and the standard-normal distribution """ @@ -212,6 +212,63 @@ def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterp self.g_0 = self.rho/self.gravity*self.vs**2 + self.save_conditioning_points_to_vtk(variable_names = ['vs','rho']) + + def save_conditioning_points_to_vtk(self, + filename = 'conditioning_data.vtk', + variable_names ): + """Saves the conditioning data to a .vtk file for visualisation. + + Args: + - filename (str, optional): . Defaults to 'conditioning_data.vtk'. + - variable_names (list[str]): list lf variables to save. variables should be attributes. + + Raises: + ValueError: if type and dimensions of `coordinates` not correct + ValueError: if variables in `variable_names` are not an attribute + ValueError: if variable dimensions are not correct. + """ + coordinates = self.data_coords + + if not isinstance(coordinates, np.ndarray) or coordinates.shape[1] != 3: + raise ValueError("Coordinates should be a numpy array with shape (N, 3).") + + n_points = len(coordinates) + n_values = len(variable_names) + + scalars = [] + for varname in variable_names: + if hasattr(self,varname): + scalar = self.__getattribute__(varname) + if len(scalar) == n_points and isinstance(scalar,np.ndarray): + scalars.append(scalar) + else: + raise ValueError(f"`{varname}` should be a numpy array of length {n_points}.") + else: + raise ValueError(f"{varname} is not an instance of `conditioning_data`") + + if not isinstance(scalars, list) or len(scalars) != len(variable_names) or len(scalars[0]) != coordinates.shape[0]: + raise ValueError("Scalars should be a numpy array with shape (N, 3), matching the number of coordinates.") + + with open(filename,'w') as file: + file.write('# vtk DataFile Version 5.1\n') + file.write('vtk output\n') + file.write('ASCII\n') + file.write('DATASET POLYDATA\n') + file.write(f'POINTS {n_points} float\n') + for point in coordinates: + file.write('%10.2f %10.2f %10.2f\n'%(point[0],point[1],point[2])) + file.write(f'POINT_DATA {n_points}\n') + file.write(f'FIELD FieldData {n_values}\n') + + for z,name in zip(scalars,variable_names): + file.write(f'{name} 1 {n_points} float\n') + for x in z: + file.write('%.g '%(x)) + file.write('\n') + + print(f"File saved as {filename}") + def data_coordinate_change(self, x_ref:float = 0., @@ -461,9 +518,11 @@ def generate(self,coordinates): self.rho = self.trans_model_rho.ZtoX(self.rf_rho.conditioned_random_field) self.g0 = self.rho * self.vs**2 + + if __name__ == '__main__': - folder = r'C:\Users\eijnden\OneDrive - Stichting Deltares\Desktop\DS_cpt\100' + folder = r'...' x = np.linspace( -50, 50, 41) y = np.linspace(-10, -0, 41) From 0ccabe9f0801ef8543db8f766e3410c37f18d90b Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 25 Oct 2024 13:40:51 +0200 Subject: [PATCH 17/62] modified: random_fields/geostatistical_cpt_interpretation.py --- random_fields/geostatistical_cpt_interpretation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 6210076..18f2aaf 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -506,7 +506,7 @@ def calibrate_geostat_model(self, def generate(self,coordinates): - """generate _summary_ + """generate random fields Args: coordinates (numpy.typing.NDArray[np.float64]): 3D coordinated of points to generate random field values for @@ -522,7 +522,7 @@ def generate(self,coordinates): if __name__ == '__main__': - folder = r'...' + folder = r'.' x = np.linspace( -50, 50, 41) y = np.linspace(-10, -0, 41) From 29e0d3706392652bd8acc6fdd0b20d3992402619 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Wed, 4 Dec 2024 14:49:14 +0100 Subject: [PATCH 18/62] Several minor changes and fixes modified: random_fields/geostatistical_cpt_interpretation.py modified: requirements.txt modified: requirements_dev.txt new file: tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef new file: tests/data/DelftSchiedam_cpts/CPT000000217402_IMBRO.gef new file: tests/data/DelftSchiedam_cpts/CPT000000217406_IMBRO.gef new file: tests/data/DelftSchiedam_cpts/CPT000000217408_IMBRO.gef new file: tests/data/DelftSchiedam_cpts/CPT000000217409_IMBRO.gef new file: tests/data/DelftSchiedam_cpts/locatie_levering.kml --- .../geostatistical_cpt_interpretation.py | 24 +- requirements.txt | 3 +- requirements_dev.txt | 3 +- .../112-074.400-2022-01-CPT-01.gef | 1325 ++++++++++++++++ .../112-074.500-2022-01-CPT-01.gef | 1326 ++++++++++++++++ .../112-074.600-2022-01-CPT-01.gef | 1332 ++++++++++++++++ .../112-074.700-2022-01-CPT-01.gef | 1334 ++++++++++++++++ .../112-074.800-2022-01-CPT-01.gef | 1323 ++++++++++++++++ .../CPT000000217393_IMBRO.gef | 1362 +++++++++++++++++ .../CPT000000217402_IMBRO.gef | 1355 ++++++++++++++++ .../CPT000000217406_IMBRO.gef | 1353 ++++++++++++++++ .../CPT000000217408_IMBRO.gef | 1356 ++++++++++++++++ .../CPT000000217409_IMBRO.gef | 1362 +++++++++++++++++ .../DelftSchiedam_cpts/locatie_levering.kml | 117 ++ 14 files changed, 13564 insertions(+), 11 deletions(-) create mode 100644 tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef create mode 100644 tests/data/DelftSchiedam_cpts/112-074.500-2022-01-CPT-01.gef create mode 100644 tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef create mode 100644 tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef create mode 100644 tests/data/DelftSchiedam_cpts/112-074.800-2022-01-CPT-01.gef create mode 100644 tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef create mode 100644 tests/data/DelftSchiedam_cpts/CPT000000217402_IMBRO.gef create mode 100644 tests/data/DelftSchiedam_cpts/CPT000000217406_IMBRO.gef create mode 100644 tests/data/DelftSchiedam_cpts/CPT000000217408_IMBRO.gef create mode 100644 tests/data/DelftSchiedam_cpts/CPT000000217409_IMBRO.gef create mode 100644 tests/data/DelftSchiedam_cpts/locatie_levering.kml diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 18f2aaf..253d1c7 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -210,18 +210,17 @@ def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterp self.rho = np.hstack([self.rho,cpt.rho]) self.vs = np.hstack([self.vs,cpt.vs]) - self.g_0 = self.rho/self.gravity*self.vs**2 - - self.save_conditioning_points_to_vtk(variable_names = ['vs','rho']) + self.g0 = self.rho*self.vs**2 + self.young_modulus = 2.*self.g0*(1.+0.2) def save_conditioning_points_to_vtk(self, filename = 'conditioning_data.vtk', - variable_names ): + variable_names = ['vs'] ): """Saves the conditioning data to a .vtk file for visualisation. Args: - filename (str, optional): . Defaults to 'conditioning_data.vtk'. - - variable_names (list[str]): list lf variables to save. variables should be attributes. + - variable_names (list[str]): list of variables to save. variables should be attributes. Raises: ValueError: if type and dimensions of `coordinates` not correct @@ -245,7 +244,7 @@ def save_conditioning_points_to_vtk(self, else: raise ValueError(f"`{varname}` should be a numpy array of length {n_points}.") else: - raise ValueError(f"{varname} is not an instance of `conditioning_data`") + raise ValueError(f"{varname} is not an attribute of `conditioning_data`") if not isinstance(scalars, list) or len(scalars) != len(variable_names) or len(scalars[0]) != coordinates.shape[0]: raise ValueError("Scalars should be a numpy array with shape (N, 3), matching the number of coordinates.") @@ -264,7 +263,7 @@ def save_conditioning_points_to_vtk(self, for z,name in zip(scalars,variable_names): file.write(f'{name} 1 {n_points} float\n') for x in z: - file.write('%.g '%(x)) + file.write('%.3g '%(x)) file.write('\n') print(f"File saved as {filename}") @@ -306,6 +305,9 @@ def data_coordinate_change(self, self.data_coords = self.data_coords @ rotation.T self.cpt_locations = self.cpt_locations @ rotation[::2,::2].T + self.save_conditioning_points_to_vtk(variable_names = ['vs','rho','young_modulus']) + + def plot_coordinates(self,original_coordinates = False): """plot_coordinates Plots the coordinates of the CPTs @@ -435,6 +437,7 @@ def __init__(self, based_on_midpoint:bool = False, max_conditioning_points:int = 2000): + self.poisson_ratio = 0.2 self.cpt_file_folder = cpt_file_folder self.x_ref = x_ref self.y_ref = y_ref @@ -517,8 +520,11 @@ def generate(self,coordinates): self.vs = self.trans_model_vs.ZtoX(self.rf_vs.conditioned_random_field) self.rho = self.trans_model_rho.ZtoX(self.rf_rho.conditioned_random_field) self.g0 = self.rho * self.vs**2 + self.young_modulus = 2*self.g0*(1. + self.poisson_ratio) - + # + # only the Young modulus is communicated back as the generated field... + self.generated_field = list(self.young_modulus) if __name__ == '__main__': @@ -538,7 +544,7 @@ def generate(self,coordinates): print(efc.geostat_model.gpr.kernel_) - %matplotlib qt + #%matplotlib qt efc.generate(xy_mesh) diff --git a/requirements.txt b/requirements.txt index 87c6dbd..cf547c7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ numpy==1.24.2 gstools==1.5.0 matplotlib>=3.7.0 -scikit-learn==1.5.1 +scikit-learn==1.5.2 +d-geolib-plus==0.4.0 diff --git a/requirements_dev.txt b/requirements_dev.txt index 877675e..d4caef6 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,6 +1,7 @@ numpy==1.24.2 gstools==1.5.0 matplotlib>=3.7.0 -scikit-learn==1.5.1 +scikit-learn==1.5.2 +d-geolib-plus==0.4.0 pytest==7.4.2 pytest-cov==4.1.0 diff --git a/tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef b/tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef new file mode 100644 index 0000000..5226d3e --- /dev/null +++ b/tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef @@ -0,0 +1,1325 @@ +#GEFID= 1, 1, 0 +#COLUMN= 8 +#COLUMNINFO= 1, m, Sondeerlengte, 1 +#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 +#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 +#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 +#COLUMNINFO= 5, graden, Helling X, 21 +#COLUMNINFO= 6, graden, Helling Y, 22 +#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 +#COLUMNINFO= 8, s, Tijd, 12 +#COLUMNTEXT= 0, off +#COLUMNVOID= 1, -99999.00 +#COLUMNVOID= 2, -99999.000 +#COLUMNVOID= 3, -99999.000 +#COLUMNVOID= 4, -99999.000 +#COLUMNVOID= 5, -99999.000 +#COLUMNVOID= 6, -99999.000 +#COLUMNVOID= 7, -99999.00 +#COLUMNVOID= 8, -99999.0 +#COMPANYID= Fugro NL Land B.V., 27114147, 31 +#DATAFORMAT= ASCII +#FILEDATE= 2022,12,21 +#FILEOWNER= P. Gommer-Reniers +#LASTSCAN= 1256 +#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever +#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam +#MEASUREMENTTEXT= 3, Schiedam, plaatsnaam +#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-2757, conus type/serienummer +#MEASUREMENTTEXT= 5, SSC/NDL/JBS, sondeerapparaat/operator +#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm +#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem +#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau +#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak +#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten +#MEASUREMENTTEXT= 21, ja, correctie meterstrepen +#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling +#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling +#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt +#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel +#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt +#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel +#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel +#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig +#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig +#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig +#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig +#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig +#MEASUREMENTVAR= 12, 0, -, elektrische sondering +#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte +#MEASUREMENTVAR= 16, 25.09, m, einddiepte sondering +#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt +#MEASUREMENTVAR= 20, -0.041, MPa, nulmeting conus voor sondering +#MEASUREMENTVAR= 21, -0.083, MPa, nulmeting conus na sondering +#MEASUREMENTVAR= 22, 0.002, MPa, nulmeting wrijving voor sondering +#MEASUREMENTVAR= 23, 0.001, MPa, nulmeting wrijving na sondering +#MEASUREMENTVAR= 30, 0.8, graden, nulmeting helling voor sondering +#MEASUREMENTVAR= 31, 0.5, graden, nulmeting helling na sondering +#REPORTCODE= GEF-CPT-Report, 1,1,1,- +#PROJECTID= CPT, 2422-220749, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM +#STARTDATE= 2022,12,11 +#STARTTIME= 08,18,21 +#TESTID= 112-074.400-2022-01-CPT-01 +#XYID= 31000, 85712.1204, 442265.2594, 0.03, 0.03 +#ZID= 31000, -0.9055, 0.03 +#ANALYSISTEXT= 131, Discovery GeoVisual v.3.9.0.53137, conversie programma +#ANALYSISTEXT= 1401, a13f8a79-7dde-4f39-8d2a-d071598d7be2, Fugro logging session ID +#ANALYSISTEXT= 1402, 76bebae6-f2cc-44a6-bb3b-2605af042d43, Fugro logging session version ID +#ANALYSISTEXT= 1403, 81d4b150-9157-415f-b791-095ae6444502, Fugro test ID +#EOH= +0.00 0.797 -99999.000 -99999.000 -0.791 -0.523 0.00 110.5 +0.02 1.029 -99999.000 -99999.000 -1.118 -0.131 0.02 111.8 +0.04 1.448 -99999.000 -99999.000 -1.133 -0.662 0.04 112.9 +0.06 2.857 -99999.000 -99999.000 -1.634 0.323 0.06 114.1 +0.08 1.439 0.001 0.035 -0.810 -0.270 0.08 115.2 +0.10 1.750 0.003 0.189 -1.131 0.071 0.10 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+1,1326 @@ +#GEFID= 1, 1, 0 +#COLUMN= 8 +#COLUMNINFO= 1, m, Sondeerlengte, 1 +#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 +#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 +#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 +#COLUMNINFO= 5, graden, Helling X, 21 +#COLUMNINFO= 6, graden, Helling Y, 22 +#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 +#COLUMNINFO= 8, s, Tijd, 12 +#COLUMNTEXT= 0, off +#COLUMNVOID= 1, -99999.00 +#COLUMNVOID= 2, -99999.000 +#COLUMNVOID= 3, -99999.000 +#COLUMNVOID= 4, -99999.000 +#COLUMNVOID= 5, -99999.000 +#COLUMNVOID= 6, -99999.000 +#COLUMNVOID= 7, -99999.00 +#COLUMNVOID= 8, -99999.0 +#COMPANYID= Fugro NL Land B.V., 27114147, 31 +#DATAFORMAT= ASCII +#FILEDATE= 2022,12,21 +#FILEOWNER= P. 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index 0000000..da10b8d --- /dev/null +++ b/tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef @@ -0,0 +1,1332 @@ +#GEFID= 1, 1, 0 +#COLUMN= 8 +#COLUMNINFO= 1, m, Sondeerlengte, 1 +#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 +#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 +#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 +#COLUMNINFO= 5, graden, Helling X, 21 +#COLUMNINFO= 6, graden, Helling Y, 22 +#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 +#COLUMNINFO= 8, s, Tijd, 12 +#COLUMNTEXT= 0, off +#COLUMNVOID= 1, -99999.00 +#COLUMNVOID= 2, -99999.000 +#COLUMNVOID= 3, -99999.000 +#COLUMNVOID= 4, -99999.000 +#COLUMNVOID= 5, -99999.000 +#COLUMNVOID= 6, -99999.000 +#COLUMNVOID= 7, -99999.00 +#COLUMNVOID= 8, -99999.0 +#COMPANYID= Fugro NL Land B.V., 27114147, 31 +#DATAFORMAT= ASCII +#FILEDATE= 2022,12,21 +#FILEOWNER= P. Gommer-Reniers +#LASTSCAN= 1262 +#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever +#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam +#MEASUREMENTTEXT= 3, Schiedam, plaatsnaam +#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-2757, conus type/serienummer +#MEASUREMENTTEXT= 5, SSC/NDL/JBS, sondeerapparaat/operator +#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm +#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem +#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau +#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak +#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten +#MEASUREMENTTEXT= 21, ja, correctie meterstrepen +#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling +#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling +#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt +#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel +#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt +#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel +#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel +#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig +#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig +#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig +#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig +#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig +#MEASUREMENTVAR= 12, 0, -, elektrische sondering +#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte +#MEASUREMENTVAR= 16, 25.01, m, einddiepte sondering +#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt +#MEASUREMENTVAR= 20, -0.092, MPa, nulmeting conus voor sondering +#MEASUREMENTVAR= 21, -0.094, MPa, nulmeting conus na sondering +#MEASUREMENTVAR= 22, 0.003, MPa, nulmeting wrijving voor sondering +#MEASUREMENTVAR= 23, -0.001, MPa, nulmeting wrijving na sondering +#MEASUREMENTVAR= 30, 0.5, graden, nulmeting helling voor sondering 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b/tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef new file mode 100644 index 0000000..7a5d2cb --- /dev/null +++ b/tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef @@ -0,0 +1,1334 @@ +#GEFID= 1, 1, 0 +#COLUMN= 9 +#COLUMNINFO= 1, m, Sondeerlengte, 1 +#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 +#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 +#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 +#COLUMNINFO= 5, MPa, Waterspanning u2, 6 +#COLUMNINFO= 6, graden, Helling X, 21 +#COLUMNINFO= 7, graden, Helling Y, 22 +#COLUMNINFO= 8, m, Gecorrigeerde diepte, 11 +#COLUMNINFO= 9, s, Tijd, 12 +#COLUMNTEXT= 0, off +#COLUMNVOID= 1, -99999.00 +#COLUMNVOID= 2, -99999.000 +#COLUMNVOID= 3, -99999.000 +#COLUMNVOID= 4, -99999.000 +#COLUMNVOID= 5, -99999.000 +#COLUMNVOID= 6, -99999.000 +#COLUMNVOID= 7, -99999.000 +#COLUMNVOID= 8, -99999.00 +#COLUMNVOID= 9, -99999.0 +#COMPANYID= Fugro NL Land B.V., 27114147, 31 +#DATAFORMAT= ASCII +#FILEDATE= 2022,12,21 +#FILEOWNER= P. 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oppervlaktequotient van de conuspunt +#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel +#MEASUREMENTVAR= 5, 87.8, mm, afstand conus tot midden kleefmantel +#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig +#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig +#MEASUREMENTVAR= 8, 1, -, waterdruk-meter u2 is aanwezig +#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig +#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig +#MEASUREMENTVAR= 12, 0, -, elektrische sondering +#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte +#MEASUREMENTVAR= 16, 25.06, m, einddiepte sondering +#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt +#MEASUREMENTVAR= 20, -0.090, MPa, nulmeting conus voor sondering +#MEASUREMENTVAR= 21, -0.114, MPa, nulmeting conus na sondering +#MEASUREMENTVAR= 22, 0.008, MPa, nulmeting wrijving voor sondering +#MEASUREMENTVAR= 23, 0.009, MPa, nulmeting wrijving na sondering +#MEASUREMENTVAR= 26, -0.003, MPa, nulmeting WSM u2 voor 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+++ b/tests/data/DelftSchiedam_cpts/112-074.800-2022-01-CPT-01.gef @@ -0,0 +1,1323 @@ +#GEFID= 1, 1, 0 +#COLUMN= 8 +#COLUMNINFO= 1, m, Sondeerlengte, 1 +#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 +#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 +#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 +#COLUMNINFO= 5, graden, Helling X, 21 +#COLUMNINFO= 6, graden, Helling Y, 22 +#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 +#COLUMNINFO= 8, s, Tijd, 12 +#COLUMNTEXT= 0, off +#COLUMNVOID= 1, -99999.00 +#COLUMNVOID= 2, -99999.000 +#COLUMNVOID= 3, -99999.000 +#COLUMNVOID= 4, -99999.000 +#COLUMNVOID= 5, -99999.000 +#COLUMNVOID= 6, -99999.000 +#COLUMNVOID= 7, -99999.00 +#COLUMNVOID= 8, -99999.0 +#COMPANYID= Fugro NL Land B.V., 27114147, 31 +#DATAFORMAT= ASCII +#FILEDATE= 2022,12,21 +#FILEOWNER= P. Gommer-Reniers +#LASTSCAN= 1254 +#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever +#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam +#MEASUREMENTTEXT= 3, Schiedam, plaatsnaam +#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-2757, conus type/serienummer +#MEASUREMENTTEXT= 5, SSC/NDL/JBS, sondeerapparaat/operator +#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm +#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem +#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau +#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak +#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten +#MEASUREMENTTEXT= 21, ja, correctie meterstrepen +#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling +#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling +#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt +#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel +#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt +#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel +#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel +#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig +#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig +#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig +#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig +#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig +#MEASUREMENTVAR= 12, 0, -, elektrische sondering +#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte +#MEASUREMENTVAR= 16, 25.02, m, einddiepte sondering +#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt +#MEASUREMENTVAR= 20, -0.064, MPa, nulmeting conus voor sondering +#MEASUREMENTVAR= 21, -0.076, MPa, nulmeting conus na sondering +#MEASUREMENTVAR= 22, -0.001, MPa, nulmeting wrijving voor sondering +#MEASUREMENTVAR= 23, -0.001, MPa, nulmeting wrijving na sondering +#MEASUREMENTVAR= 30, 0.7, graden, nulmeting helling voor sondering 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17.592 0.112 0.635 1.522 2.577 24.03 1781.2 +24.08 17.870 0.124 0.680 1.536 2.591 24.05 1782.3 +24.10 18.454 0.132 0.695 1.522 2.596 24.07 1783.4 +24.12 19.273 0.133 0.669 1.512 2.634 24.09 1784.5 +24.14 20.389 0.133 0.628 1.544 2.666 24.11 1785.6 +24.16 22.151 0.136 0.603 1.578 2.659 24.13 1786.7 +24.18 24.348 0.145 0.605 1.579 2.696 24.15 1787.8 +24.20 26.495 0.142 0.569 1.613 2.703 24.17 1788.9 +24.22 28.199 0.139 0.532 1.644 2.712 24.19 1789.9 +24.24 29.122 0.141 0.518 1.622 2.701 24.21 1791.1 +24.26 29.698 0.163 0.570 1.630 2.657 24.23 1792.1 +24.28 29.912 0.180 0.607 1.647 2.665 24.25 1794.8 +24.30 29.928 0.192 0.626 1.631 2.652 24.27 1798.2 +24.32 30.039 0.207 0.652 1.611 2.642 24.29 1801.6 +24.34 31.585 0.209 0.637 1.601 2.669 24.31 1802.7 +24.36 33.172 0.224 0.662 1.623 2.658 24.33 1803.9 +24.38 34.647 0.241 0.696 1.636 2.656 24.35 1805.1 +24.40 35.993 0.258 0.723 1.626 2.669 24.37 1806.4 +24.42 37.252 0.269 0.726 1.633 2.685 24.39 1807.8 +24.44 38.505 0.288 0.751 1.659 2.677 24.41 1809.1 +24.46 39.717 0.304 0.769 1.677 2.688 24.43 1810.6 +24.48 40.843 0.323 0.795 1.734 2.652 24.45 1812.2 +24.50 41.927 0.333 0.801 1.729 2.692 24.47 1813.8 +24.52 42.799 0.349 0.821 1.704 2.685 24.49 1815.4 +24.54 43.502 0.358 0.831 1.739 2.698 24.51 1817.2 +24.56 44.162 0.363 0.832 1.763 2.734 24.53 1819.0 +24.58 44.662 0.348 0.792 1.778 2.759 24.55 1820.8 +24.60 44.838 0.332 0.752 1.753 2.755 24.57 1822.8 +24.62 44.689 0.320 0.719 1.787 2.754 24.59 1824.7 +24.64 44.184 0.329 0.743 1.815 2.753 24.61 1826.7 +24.66 43.211 0.337 0.765 1.812 2.779 24.63 1833.3 +24.68 42.225 0.350 0.809 1.810 2.786 24.65 1840.3 +24.70 41.239 0.361 0.891 1.808 2.794 24.67 1847.3 +24.72 40.268 0.366 0.970 1.805 2.782 24.69 1853.4 +24.74 39.101 0.369 1.012 1.798 2.799 24.71 1860.4 +24.76 36.863 0.361 1.025 1.776 2.811 24.73 1862.8 +24.78 34.170 0.352 1.051 1.768 2.751 24.75 1864.1 +24.80 31.216 0.334 1.038 1.768 2.775 24.77 1865.3 +24.82 28.653 0.315 1.028 1.786 2.781 24.79 1866.5 +24.84 26.950 0.295 1.027 1.777 2.731 24.81 1867.6 +24.86 25.884 0.269 0.993 1.747 2.779 24.83 1868.7 +24.88 25.240 0.248 0.958 1.750 2.770 24.85 1869.8 +24.90 24.553 0.235 0.937 1.743 2.755 24.87 1870.9 +24.92 23.947 0.218 0.896 1.754 2.779 24.89 1872.0 +24.94 23.527 0.204 0.860 1.721 2.780 24.91 1873.1 +24.96 23.167 0.192 0.823 1.729 2.772 24.93 1874.1 +24.98 22.653 -99999.000 -99999.000 1.754 2.768 24.95 1875.2 +25.00 22.206 -99999.000 -99999.000 1.768 2.733 24.97 1876.3 +25.02 21.871 -99999.000 -99999.000 1.777 2.725 24.99 1877.4 +25.04 21.323 -99999.000 -99999.000 1.765 2.689 25.01 1878.4 +25.05 20.743 -99999.000 -99999.000 1.752 2.710 25.02 1879.0 diff --git a/tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef b/tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef new file mode 100644 index 0000000..5239f4b --- /dev/null +++ b/tests/data/DelftSchiedam_cpts/CPT000000217393_IMBRO.gef @@ -0,0 +1,1362 @@ +#GEFID= 1, 1, 0 +#COLUMN= 13 +#COLUMNINFO= 1, m (meter), sondeertrajectlengte, 1 +#COLUMNINFO= 2, MPa (megaPascal), conusweerstand, 2 +#COLUMNINFO= 3, m (meter), diepte, 11 +#COLUMNINFO= 4, s (seconde), verlopen tijd, 12 +#COLUMNINFO= 5, MPa (megaPascal), gecorrigeerde conusweerstand, 13 +#COLUMNINFO= 6, MPa (megaPascal), netto conusweerstand, 14 +#COLUMNINFO= 7, ° (graden), helling x, 21 +#COLUMNINFO= 8, ° (graden), helling y, 22 +#COLUMNINFO= 9, ° (graden), hellingresultante, 8 +#COLUMNINFO= 10, MPa (megaPascal), plaatselijke wrijving, 3 +#COLUMNINFO= 11, geen, porienratio, 15 +#COLUMNINFO= 12, MPa (megaPascal), waterspanning u2, 6 +#COLUMNINFO= 13, % (procent; MPa/MPa), wrijvingsgetal, 4 +#COLUMNSEPARATOR= ; +#COLUMNTEXT= 1, aan +#COLUMNVOID= 1, 999.999 +#COLUMNVOID= 2, 999.999 +#COLUMNVOID= 3, 999.999 +#COLUMNVOID= 4, 99999.9 +#COLUMNVOID= 5, 999.999 +#COLUMNVOID= 6, 999.999 +#COLUMNVOID= 7, 99 +#COLUMNVOID= 8, 99 +#COLUMNVOID= 9, 99 +#COLUMNVOID= 10, 9.999 +#COLUMNVOID= 11, 99.999 +#COLUMNVOID= 12, 99.999 +#COLUMNVOID= 13, 999.9 +#COMPANYID= uitvoerder onderzoek, wordt niet uitgeleverd, - +#FILEDATE= 2024, 12, 04 +#FILEOWNER= Basisregistratie Ondergrond +#LASTSCAN= 1261 +#MEASUREMENTTEXT= 4, CP15-CF75PB1SN2-P1E1M4-V12-S1/1701-3420, conustype +#MEASUREMENTTEXT= 5, Fugro Cone Penetrometer MkII, omschrijving sondeerapparaat +#MEASUREMENTTEXT= 6, NEN-EN-ISO 22476-1 / klasse 2, sondeernorm en kwaliteitsklasse +#MEASUREMENTTEXT= 9, maaiveld, lokaal verticaal referentiepunt +#MEASUREMENTTEXT= 20, nee, signaalbewerking uitgevoerd +#MEASUREMENTTEXT= 21, ja, bewerking onderbrekingen uitgevoerd +#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling +#MEASUREMENTTEXT= 43, LRG3, methode locatiebepaling +#MEASUREMENTTEXT= 101, bronhouder, 30124359, - +#MEASUREMENTTEXT= 102, opdracht publieke taakuitvoering, kader aanlevering +#MEASUREMENTTEXT= 103, infrastructuur land, kader inwinning +#MEASUREMENTTEXT= 104, uitvoerder locatiebepaling, wordt niet uitgeleverd, - +#MEASUREMENTTEXT= 105, 2022, 12, 12 +#MEASUREMENTTEXT= 106, uitvoerder verticale positiebepaling, wordt niet uitgeleverd, - +#MEASUREMENTTEXT= 107, 2022, 12, 12 +#MEASUREMENTTEXT= 109, nee, dissipatietest uitgevoerd +#MEASUREMENTTEXT= 110, ja, expertcorrectie uitgevoerd +#MEASUREMENTTEXT= 111, nee, aanvullend onderzoek uitgevoerd +#MEASUREMENTTEXT= 112, 2023, 04, 17 +#MEASUREMENTTEXT= 113, 2022, 12, 21 +#MEASUREMENTTEXT= 115, IMBRO, kwaliteitsregime +#MEASUREMENTTEXT= 116, 2023, 06, 01, 13, 45, 22 +#MEASUREMENTTEXT= 117, voltooid, registratiestatus +#MEASUREMENTTEXT= 118, 2023, 06, 01, 13, 45, 22 +#MEASUREMENTTEXT= 119, nee, gecorrigeerd +#MEASUREMENTTEXT= 121, nee, in onderzoek +#MEASUREMENTTEXT= 123, nee, uit registratie genomen +#MEASUREMENTTEXT= 125, nee, weer in registratie genomen +#MEASUREMENTTEXT= 127, 4258, 51.961881, 4.380341 +#MEASUREMENTTEXT= 128, RDNAPTRANS2018, toegepaste transformatie +#MEASUREMENTVAR= 1, 1510, mm2 (vierkante millimeter), oppervlakte conuspunt +#MEASUREMENTVAR= 2, 19895, mm2 (vierkante millimeter), oppervlakte kleefmantel +#MEASUREMENTVAR= 3, 0.58, geen, oppervlaktequoti�nt conuspunt +#MEASUREMENTVAR= 4, 1.0, geen, oppervlaktequoti�nt kleefmantel +#MEASUREMENTVAR= 5, 88, mm (millimeter), afstand conus tot midden kleefmantel +#MEASUREMENTVAR= 12, 4, -, sondeermethode +#MEASUREMENTVAR= 13, 0.00, m (meter), voorgeboord tot +#MEASUREMENTVAR= 16, 25.057, m (meter), einddiepte +#MEASUREMENTVAR= 17, 0, -, stopcriterium +#MEASUREMENTVAR= 20, -0.090, MPa (megaPascal), conusweerstand vooraf +#MEASUREMENTVAR= 21, -0.114, MPa (megaPascal), conusweerstand achteraf +#MEASUREMENTVAR= 22, 0.008, MPa (megaPascal), plaatselijke wrijving vooraf +#MEASUREMENTVAR= 23, 0.009, MPa (megaPascal), plaatselijke wrijving achteraf +#MEASUREMENTVAR= 24, -, MPa (megaPascal), waterspanning u1 vooraf +#MEASUREMENTVAR= 25, -, MPa (megaPascal), waterspanning u1 achteraf +#MEASUREMENTVAR= 26, -0.003, MPa (megaPascal), waterspanning u2 vooraf +#MEASUREMENTVAR= 27, -0.004, MPa (megaPascal), waterspanning u2 achteraf +#MEASUREMENTVAR= 28, -, 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Cone Penetrometer MkII, omschrijving sondeerapparaat +#MEASUREMENTTEXT= 6, NEN-EN-ISO 22476-1 / klasse 2, sondeernorm en kwaliteitsklasse +#MEASUREMENTTEXT= 9, maaiveld, lokaal verticaal referentiepunt +#MEASUREMENTTEXT= 20, nee, signaalbewerking uitgevoerd +#MEASUREMENTTEXT= 21, ja, bewerking onderbrekingen uitgevoerd +#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling +#MEASUREMENTTEXT= 43, LRG3, methode locatiebepaling +#MEASUREMENTTEXT= 101, bronhouder, 30124359, - +#MEASUREMENTTEXT= 102, opdracht publieke taakuitvoering, kader aanlevering +#MEASUREMENTTEXT= 103, infrastructuur land, kader inwinning +#MEASUREMENTTEXT= 104, uitvoerder locatiebepaling, wordt niet uitgeleverd, - +#MEASUREMENTTEXT= 105, 2022, 12, 12 +#MEASUREMENTTEXT= 106, uitvoerder verticale positiebepaling, wordt niet uitgeleverd, - +#MEASUREMENTTEXT= 107, 2022, 12, 12 +#MEASUREMENTTEXT= 109, nee, dissipatietest uitgevoerd +#MEASUREMENTTEXT= 110, ja, expertcorrectie uitgevoerd +#MEASUREMENTTEXT= 111, nee, 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121, nee, in onderzoek +#MEASUREMENTTEXT= 123, nee, uit registratie genomen +#MEASUREMENTTEXT= 125, nee, weer in registratie genomen +#MEASUREMENTTEXT= 127, 4258, 51.962745, 4.379945 +#MEASUREMENTTEXT= 128, RDNAPTRANS2018, toegepaste transformatie +#MEASUREMENTVAR= 1, 1510, mm2 (vierkante millimeter), oppervlakte conuspunt +#MEASUREMENTVAR= 2, 19895, mm2 (vierkante millimeter), oppervlakte kleefmantel +#MEASUREMENTVAR= 3, 0.58, geen, oppervlaktequoti�nt conuspunt +#MEASUREMENTVAR= 4, 1.0, geen, oppervlaktequoti�nt kleefmantel +#MEASUREMENTVAR= 5, 87, mm (millimeter), afstand conus tot midden kleefmantel +#MEASUREMENTVAR= 12, 4, -, sondeermethode +#MEASUREMENTVAR= 13, 0.00, m (meter), voorgeboord tot +#MEASUREMENTVAR= 16, 25.012, m (meter), einddiepte +#MEASUREMENTVAR= 17, 0, -, stopcriterium +#MEASUREMENTVAR= 20, -0.092, MPa (megaPascal), conusweerstand vooraf +#MEASUREMENTVAR= 21, -0.094, MPa (megaPascal), conusweerstand achteraf +#MEASUREMENTVAR= 22, 0.003, MPa (megaPascal), 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CPT000000217408_IMBRO.gef + #place_mark_circle_highlight + + true + relativeToGround + 4.379426638,51.963590388 + + + + CPT000000217402.xml + #place_mark_circle_highlight + + true + relativeToGround + 4.379010355,51.964487234 + + + + CPT000000217402_IMBRO.gef + #place_mark_circle_highlight + + true + relativeToGround + 4.379010355,51.964487234 + + + + CPT000000217409.xml + #place_mark_circle_highlight + + true + relativeToGround + 4.37994462,51.962745153 + + + + CPT000000217409_IMBRO.gef + #place_mark_circle_highlight + + true + relativeToGround + 4.37994462,51.962745153 + + + + CPT000000217393.xml + #place_mark_circle_highlight + + true + relativeToGround + 4.380341466,51.961880998 + + + + CPT000000217393_IMBRO.gef + #place_mark_circle_highlight + + true + relativeToGround + 4.380341466,51.961880998 + + + + CPT000000217406.xml + #place_mark_circle_highlight + + true + relativeToGround + 4.380857047,51.961036002 + + + + CPT000000217406_IMBRO.gef + #place_mark_circle_highlight + + true + relativeToGround + 4.380857047,51.961036002 + + + + + From ae546bbe598c1458e0cba8bc826026ded3b0a61c Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Wed, 4 Dec 2024 15:00:35 +0100 Subject: [PATCH 19/62] added dependencies scikit-learn and d-geolib-plus modified: random_fields/geostatistical_cpt_interpretation.py modified: setup.cfg --- random_fields/geostatistical_cpt_interpretation.py | 13 +++++++++++-- setup.cfg | 3 ++- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 253d1c7..56373e2 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -19,6 +19,10 @@ from geolib_plus.robertson_cpt_interpretation import OCRMethod from geolib_plus.robertson_cpt_interpretation import ShearWaveVelocityMethod +import logging + + + class MarginalTransformation(): """Class for the transformation model between the distributions of the pysical values and the standard-normal distribution @@ -155,6 +159,9 @@ def read_cpt_data(self,addition = False): self.cpt_list = [] self.cpt_filepaths = [] + # temporarily diable warning logging + initial_logging_level = logging.getLogger().getEffectiveLevel() + logging.disable(logging.ERROR) for gef_name in os.listdir(self.cpt_directory): if gef_name.endswith('.gef'): @@ -180,6 +187,8 @@ def read_cpt_data(self,addition = False): except: raise Warning('Ignoring file {gef_name}. xml-files for CPT data may not be supported by geolib_plus?') + # set logging level back to initial level + logging.disable(initial_logging_level) def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterpretation): @@ -366,8 +375,8 @@ def __init__(self,nb_dimensions: int,v_dim: int, kernel = None): # if kernel does not exist, create a default if not kernel: length_scale = [100.]*nb_dimensions - length_scale_bounds = [[1.0,1000.],[1.0,1000.],[1.0,1000.]] - length_scale[v_dim] = 1. + length_scale_bounds = [[1.0,1000.]]*nb_dimensions + length_scale[v_dim] = self.v_dim length_scale_bounds[v_dim] = [0.01,10.] diff --git a/setup.cfg b/setup.cfg index a0756ad..555a20e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,7 +17,8 @@ install_requires = numpy>=1.24 gstools>=1.5.0 matplotlib>=3.7.1 - scikit-learn + scikit-learn>=1.5.2 + d-geolib-plus>=0.4.0 python_requires = >=3.8 [options.extras_require] From 5bc2477776f5643699a97870b5a1b741d0dc8b9d Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Wed, 4 Dec 2024 15:11:53 +0100 Subject: [PATCH 20/62] minor fix modified: random_fields/geostatistical_cpt_interpretation.py --- .../geostatistical_cpt_interpretation.py | 44 +------------------ 1 file changed, 1 insertion(+), 43 deletions(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 56373e2..b028551 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -533,46 +533,4 @@ def generate(self,coordinates): # # only the Young modulus is communicated back as the generated field... - self.generated_field = list(self.young_modulus) - -if __name__ == '__main__': - - folder = r'.' - - x = np.linspace( -50, 50, 41) - y = np.linspace(-10, -0, 41) - z = np.linspace(-20, 20, 41) - x, y, z = np.meshgrid(x, y, z) - xy_mesh = np.array([x.ravel(), y.ravel(), z.ravel()]).T - - efc = ElasticityFieldsFromCpt(cpt_file_folder = folder, - based_on_midpoint = True, - max_conditioning_points = 1000, - orientation_x_axis = 162) - efc.calibrate_geostat_model(calibration_indices=(0,1,2)) - - print(efc.geostat_model.gpr.kernel_) - - #%matplotlib qt - - efc.generate(xy_mesh) - - fig = plt.figure() - ax = fig.add_subplot(projection='3d') - x = xy_mesh[:,0] - y = xy_mesh[:,2] - z = xy_mesh[:,1] - xd = efc.conditioning_data.data_coords[:,0] - yd = efc.conditioning_data.data_coords[:,2] - zd = efc.conditioning_data.data_coords[:,1] - #%% - - fig = plt.figure() - ax = fig.add_subplot(projection='3d') - ax.scatter(x, y, z,c=efc.rho, edgecolors=None, marker="s") - ax.scatter(xd, yd, zd,c=efc.conditioning_data.rho) - ax.set_xlabel('x') - ax.set_ylabel('y') - ax.set_zlabel('z') - ax.set_zlim([-25,5]) - plt.axis('equal') \ No newline at end of file + self.generated_field = list(self.young_modulus) \ No newline at end of file From 59eebfc3c07cef34dd06228b65a9397f5b162863 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Wed, 4 Dec 2024 15:22:35 +0100 Subject: [PATCH 21/62] set numpy version from ==1.24 to >= 1.24 modified: requirements.txt modified: requirements_dev.txt --- requirements.txt | 2 +- requirements_dev.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index cf547c7..8e4ee46 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy==1.24.2 +numpy>=1.24.2 gstools==1.5.0 matplotlib>=3.7.0 scikit-learn==1.5.2 diff --git a/requirements_dev.txt b/requirements_dev.txt index d4caef6..8a217aa 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,4 +1,4 @@ -numpy==1.24.2 +numpy>=1.24.2 gstools==1.5.0 matplotlib>=3.7.0 scikit-learn==1.5.2 From 5a1b43156a705b286e4fae738bbe3671abf0a6dc Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 6 Dec 2024 10:15:51 +0100 Subject: [PATCH 22/62] updated docstrings and cleanup modified: random_fields/geostatistical_cpt_interpretation.py modified: requirements.txt modified: requirements_dev.txt modified: setup.cfg deleted: tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef deleted: tests/data/DelftSchiedam_cpts/112-074.500-2022-01-CPT-01.gef deleted: tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef deleted: tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef deleted: tests/data/DelftSchiedam_cpts/112-074.800-2022-01-CPT-01.gef --- .../geostatistical_cpt_interpretation.py | 237 +-- requirements.txt | 1 + requirements_dev.txt | 1 + setup.cfg | 1 + .../112-074.400-2022-01-CPT-01.gef | 1325 ---------------- .../112-074.500-2022-01-CPT-01.gef | 1326 ---------------- .../112-074.600-2022-01-CPT-01.gef | 1332 ---------------- .../112-074.700-2022-01-CPT-01.gef | 1334 ----------------- .../112-074.800-2022-01-CPT-01.gef | 1323 ---------------- 9 files changed, 156 insertions(+), 6724 deletions(-) delete mode 100644 tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef delete mode 100644 tests/data/DelftSchiedam_cpts/112-074.500-2022-01-CPT-01.gef delete mode 100644 tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef delete mode 100644 tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef delete mode 100644 tests/data/DelftSchiedam_cpts/112-074.800-2022-01-CPT-01.gef diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index b028551..1ec6237 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -1,12 +1,13 @@ #%% -from typing import Optional +from typing import Optional, Tuple import os import numpy as np import numpy.typing as npt import matplotlib.pyplot as plt import scipy.stats as st +import logging from generate_field import RandomFields, ModelName @@ -19,13 +20,17 @@ from geolib_plus.robertson_cpt_interpretation import OCRMethod from geolib_plus.robertson_cpt_interpretation import ShearWaveVelocityMethod -import logging - - +class MarginalTransformation(): + """Class for the transformation model between the distributions of the pysical values and the standard-normal distribution. + Attributes: + - data (npt.NDArray[np.float64]): sampling data to construct transformation + - n (int): number of samples: length of `data` + - lognormal (bool): If True, treats the data as lognormally distributed. Defaults to False. + - _interp_z (npt.NDArray[np.float64]): internal interpolation data for transformation + - _interp_data (npt.NDArray[np.float64]): internal interpolation data for transformation + - _interp_log_data (npt.NDArray[np.float64]): internal interpolation data for transformation -class MarginalTransformation(): - """Class for the transformation model between the distributions of the pysical values and the standard-normal distribution """ def __init__(self, data: npt.NDArray[np.float64], @@ -36,55 +41,56 @@ def __init__(self, Initiate class Args: - - data (array-like): sampling data to construct transformation - - min (float, optional): _description_. Defaults to None. - - max (float, optional): _description_. Defaults to None. - - lognormal (bool, optional): _description_. Defaults to False. + - data (array-like): sample data to construct transformation + - min (float, optional): minimum value of the population. Defaults to None. + - max (float, optional): maximum value of the population. Defaults to None. + - lognormal (bool, optional): If True, treats the data as lognormally distributed. Defaults to False. """ self.data = np.sort(data) self.n = len(data) self.lognormal = lognormal + # if `max` and `min` are not given: add a 1% margin on the data if type(min) == type(None): - self.min = self.data.min() - (self.data.max() - self.data.min())/100 - self.min_ln = np.log(self.data.min()) - (np.log(self.data.max()) - np.log(self.data.min()))/100 + min = self.data.min() - (self.data.max() - self.data.min())/100 + min_ln = np.log(self.data.min()) - (np.log(self.data.max()) - np.log(self.data.min()))/100 else: - self.min = min - self.min_ln = np.log(min) + min_ln = np.log(min) if type(max) == type(None): - self.max = self.data.max() + (self.data.max() - self.data.min())/100 - self.max_ln = np.log(self.data.max()) + (np.log(self.data.max()) - np.log(self.data.min()))/100 + max = self.data.max() + (self.data.max() - self.data.min())/100 + max_ln = np.log(self.data.max()) + (np.log(self.data.max()) - np.log(self.data.min()))/100 else: - self.max = max - self.max_ln = np.log(max) + max_ln = np.log(max) self.z = st.norm.ppf(np.linspace(.5/self.n,1-.5/self.n,self.n)) - x_points = np.hstack([self.min,self.data,self.max]) - x_points_ln = np.hstack([self.min_ln,np.log(self.data),self.max_ln]) + # adds minimum and maximum to data-set + x_points = np.hstack([min,self.data,max]) + x_points_ln = np.hstack([min_ln,np.log(self.data),max_ln]) z_points = np.hstack([-8.,self.z,8.]) - self._z = np.linspace(-8,8,101) - self._data = np.interp(self._z,z_points,x_points) - self._log_data = np.interp(self._z,z_points,x_points_ln) + # initiates internal points for interpolation + self._interp_z = np.linspace(-8,8,101) + self._interp_data = np.interp(self._interp_z,z_points,x_points) + self._interp_log_data = np.interp(self._interp_z,z_points,x_points_ln) def plot(self, title: str = "Marginal distribution transformation ", x_label: str = '$Z\sim N(0,1)$', y_label: str = '$X $', - figsize: tuple = (6,5)): + figsize: tuple = (6.,5.)): """Plots the transformation model for the marginal distribution. Args: title (str, optional): plot title. Defaults to "Marginal distribution transformation ". x_label (str, optional): horizontal axis label. Defaults to '\sim N(0,1)$'. y_label (str, optional): vertical axis label. Defaults to ' $'. - figsize (tuple, optional): figure size. Defaults to (6,5). + figsize (Tuple(float), optional): figure size. Defaults to (6.,5.). """ plt.figure(figsize = figsize) plt.plot(self.z,self.data,'.',color = 'gray',alpha = 0.5,label = 'data') - plt.plot(self._z,self._d,'-',color = 'r',label = 'transformation model') + plt.plot(self._interp_z,self._interp_data,'-',color = 'r',label = 'transformation model') plt.xlabel(x_label) plt.ylabel(y_label) plt.legend() @@ -92,8 +98,9 @@ def plot(self, plt.grid() plt.show() - def XtoZ(self,x: npt.NDArray[np.float64]): - """XtoZ transforms data from the distribution of $$X$$ to the standard normal + def x_to_z(self, + x: npt.NDArray[np.float64]): + """Transforms data from the distribution of $$X$$ to the standard normal equivalent distribution $$Z\sim N(0,1)$$ Args: @@ -103,13 +110,14 @@ def XtoZ(self,x: npt.NDArray[np.float64]): z (npt.NDArray[np.float64]): standard-normal equivalent to `x` """ if self.lognormal: - z = np.interp(np.log(x),self._log_data,self._z) + z = np.interp(np.log(x),self._interp_log_data,self._interp_z) else: - z = np.interp(x,self._data,self._z) + z = np.interp(x,self._interp_data,self._interp_z) return z - def ZtoX(self,Z): - """ZtoX transforms from a standard-normal values `z` to the corresponding values in `x`. + def z_to_x(self, + z:npt.NDArray[np.float64]): + """Transforms from a standard-normal values `z` to the corresponding values in `x`. Args: z (npt.NDArray[np.float64]): standard normal values $z \in Z\sim N(0,1)$ @@ -118,22 +126,34 @@ def ZtoX(self,Z): x (npt.NDArray[np.float64]): standard-normal equivalent to `x` """ if self.lognormal: - x = np.exp(np.interp(Z,self._z,self._log_data)) + x = np.exp(np.interp(z,self._interp_z,self._interp_log_data)) else: - x = np.interp(Z,self._z,self._data) + x = np.interp(z,self._interp_z,self._interp_data) return x class CPT_data(): - """CPT_data _summary_ - - + """CPT_data class for the handling and interpretation of a series of CPT files in .GEF data format. + + Attributes: + - gravity (float): gravity in meters per second squared + - cpt_list (List(class:`GefCPT`)): list of cpt instances + - cpt_directory (str): path from which the .gef files must be read + - cpt_filepaths (list): list of interpreted cpt filepaths + - interpreter (class:`RobertsonCptInterpretation`): instance of D-GeoLib+ CPT interpretation class + - data_coords (npt.NDArray(np.float64)): XYZ coordinates of the data + - cpt_locations (npt.NDArray(np.float64)): XZ coordinates of the data + - rho (npt.NDArray(np.float64)): interpreted density at CPT coordinates in `data_coord` + - vs (npt.NDArray(np.float64)): interpreted shear wave velocity at CPT coordinates in `data_coord` + - g0 (npt.NDArray(np.float64)): interpreted g0 at CPT coordinates in `data_coord` + - young_modulus (npt.NDArray(np.float64)): interpreted Young modulus at CPT coordinates in `data_coord` + """ def __init__(self,cpt_directory = './'): - """__init__ _summary_ + """Initiator of instances of the class. Args: - cpt_directory (str, optional): _description_. Defaults to './'. + cpt_directory (str, optional): Path to the folder containing the cpt files to be interpreted and used for the geostatistical model. Defaults to './'. """ self.gravity = 9.81 self.cpt_list = [] @@ -195,7 +215,7 @@ def interpret_cpt_data(self,interpreter = None,v_dim = 1):# = RobertsonCptInterp """interpret_cpt_data interprets the CPT data based on the provided interpreter settings Args: - interpreter (:class:`geolib_plus.robertson_cpt_interpretation.RobertsonCptInterpretation`, optional): Interpretation methods. Defaults to RobertsonCptInterpretation. + interpreter (:class:`geolib_plus.robertson_cpt_interpretation.RobertsonCptInterpretation`, optional): Interpretation methods. Defaults to :class:`RobertsonCptInterpretation`. """ @@ -336,15 +356,17 @@ def plot_coordinates(self,original_coordinates = False): plt.show() class GeostatisticalModel(): - """GeostatisticalModel + """Class for the characterisation of spatial variability by means of a geostatistical model Attr: - - nb_dimensions (int): - - v_dim (int): + - nb_dimensions (int): number of physical dimensions (1, 2 or 3) + - v_dim (int): index corresponding to vertical - gpr (:class:`sklearn.gaussian_process.GaussianProcessRegressor`). Gaussian process regressor. - - data_coords (npt.NDArray[np.float64]): - - data_values (npt.NDArray[np.float64]): - - noise_level (float): Defaults to None + - data_coords (npt.NDArray[np.float64]): coordinates of the data used for the calibtration of the geostatistical model. + - data_values (npt.NDArray[np.float64]): values of the data used for the calibtration of the geostatistical model. + - noise_level (float): noise level b + - vertical_scale_fluctuation (float): vertical scale of fluctuation + - anisotropy (list): anisotropy in the scale of fluctuation, relative to the vertical scale of fluctuation. """ @@ -377,12 +399,10 @@ def __init__(self,nb_dimensions: int,v_dim: int, kernel = None): length_scale = [100.]*nb_dimensions length_scale_bounds = [[1.0,1000.]]*nb_dimensions length_scale[v_dim] = self.v_dim - length_scale_bounds[v_dim] = [0.01,10.] - + length_scale_bounds[v_dim] = [0.01,10.] kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale, length_scale_bounds = length_scale_bounds) - self.gpr = GPR(kernel = kernel) @@ -393,8 +413,6 @@ def calibrate(self,coords: npt.NDArray[np.float64],values: npt.NDArray[np.float6 coords (npt.NDArray[np.float64]): data spatial coordinates values (npt.NDArray[np.float64]): data values - Raises: - Exception: """ if coords.shape[0] == self.nb_dimensions and coords.shape[1] != self.nb_dimensions: self.data_coords = coords.T @@ -409,7 +427,6 @@ def calibrate(self,coords: npt.NDArray[np.float64],values: npt.NDArray[np.float6 # calibrate self.gpr.fit(self.data_coords,self.data_values) - # extract noise level try: self.noise_level = self.gpr.kernel_.get_params()['k1__noise_level'] @@ -431,12 +448,37 @@ def calibrate(self,coords: npt.NDArray[np.float64],values: npt.NDArray[np.float6 self.anisotropy = None class ElasticityFieldsFromCpt(): - """ElasticityFieldsFromCpt - - - Class for the automatic generation of fields of elastic parameters (vs and rho) + """Class for the automatic generation of fields of elastic parameters (vs, rho, g0 and e) + + Attr: + - poisson_ratio (float): poisson ratio. + - cpt_file_folder (str): path of the folder containing the CPT files (.gef) + - x_ref (float): physical x-coordinate of the origin of the model + - y_ref (float): physical y-coordinate of the origin of the model + - orientation_x_axis (float): orientation of the x-axis of the model (azimuth, clockwise g=from the North) + - based_on_midpoint (str): If True, ignores x_ref and y_ref and places origin of the model in the center of the set of CPT locations. + - max_conditioning_points (int): Maximum number of data points to use in the calibration of the geostatistical model. + - return_property (str): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `g0` + + - conditioning_data (class:`CPT_data`): instance of the `class:CPT_data` class. + - coord_calibration (npt.NDArray[np.numpy64]): coordinates of the thinned data used for the calibration of the geostatistical model. + - thinning_sample_index (npt.NDArray[int]): Indices of the sample from the conditioning data, selected for the calibration. + - trans_model_rho (class:`MarginalTransformation`): Transformation model of the marginal distribution of rho. + - trans_model_vs (class:`MarginalTransformation`): Transformation model of the marginal distribution of shear wave velocity vs. + - geostat_model (class:`GeostatisticalModel`): Geostatistical model + - random_fields_vs (class:`RandomFields`): Random field generator for shear wave velocity `vs` + - random_fields_rho (class:`RandomFields`): Random field generator for shear wave velocity `rho` + + - vs (npt.NDArray[np.float64]): generated conditioned random field of shear wave velocity `vs` + - rho (npt.NDArray[np.float64]): generated conditioned random field of density `rho` + - g0 (npt.NDArray[np.float64]): generated conditioned random field of shear modulus `g0 = rho * vs**2` + - young_modulus (npt.NDArray[np.float64]): generated conditioned random field of elastic modulus `young_modulus = 2 * g0 * (1. + poisson_ratio)` + - generated_field (list): list of randopm field values corresponding to parameter `return_property` + + """ - + + def __init__(self, cpt_file_folder:str = './', @@ -444,8 +486,19 @@ def __init__(self, y_ref:float = 0., orientation_x_axis:float = 0., based_on_midpoint:bool = False, - max_conditioning_points:int = 2000): - + max_conditioning_points:int = 2000, + return_property:str = 'young_modulus'): + """initiation + + Args: + - cpt_file_folder (str, optional): path to tyhe folder containing the CPT files (.gef) from which the . Defaults to './'. + - x_ref (float, optional): physical x-coordinate of the origin of the model. Defaults to 0.. + - y_ref (float, optional): physical y-coordinate of the origin of the model. Defaults to 0.. + - orientation_x_axis (float, optional): orientation of the x-axis of the model (azimuth, clockwise g=from the North). Defaults to 0.. + - based_on_midpoint (bool, optional): If True, ignores x_ref and y_ref and places origin of the model in the center of the set of CPT locations. Defaults to False. + - max_conditioning_points (int, optional): Maximum number of data points to use in the calibration of the geostatistical model. Defaults to 2000. + - return_property (str, optional): property to return through attribute `ElasticityFieldsFromCpt.generated_field`. Options are `young_modulus`, `rho`, `vs`, `g0`. Defaults to `young_modulus`. + """ self.poisson_ratio = 0.2 self.cpt_file_folder = cpt_file_folder self.x_ref = x_ref @@ -453,6 +506,7 @@ def __init__(self, self.orientation_x_axis = orientation_x_axis self.based_on_midpoint = based_on_midpoint self.max_conditioning_points = max_conditioning_points + self.return_property = return_property self.conditioning_data = CPT_data(cpt_directory = self.cpt_file_folder) self.conditioning_data.read_cpt_data() @@ -463,35 +517,39 @@ def __init__(self, self.trans_model_rho = MarginalTransformation(self.conditioning_data.rho) self.trans_model_vs = MarginalTransformation(self.conditioning_data.vs,lognormal = True) - + self.geostat_model = None + self.random_fields_vs = None + self.random_fields_rho = None + self.vs = None + self.rho = None + self.g0 = None + self.young_modulus = None + self.generated_field = None def calibrate_geostat_model(self, v_dim:int = 1, calibration_indices:tuple = (0,1), seed:int = 13): """calibrates the geostatistical model + Args: v_dim (int, optional): vertical dimension. Defaults to 1. - calibration_indices (tuple, optional): _description_. Defaults to (0,1). + calibration_indices (Tuple, optional): _description_. Defaults to (0,1). seed (int, optional): Seed for the `:class:RandomFields`. Defaults to 13. """ + # ndim_calibrate = len(calibration_indices) - - length_scale_prior = [100.]*ndim_calibrate - length_scale_prior[v_dim] = 1. - - #kernel = WhiteKernel(0.01) + RBF(length_scale=length_scale_prior) - self.coord_calibration = np.zeros([self.max_conditioning_points,3]) for i in calibration_indices: self.coord_calibration[:,i] = self.conditioning_data.data_coords[self.thinning_sample_index,i] - + # calibrate geostatistical model based on standard-normal equivalent values of the shear wave velocity data self.geostat_model = GeostatisticalModel(nb_dimensions = ndim_calibrate, v_dim = v_dim) - self.geostat_model.calibrate(self.coord_calibration[:,calibration_indices],self.trans_model_vs.XtoZ(self.conditioning_data.vs[self.thinning_sample_index])) + self.geostat_model.calibrate(self.coord_calibration[:,calibration_indices],self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.thinning_sample_index])) - self.rf_vs = RandomFields(model_name = ModelName.Gaussian, + # initiate two independent random field generators for shear wave velocity `vs` and density `rho` + self.random_fields_vs = RandomFields(model_name = ModelName.Gaussian, n_dim = 3, mean = 0, variance = 1, @@ -500,10 +558,10 @@ def calibrate_geostat_model(self, angle = [0]*2, seed = seed, max_conditioning_points=self.max_conditioning_points) - self.rf_vs.set_conditioning_points(points = self.coord_calibration, - values = self.trans_model_vs.XtoZ(self.conditioning_data.vs[self.thinning_sample_index]), + self.random_fields_vs.set_conditioning_points(points = self.coord_calibration, + values = self.trans_model_vs.x_to_z(self.conditioning_data.vs[self.thinning_sample_index]), noise_level = self.geostat_model.noise_level) - self.rf_rho = RandomFields(model_name = ModelName.Gaussian, + self.random_fields_rho = RandomFields(model_name = ModelName.Gaussian, n_dim = 3, mean = 0, variance = 1, @@ -512,25 +570,36 @@ def calibrate_geostat_model(self, angle = [0]*2, seed = seed+1, max_conditioning_points=self.max_conditioning_points) - self.rf_rho.set_conditioning_points(points = self.coord_calibration, - values = self.trans_model_rho.XtoZ(self.conditioning_data.rho[self.thinning_sample_index]), + self.random_fields_rho.set_conditioning_points(points = self.coord_calibration, + values = self.trans_model_rho.x_to_z(self.conditioning_data.rho[self.thinning_sample_index]), noise_level = self.geostat_model.noise_level) def generate(self,coordinates): - """generate random fields + """generate random fields for `rho`, `vs` and derive fields `g0` and `young_modulus`. Args: coordinates (numpy.typing.NDArray[np.float64]): 3D coordinated of points to generate random field values for - """ - self.rf_vs.generate_conditioned(nodes = coordinates) - self.rf_rho.generate_conditioned(nodes = coordinates) + """ + + # generate conditioned standard-normal equivalent + self.random_fields_vs.generate_conditioned(nodes = coordinates) + self.random_fields_rho.generate_conditioned(nodes = coordinates) - self.vs = self.trans_model_vs.ZtoX(self.rf_vs.conditioned_random_field) - self.rho = self.trans_model_rho.ZtoX(self.rf_rho.conditioned_random_field) + # transform standard-normal fields to physical (marginal) distributions: + self.vs = self.trans_model_vs.z_to_x(self.random_fields_vs.conditioned_random_field) + self.rho = self.trans_model_rho.z_to_x(self.random_fields_rho.conditioned_random_field) + + # derive dependent fields self.g0 = self.rho * self.vs**2 self.young_modulus = 2*self.g0*(1. + self.poisson_ratio) - # - # only the Young modulus is communicated back as the generated field... - self.generated_field = list(self.young_modulus) \ No newline at end of file + # only one parameter is communicated back as the generated field to STEM / KRATOS + if self.return_property.lower() == 'young_modulus': + self.generated_field = list(self.young_modulus) + elif self.return_property.lower() == 'rho': + self.generated_field = list(self.rho) + elif self.return_property.lower() == 'g0': + self.generated_field = list(self.g0) + elif self.return_property.lower() == 'vs': + self.generated_field = list(self.vs) diff --git a/requirements.txt b/requirements.txt index 8e4ee46..d6f97cf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ numpy>=1.24.2 +scipy==1.14.1 gstools==1.5.0 matplotlib>=3.7.0 scikit-learn==1.5.2 diff --git a/requirements_dev.txt b/requirements_dev.txt index 8a217aa..2d6ac22 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,4 +1,5 @@ numpy>=1.24.2 +scipy==1.14.1 gstools==1.5.0 matplotlib>=3.7.0 scikit-learn==1.5.2 diff --git a/setup.cfg b/setup.cfg index 555a20e..f33ff2b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -15,6 +15,7 @@ packages = random_fields include_package_data = True install_requires = numpy>=1.24 + scipy>=1.14.1 gstools>=1.5.0 matplotlib>=3.7.1 scikit-learn>=1.5.2 diff --git a/tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef b/tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef deleted file mode 100644 index 5226d3e..0000000 --- a/tests/data/DelftSchiedam_cpts/112-074.400-2022-01-CPT-01.gef +++ /dev/null @@ -1,1325 +0,0 @@ -#GEFID= 1, 1, 0 -#COLUMN= 8 -#COLUMNINFO= 1, m, Sondeerlengte, 1 -#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 -#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 -#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 -#COLUMNINFO= 5, graden, Helling X, 21 -#COLUMNINFO= 6, graden, Helling Y, 22 -#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 -#COLUMNINFO= 8, s, Tijd, 12 -#COLUMNTEXT= 0, off -#COLUMNVOID= 1, -99999.00 -#COLUMNVOID= 2, -99999.000 -#COLUMNVOID= 3, -99999.000 -#COLUMNVOID= 4, -99999.000 -#COLUMNVOID= 5, -99999.000 -#COLUMNVOID= 6, -99999.000 -#COLUMNVOID= 7, -99999.00 -#COLUMNVOID= 8, -99999.0 -#COMPANYID= Fugro NL Land B.V., 27114147, 31 -#DATAFORMAT= ASCII -#FILEDATE= 2022,12,21 -#FILEOWNER= P. Gommer-Reniers -#LASTSCAN= 1256 -#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever -#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam -#MEASUREMENTTEXT= 3, Schiedam, plaatsnaam -#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-2757, conus type/serienummer -#MEASUREMENTTEXT= 5, SSC/NDL/JBS, sondeerapparaat/operator -#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm -#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem -#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau -#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak -#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten -#MEASUREMENTTEXT= 21, ja, correctie meterstrepen -#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling -#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling -#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt -#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel -#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt -#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel -#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel -#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig -#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig -#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig -#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig -#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig -#MEASUREMENTVAR= 12, 0, -, elektrische sondering -#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte -#MEASUREMENTVAR= 16, 25.09, m, einddiepte sondering -#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt -#MEASUREMENTVAR= 20, -0.041, MPa, nulmeting conus voor sondering -#MEASUREMENTVAR= 21, -0.083, MPa, nulmeting conus na sondering -#MEASUREMENTVAR= 22, 0.002, MPa, nulmeting wrijving voor sondering -#MEASUREMENTVAR= 23, 0.001, MPa, nulmeting wrijving na sondering -#MEASUREMENTVAR= 30, 0.8, graden, nulmeting helling voor sondering -#MEASUREMENTVAR= 31, 0.5, graden, nulmeting helling na sondering -#REPORTCODE= GEF-CPT-Report, 1,1,1,- -#PROJECTID= CPT, 2422-220749, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM -#STARTDATE= 2022,12,11 -#STARTTIME= 08,18,21 -#TESTID= 112-074.400-2022-01-CPT-01 -#XYID= 31000, 85712.1204, 442265.2594, 0.03, 0.03 -#ZID= 31000, -0.9055, 0.03 -#ANALYSISTEXT= 131, Discovery GeoVisual v.3.9.0.53137, conversie programma -#ANALYSISTEXT= 1401, a13f8a79-7dde-4f39-8d2a-d071598d7be2, Fugro logging session ID -#ANALYSISTEXT= 1402, 76bebae6-f2cc-44a6-bb3b-2605af042d43, Fugro logging session version ID -#ANALYSISTEXT= 1403, 81d4b150-9157-415f-b791-095ae6444502, Fugro test ID -#EOH= -0.00 0.797 -99999.000 -99999.000 -0.791 -0.523 0.00 110.5 -0.02 1.029 -99999.000 -99999.000 -1.118 -0.131 0.02 111.8 -0.04 1.448 -99999.000 -99999.000 -1.133 -0.662 0.04 112.9 -0.06 2.857 -99999.000 -99999.000 -1.634 0.323 0.06 114.1 -0.08 1.439 0.001 0.035 -0.810 -0.270 0.08 115.2 -0.10 1.750 0.003 0.189 -1.131 0.071 0.10 116.3 -0.12 0.938 0.004 0.188 -1.306 -0.052 0.12 117.4 -0.14 1.411 0.008 0.372 -1.341 0.176 0.14 118.6 -0.16 2.741 0.041 1.573 -1.300 0.259 0.16 119.7 -0.18 3.117 0.040 1.147 -1.177 0.655 0.18 120.9 -0.20 3.891 0.011 0.281 -1.321 0.628 0.20 122.0 -0.22 5.320 0.010 0.229 -4.588 1.762 0.22 123.2 -0.24 7.841 0.009 0.177 -1.423 0.677 0.24 124.3 -0.26 6.213 0.086 1.511 -4.700 -0.043 0.26 125.4 -0.28 9.267 0.069 0.908 -5.804 0.799 0.28 126.7 -0.30 10.683 0.032 0.361 -5.666 0.441 0.30 131.5 -0.32 12.099 0.027 0.259 -5.529 0.082 0.32 136.2 -0.34 11.925 0.034 0.320 -6.560 -1.168 0.34 139.9 -0.36 11.217 0.026 0.230 -0.569 0.805 0.36 141.4 -0.38 13.986 0.017 0.141 -0.922 0.737 0.38 142.8 -0.40 14.484 0.104 0.823 -1.342 0.508 0.40 144.1 -0.42 14.397 0.050 0.367 -2.104 0.508 0.42 146.9 -0.44 16.088 0.041 0.267 -2.244 0.525 0.44 153.2 -0.46 17.779 0.092 0.528 -2.383 0.542 0.46 159.6 -0.48 15.981 0.105 0.539 -1.782 0.749 0.48 164.3 -0.50 17.688 0.069 0.327 -0.780 0.681 0.50 165.5 -0.52 23.148 0.082 0.371 -0.934 0.789 0.52 166.6 -0.54 24.673 0.103 0.455 -0.845 0.665 0.54 167.7 -0.56 25.882 0.108 0.447 -1.088 0.480 0.56 168.9 -0.58 27.666 0.111 0.433 -1.186 0.649 0.58 170.0 -0.60 27.274 0.100 0.381 -0.763 0.607 0.60 171.2 -0.62 25.742 0.106 0.398 -1.012 0.443 0.62 172.3 -0.64 27.328 0.121 0.443 -0.913 0.302 0.64 173.4 -0.66 26.747 0.130 0.470 -0.844 0.348 0.66 174.5 -0.68 27.278 0.121 0.431 -1.174 0.472 0.68 175.7 -0.70 30.000 0.113 0.395 -1.143 0.437 0.70 176.8 -0.72 28.895 0.123 0.421 -0.843 0.565 0.72 177.9 -0.74 29.661 0.142 0.475 -0.779 0.632 0.74 179.1 -0.76 31.231 0.148 0.485 -0.764 0.716 0.76 180.2 -0.78 31.435 0.153 0.489 -0.733 0.671 0.78 181.3 -0.80 31.876 0.141 0.442 -0.688 0.608 0.80 182.4 -0.82 32.337 0.108 0.334 -0.632 0.611 0.82 183.6 -0.84 33.445 0.075 0.226 -0.577 0.652 0.84 184.8 -0.86 35.918 0.091 0.265 -0.505 0.673 0.86 185.9 -0.88 38.115 0.132 0.364 -0.479 0.694 0.88 187.2 -0.90 38.665 0.147 0.390 -0.427 0.706 0.90 191.3 -0.92 39.214 0.198 0.499 -0.375 0.718 0.92 195.4 -0.94 41.369 0.216 0.522 -0.363 0.705 0.94 198.1 -0.96 42.826 0.234 0.556 -0.381 0.679 0.96 199.4 -0.98 43.332 0.227 0.540 -0.393 0.679 0.98 200.7 -1.00 42.498 0.258 0.615 -0.373 0.659 1.00 201.9 -1.02 42.555 0.272 0.649 -0.345 0.661 1.02 203.3 -1.04 42.411 0.294 0.712 -0.294 0.618 1.04 204.6 -1.06 41.060 0.315 0.776 -0.367 0.482 1.06 205.8 -1.08 39.330 0.329 0.829 -0.378 0.458 1.08 207.1 -1.10 38.287 0.350 0.902 -0.357 0.432 1.10 208.2 -1.12 37.182 0.363 0.965 -0.376 0.425 1.12 209.4 -1.14 35.957 0.361 0.985 -0.370 0.413 1.14 210.6 -1.16 35.267 0.388 1.085 -0.413 0.414 1.16 211.8 -1.18 34.829 0.397 1.132 -0.400 0.424 1.18 212.9 -1.20 34.136 0.388 1.132 -0.434 0.405 1.20 214.1 -1.22 33.602 0.385 1.142 -0.453 0.461 1.22 215.3 -1.24 32.904 0.393 1.186 -0.477 0.474 1.24 216.4 -1.26 32.411 0.395 1.212 -0.499 0.489 1.26 217.6 -1.28 32.107 0.373 1.165 -0.485 0.559 1.28 218.8 -1.30 31.293 0.353 1.116 -0.472 0.575 1.30 219.9 -1.32 30.856 0.334 1.073 -0.407 0.555 1.32 221.1 -1.34 30.348 0.318 1.036 -0.379 0.584 1.34 222.3 -1.36 30.641 0.269 0.887 -0.354 0.586 1.36 223.4 -1.38 29.780 0.221 0.737 -0.311 0.569 1.38 224.5 -1.40 28.755 0.209 0.711 -0.312 0.586 1.40 225.7 -1.42 28.303 0.235 0.822 -0.325 0.631 1.42 226.8 -1.44 27.294 0.266 0.960 -0.304 0.653 1.44 232.9 -1.46 26.007 0.263 0.981 -0.284 0.649 1.46 240.3 -1.48 25.914 0.279 1.069 -0.273 0.609 1.48 244.6 -1.50 25.524 0.288 1.131 -0.260 0.647 1.50 245.7 -1.52 25.257 0.286 1.131 -0.213 0.639 1.52 246.8 -1.54 24.909 0.294 1.177 -0.154 0.645 1.54 247.9 -1.56 24.694 0.306 1.243 -0.089 0.672 1.56 249.0 -1.58 24.384 0.309 1.277 -0.057 0.681 1.58 250.1 -1.60 23.821 0.299 1.259 -0.041 0.733 1.60 251.2 -1.62 23.261 0.294 1.259 -0.032 0.760 1.62 252.4 -1.64 22.759 0.286 1.252 -0.019 0.746 1.64 253.5 -1.66 22.250 0.283 1.274 -0.013 0.782 1.66 254.6 -1.68 21.833 0.278 1.295 -0.022 0.735 1.68 255.7 -1.70 21.102 0.269 1.296 -0.015 0.755 1.70 256.8 -1.72 19.935 0.263 1.308 -0.006 0.745 1.72 257.9 -1.74 18.808 0.262 1.350 -0.018 0.747 1.74 259.0 -1.76 18.137 0.259 1.390 -0.037 0.699 1.76 260.1 -1.78 17.835 0.253 1.407 -0.047 0.691 1.78 261.2 -1.80 17.252 0.244 1.406 -0.074 0.714 1.80 262.3 -1.82 16.423 0.236 1.402 -0.105 0.695 1.82 263.4 -1.84 16.351 0.228 1.399 -0.107 0.680 1.84 264.6 -1.86 15.890 0.221 1.398 -0.114 0.696 1.86 265.7 -1.88 15.311 0.217 1.420 -0.086 0.702 1.88 266.8 -1.90 14.793 0.213 1.448 -0.061 0.696 1.90 267.9 -1.92 14.237 0.205 1.450 -0.075 0.705 1.92 269.0 -1.94 13.405 0.193 1.438 -0.058 0.712 1.94 270.1 -1.96 12.471 0.186 1.450 -0.059 0.730 1.96 271.2 -1.98 11.869 0.171 1.395 -0.021 0.747 1.98 272.3 -2.00 11.248 0.154 1.307 -0.010 0.789 2.00 273.4 -2.02 10.698 0.138 1.220 0.036 0.768 2.02 274.5 -2.04 10.098 0.136 1.283 0.057 0.803 2.04 275.6 -2.06 9.386 0.133 1.387 0.087 0.794 2.06 277.2 -2.08 8.423 0.130 1.511 0.098 0.781 2.08 280.7 -2.10 7.461 0.126 1.664 0.108 0.769 2.10 284.1 -2.12 6.797 0.120 1.815 0.082 0.800 2.12 286.2 -2.14 6.241 0.110 1.879 0.071 0.783 2.14 287.3 -2.16 5.607 0.104 1.880 0.082 0.758 2.16 288.4 -2.18 4.849 0.096 1.923 0.072 0.734 2.18 289.5 -2.20 4.150 0.087 2.044 0.080 0.738 2.20 290.6 -2.22 3.425 0.082 2.223 0.062 0.745 2.22 291.7 -2.24 2.825 0.079 2.582 0.051 0.747 2.24 292.8 -2.26 2.225 0.079 3.087 0.046 0.758 2.26 293.9 -2.28 1.750 0.079 3.751 0.059 0.754 2.28 295.1 -2.30 1.447 0.079 4.564 -0.018 0.806 2.30 296.1 -2.32 1.213 0.073 5.087 0.067 0.801 2.32 297.3 -2.34 1.045 0.063 5.235 0.070 0.792 2.34 298.4 -2.36 0.930 0.051 4.987 0.073 0.796 2.36 299.4 -2.38 0.838 0.047 4.898 0.079 0.822 2.38 300.6 -2.40 0.760 0.044 4.870 0.074 0.827 2.40 301.7 -2.42 0.734 0.041 4.842 0.090 0.829 2.42 302.8 -2.44 0.729 0.038 4.814 0.110 0.788 2.44 303.9 -2.46 0.728 0.027 3.560 0.098 0.803 2.46 305.6 -2.48 0.723 0.034 4.843 0.106 0.803 2.48 307.9 -2.50 0.718 0.037 5.535 0.114 0.803 2.50 310.2 -2.52 0.713 0.039 6.199 0.121 0.803 2.52 312.5 -2.54 0.634 0.042 6.639 0.117 0.813 2.54 314.7 -2.56 0.624 0.043 6.807 0.079 0.855 2.56 315.8 -2.58 0.620 0.042 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21.775 0.192 0.870 1.044 0.280 24.46 1863.3 -24.48 23.671 0.202 0.864 1.049 0.242 24.48 1864.4 -24.50 25.453 0.215 0.870 1.026 0.272 24.50 1865.5 -24.52 27.207 0.226 0.868 1.049 0.286 24.52 1866.6 -24.54 28.436 0.239 0.880 1.068 0.271 24.54 1867.7 -24.56 29.137 0.252 0.896 1.065 0.289 24.56 1868.8 -24.58 29.447 0.260 0.904 1.083 0.267 24.58 1869.9 -24.60 29.640 0.267 0.915 1.086 0.272 24.60 1871.0 -24.62 29.770 0.251 0.859 1.094 0.241 24.62 1872.1 -24.64 29.695 0.236 0.804 1.080 0.233 24.64 1873.2 -24.66 29.243 0.224 0.770 1.071 0.231 24.66 1874.3 -24.68 28.339 0.236 0.835 1.094 0.222 24.68 1875.4 -24.70 26.972 0.247 0.900 1.081 0.224 24.70 1879.4 -24.72 25.581 0.253 0.969 1.069 0.216 24.72 1883.6 -24.74 25.218 0.255 1.026 1.084 0.262 24.74 1886.5 -24.76 24.393 0.253 1.077 1.078 0.236 24.76 1887.6 -24.78 23.252 0.246 1.081 1.073 0.189 24.78 1888.7 -24.80 22.072 0.242 1.099 1.083 0.213 24.80 1889.8 -24.82 20.691 0.231 1.115 1.096 0.218 24.82 1890.8 -24.84 18.918 0.219 1.139 1.064 0.234 24.84 1891.9 -24.86 17.132 0.207 1.154 1.058 0.289 24.86 1893.0 -24.88 15.602 0.191 1.154 1.038 0.261 24.88 1894.1 -24.90 14.333 0.176 1.146 1.033 0.250 24.90 1895.2 -24.92 13.378 0.161 1.130 1.046 0.248 24.92 1896.2 -24.94 12.642 0.148 1.107 1.061 0.207 24.94 1897.3 -24.96 12.073 0.144 1.139 1.055 0.258 24.96 1898.4 -24.98 11.659 0.128 1.059 1.073 0.238 24.98 1899.4 -25.00 11.315 0.113 0.977 1.067 0.291 25.00 1900.6 -25.02 11.103 -99999.000 -99999.000 1.043 0.277 25.02 1901.6 -25.04 10.597 -99999.000 -99999.000 1.053 0.256 25.04 1902.7 -25.06 10.393 -99999.000 -99999.000 1.046 0.250 25.06 1903.8 -25.08 10.167 -99999.000 -99999.000 1.057 0.262 25.08 1904.8 -25.09 9.910 -99999.000 -99999.000 1.064 0.270 25.09 1906.0 diff --git a/tests/data/DelftSchiedam_cpts/112-074.500-2022-01-CPT-01.gef b/tests/data/DelftSchiedam_cpts/112-074.500-2022-01-CPT-01.gef deleted file mode 100644 index e7c1c05..0000000 --- a/tests/data/DelftSchiedam_cpts/112-074.500-2022-01-CPT-01.gef +++ /dev/null @@ -1,1326 +0,0 @@ -#GEFID= 1, 1, 0 -#COLUMN= 8 -#COLUMNINFO= 1, m, Sondeerlengte, 1 -#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 -#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 -#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 -#COLUMNINFO= 5, graden, Helling X, 21 -#COLUMNINFO= 6, graden, Helling Y, 22 -#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 -#COLUMNINFO= 8, s, Tijd, 12 -#COLUMNTEXT= 0, off -#COLUMNVOID= 1, -99999.00 -#COLUMNVOID= 2, -99999.000 -#COLUMNVOID= 3, -99999.000 -#COLUMNVOID= 4, -99999.000 -#COLUMNVOID= 5, -99999.000 -#COLUMNVOID= 6, -99999.000 -#COLUMNVOID= 7, -99999.00 -#COLUMNVOID= 8, -99999.0 -#COMPANYID= Fugro NL Land B.V., 27114147, 31 -#DATAFORMAT= ASCII -#FILEDATE= 2022,12,21 -#FILEOWNER= P. Gommer-Reniers -#LASTSCAN= 1257 -#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever -#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam -#MEASUREMENTTEXT= 3, Delft/Schiedam, plaatsnaam -#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-3470, conus type/serienummer -#MEASUREMENTTEXT= 5, Reo62/JWV/DVP, sondeerapparaat/operator -#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm -#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem -#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau -#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak -#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten -#MEASUREMENTTEXT= 21, ja, correctie meterstrepen -#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling -#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling -#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt -#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel -#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt -#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel -#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel -#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig -#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig -#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig -#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig -#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig -#MEASUREMENTVAR= 12, 0, -, elektrische sondering -#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte -#MEASUREMENTVAR= 16, 25.05, m, einddiepte sondering -#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt -#MEASUREMENTVAR= 20, 0.016, MPa, nulmeting conus voor sondering -#MEASUREMENTVAR= 21, -0.030, MPa, nulmeting conus na sondering -#MEASUREMENTVAR= 22, -0.003, MPa, nulmeting wrijving voor sondering -#MEASUREMENTVAR= 23, -0.003, MPa, nulmeting wrijving na sondering -#MEASUREMENTVAR= 30, 0.8, graden, nulmeting helling voor sondering -#MEASUREMENTVAR= 31, 0.7, graden, nulmeting helling na sondering -#REPORTCODE= GEF-CPT-Report, 1,1,1,- -#PROJECTID= CPT, 2422-220749, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM -#STARTDATE= 2022,12,11 -#STARTTIME= 11,52,57 -#TESTID= 112-074.500-2022-01-CPT-01 -#XYID= 31000, 85739.3448, 442165.0888, 0.03, 0.03 -#ZID= 31000, -0.8103, 0.03 -#ANALYSISTEXT= 131, Discovery GeoVisual v.3.9.0.53137, conversie programma -#ANALYSISTEXT= 1401, a8026119-a3ce-4359-be12-de6d6ee559a6, Fugro logging session ID -#ANALYSISTEXT= 1402, d3788594-30a4-49e3-b382-cd704bf6b7d8, Fugro logging session version ID -#ANALYSISTEXT= 1403, 487b1bdd-6b14-48a9-817f-cdf035f7177b, Fugro test ID -#EOH= -0.00 0.051 -99999.000 -99999.000 -1.039 0.258 0.00 105.5 -0.02 0.084 -99999.000 -99999.000 -0.915 -0.010 0.02 109.3 -0.04 0.174 -99999.000 -99999.000 -0.732 -0.410 0.04 111.4 -0.06 2.112 -99999.000 -99999.000 -0.770 -0.528 0.06 113.4 -0.08 2.022 0.005 0.273 -0.726 -0.660 0.08 115.2 -0.10 2.484 0.009 0.401 -0.379 -0.841 0.10 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1467.4 -24.46 12.093 0.119 0.980 2.657 -0.460 24.41 1468.2 -24.48 12.216 0.119 0.967 2.663 -0.503 24.43 1469.0 -24.50 12.401 0.118 0.953 2.696 -0.497 24.45 1469.7 -24.52 12.656 0.118 0.933 2.667 -0.526 24.47 1470.5 -24.54 12.928 0.118 0.912 2.669 -0.536 24.49 1471.3 -24.56 13.298 0.119 0.902 2.653 -0.519 24.51 1472.1 -24.58 13.631 0.119 0.883 2.671 -0.522 24.53 1472.8 -24.60 13.993 0.121 0.877 2.641 -0.533 24.55 1473.6 -24.62 14.351 0.123 0.867 2.671 -0.552 24.57 1474.4 -24.64 14.676 0.125 0.859 2.668 -0.536 24.59 1475.2 -24.66 14.953 0.128 0.859 2.651 -0.508 24.61 1476.0 -24.68 15.284 0.132 0.864 2.686 -0.510 24.63 1476.8 -24.70 15.709 0.133 0.855 2.646 -0.540 24.65 1477.6 -24.72 16.181 0.137 0.854 2.651 -0.582 24.67 1478.3 -24.74 16.649 0.139 0.846 2.652 -0.555 24.69 1479.1 -24.76 17.019 0.141 0.842 2.643 -0.570 24.71 1479.9 -24.78 17.312 0.144 0.839 2.652 -0.572 24.73 1480.7 -24.80 17.567 0.147 0.840 2.651 -0.640 24.75 1481.5 -24.82 17.923 0.148 0.834 2.673 -0.579 24.77 1482.3 -24.84 18.263 0.153 0.849 2.673 -0.597 24.79 1483.1 -24.86 18.578 0.158 0.859 2.655 -0.614 24.81 1483.9 -24.88 18.789 0.162 0.869 2.646 -0.587 24.83 1484.7 -24.90 18.943 0.166 0.878 2.659 -0.628 24.85 1485.5 -24.92 19.031 0.170 0.880 2.629 -0.625 24.87 1486.2 -24.94 19.283 0.173 0.868 2.669 -0.647 24.88 1487.0 -24.96 20.008 0.179 0.873 2.662 -0.638 24.90 1487.8 -24.98 21.118 0.181 0.862 2.678 -0.665 24.92 1488.7 -25.00 22.401 0.183 0.850 2.676 -0.684 24.94 1489.5 -25.02 23.996 -99999.000 -99999.000 2.672 -0.718 24.96 1490.4 -25.04 25.234 -99999.000 -99999.000 2.675 -0.698 24.98 1491.8 -25.06 25.782 -99999.000 -99999.000 2.681 -0.700 25.00 1494.8 -25.08 26.329 -99999.000 -99999.000 2.686 -0.701 25.02 1497.8 -25.09 26.876 -99999.000 -99999.000 2.692 -0.703 25.04 1500.8 -25.10 26.904 -99999.000 -99999.000 2.692 -0.703 25.05 1501.0 diff --git a/tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef b/tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef deleted file mode 100644 index da10b8d..0000000 --- a/tests/data/DelftSchiedam_cpts/112-074.600-2022-01-CPT-01.gef +++ /dev/null @@ -1,1332 +0,0 @@ -#GEFID= 1, 1, 0 -#COLUMN= 8 -#COLUMNINFO= 1, m, Sondeerlengte, 1 -#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 -#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 -#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 -#COLUMNINFO= 5, graden, Helling X, 21 -#COLUMNINFO= 6, graden, Helling Y, 22 -#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 -#COLUMNINFO= 8, s, Tijd, 12 -#COLUMNTEXT= 0, off -#COLUMNVOID= 1, -99999.00 -#COLUMNVOID= 2, -99999.000 -#COLUMNVOID= 3, -99999.000 -#COLUMNVOID= 4, -99999.000 -#COLUMNVOID= 5, -99999.000 -#COLUMNVOID= 6, -99999.000 -#COLUMNVOID= 7, -99999.00 -#COLUMNVOID= 8, -99999.0 -#COMPANYID= Fugro NL Land B.V., 27114147, 31 -#DATAFORMAT= ASCII -#FILEDATE= 2022,12,21 -#FILEOWNER= P. Gommer-Reniers -#LASTSCAN= 1262 -#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever -#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam -#MEASUREMENTTEXT= 3, Schiedam, plaatsnaam -#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-2757, conus type/serienummer -#MEASUREMENTTEXT= 5, SSC/NDL/JBS, sondeerapparaat/operator -#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm -#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem -#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau -#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak -#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten -#MEASUREMENTTEXT= 21, ja, correctie meterstrepen -#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling -#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling -#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt -#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel -#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt -#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel -#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel -#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig -#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig -#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig -#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig -#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig -#MEASUREMENTVAR= 12, 0, -, elektrische sondering -#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte -#MEASUREMENTVAR= 16, 25.01, m, einddiepte sondering -#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt -#MEASUREMENTVAR= 20, -0.092, MPa, nulmeting conus voor sondering -#MEASUREMENTVAR= 21, -0.094, MPa, nulmeting conus na sondering -#MEASUREMENTVAR= 22, 0.003, MPa, nulmeting wrijving voor sondering -#MEASUREMENTVAR= 23, -0.001, MPa, nulmeting wrijving na sondering -#MEASUREMENTVAR= 30, 0.5, graden, nulmeting helling voor sondering -#MEASUREMENTVAR= 31, 0.4, graden, nulmeting helling na sondering -#REPORTCODE= GEF-CPT-Report, 1,1,1,- -#PROJECTID= CPT, 2422-220749, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM -#STARTDATE= 2022,12,11 -#STARTTIME= 09,21,39 -#TESTID= 112-074.600-2022-01-CPT-01 -#XYID= 31000, 85773.6389, 442070.5626, 0.03, 0.03 -#ZID= 31000, -0.8544, 0.03 -#ANALYSISTEXT= 131, Discovery GeoVisual v.3.9.0.53137, conversie programma -#ANALYSISTEXT= 1401, 60e7531d-01ff-4410-baa8-8a6b29a954eb, Fugro logging session ID -#ANALYSISTEXT= 1402, 1a01f71f-3f9a-4bb2-a6fa-ff15d45f5f3f, Fugro logging session version ID -#ANALYSISTEXT= 1403, a7c60be7-a9d3-436b-b4dd-1159010a5196, Fugro test ID -#EOH= -0.00 0.394 -99999.000 -99999.000 0.194 0.398 0.00 66.5 -0.02 0.589 -99999.000 -99999.000 0.376 0.439 0.02 67.6 -0.04 0.786 -99999.000 -99999.000 0.349 0.529 0.04 68.7 -0.06 1.479 -99999.000 -99999.000 -0.572 0.524 0.06 69.8 -0.08 2.493 -99999.000 -99999.000 -0.652 0.117 0.08 70.9 -0.10 1.612 -99999.000 -99999.000 0.049 0.687 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b/tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef deleted file mode 100644 index 7a5d2cb..0000000 --- a/tests/data/DelftSchiedam_cpts/112-074.700-2022-01-CPT-01.gef +++ /dev/null @@ -1,1334 +0,0 @@ -#GEFID= 1, 1, 0 -#COLUMN= 9 -#COLUMNINFO= 1, m, Sondeerlengte, 1 -#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 -#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 -#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 -#COLUMNINFO= 5, MPa, Waterspanning u2, 6 -#COLUMNINFO= 6, graden, Helling X, 21 -#COLUMNINFO= 7, graden, Helling Y, 22 -#COLUMNINFO= 8, m, Gecorrigeerde diepte, 11 -#COLUMNINFO= 9, s, Tijd, 12 -#COLUMNTEXT= 0, off -#COLUMNVOID= 1, -99999.00 -#COLUMNVOID= 2, -99999.000 -#COLUMNVOID= 3, -99999.000 -#COLUMNVOID= 4, -99999.000 -#COLUMNVOID= 5, -99999.000 -#COLUMNVOID= 6, -99999.000 -#COLUMNVOID= 7, -99999.000 -#COLUMNVOID= 8, -99999.00 -#COLUMNVOID= 9, -99999.0 -#COMPANYID= Fugro NL Land B.V., 27114147, 31 -#DATAFORMAT= ASCII -#FILEDATE= 2022,12,21 -#FILEOWNER= P. Gommer-Reniers -#LASTSCAN= 1261 -#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever -#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam -#MEASUREMENTTEXT= 3, Delft/Schiedam, plaatsnaam -#MEASUREMENTTEXT= 4, CP15-CF75PB1SN2-P1E1M4-V12-S1/1701-3420, conus type/serienummer -#MEASUREMENTTEXT= 5, Reo62/JWV/DVP, sondeerapparaat/operator -#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm -#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem -#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau -#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak -#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten -#MEASUREMENTTEXT= 21, ja, correctie meterstrepen -#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling -#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling -#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt -#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel -#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt -#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel -#MEASUREMENTVAR= 5, 87.8, mm, afstand conus tot midden kleefmantel -#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig -#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig -#MEASUREMENTVAR= 8, 1, -, waterdruk-meter u2 is aanwezig -#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig -#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig -#MEASUREMENTVAR= 12, 0, -, elektrische sondering -#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte -#MEASUREMENTVAR= 16, 25.06, m, einddiepte sondering -#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt -#MEASUREMENTVAR= 20, -0.090, MPa, nulmeting conus voor sondering -#MEASUREMENTVAR= 21, -0.114, MPa, nulmeting conus na sondering -#MEASUREMENTVAR= 22, 0.008, MPa, nulmeting wrijving voor sondering -#MEASUREMENTVAR= 23, 0.009, MPa, nulmeting wrijving na sondering -#MEASUREMENTVAR= 26, -0.003, MPa, nulmeting WSM u2 voor sondering -#MEASUREMENTVAR= 27, -0.004, MPa, nulmeting WSM u2 na sondering -#MEASUREMENTVAR= 30, 0.3, graden, nulmeting helling voor sondering -#MEASUREMENTVAR= 31, 0.8, graden, nulmeting helling na sondering -#REPORTCODE= GEF-CPT-Report, 1,1,1,- -#PROJECTID= CPT, 2422-220749, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM -#STARTDATE= 2022,12,11 -#STARTTIME= 10,55,42 -#TESTID= 112-074.700-2022-01-CPT-01 -#XYID= 31000, 85799.5791, 441974.0474, 0.03, 0.03 -#ZID= 31000, -0.8236, 0.03 -#ANALYSISTEXT= 131, Discovery GeoVisual v.3.9.0.53137, conversie programma -#ANALYSISTEXT= 1401, e96a5768-7599-4d67-9abc-36efb36a3807, Fugro logging session ID -#ANALYSISTEXT= 1402, 35546c44-2624-4977-82cb-92b070da61fa, Fugro logging session version ID -#ANALYSISTEXT= 1403, 0d1a384a-6206-4a12-b53a-d46a0d524a6d, Fugro test ID -#EOH= -0.00 0.557 -99999.000 -99999.000 -99999.000 -0.303 0.775 0.00 242.0 -0.02 9.030 -99999.000 -99999.000 0.000 -0.851 0.234 0.02 245.3 -0.04 8.478 -99999.000 -99999.000 0.000 -4.267 -0.774 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a/tests/data/DelftSchiedam_cpts/112-074.800-2022-01-CPT-01.gef +++ /dev/null @@ -1,1323 +0,0 @@ -#GEFID= 1, 1, 0 -#COLUMN= 8 -#COLUMNINFO= 1, m, Sondeerlengte, 1 -#COLUMNINFO= 2, MPa, Conusweerstand qc, 2 -#COLUMNINFO= 3, MPa, Wrijvingsweerstand fs, 3 -#COLUMNINFO= 4, %, Wrijvingsgetal Rf, 4 -#COLUMNINFO= 5, graden, Helling X, 21 -#COLUMNINFO= 6, graden, Helling Y, 22 -#COLUMNINFO= 7, m, Gecorrigeerde diepte, 11 -#COLUMNINFO= 8, s, Tijd, 12 -#COLUMNTEXT= 0, off -#COLUMNVOID= 1, -99999.00 -#COLUMNVOID= 2, -99999.000 -#COLUMNVOID= 3, -99999.000 -#COLUMNVOID= 4, -99999.000 -#COLUMNVOID= 5, -99999.000 -#COLUMNVOID= 6, -99999.000 -#COLUMNVOID= 7, -99999.00 -#COLUMNVOID= 8, -99999.0 -#COMPANYID= Fugro NL Land B.V., 27114147, 31 -#DATAFORMAT= ASCII -#FILEDATE= 2022,12,21 -#FILEOWNER= P. Gommer-Reniers -#LASTSCAN= 1254 -#MEASUREMENTTEXT= 1, ProRail B.V., opdrachtgever -#MEASUREMENTTEXT= 2, BAANSTABILITEIT DELFT CAMPUS-SCHIEDAM, projectnaam -#MEASUREMENTTEXT= 3, Schiedam, plaatsnaam -#MEASUREMENTTEXT= 4, CP15-CF75SN2-P1E1M4-V1-S1/1701-2757, conus type/serienummer -#MEASUREMENTTEXT= 5, SSC/NDL/JBS, sondeerapparaat/operator -#MEASUREMENTTEXT= 6, ISO 22476-1 2012 Application Class 2, gevolgde norm -#MEASUREMENTTEXT= 7, Amersfoort / RD New, eigen coordinatensysteem -#MEASUREMENTTEXT= 8, NAP height, eigen referentieniveau -#MEASUREMENTTEXT= 9, maaiveld, vast horizontaal vlak -#MEASUREMENTTEXT= 20, nee, extra correctie nulpunten -#MEASUREMENTTEXT= 21, ja, correctie meterstrepen -#MEASUREMENTTEXT= 42, MRG2, methode verticale positiebepaling -#MEASUREMENTTEXT= 43, LRG3, methode horizontale positiebepaling -#MEASUREMENTVAR= 1, 1510, mm2, nom. opp. conuspunt -#MEASUREMENTVAR= 2, 19895, mm2, nom. opp. wrijvingsmantel -#MEASUREMENTVAR= 3, 0.58, -, netto oppervlaktequotient van de conuspunt -#MEASUREMENTVAR= 4, 0.98608, -, netto oppervlaktequotient van de kleefmantel -#MEASUREMENTVAR= 5, 86.9, mm, afstand conus tot midden kleefmantel -#MEASUREMENTVAR= 6, 1, -, wrijvingsmeter is aanwezig -#MEASUREMENTVAR= 7, 0, -, waterdruk-meter u1 is niet aanwezig -#MEASUREMENTVAR= 8, 0, -, waterdruk-meter u2 is niet aanwezig -#MEASUREMENTVAR= 9, 0, -, waterdruk-meter u3 is niet aanwezig -#MEASUREMENTVAR= 10, 1, -, helling-meter is aanwezig -#MEASUREMENTVAR= 12, 0, -, elektrische sondering -#MEASUREMENTVAR= 13, 0.00, m, voorgegraven diepte -#MEASUREMENTVAR= 16, 25.02, m, einddiepte sondering -#MEASUREMENTVAR= 17, 0, -, einddiepte bereikt -#MEASUREMENTVAR= 20, -0.064, MPa, nulmeting conus voor sondering -#MEASUREMENTVAR= 21, -0.076, MPa, nulmeting conus na sondering -#MEASUREMENTVAR= 22, -0.001, MPa, nulmeting wrijving voor sondering -#MEASUREMENTVAR= 23, -0.001, MPa, nulmeting wrijving na sondering -#MEASUREMENTVAR= 30, 0.7, graden, nulmeting helling voor sondering 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2.566 23.63 1754.6 -23.68 17.352 0.132 0.750 1.492 2.559 23.65 1759.5 -23.70 17.326 0.129 0.747 1.500 2.566 23.67 1761.8 -23.72 17.110 0.128 0.743 1.504 2.578 23.69 1762.9 -23.74 16.889 0.125 0.726 1.497 2.585 23.71 1763.9 -23.76 16.909 0.123 0.716 1.505 2.564 23.73 1765.0 -23.78 17.170 0.124 0.727 1.516 2.609 23.75 1766.1 -23.80 17.192 0.123 0.723 1.486 2.626 23.77 1767.2 -23.82 17.151 0.123 0.725 1.502 2.559 23.79 1768.3 -23.84 17.078 0.124 0.730 1.503 2.575 23.81 1769.3 -23.86 16.983 0.122 0.725 1.513 2.596 23.83 1770.4 -23.88 16.853 0.120 0.716 1.529 2.601 23.85 1771.5 -23.90 16.526 0.119 0.720 1.525 2.609 23.87 1772.6 -23.92 16.138 0.118 0.717 1.518 2.580 23.89 1773.7 -23.94 15.898 0.117 0.714 1.536 2.559 23.91 1774.7 -23.96 15.847 0.114 0.697 1.556 2.582 23.93 1775.8 -23.98 15.994 0.108 0.661 1.534 2.568 23.95 1776.9 -24.00 16.465 0.108 0.650 1.532 2.558 23.97 1777.9 -24.02 16.849 0.106 0.634 1.508 2.601 23.99 1779.1 -24.04 17.241 0.107 0.627 1.498 2.627 24.01 1780.1 -24.06 17.592 0.112 0.635 1.522 2.577 24.03 1781.2 -24.08 17.870 0.124 0.680 1.536 2.591 24.05 1782.3 -24.10 18.454 0.132 0.695 1.522 2.596 24.07 1783.4 -24.12 19.273 0.133 0.669 1.512 2.634 24.09 1784.5 -24.14 20.389 0.133 0.628 1.544 2.666 24.11 1785.6 -24.16 22.151 0.136 0.603 1.578 2.659 24.13 1786.7 -24.18 24.348 0.145 0.605 1.579 2.696 24.15 1787.8 -24.20 26.495 0.142 0.569 1.613 2.703 24.17 1788.9 -24.22 28.199 0.139 0.532 1.644 2.712 24.19 1789.9 -24.24 29.122 0.141 0.518 1.622 2.701 24.21 1791.1 -24.26 29.698 0.163 0.570 1.630 2.657 24.23 1792.1 -24.28 29.912 0.180 0.607 1.647 2.665 24.25 1794.8 -24.30 29.928 0.192 0.626 1.631 2.652 24.27 1798.2 -24.32 30.039 0.207 0.652 1.611 2.642 24.29 1801.6 -24.34 31.585 0.209 0.637 1.601 2.669 24.31 1802.7 -24.36 33.172 0.224 0.662 1.623 2.658 24.33 1803.9 -24.38 34.647 0.241 0.696 1.636 2.656 24.35 1805.1 -24.40 35.993 0.258 0.723 1.626 2.669 24.37 1806.4 -24.42 37.252 0.269 0.726 1.633 2.685 24.39 1807.8 -24.44 38.505 0.288 0.751 1.659 2.677 24.41 1809.1 -24.46 39.717 0.304 0.769 1.677 2.688 24.43 1810.6 -24.48 40.843 0.323 0.795 1.734 2.652 24.45 1812.2 -24.50 41.927 0.333 0.801 1.729 2.692 24.47 1813.8 -24.52 42.799 0.349 0.821 1.704 2.685 24.49 1815.4 -24.54 43.502 0.358 0.831 1.739 2.698 24.51 1817.2 -24.56 44.162 0.363 0.832 1.763 2.734 24.53 1819.0 -24.58 44.662 0.348 0.792 1.778 2.759 24.55 1820.8 -24.60 44.838 0.332 0.752 1.753 2.755 24.57 1822.8 -24.62 44.689 0.320 0.719 1.787 2.754 24.59 1824.7 -24.64 44.184 0.329 0.743 1.815 2.753 24.61 1826.7 -24.66 43.211 0.337 0.765 1.812 2.779 24.63 1833.3 -24.68 42.225 0.350 0.809 1.810 2.786 24.65 1840.3 -24.70 41.239 0.361 0.891 1.808 2.794 24.67 1847.3 -24.72 40.268 0.366 0.970 1.805 2.782 24.69 1853.4 -24.74 39.101 0.369 1.012 1.798 2.799 24.71 1860.4 -24.76 36.863 0.361 1.025 1.776 2.811 24.73 1862.8 -24.78 34.170 0.352 1.051 1.768 2.751 24.75 1864.1 -24.80 31.216 0.334 1.038 1.768 2.775 24.77 1865.3 -24.82 28.653 0.315 1.028 1.786 2.781 24.79 1866.5 -24.84 26.950 0.295 1.027 1.777 2.731 24.81 1867.6 -24.86 25.884 0.269 0.993 1.747 2.779 24.83 1868.7 -24.88 25.240 0.248 0.958 1.750 2.770 24.85 1869.8 -24.90 24.553 0.235 0.937 1.743 2.755 24.87 1870.9 -24.92 23.947 0.218 0.896 1.754 2.779 24.89 1872.0 -24.94 23.527 0.204 0.860 1.721 2.780 24.91 1873.1 -24.96 23.167 0.192 0.823 1.729 2.772 24.93 1874.1 -24.98 22.653 -99999.000 -99999.000 1.754 2.768 24.95 1875.2 -25.00 22.206 -99999.000 -99999.000 1.768 2.733 24.97 1876.3 -25.02 21.871 -99999.000 -99999.000 1.777 2.725 24.99 1877.4 -25.04 21.323 -99999.000 -99999.000 1.765 2.689 25.01 1878.4 -25.05 20.743 -99999.000 -99999.000 1.752 2.710 25.02 1879.0 From 13f246a53ea57312297b1d06d08ded5a5febae60 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden Date: Fri, 6 Dec 2024 10:24:32 +0100 Subject: [PATCH 23/62] scipy requirements weakened modified: requirements.txt modified: requirements_dev.txt modified: setup.cfg --- requirements.txt | 2 +- requirements_dev.txt | 2 +- setup.cfg | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index d6f97cf..804e0e8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy>=1.24.2 -scipy==1.14.1 +scipy>=1.6 gstools==1.5.0 matplotlib>=3.7.0 scikit-learn==1.5.2 diff --git a/requirements_dev.txt b/requirements_dev.txt index 2d6ac22..e2f239e 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,5 +1,5 @@ numpy>=1.24.2 -scipy==1.14.1 +scipy>=1.6 gstools==1.5.0 matplotlib>=3.7.0 scikit-learn==1.5.2 diff --git a/setup.cfg b/setup.cfg index f33ff2b..b693fec 100644 --- a/setup.cfg +++ b/setup.cfg @@ -15,7 +15,7 @@ packages = random_fields include_package_data = True install_requires = numpy>=1.24 - scipy>=1.14.1 + scipy>=1.6 gstools>=1.5.0 matplotlib>=3.7.1 scikit-learn>=1.5.2 From be3acd27f8d21dc5a750cee0432d7d3bf312e001 Mon Sep 17 00:00:00 2001 From: Bram van den Eijnden <38314273+bramvandeneijnden@users.noreply.github.com> Date: Tue, 10 Dec 2024 13:42:31 +0100 Subject: [PATCH 24/62] Update geostatistical_cpt_interpretation.py fix package reference to `random_fields` --- random_fields/geostatistical_cpt_interpretation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/random_fields/geostatistical_cpt_interpretation.py b/random_fields/geostatistical_cpt_interpretation.py index 1ec6237..6dfac92 100644 --- a/random_fields/geostatistical_cpt_interpretation.py +++ b/random_fields/geostatistical_cpt_interpretation.py @@ -9,7 +9,7 @@ import scipy.stats as st import logging -from generate_field import RandomFields, ModelName +from random_fields.generate_field import RandomFields, ModelName from sklearn.gaussian_process import GaussianProcessRegressor as GPR from sklearn.gaussian_process.kernels import RBF, WhiteKernel, Sum From d1a77a0a4abcf4a027ed037755f7734a6ad09700 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 7 Jan 2025 13:36:42 +0100 Subject: [PATCH 25/62] updated authors.rst --- docs/authors.rst | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/docs/authors.rst b/docs/authors.rst index cf6cb99..d1d1536 100644 --- a/docs/authors.rst +++ b/docs/authors.rst @@ -10,10 +10,7 @@ Maintainers Developers ---------- -- Marjan Fathian `@indigocoral `_ -- Jonathan Nuttall `@mcgicjn2 `_ -- Davide Moretti `@morettid `_ -- Jelme Pennings `@jkpennings `_ - Bram van den Eijnden `@bramvandeneijnden `_ +- Davide Moretti `@morettid `_ From ec7bc7266bc4fa5051f664d8a6f7625fe2de0e09 Mon Sep 17 00:00:00 2001 From: brunozc Date: Tue, 7 Jan 2025 13:39:11 +0100 Subject: [PATCH 26/62] Correct GitHub username casing in documentation configuration --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 60f76a3..5aeb1f3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -55,7 +55,7 @@ # Link to the github html_context = { "display_github": True, - "github_user": "stemVibrations", + "github_user": "StemVibrations", "github_repo": "RanndomFields", "github_version": "main", "conf_py_path": "/docs/", From bb76a5c1c82f84123d3bb0f533ec66610d1c1458 Mon Sep 17 00:00:00 2001 From: brunozc Date: Thu, 9 Jan 2025 11:21:47 +0100 Subject: [PATCH 27/62] Update dependency versions in requirements and setup configuration --- requirements.txt | 12 ++++++------ requirements_dev.txt | 21 +++++++++++++-------- setup.cfg | 22 +++++++++++++--------- 3 files changed, 32 insertions(+), 23 deletions(-) diff --git a/requirements.txt b/requirements.txt index 804e0e8..770a054 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ -numpy>=1.24.2 -scipy>=1.6 -gstools==1.5.0 -matplotlib>=3.7.0 -scikit-learn==1.5.2 -d-geolib-plus==0.4.0 +numpy==2.0.2 +matplotlib==3.9.3 +scipy==1.15.0 +gstools==1.6.0 +scikit-learn==1.6.0 +CPTtool==2.2.1 diff --git a/requirements_dev.txt b/requirements_dev.txt index e2f239e..b20cb34 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,8 +1,13 @@ -numpy>=1.24.2 -scipy>=1.6 -gstools==1.5.0 -matplotlib>=3.7.0 -scikit-learn==1.5.2 -d-geolib-plus==0.4.0 -pytest==7.4.2 -pytest-cov==4.1.0 +numpy==2.0.2 +matplotlib==3.9.3 +scipy==1.15.0 +gstools==1.6.0 +scikit-learn==1.6.0 +CPTtool==2.2.1 +mypy==1.13.0 +pytest==8.3.4 +pytest-cov==6.0.0 +yapf==0.43.0 +tox==4.23.2 +flake8==7.0.0 +pre-commit==3.6.2 diff --git a/setup.cfg b/setup.cfg index b693fec..eef0a98 100644 --- a/setup.cfg +++ b/setup.cfg @@ -14,16 +14,20 @@ classifiers = packages = random_fields include_package_data = True install_requires = - numpy>=1.24 - scipy>=1.6 - gstools>=1.5.0 - matplotlib>=3.7.1 - scikit-learn>=1.5.2 - d-geolib-plus>=0.4.0 -python_requires = >=3.8 + numpy>=2.0.2 + scipy>=1.13.1 + matplotlib>=3.9.3 + gstools>=1.6.0 + scikit-learn>=1.6.0 + CPTtool==2.2.1 +python_requires = >=3.9 [options.extras_require] testing = pytest>=7.4.2 - pytest-cov>=4.1.0 - tox>=4.4 + pytest-cov>=6.0 + tox>=4.23 + mypy>=1.13 + pre-commit==3.6.2 + yapf==0.43.0 + flake8==7.0.0 \ No newline at end of file From 6ac10e08dc135f2a014d9a9a915a65b223530930 Mon Sep 17 00:00:00 2001 From: brunozc Date: Thu, 9 Jan 2025 13:34:04 +0100 Subject: [PATCH 28/62] Add configuration files for flake8, mypy, and pre-commit; update CPTtool version --- .flake8 | 4 ++++ .pre-commit-config.yaml | 21 +++++++++++++++++++++ .style.yapf | 12 ++++++++++++ pyproject.toml | 17 +++++++++++++++++ requirements.txt | 2 +- requirements_dev.txt | 2 +- setup.cfg | 7 +++++-- 7 files changed, 61 insertions(+), 4 deletions(-) create mode 100644 .flake8 create mode 100644 .pre-commit-config.yaml create mode 100644 .style.yapf diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..bcb2786 --- /dev/null +++ b/.flake8 @@ -0,0 +1,4 @@ +[flake8] +ignore = F403, F405, W503, W504 +max-line-length = 120 +per-file-ignores = __init__.py:F401 \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..a195abe --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,21 @@ +repos: + - repo: local + hooks: + - id: yapf + name: yapf + language: python + entry: yapf + args: [-i] + types: [python] + - id: mypy + name: mypy + language: python + entry: mypy + files: "^random_fields/" + args: [ --config-file, "pyproject.toml" ] + types: [python] + - id: flake8 + name: flake8 + language: python + entry: flake8 + files: "^random_fields/" \ No newline at end of file diff --git a/.style.yapf b/.style.yapf new file mode 100644 index 0000000..a5d75df --- /dev/null +++ b/.style.yapf @@ -0,0 +1,12 @@ +[style] +based_on_style = pep8 +SPLIT_ALL_TOP_LEVEL_COMMA_SEPARATED_VALUES = False +dedent_closing_brackets = False +EACH_DICT_ENTRY_ON_SEPARATE_LINE = True +FORCE_MULTILINE_DICT = False +coalesce_brackets = True +column_limit = 120 +split_arguments_when_comma_terminated = True +SPLIT_BEFORE_FIRST_ARGUMENT = False +#SPLIT_PENALTY_FOR_ADDED_LINE_SPLIT = 1 +ALLOW_SPLIT_BEFORE_DICT_VALUE = False \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 8fe2f47..141469b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,20 @@ [build-system] requires = ["setuptools>=42", "wheel"] build-backend = "setuptools.build_meta" + +[tool.mypy] +check_untyped_defs = true +disallow_any_generics = true +ignore_missing_imports = true +no_implicit_optional = true +show_error_codes = true +strict_equality = true +warn_redundant_casts = true +warn_return_any = true +warn_unreachable = true +warn_unused_configs = true +no_implicit_reexport = true + +[[tool.mypy.overrides]] +module = ["gstools.*", "sklearn.*"] +follow_untyped_imports = true diff --git a/requirements.txt b/requirements.txt index 770a054..f1ba7e8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,4 @@ matplotlib==3.9.3 scipy==1.15.0 gstools==1.6.0 scikit-learn==1.6.0 -CPTtool==2.2.1 +CPTtool==2.2.2 diff --git a/requirements_dev.txt b/requirements_dev.txt index b20cb34..4d5acc9 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -3,7 +3,6 @@ matplotlib==3.9.3 scipy==1.15.0 gstools==1.6.0 scikit-learn==1.6.0 -CPTtool==2.2.1 mypy==1.13.0 pytest==8.3.4 pytest-cov==6.0.0 @@ -11,3 +10,4 @@ yapf==0.43.0 tox==4.23.2 flake8==7.0.0 pre-commit==3.6.2 +CPTtool==2.2.2 \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index eef0a98..67e0193 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,7 +19,7 @@ install_requires = matplotlib>=3.9.3 gstools>=1.6.0 scikit-learn>=1.6.0 - CPTtool==2.2.1 + CPTtool==2.2.2 python_requires = >=3.9 [options.extras_require] @@ -30,4 +30,7 @@ testing = mypy>=1.13 pre-commit==3.6.2 yapf==0.43.0 - flake8==7.0.0 \ No newline at end of file + flake8==7.0.0 +docs = + Sphinx==6.2.1 + sphinx-rtd-theme==1.2.2 \ No newline at end of file From 72105fc630163fc34cfdfbe69852047018179b31 Mon Sep 17 00:00:00 2001 From: brunozc Date: Fri, 10 Jan 2025 10:04:22 +0100 Subject: [PATCH 29/62] updated documentation --- .gitignore | 3 +- docs/_static/conditioned_random_field_3D.png | Bin 93281 -> 93396 bytes docs/_static/kriging_mean_2D.png | Bin 23991 -> 23992 bytes docs/_static/kriging_mean_3D.png | Bin 90389 -> 90444 bytes docs/_static/kriging_std_2D.png | Bin 27517 -> 25367 bytes docs/_static/kriging_std_3D.png | Bin 82441 -> 82494 bytes docs/_static/random_field_2.png | Bin 98428 -> 77054 bytes docs/_static/random_field_3.png | Bin 120058 -> 95143 bytes docs/_static/random_field_4.png | Bin 49390 -> 49390 bytes docs/_static/random_field_5.png | Bin 51402 -> 51401 bytes docs/authors.rst | 2 - docs/conf.py | 2 - docs/contributions.rst | 24 +- docs/index.rst | 13 +- docs/make.bat | 70 +++--- docs/theory.rst | 6 - docs/tutorial_1.rst | 116 +++++++++ docs/tutorial_2.rst | 92 +++++++ docs/tutorial_3.rst | 86 +++++++ docs/tutorial_4.rst | 8 + docs/tutorials.rst | 252 ------------------- random_fields/generate_field.py | 215 ++++++++-------- 22 files changed, 458 insertions(+), 431 deletions(-) delete mode 100644 docs/theory.rst create mode 100644 docs/tutorial_1.rst create mode 100644 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