From 0ff7e19425004eb41b5063ff3ee826bde5952ae1 Mon Sep 17 00:00:00 2001 From: plaresmedima Date: Tue, 3 Dec 2024 12:46:22 +0000 Subject: [PATCH] update docs --- dev/sops.txt | 2 +- ...fitting.py => _plot_poor_model_fitting.py} | 22 +- docs/examples/use_cases/deformationField.mhd | 14 + docs/examples/use_cases/deformationField.raw | Bin 0 -> 3234816 bytes docs/examples/use_cases/plot_3Dexample_vfa.py | 18 +- .../backreferences/mdreg.animation.examples | 10 +- .../mdreg.elastix.params.examples | 10 +- .../backreferences/mdreg.fetch.examples | 10 +- .../backreferences/mdreg.fit.examples | 10 +- .../backreferences/mdreg.plot.examples | 6 +- .../backreferences/mdreg.plot_series.examples | 10 +- .../mdreg.spgr_vfa_nonlin.examples | 6 +- docs/source/generated/examples/index.rst | 4 +- .../_plot_poor_model_fitting.codeobj.json | 457 + .../use_cases/_plot_poor_model_fitting.ipynb | 219 + .../use_cases/_plot_poor_model_fitting.py | 273 + .../use_cases/_plot_poor_model_fitting.rst | 376 + .../use_cases/_plot_poor_model_fitting.zip | Bin 0 -> 24680 bytes .../sphx_glr_plot_3Dexample_vfa_001.gif | Bin 70092 -> 70092 bytes .../sphx_glr_plot_3Dexample_vfa_002.gif | Bin 69983 -> 70533 bytes .../sphx_glr_plot_3Dexample_vfa_003.gif | Bin 70449 -> 70359 bytes ...phx_glr__plot_poor_model_fitting_thumb.png | Bin 0 -> 26794 bytes .../generated/examples/use_cases/index.rst | 6 +- .../examples/use_cases/plot_3Dexample_vfa.py | 18 +- .../use_cases/plot_3Dexample_vfa.py.md5 | 2 +- .../examples/use_cases/plot_3Dexample_vfa.rst | 288415 +++++++-------- .../examples/use_cases/plot_3Dexample_vfa.zip | Bin 18186 -> 18196 bytes .../use_cases/plot_poor_model_fitting.py | 22 +- docs/source/releases/index.rst | 1 + docs/source/releases/release_0.4.0.rst | 5 +- docs/source/releases/release_0.4.1.rst | 25 + tests/requirements.txt | 2 + 32 files changed, 145643 insertions(+), 144300 deletions(-) rename docs/examples/use_cases/{plot_poor_model_fitting.py => _plot_poor_model_fitting.py} (96%) create mode 100644 docs/examples/use_cases/deformationField.mhd create mode 100644 docs/examples/use_cases/deformationField.raw create mode 100644 docs/source/generated/examples/use_cases/_plot_poor_model_fitting.codeobj.json create mode 100644 docs/source/generated/examples/use_cases/_plot_poor_model_fitting.ipynb create mode 100644 docs/source/generated/examples/use_cases/_plot_poor_model_fitting.py create mode 100644 docs/source/generated/examples/use_cases/_plot_poor_model_fitting.rst create mode 100644 docs/source/generated/examples/use_cases/_plot_poor_model_fitting.zip create mode 100644 docs/source/generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png create mode 100644 docs/source/releases/release_0.4.1.rst create mode 100644 tests/requirements.txt diff --git a/dev/sops.txt b/dev/sops.txt index e8c0bb9d..de940d3f 100644 --- a/dev/sops.txt +++ b/dev/sops.txt @@ -103,7 +103,7 @@ Building release notes - Save the token in the environment variable GH_TOKEN. On windows: >>> $env:GH_TOKEN='token' - Build the changelog from current version to current dev. For instance: ->>> changelist QIB-Sheffield/mdreg 0.4.0 dev --format rst +>>> changelist QIB-Sheffield/mdreg 0.4.0 development --format rst - Copy-paste the terminal output in an .rst file and save it in docs/source/releases - Edit the content of the file manually as needed - Add an entry in the index of docs/source/releases diff --git a/docs/examples/use_cases/plot_poor_model_fitting.py b/docs/examples/use_cases/_plot_poor_model_fitting.py similarity index 96% rename from docs/examples/use_cases/plot_poor_model_fitting.py rename to docs/examples/use_cases/_plot_poor_model_fitting.py index 47e075a1..4479b46e 100644 --- a/docs/examples/use_cases/plot_poor_model_fitting.py +++ b/docs/examples/use_cases/_plot_poor_model_fitting.py @@ -18,7 +18,7 @@ #%% # Import packages and data -# ---------------------------- +# ------------------------ # Example data can be easily loaded in using the `fetch` function. import numpy as np @@ -31,14 +31,14 @@ #%% # Extract the desired slice from the data array -# ---------------------------- +# --------------------------------------------- # As an intial step, we will extract the 4D data (x,y,z,t) from the fetched data dictionary. array = data['array'] #%% # Signal model theory -# ---------------------------- +# ------------------- # The signal model used in this example is the non-linear variable flip angle # SPGR model. The signal model is defined by the following equation: # @@ -52,7 +52,7 @@ #%% # Define model fit parameters -# ---------------------------- +# --------------------------- # The image fitting settings dictionary (`vfa_fit` in this case) is required by # `mdreg.fit` to fit a specific signal model to the data. Leaving this as None # will fit a constant model to the data as a default. @@ -71,7 +71,7 @@ #%% # Define the coregistration parameters -# ---------------------------- +# ------------------------------------ # The coregistration parameters are set in the `coreg_params` dictionary. # The `package` key specifies the coregistration package to be used, with a # choice of elastix, skimage, or dipy. @@ -91,7 +91,7 @@ #%% # Define the plotting parameters -# ---------------------------- +# ------------------------------ # The plotting parameters are set in the `plot_settings` dictionary. # # The `interval` key specifies the time interval between frames in @@ -121,7 +121,7 @@ #%% # Perform model-driven coregistration -# ---------------------------- +# ----------------------------------- # The `mdreg.fit` function is used to perform the model-driven coregistration. # The function requires the 4D data array, the fit_image dictionary, and the # coregistration parameters we have already defined. @@ -142,7 +142,7 @@ #%% # Visualise coregistration results -# --------------------------------- +# -------------------------------- # To easily visualise the output of the employ the `mdreg.plot` module to # easily produce animations that render on screen or save as a gif. # Here we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data @@ -167,7 +167,7 @@ #%% # Export all series at once -# ---------------------------- +# ------------------------- # The `mdreg.plot_series` function can be used to plot the original, fitted and # coregistered data for all slices in the data array. This function can also # be used to save the animations to a file. @@ -178,7 +178,7 @@ # >>> anims = mdreg.plot_series(array, fit, coreg, **plot_settings) #%% -# Identifiying poor model fitting +# Identifying poor model fitting # ------------------------------ # # The coregistration results show significant defects in the coregistered data. @@ -256,7 +256,7 @@ #%% # Goodness of fit -# ---------------------------- +# --------------- # The chi squared values in the figure above show large regions of poor fit # across the data array. These regions are highlighted in red as above the 99th # percentile of the chi squared values. These region also correspond to the diff --git a/docs/examples/use_cases/deformationField.mhd b/docs/examples/use_cases/deformationField.mhd new file mode 100644 index 00000000..9ec571fd --- /dev/null +++ b/docs/examples/use_cases/deformationField.mhd @@ -0,0 +1,14 @@ +ObjectType = Image +NDims = 3 +BinaryData = True +BinaryDataByteOrderMSB = False +CompressedData = False +TransformMatrix = 1 0 0 0 1 0 0 0 1 +Offset = 0 0 0 +CenterOfRotation = 0 0 0 +AnatomicalOrientation = RAI +ElementSpacing = 5 4.6875 4.6875 +DimSize = 36 96 78 +ElementNumberOfChannels = 3 +ElementType = MET_FLOAT +ElementDataFile = deformationField.raw diff --git a/docs/examples/use_cases/deformationField.raw b/docs/examples/use_cases/deformationField.raw new file mode 100644 index 0000000000000000000000000000000000000000..b049ac9ce7f0514a443b729f23dbfafc94ce6f4c GIT binary patch literal 3234816 zcmWJs_ajz)6gM(6B4m?Q2vLZh`#pyuMJh=`$x1?HCS>ouXEKtgBn=gwdlgzLMWrH< 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.. only:: not html - * :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/backreferences/mdreg.elastix.params.examples b/docs/source/generated/backreferences/mdreg.elastix.params.examples index 1a74f099..3223b82d 100644 --- a/docs/source/generated/backreferences/mdreg.elastix.params.examples +++ b/docs/source/generated/backreferences/mdreg.elastix.params.examples @@ -15,24 +15,24 @@ Examples using ``mdreg.elastix.params`` .. raw:: html -
+
.. only:: html - .. image:: /generated/examples/tutorials/images/thumb/sphx_glr_plot_getting_started_defo_thumb.gif + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html -
Customizing the coregistration
+
Example: Poor Model Fitting
.. only:: not html - * :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/backreferences/mdreg.fetch.examples b/docs/source/generated/backreferences/mdreg.fetch.examples index dfd75982..8b068ec0 100644 --- a/docs/source/generated/backreferences/mdreg.fetch.examples +++ b/docs/source/generated/backreferences/mdreg.fetch.examples @@ -15,24 +15,24 @@ Examples using ``mdreg.fetch`` .. raw:: html -
+
.. only:: html - .. image:: /generated/examples/tutorials/images/thumb/sphx_glr_plot_getting_started_defo_thumb.gif + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html -
Customizing the coregistration
+
Example: Poor Model Fitting
.. only:: not html - * :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/backreferences/mdreg.fit.examples b/docs/source/generated/backreferences/mdreg.fit.examples index 5d378141..478906f2 100644 --- a/docs/source/generated/backreferences/mdreg.fit.examples +++ b/docs/source/generated/backreferences/mdreg.fit.examples @@ -15,24 +15,24 @@ Examples using ``mdreg.fit`` .. raw:: html -
+
.. only:: html - .. image:: /generated/examples/tutorials/images/thumb/sphx_glr_plot_getting_started_defo_thumb.gif + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html -
Customizing the coregistration
+
Example: Poor Model Fitting
.. only:: not html - * :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/backreferences/mdreg.plot.examples b/docs/source/generated/backreferences/mdreg.plot.examples index 31d7b85d..117d1f6b 100644 --- a/docs/source/generated/backreferences/mdreg.plot.examples +++ b/docs/source/generated/backreferences/mdreg.plot.examples @@ -19,10 +19,10 @@ Examples using ``mdreg.plot`` .. only:: html - .. image:: /generated/examples/use_cases/images/thumb/sphx_glr_plot_poor_model_fitting_thumb.png + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_use_cases_plot_poor_model_fitting.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html @@ -32,7 +32,7 @@ Examples using ``mdreg.plot`` .. only:: not html - * :ref:`sphx_glr_generated_examples_use_cases_plot_poor_model_fitting.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/backreferences/mdreg.plot_series.examples b/docs/source/generated/backreferences/mdreg.plot_series.examples index af1c3b30..7ca85b00 100644 --- a/docs/source/generated/backreferences/mdreg.plot_series.examples +++ b/docs/source/generated/backreferences/mdreg.plot_series.examples @@ -15,24 +15,24 @@ Examples using ``mdreg.plot_series`` .. raw:: html -
+
.. only:: html - .. image:: /generated/examples/tutorials/images/thumb/sphx_glr_plot_getting_started_defo_thumb.gif + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html -
Customizing the coregistration
+
Example: Poor Model Fitting
.. only:: not html - * :ref:`sphx_glr_generated_examples_tutorials_plot_getting_started_defo.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/backreferences/mdreg.spgr_vfa_nonlin.examples b/docs/source/generated/backreferences/mdreg.spgr_vfa_nonlin.examples index 1a8596f2..957a6f49 100644 --- a/docs/source/generated/backreferences/mdreg.spgr_vfa_nonlin.examples +++ b/docs/source/generated/backreferences/mdreg.spgr_vfa_nonlin.examples @@ -19,10 +19,10 @@ Examples using ``mdreg.spgr_vfa_nonlin`` .. only:: html - .. image:: /generated/examples/use_cases/images/thumb/sphx_glr_plot_poor_model_fitting_thumb.png + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_use_cases_plot_poor_model_fitting.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html @@ -32,7 +32,7 @@ Examples using ``mdreg.spgr_vfa_nonlin`` .. only:: not html - * :ref:`sphx_glr_generated_examples_use_cases_plot_poor_model_fitting.py` + * :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. thumbnail-parent-div-close diff --git a/docs/source/generated/examples/index.rst b/docs/source/generated/examples/index.rst index 516e2500..3164b576 100644 --- a/docs/source/generated/examples/index.rst +++ b/docs/source/generated/examples/index.rst @@ -133,10 +133,10 @@ Use cases .. only:: html - .. image:: /generated/examples/use_cases/images/thumb/sphx_glr_plot_poor_model_fitting_thumb.png + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_use_cases_plot_poor_model_fitting.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html diff --git a/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.codeobj.json b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.codeobj.json new file mode 100644 index 00000000..95214a3c --- /dev/null +++ b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.codeobj.json @@ -0,0 +1,457 @@ +{ + "FA": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "FA" + } + ], + "S": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "S" + } + ], + "S_{0}": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "S_{0}" + }, + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "S_{0}" + } + ], + "TR": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "TR" + } + ], + "T_{1}": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "T_{1}" + }, + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "T_{1}" + } + ], + "T_{R}": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "T_{R}" + } + ], + "\\frac{\\sin{\\phi}e^{-\\frac{T_{R}}{T_{1}}}}{1-\\cos{\\phi}e^{-\\frac{T_{R}}{T_{1}}}}": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "\\frac{\\sin{\\phi}e^{-\\frac{T_{R}}{T_{1}}}}{1-\\cos{\\phi}e^{-\\frac{T_{R}}{T_{1}}}}" + } + ], + "\\phi": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "\\phi" + } + ], + "coreg_params": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "coreg_params" + } + ], + "data": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "data" + } + ], + "fetch": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "fetch" + }, + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "fetch" + } + ], + "filename": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "filename" + } + ], + "func": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "func" + } + ], + "interval": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "interval" + } + ], + "math.ceil": [ + { + "is_class": false, + "is_explicit": false, + "module": "math", + "module_short": "math", + "name": "ceil" + } + ], + "math.sqrt": [ + { + "is_class": false, + "is_explicit": false, + "module": "math", + "module_short": "math", + "name": "sqrt" + } + ], + "mdreg": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "mdreg" + } + ], + "mdreg.animation": [ + { + "is_class": false, + "is_explicit": false, + "module": "mdreg", + "module_short": "mdreg", + "name": "animation" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "animation" + } + ], + "mdreg.elastix.params": [ + { + "is_class": false, + "is_explicit": false, + "module": "mdreg.elastix", + "module_short": "mdreg.elastix", + "name": "params" + } + ], + "mdreg.fetch": [ + { + "is_class": false, + "is_explicit": false, + "module": "mdreg", + "module_short": "mdreg", + "name": "fetch" + } + ], + "mdreg.fit": [ + { + "is_class": false, + "is_explicit": false, + "module": "mdreg", + "module_short": "mdreg", + "name": "fit" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "fit" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "fit" + } + ], + "mdreg.plot": [ + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "plot" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "plot" + } + ], + "mdreg.plot_series": [ + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "plot_series" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "plot_series" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "plot_series" + } + ], + "mdreg.spgr_vfa_nonlin": [ + { + "is_class": false, + "is_explicit": false, + "module": "mdreg", + "module_short": "mdreg", + "name": "spgr_vfa_nonlin" + }, + { + "is_class": false, + "is_explicit": true, + "module": "mdreg", + "module_short": "mdreg", + "name": "spgr_vfa_nonlin" + } + ], + "np.divide": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "divide" + } + ], + "np.percentile": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "percentile" + } + ], + "np.round": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "round" + } + ], + "np.sum": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "sum" + } + ], + "package": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "package" + } + ], + "params": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "params" + } + ], + "path": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "path" + } + ], + "plot_settings": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "plot_settings" + } + ], + "plt.get_cmap": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "get_cmap" + } + ], + "plt.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "show" + } + ], + "plt.subplots": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "subplots" + } + ], + "plt.tight_layout": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "tight_layout" + } + ], + "slice": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "slice" + }, + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "slice" + } + ], + "time.time": [ + { + "is_class": false, + "is_explicit": false, + "module": "time", + "module_short": "time", + "name": "time" + } + ], + "verbose": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "verbose" + } + ], + "vfa_fit": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "vfa_fit" + } + ], + "vmax": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "vmax" + } + ], + "vmin": [ + { + "is_class": false, + "is_explicit": true, + "module": "builtins", + "module_short": "builtins", + "name": "vmin" + } + ] +} \ No newline at end of file diff --git a/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.ipynb b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.ipynb new file mode 100644 index 00000000..0fe4dad5 --- /dev/null +++ b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Example: Poor Model Fitting\n\nThis example illustrates the effect of poor model fitting on coregistration\nfor the 3D Variable Flip Angle (VFA) dataset used in the 3D data example.\n\nAs in the related 3D VFA example the data are fetched using the `fetch` \nfunction, the VFA parameters are defined, and the coregistration parameters are\nset. The model-driven coregistration is performed, and the results are \nvisualized.\n\nDifferently from the previous example, the model fit shows poor fitting and the\nresulting coregistration creates significant defects in the coregistered data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import packages and data\nExample data can be easily loaded in using the `fetch` function.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nimport mdreg\nimport time\nimport matplotlib.pyplot as plt\nimport math\n\ndata = mdreg.fetch('VFA')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract the desired slice from the data array\nAs an intial step, we will extract the 4D data (x,y,z,t) from the fetched data dictionary.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "array = data['array']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Signal model theory\nThe signal model used in this example is the non-linear variable flip angle \nSPGR model. The signal model is defined by the following equation:\n\n$S(\\phi)=S_{0} \\frac{\\sin{\\phi}e^{-\\frac{T_{R}}{T_{1}}}}{1-\\cos{\\phi}e^{-\\frac{T_{R}}{T_{1}}}}$\n\nWhere $S$ is the signal, $S_{0}$ the initial signal, $\\phi$\nthe flip angle, $T_{R}$ the reptition time and $T_{1}$ the \nlongitudinal relaxtion time. Using this equation, $T_{1}$ and \n$S_{0}$ are optimised via a least squares method.\n\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define model fit parameters\nThe image fitting settings dictionary (`vfa_fit` in this case) is required by \n`mdreg.fit` to fit a specific signal model to the data. Leaving this as None \nwill fit a constant model to the data as a default.\n\nHere, we select the model function `func` to be the non-linear varaible flip \nangle SPGR model from the model library (`mdreg.spgr_vfa_nonlin`). This model \nfit requires the flip angle values in radians (`FA`) and the repetition time \nin seconds (`TR`). This information is provided in the `data` dictionary for \nthis example.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "vfa_fit = {\n 'func': mdreg.spgr_vfa_nonlin, # The function to fit the data\n 'FA': data['FA'], # The flip angles in degrees\n 'TR': 3.71/1000 # The TR value in s\n}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the coregistration parameters\nThe coregistration parameters are set in the `coreg_params` dictionary.\nThe `package` key specifies the coregistration package to be used, with a \nchoice of elastix, skimage, or dipy.\n\nThe `params` key specifies the parameters required by the chosen \ncoregistration package. Here None is used to specify default parameters for \nfreeform registration included by `mdreg`.\n\nHere, we use the elastix package with the following parameters:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "coreg_params = {\n 'package': 'elastix',\n 'params': mdreg.elastix.params(FinalGridSpacingInPhysicalUnits='150.0'),\n 'spacing': data['spacing']\n}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the plotting parameters\nThe plotting parameters are set in the `plot_settings` dictionary.\n\nThe `interval` key specifies the time interval between frames in \nmilliseconds, and the `vmin`/`vmax` keys specify the minimum/maximum value of \nthe colorbar. \n\nThe `slice` key specifies the slice to be displayed in the \nanimation. If unset or set to None, the central slice is displayed by \ndefault. \n\nIf you are interested to save the resulting animation, you can use \nthe `path` key to the desired file path and the `filename` key to the desired \nfilename. As a default these are set to None resulting in the animation being \ndisplayed on screen only. For more plotting keyword arguements, see the \n`mdreg.plot` module.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plot_settings = {\n 'interval' : 500, # Time interval between animation frames in ms\n 'vmin' : 0, # Minimum value of the colorbar\n 'vmax' : np.percentile(array,99), # Maximum value of the colorbar\n 'path' : None, # Path to save the animation\n 'show' : True, # Display the animation on screen\n 'filename' : None, # Filename to save the animation\n 'slice' : None # No slice specified, show all slices for 3D data\n}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform model-driven coregistration\nThe `mdreg.fit` function is used to perform the model-driven coregistration.\nThe function requires the 4D data array, the fit_image dictionary, and the \ncoregistration parameters we have already defined.\nThe `verbose` parameter can be set to 0, 1, 2, or 3 to control the level of \noutput.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "stime = time.time()\n\ncoreg, defo, fit, pars = mdreg.fit(array,\n fit_image = vfa_fit, \n fit_coreg = coreg_params,\n maxit=3, \n verbose=0)\n\ntot_time = time.time() - stime\n\nprint(f\"Non-linear fitting time elapsed: {(int(tot_time/60))} mins, {np.round(tot_time-(int(tot_time/60)*60),1)} s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualise coregistration results\nTo easily visualise the output of the employ the `mdreg.plot` module to \neasily produce animations that render on screen or save as a gif.\nHere we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data \narrays which change over an additional dimension (e.g. time or FA in this \ncase). This displays the orginal data, the fitted data and the coregistered \ndata. \n\nFor the case of 3D data, by default the function renders animations for all \nslices for the original, fitted and coregistered data in seperate figures. If\na `slice` parameter is specified in the plotting parameters, the function\nwill produce a single figure for the specified slice showing the original,\nfitted and coregistered data animations side-by-side.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "anim = mdreg.animation(array, title='Original Data', **plot_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "anim = mdreg.animation(coreg, title='Coregistered', **plot_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "anim = mdreg.animation(fit, title='Model Fit', **plot_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export all series at once\nThe `mdreg.plot_series` function can be used to plot the original, fitted and\ncoregistered data for all slices in the data array. This function can also\nbe used to save the animations to a file. \n\nWe include the `mdreg.animation` function to display the animations on screen\nwithin the documentation, but recommend using the `mdreg.plot_series` function\nto easily process and save the animations to a file when running locally.\n >>> anims = mdreg.plot_series(array, fit, coreg, **plot_settings)\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Identifying poor model fitting\n\nThe coregistration results show significant defects in the coregistered data.\nThis is due to poor model fitting, which usually results from optimisation to\na local minimum rather than the global minimum.\n\nThis poor model fitting is evident from\nthe model fitting from the initial iteration. For subsequent iterations the \nmodel fitting become increasing unstable due to the effect of compenstating\ndeformation fields.\n\nTo identify poor model fitting, the user can inspect the model fitting by \noutputing the model fitting outcomes an probing a goodness of fit metric \nagainst the original uncoregistred data.\n\nHere we calculate the chi squared value for each pixel in the data array\nafter two iteration of the model fitting, and visualise the results.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "coreg_1iter, defo_1iter, fit_1iter, pars_1iter = mdreg.fit(array,\n fit_image = vfa_fit, \n fit_coreg = coreg_params,\n maxit=1, \n verbose=0)\n\n# Determine chi squared of fit pixel wise, regions where data == 0 are ignored\n# and the output is zero\nchi_squared = np.sum(np.divide((fit_1iter - array) ** 2, array, where=array != 0), axis=-1)\n\n# Determine the grid size for the panels\nnum_slices = array.shape[2]\ngrid_size = math.ceil(math.sqrt(num_slices))\n\n# Set the colormap and color limits\ncmap = plt.get_cmap('viridis')\nvmin = np.percentile(chi_squared, 1)\nvmax = np.percentile(chi_squared, 99)\n\n# Setup the figure\nfig, axes1 = plt.subplots(grid_size, grid_size, figsize=(grid_size*2, grid_size*2))\nfig.subplots_adjust(wspace=0.5, hspace=0.01)\n\n# Add a title to the figure\ntitlesize = 10\nfig.suptitle('Goodness of Fit\\n \\n', fontsize=titlesize+2)\n\n# Improve the layout\nplt.tight_layout()\n\n# Plot the chi squared values for each slice\nfor i in range(grid_size * grid_size):\n row = i // grid_size\n col = i % grid_size\n if i < num_slices:\n im = axes1[row, col].imshow(chi_squared[:, :, i].T, cmap=cmap, vmin=vmin, vmax=vmax)\n axes1[row, col].set_title('Slice {}'.format(i+1), fontsize=titlesize)\n else:\n axes1[row, col].axis('off') # Turn off unused subplots\n axes1[row, col].set_xticks([]) # Remove x-axis ticks\n axes1[row, col].set_yticks([]) # Remove y-axis ticks\n\n# Set the colorbar to have overflow and underflow colors\nim.cmap.set_over('red')\nim.cmap.set_under('white')\n\n# set colorbar axis: [left, bottom, width, height]\ncbar_ax = fig.add_axes([0.90, 0.15, 0.02, 0.7])\ncbar = fig.colorbar(im, cax=cbar_ax, extend='both')\n\n# Add a label to the colorbar and adjust the layout to make space for the \n# colorbar\ncbar.set_label('Chi Squared', rotation=270, labelpad=15) \nfig.subplots_adjust(right=0.89)\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Goodness of fit\nThe chi squared values in the figure above show large regions of poor fit\nacross the data array. These regions are highlighted in red as above the 99th\npercentile of the chi squared values. These region also correspond to the \nregions of strange coregtistration behaviour shown in the previous animations.\n\nThe user can use this information to identify regions of poor fit and decide \nwhether to adjust the model fitting parameters or the coregistration options.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.py b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.py new file mode 100644 index 00000000..4479b46e --- /dev/null +++ b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.py @@ -0,0 +1,273 @@ +""" +=============================================== +Example: Poor Model Fitting +=============================================== + +This example illustrates the effect of poor model fitting on coregistration +for the 3D Variable Flip Angle (VFA) dataset used in the 3D data example. + +As in the related 3D VFA example the data are fetched using the `fetch` +function, the VFA parameters are defined, and the coregistration parameters are +set. The model-driven coregistration is performed, and the results are +visualized. + +Differently from the previous example, the model fit shows poor fitting and the +resulting coregistration creates significant defects in the coregistered data. + +""" + +#%% +# Import packages and data +# ------------------------ +# Example data can be easily loaded in using the `fetch` function. + +import numpy as np +import mdreg +import time +import matplotlib.pyplot as plt +import math + +data = mdreg.fetch('VFA') + +#%% +# Extract the desired slice from the data array +# --------------------------------------------- +# As an intial step, we will extract the 4D data (x,y,z,t) from the fetched data dictionary. + +array = data['array'] + +#%% +# Signal model theory +# ------------------- +# The signal model used in this example is the non-linear variable flip angle +# SPGR model. The signal model is defined by the following equation: +# +# :math:`S(\phi)=S_{0} \frac{\sin{\phi}e^{-\frac{T_{R}}{T_{1}}}}{1-\cos{\phi}e^{-\frac{T_{R}}{T_{1}}}}` +# +# Where :math:`S` is the signal, :math:`S_{0}` the initial signal, :math:`\phi` +# the flip angle, :math:`T_{R}` the reptition time and :math:`T_{1}` the +# longitudinal relaxtion time. Using this equation, :math:`T_{1}` and +# :math:`S_{0}` are optimised via a least squares method. +# + +#%% +# Define model fit parameters +# --------------------------- +# The image fitting settings dictionary (`vfa_fit` in this case) is required by +# `mdreg.fit` to fit a specific signal model to the data. Leaving this as None +# will fit a constant model to the data as a default. +# +# Here, we select the model function `func` to be the non-linear varaible flip +# angle SPGR model from the model library (`mdreg.spgr_vfa_nonlin`). This model +# fit requires the flip angle values in radians (`FA`) and the repetition time +# in seconds (`TR`). This information is provided in the `data` dictionary for +# this example. + +vfa_fit = { + 'func': mdreg.spgr_vfa_nonlin, # The function to fit the data + 'FA': data['FA'], # The flip angles in degrees + 'TR': 3.71/1000 # The TR value in s +} + +#%% +# Define the coregistration parameters +# ------------------------------------ +# The coregistration parameters are set in the `coreg_params` dictionary. +# The `package` key specifies the coregistration package to be used, with a +# choice of elastix, skimage, or dipy. +# +# The `params` key specifies the parameters required by the chosen +# coregistration package. Here None is used to specify default parameters for +# freeform registration included by `mdreg`. +# +# Here, we use the elastix package with the following parameters: + + +coreg_params = { + 'package': 'elastix', + 'params': mdreg.elastix.params(FinalGridSpacingInPhysicalUnits='150.0'), + 'spacing': data['spacing'] +} + +#%% +# Define the plotting parameters +# ------------------------------ +# The plotting parameters are set in the `plot_settings` dictionary. +# +# The `interval` key specifies the time interval between frames in +# milliseconds, and the `vmin`/`vmax` keys specify the minimum/maximum value of +# the colorbar. +# +# The `slice` key specifies the slice to be displayed in the +# animation. If unset or set to None, the central slice is displayed by +# default. +# +# If you are interested to save the resulting animation, you can use +# the `path` key to the desired file path and the `filename` key to the desired +# filename. As a default these are set to None resulting in the animation being +# displayed on screen only. For more plotting keyword arguements, see the +# `mdreg.plot` module. +# + +plot_settings = { + 'interval' : 500, # Time interval between animation frames in ms + 'vmin' : 0, # Minimum value of the colorbar + 'vmax' : np.percentile(array,99), # Maximum value of the colorbar + 'path' : None, # Path to save the animation + 'show' : True, # Display the animation on screen + 'filename' : None, # Filename to save the animation + 'slice' : None # No slice specified, show all slices for 3D data +} + +#%% +# Perform model-driven coregistration +# ----------------------------------- +# The `mdreg.fit` function is used to perform the model-driven coregistration. +# The function requires the 4D data array, the fit_image dictionary, and the +# coregistration parameters we have already defined. +# The `verbose` parameter can be set to 0, 1, 2, or 3 to control the level of +# output. + +stime = time.time() + +coreg, defo, fit, pars = mdreg.fit(array, + fit_image = vfa_fit, + fit_coreg = coreg_params, + maxit=3, + verbose=0) + +tot_time = time.time() - stime + +print(f"Non-linear fitting time elapsed: {(int(tot_time/60))} mins, {np.round(tot_time-(int(tot_time/60)*60),1)} s") + +#%% +# Visualise coregistration results +# -------------------------------- +# To easily visualise the output of the employ the `mdreg.plot` module to +# easily produce animations that render on screen or save as a gif. +# Here we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data +# arrays which change over an additional dimension (e.g. time or FA in this +# case). This displays the orginal data, the fitted data and the coregistered +# data. +# +# For the case of 3D data, by default the function renders animations for all +# slices for the original, fitted and coregistered data in seperate figures. If +# a `slice` parameter is specified in the plotting parameters, the function +# will produce a single figure for the specified slice showing the original, +# fitted and coregistered data animations side-by-side. +# + +anim = mdreg.animation(array, title='Original Data', **plot_settings) + +#%% +anim = mdreg.animation(coreg, title='Coregistered', **plot_settings) + +#%% +anim = mdreg.animation(fit, title='Model Fit', **plot_settings) + +#%% +# Export all series at once +# ------------------------- +# The `mdreg.plot_series` function can be used to plot the original, fitted and +# coregistered data for all slices in the data array. This function can also +# be used to save the animations to a file. +# +# We include the `mdreg.animation` function to display the animations on screen +# within the documentation, but recommend using the `mdreg.plot_series` function +# to easily process and save the animations to a file when running locally. +# >>> anims = mdreg.plot_series(array, fit, coreg, **plot_settings) + +#%% +# Identifying poor model fitting +# ------------------------------ +# +# The coregistration results show significant defects in the coregistered data. +# This is due to poor model fitting, which usually results from optimisation to +# a local minimum rather than the global minimum. +# +# This poor model fitting is evident from +# the model fitting from the initial iteration. For subsequent iterations the +# model fitting become increasing unstable due to the effect of compenstating +# deformation fields. +# +# To identify poor model fitting, the user can inspect the model fitting by +# outputing the model fitting outcomes an probing a goodness of fit metric +# against the original uncoregistred data. +# +# Here we calculate the chi squared value for each pixel in the data array +# after two iteration of the model fitting, and visualise the results. + +coreg_1iter, defo_1iter, fit_1iter, pars_1iter = mdreg.fit(array, + fit_image = vfa_fit, + fit_coreg = coreg_params, + maxit=1, + verbose=0) + +# Determine chi squared of fit pixel wise, regions where data == 0 are ignored +# and the output is zero +chi_squared = np.sum(np.divide((fit_1iter - array) ** 2, array, where=array != 0), axis=-1) + +# Determine the grid size for the panels +num_slices = array.shape[2] +grid_size = math.ceil(math.sqrt(num_slices)) + +# Set the colormap and color limits +cmap = plt.get_cmap('viridis') +vmin = np.percentile(chi_squared, 1) +vmax = np.percentile(chi_squared, 99) + +# Setup the figure +fig, axes1 = plt.subplots(grid_size, grid_size, figsize=(grid_size*2, grid_size*2)) +fig.subplots_adjust(wspace=0.5, hspace=0.01) + +# Add a title to the figure +titlesize = 10 +fig.suptitle('Goodness of Fit\n \n', fontsize=titlesize+2) + +# Improve the layout +plt.tight_layout() + +# Plot the chi squared values for each slice +for i in range(grid_size * grid_size): + row = i // grid_size + col = i % grid_size + if i < num_slices: + im = axes1[row, col].imshow(chi_squared[:, :, i].T, cmap=cmap, vmin=vmin, vmax=vmax) + axes1[row, col].set_title('Slice {}'.format(i+1), fontsize=titlesize) + else: + axes1[row, col].axis('off') # Turn off unused subplots + axes1[row, col].set_xticks([]) # Remove x-axis ticks + axes1[row, col].set_yticks([]) # Remove y-axis ticks + +# Set the colorbar to have overflow and underflow colors +im.cmap.set_over('red') +im.cmap.set_under('white') + +# set colorbar axis: [left, bottom, width, height] +cbar_ax = fig.add_axes([0.90, 0.15, 0.02, 0.7]) +cbar = fig.colorbar(im, cax=cbar_ax, extend='both') + +# Add a label to the colorbar and adjust the layout to make space for the +# colorbar +cbar.set_label('Chi Squared', rotation=270, labelpad=15) +fig.subplots_adjust(right=0.89) + +plt.show() + +#%% +# Goodness of fit +# --------------- +# The chi squared values in the figure above show large regions of poor fit +# across the data array. These regions are highlighted in red as above the 99th +# percentile of the chi squared values. These region also correspond to the +# regions of strange coregtistration behaviour shown in the previous animations. +# +# The user can use this information to identify regions of poor fit and decide +# whether to adjust the model fitting parameters or the coregistration options. + +# sphinx_gallery_start_ignore + +# Choose the last image as a thumbnail for the gallery +# sphinx_gallery_thumbnail_number = -1 + +# sphinx_gallery_end_ignore \ No newline at end of file diff --git a/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.rst b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.rst new file mode 100644 index 00000000..6af53c88 --- /dev/null +++ b/docs/source/generated/examples/use_cases/_plot_poor_model_fitting.rst @@ -0,0 +1,376 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "generated\examples\use_cases\_plot_poor_model_fitting.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py: + + +=============================================== +Example: Poor Model Fitting +=============================================== + +This example illustrates the effect of poor model fitting on coregistration +for the 3D Variable Flip Angle (VFA) dataset used in the 3D data example. + +As in the related 3D VFA example the data are fetched using the `fetch` +function, the VFA parameters are defined, and the coregistration parameters are +set. The model-driven coregistration is performed, and the results are +visualized. + +Differently from the previous example, the model fit shows poor fitting and the +resulting coregistration creates significant defects in the coregistered data. + +.. GENERATED FROM PYTHON SOURCE LINES 20-23 + +Import packages and data +------------------------ +Example data can be easily loaded in using the `fetch` function. + +.. GENERATED FROM PYTHON SOURCE LINES 23-32 + +.. code-block:: Python + + + import numpy as np + import mdreg + import time + import matplotlib.pyplot as plt + import math + + data = mdreg.fetch('VFA') + + +.. GENERATED FROM PYTHON SOURCE LINES 33-36 + +Extract the desired slice from the data array +--------------------------------------------- +As an intial step, we will extract the 4D data (x,y,z,t) from the fetched data dictionary. + +.. GENERATED FROM PYTHON SOURCE LINES 36-39 + +.. code-block:: Python + + + array = data['array'] + + +.. GENERATED FROM PYTHON SOURCE LINES 40-52 + +Signal model theory +------------------- +The signal model used in this example is the non-linear variable flip angle +SPGR model. The signal model is defined by the following equation: + +:math:`S(\phi)=S_{0} \frac{\sin{\phi}e^{-\frac{T_{R}}{T_{1}}}}{1-\cos{\phi}e^{-\frac{T_{R}}{T_{1}}}}` + +Where :math:`S` is the signal, :math:`S_{0}` the initial signal, :math:`\phi` +the flip angle, :math:`T_{R}` the reptition time and :math:`T_{1}` the +longitudinal relaxtion time. Using this equation, :math:`T_{1}` and +:math:`S_{0}` are optimised via a least squares method. + + +.. GENERATED FROM PYTHON SOURCE LINES 54-65 + +Define model fit parameters +--------------------------- +The image fitting settings dictionary (`vfa_fit` in this case) is required by +`mdreg.fit` to fit a specific signal model to the data. Leaving this as None +will fit a constant model to the data as a default. + +Here, we select the model function `func` to be the non-linear varaible flip +angle SPGR model from the model library (`mdreg.spgr_vfa_nonlin`). This model +fit requires the flip angle values in radians (`FA`) and the repetition time +in seconds (`TR`). This information is provided in the `data` dictionary for +this example. + +.. GENERATED FROM PYTHON SOURCE LINES 65-72 + +.. code-block:: Python + + + vfa_fit = { + 'func': mdreg.spgr_vfa_nonlin, # The function to fit the data + 'FA': data['FA'], # The flip angles in degrees + 'TR': 3.71/1000 # The TR value in s + } + + +.. GENERATED FROM PYTHON SOURCE LINES 73-84 + +Define the coregistration parameters +------------------------------------ +The coregistration parameters are set in the `coreg_params` dictionary. +The `package` key specifies the coregistration package to be used, with a +choice of elastix, skimage, or dipy. + +The `params` key specifies the parameters required by the chosen +coregistration package. Here None is used to specify default parameters for +freeform registration included by `mdreg`. + +Here, we use the elastix package with the following parameters: + +.. GENERATED FROM PYTHON SOURCE LINES 84-92 + +.. code-block:: Python + + + + coreg_params = { + 'package': 'elastix', + 'params': mdreg.elastix.params(FinalGridSpacingInPhysicalUnits='150.0'), + 'spacing': data['spacing'] + } + + +.. GENERATED FROM PYTHON SOURCE LINES 93-111 + +Define the plotting parameters +------------------------------ +The plotting parameters are set in the `plot_settings` dictionary. + +The `interval` key specifies the time interval between frames in +milliseconds, and the `vmin`/`vmax` keys specify the minimum/maximum value of +the colorbar. + +The `slice` key specifies the slice to be displayed in the +animation. If unset or set to None, the central slice is displayed by +default. + +If you are interested to save the resulting animation, you can use +the `path` key to the desired file path and the `filename` key to the desired +filename. As a default these are set to None resulting in the animation being +displayed on screen only. For more plotting keyword arguements, see the +`mdreg.plot` module. + + +.. GENERATED FROM PYTHON SOURCE LINES 111-122 + +.. code-block:: Python + + + plot_settings = { + 'interval' : 500, # Time interval between animation frames in ms + 'vmin' : 0, # Minimum value of the colorbar + 'vmax' : np.percentile(array,99), # Maximum value of the colorbar + 'path' : None, # Path to save the animation + 'show' : True, # Display the animation on screen + 'filename' : None, # Filename to save the animation + 'slice' : None # No slice specified, show all slices for 3D data + } + + +.. GENERATED FROM PYTHON SOURCE LINES 123-130 + +Perform model-driven coregistration +----------------------------------- +The `mdreg.fit` function is used to perform the model-driven coregistration. +The function requires the 4D data array, the fit_image dictionary, and the +coregistration parameters we have already defined. +The `verbose` parameter can be set to 0, 1, 2, or 3 to control the level of +output. + +.. GENERATED FROM PYTHON SOURCE LINES 130-143 + +.. code-block:: Python + + + stime = time.time() + + coreg, defo, fit, pars = mdreg.fit(array, + fit_image = vfa_fit, + fit_coreg = coreg_params, + maxit=3, + verbose=0) + + tot_time = time.time() - stime + + print(f"Non-linear fitting time elapsed: {(int(tot_time/60))} mins, {np.round(tot_time-(int(tot_time/60)*60),1)} s") + + +.. GENERATED FROM PYTHON SOURCE LINES 144-159 + +Visualise coregistration results +-------------------------------- +To easily visualise the output of the employ the `mdreg.plot` module to +easily produce animations that render on screen or save as a gif. +Here we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data +arrays which change over an additional dimension (e.g. time or FA in this +case). This displays the orginal data, the fitted data and the coregistered +data. + +For the case of 3D data, by default the function renders animations for all +slices for the original, fitted and coregistered data in seperate figures. If +a `slice` parameter is specified in the plotting parameters, the function +will produce a single figure for the specified slice showing the original, +fitted and coregistered data animations side-by-side. + + +.. GENERATED FROM PYTHON SOURCE LINES 159-162 + +.. code-block:: Python + + + anim = mdreg.animation(array, title='Original Data', **plot_settings) + + +.. GENERATED FROM PYTHON SOURCE LINES 163-165 + +.. code-block:: Python + + anim = mdreg.animation(coreg, title='Coregistered', **plot_settings) + + +.. GENERATED FROM PYTHON SOURCE LINES 166-168 + +.. code-block:: Python + + anim = mdreg.animation(fit, title='Model Fit', **plot_settings) + + +.. GENERATED FROM PYTHON SOURCE LINES 169-179 + +Export all series at once +------------------------- +The `mdreg.plot_series` function can be used to plot the original, fitted and +coregistered data for all slices in the data array. This function can also +be used to save the animations to a file. + +We include the `mdreg.animation` function to display the animations on screen +within the documentation, but recommend using the `mdreg.plot_series` function +to easily process and save the animations to a file when running locally. + >>> anims = mdreg.plot_series(array, fit, coreg, **plot_settings) + +.. GENERATED FROM PYTHON SOURCE LINES 181-199 + +Identifying poor model fitting +------------------------------ + +The coregistration results show significant defects in the coregistered data. +This is due to poor model fitting, which usually results from optimisation to +a local minimum rather than the global minimum. + +This poor model fitting is evident from +the model fitting from the initial iteration. For subsequent iterations the +model fitting become increasing unstable due to the effect of compenstating +deformation fields. + +To identify poor model fitting, the user can inspect the model fitting by +outputing the model fitting outcomes an probing a goodness of fit metric +against the original uncoregistred data. + +Here we calculate the chi squared value for each pixel in the data array +after two iteration of the model fitting, and visualise the results. + +.. GENERATED FROM PYTHON SOURCE LINES 199-257 + +.. code-block:: Python + + + coreg_1iter, defo_1iter, fit_1iter, pars_1iter = mdreg.fit(array, + fit_image = vfa_fit, + fit_coreg = coreg_params, + maxit=1, + verbose=0) + + # Determine chi squared of fit pixel wise, regions where data == 0 are ignored + # and the output is zero + chi_squared = np.sum(np.divide((fit_1iter - array) ** 2, array, where=array != 0), axis=-1) + + # Determine the grid size for the panels + num_slices = array.shape[2] + grid_size = math.ceil(math.sqrt(num_slices)) + + # Set the colormap and color limits + cmap = plt.get_cmap('viridis') + vmin = np.percentile(chi_squared, 1) + vmax = np.percentile(chi_squared, 99) + + # Setup the figure + fig, axes1 = plt.subplots(grid_size, grid_size, figsize=(grid_size*2, grid_size*2)) + fig.subplots_adjust(wspace=0.5, hspace=0.01) + + # Add a title to the figure + titlesize = 10 + fig.suptitle('Goodness of Fit\n \n', fontsize=titlesize+2) + + # Improve the layout + plt.tight_layout() + + # Plot the chi squared values for each slice + for i in range(grid_size * grid_size): + row = i // grid_size + col = i % grid_size + if i < num_slices: + im = axes1[row, col].imshow(chi_squared[:, :, i].T, cmap=cmap, vmin=vmin, vmax=vmax) + axes1[row, col].set_title('Slice {}'.format(i+1), fontsize=titlesize) + else: + axes1[row, col].axis('off') # Turn off unused subplots + axes1[row, col].set_xticks([]) # Remove x-axis ticks + axes1[row, col].set_yticks([]) # Remove y-axis ticks + + # Set the colorbar to have overflow and underflow colors + im.cmap.set_over('red') + im.cmap.set_under('white') + + # set colorbar axis: [left, bottom, width, height] + cbar_ax = fig.add_axes([0.90, 0.15, 0.02, 0.7]) + cbar = fig.colorbar(im, cax=cbar_ax, extend='both') + + # Add a label to the colorbar and adjust the layout to make space for the + # colorbar + cbar.set_label('Chi Squared', rotation=270, labelpad=15) + fig.subplots_adjust(right=0.89) + + plt.show() + + +.. GENERATED FROM PYTHON SOURCE LINES 258-267 + +Goodness of fit +--------------- +The chi squared values in the figure above show large regions of poor fit +across the data array. These regions are highlighted in red as above the 99th +percentile of the chi squared values. These region also correspond to the +regions of strange coregtistration behaviour shown in the previous animations. + +The user can use this information to identify regions of poor fit and decide +whether to adjust the model fitting parameters or the coregistration options. + + +.. _sphx_glr_download_generated_examples_use_cases__plot_poor_model_fitting.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: _plot_poor_model_fitting.ipynb <_plot_poor_model_fitting.ipynb>` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: _plot_poor_model_fitting.py <_plot_poor_model_fitting.py>` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: _plot_poor_model_fitting.zip <_plot_poor_model_fitting.zip>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery 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z^xneX7r@l_5@Qrcs`J8^7UDb$-WeK4m+XtJ3&KZ7S6+^$NZ>!&X`PU!KTz#P_GAY} zUxpQ}?a#t%EUg?CV!M87FVE7`zVtM28Ws)SA`7AtOq~2ECaAz`qv_?F~C3qSUCUM{7shv@F07{vSkdl;zdrs${-|{2xkbS>yl! literal 0 HcmV?d00001 diff --git a/docs/source/generated/examples/use_cases/index.rst b/docs/source/generated/examples/use_cases/index.rst index a745fe61..2636c61d 100644 --- a/docs/source/generated/examples/use_cases/index.rst +++ b/docs/source/generated/examples/use_cases/index.rst @@ -39,10 +39,10 @@ Use cases .. only:: html - .. image:: /generated/examples/use_cases/images/thumb/sphx_glr_plot_poor_model_fitting_thumb.png + .. image:: /generated/examples/use_cases/images/thumb/sphx_glr__plot_poor_model_fitting_thumb.png :alt: - :ref:`sphx_glr_generated_examples_use_cases_plot_poor_model_fitting.py` + :ref:`sphx_glr_generated_examples_use_cases__plot_poor_model_fitting.py` .. raw:: html @@ -61,5 +61,5 @@ Use cases :hidden: /generated/examples/use_cases/plot_3Dexample_vfa - /generated/examples/use_cases/plot_poor_model_fitting + /generated/examples/use_cases/_plot_poor_model_fitting diff --git a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py index c5bbbea6..c8985f8e 100644 --- a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py +++ b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py @@ -13,7 +13,7 @@ #%% # Import packages and data -# ---------------------------- +# ------------------------ # Example data can be easily loaded in using the `fetch` function. import numpy as np @@ -24,7 +24,7 @@ #%% # Extract the desired slice from the data array -# ---------------------------- +# --------------------------------------------- # As an intial step, we will extract the 4D data (x,y,z,t) from the fetched # data dictionary. @@ -32,7 +32,7 @@ #%% # Signal model theory -# ---------------------------- +# ------------------- # The signal model used in this example is the variable flip angle # SPGR model. The signal model is defined by the following equation: # @@ -64,7 +64,7 @@ #%% # Define model fit parameters -# ---------------------------- +# --------------------------- # The image fitting settings dictionary (`vfa_fit` in this case) is # required by `mdreg.fit` to fit a specific signal model to the data. Leaving # this as None will fit a constant model to the data as a default. @@ -80,7 +80,7 @@ } #%% # Define the coregistration parameters -# ---------------------------- +# ------------------------------------ # The coregistration parameters are set in the `coreg_params` dictionary. # The `package` key specifies the coregistration package to be used, with a # choice of elastix, skimage, or dipy. @@ -99,7 +99,7 @@ #%% # Define the plotting parameters -# ---------------------------- +# ------------------------------ # The plotting parameters are set in the `plot_settings` dictionary. # # The `interval` key specifies the time interval between frames in @@ -136,7 +136,7 @@ #%% # Perform model-driven coregistration -# ---------------------------- +# ----------------------------------- # The `mdreg.fit` function is used to perform the model-driven coregistration. # The function requires the 4D data array, the fit_image dictionary, and the # coregistration parameters we have already defined. @@ -158,7 +158,7 @@ #%% # Visualize the results -# ---------------------------- +# --------------------- # To easily visualise the output of the employ the `mdreg.plot` module to # easily produce animations that render on screen or save as a gif. # Here we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data @@ -179,7 +179,7 @@ #%% # Export all series at once -# ---------------------------- +# ------------------------- # The `mdreg.plot_series` function can be used to plot the original, fitted and # coregistered data for all slices in the data array. This function can also # be used to save the animations to a file. diff --git a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py.md5 b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py.md5 index ce4edcad..71fc1958 100644 --- a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py.md5 +++ b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.py.md5 @@ -1 +1 @@ -dc1bcdd3ebe3ed7863e91ec87ca1dffb \ No newline at end of file +9b3fcdc21e25a97c25dbe6d1b123ff2a \ No newline at end of file diff --git a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.rst b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.rst index 44219ad6..c06d1c59 100644 --- a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.rst +++ b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.rst @@ -31,7 +31,7 @@ performed, and the results are visualized. .. GENERATED FROM PYTHON SOURCE LINES 15-18 Import packages and data ----------------------------- +------------------------ Example data can be easily loaded in using the `fetch` function. .. GENERATED FROM PYTHON SOURCE LINES 18-25 @@ -55,7 +55,7 @@ Example data can be easily loaded in using the `fetch` function. .. GENERATED FROM PYTHON SOURCE LINES 26-30 Extract the desired slice from the data array ----------------------------- +--------------------------------------------- As an intial step, we will extract the 4D data (x,y,z,t) from the fetched data dictionary. @@ -76,7 +76,7 @@ data dictionary. .. GENERATED FROM PYTHON SOURCE LINES 34-64 Signal model theory ----------------------------- +------------------- The signal model used in this example is the variable flip angle SPGR model. The signal model is defined by the following equation: @@ -109,7 +109,7 @@ signal :math:`S`. .. GENERATED FROM PYTHON SOURCE LINES 66-76 Define model fit parameters ----------------------------- +--------------------------- The image fitting settings dictionary (`vfa_fit` in this case) is required by `mdreg.fit` to fit a specific signal model to the data. Leaving this as None will fit a constant model to the data as a default. @@ -138,7 +138,7 @@ provided in the `data` dictionary for this example. .. GENERATED FROM PYTHON SOURCE LINES 82-92 Define the coregistration parameters ----------------------------- +------------------------------------ The coregistration parameters are set in the `coreg_params` dictionary. The `package` key specifies the coregistration package to be used, with a choice of elastix, skimage, or dipy. @@ -170,7 +170,7 @@ with the following parameters: .. GENERATED FROM PYTHON SOURCE LINES 101-126 Define the plotting parameters ----------------------------- +------------------------------ The plotting parameters are set in the `plot_settings` dictionary. The `interval` key specifies the time interval between frames in @@ -220,7 +220,7 @@ of the final outputs. .. GENERATED FROM PYTHON SOURCE LINES 138-145 Perform model-driven coregistration ----------------------------- +----------------------------------- The `mdreg.fit` function is used to perform the model-driven coregistration. The function requires the 4D data array, the fit_image dictionary, and the coregistration parameters we have already defined. @@ -254,8 +254,7 @@ output. .. code-block:: none divide by zero encountered in divide - invalid value encountered in divide - Linear fitting time elapsed: 0 mins, 41.9 s + Linear fitting time elapsed: 3 mins, 49.0 s @@ -263,7 +262,7 @@ output. .. GENERATED FROM PYTHON SOURCE LINES 160-171 Visualize the results ----------------------------- +--------------------- To easily visualise the output of the employ the `mdreg.plot` module to easily produce animations that render on screen or save as a gif. Here we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data @@ -473,42 +472,42 @@ results.
    - +
    - + oninput="anime1fe5a61270d4481a8349057b115d8cf.set_frame(parseInt(this.value));">
    - - - - - - - - -
    -
    - - - Once + - - Loop + - +
    @@ -518,48172 +517,48170 @@ results. /* Instantiate the Animation class. */ /* The IDs given should match those used in the template above. */ (function() { - var img_id = "_anim_img93caeaf2e85343f0b11d1bceb16d7ff6"; - var slider_id = "_anim_slider93caeaf2e85343f0b11d1bceb16d7ff6"; - var loop_select_id = "_anim_loop_select93caeaf2e85343f0b11d1bceb16d7ff6"; + var img_id = "_anim_imge1fe5a61270d4481a8349057b115d8cf"; + var slider_id = "_anim_slidere1fe5a61270d4481a8349057b115d8cf"; + var loop_select_id = "_anim_loop_selecte1fe5a61270d4481a8349057b115d8cf"; var frames = new Array(5); - frames[0] = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABLAAAASwCAYAAADrIbPPAAAAP3RFWHRTb2Z0d2FyZQBNYXRwbG90\ - bGliIHZlcnNpb24zLjkuMS5wb3N0MSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8kixA/AAAACXBI\ - WXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9eZRkWVU1vmPIjHnIoaauHqEnpgZFlAZkFBpEGkGh\ - EZRpOTHLt/gE1B9N8yk4MIiACvoBKg0KKK2i2CC2wqL1Yx4Uwabpgh6qKqeYx8yM9/uj1r6x380X\ - 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slider_id, 500.0, + anime1fe5a61270d4481a8349057b115d8cf = new Animation(frames, img_id, slider_id, 500.0, loop_select_id); }, 0); })() @@ -48892,42 +48889,42 @@ results.
    - +
    - + oninput="anim73fc0346ed1a4cb5904998ad4f5e449a.set_frame(parseInt(this.value));">
    - - - - - - - - -
    -
    - - - Once + - - Loop + - +
    @@ -48937,48004 +48934,47976 @@ results. /* Instantiate the Animation class. */ /* The IDs given should match those used in the template above. */ (function() { - var img_id = "_anim_img4df98e796fff4f35843c2419506077ac"; - var slider_id = "_anim_slider4df98e796fff4f35843c2419506077ac"; - var loop_select_id = "_anim_loop_select4df98e796fff4f35843c2419506077ac"; + var img_id = "_anim_img73fc0346ed1a4cb5904998ad4f5e449a"; + var slider_id = "_anim_slider73fc0346ed1a4cb5904998ad4f5e449a"; + var loop_select_id = "_anim_loop_select73fc0346ed1a4cb5904998ad4f5e449a"; var frames = new Array(5); - frames[0] = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABLAAAASwCAYAAADrIbPPAAAAP3RFWHRTb2Z0d2FyZQBNYXRwbG90\ - bGliIHZlcnNpb24zLjkuMS5wb3N0MSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8kixA/AAAACXBI\ - WXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9eZRsaVUlviMyM+aIHN9QcxUFVEFBoQJqUTRVWAIi\ - AorIpILSyFImtZcopT8sQBGnFgS1LcBGW4Ylo9gqg0IBNmALNFWgUlCFBdb03ssp5iGHuL8/3tpf\ - 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Cp40CoFVUFBQUFBQUFBQUFBQUFBQUPCkUQisgoKCgoKCgoKCgoKCgoKCgoInjUJgFRQUFBQUFBQU\ + FBQUFBQUFBQ8aRQCq6CgoKCgoKCgoKCgoKCgoKDgSeP/AW94tH3iN8W6AAAAAElFTkSuQmCC\ " /* set a timeout to make sure all the above elements are created before the object is initialized. */ setTimeout(function() { - anim4df98e796fff4f35843c2419506077ac = new Animation(frames, img_id, slider_id, 500.0, + anim73fc0346ed1a4cb5904998ad4f5e449a = new Animation(frames, img_id, slider_id, 500.0, loop_select_id); }, 0); })() @@ -97143,42 +97112,42 @@ results.
    - +
    - + oninput="anim56d7c3f9489c4331b528a61dba081dad.set_frame(parseInt(this.value));">
    - - - - - - - - -
    -
    - - - Once + - - Loop + - +
    @@ -97188,48036 +97157,48040 @@ results. /* Instantiate the Animation class. */ /* The IDs given should match those used in the template above. */ (function() { - var img_id = "_anim_imgc673d64c365e4322a415ca2d13f95e79"; - var slider_id = "_anim_sliderc673d64c365e4322a415ca2d13f95e79"; - var loop_select_id = "_anim_loop_selectc673d64c365e4322a415ca2d13f95e79"; + var img_id = "_anim_img56d7c3f9489c4331b528a61dba081dad"; + var slider_id = "_anim_slider56d7c3f9489c4331b528a61dba081dad"; + var loop_select_id = "_anim_loop_select56d7c3f9489c4331b528a61dba081dad"; var frames = new Array(5); - frames[0] = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABLAAAASwCAYAAADrIbPPAAAAP3RFWHRTb2Z0d2FyZQBNYXRwbG90\ - bGliIHZlcnNpb24zLjkuMS5wb3N0MSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8kixA/AAAACXBI\ - WXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9eZSlVXnv/z1TnXmoU0NX9UjTjTI7ICEIUZQoIlc0\ - iJBoAuqK3jhFk5WfkcQE0RhzEyMmkESNN2oMeB2J8aKCV0GImIgILTPd0N30VPOZxzrnvL8/an13\ - 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GENERATED FROM PYTHON SOURCE LINES 181-192 Export all series at once ----------------------------- +------------------------- The `mdreg.plot_series` function can be used to plot the original, fitted and coregistered data for all slices in the data array. This function can also be used to save the animations to a file. @@ -145245,7 +145218,7 @@ locally. .. rst-class:: sphx-glr-timing - **Total running time of the script:** (0 minutes 56.666 seconds) + **Total running time of the script:** (4 minutes 27.801 seconds) .. _sphx_glr_download_generated_examples_use_cases_plot_3Dexample_vfa.py: diff --git a/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.zip b/docs/source/generated/examples/use_cases/plot_3Dexample_vfa.zip index 35a4f5c007435d71c7dbd7f1300a717b12dff884..94223a74b9577dcbcae6a8a80a06491d1e09f588 100644 GIT binary patch delta 173 zcmeC`W1P~*C?4R=%p$@70zJ;nkz7Y_CCSS%FvxEd_hp(Kz|Ar_hw1s`25H{O3Zfj7 z!`L<%p$@70&94(BmKFmxJBg{7{GY)0yl|`#)eFj6PVslPGH_JIe?jS zvj9sz%Vd5Q&du!{XP71nuy9O1q9eJvhI=ZQvss!?j2$Gp*+JBgdGZ8lp3RdbSePdZ z@NrLe5M`g7<>a*ag>(rM^J$*!&GB+d8oVgBPVRFvQvgauPCK3X_pLeu0|-liT?Zky VHG0TRUg#z|`KOyGTecfW5CB`#G-m(+ diff --git a/docs/source/generated/examples/use_cases/plot_poor_model_fitting.py b/docs/source/generated/examples/use_cases/plot_poor_model_fitting.py index 47e075a1..4479b46e 100644 --- a/docs/source/generated/examples/use_cases/plot_poor_model_fitting.py +++ b/docs/source/generated/examples/use_cases/plot_poor_model_fitting.py @@ -18,7 +18,7 @@ #%% # Import packages and data -# ---------------------------- +# ------------------------ # Example data can be easily loaded in using the `fetch` function. import numpy as np @@ -31,14 +31,14 @@ #%% # Extract the desired slice from the data array -# ---------------------------- +# --------------------------------------------- # As an intial step, we will extract the 4D data (x,y,z,t) from the fetched data dictionary. array = data['array'] #%% # Signal model theory -# ---------------------------- +# ------------------- # The signal model used in this example is the non-linear variable flip angle # SPGR model. The signal model is defined by the following equation: # @@ -52,7 +52,7 @@ #%% # Define model fit parameters -# ---------------------------- +# --------------------------- # The image fitting settings dictionary (`vfa_fit` in this case) is required by # `mdreg.fit` to fit a specific signal model to the data. Leaving this as None # will fit a constant model to the data as a default. @@ -71,7 +71,7 @@ #%% # Define the coregistration parameters -# ---------------------------- +# ------------------------------------ # The coregistration parameters are set in the `coreg_params` dictionary. # The `package` key specifies the coregistration package to be used, with a # choice of elastix, skimage, or dipy. @@ -91,7 +91,7 @@ #%% # Define the plotting parameters -# ---------------------------- +# ------------------------------ # The plotting parameters are set in the `plot_settings` dictionary. # # The `interval` key specifies the time interval between frames in @@ -121,7 +121,7 @@ #%% # Perform model-driven coregistration -# ---------------------------- +# ----------------------------------- # The `mdreg.fit` function is used to perform the model-driven coregistration. # The function requires the 4D data array, the fit_image dictionary, and the # coregistration parameters we have already defined. @@ -142,7 +142,7 @@ #%% # Visualise coregistration results -# --------------------------------- +# -------------------------------- # To easily visualise the output of the employ the `mdreg.plot` module to # easily produce animations that render on screen or save as a gif. # Here we utilise `mdreg.plot_series` which accepts both 2D and 3D spatial data @@ -167,7 +167,7 @@ #%% # Export all series at once -# ---------------------------- +# ------------------------- # The `mdreg.plot_series` function can be used to plot the original, fitted and # coregistered data for all slices in the data array. This function can also # be used to save the animations to a file. @@ -178,7 +178,7 @@ # >>> anims = mdreg.plot_series(array, fit, coreg, **plot_settings) #%% -# Identifiying poor model fitting +# Identifying poor model fitting # ------------------------------ # # The coregistration results show significant defects in the coregistered data. @@ -256,7 +256,7 @@ #%% # Goodness of fit -# ---------------------------- +# --------------- # The chi squared values in the figure above show large regions of poor fit # across the data array. These regions are highlighted in red as above the 99th # percentile of the chi squared values. These region also correspond to the diff --git a/docs/source/releases/index.rst b/docs/source/releases/index.rst index b6d9cb5f..7f964ea0 100644 --- a/docs/source/releases/index.rst +++ b/docs/source/releases/index.rst @@ -9,6 +9,7 @@ Releases .. toctree:: :maxdepth: 2 + release_0.4.1 release_0.4.0 release_0.3.9 release_0.3.0 diff --git a/docs/source/releases/release_0.4.0.rst b/docs/source/releases/release_0.4.0.rst index 29d04c6a..4969ea7a 100644 --- a/docs/source/releases/release_0.4.0.rst +++ b/docs/source/releases/release_0.4.0.rst @@ -5,7 +5,10 @@ We're happy to announce the release of mdreg 0.4.0! Summary -------- -Major new release with breaking changes in the API. As of v0.4.0 mdreg has moved over to a simpler functional programming interface with significant customization options. Older versions are deprecated and should no longer be used. +Major new release with breaking changes in the API. As of v0.4.0 mdreg has +moved over to a simpler functional programming interface with significant +customization options. Older versions are deprecated and should no longer be +used. Contributors diff --git a/docs/source/releases/release_0.4.1.rst b/docs/source/releases/release_0.4.1.rst new file mode 100644 index 00000000..6ec9b996 --- /dev/null +++ b/docs/source/releases/release_0.4.1.rst @@ -0,0 +1,25 @@ +mdreg 0.4.1 +=========== + +Summary +-------- + +- Extended documentation including function-level docstrings +- New examples +- 3D and multi-slice 2D +- utilities: jacobian and norm of deformation field +- improved error handling for elastix coregistration +- correct handling of Nan and non-contiguous arrays in elastix +- proper handling of floating point images in skimage registration + + +Contributors +------------ + +3 authora added to this release: + +- Alex Daniel (`@alexdaniel654 `_) +- Eve Shalom (`@EShalom `_) +- Steven Sourbron (`@plaresmedima `_) + + diff --git a/tests/requirements.txt b/tests/requirements.txt new file mode 100644 index 00000000..cffeec65 --- /dev/null +++ b/tests/requirements.txt @@ -0,0 +1,2 @@ +pytest +pytest-cov \ No newline at end of file

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