work on documentation update complete

dkazanc · Jan 6, 2025 · 7651843 · 7651843
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diff --git a/CHANGES.md b/CHANGES.md
@@ -1,5 +1,11 @@
 # ChangeLog
 
+## v3.0.1 (2024.12)
+
+* Documentation has been updated in January 2025 with a lot of additional information, API links, tutorials, etc.
+* Docstrings of the modules were updated with the references to documentation. 
+
+
 ## v3.0 (2023.12)
 * Project reorganised into two parts: a library that is build as a shared object using Cmake and Ctypes bindings and pure Python part that can be 
 installed separately. 

diff --git a/Demos/2D/Model.py b/Demos/2D/Model.py
@@ -16,7 +16,6 @@
 
 model = 4  # select a model number from the library file (Phantom2DLibrary)
 N_size = 512  # set the desired dimension of the phantom
-# one can specify an exact path to the parameters file
 path = os.path.dirname(tomophantom.__file__)
 path_library2D = os.path.join(path, "phantomlib", "Phantom2DLibrary.dat")
 
@@ -29,6 +28,7 @@
 plt.imshow(phantom_2D, vmin=0, vmax=1, cmap="BuPu")
 plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
 plt.title("{}" "{}".format("2D Phantom using model no.", model))
+plt.show()
 
 # %%
 # Parameters to generate a sinogram
@@ -51,110 +51,111 @@
 plt.imshow(sino_an, vmin=0, vmax=300, cmap="BuPu")
 plt.colorbar(ticks=[0, 150, 300], orientation="vertical")
 plt.title("{}" "{}".format("Analytical sinogram of model no.", model))
-# %%
-# generate numerical sinogram
-angles_num = int(0.5 * np.pi * N_size)
-# angles number
-angles = np.linspace(0.0, 179.9, angles_num, dtype="float32")
-P = int(np.sqrt(2) * N_size)  # detectors
-
-tic = timeit.default_timer()
-sino_num = TomoP2D.SinoNum(phantom_2D, P, angles)
-toc = timeit.default_timer()
-Run_time = toc - tic
-print("Numerical sinogram has been generated in {} seconds".format(Run_time))
-
-plt.figure()
-plt.rcParams.update({"font.size": 21})
-plt.imshow(sino_num, vmin=0, vmax=300, cmap="BuPu")
-plt.colorbar(ticks=[0, 150, 300], orientation="vertical")
-plt.title("{}" "{}".format("Numerical sinogram of model no.", model))
+plt.show()
+# # %%
+# # generate numerical sinogram
+# angles_num = int(0.5 * np.pi * N_size)
+# # angles number
+# angles = np.linspace(0.0, 179.9, angles_num, dtype="float32")
+# P = int(np.sqrt(2) * N_size)  # detectors
+
+# tic = timeit.default_timer()
+# sino_num = TomoP2D.SinoNum(phantom_2D, P, angles)
+# toc = timeit.default_timer()
+# Run_time = toc - tic
+# print("Numerical sinogram has been generated in {} seconds".format(Run_time))
 
 # plt.figure()
-# plt.imshow(abs(sino_an-sino_num), vmin=0, vmax=0.001, cmap="BuPu")
-# plt.colorbar(ticks=[0, 0.02, 0.05], orientation='vertical')
-# plt.title('Analytical vs Numerical singograms')
-# %%
-
-
-###################################################################
-from tomobar.methodsDIR import RecToolsDIR
-
-angles_rad = angles * (np.pi / 180.0)
-
-# get numerical sinogram (ASTRA-toolbox)
-Rectools = RecToolsDIR(
-    DetectorsDimH=P,  # DetectorsDimH # detector dimension (horizontal)
-    DetectorsDimV=None,  # DetectorsDimV # detector dimension (vertical) for 3D case only
-    CenterRotOffset=0.0,  # Center of Rotation (CoR) scalar (for 3D case only)
-    AnglesVec=angles_rad,  # array of angles in radians
-    ObjSize=N_size,  # a scalar to define reconstructed object dimensions
-    device_projector="cpu",
-)
-
-tic = timeit.default_timer()
-sino_num_ASTRA = Rectools.FORWPROJ(phantom_2D)  # generate numerical sino (Ax)
-toc = timeit.default_timer()
-Run_time = toc - tic
-print("Numerical (ASTRA) sinogram has been generated in {} seconds".format(Run_time))
-
-plt.figure()
-plt.rcParams.update({"font.size": 21})
-plt.imshow(sino_num_ASTRA, vmin=0, vmax=150, cmap="BuPu")
-plt.colorbar(ticks=[0, 150, 250], orientation="vertical")
-plt.title("{}" "{}".format("Numerical sinogram (ASTRA) of model no.", model))
-# %%
-print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print("Reconstructing analytical sinogram using Fourier Slice method")
-print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# plt.rcParams.update({"font.size": 21})
+# plt.imshow(sino_num, vmin=0, vmax=300, cmap="BuPu")
+# plt.colorbar(ticks=[0, 150, 300], orientation="vertical")
+# plt.title("{}" "{}".format("Numerical sinogram of model no.", model))
+
+# # plt.figure()
+# # plt.imshow(abs(sino_an-sino_num), vmin=0, vmax=0.001, cmap="BuPu")
+# # plt.colorbar(ticks=[0, 0.02, 0.05], orientation='vertical')
+# # plt.title('Analytical vs Numerical singograms')
+# # %%
+
+
+# ###################################################################
+# from tomobar.methodsDIR import RecToolsDIR
+
+# angles_rad = angles * (np.pi / 180.0)
+
+# # get numerical sinogram (ASTRA-toolbox)
+# Rectools = RecToolsDIR(
+#     DetectorsDimH=P,  # DetectorsDimH # detector dimension (horizontal)
+#     DetectorsDimV=None,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+#     CenterRotOffset=0.0,  # Center of Rotation (CoR) scalar (for 3D case only)
+#     AnglesVec=angles_rad,  # array of angles in radians
+#     ObjSize=N_size,  # a scalar to define reconstructed object dimensions
+#     device_projector="cpu",
+# )
+
+# tic = timeit.default_timer()
+# sino_num_ASTRA = Rectools.FORWPROJ(phantom_2D)  # generate numerical sino (Ax)
+# toc = timeit.default_timer()
+# Run_time = toc - tic
+# print("Numerical (ASTRA) sinogram has been generated in {} seconds".format(Run_time))
 
-RecFourier = Rectools.FOURIER(sino_an, "linear")
+# plt.figure()
+# plt.rcParams.update({"font.size": 21})
+# plt.imshow(sino_num_ASTRA, vmin=0, vmax=150, cmap="BuPu")
+# plt.colorbar(ticks=[0, 150, 250], orientation="vertical")
+# plt.title("{}" "{}".format("Numerical sinogram (ASTRA) of model no.", model))
+# # %%
+# print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# print("Reconstructing analytical sinogram using Fourier Slice method")
+# print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
-plt.figure()
-plt.imshow(RecFourier, vmin=0, vmax=1, cmap="BuPu")
-plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
-plt.title("Fourier slice reconstruction")
-# %%
-print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print("Reconstructing analytical sinogram using FBP (tomobar)...")
-print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-# x = Atools.backproj(sino_an) # generate backprojection (A'b)
+# RecFourier = Rectools.FOURIER(sino_an, "linear")
 
-plt.figure()
-plt.subplot(121)
-plt.imshow(sino_an, cmap="BuPu")
-plt.title("Analytical sinogram")
-plt.subplot(122)
-plt.imshow(sino_num_ASTRA, cmap="BuPu")
-plt.title("Numerical sinogram")
-plt.show()
-# calculate norm
-# rmse1 = np.linalg.norm(sino_an - sino_num_ASTRA)/np.linalg.norm(sino_num_ASTRA)
+# plt.figure()
+# plt.imshow(RecFourier, vmin=0, vmax=1, cmap="BuPu")
+# plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
+# plt.title("Fourier slice reconstruction")
+# # %%
+# print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# print("Reconstructing analytical sinogram using FBP (tomobar)...")
+# print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# # x = Atools.backproj(sino_an) # generate backprojection (A'b)
 
-print("Reconstructing analytical sinogram using FBP (astra TB)...")
-FBPrec1 = Rectools.FBP(sino_an)
-plt.figure()
-plt.imshow(FBPrec1, vmin=0, vmax=1, cmap="BuPu")
-plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
-plt.title("FBP Reconstructed Phantom (analyt)")
+# plt.figure()
+# plt.subplot(121)
+# plt.imshow(sino_an, cmap="BuPu")
+# plt.title("Analytical sinogram")
+# plt.subplot(122)
+# plt.imshow(sino_num_ASTRA, cmap="BuPu")
+# plt.title("Numerical sinogram")
+# plt.show()
+# # calculate norm
+# # rmse1 = np.linalg.norm(sino_an - sino_num_ASTRA)/np.linalg.norm(sino_num_ASTRA)
+
+# print("Reconstructing analytical sinogram using FBP (astra TB)...")
+# FBPrec1 = Rectools.FBP(sino_an)
+# plt.figure()
+# plt.imshow(FBPrec1, vmin=0, vmax=1, cmap="BuPu")
+# plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
+# plt.title("FBP Reconstructed Phantom (analyt)")
 
-print("Reconstructing numerical sinogram using FBP (astra TB)...")
-FBPrec2 = Rectools.FBP(sino_num_ASTRA)
+# print("Reconstructing numerical sinogram using FBP (astra TB)...")
+# FBPrec2 = Rectools.FBP(sino_num_ASTRA)
 
-plt.figure()
-plt.imshow(FBPrec2, vmin=0, vmax=1, cmap="BuPu")
-plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
-plt.title("FBP Reconstructed Phantom (numeric)")
+# plt.figure()
+# plt.imshow(FBPrec2, vmin=0, vmax=1, cmap="BuPu")
+# plt.colorbar(ticks=[0, 0.5, 1], orientation="vertical")
+# plt.title("FBP Reconstructed Phantom (numeric)")
 
-plt.figure()
-plt.imshow(abs(FBPrec1 - FBPrec2), vmin=0, vmax=0.05, cmap="BuPu")
-plt.colorbar(ticks=[0, 0.02, 0.05], orientation="vertical")
-plt.title("FBP rec differences")
-# rmse2 = np.linalg.norm(FBPrec1 - FBPrec2)/np.linalg.norm(FBPrec2)
-
-Qtools = QualityTools(phantom_2D, FBPrec1)
-RMSE_FBP1 = Qtools.rmse()
-Qtools = QualityTools(phantom_2D, FBPrec2)
-RMSE_FBP2 = Qtools.rmse()
-print("RMSE for FBP (analyt) {}".format(RMSE_FBP1))
-print("RMSE for FBP (numeric) {}".format(RMSE_FBP2))
+# plt.figure()
+# plt.imshow(abs(FBPrec1 - FBPrec2), vmin=0, vmax=0.05, cmap="BuPu")
+# plt.colorbar(ticks=[0, 0.02, 0.05], orientation="vertical")
+# plt.title("FBP rec differences")
+# # rmse2 = np.linalg.norm(FBPrec1 - FBPrec2)/np.linalg.norm(FBPrec2)
+
+# Qtools = QualityTools(phantom_2D, FBPrec1)
+# RMSE_FBP1 = Qtools.rmse()
+# Qtools = QualityTools(phantom_2D, FBPrec2)
+# RMSE_FBP2 = Qtools.rmse()
+# print("RMSE for FBP (analyt) {}".format(RMSE_FBP1))
+# print("RMSE for FBP (numeric) {}".format(RMSE_FBP2))
diff --git a/Demos/2D/Object2D.py b/Demos/2D/Object2D.py
@@ -7,11 +7,6 @@
 Script to generate 2D/3D analytical objects and their sinograms
 Recursively adding objects and sinos one can build a required model
 
->>>>> Optional dependencies (reconstruction mainly): <<<<<
-1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
-2. tomobar: conda install -c dkazanc tomobar
-or install from https://github.com/dkazanc/ToMoBAR
-
 @author: Daniil Kazantsev
 """
 import numpy as np

diff --git a/Demos/jupyter-notebooks/.ipynb_checkpoints/Random2Dphantom_artifacts-checkpoint.ipynb b/Demos/jupyter-notebooks/.ipynb_checkpoints/Random2Dphantom_artifacts-checkpoint.ipynb
diff --git a/Demos/jupyter-notebooks/.ipynb_checkpoints/model2D-checkpoint.ipynb b/Demos/jupyter-notebooks/.ipynb_checkpoints/model2D-checkpoint.ipynb
diff --git a/Demos/jupyter-notebooks/.ipynb_checkpoints/model3D-checkpoint.ipynb b/Demos/jupyter-notebooks/.ipynb_checkpoints/model3D-checkpoint.ipynb
diff --git a/Demos/jupyter-notebooks/Random2Dphantom_artifacts.ipynb b/Demos/jupyter-notebooks/Random2Dphantom_artifacts.ipynb
diff --git a/Demos/jupyter-notebooks/model2D.ipynb b/Demos/jupyter-notebooks/model2D.ipynb
diff --git a/Demos/jupyter-notebooks/model3D.ipynb b/Demos/jupyter-notebooks/model3D.ipynb
diff --git a/README.md b/README.md
@@ -18,8 +18,9 @@
 
 ****************
 
-### NEW! VERSION 3.0
-TomoPhantom has been refactored, please see [changes](CHANGES.md). Everyone is welcome to <a href="https://dkazanc.github.io/TomoPhantom/index.html">Documentation</a> page.
+### NEW!
+Please see [changes](CHANGES.md). <a href="https://dkazanc.github.io/TomoPhantom/index.html">Documentation</a> has been updated in January 2025 with 
+a lot of additional information, API links, tutorials, etc.
 
  <div class="post-content">
         <h3 class="post-title">About TomoPhantom </h3>
@@ -34,7 +35,7 @@ TomoPhantom has been refactored, please see [changes](CHANGES.md). Everyone is w
  * Model a variety of tomographic data artefacts (noise models, zingers, rings, shifts, partial volume effect and others). 
 
 ### Installation:
-Tomophantom is distributed as a Python conda package for Linux/Windows/Mac OS's:
+TomoPhantom is distributed as a conda package in Python for Linux & Windows:
 ```
 conda install -c httomo tomophantom
 ```

diff --git a/README.rst b/README.rst
@@ -28,17 +28,19 @@ for parallel-beam scanning geometry.
 
 Documentation
 -------------
-Please check the full `documentation <https://dkazanc.github.io/Tomophantom/>`_.
+
+`Documentation <https://dkazanc.github.io/Tomophantom/>`_ is a good place to start.
 
 Install TomoPhantom
---------------------------------------------------------
-Tomophantom is distributed as a Python conda package for Linux/Windows/Mac OS's:
+-------------------
+
+TomoPhantom is distributed as a conda package in Python for Linux & Windows:
 
 .. code-block:: console
 
    $ conda install -c httomo tomophantom
    
-See the detailed page on :ref:`ref_installation`.
+See the detailed installation page on :ref:`ref_installation`.
 
 .. [SX2018] D. Kazantsev et al. 2018, TomoPhantom, a software package to 
    generate 2D-4D analytical phantoms for CT image reconstruction 

diff --git a/conda-recipe_library/conda_build_config.yaml b/conda-recipe_library/conda_build_config.yaml
@@ -1,9 +1,11 @@
 python:
   - 3.10
   - 3.11
+  - 3.12
 numpy:
   - 1.23
   - 1.26
+  - 1.26
 zip_keys:
   - python
   - numpy

diff --git a/docs/source/_static/tutorials/model4.png b/docs/source/_static/tutorials/model4.png
diff --git a/docs/source/_static/tutorials/model_sino4.png b/docs/source/_static/tutorials/model_sino4.png
diff --git a/docs/source/_static/tutorials/object2d.png b/docs/source/_static/tutorials/object2d.png
diff --git a/docs/source/_static/tutorials/object_sino2d.png b/docs/source/_static/tutorials/object_sino2d.png
diff --git a/docs/source/api/tomophantom.generator.rst b/docs/source/api/tomophantom.generator.rst
@@ -0,0 +1,6 @@
+:mod:`tomophantom.generator`
+============================================
+.. automodule:: tomophantom.generator
+   :members:
+   :show-inheritance:
+   :undoc-members:
diff --git a/docs/source/howto/build_phantoms.rst b/docs/source/howto/build_phantoms.rst
@@ -5,6 +5,6 @@ Build Phantoms
 
 This sections explains how to build phantoms by presenting two main ways of doing that. 
 
-* 1. The easiest and the quickest way to build a phantom is to use provided library files, see more in :ref:`howto_libraries`.  This is the most suitable way to start for someone who is new to TomoPhantom. We recommend to try building already pre-existing models by following this guide on :ref:`tutorial_model`.
+* 1. The easiest and the quickest way to build a phantom is to use provided library files, see more in :ref:`howto_libraries`.  This is the most suitable way to start for someone who is new to TomoPhantom. We recommend to try building already pre-existing models by following this guide on how to generate :ref:`tutorial_model`.
 
-* 2. For more advanced approach in building bespoke phantoms consisting of multiple objects, we recommend to use :ref:`ref_object_api`. This is how one can build a phantom directly, i.e., without the use of the library file. One can specify parameters in Python as a dictionary and pass into a function that works with the objects. We recommend to have a look at this tutorial :ref:`tutorial_object`.
+* 2. For more advanced approach in building bespoke phantoms consisting of multiple objects, we recommend to follow the guidelines of :ref:`ref_object_api`. This is how one can build a phantom directly, i.e., without the use of the library file. One can specify parameters as a dictionary in Python and pass into a function that works with the objects. This can be more versatile way of building phantoms when one need to update or manipulate with parameters of objects. We recommend to have a look at the tutorial how to generate :ref:`tutorial_object`.
-Original file line number
+Diff line change
@@ -1,9 +1,11 @@
     python:
       - 3.10
       - 3.11
+      - 3.12
     numpy:
       - 1.23
       - 1.26
+      - 1.26
     zip_keys:
       - python
       - numpy
@@ Expand Down @@