Merge pull request #71 from dkazanc/pyproject

reformating the project with pyproject

.github/workflows/tomobar_conda_upload.yml

@@ -0,0 +1,32 @@
+name: tomobar_conda_upload
+
+on: [push]
+
+jobs:
+  build-linux:
+    runs-on: ubuntu-latest
+
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+
+    - name: Install dependencies with Conda
+      run: |
+        $CONDA/bin/conda env update --file conda-recipe/environment/environment.yml --name base
+
+    - name: Decrypt a secret
+      run: ./.scripts/decrypt_secret.sh
+      env:
+        LARGE_SECRET_PASSPHRASE: ${{ secrets.LARGE_SECRET_PASSPHRASE }}
+
+    - name: Upload the tested package to conda cloud
+      run: |
+        chmod +x ./.scripts/conda_upload.sh
+        ./.scripts/conda_upload.sh

.scripts/conda_upload.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+
+PKG_NAME=tomobar
+USER=dkazanc
+OS=linux-64
+CONDA_TOKEN=$(cat $HOME/.secrets/my_secret.json)
+
+mkdir ~/conda-bld
+conda config --set anaconda_upload no
+export CONDA_BLD_PATH=~/conda-bld
+#export VERSION=`date +%Y.%m`
+#conda install --yes anaconda-client
+#conda build . --numpy=1.20 --python=3.9
+#conda build . --numpy=1.21 --python=3.9
+#conda build . --numpy=1.22 --python=3.9
+#conda build . --numpy=1.20 --python=3.10
+#conda build . --numpy=1.21 --python=3.10
+#conda build . --numpy=1.22 --python=3.10
+
+for python_ver in 3.9 3.10; do   
+    for numpy_ver in 1.21 1.22 1.23 1.24; do
+        export VERSION=`date +%Y.%m`"-py"$python_ver"_np"$numpy_ver
+        conda build . --numpy $numpy_ver --python $python_ver
+    done
+done
+
+# upload packages to conda
+find $CONDA_BLD_PATH/$OS -name *.tar.bz2 | while read file
+do
+    echo $file
+    $CONDA/bin/anaconda -v --show-traceback --token $CONDA_TOKEN upload $file --force
+done

scripts/decrypt_secret.sh → .scripts/decrypt_secret.sh

@@ -1,8 +1,8 @@
 #!/bin/sh
 
 # Decrypt the file
-mkdir $HOME/secrets
+mkdir $HOME/.secrets
 # --batch to prevent interactive command
 # --yes to assume "yes" for questions
 gpg --quiet --batch --yes --decrypt --passphrase="$LARGE_SECRET_PASSPHRASE" \
---output $HOME/secrets/my_secret.json ./scripts/my_secret.json.gpg
+--output $HOME/.secrets/my_secret.json ./.scripts/my_secret.json.gpg

Demos/Python/DemoFISTA_artifacts3D.py

@@ -255,33 +255,6 @@
 plt.title('3D Huber Rec, sagittal')
 plt.show()
 
-# adding RING minimisation component (better model for data with rings - different from GH!)
-#data.update({'huber_threshold' : None})
-_data_.update({'ring_weights_threshold' : 2.0})
-_data_.update({'ring_tuple_halfsizes' : (9,7,9)})
-
-# Run FISTA reconstrucion algorithm with 3D regularisation and a better ring model
-RecFISTA_HuberRING_TV = Rectools.FISTA(_data_, _algorithm_, _regularisation_)
-
-Qtools = QualityTools(phantom_tm, RecFISTA_HuberRING_TV)
-RMSE_FISTA_HUBER_RING_TV = Qtools.rmse()
-print("RMSE for FISTA-OS-Huber-Ring-TV is {}".format(RMSE_FISTA_HUBER_RING_TV))
-
-sliceSel = int(0.5*N_size)
-max_val = 1
-plt.figure() 
-plt.subplot(131)
-plt.imshow(RecFISTA_HuberRING_TV[sliceSel,:,:],vmin=0, vmax=max_val)
-plt.title('3D HuberRing Rec, axial')
-
-plt.subplot(132)
-plt.imshow(RecFISTA_HuberRING_TV[:,sliceSel,:],vmin=0, vmax=max_val)
-plt.title('3D HuberRing Rec, coronal')
-
-plt.subplot(133)
-plt.imshow(RecFISTA_HuberRING_TV[:,:,sliceSel],vmin=0, vmax=max_val)
-plt.title('3D HuberRing Rec, sagittal')
-plt.show()
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("Reconstructing with FISTA-OS-SWLS-TV using tomobar")

Readme.md

@@ -20,7 +20,7 @@
  * A wrapper around [ASTRA-toolbox](https://www.astra-toolbox.com/) to simplify access to various reconstruction methods available in ASTRA
  * Regularised iterative ordered-subsets [FISTA](https://epubs.siam.org/doi/10.1137/080716542) reconstruction algorithm with linear and non-linear data fidelities
  * Regularised iterative [ADMM](https://ieeexplore.ieee.org/document/7744574/) reconstruction algorithm
- * Access to multi-GPU capability through mpi4py
+ * Access to multi-GPU capability through mpi4py library
  * Demos to reconstruct synthetic and also real data (provided) [4-6]
 
 <div align="center">
@@ -36,7 +36,7 @@
  * The FISTA algorithm offers various modifications: convergence acceleration with ordered-subsets method; data fidelities: PWLS, Kullback-Leibler, Huber, Group-Huber[2], Students't [3,4], and SWLS [5] to deal with noise and imaging artifacts (rings, streaks).
 
 ### General software prerequisites
- * Python or [MATLAB](http://www.mathworks.com/products/matlab/)
+ * Python or [MATLAB](http://www.mathworks.com/products/matlab/) (not actively maintained currently)
 
 ### Software dependencies
  * [ASTRA-toolbox](https://www.astra-toolbox.com/) for projection operations
@@ -63,11 +63,14 @@ conda install -c savu-dep tomobar
 export VERSION=`date +%Y.%m` (unix) / set VERSION=2020.10 (Windows)
 conda build conda-recipe/ --numpy 1.15 --python 3.7
 conda install -c file://${CONDA_PREFIX}/conda-bld/ tomobar --force-reinstall
-conda install tomobar --use-local --force-reinstall # if Python2
 ```
 
-### Python standalone
-For building on Linux, modify and run the following [script](https://github.com/dkazanc/ToMoBAR/blob/master/run.sh)
+### Python development environment
+* Clone the repository from GitHub page
+* Install dependencies from the environment file
+* Alternatively you can install from the existing explicit file
+* Activate the environment with :code:`conda activate tomobar`
+* From the root directory install the enviroment in development mode with :code:`pip install -e .[dev]`
 
 ### MultiGPU capability
 ToMoBAR can be used by running in parallel across multiple GPU devices on a PC or a compute node of a cluster. In order to initiate a parallel run on multiple GPUs you will need an MPI library, such as, [mpi4py](https://mpi4py.readthedocs.io/en/stable/). See this [demo](https://github.com/dkazanc/ToMoBAR/blob/master/Demos/Python/MultiGPU_demo.py).
@@ -76,12 +79,12 @@ ToMoBAR can be used by running in parallel across multiple GPU devices on a PC o
 Use available m-functions, see [Demos](https://github.com/dkazanc/ToMoBAR/tree/master/Demos/Matlab).
 
 ## How to use ToMoBAR in Python:
-Detailed information about the methods and its parameters can be obtained with:
+Detailed information about the methods and parameters can be obtained with:
 ```
 from tomobar.methodsIR import RecToolsIR
 help(RecToolsIR)
 from tomobar.methodsDIR import RecToolsDIR
-help(RecToolsIR)
+help(RecToolsDIR)
 ```
 
 A typical setup for iterative reconstruction would include building three dictionaries: `_data_`, `_algorithm_` and `_regularisation_`

conda-recipe/bld.bat

@@ -3,12 +3,5 @@ ECHO VERSION Not Defined.
 exit 1
 )
 
-mkdir "%SRC_DIR%\test"
-ROBOCOPY /E "%RECIPE_DIR%\..\test" "%SRC_DIR%\test"
-cd %SRC_DIR%
-
-:: issue cmake to create setup.py
-cmake -G "NMake Makefiles" %RECIPE_DIR%\..\ -DBUILD_PYTHON_WRAPPERS=ON -DCONDA_BUILD=ON -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB="%CONDA_PREFIX%\lib" -DLIBRARY_INC="%CONDA_PREFIX%" -DCMAKE_INSTALL_PREFIX="%PREFIX%\Library"
-
-nmake install
-if errorlevel 1 exit 1
+"%PYTHON%" -m pip install .
+if errorlevel 1 exit 1

conda-recipe/build.sh

@@ -1,8 +1,3 @@
-set -xe
-cp -rv "$RECIPE_DIR/../test" "$SRC_DIR/"
+#!/bin/bash
 
-cd $SRC_DIR
-
-cmake -G "Unix Makefiles" $RECIPE_DIR/../ -DBUILD_PYTHON_WRAPPER=ON -DCONDA_BUILD=ON -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB=$CONDA_PREFIX/lib -DLIBRARY_INC=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$PREFIX
-
-make install
+python -m pip install .

conda-recipe/conda_build_config.yaml

@@ -1,10 +1,4 @@
 python:
-  - 3.5
-  - 3.6
-  - 3.7
+  - 3.10
 numpy:
-  - 1.12
-  - 1.14
-  - 1.15
-  - 1.16
-  - 1.21
+  - 1.24

conda-recipe/environment/environment.yml

@@ -0,0 +1,17 @@
+name: tomobar
+channels:
+  - conda-forge
+  - anaconda
+dependencies:
+  - conda-forge::numpy
+  - conda-forge::python
+  - conda-forge::pytest
+  - conda-forge::pytest-xdist
+  - conda-forge::pytest-cov
+  - conda-forge::flake8
+  - conda-forge::pyproject-flake8
+  - conda-forge::pydocstyle
+  - conda-forge::setuptools
+  - anaconda::ipython  
+  - anaconda::conda-build
+  - anaconda::anaconda-client