Merge pull request #6 from CEA-COSMIC/develop

Version 0.0.1 patch release
CEA-COSMIC · Apr 20, 2021 · 41c6395 · 41c6395
2 parents 7d56509 + cfc3155
commit 41c6395
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diff --git a/.github/workflows/ci-build.yml b/.github/workflows/ci-build.yml
@@ -0,0 +1,121 @@
+name: CI
+
+on:
+  pull_request:
+    branches:
+     - develop
+     - master
+     - main
+
+jobs:
+  test-full:
+    name: Full Test Suite
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+        python-version: [3.8]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+
+      - name: Set up Conda with Python ${{ matrix.python-version }}
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          python-version: ${{ matrix.python-version }}
+          auto-activate-base: false
+
+      - name: Check Conda
+        shell: bash -l {0}
+        run: |
+          conda info
+          conda list
+          python --version
+
+      - name: Install macOS Dependencies
+        shell: bash -l {0}
+        if: runner.os == 'macOS'
+        run: |
+          brew install libomp
+
+      - name: Install Package Dependencies
+        shell: bash -l {0}
+        run: |
+          python --version
+          python -m pip install --upgrade pip
+          python -m pip install git+https://github.com/CEA-COSMIC/pysap@develop
+          python -m pip install coverage nose
+          python -m pip install pytest pytest-cov pytest-pycodestyle pytest-pydocstyle
+          python -m pip install twine
+          python -m pip install .
+
+      - name: Run Tests
+        shell: bash -l {0}
+        run: |
+          python setup.py test
+
+      - name: Save Test Results
+        if: always()
+        uses: actions/upload-artifact@v2
+        with:
+          name: unit-test-results-${{ matrix.os }}-${{ matrix.python-version }}
+          path: pytest.xml
+
+      - name: Check Distribution
+        shell: bash -l {0}
+        run: |
+          python setup.py sdist
+          twine check dist/*
+
+      - name: Upload Coverage to Codecov
+        uses: codecov/codecov-action@v1
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          file: coverage.xml
+          flags: unittests
+
+  test-basic:
+    name: Basic Test Suite
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+        python-version: [3.6, 3.7, 3.9]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+
+      - name: Set up Conda with Python ${{ matrix.python-version }}
+        uses: conda-incubator/setup-miniconda@v2
+        with:
+          auto-update-conda: true
+          python-version: ${{ matrix.python-version }}
+          auto-activate-base: false
+
+      - name: Install macOS Dependencies
+        shell: bash -l {0}
+        if: runner.os == 'macOS'
+        run: |
+          brew install libomp
+
+      - name: Install Package Dependencies
+        shell: bash -l {0}
+        run: |
+          python --version
+          python -m pip install --upgrade pip
+          python -m pip install git+https://github.com/CEA-COSMIC/pysap@develop
+          python -m pip install coverage nose
+          python -m pip install pytest pytest-cov pytest-pycodestyle pytest-pydocstyle
+          python -m pip install .
+
+      - name: Run Tests
+        shell: bash -l {0}
+        run: |
+          python setup.py test
diff --git a/.travis.yml b/.travis.yml
diff --git a/README.rst b/README.rst
@@ -1,40 +1,22 @@
 
-pySAP-astro
+PySAP-Astro
 ===============
 
-|travis| |coveralls| |python35| |python36| |python37|
+|travis| |codecov| |pypi|
 
 .. |travis| image:: https://travis-ci.org/CEA-COSMIC/pysap-astro.svg?branch=master
   :target: https://travis-ci.org/CEA-COSMIC/pysap-astro
 
-.. |coveralls| image:: https://coveralls.io/repos/github/CEA-COSMIC/pysap-astro/badge.svg?branch=master
-  :target: https://coveralls.io/github/CEA-COSMIC/pysap-astro
-
-.. |python35| image:: https://img.shields.io/badge/python-3.5-green.svg
-  :target: https://www.python.org/
-
-.. |python36| image:: https://img.shields.io/badge/python-3.6-green.svg
-  :target: https://www.python.org/
-
-.. |python37| image:: https://img.shields.io/badge/python-3.7-green.svg
-  :target: https://www.python.org/
+.. |codecov| image:: https://codecov.io/gh/CEA-COSMIC/pysap-astro/branch/master/graph/badge.svg?token=XHJIQXV7AX
+  :target: https://codecov.io/gh/CEA-COSMIC/pysap-astro
 
+.. |pypi| image:: https://img.shields.io/pypi/v/pysap-astro
+  :target: https://pypi.org/project/pysap-astro/
 
 Python Sparse data Analysis Package external ASTRO plugin.
 
-This work is made available by a community of people, amoung which the
-CEA Neurospin UNATI and CEA CosmoStat laboratories, in particular A. Grigis,
-J.-L. Starck, P. Ciuciu, and S. Farrens.
-
-
-Important links
-===============
-
-- Official pySAP source code repo: https://github.com/cea-cosmic/pysap
-- pySAP HTML documentation (last stable release): http://cea-cosmic.github.io/pysap
-
-
-Python Sparse data Analysis Package external ASTRO plugin.
+  This package is part of the PySAP plug-in framework and is not intended to
+  work as a stand-alone package.
 
 This work is made available by a community of people, amoung which the
 CEA Neurospin UNATI and CEA CosmoStat laboratories, in particular A. Grigis,
@@ -44,5 +26,5 @@ J.-L. Starck, P. Ciuciu, and S. Farrens.
 Important links
 ===============
 
-- Official pySAP source code repo: https://github.com/cea-cosmic/pysap
-- pySAP HTML documentation (last stable release): http://cea-cosmic.github.io/pysap
+- Official PySAP source code repo: https://github.com/CEA-COSMIC/pysap
+- PySAP HTML documentation (last stable release): https://python-pysap.readthedocs.io/en/latest/
diff --git a/astro/__init__.py b/astro/__init__.py
@@ -7,9 +7,7 @@
 # for details.
 ##########################################################################
 
-"""
-This module defines all the Astro related plugins.
-"""
+"""Module defines all the Astro related plugins."""
 
 __all__ = ['deconvolution', 'denoising']
 

diff --git a/astro/deconvolution/__init__.py b/astro/deconvolution/__init__.py
@@ -7,9 +7,7 @@
 # for details.
 ##########################################################################
 
-"""
-This module defines all the Astro deconvolution plugins.
-"""
+"""Module defines all the Astro deconvolution plugins."""
 
 __all__ = ['deconvolve', 'linear', 'wavelet_filters']
 

diff --git a/astro/deconvolution/deconvolve.py b/astro/deconvolution/deconvolve.py
@@ -7,14 +7,12 @@
 # for details.
 ##########################################################################
 
-"""
-CONDA-VU Galaxy Image Deconvolution
-"""
+"""CONDA-VU Galaxy Image Deconvolution."""
 
 # System import
 import pysap
-from .linear import WaveletConvolve2
-from .wavelet_filters import get_cospy_filters
+from pysap.plugins.astro.deconvolution.linear import WaveletConvolve2
+from pysap.plugins.astro.deconvolution.wavelet_filters import get_cospy_filters
 from pysap.utils import condatvu_logo
 
 # Third party import
@@ -31,7 +29,7 @@
 
 
 def psf_convolve(data, psf, psf_rot=False):
-    """PSF Convolution
+    """PSF Convolution.
 
     Parameters
     ----------
@@ -47,15 +45,14 @@ def psf_convolve(data, psf, psf_rot=False):
     np.ndarray convolved image
 
     """
-
     if psf_rot:
         psf = rotate(psf)
 
     return convolve(data, psf)
 
 
 def get_weights(data, psf, filters, wave_thresh_factor=np.array([3, 3, 4])):
-    """Get Sparsity Weights
+    """Get Sparsity Weights.
 
     Parameters
     ----------
@@ -74,20 +71,27 @@ def get_weights(data, psf, filters, wave_thresh_factor=np.array([3, 3, 4])):
     np.ndarray weights
 
     """
-
     noise_est = sigma_mad(data)
 
     filter_conv = filter_convolve(np.rot90(psf, 2), filters)
 
-    filter_norm = np.array([np.linalg.norm(a) * b * np.ones(data.shape)
-                            for a, b in zip(filter_conv, wave_thresh_factor)])
+    filter_norm = np.array([
+        np.linalg.norm(a) * b * np.ones(data.shape)
+        for a, b in zip(filter_conv, wave_thresh_factor)
+    ])
 
     return noise_est * filter_norm
 
 
-def sparse_deconv_condatvu(data, psf, n_iter=300, n_reweights=1, verbose=False,
-                           progress=True):
-    """Sparse Deconvolution with Condat-Vu
+def sparse_deconv_condatvu(
+    data,
+    psf,
+    n_iter=300,
+    n_reweights=1,
+    verbose=False,
+    progress=True,
+):
+    """Sparse Deconvolution with Condat-Vu.
 
     Parameters
     ----------
@@ -109,14 +113,15 @@ def sparse_deconv_condatvu(data, psf, n_iter=300, n_reweights=1, verbose=False,
     np.ndarray deconvolved image
 
     """
-
     # Print the algorithm set-up
     if verbose:
         print(condatvu_logo())
 
     # Define the wavelet filters
-    filters = (get_cospy_filters(data.shape,
-               transform_name='LinearWaveletTransformATrousAlgorithm'))
+    filters = get_cospy_filters(
+        data.shape,
+        transform_name='LinearWaveletTransformATrousAlgorithm'
+    )
 
     # Set the reweighting scheme
     reweight = cwbReweight(get_weights(data, psf, filters))
@@ -126,8 +131,11 @@ def sparse_deconv_condatvu(data, psf, n_iter=300, n_reweights=1, verbose=False,
     dual = np.ones(filters.shape)
 
     # Set the gradient operators
-    grad_op = GradBasic(data, lambda x: psf_convolve(x, psf),
-                        lambda x: psf_convolve(x, psf, psf_rot=True))
+    grad_op = GradBasic(
+        data,
+        lambda x: psf_convolve(x, psf),
+        lambda x: psf_convolve(x, psf, psf_rot=True),
+    )
 
     # Set the linear operator
     linear_op = WaveletConvolve2(filters)
@@ -137,13 +145,29 @@ def sparse_deconv_condatvu(data, psf, n_iter=300, n_reweights=1, verbose=False,
     prox_dual_op = SparseThreshold(linear_op, reweight.weights)
 
     # Set the cost function
-    cost_op = costObj([grad_op, prox_op, prox_dual_op], tolerance=1e-6,
-                      cost_interval=1, plot_output=True, verbose=verbose)
+    cost_op = costObj(
+        [grad_op, prox_op, prox_dual_op],
+        tolerance=1e-6,
+        cost_interval=1,
+        plot_output=True,
+        verbose=verbose,
+    )
 
     # Set the optimisation algorithm
-    alg = Condat(primal, dual, grad_op, prox_op, prox_dual_op, linear_op,
-                 cost_op, rho=0.8, sigma=0.5, tau=0.5, auto_iterate=False,
-                 progress=progress)
+    alg = Condat(
+        primal,
+        dual,
+        grad_op,
+        prox_op,
+        prox_dual_op,
+        linear_op,
+        cost_op,
+        rho=0.8,
+        sigma=0.5,
+        tau=0.5,
+        auto_iterate=False,
+        progress=progress,
+    )
 
     # Run the algorithm
     alg.iterate(max_iter=n_iter)