Release v0.3.1 (#41)

* Fix docstring typo * Fix format of output diagram (#38) * Fix format of output diagram Signed-off-by: Guillaume Tauzin <[email protected]> * Add auditwheel repair and twine check, remove tarball from CI (#40) * Wording tweaks in README, removal of gcc install step * Remove trailing whitespace * Create release notes for v0.2.1, bump version * Remove source distribution, use auditwheel repair on manylinux, add twine check * Update for v0.3.1 (#39) * Update for v0.3.1 Signed-off-by: Guillaume Tauzin <[email protected]> * Fix rst typos * Retroactively fix more rst typos in RELEASE.rst * Final round of typo fixes in RELEASE.rst * Fix issue with boldfacing in RELEASE.rst * Problematic spaces removes from RELEASE.rst * Retroactively fix problematic spaces in RELEASE.rst Co-authored-by: Umberto Lupo <[email protected]>
giotto-ai · May 21, 2020 · 6b33964 · 6b33964
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diff --git a/.azure-ci/docker_scripts.sh b/.azure-ci/docker_scripts.sh
@@ -22,5 +22,10 @@ flake8 --exit-zero /io/
 pip uninstall -y pyflagser
 
 # Build wheels
-pip install wheel
-python setup.py sdist bdist_wheel
+pip install wheel==0.34.1 auditwheel==3.1.0
+python setup.py bdist_wheel
+
+# Repair wheels with auditwheel
+auditwheel repair dist/*whl -w dist/
+# remove wheels that are not manylinux2010
+rm -rf dist/*-linux*.whl
diff --git a/RELEASE.rst b/RELEASE.rst
@@ -1,3 +1,31 @@
+Release 0.3.1
+==============
+
+Major Features and Improvements
+-------------------------------
+- Clarity of the code of ``flagser_unweighted`` and ``flagser_weighted`` was improved.
+- Auditwheel repair is now run in the manylinux jobs.
+- ``twine check`` is now run as part of the CI.
+
+Bug Fixes
+---------
+- Fix bug causing ``flagser_weighted``'s output persistence diagrams to be a list of list of tuples instead of a list of ``numpy.ndarrays`` of shape (n_points, 2).
+
+Backwards-Incompatible Changes
+------------------------------
+- Installation from tarballs is no longer supported.
+
+Thanks to our Contributors
+--------------------------
+
+This release contains contributions from many people:
+
+Umberto Lupo and Guillaume Tauzin.
+
+We are also grateful to all who filed issues or helped resolve them, asked and
+answered questions, and were part of inspiring discussions.
+
+
 Release 0.3.0
 ==============
 
@@ -6,49 +34,56 @@ Major Features and Improvements
 
 This is a major release. The whole library has been fully refactored and all functions have been renamed. In particular:
 
-- All functions have been split into an `unweighted` and a `weighted` version.
+- All functions have been split into an ``unweighted`` and a ``weighted`` version.
 
-  - The `unweighted` functions process unweighted graphs. In the adjacency matrices passed to them, off-diagonal, ``0`` or ``False`` values denote absent edges while non-``0`` or ``True`` values denote edges which are present. Diagonal values are ignored.
-  - The `weighted` functions process weighted graphs. In the adjacency matrices passed to them, the way zero values are handled depends on the format of the matrix. If the matrix is a dense ``numpy.ndarray``, zero values denote zero-weighted edges. If the matrix is a sparse ``scipy.sparse`` matrix, explicitly stored off-diagonal zeros and all diagonal zeros denote zero-weighted edges. Off-diagonal values that have not been explicitely stored are treated by ``scipy.sparse`` as zeros but will be understood as infinitely-valued edges, i.e., edges absent from the filtration. Diagonal elements are vertex weights.
-
-- `saveflag` has been split into `save_unweighted_flag` and a `save_weighted_flag`:
+  - The ``unweighted`` functions process unweighted graphs. In the adjacency matrices passed to them, off-diagonal, ``0`` or ``False`` values denote absent edges while non-``0`` or ``True`` values denote edges which are present. Diagonal values are ignored.
+  - The ``weighted`` functions process weighted graphs. In the adjacency matrices passed to them, the way zero values are handled depends on the format of the matrix. If the matrix is a dense ``numpy.ndarray``, zero values denote zero-weighted edges. If the matrix is a sparse ``scipy.sparse`` matrix, explicitly stored off-diagonal zeros and all diagonal zeros denote zero-weighted edges. Off-diagonal values that have not been explicitely stored are treated by ``scipy.sparse`` as zeros but will be understood as infinitely-valued edges, i.e., edges absent from the filtration. Diagonal elements are vertex weights.
 
-  - `save_unweighted_flag` focuses on saving adjacency matrices of unweighted graphs into a `.flag` file understandable by C++ `flagser`.
-  - `save_weighted_flag` focuses on saving adjacency matrices of weighted graphs into a `.flag` file understandable by C++ `flagser`.  It now takes a `max_edge_weight` argument. All edge weights greater than that value will be considered as infinitely-valued, i.e., absent from the filtration.
-
-- `loadflag` has been split into `load_unweighted_flag` and a `load_weighted_flag`.
+- ``saveflag`` has been split into ``save_unweighted_flag`` and a ``save_weighted_flag``:
 
-  - `load_unweighted_flag` focuses on loading `.flag` files as adjacency matrices of unweighted graphs.
-  - `load_weighted_flag` focuses on loading `.flag` files as adjacency matrices of weighted graphs. It now take an `infinity_value` parameter which is the value to use to denote an absence of edge. It is only useful when the output adjacency matrix is set to be a ``numpy.ndarray`` by passing `fmt` as ``'dense``. If ``None``, it is set to the maximum value allowed by the passed `dtype`.
-
-- `flagser` has been split into `flagser_unweighted` and a `flagser_weighted`.
+  - ``save_unweighted_flag`` focuses on saving adjacency matrices of unweighted graphs into a `.flag` file understandable by C++ `flagser`.
+  - ``save_weighted_flag`` focuses on saving adjacency matrices of weighted graphs into a `.flag` file understandable by C++ `flagser`.  It now takes a ``max_edge_weight`` argument. All edge weights greater than that value will be considered as infinitely-valued, i.e., absent from the filtration.
 
-  - `flagser_unweighted` focuses on the computation of homology and outputs Betti numbers, cell counts per dimension, and Euler characteristic.
-  - `flagser_weighted` focuses on the computation of persistent homology  and outputs persistence diagrams, Betti numbers, cell counts per dimension, and Euler characteristic. It now takes a `max_edge_weight` argument. All edge weights greater than that value will be considered as infinitely-valued, i.e., absent from the filtration. 
+- ``loadflag`` has been split into ``load_unweighted_flag`` and a ``load_weighted_flag``.
+
+  - ``load_unweighted_flag`` focuses on loading ``.flag`` files as adjacency matrices of unweighted graphs.
+  - ``load_weighted_flag`` focuses on loading ``.flag`` files as adjacency matrices of weighted graphs. It now take an ``infinity_value`` parameter which is the value to use to denote an absence of edge. It is only useful when the output adjacency matrix is set to be a ``numpy.ndarray`` by passing `fmt` as ``'dense'``. If ``None``, it is set to the maximum value allowed by the passed `dtype`.
+
+- ``flagser`` has been split into ``flagser_unweighted`` and a ``flagser_weighted``.
+
+  - ``flagser_unweighted`` focuses on the computation of homology and outputs Betti numbers, cell counts per dimension, and Euler characteristic.
+  - ``flagser_weighted`` focuses on the computation of persistent homology  and outputs persistence diagrams, Betti numbers, cell counts per dimension, and Euler characteristic. It now takes a ``max_edge_weight`` argument. All edge weights greater than that value will be considered as infinitely-valued, i.e., absent from the filtration.
 
 Additionally,
- - The documentation have been strongly improved both in docstrings and in the code.
- - The handling of default parameters has been improved and warnings are now issued.
- - Sparse matrix efficiency warnings have been turned off (`lil_matrix` cannot be used because it ignores explicitly set 0 values).
- - Core functions to transform an adjacency matrix into the data structures understood by C++ `flagser` have been moved to the new `_utils.py`.
- - Tests have been extended according to cover the new functionalities.
+
+- The documentation have been strongly improved both in docstrings and in the code.
+- The handling of default parameters has been improved and warnings are now issued.
+- Sparse matrix efficiency warnings have been turned off (``lil_matrix`` cannot be used because it ignores explicitly set 0 values).
+- Core functions to transform an adjacency matrix into the data structures understood by C++ ``flagser`` have been moved to the new ``_utils.py``.
+- Tests have been extended according to cover the new functionalities.
 
 Bug Fixes
 ---------
 
 The following bug fixes were introduced:
- - A bug fix from C++ `flagser` on ``vertex_degree`` filtration has been propagated to pyflagser.
- - A bug in the C++ `flagser` bindings causing persistence diagrams and cell counts to be wrong based on the values of `min_dimension` and `max_dimension` has been fixed.
- - Tests were updated accordingly and `conftest.py` has been improved.
- - Bugs in the `pyflagser` `flagser` functions causing incompatibilities with sparse matrix and non-float datatype have been fixed.
- - ``CMakeLists`` has been updated to use C++14. This addresses problem when compiling on MacOS.
+
+- A bug fix from C++ ``flagser`` on ``vertex_degree`` filtration has been propagated to pyflagser.
+
+- A bug in the C++ ``flagser`` bindings causing persistence diagrams and cell counts to be wrong based on the values of ``min_dimension`` and ``max_dimension`` has been fixed.
+
+- Tests were updated accordingly and `conftest.py` has been improved.
+
+- Bugs in the ``pyflagser`` ``flagser`` functions causing incompatibilities with sparse matrix and non-float datatype have been fixed.
+
+- ``CMakeLists`` has been updated to use C++14. This addresses problem when compiling on MacOS.
 
 Backwards-Incompatible Changes
 ------------------------------
 
 The library has been fully refactored, which means that most changes were backwards-incompatible. In particular:
-- All functions have been renamed as they now include an `unweighted` and a `weighted` version. 
-- The `flag_matrix` argument have been renamed `adjacency_matrix`.
+
+- All functions have been renamed as they now include an ``unweighted`` and a ``weighted`` version.
+- The ``flag_matrix`` argument have been renamed ``adjacency_matrix``.
 
 Please check the documentation for more information.
 
@@ -76,8 +111,8 @@ Bug Fixes
 
 Hotfix addressing multiples issues where forwarding arguments to C++ ``flagser``:
 
- - ``filtration`` was not correctly forwarded and it always fallback to zero filtration.
- - ``max-dim`` and ``min-dim`` were always equal to 0.
+- ``filtration`` was not correctly forwarded and it always fallback to zero filtration.
+- ``max-dim`` and ``min-dim`` were always equal to 0.
 
 ``CMakeLists`` updated to disable AVX instructions. This addresses incompatibilities observed with specific hardware setups.
 

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -213,10 +213,16 @@ jobs:
 
   - script: |
       pip install wheel
-      python setup.py sdist bdist_wheel
+      python setup.py bdist_wheel
     failOnStderr: false
     displayName: 'Build the wheels'
 
+  - script: |
+      set -e
+      pip install twine
+      twine check dist/*
+    displayName: 'Check distribution with twine'
+
   - script: pip install dist/*.whl
     failOnStderr: true
     displayName: 'Install the wheels'
@@ -302,7 +308,7 @@ jobs:
   - bash: |
       sed -i $'s/\r$//' README.rst
       pip install wheel
-      python setup.py sdist bdist_wheel
+      python setup.py bdist_wheel
     failOnStderr: false
     displayName: 'Build the wheels'
 

diff --git a/pyflagser/_version.py b/pyflagser/_version.py
@@ -17,4 +17,4 @@
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
 
-__version__ = '0.3.0'
+__version__ = '0.3.1'
diff --git a/pyflagser/flagio.py b/pyflagser/flagio.py
@@ -17,7 +17,7 @@ def load_unweighted_flag(fname, fmt='csr', dtype=np.bool):
         Filename of extension ``.flag`` containing the information of a flag
         matrix.
 
-    fmt : {'dense', 'dia', 'csr', 'csc', 'lil', ...}, optional,
+    fmt : {'dense', 'dia', 'csr', 'csc', 'lil', ...}, optional, \
         default: ``'csr'``
         Matrix format of the result. By default, a CSR sparse matrix is
         returned. Keep in mind that some matrix formats do not track zero

diff --git a/pyflagser/flagser.py b/pyflagser/flagser.py
@@ -93,13 +93,15 @@ def flagser_unweighted(adjacency_matrix, min_dimension=0, max_dimension=np.inf,
 
     # Call flagser binding
     homology = compute_homology(vertices, edges, min_dimension, _max_dimension,
-                                directed, coeff, _approximation, _filtration)
+                                directed, coeff, _approximation,
+                                _filtration)[0]
 
     # Creating dictionary of return values
-    out = dict()
-    out['betti'] = homology[0].get_betti_numbers()
-    out['cell_count'] = homology[0].get_cell_count()
-    out['euler'] = homology[0].get_euler_characteristic()
+    out = {
+        'betti': homology.get_betti_numbers(),
+        'cell_count': homology.get_cell_count(),
+        'euler': homology.get_euler_characteristic()
+    }
     return out
 
 
@@ -227,14 +229,13 @@ def flagser_weighted(adjacency_matrix, max_edge_weight=None, min_dimension=0,
 
     # Call flagser binding
     homology = compute_homology(vertices, edges, min_dimension, _max_dimension,
-                                directed, coeff, _approximation, filtration)
+                                directed, coeff, _approximation, filtration)[0]
 
     # Create dictionary of return values
-    out = dict()
-    out['dgms'] = [homology[0].get_persistence_diagram()[i]
-                   for i in range(len(homology[0].get_persistence_diagram()))]
-    out['betti'] = homology[0].get_betti_numbers()
-    out['cell_count'] = homology[0].get_cell_count()
-    out['euler'] = homology[0].get_euler_characteristic()
-
+    out = {
+        'dgms': [np.asarray(d) for d in homology.get_persistence_diagram()],
+        'betti': homology.get_betti_numbers(),
+        'cell_count': homology.get_cell_count(),
+        'euler': homology.get_euler_characteristic()
+    }
     return out
diff --git a/setup.py b/setup.py
@@ -28,7 +28,7 @@
 MAINTAINER_EMAIL = '[email protected]'
 URL = 'https://github.com/giotto-ai/pyflagser'
 LICENSE = 'GNU AGPLv3'
-DOWNLOAD_URL = 'https://github.com/giotto-ai/pyflagser/tarball/v0.3.0'
+DOWNLOAD_URL = 'https://github.com/giotto-ai/pyflagser/tarball/v0.3.1'
 VERSION = __version__ # noqa
 CLASSIFIERS = ['Intended Audience :: Science/Research',
                'Intended Audience :: Developers',