PythonOT · rflamary · Apr 28, 2020 · Apr 24, 2020 · Apr 24, 2020 · Apr 24, 2020
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
@@ -2,6 +2,9 @@ name: Build dist and wheels
 
 on:
   release:
+  push:
+    branches:
+      - "master"
 
 jobs:
   build_wheels:

diff --git a/Makefile b/Makefile
@@ -59,6 +59,11 @@ release_test :
 rdoc :
 	pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md
 	sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/readme.rst 
+	pandoc --from=markdown --to=rst --output=docs/source/releases.rst RELEASES.md
+	sed -i 's,https://pot.readthedocs.io/en/latest/,,g' docs/source/releases.rst
+	sed -i 's,https://github.com/rflamary/POT/blob/master/notebooks/,auto_examples/,g' docs/source/releases.rst 
+	sed -i 's,.ipynb,.html,g' docs/source/releases.rst 
+	sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/releases.rst
 
 notebook :
 	ipython notebook --matplotlib=inline  --notebook-dir=notebooks/

diff --git a/README.md b/README.md
@@ -22,29 +22,29 @@ POT provides the following generic OT solvers (links to examples):
 * [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
 * [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
 * Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy).
-* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html) [21]  and unmixing [4].
+* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21]  and unmixing [4].
 * Sinkhorn divergence [23] and entropic regularization OT  from empirical data.
 * [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17].
-* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
-* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html)  (exact [13] and regularized [12])
- * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html) [24]
+* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
+* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html)  (exact [13] and regularized [12])
+ * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24]
 * [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
-* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20].
-* [Unbalanced OT](https://pythonot.github.io/auto_examples/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html) [10, 25].
-* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
+* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
+* [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25].
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
   formulations).
 
 POT provides the following Machine Learning related solvers:
 
 * [Optimal transport for domain
-  adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html)
-  with [group lasso regularization](https://pythonot.github.io/auto_examples/plot_otda_classes.html),   [Laplacian regularization](https://pythonot.github.io/auto_examples/plot_otda_laplacian.html) [5] [30] and [semi
-  supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html).
-* [Linear OT mapping](https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8].
-* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt).
-* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27].
+  adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html)
+  with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html),   [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi
+  supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html).
+* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8].
+* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt).
+* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27].
 
-Some demonstrations  are available in the  [documentation](https://pythonot.github.io/auto_examples/index.html).
+Some other examples are available in the  [documentation](https://pythonot.github.io/auto_examples/index.html).
 
 #### Using and citing the toolbox
 
@@ -153,7 +153,7 @@ ba=ot.barycenter(A,M,reg) # reg is regularization parameter
 
 ### Examples and Notebooks
 
-The examples folder contain several examples and use case for the library. The full documentation is available on [https://PythonOT.github.io/](https://PythonOT.github.io/).
+The examples folder contain several examples and use case for the library. The full documentation with examples and output is available on [https://PythonOT.github.io/](https://PythonOT.github.io/).
 
 
 ## Acknowledgements

diff --git a/RELEASES.md b/RELEASES.md
@@ -1,7 +1,9 @@
-# POT Releases
+# Releases
 
 
-## 0.6 Year 3
+
+
+## 0.6 
 *July 2019*
 
 This is the first official stable release of POT and this means a jump to 0.6! 
@@ -69,7 +71,7 @@ bring new features and solvers to the library.
 - Issue #72 Macosx build problem
 
 
-## 0.5.0 Year 2
+## 0.5.0 
 *Sep 2018*
 
 POT is 2 years old! This release brings numerous new features to the 
@@ -127,7 +129,7 @@ Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5
 * Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip 
 
 
-## 0.4 Community edition
+## 0.4 
 *15 Sep 2017*
 
 This release contains a lot of contribution from new contributors.
@@ -152,7 +154,7 @@ This release contains a lot of contribution from new contributors.
 
 * Correct bug in emd on windows
 
-## 0.3 Summer release
+## 0.3 
 *7 Jul 2017*
 
 * emd* and sinkhorn* are now performed in parallel for multiple target distributions
@@ -173,32 +175,30 @@ This release contains a lot of contribution from new contributors.
 
 
 
-## V0.1.11 New years resolution
+## 0.1.11 
 *5 Jan 2017*
 
 * Add sphinx gallery for better documentation
 * Small efficiency tweak in sinkhorn
 * Add simple tic() toc() functions for timing
 
 
-## V0.1.10 
+## 0.1.10 
 *7 Nov 2016*
 * numerical stabilization for sinkhorn (log domain and epsilon scaling)
 
-## V0.1.9 DA classes and mapping
+## 0.1.9
 *4 Nov 2016*
 
 * Update classes and examples for domain adaptation
 * Joint OT matrix and mapping estimation
 
-## V0.1.7
+## 0.1.7
 *31 Oct 2016*
 
 * Original Domain adaptation classes
 
-
-
-## PyPI version 0.1.3
+## 0.1.3
 
 * pipy works
 

diff --git a/docs/source/_templates/module.rst b/docs/source/_templates/module.rst
@@ -0,0 +1,57 @@
+{{ fullname }}
+{{ underline }}
+
+.. automodule:: {{ fullname }}
+
+   {% block functions %}
+   {% if functions %}
+
+   Functions
+   ---------
+
+   {% for item in functions %}
+
+   .. autofunction:: {{ item }}
+
+   .. include:: backreferences/{{fullname}}.{{item}}.examples
+
+   .. raw:: html
+
+	       <div class="sphx-glr-clear"></div>
+
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+
+   Classes
+   -------
+
+   {% for item in classes %}
+   .. autoclass:: {{ item }}
+      :members:
+
+   .. include:: backreferences/{{fullname}}.{{item}}.examples
+
+   .. raw:: html
+
+	       <div class="sphx-glr-clear"></div>
+
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+
+   Exceptions
+   ----------
+
+   .. autosummary::
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/docs/source/all.rst b/docs/source/all.rst
@@ -1,94 +1,36 @@
 
+.. _sphx_glr_api_reference:
 
-Python modules
-==============
+API and modules
+===============
 
-ot
---
+.. currentmodule:: ot
 
-.. automodule:: ot
-   :members:
-
-ot.lp
------
-.. automodule:: ot.lp
-   :members:
-
-ot.bregman
-----------
-
-.. automodule:: ot.bregman
-   :members:
-
-ot.smooth
------
-.. automodule:: ot.smooth
-   :members:
-
-ot.gromov
-----------
-
-.. automodule:: ot.gromov
-   :members:
-
-
-ot.optim
---------
-
-.. automodule:: ot.optim
-   :members:
-
-ot.da
---------
 
-.. automodule:: ot.da
-  :members:
+:py:mod:`ot`:
 
-ot.gpu
---------
+.. autosummary::
+   :toctree: gen_modules/
+   :template: module.rst
 
-.. automodule:: ot.gpu
-  :members:
+   lp
+   bregman
+   smooth
+   gromov
+   optim
+   da
+   gpu
+   dr
+   utils
+   datasets
+   plot
+   stochastic
+   unbalanced
+   partial
 
-ot.dr
---------
+.. autosummary::
+   :toctree: ../modules/generated/
+   :template: module.rst
 
-.. automodule:: ot.dr
-  :members:
-
-
-ot.utils
---------
-
-.. automodule:: ot.utils
-   :members:
-
-ot.datasets
------------
-
-.. automodule:: ot.datasets
-   :members:
-
-ot.plot
--------
-
-.. automodule:: ot.plot
-   :members:
-
-ot.stochastic
--------------
-
-.. automodule:: ot.stochastic
+.. automodule:: ot
    :members:
-
-ot.unbalanced
--------------
-
-.. automodule:: ot.unbalanced
-  :members:
-
-ot.partial
--------------
-
-.. automodule:: ot.partial
-  :members: