[MRG] Release 8.2 (#365)

* release text and number

* add examples in release fil build wheels

* switch gallery to release

* add much needed contributors file

* debug circleci

* une line of logos

* working logo

* back to stable sphinx galery

.circleci/config.yml

@@ -48,9 +48,8 @@ jobs:
               python -m pip install --user -e .
               python -m pip install --user --upgrade --no-cache-dir --progress-bar off -r requirements.txt 
               python -m pip install --user --upgrade --progress-bar off -r docs/requirements.txt
-              python -m pip install --user --upgrade --progress-bar off ipython "https://api.github.com/repos/sphinx-gallery/sphinx-gallery/zipball/master" memory_profiler
-              
-
+              python -m pip install --user --upgrade --progress-bar off ipython sphinx-gallery memory_profiler              
+          # python -m pip install --user --upgrade --progress-bar off ipython "https://api.github.com/repos/sphinx-gallery/sphinx-gallery/zipball/master" memory_profiler
         - save_cache:
             key: pip-cache
             paths:

CONTRIBUTORS.md

@@ -0,0 +1,52 @@
+
+
+## Creators and Maintainers 
+
+This toolbox has been created and is maintained by:
+
+* [Rémi Flamary](http://remi.flamary.com/)
+* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
+
+## Contributors
+
+The contributors to this library are:
+
+* [Rémi Flamary](http://remi.flamary.com/) (EMD wrapper, Pytorch backend, DA
+  classes, conditional gradients, WDA, weak OT, linear OT mapping, documentation)
+* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) (Original sinkhorn,
+  Wasserstein barycenters and convolutional barycenters, 1D wasserstein)
+* [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI, documentation)
+* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) (Partial OT,
+  Unbalanced OT non-regularized)
+* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation)
+* [Léo Gautheron](https://github.com/aje) (Initial GPU implementation)
+* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) (DA classes)
+* [Stanislas Chambon](https://slasnista.github.io/) (DA classes)
+* [Antoine Rolet](https://arolet.github.io/) (EMD solver debug)
+* Erwan Vautier (Gromov-Wasserstein)
+* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic solvers,
+  empirical sinkhorn)
+* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) (Greenkhorn)
+* [Vayer Titouan](https://tvayer.github.io/) (Gromov-Wasserstein, Fused-Gromov-Wasserstein)
+* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT, Debiased barycenters)
+* [Romain Tavenard](https://rtavenar.github.io/) (1D Wasserstein)
+* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn)
+* [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT)
+* [Adrien Corenflos](https://adriencorenflos.github.io/) (Sliced Wasserstein Distance)
+* [Tanguy Kerdoncuff](https://hv0nnus.github.io/) (Sampled Gromov Wasserstein)
+* [Minhui Huang](https://mhhuang95.github.io) (Projection Robust Wasserstein Distance)
+* [Nathan Cassereau](https://github.com/ncassereau-idris) (Backends)
+* [Cédric Vincent-Cuaz](https://github.com/cedricvincentcuaz) (Graph Dictionary Learning)
+
+## Acknowledgments
+
+This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
+
+* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab)
+* [Mathieu Blondel](https://mblondel.org/) (original implementation smooth OT)
+* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) (C++ code for EMD)
+* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda)
+
+POT has benefited from the financing or manpower from the following partners:
+
+<img src="https://pythonot.github.io/master/_static/images/logo_anr.jpg" alt="ANR" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_cnrs.jpg" alt="CNRS" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_3ia.jpg" alt="3IA" style="height:60px;"/>

README.md

@@ -180,35 +180,12 @@ This toolbox has been created and is maintained by
 * [Rémi Flamary](http://remi.flamary.com/)
 * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
 
-The contributors to this library are 
-
-* [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI, documentation)
-* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) (Partial OT)
-* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation)
-* [Léo Gautheron](https://github.com/aje) (Initial GPU implementation)
-* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) (DA classes)
-* [Stanislas Chambon](https://slasnista.github.io/) (DA classes)
-* [Antoine Rolet](https://arolet.github.io/) (EMD solver debug)
-* Erwan Vautier (Gromov-Wasserstein)
-* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic solvers)
-* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home)
-* [Vayer Titouan](https://tvayer.github.io/) (Gromov-Wasserstein -, Fused-Gromov-Wasserstein)
-* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT, Debiased barycenters)
-* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein)
-* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn)
-* [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT)
-* [Adrien Corenflos](https://adriencorenflos.github.io/) (Sliced Wasserstein Distance)
-* [Tanguy Kerdoncuff](https://hv0nnus.github.io/) (Sampled Gromov Wasserstein)
-* [Minhui Huang](https://mhhuang95.github.io) (Projection Robust Wasserstein Distance)
-* [Nathan Cassereau](https://github.com/ncassereau-idris) (Backends)
-* [Cédric Vincent-Cuaz](https://github.com/cedricvincentcuaz) (Graph Dictionary Learning)
-
-This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
-
-* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab)
-* [Mathieu Blondel](https://mblondel.org/) (original implementation smooth OT)
-* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) (C++ code for EMD)
-* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda)
+The numerous contributors to this library are listed [here](CONTRIBUTORS.md).
+
+POT has benefited from the financing or manpower from the following partners:
+
+<img src="https://pythonot.github.io/master/_static/images/logo_anr.jpg" alt="ANR" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_cnrs.jpg" alt="CNRS" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_3ia.jpg" alt="3IA" style="height:60px;"/>
+
 
 ## Contributions and code of conduct
 

RELEASES.md

@@ -1,22 +1,61 @@
 # Releases
 
 
-## 0.8.2dev Development
+## 0.8.2
+
+This releases introduces several new notable features. The less important
+but most exiting one being that we now have a logo for the toolbox (color
+and dark background) :
+
+![](https://pythonot.github.io/master/_images/logo.svg)![](https://pythonot.github.io/master/_static/logo_dark.svg)
+
+This logo is generated using with matplotlib and using the solution of an OT
+problem provided by POT (with `ot.emd`). Generating the logo can be done with a
+simple python script also provided in the [documentation gallery](https://pythonot.github.io/auto_examples/others/plot_logo.html#sphx-glr-auto-examples-others-plot-logo-py).
+
+New OT solvers include [Weak
+OT](https://pythonot.github.io/gen_modules/ot.weak.html#ot.weak.weak_optimal_transport)
+ and [OT with factored
+coupling](https://pythonot.github.io/gen_modules/ot.factored.html#ot.factored.factored_optimal_transport)
+that can be used on large datasets. The [Majorization Minimization](https://pythonot.github.io/gen_modules/ot.unbalanced.html?highlight=mm_#ot.unbalanced.mm_unbalanced) solvers for
+non-regularized Unbalanced OT are now also available. We also now provide an
+implementation of [GW and FGW unmixing](https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_linear_unmixing) and [dictionary learning](https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_dictionary_learning). It is now
+possible to use autodiff to solve entropic an quadratic regularized OT in the
+dual for full or stochastic optimization thanks to the new functions to compute
+the dual loss for [entropic](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_entropic) and [quadratic](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_quadratic) regularized OT and reconstruct the [OT
+plan](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.plan_dual_entropic) on part or all of the data. They can be used for instance to solve OT
+problems with stochastic gradient or for estimating the [dual potentials as
+neural networks](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py).
+
+On the backend front, we now have backend compatible functions and classes in
+the domain adaptation [`ot.da`](https://pythonot.github.io/gen_modules/ot.da.html#module-ot.da) and unbalanced OT [`ot.unbalanced`](https://pythonot.github.io/gen_modules/ot.unbalanced.html) modules. This
+means that the DA classes can be used on tensors from all compatible backends.
+The [free support Wasserstein barycenter](https://pythonot.github.io/gen_modules/ot.lp.html?highlight=free%20support#ot.lp.free_support_barycenter) solver is now also backend compatible.
+
+Finally we have worked on the documentation to provide an update of existing
+examples in the gallery and and several new examples including [GW dictionary
+learning](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html#sphx-glr-auto-examples-gromov-plot-gromov-wasserstein-dictionary-learning-py)
+[weak Optimal
+Transport](https://pythonot.github.io/auto_examples/others/plot_WeakOT_VS_OT.html#sphx-glr-auto-examples-others-plot-weakot-vs-ot-py),
+[NN based dual potentials
+estimation](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py)
+and [Factored coupling OT](https://pythonot.github.io/auto_examples/others/plot_factored_coupling.html#sphx-glr-auto-examples-others-plot-factored-coupling-py).
+.
 
 #### New features
 
 - Remove deprecated `ot.gpu` submodule (PR #361)
-- Update examples in the gallery (PR #359).
+- Update examples in the gallery (PR #359)
 - Add stochastic loss and OT plan computation for regularized OT and 
-  backend examples(PR #360).
-- Implementation of factored OT with emd and sinkhorn (PR #358).
+  backend examples(PR #360)
+- Implementation of factored OT with emd and sinkhorn (PR #358)
 - A brand new logo for POT (PR #357)
-- Better list of related examples in quick start guide with `minigallery` (PR #334).
+- Better list of related examples in quick start guide with `minigallery` (PR #334)
 - Add optional log-domain Sinkhorn implementation in WDA to support smaller values
-  of the regularization parameter (PR #336).
-- Backend implementation for `ot.lp.free_support_barycenter` (PR #340).
-- Add weak OT solver + example  (PR #341).
-- Add backend support for Domain Adaptation and Unbalanced solvers (PR #343).
+  of the regularization parameter (PR #336)
+- Backend implementation for `ot.lp.free_support_barycenter` (PR #340)
+- Add weak OT solver + example  (PR #341)
+- Add backend support for Domain Adaptation and Unbalanced solvers (PR #343)
 - Add (F)GW linear dictionary learning solvers + example  (PR #319)
 - Add links to related PR and Issues in the doc release page (PR #350)
 - Add new minimization-maximization algorithms for solving exact Unbalanced OT + example (PR #362)

docs/source/contributors.rst

@@ -0,0 +1,6 @@
+Contributors
+============
+
+.. include:: ../../CONTRIBUTORS.md
+    :parser: myst_parser.sphinx_
+    :start-line: 2

docs/source/index.rst

@@ -22,6 +22,7 @@ Contents
    auto_examples/index
    releases
    .github/CONTRIBUTING
+   contributors
    .github/CODE_OF_CONDUCT
 
 

docs/source/releases.rst

@@ -3,4 +3,4 @@ Releases
 
 .. include:: ../../RELEASES.md
     :parser: myst_parser.sphinx_
-    :start-line: 3
+    :start-line: 2

examples/others/plot_logo.py

@@ -18,7 +18,7 @@
 
 # sphinx_gallery_thumbnail_number = 1
 
-# %%
+# %% Load modules
 import numpy as np
 import matplotlib.pyplot as pl
 import ot
@@ -36,21 +36,21 @@
 o1 = np.array([[0, 6.], [-1, 5], [-1.5, 4], [-1.5, 3], [-1, 2], [0, 1], ])
 o2 = np.array([[1, 6.], [2, 5], [2.5, 4], [2.5, 3], [2, 2], [1, 1], ])
 
-# scaling and translation for letter O
+# Scaling and translation for letter O
 o1[:, 0] += 6.4
 o2[:, 0] += 6.4
 o1[:, 0] *= 0.6
 o2[:, 0] *= 0.6
 
-# letter T
+# Letter T
 t1 = np.array([[-1, 6.], [-1, 5], [0, 4], [0, 3], [0, 2], [0, 1], ])
 t2 = np.array([[1.5, 6.], [1.5, 5], [0.5, 4], [0.5, 3], [0.5, 2], [0.5, 1], ])
 
-# translatin the T
+# Translating the T
 t1[:, 0] += 7.1
 t2[:, 0] += 7.1
 
-# Cocatenate all letters
+# Concatenate all letters
 x1 = np.concatenate((p1, o1, t1), axis=0)
 x2 = np.concatenate((p2, o2, t2), axis=0)
 

ot/__init__.py

@@ -51,7 +51,7 @@
 # utils functions
 from .utils import dist, unif, tic, toc, toq
 
-__version__ = "0.8.2dev"
+__version__ = "0.8.2"
 
 __all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils',
            'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov',