[MRG] Actually run sphinx-gallery (#146)

* generate gallery

* remove mock

* add sklearn to requirermnt?txt for example

* remove latex from fgw example

* add networks for graph example

* remove all

* add requirement.txt rtd

* rtd debug

* update readme

* eradthedoc with redirection

* add conf rtd

1 files changed, 0 insertions, 310 deletions

diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst
deleted file mode 100644
index 460bb95..0000000
--- a/docs/source/auto_examples/plot_OT_2D_samples.rst
+++ /dev/null
@@ -1,310 +0,0 @@
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_auto_examples_plot_OT_2D_samples.py>`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
-
-    .. _sphx_glr_auto_examples_plot_OT_2D_samples.py:
-
-
-====================================================
-2D Optimal transport between empirical distributions
-====================================================
-
-Illustration of 2D optimal transport between discributions that are weighted
-sum of diracs. The OT matrix is plotted with the samples.
-
-
-
-.. code-block:: default
-
-
-    # Author: Remi Flamary <remi.flamary@unice.fr>
-    #         Kilian Fatras <kilian.fatras@irisa.fr>
-    #
-    # License: MIT License
-
-    import numpy as np
-    import matplotlib.pylab as pl
-    import ot
-    import ot.plot
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-.. code-block:: default
-
-
-    n = 50  # nb samples
-
-    mu_s = np.array([0, 0])
-    cov_s = np.array([[1, 0], [0, 1]])
-
-    mu_t = np.array([4, 4])
-    cov_t = np.array([[1, -.8], [-.8, 1]])
-
-    xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
-    xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
-
-    a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples
-
-    # loss matrix
-    M = ot.dist(xs, xt)
-    M /= M.max()
-
-
-
-
-
-
-
-
-Plot data
----------
-
-
-.. code-block:: default
-
-
-    pl.figure(1)
-    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-    pl.legend(loc=0)
-    pl.title('Source and target distributions')
-
-    pl.figure(2)
-    pl.imshow(M, interpolation='nearest')
-    pl.title('Cost matrix M')
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png
-            :class: sphx-glr-multi-img
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png
-            :class: sphx-glr-multi-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-
-    Text(0.5, 1.0, 'Cost matrix M')
-
-
-
-Compute EMD
------------
-
-
-.. code-block:: default
-
-
-    G0 = ot.emd(a, b, M)
-
-    pl.figure(3)
-    pl.imshow(G0, interpolation='nearest')
-    pl.title('OT matrix G0')
-
-    pl.figure(4)
-    ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
-    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-    pl.legend(loc=0)
-    pl.title('OT matrix with samples')
-
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png
-            :class: sphx-glr-multi-img
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png
-            :class: sphx-glr-multi-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-
-    Text(0.5, 1.0, 'OT matrix with samples')
-
-
-
-Compute Sinkhorn
-----------------
-
-
-.. code-block:: default
-
-
-    # reg term
-    lambd = 1e-3
-
-    Gs = ot.sinkhorn(a, b, M, lambd)
-
-    pl.figure(5)
-    pl.imshow(Gs, interpolation='nearest')
-    pl.title('OT matrix sinkhorn')
-
-    pl.figure(6)
-    ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
-    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-    pl.legend(loc=0)
-    pl.title('OT matrix Sinkhorn with samples')
-
-    pl.show()
-
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png
-            :class: sphx-glr-multi-img
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png
-            :class: sphx-glr-multi-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:103: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      pl.show()
-
-
-
-
-Emprirical Sinkhorn
-----------------
-
-
-.. code-block:: default
-
-
-    # reg term
-    lambd = 1e-3
-
-    Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)
-
-    pl.figure(7)
-    pl.imshow(Ges, interpolation='nearest')
-    pl.title('OT matrix empirical sinkhorn')
-
-    pl.figure(8)
-    ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])
-    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-    pl.legend(loc=0)
-    pl.title('OT matrix Sinkhorn from samples')
-
-    pl.show()
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_007.png
-            :class: sphx-glr-multi-img
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_008.png
-            :class: sphx-glr-multi-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/ot/bregman.py:363: RuntimeWarning: divide by zero encountered in true_divide
-      v = np.divide(b, KtransposeU)
-    Warning: numerical errors at iteration 0
-    /home/rflamary/PYTHON/POT/ot/plot.py:90: RuntimeWarning: invalid value encountered in double_scalars
-      if G[i, j] / mx > thr:
-    /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      pl.show()
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  2.154 seconds)
-
-
-.. _sphx_glr_download_auto_examples_plot_OT_2D_samples.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: plot_OT_2D_samples.py <plot_OT_2D_samples.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb <plot_OT_2D_samples.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_