[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, 343 deletions

diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
deleted file mode 100644
index 16b20f9..0000000
--- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst
+++ /dev/null
@@ -1,343 +0,0 @@
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_auto_examples_plot_OT_L1_vs_L2.py>`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
-
-    .. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py:
-
-
-==========================================
-2D Optimal transport for different metrics
-==========================================
-
-2D OT on empirical distributio  with different gound metric.
-
-Stole the figure idea from Fig. 1 and 2 in
-https://arxiv.org/pdf/1706.07650.pdf
-
-
-
-
-.. code-block:: default
-
-
-    # Author: Remi Flamary <remi.flamary@unice.fr>
-    #
-    # License: MIT License
-
-    import numpy as np
-    import matplotlib.pylab as pl
-    import ot
-    import ot.plot
-
-
-
-
-
-
-
-
-Dataset 1 : uniform sampling
-----------------------------
-
-
-.. code-block:: default
-
-
-    n = 20  # nb samples
-    xs = np.zeros((n, 2))
-    xs[:, 0] = np.arange(n) + 1
-    xs[:, 1] = (np.arange(n) + 1) * -0.001  # to make it strictly convex...
-
-    xt = np.zeros((n, 2))
-    xt[:, 1] = np.arange(n) + 1
-
-    a, b = ot.unif(n), ot.unif(n)  # uniform distribution on samples
-
-    # loss matrix
-    M1 = ot.dist(xs, xt, metric='euclidean')
-    M1 /= M1.max()
-
-    # loss matrix
-    M2 = ot.dist(xs, xt, metric='sqeuclidean')
-    M2 /= M2.max()
-
-    # loss matrix
-    Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
-    Mp /= Mp.max()
-
-    # Data
-    pl.figure(1, figsize=(7, 3))
-    pl.clf()
-    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-    pl.axis('equal')
-    pl.title('Source and target distributions')
-
-
-    # Cost matrices
-    pl.figure(2, figsize=(7, 3))
-
-    pl.subplot(1, 3, 1)
-    pl.imshow(M1, interpolation='nearest')
-    pl.title('Euclidean cost')
-
-    pl.subplot(1, 3, 2)
-    pl.imshow(M2, interpolation='nearest')
-    pl.title('Squared Euclidean cost')
-
-    pl.subplot(1, 3, 3)
-    pl.imshow(Mp, interpolation='nearest')
-    pl.title('Sqrt Euclidean cost')
-    pl.tight_layout()
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_001.png
-            :class: sphx-glr-multi-img
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png
-            :class: sphx-glr-multi-img
-
-
-
-
-
-Dataset 1 : Plot OT Matrices
-----------------------------
-
-
-.. code-block:: default
-
-    G1 = ot.emd(a, b, M1)
-    G2 = ot.emd(a, b, M2)
-    Gp = ot.emd(a, b, Mp)
-
-    # OT matrices
-    pl.figure(3, figsize=(7, 3))
-
-    pl.subplot(1, 3, 1)
-    ot.plot.plot2D_samples_mat(xs, xt, G1, 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.axis('equal')
-    # pl.legend(loc=0)
-    pl.title('OT Euclidean')
-
-    pl.subplot(1, 3, 2)
-    ot.plot.plot2D_samples_mat(xs, xt, G2, 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.axis('equal')
-    # pl.legend(loc=0)
-    pl.title('OT squared Euclidean')
-
-    pl.subplot(1, 3, 3)
-    ot.plot.plot2D_samples_mat(xs, xt, Gp, 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.axis('equal')
-    # pl.legend(loc=0)
-    pl.title('OT sqrt Euclidean')
-    pl.tight_layout()
-
-    pl.show()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_003.png
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:113: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      pl.show()
-
-
-
-
-Dataset 2 : Partial circle
---------------------------
-
-
-.. code-block:: default
-
-
-    n = 50  # nb samples
-    xtot = np.zeros((n + 1, 2))
-    xtot[:, 0] = np.cos(
-        (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
-    xtot[:, 1] = np.sin(
-        (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
-
-    xs = xtot[:n, :]
-    xt = xtot[1:, :]
-
-    a, b = ot.unif(n), ot.unif(n)  # uniform distribution on samples
-
-    # loss matrix
-    M1 = ot.dist(xs, xt, metric='euclidean')
-    M1 /= M1.max()
-
-    # loss matrix
-    M2 = ot.dist(xs, xt, metric='sqeuclidean')
-    M2 /= M2.max()
-
-    # loss matrix
-    Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
-    Mp /= Mp.max()
-
-
-    # Data
-    pl.figure(4, figsize=(7, 3))
-    pl.clf()
-    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
-    pl.axis('equal')
-    pl.title('Source and traget distributions')
-
-
-    # Cost matrices
-    pl.figure(5, figsize=(7, 3))
-
-    pl.subplot(1, 3, 1)
-    pl.imshow(M1, interpolation='nearest')
-    pl.title('Euclidean cost')
-
-    pl.subplot(1, 3, 2)
-    pl.imshow(M2, interpolation='nearest')
-    pl.title('Squared Euclidean cost')
-
-    pl.subplot(1, 3, 3)
-    pl.imshow(Mp, interpolation='nearest')
-    pl.title('Sqrt Euclidean cost')
-    pl.tight_layout()
-
-
-
-
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_004.png
-            :class: sphx-glr-multi-img
-
-    *
-
-      .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png
-            :class: sphx-glr-multi-img
-
-
-
-
-
-Dataset 2 : Plot  OT Matrices
------------------------------
-
-
-.. code-block:: default
-
-    G1 = ot.emd(a, b, M1)
-    G2 = ot.emd(a, b, M2)
-    Gp = ot.emd(a, b, Mp)
-
-    # OT matrices
-    pl.figure(6, figsize=(7, 3))
-
-    pl.subplot(1, 3, 1)
-    ot.plot.plot2D_samples_mat(xs, xt, G1, 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.axis('equal')
-    # pl.legend(loc=0)
-    pl.title('OT Euclidean')
-
-    pl.subplot(1, 3, 2)
-    ot.plot.plot2D_samples_mat(xs, xt, G2, 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.axis('equal')
-    # pl.legend(loc=0)
-    pl.title('OT squared Euclidean')
-
-    pl.subplot(1, 3, 3)
-    ot.plot.plot2D_samples_mat(xs, xt, Gp, 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.axis('equal')
-    # pl.legend(loc=0)
-    pl.title('OT sqrt Euclidean')
-    pl.tight_layout()
-
-    pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_006.png
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:208: 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  1.002 seconds)
-
-
-.. _sphx_glr_download_auto_examples_plot_OT_L1_vs_L2.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_L1_vs_L2.py <plot_OT_L1_vs_L2.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb <plot_OT_L1_vs_L2.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_