[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, 92 deletions

diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.py b/docs/source/auto_examples/plot_convolutional_barycenter.py
deleted file mode 100644
index e74db04..0000000
--- a/docs/source/auto_examples/plot_convolutional_barycenter.py
+++ /dev/null
@@ -1,92 +0,0 @@
-
-#%%
-# -*- coding: utf-8 -*-
-"""
-============================================
-Convolutional Wasserstein Barycenter example
-============================================
-
-This example is designed to illustrate how the Convolutional Wasserstein Barycenter
-function of POT works.
-"""
-
-# Author: Nicolas Courty <ncourty@irisa.fr>
-#
-# License: MIT License
-
-
-import numpy as np
-import pylab as pl
-import ot
-
-##############################################################################
-# Data preparation
-# ----------------
-#
-# The four distributions are constructed from 4 simple images
-
-
-f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]
-f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]
-f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]
-f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]
-
-A = []
-f1 = f1 / np.sum(f1)
-f2 = f2 / np.sum(f2)
-f3 = f3 / np.sum(f3)
-f4 = f4 / np.sum(f4)
-A.append(f1)
-A.append(f2)
-A.append(f3)
-A.append(f4)
-A = np.array(A)
-
-nb_images = 5
-
-# those are the four corners coordinates that will be interpolated by bilinear
-# interpolation
-v1 = np.array((1, 0, 0, 0))
-v2 = np.array((0, 1, 0, 0))
-v3 = np.array((0, 0, 1, 0))
-v4 = np.array((0, 0, 0, 1))
-
-
-##############################################################################
-# Barycenter computation and visualization
-# ----------------------------------------
-#
-
-pl.figure(figsize=(10, 10))
-pl.title('Convolutional Wasserstein Barycenters in POT')
-cm = 'Blues'
-# regularization parameter
-reg = 0.004
-for i in range(nb_images):
-    for j in range(nb_images):
-        pl.subplot(nb_images, nb_images, i * nb_images + j + 1)
-        tx = float(i) / (nb_images - 1)
-        ty = float(j) / (nb_images - 1)
-
-        # weights are constructed by bilinear interpolation
-        tmp1 = (1 - tx) * v1 + tx * v2
-        tmp2 = (1 - tx) * v3 + tx * v4
-        weights = (1 - ty) * tmp1 + ty * tmp2
-
-        if i == 0 and j == 0:
-            pl.imshow(f1, cmap=cm)
-            pl.axis('off')
-        elif i == 0 and j == (nb_images - 1):
-            pl.imshow(f3, cmap=cm)
-            pl.axis('off')
-        elif i == (nb_images - 1) and j == 0:
-            pl.imshow(f2, cmap=cm)
-            pl.axis('off')
-        elif i == (nb_images - 1) and j == (nb_images - 1):
-            pl.imshow(f4, cmap=cm)
-            pl.axis('off')
-        else:
-            # call to barycenter computation
-            pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)
-            pl.axis('off')
-pl.show()