From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Tue, 21 Apr 2020 17:48:37 +0200
Subject: [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
---
 .../plot_convolutional_barycenter.ipynb            | 90 ----------------------
 1 file changed, 90 deletions(-)
 delete mode 100644 docs/source/auto_examples/plot_convolutional_barycenter.ipynb

(limited to 'docs/source/auto_examples/plot_convolutional_barycenter.ipynb')

diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb
deleted file mode 100644
index f94a32e..0000000
--- a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Convolutional Wasserstein Barycenter example\n\n\nThis example is designed to illustrate how the Convolutional Wasserstein Barycenter\nfunction of POT works.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Author: Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\n\nimport numpy as np\nimport pylab as pl\nimport ot"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\nf2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\nf3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\nf4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n\nA = []\nf1 = f1 / np.sum(f1)\nf2 = f2 / np.sum(f2)\nf3 = f3 / np.sum(f3)\nf4 = f4 / np.sum(f4)\nA.append(f1)\nA.append(f2)\nA.append(f3)\nA.append(f4)\nA = np.array(A)\n\nnb_images = 5\n\n# those are the four corners coordinates that will be interpolated by bilinear\n# interpolation\nv1 = np.array((1, 0, 0, 0))\nv2 = np.array((0, 1, 0, 0))\nv3 = np.array((0, 0, 1, 0))\nv4 = np.array((0, 0, 0, 1))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Barycenter computation and visualization\n----------------------------------------\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "pl.figure(figsize=(10, 10))\npl.title('Convolutional Wasserstein Barycenters in POT')\ncm = 'Blues'\n# regularization parameter\nreg = 0.004\nfor i in range(nb_images):\n    for j in range(nb_images):\n        pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n        tx = float(i) / (nb_images - 1)\n        ty = float(j) / (nb_images - 1)\n\n        # weights are constructed by bilinear interpolation\n        tmp1 = (1 - tx) * v1 + tx * v2\n        tmp2 = (1 - tx) * v3 + tx * v4\n        weights = (1 - ty) * tmp1 + ty * tmp2\n\n        if i == 0 and j == 0:\n            pl.imshow(f1, cmap=cm)\n            pl.axis('off')\n        elif i == 0 and j == (nb_images - 1):\n            pl.imshow(f3, cmap=cm)\n            pl.axis('off')\n        elif i == (nb_images - 1) and j == 0:\n            pl.imshow(f2, cmap=cm)\n            pl.axis('off')\n        elif i == (nb_images - 1) and j == (nb_images - 1):\n            pl.imshow(f4, cmap=cm)\n            pl.axis('off')\n        else:\n            # call to barycenter computation\n            pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n            pl.axis('off')\npl.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
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