From b5e45bbc83fd8cd8c1634a78f2f983d1cf28af73 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Wed, 30 May 2018 09:58:51 +0200
Subject: update examples and notebooks

---
 docs/source/auto_examples/plot_OT_1D.ipynb | 68 +++++++++++++++---------------
 1 file changed, 34 insertions(+), 34 deletions(-)

(limited to 'docs/source/auto_examples/plot_OT_1D.ipynb')

diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb
index 649efa6..bd0439e 100644
--- a/docs/source/auto_examples/plot_OT_1D.ipynb
+++ b/docs/source/auto_examples/plot_OT_1D.ipynb
@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "cell_type": "code",
       "execution_count": null,
       "metadata": {
         "collapsed": false
@@ -8,35 +9,35 @@
       "outputs": [],
       "source": [
         "%matplotlib inline"
-      ],
-      "cell_type": "code"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
       "execution_count": null,
       "metadata": {
         "collapsed": false
       },
       "outputs": [],
       "source": [
-        "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import get_1D_gauss as gauss"
-      ],
-      "cell_type": "code"
+        "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "Generate data\n-------------\n\n"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
       "execution_count": null,
       "metadata": {
         "collapsed": false
@@ -44,17 +45,17 @@
       "outputs": [],
       "source": [
         "#%% parameters\n\nn = 100  # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5)  # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()"
-      ],
-      "cell_type": "code"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "Plot distributions and loss matrix\n----------------------------------\n\n"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
       "execution_count": null,
       "metadata": {
         "collapsed": false
@@ -62,17 +63,17 @@
       "outputs": [],
       "source": [
         "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
-      ],
-      "cell_type": "code"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "Solve EMD\n---------\n\n"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
       "execution_count": null,
       "metadata": {
         "collapsed": false
@@ -80,17 +81,17 @@
       "outputs": [],
       "source": [
         "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')"
-      ],
-      "cell_type": "code"
+      ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {},
       "source": [
         "Solve Sinkhorn\n--------------\n\n"
-      ],
-      "cell_type": "markdown"
+      ]
     },
     {
+      "cell_type": "code",
       "execution_count": null,
       "metadata": {
         "collapsed": false
@@ -98,29 +99,28 @@
       "outputs": [],
       "source": [
         "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()"
-      ],
-      "cell_type": "code"
+      ]
     }
   ],
   "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
     "language_info": {
-      "name": "python",
       "codemirror_mode": {
         "name": "ipython",
         "version": 3
       },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
       "nbconvert_exporter": "python",
-      "version": "3.5.2",
       "pygments_lexer": "ipython3",
-      "file_extension": ".py",
-      "mimetype": "text/x-python"
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3",
-      "language": "python"
+      "version": "3.6.5"
     }
   },
-  "nbformat_minor": 0,
-  "nbformat": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file
-- 
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