update exmaple bary 1D for better notebook

10 files changed, 162 insertions, 54 deletions

diff --git a/docs/cache_nbrun b/docs/cache_nbrun
index 2781d81..0745a21 100644
--- a/docs/cache_nbrun
+++ b/docs/cache_nbrun
@@ -1 +1 @@
-{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}
\ No newline at end of file
+{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}
\ No newline at end of file
diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip
index 2325d59..c6a7e90 100644
--- a/docs/source/auto_examples/auto_examples_jupyter.zip
+++ b/docs/source/auto_examples/auto_examples_jupyter.zip
diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip
index 694d704..28ff08e 100644
--- a/docs/source/auto_examples/auto_examples_python.zip
+++ b/docs/source/auto_examples/auto_examples_python.zip
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
index 81cee52..d8db85e 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png
index eac9230..81cee52 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png
index 2e29ff9..bfa0873 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png
diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst
index 77a46aa..5cbfba6 100644
--- a/docs/source/auto_examples/index.rst
+++ b/docs/source/auto_examples/index.rst
@@ -229,13 +229,13 @@ This is a gallery of all the POT example files.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="OT for domain adaptation with image color adaptation [6] with mapping estimation [8].">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [...">
 
 .. only:: html
 
-    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png
+    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png
 
-        :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py`
+        :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py`
 
 .. raw:: html
 
@@ -245,17 +245,17 @@ This is a gallery of all the POT example files.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_otda_mapping_colors_images
+   /auto_examples/plot_barycenter_1D
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [...">
+    <div class="sphx-glr-thumbcontainer" tooltip="OT for domain adaptation with image color adaptation [6] with mapping estimation [8].">
 
 .. only:: html
 
-    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png
+    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png
 
-        :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py`
+        :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py`
 
 .. raw:: html
 
@@ -265,7 +265,7 @@ This is a gallery of all the POT example files.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_barycenter_1D
+   /auto_examples/plot_otda_mapping_colors_images
 
 .. raw:: html
 
diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb
index 5866088..fc60e1f 100644
--- a/docs/source/auto_examples/plot_barycenter_1D.ipynb
+++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb
@@ -26,7 +26,79 @@
       },
       "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\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100  # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5)  # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2  # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n    alpha = alpha_list[i]\n    weights = np.array([1 - alpha, alpha])\n    B_l2[:, i] = A.dot(weights)\n    B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_l2[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_wass[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()"
+        "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa\nfrom matplotlib.collections import PolyCollection"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Generate data\n-------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "#%% parameters\n\nn = 100  # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5)  # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot data\n---------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Barycenter computation\n----------------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "#%% barycenter computation\n\nalpha = 0.2  # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Barycentric interpolation\n-------------------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n    alpha = alpha_list[i]\n    weights = np.array([1 - alpha, alpha])\n    B_l2[:, i] = A.dot(weights)\n    B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_l2[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_wass[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()"
       ]
     }
   ],
diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py
index 5ed9f3f..6864301 100644
--- a/docs/source/auto_examples/plot_barycenter_1D.py
+++ b/docs/source/auto_examples/plot_barycenter_1D.py
@@ -25,7 +25,7 @@ import ot
 from mpl_toolkits.mplot3d import Axes3D  # noqa
 from matplotlib.collections import PolyCollection
 
-#
+##############################################################################
 # Generate data
 # -------------
 
@@ -48,7 +48,7 @@ n_distributions = A.shape[1]
 M = ot.utils.dist0(n)
 M /= M.max()
 
-#
+##############################################################################
 # Plot data
 # ---------
 
@@ -60,7 +60,7 @@ for i in range(n_distributions):
 pl.title('Distributions')
 pl.tight_layout()
 
-#
+##############################################################################
 # Barycenter computation
 # ----------------------
 
@@ -90,7 +90,7 @@ pl.legend()
 pl.title('Barycenters')
 pl.tight_layout()
 
-#
+##############################################################################
 # Barycentric interpolation
 # -------------------------
 
diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst
index b314dc1..66ac042 100644
--- a/docs/source/auto_examples/plot_barycenter_1D.rst
+++ b/docs/source/auto_examples/plot_barycenter_1D.rst
@@ -18,52 +18,34 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
 
 
 
+.. code-block:: python
 
-.. rst-class:: sphx-glr-horizontal
-
-
-    *
 
-      .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png
-            :scale: 47
+    # Author: Remi Flamary <remi.flamary@unice.fr>
+    #
+    # License: MIT License
 
-    *
+    import numpy as np
+    import matplotlib.pylab as pl
+    import ot
+    # necessary for 3d plot even if not used
+    from mpl_toolkits.mplot3d import Axes3D  # noqa
+    from matplotlib.collections import PolyCollection
 
-      .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png
-            :scale: 47
 
-    *
 
-      .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
-            :scale: 47
 
-    *
 
-      .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png
-            :scale: 47
 
 
+Generate data
+-------------
 
 
 
 .. code-block:: python
 
 
-    # Author: Remi Flamary <remi.flamary@unice.fr>
-    #
-    # License: MIT License
-
-    import numpy as np
-    import matplotlib.pylab as pl
-    import ot
-    # necessary for 3d plot even if not used
-    from mpl_toolkits.mplot3d import Axes3D  # noqa
-    from matplotlib.collections import PolyCollection
-
-    #
-    # Generate data
-    # -------------
-
     #%% parameters
 
     n = 100  # nb bins
@@ -83,9 +65,19 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
     M = ot.utils.dist0(n)
     M /= M.max()
 
-    #
-    # Plot data
-    # ---------
+
+
+
+
+
+
+Plot data
+---------
+
+
+
+.. code-block:: python
+
 
     #%% plot the distributions
 
@@ -95,9 +87,22 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
     pl.title('Distributions')
     pl.tight_layout()
 
-    #
-    # Barycenter computation
-    # ----------------------
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png
+    :align: center
+
+
+
+
+Barycenter computation
+----------------------
+
+
+
+.. code-block:: python
+
 
     #%% barycenter computation
 
@@ -125,9 +130,22 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
     pl.title('Barycenters')
     pl.tight_layout()
 
-    #
-    # Barycentric interpolation
-    # -------------------------
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
+    :align: center
+
+
+
+
+Barycentric interpolation
+-------------------------
+
+
+
+.. code-block:: python
+
 
     #%% barycenter interpolation
 
@@ -194,7 +212,25 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
 
     pl.show()
 
-**Total running time of the script:** ( 0 minutes  0.363 seconds)
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png
+            :scale: 47
+
+    *
+
+      .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png
+            :scale: 47
+
+
+
+
+**Total running time of the script:** ( 0 minutes  0.413 seconds)