Merge pull request #27 from rflamary/autonb

auto notebooks + release update (fixes #16)

1 files changed, 135 insertions, 76 deletions

diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst
index 1b15c77..f17f2c2 100644
--- a/docs/source/auto_examples/plot_barycenter_1D.rst
+++ b/docs/source/auto_examples/plot_barycenter_1D.rst
@@ -7,171 +7,230 @@
 1D Wasserstein barycenter demo
 ==============================
 
+This example illustrates the computation of regularized Wassersyein Barycenter
+as proposed in [3].
 
-@author: rflamary
 
+[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).
+Iterative Bregman projections for regularized transportation problems
+SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
 
 
 
-.. rst-class:: sphx-glr-horizontal
 
+.. code-block:: python
 
-    *
 
-      .. 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
 
 
-    import numpy as np
-    import matplotlib.pylab as pl
-    import ot
-    from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used
-    from matplotlib.collections import PolyCollection
-
-
     #%% parameters
 
-    n=100 # nb bins
+    n = 100  # nb bins
 
     # bin positions
-    x=np.arange(n,dtype=np.float64)
+    x = np.arange(n, dtype=np.float64)
 
     # Gaussian distributions
-    a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std
-    a2=ot.datasets.get_1D_gauss(n,m=60,s=8)
+    a1 = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+    a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)
 
     # creating matrix A containing all distributions
-    A=np.vstack((a1,a2)).T
-    nbd=A.shape[1]
+    A = np.vstack((a1, a2)).T
+    n_distributions = A.shape[1]
 
     # loss matrix + normalization
-    M=ot.utils.dist0(n)
-    M/=M.max()
+    M = ot.utils.dist0(n)
+    M /= M.max()
+
+
+
+
+
+
+
+Plot data
+---------
+
+
+
+.. code-block:: python
+
 
     #%% plot the distributions
 
-    pl.figure(1)
-    for i in range(nbd):
-        pl.plot(x,A[:,i])
+    pl.figure(1, figsize=(6.4, 3))
+    for i in range(n_distributions):
+        pl.plot(x, A[:, i])
     pl.title('Distributions')
+    pl.tight_layout()
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png
+    :align: center
+
+
+
+
+Barycenter computation
+----------------------
+
+
+
+.. code-block:: python
+
 
     #%% barycenter computation
 
-    alpha=0.2 # 0<=alpha<=1
-    weights=np.array([1-alpha,alpha])
+    alpha = 0.2  # 0<=alpha<=1
+    weights = np.array([1 - alpha, alpha])
 
     # l2bary
-    bary_l2=A.dot(weights)
+    bary_l2 = A.dot(weights)
 
     # wasserstein
-    reg=1e-3
-    bary_wass=ot.bregman.barycenter(A,M,reg,weights)
+    reg = 1e-3
+    bary_wass = ot.bregman.barycenter(A, M, reg, weights)
 
     pl.figure(2)
     pl.clf()
-    pl.subplot(2,1,1)
-    for i in range(nbd):
-        pl.plot(x,A[:,i])
+    pl.subplot(2, 1, 1)
+    for i in range(n_distributions):
+        pl.plot(x, A[:, i])
     pl.title('Distributions')
 
-    pl.subplot(2,1,2)
-    pl.plot(x,bary_l2,'r',label='l2')
-    pl.plot(x,bary_wass,'g',label='Wasserstein')
+    pl.subplot(2, 1, 2)
+    pl.plot(x, bary_l2, 'r', label='l2')
+    pl.plot(x, bary_wass, 'g', label='Wasserstein')
     pl.legend()
     pl.title('Barycenters')
+    pl.tight_layout()
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png
+    :align: center
+
+
+
+
+Barycentric interpolation
+-------------------------
+
+
+
+.. code-block:: python
 
 
     #%% barycenter interpolation
 
-    nbalpha=11
-    alphalist=np.linspace(0,1,nbalpha)
+    n_alpha = 11
+    alpha_list = np.linspace(0, 1, n_alpha)
 
 
-    B_l2=np.zeros((n,nbalpha))
+    B_l2 = np.zeros((n, n_alpha))
 
-    B_wass=np.copy(B_l2)
+    B_wass = np.copy(B_l2)
 
-    for i in range(0,nbalpha):
-        alpha=alphalist[i]
-        weights=np.array([1-alpha,alpha])
-        B_l2[:,i]=A.dot(weights)
-        B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)
+    for i in range(0, n_alpha):
+        alpha = alpha_list[i]
+        weights = np.array([1 - alpha, alpha])
+        B_l2[:, i] = A.dot(weights)
+        B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)
 
     #%% plot interpolation
 
-    pl.figure(3,(10,5))
+    pl.figure(3)
 
-    #pl.subplot(1,2,1)
-    cmap=pl.cm.get_cmap('viridis')
+    cmap = pl.cm.get_cmap('viridis')
     verts = []
-    zs = alphalist
-    for i,z in enumerate(zs):
-        ys = B_l2[:,i]
+    zs = alpha_list
+    for i, z in enumerate(zs):
+        ys = B_l2[:, i]
         verts.append(list(zip(x, ys)))
 
     ax = pl.gcf().gca(projection='3d')
 
-    poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])
+    poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])
     poly.set_alpha(0.7)
     ax.add_collection3d(poly, zs=zs, zdir='y')
-
     ax.set_xlabel('x')
     ax.set_xlim3d(0, n)
     ax.set_ylabel('$\\alpha$')
-    ax.set_ylim3d(0,1)
+    ax.set_ylim3d(0, 1)
     ax.set_zlabel('')
-    ax.set_zlim3d(0, B_l2.max()*1.01)
+    ax.set_zlim3d(0, B_l2.max() * 1.01)
     pl.title('Barycenter interpolation with l2')
+    pl.tight_layout()
 
-    pl.show()
-
-    pl.figure(4,(10,5))
-
-    #pl.subplot(1,2,1)
-    cmap=pl.cm.get_cmap('viridis')
+    pl.figure(4)
+    cmap = pl.cm.get_cmap('viridis')
     verts = []
-    zs = alphalist
-    for i,z in enumerate(zs):
-        ys = B_wass[:,i]
+    zs = alpha_list
+    for i, z in enumerate(zs):
+        ys = B_wass[:, i]
         verts.append(list(zip(x, ys)))
 
     ax = pl.gcf().gca(projection='3d')
 
-    poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])
+    poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])
     poly.set_alpha(0.7)
     ax.add_collection3d(poly, zs=zs, zdir='y')
-
     ax.set_xlabel('x')
     ax.set_xlim3d(0, n)
     ax.set_ylabel('$\\alpha$')
-    ax.set_ylim3d(0,1)
+    ax.set_ylim3d(0, 1)
     ax.set_zlabel('')
-    ax.set_zlim3d(0, B_l2.max()*1.01)
+    ax.set_zlim3d(0, B_l2.max() * 1.01)
     pl.title('Barycenter interpolation with Wasserstein')
+    pl.tight_layout()
 
     pl.show()
-**Total running time of the script:** ( 0 minutes  2.274 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  0.814 seconds)