CircleCI update of dev docs (2678).

Author: Circle CI

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -104,9 +104,10 @@ <h1>Source code for ot.optim</h1><div class="highlight"><pre>
 <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
     <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
     <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">scalar_search_armijo</span>
-    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
         <span class="kn">from</span> <span class="nn">scipy.optimize._linesearch</span> <span class="kn">import</span> <span class="n">scalar_search_armijo</span>
+    <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+        <span class="c1"># scipy&lt;1.8.0</span>
+        <span class="kn">from</span> <span class="nn">scipy.optimize.linesearch</span> <span class="kn">import</span> <span class="n">scalar_search_armijo</span>
 
 <span class="c1"># The corresponding scipy function does not work for matrices</span>
 

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -195,26 +195,26 @@ Estimating dual variables for entropic OT
 
  .. code-block:: none
 
-    Iter:   0, loss=0.2020494900224745
-    Iter:  10, loss=-19.57618259614961
-    Iter:  20, loss=-31.66301265244961
-    Iter:  30, loss=-36.50666181688356
-    Iter:  40, loss=-39.95100518352593
-    Iter:  50, loss=-41.47867745038081
-    Iter:  60, loss=-42.40982244225048
-    Iter:  70, loss=-42.75576775807454
-    Iter:  80, loss=-42.96194349519
-    Iter:  90, loss=-43.010399265246384
-    Iter: 100, loss=-43.04617124235882
-    Iter: 110, loss=-43.05966450080063
-    Iter: 120, loss=-43.06794354289777
-    Iter: 130, loss=-43.073006381620885
-    Iter: 140, loss=-43.076636666041324
-    Iter: 150, loss=-43.079366227386934
-    Iter: 160, loss=-43.08154681843257
-    Iter: 170, loss=-43.08329470262464
-    Iter: 180, loss=-43.08470031156932
-    Iter: 190, loss=-43.08581430429776
+    Iter:   0, loss=0.202049490022473
+    Iter:  10, loss=-19.49942521965765
+    Iter:  20, loss=-31.658280394643207
+    Iter:  30, loss=-35.907837287180875
+    Iter:  40, loss=-38.95231618671824
+    Iter:  50, loss=-40.63472110777525
+    Iter:  60, loss=-41.428437008367894
+    Iter:  70, loss=-41.57292501821869
+    Iter:  80, loss=-41.65390714976431
+    Iter:  90, loss=-41.68837925711983
+    Iter: 100, loss=-41.69859895901433
+    Iter: 110, loss=-41.70101376621021
+    Iter: 120, loss=-41.702842572879625
+    Iter: 130, loss=-41.70357118936795
+    Iter: 140, loss=-41.70394891940299
+    Iter: 150, loss=-41.704190391352505
+    Iter: 160, loss=-41.70437160490415
+    Iter: 170, loss=-41.70451018830662
+    Iter: 180, loss=-41.7046236183371
+    Iter: 190, loss=-41.70471893714058
 
 
 
@@ -319,25 +319,25 @@ Estimating dual variables for quadratic OT
  .. code-block:: none
 
     Iter:   0, loss=-0.0018442196020623663
-    Iter:  10, loss=-19.544292446025445
-    Iter:  20, loss=-31.280524158660008
-    Iter:  30, loss=-36.19856648665331
-    Iter:  40, loss=-39.62982578094831
-    Iter:  50, loss=-41.171229459917676
-    Iter:  60, loss=-42.12753723269981
-    Iter:  70, loss=-42.55976656784887
-    Iter:  80, loss=-42.761741861484026
-    Iter:  90, loss=-42.86048643148755
-    Iter: 100, loss=-42.896210186167515
-    Iter: 110, loss=-42.912518604496654
-    Iter: 120, loss=-42.92196834252769
-    Iter: 130, loss=-42.92766529649812
-    Iter: 140, loss=-42.93143626004543
-    Iter: 150, loss=-42.93430243457384
-    Iter: 160, loss=-42.93669507664405
-    Iter: 170, loss=-42.93876197077028
-    Iter: 180, loss=-42.94043926818304
-    Iter: 190, loss=-42.941767213471
+    Iter:  10, loss=-19.76260285675617
+    Iter:  20, loss=-31.602402063956877
+    Iter:  30, loss=-35.74655038942226
+    Iter:  40, loss=-38.83929304001835
+    Iter:  50, loss=-40.45321197269921
+    Iter:  60, loss=-41.152539146867575
+    Iter:  70, loss=-41.41214679503097
+    Iter:  80, loss=-41.51321463756855
+    Iter:  90, loss=-41.557348889785736
+    Iter: 100, loss=-41.56907217636828
+    Iter: 110, loss=-41.57480296157927
+    Iter: 120, loss=-41.576586788387544
+    Iter: 130, loss=-41.5772839880003
+    Iter: 140, loss=-41.577554575962814
+    Iter: 150, loss=-41.577660285916004
+    Iter: 160, loss=-41.57770125151936
+    Iter: 170, loss=-41.57772639029642
+    Iter: 180, loss=-41.577740225081975
+    Iter: 190, loss=-41.57775039353258
 
 
 
@@ -380,7 +380,7 @@ Plot the estimated quadratic OT plan
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.564 seconds)
+   **Total running time of the script:** (0 minutes 28.854 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_dual_ot_pytorch.py:

master/_images/sphx_glr_plot_OT_2D_samples_001.png

@@ -395,14 +395,14 @@ classes
     Estimated weights :  [0.29985821 0.18926744 0.51087435]
     True proportions :  [0.5 0.3 0.2]
 
-    <matplotlib.colorbar.Colorbar object at 0x7f60c09e69e0>
+    <matplotlib.colorbar.Colorbar object at 0x7f5c983172e0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.575 seconds)
+   **Total running time of the script:** (0 minutes 8.353 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_optim_gromov_pytorch.py: