PythonOT
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diff --git a/‎master/_downloads/06f3dd7c3258690ab730be0a658b5e36/plot_ssw_unif_torch.zip
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diff --git a/‎master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
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diff --git a/‎master/_downloads/135d9df766cd2fdce616d2b7c6016c04/plot_otda_jcpot.zip
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diff --git a/‎master/_downloads/172bc04ad2581ec5358a93130ebb604a/plot_lowrank_sinkhorn.zip
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diff --git a/‎master/_downloads/1ad7d23c19eb30825c699a538f007406/plot_WeakOT_VS_OT.zip
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diff --git a/‎master/_downloads/21e17efb5cdae61c14f9cbf8b4c70630/plot_factored_coupling.zip
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diff --git a/‎master/_downloads/2ed40fb3b0b532638a960dde6c1f792b/plot_otda_color_images.zip
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diff --git a/‎master/_downloads/3337d33da9a747dfa3fadd7e712d540f/plot_COOT.zip
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diff --git a/‎master/_downloads/36386058d37d3a0dde5bef75520cbc51/plot_learning_weights_with_COOT.zip
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diff --git a/‎master/_downloads/36d7c77a075d7f2ded4ced874493f94c/plot_OT_L1_vs_L2.zip
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diff --git a/‎master/_downloads/3cc7f7b96d00caffb0f38c90ccf63c29/plot_stochastic.zip
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diff --git a/‎master/_downloads/4bf5b36d4750ccf400a6581dc9be238b/plot_optim_OTreg.zip
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diff --git a/‎master/_downloads/688461c65828cf854aeed83edf7c31dc/plot_screenkhorn_1D.zip
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diff --git a/‎master/_downloads/68e34f6a20333935dad365f1fd8bd905/plot_regpath.zip
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diff --git a/‎master/_downloads/690f67f4e935a78cd58e89e7460c1ba8/plot_unmix_optim_torch.zip
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diff --git a/‎master/_downloads/6a14021cc059297499201ffe925ba6bd/plot_GMM_flow.zip
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diff --git a/‎master/_downloads/6c61e837dc1e6b38d33b792d4ad27f64/plot_barycenter_1D.zip
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diff --git a/‎master/_downloads/6e3e83f0c6c46ee19892cc99e4dd87e0/plot_variance_ssw.zip
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diff --git a/‎master/_downloads/6e527a47ab91993455292edd573fe7fc/plot_OT_1D.zip
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diff --git a/‎master/_downloads/70a9e8bc2c10d895d424b26e109a7bf7/plot_UOT_1D.zip
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diff --git a/‎master/_downloads/766666d15248defb8aa962690e1bd50c/plot_solve_variants.zip
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diff --git a/‎master/_downloads/773907dd64516318f59bfbc3e6cc4c57/plot_Intro_OT.zip
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diff --git a/‎master/_downloads/7d2ab963aa345d0e9446612046c2c696/plot_free_support_sinkhorn_barycenter.zip
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diff --git a/‎master/_downloads/7d3f7f21ed21e6090c92c2c9a1e92cfc/plot_dmmot.zip
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diff --git a/‎master/_downloads/7d708f53377dd33525d6b70fa47a8803/plot_otda_d2.zip
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diff --git a/‎master/_downloads/7e335ca34b7289a239bd9118ea4a9802/plot_GMMOT_plan.zip
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diff --git a/‎master/_downloads/7f27ffa56b6f5e9c0b1b3ef34dfe846e/plot_semirelaxed_fgw.zip
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diff --git a/‎master/_downloads/7fce29b781b1d403c12a6e447812ec53/plot_sliced_wass_grad_flow_pytorch.zip
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diff --git a/‎master/_downloads/7ffae8ea50cc16d7cf5b6ad4a6f3aeb0/plot_stoch_continuous_ot_pytorch.zip
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diff --git a/‎master/_downloads/878e51a4f4c84a76650e21301ecf5e08/plot_gaussian_barycenter.zip
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diff --git a/‎master/_downloads/89c5b8bff0577721f3c9d08ecc75685a/plot_entropic_semirelaxed_fgw.zip
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diff --git a/‎master/_downloads/8fa63bc21a9d5f96d8cbb59966610011/plot_compute_emd.zip
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diff --git a/‎master/_downloads/8fea9484c05c48b6245f1eb28345644b/plot_gnn_TFGW.zip
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diff --git a/‎master/_downloads/903e7b409a49e7d781232078604898bf/plot_variance.zip
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diff --git a/‎master/_downloads/97aed841d11bc7d4a590b2d2c242a574/plot_wass2_gan_torch.zip
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diff --git a/‎master/_downloads/993b8d5b2ad8de778ee0ae6506032733/plot_quantized_gromov_wasserstein.zip
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diff --git a/‎master/_downloads/9ce5f5c629d26a28be8eee15e86089de/plot_debiased_barycenter.zip
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diff --git a/‎master/_downloads/a2a9304a2c53b5d5d9e76c47f0086813/plot_otda_semi_supervised.zip
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diff --git a/‎master/_downloads/a63a99e8f7f99acea43a0d90dac8154a/plot_gromov_barycenter.zip
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diff --git a/‎master/_modules/ot/lp/solver_1d.html
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+14-9
diff --git a/‎master/_sources/auto_examples/backends/plot_dual_ot_pytorch.rst.txt
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@@ -245,7 +245,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 <span class="sd">    - x_a and x_b are the samples</span>
 <span class="sd">    - a and b are the sample weights</span>
 
-<span class="sd">    When &#39;minkowski&#39; is used as a metric, :math:`d(x, y) = |x - y|^p`.</span>
+<span class="sd">    This implementation only supports metrics</span>
+<span class="sd">    of the form :math:`d(x, y) = |x - y|^p`.</span>
 
 <span class="sd">    Uses the algorithm detailed in [1]_</span>
 
@@ -260,9 +261,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 <span class="sd">    b : (nt,) ndarray, float64, optional</span>
 <span class="sd">        Target histogram (default is uniform weight)</span>
 <span class="sd">    metric: str, optional (default=&#39;sqeuclidean&#39;)</span>
-<span class="sd">        Metric to be used. Only strings listed in :func:`ot.dist` are accepted.</span>
-<span class="sd">        Due to implementation details, this function runs faster when</span>
-<span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;` metrics are used.</span>
+<span class="sd">        Metric to be used. Only works with either of the strings</span>
+<span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;`.</span>
 <span class="sd">    p: float, optional (default=1.0)</span>
 <span class="sd">         The p-norm to apply for if metric=&#39;minkowski&#39;</span>
 <span class="sd">    dense: boolean, optional (default=True)</span>
@@ -327,6 +327,12 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
         <span class="s2">&quot;emd_1d should only be used with monodimensional data&quot;</span>
     <span class="k">assert</span> <span class="p">(</span><span class="n">x_b</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">x_b</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">x_b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">),</span> \
         <span class="s2">&quot;emd_1d should only be used with monodimensional data&quot;</span>
+    <span class="k">if</span> <span class="n">metric</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;sqeuclidean&#39;</span><span class="p">,</span> <span class="s1">&#39;minkowski&#39;</span><span class="p">,</span> <span class="s1">&#39;cityblock&#39;</span><span class="p">,</span> <span class="s1">&#39;euclidean&#39;</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;Solver for EMD in 1d only supports metrics &quot;</span> <span class="o">+</span>
+            <span class="s2">&quot;from the following list: &quot;</span> <span class="o">+</span>
+            <span class="s2">&quot;`[&#39;sqeuclidean&#39;, &#39;minkowski&#39;, &#39;cityblock&#39;, &#39;euclidean&#39;]`&quot;</span>
+        <span class="p">)</span>
 
     <span class="c1"># if empty array given then use uniform distributions</span>
     <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">a</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
@@ -396,7 +402,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 <span class="sd">    - x_a and x_b are the samples</span>
 <span class="sd">    - a and b are the sample weights</span>
 
-<span class="sd">    When &#39;minkowski&#39; is used as a metric, :math:`d(x, y) = |x - y|^p`.</span>
+<span class="sd">    This implementation only supports metrics</span>
+<span class="sd">    of the form :math:`d(x, y) = |x - y|^p`.</span>
 
 <span class="sd">    Uses the algorithm detailed in [1]_</span>
 
@@ -411,10 +418,8 @@ <h1>Source code for ot.lp.solver_1d</h1><div class="highlight"><pre>
 <span class="sd">    b : (nt,) ndarray, float64, optional</span>
 <span class="sd">        Target histogram (default is uniform weight)</span>
 <span class="sd">    metric: str, optional (default=&#39;sqeuclidean&#39;)</span>
-<span class="sd">        Metric to be used. Only strings listed in :func:`ot.dist` are accepted.</span>
-<span class="sd">        Due to implementation details, this function runs faster when</span>
-<span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;` metrics</span>
-<span class="sd">        are used.</span>
+<span class="sd">        Metric to be used. Only works with either of the strings</span>
+<span class="sd">        `&#39;sqeuclidean&#39;`, `&#39;minkowski&#39;`, `&#39;cityblock&#39;`,  or `&#39;euclidean&#39;`.</span>
 <span class="sd">    p: float, optional (default=1.0)</span>
 <span class="sd">         The p-norm to apply for if metric=&#39;minkowski&#39;</span>
 <span class="sd">    dense: boolean, optional (default=True)</span>