Deploying to gh-pages from @ 7f5e6d0 🚀

_downloads/2d62e710e011551b615a935858a62b71/plot_oinfo.ipynb

@@ -115,7 +115,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### S-information\nFinally, the S-information is defined as the sum of the TC and DTC :\n\n"
+        "### S-information\nFinally, the S-information is defined as the sum of the TC and DTC :\n\n\\begin{align}\\Omega(X^{n})  &=  TC(X^{n}) + DTC(X^{n}) \\\\\n                   &=  nH(X^{n}) + \\sum_{j=1}^{n} [H(X_{j}) + H(\n                   X_{-j}^{n})]\\end{align}\n\n"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "# .. math::\n#     \\Omega(X^{n})  &=  TC(X^{n}) + DTC(X^{n}) \\\\\n#                    &=  nH(X^{n}) + \\sum_{j=1}^{n} [H(X_{j}) + H(\n#                    X_{-j}^{n})]\n\n# compute hoi using the S-information :\nmodel = Sinfo(x)\nhoi = model.fit(method=\"gc\", minsize=3, maxsize=3)\n\n# get the multiplets with largest values of hoi\nprint(get_nbest_mult(hoi, model))"
+        "# compute hoi using the S-information :\nmodel = Sinfo(x)\nhoi = model.fit(method=\"gc\", minsize=3, maxsize=3)\n\n# get the multiplets with largest values of hoi\nprint(get_nbest_mult(hoi, model))"
       ]
     },
     {

_downloads/f089696c3572cc479d2da98315c1bf83/plot_oinfo.py

@@ -145,7 +145,7 @@
 # S-information
 # ^^^^^^^^^^^^^
 # Finally, the S-information is defined as the sum of the TC and DTC :
-
+#
 # .. math::
 #     \Omega(X^{n})  &=  TC(X^{n}) + DTC(X^{n}) \\
 #                    &=  nH(X^{n}) + \sum_{j=1}^{n} [H(X_{j}) + H(

auto_examples/it/plot_entropies.html

@@ -606,7 +606,7 @@ <h2>Entropy of data sampled from an exponential distribution<a class="headerlink
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../../_images/sphx_glr_plot_entropies_003.png" srcset="../../_images/sphx_glr_plot_entropies_003.png" alt="Comparison of entropy estimators when the data are sampled from a exponential distribution" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.860 seconds)</p>
+<img src="../../_images/sphx_glr_plot_entropies_003.png" srcset="../../_images/sphx_glr_plot_entropies_003.png" alt="Comparison of entropy estimators when the data are sampled from a exponential distribution" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.910 seconds)</p>
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auto_examples/it/plot_entropies_mvar.html

@@ -581,7 +581,7 @@ <h2>Comparison of estimators<a class="headerlink" href="#comparison-of-estimator
 <span class="n">plot</span><span class="p">(</span><span class="n">h_x</span><span class="p">,</span> <span class="n">h_theoric</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
 </pre></div>
 </div>
-<img src="../../_images/sphx_glr_plot_entropies_mvar_001.png" srcset="../../_images/sphx_glr_plot_entropies_mvar_001.png" alt="2-dimensional multivariate normal, 4-dimensional multivariate normal, 6-dimensional multivariate normal, 8-dimensional multivariate normal" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 26.034 seconds)</p>
+<img src="../../_images/sphx_glr_plot_entropies_mvar_001.png" srcset="../../_images/sphx_glr_plot_entropies_mvar_001.png" alt="2-dimensional multivariate normal, 4-dimensional multivariate normal, 6-dimensional multivariate normal, 8-dimensional multivariate normal" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 26.454 seconds)</p>
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auto_examples/it/plot_mi.html

@@ -564,7 +564,7 @@ <h2>MI of data sampled from normal distribution<a class="headerlink" href="#mi-o
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../../_images/sphx_glr_plot_mi_001.png" srcset="../../_images/sphx_glr_plot_mi_001.png" alt="Comparison of MI estimators when the data are sampled from a normal distribution" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.125 seconds)</p>
+<img src="../../_images/sphx_glr_plot_mi_001.png" srcset="../../_images/sphx_glr_plot_mi_001.png" alt="Comparison of MI estimators when the data are sampled from a normal distribution" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 17.317 seconds)</p>
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auto_examples/it/plot_tutorial_core.html

@@ -510,7 +510,7 @@ <h2>Mutual information<a class="headerlink" href="#mutual-information" title="Li
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Mutual information between x and y: 0.00
 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.232 seconds)</p>
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 <p><a class="reference download internal" download="" href="../../_downloads/5b339cefcfab9a7df160c5f43bed58df/plot_tutorial_core.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tutorial_core.ipynb</span></code></a></p>

auto_examples/it/sg_execution_times.html

@@ -420,7 +420,7 @@ <h1>Computation times</h1>
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-it-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
-<p><strong>01:01.247</strong> total execution time for 4 files <strong>from auto_examples/it</strong>:</p>
+<p><strong>01:01.914</strong> total execution time for 4 files <strong>from auto_examples/it</strong>:</p>
 <div class="docutils container">
 <style scoped>
 <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -442,19 +442,19 @@ <h1>Computation times</h1>
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_entropies_mvar.html#sphx-glr-auto-examples-it-plot-entropies-mvar-py"><span class="std std-ref">Comparison of entropy estimators for a multivariate normal</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_entropies_mvar.py</span></code>)</p></td>
-<td><p>00:26.034</p></td>
+<td><p>00:26.454</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_entropies.html#sphx-glr-auto-examples-it-plot-entropies-py"><span class="std std-ref">Comparison of entropy estimators for various distributions</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_entropies.py</span></code>)</p></td>
-<td><p>00:17.860</p></td>
+<td><p>00:17.910</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_mi.html#sphx-glr-auto-examples-it-plot-mi-py"><span class="std std-ref">Comparison of mutual-information estimators</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mi.py</span></code>)</p></td>
-<td><p>00:17.125</p></td>
+<td><p>00:17.317</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_tutorial_core.html#sphx-glr-auto-examples-it-plot-tutorial-core-py"><span class="std std-ref">Introduction to core information theoretical metrics</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tutorial_core.py</span></code>)</p></td>
-<td><p>00:00.228</p></td>
+<td><p>00:00.232</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>

auto_examples/metrics/plot_infotopo.html

@@ -487,8 +487,8 @@ <h2>Spatial spreading : the problem with the O-information<a class="headerlink"
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  0%|          |  0/5 [00:00&lt;?,       ?it/s]
  20%|██        | Oinfo (3):  1/5 [00:00&lt;00:01,    2.83it/s]
- 40%|████      | Oinfo (4):  2/5 [00:00&lt;00:00,    3.45it/s]
- 60%|██████    | Oinfo (5):  3/5 [00:01&lt;00:00,    2.81it/s]
+ 40%|████      | Oinfo (4):  2/5 [00:00&lt;00:00,    3.40it/s]
+ 60%|██████    | Oinfo (5):  3/5 [00:01&lt;00:00,    2.78it/s]
  80%|████████  | Oinfo (6):  4/5 [00:01&lt;00:00,    2.57it/s]
 100%|██████████| Oinfo (7):  5/5 [00:01&lt;00:00,    2.63it/s]
 </pre></div>
@@ -544,16 +544,16 @@ <h2>Multiplet isolation using the Topological Information<a class="headerlink" h
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  0%|          |  0/7 [00:00&lt;?,       ?it/s]
  14%|█▍        | Entropy H(1):  1/7 [00:00&lt;00:00,    8.35it/s]
- 29%|██▊       | Entropy H(2):  2/7 [00:00&lt;00:00,    8.18it/s]
- 43%|████▎     | Entropy H(3):  3/7 [00:00&lt;00:00,    8.28it/s]
- 57%|█████▋    | Entropy H(4):  4/7 [00:00&lt;00:00,    8.71it/s]
- 71%|███████▏  | Entropy H(5):  5/7 [00:00&lt;00:00,    8.96it/s]
- 86%|████████▌ | Entropy H(6):  6/7 [00:00&lt;00:00,    9.17it/s]
-100%|██████████| Entropy H(7):  7/7 [00:00&lt;00:00,    9.37it/s]
+ 29%|██▊       | Entropy H(2):  2/7 [00:00&lt;00:00,    8.22it/s]
+ 43%|████▎     | Entropy H(3):  3/7 [00:00&lt;00:00,    8.32it/s]
+ 57%|█████▋    | Entropy H(4):  4/7 [00:00&lt;00:00,    8.72it/s]
+ 71%|███████▏  | Entropy H(5):  5/7 [00:00&lt;00:00,    8.98it/s]
+ 86%|████████▌ | Entropy H(6):  6/7 [00:00&lt;00:00,    9.19it/s]
+100%|██████████| Entropy H(7):  7/7 [00:00&lt;00:00,    9.42it/s]
 
 
   0%|          |  0/99 [00:00&lt;?,       ?it/s]
-100%|██████████| Mutual information:  99/99 [00:00&lt;00:00, 149097.34it/s]
+100%|██████████| Mutual information:  99/99 [00:00&lt;00:00, 134598.41it/s]
 </pre></div>
 </div>
 <p>Again, we can plot the landscape. This time, as we can see, there’s no more
@@ -587,7 +587,7 @@ <h2>Multiplet isolation using the Topological Information<a class="headerlink" h
 9     33      3 -0.090897     [3, 5, 6]
 </pre></div>
 </div>
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+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 3.641 seconds)</p>
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