Deploying to gh-pages from @ c8a53d9 🚀

Author: EtienneCmb

_downloads/007eb65766f3b4811d99ff5b36a7b49b/plot_ml_vs_it.ipynb

@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "def it(x, y):\n    model = GradientOinfo(x, y, verbose=False)\n    return model.fit(minsize=3, maxsize=3)\n\n\nfig, axs = plt.subplots(2, 1, figsize=(8, 12), sharex=True, sharey=True)\naxs = np.ravel(axs)\nplt.sca(axs[0])\nplt.plot(it(x_red, y_red).squeeze())\nplt.ylabel(\"$\\partial \\Omega_{[X_{1}, ..., X_{N}]}$ [Bits]\")\nplt.title(\"Redundant interactions\", fontweight=\"bold\")\nplt.sca(axs[1])\nplt.plot(it(x_syn, y_syn).squeeze())\nplt.ylabel(\"$\\partial \\Omega_{[X_{1}, ..., X_{N}]}$ [Bits]\")\nplt.title(\"Synergistic interactions\", fontweight=\"bold\")\nplt.show()"
+        "def it(x, y):\n    model = GradientOinfo(x, y, verbose=False)\n    return model.fit(minsize=3, maxsize=3)\n\n\nfig, axs = plt.subplots(2, 1, figsize=(8, 12), sharex=True, sharey=True)\naxs = np.ravel(axs)\nplt.sca(axs[0])\nplt.plot(it(x_red, y_red).squeeze())\nplt.ylabel(r\"$\\partial \\Omega_{[X_{1}, ..., X_{N}]}$ [Bits]\")\nplt.title(\"Redundant interactions\", fontweight=\"bold\")\nplt.sca(axs[1])\nplt.plot(it(x_syn, y_syn).squeeze())\nplt.ylabel(r\"$\\partial \\Omega_{[X_{1}, ..., X_{N}]}$ [Bits]\")\nplt.title(\"Synergistic interactions\", fontweight=\"bold\")\nplt.show()"
       ]
     },
     {

_downloads/071683bb9f5d321841ee9dd62eee27a4/plot_sim_red_syn.zip

@@ -185,11 +185,11 @@ def it(x, y):
 axs = np.ravel(axs)
 plt.sca(axs[0])
 plt.plot(it(x_red, y_red).squeeze())
-plt.ylabel("$\partial \Omega_{[X_{1}, ..., X_{N}]}$ [Bits]")
+plt.ylabel(r"$\partial \Omega_{[X_{1}, ..., X_{N}]}$ [Bits]")
 plt.title("Redundant interactions", fontweight="bold")
 plt.sca(axs[1])
 plt.plot(it(x_syn, y_syn).squeeze())
-plt.ylabel("$\partial \Omega_{[X_{1}, ..., X_{N}]}$ [Bits]")
+plt.ylabel(r"$\partial \Omega_{[X_{1}, ..., X_{N}]}$ [Bits]")
 plt.title("Synergistic interactions", fontweight="bold")
 plt.show()
 

_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -591,7 +591,7 @@ <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>
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+<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 20.483 seconds)</p>
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_downloads/17dd9833b54c0c0905f696d14e67b05d/plot_bootstrapping.zip

@@ -567,7 +567,7 @@ <h2>Comparison of estimators<a class="headerlink" href="#comparison-of-estimator
 <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_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 32.398 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 32.304 seconds)</p>
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_downloads/1e58f0eaeee8456990c2959bfc1babe9/plot_oinfo.zip

@@ -564,7 +564,7 @@ <h2>Definition of entropy estimators<a class="headerlink" href="#definition-of-e
 <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_entropy_high_dimensional_001.png" srcset="../../_images/sphx_glr_plot_entropy_high_dimensional_001.png" alt="Comparison of entropy estimators when the data is high-dimensional, Mode: dense, Mode: sparse" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 24.189 seconds)</p>
+<img src="../../_images/sphx_glr_plot_entropy_high_dimensional_001.png" srcset="../../_images/sphx_glr_plot_entropy_high_dimensional_001.png" alt="Comparison of entropy estimators when the data is high-dimensional, Mode: dense, Mode: sparse" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 24.201 seconds)</p>
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_downloads/31d2b25c7ef60a5789246c25d83787c6/plot_ml_vs_it.zip

@@ -556,7 +556,7 @@ <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 20.835 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 20.714 seconds)</p>
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 <p><a class="reference download internal" download="" href="../../_downloads/f14e92090ebeb63084124f7809315b90/plot_mi.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_mi.ipynb</span></code></a></p>

_downloads/34da59110fbcc6d2797595ef80bd7328/plot_inspect_results.zip

@@ -404,7 +404,7 @@ <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>02:08.116</strong> total execution time for 6 files <strong>from auto_examples/it</strong>:</p>
+<p><strong>02:07.944</strong> total execution time for 6 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" />
@@ -426,27 +426,27 @@ <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>00:32.398</p></td>
+<td><p>00:32.304</p></td>
 <td><p>0.0</p></td>
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 <tr class="row-odd"><td><p><a class="reference internal" href="plot_mi_high_dimensional.html#sphx-glr-auto-examples-it-plot-mi-high-dimensional-py"><span class="std std-ref">Comparison of MI estimators with high-dimensional data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mi_high_dimensional.py</span></code>)</p></td>
 <td><p>00:30.009</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_entropy_high_dimensional.html#sphx-glr-auto-examples-it-plot-entropy-high-dimensional-py"><span class="std std-ref">Comparison of entropy estimators with high-dimensional data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_entropy_high_dimensional.py</span></code>)</p></td>
-<td><p>00:24.189</p></td>
+<td><p>00:24.201</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><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>00:20.835</p></td>
+<td><p>00:20.714</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-even"><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>00:20.450</p></td>
+<td><p>00:20.483</p></td>
 <td><p>0.0</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="tuto_core.html#sphx-glr-auto-examples-it-tuto-core-py"><span class="std std-ref">Introduction to core information theoretical metrics</span></a> (<code class="docutils literal notranslate"><span class="pre">tuto_core.py</span></code>)</p></td>
-<td><p>00:00.235</p></td>
+<td><p>00:00.233</p></td>
 <td><p>0.0</p></td>
 </tr>
 </tbody>

_downloads/4edd620b8310fd78193d26c454d1ca13/plot_rsi.zip

@@ -494,7 +494,7 @@ <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>
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_downloads/523f753ee18fc36accf251c60a59f685/tuto_jax.zip

@@ -471,11 +471,11 @@ <h2>Spatial spreading : the problem with the O-information<a class="headerlink"
 </pre></div>
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- 20%|██        | Oinfo (3):  1/5 [00:00&lt;00:01,    2.83it/s]
- 40%|████      | Oinfo (4):  2/5 [00:00&lt;00:00,    3.48it/s]
- 60%|██████    | Oinfo (5):  3/5 [00:01&lt;00:00,    2.77it/s]
- 80%|████████  | Oinfo (6):  4/5 [00:01&lt;00:00,    2.59it/s]
-100%|██████████| Oinfo (7):  5/5 [00:01&lt;00:00,    2.65it/s]
+ 20%|██        | Oinfo (3):  1/5 [00:00&lt;00:01,    2.74it/s]
+ 40%|████      | Oinfo (4):  2/5 [00:00&lt;00:00,    3.42it/s]
+ 60%|██████    | Oinfo (5):  3/5 [00:01&lt;00:00,    2.75it/s]
+ 80%|████████  | Oinfo (6):  4/5 [00:01&lt;00:00,    2.55it/s]
+100%|██████████| Oinfo (7):  5/5 [00:01&lt;00:00,    2.60it/s]
 </pre></div>
 </div>
 <p>Now we can plot the landscape. This landscape show the values of HOI for
@@ -528,19 +528,19 @@ <h2>Multiplet isolation using the Topological Information<a class="headerlink" h
 </pre></div>
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- 57%|█████▋    | Entropy H(4):  4/7 [00:00&lt;00:00,    8.20it/s]
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- 86%|████████▌ | Entropy H(6):  6/7 [00:00&lt;00:00,    8.77it/s]
-100%|██████████| Entropy H(7):  7/7 [00:00&lt;00:00,    9.00it/s]
+ 14%|█▍        | Entropy H(1):  1/7 [00:00&lt;00:00,    8.44it/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,    8.27it/s]
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+ 86%|████████▌ | Entropy H(6):  6/7 [00:00&lt;00:00,    9.16it/s]
+100%|██████████| Entropy H(7):  7/7 [00:00&lt;00:00,    9.37it/s]
 
 
 Mutual information:   0%|          | 0/99 [00:00&lt;?, ?it/s]
 
 
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+Mutual information: 100%|██████████| 99/99 [00:00&lt;00:00, 29289.42it/s]
 </pre></div>
 </div>
 <p>Again, we can plot the landscape. This time, as we can see, there’s no more
@@ -574,7 +574,7 @@ <h2>Multiplet isolation using the Topological Information<a class="headerlink" h
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