Update documentation

docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt

@@ -554,9 +554,9 @@ randomization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 3.161 seconds)
+   **Total running time of the script:** (1 minutes 7.006 seconds)
 
-**Estimated memory usage:**  691 MB
+**Estimated memory usage:**  708 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_2D_simulation_example.py:

docs/_sources/auto_examples/plot_dcrt_example.rst.txt

@@ -162,9 +162,9 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 1.529 seconds)
+   **Total running time of the script:** (1 minutes 1.639 seconds)
 
-**Estimated memory usage:**  623 MB
+**Estimated memory usage:**  640 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_dcrt_example.py:

docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt

@@ -500,9 +500,9 @@ Analyze the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 7.601 seconds)
+   **Total running time of the script:** (0 minutes 8.555 seconds)
 
-**Estimated memory usage:**  609 MB
+**Estimated memory usage:**  626 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_diabetes_variable_importance_example.py:

docs/_sources/auto_examples/plot_fmri_data_example.rst.txt

@@ -184,7 +184,7 @@ You may choose a subject in [1, 2, 3, 4, 5, 6]. By default subject=2.
     Downloading data from http://data.pymvpa.org/datasets/haxby2001/MD5SUMS ...
      ...done. (0 seconds, 0 min)
     Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...
-    Downloaded 115441664 of 291168628 bytes (39.6%,    1.5s remaining)    Downloaded 262291456 of 291168628 bytes (90.1%,    0.2s remaining) ...done. (2 seconds, 0 min)
+    Downloaded 16596992 of 291168628 bytes (5.7%,   16.8s remaining)    Downloaded 55353344 of 291168628 bytes (19.0%,    8.6s remaining)    Downloaded 94068736 of 291168628 bytes (32.3%,    6.4s remaining)    Downloaded 133029888 of 291168628 bytes (45.7%,    4.8s remaining)    Downloaded 172138496 of 291168628 bytes (59.1%,    3.5s remaining)    Downloaded 211009536 of 291168628 bytes (72.5%,    2.3s remaining)    Downloaded 249634816 of 291168628 bytes (85.7%,    1.2s remaining)    Downloaded 288653312 of 291168628 bytes (99.1%,    0.1s remaining) ...done. (8 seconds, 0 min)
     Extracting data from /home/runner/nilearn_data/haxby2001/def37a305edfda829916fa14c9ea08f8/subj2-2010.01.14.tar.gz..... done.
     /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/nilearn/image/resampling.py:492: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected.
       warnings.warn(
@@ -284,7 +284,7 @@ Now, we compute p-values thanks to permutation tests applied to
  .. code-block:: none
 
     [Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.6s
-    [Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.3s
+    [Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.6s
 
 
 
@@ -365,7 +365,7 @@ However you might benefit from clustering randomization taking
  .. code-block:: none
 
     [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
-    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   32.3s finished
+    [Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   35.2s finished
 
 
 
@@ -610,9 +610,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 22.789 seconds)
+   **Total running time of the script:** (1 minutes 34.855 seconds)
 
-**Estimated memory usage:**  3157 MB
+**Estimated memory usage:**  3267 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_fmri_data_example.py:

docs/_sources/auto_examples/plot_knockoff_aggregation.rst.txt

@@ -205,9 +205,9 @@ Imports needed for this script
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (5 minutes 23.029 seconds)
+   **Total running time of the script:** (5 minutes 34.048 seconds)
 
-**Estimated memory usage:**  752 MB
+**Estimated memory usage:**  763 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_knockoff_aggregation.py:

docs/_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**08:58.109** total execution time for 5 files **from auto_examples**:
+**09:26.105** total execution time for 5 files **from auto_examples**:
 
 .. container::
 
@@ -33,17 +33,17 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_knockoff_aggregation.py` (``plot_knockoff_aggregation.py``)
-     - 05:23.029
-     - 752.0
+     - 05:34.048
+     - 763.3
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``plot_fmri_data_example.py``)
-     - 01:22.789
-     - 3157.1
+     - 01:34.855
+     - 3266.9
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:03.161
-     - 691.2
+     - 01:07.006
+     - 707.8
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``plot_dcrt_example.py``)
-     - 01:01.529
-     - 622.9
+     - 01:01.639
+     - 640.1
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``plot_diabetes_variable_importance_example.py``)
-     - 00:07.601
-     - 608.7
+     - 00:08.555
+     - 625.5

docs/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**08:58.109** total execution time for 5 files **from all galleries**:
+**09:26.105** total execution time for 5 files **from all galleries**:
 
 .. container::
 
@@ -33,17 +33,17 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_knockoff_aggregation.py` (``../examples/plot_knockoff_aggregation.py``)
-     - 05:23.029
-     - 752.0
+     - 05:34.048
+     - 763.3
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``../examples/plot_fmri_data_example.py``)
-     - 01:22.789
-     - 3157.1
+     - 01:34.855
+     - 3266.9
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:03.161
-     - 691.2
+     - 01:07.006
+     - 707.8
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``../examples/plot_dcrt_example.py``)
-     - 01:01.529
-     - 622.9
+     - 01:01.639
+     - 640.1
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``../examples/plot_diabetes_variable_importance_example.py``)
-     - 00:07.601
-     - 608.7
+     - 00:08.555
+     - 625.5

docs/auto_examples/plot_2D_simulation_example.html

@@ -475,8 +475,8 @@ <h2>Analysis of the results<a class="headerlink" href="#analysis-of-the-results"
 conservative. In practice, Type-1 Error guarantees seem to hold
 for a lower spatial tolerance. This is an additional benefit of clustering
 randomization.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 3.161 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  691 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 7.006 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  708 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-2d-simulation-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7d2770a07fbe419760c9ac177df4f69e/plot_2D_simulation_example.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_2D_simulation_example.ipynb</span></code></a></p>

docs/auto_examples/plot_dcrt_example.html

@@ -210,8 +210,8 @@ <h2>Plotting the comparison<a class="headerlink" href="#plotting-the-comparison"
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_dcrt_example_001.png" srcset="../_images/sphx_glr_plot_dcrt_example_001.png" alt="Type-I Error, Power" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 1.529 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  623 MB</p>
+<img src="../_images/sphx_glr_plot_dcrt_example_001.png" srcset="../_images/sphx_glr_plot_dcrt_example_001.png" alt="Type-I Error, Power" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 1.639 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  640 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-dcrt-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/642b61154cca48af8e3feb505b920e16/plot_dcrt_example.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_dcrt_example.ipynb</span></code></a></p>

docs/auto_examples/plot_diabetes_variable_importance_example.html

@@ -476,8 +476,8 @@ <h2>Analyze the results<a class="headerlink" href="#analyze-the-results" title="
   ax.set_xticklabels(diabetes.feature_names)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.601 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  609 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.555 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  626 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-diabetes-variable-importance-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a70e28075a283d5e3fe675ced733c459/plot_diabetes_variable_importance_example.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_diabetes_variable_importance_example.ipynb</span></code></a></p>

docs/auto_examples/plot_fmri_data_example.html

@@ -252,8 +252,14 @@ <h2>Gathering and preprocessing Haxby dataset for a given subject<a class="heade
  ...done. (0 seconds, 0 min)
 Downloading data from http://data.pymvpa.org/datasets/haxby2001/subj2-2010.01.14.tar.gz ...
 
-Downloaded 115441664 of 291168628 bytes (39.6%,    1.5s remaining)
-Downloaded 262291456 of 291168628 bytes (90.1%,    0.2s remaining) ...done. (2 seconds, 0 min)
+Downloaded 16596992 of 291168628 bytes (5.7%,   16.8s remaining)
+Downloaded 55353344 of 291168628 bytes (19.0%,    8.6s remaining)
+Downloaded 94068736 of 291168628 bytes (32.3%,    6.4s remaining)
+Downloaded 133029888 of 291168628 bytes (45.7%,    4.8s remaining)
+Downloaded 172138496 of 291168628 bytes (59.1%,    3.5s remaining)
+Downloaded 211009536 of 291168628 bytes (72.5%,    2.3s remaining)
+Downloaded 249634816 of 291168628 bytes (85.7%,    1.2s remaining)
+Downloaded 288653312 of 291168628 bytes (99.1%,    0.1s remaining) ...done. (8 seconds, 0 min)
 Extracting data from /home/runner/nilearn_data/haxby2001/def37a305edfda829916fa14c9ea08f8/subj2-2010.01.14.tar.gz..... done.
 /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/nilearn/image/resampling.py:492: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected.
   warnings.warn(
@@ -307,7 +313,7 @@ <h2>Making the inference with several algorithms<a class="headerlink" href="#mak
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    1.6s
-[Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.3s
+[Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    6.6s
 </pre></div>
 </div>
 <p>Now, let us run the algorithm introduced by Gaonkar et al. (c.f. References).
@@ -343,7 +349,7 @@ <h2>Making the inference with several algorithms<a class="headerlink" href="#mak
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
-[Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   32.3s finished
+[Parallel(n_jobs=2)]: Done   5 out of   5 | elapsed:   35.2s finished
 </pre></div>
 </div>
 </section>
@@ -466,8 +472,8 @@ <h2>Analysis of the results<a class="headerlink" href="#analysis-of-the-results"
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 22.789 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  3157 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 34.855 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  3267 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-fmri-data-example-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/931385a6992917f918857d6a3ee9f780/plot_fmri_data_example.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_fmri_data_example.ipynb</span></code></a></p>

docs/auto_examples/plot_knockoff_aggregation.html

@@ -287,8 +287,8 @@ <h2>Imports needed for this script<a class="headerlink" href="#imports-needed-fo
 <li><img src="../_images/sphx_glr_plot_knockoff_aggregation_001.png" srcset="../_images/sphx_glr_plot_knockoff_aggregation_001.png" alt="FDR = 0.1, n = 500, p = 500, rho = 0.7" class = "sphx-glr-multi-img"/></li>
 <li><img src="../_images/sphx_glr_plot_knockoff_aggregation_002.png" srcset="../_images/sphx_glr_plot_knockoff_aggregation_002.png" alt="FDR = 0.1, n = 500, p = 500, rho = 0.7" class = "sphx-glr-multi-img"/></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (5 minutes 23.029 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  752 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (5 minutes 34.048 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  763 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-knockoff-aggregation-py">
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/8635bd4b58b2828c710e4331f35d14f6/plot_knockoff_aggregation.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_knockoff_aggregation.ipynb</span></code></a></p>

docs/auto_examples/sg_execution_times.html

@@ -110,7 +110,7 @@
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
-<p><strong>08:58.109</strong> total execution time for 5 files <strong>from auto_examples</strong>:</p>
+<p><strong>09:26.105</strong> total execution time for 5 files <strong>from auto_examples</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" />
@@ -132,24 +132,24 @@
 </thead>
 <tbody>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_knockoff_aggregation.html#sphx-glr-auto-examples-plot-knockoff-aggregation-py"><span class="std std-ref">Knockoff aggregation on simulated data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_knockoff_aggregation.py</span></code>)</p></td>
-<td><p>05:23.029</p></td>
-<td><p>752.0</p></td>
+<td><p>05:34.048</p></td>
+<td><p>763.3</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_fmri_data_example.html#sphx-glr-auto-examples-plot-fmri-data-example-py"><span class="std std-ref">Support recovery on fMRI data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_fmri_data_example.py</span></code>)</p></td>
-<td><p>01:22.789</p></td>
-<td><p>3157.1</p></td>
+<td><p>01:34.855</p></td>
+<td><p>3266.9</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_2D_simulation_example.html#sphx-glr-auto-examples-plot-2d-simulation-example-py"><span class="std std-ref">Support recovery on simulated data (2D)</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_2D_simulation_example.py</span></code>)</p></td>
-<td><p>01:03.161</p></td>
-<td><p>691.2</p></td>
+<td><p>01:07.006</p></td>
+<td><p>707.8</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_dcrt_example.html#sphx-glr-auto-examples-plot-dcrt-example-py"><span class="std std-ref">Distilled Conditional Randomization Test (dCRT) using Lasso vs Random Forest learners</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dcrt_example.py</span></code>)</p></td>
-<td><p>01:01.529</p></td>
-<td><p>622.9</p></td>
+<td><p>01:01.639</p></td>
+<td><p>640.1</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_diabetes_variable_importance_example.html#sphx-glr-auto-examples-plot-diabetes-variable-importance-example-py"><span class="std std-ref">Variable Importance on diabetes dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_diabetes_variable_importance_example.py</span></code>)</p></td>
-<td><p>00:07.601</p></td>
-<td><p>608.7</p></td>
+<td><p>00:08.555</p></td>
+<td><p>625.5</p></td>
 </tr>
 </tbody>
 </table>