Update documentation

docs/_sources/auto_examples/plot_2D_simulation_example.rst.txt

@@ -554,7 +554,7 @@ randomization.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 9.382 seconds)
+   **Total running time of the script:** (1 minutes 7.044 seconds)
 
 **Estimated memory usage:**  705 MB
 

docs/_sources/auto_examples/plot_dcrt_example.rst.txt

@@ -162,7 +162,7 @@ Plotting the comparison
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 1.772 seconds)
+   **Total running time of the script:** (1 minutes 1.764 seconds)
 
 **Estimated memory usage:**  643 MB
 

docs/_sources/auto_examples/plot_diabetes_variable_importance_example.rst.txt

@@ -491,7 +491,7 @@ Analyze the results
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 8.364 seconds)
+   **Total running time of the script:** (0 minutes 8.271 seconds)
 
 **Estimated memory usage:**  626 MB
 

docs/_sources/auto_examples/plot_fmri_data_example.rst.txt

@@ -180,11 +180,11 @@ You may choose a subject in [1, 2, 3, 4, 5, 6]. By default subject=2.
     Dataset created in /home/runner/nilearn_data/haxby2001
 
     Downloading data from https://www.nitrc.org/frs/download.php/7868/mask.nii.gz ...
-     ...done. (1 seconds, 0 min)
+     ...done. (0 seconds, 0 min)
     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 15917056 of 291168628 bytes (5.5%,   17.6s remaining)    Downloaded 52502528 of 291168628 bytes (18.0%,    9.2s remaining)    Downloaded 91537408 of 291168628 bytes (31.4%,    6.6s remaining)    Downloaded 130457600 of 291168628 bytes (44.8%,    5.0s remaining)    Downloaded 169959424 of 291168628 bytes (58.4%,    3.6s remaining)    Downloaded 209125376 of 291168628 bytes (71.8%,    2.4s remaining)    Downloaded 248020992 of 291168628 bytes (85.2%,    1.2s remaining)    Downloaded 287047680 of 291168628 bytes (98.6%,    0.1s remaining) ...done. (8 seconds, 0 min)
+    Downloaded 110182400 of 291168628 bytes (37.8%,    1.6s remaining)    Downloaded 249692160 of 291168628 bytes (85.8%,    0.3s remaining) ...done. (2 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:    6.4s
 
 
 
@@ -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:   33.4s finished
 
 
 
@@ -610,9 +610,9 @@ spurious discoveries.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 29.290 seconds)
+   **Total running time of the script:** (1 minutes 25.117 seconds)
 
-**Estimated memory usage:**  3331 MB
+**Estimated memory usage:**  3349 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 31.198 seconds)
+   **Total running time of the script:** (5 minutes 27.443 seconds)
 
-**Estimated memory usage:**  782 MB
+**Estimated memory usage:**  769 MB
 
 
 .. _sphx_glr_download_auto_examples_plot_knockoff_aggregation.py:

docs/_sources/auto_examples/plot_variable_importance_classif.rst.txt

@@ -176,7 +176,7 @@ Visualize the data
  .. code-block:: none
 
 
-    [<matplotlib.axis.YTick object at 0x7f2e590de7b0>, <matplotlib.axis.YTick object at 0x7f2e590bc710>, <matplotlib.axis.YTick object at 0x7f2e59034a70>, <matplotlib.axis.YTick object at 0x7f2d611c46e0>, <matplotlib.axis.YTick object at 0x7f2d611c7c50>, <matplotlib.axis.YTick object at 0x7f2d611c4950>, <matplotlib.axis.YTick object at 0x7f2d611c7110>, <matplotlib.axis.YTick object at 0x7f2d481c4d10>, <matplotlib.axis.YTick object at 0x7f2d611c5430>, <matplotlib.axis.YTick object at 0x7f2d611c60c0>]
+    [<matplotlib.axis.YTick object at 0x7fcb297db2c0>, <matplotlib.axis.YTick object at 0x7fcb297daf90>, <matplotlib.axis.YTick object at 0x7fcc17b51370>, <matplotlib.axis.YTick object at 0x7fcb299c8e90>, <matplotlib.axis.YTick object at 0x7fcb299c9ca0>, <matplotlib.axis.YTick object at 0x7fcb299c8170>, <matplotlib.axis.YTick object at 0x7fcb299c90a0>, <matplotlib.axis.YTick object at 0x7fcb297d8b00>, <matplotlib.axis.YTick object at 0x7fcb299c8cb0>, <matplotlib.axis.YTick object at 0x7fcb299cbef0>]
 
 
 
@@ -422,14 +422,14 @@ the features.
  .. code-block:: none
 
 
-    <matplotlib.legend.Legend object at 0x7f2d708236e0>
+    <matplotlib.legend.Legend object at 0x7fcb2945d6d0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 44.372 seconds)
+   **Total running time of the script:** (0 minutes 43.289 seconds)
 
 **Estimated memory usage:**  622 MB
 

docs/_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**10:04.378** total execution time for 6 files **from auto_examples**:
+**09:52.927** total execution time for 6 files **from auto_examples**:
 
 .. container::
 
@@ -33,20 +33,20 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_knockoff_aggregation.py` (``plot_knockoff_aggregation.py``)
-     - 05:31.198
-     - 782.5
+     - 05:27.443
+     - 769.2
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``plot_fmri_data_example.py``)
-     - 01:29.290
-     - 3331.0
+     - 01:25.117
+     - 3348.8
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``plot_2D_simulation_example.py``)
-     - 01:09.382
+     - 01:07.044
      - 704.9
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``plot_dcrt_example.py``)
-     - 01:01.772
-     - 642.6
+     - 01:01.764
+     - 643.1
    * - :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py` (``plot_variable_importance_classif.py``)
-     - 00:44.372
-     - 622.2
+     - 00:43.289
+     - 621.8
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``plot_diabetes_variable_importance_example.py``)
-     - 00:08.364
-     - 626.5
+     - 00:08.271
+     - 626.2

docs/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**10:04.378** total execution time for 6 files **from all galleries**:
+**09:52.927** total execution time for 6 files **from all galleries**:
 
 .. container::
 
@@ -33,20 +33,20 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_plot_knockoff_aggregation.py` (``../examples/plot_knockoff_aggregation.py``)
-     - 05:31.198
-     - 782.5
+     - 05:27.443
+     - 769.2
    * - :ref:`sphx_glr_auto_examples_plot_fmri_data_example.py` (``../examples/plot_fmri_data_example.py``)
-     - 01:29.290
-     - 3331.0
+     - 01:25.117
+     - 3348.8
    * - :ref:`sphx_glr_auto_examples_plot_2D_simulation_example.py` (``../examples/plot_2D_simulation_example.py``)
-     - 01:09.382
+     - 01:07.044
      - 704.9
    * - :ref:`sphx_glr_auto_examples_plot_dcrt_example.py` (``../examples/plot_dcrt_example.py``)
-     - 01:01.772
-     - 642.6
+     - 01:01.764
+     - 643.1
    * - :ref:`sphx_glr_auto_examples_plot_variable_importance_classif.py` (``../examples/plot_variable_importance_classif.py``)
-     - 00:44.372
-     - 622.2
+     - 00:43.289
+     - 621.8
    * - :ref:`sphx_glr_auto_examples_plot_diabetes_variable_importance_example.py` (``../examples/plot_diabetes_variable_importance_example.py``)
-     - 00:08.364
-     - 626.5
+     - 00:08.271
+     - 626.2

docs/auto_examples/plot_2D_simulation_example.html

@@ -475,7 +475,7 @@ <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 9.382 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 7.044 seconds)</p>
 <p><strong>Estimated memory usage:</strong>  705 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">

docs/auto_examples/plot_dcrt_example.html

@@ -210,7 +210,7 @@ <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 1.772 seconds)</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 1.764 seconds)</p>
 <p><strong>Estimated memory usage:</strong>  643 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">

docs/auto_examples/plot_diabetes_variable_importance_example.html

@@ -467,7 +467,7 @@ <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 8.364 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 8.271 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">

docs/auto_examples/plot_fmri_data_example.html

@@ -247,19 +247,13 @@ <h2>Gathering and preprocessing Haxby dataset for a given subject<a class="heade
 Dataset created in /home/runner/nilearn_data/haxby2001
 
 Downloading data from https://www.nitrc.org/frs/download.php/7868/mask.nii.gz ...
- ...done. (1 seconds, 0 min)
+ ...done. (0 seconds, 0 min)
 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 15917056 of 291168628 bytes (5.5%,   17.6s remaining)
-Downloaded 52502528 of 291168628 bytes (18.0%,    9.2s remaining)
-Downloaded 91537408 of 291168628 bytes (31.4%,    6.6s remaining)
-Downloaded 130457600 of 291168628 bytes (44.8%,    5.0s remaining)
-Downloaded 169959424 of 291168628 bytes (58.4%,    3.6s remaining)
-Downloaded 209125376 of 291168628 bytes (71.8%,    2.4s remaining)
-Downloaded 248020992 of 291168628 bytes (85.2%,    1.2s remaining)
-Downloaded 287047680 of 291168628 bytes (98.6%,    0.1s remaining) ...done. (8 seconds, 0 min)
+Downloaded 110182400 of 291168628 bytes (37.8%,    1.6s remaining)
+Downloaded 249692160 of 291168628 bytes (85.8%,    0.3s remaining) ...done. (2 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(
@@ -313,7 +307,7 @@ <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:    6.4s
 </pre></div>
 </div>
 <p>Now, let us run the algorithm introduced by Gaonkar et al. (c.f. References).
@@ -349,7 +343,7 @@ <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:   33.4s finished
 </pre></div>
 </div>
 </section>
@@ -472,8 +466,8 @@ <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 29.290 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  3331 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 25.117 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  3349 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 31.198 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  782 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (5 minutes 27.443 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  769 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>