Dev docs nilearn/nilearn@c10b1a5 : [MAINT] Make sure only changed exa…

…mples are rebuilt on PR (#4780)
nilearn · Nov 22, 2024 · edc596e · edc596e
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diff --git a/dev/_downloads/0140eeb6da671d73b808df162f8895e8/plot_simulated_data.zip b/dev/_downloads/0140eeb6da671d73b808df162f8895e8/plot_simulated_data.zip
diff --git a/dev/_downloads/04502003935d64eedeb72956946fe91b/plot_nilearn_101.zip b/dev/_downloads/04502003935d64eedeb72956946fe91b/plot_nilearn_101.zip
diff --git a/dev/_downloads/078678c45812ee1e319ed2d2c21c554f/plot_python_101.zip b/dev/_downloads/078678c45812ee1e319ed2d2c21c554f/plot_python_101.zip
diff --git a/dev/_downloads/07bd43f5f35abcb693398fbe82adc61d/plot_oasis_vbm.zip b/dev/_downloads/07bd43f5f35abcb693398fbe82adc61d/plot_oasis_vbm.zip
diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/09c9580584f4e64ef9778ef8f3417e60/plot_resample_to_template.zip b/dev/_downloads/09c9580584f4e64ef9778ef8f3417e60/plot_resample_to_template.zip
diff --git a/dev/_downloads/0b08503308b188b3029a4de1b9022119/plot_visualization.zip b/dev/_downloads/0b08503308b188b3029a4de1b9022119/plot_visualization.zip
diff --git a/dev/_downloads/12e9d2a7add7eab2059f4f619ad12621/plot_data_driven_parcellations.zip b/dev/_downloads/12e9d2a7add7eab2059f4f619ad12621/plot_data_driven_parcellations.zip
diff --git a/dev/_downloads/2442f69e6be8788c989e28093310602e/plot_second_level_two_sample_test.zip b/dev/_downloads/2442f69e6be8788c989e28093310602e/plot_second_level_two_sample_test.zip
diff --git a/dev/_downloads/24646bf88295d9b694826f1c88efdaf9/plot_3d_and_4d_niimg.zip b/dev/_downloads/24646bf88295d9b694826f1c88efdaf9/plot_3d_and_4d_niimg.zip
diff --git a/dev/_downloads/24c23a56893b4488de131db2c3fbbe9c/plot_extract_rois_smith_atlas.zip b/dev/_downloads/24c23a56893b4488de131db2c3fbbe9c/plot_extract_rois_smith_atlas.zip
diff --git a/dev/_downloads/2ce0f24d145edf0cb7091fe8dae3bd07/plot_carpet.zip b/dev/_downloads/2ce0f24d145edf0cb7091fe8dae3bd07/plot_carpet.zip
diff --git a/dev/_downloads/2db23df6fc02e7fb53e6ee82f49eb2bc/plot_multiscale_parcellations.zip b/dev/_downloads/2db23df6fc02e7fb53e6ee82f49eb2bc/plot_multiscale_parcellations.zip
diff --git a/dev/_downloads/330cb23bdaeeb988f77c245c2f701a0c/plot_haxby_mass_univariate.zip b/dev/_downloads/330cb23bdaeeb988f77c245c2f701a0c/plot_haxby_mass_univariate.zip
diff --git a/dev/_downloads/3501b5161527823b76e121d0c20043a3/plot_mask_computation.zip b/dev/_downloads/3501b5161527823b76e121d0c20043a3/plot_mask_computation.zip
diff --git a/dev/_downloads/35e89a0fddeee2ec3084f9e9b94edcbe/plot_beta_series.zip b/dev/_downloads/35e89a0fddeee2ec3084f9e9b94edcbe/plot_beta_series.zip
diff --git a/dev/_downloads/362b0c50c649196a3fb4b2cf11a39304/plot_smooth_mean_image.zip b/dev/_downloads/362b0c50c649196a3fb4b2cf11a39304/plot_smooth_mean_image.zip
diff --git a/dev/_downloads/387769616b4a5f86cdd1e6b0b3f7597e/plot_surface_101.py b/dev/_downloads/387769616b4a5f86cdd1e6b0b3f7597e/plot_surface_101.py
@@ -18,7 +18,8 @@
 # brain.
 #
 # Practically, this means that the main difference between the two is the basic
-# unit that holds the data. For volumetric images, that basic unit is a voxel,
+# unit that holds the data.
+# For volumetric images, that basic unit is a voxel,
 # while for surface images it is a :term:`vertex`.
 #
 # The goal of this tutorial is to show you how to work with surface images in
@@ -44,7 +45,8 @@
 #           representation.
 #
 # For brain surfaces we typically have two meshes: one for the left hemisphere
-# and one for the right hemisphere. Nilearn represents this as a
+# and one for the right hemisphere.
+# Nilearn represents this as a
 # :class:`~nilearn.surface.PolyMesh` object with two ``parts``:
 # ``left`` and ``right``.
 #
@@ -97,7 +99,8 @@
 # ----
 #
 # The data is the information stored at each :term:`vertex` of the
-# :term:`mesh`. This can be anything from the thickness of the cortex to the
+# :term:`mesh`.
+# This can be anything from the thickness of the cortex to the
 # activation level at that :term:`vertex`.
 #
 # For this example, let's create some random data for the vertices of the
@@ -123,7 +126,8 @@
 # --------------------------
 #
 # The surface image can be plotted using the different functions from the
-# :mod:`nilearn.plotting` module. Here we will show how to use the
+# :mod:`nilearn.plotting` module.
+# Here we will show how to use the
 # :func:`~nilearn.plotting.view_surf` function:
 from nilearn import plotting
 
@@ -163,7 +167,8 @@
 
 # %%
 # You will see that this creates four files in total -- two for the
-# :term:`mesh` and two for the data. The files ending with ``_hemi-L.gii``
+# :term:`mesh` and two for the data.
+# The files ending with ``_hemi-L.gii``
 # correspond to the left part and those ending with ``_hemi-R.gii`` correspond
 # to the right part.
 
@@ -202,7 +207,8 @@
 # ---------------
 #
 # Most things that can be done with volumetric images can also be done with
-# surface images. See following examples for more details:
+# surface images.
+# See following examples for more details:
 #
 # * For plotting statistical maps on the surface, see
 #   :ref:`sphx_glr_auto_examples_01_plotting_plot_surf_stat_map.py`

diff --git a/dev/_downloads/39715408852c4c991549b6f510431022/plot_surf_stat_map.zip b/dev/_downloads/39715408852c4c991549b6f510431022/plot_surf_stat_map.zip
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diff --git a/dev/_downloads/42b3bd92b4565279744cf95e7263db7d/plot_visualize_megatrawls_netmats.zip b/dev/_downloads/42b3bd92b4565279744cf95e7263db7d/plot_visualize_megatrawls_netmats.zip
diff --git a/dev/_downloads/44ace3c1d0ab418c0f97134024ad0a6d/plot_3d_map_to_surface_projection.zip b/dev/_downloads/44ace3c1d0ab418c0f97134024ad0a6d/plot_3d_map_to_surface_projection.zip
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diff --git a/dev/_downloads/4d17a3136b19f949ccdbf7f04a64bae1/plot_haxby_anova_svm.zip b/dev/_downloads/4d17a3136b19f949ccdbf7f04a64bae1/plot_haxby_anova_svm.zip
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diff --git a/dev/_downloads/5f7a238835a6ffe32e1edfb58a614869/plot_extract_regions_labels_image.zip b/dev/_downloads/5f7a238835a6ffe32e1edfb58a614869/plot_extract_regions_labels_image.zip
diff --git a/dev/_downloads/5f8b981c74d82c0a0d77446a11d50f5f/plot_surface_projection_strategies.zip b/dev/_downloads/5f8b981c74d82c0a0d77446a11d50f5f/plot_surface_projection_strategies.zip
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diff --git a/dev/_downloads/ea6bb73e12fcd9f496491b01c1be157d/plot_surface_image_and_maskers.zip b/dev/_downloads/ea6bb73e12fcd9f496491b01c1be157d/plot_surface_image_and_maskers.zip
diff --git a/dev/_downloads/eb173a3a75ae72eb6bfd3d63625d0f10/plot_haxby_multiclass.zip b/dev/_downloads/eb173a3a75ae72eb6bfd3d63625d0f10/plot_haxby_multiclass.zip
diff --git a/dev/_downloads/ec9ec7cc0c1ea84207d4b1895aa51c35/plot_atlas.zip b/dev/_downloads/ec9ec7cc0c1ea84207d4b1895aa51c35/plot_atlas.zip
diff --git a/dev/_downloads/edc5e89b51e77bec19a6b031b501cbe0/plot_haxby_understand_decoder.zip b/dev/_downloads/edc5e89b51e77bec19a6b031b501cbe0/plot_haxby_understand_decoder.zip
diff --git a/dev/_downloads/ef891579a60b1bdf68937149b8c6c6e9/plot_negate_image.zip b/dev/_downloads/ef891579a60b1bdf68937149b8c6c6e9/plot_negate_image.zip
diff --git a/dev/_downloads/f395544c92d89d41f165c66b83663359/plot_localizer_simple_analysis.zip b/dev/_downloads/f395544c92d89d41f165c66b83663359/plot_localizer_simple_analysis.zip
diff --git a/dev/_downloads/f50b3205028cf5c0f8e176b94f00db09/plot_roi_extraction.zip b/dev/_downloads/f50b3205028cf5c0f8e176b94f00db09/plot_roi_extraction.zip
diff --git a/dev/_downloads/fa2474da6eefd506beffd0271a40d30a/plot_surface_101.ipynb b/dev/_downloads/fa2474da6eefd506beffd0271a40d30a/plot_surface_101.ipynb
@@ -11,14 +11,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## What is a surface image?\n\nWithin the context of neuroimaging, a surface image is an alternative way of\nrepresenting MRI data as opposed to a volumetric image.\n\nWhile volumetric images are 3D grids of voxels, surface images consist of\npoints (vertices) in 3D space connected to represent the surface of the\nbrain.\n\nPractically, this means that the main difference between the two is the basic\nunit that holds the data. For volumetric images, that basic unit is a voxel,\nwhile for surface images it is a :term:`vertex`.\n\nThe goal of this tutorial is to show you how to work with surface images in\nNilearn. For more existential questions like why surface images are useful,\nhow they are created etc., [Andy Jahn's blog](https://andysbrainbook.readthedocs.io/en/latest/FreeSurfer/FreeSurfer_Introduction.html)\nis a good starting point.\n\nSurface images have two main components:\n 1. The :term:`mesh`, which is the geometry of the surface.\n 2. The data, which is the information stored at each vertex of the mesh.\n\n"
+        "## What is a surface image?\n\nWithin the context of neuroimaging, a surface image is an alternative way of\nrepresenting MRI data as opposed to a volumetric image.\n\nWhile volumetric images are 3D grids of voxels, surface images consist of\npoints (vertices) in 3D space connected to represent the surface of the\nbrain.\n\nPractically, this means that the main difference between the two is the basic\nunit that holds the data.\nFor volumetric images, that basic unit is a voxel,\nwhile for surface images it is a :term:`vertex`.\n\nThe goal of this tutorial is to show you how to work with surface images in\nNilearn. For more existential questions like why surface images are useful,\nhow they are created etc., [Andy Jahn's blog](https://andysbrainbook.readthedocs.io/en/latest/FreeSurfer/FreeSurfer_Introduction.html)\nis a good starting point.\n\nSurface images have two main components:\n 1. The :term:`mesh`, which is the geometry of the surface.\n 2. The data, which is the information stored at each vertex of the mesh.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Mesh\n\nA :term:`mesh` can be defined by two arrays:\n 1. The coordinates of the vertices.\n 2. Which vertices need to be connected to form :term:`faces`.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This representation of a mesh is known as [Face-Vertex](https://en.wikipedia.org/wiki/Polygon_mesh#Face-vertex_meshes)\n          representation.</p></div>\n\nFor brain surfaces we typically have two meshes: one for the left hemisphere\nand one for the right hemisphere. Nilearn represents this as a\n:class:`~nilearn.surface.PolyMesh` object with two ``parts``:\n``left`` and ``right``.\n\nSo you can define your own :term:`mesh`, say, for the left part a tetrahedron\nand for the right part a pyramid, using numpy arrays and create a\n:class:`~nilearn.surface.PolyMesh` object as follows:\n\n"
+        "## Mesh\n\nA :term:`mesh` can be defined by two arrays:\n 1. The coordinates of the vertices.\n 2. Which vertices need to be connected to form :term:`faces`.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This representation of a mesh is known as [Face-Vertex](https://en.wikipedia.org/wiki/Polygon_mesh#Face-vertex_meshes)\n          representation.</p></div>\n\nFor brain surfaces we typically have two meshes: one for the left hemisphere\nand one for the right hemisphere.\nNilearn represents this as a\n:class:`~nilearn.surface.PolyMesh` object with two ``parts``:\n``left`` and ``right``.\n\nSo you can define your own :term:`mesh`, say, for the left part a tetrahedron\nand for the right part a pyramid, using numpy arrays and create a\n:class:`~nilearn.surface.PolyMesh` object as follows:\n\n"
       ]
     },
     {
@@ -36,7 +36,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Data\n\nThe data is the information stored at each :term:`vertex` of the\n:term:`mesh`. This can be anything from the thickness of the cortex to the\nactivation level at that :term:`vertex`.\n\nFor this example, let's create some random data for the vertices of the\n:term:`mesh`:\n\n"
+        "## Data\n\nThe data is the information stored at each :term:`vertex` of the\n:term:`mesh`.\nThis can be anything from the thickness of the cortex to the\nactivation level at that :term:`vertex`.\n\nFor this example, let's create some random data for the vertices of the\n:term:`mesh`:\n\n"
       ]
     },
     {
@@ -72,7 +72,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plotting the surface image\n\nThe surface image can be plotted using the different functions from the\n:mod:`nilearn.plotting` module. Here we will show how to use the\n:func:`~nilearn.plotting.view_surf` function:\n\n"
+        "## Plotting the surface image\n\nThe surface image can be plotted using the different functions from the\n:mod:`nilearn.plotting` module.\nHere we will show how to use the\n:func:`~nilearn.plotting.view_surf` function:\n\n"
       ]
     },
     {
@@ -151,7 +151,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You will see that this creates four files in total -- two for the\n:term:`mesh` and two for the data. The files ending with ``_hemi-L.gii``\ncorrespond to the left part and those ending with ``_hemi-R.gii`` correspond\nto the right part.\n\n"
+        "You will see that this creates four files in total -- two for the\n:term:`mesh` and two for the data.\nThe files ending with ``_hemi-L.gii``\ncorrespond to the left part and those ending with ``_hemi-R.gii`` correspond\nto the right part.\n\n"
       ]
     },
     {
@@ -194,7 +194,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "And that's it! Now you know how to create, plot, save and load surface images\nwith Nilearn.\n\n## Further reading\n\nMost things that can be done with volumetric images can also be done with\nsurface images. See following examples for more details:\n\n* For plotting statistical maps on the surface, see\n  `sphx_glr_auto_examples_01_plotting_plot_surf_stat_map.py`\n\n* For performing GLM analysis on surface data organized in BIDS\n  format, see\n  `sphx_glr_auto_examples_07_advanced_plot_surface_bids_analysis.py`\n\n* For performing first-level GLM analysis on surface data, see [this \\\n  example](../04_glm_first_level/plot_localizer_surface_analysis.html).\n\n"
+        "And that's it! Now you know how to create, plot, save and load surface images\nwith Nilearn.\n\n## Further reading\n\nMost things that can be done with volumetric images can also be done with\nsurface images.\nSee following examples for more details:\n\n* For plotting statistical maps on the surface, see\n  `sphx_glr_auto_examples_01_plotting_plot_surf_stat_map.py`\n\n* For performing GLM analysis on surface data organized in BIDS\n  format, see\n  `sphx_glr_auto_examples_07_advanced_plot_surface_bids_analysis.py`\n\n* For performing first-level GLM analysis on surface data, see [this \\\n  example](../04_glm_first_level/plot_localizer_surface_analysis.html).\n\n"
       ]
     }
   ],

diff --git a/dev/_downloads/fea3af77ca714bc79a6ef19a9de3e2bd/plot_haxby_searchlight_surface.zip b/dev/_downloads/fea3af77ca714bc79a6ef19a9de3e2bd/plot_haxby_searchlight_surface.zip
diff --git a/dev/_downloads/ff1c3f1839b56e730a233c58d5a283c9/plot_haxby_different_estimators.zip b/dev/_downloads/ff1c3f1839b56e730a233c58d5a283c9/plot_haxby_different_estimators.zip
diff --git a/dev/_images/sphx_glr_plot_advanced_decoding_scikit_001.png b/dev/_images/sphx_glr_plot_advanced_decoding_scikit_001.png
diff --git a/dev/_images/sphx_glr_plot_advanced_decoding_scikit_thumb.png b/dev/_images/sphx_glr_plot_advanced_decoding_scikit_thumb.png
diff --git a/dev/_images/sphx_glr_plot_first_level_details_024.png b/dev/_images/sphx_glr_plot_first_level_details_024.png
diff --git a/dev/_images/sphx_glr_plot_haxby_anova_svm_001.png b/dev/_images/sphx_glr_plot_haxby_anova_svm_001.png
diff --git a/dev/_images/sphx_glr_plot_haxby_anova_svm_thumb.png b/dev/_images/sphx_glr_plot_haxby_anova_svm_thumb.png
diff --git a/dev/_images/sphx_glr_plot_haxby_different_estimators_001.png b/dev/_images/sphx_glr_plot_haxby_different_estimators_001.png
diff --git a/dev/_images/sphx_glr_plot_haxby_different_estimators_002.png b/dev/_images/sphx_glr_plot_haxby_different_estimators_002.png
diff --git a/dev/_images/sphx_glr_plot_haxby_different_estimators_005.png b/dev/_images/sphx_glr_plot_haxby_different_estimators_005.png
diff --git a/dev/_images/sphx_glr_plot_haxby_different_estimators_thumb.png b/dev/_images/sphx_glr_plot_haxby_different_estimators_thumb.png
diff --git a/dev/_images/sphx_glr_plot_haxby_grid_search_001.png b/dev/_images/sphx_glr_plot_haxby_grid_search_001.png
diff --git a/dev/_images/sphx_glr_plot_haxby_grid_search_thumb.png b/dev/_images/sphx_glr_plot_haxby_grid_search_thumb.png
diff --git a/dev/_images/sphx_glr_plot_haxby_mass_univariate_002.png b/dev/_images/sphx_glr_plot_haxby_mass_univariate_002.png
diff --git a/dev/_images/sphx_glr_plot_haxby_searchlight_001.png b/dev/_images/sphx_glr_plot_haxby_searchlight_001.png
diff --git a/dev/_images/sphx_glr_plot_haxby_searchlight_thumb.png b/dev/_images/sphx_glr_plot_haxby_searchlight_thumb.png
diff --git a/dev/_images/sphx_glr_plot_localizer_mass_univariate_methods_001.png b/dev/_images/sphx_glr_plot_localizer_mass_univariate_methods_001.png
diff --git a/dev/_images/sphx_glr_plot_localizer_mass_univariate_methods_thumb.png b/dev/_images/sphx_glr_plot_localizer_mass_univariate_methods_thumb.png
diff --git a/dev/_images/sphx_glr_plot_oasis_vbm_004.png b/dev/_images/sphx_glr_plot_oasis_vbm_004.png
diff --git a/dev/_images/sphx_glr_plot_second_level_association_test_003.png b/dev/_images/sphx_glr_plot_second_level_association_test_003.png
diff --git a/dev/_images/sphx_glr_plot_second_level_one_sample_test_003.png b/dev/_images/sphx_glr_plot_second_level_one_sample_test_003.png
diff --git a/dev/_images/sphx_glr_plot_simulated_data_002.png b/dev/_images/sphx_glr_plot_simulated_data_002.png
diff --git a/dev/_images/sphx_glr_plot_simulated_data_003.png b/dev/_images/sphx_glr_plot_simulated_data_003.png
diff --git a/dev/_images/sphx_glr_plot_simulated_data_006.png b/dev/_images/sphx_glr_plot_simulated_data_006.png
diff --git a/dev/_images/sphx_glr_plot_surface_image_and_maskers_004.png b/dev/_images/sphx_glr_plot_surface_image_and_maskers_004.png
diff --git a/dev/_sources/auto_examples/00_tutorials/plot_3d_and_4d_niimg.rst.txt b/dev/_sources/auto_examples/00_tutorials/plot_3d_and_4d_niimg.rst.txt
@@ -127,7 +127,7 @@ statistical map:
  .. code-block:: none
 
 
-    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff4c77fc940>
+    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f2ab2e03fd0>
 
 
 
@@ -156,7 +156,7 @@ Visualizing works better with a threshold
  .. code-block:: none
 
 
-    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff4df9d9160>
+    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f2acafd2880>
 
 
 
@@ -186,7 +186,6 @@ correspondence between rest and task
     [get_dataset_dir] Dataset created in /home/runner/nilearn_data/smith_2009
     [fetch_single_file] Downloading data from 
     https://www.fmrib.ox.ac.uk/datasets/brainmap+rsns/PNAS_Smith09_rsn10.nii.gz ...
-    [_chunk_report_] Downloaded 1753088 of 7565016 bytes (23.2%%,    3.5s remaining)
     [fetch_single_file]  ...done. (2 seconds, 0 min)
 
 
@@ -271,7 +270,7 @@ We can then plot it
  .. code-block:: none
 
 
-    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7ff4d5c1f040>
+    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f2acafab160>
 
 
 
@@ -489,7 +488,7 @@ image.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 21.791 seconds)
+   **Total running time of the script:** (0 minutes 22.810 seconds)
 
 **Estimated memory usage:**  352 MB