CircleCI update of dev docs (build: 14534).

nilearn · Feb 26, 2024 · 59fe946 · 59fe946
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diff --git a/dev/.buildinfo b/dev/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 3f1763c03d92f95a5116d0f64a7025b2
+config: 536979c79997a64bb7550fa11411abc5
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_downloads/8f8e364ed231d7f514803f54b0316651/plot_sphere_based_connectome.ipynb b/dev/_downloads/8f8e364ed231d7f514803f54b0316651/plot_sphere_based_connectome.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Extract signals on spheres and plot a connectome\n\nThis example shows how to extract signals from spherical regions.\nWe show how to build spheres around user-defined coordinates, as well as\ncentered on coordinates from the Power-264 atlas (:footcite:t:`Power2011`),\nand the Dosenbach-160 atlas (:footcite:t:`Dosenbach2010`).\n\nWe estimate connectomes using two different methods: **sparse inverse\ncovariance** and **partial_correlation**, to recover the functional brain\n**networks structure**.\n\nWe'll start by extracting signals from Default Mode Network regions and\ncomputing a connectome from them.\n\n.. include:: ../../../examples/masker_note.rst\n"
+        "\n# Extract signals on spheres and plot a connectome\n\nThis example shows how to extract signals from spherical regions.\nWe show how to build spheres around user-defined coordinates, as well as\ncentered on coordinates from the Power-264 atlas (:footcite:t:`Power2011`),\nand the Dosenbach-160 atlas (:footcite:t:`Dosenbach2010`).\n\nWe estimate connectomes using two different methods: **sparse inverse\ncovariance** and **partial_correlation**,\nto recover the functional brain **networks structure**.\n\nWe'll start by extracting signals from Default Mode Network regions and\ncomputing a connectome from them.\n\n.. include:: ../../../examples/masker_note.rst\n"
       ]
     },
     {
@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Display spheres summary report\nBy default all spheres are displayed. This can be tweaked by passing an\ninteger or list/array of indices to the ``displayed_spheres`` argument of\n``generate_report``.\n\n"
+        "## Display spheres summary report\nBy default all spheres are displayed.\nThis can be tweaked by passing an integer or list/array of indices\nto the ``displayed_spheres`` argument of ``generate_report``.\n\n"
       ]
     },
     {
@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Compute partial correlation matrix\nUsing object :class:`nilearn.connectome.ConnectivityMeasure`: Its\ndefault covariance estimator is Ledoit-Wolf, allowing to obtain accurate\npartial correlations.\n\n"
+        "## Compute partial correlation matrix\nUsing object :class:`nilearn.connectome.ConnectivityMeasure`:\nits default covariance estimator is Ledoit-Wolf,\nallowing to obtain accurate partial correlations.\n\n"
       ]
     },
     {

diff --git a/dev/_downloads/cb064f9ac4369c8f91e970b8f1ae7965/plot_sphere_based_connectome.py b/dev/_downloads/cb064f9ac4369c8f91e970b8f1ae7965/plot_sphere_based_connectome.py
@@ -8,8 +8,8 @@
 and the Dosenbach-160 atlas (:footcite:t:`Dosenbach2010`).
 
 We estimate connectomes using two different methods: **sparse inverse
-covariance** and **partial_correlation**, to recover the functional brain
-**networks structure**.
+covariance** and **partial_correlation**,
+to recover the functional brain **networks structure**.
 
 We'll start by extracting signals from Default Mode Network regions and
 computing a connectome from them.
@@ -83,9 +83,9 @@
 # %%
 # Display spheres summary report
 # ------------------------------
-# By default all spheres are displayed. This can be tweaked by passing an
-# integer or list/array of indices to the ``displayed_spheres`` argument of
-# ``generate_report``.
+# By default all spheres are displayed.
+# This can be tweaked by passing an integer or list/array of indices
+# to the ``displayed_spheres`` argument of ``generate_report``.
 report = masker.generate_report()
 report
 
@@ -106,9 +106,9 @@
 # %%
 # Compute partial correlation matrix
 # -----------------------------------
-# Using object :class:`nilearn.connectome.ConnectivityMeasure`: Its
-# default covariance estimator is Ledoit-Wolf, allowing to obtain accurate
-# partial correlations.
+# Using object :class:`nilearn.connectome.ConnectivityMeasure`:
+# its default covariance estimator is Ledoit-Wolf,
+# allowing to obtain accurate partial correlations.
 
 from nilearn.connectome import ConnectivityMeasure
 

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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_004.png b/dev/_images/sphx_glr_plot_simulated_data_004.png
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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
@@ -126,7 +126,7 @@ statistical map:
  .. code-block:: none
 
 
-    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fbb25023d10>
+    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f005c151730>
 
 
 
@@ -155,7 +155,7 @@ Visualizing works better with a threshold
  .. code-block:: none
 
 
-    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fbb245701d0>
+    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f0057add460>
 
 
 
@@ -264,7 +264,7 @@ We can then plot it
  .. code-block:: none
 
 
-    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fbb245b0c50>
+    <nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f005c2dc4d0>
 
 
 
@@ -475,9 +475,9 @@ image.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 15.328 seconds)
+   **Total running time of the script:** (0 minutes 14.593 seconds)
 
-**Estimated memory usage:**  215 MB
+**Estimated memory usage:**  217 MB
 
 
 .. _sphx_glr_download_auto_examples_00_tutorials_plot_3d_and_4d_niimg.py: