Dev docs nilearn/nilearn@4a37172 : [ENH] Handling of SurfaceData in c…

…ompute_contrast (#4610) Co-authored-by: Remi Gau <[email protected]>
nilearn · Nov 12, 2024 · 0562732 · 0562732
1 parent 48ff05b
commit 0562732
Show file tree

Hide file tree

Showing 744 changed files with 18,566 additions and 19,689 deletions.
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/14c468d44123efe56ebabebf18f1d7e1/plot_surface_image_and_maskers.ipynb b/dev/_downloads/14c468d44123efe56ebabebf18f1d7e1/plot_surface_image_and_maskers.ipynb
diff --git a/dev/_downloads/193d69e0f7475d4607f305617999a80e/plot_surf_stat_map.py b/dev/_downloads/193d69e0f7475d4607f305617999a80e/plot_surf_stat_map.py
@@ -124,8 +124,7 @@
 
 # %%
 # Display ROI on surface
-from nilearn.experimental.plotting import plot_surf_roi, plot_surf_stat_map
-from nilearn.plotting import show
+from nilearn.plotting import plot_surf_roi, plot_surf_stat_map, show
 
 # Transform ROI indices in ROI map
 pcc_map = np.zeros(parcellation.shape[0], dtype=int)

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/2b452a6ad2276d7c92efa36b4a5f800e/plot_haxby_searchlight.py b/dev/_downloads/2b452a6ad2276d7c92efa36b4a5f800e/plot_haxby_searchlight.py
@@ -89,16 +89,39 @@
     cv=cv,
 )
 searchlight.fit(fmri_img, y)
+
 # %%
-# Accessing the searchlight results
-# ---------------------------------
+# Visualization
+# -------------
+from nilearn import image
+from nilearn.plotting import plot_img, plot_stat_map, show
+
 # After fitting the searchlight, we can access the searchlight scores
 # as a NIfTI image using the `scores_img_` attribute.
 scores_img = searchlight.scores_img_
-print("Searchlight scores image created:", scores_img)
+
+# Use the :term:`fMRI` mean image as a surrogate of anatomical data
+mean_fmri = image.mean_img(fmri_img, copy_header=True)
+
+# Because scores are not a zero-center test statistics,
+# we cannot use plot_stat_map
+plot_img(
+    scores_img,
+    bg_img=mean_fmri,
+    title="Searchlight scores image",
+    display_mode="z",
+    cut_coords=[-9],
+    vmin=0.42,
+    cmap="hot",
+    threshold=0.2,
+    black_bg=True,
+)
+
 # %%
 # F-scores computation
 # --------------------
+from sklearn.feature_selection import f_classif
+
 from nilearn.maskers import NiftiMasker
 
 # For decoding, standardizing is often very important
@@ -111,37 +134,11 @@
 )
 fmri_masked = nifti_masker.fit_transform(fmri_img)
 
-from sklearn.feature_selection import f_classif
-
 _, p_values = f_classif(fmri_masked, y)
 p_values = -np.log10(p_values)
 p_values[p_values > 10] = 10
 p_unmasked = get_data(nifti_masker.inverse_transform(p_values))
 
-# %%
-# Visualization
-# -------------
-# Use the :term:`fMRI` mean image as a surrogate of anatomical data
-from nilearn import image
-
-mean_fmri = image.mean_img(fmri_img, copy_header=True)
-
-from nilearn.plotting import plot_img, plot_stat_map, show
-
-# Because scores are not a zero-center test statistics, we cannot use
-# plot_stat_map
-plot_img(
-    scores_img,
-    bg_img=mean_fmri,
-    title="Searchlight scores image",
-    display_mode="z",
-    cut_coords=[-9],
-    vmin=0.42,
-    cmap="hot",
-    threshold=0.2,
-    black_bg=True,
-)
-
 # F_score results
 p_ma = np.ma.array(p_unmasked, mask=np.logical_not(process_mask))
 f_score_img = new_img_like(mean_fmri, p_ma)

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/3597ffc7be60fdef7ae16aaad5f2a265/plot_surf_atlas.ipynb b/dev/_downloads/3597ffc7be60fdef7ae16aaad5f2a265/plot_surf_atlas.ipynb
@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.experimental import plotting\nfrom nilearn.plotting import show\n\nplotting.plot_surf_roi(\n    roi_map=destrieux_atlas,\n    hemi=\"left\",\n    view=\"lateral\",\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    darkness=0.5,\n    title=\"Destrieux parcellation on sulcal surface\",\n)"
+        "from nilearn.plotting import plot_surf_roi, show\n\nplot_surf_roi(\n    roi_map=destrieux_atlas,\n    hemi=\"left\",\n    view=\"lateral\",\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    darkness=0.5,\n    title=\"Destrieux parcellation on sulcal surface\",\n)"
       ]
     },
     {
@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "for view in [\"lateral\", \"posterior\", \"ventral\"]:\n    plotting.plot_surf_roi(\n        surf_mesh=fsaverage_meshes[\"inflated\"],\n        roi_map=destrieux_atlas,\n        hemi=\"left\",\n        view=view,\n        bg_map=fsaverage_sulcal,\n        bg_on_data=True,\n        darkness=0.5,\n        title=f\"Destrieux parcellation on inflated surface\\n{view} view\",\n    )\n\nshow()"
+        "for view in [\"lateral\", \"posterior\", \"ventral\"]:\n    plot_surf_roi(\n        surf_mesh=fsaverage_meshes[\"inflated\"],\n        roi_map=destrieux_atlas,\n        hemi=\"left\",\n        view=view,\n        bg_map=fsaverage_sulcal,\n        bg_on_data=True,\n        darkness=0.5,\n        title=f\"Destrieux parcellation on inflated surface\\n{view} view\",\n    )\n\nshow()"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "elev, azim = 210.0, 90.0  # appropriate for visualizing, e.g., the OTS\nplotting.plot_surf_roi(\n    surf_mesh=fsaverage_meshes[\"inflated\"],\n    roi_map=destrieux_atlas,\n    hemi=\"left\",\n    view=(elev, azim),\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    darkness=0.5,\n    title=\"Arbitrary view of Destrieux parcellation\",\n)"
+        "elev, azim = 210.0, 90.0  # appropriate for visualizing, e.g., the OTS\nplot_surf_roi(\n    surf_mesh=fsaverage_meshes[\"inflated\"],\n    roi_map=destrieux_atlas,\n    hemi=\"left\",\n    view=(elev, azim),\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    darkness=0.5,\n    title=\"Arbitrary view of Destrieux parcellation\",\n)"
       ]
     },
     {
@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "view = plotting.view_surf(\n    surf_mesh=fsaverage_meshes[\"inflated\"],\n    surf_map=destrieux_atlas,\n    cmap=\"gist_ncar\",\n    symmetric_cmap=False,\n    colorbar=False,\n)\n# In a Jupyter notebook, if ``view`` is the output of a cell,\n# it will be displayed below the cell\nview\n\n# uncomment this to open the plot in a web browser:\n# view.open_in_browser()"
+        "from nilearn.plotting import view_surf\n\nview = view_surf(\n    surf_mesh=fsaverage_meshes[\"inflated\"],\n    surf_map=destrieux_atlas,\n    cmap=\"gist_ncar\",\n    symmetric_cmap=False,\n    colorbar=False,\n)\n# In a Jupyter notebook, if ``view`` is the output of a cell,\n# it will be displayed below the cell\nview\n\n# uncomment this to open the plot in a web browser:\n# view.open_in_browser()"
       ]
     },
     {

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/39715408852c4c991549b6f510431022/plot_surf_stat_map.zip b/dev/_downloads/39715408852c4c991549b6f510431022/plot_surf_stat_map.zip
diff --git a/dev/_downloads/42459a562621fd56db76c2274f38f004/plot_bids_analysis.zip b/dev/_downloads/42459a562621fd56db76c2274f38f004/plot_bids_analysis.zip
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/432c8b6d7f95b7a48cce237073c3045e/plot_3d_map_to_surface_projection.py b/dev/_downloads/432c8b6d7f95b7a48cce237073c3045e/plot_3d_map_to_surface_projection.py
@@ -56,7 +56,7 @@
 # You can visualize the texture on the surface using the function
 # :func:`~nilearn.plotting.plot_surf_stat_map` which uses ``matplotlib``
 # as the default plotting engine.
-from nilearn.experimental.plotting import plot_surf_stat_map
+from nilearn.plotting import plot_surf_stat_map
 
 fig = plot_surf_stat_map(
     stat_map=img,
@@ -172,7 +172,7 @@
 # %%
 # Display outlines of the regions of interest on top of a statistical map
 # -----------------------------------------------------------------------
-from nilearn.experimental.plotting import plot_surf_contours
+from nilearn.plotting import plot_surf_contours
 
 fsaverage_sulcal = load_fsaverage_data(data_type="sulcal", mesh_type="pial")
 
@@ -199,10 +199,11 @@
     show()
 elif engine == "plotly":
     figure.add_contours(
-        roi_map=destrieux_atlas.data.parts["right"],
+        roi_map=destrieux_atlas,
         levels=regions_indices,
         labels=labels,
         lines=[{"width": 5}],
+        hemi="right",
     )
     # view the contours in a browser
     # figure.show()
@@ -229,7 +230,7 @@
 )
 
 plot_surf_stat_map(
-    big_img,
+    stat_map=big_img,
     surf_mesh=big_fsaverage_meshes["inflated"],
     hemi="right",
     colorbar=True,
@@ -272,7 +273,7 @@
 # :func:`nilearn.plotting.view_img_on_surf` that give
 # more interactive visualizations in a web browser.
 # See :ref:`interactive-surface-plotting` for more details.
-from nilearn.experimental.plotting import view_surf
+from nilearn.plotting import view_surf
 
 view = view_surf(
     surf_mesh=fsaverage_meshes["inflated"],

diff --git a/dev/_downloads/44ace3c1d0ab418c0f97134024ad0a6d/plot_3d_map_to_surface_projection.zip b/dev/_downloads/44ace3c1d0ab418c0f97134024ad0a6d/plot_3d_map_to_surface_projection.zip
diff --git a/dev/_downloads/44cb3a7d606fae23fb2b04d989cc6947/plot_hrf.zip b/dev/_downloads/44cb3a7d606fae23fb2b04d989cc6947/plot_hrf.zip
diff --git a/dev/_downloads/45fc876cbbe833a7942ff19b6aeb70fe/plot_age_group_prediction_cross_val.zip b/dev/_downloads/45fc876cbbe833a7942ff19b6aeb70fe/plot_age_group_prediction_cross_val.zip