dev docs: nilearn/nilearn@1f0c757 : [ENH] make colorbar=True the default for plotting functions (#5172)

Author: actions-user

dev/_downloads/0140eeb6da671d73b808df162f8895e8/plot_simulated_data.zip

@@ -129,7 +129,6 @@
 display = plotting.plot_stat_map(
     z_map,
     threshold=threshold,
-    colorbar=True,
     display_mode="z",
     cut_coords=[-4, 26],
     figure=fig,
@@ -148,7 +147,6 @@
 plotting.plot_stat_map(
     z_map,
     threshold=threshold,
-    colorbar=True,
     title="sex effect on gray matter density (FDR = .05)",
 )
 

dev/_downloads/04502003935d64eedeb72956946fe91b/plot_nilearn_101.zip

@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.plotting import plot_matrix, show\n\ntitle = \"Partial correlation matrices\\n for d=300\"\ndisplay = plot_matrix(partial_correlation, colorbar=True, title=title)\nshow()"
+        "from nilearn.plotting import plot_matrix, show\n\ntitle = \"Partial correlation matrices\\n for d=300\"\ndisplay = plot_matrix(partial_correlation, title=title)\nshow()"
       ]
     }
   ],

dev/_downloads/078678c45812ee1e319ed2d2c21c554f/plot_python_101.zip

@@ -195,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "plot_glass_brain(\n    stat_maps_unpaired[\"effect_variance\"],\n    colorbar=True,\n    vmin=0,\n    vmax=6,\n    cmap=\"inferno\",\n    title=\"vertical vs horizontal effect variance, unpaired\",\n)\n\nplot_glass_brain(\n    stat_maps_paired[\"effect_variance\"],\n    colorbar=True,\n    vmin=0,\n    vmax=6,\n    cmap=\"inferno\",\n    title=\"vertical vs horizontal effect variance, paired\",\n)\n\nshow()"
+        "plot_glass_brain(\n    stat_maps_unpaired[\"effect_variance\"],\n    vmin=0,\n    vmax=6,\n    cmap=\"inferno\",\n    title=\"vertical vs horizontal effect variance, unpaired\",\n)\n\nplot_glass_brain(\n    stat_maps_paired[\"effect_variance\"],\n    vmin=0,\n    vmax=6,\n    cmap=\"inferno\",\n    title=\"vertical vs horizontal effect variance, paired\",\n)\n\nshow()"
       ]
     },
     {
@@ -213,7 +213,7 @@
       },
       "outputs": [],
       "source": [
-        "threshold = 3.1  # corresponds to  p < .001, uncorrected\nplot_glass_brain(\n    stat_maps_unpaired[\"z_score\"],\n    threshold=threshold,\n    colorbar=True,\n    plot_abs=False,\n    vmax=5.8,\n    title=\"vertical vs horizontal (unc p<0.001), unpaired\",\n)\n\nplot_glass_brain(\n    stat_maps_paired[\"z_score\"],\n    threshold=threshold,\n    colorbar=True,\n    plot_abs=False,\n    vmax=5.8,\n    title=\"vertical vs horizontal (unc p<0.001), paired\",\n)\n\nshow()"
+        "threshold = 3.1  # corresponds to  p < .001, uncorrected\nplot_glass_brain(\n    stat_maps_unpaired[\"z_score\"],\n    threshold=threshold,\n    plot_abs=False,\n    vmax=5.8,\n    title=\"vertical vs horizontal (unc p<0.001), unpaired\",\n)\n\nplot_glass_brain(\n    stat_maps_paired[\"z_score\"],\n    threshold=threshold,\n    plot_abs=False,\n    vmax=5.8,\n    title=\"vertical vs horizontal (unc p<0.001), paired\",\n)\n\nshow()"
       ]
     },
     {

dev/_downloads/07bd43f5f35abcb693398fbe82adc61d/plot_oasis_vbm.zip

@@ -123,6 +123,7 @@
     bg_map=fsaverage_sulcal,
     bg_on_data=True,
     title="PCC Seed",
+    colorbar=False,
 )
 
 show()
@@ -151,6 +152,7 @@
     bg_map=fsaverage_sulcal,
     bg_on_data=True,
     title="PCC Seed on flat map",
+    colorbar=False,
 )
 
 show()
@@ -205,7 +207,6 @@
     stat_map=stat_map_surf,
     hemi=hemisphere,
     view="medial",
-    colorbar=True,
     bg_map=fsaverage_sulcal,
     bg_on_data=True,
     darkness=0.3,
@@ -222,7 +223,6 @@
     stat_map=stat_map_surf,
     hemi=hemisphere,
     view="medial",
-    colorbar=True,
     bg_map=fsaverage_sulcal,
     bg_on_data=True,
     cmap="bwr",
@@ -242,7 +242,6 @@
     stat_map=stat_map_surf,
     hemi=hemisphere,
     view="lateral",
-    colorbar=True,
     cmap="bwr",
     threshold=0.5,
     title="Plotting without background",
@@ -266,7 +265,6 @@
     bg_map=fsaverage_sulcal,
     bg_on_data=True,
     threshold=0.5,
-    colorbar=True,
     output_file=output_dir / "plot_surf_stat_map.png",
     cmap="bwr",
 )

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -165,15 +165,13 @@
 
 plot_glass_brain(
     z_map,
-    colorbar=True,
     threshold=norm.isf(0.001),
     title='Nilearn Z map of "StopSuccess - Go" (unc p<0.001)',
     plot_abs=False,
     display_mode="ortho",
 )
 plot_glass_brain(
     fsl_z_map,
-    colorbar=True,
     threshold=norm.isf(0.001),
     title='FSL Z map of "StopSuccess - Go" (unc p<0.001)',
     plot_abs=False,
@@ -204,7 +202,6 @@
 plot_contrast_matrix("StopSuccess - Go", design_matrix)
 plot_glass_brain(
     z_map,
-    colorbar=True,
     threshold=norm.isf(0.001),
     plot_abs=False,
     display_mode="z",

dev/_downloads/09c9580584f4e64ef9778ef8f3417e60/plot_resample_to_template.zip

@@ -67,6 +67,7 @@
     components_img,
     view_type="filled_contours",
     title="Dictionary Learning maps",
+    draw_cross=False,
 )
 
 show()
@@ -107,7 +108,10 @@
     "Each separate color of region indicates extracted region"
 )
 plot_prob_atlas(
-    regions_extracted_img, view_type="filled_contours", title=title
+    regions_extracted_img,
+    view_type="filled_contours",
+    title=title,
+    draw_cross=False,
 )
 
 show()
@@ -164,7 +168,7 @@
 title = f"Correlation between {int(n_regions_extracted)} regions"
 
 # First plot the matrix
-plot_matrix(mean_correlations, vmax=1, vmin=-1, colorbar=True, title=title)
+plot_matrix(mean_correlations, vmax=1, vmin=-1, title=title)
 
 # Then find the center of the regions and plot a connectome
 regions_img = regions_extracted_img
@@ -190,7 +194,6 @@
 plot_stat_map(
     img,
     cut_coords=coords,
-    colorbar=True,
     title="Showing one specific network",
 )
 
@@ -208,7 +211,9 @@
 
 regions_indices_of_map3 = np.where(np.array(regions_index) == 4)
 
-display = plot_anat(cut_coords=coords, title="Regions from this network")
+display = plot_anat(
+    cut_coords=coords, title="Regions from this network", colorbar=False
+)
 
 # Add as an overlay all the regions of index 4
 colors = "rgbcmyk"

dev/_downloads/0b08503308b188b3029a4de1b9022119/plot_visualization.zip

@@ -83,7 +83,6 @@
 plot_stat_map(
     z_map,
     threshold=threshold,
-    colorbar=True,
     title="Group-level association between motor activity \n"
     "and reading fluency (fdr=0.05)",
     cut_coords=cut_coords,
@@ -119,7 +118,6 @@
 )
 plot_stat_map(
     neg_log_pval,
-    colorbar=True,
     cut_coords=cut_coords,
     threshold=threshold,
     title=title,
@@ -153,7 +151,6 @@
 )
 plot_stat_map(
     neg_log_pvals_permuted_ols_unmasked,
-    colorbar=True,
     cut_coords=cut_coords,
     threshold=threshold,
     title=title,

dev/_downloads/0b94243b28882337066a6039ca45a95f/plot_mask_large_fmri.zip

@@ -166,7 +166,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.plotting import plot_stat_map, show\n\ncut_coords = [10, -5, 10]\n\nplot_stat_map(\n    z_map,\n    threshold=threshold,\n    colorbar=True,\n    title=\"Group-level association between motor activity \\n\"\n    \"and reading fluency (fdr=0.05)\",\n    cut_coords=cut_coords,\n    draw_cross=False,\n)\n\nshow()"
+        "from nilearn.plotting import plot_stat_map, show\n\ncut_coords = [10, -5, 10]\n\nplot_stat_map(\n    z_map,\n    threshold=threshold,\n    title=\"Group-level association between motor activity \\n\"\n    \"and reading fluency (fdr=0.05)\",\n    cut_coords=cut_coords,\n    draw_cross=False,\n)\n\nshow()"
       ]
     },
     {
@@ -202,7 +202,7 @@
       },
       "outputs": [],
       "source": [
-        "# Since we are plotting negative log p-values and using a threshold equal to 1,\n# it corresponds to corrected p-values lower than 10%, meaning that there\n# is less than 10% probability to make a single false discovery\n# (90% chance that we make no false discoveries at all).\n# This threshold is much more conservative than the previous one.\nthreshold = 1\ntitle = (\n    \"Group-level association between motor activity and reading: \\n\"\n    \"neg-log of parametric corrected p-values (FWER < 10%)\"\n)\nplot_stat_map(\n    neg_log_pval,\n    colorbar=True,\n    cut_coords=cut_coords,\n    threshold=threshold,\n    title=title,\n    vmin=threshold,\n    cmap=\"inferno\",\n    draw_cross=False,\n)\nshow()"
+        "# Since we are plotting negative log p-values and using a threshold equal to 1,\n# it corresponds to corrected p-values lower than 10%, meaning that there\n# is less than 10% probability to make a single false discovery\n# (90% chance that we make no false discoveries at all).\n# This threshold is much more conservative than the previous one.\nthreshold = 1\ntitle = (\n    \"Group-level association between motor activity and reading: \\n\"\n    \"neg-log of parametric corrected p-values (FWER < 10%)\"\n)\nplot_stat_map(\n    neg_log_pval,\n    cut_coords=cut_coords,\n    threshold=threshold,\n    title=title,\n    vmin=threshold,\n    cmap=\"inferno\",\n    draw_cross=False,\n)\nshow()"
       ]
     },
     {
@@ -238,7 +238,7 @@
       },
       "outputs": [],
       "source": [
-        "title = (\n    \"Group-level association between motor activity and reading: \\n\"\n    \"neg-log of non-parametric corrected p-values (FWER < 10%)\"\n)\nplot_stat_map(\n    neg_log_pvals_permuted_ols_unmasked,\n    colorbar=True,\n    cut_coords=cut_coords,\n    threshold=threshold,\n    title=title,\n    vmin=threshold,\n    cmap=\"inferno\",\n    draw_cross=False,\n)\nshow()\n\n# The neg-log p-values obtained with non parametric testing are capped at 3\n# since the number of permutations is 1e3.\n# The non parametric test yields a few more discoveries\n# and is then more powerful than the usual parametric procedure."
+        "title = (\n    \"Group-level association between motor activity and reading: \\n\"\n    \"neg-log of non-parametric corrected p-values (FWER < 10%)\"\n)\nplot_stat_map(\n    neg_log_pvals_permuted_ols_unmasked,\n    cut_coords=cut_coords,\n    threshold=threshold,\n    title=title,\n    vmin=threshold,\n    cmap=\"inferno\",\n    draw_cross=False,\n)\nshow()\n\n# The neg-log p-values obtained with non parametric testing are capped at 3\n# since the number of permutations is 1e3.\n# The non parametric test yields a few more discoveries\n# and is then more powerful than the usual parametric procedure."
       ]
     }
   ],

dev/_downloads/1020ea2b51f8e38daf607e192d4bdc5d/plot_oasis.py

@@ -119,7 +119,7 @@
       },
       "outputs": [],
       "source": [
-        "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()"
+        "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)\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()"
       ]
     },
     {
@@ -137,7 +137,7 @@
       },
       "outputs": [],
       "source": [
-        "view = view_connectome(\n    corr,\n    coordinates,\n    edge_threshold=\"90%\",\n    colorbar=False,\n)\n\n# uncomment this to open the plot in a web browser:\n# view.open_in_browser()\nview"
+        "view = view_connectome(\n    corr,\n    coordinates,\n    edge_threshold=\"90%\",\n)\n\n# uncomment this to open the plot in a web browser:\n# view.open_in_browser()\nview"
       ]
     },
     {

dev/_downloads/126eff4ec954cc958192cc36d5123433/plot_visualize_megatrawls_netmats.ipynb

@@ -141,7 +141,6 @@
 # But the variance in the unpaired image is larger.
 plot_glass_brain(
     stat_maps_unpaired["effect_variance"],
-    colorbar=True,
     vmin=0,
     vmax=6,
     cmap="inferno",
@@ -150,7 +149,6 @@
 
 plot_glass_brain(
     stat_maps_paired["effect_variance"],
-    colorbar=True,
     vmin=0,
     vmax=6,
     cmap="inferno",
@@ -166,7 +164,6 @@
 plot_glass_brain(
     stat_maps_unpaired["z_score"],
     threshold=threshold,
-    colorbar=True,
     plot_abs=False,
     vmax=5.8,
     title="vertical vs horizontal (unc p<0.001), unpaired",
@@ -175,7 +172,6 @@
 plot_glass_brain(
     stat_maps_paired["z_score"],
     threshold=threshold,
-    colorbar=True,
     plot_abs=False,
     vmax=5.8,
     title="vertical vs horizontal (unc p<0.001), paired",

dev/_downloads/12cc2d26d59ec4b13cb8816906c03e68/plot_second_level_two_sample_test.ipynb

@@ -70,7 +70,6 @@
     surf_mesh=fsaverage_meshes["inflated"],
     hemi=hemi,
     title="Surface with matplotlib",
-    colorbar=True,
     threshold=1.0,
     bg_map=curv_sign,
 )
@@ -96,7 +95,6 @@
     surf_mesh=fsaverage_meshes["inflated"],
     hemi=hemi,
     title=f"Surface with {engine}",
-    colorbar=True,
     threshold=1.0,
     bg_map=curv_sign,
     bg_on_data=True,
@@ -183,7 +181,6 @@
     surf_mesh=fsaverage_meshes["inflated"],
     hemi=hemi,
     title="ROI outlines on surface",
-    colorbar=True,
     threshold=1.0,
     bg_map=fsaverage_sulcal,
     engine=engine,
@@ -240,7 +237,6 @@
     stat_map=big_img,
     surf_mesh=big_fsaverage_meshes["inflated"],
     hemi=hemi,
-    colorbar=True,
     title="Surface fine mesh",
     threshold=1.0,
     bg_map=big_fsaverage_sulcal,
@@ -264,7 +260,6 @@
     stat_map=stat_img,
     views=["lateral", "medial"],
     hemispheres=["left", "right"],
-    colorbar=True,
     title="multiple views of the 3D volume",
     bg_on_data=True,
 )

dev/_downloads/12e9d2a7add7eab2059f4f619ad12621/plot_data_driven_parcellations.zip

@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "let's create a figure with all the views for both hemispheres\n\n"
+        "let's create a figure with several views for both hemispheres\n\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "views = [\n    \"lateral\",\n    \"medial\",\n    \"dorsal\",\n    \"ventral\",\n    \"anterior\",\n    \"posterior\",\n]\nhemispheres = [\"left\", \"right\", \"both\"]"
+        "views = [\n    \"lateral\",\n    \"dorsal\",\n]\nhemispheres = [\"left\", \"right\", \"both\"]"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "vmax = max(np.absolute(hemi).max() for hemi in mean_img.data.parts.values())\nvmin = -vmax\n\nfor view, ax_row in zip(views, axes):\n    for ax, hemi in zip(ax_row, hemispheres):\n        if hemi == \"both\" and view == \"lateral\":\n            view = \"left\"\n        elif hemi == \"both\" and view == \"medial\":\n            view = \"right\"\n        plot_surf(\n            surf_map=mean_img,\n            hemi=hemi,\n            view=view,\n            figure=fig,\n            axes=ax,\n            title=f\"mean image - {hemi} - {view}\",\n            colorbar=False,\n            symmetric_cmap=True,\n            bg_on_data=True,\n            vmin=vmin,\n            vmax=vmax,\n            bg_map=fsaverage_sulcal,\n            cmap=\"seismic\",\n        )\nfig.set_size_inches(12, 8)\n\nshow()"
+        "vmax = max(np.absolute(hemi).max() for hemi in mean_img.data.parts.values())\nvmin = -vmax\n\nfor view, ax_row in zip(views, axes):\n    for ax, hemi in zip(ax_row, hemispheres):\n        if hemi == \"both\" and view == \"lateral\":\n            view = \"left\"\n        elif hemi == \"both\" and view == \"medial\":\n            view = \"right\"\n        plot_surf(\n            surf_map=mean_img,\n            hemi=hemi,\n            view=view,\n            figure=fig,\n            axes=ax,\n            title=f\"{hemi} - {view}\",\n            colorbar=False,\n            symmetric_cmap=True,\n            bg_on_data=True,\n            vmin=vmin,\n            vmax=vmax,\n            bg_map=fsaverage_sulcal,\n            cmap=\"seismic\",\n        )\nfig.set_size_inches(12, 8)\n\nshow()"
       ]
     },
     {
@@ -177,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.decoding import Decoder\n\n# create some random labels\nrng = np.random.RandomState(0)\nn_time_points = surf_img_nki.shape[1]\ny = rng.choice(\n    [0, 1],\n    replace=True,\n    size=n_time_points,\n)\n\ndecoder = Decoder(\n    mask=SurfaceMasker(),\n    param_grid={\"C\": [0.01, 0.1]},\n    cv=3,\n    screening_percentile=1,\n)\ndecoder.fit(surf_img_nki, y)\nprint(\"CV scores:\", decoder.cv_scores_)\n\nplot_surf(\n    surf_map=decoder.coef_img_[0],\n    threshold=1e-6,\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    colorbar=True,\n    cmap=\"inferno\",\n    vmin=0,\n)\nshow()"
+        "from nilearn.decoding import Decoder\n\n# create some random labels\nrng = np.random.RandomState(0)\nn_time_points = surf_img_nki.shape[1]\ny = rng.choice(\n    [0, 1],\n    replace=True,\n    size=n_time_points,\n)\n\ndecoder = Decoder(\n    mask=SurfaceMasker(),\n    param_grid={\"C\": [0.01, 0.1]},\n    cv=3,\n    screening_percentile=1,\n)\ndecoder.fit(surf_img_nki, y)\nprint(\"CV scores:\", decoder.cv_scores_)\n\nplot_surf(\n    surf_map=decoder.coef_img_[0],\n    threshold=1e-6,\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    cmap=\"inferno\",\n    vmin=0,\n)\nshow()"
       ]
     },
     {
@@ -195,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn import feature_selection, linear_model, pipeline, preprocessing\n\ndecoder = pipeline.make_pipeline(\n    SurfaceMasker(),\n    preprocessing.StandardScaler(),\n    feature_selection.SelectKBest(\n        score_func=feature_selection.f_regression, k=500\n    ),\n    linear_model.Ridge(),\n)\ndecoder.fit(surf_img_nki, y)\n\ncoef_img = decoder[:-1].inverse_transform(np.atleast_2d(decoder[-1].coef_))\n\nvmax = max(np.absolute(hemi).max() for hemi in coef_img.data.parts.values())\nvmin = -vmax\nplot_surf(\n    surf_map=coef_img,\n    cmap=\"RdBu_r\",\n    vmin=vmin,\n    vmax=vmax,\n    threshold=1e-6,\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n    colorbar=True,\n)\nshow()"
+        "from sklearn import feature_selection, linear_model, pipeline, preprocessing\n\ndecoder = pipeline.make_pipeline(\n    SurfaceMasker(),\n    preprocessing.StandardScaler(),\n    feature_selection.SelectKBest(\n        score_func=feature_selection.f_regression, k=500\n    ),\n    linear_model.Ridge(),\n)\ndecoder.fit(surf_img_nki, y)\n\ncoef_img = decoder[:-1].inverse_transform(np.atleast_2d(decoder[-1].coef_))\n\nvmax = max(np.absolute(hemi).max() for hemi in coef_img.data.parts.values())\nvmin = -vmax\nplot_surf(\n    surf_map=coef_img,\n    cmap=\"RdBu_r\",\n    vmin=vmin,\n    vmax=vmax,\n    threshold=1e-6,\n    bg_map=fsaverage_sulcal,\n    bg_on_data=True,\n)\nshow()"
       ]
     }
   ],

dev/_downloads/193d69e0f7475d4607f305617999a80e/plot_surf_stat_map.py

@@ -32,7 +32,7 @@
 
 from nilearn.plotting import plot_epi, show
 
-plot_epi(mean_haxby, colorbar=True, cbar_tick_format="%i")
+plot_epi(mean_haxby, cbar_tick_format="%i")
 
 # %%
 # Extracting a brain mask
@@ -45,7 +45,7 @@
 # Visualize it as an ROI
 from nilearn.plotting import plot_roi
 
-plot_roi(mask_img, mean_haxby)
+plot_roi(mask_img, mean_haxby, colorbar=False)
 
 # %%
 # Applying the mask to extract the corresponding time series

dev/_downloads/1c5676eb2c85c27dffcfba93ccaa900c/plot_bids_features.py

@@ -105,7 +105,6 @@
 display = plotting.plot_glass_brain(
     z_map,
     threshold=p001_unc,
-    colorbar=True,
     display_mode="z",
     plot_abs=False,
     title="group left-right button press (unc p<0.001)",
@@ -212,7 +211,6 @@
     ax = axes[i_row, j_col]
     plotting.plot_glass_brain(
         IMAGES[img_counter],
-        colorbar=True,
         vmax=vmax,
         vmin=threshold,
         display_mode="z",