dev docs nilearn/nilearn@7ce39ab : [ENH] update default colors (#4959)

dev/_downloads/00b63a2f3c19f56521d9ac182410c025/plot_simulated_data.py

@@ -258,4 +258,4 @@ def plot_slices(data, title=None):
 # References
 # ----------
 #
-#  .. footbibliography::
+# .. footbibliography::

dev/_downloads/01cfe421c270935a30bc814b91794c28/plot_haxby_mass_univariate.ipynb

@@ -11,7 +11,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Load Haxby dataset\n\n"
+        "## Load Haxby dataset\n\n"
       ]
     },
     {
@@ -29,7 +29,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Restrict to faces and houses\n\n"
+        "## Restrict to faces and houses\n\n"
       ]
     },
     {
@@ -47,7 +47,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Mask data\n\n"
+        "## Mask data\n\n"
       ]
     },
     {
@@ -65,7 +65,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Perform massively univariate analysis with permuted OLS\n\nWe use a two-sided t-test to compute p-values, but we keep trace of the\neffect sign to add it back at the end and thus observe the signed effect\n\n"
+        "## Perform massively univariate analysis with permuted OLS\n\nWe use a two-sided t-test to compute p-values, but we keep trace of the\neffect sign to add it back at the end and thus observe the signed effect\n\n"
       ]
     },
     {
@@ -101,7 +101,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Visualization\n\n"
+        "## Visualization\n\n"
       ]
     },
     {
@@ -112,14 +112,14 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nfrom nilearn.image import get_data\n\n# Use the fMRI mean image as a surrogate of anatomical data\nmean_fmri_img = image.mean_img(func_filename, copy_header=True)\n\nthreshold = -np.log10(0.1)  # 10% corrected\n\nvmax = min(signed_neg_log_pvals.max(), neg_log_pvals_bonferroni.max())\n\n# Plot thresholded p-values map corresponding to F-scores\ndisplay = plot_stat_map(\n    neg_log_pvals_bonferroni_unmasked,\n    mean_fmri_img,\n    threshold=threshold,\n    cmap=plt.cm.RdBu_r,\n    display_mode=\"z\",\n    cut_coords=[-1],\n    vmax=vmax,\n)\n\nneg_log_pvals_bonferroni_data = get_data(neg_log_pvals_bonferroni_unmasked)\nn_detections = (neg_log_pvals_bonferroni_data > threshold).sum()\ntitle = (\n    \"Negative $\\\\log_{10}$ p-values\"\n    \"\\n(Parametric two-sided F-test\"\n    \"\\n+ Bonferroni correction)\"\n    f\"\\n{n_detections} detections\"\n)\n\ndisplay.title(title, size=10)\n\n# Plot permutation p-values map\ndisplay = plot_stat_map(\n    signed_neg_log_pvals_unmasked,\n    mean_fmri_img,\n    threshold=threshold,\n    cmap=plt.cm.RdBu_r,\n    display_mode=\"z\",\n    cut_coords=[-1],\n    vmax=vmax,\n)\n\nn_detections = (np.abs(signed_neg_log_pvals) > threshold).sum()\ntitle = (\n    \"Negative $\\\\log_{10}$ p-values\"\n    \"\\n(Non-parametric two-sided test\"\n    \"\\n+ max-type correction)\"\n    f\"\\n{n_detections} detections\"\n)\n\ndisplay.title(title, size=10)\n\nshow()"
+        "from nilearn.image import get_data\n\n# Use the fMRI mean image as a surrogate of anatomical data\nmean_fmri_img = image.mean_img(func_filename, copy_header=True)\n\nthreshold = -np.log10(0.1)  # 10% corrected\n\nvmax = min(signed_neg_log_pvals.max(), neg_log_pvals_bonferroni.max())\n\n# Plot thresholded p-values map corresponding to F-scores\n\nneg_log_pvals_bonferroni_data = get_data(neg_log_pvals_bonferroni_unmasked)\nn_detections = (neg_log_pvals_bonferroni_data > threshold).sum()\ntitle = (\n    \"Negative $\\\\log_{10}$ p-values\"\n    \"\\n(Parametric two-sided F-test\"\n    \"\\n+ Bonferroni correction)\"\n    f\"\\n{n_detections} detections\"\n)\n\ndisplay = plot_stat_map(\n    neg_log_pvals_bonferroni_unmasked,\n    mean_fmri_img,\n    threshold=threshold,\n    display_mode=\"z\",\n    cut_coords=[-1],\n    vmax=vmax,\n    vmin=threshold,\n    cmap=\"inferno\",\n)\n\ndisplay.title(title, size=10)\n\n# Plot permutation p-values map\nn_detections = (np.abs(signed_neg_log_pvals) > threshold).sum()\ntitle = (\n    \"Negative $\\\\log_{10}$ p-values\"\n    \"\\n(Non-parametric two-sided test\"\n    \"\\n+ max-type correction)\"\n    f\"\\n{n_detections} detections\"\n)\n\ndisplay = plot_stat_map(\n    signed_neg_log_pvals_unmasked,\n    mean_fmri_img,\n    threshold=threshold,\n    display_mode=\"z\",\n    cut_coords=[-1],\n    vmax=vmax,\n    vmin=threshold,\n    cmap=\"inferno\",\n)\n\ndisplay.title(title, size=10)\n\nshow()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## References\n\n .. footbibliography::\n\n"
+        "## References\n\n.. footbibliography::\n\n"
       ]
     }
   ],

dev/_downloads/034e4a0d5c805cf4938eba117765c0ee/plot_predictions_residuals.ipynb

@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "plot_stat_map(\n    fmri_glm.r_square[0],\n    bg_img=mean_img,\n    threshold=0.1,\n    display_mode=\"z\",\n    cut_coords=7,\n    cmap=\"black_red\",\n    title=\"R-squared\",\n    vmin=0,\n    symmetric_cbar=False,\n)"
+        "plot_stat_map(\n    fmri_glm.r_square[0],\n    bg_img=mean_img,\n    threshold=0.1,\n    display_mode=\"z\",\n    cut_coords=7,\n    cmap=\"inferno\",\n    title=\"R-squared\",\n    vmin=0,\n    symmetric_cbar=False,\n)"
       ]
     },
     {
@@ -166,7 +166,7 @@
       },
       "outputs": [],
       "source": [
-        "# f-test for 'listening'\nz_map_ftest = fmri_glm.compute_contrast(\n    \"listening\", stat_type=\"F\", output_type=\"z_score\"\n)\n\nplot_stat_map(\n    z_map_ftest,\n    bg_img=mean_img,\n    threshold=threshold,\n    display_mode=\"z\",\n    cut_coords=7,\n    cmap=\"black_red\",\n    title=f\"listening > rest (F-test; Z>{threshold})\",\n    symmetric_cbar=False,\n    vmin=0,\n)\n\nshow()"
+        "# f-test for 'listening'\nz_map_ftest = fmri_glm.compute_contrast(\n    \"listening\", stat_type=\"F\", output_type=\"z_score\"\n)\n\nplot_stat_map(\n    z_map_ftest,\n    bg_img=mean_img,\n    threshold=threshold,\n    display_mode=\"z\",\n    cut_coords=7,\n    cmap=\"inferno\",\n    title=f\"listening > rest (F-test; Z>{threshold})\",\n    symmetric_cbar=False,\n    vmin=0,\n)\n\nshow()"
       ]
     }
   ],

dev/_downloads/0819560eb93050d61651e3d779f2047b/plot_haxby_multiclass.ipynb

@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import confusion_matrix\n\nsvc_ovo.fit(X[run < 10], y[run < 10])\ny_pred_ovo = svc_ovo.predict(X[run >= 10])\n\nplot_matrix(\n    confusion_matrix(y_pred_ovo, y[run >= 10]),\n    labels=unique_conditions,\n    title=\"Confusion matrix: One vs One\",\n    cmap=\"hot_r\",\n)\n\nsvc_ova.fit(X[run < 10], y[run < 10])\ny_pred_ova = svc_ova.predict(X[run >= 10])\n\nplot_matrix(\n    confusion_matrix(y_pred_ova, y[run >= 10]),\n    labels=unique_conditions,\n    title=\"Confusion matrix: One vs All\",\n    cmap=\"hot_r\",\n)\n\nshow()"
+        "from sklearn.metrics import confusion_matrix\n\nsvc_ovo.fit(X[run < 10], y[run < 10])\ny_pred_ovo = svc_ovo.predict(X[run >= 10])\n\nplot_matrix(\n    confusion_matrix(y_pred_ovo, y[run >= 10]),\n    labels=unique_conditions,\n    title=\"Confusion matrix: One vs One\",\n    cmap=\"inferno\",\n)\n\nsvc_ova.fit(X[run < 10], y[run < 10])\ny_pred_ova = svc_ova.predict(X[run >= 10])\n\nplot_matrix(\n    confusion_matrix(y_pred_ova, y[run >= 10]),\n    labels=unique_conditions,\n    title=\"Confusion matrix: One vs All\",\n    cmap=\"inferno\",\n)\n\nshow()"
       ]
     }
   ],

dev/_downloads/0a08d2a6f3fe90c61fff00f683f2fa0a/plot_3d_and_4d_niimg.py

@@ -33,13 +33,13 @@
 #
 # The file contains a 3D volume, we can easily visualize it as a
 # statistical map:
-from nilearn import plotting
+from nilearn.plotting import plot_stat_map, show
 
-plotting.plot_stat_map(tmap_filename)
+plot_stat_map(tmap_filename)
 
 # %%
 # Visualizing works better with a threshold
-plotting.plot_stat_map(tmap_filename, threshold=3)
+plot_stat_map(tmap_filename, threshold=3)
 
 
 # %%
@@ -67,7 +67,7 @@
 # first_rsn is a 3D image.
 #
 # We can then plot it
-plotting.plot_stat_map(first_rsn)
+plot_stat_map(first_rsn)
 
 
 # %%
@@ -81,7 +81,7 @@
 # compact display.
 for img in image.iter_img(rsn):
     # img is now an in-memory 3D img
-    plotting.plot_stat_map(
+    plot_stat_map(
         img, threshold=3, display_mode="z", cut_coords=1, colorbar=False
     )
 
@@ -103,13 +103,13 @@
 # to the previous plot above.
 
 for img in image.iter_img(selected_volumes):
-    plotting.plot_stat_map(img)
+    plot_stat_map(img)
 
 
 # %%
 # plotting.show is useful to force the display of figures when running
 # outside IPython
-plotting.show()
+show()
 
 # %%
 # |

dev/_downloads/15bf7d6d499dfe919be74278af66d069/plot_simulated_data.ipynb

@@ -130,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## References\n\n .. footbibliography::\n\n"
+        "## References\n\n.. footbibliography::\n\n"
       ]
     }
   ],

dev/_downloads/1762b321e2d54728de2d7ecfc8d506f2/plot_oasis_vbm.py

@@ -60,7 +60,7 @@
 from nilearn.image import get_data
 from nilearn.maskers import NiftiMasker
 
-n_subjects = 100  # more subjects requires more memory
+n_subjects = 200  # more subjects requires more memory
 
 # %%
 # Load Oasis dataset
@@ -154,9 +154,13 @@
 bg_filename = gray_matter_map_filenames[0]
 z_slice = 0
 display = plot_stat_map(
-    weight_img, bg_img=bg_filename, display_mode="z", cut_coords=[z_slice]
+    weight_img,
+    bg_img=bg_filename,
+    display_mode="z",
+    cut_coords=[z_slice],
+    title="SVM weights",
+    cmap="cold_hot",
 )
-display.title("SVM weights")
 show()
 
 # %%
@@ -206,24 +210,22 @@
 # Show results
 threshold = -np.log10(0.1)  # 10% corrected
 
-fig = plt.figure(figsize=(5.5, 7.5), facecolor="k")
+n_detections = (get_data(signed_neg_log_pvals_unmasked) > threshold).sum()
 
-display = plot_stat_map(
+title = (
+    "Negative $\\log_{10}$ p-values\n(Non-parametric + max-type correction)"
+    f"\n{int(n_detections)} detections"
+)
+
+plot_stat_map(
     signed_neg_log_pvals_unmasked,
     bg_img=bg_filename,
     threshold=threshold,
-    cmap=plt.cm.RdBu_r,
     display_mode="z",
     cut_coords=[z_slice],
-    figure=fig,
+    figure=plt.figure(figsize=(5.5, 7.5), facecolor="k"),
+    title=title,
 )
-title = (
-    "Negative $\\log_{10}$ p-values\n(Non-parametric + max-type correction)"
-)
-display.title(title)
-
-n_detections = (get_data(signed_neg_log_pvals_unmasked) > threshold).sum()
-print(f"\n{int(n_detections)} detections")
 
 show()
 

dev/_downloads/193d69e0f7475d4607f305617999a80e/plot_surf_stat_map.py

@@ -18,10 +18,12 @@
 The :func:`~nilearn.plotting.plot_surf_stat_map` function is used
 to plot the resulting statistical map on the pial surface.
 
-See also :ref:`for a similar example but using volumetric input data
-<sphx_glr_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py>`.
+.. seealso::
 
-See :ref:`plotting` for more details on plotting tools.
+    :ref:`for a similar example but using volumetric input data
+    <sphx_glr_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py>`.
+
+    See :ref:`plotting` for more details on plotting tools.
 """
 
 # %%
@@ -271,7 +273,7 @@
 # References
 # ----------
 #
-#  .. footbibliography::
+# .. footbibliography::
 
 
 # sphinx_gallery_thumbnail_number = 2

dev/_downloads/2002f5d0c70b1223f14a1b2bac383088/plot_extract_regions_dictlearning_maps.py

@@ -176,7 +176,8 @@
 display = plotting.plot_stat_map(
     img,
     cut_coords=coords,
-    colorbar=False,
+    colorbar=True,
+    cmap="inferno",
     title="Showing one specific network",
 )
 

dev/_downloads/22639334ce8d30070c745bfc1e0d1bc8/plot_second_level_association_test.py

@@ -86,12 +86,16 @@
 # Let us plot the second level :term:`contrast` at the computed thresholds.
 from nilearn.plotting import plot_stat_map, show
 
+cut_coords = [10, -5, 10]
+
 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,
+    draw_cross=False,
 )
 
 show()
@@ -110,7 +114,7 @@
 
 # %%
 # Let us plot the (corrected) negative log  p-values for the parametric test
-cut_coords = [38, -17, -3]
+
 # Since we are plotting negative log p-values and using a threshold equal to 1,
 # it corresponds to corrected p-values lower than 10%, meaning that there
 # is less than 10% probability to make a single false discovery
@@ -127,6 +131,9 @@
     cut_coords=cut_coords,
     threshold=threshold,
     title=title,
+    vmin=threshold,
+    cmap="inferno",
+    draw_cross=False,
 )
 show()
 
@@ -158,6 +165,9 @@
     cut_coords=cut_coords,
     threshold=threshold,
     title=title,
+    vmin=threshold,
+    cmap="inferno",
+    draw_cross=False,
 )
 show()
 

dev/_downloads/228040370ea48e92e9ffdf4d7fe54686/plot_compare_decomposition.ipynb

@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.image import iter_img\nfrom nilearn.plotting import plot_stat_map, show\n\nfor i, cur_img in enumerate(iter_img(canica_components_img)):\n    plot_stat_map(\n        cur_img,\n        display_mode=\"z\",\n        title=f\"IC {int(i)}\",\n        cut_coords=1,\n        colorbar=False,\n    )"
+        "from nilearn.image import iter_img\nfrom nilearn.plotting import plot_stat_map, show\n\nfor i, cur_img in enumerate(iter_img(canica_components_img)):\n    plot_stat_map(\n        cur_img,\n        display_mode=\"z\",\n        title=f\"IC {int(i)}\",\n        cut_coords=1,\n        vmax=0.05,\n        vmin=-0.05,\n        colorbar=False,\n    )\n\n\nshow()"
       ]
     },
     {
@@ -137,7 +137,7 @@
       },
       "outputs": [],
       "source": [
-        "for i, cur_img in enumerate(iter_img(dictlearning_components_img)):\n    plot_stat_map(\n        cur_img,\n        display_mode=\"z\",\n        title=f\"Comp {int(i)}\",\n        cut_coords=1,\n        colorbar=False,\n    )"
+        "for i, cur_img in enumerate(iter_img(dictlearning_components_img)):\n    plot_stat_map(\n        cur_img,\n        display_mode=\"z\",\n        title=f\"Comp {int(i)}\",\n        cut_coords=1,\n        vmax=0.1,\n        vmin=-0.1,\n        colorbar=False,\n    )"
       ]
     },
     {
@@ -169,7 +169,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## References\n\n .. footbibliography::\n\n"
+        "## References\n\n.. footbibliography::\n\n"
       ]
     }
   ],