dev docs: nilearn/nilearn@5de9169 : [MAINT] clean up architecture restriction (#5890)

Author: actions-user

dev/_downloads/0140eeb6da671d73b808df162f8895e8/plot_simulated_data.zip

@@ -65,7 +65,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Extract the largest clusters\n\n"
+        "## Extract the largest clusters\nWe can extract the 6 largest clusters surviving our threshold.\nand get the x, y, and z coordinates of their peaks.\nWe then extract the time series from a sphere around each coordinate.\n\n\n"
       ]
     },
     {
@@ -76,7 +76,25 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.maskers import NiftiSpheresMasker\nfrom nilearn.reporting import get_clusters_table\n\ntable = get_clusters_table(\n    z_map, stat_threshold=threshold, cluster_threshold=20\n)\ntable.set_index(\"Cluster ID\", drop=True)\ntable.head()\n\n# get the 6 largest clusters' max x, y, and z coordinates\ncoords = table.loc[range(1, 7), [\"X\", \"Y\", \"Z\"]].to_numpy()\n\n# extract time series from each coordinate\nmasker = NiftiSpheresMasker(coords)\nreal_timeseries = masker.fit_transform(fmri_img)\npredicted_timeseries = masker.fit_transform(fmri_glm.predicted[0])"
+        "from nilearn.maskers import NiftiSpheresMasker\nfrom nilearn.reporting import get_clusters_table\n\ntable = get_clusters_table(\n    z_map, stat_threshold=threshold, cluster_threshold=20\n)\ntable.set_index(\"Cluster ID\", drop=True)\nprint(table)\n\ncoords = table.loc[range(1, 7), [\"X\", \"Y\", \"Z\"]].to_numpy()\nprint(coords)\n\nmasker = NiftiSpheresMasker(coords)\nreal_timeseries = masker.fit_transform(fmri_img)\npredicted_timeseries = masker.fit_transform(fmri_glm.predicted[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's have a look at the report to make sure\nthe spheres are well placed.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "report = masker.generate_report()\nreport"
       ]
     },
     {

dev/_downloads/034e4a0d5c805cf4938eba117765c0ee/plot_predictions_residuals.ipynb

@@ -166,47 +166,23 @@
 # gray matter density on that dataset.
 
 # %%
-# Saving model outputs to disk
-# ----------------------------
-# It can be useful to quickly generate a portable, ready-to-view report with
-# most of the pertinent information.
-# We can do this by saving the output of the GLM to disk
-# including an HTML report.
-# This is easy to do if you have a fitted model and the list of contrasts,
-# which we do here.
-from pathlib import Path
-
-from nilearn.glm import save_glm_to_bids
-
-output_dir = Path.cwd() / "results" / "plot_oasis"
-output_dir.mkdir(exist_ok=True, parents=True)
+# Generate a report for the GLM
+# -----------------------------
+#
+# Generate a report and view it.
+#
 
 icbm152_2009 = fetch_icbm152_2009()
 
-second_level_model = save_glm_to_bids(
-    second_level_model,
-    contrasts=["age", "sex"],
-    out_dir=output_dir / "derivatives" / "nilearn_glm",
-    prefix="ageEffectOnGM",
-    bg_img=icbm152_2009["t1"],
-    alpha=0.05,
-    height_control="fdr",
-)
-
-# %%
-# View the generated files
-files = sorted((output_dir / "derivatives" / "nilearn_glm").glob("**/*"))
-print("\n".join([str(x.relative_to(output_dir)) for x in files]))
-
-#  %%
-# Generate a report and view it.
-# If no new contrast is passed to ``generate_report``,
-# the results saved to disk will be reused to generate the report.
-
 report = second_level_model.generate_report(
     bg_img=icbm152_2009["t1"],
     plot_type="glass",
     alpha=0.05,
     height_control=None,
 )
-report.save_as_html(output_dir / "report.html")
+
+# %%
+#
+# .. include:: ../../../examples/report_note.rst
+#
+report