dev docs: nilearn/nilearn@329d901 : [FIX] standardize verbosity (#5778)

Author: actions-user

dev/_downloads/0140eeb6da671d73b808df162f8895e8/plot_simulated_data.zip

@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.image import index_img\nfrom nilearn.maskers import NiftiMasker\n\nmask_filename = haxby_dataset.mask\n\nnifti_masker = NiftiMasker(\n    smoothing_fwhm=8,\n    mask_img=mask_filename,\n    memory=\"nilearn_cache\",  # cache options\n    memory_level=1,\n)\nfunc_filename = haxby_dataset.func[0]\nfunc_reduced = index_img(func_filename, condition_mask)\nfmri_masked = nifti_masker.fit_transform(func_reduced)\n\n# We consider the mean image per run and per condition.\n# Otherwise, the observations cannot be exchanged at random because\n# a time dependence exists between observations within a same run.\nn_runs = np.unique(runs).size\nconditions_per_run = 2\ngrouped_fmri_masked = np.empty(\n    (conditions_per_run * n_runs, fmri_masked.shape[1])\n)\ngrouped_conditions_encoded = np.empty((conditions_per_run * n_runs, 1))\n\nfor s in range(n_runs):\n    run_mask = runs[condition_mask] == s\n    run_house_mask = np.logical_and(\n        run_mask, conditions[condition_mask] == \"house\"\n    )\n    run_face_mask = np.logical_and(\n        run_mask, conditions[condition_mask] == \"face\"\n    )\n    grouped_fmri_masked[2 * s] = fmri_masked[run_house_mask].mean(0)\n    grouped_fmri_masked[2 * s + 1] = fmri_masked[run_face_mask].mean(0)\n    grouped_conditions_encoded[2 * s] = conditions_encoded[run_house_mask][0]\n    grouped_conditions_encoded[2 * s + 1] = conditions_encoded[run_face_mask][\n        0\n    ]"
+        "from nilearn.image import index_img\nfrom nilearn.maskers import NiftiMasker\n\nmask_filename = haxby_dataset.mask\n\nnifti_masker = NiftiMasker(\n    smoothing_fwhm=8,\n    mask_img=mask_filename,\n    memory=\"nilearn_cache\",  # cache options\n    memory_level=1,\n    verbose=1,\n)\nfunc_filename = haxby_dataset.func[0]\nfunc_reduced = index_img(func_filename, condition_mask)\nfmri_masked = nifti_masker.fit_transform(func_reduced)\n\n# We consider the mean image per run and per condition.\n# Otherwise, the observations cannot be exchanged at random because\n# a time dependence exists between observations within a same run.\nn_runs = np.unique(runs).size\nconditions_per_run = 2\ngrouped_fmri_masked = np.empty(\n    (conditions_per_run * n_runs, fmri_masked.shape[1])\n)\ngrouped_conditions_encoded = np.empty((conditions_per_run * n_runs, 1))\n\nfor s in range(n_runs):\n    run_mask = runs[condition_mask] == s\n    run_house_mask = np.logical_and(\n        run_mask, conditions[condition_mask] == \"house\"\n    )\n    run_face_mask = np.logical_and(\n        run_mask, conditions[condition_mask] == \"face\"\n    )\n    grouped_fmri_masked[2 * s] = fmri_masked[run_house_mask].mean(0)\n    grouped_fmri_masked[2 * s + 1] = fmri_masked[run_face_mask].mean(0)\n    grouped_conditions_encoded[2 * s] = conditions_encoded[run_house_mask][0]\n    grouped_conditions_encoded[2 * s + 1] = conditions_encoded[run_face_mask][\n        0\n    ]"
       ]
     },
     {

dev/_downloads/01cfe421c270935a30bc814b91794c28/plot_haxby_mass_univariate.ipynb

@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.glm.first_level import FirstLevelModel\n\nfmri_glm = FirstLevelModel(\n    t_r=subject_data.t_r,\n    drift_model=\"cosine\",\n    signal_scaling=False,\n    mask_img=mask,\n    minimize_memory=False,\n)\n\nfmri_glm = fmri_glm.fit(fmri_img, events)"
+        "from nilearn.glm.first_level import FirstLevelModel\n\nfmri_glm = FirstLevelModel(\n    t_r=subject_data.t_r,\n    drift_model=\"cosine\",\n    signal_scaling=False,\n    mask_img=mask,\n    minimize_memory=False,\n    verbose=1,\n)\n\nfmri_glm = fmri_glm.fit(fmri_img, events)"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "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)\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])"
+        "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, verbose=1)\nreal_timeseries = masker.fit_transform(fmri_img)\npredicted_timeseries = masker.fit_transform(fmri_glm.predicted[0])"
       ]
     },
     {

dev/_downloads/034e4a0d5c805cf4938eba117765c0ee/plot_predictions_residuals.ipynb

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from nilearn.maskers import NiftiMasker\n\n# For decoding, standardizing is often very important\nnifti_masker = NiftiMasker(\n    mask_img=mask_filename,\n    standardize=\"zscore_sample\",\n    runs=run,\n    smoothing_fwhm=4,\n    memory=\"nilearn_cache\",\n    memory_level=1,\n)\nX = nifti_masker.fit_transform(func_filename)\n\n# Remove the \"rest\" condition\nX = X[non_rest]\nrun = run[non_rest]"
+        "from nilearn.maskers import NiftiMasker\n\n# For decoding, standardizing is often very important\nnifti_masker = NiftiMasker(\n    mask_img=mask_filename,\n    standardize=\"zscore_sample\",\n    runs=run,\n    smoothing_fwhm=4,\n    memory=\"nilearn_cache\",\n    memory_level=1,\n    verbose=1,\n)\nX = nifti_masker.fit_transform(func_filename)\n\n# Remove the \"rest\" condition\nX = X[non_rest]\nrun = run[non_rest]"
       ]
     },
     {