neurostuff · tsalo · Mar 21, 2022 · Mar 21, 2022 · Mar 21, 2022 · Mar 21, 2022
diff --git a/examples/01_datasets/01_plot_dataset_io.py b/examples/01_datasets/01_plot_dataset_io.py
@@ -11,7 +11,7 @@
 
 ###############################################################################
 # Start with the necessary imports
-# --------------------------------
+# -----------------------------------------------------------------------------
 import os
 
 from nimare.dataset import Dataset

diff --git a/examples/01_datasets/02_download_neurosynth.py b/examples/01_datasets/02_download_neurosynth.py
@@ -32,7 +32,7 @@
 """
 ###############################################################################
 # Start with the necessary imports
-# --------------------------------
+# -----------------------------------------------------------------------------
 import os
 from pprint import pprint
 
@@ -41,7 +41,7 @@
 
 ###############################################################################
 # Download Neurosynth
-# -------------------
+# -----------------------------------------------------------------------------
 # Neurosynth's data files are stored at https://github.com/neurosynth/neurosynth-data.
 out_dir = os.path.abspath("../example_data/")
 os.makedirs(out_dir, exist_ok=True)
@@ -59,7 +59,7 @@
 
 ###############################################################################
 # Convert Neurosynth database to NiMARE dataset file
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 neurosynth_dset = convert_neurosynth_to_dataset(
     coordinates_file=neurosynth_db["coordinates"],
     metadata_file=neurosynth_db["metadata"],
@@ -70,7 +70,7 @@
 
 ###############################################################################
 # Add article abstracts to dataset
-# --------------------------------
+# -----------------------------------------------------------------------------
 # This is only possible because Neurosynth uses PMIDs as study IDs.
 #
 # Make sure you replace the example email address with your own.
@@ -79,7 +79,7 @@
 
 ###############################################################################
 # Do the same with NeuroQuery
-# ---------------------------
+# -----------------------------------------------------------------------------
 # NeuroQuery's data files are stored at https://github.com/neuroquery/neuroquery_data.
 files = fetch_neuroquery(
     data_dir=out_dir,

diff --git a/examples/01_datasets/03_plot_neurovault_io.py b/examples/01_datasets/03_plot_neurovault_io.py
@@ -14,7 +14,7 @@
 
 ###############################################################################
 # Neurovault + NiMARE: Load freely shared statistical maps for Meta-Analysis
-# --------------------------------------------------------------------------
+# -----------------------------------------------------------------------------
 # `Neurovault <https://neurovault.org/>`_ is an online platform that hosts
 # unthresholded statistical maps, including group statistical maps.
 # NiMARE can read these statistical maps when given a list of collection_ids.
@@ -64,7 +64,7 @@
 
 ###############################################################################
 # Conversion of Statistical Maps
-# ------------------------------
+# -----------------------------------------------------------------------------
 # Some of the statistical maps are T statistics and others are Z statistics.
 # To perform a Fisher's meta analysis, we need all Z maps.
 # Thoughtfully, NiMARE has a class named ``ImageTransformer`` that will
@@ -84,7 +84,7 @@
 
 ###############################################################################
 # Run a Meta-Analysis
-# -------------------
+# -----------------------------------------------------------------------------
 # With the missing Z maps filled in, we can run a Meta-Analysis
 # and plot our results
 from nimare.meta.ibma import Fishers

diff --git a/examples/01_datasets/04_transform_images_to_coordinates.py b/examples/01_datasets/04_transform_images_to_coordinates.py
@@ -27,12 +27,12 @@
 
 ###############################################################################
 # Download data
-# --------------------------------
+# -----------------------------------------------------------------------------
 dset_dir = download_nidm_pain()
 
 ###############################################################################
 # Load Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json")
 dset = Dataset(dset_file)
 dset.update_path(dset_dir)
@@ -53,7 +53,7 @@
 
 ###############################################################################
 # Inspect Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 
 # There is only one study contrast with coordinates, but no images
 print(f"studies with only coordinates: {set(dset.coordinates['id']) - set(dset.images['id'])}\n")
@@ -69,7 +69,7 @@
 
 ###############################################################################
 # Use different strategies to overwrite existing coordinate data
-# --------------------------------------------------------------
+# -----------------------------------------------------------------------------
 # There are three choices for how to treat existing coordinate
 # data in the dataset which are named: 'fill', 'replace', and 'demolish'.
 #
@@ -101,7 +101,7 @@
 
 ###############################################################################
 # Inspect generated datasets
-# --------------------------
+# -----------------------------------------------------------------------------
 
 example_study = "pain_01.nidm-1"
 

diff --git a/examples/02_meta-analyses/05_plot_correctors.py b/examples/02_meta-analyses/05_plot_correctors.py
@@ -15,14 +15,14 @@
 
 ###############################################################################
 # Download data
-# --------------------------------
+# -----------------------------------------------------------------------------
 from nimare.extract import download_nidm_pain
 
 dset_dir = download_nidm_pain()
 
 ###############################################################################
 # Load Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 import os
 
 from nimare.dataset import Dataset
@@ -38,7 +38,7 @@
 # .. _corrector-cbma-example:
 #
 # Multiple comparisons correction in coordinate-based meta-analyses
-# -----------------------------------------------------------------
+# -----------------------------------------------------------------------------
 # .. tip::
 #   For more information multiple comparisons correction and CBMA in NiMARE,
 #   see :ref:`multiple comparisons correction`.
@@ -56,7 +56,7 @@
 
 ###############################################################################
 # Apply the Corrector to the MetaResult
-# =====================================
+# =============================================================================
 # Now that we know what FWE correction methods are available, we can use one.
 #
 # The "montecarlo" method is a special one that is implemented within the
@@ -89,7 +89,7 @@
 
 ###############################################################################
 # Show corrected results
-# ======================
+# =============================================================================
 MAPS_TO_PLOT = [
     "z",
     "z_desc-size_level-cluster_corr-FWE_method-montecarlo",
@@ -122,7 +122,7 @@
 
 ###############################################################################
 # Multiple comparisons correction in image-based meta-analyses
-# ------------------------------------------------------------
+# -----------------------------------------------------------------------------
 from nimare.meta.ibma import Stouffers
 
 meta = Stouffers(resample=True)
@@ -138,13 +138,13 @@
 
 ###############################################################################
 # Apply the Corrector to the MetaResult
-# =====================================
+# =============================================================================
 corr = FDRCorrector(method="indep", alpha=0.05)
 cres = corr.transform(results)
 
 ###############################################################################
 # Show corrected results
-# ======================
+# =============================================================================
 fig, axes = plt.subplots(figsize=(8, 6), nrows=2)
 plot_stat_map(
     cres.get_map("z"),

diff --git a/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py b/examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py
@@ -25,12 +25,12 @@
 
 ###############################################################################
 # Download data
-# --------------------------------
+# -----------------------------------------------------------------------------
 dset_dir = download_nidm_pain()
 
 ###############################################################################
 # Load Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json")
 dset = Dataset(dset_file)
 dset.update_path(dset_dir)

diff --git a/examples/02_meta-analyses/07_macm.py b/examples/02_meta-analyses/07_macm.py
@@ -23,15 +23,15 @@
 
 ###############################################################################
 # Load Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 # We will assume that the Neurosynth database has already been downloaded and
 # converted to a NiMARE dataset.
 dset_file = "neurosynth_nimare_with_abstracts.pkl.gz"
 dset = Dataset.load(dset_file)
 
 ###############################################################################
 # Define a region of interest
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 # We'll use the right amygdala from the Harvard-Oxford atlas
 atlas = datasets.fetch_atlas_harvard_oxford("sub-maxprob-thr50-2mm")
 img = nib.load(atlas["maps"])
@@ -43,22 +43,22 @@
 
 ###############################################################################
 # Select studies with a reported coordinate in the ROI
-# ----------------------------------------------------
+# -----------------------------------------------------------------------------
 roi_ids = dset.get_studies_by_mask(roi_img)
 dset_sel = dset.slice(roi_ids)
 print(f"{len(roi_ids)}/{len(dset.ids)} studies report at least one coordinate in the ROI")
 
 ###############################################################################
 # Select studies with *no* reported coordinates in the ROI
-# --------------------------------------------------------
+# -----------------------------------------------------------------------------
 no_roi_ids = list(set(dset.ids).difference(roi_ids))
 dset_unsel = dset.slice(no_roi_ids)
 print(f"{len(no_roi_ids)}/{len(dset.ids)} studies report zero coordinates in the ROI")
 
 
 ###############################################################################
 # MKDA Chi2 with FWE correction
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 mkda = MKDAChi2(kernel__r=10)
 mkda.fit(dset_sel, dset_unsel)
 
@@ -75,7 +75,7 @@
 
 ###############################################################################
 # SCALE
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 # Another good option for a MACM analysis is the SCALE algorithm, which was
 # designed specifically for MACM. Unfortunately, SCALE does not support
 # multiple-comparisons correction.

diff --git a/examples/02_meta-analyses/09_plot_simulated_data.py b/examples/02_meta-analyses/09_plot_simulated_data.py
@@ -18,7 +18,7 @@
 
 ###############################################################################
 # Create function to perform a meta-analysis and plot results
-# -----------------------------------------------------------
+# -----------------------------------------------------------------------------
 
 
 def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=False):
@@ -61,7 +61,7 @@ def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=Fal
 
 ###############################################################################
 # Create Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 # In this example, each of the 30 generated fake studies
 # select 4 coordinates from a probability map representing the probability
 # that particular coordinate will be chosen.
@@ -76,7 +76,7 @@ def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=Fal
 
 ###############################################################################
 # Analyze and plot simple dataset
-# -------------------------------
+# -----------------------------------------------------------------------------
 # The red dots in this plot and subsequent plots represent the
 # simulated ground truth foci, and the clouds represent the statistical
 # maps of the simulated data.
@@ -85,7 +85,7 @@ def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=Fal
 
 ###############################################################################
 # Fine-tune dataset creation
-# --------------------------
+# -----------------------------------------------------------------------------
 # Perhaps you want more control over the studies being generated.
 # you can set:
 #
@@ -110,14 +110,14 @@ def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=Fal
 
 ###############################################################################
 # Analyze and plot manual dataset
-# -------------------------------
+# -----------------------------------------------------------------------------
 
 fig = analyze_and_plot(manual_dset, ground_truth_foci)
 fig.show()
 
 ###############################################################################
 # Control percentage of studies with the foci of interest
-# -------------------------------------------------------
+# -----------------------------------------------------------------------------
 # Often times a converging peak is not found in all studies within
 # the meta-analysis, but only a portion.
 # We can select a percentage of studies where a coordinate
@@ -129,14 +129,14 @@ def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=Fal
 
 ###############################################################################
 # Analyze and plot the 50% foci dataset
-# -------------------------------------
+# -----------------------------------------------------------------------------
 
 fig = analyze_and_plot(perc_foci_dset, ground_truth_foci[0:2])
 fig.show()
 
 ###############################################################################
 # Create a null dataset
-# --------------------------------------------------------------------
+# -----------------------------------------------------------------------------
 # Perhaps you are interested in the number of false positives your favorite
 # meta-analysis algorithm typically gives.
 # At an alpha of 0.05 we would expect no more than 5% of results to be false positives.
@@ -149,7 +149,7 @@ def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=Fal
 
 ###############################################################################
 # Analyze and plot no foci dataset
-# --------------------------------
+# -----------------------------------------------------------------------------
 # When not performing a multiple comparisons correction,
 # there is a false positive rate of approximately 5%.
 

diff --git a/examples/02_meta-analyses/10_peaks2maps.py b/examples/02_meta-analyses/10_peaks2maps.py
@@ -11,7 +11,7 @@
 """
 ###############################################################################
 # Start with the necessary imports
-# --------------------------------
+# -----------------------------------------------------------------------------
 import os
 
 from nilearn.plotting import plot_glass_brain
@@ -22,19 +22,19 @@
 
 ###############################################################################
 # Load Dataset
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json")
 dset = Dataset(dset_file)
 
 ###############################################################################
 # Run peaks2maps
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 k = Peaks2MapsKernel()
 imgs = k.transform(dset, return_type="image")
 
 ###############################################################################
 # Plot modeled activation maps
-# --------------------------------------------------
+# -----------------------------------------------------------------------------
 for img in imgs:
     display = plot_glass_brain(
         img, display_mode="lyrz", plot_abs=False, colorbar=True, vmax=1, threshold=0

diff --git a/examples/03_annotation/01_plot_tfidf.py b/examples/03_annotation/01_plot_tfidf.py
@@ -14,15 +14,15 @@
 
 ###############################################################################
 # Load dataset with abstracts
-# ---------------------------
+# -----------------------------------------------------------------------------
 # We'll load a small dataset composed only of studies in Neurosynth with
 # Angela Laird as a coauthor, for the sake of speed.
 dset = dataset.Dataset(os.path.join(utils.get_resource_path(), "neurosynth_laird_studies.json"))
 dset.texts.head(2)
 
 ###############################################################################
 # Generate term counts
-# --------------------
+# -----------------------------------------------------------------------------
 # Let's start by extracting terms and their associated counts from article
 # abstracts.
 counts_df = annotate.text.generate_counts(
@@ -36,7 +36,7 @@
 
 ###############################################################################
 # Generate term counts
-# --------------------
+# -----------------------------------------------------------------------------
 # We can also extract term frequency-inverse document frequency (tf-idf)
 # values from text using the same function.
 # While the terms and values will differ based on the dataset provided and the