ENH: extract ROI time series

Connectivity.py

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+# This is script to calculate connectivity using raw np arrays,
+# each assumed to be in the shape (channels/ROI's, time points)
+
+import numpy as np
+import time
+import os
+import dyconnmap
+import bct
+from scipy import stats
+import pathlib
+start = time.time()
+
+def operations_to_perform():    # Change as desired
+    Cal_wpli = True
+    Cal_graph_metrics = True
+    return Cal_wpli, Cal_graph_metrics
+
+
+def setup_dependencies():    # Change as desired
+    input_dir = r''     # The directory containing files of interest
+    output_dir = r''    # The output directory
+    array_type = 'participant_conversation_alpha'     # This is the type of array that will be loaded for further calculations, eg. "participant_conversation_alpha"
+    target_fb = [8,12]  # The target frequency band     TODO: set this up so variable can be a this can be list of bands and each generates it's own adjacency matrix
+    fs = 250    # The sampling frequency
+    saving = True
+    os.makedirs(output_dir, exist_ok=True)
+    return input_dir, output_dir, array_type, fs, target_fb, saving
+
+
+def prep_data(input_dir, array_type, target_fb, fs):
+    data = []
+    folder = pathlib.Path(input_dir).glob('*')
+    for file in folder:
+        if (os.path.basename(file)).endswith('.npy'):
+            if array_type in os.path.basename(file):
+                array = np.load(file)
+                _, _, filtered_array = dyconnmap.analytic_signal(array, fb=target_fb, fs=fs)
+                data.append(filtered_array)
+    return np.array(data)
+
+
+def wpli_conn(array, target_fb, fs):   # (participants, roi's, time_points)
+    adj_matrix = []
+    for participant in array:
+        adj_matrix.append(dyconnmap.fc.wpli(participant, fs=fs, fb=target_fb))
+    return np.array(adj_matrix)
+
+
+def graph_metrics(adj_matrix):  # TODO: Add in stats, maybe nbs? Could use fdc as well?
+    # Strength calculator
+    Strength = []
+    for participant in adj_matrix:
+        Strength.append(bct.strengths_und(np.nan_to_num(participant)))
+    Strength = np.array(Strength)
+
+    # Zeroing negative phasing
+    Strength[Strength < 0] = 0
+
+    # Betweenness centrality calculator
+    Betweenness = []
+    for participant in adj_matrix:
+        Betweenness.append(bct.betweenness_wei(np.nan_to_num(participant)))
+    Betweenness = np.array(Betweenness)
+
+    # Eigenvector centrality calculator
+    Eigenvector = []
+    for participant in adj_matrix:
+        Eigenvector.append(bct.eigenvector_centrality_und(np.nan_to_num(participant)))
+    Eigenvector = np.array(Eigenvector)
+
+    # Clustering calculator
+    Clustering = []
+    for participant in adj_matrix:
+        Clustering.append(bct.clustering_coef_wu(np.nan_to_num(participant)))
+    Clustering = np.array(Clustering)
+
+    return Strength, Betweenness, Eigenvector, Clustering
+
+
+def main():     # TODO: put in the elif statements
+    # Prep
+    Cal_wpli, Cal_graph_metrics = operations_to_perform()
+    input_dir, output_dir, array_type, fs, target_fb, saving = setup_dependencies()
+    data = prep_data(input_dir, array_type, target_fb, fs)
+
+    # Core functions
+    if Cal_wpli:
+        adj_matrix = wpli_conn(data, target_fb, fs)
+    if Cal_graph_metrics:
+        Strength, Betweenness, Eigenvector, Clustering = graph_metrics(adj_matrix)
+
+    # Saving
+    if saving:
+        np.save(output_dir + '_All_Strength_' + array_type, Strength)
+        np.save(output_dir + '_All_Betweenness_' + array_type, Betweenness)
+        np.save(output_dir + '_All_Eigenvector_' + array_type, Eigenvector)
+        np.save(output_dir + '_All_Clustering_' + array_type, Clustering)
+        np.save(output_dir + '_Master_adj_matrix_' + array_type, adj_matrix)
+
+
+if __name__ == "__main__":
+    main()
+
+print('\n' + "EXECUTION TIME: " + str(time.time()-start))