read the actual highest modularity instead of hardcoding it

python/brainNetwork/scripts/community_repeated.py

@@ -42,7 +42,9 @@ df_rand = connectome.measure_rand_index(unique_partitions)
 # turn index into frequency
 fractions = (counts*100) / counts.sum()
 unique_partitions.index = fractions
-
+# Save fractions and their modularities
+frac_modularities = pd.DataFrame((fractions, vals), index = ['fractions', 'modularities']).transpose()
+frac_modularities.to_csv(Path(save_dir,'freq_modularity.csv'))
 # Read paper partition and add
 csv_dir = Path(get_local_datadir(),'partition_paper.csv')
 partition_paper = pd.read_csv(csv_dir,sep=';')

python/brainNetwork/scripts/shuffle_connectivity.py

@@ -23,13 +23,18 @@ G = nx.convert_matrix.from_pandas_adjacency(connect_mat)
 df_pre_post = nx.to_pandas_edgelist(G)
 
 # calculate and save permutated modularities
-get_shuffling = True
+get_shuffling = False
 if get_shuffling:
     modularities = connectome.permute_modularity(pre_post_list=df_pre_post, 
                                                 output_path=save_dir)
 
+# Get the observed modularity 
+dir_csv = Path(get_local_datadir(), 'repeat_modularity','freq_modularity.csv')
+freq_modularity = pd.read_csv(dir_csv)
+highest_modularity = freq_modularity.modularities.max()
+
 # Plot them
 fig, ax = plt.subplots(figsize=util.cm2inch(3.3,1.8))
-connectome.plot_histogram(real_modularity=0.035011758462517,
+connectome.plot_histogram(real_modularity=highest_modularity,
                           path_modularity_file=save_dir)
             
\ No newline at end of file

python/data/repeat_modularity/freq_modularity.csv

+,fractions,modularities
+0,0.03,0.0310551359014252
+1,0.07,0.0325829252651335
+2,0.02,0.0328635156285163
+3,0.01,0.0344021801308508
+4,89.51,0.0349689202641701
+5,10.36,0.035011758462517