Fix colors in the repetition figure

python/brainNetwork/scripts/community_repeated.py

@@ -8,7 +8,7 @@ import matplotlib.pyplot as plt
 
 import networkx as nx
 from brainNetwork.util.path import get_local_datadir, get_fig_dir
-from brainNetwork.util import connectome
+from brainNetwork.util import connectome, util
 
 # Paths
 csv_dir = Path(get_local_datadir(),'connectivity_tastant.csv')
@@ -19,7 +19,7 @@ connect_mat = pd.read_csv(csv_dir, index_col=0)
 # Get the graph
 G = nx.convert_matrix.from_pandas_adjacency(connect_mat)
 
-# Use np.random.randint to get 10K int between 0, 2**32-1 and use them as seeds for the random number generator
+# Perform repeated communities with different seeds
 perform_repetition = False
 df_partitions, modularities = connectome.repeat_community(G, num_repeats=10000, 
                                                           save_dir=save_dir, 
@@ -30,7 +30,7 @@ assert (df_partitions.values.max(axis=1) == 2).all() # Always only 3 clusters
 # create consistent indices
 df_partitions = connectome.consistent_cluster_id(df_partitions)
 
-# Make sure the same modularity means the same clustering and 
+# Make sure the same modularity means the same clustering
 [vals, idx, counts] = np.unique(modularities.values, return_index=True, return_counts=True)
 for val in vals: 
     cur_same_mod = df_partitions.loc[modularities.values == val,:]
@@ -45,11 +45,12 @@ 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=';')
-partition_paper.index = ['partition_paper']
-unique_partitions = pd.concat([unique_partitions, partition_paper])
+# csv_dir = Path(get_local_datadir(),'partition_paper.csv')
+# partition_paper = pd.read_csv(csv_dir,sep=';')
+# partition_paper.index = ['partition_paper']
+# unique_partitions = pd.concat([unique_partitions, partition_paper])
 
 # Save unique partitions
 unique_partitions_tosave = unique_partitions.copy()
@@ -57,12 +58,14 @@ unique_partitions_tosave = unique_partitions_tosave.sort_values(by=[10.36], axis
 unique_partitions_tosave.to_csv(Path(save_dir,'unique_partitions.csv'))
 
 # Plot
-fig, ax = plt.subplots(1, 1, figsize=[3, 9])
+fig, ax = plt.subplots(1, 1, figsize=[4, 8])
+c_map_mods = util.colors()
 sorted_partition = unique_partitions.sort_values(by=[10.36], axis=1)
+sorted_partition = sorted_partition.T
 shape_part = sorted_partition.shape
 
-plt.imshow(sorted_partition.T.values, cmap='cool')
-plt.yticks(range(shape_part[1]), sorted_partition.T.index)
-plt.xticks(range(shape_part[0]), (*fractions, 'paper'), rotation=90)
-fig_name = Path(get_fig_dir(), 'the_communities.svg')
+plt.imshow(c_map_mods[sorted_partition.values])
+plt.yticks(range(shape_part[0]), sorted_partition.index)
+plt.xticks(range(shape_part[1]), fractions, rotation=90)
+fig_name = Path(get_fig_dir(), 'the_communities_repetition_tastant.svg')
 fig.savefig(fig_name)
\ No newline at end of file

python/brainNetwork/util/util.py

+import numpy as np
+
 def cm2inch(*tupl):
     """
     Convert centimeter tuple to inch tuple for setting figure size in matplotlib
@@ -20,3 +22,10 @@ def remove_box(ax, type = 'vertical'):
         ax.spines['bottom'].set_visible(False)
         ax.spines['right'].set_visible(False)
     return ax
+
+
+def colors():
+    modules = np.asarray([[0.941176470588235, 0.501960784313726, 0.501960784313726],
+                          [0.250980392156863, 0.878431372549020, 0.815686274509804],
+                          [0.254901960784314, 0.411764705882353, 0.882352941176471]])
+    return modules

python/data/repeat_modularity/unique_partitions.csv

@@ -5,4 +5,3 @@
 0.01,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,2,2
 89.51,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,2,2
 10.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2
-partition_paper,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,1,2,2,2,1,2,2,1,1,2,2,2,1,2,2