Updating the utils module

netbone/backbone.py

 import networkx as nx
 from pandas import DataFrame
-from netbone.Utils.utils import edge_properties
+from netbone.utils.utils import edge_properties
 
 class Backbone:
 

netbone/compare.py

 import pandas as pd
 import networkx as nx
-from netbone.Utils.utils import cumulative_dist
+from netbone.utils.utils import cumulative_dist
 from netbone.filters import threshold_filter, boolean_filter
 from pandas import DataFrame
 from scipy.stats import kstest

netbone/filters.py

 import math
 
 import networkx as nx
-from netbone.Utils.utils import edge_properties
+from netbone.utils.utils import edge_properties
 def boolean_filter(backbone, narrate=True, value=[]):
     if boolean_filter in backbone.filters():
         data = backbone.graph

netbone/measures.py

 import igraph as ig
 import networkx as nx
 import numpy as np
-from netbone.Utils.utils import lcc
+from netbone.utils.utils import lcc
 from scipy.stats import entropy
 
 

netbone/statistical/global_threshold.py

 import networkx as nx
 from pandas import DataFrame
 from networkx import Graph,to_pandas_edgelist
-from netbone.Utils.utils import edge_properties
+from netbone.utils.utils import edge_properties
 from netbone.backbone import Backbone
 from netbone.filters import fraction_filter, threshold_filter
 def global_threshold(data):

netbone/utils/utils.py

+import numpy as np
+import pandas as pd
+import networkx as nx
+
+
+def lcc(G):
+    return G.subgraph(max(nx.connected_components(G), key=len)).copy()
+def cumulative_dist(name, method, values, increasing=True):
+    if increasing:
+        x = -np.sort(-np.array(values))
+    else:
+        x = np.sort(values)
+    y = np.arange(1, len(x) + 1)/len(x)
+
+    df = pd.DataFrame(index=x)
+    df.index.name = name
+    df[method] = y
+    return df
+
+def edge_properties(df):
+    columns = list(df.columns)
+    columns.remove('source')
+    columns.remove('target')
+    return columns