Adding new methods and the consensual backbone extraction

netbone/__init__.py

@@ -26,6 +26,7 @@ from netbone.structural.mlam import mlam
 from netbone.structural.gspar import gspar
 from netbone.structural.degree import degree
 from netbone.structural.betweenness import betweenness
+from netbone.structural.mad import mad
 # from netbone.statistical.correlation_and_statistic import correlation_and_statistic
 
 from netbone.filters import threshold_filter, fraction_filter

netbone/compare.py

@@ -97,7 +97,7 @@ class Compare:
             raise Exception('Please enter the filter values.')
         cons = []
         if consent == False:
-            for backbon in self.backbones:
+            for backbone in self.backbones:
                 cons.append(False)
             consent = cons
 

netbone/structural/global_threshold.py

@@ -7,15 +7,11 @@ from netbone.filters import fraction_filter, threshold_filter
 def global_threshold(data):
 
     if isinstance(data, DataFrame):
-        table = data.copy()
+        g = nx.from_pandas_edgelist(data, edge_attr=edge_properties(data))
     elif isinstance(data, Graph):
-        table = nx.to_pandas_edgelist(data)
+        g = data.copy()
     else:
         print("data should be a panads dataframe or nx graph")
         return
 
-    table['score'] = table['weight']
-
+    return Backbone(g, method_name="Global Threshold Filter", property_name="weight", ascending=False, compatible_filters=[fraction_filter, threshold_filter], filter_on='Edges')
\ No newline at end of file
-    g = nx.from_pandas_edgelist(table, edge_attr=edge_properties(table))
-
-    return Backbone(g, method_name="Global Threshold Filter", property_name="score", ascending=False, compatible_filters=[fraction_filter, threshold_filter], filter_on='Edges')
\ No newline at end of file