Adding new methods and the consensual backbone

Adding new methods and the consensual backbone

netbone/backbone.py

 import networkx as nx
+import pandas as pd
 from pandas import DataFrame
 from netbone.utils.utils import edge_properties
 
+
 class Backbone:
 
-    def __init__(self, graph, name, column, ascending, filters):
+    def __init__(self, graph, method_name, property_name, ascending, compatible_filters, filter_on):
         if isinstance(graph, DataFrame):
             graph = nx.from_pandas_edgelist(graph, edge_attr=edge_properties(graph))
 
         self.graph = graph
-        self.name = name
-        self.column = column
+        self.method_name = method_name
+        self.property_name = property_name
         self.ascending = ascending
-        self.compatible_filters = filters
-
+        self.filters = compatible_filters
+        self.filter_on = filter_on
 
     def to_dataframe(self):
+        if self.filter_on == 'Edges':
             return nx.to_pandas_edgelist(self.graph)
-
+        else:
+            node_attrs = {}
+            for node in self.graph.nodes():
+                node_attrs[node] = self.graph.nodes[node]
+            # Convert the dictionary to a Pandas DataFrame
+            return pd.DataFrame.from_dict(node_attrs, orient='index')
 
     def narrate(self):
-        match self.name:
+        match self.method_name:
             case "Disparity Filter":
-                print(self.name)
+                print(self.method_name)
             case "Enhanced Configuration Model Filter":
-                print(self.name)
+                print(self.method_name)
             case "Marginal Likelihood Filter":
-                print(self.name)
+                print(self.method_name)
             case "Locally Adaptive Network Sparsification Filter":
-                print(self.name)
+                print(self.method_name)
             case "Noise Corrected Filter":
-                print(self.name)
+                print(self.method_name)
             case 'High Salience Skeleton Filter':
-                print(self.name)
+                print(self.method_name)
             case 'Modularity Filter':
-                print(self.name)
+                print(self.method_name)
             case 'Ultrametric Distance Filter':
-                print(self.name)
+                print(self.method_name)
             case 'Maximum Spanning Tree':
-                print(self.name)
+                print(self.method_name)
             case 'Metric Distance Filter':
-                print(self.name)
+                print(self.method_name)
             case 'H-Backbone Filter':
-                print(self.name)
+                print(self.method_name)
             case 'Doubly Stochastic Filter':
-                print(self.name)
+                print(self.method_name)
             case 'Global Threshold Filter':
-                print(self.name)
+                print(self.method_name)
             case _:
                 print("Citation here")
 
-
-    def filters(self):
-        return self.compatible_filters
-        # match self.name:
-        #     case "Disparity Filter" | 'Noise Corrected Filter' | "Enhanced Configuration Model Filter" | "Marginal Likelihood Filter" | 'Locally Adaptive Network Sparsification Filter' | 'Global Threshold Filter':
-        #         return [fraction_filter, threshold_filter]
-        #     case "H-Backbone Filter" | 'Metric Distance Filter' | 'Maximum Spanning Tree' | 'Ultrametric Distance Filter' | 'Modularity Filter':
-        #         return [boolean_filter]
-        #     case "Doubly Stochastic Filter" | "High Salience Skeleton Filter":
-        #         return [boolean_filter, fraction_filter, threshold_filter]
-        #     case _:
-        #         print("Error " + self.name + " does not exist")
+    def compatible_filters(self):
+        return self.filters