Delete InheritanceAlgorithm_v08042021.py

InheritanceAlgorithm_v08042021.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Sep  5 15:54:10 2018
-
-@author: mhennart
-"""
-import pandas as pd 
-import networkx as nx
-
-
-def Mappind_cluster_ST (data, seuil ) :
-        Df = pd.crosstab(data['ST'], data[seuil])
-        Df = Df.drop('NA')
-        
-        B = nx.Graph()
-        B.add_nodes_from(Df.index, bipartite=1)
-        B.add_nodes_from(Df.columns, bipartite=0)
-        
-        for i in Df.index :
-            for j in Df.columns :
-                if Df[j][i] > 0 :
-                    B.add_edge(i,j, weight=Df[j][i])
-        
-        dico = {}
-        ST_attribute = []
-        while len(B.edges) > 0 : 
-            edges = B.edges(data=True)
-            max_weight_edges = max([edge[2]['weight'] for edge in edges])
-            edges_subgraph = [(u,v) for (u,v,d) in B.edges(data=True) if d['weight'] == max_weight_edges]
-            C = B.edge_subgraph(edges_subgraph).copy()
-            
-            for connected_component in nx.connected_components(C) :
-                S = C.subgraph(connected_component).copy()         
-                top_nodes = {n for n, d in S.nodes(data=True) if d["bipartite"] == 0}      
-                bottom_nodes = set(S) - top_nodes 
-                
-                if len(top_nodes) == 1 :         
-                    u = list(top_nodes)[0]
-                    v = min(bottom_nodes)
-                    B.remove_nodes_from((u,v)) 
-                    dico[u]= [ v , 'relative_ST' ]
-                    ST_attribute.append(v)
-                else :
-                    no_strains_nodes = [(sum(d['weight'] for (u,v,d) in C.edges(top_node, data=True)), top_node) for top_node in top_nodes]            
-                    no_strains_nodes.sort()
-                    for (no_strains_node, node)  in no_strains_nodes : 
-                        u = node
-                        degree = [(S.degree[x], x) for x in S.adj[u] if x not in ST_attribute ]
-                        if len(degree) > 0 : 
-                            v = min(degree)[1]                
-                            B.remove_nodes_from((u,v))                    
-                            dico[u]= [ v , 'relative_ST' ] 
-                            ST_attribute.append(v)
-                            
-                            
-        top_nodes = {n for n, d in B.nodes(data=True) if d["bipartite"] == 0}                 
-        lambda_ = 10000
-        for u in top_nodes :  
-              dico[u]=  [lambda_ , "arbitrary"]
-              lambda_ += 1 
-        
-        mapping = data[seuil].apply(lambda x : dico[x][0])
-        attribute = data[seuil].apply(lambda x : dico[x][1])
-        
-        return mapping, attribute
-    
-in_ = "PartionnementMLST_ALL.txt"
-data = pd.read_csv(in_, sep='\t', index_col=0, dtype=str)
-data = data.fillna('NA')
-
-seuils = ['Seuil43', 'Seuil190'] 
-for seuil in seuils :     
-    mapping, attribute = Mappind_cluster_ST (data, seuil) 
-    data['Mapping_'+seuil] = mapping
-    data['Attribute_'+seuil] = attribute
-    
-data.to_csv('ResultatsMapping_CG_SL.txt', sep="\t")
-