diff --git a/src/InheritanceAlgorithm.py b/src/InheritanceAlgorithm.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+###############################################################################
+# #
+# Copyright (C) 2021 Melanie HENNART #
+# #
+# This program is free software: you can redistribute it and/or modify #
+# it under the terms of the GNU General Public License as published by #
+# the Free Software Foundation, either version 3 of the License, or #
+# (at your option) any later version. #
+# #
+# This program is distributed in the hope that it will be useful, #
+# but WITHOUT ANY WARRANTY; without even the implied warranty of #
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
+# GNU General Public License for more details. #
+# #
+# You should have received a copy of the GNU General Public License #
+# along with this program. If not, see <https://www.gnu.org/licenses/>. #
+# #
+# #
+# Contact: #
+# #
+# Melanie HENNART, PhD Student #
+# melanie.hennart@pasteur.fr #
+# Biodiversity and Epidemiology of Bacterial Pathogens #
+# Institut Pasteur #
+# 25-28, Rue du Docteur Roux #
+# 75015 Paris Cedex 15 #
+# France #
+# #
+###############################################################################
+
+import pandas as pd
+import networkx as nx
+import argparse
+
+
+def mapping_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 , 'Inheritance from 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 , 'Inheritance from 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, dico
+
+#========= MAIN PROGRAM =====================================================#
+
+#=== Parameters
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-i", dest="input_file", type=str, required=True, help="input tab-delimited file (mandatory)")
+parser.add_argument("-c", dest="colunms", type=str, required=True, help="name of the clustering column(s) (mandatory)")
+parser.add_argument("-o", dest="output_file", type=str, required=True, help="basename for output files (mandatory)")
+
+args = parser.parse_args()
+
+input_file = args.input_file
+colunms = args.colunms
+output_file = args.output_file
+
+
+#=== Algorithms
+
+data = pd.read_csv(input_file, sep='\t', index_col=0, dtype=str)
+data = data.fillna('NA')
+
+colunms = colunms.split(',')
+for colunm in colunms :
+ if colunm in data.columns :
+ mapping, attribute, dico = mapping_cluster_ST (data, colunm)
+
+ data['Mapping_'+colunm] = mapping
+ data['Attribution_'+colunm] = attribute
+
+ GroupData=pd.DataFrame.from_dict(dico, orient='index', columns=['Mapping', 'Attribution'])
+ GroupData.to_csv(output_file+'.'+colunm+'.txt', sep="\t")
+ else :
+ print ('Error: '+ colunm + ' is not a column in the input file.')
+data.to_csv(output_file + '.out', sep="\t")
+