diff --git a/tool_of_mapping_results_treatments.py b/tool_of_mapping_results_treatments.py
index d4f966942c0d9d3177cdd28cd25bbb885a6dfe15..c1b9dd760f054cfd138ab33fc5e0be1dbf122c02 100644
--- a/tool_of_mapping_results_treatments.py
+++ b/tool_of_mapping_results_treatments.py
@@ -17,12 +17,12 @@ import csv
 
 ### Variables
 
-Pathways_a_enlever=['Metabolic pathways','Biosynthesis of secondary metabolites','Microbial metabolism in diverse environments','Carbon metabolism''2-Oxocarboxylic acid metabolism','Fatty acid metabolism','Biosynthesis of amino acids','Nucleotide metabolism','Biosynthesis of nucleotide sugars','Biosynthesis of cofactors','Degradation of aromatic compounds','Carbohydrate metabolism','Energy metabolism','Lipid metabolism','Nucleotide metabolism','Amino acid metabolism','Metabolism of other amino acids','Glycan biosynthesis and metabolism','Metabolism of cofactors and vitamins','Metabolism of terpenoids and polyketides','Biosynthesis of other secondary metabolites','Xenobiotics biodegradation and metabolism','Chemical structure transformation maps','Genetic Information Processing','Transcription','Translation','Folding, sorting and degradation','Replication and repair','Information processing in viruses','Environmental Information Processing','Membrane transport','Signal transduction','Signaling molecules and interaction','Cellular Processes','Transport and catabolism','Cell growth and death','Cellular community - eukaryotes','Cellular community - prokaryotes','Cell motility','Immune system','Organismal Systems','Endocrine system','Circulatory system','Digestive system','Excretory system','Nervous system','Sensory system','Development and regeneration','Aging','Environmental adaptation','Human Diseases','Cancer: overview''Cancer: specific types','Infectious disease: viral','Infectious disease: bacterial','Infectious disease: parasitic','Immune disease','Neurodegenerative disease','Substance dependence','Cardiovascular disease','Endocrine and metabolic disease','Drug resistance: antimicrobial','Drug resistance: antineoplastic','Drug Development','Chronology: Antiinfectives','Chronology: Antineoplastics','Chronology: Nervous system agents','Chronology: Other drugs','Target-based classification: G protein-coupled receptors','Target-based classification: Nuclear receptors','Target-based classification: Ion channels','Target-based classification: Transporters','Target-based classification: Enzymes','Structure-based classification','Skeleton-based classification']
+Pathways_a_enlever=['Metabolic pathways','Biosynthesis of secondary metabolites','Microbial metabolism in diverse environments','Carbon metabolism','2-Oxocarboxylic acid metabolism','Fatty acid metabolism','Biosynthesis of amino acids','Biosynthesis of nucleotide sugars','Biosynthesis of cofactors','Degradation of aromatic compounds','Carbohydrate metabolism','Energy metabolism','Lipid metabolism','Nucleotide metabolism','Amino acid metabolism','Metabolism of other amino acids','Glycan biosynthesis and metabolism','Metabolism of cofactors and vitamins','Metabolism of terpenoids and polyketides','Biosynthesis of other secondary metabolites','Xenobiotics biodegradation and metabolism','Chemical structure transformation maps','Genetic Information Processing','Transcription','Translation','Folding, sorting and degradation','Replication and repair','Information processing in viruses','Environmental Information Processing','Membrane transport','Signal transduction','Signaling molecules and interaction','Cellular Processes','Transport and catabolism','Cell growth and death','Cellular community - eukaryotes','Cellular community - prokaryotes','Cell motility','Immune system','Organismal Systems','Endocrine system','Circulatory system','Digestive system','Excretory system','Nervous system','Sensory system','Development and regeneration','Aging','Environmental adaptation','Human Diseases','Cancer: overview''Cancer: specific types','Infectious disease: viral','Infectious disease: bacterial','Infectious disease: parasitic','Immune disease','Neurodegenerative disease','Substance dependence','Cardiovascular disease','Endocrine and metabolic disease','Drug resistance: antimicrobial','Drug resistance: antineoplastic','Drug Development','Chronology: Antiinfectives','Chronology: Antineoplastics','Chronology: Nervous system agents','Chronology: Other drugs','Target-based classification: G protein-coupled receptors','Target-based classification: Nuclear receptors','Target-based classification: Ion channels','Target-based classification: Transporters','Target-based classification: Enzymes','Structure-based classification','Skeleton-based classification']
 
-Fichier_a_traiter="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\fichier_test_programme_automatisation.csv"
-Sortie_Resemblance_des_pathways="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\table_de_resemblance_fichier_test_programme_automatisation.xlsx"
-Sortie_fréquence_des_metabolites="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\frequence_des_metabolites_fichier_test_programme_automatisation.xlsx"
-Sortie_pathways_de_chaque_metabolites="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\Metabolites_et_leurs_pathways_fichier_test_programme_automatisation.xlsx"
+Fichier_a_traiter="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\L100_Ramp_Chebi_IDs_Pathways_mis_en_forme_pour_programme.csv"
+Sortie_Resemblance_des_pathways="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\table_de_resemblance_L100_Ramp_Chebi_IDs_Pathways.xlsx"
+Sortie_fréquence_des_metabolites="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\frequence_des_metabolites_L100_Ramp_Chebi_IDs_Pathways.xlsx"
+Sortie_pathways_de_chaque_metabolites="C:\\Users\\mumec\\Desktop\\Dossier_gitlab_local\\Metabolites_et_leurs_pathways_L100_Ramp_Chebi_IDs_Pathways.xlsx"
 
 ### Function initialization
 
@@ -33,7 +33,7 @@ def lecture_colonne(file, n, sep=";"):          # fill opening and recovery
     res=[]
     if (n < len(lignes[0])) and (n >= -len(lignes[0])):
       for l in lignes :
-        res.append(l[n])
+        res.append(l[n].strip())
     return res
 
 
@@ -81,15 +81,16 @@ def traitement_des_pathways (file,sep=";"):
                 indice=b
                 Tout_les_pathways[b]=Tout_les_pathways[b][0:r]
                 break
+                
+    for i_path in range (Nombre_de_pathways_totale) :
+        Tout_les_pathways[i_path]=[ele for ele in Tout_les_pathways[i_path] if ele!="NA"]
 
             
     tableau_de_resemblance=np.zeros([Nombre_de_pathways_totale,Nombre_de_pathways_totale+2])
 
 ### Nettoyage des données d'entrées 
 
-    for i_ligne in range(Nombre_de_pathways_totale):
-        for i_element in range(len(Tout_les_pathways[i_ligne])) :
-            Tout_les_pathways[i_ligne][i_element]=Tout_les_pathways[i_ligne][i_element].strip()
+
     P1=[]
     P2=[]
     for ligne in range(Nombre_de_pathways_totale):