diff --git a/data/data_processing.py b/data/data_processing.py
index 9f7fd3e5c8dbc7234e7ec6d877e4e35ba63c1215..b31655122660562d396357e9cdf91d19bc5f23ea 100644
--- a/data/data_processing.py
+++ b/data/data_processing.py
@@ -100,15 +100,18 @@ def select_best_data(df_list,threshold):
     num = len(df_list)
     l=[]
     i=0
+    print('Error calc')
     for df in df_list :
         df['abs err {}'.format(i)] = abs(df['rt pred'] - df['true rt'])
         df_group = df.groupby(['seq'])['abs err {}'.format(i)].mean().to_frame().reset_index()
         l.append(df_group)
         i += 1
+        print(str(i)+'/'+str(num))
     df = pd.concat(l, axis=1)
     df['mean'] = df['abs err 0']
     for i in range(1,num):
         df['mean']=df['mean']+df['abs err {}'.format(i)]
+        print('filtering')
     df['mean'] = df['mean']/num
     df_res = df[df['mean']<threshold]
     c_name=['seq{}'.format(i) for i in range(num)]+['mean']
@@ -117,10 +120,14 @@ def select_best_data(df_list,threshold):
     df_res = df_res[['seq0']]
     good_seq=[]
     good_rt=[]
+    print('selecting')
+    i=0
     for r in df_list[0].iterrows() :
+        print(str(i) + '/' + str(len(df_list[0])))
         if r[1]['seq'] in df_res.values :
             good_rt.append(r[1]['true rt'])
             good_seq.append(r[1]['seq'])
+    print('merging')
     return pd.DataFrame({'sequence' : good_seq, 'irt_scaled': good_rt})
 
 
@@ -156,16 +163,16 @@ def numerical_to_alphabetical_str(s):
 if __name__ == '__main__':
     # main()
 
-    df_base = pd.read_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/data/data_PXD006109/e_coli/data_aligned_train_coli.csv')
+    df_base = pd.read_csv('./data_PXD006109/e_coli/data_aligned_train_coli.csv')
     df_base = df_base[['sequence', 'irt_scaled','state']]
     t = [0.05,0.1,0.2,0.3,0.4,0.5,0.7,1,10]
     #reste 07 1 et all
     name = ['005','01','02','03','04','05','07','1','all']
-    df_0 = pd.read_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/output/out_coli_aligned_train_0.csv')
-    df_1 = pd.read_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/output/out_coli_aligned_train_1.csv')
-    df_2 = pd.read_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/output/out_coli_aligned_train_2.csv')
-    df_3 = pd.read_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/output/out_coli_aligned_train_3.csv')
-    df_4 = pd.read_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/output/out_coli_aligned_train_4.csv')
+    df_0 = pd.read_csv('../output/out_coli_aligned_train_0.csv')
+    df_1 = pd.read_csv('../output/out_coli_aligned_train_1.csv')
+    df_2 = pd.read_csv('../output/out_coli_aligned_train_2.csv')
+    df_3 = pd.read_csv('../output/out_coli_aligned_train_3.csv')
+    df_4 = pd.read_csv('../output/out_coli_aligned_train_4.csv')
 
     list_df = [df_0, df_1, df_2, df_3, df_4]
     for i in range(len(name)):
@@ -173,12 +180,12 @@ if __name__ == '__main__':
         print('thresold {} en cours'.format(name[i]))
         #
         df = select_best_data(list_df, t[i])
-        df.to_pickle('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/data/data_PXD006109/e_coli/data_ISA_additionnal_{}.pkl'.format(name[i]))
-        df = pd.read_pickle('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/data/data_PXD006109/e_coli/data_ISA_additionnal_{}.pkl'.format(name[i]))
+        df.to_pickle('./data_PXD006109/e_coli/data_ISA_additionnal_{}.pkl'.format(name[i]))
+        df = pd.read_pickle('./data_PXD006109/e_coli/data_ISA_additionnal_{}.pkl'.format(name[i]))
         df['state'] = 'train'
         df['sequence'] = df['sequence'].map(numerical_to_alphabetical_str)
         df_augmented_1 = pd.concat([df, df_base], axis=0).reset_index(drop=True)
         df_augmented_1.columns = ['sequence', 'irt_scaled','state']
 
-        df_augmented_1.to_csv('/lustre/fswork/projects/rech/bun/ucg81ws/these/dia-augmentation/data/data_PXD006109/e_coli/plasma_data_augmented_{}.csv'.format(name[i]), index=False)
+        df_augmented_1.to_csv('./data_PXD006109/e_coli/plasma_data_augmented_{}.csv'.format(name[i]), index=False)
         print(df_augmented_1.shape)
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