diff --git a/local_integration_msms.py b/local_integration_msms.py
index b2f2c1b4cd7f8aa915d8ebed5e0c1b80711f896b..55950ed9501ccaa73a3defd5c6030f241b8afcda 100644
--- a/local_integration_msms.py
+++ b/local_integration_msms.py
@@ -112,27 +112,82 @@ if __name__ == "__main__":
     #
     # plt.savefig('temp.png')
     #
-    # #pic d'étude 280-310
-    # #mz 411.5 418.5
-    df_peak = df[df['MSlevel'] == 2]
-    df_peak = df_peak[411 < df_peak['MS1_mz_max']]
-    df_peak = df_peak[419 > df_peak['MS1_mz_min']]
-    df_peak = df_peak[280 < df_peak['RT']]
-    df_peak = df_peak[310 > df_peak['RT']]
-
-#
-    df_peak2 = df[df['MSlevel'] == 2]
-    df_peak2 = df_peak2[411 < df_peak2['MS1_mz_max']]
-    df_peak2 = df_peak2[419> df_peak2['MS1_mz_min']]
-    df_peak2 = df_peak2[296 < df_peak2['RT']]
-    df_peak2 = df_peak2[297 > df_peak2['RT']]
-
-    mz1, inty1 = integrate_ms_ms(df_peak, 1)
-    mz2, inty2 = integrate_ms_ms(df_peak2, 1)
+#etude 1 : RT 403 env mz 586
+#etude 2 : RT 417 env mz 620
+    df_large_1 = df[df['MSlevel'] == 2]
+    df_large_1 = df_large_1[583 < df_large_1['MS1_mz_max']]
+    df_large_1 = df_large_1[590 > df_large_1['MS1_mz_min']]
+    df_large_1 = df_large_1[400 < df_large_1['RT']]
+    df_large_1 = df_large_1[407 > df_large_1['RT']]
+    df_peak_1 = df[df['MSlevel'] == 2]
+    df_peak_1 = df_peak_1[583 < df_peak_1['MS1_mz_max']]
+    df_peak_1 = df_peak_1[590 > df_peak_1['MS1_mz_min']]
+    df_peak_1 = df_peak_1[402.61 < df_peak_1['RT']]
+    df_peak_1 = df_peak_1[404.70 > df_peak_1['RT']]
+
+    peak_labels = ['y3','y4','y5','y6','y7','y8','y9','y10','y10 - H2O','b10']
+    intensities_1 = [72.98152923583984,515.2999267578125,233.5383758544922,195.87428283691406,1547.2972412109375,565.280029296875,751.7384033203125,326.5546875,1582.326904296875,62.46744918823242,28.2091007232666,28.175247192382812,56.511573791503906,66.04779052734375,162.41555786132812,513.6303100585938,89.15145111083984,32.340614318847656]
+    masses_1 = [260.19452231941415,317.21887057506785,416.28718638590306,529.3694932073394,658.4082871894378,757.4859952659489,886.516076743466,1015.5580941852136,1072.5841392915013,640.4013354636426,739.4767875172264,868.5072379164048,997.5687132366882,1054.5537160953004,286.1350031265497,415.1792848734148,514.2414463061315,912.4601973498769]
+
+    mz1_large, inty1_large = integrate_ms_ms(df_large_1, 0.25)
+    mz1, inty1 = integrate_ms_ms(df_peak_1, 0.25)
+
+
+    intensities_1=np.array(intensities_1) / np.linalg.norm(intensities_1)
+    inty1 = np.array(inty1) / np.linalg.norm(inty1)
+    inty1_large = np.array(inty1_large) / np.linalg.norm(inty1_large)
     plt.clf()
     fig, ax = plt.subplots()
     ax.plot(mz1, inty1, linewidth=0.3)
+    ax.vlines(masses_1, 0, -intensities_1, color="tab:red", linewidth=0.3)
+    ax.set_xlim(200, 1200)
+    plt.savefig('fig/local_inte_res/spec_peak_theo_1.png')
+
+    plt.clf()
+    fig, ax = plt.subplots()
+    ax.plot(mz1_large, inty1_large, linewidth=0.3)
+    ax.vlines(masses_1, 0, -intensities_1, color="tab:red", linewidth=0.3)
+    ax.set_xlim(200, 1200)
+    plt.savefig('fig/local_inte_res/spec_large_theo_1.png')
+    plt.clf()
+
+    df_large_2 = df[df['MSlevel'] == 2]
+    df_large_2 = df_large_2[619 < df_large_2['MS1_mz_max']]
+    df_large_2 = df_large_2[623 > df_large_2['MS1_mz_min']]
+    df_large_2 = df_large_2[414.8 < df_large_2['RT']]
+    df_large_2 = df_large_2[420.3 > df_large_2['RT']]
+
+    df_peak_2 = df[df['MSlevel'] == 2]
+    df_peak_2 = df_peak_2[619 < df_peak_2['MS1_mz_max']]
+    df_peak_2 = df_peak_2[623 > df_peak_2['MS1_mz_min']]
+    df_peak_2 = df_peak_2[417.2 < df_peak_2['RT']]
+    df_peak_2 = df_peak_2[419.3 > df_peak_2['RT']]
+    mz2, inty2 = integrate_ms_ms(df_peak_2, 0.25)
+    mz2_large, inty2_large = integrate_ms_ms(df_large_2, 0.25)
+
+    intensities_2 = [28.185697555541992,190.35972595214844,369.7372741699219,218.33245849609375,450.749755859375,289.6231994628906,282.96661376953125,151.11683654785156,56.205596923828125,151.97030639648438,145.062255859375,43.23415756225586]
+    masses_2 = [246.1807143935358,560.2988894900997,673.3880444971912,730.3965872674734,801.4428960350862,872.4753078336571,943.5086099811606,1056.596262153857,1038.5789221617815,298.2142932725484,369.2477520599847,440.2814643814369]
+
+
+    intensities_2=np.array(intensities_2) / np.linalg.norm(intensities_2)
+    inty2 = np.array(inty2) / np.linalg.norm(inty2)
+    inty2_large = np.array(inty2_large) / np.linalg.norm(inty2_large)
+
+    plt.clf()
+    fig, ax = plt.subplots()
     ax.plot(mz2, inty2, linewidth=0.3)
+    ax.vlines(masses_2, 0, -intensities_2, color="tab:red", linewidth=0.3)
     ax.set_xlim(200, 1200)
-    plt.savefig('spec_combined.png')
+    plt.savefig('fig/local_inte_res/spec_peak_theo_2.png')
     plt.clf()
+
+    plt.clf()
+    fig, ax = plt.subplots()
+    ax.plot(mz2_large, inty2_large, linewidth=0.3)
+    ax.vlines(masses_2, 0, -intensities_2, color="tab:red", linewidth=0.3)
+    ax.set_xlim(200, 1200)
+    plt.savefig('fig/local_inte_res/spec_large_theo_2.png')
+    plt.clf()
+
+
+