diff --git a/data/COLI-189-AER_100vW_100SPD.mzML b/data/COLI-189-AER_100vW_100SPD.mzML
deleted file mode 100644
index f50213c788d5e3968ec0d2d29faed5e138a71faf..0000000000000000000000000000000000000000
Binary files a/data/COLI-189-AER_100vW_100SPD.mzML and /dev/null differ
diff --git a/data/COLI-189-ANA_100vW_100SPD.mzML b/data/COLI-189-ANA_100vW_100SPD.mzML
deleted file mode 100644
index 050304ec58776ad6614abed9d1670a1e8ad75890..0000000000000000000000000000000000000000
Binary files a/data/COLI-189-ANA_100vW_100SPD.mzML and /dev/null differ
diff --git a/data/Staph140.mzML b/data/Staph140.mzML
deleted file mode 100644
index 232d55fb5fdfc9c9b368457e6d269c1c94fb5c1e..0000000000000000000000000000000000000000
Binary files a/data/Staph140.mzML and /dev/null differ
diff --git a/local_integration_msms.py b/local_integration_msms.py
index bba47338e9a3cfe188798bff67ebc6ecb904726a..b2f2c1b4cd7f8aa915d8ebed5e0c1b80711f896b 100644
--- a/local_integration_msms.py
+++ b/local_integration_msms.py
@@ -94,51 +94,45 @@ def integrate_ms_ms(df, mz_bin, output='temp.png', display = False):
 
 if __name__ == "__main__":
     e = oms.MSExperiment()
-    oms.MzMLFile().load("data/Staph140.mzML", e)
+    oms.MzMLFile().load("data/echantillons données DIA/CITAMA-5-AER-d200.mzML", e)
     # generate_RT_int_imgs(e, 350, 1250)
 
     df = get_df(e, long=True)
-    df1 = df[df['MSlevel'] == 1]
-    df_slide = df1[750.1< df1['mz']]
-    df_slide = df_slide[750.15 > df_slide['mz']]
-
-    inty_sorted = [x for y, x in sorted(zip(df_slide['RT'], df_slide['inty']))]
-    mz_sorted = sorted(df_slide['RT'])
-    plt.clf()
-    fig, ax = plt.subplots()
-    ax.set_xlim(400,500)
-    ax.plot(mz_sorted,inty_sorted)
-
-    plt.savefig('temp.png')
-
-    #pic d'étude 462-468
-    #mz 750.13
+    # df1 = df[df['MSlevel'] == 1]
+    # df_slide = df1[411.5< df1['mz']]
+    # df_slide = df_slide[418.5 > df_slide['mz']]
+    #
+    #
+    # inty_sorted = [x for y, x in sorted(zip(df_slide['RT'], df_slide['inty']))]
+    # mz_sorted = sorted(df_slide['RT'])
+    # plt.clf()
+    # fig, ax = plt.subplots()
+    # ax.set_xlim(280,310)
+    # ax.plot(mz_sorted,inty_sorted)
+    #
+    # plt.savefig('temp.png')
+    #
+    # #pic d'étude 280-310
+    # #mz 411.5 418.5
     df_peak = df[df['MSlevel'] == 2]
-    df_peak = df_peak[750.1 < df_peak['MS1_mz_max']]
-    df_peak = df_peak[750.1 > df_peak['MS1_mz_min']]
-    df_peak = df_peak[465 < df_peak['RT']]
-    df_peak = df_peak[466 > df_peak['RT']]
+    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[750.1 < df_peak2['MS1_mz_max']]
-#     df_peak2 = df_peak2[750.1 > df_peak2['MS1_mz_min']]
-#     df_peak2 = df_peak2[463 < df_peak2['RT']]
-#     df_peak2 = df_peak2[467 > df_peak2['RT']]
-#
-#     mz1, inty1 = integrate_ms_ms(df_peak, 1)
-#     mz2, inty2 = integrate_ms_ms(df_peak2, 1)
-#     plt.clf()
-#     fig, ax = plt.subplots()
-#     ax.plot(mz1, inty1, linewidth=0.3)
-#     ax.plot(mz2, inty2, linewidth=0.3)
-#     ax.set_xlim(200, 1800)
-#     plt.savefig('spec_combined.png')
-#     plt.clf()
-
-    df = pd.read_csv('data/staph140_maxquant.csv')
-    df['Retention time'] = df['Retention time']*60
-    df_filtered = df[df['Retention time']>463 ]
-    df_filtered = df_filtered[df_filtered['Retention time']<467 ]
-
-#358.1 358.32
\ No newline at end of file
+    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)
+    plt.clf()
+    fig, ax = plt.subplots()
+    ax.plot(mz1, inty1, linewidth=0.3)
+    ax.plot(mz2, inty2, linewidth=0.3)
+    ax.set_xlim(200, 1200)
+    plt.savefig('spec_combined.png')
+    plt.clf()
diff --git a/prosit_rt_ori.py b/prosit_rt_ori.py
index 950c7c5f7bd6e3ca8889f5de097b26c35b9784c9..336a323af68d0d78ff8b483616d1f6441c5c0add 100644
--- a/prosit_rt_ori.py
+++ b/prosit_rt_ori.py
@@ -59,19 +59,16 @@ history = model.fit(
 predictions = model.predict(test_sequences)
 predictions = predictions.ravel()
 
-print(test_sequences[:5])
-print(test_targets[:5])
-print(predictions[:5])
 
 
 report = RetentionTimeReport(output_path="./output", history=history)
 
 print("R2: ", report.calculate_r2(test_targets, predictions))
 
-pd.DataFrame(
-    {
-        "sequence": d["test"]["_parsed_sequence"],
-        "irt": test_targets,
-        "predicted_irt": predictions,
-    }
-).to_csv("./predictions_prosit_fullrun.csv", index=False)
\ No newline at end of file
+# pd.DataFrame(
+#     {
+#         "sequence": d["test"]["_parsed_sequence"],
+#         "irt": test_targets,
+#         "predicted_irt": predictions,
+#     }
+# ).to_csv("./predictions_prosit_fullrun.csv", index=False)
\ No newline at end of file