datasets

local_integration_msms.py

@@ -2,7 +2,7 @@ import pyopenms as oms
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
-
+import scipy.signal
 
 
 def compute_chromatograms(rt, mz, intensity, start_c, end_c):
@@ -92,6 +92,13 @@ def integrate_ms_ms(df, mz_bin, output='temp.png', display = False):
         plt.clf()
     return mz_list,int_list
 
+def integrated_mz_int(mz_list,int_list,mz_bin, mz_ref):
+    min_mz, max_mz = min(mz_ref), max(mz_ref)
+    res = np.zeros(int((max_mz-min_mz)//mz_bin)+1)
+    for i in range(len(mz_list)):
+        res[int((mz_list[i] - min_mz) // mz_bin)] += int_list[i]
+    return res
+
 if __name__ == "__main__":
     e = oms.MSExperiment()
     oms.MzMLFile().load("data/echantillons données DIA/CITAMA-5-AER-d200.mzML", e)
@@ -151,6 +158,11 @@ if __name__ == "__main__":
     plt.savefig('fig/local_inte_res/spec_large_theo_1.png')
     plt.clf()
 
+    int_theo_inte_peak = integrated_mz_int(masses_1, intensities_1, 0.25, mz1)
+    int_theo_inte_large = integrated_mz_int(masses_1, intensities_1, 0.25, mz1_large)
+    res_peak_1 = scipy.stats.pearsonr(int_theo_inte_peak, inty1)
+    res_large_1 = scipy.stats.pearsonr(int_theo_inte_large, inty1_large)
+
     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']]