datasets

local_integration_msms.py

@@ -3,6 +3,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 import scipy.signal
+from PIL.ImageOps import cover
 
 
 def compute_chromatograms(rt, mz, intensity, start_c, end_c):
@@ -99,6 +100,10 @@ def integrated_mz_int(mz_list,int_list,mz_bin, mz_ref):
         res[int((mz_list[i] - min_mz) // mz_bin)] += int_list[i]
     return res
 
+def intensity_coverage(int_expe,int_theo):
+    coverage = np.sum(np.where(int_theo>0,int_expe,0))/np.sum(int_expe)
+    return coverage
+
 if __name__ == "__main__":
     e = oms.MSExperiment()
     oms.MzMLFile().load("data/echantillons données DIA/CITAMA-5-AER-d200.mzML", e)
@@ -158,10 +163,12 @@ 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)
+    int_theo_inte_peak_1 = integrated_mz_int(masses_1, intensities_1, 0.25, mz1)
+    int_theo_inte_large_1 = integrated_mz_int(masses_1, intensities_1, 0.25, mz1_large)
+    res_peak_1 = scipy.stats.pearsonr(int_theo_inte_peak_1, inty1)
+    coverage_peak_1 = intensity_coverage(inty1,int_theo_inte_peak_1)
+    res_large_1 = scipy.stats.pearsonr(int_theo_inte_large_1, inty1_large)
+    coverage_large_1 = intensity_coverage(inty1_large, int_theo_inte_large_1)
 
     df_large_2 = df[df['MSlevel'] == 2]
     df_large_2 = df_large_2[619 < df_large_2['MS1_mz_max']]
@@ -201,5 +208,11 @@ if __name__ == "__main__":
     plt.savefig('fig/local_inte_res/spec_large_theo_2.png')
     plt.clf()
 
+    int_theo_inte_peak_2 = integrated_mz_int(masses_2, intensities_2, 0.25, mz2)
+    int_theo_inte_large_2 = integrated_mz_int(masses_2, intensities_2, 0.25, mz2_large)
+    res_peak_2 = scipy.stats.pearsonr(int_theo_inte_peak_2, inty2)
+    coverage_peak_2 = intensity_coverage(inty2,int_theo_inte_peak_2)
+    res_large_2 = scipy.stats.pearsonr(int_theo_inte_large_2, inty2_large)
+    coverage_large_2 = intensity_coverage(inty2_large, int_theo_inte_large_2)