data viz + df from raw spectra

data_viz.py

@@ -275,9 +275,11 @@ def add_length(dataframe):
 # scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_prosit_ISA_eval.png', color=True)
 # histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_prosit_ISA_eval.png')
 
-df = pd.read_csv('output/out_common_ISA_ISA_eval_2.csv')
-add_length(df)
-df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
-histo_abs_error(df, display=False, save=True, path='fig/custom model res/histo_ISA_ISA_eval_2.png')
-scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_ISA_ISA_eval_2_seq.png', color=True, col = 'seq')
-histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_ISA_ISA_eval_2.png')
\ No newline at end of file
+# df = pd.read_csv('output/out_common_ISA_ISA_eval_2.csv')
+# add_length(df)
+# df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
+# histo_abs_error(df, display=False, save=True, path='fig/custom model res/histo_ISA_ISA_eval_2.png')
+# scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_ISA_ISA_eval_2_seq.png', color=True, col = 'seq')
+# histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_ISA_ISA_eval_2.png')
+
+

local_integration_msms.py

@@ -3,6 +3,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
+
+
 def compute_chromatograms(rt, mz, intensity, start_c, end_c):
     value=[]
 
@@ -23,15 +25,29 @@ def get_df(expe, long: bool = False):
     Returns:
     pandas.DataFrame: feature information stored in a DataFrame
     """
-    if long:
-        cols = ["RT", "mz", "inty", 'MSlevel']
-        expe.updateRanges()
-        spectraarrs2d = expe.get2DPeakDataLong(expe.getMinRT(), expe.getMaxRT(), expe.getMinMZ(), expe.getMaxMZ())
-        return pd.DataFrame(dict(zip(cols, spectraarrs2d))) #TODO ajouter MSlevel
+
 
     cols = ["RT", "mzarray", "intarray", 'MSlevel','MS1 MZ']
 
-    return pd.DataFrame(data=((spec.getRT(), *spec.get_peaks(), spec.getMSLevel(), spec.getPrecursors()[0].getMZ() if  spec.getMSLevel() ==2 else None) for spec in expe), columns=cols)
+    df = pd.DataFrame(data=((spec.getRT(), *spec.get_peaks(), spec.getMSLevel(), spec.getPrecursors()[0].getMZ() if  spec.getMSLevel() ==2 else None) for spec in expe), columns=cols)
+
+    if long:
+        RT = []
+        mz = []
+        inty = []
+        ms_lv = []
+        ms1_mz = []
+        for index, row in df.iterrows():
+            mz.extend(row['mzarray'])
+            inty.extend(row['intarray'])
+            RT.extend([row['RT']]*len(row['intarray']))
+            ms_lv.extend([row['MSlevel']] * len(row['intarray']))
+            ms1_mz.extend([row['MS1 MZ']] * len(row['intarray']))
+        dico = {"RT": RT, "mz": mz, "inty": inty, "MSlevel": ms_lv, "MS1 MZ": ms1_mz}
+        return pd.DataFrame(dico)
+
+    else :
+        return df
 
 def generate_RT_int_imgs(exp,star_mz,stop_mz):
     exp.updateRanges()
@@ -58,7 +74,7 @@ def generate_RT_int_imgs(exp,star_mz,stop_mz):
 def integrate_ms_ms(time_start, time_end, df):
     df_useful = df[(df['MS1 RT']>time_start) & (df['MS1 RT']<time_end) & (df['MSlevel']==2)].reset_index(inplace=True)
 
-
+    value = 0
 
 
     return value
@@ -68,19 +84,17 @@ if __name__ == "__main__":
     oms.MzMLFile().load("data/Staph140.mzML", e)
     # generate_RT_int_imgs(e, 350, 1250)
 
-    df = get_df(e)
+    df = get_df(e, long=True)
     df1 = df[df['MSlevel'] == 1]
-    df1.reset_index(inplace=True, drop=True)
-    for i in range(len(df1)):
-        fig, ax = plt.subplots()
-        ax.plot(df1['mzarray'][i], df1['intarray'][i],linewidth=0.1)
-        ax.set_xlabel('mz')
-        ax.set_xlim(350,750)
-        ax.set_ylabel('Intensity')
-        ax.set_title('RT : {}'.format(df1['RT'][i]))
-        plt.savefig('fig/rt_local/RT{}.png'.format(df1['RT'][i]))
-        plt.close()
-
+    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')
 #358.1 358.32
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