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Convert carbon markers RT in minutes to match library spectral library #727

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Convert carbon markers RT in minutes to match library spectral library #727

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0f5e1af
Convert carbon markers RT in minutes to match library spectral library
lfnothias May 11, 2021
bd038fc
Correct Kovatz retention index calculation formula
lfnothias May 11, 2021
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5 changes: 3 additions & 2 deletions molecular-librarysearch-gc/tools/molecularsearch-gc/calculate_kovats.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,15 @@
import pandas as pd
import argparse
import shutil
import math
import numpy as np

def loadMarkers(marker):
df = pd.read_csv(marker, sep=',')

# compounds name has to be in the format of "name(C#)"
df["Compound_Name"] = df["Carbon_Number"].astype('int32')
df["RT"] = df["RT"].astype('float32')
df["RT"] = df["RT"].astype('float32')/60
df = df.sort_values(['Compound_Name'], ascending=[True])

return df
Expand All @@ -22,7 +23,7 @@ def kovatIndex(rt, markerDic):
or (rt == markerDic.RT[i] or rt == markerDic.RT[i+1]):
N,n,tr_N,tr_n = markerDic['Compound_Name'][i+1],markerDic['Compound_Name'][i], \
markerDic.RT[i+1],markerDic.RT[i]
Original_Annotation_KI_calculated = 100.0*(n+(N-n)*(rt-tr_n)/(tr_N - tr_n))
Original_Annotation_KI_calculated = 100.0*(n+(N-n)*((math.log(rt)-math.log(tr_n))/(math.log(tr_N) - math.log(tr_n))))
return Original_Annotation_KI_calculated
return 0.0

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