Mercurial > repos > jasmine_amir > snrqk2
diff SNRQK2.py @ 0:2547394443a0
"planemo upload commit 8b4c59e523d28c30368aebfa0416d9ff8e27d257"
author | jasmine_amir |
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date | Thu, 09 Jun 2022 19:29:02 -0400 |
parents | |
children | 1ab67c0c0054 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/SNRQK2.py Thu Jun 09 19:29:02 2022 -0400 @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# coding: utf-8 +########################################################### +########################################################### +## Jasmine Amirzadegan +## 2022 APRIL 14 +########################################################### +## SNRQC.py: +## compute QC metrics specific to SSQuAWK v4 + workflows +## usage: python SNRQC.py intermediateSSQuAWKfile.txt +## python 3.7 +########################################################### +import pandas as pd +import sys + +fn = sys.argv[1] +df = pd.read_csv(fn, sep = "\t", header = 0) + + + +if (df['Sample'].str.contains('negativeControl')).any(): + m = df.loc[ (df['Sample'].str.contains('negativeControl')) ] + noise = m['readsAlignPassFilt'] + SNR = [] + + for i in df['readsAlignPassFilt']: + SNR.append(i/noise.item()) + #[float(j) for j in SNR] + df['SNR'] = SNR + +else: + df['SNR'] = "NA" + +#print(df) +#fn1 = fn.split("/")[1] +#df.to_csv('test-data/snrqk_result' + fn1 + '.tsv', sep="\t") +df.to_csv('snrqk_result.tsv', sep='\t')