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Update msdial_formatter.py #804

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51 changes: 21 additions & 30 deletions ...molecular-networking/tools/feature-based-molecular-networking/scripts/msdial_formatter.py
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"""
Created on Thu Oct 4 17:20:37 2018, and updated April 2 2019

@author: zheng zhang, louis felix nothias, and mingxun wang
@author: zheng zhang, louis felix nothias, mingxun wang & Chris Pook
@purpose: to convert the MS-DIAL file into a diserable format
"""
import pandas as pd
import sys

def convert_to_feature_csv(input_filename, output_filename):
input_format = pd.read_csv(input_filename, sep='\t', skiprows=3)


# first enumerate samples in the input
n_df = pd.read_csv(input_filename, sep='\t', skiprows=3, nrows = 5)
sample_columns = list(n_df)
last_sample_column = sample_columns.index('Average')

# now process the rest of the data
input_df = pd.read_csv(input_filename, sep='\t', skiprows=4)

#Check IMS data columns and drop them
if 'Average drift time' in input_format.columns:
input_format = input_format.drop(['Average drift time','Average CCS'], axis=1)
if 'Average drift time' in input_df.columns:
input_df = input_df.drop(['Average drift time','Average CCS'], axis=1)

#Continue with the processing
headers = list(input_format.keys())
sample_names = headers[22:]

input_records = input_format.to_dict(orient="records")
output_records = []

for record in input_records:
scan = record["Alignment ID"]
mz = record["Average Mz"]
rt = record["Average Rt(min)"]

output_record = {}
output_record["row ID"] = str(scan)
output_record["row m/z"] = str(mz)
output_record["row retention time"] = str(rt)

for sample_name in sample_names:
output_record[sample_name + " Peak area"] = record[sample_name]

output_records.append(output_record)

output_headers = ["row ID", "row m/z", "row retention time"]
output_headers += [sample_name + " Peak area" for sample_name in sample_names]
headers = list(input_df.keys())
sample_names = headers[32:last_sample_column]

output_df = pd.DataFrame(output_records)
output_df.to_csv(output_filename, sep=",", index=False, columns=output_headers)
columns = ["Alignment ID", "Average Mz", "Average Rt(min)"] + sample_names
output_df = input_df[columns].copy()

output_columns = ["row ID", "row m/z", "row retention time"]
output_columns += [sample_name + " Peak area" for sample_name in sample_names]
column_name_dict = dict(zip(columns, output_columns))
output_df.rename(columns = column_name_dict, inplace=True)
output_df.to_csv(output_filename, sep=",", index=False)
return

if __name__=="__main__":
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