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aggregate_rescaling_limits.py
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aggregate_rescaling_limits.py
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import pandas as pd
import numpy as np
import argparse
import os
import os.path
def parse_arguments():
parser = argparse.ArgumentParser(
prog='aggregate_rescaling_limits',
description=('')
)
parser.add_argument(
'-i', '--input_files', type=str, nargs='+', required=True,
help='list of input files to be aggregated (.pkl)'
)
parser.add_argument(
'-o', '--output_file', type=str, default='aggregated_limits.pkl',
help='filename for output file (.pkl)'
)
return(parser.parse_args())
def percentile(n):
def percentile_(x):
return np.percentile(x, n)
percentile_.__name__ = 'percentile_%s' % n
return percentile_
def main(args):
rescaling_limits_list = []
for index, filename in enumerate(args.input_files):
rescaling_limits_list.append(
pd.read_pickle(filename)
)
rescaling_limits = pd.concat(rescaling_limits_list)
grouped = rescaling_limits.groupby('control')
columns = grouped['lower_limit', 'upper_limit']
aggregated_limits = columns.agg([
np.mean, np.min, np.max,
percentile(10), percentile(40),
percentile(60), percentile(80)
])
aggregated_limits.to_pickle(args.output_file)
aggregated_limits['lower_limit'].to_csv(
os.path.splitext(
os.path.basename(args.output_file))[0] + '_lower_limit.csv'
)
aggregated_limits['upper_limit'].to_csv(
os.path.splitext(
os.path.basename(args.output_file))[0] + '_upper_limit.csv'
)
return
if __name__ == '__main__':
arguments = parse_arguments()
main(arguments)