mdispCompare.py

import numpy as np
import argparse
from multiprocessing import Pool


parser = argparse.ArgumentParser(description='Compare 2 spectra files.')
parser.add_argument('bins', help='Bins on which the comparison should be made, should be the same file as the used in reducing the binned spectra.', type=str)
parser.add_argument('spectra1', help='First spectra, the one that will be devided.', type=str)
parser.add_argument('spectra2', help='Second spectra, the one we will divide BY.', type=str)
args = parser.parse_args()

cpuNumber = 2

print('Loading files.')
binData = np.loadtxt(args.bins, ndmin=2)
print('bins loaded')
data = np.loadtxt(args.spectra1)
print('first spectra loaded')
data2 = np.loadtxt(args.spectra2)
print('All files loaded, starting comparison!')


def compare(data):
    average = []
    global binData
    summ, count, i, j = 0, 0, 0, 0
    spectraLength = len(data)
    while i < spectraLength:
        if j == len(binData):
            break
        if (data[i][0] >= binData[j][0]) and ((data[i][0]) < binData[j][1]):
            count += 1
            summ += data[i][1]
            i += 1
            if i == spectraLength - 1:
                    average.append(summ / count)
                    for k in range(j + 1, len(binData)):
                        average.append(0)
                    break
        elif data[i][0] < binData[j][0]:
            i += 1
        elif data[i][0] >= binData[j][0]:
            j += 1
            if summ > 0:
                average.append(summ / count)
            else:
                average.append(0)
            summ, count = 0, 0
    return average


p = Pool(cpuNumber)
average = p.map(compare, [data, data2])
output = np.c_[binData, np.divide(average[0], average[1])]
np.savetxt(args.spectra1 + 'Comparison', output, fmt='%.7e')
np.savetxt(args.spectra1 + '.averaged', np.c_[binData, average[0]], fmt='%.7e')
np.savetxt(args.spectra2 + '.averaged', np.c_[binData, average[1]], fmt='%.7e')