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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Aaron LI
# Created: 2016-04-29
# Updated: 2016-04-29
#
# TODO:
# * to calculate the PEI error
# * to save a plot with the rectangular and interpolation marked
#
"""
Calculate the power excess index (PEI), which is defined the area ratio of
the lower-left part with respect to the total rectangulr, which is further
defined by the radial power spectrum and the scale of 0.035R500 and 0.35R500,
in the logarithmic space.
Reference:
Zhang, C., et al. 2016, ApJ
"""
__version__ = "0.1.0"
__date__ = "2016-04-29"
import sys
import glob
import argparse
import json
from collections import OrderedDict
import numpy as np
import scipy as sp
import scipy.interpolate
import scipy.integrate
from make_r500_regions import get_r500
def calc_pei(data, r500, interp_np=101):
"""
Calculate the power excess index (PEI), which is defined the area ratio
of the lower-left part with respect to the total rectangulr.
Arguments:
* data: 3-column power spectrum data (frequency, power, power_err)
* r500: R500 value in unit of the inverse of the above "frequency"
* interp_np: number of data points interpolated to calculate PEI
"""
freqs = data[:, 0]
psd1d = data[:, 1]
psd1d_err = data[:, 2]
# frequency values corresponding to 0.35R500 and 0.035R500
f1 = 1.0 / (0.350 * r500)
f2 = 1.0 / (0.035 * r500)
# switch to the logarithmic scale
# XXX: how to deal with the errors
mask = (freqs > 0.0)
x = np.log10(freqs[mask])
y = np.log10(psd1d[mask])
x1 = np.log10(f1)
x2 = np.log10(f2)
# interpolate the power spectrum
f_interp = sp.interpolate.interp1d(x, y, kind="cubic", assume_sorted=True)
y1 = f_interp(x1)
y2 = f_interp(x2)
if interp_np % 2 == 0:
# Simpson's rule requires an even number of intervals
interp_np += 1
x_interp = np.linspace(x1, x2, num=interp_np)
y_interp = f_interp(x_interp)
# calculate the PEI
area_total = abs(x1 - x2) * abs(y1 - y2)
area_below = sp.integrate.simps((y_interp-y2), x_interp)
pei_value = area_below / area_total
results = {
"area_total": area_total,
"area_below": area_below,
"pei_value": pei_value,
"pei_err": None,
}
return results
def main():
# default arguments
default_infile = "psd.txt"
default_outfile = "pei.json"
default_infojson = "../*_INFO.json"
parser = argparse.ArgumentParser(
description="Calculate the power excess index (PEI)",
epilog="Version: %s (%s)" % (__version__, __date__))
parser.add_argument("-V", "--version", action="version",
version="%(prog)s " + "%s (%s)" % (__version__, __date__))
parser.add_argument("-j", "--json", dest="json", required=False,
help="the *_INFO.json file (default: find %s)" % default_infojson)
parser.add_argument("-i", "--infile", dest="infile",
required=False, default=default_infile,
help="input data of the radial power spectrum " + \
"(default: %s)" % default_infile)
parser.add_argument("-o", "--outfile", dest="outfile",
required=False, default=default_outfile,
help="output json file to save the results " + \
"(default: %s)" % default_outfile)
args = parser.parse_args()
info_json = glob.glob(default_infojson)[0]
if args.json:
info_json = args.json
json_str = open(info_json).read().rstrip().rstrip(",")
info = json.loads(json_str)
name = info["Source Name"]
obsid = int(info["Obs. ID"])
r500 = get_r500(info)
r500_kpc = r500["r500_kpc"]
r500_pix = r500["r500_pix"]
kpc_per_pix = r500["kpc_per_pix"]
psd_data = np.loadtxt(args.infile)
pei = calc_pei(psd_data, r500=r500_pix)
pei_data = OrderedDict([
("name", name),
("obsid", obsid),
("r500_kpc", r500_kpc),
("r500_pix", r500_pix),
("kpc_per_pix", kpc_per_pix),
("area_total", pei["area_total"]),
("area_below", pei["area_below"]),
("pei", pei["pei_value"]),
("pei_err", pei["pei_err"]),
])
pei_data_json = json.dumps(pei_data, indent=2)
print(pei_data_json)
open(args.outfile, "w").write(pei_data_json+"\n")
if __name__ == "__main__":
main()
# vim: set ts=4 sw=4 tw=0 fenc=utf-8 ft=python: #
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