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-rwxr-xr-xastro/fitting/fit_beta_model.py133
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diff --git a/astro/fitting/fit_beta_model.py b/astro/fitting/fit_beta_model.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# Fitting data according to (SBP) beta model.
+#
+# Aaron LI
+# 2015/05/29
+
+
+import numpy as np
+from scipy.optimize import curve_fit
+
+import os
+import sys
+import getopt
+import re
+
+
+USAGE = """Usage:
+ %(prog)s [ -h -c -o outfile ] -i infile
+
+Required arguments:
+ -i, --infile
+ input data file with the following *four* columns:
+ r, rerr, s, serr
+ -o, --outfile
+ output file to store the fitted data
+ if not provided, then print results to screen
+
+Optional arguments:
+ -h, --help
+ print this usage
+ -c, --cut
+ accept an integer number
+ ignore the inner-most n data points
+""" % { 'prog': os.path.basename(sys.argv[0]) }
+
+
+def usage():
+ print(USAGE)
+
+
+def beta_model(r, s0, rc, beta, c):
+ """
+ SBP beta model.
+ """
+ return s0 * np.power((1.0+(r/rc)**2), 0.5-3*beta) + c
+
+
+def main():
+ infile = None
+ outfilename= None
+ cut = 0
+
+ try:
+ opts, args = getopt.getopt(sys.argv[1:], 'c:hi:o:',
+ ['cut=', 'help', 'infile=', 'outfile='])
+ except getopt.GetoptError as e:
+ print(e, file=sys.stderr)
+ usage()
+ sys.exit(2)
+
+ for opt, arg in opts:
+ if opt in ('-h', '--help'):
+ usage()
+ sys.exit(0)
+ elif opt in ('-i', '--infile'):
+ infile = arg
+ elif opt in ('-o', '--outfile'):
+ outfilename = arg
+ elif opt in ('-c', '--cut'):
+ cut = int(arg)
+ else:
+ assert False, 'unhandled option'
+
+ if not infile:
+ print('ERROR: --infile required', file=sys.stderr)
+ sys.exit(11)
+ if outfilename:
+ outfile = open(outfilename, 'w')
+ else:
+ outfile = sys.stdout
+
+ # input data list
+ r_data = []
+ rerr_data = []
+ s_data = []
+ serr_data = []
+
+ for line in open(infile, 'r'):
+ if re.match(r'^\s*#', line):
+ continue
+ if re.match(r'^\s*$', line):
+ continue
+ r, rerr, s, serr = map(float, line.split())
+ r_data.append(r)
+ rerr_data.append(rerr)
+ s_data.append(s)
+ serr_data.append(serr)
+
+ # convert to numpy array
+ r_data = np.array(r_data)
+ rerr_data = np.array(rerr_data)
+ s_data = np.array(s_data)
+ serr_data = np.array(serr_data)
+
+ # initial parameter values for beta model
+ s0_0 = 1.0e-7
+ rc_0 = 10.0
+ beta_0 = 0.6
+ c_0 = 0.0
+ par_0 = [s0_0, rc_0, beta_0, c_0]
+
+ par_fit, cov_fit = curve_fit(beta_model, r_data[cut:], s_data[cut:],
+ p0=par_0, sigma=serr_data[cut:])
+
+ s_fitted = beta_model(r_data, *par_fit)
+
+ print("# beta-model fitting results:", file=outfile)
+ print("# s0 * power((1.0+(r/rc)**2), 0.5-3*beta) + c", file=outfile)
+ print("# s0 = %g, rc = %g, beta = %g, c = %g" % tuple(par_fit),
+ file=outfile)
+ print("# radius(input) brightness(fitted)", file=outfile)
+ for i in range(len(s_fitted)):
+ print("%g %g" % (r_data[i], s_fitted[i]), file=outfile)
+
+ if not outfilename:
+ outfile.close()
+
+
+if __name__ == '__main__':
+ main()
+