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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Aaron LI
# Created: 2016-04-26
# Updated: 2016-05-06
#
# Changelog:
# 2016-05-06:
# * Add function `estimate_excess_error()` to estimate uncertainty
# * update according to `sbp_fit` renamed to `fit_sbp`
# * PEP8 fixes
#
#
"""
Calculate the central brightness excess value and ratio with respect to the
fitted SBP model (i.e., single-beta model).
NOTE:
* excess value: brightness_observed - brightness_model_predicted
* excess ratio: excess_value / brightness_observed
"""
import sys
import argparse
import json
from collections import OrderedDict
import numpy as np
from configobj import ConfigObj
from fit_sbp import make_model, make_sbpfit
__version__ = "0.2.0"
__date__ = "2016-05-06"
def calc_excess(data, fitted_model):
"""
Calculate the central brightness excess value and ratio with respect
to the fitted SBP model (single-beta).
TODO/XXX:
* whether to interpolate the SBP?
Arguments:
* data: raw 4-column SBP data
* fitted_model: fitted SBP model
"""
radii = data[:, 0]
radii_err = data[:, 1]
brightness = data[:, 2]
brightness_model = fitted_model.f(radii)
rin = radii - radii_err
rout = radii + radii_err
areas = np.pi * (rout**2 - rin**2)
bsum_obs = np.sum(brightness * areas)
bsum_model = np.sum(brightness_model * areas)
excess_value = bsum_obs - bsum_model
excess_ratio = excess_value / bsum_obs
excess = {
"brightness_obs": bsum_obs,
"brightness_model": bsum_model,
"excess_value": excess_value,
"excess_ratio": excess_ratio,
}
return excess
def estimate_excess_error(data, sbpfit, mctimes, verbose=False):
"""
Estimate the uncertainty of central excess value by Monte Carlo method.
XXX/TODO:
* whether also consider the uncertainty of R500?
Arguments:
* data: 4-column SBP data (radius, r_err, brightness, brightness_err)
* sbpfit: `SbpFit` object used to perform SBP fitting
* mctimes: number of Monte Carlo iterations
"""
brightness = data[:, 2]
brightness_err = data[:, 3]
params = sbpfit.dump_params()
ev_results = []
er_results = []
if verbose:
print("Estimating excess uncertainty by Monte Carlo " +
"(%d times) ..." % mctimes, end="", flush=True)
for i in range(mctimes):
if verbose and i % 100 == 0:
print("%d..." % i, end="", flush=True)
# randomize SBP data
brightness_rand = np.random.normal(brightness, scale=brightness_err)
sbpdata_rand = data.copy()
sbpdata_rand[:, 2] = brightness_rand
# load randomized data and perform SBP fit
sbpfit.reset(keep_source=True)
sbpfit.load_data(sbpdata_rand, keep_mask=True)
sbpfit.load_params(params)
sbpfit.fit()
model_rand = sbpfit.get_model()
excess = calc_excess(data=sbpdata_rand, fitted_model=model_rand)
ev_results.append(excess["excess_value"])
er_results.append(excess["excess_ratio"])
if verbose:
print("DONE!", flush=True)
ev_mean = np.mean(ev_results)
ev_std = np.std(ev_results)
ev_q25, ev_median, ev_q75 = np.percentile(ev_results, q=(25, 50, 75))
er_mean = np.mean(er_results)
er_std = np.std(er_results)
er_q25, er_median, er_q75 = np.percentile(er_results, q=(25, 50, 75))
results = {
"excess_value_mean": ev_mean,
"excess_value_median": ev_median,
"excess_value_q25": ev_q25,
"excess_value_q75": ev_q75,
"excess_value_std": ev_std,
"excess_ratio_mean": er_mean,
"excess_ratio_median": er_median,
"excess_ratio_q25": er_q25,
"excess_ratio_q75": er_q75,
"excess_ratio_std": er_std,
}
return results
def main():
# default arguments
default_outfile = "excess.json"
default_mctimes = 1000
parser = argparse.ArgumentParser(
description="Calculate the central brightness excess value",
epilog="Version: %s (%s)" % (__version__, __date__))
parser.add_argument("-V", "--version", action="version",
version="%(prog)s " +
"%s (%s)" % (__version__, __date__))
parser.add_argument("-v", "--verbose", dest="verbose",
required=False, action="store_true",
help="show verbose information")
parser.add_argument("-m", "--mctimes", dest="mctimes", required=False,
type=int, default=default_mctimes,
help="number of MC times to estimate excess error " +
"(default: %d)" % default_mctimes)
parser.add_argument("config", help="Config file for SBP fitting")
parser.add_argument("outfile", nargs="?", default=default_outfile,
help="Output json file to save the results " +
"(default: %s)" % default_outfile)
args = parser.parse_args()
config = ConfigObj(args.config)
modelname = config["model"]
config_model = config[modelname]
model = make_model(config, modelname=modelname)
print("SBP model: %s" % model.long_name, file=sys.stderr)
sbpfit_outfile = config.get("outfile")
sbpfit_outfile = config_model.get("outfile", sbpfit_outfile)
sbpfit_results = json.load(open(sbpfit_outfile),
object_pairs_hook=OrderedDict)
# Load fitted parameters for model
for pname, pvalue in sbpfit_results["params"].items():
model.set_param(name=pname, value=pvalue[0])
sbpfit = make_sbpfit(config, model=model)
sbpdata = np.loadtxt(config["sbpfile"])
excess = calc_excess(data=sbpdata, fitted_model=model)
excess_err = estimate_excess_error(data=sbpdata, sbpfit=sbpfit,
mctimes=args.mctimes,
verbose=args.verbose)
excess_data = OrderedDict([
("name", config["name"]),
("obsid", int(config["obsid"])),
("model", modelname),
("brightness_obs", excess["brightness_obs"]),
("brightness_model", excess["brightness_model"]),
("excess_value", excess["excess_value"]),
("excess_value_mean", excess_err["excess_value_mean"]),
("excess_value_median", excess_err["excess_value_median"]),
("excess_value_q25", excess_err["excess_value_q25"]),
("excess_value_q75", excess_err["excess_value_q75"]),
("excess_value_std", excess_err["excess_value_std"]),
("excess_ratio", excess["excess_ratio"]),
("excess_ratio_mean", excess_err["excess_ratio_mean"]),
("excess_ratio_median", excess_err["excess_ratio_median"]),
("excess_ratio_q25", excess_err["excess_ratio_q25"]),
("excess_ratio_q75", excess_err["excess_ratio_q75"]),
("excess_ratio_std", excess_err["excess_ratio_std"]),
("mc_times", args.mctimes),
])
excess_data_json = json.dumps(excess_data, indent=2)
print(excess_data_json)
open(args.outfile, "w").write(excess_data_json+"\n")
if __name__ == "__main__":
main()
# vim: set ts=4 sw=4 tw=0 fenc=utf-8 ft=python: #
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