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#!/usr/bin/env python3
#
# Copyright (c) 2016 Aaron LI
# MIT license
#
# Extract the sbpfit results, and output in CSV format.
#
# Created: 2016-04-27
#
import sys
import os
import json
import csv
import argparse
from collections import OrderedDict
from configobj import ConfigObj
def extract_sbpfit(data, config):
"""
Extract the SBP fitting results (sbpfit) as well as some config
information, and return the results as a Python dictionary.
"""
results = OrderedDict([
# basic information from config
("name", config["name"]),
("obsid", int(config["obsid"])),
("r500_pix", float(config["r500_pix"])),
("r500_kpc", float(config["r500_kpc"])),
("unit", config["unit"]),
("model", config["model"]),
# basic fitting results
("ndata", data["ndata"]),
("nvarys", data["nvarys"]),
("nfree", data["nfree"]),
("nfev", data["nfev"]),
("chisqr", data["chisqr"]),
("redchi", data["redchi"]),
("aic", data["aic"]),
("bic", data["bic"]),
])
# fitted paramters value and confidence intervals
results_params = extract_params(data)
results.update(results_params)
return results
def extract_params(data):
"""
Extract the values and confidence intervals (if present)
for each parameter.
"""
results = OrderedDict()
for pname, pvalue in data["params"].items():
# best value
results[pname] = pvalue[0]
# confidence intervals (if present)
if "ci" in data:
for ci_name, ci_value in data["ci"][pname].items():
if ci_name == "best":
continue
results["%s_%s_L" % (pname, ci_name)] = ci_value[0]
results["%s_%s_U" % (pname, ci_name)] = ci_value[1]
return results
def main():
parser = argparse.ArgumentParser(
description="Extract SBP fitting results")
parser.add_argument("config", nargs="?", default="sbpfit.conf",
help="config used for SBP fitting (default: sbpfit.conf)")
args = parser.parse_args()
config = ConfigObj(args.config)
modelname = config["model"]
sbpfit_outfile = config.get("outfile")
sbpfit_outfile = config[modelname].get("outfile", sbpfit_outfile)
sbpfit_outfile = os.path.join(os.path.dirname(args.config), sbpfit_outfile)
sbpfit_results = json.load(open(sbpfit_outfile),
object_pairs_hook=OrderedDict)
results = extract_sbpfit(data=sbpfit_results, config=config)
# output results
csv_writer = csv.writer(sys.stdout)
csv_writer.writerow(results.keys())
csv_writer.writerow(results.values())
if __name__ == "__main__":
main()
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