Source code for hendrics.modeling

import os
import copy

import numpy as np
from astropy import log
from stingray.modeling import fit_powerspectrum
from .io import load_model, load_pds, save_model, save_pds, HEN_FILE_EXTENSION

[docs] def main_model(args=None): """Main function called by the `HENfspec` command line script.""" import argparse from .base import _add_default_args, check_negative_numbers_in_args description = ( "Fit frequency spectra (PDS, CPDS, cospectrum) " "with user-defined models" ) parser = argparse.ArgumentParser(description=description) parser.add_argument("files", help="List of light curve files", nargs="+") parser.add_argument( "-m", "--modelfile", type=str, help="File containing an Astropy model with or without" " constraints", ) parser.add_argument( "--fitmethod", type=str, default="L-BFGS-B", help="Any scipy-compatible fit method", ) parser.add_argument( "--frequency-interval", type=float, nargs="+", default=None, help="Select frequency interval(s) to fit. Must be " "an even number of frequencies in Hz, like " '"--frequency-interval 0 2" or ' '"--frequency-interval 0 2 5 10", meaning that ' "the spectrum will be fitted between 0 and 2 Hz, " "or using the intervals 0-2 Hz and 5-10 Hz.", ) _add_default_args(parser, ["loglevel", "debug"]) args = check_negative_numbers_in_args(args) args = parser.parse_args(args) if args.debug: args.loglevel = "DEBUG" freqs = args.frequency_interval if freqs is not None and len(freqs) % 2 != 0: raise ValueError("Invalid number of frequencies specified") log.setLevel(args.loglevel) with log.log_to_file("HENmodel.log"): model, kind, constraints = load_model(args.modelfile) if kind != "Astropy": raise TypeError("At the moment, only Astropy models are accepted") for f in args.files: root = os.path.splitext(f)[0] spectrum = load_pds(f) if freqs is not None: good = np.zeros(len(spectrum.freq), dtype=bool) for f0, f1 in zip(freqs[::2], freqs[1::2]): local_good = (spectrum.freq >= f0) & (spectrum.freq < f1) good[local_good] = True spectrum_filt = copy.copy(spectrum) spectrum_filt.power = spectrum.power[good] spectrum_filt.freq = spectrum.freq[good] spectrum_filt.power_err = spectrum.power_err[good] priors = None max_post = False if constraints is not None and "priors" in constraints: priors = constraints["priors"] max_post = True parest, res = fit_powerspectrum( spectrum, model, model.parameters, max_post=max_post, priors=priors, fitmethod=args.fitmethod, ) save_model(res.model, root + "_bestfit.p") spectrum.best_fits = [res.model]"Best-fit model:") save_pds(spectrum, root + "_fit" + HEN_FILE_EXTENSION)