Source code for beast.plotting.plot_noisemodel

import numpy as np
import matplotlib.pyplot as plt
import argparse
import re

from beast.physicsmodel.grid import SEDGrid
import beast.observationmodel.noisemodel.generic_noisemodel as noisemodel

__all__ = ["plot_noisemodel"]

[docs] def plot_noisemodel( sed_file, noise_file_list, plot_file, samp=100, cmap_name='viridis', ): """ Make a plot of the noise model: for each of the bandsm make plots of bias and uncertainty as a function of flux If there are multiple files in noise_file_list, each of them will be overplotted in each panel. Parameters ---------- sed_file : string path+name of the SED grid file noise_file_list : list of strings path+name of the noise model file(s) plot_file : string name of the file to save the plot samp : int (default=100) plotting all of the SED points takes a long time for a viewer to load, so set this to plot every Nth point cmap_name : string ( name of a color map to use """ # read in the SED grid print("* reading SED grid file") sed_object = SEDGrid(sed_file) if hasattr(sed_object.seds, "read"): sed_grid = else: sed_grid = sed_object.seds filter_list = sed_object.filters n_filter = len(filter_list) # figure fig, ax = plt.subplots(nrows=3, ncols=n_filter, figsize=(25, 15)) # setup the plots fontsize = 12 font = {"size": fontsize} plt.rc("font", **font) plt.rc("lines", linewidth=2) plt.rc("axes", linewidth=2) plt.rc("xtick.major", width=2) plt.rc("ytick.major", width=2) plt.set_cmap(cmap_name) # go through noise files after sorting them according to # their SD bin number noise_file_list.sort(key=lambda f: int(''.join(filter(str.isdigit, f)))) bin_label = [re.findall(r"bin\d+", x)[0] for x in noise_file_list] for n, nfile in enumerate(np.atleast_1d(noise_file_list)): print("* reading " + nfile) # read in the values noisemodel_vals = noisemodel.get_noisemodelcat(nfile) # extract error and bias noise_err = noisemodel_vals["error"] noise_bias = noisemodel_vals["bias"] noise_compl = noisemodel_vals["completeness"] # plot things for f, filt in enumerate(filter_list): # error is negative where it's been extrapolated -> trim those good_err = np.where(noise_err[:, f] > 0)[0] plot_sed = sed_grid[good_err, f][::samp] plot_err = noise_err[good_err, f][::samp] plot_bias = noise_bias[good_err, f][::samp] plot_compl = noise_compl[good_err, f][::samp] # bias bax = ax[0, f] bax.plot( np.log10(plot_sed), plot_bias / plot_sed, marker="o", linestyle="none", mew=0, ms=2, alpha=0.1, label='SD %s' % (bin_label[n]), ) bax.tick_params(axis="both", which="major") bax.set_xlabel("log " + filt) bax.set_ylabel(r"Bias ($\mu$/F)") leg = bax.legend(loc='lower right', markerscale=3) for lh in leg.legendHandles: lh._legmarker.set_alpha(1) # error eax = ax[1, f] eax.plot( np.log10(plot_sed), plot_err / plot_sed, marker="o", linestyle="none", mew=0, ms=2, alpha=0.1, ) eax.tick_params(axis="both", which="major") eax.set_xlabel("log " + filt) eax.set_ylabel(r"Error ($\sigma$/F)") # completeness cax = ax[2, f] cax.plot( np.log10(plot_sed), plot_compl, marker="o", linestyle="none", mew=0, ms=2, alpha=0.1, ) cax.tick_params(axis="both", which="major") cax.set_xlabel("log " + filt) cax.set_ylabel(r"Completeness") plt.tight_layout() fig.savefig(plot_file, dpi=300) plt.close(fig)
if __name__ == "__main__": # pragma: no cover # commandline parser parser = argparse.ArgumentParser() parser.add_argument( "sed_file", type=str, help="path+name of the sed grid file", ) parser.add_argument( "noise_file_list", type=str, nargs="+", help="path+name of the noise model file(s)", ) parser.add_argument( "plot_file", type=str, help="name of the file to save the plot", ) parser.add_argument( "--samp", type=int, default=100, help="plot every Nth point", ) parser.add_argument( "--cmap_name", type=str, default="viridis", help="color map to use when making plots", ) args = parser.parse_args() plot_noisemodel( args.sed_file, args.noise_file_list, args.plot_file, samp=args.samp, cmap_name=args.cmap_name, ) # print help if no arguments if not any(vars(args).values()): parser.print_help()