Source code for pycbc.results.pygrb_plotting_utils

# Copyright (C) 2019 Francesco Pannarale, Gino Contestabile, Cameron Mills
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# =============================================================================
# Preamble
# =============================================================================

Module to generate PyGRB figures: scatter plots and timeseries.

import copy
import numpy
from pycbc.results import save_fig_with_metadata

# Used locally

# =============================================================================
# Function to calculate chi-square weight for the reweighted SNR
# =============================================================================
[docs]def new_snr_chisq(snr, new_snr, chisq_dof, chisq_index=4.0, chisq_nhigh=3.0): """Returns the chi-square value needed to weight SNR into new SNR""" chisqnorm = (snr/new_snr)**chisq_index if chisqnorm <= 1: return 1E-20 return chisq_dof * (2*chisqnorm - 1)**(chisq_nhigh/chisq_index)
# ============================================================================= # Plot contours in a scatter plot where SNR is on the horizontal axis # =============================================================================
[docs]def contour_plotter(axis, snr_vals, contours, colors, vert_spike=False): """Plot contours in a scatter plot where SNR is on the horizontal axis""" for i, _ in enumerate(contours): plot_vals_x = [] plot_vals_y = [] if vert_spike: for j, _ in enumerate(snr_vals): # Workaround to ensure vertical spike is shown on veto plots if contours[i][j] > 1E-15 and not plot_vals_x: plot_vals_x.append(snr_vals[j]) plot_vals_y.append(0.1) if contours[i][j] > 1E-15 and plot_vals_x: plot_vals_x.append(snr_vals[j]) plot_vals_y.append(contours[i][j]) else: plot_vals_x = snr_vals plot_vals_y = contours[i] axis.plot(plot_vals_x, plot_vals_y, colors[i])
# # Used (also) in executables # # ============================================================================= # Given the trigger and injection values of a quantity, determine the maximum # =============================================================================
[docs]def axis_max_value(trig_values, inj_values, inj_file): """Deterime the maximum of a quantity in the trigger and injection data""" axis_max = trig_values.max() if inj_file and inj_values.size and inj_values.max() > axis_max: axis_max = inj_values.max() return axis_max
# ============================================================================= # Master plotting function: fits all plotting needs in for PyGRB results # =============================================================================
[docs]def pygrb_plotter(trigs, injs, xlabel, ylabel, opts, snr_vals=None, conts=None, shade_cont_value=None, colors=None, vert_spike=False, cmd=None): """Master function to plot PyGRB results""" from matplotlib import pyplot as plt # Set up plot fig = plt.figure() cax = fig.gca() # Plot trigger-related and (if present) injection-related quantities cax_plotter = cax.loglog if opts.use_logs else cax.plot cax_plotter(trigs[0], trigs[1], 'bx') if not (injs[0] is None and injs[1] is None): cax_plotter(injs[0], injs[1], 'r+') cax.grid() # Plot contours if conts is not None: contour_plotter(cax, snr_vals, conts, colors, vert_spike=vert_spike) # Add shading above a specific contour (typically used for vetoed area) if shade_cont_value is not None: limy = cax.get_ylim()[1] polyx = copy.deepcopy(snr_vals) polyy = copy.deepcopy(conts[shade_cont_value]) polyx = numpy.append(polyx, [max(snr_vals), min(snr_vals)]) polyy = numpy.append(polyy, [limy, limy]) cax.fill(polyx, polyy, color='#dddddd') # Axes: labels and limits cax.set_xlabel(xlabel) cax.set_ylabel(ylabel) if opts.x_lims: x_lims = map(float, opts.x_lims.split(',')) cax.set_xlim(x_lims) if opts.y_lims: y_lims = map(float, opts.y_lims.split(',')) cax.set_ylim(y_lims) # Wrap up plt.tight_layout() save_fig_with_metadata(fig, opts.output_file, cmd=cmd, title=opts.plot_title, caption=opts.plot_caption) plt.close()