Combined plotifar

bin/plotting/pycbc_page_ifar_vs_stat

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+#!/usr/bin/env python
+import sys, h5py
+import matplotlib
+matplotlib.use('agg')
+from matplotlib import pyplot as plt
+import numpy as np
+import argparse, logging
+from pycbc import init_logging, add_common_pycbc_options, results
+from pycbc.events import significance
+from pycbc.conversions import sec_to_year as convert_s_to_y
+
+plt.rcParams.update({
+    "text.usetex": True,
+})
+
+parser = argparse.ArgumentParser(usage='pycbc_page_ifar_vs_stat [--options]',
+                    description='Plots cumulative IFAR vs stat for'
+                          'backgrounds of different event types.')
+add_common_pycbc_options(parser)
+# get singles fit options
+significance.insert_significance_option_group(parser)
+parser.add_argument('--trigger-files', nargs='+',
+                    help='Paths to separate-event-type statmap files.')
+parser.add_argument('--ifo-combos', nargs='+',
+                    help='Which event types (detector combinations) to plot.')
+parser.add_argument('--output-file', required=True,
+                    help='Path to output plot.')
+parser.add_argument("--loudest-keep-values",
+                    default='[6:1]',
+                    help="Apply successive multiplicative levels of"
+                         " decimation to coincs with stat value below the"
+                         " given thresholds. Supply as a comma-separated list"
+                         " of threshold:decimation value pairs surrounded by"
+                         " square brackets (no spaces!). Decimation values must"
+                         " be positive integers."
+                         " Ex. [15:5,10:30,5:30,0:30]."
+                         " Default: no decimation")
+opts = parser.parse_args()
+init_logging(opts.verbose)
+
+sig_dict = significance.digest_significance_options(opts.ifo_combos, opts)
+
+coinc_color_map = {
+    'H':  '#ee0000',  # red
+    'L':  '#4ba6ff',  # blue
+    'V':  '#9b59b6',  # magenta/purple
+    'HL': '#0000DD',
+    'LV': '#FFCC33',
+    'HV': '#990000',
+    'HLV': np.array([20, 120, 20]) / 255.}
+
+coinc_line_map = {
+    'H':  '--',
+    'L':  '--',
+    'V':  '--',
+    'HL': '-',
+    'LV': '-',
+    'HV': '-',
+    'HLV':'-'
+}
+
+exc_zlag_filenames = []
+# Not sure what this step does precisely - select the first matching file ?
+for coinc_key in [f'/{k}-' for k in opts.ifo_combos]:
+    exc_zlag_filenames.append([f for f in opts.trigger_files if coinc_key in f][0])
+logging.info('Got files: ', ', '.join(exc_zlag_filenames))
+
+loudest_keep_vals = opts.loudest_keep_values.strip('[]').split(',')
+threshes = []
+factors = []
+for decstr in loudest_keep_vals:
+    thresh, factor = decstr.split(':')
+    if float(factor) % 1:
+        raise RuntimeError("Non-integer decimation is not supported")
+    if int(factor) < 1:
+        raise RuntimeError("Negative or zero decimation does not make sense")
+    if int(factor) == 1:
+        continue
+    threshes.append(float(thresh))
+    factors.append(int(factor))
+
+# Sort the threshes into descending order
+# - allows decimation factors to be given in any order
+threshorder = np.argsort(threshes)[::-1]
+decimation_stat = np.array(threshes)[threshorder]
+decimation_fact = np.array(factors)[threshorder]
+
+fig = plt.figure(figsize=(9, 6))
+ax = fig.add_subplot(111)
+
+ifar_calc_points = np.linspace(-10, 30, 100)
+
+singles = []
+for exc_zlag_filename in exc_zlag_filenames:
+    logging.info(f"opening file {exc_zlag_filename}")
+    with h5py.File(exc_zlag_filename,'r') as exc_zlag_f:
+        event_ifos = exc_zlag_f.attrs['ifos'].split()
+        coinc_key = ''.join(event_ifos).replace('1', '')
+        stat_exc = exc_zlag_f['background_exc']['stat'][:]
+        stat = exc_zlag_f['background']['stat'][:]
+        dec_fac = exc_zlag_f['background']['decimation_factor'][:]
+        dec_fac_exc = exc_zlag_f['background_exc']['decimation_factor'][:]
+        bg_time = convert_s_to_y(exc_zlag_f.attrs['background_time_exc'])
+        ifar_file = exc_zlag_f['background_exc']['ifar'][:]
+
+    logging.info(f"Min {coinc_key} IFAR: {min(ifar_file)}")
+    logging.info(f"{coinc_key} background time (y): {bg_time}")
+    if len(event_ifos) == 1:
+        ifo = event_ifos[0]
+        singles.append(ifo)
+        fit_thresh = sig_dict[ifo]['fit_threshold']
+        _, far, _ = significance.get_far(stat_exc,
+                                        ifar_calc_points,
+                                        dec_fac_exc,
+                                        bg_time,
+                                        **sig_dict[ifo])
+
+        # Plot n-louder points with a solid line and sngl fits with dashed
+        ax.plot(ifar_calc_points[ifar_calc_points < fit_thresh],
+                far[ifar_calc_points < fit_thresh],
+                '-', c=coinc_color_map[coinc_key], zorder=-5)
+        ax.plot(ifar_calc_points[ifar_calc_points > fit_thresh],
+                far[ifar_calc_points > fit_thresh],
+                coinc_line_map[coinc_key],
+                c=coinc_color_map[coinc_key], label=coinc_key, zorder=-5)
+        del ifo, fit_thresh  # avoid variables hanging around
+
+    else:
+        _, far = significance.get_far(stat_exc,
+                                      ifar_calc_points,
+                                      dec_fac_exc,
+                                      bg_time,
+                                      method='n_louder')
+
+        ax.plot(ifar_calc_points, far, '-',
+                c=coinc_color_map[coinc_key], label=coinc_key, zorder=-5)
+
+    del far  # avoid variable hanging around
+
+# Plot the thresholds
+for ifo in singles:
+    ax.axvline(sig_dict[ifo]['fit_threshold'], ls='--', zorder=-10,
+               c=coinc_color_map[ifo.rstrip('1')])
+
+ax.semilogy()
+ax.legend(ncol=2, fontsize=12)
+ax.grid()
+ax.set_xlim([-10, 30])
+ax.set_ylim([1e-3, 1e6])
+yticks = ax.get_yticks()
+ax.set_yticklabels(['$10^{%d}$' % np.log10(t) for t in yticks], fontsize=14)
+xticks = ax.get_xticks()
+ax.set_xticklabels(['%d' % t for t in xticks], fontsize=14)
+ax.set_xlabel('Ranking Statistic', fontsize=16)
+ax.set_ylabel('Cumulative Event Rate [y$^{-1}$]', fontsize=16)
+
+# Save
+caption = 'Cumulative rates of noise events for separate event types. Solid ' \
+          'lines represent estimates from counts of louder (zerolag or time ' \
+          'shifted) events. Dashed lines represent estimates from fit ' \
+          'extrapolation for single-detector events.'
+
+results.save_fig_with_metadata(fig, opts.output_file,
+     fig_kwds={'bbox_inches': 'tight'},
+     title='Cumulative Rate vs. Statistic',
+     caption=caption,
+     cmd=' '.join(sys.argv))
+