Plot horizon distance from ranking statistics

This MR will change the horizon distance plotting program in the DAG: instead of creating one horizon distance plot from reference PSDs, the new program create the horizon distance from the ranking statistic for each template bank bin. The main changes that affect the DAG workflow are summarized below:

• gstlal_inspiral_pipe: change plotting program from gstlal_plot_psd_horizon to gstlal_inspiral_plot_rankingstats_horizon (previously called gstlal_inspiral_plot_psd_horizon which does not accurately describe what it actually does, also see miscellaneous changes below).
• inspiral.py: update the horizon_dist_layer for the new plotting program.
• gstlal_inspiral_summary_page: horizon distances plots are put inside a new UI component (report.ImagePagination) so that all the plots will not be fetched at the same time and can be paginated.
• make the above changes to new style DAG workflow as well.

Miscellaneous changes:

• add new plotting module called horizon.py
• update the gstlal_plot_psd_horizon to use horizon.py module,
• renamed gstlal_inspiral_plot_psd_horizon --> gstlal_inspiral_plot_rankingstats_horizon

Note: this MR depends on gstlal-visualisation/ligo-scald!36 (merged), it's better not to merged before that request has been merged.

gstlal-inspiral/bin/gstlal_inspiral

@@ -775,27 +775,20 @@ for output_file_number, (svd_bank_url_dict, output_url, ranking_stat_output_url,
 		options.upload_time_before_merger = {row.template_id: abs(row.end.gpsSeconds) + 1e-9*abs(row.end.gpsNanoSeconds) for bank in list(banks.items())[0][-1] for row in bank.sngl_inspiral_table}
 
 	#
-	# calculate expected injection SNR if processing injections
-	# and a PSD is available. if so, this will filter the set
-	# of injections, calculate the expected the SNR and write
-	# the new injection file to disk before the pipeline starts up
+	# trim down injection list to the job's analysis segments if processing
+	# injections. this is done for two reasons:
+	#   1. increase efficiency for the injection element
+	#   2. decrease size of sim_inspiral table in output trigger files
 	#
 
-	if (options.injections and options.reference_psd) and not options.data_source in ("lvshm", "framexmit"):
-		if options.verbose:
-			print("calculating expected SNR for injections...", file=sys.stderr)
-
+	if options.injections:
 		# read in injections
 		xmldoc = ligolw_utils.load_filename(options.injections, verbose = options.verbose, contenthandler = LIGOLWContentHandler)
 		sim_inspiral_table = lsctables.SimInspiralTable.get_table(xmldoc)
 
-		# calculate expected SNR
-		# NOTE: f_high is determined from the bank's highest sampling
-		#       rate with a fudge factor to safely deal with roll-off
-		f_low = min(set(bank.f_low for ifo in banks.keys() for bank in banks[ifo]))
-		max_rate = max(set(rate for ifo in banks.keys() for bank in banks[ifo] for rate in bank.get_rates()))
-		f_high = 0.8 * (max_rate / 2.)
-		inspiral.calc_sim_inspiral_table_snrs(sim_inspiral_table, psd, detectors.seg, f_low = f_low, f_high = f_high, sample_rate = max_rate)
+		# trim injection list to analysis segments
+		injections = [inj for inj in sim_inspiral_table if inj.time_geocent in detectors.seg]
+		sim_inspiral_table[:] = injections
 
 		# write the injection file back out to a temporary directory
 		f, fname = tempfile.mkstemp(".xml.gz")