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-ugly/bin/gstlal_ll_dq

@@ -82,26 +82,25 @@ class NoiseTracker(object):
 
 	def on_buffer(self, buf):
 		if self.last_reduce_time is None:
-			self.last_reduce_time = int(round(buf.t0, -2))
-		if buf.data is None:
-			return
-
+			self.last_reduce_time = round(int(buf.t0), -2)
 		logging.debug(f"found buffer at t = {buf.t0}")
+
 		# First noise
-		ix = numpy.argmax(buf.data, axis=0)[1]
-		self.timedeq.append(buf.t0)
-		self.datadeq['noise'].append(buf.data[ix,1])
+		ix = numpy.argmax(buf.data[0])
+		self.timedeq.append(int(buf.t0))
+		self.datadeq['noise'].append(buf.data[0,ix])
 
-		# Then range
-		self.datadeq['range_history'].append(self.horizon_distance_func(self.psd, 8)[0] / 2.25)
+		if self.psd:
+			# Then range
+			self.datadeq['range_history'].append(self.horizon_distance_func(self.psd, 8)[0] / 2.25)
 
-		# The PSD
-		psd_freq = numpy.arange(self.psd.data.length / 4) * self.psd.deltaF * 4
-		psd_data = signal.decimate(self.psd.data.data[:], 4, ftype='fir', zero_phase=False)[:-1]**.5
+			# The PSD
+			psd_freq = numpy.arange(self.psd.data.length / 4) * self.psd.deltaF * 4
+			psd_data = signal.decimate(self.psd.data.data[:], 4, ftype='fir', zero_phase=False)[:-1]**.5
 
 		# Only reduce every 100s
 		if (buf.t0 - self.last_reduce_time) >= 100:
-			self.last_reduce_time = int(round(buf.t0, -2))
+			self.last_reduce_time = round(int(buf.t0), -2)
 			logging.info("reducing data and writing PSD snapshot for %d @ %d" % (buf.t0, int(utils.gps_now())))
 
 			data = {route: {self.instrument: {'time': list(self.timedeq), 'fields': {'data': list(self.datadeq[route])}}} for route in self.routes}
@@ -119,8 +118,8 @@ class NoiseTracker(object):
 			# NOTE: The PSD is special, we just record it. No min/median/max
 			thisdir = os.path.join(self.out_path, core.gps_to_leaf_directory(buf.t0))
 			core.makedir(thisdir)
-			psd_name = "%s-PSD-%d-100.hdf5" % (self.instrument, int(round(buf.t0, -2)))
-			self.to_hdf5(os.path.join(thisdir, psd_name), {"freq": psd_freq, "asd": psd_data, "time": numpy.array([buf.t0])})
+			psd_name = "%s-PSD-%d-100.hdf5" % (self.instrument, round(int(buf.t0), -2))
+			self.to_hdf5(os.path.join(thisdir, psd_name), {"freq": psd_freq, "asd": psd_data, "time": numpy.array([int(buf.t0)])})
 
 	def to_hdf5(self, path, datadict):
 		tmppath = "/dev/shm/%s" % path.replace("/","_") + ".tmp"
@@ -177,8 +176,7 @@ if __name__ == '__main__':
 		.whiten(fft_length=options.psd_fft_length, expand_gaps=True) \
 		.queue() \
 		.reblock() \
-		.nxydump() \
-		.sink(tracker.on_buffer)
+		.bufsink(tracker.on_buffer)
 
 	#
 	# process segment