diff --git a/gstlal-ugly/bin/gstlal_feature_extractor b/gstlal-ugly/bin/gstlal_feature_extractor
index e88d97150b11631a9ee4901918e54a385dcb4382..0e3e5fa146b8ce99633caba503a2383d2c29328a 100755
--- a/gstlal-ugly/bin/gstlal_feature_extractor
+++ b/gstlal-ugly/bin/gstlal_feature_extractor
@@ -115,9 +115,9 @@ class MultiChannelHandler(simplehandler.Handler):
self.instrument = data_source_info.instrument
self.frame_segments = data_source_info.frame_segments
self.keys = kwargs.pop("keys")
- self.basis_params = kwargs.pop("basis_params")
+ self.waveforms = kwargs.pop("waveforms")
self.basename = kwargs.pop("basename")
- self.waveform = options.waveform
+ self.waveform_type = options.waveform
# format id for aesthetics
self.job_id = str(options.job_id).zfill(4)
@@ -286,17 +286,17 @@ class MultiChannelHandler(simplehandler.Handler):
trigger_seg = segments.segment(LIGOTimeGPS(row.end_time, row.end_time_ns), LIGOTimeGPS(row.end_time, row.end_time_ns))
if not self.frame_segments[self.instrument] or self.frame_segments[self.instrument].intersects_segment(trigger_seg):
- freq, q, duration = self.basis_params[(channel, rate)].parameter_grid[row.channel_index]
- filter_duration = self.basis_params[(channel, rate)].max_duration
+ freq, q, duration = self.waveforms[channel].parameter_grid[rate][row.channel_index]
+ filter_duration = self.waveforms[channel].filter_duration[rate]
filter_stop_time = row.end_time + row.end_time_ns * 1e-9
# set trigger time based on waveform
- if self.waveform == 'sine_gaussian':
+ if self.waveform_type == 'sine_gaussian':
trigger_time = filter_stop_time - (filter_duration / 2.)
start_time = trigger_time - duration / 2.
stop_time = trigger_time + duration / 2.
- elif self.waveform == 'half_sine_gaussian':
+ elif self.waveform_type == 'half_sine_gaussian':
trigger_time = filter_stop_time
start_time = trigger_time - duration
stop_time = trigger_time
@@ -581,6 +581,7 @@ options, filenames = parse_command_line()
data_source_info = multichannel_datasource.DataSourceInfo(options)
instrument = data_source_info.instrument
basename = '%s-%s' % (instrument[:1], options.description)
+waveforms = {}
# only load kafka library if triggers are transferred via kafka topic
if options.use_kafka:
@@ -713,9 +714,6 @@ for subset_id, channel_subset in enumerate(data_source_info.channel_subsets, 1):
mainloop = GObject.MainLoop()
pipeline = Gst.Pipeline(sys.argv[0])
- # dictionary of basis parameters keyed by ifo, rate
- basis_params = {}
-
# generate multiple channel sources, and link up pipeline
head = multichannel_datasource.mkbasicmultisrc(pipeline, data_source_info, channel_subset, verbose = options.verbose)
src = {}
@@ -728,6 +726,25 @@ for subset_id, channel_subset in enumerate(data_source_info.channel_subsets, 1):
n_rates = int(numpy.log2(max_rate/min_rate) + 1)
rates = [min_rate*2**i for i in range(n_rates)]
+ # choose range of basis parameters
+ # NOTE: scale down frequency range by downsample_factor to deal with rolloff from downsampler
+ downsample_factor = 0.8
+ flow = min_rate/4.
+ fhigh = max_rate/2.
+ qlow = 3.3166
+ if data_source_info.extension == 'ini':
+ qhigh = data_source_info.channel_dict[channel]['qhigh']
+ else:
+ qhigh = options.qhigh
+
+ # generate templates
+ if options.waveform == 'half_sine_gaussian':
+ waveforms[channel] = idq_utils.HalfSineGaussianGenerator((flow, fhigh), (qlow, qhigh), rates, mismatch=options.mismatch, downsample_factor=downsample_factor)
+ elif options.waveform == 'sine_gaussian':
+ waveforms[channel] = idq_utils.SineGaussianGenerator((flow, fhigh), (qlow, qhigh), rates, mismatch=options.mismatch, downsample_factor=downsample_factor)
+ else:
+ raise NotImplementedError
+
if options.latency_output:
head[channel] = pipeparts.mklatency(pipeline, head[channel], name=idq_utils.latency_name('beforewhitening', 2, channel))
@@ -736,34 +753,13 @@ for subset_id, channel_subset in enumerate(data_source_info.channel_subsets, 1):
if options.latency_output:
thishead = pipeparts.mklatency(pipeline, thishead, name=idq_utils.latency_name('afterwhitening', 3, channel, rate))
- # choose range of basis parameters
- # NOTE: scale down frequency range by downsample_factor to deal with rolloff from downsampler
- downsample_factor = 0.8
- flow = rate/4. * downsample_factor
- fhigh = rate/2. * downsample_factor
- qlow = 3.3166
- if data_source_info.extension == 'ini':
- qhigh = data_source_info.channel_dict[channel]['qhigh']
- else:
- qhigh = options.qhigh
-
- # generate templates
- if options.waveform == 'half_sine_gaussian':
- waveforms = idq_utils.HalfSineGaussianGenerator((flow, fhigh), (qlow, qhigh), rate, mismatch = options.mismatch)
- elif options.waveform == 'sine_gaussian':
- waveforms = idq_utils.SineGaussianGenerator((flow, fhigh), (qlow, qhigh), rate, mismatch = options.mismatch)
- else:
- raise NotImplementedError
-
- basis_params[(channel, rate)] = waveforms
- thishead = pipeparts.mkqueue(pipeline, thishead, max_size_buffers = 0, max_size_bytes = 0, max_size_time = Gst.SECOND * 30)
-
# determine whether to do time-domain or frequency-domain convolution
- time_domain = (waveforms.max_duration*rate**2) < (5*waveforms.max_duration*rate*numpy.log2(rate))
+ time_domain = (waveforms[channel].sample_pts[rate]*rate) < (5*waveforms[channel].sample_pts[rate]*numpy.log2(rate))
# create FIR bank of half sine-gaussian templates
- fir_matrix = numpy.array([waveform for waveform in waveforms.generate_templates()])
- thishead = pipeparts.mkfirbank(pipeline, thishead, fir_matrix = fir_matrix, time_domain = time_domain, block_stride = int(rate), latency = int(numpy.ceil(waveforms.latency * rate)))
+ fir_matrix = numpy.array([waveform for waveform in waveforms[channel].generate_templates(rate)])
+ thishead = pipeparts.mkqueue(pipeline, thishead, max_size_buffers = 0, max_size_bytes = 0, max_size_time = Gst.SECOND * 30)
+ thishead = pipeparts.mkfirbank(pipeline, thishead, fir_matrix = fir_matrix, time_domain = time_domain, block_stride = int(rate), latency = waveforms[channel].latency[rate])
# add queues, change stream format, add tags
if options.latency_output:
@@ -789,7 +785,7 @@ for subset_id, channel_subset in enumerate(data_source_info.channel_subsets, 1):
# define structures to synchronize output streams and extract triggers from buffer
logger.info("setting up pipeline handler...")
- handler = MultiChannelHandler(mainloop, pipeline, data_source_info, options, keys = src.keys(), basis_params = basis_params, basename = basename, subset_id = subset_id)
+ handler = MultiChannelHandler(mainloop, pipeline, data_source_info, options, keys = src.keys(), waveforms = waveforms, basename = basename, subset_id = subset_id)
logger.info("attaching appsinks to pipeline...")
appsync = LinkedAppSync(appsink_new_buffer = handler.bufhandler)
diff --git a/gstlal-ugly/python/idq_utils.py b/gstlal-ugly/python/idq_utils.py
index e43af5a60a11ddd5d1dd5df92952873bfd37619d..99fbff65fdd497122d9c0b779da5318ec8c749fb 100644
--- a/gstlal-ugly/python/idq_utils.py
+++ b/gstlal-ugly/python/idq_utils.py
@@ -349,7 +349,7 @@ class HalfSineGaussianGenerator(object):
for f, q, _ in self.parameter_grid[rate]:
if quadrature:
for phase in self.phases:
- yield self.waveform(f, q, phase)
+ yield self.waveform(f, q, phase, rate)
else:
yield self.waveform(f, q, self.phases[0], rate)
@@ -416,7 +416,7 @@ class SineGaussianGenerator(HalfSineGaussianGenerator):
def __init__(self, f_range, q_range, rates, mismatch=0.2, tolerance=5e-3, downsample_factor=0.8):
super(SineGaussianGenerator, self).__init__(f_range, q_range, rates, mismatch=mismatch, tolerance=tolerance, downsample_factor=0.8)
self.times = {rate: numpy.linspace(-((self.sample_pts[rate] - 1) / 2.) / rate, ((self.sample_pts[rate] - 1) / 2.) / rate, self.sample_pts[rate], endpoint=True) for rate in self.rates}
- self.latency = {rate: self.times[rate][-1] for rate in self.rates}
+ self.latency = {rate: int((self.sample_pts[rate] - 1) / 2) for rate in self.rates}
self.filter_duration = {rate: (self.times[rate][-1] - self.times[rate][0]) for rate in self.rates}
def duration(self, f, q):