snax.pipeeparts: add queue after matrixmixer

gstlal-burst/python/snax/pipeparts.py

@@ -565,16 +565,15 @@ def mkextract(
 	# split into distinct frequency bins
 	out = {idx: tee for idx, wfs in enumerate(waveforms.index_by_bin(rate)) if len(wfs) > 0}
 	for idx, snrhead in out.items():
-		if len(out) == 1:  # don't unnecessarily use a matrixmixer if we don't need to split up the stream
-			out[idx] = snrhead
-		else:
+		if len(out) > 1:  # don't unnecessarily use a matrixmixer if we don't need to split up the stream
+			snrhead = pipeparts.mkqueue(pipeline, snrhead, max_size_buffers=1, max_size_bytes=0, max_size_time=0)
+			snrhead = pipeparts.mkmatrixmixer(pipeline, snrhead, matrix=waveforms.bin_mixer_coeffs(rate, idx))
 			snrhead = pipeparts.mkqueue(pipeline, snrhead, max_size_buffers=1, max_size_bytes=0, max_size_time=0)
-			out[idx] = pipeparts.mkmatrixmixer(pipeline, snrhead, matrix=waveforms.bin_mixer_coeffs(rate, idx))
 
 		# extract features from time series
 		if feature_mode == 'timeseries':
-			out[idx] = pipeparts.mktrigger(pipeline, out[idx], int(sample_rate // feature_sample_rate), max_snr=True)
+			out[idx] = pipeparts.mktrigger(pipeline, snrhead, int(sample_rate // feature_sample_rate), max_snr=True)
 		elif feature_mode == 'etg':
-			out[idx] = pipeparts.mktrigger(pipeline, out[idx], sample_rate, snr_thresh=snr_threshold)
+			out[idx] = pipeparts.mktrigger(pipeline, snrhead, sample_rate, snr_thresh=snr_threshold)
 
 	return out