X.append(
test_common.complex_test_src(pipeline,
buffer_length=buffer_length,
rate=rate_in,
width=64,
test_duration=test_duration,
wave=5,
freq=0,
src_suffix=str(i)))
kappas = calibration_parts.compute_exact_kappas_from_filters_file(
pipeline, X, freqs, EPICS, rate_in)
for i in range(4 + 2 * num_stages):
pipeparts.mknxydumpsink(pipeline, kappas[i], "kappas_%d.txt" % i)
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
test_common.build_and_run(exact_kappas_01, "exact_kappas_01")
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
test_common.build_and_run(
pcal2darm,
"pcal2darm",
segment=segments.segment(
(LIGOTimeGPS(0, 1000000000 * options.gps_start_time),
LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
# Read data from files and plot it
colors = [
'mediumblue', 'gold', 'c', 'b', 'm'
] # Hopefully the user will not want to plot more than six datasets on one plot.
tau_colors = ['red', 'skyblue', 'purple']
channels = calcs_channels
channels.extend(gstlal_channels)
num_rows = 2 + (1 if any(kappa_channels) else 0)
num_columns = len(frequencies)
plot_labels = []
if options.latex_labels:
compression_scheme=6,
compression_level=3)
mux = pipeparts.mkprogressreport(pipeline, mux, "end")
pipeparts.mkframecppfilesink(pipeline,
mux,
frame_type=frame_type,
path=output_path,
instrument=ifo)
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
test_common.build_and_run(
frame_manipulator,
"frame_manipulator",
segment=segments.segment(
(LIGOTimeGPS(0, 1000000000 * options.gps_start_time),
LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
# Run pipeline
test_common.build_and_run(compute_RMS_timeseries, "compute_RMS_timeseries", segment = segments.segment((LIGOTimeGPS(0, 1000000000 * gps_start_time), LIGOTimeGPS(0, 1000000000 * gps_end_time))))
colors = ['blue', 'limegreen', "royalblue", "deepskyblue", "red", "yellow", "purple", "pink"]
# Read data from txt file.
rms_data = []
tdata = []
for i in range(0, len(labels)):
data = numpy.loadtxt('%s_RMS_%s_%d-%d.txt' % (ifo, labels[i].replace(' ', '_'), gps_start_time, data_duration))
if not any(tdata):
t_start = data[0][0]
dur = data[len(data) - 1][0] - t_start
dur_in_seconds = dur
t_unit = 'seconds'
sec_per_t_unit = 1.0
if dur > 60 * 60 * 100:
buffer_length=buffer_length,
rate=rate_in,
width=64,
channels=channels,
test_duration=test_duration,
wave=5,
freq=0)
streams = calibration_parts.mkdeinterleave(pipeline, head, channels)
head = calibration_parts.mkinterleave(pipeline, streams)
solutions = pipeparts.mkgeneric(pipeline, head, "lal_matrixsolver")
solutions = list(calibration_parts.mkdeinterleave(pipeline, solutions, 10))
for i in range(10):
pipeparts.mknxydumpsink(pipeline, solutions[i], "solutions_%d.txt" % i)
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
test_common.build_and_run(lal_matrixsolver_01, "lal_matrixsolver_01")
f_max=500)
rms_bp400to500 = calibration_parts.compute_rms(pipeline,
white_noise_bp400to500,
1024,
1.0,
f_min=100,
f_max=500)
pipeparts.mknxydumpsink(pipeline, rms, "rms.txt")
pipeparts.mknxydumpsink(pipeline, rms_over10, "rms_over10.txt")
pipeparts.mknxydumpsink(pipeline, rms_bp100to150, "rms_bp100to150.txt")
pipeparts.mknxydumpsink(pipeline, rms_bp250to300, "rms_bp250to300.txt")
pipeparts.mknxydumpsink(pipeline, rms_bp450to500, "rms_bp450to500.txt")
pipeparts.mknxydumpsink(pipeline, rms_bp400to500, "rms_bp400to500.txt")
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
test_common.build_and_run(rms_01, "rms_01")
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
# Run pipeline
test_common.build_and_run(
plot_transfer_function,
"plot_transfer_function",
segment=segments.segment(
(LIGOTimeGPS(0, 1000000000 * options.gps_start_time),
LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
# Get any zeros and poles that we want to use to filter the transfer functions
zeros = []
real_zeros = [''] if options.zeros is None else options.zeros.split(';')
if len(real_zeros) < len(labels):
# Then copy the last element until they are the same length
for i in range(len(labels) - len(real_zeros)):
real_zeros.append(real_zeros[-1])
for i in range(len(real_zeros)):
real_zeros[i] = real_zeros[i].split(',')
zeros.append([])
for j in range(0, len(real_zeros[i]) // 2):
zeros[i].append(
# your calculations. You will also need to demodulate the Pcal channel at f1. And
# you will have to do several more demodulations (10 total I think, since each of
# the five lines is present in darm_err and in one injection channel). Then, you
# will need to do additions, multiplications, divisions, powers, etc. I would
# suggest looking in gstlal_compute_strain (the calibration pipeline) and
# calibration_parts.py for examples of how to do these. calibration_parts has lots
# of functions that should be helpful.
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
# This function calls the above pipeline function to build and run the pipeline.
test_common.build_and_run(
gstlal_compute_kappas_without_D,
"gstlal_compute_kappas_without_D",
segment=segments.segment(
(LIGOTimeGPS(0, 1000000000 * options.gps_start_time),
LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
# Run pipeline
test_common.build_and_run(
demod_ratio,
"demod_ratio",
segment=segments.segment(
(LIGOTimeGPS(0, 1000000000 * options.gps_start_time),
LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
# Read data from files and plot it
colors = [
'blueviolet', 'darkgreen', 'limegreen', 'khaki', 'b', 'r'
] # Hopefully the user will not want to plot more than six datasets on one plot.
for i in range(0, len(freq_list)):
data = numpy.loadtxt("%s_%s_over_%s_%0.1fHz.txt" %
(ifo, options.numerator_channel_name,
options.denominator_channel_name, freq_list[i][0]))
t_start = data[0][0]
dur = data[len(data) - 1][0] - t_start
t_unit = 'seconds'
sec_per_t_unit = 1.0
#smooth = pipeparts.mkgeneric(pipeline, head, "lal_smoothkappas", array_size = rate * 128, avg_array_size = rate * 10, filter_latency = 1)
#smooth = pipeparts.mkgeneric(pipeline, smooth, "splitcounter", name = "smooth")
#head = pipeparts.mkgeneric(pipeline, head, "splitcounter", name = "unsmooth")
#channelmux_input_dict = {}
#channelmux_input_dict["unsmooth"] = calibration_parts.mkqueue(pipeline, head)
#channelmux_input_dict["smooth"] = calibration_parts.mkqueue(pipeline, smooth)
#mux = pipeparts.mkframecppchannelmux(pipeline, channelmux_input_dict, frame_duration = 64, frames_per_file = 1, compression_scheme = 6, compression_level = 3)
head = calibration_parts.mkadder(
pipeline, calibration_parts.list_srcs(pipeline, head, smooth))
#mux = pipeparts.mkgeneric(pipeline, mux, "splitcounter", name = "sum")
#head = calibration_parts.mkgate(pipeline, smooth, head, 0)
pipeparts.mknxydumpsink(pipeline, head, "%s_out.txt" % name)
#pipeparts.mkframecppfilesink(pipeline, mux, frame_type = "H1DCS", instrument = "H1")
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
test_common.build_and_run(fill_silence_test_01, "fill_silence_test_01")
pipeline,
summed_streams,
"lal_detectchange",
average_samples=int(options.average_time * options.sample_rate),
detection_threshold=options.detection_threshold,
filename=options.filename)
pipeparts.mkfakesink(pipeline, summed_streams)
#
# done
#
return pipeline
#
# =============================================================================
#
# Main
#
# =============================================================================
#
# Run pipeline
test_common.build_and_run(
detect_change,
"detect_change",
segment=segments.segment(
(LIGOTimeGPS(0, 1000000000 * options.gps_start_time),
LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
#
# =============================================================================
#
# Main
#
# =============================================================================
#
line_seps = []
signal_rmss = []
for i in range(0, 1000):
line_sep = random.random()
line_seps.append(line_sep)
test_common.build_and_run(line_separation_nonoise_test_01,
"line_separation_nonoise_test_01",
line_sep=line_sep)
data = numpy.loadtxt("rms_signal_nonoise.txt")
signal_rmss.append(data[-1][1])
plt.figure(figsize=(10, 10))
plt.plot(line_seps,
signal_rmss,
'g.',
markersize=5,
label='line with no noise')
leg = plt.legend(fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.gca().set_xscale('linear')
plt.gca().set_yscale('log')
plt.title('Cross-contamination of Lines')
plt.ylabel('RMS of demodulated line')