How to run omicron on simulated data
The pyomicron system can be used to process data in frames outside the usual process. That is when the data frames are not in the datafind system and data quality segments and Guardian states do not apply.
The steps are:
Create a configuration file
Create a frame cache
Specify to not use the data quality segments
Below is a sample Python program which creates an example frame file with 200 seconds of gaussian noise and adds a few sine-gaussiasn glitches. It produces a frame file in /tmp.
from gwpy.timeseries import TimeSeries
from numpy.random import normal
from numpy import linspace
from scipy.signal import gausspulse
from gpstime import tconvert
from pathlib import Path
# Generate a TimeSeries containing Gaussian noise sampled at 4096 Hz,
# with several sine-Gaussian pulses (‘glitch’) at different frequencies:
strt = tconvert('9/1/2022')
fs = 4096
dur = 200
ndata = fs * dur
outdir = Path('/tmp')
# sine gaussian "glitches"
t = linspace(-1, 1, 2 * fs, endpoint=False)
sg = gausspulse(t, fc=250, retquad=False, retenv=False)
sg2 = gausspulse(t, fc=500, retquad=False, retenv=False)
sg3 = gausspulse(t, fc=750, retquad=False, retenv=False)
# add several sets of glitches to the noise
data = normal(loc=1, size=ndata) # Gaussian noise
for t in range(int(dur/4), int(3*dur/4), int(dur/10)):
data[fs*(t-1):fs*(t+1)] += sg * 12
data[fs*(t+6):fs*(t+8)] += sg2 * 10
data[fs*(t+13):fs*(t+15)] += sg3 * 8
ts = TimeSeries(data, t0=strt, sample_rate=fs, name='Z1:SIM-SINE_GAUSS',
channel='Z1:SIM-SINE_GAUSS')
out_file = str(outdir / f'Z-Z1_SIM-{int(strt)}-{dur}.gwf')
ts.write(out_file)
print(f'Wrote {out_file}')
Create a LAL format cache file such as sim.lcf:
file://localhost/tmp/Z-Z1_SIM-1346050818-200.gwf
Create a pyomicron configuration file named sim.ini:
[SIM]
q-range = 3.3166 150
frequency-range = 4.0 2048.0
frametype = Z-Z1_SIM
sample-frequency = 4096
chunk-duration = 124
segment-duration = 64
overlap-duration = 4
mismatch-max = 0.2
snr-threshold = 6
channels = Z1:SIM-SINE_GAUSS
We can now run pyomicron:
omicron-process --gps 1346050818 1346051018 --ifo Z1 --config-file ./sim.ini \
--output-dir ./run --no-segdb --cache-file ./sim.lcf -vvv --file-tag SIM SIM
The results will be in the directory:
./run/merge/Z1:SIM-SINE_GAUSS/
One way to view them is to use ligolw_print. The results, formatted for readability:
ligolw_print -t sngl_burst -c 'peak_time' -c 'snr' -c 'peak_frequency' \
'run/merge/Z1:SIM-SINE_GAUSS/Z1-SIM_SINE_GAUSS_OMICRON-1346050820-196.xml.gz'
+------------+-----------+--------------+
| peak_time | SNR | Frequency |
+============+===========+==============|
| 1346050868 | 37.1 | 236.4 |
| 1346050875 | 22.1 | 480.7 |
| 1346050882 | 15.2 | 771.3 |
| 1346050888 | 37.1 | 243.9 |
| 1346050894 | 21.4 | 459.1 |
| 1346050902 | 13.7 | 699.8 |
| 1346050908 | 36.5 | 266.1 |
| 1346050915 | 22.4 | 480.7 |
| 1346050922 | 13.9 | 771.3 |
| 1346050928 | 36.0 | 236.4 |
| 1346050935 | 22.1 | 480.6 |
| 1346050942 | 14.2 | 771.3 |
| 1346050948 | 38.7 | 243.9 |
| 1346050954 | 24.3 | 459.1 |
| 1346050962 | 15.0 | 699.7 |
+------------+-----------+--------------+