`pycbc_make_psd_estimation_workflow`: A workflow generator for noise estimation¶

It can be useful to estimate the average noise PSD of a long period of data, for instance for building template banks manually or doing bank simulations. The program pycbc_make_psd_estimation_workflow is devoted to this task.

The program sets up a Pegasus workflow which is basically a small subset of a coincident search workflow:

Find the data frames and the segments

Segment the analyzable data in each detector

Run pycbc_calculate_psd to estimate the PSD in each segment in each detector

Run pycbc_average_psd to combine the PSD estimates over time for each detector, as well as over time and detectors.

Configuration file¶

pycbc_make_psd_estimation_workflow is configured through an .ini file, similarly to search workflows. An example for ER8 data, broken by sections, is given here.

The sections below control which data are being used and they are basically the same as found in a coincidence workflow. In this example, ER8 data are used, the analyzable time is broken into 2048 s segments for PSD estimation and each PSD estimation job processes up to 100 segments.:

[workflow]
start-time = 1123858817
end-time = 1125217722
h1-channel-name = H1:GDS-CALIB_STRAIN
l1-channel-name = L1:GDS-CALIB_STRAIN
file-retention-level = no_intermediates

[workflow-ifos]
h1 =
l1 =

[workflow-datafind]
datafind-h1-frame-type = H1_HOFT_C00
datafind-l1-frame-type = L1_HOFT_C00
datafind-method = AT_RUNTIME_SINGLE_FRAMES
datafind-check-segment-gaps = update_times
datafind-check-frames-exist = raise_error
datafind-check-segment-summary = warn

[workflow-segments]
segments-h1-science-name = H1:DMT-ANALYSIS_READY:1
segments-l1-science-name = L1:DMT-ANALYSIS_READY:1
segments-database-url = https://segments.ligo.org
segments-veto-definer-url = https://code.pycbc.phy.syr.edu/detchar/veto-definitions/download/d06231daa8edf28c4760106599f86c8d8659cc3e/cbc/ER8/H1L1-HOFT_C00_ER8B_CBC.xml
segments-science-veto = 1
segments-veto-groups =
segments-final-veto-group = 12H
segments-method = ALL_SINGLE_IFO_TIME

[datafind]
urltype = file

[workflow-matchedfilter]
matchedfilter-method = WORKFLOW_INDEPENDENT_IFOS
analysis-length = 2048
max-segments-per-job = 100
min-analysis-segments = 15
max-analysis-segments = 15
output-type = hdf

The sections below specifies the location of the various executables called by the workflow. The ${which:X} syntax replaces the line with the full path to the executable, wherever that happens to be at the time of running pycbc_make_psd_estimation_workflow:

[executables]
segment_query = ${which:ligolw_segment_query_dqsegdb}
segments_from_cats = ${which:ligolw_segments_from_cats_dqsegdb}
llwadd = ${which:ligolw_add}
ligolw_combine_segments = ${which:ligolw_combine_segments}
plot_segments = ${which:pycbc_page_segments}
calculate_psd = ${which:pycbc_calculate_psd}
average_psd = ${which:pycbc_average_psd}
merge_psds = ${which:pycbc_merge_psds}
plot_spectrum = ${which:pycbc_plot_psd_file}
plot_range = ${which:pycbc_plot_range}
page_segtable = ${which:pycbc_page_segtable}
page_segplot = ${which:pycbc_page_segplot}
results_page = ${which:pycbc_make_html_page}

The sections below control how the PSD is estimated in each segment. The program devoted to this is pycbc_calculate_psd, see its --help for details. In this example, two instances of pycbc_calculate_psd are launched (one per detector) and each instance uses 4 CPU cores. For details on PSD estimation, see for instance the FindChirp paper.:

[calculate_psd]
cores = 4
low-frequency-cutoff = 10
pad-data = 8
strain-high-pass = 8
sample-rate = 4096
segment-length = 256
segment-start-pad = 64
segment-end-pad = 64
psd-estimation = median
psd-segment-length = 16
psd-segment-stride = 8

[calculate_psd-h1]
channel-name = H1:GDS-CALIB_STRAIN

[calculate_psd-l1]
channel-name = L1:GDS-CALIB_STRAIN

[pegasus_profile-calculate_psd]
condor|request_cpus = 4

The next section is related to pycbc_merge_psds which has no options.:

[merge_psds]

The section below controls how the averaging of the PSDs over time and detector is done, i.e. it contains options for the pycbc_average_psd program. Currently the program does not take options and the only supported averaging method is the harmonic mean.:

[average_psd]

The sections below control plotting jobs.:

[plot_segments]

[plot_range]
mass1 = 1.4
mass2 = 1.4
approximant = SPAtmplt

[plot_spectrum]
psd-model = aLIGOZeroDetHighPower

[page_segtable]

[page_segplot]

[results_page]
output-path=../../html
analysis-title="PSD Estimation"
analysis-subtitle="..."

Generating and running the workflow¶

Once you have an .ini file at /path/to/ini/file, create the workflow in the following way:

pycbc_make_psd_estimation_workflow \
    --workflow-name RUN_NAME \
    --output-dir /path/to/run/directory \
    --config-files /path/to/ini/file

RUN_NAME should be replaced with a meaningful descriptive name for the workflow and /path/to/run/directory should point to the directory where the run is supposed to take place. Once the workflow is generated, move to /path/to/run/directory and start the workflow with

pycbc_submit_dax \
    --dax RUN_NAME.dax \
    --accounting-group ACCOUNTING_TAG

where again RUN_NAME and ACCOUNTING_TAG should be given meaningful values. When the workflow completes, the average PSDs should be available in /path/to/run/directory/psds and diagnostic plots should be in /path/to/run/directory/plots.

pycbc_make_psd_estimation_workflow: A workflow generator for noise estimation¶

Configuration file¶

Generating and running the workflow¶

`pycbc_make_psd_estimation_workflow`: A workflow generator for noise estimation¶