Simulate ======== This page contains the documentation for 'simulate.py'. This module provides functions to generate strain time series with both gravitational wave signals and glitches. Glitch Simulations ---------------------- .. raw:: html
GlitchPop.simulate.random_glitch(ifo, run, mu=0.1, duration=100, gps=0, interval=None, probs=[0.25, 0.25, 0.25, 0.25])
Randomly generates time series with Gaussian noise and glitches. Probabilities are in this order: blip, low frequency blip, tomte and koi fish. **Parameters:** - **ifo** (string) - Interferometer abbreviation - **run** (string) - Observing run - **mu** (float) - Poisson rate parameter; average number of glitches per second - **duration** (float) - Duration of time series - **gps** (float) - Starting GPS time of time series - **interval** (NoneType or float) - Optional interval of repeating glitches - **probs** (list) - List of probabilities of each type of glitch occurring; probabilities are blip, lfb, tomte, koi and sum to one **Returns:** - **gwpy.timeseries.TimeSeries** - h(t) w/ Gaussian noise and glitches .. raw:: html
GlitchPop.simulate.determ_glitch(ifo, run, glitches, times, seeds, duration=100, gps=0, scale=1, suggest=True)
Generates deterministic time series w/ coloured Gaussian noise. **Parameters:** - **ifo** (string) - Interferometer abbreviation - **run** (string) - Observing run - **glitches** (list) - List containing strings of glitch types - **seeds** (list) - List containing integers of random seeds - **duration** (float) - Duration of time series - **gps** (float) - Starting GPS time of time series - **scale** (float) - Scale of gaussian noise - **suggest** (bool) - Option to use suggest bounds on parameter distributions **Returns:** - **gwpy.timeseries.TimeSeries** - h(t) w/ Gaussian noise and glitches GW + Glitch Simulations ---------------------- .. raw:: html
GlitchPop.simulate.random_gw(ifo, run, gps=0, duration=500, mu_glitch=0.1, mu_gw=10e-5, interval=None, probs_glitch=[0.25,0.25,0.25,0.25], probs_gw=[1/3,1/3,1/3], precession=False)
Randomly simulates a single detector's calibrated strain channel given two Poissonian rates. The probabilities for the GW signals are in this order: 'BHBH', 'NSBH' and 'BNS'. This function can take a long time to run if the GW Poissonian rate is high and/or precession is enabled. The minimum suggested duration is 85 seconds. **Parameters:** - **ifo** (string) - Interferometer abbreviation - **run** (string) - Observing run - **gps** (float) - Starting GPS time of time series - **duration** (float) - Duration of time series - **mu_glitch** (float) - Poisson rate parameter; average number of glitches per second - **mu_gw** (float) - Poisson rate parameter; average number of GWs per second - **interval** (NoneType or float) - Optional, repeating interval of glitches - **probs_glitch** (list) - List of floats containing probabilities of generating each glitch; Probabilities are blip, LFB, tomte, koi and sum to 1 - **probs_gw** (list) - List of floats containing probabilities of generating each type of CBC signal; Probabilities are BHBH, NSBH, BNS and sum to 1 - **precession** (bool) - Optional argument to enable x & y spins **Returns:** - **gwpy.timeseries.TimeSeries** - h(t) w/ gaussian noise, glitches and GWs .. raw:: html
GlitchPop.simulate.determ_gw(ifo, ts, t_merge, signals, seeds, precession=False)
Deterministically simulates a single detector's calibrated strain channel given random seeds. **Parameters:** - **ifo** (string) - Interferometer abbreviation - **ts** (gwpy.timeseries.TimeSeries) - Time series to append GW signals to - **t_merge** (list) - List of CBC merger times - **signals** (list) - List of strings of CBC type, in order with merger times - **seeds** (list) - List of random seed integers, in order with merger times - **precession** (bool) - Optional argument to enable x & y spins **Returns:** - **gwpy.timeseries.TimeSeries** - h(t) w/ gaussian noise, glitches and appended GWs Tri-Detector Simulations ---------------------- .. raw:: html
GlitchPop.simulate.random_tri_ifo(run, gps=0, duration=500, mu_glitch=0.1, mu_gw=10e-5, interval=None, probs_glitch=[0.25,0.25,0.25,0.25], probs_gw=[1/3,1/3,1/3], precession=False)
Randomly simulates a tri-detector network given two Poissonian rates. The tri-detector network used here is Hanford (H1), Livingston (L1) and Virgo (V1). The minimum suggested duration is 85 seconds. **Parameters:** - **run** (string) - Observing run - **gps** (float) - Starting GPS time of time series - **duration** (float) - Duration of time series - **mu_glitch** (float) - Poisson rate parameter; average number of glitches per second - **mu_gw** (float) - Poisson rate parameter; average number of GWs per second - **interval** (NoneType or float) - Optional, repeating interval of glitches - **probs_glitch** (list) - List of floats containing probabilities of generating each glitch; Probabilities are blip, LFB, tomte, koi and sum to 1 - **probs_gw** (list) - List of floats containing probabilities of generating each type of CBC signal; Probabilities are BHBH, NSBH, BNS and sum to 1 - **precession** (bool) - Optional argument to enable x & y spins **Returns:** - **gwpy.timeseries.TimeSeries** - h(t) of H1 strain - **gwpy.timeseries.TimeSeries** - h(t) of L1 strain - **gwpy.timeseries.TimeSeries** - h(t) V1 strain .. raw:: html
GlitchPop.simulate.determ_tri_ifo(h1, l1, v1, t_merge, signals=['BHBH'], seeds=[0], precession=False)
Deterministically simulates the 3 detector network. **Parameters:** - **h1** (gwpy.timeseries.TimeSeries) - H1 strain w/out GW signals - **l1** (gwpy.timeseries.TimeSeries) - L1 strain w/out GW signals - **v1** (gwpy.timeseries.TimeSeries) - V1 strain w/out GW signals - **t_merge** (list) - List of CBC merger times - **signals** (list) - List of strings of CBC type, in order with merger times - **seeds** (list) - List of random seed integers, in order with merger times - **precession** (bool) - Optional argument to enable x & y spins **Returns:** - **gwpy.timeseries.TimeSeries** - h(t) of H1 strain - **gwpy.timeseries.TimeSeries** - h(t) of L1 strain - **gwpy.timeseries.TimeSeries** - h(t) V1 strain