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Resource.cpp

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  • Forked from einsteinathome / graphicsframework
    Source project has a limited visibility.
    weak_signal_follow_up.py 1.62 KiB
    import pyfstat
    
    F0 = 30.0
    F1 = 0
    F2 = 0
    Alpha = 1.0
    Delta = 0.5
    
    # Properties of the GW data
    sqrtSX = 1e-23
    tstart = 1000000000
    duration = 100*86400
    tend = tstart+duration
    tref = .5*(tstart+tend)
    
    depth = 70
    data_label = 'weak_signal_follow_up_depth_{:1.0f}'.format(depth)
    
    h0 = sqrtSX / depth
    
    data = pyfstat.Writer(
        label=data_label, outdir='data', tref=tref,
        tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
        Delta=Delta, h0=h0, sqrtSX=sqrtSX)
    data.make_data()
    
    # The predicted twoF, given by lalapps_predictFstat can be accessed by
    twoF = data.predict_fstat()
    print 'Predicted twoF value: {}\n'.format(twoF)
    
    # Search
    theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6),
                          'upper': F0*(1+1e-6)},
                   'F1': F1, #{'type': 'unif', 'lower': F1*(1+1e-2),
                          #'upper': F1*(1-1e-2)},
                   'F2': F2,
                   'Alpha': {'type': 'unif', 'lower': Alpha-1e-2,
                             'upper': Alpha+1e-2},
                   'Delta': {'type': 'unif', 'lower': Delta-1e-2,
                             'upper': Delta+1e-2},
                   }
    
    ntemps = 3
    log10temperature_min = -1
    nwalkers = 200
    scatter_val = 1e-10
    nsteps = [100, 100]
    
    mcmc = pyfstat.MCMCFollowUpSearch(
        label='weak_signal_follow_up', outdir='data',
        sftfilepath='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
        minStartTime=tstart, maxStartTime=tend, nwalkers=nwalkers, nsteps=nsteps,
        ntemps=ntemps, log10temperature_min=log10temperature_min,
        scatter_val=scatter_val)
    mcmc.run(R0=10, Vmin=100)
    mcmc.plot_corner(add_prior=True)
    mcmc.print_summary()
    #mcmc.generate_loudest()