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generate_table.py

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  • Forked from Gregory Ashton / PyFstat
    296 commits behind the upstream repository.
    generate_table.py 2.47 KiB
    import pyfstat
    import numpy as np
    
    outdir = 'data'
    
    label = 'allsky_setup'
    data_label = '{}_data'.format(label)
    
    # Properties of the GW data
    sqrtSX = 2e-23
    tstart = 1000000000
    Tspan = 100*86400
    tend = tstart + Tspan
    
    # Fixed properties of the signal
    F0_center = 30
    F1_center = 1e-10
    F2 = 0
    tref = .5*(tstart+tend)
    
    
    VF0 = VF1 = 100
    DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
    DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
    
    DeltaAlpha = 0.02
    DeltaDelta = 0.02
    
    depth = 100
    
    nsteps = 50
    run_setup = [((nsteps, 0), 20, False),
                 ((nsteps, 0), 11, False),
                 ((nsteps, 0), 6, False),
                 ((nsteps, 0), 3, False),
                 ((nsteps, nsteps), 1, False)]
    
    h0 = sqrtSX / float(depth)
    r = np.random.uniform(0, 1)
    theta = np.random.uniform(0, 2*np.pi)
    F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
    F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
    
    Alpha_center = 0
    Delta_center = 0
    Alpha = Alpha_center + np.random.uniform(-0.5, 0.5)*DeltaAlpha
    Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta
    
    psi = np.random.uniform(-np.pi/4, np.pi/4)
    phi = np.random.uniform(0, 2*np.pi)
    cosi = np.random.uniform(-1, 1)
    
    data = pyfstat.Writer(
        label=data_label, outdir=outdir, tref=tref,
        tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
        Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
        detector='H1,L1')
    data.make_data()
    predicted_twoF = data.predict_fstat()
    
    theta_prior = {'F0': {'type': 'unif',
                          'lower': F0_center-DeltaF0,
                          'upper': F0_center+DeltaF0},
                   'F1': {'type': 'unif',
                          'lower': F1_center-DeltaF1,
                          'upper': F1_center+DeltaF1},
                   'F2': F2,
                   'Alpha': {'type': 'unif',
                             'lower': Alpha_center-DeltaAlpha,
                             'upper': Alpha_center+DeltaAlpha},
                   'Delta': {'type': 'unif',
                             'lower': Delta_center-DeltaDelta,
                             'upper': Delta_center+DeltaDelta},
                   }
    
    ntemps = 2
    log10temperature_min = -1
    nwalkers = 100
    
    mcmc = pyfstat.MCMCFollowUpSearch(
        label=label, outdir=outdir,
        sftfilepath='{}/*{}*sft'.format(outdir, data_label),
        theta_prior=theta_prior,
        tref=tref, minStartTime=tstart, maxStartTime=tend,
        nwalkers=nwalkers, ntemps=ntemps, nsteps=[nsteps, nsteps],
        log10temperature_min=log10temperature_min)
    mcmc.run(Nsegs0=20, R=10)
    #mcmc.run(run_setup)