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follow_up.py

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  • follow_up.py 2.24 KiB
    import pyfstat
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib
    
    F0 = 30.0
    F1 = -1e-10
    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 = 50
    data_label = 'follow_up'
    
    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
    VF0 = VF1 = 500
    DeltaF0 = VF0 * np.sqrt(3)/(np.pi*duration)
    DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*duration**2)
    DeltaAlpha = 1e-1
    DeltaDelta = 1e-1
    theta_prior = {'F0': {'type': 'unif', 'lower': F0-DeltaF0/2.,
                          'upper': F0+DeltaF0/2},
                   'F1': {'type': 'unif', 'lower': F1-DeltaF1/2.,
                          'upper': F1+DeltaF1/2},
                   'F2': F2,
                   'Alpha': {'type': 'unif', 'lower': Alpha-DeltaAlpha,
                             'upper': Alpha+DeltaAlpha},
                   'Delta': {'type': 'unif', 'lower': Delta-DeltaDelta,
                             'upper': Delta+DeltaDelta},
                   }
    
    ntemps = 3
    log10temperature_min = -0.5
    nwalkers = 100
    scatter_val = 1e-10
    nsteps = [200, 200]
    
    mcmc = pyfstat.MCMCFollowUpSearch(
        label='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)
    
    fig, axes = plt.subplots(nrows=2, ncols=2)
    fig, axes = mcmc.run(
        R=10, Nsegs0=100, subtractions=[F0, F1, Alpha, Delta], labelpad=0.01,
        fig=fig, axes=axes, plot_det_stat=False, return_fig=True)
    axes[3].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1)
    for ax in axes:
        ax.set_xlim(0, axes[0].get_xlim()[-1])
        ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(5))
    fig.tight_layout()
    fig.savefig('{}/{}_walkers.png'.format(mcmc.outdir, mcmc.label), dpi=400)
    
    mcmc.plot_corner(add_prior=True)
    mcmc.print_summary()