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checkin_notes_samples

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  • semi_coherent_glitch_search_using_MCMC.py 1.12 KiB
    import pyfstat
    
    F0 = 30.0
    F1 = -1e-10
    F2 = 0
    Alpha = 5e-3
    Delta = 6e-2
    tref = 362750407.0
    
    tstart = 1000000000
    duration = 100*86400
    tend = tstart + duration
    
    theta_prior = {'F0': {'type': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
                   'F1': {'type': 'norm', 'loc': F1, 'scale': abs(1e-6*F1)},
                   'F2': F2,
                   'Alpha': Alpha,
                   'Delta': Delta,
                   'delta_F0': {'type': 'halfnorm', 'loc': 0,
                                'scale': 1e-5*F0},
                   'delta_F1': 0,
                   'tglitch': {'type': 'unif',
                               'lower': tstart+0.1*duration,
                               'upper': tstart+0.9*duration},
                   }
    
    ntemps = 4
    log10beta_min = -1
    nwalkers = 100
    nsteps = [5000, 1000, 1000]
    
    mcmc = pyfstat.MCMCGlitchSearch(
        'semi_coherent_glitch_search_using_MCMC', 'data',
        sftfilepattern='data/*_glitch*sft', theta_prior=theta_prior, tref=tref,
        tstart=tstart, tend=tend, nsteps=nsteps, nwalkers=nwalkers,
        scatter_val=1e-10, nglitch=1, ntemps=ntemps,
        log10beta_min=log10beta_min)
    
    mcmc.run()
    mcmc.plot_corner(add_prior=True)
    mcmc.print_summary()