Skip to content
Snippets Groups Projects
Select Git revision
  • 91b382fb848892a6edb41be8bae1cfa691c16705
  • master default
  • mingw_gcc44
  • release_ABP1_012
  • release_ABP1_008
  • release_ABP1_006
  • release_ABP1_007
  • release_ABP1_005
  • release_ABP1_004
  • release_ABP1_003
  • pre_release_0.15
  • release_ABP1_001
  • release_ABP1_002
  • pre_release_0.13
  • pre_release_0.14
  • pre_release_0.11
  • pre_release_0.12
  • pre_release_0.10
  • pre_release_0.09
  • pre_release_0.08
20 results

footer.html

Blame
  • Forked from einsteinathome / graphicsframework
    Source project has a limited visibility.
    semi_coherent_directed_follow_up.py 2.04 KiB
    import pyfstat
    import numpy as np
    import matplotlib.pyplot as plt
    
    F0 = 30.0
    F1 = -1e-10
    F2 = 0
    Alpha = np.radians(83.6292)
    Delta = np.radians(22.0144)
    
    # Properties of the GW data
    sqrtSX = 1e-23
    tstart = 1000000000
    duration = 100*86400
    tend = tstart+duration
    tref = .5*(tstart+tend)
    
    depth = 40
    label = 'semicoherent_directed_follow_up'
    outdir = 'data'
    
    h0 = sqrtSX / depth
    
    data = pyfstat.Writer(
        label=label, outdir=outdir, 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 = 1e5
    DeltaF0 = np.sqrt(VF0) * np.sqrt(3)/(np.pi*duration)
    DeltaF1 = np.sqrt(VF1) * np.sqrt(180)/(np.pi*duration**2)
    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': Alpha,
                   'Delta': Delta
                   }
    
    ntemps = 3
    log10beta_min = -0.5
    nwalkers = 100
    nsteps = [100, 100]
    
    mcmc = pyfstat.MCMCFollowUpSearch(
        label=label, outdir=outdir,
        sftfilepattern='{}/*{}*sft'.format(outdir, label),
        theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
        nwalkers=nwalkers, nsteps=nsteps, ntemps=ntemps,
        log10beta_min=log10beta_min)
    
    NstarMax = 1000
    Nsegs0 = 100
    fig, axes = plt.subplots(nrows=2, figsize=(3.4, 3.5))
    fig, axes = mcmc.run(
        NstarMax=NstarMax, Nsegs0=Nsegs0, labelpad=0.01,
        plot_det_stat=False, return_fig=True, fig=fig,
        axes=axes)
    for ax in axes:
        ax.grid()
        ax.set_xticks(np.arange(0, 600, 100))
        ax.set_xticklabels([str(s) for s in np.arange(0, 700, 100)])
    axes[-1].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1)
    fig.tight_layout()
    fig.savefig('{}/{}_walkers.png'.format(mcmc.outdir, mcmc.label), dpi=400)