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11 results

fully_coherent_search_using_MCMC.py

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  • generate_data.py 2.46 KiB
    import pyfstat
    import numpy as np
    import os
    
    # 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
    Alpha = 5e-3
    Delta = 6e-2
    tref = .5*(tstart+tend)
    
    data_label = 'temp_data_{}'.format(os.getpid())
    results_file_name = 'MCResults.txt'
    
    VF0 = VF1 = 100
    DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
    DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
    
    depths = np.linspace(100, 250, 13)
    
    run_setup = [((10, 0), 16, False),
                 ((10, 0), 5, False),
                 ((10, 10), 1, False)]
    for depth in depths:
        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)
    
        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='data', 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-DeltaF0/2.,
                              'upper': F0+DeltaF0/2.},
                       'F1': {'type': 'unif',
                              'lower': F1-DeltaF1/2.,
                              'upper': F1+DeltaF1/2.},
                       'F2': F2,
                       'Alpha': Alpha,
                       'Delta': Delta
                       }
    
        ntemps = 1
        log10temperature_min = -1
        nwalkers = 50
        nsteps = [50, 50]
    
        mcmc = pyfstat.MCMCFollowUpSearch(
            label='temp', outdir='data',
            sftfilepath='data/*'+data_label+'*sft', theta_prior=theta_prior,
            tref=tref, minStartTime=tstart, maxStartTime=tend, nsteps=nsteps,
            nwalkers=nwalkers, ntemps=ntemps,
            log10temperature_min=log10temperature_min)
        mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
                 gen_tex_table=False)
        d, maxtwoF = mcmc.get_max_twoF()
        dF0 = F0 - d['F0']
        dF1 = F1 - d['F1']
        with open(results_file_name, 'a') as f:
            f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e}\n'
                    .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF))
        os.system('rm data/temp*')