コード例 #1
0
    ax4 = [plt.subplot( gs[-1,i] , sharex=ax1[i]) for i in range(Nzs)]
else:
    fig = plt.figure()
    gs = gridspec.GridSpec(1,Nzs)
    gs.update(hspace=0.0, wspace=0.2)
    ax1 = [plt.subplot( gs[:,i]) for i in range(Nzs)]

    fig2 = plt.figure()
    gs = gridspec.GridSpec(1,Nzs)
    gs.update(hspace=0.0, wspace=0.2)
    ax3 = [plt.subplot( gs[:,i]) for i in range(Nzs)]

noise_freqs, noise_ks, noises = py21cm.load_noise_files(
        glob.glob(opts.noisefiles),polyfit_deg=3
        )
POBER_NOISE = py21cm.noise_interp2d(noise_freqs,noise_ks,noises,interp_kind='linear')

for key in data:
    z,ks,k3pk,k3err = capo.eor_results.get_k3pk_from_npz( data[key] )
    _,kpls,pk,pkerr = capo.eor_results.get_pk_from_npz( data[key] )
    fqs = capo.pspec.z2f(z)*1e3 ##put freqs in Mhz
    for i, redshift in enumerate(z):

        ax1[i].plot(ks[i], np.abs(k3pk[i]) + k3err[i],
                '--', color=colors[key],label=key)

        ax1[i].plot(ks[i], 2*POBER_NOISE(fqs[i],ks[i]), 'k-')

        ax1[i].set_yscale('log')
        if i > 0:
            ax1[i].get_shared_y_axes().join(ax1[0],ax1[i])
コード例 #2
0
else:
    fig = plt.figure()
    gs = gridspec.GridSpec(1, Nzs)
    gs.update(hspace=0.0, wspace=0.2)
    ax1 = [plt.subplot(gs[:, i]) for i in range(Nzs)]

    fig2 = plt.figure()
    gs = gridspec.GridSpec(1, Nzs)
    gs.update(hspace=0.0, wspace=0.2)
    ax3 = [plt.subplot(gs[:, i]) for i in range(Nzs)]

noise_freqs, noise_ks, noises = py21cm.load_noise_files(glob.glob(
    opts.noisefiles),
                                                        polyfit_deg=3)
POBER_NOISE = py21cm.noise_interp2d(noise_freqs,
                                    noise_ks,
                                    noises,
                                    interp_kind='linear')

for key in data:
    z, ks, k3pk, k3err = capo.eor_results.get_k3pk_from_npz(data[key])
    _, kpls, pk, pkerr = capo.eor_results.get_pk_from_npz(data[key])
    fqs = capo.pspec.z2f(z) * 1e3  ##put freqs in Mhz
    for i, redshift in enumerate(z):

        ax1[i].plot(ks[i],
                    np.abs(k3pk[i]) + k3err[i],
                    '--',
                    color=colors[key],
                    label=key)

        ax1[i].plot(ks[i], 2 * POBER_NOISE(fqs[i], ks[i]), 'k-')
コード例 #3
0
    print 'frequency not found in boots. Searching in pspec.npz'
    f_name = ('/home/mkolopanis/psa64/sigloss_verification/'
              'Jul6_noise_3Jy_inttime_44/95_115/I/'
              'pspec_Jul6_noise_3Jy_inttime_44_95_115_I.npz')
    npz = n.load(f_name)  # matt's data
    freq = npz['freq']
    z_bin = f2z(freq)

# load 21cmSense Noise models used to compute Beta
# Beta = (P_noise + P_inj)/P_out

noise_files = ('/home/mkolopanis/psa64/'
               '21cmsense_noise/dish_size_1/*drift_mod*.npz')
n_fqs, n_ks, noise = py21cm.load_noise_files(glob.glob(noise_files))

noise_interp = py21cm.noise_interp2d(n_fqs, n_ks, noise)

kpls_pos = n.concatenate(n.array_split(kpls, [10, 11])[1:])
umag = 30 / (3e8 / (freq * 1e9))
kpr = dk_du(z_bin) * umag
n_k = n.array(n.sqrt(kpls_pos**2 + kpr**2))
d2_n = noise_interp(freq * 1e3, n_k)
pk_n = 2 * n.pi**2 / (n_k**3) * d2_n  # * 3887./2022
p_n = n.median(pk_n)
# p_n = n.max( pk_n )

print 'This script does not Bootstrap'

# take the median over k's
# Now has shape (inj, boots*times)
pIs = n.ma.median(pIs, 1)
コード例 #4
0
except:
    print 'frequency not found in boots. Searching in pspec.npz'
    npz = n.load('/home/mkolopanis/psa64/sigloss_verification/'
                 'Jul6_noise_3Jy_inttime_44/95_115/I/'
                 'pspec_Jul6_noise_3Jy_inttime_44_95_115_I.npz')  # matt's data
    freq = npz['freq']
    z_bin = f2z(freq)

# load 21cmSense Noise models used to compute Beta
# Beta = (P_noise + P_inj)/P_out

n_fqs, n_ks, noise = py21cm.load_noise_files(
        glob.glob('/home/mkolopanis/psa64/21cmsense_noise/'
                  'dish_size_1/*drift_mod*.npz'))

noise_interp = py21cm.noise_interp2d(n_fqs, n_ks, noise)

kpls_pos = n.concatenate(n.array_split(kpls, [10, 11])[1:])
umag = 30/(3e8/(freq*1e9))
kpr = dk_du(z_bin) * umag
n_k = n.array(n.sqrt(kpls_pos**2 + kpr**2))
d2_n = noise_interp(freq*1e3, n_k)
pk_n = 2*n.pi**2/(n_k**3) * d2_n  # * 3887./2022
p_n = n.median(pk_n)
# p_n = n.max( pk_n )

pIvs_boot = []
pCvs_boot = []
pCs_boot = []
pIs_boot = []