gs.update(hspace=0.0, wspace=0.2)
ax3 = [plt.subplot( gs[:-1,i]) for i in range(Nzs)]
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')
gs.update(hspace=0.0, wspace=0.2)
ax3 = [plt.subplot(gs[:-1, i]) for i in range(Nzs)]
ax4 = [plt.subplot(gs[-1, i], sharex=ax1[gs_ind]) 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)
freqs = capo.pspec.z2f(z_ref) * 1e3
noise_ind = np.array(
[np.argmin(abs(np.array(noise_freqs) - fq)) for fq in freqs])
noise_freqs = np.take(noise_freqs, noise_ind).tolist()
noise_ks = np.take(noise_ks, noise_ind, axis=0).tolist()
noises = np.take(noises, noise_ind, axis=0).tolist()
# POBER_NOISE = py21cm.noise_interp2d(
# noise_freqs,noise_ks,noises,interp_kind='linear')
if opts.psa32_noise:
noise_freqs_32, noise_ks_32, noises_32 = py21cm.load_noise_files(
glob.glob(opts.psa32_noise), polyfit_deg=3)
z_bin = f2z(freq)
except:
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
gs.update(hspace=0.0, wspace=0.2)
ax3 = [plt.subplot( gs[:-1,i]) for i in range(Nzs)]
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')
if opts.psa32_noise:
noise_freqs_32, noise_ks_32, noises_32 = py21cm.load_noise_files(
glob.glob(opts.psa32_noise),polyfit_deg=3
)
# POBER_NOISE_32 = py21cm.noise_interp2d(noise_freqs_32,noise_ks_32,noises_32,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):
gs.update(hspace=0.0, wspace=0.2)
ax3 = [plt.subplot(gs[:-1, i]) for i in range(Nzs)]
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],
'--',
try:
z_bin = f2z(freq)
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 = []
try:
z_bin = f2z(freq)
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 = []
# Create figure and prep subplot sizes for P(k) Noise
fig5 = plt.figure(figsize=figsize)
gs = gridspec.GridSpec(1, Nzs)
gs.update(hspace=0.0, wspace=0.2)
ax5 = [plt.subplot(gs[:, i]) for i in range(Nzs)]
plt.subplots_adjust(left=0.2)
# Create figure and prep subplot sizes for P(k) Noise
fig6 = plt.figure(figsize=figsize)
gs = gridspec.GridSpec(1, Nzs)
gs.update(hspace=0.0, wspace=0.2)
ax6 = [plt.subplot(gs[:, i]) for i in range(Nzs)]
plt.subplots_adjust(left=0.2)
if args.noisefiles:
noise_freqs, noise_ks, noises = py21cm.load_noise_files(
args.noisefiles, polyfit_deg=3)
freqs = capo.pspec.z2f(zs) * 1e3
noise_ind = np.array([np.argmin(abs(np.array(noise_freqs) - fq))
for fq in freqs])
noise_freqs = np.take(noise_freqs, noise_ind).tolist()
noise_ks = np.take(noise_ks, noise_ind, axis=0).tolist()
noises = np.take(noises, noise_ind, axis=0).tolist()
# Plot the Noise files on the plot
for gs_ind in xrange(Nzs):
ax1[gs_ind].plot(noise_ks[gs_ind], noises[gs_ind], 'c-', label='21cmSense')
d2_n = noises[gs_ind]
pk_n = 2*np.pi**2/(np.array(noise_ks[gs_ind])**3)*d2_n
z_bin = f2z(freq)
except:
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