def test_k_from_k3pk(self):
ref_k_par = .2
ref_z = f212z(119.7e6)
ref_umag = 30 / (3e8 / (119.7 * 1e6))
ref_k_perp = ref_umag * pspec.dk_du(ref_z)
ref_k_mag = np.sqrt(ref_k_par**2 + ref_k_perp**2)
_, out_k_mag, _, _ = load_k3pk(test_data_file, verbose=False)
self.assertEqual(ref_k_mag, out_k_mag,
msg='Expected k_mag {0} but ' \
'retruned k_mag {1}'.format(ref_k_mag,out_k_mag)
)
ndelays = len(delays)
nkparr = n.sum(delays >= 0)
#P = F['P'].T
#FOLD UP THE PSPEC AROUND ZERO RELATIVE DELAY!!!
#XXX THIS WILL BE INCORRECT FOR PHASED UP STUFF.
wedge_X = n.sqrt(n.abs(P[nkparr:,:,0]*n.conj(P[nkparr:,:,0]) + \
n.flipud(P[:nkparr,:,0]*n.conj(P[:nkparr,:,0]))/2))
wedge_Y = n.sqrt(n.abs(P[nkparr:,:,1]*n.conj(P[nkparr:,:,1]) + \
n.flipud(P[:nkparr,:,1]*n.conj(P[:nkparr,:,1]))/2))
print n.sum(n.isnan(P)), n.sum(P < 0)
wedge_X = n.ma.masked_invalid(n.log10(wedge_X))
wedge_Y = n.ma.masked_invalid(n.log10(wedge_Y))
kparr = delays[delays >= 0] * pspec.dk_deta(z)
kperp = bl_lengths * pspec.dk_du(z)
horizon = bl_lengths * pspec.dk_deta(z)
print bl_lengths, pspec.dk_deta(z), z
print horizon
windowpad = windowpad_ns * pspec.dk_deta(z)
KPERP, KPARR = n.meshgrid(kperp, kparr)
MIN = n.min([
n.mean(wedge_X[KPARR > (horizon + windowpad)]),
n.mean(wedge_X[KPARR > (horizon + windowpad)])
]) * 0.8
dfcoarse = 30 / 24. * 1e-3
if PLOTWINDOWCONTOUR:
#calculate danny's stat window
BL, D = n.meshgrid(bl_lengths, delays[delays >= 0])
coarse_chan_delay = 1 / (1.28 / 1e3) #
# compute Pk vs kpl vs bootstraps
print " Bootstrapping..."
pk_pspecs, vals = average_bootstraps(pspecs, Nt_eff=Neff_lst,
Nboots=args.nboots,
avg_func=eval(args.avg_func),
version=args.version)
outname = 'pspec_2d_to_1d.npz'
print ' Saving', outname # save all values used in bootstrapping
np.savez(args.output + outname, **vals)
# Compute |k|
bl_length = np.linalg.norm(pspecs['uvw'])
wavelength = cosmo_units.c / (pspecs['freq'] * 1e9)
ubl = bl_length / wavelength
kperp = dk_du(pspecs['freq']) * ubl
print " freq = ", pspecs['freq']
print " kperp = ", kperp
pk_pspecs['k'] = np.sqrt(kperp**2 + pk_pspecs['kpl_fold']**2)
pk_pspecs['kperp'] = np.ma.masked_invalid(kperp)
pk_pspecs['cmd'] = pk_pspecs['cmd'].item() + ' \n ' + ' '.join(sys.argv)
if NGPS_LST > 0: pk_pspecs['nlsts_g'] = Neff_lst / NGPS_LST # number of lsts in one group
else: pk_pspecs['nlsts_g'] = Neff_lst
if args.version == 4: pk_pspecs['nPS'] = pspecs['pCv'].shape[0] * pspecs['pCv'].shape[2]
else: pk_pspecs['nPS'] = pspecs['pCv'].shape[0]
# Important numbers
print " Total number of bls = ", pk_pspecs['nbls']
print " number of bl groups = ", pk_pspecs['ngps']
print " nbls in a group = ", pk_pspecs['nbls'] / pk_pspecs['ngps']
print " Total number of lsts = ", Neff_lst
print files, np.argsort(x)
files = [files[xi] for xi in n.argsort(x)]
freqs = n.array(freqs)
freqs = freqs[n.argsort(x)]
noise_scale = noise_scale[np.argsort(x)]
print "Power Spectrum at {0:.2f}Mhz".format(freqs[0])
x = n.sort(x)
print "loading files", files
z = f212z(freqs * 1e6)
print "processing redshifts:", z
#redshift_files_pads = dict(zip(z,files,x))
umags = 30 / (c / (freqs * 1e6))
print "umags = ", umags
kperps = umags * pspec.dk_du(z)
Pkk = []
Pkk_err = []
k3Pk = []
k3err = []
neg = []
kpars = []
kmags = []
Pks = []
Pkerr = []
for i, FILE in enumerate(files):
F = n.load(FILE)
print FILE, z[i]
k = n.sqrt(F['kpl']**2 + kperps[i]**2)
k3Pk.append(F['k3pk'])
Pks.append(F['pk'])
pspecs[key] = np.ma.masked_invalid(np.array(pspecs[key]))
except:
import ipdb
ipdb.set_trace()
if args.add_pCv:
pspecs['pk_vs_t'] += pspecs['pCv']
# compute Pk vs kpl vs bootstraps
pk_pspecs = average_bootstraps(pspecs,
Nt_eff=Neff_lst,
Nboots=100,
avg_func=np.mean)
# Compute |k|
pspecs['freq'] = np.mean(pspecs['afreqs'])
wavelength = cosmo_units.c / (pspecs['freq'] * 1e9)
ubl = args.bl_length / wavelength
kperp = dk_du(pspecs['freq']) * ubl
print "freq = ", pspecs['freq']
print "kperp = ", kperp
pk_pspecs['k'] = np.sqrt(kperp**2 + pk_pspecs['kpl_fold']**2)
# apply corrections to all the various channels
pspec_channels = ['pC', 'pI', 'pCv', 'pIv']
corrections = [
1 / np.sqrt(2), # the median overestimates by sqrt(2)
1.39
] # beam^2
# for chan in pspec_channels:
# for c in [chan,chan+'_fold']: #no need to correct error bars
# for correction in corrections:
# pk_pspecs[c] *= correction
# make a pspec file
for key in pk_pspecs.keys():
N = 100
fig = figure()
#plot a fringe of constant kperp
i,j = np.indices((N,N))
kperp = np.linspace(0.0,0.35,N)
fq = np.linspace(0.12,0.18,N)
fringe = np.cos(2.*np.pi*10.*kperp[j])
ax = fig.add_subplot(111)
imshow(fringe,
extent=[kperp[0],kperp[-1],fq[0],fq[-1]],
origin=(0,0),
aspect='auto',
cmap = cm.Blues,
alpha=0.5
)
for b in [30.,100.,200.,350.,500.]:
b *= fq/0.3 #in lambda.
b *= pspec.dk_du(7.5)
plot(b, fq, 'k', lw=2)
ax.set_xlabel(r'$k_{||}\ [h{\rm Mpc}^{-1}]$', size=16)
ax.set_ylabel(r'${\rm Frequency}\ [{\rm GHz}]$', size=16)
fig.savefig('introduction/figures/BaselineTrack.eps', fmt='eps')
show()
nkparr = n.sum(delays>=0)
#P = F['P'].T
#FOLD UP THE PSPEC AROUND ZERO RELATIVE DELAY!!!
#XXX THIS WILL BE INCORRECT FOR PHASED UP STUFF.
wedge_X = n.sqrt(n.abs(P[nkparr:,:,0]*n.conj(P[nkparr:,:,0]) + \
n.flipud(P[:nkparr,:,0]*n.conj(P[:nkparr,:,0]))/2))
wedge_Y = n.sqrt(n.abs(P[nkparr:,:,1]*n.conj(P[nkparr:,:,1]) + \
n.flipud(P[:nkparr,:,1]*n.conj(P[:nkparr,:,1]))/2))
print n.sum(n.isnan(P)),n.sum(P<0)
wedge_X = n.ma.masked_invalid(n.log10(wedge_X))
wedge_Y = n.ma.masked_invalid(n.log10(wedge_Y))
kparr = delays[delays>=0]*pspec.dk_deta(z)
kperp = bl_lengths*pspec.dk_du(z)
horizon = bl_lengths*pspec.dk_deta(z)
print bl_lengths,pspec.dk_deta(z),z
print horizon
windowpad = windowpad_ns*pspec.dk_deta(z)
KPERP,KPARR = n.meshgrid(kperp,kparr)
MIN = n.min([n.mean(wedge_X[KPARR>(horizon+windowpad)]),n.mean(wedge_X[KPARR>(horizon+windowpad)])])*0.8
dfcoarse=30/24.*1e-3
if PLOTWINDOWCONTOUR:
#calculate danny's stat window
BL,D = n.meshgrid(bl_lengths,delays[delays>=0])
coarse_chan_delay = 1/(1.28/1e3)#
print "coarse_chan_delay",coarse_chan_delay
WINDOW = n.zeros_like(D)
WINDOW[n.logical_and(D>(windowpad_ns + bl_lengths),D<coarse_chan_delay)] = 1
def posterior(kpl, pk, err, pkfold=None, errfold=None, f0=.151, umag=16.,
theo_noise=None, verbose=False):
"""Find posterior of Delta^2."""
import scipy.interpolate as interp
k0 = n.abs(kpl).argmin()
kpl = kpl[k0:]
z = pspec.f2z(f0)
kpr = pspec.dk_du(z) * umag
k = n.sqrt(kpl**2 + kpr**2)
if pkfold is None:
if verbose:
print 'Folding for posterior'
pkfold = pk[k0:].copy()
errfold = err[k0:].copy()
pkpos, errpos = pk[k0 + 1:].copy(), err[k0 + 1:].copy()
pkneg, errneg = pk[k0 - 1:0:-1].copy(), err[k0 - 1:0:-1].copy()
pkfold[1:] = ((pkpos / errpos**2 + pkneg / errneg**2)
/ (1. / errpos**2 + 1. / errneg**2))
errfold[1:] = n.sqrt(1. / (1. / errpos**2 + 1. / errneg**2))
# ind = n.logical_and(kpl>.2, kpl<.5)
ind = n.logical_and(k > .15, k < .5)
# ind = n.logical_and(kpl>.12, kpl<.5)
# print kpl,pk.real,err
k = k[ind]
pkfold = pkfold[ind]
errfold = errfold[ind]
# if not theo_noise is None:
# theo_noise=theo_noise[ind]
pk = pkfold
err = errfold
err_omit = err.copy()
# s = n.logspace(1,3.5,100)
s = n.linspace(-5000, 5000, 10000)
# print s
data = []
data_omit = []
for _k, _pk, _err in zip(k, pk, err):
if verbose:
print _k, _pk.real, _err
# print '%6.3f %9.5f 9.5f'%(_k, _pk.real, _err)
for ss in s:
data.append(n.exp(-.5 * n.sum((pk.real - ss)**2 / err**2)))
data_omit.append(n.exp(-.5 * n.sum((pk.real - ss)**2 / err_omit**2)))
# print data[-1]
data = n.array(data)
data_omit = n.array(data_omit)
# print data
# print s
# data/=n.sum(data)
data /= n.max(data)
data_omit /= n.max(data_omit)
p.figure(5, figsize=(6.5, 5.5))
p.plot(s, data, 'k', linewidth=2)
# p.plot(s, data_omit, 'k--', linewidth=1)
# use a spline interpolator to get the 1 and 2 sigma limits.
# spline = interp.interp1d(data,s)
# print spline
# print max(data), min(data)
# print spline(.68), spline(.95)
# p.plot(spline(n.linspace(.0,1,100)),'o')
# p.plot(s, n.exp(-.5)*n.ones_like(s))
# p.plot(s, n.exp(-.5*2**2)*n.ones_like(s))
data_c = n.cumsum(data)
data_omit_c = n.cumsum(data_omit)
data_c /= data_c[-1]
data_omit_c /= data_omit_c[-1]
mean = s[n.argmax(data)]
s1lo, s1hi = s[data_c < 0.1586][-1], s[data_c > 1 - 0.1586][0]
s2lo, s2hi = s[data_c < 0.0227][-1], s[data_c > 1 - 0.0227][0]
if verbose:
print 'Posterior: Mean, (1siglo,1sighi), (2siglo,2sighi)'
if verbose:
print 'Posterior:', mean, (s1lo, s1hi), (s2lo, s2hi)
mean_o = s[n.argmax(data_omit)]
s1lo_o, s1hi_o = s[data_omit_c < 0.1586][-1], s[data_omit_c > 1 - 0.1586][0]
s2lo_o, s2hi_o = s[data_omit_c < 0.0227][-1], s[data_omit_c > 1 - 0.0227][0]
if verbose:
print 'Posterior (omit):', mean_o, (s1lo_o, s1hi_o), (s2lo_o, s2hi_o)
p.vlines(s1lo, 0, 1, color=(0, 107 / 255., 164 / 255.), linewidth=2)
p.vlines(s1hi, 0, 1, color=(0, 107 / 255., 164 / 255.), linewidth=2)
# limits for data_omit
p.vlines(s2lo, 0, 1, color=(1, 128 / 255., 14 / 255.), linewidth=2)
p.vlines(s2hi, 0, 1, color=(1, 128 / 255., 14 / 255.), linewidth=2)
if theo_noise is not None:
s2l_theo = n.sqrt(1. / n.mean(1. / theo_noise**2))
p.vlines(s2l_theo, 0, 1, color='black', linewidth=2)
if verbose:
print('Noise level: {0:0>5.3f} mk^2'.format(s2l_theo))
p.xlabel(r'$k^3/2\pi^2\ P(k)\ [{\rm mK}^2]$', fontsize='large')
p.ylabel('Posterior Distribution', fontsize='large')
p.xlim(0, 700)
p.title('z = {0:.2f}'.format(z))
if (s2lo > 700) or (s2hi > 1000):
p.xlim(0, 1500)
p.grid(1)
p.subplots_adjust(left=.15, top=.95, bottom=.15, right=.95)
p.savefig('posterior_{0:.2f}.png'.format(z))
f = open('posterior_{0:.2f}.txt'.format(z), 'w')
f.write('Posterior: Mean,\t(1siglo,1sighi),\t(2siglo,2sighi)\n')
f.write('Posterior: {0:.4f},\t({1:.4f},{2:.4f}),\t({3:.4f},'
'{4:.4f})\n'.format(mean, s1lo, s1hi, s2lo, s2hi))
f.write('Posterior (omit): {0:.4f},\t({1:.4f},{2:.4f}),t({3:.4f},'
'{4:.4f})\n'.format(mean_o, s1lo_o, s1hi_o, s2lo_o, s2hi_o))
f.write('Noise level: {0:0>5.3f} mk^2\n'.format(s2l_theo))
f.close()
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)
pCs = n.ma.median(pCs, 1)
pIvs = n.ma.median(pIvs, 1)
pCvs = n.ma.median(pCvs, 1)
pCvs_pk = n.ma.median(pCvs_pk, 1)
freqs.append(chan/2. + 100) #a pretty good apprximation of chan 2 freq for 500kHz channels
except(IndexError):
print "[FAIL] no freq found. Skipping..."
print "sorting input files"
files = files[n.argsort(freqs)]
freqs = n.sort(freqs)
print "found freqs"
freqs = n.array(freqs)
print freqs
z = f212z(freqs*1e6)
print "processing redshifts:",z
redshift_files = dict(zip(z,files))
umags = 30/(c/(freqs*1e6))
print "umags = ",umags
kperps = umags*pspec.dk_du(z)
Pkk = []
Pkk_err = []
k3Pk = []
k3err = []
neg = []
kpars = []
kmags = []
Pks = []
Pkerr =[]
for i,FILE in enumerate(files):
F = n.load(FILE)
print FILE,z[i]
k = n.sqrt(F['kpl']**2 + kperps[i]**2)
k3Pk.append(F['k3pk'])
Pks.append(F['pk'])
files = sort(args)
#form up the frequency
#dchan = n.array([int(F.split('/')[1].split('_')[1])-int(F.split('/')[1].split('_')[0]) for F in files])
#chans = n.array([int(F.split('/')[1].split('_')[0]) for F in files])+dchan/2
chans = n.array([f['chans'] for f in files])
freqs = n.array([f['afreqs'] for f in files])
I = n.argsort(chans)
chans = chans[I]
freqs = freqs[I]
#freqs = chans/2. + 100
print freqs
z = f212z(freqs*1e6)
umags = 30/(c/freqs*1e6)
kperps = umags*pspec.dk_du(z)
#k = n.sqrt(kpl**2 + kpr**2)
#k3 = n.abs(k**3 / (2*n.pi**2))
#print [len(p) for p in P]
#plot a single k bin
#mykpl = 0.35
#assume 30m baselines at 150Mhz
#print "plotting k_parallel = ",mykpl
Pkk = []
Pkk_err = []
k3Pk = []
k3err = []
neg = []
for i,FILE in enumerate(files):
def posterior(kpl,
pk,
err,
pkfold=None,
errfold=None,
f0=.151,
umag=16.,
theo_noise=None,
verbose=False):
import scipy.interpolate as interp
k0 = n.abs(kpl).argmin()
kpl = kpl[k0:]
z = pspec.f2z(f0)
kpr = pspec.dk_du(z) * umag
k = n.sqrt(kpl**2 + kpr**2)
if pkfold is None:
if verbose: print 'Folding for posterior'
pkfold = pk[k0:].copy()
errfold = err[k0:].copy()
pkpos, errpos = pk[k0 + 1:].copy(), err[k0 + 1:].copy()
pkneg, errneg = pk[k0 - 1:0:-1].copy(), err[k0 - 1:0:-1].copy()
pkfold[1:] = (pkpos / errpos**2 +
pkneg / errneg**2) / (1. / errpos**2 + 1. / errneg**2)
errfold[1:] = n.sqrt(1. / (1. / errpos**2 + 1. / errneg**2))
#ind = n.logical_and(kpl>.2, kpl<.5)
ind = n.logical_and(k > .15, k < .5)
#ind = n.logical_and(kpl>.12, kpl<.5)
#print kpl,pk.real,err
k = k[ind]
pkfold = pkfold[ind]
errfold = errfold[ind]
#if not theo_noise is None:
# theo_noise=theo_noise[ind]
pk = pkfold
err = errfold
err_omit = err.copy()
#s = n.logspace(1,3.5,100)
s = n.linspace(-5000, 5000, 10000)
# print s
data = []
data_omit = []
for _k, _pk, _err in zip(k, pk, err):
if verbose: print _k, _pk.real, _err
# print '%6.3f %9.5f 9.5f'%(_k, _pk.real, _err)
for ss in s:
data.append(n.exp(-.5 * n.sum((pk.real - ss)**2 / err**2)))
data_omit.append(n.exp(-.5 * n.sum((pk.real - ss)**2 / err_omit**2)))
# print data[-1]
data = n.array(data)
data_omit = n.array(data_omit)
#print data
#print s
#data/=n.sum(data)
data /= n.max(data)
data_omit /= n.max(data_omit)
p.figure(5, figsize=(6.5, 5.5))
p.plot(s, data, 'k', linewidth=2)
# p.plot(s, data_omit, 'k--', linewidth=1)
#use a spline interpolator to get the 1 and 2 sigma limits.
#spline = interp.interp1d(data,s)
#print spline
#print max(data), min(data)
#print spline(.68), spline(.95)
#p.plot(spline(n.linspace(.0,1,100)),'o')
# p.plot(s, n.exp(-.5)*n.ones_like(s))
# p.plot(s, n.exp(-.5*2**2)*n.ones_like(s))
data_c = n.cumsum(data)
data_omit_c = n.cumsum(data_omit)
data_c /= data_c[-1]
data_omit_c /= data_omit_c[-1]
mean = s[n.argmax(data)]
s1lo, s1hi = s[data_c < 0.1586][-1], s[data_c > 1 - 0.1586][0]
s2lo, s2hi = s[data_c < 0.0227][-1], s[data_c > 1 - 0.0227][0]
if verbose: print 'Posterior: Mean, (1siglo,1sighi), (2siglo,2sighi)'
if verbose: print 'Posterior:', mean, (s1lo, s1hi), (s2lo, s2hi)
mean_o = s[n.argmax(data_omit)]
s1lo_o, s1hi_o = s[data_omit_c < 0.1586][-1], s[data_omit_c > 1 -
0.1586][0]
s2lo_o, s2hi_o = s[data_omit_c < 0.0227][-1], s[data_omit_c > 1 -
0.0227][0]
if verbose:
print 'Posterior (omit):', mean_o, (s1lo_o, s1hi_o), (s2lo_o, s2hi_o)
p.vlines(s1lo, 0, 1, color=(0, 107 / 255., 164 / 255.), linewidth=2)
p.vlines(s1hi, 0, 1, color=(0, 107 / 255., 164 / 255.), linewidth=2)
# limits for data_omit
p.vlines(s2lo, 0, 1, color=(1, 128 / 255., 14 / 255.), linewidth=2)
p.vlines(s2hi, 0, 1, color=(1, 128 / 255., 14 / 255.), linewidth=2)
if not theo_noise is None:
s2l_theo = n.sqrt(1. / n.mean(1. / theo_noise**2))
p.vlines(s2l_theo, 0, 1, color='black', linewidth=2)
if verbose: print('Noise level: {0:0>5.3f} mk^2'.format(s2l_theo))
p.xlabel(r'$k^3/2\pi^2\ P(k)\ [{\rm mK}^2]$', fontsize='large')
p.ylabel('Posterior Distribution', fontsize='large')
p.xlim(0, 700)
p.title('z = {0:.2f}'.format(z))
if (s2lo > 700) or (s2hi > 1000):
p.xlim(0, 1500)
p.grid(1)
p.subplots_adjust(left=.15, top=.95, bottom=.15, right=.95)
p.savefig('posterior_{0:.2f}.png'.format(z))
f = open('posterior_{0:.2f}.txt'.format(z), 'w')
f.write('Posterior: Mean,\t(1siglo,1sighi),\t(2siglo,2sighi)\n')
f.write(
'Posterior: {0:.4f},\t({1:.4f},{2:.4f}),\t({3:.4f},{4:.4f})\n'.format(
mean, s1lo, s1hi, s2lo, s2hi))
f.write(
'Posterior (omit): {0:.4f}, ({1:.4f},{2:.4f}),\t({3:.4f},{4:.4f})\n'.
format(mean_o, s1lo_o, s1hi_o, s2lo_o, s2hi_o))
f.write('Noise level: {0:0>5.3f} mk^2\n'.format(s2l_theo))
f.close()
'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 = []
print 'Boostrapping the Bootstraps'
for nboot in xrange(NBOOT):
if (nboot+1) % 10 == 0:
print ' ', nboot+1, '/', NBOOT
def kpr_from_sep(sepstr, fq, grid_spacing=[4., 32.]):
kx, ky = uv_from_sep(sepstr, grid_spacing=grid_spacing)
scalar = pspec.dk_du(pspec.f2z(fq)) * (fq / 0.3)
return scalar * np.sqrt(kx**2 + ky**2)
def get_k3pk_from_npz(files=None, verbose=False):
'''
Loads output from plot_pk_k3pk.npz and returns Delta^2 spectrum
returns lists of k, Delta^2, Delta^2_err ordered by decreasing redshift
return format: z, k_magnitude, Delta^2, Delta^2_err
'''
if files is None: #check that files are passed
print 'No Files gives for loading'
return 0,'','',''
one_file_flag=False
if len(n.shape(files)) ==0: files = [files];
if len(files) == 1: one_file_flag=True
freqs = []
if verbose: print "parsing npz file frequencies"
for filename in files:
if verbose: print filename,
try:
if verbose: print "npz..",
freqs.append(n.load(filename)['freq']*1e3) #load freq in MHz
if verbose: print "[Success]"
except(KeyError):
if verbose: print "[FAIL]"
try:
if verbose: print "looking for path like RUNNAME/chan_chan/I/pspec.npz"
dchan = int(filename.split('/')[1].split('_')[1])-int(filename.split('/')[1].split('_')[0])
chan = int(filename.split('/')[1].split('_')[0]) + dchan/2
freqs.append(chan/2. + 100) #a pretty good apprximation of chan 2 freq for 500kHz channels
except(IndexError):
if verbose: print "[FAIL] no freq found. Skipping..."
if len(freqs) ==0: #check if any files were loaded correctly
print 'No parsable frequencies found'
print 'Exiting'
return 0,'','',''
if verbose: print "sorting input files"
files = n.array(files)
files = files[n.argsort(freqs)]
freqs = n.sort(freqs)
if verbose: print "found freqs"
freqs = n.array(freqs)
if verbose: print freqs
z = f212z(freqs*1e6)
if verbose: print "processing redshifts:",z
#redshift_files = dict(zip(z,files))
umags = 30/(c/(freqs*1e6))
# if verbose: print "umags = ",umags
kperps = umags*pspec.dk_du(z)
k3Pk = []
k3err = []
kmags = []
for i,FILE in enumerate(files):
F = n.load(FILE)
if verbose: print FILE.split('/')[-1],z[i]
# k = n.sqrt(F['kpl']**2 + kperps[i]**2)
k3Pk.append(F['k3pk'])
k3err.append(F['k3err'])
kmags.append(F['k'])
# if one_file_flag:
# z = n.squeeze(z)
# kmags = n.squeeze(kmags)
# k3Pk = n.squeeze(k3Pk)
# k3err = n.squeeze(k3err)
return z, kmags, k3Pk, k3err
