def PAPER_64_all():
"""
Return results from PAPER 64 Ali et.al 2015.
outputs results[z] = n.array([k, Delta^2, 2-sigma upper, 2-sigma lower])
"""
PAPER_RESULTS_FILES = (glob.glob(os.path.dirname(__file__)
+ '/data/psa64_apj/pspec_*.npz'))
PAPER_RESULTS_FILES.sort()
freqs = []
for filename in PAPER_RESULTS_FILES:
try:
freqs.append(n.load(filename)['freq'] * 1e3)
except(KeyError):
try:
dchan = (int(filename.split('/')[-1].split('.')[0]
.split('_')[2])
- int(filename.split('.')[0].split('_')[1]))
chan = (int(filename.split('/')[-1].split('.')[0]
.split('_')[1])
+ dchan / 2.)
# a pretty good apprximation of chan 2 freq for 500kHz channels
freqs.append(chan / 2. + 100)
except:
continue
freqs = n.array(freqs)
zs = pspec.f2z(freqs * 1e-3)
results = {}
for files, z in zip(PAPER_RESULTS_FILES, zs):
f = n.load(files)
results[z] = n.array([f['k'], f['k3pk'], f['k3pk'] + f['k3err'],
f['k3pk'] - f['k3err']]).T
return results
def gather_metadata(uv, chans, window):
M = {}
chans = a.scripting.parse_chans(chans, uv['nchan'])
freqs = a.cal.get_freqs(uv['sdf'], uv['sfreq'], uv['nchan'])
freqs = freqs.take(chans)
M['sdf'] = uv['sdf']
M['fq'] = np.average(freqs)
M['z'] = pspec.f2z(M['fq'])
M['B'] = M['sdf'] * freqs.size / pfb.NOISE_EQUIV_BW[window]
M['etas'] = np.fft.fftshift(pspec.f2eta(freqs))
M['kpl'] = M['etas'] * pspec.dk_deta(M['z'])
M['bm'] = np.polyval(pspec.DEFAULT_BEAM_POLY, M['fq'])
M['scalar'] = pspec.X2Y(M['z']) * M['bm'] * M['B']
return chans, freqs, M
def PAPER_64_all():
'''
Results from PAPER 64 Ali et.al 2015
outputs results[z] = n.array([k, Delta^2, 2-sigma upper, 2-sigma lower])
'''
PAPER_RESULTS_FILES = glob.glob(
os.path.dirname(__file__) + '/data/psa64_apj/pspec_*.npz')
PAPER_RESULTS_FILES.sort()
freqs = []
for filename in PAPER_RESULTS_FILES:
try:
freqs.append(n.load(filename)['freq'] * 1e3)
except (KeyError):
try:
dchan = int(
filename.split('/')[-1].split('.')[0].split('_')[2]) - int(
filename.split('.')[0].split('_')[1])
chan = int(
filename.split('/')[-1].split('.')[0].split('_')
[1]) + dchan / 2.
freqs.append(
chan / 2. + 100
) #a pretty good apprximation of chan 2 freq for 500kHz channels
except:
continue
freqs = n.array(freqs)
zs = pspec.f2z(freqs * 1e-3)
#zs = n.array([8.31])
results = {}
for files, z in zip(PAPER_RESULTS_FILES, zs):
f = n.load(files)
results[z] = n.array([
f['k'], f['k3pk'], f['k3pk'] + f['k3err'], f['k3pk'] - f['k3err']
]).T
return results
def RM_bad(fq, k):
_z = pspec.f2z(fq)
return k * c * (1.0 + _z) / (4.0 * H(_z) * nu2l2(fq))
windowpad = F['windowpad']
plotres=True
PLOTWINDOWCONTOUR=False
figsize=(20,25)
try:
observation_num = int(sys.argv[1].split('.')[0])
observation=MWA_Observation(observation_num,db=db)
except(ValueError):
gpstime = int(n.round(Time(F['time'],scale='utc',format='jd').gps))
observation_num = find_closest_observation(gpstime,db=db)
observation=MWA_Observation(observation_num,db=db)
for plotres in [False,True]:
z = pspec.f2z(freq/1e9)
#windowpad = 0.01 #should match whatever I used in uvfitswedge.py
if len(sys.argv[1:])==1 and not sys.argv[1].endswith('.p.npz'):
amps = n.ma.masked_invalid(n.log10(n.abs(F['P'].T)))
outname = sys.argv[1].replace('.npz','')
else:
if len(sys.argv[1:])==1:outname = sys.argv[1].replace('.npz','')
else:outname = 'mywedge'
if plotres: outname += '_res'
P=0
P2 = 0
C = 0
for Pfile in sys.argv[1:]:
F = n.load(Pfile)
if plotres:
P += F['P_res']
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()
for filename in files:
try:
freqs.append(n.load(filename)['freq']*1e3)
print('Frequency found: {0}'.format(freqs[-1]))
except(KeyError):
try:
dchan = int(filename.split('/')[-1].split('.')[0].split('_')[2])-int(filename.split('.')[0].split('_')[1])
chan = int(filename.split('/')[-1].split('.')[0].split('_')[1]) + dchan/2.
freqs.append(chan/2. + 100) #a pretty good apprximation of chan 2 freq for 500kHz channels
print('Frequency found: {0}'.format(freqs[-1]))
except:
print('No frequency found for file: '+filename+'\n')
print('Skipping')
continue
freqs = n.array(freqs)
zs = pspec.f2z(freqs*1e-3)
fig = p.figure(figsize=(10,5))
ax = fig.add_subplot(111)
#plot the GMRT paciga 2014 data
GMRT = eor_results.GMRT_2014_all()
GMRT_results = {}
print('GMRT')
for i,z in enumerate(GMRT.keys()):
#index = n.argwhere(GMRT[z][:,0] - .2 < 0.1).squeeze()
freq= pspec.z2f(z)
k_horizon = n.sqrt(cosmo_units.eta2kparr(30./c,z)**2 + \
cosmo_units.u2kperp(15*freq*1e6/c,z)**2)
D = F[0]
times = D.data.field('DATE')
t_int = n.diff(times).max()*24*3600
print "t_int = ",t_int
totalt = n.ceil((times.max() - times.min())*24*3600 + t_int)
print "total observing time = ",totalt
Ntimes = n.ceil(totalt/t_int)
print "ntimes = ",Ntimes
bls = D.data.field('BASELINE')
ant2 = (bls%256).astype(int)
ant1 = ((bls-ant2)/256).astype(int)
uvws = n.array(zip(D.data.field('UU'),D.data.field('VV'),D.data.field('WW')))*1e9 #bl vectors in ns
Nants = len(set(ant2))
Nmax = ant2.max()
freqs = gethdufreqs(D)
z = pspec.f2z(n.mean(freqs)/1e9)
print "padding the horizon by k_parr = ",windowpad
windowpad_delay = windowpad / pspec.dk_deta(z)
print "at z=%4.1f this is %7.5f ns"%(z,windowpad_delay)
df = n.diff(freqs)[0]
delays = n.fft.fftfreq(freqs.shape[0],df/1e9) #delays in ns
print "n_ant = ",Nants
print n.sum((ant2-ant1)==0)
Nblt = D.data.field('DATA').shape[0]
Nbl = Nblt/Ntimes
MAXBL = n.max(bls)
Nfreqs = D.data.field('DATA').shape[3]
Npol = D.data.field('DATA').shape[4]
#form up a list of baselines, sorted by length
bl_lengths = []
bls_sorted = []
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)
pIvs.fill_value = 1e-5
pIvs_pk = n.ma.masked_invalid(pIvs_pk)
pCvs_pk = n.ma.masked_invalid(pCvs_pk)
pIvs_pk_mask, pCvs_pk_mask = pIvs_pk.mask, pCvs_pk.mask
pIvs_pk = n.ma.masked_less(pIvs_pk, 0)
pCvs_pk = n.ma.masked_less(pCvs_pk, 0)
pIvs_pk.mask = n.logical_or(pIvs_pk.mask, pIvs_pk_mask)
pCvs_pk.mask = n.logical_or(pCvs_pk.mask, pCvs_pk_mask)
pIvs_pk.fill_value = 1e-5
pCvs_pk.fill_value = 1e-5
try:
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))
def k_bad(fq,RM):
_z = pspec.f2z(fq)
k = nu2l2(fq) * RM * 4.* H(_z) / ( (1+_z) * c)
print k / 0.71
return k / 0.71
def k3pk(T,k,fq,B):
z = pspec.f2z(fq)
bm = 0.76
scalar = pspec.X2Y(z) * k**3 / (2*np.pi**2* B*1e9 * bm)
return scalar * 1e6 *np.abs(T)**2
fig = figure()
colors='kcmb'
for j,RM in enumerate(RMs):
Nann = 3
for i in range(Nann):
if i == 0 or i+1 == Nann: continue
#if i%4 != 0: continue
nchan_i = int(Nchan/Nann)
ntaps = 3
ch1,ch2 = i*nchan_i,(i+1)*nchan_i
ch_in,ch_out = ch1-nchan_i, ch2+nchan_i
freqs_i = freqs[ch_in:ch_out]
dnu = freqs_i[1] - freqs_i[0]
eta = np.fft.fftshift(np.fft.fftfreq(nchan_i,dnu))
z_mid = np.median(pspec.f2z(freqs_i))
k = np.abs(eta*pspec.dk_deta(z_mid))
V_nu = RMvis(freqs_i,RM)
V_nu *= pspec.jy2T(freqs_i)
Trms = pfb.pfb(V_nu,taps=ntaps,window=window,fft=np.fft.ifft)
Trms = np.fft.fftshift(Trms)
DELTA = k3pk(Trms,k=k,fq=np.mean(freqs_i),B=0.1/Nann)
valid = k!=0.
DELTA = np.extract(valid,DELTA)
DELTA /= DELTA.sum()
k = np.extract(valid,k)
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()
p.xlabel(r'$P_{\rm in}(k)\ [{\rm mK}^2\ (h^{-1}\ {\rm Mpc})^3]$', fontsize=14)
p.ylabel(r'$P_{\rm out}(k)\ [{\rm mK}^2\ (h^{-1}\ {\rm Mpc})^3]$', fontsize=14)
p.grid()
#npz = n.load('/home/mkolopanis/psa64/psa64_multiz/data/pspec_Nov18_vanilla_skew_frpad_2_'+chan+'_I.npz')
sig_factor_interp = interp1d(pIs, pIs/pCs,kind='linear',bounds_error=False,fill_value=0)
npz_file = opts.path+'/pspec_'+opts.pspec+'_'+chan+'_'+opts.pol+'.npz'
npz = n.load(npz_file)
#npz = n.load('/home/mkolopanis/psa64/pspec_Mar17_vanilla_ali_frf_recon_'+chan+'_I.npz')
#print npz.files
kpls,pks,errs = npz['kpl'], npz['pk'], npz['err']
freq = npz['freq']
z_bin = f2z(freq)
sig_factors= []
for kpl,pk,err in zip(kpls,pks,errs):
#p.([pklo,pkhi], [pk,pk], 'r')
pkup = max(pk+err,1e-6)
pkdn = max(pk-err,1e-6)
p.subplot(gs[2])
p.fill_between([pklo,pkhi], [pkdn,pkdn], [pkup,pkup], facecolor='gray', edgecolor='gray')
p.subplot(gs[3])
p.fill_between([1e-3,20], [pkdn,pkdn], [pkup,pkup], facecolor='gray', edgecolor='gray')
if pkup > 1e-6:
sig_factors.append( sig_factor_interp(pkup))
sig_factors = n.array(sig_factors)
freq = F['freq']
windowpad = F['windowpad']
plotres = True
PLOTWINDOWCONTOUR = False
figsize = (20, 25)
try:
observation_num = int(sys.argv[1].split('.')[0])
observation = MWA_Observation(observation_num, db=db)
except (ValueError):
gpstime = int(n.round(Time(F['time'], scale='utc', format='jd').gps))
observation_num = find_closest_observation(gpstime, db=db)
observation = MWA_Observation(observation_num, db=db)
for plotres in [False, True]:
z = pspec.f2z(freq / 1e9)
#windowpad = 0.01 #should match whatever I used in uvfitswedge.py
if len(sys.argv[1:]) == 1 and not sys.argv[1].endswith('.p.npz'):
amps = n.ma.masked_invalid(n.log10(n.abs(F['P'].T)))
outname = sys.argv[1].replace('.npz', '')
else:
if len(sys.argv[1:]) == 1: outname = sys.argv[1].replace('.npz', '')
else: outname = 'mywedge'
if plotres: outname += '_res'
P = 0
P2 = 0
C = 0
for Pfile in sys.argv[1:]:
F = n.load(Pfile)
if plotres:
P += F['P_res']
try:
dchan = int(
filename.split('/')[-1].split('.')[0].split('_')[2]) - int(
filename.split('.')[0].split('_')[1])
chan = int(filename.split('/')[-1].split('.')[0].split('_')
[1]) + dchan / 2.
freqs.append(
chan / 2. + 100
) #a pretty good apprximation of chan 2 freq for 500kHz channels
print('Frequency found: {0}'.format(freqs[-1]))
except:
print('No frequency found for file: ' + filename + '\n')
print('Skipping')
continue
freqs = n.array(freqs)
zs = pspec.f2z(freqs * 1e-3)
fig = p.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
#plot the GMRT paciga 2014 data
GMRT = eor_results.GMRT_2014_all()
GMRT_results = {}
print('GMRT')
for i, z in enumerate(GMRT.keys()):
#index = n.argwhere(GMRT[z][:,0] - .2 < 0.1).squeeze()
freq = pspec.z2f(z)
k_horizon = n.sqrt(cosmo_units.eta2kparr(30./c,z)**2 + \
cosmo_units.u2kperp(15*freq*1e6/c,z)**2)
if np.min(abs(GMRT[z][:, 0] - myk)) > .07: continue
index = n.argwhere(abs(GMRT[z][:, 0] - myk)).squeeze()
