datatau = dl_tr.nu2tau(datnu)
data2.append(datatau.transpose())
#print tauchan, datatau[tauchan]
#print "data shapes", data1.shape, data2.shape
print "Average over %d time points" % len(data1)
data1, data2 = n.ma.array(data1), n.ma.array(data2)
data = n.multiply(n.conjugate(data1), data2) * norm
#data is shaped [timesample, channel]
P = n.ma.array([])
for ind in n.arange(len(taulist)):
P = n.append(P, n.mean(data.T[ind]))
print P
#print len(taulist), len(P)
#kz = taulist*2*n.pi/Y
kz = cosmo_units.eta2kparr(taulist * 1.E-9,
z) #This function needs tau in Hz^-1
#print "shapes of arrays:", data1.shape, data2.shape
#Bootstrap resampling
B = 10
bootmean, booterr = boot_simple.bootstrap(B, data)
#plotting
fig = p.figure()
ax = fig.add_subplot(311)
#plotp.P_v_Eta(ax,kz,P)
ax.set_xlabel('kz')
ax.set_ylabel(r'$P(k) mK^{2} (h^{-1} Mpc)^{3}$')
p.plot(kz, P, 'bo')
ax.set_yscale('log')
ax = fig.add_subplot(312)
def plot_eor_summary(files=None, title='Input Files', k_mag=.2,
models=True, verbose=False, capsize=3.5, **kwargs):
"""Create summary plot of known EoR results.
All inputs are optional.
files: capo formated pspec k3pk files
title: legend handle for input files
k_mag: the k value to take the limits near (find limits close to k_mag)
modles: boolean, plot fiducal 21cmfast models (included in capo)
capsize: defines the size of the upper limits caps.
verbose: print info while plotting
"""
fig = p.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
# plot the GMRT paciga 2014 data
GMRT = GMRT_2014_all()
GMRT_results = {}
if verbose:
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/cosmo_units.c, z)**2)
index = n.argwhere(abs(GMRT[z][:, 0] - k_mag)).squeeze()
GMRT_results[z] = n.min(GMRT[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(GMRT_results[z])))
ax.errorbar(float(z), GMRT_results[z], GMRT_results[z]/1.5,
fmt='p', ecolor='gray', color='gray', uplims=True,
label='Paciga, 2013' if i == 0 else "",
capsize=capsize)
# Get MWA 32 data
MWA_results = {}
MWA = MWA_32T_all()
if verbose:
print('Results: Z,\t Upper Limits')
print('MWA 32')
for i, z in enumerate(MWA.keys()):
# index = n.argwhere(MWA[z][:,0] - .2 < .01).squeeze()
freq = pspec.z2f(z)
k_horizon = n.sqrt(cosmo_units.eta2kparr(30./c, z)**2 +
cosmo_units.u2kperp(15*freq*1e6/cosmo_units.c, z)**2)
index = n.argwhere(abs(MWA[z][:, 0] > k_horizon)).squeeze()
MWA_results[z] = n.min(MWA[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(MWA_results[z])))
ax.errorbar(float(z), MWA_results[z], MWA_results[z]/1.5,
fmt='r*', uplims=True,
label='Dillon, 2014' if i == 0 else "",
capsize=capsize)
MWA128_results = {}
MWA128 = MWA_128_all()
if verbose:
print('MWA 128')
for i, z in enumerate(MWA128.keys()):
index = n.argmin(abs(MWA128[z][:, 0] - k_mag)).squeeze()
MWA128_results[z] = n.min(MWA128[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(MWA128_results[z])))
ax.errorbar(float(z), MWA128_results[z], MWA128_results[z]/1.5,
fmt='y*', uplims=True, alpha=.5,
label='Dillon, 2015' if i == 0 else "", capsize=capsize)
MWA_beards = MWA_128_beards()
MWA_beards_results = {}
if verbose:
print('MWA Beardsley')
for i, z in enumerate(MWA_beards.keys()):
index = n.argmin(abs(MWA_beards[z][:, 0] - k_mag).squeeze())
MWA_beards_results[z] = n.min(MWA_beards[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(MWA_beards_results[z])))
ax.errorbar(float(z), MWA_beards_results[z], MWA_beards_results[z]/1.5,
fmt='g*', uplims=True,
label='Beardsley, 2016' if i == 0 else "",
capsize=capsize)
# Get Paper-32 data
PSA32 = PAPER_32_all()
PSA32_results = {}
Jacobs_et_al = [0, 1, 2, 4]
if verbose:
print('PSA32')
for i, z in enumerate(PSA32.keys()):
index = n.argmin(abs(PSA32[z][:, 0] - k_mag)).squeeze()
PSA32_results[z] = n.min(PSA32[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(PSA32_results[z])))
if i in Jacobs_et_al:
ax.errorbar(float(z), PSA32_results[z], PSA32_results[z]/1.5,
fmt='md', uplims=True,
label='Jacobs, 2015' if i == 0 else "",
capsize=capsize)
else:
ax.errorbar(float(z), PSA32_results[z], PSA32_results[z]/1.5,
fmt='cv', uplims=True, label='Parsons, 2014',
capsize=capsize)
# Get PAPER-64 results
PSA64 = PAPER_64_all()
PSA64_results = {}
if verbose:
print('PSA64')
for z in PSA64.keys():
index = n.argmin(abs(PSA64[z][:, 0] - k_mag)).squeeze()
PSA64_results[z] = n.min(abs(PSA64[z][index, 2]))
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(PSA64_results[z])))
ax.errorbar(float(z), PSA64_results[z], PSA64_results[z]/1.5,
fmt='bs', uplims=True, label='Ali, 2015', capsize=capsize)
# zs = [10.87,8.37]
results = {}
if verbose:
print('Input files')
zs, ks, k3pk, k3err = get_k3pk_from_npz(files)
for i, z in enumerate(zs):
results_array = n.array([ks[i], k3pk[i], k3pk[i] + k3err[i],
k3pk[i] - k3err[i]]).T
negs = n.argwhere(k3pk[i] < 0).squeeze()
try:
len(negs)
except:
negs = n.array([negs.item()])
if len(negs) > 0:
results_array[negs, -2], results_array[negs, -1] = abs(results_array[negs, -1]), -1 * results_array[negs, -2]
index = n.argmin(abs(results_array[:, 0] - k_mag)).squeeze()
results[z] = n.min(abs(results_array[index, 2]))
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(results[z])))
ax.errorbar(float(z), results[z], results[z]/1.5,
fmt='ko', uplims=True,
label=title if i == 0 else "",
capsize=capsize)
ax.set_yscale('log')
ax.set_ylabel('$\Delta^{2} (mK)^{2}$')
ax.set_ylim([1e0, 1e7])
ax.set_xlabel('z')
ax.grid(axis='y')
# Add model data.
if models:
if verbose:
print 'Plotting 21cmFAST Model'
simk = 0.2
xlim = ax.get_xlim() # save the data xlimits
parm_array, k_array, delta2_array, delta2_err_array = load_andre_models()
k_index = n.abs(k_array[0]-simk).argmin()
alphaXs = n.sort(list(set(parm_array[:, 3])))
Mmins = n.sort(list(set(parm_array[:, 4])))
Nxs = n.sort(list(set(parm_array[:, 2])))
for Nx in Nxs:
for alphaX in alphaXs:
for Mmin in Mmins:
_slice = n.argwhere(all_and([
parm_array[:, 2] == Nx,
parm_array[:, 3] == alphaX,
parm_array[:, 4] == Mmin]
))
ax.plot(parm_array[_slice, 0],
delta2_array[_slice, k_index], '-k',
label='Fiducal 21cmFAST model')
ax.set_xlim(xlim) # reset to the data xlimits
handles, labels = ax.get_legend_handles_labels()
handles = [h[0] if cnt > 0 else h for cnt, h in enumerate(handles)]
num_hands = len(handles)
handles.insert(num_hands, handles.pop(0))
labels.insert(num_hands, labels.pop(0))
box = ax.get_position()
ax.set_position([box.x0, box.height * .2 + box.y0,
box.width, box.height*.8])
# fig.subplots_adjust(bottom=.275,top=.8)
ax.legend(handles, labels, loc='lower center',
bbox_to_anchor=(.5, -.425), ncol=3, **kwargs)
# ax.legend(loc='bottom',ncol=3)
return fig
#print "data lenth", len(datnu)
datatau = dl_tr.nu2tau(datnu)
data2.append(n.array(datatau).transpose())
#print tauchan, datatau[tauchan]
#print "data shapes", data1.shape, data2.shape
print "Average over %d time points" % len(data1)
data1, data2 = n.array(data1), n.array(data2)
data = n.multiply(n.conjugate(data1), data2)*norm
#data is shaped [timesample, channel]
P=[]
for ind in n.arange(len(taulist)): P.append(n.mean(data.T[ind]))
#print len(taulist), len(P)
#kz = taulist*2*n.pi/Y
kz = cosmo_units.eta2kparr(taulist*1.E-9,z) #This function needs tau in Hz^-1
#print "shapes of arrays:", data1.shape, data2.shape
#Bootstrap resampling
B = 100
bootmean, booterr = boot_simple.bootstrap(B, data)
#plotting
fig = p.figure()
ax = fig.add_subplot(311)
#plotp.P_v_Eta(ax,kz,P)
ax.set_xlabel('kz')
ax.set_ylabel(r'$P(k) K^{2} (h^{-1} Mpc)^{3}$')
p.plot(kz,P,'bo')
print k3err[i][j],
print 2*POBER_NOISE(kmags [i][j],freqs[i]).squeeze()
print "%%%"*10
#make a nice big 2x4 plot of all the new pspectra
# with Pk on top
# and Delta^2 on the bottom
figure(10,figsize=(17,10))
clf()
Nzs = len(z)-1#all points except P14
print Nzs
j = 0
for i,redshift in enumerate(z):#gotta index z to get the right data
if n.abs(redshift-7.67)<0.01:continue #skip the P14 point
k_horizon = n.sqrt(cosmo_units.eta2kparr(30./3e8,redshift)**2 + \
cosmo_units.u2kperp(15*freqs[i]*1e6/3e8,redshift)**2)
print "cutting %d points below k= %4.3f"%(n.sum(kmags[i]<k_horizon),k_horizon)
window_points = n.argwhere(kmags[i]>k_horizon).squeeze()
j += 1
ax_pk = subplot(2,Nzs,j)
ax_delta = subplot(2,Nzs,Nzs+j)
#plot PAPER
# PAPER Pk
ax_pk.errorbar(kpars[i],n.abs(Pks[i]),yerr=Pkerr[i],fmt='k+')
# PAPER Delta^2
ax_delta.errorbar(kmags[i][window_points],k3Pk[i][window_points],yerr=k3err[i][window_points],fmt='k.', capsize=0)
#plot PAPER sensitivity
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()
GMRT_results[z] = n.min(GMRT[z][2:,2])
print('results: {0},\t{1}'.format(z,n.sqrt(GMRT_results[z])))
ax.errorbar(float(z), GMRT_results[z], GMRT_results[z]/1.5, fmt='p',ecolor='gray',color='gray', uplims=True, label='Paciga, 2013' if i ==0 else "",capsize = capsize)
#Get MWA 32 data
MWA_results = {}
MWA = eor_results.MWA_32T_all()
print('Results: Z,\t Upper Limits')
print('MWA 32')
for i,z in enumerate(MWA.keys()):
#index = n.argwhere(MWA[z][:,0] - .2 < .01).squeeze()
freq= pspec.z2f(z)
print k3err[i][j],
print 2*POBER_NOISE(kmags [i][j],freqs[i]).squeeze()
print "%%%"*10
#make a nice big 2x4 plot of all the new pspectra
# with Pk on top
# and Delta^2 on the bottom
figure(10,figsize=(17,10))
clf()
Nzs = len(z)-1#all points except P14
print Nzs
j = 0
for i,redshift in enumerate(z):#gotta index z to get the right data
if n.abs(redshift-7.67)<0.01:continue #skip the P14 point
k_horizon = n.sqrt(cosmo_units.eta2kparr(30./3e8,redshift)**2 + \
cosmo_units.u2kperp(15*freqs[i]*1e6/3e8,redshift)**2)
print "cutting %d points below k= %4.3f"%(n.sum(kmags[i]<k_horizon),k_horizon)
window_points = n.argwhere(kmags[i]>k_horizon).squeeze()
j += 1
ax_pk = subplot(2,Nzs,j)
ax_delta = subplot(2,Nzs,Nzs+j)
#plot PAPER
# PAPER Pk
ax_pk.errorbar(kpars[i],n.abs(Pks[i]),yerr=Pkerr[i],fmt='k+')
# PAPER Delta^2
ax_delta.errorbar(kmags[i][window_points],k3Pk[i][window_points],yerr=k3err[i][window_points],fmt='k.', capsize=0)
#plot PAPER sensitivity
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()
GMRT_results[z] = n.min(GMRT[z][2:, 2])
print('results: {0},\t{1}'.format(z, n.sqrt(GMRT_results[z])))
ax.errorbar(float(z),
GMRT_results[z],
GMRT_results[z] / 1.5,
fmt='p',
ecolor='gray',
color='gray',
uplims=True,
label='Paciga, 2013' if i == 0 else "",
capsize=capsize)
def plot_eor_summary(files=None,
title='Input Files',
k_mag=.2,
models=True,
verbose=False,
capsize=3.5,
**kwargs):
"""Create summary plot of known EoR results.
All inputs are optional.
files: capo formated pspec k3pk files
title: legend handle for input files
k_mag: the k value to take the limits near (find limits close to k_mag)
modles: boolean, plot fiducal 21cmfast models (included in capo)
capsize: defines the size of the upper limits caps.
verbose: print info while plotting
"""
fig = p.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
# plot the GMRT paciga 2014 data
GMRT = GMRT_2014_all()
GMRT_results = {}
if verbose:
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 / cosmo_units.c, z)**2)
index = n.argwhere(abs(GMRT[z][:, 0] - k_mag)).squeeze()
GMRT_results[z] = n.min(GMRT[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(GMRT_results[z])))
ax.errorbar(float(z),
GMRT_results[z],
GMRT_results[z] / 1.5,
fmt='p',
ecolor='gray',
color='gray',
uplims=True,
label='Paciga, 2013' if i == 0 else "",
capsize=capsize)
# Get MWA 32 data
MWA_results = {}
MWA = MWA_32T_all()
if verbose:
print('Results: Z,\t Upper Limits')
print('MWA 32')
for i, z in enumerate(MWA.keys()):
# index = n.argwhere(MWA[z][:,0] - .2 < .01).squeeze()
freq = pspec.z2f(z)
k_horizon = n.sqrt(
cosmo_units.eta2kparr(30. / c, z)**2 +
cosmo_units.u2kperp(15 * freq * 1e6 / cosmo_units.c, z)**2)
index = n.argwhere(abs(MWA[z][:, 0] > k_horizon)).squeeze()
MWA_results[z] = n.min(MWA[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(MWA_results[z])))
ax.errorbar(float(z),
MWA_results[z],
MWA_results[z] / 1.5,
fmt='r*',
uplims=True,
label='Dillon, 2014' if i == 0 else "",
capsize=capsize)
MWA128_results = {}
MWA128 = MWA_128_all()
if verbose:
print('MWA 128')
for i, z in enumerate(MWA128.keys()):
index = n.argmin(abs(MWA128[z][:, 0] - k_mag)).squeeze()
MWA128_results[z] = n.min(MWA128[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(MWA128_results[z])))
ax.errorbar(float(z),
MWA128_results[z],
MWA128_results[z] / 1.5,
fmt='y*',
uplims=True,
alpha=.5,
label='Dillon, 2015' if i == 0 else "",
capsize=capsize)
MWA_beards = MWA_128_beards()
MWA_beards_results = {}
if verbose:
print('MWA Beardsley')
for i, z in enumerate(MWA_beards.keys()):
index = n.argmin(abs(MWA_beards[z][:, 0] - k_mag).squeeze())
MWA_beards_results[z] = n.min(MWA_beards[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z,
n.sqrt(MWA_beards_results[z])))
ax.errorbar(float(z),
MWA_beards_results[z],
MWA_beards_results[z] / 1.5,
fmt='g*',
uplims=True,
label='Beardsley, 2016' if i == 0 else "",
capsize=capsize)
# Get Paper-32 data
PSA32 = PAPER_32_all()
PSA32_results = {}
Jacobs_et_al = [0, 1, 2, 4]
if verbose:
print('PSA32')
for i, z in enumerate(PSA32.keys()):
index = n.argmin(abs(PSA32[z][:, 0] - k_mag)).squeeze()
PSA32_results[z] = n.min(PSA32[z][index, 2])
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(PSA32_results[z])))
if i in Jacobs_et_al:
ax.errorbar(float(z),
PSA32_results[z],
PSA32_results[z] / 1.5,
fmt='md',
uplims=True,
label='Jacobs, 2015' if i == 0 else "",
capsize=capsize)
else:
ax.errorbar(float(z),
PSA32_results[z],
PSA32_results[z] / 1.5,
fmt='cv',
uplims=True,
label='Parsons, 2014',
capsize=capsize)
# Get PAPER-64 results
PSA64 = PAPER_64_all()
PSA64_results = {}
if verbose:
print('PSA64')
for z in PSA64.keys():
index = n.argmin(abs(PSA64[z][:, 0] - k_mag)).squeeze()
PSA64_results[z] = n.min(abs(PSA64[z][index, 2]))
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(PSA64_results[z])))
ax.errorbar(float(z),
PSA64_results[z],
PSA64_results[z] / 1.5,
fmt='bs',
uplims=True,
label='Ali, 2015',
capsize=capsize)
# zs = [10.87,8.37]
results = {}
if verbose:
print('Input files')
zs, ks, k3pk, k3err = get_k3pk_from_npz(files)
for i, z in enumerate(zs):
results_array = n.array(
[ks[i], k3pk[i], k3pk[i] + k3err[i], k3pk[i] - k3err[i]]).T
negs = n.argwhere(k3pk[i] < 0).squeeze()
try:
len(negs)
except:
negs = n.array([negs.item()])
if len(negs) > 0:
results_array[negs, -2], results_array[negs, -1] = abs(
results_array[negs, -1]), -1 * results_array[negs, -2]
index = n.argmin(abs(results_array[:, 0] - k_mag)).squeeze()
results[z] = n.min(abs(results_array[index, 2]))
if verbose:
print('results: {0},\t{1}'.format(z, n.sqrt(results[z])))
ax.errorbar(float(z),
results[z],
results[z] / 1.5,
fmt='ko',
uplims=True,
label=title if i == 0 else "",
capsize=capsize)
ax.set_yscale('log')
ax.set_ylabel('$\Delta^{2} (mK)^{2}$')
ax.set_ylim([1e0, 1e7])
ax.set_xlabel('z')
ax.grid(axis='y')
# Add model data.
if models:
if verbose:
print 'Plotting 21cmFAST Model'
simk = 0.2
xlim = ax.get_xlim() # save the data xlimits
parm_array, k_array, delta2_array, delta2_err_array = load_andre_models(
)
k_index = n.abs(k_array[0] - simk).argmin()
alphaXs = n.sort(list(set(parm_array[:, 3])))
Mmins = n.sort(list(set(parm_array[:, 4])))
Nxs = n.sort(list(set(parm_array[:, 2])))
for Nx in Nxs:
for alphaX in alphaXs:
for Mmin in Mmins:
_slice = n.argwhere(
all_and([
parm_array[:, 2] == Nx, parm_array[:, 3] == alphaX,
parm_array[:, 4] == Mmin
]))
ax.plot(parm_array[_slice, 0],
delta2_array[_slice, k_index],
'-k',
label='Fiducal 21cmFAST model')
ax.set_xlim(xlim) # reset to the data xlimits
handles, labels = ax.get_legend_handles_labels()
handles = [h[0] if cnt > 0 else h for cnt, h in enumerate(handles)]
num_hands = len(handles)
handles.insert(num_hands, handles.pop(0))
labels.insert(num_hands, labels.pop(0))
box = ax.get_position()
ax.set_position(
[box.x0, box.height * .2 + box.y0, box.width, box.height * .8])
# fig.subplots_adjust(bottom=.275,top=.8)
ax.legend(handles,
labels,
loc='lower center',
bbox_to_anchor=(.5, -.425),
ncol=3,
**kwargs)
# ax.legend(loc='bottom',ncol=3)
return fig
print "%%%" * 10
# make a nice big 2x4 plot of all the new pspectra
# with Pk on top
# and Delta^2 on the bottom
figure(10, figsize=(17, 10))
clf()
Nzs = len(z) - 1 # all points except P14
print Nzs
j = 0
for i, redshift in enumerate(z): # gotta index z to get the right data
if n.abs(redshift - 7.67) < 0.01:
continue # skip the P14 point
k_horizon = n.sqrt(
cosmo_units.eta2kparr(30.0 / 3e8, redshift) ** 2 + cosmo_units.u2kperp(15 * freqs[i] * 1e6 / 3e8, redshift) ** 2
)
print "cutting %d points below k= %4.3f" % (n.sum(kmags[i] < k_horizon), k_horizon)
window_points = n.argwhere(kmags[i] > k_horizon).squeeze()
j += 1
ax_pk = subplot(2, Nzs, j)
ax_delta = subplot(2, Nzs, Nzs + j)
# plot PAPER
# PAPER Pk
ax_pk.errorbar(kpars[i], n.abs(Pks[i]), yerr=Pkerr[i], fmt="k+")
# PAPER Delta^2
ax_delta.errorbar(
kmags[i][window_points], k3Pk[i][window_points], yerr=k3err[i][window_points], fmt="k.", capsize=0
)
print k3err[i][j],
print 2 * POBER_NOISE(kmags[i][j], freqs[i]).squeeze()
print "%%%" * 10
#make a nice big 2x4 plot of all the new pspectra
# with Pk on top
# and Delta^2 on the bottom
figure(10, figsize=(17, 10))
clf()
Nzs = len(z) - 1 #all points except P14
print Nzs
j = 0
for i, redshift in enumerate(z): #gotta index z to get the right data
if n.abs(redshift - 7.67) < 0.01: continue #skip the P14 point
k_horizon = n.sqrt(cosmo_units.eta2kparr(30./3e8,redshift)**2 + \
cosmo_units.u2kperp(15*freqs[i]*1e6/3e8,redshift)**2)
print "cutting %d points below k= %4.3f" % (n.sum(kmags[i] < k_horizon),
k_horizon)
window_points = n.argwhere(kmags[i] > k_horizon).squeeze()
j += 1
ax_pk = subplot(2, Nzs, j)
ax_delta = subplot(2, Nzs, Nzs + j)
#plot PAPER
# PAPER Pk
ax_pk.errorbar(kpars[i], n.abs(Pks[i]), yerr=Pkerr[i], fmt='k+')
# PAPER Delta^2
ax_delta.errorbar(kmags[i][window_points],
k3Pk[i][window_points],
