os.makedirs(outDir)
# Just save the H2 estimate and MC values to reduce file space
np.savez(outDir+'id%s_fwhm%s_power%s.npz' %(map_id,FWHM,noise_power),\
H2=H2_est,H2_MC=H2_MC,ang=ang_est,A=A_est)
print 'Map %s of %s complete in %s seconds' % (
mi + 1, len(all_map_id), time.time() - start_time)
del H2_est, H2_MC, ang_est, A_est, A_MC, output
print 'Job %s of %s complete in %s seconds' % (index + 1, length,
time.time() - start_time)
# send email notification
if index == LEN - 1:
from hades.NoiseParams import sendMail
sendMail('Noise Parameter Space')
sys.exit()
def check_files(map_size=a.map_size, sep=a.sep, freq=a.freq):
import os
paramFile = np.load(a.root_dir + '%sdeg%sBatchNoiseParams.npz' %
(map_size, sep))
for mi, map_id in enumerate(paramFile['map_id']):
print 'Checking tile %s of %s' % (mi + 1, len(paramFile['map_id']))
for index in range(len(paramFile['FWHM'])):
delensing_fraction = paramFile['delensing_fraction'][index]
noise_power = paramFile['noise_power'][index]
return estimator_wrap(map_id,l_step=l_step)
output=runner(map_id)
# Save output to file
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir+'%s_%s.npy' %(batch_id,param_id), output) # save output
print "Task %s tile %s complete in %s seconds" %(param_id,batch_id,time.time()-start_time)
if batch_id==len(l_step_dat)*len(goodIDs)-2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Single Map Params')
def create_significances():
""" Recreate significances from parameters."""
import warnings
for i in range(len(l_step_dat)):
from hades.wrapper import hex_patch_anisotropy
suffix='_'+str(i)
try:
sigs,sigsA=hex_patch_anisotropy(suffix=suffix)
except ValueError:
print 'err'
continue
print 'l_step %s, Hex sig: %s, A sig: %s' %(l_step_dat[i],sigs,sigsA)
# from hades.debiased_wrapper import tile_wrap
# output=tile_wrap(map_id)
#print output
# Save output to file
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir+'%s.npy' %batch_id, output) # save output
print "Job %s complete in %s seconds" %(batch_id,time.time()-start_time)
if batch_id==len(goodIDs)-2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Debiased Padded Single Map')
def padded_wrap(map_id,map_size=a.map_size,\
sep=a.sep,N_sims=a.N_sims,N_bias=a.N_bias,noise_power=a.noise_power,FWHM=a.FWHM,\
slope=a.slope,l_step=a.l_step,lMin=a.lMin,lMax=a.lMax,rot=a.rot,freq=a.freq,\
delensing_fraction=a.delensing_fraction,useTensors=a.useTensors,f_dust=a.f_dust,\
rot_average=a.rot_average,useBias=a.useBias,padding_ratio=a.padding_ratio,unPadded=a.unPadded,flipU=a.flipU,root_dir=a.root_dir,\
KKdebiasH2=a.KKdebiasH2,cutFactor=1.25,ffp10_spectrum=a.ffp10_spectrum):
""" Compute the estimated angle, amplitude and polarisation fraction with noise, correcting for bias.
Noise model is from Hu & Okamoto 2002 and errors are estimated using MC simulations, which are all saved.
Input: map_id (tile number)
map_size (tile width in degrees)
sep (separation of tile centres in degrees)
outDir = a.root_dir + 'BatchData/f%s_ms%s_s%s_fw%s_np%s_d%s/' % (
freq, a.map_size, a.sep, a.FWHM, a.noise_power, a.delensing_fraction)
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir + '%s.npy' % (batch_id % len(goodIDs)),
output) # save output
print "Job %s complete in %s seconds" % (batch_id,
time.time() - start_time)
if batch_id == maxID - 2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Single Map')
def reconstructor(map_size, sep, root_dir):
""" Reconstruct the patch epsilon values for different frequencies."""
from hades.params import BICEP
a = BICEP()
import numpy as np
# Load good ids
goodFile = root_dir + '%sdeg%sGoodIDs.npy' % (map_size, sep)
goodIDs = np.load(goodFile)
freqs = np.arange(50, 500, 50)
sig = np.zeros(len(freqs))
meanA = np.zeros(len(freqs))
os.makedirs(outDir)
for j in range(err_repeats): # repeat for MC errors
print 'f_dust %s tile %s healpy run %s starting for map_id %s' % (
f_dust, batch_id, j, map_id)
output = runner(map_id)
np.save(outDir + '%s_%s_%s.npy' % (batch_id, param_id, j),
output) # save output
print "Task %s tile %s complete in %s seconds" % (param_id, batch_id,
time.time() - start_time)
if (map_id == len(goodIDs) - 1) and (param_id == len(a.f_dust_all) - 1):
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Full Map Healpy Null Test')
def create_significances(map_size=a.map_size,sep=a.sep,FWHM=a.FWHM,noise_power=a.noise_power,delensing_fraction=a.delensing_fraction,freq=a.freq,root_dir=a.root_dir,\
folder=None,plot=False):
""" Recreate + plot significances from parameters."""
if folder == None:
folder = 'HealpyFullMapNullTestData'
from hades.hex_wrap import patch_hexadecapole
mean, err, monopole, true_monopole = [
np.zeros(len(a.f_dust_all)) for _ in range(4)
]
for i in range(len(a.f_dust_all)):
trueA = []
A = []
sigs = []
# output=tile_wrap(map_id)
#print output
# Save output to file
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir + '%s.npy' % batch_id, output) # save output
print "Job %s complete in %s seconds" % (batch_id,
time.time() - start_time)
if batch_id == len(goodIDs) - 2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Healpy Full Map')
def wrapper(map_id,map_size=a.map_size,\
sep=a.sep,N_sims=a.N_sims,N_bias=a.N_bias,noise_power=a.noise_power,FWHM=a.FWHM,\
slope=a.slope,l_step=a.l_step,lMin=a.lMin,lMax=a.lMax,rot=a.rot,freq=a.freq,\
delensing_fraction=a.delensing_fraction,useTensors=a.useTensors,f_dust=a.f_dust,\
rot_average=a.rot_average,useBias=a.useBias,padding_ratio=a.padding_ratio,unPadded=a.unPadded,flipU=a.flipU,root_dir=a.root_dir,\
KKdebiasH2=a.KKdebiasH2,cutFactor=1.35,camb_spectrum=a.camb_spectrum):
""" Compute the estimated angle, amplitude and polarisation fraction with noise, correcting for bias.
Noise model is from Hu & Okamoto 2002 and errors are estimated using MC simulations, which are all saved.
This uses cutouts from input dust + lensing + noise map. (Pre-corrected for frequency dependence)
Input: map_id (tile number)
os.makedirs(outDir)
for j in range(err_repeats): # repeat for MC errors
print 'f_dust %s tile %s run %s starting for map_id %s' % (
f_dust, batch_id, j, map_id)
output = runner(map_id)
np.save(outDir + '%s_%s_%s.npy' % (batch_id, param_id, j),
output) # save output
print "Task %s tile %s complete in %s seconds" % (param_id, batch_id,
time.time() - start_time)
if (map_id == len(goodIDs) - 1) and (param_id == len(a.f_dust_all) - 1):
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Single Map Null Test')
def create_significances(map_size=a.map_size,sep=a.sep,FWHM=a.FWHM,noise_power=a.noise_power,delensing_fraction=a.delensing_fraction,freq=a.freq,root_dir=a.root_dir,\
folder=None,plot=False):
""" Recreate + plot significances from parameters."""
if folder == None:
if a.true_lensing:
folder = 'NullTestLensedBatchData'
else:
folder = 'NullTestBatchData'
from hades.hex_wrap import patch_hexadecapole
mean, err, monopole, true_monopole = [
np.zeros(len(a.f_dust_all)) for _ in range(4)
]
for i in range(len(a.f_dust_all)):
trueA = []
# output=tile_wrap(map_id)
#print output
# Save output to file
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir + '%s.npy' % batch_id, output) # save output
print "Job %s complete in %s seconds" % (batch_id,
time.time() - start_time)
if batch_id == len(goodIDs) - 2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Healpy Single Map')
def wrapper(map_id,map_size=a.map_size,\
sep=a.sep,N_sims=a.N_sims,N_bias=a.N_bias,noise_power=a.noise_power,FWHM=a.FWHM,\
slope=a.slope,l_step=a.l_step,lMin=a.lMin,lMax=a.lMax,rot=a.rot,freq=a.freq,\
delensing_fraction=a.delensing_fraction,useTensors=a.useTensors,f_dust=a.f_dust,\
rot_average=a.rot_average,useBias=a.useBias,padding_ratio=a.padding_ratio,unPadded=a.unPadded,flipU=a.flipU,root_dir=a.root_dir,\
KKdebiasH2=a.KKdebiasH2,cutFactor=1.35):
""" Compute the estimated angle, amplitude and polarisation fraction with noise, correcting for bias.
Noise model is from Hu & Okamoto 2002 and errors are estimated using MC simulations, which are all saved.
This uses cutouts from input dust + lensing + noise map. (Pre-corrected for frequency dependence)
Input: map_id (tile number)
from hades.dedusting import compute_angle
angle,ratio=compute_angle(map_id)
# Save output to file
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir+'A%s.npy' %batch_id, angle) # save output
np.save(outDir+'R%s.npy' %batch_id, ratio) # save output
print "Job %s complete in %s seconds" %(batch_id,time.time()-start_time)
if batch_id==len(goodIDs)-2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Angle + Ratio Maps')
def compute_angle(map_id,padding_ratio=a.padding_ratio,map_size=a.map_size,sep=a.sep,freq=a.freq,\
f_dust=a.f_dust,lMax=a.lMax,lMin=a.lMin,l_step=a.l_step,FWHM=a.FWHM,noise_power=a.noise_power,\
slope=a.slope,delensing_fraction=a.delensing_fraction,useQU=a.useQU,N_bias=a.N_bias):
"""Compute the polarisation angle for a specific tile, creating a model B-power spectrum + cross-spectra
in order to find the angle including the ambiguity in sin(2alpha), cos(2alpha) due to initial computation
of sin(4alpha), cos(4alpha).
Returns angle in degrees.
"""
# Step 1, create actual B-mode map
from hades.padded_debiased_wrap import padded_wrap
output=padded_wrap(map_id)
#print output
# Save output to file
if not os.path.exists(outDir): # make directory
os.makedirs(outDir)
np.save(outDir+'%s.npy' %batch_id, output) # save output
print "Job %s complete in %s seconds" %(batch_id,time.time()-start_time)
if batch_id==len(goodIDs)-2:
if a.send_email:
from hades.NoiseParams import sendMail
sendMail('Debiased Lensed Single Map')
def lensed_wrap(map_id,map_size=a.map_size,\
sep=a.sep,N_sims=a.N_sims,N_bias=a.N_bias,noise_power=a.noise_power,FWHM=a.FWHM,\
slope=a.slope,l_step=a.l_step,lMin=a.lMin,lMax=a.lMax,rot=a.rot,freq=a.freq,\
delensing_fraction=a.delensing_fraction,useTensors=a.useTensors,f_dust=a.f_dust,\
rot_average=a.rot_average,useBias=a.useBias,padding_ratio=a.padding_ratio,unPadded=a.unPadded,flipU=a.flipU,root_dir=a.root_dir,\
KKdebiasH2=a.KKdebiasH2,cutFactor=1.35):
""" Compute the estimated angle, amplitude and polarisation fraction with noise, correcting for bias.
Noise model is from Hu & Okamoto 2002 and errors are estimated using MC simulations, which are all saved.
Input: map_id (tile number)
map_size (tile width in degrees)
sep (separation of tile centres in degrees)
