def _get_response(comp,param_override=None):
if bandpasses:
if no_act_color_correction:
r = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts,
param_dict_override=param_override)
else:
r = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts,
ccor_cen_nus=cfreqs, ccor_beams=lbeams,
ccor_exps = [ccor_exp] * narrays,
param_dict_override=param_override)
else:
r = tfg.get_mix(bps, comp,param_dict_override=param_override)
return r
# Reconvolve to the same beam and filter out modes < ellmin
ls, obells = np.loadtxt(bfile, unpack=True)
obeam = maps.interp(ls, obells)(modlmap)
beam_ratio = kbeam / obeam
beam_ratio[~np.isfinite(beam_ratio)] = 0
kmask = maps.mask_kspace(mask.shape, mask.wcs, lmin=lmin, lmax=30000)
# Get coadd map of reference array
kmap = enmap.fft(
dm.get_coadd("s15", region, array, srcfree=True, ncomp=1)[0] * mask)
# Get pixel window correction
pwin = tutils.get_pixwin(mask.shape[-2:])
bpfile = "data/" + dm.get_bandpass_file_name(array)
# Apply a filter that converts array map to Compton Y units (Eq 8)
f = tfg.get_mix_bandpassed([bpfile],
'tSZ',
ccor_cen_nus=[freqs[array]],
ccor_beams=[lbeam])[0]
f2d = maps.interp(ells, f)(modlmap)
filt = kmask / pwin / f2d
filt[~np.isfinite(filt)] = 0
imap = enmap.ifft(kmap * filt).real
# Reconvolve Y map
ymap = maps.filter_map(enmap.read_map(yfile), beam_ratio * kmask)
# Get CMB maps
smap = enmap.read_map(yfile2)
cmap = enmap.read_map(yfile3)
# Width in pixels of stamp cutout
pix = 60
def __init__(self,qids,fg_res_version=None,ellmax=8101,bandpassed=True,no_act_color_correction=False,ccor_exp=-1):
self.qids = qids
self.alms = {}
ells = np.arange(ellmax)
self.ellmax = ellmax
self.cyy = fgs.power_y(ells)[None,None]
self.cyy[0,0][ells<2] = 0
narrays = len(qids)
if fg_res_version is not None:
fpath = sints.dconfig['actsims']['fg_res_path'] + "/" + fg_res_version + "/"
self.cfgres = np.zeros((narrays,narrays,ellmax))
for i in range(narrays):
for j in range(i,narrays):
qid1 = qids[i]
qid2 = qids[j]
clfilename = "%shfgcov_%s_%s.txt" % (fpath,qid1,qid2) #!!!! Changed fgcov to hfgcov # !!!!
clfilename_alt = "%shfgcov_%s_%s.txt" % (fpath,qid2,qid1) #!!!! Changed fgcov to hfgcov
try:
ls,cls = np.loadtxt(clfilename,unpack=True)
except:
ls,cls = np.loadtxt(clfilename_alt,unpack=True)
assert np.all(np.isclose(ls,ells))
self.cfgres[i,j] = cls.copy()
if i!=j:
self.cfgres[j,i] = cls.copy()
# self.cfgres[j,i] = self.cfgres[i,j] = cls.copy()*0 # !!!! REMOVED CORR
#if (qid1 in ['p01','p02']) or (qid2 in ['p01','p02']): self.cfgres[j,i] = self.cfgres[i,j] = 0 # !!!!
# self.cfgres[j,i][ls<300] = self.cfgres[i,j][ls<300] = 0 # !!!!
else:
self.cfgres = None
# get tSZ response, and also zero out parts of map that are not observed
aspecs = tutils.ASpecs().get_specs
bps = []
cfreqs = []
lbeams = []
ells = np.arange(0,35000)
for qid in qids:
dm = sints.models[sints.arrays(qid,'data_model')]()
if dm.name=='act_mr3':
season,array1,array2 = sints.arrays(qid,'season'),sints.arrays(qid,'array'),sints.arrays(qid,'freq')
array = '_'.join([array1,array2])
lbeam = tutils.get_kbeam(qid,ells,sanitize=True,planck_pixwin=False) # note no pixwin but doesnt matter since no ccorr for planck
elif dm.name=='planck_hybrid':
season,patch,array = None,None,sints.arrays(qid,'freq')
lbeam = None
else:
raise ValueError
lbeams.append(lbeam)
lmin,lmax,hybrid,radial,friend,cfreq,fgroup,wrfit = aspecs(qid)
if bandpassed:
bps.append("data/"+dm.get_bandpass_file_name(array))
else:
bps.append(cfreq)
cfreqs.append(cfreq)
if bandpassed:
if no_act_color_correction:
self.tsz_fnu = tfg.get_mix_bandpassed(bps, 'tSZ')
else:
self.tsz_fnu = tfg.get_mix_bandpassed(bps, 'tSZ',
ccor_cen_nus=cfreqs, ccor_beams=lbeams,
ccor_exps = [ccor_exp] * narrays)
else:
self.tsz_fnu = tfg.get_mix(bps, 'tSZ')
def build_and_save_ilc(arrays,region,version,cov_version,beam_version,
solutions,beams,chunk_size,
effective_freq,overwrite,maxval,unsanitized_beam=False,do_weights=False,
pa1_shift = None,
pa2_shift = None,
pa3_150_shift = None,
pa3_090_shift = None,
no_act_color_correction=False, ccor_exp = -1,
isotropize=False, isotropize_width=20):
print("Chunk size is ", chunk_size*64./8./1024./1024./1024., " GB.")
def warn(): print("WARNING: no bandpass file found. Assuming array ",dm.c['id']," has no response to CMB, tSZ and CIB.")
aspecs = tutils.ASpecs().get_specs
bandpasses = not(effective_freq)
savedir = tutils.get_save_path(version,region)
covdir = tutils.get_save_path(cov_version,region)
assert os.path.exists(covdir)
if not(overwrite):
assert not(os.path.exists(savedir)), \
"This version already exists on disk. Please use a different version identifier."
try: os.makedirs(savedir)
except:
if overwrite: pass
else: raise
mask = enmap.read_map(covdir+"tilec_mask.fits")
shape,wcs = mask.shape,mask.wcs
Ny,Nx = shape
modlmap = enmap.modlmap(shape,wcs)
arrays = arrays.split(',')
narrays = len(arrays)
kcoadds = []
kbeams = []
bps = []
names = []
lmins = []
lmaxs = []
shifts = []
cfreqs = []
lbeams = []
ells = np.arange(0,modlmap.max())
for i,qid in enumerate(arrays):
dm = sints.models[sints.arrays(qid,'data_model')](region=mask,calibrated=True)
lmin,lmax,hybrid,radial,friend,cfreq,fgroup,wrfit = aspecs(qid)
cfreqs.append(cfreq)
lmins.append(lmin)
lmaxs.append(lmax)
names.append(qid)
if dm.name=='act_mr3':
season,array1,array2 = sints.arrays(qid,'season'),sints.arrays(qid,'array'),sints.arrays(qid,'freq')
array = '_'.join([array1,array2])
elif dm.name=='planck_hybrid':
season,patch,array = None,None,sints.arrays(qid,'freq')
else:
raise ValueError
kcoadd_name = covdir + "kcoadd_%s.npy" % qid
kmask = maps.mask_kspace(shape,wcs,lmin=lmin,lmax=lmax)
kcoadd = enmap.enmap(np.load(kcoadd_name),wcs)
dtype = kcoadd.dtype
kcoadds.append(kcoadd.copy()*kmask)
kbeam = tutils.get_kbeam(qid,modlmap,sanitize=not(unsanitized_beam),version=beam_version,planck_pixwin=True)
if dm.name=='act_mr3':
lbeam = tutils.get_kbeam(qid,ells,sanitize=not(unsanitized_beam),version=beam_version,planck_pixwin=False) # note no pixwin but doesnt matter since no ccorr for planck
elif dm.name=='planck_hybrid':
lbeam = None
else:
raise ValueError
lbeams.append(lbeam)
kbeams.append(kbeam.copy())
if bandpasses:
try:
fname = dm.get_bandpass_file_name(array)
bps.append("data/"+fname)
if (pa1_shift is not None) and 'PA1' in fname:
shifts.append(pa1_shift)
elif (pa2_shift is not None) and 'PA2' in fname:
shifts.append(pa2_shift)
elif (pa3_150_shift is not None) and ('PA3' in fname) and ('150' in fname):
shifts.append(pa3_150_shift)
elif (pa3_090_shift is not None) and ('PA3' in fname) and ('090' in fname):
shifts.append(pa3_90_shift)
else:
shifts.append(0)
except:
warn()
bps.append(None)
else:
try: bps.append(cfreq)
except:
warn()
bps.append(None)
kcoadds = enmap.enmap(np.stack(kcoadds),wcs)
# Read Covmat
cov = maps.SymMat(narrays,shape[-2:])
for aindex1 in range(narrays):
for aindex2 in range(aindex1,narrays):
icov = enmap.enmap(np.load(covdir+"tilec_hybrid_covariance_%s_%s.npy" % (names[aindex1],names[aindex2])),wcs)
if isotropize:
bin_edges = np.append([0.],np.arange(min(lmins),modlmap.max(),isotropize_width))
binner = stats.bin2D(modlmap,bin_edges)
ls,c1d = binner.bin(icov)
icov = maps.interp(ls,c1d)(modlmap)
if aindex1==aindex2:
icov[modlmap<lmins[aindex1]] = maxval
icov[modlmap>lmaxs[aindex1]] = maxval
cov[aindex1,aindex2] = icov
cov.data = enmap.enmap(cov.data,wcs,copy=False)
covfunc = lambda sel: cov.to_array(sel,flatten=True)
assert cov.data.shape[0]==((narrays*(narrays+1))/2) # FIXME: generalize
assert np.all(np.isfinite(cov.data))
# Make responses
responses = {}
for comp in ['tSZ','CMB','CIB']:
if bandpasses:
if no_act_color_correction:
responses[comp] = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts)
else:
responses[comp] = tfg.get_mix_bandpassed(bps, comp, bandpass_shifts=shifts,
ccor_cen_nus=cfreqs, ccor_beams=lbeams,
ccor_exps = [ccor_exp] * narrays)
else:
responses[comp] = tfg.get_mix(bps, comp)
ilcgen = ilc.chunked_ilc(modlmap,np.stack(kbeams),covfunc,chunk_size,responses=responses,invert=True)
# Initialize containers
solutions = solutions.split(',')
data = {}
kcoadds = kcoadds.reshape((narrays,Ny*Nx))
for solution in solutions:
data[solution] = {}
comps = solution.split('-')
data[solution]['comps'] = comps
if len(comps)<=2:
data[solution]['noise'] = enmap.zeros((Ny*Nx),wcs)
if len(comps)==2:
data[solution]['cnoise'] = enmap.zeros((Ny*Nx),wcs)
data[solution]['kmap'] = enmap.zeros((Ny*Nx),wcs,dtype=dtype) # FIXME: reduce dtype?
if do_weights and len(comps)<=2:
for qid in arrays:
data[solution]['weight_%s' % qid] = enmap.zeros((Ny*Nx),wcs)
for chunknum,(hilc,selchunk) in enumerate(ilcgen):
print("ILC on chunk ", chunknum+1, " / ",int(modlmap.size/chunk_size)+1," ...")
for solution in solutions:
comps = data[solution]['comps']
if len(comps)==1: # GENERALIZE
data[solution]['noise'][selchunk] = hilc.standard_noise(comps[0])
if do_weights: weight = hilc.standard_weight(comps[0])
data[solution]['kmap'][selchunk] = hilc.standard_map(kcoadds[...,selchunk],comps[0])
elif len(comps)==2:
data[solution]['noise'][selchunk] = hilc.constrained_noise(comps[0],comps[1])
data[solution]['cnoise'][selchunk] = hilc.cross_noise(comps[0],comps[1])
ret = hilc.constrained_map(kcoadds[...,selchunk],comps[0],comps[1],return_weight=do_weights)
if do_weights:
data[solution]['kmap'][selchunk],weight = ret
else:
data[solution]['kmap'][selchunk] = ret
elif len(comps)>2:
data[solution]['kmap'][selchunk] = np.nan_to_num(hilc.multi_constrained_map(kcoadds[...,selchunk],comps[0],*comps[1:]))
if len(comps)<=2 and do_weights:
for qind,qid in enumerate(arrays):
data[solution]['weight_%s' % qid][selchunk] = weight[qind]
del ilcgen,cov
# Reshape into maps
name_map = {'CMB':'cmb','tSZ':'comptony','CIB':'cib'}
beams = beams.split(',')
for solution,beam in zip(solutions,beams):
comps = "tilec_single_tile_"+region+"_"
comps = comps + name_map[data[solution]['comps'][0]]+"_"
if len(data[solution]['comps'])>1: comps = comps + "deprojects_"+ '_'.join([name_map[x] for x in data[solution]['comps'][1:]]) + "_"
comps = comps + version
if do_weights and len(data[solution]['comps'])<=2:
for qind,qid in enumerate(arrays):
enmap.write_map("%s/%s_%s_weight.fits" % (savedir,comps,qid), enmap.enmap(data[solution]['weight_%s' % qid].reshape((Ny,Nx)),wcs))
try:
noise = enmap.enmap(data[solution]['noise'].reshape((Ny,Nx)),wcs)
enmap.write_map("%s/%s_noise.fits" % (savedir,comps),noise)
except: pass
try:
cnoise = enmap.enmap(data[solution]['cnoise'].reshape((Ny,Nx)),wcs)
enmap.write_map("%s/%s_cross_noise.fits" % (savedir,comps),cnoise)
except: pass
ells = np.arange(0,modlmap.max(),1)
try:
fbeam = float(beam)
kbeam = maps.gauss_beam(modlmap,fbeam)
lbeam = maps.gauss_beam(ells,fbeam)
except:
qid = beam
bfunc = lambda x: tutils.get_kbeam(qid,x,version=beam_version,sanitize=not(unsanitized_beam),planck_pixwin=False)
kbeam = bfunc(modlmap)
lbeam = bfunc(ells)
kmap = enmap.enmap(data[solution]['kmap'].reshape((Ny,Nx)),wcs)
smap = enmap.ifft(kbeam*kmap,normalize='phys').real
enmap.write_map("%s/%s.fits" % (savedir,comps),smap)
io.save_cols("%s/%s_beam.txt" % (savedir,comps),(ells,lbeam),header="ell beam")
enmap.write_map(savedir+"/tilec_mask.fits",mask)
maxval=maxval,
theory_signal=args.theory)
cov.data = enmap.enmap(cov.data, ewcs, copy=False)
covfunc = lambda sel: cov.to_array(sel, flatten=True)
assert cov.data.shape[0] == (
(narrays * (narrays + 1)) / 2) # FIXME: generalize
assert np.all(np.isfinite(cov.data))
# bps, kbeams,
# Make responses
responses = {}
for comp in ['tSZ', 'CMB', 'CIB']:
if bandpasses:
responses[comp] = tfg.get_mix_bandpassed(bps, comp)
else:
responses[comp] = tfg.get_mix(bps, comp)
ilcgen = ilc.chunked_ilc(modlmap,
np.stack(kbeams),
covfunc,
chunk_size,
responses=responses,
invert=True)
Ny, Nx = eshape[-2:]
# Initialize containers
data = {}
for qind, qid in enumerate(aids):
lmin = lmins[qind]
lmin, lmax, hybrid, radial, friend, cfreq, fgroup, wrfit = aspecs(
qid)
dm = sints.models[sints.arrays(qid, 'data_model')]()
if dm.name == 'act_mr3':
season, array1, array2 = sints.arrays(
qid, 'season'), sints.arrays(qid, 'array'), sints.arrays(
qid, 'freq')
array = '_'.join([array1, array2])
marker = 's'
elif dm.name == 'planck_hybrid':
season, patch, array = None, None, sints.arrays(qid, 'freq')
marker = 'o'
bps = ["data/" + dm.get_bandpass_file_name(array)]
fr = tfg.get_mix_bandpassed(bps, comp, normalize_cib=False)[0]
fc = tfg.get_mix(cfreq, comp=comp)[0]
if comp == 'CIB': print(comp, fr, fc, f0)
f0 = tfg.get_mix_bandpassed(
['data/PA2_avg_passband_wErr_trunc.txt'],
comp,
normalize_cib=False)[0]
fs = np.abs(fr / f0)
if cfreq == 93: cfreq = 97
if cfreq == 148: cfreq = 149
print(cfreq)
pl._ax.scatter(cfreq,
fs,
color=col,
edgecolors='k',
zorder=1,
'weight': 'bold',
'size': 16,
}
freqs = [30, 44, 70, 100, 143, 217, 353, 545]
for f in freqs:
pl._ax.text(f * 0.9, 0.02, "%d" % f, fontdict=font)
# pl._ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
#pl._ax.get_xaxis().get_major_formatter().labelOnlyBase = False
pl.legend(loc='upper right', labsize=ftsize)
pl.done("fig_bandpass.pdf")
sys.exit()
comp = 'tSZ'
mix = fg.get_mix_bandpassed(fnames, comp)
mixp = fg.get_mix_bandpassed(fnames,
comp,
shifts=[2.4, 2.4, 1.5, 2.4] + [0] * 8)
mixn = fg.get_mix_bandpassed(fnames,
comp,
shifts=[-2.4, -2.4, -1.5, -2.4] + [0] * 8)
diff = (mixp - mix) * 100. / mix
diff2 = (mixn - mix) * 100. / mix
diff3 = (mixp - mixn) * 100. / mix
print(diff, diff2, diff3)
sys.exit()
dm = sints.ACTmr3()
for season in dm.cals.keys():
for qid in qids:
dm = sints.models[sints.arrays(qid, 'data_model')]()
lmin, lmax, hybrid, radial, friend, cfreq, fgroup, wrfit = aspecs(qid)
cfreqs.append(cfreq)
season, array1, array2 = sints.arrays(qid, 'season'), sints.arrays(
qid, 'array'), sints.arrays(qid, 'freq')
array = '_'.join([array1, array2])
bps.append("data/" + dm.get_bandpass_file_name(array))
lbeams.append(tutils.get_kbeam(qid, ells, sanitize=False))
lexps1.append(exp1[cfreq])
lexps2.append(exp2[cfreq])
r1 = tfg.get_mix_bandpassed(bps,
'tSZ',
ccor_cen_nus=cfreqs,
ccor_beams=lbeams,
ccor_exps=lexps1)
r2 = tfg.get_mix_bandpassed(bps,
'tSZ',
ccor_cen_nus=cfreqs,
ccor_beams=lbeams,
ccor_exps=lexps2)
pl = io.Plotter(xlabel='$\\ell$', ylabel='$\\Delta R/R$')
for i, qid in enumerate(qids):
season, array1, array2, region = sints.arrays(qid, 'season'), sints.arrays(
qid, 'array'), sints.arrays(qid, 'freq'), sints.arrays(qid, 'region')
array = '_'.join([array1, array2])
pl.add(ells, (r2[i] - r1[i]) / r1[i],
from __future__ import print_function
from orphics import maps, io, cosmology
from pixell import enmap
import numpy as np
import os, sys
from soapack import interfaces as sints
import healpy as hp
from tilec import fg as tfg
import glob
for freq in [30, 44, 70, 100, 143, 217, 353, 545, 857]:
v1 = tfg.get_mix_bandpassed([
glob.glob('data/*_BANDPASS_F%s_reformat.txt' % str(freq).zfill(3))[0]
],
'CIB',
normalize_cib=False)
v2 = tfg.get_mix(freq, 'CIB_Jysr')
v3 = tfg.get_mix(freq, 'CIB')
print(v1 / 1e26, v2 / 1e26, v3 / 1e26)
sys.exit()
# print(tfg.ItoDeltaT(545))
# sys.exit()
lmax = 6000 #8192*3
cmb_set = 0
nu_ghz = 92
def get_cls(alms):
calm = maps.change_alm_lmax(alms, lmax)
cls = hp.alm2cl(calm)