import healpy as hp
from pixell import enmap, curvedsky
from orphics import maps,io
from soapack import interfaces as sints
from tilec.pipeline import JointSim
qids = ['d56_03','p05']
jsim = JointSim(qids,None)
jsim.update_signal_index(0)
mask = sints.get_act_mr3_crosslinked_mask('deep6')
imap = jsim.compute_map(mask.shape,mask.wcs,'p05',include_cmb=True,include_tsz=True,include_fgres=True)
io.hplot(imap,'planck')
imap = jsim.compute_map(mask.shape,mask.wcs,'d56_03',include_cmb=True,include_tsz=True,include_fgres=True)
io.hplot(imap,'act')
ystack = ystack / i * 1e6
sstack = sstack / i
cstack = cstack / i
# Approximate geometry of stack
_, nwcs = enmap.geometry(pos=(0, 0),
shape=istack.shape,
res=np.deg2rad(0.5 / 60.))
# Plot images
if do_images:
io.hplot(enmap.enmap(istack, nwcs),
"istack",
ticks=5,
tick_unit='arcmin',
grid=True,
colorbar=True,
color='gray',
upgrade=4,
min=-5,
max=25)
io.hplot(enmap.enmap(ystack, nwcs),
"ystack",
ticks=5,
tick_unit='arcmin',
grid=True,
colorbar=True,
color='gray',
upgrade=4,
min=-5,
max=25)
kmask = maps.mask_kspace(shape,wcs,lmin=Lmin,lmax=Lmax)
feed_dict['uC_T_T'] = cov
feed_dict['tC_T_T'] = cov + (wnoise*np.pi/180/60)**2 / kbeam**2
qe = symlens.QE(shape,wcs,feed_dict,'hu_ok','TT',xmask=xmask,ymask=ymask,kmask=kmask)
s = stats.Stats()
for task in my_tasks:
cmb = maps.filter_map(mgen.get_map(seed=(1,task))[0],kbeam)
nseed = (2,task)
nmap = maps.white_noise(shape,wcs,noise_muK_arcmin=None,seed=nseed,ipsizemap=pmap,div=ivar)
obs = cmb + nmap
kobs = enmap.fft(obs,normalize='phys')/kbeam
kobs[~np.isfinite(kobs)] = 0
feed_dict['X'] = kobs
feed_dict['Y'] = kobs
krecon = qe.reconstruct(feed_dict)
print(cmb.shape,nmap.shape,krecon.shape)
s.add_to_stack('kreal',krecon.real)
s.add_to_stack('kimag',krecon.imag)
s.get_stacks()
mf = enmap.ifft(s.stacks['kreal'] + 1j*s.stacks['kimag'],normalize='phys')
io.hplot(mf,'mf.png')
"""
Loads a catalog
Maps it
Smooths it
Thresholds it
Projects it onto ACT
This gives a mask of 1s and 0s from which a random catalog can be made
"""
paths = cutils.paths
#cat_type = "wise_panstarrs"
#cat_type = "madcows_photz"
cat_type = args.sys[1]
meanfield = False
# cat_type = "sdss_redmapper"
# meanfield = True
shape,wcs = enmap.fullsky_geometry(res=1 * utils.degree)
ras,decs,_ = cutils.catalog_interface(cat_type,is_meanfield=meanfield)
cmapper = catalogs.CatMapper(ras,decs,shape=shape,wcs=wcs)
cmap = maps.binary_mask(enmap.smooth_gauss(cmapper.counts,2 * utils.degree),1e-3)
io.hplot(cmap,'counts')
shape,wcs = enmap.read_map_geometry(paths.coadd_data + f"act_planck_s08_s18_cmb_f150_daynight_srcfree_map.fits")
omap = enmap.project(cmap,shape,wcs,order=0)
io.plot_img(omap,'pcounts')
enmap.write_map(f'{paths.scratch}{cat_type}_mask.fits',omap)
imap = enmap.rand_map(shape, wcs, np.ones((1, 1, shape[0], shape[1])))
ta = tiling.TiledAnalysis(shape, wcs, comm)
ta.initialize_output("out")
for ext, ins in ta.tiles():
emap = ext(imap)
emap = filter_map(emap)
ta.update_output("out", emap, ins)
outmap = ta.get_final_output("out")
# print(comm.rank)
# io.plot_img(outmap,"rank_%d" % comm.rank)
if comm.rank == 0:
fcmap = filter_map(imap)
io.hplot(enmap.downgrade(imap, 8))
io.hplot(enmap.downgrade(outmap, 8))
io.hplot(enmap.downgrade(fcmap, 8))
io.plot_img(enmap.downgrade(outmap - fcmap, 8), lim=1)
# brmap = enmap.zeros(observed.shape[-2:],observed.wcs)
# bwrmap = enmap.zeros(observed.shape[-2:],observed.wcs)
# shape,wcs = observed.shape,observed.wcs
# epboxes = get_pixboxes(shape,wcs,width_deg,pad_deg)
# pboxes = get_pixboxes(shape,wcs,width_deg,pad_deg-2.*rtap_deg)
# for i in range(pboxes.shape[0]):
# for j in range(pboxes.shape[1]):
# omap = observed.copy()
# #print(npix(pboxes[i,j]))
# emap = enmap.extract_pixbox(omap,epboxes[i,j],wrap=shape[-2:])
# print("Min ell: ", maps.minimum_ell(emap.shape,emap.wcs))
mgen = maps.MapGen(shape,wcs,ps_signal[None,None])
ngen = maps.MapGen(shape,wcs,ps_noise[None,None]*nsplits)
cmb = mgen.get_map(seed=(1,0))
observed = []
for i in range(nsplits):
nreal = ngen.get_map(seed=(2,i))
observed.append(cmb+nreal)
kmaps = []
fc = maps.FourierCalc(shape,wcs)
for i in range(nsplits):
kmaps.append(fc.power2d(observed[i])[1])
io.hplot(maps.ftrans(ps_noise),"noise_orig",grid=False)
io.hplot(maps.ftrans(ps_signal),"signal_orig",grid=False)
io.hplot(maps.ftrans(ps_noise+ps_signal),"total_orig",grid=False)
scov,ncov,_ = tutils.ncalc(np.array(kmaps)[None],0,0,fc)
io.hplot(maps.ftrans(ncov),"noise_real",grid=False)
io.hplot(maps.ftrans(scov),"signal_real",grid=False)
dscov = covtools.signal_average(scov,bin_width=80,kind=0)
dncov0 = covtools.signal_average(ncov,bin_width=80,kind=0)
dncov = covtools.noise_average(ncov,dfact=(16,16),lmin=300,lmax=8000,wnoise_annulus=500,bin_annulus=20,
lknee_guess=3000,alpha_guess=-4,nparams=None,modlmap=None,
verbose=False,method="fft",radial_fit=True,
oshape=None,upsample=True)[0]
# print(ksol[px[0],px[1]])
# print(ksol[px[0]-11,px[1]])
# print(optile[px[0],px[1]])
# print(optile[px[0]-1,px[1]])
# print(modlmap[px[0],px[1]])
# print(modlmap[px[0]-1,px[1]])
# # pftile = ptile
# # pftile[modlmap>300] = 0
# # print(np.sort(pftile[pftile>0]))
# # print(modlmap[np.isclose(ptile,1.52256073e+02)])
# # io.plot_img(np.log10(np.fft.fftshift(ptile)),os.environ['WORK']+"/tiling/ptile_%d_smap" % i)
# # io.hplot(enmap.enmap(np.log10(np.fft.fftshift(ptile)),ewcs),os.environ['WORK']+"/tiling/phtile_%d_smap" % i)
smap = enmap.ifft(kbeam * enmap.enmap(ksol, ewcs),
normalize='phys').real
if solution == 'CMB':
io.hplot(smap, os.environ['WORK'] + "/tiling/tile_%d_smap" % i)
io.power_crop(np.real(ksol * ksol.conj()), 100,
os.environ['WORK'] + "/tiling/ptile_%d.png" % i)
# sys.exit()
ta.update_output(solution, smap, inserter)
#ta.update_output("processed",c*civar,inserter)
#ta.update_output("processed_ivar",civar,inserter)
#pmap = ilc.do_ilc
#ta.update_output("processed",pmap,inserter)
print("Rank %d done" % comm.rank)
for solution in solutions:
pmap = ta.get_final_output(solution)
if comm.rank == 0:
io.hplot(pmap, os.environ['WORK'] + "/tiling/map_%s" % solution)
mask = sints.get_act_mr3_crosslinked_mask("deep56")
io.hplot(
"""
Given the mask saved from mapcat.py, generate and save
a random catalog.
"""
paths = cutils.paths
#cat_type = "wise_panstarrs"
#cat_type = "madcows_photz"
cat_type = args.sys[1]
Nx = int(args.sys[2])
ras,decs,_,_,_ = cutils.catalog_interface(cat_type,is_meanfield=False)
N = Nx * len(ras)
mask = enmap.read_map(f'{paths.scratch}{cat_type}_mask.fits')
shape,wcs = mask.shape,mask.wcs
Npix = mask[mask>0].size
inds = np.random.choice(Npix,size=N,replace=False)
pixs = enmap.pixmap(shape,wcs)
print(pixs.shape)
coords = mask.pix2sky(pixs[:,mask>0][:,inds]) / utils.degree
io.save_cols(paths.data+f"{cat_type}_randoms.txt",(coords[1],coords[0]))
print(coords.shape)
cmapper = catalogs.CatMapper(coords[1],coords[0],shape=shape,wcs=wcs)
io.hplot(enmap.downgrade(cmapper.counts,16),'randcounts',mask=0)
def pshow(cov,fname=None): io.hplot(np.log10(enmap.downgrade(enmap.enmap(np.fft.fftshift(cov),cov.wcs),2)),fname) """
# rot="gal,equ", first=0, is_alm=True, return_alm=False, f_ell=None)
# io.hplot(nmap[sels[region]],'fig_full_nmap_%s' % region,color='gray',grid=True,colorbar=True,
# annotate=annot,min=-1.25e-5,max=3.0e-5,ticks=t[region],mask=0,downgrade=down,mask_tol=1e-14)
# io.hplot(pmap[sels[region]],'fig_full_pmap_%s' % region,color='planck',grid=True,colorbar=True,
# ticks=t[region],downgrade=down)
# io.hplot(ymap[sels[region]],'fig_full_ymap_%s' % region,color='gray',grid=True,colorbar=True,
# annotate=annot,min=-1.25e-5,max=3.0e-5,ticks=t[region],mask=0,downgrade=down,mask_tol=1e-14,font_size=fontsize)
io.hplot(ymap[sels[region]],
'fig_full_ymap_%s' % region,
color='gray',
grid=True,
colorbar=True,
annotate=annot,
min=-0.7e-5,
max=2.0e-5,
ticks=t[region],
mask=0,
downgrade=down,
mask_tol=1e-14,
font_size=fontsize)
io.hplot(smap[sels[region]],
'fig_full_smap_%s' % region,
color='planck',
grid=True,
colorbar=True,
range=300,
ticks=t[region],
mask=0,
downgrade=down,
from orphics import maps,io,cosmology,catalogs
from pixell import enmap,reproject
import numpy as np
import os,sys,shutil
from soapack import interfaces as sints
import healpy as hp
box = np.deg2rad([ [11,-173.5],
[13.5,-171] ])
rosat = enmap.read_map("/scratch/r/rbond/msyriac/data/for_sigurd/rosat_r7_boss.fits").submap(box)
print(rosat.shape,maps.resolution(rosat.shape,rosat.wcs) * 180.*60./np.pi)
version = "map_v1.0.0_rc_joint"
cversion = "v1.0.0_rc"
region = 'boss'
yname = "/scratch/r/rbond/msyriac/data/depot/tilec/v1.0.0_rc_20190919/%s_%s/tilec_single_tile_%s_comptony_%s.fits" % (version,region,region,version)
ymap = enmap.read_map(yname).submap(box)
print(ymap.center()*180./np.pi)
rosat = enmap.project(rosat,ymap.shape,ymap.wcs)
io.hplot(np.log10(rosat),'fig_virgo_rosat')
#io.hplot(enmap.smooth_gauss(ymap,np.deg2rad(3./60.)),'fig_virgo_act',color='gray') #,min=-1.25e-5,max=3.0e-5
io.hplot(ymap,'fig_virgo_act',color='gray') #,min=-1.25e-5,max=3.0e-5
fg = "tsz"
#ppath = "/scratch/r/rbond/msyriac/data/depot/tilec/map_rad_src_test_v1.0.0_rc_deep56/"
ppath = "/scratch/r/rbond/msyriac/data/depot/tilec/map_%s_src_test_v1.0.0_rc_deep56/" % fg
dec = 0
ra = 10
#imap = enmap.read_map(ppath + "tilec_single_tile_deep56_comptony_map_rad_src_test_v1.0.0_rc.fits")
imap = enmap.read_map(
ppath +
"tilec_single_tile_deep56_comptony_map_%s_src_test_v1.0.0_rc.fits" % fg)
cut = reproject.cutout(imap, ra=np.deg2rad(ra), dec=np.deg2rad(dec),
npix=80) #* 1e6
_, nwcs = enmap.geometry(pos=(0, 0),
shape=cut.shape,
res=np.deg2rad(0.5 / 60.))
print(cut.shape)
io.hplot(enmap.enmap(cut, nwcs),
"%scut" % fg,
color='gray',
colorbar=True,
ticks=5,
tick_unit='arcmin',
font_size=12,
upgrade=4,
quantile=1e-3)
ystack = s.stacks['ystack']
cstack = s.stacks['cstack']
c150 = s.stacks['c150']
c1502 = s.stacks['c1502']
c1503 = s.stacks['c1503']
c1504 = s.stacks['c1504']
c1505 = s.stacks['c1505']
c090 = s.stacks['c090']
_, nwcs = enmap.geometry(pos=(0, 0),
shape=ystack.shape,
res=np.deg2rad(0.5 / 60.))
io.hplot(enmap.enmap(ystack, nwcs),
"fig_qso_ystack_%s_%s" % (cversion, region),
ticks=5,
tick_unit='arcmin',
grid=True,
colorbar=True,
color='gray',
upgrade=4,
quantile=1e-3)
io.hplot(enmap.enmap(cstack, nwcs),
"fig_qso_cstack_%s_%s" % (cversion, region),
ticks=5,
tick_unit='arcmin',
grid=True,
colorbar=True,
color='gray',
upgrade=4,
quantile=1e-3)
io.hplot(enmap.enmap(c150, nwcs),
"fig_qso_c150_%s_%s" % (cversion, region),
from __future__ import print_function
from orphics import maps,io,cosmology,stats
from pixell import enmap
import numpy as np
import os,sys
from tilec import utils as tutils,covtools,ilc
c = tutils.Config()
for ai in range(c.narrays):
coadd = c.load_coadd_real(ai)
io.hplot(coadd*c.mask,"fcoadd_%d" % ai)
cents, ac1d = binner.bin(c.fc.f2power(kcmap, kcmap))
pl = io.Plotter(yscale='log', scalefn=lambda x: x**2. / np.pi)
pl.add(cents, as1d, ls="-")
pl.add(cents, ac1d, ls="-")
pl.add(cents, s1d, ls="--")
pl.add(cents, c1d, ls="--")
pl.done("snoise_data.png")
smap = enmap.enmap(c.fc.ifft(ksmap).real, wcs)
cmap = enmap.enmap(c.fc.ifft(kcmap).real, wcs)
ysmap = enmap.enmap(c.fc.ifft(yksmap).real, wcs)
ycmap = enmap.enmap(c.fc.ifft(ykcmap).real, wcs)
ycmap_d = enmap.enmap(c.fc.ifft(ykcmap_d).real, wcs)
# ycmap_dc = enmap.enmap(c.fc.ifft(ykcmap_dc).real,wcs)
io.hplot(smap, "usmap")
io.hplot(cmap, "ucmap")
io.plot_img(smap, "umsmap.png", lim=300)
io.plot_img(cmap, "umcmap.png", lim=300)
#sbeam = 1.4
#cbeam = 2.2
skbeam = c.get_beam(modlmap, 's15_pa3_150')
ckbeam = c.get_beam(modlmap, 's15_pa3_90')
# Uncomment this back
# smap = maps.filter_map(enmap.enmap(smap,wcs),skbeam)
# cmap = maps.filter_map(enmap.enmap(cmap,wcs),ckbeam)
# ysmap = maps.filter_map(enmap.enmap(ysmap,wcs),skbeam)
# ycmap = maps.filter_map(enmap.enmap(ycmap,wcs),ckbeam)
# ycmap_d = maps.filter_map(enmap.enmap(ycmap_d,wcs),ckbeam)
mask = enmap.read_map("%s/tilec_mask.fits" % savedir)
dm = sints.ACTmr3(region=mask)
fbeam = lambda x: dm.get_beam(
x, "s15", "deep56", "pa3_f090", kind='normalized')
name_map = {'CMB': 'cmb', 'tSZ': 'comptony', 'CIB': 'cib'}
comps = "tilec_single_tile_" + args.region + "_" + name_map[
args.solution] + "_" + args.version
lmin = args.lmin
lmax = args.lmax
w2 = np.mean(mask**2.)
imap = enmap.read_map("%s/%s.fits" % (savedir, comps))
color = 'planck' if args.solution == 'CMB' else 'gray'
io.hplot(imap,
"map_%s_%s" % (args.solution, args.region),
color='planck',
grid=True)
imap1 = dm.get_coadd(season="s15",
patch=args.region,
array="pa3_f090",
srcfree=True,
ncomp=1)
imap2 = dm.get_coadd(season="s15",
patch=args.region,
array="pa3_f150",
srcfree=True,
ncomp=1)
#io.hplot(imap2,"cmb_map_%s_s15_pa3_f150" % (args.region),color="planck",grid=True)
#io.hplot(imap1,"cmb_map_%s_s15_pa3_f090" % (args.region),color="planck",grid=True)
nmap = enmap.read_map("%s/%s_noise.fits" % (savedir, comps))
def process(dm,
patch,
array_id,
mask,
skip_splits=False,
splits_fname=None,
inpaint=True,
fn_beam=None,
cache_inpaint_geometries=True,
verbose=True,
plot_inpaint_path=None,
do_pol=False,
split_set=None):
"""
Return (nsplits,Ny,Nx) fourier transform
Return (Ny,Nx) fourier transform of coadd
This function applies no corrections for masks.
split_set : None by default means it returns all splits. Otherwise, can be 0 or 1, which
determines which half of the splits to use. E.g. for ACT with 4 splits, 0 will return
split 0,1 and 1 will return split 2,3.
"""
qid = array_id
if dm.name == 'act_mr3':
season, array1, array2 = sints.arrays(qid, 'season'), sints.arrays(
qid, 'array'), sints.arrays(qid, 'freq')
array = '_'.join([array1, array2])
pixwin = True
elif dm.name == 'planck_hybrid':
season, patch, array = None, None, sints.arrays(qid, 'freq')
pixwin = False
wins = dm.get_splits_ivar(season=season,
patch=patch,
arrays=[array],
ncomp=None)[0, :, 0, :, :]
if splits_fname is None:
splits = dm.get_splits(season=season,
patch=patch,
arrays=[array],
ncomp=3,
srcfree=True)[0, :, :, :, :]
else:
splits = enmap.read_map(splits_fname)
if split_set is not None:
fsplits = splits.shape[0]
assert fsplits == 2 or fsplits == 4
hsplits = fsplits // 2
assert split_set in [0, 1]
if split_set == 0:
wins = wins[:hsplits]
splits = splits[:hsplits]
elif split_set == 1:
wins = wins[hsplits:]
splits = splits[hsplits:]
assert splits.ndim == 4
nsplits = splits.shape[0]
if split_set is None: assert nsplits == 2 or nsplits == 4
else: assert nsplits == 1 or nsplits == 2
assert nsplits == wins.shape[0]
# Inpaint
if inpaint and dm.name == 'act_mr3':
rsplits = []
for i in range(nsplits):
result = inpainting.inpaint_map_white(
splits[i],
wins[i],
fn_beam,
union_sources_version=None,
plots=False,
cache_name="qid_%s_splitnum_%d" %
(qid, i) if cache_inpaint_geometries else None,
verbose=verbose)
rsplits.append(result[0, :, :].copy())
if plot_inpaint_path is not None:
io.hplot(splits[i][0, :, :],
"%s/uninpainted_qid_%s_splitnum_%d" %
(plot_inpaint_path, qid, i),
grid=True,
color='planck')
io.hplot(result[0, :, :],
"%s/inpainted_qid_%s_splitnum_%d" %
(plot_inpaint_path, qid, i),
grid=True,
color='planck')
# if do_pol:
# for i in range(1,3):
# io.hplot(splits[i][0,:,:],"%s/uninpainted_qid_%s_splitnum_%d" % (plot_inpaint_path,qid,i),grid=True,color='planck')
# io.hplot(result[0,:,:],"%s/inpainted_qid_%s_splitnum_%d" % (plot_inpaint_path,qid,i),grid=True,color='planck')
rsplits = enmap.enmap(np.stack(rsplits), splits.wcs)
else:
rsplits = splits[:, 0, :, :]
kdiffs, kcoadd = process_splits(rsplits,
wins,
mask,
skip_splits=skip_splits,
do_fft_splits=False,
pixwin=pixwin)
return kdiffs, kcoadd, wins
import os, sys
from pixell import enmap
from orphics import io
tdir = '/scratch/r/rbond/msyriac/data/depot/tilec/v1.0.0_rc_20190919'
def get_tsz_map():
pass
for region in ['deep56', 'boss']:
for dcomb in ['joint', 'act', 'planck']:
for solution in ['cmb', 'tsz']:
for deproj in [None, 'cib'] + [{
'cmb': 'tsz',
'tsz': 'cmb'
}[solution]]:
fname = tutils.get_generic_fname(tdir, region, solution,
deproj, dcomb)
imap = enmap.downgrade(enmap.read_map(fname), 4)
bname = 'plot_' + os.path.basename(fname).replace('.fits', '')
io.hplot(imap,
bname,
grid=True,
color={
'cmb': 'planck',
'tsz': 'gray'
}[solution])
#print(os.path.basename(fname))
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
mask = sints.get_act_mr3_crosslinked_mask("boss", version='padded_v1')
shape, wcs = mask.shape, mask.wcs
print(np.rad2deg(enmap.pix2sky(shape, wcs, (shape[-2], shape[-1]))))
sys.exit()
io.hplot(mask, "mask.png")
print(mask.shape)
dm = sints.PlanckHybrid(region=mask)
ivars = dm.get_splits_ivar(['100'], ncomp=1)
splits = dm.get_splits(['100'], ncomp=1)
# print(ivars.shape)
# print(ivars[ivars<=0].size/ivars.size*100.)
# print(ivars[ivars<=0].size)
# print(splits[ivars<=0])
# print(splits[ivars<=0].max())
# splits[ivars>0] = np.nan
# splits[ivars<=0] = 1
io.hplot(splits[0, 1, 0], "boss.png")
ymap = reconvolve(ymap, ybname, fwhm=2.2)
cmap = reconvolve(cmap, cbname, fwhm=2.2)
smap = reconvolve(smap, sbname, fwhm=2.2)
box = np.deg2rad(np.array([[-2, 12], [2, 18]]))
enmap.write_map("ymap_%s.fits" % region, ymap)
enmap.write_map("smap_%s.fits" % region, smap)
enmap.write_map("ycutout.fits", ymap.submap(box))
io.hplot(ymap.submap(box),
'fig_region_ymap',
color='gray',
grid=True,
colorbar=True,
annotate='paper/public_clusters.csv',
min=-1.25e-5,
max=3.0e-5)
io.hplot(cmap.submap(box),
'fig_region_cmap',
color='planck',
grid=True,
colorbar=True,
range=300,
annotate='paper/public_clusters.csv')
io.hplot(smap.submap(box),
'fig_region_smap',
color='planck',
grid=True,
colorbar=True,
pl.add(ls,nls,ls="--",label='theory noise per mode') pl._ax.set_xlim(20,4000) pl.done(config['data_path']+"xcls_%s.png" % polcomb) # Filtered input fikalm = hp.almxfl(ikalm,wfilt) fimap = hp.alm2map(fikalm,nside=256) # Resampled mask dmask = hp.ud_grade(mask,nside_out=256) dmask[dmask<0] = 0 # Mollview plots io.mollview(frmap*dmask,config['data_path']+"wrmap1.png",xsize=1600,lim=8e-6) io.mollview(fimap*dmask,config['data_path']+"wimap1.png",xsize=1600,lim=8e-6) # CAR plots shape,wcs = enmap.band_geometry(np.deg2rad((-70,30)),res=np.deg2rad(0.5*8192/512/60.)) omask = reproject.enmap_from_healpix(dmask, shape, wcs,rot=None) omap = cs.alm2map(fkalm, enmap.empty(shape,wcs)) io.hplot(omap*omask,config['data_path']+"cwrmap1",grid=True,ticks=20,color='gray') omap = cs.alm2map(fikalm, enmap.empty(shape,wcs)) io.hplot(omap*omask,config['data_path']+"cwimap1",grid=True,ticks=20,color='gray')
p18 = enmap.downgrade(
enmap.read_map(fname, box=box, sel=np.s_[0, ...]), 4)
ip18 = enmap.downgrade(enmap.read_map(ifname,
box=box,
sel=np.s_[0, ...]),
4,
op=np.sum)
print(p18.shape, ip18.shape)
return p18
#%%
p18 = load_map('v20')
io.hplot(p18, colorbar=True, range=40000)
# %%
p20 = load_map()
io.hplot(p20, colorbar=True, range=40000)
# %%
taper, _ = maps.get_taper_deg(p18.shape, p18.wcs)
bin_edges = np.arange(100, 3000, 80)
cents, c18 = maps.binned_power(p18, bin_edges=bin_edges, mask=taper)
cents, c20 = maps.binned_power(p20, bin_edges=bin_edges, mask=taper)
# %%
pl = io.Plotter('Dell')
pl.add(cents, c18, label='Planck 2018')
pl.add(cents, c20, label='Planck 2020')
pl.done()
# %%
_, nwcs = enmap.geometry(pos=(0, 0),
shape=ystack.shape,
res=np.deg2rad(0.5 / 60.))
return rank, enmap.enmap(
ystack, nwcs), enmap.enmap(cstack, nwcs), enmap.enmap(
dstack, nwcs), N, cents, y1ds, c1ds, d1ds, ry1d, rc1d, rd1d
else:
return rank, None, None, None, None, None, None, None, None, None, None, None
hplot = lambda x, y: io.hplot(x,
os.environ['WORK'] + "/" + y,
ticks=5,
tick_unit='arcmin',
grid=True,
colorbar=True,
color='gray',
upgrade=4,
quantile=1e-3)
print("Starting deep56")
rank, ystack1, cstack1, dstack1, i1, cents, y1ds1, c1ds1, d1ds1, ry1d1, rc1d1, rd1d1 = do(
ymap1, cmap1, dmap1, mask1, ras1, decs1, wt1)
if rank == 0:
print(i1)
hplot(ystack1, "fig_all_cmass_ystack_%s_%s" % (cversion, 'deep56'))
hplot(cstack1, "fig_all_cmass_cstack_%s_%s" % (cversion, 'deep56'))
hplot(dstack1, "fig_all_cmass_dstack_%s_%s" % (cversion, 'deep56'))
print("Starting boss")
random = True
cversion = 'joint'
region = 'deep56'
fname = os.environ['WORK'] + "/data/boss/eboss_dr14/data_DR14_QSO_S.fits"
cols = catalogs.load_fits(fname, ['RA', 'DEC'])
ras = cols['RA']
decs = cols['DEC']
sns = np.array(ras) * 0 + 6
tdir = '/scratch/r/rbond/msyriac/data/depot/tilec/v1.0.0_rc_20190919'
mask = sints.get_act_mr3_crosslinked_mask(region)
bmask = mask.copy()
bmask[bmask < 0.99] = 0
io.hplot(enmap.downgrade(bmask, 4), "fig_qso_bmask")
solution = 'tsz'
bfile = tutils.get_generic_fname(tdir, region, 'cmb', None, 'act', beam=True)
yfile = tutils.get_generic_fname(tdir, region, 'cmb', None, 'act')
bfile2 = tutils.get_generic_fname(tdir,
region,
'cmb',
None,
'planck',
beam=True)
yfile2 = tutils.get_generic_fname(tdir, region, 'cmb', None, 'planck')
cmap = enmap.read_map(yfile2)
box = np.deg2rad([[-1, -1], [10, 10]])
#shape,wcs = enmap.fullsky_geometry(res=np.deg2rad(res/60.))
# shape,wcs = enmap.geometry(pos=box,res=np.deg2rad(res/60.))
hmap = hp.read_map(hfile)
ofunc = lambda shape, wcs: reproject.enmap_from_healpix_interp(
hmap, shape, wcs, rot=None, interpolate=False)
cmap = populate(shape, wcs, ofunc, maxpixy=2000,
maxpixx=2000) #enmap.downgrade(populate(shape,wcs,ofunc),2)
# enmap.write_map("cmap.fits",cmap)
# io.plot_img(cmap,"fmap.png")
cmap = cmap.submap(box)
io.hplot(cmap, "cmap", grid=False)
sys.exit()
pcls = hp.alm2cl(curvedsky.map2alm(cmap, lmax=6000))
hcls = hp.anafast(hmap, lmax=6000)
pells = range(len(pcls))
hells = range(len(hcls))
pl = io.Plotter()
pl.add(pells, (pcls - hcls) / pcls, label='car')
#pl.add(pells,pcls*pells,label='car')
#pl.add(hells,hcls*hells,label='healpix')
pl._ax.set_xlim(200, 4000)
pl._ax.set_ylim(-0.2, 0.1)
pl.done("cls.png")
try:
splits = dm.get_splits(season=season,
patch=apatch,
arrays=[array],
ncomp=3,
srcfree=False)[0, :, :, ...]
cmap = dm.get_coadd(season=season,
patch=apatch,
array=array,
ncomp=3,
srcfree=False)
except:
continue
io.hplot(splits,
os.environ['WORK'] + "/new_mr3f/splits_%s" % fname,
min=-300,
max=600,
grid=True)
# ivars = dm.get_splits_ivar(season=season,patch=apatch,arrays=[array],ncomp=None)[0,:,0,...]
# cmap,_ = noise.get_coadd(splits,ivars,axis=0)
rmap = maps.filter_map(cmap * mask, mfilter)
io.hplot(rmap,
os.environ['WORK'] +
"/new_mr3f/coadd_filtered_%s" % fname,
grid=True)
rmap = maps.filter_map(splits * mask, mfilter)
io.hplot(rmap,
os.environ['WORK'] +
"/new_mr3f/splits_filtered_%s" % fname,
grid=True)
dtype=dm.dtype)
ndown, nfitted, nparams = covtools.noise_average(npower[0, 0],
dfact=(16, 16),
lmin=300,
lmax=8000,
wnoise_annulus=500,
bin_annulus=20,
lknee_guess=3000,
alpha_guess=-4,
method="fft",
radial_fit=True)
#io.plot_img(np.fft.fftshift(np.log10(ndown)),"ndown.png",aspect='auto',lim=[-6,3])
#io.hplot(np.fft.fftshift(np.log10(ndown)),"hndown")
io.hplot(np.fft.fftshift((ndown)), "hndown")
io.plot_img(np.fft.fftshift(ndown / nfitted), "nunred.png", aspect='auto')
nmod = ndown / nfitted
enmap.write_map("anisotropy_template.fits", enmap.samewcs(nmod, npower))
shape, wcs = maps.rect_geometry(width_deg=50.,
height_deg=30,
px_res_arcmin=0.5)
rms = 10.0
lknee = 3000
alpha = -3
n2d = covtools.get_anisotropic_noise(shape, wcs, rms, lknee, alpha)
modlmap = enmap.modlmap(shape, wcs)
bin_edges = np.arange(100, 8000, 100)
binner = stats.bin2D(modlmap, bin_edges)
arrays.s16_01.rms = 40.
arrays.s16_02.freq = 148
arrays.s16_02.fwhm = 1.4
arrays.s16_02.rms = 60.
arrays.s16_03.freq = 93
arrays.s16_03.fwhm = 1.4 * (148. / 93.)
arrays.s16_03.rms = 60.
np.random.seed((1, seed))
ras, decs = catalogs.random_catalog(shape, wcs, Nclusters, edge_avoid_deg=0.)
for qind, qid in enumerate(arrays.keys()):
fwhm = arrays[qid].fwhm
freq = arrays[qid].freq
rms = arrays[qid].rms
amps = get_amps(freq)
srcs = np.stack((decs * putils.degree, ras * putils.degree, amps)).T
szmap = sim_srcs(shape, wcs, srcs, beam=fwhm * putils.arcmin)
kbeam = maps.gauss_beam(modlmap, fwhm)
signal = maps.filter_map(cmb, kbeam) + szmap
# io.hplot(signal,os.environ['WORK'] + '/tiling/signal_%s' % qid)
for i in range(nsplits):
noise = maps.white_noise(shape, wcs, rms, seed=(2, qind, seed, i))
omap = signal + noise
fname = os.environ['WORK'] + '/sim_tiling/%s_split_%d.fits' % (qid, i)
enmap.write_map(fname, omap)
io.hplot(omap, os.environ['WORK'] + '/tiling/total_%s_%d' % (qid, i))
# If debugging, get unfiltered maps and plot Cls
if task==0 and debug_cmb:
t_alm,e_alm,b_alm = solint.get_kmap(channel,seed,lmin,lmax,filtered=False)
tcl = hp.alm2cl(t_alm)
ls = np.arange(tcl.size)
pl = io.Plotter('Cell')
pl.add(ls,tcl/w2)
ls2,nells,nells_P = solint.get_noise_power(channel,beam_deconv=True)
theory = cosmology.default_theory()
pl.add(ls,theory.lCl('TT',ls) + maps.interp(ls2,nells)(ls),color='k')
pl._ax.set_xlim(1,6000)
pl._ax.set_ylim(1e-6,1e3)
pl.done(f'{solenspipe.opath}/tcl.png')
imap = enmap.downgrade(solint.alm2map(np.asarray([t_alm,e_alm,b_alm]),ncomp=3) * maps.binary_mask(mask),2)
for i in range(3): io.hplot(imap[i],f'{solenspipe.opath}/imap_{i}',mask=0)
with bench.show("sim"):
# Get simulated, prepared filtered T, E, B maps, i.e. (1/(C+N) * teb_alm)
t_alm,e_alm,b_alm = solint.get_kmap(channel,seed,lmin,lmax,filtered=True)
# Get the reconstructed kappa map alms and filter it with the normalization
recon_alms = qe.filter_alms(solint.get_mv_kappa(polcomb,t_alm,e_alm,b_alm),maps.interp(Als['L'],Als[polcomb]))
# Subtract a meanfield if necessary
recon_alms = recon_alms - mf_alm
if task==0 and debug_cmb:
rmap = solint.alm2map(recon_alms,ncomp=1)[0] * maps.binary_mask(mask)
io.hplot(rmap,f'{solenspipe.opath}/rmap',mask=0,color='gray')
falms = recon_alms.copy()
mask = sints.get_act_mr3_crosslinked_mask(
mpatch,
version=in_versions[survey],
kind='binary_apod',
season="s16" if survey == 'advact' else None,
array=None,
pad=pad)
print(survey, patch)
# FFT friendliness
Ny, Nx = mask.shape[-2:]
dNy = fft.fft_len(Ny, "above")
dNx = fft.fft_len(Nx, "above")
pny = dNy - Ny
pnx = dNx - Nx
pady1 = pny // 2
pady2 = pny - pady1
padx1 = pnx // 2
padx2 = pnx - padx1
mask = enmap.pad(mask, ((pady1, padx1), (pady2, padx2)))
assert mask.shape[-2] == dNy
assert mask.shape[-1] == dNx
enmap.write_map(out_path + "%s.fits" % patch, mask)
io.hplot(enmap.downgrade(mask, 8), out_path + "%s" % patch)
sN = get_smooth_N(patch)
smoothed = mask_smoothing(mask, sN)
enmap.write_map(out_path + "%s_smoothed.fits" % patch, smoothed)
io.hplot(enmap.downgrade(smoothed, 8),
out_path + "%s_smoothed" % patch)
