def test():
# season = "s15"
# patch = "deep56"
# array = "pa3_f090"
season = "s15"
patch = "boss"
array = "pa3_f150"
dm = sints.ACTmr3()
imap = dm.get_split(season, patch, array, 0, ncomp=None, srcfree=True)
ivar = dm.get_split_ivar(season, patch, array, 0)
fbeam = lambda x: dm.get_beam(x, season, patch, array)
with bench.show("inpaint with plots"):
inpainted = inpaint_map_white(imap, ivar, fbeam, plots=True)
with bench.show("inpaint"):
inpainted = inpaint_map_white(imap, ivar, fbeam, plots=False)
w2 = np.mean(mask**2.)
modlmap = enmap.modlmap(map1.shape,map1.wcs)
binned_power = bin(power/w2/beamf1(modlmap)/beamf2(modlmap),modlmap,bin_edges)
return centers, binned_power
else:
ells,cls = pcalc.get_power_scalarXscalar(map1*mask, map2*mask,ret_dl=False)
return ells,cls/beamf1(ells)/beamf2(ells)
# # ACTxPlanck
# coadded ACT x coadded Planck
patch = args.region
dma = interfaces.ACTmr3(region=mask)
dmp = interfaces.PlanckHybrid(region=mask)
pfreqs = ['030','044','070','100','143','217','353','545']
nfreqs = len(pfreqs)
# we loop over all pairs of Planck x ACT
# combs = []
# for planckfreq in ['030','044','070','100','143','217','353','545']: # no '857'
# for actseason in ['s14','s15']:
# for array in ['pa1_f150', 'pa2_f150', 'pa3_f090', 'pa3_f150']:
# try:
# actbeam = lambda x: dma.get_beam(x, actseason,
# patch, array)
# actbeam(100)
# combs.append((planckfreq,actseason,array))
cols = catalogs.load_fits("AdvACT_S18dn_confirmed_clusters.fits",
['RAdeg', 'DECdeg', 'SNR'])
ras = cols['RAdeg']
decs = cols['DECdeg']
sns = cols['SNR']
# Rough central frequencies
freqs = {"pa3_f090": 97, "pa3_f150": 149}
# Get a region mask
mask = sints.get_act_mr3_crosslinked_mask(region)
bmask = mask.copy()
bmask[bmask < 0.99] = 0
# Map loader
dm = sints.ACTmr3(region=mask, calibrated=True)
modlmap = mask.modlmap()
ells = np.arange(0, modlmap.max())
# 2D beam
kbeam = dm.get_beam(modlmap, "s15", region, array, sanitize=True)
# 1D beam
lbeam = dm.get_beam(ells, "s15", region, array, sanitize=True)
# beam and map files
# Y-maps
bfile = os.environ[
"WORK"] + "/data/depot/tilec/v1.2.0_20200324/map_v1.2.0_%s_%s/tilec_single_tile_%s_comptony_map_v1.2.0_%s_beam.txt" % (
cversion, region, region, cversion)
yfile = os.environ[
"WORK"] + "/data/depot/tilec/v1.2.0_20200324/map_v1.2.0_%s_%s/tilec_single_tile_%s_comptony_map_v1.2.0_%s.fits" % (
cversion, region, region, cversion)
# iras = np.append(iras1,iras2)
# idecs = np.append(idecs1,idecs2)
fname = os.environ[
'WORK'] + "/data/boss/sdss_dr8/redmapper_dr8_public_v6.3_catalog.fits"
cols = catalogs.load_fits(fname, ['RA', 'DEC'])
iras = cols['RA']
idecs = cols['DEC']
mask1 = sints.get_act_mr3_crosslinked_mask(region1)
mask1[mask1 < 0.99] = 0
mask2 = sints.get_act_mr3_crosslinked_mask(region2)
mask2[mask2 < 0.99] = 0
dm1 = sints.ACTmr3(region=mask1, calibrated=True)
dm2 = sints.ACTmr3(region=mask2, calibrated=True)
wt1 = dm1.get_coadd_ivar("s15", region1, "pa2_f150")
wt2 = dm2.get_coadd_ivar("s15", region2, "pa2_f150")
ras1, decs1 = catalogs.select_based_on_mask(iras, idecs, mask1)
ras2, decs2 = catalogs.select_based_on_mask(iras, idecs, mask2)
tdir = '/scratch/r/rbond/msyriac/data/depot/tilec/v1.0.0_rc_20190919'
solution = 'tsz'
yfile1 = tutils.get_generic_fname(tdir, region1, solution, None, cversion)
cfile1 = tutils.get_generic_fname(tdir, region1, solution, 'cib', cversion)
dfile1 = tutils.get_generic_fname(tdir, region1, solution, 'cmb', cversion)
ybfile1 = tutils.get_generic_fname(tdir,
from enlib import bench
from actsims import noise
for season in ['s13', 's14', 's15']:
for apatch in ['boss', 'deep1', 'deep5', 'deep6', 'deep8', 'deep56']:
#for apatch in ['deep1','deep5','deep6','deep8','deep56','boss']:
mask = sints.get_act_mr3_crosslinked_mask(apatch)
ellcen = 5000
ellsig = 1000
modlmap = mask.modlmap()
ells = np.arange(0, modlmap.max())
mfilter = maps.interp(ells,
np.exp(-(ells - ellcen)**2. / 2. /
ellsig**2.))(modlmap)
for array in ['pa1_f150', 'pa2_f150', 'pa3_f150', 'pa3_f090']:
dm = sints.ACTmr3(calibrated=False, region=mask)
fname = "%s_%s_%s" % (season, apatch, array)
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,
# 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 patch in dm.cals[season].keys():
for array in dm.cals[season][patch].keys():
cal = dm.cals[season][patch][array]['cal']
cal_err = dm.cals[season][patch][array]['cal_err']
print(season, patch, array, cal_err * 100. / cal)
"""
import argparse
# Parse command line
parser = argparse.ArgumentParser(description='Do a thing.')
parser.add_argument("version", type=str, help='Region name.')
parser.add_argument("region", type=str, help='Region name.')
parser.add_argument("solution", type=str, help='Solution.')
parser.add_argument("--lmin", type=int, default=80, help="lmin.")
parser.add_argument("--lmax", type=int, default=6000, help="lmin.")
args = parser.parse_args()
save_path = sints.dconfig['tilec']['save_path']
savedir = save_path + args.version + "_" + args.region
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',
iname = "%s%s_%s_%s_split_%d_%d" % (tag, season, patch, array, s, sid)
if not (skip_plots): plot_img(masked, "ivars_masked_%s.png" % iname)
retvars[s, 0,
modrmap < cutradius] = retvars[s, 0,
modrmap >= cutradius].mean()
if not (skip_plots):
plot_img(retvars[s, 0], "ivars_filled_%s.png" % iname)
return retvars
ras, decs = np.loadtxt(
"/home/r/rbond/sigurdkn/project/actpol/maps/mr3f_20190502/cat_bright_tot.txt",
unpack=True,
usecols=[0, 1])
dm = sints.ACTmr3(calibrated=False)
for season in dm.seasons:
for patch in dm.patches:
if patch != apatch: continue
for array in dm.arrays:
if array != "pa3_f090": continue # !!!
try:
splits = dm.get_splits(season,
patch,
array,
ncomp=None,
srcfree=True)[0]
print("Found %s %s %s" % (season, patch, array))
except:
continue
(opath, region))
umap = enmap.read_map("%sdataCoadd_combined_U_s14&15_%s.fits" %
(opath, region))
#oq_noise2d = pow(qmap)
#ou_noise2d = pow(umap)
_, oe_p2d, ob_p2d = get_pol_powers(tmap, qmap, umap, kbeam, w2)
io.power_crop(oe_p2d, Nplot, "oe_noise2d.png", ftrans=True)
#io.power_crop(ob_p2d,Nplot,"ob_noise2d.png",ftrans=True)
dmask = sints.get_act_mr3_crosslinked_mask(region='deep6')
dw2 = np.mean(dmask**2)
dm = sints.ACTmr3(calibrated=True, region=dmask)
imap = dm.get_coadd(season='s13',
array='pa1_f150',
patch='deep6',
srcfree=True)
tmap = imap[0] * dmask
qmap = imap[1] * dmask
umap = imap[2] * dmask
dbeam = tutils.get_kbeam("d6", dmask.modlmap(), sanitize=False)
_, de_p2d, db_p2d = get_pol_powers(tmap, qmap, umap, dbeam, dw2)
io.power_crop(de_p2d, Nplot, "de_noise2d.png", ftrans=True)
dbinner = stats.bin2D(dmask.modlmap(), bin_edges)
cents, de_noise1d = dbinner.bin(de_p2d)
cents, db_noise1d = dbinner.bin(db_p2d)
