def test_project_nn(self):
shape,wcs = enmap.fullsky_geometry(res=np.deg2rad(12/60.),proj='car')
shape2,wcs2 = enmap.fullsky_geometry(res=np.deg2rad(6/60.),proj='car')
shape3,wcs3 = enmap.fullsky_geometry(res=np.deg2rad(24/60.),proj='car')
imap = enmap.ones(shape,wcs)
omap2 = enmap.project(imap,shape2,wcs2,order=0,mode='wrap')
omap3 = enmap.project(imap,shape3,wcs3,order=0,mode='wrap')
assert np.all(np.isclose(omap2,1))
assert np.all(np.isclose(omap3,1))
def build_foreground_var(maps, bsize1=8, bsize2=4, scale=0.5):
"""Given maps[ncomp,ny,nx] that may contain point sources etc., return
an estimate of the foreground variance. This can be used to downweight
high-foreground regions."""
vbox = np.array([[-32, -88], [-31, -90]]) * utils.degree
# Can't use op=np.var here because downgrade does the operation in two steps
v1 = enmap.downgrade(maps**2, bsize1, op=np.mean, inclusive=True)
v1 -= enmap.downgrade(maps, bsize1, op=np.mean, inclusive=True)**2
v2 = enmap.downgrade(v1, bsize2, op=np.median, inclusive=True)
for v in v2:
v[v == 0] = np.median(v[v != 0])
res = np.exp(enmap.project(np.log(v2), maps.shape, maps.wcs, order=1))
res *= scale
return res
def smooth(m):
'''
Local smoothing of input map.
Parameters
----------
m : enmap
Input map
Returns
-------
m_smoothed : enmap
Output map with same shape as input.
'''
m_smoothed = enmap.downgrade(m, 2)
m_smoothed = enmap.project(m_smoothed, m.shape, m.wcs, order=1)
return m_smoothed
pathMap2 = "./output/cmb_map/tilec_pact_cmbksz_v1.2.0/" + "tilec_map.fits"
pathMask = "./output/cmb_map/tilec_pact_cmbksz_v1.2.0/" + "mask_full_foot_gal_ps.fits"
pathDirOut = "./output/cmb_map/pactf150daynight20200228maskgal60r2_minus_tilec_pact_cmbksz/"
if not os.path.exists(pathDirOut):
os.makedirs(pathDirOut)
pathOut = pathDirOut + "diff_map.fits"
# read the maps
map1 = enmap.read_map(pathMap1)[0]
map2 = enmap.read_map(pathMap2)
# reproject to the smaller TileC geometry
map1 = enmap.project(map1,
map2.shape,
map2.wcs,
order=1,
mode='constant',
cval=0.0,
force=False,
prefilter=True,
mask_nan=True,
safe=True)
# take the difference
diffMap = map1 - map2
enmap.write_map(pathOut, diffMap)
# copy the mask
copyfile(pathMask, pathDirOut + "mask_full_foot_gal_ps.fits")
###################################################################################3
# conversion from y to muK at 150 GHz
Tcmb = 2.726 # K
h = 6.63e-34 # SI
kB = 1.38e-23 # SI
# ACT point source mask
pathPSMask = "/global/cscratch1/sd/eschaan/project_ucsc/data/act_ps_mask/source_mask_s16_simonecat_sn5_cross_20171105.fits"
psMask = enmap.read_map(pathPSMask)
#########################################################################
# combine Deep56 and BOSS North maps, on the total ACT footprint
# reproject the D56 and BN maps to the overall ACT footprint
# then crop the map to reduce size
# combine the masks
tilecMaskD56 = enmap.project(tilecMaskD56,
psMask.shape,
psMask.wcs,
order=1,
mode='constant',
cval=0.0,
force=False,
prefilter=True,
mask_nan=True,
safe=True)
tilecMaskBN = enmap.project(tilecMaskBN,
psMask.shape,
psMask.wcs,
order=1,
mode='constant',
cval=0.0,
force=False,
prefilter=True,
mask_nan=True,
safe=True)
tilecMask = tilecMaskD56 + tilecMaskBN
def generate_map(qids, overwrite=False, verbose=True, dg=2, **kwargs):
# Here we compute the real coadd map and simulated maps from the noise covariance and pre-computed fullsky signal
# This function uses actsims "simgen" code
for qid in qids:
if qid in ['boss_d04', 's16_d03', 'boss_04']:
# Here, the simulation is performed for each array, not frequency, to take into account their correlation
# For pa3, there are two frequency bands
if verbose:
print('skip ' + qid +
': the map is generated by running f090 case.')
continue
if '_d0' in qid:
version = 'v6.3.0_calibrated_mask_version_masks_20200723'
else:
version = 'v6.3.0_calibrated_mask_version_padded_v1'
# define qid_array to take into account multi-frequency case
if qid == 'boss_d03':
qid_array = ['boss_d03', 'boss_d04']
elif qid == 's16_d02':
qid_array = ['s16_d02', 's16_d03']
elif qid == 'boss_03':
qid_array = ['boss_03', 'boss_04']
else:
# single frequency case
qid_array = [qid]
# define filename
aobj = {
q: local.init_analysis_params(qid=q, **kwargs)
for q in qid_array
}
# load survey mask
mask = load_survey_mask(qid, dg=1)
# Define an object for sim generation
model, season, array, patch, freq = local.qid_info(qid)
if model == 'dr5':
simobj = simgen.SimGen(version=version, qid=qid_array, model=model)
if model == 'act_mr3':
simobj = simgen.SimGen(version=version, model=model)
# save 1/var map
if not misctools.check_path(aobj[qid_array[0]].fivar,
overwrite=overwrite,
verbose=verbose):
ivars = get_ivar(qid_array, mask)
civars = np.average(ivars, axis=1) # coadd ivar
for qi, q in enumerate(qid_array):
enmap.write_map(aobj[q].fivar, civars[qi, :, :, :])
# loop over realizations
for i in tqdm.tqdm(aobj[qid].rlz):
if misctools.check_path(aobj[qid].fmap['s'][i],
overwrite=overwrite,
verbose=verbose):
continue
# maps will have signal ('s') and noise ('n') and each has [Nfreq, NTPol, Coord1, Coord2]
maps = {}
# real data
if i == 0:
# take coadd data
maps['s'] = coadd_real_data(qid_array, mask)
# SZ map subtraction
shape, wcs = maps['s'][0, 0, :, :].shape, maps['s'].wcs
if q in [
'boss_d03', 's16_d02', 'boss_03'
]: # need both freqs for combined data array of two freqs
sz_map_090 = enmap.project(
enmap.read_map(
'data_local/input/S18d_202006_confirmed_model_f090.fits'
), shape, wcs) / local.Tcmb
sz_map_150 = enmap.project(
enmap.read_map(
'data_local/input/S18d_202006_confirmed_model_f150.fits'
), shape, wcs) / local.Tcmb
maps['s'][0, 0, :, :] -= sz_map_090
maps['s'][1, 0, :, :] -= sz_map_150
else:
sz_map = enmap.project(
enmap.read_map(
'data_local/input/S18d_202006_confirmed_model_f150.fits'
), shape, wcs) / local.Tcmb
maps['s'][0, 0, :, :] -= sz_map
# simulation
else:
maps['s'], maps['n'], ivars = simobj.get_sim(season,
patch,
array,
sim_num=i,
set_idx=0)
# coadd with civars weight implicitly multiplied
civars = np.average(ivars, axis=1)
civars[civars == 0] = np.inf
maps['s'] = mask[None, None, :, :] * np.average(
maps['s'] * ivars, axis=1) / civars / local.Tcmb
maps['n'] = mask[None, None, :, :] * np.average(
maps['n'] * ivars, axis=1) / civars / local.Tcmb
# downgrade
maps['s'] = enmap.downgrade(maps['s'], dg)
maps['n'] = enmap.downgrade(maps['n'], dg)
# save signal and noise to files
for s in ['s', 'n']:
if s == 'n' and i == 0:
continue # real data is saved to a signal file
for qi, q in enumerate(qid_array):
enmap.write_map(aobj[q].fmap[s][i], maps[s][qi, :, :, :])
ratio[hitmap == 0] = np.nan
plot = enplot.plot(hitmap, **plot_opts)
enplot.write(opj(mapdir, 'hits'), plot)
plot = enplot.plot(cutmap, **plot_opts)
enplot.write(opj(mapdir, 'cuts'), plot)
plot = enplot.plot(ratio, min=0, max=args.range, **plot_opts)
enplot.write(opj(mapdir, 'ratio'), plot)
if args.signal_map is not None:
ratio[hitmap == 0] = 0.
ratio_sm = enmap.downgrade(ratio, 32 / float(args.downgrade))
ratio_sm = enmap.project(ratio_sm, ratio.shape, ratio.wcs, order=1)
contours = '0.003, 0.01'
contour_layer = enplot.plot(ratio_sm,
contours=contours,
no_image=True,
contour_width=2,
contour_color='hotcold',
min=0,
max=args.range,
**plot_opts)
enplot.write(opj(mapdir, 'ratio_contour'), contour_layer)
signal_layer = enplot.plot(signal,
layers=True,
min=-300,
"""
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)
def project_mask(mask, shape, wcs, fname=None):
sim_mask = enmap.project(mask, shape, wcs, order=1)
if fname != None:
enmap.write_fits(fname, sim_mask, extra={})
pathPSMask = "/global/cscratch1/sd/eschaan/project_ksz_act_planck/data/act_ps_mask/source_mask_s16_simonecat_sn5_cross_20171105.fits"
psMask = enmap.read_map(pathPSMask)
# In[16]:
psMask = (psMask > 0.95).astype(np.float)
# In[17]:
# project to TileC footprint
psMask = enmap.project(psMask,
footMask.shape,
footMask.wcs,
order=1,
mode='constant',
cval=0.0,
force=False,
prefilter=True,
mask_nan=True,
safe=True)
# In[18]:
# plot=enplot.plot(psMask, downgrade=1, min=0., max=1.)
# enplot.show(plot)
# # Full mask: footprint + Galactic + point sources
# In[23]:
fullMask = footMask * planckMask * psMask
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
map1_srfree = map_path + 's17_cmb_pa4_f150_nohwp_night_1pass_2way_set1_map_srcfree.fits'
ivar1 = map_path + 's17_cmb_pa4_f150_nohwp_night_1pass_2way_set1_ivar.fits'
map1 = map_path + 's17_cmb_pa4_f150_nohwp_night_1pass_2way_set1_map.fits'
map1sz = map_path + 's17_cmb_pa4_f150_nohwp_night_1pass_2way_set1_map_src_sz.fits'
imap0 = enmap.read_map(map0_srfree, )
imap1 = enmap.read_map(map1_srfree, )
ivar0 = enmap.read_map(ivar0, )
ivar1 = enmap.read_map(ivar1, )
#################################################################################################################################
#create pixel map
shape = imap0.shape
wcs = imap0.wcs
mask = enmap.project(mask, shape, wcs, order=1)
pmap = enmap.pixsizemap(shape, wcs)
map_list = [imap0, imap1]
ivar_list = [ivar0, ivar1]
pol_a = 0
pol_b = 0
#calculate the flattened power spectrum used to produce simulations
cls = simgen.get_power(map_list, ivar_list, pol_a, pol_b, mask, N=5)
np.savetxt(f'/global/homes/j/jia_qu/sim_gen/flattened{pol_a}{pol_b}.txt', cls)
plot_cl = False
if plot_cl:
ls = np.arange(cls.size)
print(cls)
plt.plot(ls, cls, label='cls')
