def get_cuts(mask, ymap, cmap, dmap, wtmap, ra, dec, arcmin, pix):
mcut = reproject.cutout(mask,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=int(arcmin / pix))
if mcut is None:
return None, None, None, None, None
if np.any(mcut) <= 0:
return None, None, None, None, None
ycut = reproject.cutout(ymap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=int(arcmin / pix))
ccut = reproject.cutout(cmap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=int(arcmin / pix))
dcut = reproject.cutout(dmap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=int(arcmin / pix))
wcut = reproject.cutout(wtmap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=int(arcmin / pix))
weight = wcut.mean()
return mcut, ycut, ccut, dcut, weight
def inpaint_map_const_cov(imap,mask,union_sources_version=None,noise_pix = 20,hole_radius = 3.,plots=False):
"""
Inpaints a map under the assumption of constant 2D Fourier covariance. This uses the average PS
of the full map for the noise model at each source location and thus does not handle inhomogenity.
Pros: no products needed other than map-maker outputs of map
Cons:
imap -- (npol,Ny,Nx)
ivar -- (Ny,Nx)
"""
ras,decs = sints.get_act_mr3f_union_sources(version=union_sources_version)
kmap = enmap.fft(mask*imap,normalize='phys')
gtags = []
gdicts = {}
pcoords = []
for i,(ra,dec) in enumerate(zip(ras,decs)):
sel = reproject.cutout(ivar, ra=np.deg2rad(ra), dec=np.deg2rad(dec), pad=1, corner=False,npix=noise_pix,return_slice=True)
if sel is None: continue
civar = ivar[sel]
if np.any(civar<=0): continue
modrmap = civar.modrmap()
modlmap = civar.modlmap()
res = maps.resolution(civar.shape,civar.wcs)
cimap = imap[sel]
print(ra,dec)
if plots:
for p in range(3): io.plot_img(cimap[p],os.environ['WORK']+"/cimap_%d_%s" % (p,str(i).zfill(2)))
mimap = cimap.copy()
mimap[...,modrmap<np.deg2rad(hole_radius/60.)] = np.nan
for p in range(3): io.plot_img(mimap[p],os.environ['WORK']+"/masked_cimap_%d_%s" % (p,str(i).zfill(2)))
scov = pixcov.scov_from_theory(modlmap,cmb_theory_fn,fn_beam,iau=False)
ncov = pixcov.ncov_from_ivar(civar)
pcov = scov + ncov
gdicts[i] = pixcov.make_geometry(hole_radius=np.deg2rad(hole_radius/60.),n=noise_pix,deproject=True,iau=False,pcov=pcov,res=res)
pcoords.append(np.array((dec,ra)))
gtags.append(i)
if len(gtags)>0:
pcoords = np.stack(pcoords).swapaxes(0,1)
result = pixcov.inpaint(imap,pcoords,deproject=True,iau=False,geometry_tags=gtags,geometry_dicts=gdicts,verbose=True)
if plots:
for i,(ra,dec) in enumerate(zip(ras,decs)):
sel = reproject.cutout(ivar, ra=np.deg2rad(ra), dec=np.deg2rad(dec), pad=1, corner=False,npix=noise_pix,return_slice=True)
if sel is None: continue
civar = ivar[sel]
if np.any(civar<=0): continue
modrmap = civar.modrmap()
modlmap = civar.modlmap()
res = maps.resolution(civar.shape,civar.wcs)
cimap = result[sel]
print("Inpainted ", ra,dec)
if plots:
for p in range(3): io.plot_img(cimap[p],os.environ['WORK']+"/inpainted_cimap_%d_%s" % (p,str(i).zfill(2)))
return result
if random:
box = mask.box()
ramin = min(box[0, 1], box[1, 1])
ramax = max(box[0, 1], box[1, 1])
decmin = min(box[0, 0], box[1, 0])
decmax = max(box[0, 0], box[1, 0])
ra = np.rad2deg(np.random.uniform(ramin, ramax))
dec = np.rad2deg(np.random.uniform(decmin, decmax))
# SNR cut
if sn < 5:
continue
# Reject based on mask cutout
mcut = reproject.cutout(bmask,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=pix)
if mcut is None:
continue
if np.any(mcut) <= 0:
continue
# Stamp cutouts
icut = reproject.cutout(imap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=pix)
ycut = reproject.cutout(ymap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=pix)
div = dm.get_split_ivar(freq, i, ncomp=None)[0]
sras = []
sdecs = []
amps = []
#ras,decs,iamps = np.loadtxt("/scratch/r/rbond/msyriac/data/planck/data/hybrid/planck_hybrid_545_2way_1_map_srcfree_pccs_sub_catalog.txt",unpack=True) # !!!
for k, (ra, dec) in enumerate(zip(ras, decs)):
if np.abs(np.rad2deg(dec)) > decmax: continue
#if iamps[k]>-500: continue # !!!
# if sns[k]<20: continue
# if k<300 or k>400: continue
if evals[k] < 3: continue
stamp = reproject.cutout(imap, ra=ra, dec=dec, pad=1, npix=npix)
if stamp is None: continue
divstamp = reproject.cutout(div, ra=ra, dec=dec, pad=1, npix=npix)
famp, cov, pfit = ptfit.ptsrc_fit(stamp,
dec,
ra,
maps.sigma_from_fwhm(
np.deg2rad(pfwhm / 60.)),
div=divstamp,
ps=ps,
beam=pfwhm,
n2d=None)
# model = pointsrcs.sim_srcs(stamp.shape, stamp.wcs,
# np.array((dec,ra,famp.reshape(-1)[0]))[None],
# maps.sigma_from_fwhm(np.deg2rad(pfwhm/60.)))
array,
ncomp=None,
srcfree=False)[0]
print("Found %s %s %s" % (season, patch, array))
except:
continue
ivars = dm.get_splits_ivar(season, patch, array, ncomp=None)[0]
wcs = ivars.wcs
nsplits = ivars.shape[0]
ids = []
sel_ivars = []
for sid, (ra, dec) in enumerate(zip(ras, decs)):
cutout = reproject.cutout(splits,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
pad=1,
corner=False,
npix=noise_pix)
pcutout = reproject.cutout(psplits,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
pad=1,
corner=False,
npix=noise_pix)
if (cutout is not None):
sel_ivar = reproject.cutout(ivars,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
pad=1,
corner=False,
npix=noise_pix,
def inpaint_map_white(imap,ivar,fn_beam,union_sources_version=None,noise_pix = 40,hole_radius = 6.,plots=False,cache_name=None,verbose=True):
"""
Inpaints a map under the assumption of inhomogenous but white uncorrelated instrument noise.
Pros: no products needed other than map-maker outputs of map and ivar, good inpainting despite crappy
noise model.
Cons: noise model has to be built for each source, so this is actually quite slow.
imap -- (npol,Ny,Nx)
ivar -- (Ny,Nx)
fn_beam -- lambda ells: beam(ells)
cache_name -- a unique string identifying the catalog+map/array/frequency/split combination to/from which the geometries are cached
"""
if cache_name is not None:
cache_name = cache_name + "_catversion_%s" % union_sources_version
try:
ras,decs,gtags,pcoords,gdicts = load_cached_inpaint_geometries(cache_name)
do_geoms = False
if verbose: print("actsims.inpaint: loaded cached geometries for ", cache_name)
except:
if verbose: print("actsims.inpaint: no cached geometries found for ", cache_name, ". Generating and saving...")
do_geoms = True
else:
do_geoms = True
if do_geoms:
ras,decs = sints.get_act_mr3f_union_sources(version=union_sources_version)
cmb_theory_fn = lambda s,l: cosmology.default_theory().lCl(s,l)
gtags = []
gdicts = {}
pcoords = []
for i,(ra,dec) in enumerate(zip(ras,decs)):
sel = reproject.cutout(ivar, ra=np.deg2rad(ra), dec=np.deg2rad(dec), pad=1, corner=False,npix=noise_pix,return_slice=True)
if sel is None: continue
civar = ivar[sel]
if np.any(civar<=0): continue
modrmap = civar.modrmap()
modlmap = civar.modlmap()
res = maps.resolution(civar.shape,civar.wcs)
cimap = imap[sel]
if verbose: print("actsims.inpaint: built noise model for source ",i," / ",len(ras))
scov = pixcov.scov_from_theory(modlmap,cmb_theory_fn,fn_beam,iau=False)
ncov = pixcov.ncov_from_ivar(civar)
pcov = scov + ncov
gdicts[i] = pixcov.make_geometry(hole_radius=np.deg2rad(hole_radius/60.),n=noise_pix,deproject=True,iau=False,pcov=pcov,res=res)
pcoords.append(np.array((dec,ra)))
gtags.append(i)
if len(gtags)>0:
pcoords = np.stack(pcoords).swapaxes(0,1)
if cache_name is not None:
save_cached_inpaint_geometries(cache_name,ras,decs,gtags,pcoords,gdicts)
if verbose: print("actsims.inpaint: cached geometries for ",cache_name)
if len(gtags)>0: result = pixcov.inpaint(imap,pcoords,deproject=True,iau=False,geometry_tags=gtags,geometry_dicts=gdicts,verbose=verbose)
if plots:
for i,(ra,dec) in enumerate(zip(ras,decs)):
sel = reproject.cutout(ivar, ra=np.deg2rad(ra), dec=np.deg2rad(dec), pad=1, corner=False,npix=noise_pix,return_slice=True)
if sel is None: continue
civar = ivar[sel]
if np.any(civar<=0): continue
modrmap = civar.modrmap()
modlmap = civar.modlmap()
res = maps.resolution(civar.shape,civar.wcs)
cimap = imap[sel]
for p in range(3): io.plot_img(cimap[p],os.environ['WORK']+"/cimap_%d_%s" % (p,str(i).zfill(2)))
mimap = cimap.copy()
mimap[...,modrmap<np.deg2rad(hole_radius/60.)] = np.nan
for p in range(3): io.plot_img(mimap[p],os.environ['WORK']+"/masked_cimap_%d_%s" % (p,str(i).zfill(2)))
cimap = result[sel]
for p in range(3): io.plot_img(cimap[p],os.environ['WORK']+"/inpainted_cimap_%d_%s" % (p,str(i).zfill(2)))
return result
array,
ncomp=None,
srcfree=True)[0]
print("Found %s %s %s" % (season, patch, array))
except:
continue
ivars = dm.get_splits_ivar(season, patch, array, ncomp=None)[0]
wcs = ivars.wcs
nsplits = ivars.shape[0]
ids = []
sel_ivars = []
for sid, (ra, dec) in enumerate(zip(ras, decs)):
cutout = reproject.cutout(splits,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
pad=1,
corner=False,
npix=noise_pix)
if (cutout is not None):
sel_ivar = reproject.cutout(ivars,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
pad=1,
corner=False,
npix=noise_pix,
return_slice=True)
cut_ivar = ivars[sel_ivar]
retv = plot_cutout(nsplits,
cutout,
cut_ivar,
skip_plots=True)
import numpy as np
import os, sys
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)
if random:
box = mask.box()
ramin = min(box[0, 1], box[1, 1])
ramax = max(box[0, 1], box[1, 1])
decmin = min(box[0, 0], box[1, 0])
decmax = max(box[0, 0], box[1, 0])
ra = np.rad2deg(np.random.uniform(ramin, ramax))
dec = np.rad2deg(np.random.uniform(decmin, decmax))
if sn < 5:
continue
# if ra>15: continue
mcut = reproject.cutout(bmask,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=pix)
if mcut is None:
continue
if np.any(mcut) <= 0:
continue
ycut = reproject.cutout(ymap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=pix)
ccut = reproject.cutout(cmap,
ra=np.deg2rad(ra),
dec=np.deg2rad(dec),
npix=pix)
cut150 = reproject.cutout(m150,
