def plot(fields, filename, lbin, Cb, cov=None, theoryfile=None):
lmax = int(numpy.max(lbin))
if theoryfile != None:
ps = powspec.read_spectrum(theoryfile)[:3, :3]
clth = {}
clth['TT'] = ps[0, 0, :][:lmax + 100]
clth['EE'] = ps[1, 1, :][:lmax + 100]
clth['BB'] = ps[2, 2, :][:lmax + 100]
clth['TE'] = ps[0, 1, :][:lmax + 100]
clth['TB'] = ps[0, 1, :][:lmax + 100] * 0
clth['EB'] = ps[0, 1, :][:lmax + 100] * 0
lth = numpy.arange(len(clth['TT']))
fth = lth * (lth + 1) / (2 * numpy.pi)
for l1 in fields:
if theoryfile != None:
pylab.plot(lth, clth[l1] * fth, color='gray')
if cov != None:
pylab.errorbar(lbin, Cb[l1], numpy.sqrt(cov[l1 + l1]), fmt='.')
else:
pylab.errorbar(lbin, Cb[l1], fmt='.')
pylab.xlabel(r'$\ell$', fontsize=22)
pylab.ylabel(r'$\ell(\ell+1) C^{%s}_\ell/(2 \pi)$' % l1, fontsize=22)
pylab.savefig(filename + '_%s.png' % l1)
pylab.clf()
pylab.close()
class dprint:
def __init__(self, desc):
self.desc = desc
def __enter__(self):
self.t1 = time.time()
def __exit__(self, type, value, traceback):
sys.stderr.write("%6.2f %6.3f %6.3f %s\n" %
(time.time() - self.t1, memory.current() / 1024.**3,
memory.max() / 1024.**3, self.desc))
with dprint("spec"):
ps = powspec.read_spectrum(args.powspec)[:ncomp, :ncomp]
# Construct our output coordinates, a zea system. My standard
# constructor doesn't handle pole crossing, so do it manually.
with dprint("construct omap"):
R = args.radius * deg2rad
res = args.res * min2rad
wo = wcsutils.WCS(naxis=2)
wo.wcs.ctype = ["RA---ZEA", "DEC--ZEA"]
wo.wcs.crval = [0, 90]
wo.wcs.cdelt = [res / deg2rad, res / deg2rad]
wo.wcs.crpix = [1, 1]
x, y = wo.wcs_world2pix(0, 90 - R / deg2rad, 1)
y = int(np.ceil(y))
n = 2 * y - 1
wo.wcs.crpix = [y, y]
pitas_lib = pitas.power.PITAS(mcm_identifier, taper, taper, bin_edges, lmax,
transfer, overwrite)
binner = pitas_lib.binner
lbin = pitas_lib.bin_center
# load sims (unlensed 15x15 sq deg)
tmap, qmap, umap = enmap.read_fits(resource_path('test_tqu2.fits'))
tmap -= np.mean(tmap)
qmap -= np.mean(qmap)
umap -= np.mean(umap)
tmap *= taper
qmap *= taper
umap *= taper
# load theory
theo = powspec.read_spectrum(resource_path('cosmo2017_10K_acc3_scalCls.dat'))
cltt_th = theo[0, 0, 2:]
clee_th = theo[1, 1, 2:]
clte_th = theo[2, 2, 2:]
cleb_th = clbb_th = np.zeros(cltt_th.shape)
l_th = np.arange(len(cltt_th)) + 2.
# bin theory (cls)
lbin, clttbin_th = pitas_lib.bin_theory_scalarxscalar(l_th, cltt_th)
lbin, cltebin_th = pitas_lib.bin_theory_scalarxvector(l_th, clte_th)
lbin, cleebin_th, clebbin_th, clbbbin_th = pitas_lib.bin_theory_pureeb(
l_th, clee_th, cleb_th, clbb_th)
# bin theory (dls)
lbin, dlttbin_th = pitas_lib.bin_theory_scalarxscalar(l_th,
cltt_th,
raise NotImplementedError("Beam not supported for lensed sims yet")
if args.geometry == "curved":
m, = lensing.rand_map(shape,
wcs,
ps,
lmax=lmax,
maplmax=maplmax,
seed=(seed, i),
verbose=verbose,
dtype=dtype)
else:
maps = enmap.rand_map((shape[0] + 1, ) + shape[1:], wcs, ps)
phi, unlensed = maps[0], maps[1:]
m = lensing.lens_map_flat(unlensed, phi)
else:
ps = powspec.read_spectrum(args.powspec).astype(type)
beam = make_beam(ps.shape[-1], args.beam * utils.arcmin * utils.fwhm)
ps *= beam
if args.geometry == "curved":
m = curvedsky.rand_map(shape,
wcs,
ps,
lmax=lmax,
seed=(seed, i),
method=args.method,
direct=args.direct,
dtype=dtype,
verbose=verbose)
else:
m = enmap.rand_map(shape, wcs, ps)
ncomp = 1
comm = mpi.COMM_WORLD
signals = args.signals.split(",")
verbosity = args.verbose - args.quiet
highpass_alpha = 3
utils.mkdir(args.odir)
debug_tile = None if args.debug_tile is None else [int(w) for w in args.debug_tile.split(",")]
# Get the set of bounding boxes, after normalizing them
boxes = np.sort(np.array([d.box for d in mapinfo.datasets]),-2)
# Read the cmb power spectrum, which is an effective noise
# component. T-only
cl_path = os.path.join(os.path.dirname(args.config),config.cl_background)
cl_bg = powspec.read_spectrum(cl_path)[0,0]
# Read our mask. High-resolution and per-dataset masks would be handled otherwise.
# This approach only works for low-resolution masks
if config.mask_mode == "none": mask = None
elif config.mask_mode == "lowres":
mask_path = os.path.join(os.path.dirname(args.config), config.mask)
mask = enmap.read_map(mask_path).preflat[0]
else: raise ValueError("Unrecognized mask mode '%s'" % config.mask_mode)
def overlaps_any(box, refboxes):
box = np.sort(box, 0)
rdec, rra = utils.moveaxis(refboxes - box[0,:], 2,0)
wdec, wra = box[1] - box[0]
rra -= np.floor(rra[:,0,None]/(2*np.pi)+0.5)*(2*np.pi)
for i in range(-1,2):
import numpy
import pylab
import spec_utils
print "Reading dict file"
p = dict_utils.flipperDict()
p.read_from_file(sys.argv[1])
nSims = p['nSims']
lmax = p['lmax']
binsize = p['binsize']
nbin = lmax / binsize - 1
fields = ['TT', 'EE', 'BB', 'TE', 'EB', 'TB']
ps = powspec.read_spectrum(p['theoryPS'])[:3, :3]
clth = {}
clth['TT'] = ps[0, 0, :][:lmax + 100]
clth['EE'] = ps[1, 1, :][:lmax + 100]
clth['BB'] = ps[2, 2, :][:lmax + 100]
clth['TE'] = ps[0, 1, :][:lmax + 100]
clth['TB'] = ps[0, 1, :][:lmax + 100] * 0
clth['EB'] = ps[0, 1, :][:lmax + 100] * 0
lth = numpy.arange(len(clth['TT']))
lb, Cb = spec_utils.binCl(lth, clth, nbin, binsize, fields)
print lb.shape
for l1 in fields:
cl_all = []
def get_scans(area, signal, bore, dets, noise, seed=0, real=None, noise_override=None):
scans = []
# Get real scan information if necessary
L.debug("real")
if real:
real_scans = []
filedb.init()
db = filedb.data
ids = fileb.scans[real].ids
for id in ids:
try:
real_scans.append(actscan.ACTScan(db[id]))
except errors.DataMissing as e:
L.debug("Skipped %s (%s)" % (id, e.message))
# Dets
L.debug("dets")
sim_dets = []
toks = dets.split(":")
if toks[0] == "scattered":
ngroup, nper, rad = int(toks[1]), int(toks[2]), float(toks[3])
sim_dets = [scansim.dets_scattered(ngroup, nper,rad=rad*np.pi/180/60)]
margin = rad*np.pi/180/60
elif toks[0] == "real":
ndet = int(toks[1])
dslice = slice(0,ndet) if ndet > 0 else slice(None)
sim_dets = [bunch.Bunch(comps=s.comps[dslice], offsets=s.offsets[dslice]) for s in real_scans]
margin = np.max([np.sum(s.offsets**2,1)**0.5 for s in sim_dets])
else: raise ValueError
# Boresight. Determines our number of scans
L.debug("bore")
sim_bore = []
toks = bore.split(":")
if toks[0] == "grid":
nscan, density, short = int(toks[1]), float(toks[2]), float(toks[3])
for i in range(nscan):
tbox = shorten(area.box(),i%2,short)
sim_bore.append(scansim.scan_grid(tbox, density*np.pi/180/60, dir=i, margin=margin))
elif toks[0] == "ces":
nscan = int(toks[1])
azs = [float(w)*utils.degree for w in toks[2].split(",")]
els = [float(w)*utils.degree for w in toks[3].split(",")]
mjd0 = float(toks[4])
dur = float(toks[5])
azrate= float(toks[6]) if len(toks) > 6 else 1.5*utils.degree
srate = float(toks[7]) if len(toks) > 7 else 400
nsamp = utils.nint(dur*srate)
for i in range(nscan):
mjd = mjd0 + dur*(i//(2*len(els)))/(24*3600)
el = els[(i//2)%len(els)]
az1, az2 = azs
if i%2 == 1: az1, az2 = -az2, -az1
box = np.array([[az1,el],[az2,el]])
sim_bore.append(scansim.scan_ceslike(nsamp, box, mjd0=mjd, srate=srate, azrate=azrate))
elif toks[0] == "real":
sim_bore = [bunch.Bunch(boresight=s.boresight, hwp_phase=s.hwp_phase, sys=s.sys, site=s.site, mjd0=s.mjd0) for s in real_scans]
else: raise ValueError
nsim = len(sim_bore)
# Make one det info per scan
sim_dets = sim_dets*(nsim/len(sim_dets))+sim_dets[:nsim%len(sim_dets)]
# Noise
L.debug("noise")
sim_nmat = []
toks = noise.split(":")
nonoise = False
if toks[0] == "1/f":
sigma, alpha, fknee = [float(v) for v in toks[1:4]]
nonoise = sigma < 0
for i in range(nsim):
sim_nmat.append(scansim.oneoverf_noise(sim_dets[i].comps.shape[0], sim_bore[i].boresight.shape[0], sigma=np.abs(sigma), alpha=alpha, fknee=fknee))
elif toks[0] == "detcorr":
sigma, alpha, fknee = [float(v) for v in toks[1:4]]
nonoise = sigma < 0
for i in range(nsim):
sim_nmat.append(scansim.oneoverf_detcorr_noise(sim_dets[i].comps.shape[0], sim_bore[i].boresight.shape[0], sigma=np.abs(sigma), alpha=alpha, fknee=fknee))
elif toks[0] == "real":
scale = 1.0 if len(toks) < 2 else float(toks[1])
for i,s in enumerate(real_scans):
ndet = len(sim_dets[i].offsets)
nmat = s.noise[:ndet]*scale**-2
sim_nmat.append(nmat)
else: raise ValueError
noise_scale = not nonoise if noise_override is None else noise_override
sim_nmat = sim_nmat*(nsim/len(sim_nmat))+sim_nmat[:nsim%len(sim_nmat)]
# Signal
L.debug("signal")
toks = signal.split(":")
if toks[0] == "none":
for i in range(nsim):
scans.append(scansim.SimPlain(sim_bore[i], sim_dets[i], sim_nmat[i], seed=seed+i, noise_scale=noise_scale))
elif toks[0] == "ptsrc":
# This one always operates in the same coordinates as
nsrc, amp, fwhm = int(toks[1]), float(toks[2]), float(toks[3])
np.random.seed(seed)
sim_srcs = scansim.rand_srcs(area.box(), nsrc, amp, abs(fwhm)*np.pi/180/60, rand_fwhm=fwhm<0)
for i in range(nsim):
scans.append(scansim.SimSrcs(sim_bore[i], sim_dets[i], sim_srcs, sim_nmat[i], seed=seed+i, noise_scale=noise_scale))
elif toks[0] == "vsrc":
# Create a single variable source
ra, dec, fwhm = float(toks[1])*np.pi/180, float(toks[2])*np.pi/180, float(toks[3])*np.pi/180/60
amps = [float(t) for t in toks[4].split(",")]
for i in range(nsim):
sim_srcs = bunch.Bunch(pos=np.array([[dec,ra]]),amps=np.array([[amps[i],0,0,0]]), beam=np.array([fwhm/(8*np.log(2)**0.5)]))
scans.append(scansim.SimSrcs(sim_bore[i], sim_dets[i], sim_srcs, sim_nmat[i], seed=seed+i, noise_scale=noise_scale, nsigma=20))
elif toks[0] == "cmb":
np.random.seed(seed)
ps = powspec.read_spectrum(toks[1])
sim_map = enmap.rand_map(area.shape, area.wcs, ps)
for i in range(nsim):
scans.append(scansim.SimMap(sim_bore[i], sim_dets[i], sim_map, sim_nmat[i], seed=seed+i, noise_scale=noise_scale))
else: raise ValueError
return scans
try:
os.makedirs(specDir)
except:
pass
try:
os.makedirs(plotDir)
except:
pass
minfo = sharp.map_info_healpix(nside)
ainfo = sharp.alm_info(lmax)
sht = sharp.sht(minfo, ainfo)
# Initialize alm but should not be needed ####################
ps = powspec.read_spectrum(p['theoryPS'])[:ncomp, :ncomp]
alm = curvedsky.rand_alm(ps, ainfo)
##########################################################
mask = healpy.read_map(p['maskfile'])
fields = ['TT', 'EE', 'BB', 'TE', 'EB', 'TB']
alm_all = []
for j in range(nd):
maps = healpy.read_map(patchDir + '/map_%d.fits' % j, field=[0, 1, 2])
T = maps[0] * mask
Q = maps[1] * mask
U = maps[2] * mask
sht.map2alm(T, alm[0])
sht.map2alm((Q, U), alm[1:], spin=2)
# Kill extreme values
L.info("Removing extreme outliers above 1e6")
map = np.minimum(1e6, np.maximum(-1e6, map))
nmin = 50
#cut = 0x100
#map = map[:,h/2-cut:h/2+cut,w/2-cut:w/2+cut]
#inoise = inoise[:,:,h/2-cut:h/2+cut,w/2-cut:w/2+cut]
#map = map[:,250:750,700:1700]
#inoise = inoise[:,:,250:750,700:1700]
L.info("Writing preprocessed map")
enmap.write_map(args.odir + "/map.fits", map[0])
L.info("Reading spectrum " + args.powspec)
ps_cmb_tmp = powspec.read_spectrum(args.powspec)[:1, :1, :]
# Extend to max l
lmax = np.max(np.sum(map.lmap()**2, 0)**0.5).astype(int) + 1
lmax_tmp = min(ps_cmb_tmp.shape[-1], lmax)
ps_cmb = np.zeros([1, 1, lmax])
ps_cmb[:, :, :lmax_tmp] = ps_cmb_tmp[:, :, :lmax_tmp]
ps_cmb[:, :, lmax_tmp:] = ps_cmb_tmp[:, :, -1]
sigma = args.beam * np.pi / 180 / 60 / (8 * np.log(2))
l = np.arange(ps_cmb.shape[-1])
ps_src = np.exp(-l * (l + 1) * sigma**2)[None, None, :] * (args.srcrms *
np.pi / 180 / 60)**2
L.info("Setting up signal and noise matrices")
S = enmap.spec2flat(map.shape, map.wcs, ps_cmb, 1.0)
iP = enmap.spec2flat(map.shape, map.wcs, ps_src, -1.0)
def get_scans(area, signal, bore, dets, noise, seed=0, real=None, noise_override=None):
scans = []
# Get real scan information if necessary
L.debug("real")
if real:
real_scans = []
filedb.init()
db = filedb.data
ids = fileb.scans[real].ids
for id in ids:
try:
real_scans.append(actscan.ACTScan(db[id]))
except errors.DataMissing as e:
L.debug("Skipped %s (%s)" % (id, str(e)))
# Dets
L.debug("dets")
sim_dets = []
toks = dets.split(":")
if toks[0] == "scattered":
ngroup, nper, rad = int(toks[1]), int(toks[2]), float(toks[3])
sim_dets = [scansim.dets_scattered(ngroup, nper,rad=rad*np.pi/180/60)]
margin = rad*np.pi/180/60
elif toks[0] == "real":
ndet = int(toks[1])
dslice = slice(0,ndet) if ndet > 0 else slice(None)
sim_dets = [bunch.Bunch(comps=s.comps[dslice], offsets=s.offsets[dslice]) for s in real_scans]
margin = np.max([np.sum(s.offsets**2,1)**0.5 for s in sim_dets])
else: raise ValueError
# Boresight. Determines our number of scans
L.debug("bore")
sim_bore = []
toks = bore.split(":")
if toks[0] == "grid":
nscan, density, short = int(toks[1]), float(toks[2]), float(toks[3])
for i in range(nscan):
tbox = shorten(area.box(),i%2,short)
sim_bore.append(scansim.scan_grid(tbox, density*np.pi/180/60, dir=i, margin=margin))
elif toks[0] == "ces":
nscan = int(toks[1])
azs = [float(w)*utils.degree for w in toks[2].split(",")]
els = [float(w)*utils.degree for w in toks[3].split(",")]
mjd0 = float(toks[4])
dur = float(toks[5])
azrate= float(toks[6]) if len(toks) > 6 else 1.5*utils.degree
srate = float(toks[7]) if len(toks) > 7 else 400
nsamp = utils.nint(dur*srate)
for i in range(nscan):
mjd = mjd0 + dur*(i//(2*len(els)))/(24*3600)
el = els[(i//2)%len(els)]
az1, az2 = azs
if i%2 == 1: az1, az2 = -az2, -az1
box = np.array([[az1,el],[az2,el]])
sim_bore.append(scansim.scan_ceslike(nsamp, box, mjd0=mjd, srate=srate, azrate=azrate))
elif toks[0] == "real":
sim_bore = [bunch.Bunch(boresight=s.boresight, hwp_phase=s.hwp_phase, sys=s.sys, site=s.site, mjd0=s.mjd0) for s in real_scans]
else: raise ValueError
nsim = len(sim_bore)
# Make one det info per scan
sim_dets = sim_dets*(nsim/len(sim_dets))+sim_dets[:nsim%len(sim_dets)]
# Noise
L.debug("noise")
sim_nmat = []
toks = noise.split(":")
nonoise = False
if toks[0] == "1/f":
sigma, alpha, fknee = [float(v) for v in toks[1:4]]
nonoise = sigma < 0
for i in range(nsim):
sim_nmat.append(scansim.oneoverf_noise(sim_dets[i].comps.shape[0], sim_bore[i].boresight.shape[0], sigma=np.abs(sigma), alpha=alpha, fknee=fknee))
elif toks[0] == "detcorr":
sigma, alpha, fknee = [float(v) for v in toks[1:4]]
nonoise = sigma < 0
for i in range(nsim):
sim_nmat.append(scansim.oneoverf_detcorr_noise(sim_dets[i].comps.shape[0], sim_bore[i].boresight.shape[0], sigma=np.abs(sigma), alpha=alpha, fknee=fknee))
elif toks[0] == "real":
scale = 1.0 if len(toks) < 2 else float(toks[1])
for i,s in enumerate(real_scans):
ndet = len(sim_dets[i].offsets)
nmat = s.noise[:ndet]*scale**-2
sim_nmat.append(nmat)
else: raise ValueError
noise_scale = not nonoise if noise_override is None else noise_override
sim_nmat = sim_nmat*(nsim/len(sim_nmat))+sim_nmat[:nsim%len(sim_nmat)]
# Signal
L.debug("signal")
toks = signal.split(":")
if toks[0] == "none":
for i in range(nsim):
scans.append(scansim.SimPlain(sim_bore[i], sim_dets[i], sim_nmat[i], seed=seed+i, noise_scale=noise_scale))
elif toks[0] == "ptsrc":
# This one always operates in the same coordinates as
nsrc, amp, fwhm = int(toks[1]), float(toks[2]), float(toks[3])
np.random.seed(seed)
sim_srcs = scansim.rand_srcs(area.box(), nsrc, amp, abs(fwhm)*np.pi/180/60, rand_fwhm=fwhm<0)
for i in range(nsim):
scans.append(scansim.SimSrcs(sim_bore[i], sim_dets[i], sim_srcs, sim_nmat[i], seed=seed+i, noise_scale=noise_scale))
elif toks[0] == "vsrc":
# Create a single variable source
ra, dec, fwhm = float(toks[1])*np.pi/180, float(toks[2])*np.pi/180, float(toks[3])*np.pi/180/60
amps = [float(t) for t in toks[4].split(",")]
for i in range(nsim):
sim_srcs = bunch.Bunch(pos=np.array([[dec,ra]]),amps=np.array([[amps[i],0,0,0]]), beam=np.array([fwhm/(8*np.log(2)**0.5)]))
scans.append(scansim.SimSrcs(sim_bore[i], sim_dets[i], sim_srcs, sim_nmat[i], seed=seed+i, noise_scale=noise_scale, nsigma=20))
elif toks[0] == "cmb":
np.random.seed(seed)
ps = powspec.read_spectrum(toks[1])
sim_map = enmap.rand_map(area.shape, area.wcs, ps)
for i in range(nsim):
scans.append(scansim.SimMap(sim_bore[i], sim_dets[i], sim_map, sim_nmat[i], seed=seed+i, noise_scale=noise_scale))
else: raise ValueError
return scans
spectra.
"""
import os, sys
from enlib import enmap, powspec, utils
from orphics.theory.cosmology import Cosmology
import orphics.tools.io as io
import numpy as np
from scipy.interpolate import interp1d
import orphics.tools.stats as stats
out_dir = os.environ['WWW']
ps = powspec.read_spectrum("data/cl_lensed.dat")
print((ps.shape))
TCMB = 2.7255e6
cc = Cosmology(lmax=3000, pickling=True)
ells = np.arange(2, 3000, 1)
cctt = cc.theory.lCl('TT', ells) * TCMB**2.
ccte = cc.theory.lCl('TE', ells) * TCMB**2.
ccee = cc.theory.lCl('EE', ells) * TCMB**2.
ccbb = cc.theory.lCl('BB', ells) * TCMB**2.
enells = np.asarray(list(range(ps.shape[2])))[2:]
entt = ps[0, 0, 2:]
ente = ps[0, 1, 2:]
enet = ps[1, 0, 2:]
def apod_butter(x, r):
return (1 + (x / r)**-4)**-1
def apod_cos(x, r):
return (1 - np.cos(np.min(1, nx / r) * np.pi)) / 2
# Read our inputs
freqs = parse_floats(args.freqs)
maps = read_maps(args.maps, len(freqs))
ncomp = maps.shape[-3]
nfreq = maps.shape[-4]
noise = read_noise(args.noise, maps.shape, maps.wcs, len(freqs))
ps = powspec.read_spectrum(args.powspec, expand="diag")[:ncomp, :ncomp]
poss = np.loadtxt(args.posfile)[:, :2] / r2c
R = args.radius / r2c / 60
beam_fiducial = 1.5 / r2b
beam_range = [0.8 / r2b, 3.0 / r2b]
beam_max_asym = 2
apod_rad = R / 10
# We will cut out small mini-maps around each source candadate and
# sample the CMB and source parameters jointly. But some candiates
# are so near each other that they aren't independent. These must
# be grouped into groups.
def build_groups(poss):
def dist(a, b):
return np.sum((poss[a] - poss[b])**2)**0.5 * 180 * 60 / np.pi
parser.add_argument("-s", "--sigma", type=float, default=50)
parser.add_argument("-n", "--nsamp", type=int, default=4)
args = parser.parse_args()
def sim_scan_fast(imap, sigma, rad, n):
mask = np.zeros(imap.shape[-2:])
mask[rad:-rad,rad:-rad] = 1
w = np.maximum(1 - ndimage.distance_transform_edt(1-mask)/rad,0)**3
w = w[None,:,:]*np.array([1,0.5,0.5])[:,None,None]*sigma**-2*n
w = enmap.samewcs(w, imap)
m = imap + np.random.standard_normal(imap.shape)*w**-0.5
m[~np.isfinite(m)] = 0
return m, w
template = enmap.read_map(args.template)[:,::2,::2]
ps = powspec.read_spectrum(args.powspec, expand="diag")
sim = enmap.rand_map(template.shape, template.wcs, ps)
# Simulate noisy data
m, w = sim_scan_fast(sim, args.sigma, 128, 1)
ncomp = m.shape[0]
iN = enmap.zeros((ncomp,ncomp)+w.shape[-2:])
for i in range(ncomp): iN[i,i] = w[i]
iS = enmap.spec2flat(m.shape, m.wcs, ps, -1)
# Draw samples
sampler = gibbs.FieldSampler(iS, iN, m)
res = enmap.zeros((1+args.nsamp,)+m.shape,m.wcs)
res[0] = m
for i in range(args.nsamp):
phi, fphi = kappa_to_phi(kappaMap, modlmap, return_fphi=True)
io.quickPlot2d(phi, out_dir + "phi.png")
alpha_pix = enmap.grad_pixf(fphi)
# alpha_pix2 = enmap.grad_pix(phi)
print((alpha_pix.shape))
# print alpha_pix2.shape
io.quickPlot2d(alpha_pix[0], out_dir + "alpha_pixx.png")
io.quickPlot2d(alpha_pix[1], out_dir + "alpha_pixy.png")
io.quickPlot2d(np.sum(alpha_pix**2, 0)**0.5, out_dir + "alpha_pix.png")
# io.quickPlot2d(alpha_pix2[0],out_dir+"alpha_pixx2.png")
# io.quickPlot2d(alpha_pix2[1],out_dir+"alpha_pixy2.png")
TCMB = 2.7255e6
ps = powspec.read_spectrum("../alhazen/data/cl_lensinput.dat")
cmb_map = old_div(enmap.rand_map(shape, wcs, ps), TCMB)
lensed = lensing.lens_map_flat_pix(cmb_map, alpha_pix, order=5)
#lensed = lensing.lens_map_flat(cmb_map, phi)
io.quickPlot2d(cmb_map, out_dir + "unlensed.png")
io.quickPlot2d(lensed, out_dir + "lensed.png")
io.quickPlot2d(lensed - cmb_map, out_dir + "diff.png")
alpha = enmap.gradf(fphi)
io.quickPlot2d(alpha[0], out_dir + "alphax.png")
io.quickPlot2d(alpha[1], out_dir + "alphay.png")
io.quickPlot2d(np.sum(alpha**2, 0)**0.5, out_dir + "alpha.png")
kappa_inverted = -0.5 * enmap.div(alpha, normalize=False)
io.quickPlot2d(kappa_inverted, out_dir + "kappainv.png")
shapeDown, wcsDown = enmap.geometry(pos=[[-hwidth*arcmin,-hwidth*arcmin],[hwidth*arcmin,hwidth*arcmin]], res=pxDown*arcmin, proj="car")
thetaMap = enmap.posmap(shape, wcs)
thetaMap = np.sum(thetaMap**2,0)**0.5
thetaMapDown = enmap.posmap(shapeDown, wcsDown)
thetaMapDown = np.sum(thetaMapDown**2,0)**0.5
comL = cc.results.comoving_radial_distance(zL)*cc.h
comS = cc.results.comoving_radial_distance(sourceZ)*cc.h
winAtLens = (comS-comL)/comS
kappaMap,r500 = NFWkappa(cc,massOverh,concentration,zL,thetaMap*180.*60./np.pi,winAtLens,overdensity=overdensity,critical=critical,atClusterZ=atClusterZ)
# === CMB POWER SPECTRUM ===
ps = powspec.read_spectrum("data/cl_lensinput.dat")
# === DEFLECTION MAP ===
a = alphaMaker(thetaMap)
alpha = a.kappaToAlpha(kappaMap,test=False)
pos = thetaMap.posmap() + alpha
pix = thetaMap.sky2pix(pos, safe=False)
gradfit = 2.*np.pi/180./60.
from scipy.ndimage.interpolation import rotate
N = 100
avgrot = 0.
for i in range(N):
def make_beam(nl, bsize):
l = np.arange(nl)
return np.exp(-l*(l+1)*bsize**2)
for i in range(comm.rank, args.nsim, comm.size):
if args.lensed:
ps = powspec.read_camb_full_lens(args.powspec).astype(dtype)
if args.beam:
raise NotImplementedError("Beam not supported for lensed sims yet")
if args.geometry == "curved":
m, = lensing.rand_map(shape, wcs, ps, lmax=lmax, maplmax=maplmax, seed=(seed,i), verbose=verbose, dtype=dtype)
else:
maps = enmap.rand_map((shape[0]+1,)+shape[1:], wcs, ps)
phi, unlensed = maps[0], maps[1:]
m = lensing.lens_map_flat(unlensed, phi)
else:
ps = powspec.read_spectrum(args.powspec).astype(type)
beam = make_beam(ps.shape[-1], args.beam*utils.arcmin*utils.fwhm)
ps *= beam
if args.geometry == "curved":
m = curvedsky.rand_map(shape, wcs, ps, lmax=lmax, seed=(seed,i), method=args.method, direct=args.direct, dtype=dtype, verbose=verbose)
else:
m = enmap.rand_map(shape, wcs, ps)
if args.nsim == 1:
if verbose: print "Writing %s" % args.ofile
enmap.write_map(args.ofile, m)
else:
if verbose: print "Writing %s" % (args.ofile % i)
enmap.write_map(args.ofile % i, m)
args = parser.parse_args()
deg2rad = np.pi/180
min2rad = np.pi/180/60
ncomp = args.ncomp
class dprint:
def __init__(self, desc):
self.desc = desc
def __enter__(self):
self.t1 = time.time()
def __exit__(self, type, value, traceback):
sys.stderr.write("%6.2f %6.3f %6.3f %s\n" % (time.time()-self.t1,memory.current()/1024.**3, memory.max()/1024.**3, self.desc))
with dprint("spec"):
ps = powspec.read_spectrum(args.powspec)[:ncomp,:ncomp]
# Construct our output coordinates, a zea system. My standard
# constructor doesn't handle pole crossing, so do it manually.
with dprint("construct omap"):
R = args.radius*deg2rad
res = args.res*min2rad
wo = wcsutils.WCS(naxis=2)
wo.wcs.ctype = ["RA---ZEA","DEC--ZEA"]
wo.wcs.crval = [0,90]
wo.wcs.cdelt = [res/deg2rad, res/deg2rad]
wo.wcs.crpix = [1,1]
x, y = wo.wcs_world2pix(0,90-R/deg2rad,1)
y = int(np.ceil(y))
n = 2*y-1
wo.wcs.crpix = [y,y]
def add(res, m, desc):
m2 = enmap.ifft(enmap.map2harm(m)).real
res.tqu.append(m.copy())
res.teb.append(m2)
res.desc.append(desc)
print desc
def output(res, dir):
utils.mkdir(dir)
for i, (tqu, teb, desc) in enumerate(zip(res.tqu, res.teb, res.desc)):
enmap.write_map("%s/%02d_%s_tqu.hdf" % (dir,i+1,desc), tqu)
enmap.write_map("%s/%02d_%s_teb.hdf" % (dir,i+1,desc), teb)
cl_scal, cl_phi = powspec.read_camb_scalar(args.scal_cls, expand=True)
cl_tens = powspec.read_spectrum(args.tens_cls, expand="diag")
cl_cmb = cl_scal+cl_tens
np.random.seed(args.seed)
res = bunch.Bunch(tqu=[],teb=[],desc=[])
# 1. Start with last scattering
cmb = enmap.rand_map(shape, wcs, cl_cmb)
m = cmb; add(res, m, "cmb")
# 2. Add lensing
phi = enmap.rand_map(shape[-2:], wcs, cl_phi)
lcmb = lensing.lens_map_flat(cmb, phi)
m = lcmb; add(res, m, "lens")
# 3. Add point sources
pinfo = parse_points(args.ptsrc)
pmap = sim_points(shape, wcs, pinfo)
ncomp = 1
comm = mpi.COMM_WORLD
signals = args.signals.split(",")
verbosity = args.verbose - args.quiet
highpass_alpha = 3
utils.mkdir(args.odir)
debug_tile = None if args.debug_tile is None else [int(w) for w in args.debug_tile.split(",")]
# Get the set of bounding boxes, after normalizing them
boxes = np.sort(np.array([d.box for d in mapinfo.datasets]),-2)
# Read the cmb power spectrum, which is an effective noise
# component. T-only
cl_path = os.path.join(os.path.dirname(args.config),config.cl_background)
cl_bg = powspec.read_spectrum(cl_path)[0,0]
# Read our mask. High-resolution and per-dataset masks would be handled otherwise.
# This approach only works for low-resolution masks
if config.mask_mode == "none": mask = None
elif config.mask_mode == "lowres":
mask_path = os.path.join(os.path.dirname(args.config), config.mask)
mask = enmap.read_map(mask_path).preflat[0]
else: raise ValueError("Unrecognized mask mode '%s'" % config.mask_mode)
def overlaps_any(box, refboxes):
box = np.sort(box, 0)
rdec, rra = utils.moveaxis(refboxes - box[0,:], 2,0)
wdec, wra = box[1] - box[0]
rra -= np.floor(rra[:,0,None]/(2*np.pi)+0.5)*(2*np.pi)
for i in range(-1,2):
def get_scans(area, signal, bore, dets, noise, seed=0, real=None):
scans = []
# Get real scan information if necessary
L.debug("real")
if real:
real_scans = []
toks = real.split(":")
filelist, fslice = toks[0], ":".join(toks[1:])
filelist = [
line.split()[0] for line in open(filelist, "r") if line[0] != "#"
]
filelist = eval("filelist" + fslice)
db = filedb.ACTdb(config.get("filedb"))
for id in filelist:
try:
real_scans.append(data.ACTScan(db[id]))
except errors.DataMissing as e:
L.debug("Skipped %s (%s)" % (filelist[ind], e.message))
# Dets
L.debug("dets")
sim_dets = []
toks = dets.split(":")
if toks[0] == "scattered":
ngroup, nper, rad = int(toks[1]), int(toks[2]), float(toks[3])
sim_dets = [
scansim.dets_scattered(ngroup, nper, rad=rad * np.pi / 180 / 60)
]
margin = rad * np.pi / 180 / 60
elif toks[0] == "real":
ndet = int(toks[1])
dslice = slice(0, ndet) if ndet > 0 else slice(None)
sim_dets = [
bunch.Bunch(comps=s.comps[dslice], offsets=s.offsets[dslice])
for s in real_scans
]
margin = np.max([np.sum(s.offsets**2, 1)**0.5 for s in sim_dets])
else:
raise ValueError
# Boresight. Determines our number of scans
L.debug("bore")
sim_bore = []
toks = bore.split(":")
if toks[0] == "grid":
nscan, density, short = int(toks[1]), float(toks[2]), float(toks[3])
for i in range(nscan):
tbox = shorten(area.box(), i % 2, short)
sim_bore.append(
scansim.scan_grid(tbox,
density * np.pi / 180 / 60,
dir=i,
margin=margin))
elif toks[0] == "real":
sim_bore = [
bunch.Bunch(boresight=s.boresight,
sys=s.sys,
site=s.site,
mjd0=s.mjd0) for s in real_scans
]
else:
raise ValueError
nsim = len(sim_bore)
# Make one det info per scan
sim_dets = sim_dets * (nsim / len(sim_dets)) + sim_dets[:nsim %
len(sim_dets)]
# Noise
L.debug("noise")
sim_nmat = []
toks = noise.split(":")
noiseless = False
if toks[0] == "1/f":
sigma, alpha, fknee = [float(v) for v in toks[1:4]]
noiseless = sigma < 0
for i in range(nsim):
sim_nmat.append(
scansim.oneoverf_noise(sim_dets[i].comps.shape[0],
sim_bore[i].boresight.shape[0],
sigma=np.abs(sigma),
alpha=alpha,
fknee=fknee))
elif toks[0] == "detcorr":
sigma, alpha, fknee = [float(v) for v in toks[1:4]]
noiseless = sigma < 0
for i in range(nsim):
sim_nmat.append(
scansim.oneoverf_detcorr_noise(sim_dets[i].comps.shape[0],
sim_bore[i].boresight.shape[0],
sigma=np.abs(sigma),
alpha=alpha,
fknee=fknee))
elif toks[0] == "real":
scale = 1.0 if len(toks) < 2 else float(toks[1])
for i, s in enumerate(real_scans):
ndet = len(sim_dets[i].offsets)
nmat = s.noise[:ndet] * scale**-2
sim_nmat.append(nmat)
else:
raise ValueError
sim_nmat = sim_nmat * (nsim / len(sim_nmat)) + sim_nmat[:nsim %
len(sim_nmat)]
# Signal
L.debug("signal")
toks = signal.split(":")
if toks[0] == "ptsrc":
# This one always operates in the same coordinates as
nsrc, amp, fwhm = int(toks[1]), float(toks[2]), float(toks[3])
sim_srcs = scansim.rand_srcs(area.box(),
nsrc,
amp,
abs(fwhm) * np.pi / 180 / 60,
rand_fwhm=fwhm < 0)
for i in range(nsim):
scans.append(
scansim.SimSrcs(sim_bore[i],
sim_dets[i],
sim_srcs,
sim_nmat[i],
seed=seed + i,
nonoise=noiseless))
elif toks[0] == "cmb":
np.random.seed(seed)
ps = powspec.read_spectrum(toks[1])
sim_map = enmap.rand_map(area.shape, area.wcs, ps)
for i in range(nsim):
scans.append(
scansim.SimMap(sim_bore[i],
sim_dets[i],
sim_map,
sim_nmat[i],
seed=seed + i,
nonoise=noiseless))
else:
raise ValueError
return scans
