def calc_model_constrained(tod,
cut,
srate=400,
mask_scale=0.3,
lim=3e-4,
maxiter=50,
verbose=False):
# First do some simple gapfilling to avoid messing up the noise model
tod = sampcut.gapfill_linear(cut, tod, inplace=False)
ft = fft.rfft(tod) * tod.shape[1]**-0.5
iN = nmat_measure.detvecs_jon(ft, srate)
del ft
iV = iN.ivar * mask_scale
def A(x):
x = x.reshape(tod.shape)
Ax = iN.apply(x.copy())
Ax += sampcut.gapfill_const(cut, x * iV[:, None], 0, inplace=True)
return Ax.reshape(-1)
b = sampcut.gapfill_const(cut, tod * iV[:, None], 0,
inplace=True).reshape(-1)
x0 = sampcut.gapfill_linear(cut, tod).reshape(-1)
solver = cg.CG(A, b, x0)
while solver.i < maxiter and solver.err > lim:
solver.step()
if verbose:
print("%5d %15.7e" % (solver.i, solver.err))
res = solver.x.reshape(tod.shape)
res = smooth(res, srate)
return res
def map_cuts(entry,
data,
hitmap,
cutmap,
keep_buffer=False,
nocommon_frac=None):
'''
Project hits and hits-cuts onto the sky.
parameters
----------
entry : filedb.data object
data : actdata instance
hitmap : (1, ny, nx) enmap
cutmap : (1, ny, nx) enmap
keep_buffer : bool, optional
Do not remove the 200 sample buffers before and after cuts.
common_frac : float, optional
Do not consider cuts that are at least common to this
fraction of detectors.
'''
scan = actscan.ACTScan(entry, d=data)
pmap = pmat.PmatMap(scan, hitmap)
cut = data.cut
if nocommon_frac is not None:
cut = remove_common(cut, frac=nocommon_frac)
if keep_buffer is False:
cut = remove_buffer(cut)
tod = np.full([data.ndet, data.nsamp], 1.0, dtype)
pmap.backward(tod, hitmap)
sampcut.gapfill_const(cut, tod, 0.0, inplace=True)
pmap.backward(tod, cutmap)
def calc_model_constrained(tod, cut, srate=400, mask_scale=0.3, lim=3e-4, maxiter=50, verbose=False): # First do some simple gapfilling to avoid messing up the noise model tod = sampcut.gapfill_linear(cut, tod, inplace=False) ft = fft.rfft(tod) * tod.shape[1]**-0.5 iN = nmat_measure.detvecs_jon(ft, srate) del ft iV = iN.ivar*mask_scale def A(x): x = x.reshape(tod.shape) Ax = iN.apply(x.copy()) Ax += sampcut.gapfill_const(cut, x*iV[:,None], 0, inplace=True) return Ax.reshape(-1) b = sampcut.gapfill_const(cut, tod*iV[:,None], 0, inplace=True).reshape(-1) x0 = sampcut.gapfill_linear(cut, tod).reshape(-1) solver = cg.CG(A, b, x0) while solver.i < maxiter and solver.err > lim: solver.step() if verbose: print "%5d %15.7e" % (solver.i, solver.err) return solver.x.reshape(tod.shape)
]
# Process all our tods
L.info("Processing %d tods" % data.n)
for fi, scan in enumerate(data.scans):
L.debug("Processing %4d/%d %s" %
(data.rinds[fi] + 1, data.n, ids[data.inds[fi]]))
tod = scan.get_samples().astype(dtype)
# Apply our filters
for f in filters:
f(scan, tod)
# Measure our noise properties and apply inverse variance weight
ivar = 1 / np.var(tod, 1)
tod *= ivar[:, None]
# Apply our cuts and accumulate
sampcut.gapfill_const(scan.cut, tod, 0, inplace=True)
signal.backward(scan, tod, rhs_work)
# Build our denominator too
for ci in range(ncomp):
div_tmp *= 0
div_tmp[ci] = 1
signal.forward(scan, tod, div_tmp, tmul=0)
tod *= ivar[:, None]
sampcut.gapfill_const(scan.cut, tod, 0, inplace=True)
signal.backward(scan, tod, div_work[ci])
# and hits
tod[:] = 1
sampcut.gapfill_const(scan.cut, tod, 0, inplace=True)
signal.backward(scan, tod, hit_work)
def A(x):
x = x.reshape(tod.shape)
Ax = iN.apply(x.copy())
Ax += sampcut.gapfill_const(cut, x * iV[:, None], 0, inplace=True)
return Ax.reshape(-1)
def A(x): x = x.reshape(tod.shape) Ax = iN.apply(x.copy()) Ax += sampcut.gapfill_const(cut, x*iV[:,None], 0, inplace=True) return Ax.reshape(-1)
src_cut *= scan.cut
del src_mask
with bench.show("atm model"):
if args.model == "joneig":
model = calc_model_joneig(tod, src_cut, d.srate)
elif args.model == "constrained":
model = calc_model_constrained(tod, src_cut, d.srate, verbose=True)
with bench.show("atm subtract"):
tod -= model
del model
tod = tod.astype(dtype, copy=False)
# Should now be reasonably clean of correlated noise.
# Proceed to make simple binned map for each point source. We need a separate
# pointing matrix for each because each has its own local coordinate system.
tod *= ivar[:,None]
sampcut.gapfill_const(scan.cut, tod, inplace=True)
for sid in tod_srcs[id]:
src = srcs[sid]
if src.type == "fixed": sys = "hor:%.6f_%.6f:cel/0_0:hor" % (src.ra/utils.degree, src.dec/utils.degree)
elif src.type == "planet": sys = "hor:%s/0_0" % src.name
else: raise ValueError("Invalid source type '%s'" % src.type)
rhs = enmap.zeros((ncomp,)+shape, area.wcs, dtype)
div = enmap.zeros((ncomp,ncomp)+shape, area.wcs, dtype)
with bench.show("pmat %s" % sid):
pmap = pmat.PmatMap(scan, area, sys=sys)
with bench.show("rhs %s" % sid):
pmap.backward(tod, rhs)
with bench.show("hits"):
for i in range(ncomp):
div[i,i] = 1
pmap.forward(tod, div[i])
def apply_cut(tod, inplace=True):
return sampcut.gapfill_const(scan.cut, tod, inplace=inplace)
def gapfill_constant(tod, cut, inplace=False, value=0.0, overlap=None):
return sampcut.gapfill_const(cut, tod, value, inplace=inplace)
