def estimate_atmosphere(tod, region_cut, srate, fknee, alpha):
model = gapfill.gapfill_joneig(tod, region_cut, inplace=False)
ft = fft.rfft(model)
freq = fft.rfftfreq(model.shape[-1]) * srate
flt = 1 / (1 + (freq / fknee)**alpha)
ft *= flt
fft.ifft(ft, model, normalize=True)
return model
def estimate_atmosphere(tod, region_cut, srate, fknee, alpha): model = gapfill.gapfill_joneig(tod, region_cut, inplace=False) ft = fft.rfft(model) freq = fft.rfftfreq(model.shape[-1])*srate flt = 1/(1+(freq/fknee)**alpha) ft *= flt fft.ifft(ft, model, normalize=True) return model
with bench.show("ivar"):
tod = d.tod
del d.tod
tod -= np.mean(tod,1)[:,None]
tod = tod.astype(dtype)
diff = tod[:,1:]-tod[:,:-1]
diff = diff[:,:diff.shape[-1]/csize*csize].reshape(d.ndet,-1,csize)
ivar = 1/(np.median(np.mean(diff**2,-1),-1)/2**0.5)
del diff
# Generate planet cut
with bench.show("planet cut"):
planet_cut = cuts.avoidance_cut(d.boresight, d.point_offset, d.site,
args.planet, R)
# Subtract atmospheric model
with bench.show("atm model"):
model= gapfill.gapfill_joneig(tod, planet_cut, inplace=False)
# Estimate noise level
asens = np.sum(ivar)**-0.5 / d.srate**0.5
print asens
with bench.show("smooth"):
ft = fft.rfft(model)
freq = fft.rfftfreq(model.shape[-1])*d.srate
flt = 1/(1+(freq/model_fknee)**model_alpha)
ft *= flt
fft.ifft(ft, model, normalize=True)
del ft, flt, freq
with bench.show("atm subtract"):
tod -= model
del model
tod = tod.astype(dtype, copy=False)
# Should now be reasonably clean of correlated noise.
rhs = enmap.zeros((ncomp, ) + shape, area.wcs, dtype)
div = enmap.zeros((ncomp, ncomp) + shape, area.wcs, dtype)
hits = enmap.zeros((ncomp, ncomp) + shape, area.wcs, dtype)
junk = np.zeros(pcut.njunk, dtype)
# Generate planet cut.
with bench.show("planet cut"):
planet_cut = cuts.avoidance_cut(d.boresight, d.point_offset, d.site,
args.planet, R)
if args.sim:
if args.noiseless: tod_orig = tod.copy()
with bench.show("inject"):
pmap.forward(tod, area)
# Compute atmospheric model
with bench.show("atm model"):
model = smooth(gapfill.gapfill_joneig(tod, planet_cut, inplace=False),
d.srate)
if args.sim and args.noiseless:
model -= smooth(
gapfill.gapfill_joneig(tod_orig, planet_cut, inplace=False),
d.srate)
tod -= tod_orig
del tod_orig
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
with bench.show("rhs"):
tod *= ivar[:, None]
def calc_model_joneig(tod, cut, srate=400):
return smooth(gapfill.gapfill_joneig(tod, cut, inplace=False), srate)
with bench.show("ivar"):
tod = d.tod
del d.tod
tod -= np.mean(tod,1)[:,None]
tod = tod.astype(dtype)
diff = tod[:,1:]-tod[:,:-1]
diff = diff[:,:diff.shape[-1]//csize*csize].reshape(d.ndet,-1,csize)
ivar = 1/(np.median(np.mean(diff**2,-1),-1)/2**0.5)
del diff
# Generate planet cut
with bench.show("planet cut"):
planet_cut = cuts.avoidance_cut(d.boresight, d.point_offset, d.site,
args.planet, R)
# Subtract atmospheric model
with bench.show("atm model"):
model= gapfill.gapfill_joneig(tod, planet_cut, inplace=False)
# Estimate noise level
asens = np.sum(ivar)**-0.5 / d.srate**0.5
print(asens)
with bench.show("smooth"):
ft = fft.rfft(model)
freq = fft.rfftfreq(model.shape[-1])*d.srate
flt = 1/(1+(freq/model_fknee)**model_alpha)
ft *= flt
fft.ifft(ft, model, normalize=True)
del ft, flt, freq
with bench.show("atm subtract"):
tod -= model
del model
tod = tod.astype(dtype, copy=False)
# Should now be reasonably clean of correlated noise, so we can from now on use
def calc_model_joneig(tod, cut, srate=400): return smooth(gapfill.gapfill_joneig(tod, cut, inplace=False), srate)
pmap = pmat.PmatMap(scan, area, sys=sys)
pcut = pmat.PmatCut(scan)
rhs = enmap.zeros((ncomp,)+shape, area.wcs, dtype)
div = enmap.zeros((ncomp,ncomp)+shape, area.wcs, dtype)
junk = np.zeros(pcut.njunk, dtype)
# Generate planet cut
with bench.show("planet cut"):
planet_cut = cuts.avoidance_cut(d.boresight, d.point_offset, d.site,
args.planet, R)
if args.sim:
if args.noiseless: tod_orig = tod.copy()
with bench.show("inject"):
pmap.forward(tod, area)
# Compute atmospheric model
with bench.show("atm model"):
model = smooth(gapfill.gapfill_joneig(tod, planet_cut, inplace=False), d.srate)
if args.sim and args.noiseless:
model -= smooth(gapfill.gapfill_joneig(tod_orig, planet_cut, inplace=False), d.srate)
tod -= tod_orig
del tod_orig
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
with bench.show("rhs"):
tod *= ivar[:,None]
pcut.backward(tod, junk)
pmap.backward(tod, rhs)
with bench.show("hits"):
if bleh:
sim_rhs = np.zeros(len(inject_params))
sim_div = np.zeros(len(inject_params))
for si, scan in zip(myinds, myscans):
L.debug("Processing %s" % scan.id)
# Read the tod
tod = scan.get_samples().astype(dtype)
tod = utils.deslope(tod)
if args.mapsub:
# Subtract the reference map. If the reference map is not source free,
# then this could reintroduce strong point sources that were cut earlier.
# To avoid this we do another round of gapfilling
signal.forward(scan, tod, refmap, tmul=1, mmul=-1)
gapfill.gapfill_joneig(tod, scan.cut, inplace=True)
# Inject simulated signal if requested
if args.inject:
dmjd = scan.mjd0 - mjd0
earth_pos = -ephemeris.ephem_vec("Sun", scan.mjd0)[:, 0]
# Set the position and amplitude uK of each simulated source
sim_srcs = np.zeros([len(inject_params), 8])
# TODO: inject and analyze with no displacement to see if interpolation is the
# cause of the low bias in amplitude.
sim_srcs[:, :2] = inject_params[:, 1::-1] * utils.degree
if not args.static:
sim_srcs[:, :2] = planet9.displace_pos(
sim_srcs[:, :2].T, earth_pos, inject_params.T[2],
inject_params.T[4:2:-1] * ym * dmjd).T
#print "params", inject_params[:,5]