def white_noise(ndet, nsamp, sigma): bins = linbin(1.0, 1) ebins = np.array([[0,0]]) D = np.zeros([1,ndet])+sigma**2 V = np.zeros([1,ndet]) E = np.zeros([1]) return nmat.NmatDetvecs(D, V, E, bins, ebins)
def oneoverf_noise(ndet, nsamp, sigma, fknee=0.2, alpha=1): nbin = 1000 bins = linbin(1.0, nbin) freq = np.mean(bins,1) Nu = np.empty([nbin,ndet]) Nu[:,:] = ((1+(freq/fknee)**-alpha)*sigma**2)[:,None] #Nu[:,:] = ((0+(freq/fknee)**-alpha)*sigma**2)[:,None] ebins = np.zeros([nbin,2],dtype=int) return nmat.NmatDetvecs(Nu, np.zeros([1,ndet]), np.zeros([1]), bins, ebins)
def prepare_detvecs(D, Vlist, Elist, ibins, srate, dets): D = np.asarray(D) if dets is None: dets = np.arange(D.shape[1]) assert len(dets) == D.shape[1] fbins = ibins*(srate/2.)/ibins[-1,-1] etmp = np.concatenate([[0],np.cumsum(np.array([len(e) for e in Elist]))]) ebins= np.array([etmp[0:-1],etmp[1:]]).T E, V = np.hstack(Elist), np.hstack(Vlist).T return nmat.NmatDetvecs(D, V, E, fbins, ebins, dets)
def oneoverf_detcorr_noise(ndet, nsamp, sigma, fknee=0.2, alpha=1): # A single, atmospheric mode nbin = 1000 bins = linbin(1.0, nbin) freq = np.mean(bins,1) Nu = np.zeros([nbin,ndet])+sigma**2 E = (freq/fknee)**-alpha * sigma**2 V = np.zeros([nbin,ndet])+1 ebins = linbin(nbin,nbin) return nmat.NmatDetvecs(Nu, V, E, bins, ebins)
if scan.ndet == 0 or scan.nsamp == 0:
raise errors.DataMissing("No samples in scan")
if args.ndet:
scan = scan[:args.ndet]
if downsample > 1:
scan = scan[:, ::downsample]
scan.pmap = pmat.PmatMap(scan, area)
scan.pcut = pmat.PmatCut(scan)
# Build the noise model
tod = scan.get_samples()
tod -= np.mean(tod, 1)[:, None]
tod = tod.astype(dtype)
scan.noise = scan.noise.update(tod, scan.srate)
scan.T = np.min(scan.noise.D) * Tscale
scan.noise_bar = nmat.NmatDetvecs(scan.noise.D - scan.T, scan.noise.V,
scan.noise.E, scan.noise.bins,
scan.noise.ebins, scan.noise.dets)
# Set up cuts
scan.cut_range = [njunk_tot, njunk_tot + scan.pcut.njunk]
njunk_tot += scan.pcut.njunk
# Prepare our filtered data. We do this one of two ways.
# Either store Nb"d for each TOD, which can end up taking
# up a lot of memory, or precompute P'(Nb"+(lT)")"Nbd"d for
# each value of lambda. This saves memory if the maps aren't
# too big and if the number of lambdas is reasonably small.
# For 6 lambdas and deep56 size, we get 240 MB * 6 = 1.4 GB.
# That corresponds to storing 4 downsampled tods.
if args.precompute:
iNbd = scan.noise_bar.apply(tod.copy())
for lam in np.unique(cooldown):
# Could cache this too, but it's fast to compute