def apply(self, tod):
nmat.apply_window(tod, self.window)
ft = fft.rfft(tod)
self.nmat.apply_ft(ft, tod.shape[-1], tod.dtype)
if self.filter is not None: ft *= self.filter
fft.irfft(ft, tod, flags=['FFTW_ESTIMATE','FFTW_DESTROY_INPUT'])
nmat.apply_window(tod, self.window)
return tod
def __init__(self, data, model=None, window=None):
model = config.get("noise_model", model)
window = config.get("tod_window", window)*data.srate
nmat.apply_window(data.tod, window)
self.nmat = nmat_measure.NmatBuildDelayed(model, cut=data.cut_noiseest, spikes=data.spikes[:2].T)
self.nmat = self.nmat.update(data.tod, data.srate)
nmat.apply_window(data.tod, window, inverse=True)
self.model, self.window = model, window
self.ivar = self.nmat.ivar
self.cut = data.cut
def __init__(self, scan, model=None, window=None, filter=None):
model = config.get("noise_model", model)
window = config.get("tod_window", window)*scan.srate
nmat.apply_window(scan.tod, window)
self.nmat = nmat_measure.NmatBuildDelayed(model, cut=scan.cut_noiseest, spikes=scan.spikes)
self.nmat = self.nmat.update(scan.tod, scan.srate)
nmat.apply_window(scan.tod, window, inverse=True)
self.model, self.window = model, window
self.ivar = self.nmat.ivar
self.cut = scan.cut
# Optional extra filter
if filter:
freq = fft.rfftfreq(scan.nsamp, 1/scan.srate)
fknee, alpha = filter
with utils.nowarn():
self.filter = (1 + (freq/fknee)**-alpha)**-1
else: self.filter = None
def white(self, tod):
nmat.apply_window(tod, self.window)
self.nmat.white(tod)
nmat.apply_window(tod, self.window)
def __call__(self, scan, tod):
nsamp = int(self.width * scan.srate)
nmat.apply_window(tod, nsamp)
d = actscan.ACTScan(entry)
if d.ndet == 0 or d.nsamp == 0:
raise errors.DataMissing("Tod contains no valid data")
except errors.DataMissing as e:
L.debug("Skipped %s (%s)" % (str(id), str(e)))
continue
L.debug("Read %s" % id)
d = d[:, ::config.get("downsample")]
if args.ndet > 0: d = d[:args.ndet]
# Get our samples
tod = d.get_samples()
tod -= np.mean(tod, 1)[:, None]
tod = tod.astype(dtype)
# Construct noise model for this tod
winsize = int(config.get("tod_window") * d.srate)
nmat.apply_window(tod, winsize)
d.noise = d.noise.update(tod, d.srate)
L.debug("Noise %s" % id)
# Apply it, to get N"d
d.noise.apply(tod)
nmat.apply_window(tod, winsize)
# Compute the variance per detector. If our noise model
# were correct and our data were pure noise, this would be
# N"<nn'>N" = N". But our noise model isn't totally accurate.
vars = np.var(tod, 1)
# Project each detectors result on the sky
tod[:] = vars[:, None]
pmap = pmat.PmatMap(d, osig)
pcut = pmat.PmatCut(d)
junk = np.zeros(pcut.njunk, dtype=dtype)
pcut.backward(tod, junk)
d = actscan.ACTScan(entry)
if d.ndet == 0 or d.nsamp == 0:
raise errors.DataMissing("Tod contains no valid data")
except errors.DataMissing as e:
L.debug("Skipped %s (%s)" % (str(id), e.message))
continue
L.debug("Read %s" % id)
d = d[:,::config.get("downsample")]
if args.ndet > 0: d = d[:args.ndet]
# Get our samples
tod = d.get_samples()
tod -= np.mean(tod,1)[:,None]
tod = tod.astype(dtype)
# Construct noise model for this tod
winsize = int(config.get("tod_window")*d.srate)
nmat.apply_window(tod, winsize)
d.noise = d.noise.update(tod, d.srate)
L.debug("Noise %s" % id)
# Apply it, to get N"d
d.noise.apply(tod)
nmat.apply_window(tod, winsize)
# Compute the variance per detector. If our noise model
# were correct and our data were pure noise, this would be
# N"<nn'>N" = N". But our noise model isn't totally accurate.
vars = np.var(tod,1)
# Project each detectors result on the sky
tod[:] = vars[:,None]
pmap = pmat.PmatMap(d, osig)
pcut = pmat.PmatCut(d)
junk = np.zeros(pcut.njunk, dtype=dtype)
pcut.backward(tod, junk)
def apply(self, tod): nmat.apply_window(tod, self.window) self.nmat.apply(tod) nmat.apply_window(tod, self.window) return tod