def forward(self, tod, amps, pmul=1):
# Amps should be [nsrc,ndir,ndet|1,npol]
params = self.params.copy()
params[...,2:2+amps.shape[-1]] = amps
self.psrc.forward(tod, params, pmul=pmul)
sampcut.gapfill_linear(self.cut, tod, inplace=True)
return tod
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 forward(self, tod, amps, pmul=1):
# Amps should be [nsrc,ndir,ndet|1,npol]
params = self.params.copy()
# this assumes that the input amps params start from tqu onwards (no ra,dec)
params[...,2:2+amps.shape[-1]] = amps
self.psrc.forward(tod, params, pmul=pmul)
sampcut.gapfill_linear(self.cut, tod, inplace=True)
return tod
def backward(self, tod, amps=None, pmul=1, ncomp=3):
params = self.params.copy()
tod = sampcut.gapfill_linear(self.cut, tod, inplace=False, transpose=True)
self.psrc.backward(tod, params, pmul=pmul)
if amps is None: amps = params[...,2:2+ncomp]
else: amps[:] = params[...,2:2+amps.shape[-1]]
return amps
def backward(self, tod, amps=None, pmul=1): params = self.params.copy() tod = sampcut.gapfill_linear(self.cut, tod, inplace=False, transpose=True) self.psrc.backward(tod, params, pmul=pmul) if amps is None: amps = params[...,2] else: amps[:] = params[...,2] return amps
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)
def gapfill_linear(tod, cut, inplace=False, overlap=None):
overlap = config.get("gapfill_context", overlap)
return sampcut.gapfill_linear(cut, tod, inplace=inplace, context=overlap)
def forward(self, tod, amps, pmul=1): params = self.params.copy() params[...,2] = amps self.psrc.forward(tod, params, pmul=pmul) sampcut.gapfill_linear(self.cut, tod, inplace=True)