def __new__(cls, fft_filter=None, ncorrelations=None, nsamples=None):
if fft_filter is None:
return Operator.__new__(cls)
filter_length = fft_filter.shape[-1]
invntt = DiagonalOperator(fft_filter)
fft = FftHalfComplexOperator(filter_length)
padding = PadOperator(left=ncorrelations, right=filter_length - \
nsamples - ncorrelations)
return padding.T * fft.T * invntt * fft * padding
def __new__(cls, fft_filter=None, ncorrelations=None, nsamples=None):
if fft_filter is None:
return Operator.__new__(cls)
filter_length = fft_filter.shape[-1]
invntt = DiagonalOperator(fft_filter)
fft = FftHalfComplexOperator(filter_length)
padding = PadOperator(left=ncorrelations, right=filter_length - \
nsamples - ncorrelations)
return padding.T * fft.T * invntt * fft * padding
def __new__(cls, obs=None, method='uncorrelated', **keywords):
if obs is None:
return Operator.__new__(cls)
nsamples = obs.get_nsamples()
comm_tod = obs.instrument.comm
method = method.lower()
if method not in ('uncorrelated', 'uncorrelated python'):
raise ValueError("Invalid method '{0}'.".format(method))
filter = obs.get_filter_uncorrelated(**keywords)
if filter.ndim == 2:
filter = filter[np.newaxis, ...]
nfilters = filter.shape[0]
if nfilters != 1 and nfilters != len(nsamples):
raise ValueError(
"Incompatible number of filters '{0}'. Expected nu"
"mber is '{1}'".format(nfilters, len(nsamples)))
ncorrelations = filter.shape[-1] - 1
filter_length = np.asarray(2**np.ceil(np.log2(np.array(nsamples) + \
2 * ncorrelations)), int)
fft_filters = []
for i, n in enumerate(filter_length):
i = min(i, nfilters - 1)
fft_filter, status = tmf.fft_filter_uncorrelated(filter[i].T, n)
if status != 0: raise RuntimeError()
np.maximum(fft_filter, 0, fft_filter)
fft_filters.append(fft_filter.T)
norm = comm_tod.allreduce(max(np.max(f) for f in fft_filters),
op=MPI.MAX)
for f in fft_filters:
np.divide(f, norm, f)
if method == 'uncorrelated':
cls = InvNttUncorrelatedOperator
else:
cls = InvNttUncorrelatedPythonOperator
#XXX should generate BlockDiagonalOperator with no duplicates...
return BlockDiagonalOperator([cls(f, ncorrelations, n) \
for f, n in zip(fft_filters, nsamples)], axisin=-1)
def __new__(cls, obs=None, method='uncorrelated', **keywords):
if obs is None:
return Operator.__new__(cls)
nsamples = obs.get_nsamples()
comm_tod = obs.instrument.comm
method = method.lower()
if method not in ('uncorrelated', 'uncorrelated python'):
raise ValueError("Invalid method '{0}'.".format(method))
filter = obs.get_filter_uncorrelated(**keywords)
if filter.ndim == 2:
filter = filter[np.newaxis,...]
nfilters = filter.shape[0]
if nfilters != 1 and nfilters != len(nsamples):
raise ValueError("Incompatible number of filters '{0}'. Expected nu"
"mber is '{1}'".format(nfilters, len(nsamples)))
ncorrelations = filter.shape[-1] - 1
filter_length = np.asarray(2**np.ceil(np.log2(np.array(nsamples) + \
2 * ncorrelations)), int)
fft_filters = []
for i, n in enumerate(filter_length):
i = min(i, nfilters-1)
fft_filter, status = tmf.fft_filter_uncorrelated(filter[i].T, n)
if status != 0: raise RuntimeError()
np.maximum(fft_filter, 0, fft_filter)
fft_filters.append(fft_filter.T)
norm = comm_tod.allreduce(max(np.max(f) for f in fft_filters),
op=MPI.MAX)
for f in fft_filters:
np.divide(f, norm, f)
if method == 'uncorrelated':
cls = InvNttUncorrelatedOperator
else:
cls = InvNttUncorrelatedPythonOperator
#XXX should generate BlockDiagonalOperator with no duplicates...
return BlockDiagonalOperator([cls(f, ncorrelations, n) \
for f, n in zip(fft_filters, nsamples)], axisin=-1)
