def binning3d(tod, factors):
shape = tod.shape
if len(shape) is not 3:
raise ValueError('Expected 3d shape')
B0 = lo.binning(shape, factor=factors[0], axis=0, dtype=np.float64)
shape1 = np.asarray(copy(shape))
shape1[0] /= factors[0]
B1 = lo.binning(shape1, factor=factors[1], axis=1, dtype=np.float64)
shape2 = np.asarray(copy(shape1))
shape2[1] /= factors[1]
B2 = lo.binning(shape2, factor=factors[2], axis=2, dtype=np.float64)
return B2 * B1 * B0
def binning3d(tod, factors):
shape = tod.shape
if len(shape) is not 3:
raise ValueError('Expected 3d shape')
B0 = lo.binning(shape, factor=factors[0], axis=0, dtype=np.float64)
shape1 = np.asarray(copy(shape))
shape1[0] /= factors[0]
B1 = lo.binning(shape1, factor=factors[1], axis=1, dtype=np.float64)
shape2 = np.asarray(copy(shape1))
shape2[1] /= factors[1]
B2 = lo.binning(shape2, factor=factors[2], axis=2, dtype=np.float64)
return B2 * B1 * B0
def averaging(tod, factor, dtype=np.float64):
"""Averaging compression mode"""
shape = tod.shape
nsamples = tod.nsamples
shapes = [(tod.shape[0], n) for n in nsamples]
Bs = [lo.binning(s, factor=factor, axis=1, dtype=dtype) for s in shapes]
B = lo.block_diagonal(Bs)
S = lo.ndhomothetic(shape, 1. / factor, dtype=dtype)
return B * S
def averaging(tod, factor, dtype=np.float64):
"""Averaging compression mode"""
shape = tod.shape
nsamples = tod.nsamples
shapes = [(tod.shape[0], n) for n in nsamples]
Bs = [lo.binning(s, factor=factor, axis=1, dtype=dtype) for s in shapes]
B = lo.block_diagonal(Bs)
S = lo.ndhomothetic(shape, 1. / factor, dtype=dtype)
return B * S
