def fht(shapein, **kargs):
"""
Fast Hadamard transform LinearOperator
"""
def matvec(arr):
return fht_mod.fht(arr, **kargs)
return lo.ndoperator(shapein, shapein, matvec=matvec, rmatvec=matvec, dtype=np.float64)
def fht(shapein, **kargs):
"""
Fast Hadamard transform LinearOperator
"""
def matvec(arr):
return fht_mod.fht(arr, **kargs)
return lo.ndoperator(shapein,
shapein,
matvec=matvec,
rmatvec=matvec,
dtype=np.float64)
def group_sum(ind, cube, data):
from lo import ndsubclass
# shapes
shapein = cube.shape + (data.shape[-1],)
shapeout = cube.shape + (len(ind),)
# slicing indexes
ind1 = ind
ind2 = ind1[1:] + [None,]
axis = -1
def matvec(x):
out = np.zeros(shapeout)
for i, j, k in zip(ind1, ind2, np.arange(len(ind1))):
out[..., k] = x[..., i:j].sum(axis=-1)
return out
def rmatvec(x):
out = np.zeros(shapein)
for i, j, k in zip(ind1, ind2, np.arange(len(ind1))):
tmp_shape = out[..., i:j].shape
out[..., i:j] = x[..., k].repeat(tmp_shape[-1]).reshape(tmp_shape)
return out
return lo.ndoperator(shapein, shapeout, matvec, rmatvec, dtype=np.float64)