def convolve_third(S2, E3=(1,)):
dL = len(E3) - 1
L3, L2, L1 = S2.shape
S3 = np.zeros((L3 + dL, L2, L1), dtype=S2.dtype)
A = convolution_operator(E3, L3, False, False)
for sum23 in xrange(L2):
for T1 in xrange(L1):
S3[:, sum23, T1] = A.dot(S2[:, sum23, T1])
return S3
def convolve_first(R, E1=(1,)):
dL = len(E1) - 1
L3, L2, L1 = R.shape
S1 = np.zeros((L3, L2, L1 + L2 + L3 + dL), dtype=R.dtype)
A = convolution_operator(E1, L1, False, False)
for t3 in xrange(L3):
for t2 in xrange(L2):
S1[t3, t2, (t3 + t2):(t3 + t2 + L1 + dL)] = A.dot(R[t3, t2, :])
return S1
def convolve_second(S1, E2=(1,)):
dL = len(E2) - 1
L3, L2, L1 = S1.shape
S2 = np.zeros((L3, L2 + L3 + dL, L1), dtype=S1.dtype)
A = convolution_operator(E2, L2, False, False)
for t3 in xrange(L3):
for sum12 in xrange(L1):
S2[t3, t3:(t3 + L2 + dL), sum12] = A.dot(S1[t3, :, sum12])
return S2
def convolve_third(S2, E3=(1, )):
dL = len(E3) - 1
L3, L2, L1 = S2.shape
S3 = np.zeros((L3 + dL, L2, L1), dtype=S2.dtype)
A = convolution_operator(E3, L3, False, False)
for sum23 in xrange(L2):
for T1 in xrange(L1):
S3[:, sum23, T1] = A.dot(S2[:, sum23, T1])
return S3
def convolve_second(S1, E2=(1, )):
dL = len(E2) - 1
L3, L2, L1 = S1.shape
S2 = np.zeros((L3, L2 + L3 + dL, L1), dtype=S1.dtype)
A = convolution_operator(E2, L2, False, False)
for t3 in xrange(L3):
for sum12 in xrange(L1):
S2[t3, t3:(t3 + L2 + dL), sum12] = A.dot(S1[t3, :, sum12])
return S2
def convolve_first(R, E1=(1, )):
dL = len(E1) - 1
L3, L2, L1 = R.shape
S1 = np.zeros((L3, L2, L1 + L2 + L3 + dL), dtype=R.dtype)
A = convolution_operator(E1, L1, False, False)
for t3 in xrange(L3):
for t2 in xrange(L2):
S1[t3, t2, (t3 + t2):(t3 + t2 + L1 + dL)] = A.dot(R[t3, t2, :])
return S1
def R3_to_P3_alt(R, E_all=((1,), (1,), (1,)), trim=True, include_margin=True):
conv_mat, shapes, slice_maps = convolution_parameters(R.shape, E_all)
P3 = shift_time_indices(
reduce(loop_matvec, zip(conv_mat, shapes[1:], slice_maps), R),
E_all, trim, include_margin)
try:
return MetaArray(P3, ticks=expand_ticks(R, E_all), rw_freq=R.rw_freq)
except AttributeError:
return P3
def convolution_parameters((L3, L2, L1), E_all=((1,), (1,), (1,))):
E3, E2, E1 = E_all
conv_matrices = [convolution_operator(E1, L1, False, False),
convolution_operator(E2, L2, False, False),
convolution_operator(E3, L3, False, False)]
d1, d2, d3 = (len(conv_matrices[i]) for i in xrange(3))
M1 = L3 + L2 + d1
M2 = L3 + d2
M3 = d3
shapes = [(L3, L2, L1), (L3, L2, M1), (L3, M2, M1), (M3, M2, M1)]
slice_maps = [[((t3, t2, slice(t3 + t2, t3 + t2 + d1)), (t3, t2))
for t3 in xrange(L3) for t2 in xrange(L2)],
[((t3, slice(t3, t3 + d2), sum12), (t3, slice(None), sum12))
for t3 in xrange(L3) for sum12 in xrange(M1)],
[((slice(None), sum23, T1), (slice(None), sum23, T1))
for sum23 in xrange(M2) for T1 in xrange(M1)]]
return conv_matrices, shapes, slice_maps
include_margin=True):
conv_mat, shapes, slice_maps = convolution_parameters(R.shape, E_all)
P3 = shift_time_indices(
reduce(loop_matvec, zip(conv_mat, shapes[1:], slice_maps), R), E_all,
trim, include_margin)
try:
return MetaArray(P3, ticks=expand_ticks(R, E_all), rw_freq=R.rw_freq)
except AttributeError:
return P3
def convolution_parameters((L3, L2, L1), E_all=((1, ), (1, ), (1, ))):
E3, E2, E1 = E_all
conv_matrices = [
convolution_operator(E1, L1, False, False),
convolution_operator(E2, L2, False, False),
convolution_operator(E3, L3, False, False)
]
d1, d2, d3 = (len(conv_matrices[i]) for i in xrange(3))
M1 = L3 + L2 + d1
M2 = L3 + d2
M3 = d3
shapes = [(L3, L2, L1), (L3, L2, M1), (L3, M2, M1), (M3, M2, M1)]
slice_maps = [[((t3, t2, slice(t3 + t2, t3 + t2 + d1)), (t3, t2))
for t3 in xrange(L3) for t2 in xrange(L2)],
[((t3, slice(t3, t3 + d2), sum12), (t3, slice(None), sum12))
for t3 in xrange(L3) for sum12 in xrange(M1)],
[((slice(None), sum23, T1), (slice(None), sum23, T1))
for sum23 in xrange(M2) for T1 in xrange(M1)]]
return conv_matrices, shapes, slice_maps
