def _correlate_and_convolve_body(vector, filter, d, mode):
""" The body of correlate() and convolve() are identical"""
# Anything to do?
if vector.size <= 0:
return vector.copy()
# Complex correlate includes a conjugation
if numpy.iscomplexobj(filter):
filter = numpy.conj(filter)
dtype = numpy.result_type(vector, filter)
rows = int(
ufuncs.ceil((vector.size + 2 * filter.size - 2) / float(filter.size)))
padded = array_create.empty([rows * filter.size], dtype=dtype)
padded[0:filter.size - 1] = 0
padded[filter.size - 1:vector.size + filter.size - 1] = vector
padded[vector.size + filter.size - 1:] = 0
s = numpy_backport.as_strided(padded,
shape=(padded.shape[0] - filter.size + 1,
filter.size),
strides=(padded.strides[0],
padded.strides[0]))
result = linalg.dot(s, filter)
if mode == 'same':
return result[d:vector.size + d]
elif mode == 'full':
return result[0:vector.size + filter.size - 1]
elif mode == 'valid':
return result[filter.size - 1:vector.size]
else:
raise ValueError("correlate1d: invalid mode '%s'" % mode)
def _correlate_and_convolve_body(vector, filter, d, mode):
""" The body of correlate() and convolve() are identical"""
# Anything to do?
if vector.size <= 0:
return vector.copy()
# Complex correlate includes a conjugation
if numpy.iscomplexobj(filter):
filter = numpy.conj(filter)
dtype = numpy.result_type(vector, filter)
rows = int(ufuncs.ceil((vector.size + 2 * filter.size - 2) / float(filter.size)))
padded = array_create.empty([rows * filter.size], dtype=dtype)
padded[0:filter.size - 1] = 0
padded[filter.size - 1:vector.size + filter.size - 1] = vector
padded[vector.size + filter.size - 1:] = 0
s = numpy_backport.as_strided(padded, shape=(padded.shape[0] - filter.size + 1, filter.size),
strides=(padded.strides[0], padded.strides[0]))
result = linalg.dot(s, filter)
if mode == 'same':
return result[d:vector.size + d]
elif mode == 'full':
return result[0:vector.size + filter.size - 1]
elif mode == 'valid':
return result[filter.size - 1:vector.size]
else:
raise ValueError("correlate1d: invalid mode '%s'" % mode)
def _correlate_kernel(Array, Filter, mode):
# Anything to do?
if Array.size <= 0:
return Array.copy()
# Complex correlate includes a conjugation
if numpy.iscomplexobj(Filter):
Filter = numpy.conj(Filter)
# Get sizes as arrays for easier manipulation
ArrSize = numpy.array(Array.shape, dtype=numpy.int32)
FilterSize = numpy.array(Filter.shape, dtype=numpy.int32)
# Check that mode='valid' is allowed given the array sizes
if mode == 'valid':
diffSize = ArrSize[:FilterSize.size] - FilterSize
nSmaller = summations.sum(diffSize < 0)
if nSmaller > 0:
raise ValueError(
"correlateNd: For 'valid' mode, one must be at least as large as the other in every dimension")
# Use numpy convention for result dype
dtype = numpy.result_type(Array, Filter)
# Add zeros along relevant dimensions
Padded = _addZerosNd(Array, FilterSize, dtype)
PaddedSize = numpy.array(Padded.shape, dtype=numpy.int32)
# Get positions of first view
IndiVec = _findIndices(PaddedSize, FilterSize)
CenterPos = tuple((FilterSize - 1) // 2)
IndiMat = IndiVec.reshape(FilterSize, order='F')
nPre = IndiMat[CenterPos] # Required zeros before Array for correct alignment
nPost = IndiVec[Filter.size - 1] - nPre
n = Padded.size
nTot = n + nPre + nPost # Total size after pre/post padding
V = array_create.empty([nTot], dtype=dtype, bohrium=bhary.check(Array))
V[nPre:n + nPre] = Padded.flatten(order='F')
V[:nPre] = 0
V[n + nPre:] = 0
A = Filter.flatten(order='F')
# Actual correlation calculation
Correlated = V[IndiVec[0]:n + IndiVec[0]] * A[0]
for i in range(1, Filter.size):
Correlated += V[IndiVec[i]:n + IndiVec[i]] * A[i]
# TODO: we need this flush because of very slow fusion
if bhary.check(V):
_bh.flush()
Full = Correlated.reshape(PaddedSize, order='F')
if mode == 'full':
return Full
elif mode == 'same':
return _findSame(Full, FilterSize)
elif mode == 'valid':
return _findValid(Full, FilterSize)
else:
raise ValueError("correlateNd: invalid mode '%s'" % mode)
def _correlate_kernel(Array, Filter, mode):
# Anything to do?
if Array.size <= 0:
return Array.copy()
# Complex correlate includes a conjugation
if numpy.iscomplexobj(Filter):
Filter = numpy.conj(Filter)
# Get sizes as arrays for easier manipulation
ArrSize = array_create.array(Array.shape, dtype=numpy.int32)
FilterSize = array_create.array(Filter.shape, dtype=numpy.int32)
# Check that mode='valid' is allowed given the array sizes
if mode == 'valid':
diffSize = ArrSize[:FilterSize.size] - FilterSize
nSmaller = summations.sum(diffSize < 0)
if nSmaller > 0:
raise ValueError(
"correlateNd: For 'valid' mode, one must be at least as large as the other in every dimension"
)
# Use numpy convention for result dype
dtype = numpy.result_type(Array, Filter)
# Add zeros along relevant dimensions
Padded = _addZerosNd(Array, FilterSize, dtype)
PaddedSize = array_create.array(Padded.shape, dtype=numpy.int32)
# Get positions of first view
IndiVec = _findIndices(PaddedSize, FilterSize)
CenterPos = tuple((FilterSize - 1) // 2)
IndiMat = IndiVec.reshape(FilterSize, order='F')
nPre = IndiMat[
CenterPos] # Required zeros before Array for correct alignment
nPost = IndiVec[Filter.size - 1] - nPre
n = Padded.size
nTot = n + nPre + nPost # Total size after pre/post padding
V = array_create.empty([nTot], dtype=dtype)
V[nPre:n + nPre] = Padded.flatten(order='F')
V[:nPre] = 0
V[n + nPre:] = 0
A = Filter.flatten(order='F')
# Actual correlation calculation
Correlated = V[IndiVec[0]:n + IndiVec[0]] * A[0]
for i in range(1, Filter.size):
Correlated += V[IndiVec[i]:n + IndiVec[i]] * A[i]
Full = Correlated.reshape(PaddedSize, order='F')
if mode == 'full':
return Full
elif mode == 'same':
return _findSame(Full, FilterSize)
elif mode == 'valid':
return _findValid(Full, FilterSize)
else:
raise ValueError("correlateNd: invalid mode '%s'" % mode)