def _correlate_or_convolve(input, weights, output, mode, cval, origin,
convolution):
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
origins = _ni_support._normalize_sequence(origin, input.rank)
weights = numarray.asarray(weights, type=numarray.Float64)
wshape = [ii for ii in weights.shape if ii > 0]
if len(wshape) != input.rank:
raise RuntimeError, 'filter weights array has incorrect shape.'
if convolution:
weights = weights[tuple([slice(None, None, -1)] * weights.rank)]
for ii in range(len(origins)):
origins[ii] = -origins[ii]
if not weights.shape[ii] & 1:
origins[ii] -= 1
for origin, lenw in zip(origins, wshape):
if (lenw // 2 + origin < 0) or (lenw // 2 + origin > lenw):
raise ValueError, 'invalid origin'
if not weights.iscontiguous():
weights = weights.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate(input, weights, output, mode, cval, origins)
return return_value
def _correlate_or_convolve(input, weights, output, mode, cval, origin,
convolution):
input = numpy.asarray(input)
if numpy.iscomplexobj(int):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
weights = numpy.asarray(weights, dtype=numpy.float64)
wshape = [ii for ii in weights.shape if ii > 0]
if len(wshape) != input.ndim:
raise RuntimeError('filter weights array has incorrect shape.')
if convolution:
weights = weights[tuple([slice(None, None, -1)] * weights.ndim)]
for ii in range(len(origins)):
origins[ii] = -origins[ii]
if not weights.shape[ii] & 1:
origins[ii] -= 1
for origin, lenw in zip(origins, wshape):
if (lenw // 2 + origin < 0) or (lenw // 2 + origin > lenw):
raise ValueError('invalid origin')
if not weights.flags.contiguous:
weights = weights.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate(input, weights, output, mode, cval, origins)
return return_value
def _correlate_or_convolve(input, weights, output, mode, cval, origin, convolution):
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, "Complex type not supported"
origins = _ni_support._normalize_sequence(origin, input.rank)
weights = numarray.asarray(weights, type=numarray.Float64)
wshape = [ii for ii in weights.shape if ii > 0]
if len(wshape) != input.rank:
raise RuntimeError, "filter weights array has incorrect shape."
if convolution:
weights = weights[tuple([slice(None, None, -1)] * weights.rank)]
for ii in range(len(origins)):
origins[ii] = -origins[ii]
if not weights.shape[ii] & 1:
origins[ii] -= 1
for origin, lenw in zip(origins, wshape):
if (lenw // 2 + origin < 0) or (lenw // 2 + origin > lenw):
raise ValueError, "invalid origin"
if not weights.iscontiguous():
weights = weights.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate(input, weights, output, mode, cval, origins)
return return_value