def _correlate(in1, in2, mode='full', method='auto', convolution=False):
quick_out = _st_core._check_conv_inputs(in1, in2, mode, convolution)
if quick_out is not None:
return quick_out
if method not in ('auto', 'direct', 'fft'):
raise ValueError('acceptable methods are "auto", "direct", or "fft"')
if method == 'auto':
method = choose_conv_method(in1, in2, mode=mode)
if method == 'direct':
return _st_core._direct_correlate(in1, in2, mode, in1.dtype,
convolution)
# if method == 'fft':
inputs_swapped = _st_core._inputs_swap_needed(mode, in1.shape, in2.shape)
if inputs_swapped:
in1, in2 = in2, in1
if not convolution:
in2 = _st_core._reverse_and_conj(in2)
out = fftconvolve(in1, in2, mode)
result_type = cupy.result_type(in1, in2)
if result_type.kind in 'ui':
out = out.round()
out = out.astype(result_type, copy=False)
if not convolution and inputs_swapped:
out = cupy.ascontiguousarray(_st_core._reverse_and_conj(out))
return out
def _correlate(in1, in2, mode='full', method='auto', convolution=False):
quick_out = _st_core._check_conv_inputs(in1, in2, mode, convolution)
if quick_out is not None:
return quick_out
if method not in ('auto', 'direct', 'fft'):
raise ValueError("acceptable methods are 'auto', 'direct', or 'fft'")
if method == 'auto':
method = choose_conv_method(in1, in2, mode=mode)
if method == 'direct':
return _st_core._direct_correlate(in1, in2, mode, in1.dtype,
convolution)
# if method == 'fft':
raise ValueError('fftconvolve currently not supported')
def _correlate2d(in1, in2, mode, boundary, fillvalue, convolution=False):
if not (in1.ndim == in2.ndim == 2):
raise ValueError('{} inputs must both be 2-D arrays'.format(
'convolve2d' if convolution else 'correlate2d'))
_boundaries = {
'fill': 'constant', 'pad': 'constant',
'wrap': 'wrap', 'circular': 'wrap',
'symm': 'reflect', 'symmetric': 'reflect',
}
boundary = _boundaries.get(boundary)
if boundary is None:
raise ValueError('Acceptable boundary flags are "fill" (or "pad"), '
'"circular" (or "wrap"), and '
'"symmetric" (or "symm").')
quick_out = _st_core._check_conv_inputs(in1, in2, mode, convolution)
if quick_out is not None:
return quick_out
return _st_core._direct_correlate(in1, in2, mode, in1.dtype, convolution,
boundary, fillvalue, not convolution)
