def forward(self,
input_array=None,
output_array=None,
fast_transform=True,
padding_factor=1):
if padding_factor > 1:
assert self._padded_basis is not None
output_array = self._padded_basis.forward(
input_array, output_array, fast_transform=fast_transform)
return output_array
if fast_transform is False:
return SpectralBase.forward(self, input_array, output_array, False)
if input_array is not None:
self.forward.input_array[...] = input_array
self.forward.xfftn()
self._truncation_forward(self.forward.tmp_array,
self.forward.output_array)
M = self.get_normalization()
self.forward._output_array *= M
if output_array is not None:
output_array[...] = self.forward.output_array
return output_array
return self.forward.output_array
def forward(self, input_array=None, output_array=None, fast_transform=True):
if fast_transform is False:
return SpectralBase.forward(self, input_array, output_array, False)
if input_array is not None:
self.forward.input_array[...] = input_array
self.forward.xfftn()
self._truncation_forward(self.forward.tmp_array,
self.forward.output_array)
M = self.get_normalization()
self.forward._output_array *= M
if output_array is not None:
output_array[...] = self.forward.output_array
return output_array
return self.forward.output_array
def forward(self,
input_array=None,
output_array=None,
fast_transform=True):
if fast_transform is False:
return SpectralBase.forward(self, input_array, output_array, False)
if input_array is not None:
self.forward.input_array[...] = input_array
self.forward.xfftn()
self._truncation_forward(self.forward.tmp_array,
self.forward.output_array)
self.forward._output_array *= (1. / self.N / self.padding_factor)
if output_array is not None:
output_array[...] = self.forward.output_array
return output_array
return self.forward.output_array
