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