diff --git a/nifty/minimization/line_searching/line_search.py b/nifty/minimization/line_searching/line_search.py index f65b3ea6b0eeb94d591788f7667eefbb3549b5d4..558e1bc4ad0460e9151c70d263dc34f07a101d91 100644 --- a/nifty/minimization/line_searching/line_search.py +++ b/nifty/minimization/line_searching/line_search.py @@ -7,7 +7,7 @@ from nifty import LineEnergy class LineSearch(object, Loggable): """ - Class for finding a step size.◙ + Class for finding a step size. """ __metaclass__ = abc.ABCMeta diff --git a/nifty/operators/fft_operator/transformations/rg_transforms.py b/nifty/operators/fft_operator/transformations/rg_transforms.py index 01105710ff521af74ea51171d3954695bc28e371..7e9f59c660c7cc8cc97fa46ff98379077c26ac80 100644 --- a/nifty/operators/fft_operator/transformations/rg_transforms.py +++ b/nifty/operators/fft_operator/transformations/rg_transforms.py @@ -248,17 +248,15 @@ class FFTW(Transform): # val must be numpy array or d2o with slicing distributor ### - local_offset_Q = False try: local_val = val.get_local_data(copy=False) - if axes is None or 0 in axes: - local_offset_Q = val.distributor.local_shape[0] % 2 except(AttributeError): local_val = val + current_info = self._get_transform_info(self.domain, self.codomain, local_shape=local_val.shape, - local_offset_Q=local_offset_Q, + local_offset_Q=False, is_local=True, **kwargs) @@ -309,14 +307,10 @@ class FFTW(Transform): def _mpi_transform(self, val, axes, **kwargs): - if axes is None or 0 in axes: - local_offset_list = np.cumsum( - np.concatenate([[0, ], val.distributor.all_local_slices[:, 2]]) - ) - local_offset_Q = bool( - local_offset_list[val.distributor.comm.rank] % 2) - else: - local_offset_Q = False + local_offset_list = np.cumsum( + np.concatenate([[0, ], val.distributor.all_local_slices[:, 2]]) + ) + local_offset_Q = bool(local_offset_list[val.distributor.comm.rank] % 2) return_val = val.copy_empty(global_shape=val.shape, dtype=self.codomain.dtype)