diff options
Diffstat (limited to 'src/silx/utils/array_like.py')
| -rw-r--r-- | src/silx/utils/array_like.py | 84 |
1 files changed, 39 insertions, 45 deletions
diff --git a/src/silx/utils/array_like.py b/src/silx/utils/array_like.py index d9c7b73..b9b976b 100644 --- a/src/silx/utils/array_like.py +++ b/src/silx/utils/array_like.py @@ -46,8 +46,6 @@ Functions: """ -import sys - import numpy import numbers @@ -211,10 +209,9 @@ class ListOfImages(object): :param images: list of 2D numpy arrays, or :class:`ListOfImages` object :param transposition: Tuple of dimension numbers in the wanted order """ - def __init__(self, images, transposition=None): - """ - """ + def __init__(self, images, transposition=None): + """ """ super(ListOfImages, self).__init__() # if images is a ListOfImages instance, get the underlying data @@ -223,19 +220,16 @@ class ListOfImages(object): images = images.images # test stack of images is as expected - assert is_list_of_arrays(images), \ - "Image stack must be a list of arrays" + assert is_list_of_arrays(images), "Image stack must be a list of arrays" image0_shape = images[0].shape for image in images: - assert image.ndim == 2, \ - "Images must be 2D numpy arrays" - assert image.shape == image0_shape, \ - "All images must have the same shape" + assert image.ndim == 2, "Images must be 2D numpy arrays" + assert image.shape == image0_shape, "All images must have the same shape" self.images = images """List of images""" - self.shape = (len(images), ) + image0_shape + self.shape = (len(images),) + image0_shape """Tuple of array dimensions""" self.dtype = get_concatenated_dtype(images) """Data-type of the global array""" @@ -257,14 +251,14 @@ class ListOfImages(object): if transposition is not None: assert len(transposition) == self.ndim - assert set(transposition) == set(list(range(self.ndim))), \ - "Transposition must be a sequence containing all dimensions" + assert set(transposition) == set( + list(range(self.ndim)) + ), "Transposition must be a sequence containing all dimensions" self.transposition = transposition self.__sort_shape() def __sort_shape(self): - """Sort shape in the order defined in :attr:`transposition` - """ + """Sort shape in the order defined in :attr:`transposition`""" new_shape = tuple(self.shape[dim] for dim in self.transposition) self.shape = new_shape @@ -277,8 +271,9 @@ class ListOfImages(object): :return: Sorted tuple of indices, to access original data """ assert len(indices) == self.ndim - sorted_indices = tuple(idx for (_, idx) in - sorted(zip(self.transposition, indices))) + sorted_indices = tuple( + idx for (_, idx) in sorted(zip(self.transposition, indices)) + ) return sorted_indices def __array__(self, dtype=None): @@ -286,8 +281,9 @@ class ListOfImages(object): If a transposition has been done on this images, return a transposed view of a numpy array.""" - return numpy.transpose(numpy.array(self.images, dtype=dtype), - self.transposition) + return numpy.transpose( + numpy.array(self.images, dtype=dtype), self.transposition + ) def __len__(self): return self.shape[0] @@ -313,8 +309,7 @@ class ListOfImages(object): elif list(self.transposition) != list(range(self.ndim)): transposition = [self.transposition[i] for i in transposition] - return ListOfImages(self.images, - transposition) + return ListOfImages(self.images, transposition) @property def T(self): @@ -346,8 +341,9 @@ class ListOfImages(object): # n-dimensional slicing if len(item) != self.ndim: raise IndexError( - "N-dim slicing requires a tuple of N indices/slices. " + - "Needed dimensions: %d" % self.ndim) + "N-dim slicing requires a tuple of N indices/slices. " + + "Needed dimensions: %d" % self.ndim + ) # get list of indices sorted in the original images order sorted_indices = self.__sort_indices(item) @@ -379,8 +375,7 @@ class ListOfImages(object): # single list elements selected if isinstance(images_selection, numpy.ndarray): - return numpy.transpose(images_selection[array_idx], - axes=output_dimensions) + return numpy.transpose(images_selection[array_idx], axes=output_dimensions) # muliple list elements selected else: # apply selection first @@ -388,8 +383,7 @@ class ListOfImages(object): for img in images_selection: output_stack.append(img[array_idx]) # then cast into a numpy array, and transpose - return numpy.transpose(numpy.array(output_stack), - axes=output_dimensions) + return numpy.transpose(numpy.array(output_stack), axes=output_dimensions) def min(self): """ @@ -428,10 +422,9 @@ class DatasetView(object): :param transposition: List of dimensions sorted in the order of transposition (relative to the original h5py dataset) """ - def __init__(self, dataset, transposition=None): - """ - """ + def __init__(self, dataset, transposition=None): + """ """ super(DatasetView, self).__init__() self.dataset = dataset """original dataset""" @@ -463,14 +456,14 @@ class DatasetView(object): if transposition is not None: assert len(transposition) == self.ndim - assert set(transposition) == set(list(range(self.ndim))), \ - "Transposition must be a list containing all dimensions" + assert set(transposition) == set( + list(range(self.ndim)) + ), "Transposition must be a list containing all dimensions" self.transposition = transposition self.__sort_shape() def __sort_shape(self): - """Sort shape in the order defined in :attr:`transposition` - """ + """Sort shape in the order defined in :attr:`transposition`""" new_shape = tuple(self.shape[dim] for dim in self.transposition) self.shape = new_shape @@ -483,8 +476,9 @@ class DatasetView(object): :return: Sorted tuple of indices, to access original data """ assert len(indices) == self.ndim - sorted_indices = tuple(idx for (_, idx) in - sorted(zip(self.transposition, indices))) + sorted_indices = tuple( + idx for (_, idx) in sorted(zip(self.transposition, indices)) + ) return sorted_indices def __getitem__(self, item): @@ -516,8 +510,9 @@ class DatasetView(object): # n-dimensional slicing if len(item) != self.ndim: raise IndexError( - "N-dim slicing requires a tuple of N indices/slices. " + - "Needed dimensions: %d" % self.ndim) + "N-dim slicing requires a tuple of N indices/slices. " + + "Needed dimensions: %d" % self.ndim + ) # get list of indices sorted in the original dataset order sorted_indices = self.__sort_indices(item) @@ -546,16 +541,16 @@ class DatasetView(object): assert (len(output_dimensions) + len(frozen_dimensions)) == self.ndim assert set(output_dimensions) == set(range(len(output_dimensions))) - return numpy.transpose(output_data_not_transposed, - axes=output_dimensions) + return numpy.transpose(output_data_not_transposed, axes=output_dimensions) def __array__(self, dtype=None): """Cast the dataset into a numpy array, and return it. If a transposition has been done on this dataset, return a transposed view of a numpy array.""" - return numpy.transpose(numpy.array(self.dataset, dtype=dtype), - self.transposition) + return numpy.transpose( + numpy.array(self.dataset, dtype=dtype), self.transposition + ) def __len__(self): return self.shape[0] @@ -580,8 +575,7 @@ class DatasetView(object): elif list(self.transposition) != list(range(self.ndim)): transposition = [self.transposition[i] for i in transposition] - return DatasetView(self.dataset, - transposition) + return DatasetView(self.dataset, transposition) @property def T(self): |