impy.arrays.bases package¶
Submodules¶
impy.arrays.bases.metaarray module¶
- class impy.arrays.bases.metaarray.MetaArray(obj, name: str | None = None, axes: str | None = None, source: str | Path | None = None, metadata: dict[str, Any] | None = None, dtype: DTypeLike = None)[source]¶
Bases:
impy.arrays.axesmixin.AxesMixin
,numpy.ndarray
- NP_DISPATCH = {<function squeeze>: <function _>, <function take>: <function _>, <function stack>: <function _>, <function concatenate>: <function _>, <function block>: <function _>, <function zeros_like>: <function _>, <function empty_like>: <function _>, <function expand_dims>: <function _>, <function transpose>: <function _>, <function split>: <function _>}¶
- additional_props = ['_source', '_metadata', '_name']¶
- apply_dask(func: Callable, c_axes: str | None = None, drop_axis: Iterable[int] = [], new_axis: Iterable[int] = None, dtype=<class 'numpy.float32'>, out_chunks: tuple[int, ...] = None, args: tuple[Any] = None, kwargs: dict[str, Any] = None) Self [source]¶
Convert array into dask array and run a batch process in parallel. In many cases batch process in this way is faster than multiprocess module.
- Parameters
func (callable) – Function to apply.
c_axes (str, optional) – Axes to iterate.
drop_axis (Iterable[int], optional) – Passed to map_blocks.
new_axis (Iterable[int], optional) – Passed to map_blocks.
dtype (any that can be converted to np.dtype object, default is np.float32) – Output data type.
out_chunks (tuple of int, optional) – Output chunks. This argument is important when the output shape will change.
args (tuple, optional) – Arguments that will passed to func.
kwargs (dict) – Keyword arguments that will passed to func.
- Returns
Processed array.
- Return type
- argmax_nd() tuple[int, ...] [source]¶
N-dimensional version of argmax.
For instance, if yx-array takes its maximum at (5, 8), this function returns
AxesShape(y=5, x=8)
.- Returns
Argmax of the array.
- Return type
AxesShape
- classmethod implements(numpy_function)[source]¶
Add functions to NP_DISPATCH so that numpy functions can be overloaded.
- property metadata: dict[str, typing.Any]¶
Metadata dictionary of the array.
- property name: str¶
Name of the array.
- property shape¶
Tuple of array dimensions.
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.
Examples
>>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]]) >>> y.shape = (3, 6) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: total size of new array must be unchanged >>> np.zeros((4,2))[::2].shape = (-1,) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: Incompatible shape for in-place modification. Use `.reshape()` to make a copy with the desired shape.
See also
numpy.reshape
similar function
ndarray.reshape
similar method
- property source¶
The source file path.
- split(axis=None) DataList[Self] [source]¶
Split n-dimensional image into (n-1)-dimensional images.
- Parameters
axis (str or int, optional) – Along which axis the original image will be split, by default “c”
- Returns
Separate images
- Return type
list of arrays
- property value: numpy.ndarray¶
Numpy view of the array.