xorbits.numpy.asfortranarray#
- xorbits.numpy.asfortranarray(a, dtype=None, *, like=None)[source]#
Return an array (ndim >= 1) laid out in Fortran order in memory.
- Parameters
a (array_like) – Input array.
dtype (str or dtype object, optional) – By default, the data-type is inferred from the input data.
like (array_like, optional) –
Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as
like
supports the__array_function__
protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.New in version 1.20.0(numpy).
- Returns
out – The input a in Fortran, or column-major, order.
- Return type
ndarray
See also
ascontiguousarray
Convert input to a contiguous (C order) array.
asanyarray
Convert input to an ndarray with either row or column-major memory order.
require
Return an ndarray that satisfies requirements.
ndarray.flags
Information about the memory layout of the array.
Examples
Starting with a C-contiguous array:
>>> x = np.ones((2, 3), order='C') >>> x.flags['C_CONTIGUOUS'] True
Calling
asfortranarray
makes a Fortran-contiguous copy:>>> y = np.asfortranarray(x) >>> y.flags['F_CONTIGUOUS'] True >>> np.may_share_memory(x, y) False
Now, starting with a Fortran-contiguous array:
>>> x = np.ones((2, 3), order='F') >>> x.flags['F_CONTIGUOUS'] True
Then, calling
asfortranarray
returns the same object:>>> y = np.asfortranarray(x) >>> x is y True
Note: This function returns an array with at least one-dimension (1-d) so it will not preserve 0-d arrays.
This docstring was copied from numpy.