jax.numpy.asarrayΒΆ
-
jax.numpy.
asarray
(a, dtype=None, order=None)[source]ΒΆ Convert the input to an array.
LAX-backend implementation of
asarray()
. Original docstring below.- Parameters
a (array_like) β Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.
dtype (data-type, optional) β By default, the data-type is inferred from the input data.
order ({'C', 'F'}, optional) β Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to βCβ.
- Returns
out β Array interpretation of a. No copy is performed if the input is already an ndarray with matching dtype and order. If a is a subclass of ndarray, a base class ndarray is returned.
- Return type
See also
asanyarray()
Similar function which passes through subclasses.
ascontiguousarray()
Convert input to a contiguous array.
asfarray()
Convert input to a floating point ndarray.
asfortranarray()
Convert input to an ndarray with column-major memory order.
asarray_chkfinite()
Similar function which checks input for NaNs and Infs.
fromiter()
Create an array from an iterator.
fromfunction()
Construct an array by executing a function on grid positions.
Examples
Convert a list into an array:
>>> a = [1, 2] >>> np.asarray(a) array([1, 2])
Existing arrays are not copied:
>>> a = np.array([1, 2]) >>> np.asarray(a) is a True
If dtype is set, array is copied only if dtype does not match:
>>> a = np.array([1, 2], dtype=np.float32) >>> np.asarray(a, dtype=np.float32) is a True >>> np.asarray(a, dtype=np.float64) is a False
Contrary to asanyarray, ndarray subclasses are not passed through:
>>> issubclass(np.recarray, np.ndarray) True >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray) >>> np.asarray(a) is a False >>> np.asanyarray(a) is a True