jax.numpy.meshgrid#
- jax.numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy')[source]#
Return coordinate matrices from coordinate vectors.
LAX-backend implementation of
numpy.meshgrid()
.The JAX version of this function may in some cases return a copy rather than a view of the input.
Original docstring below.
Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.
Changed in version 1.9: 1-D and 0-D cases are allowed.
- Parameters
indexing ({'xy', 'ij'}, optional) – Cartesian (‘xy’, default) or matrix (‘ij’) indexing of output. See Notes for more details.
sparse (bool, optional) –
If True the shape of the returned coordinate array for dimension i is reduced from
(N1, ..., Ni, ... Nn)
to(1, ..., 1, Ni, 1, ..., 1)
. These sparse coordinate grids are intended to be use with Broadcasting. When all coordinates are used in an expression, broadcasting still leads to a fully-dimensonal result array.Default is False.
copy (bool, optional) – If False, a view into the original arrays are returned in order to conserve memory. Default is True. Please note that
sparse=False, copy=False
will likely return non-contiguous arrays. Furthermore, more than one element of a broadcast array may refer to a single memory location. If you need to write to the arrays, make copies first.xi (
Union
[Array
,ndarray
,bool_
,number
,bool
,int
,float
,complex
]) –
- Returns
X1, X2,…, XN – For vectors x1, x2,…, xn with lengths
Ni=len(xi)
, returns(N1, N2, N3,..., Nn)
shaped arrays if indexing=’ij’ or(N2, N1, N3,..., Nn)
shaped arrays if indexing=’xy’ with the elements of xi repeated to fill the matrix along the first dimension for x1, the second for x2 and so on.- Return type
ndarray