dask.array.meshgrid
dask.array.meshgrid¶
- dask.array.meshgrid(*xi, sparse=False, indexing='xy', **kwargs)[source]¶
Return coordinate matrices from coordinate vectors.
This docstring was copied from numpy.meshgrid.
Some inconsistencies with the Dask version may exist.
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
- x1, x2,…, xnarray_like
1-D arrays representing the coordinates of a grid.
- indexing{‘xy’, ‘ij’}, optional
Cartesian (‘xy’, default) or matrix (‘ij’) indexing of output. See Notes for more details.
New in version 1.7.0.
- sparsebool, 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.
New in version 1.7.0.
- copybool, optional (Not supported in Dask)
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.New in version 1.7.0.
- Returns
- X1, X2,…, XNndarray
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.
See also
mgrid
Construct a multi-dimensional “meshgrid” using indexing notation.
ogrid
Construct an open multi-dimensional “meshgrid” using indexing notation.
Notes
This function supports both indexing conventions through the indexing keyword argument. Giving the string ‘ij’ returns a meshgrid with matrix indexing, while ‘xy’ returns a meshgrid with Cartesian indexing. In the 2-D case with inputs of length M and N, the outputs are of shape (N, M) for ‘xy’ indexing and (M, N) for ‘ij’ indexing. In the 3-D case with inputs of length M, N and P, outputs are of shape (N, M, P) for ‘xy’ indexing and (M, N, P) for ‘ij’ indexing. The difference is illustrated by the following code snippet:
xv, yv = np.meshgrid(x, y, indexing='ij') for i in range(nx): for j in range(ny): # treat xv[i,j], yv[i,j] xv, yv = np.meshgrid(x, y, indexing='xy') for i in range(nx): for j in range(ny): # treat xv[j,i], yv[j,i]
In the 1-D and 0-D case, the indexing and sparse keywords have no effect.
Examples
>>> nx, ny = (3, 2) >>> x = np.linspace(0, 1, nx) >>> y = np.linspace(0, 1, ny) >>> xv, yv = np.meshgrid(x, y) >>> xv array([[0. , 0.5, 1. ], [0. , 0.5, 1. ]]) >>> yv array([[0., 0., 0.], [1., 1., 1.]]) >>> xv, yv = np.meshgrid(x, y, sparse=True) # make sparse output arrays >>> xv array([[0. , 0.5, 1. ]]) >>> yv array([[0.], [1.]])
meshgrid is very useful to evaluate functions on a grid. If the function depends on all coordinates, you can use the parameter
sparse=True
to save memory and computation time.>>> x = np.linspace(-5, 5, 101) >>> y = np.linspace(-5, 5, 101) >>> # full coordinate arrays >>> xx, yy = np.meshgrid(x, y) >>> zz = np.sqrt(xx**2 + yy**2) >>> xx.shape, yy.shape, zz.shape ((101, 101), (101, 101), (101, 101)) >>> # sparse coordinate arrays >>> xs, ys = np.meshgrid(x, y, sparse=True) >>> zs = np.sqrt(xs**2 + ys**2) >>> xs.shape, ys.shape, zs.shape ((1, 101), (101, 1), (101, 101)) >>> np.array_equal(zz, zs) True
>>> import matplotlib.pyplot as plt >>> h = plt.contourf(x, y, zs) >>> plt.axis('scaled') >>> plt.colorbar() >>> plt.show()