numpy.meshgrid(*xi, **kwargs)
[source]
Return coordinate matrices from coordinate vectors.
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,…, xn : array_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. sparse : bool, optional If True a sparse grid is returned in order to conserve memory. Default is False. New in version 1.7.0. copy : bool, optional If False, a view into the original arrays are returned in order to conserve memory. Default is True. Please note that New in version 1.7.0. |
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Returns: |
X1, X2,…, XN : ndarray For vectors |
See also
index_tricks.mgrid
index_tricks.ogrid
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, sparse=False, 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, sparse=False, 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.
>>> 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.
>>> x = np.arange(-5, 5, 0.1) >>> y = np.arange(-5, 5, 0.1) >>> xx, yy = np.meshgrid(x, y, sparse=True) >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2) >>> h = plt.contourf(x,y,z)
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https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.meshgrid.html