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numpy.meshgrid(*xi, **kwargs)
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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
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,..., XN : ndarray
For vectors
x1
,x2
,..., ?xn? with lengthsNi=len(xi)
, return(N1, N2, N3,...Nn)
shaped arrays if indexing=?ij? or(N2, N1, N3,...Nn)
shaped arrays if indexing=?xy? with the elements ofxi
repeated to fill the matrix along the first dimension forx1
, the second forx2
and so on.See also
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index_tricks.mgrid
- Construct a multi-dimensional ?meshgrid? using indexing notation.
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index_tricks.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 = 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 = 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.
Examples
>>> nx, ny = (3, 2) >>> x = np.linspace(0, 1, nx) >>> y = np.linspace(0, 1, ny) >>> xv, yv = meshgrid(x, y) >>> xv array([[ 0. , 0.5, 1. ], [ 0. , 0.5, 1. ]]) >>> yv array([[ 0., 0., 0.], [ 1., 1., 1.]]) >>> xv, yv = 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 = 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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numpy.meshgrid()
2017-01-10 18:16:05
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