meshgrid(*xi, copy=True, sparse=False, indexing='xy')
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.
1-D and 0-D cases are allowed.
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.
1-D arrays representing the coordinates of a grid.
Cartesian ('xy', default) or matrix ('ij') indexing of output. See Notes for more details.
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 basics.broadcasting
. When all coordinates are used in an expression, broadcasting still leads to a fully-dimensonal result array.
Default is False.
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.
For vectors x1
, :None:None:`x2`,..., 'xn' with lengths Ni=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 of :None:None:`xi` repeated to fill the matrix along the first dimension for x1
, the second for :None:None:`x2` and so on.
Return coordinate matrices from coordinate vectors.
mgrid
Construct a multi-dimensional "meshgrid" using indexing notation.
ogrid
Construct an open multi-dimensional "meshgrid" using indexing notation.
>>> 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 coorindate 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()
The following pages refer to to this document either explicitly or contain code examples using this.
matplotlib.axes._axes.Axes.contour
matplotlib.pyplot.contourf
scipy.interpolate._interpolate.RegularGridInterpolator
numpy.meshgrid
matplotlib.axes._axes.Axes.contourf
scipy.interpolate._fitpack2.RectSphereBivariateSpline
numpy.ix_
scipy.fft._basic.fftn
scipy.interpolate._fitpack2.LSQSphereBivariateSpline
numpy.fromfunction
dask.array.creation.meshgrid
scipy.interpolate._fitpack2.SmoothSphereBivariateSpline
scipy.optimize._optimize.rosen
scipy.interpolate._interpolate.interpn
scipy.interpolate._interpolate.interp2d
matplotlib.pyplot.contour
scipy.interpolate.interpnd.LinearNDInterpolator
numpy.indices
scipy.interpolate._ndgriddata.NearestNDInterpolator
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