`np.meshgrid()`

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 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 xi repeated to fill the matrix along the first dimension for x1, the second for x2 and so on.



In [1]:

    
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline



In [2]:

    
x = np.linspace(1.0, 6.0, 5)
y = np.linspace(12.0, 15.0, 3)



In [3]:

    
X,Y = np.meshgrid(x,y, indexing='xy')

indexing='xy' (default)

In this case X and Y are of size: (len(y), len(x))

For the above example:
- X.shape = (3,5)
- Y.shape = (3,5)
If indexing='ij':

In this case X and Y are of size: (len(x), len(y))

For the above example:
- X.shape = (5,3)
- Y.shape = (5,3)



In [4]:

    
X.shape









    Out[4]:





(3, 5)



In [5]:

    
X









    Out[5]:





array([[ 1.  ,  2.25,  3.5 ,  4.75,  6.  ],
       [ 1.  ,  2.25,  3.5 ,  4.75,  6.  ],
       [ 1.  ,  2.25,  3.5 ,  4.75,  6.  ]])



In [6]:

    
Y.shape









    Out[6]:





(3, 5)



In [7]:

    
Y









    Out[7]:





array([[ 12. ,  12. ,  12. ,  12. ,  12. ],
       [ 13.5,  13.5,  13.5,  13.5,  13.5],
       [ 15. ,  15. ,  15. ,  15. ,  15. ]])

Notice that the starting point (X,Y)[0,0] is in the top-left corner.



In [8]:

    
im = plt.imshow(X)
cb = plt.colorbar(im)



In [9]:

    
im = plt.imshow(Y)
cb = plt.colorbar(im)