# numpy.mgrid#

numpy.mgrid = <numpy.lib._index_tricks_impl.MGridClass object>#

An instance which returns a dense multi-dimensional “meshgrid”.

An instance which returns a dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. The dimensions and number of the output arrays are equal to the number of indexing dimensions. If the step length is not a complex number, then the stop is not inclusive.

However, if the step length is a complex number (e.g. 5j), then the integer part of its magnitude is interpreted as specifying the number of points to create between the start and stop values, where the stop value is inclusive.

Returns:
mesh-gridndarray

A single array, containing a set of `ndarray`s all of the same dimensions. stacked along the first axis.

`ogrid`

like `mgrid` but returns open (not fleshed out) mesh grids

`meshgrid`

return coordinate matrices from coordinate vectors

`r_`

array concatenator

How to create arrays with regularly-spaced values

Examples

```>>> import numpy as np
>>> np.mgrid[0:5, 0:5]
array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]])
>>> np.mgrid[-1:1:5j]
array([-1. , -0.5,  0. ,  0.5,  1. ])
```
```>>> np.mgrid[0:4].shape
(4,)
>>> np.mgrid[0:4, 0:5].shape
(2, 4, 5)
>>> np.mgrid[0:4, 0:5, 0:6].shape
(3, 4, 5, 6)
```