# NumPy mgrid vs. meshgrid

The
meshgrid function is useful for creating coordinate arrays to
vectorize function evaluations over a grid. Experienced NumPy users will have
noticed some discrepancy between `meshgrid`

and the `mgrid`

, a function
that is used just as often, for exactly the same purpose. What is the
discrepancy, and why does a discrepancy even exist when^{1} *“there should be one –
and preferably only one – obvious way to do it."*

First, recall that `meshgrid`

behaves as follows:

```
>>> import numpy as np
>>> x1, y1 = np.meshgrid(np.arange(1, 11, 2), np.arange(-12, -3, 3))
>>> x1 # 3x5 array
array([[1, 3, 5, 7, 9],
[1, 3, 5, 7, 9],
[1, 3, 5, 7, 9]])
>>> y1 # 3x5 array
array([[-12, -12, -12, -12, -12],
[ -9, -9, -9, -9, -9],
[ -6, -6, -6, -6, -6]])
```

If you have used NumPy for a while or are familiar enough with how
Broadcasting works, you will have realized that `meshgrid`

is actually
superfluous for NumPy arrays, and that it is actually just an implementation
of
MATLAB’s meshgrid, probably to cater to users coming from a MATLAB
background.

Observe the behavior of `mgrid`

, which essentially returns the transpose of
`meshgrid`

:

```
>>> x2, y2 = np.mgrid[1:11:2, -12:-3:3]
>>> x2 # 5x3 array
array([[1, 1, 1],
[3, 3, 3],
[5, 5, 5],
[7, 7, 7],
[9, 9, 9]])
>>> y2 # 5x3 array
array([[-12, -9, -6],
[-12, -9, -6],
[-12, -9, -6],
[-12, -9, -6],
[-12, -9, -6]])
>>> np.all(x1 == x2.T)
True
>>> np.all(y2 == y2.T)
True
```

Note this this order is actually more natural, since `mgrid`

just fleshes
out the open (not fleshed out) grids given by
ogrid by broadcasting them to
form dense grids, i.e.

```
>>> a, b = np.ogrid[1:11:2, -12:-3:3]
>>> a # 5x1 array
array([[1],
[3],
[5],
[7],
[9]])
>>> b # 1x3 array
array([[-12, -9, -6]])
```

and the *5x1* array `a`

is broadcasted with the *1x3* array `b`

to form
two *5x3* arrays

```
>>> x2, y2 = np.broadcast_arrays(a, b)
>>> x2 # 5x3 array
array([[1, 1, 1],
[3, 3, 3],
[5, 5, 5],
[7, 7, 7],
[9, 9, 9]])
>>> y2 # 5x3 array
array([[-12, -9, -6],
[-12, -9, -6],
[-12, -9, -6],
[-12, -9, -6],
[-12, -9, -6]])
```

which behaves exactly the same way as `mgrid`

. Note that you seldom have to
broadcast arrays explicitly, let alone use functions like `mgrid`

or
`meshgrid`

, since all arithmetic operations on NumPy arrays already perform
broadcasting implicitly. E.g.

```
>>> x2 + y2 # adding two 5x3 arrays
array([[-11, -8, -5],
[ -9, -6, -3],
[ -7, -4, -1],
[ -5, -2, 1],
[ -3, 0, 3]])
>>> a + b # adding a 5x1 array to a 1x3 array
array([[-11, -8, -5],
[ -9, -6, -3],
[ -7, -4, -1],
[ -5, -2, 1],
[ -3, 0, 3]])
```

Finally, if for some reason you must have output like that of `meshgrid`

,
just use `mgrid`

with the arguments and unpacking targets reversed.

```
>>> y3, x3 = np.mgrid[-12:-3:3, 1:11:2]
>>> x3 # 3x5 array
array([[1, 3, 5, 7, 9],
[1, 3, 5, 7, 9],
[1, 3, 5, 7, 9]])
>>> y3 # 3x5 array
array([[-12, -12, -12, -12, -12],
[ -9, -9, -9, -9, -9],
[ -6, -6, -6, -6, -6]])
>>> np.all(x1 == x3)
True
>>> np.all(y1 == y3)
True
```

## Uniformly-spaced meshgrids

At the very beginning, we created a meshgrid by specifying ranges and step
lengths using `np.arange`

. Suppose instead we just want to specify the number
of evenly-spaced points we’d like the meshgrid to include between some ranges.
In other words, we’re instead interested in using `np.linspace`

instead of
`np.arange`

:

```
>>> x1, y1 = np.meshgrid(np.linspace(-5, 5, 5),
... np.linspace(-12, -3, 3))
>>> x1 # 3x5 array
array([[-5. , -2.5, 0. , 2.5, 5. ],
[-5. , -2.5, 0. , 2.5, 5. ],
[-5. , -2.5, 0. , 2.5, 5. ]])
>>> y1 # 3x5 array
array([[-12. , -12. , -12. , -12. , -12. ],
[ -7.5, -7.5, -7.5, -7.5, -7.5],
[ -3. , -3. , -3. , -3. , -3. ]])
```

The `mgrid`

allows you to specify this by using a complex number (e.g. `5j`

)
as a step length. When the step length is a complex number, 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. Hence, to
achieve the above using `mgrid`

:

```
>>> y3, x3 = np.mgrid[-12:-3:3j,-5:5:5j]
>>> np.all(x1 == x3)
True
>>> np.all(y1 == y3)
True
```

In summary, while the `mgrid`

function is often overlooked, it is very general
and powerful, and subsumes many other functions in NumPy as special cases. It is
related to the `ogrid`

, and demonstrates the flexibility of NumPy
Broadcasting.

## Further Reading

PEP20 - The Zen of Python (https://www.python.org/dev/peps/pep-0020/) ↩︎