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NumPy | meshgrid method

schedule Aug 12, 2023
Last updated
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Numpy's meshgrid(~) method returns a grid that is useful in plotting contour plots of 3D graphs. Since it is difficult to explain in words what this method does, consult the examples below.

Parameters

1. x1 | array-like

The input array to make a grid out of.

2. x2 | array-like | optional

If you want a 2D grid, then this is required.

* Note that you could add in more arrays (i.e. x3, x4,...) if desired.

3. sparse | boolean | optional

Whether to return a sparse grid. Set this to True if you're dealing with large volumes of data that cannot be fit into memory. By default, sparse=False.

4. copy | boolean | optional

If True, then a new Numpy array is created and returned. If False, then view is returned so as to save memory. By default, copy=True.

Return value

If only x1 is specified, then a Numpy array is returned. Otherwise, a tuple of Numpy arrays will be returned.

Examples

Visualising the mesh grid

Let us create a 2D mesh grid:

x = [1,2,3,4]
y = [5,6,7,8]
xx, yy = np.meshgrid(x, y)

Let's unveil the content of the returned arrays:

print(xx)
[[1 2 3 4]
[1 2 3 4]
[1 2 3 4]
[1 2 3 4]]

Here's yy:

print(yy)
[[5 5 5 5]
[6 6 6 6]
[7 7 7 7]
[8 8 8 8]]

Using Matplotlib, we can graph them like so:

plt.scatter(xx, yy)
plt.show()

This gives us the following:

Drawing a contour plot

Let's now talk about the practical benefits of having these data-points. They come in handy when we draw contour plots of 3D functions.

Here's an example:

def f(x, y):
return x**2 + y **2

We can draw the contour like so:

x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
xx, yy = np.meshgrid(x, y)
zz = f(xx, yy)
plt.contour(xx, yy, zz)
plt.show()

This gives us the following contour plot:

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Published by Isshin Inada
Edited by 0 others
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