The purpose of `meshgrid`

is to create a rectangular grid out of an array of x values and an array of y values.

So, for example, if we want to create a grid where we have a point at each integer value between 0 and 4 in both the x and y directions. To create a rectangular grid, we need every combination of the `x`

and `y`

points.

This is going to be 25 points, right? So if we wanted to create an x and y array for all of these points, we *could* do the following.

x[0,0] = 0 y[0,0] = 0 x[0,1] = 1 y[0,1] = 0 x[0,2] = 2 y[0,2] = 0 x[0,3] = 3 y[0,3] = 0 x[0,4] = 4 y[0,4] = 0 x[1,0] = 0 y[1,0] = 1 x[1,1] = 1 y[1,1] = 1 ... x[4,3] = 3 y[4,3] = 4 x[4,4] = 4 y[4,4] = 4

This would result in the following `x`

and `y`

matrices, such that the pairing of the corresponding element in each matrix gives the x and y coordinates of a point in the grid.

x = 0 1 2 3 4 y = 0 0 0 0 0 0 1 2 3 4 1 1 1 1 1 0 1 2 3 4 2 2 2 2 2 0 1 2 3 4 3 3 3 3 3 0 1 2 3 4 4 4 4 4 4

We can then plot these to verify that they are a grid:

plt.plot(x,y, marker='.', color='k', linestyle='none')

Obviously, this gets very tedious especially for large ranges of `x`

and `y`

. Instead, `meshgrid`

can actually generate this for us: all we have to specify are the unique `x`

and `y`

values.

xvalues = np.array([0, 1, 2, 3, 4]); yvalues = np.array([0, 1, 2, 3, 4]);

Now, when we call `meshgrid`

, we get the previous output automatically.

xx, yy = np.meshgrid(xvalues, yvalues) plt.plot(xx, yy, marker='.', color='k', linestyle='none')

Creation of these rectangular grids is useful for a number of tasks. In the example that you have provided in your post, it is simply a way to sample a function (`sin(x**2 + y**2) / (x**2 + y**2)`

) over a range of values for `x`

and `y`

.

Because this function has been sampled on a rectangular grid, the function can now be visualized as an “image”.

Additionally, the result can now be passed to functions which expect data on rectangular grid (i.e. `contourf`

)