Dominik Farhan

A Simple Way to Show 2D Transformations with Matplotlib


While studying linear algebra it might be nice to visualize some 2D transformations to get a feeling what those different matrices actually do. It is also an amazing way to procrastinate when you are in need to study for a midterm :).


We will be using matplotlib.pyplot. By searching the web it seems there are two obvious ways to do it. You can use streamplot or simple quiver. Although you can do some really cool stuff with streamplot (like this) I think quiver can look good without much work whereas to make streamplot appealing one must fiddle with more parameters. To keep this simple (and allow me to get back to studying) we will stick with quiver.

We can construct the plot with simple function.

def show_plot(X, Y, U, V):
      plt.figure(figsize=(7, 7))
      plt.quiver(X, Y, X, Y, color='dodgerblue')
      plt.quiver(X, Y, U, V, color='red')

All parameters are matrices. The first two specify the starting points of vectors. The other two their directions. Observe the line plt.quiver(X, Y, X, Y, color='dodgerblue'). All it does is that it displays blue vectors as if no transformation was applied. This makes it easier to see the difference between the original vectors and the resulting.

To generate the first two matrices we can use numpy:

X = np.arange(-10, 10, 1.5)
		Y = np.arange(-10, 10, 1.5)
		X, Y = np.meshgrid(X, Y)
With this, we can easily change the density of vectors by changing the 1.51.5 step parameter.

Now, because we would like to study matrices we need to find a way to apply them to the given X/Y field. Here is not very effective but a straightforward way to transform the plane with a 2×22\times 2 matrix.

def make_vec(x, y):
      return np.array([x, y])

  def apply_transformation(f = np.eye(2)):
      np.testing.assert_array_equal(X.shape, Y.shape)
      out_X = np.empty(X.shape)
      out_Y = np.empty(X.shape)
      for i, (row, column) in enumerate(zip(X, Y)):
          for j, (x, y) in enumerate(zip(row, column)):
              vec = make_vec(x, y)
              out_vec = f @ vec
              out_X[i][j] = out_vec[0]
              out_Y[i][j] = out_vec[1]
       return out_X, out_Y


This is pretty much all we need. Here are some results :)

Reflection, axis of the 1st and 4th quadrants
45°45 \degree rotation
Something quite random where no vector should end where it started
Orthogonal projection on the line (2,1)T(2, 1)^T

I realized this only later but this approach won't allow us to rescale vectors easily (no homothety, sorry). However, it should be possible to change the scale with parameters to quiver.

Lastly, here is a link for the code.