Can matplotlib outlines match pixel edges?

Question

How to matplotlib pixel borders in matplotlib ? For example, for a semi-random dataset like the one below,

 # the code block that follows is irrelevant import numpy as np k = [] for s in [2103, 1936, 2247, 2987]: np.random.seed(s) k.append(np.random.randint(0, 2, size=(2,6))) arr = np.hstack([np.vstack(k)[:, :-1], np.vstack(k).T[::-1].T ]) image = np.zeros(shape=(arr.shape[0]+2, arr.shape[1]+2)) image[1:-1, 1:-1] = arr 

it is clear that the outline corresponding to the edges of the image pixel will be preferable to the default behavior for the outline function, where the outline lines are effectively drawn along the diagonal of the edge pixels.

 import matplotlib.pyplot as plt plt.contour(image[::-1], [0.5], colors='r') 

How to make outlines coincide with pixels? I am looking for a solution in the numpy and matplotlib .

Answer 1

If the image has a resolution of 1 pixel per unit, how would you define the "edge" of a pixel? The concept of "edge" only makes sense in a frame with an increased resolution compared to the pixel itself, and contour cannot draw any edges if it works with the same resolution as the image itself.

On the other hand, of course, you can increase the resolution so that the concept of "edge" makes sense. So, let's say we increase the resolution by 100 times, we can easily draw edges using the contour graph.

 import matplotlib.pyplot as plt import numpy as np k = [] for s in [2103, 1936, 2247, 2987]: np.random.seed(s) k.append(np.random.randint(0, 2, size=(2,6))) arr = np.hstack([np.vstack(k)[:, :-1], np.vstack(k).T[::-1].T ]) image = np.zeros(shape=(arr.shape[0]+2, arr.shape[1]+2)) image[1:-1, 1:-1] = arr f = lambda x,y: image[int(y),int(x) ] g = np.vectorize(f) x = np.linspace(0,image.shape[1], image.shape[1]*100) y = np.linspace(0,image.shape[0], image.shape[0]*100) X, Y= np.meshgrid(x[:-1],y[:-1]) Z = g(X[:-1],Y[:-1]) plt.imshow(image[::-1], origin="lower", interpolation="none", cmap="Blues") plt.contour(Z[::-1], [0.5], colors='r', linewidths=[3], extent=[0-0.5, x[:-1].max()-0.5,0-0.5, y[:-1].max()-0.5]) plt.show() 

For comparison, we can also draw an image in the same plot using imshow .