import matplotlib.pyplot as plt
import numpy as np
from matplotlib.transforms import Transform

# Create a nonaffine identity to easily convert an affine transform to its nonaffine equivalent
class NonAffineIdentityTransform(Transform):
    input_dims = 2
    output_dims = 2

    def inverted(self):
        return self
nonaffine_identity = NonAffineIdentityTransform()

fig = plt.figure(figsize=(10, 5))
fig.set_facecolor('g')

# All values in this test are chosen carefully so that many display pixels are
# aligned with an edge or a corner of an input pixel

# Layout:
# Top row is origin='upper', bottom row is origin='lower'
# Column 1: affine transform, anchored at whole pixel
# Column 2: affine transform, anchored at half pixel
# Column 3: nonaffine transform, anchored at whole pixel
# Column 4: nonaffine transform, anchored at half pixel
# Column 5: affine transform, anchored at half pixel, interpolation='hanning'

# Each axes patch is magenta, so seeing a magenta line at an edge of the image
# means that the image is not filling the axes

corner_x = [0.01, 0.199, 0.41, 0.599, 0.81]
corner_y = [0.1, 0.5]

axs = []
for cy in corner_y:
    for ix, cx in enumerate(corner_x):
        mx = cx + 0.0005 if ix in [1, 3, 4] else cx
        my = cy + 0.011 if ix in [1, 3, 4] else cy
        axs.append(fig.add_axes([mx, my, 0.175, 0.35], xticks=[], yticks=[]))

N = 10

data = np.arange(N**2).reshape((N, N)) % 9
seps = np.arange(-0.5, N)

for i, ax in enumerate(axs):
    ax.set_facecolor('m')

    ax.imshow(data, cmap='Blues',
              interpolation='hanning' if i % 5 == 4 else 'nearest',
              origin='upper' if i >= 5 else 'lower',
              transform=nonaffine_identity + ax.transData if i % 4 >= 2 else ax.transData)

    ax.vlines(seps, -0.5, N - 0.5, linewidth=0.5, color='red', linestyle=(0, (3, 6)))
    ax.hlines(seps, -0.5, N - 0.5, linewidth=0.5, color='red', linestyle=(0, (3, 6)))

    for spine in ax.spines:
        ax.spines[spine].set_linestyle((0, (5, 10)))

plt.show()