There might be better ways, but at least you can always make a planar mesh and color it: import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np # create a 21 x 21 vertex mesh xx, yy = np.meshgrid(np.linspace(0,1,21), np.linspace(0,1,21)) # create some dummy data (20 x 20) for the image data … Read more
Setting the extent Assuming you have real_x=np.array([15,16,17]) real_y=np.array([20,21,22,23]) you would set the image extent as dx = (real_x[1]-real_x[0])/2. dy = (real_y[1]-real_y[0])/2. extent = [real_x[0]-dx, real_x[-1]+dx, real_y[0]-dy, real_y[-1]+dy] plt.imshow(data, extent=extent) Changing ticklabels An alternative would be to just change the ticklabels real_x=np.array([15,16,17]) real_y=np.array([20,21,22,23]) plt.imshow(data) plt.gca().set_xticks(range(len(real_x))) plt.gca().set_yticks(range(len(real_x))) plt.gca().set_xticklabels(real_x) plt.gca().set_yticklabels(real_y)
You can do it by setting the aspect of the image manually (or by letting it auto-scale to fill up the extent of the figure). By default, imshow sets the aspect of the plot to 1, as this is often what people want for image data. In your case, you can do something like: import … Read more