How to use matplotlib's imshow and contour plot on the same numpy array?

With the following code I am producing an obstacle on a 2D Cartesian grid (used later as input for some simulations) by defining the area where the obstacle is located as 1 and 0 everywhere else. I wanted to plot it using a contour plot, but had some troubles producing a binary filled contour plot (side question: how to achieve that?) and so decided to plot the array as an image. Here is the code: ``` import numpy as np, matplotlib.pyplot as plt # create spatial coordinates N_x, N_y = 161, 161 x = np.linspace( .0, .80, N_x ) y = np.linspace( .0, .80, N_y ) # define obstacle obst_diameter = .3 obst_center = (.4,.2) # 2D array defining obstacle structure: 1=obstacle, 0=nothing metal = np.zeros( (N_x, N_y), dtype=int ) for ii in range(N_x): for jj in range(N_y): if ( x[ii] >= (obst_center[0]-obst_diameter/2) and x[ii] <= (obst_center[0]+obst_diameter/2) and y[jj] >= (obst_center[1]-obst_diameter/2) and y[jj] <= (obst_center[1]+obst_diameter/2) ): metal[jj,ii] = 1. # do the plotting (contour and imshow) xx, yy = np.meshgrid( x, y ) fig = plt.figure( figsize=(20,10) ) ax1 = fig.add_subplot( 1,2,1, aspect='equal' ) cont_obst = ax1.contour( xx, yy, metal, colors='k', levels=[0,1] ) ax1.plot( obst_center[0], obst_center[1], marker='*', markersize=30, color='yellow' ) ax1.set_xlabel( 'x in m' ) ax1.set_ylabel( 'y in m' ) ax2 = fig.add_subplot( 1,2, 2, aspect='equal' ) ax2.imshow( metal, cmap='Greys', interpolation='none', extent=[np.min(x), np.max(x), np.min(y), np.max(y)] ) ax2.plot( obst_center[0], obst_center[1], marker='*', markersize=30, color='yellow' ) ax2.set_xlabel( 'x in m' ) ax2.set_ylabel( 'y in m' ) plt.savefig( 'another_plot.png', bbox_inches='tight' ) ``` The resulting image looks as follows, with the `contour` plot on the left and the `imshow` plot on the right (the center of the obstacle is marked with a yellow star). Obviously, the two plots are different. What is the reason for this, i.e. what am I missing here?