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    <p><a href="http://matplotlib.sourceforge.net/users/image_tutorial.html" rel="noreferrer">Matplotlib</a> has the <code>imshow</code> method for plotting arrays:</p> <pre><code>from matplotlib import mpl,pyplot import numpy as np # make values from -5 to 5, for this example zvals = np.random.rand(100,100)*10-5 # make a color map of fixed colors cmap = mpl.colors.ListedColormap(['blue','black','red']) bounds=[-6,-2,2,6] norm = mpl.colors.BoundaryNorm(bounds, cmap.N) # tell imshow about color map so that only set colors are used img = pyplot.imshow(zvals,interpolation='nearest', cmap = cmap,norm=norm) # make a color bar pyplot.colorbar(img,cmap=cmap, norm=norm,boundaries=bounds,ticks=[-5,0,5]) pyplot.show() </code></pre> <p>This is what it looks like: </p> <p><img src="https://i.stack.imgur.com/qnV0Z.png" alt="enter image description here"></p> <p>The details for the color bar setup were taken from a matplotlib example: <a href="http://matplotlib.sourceforge.net/examples/api/colorbar_only.html" rel="noreferrer">colorbar_only.py.</a> It explains that the number of <code>boundaries</code> need to be one larger then then number of colors. </p> <p><strong>EDIT</strong> </p> <p>You should <a href="http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.imshow" rel="noreferrer">note</a>, that <code>imshow</code> accepts the <code>origin</code> keyword, which sets the where the first point is assigned. The default is 'upper left', which is why in my posted plot the y axis has 0 in the upper left and 99 (not shown) in the lower left. The alternative is to set <code>origin="lower"</code>, so that first point is plotted in the lower left corner. </p> <p><strong>EDIT 2</strong></p> <p>If you want a gradient and not a discrete color map, make a color map by <a href="http://matplotlib.sourceforge.net/api/colors_api.html#matplotlib.colors.LinearSegmentedColormap.from_list" rel="noreferrer">linearly interpolating</a> through a series of colors:</p> <pre><code>fig = pyplot.figure(2) cmap2 = mpl.colors.LinearSegmentedColormap.from_list('my_colormap', ['blue','black','red'], 256) img2 = pyplot.imshow(zvals,interpolation='nearest', cmap = cmap2, origin='lower') pyplot.colorbar(img2,cmap=cmap2) fig.savefig("image2.png") </code></pre> <p>This produces: <img src="https://i.stack.imgur.com/bCttt.png" alt="enter image description here"></p> <p><strong>EDIT 3</strong></p> <p>To add a grid, as shown in this <a href="http://matplotlib.sourceforge.net/examples/pylab_examples/axes_props.html" rel="noreferrer">example</a>, use the <code>grid</code> method. Setting the grid color to 'white' works well with the colors used by the colormap (ie the default black does not show up well). </p> <pre><code>pyplot.grid(True,color='white') </code></pre> <p>Including this before the <code>savefig</code> call produces this plot (made using 11x11 grid for clarity): <img src="https://i.stack.imgur.com/oPpby.png" alt="enter image description here"> There are many options for <code>grid</code>, which are described in the matplotlib <a href="http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.grid" rel="noreferrer">documentation</a>. One you might be interested in is <code>linewidth</code>.</p>
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    1. COIt's great! That's exactly what I meant. Thanks! But one more thing. Is there any chance to change coloring? I mean that I need sth like [here](http://matplotlib.sourceforge.net/plot_directive/mpl_examples/pylab_examples/contour_image.hires.png), since my data looks like this: (5.541036443901248E-5, 3.51784925442189E-4, -2.0).
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    2. COWhat do you mean by sth?
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    3. COI mean that I need as colorbar gradient, which will be calculated automatically since I have many different values in range from -2.0 to 2.0. It seems that your solution colors -5 by blue, 0 by black, and 5 as red. I need to color values between them as well. After 'sth' I've put [link](http://matplotlib.sourceforge.net/plot_directive/mpl_examples/pylab_examples/contour_image.hires.png) to example. I don't need map like there, I just need similar coloring. EDIT: Bar like in answer below (one with 3D plot).
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