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I am graphing data from a numpy array using matplotlib imshow. However, some points have no data in them. I initialized the array using np.zeroes, so these points are dragging down the whole map. I know that none of the data will ever have a value of 0.0. Is there some way for me to tell the imshow routine to ignore these points (ie leave them white so it is clear they are empty)?
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The most direct way is to just render your array to RGB using the colormap, and then change the pixels you want.
Just specify vmin=0, vmax=1 . By default, imshow normalizes the data to its min and max. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling).
This example displays the difference between interpolation methods for imshow . If interpolation is None, it defaults to the rcParams["image. interpolation"] (default: 'antialiased' ). If the interpolation is 'none' , then no interpolation is performed for the Agg, ps and pdf backends.
How to hide axis in matplotlib figure? The matplotlib. pyplot. axis('off') command us used to hide the axis(both x-axis & y-axis) in the matplotlib figure.
Have you tried instantiating your array with NaNs instead of zeros to see if matplotlib's default will ignore the NaNs in a way that works for you? You could also try just using logical indexing to make the locations of 0 equal to NaN right before plotting:
my_data[my_data == 0.0] = numpy.nan
Alternatively, you can use the NaN idea and follow this link's advice and use NumPy masked arrays in order to plot the NaN entries as a color you prefer.
I think you could also use that link's idea to make a masked array at the zero locations too, without going to the NaN option if you don't like it.
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