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Hey Guys,</span></font>
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I've got it!  Mucho thanks for the very cool numpy example from Chris and of course to
everyone else for the assistance.  I can now add this code to a program that upsamples
these images so they look better when plotted on a GIS map.  I realize that I'm technically
destroying data by blasting pixels from the image, but sometimes it's better to have a pretty
presentation!</span></font>
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You can try this code by grabbing a radar image from the source server:</span></font>
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http://radar.weather.gov/ridge/RadarImg/N0Z/</span></font>
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##############################################</span></font>
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from PIL import Image</span></font>
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import numpy as np</span></font>
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#Open the raw radar image</span></font>
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raw_image = Image.open("ABR_N0Z_0.gif")</span></font>
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# make an array out of the image:</span></font>
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a = np.asarray(raw_image).copy()</span></font>
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# The noise colors are index 0,7,8 in the palette</span></font>
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# I used IrfanView to determine this</span></font>
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noise_colors = [0,7,8]</span></font>
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# white is index 15</span></font>
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white = 15</span></font>
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# Replace the noise colors with white</span></font>
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for color in noise_colors:</span></font>
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    a[a==color] = white</span></font>
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# Build the clean image from the array</span></font>
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im1 = Image.fromarray(a, mode='P')</span></font>
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# Give it the original palette</span></font>
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im1.putpalette(palette)</span></font>
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# Save it out</span></font>
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im1.save("clean_image.gif")</span></font>
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