Replace NaN values with zero #7
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SNAPHU errors if the input interferogram contains NaN or infinity values, but NaN is commonly used as a fill value to represent invalid samples, which SNAPHU should mask out for purposes of unwrapping. It's convenient for the Python wrapper to replace NaN values with zeros, since SNAPHU treats zero-magnitude samples as invalid (masked out).
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Closes #2
SNAPHU errors if the input interferogram contains NaN or infinity values, but NaN is commonly used as a fill value to represent invalid samples, which we want to mask out for purposes of unwrapping.
As @scottstanie reminded me, it's convenient for the Python wrapper to replace NaN values with zeros, since SNAPHU treats zero-magnitude samples as invalid (masked out).