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POPropagation of NaN through calculations
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  1. POPropagation of NaN through calculations
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    copied!<p>Normally, NaN (not a number) propagates through calculations, so I don't need to check for NaN in each step. This works almost always, but apparently there are exceptions. For example:</p> <pre><code>&gt;&gt;&gt; nan = float('nan') &gt;&gt;&gt; pow(nan, 0) 1.0 </code></pre> <p>I found the <a href="http://en.wikipedia.org/wiki/NaN#Function_definition" rel="nofollow">following comment</a> on this:</p> <blockquote> <p>The propagation of quiet NaNs through arithmetic operations allows errors to be detected at the end of a sequence of operations without extensive testing during intermediate stages. However, note that depending on the language and the function, NaNs can silently be removed in expressions that would give a constant result for all other floating-point values e.g. NaN^0, which may be defined as 1, so in general a later test for a set INVALID flag is needed to detect all cases where NaNs are introduced.</p> <p>To satisfy those wishing a more strict interpretation of how the power function should act, the 2008 standard defines two additional power functions; pown(x, n) where the exponent must be an integer, and powr(x, y) which returns a NaN whenever a parameter is a NaN or the exponentiation would give an indeterminate form.</p> </blockquote> <p>Is there a way to check the INVALID flag mentioned above through Python? Alternatively, is there any other approach to catch cases where NaN does not propagate?</p> <p>Motivation: I decided to use NaN for missing data. In my application, missing inputs should result in missing result. It works great, with the exception I described. </p>
 

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