Re: [Numpy-discussion] Medians that ignore values
On Fri, Sep 19, 2008 at 11:41 PM, David Cournapeau [EMAIL PROTECTED] wrote: Anne Archibald wrote: I, on the other hand, was making specifically that suggestion: users should not use nans to indicate missing values. Users should use masked arrays to indicate missing values. I agree it is the nicest solution in theory, but I think it is impractical (as mentioned by Eric Firing in his email). This part I pretty much agree with. I can't really see which one is better (failing or returning NaN for sort/min/max and their sort counterpat), or if we should let the choice be left to the user. I am fine with both, and they both require the same amount of work. Or we can make them behave drastically differently. Masked arrays clearly need to be able to handle masked values flexibly and explicitly. So I think nans should be handled simply and conservatively: propagate them if possible, raise if not. I agree about this behavior being the default. I just think that for a couple of functions, we could we give either separate functions, or additional arguments to existing functions to ignore them: I am thinking about min/max/sort and their arg* counterpart, because those are really basic, and because we already have nanmean/nanstd/nanmedian (e.g. having a nansort would help for nanmean to be much faster). I would be happy to implement nan sorts if someone can provide me with a portable and easy way to detect nans for single, double, and long double floats. And not have it fail if the architecture doesn't support nans. I think getting all the needed nan detection and setup in place is the first step for anything else. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Medians that ignore values
On Sat, Sep 20, 2008 at 01:15, Charles R Harris [EMAIL PROTECTED] wrote: I would be happy to implement nan sorts if someone can provide me with a portable and easy way to detect nans for single, double, and long double floats. And not have it fail if the architecture doesn't support nans. I think getting all the needed nan detection and setup in place is the first step for anything else. We explicitly only support IEEE-754 architectures, so we are always on an architecture that supports NaNs. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Medians that ignore values
2008/9/19 Eric Firing [EMAIL PROTECTED]: Pierre GM wrote: It seems to me that there are pragmatic reasons why people work with NaNs for missing values, that perhaps shd not be dismissed so quickly. But maybe I am overlooking a simple solution. nansomething solutions tend to be considerably faster, that might be one reason. A lack of visibility of numpy.ma could be a second. In any case, I can't but agree with other posters: a NaN in an array usually means something went astray. Additional reasons for using nans: 1) years of experience with Matlab, in which using nan for missing values is the standard idiom. Users are already retraining to use zero-based indexing; I don't think asking them to use a full-featured masked array package is an unreasonable retraining burden, particularly since this idiom breaks as soon as they want to work with arrays of integers or records. 2) convenient interfacing with extension code in C or C++. The latter is a factor in the present use of nan in matplotlib; using nan for missing values in an array passed into extension code saves having to pass and process a second (mask) array. It is fast and simple. How hard is it to pass an array where the masked values have been filled with nans? It's certainly easy to go the other way (mask all nans). I think this is less painful than supporting two differently-featured sets of functions for dealing with arrays containing some invalid values. Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Medians that ignore values
Charles R Harris wrote: I would be happy to implement nan sorts if someone can provide me with a portable and easy way to detect nans for single, double, and long double floats. And not have it fail if the architecture doesn't support nans. I think getting all the needed nan detection and setup in place is the first step for anything else. I guess you mean when isnan is available but broken, since we do not support platforms without IEEE 754 support ? I want to take care of this for my umathmodule cleaning (all the configuration checks/replacements are in place; if we want to be paranoid, we could check whether isnan works for all types if found on the system). cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] NEW GENERATED DLL ERROR FOUND WITHIN f2PY.py
Blubaugh, David A. wrote: ImportError: DLL load with error code 193 Likely to be a build error: how did you build the .pyd file ? cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Medians that ignore values
On Sat, Sep 20, 2008 at 11:02 AM, Jake Harris [EMAIL PROTECTED]wrote: Because you're always working with probabilities, there is almost always no ambiguity...whenever NaN is encounter, 0 is what is desired. ...of course, division presents a good counterexample. Bad idea? So probably. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
