was: Re: [Numpy-discussion] hstack(arr_Int32, arr_float32) fails because of casting rules Travis Oliphant wrote: > Sebastian Haase wrote: >> On Thursday 24 August 2006 17:28, Travis Oliphant wrote: >> >>> Sebastian Haase wrote: >>> >>>> Hi, >>>> I get >>>> TypeError: array cannot be safely cast to required type >>>> >>>> when calling hstack() ( which calls concatenate() ) >>>> on two arrays being a int32 and a float32 respectively. >>>> >>>> I understand now that a int32 cannot be safely converted into a float32 >>>> but why does concatenate not automatically >>>> up(?) cast to float64 ?? >>>> >>> Basically, NumPy is following Numeric's behavior of raising an error in >>> this case of unsafe casting in concatenate. For functions that are not >>> universal-function objects, mixed-type behavior works basically just >>> like Numeric did (using the ordering of the types to determine which one >>> to choose as the output). >>> >>> It could be argued that the ufunc-rules should be followed instead. >>> >>> -Travis >>> >>> >> Are you saying the ufunc-rules would convert "int32-float32" to float64 >> and >> hence make my code "just work" !? >> > > This is now the behavior in SVN. Note that this is different from both > Numeric (which gave an error) and numarray (which coerced to float32). > > But, it is consistent with how mixed-types are handled in calculations > and is thus an easier rule to explain. > > Thanks for the testing. > > -Travis After sleeping over this, I am contemplating about the cases where one would use float32 in the first place. My case yesterday, where I only had a 1d line profile of my data, I was of course OK with coercion to float64. But if you are working with 3D image data (as in medicine) or large 2D images as in astronomy I would assume the reason use float32 is that computer memory is to tight to afford 64bits per pixel. This is probably why numarray tried to keep float32. Float32 can handle a few more digits of precision than int16, but not as much as int32. But I find that I most always have int32s only because its the default, whereas I have float32 as a clear choice to save memory.
How hard would it be to change the rules back to the numarray behavior ? Who would be negatively affected ? And who positively ? Thanks for the great work. Sebastian ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
