On 12/11/06, Erin Sheldon <[EMAIL PROTECTED]> wrote: > Actually, there is a problem with that approach. It first converts > the entire array to a single type, by default a floating type. For > very large integers this precision is insufficient. For example, I > have the following integer in my arrays: > 94137100072000193L > which ends up as > 94137100072000192 > after going to a float and then back to an integer.
That's an unfortunate limitation of numpy; it views double-precision floats as higher precision than 64-bit integers, but of course they aren't. If you want to put all your data in a record array, you could try transposing the lists using a list comprehension - numpy is not always as much faster than pure python as it looks. You could then convert that to a list of four arrays and do the assignment as appropriate. Alternatively, you could convert your array into a higher-precision floating-point format (if one is available on your machine) before transposing and storing in a record array. A. M. Archibald ------------------------------------------------------------------------- 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
