I'm using windows datetimes (100nano-seconds since 0001,1,1) as time in a numpy array and was hit by this behaviour.
>>> numpy.__version__ '1.0b4' >>> a=numpy.array([632925394330000000L],numpy.uint64) >>> t=a[0] >>> t 632925394330000000L >>> type(t) <type 'numpy.uint64'> >>> t+1 6.3292539433e+017 >>> type(t+1) <type 'numpy.float64'> >>> t==(t+1) True I was trying to set t larger than any time in an array. Is there any reason for the scalar to upcast in this case? //Torgil ------------------------------------------------------------------------- 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
