On Tue, Oct 11, 2011 at 6:33 PM, <josef.pktd@gmail.com> wrote:
On Tue, Oct 11, 2011 at 7:13 PM, Benjamin Root <ben.root@ou.edu> wrote:
On Tue, Oct 11, 2011 at 2:51 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi
On Tue, Oct 11, 2011 at 3:16 PM, Charles R Harris <charlesr.harris@gmail.com> wrote:
On Tue, Oct 11, 2011 at 12:23 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi,
I recently ran into this:
In [68]: arr = np.array(-128, np.int8)
In [69]: arr Out[69]: array(-128, dtype=int8)
In [70]: np.abs(arr) Out[70]: -128
This has come up for discussion before, but no consensus was ever reached. One solution is for abs to return an unsigned type, but then combining that with signed type of the same number of bits will cause both to be cast to higher precision. IIRC, matlab was said to return +127 as abs(-128), which, if true, is quite curious.
octave-3.2.3:1> a = int8([-128, 127]) a =
-128 127
octave-3.2.3:2> abs(a) ans =
127 127
Matlab is the same. That is curious...
See you,
Matthew
Well, it _is_ only off by 0.78%. That should be good enough for government work, right?
So, which government is using numpy, only off by 200%
Josef
Not government, but maybe Lockheed-Martin when they were doing that Mars probe? "What? It was negative? Well, that explains why it went down, not up!" ::rimshot:: Thank you folks! I will be here all week! Ben Root