I ran into this weird behavior with astype(int)
In [57]: a = np.array(1E13)
In [58]: a.astype(int)
Out[58]: array(-2147483648)
I understand why large numbers need to be clipped when converting to
int (although I would have expected some sort of warning), but I'm
puzzled by the negative
Hi,
This a usual thing in integers conversions. If you transform an
integer like 0x from 16 bits to 8bits, you get 0x, thus a
negative number. As there are no processor instructions that do
saturations (DSP instructions), the behavior is to be expected.
Matthieu
2008/10/17 Tony S Yu
On Fri, Oct 17, 2008 at 1:27 PM, Tony S Yu [EMAIL PROTECTED] wrote:
I ran into this weird behavior with astype(int)
In [57]: a = np.array(1E13)
In [58]: a.astype(int)
Out[58]: array(-2147483648)
I understand why large numbers need to be clipped when converting to
int (although I would
