I got bit again by this bug with unsigned integers. (My original changes got
overwritten when I updated from svn and, unfortunately, merged conflicts
without actually looking over the changes.)
In any case, I thought it'd be a good time to bump the issue (with patch).
Cheers,
-Tony
PS: Just f
On May 25, 2010, at 10:57 PM, Charles R Harris wrote:
>
>
> On Tue, May 25, 2010 at 8:21 PM, Tony S Yu wrote:
> I got bit again by this bug with unsigned integers. (My original changes got
> overwritten when I updated from svn and, unfortunately, merged conflicts
> withou
I came across some strange behavior when multiplying numpy floats and python
lists: the list is returned unchanged:
> In [18]: np.float64(1.2) * [1, 2]
>
> Out[18]: [1, 2]
On the other hand, multiplying an array scalar and a python list gives the
expected answer
> In [19]: np.array(1.2) * [1,
On Jun 20, 2010, at 2:28 PM, Pauli Virtanen wrote:
> su, 2010-06-20 kello 13:56 -0400, Tony S Yu kirjoitti:
>> I came across some strange behavior when multiplying numpy floats and
>> python lists: the list is returned unchanged:
>>
>>> In [18]: np.float64(1.2) *
On Nov 2, 2009, at 11:09 AM, numpy-discussion-requ...@scipy.org wrote:
From: David Cournapeau
Subject: [Numpy-discussion] 1.4.0: Setting a firm release date for 1st
december.
To: Discussion of Numerical Python
Message-ID:
<5b8d13220911020029q1d9f1bd7ia1770e3b93e6e...@mail.gmai
Hi,
Functions that call _nanop (i.e. nan[arg]min, nan[arg]max) currently fail with
unsigned integers. For example:
>>> np.nanmin(np.array([0, 1], dtype=np.uint8))
OverflowError: cannot convert float infinity to integer
It seems that unsigned integers don't get identified as integers in the _na
Is there any way to name the columns of an array so that they can be
called with a string, but still used like an ordinary array? What I
have in mind is very similar to a record array, but with homogenous
types. Having different types implies that certain operations (e.g.,
mean, sum) don't
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 valu
Hello,
This is something that's been bothering for awhile. When numpy raises
the following divide by zero error:
>>> Warning: divide by zero encountered in double_scalars
is there a way to get a Traceback on where that warning occurred.
Thanks,
Tony
On Dec 13, 2007, at 2:21 PM, Robert Kern wrote:
> Tony S Yu wrote:
>> Hello,
>>
>> This is something that's been bothering for awhile. When numpy raises
>> the following divide by zero error:
>>>>> Warning: divide by zero encountered in double_
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