Ignore this query -- silly mistake on my part. Sorry.
On Sep 25, 2006, at 2:30 PM, [EMAIL PROTECTED] wrote:
> I've just tried building RC1 on OSX 10.4 (Intel), and it fails
> almost immediately. This did not fail in the past with beta5 or
> from SVN:
>
> Osoyoos:~/Development/numpy-1.0rc1 chr
[EMAIL PROTECTED] wrote:
> I've just tried building RC1 on OSX 10.4 (Intel), and it fails almost
> immediately. This did not fail in the past with beta5 or from SVN:
It looks like a problem on your end:
> gcc: installation problem, cannot exec `cc1': No such file or directory
Can you build any
I've just tried building RC1 on OSX 10.4 (Intel), and it fails almost
immediately. This did not fail in the past with beta5 or from SVN:
Osoyoos:~/Development/numpy-1.0rc1 chris$ python setup.py build
Running from numpy source directory.
F2PY Version 2_3198
blas_opt_info:
FOUND:
extra_li
Francesc Altet wrote:
>El dl 25 de 09 del 2006 a les 11:08 -0600, en/na Travis Oliphant va
>escriure:
>
>
>>Francesc Altet wrote:
>>
>>
>>
>>>Hi,
>>>
>>>Anybody know if there is a map between NumPy types and Numeric
>>>typecodes? Something like 'typecodes' for numarray:
>>>
>>>
Oh, yo
El dl 25 de 09 del 2006 a les 11:08 -0600, en/na Travis Oliphant va
escriure:
> Francesc Altet wrote:
>
> >Hi,
> >
> >Anybody know if there is a map between NumPy types and Numeric
> >typecodes? Something like 'typecodes' for numarray:
> >
> >
> How about
>
> dtype(obj).char?
This doesn't work
Francesc Altet wrote:
>Hi,
>
>Anybody know if there is a map between NumPy types and Numeric
>typecodes? Something like 'typecodes' for numarray:
>
>
How about
dtype(obj).char?
-Travis
-
Take Surveys. Earn Cash. Influenc
Hi,
Anybody know if there is a map between NumPy types and Numeric
typecodes? Something like 'typecodes' for numarray:
>>> numarray.typecode
{'UInt64': 'U', 'Int32': 'i', 'Int16': 's', 'Float64': 'd', 'Object':
'O', 'UInt8': 'b', 'UInt32': 'u', 'Complex64': 'D', 'UInt16': 'w',
'Bool': 'B', 'Compl
Christopher Hanley wrote:
> Hi,
>
> Change set 3213 changed the data type printing with an array from
> something like dtype=int64 to dtype='int64'. Although this is a small
> cosmetic change it has broken all of the doctests I have written for
> numpy code.
I was changing the way dtypes print
Hi,
I have been bitten by a subtlety in numpy scalar divisions. The next
exposes the issue:
>>> -1/20
-1
>>> Numeric.array([-1])[0] / Numeric.array([20])[0]
-1
>>> numarray.array([-1])[0] / numarray.array([20])[0]
-1
>>> numpy.array([-1])[0] / numpy.array([20])[0]
0
After some digging, I've foun
Hi,
I am sorry if I have missed anything obvious here, but is there a fast
simple way to downcast an array to the smallest storage that hold
array data within a specified precision - e.g.
a = array([1.0])
small_a = fantasy_function(a, rtol=1.0001e-05, atol=1e-08 )
b = array([1.2])
sm
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Hi,
Change set 3213 changed the data type printing with an array from
something like dtype=int64 to dtype='int64'. Although this is a small
cosmetic change it has broken all of the doctests I have written for
numpy code. I expect that I am not the only person this change has
caught. Please
Hi all, these are just a couple of small fixes to the string support in
Numexpr, and a test case for the string copy operation.
For the base patches:
1.http://www.mail-archive.com/numpy-discussion@lists.sourceforge.net/msg01551.html
2.http://www.mail-archive.com/numpy-discussion@lists.sourceforge
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