On 15 November 2010 14:15, srinivas zinka wrote:
> Thank you for the reply.
> I just downloaded the following zip file:
> http://docs.scipy.org/doc/numpy-1.5.x/numpy-html.zip
> When I try to search for some thing (e.g., "array"), it keeps on
> searching (see the attached file).
> At the same time,
Bruce Southey gmail.com> writes:
> On 11/15/2010 11:48 AM, Felix wrote:
> > On Nov 15, 2:00 am, Dag Sverre Seljebotn wrote:
> >> On 11/15/2010 06:23 AM, Felix wrote:
> >>
> >>> is there any workaround or fix for the problem described in Ticket
> >>> 1504?
> >>> http://projects.scipy.org/numpy/ti
On Mon, Nov 15, 2010 at 4:57 PM, Pierre GM wrote:
>
> On Nov 15, 2010, at 11:43 PM, Benjamin Root wrote:
>
> > Hello,
> >
> > I was using append_fields() in numpy.lib.recfunctions when I discovered a
> slight logic mistake in handling the dtypes argument.
> >
> > The code first checks to see if d
On Nov 15, 2010, at 11:43 PM, Benjamin Root wrote:
> Hello,
>
> I was using append_fields() in numpy.lib.recfunctions when I discovered a
> slight logic mistake in handling the dtypes argument.
>
> The code first checks to see if dtypes is None. If so, it then guesses the
> dtype info from t
Hi,
I'm trying to use numpy.testing.Tester to run tests for another, numpy-based
project. It works beautifully, except for the fact that I can't seem to
silence output (i.e. NOSE_NOCAPTURE/--nocapture/-s). I've tried to call test
with extra_argv=['-s'] and also tried subclassing to muck with
pre
Hello,
I was using append_fields() in numpy.lib.recfunctions when I discovered a
slight logic mistake in handling the dtypes argument.
The code first checks to see if dtypes is None. If so, it then guesses the
dtype info from the input data. Then, it goes to see if the dtypes is not a
sequence
On Sun, Nov 14, 2010 at 1:29 PM, Mark Wiebe wrote:
> On Sun, Nov 14, 2010 at 12:21 PM, Charles R Harris <
> charlesr.har...@gmail.com> wrote:
>
>> Good point. I was trying to keep the fpeclear in front of the code to be
>> tested.
>>
>>
> Yeah, I hadn't considered that possibility too seriously.
On 11/15/10 11:35 AM, Pauli Virtanen wrote:
> One can argue that this is a bug in Python or Numpy:
>
> "%d" % numpy.int16(1)
>
> "{0:d}".format(numpy.int16(1))
> To make it work via changes in Numpy: scalars should implement a
> __format__ method. Two choices: either we parse the forma
On Mon, 15 Nov 2010 09:20:43 -0600, Robert Kern wrote:
[clip]
> Correct. On a 64-bit system, numpy.int32 does not subtype from int. The
> format codes do strict type-checking.
One can argue that this is a bug in Python or Numpy:
"%d" % numpy.int16(1)
"{0:d}".format(numpy.int16(1)
On 11/15/2010 07:01 PM, Bruce Southey wrote:
> On 11/15/2010 11:48 AM, Felix wrote:
>
>> On Nov 15, 2:00 am, Dag Sverre Seljebotn wrote:
>>
>>> On 11/15/2010 06:23 AM, Felix wrote:
>>>
>>>
is there any workaround or fix for the problem described in Ticket
1504?
htt
On 11/15/2010 11:48 AM, Felix wrote:
> On Nov 15, 2:00 am, Dag Sverre Seljebotn wrote:
>> On 11/15/2010 06:23 AM, Felix wrote:
>>
>>> is there any workaround or fix for the problem described in Ticket
>>> 1504?
>>> http://projects.scipy.org/numpy/ticket/1504
>> You can try to see if sys.setdlopenf
On Nov 15, 2:00 am, Dag Sverre Seljebotn wrote:
> On 11/15/2010 06:23 AM, Felix wrote:
>
> > is there any workaround or fix for the problem described in Ticket
> > 1504?
> >http://projects.scipy.org/numpy/ticket/1504
>
> You can try to see if sys.setdlopenflags works for you, it does for me:
>
But, I have no problem searching "python HTML documentation" which is also
created by Sphinx.
I don't know much about JavaScript.
But, don't they both use the same JavaScript?
On Mon, Nov 15, 2010 at 11:02 PM, Robert Kern wrote:
> On Mon, Nov 15, 2010 at 06:15, srinivas zinka wrote:
> > Than
Is there a convention for dealing with NaN and Inf? I've found that
trusting the default behavior is a very bad idea:
---
from numpy import *
x = zeros((5,7))
x[:,3:] = nan
x[:,-1] = inf
savetxt('problem_array.txt',x,delimiter='\t')
x2 = loadtxt('problem_array.txt'
math.duke.edu> writes:
>
> Hi, what is the best way to print (to a file or to stdout) formatted
> numerical values? Analogously to C's printf("%d %g",x,y) etc?
>
For stdout you can simply do:
In [26]: w, x, y, z = np.randint(0,100,4)
In [27]: type(w)
Out[27]:
In [28]: print("%f %g %e %d"
On Mon, Nov 15, 2010 at 08:57, Keith Goodman wrote:
> There is more than one way to form the same slice. For example, a[:2]
> and a[0:2] and a[0:2:] pass slice objects to getitem with the same
> start, stop and step.
Not quite correct. a[:2] passes slice(None, 2, None) whereas the next
two pass s
On Mon, Nov 15, 2010 at 08:32, wrote:
> Hi, what is the best way to print (to a file or to stdout) formatted
> numerical values? Analogously to C's printf("%d %g",x,y) etc?
>
> Numpy Documentation only discusses input *from* a file, or output of
> entire arrays. (np.savetxt()) I just want tab or
On 15 November 2010 15:32, wrote:
> Hi, what is the best way to print (to a file or to stdout) formatted
> numerical values? Analogously to C's printf("%d %g",x,y) etc?
Use the .tofile() method:
numpy.random.random(5).tofile(sys.stdout, ' ', '%s')
0.230466435867 0.609443784908 0.353855676828 0.
There is more than one way to form the same slice. For example, a[:2]
and a[0:2] and a[0:2:] pass slice objects to getitem with the same
start, stop and step. Is there any way to get a hold of the exact
character sequence the user used to form the slice? That is, I'd like
to know if the user entere
Hi, what is the best way to print (to a file or to stdout) formatted
numerical values? Analogously to C's printf("%d %g",x,y) etc?
Numpy Documentation only discusses input *from* a file, or output of
entire arrays. (np.savetxt()) I just want tab or space-delimited output of
selected formatted v
On Mon, Nov 15, 2010 at 06:15, srinivas zinka wrote:
> Thank you for the reply.
> I just downloaded the following zip file:
> http://docs.scipy.org/doc/numpy-1.5.x/numpy-html.zip
> When I try to search for some thing (e.g., "array"), it keeps on
> searching (see the attached file).
> At the same t
On Nov 15, 2010, at 1:07 PM, Lluís wrote:
> Pierre GM writes:
>
>> On Nov 14, 2010, at 9:30 PM, Lluís wrote:
>
>>> This will work as long as 'first_values' is assured to always contain
>>> valid data and as long as its indexes are equivalent to those in
>>> converters (which I simply haven't ch
Pierre GM writes:
> On Nov 14, 2010, at 9:30 PM, Lluís wrote:
>> This will work as long as 'first_values' is assured to always contain
>> valid data and as long as its indexes are equivalent to those in
>> converters (which I simply haven't checked).
> I beat you to it, actually ;)
Argh! :)
>
On 15 November 2010 12:02, srinivas zinka wrote:
> I downloaded the "Numpy reference guide" in HTML format from the following
> link:
> http://docs.scipy.org/doc/
> My intension is to use this documentation in "offline mode".
> But, in offline mode, I am unable to search the document using "quick
Hi,
I downloaded the "Numpy reference guide" in HTML format from the following
link:
http://docs.scipy.org/doc/
My intension is to use this documentation in "offline mode".
But, in offline mode, I am unable to search the document using "quick
search" option.
(However, I can search the same docum
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