Hi All,
I've added ufuncs fmin and fmax that behave as follows:
In [3]: a = array([NAN, 0, NAN, 1])
In [4]: b = array([0, NAN, NAN, 0])
In [5]: fmax(a,b)
Out[5]: array([ 0., 0., NaN, 1.])
In [6]: fmin(a,b)
Out[6]: array([ 0., 0., NaN, 0.])
In [7]: fmax.reduce(a)
Out[7]: 1.0
In [8
Hello,
I am using numpy in conjunction with pyTables. The data that I read in from
pyTables seem to have the following dtype:
p = hdf5.root.myTable.read()
p.__class__
p[0].__class__
p.dtype
dtype([('time', ' Traceback (most recent call last)
p:\AsiaDesk\johngu\projects\deltaFor
The normal signbit function of gcc without the -std=c99 flag doesn't work
correctly for nans and infs. I found the following code on a boost mailing
list and it might be helpful here for portability.
const boost::uint32_t signbit_mask
= binary_cast(1.0f)
^ binary_cast(-1.0f);
inline boo
On Wed, Oct 1, 2008 at 7:16 PM, Charles R Harris
<[EMAIL PROTECTED]>wrote:
>
>
> On Wed, Oct 1, 2008 at 2:46 PM, oc-spam66 <[EMAIL PROTECTED]> wrote:
>
>> Hello and thank you for your answer.
>>
>> > There are at least three methods I can think of, but choosing the best
>> one
>> > requires more i
Hi,
I have a large data file which contains 2 columns of data. The two columns only
have zero and one. Now I want to cound how many one in between if both columns
are one. For example, if my data is:
1 0
0 0
1 1
0 0
0 1x
0 1x
0 0
0 1x
1 1
0 0
0 1x
0 1x
1 1
Then my
On Wed, Oct 1, 2008 at 2:46 PM, oc-spam66 <[EMAIL PROTECTED]> wrote:
> Hello and thank you for your answer.
>
> > There are at least three methods I can think of, but choosing the best
> one
> > requires more information. How long are the lists? Do the arrays have
> > variable dimensions? The simp
Hi,
Are there any plans to tape the presentations? Unfortunately some of
us can't make it down to Texas, but the talks look quite interesting.
Thanks,
-steve
On Oct 1, 2008, at 10:36 AM, Travis Vaught wrote:
> Greetings,
>
> The Texas Python Regional Unconference is coming up this weekend
> (
Hello and thank you for your answer.
> There are at least three methods I can think of, but choosing the best one
> requires more information. How long are the lists? Do the arrays have
> variable dimensions? The simplest and most adaptable method is probably
The lists would be made of 4x4 matric
To all,
I have now been able to develop a stable file via f2py!! However, I had
to execute the following:
1.) First, I had to copy all required library files from my selected
Compaq visual Fortran compiler under python's scripts directory along
with f2py itself.
2.) I also had to include a
On Tuesday 30 September 2008 18:54:21 Travis E. Oliphant wrote:
> I just went to the code and noticed that PyArray_Resize returns None.
> So, you certainly don't want to point array to it. The array does not
> get any reference count changes.
Thanks for the very clear explanation.
> PyObject *
Greetings,
The Texas Python Regional Unconference is coming up this weekend
(October 4-5) and I wanted to send out some more details of the
meeting. The web page for the meeting is here:
http://www.scipy.org/TXUncon2008
The meeting is _absolutely free_, so please add yourself to the
Atten
Alan G Isaac wrote:
> On 10/1/2008 9:04 AM dmitrey apparently wrote:
>
>> why array(1).tolist() returns 1? I expected to get [1] instead.
>>
>
> I guess I would expect it not to work at all.
> Given that it does work, this seems the best result.
> What list shape matches the shape of a 0-d
dmitrey wrote:
> hi all,
> will array(Python set) (and asarray, asfarray etc) ever be implemented
> as cast method?
>
Use fromiter instead.We could special case set objects in array(...)
if that is deemed desirable.
-Travis
___
Numpy-discuss
dmitrey wrote:
> let me also note that list(array((1))) returns
>
> Traceback (innermost last):
> File "", line 1, in
> TypeError: iteration over a 0-d array
>
> D.
>
This is expected. 0-d arrays are currently not iterable.
-Travis
___
Numpy-disc
On 10/1/2008 9:04 AM dmitrey apparently wrote:
> why array(1).tolist() returns 1? I expected to get [1] instead.
I guess I would expect it not to work at all.
Given that it does work, this seems the best result.
What list shape matches the shape of a 0-d array?
What is the use case that makes thi
hi all,
will array(Python set) (and asarray, asfarray etc) ever be implemented
as cast method?
Now it just puts the set into 1st element:
>>> asarray(set([11, 12, 13, 14]))
array(set([11, 12, 13, 14]), dtype=object)
>>> array(set([11, 12, 13, 14]))
array(set([11, 12, 13, 14]), dtype=object)
let me also note that list(array((1))) returns
Traceback (innermost last):
File "", line 1, in
TypeError: iteration over a 0-d array
D.
dmitrey wrote:
> hi all,
> why array(1).tolist() returns 1? I expected to get [1] instead.
> D.
>
> ___
> Numpy-di
hi all,
why array(1).tolist() returns 1? I expected to get [1] instead.
D.
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
> distances, indices = T.query(xs) # single nearest neighbor
I'm not sure if it's implied, but can xs be a NxD matrix here i.e.
query for all N points rather than just one. This will reduce the
python call overhead for large queries.
Also, I have some c++ code for locality sensitive hashing which
2008/10/1 Barry Wark <[EMAIL PROTECTED]>:
> Thanks for taking this on. The scikits.ann has licensing issues (as
> noted above), so it would be nice to have a clean-room implementation
> in scipy. I am happy to port the scikits.ann API to the final API that
> you choose, however, if you think that
On Sep 30, 2008, at 23:16 , Lisandro Dalcin wrote:
On Tue, Sep 30, 2008 at 9:27 PM, Brian Blais <[EMAIL PROTECTED]>
thanks for all of the help. My initial solution is to pickle my
object,
with the text-based version of pickle, and send it across rpc. I
do this
because the actual thing I am
2008/10/1 Gael Varoquaux <[EMAIL PROTECTED]>:
> On Tue, Sep 30, 2008 at 06:10:46PM -0400, Anne Archibald wrote:
>> > k=None in the third call to T.query seems redundant. It should be
>> > possible do put some logics so that the call is simply
>
>> > distances, indices = T.query(xs, distance_upper_b
Lisandro Dalcin wrote:
>
> I believe xmlrpclib is currently the simpler approach. Some day I'll
> have the time to implement something similar using MPI communication
> with mpi4py. However, I believe it can be done even better: local,
> client-side proxies should automatically provide access to a
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