On Tue, Feb 7, 2012 at 9:01 PM, Travis Oliphant tra...@continuum.io wrote:
like so: x[ind1, :, ind2], the question is what should the shape of the
output me. If ind1 is a scalar there is no ambiguity (and this should be
special cased --- but unfortunately isn't).
If
x.shape == (a0, a1,
2012/2/8 Stéfan van der Walt ste...@sun.ac.za:
On Tue, Feb 7, 2012 at 2:03 PM, Travis Oliphant tra...@continuum.io wrote:
John Turner at ORNL has the numpy.org domain and perhaps we could get him to
point it to numpy.github.com
Remember to also put a CNAME file in the root of the repository:
Hi,
08.02.2012 11:22, Scott Sinclair kirjoitti:
[clip]
I see that you've added the CNAME file. Now numpy.github.com is being
redirected to numpy.scipy.org (the old site).
As I understand it, whoever controls the scipy.org DNS settings needs
point numpy.scipy.org at numpy.github.com so that
Le 8 février 2012 00:01, Travis Oliphant tra...@continuum.io a écrit :
On Feb 7, 2012, at 12:24 PM, Sturla Molden wrote:
On 07.02.2012 19:17, Benjamin Root wrote:
print x.shape
(2, 3, 4)
print x[0, :, :].shape
(3, 4)
print x[0, :, idx].shape
(2, 3)
That looks like a bug to
On 08.02.2012 06:01, Travis Oliphant wrote:
Recall that the shape of the output with fancy indexing is determined by
broadcasting together the indexing objects and using that as the shape of the
output:
x[ind1, ind2] will produce an output with the shape of broadcast(ind1,
ind2) whose
On Feb 8, 2012, at 8:29 AM, Sturla Molden wrote:
On 08.02.2012 06:01, Travis Oliphant wrote:
Recall that the shape of the output with fancy indexing is determined by
broadcasting together the indexing objects and using that as the shape of
the output:
x[ind1, ind2] will produce an
On 08.02.2012 15:11, Olivier Delalleau wrote:
From a user perspective, I would definitely prefer cross-product
semantics for fancy indexing. In fact, I had never used fancy indexing
with more than one array index, so the behavior described in this thread
totally baffled me. If for instance x
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not out of the question in my mind
because I suspect the number of users and use-cases of broadcasted
fancy-indexing is small.
In Matlab this (misfeature?) is generally used to compensate for the
lack of
Hello,
When I try to use the command hstack, I am given the error message
TypeError: hstack() takes exactly 1 argument (2 given). I have a 9X1
array (called array) that I would like to concatenate to a 9X2 matrix
(called matrix), and I try to do this by typing the command
hstack(array,matrix). I
You
On Wed, Feb 8, 2012 at 4:32 PM, Stephanie Cooke
cooke.stepha...@gmail.com wrote:
Hello,
When I try to use the command hstack, I am given the error message
TypeError: hstack() takes exactly 1 argument (2 given). I have a 9X1
array (called array) that I would like to concatenate to a 9X2
Hello, is there a good way to get just the date part of a datetime64?
Frequently datetime datatypes have month(), date(), hour(), etc functions
that pull out part of the datetime, but I didn't see those mentioned in the
datetime64 docs. Casting to a 'D' dtype didn't work as I would have hoped:
In
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not out of the question in my
mind because I suspect the number of users and use-cases of broadcasted
fancy-indexing is small.
I
how to insert some specific delay in python programming using numpy command.
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On 08.02.2012 18:17, josef.p...@gmail.com wrote:
I think I use it quite a bit, and I like that the broadcasting in
indexing is as flexible as the broadcasting of numpy arrays
themselves.
x[np.arange(len(x)), np.arange(len(x))] gives the diagonal for example.
Personally I would prefer that
On Wed, Feb 8, 2012 at 10:18 AM, Debashish Saha silid...@gmail.com wrote:
how to insert some specific delay in python programming using numpy command.
do you mean a time delay? If so -- numpy doesn't (and shouldn't) have
such a thing.
however, the time module has time.sleep()
whether it's a
On Wed, Feb 8, 2012 at 8:49 AM, Travis Oliphant tra...@continuum.io wrote:
On Feb 8, 2012, at 8:29 AM, Sturla Molden wrote:
On 08.02.2012 06:01, Travis Oliphant wrote:
Recall that the shape of the output with fancy indexing is determined
by broadcasting together the indexing objects and
On Feb 8, 2012, at 11:17 AM, josef.p...@gmail.com wrote:
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not out of the question in my
mind because I suspect the number of users
On Wed, Feb 8, 2012 at 22:11, Travis Oliphant tra...@continuum.io wrote:
On Feb 8, 2012, at 11:17 AM, josef.p...@gmail.com wrote:
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not
On Feb 8, 2012, at 4:19 PM, Robert Kern wrote:
On Wed, Feb 8, 2012 at 22:11, Travis Oliphant tra...@continuum.io wrote:
On Feb 8, 2012, at 11:17 AM, josef.p...@gmail.com wrote:
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant
On Wed, Feb 8, 2012 at 6:49 AM, Travis Oliphant tra...@continuum.io wrote:
There are also some very nice applications where you can select out of a 3-d
volume a depth-surface defined by indexes like so:
arr[ i[:,newaxis], j, depth]
where arr is a 3-d array, i and j are 1-d index
Converting between date and datetime requires caution, because it depends
on your time zone. Because all datetime64's are internally stored in UTC,
simply casting as in your example treats it in UTC. The 'astype' function
does not raise an error to tell you that this is problematic, because
Hi,
Am I wrong or the numpy.arange() function is not correct 100%?
Try to do this:
In [7]: len(np.arange(3.1, 4.9, 0.1))
Out[7]: 18
In [8]: len(np.arange(8.1, 9.9, 0.1))
Out[8]: 19
I would expect the same result for each command.
All the best
--
View this message in context:
On 02/08/2012 09:31 PM, teomat wrote:
Hi,
Am I wrong or the numpy.arange() function is not correct 100%?
Try to do this:
In [7]: len(np.arange(3.1, 4.9, 0.1))
Out[7]: 18
In [8]: len(np.arange(8.1, 9.9, 0.1))
Out[8]: 19
I would expect the same result for each command.
Not after more
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