> http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.transpose.html
And I completely missed its general case. D'oh!
Thank you.
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-disc
Is there a clean way to create a view on an existing ND-array with its
axes in a different order.
For example, suppose I have an array of shape (100,200,300,3) and I
want to create a view of this where the vector coordinate is axis 0, not
axis 3. (So the view will have shape (3,100,200,300).)
On 04/01/2014 04:25 PM, Nathaniel Smith wrote:
> On Tue, Apr 1, 2014 at 3:57 PM, Sebastian Berg
> wrote:
>> If `a` has exactly one dimension more then `b`, the first case is used.
>> Otherwise (..., M, K) is used instead. To make sure you always get the
>> expected result, it may be best to make s
Versions:
>>> sys.version
'3.3.2 (default, Mar 5 2014, 08:21:05) \n[GCC 4.8.2 20131212 (Red Hat
4.8.2-7)]'
>>> numpy.__version__
'1.8.0'
Problem:
I'm trying to unpick the shape requirements of numpy.linalg.solve().
The help text says:
solve(a, b) -
a : (..., M, M) array_like
C
> You may want to use this:
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.piecewise.html
Thank you. That's just what I needed.
Works a treat:
--- start ---
import numpy
def chebyshev(x, m):
'''Calculates Chebyshev functions of the first kind using the
trigonometric identi
I have a simple function defined in the following snippet:
--- start ---
import numpy
def chebyshev(x, m):
'''Calculates Chebyshev functions of the first kind using the
trigonometric identities.'''
theta = numpy.where(
numpy.abs(x)<=1.0,
numpy.arccos(x),
n
On 08/06/12 14:14, Neal Becker wrote:
> The fact that this proposed numpy behavior would not match python list
> behavior
> holds little weight for me. I would still favor this change, unless it added
> significant overhead. My opinion, of course.
It holds enormous weight for me. My opinion i
numpy.dot and numpy.tensordot do exactly what I need.
Thank you all (and especially the gentleman who spotted I was rotating
in the wrong direction).
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/nu
Conceptually, I have a 2-d grid of 2-d vectors. I am representing this
as an ndarray of shape (2,M,N). I want to apply a 2x2 matrix
individually to each vector in the grid.
However, I can't work out the appropriate syntax for getting the matrix
multiplication to broadcast over the grid. All
B[i] in A. In the (very rare) case when B[i] is not
> in A C[i] should be equal to -1.
May we assume that there are no repeats in A? (i.e. no cases where two
different indices are both valid?)
--
Bob Dowling
___
NumPy-Discussion mailing list
NumPy-
On 19/08/11 15:49, Chris Withers wrote:
> On 18/08/2011 07:58, Bob Dowling wrote:
>>
>> >>> numpy.add.accumulate(a)
>> array([ 0, 1, 3, 6, 10])
>>
>> >>> numpy.add.accumulate(a, out=a)
>> array([ 0, 1, 3, 6, 10])
>
> What's th
On 18/08/11 15:19, Chris Withers wrote:
> Hopefully a simple newbie question, if I have an array such as :
>
> array([0, 1, 2, 3, 4])
>
> ...what's the best way to cummulatively sum it so that I end up with:
>
> array([0, 1, 3, 6, 10])
>
> How would I do this both in-place and to create a new arr
> There is not supposed to be a one-to-one correspondence between the
> functions in numpy and the methods on an ndarray. There is some
> duplication between the two, but that is not a reason to make more
> duplication.
I would make a plea for consistency, to start with.
Those of us who write in
[ I'm new here and this has the feel of an FAQ but I couldn't find
anything at http://www.scipy.org/FAQ . If I should have looked
somewhere else a URL would be gratefully received. ]
What's the reasoning behind functions like sum() and cumsum() being
provided both as module functions (numpy.s
14 matches
Mail list logo
