Hi everyone,
A long time ago, Aditya Sethi I am facing an issue upgrading numpy from 1.5.1 to 1.6.1.
> In numPy 1.6, the casting behaviour for ufunc has changed and has become
> stricter.
>
> Can someone advise how to implement the below simple example which worked in
> 1.5.1 but fails in 1.6.1?
On Fri, Jan 20, 2012 at 3:53 AM, David Verelst wrote:
> I would like to assist on the website. Although I have not made any code
> contributions to Numpy/SciPy (yet), I do follow the mailing lists and
> try to keep up to date on the scientific python scene. However, I need
> to hold my breath unti
Le 20/01/2012 16:30, Sturla Molden a écrit :
> Often we just want the upper-right p x p quadrant.
Thanks for the explanation.
If I understood it correctly, you're interested in the
*cross*-covariance block of the matrix (and now I understand better
Elliot's message). Actually, I thought that was th
Den 20.01.2012 13:39, skrev Pierre Haessig:
> I don't see how does your function relates to numpy.cov [1]. Is it an
> "extended case" function or is there a difference in the underlying math ?
>
If X is rank n x p, then np.cov(X, rowvar=False) is equal
to S after
cX = X - X.mean(axis=0)[np.n
What do you mean by "summarize"?
If for instance you want to sum along Y, just do
my_array.sum(axis=1)
-=- Olivier
2012/1/20 Ruby Stevenson
> hi, all
>
> Say I have a three dimension array, X, Y, Z, how can I condense into
> two dimensions: for example, compute 2-D array with (X, Z) and
> su
hi, all
Say I have a three dimension array, X, Y, Z, how can I condense into
two dimensions: for example, compute 2-D array with (X, Z) and
summarize along Y dimensions ... is it possible?
thanks
Ruby
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Exactly what I need - thank you very much.
Ruby
On Thu, Jan 19, 2012 at 11:33 PM, Benjamin Root wrote:
>
>
> On Thursday, January 19, 2012, Ruby Stevenson wrote:
>> hi, all
>>
>> I am a newbie on numpy ... I am trying to figure out, given an array,
>> how to get back position value based on som
Hi Eliot,
Le 19/01/2012 07:50, Elliot Saba a écrit :
> I recently needed to calculate the cross-covariance of two random
> vectors, (e.g. I have two matricies, X and Y, the columns of which are
> observations of one variable, and I wish to generate a matrix pairing
> each value of X and Y)
I d
* Olivier Delalleau [120120]:
> Not sure if there's a better way, but you can do it with
>
> assert not numpy.allclose(numpy_result, result)
Okay, thats already better than what I have.
thanks
V-
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I would like to assist on the website. Although I have not made any code
contributions to Numpy/SciPy (yet), I do follow the mailing lists and
try to keep up to date on the scientific python scene. However, I need
to hold my breath until the end of my wind tunnel test campaign mid
February.
An
Not sure if there's a better way, but you can do it with
assert not numpy.allclose(numpy_result, result)
-=- Olivier
2012/1/20 Hänel Nikolaus Valentin
> Hi,
>
> I would like to make a sanity test to check that calling the same
> function with different parameters actually gives different resul
Hi,
I would like to make a sanity test to check that calling the same
function with different parameters actually gives different results.
I am currently using::
try:
npt.assert_almost_equal(numpy_result, result)
except AssertionError:
assert True
else:
assert
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