> > Unfortunately if the value is changed to masked, this is not updated
> > in the parent array. This seems very inconsistent. I don't view masked
> > values any different than any other value.
>
> Inconsistent, maybe, useful definitely:
> Masking a view and getting the original masked accordingly
Robert Kern wrote:
> Tim Hochberg wrote:
>> Robert Kern wrote:
>
>>> One possibility is to check if the object is an ndarray (or subclass) and
>>> use
>>> .copy() if so; otherwise, use the current implementation and hope that you
>>> didn't pass it a Numeric or numarray array (or some other view-
On 22 Nov 2006 16:44:07 -
[EMAIL PROTECTED] wrote:
> (Reposting to numpy-discussion@scipy.org instead of the SourceForge list.)
>
>
> It is my understanding that Numpy has "lite" versions of BLAS and LAPACK
> that it will use if it cannot find system libraries. Is it possible to FORCE
> it t
Christopher Barker wrote:
> Robert Kern wrote:
>> I don't want to assume that the only two sequence types are lists and arrays.
>
> Does numpy.random.shuffle really have to work on any sequence? and
> without making a copy? I'm not so sure -- having num* functions operate
> on any sequence has b
Robert Kern wrote:
> I don't want to assume that the only two sequence types are lists and arrays.
Does numpy.random.shuffle really have to work on any sequence? and
without making a copy? I'm not so sure -- having num* functions operate
on any sequence has been a design goal of Numeric from the
Robert Kern wrote:
> Tim Hochberg wrote:
>
>> Robert Kern wrote:
>>
>
>
>>> One possibility is to check if the object is an ndarray (or subclass) and
>>> use
>>> .copy() if so; otherwise, use the current implementation and hope that you
>>> didn't pass it a Numeric or numarray array (or
Tim Hochberg wrote:
> Robert Kern wrote:
>> One possibility is to check if the object is an ndarray (or subclass) and use
>> .copy() if so; otherwise, use the current implementation and hope that you
>> didn't pass it a Numeric or numarray array (or some other view-based object).
>>
> I think I
Robert Kern wrote:
> Christopher Barker wrote:
>
>> Robert Kern wrote:
>>
[SNIP]
>>> I copied the algorithm from Python's random
>>> module. At the core of it is a set of swaps:
>>>
>>> x[i], x[j] = x[j], x[i]
>>>
>>> With the kind of sequences that the stdlib random module is expecting
Christopher Barker wrote:
> Robert Kern wrote:
>> This is really a thinko on my part.
>
> What, exactly, is a thinko?
Like a typo except that the fault lies with the brain, not the fingers.
>> I copied the algorithm from Python's random
>> module. At the core of it is a set of swaps:
>>
>> x
Robert Kern wrote:
> This is really a thinko on my part.
What, exactly, is a thinko?
> I copied the algorithm from Python's random
> module. At the core of it is a set of swaps:
>
> x[i], x[j] = x[j], x[i]
>
> With the kind of sequences that the stdlib random module is expecting, that
> mak
(Reposting to numpy-discussion@scipy.org instead of the SourceForge list.)
It is my understanding that Numpy has "lite" versions of BLAS and LAPACK
that it will use if it cannot find system libraries. Is it possible to FORCE
it to use the lite versions rather than existing system libraries?
(Whe
Alan G Isaac wrote:
rr=N.array(zip(range(20),range(20)))
pid = N.random.permutation(rr.shape[0])
rr=rr[pid]
rr
> array([[ 9, 9],
>[ 2, 2],
>[ 3, 3],
>[15, 15],
>[ 4, 4],
>[11, 11],
>[18, 18],
>[17, 17],
>[ 5,
On Wed, 22 Nov 2006, Robert apparently wrote:
numpy.random.shuffle(rr)
The docstring is incomplete. From the NumPy Book:
shuffle (sequence)
Randomly permute the items of any sequence. If sequence is an array, then it
must be 1-d.
>>> rr=N.array(zip(range(20),range(20)))
>>> N.
for the computation of a p_value I need the betainc function, which draws the
big _cephes module into a (numpy-only) app - the only reason for this import.
I need it just for the computation of a p-value like:
p_value = scipy.special.betainc(0.5*nf, 0.5, nf/(nf+t_value*t_value))
Is there a simpl
is this an error when shuffle doubles&looses items on a 2-d array? :
>>> r=arange(20)
>>> rr=ziparrays(r,r) #numpy.vstack(*args).transpose()
>>> rr
array([[ 0, 0],
[ 1, 1],
[ 2, 2],
[ 3, 3],
[ 4, 4],
[ 5, 5],
[ 6, 6],
[ 7, 7],
[ 8,
Hi,
I am about to release some code to compute LPC coefficients (Auto
regressive modeling, using Levinson Durbin algorithm for autocorrelation
matrix inversion), and I was wondering how to handle cases of divide by
0, or values really near 0.
I understand this is a complex issue, and I
Since some months now the latest scipy (0.5.1) binary (for Windows) is not
compatible with current numpy (1.0) - obviously because of a C-library mismatch.
Does somebody have a compatible binary - or when is the next scipy (binary)
planned?
Robert
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