> 2009/7/8 Pierre GM :
>
> On Jul 8, 2009, at 3:18 AM, Scott Sinclair wrote:
>
>>> 2009/7/8 Robert Kern :
>>> 2009/7/4 Stéfan van der Walt :
Thanks, Scott. This should now be fixed in SVN.
>>>
>>> You should probably change that to asanyarray() before the masked
>>> array crowd gets upset. :-
Hey All
I am reading thru a file and trying to store the values into another
array, but instead of storing the values 1 by 1, I would like to store
them in bulk sets for optimization purposes.
Here is what I have, which does it 1x1:
z={} #dictionary
r=csv.reader(file)
for i,row in enumerate(r):
>
>>On Fri, Jul 3, 2009 at 10:21 PM, Alan Jackson wrote:
>>>
>>
>>I don't see any problem here. If you can replicate your results, we
>>would need more information about the versions.
>>
>>Josef
>>
>>'''
> np.version.version
>>'1.3.0'
> scipy.version.version
>>'0.8.0.dev5789'
>>'''
>>
>
>In
Hello
The problem is not PyArray_Conjugate itself.
The problem is that whenever you call a function from the C side
and one of the inputs has ref_count 1, it can be overwritten.
This is not a problem from the python side because if the
ufunc sees a ref_count=1 it means that no python object is ref
On Wed, Jul 8, 2009 at 19:11, John [H2O] wrote:
>
> Also, could someone please explain why:
>
> Tsub = T[ (T[:,0]>t1) & (T[:,0]
> Works, but:
>
> Tsub = T[ (t1
> Does not???
I'm not positive, but I think it boils down to this: Python tries to
look up the method from the first operand before the se
Also, could someone please explain why:
Tsub = T[ (T[:,0]>t1) & (T[:,0]http://www.nabble.com/Help-with-np.where-and-datetime-functions-tp24389447p24401687.html
Sent from the Numpy-discussion mailing list archive at Nabble.com.
___
NumPy-Discussion mail
nhmc wrote:
>
>
>
> Also, if you don't need the indices, you can just use the conditional
> expression as a boolean mask:
>
condition = (t1 < Y[:,0]) & (Y[:,0] < t2)
Y[:,0][condition]
>
> Neil
>
'condition' is not an index array? Wouldn't it just be the indices as well?
Would i
Pierre GM-2 wrote:
>
>
>
> Would you like to give the scikits.timeseries package a try ? It's
> available at pytseries.sourceforge.net.
> Calculatng the hourly average should be straightforward.
>
I would, in fact I have been investigating it, but I didn't have numpy1.3 up
and running unti
On Wed, Jul 8, 2009 at 18:28, Pauli Virtanen wrote:
> On 2009-07-08, Charles R Harris wrote:
>> In that case I don't see a problem offhand. That said, I haven't looked at
>> the code yet.
>
> I'm a bit worried about the problem that cropped up in the ticket
> with the complex ufuncs. As Luca noted
On 2009-07-08, Charles R Harris wrote:
> In that case I don't see a problem offhand. That said, I haven't looked at
> the code yet.
I'm a bit worried about the problem that cropped up in the ticket
with the complex ufuncs. As Luca noted in the ticket,
obj3 = PyArray_Conjugate((PyAO *)obj1,
On Wed, Jul 8, 2009 at 5:02 PM, Pauli Virtanen
> wrote:
> On 2009-07-08, Stéfan van der Walt wrote:
> > I know very little about cache optimality, so excuse the triviality of
> > this question: Is it possible to design this loop optimally (taking
> > into account certain build-time measurable pa
Hello all,
(resending for the Nth time, as the previous attempts
didn't make it to the list)
I'm new to this list (and numpy is mostly new to me :-).
Using python 2.6 and numpy 1.3.
My plan is to write some C extensions that will perform
rather specialised processing on multichannel digital au
On Thu, Jul 9, 2009 at 8:02 AM, Pauli Virtanen wrote:
>
> I don't think we want to go the ATNumPy route, or even have
> tunable parameters chosen at build or compile time.
Detecting things like cache size at compile time should not be too
difficult, at least for common platforms. Even detecting i
On Wed, Jul 8, 2009 at 18:02, Pauli Virtanen wrote:
> On 2009-07-08, Stéfan van der Walt wrote:
>> I know very little about cache optimality, so excuse the triviality of
>> this question: Is it possible to design this loop optimally (taking
>> into account certain build-time measurable parameters)
On 2009-07-08, Stéfan van der Walt wrote:
> I know very little about cache optimality, so excuse the triviality of
> this question: Is it possible to design this loop optimally (taking
> into account certain build-time measurable parameters), or is it the
> kind of thing that can only be discovere
On Wed, Jul 8, 2009 at 17:53, Fons Adriaensen wrote:
> On Wed, Jul 08, 2009 at 11:01:55PM +0100, Peter wrote:
>> Anyway - fingers crossed the list is working for you
>> now...
>
> I'm not convinced... Will try posting again.
And here you are.
--
Robert Kern
"I have come to believe that the who
On Wed, Jul 08, 2009 at 11:01:55PM +0100, Peter wrote:
> On Wed, Jul 8, 2009 at 10:00 PM, Fons Adriaensen wrote:
> >
> > On Sun, Jul 05, 2009 at 12:22:37AM +0200, Stéfan van der Walt wrote:
> >
> >> 2009/7/5 Pauli Virtanen :
> >> > 2009-07-04 22:52 +0200, Fons Adriaensen wrote:
> >> > [clip]
> >>
On 2009-07-08, Charles R Harris wrote:
[clip]
> How do the benchmarks compare with just making contiguous copies? Which is
> blocking of sort, I suppose.
I think that's slower than just walking over the discontiguous
array:
1) The new code: (on the Athlon machine)
$ ./bench-red
Hi Pauli
2009/7/9 Pauli Virtanen :
> Unfortunately, improving the performance using the above scheme
> comes at the cost of some slightly murky heuristics. I didn't
> manage to come up with an optimal decision rule, so they are
> partly empirical. There is one parameter tuning the cross-over
> be
On Wed, Jul 8, 2009 at 4:17 PM, Citi, Luca wrote:
>
> > On thing to keep in mind is that the inputs might be different views of
> the
> > same array so the elements might accessed in an unexpected order.
>
> Only inputs owning their own data and with refcount 1 (i.e. no other array
> can be a vie
On Wed, Jul 8, 2009 at 4:16 PM, Pauli Virtanen
> wrote:
> Hi,
>
> Ticket #1143 points out that Numpy's reduction operations are not
> always cache friendly. I worked a bit on tuning them.
>
>
> Just to tickle some interest, a "pathological" case before optimization:
>
>In [1]: import numpy as
> On thing to keep in mind is that the inputs might be different views of the
> same array so the elements might accessed in an unexpected order.
Only inputs owning their own data and with refcount 1 (i.e. no other array can
be a view of it)
are re-used as outputs.
__
Hi,
Ticket #1143 points out that Numpy's reduction operations are not
always cache friendly. I worked a bit on tuning them.
Just to tickle some interest, a "pathological" case before optimization:
In [1]: import numpy as np
In [2]: x = np.zeros((8, 256))
In [3]: %timeit x.sum(ax
On Wed, Jul 8, 2009 at 4:10 PM, Citi, Luca wrote:
> @Charles R Harris
>
> >> For example 'sqrt(a**2 + b**2)' can be performed...
> > I think this particular function is already available as a ufunc.
>
> I am not sure it is implemented as ufunc.
> But in any case it was just an example.
>
> Anothe
On Wed, Jul 8, 2009 at 17:10, Citi, Luca wrote:
> @Charles R Harris
>
>>> For example 'sqrt(a**2 + b**2)' can be performed...
>> I think this particular function is already available as a ufunc.
>
> I am not sure it is implemented as ufunc.
hypot(a, b)
--
Robert Kern
"I have come to believe tha
@Charles R Harris
>> For example 'sqrt(a**2 + b**2)' can be performed...
> I think this particular function is already available as a ufunc.
I am not sure it is implemented as ufunc.
But in any case it was just an example.
Another example is
sin(2*pi*w+phi)
that is currently implemented allocat
On Wed, Jul 8, 2009 at 3:57 PM, Citi, Luca wrote:
> Hi Stefan,
> I am afraid I did not explain myself clear enough.
>
> Of course
> c = a + b + d
> leaves a, b, and d unchanged.
> The only array that is overwritten is (a+b) which is a temporary
> array that would be destroyed anyway.
> Normally t
On Wed, Jul 8, 2009 at 10:00 PM, Fons Adriaensen wrote:
>
> On Sun, Jul 05, 2009 at 12:22:37AM +0200, Stéfan van der Walt wrote:
>
>> 2009/7/5 Pauli Virtanen :
>> > 2009-07-04 22:52 +0200, Fons Adriaensen wrote:
>> > [clip]
>> >> I subscribed to numpy-discussion almost two days ago.
>> >> I do rec
Hi Stefan,
I am afraid I did not explain myself clear enough.
Of course
c = a + b + d
leaves a, b, and d unchanged.
The only array that is overwritten is (a+b) which is a temporary
array that would be destroyed anyway.
Normally the operation above is performed like this:
1) allocation of a tempora
On Wed, Jul 8, 2009 at 11:34 AM, Citi, Luca wrote:
> Hello guys,
> I made a patch for numpy which allows performing
> operations in-place to save memory allocations.
> For example 'sqrt(a**2 + b**2)' can be performed
I think this particular function is already available as a ufunc. What might
b
On Sun, Jul 05, 2009 at 12:22:37AM +0200, Stéfan van der Walt wrote:
> 2009/7/5 Pauli Virtanen :
> > 2009-07-04 22:52 +0200, Fons Adriaensen wrote:
> > [clip]
> >> I subscribed to numpy-discussion almost two days ago.
> >> I do receive messages from the list, but the ones I post
> >> don't appear
2009/7/8 Gael Varoquaux :
> On Wed, Jul 08, 2009 at 12:48:17PM -0700, Matthew Brett wrote:
>> > Yup. It's not even very idiomatic Python. readlines() is probably a
>> > bad idea unless your file is trivial length, and even ignoring
>> > numpy.loadtxt(), all of this could be considerably simplified
2009/7/8 Robert Kern :
> 2009/7/4 Stéfan van der Walt :
>> Thanks, Scott. This should now be fixed in SVN.
>
> You should probably change that to asanyarray() before the masked
> array crowd gets upset. :-)
Thanks, fixed!
Stéfan
___
Numpy-discussion ma
Hi Luca
2009/7/8 Citi, Luca :
> Hello guys,
> I made a patch for numpy which allows performing
> operations in-place to save memory allocations.
> For example 'sqrt(a**2 + b**2)' can be performed
> allocating only two arrays instead of four.
> You find the details in ticket 1153 of numpy-core.
> I
On Wed, Jul 08, 2009 at 12:48:17PM -0700, Matthew Brett wrote:
> > Yup. It's not even very idiomatic Python. readlines() is probably a
> > bad idea unless your file is trivial length, and even ignoring
> > numpy.loadtxt(), all of this could be considerably simplified with the
> > built-in csv modul
Hi,
> Yup. It's not even very idiomatic Python. readlines() is probably a
> bad idea unless your file is trivial length, and even ignoring
> numpy.loadtxt(), all of this could be considerably simplified with the
> built-in csv module.
or a 1-liner with scipy.io.loadmat ...
Best,
Matthew
___
On 8-Jul-09, at 4:19 AM, Pauli Virtanen wrote:
> Tue, 07 Jul 2009 21:30:06 -0500, alan kirjoitti:
>> Mathematica vs Matlab vs Python
>>
>> http://www.larssono.com/musings/matmatpy/index.html
>
> The Python code there is not very idiomatic Numpy code. It's written
> for
> Numeric, and fancy index
On Jul 8, 2009, at 7:03 AM, John [H2O] wrote:
>
> Hello,
>
> I have several issues which require me to iterate through a fairly
> large
> array (30+ records).
>
> The first case is calculating and hourly average from non-regularly
> sampled
> data.
Would you like to give the scikits.time
On Jul 8, 2009, at 3:18 AM, Scott Sinclair wrote:
>> 2009/7/8 Robert Kern :
>> 2009/7/4 Stéfan van der Walt :
>>> Thanks, Scott. This should now be fixed in SVN.
>>
>> You should probably change that to asanyarray() before the masked
>> array crowd gets upset. :-)
>
> I hadn't thought about that
Thanks, you two. That helps alot. The PyCObject_FromVoidPtr() trick is
good to know--I still have to have my class because it holds other data, but
this definitely points me in the right direction.
On Tue, Jul 7, 2009 at 9:55 PM, Lisandro Dalcin wrote:
> 2009/7/7 Stéfan van der Walt :
> > Hi K
Hello guys,
I made a patch for numpy which allows performing
operations in-place to save memory allocations.
For example 'sqrt(a**2 + b**2)' can be performed
allocating only two arrays instead of four.
You find the details in ticket 1153 of numpy-core.
I thought maybe you could be interested.
I am
John [H2O] gmail.com> writes:
> What I am trying to do (obviously?) is find all the values of X that fall
> within a time range.
>
> Specifically, one point I do not understand is why the following two methods
> fail:
>
> --> 196 ind = np.where( (t1 < Y[:,0] < t2) ) #same result
> with/
Le lundi 06 juillet 2009 à 17:57 +0200, Fabrice Silva a écrit :
> Le lundi 06 juillet 2009 à 17:13 +0200, Nils Wagner a écrit :
> > IIRC, the coefficients of your polynomial are complex.
> > So, you cannot guarantee that the roots are complex
> > conjugate pairs.
>
> Correct! If the construction
On Fri, Jun 12, 2009 at 7:46 PM, David
Cournapeau wrote:
> Hi,
>
> I have finally spent some time so that we can install pure C
> libraries using numpy.distutils. With this, one could imagine having a C
> library for fft, special functions in numpy or scipy, so that the
> library could be reused
Hello,
I have several issues which require me to iterate through a fairly large
array (30+ records).
The first case is calculating and hourly average from non-regularly sampled
data. The second is screening one array, based on data in the second array.
The functions are defined below, but in
On Wed, Jul 8, 2009 at 5:37 AM, Charles R
Harris wrote:
> David,
>
> Should any standard c functions used in loops.c.src be the npy_* version?
> I've been using fabs, but I'm wondering if that should be npy_fabs.
Yes. Although fabs is available on any platform in theory, we should
standardize on u
Tue, 07 Jul 2009 21:30:06 -0500, alan kirjoitti:
> Mathematica vs Matlab vs Python
>
> http://www.larssono.com/musings/matmatpy/index.html
The Python code there is not very idiomatic Numpy code. It's written for
Numeric, and fancy indexing etc. are not used.
Seems like the author also left it a
> 2009/7/8 Robert Kern :
> 2009/7/4 Stéfan van der Walt :
>> Thanks, Scott. This should now be fixed in SVN.
>
> You should probably change that to asanyarray() before the masked
> array crowd gets upset. :-)
I hadn't thought about that, but I'm don't think it matters in this
case. MaskedArray.no
48 matches
Mail list logo
