Hi All,
I changed the sort routines to sort nans to the end and got some timings.
Sorting 10 random doubles 100 times yields:
current nan version
quicksort 1.17 sec1.29 sec
mergesort 1.37 sec1.36 sec
heapsort 1.83 sec2.12 sec
Curiously, mergesort doe
On Wed, Jul 22, 2009 at 13:35, Keith Goodman wrote:
> On Ubuntu 9.04, python 2.6.2, numpy 1.2.1 this gives a ValueError:
>
> x = np.array([1,2,3])
> x[0] = np.nan
> ValueError: cannot convert float to NaN to integer
>
> But on Debian squeeze, python 2.5.4, numpy 1.2.1 the assignment works
> (well,
Sorry. I meant to update this thread after I had resolved my issue.
This was indeed one problem. I had to set LD_LIBRARY_PATH.
I also had another odd problem that I will spell out here in hopes
that I save someone some trouble. Specifically, one should be very
sure that the path to the blas tha
By the way, Chris, what's it gonna take to get you contributing to the doc? ;-)
DG
--- On Wed, 7/22/09, Christopher Barker wrote:
> From: Christopher Barker
> Subject: Re: [Numpy-discussion] String manipulation
> To: "Discussion of Numerical Python"
> Date: Wednesday, July 22, 2009, 11:33 AM
On Ubuntu 9.04, python 2.6.2, numpy 1.2.1 this gives a ValueError:
x = np.array([1,2,3])
x[0] = np.nan
ValueError: cannot convert float to NaN to integer
But on Debian squeeze, python 2.5.4, numpy 1.2.1 the assignment works
(well, the float nan is convert to the int 0):
x[0] = np.nan
x
array(
Stéfan van der Walt wrote:
You cannot use view on an array scalar, so you'll have to convert to
an array first:
x = x.reshape((1,))
x.view('S1')
Bingo!
Thanks, Stéfan.
I don't know why one can't uwe view on a scalar -- there is still a dat
pointer isn't there?, and it can be reshaped. Butno
2009/7/22 Hans Meine
> Hi!
>
> (This mail is a reply to a personal conversation with Ullrich Köthe, but is
> obviously of a greater concern. This is about VIGRA's new NumPy-based
> python
> bindings.) Ulli considers this behaviour of NumPy to be a bug:
>
> In [1]: a = numpy.array([200], numpy.u
Hans Meine wrote:
> In [3]: numpy.add(a, a, numpy.empty((1, ), dtype = numpy.uint32))
> Out[3]: array([144], dtype=uint32)
yes, it sure would be nice to fix this...
> one will often end up with uint8 arrays which cannot be passed
> into many algorithms without an explicit conversion. However, i
I am afraid I misunderstand your question
because I do not get the results you expected.
def pdyn(a, p):
a = np.sort(a)
n = round((1-p) * len(a))
return a[int((n+1)/2)], a[len(a)-1-int(n/2)] # a[-int(n/2)] would not work
if n<=1
>>> pdyn([0, 0, 0, 0, 1, 2, 3, 4, 5, 2000], 1)
(0, 2000
On Jul 22, 2009, at 11:34 AM, Peter Alexander wrote:
> Hi all,
>
> I see life in a feature I've been dreaming about for years now. :-)
> I'm wondering how stable this branch is and if it's ready for
> playing with.
I may speak out of turn here, but I don't think so. Besides Travis O.
who doe
On Jul 22, 2009, at 12:36 PM, Johannes Bauer wrote:
> Hello list,
>
> is there some possibilty to get a p-dynamic of an array, i.e. if p=1
> then the result would be (arr.min(), arr.max()), but if 0 < p < 1,
> then
> the result is so that the pth percentile of the picture is withing the
> range
You can do it "by hand" by sorting the array and taking the
corresponding elements or you can use
scipy.stats.scoreatpercentile
that also interpolates.
Best,
Luca
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Hello list,
is there some possibilty to get a p-dynamic of an array, i.e. if p=1
then the result would be (arr.min(), arr.max()), but if 0 < p < 1, then
the result is so that the pth percentile of the picture is withing the
range given?
I cannot explain this very well, so please let me illustrate
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On Wednesday 22 July 2009 11:48:29 Gael Varoquaux wrote:
> On Fri, Jul 17, 2009 at 04:30:38PM +0200, Hans Meine wrote:
> > I have a simple question: How can I detect whether two arrays share the
> > same data?
>
> np.may_share_memory
Thanks a lot, that (and it's implementation, cf. byte_bounds) do
2009/7/22 Hans Meine
> On Wednesday 22 July 2009 17:16:31 Ralf Gommers wrote:
> > 2009/7/22 Hans Meine
> > > type = min(float32, a.dtype.type, b.dtype.type)
> >
> > Are you looking for the type to cast to? In that case I guess you meant
> > max() not min().
>
> No, at least for integers min(..)
On Wednesday 22 July 2009 17:16:31 Ralf Gommers wrote:
> 2009/7/22 Hans Meine
> > type = min(float32, a.dtype.type, b.dtype.type)
>
> Are you looking for the type to cast to? In that case I guess you meant
> max() not min().
No, at least for integers min(..) does what one would expect max(..) to
Hi all,
I see life in a feature I've been dreaming about for years now. :-)
I'm wondering how stable this branch is and if it's ready for playing with.
I ask because I'm (once again) about to write an cython extension to
process vectors of epochs and records of struct tm.
Naturally, I'd love to be
2009/7/22 Hans Meine
> Hi,
>
> Ullrich Köthe found an interesting way to compute a promoted dtype, given
> two
> arrays a and b:
>
> type = min(float32, a.dtype.type, b.dtype.type)
>
Are you looking for the type to cast to? In that case I guess you meant
max() not min().
>
> How hackish is thi
Hello Hans,
> Although it should be noted that in C/C++,
> the result of uint8+uint8 is int.
But C/C++ works with scalars and often temporary results
are kept in registers.
On the contrary, numpy works with arrays. We cannot expect
(a+b)*c
to grow from uint8 to uint16 and then uint32 :-D
> For e
On Wednesday 22 July 2009 15:14:32 Citi, Luca wrote:
> In [2]: a + a
> Out[2]: array([144], dtype=uint8)
>
> Please do not "fix" this, that IS the correct output.
No, I did not mean to fix this. (Although it should be noted that in C/C++,
the result of uint8+uint8 is int.)
> If instead, you ref
Hi Hans!
> Ideally, I'd like numpy to be "fixed"
what do you mean by "fixed"?
Are you referring to Out[2] or Out[3]?
In [1]: a = numpy.array([200], numpy.uint8)
In [2]: a + a
Out[2]: array([144], dtype=uint8)
Please do not "fix" this, that IS the correct output.
What should numpy do? Promote eve
On Friday 17 July 2009 22:15:31 Pauli Virtanen wrote:
> On 2009-07-17, Hans Meine wrote:
> > If I understood Travis' comments in the above-mentioned thread [1]
> > correctly, this would already fix some of the performance issues along
> > the way (since it would suddenly allow the use of special,
Hi,
Ullrich Köthe found an interesting way to compute a promoted dtype, given two
arrays a and b:
type = min(float32, a.dtype.type, b.dtype.type)
How hackish is this? Is this likely to break on other platforms/numpy
versions? Is there a better API for type promotion?
Have a nice day,
Han
Hi!
(This mail is a reply to a personal conversation with Ullrich Köthe, but is
obviously of a greater concern. This is about VIGRA's new NumPy-based python
bindings.) Ulli considers this behaviour of NumPy to be a bug:
In [1]: a = numpy.array([200], numpy.uint8)
In [2]: a + a
Out[2]: array(
Hi all,
Pieter Holtzhausen sent me this interesting link:
http://morepypy.blogspot.com/2009/07/pypy-numeric-experiments.html
"""
I have been playing recently with the idea of NumPy and PyPy
integration. My idea is to integrate PyPy's JIT with NumPy or at least
a very basic subset of it. Time con
On Fri, Jul 17, 2009 at 04:30:38PM +0200, Hans Meine wrote:
> Hi,
> I have a simple question: How can I detect whether two arrays share the same
> data?
np.may_share_memory
G.
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Hi,
I have a simple question: How can I detect whether two arrays share the same
data?
>>> a = numpy.arange(10)
>>> b = a.view(numpy.ndarray)
>>>
>>> a is not b # False, as expected
True
>>> a.data is b.data # I expected this to be True
False
>>>
>>> a.data
>>> b.data # even the memory address
2009/7/21 Christopher Barker :
> In [35]: line
> Out[35]: '-1.00E+00-1.00E+00-1.00E+00-1.00E+00
> 1.25E+00 1.25E+00'
>
> In [36]: a = np.array(line)
>
> In [37]: a
> Out[37]:
> array('-1.00E+00-1.00E+00-1.00E+00-1.00E+00 1.25E+00
> 1.25E+00',
>
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