>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
>
I've tried the same scheme using R and it seems to give the right
answers
> quantile( rf(1000,10,10), .99)
99%
4.84548
> quantile( rf(1000,11,10), .99)
99%
4.770002
> quantile( rf(1000,11,11), .99)
99%
4.465655
> quantile( rf(1000,10,11), .99)
99%
4.539423
I either found a bug in the F distribution, or I'm really messed up.
>From a table I find
dfnum dfden F(P<.01)
10 10 4.85
11 10 4.78
11 11 4.46
10 11 4.54
So let's calculate the same quantities using numpy...
import scipy.stats as stats
import numpy as np
I
On Thu, 05 Feb 2009 22:17:54 -0600
Travis Oliphant wrote:
> Gael Varoquaux wrote:
> > On Thu, Feb 05, 2009 at 05:08:49PM -0600, Travis E. Oliphant wrote:
> >
> >> I've been fairly quiet on this list for awhile due to work and family
> >> schedule, but I think about how things can improve regu
On Fri, 16 Jan 2009 19:24:56 -0500
"Kevin Jacobs " wrote:
> On Fri, Jan 16, 2009 at 7:07 PM, Matthew Brett wrote:
>
> > So, please, if you are considering google code for hosting, consider
> > other options.
> >
>
> Seems odd that you'd post that from a gmail account. I do sympathize with
> yo
gt; מאת: [EMAIL PROTECTED] בשם Brent Pedersen
> נשלח: ו 12-ספטמבר-08 04:19
> אל: Discussion of Numerical Python
> נושא: Re: [Numpy-discussion] array gymnastics
>
> On Thu, Sep 11, 2008 at 6:03 PM, Alan Jackson <[EMAIL PROTECTED]> wrote:
> > There has got to be a
There has got to be a simple way to do this, but I'm just not seeing it.
>>> a = array([[1,2,3,4,5,6],
[7,8,9,10,11,12]])
>>> b = array([21,22,23,24,25,26])
What I want to end up with is :
c = array([[1,7,21],
[2,8,22],
..
[6,12,26]])
--
---
Thanks. I'm still struggling with learning the idiom. Too much perl
to unlearn. 8-)
On Sat, 30 Aug 2008 23:10:10 -0400
Zachary Pincus <[EMAIL PROTECTED]> wrote:
> Hi Alan,
>
> > Traceback (most recent call last):
> > File "/usr/local/lib/python2.5/site-packages/enthought.traits-2.0.4-
> > py2.
I been beating myself up over this bit of code for far too long now - I
know I must be missing something really simple, but what is it?
TYPICALLY_UINT_COLUMNS = ['Track', 'Bin', 'code', 'horizon']
..
dtypes = [ ]
for i in range(0, len(self.var_list)) :
if TYPICALLY_
I tested all three offered solutions :
t = table[:] # convert to structured array
collections = np.unique(t['collection'])
for collection in collections:
cond = t['collection'] == collection
energy_this_collection = t['energy'][cond]
--
energies = {}
for ro
Looking for advice on a good way to handle this problem.
I'm dealing with large tables (Gigabyte large). I would like to
efficiently subset values from one column based on the values in
another column, and get arrays out of the operation. For example,
say I have 2 columns, "energy" and "collectio
Just a question - I'm gradually working through the distributions for
the documentation marathon and I realised that there is a whole nest
of them named "standard-".
For several (e.g., normal) they are just the regular distribution with all the
parameters except size set to "standard" values.
On Fri, 13 Jun 2008 15:21:11 +0200
Gael Varoquaux <[EMAIL PROTECTED]> wrote:
> On Thu, Jun 12, 2008 at 02:51:51AM +0200, Stéfan van der Walt wrote:
> > > Build instructions don't really belong in docstrings. Put it in the
> > > README.txt or DEV_README.txt.
>
> > No, but running the tests is some
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