A Thursday 02 July 2009 20:15:13 Dan Yamins escrigué:
What's wrong with recarrays? In any case, if you need a true ndarray
object
you can always do:
ndarr = recarr.view(np.ndarray)
and you are done.
I have a question about this though. The object ndarr will consist of
records,
Pierre GM pgmdevlist at gmail.com writes:
What about
'formats':[eval(b) for b in event_format]
Should it fail, try something like:
dtype([(x,eval(b)) for (x,b) in zip(event_fields, event_format)])
At least you force dtype to have the same nb of names formats.
You could use
data =
Hi,
should this not be accepted:
N.argwhere([4,0,2,1,3])
?
instead I get
Traceback (most recent call last):
File input, line 1, in module
File ./numpy/core/numeric.py, line 510, in argwhere
AttributeError: 'list' object has no attribute 'nonzero'
N.argwhere(N.array([4,0,2,1,3]))
[[0]
[2]
Hello
Has anyone looked at the behaviour of the (polynomial) roots function
for high-order polynomials ? I have an application which internally
searches for the roots of a polynomial. It works nicely for order less
than 20, and then has an erratic behaviour for upper values...
I looked into the
On Fri, 03 Jul 2009 11:48:45 +0200
Fabrice Silva si...@lma.cnrs-mrs.fr wrote:
Hello
Has anyone looked at the behaviour of the (polynomial)
roots function
for high-order polynomials ? I have an application which
internally
searches for the roots of a polynomial. It works nicely
for order
2009/7/3 Sebastian Haase seb.ha...@gmail.com:
Hi,
should this not be accepted:
N.argwhere([4,0,2,1,3])
?
instead I get
Traceback (most recent call last):
File input, line 1, in module
File ./numpy/core/numeric.py, line 510, in argwhere
AttributeError: 'list' object has no attribute
Le vendredi 03 juillet 2009 à 11:52 +0200, Nils Wagner a écrit :
You will need multiprecision arithmetic in that case.
It's an ill-conditioned problem.
I may have said that the solution are of the same order of magnitude, so
that the ratio between the lowest and the highest absolute values of
Le vendredi 03 juillet 2009 à 14:43 +0200, Nils Wagner a écrit :
Just curious - Can you provide us with the coefficients of
your polynomial ?
Working case :
Polynomial.c =
[ -1.34100085e+57 +0.e+00j -2.28806781e+55 +0.e+00j
-4.34808480e+54 -3.27208577e+36j
On Fri, Jul 3, 2009 at 3:48 AM, Fabrice Silva si...@lma.cnrs-mrs.fr wrote:
Hello
Has anyone looked at the behaviour of the (polynomial) roots function
for high-order polynomials ? I have an application which internally
searches for the roots of a polynomial. It works nicely for order less
Fabrice Silva wrote:
Le vendredi 03 juillet 2009 à 11:52 +0200, Nils Wagner a écrit :
You will need multiprecision arithmetic in that case.
It's an ill-conditioned problem.
I may have said that the solution are of the same order of magnitude, so
that the ratio between the lowest and the
On 2009-07-03, Charles R Harris charlesr.har...@gmail.com wrote:
roots? The connection between polynomial coefficients and polynomial values
becomes somewhat vague when the polynomial degree becomes large, it is
numerically ill conditioned.
In addition to switching to higher precision than
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
In
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
If I have two recarrays with the same len and column headers, the __eq__
method returns the rich comparison, which is great. E.g.
In [20]: x =
np.rec.fromrecords([(1,2,'dd',.3),(33,2,'y',2.2),(2,3,'a',21.4),(3,4,'b',33.2)],names=['A','B','C','D'])
In [21]: y =
14 matches
Mail list logo
