Hi,
I have worked on porting scipy to py3k, and it is mostly working. One
thing which would be useful is to install something similar to
npy_3kcompat.h in numpy, so that every scipy extension could share the
compat header. Is the current python 3 compatibility header usable in
the wild, or will
Yes, that is very likely the solution. It's clear that the module is in
the list. I say likely, since I've never done it before and there always
seems to be something that gets overlooked in what seems to be something
so simple. :-)
However, my colleague is on XP. Ah, same idea there.
I find
The general guideline:
Suppose the function definition is:
def func(x,y):
# x and y are scalars
bla bla bla ...
return z # a scalar
So,
import numpy as np
vecfun = np.vectorize(func)
vecfun.ufunc.accumulate(array((0,1,2,3,4,5,6,7,8,9))
Nadav.
-Original Message-
On 03/27/2010 01:31 PM, Ryan May wrote:
On Sat, Mar 27, 2010 at 11:12 AM,josef.p...@gmail.com wrote:
On Sat, Mar 27, 2010 at 1:00 PM, Ryan Mayrma...@gmail.com wrote:
On Mon, Mar 22, 2010 at 8:14 AM, Ryan Mayrma...@gmail.com wrote:
On Sun, Mar 21, 2010 at 11:57
On Mon, Mar 29, 2010 at 4:13 AM, David Cournapeau courn...@gmail.comwrote:
Hi,
I have worked on porting scipy to py3k, and it is mostly working. One
thing which would be useful is to install something similar to
npy_3kcompat.h in numpy, so that every scipy extension could share the
compat
ma, 2010-03-29 kello 19:13 +0900, David Cournapeau kirjoitti:
I have worked on porting scipy to py3k, and it is mostly working. One
thing which would be useful is to install something similar to
npy_3kcompat.h in numpy, so that every scipy extension could share the
compat header. Is the
On Mon, Mar 29, 2010 at 8:00 AM, Bruce Southey bsout...@gmail.com wrote:
On 03/27/2010 01:31 PM, Ryan May wrote:
Because of the call to asarray(), the mask is completely discarded and
you end up with identical results to an unmasked array,
which is not what I'd expect. Worse, the actual
Hi,
I decided that having actual code that does what I want and keeps
backwards compatibility (and adds tests) might be better than arguing
semantics. I've updated my patch to:
* Uses the array.sum() method instead of add.reduce to make subclasses
fully work (this was still breaking masked
On 03/29/2010 10:17 AM, Ryan May wrote:
On Mon, Mar 29, 2010 at 8:00 AM, Bruce Southeybsout...@gmail.com wrote:
On 03/27/2010 01:31 PM, Ryan May wrote:
Because of the call to asarray(), the mask is completely discarded and
you end up with identical results to an unmasked array,
Hi,
Does anyone have an idea how fft functions are implemented? Is it pure
python? based on BLAS/LAPACK? or is it using fftw?
I successfully used numpy.fft in 3D. I would like to know if I can
calculate a specific a plane using the numpy.fft.
I have in 3D:
r(x, y, z)=\sum_h^N-1 \sum_k^M-1
On Mon, Mar 29, 2010 at 16:00, Pascal pascal...@parois.net wrote:
Hi,
Does anyone have an idea how fft functions are implemented? Is it pure
python? based on BLAS/LAPACK? or is it using fftw?
Using FFTPACK converted from FORTRAN to C.
--
Robert Kern
I have come to believe that the whole
Hi All,
On 29 March 2010 00:59, Andrea Gavana wrote:
On 29 March 2010 00:34, Robert Kern wrote:
Scaling each axis by its standard deviation is a typical first start.
Shifting and scaling the values such that they each go from 0 to 1 is
another useful thing to try.
Ah, magnifico! Thank you
Hi Kevin,
On 29 March 2010 01:38, Kevin Dunn wrote:
Message: 5
Date: Sun, 28 Mar 2010 00:24:01 +
From: Andrea Gavana andrea.gav...@gmail.com
Subject: [Numpy-discussion] Interpolation question
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID:
Hi Brennan All,
On 29 March 2010 00:46, Brennan Williams wrote:
Andrea Gavana wrote:
As for your question, the parameter are not spread completely
randomly, as this is a collection of simulations done over the years,
trying manually different scenarios, without having in mind a proper
Hi Chris and All,
On 29 March 2010 22:35, Christopher Barker wrote:
Andrea Gavana wrote:
Scaling each axis by its standard deviation is a typical first start.
Shifting and scaling the values such that they each go from 0 to 1 is
another useful thing to try.
Ah, magnifico! Thank you Robert
Andrea Gavana wrote:
Scaling each axis by its standard deviation is a typical first start.
Shifting and scaling the values such that they each go from 0 to 1 is
another useful thing to try.
Ah, magnifico! Thank you Robert and Friedrich, it seems to be working
now...
One other thought -- core
Hi,
In my setup.py, I have
from numpy.distutils.misc_util import Configuration
fflags= '-fdefault-real-8 -ffixed-form'
config = Configuration(
'foo',
parent_package=None,
top_path=None,
f2py_options='--f77flags=\'%s\' --f90flags=\'%s\'' % (fflags,
fflags)
)
However I am
On Mon, Mar 29, 2010 at 3:00 PM, Pascal pascal...@parois.net wrote:
Hi,
Does anyone have an idea how fft functions are implemented? Is it pure
python? based on BLAS/LAPACK? or is it using fftw?
I successfully used numpy.fft in 3D. I would like to know if I can
calculate a specific a plane
Andrea Gavana wrote:
Hi Chris and All,
On 29 March 2010 22:35, Christopher Barker wrote:
Andrea Gavana wrote:
Scaling each axis by its standard deviation is a typical first start.
Shifting and scaling the values such that they each go from 0 to 1 is
another useful thing to try.
On 29 March 2010 23:13, Brennan Williams wrote:
Andrea Gavana wrote:
Hi Chris and All,
On 29 March 2010 22:35, Christopher Barker wrote:
Andrea Gavana wrote:
Scaling each axis by its standard deviation is a typical first start.
Shifting and scaling the values such that they each go from 0
2010/3/29 Andrea Gavana andrea.gav...@gmail.com:
If anyone is interested in a follow up, I have tried a time-based
interpolation of my oil profile (and gas and gas injection profiles)
using those 40 interpolators (and even more, up to 400, one every
month of fluid flow simulation time step).
Thanks Nadav!
On Mon, Mar 29, 2010 at 4:07 AM, Nadav Horesh nad...@visionsense.comwrote:
The general guideline:
Suppose the function definition is:
def func(x,y):
# x and y are scalars
bla bla bla ...
return z # a scalar
So,
import numpy as np
vecfun = np.vectorize(func)
HI Friedrich All,
On 29 March 2010 23:44, Friedrich Romstedt wrote:
2010/3/29 Andrea Gavana andrea.gav...@gmail.com:
If anyone is interested in a follow up, I have tried a time-based
interpolation of my oil profile (and gas and gas injection profiles)
using those 40 interpolators (and even
On Mon, Mar 29, 2010 at 17:57, Andrea Gavana andrea.gav...@gmail.com wrote:
HI Friedrich All,
On 29 March 2010 23:44, Friedrich Romstedt wrote:
Something completely different: Are you going to do more simulations?
110% surely undeniably yes. The little interpolation tool I have is
just a
Pauli Virtanen wrote:
ma, 2010-03-29 kello 19:13 +0900, David Cournapeau kirjoitti:
I have worked on porting scipy to py3k, and it is mostly working. One
thing which would be useful is to install something similar to
npy_3kcompat.h in numpy, so that every scipy extension could share the
Thank you all for your suggestions. I ended up multiplying by 10 and
rounding, while casting the array to an int. Certainly not the most
universal solution, but it worked for my data.
code, for anyone searching for examples:
np.array(np.round((hlspec[:,0]-offset)*10),dtype=np.int)
-Mike
On
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