Hello folks,
I have Ubuntu 10.10 server on EC2. I installed Python 3.1, and now I want to
install NumPy on it. How do I do it? I tried `easy_install-3.1 numpy` but
got this error:
[...]
RefactoringTool: Refactored
/tmp/easy_install-MiUli2/numpy-1.5.1/build/py3k/numpy/core/defchararray.py
On Mon, Jan 24, 2011 at 8:22 PM, cool-RR cool...@cool-rr.com wrote:
Hello folks,
I have Ubuntu 10.10 server on EC2. I installed Python 3.1, and now I want
to install NumPy on it. How do I do it? I tried `easy_install-3.1 numpy` but
got this error:
Just do python3.1 setup.py install. That's
Hello,
I'm trying to cycle over some vectors (lat,lon,emissions) of
irregularly spaced lat/lon spots, and values. I need to sum the values
each contributing to grid on a regular lat lon grid.
This is what I have presently, but it is too slow. Is there a more
efficient way to do this? I would
Hi John,
Since you have a regular grid, you should be able to find the x and y
indices without np.where, ie something like
I = (lon-grid.outlon0 / grid.dx).astype(int)
J = (lat-grid.outlat0 / grid.dy).astype(int)
for i, j, e in zip(I, J, emissions):
Z[i,j] += e
David
On Mon, Jan 24, 2011
On 01/24/2011 02:53 PM, John wrote:
Hello,
I'm trying to cycle over some vectors (lat,lon,emissions) of
irregularly spaced lat/lon spots, and values. I need to sum the values
each contributing to grid on a regular lat lon grid.
This is what I have presently, but it is too slow. Is there a
I know we're not supposed to 'broadcast' thanks, but Thanks! This
works much more efficiently!
On Mon, Jan 24, 2011 at 3:50 PM, David Huard david.hu...@gmail.com wrote:
Hi John,
Since you have a regular grid, you should be able to find the x and y
indices without np.where, ie something like
I will try this as well and report back with a timing...
On Mon, Jan 24, 2011 at 3:56 PM, Vincent Schut sc...@sarvision.nl wrote:
On 01/24/2011 02:53 PM, John wrote:
Hello,
I'm trying to cycle over some vectors (lat,lon,emissions) of
irregularly spaced lat/lon spots, and values. I need to
Thanks again everyone,
Just for completeness. First, there seems to be a problem with my
original method, but it must have to do with indexing. Apart from
being slow, it reports back maximum values a factor of two greater
than the other two methods, so something is amiss there. The other two
Hello,
I have discovered a strange bug with numexpr. numexpr.evaluate gives
randomized results on arrays larger than 2047 elements. The following
program demonstrates this:
from numpy import *
from numexpr import evaluate
def func(x):
return evaluate(sum(x, axis = 0))
x = zeros(2048)+.01
Forgot to mention that I am using numexpr 1.4.1 and numpy 1.5.1
On Mon, Jan 24, 2011 at 9:47 AM, John Salvatier
jsalv...@u.washington.eduwrote:
Hello,
I have discovered a strange bug with numexpr. numexpr.evaluate gives
randomized results on arrays larger than 2047 elements. The following
I have the same problem here. I'm using numexpr 1.4.1 and numpy 1.3.0.
On Mon, Jan 24, 2011 at 4:19 PM, John Salvatier
jsalv...@u.washington.edu wrote:
Forgot to mention that I am using numexpr 1.4.1 and numpy 1.5.1
On Mon, Jan 24, 2011 at 9:47 AM, John Salvatier jsalv...@u.washington.edu
I see the same randomness, but at a different array size:
In [23]: numpy.__version__
Out[23]: '1.4.0'
In [24]: import numexpr
In [25]: numexpr.__version__
Out[25]: '1.4.1'
In [26]: x = zeros(8192)+0.01
In [27]: print evaluate('sum(x, axis=0)')
72.97
In [28]: print evaluate('sum(x, axis=0)')
I also get the same issue with prod()
On Mon, Jan 24, 2011 at 10:23 AM, Warren Weckesser
warren.weckes...@enthought.com wrote:
I see the same randomness, but at a different array size:
In [23]: numpy.__version__
Out[23]: '1.4.0'
In [24]: import numexpr
In [25]: numexpr.__version__
Looks like this is related to issue 41 (
http://code.google.com/p/numexpr/issues/detail?id=41can=1).
On Mon, Jan 24, 2011 at 10:29 AM, John Salvatier
jsalv...@u.washington.eduwrote:
I also get the same issue with prod()
On Mon, Jan 24, 2011 at 10:23 AM, Warren Weckesser
On Mon, Jan 24, 2011 at 1:13 PM, John Salvatier
jsalv...@u.washington.eduwrote:
Looks like this is related to issue 41 (
http://code.google.com/p/numexpr/issues/detail?id=41can=1).
That might not be the same issue.
You can fix the randomness by setting the number of threads to 1, as in
Hi,
Running on:
In []: np.__version__
Out[]: '1.5.1'
In []: sys.version
Out[]: '2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)]'
For the reference:
In []: X= randn(10, 125)
In []: timeit dot(X.T, X)
1 loops, best of 3: 170 us per loop
In []: X= randn(10, 250)
In []:
Am I misreading the docs or missing something? Consider the following
adapted from here:
http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html
from StringIO import StringIO
import numpy as np
data = 1, 2, 3\n4, ,5
np.genfromtxt(StringIO(data), delimiter=,, names=a,b,c,
missing_values=
On Mon, Jan 24, 2011 at 4:29 PM, eat e.antero.ta...@gmail.com wrote:
Hi,
Running on:
In []: np.__version__
Out[]: '1.5.1'
In []: sys.version
Out[]: '2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)]'
For the reference:
In []: X= randn(10, 125)
In []: timeit dot(X.T,
On Mon, Jan 24, 2011 at 6:49 PM, josef.p...@gmail.com wrote:
On Mon, Jan 24, 2011 at 4:29 PM, eat e.antero.ta...@gmail.com wrote:
Hi,
Running on:
In []: np.__version__
Out[]: '1.5.1'
In []: sys.version
Out[]: '2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit
(Intel)]'
For
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