I have had good luck with Continuum's Miniconda Python distributions on
Linux.
http://conda.pydata.org/miniconda.html
The `conda` command makes it very easy to create specific testing
environments for Python 2 and 3 with many different packages. Everything is
precompiled, so you won't have to worry
Arnd,
I can see where this is an issue. If you are trying to update your code for
Py3, I still think that it would really help to add a version attribute of
some sort to your new HDF files. You can then write a little check in your
access code that looks for this variable. If it is not present, yo
This works if run from Py3. Don't know if it will *always* work. From that
GH discussion you linked, it sounds like that is a bit of a hack.
##
"""Illustrate problem with pytables data - python 2 to python 3."""
from __future__ import print_function
import sys
import numpy as np
impor
he Matrix stuff from here to make a separate
project with just the Matrix stuff, if you don't want to go through the
Sage interface.
On Wed, Feb 11, 2015 at 11:54 AM, cjw wrote:
>
> On 11-Feb-15 10:21 AM, Ryan Nelson wrote:
>
> So:
>
> In [2]: np.mat([4,'5&#x
So:
In [2]: np.mat([4,'5',6])
Out[2]:
matrix([['4', '5', '6']], dtype=' wrote:
> It seems to be agreed that there are weaknesses in the existing Numpy
> Matrix
> Class.
>
> Some problems are illustrated below.
>
> I'll try to put some suggestions over the coming weeks and would appreciate
> comme
Colin,
I'll second the endorsement of Sage; however, for teaching purposes, I
would suggest Sage Math Cloud. It is a free, web-based version of Sage, and
it does not require you or the students to install any software (besides a
new-ish web browser). It also make sharing/collaborative work quite e
Maniteja,
Ralf's suggestion for Numpy works very well. In a more general case,
though, you might want to play around with conda, the package manager for
Anaconda's Python distribution (http://continuum.io/downloads).
I use the Miniconda package, which is pretty much just conda, to create new
"env
Alok Singhal edgestreamlp.com> writes:
>
> On Thu, Dec 4, 2014 at 4:25 PM, Ryan Nelson
gmail.com> wrote:
> >
> > I guess I'm a little confused about how the spacing values are
calculated.
>
> np.spacing(x) is basically the same as np.nextafter(x, np.inf)
Hello everyone,
I was working through the example usage for the test function
`assert_array_almost_equal_nulp`, and it brought up a question regarding
the function `spacing`. Here's some example code:
import numpy as np
from numpy.testing import assert_array_almost_equal_nulp
np.set_printopt
Emanuele Olivetti relativita.com> writes:
>
> Hi,
>
> I am using 2D arrays where only one dimension remains constant, e.g.:
> ---
> import numpy as np
> a = np.array([[1, 2, 3], [4, 5, 6]]) # 2 x 3
> b = np.array([[9, 8, 7]]) # 1 x 3
> c = np.array([[1, 3, 5], [7, 9, 8], [6, 4, 2]]) # 3 x 3
> d
much smaller "xs" array (~100 points) and the
broadcasting version was much faster.
Thanks
Ryan
Note: The link to the Scipy wiki page above is broken at the bottom of
Numpy's broadcasting page, otherwise I would have seen that earlier. Sorry
for the noise.
On Sun, Sep 14, 2014 at
Hello all,
I have a question about the performance of broadcasting versus Python for
loops. I have the following sample code that approximates some simulation
I'd like to do:
## Test Code ##
import numpy as np
def lorentz(x, pos, inten, hwhm):
return inten*( hwhm**2 / ( (x - pos)**2 + hwh
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