On 12/11/11 8:40 AM, Ralf Gommers wrote:
On Wed, Dec 7, 2011 at 7:50 PM, Chris.Barker chris.bar...@noaa.gov
* If we have a good, fast ascii (or unicode?) to array reader, hopefully
it could be leveraged for use in the more complex cases. So that rather
than genfromtxt() being
On 12/9/11 11:25 AM, Ng, Enrico wrote:
I am trying to pass a multi-dimensional ndarray to C as a multi-dimensional C
array for the purposes of passing it to mathematica. I am using
PyArray_AsCArray but getting an error.
I understand that SWIG, Boost, et. al are perhaps too heavyweight for
Hi folks,
This is a continuation of a conversation already started, but i gave it
a new, more appropriate, thread and subject.
On 12/6/11 2:13 PM, Wes McKinney wrote:
we should start talking
about building a *high performance* flat file loading solution with
good column type inference and
Hi folks,
I'm working on a ragged array class -- an array that can store and
work with what can be considered tabular data, with the rows of
different lengths:
ragged_array
A ragged array class -- build on numpy
The idea is to be able to store data that is essentially 2-d, but each
row
On 11/13/11 9:55 AM, Olivier Delalleau wrote:
idea, since it will throw out a lot of information if you decrease the
number of bins:
I agree -- I'd think about looking at a smooth interpolation -- maybe
kernel density estimation?
On 11/14/11 8:12 AM, Sturla Molden wrote:
Fit a poisson
On 11/10/11 3:57 AM, Olivier Delalleau wrote:
In such a situation you should probably use a dictionary from the start,
all good suggestions, but while we're at it:
On 11/10/11 2:17 AM, Chao YUE wrote:
Does anyone know how I can quickly use the name of a ndarray as a string?
This reflects a
On 11/7/11 10:16 AM, Carlos Neves wrote:
I am a robotics student and I have used numpy to develop a controller
for a humanoid robot. Now I am trying to implement the same controller
to a different robot, NAO - http://www.aldebaran-robotics.com/
This robot has a Linux based OS with Python
On 11/2/11 7:16 PM, Nathaniel Smith wrote:
By R compatibility, I specifically had in mind in-memory
compatibility.
The R crowd has had a big voice in this discussion, and I understand
that there are some nice lessons to be learned from it with regard to
the NA issues.
However, I think making
On 10/31/11 6:38 PM, Stéfan van der Walt wrote:
On Mon, Oct 31, 2011 at 6:25 PM, Matthew Brettmatthew.br...@gmail.com
wrote:
Oh, dear, I'm suffering now:
In [12]: res 2**31-1
Out[12]: array([False], dtype=bool)
I'm seeing:
...
Your result seems very strange, because the numpy scalars
On 10/27/11 7:51 PM, Travis Oliphant wrote:
As I mentioned. I find the ability to separate an ABSENT idea from an
IGNORED idea convincing. In other words, I think distinguishing between
masks and bit-patterns is not just an implementation detail, but
provides a useful concept for multiple
On 10/28/11 11:37 AM, Matthew Brett wrote:
The main motivation for the alterNEP was our strong feeling that
separating ABSENT and IGNORE was easier to comprehend and cleaner.
I don't know about easier to comprehend, or cleaner, but it is more
feature-full.
I see two issues here:
1) being
On 10/14/11 5:04 AM, Neal Becker wrote:
suppose I have:
In [10]: u
Out[10]:
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
And I have a vector v:
v = np.array ((0,1,0,1,0))
I want to form an output vector which selects items from u where v is the
index
of the row of u to be
On 10/13/11 6:03 AM, Linus Jundén wrote:
I am about to make a NumPy presentation for my colleges in about a
week. I want to tell them something about the history of the library
and what kind of code it relies on.
Is NumPy based on some external code like e.g. BLAS, LAPACK etc or is
it coded
On 9/27/11 2:14 AM, oc-spam66 wrote:
if you want to write to a string, why not use .tostring()?
Because A.tostring() returns the binary data, while I would like the text
representation.
More precisely, I would like to use A.tofile(sep=\t).
I see -- I've always thought mingling binary and
On 9/12/11 4:38 PM, Christopher Jordan-Squire wrote:
I did some timings to see what the advantage would be, in the simplest
case possible, of taking multiple lines from the file to process at a
time.
Nice work, only a minor comment:
f6 and f7 use stripped down versions of Chris
Barker's
On 9/8/11 1:43 PM, Christopher Jordan-Squire wrote:
I just ran a quick test on my machine of this idea. With
dt = np.dtype([('x',np.float32),('y', np.int32),('z', np.float64)])
temp = np.empty((), dtype=dt)
temp2 = np.zeros(1,dtype=dt)
In [96]: def f():
...: l=[0]*3
...:
On 9/2/11 2:45 PM, Christopher Jordan-Squire wrote:
It doesn't have to parse the entire file to determine the dtypes. It
builds up a regular expression for what it expects to see, in terms of
dtypes. Then it just loops over the lines, only parsing if the regular
expression doesn't match. It
On 9/2/11 8:22 AM, Derek Homeier wrote:
I agree it would make a very nice addition, and could complement my
pre-allocation option for loadtxt - however there I've also been made
aware that this approach breaks streamed input etc., so the buffer.resize(…)
methods in accumulator would be the
On 9/2/11 9:16 AM, Christopher Jordan-Squire wrote:
I agree it would make a very nice addition, and could complement my
pre-allocation option for loadtxt - however there I've also been made
aware that this approach breaks streamed input etc., so the buffer.resize(…)
methods in accumulator
On 8/31/11 3:58 AM, Dieter Weber wrote:
just wanted to show an example of how python3 + numpy compares with just
python3 and many other languages and language implementations:
http://shootout.alioth.debian.org/u64q/performance.php?test=mandelbrot#about
hmmm - it would be interesting to see
On 8/27/11 11:08 AM, Christopher Jordan-Squire wrote:
I've submitted a pull request for a new method for loading data from
text files into a record array/masked record array.
Click on the link for more info, but the general idea is to create a
regular expression for what entries should look
On 8/26/11 5:04 AM, Derek Homeier wrote:
Hmm, the pure Python version might be, but, I've used cPickle for a long time
and never noted any stability problems.
well, here is the NEP:
https://github.com/numpy/numpy/blob/master/doc/neps/npy-format.txt
It addresses the why's and hows of the
On 8/24/11 9:22 AM, Anthony Scopatz wrote:
You can use Python pickling, if you do *not* have a requirement for:
I can't recall why, but it seem pickling of numpy arrays has been
fragile and not very performant.
I like the npy / npz format, built in to numpy, if you don't need:
-
Simon Palmer wrote:
Does JSON have a representation for n-d arrays? In my little work with
it, it looked pretty lame for arrays of number, so I'd be surprised.
yes it does, thet are just treated as nested lists and the square
bracket notation is used.
then it looks like one of str(array)
Bill Baxter wrote:
import numpy as npy
Bill,
for what it's worth, I *think* this group has reached a consensus to use:
import numpy as np
We all have different tastes for how they might want to spell it, but
the more consistent we are, the easier it will be for newbies.
-Chris
--
Linda Seltzer wrote:
I would appreciate it if someone could answer my question without
referring to subjects such as APIs and interfaces, since I am only
concerned with a mathematical application at this time.
caution: this is a bit rude -- that was an excellent and informative
answer to your
Keith Goodman wrote:
Interestingly, MATLAB (v7.5.0) takes a different approach:
ans =
1271
-A
ans =
127 -1
can anyone explain that? -- just curious.
Charles R Harris wrote:
We could simply define the range of int8 as [-127,127], but that is
somewhat problematical also.
That
Could we add a from __future__ import something along with a
deprecation warning?
This could be used for Tim's new matrix class, or any other API change.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/ORR(206) 526-6959 voice
7600 Sand
Hi all,
I have a n X m X 3 array, and I a n X M array. I want to assign the
values in the n X m to all three of the slices in the bigger array:
A1 = np.zeros((5,4,3))
A2 = np.ones((5,4))
A1[:,:,0] = A2
A1[:,:,1] = A2
A1[:,:,2] = A2
However,it seems I should be able to broadcast that, so I
Travis E. Oliphant wrote:
Stéfan van der Walt wrote:
2008/4/30 Christopher Barker [EMAIL PROTECTED]:
Since it is optional, shouldn't it be keyword argument?
Thanks, fixed in r5115.
This was too hasty. I had considered this before.
The problem with this is that the object can
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