Is there any possibility of incorporating this work into numpy?
http://icl.cs.utk.edu/magma/software/index.html
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My suggestion is: don't.
It's easier to script runs if you read parameters from the command line.
I recommend argparse.
Giovanni Plantageneto wrote:
Dear all,
I have a simple question. I would like to have all the parameters of a
model written in a configuration file (text), and I would like
I made 2 mistakes here, the 1st argument had the wrong shape, and I really
wanted to use 'where', not 'choose'. But shouldn't segfault:
ValueError: Need between 2 and (32) array objects (inclusive).
Segmentation fault (core dumped)
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Michael Hull wrote:
Hi Everyone,
First off, thanks for all your hard work on numpy, its a really great help!
I was wondering if there was a standard 'groupby' in numpy, that
similar to that in itertools.
I know its not hard to write with np.diff, but I have found myself
writing it on more
I was just bitten by this unexpected behavior:
In [24]: all ([i 0 for i in xrange (10)])
Out[24]: False
In [25]: all (i 0 for i in xrange (10))
Out[25]: True
Turns out:
In [31]: all is numpy.all
Out[31]: True
So numpy.all doesn't seem to do what I would expect when given a generator.
Bug?
Dag Sverre Seljebotn wrote:
On 01/31/2012 03:07 PM, Robert Kern wrote:
On Tue, Jan 31, 2012 at 13:26, Neal Beckerndbeck...@gmail.com wrote:
I was just bitten by this unexpected behavior:
In [24]: all ([i0 for i in xrange (10)])
Out[24]: False
In [25]: all (i0 for i in xrange
And where do we find this gem?
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Mark Wiebe wrote:
On Fri, Feb 17, 2012 at 11:52 AM, Eric Firing efir...@hawaii.edu wrote:
On 02/17/2012 05:39 AM, Charles R Harris wrote:
On Fri, Feb 17, 2012 at 8:01 AM, David Cournapeau courn...@gmail.com
mailto:courn...@gmail.com wrote:
Hi Travis,
On Thu, Feb 16,
Sturla Molden wrote:
Den 19.02.2012 01:12, skrev Nathaniel Smith:
I don't oppose it, but I admit I'm not really clear on what the
supposed advantages would be. Everyone seems to agree that
-- Only a carefully-chosen subset of C++ features should be used
-- But this subset would be
Nathaniel Smith wrote:
On Sun, Feb 19, 2012 at 9:16 AM, David Cournapeau courn...@gmail.com wrote:
On Sun, Feb 19, 2012 at 8:08 AM, Mark Wiebe mwwi...@gmail.com wrote:
Is there a specific
target platform/compiler combination you're thinking of where we can do
tests on this? I don't believe
Sturla Molden wrote:
Den 18. feb. 2012 kl. 01:58 skrev Charles R Harris
charlesr.har...@gmail.com:
On Fri, Feb 17, 2012 at 4:44 PM, David Cournapeau courn...@gmail.com wrote:
I don't think c++ has any significant advantage over c for high performance
libraries. I am not convinced by
Charles R Harris wrote:
On Fri, Feb 17, 2012 at 12:09 PM, Benjamin Root ben.r...@ou.edu wrote:
On Fri, Feb 17, 2012 at 1:00 PM, Christopher Jordan-Squire
cjord...@uw.edu wrote:
On Fri, Feb 17, 2012 at 10:21 AM, Mark Wiebe mwwi...@gmail.com wrote:
On Fri, Feb 17, 2012 at 11:52 AM, Eric
What is the correct way to find the installed location of arrayobject.h?
On fedora, I had been using:
(via scons):
import distutils.sysconfig
PYTHONINC = distutils.sysconfig.get_python_inc()
PYTHONLIB = distutils.sysconfig.get_python_lib(1)
NUMPYINC = PYTHONLIB + '/numpy/core/include'
But on
It's great advice to say
avoid using new
instead rely on scope and classes such as std::vector.
I just want to point out, that sometimes objects must outlive scope.
For those cases, std::shared_ptr can be helpful.
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Is mkl only used for linear algebra? Will it speed up e.g., elementwise
transendental functions?
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Pauli Virtanen wrote:
23.02.2012 20:44, Francesc Alted kirjoitti:
On Feb 23, 2012, at 1:33 PM, Neal Becker wrote:
Is mkl only used for linear algebra? Will it speed up e.g., elementwise
transendental functions?
Yes, MKL comes with VML that has this type of optimizations:
And also
Francesc Alted wrote:
On Feb 23, 2012, at 2:19 PM, Neal Becker wrote:
Pauli Virtanen wrote:
23.02.2012 20:44, Francesc Alted kirjoitti:
On Feb 23, 2012, at 1:33 PM, Neal Becker wrote:
Is mkl only used for linear algebra? Will it speed up e.g., elementwise
transendental functions
Keith Goodman wrote:
Is this a reasonable (and fast) way to create a bool array in cython?
def makebool():
cdef:
int n = 2
np.npy_intp *dims = [n]
np.ndarray[np.uint8_t, ndim=1] a
a = PyArray_EMPTY(1, dims, NPY_UINT8, 0)
Charles R Harris wrote:
On Tue, Feb 28, 2012 at 12:05 PM, John Hunter jdh2...@gmail.com wrote:
On Sat, Feb 18, 2012 at 5:09 PM, David Cournapeau courn...@gmail.comwrote:
There are better languages than C++ that has most of the technical
benefits stated in this discussion (rust and D
What is a simple, efficient way to determine if all elements in an array (in my
case, 1D) are equal? How about close?
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Keith Goodman wrote:
On Mon, Mar 5, 2012 at 11:14 AM, Neal Becker ndbeck...@gmail.com wrote:
What is a simple, efficient way to determine if all elements in an array (in
my case, 1D) are equal? How about close?
For the exactly equal case, how about:
I[1] a = np.array([1,1,1,1])
I[2
Keith Goodman wrote:
On Mon, Mar 5, 2012 at 11:52 AM, Benjamin Root ben.r...@ou.edu wrote:
Another issue to watch out for is if the array is empty. Technically
speaking, that should be True, but some of the solutions offered so far
would fail in this case.
Good point.
For fun, here's
I'm wondering what is the use for the ignored data feature?
I can use:
A[valid_A_indexes] = whatever
to process only the 'non-ignored' portions of A. So at least some simple cases
of ignored data are already supported without introducing a new type.
OTOH:
w = A[valid_A_indexes]
will copy
Charles R Harris wrote:
On Wed, Mar 7, 2012 at 1:05 PM, Neal Becker ndbeck...@gmail.com wrote:
I'm wondering what is the use for the ignored data feature?
I can use:
A[valid_A_indexes] = whatever
to process only the 'non-ignored' portions of A. So at least some simple
cases
I see unique does not take an axis arg.
Suggested way to apply unique to each column of a 2d array?
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I have an array of object.
How can I apply attribute access to each element?
I want to do, for example,
np.all (u.some_attribute == 0) for all elements in u?
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Ken Watford wrote:
On Thu, Apr 5, 2012 at 11:57 AM, Olivier Delalleau sh...@keba.be wrote:
Le 5 avril 2012 11:45, Neal Becker ndbeck...@gmail.com a écrit :
You can do:
f = numpy.frompyfunc(lambda x: x.some_attribute == 0, 1, 1)
Then
f(array_of_objects_x)
This is handy too:
agetattr
Nathaniel Smith wrote:
On Sat, Apr 28, 2012 at 7:38 AM, Richard Hattersley
rhatters...@gmail.com wrote:
So, assuming numpy.ndarray became a strict subclass of some new masked
array, it looks plausible that adding just a few checks to numpy.ndarray to
exclude the masked superclass would
I am quite interested in a fixed point data type. I had produced a working
model some time ago.
Maybe I can use some of these new efforts to provide good examples as a guide.
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Will copying slices always work correctly w/r to aliasing?
That is, will:
u[a:b] = u[c:d]
always work (assuming the ranges of a:b, d:d are equal, or course)
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I think it's unfortunate that functions like logical_or are limited to binary.
As a workaround, I've been using this:
def apply_binary (func, *args):
if len (args) == 1:
return args[0]
elif len (args) == 2:
return func (*args)
else:
return func (
Would lazy eval be able to eliminate temps in doing operations such as:
np.sum (u != 23)?
That is, now ops involving selecting elements of matrixes are often performed
by
first constructing temp matrixes, and the operating on them.
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In [3]: u = np.arange(10)
In [4]: u
Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [5]: u[-2:]
Out[5]: array([8, 9])
In [6]: u[-2:2]
Out[6]: array([], dtype=int64)
I would argue for consistency it would be desirable for this to return
[8, 9, 0, 1]
Robert Kern wrote:
On Thu, Jun 7, 2012 at 7:55 PM, Neal Becker ndbeck...@gmail.com wrote:
In [3]: u = np.arange(10)
In [4]: u
Out[4]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [5]: u[-2:]
Out[5]: array([8, 9])
In [6]: u[-2:2]
Out[6]: array([], dtype=int64)
I would argue
Maybe I'm being slow, but is there any convenient function to calculate,
for 2 vectors:
\sum_i \sum_j x_i y_j
(I had a matrix once, but it vanished without a trace)
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I've been bitten several times by this.
logical_or (a, b, c)
is silently accepted when I really meant
logical_or (logical_or (a, b), c)
because the logic functions are binary, where I expected them to be m-ary.
Dunno if anything can be done about it.
Sure would like it if they were m-ary and
Perhaps of some interest here:
http://lwn.net/Articles/507756/rss
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This looks interesting:
http://code.google.com/p/blaze-lib/
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I think this should be simple, but I'm drawing a blank
I have 2 2d matrixes
Matrix A has indexes (i, symbol)
Matrix B has indexes (state, symbol)
I combined them into a 3d matrix:
C = A[:,newaxis,:] + B[newaxis,:,:]
where C has indexes (i, state, symbol)
That works fine.
Now suppose I want
In [19]: u = np.arange (10)
In [20]: v = np.arange (10)
In [21]: u[v] = u
In [22]: u[v] = np.arange(11)
silence...
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Sounds like I'm not the only one surprised then:
http://projects.scipy.org/numpy/ticket/2220
Matthew Brett wrote:
Hi,
On Mon, Oct 1, 2012 at 9:04 AM, Pierre Haessig pierre.haes...@crans.org
wrote:
Hi,
Le 28/09/2012 21:02, Neal Becker a écrit :
In [19]: u = np.arange (10)
In [20]: v
I find it annoying that in casual use, if I print an array, that form can't be
directly used as subsequent input (or can it?).
What do others do about this? When I say casual, what I mean is, I write some
long-running task and at the end, print some small array. Now I decide I'd
like
to
I'm trying to convert some matlab code. I see this:
b(1)=[];
AFAICT, this removes the first element of the array, shifting the others.
What is the preferred numpy equivalent?
I'm not sure if
b[:] = b[1:]
is safe or not
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I'm trying to do a bit of benchmarking to see if amd libm/acml will help me.
I got an idea that instead of building all of numpy/scipy and all of my custom
modules against these libraries, I could simply use:
David Cournapeau wrote:
On Wed, Nov 7, 2012 at 12:35 PM, Neal Becker ndbeck...@gmail.com wrote:
I'm trying to do a bit of benchmarking to see if amd libm/acml will help me.
I got an idea that instead of building all of numpy/scipy and all of my
custom modules against these libraries, I could
David Cournapeau wrote:
On Wed, Nov 7, 2012 at 1:56 PM, Neal Becker ndbeck...@gmail.com wrote:
David Cournapeau wrote:
On Wed, Nov 7, 2012 at 12:35 PM, Neal Becker ndbeck...@gmail.com wrote:
I'm trying to do a bit of benchmarking to see if amd libm/acml will help
me.
I got an idea
David Cournapeau wrote:
On Wed, Nov 7, 2012 at 1:56 PM, Neal Becker ndbeck...@gmail.com wrote:
David Cournapeau wrote:
On Wed, Nov 7, 2012 at 12:35 PM, Neal Becker ndbeck...@gmail.com wrote:
I'm trying to do a bit of benchmarking to see if amd libm/acml will help
me.
I got an idea
Would you expect numexpr without MKL to give a significant boost?
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I'm interested in trying numexpr, but have a question (not sure where's the
best
forum to ask).
The examples I see use
ne.evaluate (some string...)
When used within a loop, I would expect the compilation from the string form to
add significant overhead. I would have thought a pre-compiled
I don't understand why the plot of the spline continues on a negative slope at
the end, but the plot of the integral of it flattens.
-
import numpy as np
import matplotlib.pyplot as plt
ibo = np.array ((12, 14, 16, 18, 20, 22, 24, 26, 28, 29,
Pauli Virtanen wrote:
20.11.2012 21:11, Neal Becker kirjoitti:
import numpy as np
import matplotlib.pyplot as plt
ibo = np.array ((12, 14, 16, 18, 20, 22, 24, 26, 28, 29, 29.8, 30.2))
gain_deriv = np.array ((0, 0, 0, 0, 0, 0, .2, .4, .5, .5, 0,-2))
import scipy.interpolate
s
I think it's a misfeature that a floating point is silently accepted as an
index. I would prefer a warning for:
bins = np.arange (...)
for b in bins:
...
w[b] = blah
when I meant:
for ib,b in enumerate (bins):
w[ib] = blah
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I'd be happy with disallowing floating point index at all. I would think it
was
almost always a mistake.
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Are release notes available?
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np.unwrap was too slow, so I rolled by own (in c++).
I wanted to be able to handle the case of
unwrap (arg (x1) + arg (x2))
Here, phase can change by more than 2pi.
I came up with the following algorithm, any thoughts?
In the following, y is normally set to pi.
o points to output
i points to
Nadav Horesh wrote:
There is an unwrap function in numpy. Doesn't it work for you?
Like I had said, np.unwrap was too slow. Profiling showed it eating up an
absurd proportion of time. My c++ code was much better (although still
surprisingly slow).
* np.sin (u)
plot (arg(v))
plot (arg(v) + arg (v))
plot (unwrap (arg (v)))
plot (unwrap (arg (v) + arg (v)))
---
Pierre Haessig wrote:
Hi Neal,
Le 11/01/2013 16:40, Neal Becker a écrit :
I wanted to be able to handle the case of
unwrap (arg (x1) + arg (x2))
Here
Any suggestion how to take a 2d complex array and find the set of points that
are unique within some tolerance? (My preferred metric here would be Euclidean
distance)
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I have an array to be used for indexing. It is 2d, where the rows are all the
permutations of some numbers. So:
array([[-2, -2, -2],
[-2, -2, -1],
[-2, -2, 0],
[-2, -2, 1],
[-2, -2, 2],
...
[ 2, 1, 2],
[ 2, 2, -2],
[ 2, 2, -1],
[
Is there a way to add '-march=native' flag to gcc for the build?
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In the following code
c = np.multiply (a, b.conj())
d = np.abs (np.sum (c, axis=0)/rows)
d2 = np.abs (np.tensordot (a, b.conj(), ((0,),(0,)))/rows)
print a.shape, b.shape, d.shape, d2.shape
The 1st compute steps, where I do multiply and then sum
Bradley M. Froehle wrote:
Hi Neal:
The tensordot part:
np.tensordot (a, b.conj(), ((0,),(0,))
is returning a (13, 13) array whose [i, j]-th entry is sum( a[k, i] *
b.conj()[k, j] for k in xrange(1004) ).
-Brad
The print statement outputs this:
(1004, 13) (1004, 13) (13,)
Henry Gomersall wrote:
Some of you may be interested in the latest release of my FFTW bindings.
It can now serve as a drop in replacement* for numpy.fft and
scipy.fftpack.
This means you can get most of the speed-up of FFTW with a one line code
change or monkey patch existing libraries.
I tried to save a vector as a csv, but it didn't work.
The vector is:
a[0,0]
array([-0.70710678-0.70710678j, 0.70710678+0.70710678j,
0.70710678-0.70710678j, 0.70710678+0.70710678j,
-0.70710678-0.70710678j, 0.70710678-0.70710678j,
-0.70710678+0.70710678j,
Robert Kern wrote:
On Wed, Feb 20, 2013 at 1:25 PM, Neal Becker ndbeck...@gmail.com wrote:
I tried to save a vector as a csv, but it didn't work.
The vector is:
a[0,0]
array([-0.70710678-0.70710678j, 0.70710678+0.70710678j,
0.70710678-0.70710678j, 0.70710678+0.70710678j
Nathaniel Smith wrote:
On Tue, Mar 12, 2013 at 9:25 PM, Nathaniel Smith n...@pobox.com wrote:
On Mon, Mar 11, 2013 at 9:46 AM, Robert Kern robert.k...@gmail.com wrote:
On Sun, Mar 10, 2013 at 6:12 PM, Siu Kwan Lam s...@continuum.io wrote:
My suggestion to overcome (1) and (2) is to allow the
I guess I talked to you about 100 years ago about sharing state between numpy
rng and code I have in c++ that wraps boost::random. So is there a C-api for
this RandomState object I could use to call from c++? Maybe I could do
something with that.
The c++ code could invoke via the python api,
Neal Becker wrote:
I guess I talked to you about 100 years ago about sharing state between numpy
rng and code I have in c++ that wraps boost::random. So is there a C-api for
this RandomState object I could use to call from c++? Maybe I could do
something with that.
The c++ code could
Neal Becker wrote:
Neal Becker wrote:
I guess I talked to you about 100 years ago about sharing state between numpy
rng and code I have in c++ that wraps boost::random. So is there a C-api for
this RandomState object I could use to call from c++? Maybe I could do
something
Grabbed numpy-1.7.0 source.
Cython is 0.18
cython mtrand.pyx produces lots of errors.
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Robert Kern wrote:
On Wed, Mar 13, 2013 at 6:40 PM, Neal Becker ndbeck...@gmail.com wrote:
Grabbed numpy-1.7.0 source.
Cython is 0.18
cython mtrand.pyx produces lots of errors.
It helps to copy-and-paste the errors that you are seeing.
In any case, Cython 0.18 works okay on master's
Robert Kern wrote:
On Wed, Mar 13, 2013 at 12:16 AM, Neal Becker ndbeck...@gmail.com wrote:
I guess I talked to you about 100 years ago about sharing state between numpy
rng and code I have in c++ that wraps boost::random. So is there a C-api for
this RandomState object I could use to call
Robert Kern wrote:
On Wed, Mar 13, 2013 at 12:16 AM, Neal Becker ndbeck...@gmail.com wrote:
I guess I talked to you about 100 years ago about sharing state between numpy
rng and code I have in c++ that wraps boost::random. So is there a C-api for
this RandomState object I could use to call
Robert Kern wrote:
On Thu, Mar 14, 2013 at 11:00 AM, Neal Becker ndbeck...@gmail.com wrote:
Robert Kern wrote:
On Wed, Mar 13, 2013 at 12:16 AM, Neal Becker ndbeck...@gmail.com wrote:
I guess I talked to you about 100 years ago about sharing state between
numpy
rng and code I have in c
I frequently find I have my 1d function that performs some reduction that I'd
like to apply-along some axis of an n-d array.
As a trivial example,
def sum(u):
return np.sum (u)
In this case the function is probably C/C++ code, but that is irrelevant (I
think).
Is there a reasonably
Neal Becker wrote:
starting with a NxM array, I want to select elements of the array using a set
of
boolean masks. The masks are simply where the indexes have a 0 or 1 in the
corresponding bit position. For example, consider the case where M = 4.
all_syms = np.arange (4)
all_bits
In the following code, the function maxstar is applied along the
last axis. Can anyone suggest how to modify this to apply reduction along
a user-specified axis?
def maxstar2 (a, b):
return max (a, b) + log1p (exp (-abs (a - b)))
def maxstar (u):
s = u.shape[-1]
if s == 1:
What sorts of functions take advantage of MKL?
Linear Algebra (equation solving)?
Something like dot product?
exp, log, trig of matrix?
basic numpy arithmetic? (add matrixes)
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KACVINSKY Tom wrote:
You also get highly optimized BLAS routines, like dgemm and degemv.
And does numpy/scipy just then automatically use them? When I do a matrix
multiply, for example?
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np.array ((0,0))
Out[10]: array([0, 0]) ok, it's 2 dimensional
In [11]: np.array ((0,0)).shape
Out[11]: (2,) except, it isn't
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Neal Becker wrote:
np.array ((0,0))
Out[10]: array([0, 0]) ok, it's 2 dimensional
In [11]: np.array ((0,0)).shape
Out[11]: (2,) except, it isn't
Sorry for the stupid question - please ignore
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It would be convenient if in arithmetic 0-d arrays were just ignored - it would
seem to me to be convenient in generic code where a degenerate array is treated
as nothing
np.zeros ((0,0)) + np.ones ((2,2))
---
ValueError
Sebastian Berg wrote:
On Fri, 2013-05-10 at 19:57 -0400, Neal Becker wrote:
It would be convenient if in arithmetic 0-d arrays were just ignored - it
would seem to me to be convenient in generic code where a degenerate array is
treated as nothing
Small naming detail. A 0-d array
Nathaniel Smith wrote:
On 16 May 2013 19:48, Jonathan Helmus jjhel...@gmail.com wrote:
On 05/16/2013 01:42 PM, Neal Becker wrote:
Is there a way to get a traceback instead of just printing the
line that triggered the error?
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I have a system that transmits signals for an alphabet of M symbols
over and additive Gaussian noise channel. The receiver has a
1-d array of complex received values. I'd like to find the means
of the received values according to the symbol that was transmitted.
So transmit symbol indexes might
I thought the topic of this article might be of interest here:
https://groups.google.com/forum/?fromgroups#!topic/julia-dev/GAdcYzmibyo
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I tried running python2 -3 on some code, and found numpy
produces a lot of warnings.
Many like:
python -3 -c 'import numpy'
...
/usr/lib64/python2.7/site-packages/numpy/lib/polynomial.py:928:
DeprecationWarning: Overriding __eq__ blocks inheritance of __hash__ in 3.x
But also:
Failed building on fedora 19 x86_64 using atlas:
creating build/temp.linux-x86_64-2.7/numpy/linalg
creating build/temp.linux-x86_64-2.7/numpy/linalg/lapack_lite
compile options: '-DATLAS_INFO=\3.8.4\ -I/usr/include -Inumpy/core/include -
Ibuild/src.linux-x86_64-2.7/numpy/core/include/numpy
Built on fedora linux 19 x86_64 using mkl:
build OK using:
env ATLAS=/usr/lib64 FFTW=/usr/lib64 BLAS=/usr/lib64 LAPACK=/usr/lib64
CFLAGS=-mtune=native -march=native -O3 LDFLAGS=-Wl,-
rpath=/opt/intel/mkl/lib/intel64 python setup.py build
and attached site.cfg:
David Cournapeau wrote:
On Wed, Sep 4, 2013 at 1:00 PM, Neal Becker ndbeck...@gmail.com wrote:
Failed building on fedora 19 x86_64 using atlas:
creating build/temp.linux-x86_64-2.7/numpy/linalg
creating build/temp.linux-x86_64-2.7/numpy/linalg/lapack_lite
compile options: '-DATLAS_INFO
Just want to make sure this post had been noted:
Neal Becker wrote:
Built on fedora linux 19 x86_64 using mkl:
build OK using:
env ATLAS=/usr/lib64 FFTW=/usr/lib64 BLAS=/usr/lib64 LAPACK=/usr/lib64
CFLAGS=-mtune=native -march=native -O3 LDFLAGS=-Wl,-
rpath=/opt/intel/mkl/lib/intel64
Charles R Harris wrote:
On Thu, Sep 5, 2013 at 5:34 AM, Neal Becker ndbeck...@gmail.com wrote:
Just want to make sure this post had been noted:
Neal Becker wrote:
Built on fedora linux 19 x86_64 using mkl:
build OK using:
env ATLAS=/usr/lib64 FFTW=/usr/lib64 BLAS=/usr/lib64
LAPACK
Charles R Harris wrote:
Hi all,
I'm happy to announce the second beta release of Numpy 1.8.0. This release
should solve the Windows problems encountered in the first beta. Many
thanks to Christolph Gohlke and Julian Taylor for their hard work in
getting those issues settled.
It would be
Here's code I use for basic 2d histogramimport numpy as np
def nint (x):
if x = 0:
return int (x + 0.5)
else:
return int (x - 0.5)
class histogram2d (object):
def __init__ (self, min, max, delta, clip=True):
self.min = min
self.max = max
Does numpy/scipy support building with openblas for blas,lapack instead of
atlas?
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David Goldsmith wrote:
Is this a valid algorithm for generating a 3D Wiener process? (When I
graph the results, they certainly look like potential Brownian motion
tracks.)
def Wiener3D(incr, N):
r = incr*(R.randint(3, size=(N,))-1)
r[0] = 0
r = r.cumsum()
t =
isinstance (np.zeros (10), collections.Sequence)
Out[36]: False
That's unfortunate.
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Thanks for the release!
I am having a hard time finding the build instructions. Could you please add
this to the announcement?
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Charles R Harris wrote:
On Thu, Oct 31, 2013 at 6:58 AM, Neal Becker ndbeck...@gmail.com wrote:
Thanks for the release!
I am having a hard time finding the build instructions. Could you please
add
this to the announcement?
What sort of build instructions are you looking for?
Chuck
import numpy as np
#from accumulator import stat2nd_double
## Just to make this really clear, I'm making a dummy
## class here that overloads +=
class stat2nd_double (object):
def __iadd__ (self, x):
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