Hi,
I have encountered a very weird behaviour with einsum. I try to compute
something like R*A*R', where * denotes a kind of matrix
multiplication. However, for particular shapes of R and A, the results
are extremely bad.
I compare two einsum results:
First, I compute in two einsum calls as
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 with that.
Hi everybody, I hope this has not been discussed before, I couldn't find a
solution elsewhere.
I need to read some binary data, and I am using numpy.fromfile to do this.
Since the files are huge, and would make me run out of memory, I need to read
data skipping some records (I am reading data
On Wed, Mar 13, 2013 at 1:45 PM, Andrea Cimatoribus
andrea.cimatori...@nioz.nl wrote:
Hi everybody, I hope this has not been discussed before, I couldn't find a
solution elsewhere.
I need to read some binary data, and I am using numpy.fromfile to do this.
Since the files are huge, and would
Is it possible to create a numpy array which points to the same data in a
different numpy array (but in different order etc)?
For example:
Code:
--
import numpy as np
a = np.arange(10)
ids = np.array([0,0,5,5,9,9,1,1])
This solution does not work for me since I have an offset before the data that
is not a multiple of the datatype (it's a header containing various stuff).
I'll at pytables.
# Exploit the operating system's virtual memory manager to get a
virtual copy of the entire file in memory
# (This does not
Hi!
How can I compute dot product (or similar multiplysum operations)
efficiently so that broadcasting is utilized?
For multi-dimensional arrays, NumPy's inner and dot functions do not
match the leading axes and use broadcasting, but instead the result has
first the leading axes of the first
I see that pytables deals with hdf5 data. It would be very nice if the data
were in such a standard format, but that is not the case, and that can't be
changed.
Da: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org] per
conto di
On Wed, Mar 13, 2013 at 2:18 PM, Andrea Cimatoribus
andrea.cimatori...@nioz.nl wrote:
This solution does not work for me since I have an offset before the data
that is not a multiple of the datatype (it's a header containing various
stuff).
np.memmap takes an offset= argument.
-n
Indeed, but that offset it should be a multiple of the byte-size of dtype as
the help says.
Indeed, this is silly.
Da: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org] per
conto di Nathaniel Smith [n...@pobox.com]
Inviato:
On top of that, there is another issue: it can be that the data available
itself is not a multiple of dtype, since there can be write errors at the end
of the file, and I don't know how to deal with that.
Da: numpy-discussion-boun...@scipy.org
Indeed, but that offset it should be a multiple of the byte-size of dtype as
the help says.
My mistake, sorry, even if the help says so, it seems that this is not the case
in the actual code. Still, the problem with the size of the available data
(which is not necessarily a multiple of dtype
On Wed, Mar 13, 2013 at 2:46 PM, Andrea Cimatoribus
andrea.cimatori...@nioz.nl wrote:
Indeed, but that offset it should be a multiple of the byte-size of dtype
as the help says.
My mistake, sorry, even if the help says so, it seems that this is not the
case in the actual code. Still, the
On 3/13/13 2:45 PM, Andrea Cimatoribus wrote:
Hi everybody, I hope this has not been discussed before, I couldn't find a
solution elsewhere.
I need to read some binary data, and I am using numpy.fromfile to do this.
Since the files are huge, and would make me run out of memory, I need to
On 3/13/13 3:53 PM, Francesc Alted wrote:
On 3/13/13 2:45 PM, Andrea Cimatoribus wrote:
Hi everybody, I hope this has not been discussed before, I couldn't
find a solution elsewhere.
I need to read some binary data, and I am using numpy.fromfile to do
this. Since the files are huge, and
Ok, this seems to be working (well, as soon as I get the right offset and
things like that, but that's a different story).
The problem is that it does not go any faster than my initial function compiled
with cython, and it is still a lot slower than fromfile. Is there a reason why,
even with
On 13 Mar 2013 15:16, Andrea Cimatoribus andrea.cimatori...@nioz.nl
wrote:
Ok, this seems to be working (well, as soon as I get the right offset and
things like that, but that's a different story).
The problem is that it does not go any faster than my initial function
compiled with cython, and
Hi,
I was wondering if someone could provide some intuition on the
performance of einsum?
I have found that sometimes it is extremely efficient but sometimes it
is several orders of magnitudes slower compared to some other
approaches, for instance, using multiple dot-calls.
My intuition is that
Thanks a lot for the feedback, I'll try to modify my function to overcome this
issue.
Since I'm in the process of buying new hardware too, a slight OT (but
definitely related).
Does an ssd provide substantial improvement in these cases?
Da:
Many thanks Pat - the numpy discussion list is brill.
Go ahead and see if the CPython developers would be interested as it is a
problem that appears all the time on boards/lists.
Best ... Dinesh
From: Pat Marion
Sent: Tuesday, March 12, 2013 7:23 AM
To: Aron Ahmadia
Cc: Discussion of
Hi Chris
Darn! It worked this morning and I don't know why.
Focused on PyInstaller because it creates a single executable. Testing on
all major versions of Windows (32-bit and 64-bit), Linux and OSX. The
problem OS is unsurprisingly, Windows XP (SP3).
Numpy was upgraded to the mkl-version
Since the files are huge, and would make me run out of memory, I need to
read data skipping some records
Is it possible to describe what you're doing with the data once you have
subsampled it? And if there were a way to work with the full resolution
data, would that be desirable?
I ask because
On 13 March 2013 16:54, Andrea Cimatoribus andrea.cimatori...@nioz.nlwrote:
Since I'm in the process of buying new hardware too, a slight OT (but
definitely related).
Does an ssd provide substantial improvement in these cases?
It should help. Nevertheless, when talking about performance, it
Grabbed numpy-1.7.0 source.
Cython is 0.18
cython mtrand.pyx produces lots of errors.
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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 mtrand.pyx sources.
--
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
On Wed, Mar 13, 2013 at 9:54 AM, Andrea Cimatoribus
andrea.cimatori...@nioz.nl wrote:
Thanks a lot for the feedback, I'll try to modify my function to overcome
this issue.
Since I'm in the process of buying new hardware too, a slight OT (but
definitely related).
Does an ssd provide
On Wed, Mar 13, 2013 at 6:56 AM, Matt U mpuec...@mit.edu wrote:
Is it possible to create a numpy array which points to the same data in a
different numpy array (but in different order etc)?
You can do this (easily), but only if the different order can be
defined in terms of strides. A simple
*
Any help from Numpy community
[[ 0. 1.540. 0. 0.1.08
1.08 1.08 ]
[ 1.540. 1.081.08 1.080. 0.
0. ]
[0. 1.08 0. 0. 0.0.
0.
Glad you got it working! For those who might be interested, the
distinction between the example I linked to and packaging tools like
PyInstaller or py2exe, is that
NumpyBuiltinExamplehttps://github.com/patmarion/NumpyBuiltinExampleuses
static linking to embed numpy as a builtin module. At
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