[Numpy-discussion] ANN: BayesPy 0.3 released
Dear all, I am pleased to announce that BayesPy 0.3 has been released. BayesPy provides tools for variational Bayesian inference. The user can easily constuct conjugate exponential family models from nodes and run approximate posterior inference. BayesPy aims to be efficient and flexible enough for experts but also accessible for casual users. --- This release adds several state-of-the-art VB features. Below is a list of significant new features in this release: * Gradient-based optimization of the nodes by using either the Euclidean or Riemannian/natural gradient. This enables, for instance, the Riemannian conjugate gradient method. * Collapsed variational inference to improve the speed of learning. * Stochastic variational inference to improve scalability. * Pattern search to improve the speed of learning. * Deterministic annealing to improve robustness against initializations. * Gaussian Markov chains can use input signals. More details about the new features can be found here: http://www.bayespy.org/user_guide/advanced.html -- PyPI: https://pypi.python.org/pypi/bayespy/0.3 Git repository: https://github.com/bayespy/bayespy Documentation: http://www.bayespy.org/ Best regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANN: BayesPy 0.2
Dear all, I am pleased to announce the release of BayesPy version 0.2. BayesPy provides tools for Bayesian inference in Python. In particular, it implements variational message passing framework, which enables modular and efficient way to construct models and perform approximate posterior inference. Download: https://pypi.python.org/pypi/bayespy/ Documentation: http://www.bayespy.org Repository: https://github.com/bayespy/bayespy Comments, feedback and contributions are welcome. Best regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Installation bug on Python 3.3?
Hi,
I'm trying to install NumPy 1.7.1 for Python 3.3 using
pip install numpy
However, I get the following error after a while:
error: numpy.egg-info/dependency_links.txt: Operation not supported
Is this a bug or am I doing something wrong? If it matters, I'm using
virtualenv as I do not have root permission on this computer.
Thanks for any help!
Jaakko
PS. The end of the error log:
Command /home/jluttine/.virtualenvs/bayespy-3.3/bin/python3.3 -c import
setuptools;__file__='/home/jluttine/.virtualenvs/bayespy-3.3/build/numpy/setup.py';exec(compile(open(__file__).read().replace('\r\n',
'\n'), __file__, 'exec')) install --record
/tmp/pip-309wd8-record/install-record.txt
--single-version-externally-managed --install-headers
/home/jluttine/.virtualenvs/bayespy-3.3/include/site/python3.3 failed
with error code 1 in /home/jluttine/.virtualenvs/bayespy-3.3/build/numpy
Exception information:
Traceback (most recent call last):
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/basecommand.py,
line 107, in main
status = self.run(options, args)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/commands/install.py,
line 261, in run
requirement_set.install(install_options, global_options)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/req.py,
line 1166, in install
requirement.install(install_options, global_options)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/req.py,
line 589, in install
cwd=self.source_dir, filter_stdout=self._filter_install,
show_stdout=False)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/util.py,
line 612, in call_subprocess
% (command_desc, proc.returncode, cwd))
pip.exceptions.InstallationError: Command
/home/jluttine/.virtualenvs/bayespy-3.3/bin/python3.3 -c import
setuptools;__file__='/home/jluttine/.virtualenvs/bayespy-3.3/build/numpy/setup.py';exec(compile(open(__file__).read().replace('\r\n',
'\n'), __file__, 'exec')) install --record
/tmp/pip-309wd8-record/install-record.txt
--single-version-externally-managed --install-headers
/home/jluttine/.virtualenvs/bayespy-3.3/include/site/python3.3 failed
with error code 1 in /home/jluttine/.virtualenvs/bayespy-3.3/build/numpy
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Re: [Numpy-discussion] Installation bug on Python 3.3?
I was able to install by downloading the package for version 1.7.1 from
github and then running
python3.3 setup.py install
No errors given. So, the problem might be related to pip and the fact
that python3.3 is installed locally in my personal home folder which is
in a different filesystem than the other python versions. So it might be
because of some hardlinks over different partitions or something similar.
Anyway, it seems that I have the same problem installing other python
packages too, so it is not related to numpy but either python 3.3, pip,
virtualenv or my system set up.
But still, any help is appreciated.
-Jaakko
On 08/05/2013 12:53 PM, Jaakko Luttinen wrote:
Hi,
I'm trying to install NumPy 1.7.1 for Python 3.3 using
pip install numpy
However, I get the following error after a while:
error: numpy.egg-info/dependency_links.txt: Operation not supported
Is this a bug or am I doing something wrong? If it matters, I'm using
virtualenv as I do not have root permission on this computer.
Thanks for any help!
Jaakko
PS. The end of the error log:
Command /home/jluttine/.virtualenvs/bayespy-3.3/bin/python3.3 -c import
setuptools;__file__='/home/jluttine/.virtualenvs/bayespy-3.3/build/numpy/setup.py';exec(compile(open(__file__).read().replace('\r\n',
'\n'), __file__, 'exec')) install --record
/tmp/pip-309wd8-record/install-record.txt
--single-version-externally-managed --install-headers
/home/jluttine/.virtualenvs/bayespy-3.3/include/site/python3.3 failed
with error code 1 in /home/jluttine/.virtualenvs/bayespy-3.3/build/numpy
Exception information:
Traceback (most recent call last):
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/basecommand.py,
line 107, in main
status = self.run(options, args)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/commands/install.py,
line 261, in run
requirement_set.install(install_options, global_options)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/req.py,
line 1166, in install
requirement.install(install_options, global_options)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/req.py,
line 589, in install
cwd=self.source_dir, filter_stdout=self._filter_install,
show_stdout=False)
File
/home/jluttine/.virtualenvs/bayespy-3.3/lib/python3.3/site-packages/pip-1.2.1-py3.3.egg/pip/util.py,
line 612, in call_subprocess
% (command_desc, proc.returncode, cwd))
pip.exceptions.InstallationError: Command
/home/jluttine/.virtualenvs/bayespy-3.3/bin/python3.3 -c import
setuptools;__file__='/home/jluttine/.virtualenvs/bayespy-3.3/build/numpy/setup.py';exec(compile(open(__file__).read().replace('\r\n',
'\n'), __file__, 'exec')) install --record
/tmp/pip-309wd8-record/install-record.txt
--single-version-externally-managed --install-headers
/home/jluttine/.virtualenvs/bayespy-3.3/include/site/python3.3 failed
with error code 1 in /home/jluttine/.virtualenvs/bayespy-3.3/build/numpy
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[Numpy-discussion] einsum and broadcasting
I don't quite understand how einsum handles broadcasting. I get the
following error, but I don't understand why:
In [8]: import numpy as np
In [9]: A = np.arange(12).reshape((4,3))
In [10]: B = np.arange(6).reshape((3,2))
In [11]: np.einsum('ik,k...-i...', A, B)
---
ValueError: operand 0 did not have enough dimensions to match the
broadcasting, and couldn't be extended because einstein sum subscripts
were specified at both the start and end
However, if I use explicit indexing, it works:
In [12]: np.einsum('ik,kj-ij', A, B)
Out[12]:
array([[10, 13],
[28, 40],
[46, 67],
[64, 94]])
It seems that it also works if I add '...' to the first operand:
In [12]: np.einsum('ik...,k...-i...', A, B)
Out[12]:
array([[10, 13],
[28, 40],
[46, 67],
[64, 94]])
However, as far as I understand, the syntax
np.einsum('ik,k...-i...', A, B)
should work. Have I misunderstood something or is there a bug?
Thanks for your help!
Jaakko
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Re: [Numpy-discussion] Dot/inner products with broadcasting?
I tried using this inner1d as an alternative to dot because it uses
broadcasting. However, I found something surprising: Not only is inner1d
much much slower than dot, it is also slower than einsum which is much
more general:
In [68]: import numpy as np
In [69]: import numpy.core.gufuncs_linalg as gula
In [70]: K = np.random.randn(1000,1000)
In [71]: %timeit gula.inner1d(K[:,np.newaxis,:],
np.swapaxes(K,-1,-2)[np.newaxis,:,:])
1 loops, best of 3: 6.05 s per loop
In [72]: %timeit np.dot(K,K)
1 loops, best of 3: 392 ms per loop
In [73]: %timeit np.einsum('ik,kj-ij', K, K)
1 loops, best of 3: 1.24 s per loop
Why is it so? I thought that the performance of inner1d would be
somewhere in between dot and einsum, probably closer to dot. Now I don't
see any reason to use inner1d instead of einsum..
-Jaakko
On 03/15/2013 04:22 PM, Oscar Villellas wrote:
In fact, there is already an inner1d implemented in
numpy.core.umath_tests.inner1d
from numpy.core.umath_tests import inner1d
It should do the trick :)
On Thu, Mar 14, 2013 at 12:54 PM, Jaakko Luttinen
jaakko.lutti...@aalto.fi wrote:
Answering to myself, this pull request seems to implement an inner
product with broadcasting (inner1d) and many other useful functions:
https://github.com/numpy/numpy/pull/2954/
-J
On 03/13/2013 04:21 PM, Jaakko Luttinen wrote:
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 input array and then the leading
axes of the second input array.
For instance, I would like to compute the following inner-product:
np.sum(A*B, axis=-1)
But numpy.inner gives:
A = np.random.randn(2,3,4)
B = np.random.randn(3,4)
np.inner(A,B).shape
# - (2, 3, 3) instead of (2, 3)
Similarly for dot product, I would like to compute for instance:
np.sum(A[...,:,:,np.newaxis]*B[...,np.newaxis,:,:], axis=-2)
But numpy.dot gives:
In [12]: A = np.random.randn(2,3,4); B = np.random.randn(2,4,5)
In [13]: np.dot(A,B).shape
# - (2, 3, 2, 5) instead of (2, 3, 5)
I could use einsum for these operations, but I'm not sure whether that's
as efficient as using some BLAS-supported(?) dot products.
I couldn't find any function which could perform this kind of
operations. NumPy's functions seem to either flatten the input arrays
(vdot, outer) or just use the axes of the input arrays separately (dot,
inner, tensordot).
Any help?
Best regards,
Jaakko
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Re: [Numpy-discussion] Dot/inner products with broadcasting?
Well, thanks to seberg, I finally noticed that there is a dot product
function in this new module numpy.core.gufuncs_linalg, it was just named
differently (matrix_multiply instead of dot).
However, I may have found a bug in it:
import numpy.core.gufuncs_linalg as gula
A = np.arange(2*2).reshape((2,2))
B = np.arange(2*1).reshape((2,1))
gula.matrix_multiply(A, B)
ValueError: On entry to DGEMM parameter number 10 had an illegal value
-Jaakko
On 03/20/2013 03:33 PM, Jaakko Luttinen wrote:
I tried using this inner1d as an alternative to dot because it uses
broadcasting. However, I found something surprising: Not only is inner1d
much much slower than dot, it is also slower than einsum which is much
more general:
In [68]: import numpy as np
In [69]: import numpy.core.gufuncs_linalg as gula
In [70]: K = np.random.randn(1000,1000)
In [71]: %timeit gula.inner1d(K[:,np.newaxis,:],
np.swapaxes(K,-1,-2)[np.newaxis,:,:])
1 loops, best of 3: 6.05 s per loop
In [72]: %timeit np.dot(K,K)
1 loops, best of 3: 392 ms per loop
In [73]: %timeit np.einsum('ik,kj-ij', K, K)
1 loops, best of 3: 1.24 s per loop
Why is it so? I thought that the performance of inner1d would be
somewhere in between dot and einsum, probably closer to dot. Now I don't
see any reason to use inner1d instead of einsum..
-Jaakko
On 03/15/2013 04:22 PM, Oscar Villellas wrote:
In fact, there is already an inner1d implemented in
numpy.core.umath_tests.inner1d
from numpy.core.umath_tests import inner1d
It should do the trick :)
On Thu, Mar 14, 2013 at 12:54 PM, Jaakko Luttinen
jaakko.lutti...@aalto.fi wrote:
Answering to myself, this pull request seems to implement an inner
product with broadcasting (inner1d) and many other useful functions:
https://github.com/numpy/numpy/pull/2954/
-J
On 03/13/2013 04:21 PM, Jaakko Luttinen wrote:
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 input array and then the leading
axes of the second input array.
For instance, I would like to compute the following inner-product:
np.sum(A*B, axis=-1)
But numpy.inner gives:
A = np.random.randn(2,3,4)
B = np.random.randn(3,4)
np.inner(A,B).shape
# - (2, 3, 3) instead of (2, 3)
Similarly for dot product, I would like to compute for instance:
np.sum(A[...,:,:,np.newaxis]*B[...,np.newaxis,:,:], axis=-2)
But numpy.dot gives:
In [12]: A = np.random.randn(2,3,4); B = np.random.randn(2,4,5)
In [13]: np.dot(A,B).shape
# - (2, 3, 2, 5) instead of (2, 3, 5)
I could use einsum for these operations, but I'm not sure whether that's
as efficient as using some BLAS-supported(?) dot products.
I couldn't find any function which could perform this kind of
operations. NumPy's functions seem to either flatten the input arrays
(vdot, outer) or just use the axes of the input arrays separately (dot,
inner, tensordot).
Any help?
Best regards,
Jaakko
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Re: [Numpy-discussion] Dot/inner products with broadcasting?
Answering to myself, this pull request seems to implement an inner product with broadcasting (inner1d) and many other useful functions: https://github.com/numpy/numpy/pull/2954/ -J On 03/13/2013 04:21 PM, Jaakko Luttinen wrote: 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 input array and then the leading axes of the second input array. For instance, I would like to compute the following inner-product: np.sum(A*B, axis=-1) But numpy.inner gives: A = np.random.randn(2,3,4) B = np.random.randn(3,4) np.inner(A,B).shape # - (2, 3, 3) instead of (2, 3) Similarly for dot product, I would like to compute for instance: np.sum(A[...,:,:,np.newaxis]*B[...,np.newaxis,:,:], axis=-2) But numpy.dot gives: In [12]: A = np.random.randn(2,3,4); B = np.random.randn(2,4,5) In [13]: np.dot(A,B).shape # - (2, 3, 2, 5) instead of (2, 3, 5) I could use einsum for these operations, but I'm not sure whether that's as efficient as using some BLAS-supported(?) dot products. I couldn't find any function which could perform this kind of operations. NumPy's functions seem to either flatten the input arrays (vdot, outer) or just use the axes of the input arrays separately (dot, inner, tensordot). Any help? Best regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Bug in einsum?
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 (R*A)*R'.
Second, I compute the whole result in one einsum call.
However, the results are significantly different for some shapes.
My test:
import numpy as np
for D in range(30):
A = np.random.randn(100,D,D)
R = np.random.randn(D,D)
Y1 = np.einsum('...ik,...kj-...ij', R, A)
Y1 = np.einsum('...ik,...kj-...ij', Y1, R.T)
Y2 = np.einsum('...ik,...kl,...lj-...ij', R, A, R.T)
print(D=%d % D, np.allclose(Y1,Y2), np.linalg.norm(Y1-Y2))
Output:
D=0 True 0.0
D=1 True 0.0
D=2 True 8.40339658678e-15
D=3 True 8.09995399928e-15
D=4 True 3.59428803435e-14
D=5 False 34.755610184
D=6 False 28.3576558351
D=7 False 41.5402690906
D=8 True 2.31709582841e-13
D=9 False 36.0161112799
D=10 True 4.76237746912e-13
D=11 True 4.5790782e-13
D=12 True 4.90302218301e-13
D=13 True 6.96175851271e-13
D=14 True 1.10067181384e-12
D=15 True 1.29095933163e-12
D=16 True 1.3466837332e-12
D=17 True 1.52265065763e-12
D=18 True 2.05407923852e-12
D=19 True 2.33327630748e-12
D=20 True 2.96849358082e-12
D=21 True 3.31063706175e-12
D=22 True 4.28163620455e-12
D=23 True 3.58951880681e-12
D=24 True 4.69973694769e-12
D=25 True 5.47385264567e-12
D=26 True 5.49643316347e-12
D=27 True 6.75132988402e-12
D=28 True 7.86435437892e-12
D=29 True 7.85453681029e-12
So, for D={5,6,7,9}, allclose returns False and the error norm is HUGE.
It doesn't seem like just some small numerical inaccuracy because the
error norm is so large. I don't know which one is correct (Y1 or Y2) but
at least either one is wrong in my opinion.
I ran the same test several times, and each time same values of D fail.
If I change the shapes somehow, the failing values of D might change
too, but I usually have several failing values.
I'm running the latest version from github (commit bd7104cef4) under
Python 3.2.3. With NumPy 1.6.1 under Python 2.7.3 the test crashes and
Python exits printing Floating point exception.
This seems so weird to me that I wonder if I'm just doing something stupid..
Thanks a lot for any help!
Jaakko
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[Numpy-discussion] Dot/inner products with broadcasting?
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 input array and then the leading axes of the second input array. For instance, I would like to compute the following inner-product: np.sum(A*B, axis=-1) But numpy.inner gives: A = np.random.randn(2,3,4) B = np.random.randn(3,4) np.inner(A,B).shape # - (2, 3, 3) instead of (2, 3) Similarly for dot product, I would like to compute for instance: np.sum(A[...,:,:,np.newaxis]*B[...,np.newaxis,:,:], axis=-2) But numpy.dot gives: In [12]: A = np.random.randn(2,3,4); B = np.random.randn(2,4,5) In [13]: np.dot(A,B).shape # - (2, 3, 2, 5) instead of (2, 3, 5) I could use einsum for these operations, but I'm not sure whether that's as efficient as using some BLAS-supported(?) dot products. I couldn't find any function which could perform this kind of operations. NumPy's functions seem to either flatten the input arrays (vdot, outer) or just use the axes of the input arrays separately (dot, inner, tensordot). Any help? Best regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Performance of einsum?
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 the computation time of einsum is linear with
respect to the size of the index space, that is, the product of the
maximums of the indices.
So, for instance computing the dot product of three matrices A*B*C would
not be efficient as einsum('ij,jk,kl-il', A, B, C) because there are
four indices i=1,...,I, j=1,...,J, k=1,...,K and l=1,...,L so the total
computation time is O(I*J*K*L) which is much worse than with two dot
products O(I*J*K+J*K*L), or with two einsum-calls for Y=A*B and Y*C.
On the other hand, computing einsum('ij,ij,ij-i', A, B, C) would be
efficient because the computation time is only O(I*J).
Is this intuition roughly correct or how could I intuitively understand
when the usage of einsum is bad?
Best regards,
Jaakko
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[Numpy-discussion] Leaking memory problem
Hi! I was wondering if anyone could help me in finding a memory leak problem with NumPy. My project is quite massive and I haven't been able to construct a simple example which would reproduce the problem.. I have an iterative algorithm which should not increase the memory usage as the iteration progresses. However, after the first iteration, 1GB of memory is used and it steadily increases until at about 100-200 iterations 8GB is used and the program exits with MemoryError. I have a collection of objects which contain large arrays. In each iteration, the objects are updated in turns by re-computing the arrays they contain. The number of arrays and their sizes are constant (do not change during the iteration). So the memory usage should not increase, and I'm a bit confused, how can the program run out of memory if it can easily compute at least a few iterations.. I've tried to use Pympler, but I've understood that it doesn't show the memory usage of NumPy arrays.. ? I also tried gc.set_debug(gc.DEBUG_UNCOLLECTABLE) and then printing gc.garbage at each iteration, but that doesn't show anything. Does anyone have any ideas how to debug this kind of memory leak bug? And how to find out whether the bug is in my code, NumPy or elsewhere? Thanks for any help! Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.einsum bug?
Hi, Is this a bug in numpy.einsum? np.einsum(3, [], 2, [], []) ValueError: If 'op_axes' or 'itershape' is not NULL in theiterator constructor, 'oa_ndim' must be greater than zero I think it should return 6 (i.e., 3*2). Regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpydoc for python 3?
On 01/14/2013 02:44 PM, Matthew Brett wrote:
On Mon, Jan 14, 2013 at 10:35 AM, Jaakko Luttinen
jaakko.lutti...@aalto.fi wrote:
On 01/14/2013 12:53 AM, Matthew Brett wrote:
You might be able to get away without 2to3, using the kind of stuff
that Pauli has used for scipy recently:
https://github.com/scipy/scipy/pull/397
Ok, thanks, maybe I'll try to make the tests valid in all Python
versions. It seems there's only one line which I'm not able to transform.
In doc/sphinxext/tests/test_docscrape.py, on line 559:
assert doc['Summary'][0] == u'öäöäöäöäö'.encode('utf-8')
This is invalid in Python 3.0-3.2. How could I write this in such a way
that it is valid in all Python versions? I'm a bit lost with these
unicode encodings in Python (and in general).. And I didn't want to add
dependency on 'six' package.
Pierre's suggestion is good; you can also do something like this:
# -*- coding: utf8 -*-
import sys
if sys.version_info[0] = 3:
a = 'öäöäöäöäö'
else:
a = unicode('öäöäöäöäö', 'utf8')
The 'coding' line has to be the first or second line in the file.
Thanks for all the comments!
I reported an issue and made a pull request:
https://github.com/numpy/numpy/pull/2919
However, I haven't been able to make nosetests work. I get error:
ValueError: Attempted relative import in non-package
Don't know how to fix it properly..
-Jaakko
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Re: [Numpy-discussion] numpydoc for python 3?
On 2013-01-10 17:16, Jaakko Luttinen wrote: On 01/10/2013 05:04 PM, Pauli Virtanen wrote: Jaakko Luttinen jaakko.luttinen at aalto.fi writes: The files in numpy/doc/sphinxext/ and numpydoc/ (from PyPI) are a bit different. Which ones should be modified? The stuff in sphinxext/ is the development version of the package on PyPi, so the changes should be made in sphinxext/ Thanks! I'm trying to run the tests with Python 2 using nosetests, but I get some errors http://pastebin.com/Mp9i8T2f . Am I doing something wrong? How should I run the tests? If I run nosetests on the numpydoc folder from PyPI, all the tests are successful. I'm a bit stuck trying to make numpydoc Python 3 compatible. I made setup.py try to use distutils.command.build_py.build_py_2to3 in order to transform installed code automatically to Python 3. However, the tests (in tests folder) are not part of the package but rather package_data, so they won't get transformed. How can I automatically transform the tests too? Probably there is some easy and right solution to this, but I haven't been able to figure out a nice and simple solution.. Any ideas? Thanks. -Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpydoc for python 3?
The files in numpy/doc/sphinxext/ and numpydoc/ (from PyPI) are a bit different. Which ones should be modified? -Jaakko On 01/10/2013 02:04 PM, Pauli Virtanen wrote: Hi, Jaakko Luttinen jaakko.luttinen at aalto.fi writes: I'm trying to use numpydoc (Sphinx extension) for my project written in Python 3.2. However, installing numpydoc gives errors shown at http://pastebin.com/MPED6v9G and although it says Successfully installed numpydoc, trying to import numpydoc raises errors.. Could this be fixed or am I doing something wrong? Numpydoc hasn't been ported to Python 3 so far. This probably wouldn't a very large amount of work --- patches are accepted! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpydoc for python 3?
On 01/10/2013 05:04 PM, Pauli Virtanen wrote: Jaakko Luttinen jaakko.luttinen at aalto.fi writes: The files in numpy/doc/sphinxext/ and numpydoc/ (from PyPI) are a bit different. Which ones should be modified? The stuff in sphinxext/ is the development version of the package on PyPi, so the changes should be made in sphinxext/ Thanks! I'm trying to run the tests with Python 2 using nosetests, but I get some errors http://pastebin.com/Mp9i8T2f . Am I doing something wrong? How should I run the tests? If I run nosetests on the numpydoc folder from PyPI, all the tests are successful. -Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpydoc for python 3?
Hi! I'm trying to use numpydoc (Sphinx extension) for my project written in Python 3.2. However, installing numpydoc gives errors shown at http://pastebin.com/MPED6v9G and although it says Successfully installed numpydoc, trying to import numpydoc raises errors.. Could this be fixed or am I doing something wrong? Thanks! Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Special matrices with structure?
Hi! I was wondering whether it would be easy/possible/reasonable to have classes for arrays that have special structure in order to use less memory and speed up some computations? For instance: - symmetric matrix could be stored in almost half the memory required by a non-symmetric matrix - diagonal matrix only needs to store the diagonal vector - Toeplitz matrix only needs to store one or two vectors - sparse matrix only needs to store non-zero elements (some implementations in scipy.sparse) - and so on If such classes were implemented, it would be nice if they worked with numpy functions (dot, diag, ...) and operations (+, *, +=, ...) easily. I believe this has been discussed before but google didn't help a lot.. Regards, Jaakko ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
