foo.bar using the np.ctypeslib.ndpointer approach always fails
with a ctypes.ArgumentError:
In [5]: foo = ctypes.CDLL('foo.dll')
In [6]: foo.bar.argtpes = [np.ctypeslib.ndpointer(dtype=np.float64,
ndim=1, flags='C_CONTIGUOUS')]
Typo: argtpes - argtypes
Probably not Windows-specific :)
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accumulates the cost of passing objects through the emulation layer.
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10.03.2012 23:19, Sturla Molden kirjoitti:
[clip]
If the intention is to avoid Fortran dependency in NumPy, I am not sure
why this is better than a dependency on CBLAS and LAPACKE.
The CBLAS parts aren't compiled if the library is not available --- in
that case Numpy just falls back to a
breakpoints to relevant routines in your class and inside Numpy, and
then step through the execution with (c)gdb.
Automatic memory leak detection tools maybe don't help so much here, as
the problem is likely in wrong reference counting.
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09.03.2012 21:05, Sameer Grover kirjoitti:
subroutine union ()
write(*,*)'Hello from Fortran90!!!'
end subroutine union
f2py is unable to wrap this function. Changing the name of the
subroutine from 'union' to something else works. I understand that
problems like these have been
the size specified by the subtype.
A workaround is probably to specify a suitable tp_alloc routine yourself:
PyType_GenericAlloc,/* tp_alloc */
unitArray_new, /* tp_new */
_PyObject_Del /* tp_free */
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the conversion script in the public sounds good. We could
perhaps also consider applying hosting for the Scipy tracker, too.
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27.02.2012 19:07, Alan G Isaac kirjoitti:
On 2/27/2012 1:00 PM, Paulo Jabardo wrote:
First of all '.' isn't international notation
That is in fact a standard designation.
http://en.wikipedia.org/wiki/Decimal_mark#Influence_of_calculators_and_computers
ISO specifies comma to be used in
and Scipy don't use VML.
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loading.
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of the work in Numpy implementation is not actually in numerics,
but in figuring out the correct operation to dispatch the computations
to. So, this is one reason why Fortran is not considered.
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19.02.2012 05:38, Travis Oliphant kirjoitti:
[clip]
Sure. This list actually deserves a long writeup about that.
First, there wasn't a Cython-refactor of NumPy. There was a
Cython-refactor of SciPy. I'm not sure of it's current status.
I'm still very supportive of that sort of thing.
with it is not that much fun.
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Hi,
16.02.2012 23:39, Travis Oliphant kirjoitti:
[clip]
* NumPy 1.8 to come out in July which will have as many ABI-compatible
feature enhancements as we can add while improving test coverage and code
cleanup. I will post to this list more details of what we plan to address
with it
rounding error.
See http://www.nccs.nasa.gov/images/FloatingPoint_consistency.pdf
That would explain why the pattern you see is quasi-deterministic. The
other explanation would be using uninitialized memory at some point, but
that seems quite unlikely.
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16.02.2012 14:14, josef.p...@gmail.com kirjoitti:
[clip]
We had other cases of several patterns in quasi-deterministic linalg
before, but as far as I remember only in the final digits of
precision, where it didn't matter much except for reducing test
precision in my cases.
In the random
16.02.2012 14:54, josef.p...@gmail.com kirjoitti:
[clip]
If I interpret you correctly, this should be a svd ticket, or an svd
ticket as duplicate ?
I think it should be a multivariate normal ticket.
Fixing SVD is in my opinion not sensible: its only guarantee is that A
= U S V^H down to
Hi,
16.02.2012 18:00, Nathaniel Smith kirjoitti:
[clip]
I agree, but the behavior is still surprising -- people reasonably
expect something like svd to be deterministic. So there's probably a
doc bug for alerting people that their reasonable expectation is, in
fact, wrong :-).
The problem
Hi,
15.02.2012 14:59, Ognen Duzlevski kirjoitti:
[clip]
Alright, it will happen sometime today and I will post a message announcing
so.
Ognen
Great! Once you have changed the records, we can adjust [1] the
numpy.github.com page [1] to deal with the new virtual host name.
Thanks,
Pauli
[1]
-api.array.html#import_array
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, as
i = arange(n)
a[i].shape == i.shape
It's probably possible to cut the overhead, though.
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11.02.2012 20:44, Travis Oliphant kirjoitti:
How to people feel about moving the issue tracking for NumPy to Github?
It looks like they have improved their issue tracking quite a bit and
the workflow and integration with commits looks quite good from what I
can see.
The lack of
11.02.2012 22:02, Jason Grout kirjoitti:
[clip]
Are there any good github trac plugins? For example:
http://davglass.lighthouseapp.com/projects/21212/home
http://trac-hacks.org/wiki/GitPlugin (git, not github, but still maybe
useful)
The Numpy Scipy Trac is currently running on this:
the heuristics in place for elementwise operations.
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Hi,
08.02.2012 11:22, Scott Sinclair kirjoitti:
[clip]
I see that you've added the CNAME file. Now numpy.github.com is being
redirected to numpy.scipy.org (the old site).
As I understand it, whoever controls the scipy.org DNS settings needs
point numpy.scipy.org at numpy.github.com so that
Hi,
06.02.2012 20:41, Ralf Gommers kirjoitti:
[clip]
I've created https://github.com/scipy/scipy.github.com and gave you
permissions on that. So with that for the built html and
https://github.com/scipy/scipy.org-new for the sources, that should do it.
On the numpy org I don't have the
is not a bug, but a documented feature that has
been around probably already since Numeric.
However, adding a new function could be possible.
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19.01.2012 18:21, Travis Oliphant kirjoitti:
I think the problem here is one of delegation and information.
I'm not even sure how the web-pages get updated at this point.
Does anyone on this list know? I think it would be a great idea
to move to github pages for the NumPy project at least.
]
Since Numpy version 1.5, the new buffer protocol is supported.
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really know what is supposed to happen if the new
buffer protocol is supported by some class named say Foo. Could I then
do
x=Foo([1,2,3])
numpy.array([2,2,2])+x
and such operations?
Yes. You can try it out with Python's builtin memoryview class.
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against MKL (check the build log), then one
possible reason could be different threading options passed to MKL.
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docstring, especially `axis` and `shape`
are very common.
However, an example with the axis keyword could be useful.
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01.12.2011 03:31, josef.p...@gmail.com kirjoitti:
[clip]
I thought np.dot is Lapack based and favors fortran order, but if the
second array is fortran ordered, then dot takes twice as long.
It uses C-LAPACK, and will make copies if the arrays are not in C-order.
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the glossary list from `glossary.py` to
`doc/source/glossary.txt` in place of `automodule:: numpy.doc.glossary`
If that doesn't help, then something in the formatting of the glossary
list that makes Sphinx to choke (and it's certainly then a Sphinx bug).
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23.11.2011 15:01, Fabrice Silva kirjoitti:
Hi folks,
how should I specify a PEP3118 buffer format that could be understand by
numpy as the following dtype:
dtype = [('t11', '|f8'), ('t22', '|f8'), ('t33', '|f8'),
('t23', '|f8'), ('t13', '|f8'), ('t12', '|f8')]
so that I can
]` unchanged if it's ignored, and
having ignored values propagate creates the problem.
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04.11.2011 19:59, Pauli Virtanen kirjoitti:
[clip]
This makes inline binary ops
behave like Nn. Reductions are N. (Assignment: dC, reductions: N, binary
ops: PX, unary ops: PC, inline binary ops: Nn).
Sorry, inline binary ops are also PdX, not Nn.
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; the payload of the RHS is neglected,
# the assigned value has the original LHS
# as the payload
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.
However, doesn't this have the issue that Nathaniel brought up earlier:
commutativity
unignore(x + y) != unignore(y + x)
As the definition concerns only what happens on assignment, it does not
have problems with commutativity.
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change the masked data:
a[:] = b
which changes also masked values in `a`. That may be a bug.
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04.11.2011 23:29, Pauli Virtanen kirjoitti:
[clip]
As the definition concerns only what happens on assignment, it does not
have problems with commutativity.
This is of course then not really true in a wider sense, as an example
from T J shows:
a = 1
a += IGNORE(3)
# - a := a + IGNORE(3
operations rather than having it in binary ops.
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values?
Is there a real-word need to have (a) be true?
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04.11.2011 17:31, Gary Strangman kirjoitti:
[clip]
The question does still remain what to do when performing operations like
those above in IGNORE cases. Perform the operation underneath? Or not?
I have a feeling that if you don't start by mathematically defining the
scalar operations first,
31.10.2011 09:44, mark florisson kirjoitti:
[clip]
Ah, that's too bad. Is it anywhere near ready, or was it abandoned for
ironclad? Could you point me to the code?
It's quite ready and working, and as far as I understand, Enthought is
shipping it. I haven't used it, though.
The code is here:
C library. It's been in a
merge-limbo for some time now, however.
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why the error occurs in
only some of the cases.
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26.10.2011 10:07, Nathaniel Smith kirjoitti:
On Tue, Oct 25, 2011 at 4:49 PM, Matthew Brett matthew.br...@gmail.com
wrote:
I guess from your answer that such a warning would be complicated to
implement, and if that's the case, I can imagine it would be low
priority.
I assume the problem
25.10.2011 06:59, Matthew Brett kirjoitti:
res = np.longdouble(2)**64
res-1
36893488147419103231.0
Can you check if long double works properly (not a given) in C on that
platform:
long double x;
x = powl(2, 64);
x -= 1;
printf(%g %Lg\n, (double)x, x);
or, in
gives something wrong.
Can you try to check this by doing something like:
- do some set of calculations using np.longdouble in Numpy
(that requires the extra accuracy)
- at the end, cast the result back to double
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before formatting one. In the latter case, one would have to maintain a
list of broken C libraries (ugh).
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(or explicitly creates masked arrays), but
as far as I understand that's about the same situation as with np.ma.
.. [1] http://docs.scipy.org/doc/numpy/reference/arrays.maskna.html
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understand.
No, master is supposed to be the integration branch with only finished
stuff in it.
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13.10.2011 12:59, Alex van der Spek kirjoitti:
gives me a confusing result. I only asked to name the columns and change
their
types to half precision floats.
Structured arrays shouldn't be thought as an array with named columns,
as they are somewhat different.
What am I missing? How to do
17.10.2011 15:48, josef.p...@gmail.com kirjoitti:
On Mon, Oct 17, 2011 at 6:17 AM, Pauli Virtanen p...@iki.fi wrote:
[clip]
What am I missing? How to do this?
np.rec.fromarrays(arr.T, dtype=dt)
y.astype(float16).view(dt)
I think this will give surprises if the original array is not in C
' in it on the upper right
corner of the page.
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that it can be vectorized. Without knowing
more about the actual algorithm you are trying to implement, it's not
easy to give more detailed help.
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as all the lattice points inside
the bounding box are checked.
The only way to vectorize this I see is to do write the floodfill
algorithm on rectangular supercells, so that the constant costs are
amortized. Sounds a bit messy to do, though.
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10.10.2011 23:02, Jesper Larsen kirjoitti:
Hi numpy-users
I have a 2d array of shape (ny, nx). I want to broadcast (copy) this
array to a target array of shape (nt, nz, ny, nz) or (nt, ny, nx) so
that the 2d array is repeated for each t and z index (corresponding to
nt and nz). I am not sure
(words):
i = np.arange(len(word))
canvas[i+j, 2*i] = list(word)
canvas[:,-1] = '\n'
return canvas.tostring().rstrip()
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: your qrupdate code is licensed under the
GPL. To include it in Scipy, we need to have it distributable under a
BSD-compatible license. Would you be willing to relicense it?
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**
Yes, this is a known shortcoming of .tofile().
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On Fri, 23 Sep 2011 11:57:15 -0600, Charles R Harris wrote:
[clip]
We're falling behind. The real world is impacting hard at the moment.
Matthew's suggestion is a good one.
Should we stop recommending using Trac for contributions, and just
say use git? (I think we recommend it somewhere.)
I'd
Thu, 22 Sep 2011 08:12:12 +0200, Han Genuit wrote:
[clip]
I also noticed that it does strange things when using a list:
c[[True, False, True]]
array([[3, 4, 5],
[0, 1, 2],
[3, 4, 5]])
It casts the list with booleans to an integer array. Probably shouldn't
work like that...
Hi,
Thu, 22 Sep 2011 13:09:51 +0200, Marijn Verkerk wrote:
[clip]
ImportError: libptf77blas.so.3: cannot open shared object file: No such
file or directory
In the __config.py file the folder where libptf77 should be is present.
Any suggestions?
You need to make the dynamic linker able to
Sun, 11 Sep 2011 03:03:26 -0500, Pengkui Luo wrote:
[clip]
However, converting a large sparse matrix to dense would easily eat up
the memory. Is there a way for np.sign (as well as other ufunc) to take
a sparse matrix as parameter, and return a sparse matrix?
For CSR, CSC, and DIA you can do
Wed, 07 Sep 2011 12:52:44 -0700, Chris.Barker wrote:
[clip]
In [9]: temp['x'] = 3
In [10]: temp['y'] = 4
In [11]: temp['z'] = 5
[clip]
maybe it wouldn't be any faster, but with re-using temp, and one less
list-tuple conversion, and fewer python type to numpy type conversions,
maybe it
in exact arithmetic, for a computer it may still be
invertible (by a given algorithm). This type of things are not
unusual in floating-point computations.
The matrix condition number (`np.linalg.cond`) is a better measure
of whether a matrix is invertible or not.
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Hey,
Sat, 27 Aug 2011 12:28:14 +0200, David Cournapeau wrote:
I am finally at a stage where bento can do most of what numscons could
do. I would rather avoid having 3 different set of build scripts
(distutils+bento+numscons) to maintain in the long term, so I would
favor removing numscons
in several Python-for-science distributions),
and how difficult would it be for you to ship them with your stuff,
or to require the users to install them.
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Fri, 19 Aug 2011 12:48:29 +0200, Ralf Gommers wrote:
[clip]
Hi Ognen,
Could you please disable http access to numpy and scipy svn?
Turns out also I had enough permissions to disable this. Now:
$ svn co http://svn.scipy.org/svn/numpy/trunk numpy
svn: Repository moved permanently to
of lines instead of a number of bytes?
First use standard Python I/O functions to determine the number of
bytes to skip at the beginning and the number of data items. Then pass
in `offset` and `shape` parameters to numpy.memmap.
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Sat, 13 Aug 2011 22:00:33 -0400, josef.pktd wrote:
[clip]
Does Trac require svn access to dig out old information? for example
links to old changesets, annotate/blame, ... ?
It does not require HTTP access to SVN, as it looks directly at the
SVN repo on the local disk.
It also probably doesn't
Sat, 13 Aug 2011 18:14:11 +0200, Ralf Gommers wrote:
[clip]
We should check if there's still any code in SVN branches that is
useful.
If so the people who are interested in it should move it somewhere else.
Anyone?
All the SVN branches are available in Git, though some are hidden. Do
)
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Fri, 29 Jul 2011 10:52:12 +0200, Yoshi Rokuko wrote:
[clip]
A, B = mod.meth(C, prob=.95)
is ith possible to return two arrays?
The way to do this in Python is to build a tuple with
Py_BuildValue(OO, A, B) and return that.
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doesn't vectorize the slice operations such as
b[:,j-g]
but falls back to Numpy on them. You'll need to convert the slice
notation to a loop to get speed gains.
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Tue, 19 Jul 2011 21:55:28 +0200, Ralf Gommers wrote:
On Sun, Jul 17, 2011 at 11:48 PM, Darren Dale dsdal...@gmail.com
wrote:
In numpy.distutils.system info:
default_x11_lib_dirs = libpaths(['/usr/X11R6/lib','/usr/X11/lib',
'/usr/lib'], platform_bits)
the same speed
as an equivalent C implementation, which *does* pre-allocation, using
exactly the same benchmark codes as you have posted?
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Wed, 20 Jul 2011 09:04:09 +, Pauli Virtanen wrote:
Wed, 20 Jul 2011 08:49:21 +0200, Carlos Becker wrote:
Those are very interesting examples. I think that pre-allocation is
very important, and something similar happens in Matlab if no
pre-allocation is done: it takes 3-4x longer than
Wed, 20 Jul 2011 11:31:41 +, Pauli Virtanen wrote:
[clip]
There is a sharp order-of-magnitude change of speed in malloc+memset of
an array, which is not present in memset itself. (This is then also
reflected in the Numpy performance -- floating point operations probably
don't cost much
Tue, 19 Jul 2011 11:05:18 +0200, Carlos Becker wrote:
Hi, I started with numpy a few days ago. I was timing some array
operations and found that numpy takes 3 or 4 times longer than Matlab on
a simple array-minus-scalar operation.
This looks as if there is a lack of vectorization, even though
On Tue, 19 Jul 2011 17:49:14 +0200, Carlos Becker wrote:
I made more tests with the same operation, restricting Matlab to use a
single processing unit. I got:
- Matlab: 0.0063 sec avg
- Numpy: 0.026 sec avg
- Numpy with weave.blitz: 0.0041
To check if it's an issue with building without
operation
runs exactly at the same speed as C, so this issue must have
a platform-dependent explanation.
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Sat, 16 Jul 2011 12:42:36 +0200, Stefan Krah wrote:
x = ndarray(buffer=bytearray([1,2,3,4,5,6,7,8,9,10]),
shape=[10], strides=[-1], dtype=B, offset=9)
[clip]
I do not understand the PyBUF_SIMPLE result. According to the C-API docs
a consumer would be allowed to access buf[9], which
Hi,
Fri, 01 Jul 2011 16:45:47 +0200, Marc Labadens wrote:
I am trying to interface some python code using numpy array with some C
code.
You can read:
http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html#writing-an-extension-module
However, using Cython often saves you from writing
Tue, 21 Jun 2011 16:43:13 -0500, Mark Wiebe wrote:
[clip: __array_wrap__]
Those could stay as they are, and just the ufunc usage of __array_wrap__
can be deprecated. For classes which currently use __array_wrap__, they
would just need to also implement _numpy_ufunc_ to eliminate any
On Fri, 17 Jun 2011 15:39:21 -0500, Robert Kern wrote:
[clip]
File
/home/bvr/Programs/scipy/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py,
line 378 in test_ticket_1459_arpack_crash
Well, if it's testing that it crashes, test passed!
Here is the referenced ticket and the relevant
On Tue, 14 Jun 2011 15:37:36 -0500, Mark Wiebe wrote:
[clip]
It would be nice to get a fix to this newly reported regression in:
http://projects.scipy.org/numpy/ticket/1867
I see that regression only in master, not in 1.6.x, so I don't think
it will delay 1.6.1.
Pauli
On Mon, 13 Jun 2011 11:08:18 -0500, Bruce Southey wrote:
[clip]
OSError:
/usr/local/lib/python3.2/site-packages/numpy/core/multiarray.pyd: cannot
open shared object file: No such file or directory
I think that's a result of Python 3.2 changing the extension module
file naming scheme (IIRC to a
. Maybe they can be memcpy'd to make aliases,
although that sounds dirty...
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On Fri, 10 Jun 2011 18:51:14 -0700, Keith Goodman wrote:
[clip]
Maybe this is the same question, but are you maybe yes, maybe no on this
too:
type(np.sum([1, 2, 3], dtype=np.int32)) == np.int32
False
Note that this is a comparison between two Python types...
Ben, what happens if
Tue, 31 May 2011 11:44:15 -0500, Mark Wiebe wrote:
[clip]
I find very commendable to strive for consistency, mind you. I'm just
not not very comfortable with the idea of modifying old records a
posteriori to adjust to new policies...
I was under the impression this already was the policy,
Fri, 27 May 2011 21:06:45 +0300, Talla wrote:
[clip: AbinitBandStructuremaker.py]
FYI: try getting a newer version of the abinit software from abinit.org
They seem to have an updated version of this particular script
in their source package.
Any further discussion should probably go to their
the new page.
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%20Numeric/24.2/
No pre-made binaries for Python 2.7, for obvious reasons, however.
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at the same time as their
product.
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Tue, 24 May 2011 11:41:28 +0200, Jan Wuyts wrote:
bmidata=np.asarray([x for x in data if x['paramname']=='bmi'],
dtype=data.dtype)
Use indexing:
bmidata = data[data['paramname'] == 'bmi']
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to it in the changelog or release notes.
Looks like a bug -- the change was not intentional.
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Wed, 18 May 2011 07:39:18 -0400, Neal Becker wrote:
The file is pickle saved on i386 and loaded on x86_64. It contains a
numpy array (amoungst other things).
On load it says:
RuntimeError: invalid signature
There's no such message in Numpy source code, so the error does
not come directly
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