Hi,
I think Group 1 is a negligible epsilon of Group 2, and moreover,
Group 1 is the most likely to be able to deal with those issues.
It is time for an on list vote?
I must say, although I know it is not straightforward, that I agree
with David that, we should act in favor of our new and
Hi,
Over on the scipy list, someone pointed out an oddness in the output
of the matlab reader, which revealed this - to me - unexpected
behavior in numpy:
In [20]: dt = np.dtype('f8')
In [21]: dt.isbuiltin
Out[21]: 1
In [22]: ndt = dt.newbyteorder('')
In [23]: ndt.isbuiltin
Out[23]: 0
I was
Hi,
It is at least undesirable. It may not be a bug per se as I don't
think that we guarantee that .isbuiltin is free from false negatives
(though we do guarantee that it is free from false positives). The
reason is that we would have to search the builtin dtypes for a match
every time we
Hi,
On Thu, Dec 31, 2009 at 2:08 AM, Christopher Barker
chris.bar...@noaa.gov wrote:
Charles R Harris wrote:
That is due to type promotion for the ufunc call:
In [17]: a1 = np.array('a\x00\x00\x00')
n [21]: np.array(['a'], dtype=a1.dtype)[0]
Out[21]: 'a'
In [22]: np.array(['a'],
Hi.
It isn't empty:
In [3]: array(['\x00']).dtype
Out[3]: dtype('|S1')
In [4]: array(['\x00']).tostring()
Out[4]: '\x00'
In [5]: array(['\x00'])[0]
Out[5]: ''
No, but my problem was that an empty string is not empty either, and
that you can't therefore distinguish between an empty
Hi,
I was surprised by this - should I have been?
In [35]: e = np.array(['a'])
In [36]: e.shape
Out[36]: (1,)
In [37]: e.size
Out[37]: 1
In [38]: e.tostring()
Out[38]: 'a'
In [39]: f = np.array(['a'])
In [40]: f.shape == e.shape
Out[40]: True
In [41]: f.size == e.size
Out[41]: True
In
Hi,
I don't see any empty string in your code ? They all have one byte.
The last one is slightly confusing as far as printing is concerned (I
would have expected array([¥x00]...) instead). It may be a bug in
numpy because a byte with value 0 is used a string delimiter in C.
Sorry - I pasted
Hi,
Ok, it looks like there are at least two issues:
- if an item in a string array is set to '¥x00', this seems to be
replace with '', but '' != '¥x00']
Sorry - I'm afraid I don't understand. It looks to me as though the
buffer contents of [''] is a length 1 string with a 0 byte, and an
Hi,
x = ¥00
arr = np.array([x])
lst = [x]
arr[0] == x # False
arr[0] == # True
lst[0] == x # True
lst[0] == # False
Ah - thanks - got it.
It looks to me as though the
buffer contents of [''] is a length 1 string with a 0 byte, and an
array.size of 1 - is that also what you think?
Hi,
An obvious idea is to allow the testing machinery to parse all
the configs, including the doctest configs, then throw away the native
(non-numpy) doctest plugin before we get to collecting and running the
tests.
I've attached a patch that does this; it's a little bit magic because
of
Hi,
Because I like doctests, I have the following set in my .noserc file:
with-doctest=1
This setting breaks numpy.test() like this:
In [2]: numpy.test()
Running unit tests for numpy
NumPy version 1.5.0.dev8029
NumPy is installed in /Users/mb312/usr/local/lib/python2.6/site-packages/numpy
Hi,
I'm happy to write the doctests as tests. My feeling is there is no
objection to this function at the moment, so it would be reasonable,
unless I hear otherwise, to commit to SVN.
Committed - with tests in tests_linalg.py - in revision 8029
Cheers,
Matthew
Hi,
Is it reasonable to summarize that, to avoid confusion, we keep
'matrix_rank' as the name?
I've edited as Robert suggested, attempting to adopt a more suitable
tone in the docstring...
What comes next when someone offers up a useful function like this?
We are using an earlier version
Hi,
On Wed, Dec 16, 2009 at 2:16 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Wed, Dec 16, 2009 at 02:13:08PM -0500, Matthew Brett wrote:
I'm happy to write the doctests as tests. My feeling is there is no
objection to this function at the moment, so it would be reasonable
Hi,
Following on from the occasional discussion on the list, can I propose
a small matrix_rank function for inclusion in numpy/linalg?
I suggest it because it seems rather a basic need for linear algebra,
and it's very small and simple...
I've appended an implementation with some doctests in
Hi,
+1 for the function but we can not shorten the name because of existing
numpy.rank() function.
I don't feel strongly about the name, but I imagine you could do
from numpy.linalg import rank as matrix_rank
if you weren't using the numpy.linalg namespace already...
Best,
Matthew
Hi,
Is it reasonable to summarize that, to avoid confusion, we keep
'matrix_rank' as the name?
I've edited as Robert suggested, attempting to adopt a more suitable
tone in the docstring...
Thanks a lot,
Matthew
def matrix_rank(M, tol=None):
''' Return rank of matrix using SVD method
Hi,
On Wed, Dec 9, 2009 at 4:31 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
In the nipy project, we have cython-generated C files checked in.
With the latest numpy I get the following run-time failure:
/home/varoquau/dev/nipy-trunk/nipy/neurospin/register/iconic_matcher.py,
line
Hi,
I was surprised by this - is it a bug or a feature or me
misunderstanding something?
a = np.zeros((1,), dtype=[('f1', 'u2')])
b = a.copy()
b == a
(array([True], dtype=bool)) # as expected
c = a.byteswap().newbyteorder()
c == a
(False) # to me, unexpected, note bool rather than array
Thanks
Hi,
c = a.byteswap().newbyteorder()
c == a
In the last two lines, a variable c is assigned to a modified a. The
next line tests (==) to see if c is the same as (==) the unmodified a.
It isn't, because c is the modified a. Hence, False.
Sorry, I wasn't very clear - the stuff in brackets
Hi,
Nicolas Devillard discusses several algorithms at
http://ndevilla.free.fr/median/median/index.html
Thanks for this. A loud 'yes' from the back of the internet too.
I contacted Nicolas Devillard a year or so ago to ask him if we could
include his code in Scipy, and he said 'yes'. I can
Hi,
Indeed. In the future, if OpenCL is the way to go, it may even be
helpful to have Numpy using OpenCL directly, as AMD provides an SDK
for OpenCL, and with Larrabee approaching, Intel will surely provide
one of its own.
I was just in a lecture by one of the Intel people about OpenCL:
On Fri, Aug 21, 2009 at 11:33 AM, Matthew Brettmatthew.br...@gmail.com
wrote:
Nicolas investigated algorithms that find the lower (or upper) median
value. The lower median is the median iff there are an odd number of
entries in our list, or the lower of the central values in the sort,
when
Hi Jonathan,
http://www.cs.toronto.edu/~jtaylor/crash/
I would be interested to know if this bug can be duplicated and/or if anyone
has any suggestions as to why:
import numpy as np
A = np.load('A.npy')
b = np.load('b.npy')
rc = np.linalg.lstsq(A,b)
produces:
*** glibc detected ***
Hi,
On Tue, Aug 4, 2009 at 9:31 AM, David Cournapeaucourn...@gmail.com wrote:
On Tue, Aug 4, 2009 at 8:13 PM, David
Cournapeauda...@ar.media.kyoto-u.ac.jp wrote:
I think I understand the problem. Unfortunately, that's looks tricky to
solve... I hate distutils.
Ok - should be fixed in
Hi,
We are using numpy.distutils, and have run into this odd behavior in windows:
I have XP, Mingw, latest numpy SVN, python.org python 2.6. All the
commands below I am running from within the 'numpy' root directory
(where 'numpy' is a subdirectory).
If I run
python setup.py build
I get the
Hi,
In short, I want to create a class Dummy, inherited from np.ndarray, which
returns a plain array whenever an instance is sliced or viewed. I cannot
figure
out how.
class Dummy(np.ndarray):
def __new__(cls, array):
obj=array.view(cls)
return obj
def __array_finalize__(self,
Hi,
It's been there ever since Travis added the test, but it is sporadic. It's
probably a reference counting error somewhere but no one has tracked it down
yet. There is a ticket open on it.
As a demonstration that this is not fully predictable; here's me
copying the test file, running it
Hi,
I just got an email about a broken buildbot build. Looking at the
logs, I see this:
==
FAIL: Test bug in reduceat with structured arrays copied for speed.
Hi,
The words of caution were intended to remind that setting the value
of new array attributes in __new__ only could lead to problems, as
it's possible to transform a ndarray into a subclass with a view (that
is, calling __array_finalize__ without calling __new__). In the Simple
Example of
Hi,
I'm the person who removed the reference to thread-safeness in the
numpy version of the subclassing docs. I remember at the time going
through all the email discussions that led up to the thread-safeness
question, and concluding (in discussion with Travis O and others) that
there was
Hi,
Yup. It's not even very idiomatic Python. readlines() is probably a
bad idea unless your file is trivial length, and even ignoring
numpy.loadtxt(), all of this could be considerably simplified with the
built-in csv module.
or a 1-liner with scipy.io.loadmat ...
Best,
Matthew
Hi,
I am having problem while trying to memory map a simple file (attached as
test.txt)
The file looks like a text file, but memmap is for binary files.
Could that be the problem?
Best,
Matthew
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I think something close to this would be possible:
add dot as an array method.
A .dot(B) .dot(C)
is not as pretty as
A * B * C
but it is much better than
np.dot(np.dot(A,B),C)
That is much better.
Matthew
___
Hi,
The RC1 for 0.7.1 scipy release has just been tagged. This is a
bug-only release
I feel (y)our pain, but don't you mean 'bug-fix only release'? ;-)
Matthew
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Hi,
Whine. I was afraid of something like that...
2 options, then:
* We revert to computing a mask beforehand. That looks like the part
that takes the most time w/ domained operations (according to Robert
K's profiler. Robert, you deserve a statue for this tool). And that
doesn't solve the
Hi,
I'm trying to fix a bug in the scipy matlab loading routines, and this
requires me to somehow represent an empty structured array.
Do you need the struct to be empty (size is 0) or to have no fields ?
What would you expect np.zeros((), dtype=np.dtype([])) to return, for
example ?
Yes,
Hello,
I'm trying to fix a bug in the scipy matlab loading routines, and this
requires me to somehow represent an empty structured array.
In matlab this is:
a = struct()
In numpy, you can do this:
In [1]: dt = np.dtype([])
but then you can't use it:
In [2]: np.zeros((),dtype=dt)
Hi,
Sturla Molden has a patch adding offset support to memmap. However... the
feature requires Python 2.5 and above. The Python version is checked for in
the code but I am wondering if we want to do that sort of thing. Using
advanced features here and there doesn't bother me too much as long
Hi,
I enjoyed this quote from http://www.eecs.harvard.edu/~cduan/technical/git/
Summary: You can only really use Git if you understand how Git works.
When I first started using Git, I read plenty of tutorials, as well
as the user manual. Though I picked up the basic usage patterns and
commands,
Hello,
Summary: is it possible to distribute, optionally or not, the blas /
lapack libraries that numpy is built against, with the numpy binary
installers?
We at the NIPY project have run into what seems like a recurring
problem; we want to build our code against both numpy and lapack, on
Hi,
We at the NIPY project have run into what seems like a recurring
problem; we want to build our code against both numpy and lapack, on
windows, linux and OS X.
No problem of course if we've done a development install - we already
needed to have blas/lapack.
I am not sure I understand:
Hi,
1) Numerical Recipes has an out-of-memory FFT algorithm, but looking
through the numpy and scipy docs and modules, I didn't find a function
that does the same thing. Did I miss it? Should I get to work typing
it in?
No please don't do that; I'm afraid the Numerical Recipes book has a
Hi,
I found this a little confusing:
In [11]: n = 25
In [12]: np.arange(n).shape
Out[12]: (0,)
Maybe this should raise an error instead.
This was a little more obvious, but perhaps again a more explicit
error would be helpful?
In [13]: np.zeros((n,))
Hi,
On Sun, Mar 22, 2009 at 1:31 AM, David Cournapeau courn...@gmail.com wrote:
Hi Matthew,
On Sun, Mar 22, 2009 at 3:08 PM, Matthew Brett matthew.br...@gmail.com
wrote:
I notice this gives much more helpful memory errors on a 64 bit
machine with 4GB of memory.
Can you tell me which
Hello,
I found this a little confusing:
In [11]: n = 25
In [12]: np.arange(n).shape
Out[12]: (0,)
Maybe this should raise an error instead.
This was a little more obvious, but perhaps again a more explicit
error would be helpful?
In [13]: np.zeros((n,))
Hi,
I'm having some difficulty understanding how these work and would be
grateful for any help.
In the simple case, I get what I expect:
In [42]: a = np.zeros((), dtype=[('f1', 'f8'),('f2', 'f8')])
In [43]: a == a
Out[43]: True
If one of the fields is itself an array, and the other is a
Hi,
The idea behind the current syntax was to keep things as close as
possible to Python/NumPy, and only provide some hints to Cython for
optimization. My problem with this now is that a) it's too easy to get
non-optimized code without a warning by letting in untyped indices, b) I
think the
Hi,
I was just trying to write a docstring for np.dtype.isbuiltin, when I
realized I didn't understand it.
As far as I can see, isbuitin should return:
0 for structured array dtypes
1 for types compiled into numpy
2 for extension types using the numpy C-API type extension machinery.
Here's the
Hi,
When converting arrays from float to ints, I notice that NaNs, Infs,
and -Infs all get the minimum integer value:
flts = np.array([np.nan, np.inf, -np.inf])
flts.astype(np.int16)
array([-32768, -32768, -32768], dtype=int16)
However, setting NaNs into integer arrays gives a value of 0
Did you say matlab ? ;-)
I have to debug a matlab program -- is there a way to do a post
mortem debugging ?
(I don't even know if this term even exists outside of python ;-)
((I want to know some local variable values and find out why our
program crashes when there are no particles
Hi,
#This does not work
import numpy
a=numpy.zeros(10)
b=numpy.ones(4, numpy.int)
a[b] += 1
The problem here is that you are setting a[1] to a[1]+1.
I think you want:
import numpy
a=numpy.zeros(10)
b=numpy.ones(4, numpy.bool)
a[b] += 1
Best,
Matthew
Judging from his for loop, he does want the integer array. He's doing
something like histogramming.
Yup, thanks, just goes to show that it's not good to send emails after
a glass of wine late at night with slight jetlag.
Matthew
___
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Hi,
Do you also know how the situation is with sourceforge/launchpad/trac...
and other popular hosting systems ?
Do they also have these restrictions ?
I've not noticed any problems with sourceforge, nor launchpad - I'm
using them regularly from here. You'd hope that was the case for
Hi,
I am just visiting colleagues in the Cuban Neuroscience Center, and of
course I'm trying to persuade them that Python and open-source are the
way forward.
This is made more difficult because several projects - for example
pyglet - have their repositories on Google code. Google, unlike any
I know, I know, last one...
http://www.catb.org/~esr/faqs/smart-questions.html
I had forgotten this wise quote from the smart questions FAQ:
Be gentle. Problem-related stress can make people seem rude or stupid
even when they're not.
Best,
Matthew
Friends,
Those statements are not demeaning; lighten up.
STOP IT. JUST STOP IT. STOP IT RIGHT NOW.
Let us not go to this place, honestly, there is no need. Let's go
back to the technical problem again.
Linda, did you have time to try Alan's example?
Best,
Matthew
Hi Stefan,
Thanks for the kindly reply...
Since both those objects have an __array_priority__ of 0.0, I guess it
just takes whichever class comes first.
In [15]: class A(np.ndarray):
: __array_priority__ = -1.0
I think, playing more...
For np.multiply, it does not seem possible
Hi Nils,
I don't have an example.py in a checkout from a few seconds ago - it
is possible it could be a stray file?
Best,
Matthew
On Thu, Aug 28, 2008 at 3:29 AM, Nils Wagner
[EMAIL PROTECTED] wrote:
==
ERROR: Failure:
Hi,
find . -name example.py
in my svn/numpy directory yields
./doc/example.py
./doc/newdtype_example/example.py
Ah - how strange - I just tried deleting my doc directory and checking
it out again, and don't have those files or directory...
Matthew
Hi Travis and team,
I am just writing some docs for subclassing, and ran into some
behavior I didn't understand:
In [143]: class A(np.ndarray): pass
In [144]: arr = np.arange(5)
In [145]: obj = arr.copy().view(A)
In [146]: type(obj)
Out[146]: class '__main__.A'
In [147]:
Hi,
First the web sites. I can find three different websites that appear to
be relevant to NumPy. Let's take a look, shall we?
1. http://numpy.scipy.org
This is the first link on Google and appears to be the proper home of
Numpy ...
Thanks for this - no really - it's funny, and you're
Hi,
The idea behind set_local_path is that it allows running tests
inside subpackages without the need to rebuild the entire package.
Ah, thanks; I'd forgotten about that. I'll leave them alone, then. I
made a note for myself to make sure it's possible to run tests locally
without doing a
Hi,
Thanks a lot for the email - it's an exciting project.
cdef int i = 4, j = 6
cdef np.ndarray[np.float64, 2] arr = np.zeros((10, 10), dtype=np.float64)
arr[i, j] = 1
I'm afraid I'm going to pitch into an area I'm ignorant of, and I'm
sorry for that, but do you think there is any way of
Hi,
The feature of compiling code for multiple types is somewhat orthogonal to
ndarray support; better treat them seperately and take one at the time.
Well, it's relevant to numpy because if you want to implement - for
example - a numpy sort, then you've got to deal with an unspecified
number
Hi,
As a matter of interest, what is the relationship, if any, between (in Cython)
import numpy
and
cnp.import_array()
Are they initializing different copies of the same thing?
Best,
Matthew
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Hi,
- There's a bug in the latest Cython annotation, preventing the
example from compiling. Workaround: replace cython -a with
cython.
Mmh, I have 0.9.8 (their last release) and it works for me...
I've got the same version, works for me too...
Matthew
Hi,
Following on from Fernando's post about his Cython example, I would
like to suggest adding his .pxd files to the standard numpy include
directory, fetched with np.get_include()
Why:
Because anyone writing a Cython extension for numpy will need these
files. At the moment, this means that
Following on from Fernando's post about his Cython example, I would
like to suggest adding his .pxd files to the standard numpy include
directory, fetched with np.get_include()
I am a bit hesitant to add them to a standard public path until they
are close to complete.
As usual, fair
Hi,
Cython looks in the include_dirs for .pxd files, right? Then yes, I
would support putting them alongside arrayobject.h.
Yes, right - Fernando just pointed me to the reference - quoting him:
As indicated here:
Hi,
As long as it doesn't cause problems, then I'd vote for doing it.
This only happens at module import time, so even if several modules
using cython call it, it's only when they get imported. I think the
API simplification is worth it (minor, but still nice).
I just tested the changes
Hi,
I am sure you know this already, but you can just replace the tests
using ParametricTestCase with a nose test generator. See:
http://www.scipy.org/scipy/scipy/wiki/TestingGuidelines
under Parametric tests.
Best,
Matthew
On Mon, Jun 9, 2008 at 3:37 PM, Alan McIntyre [EMAIL PROTECTED]
Hi,
By being consistent in importing modules using the 'import numpy.fft
as fft', it can make it more clear that we are importing a module.
I already recommend this usage in the matplotlib coding guide, and
numpy may want to adopt it as well.
That's an excellent suggestion, seconded.
Hi,
I hope you're the right person to ask about this - sorry if not.
I have just noticed that our (neuroimaging.scipy.org) wiki link no longer works:
http://projects.scipy.org/neuroimaging/ni/wiki
gives a 502 proxy error:
Proxy Error
The proxy server received an invalid response from an
Hi,
Stefan, sometimes the fix really is clear and a test is like closing the
barn door after the horse has bolted. Sometimes it isn't even clear *how* to
test. I committed one fix and omitted a test because I couldn't think of
anything really reasonable. I think concentrating on unit tests is
Hi,
I must say, I agree with the other posters here, that it is not
completely obvious to me that:
a = np.array([True, False])
bool(a)
should return False. Especially given:
L = [True, False]
bool(L)
returns True.
Given that it's not completely obvious, and
a.all()
is completely
Hi Joe,
I do see your point - and agree that typing and cruft make code harder
to write and read, to some extent. But I think the point is - and I'm
increasingly finding this - that a judicious use of namespaces and
'from' statements makes the code much easier to read, as in
import numpy as np
Hi,
On Mon, Apr 7, 2008 at 4:25 PM, Stéfan van der Walt [EMAIL PROTECTED] wrote:
On 07/04/2008, Matthieu Brucher [EMAIL PROTECTED] wrote:
BTW, I stumbled on something strange with the nose framework. If you from
numpy.testing import * in a test file, the nose framework will try to test
+1 (and s/students/colleagues).
Surely you mean:
s.replace('students', colleagues')
!
Matthew
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Hi,
Note that the plug-in idea is just my own idea, it is not something
agreed by anyone else. So maybe it won't be done for numpy 1.1, or at
all. It depends on the main maintainers of numpy.
I'm +3 for the plugin idea - it would have huge benefits for
installation and automatic
Hi,
I think I found a bug in numpy/distutils/ccompiler.py - and wanted to
check that no-one has any objections before I fix it.
These lines (390ff distutils.ccompiler.py)
for _cc in ['msvc', 'bcpp', 'cygwinc', 'emxc', 'unixc']:
_m = sys.modules.get('distutils.'+_cc+'compiler')
if _m
Hi,
#558: 'axis' support for numpy.median()
This one should be fixed, and can be closed. I don't think I have wiki
login rights though.
Thanks,
Matthew
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Ah,
To answer my own question:
Suggestion 1:
Wrap the .sort method call in a tiny python wrapper of the form:
def sort(self, axis=-1, kind='quicksort', order=None):
if axis=None:
_c_sort(self.ravel(), axis, kind, order)
else:
_c_sort(self, axis, kind, order)
I guess
Hi,
Is it possible, in fact, to do an inplace sort on an array with
axis=None (ie flat sort)?
Should the sort method have its docstring changed to reflect the fact
that axis=None is not valid?
Sorry to press on, but it would be good to resolve this somehow.
Is there some reason not to:
Hi,
To rephrase:
Is it possible, in fact, to do an inplace sort on an array with
axis=None (ie flat sort)?
Should the sort method have its docstring changed to reflect the fact
that axis=None is not valid?
Matthew
On Feb 10, 2008 7:50 PM, Matthew Brett [EMAIL PROTECTED] wrote:
Hi,
I just
Hi,
On Feb 12, 2008 8:48 PM, Anne Archibald [EMAIL PROTECTED] wrote:
On 12/02/2008, Matthew Brett [EMAIL PROTECTED] wrote:
Suggestion 1:
def median(a, axis=0, out=None)
[...]
Suggestion 2:
def median(a, axis=0, scratch_input=False)
No reason not to combine the two. It's a pretty
Hi,
I can also see that this could possibly be improved by using a for
loop to iterate over the output elements, so that there was no need to
duplicate the large input array, or perhaps a blocked iteration that
duplicated arrays of modest size would be better. But how can a single
float per
Hi,
I am sorry if I have missed something obvious, but is there any way in
python of doing this:
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Thanks a lot for any pointers.
Matthew
import numpy as np
a = np.arange(10)
b = np.arange(10)+1
a.data = b.data # raises error, but I hope you see what I mean
?
Not really, no. Can you describe your use case in more detail?
Yes - I am just writing the new median implementation. To allow
future optimization, I would
Ah, I see. You definitely do not want to reassign the .data buffer in
this case. An out= parameter does not reassign the memory location
that the array object points to. It should use the allocated memory
that was already there. It shouldn't copy anything at all;
otherwise, median(x, out=out)
Hi,
I was wondering whether there was any plan to change the anomalous
interface of median, for example compared to 'mean', 'min', 'max'
np.mean(a, axis=None, dtype=None, out=None)
whereas:
np.median(m)
'median(m) returns a median of m along the first dimension of m.'
I think it would be a
Hi,
http://projects.scipy.org/scipy/numpy/browser/branches/build_with_scons/
Excellent idea - and congratulations to David for all his hard work -
but this is a large change, and I wonder if we need more time with the
scons build system in the svn trunk? Not to say it should not be in
1.0.5,
Hi,
median moved mediandim0
implementation of medianwithaxis or similar, with same call
signature as mean.
Deprecation warning for use of median, and return of mediandim0 for
now. Eventual move of median to return medianwithaxis.
This would confuse people even more, I'm afraid.
Hi,
All that is going to be merged are the hooks necessary to allow his
numscons package to work.
This is not a big change, as far as I know.
Yes, sorry, I misunderstood - and had thought the plan was to move to
the numscons build system as the default. Now I've understood it, it
sounds
Hi,
median moved mediandim0
implementation of medianwithaxis or similar, with same call
signature as mean.
But - for the median function change - do we agree that this should be
changed? I think it is a significant wart in the numpy API, and has
caught quite a few people...
Matthew
Hi,
I've attached the build logs. I noticed that, for atlas, you check
for atlas_enum.c - but do you in fact need this for the build?
Now. I just wanted one header specific to atlas. It looks like not all
version of ATLAS install this one, unfortunately (3.8, for example).
Hi,
When would the function signature be missing? In C functions we copy
the signature into the docstring. I am concerned about duplicating
information that may change.
I guess that would be
def my_func(*args, **kwargs):
although, if you're going to document the parameters in detail for
Also,
Perhaps we could have that discussion on the IRC channel on Friday at
some specified time?
I suppose it's possible to hack up some processor of the form:
doc_me('my_file.py')
that can introspect things like argument lists, add boilerplate and
process the resulting text according to the
Hi,
Search for the docstring standard, I hit this:
http://www.scipy.org/DocstringStandard
but I think the current thinking is this:
http://projects.scipy.org/scipy/numpy/wiki/CodingStyleGuidelines
Is that correct? Does the first page apply to matplotlib in some way?
Should we change the first
Hi,
I've just finished moving the scipy tests over to nose.
Thinking about it, it seems to me to be a good idea to do the same for numpy.
The advantages of doing this now are that numpy and scipy would be in
parallel, that we can continue to have one testing system for both,
and that it would
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