On Tue, Jun 26, 2012 at 10:25 PM, Ralf Gommers
ralf.gomm...@googlemail.com wrote:
On Tue, Jun 26, 2012 at 5:33 AM, Travis Oliphant tra...@continuum.io
wrote:
...
What should have happened in this case, in my mind, is that NumPy 1.4.0
should have been 1.5.0 and advertised that there was a
I do understand the issues around ABI breakage. I just want to speak up for
the people who are affected by API breakage who are not as vocal on this list.
I believe we should have similar frustration and concern at talk of API
breakage as there is about talk of ABI breakage.
-Travis
On
Damn it, N is inverted and I noticed it now after posting. Sorry about
that, here is correct one:
from numpy import arange, ones
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111,
On Tue, Jun 26, 2012 at 11:02 PM, Travis Oliphant tra...@continuum.io wrote:
I just want to speak up for the people who are affected by API breakage who
are not as vocal on this list.
Certainly! And indeed I bet you that's a community underrepresented
here: those of us who are on this list
Hi Fernando,
On Tue, Jun 26, 2012 at 6:00 PM, Fernando Perez fperez@gmail.com wrote:
On Tue, Jun 26, 2012 at 5:40 PM, Ondřej Čertík ondrej.cer...@gmail.com
wrote:
Do you use anything else besides Travis CI?
Yes, we use both Shining Panda and Travis CI:
Hi Matthew,
On Tue, Jun 26, 2012 at 7:07 PM, Matthew Brett matthew.br...@gmail.com wrote:
Hi,
On Tue, Jun 26, 2012 at 6:00 PM, Fernando Perez fperez@gmail.com wrote:
On Tue, Jun 26, 2012 at 5:40 PM, Ondřej Čertík ondrej.cer...@gmail.com
wrote:
Do you use anything else besides Travis
On 6/26/2012 8:13 PM, Travis Oliphant wrote:
For the main repos we use buildbot and test on:
Ubuntu Maverick 32-bit
Debian sid 64-bit
OSX 10.4 PPC
OSX 10.5 Intel
Debian wheezy PPC
Debian squeeze ARM (a Raspberry PI no less)
WIndows XP 32 bit
SPARC (courtesy of our friends at NeuroDebian)
I continued in this mpl trip, with small animation sequence:
# animation
ax.view_init(90,-90)
plt.ion()
plt.draw()
plt.show()
for l in arange(25):
ax.set_xlim3d(1.5-.1*l,2.5+.1*l)
ax.set_ylim3d(1.5-.1*l,2.5+.1*l)
ax.view_init(90-3*l, -90+l)
You're now reminding me of the old spinning SGI logo
http://www.youtube.com/watch?v=Nqf6TjE49N8
Brennan
On 27/06/2012 10:40 a.m., klo uo wrote:
I continued in this mpl trip, with small animation sequence:
# animation
ax.view_init(90,-90)
plt.ion()
Yeah, camera is in cliche, I know :D
Something more original can be done, perhaps some idea of transforming
grid in 2D (in Z plane) for opening sequence and then emerging latices
in some analogy with numpy arrays, finishing with complete figure, but
I guess not in matplotlib ;)
On Tue, Jun 26, 2012 at 11:08 PM, Travis Oliphant tra...@continuum.iowrote:
In my enthusiasm of finding someone to help with the release of NumPy 1.7
and my desire to get something released by the SciPy conference, I was
hasty and didn't gather enough feedback from others about the release of
Great. Yes, the Travis-CI stuff looks great. There are a lot of good CI
things happening on a lot of fronts.
It is encouraging to see.
It would be good to consolidate them --- or at least have a place to go to look
at output from many of them.
Travis
--
Travis Oliphant
(on a mobile)
On 06/26/2012 06:15 PM, Skipper Seabold wrote:
My uninformed opinion from the sidelines: For me, this begged the
question of why projects would wait so long and be upgrading 1.5.x -
1.7.x. it sounded to me like an outreach problem.
lenny: none
squeeze: 1.4.1
wheezy: 1.6.2
hardy: 1.0.4
lucid:
Some examples would be nice. A lot of people did move already. And I haven't
seen reports of those that tried and got stuck. Also, Debian and Python(x,
y) have 1.6.2, EPD has 1.6.1.
In my company, the numpy for our production python install is well
behind 1.6. In the world of trading, the
On Wed, Jun 27, 2012 at 7:20 AM, John Hunter jdh2...@gmail.com wrote:
Some examples would be nice. A lot of people did move already. And I
haven't
seen reports of those that tried and got stuck. Also, Debian and
Python(x,
y) have 1.6.2, EPD has 1.6.1.
In my company, the numpy for our
On Wed, Jun 27, 2012 at 9:33 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Jun 27, 2012 at 7:20 AM, John Hunter jdh2...@gmail.com wrote:
because the original developers are gone. So people are loathe to
upgrade. It is certainly true that deprecations that have lived for a
On Wed, Jun 27, 2012 at 8:43 AM, Peter Wang pw...@continuum.io wrote:
On Wed, Jun 27, 2012 at 9:33 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Jun 27, 2012 at 7:20 AM, John Hunter jdh2...@gmail.com wrote:
because the original developers are gone. So people are loathe to
On 27-Jun-2012 11:40, klo uo wrote:
I continued in this mpl trip, with small animation sequence:
# animation
ax.view_init(90,-90)
plt.ion()
plt.draw()
plt.show()
for l in arange(25):
ax.set_xlim3d(1.5-.1*l,2.5+.1*l)
This is cool. It would be nice to put these things somewhere where they could
be available for reference.
-Travis
On Jun 27, 2012, at 10:20 AM, Virgil Stokes wrote:
On 27-Jun-2012 11:40, klo uo wrote:
I continued in this mpl trip, with small animation sequence:
On Tue, Jun 26, 2012 at 10:53 PM, John Salvatier
jsalv...@u.washington.edu wrote:
I want to support multiple types in the index_increment function that I've
written here:
https://github.com/jsalvatier/numpy/blob/master/numpy/core/src/multiarray/mapping.c
I need to check that the first
On 27-Jun-2012 08:04, klo uo wrote:
from numpy import arange, ones
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
o = ones(4)
r = arange(4)
# planes:
for z in arange(3)+1:
ax.bar(r, o*4, zs=z,
On Tue, Jun 26, 2012 at 10:02 PM, Ralf Gommers
ralf.gomm...@googlemail.com wrote:
I agree with what you're arguing for here (as little impact as possible on
existing users), but your view of especially 1.6.x seems to be skewed by
regressions and changes that were either unintended or thought to
Currently the numpy build system(s) support two ways of building
numpy: either by compiling a giant concatenated C file, or by the more
conventional route of first compiling each .c file to a .o file, and
then linking those together. I gather from comments in the source code
that the former is the
According to the Travis-CI build logs, this code produces
non-deterministic behaviour in master:
a = np.arange(5)
a[:3] = a[2:]
assert_equal(a, [2, 3, 4, 3, 4])
Sometimes 'a' is [2, 3, 4, 3, 4], and sometimes it is [4, 3, 4, 3, 4].
The latter is what you get if the assignment is done
On Wed, Jun 27, 2012 at 7:17 PM, Nathaniel Smith n...@pobox.com wrote:
Currently the numpy build system(s) support two ways of building
numpy: either by compiling a giant concatenated C file, or by the more
conventional route of first compiling each .c file to a .o file, and
then linking those
On Wed, Jun 27, 2012 at 7:50 PM, David Cournapeau courn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 7:17 PM, Nathaniel Smith n...@pobox.com wrote:
Currently the numpy build system(s) support two ways of building
numpy: either by compiling a giant concatenated C file, or by the more
On Wed, Jun 27, 2012 at 8:07 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Jun 27, 2012 at 7:50 PM, David Cournapeau courn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 7:17 PM, Nathaniel Smith n...@pobox.com wrote:
Currently the numpy build system(s) support two ways of building
numpy:
On Wed, Jun 27, 2012 at 8:29 PM, David Cournapeau courn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 8:07 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Jun 27, 2012 at 7:50 PM, David Cournapeau courn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 7:17 PM, Nathaniel Smith n...@pobox.com wrote:
On 06/27/2012 09:53 PM, Nathaniel Smith wrote:
On Wed, Jun 27, 2012 at 8:29 PM, David Cournapeaucourn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 8:07 PM, Nathaniel Smithn...@pobox.com wrote:
On Wed, Jun 27, 2012 at 7:50 PM, David Cournapeaucourn...@gmail.com
wrote:
On Wed, Jun 27, 2012
On Wed, Jun 27, 2012 at 8:53 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Jun 27, 2012 at 8:29 PM, David Cournapeau courn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 8:07 PM, Nathaniel Smith n...@pobox.com wrote:
On Wed, Jun 27, 2012 at 7:50 PM, David Cournapeau courn...@gmail.com
wrote:
On Wed, Jun 27, 2012 at 8:57 PM, Dag Sverre Seljebotn
d.s.seljeb...@astro.uio.no wrote:
On 06/27/2012 09:53 PM, Nathaniel Smith wrote:
On Wed, Jun 27, 2012 at 8:29 PM, David Cournapeaucourn...@gmail.com wrote:
On Wed, Jun 27, 2012 at 8:07 PM, Nathaniel Smithn...@pobox.com wrote:
On Wed, Jun
Thanks nathaniel, that does tricky...
On Wed, Jun 27, 2012 at 9:25 AM, Nathaniel Smith n...@pobox.com wrote:
On Tue, Jun 26, 2012 at 10:53 PM, John Salvatier
jsalv...@u.washington.edu wrote:
I want to support multiple types in the index_increment function that
I've
written here:
On Wed, Jun 27, 2012 at 1:45 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Tue, Jun 26, 2012 at 11:08 PM, Travis Oliphant tra...@continuum.iowrote:
In my enthusiasm of finding someone to help with the release of NumPy 1.7
and my desire to get something released by the SciPy
On Mon, Jun 18, 2012 at 9:47 AM, Aron Ahmadia a...@ahmadia.net wrote:
f2py, by default, seems to prefer g77 (no longer maintained, deprecated,
speedy, doesn't support Fortran 90 or Fortran 95) over gfortran
(maintained, slower, Fortran 90 and Fortran 95 support).
This causes problems when we
I've promoted gfortran to be the default compiler on OS X over vendor
compilers (to be more compatible with Linux), and made a similar adjustment
for the platform detection. I've promoted gfortran over g77 but not vendor
compilers on the other 'nixes. I left the Windows compiler options alone.
Hi list.
I have got completely cunfused with the numpy.dot() function.
dot(A,B) does:
- matrix multiplication if A and B are of MxN and NxK sizey
- dot product if A and B are of size M
How how can I perform matrix multiplication of two vectors?
(in matlab I do it like a*a')
Thanks.
Petro.
On Wed, Jun 27, 2012 at 4:38 PM, x.pi...@gmail.com wrote:
Hi list.
I have got completely cunfused with the numpy.dot() function.
dot(A,B) does:
- matrix multiplication if A and B are of MxN and NxK sizey
- dot product if A and B are of size M
How how can I perform matrix multiplication of
Hi All,
I am wondering if there any difference in memory overhead between the
following code.
a=numpy.arange(10)
b=numpy.arange(10)
c=a+b
and
a=numpy.arange(10)
b=numpy.arange(10)
c=numpy.empty_likes(a)
c[:]=a+b
Does the later code make a temproray array for the result of (a+b) and then
copy
I've submitted a pull request ( https://github.com/numpy/numpy/pull/326 ).
I'm new to the numpy and python internals, so feedback is greatly
appreciated.
On Tue, Jun 26, 2012 at 12:10 PM, Travis Oliphant tra...@continuum.iowrote:
On Jun 26, 2012, at 1:34 PM, Frédéric Bastien wrote:
Hi,
On Thu, Jun 28, 2012 at 12:38 AM, astronomer shailendra.vi...@gmail.com wrote:
Hi All,
I am wondering if there any difference in memory overhead between the
following code.
a=numpy.arange(10)
b=numpy.arange(10)
c=a+b
and
a=numpy.arange(10)
b=numpy.arange(10)
c=numpy.empty_likes(a)
In the first version this line:
ax.bar3d([i], [0], [i], [.9], [.1], [.9], color='y', linewidth=.1)
is responsible for diagonal in N, and it is inverted.
In the second version you quoted this is corrected with:
ax.bar3d([3-i], [0], [i], [.9], [.1], [.9], color='y', linewidth=.1)
Also
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