On 02/08/2012 09:31 PM, teomat wrote:
Hi,
Am I wrong or the numpy.arange() function is not correct 100%?
Try to do this:
In [7]: len(np.arange(3.1, 4.9, 0.1))
Out[7]: 18
In [8]: len(np.arange(8.1, 9.9, 0.1))
Out[8]: 19
I would expect the same result for each command.
Not after more
On 02/09/2012 09:20 AM, Drew Frank wrote:
Eric Firingefiringat hawaii.edu writes:
On 02/08/2012 09:31 PM, teomat wrote:
Hi,
Am I wrong or the numpy.arange() function is not correct 100%?
Try to do this:
In [7]: len(np.arange(3.1, 4.9, 0.1))
Out[7]: 18
In [8]: len(np.arange(8.1,
On 02/11/2012 10:44 AM, Travis Oliphant wrote:
This is good feedback.
It looks like there are 2 concerns:
1) no way to add attachments --- it would seem that gists and indeed
other github repos solves that problem.
Not really, in practice. Yes one can use these mechanisms, but they are
On 02/13/2012 08:07 PM, Charles R Harris wrote:
Let it go, Travis. It's a waste of time.
(Off-list) Chuck, I really appreciate your consistent good sense; this
is just one of many examples. Thank you for all your numpy work.
Eric
___
On 02/15/2012 08:50 AM, Matthew Brett wrote:
Hi,
On Wed, Feb 15, 2012 at 5:51 AM, Alan G Isaacalan.is...@gmail.com wrote:
On 2/14/2012 10:07 PM, Bruce Southey wrote:
The one thing that gets over looked here is that there is a huge
diversity of users with very different skill levels. But
On 02/17/2012 05:39 AM, Charles R Harris wrote:
On Fri, Feb 17, 2012 at 8:01 AM, David Cournapeau courn...@gmail.com
mailto:courn...@gmail.com wrote:
Hi Travis,
On Thu, Feb 16, 2012 at 10:39 PM, Travis Oliphant
tra...@continuum.io mailto:tra...@continuum.io wrote:
Mark
On 02/17/2012 09:55 PM, David Cournapeau wrote:
I may not have explained it very well: my whole point is that we don't
recruite people, where I understand recruit as hiring full time,
profesional programmers.We need more people who can casually spend a few
hours - typically grad students,
On 02/18/2012 05:52 AM, Chao YUE wrote:
Dear all,
I built a new empty masked array:
In [91]: a=np.ma.empty((2,5))
Of course this only makes sense if you are going to immediately populate
the array.
In [92]: a
Out[92]:
masked_array(data =
[[ 1.20569155e-312 3.34730819e-316
On 03/07/2012 09:26 AM, Nathaniel Smith wrote:
On Wed, Mar 7, 2012 at 5:17 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Mar 7, 2012 at 9:35 AM, Pierre Haessigpierre.haes...@crans.org
Coming back to Travis proposition bit-pattern approaches to missing
data (*at least* for
On 03/07/2012 11:15 AM, Pierre Haessig wrote:
Hi,
Le 07/03/2012 20:57, Eric Firing a écrit :
In other words, good low-level support for numpy.ma functionality?
Coming back to *existing* ma support, I was just wondering whether it
was now possible to np.save a masked array.
(I'm using numpy
On 03/25/2012 06:55 AM, Pierre Haessig wrote:
Hi,
I have an off topic but somehow related question :
Le 19/03/2012 12:04, Matthieu Rigal a écrit :
array = numpy.logical_and(numpy.logical_and(aBlueChannel 1.0, aNirChannel
(aBlueChannel * 1.0)), aNirChannel (aBlueChannel * 1.8))
Is there
On 03/25/2012 12:22 PM, Pierre Haessig wrote:
Hi Eric,
Thanks for the hints !
Le 25/03/2012 20:33, Eric Firing a écrit :
Using the bitwise operators in place of logical operators is a hack to
get around limitations of the language; but, if done carefully, it is a
useful one.
What
On 04/09/2012 06:52 PM, Travis Oliphant wrote:
Hey all,
I've been waiting for Mark Wiebe to arrive in Austin where he will
spend several weeks, but I also know that masked arrays will be only
one of the things he and I are hoping to make head-way on while he is
in Austin.Nevertheless, we
On 04/17/2012 08:40 AM, Matthew Brett wrote:
Hi,
On Tue, Apr 17, 2012 at 7:24 AM, Nathaniel Smithn...@pobox.com wrote:
On Tue, Apr 17, 2012 at 5:59 AM, Matthew Brettmatthew.br...@gmail.com
wrote:
Hi,
On Mon, Apr 16, 2012 at 8:40 PM, Travis Oliphanttra...@continuum.io
wrote:
Mark and
On 05/14/2012 06:03 PM, Travis Oliphant wrote:
What happens, though when you have
a[:, in1 :, in2]?
in1 and in2 are broadcasted together to create a two-dimensional
sub-space that must fit somewhere. Where should it go? Should
it replace in1 or in2?I.e. should the output be
On 2012/09/18 7:40 AM, Benjamin Root wrote:
On Fri, Sep 7, 2012 at 12:05 PM, Nathaniel Smith n...@pobox.com
mailto:n...@pobox.com wrote:
On 7 Sep 2012 14:38, Benjamin Root ben.r...@ou.edu
mailto:ben.r...@ou.edu wrote:
An issue just reported on the matplotlib-users list
On 2012/09/18 9:25 AM, Charles R Harris wrote:
On Tue, Sep 18, 2012 at 1:13 PM, Benjamin Root ben.r...@ou.edu
mailto:ben.r...@ou.edu wrote:
On Tue, Sep 18, 2012 at 2:47 PM, Charles R Harris
charlesr.har...@gmail.com mailto:charlesr.har...@gmail.com wrote:
On Tue, Sep
On 2012/09/21 12:20 PM, Nathaniel Smith wrote:
On Fri, Sep 21, 2012 at 10:04 PM, Chris Barker chris.bar...@noaa.gov wrote:
On Fri, Sep 21, 2012 at 10:03 AM, Nathaniel Smith n...@pobox.com wrote:
You're right of course. What I meant is that
a += b
should produce the same result as
On 2013/01/13 7:27 AM, Nathaniel Smith wrote:
Hi all,
PR 2875 adds two new functions, that generalize zeros(), ones(),
zeros_like(), ones_like(), by simply taking an arbitrary fill value:
https://github.com/numpy/numpy/pull/2875
So
np.ones((10, 10))
is the same as
np.filled((10,
On 2013/01/14 6:15 AM, Olivier Delalleau wrote:
- I agree the name collision with np.ma.filled is a problem. I have no
better suggestion though at this point.
How about initialized()?
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On 2013/01/17 4:13 AM, Pierre Haessig wrote:
Hi,
Le 14/01/2013 20:05, Benjamin Root a écrit :
I do like the way you are thinking in terms of the broadcasting
semantics, but I wonder if that is a bit awkward. What I mean is, if
one were to use broadcasting semantics for creating an array,
On 2013/03/04 9:01 PM, Nicolas Rougier wrote:
This made me think of a serious performance limitation of structured
dtypes: a
structured dtype is always packed, which may lead to terrible byte
alignment
for common types. For instance, `dtype([('a', 'u1'), ('b',
'u8')]).itemsize == 9`,
On 2013/03/05 8:14 AM, Kurt Smith wrote:
On Tue, Mar 5, 2013 at 1:45 AM, Eric Firing efir...@hawaii.edu wrote:
On 2013/03/04 9:01 PM, Nicolas Rougier wrote:
This made me think of a serious performance limitation of structured
dtypes: a
structured dtype is always packed, which may lead
On 2013/06/04 2:05 PM, Charles R Harris wrote:
On Tue, Jun 4, 2013 at 12:07 PM, Slavin, Jonathan
jsla...@cfa.harvard.edu mailto:jsla...@cfa.harvard.edu wrote:
Hi,
I would like to suggest that the behavior of numpy.interp be changed
regarding treatment of situations in which
think might reasonably be an option but that should not be
required.
Eric
I have been bitten by this problem too.
Cheers!
Ben Root
On Jun 4, 2013 9:08 PM, Eric Firing efir...@hawaii.edu
mailto:efir...@hawaii.edu wrote:
On 2013/06/04 2:05 PM, Charles R Harris wrote:
On Tue
On 2013/06/10 10:17 AM, Aldcroft, Thomas wrote:
I use np.ma http://np.ma, and for me the most intuitive would be the
second option where the new array matches the original array in shape
and dtype, but always has an empty mask. I always think of the *_like()
functions as just copying shape
On 2013/06/12 2:10 AM, Nathaniel Smith wrote:
Hi all,
It looks like we've gotten a bit confused and need to untangle
something. There's a PR to add new functions 'np.filled' and
'np.filled_like':
https://github.com/numpy/numpy/pull/2875
And there was a discussion about this on the list
On 2013/06/12 4:18 AM, Nathaniel Smith wrote:
Now imagine a different new version of this page, if we overload
'empty' to add a fill= option. I don't even know how we document that
on this page. The list will remain:
empty
ones
zeros
Opposite of empty: full. So that is another
On 2013/06/12 8:13 AM, Warren Weckesser wrote:
That's why I suggested 'filledwith' (add the underscore if you like).
This also allows a corresponding masked implementation, 'ma.filledwith',
without clobbering the existing 'ma.filled'.
Consensus on np.filled? absolutely not, you do not have a
On 2013/06/13 10:36 AM, Benjamin Root wrote:
On Thu, Jun 13, 2013 at 9:36 AM, Aldcroft, Thomas
aldcr...@head.cfa.harvard.edu mailto:aldcr...@head.cfa.harvard.edu
wrote:
On Wed, Jun 12, 2013 at 2:55 PM, Eric Firing efir...@hawaii.edu
mailto:efir...@hawaii.edu wrote
On 2013/06/14 5:15 AM, Alan G Isaac wrote:
On 6/14/2013 9:27 AM, Aldcroft, Thomas wrote:
If I just saw np.values(..) in some code I would never guess what it is
doing from the name
That suggests np.fromvalues.
But more important than the name I think
is allowing broadcasting of the values,
On 2013/06/14 7:22 AM, Nathaniel Smith wrote:
On Wed, Jun 12, 2013 at 7:43 PM, Eric Firing efir...@hawaii.edu wrote:
On 2013/06/12 2:10 AM, Nathaniel Smith wrote:
Personally I think that overloading np.empty is horribly ugly, will
continue confusing newbies and everyone else indefinitely
A nice summary of the discussions from a year ago is here:
http://www.numpy.org/NA-overview.html
It provides food for thought.
Eric
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On 2013/06/15 6:06 AM, Pierre GM wrote:
On Jun 15, 2013, at 17:35 , Matthew Brett matthew.br...@gmail.com wrote:
Hi,
On Sat, Jun 15, 2013 at 2:51 PM, Sudheer Joseph
sudheer.jos...@yahoo.com wrote:
Thank you very much for this tip.
Is there a typical way to save masked and the rest
What is the preferred strategy for handling bug fix PRs? Initial fix on
master, and then a separate PR to backport to v1.7.x? Or the reverse?
It doesn't look like v1.7.x is being merged into master regularly, so
the matplotlib pattern (fix on maintenance, merge maintenance into
master) seems
Github issues 611, 629, and 2490 are duplicates. 611 included patches
with a test and a fix, both of which were committed long ago, so all
three issues should be closed.
Please see my comment on 2264 as to why that should be closed.
On 1417, please remove the component:numpy.ma label and add
On 2013/08/17 9:49 PM, Sudheer Joseph wrote:
Hi,
I have defined a small function to find the n maximum values
of an array as below. With in it I assign the input array to a second
array and temporarily make the array location after first iteration as
nan. I expected this temporary
On 2013/08/25 2:30 PM, Cera, Tim wrote:
I have done this before, but am now really confused.
Created an array 'day' specifying the 'f' type
In [29]: day
Out[29]: array([ 5., 5.], dtype=float32)
# Have a mask...
In [30]: mask
Out[30]: array([ True, False], dtype=bool)
# So far, so
On 2013/08/25 2:30 PM, Cera, Tim wrote:
I have done this before, but am now really confused.
Created an array 'day' specifying the 'f' type
In [29]: day
Out[29]: array([ 5., 5.], dtype=float32)
# Have a mask...
In [30]: mask
Out[30]: array([ True, False], dtype=bool)
# So far, so
On 2013/09/30 4:05 PM, josef.p...@gmail.com wrote:
On Mon, Sep 30, 2013 at 9:38 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Sep 30, 2013 at 7:05 PM, Ondřej Čertík ondrej.cer...@gmail.com
wrote:
Hi,
What is the rationale for using False in 'mask' for elements that
On 2013/09/30 4:57 PM, Ondřej Čertík wrote:
On Mon, Sep 30, 2013 at 8:29 PM, Eric Firing efir...@hawaii.edu wrote:
On 2013/09/30 4:05 PM, josef.p...@gmail.com wrote:
On Mon, Sep 30, 2013 at 9:38 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Mon, Sep 30, 2013 at 7:05 PM, Ondřej
On 2013/12/05 5:14 PM, Faraz Mirzaei wrote:
Hi,
If I pass a masked array through np.asarray, I get original unmasked array.
Example:
test = np.array([[1, 0], [-1, 3]])
testMasked = ma.masked_less_equal(test, 0)
print testMasked
[[1 --]
[-- 3]]
print testMasked.fill_value
On 2014/03/13 9:09 PM, Sudheer Joseph wrote:
Dear Oslen,
I had a detailed look at the example you send and points I got were below
a = np.arange(-8, 8).reshape((4, 4))
b = ma.masked_array(a, mask=a 0)
Out[33]: b[b4]
masked_array(data = [-- -- -- -- -- -- -- -- 0 1 2 3],
On 2014/03/22 8:13 AM, Nathaniel Smith wrote:
Hi all,
After 88 emails we don't have a conclusion in the other thread (see
[1] for background). But we have to come to some conclusion or another
if we want @ to exist:-). So I'll summarize where the discussion
stands and let's see if we can
On 2014/05/07 2:14 PM, mfm24 wrote:
I'm having a problem I haven't seen elsewhere (and apologies if it has
been answered before).
I see the following behavior (copied verbatim from a python session):
Python 2.7.4 (default, Apr 6 2013, 19:55:15) [MSC v.1500 64 bit (AMD64)] on
win32
Type
On 2014/05/07 11:26 PM, Robert McGibbon wrote:
Hey all,
The travis tests for a library I work on just stopped working, and I
tracked down the bug to the following test case. The file
MDTraj/testing/reference/mdcrd.nc http://mdcrd.nc is a netcdf3 file
in our repository
On 2014/07/06, 11:43 AM, Nathaniel Smith wrote:
On Sun, Jul 6, 2014 at 9:35 PM, Daniel da Silva
var.mail.dan...@gmail.com wrote:
The idea is that there be a short-hand for creating arrays as there is for
matrices:
np.mat('.2 .7 .1; .3 .5 .2; .1 .1 .9')
It was suggested in GitHub issue
On 2014/07/06, 4:27 PM, Alexander Belopolsky wrote:
On Sun, Jul 6, 2014 at 6:06 PM, Eric Firing efir...@hawaii.edu
mailto:efir...@hawaii.edu wrote:
(I'm not entirely convinced
np.arr() is a good idea at all; but if it is, it must be kept simple.)
If you are going to introduce
On 11/14/2010 05:03 AM, Vincent Davis wrote:
On Sun, Nov 14, 2010 at 7:20 AM, Ralf Gommers
ralf.gomm...@googlemail.com wrote:
On Sun, Nov 14, 2010 at 9:16 AM, Vincent Davisvinc...@vincentdavis.net
wrote:
The questions below regard the osx dmg installer, not sure about how
this applies to
On 12/31/2010 06:29 PM, Erik Rigtorp wrote:
Hi,
Implementing moving average, moving std and other functions working
over rolling windows using python for loops are slow. This is a
effective stride trick I learned from Keith Goodman's
kwgood...@gmail.com Bottleneck code but generalized into
On 05/22/2011 08:17 AM, Jeffrey Spencer wrote:
from numpy import arange, sum
for x in range(1000):
inhibVal = sum(arange(15))
Memory usage stays constant with Ubuntu 11.04, 64-bit, using the numpy
1.5.1 package from ubuntu, and using 1.6.1.dev-a265004.
efiring@manini:~$ uname
On 06/17/2011 06:56 AM, Benjamin Root wrote:
It does not appear that unwrap works properly for masked arrays. First,
it uses np.asarray() at the start of the function. However, that alone
would not fix the problem given the nature of how unwrap works
(performing diff operations). I tried a
On 06/20/2011 10:41 AM, Zachary Pincus wrote:
You could try:
src_mono = src_rgb.astype(float).sum(axis=-1) / 3.
But that speed does seem slow. Here are the relevant timings on my machine (a
recent MacBook Pro) for a 3.1-megapixel-size array:
In [16]: a = numpy.empty((2048, 1536, 3),
On 06/23/2011 11:19 AM, Nathaniel Smith wrote:
I'd like to see a statement of what the missing data problem is, and
how this solves it? Because I don't think this is entirely intuitive,
or that everyone necessarily has the same idea.
Reduction operations like 'sum', 'prod', 'min', and 'max'
On 06/25/2011 09:09 AM, Benjamin Root wrote:
On Sat, Jun 25, 2011 at 1:57 PM, Nathaniel Smith n...@pobox.com
mailto:n...@pobox.com wrote:
On Sat, Jun 25, 2011 at 11:50 AM, Eric Firing efir...@hawaii.edu
mailto:efir...@hawaii.edu wrote:
On 06/25/2011 07:05 AM, Nathaniel Smith
On 06/28/2011 07:26 AM, Nathaniel Smith wrote:
On Tue, Jun 28, 2011 at 9:38 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
Nathaniel, an implementation using masks will look *exactly* like an
implementation using na-dtypes from the user's point of view. Except that
taking a masked
On 06/29/2011 09:32 AM, Matthew Brett wrote:
Hi,
[...]
Clearly there are some overlaps between what masked arrays are trying
to achieve and what Rs NA mechanisms are trying to achieve. Are they
really similar enough that they should function using the same API?
And if so, won't that be
On 06/30/2011 08:53 AM, Nathaniel Smith wrote:
On Wed, Jun 29, 2011 at 2:21 PM, Eric Firingefir...@hawaii.edu wrote:
In addition, for new code, the full-blown masked array module may not be
needed. A convenience it adds, however, is the automatic masking of
invalid values:
In [1]:
On 07/01/2011 10:27 AM, Charles R Harris wrote:
On Fri, Jul 1, 2011 at 1:39 PM, Christopher Barker
chris.bar...@noaa.gov mailto:chris.bar...@noaa.gov wrote:
Joe Harrington wrote:
All
that has to happen is to allow the sense of the mask to be FALSE
= the
data
On 07/01/2011 06:40 PM, Nathaniel Smith wrote:
On Fri, Jul 1, 2011 at 9:29 AM, Christopher Jordan-Squire
BTW, you can't access the memory of a masked value by taking a view,
at least if I'm reading this version of the NEP correctly, and it
seems to be the latest:
On 07/06/2011 07:51 PM, Chris Barker wrote:
On 7/6/11 11:57 AM, Mark Wiebe wrote:
On Wed, Jul 6, 2011 at 1:25 PM, Christopher Barker
Is this really true? if you use a bitpattern for IGNORE, haven't you
just lost the ability to get the original value back if you want to stop
On 07/08/2011 01:31 PM, Mark Wiebe wrote:
I've just made pull request 105:
https://github.com/numpy/numpy/pull/105
This adds public API PyArray_MaskedCopyInto and PyArray_MaskedMoveInto,
which behave analogously to the corresponding unmasked functions. To
expose this with a reasonable
On 07/08/2011 01:31 PM, Mark Wiebe wrote:
I've just made pull request 105:
https://github.com/numpy/numpy/pull/105
This adds public API PyArray_MaskedCopyInto and PyArray_MaskedMoveInto,
which behave analogously to the corresponding unmasked functions. To
expose this with a reasonable
On 07/08/2011 01:31 PM, Mark Wiebe wrote:
I've just made pull request 105:
https://github.com/numpy/numpy/pull/105
It's merged, which is good, but I have a suggestion relevant to that
pull and I suspect to many others to come: use defines and macros to
consolidate some of the implementation
On 07/29/2011 11:18 AM, Timo Kluck wrote:
Dear numpy developers,
The current implementation of numpy.interp(x,xp,fp) comes down to: first
calculating all the slopes of the linear interpolant (these are
len(xp)-1), then use a binary search to find where x is in xp (running
time log(len(xp)).
On 08/03/2011 11:24 AM, Gökhan Sever wrote:
I[1]: timeit a = np.fromfile('temp.npa', dtype=np.uint16)
1 loops, best of 3: 263 ms per loop
You need to clear your cache and then run timeit with options -n1 -r1.
Eric
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On 08/16/2011 04:22 AM, Timo Kluck wrote:
2011/8/1 Timo Klucktkl...@infty.nl:
I just submitted a patch at
http://projects.scipy.org/numpy/ticket/1920 . It implements Eric's
suggestion. Please review, I'll be happy to adapt it to any of your
feedback.
I submitted a minor patch a while ago.
On 10/13/2011 12:22 PM, Gökhan Sever wrote:
On Thu, Oct 13, 2011 at 4:15 PM, Benjamin Root ben.r...@ou.edu
mailto:ben.r...@ou.edu wrote:
Myself and other developers would greatly appreciate help from the
community to point out which examples are too confusing or out of
date. We
On 10/23/2011 10:49 AM, Nathaniel Smith wrote:
But I (and presumably others) were unaware of the pull request,
because it turns out that actually Mark did*not* point to the pull
request, at least in email to either me or numpy-discussion. As far as
I can tell, the first time that pull request
On 10/23/2011 12:34 PM, Nathaniel Smith wrote:
like. And in this case I do think we can come up with an API that will
make everyone happy, but that Mark's current API probably can't be
incrementally evolved to become that API.)
No one could object to coming up with an API that makes everyone
On 10/25/2011 04:56 PM, Travis Oliphant wrote:
So, I am very interested in making sure I remember the details of the
counterproposal.What I recall is that you wanted to be able to
differentiate between a bit-pattern mask and a boolean-array mask
in the API. I believe currently even when
On 10/29/2011 12:26 AM, Ralf Gommers wrote:
The history of this discussion doesn't suggest it straightforward to get
a design right first time. It's a complex subject.
The second part of your statement, and then implement, sounds so
simple. The reality is that there are only a handful of
On 10/29/2011 12:02 PM, Olivier Delalleau wrote:
I haven't been following the discussion closely, but wouldn't it be instead:
a.mask[0:2] = True?
That would be consistent with numpy.ma and the opposite of Mark's
implementation.
I can live with either, but I much prefer the numpy.ma version
On 10/29/2011 12:57 PM, Charles R Harris wrote:
On Sat, Oct 29, 2011 at 4:47 PM, Eric Firing efir...@hawaii.edu
mailto:efir...@hawaii.edu wrote:
On 10/29/2011 12:02 PM, Olivier Delalleau wrote:
I haven't been following the discussion closely, but wouldn't
David Cournapeau wrote:
Hi,
When trying to speed up some matplotlib routines with the matplotlib
dev team, I noticed that numpy.clip is pretty slow: clip(data, m, M) is
slower than a direct numpy implementation (that is data[datam] = m;
data[dataM] = M; return data.copy()). My
David,
I think my earlier post got lost in the exchange between you and Stefan,
so I will reiterate the central point: numpy.clip *is* slow, in that an
implementation using putmask is substantially faster:
def fastclip(a, vmin, vmax):
a = a.copy()
putmask(a, a=vmin, vmin)
David Cournapeau wrote:
Eric Firing wrote:
David,
I think my earlier post got lost in the exchange between you and Stefan,
so I will reiterate the central point: numpy.clip *is* slow, in that an
implementation using putmask is substantially faster:
def fastclip(a, vmin, vmax
John,
The current version of __call__ already includes substantial speedups
prompted by David's profiling, and if I understand correctly the present
bottleneck is actually the numpy take function. That is not to say that
other improvements can't be made, of course.
Eric
John Hunter wrote:
belinda thom wrote:
Eric,
Thanks for the well-thought-out answers to some of my recent posts.
I've been using:
http://pythonmac.org/packages/py24-fat/index.html
for installing scipy, numpy, and matplotlib, as I didn't feel as
confident installing things manually.
Should I be
Pierre GM wrote:
All,
I've updated this famous reimplementation of maskedarray I keep ranting about.
[...]
I also put the file `timer_comparison.py`, that runs some unittests with each
implementation
(numpy.core.ma and maskedarray), and outputs the minimum times.
On my machine, there
I have been using Jeff Whitaker's netcdf4 interface with good results.
I could not find the web page for it on a NOAA site--I think NOAA is
reorganizing--but a search turned it up here. Maybe Jeff can provide a
better link.
Sebastian Haase wrote:
On 3/22/07, Stefan van der Walt [EMAIL PROTECTED] wrote:
On Thu, Mar 22, 2007 at 08:13:22PM -0400, Brian Blais wrote:
Hello,
I'd like to concatenate a couple of 1D arrays to make it a 2D array, with
two columns
(one for each of the original 1D arrays). I thought
Travis and others,
In the course of trying to understand memory leaks in matplotlib I have
been trying to understand a bit about the garbage collector. If I
understand correctly, any container that can can hold references to
other containers could lead to a reference cycle; if the container
Travis Oliphant wrote:
[...]
I'm inclined to move his masked array over to ma wholesale. The fact
that Pierre sees it as his baby is very important to me. If it doesn't
have significant compatibility issues than I'm all for it. I'm mainly
interested in hearing how people actually using
: switching may have some subtle consequences in
matplotlib (nothing that can't be quickly fiexed, however). What do Eric
Firing, John Hunter and the other mpl developer think ?
I think this would be a good time to make the switch. We are going to
be stripping out the Numeric and numarray support, so
Robert Kern wrote:
Geoffrey Zhu wrote:
Hi Everyone,
I am finding that numpy cannot operate on boolean arrays. For example,
the following does not work:
x=3Darray([(1,2),(2,1),(3,1),(4,1)])
x[x[:,0]x[:,1] and x[1:]1,:]
It gives me an syntax error:
---
Traceback (most
Ludwig,
Masked arrays will do exactly what you want. You have your choice of
the numpy.ma version or the external maskedarray class.
Eric
Ludwig M Brinckmann wrote:
Hi there,
I have a 2D array of size, lets say 4 * 512, which I need to
downsample by a step of 4 in the y direction,
Ludwig M Brinckmann wrote:
This is a follow-up to an earlier mail that reported a suspected bug in
the reduce/minimum operation of numpy.ma http://numpy.ma.
I have tried the same code with the scipy sandbox maskedarray
implementation and that gives me the correct output. For comparison:
be that the P module (short for pylab) would only
contain the stuff described in the __doc__ strings of `pylab.py` and
`__init__.py`(in matplotlib) (+ plus some extra, undocumented, yet
pylab specific things)
Thanks
-Sebastian
On 3/16/07, Eric Firing [EMAIL PROTECTED] wrote:
Sebastian Haase wrote
In looking at maskedarray performance, I found that the filled()
function or method is a bottleneck. I think it can be sped up by using
putmask instead of indexed assignment, but I found that putmask itself
is slower than it needs to be. So I followed David Cournapeau's example
of fastclip
David Cournapeau wrote:
On 8/17/07, Eric Firing [EMAIL PROTECTED] wrote:
In looking at maskedarray performance, I found that the filled()
function or method is a bottleneck. I think it can be sped up by using
putmask instead of indexed assignment, but I found that putmask itself
is slower than
David M. Cooke wrote:
On Thu, Aug 16, 2007 at 04:39:02PM -1000, Eric Firing wrote:
As far as I can see there is no way of using svn diff to deal with
this automatically, so in the attached revision I have manually removed
chunks resulting solely from whitespace.
Is there a better way
David M. Cooke wrote:
On Thu, Aug 16, 2007 at 04:39:02PM -1000, Eric Firing wrote:
As far as I can see there is no way of using svn diff to deal with
this automatically, so in the attached revision I have manually removed
chunks resulting solely from whitespace.
Is there a better way
Pierre GM wrote:
On Saturday 25 August 2007 12:50:38 Eric Firing wrote:
Alexander Michael wrote:
Is there any documentation available for your maskedarray?
Pierre wrote some notes about maskedarray here:
http://projects.scipy.org/scipy/numpy/wiki/MaskedArray
starting half-way down the page
Timothy Hochberg wrote:
On 8/29/07, *Charles R Harris* [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
I still don't see why the method is needed at all. Given the
conditions on the array, the only thing it buys you over the resize
function or a reshape is the automatic
stefan wrote:
On Tue, 18 Sep 2007 13:07:29 +0200, Gael Varoquaux
[EMAIL PROTECTED] wrote:
On Tue, Sep 18, 2007 at 10:33:29AM -, mark wrote:
Does that make sense? I know, I should probably use a.min() rather
than min(a), but why does min() not get imported on an import * ?
Because min
Stuart Brorson wrote:
On Fri, 21 Sep 2007, Robert Kern wrote:
Stuart Brorson wrote:
Is it NumPy's goal to be as compatible with Matlab as possible?
No.
OK, so that's fair enough. But how about self-consistency?
I was thinking about this issue as I was biking home this evening.
To review
A quick scan of the tickets did not show me anything like the following,
but I might have missed it. The attached script generates a segfault on
my ubuntu feisty system with svn numpy. Running inside of ipython, the
segfault occurs upon exiting ipython, not upon running the script.
Running
Stefan,
Ticket #607 should be closed now also. It looks like I can't do that,
even though I created the ticket.
I'm not sure whether it was the fix for #614 that did it, or whether it
is the code it referred to, but now a proper exception is raised instead
of a segfault.
Eric
Stefan van
Stefan,
I think the description of the putmask difference is missing the point.
The real difference is not in the way the third argument is handled,
or its required shape, but in whether the mask is updated or not.
numpy.ma.putmask updates the mask; that is, if it puts something into
the
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