matplotlib-devel Mailing List for matplotlib

On 4/5/07, Eric Firing <ef...@ha...> wrote:
> John Hunter wrote:
> [...]
> > As for your specific points:
> >
> > * inside matplotlib we should just use numpy everywhere. We need to
> > agree on some import convention. I'm happy with 'import numpy as nx'
> > though this might be confusing for a while since people might confuse
> > it with the numerix layer. I like nx because numpy is too long, and N
> > occurs too frequently in regular code. I don't like capital
> > letters.... I could see ns too, since that is what we have been using
> > for the numpy extensions when numpy was originally discussed in the
> > context of the scipy core.
>
> ns is not very mnemonic, and I think we should avoid the confusion
> between nx as numerix and nx as numpy, so I suggest "np" for numpy. It
> is mnemonic, and it will make it easier to keep track of the conversion
> process. An alternative would be "nu".
My opinion is that all of N,np,nu,ns,nx look too much like variable
names I commonly use (nx == number of x values, etc). And none of
them looks particularly to me like the name of a big package for doing
numerical work.
So I suggest instead 'npy'. It is already used extensively in the C
API of Numpy so it has strong precedent as an abbreviation. It also
*looks* to me like an abbreviation for 'numpy' as opposed to "number
of points (np)" or something else. Having 3 letters also makes it
slightly less likely to clash with typical short user variable names.
One thing it doesn't have is a nice analogous abbreviation for scipy.
The direct analog would of course be 'spy', but that's obviously out.
Even if pylab.spy didn't exist, it still doesn't look like an
abbreviation for scipy. I guess I like sci for scipy. npy and sci.
--bb

John Hunter wrote:
> * I suppose we should deprecate it for a release, but I'm inclined
> just to push the thing through quickly
+1
You can't do it too fast for me.
 >* when we do the cleanup, we should replace all the 'from numerix
 >import something' with 'import numpy as nx; nx.something'
+1
 > Where possible when cleaning a given module for numerix, we should
 > standardize the other imports. Eg, instead of 'from cbook import
 > iterable' we should do 'import matplotlib.cbook as cbook;
 > cbook.iterable'
+1
all around a good plan.
-Chris
-- 
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Chr...@no...

John Hunter wrote:
[...]
> As for your specific points:
> 
> * inside matplotlib we should just use numpy everywhere. We need to
> agree on some import convention. I'm happy with 'import numpy as nx'
> though this might be confusing for a while since people might confuse
> it with the numerix layer. I like nx because numpy is too long, and N
> occurs too frequently in regular code. I don't like capital
> letters.... I could see ns too, since that is what we have been using
> for the numpy extensions when numpy was originally discussed in the
> context of the scipy core.
ns is not very mnemonic, and I think we should avoid the confusion 
between nx as numerix and nx as numpy, so I suggest "np" for numpy. It 
is mnemonic, and it will make it easier to keep track of the conversion 
process. An alternative would be "nu".
> 
> * I suppose we should deprecate it for a release, but I'm inclined
> just to push the thing through quickly because it is a big change and
> if and when we have energy for it we should just get it done. I'm
> also happy to have some sense talked into me. I suppose one
I won't be the one to talk sense into you! This change is going to take 
a some time, and I would like to be able to get started on it. It 
doesn't have to be done all at once.
> possibility is to deprecate it *now* and push out 0.91 ASAP and then
> immediately pull the old support out. I'll post on user's list to get
If you want to make one more release, I would like to have a few days 
notice to see if I can clear up at least one thing, and maybe a couple more.
> a sense of how many people both need the latest mpl and the older
> array packages. I can't imagine there are too many... I'm sure some
> people need Numeric or numarray, but if they are that curmudgeonly,
> surely they can live on the older mpl branch.
[...]
Eric

On Apr 4, 2007, at 9:41 AM, John Hunter wrote:
> On 4/4/07, Andrew Straw <str...@as...> wrote:
>
>> Do we add deprecation warnings for the 0.90+1 release cycle and then
>> stop building the numarray and numeric numerix backends at some point
>> after that? When? Do we keep the "numerix" name or just switch
>> everything to numpy?
>
> I agree that it is about time to begin preparing for the switch. I
> was talking to Perry the other day about what an irony it was for him
> when he was writing the colormap support in mpl that he had to use all
> the takes and puts after spending so much effort in numarray to get
> more natural indexing and other features. So I think most everyone is
> ready to jettison the old stuff and move forward with the new. I've
> been waiting for the green light from STScI that they are mostly
> finished with their numarray->numpy migration since they have made
> significant contributions to mpl (and numerix) and if I recall
> correctly, I think Perry said they were mostly done, which means we
> should go forward. Perry?
We are done internally for all our released software and have 
propagated these changes to our internal users (just this week as a 
matter of fact). The most convenient time to remove the support for 
numarray for us is when we make a public release of our software. The 
date isn't fixed yet but that would probably be in June sometime. The 
reason it is convenient for us to retain the numarray compatibility 
until then is that we release a bunch of things together that people 
can get as one download; taking numarray support out of mpl before 
then means that people with the existing release will have to install 
numpy if they want to upgrade mpl (and also face some confusion about 
what kind of array object they are dealing with if they use functions 
within mpl that create arrays).
Having said that, I've told John that I hate having held up the date 
that the transition to pure numpy in mpl can be accomplished by, and 
that if he wants to he can go ahead with it. So far he has been very 
kind in waiting for us to finish our transition to numpy. So to 
summarize on our end, the conversion to numpy has been completed and 
tested by our developers, and now is being tested by our 
institutional users, and sometime around June we will release our new 
software. At that point, we have no desire or need to have any 
further numarray option in mpl. I'll leave it to John to decide if he 
wants to go ahead with that conversion in mpl now. The effect on our 
user community probably isn't going to be great. By the time it is 
done in mpl and is available to our community there should only be a 
couple months, at most, where our users will have to deal with the 
issue (and they can either wait to upgrade mpl after we release, or 
deal with the installation/array issues that arise for the relatively 
short duration.
Perry

On 4/4/07, Andrew Straw <str...@as...> wrote:
> Do we add deprecation warnings for the 0.90+1 release cycle and then
> stop building the numarray and numeric numerix backends at some point
> after that? When? Do we keep the "numerix" name or just switch
> everything to numpy?
I agree that it is about time to begin preparing for the switch. I
was talking to Perry the other day about what an irony it was for him
when he was writing the colormap support in mpl that he had to use all
the takes and puts after spending so much effort in numarray to get
more natural indexing and other features. So I think most everyone is
ready to jettison the old stuff and move forward with the new. I've
been waiting for the green light from STScI that they are mostly
finished with their numarray->numpy migration since they have made
significant contributions to mpl (and numerix) and if I recall
correctly, I think Perry said they were mostly done, which means we
should go forward. Perry?
As for your specific points:
 * inside matplotlib we should just use numpy everywhere. We need to
agree on some import convention. I'm happy with 'import numpy as nx'
though this might be confusing for a while since people might confuse
it with the numerix layer. I like nx because numpy is too long, and N
occurs too frequently in regular code. I don't like capital
letters.... I could see ns too, since that is what we have been using
for the numpy extensions when numpy was originally discussed in the
context of the scipy core.
 * I suppose we should deprecate it for a release, but I'm inclined
just to push the thing through quickly because it is a big change and
if and when we have energy for it we should just get it done. I'm
also happy to have some sense talked into me. I suppose one
possibility is to deprecate it *now* and push out 0.91 ASAP and then
immediately pull the old support out. I'll post on user's list to get
a sense of how many people both need the latest mpl and the older
array packages. I can't imagine there are too many... I'm sure some
people need Numeric or numarray, but if they are that curmudgeonly,
surely they can live on the older mpl branch.
 * I would like to see the numerix layer live on, not for use in mpl
but for use outside it for folks who have written a lot of code around
it in external scripts. So people who have done
 from pylab import nx
or
 import matplotlib.numerix as nx
will still have working code. Of course we will lose all the mpl
extensions compiled against the other array packages, but with the
array interface I don't think this will pose a problem for people
using mpl with recent versions of Numeric or numarray
 * when we do the cleanup, we should replace all the 'from numerix
import something' with 'import numpy as nx; nx.something' as above.
Where possible when cleaning a given module for numerix, we should
standardize the other imports. Eg, instead of 'from cbook import
iterable' we should do 'import matplotlib.cbook as cbook;
cbook.iterable' Let's use this convention where we use absolute
imports renamed to relative imports, and qualify all module functions
in the code with the module names.
Anything else?
JDH

I think David Cournapeau's email to the -user list (included below) 
brings up the general issue of whether and how and when we want to go 
about deprecating the use of Numeric and numarray in MPL. Their 
continued inclusion in the core of MPL increases complexity (thereby 
slowing development and making bugs more likely) and limits features by 
introducing a least-common-denominator situation. For example, Eric 
Firing and I recently fixed a bug involving masked arrays being passed 
to quiver() that illustrated this issue. I think it's obvious that 
Travis Oliphant is succeeding (or is that "has succeeded"?) in creating 
the definitive array package for Python and people are crazy if they 
write new code with the older packages. That said, I'm sure there's lots 
of old code not yet ported, but numpy has pretty good (copy-less) 
support for Numeric and numarray arrays, too -- just because they won't 
be in the core of MPL doesn't mean they can't be used.
So, this email is just to ask the questions, not to actually propose 
anything concrete:
Do we add deprecation warnings for the 0.90+1 release cycle and then 
stop building the numarray and numeric numerix backends at some point 
after that? When? Do we keep the "numerix" name or just switch 
everything to numpy?
-Andrew
David Cournapeau wrote:
> Hi there,
>
> A few months back, I complained about the slowness of the image 
> function in matplotlib. One of the cullprit was a slow clip function; 
> I've done a bit some work to improve the situation on numpy's side, 
> efforts which were integrated in numpy 1.0.2. Now, when you clip a numpy 
> array with scalar min and max values, you get a 5 to 30 fold speed-up; 
> to get the maximum efficiency, you need inplace clipping (using the 
> syntax a.clip(min, max, a) for a a numpy array). This makes image 
> significantly faster (between 100 and 200 ms on recent computers), and I 
> am sure in other functionalities of matplotlib as well.
> cheers,
>
> David
>
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