Re: [matplotlib-devel] [Matplotlib-users] License, freetype

[Paul Hobson]

Chad,

My recollections is that matplotlib doesn't distribute the source code to
FreeType, it only uses it as a dependency. As such, MPL is in the clear
with its more permissive licensing.
-Paul

On Fri, Feb 17, 2017 at 12:45 PM, CAB  wrote:

> Hi, All,
>
> I just went to install matplotlib version 2.0.0, and it has a dependency
> called "freetype". This software appears to be licensed under GPL3.  My
> reading of that latter license is that, if someone wanted to distribute a
> compiled version of a program requiring matplotlib, that entire program
> would fall under the GPL3 license.  I'm sure that would be a non-starter
> for many, many projects.
>
> Does anyone have any takes on this?
>
> Chad
>
> 
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Re: [matplotlib-devel] PyQt4 in Matplotlib v2.0.0

[Paul Hobson]

It appears that the build of matplotlib in conda-forge does not require
pyqt5:
https://github.com/conda-forge/matplotlib-feedstock/blob/master/recipe/meta.yaml

So in your case, I would do:

conda remove pyqt5 matplotlib
conda install --channel=conda-forge matplotlib

On Thu, Feb 2, 2017 at 8:48 AM, Benjamin Root  wrote:

> no, pyqt5 is not a dependency for matplotlib. this looks like a conda
> packaging issue.
>
> Side note: the sourceforge mailing list is deprecated. Please use the
> python.org version instead (you'll have to re-register).
>
> Ben Root
>
>
> On Thu, Feb 2, 2017 at 11:43 AM, Osborn, Raymond  wrote:
>
>> I just created a new conda environment with Matplotlib included, and it
>> duly installed matplotlib: 2.0.0-np111py27_0. It installed PyQt5 by
>> default, whereas I need PyQt4, but when I installed PyQt4 (conda install
>> pyqt=4), it automatically downgraded Matplotlib to 1.5.1.
>> 
>> The following packages will be DOWNGRADED due to dependency conflicts:
>>
>> matplotlib: 2.0.0-np111py27_0 --> 1.5.1-np111py27_0
>> pyqt:   5.6.0-py27_2  --> 4.11.4-py27_4
>> qt: 5.6.2-0   --> 4.8.7-4
>> 
>> Is PyQt5 really a dependency for Matplotlib v2? I have been testing
>> release candidate versions of Matplotlib v2 without any problem. This will
>> cause headaches to my users who want the latest features.
>>
>> Ray
>> --
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>> Materials Science Division
>> Argonne National Laboratory
>> Argonne, IL 60439, USA
>> Phone: +1 (630) 252-9011 <(630)%20252-9011>
>> Email: rosb...@anl.gov
>>
>>
>>
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Re: [matplotlib-devel] Heatmap for 7K X 7K matrix

[Paul Hobson]

Madhav,

Do I understand correctly that you want to generate a 14k x 14k image in
matplotlib? Are there computer monitors available that can display such an
image in native quality?
-p

On Thu, Aug 25, 2016 at 11:59 PM, Jens Nielsen 
wrote:

> Hi Madhav
>
> We have moved our mailing lists off source forge to python.org I
> recommend that you use one of the mailing lists here see the readme
> https://github.com/matplotlib/matplotlib/blob/master/README.rst this type
> of question is probably most suited for the user list rather than the
> developer list.
>
> Did you recently find a link to the old one? In which case we would like
> to know where so we can update the reference.
>
> Back to your question, If I understand correctly I think what you are
> looking for is a data cursor that can show you information when you click
> on a specific point.
>
> https://pypi.python.org/pypi/mpldatacursor/ is one such package.
>
> Best Jens
> On Thu, 25 Aug 2016 at 21:57, Madhav Sharan  wrote:
>
>> *Sending again after joining list*
>>
>> Hi matplotlib users,
>>
>> I am trying to generate a fairly huge heatmap of a 7000 X 7000. This is a
>> 2D matrix in which each cell i,j have a similarity score between ith and
>> jth label.
>>
>> PFA my first attempt (code - [0]).
>>
>> Now I am looking for better visualization which can also tell me more
>> about *cell level information*. To start with can I create a higher
>> resolution image such that I have at least 2X2 pixel for every cell.
>> Resultant should be of resolution 14000 X 14000
>>
>> Are there more features like- By some user interaction can I know what
>> pair it represent? and what's the similarity score?
>>
>> [0] https://github.com/smadha/hadoop-pot/blob/cartesian/src/
>> main/bin/similarity_heatmap.py
>>
>> --
>> Madhav Sharan
>>
>>
>>
>> 
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Re: [matplotlib-devel] RFC: candidates for a new default colormap

[Paul Hobson]

A brief poll of my office gave
3 A's and a B.

One of the A's came from someone who can't remember their distinct flavor
of color blindness, but definitely gets tripped up by reds and greens.
-p

On Wed, Jun 3, 2015 at 1:29 PM, Arnd Baecker arnd.baec...@web.de wrote:

 In our group I also recieved quite mixed responses:
 - C B A   (2 x)
 - B A C
 - A B C
 - C
 - B

 One collegue having anomalous color vision
 (something between protanomaly and protanopia)
 called *all* three versions harsh to his eye (like looking into a cars
 lights at night) and rather unpleasant.
 He considered C as the least unpleasant, but not that easy to look at.

 Moreover, he stated that,  the parula may be flawed, but at least it
 doesn’t make one want to look away immediately.

 Best, Arnd




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Re: [matplotlib-devel] RFC: candidates for a new default colormap

[Paul Hobson]

I'm really digging option D too -- it has the bonus of being unambiguously
distinct from GNUPlot,


On Wed, Jun 3, 2015 at 5:29 PM, Benjamin Root ben.r...@ou.edu wrote:

 May I suggest an update to the code showing the 3d sRGB colorspace? Can
 you add a shade=False to it? Currently, in pycam02ucs.viscm.py, around
 line 279, it calls the 3d scatter function without the kwarg. This means
 that mplot3d will apply an alpha transparancy to dots that are farther away
 to give the perception of depth. Since we actually want to see the correct
 color, we probably shouldn't have that feature on.

 Ben Root


 On Wed, Jun 3, 2015 at 8:17 PM, Stéfan van der Walt ste...@sun.ac.za
 wrote:

 On Wed, Jun 3, 2015 at 5:08 PM, Nathan Goldbaum nathan12...@gmail.com
 wrote:
  I'm a big fan of option D.  So much so that when I needed to make a
 movie of
  ony my galaxy simulations today I went ahead and used it:
 
  https://youtu.be/bnm554et0T8

 Beautiful!  How hard would it be to also do this for the other
 proposed colormaps?

 Stéfan


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Re: [matplotlib-devel] RFC: candidates for a new default colormap

[Paul Hobson]

Just want to chime in and say that they colorblind versions of the maps are
pretty nice too. Can those be made available?

It also occurs to me that these are pretty similar to the existing colormap
GNUPlot. I don't know if that's good or bad, but something to keep in mind
if the desire is for matplotlib to standout away from other plotting
packages.
-p

On Wed, Jun 3, 2015 at 8:38 AM, Benjamin Root ben.r...@ou.edu wrote:

 One of the big advantage of jet as evidenced by these graphs is that for
 most of its range, the perceptual delta is above 200 (although it loses
 that advantage in blackwhite). Parula sacrafices a fair amount of
 perceptual delta, but stays mostly above 100. All of the options beat or
 matches Parula in this respect overall, even in BW mode.

 However, I wonder just how much should we hold fast to a constant
 perceptual delta? As we see with grayscale, perceptual delta is not
 constant with respect to luminosity. Keep in mind that our perceptual
 delta measure is just a model, and I don't think it properly takes into
 account luminosity. So, perhaps it might make sense to be a little bit
 flexible with perceptual delta (maybe something like an exponental decay).
 Nothing jerky like Parula or Jet, but something to help us out on the ends
 of the map?

 By the way, I have seen Parula in action for the display of water vapor
 over Africa, and it looks very nice. Perhaps a real-world example image
 might be some sort of geographical map of something familiar across all
 disciplines like a terrain map of a continent?

 Ben Root

 On Wed, Jun 3, 2015 at 9:55 AM, Paul Ganssle pgans...@gmail.com wrote:


 -BEGIN PGP SIGNED MESSAGE-
 Hash: SHA1

 I'm also in the B  A  C camp, FWIW. I agree with OceanWolf in that B
 looks most professional. It looks much crisper than the others as well.

 On 6/3/2015 08:50, Tony Yu wrote:
  It doesn't sound like this is going to be decided by email votes, but
 just so the arguments for C don't dominate, my vote would be:
 
  B  A  C
 
  C has the least perceptual range (that's quantifiable, right?). Also, I
 find A and B much more aesthetically pleasing (that's obviously debatable).
 In particular, the yellows and blues in C have a slight visual vibration.
 Actually, if you google visual vibration, one of the first hits is a
 yellow and violet image
 https://web.njit.edu/~mmp57/visual%20vibration.jpg
 https://web.njit.edu/~mmp57/visual%20vibration.jpg. B would have this
 to a certain extent, but it's much more problematic if those colors are at
 the limits of the colormap range. It looks like A wouldn't have this
 problem at all since it's white point has a very muted yellow tone, so
 maybe I'll switch my vote to A. (Personally, it's a toss up between the
 two; anything but C, if I haven't made myself clear ;)
 
  Thanks to Nathaniel and Stéfan for putting this together! Hopefully
 jet can be banished soon :)
 
  -Tony
 
  On Wed, Jun 3, 2015 at 5:20 AM, OceanWolf 
 juichenieder-n...@yahoo.co.uk mailto:juichenieder-n...@yahoo.co.uk
 juichenieder-n...@yahoo.co.uk wrote:
 
  Personally, just looking at the images I think B looks more
  professional, the others look faded.  With A and B I see more of
  contrast in the core of the radial image (though that might arise
 from
  a combination of my monitor/eyes, though I usually do quite well in
  colour perception tests).
 
  I think we really need to see a variety of real examples before we
 make
  a decision though, both in application a.k.a different type of
 datasets,
  including ones with NaNs; and different graph types, the 3d example
 will
  make for a good test as we get the same information twice, from
 height
  and colour, which gives us a reference for comparison.
 
  With the NaNs Andreas, why did you pick B over C?  My eyes see B
 going
  to white as well, only C as far as I can tell does not go to white.
 
  Looking forward to having a play later :).  I wonder what
 Parula-based
  colormap would look like if we were to make it linear... one other
  thing, mpl currently doesn't select good bounds with pure
  horizontal/vertical lines, making it very difficult (at least for
 me) to
  see the perceptual deltas, zoomed in to option_c the line gets
  completely hidden by the axes...
 
  On 03/06/15 09:04, Andreas Hilboll wrote:
   On 03.06.2015 08:54, Juan Nunez-Iglesias wrote:
   You can always use green for NaN with any of these maps...
   In grayscale that then wouldn't be distinguishable at all ...
  
   On Wed, Jun 3, 2015 at 4:30 PM, Andreas Hilboll 
 li...@hilboll.de mailto:li...@hilboll.de li...@hilboll.de
   mailto:li...@hilboll.de li...@hilboll.de
 mailto:li...@hilboll.de li...@hilboll.de wrote:
  
 I particularly like that A ends on the white end of the
 spectrum
  
That's exactly why I don't like A that much.
  
In many plots, I need a color 

Re: [matplotlib-devel] RFC: candidates for a new default colormap

[Paul Hobson]

Sorry for send you two emails, Nathaniel.

I'm going to vote for A with C as a close second.

Of the three, B looks the most bandy to me (but not overly so).
-p

On Tue, Jun 2, 2015 at 6:46 PM, Nathaniel Smith n...@pobox.com wrote:

 Hi all,

 As was hinted at in a previous thread, Stéfan van der Walt and I have
 been using some Fancy Color Technology to attempt to design a new
 colormap intended to become matplotlib's new default. (Down with jet!)

 Unfortunately, while our Fancy Color Technology includes a
 computational model of perceptual distance, it does not include a
 computational model of aesthetics. So this is where you come in.

 We've put up three reasonable candidates at:
 https://bids.github.io/colormap/
 (along with some well-known colormaps for comparison), and we'd like
 your feedback.

 They are all optimal on all of the objective criteria we know how to
 measure. What we need judgements on is which one you like best, both
 aesthetically and as a way of visualizing data. (There are some sample
 plots to look at there, plus you can download them and play with them
 on your own data if you want.)

 We especially value input from anyone with anomalous color vision.
 There are some simulations there, but computational models are
 inherently limited here. (It's difficult to ask someone with
 colorblindness does this look to you, the same way this other picture
 looks to me?)

 -n

 --
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Re: [matplotlib-devel] Easy problem working with dev library

[Paul Hobson]

After you've setup your development environment with all of the MPL
dependencies, navigate to the MPL source directory and install it with:

$ python setup.py develop

or

$ pip install -e .

That'll create a link in site-packages (or whatever that directory is) to
the source directory. After you make changes to the source code, you'll
either need to use the imp module to reload MPL and the submodule you
changed. I typically find it easier to just restart my python interpreter.
-Paul

On Sat, May 16, 2015 at 1:45 PM, Neil Girdhar mistersh...@gmail.com wrote:

 How do I set it up so that I can import my local matplotlib dev copy?

 I tried making a sym-link to matplotlib/lib/matplotlib, but it's giving me
 errors:

 import matplotlib.transforms as mtransforms
 AttributeError: 'module' object has no attribute 'transforms'

 Thanks,

 Neil


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Re: [matplotlib-devel] Fwd: SciPy 2015 CFP Email 2

[Paul Hobson]

Joe,




You should introduce yourself as that guy who did that paw detection post that 
saved that one guy's research. 

-P



—
Sent from Mailbox

On Mon, Mar 30, 2015 at 4:52 PM, Thomas Caswell tcasw...@gmail.com
wrote:

 +1 from me.  I suspect many people got their start learning mpl from you on
 SO ;)
 Tom
 On Mon, Mar 30, 2015 at 7:17 PM Joe Kington joferking...@gmail.com wrote:
 If you don't mind a non-core person doing the tutorial, I'll be there
 this year, and I'd be happy to be Ben's backup for teaching it.
 Cheers!
 -Joe

 On Sun, Mar 29, 2015 at 9:17 PM, Thomas Caswell tcasw...@gmail.com
 wrote:

 Ben,

 Have you sorted out if you can make scipy this year and does anyone want
 to be back up on teaching the tutorial?

 It seems a shame to not have a mpl tutorial available.

 I am probably going to submit a 'state of the library' talk and do not
 want to do both.

 Tom

 On Thu, Mar 26, 2015 at 5:06 PM Michael Droettboom md...@stsci.edu
 wrote:

  This sounds great.  Unfortunately, I can't attend Scipy this year due
 to a family commitment, but would be more than happy to help put together
 and review materials beforehand.

 Cheers,
 Mike


 On 03/26/2015 10:59 AM, Thomas Caswell wrote:


  I also think we should have a 'state of the library' talk.

 We definitely have a few important things to announce/show off:
   - FSA
   - nbagg/notebook
   - new default colors
   - style module

  and should have a couple more by July
   - sane serialize/deserialize + interop with plotly/bokeh
   - better toolbar
   - better interactive OO
   - improved docs

  I will be there for the main conference and the sprints and am willing
 to give this talk, but will defer if someone else wants to do it.

  Does anyone want to volunteer to be Ben's second on his tutorial?



 On Fri, Mar 13, 2015 at 2:46 PM Olga Botvinnik obotv...@ucsd.edu
 wrote:

 I'd be very interested in hearing a state of matplotlib talk.

 On Fri, Mar 13, 2015, 11:29 Phil Elson pelson@gmail.com wrote:

 Orchestrating MPL tutorials and talks in this thread would be a good
 idea. I'd be happy to help anybody planning on submitting anything 
 relating
 specifically to matplotlib, and wonder if we should do a state of
 matplotlib type talk similar to the one Mike did 2 years ago.

 On 13 March 2015 at 02:05, Benjamin Root ben.r...@ou.edu wrote:

  Yes, I plan to submit my time-honored, and requested Anatomy of
 Matplotlib tutorial. Now, I am not entirely sure I will be able to 
 attend
 the conference this year, so perhaps someone else might be willing to 
 step
 in and give it this year?

  Note that my tutorial is geared for beginners. So there is still
 plenty of opportunity for someone else to submit a tutorial for more
 advanced users!

  Cheers!
  Ben Root

  On Thu, Mar 12, 2015 at 6:46 PM, Nelle Varoquaux 
 nelle.varoqu...@gmail.com wrote:

  Hi everyone,

  Is someone submitting a tutorial on matplotlib? The call for
 tutorial is open, and I think it would be nice to have one on 
 matplotlib.

  Cheers,
 N

 -- Forwarded message --
 From: SciPy 2015 Organizers scipy-organiz...@scipy.org
 Date: 11 March 2015 at 01:02
 Subject: SciPy 2015 CFP Email 2
 To: nelle.varoqu...@gmail.com


[image: SciPy 2015 Logo]
 https://www.eiseverywhere.com/emarketing/go.php?i=182077e=bmVsbGUudmFyb3F1YXV4QGdtYWlsLmNvbQ==l=http://scipy2015.scipy.org/ehome/index.php%7CQ%7Ceventid%7CE%7C115969%7CA%7C

Tick-Tock, Tick-Tock:
 T-Minus 6 Days for Tutorial Submissions
  *Due Date: March 16, 2015*

 The SciPy experience kicks off with two days of tutorials
 https://www.eiseverywhere.com/emarketing/go.php?i=182077e=bmVsbGUudmFyb3F1YXV4QGdtYWlsLmNvbQ==l=http://scipy2015.scipy.org/ehome/115969/259288/%7CQ%7C
 (July 6-7). These sessions provide extremely affordable access to 
 expert
 training, and consistently receive fantastic feedback from 
 participants.
 We're looking for submissions on topics from introductory to advanced -
 we'll have attendees across the gamut looking to learn. Plus, you can 
 earn
 an instructor stipend to apply towards your conference participation. 
 Visit
 the SciPy 2015 website for details
 https://www.eiseverywhere.com/emarketing/go.php?i=182077e=bmVsbGUudmFyb3F1YXV4QGdtYWlsLmNvbQ==l=http://scipy2015.scipy.org
 or submit a proposal here
 https://www.eiseverywhere.com/emarketing/go.php?i=182077e=bmVsbGUudmFyb3F1YXV4QGdtYWlsLmNvbQ==l=http://www.scipy2015.scipy.org/eselectv2/frontend/index/115969
 .

   Submit a Tutorial Proposal Here
 https://www.eiseverywhere.com/emarketing/go.php?i=182077e=bmVsbGUudmFyb3F1YXV4QGdtYWlsLmNvbQ==l=http://www.scipy2015.scipy.org/eselectv2/frontend/index/115969
  Talk
 and Poster Proposals Due April 1st

 There's always something new and exciting going on in the world of
 Science + Python, this is your chance to get up and talk about it!

 *Visit the SciPy 2015 website
 

Re: [matplotlib-devel] Histogram normalization and overflow bins

[Paul Hobson]

You can comment on specific lines of code in the pull request interface,
but that's not what I think you're describing. A better practice, IMO is to
raise a DeprecationWarning when the soon-to-be-removed code is executed.
Then you can just grep for those and get cracking.
-p

On Tue, Mar 10, 2015 at 8:51 AM OceanWolf juichenieder-n...@yahoo.co.uk
wrote:

 Slightly off-topic, does github allow for one to tag lines/code-blocks
 with notes to the future like ``numpy1.5 removal'', similar to tags for
 issues and milestones?  This would save us from trawling through the
 code looking for comments.  If github does not have this feature, do you
 think github would add that as a feature if asked?

 
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Re: [matplotlib-devel] Histogram normalization and overflow bins

[Paul Hobson]

IMO, this seems like a bug. I would expect bars to change height with 
zoom/limit levels. 

-p



—
Sent from Mailbox

On Sat, Mar 7, 2015 at 4:20 PM, Tomo Lazovich lazov...@gmail.com wrote:

 Hello matplotlib developers,
 I'm not sure if this is the right mailing list for this question, so please
 re-direct me if it is not.
 I am wondering whether it is possible to have a histogram in pyplot
 normalized to the total length of the list input, rather than just the bins
 showing on the plot (i.e. include those entries in the overflow and
 underflow, off the right and left edges of the plot). As far as I can
 tell, the normed option of pyplot.hist currently makes it so that the area
 under the bins showing is 1. This can lead to a situation like the one
 pasted below, where when I look at the whole histogram the bins have
 certain values but when I try to zoom in to see one part of the plot better
 those values change.
 I can think of two ways to solve this as of now:
 1) Use the weights option to scale each entry by 1/len(input) rather than
 using normed=True.
 2) Somehow add the contents of the overflow to the last bin of the plot,
 which would keep the normalizations constant for earlier bins even if you
 extend the axes.
 Is there a better way of doing this? If the option does not currently
 exist, I am also happy to help implement it if the community would find it
 desirable.
 Thanks for your help!
 Tomo Lazovich
 P.S. Here is a toy example of what I mean:
 import numpy as np
 import matplotlib.pyplot as plt
 h1 = [0, 0, 0, 1, 1, 2, 3]
 my_bins = np.linspace(-0.5, 4.5, 6)
 plt.hist(h1, bins=my_bins, normed=True)
 plt.show()
 gives
 [image: Inline image 1]
 Now, if I change the range on the x axis that I would like plot:
 my_bins2 = np.linspace(-0.5, 1.5, 3)
 plt.hist(h1, bins=my_bins2, normed=True)
 plt.show()
 [image: Inline image 2]
 The y values have changed to 0.6 and 0.4 because the normalization does not
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Re: [matplotlib-devel] Capitalization of Matplotlib

[Paul Hobson]

Perhaps this is a bit a of tangent, but what is exactly is the distinction
between the project and the software?

Is it as simple as: software = code and project = code + mailing list +
documentation + managing issues on github?

On Mon, Feb 16, 2015 at 11:04 AM, Matthew Brett matthew.br...@gmail.com
wrote:

 Hi,

 On Mon, Feb 16, 2015 at 10:53 AM, Nelle Varoquaux
 nelle.varoqu...@gmail.com wrote:
 
  IMO, never.
 
 
  Rationale, please?
 
 
  Consistency: it is never capitalized in matplotlib's documentation.
 
 
  True, and a valid point--but we could easily change that.  Wouldn't it
 make
  it bit more readable if sentences always started with a capital letter?
  Starting with lower case just looks wrong and artificial.
 
  I'm going to give two bad reasons to keep it this way:
  1. backward compatibility :p
  2. you are used to having sentences start with capital letter, but
  this is mostly cultural. German People capitalize almost all Words in
  a Sentence. It just looks weird too… (There is the other extreme:
  people who don't seem to know where the shift button on their keyboard
  is when writing an email.)
 
  I'm actually fine with changing it, but I think we should try as much
  as possible to be consistent.

 I suppose everyone would agree that prematplotlib/pre should never
 be capitalized.  I guess also that your (Ben's) typsetters will not
 often be using pre/pre formatted matplotlib.  In that case I would
 say Matplotlib is a English proper noun and standard capitalization
 rules apply.   It would probably be confusing to try and distinguish
 between the project and the software with capitalization.

 Does it matter that the book and the mpl documentation have a
 different convention?

 Cheers,

 Matthew


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Re: [matplotlib-devel] release strategy and the color revolution

[Paul Hobson]

There are several cycles in seaborn. Is it safe to assume that you mean the
'deep' palette?
On Mon, Feb 16, 2015 at 14:40 Eric Firing efir...@hawaii.edu wrote:

 On 2015/02/16 12:01 PM, Eric Firing wrote:

 
  Proposals for the new color cycle for line plots?

 Here is a proposal: we simply adopt seaborn's cycle.

 Eric


 
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Re: [matplotlib-devel] ginput in nbagg backend to use in IPython Notebooks

[Paul Hobson]

I'm 99% sure you can do this in a GUI window. Does your solution have to be
in the notebook?

On Tue, Jan 27, 2015 at 12:37 AM, Mark Bakker mark...@gmail.com wrote:

 Thanks, Tom.

 I want to use ginput to draw a straight line on a graph.
 The line is used to select a cross-section of a contour plot.

 I was afraid it wasn't going to be easy.

 Getting to it from the other side, is there a matplotlib widget in the
 works where I can type text or numbers in a box? Like the FloatTextWidget
 in IPython?

 Problem is I want to make a small GUI that includes both a text widget
 (which is available in IPython) and a 'select points in graph' widget like
 ginput in matplotlib.

 Mark


 On Mon, Jan 26, 2015 at 11:47 PM, Thomas Caswell tcasw...@gmail.com
 wrote:

 nbagg is always running in the IPython event loop (as I understand it),
 so I am not sure how to integrate that with the blocking.

 On the 1.4.x/master branch we have support for (almost, one PR still
 pending) all mouse and keyboard events so all of the mpl widgets should
 work (big thanks to Steven Silvester).  T

 What do you want to use that relies on ginput?

 You can fake up a non-blocking version something like:

 from collections import deque
 ```
 class accumulator(object):
 def __init__(self, n=5):
 self.list_of_points = deque(maxlen=n)

 def on_event(self, event):
 self.list_of_points.append(event)

 import matplotlib
 import itertools
 import numpy as np
 matplotlib.use('nbagg')
 import matplotlib.pyplot as plt
 plt.close('all')
 fig, ax = plt.subplots()
 x = np.linspace(0,10,1)
 y = np.sin(x)
 ln, = ax.plot(x,y)

 dd = accumulator(15)
 fig.canvas.mpl_connect('button_press_event', dd.on_event)
 plt.show()
 ```

 and then get the points by

 ```
 dd.lest_of_points
 ```

 This code obviously needs lots of bells and whistles, but points in the
 right direction.

 Tom

 On Mon Jan 26 2015 at 2:45:45 PM Mark Bakker mark...@gmail.com wrote:

 Hello List,

 Are there any plans to make ginput work in the nbagg backend?

 It would be so cool if I could use that in an IPython Notebook together
 with the other widgets.

 Thanks,

 Mark
 
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Re: [matplotlib-devel] Matplotlib style gallery

[Paul Hobson]

Tony! This is very cool. Bravo.

On Mon, Jan 5, 2015 at 8:42 PM, Tony Yu tsy...@gmail.com wrote:

 I've been playing around with learning Javascript lately. As part of the
 process, I created a Flask app to build a gallery for matplotlib style
 sheets:

 https://github.com/tonysyu/matplotlib-style-gallery

 If you run that locally, you can actually input styles, either with a URL
 to a *.mplstyle file or with matplotlibrc commands. Here's a static version
 without the custom inputs:

 http://tonysyu.github.io/raw_content/matplotlib-style-gallery/gallery.html

 Ideally, I'd get this into a form that could be submitted as a PR for the
 matplotlib website, but I'll need a bit more spare time to learn some more
 web development (sessions, client storage, etc).

 Cheers!
 -Tony


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Re: [matplotlib-devel] Who runs our twitter account?

[Paul Hobson]

For Q and A no. But it's great for announcements, links to example of upcoming 
or new features, etc. 

—
Sent from Mailbox

On Sat, Dec 20, 2014 at 2:11 PM, Eric Firing efir...@hawaii.edu wrote:

 On 2014/12/20, 10:45 AM, Thomas Caswell wrote:
 We have a Twitter account?!?
 It's news to me, too.  Maybe it was started by someone not closely 
 connected with matplotlib.  I've never paid any attention to twitter, 
 and don't expect to in the future.  If I had any control over it, I 
 would oppose any matplotlib presence on twitter.  It's not a suitable 
 medium for software-related QA.
 Eric

 On Fri, Dec 19, 2014, 20:05 Benjamin Root ben.r...@ou.edu
 mailto:ben.r...@ou.edu wrote:

 I just realized today that people have been posting questions to a
 matplotlib handle on twitter, but it hasn't posted any tweets since
 April.

 Same issue for numpy as well, it seems.

 Ben Root

 
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Re: [matplotlib-devel] Better defaults all around?

[Paul Hobson]

I'd like to propose an update to the default boxplot symbology: all black

Q: How much more black could the boxplots be?
A: None. None more black.

(sorry, ben)

On Fri, Nov 21, 2014 at 7:18 PM, Benjamin Root ben.r...@ou.edu wrote:

 With regards to defaults for 2.0, I am actually all for breaking them for
 the better. What I find important is giving users an easy mechanism to use
 an older style, if it is important to them. The current behavior isn't
 buggy (for the most part) and failing to give users a way to get behavior
 that they found desirable would be alienating. I think this is why projects
 like prettyplotlib and seaborn have been so important to matplotlib. It
 enables those who are in the right position to judge styles to explore the
 possibilities easily without commiting matplotlib to any early decision and
 allowing it to have a level of stability that many users find attractive.

 At the moment, the plans for the OO interface changes should not result in
 any (major) API breaks, so I am not concerned about that at the moment.
 Let's keep focused on style related issues in this thread.

 Tabbed figures? Intriguing... And I really do need to review that MEP of
 yours...

 Cheers!
 Ben Root

 On Fri, Nov 21, 2014 at 9:36 PM, Federico Ariza ariza.feder...@gmail.com
 wrote:

 I like the idea of aligning a set of changes for 2.0 even if still far
 away.

 Regarding to backwards compatibility I think that indeed it is important
 but when changing mayor version (1.x to 2.0) becomes less important and we
 must take care of prioritizing evolution.
 Take for example the  OO interface (not defined yet) this is very
 probable to break the current pyplot interface but still this is a change
 that needs to be done.

 In terms of defaults. I would like to see the new Navigation as default
 (if it gets merged) and tabbed figures (to come after navigation), having
 separate figures feel kind of ...old
 On 21 Nov 2014 21:23, Benjamin Root ben.r...@ou.edu wrote:

 Some of your wishes are in progress already:
 https://github.com/matplotlib/matplotlib/pull/3818
 There is also an issue open about scaling the dashes with the line
 width, and you are right, the spacing for the dashes are terrible.

 I can definitely see the argument to making a bunch of these visual
 changes together. Preferably, I would like to do these changes via style
 sheets so that we can provide a classic stylesheet for backwards
 compatibility.

 I do actually like the autoscaling system as it exists now. The problem
 is that the data margins feature is applied haphazardly. The power spectra
 example is a good example of where we could smarten the system. As for
 the ticks... I think that is a very obscure edge-case. I personally prefer
 inward.

 It is good to get these grievances enumerated. I am interested in seeing
 where this discussion goes.

 Cheers!
 Ben Root

 On Fri, Nov 21, 2014 at 6:22 PM, Nathaniel Smith n...@pobox.com wrote:

 Hi all,

 Since we're considering the possibility of making a matplotlib 2.0
 release with a better default colormap, it occurred to me that it
 might make sense to take this opportunity to improve other visual
 defaults.

 Defaults are important. Obviously for publication graphs you'll want
 to end up tweaking every detail, but (a) not everyone does but we
 still have to read their graphs, and (b) probably only 1% of the plots
 I make are for publication; the rest are quick one-offs that I make
 on-the-fly to help me understand my own data. For such plots it's
 usually not worth spending much/any time tweaking layout details, I
 just want something usable, quickly. And I think there's a fair amount
 of low-hanging improvements possible.

 Batching multiple visual changes like this together seems much better
 than spreading them out over multiple releases. It keeps the messaging
 super easy to understand: matplotlib 2.0 is just like 1.x, your code
 will still work, the only difference is that your plots will look
 better by default. And grouping these changes together makes it
 easier to provide for users who need to revert back to the old
 defaults -- it's easy to provide simple binary choice between before
 2.0 versus after 2.0, harder to keep track of a bunch of different
 changes spread over multiple releases.

 Some particular annoyances I often run into and that might be
 candidates for changing:

 - The default method of choosing axis limits is IME really, really
 annoying, because of the way it tries to find round number
 boundaries. It's a clever idea, but in practice I've almost never seen
 this pick axis limits that are particularly meaningful for my data,
 and frequently it picks particularly bad ones. For example, suppose
 you want to plot the spectrum of a signal; because of FFT's preference
 for power-of-two sizes works it's natural to end up with samples
 ranging from 0 to 255. If you plot this, matplotlib will give you an
 xlim of (0, 300), which looks pretty ridiculous. But even worse 

Re: [matplotlib-devel] Setting default labelpad

[Paul Hobson]

Peter,

Can you submit this as a pull request on github?
http://matplotlib.org/devel/gitwash/git_development.html


On Fri, May 30, 2014 at 12:37 PM, Peter St. John peterc.stj...@gmail.com
wrote:

 Finally made this into a patch to allow the dynamic updating of the
 axes.labelpad parameter.
 Hope this is in the appropriate format!

 Thanks,
 -- Peter


 On Mon, Oct 28, 2013 at 10:45 AM, Peter St. John peterc.stj...@gmail.com
 wrote:

 Hi Matplotlib-users,

 I found it was useful to be able to change the default 'Axis.labelpad'
 parameter, since this value didn't scale when changing the default figure
 size (in my opinion its easier to prepare figures for publication assuming
 they'll need to fit in a 1-column figure). I don't consider myself
 experienced enough to attempt to contribute a patch, but nevertheless here
 is the hack I used in case anyone has a similar problem:

 axis.py, line 652:
 original: self.labelpad = 5
 changed : self.labelpad = rcParams['axes.labelpad']

 Then, in rcsetup.py, I added the line (at 578):
 'axes.labelpad' :[5.0, validate_float],

 This lets you put
 axes.labelpad   : 3
 for instance, in your matplotlibrc to change the default label padding.

 Anyways, not sure if this is the right mailing list for this type of
 thing, but just thought I'd contribute it nevertheless.

 Best,
 -- Peter




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Re: [matplotlib-devel] Axes properties are cleared after offsetting spines

[Paul Hobson]

Wow. Thanks so much, Stan! This is a huge help and works just as I need it
to.

Much appreciated!


On Mon, Mar 24, 2014 at 11:26 AM, Stan West stan.w...@nrl.navy.mil wrote:

  On 2014-03-24 14:08, Stan West wrote:

 May I suggest that you look at the mailing list thread from that time [1],
 try the patch in the thread, and see whether your issue is resolved? This
 solution doesn't provide a work-around in your code, but it may fix the
 problem at the root.

 Paul, I just found the work-around that I used in my code. Define the
 following function:

 def set_spine_position(spine, position):
 
 Set the spine's position without resetting an associated axis.

 As of matplotlib v. 1.0.0, if a spine has an associated axis, then
 spine.set_position() calls axis.cla(), which resets locators, formatters,
 etc.  We temporarily replace that call with axis.reset_ticks(), which is
 sufficient for our purposes.
 
 axis = spine.axis
 if axis is not None:
 cla = axis.cla
 axis.cla = axis.reset_ticks
 spine.set_position(position)
 if axis is not None:
 axis.cla = cla

  (The mention of v. 1.0.0 in the docstring is just the version I was using
 at the time, not necessarily the earliest version with this issue.) Then
 replace method calls like spine.set_position(pos) with the function call
 set_spine_position(spine, pos). I hope this helps.

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Re: [matplotlib-devel] is R wrong? (boxplot)

[Paul Hobson]

Hey Yaroslav,

As the author of the fix and the recent overhaul to boxplots, I can say
with certainty that R is wrong! ;-)

More seriously, the main thing that I take away from Tukey's paper about
boxplots, is that there are many valid ways to draw them. I personally set
up the new boxplot functionality to take the most basic boxplot definition
very literally. My guess is that R is fudging those rules a bit for the
purpose of completeness, or aesthetics, or ...(?)

Perhaps one can look at the purpose of boxplots in two different fashions:
1) Matplotlib: show some of the data and some basic stats
2) R (I'm guession): show how the data are /probably/ distributed.

Obviously, I prefer #1. But I'm not going to say that #2 is wrong just yet.




On Sat, Feb 15, 2014 at 5:00 AM, Yaroslav Halchenko s...@onerussian.comwrote:

 Dear Matplotlib gurus,

 Following the code to demonstrate recent(ish) fix for whiskers in boxplots:
 https://github.com/matplotlib/matplotlib/pull/1855 I have compared it
 against
 R's boxplot.  Description seems to correspond, and all the percentiles are
 the
 same in numpy and R (3.0.1) but R's boxplot seems to have extended IQR box
 and
 still have an upper whisker (corresponds to 9000, which is not within
 75%+1.5*IQR), when it shouldn't:

 http://nbviewer.ipython.org/url/www.onerussian.com/tmp/boxplot-Python-vs-R.ipynb

 is R's plot incorrect or am I missing something (e.g. documented feature
 in R's boxplot) warranting such a difference?

 Thanks in advance
 --
 Yaroslav O. Halchenko, Ph.D.
 http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
 Senior Research Associate, Psychological and Brain Sciences Dept.
 Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
 Phone: +1 (603) 646-9834   Fax: +1 (603) 646-1419
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Re: [matplotlib-devel] is R wrong? (boxplot)

[Paul Hobson]

Yaroslav,

Those figures look great. Seaborn has some similar functionality (scroll
down a bit):
http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb#Comparing-distributions:-boxplot-and-violinplot

The main point of the most recent overhaul of boxplots was to allow users
to just what you describe. The methods plt.boxplot and ax.boxplot now do
very little on their own. Input data are passed to
matplotlib.cbook.boxplot_stats, that function returns a list of
dictionaries of statistics, and then ax.bxp actually does the drawing. All
of this is to say that you can write your own function to modify
boxplot_stats' output or generate independently the list of dictionaries
expected by ax.bxp.

The keys of those dictionaries can include:
 - label  - tick label for the boxplot
 - mean - mean value (can plot as a line or point)
 - median - 50th percentile
 - q1 - first quartile (25th pctl)
 - q3 - third quartile (75 (pctl)
 - cilo - lower notch around the median
 - ciho - upper notch around the median
 - whislo - end of the lower whisker
 - whishi - end of the upper whisker
 - fliers - outliers

Basically, you can set the appropriate values to whatever you want to draw
boxplots however you wish (like open/close diagrams for pandas).

Also, the `whis` kwarg accepted by boxplot and cbook.boxplot_stats can
either be a float (1.5 by default), a list of integer percentiles (like 5,
95), or the strings 'range', 'limits', or 'min/max', all of which will
extend the whiskers to over all of the data.

Since you're running off of master, you should access to this new
functionality.

Here's a link to the PR that overhauled ax.boxplot and created ax.bxp:
https://github.com/matplotlib/matplotlib/pull/2643

Looking at it now -- it looks like cbook.boxplot_stats' docstring got
cutoff. I'll pull together a PR to fix that soon.

Feel free to hit me up with any other questions!

-paul



On Sat, Feb 15, 2014 at 2:20 PM, Yaroslav Halchenko s...@onerussian.comwrote:

 Hi Paul,

 On Sat, 15 Feb 2014, Paul Hobson wrote:
 As the author of the fix and the recent overhaul to boxplots

 Thanks for that!

   I can say with certainty that R is wrong! ;-)

 phew -- thanks ;)

 More seriously, the main thing that I take away from Tukey's paper
 about
 boxplots, is that there are many valid ways to draw them. I
 personally set
 up the new boxplot functionality to take the most basic boxplot
 definition
 very literally. My guess is that R is fudging those rules a bit for
 the
 purpose of completeness, or aesthetics, or ...(?)

 well -- I was trying to figure out why the divergence from R's boxplot
 help, but so far it seemed to match description/definition for boxplot
 as in matplotlib.  I guess the next step would be to look inside
 (running apt-get source r-base now ;-) )

 Perhaps one can look at the purpose of boxplots in two different
 fashions:
 1) Matplotlib: show some of the data and some basic stats
 2) R (I'm guession): show how the data are /probably/ distributed.�
 Obviously, I prefer #1. But I'm not going to say that #2 is wrong just
 yet.

 would you may be interested to adopt (or just do independently) an
 option to e.g. plot the data point?  once I shared this one
 http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
 and the actual code https://gist.github.com/yarikoptic/9023331

 I just never got to formalize it into mpl pull request :-/
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[matplotlib-devel] Axes properties are cleared after offsetting spines

[Paul Hobson]

I noticed that when you offset the spines of an Axes object, the labels,
ticks, and ticklabels/formatting get mostly cleared. Is this intentional
and is there a way to prevent (or undo) it?

It's probably easiest to just look at a notebook:
http://nbviewer.ipython.org/gist/phobson/8818648

That notebook contains a proposed solution from Stack Overflow.
Unfortunately, minor ticks and labels are missed (and I can't understand
why as the values are contained in the properties dictionary of the spines).

Background: I'm trying to add an offset kwarg to the despine function in
seaborn (https://github.com/mwaskom/seaborn/pull/92). Point of mentioning
that is that to make this work, we need to be able to offset the spines
*after* plotting and formatting ticks.

Alternatively, if there was a way to specify a default offset in rcParams
before a figure and axes were even created, that might work too.

--
Related to that, when I use the SO solution, about 50% of the time the axes
labels are rendered as the label objects, not text. Whatever triggers that
doesn't seem to be deterministic. Resetting the notebook will fix it or
break it -- there's no telling how it's going to go. Here's the exact same
notebook as above, with the mangled figure at the bottom.
http://nbviewer.ipython.org/gist/phobson/8818680

Cheers,
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Re: [matplotlib-devel] PDF not readable by Adobe PDF readers

[Paul Hobson]

You mentioned in the comments that it only occurs with a lot (~4 GB)of
data. Could it be that you're on a 32-bit system and you're generating a
file that's too big for your OS to handle?


On Tue, Dec 3, 2013 at 1:21 AM, buckeliger buckeli...@gmx.ch wrote:

 I have described and uploaded a sample file with the problem to


 http://stackoverflow.com/questions/20314255/matplotlib-generated-pdf-cannot-be-viewed-in-acrobat-reader
 
 http://stackoverflow.com/questions/20314255/matplotlib-generated-pdf-cannot-be-viewed-in-acrobat-reader
 

 there they ment, I should maybe report a bug. What do you think?



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[matplotlib-devel] Strange Error on Travis 2.6 build (can't find cbook)

[Paul Hobson]

Hey folks,

I've got a branch going to refactor the boxplot function and address
several issues that have cropped up lately.

Currently, everything on my feature branch is working well except for
Travis' Python 2.6 build.

Here's a link directly the error on the build:
https://travis-ci.org/phobson/matplotlib/jobs/14820842#L9502

And here's a link to my current branch (still a work-in-progress)
https://github.com/phobson/matplotlib/compare/wip2-boxplot-refactor

Thanks,
-paul
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Re: [matplotlib-devel] Strange Error on Travis 2.6 build (can't find cbook)

[Paul Hobson]

Thanks, Thomas. That sounds like enough to get me going.
-paul


On Mon, Dec 2, 2013 at 1:48 PM, Thomas A Caswell tcasw...@uchicago.eduwrote:

 That tends to mean you have something that fails to import (raises an
 exception on import that get silently suppressed) so I would guess
 something in there is non 2.6 compatible, but don't know enough to
 tell you what.

 On Mon, Dec 2, 2013 at 1:00 PM, Paul Hobson pmhob...@gmail.com wrote:
  Hey folks,
 
  I've got a branch going to refactor the boxplot function and address
 several
  issues that have cropped up lately.
 
  Currently, everything on my feature branch is working well except for
  Travis' Python 2.6 build.
 
  Here's a link directly the error on the build:
  https://travis-ci.org/phobson/matplotlib/jobs/14820842#L9502
 
  And here's a link to my current branch (still a work-in-progress)
  https://github.com/phobson/matplotlib/compare/wip2-boxplot-refactor
 
  Thanks,
  -paul
 
 
 
 
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Re: [matplotlib-devel] Image comparison decorators outside matplotlib

[Paul Hobson]

Eduard,

Did you make any progress on this? I'm trying to do the same thing and it's
skipping my tests entirely.
-paul


On Thu, Oct 10, 2013 at 5:41 AM, Eduard Bopp eduard.b...@aepsil0n.dewrote:

 Hello,

 I am developing a toolkit to parse, analyse and plot some scientific
 data using matplotlib. Among them are some application-specific plotting
 functions that sort of extend matplotlib.

 There are these nice image comparison decorators to test code like that
 but I am not sure how to use them for unit testing outside the scope of
 matplotlib itself. Is this use case intended and possible for the
 decorator?

 I have experimented with this unsuccessfully in the following way:

 There is a tests directory within my package with test functions
 decorated like so

 @image_comparison(baseline_images=['custom_function'])
 def test_custom_function():
 # plot stuff...

 When I run nosetests, it fails creating some output images in
 result_images.

 Copying the appropriate files according to [1] to
 my_package/tests/baseline_images does not seem to have any effect. There
 are neither *-expected* nor *_{pdf,svg}.png files in there, only
 custom_function.{pdf,svg,png}. What am I doing wrong?

 Eduard

 [1] http://matplotlib.org/devel/testing.html


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[matplotlib-devel] Autogenerated docstrings, pyplot, and Windows code completion

[Paul Hobson]

Hey folks,

While I'm primarily a Vim/Sublime Text kind of guy, my employer provides me
with a Windows machine with Visual Studio and whatnot. So I've been giving
PTVS a shot (https://pytools.codeplex.com/). Seems perfectly nice so far.

But the point is that I just wanted the dev team to know that they've got
an open ticket about their Intellisense/code completion with the pyplot
module:
https://pytools.codeplex.com/workitem/1841

Basic summary is the autogenerated docstrings maybe the culprit.

I'm not suggesting any wholesale changes to MPL to accommodate this. It
just seemed like something y'all might want to know about in case any big
decisions in that area come up.

Note: that in my experience, you get good code completion and tooltips when
working directly with axes objects. So maybe that's all the more reason to
push users away from pyplot?

Cheers and as always, many thanks for the great tools!
-paul
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Re: [matplotlib-devel] I have a Mac!

[Paul Hobson]

Mike,

That's great news. Is there any chance we can look forward to official
instructions for setting up a Mac to develop matplotlib?

I gave up a long time ago and started piecing to together my meager PRs in
a linux VM.
-paul


On Fri, Aug 16, 2013 at 6:52 AM, Michael Droettboom md...@stsci.edu wrote:

 Thanks to the gracious donation from Hans Petter Langtangen and the
 Center for Biomedical Computing at Simula (http://home.simula.no/~hpl),
 I now have a new Mac Mini sitting at my desk.  This should allow me to
 keep on top of changes that affect the Mac builds and to better track
 down Mac-only issues.

 Stay tuned over the next few weeks and months as we will most likely be
 using some more of these funds to pay for hosted continuous integration
 services (as discussed yesterday in our MEP19 Google Hangout).

 Cheers,
 Mike




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Re: [matplotlib-devel] I have a Mac!

[Paul Hobson]

On Fri, Aug 16, 2013 at 7:42 AM, Jens Nielsen jenshniel...@gmail.comwrote:

 On my mac box I'm just using homebrew www.brew.sh to install the latest
 python along with all non python dependencies and the python dependencies
 via pip. This seems to work great most of the time.
 Jens


Yeah. I'm sure Homebrew has made a lot of progress since I last tried. A
couple of years ago I found a couple of blog posts that all walked you
through different ways of getting a dev environment going for the various
flavors of python installed (it was never /all/ available through
Homebrew). Sometimes one would work for me. Eventually I just said, screw
it and installed Anaconda for use and fired up a VM for development.

I guess my point is that it'd be nice to have documentation on
matplotlib.org that says, Here's how Mike D. sets up his Mac to build mpl
from source. Because if it works well enough for Mike, it'll probably work
well enough for me. (Now if only pandas would do the same thing.)

A similar document would be great for Windows too.
-paul



 On Fri, Aug 16, 2013 at 3:32 PM, Michael Droettboom md...@stsci.eduwrote:

  We actually discussed this very issue yesterday in our Google hangout
 about continuous integration.  We're probably going to need to script a
 full setup from a clean Mac + XCode to a working matplotlib development
 environment in order to make that happen, and obviously that will be shared
 with the world.  Things are even more complex on Windows, and I'd like to
 do that there, too.  So stay tuned.

 Mike


 On 08/16/2013 10:02 AM, Paul Hobson wrote:

 Mike,

  That's great news. Is there any chance we can look forward to
 official instructions for setting up a Mac to develop matplotlib?

  I gave up a long time ago and started piecing to together my meager PRs
 in a linux VM.
 -paul


 On Fri, Aug 16, 2013 at 6:52 AM, Michael Droettboom md...@stsci.eduwrote:

 Thanks to the gracious donation from Hans Petter Langtangen and the
 Center for Biomedical Computing at Simula (http://home.simula.no/~hpl),
 I now have a new Mac Mini sitting at my desk.  This should allow me to
 keep on top of changes that affect the Mac builds and to better track
 down Mac-only issues.

 Stay tuned over the next few weeks and months as we will most likely be
 using some more of these funds to pay for hosted continuous integration
 services (as discussed yesterday in our MEP19 Google Hangout).

 Cheers,
 Mike



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Re: [matplotlib-devel] I have a Mac!

[Paul Hobson]

On Fri, Aug 16, 2013 at 9:08 AM, Chris Barker - NOAA Federal 
chris.bar...@noaa.gov wrote:

 On Fri, Aug 16, 2013 at 7:32 AM, Michael Droettboom md...@stsci.edu
 wrote:
  We actually discussed this very issue yesterday in our Google hangout
 about
  continuous integration.  We're probably going to need to script a full
 setup
  from a clean Mac + XCode to a working matplotlib development environment
 in
  order to make that happen,

 Just a note -- this did NOT just work the other day for me -- it
 found the freetype libs that OS-X has in the X11 build, but didn't
 like them at compile time. I haven't debugged it yet, sorry.

 But the real trick here is what you want to build: which OS-X versions
 do you want to support? which architectures? which Python Build(s)?

 What I've been planning on doing is setting up a gitHub (or something)
 project for building the various dependencies that various python
 packages need -- there are a few that are broadly used: libpng,
 libfreetype (used by MPL, PIL, wxPython, ???). The idea is that if you
 wanted to build MPL (or PIL, or ???) you'd grab the
 MacPyton_Dependencies project, build it, then go from there.

 Anyone want to help? It just feels like we are all repeating
 each-others work a LOT here!

 NOTE: the big issues come up if you want to build binaries that are
 re-distributable (as a package, or with py2app, or???). In this case,
 you need binaries that can run on perhaps older machines than the one
 you're building on, or a different architecture. Building to run on
 the machine it's built-on is a lot easier. (particularly with macport
 or homebrew)

 -CHB


Wow! Ambitious. I'll try to keep track of this and help out where possible.

Side note: it'll probably be good to alert the pandas folks to such a
project. I feel like they're always on Macs at their presentations. They
probably have some good ideas for this stuff.
-p
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Re: [matplotlib-devel] matplotlib 1.3.0rc4 (was: matplotlib 1.3.0rc3 tomorrow)

[Paul Hobson]

On Fri, Jun 21, 2013 at 10:42 AM, Michael Droettboom md...@stsci.eduwrote:

  I have made a new release candidate (1.3.0rc4) that fixes the following
 vs. 1.3.0rc3:

 - It doesn't add a setup.cfg file to the tarball

 - It doesn't install the KnownFailure nose plugin as a pkg_resources
 entry_point (this conflicted with IPython's plugin of the same name)

 - We get a known failure from the pep8 test if pep8 isn't installed

 Hopefully that's enough to get to the point of giving this release
 candidate some wider exposure before putting out a final release.  We can
 use some sprint time at Scipy to get this release polished if desired and
 necessary, too.

 Mike


Hey folks,

I just downloaded the RC4 Gohlke binary for 64-bit Python 3.3, and the test
suite is blowing up. Mostly related to the baseline images not being
included. Is there a trick to get the test suite to meaningfully run on
Windows? The test suite for Python 2.7.5 (via Anaconda) went relevatively
well(1 failure + 1 error, I think).

Thanks,
-paul
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Re: [matplotlib-devel] MEP14: Improve text handling

[Paul Hobson]

On Thu, May 30, 2013 at 5:03 PM, Michael Droettboom md...@stsci.edu wrote:

 On 05/30/2013 02:27 PM, Chris Barker - NOAA Federal wrote:
 
  With a fully-function mathtex, it could be the default (only?) text
  layout system for MPL, simplifying things quite a bit.

 I'm not sure that's realistic.  The usetex backend gets a great deal of
 use, and I don't think it's only because it handles multiline text
 better -- it's also the easiest way to make the text match that of a
 larger TeX document in which it's included (though the new PGF backend
 goes some way to helping that in an entirely different way).


Exactly! I like that I can set text.usetex=True and add
\usepackage{fourier} and I *know* that my figures and document will look
the same.

That said, I've never been able to get the PGF backend to work well. Random
elements are pixelated. It's surely user-error on my end, but the usetex is
comparatively easy to set up.


 It might
 be worth collating a list of reasons that users are using usetex to
 include in the MEP -- if we can address them all in another way, great,
 but if not it's not too difficult to keep something that already works
 fairly well working.  The problem I have with it is not really that it
 exists, only that it has tendrils all throughout matplotlib that could
 be better localized into a single set of modules.


As I state above -- I absolutely require One Font throughout my documents.
If it's a serif font, I use the fourier TeX package. If it's a sans-serif
font, I do the weird \sansmath voodoo (I still owe you a PR with an example
of setting that up). Point is, it works well.

Cheers,
-paul
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Re: [matplotlib-devel] How to contribute a new kind of plot

[Paul Hobson]

On Thu, Mar 14, 2013 at 1:50 AM, Pierre Barbier de Reuille 
pie...@barbierdereuille.net wrote:

 Hello,

 for my own research, I have implemented a function plotting datasets as
 violin plots ( http://en.wikipedia.org/wiki/Violin_plot ). I attach an
 example output that I am using.

 It is fairly stand-alone, with the expection of a scipy dependency for
 computation of the estimated density of probability of the shown. Before
 distribution, I will need to document the function and classes associated.

 My question is: what is the process if there a place I can make the code
 available for consideration?

 Thanks,


Pierre,

The scipy dependency is unfortunately a deal breaker for matplotlib. I'm
not a core dev, but typically you have a work around the dependency in some
way (e.g., make the user specify the KDE for each dataset).

Also, the statsmodels project has this functionality:
http://statsmodels.sourceforge.net/devel/generated/statsmodels.graphics.boxplots.violinplot.html#statsmodels.graphics.boxplots.violinplot

Perhaps your ideas and approach could be used to improve
their implementation.
-paul
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Re: [matplotlib-devel] matplotlib.org website DOWN (again)

[Paul Hobson]

On Sat, Dec 22, 2012 at 3:25 PM, Patrick Marsh patrickmars...@gmail.comwrote:

 The matplotlib.org website is down right now -- at least for me.

 Also, over the last 2-3 months or so, I'd guess that about 50% of the time
 I go to the website it's down. Granted, I don't go daily, so I'm guessing
 that part of this is just awesome timing on my part, but my general feeling
 is that this is becoming a more frequent occurrence. Is it just me?


I can't say for sure. But I think there was a github outage that caused
this. As for the site being down, I'm probably looking at examples or the
gallery 3x a day and haven't noticed it being down once. Who knows?
-p
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Re: [matplotlib-devel] MEP12: Reorganize example gallery and clean up examples

[Paul Hobson]

On Wed, Dec 12, 2012 at 5:59 AM, Michael Droettboom md...@stsci.edu wrote:


 http://matplotlib.org/examples/api/clippath_demo.html

 It's perfectly reasonable code, but seems strange that it's clipped off to
 the corner which I think makes it less useful as an example.


If I understand correctly, you're proposing that that such an example would
be deleted after a more practical example was available to replace it?

While I'm 100% in favor of cleaning out the closet, I used this exact
example two days ago to clip a depressed groundwater surface to a landfill
boundary :)
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Re: [matplotlib-devel] MEP12: Reorganize example gallery and clean up examples

[Paul Hobson]

On Mon, Dec 10, 2012 at 2:18 PM, Tony Yu tsy...@gmail.com wrote:

 Hi all,

 I'm not sure if non-core-developers are allowed to post MEPs, but I just
 did ;). MEP 12 outlines the reorganization of the example gallery and
 subsequent clean up of the examples:

 https://github.com/matplotlib/matplotlib/wiki/MEP12

 In my opinion, there are two open questions in the MEP:

 * Section names (may seem trivial to some, but I think it's really
 important)
 * Guidelines for cleaning up examples

 I've added proposed section names and clean-up guidelines to the MEP, but
 I'm sure these will evolve over time.

 Best,
 -Tony


Here here! I've been waiting for a really nasty, cold, and rainy weekend
here in Portland to make some PRs at least cleaning up the code contained
within the examples. Just hasn't happened yet ;)

 Tony, if you make your own branch for this, I'd be happy to contribute.
-paul
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Re: [matplotlib-devel] RFC: boxplot_enhanced paired_stats

[Paul Hobson]

On Fri, Nov 16, 2012 at 7:58 AM, Skipper Seabold jsseab...@gmail.comwrote:

 On Fri, Nov 16, 2012 at 10:19 AM, Yaroslav Halchenko s...@onerussian.com
 wrote:
  I just found some code (http://www.onerussian.com/tmp/plots.py and
  pasted below for review/feedback) laying around which I wrote around
  matplotlib for plotting primarily pair-wise stats results.  Here is a
  demonstration:
  http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
 
  I wonder if there is a need/place for it in matplotlib and what changes
 would
  you advise. Sorry for the lack of documentation -- I guess I have not
 finished
  it at that point (scipy dependency can easily be dropped, used only for
  standard error function iirc):
 

 Looks nice. We'd certainly be interesting in including it in
 statsmodels/graphics if there isn't sufficient interest here and/or
 you'd like to keep the scipy dependency. ;)

 Skipper


I was going to suggest either the same thing or adding it to pandas. I
think statsmodels if the better fit, though. I also noticed scipy is only
used for scipy.stats.sem -- so it might be easy enough to loose the scipy
dependency. Just a thought.
-paul
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Re: [matplotlib-devel] 1.2.0rc1 is cut

[Paul Hobson]

On Tue, Sep 18, 2012 at 3:57 PM, Russell E. Owen ro...@uw.edu wrote:
 In article 50509fb1.7070...@stsci.edu,
  Michael Droettboom md...@stsci.edu
  wrote:

 I have tagged and created a tarball for 1.2.0rc1.  The githash is
 bda6dd9feab8.  The tarball is on the github download page here:

 https://github.com/matplotlib/matplotlib/downloads

 I have created a new branch, v1.2.x, for continuing 1.2.x development.
 The feature freeze on master is now lifted and big experimental changes
 can be merged into master.  Any bugfixes that need to go into 1.2.x
 should be merged into both places.  Please mark any PRs for the 1.2.x
 branch with the 1.2.x milestone so we can verify that things are merged
 in both places.

 It appears that
 import matplotlib
 no longer imports matplotlib.dates -- that I must do that explicitly:
 import matplotlib.dates

 Is that an intentional change? It breaks existing code of mine, which is
 easily fixed and perhaps was making unwarranted assumptions. But I
 wonder what else will break.

Russel,

Which version were you on? with MPL v1.1 i get:
In [28]: import matplotlib

In [29]: matplotlib.dates
---
AttributeErrorTraceback (most recent call last)
ipython-input-29-a13aa8cf36d8 in module()
 1 matplotlib.dates

AttributeError: 'module' object has no attribute 'dates'

In [30]: matplotlib.__version__
Out[30]: '1.1.0'

In [31]: import matplotlib.dates

In [32]: matplotlib.dates
Out[32]: module 'matplotlib.dates' from
'C:\Python27\lib\site-packages\matplotlib\dates.pyc'

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Re: [matplotlib-devel] Q-Q Plot

[Paul Hobson]

Damon,

When the current state of the relevant python libraries, scipy is
required to create a QQ plot. Therefore, matplotib will never be able
to natively make QQ or probability plots. I've got a PR into the the
statsmodels project to do just what you need (and more!).
https://github.com/statsmodels/statsmodels/pull/412

It'll probably be a while before I get that PR up to par with the rest
of statsmodels, so in the meantime, I suggest you use the existing
functionality in statsmodels or roll your own.

Good luck,
-paul

On Wed, Aug 15, 2012 at 4:02 PM, Damon McDougall
damon.mcdoug...@gmail.com wrote:
 Hi all,

 If anyone hasn't noticed already. I've been somewhat semi-perusing the
 Matlab interface and trying to port over any plotting functionality not
 current present in mpl.

 The other day I made a bit of a mess up regarding functionality I didn't
 think existed but actually did (discussion here:
 https://github.com/matplotlib/matplotlib/pull/1068)

 I'm sending a message here because I've found another one I don't think
 currently exists in mpl:
 http://www.mathworks.co.uk/help/toolbox/stats/qqplot.html

 I wanted to check with everyone here (who probably know better!) before
 I started anything.

 Thanks!
 Damon

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Re: [matplotlib-devel] re lease schedule for next version

[Paul Hobson]

I'll be more than happy to provide similar help testing on Windows 7.
-paul

On Sun, Feb 26, 2012 at 5:52 PM, John Hunter jdh2...@gmail.com wrote:


 On Sun, Feb 26, 2012 at 6:16 PM, Mark Lawrence breamore...@yahoo.co.uk
 wrote:

 On 25/02/2012 17:13, John Hunter wrote:

  After we get the bugfix out I'd like to gear up for a major python3
  release.

 Huge +1.

 I understand that the majority of Python and hence matplotlib people
 work on *nix boxes, so if you'd like a hand with testing, or anything
 else come to think of it, on my Windows Vista box feel free to ask, as
 I've been using matplotlib for around seven years and don't mind trying
 to put a bit back in.


 That would be great-- you can cut your teeth testing the release candidates
 for the bugfix release in the current cycle, familiarize yourself with how
 to run the tests, etc.

 We definitely have a shortage of OSX and windows testing, especially the
 latter, because few people run from git HEAD  on windows, but lots of people
 do on linux.

 Just chime in when you see the announcements for rc release candidates on
 the users or devel lists.

 Thanks!
 JDH

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Re: [matplotlib-devel] Code review request: minor modifications to axes.boxplot

[Paul Hobson]

Ben,

Thanks for the reply. I definitely like your idea. Seems like we could
include some logic in axes.errorbar to look at the shapes of xerr and
yerr in a similar fashion to what I propose for axes.boxplots,
allowing the user to have custom lower and upper errors for each data
point (in a time series as I would use it).

I'll try to bang that out this weekend while this is still fresh.
-p


On Fri, Dec 9, 2011 at 1:29 PM, Benjamin Root ben.r...@ou.edu wrote:
 On Thu, Dec 8, 2011 at 10:45 AM, Paul Hobson pmhob...@gmail.com wrote:

 Matplotlib gurus:

 I took at stab at the git work flow and incorporated my personal
 modifications to the boxplot function. Github's diff can be found
 here:
 https://github.com/phobson/matplotlib/compare/master...manual_boxplots

 In summary, if your data is MxN, you can manually specify medians and
 the confidence intervals around the medians using Nx1 and Nx2 arrays,
 respectively. Alternatively, you can use lists or tuples and use Nones
 if you want to specify those values only for some columns in your MxN
 data set. In other words, with an Mx5 data array, you can specify
 conf_intervals=[(ci1a,ci2a), (ci1b,ci2b), (ci1c,ci2c), None,
 (ci1e,ci2e)]. Within the conf_intervals array, the CIs can be listed
 in any order as I use np.max() and np.min() to pull the upper and
 lower values, respectively.

 The motivation behind this is that sometimes I need the confidence
 levels to be different than 95%, and also that I compute those
 confidence intervals with a bootstrapping routine that is more robust
 than mpl-compatible one I submitted some time ago.

 I hope y'all find this to be a useful contribution. I'm an avid
 matplotlib user. It really is a wonderful tool.

 Cheers,
 paul h


 Paul,

 Interesting.  I haven't had much time to really look over your changes, but
 I have been wondering if the errorbar() and boxplot() functions could be
 treated as two different ways to display similar information.  Therefore,
 perhaps their call signatures could be made more similar to each other.
 What do you think?

 Ben Root


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[matplotlib-devel] Code review request: minor modifications to axes.boxplot

[Paul Hobson]

Matplotlib gurus:

I took at stab at the git work flow and incorporated my personal
modifications to the boxplot function. Github's diff can be found
here:
https://github.com/phobson/matplotlib/compare/master...manual_boxplots

In summary, if your data is MxN, you can manually specify medians and
the confidence intervals around the medians using Nx1 and Nx2 arrays,
respectively. Alternatively, you can use lists or tuples and use Nones
if you want to specify those values only for some columns in your MxN
data set. In other words, with an Mx5 data array, you can specify
conf_intervals=[(ci1a,ci2a), (ci1b,ci2b), (ci1c,ci2c), None,
(ci1e,ci2e)]. Within the conf_intervals array, the CIs can be listed
in any order as I use np.max() and np.min() to pull the upper and
lower values, respectively.

The motivation behind this is that sometimes I need the confidence
levels to be different than 95%, and also that I compute those
confidence intervals with a bootstrapping routine that is more robust
than mpl-compatible one I submitted some time ago.

I hope y'all find this to be a useful contribution. I'm an avid
matplotlib user. It really is a wonderful tool.

Cheers,
paul h

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Re: [matplotlib-devel] Location of bleeding edge repository

[Paul Hobson]

Thanks, Scott. That's a huge help.
-p

On Tue, Mar 8, 2011 at 12:49 AM, Scott Sinclair
scott.sinclair...@gmail.com wrote:
 On 8 March 2011 09:40, Paul Hobson pmhob...@gmail.com wrote:
 Is it in git or svn? I just cloned and installed from git using:
 git clone git://github.com/astraw/matplotlib.git
 cd matplotlib
 sudo python setupegg.py develop

 Starting ipython, and importing matplotlib, I get:
 In [2]: matplotlib.__version__
 Out[2]: '1.0.0'

 I thought v1.0.1 was available. Should I install from svn?

 The main repository is at https://github.com/matplotlib/matplotlib you
 cloned a forked copy by mistake.

 As an aside, I first tried the proceedure outlined here:
 http://matplotlib.sourceforge.net/devel/coding_guide.html#using-git
 and got a public key error.

 That doc appears to be out of date. The repository move was quite recent.

 Cheers,
 Scott

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[matplotlib-devel] Location of bleeding edge repository

[Paul Hobson]

Is it in git or svn? I just cloned and installed from git using:
git clone git://github.com/astraw/matplotlib.git
cd matplotlib
sudo python setupegg.py develop

Starting ipython, and importing matplotlib, I get:
In [2]: matplotlib.__version__
Out[2]: '1.0.0'

I thought v1.0.1 was available. Should I install from svn?

As an aside, I first tried the proceedure outlined here:
http://matplotlib.sourceforge.net/devel/coding_guide.html#using-git
and got a public key error.

Thanks,
-paul

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