matplotlib-devel Mailing List for matplotlib (Page 2)

John Hunter wrote:
> On Mon, Jan 26, 2009 at 6:02 PM, Jae-Joon Lee <lee...@gm...> wrote:
>> Michael,
>>
>> It seems that the gtk backend in the current svn silently ignores ALL
>> exceptions raised during the drawing.
>>
>> http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/lib/matplotlib/backends/backend_gtk.py?r1=6696&r2=6793
>>
>> Is this necessary? I don't think we want to do this.
> 
> No, it is a bug.  Catching blanket exceptions and ignoring them is
> never OK -- we need to add a section to the coding guide to this
> effect.  If absolutely necessary, one can catch blanket exceptions and
> log them, eg using cbook.exception_to_str, but they must be reported.
> Michael has already fixed this (perhaps it was some detritus left in
> from a debugging session?) and I'll make a note in the developer docs
> coding guide.

John,

Not quite "always": I think that for something like cbook.is_string_like 
we actually *do* want to silently catch all exceptions.  The problem is 
that if you know nothing about the type of object that you might have to 
deal with, you have no way of knowing what exception it might raise.  We 
ran into an example of this recently.

Eric

> 
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On Tue, Jan 27, 2009 at 12:28 AM, Eric Firing <ef...@ha...> wrote:
> Martin Spacek wrote:
>>
>> Hi,
>>
>> I just updated my checkout to rev 6829, and it seems
>> lines.Line2D.set_pickradius has been renamed to setpickradius. Is this a
>> typo? get_pickradius still exists. This is on line 318 in lines.py. Renaming
>> it back to set_pickradius seems make it work the way it used to.
>>
>
> John, you made the change on Dec. 10:
>
> http://currents.soest.hawaii.edu/hg/hgwebdir.cgi/matplotlib_mirror/diff/0a8f5203a8fd/matplotlib/lib/matplotlib/lines.py
>
> Looks accidental to me.

Yep, it must have been a bug because it doesn't follow the naming
conventions for the set_property funcs. Fixed on branch and trunk.

Thanks for tracking me down as the culprit :-)
JDH

On Mon, Jan 26, 2009 at 6:02 PM, Jae-Joon Lee <lee...@gm...> wrote:
> Michael,
>
> It seems that the gtk backend in the current svn silently ignores ALL
> exceptions raised during the drawing.
>
> http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/lib/matplotlib/backends/backend_gtk.py?r1=6696&r2=6793
>
> Is this necessary? I don't think we want to do this.

No, it is a bug.  Catching blanket exceptions and ignoring them is
never OK -- we need to add a section to the coding guide to this
effect.  If absolutely necessary, one can catch blanket exceptions and
log them, eg using cbook.exception_to_str, but they must be reported.
Michael has already fixed this (perhaps it was some detritus left in
from a debugging session?) and I'll make a note in the developer docs
coding guide.

Sorry.  That was a mistake to commit it -- I did this while I was trying 
to track down a segfault.  I will revert it.

Mike

Jae-Joon Lee wrote:
> Michael,
>
> It seems that the gtk backend in the current svn silently ignores ALL
> exceptions raised during the drawing.
>
> http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/lib/matplotlib/backends/backend_gtk.py?r1=6696&r2=6793
>
> Is this necessary? I don't think we want to do this.
>
> Regards,
>
> -JJ
>
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>   

Martin Spacek wrote:
> Hi,
> 
> I just updated my checkout to rev 6829, and it seems lines.Line2D.set_pickradius 
> has been renamed to setpickradius. Is this a typo? get_pickradius still exists. 
> This is on line 318 in lines.py. Renaming it back to set_pickradius seems make 
> it work the way it used to.
> 

John, you made the change on Dec. 10:

http://currents.soest.hawaii.edu/hg/hgwebdir.cgi/matplotlib_mirror/diff/0a8f5203a8fd/matplotlib/lib/matplotlib/lines.py

Looks accidental to me.

Eric

Hi,

I just updated my checkout to rev 6829, and it seems lines.Line2D.set_pickradius 
has been renamed to setpickradius. Is this a typo? get_pickradius still exists. 
This is on line 318 in lines.py. Renaming it back to set_pickradius seems make 
it work the way it used to.

Cheers,

Martin

Michael,

It seems that the gtk backend in the current svn silently ignores ALL
exceptions raised during the drawing.

http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/lib/matplotlib/backends/backend_gtk.py?r1=6696&r2=6793

Is this necessary? I don't think we want to do this.

Regards,

-JJ

Okay -- I think I've at least narrowed it down to a cause.  Agg uses 
fixed-point arithmetic to render at the low-level -- by default it uses 
24.8 (i.e. 24 integer bits and 8 fractional bits).  Therefore, it can 
only handle pixel coordinates in the range -2^23 to 2^23.  Both of the 
provided examples, after the data has been scaled, draw outside of this 
range, which results in integer overflow, hence things going in the 
wrong direction etc.

We could change the fixed point in agg_basics.h, but I hesitate to do 
so, as it's at the expense of fine detail.  We could possibly move to 
64-bits, but I'm not sure how easy that would be, or what the impact 
might be on 32-bit platforms.

    
//----------------------------------------------------poly_subpixel_scale_e
    // These constants determine the subpixel accuracy, to be more precise,
    // the number of bits of the fractional part of the coordinates.
    // The possible coordinate capacity in bits can be calculated by 
formula:
    // sizeof(int) * 8 - poly_subpixel_shift, i.e, for 32-bit integers and
    // 8-bits fractional part the capacity is 24 bits.
    enum poly_subpixel_scale_e
    {
        poly_subpixel_shift = 8,                      
//----poly_subpixel_shift
        poly_subpixel_scale = 1<<poly_subpixel_shift, 
//----poly_subpixel_scale
        poly_subpixel_mask  = poly_subpixel_scale-1,  
//----poly_subpixel_mask
    };


One thing I will look into is whether the line simplification algorithm 
can be extended to actually clip the lines when they go outside of the 
image range.  At the moment, it does some work to reduce the number of 
points outside of the image, but it always draws at least one point 
outside at its original location.  It looks like Agg has some of the 
pieces necessary to do this -- whether it's feasible to integrate that 
into our existing line simplification algorithm remains to be seen.

Mike


Michael Droettboom wrote:
> Thanks for this.
>
> I believe both of these examples illustrate a shortcoming in Agg when 
> the distance between two points on either end of a line is too great.
>
> I'll do some digging around and see what may be causing this and if any 
> limits can be adjusted -- I may not get to this today, however.
>
> Mike
>
> João Luís Silva wrote:
>   
>> Jan Müller wrote:
>>   
>>     
>>> Hi,
>>>
>>> The simple code snippet at the end of this mail should plot a single line. 
>>>
>>>     
>>>       
>> I can confirm this bug on Ubuntu running matplotlib svn revision 6827. 
>> However I think it doesn't have to do with the log-scale but with the 
>> big variations on the x-scale and the custom xscale. I've reproduced a 
>> similar behavior with the following script (pan and zoom to see the 
>> buggy behavior).
>> --------------------
>>
>> import numpy as np
>> import matplotlib.pyplot as plt
>>
>> x = np.array([1.0,2.0,3.0,1.0E5,2.0E5])
>> y = np.arange(len(x))
>> plt.plot(x,y)
>> plt.xlim(xmin=2,xmax=6)
>> plt.show()
>>
>> --------------------
>>
>> Best Regards,
>> João Silva
>>
>>
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>>   
>>     
>
>   

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Thanks for this.

I believe both of these examples illustrate a shortcoming in Agg when 
the distance between two points on either end of a line is too great.

I'll do some digging around and see what may be causing this and if any 
limits can be adjusted -- I may not get to this today, however.

Mike

João Luís Silva wrote:
> Jan Müller wrote:
>   
>> Hi,
>>
>> The simple code snippet at the end of this mail should plot a single line. 
>>
>>     
>
> I can confirm this bug on Ubuntu running matplotlib svn revision 6827. 
> However I think it doesn't have to do with the log-scale but with the 
> big variations on the x-scale and the custom xscale. I've reproduced a 
> similar behavior with the following script (pan and zoom to see the 
> buggy behavior).
> --------------------
>
> import numpy as np
> import matplotlib.pyplot as plt
>
> x = np.array([1.0,2.0,3.0,1.0E5,2.0E5])
> y = np.arange(len(x))
> plt.plot(x,y)
> plt.xlim(xmin=2,xmax=6)
> plt.show()
>
> --------------------
>
> Best Regards,
> João Silva
>
>
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>   

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Jan Müller wrote:
> Hi,
> 
> The simple code snippet at the end of this mail should plot a single line. 
> 

I can confirm this bug on Ubuntu running matplotlib svn revision 6827. 
However I think it doesn't have to do with the log-scale but with the 
big variations on the x-scale and the custom xscale. I've reproduced a 
similar behavior with the following script (pan and zoom to see the 
buggy behavior).
--------------------

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1.0,2.0,3.0,1.0E5,2.0E5])
y = np.arange(len(x))
plt.plot(x,y)
plt.xlim(xmin=2,xmax=6)
plt.show()

--------------------

Best Regards,
João Silva

Hi,

The simple code snippet at the end of this mail should plot a single line. 

Unfortunately, depending on 
- the backend
- the windowsize
- and the pan/zoom position inside the plot 
one or more additional lines appear.

Under windows it looks like this:

http://img217.imageshack.us/my.php?image=matplotlibproblemei6.png
(disable Adblock Plus on Firefox, otherwise the image might not be visible)

When I pan the plot, the additional lines jump around randomly and sometimes dis- and reappear at their own will.


I get this problem for all the AGG-based backends (one additional line) and the GTK backend (several additional lines). GTKCairo does not show this behavior.

The problem appears under an up-to-date Fedora 64-bit with matplotlib 0.98.3 and 0.89.6 SVN (from today)

The output of  "python setup.py" is this:
------------------
BUILDING MATPLOTLIB
matplotlib: 0.98.6svn
python: 2.5.2 (r252:60911, Sep 30 2008, 15:42:03)  [GCC 4.3.2 20080917 (Red Hat 4.3.2-4)]
platform: linux2

REQUIRED DEPENDENCIES
numpy: 1.2.0
freetype2: 9.18.3

OPTIONAL BACKEND DEPENDENCIES
libpng: 1.2.33
Tkinter: no
wxPython: 2.8.9.1
      * WxAgg extension not required for wxPython >= 2.8
Gtk+: gtk+: 2.14.5, glib: 2.18.3, pygtk: 2.13.0,
      pygobject: 2.15.4
Qt4: Qt: 4.4.3, PyQt4: 4.4.4
Cairo: 1.4.12

OPTIONAL DATE/TIMEZONE DEPENDENCIES
datetime: present, version unknown
dateutil: 1.4
pytz: 2008i

OPTIONAL USETEX DEPENDENCIES
dvipng: no
ghostscript: 8.63
latex: no
------------------

Additionally I see this problem under 32-bit Windows XP using the Enthought EPD Py25 v4.1.30101 distribution which uses matplotlib 0.98.3

To reproduce the error:
- switch to GTKAgg
- run the script below
- enlarge or maximize the window (something larger than 1280*1024 should be fine)
- play around with zoom/pan

Expected result:
- a single line is plotted

Actually result:
- two or more lines are plotted




If you need more information just contact me,
Jan



-----------------------------------------



import numpy as np
import matplotlib.pyplot as plt

E = np.array((  
    1.00000000e+00,   1.50000000e+00,   2.00000000e+00,   3.00000000e+00,
    4.00000000e+00,   5.00000000e+00,   6.00000000e+00,   8.00000000e+00,
    1.00000000e+01,   1.50000000e+01,   2.00000000e+01,   3.00000000e+01,
    4.00000000e+01,   5.00000000e+01,   6.00000000e+01,   8.00000000e+01,
    1.00000000e+02,   1.50000000e+02,   2.00000000e+02,   3.00000000e+02,
    
    4.00000000e+02,   5.00000000e+02,   6.00000000e+02,   8.00000000e+02,
    1.00000000e+03,   1.02200000e+03,   1.25000000e+03,   1.50000000e+03,
    2.00000000e+03,   2.04400000e+03,   3.00000000e+03,   4.00000000e+03,
    5.00000000e+03,   6.00000000e+03,   7.00000000e+03,   8.00000000e+03,
    9.00000000e+03,   1.00000000e+04,   1.10000000e+04,   1.20000000e+04,
    
    1.30000000e+04,   1.40000000e+04,   1.50000000e+04,   1.60000000e+04,
    1.80000000e+04,   2.00000000e+04,   2.20000000e+04,   2.40000000e+04,
    2.60000000e+04,   2.80000000e+04,   3.00000000e+04,   4.00000000e+04,
    5.00000000e+04,   6.00000000e+04,   8.00000000e+04,   1.00000000e+05,
    1.50000000e+05,   2.00000000e+05,   3.00000000e+05,   4.00000000e+05,
    
    5.00000000e+05,   6.00000000e+05,   8.00000000e+05,   1.00000000e+06,
    1.50000000e+06,   2.00000000e+06,   3.00000000e+06,   4.00000000e+06,
    5.00000000e+06,   6.00000000e+06,   8.00000000e+06,   1.00000000e+07,
    1.50000000e+07,   2.00000000e+07,   3.00000000e+07,   4.00000000e+07,
    5.00000000e+07,   6.00000000e+07,   8.00000000e+07,   1.00000000e+08))
   
att = np.array((
    6.81740051e+00,   1.75185086e+00,   6.63815247e-01,   1.67656668e-01,
    6.29160626e-02,   2.93190047e-02,   1.56961535e-02,   5.86499592e-03,
    2.72337524e-03,   6.73972636e-04,   2.49871770e-04,   6.16613263e-05,
    2.28421754e-05,   1.05816094e-05,   5.64930077e-06,   2.10496950e-06,
    9.82279267e-07,   2.49513274e-07,   9.62561983e-08,   2.63733618e-08,
    
    1.11014287e-08,   5.93131769e-09,   3.67936480e-09,   1.86298464e-09,
    1.17168470e-09,   1.12328773e-09,   7.79131487e-10,   5.81480647e-10,
    3.70505702e-10,   3.58735080e-10,   2.10556699e-10,   1.46027404e-10,
    1.11492282e-10,   9.01020155e-11,   7.55231748e-11,   6.50072897e-11,
    5.70367268e-11,   5.08048699e-11,   4.57978746e-11,   4.16871195e-11,
    
    3.82455571e-11,   3.53357639e-11,   3.28322663e-11,   3.06573900e-11,
    2.70724292e-11,   2.42403101e-11,   2.19399603e-11,   2.00399310e-11,
    1.84446235e-11,   1.70823384e-11,   1.59052762e-11,   1.18363457e-11,
    9.42247205e-12,   7.82716448e-12,   5.84766861e-12,   4.66702151e-12,
    3.10158861e-12,   2.32245712e-12,   1.54571561e-12,   1.15853984e-12,
    
    9.26114881e-13,   7.71364072e-13,   5.78493179e-13,   4.62698944e-13,
    3.08366381e-13,   2.31229973e-13,   1.54153316e-13,   1.15555237e-13,
    9.24919894e-14,   7.70766578e-14,   5.77835936e-14,   4.62280699e-14,
    3.08187133e-14,   2.31110475e-14,   1.54093566e-14,   1.15555237e-14,
    9.24322401e-15,   7.70169085e-15,   5.77776187e-15,   4.62220950e-15))

plt.figure()
plt.plot(E,att)

plt.yscale("log")
plt.xscale("linear")

plt.xlim(xmin=np.log(20), xmax=np.log(500))

plt.ylim(ymin=-18,ymax=5)

plt.show()

All,

While looking over the polar plot code I came across the following issue:  When plotting something like 'polar( [2*pi/180, 358*pi/180], [2.0, 1.0] )' the plotted line will actually wrap around the origin of the plot before reaching its destination.  Initially I thought that this was correct behavior.  The line numerically passed through all angles between 2 and 358 degrees in a linear fashion.  However after consulting several colleagues and text books I believe that the behavior is actually wrong.

It is my understanding that for polar plots there is no linear mapping of the axes as it is currently implemented.  Rather for a simple two-point line defined in polar coordinates, the line should essentially take the direct route.  This is highlighted by the two-point equation of a line for polar plots:

r = ( r1*r2*sin(t2-t1) ) / ( (r1*sin(t-t1)) - (r2*sin(t-t2)) )

If you were to plug in the two points given above, then increment theta (t) from 2 degrees to 358 degrees, then convert to Cartesian cords, and plot the results, you will get the correct line that directly crosses the zero degree line and not one that wraps around the origin.

Is the polar plot function implemented this way on purpose?  Which way should it really be implemented?

Thanks,
--James Evans

Hello

I couldn't manage to find how to search the mailing list archive on
sourceforge, so sorry if it is already discussed/developed.

There are many backends for matplotlib and debian/ubuntu packages depend
  on at least one of the interactive once [1]. But matplotrc as
installed says "backend: TkAgg". This often results in errors upon
importing pylab  after installation saying "Please install python-tk".

Has there been any work done about providing an auto backend? The one
that would select the best one automagically.

I've pocked into code a little bit. My understanding is to hook into
pylab_setup() (lib/matplotlib/backends/__init__.py). That is if backend
is auto, run a script which returns a name of the pretties backend which
 then is used.

I think I will manage writing something like that. And I would be glad
to collaborate.

[1] One of the dependencies is:
python-tk | python-gtk2 | python-wxgtk2.8 | python-qt3 | python-qt4

ps. cloned github repo, now updating, takes ages =D Maybe git hub needs
to be updated with more revisions? =D
--
With regards,

Dmitrijs Ledkovs.

Ryan May wrote:
> Hi,
> 
> Does anyone know why set_aspect('equal', 'box') isn't accepted on shared axes?
> I'm trying to make the following type of scenario work:

Ryan,

Mark Bakker keeps asking about this, too.

I have never been able to figure out an algorithm, consistent with the 
present mpl architecture, that would cleanly handle shared axes with 
fixed aspect ratio when the box is adjustable.  If you can specify 
exactly what should happen under all such conditions that might 
arise--including single or dual shared axes, and fixed aspect ratio on 
one or more of the plots with shared axes, and any kind of zooming or 
panning--then we should be able to come up with the logic to implement it.

Maybe the problem is not that there is no such specification or 
algorithm, but that it requires a global solution, involving figuring 
out the dimensions of all the boxes at the same time, whereas with 
adjustable datalim the calculation can be done on any subplot 
individually--hence it can be in Axes.draw, as at present.  I suspect 
this is the crux of it--fixed aspect ratio, shared axes, adjustable box 
would require a level of code that does not exist at present; a method 
invoked at the Figure.draw() level that would calculate and freeze all 
of the axes positions in one whack before any of them are drawn.

Actually, even this is not right, because IIRC axes can be shared across 
figures, so this calculation would need to be done at the highest 
level--before the Figure.draw() method.

If we go this route--which sounds like going to full-fledged sizer/pack 
type algorithms--we need to be sure it does not slow down interactive 
responsiveness.  Or burden us with bugs and unmaintainable code. 
Sometimes it is worthwhile to accept some limitations and keep things 
simple.

Note that the present implementation of shared axes, unlike an earlier 
implementation, has no notion of master and slaves; all are equivalent, 
and can be calculated and drawn in any order.

Eric


> 
> import numpy as np
> from matplotlib.pyplot import figure, show
> 
> fig1 = figure()
> fig2 = figure()
> 
> ax1 = fig1.add_subplot(1, 1, 1)
> ax1.set_aspect('equal', 'datalim')
> 
> ax2 = fig2.add_subplot(1, 2, 1, sharex=ax1, sharey=ax1)
> ax2.set_aspect('equal', 'datalim')
> ax3 = fig2.add_subplot(1, 2, 2, sharex=ax2, sharey=ax2)
> 
> data = np.random.rand(50,50)
> ax1.pcolormesh(data)
> ax2.pcolormesh(data)
> ax3.pcolormesh(data)
> 
> show()
> 
> Basically, I have multiple figures with multiple subplots, all of which should be
> displaying the same range.  However, the different figures have different numbers
> of subplots.  The example above doesn't work, because once you zoom into one of
> the figures, it iteratively zooms out, adjusting data limits until both figures
> have their aspect ratio properly set again.  I thought using 'box' might
> alleviate the problem, but that's throwing an exception.
> 
> I realize making the figures have the same layout would solve the problem, I just
> wasn't sure if there was another way.
> 
> Ryan

Hi,

Does anyone know why set_aspect('equal', 'box') isn't accepted on shared axes?
I'm trying to make the following type of scenario work:

import numpy as np
from matplotlib.pyplot import figure, show

fig1 = figure()
fig2 = figure()

ax1 = fig1.add_subplot(1, 1, 1)
ax1.set_aspect('equal', 'datalim')

ax2 = fig2.add_subplot(1, 2, 1, sharex=ax1, sharey=ax1)
ax2.set_aspect('equal', 'datalim')
ax3 = fig2.add_subplot(1, 2, 2, sharex=ax2, sharey=ax2)

data = np.random.rand(50,50)
ax1.pcolormesh(data)
ax2.pcolormesh(data)
ax3.pcolormesh(data)

show()

Basically, I have multiple figures with multiple subplots, all of which should be
displaying the same range.  However, the different figures have different numbers
of subplots.  The example above doesn't work, because once you zoom into one of
the figures, it iteratively zooms out, adjusting data limits until both figures
have their aspect ratio properly set again.  I thought using 'box' might
alleviate the problem, but that's throwing an exception.

I realize making the figures have the same layout would solve the problem, I just
wasn't sure if there was another way.

Ryan
-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma

Michael Droettboom <mdroe@...> writes:



> >   
> Thanks for the pointers.
> 
> The original simplification code was written by John Hunter (I believe), 
> and I don't know if it was designed by him also or is a replication of 
> something published elsewhere.  So I take no credit for and have little 
> knowledge of its original goals.

I'm not sure on everything it does, but it seems to do clipping and removes 
line segments where the change in slope is less than some limit. There are
probably better algorithms out there, but this one works surprisingly well 
and is fast and simple. I think it should be a requirement that it returns
points which are a subset of the original points- with the change you've 
made it does this, right? 

> 
> However, IMHO the primary purpose of the path simplification in 
> matplotlib is to improve interactive performance (and smaller file size 
> is just an convenient side effect of that), I would hesitate to use an 
> algorithm that is any worse than O(n), since it must be recalculated on 
> every pan or zoom since the simplification is related to *pixels* not 
> data units.  Even on modern hardware, it is a constant battle keeping 
> the inner drawing loop fast enough.  We could, of course, make the 
> choice of algorithm user-configurable, or use something more precise 
> when using a non-interactive backend, but then we would have two 
> separate code paths to keep in sync and bug free --- not a choice I 
> take lightly.

I see your point.

I originally encountered a problem when preparing a pdf figure- I had a lot 
of high resolution data, and with path simplification the resulting pdf 
looked pretty bad (the lines were jagged). But the advantage was a massive
reduction in file size of the pdf. I adjusted perpdNorm2 and got much better
results.


> 
> The trick with the present algorithm is to keep the error rate at the 
> subpixel level through the correct selection of perpdNorm.  It seems to 
> me that the more advanced simplification algorithm is only necessary 
> when you want to simplify more aggressively than the pixel level.  But 
> what hasn't been done is a proper study of the error rate along the 
> simplified path of the current approach vs. other possible approaches.  
> Even this latest change was verified by just looking at the results 
> which seemingly are better on the data I looked at.  So I'm mostly 
> speaking from my gut rather than evidence here.
> >
> >   #src/agg_py_path_iterator.h
> >
> >     //if the perp vector is less than some number of (squared)
> >     //pixels in size, then merge the current vector
> >     if (perpdNorm2 < (1.0 / 9.0))
> >   
> That sounds like a good idea.  I'll have a look at doing that.
> 

Right, perhaps the best thing to do is make the tolerance parameter 
adjustable, so it can be reduced to speed up drawing in the interactive
backends, but it can also be easily bumped up for extra resolution in the
non-interactive backends like pdf/ps.


> Mike
> 

a

All,

 

I have a suggestion for the units.ConversionInterface class.  It seems to me that for each method of the ConversionInterface there
should be another parameter passed in, the Axis instance that is requesting the conversion/info.  Doing so would allow for any
custom conversion code to make checks on the Axis, whether it is X/Y, or even make checks on the Axes the Axis is attached to.
Additionally this would allow for converters to apply any necessary changes before they are really used (such as DateConverter
making sure that the axis bounds are >= 1).

 

Comments/Suggestions?

 

If it is okay, I would like to apply the necessary changes.

 

--James Evans

a wrote:
> Michael Droettboom <mdroe@...> writes:
>
>   
>> I've checked this change into SVN so others can test it out.
>>
>> Assuming we don't discover any cases where this is clearly inferior, it 
>> should make it into the next major release.
>>
>> Mike
>>
>>     
>
> Hi,
>
> This change looks good- it has the advantage of choosing points that actually
> lie on the curve, which is better visually, and would seem to be a better
> solution for publication quality plots.
>
> The method for simplifying the paths is quite simple and effective, but a bit
> crude- there are other algorithms you might look into for simplifying lines:
>
>   http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
>
> This one is fairly simple to implement and has the advantage that you have some
> control over the errors- the deviation from your simplified path and the actual
> path.
>   
Thanks for the pointers.

The original simplification code was written by John Hunter (I believe), 
and I don't know if it was designed by him also or is a replication of 
something published elsewhere.  So I take no credit for and have little 
knowledge of its original goals.

However, IMHO the primary purpose of the path simplification in 
matplotlib is to improve interactive performance (and smaller file size 
is just an convenient side effect of that), I would hesitate to use an 
algorithm that is any worse than O(n), since it must be recalculated on 
every pan or zoom since the simplification is related to *pixels* not 
data units.  Even on modern hardware, it is a constant battle keeping 
the inner drawing loop fast enough.  We could, of course, make the 
choice of algorithm user-configurable, or use something more precise 
when using a non-interactive backend, but then we would have two 
separate code paths to keep in sync and bug free --- not a choice I take 
lightly.

The trick with the present algorithm is to keep the error rate at the 
subpixel level through the correct selection of perpdNorm.  It seems to 
me that the more advanced simplification algorithm is only necessary 
when you want to simplify more aggressively than the pixel level.  But 
what hasn't been done is a proper study of the error rate along the 
simplified path of the current approach vs. other possible approaches.  
Even this latest change was verified by just looking at the results 
which seemingly are better on the data I looked at.  So I'm mostly 
speaking from my gut rather than evidence here.
> Also, you might consider to make the path simplification tolerance (perdNorm2)
> an adjustable parameter in the matplotlibrc file:
>
>   #src/agg_py_path_iterator.h
>
>     //if the perp vector is less than some number of (squared)
>     //pixels in size, then merge the current vector
>     if (perpdNorm2 < (1.0 / 9.0))
>   
That sounds like a good idea.  I'll have a look at doing that.

Mike

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Michael Droettboom <mdroe@...> writes:

> 
> I've checked this change into SVN so others can test it out.
> 
> Assuming we don't discover any cases where this is clearly inferior, it 
> should make it into the next major release.
> 
> Mike
> 

Hi,

This change looks good- it has the advantage of choosing points that actually
lie on the curve, which is better visually, and would seem to be a better
solution for publication quality plots.

The method for simplifying the paths is quite simple and effective, but a bit
crude- there are other algorithms you might look into for simplifying lines:

  http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm

This one is fairly simple to implement and has the advantage that you have some
control over the errors- the deviation from your simplified path and the actual
path.

Also, you might consider to make the path simplification tolerance (perdNorm2)
an adjustable parameter in the matplotlibrc file:

  #src/agg_py_path_iterator.h

    //if the perp vector is less than some number of (squared)
    //pixels in size, then merge the current vector
    if (perpdNorm2 < (1.0 / 9.0))


kind regards,

a

I've checked this change into SVN so others can test it out.

Assuming we don't discover any cases where this is clearly inferior, it 
should make it into the next major release.

Mike

Andrew Hawryluk wrote:
>> -----Original Message-----
>> From: Michael Droettboom [mailto:md...@st...]
>> Sent: 16 Jan 2009 1:31 PM
>> To: Andrew Hawryluk
>> Cc: mat...@li...
>> Subject: Re: [matplotlib-devel] path simplification can decrease the
>> smoothness of data plots
>>
>> Michael Droettboom wrote:
>>     
>
> ...
>
>   
>> I've attached a patch that will only include points from the original
>> data in the simplified path.  I hesitate to commit it to SVN, as these
>> things are very hard to get right -- and just because it appears to
>> work better on this data doesn't mean it doesn't create a regression
>>     
> on
>   
>> something else... ;)  That said, it would be nice to confirm that this
>> solution works, because it has the added benefit of being a little
>> simpler computationally.  Be sure to blitz your build directory when
>> testing the patch -- distutils won't pick it up as a dependency.
>>
>> I've attached two PDFs -- one with the original (current trunk)
>> behavior, and one with the new behavior.  I plotted the unsimplified
>> plot in thick blue behind the simplified plot in green, so you can see
>> how much deviation there is between the original data and the
>> simplified line (you'll want to zoom way in with your PDF viewer to
>>     
> see
>   
>> it.)
>>
>> I've also included a new version of your test script which detects
>> "new"
>> data values in the simplified path, and also seeds the random number
>> generator so that results are comparable.  I also set the
>> solid_joinstyle to "round", as it makes the wiggliness less
>>     
> pronounced.
>   
>> (There was another thread on this list recently about making that the
>> default setting).
>>
>> Cheers,
>> Mike
>>
>> --
>> Michael Droettboom
>> Science Software Branch
>> Operations and Engineering Division
>> Space Telescope Science Institute
>> Operated by AURA for NASA
>>     
>
> Thanks for looking into this! The new plot is much improved, and the
> simplified calculations are a pleasant surprise. I was also testing the
> previous algorithm with solid_joinstyle set to "round" as it is the
> default in my matplotlibrc.
>
> I am probably not able to build your patch here, unless building
> matplotlib from source on Windows is easier than I anticipate. May I
> send you some data off the list for you to test?
>
> Regards,
> Andrew
>
> NOVA Chemicals Research & Technology Centre
> Calgary, Canada
>   

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

I'm not able to see this here (RHEL 4, matplotlib SVN trunk) with either 
the PDF or TkAgg backends.  The attached graphs show that memory usage 
levels off after the first 3-4 iterations.  I only did 10 iterations 
here to save bandwidth, but I also tested 500 and the results were the 
same. As I'm not on a Mac, I'm unable to test the Cocoa backend.  What 
tool are you using to detect the memory leaks?  Many tools that work at 
the high level (such as Windows Task Manager etc.) are full of pitfalls, 
so in my experience you really need to use a tool designed for the 
purpose of detecting memory leaks (such as Valgrind/Massif, or the Apple 
one whose name escapes me).

I modified Damon's script to use random data (since his data was not 
attached).  If this script does not leak memory for either of you, then 
perhaps the leak is data-dependent, in which case I'll either need to 
obtain or better simulate that data.

Mike
 
Michiel de Hoon wrote:
> I am also finding the continuing increase in memory usage, but this also occurs with other backends (I tried tkagg and pdf) and also without the call to savefig. One possibility is a circular reference in the quiver function that prevents data from being cleaned up.
>
> --Michiel
>
>
> --- On Mon, 1/19/09, Damon McDougall <dam...@gm...> wrote:
>
>   
>> From: Damon McDougall <dam...@gm...>
>> Subject: [matplotlib-devel] Memory leak using savefig with MacOSX backend?
>> To: "matplotlib development list" <mat...@li...>
>> Date: Monday, January 19, 2009, 6:09 AM
>> I'm looping over several files (about 1000) to produce a
>> vector field plot for each data file I have. Doing this with
>> the MacOSX backend appears to chew memory. My guess as to
>> the source of the problem is the 'savefig' function
>> (or possibly the way the MacOSX backend handles the saving
>> of plots).
>>
>> I opened Activity Monitor to watch the usage of memory
>> increase. Below is code that recreates the problem.
>>
>> [start]
>>
>> import matplotlib
>> matplotlib.use('macosx')
>> matplotlib.rc('font',**{'family':'serif','serif':['Computer
>> Modern Roman']})
>> matplotlib.rc('text', usetex=True)
>> from pylab import *
>>
>> i = 0
>> x = []
>> y = []
>> v1 = []
>> v2 = []
>>
>> while(True):
>> 	f = open("%dresults.dat"%i,"r")
>> 	for line in f:
>> 		x.append(float(line.split()[0]))
>> 		y.append(float(line.split()[1]))
>> 		v1.append(float(line.split()[2]))
>> 		v2.append(float(line.split()[3]))
>> 	f.close()
>> 	hold(False)
>> 	figure(1)
>> 	quiver(x, y, v1, v2, color='b',
>> units='width', scale=1.0)
>> 	xlabel('$x$')
>> 	ylabel('$y$')
>> 	grid(True)
>> 	print i
>> 	savefig('graph-%05d.pdf'%i)
>> 	close(1)
>> 	x = []
>> 	y = []
>> 	v1 = []
>> 	v2 = []
>> 	i = i + 1
>>
>>
>> [end]
>>
>> Regards,
>> --Damon
>>
>> ------------------------------------------------------------------------------
>> This SF.net email is sponsored by:
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>>     
>
>
>       
>
> ------------------------------------------------------------------------------
> This SF.net email is sponsored by:
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>   

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

> Thanks for looking into this! The new plot is much improved, and the
> simplified calculations are a pleasant surprise. I was also testing the
> previous algorithm with solid_joinstyle set to "round" as it is the
> default in my matplotlibrc.
>
> I am probably not able to build your patch here, unless building
> matplotlib from source on Windows is easier than I anticipate. May I
> send you some data off the list for you to test?
>   
No problem.  I'd also want testing from others -- there aren't a lot of examples in matplotlib itself where simplification even kicks in.

Mike

-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

> -----Original Message-----
> From: Michael Droettboom [mailto:md...@st...]
> Sent: 16 Jan 2009 1:31 PM
> To: Andrew Hawryluk
> Cc: mat...@li...
> Subject: Re: [matplotlib-devel] path simplification can decrease the
> smoothness of data plots
> 
> Michael Droettboom wrote:

...

> I've attached a patch that will only include points from the original
> data in the simplified path.  I hesitate to commit it to SVN, as these
> things are very hard to get right -- and just because it appears to
> work better on this data doesn't mean it doesn't create a regression
on
> something else... ;)  That said, it would be nice to confirm that this
> solution works, because it has the added benefit of being a little
> simpler computationally.  Be sure to blitz your build directory when
> testing the patch -- distutils won't pick it up as a dependency.
> 
> I've attached two PDFs -- one with the original (current trunk)
> behavior, and one with the new behavior.  I plotted the unsimplified
> plot in thick blue behind the simplified plot in green, so you can see
> how much deviation there is between the original data and the
> simplified line (you'll want to zoom way in with your PDF viewer to
see
> it.)
> 
> I've also included a new version of your test script which detects
> "new"
> data values in the simplified path, and also seeds the random number
> generator so that results are comparable.  I also set the
> solid_joinstyle to "round", as it makes the wiggliness less
pronounced.
> (There was another thread on this list recently about making that the
> default setting).
> 
> Cheers,
> Mike
> 
> --
> Michael Droettboom
> Science Software Branch
> Operations and Engineering Division
> Space Telescope Science Institute
> Operated by AURA for NASA

Thanks for looking into this! The new plot is much improved, and the
simplified calculations are a pleasant surprise. I was also testing the
previous algorithm with solid_joinstyle set to "round" as it is the
default in my matplotlibrc.

I am probably not able to build your patch here, unless building
matplotlib from source on Windows is easier than I anticipate. May I
send you some data off the list for you to test?

Regards,
Andrew

NOVA Chemicals Research & Technology Centre
Calgary, Canada

Greetings,

This weekend I decided to try and build mpl for windows using python
2.6.1.  I was able to build numpy and mpl and I thought things were
great.  However, when I tried to run a script with a show() function,
nothing would happen.  Looking into this, I discovered that mpl didn't
recognize Tkinter during it's building process.  I checked the
setupext.py code and found that python 2.6 wast excluded in the
windows section, even though Tkinter at least imports using python2.6.
 I also noticed that there appears to be a hold over to a previous
version of mpl by including references to python 2.2, even though the
TCL/TK headers and libs for TCL/TK 8.3 aren't included in win32_static
version anymore.  I decided to build TCL/TK 8.5 (which is what python
2.6 ships with) and dumped the appropriate libs in the lib directory
of win32_static and the header files into include/tcl85 directory.  I
then changed references from python 2.2 to use python 2.6 (including
using TCL/TK 8.5 instead of 8.3), rebuilt mpl, and ran it.  This time
the show window opens, however as the figure is being drawn an error
occurs and the figure window remains blank.  Below is the traceback:

Exception in Tkinter callback
Traceback (most recent call last):
  File "D:\Python26\lib\lib-tk\Tkinter.py", line 1410, in __call__
    return self.func(*args)
  File "D:\Python26\lib\site-packages\matplotlib\backends\backend_tkagg.py",
line 212, in resize
    self.show()
  File "D:\Python26\lib\site-packages\matplotlib\backends\backend_tkagg.py",
line 216, in draw
    tkagg.blit(self._tkphoto, self.renderer._renderer, colormode=2)
  File "D:\Python26\lib\site-packages\matplotlib\backends\tkagg.py",
line 19, in blit
    tk.call("PyAggImagePhoto", photoimage, id(aggimage), colormode,
id(bbox_array))
TclError


This is as far as I've been able to go (so far) in tracking down the
issue.  I'll admit that I haven't looked much farther due to time
constraints, so if anyone else has suggestions as to where to go next,
I'm all ears.  I'm not sure who the windows guru is for mpl, but if I
(we) can figure this problem out, I'll work with them in getting mpl
ready for 2.6 on windows.

Thanks,
-Patrick



-- 
Patrick Marsh
Graduate Research Assistant
School of Meteorology
University of Oklahoma
http://www.patricktmarsh.com

On Mon, Jan 19, 2009 at 6:33 AM, Michiel de Hoon <mjl...@ya...> wrote:
> I am also finding the continuing increase in memory usage, but this also occurs with other backends (I tried tkagg and pdf) and also without the call to savefig. One possibility is a circular reference in the quiver function that prevents data from being cleaned up.

I do not know how relevant this is to the problem at hand, but I have
observed memory leaks in the Delaunay code in matplotlib 0.98.3. I
hadn't had the chance to upgrade to 0.98.5.x yet, but judging from the
release notes the issue was not fixed. Since the leak happens from
inside Sage I need to find out what exactly causes those leaks before
poking around and fixing them.

> --Michiel
>

Cheers,

Michael