matplotlib-users Mailing List for matplotlib

>>>>> "Eric" == Eric Emsellem <ems...@ob...> writes:

    Eric> Am I doing something wrong there?

No, this is a bug in the tick labeling / extent setting for aspect
preserved images - thanks for reminding me.  I had managed to suppress
it from my active memory!

JDH

Hi,

I am trying to display an image with imshow. What I am doing now is
to write a small function which plot the image preserving the aspect
ratio of the pixels in X and Y, but scaling the size of the image of the 
window
but at the same time scaling the axes either to just surround the image
(default) or to be larger.

This should be easy  as I can use 'preserve'
for the 'aspect' of imshow: that takes care of the pixel scale on the 
figure.

However then it does have axis which show incorrect coordinates, namely:
- along the direction where the image is filling the figure window better
 it shows the right coordinates
- along the other direction, the image is not filling the entire length
(as expected if the image does not follow the window aspect ratio)
but the axis are STILL showing the limits of the image.

To be clear:
 for  a square figure window.
 If my image is 100 x 50,
 then with aspect='preserve' I have the right coordinates along X
(showing 0==> 100) but the wrong coordinates for Y (showing 0=>50,
and NOT 0=>100 as it should since it fills only half the figure...).

I hope this is clear.

Am I doing something wrong there?

Thanks,

Eric E.

-- 
===============================================================
Observatoire de Lyon                 ems...@ob...
9 av. Charles-Andre                      tel: +33 4 78 86 83 84
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France               http://www-obs.univ-lyon1.fr/eric.emsellem
===============================================================

>>>>> "seberino" == seberino  <seb...@sp...> writes:

    seberino> I installed Numeric and I can import it into Python but
    seberino> newest stable Matplotlib is barfing on it.  I installed
    seberino> Matplotlib after I installed Numeric so I'm not sure
    seberino> reinstalling it will fix this problem.  How fix?

Just to be sure


  * rm -rf your "build" subdir in the matplotlib directory

  * make sure you have Numeric installed for the same python you are
    building with.  Eg, specify the full path to python when testing
    and building

    > /usr/bin/python2.3
    >>> import Numeric
    >>> CTRL-D
    > sudo /usr/bin/python2.3 setup.py install >& build.out


  * make sure you choose numerix : Numeric  in .matplotlibrc

If you encounter more problems, post build.out - the incantation for
capturing standard output and standard error may differ depending on
your shell
   
Most of the times problems like this occur from distutils not properly
rebuilding when it should and can be solved by erasing its memory, ie
the "build" directory.

Let me know...

JDH

I installed Numeric and I can import it into Python but
newest stable Matplotlib is barfing on it.  I installed
Matplotlib after I installed Numeric so I'm not sure
reinstalling it will fix this problem.  How fix?

Chris

...Here is error output....:


The import of the numeric version of the _contour module,
_nc_contour, failed.  This is is either because numeric was
unavailable when matplotlib was compiled, because a dependency of
_nc_contour could not be satisfied, or because the build flag for
this module was turned off in setup.py.  If it appears that
_nc_contour was not built, make sure you have a working copy of
numeric and then re-install matplotlib. Otherwise, the following
traceback gives more details:

Traceback (most recent call last):
  File "test_campbell_diagram.py", line 10, in ?
    import campbell_diagram
  File
  "/home/seberino/MAIN/BusinessStuff/ThomasStuff/PlottingProject/CampbellDi
  agram/campbell_diagram.py", line 14, in ?
    import matplotlib.pylab
  File "/usr/lib/python2.3/site-packages/matplotlib/pylab.py", line
  184, in ?
    from axes import Axes, PolarAxes
  File "/usr/lib/python2.3/site-packages/matplotlib/axes.py", line 11,
  in ?
    import _contour
  File "/usr/lib/python2.3/site-packages/matplotlib/_contour.py", line
  11, in ?
    from matplotlib._nc_contour import *
ImportError: No module named _nc_contour

>>>>> "Eric" == Eric Emsellem <ems...@ob...> writes:

    Eric> Hi, working more and more with matplotlib and adapt my local
    Eric> tools I find that (for me) 2 plotting functionalities are
    Eric> still missing (but are in the list of the ''goals''):

    Eric> - plotting a surface either using a mesh/grid like plotwith
    Eric> colours or a smooth rendering - 

I am not familiar with "plotwith colours".  Is that a gnuplot command?

I assume you are referring to a 3D surf or mesh plot here, no?  The
official answer here has always been we're deferring any serious work
on 3D until we have a feature complete 2D lib in part because there
are already a number of excellent 3D tools for python.  There has been
some proof-of-concept work integrating VTK ( a premier 3D lib) with
matplotlib/agg -- you can find links on the goals page.

Although there are good tools already out there, I am receptive to the
argument that it's nice to have a core set of functionality under one
roof, and so would like to include support for basic 3D plots down the
road.  If some enterprising person wanted to take this on sooner, I
wouldn't be opposed.  But I can't give you an estimate on the time
frame right now.  Like much in the open source world, it's chronically
6 months away :-)

    Eric> have the connect function update the ''key'' field when
    Eric> using the keyboard

OK, to make up for the non-committal answer above, I implemented this
in CVS and it will be available in the next release.  Here's the
keypress_demo from CVS

from pylab import *

def press(event):
    if event.key=='g':
        grid()
        draw()
    
connect('key_press_event', press)

title('press g to toggle grid')
plot(rand(12), rand(12), 'go')
show()


In the process of getting this to work, I refactored the event
handling across backends in such a way that it is now robust and seems
to work well on all the backends, supporting multiple connects and
disconnects.  

JDH

>>>>> "Jeff" == Jeff Whitaker <js...@fa...> writes:

    Jeff> I'm currently working on a map plotting module.  I've added
    Jeff> the ability to plot filled continents on various map
    Jeff> projections, using the GSHHS coastline polygon dataset
    Jeff> (http://www.soest.hawaii.edu/wessel/gshhs/gshhs.html).  This
    Jeff> dataset provides polygon segments that define continental
    Jeff> outlines.  However, before I post it here I'd like to also
    Jeff> provide the capability to fill the 'wet' areas as well.  In
    Jeff> order to do this I need to figure out how to fill the area
    Jeff> between the polygons.  Looks like this module
    Jeff> (http://www.dezentral.de/soft/Polygon/index.html) would do
    Jeff> the trick, but it requires the generaly polygon clipping C
    Jeff> library.  Do you have any suggestions?

agg actually includes a wrapper for gpc, as well as the gpc code
itself, but gpc is GPLd and I haven't included it in matplotlib for
that reason.  Agg separately implements a "scanline boolean algebra"
which I haven't studied in detail but which I understand basically
does the same thing: allow clipping to arbitrary polygons, and
supports boolean operations on polygons --
http://www.antigrain.com/demo/index.html#PAGE_DEMO_scanline_boolean

We have to expose the functionality such that it is available for use
by other backends, much as we use agg for image across backends.

If this is an area that you want to dive into, by all means.
Otherwise, it's on the list of things to do (I just added it to the
goals page in my tree, so it will appear on the site docs in the
not-too-distant-future.

JDH