matplotlib-users

[Matplotlib-users] dynamic colorbar

[Rodrigo C. <rc...@ge...>]

One of my favorite aspects of matplotlib is how easy it is to make 
dynamic plots. When doing this with 2D renderings using pcolor or 
imshow, is there an easy way to also dynamically update the  colorbar?

Thanks,
Rodrigo

Re: [Matplotlib-users] dynamic colorbar

[John H. <jdh...@ac...>]

>>>>> "Rodrigo" == Rodrigo Caballero <rc...@ge...> writes:

    Rodrigo> One of my favorite aspects of matplotlib is how easy it
    Rodrigo> is to make dynamic plots. When doing this with 2D
    Rodrigo> renderings using pcolor or imshow, is there an easy way
    Rodrigo> to also dynamically update the colorbar?

It should *just work*.  images (matplotlib.image.AxesImage) and
pcolors (matplotlib.collections.PolygonCollection) both derive from
matplotlib.cm.ScalarMappable.  This class implements an observer
pattern, and the colorbar registers itself with the ScalarMappable
instance.  If you change the normalization, or colormap, or
colorlimits, the ScalarMappable instance notifies it's observers, who
update automagically.

Let me know if you run into any troubles.

JDH

Re: [Matplotlib-users] dynamic colorbar

[Rodrigo C. <rc...@ge...>]

Ok, here's the problem, illlustrated by a simple variant of the 
dynamic_image_gtkagg.py example (see below).
I've put in a colorbar and I'm letting the amplitude of the pattern 
grow linearly in time. The result is that the colormap limits remained 
fixed at their initial values (-2,2), rather than change in time. If I 
don't put in the colorbar, the colormap limits *do* change. I'm a bit 
confused about all this.
I tried changing the normalization of the colormap doing 
im.autoscale(z) each time z is updated, but that doesn't work ...

Rodrigo

-----------

#!/usr/bin/env python
"""
An animated image
"""
import sys, time, os, gc
from matplotlib import rcParams

from matplotlib.matlab import *
import gtk

x = arange(120.0)*2*pi/120.0
x = resize(x, (100,120))
y = arange(100.0)*2*pi/100.0
y = resize(y, (120,100))
y = transpose(y)
z = sin(x) + cos(y)
im = imshow( z, cmap=cm.jet)#, interpolation='nearest')

colorbar()

manager = get_current_fig_manager()

cnt = 0
tstart = time.time()
def updatefig(*args):
     global x, y, cnt, start
     x += pi/15
     y += pi/20
     z = ( sin(x) + cos(y) ) * cnt/5.
     im.set_array(z)
     manager.canvas.draw()
     cnt += 1
     if cnt==50:
         print 'FPS', cnt/(time.time() - tstart)
         return gtk.FALSE
     return True


cnt = 0

gtk.idle_add(updatefig)
show()

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

On Monday, September 13, 2004, at 03:47  PM, John Hunter wrote:

>>>>>> "Rodrigo" == Rodrigo Caballero <rc...@ge...> writes:
>
>     Rodrigo> One of my favorite aspects of matplotlib is how easy it
>     Rodrigo> is to make dynamic plots. When doing this with 2D
>     Rodrigo> renderings using pcolor or imshow, is there an easy way
>     Rodrigo> to also dynamically update the colorbar?
>
> It should *just work*.  images (matplotlib.image.AxesImage) and
> pcolors (matplotlib.collections.PolygonCollection) both derive from
> matplotlib.cm.ScalarMappable.  This class implements an observer
> pattern, and the colorbar registers itself with the ScalarMappable
> instance.  If you change the normalization, or colormap, or
> colorlimits, the ScalarMappable instance notifies it's observers, who
> update automagically.
>
> Let me know if you run into any troubles.
>
> JDH
>
>
> -------------------------------------------------------
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Re: [Matplotlib-users] dynamic colorbar

[John H. <jdh...@ac...>]

>>>>> "Rodrigo" == Rodrigo Caballero <rc...@ge...> writes:

    Rodrigo> Ok, here's the problem, illlustrated by a simple variant
    Rodrigo> of the dynamic_image_gtkagg.py example (see below).  I've
    Rodrigo> put in a colorbar and I'm letting the amplitude of the
    Rodrigo> pattern grow linearly in time. The result is that the
    Rodrigo> colormap limits remained fixed at their initial values
    Rodrigo> (-2,2), rather than change in time. If I don't put in the
    Rodrigo> colorbar, the colormap limits *do* change. I'm a bit
    Rodrigo> confused about all this.  I tried changing the
    Rodrigo> normalization of the colormap doing im.autoscale(z) each
    Rodrigo> time z is updated, but that doesn't work ...

OK, I understand the problem now.  The reason setting the colorbar
caused the behavior you find unusual (the colormap limits remain
fixed) stems from the call to 

    if norm.vmin is None or norm.vmax is None:
        mappable.autoscale()


in matplotlib.matlab.colorbar.  When the normalization limits are
None, the normalization class automatically uses the full range.  This
call to autoscale sets the normalization limits, and after that they
are fixed.

I'll have to make some changes to the code to do this right, perhaps
creating a colorbar class instance on which you can call various
methods to get the desired behavior.  The question is: what is the
right default behavior, and what are the other kinds of behaviors that
should be possible?  I imagine that in some cases when you set the
array data for the main window, you want to colorbar and color limits
to update and autoscale it's range, and in other cases you want the
colorbar and limits to autoscale.

JDH

[Matplotlib-users] Changing the size of graphs when saved

[Chris <rea...@po...>]

Hi,

I have looked had a look around but I have been unable to find out how to 
change the size (and orientation) of graphs when I save them as eps or png  
files - using savefig - any help or directions to help would be greatly 
appreciated.

What I want to do is save a graph that is a full page size in landscape 
orientation.

Chris

Re: [Matplotlib-users] Changing the size of graphs when saved

[John H. <jdh...@ac...>]

    Chris> Hi, I have looked had a look around but I have been unable
    Chris> to find out how to change the size (and orientation) of
    Chris> graphs when I save them as eps or png files - using savefig
    Chris> - any help or directions to help would be greatly
    Chris> appreciated.

The flags you are looking for a re the figsize argument to the figure
command, and the orientation flag to the savefig command.  Complete
docstrings below.

I didn't write the landscape support for PS/EPS (there is none
currently for png), but I think the following incantation is what you
are looking for

    from matplotlib.matlab import *
    figure(figsize=(11, 8.5))
    plot([1,2,3])
    savefig('temp.ps', orientation='landscape')
    show()
     

Docstring:
    figure(num = 1, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
     
     
    Create a new figure and return a handle to it
     
    If figure(num) already exists, make it active and return the
    handle to it.
     
      figure(1)
     
    figsize - width in height x inches; defaults to rc figure.figsize
    dpi     - resolution; defaults to rc figure.dpi
    facecolor - the background color; defaults to rc figure.facecolor
    edgecolor - the border color; defaults to rc figure.edgecolor
     
    rcParams gives the default values from the .matplotlibrc file
 
 
3 >>> savefig?
Type:           function
Base Class:     <type 'function'>
String Form:    <function savefig at 0x414504fc>
Namespace:      Interactive
File:           /usr/local/lib/python2.3/site-packages/matplotlib/matlab.py
Definition:     savefig(*args, **kwargs)
Docstring:
    def savefig(fname, dpi=150, facecolor='w', edgecolor='w',
                orientation='portrait'):
    Save the current figure to filename fname.  dpi is the resolution
    in dots per inch.
     
    Output file types currently supported are jpeg and png and will be
    deduced by the extension to fname
     
    facecolor and edgecolor are the colors os the figure rectangle
     
    orientation is either 'landscape' or 'portrait' - not supported on
    all backends; currently only on postscript output.
 
 
Hope this helps,
JDH