matplotlib-users Mailing List for matplotlib

Hi,

I wanted to be more clear:

the numbers on my figure's color bar range from 0 to 1.7,
but mpl writes the following numbers:

0.0000, 0.000250,0.000050,0.000750,

etc.

This is totally not somthing I can use, it looks horrible.
I'm also attaching the image ...



--
Oz Nahum
Graduate Student
Zentrum für Angewandte Geologie
Universität Tübingen

---

Imagine there's no countries
it isn't hard to do
Nothing to kill or die for
And no religion too
Imagine all the people
Living life in peace

Christoph Gohlke wrote:
>
> On 5/31/2010 1:53 AM, Craig McQueen wrote:
>>   I just installed matplotlib-0.99.3.win32-py2.6.exe on this Win2000 PC.
>> When I try:
>>      from matplotlib import pyplot as plt
>>
>> it crashes Python with an apparent NULL-pointer reference. If I run
>>      python -v
>>
>> then it crashes just after:
>> # c:\python26\lib\site-packages\matplotlib\transforms.pyc matches
>> c:\python26\li
>> b\site-packages\matplotlib\transforms.py
>> import matplotlib.transforms # precompiled from
>> c:\python26\lib\site-packages\ma
>> tplotlib\transforms.pyc
>>
>> matplotlib-0.99.1.win32-py2.6.exe worked fine on this PC.
>>
>> Regards,
>> Craig McQueen
>
>
> I can not reproduce the crash in a Virtual Machine with Windows 2000 
> SP4, Python 2.6.5, numpy 1.4.1, and matplotlib 0.99.3.
>
> Exactly which versions are you using, and how did you install Python 
> (for all users?). What is your CPU?
>
> matplotlib-0.99.3.win32-py2.6 was built against numpy 1.4.1, libpng 
> 1.4.2 and zlib 1.2.5, while matplotlib-0.99.1.win32-py2.6 was built 
> against numpy 1.3.0, libpng 1.2.3x, and zlib 1.2.3.
>
> Christoph
>

Ah--installing numpy 1.4.1 fixed the issue. I had numpy 1.3.0 installed 
before that.

Thanks for such a helpful response. I wasn't aware that matplotlib is 
"built against" a particular version of numpy (not quite sure what that 
means either, unless numpy provides a direct C API as well as the Python 
API; please excuse my ignorance).

(To answer your questions in case that's still useful somehow... I'm 
using Windows 2000 SP4, Python 2.6.5, on a Pentium 4 PC. I installed 
Python for all users.)

Thanks and regards,
Craig McQueen

I have a small problem how to convert an image from matplotlib to PIL

right now doing somthing like this:
------------------------------------------
from scipy import *
from pylab import *
from PIL import Image

a = arange(16384).reshape(128,128)
imsave( "test.png", a, cmap=cm.summer,vmin=0,vmax=16383)
b = Image.open("test.png" )
------------------------------------------

so I have a 128x128 array, get a 128x128 size png by making use of a
colormap and get a 128x128 size PIL image. But so far I could figure out a
way to do this directly without writing a temporary png and reading it
again, which is quite slow. My important point here is to keep the the pixel
resolution.
I would be glad about some help

Regards Hans
-- 
View this message in context: http://old.nabble.com/imshow%2C-imsave-to-PIL-image-conversion-tp28736246p28736246.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Hi Guys,

I'm breaking my head how to use scientific numbering on color bar:

I use the following code to plot data from multiple files, would be great if
someone could direct me, becuase the numbers I have are 0.0013 et. and what
ever I do, the number just go over each other:


for i in xrange(len(data)):
    x,z,som=data[i]["x"],data[i]["z"],data[i]['ch2o']
    som=som*MwSOM
    som=som*1000
    x=N.unique(x)
    z=N.unique(z)
    p_w=N.unique(som)
    fig = plt.figure(len(data)+1+i)
    ax1 = fig.add_subplot(111)
    som=resize(som,(z.size,x.size))
    v=N.arange(1.15,1.18,0.002)
    CS=contourf(x,z,som,cmap=cm.pink_r)
    cb=colorbar(CS, orientation='horizontal', shrink=1.0,aspect=33)
    ax1.set_title("SOM "+titles[i])
    savefig('Fig'+str(i)+'.png')


Thanks in advance !

-- 
Oz Nahum
Graduate Student
Zentrum für Angewandte Geologie
Universität Tübingen

---

Imagine there's no countries
it isn't hard to do
Nothing to kill or die for
And no religion too
Imagine all the people
Living life in peace

On 5/31/2010 1:53 AM, Craig McQueen wrote:
>   I just installed matplotlib-0.99.3.win32-py2.6.exe on this Win2000 PC.
> When I try:
>      from matplotlib import pyplot as plt
>
> it crashes Python with an apparent NULL-pointer reference. If I run
>      python -v
>
> then it crashes just after:
> # c:\python26\lib\site-packages\matplotlib\transforms.pyc matches
> c:\python26\li
> b\site-packages\matplotlib\transforms.py
> import matplotlib.transforms # precompiled from
> c:\python26\lib\site-packages\ma
> tplotlib\transforms.pyc
>
> matplotlib-0.99.1.win32-py2.6.exe worked fine on this PC.
>
> Regards,
> Craig McQueen


I can not reproduce the crash in a Virtual Machine with Windows 2000 
SP4, Python 2.6.5, numpy 1.4.1, and matplotlib 0.99.3.

Exactly which versions are you using, and how did you install Python 
(for all users?). What is your CPU?

matplotlib-0.99.3.win32-py2.6 was built against numpy 1.4.1, libpng 
1.4.2 and zlib 1.2.5, while matplotlib-0.99.1.win32-py2.6 was built 
against numpy 1.3.0, libpng 1.2.3x, and zlib 1.2.3.

Christoph

I just installed matplotlib-0.99.3.win32-py2.6.exe on this Win2000 PC. 
When I try:
    from matplotlib import pyplot as plt

it crashes Python with an apparent NULL-pointer reference. If I run
    python -v

then it crashes just after:
# c:\python26\lib\site-packages\matplotlib\transforms.pyc matches 
c:\python26\li
b\site-packages\matplotlib\transforms.py
import matplotlib.transforms # precompiled from 
c:\python26\lib\site-packages\ma
tplotlib\transforms.pyc

matplotlib-0.99.1.win32-py2.6.exe worked fine on this PC.

Regards,
Craig McQueen

Hello,

consider the following simple code:

import matplotlib
from matplotlib import pyplot
pyplot.text(0, 0, "    <--", family='monospace')
pyplot.show()

Using matplotlib-0.99.3.win32-py2.6, the text '<--' is drawn with an 
x-offset from the y-axis, as expected from the presence of leading 
whitespace.

Using matplotlib-1.0.svn.win32-py2.6 rev 8347, the leading whitespace is 
apparently removed from the text and '<--' is drawn right at the y-axis 
with no x-offset.

In both cases the font is monospace.

Is this change intended or a regression in 1.0svn?

Thank you.

--
Christoph

On 5/30/2010 6:42 AM, Florian Lindner wrote:
>  I want to plot a simple vector arrow with a specified starting point 
and end
>  point (or direction and length).
>

http://matplotlib.sourceforge.net/users/annotations_guide.html#annotating-with-arrow 


http://matplotlib.sourceforge.net/users/annotations.html

hth,
Alan Isaac

Hello,

I want to plot a simple vector arrow with a specified starting point and end 
point (or direction and length).

I've found 
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.quiver 
but I don't see a way to set the length of the vector.

Though discussions about this topic recommend quiver.

Thanks,

Florian

Hello List,

Is there anybody who has tried to convert a Axes3D figure to U3D so it can
be imbedded in a pdf file? It would be exceedingly cool. If anybody has code
that can do this, that would be most excellent. If not, does anybody have
any other thoughts on getting a 3D image in an interactive pdf file?

Thanks,

Mark

alex arsenovic <ai...@vi...> writes:

> the main error is 
>
> Error: /nocurrentpoint in --lineto--

Sounds like this bug:

http://sourceforge.net/tracker/index.php?func=detail&aid=2879910&group_id=80706&atid=560720

It's fixed in svn, but I don't think there has been a release after the
fix. One way to test if it is indeed the same bug is to apply the
following change (svn revision 7899) and try saving a pdf file - it
should exit with an error.

On 05/28/2010 09:10 AM, Richard Ree wrote:
> On Fri, May 28, 2010 at 12:02 PM, Eric Firing wrote:
>> On 05/28/2010 06:31 AM, Richard Ree wrote:
>>> To visualize trees, I'm creating Path objects that draw horizontal and
>>> vertical lines between connected nodes.  I make sure to end these
>>> paths with a MOVETO code and the final vertex, and a STOP code and the
>>> final vertex.  But I consistently see an anomalous line segment
>>> getting rendered, angling between two disconnected nodes.  The problem
>>> is intermittent - if I pan or zoom, the anomalous segment might go
>>> away or change position.  It's also consistent - if I create two plots
>>> of the same tree structure, the anomalous segment appears in the same
>>> way.  I've tried different backends, all the same.  The attached
>>> screenshots illustrate the problem.  In each case, a detail of the
>>> tree is shown in the right pane.  path-artefact.png shows the
>>> anomalous segment angling across the view.  Any ideas on why this
>>> occurs?  I'm using the latest matplotlib release.
>>
>> Does it still happen if you turn off path simplification? E.g.
>>
>> rcParams['path.simplify'] = False
>
> Yes, this seems to fix it.  Thanks!
>
> -Rick

Good.  I think this is one of the path simplification problems that has 
already been fixed in svn by Mike D.  If you can build from svn and try 
your program without turning path simplification off, then we could be sure.

Eric

On Fri, May 28, 2010 at 12:02 PM, Eric Firing wrote:
> On 05/28/2010 06:31 AM, Richard Ree wrote:
>> To visualize trees, I'm creating Path objects that draw horizontal and
>> vertical lines between connected nodes.  I make sure to end these
>> paths with a MOVETO code and the final vertex, and a STOP code and the
>> final vertex.  But I consistently see an anomalous line segment
>> getting rendered, angling between two disconnected nodes.  The problem
>> is intermittent - if I pan or zoom, the anomalous segment might go
>> away or change position.  It's also consistent - if I create two plots
>> of the same tree structure, the anomalous segment appears in the same
>> way.  I've tried different backends, all the same.  The attached
>> screenshots illustrate the problem.  In each case, a detail of the
>> tree is shown in the right pane.  path-artefact.png shows the
>> anomalous segment angling across the view.  Any ideas on why this
>> occurs?  I'm using the latest matplotlib release.
>
> Does it still happen if you turn off path simplification? E.g.
>
> rcParams['path.simplify'] = False

Yes, this seems to fix it.  Thanks!

-Rick

>
> Eric
>
>>
>> Thanks
>> -Rick
>>
>>
>>
>> ------------------------------------------------------------------------------
>>
>>
>>
>>
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

On 05/28/2010 06:31 AM, Richard Ree wrote:
> To visualize trees, I'm creating Path objects that draw horizontal and
> vertical lines between connected nodes.  I make sure to end these
> paths with a MOVETO code and the final vertex, and a STOP code and the
> final vertex.  But I consistently see an anomalous line segment
> getting rendered, angling between two disconnected nodes.  The problem
> is intermittent - if I pan or zoom, the anomalous segment might go
> away or change position.  It's also consistent - if I create two plots
> of the same tree structure, the anomalous segment appears in the same
> way.  I've tried different backends, all the same.  The attached
> screenshots illustrate the problem.  In each case, a detail of the
> tree is shown in the right pane.  path-artefact.png shows the
> anomalous segment angling across the view.  Any ideas on why this
> occurs?  I'm using the latest matplotlib release.

Does it still happen if you turn off path simplification? E.g.

rcParams['path.simplify'] = False

Eric

>
> Thanks
> -Rick
>
>
>
> ------------------------------------------------------------------------------
>
>
>
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

To visualize trees, I'm creating Path objects that draw horizontal and
vertical lines between connected nodes.  I make sure to end these
paths with a MOVETO code and the final vertex, and a STOP code and the
final vertex.  But I consistently see an anomalous line segment
getting rendered, angling between two disconnected nodes.  The problem
is intermittent - if I pan or zoom, the anomalous segment might go
away or change position.  It's also consistent - if I create two plots
of the same tree structure, the anomalous segment appears in the same
way.  I've tried different backends, all the same.  The attached
screenshots illustrate the problem.  In each case, a detail of the
tree is shown in the right pane.  path-artefact.png shows the
anomalous segment angling across the view.  Any ideas on why this
occurs?  I'm using the latest matplotlib release.

Thanks
-Rick

On Thu, May 27, 2010 at 5:44 PM, Eric Firing <ef...@ha...> wrote:
> You can't do this via any manipulation of the colormap, or any fancier
> colormap specification--instead, you have to manipulate the data value.  For
> example, you could identify the "over" values in your data, and then use 2-D
> interpolation to replace them with the values you want.
>
> Basemap includes a 2-D interpolation routine:
>
> from mpl_toolkits.basemap import interp
>

Thanks!

>>
>> This is partially achieved with "white" (and I suppose I could pick
>> "grey" or "black"), but I think it might be nicer if it were a pure
>> mixture, rather than a mixture of the surrounding colors and the
>> "over" color.
>>
>> The script is attached below.  Sorry it is a bit long, but I needed a
>> discrete colormap.  Can we get cmap_discrete() into matplotlib?
>
> No, because it doesn't make much sense, given the mpl paradigm in which a
> colormap and a norm work together.  If you want 4 colors, make a colormap
> with 4 colors, and use a norm that maps your data to those 4 colors.
>
> For example:
>
> cm4 = get_cmap('jet', 4)
> cm4a = mpl.colors.ListedColormap(get_cmap('jet', 256)([20, 70, 150, 200]))
>
> You can select any discrete set of colors you want using ListedColormap.
>
> Then you can use the default Normalize, or a custom BoundaryNorm, to map
> data ranges to the colors.  You just don't need a lookup table with 1024
> entries to specify 4 colors--it doesn't gain you anything.
>

Wonderful.  Definitely makes the cookbook entry seem unnecessary

On 05/27/2010 01:58 PM, T J wrote:
> On Thu, May 27, 2010 at 3:23 PM, Eric Firing<ef...@ha...>  wrote:
>>
>> I'm not sure I understand the problem; could you provide a tiny example
>> to illustrate?
>>
>
> Sure, let me focus just on the interpolation and I'll leave the
> filtering issue out.
>
> In the script below, I plot a 3x3 array with the center element having
> an "over" value.  In the default case, because its value is over, the
> colormap will assign it the maximum color.  I also plot the case when
> the "over" color is explicitly set to the minimum color and also to
> white.  What I want is this:
>
>     The center element should be an equal mixture of the 4 elements around it.

You can't do this via any manipulation of the colormap, or any fancier 
colormap specification--instead, you have to manipulate the data value. 
  For example, you could identify the "over" values in your data, and 
then use 2-D interpolation to replace them with the values you want.

Basemap includes a 2-D interpolation routine:

from mpl_toolkits.basemap import interp

>
> This is partially achieved with "white" (and I suppose I could pick
> "grey" or "black"), but I think it might be nicer if it were a pure
> mixture, rather than a mixture of the surrounding colors and the
> "over" color.
>
> The script is attached below.  Sorry it is a bit long, but I needed a
> discrete colormap.  Can we get cmap_discrete() into matplotlib?

No, because it doesn't make much sense, given the mpl paradigm in which 
a colormap and a norm work together.  If you want 4 colors, make a 
colormap with 4 colors, and use a norm that maps your data to those 4 
colors.

For example:

cm4 = get_cmap('jet', 4)
cm4a = mpl.colors.ListedColormap(get_cmap('jet', 256)([20, 70, 150, 200]))

You can select any discrete set of colors you want using ListedColormap.

Then you can use the default Normalize, or a custom BoundaryNorm, to map 
data ranges to the colors.  You just don't need a lookup table with 1024 
entries to specify 4 colors--it doesn't gain you anything.

Eric



>
> ------------------
>
> import matplotlib.pyplot as plt
> import matplotlib.colors
>
> import numpy as np
> from scipy import interpolate
>
> #### http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations
> #### Can this be added to matplotlib?
> def cmap_discretize(cmap, N):
>      """Return a discrete colormap from the continuous colormap cmap.
>
>          cmap: colormap instance, eg. cm.jet.
>          N: Number of colors.
>
>      Example
>          x = resize(arange(100), (5,100))
>          djet = cmap_discretize(cm.jet, 5)
>          imshow(x, cmap=djet)
>      """
>
>      cdict = cmap._segmentdata.copy()
>      # N colors
>      colors_i = np.linspace(0,1.,N)
>      # N+1 indices
>      indices = np.linspace(0,1.,N+1)
>      for key in ('red','green','blue'):
>          # Find the N colors
>          D = np.array(cdict[key])
>          I = interpolate.interp1d(D[:,0], D[:,1])
>          colors = I(colors_i)
>          # Place these colors at the correct indices.
>          A = np.zeros((N+1,3), float)
>          A[:,0] = indices
>          A[1:,1] = colors
>          A[:-1,2] = colors
>          # Create a tuple for the dictionary.
>          L = []
>          for l in A:
>              L.append(tuple(l))
>          cdict[key] = tuple(L)
>      # Return colormap object.
>      return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)
>
> def draw(m, cm, norm, ncolors):
>      ax = plt.gca()
>      ai = ax.imshow(m, cmap=cm, norm=norm, interpolation='gaussian')
>      cb = ax.figure.colorbar(ai)
>      cb.set_ticks(np.linspace(.5, ncolors-.5, ncolors))
>      cb.set_ticklabels(['$%s$' % (i,) for i in np.arange(ncolors)])
>      return ai, cb
>
> if __name__ == '__main__':
>      ncolors = 4
>      norm = plt.Normalize(vmax=ncolors)
>      m = np.array([[0,  0, 1],
>                    [3, 10, 1],
>                    [3,  2, 2]])
>
>      for over in [None, 'min', (1,1,1,1)]:
>          f = plt.figure()
>          cm = cmap_discretize(plt.cm.jet, ncolors)
>          if over == 'min':
>              cm.set_over(cm(0.0))
>          elif over is not None:
>              cm.set_over(over)
>          ai, cb = draw(m, cm, norm, ncolors)
>
>      plt.show()

On Thu, May 27, 2010 at 3:23 PM, Eric Firing <ef...@ha...> wrote:
>
> I'm not sure I understand the problem; could you provide a tiny example
> to illustrate?
>

Sure, let me focus just on the interpolation and I'll leave the
filtering issue out.

In the script below, I plot a 3x3 array with the center element having
an "over" value.  In the default case, because its value is over, the
colormap will assign it the maximum color.  I also plot the case when
the "over" color is explicitly set to the minimum color and also to
white.  What I want is this:

   The center element should be an equal mixture of the 4 elements around it.

This is partially achieved with "white" (and I suppose I could pick
"grey" or "black"), but I think it might be nicer if it were a pure
mixture, rather than a mixture of the surrounding colors and the
"over" color.

The script is attached below.  Sorry it is a bit long, but I needed a
discrete colormap.  Can we get cmap_discrete() into matplotlib?

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

import matplotlib.pyplot as plt
import matplotlib.colors

import numpy as np
from scipy import interpolate

#### http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations
#### Can this be added to matplotlib?
def cmap_discretize(cmap, N):
    """Return a discrete colormap from the continuous colormap cmap.

        cmap: colormap instance, eg. cm.jet.
        N: Number of colors.

    Example
        x = resize(arange(100), (5,100))
        djet = cmap_discretize(cm.jet, 5)
        imshow(x, cmap=djet)
    """

    cdict = cmap._segmentdata.copy()
    # N colors
    colors_i = np.linspace(0,1.,N)
    # N+1 indices
    indices = np.linspace(0,1.,N+1)
    for key in ('red','green','blue'):
        # Find the N colors
        D = np.array(cdict[key])
        I = interpolate.interp1d(D[:,0], D[:,1])
        colors = I(colors_i)
        # Place these colors at the correct indices.
        A = np.zeros((N+1,3), float)
        A[:,0] = indices
        A[1:,1] = colors
        A[:-1,2] = colors
        # Create a tuple for the dictionary.
        L = []
        for l in A:
            L.append(tuple(l))
        cdict[key] = tuple(L)
    # Return colormap object.
    return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)

def draw(m, cm, norm, ncolors):
    ax = plt.gca()
    ai = ax.imshow(m, cmap=cm, norm=norm, interpolation='gaussian')
    cb = ax.figure.colorbar(ai)
    cb.set_ticks(np.linspace(.5, ncolors-.5, ncolors))
    cb.set_ticklabels(['$%s$' % (i,) for i in np.arange(ncolors)])
    return ai, cb

if __name__ == '__main__':
    ncolors = 4
    norm = plt.Normalize(vmax=ncolors)
    m = np.array([[0,  0, 1],
                  [3, 10, 1],
                  [3,  2, 2]])

    for over in [None, 'min', (1,1,1,1)]:
        f = plt.figure()
        cm = cmap_discretize(plt.cm.jet, ncolors)
        if over == 'min':
            cm.set_over(cm(0.0))
        elif over is not None:
            cm.set_over(over)
        ai, cb = draw(m, cm, norm, ncolors)

    plt.show()

On 05/27/2010 09:32 AM, T J wrote:
> Hi,
>
> I am plotting with imshow() and interpolation is turned on
> ('gaussian').  Part of my issue is that the distribution of values is
> such that I need to set the over/under colors to grab the most
> relevant values.  If I set the over color to be the "maximum" color,
> then the result is too dark.  Conversely for the under color.  I can
> set the over/under colors to not draw, but then I have "holes" in my
> image.  Is it possible to incorporate interpolation?  That is, I would
> like to set my over color to be interpolated among its neighboring
> cells.  This seems like it would require an (more) "intelligent"
> colormap, so my guess is that this isn't currently supported.  I'm
> growing more comfortable with the mpl internals and might give a stab
> at implementing this, but I'm not quite sure where to start.
>
> Thanks in advance.


I'm not sure I understand the problem; could you provide a tiny example 
to illustrate?

Eric

On 05/27/2010 09:08 AM, Adam Fraser wrote:
> Thanks very much,
>
> I'm getting ValueError: argument must be "box", or "datalim" at
> set_adjustable...
>
> I'm using Matplotlib version 0.98.5.3, do I need to update?
>

Yes.  Unfortunately, you will need to build from svn.  JJ added 
box-forced in January, and we have not had a release since then.

Eric

>
> On Thu, May 27, 2010 at 2:59 PM, Jae-Joon Lee <lee...@gm...
> <mailto:lee...@gm...>> wrote:
>
>     ax1 = subplot(121)
>     ax2 = subplot(122, sharex=ax1, sharey=ax1)
>
>     ax1.set_adjustable("box-forced")
>     ax2.set_adjustable("box-forced")
>
>     arr1 = np.arange(100).reshape((10, 10))
>     ax1.imshow(arr1)
>
>     arr2 = np.arange(100, 0, -1).reshape((10, 10))
>     ax2.imshow(arr2)
>
>     Note the use of set_adjustable("box-forced").
>     sharex and sharey does not get along with axes of aspect=1 &
>     adjustable="box".
>
>     -JJ

Thanks very much,

I'm getting ValueError: argument must be "box", or "datalim" at
set_adjustable...

I'm using Matplotlib version 0.98.5.3, do I need to update?


On Thu, May 27, 2010 at 2:59 PM, Jae-Joon Lee <lee...@gm...> wrote:

> ax1 = subplot(121)
> ax2 = subplot(122, sharex=ax1, sharey=ax1)
>
> ax1.set_adjustable("box-forced")
> ax2.set_adjustable("box-forced")
>
> arr1 = np.arange(100).reshape((10, 10))
> ax1.imshow(arr1)
>
> arr2 = np.arange(100, 0, -1).reshape((10, 10))
> ax2.imshow(arr2)
>
> Note the use of set_adjustable("box-forced").
> sharex and sharey does not get along with axes of aspect=1 &
> adjustable="box".
>
> -JJ
>
>
>
> On Thu, May 27, 2010 at 2:10 PM,  <PH...@ge...> wrote:
> > Do the “sharex” and “sharey” kwargs help?
> >
> >
> http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.axes
> >
> >
> http://matplotlib.sourceforge.net/examples/pylab_examples/shared_axis_demo.html
> >
> > -paul
> >
> >
> >
> > From: Adam Fraser [mailto:ada...@gm...]
> > Sent: Thursday, May 27, 2010 10:44 AM
> > To: matplotlib-users
> > Subject: [Matplotlib-users] Is there a way to link axes of imshow plots?
> >
> >
> >
> > Suppose I have a figure canvas with 3 plots... 2 are images of the same
> > dimensions plotted with imshow, and the other is a scatterplot. I'd like
> to
> > be able to link the x and y axes of the imshow plots so that when I zoom
> in
> > one, the other zooms to the same coordinates, and when I pan in one, the
> > other pans as well.
> >
> >
> >
> > I started hacking my way around this by
> subclassing NavigationToolbar2WxAgg
> > (shown below)... but there are several problems here.
> >
> > 1) This will link the axes of all plots in a canvas since all I've done
> is
> > get rid of the checks for a.in_axes()
> >
> > 2) This worked well for panning, but zooming caused all subplots to zoom
> > from the same global point, rather than from the same point in each of
> their
> > respective axes.
> >
> >
> >
> > Can anyone suggest a workaround?
> >
> >
> >
> > Much thanks!
> >
> > -Adam
> >
> >
> >
> > from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg as
> > NavigationToolbar
> >
> > class MyNavToolbar(NavigationToolbar):
> >
> >     def __init__(self, canvas, cpfig):
> >
> >         NavigationToolbar.__init__(self, canvas)
> >
> >
> >
> >     # override
> >
> >     def press_pan(self, event):
> >
> >         'the press mouse button in pan/zoom mode callback'
> >
> >
> >
> >         if event.button == 1:
> >
> >             self._button_pressed=1
> >
> >         elif  event.button == 3:
> >
> >             self._button_pressed=3
> >
> >         else:
> >
> >             self._button_pressed=None
> >
> >             return
> >
> >
> >
> >         x, y = event.x, event.y
> >
> >
> >
> >         # push the current view to define home if stack is empty
> >
> >         if self._views.empty(): self.push_current()
> >
> >
> >
> >         self._xypress=[]
> >
> >         for i, a in enumerate(self.canvas.figure.get_axes()):
> >
> >             # only difference from overridden method is that this one
> > doesn't
> >
> >             # check a.in_axes(event)
> >
> >             if x is not None and y is not None and a.get_navigate():
> >
> >                 a.start_pan(x, y, event.button)
> >
> >                 self._xypress.append((a, i))
> >
> >                 self.canvas.mpl_disconnect(self._idDrag)
> >
> >
>  self._idDrag=self.canvas.mpl_connect('motion_notify_event',
> > self.drag_pan)
> >
> >
> >
> >     def press_zoom(self, event):
> >
> >         'the press mouse button in zoom to rect mode callback'
> >
> >         if event.button == 1:
> >
> >             self._button_pressed=1
> >
> >         elif  event.button == 3:
> >
> >             self._button_pressed=3
> >
> >         else:
> >
> >             self._button_pressed=None
> >
> >             return
> >
> >
> >
> >         x, y = event.x, event.y
> >
> >
> >
> >         # push the current view to define home if stack is empty
> >
> >         if self._views.empty(): self.push_current()
> >
> >
> >
> >         self._xypress=[]
> >
> >         for i, a in enumerate(self.canvas.figure.get_axes()):
> >
> >             # only difference from overridden method is that this one
> > doesn't
> >
> >             # check a.in_axes(event)
> >
> >             if x is not None and y is not None and a.get_navigate() and
> > a.can_zoom():
> >
> >                 self._xypress.append(( x, y, a, i, a.viewLim.frozen(),
> > a.transData.frozen()))
> >
> >
> >
> >         self.press(event)
> >
> >
> >
> >
> >
> >
> >
> >
> ------------------------------------------------------------------------------
> >
> >
> > _______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >
> >
>

Hi,

I am plotting with imshow() and interpolation is turned on
('gaussian').  Part of my issue is that the distribution of values is
such that I need to set the over/under colors to grab the most
relevant values.  If I set the over color to be the "maximum" color,
then the result is too dark.  Conversely for the under color.  I can
set the over/under colors to not draw, but then I have "holes" in my
image.  Is it possible to incorporate interpolation?  That is, I would
like to set my over color to be interpolated among its neighboring
cells.  This seems like it would require an (more) "intelligent"
colormap, so my guess is that this isn't currently supported.  I'm
growing more comfortable with the mpl internals and might give a stab
at implementing this, but I'm not quite sure where to start.

Thanks in advance.

ax1 = subplot(121)
ax2 = subplot(122, sharex=ax1, sharey=ax1)

ax1.set_adjustable("box-forced")
ax2.set_adjustable("box-forced")

arr1 = np.arange(100).reshape((10, 10))
ax1.imshow(arr1)

arr2 = np.arange(100, 0, -1).reshape((10, 10))
ax2.imshow(arr2)

Note the use of set_adjustable("box-forced").
sharex and sharey does not get along with axes of aspect=1 & adjustable="box".

-JJ



On Thu, May 27, 2010 at 2:10 PM,  <PH...@ge...> wrote:
> Do the “sharex” and “sharey” kwargs help?
>
> http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.axes
>
> http://matplotlib.sourceforge.net/examples/pylab_examples/shared_axis_demo.html
>
> -paul
>
>
>
> From: Adam Fraser [mailto:ada...@gm...]
> Sent: Thursday, May 27, 2010 10:44 AM
> To: matplotlib-users
> Subject: [Matplotlib-users] Is there a way to link axes of imshow plots?
>
>
>
> Suppose I have a figure canvas with 3 plots... 2 are images of the same
> dimensions plotted with imshow, and the other is a scatterplot. I'd like to
> be able to link the x and y axes of the imshow plots so that when I zoom in
> one, the other zooms to the same coordinates, and when I pan in one, the
> other pans as well.
>
>
>
> I started hacking my way around this by subclassing NavigationToolbar2WxAgg
> (shown below)... but there are several problems here.
>
> 1) This will link the axes of all plots in a canvas since all I've done is
> get rid of the checks for a.in_axes()
>
> 2) This worked well for panning, but zooming caused all subplots to zoom
> from the same global point, rather than from the same point in each of their
> respective axes.
>
>
>
> Can anyone suggest a workaround?
>
>
>
> Much thanks!
>
> -Adam
>
>
>
> from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg as
> NavigationToolbar
>
> class MyNavToolbar(NavigationToolbar):
>
>     def __init__(self, canvas, cpfig):
>
>         NavigationToolbar.__init__(self, canvas)
>
>
>
>     # override
>
>     def press_pan(self, event):
>
>         'the press mouse button in pan/zoom mode callback'
>
>
>
>         if event.button == 1:
>
>             self._button_pressed=1
>
>         elif  event.button == 3:
>
>             self._button_pressed=3
>
>         else:
>
>             self._button_pressed=None
>
>             return
>
>
>
>         x, y = event.x, event.y
>
>
>
>         # push the current view to define home if stack is empty
>
>         if self._views.empty(): self.push_current()
>
>
>
>         self._xypress=[]
>
>         for i, a in enumerate(self.canvas.figure.get_axes()):
>
>             # only difference from overridden method is that this one
> doesn't
>
>             # check a.in_axes(event)
>
>             if x is not None and y is not None and a.get_navigate():
>
>                 a.start_pan(x, y, event.button)
>
>                 self._xypress.append((a, i))
>
>                 self.canvas.mpl_disconnect(self._idDrag)
>
>                 self._idDrag=self.canvas.mpl_connect('motion_notify_event',
> self.drag_pan)
>
>
>
>     def press_zoom(self, event):
>
>         'the press mouse button in zoom to rect mode callback'
>
>         if event.button == 1:
>
>             self._button_pressed=1
>
>         elif  event.button == 3:
>
>             self._button_pressed=3
>
>         else:
>
>             self._button_pressed=None
>
>             return
>
>
>
>         x, y = event.x, event.y
>
>
>
>         # push the current view to define home if stack is empty
>
>         if self._views.empty(): self.push_current()
>
>
>
>         self._xypress=[]
>
>         for i, a in enumerate(self.canvas.figure.get_axes()):
>
>             # only difference from overridden method is that this one
> doesn't
>
>             # check a.in_axes(event)
>
>             if x is not None and y is not None and a.get_navigate() and
> a.can_zoom():
>
>                 self._xypress.append(( x, y, a, i, a.viewLim.frozen(),
> a.transData.frozen()))
>
>
>
>         self.press(event)
>
>
>
>
>
>
>
> ------------------------------------------------------------------------------
>
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Do the "sharex" and "sharey" kwargs help?
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.axes
http://matplotlib.sourceforge.net/examples/pylab_examples/shared_axis_demo.html
-paul

From: Adam Fraser [mailto:ada...@gm...]
Sent: Thursday, May 27, 2010 10:44 AM
To: matplotlib-users
Subject: [Matplotlib-users] Is there a way to link axes of imshow plots?

Suppose I have a figure canvas with 3 plots... 2 are images of the same dimensions plotted with imshow, and the other is a scatterplot. I'd like to be able to link the x and y axes of the imshow plots so that when I zoom in one, the other zooms to the same coordinates, and when I pan in one, the other pans as well.

I started hacking my way around this by subclassing NavigationToolbar2WxAgg (shown below)... but there are several problems here.
1) This will link the axes of all plots in a canvas since all I've done is get rid of the checks for a.in_axes()
2) This worked well for panning, but zooming caused all subplots to zoom from the same global point, rather than from the same point in each of their respective axes.

Can anyone suggest a workaround?

Much thanks!
-Adam

from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg as NavigationToolbar
class MyNavToolbar(NavigationToolbar):
    def __init__(self, canvas, cpfig):
        NavigationToolbar.__init__(self, canvas)

    # override
    def press_pan(self, event):
        'the press mouse button in pan/zoom mode callback'

        if event.button == 1:
            self._button_pressed=1
        elif  event.button == 3:
            self._button_pressed=3
        else:
            self._button_pressed=None
            return

        x, y = event.x, event.y

        # push the current view to define home if stack is empty
        if self._views.empty(): self.push_current()

        self._xypress=[]
        for i, a in enumerate(self.canvas.figure.get_axes()):
            # only difference from overridden method is that this one doesn't
            # check a.in_axes(event)
            if x is not None and y is not None and a.get_navigate():
                a.start_pan(x, y, event.button)
                self._xypress.append((a, i))
                self.canvas.mpl_disconnect(self._idDrag)
                self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.drag_pan)

    def press_zoom(self, event):
        'the press mouse button in zoom to rect mode callback'
        if event.button == 1:
            self._button_pressed=1
        elif  event.button == 3:
            self._button_pressed=3
        else:
            self._button_pressed=None
            return

        x, y = event.x, event.y

        # push the current view to define home if stack is empty
        if self._views.empty(): self.push_current()

        self._xypress=[]
        for i, a in enumerate(self.canvas.figure.get_axes()):
            # only difference from overridden method is that this one doesn't
            # check a.in_axes(event)
            if x is not None and y is not None and a.get_navigate() and a.can_zoom():
                self._xypress.append(( x, y, a, i, a.viewLim.frozen(), a.transData.frozen()))

        self.press(event)

Suppose I have a figure canvas with 3 plots... 2 are images of the same
dimensions plotted with imshow, and the other is a scatterplot. I'd like to
be able to link the x and y axes of the imshow plots so that when I zoom in
one, the other zooms to the same coordinates, and when I pan in one, the
other pans as well.

I started hacking my way around this by subclassing NavigationToolbar2WxAgg
(shown below)... but there are several problems here.
1) This will link the axes of all plots in a canvas since all I've done is
get rid of the checks for a.in_axes()
2) This worked well for panning, but zooming caused all subplots to zoom
from the same global point, rather than from the same point in each of their
respective axes.

Can anyone suggest a workaround?

Much thanks!
-Adam

from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg as
NavigationToolbar
class MyNavToolbar(NavigationToolbar):
    def __init__(self, canvas, cpfig):
        NavigationToolbar.__init__(self, canvas)

    # override
    def press_pan(self, event):
        'the press mouse button in pan/zoom mode callback'

        if event.button == 1:
            self._button_pressed=1
        elif  event.button == 3:
            self._button_pressed=3
        else:
            self._button_pressed=None
            return

        x, y = event.x, event.y

        # push the current view to define home if stack is empty
        if self._views.empty(): self.push_current()

        self._xypress=[]
        for i, a in enumerate(self.canvas.figure.get_axes()):
            *# only difference from overridden method is that this one
doesn't*
*            # check a.in_axes(event) *
            if x is not None and y is not None and a.get_navigate():
                a.start_pan(x, y, event.button)
                self._xypress.append((a, i))
                self.canvas.mpl_disconnect(self._idDrag)
                self._idDrag=self.canvas.mpl_connect('motion_notify_event',
self.drag_pan)

    def press_zoom(self, event):
        'the press mouse button in zoom to rect mode callback'
        if event.button == 1:
            self._button_pressed=1
        elif  event.button == 3:
            self._button_pressed=3
        else:
            self._button_pressed=None
            return

        x, y = event.x, event.y

        # push the current view to define home if stack is empty
        if self._views.empty(): self.push_current()

        self._xypress=[]
        for i, a in enumerate(self.canvas.figure.get_axes()):
            *# only difference from overridden method is that this one
doesn't*
*            # check a.in_axes(event) *
            if x is not None and y is not None and a.get_navigate() and
a.can_zoom():
                self._xypress.append(( x, y, a, i, a.viewLim.frozen(),
a.transData.frozen()))

        self.press(event)