matplotlib-users Mailing List for matplotlib

What is the prefered way to read in images in numarray/matplotlib?

I know of PIL but it seems like a lot of overlap with numarray just to read an 
image from file.

I really need to read only one type of file (say ppm) and then i can use 
imagemagick to convert from and to that format.

Regards
Rein

>>>>> "Nino" == Nino Cucchiara <cuc...@me...> writes:

    Nino> I want to write some greek symbols in axes legend, for
    Nino> exemple nu letters or lambda. Is it possible?  Thank you.

You can use mathtext anywhere you can use text.  Just use the standard
way of specifying the string

s = r'$\lambda = 5$'

See http://matplotlib.sf.net/matplotlib.mathtext.html for more
information on mathtext.

You may be happy to hear that mathtext for postscript should be ready
soon!

Here is an example that uses mathtext in a legend

    from matplotlib.matlab import *
    t = arange(0.0, 2.0, 0.1)
    plot(t, sin(2*pi*t), '--go', t, log(1+t), '.')
    legend((r'$\nu$', r'$\lambda$'), 'upper right')
    show()

I want to write some greek symbols in axes legend, for exemple nu 
letters or lambda. Is it possible?
Thank you.

Hi,

I note that in the in the examples which display some images (e.g,
image_origin.py) the displayed images hide one of the x-axis (top one if
origin="upper" and bottom one otherwise). It seems like it may be
necessary to apply an offset before displaying the images (I looked
throughout the imshow.py code but didn't find the exact place to do it, it
may be a problem of offsetting and scaling).

Cheers,

	Eddie.

>>>>> "Leon" == Leon Brits <lj...@fa...> writes:

    Leon> Hi, I wanted to compile a old matplot program with the new
    Leon> matplotlib v0.54.2 but I get the error:

    Leon> 	NameError: name 'get_current_fig_manager' is not
    Leon> defined

    Leon> the line of code is:

    Leon> 	g_canvas = get_current_fig_manager().canvas

    Leon> This use to work with matplotlib v0.53. I use pyGtk v2.0.

    Leon> Start code: import pygtk pygtk.require('2.0') import gtk


get_current_fig_manager is definitely defined in matplotlib.matlab in
version 0.54.2.  You need to make sure you do

  from matplotlib.matlab import get_current_fig_manager

before the call to 

  g_canvas = get_current_fig_manager().canvas
  
and you should have no trouble.  If you are still having trouble,
you'll need to post a complete code example.

JDH

Hi,

I wanted to compile a old matplot program with the new matplotlib v0.54.2  
but I get the error:

	NameError: name 'get_current_fig_manager' is not defined

the line of code is:

	g_canvas = get_current_fig_manager().canvas

This use to work with matplotlib v0.53. I use pyGtk v2.0.

Start code:
	import pygtk
	pygtk.require('2.0')
	import gtk

Please help!
Leon

>>>>> "Rein" == Rein van den Boomgaard (UvA) <re...@sc...> writes:

    Rein> Dear All, as i'm new to this list let me first introduce
    Rein> myself before posting a question.

    Rein> I'm working in the field of image processing and computer
    Rein> vision for a long time now and i am a long time matlab
    Rein> user. Up to now i didn't like any of the matlab clones but
    Rein> it seems that numarray+matplotlib might be a winner here.

I've worked some with octave in the past and found it unsatisfying
(though a very impressive one man feat!), in part because I wasn't
happy with gnuplot, which it used for graphics at the time (and
perhaps still does), and in part because it really aimed at being a
matlab clone but very few of my m-files ran w/o alteration.
matplotlib tries to solve the first problem by producing better
graphics than matlab and the second by not trying to be a drop in
replacement for matlab.

I'm curious about your experiences, what you tried, and what you found
lacking.

    Rein> I started using numarray+matplotlib by coding the Gaussian
    Rein> derivative convolutions. Calculating a derivative (by
    Rein> convolving with the derivative of the Gaussian function)
    Rein> will lead to an image(array) with both positive and negative
    Rein> values. Although imshow should deal with that (as far as i
    Rein> understand the code: beware i am a Python beginner) the
    Rein> display turns black for all derivatives (except for the
    Rein> 'zero order' derivative of course).

    Rein> What is going on? Is it me (probably) or is it imshow?

In matplotlib-0.54.2, the images must be normalized to the unit
interval before color mapping or plotting.  For example, in
examples/image_demo2.py, notice that I do 

  A *= 1.0/max(A)

Your images are black, I think, because you haven't normalized them.

I've done a lot of work on matplotlib images since 0.54.2, with new
fixes and features.  Normalization is handled by default in the 0.60
release candidate.  When I run your (very nice) example with my
development version of matplotlib, it produces this image for figure 2
( I added "show" at the end of your script, but it was otherwise
unaltered)

  http://nitace.bsd.uchicago.edu:8080/files/share/fig2.png

which looks right to me.  I'm always interested in nice screenshots
for the web site, so please consider donating this example!

I've uploaded the 0.60d release candidate to 

  http://nitace.bsd.uchicago.edu:8080/files/share/matplotlib-0.60d.tar.gz

Can you compile matplotlib yourself or are you using a binary
distribution?  If you can try the development version linked above,
that would solve two problems: you'll get enhanced image support and
I'll get a tester for my image changes.  If you do so, see the updated
help for the image related commands: imshow, figimage, clim, jet, gray
and the new matplotlibrc parameters image.*

In the figure 2 image from your script, I notice that there the titles
overlap the images above.  Here is how you can control this (requires
matplotlib 0.60d)

        # pass in these keywor args to title.  The y location is the y
        # text coordinate in axes coords (0,0 is lower left, 1,1 is
        # upper right).  You can use **somedict in place of keyword
        # args in python
        offsets = {'y':1.0, 'fontsize':10}
        _gDplot(1,0,0)
        title(r'$G$', **offsets)
        _gDplot(5,1,0)
        title(r'$G_x$', **offsets)
        _gDplot(6,0,1)
        title(r'$G_y$', **offsets)
        _gDplot(9,2,0)
        title(r'$G_{xx}$', **offsets)
        _gDplot(10,1,1)
        title(r'$G_{xy}$', **offsets)
        _gDplot(11,0,2)
        title(r'$G_{yy}$', **offsets)
        _gDplot(13,3,0)
        title(r'$G_{xxx}$', **offsets)
        _gDplot(14,2,1)
        title(r'$G_{xxy}$', **offsets)
        _gDplot(15,1,2)
        title(r'$G_{xyy}$', **offsets)
        _gDplot(16,0,3)
        title(r'$G_{yyy}$', **offsets)

Cheers,
JDH

Dear All,

as i'm new to this list let me first introduce myself before posting a 
question.

I'm working in the field of image processing and computer vision for a long 
time now and i am a long time matlab user. Up to now i didn't like any of the 
matlab clones but it seems that numarray+matplotlib might be a winner here.

I started using numarray+matplotlib by coding the Gaussian derivative 
convolutions. Calculating a derivative (by convolving with the derivative of 
the Gaussian function) will lead to an image(array) with both positive and 
negative values. Although imshow should deal with that (as far as i 
understand the code: beware i am a Python beginner) the display turns black 
for all derivatives (except for the 'zero order' derivative of course).

What is going on? Is it me (probably) or is it imshow?

Attached you find the code.

Regards
Rein van den Boomgaard
University of Amsterdam
The Netherlands



# ==========================================================
# Gaussian Derivatives
#
# Copyright (c) Rein van den Boomgaard
#               Vision Consultancy & Training
#               re...@vi...
# 
# ==========================================================
import types
from numarray import *
from numarray.nd_image import *




def gD( image, scales, orders, nscales=4, mode='reflect' ):
    scales = _normalize_sequence(scales,image)
    orders = _normalize_sequence(orders,image)

    result = image.copy()
    ndims = len( image.shape )
    for i in range(0,ndims):
        N = ceil(scales[i]*nscales)
        x = arange( -N, N )
        
        w = GaussianDerivativeFunction( x, 
                                        scales[i], 
                                        orders[i] )
        result = convolve1d( result, w, i, mode = mode )
    return( result )



def HermiteH( n, x ):
    """
    Hermite Poynomial
    """
    if n==0:
        return 0*x + 1
    elif n==1:
        return 2*x
    else:
        return 2*x*HermiteH( n-1, x ) - 2*(n-1)*HermiteH(n-2,x)


def GaussianDerivativeFunction( x, scale, order ):
    scale = float(scale)
    g = 1.0 / ( scale * sqrt(2*pi) ) * exp( - x**2 / (2*scale**2) )
    return (-1.0 / (scale*sqrt(2.0)))**order * \
           HermiteH( order, x / (scale * sqrt(2)) ) * g



# the _normalize_sequence function is copied from numarray
# because i cant import it...???

def _normalize_sequence(input, array):
    """If input is a scalar, create a sequence of length equal to the
    rank of array by duplicating the input. If input is a sequence,
    check if its length is equalt to the lenght of array.

    """
    if isinstance(array, ArrayType):
        rank = len(array.shape)
    else:
        rank = 1
    if (isinstance(input, (types.IntType, types.LongType, types.FloatType))):
        normalized = [input] * rank
    else:
        if isinstance(input, numarray.numarraycore.NumArray):
            normalized = list(input)
        else:
            normalized = input
        if len(normalized) != rank:
            error = "sequence argument must have length equal to input rank"
            raise RuntimeError, error
    return normalized



if __name__ == "__main__":
    from matplotlib import use, interactive
    from matplotlib.matlab import *

    PLOT_1D_GAUSS = 1
    PLOT_GAUSSIAN_DERIVATIVES = 1
    PLOT_TIMING = 1

    if PLOT_1D_GAUSS:

        # testing the Gaussiand Derivatives
        x=arange(-5,5,.01)


        figure(1)
        clf()
        plot(x,GaussianDerivativeFunction(x,1,0))
        plot(x,GaussianDerivativeFunction(x,1,1))
        plot(x,GaussianDerivativeFunction(x,1,2))
        plot(x,GaussianDerivativeFunction(x,1,3))
        plot(x,GaussianDerivativeFunction(x,1,4))
        xlabel(r'$x$')
        ylabel(r'$G_n(x)$')
        title('Gaussian Derivatives')

    if PLOT_GAUSSIAN_DERIVATIVES:
        scale = 9
        
        def _gDplot(n,ox,oy):
            subplot(4,4,n)
            axis('off')
            imshow( gD(a,scale,(ox,oy)) )


        
        a = zeros( (64,64) ) * 1.0
        
        a[32,32] = 1.0
        figure(2)
        axis('off')
        title('Gaussian Derivatives')

        
        _gDplot(1,0,0)
        title(r'$G$')
        _gDplot(5,1,0)
        title(r'$G_x$')
        _gDplot(6,0,1)
        title(r'$G_y$')
        _gDplot(9,2,0)
        title(r'$G_{xx}$')
        _gDplot(10,1,1)
        title(r'$G_{xy}$')
        _gDplot(11,0,2)
        title(r'$G_{yy}$')
        _gDplot(13,3,0)
        title(r'$G_{xxx}$')
        _gDplot(14,2,1)
        title(r'$G_{xxy}$')
        _gDplot(15,1,2)
        title(r'$G_{xyy}$')
        _gDplot(16,0,3)
        title(r'$G_{yyy}$')
        
        

    if PLOT_TIMING:
        from timeit import Timer
        niter = 5
        
        def time_gD(scale):
            t = Timer( "b=gD(a,%d, 0)" % scale, "from __main__ import gD,a" )
            timing = t.timeit(number=niter )
            print( timing/niter )
            return( timing/niter )
            
        
        a = zeros( (256,256) ) * 1.0
        a[128,128] = 1.0
        figure(3)
        scales = array([1,3,5,7,9,11,15,19, 25,31,41,51])
        print(type(scales))
        plot( scales, map( time_gD, scales ), 'x' )
        xlabel('Scale (px)')
        ylabel('Time (s)')
        title('Gaussian Convolution Timings')