matplotlib-users Mailing List for matplotlib

Dear all,
At the moment I'm heavily using the scatter plot (great!). But if I want =
to add
a color bar (with the command colorbar(), directly following the call to
scatter) to explain the meaning of the colors of the patches, matplotlib =
(0.64)
refuses to make a colorbar with the following message:
First define a mappable image (eg imshow, figimage, pcolor, scatter
I checked how colorbar knows that the plot under consideration is a 'mapp=
able
image': it calls gci() . And indeed, if I do that by hand, after a plot w=
ith
scatter, I get the message that there is no mappable image.
According to the doc's and the error message above, the patches plotted b=
y
scatter should count as a mappable image, but apparently, they don't.
Is this a bug or a misunderstanding on my side?
Regards,
Arnold
PS: at the moment I'm using a workaround, by making my own colorbar comma=
nd:
just stealing the code from the original routine, but without getting the
colors and number ranges from the image itself (I have to define those by
hand).

> Date: 2004年12月02日 10:04:20 +0100
> From: Nils Wagner <nw...@me...>
> To: SciPy Developers List <sci...@sc...>
> Cc: mat...@li...
> Subject: [Matplotlib-users] Feature request : Comparison of sparse matrices is not implemented.
> 
> Hi all,
> 
> I tried to visualize the structure of large and sparse matrices using
> 
> from matplotlib.colors import LinearSegmentedColormap
> from matplotlib.matlab import *
> from scipy import *
> import IPython
> 
> def spy2(Z):
> """
> SPY(Z) plots the sparsity pattern of the matrix S as an image
> """
> 
> #binary colormap min white, max black
> cmapdata = {
> 'red' : ((0., 1., 1.), (1., 0., 0.)),
> 'green': ((0., 1., 1.), (1., 0., 0.)),
> 'blue' : ((0., 1., 1.), (1., 0., 0.))
> }
> binary = LinearSegmentedColormap('binary', cmapdata, 2)
> 
> Z = where(Z>0,1.,0.)
> imshow(transpose(Z), interpolation='nearest', cmap=binary)
> 
> rows, cols, entries, rep, field, symm = io.mminfo('k0.mtx')
> print 'number of rows, cols and entries', rows, cols, entries
> print 'Start reading matrix - this may take a minute'
> ma = io.mmread('k0.mtx')
> print 'Finished'
> flag = 1
> if flag == 1:
> spy2(ma)
> show()
> 
> It failed. Is it somehow possible to visualize sparse matrices ?
> Any suggestion would be appreciated.
To plot sparsity patterns, I read matrices in MatrixMarket format using 
the PySparse package. Once I have arrays with row and column indices, I 
simply use scatter() with arguments to modify the size of the 'bubbles' 
and their color (and alpha) depending on the magnitude on the nonzero 
element. The result is great. Of course when matrices are symmetric, you 
only hold the lower (or upper) triangular part, then tell scatter() to 
plot the other triangle.
Dominique

Hi all,
Can someone help me with imshow() for sparse matrices ?
Nils