matplotlib 'spy' function gives incorrect plot

I have got some funny behaviour here that looks like a bug with the spy() function. Using the latest python-matplotlib 0.87.5 package on Ubuntu 6.10, I try:

$ ipython -pylab
from scipy import io
M = io.mmread('gd.mm')
spy(M)
M.shape
M.nnz

It looks great and I get the necessary number of dots on my 'spy' plot. Then:

spy(M.todense())

This seems to plot only half of the points. At first I thought the 'todense' function must be to blame, however:

D = M.todense()
for i in range(0,231):
    for j in range(0,231):
        if D[i,j]:
            print i,j,D[i,j]

gave me the right data, so it really looks like a bug with spy().

And FWIW spy2(M) fails with an error, whereas spy2(D) shows the same bug as spy(D).

I wonder if this would warrant a backport of the 0.97.7 or 0.90 package, perhaps?

Following is the matrix file (not very big):
------8<-----
%%MatrixMarket matrix coordinate real general
% Matrix Market file format
% see http://math.nist.gov/MatrixMarket/
% RANGE: rows = 231, cols = 231, num_of_non_zeros =50
% MATRIX: rows = 231, cols = 231
231 231 50
% sparse value data:
% row#, col#, value
% whole matrix:
20 15 0.054643143142798458745
33 34 1
34 25 0.054609424508940254683
45 26 1
48 22 0.053821937145403411784
58 20 1
62 40 0.051502800816908882175
66 10 1
76 14 0.050401036017384562293
85 33 1
90 8 0.04827636527459408522
103 39 1
104 1 0.047357593452698590331
108 4 1
118 6 0.046168808547273877674
128 24 1
132 28 -4.3982297150257112506
133 11 -17.074271484359051954
134 18 -17.825720042487571249
135 5 0.049361553542753421053
136 27 -17.458078322276591621
137 23 -4.3982297150257112506
138 23 -18.849555921538758696
139 12 0.052604256284871545035
149 31 1
161 29 1
166 37 -18.849555921538758696
167 9 -18.604949812022685052
168 32 -18.215389629085230183
169 17 -4.3982297150257112506
170 17 -18.849555921538758696
171 28 -18.849555921538758696
175 13 -18.849555921538758696
176 30 -19.035616318827216276
179 13 -4.3982297150257112506
183 16 -19.314133000003423035
187 21 -18.849555921538758696
188 21 -4.3982297150257112506
192 3 -18.849555921538758696
193 3 -4.3982297150257112506
194 7 -19.326058527231953832
195 35 -18.849555921538758696
196 38 -18.849555921538758696
197 38 -4.3982297150257112506
198 2 -16.749322424296355649
199 35 -4.3982297150257112506
202 36 -18.849555921538758696
203 36 -4.3982297150257112506
204 19 -16.328875771026634567
229 37 -4.3982297150257112506