Hello,
Yeah, this helps indeed with "corner_mask='legacy' the result is
identical, even though I get a warning:
/usr/lib/python3.5/site-packages/matplotlib/cbook.py:136:
MatplotlibDeprecationWarning: The corner_mask='legacy' attribute was
deprecated in version 1.5. Use corner_mask=False or True instead.
warnings.warn(message, mplDeprecation, stacklevel=1)
I tried creating a minimal example which recreates the problem with
simpler data. The result does not exactly represent 100% of the
problem, but it is screwed anyway. Again, with the option
corner_mask='legacy' option, it looks just fine.
You can find my example here:
https://paste.kde.org/poyn7upbi
Viktoria,
Independently of the contourf bug, I suggest you consider two other
options for 2-D histogram display: pcolormesh, and hexbin. In both
cases, you would use a BoundaryNorm with a ListedColormap. Something
like this (untested):
from matplotlib import colors
cmap = colors.ListedColormap([(1, 1, 1, 1), (0, 0, 1, 0.25), (0, 0, 1,
0.5), (0, 0, 1, 0.75)])
norm = colors.BoundaryNorm([0, Lmin, L1, L2, L3, np.inf], ncolors=cmap.N)
plt.pcolormesh(xedges, yedges, hist2D, norm=norm, cmap=cmap)
An advantage of using pcolormesh or hexbin is that the value of a cell
in a histogram represents the entire cell, not a point, so it makes
sense to color in the whole cell. I think that using contour or contourf
in cases like this gives an inaccurate visual impression unless the
histogram is reasonably smoothly varying on a fine grid.
Although in the example above I have stuck to your use of alpha to
modify the shade of blue, I don't advocate this. Instead, unless you
really need the transparency to achieve a visual effect involving other
plot elements, I suggest modifying the color directly and leaving alpha
as the default (unity). So I would use something like:
cmap = colors.ListedColormap([(1, 1, 1), (0.75, 0.75, 1), (0.5, 0.5, 1),
(0.25, 0.25, 1)])
Eric
···
On 2015/11/12 7:34 AM, Viktoria Schubert via Matplotlib-devel wrote:
Thank you very much!
Viktoria
2015-11-12 17:09 GMT+01:00 Benjamin Root <ben.v.root at gmail.com>:
Interesting, but it is hard to tell what is going on without any code or
data. In particular, matplotlib's contourf polygons has always been
"unstacked". In other words, the polygons representing a contour level does
not overlap with any other polygon of another contour level, so I am not
exactly sure how you are getting the behavior you are seeing in either
image.
Now, the default algorithm for contouring did change in v1.5. You can access
the old algorithm by passing `corner_mask='legacy'` as a keyword argument to
contourf() in v1.5. Could you try that and see if at least you get identical
results for v1.5 and v1.4? Note, that keyword argument is not available in
v1.4. This should help us narrow down the source of your issue, but I
wouldn't treat it as a solution because the old algorithm is slated for
removal at some point, and it still doesn't explain the difference you are
seeing with the new contouring algorithm.
Ben
On Thu, Nov 12, 2015 at 8:11 AM, Viktoria Schubert via Matplotlib-devel >> <matplotlib-devel at python.org> wrote:
Hello together,
Since the 1.5 version of matplotlib together with python 3.5, I get
strange results from using the contourf function. What I'm actually
doing is plotting a lot of points in a 2D-space. The density equals the
likelihood of the data, so what I want is to plot the 1Sigma, 2Sigma and
3Sigma areas of the data.
For this purpose, I was using the following line of code:
cs = plt.contourf(hist2D, extent=extent, levels=[L1,L2,L3,Lmin],
linestyles=['-','-','-','-'], colors=['blue','blue','blue'], alpha=0.25)
The problem now is, that with matplotlib 1.4, the resulting image shows
the most dense area in white instead of darker blue, which was the
previous behavior. I did not change my code, I can get the old result
simply by moving back to 1.4.
I link to the two pictures I created, so maybe one of you can explain
me, what changed with the version so that the plot is getting messed up.
V1.4:
http://i.imgur.com/HH7jKBE.png
V1.5:
http://i.imgur.com/0LZ9Tso.png
Thank you very much,
Viktoria
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