[Matplotlib-devel] Use of contourf changed?

[Eric Firing]

On 2015/11/12 7:34 AM, Viktoria Schubert via Matplotlib-devel wrote:
> 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

>
> 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
>>> _______________________________________________
>>> Matplotlib-devel mailing list
>>> Matplotlib-devel at python.org
>>> https://mail.python.org/mailman/listinfo/matplotlib-devel
>>
>>
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