[Bug]: Spurious lines added with some manually add contour labels

[kafitzgerald]

Bug summary

With Matplotlib 3.8+ I'm seeing spurious lines that get added with manually labeled contours via clabel.

It seems to only happen in locations where the contours are more complex or near those areas, but I haven't been able to track down why this happens.

Code for reproduction

Example code (here working with Matplotlib <3.8): https://geocat-examples.readthedocs.io/en/latest/gallery/Contours/NCL_coneff_8.html

I'll work on stripping down this example though.

Actual outcome

when run in a script that provided a manual list of contour label locations:

when locations were manually clicked:

Expected outcome

similar, but without the spurious straight lines.

Additional information

What are the conditions under which this bug happens?

• happens with both "clicked" and specified manual contour labels in certain locations
• seems to happen only near areas with more complex contours
• interestingly, this also happens when you specify contour label locations this way on a cartopy map and the specified points are not on the projected map. however, this is not the case here.
  Has this worked in earlier versions?
• worked prior to 3.8
  Do you know why this bug is happening?
• unfortunately, no. will look into it a bit more though.

Operating system

OS/X

Matplotlib Version

3.8.1

Matplotlib Backend

MacOSX

Python version

Python 3.11.6

Jupyter version

4.0.8

Installation

conda

[ksunden]

Do you have a minimal (i.e. mpl + numpy only, preferably) reproducer of this?

I know that there were some issues in 3.8.0 that seem similar at least, but I thought I fixed them for 3.8.1 in #27045. Are you sure they persist in 3.8.1?

[kafitzgerald]

Sorry about that.

Here you go:

from matplotlib import pyplot as plt
import numpy as np

lat = [-87.8638  , -85.09653 , -82.31291 , -79.525604, -76.7369  , -73.94752 ,
       -71.15775 , -68.36776 , -65.57761 , -62.787354, -59.99702 , -57.20663 ,
       -54.4162  , -51.625732, -48.83524 , -46.044727, -43.254196, -40.46365 ,
       -37.673088, -34.882523, -32.091946, -29.30136 , -26.510769, -23.720175,
       -20.929575, -18.138971, -15.348365, -12.557756,  -9.767145,  -6.976533]
plev = [100000,  85000,  70000,  50000,  40000,  30000,  25000,  20000]
u = np.array([[ 5.281284  ,  5.281284  ,  5.281284  ,  5.281284  ,  5.281284  ,
         5.281284  ,  5.281284  ,  5.281284  ,  5.281284  ,  5.281284  ,
         5.281284  ,  5.281284  ,  5.281284  ,  5.281284  ,  4.5694838 ,
         8.355004  ,  8.594497  ,  7.364512  ,  5.3017035 ,  2.8804004 ,
         0.5346078 , -1.9970245 , -4.043903  , -5.495614  , -6.4742966 ,
        -6.86549   , -6.916672  , -6.6135497 , -5.824724  , -4.929174  ],
       [-7.8476605 , -7.8476605 , -7.8476605 , -4.9444113 , -4.6574264 ,
        -4.4306536 , -1.0847874 ,  0.23506624,  2.1887915 ,  6.6546087 ,
        10.182983  , 12.602904  , 14.024249  , 14.453691  , 14.11188   ,
        13.432743  , 12.298697  , 10.441111  ,  8.136927  ,  5.701264  ,
         3.1575031 ,  0.8824925 , -0.8204733 , -2.142562  , -3.2946696 ,
        -4.214696  , -5.014298  , -5.75503   , -5.797411  , -4.7429767 ],
       [-4.7934184 , -3.987672  , -3.880484  , -3.8545942 , -3.400934  ,
        -2.1740034 , -1.4144217 ,  1.8231349 ,  5.741105  ,  9.357849  ,
        12.589281  , 15.095114  , 16.566757  , 17.07177   , 16.893904  ,
        16.211683  , 15.016567  , 13.3146    , 11.277677  ,  9.180042  ,
         7.2710013 ,  5.7038155 ,  4.5025063 ,  3.3811352 ,  1.9761666 ,
         0.17865679, -1.848804  , -3.5658486 , -4.2557425 , -3.6108153 ],
       [-0.6427475 , -1.3300483 , -1.8785819 , -1.8878341 , -1.1554126 ,
         0.2460148 ,  2.640771  ,  5.841252  ,  9.352374  , 12.879071  ,
        16.21606   , 19.04898   , 20.9498    , 21.753773  , 21.649551  ,
        20.907742  , 19.693817  , 18.178251  , 16.707623  , 15.646201  ,
        15.057117  , 14.641205  , 13.846154  , 12.143323  ,  9.386396  ,
         5.9252477 ,  2.368536  , -0.5125828 , -2.0722165 , -2.2778945 ],
       [-0.58929294, -1.2046508 , -1.4544653 , -1.0103273 ,  0.15982199,
         1.9466033 ,  4.447282  ,  7.6771903 , 11.419675  , 15.136187  ,
        18.620575  , 21.717804  , 23.922436  , 24.840185  , 24.701748  ,
        23.987408  , 22.880804  , 21.526987  , 20.463743  , 20.20535   ,
        20.610573  , 20.982576  , 20.493448  , 18.504211  , 14.933222  ,
        10.389049  ,  5.7908807 ,  2.0268278 , -0.2654018 , -1.0479335 ],
       [-0.4088514 , -0.7439224 , -0.6603948 ,  0.0878893 ,  1.6308761 ,
         3.7945688 ,  6.51758   , 10.002526  , 14.048776  , 18.030323  ,
        21.763384  , 25.12306   , 27.494097  , 28.48131   , 28.469887  ,
        27.976196  , 27.148748  , 26.233028  , 25.929998  , 26.776876  ,
        28.395657  , 29.60503   , 29.1222    , 26.262024  , 21.399248  ,
        15.65298   , 10.07546   ,  5.45065   ,  2.312821  ,  0.5890202 ],
       [-0.30047217, -0.4094708 , -0.09946479,  0.78269076,  2.5223484 ,
         4.950463  ,  7.8729997 , 11.537473  , 15.756553  , 19.893959  ,
        23.728504  , 27.066483  , 29.343843  , 30.36043   , 30.488892  ,
        30.099907  , 29.479034  , 29.078144  , 29.513182  , 31.190378  ,
        33.604244  , 35.186615  , 34.35078   , 30.635078  , 24.895414  ,
        18.486992  , 12.361381  ,  7.0874414 ,  3.166243  ,  0.7561427 ],
       [-0.11722137,  0.06281883,  0.64088964,  1.7681435 ,  3.7674735 ,
         6.47419   ,  9.705322  , 13.647254  , 18.064777  , 22.31285   ,
        26.111181  , 29.2265    , 31.215776  , 32.078777  , 32.2106    ,
        31.913328  , 31.606083  , 31.906605  , 33.293755  , 35.880253  ,
        38.87251   , 40.392326  , 38.82986   , 34.17306   , 27.61663   ,
        20.4983    , 13.791601  ,  8.023929  ,  3.5063663 ,  0.5009983 ]])

contours = plt.contour(lat,plev,u,
                             levels=13,
                             vmin=-8,
                             vmax=40,
                             colors='black',
                             linewidths=0.5,
                             linestyles='solid')

# Label the contours
manual = [(-70, 55000), (-80, 26000), (-58, 30000), (-25, 42000),
          (-45, 69500), (-40, 34000), (-12, 39000), (-37, 75000),
          (-72, 22500)]

clabels = plt.clabel(contours,
                     fontsize=12,
                     colors="black",
                     fmt="%.0f",
                     manual=manual)

There were some similar issues in maptlotlib 3.8 that seem to have cleared up now, but this is with 3.8.1

[ksunden]

Okay, what is happening here is that the "nearest contour" logic is picking the point along the MOVETO line (which isn't drawn), and needs to filter out results with codes=MOVETO)

The above with a few things drawn:

path = contours.get_paths()[2]
fullpath = path.deepcopy()
fullpath.codes[1:] = 2
plt.gca().add_artist(mpatches.PathPatch(path, facecolor="none", edgecolor="C0", lw=4))
plt.gca().add_artist(mpatches.PathPatch(fullpath, facecolor="none", edgecolor="C3", lw=2))

plt.plot([-72], [22500], "C1o")

Shows that the offending point lies right on the omitted line.

Blue is the 0 contour, red is the 0 contour with all "LINETO" codes in the path (After the first point), orange dot is the requested manual position for the offending contour.

Should be relatively easy to fix, I think...

[ksunden]

@kafitzgerald I opened a draft PR that I think should solve it, if you could to run it through the more intense gauntlet to make sure it fully resolves the issue, that would be appreciated.

I intend to write an ever further pared-down test case and add a unit test prior to getting it merged, but I thought I'd get the code out there first.

[kafitzgerald]

Thanks!

I'd have gotten back sooner, but was struggling a bit with my dev env. For whatever reason my editable install of matplotlib wasn't working. Got a regular install to work though with the fix.

And it works great on the larger cases I had (both with specified label locations and manual clicks).

Thanks for getting this sorted so quickly!