8000 [Bug]: Change in ListedColormap behavior in 3.10.0 · Issue #29504 · matplotlib/matplotlib · GitHub
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[Bug]: Change in ListedColormap behavior in 3.10.0 #29504

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jsolbrig opened this issue Jan 22, 2025 · 5 comments
Open

[Bug]: Change in ListedColormap behavior in 3.10.0 #29504

jsolbrig opened this issue Jan 22, 2025 · 5 comments

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@jsolbrig
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Bug summary

While running integration tests on a package that I maintain I'm seeing a change in the behavior of ListedColormap in 3.10.0. This appears to be a regression and is causing significant changes in some of the imagery that my tests produce.

The tests are baked into a larger system and would be difficult for matplotlib maintainers to replicate, so I wrote up a dummy script that, I think, shows the problem. I'm not sure if it is capturing the full problem or not but it's at least capturing part of it.

Code for reproduction

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib import __version__ as mpl_vers


cld_type_colors = [
    "ghostwhite",
    "slategray",
    "blue",
    "royalblue",
    "cyan",
    "limegreen",
    "green",
    "yellow",
    "gold",
    "lightsalmon",
    "coral",
    "red",
    "maroon",
    "black",
]


rainbow_colors = [
    (255, 0, 0),     # Red
    (255, 64, 0),    # Reddish-Orange
    (255, 128, 0),   # Orange
    (255, 192, 0),   # Yellowish-Orange
    (255, 255, 0),   # Yellow
    (192, 255, 0),   # Yellowish-Green
    (128, 255, 0),   # Greenish-Yellow
    (0, 255, 0),     # Green
    (0, 255, 128),   # Greenish-Cyan
    (0, 255, 192),   # Cyan
    (0, 255, 255),   # Bright Cyan
    (0, 192, 255),   # Bluish-Cyan
    (0, 128, 255),   # Blue
    (0, 64, 255),    # Blueish-Purple
    (64, 0, 255),    # Purple
    (128, 0, 255),   # Violet
]
rainbow_colors = [(r / 255, g / 255, b / 255) for r, g, b in rainbow_colors]

cld_type_cmap = ListedColormap(cld_type_colors, name="cld_type_cmap")
rainbow_cmap = ListedColormap(rainbow_colors, name="rainbow_cmap")

# Create 1600x1600 array constant values in the vertial and float values ranging from
# 0-15 in the horizontal
size = 1600
num_values = 16
h_vals = np.linspace(0, num_values - 1, size)
flt_array = np.tile(h_vals, (size, 1))

# Plot float values using the cloud type colormap
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_title("Float - Cloud Type Color Map - MPL Version: " + mpl_vers)
ax.imshow(flt_array, cmap=cld_type_cmap)
fig.savefig(f"float_cloud_type_{mpl_vers.replace('.', '')}.png")

# Plot float values using the rainbow colormap
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_title("float - Rainbow Color Map - MPL Version: " + mpl_vers)
ax.imshow(flt_array, cmap=rainbow_cmap)
fig.savefig(f"float_rainbow_{mpl_vers.replace('.', '')}.png")

# Create a 1600x1600 array constant values in the vertial and integer values ranging
# from 0-15 in the horizontal
h_vals = np.arange(0, 16)
h_vals = np.repeat(h_vals, size // len(h_vals))
int_array = np.tile(h_vals, (size, 1))

# Plot integer values using the cloud type colormap
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_title("Integer - Cloud Type Color Map - MPL Version: " + mpl_vers)
ax.imshow(int_array, cmap=cld_type_cmap)
fig.savefig(f"int_cloud_type_{mpl_vers.replace('.', '')}.png")

# Plot integer values using the rainbow colormap
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_title("Integer - Rainbow Color Map - MPL Version: " + mpl_vers)
ax.imshow(int_array, cmap=rainbow_cmap)
fig.savefig(f"int_rainbow_{mpl_vers.replace('.', '')}.png")

Actual outcome

With v3.10.0 I get the following images. If you zoom in on the borders between color bands, you will see some lines of different color.

Image

Image

Image

Image

Zoomed in:

Image

Image

Image

Image

Expected outcome

With v3.9.4 I get the following output. If you zoom in on the borders between the color bands, there is a sharp transition between bands with no extra colors.

Image

Image

Image

Image

Zoomed in:

Image

Image

Image

Image

Additional information

Just for additional context, below are the really ugly images produced by our integration tests. The changes in how the colormaps are applied appear to be the cause of some of the differences here, though I don't think they actually address everything.

3.10.0

Image

3.9.4

Image

Operating system

Rocky

Matplotlib Version

3.10.0

Matplotlib Backend

agg

Python version

3.10.16

Jupyter version

No response

Installation

pip
@biosafetylvl5 biosafetylvl5 mentioned this issue Jan 22, 2025
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@tacaswell
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This is likely due to #28061 which moved the default interpolation stage from data space to rgba space when we are aggressively down sampling.

There is an API change note in that PR that I can not find it in the rendered docs (I need to look into that), but setting the mpl.rcParams['image.interpolation_stage'] = 'data' should restore the previous behavior.

@biosafetylvl5 biosafetylvl5 mentioned this issue Jan 22, 2025
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@tacaswell
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also, thank you for the very detailed bug report 🎉 !

@biosafetylvl5
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Wow! Thank you for such a fast response - I'm going to set mpl.rcParams['image.interpolation_stage'] = 'data' and see if that changes our outputs. 🙂

@biosafetylvl5
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That's definitely improving our outputs! @jsolbrig will likely chime in soon with more detail on the specifics.

@jsolbrig
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jsolbrig commented Jan 22, 2025 •
edited
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Thank you! As I suspected, this fixes part of our problem. The differences shown in the striped images above disappear and the image-diff from our integration tests improves. Most of our integration tests are completely fixed, but this one still has some lingering issues but I think I'll submit a different issue when I track it down.

For reference, the image-diff prior to changing this setting is shown in the first image below where red indicates some amount of change. The second image shows the image diff after changing this setting. I have a suspicion that the remaining issues have something to do with BoundaryNorm but need to investigate further.

Image

Image

The last two images show a diff that uses a threshold to look for more substantial changes. The first is without the new setting and the second is with the new setting.

Image

Image

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@tacaswell @jsolbrig @biosafetylvl5
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