Description
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.
Zoomed in:
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.
Zoomed in:
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
3.9.4
Operating system
Rocky
Matplotlib Version
3.10.0
Matplotlib Backend
agg
Python version
3.10.16
Jupyter version
No response
Installation
pip