px.density_contour() ignores color labels for negative histfunc results

Consider the following example:

import plotly.express as px
import pandas as pd
import numpy as np
import plotly


np.random.seed(0)

n_samples = 1000
df = pd.DataFrame({
    'x': np.random.randint(low=1, high=5, size=n_samples),
    'y': np.random.randint(low=1, high=4, size=n_samples),
    'z': np.random.normal(loc=-40, scale=20, size=n_samples),
})

df['z'] = df['z'] * df['x']

# uncomment to make some values above 0
# df['z'] = df['z'] + 80

# uncomment to make all values above 0
# df['z'] = df['z'].abs()

fig = px.density_contour(df, x='x', y='y', z='z', histfunc='avg', height=800, width=1200, title=f'Plotly version: {plotly.__version__}')
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()

It calculates average of mostly negative values, which results in the following plot:

Color bar has correct color range, but all data points use same color - yellow, which is not particularly useful. Similar situation happens when only part of data is negative (uncomment first commented line in the example):

Left (positive) section looks fine, while right (negative) is colored with only one color.

There are workarounds, that involve changing of input data, but this makes plot reading much harder.

先运行提供的 px.density_contour() 示例，使用负值和混合的 histfunc 结果，然后检查 density_contour 入口点以及等高线着色如何处理这些值。完成的标准是负值和混合结果使用完整的颜色范围，而不是折叠为单一颜色，同时现有的正值情况仍然正确。