histplot 2D ignores hue_norm argument

Description

If I understand correctly, when plotting a 2D histogram with histplot, the hue_norm argument should allow setting the values corresponding to data values corresponding to the extremes of the colormap, or a normalization object from matplotlib.colors such as LogNorm.

However, it seems that the hue_norm argument is being ignored. Even providing objects of some unreasonable type the same plot is produced, with no errors or complains.

Versions

seaborn 0.11.1 matplotlib 3.4.1

Example

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

x = np.random.randn(1000)
y = np.random.randn(1000)

fig, axs = plt.subplots(ncols=4, figsize=(16,3))
sns.histplot(x=x, y=y, ax=axs[0], cbar=True)
sns.histplot(x=x, y=y, ax=axs[1], cbar=True, hue_norm=(6, 10))
sns.histplot(x=x, y=y, ax=axs[2], cbar=True, hue_norm=LogNorm())
sns.histplot(x=x, y=y, ax=axs[3], cbar=True, hue_norm='I would expect strings to crash')

[mgab]

I don’t think hue_norm is especially useful in the histogram context (though perhaps moreso for univariate histograms), but it is part of the core hue mapping API and currently all relevant mapping parameters are included in each function that allows that mapping.

I suspected that. Seems totally reasonable to me! 👌

[…] Beyond the core functionality, each new example that’s relevant to you is an example that’s irrelevant to someone else, but makes it harder for them to find out how to do what they want. It can be hard to know exactly where to draw that line…

Fair enough! I guess it would have saved me (and you, sorry for the noise again) some time. But I totally see your point, so I leave it at your judgement. I would not mind having an example of a 2D histogram with log color scale, but I agree that it might be left for an answer at stackoverflow.

For controlling the histogram color scale, you can use kwargs that are passed through to pcolormesh, so vmin, vmax, and norm. I believe these should override the reparameterization that histplot does.

You’re right, sns.histplot(x=x, y=y, cbar=True, norm=LogNorm(), vmin=None, vmax=None) works. The thing is that at first I had only tried sns.histplot(x=x, y=y, cbar=True, norm=LogNorm()) -using the same data from the first message- but it fails because it conflicts with vmin and vmax set by histplot, not sure why I didn’t try to set the limits to None.

It also throws a MatplotlibDeprecationWarning about getting both norm and vmax/vmin parameters simultaneously, and threatens to become an error at v3.5.

In any case, unless you think something needs to be done about that norm and vmin/vmax conflict, I would say you can close the issue. Thanks a lot for the patience and clarity! 🙏 (and for the awesome library, of course! 😉 )

[mgab]

(Sorry for the silence, busy days)

The option I can think of would be to avoid passing vmin and vmax to pcolormesh when norm is present/not None. Then, it would be up to the user to manage same/different color norm. The ugly part is that the user providing both vmin/vmax and norm at the same would imply either raising an error (from seaborn or matplotlib) or ignoring either the parameters. And also, I guess that would require adding norm as an explicit parameter for seaborn, as it would modify/interact with other explicit parameters

Trying to set vmin/vmax to the Normalize subclass would work for LogNorm but is way less robust. Not all the subclasses in matplotlib.colors have these attributes.

Tot convinced by any option… 🤔 Do you have any approach in mind?