Wrong coloring logic when switching the `color_by` value

[hadim]

When switching the color_by value dynamically, it seems like the coloring logic is kept to the original column and does not consider the new data type.

Consider the code below:

import pandas as pd
import jscatter
import numpy as np


n = 50
categories = [f"cat_{i}" for i in range(3)]

df = pd.DataFrame({
    "x": np.random.rand(n),
    "y": np.random.rand(n),
    "length": np.random.rand(n),
    "cat": np.random.choice(categories, size=n),
})

df["cat"] = df["cat"].astype("category")


scatter = jscatter.Scatter(
    data=df,
    x="x",
    y="y",
    color_by="cat",
    size=10,
    legend=True,
    tooltip=True,
    tooltip_properties=["cat"],
    tooltip_size="large",
    height=200,
    width=500,
    legend_size="large",
)

scatter.show()

Now switch the color_by value:

scatter.color(by="length")

And the colors are now wrong:

While it should be:

---

Note that the other way around (from continuous to category) fail as well.

[flekschas]

Yeah this is a known issue. The color map setting is not unset automatically when you switch the color_by setting. A simple work around is to do

scatter.color(by=new_column, map='auto')

The devil is in the details here because it could be annoying to have the color map reset in instances where you hand picked it. I think the solution is to decouple the color map argument from the actual value such that color_map='auto' is preserved.