Avoid LossySetitemError on pandas >= 3.0

[marksteward]

Pandas 3.0 gets upset at assigning float-like values to a uint column when using .loc:

>>> import pandas as pd
>>> df = pd.DataFrame({"a": [1,  2,  3], "b": [4, 5, 6]}, dtype="uint16")
>>> df.loc[:, ["a", "b"]] *= 2  # fine
>>> df.loc[:, ["a", "b"]] /= 2  # uh oh
...
  File "/usr/local/lib/python3.12/site-packages/pandas/core/dtypes/cast.py", line 1725, in np_can_hold_element
    raise LossySetitemError
pandas.errors.LossySetitemError

The above exception was the direct cause of the following exception:
...
TypeError: Invalid value '0    1.0
1    2.0
2    3.0
Name: a, dtype: float64' for dtype 'uint16'

I've replaced this with an explicit integer division.

[Copilot]

Pull request overview

This PR updates LAS color downscaling logic to avoid pandas (>= 3.0) raising LossySetitemError when dividing unsigned-integer DataFrame columns via .loc, by switching to integer (floor) division during uint16 → uint8 conversion.

Changes:

• Replace /= 256 with //= 256 when scaling 16-bit color channels down to 8-bit.
• Keep the existing guard that avoids scaling when values already fit within [0, 255].

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