feat: use LUT for numpy AEF dequantization

[Geethen]

Problem

The plain numpy path in dequantize_aef recomputes the same int8-to-float32 formula for every pixel value. AEF stores only 256 possible raw values, so large materialized windows spend avoidable time repeating (v / 127.5) ** 2 * sign(v) and then applying a separate nodata mask.

Fix

This adds a 256-entry lookup table for the plain ndarray path and maps raw values through it with lut[raw + 128]. The -128 -> NaN nodata mapping is folded into the table. The xarray/DataArray path stays elementwise so dask-backed arrays remain lazy and blockwise.

In local checks on a 2048 x 2048 x 64 materialized AEF window, this changed numpy dequantization from about 2702 ms to 1582 ms, while preserving identical output.

Tests

Adds tests/test_utils.py coverage asserting the LUT path matches the reference formula for all 256 int8 values, for both int8 and int16 inputs.

Ran locally:

uv run pytest tests/test_utils.py
uv run ruff check aef_loader/utils.py tests/test_utils.py