Add make_palette and make_palette_from_data for palette generation

[timtreis]

Summary

Closes #210.

• Adds make_palette(n) — produce n colors from a source palette, optionally reordered for maximum perceptual contrast or colorblind accessibility.
• Adds make_palette_from_data(sdata, element, color) — derives categories from a SpatialData element. Supports all make_palette methods plus spatially-aware assignment (spaco-inspired) that maximizes contrast between spatially interleaved categories.
• Adds dict[str, str] palette support to render_shapes, render_points, and render_labels, enabling reuse of generated palettes across multiple render calls.

API overview

import spatialdata_plot as sdp

# Standalone: just produce N colors
sdp.pl.make_palette(5)
sdp.pl.make_palette(8, palette="okabe_ito")
sdp.pl.make_palette(10, palette="tab10", method="contrast")
sdp.pl.make_palette(6, method="colorblind")

# From data: spatial-aware assignment → dict[str, str]
palette = sdp.pl.make_palette_from_data(sdata, "cells", "cell_type", method="spaco")
palette = sdp.pl.make_palette_from_data(sdata, "cells", "cell_type", method="spaco_colorblind")
palette = sdp.pl.make_palette_from_data(sdata, "cells", "cell_type", palette="okabe_ito", method="spaco")

# Dict palette plugs directly into render calls
sdata.pl.render_shapes("cells", color="cell_type", palette=palette).pl.show()

Methods

Method	make_palette	make_palette_from_data	What it does
"default"	✓	✓	Source order (scanpy behaviour)
"contrast"	✓	✓	Max perceptual spread (Oklab)
"colorblind"	✓	✓	Max spread under worst-case CVD
"protanopia"	✓	✓	Max spread under protanopia
"deuteranopia"	✓	✓	Max spread under deuteranopia
"tritanopia"	✓	✓	Max spread under tritanopia
"spaco"	✗	✓	Spatial contrast (normal vision)
"spaco_colorblind"	✗	✓	Spatial contrast + worst-case CVD
"spaco_*"	✗	✓	Spatial contrast + specific CVD

Implementation details

• Spaco core reimplemented (~100 lines) to avoid GPL dependency. Uses KDTree-based spatial interlacement + randomized permutation optimizer.
• Oklab color space for perceptual distance (better than spaco's red-mean RGB).
• Brettel/Viénot CVD simulation for colorblind-aware optimization.
• Zero new dependencies — only numpy, scipy, matplotlib (all existing).
• Okabe-Ito palette built-in as palette="okabe_ito".
• Any matplotlib colormap name works as palette= (e.g., "tab10", "Set2").

[codecov-commenter]

Codecov Report

❌ Patch coverage is 83.95522% with 43 lines in your changes missing coverage. Please review.
✅ Project coverage is 76.28%. Comparing base (e8c262e) to head (165f5a7).
⚠️ Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
src/spatialdata_plot/pl/_palette.py	85.08%	21 Missing and 16 partials ⚠️
src/spatialdata_plot/pl/utils.py	62.50%	3 Missing and 3 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #581      +/-   ##
==========================================
+ Coverage   75.63%   76.28%   +0.64%     
==========================================
  Files          10       11       +1     
  Lines        2996     3259     +263     
  Branches      702      756      +54     
==========================================
+ Hits         2266     2486     +220     
- Misses        444      468      +24     
- Partials      286      305      +19     

Files with missing lines	Coverage Δ
src/spatialdata_plot/pl/__init__.py	100.00% <100.00%> (ø)
src/spatialdata_plot/pl/basic.py	84.81% <ø> (ø)
src/spatialdata_plot/pl/render_params.py	86.50% <100.00%> (ø)
src/spatialdata_plot/pl/utils.py	66.11% <62.50%> (-0.08%)	⬇️
src/spatialdata_plot/pl/_palette.py	85.08% <85.08%> (ø)

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