add squidpy way to handle segment

[giovp]

No description provided.

[giovp]

@sagar87 @timtreis this is a heavy rewrite of the rendering functions. It mostly focuses on aesthetics (colors, legend, colorbars, etc.) and doesn't change the core behavior of the library.

With this current implementation I can showcase the aggregation functions in the notebooks but I'm afraid is not enough for the datasets you have been working on. I would however suggest to re-implement the functionalities in current logic.

There are also 2 critical changes that need to be made (not in this PR).

• avoid copying of spatialdata. This is really important, the object must not be copied, but only views should be passed in the function call.
• remove all transformations in a pp functionality. The way it's handled currently is quite confusing and happens in the show method.

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[giovp]

it's ready! it should have almost all functionalities as before + handle of caterogical and continuous for points.

Docstrings are missing, will try to finish them tomorrow.

[codecov-commenter]

Codecov Report

Merging #45 (5831956) into main (b73f4aa) will increase coverage by 9.71%.
The diff coverage is 28.52%.

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #45      +/-   ##
==========================================
+ Coverage   30.62%   40.34%   +9.71%     
==========================================
  Files          12        9       -3     
  Lines         862      870       +8     
==========================================
+ Hits          264      351      +87     
+ Misses        598      519      -79     

Impacted Files	Coverage Δ
src/spatialdata_plot/_accessor.py	100.00% <ø> (ø)
src/spatialdata_plot/pl/basic.py	20.32% <9.55%> (+9.85%)	⬆️
src/spatialdata_plot/pl/utils.py	28.43% <29.37%> (-4.90%)	⬇️
src/spatialdata_plot/pl/render.py	44.30% <41.49%> (+29.15%)	⬆️
src/spatialdata_plot/__init__.py	100.00% <100.00%> (ø)
src/spatialdata_plot/pp/basic.py	76.76% <100.00%> (-0.17%)	⬇️
src/spatialdata_plot/pp/utils.py	61.53% <100.00%> (ø)