feat: remove mizani as dependency, re-implement logic internally

[machow]

This PR removes mizani as a dependency, so that we don't transitively depend on scipy and pandas. Note that our internal implementation does not depend on numpy, so that we can drop it as a dependency in a later PR.

This implementation is not as elegant as mizani's, or quite as optimized, but should do well for our purposes.

Speed

Our default implementation was about 50% slower than mizani in a simple test. But both are very fast for simple table displays (1.52 ms for 1,000 points for ours, 1ms for mizani.)

from great_tables._data_color.palettes import GradientPalette, CoeffSequence
from mizani.palettes import gradient_n_pal

palette = GradientPalette(["red", "orange", "blue", "grey", "yellow", "red", "orange"])
palette2 = GradientPalette(["red", "orange", "blue", "grey", "yellow", "red", "orange"], cls_coeff_sequence=CoeffSequence)
palette3 = gradient_n_pal(["red", "orange", "blue", "grey", "yellow", "red", "orange"])


%%timeit
# internal + bisect lookup: 1.52 ms ± 5.85 µs per loop
palette([x / 1000. for x in range(1000)])

%%timeit
# 1.76 ms ± 6.27 µs per loop
palette2([x / 1000. for x in range(1000)])

%%timeit
# mizani: 1.03 ms ± 9.82 µs per loop
palette3([x / 1000. for x in range(1000)])

The main difference is that we're using a simple bisect lookup to find cutoff corresponding to a value (in order to get coefficients for transforming within a cutoff band). Mizani uses a table lookup, which cuts the input/response space into 256 bins.

Fixes: #7

[machow]

AFAICT changes to snapshots are due to differences in rounding (and/or the resolution of mizani's table lookup).

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 93.02326% with 6 lines in your changes are missing coverage. Please review.

Project coverage is 81.87%. Comparing base (ece39e8) to head (05578a6).
Report is 125 commits behind head on main.

Files	Patch %	Lines
great_tables/_data_color/palettes.py	92.77%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #271      +/-   ##
==========================================
+ Coverage   77.01%   81.87%   +4.86%     
==========================================
  Files          40       41       +1     
  Lines        4229     4310      +81     
==========================================
+ Hits         3257     3529     +272     
+ Misses        972      781     -191     

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[rich-iannone]

Thank you for the huge amount of work you put into this and the related PR! Will review shortly.

[rich-iannone]

LGTM!

[machow]

@abstractqqq in case it's useful for polars_ds, this PR should remove pandas as a dependency (except for importing from great_tables.data, which we'll tackle later!). We should be able to release soon. Definitely let us know if you run into any issues with it.