Port HueMapping tests; with datetime mapping unimplemented/untested f…

…or now

.coveragerc

@@ -5,4 +5,9 @@ omit =
     seaborn/colors/*
     seaborn/cm.py
     seaborn/conftest.py
-    seaborn/tests/*
+
+[report]
+exclude_lines =
+    pragma: no cover
+    if TYPE_CHECKING:
+    raise NotImplementedError

seaborn/_core/mappings.py

@@ -1,10 +1,9 @@
 from __future__ import annotations
 
-from collections import abc
-
 import numpy as np
 import pandas as pd
 import matplotlib as mpl
+from matplotlib.colors import to_rgb
 
 from seaborn._core.rules import VarType, variable_type, categorical_order
 from seaborn.utils import get_color_cycle, remove_na
@@ -33,9 +32,10 @@ def __call__(self, x):  # TODO types; will need to overload (wheee)
         if isinstance(x, pd.Series):
             if x.dtype.name == "category":  # TODO! possible pandas bug
                 x = x.astype(object)
-            return x.map(self.lookup_table)
+            # TODO where is best place to ensure that LUT values are rgba tuples?
+            return np.stack(x.map(self.lookup_table).map(to_rgb))
         else:
-            return self.lookup_table[x]
+            return to_rgb(self.lookup_table[x])
 
 
 # TODO Currently, the SemanticMapping objects are also the source of the information
@@ -60,7 +60,7 @@ def __call__(self, x):  # TODO types; will need to overload (wheee)
 
 class GroupMapping(SemanticMapping):
     """Mapping that does not alter any visual properties of the artists."""
-    def setup(self, data: Series, scale: Scale | None) -> GroupMapping:
+    def setup(self, data: Series, scale: Scale | None = None) -> GroupMapping:
         self.levels = categorical_order(data)
         return self
 
@@ -77,7 +77,7 @@ def __init__(self, palette: PaletteSpec = None):
     def setup(
         self,
         data: Series,  # TODO generally rename Series arguments to distinguish from DF?
-        scale: Scale | None,  # TODO or always have a Scale?
+        scale: Scale | None = None,  # TODO or always have a Scale?
     ) -> HueMapping:
         """Infer the type of mapping to use and define it using this vector of data."""
         palette: PaletteSpec = self._input_palette
@@ -90,6 +90,7 @@ def setup(
         # e.g. specifying a numeric scale and a qualitative colormap should fail nicely.
 
         map_type = self._infer_map_type(scale, palette, data)
+        assert map_type in ["numeric", "categorical", "datetime"]
 
         # Our goal is to end up with a dictionary mapping every unique
         # value in `data` to a color. We will also keep track of the
@@ -123,9 +124,6 @@ def setup(
                 list(data), palette, order,
             )
 
-        else:
-            raise RuntimeError()  # TODO should never get here ...
-
         # TODO do we need to return and assign out here or can the
         # type-specific methods do the assignment internally
 
@@ -151,11 +149,10 @@ def _infer_map_type(
             return scale.type
         elif palette in QUAL_PALETTES:
             map_type = VarType("categorical")
-        elif isinstance(palette, (abc.Mapping, abc.Sequence)):
+        elif isinstance(palette, (dict, list)):
             map_type = VarType("categorical")
         else:
-            map_type = variable_type(data)
-
+            map_type = variable_type(data, boolean_type="categorical")
         return map_type
 
     def _setup_categorical(
@@ -212,6 +209,10 @@ def _setup_numeric(
 
             # The presence of a norm object overrides a dictionary of hues
             # in specifying a numeric mapping, so we need to process it here.
+            # TODO this functionality only exists to support the old relplot
+            # hack for linking hue orders across facets. We don't need that any
+            # more and should probably remove this, but needs deprecation.
+            # (Also what should new behavior be? I think an error probably).
             levels = list(sorted(palette))
             colors = [palette[k] for k in sorted(palette)]
             cmap = mpl.colors.ListedColormap(colors)

seaborn/_core/scales.py

@@ -10,6 +10,7 @@
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from typing import Any, Callable
+    from pandas import Series
     from matplotlib.scale import ScaleBase
     from seaborn._core.typing import VariableType
 
@@ -19,13 +20,15 @@ class ScaleWrapper:
     def __init__(
         self,
         scale: ScaleBase,
-        type: VariableType,
+        type: VariableType,  # TODO don't use builtin name?
         norm: tuple[float | None, float | None] | Normalize | None = None,
     ):
 
         transform = scale.get_transform()
         self.forward = transform.transform
         self.reverse = transform.inverted().transform
+
+        # TODO can't we get type from the scale object in most cases?
         self.type = VarType(type)
 
         if norm is None:
@@ -47,16 +50,21 @@ def cast(self, data):
 
 
 class CategoricalScale(LinearScale):
-    def __init__(self, axis: str, order: list | None, formatter: Any):
+
+    def __init__(
+        self,
+        axis: str | None = None,
+        order: list | None = None,
+        formatter: Any = None
+    ):
         # TODO what type is formatter?
 
         super().__init__(axis)
         self.order = order
         self.formatter = formatter
 
-    def cast(self, data):
+    def cast(self, data: Series) -> Series:
 
-        data = pd.Series(data)
         order = pd.Index(categorical_order(data, self.order))
         if self.formatter is None:
             order = order.astype(str)

seaborn/conftest.py

@@ -75,6 +75,7 @@ def wide_array(wide_df):
     return wide_df.to_numpy()
 
 
+# TODO s/flat/thin?
 @pytest.fixture
 def flat_series(rng):