FIX: Enable str transformations to Color/Interval scales

seaborn/_compat.py

@@ -56,6 +56,18 @@ def __call__(self, value, clip=None):
     return new_norm
 
 
+def is_registered_colormap(name):
+    """Handle changes to matplotlib colormap interface in 3.5."""
+    if _version_predates(mpl, "3.5"):
+        try:
+            mpl.cm.get_cmap(name)
+            return True
+        except ValueError:
+            return False
+    else:
+        return name in mpl.colormaps
+
+
 def get_colormap(name):
     """Handle changes to matplotlib colormap interface in 3.6."""
     try:

seaborn/_core/properties.py

@@ -14,6 +14,7 @@
 from seaborn._core.rules import categorical_order, variable_type
 from seaborn.palettes import QUAL_PALETTES, color_palette, blend_palette
 from seaborn.utils import get_color_cycle
+from seaborn._compat import is_registered_colormap
 
 from typing import Any, Callable, Tuple, List, Union, Optional
 
@@ -43,6 +44,7 @@
 
 class Property:
     """Base class for visual properties that can be set directly or be data scaling."""
+    _TRANS_ARGS = ["log", "symlog", "logit", "pow", "sqrt"]
 
     # When True, scales for this property will populate the legend by default
     legend = False
@@ -76,9 +78,8 @@ def infer_scale(self, arg: Any, data: Series) -> Scale:
         # (e.g. color). How best to handle that? One option is to call super after
         # handling property-specific possibilities (e.g. for color check that the
         # arg is not a valid palette name) but that could get tricky.
-        trans_args = ["log", "symlog", "logit", "pow", "sqrt"]
         if isinstance(arg, str):
-            if any(arg.startswith(k) for k in trans_args):
+            if any(arg.startswith(k) for k in self._TRANS_ARGS):
                 # TODO validate numeric type? That should happen centrally somewhere
                 return Continuous(trans=arg)
             else:
@@ -183,6 +184,8 @@ def infer_scale(self, arg: Any, data: Series) -> Scale:
             return Nominal(arg)
         elif var_type == "datetime":
             return Temporal(arg)
+        elif isinstance(arg, str) and any(arg.startswith(k) for k in self._TRANS_ARGS):
+            return Continuous(trans=arg)
         # TODO other variable types
         else:
             return Continuous(arg)
@@ -607,8 +610,6 @@ def infer_scale(self, arg: Any, data: Series) -> Scale:
         if callable(arg):
             return Continuous(arg)
 
-        # TODO Do we accept str like "log", "pow", etc. for semantics?
-
         if not isinstance(arg, str):
             msg = " ".join([
                 f"A single scale argument for {self.variable} variables must be",
@@ -619,7 +620,14 @@ def infer_scale(self, arg: Any, data: Series) -> Scale:
         if arg in QUAL_PALETTES:
             return Nominal(arg)
         elif var_type == "numeric":
-            return Continuous(arg)
+            # Prioritize actual colormaps, e.g. if a colormap named "pow" exists
+            if is_registered_colormap(arg):
+                return Continuous(arg)
+            elif any(arg.startswith(k) for k in self._TRANS_ARGS):
+                return Continuous(trans=arg)
+            else:
+                return Continuous(arg)
+
         # TODO implement scales for date variables and any others.
         else:
             return Nominal(arg)

tests/_core/test_properties.py

@@ -238,6 +238,17 @@ def test_inference(self, values, data_type, scale_class, vectors):
         assert isinstance(scale, scale_class)
         assert scale.values == values
 
+    @pytest.mark.parametrize(
+        "trans",
+        ["pow", "sqrt", "log", "symlog", "logit", "log2", "symlog100"]
+    )
+    def test_inference_magic_args(self, trans, num_vector):
+
+        scale = Color().infer_scale(trans, num_vector)
+        assert isinstance(scale, Continuous)
+        assert scale.trans == trans
+        assert scale.values is None
+
     def test_standardization(self):
 
         f = Color().standardize
@@ -531,6 +542,17 @@ def test_mapped_interval_categorical(self, cat_vector):
         n = cat_vector.nunique()
         assert_array_equal(mapping([n - 1, 0]), self.prop().default_range)
 
+    @pytest.mark.parametrize(
+        "trans",
+        ["pow", "sqrt", "log", "symlog", "log13", "logit", "symlog37"]
+    )
+    def test_inference_magic_args(self, trans, num_vector):
+
+        scale = self.prop().infer_scale(trans, num_vector)
+        assert isinstance(scale, Continuous)
+        assert scale.trans == trans
+        assert scale.values is None
+
     def test_bad_scale_values_numeric_data(self, num_vector):
 
         prop_name = self.prop.__name__.lower()