force LabelEnums to have labels, gives .label type str causing less mypy headache

- update LabelEnum to require label and make description default to empty str for better usability
- update tests to cover new LabelEnum behavior

Author: janosh

pymatviz/coordination/helpers.py

@@ -21,10 +21,10 @@
 class CnSplitMode(LabelEnum):
     """How to split the coordination number histogram into subplots."""
 
-    none = "none"
-    by_element = "by element"
-    by_structure = "by structure"
-    by_structure_and_element = "by structure and element"
+    none = "none", "None"
+    by_element = "by element", "By element"
+    by_structure = "by structure", "By structure"
+    by_structure_and_element = "by structure and element", "By structure and element"
 
 
 def create_hover_text(

pymatviz/enums.py

@@ -85,12 +85,7 @@ class LabelEnum(StrEnum):
     Simply add label and description as a tuple starting with the key's value.
     """
 
-    def __new__(
-        cls,
-        val: str,
-        label: str | None = None,
-        desc: str | None = None,
-    ) -> Self:
+    def __new__(cls, val: str, label: str, desc: str = "") -> Self:
         """Create a new class from a value, label, and description. Label and
         description are optional.
         """
@@ -111,14 +106,14 @@ def __reduce_ex__(self, proto: object) -> tuple[type, tuple[str]]:
         return str, (self.value,)
 
     @property
-    def label(self) -> str | None:
+    def label(self) -> str:
         """Make label read-only."""
-        return self.__dict__.get("label")
+        return self.__dict__["label"]
 
     @property
-    def description(self) -> str | None:
+    def description(self) -> str:
         """Make description read-only."""
-        return self.__dict__.get("desc")
+        return self.__dict__["desc"]
 
 
 eV_per_atom = html_tag("(eV/atom)", style="small")  # noqa: N816

pymatviz/process_data.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import itertools
-import warnings
 from typing import TYPE_CHECKING
 
 import pandas as pd
@@ -62,12 +61,6 @@ def count_elements(
     Returns:
         pd.Series: Map element symbols to heatmap values.
     """
-    # TODO: remove warning after 2025-01-01
-    if fill_value is None:
-        warnings.warn(
-            "default value of fill_value changed from zero to None.", stacklevel=2
-        )
-
     valid_count_modes = set(ElemCountMode)
     if count_mode not in valid_count_modes:
         raise ValueError(f"Invalid {count_mode=} must be one of {valid_count_modes}")

pymatviz/scatter.py

@@ -182,7 +182,8 @@ def density_scatter_plotly(
         y (str): y-values dataframe column name.
         df (pd.DataFrame): DataFrame with x and y columns.
         density ('kde' | 'interpolate' | 'empirical'): Determines the method for
-            calculating and displaying density.
+            calculating and displaying density. Default is 'empirical' when n_bins
+            is provided, else 'kde' for kernel density estimation.
         log_density (bool | None): Whether to apply logarithmic scaling to density.
             If None, automatically set based on density range.
         identity_line (bool | dict[str, Any], optional): Whether to add a parity line

tests/test_enums.py

@@ -13,7 +13,7 @@
 
 class DummyEnum(LabelEnum):
     TEST1 = "test1", "Test Label 1", "Test Description 1"
-    TEST2 = "test2"
+    TEST2 = "test2", "Test Label 2"
 
 
 def test_str_enum() -> None:
@@ -74,8 +74,6 @@ def test_label_enum_new() -> None:
 
 def test_label_enum_repr() -> None:
     assert repr(DummyEnum.TEST1) == DummyEnum.TEST1.label == "Test Label 1"
-    assert repr(DummyEnum.TEST2) == "DummyEnum.TEST2"
-    assert DummyEnum.TEST2.label is None
 
 
 @pytest.mark.parametrize(

tests/test_process_data.py

@@ -123,8 +123,7 @@ def test_count_elements_exclude_invalid_elements() -> None:
 
 def test_count_elements_fill_value() -> None:
     compositions = [Composition("Fe2O3")] * 5 + [Composition("Fe4P4O16")] * 3
-    with pytest.warns(UserWarning, match="default value of fill_value changed"):
-        series = pmv.count_elements(compositions, count_mode=ElemCountMode.composition)
+    series = pmv.count_elements(compositions, count_mode=ElemCountMode.composition)
     expected = pd.Series(
         {"Fe": 22, "O": 63, "P": 12}, index=pmv.df_ptable.index, name="count"
     )