Code cleanup for versioning updates

.pre-commit-config.yaml

@@ -1,4 +1,12 @@
 repos:
+  - repo: https://github.com/psf/black
+    rev: 26.1.0
+    hooks:
+        - id: black
+          name: black
+          stages: [pre-commit]
+          language_version: python3
+
   - repo: https://github.com/pycqa/isort
     rev: 7.0.0
     hooks:
@@ -20,14 +28,6 @@ repos:
     - id: pyupgrade
       args: [--py38-plus]
 
-  - repo: https://github.com/psf/black
-    rev: 26.1.0
-    hooks:
-        - id: black
-          name: black
-          stages: [pre-commit]
-          language_version: python3
-
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v6.0.0
     hooks:

CHANGELOG.md

@@ -27,6 +27,9 @@ All notable changes to this project will be documented in this file. If you make
 - Fixes:
   - Add a default value for `PlantData`'s `asset_distance_matrix` and `asset_direction_matrix` to
     ensure projects not utilizing location data are compatible.
+  - Fix miscellaneous pandas warnings.
+  - Rerun pre-commit and update code styling for adherence to 3.10+ standards.
+  - Replace mutable default arguments with None and handle Nones internally.
 
 ## v3.1.4 - 2026-01-29
 

examples/01_utils_examples.ipynb

@@ -1006,7 +1006,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/02a_plant_aep_analysis.ipynb

@@ -949,7 +949,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/02b_plant_aep_analysis_cubico.ipynb

@@ -1176,7 +1176,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/02c_augmented_plant_aep_analysis.ipynb

@@ -760,7 +760,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/05_eya_gap_analysis.ipynb

@@ -280,7 +280,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/06_wake_loss_analysis.ipynb

@@ -3458,9 +3458,9 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "oa-env",
+      "display_name": "Python 3 (ipykernel)",
       "language": "python",
-      "name": "oa-env"
+      "name": "python3"
     },
     "language_info": {
       "codemirror_mode": {
@@ -3472,7 +3472,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.12"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/07_static_yaw_misalignment.ipynb

@@ -2428,7 +2428,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.8"
+      "version": "3.13.11"
     },
     "toc": {
       "base_numbering": 1,

examples/__init__.py

@@ -1,5 +1,4 @@
 from pathlib import Path
 
-
 example_data_path = Path(__file__).parents[0].resolve() / "data" / "la_haute_borne"
 example_data_path_str = str(example_data_path)

examples/project_Cubico.py

@@ -44,7 +44,6 @@
 from openoa.plant import PlantData
 from openoa.logging import logging
 
-
 logger = logging.getLogger()
 
 

examples/project_ENGIE.py

@@ -30,6 +30,7 @@
 - Wind speed, wind direction, temperature, and density
 
 """
+
 from __future__ import annotations
 
 import re
@@ -45,7 +46,6 @@
 from openoa.utils import filters, timeseries
 from openoa.logging import logging
 
-
 logger = logging.getLogger()
 
 
@@ -168,7 +168,7 @@ def prepare(
     - use_cleansed (bool): Use previously prepared data if the the "cleansed" folder exists above the main `path`. Defaults to False.
     """
 
-    if type(path) == str:
+    if isinstance(path, str):
         path = Path(path).resolve()
 
     # Load the pre-cleaned data, if available

openoa/analysis/aep.py

@@ -29,7 +29,6 @@
 from openoa.utils.machine_learning_setup import MachineLearningSetup
 from openoa.analysis._analysis_validators import validate_reanalysis_selections
 
-
 logger = logging.getLogger(__name__)
 
 NDArrayFloat = npt.NDArray[np.float64]
@@ -1186,9 +1185,9 @@ def sample_long_term_reanalysis(self):
             )
 
         # Store result in dictionary
-        self.long_term_sampling[
-            (self._run.reanalysis_product, self._run.num_years_windiness)
-        ] = long_term_reg_inputs
+        self.long_term_sampling[(self._run.reanalysis_product, self._run.num_years_windiness)] = (
+            long_term_reg_inputs
+        )
 
         # Return result
         return long_term_reg_inputs.copy()
@@ -1228,9 +1227,9 @@ def plot_normalized_monthly_reanalysis_windspeed(
         xlim: tuple[datetime.datetime, datetime.datetime] = (None, None),
         ylim: tuple[float, float] = (None, None),
         return_fig: bool = False,
-        figure_kwargs: dict = {},
-        plot_kwargs: dict = {},
-        legend_kwargs: dict = {},
+        figure_kwargs: dict | None = None,
+        plot_kwargs: dict | None = None,
+        legend_kwargs: dict | None = None,
     ) -> None | tuple[plt.Figure, plt.Axes]:
         """Make a plot of the normalized annual average wind speeds from reanalysis data to show
         general trends for each, and highlighting the period of record for the plant data.
@@ -1243,11 +1242,11 @@ def plot_normalized_monthly_reanalysis_windspeed(
                 Defaults to (None, None).
             return_fig (:obj:`bool`, optional): Flag to return the figure and axes objects. Defaults to False.
             figure_kwargs (:obj:`dict`, optional): Additional figure instantiation keyword arguments
-                that are passed to ``plt.figure()``. Defaults to {}.
+                that are passed to ``plt.figure()``. Defaults to None.
             plot_kwargs (:obj:`dict`, optional): Additional plotting keyword arguments that are passed to
-                ``ax.plot()``. Defaults to {}.
+                ``ax.plot()``. Defaults to None.
             legend_kwargs (:obj:`dict`, optional): Additional legend keyword arguments that are passed to
-                ``ax.legend()``. Defaults to {}.
+                ``ax.legend()``. Defaults to None.
 
         Returns:
             None | tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]: If ``return_fig`` is
@@ -1271,9 +1270,9 @@ def plot_reanalysis_gross_energy_data(
         xlim: tuple[float, float] = (None, None),
         ylim: tuple[float, float] = (None, None),
         return_fig: bool = False,
-        figure_kwargs: dict = {},
-        plot_kwargs: dict = {},
-        legend_kwargs: dict = {},
+        figure_kwargs: dict | None = None,
+        plot_kwargs: dict | None = None,
+        legend_kwargs: dict | None = None,
     ) -> None | tuple[plt.Figure, plt.Axes]:
         """
         Makes a plot of the gross energy vs wind speed for each reanalysis product, with outliers
@@ -1290,16 +1289,23 @@ def plot_reanalysis_gross_energy_data(
                 Defaults to (None, None).
             return_fig (:obj:`bool`, optional): Flag to return the figure and axes objects. Defaults to False.
             figure_kwargs (:obj:`dict`, optional): Additional figure instantiation keyword arguments
-                that are passed to ``plt.figure()``. Defaults to {}.
+                that are passed to ``plt.figure()``. Defaults to None.
             plot_kwargs (:obj:`dict`, optional): Additional plotting keyword arguments that are passed to
-                ``ax.scatter()``. Defaults to {}.
+                ``ax.scatter()``. Defaults to None.
             legend_kwargs (:obj:`dict`, optional): Additional legend keyword arguments that are passed to
-                ``ax.legend()``. Defaults to {}.
+                ``ax.legend()``. Defaults to None.
 
         Returns:
             None | tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]: If `return_fig` is True, then
                 the figure and axes objects are returned for further tinkering/saving.
         """
+        if figure_kwargs is None:
+            figure_kwargs = {}
+        if plot_kwargs is None:
+            plot_kwargs = {}
+        if legend_kwargs is None:
+            legend_kwargs = {}
+
         figure_kwargs.setdefault("figsize", (9, 9))
         figure_kwargs.setdefault("dpi", 200)
         fig = plt.figure(**figure_kwargs)
@@ -1392,9 +1398,9 @@ def plot_aggregate_plant_data_timeseries(
         ylim_energy: tuple[float, float] = (None, None),
         ylim_loss: tuple[float, float] = (None, None),
         return_fig: bool = False,
-        figure_kwargs: dict = {},
-        plot_kwargs: dict = {},
-        legend_kwargs: dict = {},
+        figure_kwargs: dict | None = None,
+        plot_kwargs: dict | None = None,
+        legend_kwargs: dict | None = None,
     ):
         """
         Plot timeseries of monthly/daily gross energy, availability and curtailment.
@@ -1413,11 +1419,11 @@ def plot_aggregate_plant_data_timeseries(
                 limits for the loss plot (bottom figure). Defaults to (None, None).
             return_fig (:obj:`bool`, optional): Flag to return the figure and axes objects. Defaults to False.
             figure_kwargs (:obj:`dict`, optional): Additional figure instantiation keyword arguments
-                that are passed to ``plt.figure()``. Defaults to {}.
+                that are passed to ``plt.figure()``. Defaults to None.
             plot_kwargs (:obj:`dict`, optional): Additional plotting keyword arguments that are passed to
-                ``ax.scatter()``. Defaults to {}.
+                ``ax.scatter()``. Defaults to None.
             legend_kwargs (:obj:`dict`, optional): Additional legend keyword arguments that are passed to
-                ``ax.legend()``. Defaults to {}.
+                ``ax.legend()``. Defaults to None.
 
         Returns:
             None | tuple[matplotlib.pyplot.Figure, tuple[matplotlib.pyplot.Axes, matplotlib.pyplot.Axes]]:
@@ -1448,9 +1454,9 @@ def plot_result_aep_distributions(
         ylim_availability: tuple[float, float] = (None, None),
         ylim_curtail: tuple[float, float] = (None, None),
         return_fig: bool = False,
-        figure_kwargs: dict = {},
-        plot_kwargs: dict = {},
-        annotate_kwargs: dict = {},
+        figure_kwargs: dict | None = None,
+        plot_kwargs: dict | None = None,
+        annotate_kwargs: dict | None = None,
     ) -> None | tuple[plt.Figure, plt.Axes]:
         """
         Plot a distribution of AEP values from the Monte-Carlo OA method
@@ -1470,11 +1476,11 @@ def plot_result_aep_distributions(
                 y-axis plotting display limits for the curtailment subplot. Defaults to (None, None).
             return_fig (:obj:`bool`, optional): Flag to return the figure and axes objects. Defaults to False.
             figure_kwargs (:obj:`dict`, optional): Additional figure instantiation keyword arguments
-                that are passed to ``plt.figure()``. Defaults to {}.
+                that are passed to ``plt.figure()``. Defaults to None.
             plot_kwargs (:obj:`dict`, optional): Additional plotting keyword arguments that are passed to
-                ``ax.hist()``. Defaults to {}.
+                ``ax.hist()``. Defaults to None.
             annotate_kwargs (:obj:`dict`, optional): Additional annotation keyword arguments that are
-                passed to ``ax.annotate()``. Defaults to {}.
+                passed to ``ax.annotate()``. Defaults to None
 
         Returns:
             None | tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]: If `return_fig` is True, then
@@ -1502,10 +1508,10 @@ def plot_aep_boxplot(
         with_points: bool = False,
         points_label: str = "Individual AEP Estimates",
         return_fig: bool = False,
-        figure_kwargs: dict = {},
-        plot_kwargs_box: dict = {},
-        plot_kwargs_points: dict = {},
-        legend_kwargs: dict = {},
+        figure_kwargs: dict | None = None,
+        plot_kwargs_box: dict | None = None,
+        plot_kwargs_points: dict | None = None,
+        legend_kwargs: dict | None = None,
     ) -> None | tuple[plt.Figure, plt.Axes]:
         """Plot box plots of AEP results sliced by a specified Monte Carlo parameter
 
@@ -1520,13 +1526,13 @@ def plot_aep_boxplot(
                 Defaults to None.
             return_fig (:obj:`bool`, optional): Flag to return the figure and axes objects. Defaults to False.
             figure_kwargs (:obj:`dict`, optional): Additional figure instantiation keyword arguments
-                that are passed to ``plt.figure()``. Defaults to {}.
+                that are passed to ``plt.figure()``. Defaults to None.
             plot_kwargs_box (:obj:`dict`, optional): Additional plotting keyword arguments that are passed to
-                ``ax.boxplot()``. Defaults to {}.
+                ``ax.boxplot()``. Defaults to None.
             plot_kwargs_points (:obj:`dict`, optional): Additional plotting keyword arguments that are passed to
-                ``ax.boxplot()``. Defaults to {}.
+                ``ax.boxplot()``. Defaults to None.
             legend_kwargs (:obj:`dict`, optional): Additional legend keyword arguments that are passed to
-                ``ax.legend()``. Defaults to {}.
+                ``ax.legend()``. Defaults to None.
 
         Returns:
             None | tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes, dict]: If `return_fig` is