Add docstrings to tuner utilities

flasc/utilities/tuner_utilities.py

@@ -1,3 +1,12 @@
+"""Utilities for tuning FLORIS parameters.
+
+This module contains utilities for tuning FLORIS parameters. This includes
+functions for resimulating FLORIS with SCADA data, and functions for setting
+parameters in a FLORIS model.
+
+"""
+
+
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
@@ -8,27 +17,24 @@
 from flasc.data_processing import dataframe_manipulations as dfm
 from flasc.utilities.utilities_examples import load_floris_smarteole
 
-# from floris import ParallelComputingInterface
-
 
 def replicate_nan_values(df_1: pd.DataFrame, df_2: pd.DataFrame):
-    """
-    Replicate NaN Values in DataFrame df_2 to Match DataFrame df_1.
+    """Replicate NaN Values in DataFrame df_2 to Match DataFrame df_1.
 
     For columns that are common between df_1 and df_2, this function ensures that
     NaN values in df_2 appear in the same locations as NaN values in df_1. This is
     primarily useful when df_2 represents a FLORIS resimulation of
     df_1, and you want to ensure that missing data is consistent between the two DataFrames.
 
-    Parameters:
-    - df_1 (pandas.DataFrame): The reference DataFrame containing NaN values.
-    - df_2 (pandas.DataFrame): The DataFrame to be updated to match NaN positions in df_1.
+    Args:
+        df_1 (pandas.DataFrame): The reference DataFrame containing NaN values.
+        df_2 (pandas.DataFrame): The DataFrame to be updated to match NaN positions in df_1.
 
     Returns:
-    - pandas.DataFrame: A new DataFrame with NaN values in df_2 replaced to match df_1.
+        pandas.DataFrame: A new DataFrame with NaN values in df_2 replaced to match df_1.
     """
     # For columns which df_1 and df_2 have in common, make sure
-    # occurences of NaNs which appear in df_1
+    # occurrences of NaNs which appear in df_1
     # appear in the same location in df_2
     # This function is primarily for the case where df_2 is
     # a FLORIS resimulation of df_1 and making sure
@@ -47,6 +53,7 @@ def replicate_nan_values(df_1: pd.DataFrame, df_2: pd.DataFrame):
 
 def nested_get(dic: Dict[str, Any], keys: List[str]) -> Any:
     """Get a value from a nested dictionary using a list of keys.
+
     Based on: stackoverflow.com/questions/14692690/access-nested-dictionary-items-via-a-list-of-keys
 
     Args:
@@ -63,6 +70,7 @@ def nested_get(dic: Dict[str, Any], keys: List[str]) -> Any:
 
 def nested_set(dic: Dict[str, Any], keys: List[str], value: Any, idx: Optional[int] = None) -> None:
     """Set a value in a nested dictionary using a list of keys.
+
     Based on: stackoverflow.com/questions/14692690/access-nested-dictionary-items-via-a-list-of-keys
 
     Args:
@@ -89,13 +97,28 @@ def nested_set(dic: Dict[str, Any], keys: List[str], value: Any, idx: Optional[i
 
 
 def resim_floris(fm_in: FlorisModel, df_scada: pd.DataFrame, yaw_angles: np.array = None):
+    """Resimulate FLORIS with SCADA data.
+
+    This function takes a FlorisModel  and a SCADA dataframe, and resimulates the
+    FlorisModel with the SCADA data. The SCADA data is expected to contain columns
+    for wind speed, wind direction, and power reference. The function returns a
+    dataframe containing the power output of each turbine in the FlorisModel.
+
+    Args:
+        fm_in (FlorisModel): The FlorisModel to resimulate.
+        df_scada (pd.DataFrame): The SCADA data to use for resimulation.
+        yaw_angles (np.array, optional): The yaw angles to use for resimulation. Defaults to None.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing the power output of each turbine in the FlorisModel.
+    """
     # Get wind speeds and directions
     wind_speeds = df_scada["ws"].values
     wind_directions = df_scada["wd"].values
     # TODO: better handling of TIs?
     turbulence_intensities = fm_in.turbulence_intensities[0] * np.ones_like(wind_speeds)
 
-    # Get the number of turbiens
+    # Get the number of turbines
     num_turbines = dfm.get_num_turbines(df_scada)
 
     # Set up the FLORIS model