cleaning up commented out code

flasc/analysis/expected_power_analysis.py

@@ -259,8 +259,6 @@ def _total_uplift_expected_power_with_standard_error(
     percentiles: List[float] = [2.5, 97.5],
     remove_any_null_turbine_bins: bool = False,
     cov_terms: str = "zero",
-    # variance_only: bool = False,
-    # fill_cov_with_var: bool = False,
 ) -> Dict[str, Dict[str, float]]:
     """Calculate total uplift by propagating standard errors.
 
@@ -301,7 +299,6 @@ def _total_uplift_expected_power_with_standard_error(
             terms set to the product of the square root of the individual turbine variances.
             Defaults to "zero".
 
-
     Returns:
         Dict[str, Dict[str, float]]: A dictionary containing the uplift results with standard error.
     """
@@ -436,20 +433,12 @@ def _total_uplift_expected_power_with_standard_error(
             count=(pl.col(f"count_{uplift_pair[0]}") + pl.col(f"count_{uplift_pair[1]}"))
         )
 
-        # with pl.Config(tbl_cols=-1):
-        #     print(df_sub)
-
         # Compute the expected power ratio per bin
         df_sub = df_sub.with_columns(
             expected_power_ratio=pl.col(f"pow_farm_{uplift_pair[1]}")
             / pl.col(f"pow_farm_{uplift_pair[0]}")
         )
 
-        # # Compute the weighted expected power ratio
-        # df_sub = df_sub.with_columns(
-        #     weighted_expected_power_ratio=pl.col("expected_power_ratio") * pl.col("weight")
-        # )
-
         # Compute the total expected power ratio (note this is not computed by
         #  combining the expected power ratios)
         farm_power_0 = df_sub[f"weighted_farm_power_{uplift_pair[0]}"].sum()
@@ -482,9 +471,6 @@ def _total_uplift_expected_power_with_standard_error(
             )
         )
 
-        # with pl.Config(tbl_cols=-1):
-        #     print(df_sub)
-
         # The total uplift is the sum of the weighted expected power ratio and the weighted
         #  variance of the expected power ratio
         result_dict = {}
@@ -535,8 +521,6 @@ def total_uplift_expected_power(
     percentiles: List[float] = [2.5, 97.5],
     remove_any_null_turbine_bins: bool = False,
     cov_terms: str = "zero",
-    # variance_only: bool = False,
-    # fill_cov_with_var: bool = False,
 ) -> ExpectedPowerAnalysisOutput:
     """Calculate the total uplift in energy production using expected power methods.
 
@@ -650,11 +634,6 @@ def total_uplift_expected_power(
     if use_standard_error and N != 1:
         raise ValueError("N must be 1 when use_standard_error is True")
 
-    # # Raise an error if both variance_only and fill_cov_with_var are True
-    # May need to reinclude in the future but disabled for now
-    # if variance_only and fill_cov_with_var:
-    #     raise ValueError("variance_only and fill_cov_with_var cannot both be True")
-
     # Set up the column names for the wind speed and test cols
 
     if not use_predefined_ws:

flasc/analysis/expected_power_analysis_by.py

@@ -353,7 +353,6 @@ def plot_with_distributions(
             y_label = "Wind Direction (Deg)"
 
         ## AXIS 1 is histogram
-        # df_bin_counts = self.df_pandas.groupby([x_var]).size().reset_index(name='count')
         sns.histplot(
             data=self.df_pandas, x=x_var, ax=axarr[1], bins=sorted(self.df_pandas[x_var].unique())
         )

flasc/analysis/expected_power_analysis_utilities.py

@@ -286,14 +286,6 @@ def _fill_cov_with_var(
                 .alias(n_col)
             )
 
-            # # Wherever n_col is null, set cov_col to null
-            # df_cov = df_cov.with_columns(
-            #     pl.when(pl.col(n_col).is_null())
-            #     .then(None)
-            #     .otherwise(pl.col(cov_col))
-            #     .alias(cov_col)
-            # )
-
     # Remove the null_map column if exists
     if "null_map" in df_cov.columns:
         df_cov = df_cov.drop("null_map")