remove benchmarking call and run linting

examples/helpers.py

@@ -57,7 +57,7 @@ def download_zenodo_data(
         if len(files_to_download) != len(filenames):
             msg = (
                 f"Could not find all files in the Zenodo record. "
-                f"Missing files: {set(filenames) - {i['key'] for i in files_to_download} }"
+                f"Missing files: {set(filenames) - {i['key'] for i in files_to_download}}"
             )
             raise ValueError(msg)
 

tests/test_windspeed_drift.py

@@ -26,16 +26,15 @@ def test_check_windspeed_drift(test_lsa_t13_config: WindUpConfig) -> None:
     assert test_max_ws_drift_pp_period == pytest.approx(0.42913942378401204)
 
 
-def test_calc_rolling_windspeed_diff(benchmark) -> None:
+def test_calc_rolling_windspeed_diff() -> None:
     n_values = 50
     timestep = pd.Timedelta("6h")
     ts_index = pd.date_range("2020-01-01", periods=n_values, freq=timestep)
     ws_col_vals = np.linspace(5, 15, n_values)
     test_df = pd.DataFrame({"ws_col": ws_col_vals, "reanalysis_ws_col": ws_col_vals[::-1]}, index=ts_index)
 
     original = test_df.copy()
-    actual = benchmark(
-        _calculate_rolling_windspeed_diff,
+    actual = _calculate_rolling_windspeed_diff(
         wtg_df=test_df,
         ws_col="ws_col",
         reanalysis_ws_col="reanalysis_ws_col",

wind_up/detrend.py

@@ -313,7 +313,7 @@ def check_applied_detrend(
         )
     else:
         result_manager.warning(
-            f"post_r2_after_detrend < post_r2_before_detrend," f" {post_r2_after_detrend} < {post_r2_before_detrend}"
+            f"post_r2_after_detrend < post_r2_before_detrend, {post_r2_after_detrend} < {post_r2_before_detrend}"
         )
 
     if plot_cfg is not None:

wind_up/long_term.py

@@ -88,7 +88,7 @@ def calc_lt_df(
         raise RuntimeError(msg)
 
     logger.info(f"long term distribution uses data from {df_for_lt.index.min()} to {df_for_lt.index.max()}")
-    logger.info(f"long term distribution data coverage: {data_coverage*100:.1f}%")
+    logger.info(f"long term distribution data coverage: {data_coverage * 100:.1f}%")
 
     return lt_df
 

wind_up/main_analysis.py

@@ -347,7 +347,7 @@ def yaw_offset_results(
         result_name = "mean_test_yaw_offset_command_pre"
         results[result_name] = pre_df.dropna(subset=required_pp_cols)["test_yaw_offset_command"].mean()
         if results[result_name] > yaw_offset_ul:
-            result_manager.warning(f"{result_name} > 0: " f"({results[result_name]})")
+            result_manager.warning(f"{result_name} > 0: ({results[result_name]})")
 
         results["mean_test_yaw_offset_command_post"] = post_df.dropna(subset=required_pp_cols)[
             "test_yaw_offset_command"
@@ -356,12 +356,12 @@ def yaw_offset_results(
         result_name = "mean_ref_yaw_offset_command_pre"
         results[result_name] = pre_df.dropna(subset=required_pp_cols)["ref_yaw_offset_command"].mean()
         if results[result_name] > yaw_offset_ul:
-            result_manager.warning(f"{result_name} > 0 for: " f"({results[result_name]})")
+            result_manager.warning(f"{result_name} > 0 for: ({results[result_name]})")
 
         result_name = "mean_ref_yaw_offset_command_pre"
         results[result_name] = post_df.dropna(subset=required_pp_cols)["ref_yaw_offset_command"].mean()
         if results[result_name] > yaw_offset_ul:
-            result_manager.warning(f"{result_name} > 0 for: " f"({results[result_name]})")
+            result_manager.warning(f"{result_name} > 0 for: ({results[result_name]})")
     return results
 
 

wind_up/plots/combine_results_plots.py

@@ -24,7 +24,7 @@ def plot_combine_results(trdf: pd.DataFrame, tdf: pd.DataFrame, plot_cfg: PlotCo
     plt.bar(labels, values, yerr=yerrs, capsize=3)
     plt.xlabel("turbine")
     plt.ylabel("uplift [%]")
-    plot_title = f"uplift and {confidence*100:.0f}% CI by test-ref pair"
+    plot_title = f"uplift and {confidence * 100:.0f}% CI by test-ref pair"
     plt.title("uplift by test-ref pair")
     plt.grid(axis="y")
     plt.xticks(rotation=90, ha="right")
@@ -41,7 +41,7 @@ def plot_combine_results(trdf: pd.DataFrame, tdf: pd.DataFrame, plot_cfg: PlotCo
     plt.bar(labels, values, yerr=yerrs, capsize=3)
     plt.xlabel("turbine")
     plt.ylabel("uplift [%]")
-    plot_title = f"combined uplift and {confidence*100:.0f}% CI"
+    plot_title = f"combined uplift and {confidence * 100:.0f}% CI"
     plt.title(plot_title)
     plt.grid(axis="y")
     plt.xticks(rotation=90, ha="right")

wind_up/plots/pp_analysis_plots.py

@@ -447,10 +447,10 @@ def plot_pre_post_pp_analysis(
 
     plt.figure()
     plt.plot(pp_df["bin_mid"], pp_df["uplift_kw"], color="b", marker="s")
-    plt.plot(pp_df["bin_mid"], pp_df[f"uplift_p{p_low*100:.0f}_kw"], color="r", ls="--")
-    plt.plot(pp_df["bin_mid"], pp_df[f"uplift_p{p_high*100:.0f}_kw"], color="r", ls="--")
+    plt.plot(pp_df["bin_mid"], pp_df[f"uplift_p{p_low * 100:.0f}_kw"], color="r", ls="--")
+    plt.plot(pp_df["bin_mid"], pp_df[f"uplift_p{p_high * 100:.0f}_kw"], color="r", ls="--")
     plt.grid()
-    plot_title = f"test={test_name} ref={ref_name} uplift [kW] and {confidence_level*100:.0f}% CI"
+    plot_title = f"test={test_name} ref={ref_name} uplift [kW] and {confidence_level * 100:.0f}% CI"
     plt.title(plot_title)
     plt.ylabel("uplift [kW]")
     plt.xlabel("bin centre [m/s]")
@@ -478,18 +478,18 @@ def plot_pre_post_pp_analysis(
     )
     plt.plot(
         pp_df["bin_mid"],
-        pp_df[f"uplift_p{p_low*100:.0f}_kw"] * pp_df["hours_for_mwh_calc"].sum() * pp_df["f"] / 1000,
+        pp_df[f"uplift_p{p_low * 100:.0f}_kw"] * pp_df["hours_for_mwh_calc"].sum() * pp_df["f"] / 1000,
         color="r",
         ls="--",
     )
     plt.plot(
         pp_df["bin_mid"],
-        pp_df[f"uplift_p{p_high*100:.0f}_kw"] * pp_df["hours_for_mwh_calc"].sum() * pp_df["f"] / 1000,
+        pp_df[f"uplift_p{p_high * 100:.0f}_kw"] * pp_df["hours_for_mwh_calc"].sum() * pp_df["f"] / 1000,
         color="r",
         ls="--",
     )
     plt.grid()
-    plot_title = f"test={test_name} ref={ref_name} uplift [MWh] and {confidence_level*100:.0f}% CI"
+    plot_title = f"test={test_name} ref={ref_name} uplift [MWh] and {confidence_level * 100:.0f}% CI"
     plt.title(plot_title)
     plt.ylabel("uplift [MWh]")
     plt.xlabel("bin centre [m/s]")

wind_up/plots/scada_funcs_plots.py

@@ -66,7 +66,7 @@ def print_and_plot_capacity_factor(scada_df: pd.DataFrame, cfg: WindUpConfig, pl
         show_plot=plots_cfg.show_plots,
     )
 
-    logger.info(f'average capacity factor: {cf_df["CF"].mean() * 100:.1f}%')
+    logger.info(f"average capacity factor: {cf_df['CF'].mean() * 100:.1f}%")
     _table = tabulate(
         (cf_df.sort_values(by="CF", ascending=False)["CF"][0:3] * 100).to_frame(),
         tablefmt="outline",

wind_up/plots/windspeed_drift_plots.py

@@ -12,14 +12,14 @@
 
 def plot_rolling_windspeed_diff_one_wtg(
     *,
-    wtg_df: pd.DataFrame,
+    ser: pd.DataFrame,
     wtg_name: str,
     ws_col: str,
     plot_cfg: PlotConfig,
     sub_dir: str | None,
 ) -> None:
     plt.figure()
-    plt.plot(wtg_df["rolling_windspeed_diff"])
+    plt.plot(ser["rolling_windspeed_diff"])
     plot_title = f"{wtg_name} rolling {ws_col} diff to reanalysis"
     plt.title(plot_title)
     plt.xlabel("datetime")

wind_up/plots/yaw_direction_plots.py

@@ -66,13 +66,13 @@ def plot_yaw_direction_pre_post_per_signal(
         label=post_label,
     )
     plt.xlabel(f"{ref_wd_col} [deg]")
-    plt.ylabel(f'{signal_name.replace("_", " ")} [deg]')
+    plt.ylabel(f"{signal_name.replace('_', ' ')} [deg]")
     plt.grid()
 
     plot_title = (
         f"{test_name} ref {ref_name} yaw direction scatter"
         if signal_name == "yaw_offset"
-        else f'{test_name} ref {ref_name} {signal_name.replace("_", " ")} scatter'
+        else f"{test_name} ref {ref_name} {signal_name.replace('_', ' ')} scatter"
     )
     plt.suptitle(plot_title)
     if plot_cfg.show_plots:
@@ -144,7 +144,7 @@ def _get_mid(x: float | pd.Interval) -> float:
             linewidths=0.5,
             vmin=0,
             vmax=20,
-            cbar_kws={"label": f"{signal_name.replace('_',' ')} [deg]"},
+            cbar_kws={"label": f"{signal_name.replace('_', ' ')} [deg]"},
         )
 
         plt.xlabel("wind direction bin centre [deg]")
@@ -153,7 +153,7 @@ def _get_mid(x: float | pd.Interval) -> float:
         signal_descr = (
             f"{ref_name} minus {test_name} yaw direction"
             if signal_name == "yaw_offset"
-            else f'{test_name} ref {ref_name} {signal_name.replace("_", " ")}'
+            else f"{test_name} ref {ref_name} {signal_name.replace('_', ' ')}"
         )
         plot_title = f"{signal_descr} vs ws and wd {label}"
         plt.suptitle(plot_title)

wind_up/pp_analysis.py

@@ -434,7 +434,7 @@ def pre_post_pp_analysis_with_reversal(
         logger.info(f"\nresults for test={test_wtg.name} ref={ref_name}:\n")
         logger.info(f"hours pre = {pp_results['pp_valid_hours_pre']:.1f}")
         logger.info(f"hours post = {pp_results['pp_valid_hours_post']:.1f}")
-        logger.info(f"\nuplift estimate before adjustments = {100*pp_results['uplift_frc']:.1f} %")
+        logger.info(f"\nuplift estimate before adjustments = {100 * pp_results['uplift_frc']:.1f} %")
 
         logger.info(f"\npower only uplift estimate = {100 * poweronly_uplift_frc:.1f} %")
         logger.info(f"reversed (power only) uplift estimate = {100 * reversed_uplift_frc:.1f} %\n")
@@ -545,11 +545,11 @@ def pre_post_pp_analysis_with_reversal_and_bootstrapping(
 
     if plot_cfg is not None:
         msg = (
-            f"block bootstrapping uncertainty analysis results (conf={100*confidence_level:.0f}%):"
-            f"\n  median = {100*median:.1f} %"
-            f"\n  lower = {100*lower:.1f} %"
-            f"\n  upper = {100*upper:.1f} %"
-            f"\n  unc_one_sigma = {100*unc_one_sigma:.1f} %"
+            f"block bootstrapping uncertainty analysis results (conf={100 * confidence_level:.0f}%):"
+            f"\n  median = {100 * median:.1f} %"
+            f"\n  lower = {100 * lower:.1f} %"
+            f"\n  upper = {100 * upper:.1f} %"
+            f"\n  unc_one_sigma = {100 * unc_one_sigma:.1f} %"
         )
         logger.info(msg)
 
@@ -578,8 +578,8 @@ def pre_post_pp_analysis_with_reversal_and_bootstrapping(
         logger.info(f"missing bins scale factor = {pp_results['missing_bins_unc_scale_factor']:.3f}")
         logger.info(f"final 1 sigma unc = {100 * pp_results['unc_one_sigma_frc']:.1f} %\n")
 
-        logger.info(f"final uplift estimate = {100*pp_results['uplift_frc']:.1f} %")
-        logger.info(f"final P95 uplift estimate = {100*pp_results[f'uplift_p{p_high * 100:.0f}_frc']:.1f} %")
-        logger.info(f"final P5 uplift estimate = {100*pp_results[f'uplift_p{p_low * 100:.0f}_frc']:.1f} %")
+        logger.info(f"final uplift estimate = {100 * pp_results['uplift_frc']:.1f} %")
+        logger.info(f"final P95 uplift estimate = {100 * pp_results[f'uplift_p{p_high * 100:.0f}_frc']:.1f} %")
+        logger.info(f"final P5 uplift estimate = {100 * pp_results[f'uplift_p{p_low * 100:.0f}_frc']:.1f} %")
 
     return pp_results, pp_df