add format_and_print_results_table

resgroup · Sep 16, 2024 · c314a57 · c314a57
1 parent 6857afe
commit c314a57
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Showing 3 changed files with 73 additions and 56 deletions.
diff --git a/examples/helpers.py b/examples/helpers.py
@@ -6,9 +6,13 @@
 from typing import TYPE_CHECKING
 
 import requests
+from tabulate import tabulate
 
 if TYPE_CHECKING:
     from collections.abc import Collection
+
+    import pandas as pd
+
 logger = logging.getLogger(__name__)
 
 BYTES_IN_MB = 1024 * 1024
@@ -73,3 +77,61 @@ def download_zenodo_data(
         else:
             logger.info(f"File {dst_fpath} already exists. Skipping download.")
         filepaths.append(dst_fpath)
+
+
+def format_and_print_results_table(
+    results_per_test_ref_df: pd.DataFrame, *, print_small_table: bool = False
+) -> pd.DataFrame:
+    key_results_df = results_per_test_ref_df[
+        [
+            "test_wtg",
+            "ref",
+            "uplift_frc",
+            "unc_one_sigma_frc",
+            "uplift_p95_frc",
+            "uplift_p5_frc",
+            "pp_valid_hours_pre",
+            "pp_valid_hours_post",
+            "mean_power_post",
+        ]
+    ]
+
+    def _convert_frc_cols_to_pct(input_df: pd.DataFrame, dp: int = 1) -> pd.DataFrame:
+        for i, col in enumerate(x for x in input_df.columns if x.endswith("_frc")):
+            if i == 0:
+                output_df = input_df.assign(**{col: (input_df[col] * 100).round(dp).astype(str) + "%"})
+            else:
+                output_df = output_df.assign(**{col: (input_df[col] * 100).round(dp).astype(str) + "%"})
+            output_df = output_df.rename(columns={col: col.replace("_frc", "_pct")})
+        return output_df
+
+    print_df = _convert_frc_cols_to_pct(key_results_df).rename(
+        columns={
+            "test_wtg": "turbine",
+            "ref": "reference",
+            "uplift_pct": "energy uplift",
+            "unc_one_sigma_pct": "uplift uncertainty",
+            "uplift_p95_pct": "uplift P95",
+            "uplift_p5_pct": "uplift P5",
+            "pp_valid_hours_pre": "valid hours toggle off",
+            "pp_valid_hours_post": "valid hours toggle on",
+            "mean_power_post": "mean power toggle on",
+        }
+    )
+    print_df["mean power toggle on"] = print_df["mean power toggle on"].round(0).astype("int64")
+    print_df_for_tabulate = (
+        print_df[["turbine", "reference", "energy uplift", "uplift P95", "uplift P5", "valid hours toggle on"]]
+        if print_small_table
+        else print_df
+    )
+    results_table = tabulate(
+        print_df_for_tabulate,
+        headers="keys",
+        tablefmt="outline",
+        floatfmt=".1f",
+        numalign="center",
+        stralign="center",
+        showindex=False,
+    )
+    print(results_table)
+    return print_df
diff --git a/examples/smarteole_example.py b/examples/smarteole_example.py
@@ -7,7 +7,6 @@
 import pandas as pd
 from pandas.testing import assert_frame_equal
 from scipy.stats import circmean
-from tabulate import tabulate
 
 from wind_up.caching import with_parquet_cache
 from wind_up.combine_results import calc_net_uplift
@@ -18,7 +17,7 @@
 from wind_up.reanalysis_data import ReanalysisDataset
 
 sys.path.append(str(PROJECTROOT_DIR))
-from examples.helpers import download_zenodo_data, setup_logger
+from examples.helpers import download_zenodo_data, format_and_print_results_table, setup_logger
 
 CACHE_DIR = PROJECTROOT_DIR / "cache" / "smarteole_example_data"
 ANALYSIS_OUTPUT_DIR = OUTPUT_DIR / "smarteole_example"
@@ -242,58 +241,7 @@ def define_smarteole_example_config() -> WindUpConfig:
 def print_smarteole_results(
     results_per_test_ref_df: pd.DataFrame, *, print_small_table: bool = False, check_results: bool = False
 ) -> None:
-    key_results_df = results_per_test_ref_df[
-        [
-            "test_wtg",
-            "ref",
-            "uplift_frc",
-            "unc_one_sigma_frc",
-            "uplift_p95_frc",
-            "uplift_p5_frc",
-            "pp_valid_hours_pre",
-            "pp_valid_hours_post",
-            "mean_power_post",
-        ]
-    ]
-
-    def _convert_frc_cols_to_pct(input_df: pd.DataFrame, dp: int = 1) -> pd.DataFrame:
-        for i, col in enumerate(x for x in input_df.columns if x.endswith("_frc")):
-            if i == 0:
-                output_df = input_df.assign(**{col: (input_df[col] * 100).round(dp).astype(str) + "%"})
-            else:
-                output_df = output_df.assign(**{col: (input_df[col] * 100).round(dp).astype(str) + "%"})
-            output_df = output_df.rename(columns={col: col.replace("_frc", "_pct")})
-        return output_df
-
-    print_df = _convert_frc_cols_to_pct(key_results_df).rename(
-        columns={
-            "test_wtg": "turbine",
-            "ref": "reference",
-            "uplift_pct": "energy uplift",
-            "unc_one_sigma_pct": "uplift uncertainty",
-            "uplift_p95_pct": "uplift P95",
-            "uplift_p5_pct": "uplift P5",
-            "pp_valid_hours_pre": "valid hours toggle off",
-            "pp_valid_hours_post": "valid hours toggle on",
-            "mean_power_post": "mean power toggle on",
-        }
-    )
-    print_df["mean power toggle on"] = print_df["mean power toggle on"].round(0).astype("int64")
-    print_df_for_tabulate = (
-        print_df[["turbine", "reference", "energy uplift", "uplift P95", "uplift P5", "valid hours toggle on"]]
-        if print_small_table
-        else print_df
-    )
-    results_table = tabulate(
-        print_df_for_tabulate,
-        headers="keys",
-        tablefmt="outline",
-        floatfmt=".1f",
-        numalign="center",
-        stralign="center",
-        showindex=False,
-    )
-    print(results_table)
+    print_df = format_and_print_results_table(results_per_test_ref_df, print_small_table=print_small_table)
 
     if check_results:
         # raise an error if results don't match expected

diff --git a/examples/wedowind_example.py b/examples/wedowind_example.py
@@ -44,6 +44,7 @@
 from matplotlib import pyplot as plt
 from pydantic import BaseModel, Field
 
+from examples.helpers import format_and_print_results_table
 from wind_up.constants import OUTPUT_DIR, PROJECTROOT_DIR, TIMESTAMP_COL, DataColumns
 from wind_up.interface import AssessmentInputs
 from wind_up.main_analysis import run_wind_up_analysis
@@ -284,7 +285,11 @@ def generate_custom_exploratory_plots(
             plt.savefig(custom_plots_dir_timeseries / f"{title}.png")
             plt.close()
 
-    region2_df = scada_df[(scada_df["normalized_power"] > 0.2) & (scada_df["normalized_power"] < 0.8)]  # noqa PLR2004
+    region2_power_margin = 0.2
+    region2_df = scada_df[
+        (scada_df["normalized_power"] > region2_power_margin)
+        & (scada_df["normalized_power"] < (1 - region2_power_margin))
+    ]
 
     binned_by_turbine = {}
     for name, df in region2_df.groupby(DataColumns.turbine_name):
@@ -501,7 +506,9 @@ def main(analysis_name: str, *, generate_custom_plots: bool = True) -> None:
     )
 
     # Run Analysis
-    results_per_test_ref_df = run_wind_up_analysis(assessment_inputs)  # noqa: F841
+    results_per_test_ref_df = run_wind_up_analysis(assessment_inputs)
+    results_per_test_ref_df.to_csv(cfg.out_dir / "results_per_test_ref.csv", index=False)
+    _ = format_and_print_results_table(results_per_test_ref_df)
 
 
 if __name__ == "__main__":