wedowind_example.py WIP

resgroup · Sep 8, 2024 · d9d5307 · d9d5307
1 parent edff23c
commit d9d5307
Showing 1 changed file with 56 additions and 4 deletions.
diff --git a/examples/wedowind_example.py b/examples/wedowind_example.py
@@ -1,11 +1,13 @@
 import logging
+import math
 import sys
 import zipfile
 from pathlib import Path
 
+import numpy as np
 import pandas as pd
 
-from wind_up.constants import OUTPUT_DIR, PROJECTROOT_DIR
+from wind_up.constants import OUTPUT_DIR, PROJECTROOT_DIR, TIMESTAMP_COL, DataColumns
 
 sys.path.append(str(PROJECTROOT_DIR))
 from examples.helpers import download_zenodo_data, setup_logger
@@ -18,7 +20,7 @@
 ZIP_FILENAME = "Turbine_Upgrade_Dataset.zip"
 
 
-def unpack_wedowind_scada() -> pd.DataFrame:
+def unpack_wedowind_scada(rated_power_kw: float) -> pd.DataFrame:
     scada_fpath = "Turbine Upgrade Dataset(Pitch Angle Pair).csv"
     with zipfile.ZipFile(CACHE_DIR / ZIP_FILENAME) as zf:
         scada_df_raw = pd.read_csv(zf.open(scada_fpath), parse_dates=[1], index_col=0).drop(columns=["VcosD", "VsinD"])
@@ -34,10 +36,42 @@ def unpack_wedowind_scada() -> pd.DataFrame:
         .assign(TurbineName="Ref")
         .rename(columns={"y_ctrl(normalized)": "normalized_power"})
     )
+    scada_df = pd.concat([scada_df_test, scada_df_ref])
+    scada_df[DataColumns.active_power_mean] = scada_df["normalized_power"] * rated_power_kw
+    scada_df[DataColumns.wind_speed_mean] = scada_df["V"]
+    scada_df[DataColumns.yaw_angle_mean] = scada_df["D"]
+    scada_df[DataColumns.pitch_angle_mean] = 0
+    scada_df[DataColumns.gen_rpm_mean] = 1000
+    scada_df[DataColumns.shutdown_duration] = 0
+
     scada_df_mast = (
         scada_df_raw.drop(columns=["y_ctrl(normalized)", "y_test(normalized)"]).copy().assign(TurbineName="Mast")
     )
-    return pd.concat([scada_df_test, scada_df_ref, scada_df_mast])
+    scada_df = pd.concat([scada_df, scada_df_mast])
+
+    scada_df = scada_df.set_index("time")
+    scada_df.index.name = TIMESTAMP_COL
+    return scada_df
+
+
+def make_wedowind_metadata_df() -> pd.DataFrame:
+    coords_df = pd.DataFrame(
+        {
+            "Name": ["WT1", "WT2", "WT3", "WT4", "MAST1", "MAST2"],
+            "X": [500, 2200, 9836, 7571, 0, 9571],
+            "Y": [9136, 9436, 0, 2050, 9836, 50],
+        }
+    )
+    assumed_wf_lat = 40
+    assumed_wf_lon = -89
+    m_per_deglat = 40_075_000 / 360
+    coords_df["Latitude"] = assumed_wf_lat + (coords_df["Y"] - coords_df["Y"].mean()) / m_per_deglat
+    coords_df["Longitude"] = assumed_wf_lon + (coords_df["X"] - coords_df["X"].mean()) / (
+        m_per_deglat * math.cos(assumed_wf_lat * math.pi / 180)
+    )
+    return coords_df.loc[:, ["Name", "Latitude", "Longitude"]].assign(
+        TimeZone="UTC", TimeSpanMinutes=10, TimeFormat="Start"
+    )
 
 
 if __name__ == "__main__":
@@ -51,4 +85,22 @@ def unpack_wedowind_scada() -> pd.DataFrame:
     )
 
     logger.info("Preprocessing turbine SCADA data")
-    scada_df = unpack_wedowind_scada()
+    assumed_rated_power_kw = 2000
+    scada_df = unpack_wedowind_scada(rated_power_kw=assumed_rated_power_kw)
+    metadata_df = make_wedowind_metadata_df()
+
+    # make up reanalysis for now
+    from wind_up.reanalysis_data import ReanalysisDataset
+
+    rng = np.random.default_rng(0)
+    rows = 100
+    reanalysis_dataset = ReanalysisDataset(
+        id="dummy_reanalysis_data",
+        data=pd.DataFrame(
+            data={
+                "100_m_hws_mean_mps": rng.uniform(5, 10, rows),
+                "100_m_hwd_mean_deg-n_true": rng.uniform(0, 360, rows),
+            },
+            index=pd.DatetimeIndex(pd.date_range(start=scada_df.index.min(), periods=rows, freq="h", tz="UTC")),
+        ),
+    )