Add Frontend and backend in codebase

.dockerignore

@@ -0,0 +1,23 @@
+# Git
+.git
+.gitignore
+
+# Python
+__pycache__
+*.pyc
+.venv
+.env
+
+# Tests
+.pytest_cache
+.coverage
+htmlcov
+
+# IDE
+.vscode
+.idea
+
+# OA Data (Exclude large datasets, but KEEP la_haute_borne for demo)
+examples/data/kelmarsh
+examples/data/penmanshiel
+examples/data/cleansed

.gitignore

@@ -41,3 +41,6 @@ sphinx/examplesout.ipynb
 
 .ipython
 *.nc
+
+# Allow La Haute Borne data for deployment
+!examples/data/la_haute_borne/*.csv

Dockerfile

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+# Use a slim Python 3.11 image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies (some OA libs might need build tools)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the local openoa source
+COPY openoa/ ./openoa/
+COPY pyproject.toml .
+
+# Install the local package in editable mode (or just ensure it's in path)
+RUN pip install -e .
+
+# Copy application files
+COPY main.py .
+COPY index.html .
+
+# Copy example data (needed for the demo calculation)
+# Note: Ensure this directory is populated with at least the La Haute Borne CSVs
+COPY examples/data/la_haute_borne/ ./examples/data/la_haute_borne/
+
+# Expose port
+EXPOSE 8080
+
+# Run the application
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]

debug_oa.py

@@ -0,0 +1,98 @@
+import pandas as pd
+from openoa.plant import PlantData
+from openoa.analysis.aep import MonteCarloAEP
+import os
+import traceback
+
+def debug_oa():
+    # Define the base data path
+    data_path = "examples/data/la_haute_borne"
+
+    print("Loading data...")
+    try:
+        # Load Asset Data
+        asset = pd.read_csv(os.path.join(data_path, "la-haute-borne_asset_table.csv"))
+
+        # Load SCADA Data
+        scada = pd.read_csv(os.path.join(data_path, "la-haute-borne-data-2014-2015.csv"))
+        scada["Date_time"] = pd.to_datetime(scada["Date_time"], utc=True).dt.tz_localize(None)
+
+        # Load Meter Data (contains energy, availability, curtailment)
+        meter = pd.read_csv(os.path.join(data_path, "plant_data.csv"))
+        meter["time_utc"] = pd.to_datetime(meter["time_utc"], utc=True).dt.tz_localize(None)
+
+        # Use same file for curtail data as it contains those columns
+        curtail = meter.copy()
+
+        # Load Reanalysis Data - ERA5 and MERRA2 have leading index columns
+        era5 = pd.read_csv(os.path.join(data_path, "era5_wind_la_haute_borne.csv"), index_col=0)
+        era5["datetime"] = pd.to_datetime(era5["datetime"], utc=True).dt.tz_localize(None)
+
+        merra2 = pd.read_csv(os.path.join(data_path, "merra2_la_haute_borne.csv"), index_col=0)
+        merra2["datetime"] = pd.to_datetime(merra2["datetime"], utc=True).dt.tz_localize(None)
+
+        # Define Metadata
+        metadata = {
+            "capacity": 8.2,
+            "asset": {
+                "asset_id": "Wind_turbine_name",
+                "latitude": "Latitude",
+                "longitude": "Longitude"
+            },
+            "meter": {
+                "time": "time_utc",
+                "MMTR_SupWh": "net_energy_kwh"
+            },
+            "curtail": {
+                "time": "time_utc",
+                "IAVL_DnWh": "availability_kwh",
+                "IAVL_ExtPwrDnWh": "curtailment_kwh"
+            },
+            "scada": {
+                "time": "Date_time",
+                "asset_id": "Wind_turbine_name",
+                "WTUR_W": "P_avg",
+                "WMET_HorWdSpd": "Ws_avg"
+            },
+            "reanalysis": {
+                "era5": {
+                    "time": "datetime",
+                    "WMETR_HorWdSpd": "ws_100m",
+                    "WMETR_HorWdSpdU": "u_100",
+                    "WMETR_HorWdSpdV": "v_100",
+                    "WMETR_AirDen": "dens_100m"
+                },
+                "merra2": {
+                    "time": "datetime",
+                    "WMETR_HorWdSpd": "ws_50m",
+                    "WMETR_HorWdSpdU": "u_50",
+                    "WMETR_HorWdSpdV": "v_50",
+                    "WMETR_AirDen": "dens_50m"
+                }
+            }
+        }
+
+        print("Initializing PlantData...")
+        plant = PlantData(
+            metadata=metadata,
+            scada=scada,
+            meter=meter,
+            curtail=curtail,
+            asset=asset,
+            reanalysis={"era5": era5, "merra2": merra2}
+        )
+
+
+        print("Running AEP Analysis...")
+        pa = MonteCarloAEP(plant)
+        pa.run(num_sim=10, progress_bar=False)
+
+        print(f"AEP Result: {pa.results.mean()}")
+        print("Analysis finished successfully!")
+
+    except Exception as e:
+        print(f"Error during execution: {e}")
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    debug_oa()

docker-compose.yml

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+services:
+  openoa-api:
+    build: .
+    ports:
+      - "8080:8080"
+    volumes:
+      - ./examples/data:/app/examples/data
+    environment:
+      - LOG_LEVEL=INFO

examples/data/la_haute_borne/SCADA_data_description.csv

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+Variable_name;Variable_long_name;Unit_long_name;Comment
+Ws;Wind_speed;m/s;Average wind speed
+Ot;Outdoor_temperature;deg_C;
+Ya;Nacelle_angle;deg;
+Rm;Torque;Nm;
+Wa;Absolute_wind_direction;deg;
+Ba;Pitch_angle;deg;
+Ds;Generator_speed;rpm;
+Va;Vane_position;deg;
+P;Active_power;kW;