add wind PN

examples/wind_farm_pn.py

@@ -0,0 +1,204 @@
+from datetime import datetime, timedelta
+import pandas as pd
+import matplotlib.pyplot as plt
+from elexonpy.api_client import ApiClient
+from elexonpy.api.balancing_mechanism_physical_api import BalancingMechanismPhysicalApi
+
+def fetch_wind_farm_pn_data():
+    """Fetch Physical Notification (PN) data for wind farms using the Elexon API."""
+
+    # Initialize API client
+    api_client = ApiClient()
+
+    # Initialize the Balancing Mechanism Physical API
+    physical_api = BalancingMechanismPhysicalApi(api_client)
+
+    # Define dataset and settlement period parameters
+    dataset = 'PN'  # Physical Notification dataset
+    settlement_date = datetime.now().strftime('%Y-%m-%d')
+    settlement_period = 1  # Example settlement period (1-50)
+
+    # Fetch market-wide physical data for all BM Units
+    print(f"Fetching physical data for settlement date {settlement_date} and period {settlement_period}...")
+    try:
+        physical_data = physical_api.balancing_physical_all_get(
+            dataset=dataset,
+            settlement_date=settlement_date,
+            settlement_period=settlement_period,
+            format='dataframe'
+        )
+
+        # Display information about the returned DataFrame
+        print("Columns in the returned DataFrame:", physical_data.columns)
+        print(f"Total BMUs in response: {len(physical_data)}")
+        print("Sample of BMUs:", physical_data['bm_unit'].head().tolist())
+
+        # Try to identify wind farms - directly use known wind farm IDs
+        # These are the BM Unit IDs from your successful plot
+        wind_farm_bm_units = ["T_DDBRW-1", "E_WLNEY-1", "T_GWFL-1"]
+
+        # Print number of found wind farms
+        print(f"Using {len(wind_farm_bm_units)} known wind farms: {wind_farm_bm_units}")
+
+    except Exception as e:
+        print(f"Error fetching physical data: {e}")
+        return False
+
+    # Define time range for data retrieval (last 24 hours instead of 7 days)
+    # This increases the chance of finding data
+    end_time = datetime.now()
+    start_time = end_time - timedelta(hours=24)
+
+    print(f"\nRequesting data from {start_time.isoformat()} to {end_time.isoformat()}")
+
+    # Create a DataFrame to store all data
+    all_wind_data = pd.DataFrame()
+
+    # Fetch PN data for each wind farm
+    for bm_unit in wind_farm_bm_units:
+        print(f"\nFetching PN data for {bm_unit}...")
+        try:
+            # Make the API request with detailed error handling
+            print(f"Making API request for {bm_unit} with parameters:")
+            print(f"  - bm_unit: {bm_unit}")
+            print(f"  - from: {start_time.isoformat()}")
+            print(f"  - to: {end_time.isoformat()}")
+            print(f"  - dataset: PN")
+
+            wind_data = physical_api.balancing_physical_get(
+                bm_unit=[bm_unit],
+                _from=start_time.isoformat(),
+                to=end_time.isoformat(),
+                dataset=['PN'],
+                format='dataframe'
+            )
+
+            print(f"API request completed for {bm_unit}")
+
+            # Check if data was retrieved successfully
+            if not wind_data.empty:
+                # Add a column to identify the BM Unit
+                wind_data['bm_unit_id'] = bm_unit
+
+                # Append to the combined DataFrame
+                all_wind_data = pd.concat([all_wind_data, wind_data])
+
+                # Display basic statistics
+                print(f"Retrieved {len(wind_data)} data points for {bm_unit}")
+                print(f"Mean output: {wind_data['level'].mean():.2f} MW")
+                print(f"Max output: {wind_data['level'].max():.2f} MW")
+                print(f"Min output: {wind_data['level'].min():.2f} MW")
+            else:
+                print(f"No data found for {bm_unit} in the specified time period")
+                print("Trying with a different time period...")
+
+                # Try with a different time period (7 days ago)
+                alt_end_time = datetime.now() - timedelta(days=7)
+                alt_start_time = alt_end_time - timedelta(hours=24)
+
+                print(f"Trying alternative period: {alt_start_time.isoformat()} to {alt_end_time.isoformat()}")
+
+                alt_wind_data = physical_api.balancing_physical_get(
+                    bm_unit=[bm_unit],
+                    _from=alt_start_time.isoformat(),
+                    to=alt_end_time.isoformat(),
+                    dataset=['PN'],
+                    format='dataframe'
+                )
+
+                if not alt_wind_data.empty:
+                    # Add a column to identify the BM Unit
+                    alt_wind_data['bm_unit_id'] = bm_unit
+
+                    # Append to the combined DataFrame
+                    all_wind_data = pd.concat([all_wind_data, alt_wind_data])
+
+                    # Display basic statistics
+                    print(f"Retrieved {len(alt_wind_data)} data points for {bm_unit} (alternative period)")
+                    print(f"Mean output: {alt_wind_data['level'].mean():.2f} MW")
+                    print(f"Max output: {alt_wind_data['level'].max():.2f} MW")
+                    print(f"Min output: {alt_wind_data['level'].min():.2f} MW")
+                else:
+                    print(f"No data found for {bm_unit} in the alternative time period either")
+        except Exception as e:
+            print(f"Error fetching data for {bm_unit}: {e}")
+
+    # If we have data for at least one wind farm, plot it
+    if not all_wind_data.empty:
+        print("\nCreating plot with the retrieved data...")
+
+        # Create a pivot table for plotting
+        pivot_data = all_wind_data.pivot_table(
+            index='time_from',
+            columns='bm_unit_id',
+            values='level',
+            aggfunc='mean'
+        )
+
+        # Create the plot
+        plt.figure(figsize=(12, 6))
+        for bm_unit in pivot_data.columns:
+            plt.plot(pivot_data.index, pivot_data[bm_unit], label=bm_unit)
+
+        plt.title('Wind Farm Physical Notification (PN) Levels')
+        plt.xlabel('Time')
+        plt.ylabel('PN Level (MW)')
+        plt.grid(True)
+        plt.legend()
+        plt.tight_layout()
+
+        # Save the plot
+        plt.savefig('wind_farm_pn_levels.png')
+
+        print("\nPlot saved as 'wind_farm_pn_levels.png'")
+
+        # Show the plot
+        plt.show()
+
+        return True
+    else:
+        print("\nNo data was found for any of the specified wind farms")
+
+        # As a last resort, try with hardcoded sample data to demonstrate plotting functionality
+        print("\nGenerating sample data for demonstration purposes...")
+
+        # Create sample data
+        dates = pd.date_range(start=datetime.now() - timedelta(days=1), periods=24, freq='H')
+        sample_data = pd.DataFrame({
+            'time_from': dates.repeat(3),
+            'bm_unit_id': ['T_DDBRW-1', 'E_WLNEY-1', 'T_GWFL-1'] * 24,
+            'level': [100 + i % 50 for i in range(24)] * 3  # Sample values
+        })
+
+        # Create a pivot table for plotting
+        pivot_data = sample_data.pivot_table(
+            index='time_from',
+            columns='bm_unit_id',
+            values='level',
+            aggfunc='mean'
+        )
+
+        # Create the plot
+        plt.figure(figsize=(12, 6))
+        for bm_unit in pivot_data.columns:
+            plt.plot(pivot_data.index, pivot_data[bm_unit], label=bm_unit)
+
+        plt.title('Wind Farm Physical Notification (PN) Levels (SAMPLE DATA)')
+        plt.xlabel('Time')
+        plt.ylabel('PN Level (MW)')
+        plt.grid(True)
+        plt.legend()
+        plt.tight_layout()
+
+        # Save the plot
+        plt.savefig('wind_farm_pn_levels_sample.png')
+
+        print("\nSample plot saved as 'wind_farm_pn_levels_sample.png'")
+
+        # Show the plot
+        plt.show()
+
+        return False
+
+if __name__ == "__main__":
+    fetch_wind_farm_pn_data()