Update and add torch models

nilmtk_contrib/mains_stats.py

@@ -0,0 +1,76 @@
+from nilmtk import DataSet
+import numpy as np
+import pandas as pd
+
+def calculate_multi_building_mains_stats(dataset_path, building_ids, start_time, end_time, 
+                                        ac_type='active', sample_period=60):
+    """
+    Calculates mains statistics across multiple buildings by combining their data.
+    """
+    ds = DataSet(dataset_path)
+    ds.set_window(start=start_time, end=end_time)
+
+    all_mains_data = []
+
+    # 1. Loop through each specified building ID
+    for building_id in building_ids:
+        print(f"Processing Building {building_id}...")
+        try:
+            mains = ds.buildings[building_id].elec.mains()
+
+            # Use power_series_all_data for simplicity, it handles the generator loop internally
+            power_data = mains.power_series_all_data(
+                ac_type=ac_type,
+                sample_period=sample_period
+            )
+
+            if power_data is not None and not power_data.empty:
+                all_mains_data.append(power_data)
+            else:
+                print(f"  - No data found for Building {building_id} in the specified timeframe.")
+
+        except KeyError:
+            print(f"  - Building {building_id} not found in the dataset.")
+        except Exception as e:
+            print(f"  - An error occurred for Building {building_id}: {e}")
+
+    # 2. Check if any data was collected
+    if not all_mains_data:
+        print("Could not retrieve data for any of the specified buildings.")
+        return {'mean': 0, 'std': 0, 'min': 0, 'max': 0, 'data_points': 0}
+
+    # 3. Concatenate all data into a single pandas Series
+    print("\nCombining data from all buildings...")
+    combined_data = pd.concat(all_mains_data)
+    clean_data = combined_data.dropna()
+
+    # 4. Calculate statistics on the combined data
+    stats = {
+        'mean': clean_data.mean(),
+        'std': clean_data.std(),
+        'min': clean_data.min(),
+        'max': clean_data.max(),
+        'data_points': len(clean_data),
+        'ac_type': ac_type
+    }
+
+    ds.store.close()
+    return stats
+
+stats = calculate_multi_building_mains_stats(
+    dataset_path="/home/ubuntu/downloads/refit.h5",
+    building_ids=[2],  # Pass a list of buildings
+    start_time='2014-04-01',
+    end_time='2014-04-30',
+    ac_type='active',      # Pass 'active' as a string
+    sample_period=60
+)
+
+print("\n--- Combined Mains Statistics ---")
+if stats['data_points'] > 0:
+    print(f"Combined Mains Mean: {stats['mean']:.2f}W")
+    print(f"Combined Mains Std: {stats['std']:.2f}W")
+    print(f"Data Range: {stats['min']:.2f}W to {stats['max']:.2f}W")
+    print(f"Total Data Points from all buildings: {stats['data_points']}")
+else:
+    print("No data available to calculate statistics.")