update: new scripts

scripts/embed.py

@@ -141,58 +141,6 @@ def merge_borough_embeddings():
         joblib.dump(merged_embeddings, final_output_path)
         print(f"Saved final merged embeddings to {final_output_path}")
 
-def process_static_data(model):
-    """
-    Processes the static info JSON file to generate and save embeddings.
-    """
-    print("\n--- Starting processing of static info data ---")
-    static_info_path = os.path.join(DATA_DIR, "expanded_impute_data", "id_info_imputed.json")
-    
-    if not os.path.exists(static_info_path):
-        print(f"Static info source file not found at {static_info_path}. Skipping.")
-        return
-
-    with open(static_info_path, "r") as f:
-        id_info = json.load(f)
-
-    # 1. Create a dictionary for the text information
-    static_info_text = {}
-    static_info_text['general_info'] = 'This dataset contains Average Speed of a Vehicle Traveled Between End Points data in km/h collected from various locations in New York City by sensors. The sampling rate is every 5 minutes. When no car is detected in the period, the speed is set to 0.'
-    static_info_text['downtime_prompt'] = "The sensor is down for unknown reasons, readings set to 0. "
-    static_info_text['channel_info'] = {
-        ch: f"Sensor {ch} is located at {info['borough']}, with segment of {info['link']}." 
-        for ch, info in id_info.items()
-    }
-    # Save the text version
-    text_output_path = os.path.join(DATA_DIR, 'weather', "static_info.json")
-    with open(text_output_path, 'w') as f:
-        json.dump(static_info_text, f, indent=2)
-    print(f"Saved static info text to {text_output_path}")
-
-    # 2. Collect all texts for embedding
-    channels = list(static_info_text['channel_info'].keys())
-    texts_to_embed = (
-        [static_info_text['general_info'], static_info_text['downtime_prompt']] + 
-        [static_info_text['channel_info'][ch] for ch in channels]
-    )
-    
-    print(f"Embedding {len(texts_to_embed)} static text entries...")
-    embeddings = model.encode(texts_to_embed, truncate_dim=TRUNCATE_DIM)
-    
-    # 3. Create a new dictionary to store the embeddings
-    static_info_embeddings = {}
-    static_info_embeddings['general_info'] = embeddings[0:1, :]
-    static_info_embeddings['downtime_prompt'] = embeddings[1:2, :]
-    static_info_embeddings['channel_info'] = {
-        ch: embeddings[i+2:i+3, :] 
-        for i, ch in enumerate(channels)
-    }
-        
-    # 4. Save the dictionary containing the embeddings
-    output_path = os.path.join(DATA_DIR, 'weather', "static_info_embeddings.pkl")
-    joblib.dump(static_info_embeddings, output_path)
-    print(f"Saved static info embeddings to {output_path}")
-
 def main():
     """
     Main execution function
@@ -206,9 +154,6 @@ def main():
     # 2. Merge the embeddings from all cities
     merge_borough_embeddings()
     
-    # 3. Process the static information
-    process_static_data(model)
-    
     print("\nAll processing complete.")
 
 if __name__ == "__main__":

scripts/embed_static.py

@@ -20,26 +20,7 @@ def create_static_embeddings(
         input_path (str): Path to the input static_info.json file.
         output_path (str): Path to save the output .pkl file with embeddings.
     """
-    # --- 1. Sanity Checks ---
-    if not os.path.exists(input_path):
-        print(f"Error: Input file not found at '{input_path}'")
-        # Create a dummy file for demonstration if it doesn't exist
-        print("Creating a dummy 'static_info.json' for demonstration purposes.")
-        dummy_data = {
-            "general_info": "This dataset contains the solar power generation data of 16 solar panels in Calgary, Alberta, Canada. The data is collected hourly. ",
-            "downtime_prompt": "The system is shutdown, thus no power generation.",
-            "channel_info": {
-                "CFH_HQ": "The solar panel is located at Calgary Fire Hall Headquarters.",
-                "WMSC": "The solar panel is located at Whitehorn Multi-Service Centre.",
-                "SLC": "The solar panel is located at Southland Leisure Centre."
-            }
-        }
-        os.makedirs(os.path.dirname(input_path), exist_ok=True)
-        with open(input_path, 'w') as f:
-            json.dump(dummy_data, f, indent=2)
-        print(f"Dummy file created at '{input_path}'. Please run the script again.")
-        return
-
+    
     # --- 2. Initialize Model ---
     print("Initializing embedding model...")
     # Set up device (use GPU if available)