update: merged general report

README.md

@@ -14,7 +14,8 @@ size_categories:
 
 ## Data source:
 
-- [California ISO](https://www.caiso.com/todays-outlook)
+- [California ISO]
+  - https://www.caiso.com/todays-outlook
 
 ## Dataset Structure
 
@@ -25,8 +26,8 @@ The dataset is organized into the following structure:
 
 |   |-- raw_data  # Original data files
 |   |-- time_series # Rule-based Imputed data files
-|   |   |-- id_1.parquet # Time series data for each subject can be multivariate, can be in csv, parquet, etc.
-|   |   |-- id_2.parquet
+|   |   |-- all_version_1.parquet # Time series data for each subject can be multivariate, can be in csv, parquet, etc.
+|   |   |-- all_version_2.parquet
 |   |   |-- ...
 |   |   |-- id_info.json # Metadata for each subject
 
@@ -41,13 +42,13 @@ The dataset is organized into the following structure:
 |   |   |   |   |-- ...
 |   |   |   |-- weather_report (can be flattened and use regex to extract the version)
 |   |   |   |   |-- version_1
-|   |   |   |   |   |-- weather_report_????.json
+|   |   |   |   |   |-- xxx_weather_report_????.json
 |   |   |   |   |   |-- ...
 |   |   |   |   |-- version_2
 |   |   |   |   |-- ...
 |   |   |   |-- report_embedding # embedding for the weather report
 |   |   |   |   |-- version_1
-|   |   |   |   |   |-- report_embedding_????.pkl
+|   |   |   |   |   |-- xxx_report_embedding_????.pkl
 |   |   |   |   |   |-- ...
 |   |   |   |   |-- version_2
 |   |   |   |   |-- ...
@@ -56,10 +57,11 @@ The dataset is organized into the following structure:
 |   |   |-- ...
 
 |   |   |-- merged_report_embedding # merged embedding for multiple needed locations (optional)
-|   |   |   |-- version_1
-|   |   |   |   |-- report_embedding_????.pkl
-|   |   |   |   |-- ...
-|   |   |   |-- version_2
+|   |   |   |-- xxx_embeddings_????.pkl
+|   |   |   |-- ...
+
+|   |   |-- merged_general_report # merged general report for multiple needed locations (optional)
+|   |   |   |-- xxx_report.json
 |   |   |   |-- ...
 
 |   |-- scripts # Scripts for data processing, model training, and evaluation

scripts/merge_general_report.py

@@ -0,0 +1,62 @@
+import json
+import os
+import glob
+
+# Configuration for paths and filenames
+base_directory = 'weather'
+output_filename = 'merged_general_weather_forecast.json'
+
+# Define a precise search pattern to find the relevant JSON files
+report_type_folder = 'general_report'
+file_prefix = 'general_weather_forecast'
+file_pattern = os.path.join(base_directory, '*', 'weather_report', report_type_folder, f'{file_prefix}_*.json')
+
+# Find all file paths that match the specified pattern
+all_files = glob.glob(file_pattern)
+
+if not all_files:
+    print(f"No files found for pattern '{file_pattern}'. Please check the path and script location.")
+else:
+    print(f"Found {len(all_files)} files to merge. Starting process...")
+
+    # The master dictionary will hold the final nested structure.
+    # It is structured as: { 'timestamp1': {'regionA': {...}, 'regionB': {...}}, 'timestamp2': ... }
+    master_data = {}
+
+    for file_path in all_files:
+        try:
+            # Extract the region name from the file path.
+            # e.g., 'Bayreuth-TenneT' is the 3rd last part of the path.
+            path_parts = os.path.normpath(file_path).split(os.sep)
+            region = path_parts[-3] 
+
+            # The data in each file is a dictionary like {'20160101': {...}, '20160102': {...}}
+            with open(file_path, 'r', encoding='utf-8') as f:
+                region_data = json.load(f)
+
+            # Iterate through all (timestamp, report_details) pairs in the current file
+            for timestamp, report_details in region_data.items():
+                timestamp_entry = master_data.setdefault(timestamp, {})
+                # Under the entry for this timestamp, add the report details keyed by the region name.
+                timestamp_entry[region] = report_details
+            
+            print(f"  - Processed and integrated: {region}")
+
+        except Exception as e:
+            print(f"An error occurred while processing file {file_path}: {e}")
+
+    # Write the consolidated data to a new JSON file
+    if master_data:
+        print("\nWriting all data to the final file...")
+        
+        # Sort the master dictionary by timestamp (key) for better readability
+        sorted_master_data = dict(sorted(master_data.items()))
+        
+        with open(output_filename, 'w', encoding='utf-8') as f:
+            json.dump(sorted_master_data, f, indent=4, ensure_ascii=False)
+        
+        print(f"Merge complete!")
+        print(f"All data has been aggregated by timestamp into: {output_filename}")
+        print(f"The final file contains {len(master_data)} unique timestamps.")
+    else:
+        print("No files were processed successfully. The output file was not generated.")