增加web_api

src/gasflux/reporting.py

@@ -66,47 +66,52 @@ def generate_reports(name: str, processor, config: dict):
     Generates reports, configuration files, and processed output variables for gasflux processing runs.
 
     Parameters:
-        name (str): The name identifier for the current processing run.
+        name (str): The name identifier for the current processing run (task_id).
         processor (object): The processing object containing report data and output variables.
         config (dict): Configuration dictionary used for processing.
     """
     output_dir = Path(config["output_dir"]).expanduser()
     processing_time = datetime.now()
-    output_path = output_dir / name / processing_time.strftime("%Y-%m-%d_%H-%M-%S-%f_processing_run")
+    # Save directly to outputs/{task_id} directory
+    output_path = output_dir / "outputs" / name
     output_path.mkdir(parents=True, exist_ok=True)
 
     # Save reports
     for gas, report in processor.reports.items():
-        report_path = output_path / f"{name}_{gas}_report.html"
+        timestamp_str = processing_time.strftime("%Y%m%d_%H%M%S")
+        report_path = output_path / f"{gas}_report_{timestamp_str}.html"
         with open(report_path, "w", encoding="utf-8") as file:
             file.write(report)
 
     # Save config
-    header = f"# Gasflux output config for file {name} from processing run at {processing_time}\n"
-    config_path = output_path / f"{name}_config.yaml"
+    header = f"# Gasflux output config for task {name} from processing run at {processing_time}\n"
+    timestamp_str = processing_time.strftime("%Y%m%d_%H%M%S")
+    config_path = output_path / f"config_{timestamp_str}.yaml"
     with open(config_path, "w") as file:
         file.write(header)
         yaml.safe_dump(config, file)
 
-    # Save DataFrame to CSV
+    # Save DataFrame to Excel
     if hasattr(processor, 'df') and processor.df is not None:
-        csv_path = output_path / f"{name}_data.csv"
-        processor.df.to_csv(csv_path, index=False)
-        logger.info(f"DataFrame saved to {csv_path}")
+        timestamp_str = processing_time.strftime("%Y%m%d_%H%M%S")
+        excel_path = output_path / f"processed_data_{timestamp_str}.xlsx"
+        processor.df.to_excel(excel_path, index=False, engine='openpyxl')
+        logger.info(f"DataFrame saved to {excel_path}")
 
     # Save output variables
     output_vars = processor.output_vars
     # output_vars = delete_large_arrays(output_vars, threshold_size=50)
     header = (
-        f"# Gasflux output variables for file {name} from processing run at {processing_time}\n"
+        f"# Gasflux output variables for task {name} from processing run at {processing_time}\n"
     )
-    filename = output_path / f"{name}_output_vars.json"
+    timestamp_str = processing_time.strftime("%Y%m%d_%H%M%S")
+    filename = output_path / f"output_vars_{timestamp_str}.json"
     with open(filename, "w") as file:
         file.write(header)
         json.dump(
             output_vars, file, default=lambda item: item.tolist() if isinstance(item, np.ndarray) else item, indent=4
         )
-    logger.info(f"Processing run saved to {output_path}")
+    logger.info(f"Task {name} results saved to {output_path}")
 
 
 def delete_large_arrays(output_vars: dict, threshold_size: int) -> dict: