implemented response timing via time_view decorator

utils/decorators.py

@@ -0,0 +1,26 @@
+import time
+from functools import wraps
+from rest_framework.response import Response
+
+
+def time_view(view_func):
+    """Decorator to measure the execution time and add it to the JSON response."""
+
+    @wraps(view_func)
+    def wrapper(request, *args, **kwargs):
+
+        start_time = time.perf_counter()
+
+        response = view_func(request, *args, **kwargs)
+
+        end_time = time.perf_counter()
+        elapsed_time = end_time - start_time
+
+        if isinstance(response, Response) and isinstance(response.data, dict):
+            response.data["time_elapsed_seconds"] = f"{elapsed_time:.4f}"
+
+        print(f"[{view_func.__name__}] Time elapsed: {elapsed_time:.4f} seconds")
+
+        return response
+
+    return wrapper

views.py

@@ -31,6 +31,9 @@ from rest_framework.response import Response
 from rest_framework import status
 from uplim.models import Pixel, CatalogSource
 
+from .utils.decorators import time_view
+from django.utils.decorators import method_decorator
+
 # SANITIZE RESPONSE DATA BEFORE JSON CONVERSION FOR DEBUGGING NANS
 # now NaNs are converted to 'null' beforehand
 # ****************************************************************
@@ -70,6 +73,7 @@ def parse_survey_param(raw):
 # **************************************************************
 
 
+@method_decorator(time_view, name="dispatch")
 class PixelAggregateView(APIView):
 
     def get(self, request):
@@ -132,6 +136,7 @@ class PixelAggregateView(APIView):
 # **************************************************************
 
 
+@method_decorator(time_view, name="dispatch")
 class UpperLimitView(APIView):
     """
     Calculate confidence bounds based on aperture photometry using classic and bayesian methods
@@ -538,6 +543,7 @@ class UpperLimitView(APIView):
         return Response(clean, status=status.HTTP_200_OK)
 
 
+@method_decorator(time_view, name="dispatch")
 class StackedUpperLimitView(APIView):
     """
     Calculate confidence bounds based on aperture photometry using classic and bayesian methods for a set of sources