Переделал усреднение. Вариант 1

dbapp/mainapp/views/points_averaging.py

@@ -1,5 +1,6 @@
 """
 Points averaging view for satellite data grouping by day/night intervals.
+Groups points by Source, then by time intervals within each Source.
 """
 from datetime import datetime, timedelta
 from django.contrib.auth.mixins import LoginRequiredMixin
@@ -8,7 +9,7 @@ from django.shortcuts import render
 from django.views import View
 from django.utils import timezone
 
-from ..models import ObjItem, Satellite
+from ..models import ObjItem, Satellite, Source
 from ..utils import (
     calculate_mean_coords,
     calculate_distance_wgs84,
@@ -29,8 +30,9 @@ class PointsAveragingView(LoginRequiredMixin, View):
     """
     
     def get(self, request):
-        # Get satellites that have points with geo data
+        # Get satellites that have sources with points with geo data
         satellites = Satellite.objects.filter(
+            parameters__objitem__source__isnull=False,
             parameters__objitem__geo_obj__coords__isnull=False
         ).distinct().order_by('name')
         
@@ -44,13 +46,14 @@ class PointsAveragingView(LoginRequiredMixin, View):
 
 class PointsAveragingAPIView(LoginRequiredMixin, View):
     """
-    API endpoint for grouping and averaging points by day/night intervals.
+    API endpoint for grouping and averaging points by Source and day/night intervals.
     
     Groups points into:
     - Day: 08:00 - 19:00
     - Night: 19:00 - 08:00 (next day)
+    - Weekend: Friday 19:00 - Monday 08:00
     
-    For each group, calculates average coordinates and checks for outliers (>56 km).
+    For each group within each Source, calculates average coordinates and checks for outliers (>56 km).
     """
     
     def get(self, request):
@@ -76,9 +79,50 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
         except ValueError:
             return JsonResponse({'error': 'Неверный формат даты'}, status=400)
         
-        # Get all points for the satellite in the date range
-        objitems = ObjItem.objects.filter(
-            parameter_obj__id_satellite=satellite,
+        # Get all Sources for the satellite that have points in the date range
+        sources = Source.objects.filter(
+            source_objitems__parameter_obj__id_satellite=satellite,
+            source_objitems__geo_obj__coords__isnull=False,
+            source_objitems__geo_obj__timestamp__gte=date_from_obj,
+            source_objitems__geo_obj__timestamp__lt=date_to_obj,
+        ).distinct().prefetch_related(
+            'source_objitems',
+            'source_objitems__geo_obj',
+            'source_objitems__geo_obj__mirrors',
+            'source_objitems__parameter_obj',
+            'source_objitems__parameter_obj__polarization',
+            'source_objitems__parameter_obj__modulation',
+            'source_objitems__parameter_obj__standard',
+        )
+        
+        if not sources.exists():
+            return JsonResponse({'error': 'Источники не найдены в указанном диапазоне'}, status=404)
+        
+        # Process each source
+        result_sources = []
+        for source in sources:
+            source_data = self._process_source(source, date_from_obj, date_to_obj)
+            if source_data['groups']:  # Only add if has groups with points
+                result_sources.append(source_data)
+        
+        if not result_sources:
+            return JsonResponse({'error': 'Точки не найдены в указанном диапазоне'}, status=404)
+        
+        return JsonResponse({
+            'success': True,
+            'satellite': satellite.name,
+            'date_from': date_from,
+            'date_to': date_to,
+            'sources': result_sources,
+            'total_sources': len(result_sources),
+        })
+    
+    def _process_source(self, source, date_from_obj, date_to_obj):
+        """
+        Process a single Source: get its points and group them by time intervals.
+        """
+        # Get all points for this source in the date range
+        objitems = source.source_objitems.filter(
             geo_obj__coords__isnull=False,
             geo_obj__timestamp__gte=date_from_obj,
             geo_obj__timestamp__lt=date_to_obj,
@@ -89,16 +133,12 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
             'parameter_obj__modulation',
             'parameter_obj__standard',
             'geo_obj',
-            'source',
         ).prefetch_related(
             'geo_obj__mirrors'
         ).order_by('geo_obj__timestamp')
         
-        if not objitems.exists():
-            return JsonResponse({'error': 'Точки не найдены в указанном диапазоне'}, status=404)
-        
-        # Group points by source name and day/night intervals
-        groups = self._group_points_by_intervals(objitems)
+        # Group points by day/night intervals
+        groups = self._group_points_by_intervals(list(objitems))
         
         # Process each group: calculate average and check for outliers
         result_groups = []
@@ -106,21 +146,27 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
             group_result = self._process_group(group_key, points)
             result_groups.append(group_result)
         
-        return JsonResponse({
-            'success': True,
-            'satellite': satellite.name,
-            'date_from': date_from,
-            'date_to': date_to,
+        # Get source name from first point or use ID
+        source_name = f"Источник #{source.id}"
+        if objitems.exists():
+            first_point = objitems.first()
+            if first_point.name:
+                source_name = first_point.name
+        
+        return {
+            'source_id': source.id,
+            'source_name': source_name,
+            'total_points': sum(len(g['points']) for g in result_groups),
             'groups': result_groups,
-            'total_groups': len(result_groups),
-        })
+        }
     
     def _group_points_by_intervals(self, objitems):
         """
-        Group points by source name and day/night intervals.
+        Group points by day/night intervals.
         
         Day: 08:00 - 19:00
         Night: 19:00 - 08:00 (next day)
+        Weekend: Friday 19:00 - Monday 08:00
         """
         groups = {}
         
@@ -129,19 +175,14 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
                 continue
             
             timestamp = timezone.localtime(objitem.geo_obj.timestamp)
-            # timestamp = objitem.geo_obj.timestamp
-            source_name = objitem.name or f"Объект #{objitem.id}"
             
             # Determine interval
             interval_key = self._get_interval_key(timestamp)
             
-            # Create group key: (source_name, interval_key)
-            group_key = (source_name, interval_key)
+            if interval_key not in groups:
+                groups[interval_key] = []
             
-            if group_key not in groups:
-                groups[group_key] = []
-            
-            groups[group_key].append(objitem)
+            groups[interval_key].append(objitem)
         
         return groups
     
@@ -208,7 +249,7 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
             return date - timedelta(days=3)
         return date
     
-    def _process_group(self, group_key, points):
+    def _process_group(self, interval_key, points):
         """
         Process a group of points: calculate average and check for outliers.
         
@@ -218,8 +259,6 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
         3. Iteratively add points within 56 km of current average
         4. Points not within 56 km of final average are outliers
         """
-        source_name, interval_key = group_key
-        
         # Parse interval info
         date_str, interval_type = interval_key.rsplit('_', 1)
         interval_date = datetime.strptime(date_str, '%Y-%m-%d').date()
@@ -322,7 +361,7 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
         # Calculate median time from valid points using timestamp_objects array
         valid_timestamps = []
         for i in valid_indices:
-            if timestamp_objects[i]:
+            if i < len(timestamp_objects) and timestamp_objects[i]:
                 valid_timestamps.append(timestamp_objects[i])
         
         median_time_str = '-'
@@ -344,7 +383,6 @@ class PointsAveragingAPIView(LoginRequiredMixin, View):
             median_time_str = timezone.localtime(median_datetime).strftime("%d.%m.%Y %H:%M")
         
         return {
-            'source_name': source_name,
             'interval_key': interval_key,
             'interval_label': interval_label,
             'total_points': len(points_data),