-
Page Views
-
{{ total_views }}
+
Page Views (Hackathon)
+
{{ hackathon_views }}
+
All-Time: {{ all_time_views }}
diff --git a/website/views/hackathon.py b/website/views/hackathon.py
index 723cb11d8e..4568d82dc9 100644
--- a/website/views/hackathon.py
+++ b/website/views/hackathon.py
@@ -253,26 +253,39 @@ def get_context_data(self, **kwargs):
# Get the path for this hackathon
hackathon_path = f"/hackathons/{hackathon.slug}/"
- # Get the last 14 days of view data
+ # Calculate views during the hackathon timeframe
+ hackathon_start_date = hackathon.start_time.date()
+ hackathon_end_date = hackathon.end_time.date()
today = timezone.now().date()
- fourteen_days_ago = today - timedelta(days=14)
- # Query IP table for view counts by date
- view_data = (
- IP.objects.filter(path=hackathon_path, created__date__gte=fourteen_days_ago)
+ # Use the end date or today, whichever is earlier for the chart
+ chart_end_date = min(hackathon_end_date, today)
+
+ # Query IP table for view counts during hackathon period
+ # Use path__contains to match the widget's behavior
+ hackathon_view_data = (
+ IP.objects.filter(
+ path__contains=hackathon_path,
+ created__date__gte=hackathon_start_date,
+ created__date__lte=chart_end_date,
+ )
.annotate(date=TruncDate("created"))
.values("date")
.annotate(count=Sum("count"))
.order_by("date")
)
- # Prepare data for the sparkline chart
- date_counts = {item["date"]: item["count"] for item in view_data}
- dates, counts = self._get_date_range_data(fourteen_days_ago, today, date_counts)
+ # Prepare data for the sparkline chart (hackathon timeframe)
+ date_counts = {item["date"]: item["count"] for item in hackathon_view_data}
+ dates, counts = self._get_date_range_data(hackathon_start_date, chart_end_date, date_counts)
context["view_dates"] = json.dumps(dates)
context["view_counts"] = json.dumps(counts)
- context["total_views"] = sum(counts)
+ context["hackathon_views"] = sum(counts)
+
+ # Calculate all-time views (from the beginning)
+ all_time_views = IP.objects.filter(path__contains=hackathon_path).aggregate(total=Sum("count"))["total"] or 0
+ context["all_time_views"] = all_time_views
return context