[trends] Fix bug in mood trend generation

vrobbler/apps/trends/trends/mood.py

@@ -1,13 +1,10 @@
 from collections import Counter, defaultdict
-from datetime import timedelta
 
-from django.db.models import Count, Q
-from django.db.models.functions import Extract
-from django.utils import timezone
+from django.db.models import Q
 from scrobbles.models import Scrobble
 
 
-def _mood_scrobbles(user, period="all_time"):
+def _mood_scrobbles(user, period="last_30"):
     from trends.utils import get_date_range
 
     start, end = get_date_range(period)
@@ -19,17 +16,25 @@ def _mood_scrobbles(user, period="all_time"):
     return Scrobble.objects.filter(filters).select_related("mood")
 
 
+def _parse_quality(raw):
+    try:
+        return int(raw)
+    except (TypeError, ValueError):
+        return None
+
+
 def _avg_quality(values):
-    if not values:
+    nums = [v for v in values if v is not None]
+    if not nums:
         return 0.0
-    return round(sum(values) / len(values), 2)
+    return round(sum(nums) / len(nums), 2)
 
 
-def compute_mood_trajectory(user, period="all_time"):
+def compute_mood_trajectory(user, period="last_30"):
     scrobbles = _mood_scrobbles(user, period).order_by("timestamp")
     by_date = defaultdict(list)
     for s in scrobbles:
-        quality = s.log.get("mood_quality")
+        quality = _parse_quality(s.log.get("mood_quality"))
         if quality is not None:
             day_key = s.timestamp.strftime("%Y-%m-%d")
             by_date[day_key].append(quality)
@@ -48,13 +53,13 @@ def compute_mood_trajectory(user, period="all_time"):
     return {"trajectory": trajectory}
 
 
-def compute_mood_by_time(user, period="all_time"):
+def compute_mood_by_time(user, period="last_30"):
     scrobbles = _mood_scrobbles(user, period)
     by_hour = defaultdict(list)
     by_day = defaultdict(list)
 
     for s in scrobbles:
-        quality = s.log.get("mood_quality")
+        quality = _parse_quality(s.log.get("mood_quality"))
         if quality is not None and s.timestamp:
             by_hour[s.timestamp.hour].append(quality)
             by_day[s.timestamp.isoweekday()].append(quality)
@@ -94,7 +99,7 @@ def compute_mood_by_time(user, period="all_time"):
     return {"hours": hours, "days": days}
 
 
-def compute_mood_distribution(user, period="all_time"):
+def compute_mood_distribution(user, period="last_30"):
     scrobbles = _mood_scrobbles(user, period)
     mood_counts = Counter()
     type_counts = Counter()
@@ -120,7 +125,7 @@ def compute_mood_distribution(user, period="all_time"):
     }
 
 
-def compute_mood_streaks(user, period="all_time"):
+def compute_mood_streaks(user, period="last_30"):
     scrobbles = list(
         _mood_scrobbles(user, period).order_by("timestamp")
     )
@@ -169,13 +174,13 @@ def compute_mood_streaks(user, period="all_time"):
     return {"streaks": streaks[:10], "current_streak": current_streak}
 
 
-def compute_mood_weather(user, period="all_time"):
+def compute_mood_weather(user, period="last_30"):
     scrobbles = _mood_scrobbles(user, period)
     by_condition = defaultdict(list)
     by_temp_range = defaultdict(list)
 
     for s in scrobbles:
-        quality = s.log.get("mood_quality")
+        quality = _parse_quality(s.log.get("mood_quality"))
         if quality is None:
             continue
         desc = s.log.get("weather_description")
@@ -183,7 +188,11 @@ def compute_mood_weather(user, period="all_time"):
         if desc:
             by_condition[desc].append(quality)
         if temp is not None:
-            bucket = f"{(int(temp) // 10) * 10}-{(int(temp) // 10) * 10 + 9}F"
+            try:
+                temp_f = float(temp)
+            except (TypeError, ValueError):
+                continue
+            bucket = f"{(int(temp_f) // 10) * 10}-{(int(temp_f) // 10) * 10 + 9}F"
             by_temp_range[bucket].append(quality)
 
     conditions = [