mirror of
https://github.com/bnair123/MusicAnalyser.git
synced 2026-02-25 19:56:06 +00:00
af0d985253
- StatsService: Fixed N+1 queries, added missing metrics (whiplash, entropy, lifecycle), and improved correctness (boundary checks, null handling). - NarrativeService: Added payload shaping for token efficiency, improved JSON robustness, and updated prompts to align with persona specs. - Documentation: Added backend/TECHNICAL_DOCS.md detailing the logic.
714 lines
28 KiB
Python
714 lines
28 KiB
Python
from sqlalchemy.orm import Session, joinedload
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from sqlalchemy import func, distinct
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from datetime import datetime, timedelta
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from typing import Dict, Any, List, Optional
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import math
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import numpy as np
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from ..models import PlayHistory, Track, Artist
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class StatsService:
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def __init__(self, db: Session):
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self.db = db
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def compute_comparison(self, current_stats: Dict[str, Any], period_start: datetime, period_end: datetime) -> Dict[str, Any]:
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"""
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Calculates deltas vs the previous period of the same length.
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"""
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duration = period_end - period_start
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prev_end = period_start
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prev_start = prev_end - duration
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# We only need key metrics for comparison
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prev_volume = self.compute_volume_stats(prev_start, prev_end)
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prev_vibe = self.compute_vibe_stats(prev_start, prev_end)
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prev_taste = self.compute_taste_stats(prev_start, prev_end)
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deltas = {}
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# Plays
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curr_plays = current_stats["volume"]["total_plays"]
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prev_plays_count = prev_volume["total_plays"]
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deltas["plays_delta"] = curr_plays - prev_plays_count
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deltas["plays_pct_change"] = self._pct_change(curr_plays, prev_plays_count)
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# Energy & Valence
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if "mood_quadrant" in current_stats["vibe"] and "mood_quadrant" in prev_vibe:
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curr_e = current_stats["vibe"]["mood_quadrant"]["y"]
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prev_e = prev_vibe["mood_quadrant"]["y"]
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deltas["energy_delta"] = round(curr_e - prev_e, 2)
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curr_v = current_stats["vibe"]["mood_quadrant"]["x"]
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prev_v = prev_vibe["mood_quadrant"]["x"]
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deltas["valence_delta"] = round(curr_v - prev_v, 2)
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# Popularity
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if "avg_popularity" in current_stats["taste"] and "avg_popularity" in prev_taste:
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deltas["popularity_delta"] = round(current_stats["taste"]["avg_popularity"] - prev_taste["avg_popularity"], 1)
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return {
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"previous_period": {
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"start": prev_start.isoformat(),
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"end": prev_end.isoformat()
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},
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"deltas": deltas
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}
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def compute_volume_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
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"""
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Calculates volume metrics including Concentration (HHI, Gini, Entropy) and Top Lists.
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"""
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# Eager load tracks AND artists to fix the "Artist String Problem" and performance
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# Use < period_end for half-open interval to avoid double counting boundaries
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query = self.db.query(PlayHistory).options(
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joinedload(PlayHistory.track).joinedload(Track.artists)
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).filter(
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PlayHistory.played_at >= period_start,
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PlayHistory.played_at < period_end
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)
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plays = query.all()
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total_plays = len(plays)
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if total_plays == 0:
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return self._empty_volume_stats()
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total_ms = 0
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track_counts = {}
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artist_counts = {}
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genre_counts = {}
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album_counts = {}
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# Maps for resolving names later without DB hits
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track_map = {}
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artist_map = {}
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album_map = {}
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for p in plays:
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t = p.track
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if not t: continue
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total_ms += t.duration_ms if t.duration_ms else 0
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# Track Aggregation
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track_counts[t.id] = track_counts.get(t.id, 0) + 1
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track_map[t.id] = t
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# Album Aggregation
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# Prefer ID from raw_data, fallback to name
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album_id = t.album
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album_name = t.album
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if t.raw_data and "album" in t.raw_data:
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album_id = t.raw_data["album"].get("id", t.album)
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album_name = t.raw_data["album"].get("name", t.album)
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album_counts[album_id] = album_counts.get(album_id, 0) + 1
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album_map[album_id] = album_name
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# Artist Aggregation (Iterate objects, not string)
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for artist in t.artists:
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artist_counts[artist.id] = artist_counts.get(artist.id, 0) + 1
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artist_map[artist.id] = artist.name
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# Genre Aggregation
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if artist.genres:
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# artist.genres is a JSON list of strings
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for g in artist.genres:
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genre_counts[g] = genre_counts.get(g, 0) + 1
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# Derived Metrics
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unique_tracks = len(track_counts)
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one_and_done = len([c for c in track_counts.values() if c == 1])
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shares = [c / total_plays for c in track_counts.values()]
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# Top Lists (Optimized: No N+1)
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top_tracks = [
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{
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"name": track_map[tid].name,
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"artist": ", ".join([a.name for a in track_map[tid].artists]), # Correct artist display
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"count": c
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}
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for tid, c in sorted(track_counts.items(), key=lambda x: x[1], reverse=True)[:5]
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]
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top_artists = [
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{"name": artist_map.get(aid, "Unknown"), "count": c}
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for aid, c in sorted(artist_counts.items(), key=lambda x: x[1], reverse=True)[:5]
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]
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top_albums = [
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{"name": album_map.get(aid, "Unknown"), "count": c}
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for aid, c in sorted(album_counts.items(), key=lambda x: x[1], reverse=True)[:5]
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]
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top_genres = [{"name": k, "count": v} for k, v in sorted(genre_counts.items(), key=lambda x: x[1], reverse=True)[:5]]
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# Concentration Metrics
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# HHI: Sum of (share)^2
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hhi = sum([s ** 2 for s in shares])
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# Gini Coefficient
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sorted_shares = sorted(shares)
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n = len(shares)
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gini = 0
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if n > 0:
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gini = (2 * sum((i + 1) * x for i, x in enumerate(sorted_shares))) / (n * sum(sorted_shares)) - (n + 1) / n
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# Genre Entropy: -SUM(p * log(p))
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total_genre_occurrences = sum(genre_counts.values())
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genre_entropy = 0
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if total_genre_occurrences > 0:
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genre_probs = [count / total_genre_occurrences for count in genre_counts.values()]
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genre_entropy = -sum([p * math.log(p) for p in genre_probs if p > 0])
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# Top 5 Share
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top_5_plays = sum([t["count"] for t in top_tracks])
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top_5_share = top_5_plays / total_plays if total_plays else 0
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return {
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"total_plays": total_plays,
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"estimated_minutes": int(total_ms / 60000),
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"unique_tracks": unique_tracks,
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"unique_artists": len(artist_counts),
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"unique_albums": len(album_counts),
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"unique_genres": len(genre_counts),
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"top_tracks": top_tracks,
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"top_artists": top_artists,
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"top_albums": top_albums,
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"top_genres": top_genres,
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"repeat_rate": round((total_plays - unique_tracks) / total_plays, 3) if total_plays else 0,
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"one_and_done_rate": round(one_and_done / unique_tracks, 3) if unique_tracks else 0,
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"concentration": {
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"hhi": round(hhi, 4),
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"gini": round(gini, 4),
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"top_1_share": round(max(shares), 3) if shares else 0,
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"top_5_share": round(top_5_share, 3),
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"genre_entropy": round(genre_entropy, 2)
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}
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}
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def compute_time_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
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"""
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Includes Part-of-Day buckets, Listening Streaks, and Active Days stats.
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"""
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query = self.db.query(PlayHistory).filter(
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PlayHistory.played_at >= period_start,
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PlayHistory.played_at < period_end
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).order_by(PlayHistory.played_at.asc())
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plays = query.all()
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if not plays:
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return {}
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hourly_counts = [0] * 24
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weekday_counts = [0] * 7
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# Spec: Morning (6-12), Afternoon (12-18), Evening (18-24), Night (0-6)
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part_of_day = {"morning": 0, "afternoon": 0, "evening": 0, "night": 0}
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active_dates = set()
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for p in plays:
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h = p.played_at.hour
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hourly_counts[h] += 1
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weekday_counts[p.played_at.weekday()] += 1
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active_dates.add(p.played_at.date())
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if 6 <= h < 12:
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part_of_day["morning"] += 1
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elif 12 <= h < 18:
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part_of_day["afternoon"] += 1
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elif 18 <= h <= 23:
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part_of_day["evening"] += 1
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else:
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part_of_day["night"] += 1
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# Calculate Streak
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sorted_dates = sorted(list(active_dates))
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current_streak = 0
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longest_streak = 0
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if sorted_dates:
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current_streak = 1
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longest_streak = 1
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for i in range(1, len(sorted_dates)):
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delta = (sorted_dates[i] - sorted_dates[i - 1]).days
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if delta == 1:
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current_streak += 1
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else:
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longest_streak = max(longest_streak, current_streak)
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current_streak = 1
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longest_streak = max(longest_streak, current_streak)
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weekend_plays = weekday_counts[5] + weekday_counts[6]
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active_days_count = len(active_dates)
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return {
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"hourly_distribution": hourly_counts,
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"peak_hour": hourly_counts.index(max(hourly_counts)),
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"weekday_distribution": weekday_counts,
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"weekend_share": round(weekend_plays / len(plays), 2),
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"part_of_day": part_of_day,
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"listening_streak": current_streak,
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"longest_streak": longest_streak,
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"active_days": active_days_count,
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"avg_plays_per_active_day": round(len(plays) / active_days_count, 1) if active_days_count else 0
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}
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def compute_session_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
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"""
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Includes Micro-sessions, Marathon sessions, Energy Arcs, and Median metrics.
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"""
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query = self.db.query(PlayHistory).options(joinedload(PlayHistory.track)).filter(
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PlayHistory.played_at >= period_start,
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PlayHistory.played_at < period_end
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).order_by(PlayHistory.played_at.asc())
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plays = query.all()
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if not plays:
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return {"count": 0}
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sessions = []
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current_session = [plays[0]]
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# 1. Sessionization (Gap > 20 mins)
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for i in range(1, len(plays)):
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diff = (plays[i].played_at - plays[i-1].played_at).total_seconds() / 60
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if diff > 20:
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sessions.append(current_session)
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current_session = []
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current_session.append(plays[i])
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sessions.append(current_session)
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# 2. Analyze Sessions
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lengths_min = []
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micro_sessions = 0
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marathon_sessions = 0
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energy_arcs = {"rising": 0, "falling": 0, "flat": 0, "unknown": 0}
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start_hour_dist = [0] * 24
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for sess in sessions:
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# Start time distribution
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start_hour_dist[sess[0].played_at.hour] += 1
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# Durations
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if len(sess) > 1:
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duration = (sess[-1].played_at - sess[0].played_at).total_seconds() / 60
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lengths_min.append(duration)
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else:
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lengths_min.append(3.0) # Approx single song
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# Types
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if len(sess) <= 3: micro_sessions += 1
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if len(sess) >= 20: marathon_sessions += 1
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# Energy Arc
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first_t = sess[0].track
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last_t = sess[-1].track
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if first_t and last_t and first_t.energy is not None and last_t.energy is not None:
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diff = last_t.energy - first_t.energy
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if diff > 0.1: energy_arcs["rising"] += 1
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elif diff < -0.1: energy_arcs["falling"] += 1
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else: energy_arcs["flat"] += 1
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else:
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energy_arcs["unknown"] += 1
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avg_min = np.mean(lengths_min) if lengths_min else 0
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median_min = np.median(lengths_min) if lengths_min else 0
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# Sessions per day
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active_days = len(set(p.played_at.date() for p in plays))
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sessions_per_day = len(sessions) / active_days if active_days else 0
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return {
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"count": len(sessions),
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"avg_tracks": round(len(plays) / len(sessions), 1),
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"avg_minutes": round(float(avg_min), 1),
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"median_minutes": round(float(median_min), 1),
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"longest_session_minutes": round(max(lengths_min), 1) if lengths_min else 0,
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"sessions_per_day": round(sessions_per_day, 1),
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"start_hour_distribution": start_hour_dist,
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"micro_session_rate": round(micro_sessions / len(sessions), 2),
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"marathon_session_rate": round(marathon_sessions / len(sessions), 2),
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"energy_arcs": energy_arcs
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}
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def compute_vibe_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
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"""
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Aggregates Audio Features + Calculates Whiplash, Percentiles, and Profiles.
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"""
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plays = self.db.query(PlayHistory).filter(
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PlayHistory.played_at >= period_start,
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PlayHistory.played_at < period_end
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).order_by(PlayHistory.played_at.asc()).all()
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if not plays:
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return {}
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track_ids = list(set([p.track_id for p in plays]))
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tracks = self.db.query(Track).filter(Track.id.in_(track_ids)).all()
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track_map = {t.id: t for t in tracks}
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# 1. Aggregates
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feature_keys = ["energy", "valence", "danceability", "tempo", "acousticness",
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"instrumentalness", "liveness", "speechiness", "loudness"]
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features = {k: [] for k in feature_keys}
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# 2. Transition Arrays (for Whiplash)
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transitions = {"tempo": [], "energy": [], "valence": []}
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previous_track = None
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for i, p in enumerate(plays):
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t = track_map.get(p.track_id)
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if not t: continue
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# Robust Null Check: Append separately
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for key in feature_keys:
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val = getattr(t, key, None)
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if val is not None:
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features[key].append(val)
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# Calculate Transitions (Whiplash)
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if i > 0 and previous_track:
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time_diff = (p.played_at - plays[i - 1].played_at).total_seconds()
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if time_diff < 300: # 5 min gap max
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if t.tempo is not None and previous_track.tempo is not None:
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transitions["tempo"].append(abs(t.tempo - previous_track.tempo))
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if t.energy is not None and previous_track.energy is not None:
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transitions["energy"].append(abs(t.energy - previous_track.energy))
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if t.valence is not None and previous_track.valence is not None:
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transitions["valence"].append(abs(t.valence - previous_track.valence))
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previous_track = t
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# Calculate Stats (Mean, Std, Percentiles)
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stats = {}
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for key, values in features.items():
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if values:
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stats[f"avg_{key}"] = float(np.mean(values))
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stats[f"std_{key}"] = float(np.std(values))
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stats[f"p10_{key}"] = float(np.percentile(values, 10))
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stats[f"p50_{key}"] = float(np.percentile(values, 50)) # Median
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stats[f"p90_{key}"] = float(np.percentile(values, 90))
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else:
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stats[f"avg_{key}"] = None
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# Derived Metrics
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if stats.get("avg_energy") is not None and stats.get("avg_valence") is not None:
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stats["mood_quadrant"] = {
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"x": round(stats["avg_valence"], 2),
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"y": round(stats["avg_energy"], 2)
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}
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# Consistency
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avg_std = (stats.get("std_energy", 0) + stats.get("std_valence", 0)) / 2
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stats["consistency_score"] = round(1.0 - avg_std, 2)
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# Rhythm Profile
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if stats.get("avg_tempo") is not None and stats.get("avg_danceability") is not None:
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stats["rhythm_profile"] = {
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"avg_tempo": round(stats["avg_tempo"], 1),
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"avg_danceability": round(stats["avg_danceability"], 2)
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}
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# Texture Profile
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if stats.get("avg_acousticness") is not None and stats.get("avg_instrumentalness") is not None:
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stats["texture_profile"] = {
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"acousticness": round(stats["avg_acousticness"], 2),
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"instrumentalness": round(stats["avg_instrumentalness"], 2)
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}
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# Whiplash Scores
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stats["whiplash"] = {}
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for k in ["tempo", "energy", "valence"]:
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if transitions[k]:
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stats["whiplash"][k] = round(float(np.mean(transitions[k])), 2)
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else:
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stats["whiplash"][k] = 0
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return stats
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def compute_era_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
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"""
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Includes Nostalgia Gap and granular decade breakdown.
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"""
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query = self.db.query(PlayHistory).options(joinedload(PlayHistory.track)).filter(
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PlayHistory.played_at >= period_start,
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PlayHistory.played_at < period_end
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)
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plays = query.all()
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years = []
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for p in plays:
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t = p.track
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if t and t.raw_data and "album" in t.raw_data:
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rd = t.raw_data["album"].get("release_date")
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if rd:
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try:
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years.append(int(rd.split("-")[0]))
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except:
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pass
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if not years:
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return {"musical_age": None}
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avg_year = sum(years) / len(years)
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current_year = datetime.utcnow().year
|
|
|
|
decades = {}
|
|
for y in years:
|
|
dec = (y // 10) * 10
|
|
label = f"{dec}s"
|
|
decades[label] = decades.get(label, 0) + 1
|
|
|
|
total = len(years)
|
|
dist = {k: round(v / total, 3) for k, v in decades.items()}
|
|
|
|
return {
|
|
"musical_age": int(avg_year),
|
|
"nostalgia_gap": int(current_year - avg_year),
|
|
"freshness_score": dist.get(f"{int(current_year / 10) * 10}s", 0),
|
|
"decade_distribution": dist
|
|
}
|
|
|
|
def compute_skip_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
|
|
"""
|
|
Implements boredom skip detection.
|
|
"""
|
|
query = self.db.query(PlayHistory).filter(
|
|
PlayHistory.played_at >= period_start,
|
|
PlayHistory.played_at < period_end
|
|
).order_by(PlayHistory.played_at.asc())
|
|
plays = query.all()
|
|
|
|
if len(plays) < 2:
|
|
return {"skip_rate": 0, "total_skips": 0}
|
|
|
|
skips = 0
|
|
track_ids = list(set([p.track_id for p in plays]))
|
|
tracks = self.db.query(Track).filter(Track.id.in_(track_ids)).all()
|
|
track_map = {t.id: t for t in tracks}
|
|
|
|
# Denominator: transitions, which is plays - 1
|
|
transitions_count = len(plays) - 1
|
|
|
|
for i in range(transitions_count):
|
|
current_play = plays[i]
|
|
next_play = plays[i+1]
|
|
track = track_map.get(current_play.track_id)
|
|
|
|
if not track or not track.duration_ms:
|
|
continue
|
|
|
|
diff_seconds = (next_play.played_at - current_play.played_at).total_seconds()
|
|
duration_sec = track.duration_ms / 1000.0
|
|
|
|
# Logic: If diff < (duration - 10s), it's a skip.
|
|
# AND it must be a "valid" listening attempt (e.g. > 30s)
|
|
# AND it shouldn't be a huge gap (e.g. paused for 2 hours then hit next)
|
|
|
|
if 30 < diff_seconds < (duration_sec - 10):
|
|
skips += 1
|
|
|
|
return {
|
|
"total_skips": skips,
|
|
"skip_rate": round(skips / transitions_count, 3) if transitions_count > 0 else 0
|
|
}
|
|
|
|
def compute_context_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
|
|
"""
|
|
Analyzes context_uri and switching rate.
|
|
"""
|
|
query = self.db.query(PlayHistory).filter(
|
|
PlayHistory.played_at >= period_start,
|
|
PlayHistory.played_at < period_end
|
|
).order_by(PlayHistory.played_at.asc())
|
|
plays = query.all()
|
|
|
|
if not plays:
|
|
return {}
|
|
|
|
context_counts = {"playlist": 0, "album": 0, "artist": 0, "collection": 0, "unknown": 0}
|
|
unique_contexts = {}
|
|
context_switches = 0
|
|
|
|
last_context = None
|
|
|
|
for p in plays:
|
|
uri = p.context_uri
|
|
if not uri:
|
|
context_counts["unknown"] += 1
|
|
uri = "unknown"
|
|
else:
|
|
if "playlist" in uri: context_counts["playlist"] += 1
|
|
elif "album" in uri: context_counts["album"] += 1
|
|
elif "artist" in uri: context_counts["artist"] += 1
|
|
elif "collection" in uri: context_counts["collection"] += 1
|
|
else: context_counts["unknown"] += 1
|
|
|
|
if uri != "unknown":
|
|
unique_contexts[uri] = unique_contexts.get(uri, 0) + 1
|
|
|
|
# Switch detection
|
|
if last_context and uri != last_context:
|
|
context_switches += 1
|
|
last_context = uri
|
|
|
|
total = len(plays)
|
|
breakdown = {k: round(v / total, 2) for k, v in context_counts.items()}
|
|
sorted_contexts = sorted(unique_contexts.items(), key=lambda x: x[1], reverse=True)[:5]
|
|
|
|
return {
|
|
"type_breakdown": breakdown,
|
|
"album_purist_score": breakdown.get("album", 0),
|
|
"playlist_dependency": breakdown.get("playlist", 0),
|
|
"context_loyalty": round(len(plays) / len(unique_contexts), 2) if unique_contexts else 0,
|
|
"context_switching_rate": round(context_switches / (total - 1), 2) if total > 1 else 0,
|
|
"top_context_uris": [{"uri": k, "count": v} for k, v in sorted_contexts]
|
|
}
|
|
|
|
def compute_taste_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
|
|
"""
|
|
Mainstream vs. Hipster analysis.
|
|
"""
|
|
query = self.db.query(PlayHistory).filter(
|
|
PlayHistory.played_at >= period_start,
|
|
PlayHistory.played_at < period_end
|
|
)
|
|
plays = query.all()
|
|
if not plays: return {}
|
|
|
|
track_ids = list(set([p.track_id for p in plays]))
|
|
tracks = self.db.query(Track).filter(Track.id.in_(track_ids)).all()
|
|
track_map = {t.id: t for t in tracks}
|
|
|
|
pop_values = []
|
|
for p in plays:
|
|
t = track_map.get(p.track_id)
|
|
if t and t.popularity is not None:
|
|
pop_values.append(t.popularity)
|
|
|
|
if not pop_values:
|
|
return {"avg_popularity": 0, "hipster_score": 0}
|
|
|
|
avg_pop = float(np.mean(pop_values))
|
|
|
|
# Hipster Score: Percentage of tracks with popularity < 30
|
|
underground_plays = len([x for x in pop_values if x < 30])
|
|
mainstream_plays = len([x for x in pop_values if x > 70])
|
|
|
|
return {
|
|
"avg_popularity": round(avg_pop, 1),
|
|
"hipster_score": round((underground_plays / len(pop_values)) * 100, 1),
|
|
"mainstream_score": round((mainstream_plays / len(pop_values)) * 100, 1),
|
|
"obscurity_rating": round(100 - avg_pop, 1)
|
|
}
|
|
|
|
def compute_lifecycle_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
|
|
"""
|
|
Discovery, Recurrence, Comebacks, Obsessions.
|
|
"""
|
|
# 1. Current plays
|
|
current_plays = self.db.query(PlayHistory).filter(
|
|
PlayHistory.played_at >= period_start,
|
|
PlayHistory.played_at < period_end
|
|
).all()
|
|
|
|
if not current_plays: return {}
|
|
|
|
current_track_ids = set([p.track_id for p in current_plays])
|
|
|
|
# 2. Historical check
|
|
old_tracks_query = self.db.query(distinct(PlayHistory.track_id)).filter(
|
|
PlayHistory.track_id.in_(current_track_ids),
|
|
PlayHistory.played_at < period_start
|
|
)
|
|
old_track_ids = set([r[0] for r in old_tracks_query.all()])
|
|
|
|
# 3. Discovery
|
|
new_discoveries = current_track_ids - old_track_ids
|
|
|
|
# 4. Obsessions (Tracks with > 5 plays in period)
|
|
track_counts = {}
|
|
for p in current_plays:
|
|
track_counts[p.track_id] = track_counts.get(p.track_id, 0) + 1
|
|
obsessions = [tid for tid, count in track_counts.items() if count >= 5]
|
|
|
|
# 5. Comeback Detection (Old tracks not played in last 30 days)
|
|
# Simplified: If in old_track_ids but NOT in last 30 days before period_start?
|
|
# That requires a gap check. For now, we will mark 'recurrence' as general relistening.
|
|
|
|
plays_on_new = len([p for p in current_plays if p.track_id in new_discoveries])
|
|
total_plays = len(current_plays)
|
|
|
|
return {
|
|
"discovery_count": len(new_discoveries),
|
|
"discovery_rate": round(plays_on_new / total_plays, 3) if total_plays > 0 else 0,
|
|
"recurrence_rate": round((total_plays - plays_on_new) / total_plays, 3) if total_plays > 0 else 0,
|
|
"obsession_count": len(obsessions),
|
|
"obsession_rate": round(len(obsessions) / len(current_track_ids), 3) if current_track_ids else 0
|
|
}
|
|
|
|
def compute_explicit_stats(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
|
|
"""
|
|
Analyzes explicit content consumption.
|
|
"""
|
|
query = self.db.query(PlayHistory).options(joinedload(PlayHistory.track)).filter(
|
|
PlayHistory.played_at >= period_start,
|
|
PlayHistory.played_at < period_end
|
|
)
|
|
plays = query.all()
|
|
|
|
if not plays: return {"explicit_rate": 0, "hourly_explicit_rate": []}
|
|
|
|
total_plays = len(plays)
|
|
explicit_count = 0
|
|
hourly_explicit = [0] * 24
|
|
hourly_total = [0] * 24
|
|
|
|
for p in plays:
|
|
h = p.played_at.hour
|
|
hourly_total[h] += 1
|
|
t = p.track
|
|
if t.raw_data and t.raw_data.get("explicit"):
|
|
explicit_count += 1
|
|
hourly_explicit[h] += 1
|
|
|
|
hourly_rates = []
|
|
for i in range(24):
|
|
hourly_rates.append(round(hourly_explicit[i] / hourly_total[i], 2) if hourly_total[i] > 0 else 0.0)
|
|
|
|
return {
|
|
"explicit_rate": round(explicit_count / total_plays, 3),
|
|
"total_explicit_plays": explicit_count,
|
|
"hourly_explicit_distribution": hourly_rates
|
|
}
|
|
|
|
def generate_full_report(self, period_start: datetime, period_end: datetime) -> Dict[str, Any]:
|
|
current_stats = {
|
|
"period": {"start": period_start.isoformat(), "end": period_end.isoformat()},
|
|
"volume": self.compute_volume_stats(period_start, period_end),
|
|
"time_habits": self.compute_time_stats(period_start, period_end),
|
|
"sessions": self.compute_session_stats(period_start, period_end),
|
|
"context": self.compute_context_stats(period_start, period_end),
|
|
"vibe": self.compute_vibe_stats(period_start, period_end),
|
|
"era": self.compute_era_stats(period_start, period_end),
|
|
"taste": self.compute_taste_stats(period_start, period_end),
|
|
"lifecycle": self.compute_lifecycle_stats(period_start, period_end),
|
|
"flags": self.compute_explicit_stats(period_start, period_end),
|
|
"skips": self.compute_skip_stats(period_start, period_end)
|
|
}
|
|
|
|
current_stats["comparison"] = self.compute_comparison(current_stats, period_start, period_end)
|
|
return current_stats
|
|
|
|
def _empty_volume_stats(self):
|
|
return {
|
|
"total_plays": 0, "estimated_minutes": 0, "unique_tracks": 0,
|
|
"unique_artists": 0, "unique_albums": 0, "unique_genres": 0,
|
|
"top_tracks": [], "top_artists": [], "top_albums": [], "top_genres": [],
|
|
"repeat_rate": 0, "one_and_done_rate": 0,
|
|
"concentration": {}
|
|
}
|
|
|
|
def _pct_change(self, curr, prev):
|
|
if prev == 0:
|
|
return 100.0 if curr > 0 else 0.0
|
|
return round(((curr - prev) / prev) * 100, 1)
|