86 lines
2.1 KiB
Python
86 lines
2.1 KiB
Python
import logging
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from datetime import timedelta
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from django.utils import timezone
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from trends.models import PERIOD_CHOICES, TrendResult
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logger = logging.getLogger(__name__)
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PERIOD_DAYS = {
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"last_30": 30,
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"last_90": 90,
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"last_year": 365,
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}
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PERIOD_LABELS = dict(PERIOD_CHOICES)
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TIME_BOUND_TRENDS = {
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"activity-distribution",
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"concurrent-reading",
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"concurrent-listening",
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"mood-by-time",
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"mood-distribution",
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"mood-streaks",
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"mood-trajectory",
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"mood-weather",
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"peak-hours",
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"reading-pace-vs-activity",
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"time-of-day-categories",
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"trending-up",
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"weekly-rhythm",
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}
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TREND_PERIOD_OVERRIDES = {
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"trending-up": ["last_30", "last_90", "last_year"],
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}
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def get_supported_periods(trend_slug):
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if trend_slug in TREND_PERIOD_OVERRIDES:
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slugs = TREND_PERIOD_OVERRIDES[trend_slug]
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return {s: PERIOD_LABELS[s] for s in slugs}
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return dict(PERIOD_LABELS)
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def get_period_days(period):
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return PERIOD_DAYS.get(period)
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def get_date_range(period):
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days = get_period_days(period)
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if days is None:
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return None, None
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now = timezone.now()
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return now - timedelta(days=days), now
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def get_period_nav(current_period, trend_slug):
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supported = get_supported_periods(trend_slug)
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keys = list(supported.keys())
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try:
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idx = keys.index(current_period)
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except ValueError:
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return None, None
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prev_period = keys[idx - 1] if idx > 0 else None
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next_period = keys[idx + 1] if idx < len(keys) - 1 else None
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return prev_period, next_period
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def compute_and_save_trend(user, slug, period="last_30"):
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"""Compute a single trend for a given period and persist the result.
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Returns elapsed seconds on success, raises on failure.
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"""
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from trends.trends import TREND_REGISTRY
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fn = TREND_REGISTRY[slug]
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start = timezone.now()
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data = fn(user, period=period)
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TrendResult.objects.update_or_create(
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user=user,
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trend_slug=slug,
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period=period,
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defaults={"data": data, "computed_at": timezone.now()},
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)
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return (timezone.now() - start).total_seconds()
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