Files
vrobbler/tests/trends_tests/conftest.py
Colin Powell 29fc493cc2 Rewrite fasting trend algorithm and add comprehensive tests
- Replace day-iteration algorithm with pair-based approach for fasting
  detection. Iterate consecutive food scrobble pairs and mark foodless
  days between them as fasting days.

- Handle consecutive-day periodic fasting: when two food scrobbles are on
  consecutive days with a gap >= periodic_threshold but < full_threshold,
  mark the later day as a periodic fast (user's original bug fix).

- Fix _dt test helper to use replace() instead of subtracting hours,
  producing correct timestamps (e.g., '3 days ago at 18:46' instead of
  '3 days + 18h46m ago').

- Fix total_days off-by-one: removed +1 from inclusive day count so a
  'last_30' period correctly reports 30 days.

- Add _drinks_on_day() for per-day drink detection (replaces whole-
  window _drinks_in_window for more accurate liquid fasting detection).

- Fix Beer test fixture to use styles M2M field instead of non-existent
  beer_style attribute.

- Add 26 test cases covering: classification, short gaps, periodic/
  full/liquid fasting, multi-day gaps, vacation exemptions, custom
  thresholds, streaks, mixed types, edge cases.

Reasoning:
The original day-iteration algorithm had fundamental issues: it marked
days with food as fasting (since it only checked prev/next food times),
it didn't filter out normal (< periodic) gaps, and the _dt helper was
misinterpreting hour parameters. The new pair-based approach is cleaner:
for each consecutive food scrobble pair, we mark the foodless days
between them. This naturally handles multi-day fasts and correctly
avoids marking food-eating days as fasting. The consecutive-day periodic
rule preserves the user's original intent of detecting overnight fasts
between meals.
2026-07-19 13:11:29 -04:00

78 lines
1.9 KiB
Python

import pytest
from django.contrib.auth import get_user_model
from django.utils import timezone
from foods.models import Food
from profiles.models import UserProfile
from scrobbles.models import Scrobble
User = get_user_model()
@pytest.fixture
def user(db):
u = User.objects.create_user(
username="fasting", email="fasting@example.com", password="testpass"
)
UserProfile.objects.get_or_create(user=u)
return u
@pytest.fixture
def user_with_custom_thresholds(db):
u = User.objects.create_user(
username="custom", email="custom@example.com", password="testpass"
)
profile, _ = UserProfile.objects.get_or_create(user=u)
profile.fasting_periodic_hours = 12
profile.fasting_full_hours = 20
profile.save()
return u
@pytest.fixture
def food(user):
return Food.objects.create(title="Test Food", base_run_time_seconds=1200)
@pytest.fixture
def drink(user):
from drinks.models import Drink
return Drink.objects.create(
title="Test Drink", base_run_time_seconds=120, calories=50
)
@pytest.fixture
def beer(user):
from drinks.models import Beer, BeerStyle
style = BeerStyle.objects.create(name="IPA")
b = Beer.objects.create(title="Test Beer", base_run_time_seconds=120)
b.styles.add(style)
return b
def make_food_scrobble(user, food, ts):
return Scrobble.objects.create(
user=user,
food=food,
media_type=Scrobble.MediaType.FOOD,
timestamp=ts,
played_to_completion=True,
)
def make_drink_scrobble(user, drink, ts, media_type=Scrobble.MediaType.DRINK):
kwargs = {"drink": drink} if media_type == Scrobble.MediaType.DRINK else {}
if media_type == Scrobble.MediaType.BEER:
kwargs = {"beer": drink}
return Scrobble.objects.create(
user=user,
media_type=media_type,
timestamp=ts,
played_to_completion=True,
**kwargs,
)