test: add Phase 1a test suite (25 tests)
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- test_evolution_models: 12 tests for EvolutionTrack, EvolutionCardState, EvolutionTierBoost, EvolutionCosmetic, and PlayerSeasonStats models - test_evolution_seed: 7 tests for seed idempotency, thresholds, formulas - test_season_stats_update: 6 tests for batting/pitching aggregation, Decision integration, double-count prevention, multi-game accumulation Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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tests/__init__.py
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tests/__init__.py
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tests/conftest.py
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tests/conftest.py
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"""
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Shared test fixtures for the Paper Dynasty database test suite.
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Uses in-memory SQLite with foreign_keys pragma enabled. Each test
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gets a fresh set of tables via the setup_test_db fixture (autouse).
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All models are bound to the in-memory database before table creation
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so that no connection to the real storage/pd_master.db occurs during
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tests.
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"""
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import os
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import pytest
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from peewee import SqliteDatabase
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# Set DATABASE_TYPE=postgresql so that the module-level SKIP_TABLE_CREATION
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# flag is True. This prevents db_engine.py from calling create_tables()
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# against the real storage/pd_master.db during import — those calls would
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# fail if indexes already exist and would also contaminate the dev database.
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# The PooledPostgresqlDatabase object is created but never actually connects
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# because our fixture rebinds all models to an in-memory SQLite db before
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# any query is executed.
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os.environ["DATABASE_TYPE"] = "postgresql"
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from app.db_engine import (
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Rarity,
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Event,
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Cardset,
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MlbPlayer,
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Player,
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Team,
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PackType,
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Pack,
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Card,
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Roster,
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RosterSlot,
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StratGame,
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StratPlay,
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Decision,
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PlayerSeasonStats,
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EvolutionTrack,
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EvolutionCardState,
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EvolutionTierBoost,
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EvolutionCosmetic,
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ScoutOpportunity,
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ScoutClaim,
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)
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_test_db = SqliteDatabase(":memory:", pragmas={"foreign_keys": 1})
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# All models in dependency order (parents before children) so that
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# create_tables and drop_tables work without FK violations.
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_TEST_MODELS = [
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Rarity,
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Event,
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Cardset,
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MlbPlayer,
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Player,
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Team,
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PackType,
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Pack,
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Card,
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Roster,
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RosterSlot,
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StratGame,
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StratPlay,
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Decision,
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ScoutOpportunity,
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ScoutClaim,
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PlayerSeasonStats,
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EvolutionTrack,
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EvolutionCardState,
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EvolutionTierBoost,
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EvolutionCosmetic,
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]
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@pytest.fixture(autouse=True)
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def setup_test_db():
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"""Bind all models to in-memory SQLite and create tables.
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The fixture is autouse so every test automatically gets a fresh,
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isolated database schema without needing to request it explicitly.
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Tables are dropped in reverse dependency order after each test to
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keep the teardown clean and to catch any accidental FK reference
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direction bugs early.
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"""
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_test_db.bind(_TEST_MODELS)
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_test_db.connect()
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_test_db.create_tables(_TEST_MODELS)
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yield _test_db
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_test_db.drop_tables(list(reversed(_TEST_MODELS)), safe=True)
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_test_db.close()
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# ---------------------------------------------------------------------------
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# Minimal shared fixtures — create just enough data for FK dependencies
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# ---------------------------------------------------------------------------
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@pytest.fixture
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def rarity():
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"""A single Common rarity row used as FK seed for Player rows."""
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return Rarity.create(value=1, name="Common", color="#ffffff")
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@pytest.fixture
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def player(rarity):
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"""A minimal Player row with all required (non-nullable) columns filled.
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Player.p_name is the real column name (not 'name'). All FK and
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non-nullable varchar fields are provided so SQLite's NOT NULL
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constraints are satisfied even with foreign_keys=ON.
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"""
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cardset = Cardset.create(
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name="Test Set",
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description="Test cardset",
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total_cards=100,
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)
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return Player.create(
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p_name="Test Player",
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rarity=rarity,
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cardset=cardset,
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set_num=1,
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pos_1="1B",
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image="https://example.com/image.png",
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mlbclub="TST",
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franchise="TST",
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description="A test player",
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)
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@pytest.fixture
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def team():
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"""A minimal Team row.
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Team uses abbrev/lname/sname/gmid/gmname/gsheet/wallet/team_value/
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collection_value — not the 'name'/'user_id' shorthand described in
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the spec, which referred to the real underlying columns by
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simplified names.
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"""
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return Team.create(
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abbrev="TST",
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sname="Test",
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lname="Test Team",
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gmid=100000001,
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gmname="testuser",
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gsheet="https://docs.google.com/spreadsheets/test",
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wallet=500,
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team_value=1000,
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collection_value=1000,
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season=11,
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is_ai=False,
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)
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@pytest.fixture
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def track():
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"""A minimal EvolutionTrack for batter cards."""
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return EvolutionTrack.create(
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name="Batter Track",
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card_type="batter",
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formula="pa + tb * 2",
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t1_threshold=37,
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t2_threshold=149,
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t3_threshold=448,
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t4_threshold=896,
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)
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332
tests/test_evolution_models.py
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tests/test_evolution_models.py
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"""
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Tests for evolution-related models and PlayerSeasonStats.
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Covers WP-01 acceptance criteria:
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- EvolutionTrack: CRUD and unique-name constraint
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- EvolutionCardState: CRUD, defaults, unique-(player,team) constraint,
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and FK resolution back to EvolutionTrack
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- EvolutionTierBoost: CRUD and unique-(track, tier, boost_type, boost_target)
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- EvolutionCosmetic: CRUD and unique-name constraint
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- PlayerSeasonStats: CRUD with defaults, unique-(player, team, season),
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and in-place stat accumulation
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Each test class is self-contained: fixtures from conftest.py supply the
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minimal parent rows needed to satisfy FK constraints, and every assertion
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targets a single, clearly-named behaviour so failures are easy to trace.
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"""
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import pytest
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from peewee import IntegrityError
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from playhouse.shortcuts import model_to_dict
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from app.db_engine import (
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EvolutionCardState,
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EvolutionCosmetic,
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EvolutionTierBoost,
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EvolutionTrack,
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PlayerSeasonStats,
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)
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# ---------------------------------------------------------------------------
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# EvolutionTrack
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# ---------------------------------------------------------------------------
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class TestEvolutionTrack:
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"""Tests for the EvolutionTrack model.
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EvolutionTrack defines a named progression path (formula +
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tier thresholds) for a card type. The name column carries a
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UNIQUE constraint so that accidental duplicates are caught at
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the database level.
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"""
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def test_create_track(self, track):
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"""Creating a track persists all fields and they round-trip correctly.
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Reads back via model_to_dict (recurse=False) to verify the raw
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column values, not Python-object representations, match what was
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inserted.
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"""
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data = model_to_dict(track, recurse=False)
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assert data["name"] == "Batter Track"
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assert data["card_type"] == "batter"
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assert data["formula"] == "pa + tb * 2"
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assert data["t1_threshold"] == 37
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assert data["t2_threshold"] == 149
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assert data["t3_threshold"] == 448
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assert data["t4_threshold"] == 896
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def test_track_unique_name(self, track):
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"""Inserting a second track with the same name raises IntegrityError.
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The UNIQUE constraint on EvolutionTrack.name must prevent two
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tracks from sharing the same identifier, as the name is used as
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a human-readable key throughout the evolution system.
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"""
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with pytest.raises(IntegrityError):
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EvolutionTrack.create(
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name="Batter Track", # duplicate
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card_type="sp",
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formula="outs * 3",
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t1_threshold=10,
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t2_threshold=40,
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t3_threshold=120,
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t4_threshold=240,
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)
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# ---------------------------------------------------------------------------
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# EvolutionCardState
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# ---------------------------------------------------------------------------
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class TestEvolutionCardState:
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"""Tests for EvolutionCardState, which tracks per-player evolution progress.
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Each row represents one card (player) owned by one team, linked to a
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specific EvolutionTrack. The model records the current tier (0-4),
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accumulated progress value, and whether the card is fully evolved.
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"""
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def test_create_card_state(self, player, team, track):
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"""Creating a card state stores all fields and defaults are correct.
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Defaults under test:
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current_tier → 0 (fresh card, no tier unlocked yet)
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current_value → 0.0 (no formula progress accumulated)
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fully_evolved → False (evolution is not complete at creation)
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last_evaluated_at → None (never evaluated yet)
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"""
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state = EvolutionCardState.create(player=player, team=team, track=track)
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fetched = EvolutionCardState.get_by_id(state.id)
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assert fetched.player_id == player.player_id
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assert fetched.team_id == team.id
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assert fetched.track_id == track.id
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assert fetched.current_tier == 0
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assert fetched.current_value == 0.0
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assert fetched.fully_evolved is False
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assert fetched.last_evaluated_at is None
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def test_card_state_unique_player_team(self, player, team, track):
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"""A second card state for the same (player, team) pair raises IntegrityError.
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The unique index on (player, team) enforces that each player card
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has at most one evolution state per team roster slot, preventing
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duplicate evolution progress rows for the same physical card.
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"""
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EvolutionCardState.create(player=player, team=team, track=track)
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with pytest.raises(IntegrityError):
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EvolutionCardState.create(player=player, team=team, track=track)
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def test_card_state_fk_track(self, player, team, track):
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"""Accessing card_state.track returns the original EvolutionTrack instance.
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This confirms the FK is correctly wired and that Peewee resolves
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the relationship, returning an object with the same primary key and
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name as the track used during creation.
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"""
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state = EvolutionCardState.create(player=player, team=team, track=track)
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fetched = EvolutionCardState.get_by_id(state.id)
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resolved_track = fetched.track
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assert resolved_track.id == track.id
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assert resolved_track.name == "Batter Track"
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# ---------------------------------------------------------------------------
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# EvolutionTierBoost
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# ---------------------------------------------------------------------------
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class TestEvolutionTierBoost:
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"""Tests for EvolutionTierBoost, the per-tier stat/rating bonus table.
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Each row maps a (track, tier) combination to a single boost — the
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specific stat or rating column to buff and by how much. The four-
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column unique constraint prevents double-booking the same boost slot.
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"""
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def test_create_tier_boost(self, track):
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"""Creating a boost row persists all fields accurately.
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Verifies boost_type, boost_target, and boost_value are stored
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and retrieved without modification.
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"""
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boost = EvolutionTierBoost.create(
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track=track,
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tier=1,
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boost_type="rating",
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boost_target="contact_vl",
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boost_value=1.5,
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)
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fetched = EvolutionTierBoost.get_by_id(boost.id)
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assert fetched.track_id == track.id
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assert fetched.tier == 1
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assert fetched.boost_type == "rating"
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assert fetched.boost_target == "contact_vl"
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assert fetched.boost_value == 1.5
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def test_tier_boost_unique_constraint(self, track):
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"""Duplicate (track, tier, boost_type, boost_target) raises IntegrityError.
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The four-column unique index ensures that a single boost slot
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(e.g. Tier-1 contact_vl rating) cannot be defined twice for the
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same track, which would create ambiguity during evolution evaluation.
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"""
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EvolutionTierBoost.create(
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track=track,
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tier=2,
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boost_type="rating",
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boost_target="power_vr",
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boost_value=2.0,
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)
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with pytest.raises(IntegrityError):
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EvolutionTierBoost.create(
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track=track,
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tier=2,
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boost_type="rating",
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boost_target="power_vr",
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boost_value=3.0, # different value, same identity columns
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)
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# ---------------------------------------------------------------------------
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# EvolutionCosmetic
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# ---------------------------------------------------------------------------
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class TestEvolutionCosmetic:
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"""Tests for EvolutionCosmetic, decorative unlocks tied to evolution tiers.
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Cosmetics are purely visual rewards (frames, badges, themes) that a
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card unlocks when it reaches a required tier. The name column is
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the stable identifier and carries a UNIQUE constraint.
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"""
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def test_create_cosmetic(self):
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"""Creating a cosmetic persists all fields correctly.
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Verifies all columns including optional ones (css_class, asset_url)
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are stored and retrieved.
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"""
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cosmetic = EvolutionCosmetic.create(
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name="Gold Frame",
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tier_required=2,
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cosmetic_type="frame",
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css_class="evo-frame-gold",
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asset_url="https://cdn.example.com/frames/gold.png",
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)
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fetched = EvolutionCosmetic.get_by_id(cosmetic.id)
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assert fetched.name == "Gold Frame"
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assert fetched.tier_required == 2
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assert fetched.cosmetic_type == "frame"
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assert fetched.css_class == "evo-frame-gold"
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assert fetched.asset_url == "https://cdn.example.com/frames/gold.png"
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def test_cosmetic_unique_name(self):
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"""Inserting a second cosmetic with the same name raises IntegrityError.
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The UNIQUE constraint on EvolutionCosmetic.name prevents duplicate
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cosmetic definitions that could cause ambiguous tier unlock lookups.
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"""
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EvolutionCosmetic.create(
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name="Silver Badge",
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tier_required=1,
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cosmetic_type="badge",
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)
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with pytest.raises(IntegrityError):
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EvolutionCosmetic.create(
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name="Silver Badge", # duplicate
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tier_required=3,
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cosmetic_type="badge",
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)
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# ---------------------------------------------------------------------------
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# PlayerSeasonStats
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# ---------------------------------------------------------------------------
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class TestPlayerSeasonStats:
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"""Tests for PlayerSeasonStats, the per-season accumulation table.
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Each row aggregates game-by-game batting and pitching stats for one
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player on one team in one season. The three-column unique constraint
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prevents double-counting and ensures a single authoritative row for
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each (player, team, season) combination.
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"""
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def test_create_season_stats(self, player, team):
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"""Creating a stats row with explicit values stores everything correctly.
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Also verifies the integer stat defaults (all 0) for columns that
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are not provided, which is the initial state before any games are
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processed.
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"""
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stats = PlayerSeasonStats.create(
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player=player,
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team=team,
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season=11,
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games_batting=5,
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pa=20,
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ab=18,
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hits=6,
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doubles=1,
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triples=0,
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hr=2,
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bb=2,
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hbp=0,
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so=4,
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rbi=5,
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runs=3,
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sb=1,
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cs=0,
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)
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fetched = PlayerSeasonStats.get_by_id(stats.id)
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assert fetched.player_id == player.player_id
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assert fetched.team_id == team.id
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assert fetched.season == 11
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assert fetched.games_batting == 5
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assert fetched.pa == 20
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assert fetched.hits == 6
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assert fetched.hr == 2
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# Pitching fields were not set — confirm default zero values
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assert fetched.games_pitching == 0
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assert fetched.outs == 0
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assert fetched.wins == 0
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assert fetched.saves == 0
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# Nullable meta fields
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assert fetched.last_game is None
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assert fetched.last_updated_at is None
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def test_season_stats_unique_constraint(self, player, team):
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"""A second row for the same (player, team, season) raises IntegrityError.
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The unique index on these three columns guarantees that each
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player-team-season combination has exactly one accumulation row,
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preventing duplicate stat aggregation that would inflate totals.
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"""
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PlayerSeasonStats.create(player=player, team=team, season=11)
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with pytest.raises(IntegrityError):
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PlayerSeasonStats.create(player=player, team=team, season=11)
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def test_season_stats_increment(self, player, team):
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"""Manually incrementing hits on an existing row persists the change.
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Simulates the common pattern used by the stats accumulator:
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fetch the row, add the game delta, save. Verifies that save()
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writes back to the database and that subsequent reads reflect the
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updated value.
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"""
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stats = PlayerSeasonStats.create(
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player=player,
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||||
team=team,
|
||||
season=11,
|
||||
hits=10,
|
||||
)
|
||||
stats.hits += 3
|
||||
stats.save()
|
||||
|
||||
refreshed = PlayerSeasonStats.get_by_id(stats.id)
|
||||
assert refreshed.hits == 13
|
||||
159
tests/test_evolution_seed.py
Normal file
159
tests/test_evolution_seed.py
Normal file
@ -0,0 +1,159 @@
|
||||
"""
|
||||
Tests for app/seed/evolution_tracks.py — seed_evolution_tracks().
|
||||
|
||||
What: Verify that the JSON-driven seed function correctly creates, counts,
|
||||
and idempotently updates EvolutionTrack rows in the database.
|
||||
|
||||
Why: The seed is the single source of truth for track configuration. A
|
||||
regression here (duplicates, wrong thresholds, missing formula) would
|
||||
silently corrupt evolution scoring for every card in the system.
|
||||
|
||||
Each test operates on a fresh in-memory SQLite database provided by the
|
||||
autouse `setup_test_db` fixture in conftest.py. The seed reads its data
|
||||
from `app/seed/evolution_tracks.json` on disk, so the tests also serve as
|
||||
a light integration check between the JSON file and the Peewee model.
|
||||
"""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from app.db_engine import EvolutionTrack
|
||||
from app.seed.evolution_tracks import seed_evolution_tracks
|
||||
|
||||
# Path to the JSON fixture that the seed reads from at runtime
|
||||
_JSON_PATH = Path(__file__).parent.parent / "app" / "seed" / "evolution_tracks.json"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def json_tracks():
|
||||
"""Load the raw JSON definitions so tests can assert against them.
|
||||
|
||||
This avoids hardcoding expected values — if the JSON changes, tests
|
||||
automatically follow without needing manual updates.
|
||||
"""
|
||||
return json.loads(_JSON_PATH.read_text(encoding="utf-8"))
|
||||
|
||||
|
||||
def test_seed_creates_three_tracks(json_tracks):
|
||||
"""After one seed call, exactly 3 EvolutionTrack rows must exist.
|
||||
|
||||
Why: The JSON currently defines three card-type tracks (batter, sp, rp).
|
||||
If the count is wrong the system would either be missing tracks
|
||||
(evolution disabled for a card type) or have phantom extras.
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
assert EvolutionTrack.select().count() == 3
|
||||
|
||||
|
||||
def test_seed_correct_card_types(json_tracks):
|
||||
"""The set of card_type values persisted must match the JSON exactly.
|
||||
|
||||
Why: card_type is used as a discriminator throughout the evolution engine.
|
||||
An unexpected value (e.g. 'pitcher' instead of 'sp') would cause
|
||||
track-lookup misses and silently skip evolution scoring for that role.
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
expected_types = {d["card_type"] for d in json_tracks}
|
||||
actual_types = {t.card_type for t in EvolutionTrack.select()}
|
||||
assert actual_types == expected_types
|
||||
|
||||
|
||||
def test_seed_thresholds_ascending():
|
||||
"""For every track, t1 < t2 < t3 < t4.
|
||||
|
||||
Why: The evolution engine uses these thresholds to determine tier
|
||||
boundaries. If they are not strictly ascending, tier comparisons
|
||||
would produce incorrect or undefined results (e.g. a player could
|
||||
simultaneously satisfy tier 3 and not satisfy tier 2).
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
for track in EvolutionTrack.select():
|
||||
assert (
|
||||
track.t1_threshold < track.t2_threshold
|
||||
), f"{track.name}: t1 ({track.t1_threshold}) >= t2 ({track.t2_threshold})"
|
||||
assert (
|
||||
track.t2_threshold < track.t3_threshold
|
||||
), f"{track.name}: t2 ({track.t2_threshold}) >= t3 ({track.t3_threshold})"
|
||||
assert (
|
||||
track.t3_threshold < track.t4_threshold
|
||||
), f"{track.name}: t3 ({track.t3_threshold}) >= t4 ({track.t4_threshold})"
|
||||
|
||||
|
||||
def test_seed_thresholds_positive():
|
||||
"""All tier threshold values must be strictly greater than zero.
|
||||
|
||||
Why: A zero or negative threshold would mean a card starts the game
|
||||
already evolved (tier >= 1 at 0 accumulated stat points), which would
|
||||
bypass the entire progression system.
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
for track in EvolutionTrack.select():
|
||||
assert track.t1_threshold > 0, f"{track.name}: t1_threshold is not positive"
|
||||
assert track.t2_threshold > 0, f"{track.name}: t2_threshold is not positive"
|
||||
assert track.t3_threshold > 0, f"{track.name}: t3_threshold is not positive"
|
||||
assert track.t4_threshold > 0, f"{track.name}: t4_threshold is not positive"
|
||||
|
||||
|
||||
def test_seed_formula_present():
|
||||
"""Every persisted track must have a non-empty formula string.
|
||||
|
||||
Why: The formula is evaluated at runtime to compute a player's evolution
|
||||
score. An empty formula would cause either a Python eval error or
|
||||
silently produce 0 for every player, halting all evolution progress.
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
for track in EvolutionTrack.select():
|
||||
assert (
|
||||
track.formula and track.formula.strip()
|
||||
), f"{track.name}: formula is empty or whitespace-only"
|
||||
|
||||
|
||||
def test_seed_idempotent():
|
||||
"""Calling seed_evolution_tracks() twice must still yield exactly 3 rows.
|
||||
|
||||
Why: The seed is designed to be safe to re-run (e.g. as part of a
|
||||
migration or CI bootstrap). If it inserts duplicates on a second call,
|
||||
the unique constraint on EvolutionTrack.name would raise an IntegrityError
|
||||
in PostgreSQL, and in SQLite it would silently create phantom rows that
|
||||
corrupt tier-lookup joins.
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
seed_evolution_tracks()
|
||||
assert EvolutionTrack.select().count() == 3
|
||||
|
||||
|
||||
def test_seed_updates_on_rerun(json_tracks):
|
||||
"""A second seed call must restore any manually changed threshold to the JSON value.
|
||||
|
||||
What: Seed once, manually mutate a threshold in the DB, then seed again.
|
||||
Assert that the threshold is now back to the JSON-defined value.
|
||||
|
||||
Why: The seed must act as the authoritative source of truth. If
|
||||
re-seeding does not overwrite local changes, configuration drift can
|
||||
build up silently and the production database would diverge from the
|
||||
checked-in JSON without any visible error.
|
||||
"""
|
||||
seed_evolution_tracks()
|
||||
|
||||
# Pick the first track and corrupt its t1_threshold
|
||||
first_def = json_tracks[0]
|
||||
track = EvolutionTrack.get(EvolutionTrack.name == first_def["name"])
|
||||
original_t1 = track.t1_threshold
|
||||
corrupted_value = original_t1 + 9999
|
||||
track.t1_threshold = corrupted_value
|
||||
track.save()
|
||||
|
||||
# Confirm the corruption took effect before re-seeding
|
||||
track_check = EvolutionTrack.get(EvolutionTrack.name == first_def["name"])
|
||||
assert track_check.t1_threshold == corrupted_value
|
||||
|
||||
# Re-seed — should restore the JSON value
|
||||
seed_evolution_tracks()
|
||||
|
||||
restored = EvolutionTrack.get(EvolutionTrack.name == first_def["name"])
|
||||
assert restored.t1_threshold == first_def["t1_threshold"], (
|
||||
f"Expected t1_threshold={first_def['t1_threshold']} after re-seed, "
|
||||
f"got {restored.t1_threshold}"
|
||||
)
|
||||
597
tests/test_season_stats_update.py
Normal file
597
tests/test_season_stats_update.py
Normal file
@ -0,0 +1,597 @@
|
||||
"""
|
||||
Tests for app/services/season_stats.py — update_season_stats().
|
||||
|
||||
What: Verify that the incremental stat accumulation function correctly
|
||||
aggregates StratPlay and Decision rows into PlayerSeasonStats, handles
|
||||
duplicate calls idempotently, and accumulates stats across multiple games.
|
||||
|
||||
Why: This is the core bookkeeping engine for card evolution scoring. A
|
||||
double-count bug, a missed Decision merge, or a team-isolation failure
|
||||
would silently produce wrong stats that would then corrupt every
|
||||
evolution tier calculation downstream.
|
||||
|
||||
Test data is created using real Peewee models (no mocking) against the
|
||||
in-memory SQLite database provided by the autouse setup_test_db fixture
|
||||
in conftest.py. All Player and Team creation uses the actual required
|
||||
column set discovered from the model definition in db_engine.py.
|
||||
"""
|
||||
|
||||
import app.services.season_stats as _season_stats_module
|
||||
import pytest
|
||||
|
||||
from app.db_engine import (
|
||||
Cardset,
|
||||
Decision,
|
||||
Player,
|
||||
PlayerSeasonStats,
|
||||
Rarity,
|
||||
StratGame,
|
||||
StratPlay,
|
||||
Team,
|
||||
)
|
||||
from app.services.season_stats import update_season_stats
|
||||
from tests.conftest import _test_db
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Module-level patch: redirect season_stats.db to the test database
|
||||
# ---------------------------------------------------------------------------
|
||||
# season_stats.py holds a module-level reference to the `db` object imported
|
||||
# from db_engine. When test models are rebound to _test_db via bind(), the
|
||||
# `db` object inside season_stats still points at the original production db
|
||||
# (SQLite file or PostgreSQL). We replace it here so that db.atomic() in
|
||||
# update_season_stats() operates on the same in-memory connection that the
|
||||
# test fixtures write to.
|
||||
_season_stats_module.db = _test_db
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helper factories
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _make_cardset():
|
||||
"""Return a reusable Cardset row (or fetch the existing one by name)."""
|
||||
cs, _ = Cardset.get_or_create(
|
||||
name="Test Set",
|
||||
defaults={"description": "Test cardset", "total_cards": 100},
|
||||
)
|
||||
return cs
|
||||
|
||||
|
||||
def _make_rarity():
|
||||
"""Return the Common rarity singleton."""
|
||||
r, _ = Rarity.get_or_create(value=1, name="Common", defaults={"color": "#ffffff"})
|
||||
return r
|
||||
|
||||
|
||||
def _make_player(name: str, pos: str = "1B") -> Player:
|
||||
"""Create a Player row with all required (non-nullable) columns satisfied.
|
||||
|
||||
Why we need this helper: Player has many non-nullable varchar columns
|
||||
(image, mlbclub, franchise, description) and a required FK to Cardset.
|
||||
A single helper keeps test fixtures concise and consistent.
|
||||
"""
|
||||
return Player.create(
|
||||
p_name=name,
|
||||
rarity=_make_rarity(),
|
||||
cardset=_make_cardset(),
|
||||
set_num=1,
|
||||
pos_1=pos,
|
||||
image="https://example.com/image.png",
|
||||
mlbclub="TST",
|
||||
franchise="TST",
|
||||
description=f"Test player: {name}",
|
||||
)
|
||||
|
||||
|
||||
def _make_team(abbrev: str, gmid: int, season: int = 11) -> Team:
|
||||
"""Create a Team row with all required (non-nullable) columns satisfied."""
|
||||
return Team.create(
|
||||
abbrev=abbrev,
|
||||
sname=abbrev,
|
||||
lname=f"Team {abbrev}",
|
||||
gmid=gmid,
|
||||
gmname=f"gm_{abbrev.lower()}",
|
||||
gsheet="https://docs.google.com/spreadsheets/test",
|
||||
wallet=500,
|
||||
team_value=1000,
|
||||
collection_value=1000,
|
||||
season=season,
|
||||
is_ai=False,
|
||||
)
|
||||
|
||||
|
||||
def make_play(game, play_num, batter, batter_team, pitcher, pitcher_team, **stats):
|
||||
"""Create a StratPlay row with sensible defaults for all required fields.
|
||||
|
||||
Why we provide defaults for every stat column: StratPlay has many
|
||||
IntegerField columns with default=0 at the model level, but supplying
|
||||
them explicitly makes it clear what the baseline state of each play is
|
||||
and keeps the helper signature stable if defaults change.
|
||||
"""
|
||||
defaults = dict(
|
||||
on_base_code="000",
|
||||
inning_half="top",
|
||||
inning_num=1,
|
||||
batting_order=1,
|
||||
starting_outs=0,
|
||||
away_score=0,
|
||||
home_score=0,
|
||||
pa=0,
|
||||
ab=0,
|
||||
hit=0,
|
||||
run=0,
|
||||
hr=0,
|
||||
double=0,
|
||||
triple=0,
|
||||
homerun=0,
|
||||
bb=0,
|
||||
so=0,
|
||||
hbp=0,
|
||||
rbi=0,
|
||||
sb=0,
|
||||
cs=0,
|
||||
outs=0,
|
||||
sac=0,
|
||||
ibb=0,
|
||||
gidp=0,
|
||||
bphr=0,
|
||||
bpfo=0,
|
||||
bp1b=0,
|
||||
bplo=0,
|
||||
)
|
||||
defaults.update(stats)
|
||||
return StratPlay.create(
|
||||
game=game,
|
||||
play_num=play_num,
|
||||
batter=batter,
|
||||
batter_team=batter_team,
|
||||
pitcher=pitcher,
|
||||
pitcher_team=pitcher_team,
|
||||
**defaults,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Fixtures
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def rarity():
|
||||
return Rarity.create(value=1, name="Common", color="#ffffff")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def team_a():
|
||||
return _make_team("TMA", gmid=1001)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def team_b():
|
||||
return _make_team("TMB", gmid=1002)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def player_batter(rarity):
|
||||
"""A batter-type player for team A."""
|
||||
return _make_player("Batter One", pos="CF")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def player_pitcher(rarity):
|
||||
"""A pitcher-type player for team B."""
|
||||
return _make_player("Pitcher One", pos="SP")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def game(team_a, team_b):
|
||||
return StratGame.create(
|
||||
season=11,
|
||||
game_type="ranked",
|
||||
away_team=team_a,
|
||||
home_team=team_b,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Tests
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def test_single_game_batting_stats(team_a, team_b, player_batter, player_pitcher, game):
|
||||
"""Batting stat totals from StratPlay rows are correctly accumulated.
|
||||
|
||||
What: Create three plate appearances (2 hits, 1 strikeout, a walk, and a
|
||||
home run) for one batter. After update_season_stats(), the
|
||||
PlayerSeasonStats row should reflect the exact sum of all play fields.
|
||||
|
||||
Why: The core of the batting aggregation pipeline. If any field mapping
|
||||
is wrong (e.g. 'hit' mapped to 'doubles' instead of 'hits'), evolution
|
||||
scoring and leaderboards would silently report incorrect stats.
|
||||
"""
|
||||
# PA 1: single (hit=1, ab=1, pa=1)
|
||||
make_play(
|
||||
game,
|
||||
1,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
hit=1,
|
||||
outs=0,
|
||||
)
|
||||
# PA 2: home run (hit=1, homerun=1, ab=1, pa=1, rbi=1, run=1)
|
||||
make_play(
|
||||
game,
|
||||
2,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
hit=1,
|
||||
homerun=1,
|
||||
rbi=1,
|
||||
run=1,
|
||||
outs=0,
|
||||
)
|
||||
# PA 3: strikeout (ab=1, pa=1, so=1, outs=1)
|
||||
make_play(
|
||||
game,
|
||||
3,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
so=1,
|
||||
outs=1,
|
||||
)
|
||||
# PA 4: walk (pa=1, bb=1)
|
||||
make_play(
|
||||
game,
|
||||
4,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
bb=1,
|
||||
outs=0,
|
||||
)
|
||||
|
||||
result = update_season_stats(game.id)
|
||||
|
||||
assert result["batters_updated"] >= 1
|
||||
stats = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == player_batter,
|
||||
PlayerSeasonStats.team == team_a,
|
||||
PlayerSeasonStats.season == 11,
|
||||
)
|
||||
assert stats.pa == 4
|
||||
assert stats.ab == 3
|
||||
assert stats.hits == 2
|
||||
assert stats.hr == 1
|
||||
assert stats.so == 1
|
||||
assert stats.bb == 1
|
||||
assert stats.rbi == 1
|
||||
assert stats.runs == 1
|
||||
assert stats.games_batting == 1
|
||||
|
||||
|
||||
def test_single_game_pitching_stats(
|
||||
team_a, team_b, player_batter, player_pitcher, game
|
||||
):
|
||||
"""Pitching stat totals (outs, k, hits_allowed, bb_allowed) are correct.
|
||||
|
||||
What: The same plays that create batting stats for the batter are also
|
||||
the source for the pitcher's opposing stats. This test checks that
|
||||
_build_pitching_groups() correctly inverts batter-perspective fields.
|
||||
|
||||
Why: The batter's 'so' becomes the pitcher's 'k', the batter's 'hit'
|
||||
becomes 'hits_allowed', etc. Any transposition in this mapping would
|
||||
corrupt pitcher stats silently.
|
||||
"""
|
||||
# Play 1: strikeout — batter so=1, outs=1
|
||||
make_play(
|
||||
game,
|
||||
1,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
so=1,
|
||||
outs=1,
|
||||
)
|
||||
# Play 2: single — batter hit=1
|
||||
make_play(
|
||||
game,
|
||||
2,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
hit=1,
|
||||
outs=0,
|
||||
)
|
||||
# Play 3: walk — batter bb=1
|
||||
make_play(
|
||||
game,
|
||||
3,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
bb=1,
|
||||
outs=0,
|
||||
)
|
||||
|
||||
update_season_stats(game.id)
|
||||
|
||||
stats = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == player_pitcher,
|
||||
PlayerSeasonStats.team == team_b,
|
||||
PlayerSeasonStats.season == 11,
|
||||
)
|
||||
assert stats.outs == 1 # one strikeout = one out recorded
|
||||
assert stats.k == 1 # batter's so → pitcher's k
|
||||
assert stats.hits_allowed == 1 # batter's hit → pitcher hits_allowed
|
||||
assert stats.bb_allowed == 1 # batter's bb → pitcher bb_allowed
|
||||
assert stats.games_pitching == 1
|
||||
|
||||
|
||||
def test_decision_integration(team_a, team_b, player_batter, player_pitcher, game):
|
||||
"""Decision.win=1 for a pitcher results in wins=1 in PlayerSeasonStats.
|
||||
|
||||
What: Add a single StratPlay to establish the pitcher in pitching_groups,
|
||||
then create a Decision row recording a win. Call update_season_stats()
|
||||
and verify the wins column is 1.
|
||||
|
||||
Why: Decisions are stored in a separate table from StratPlay. If
|
||||
_apply_decisions() fails to merge them (wrong FK lookup, key mismatch),
|
||||
pitchers would always show 0 wins/losses/saves regardless of actual game
|
||||
outcomes, breaking standings and evolution criteria.
|
||||
"""
|
||||
make_play(
|
||||
game,
|
||||
1,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
outs=1,
|
||||
)
|
||||
Decision.create(
|
||||
season=11,
|
||||
game=game,
|
||||
pitcher=player_pitcher,
|
||||
pitcher_team=team_b,
|
||||
win=1,
|
||||
loss=0,
|
||||
is_save=0,
|
||||
hold=0,
|
||||
b_save=0,
|
||||
is_start=True,
|
||||
)
|
||||
|
||||
update_season_stats(game.id)
|
||||
|
||||
stats = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == player_pitcher,
|
||||
PlayerSeasonStats.team == team_b,
|
||||
PlayerSeasonStats.season == 11,
|
||||
)
|
||||
assert stats.wins == 1
|
||||
assert stats.losses == 0
|
||||
|
||||
|
||||
def test_double_count_prevention(team_a, team_b, player_batter, player_pitcher, game):
|
||||
"""Calling update_season_stats() twice for the same game must not double the stats.
|
||||
|
||||
What: Process a game once (pa=3), then call the function again. The
|
||||
second call should detect the already-processed state via the
|
||||
PlayerSeasonStats.last_game FK check and return early with 'skipped'=True.
|
||||
The resulting pa should still be 3, not 6.
|
||||
|
||||
Why: The bot infrastructure may deliver game-complete events more than
|
||||
once (network retries, message replays). Without idempotency, stats
|
||||
would accumulate incorrectly and could not be corrected without a full
|
||||
reset.
|
||||
"""
|
||||
for i in range(3):
|
||||
make_play(
|
||||
game,
|
||||
i + 1,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
outs=1,
|
||||
)
|
||||
|
||||
first_result = update_season_stats(game.id)
|
||||
assert "skipped" not in first_result
|
||||
|
||||
second_result = update_season_stats(game.id)
|
||||
assert second_result.get("skipped") is True
|
||||
assert second_result["batters_updated"] == 0
|
||||
assert second_result["pitchers_updated"] == 0
|
||||
|
||||
stats = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == player_batter,
|
||||
PlayerSeasonStats.team == team_a,
|
||||
PlayerSeasonStats.season == 11,
|
||||
)
|
||||
# Must still be 3, not 6
|
||||
assert stats.pa == 3
|
||||
|
||||
|
||||
def test_two_games_accumulate(team_a, team_b, player_batter, player_pitcher):
|
||||
"""Stats from two separate games are summed in a single PlayerSeasonStats row.
|
||||
|
||||
What: Process game 1 (pa=2) then game 2 (pa=3) for the same batter/team.
|
||||
After both updates the stats row should show pa=5.
|
||||
|
||||
Why: PlayerSeasonStats is a season-long accumulator, not a per-game
|
||||
snapshot. If the upsert logic overwrites instead of increments, a player's
|
||||
stats would always reflect only their most recent game.
|
||||
"""
|
||||
game1 = StratGame.create(
|
||||
season=11, game_type="ranked", away_team=team_a, home_team=team_b
|
||||
)
|
||||
game2 = StratGame.create(
|
||||
season=11, game_type="ranked", away_team=team_a, home_team=team_b
|
||||
)
|
||||
|
||||
# Game 1: 2 plate appearances
|
||||
for i in range(2):
|
||||
make_play(
|
||||
game1,
|
||||
i + 1,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
outs=1,
|
||||
)
|
||||
|
||||
# Game 2: 3 plate appearances
|
||||
for i in range(3):
|
||||
make_play(
|
||||
game2,
|
||||
i + 1,
|
||||
player_batter,
|
||||
team_a,
|
||||
player_pitcher,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
outs=1,
|
||||
)
|
||||
|
||||
update_season_stats(game1.id)
|
||||
update_season_stats(game2.id)
|
||||
|
||||
stats = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == player_batter,
|
||||
PlayerSeasonStats.team == team_a,
|
||||
PlayerSeasonStats.season == 11,
|
||||
)
|
||||
assert stats.pa == 5
|
||||
assert stats.games_batting == 2
|
||||
|
||||
|
||||
def test_two_team_game(team_a, team_b):
|
||||
"""Players from both teams in a game each get their own stats row.
|
||||
|
||||
What: Create a batter+pitcher pair for team A and another pair for team B.
|
||||
In the same game, team A bats against team B's pitcher and vice versa.
|
||||
After update_season_stats(), both batters and both pitchers must have
|
||||
correct, isolated stats rows.
|
||||
|
||||
Why: A key correctness guarantee is that stats are attributed to the
|
||||
correct (player, team) combination. If team attribution is wrong,
|
||||
a player's stats could appear under the wrong franchise or be merged
|
||||
with an opponent's row.
|
||||
"""
|
||||
batter_a = _make_player("Batter A", pos="CF")
|
||||
pitcher_a = _make_player("Pitcher A", pos="SP")
|
||||
batter_b = _make_player("Batter B", pos="CF")
|
||||
pitcher_b = _make_player("Pitcher B", pos="SP")
|
||||
|
||||
game = StratGame.create(
|
||||
season=11, game_type="ranked", away_team=team_a, home_team=team_b
|
||||
)
|
||||
|
||||
# Team A bats against team B's pitcher (away half)
|
||||
make_play(
|
||||
game,
|
||||
1,
|
||||
batter_a,
|
||||
team_a,
|
||||
pitcher_b,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
hit=1,
|
||||
outs=0,
|
||||
inning_half="top",
|
||||
)
|
||||
make_play(
|
||||
game,
|
||||
2,
|
||||
batter_a,
|
||||
team_a,
|
||||
pitcher_b,
|
||||
team_b,
|
||||
pa=1,
|
||||
ab=1,
|
||||
so=1,
|
||||
outs=1,
|
||||
inning_half="top",
|
||||
)
|
||||
|
||||
# Team B bats against team A's pitcher (home half)
|
||||
make_play(
|
||||
game,
|
||||
3,
|
||||
batter_b,
|
||||
team_b,
|
||||
pitcher_a,
|
||||
team_a,
|
||||
pa=1,
|
||||
ab=1,
|
||||
bb=1,
|
||||
outs=0,
|
||||
inning_half="bottom",
|
||||
)
|
||||
|
||||
update_season_stats(game.id)
|
||||
|
||||
# Team A's batter: 2 PA, 1 hit, 1 SO
|
||||
stats_ba = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == batter_a,
|
||||
PlayerSeasonStats.team == team_a,
|
||||
)
|
||||
assert stats_ba.pa == 2
|
||||
assert stats_ba.hits == 1
|
||||
assert stats_ba.so == 1
|
||||
|
||||
# Team B's batter: 1 PA, 1 BB
|
||||
stats_bb = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == batter_b,
|
||||
PlayerSeasonStats.team == team_b,
|
||||
)
|
||||
assert stats_bb.pa == 1
|
||||
assert stats_bb.bb == 1
|
||||
|
||||
# Team B's pitcher (faced team A's batter): 1 hit allowed, 1 K
|
||||
stats_pb = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == pitcher_b,
|
||||
PlayerSeasonStats.team == team_b,
|
||||
)
|
||||
assert stats_pb.hits_allowed == 1
|
||||
assert stats_pb.k == 1
|
||||
|
||||
# Team A's pitcher (faced team B's batter): 1 BB allowed
|
||||
stats_pa = PlayerSeasonStats.get(
|
||||
PlayerSeasonStats.player == pitcher_a,
|
||||
PlayerSeasonStats.team == team_a,
|
||||
)
|
||||
assert stats_pa.bb_allowed == 1
|
||||
Loading…
Reference in New Issue
Block a user