2569718930@qq.com
2ee00f8016
AI 解读字段扩展:
- 新增 taf_read_zh/en：解读机场预报中影响今日峰值窗口的变化
- 新增 probability_read_zh/en：描述概率分布形态（最高桶、偏左/偏右）
- stream max_tokens 900→1200 容纳新输出字段
- 缓存 key 简化为 METAR原文+观测时间，大幅提升命中率
- 兜底函数补全 TAF 和概率字段的确定性生成
异常检测:
- 纯数学计算，零 AI 延迟：实测温度 vs 全部模型预测上下限
- 三级告警：breakout_above / breakout_below / deviation
市场概览:
- 新增 POST /api/scan/terminal/overview（MiMo 批量解读，缓存10分钟）
- 前端 MarketOverviewBanner 可折叠横幅（顶栏与标签栏之间）
- 移动端适配 640px/768px 断点，暗色/亮色双主题
Scope-risk: MEDIUM — 170 测试通过，TypeScript 零错误，ruff 零告警
Tested: python -m pytest -q (170 passed), npx tsc --noEmit (0 errors), ruff check .
903 lines
33 KiB
Python
903 lines
33 KiB
Python
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from fastapi.testclient import TestClient
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from web.app import app
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import web.routes as routes
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import web.scan_terminal_cache as scan_terminal_cache
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import web.scan_terminal_service as scan_terminal_service
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from web.scan_terminal_cache import scan_terminal_cache_key
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from src.database.db_manager import DBManager
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from src.database.runtime_state import TruthRecordRepository, TrainingFeatureRecordRepository
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client = TestClient(app)
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def test_healthz_returns_ok_shape():
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response = client.get('/healthz')
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assert response.status_code == 200
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payload = response.json()
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assert payload['status'] in {'ok', 'degraded'}
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assert 'db' in payload
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assert 'state_storage_mode' in payload
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assert 'cities_count' in payload
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def test_system_status_returns_summary_shape():
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response = client.get('/api/system/status')
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assert response.status_code == 200
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payload = response.json()
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assert 'db' in payload
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assert 'state_storage_mode' in payload
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assert 'features' in payload
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assert 'integrations' in payload
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assert 'cache' in payload
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assert 'analysis' in payload['cache']
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assert 'probability' in payload
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assert 'rollout' in payload['probability']
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assert payload['probability']['rollout']['decision']['decision'] in {'hold', 'observe', 'promote'}
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assert 'training_data' in payload
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assert 'station_networks' in payload
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assert 'prewarm' in payload
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assert 'truth_records' in payload['training_data']
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assert 'training_features' in payload['training_data']
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assert 'city_coverage' in payload['training_data']
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assert 'model_city_coverage' in payload['training_data']
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assert 'artifacts' in payload['training_data']
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assert 'metar_entries' in payload['cache']
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assert 'nmc_entries' in payload['cache']
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assert 'runtime' in payload['prewarm']
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assert 'last_summary_ok' in payload['prewarm']['runtime']
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assert 'cities_count' in payload
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def test_system_status_reads_shared_prewarm_runtime(monkeypatch):
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monkeypatch.setenv("POLYWEATHER_DASHBOARD_PREWARM_ENABLED", "true")
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monkeypatch.setenv("POLYWEATHER_PREWARM_INTERVAL_SEC", "300")
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monkeypatch.setenv("POLYWEATHER_PREWARM_JITTER_SEC", "20")
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DBManager().set_payment_runtime_state(
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"dashboard_prewarm",
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{
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"cycle_count": 3,
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"success_count": 3,
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"failure_count": 0,
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"last_started_at": "2026-04-08T12:00:00",
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"last_finished_at": "2026-04-08T12:00:05",
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"last_duration_sec": 5.0,
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"last_success": True,
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"last_http_status": 200,
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"last_error": None,
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"last_requested_cities": ["shanghai", "beijing"],
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"last_requested_city_count": 2,
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"last_include_detail": True,
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"last_include_market": True,
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"last_force_refresh": False,
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"last_warmed_count": 2,
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"last_summary_ok": 2,
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"last_detail_ok": 2,
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"last_market_ok": 2,
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"last_failed_count": 0,
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"last_heartbeat_ts": __import__("time").time(),
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"writer_mode": "standalone_process",
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"writer_pid": 12345,
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"writer_thread_name": "MainThread",
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},
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)
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response = client.get('/api/system/status')
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assert response.status_code == 200
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payload = response.json()
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assert payload["prewarm"]["enabled"] is True
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assert payload["prewarm"]["thread_alive"] is True
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assert payload["prewarm"]["runtime"]["cycle_count"] >= 3
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assert payload["prewarm"]["runtime"]["last_summary_ok"] == 2
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def test_metrics_endpoint_returns_prometheus_payload():
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response = client.get('/metrics')
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assert response.status_code == 200
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assert 'polyweather_http_requests_total' in response.text
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def test_city_ai_fallback_reasoning_identifies_fast_evidence_mode():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "Tokyo",
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"temp_symbol": "°C",
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"deb": {"prediction": 17.8},
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"model_cluster": {
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"sources": [
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{"value": 17.0},
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{"value": 17.8},
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{"value": 20.6},
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]
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},
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "RJTT",
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},
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"airport_current": {
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"station_code": "RJTT",
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"temp": 16.0,
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"report_time": "21:30Z",
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"raw_metar": "RJTT 262130Z AUTO 00000KT 9999 FEW030 16/10 Q1015",
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},
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},
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locale="zh-CN",
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reason="preview",
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)
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assert "当前为快速证据模式" in payload["reasoning_zh"]
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assert "完整 AI 机场报文解读返回后再合并" in payload["reasoning_zh"]
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assert "AI 机场报文解读正常" not in payload["reasoning_zh"]
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assert "后补" not in payload["reasoning_zh"]
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assert "AI 增强可作为后续补充" not in payload["reasoning_zh"]
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def test_city_ai_fallback_revises_up_when_latest_metar_breaks_above_models():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "Manila",
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"temp_symbol": "°C",
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"deb": {"prediction": 34.0},
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"model_cluster": {
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"sources": [
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{"value": 32.5},
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{"value": 33.8},
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{"value": 34.0},
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{"value": 34.7},
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]
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},
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "RPLL",
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},
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"airport_current": {
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"station_code": "RPLL",
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"temp": 35.0,
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"report_time": "03:00Z / 当地 11:00",
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"raw_metar": "RPLL 270300Z 34004KT CAVOK 35/24 Q1009",
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},
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},
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locale="zh-CN",
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reason="stream preview",
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)
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assert payload["predicted_max"] == 35.0
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assert payload["range_high"] == 35.0
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assert "高于原先 34.0°C 中枢" in payload["final_judgment_zh"]
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assert "上修到至少 35.0°C" in payload["final_judgment_zh"]
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assert "共同支撑本轮最高温中枢" not in payload["reasoning_zh"]
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assert "超过模型上沿 34.7°C" in payload["reasoning_zh"]
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assert "继续上修最高温中枢" in payload["risks_zh"][0]
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def test_city_ai_fallback_revises_down_after_peak_when_observed_high_lags():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "London",
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"temp_symbol": "°C",
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"deb": {"prediction": 30.0},
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"model_cluster": {
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"sources": [
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{"value": 29.2},
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{"value": 30.0},
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{"value": 31.1},
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]
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},
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"window_phase": "post_peak",
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"peak_window_label": "14:00-16:59",
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "EGLL",
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},
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"airport_current": {
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"station_code": "EGLL",
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"temp": 27.0,
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"max_so_far": 27.5,
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"report_time": "16:30Z",
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"raw_metar": "EGLL 271630Z 22008KT 9999 SCT030 27/15 Q1012",
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},
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},
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locale="zh-CN",
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reason="stream preview",
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)
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assert payload["predicted_max"] == 27.5
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assert "峰值窗口（14:00-16:59）已过或接近结束" in payload["final_judgment_zh"]
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assert "最高温中枢需先下修到 27.5°C" in payload["final_judgment_zh"]
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assert "共同支撑本轮最高温中枢" not in payload["reasoning_zh"]
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assert "下修压力" in payload["reasoning_zh"]
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assert "继续下修最高温中枢" in payload["risks_zh"][0]
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def test_city_ai_fallback_does_not_downrevise_before_peak_window():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "Dubai",
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"temp_symbol": "°C",
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"deb": {"prediction": 41.0},
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"model_cluster": {
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"sources": [
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{"value": 40.5},
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{"value": 41.0},
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{"value": 41.6},
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]
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},
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"window_phase": "early_today",
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"minutes_until_peak_start": 240,
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"peak_window_label": "14:00-16:59",
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "OMDB",
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},
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"airport_current": {
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"station_code": "OMDB",
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"temp": 35.0,
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"report_time": "08:00Z",
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"raw_metar": "OMDB 270800Z 29007KT CAVOK 35/20 Q1008",
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},
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},
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locale="zh-CN",
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reason="stream preview",
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)
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assert payload["predicted_max"] == 41.0
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assert "暂不直接下修" in payload["reasoning_zh"]
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assert "峰值窗口尚未到来" in payload["reasoning_zh"]
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assert "若峰值窗口前继续偏低，需要下修最高温中枢" in payload["risks_zh"][0]
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def test_city_ai_fallback_marks_peak_window_passed_without_waiting_for_warming():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "Paris",
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"temp_symbol": "°C",
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"deb": {"prediction": 28.0},
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"model_cluster": {
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"sources": [
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{"value": 27.6},
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{"value": 28.0},
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{"value": 28.5},
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]
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},
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"window_phase": "post_peak",
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"peak_window_label": "13:00-15:59",
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "LFPG",
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},
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"airport_current": {
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"station_code": "LFPG",
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"temp": 27.0,
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"max_so_far": 27.2,
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"report_time": "17:00Z",
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"raw_metar": "LFPG 271700Z 25006KT 9999 FEW035 27/13 Q1014",
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},
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},
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locale="zh-CN",
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reason="stream preview",
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)
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assert "峰值窗口（13:00-15:59）已过" in payload["final_judgment_zh"]
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assert "不是继续按待升温路径解读" in payload["reasoning_zh"]
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assert "避免继续上调最高温中枢" in payload["risks_zh"][0]
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def test_city_ai_fallback_treats_stale_metar_as_background_not_anchor():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "Manila",
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"temp_symbol": "°C",
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"deb": {"prediction": 34.0},
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"model_cluster": {
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"sources": [
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{"value": 33.5},
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{"value": 34.0},
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{"value": 34.4},
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]
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},
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"metar_context": {
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"stale_for_today": True,
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"last_observation_time": "00:00Z",
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},
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "RPLL",
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},
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"airport_current": {
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"station_code": "RPLL",
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"temp": 36.0,
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"report_time": "00:00Z",
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"raw_metar": "RPLL 270000Z 34004KT CAVOK 36/24 Q1009",
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},
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},
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locale="zh-CN",
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reason="stream preview",
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)
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assert payload["predicted_max"] == 34.0
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assert "过旧" in payload["metar_read_zh"]
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assert "不能作为强实况锚点" in payload["metar_read_zh"]
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assert "先以 DEB 和多模型路径为主" in payload["final_judgment_zh"]
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assert "不能作为强实况锚点" in payload["reasoning_zh"]
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assert "上修到至少 36.0°C" not in payload["final_judgment_zh"]
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def test_city_ai_cache_key_changes_when_observation_fingerprint_changes():
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"""METAR 原文不变 → 缓存 key 不变（命中）；METAR 原文变了 → key 变化（miss）。"""
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base_input = {
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"city": "Manila",
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"local_date": "2026-04-28",
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"observation_anchor": {
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"source": "METAR",
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"is_airport_metar": True,
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"station_code": "RPLL",
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},
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"airport_current": {
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"obs_time": "03:00Z",
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"raw_metar": "RPLL 280300Z 34004KT CAVOK 34/24 Q1009",
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},
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"metar_context": {
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"stale_for_today": False,
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"last_observation_time": "03:00Z",
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},
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}
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changed_input = {
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**base_input,
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"airport_current": {
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**base_input["airport_current"],
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"raw_metar": "RPLL 280330Z 36006KT 9999 FEW020 33/25 Q1010",
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"obs_time": "03:30Z",
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},
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}
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assert scan_terminal_service._scan_city_ai_cache_key(base_input) != scan_terminal_service._scan_city_ai_cache_key(changed_input)
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def test_city_ai_stream_request_only_asks_provider_for_observation_read():
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request_payload = scan_terminal_service._build_city_ai_stream_request(
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{
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"city": "Tokyo",
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"city_display_name": "Tokyo",
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"temp_symbol": "°C",
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"deb": {"prediction": 17.8},
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"model_cluster": {"sources": [{"value": 17.0}, {"value": 17.8}]},
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"observation_anchor": {
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"is_airport_metar": True,
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"read_label_zh": "机场报文解读",
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},
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"airport_current": {
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"station_code": "RJTT",
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"temp": 16.0,
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"report_time": "21:30Z",
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"raw_metar": "RJTT 262130Z AUTO 00000KT 9999 FEW030 16/10 Q1015",
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},
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},
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locale="zh-CN",
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)
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user_payload = request_payload["messages"][1]["content"]
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assert request_payload["stream"] is True
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assert request_payload["max_tokens"] <= 1200
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assert request_payload["max_tokens"] < scan_terminal_service.SCAN_AI_MAX_TOKENS
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assert "taf_read_zh" in user_payload
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assert "probability_read_zh" in user_payload
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assert "predicted_max" in user_payload
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assert "final_judgment" in user_payload
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def test_city_ai_partial_json_trims_dangling_taf_clause():
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payload = scan_terminal_service._build_city_ai_fallback(
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{
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"city_display_name": "London",
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"temp_symbol": "°C",
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"deb": {"prediction": 24.3},
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"model_cluster": {
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"sources": [
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{"value": 22.3},
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{"value": 23.1},
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{"value": 24.3},
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{"value": 26.3},
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]
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},
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"observation_anchor": {
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"is_airport_metar": True,
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"station_code": "EGLL",
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},
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"airport_current": {
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"station_code": "EGLL",
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"temp": 21.0,
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"report_time": "09:00Z",
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"raw_metar": "EGLL 270900Z 34004KT CAVOK 21/09 Q1016",
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},
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},
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locale="zh-CN",
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reason="AI content is not a JSON object",
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raw_content=(
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'{"metar_read_zh":"最新METAR报文09:00观测温度21°C，西北风4节（340°），'
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'CAVOK（能见度良好，无重要云）。当前西北风弱，趋向增温但影响有限；'
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'TAF预示10-11点转南风（18012KT），南风可能带来凉爽海风抑制升温。",'
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'"reasoning_zh":"DEB预测24.3°C，多数模型集中在23-26°C，'
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'当前09时实测21°C处于快速升温路径，但TAF显示'
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),
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)
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assert "但TAF显示" not in payload["reasoning_zh"]
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assert payload["reasoning_zh"].endswith("。")
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assert "当前09时实测21°C处于快速升温路径" in payload["reasoning_zh"]
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def test_city_ai_schema_completion_trims_dangling_taf_clause():
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||
payload = scan_terminal_service._complete_city_ai_payload(
|
||
{
|
||
"predicted_max": 24.3,
|
||
"range_low": 22.3,
|
||
"range_high": 26.3,
|
||
"unit": "°C",
|
||
"confidence": "medium",
|
||
"final_judgment_zh": "London 最高温中枢暂看24°C附近。",
|
||
"final_judgment_en": "London high is centered near 24°C.",
|
||
"metar_read_zh": "最新METAR报文09:00观测温度21°C，西北风4节，CAVOK。",
|
||
"metar_read_en": "The latest METAR shows 21°C at 09:00 with northwesterly wind and CAVOK.",
|
||
"reasoning_zh": "当前09时实测21°C处于快速升温路径，但TAF显示",
|
||
"reasoning_en": "The 09:00 observation is on a fast warming path, but TAF shows",
|
||
"risks_zh": ["后续METAR若升温放缓，需要下修。"],
|
||
"risks_en": ["If later METAR warming slows, revise lower."],
|
||
"model_cluster_note_zh": "4/4 个模型落在 DEB ±2°C 内。",
|
||
"model_cluster_note_en": "4/4 models sit within 2°C of DEB.",
|
||
},
|
||
{
|
||
"city_display_name": "London",
|
||
"temp_symbol": "°C",
|
||
"deb": {"prediction": 24.3},
|
||
"model_cluster": {"sources": [{"value": 22.3}, {"value": 26.3}]},
|
||
"observation_anchor": {"is_airport_metar": True, "station_code": "EGLL"},
|
||
"airport_current": {
|
||
"station_code": "EGLL",
|
||
"temp": 21.0,
|
||
"report_time": "09:00Z",
|
||
"raw_metar": "EGLL 270900Z 34004KT CAVOK 21/09 Q1016",
|
||
},
|
||
},
|
||
locale="zh-CN",
|
||
)
|
||
|
||
assert payload["reasoning_zh"] == "当前09时实测21°C处于快速升温路径。"
|
||
assert payload["reasoning_en"] == "The 09:00 observation is on a fast warming path."
|
||
assert payload["_polyweather_meta"]["trimmed_incomplete_fields"] == [
|
||
"reasoning_en",
|
||
"reasoning_zh",
|
||
]
|
||
|
||
|
||
def test_city_ai_schema_completion_guards_stale_observation_text():
|
||
payload = scan_terminal_service._complete_city_ai_payload(
|
||
{
|
||
"predicted_max": 36.0,
|
||
"range_low": 35.0,
|
||
"range_high": 37.0,
|
||
"unit": "°C",
|
||
"confidence": "medium",
|
||
"final_judgment_zh": "Manila 最新 METAR 已经支撑 36°C 高温中枢。",
|
||
"final_judgment_en": "Manila latest METAR supports a 36°C high center.",
|
||
"metar_read_zh": "RPLL 最新 METAR 显示 36°C，当前作为强实况锚点。",
|
||
"metar_read_en": "RPLL latest METAR shows 36°C and is a strong live anchor.",
|
||
"reasoning_zh": "最新 METAR 与模型共同支撑上修。",
|
||
"reasoning_en": "Latest METAR and models jointly support an upward revision.",
|
||
"risks_zh": ["若继续升温，需要上修。"],
|
||
"risks_en": ["If it keeps warming, revise upward."],
|
||
"model_cluster_note_zh": "3/3 个模型集中。",
|
||
"model_cluster_note_en": "3/3 models are clustered.",
|
||
},
|
||
{
|
||
"city_display_name": "Manila",
|
||
"temp_symbol": "°C",
|
||
"deb": {"prediction": 34.0},
|
||
"model_cluster": {
|
||
"sources": [{"value": 33.5}, {"value": 34.0}, {"value": 34.4}]
|
||
},
|
||
"metar_context": {
|
||
"stale_for_today": True,
|
||
"last_observation_time": "00:00Z",
|
||
},
|
||
"observation_anchor": {"is_airport_metar": True, "station_code": "RPLL"},
|
||
"airport_current": {
|
||
"station_code": "RPLL",
|
||
"temp": 36.0,
|
||
"report_time": "00:00Z",
|
||
"raw_metar": "RPLL 270000Z 34004KT CAVOK 36/24 Q1009",
|
||
},
|
||
},
|
||
locale="zh-CN",
|
||
)
|
||
|
||
assert payload["predicted_max"] == 34.0
|
||
assert "过旧" in payload["metar_read_zh"]
|
||
assert "不能作为强实况锚点" in payload["metar_read_zh"]
|
||
assert "先以 DEB 和多模型路径为主" in payload["final_judgment_zh"]
|
||
assert "共同支撑上修" not in payload["reasoning_zh"]
|
||
assert "deterministic_guard_fields" in payload["_polyweather_meta"]
|
||
|
||
|
||
def test_city_ai_schema_completion_guards_observed_high_break_numbers():
|
||
payload = scan_terminal_service._complete_city_ai_payload(
|
||
{
|
||
"predicted_max": 34.0,
|
||
"range_low": 32.5,
|
||
"range_high": 34.7,
|
||
"unit": "°C",
|
||
"confidence": "medium",
|
||
"final_judgment_zh": "Manila 最高温仍以 34.0°C 为中枢。",
|
||
"final_judgment_en": "Manila high remains centered near 34.0°C.",
|
||
"metar_read_zh": "RPLL 最新 METAR 显示 35°C，CAVOK。",
|
||
"metar_read_en": "RPLL latest METAR shows 35°C and CAVOK.",
|
||
"reasoning_zh": "模型区间仍覆盖当前路径，无需上修。",
|
||
"reasoning_en": "The model range still covers the path, so no upward revision is needed.",
|
||
"risks_zh": ["后续报文偏离再修正。"],
|
||
"risks_en": ["Revise if later reports diverge."],
|
||
"model_cluster_note_zh": "4/4 个模型集中。",
|
||
"model_cluster_note_en": "4/4 models are clustered.",
|
||
},
|
||
{
|
||
"city_display_name": "Manila",
|
||
"temp_symbol": "°C",
|
||
"deb": {"prediction": 34.0},
|
||
"model_cluster": {
|
||
"sources": [
|
||
{"value": 32.5},
|
||
{"value": 33.8},
|
||
{"value": 34.0},
|
||
{"value": 34.7},
|
||
]
|
||
},
|
||
"observation_anchor": {"is_airport_metar": True, "station_code": "RPLL"},
|
||
"airport_current": {
|
||
"station_code": "RPLL",
|
||
"temp": 35.0,
|
||
"report_time": "03:00Z / 当地 11:00",
|
||
"raw_metar": "RPLL 270300Z 34004KT CAVOK 35/24 Q1009",
|
||
},
|
||
},
|
||
locale="zh-CN",
|
||
)
|
||
|
||
assert payload["predicted_max"] == 35.0
|
||
assert payload["range_high"] == 35.0
|
||
assert "上修到至少 35.0°C" in payload["final_judgment_zh"]
|
||
assert "无需上修" not in payload["reasoning_zh"]
|
||
assert "deterministic_guard_fields" in payload["_polyweather_meta"]
|
||
|
||
|
||
def test_cities_endpoint_uses_denver_display_name_for_aurora_market():
|
||
response = client.get("/api/cities")
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
denver = next(item for item in payload["cities"] if item["name"] == "denver")
|
||
assert denver["display_name"] == "Denver"
|
||
assert denver["network_provider"] == "global_metar"
|
||
assert denver["deb_recent_tier"] in {"high", "medium", "low", "other"}
|
||
assert "deb_recent_sample_count" in denver
|
||
|
||
|
||
def test_cities_endpoint_includes_new_wunderground_cities():
|
||
response = client.get("/api/cities")
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
names = {item["name"] for item in payload["cities"]}
|
||
assert {
|
||
"busan",
|
||
"qingdao",
|
||
"panama city",
|
||
"kuala lumpur",
|
||
"jakarta",
|
||
"helsinki",
|
||
"amsterdam",
|
||
}.issubset(names)
|
||
|
||
|
||
def test_payment_runtime_endpoint_returns_shape():
|
||
response = client.get('/api/payments/runtime')
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
assert 'checkout' in payload
|
||
assert 'rpc' in payload
|
||
assert 'event_loop_state' in payload
|
||
assert 'recent_audit_events' in payload
|
||
|
||
|
||
def test_auth_me_does_not_reconcile_on_status_probe(monkeypatch):
|
||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||
|
||
def _bind_identity(request):
|
||
request.state.auth_user_id = "user-1"
|
||
request.state.auth_email = "user@example.com"
|
||
|
||
monkeypatch.setattr(routes, "_bind_optional_supabase_identity", _bind_identity)
|
||
monkeypatch.setattr(routes, "_resolve_auth_points", lambda request: 0)
|
||
monkeypatch.setattr(routes, "_resolve_weekly_profile", lambda request: {"weekly_points": 0, "weekly_rank": None})
|
||
monkeypatch.setattr(routes.SUPABASE_ENTITLEMENT, "enabled", True)
|
||
|
||
calls = {"count": 0}
|
||
reconcile_calls = {"count": 0}
|
||
|
||
def _latest_subscription(user_id, respect_requirement=False):
|
||
calls["count"] += 1
|
||
return {
|
||
"plan_code": "pro_monthly",
|
||
"starts_at": "2026-03-22T00:00:00+00:00",
|
||
"expires_at": "2026-04-21T00:00:00+00:00",
|
||
}
|
||
|
||
monkeypatch.setattr(
|
||
routes.SUPABASE_ENTITLEMENT,
|
||
"get_latest_active_subscription",
|
||
_latest_subscription,
|
||
)
|
||
monkeypatch.setattr(routes.PAYMENT_CHECKOUT, "enabled", True)
|
||
|
||
def _reconcile_latest_intent(user_id):
|
||
reconcile_calls["count"] += 1
|
||
return {"ok": True, "action": "reconciled_confirmed_intent"}
|
||
|
||
monkeypatch.setattr(
|
||
routes.PAYMENT_CHECKOUT,
|
||
"reconcile_latest_intent",
|
||
_reconcile_latest_intent,
|
||
)
|
||
|
||
response = client.get("/api/auth/me")
|
||
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
assert payload["subscription_active"] is True
|
||
assert payload["subscription_plan_code"] == "pro_monthly"
|
||
assert reconcile_calls["count"] == 0
|
||
|
||
|
||
def test_ops_memberships_prefers_supabase_auth_email(monkeypatch):
|
||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||
monkeypatch.setattr(routes, "_require_ops_admin", lambda request: None)
|
||
monkeypatch.setattr(routes.PAYMENT_CHECKOUT, "enabled", False)
|
||
|
||
class _FakeDB:
|
||
@staticmethod
|
||
def get_users_by_supabase_user_ids(user_ids):
|
||
return {
|
||
"user-1": {
|
||
"supabase_email": "stale@example.com",
|
||
"username": "tester",
|
||
"telegram_id": 1,
|
||
"created_at": "2026-03-01T00:00:00+00:00",
|
||
}
|
||
}
|
||
|
||
import src.database.db_manager as db_module
|
||
|
||
monkeypatch.setattr(db_module, "DBManager", lambda: _FakeDB())
|
||
monkeypatch.setattr(
|
||
routes.SUPABASE_ENTITLEMENT,
|
||
"list_active_subscriptions",
|
||
lambda limit=200: [
|
||
{
|
||
"user_id": "user-1",
|
||
"plan_code": "pro_monthly",
|
||
"starts_at": "2026-03-22T00:00:00+00:00",
|
||
"expires_at": "2026-04-21T00:00:00+00:00",
|
||
}
|
||
],
|
||
)
|
||
monkeypatch.setattr(
|
||
routes.SUPABASE_ENTITLEMENT,
|
||
"get_auth_users",
|
||
lambda user_ids: {
|
||
"user-1": {
|
||
"email": "fresh@example.com",
|
||
"created_at": "2026-03-02T00:00:00+00:00",
|
||
}
|
||
},
|
||
)
|
||
response = client.get("/api/ops/memberships")
|
||
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
assert payload["memberships"][0]["email"] == "fresh@example.com"
|
||
|
||
|
||
def test_ops_truth_history_returns_filtered_rows(monkeypatch):
|
||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||
monkeypatch.setattr(routes, "_require_ops_admin", lambda request: None)
|
||
|
||
repo = TruthRecordRepository()
|
||
repo.upsert_truth(
|
||
city="taipei",
|
||
target_date="2026-04-02",
|
||
actual_high=26.0,
|
||
settlement_source="wunderground",
|
||
settlement_station_code="RCSS",
|
||
settlement_station_label="Taipei Songshan Airport Station",
|
||
truth_version="v1",
|
||
updated_by="test",
|
||
source_payload={"sample": True},
|
||
is_final=True,
|
||
)
|
||
|
||
response = client.get("/api/ops/truth-history?city=taipei&date_from=2026-04-01&date_to=2026-04-03&limit=10")
|
||
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
assert "items" in payload
|
||
assert payload["filters"]["city"] == "taipei"
|
||
assert payload["items"][0]["city"] == "taipei"
|
||
|
||
|
||
def test_scan_terminal_service_returns_stale_payload_after_failed_refresh(monkeypatch):
|
||
filters = {"scan_mode": "tradable", "limit": 5}
|
||
normalized_filters = scan_terminal_service._normalize_scan_terminal_filters(filters)
|
||
scan_terminal_cache._SCAN_TERMINAL_CACHE.clear()
|
||
|
||
monkeypatch.setattr(
|
||
scan_terminal_service,
|
||
"_scan_city_terminal_rows",
|
||
lambda *_args, **_kwargs: {
|
||
"city": "taipei",
|
||
"rows": [
|
||
{
|
||
"id": "row-1",
|
||
"market_key": "market-1",
|
||
"edge_percent": 12.4,
|
||
"final_score": 83.0,
|
||
"volume": 2000,
|
||
}
|
||
],
|
||
"candidate_total": 1,
|
||
"primary_scores": [83.0],
|
||
},
|
||
)
|
||
|
||
ready = scan_terminal_service.build_scan_terminal_payload(filters, force_refresh=True)
|
||
assert ready["status"] == "ready"
|
||
assert ready["rows"][0]["id"] == "row-1"
|
||
|
||
def _explode(*_args, **_kwargs):
|
||
raise RuntimeError("upstream 504")
|
||
|
||
monkeypatch.setattr(scan_terminal_service, "_scan_city_terminal_rows", _explode)
|
||
|
||
stale = scan_terminal_service.build_scan_terminal_payload(filters, force_refresh=True)
|
||
|
||
assert stale["status"] == "stale"
|
||
assert stale["stale"] is True
|
||
assert stale["rows"][0]["id"] == "row-1"
|
||
assert stale["filters"] == normalized_filters
|
||
assert stale["stale_reason"] == "upstream 504"
|
||
|
||
|
||
def test_scan_terminal_service_returns_failed_without_success_snapshot(monkeypatch):
|
||
filters = {"scan_mode": "tradable", "limit": 5}
|
||
scan_terminal_cache._SCAN_TERMINAL_CACHE.clear()
|
||
|
||
def _explode(*_args, **_kwargs):
|
||
raise RuntimeError("network down")
|
||
|
||
monkeypatch.setattr(scan_terminal_service, "_scan_city_terminal_rows", _explode)
|
||
|
||
failed = scan_terminal_service.build_scan_terminal_payload(filters, force_refresh=True)
|
||
|
||
assert failed["status"] == "failed"
|
||
assert failed["stale"] is False
|
||
assert failed["rows"] == []
|
||
assert failed["summary"]["candidate_total"] == 0
|
||
assert failed["stale_reason"] == "network down"
|
||
|
||
|
||
def test_scan_terminal_endpoint_forwards_filters(monkeypatch):
|
||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||
|
||
captured = {}
|
||
|
||
def _fake_build_scan_terminal_payload(filters, *, force_refresh=False):
|
||
captured["filters"] = dict(filters)
|
||
captured["force_refresh"] = force_refresh
|
||
return {
|
||
"generated_at": "2026-04-23T00:00:00Z",
|
||
"filters": filters,
|
||
"summary": {
|
||
"recommended_count": 1,
|
||
"visible_count": 1,
|
||
"candidate_total": 3,
|
||
"avg_edge_percent": 4.2,
|
||
"avg_primary_confidence": 88.0,
|
||
"tradable_market_count": 1,
|
||
"total_volume": 1500,
|
||
"resolved_market_type": "maxtemp",
|
||
},
|
||
"top_signal": None,
|
||
"rows": [],
|
||
}
|
||
|
||
monkeypatch.setattr(routes, "build_scan_terminal_payload", _fake_build_scan_terminal_payload)
|
||
|
||
response = client.get(
|
||
"/api/scan/terminal?scan_mode=trend&min_price=0.1&max_price=0.8&min_edge_pct=3"
|
||
"&min_liquidity=700&high_liquidity_only=true&market_type=all&time_range=week&limit=12&force_refresh=true"
|
||
)
|
||
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
assert payload["summary"]["recommended_count"] == 1
|
||
assert captured["force_refresh"] is True
|
||
assert captured["filters"]["scan_mode"] == "trend"
|
||
assert captured["filters"]["market_type"] == "all"
|
||
assert captured["filters"]["time_range"] == "week"
|
||
assert captured["filters"]["limit"] == 12
|
||
|
||
|
||
def test_scan_terminal_cache_key_includes_filter_dimensions():
|
||
first = scan_terminal_cache_key(
|
||
{
|
||
"scan_mode": "tradable",
|
||
"time_range": "today",
|
||
"limit": 25,
|
||
}
|
||
)
|
||
second = scan_terminal_cache_key(
|
||
{
|
||
"scan_mode": "trend",
|
||
"time_range": "week",
|
||
"limit": 10,
|
||
}
|
||
)
|
||
|
||
assert first != second
|
||
assert "trend" in second
|
||
assert "week" in second
|
||
|
||
|
||
def test_city_history_is_read_only_and_uses_sqlite_truth_and_features(monkeypatch):
|
||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||
|
||
def _fail_bootstrap(*args, **kwargs):
|
||
raise AssertionError("bootstrap should not be called by /api/history")
|
||
|
||
monkeypatch.setattr(routes, "bootstrap_recent_daily_history_if_missing", _fail_bootstrap, raising=False)
|
||
|
||
truth_repo = TruthRecordRepository()
|
||
truth_repo.upsert_truth(
|
||
city="ankara",
|
||
target_date="2026-04-02",
|
||
actual_high=16.0,
|
||
settlement_source="metar",
|
||
settlement_station_code="LTAC",
|
||
settlement_station_label="Ankara Esenboga Airport",
|
||
truth_version="v1",
|
||
updated_by="test",
|
||
source_payload={"sample": True},
|
||
is_final=True,
|
||
)
|
||
feature_repo = TrainingFeatureRecordRepository()
|
||
feature_repo.upsert_record(
|
||
"ankara",
|
||
"2026-04-02",
|
||
{
|
||
"deb_prediction": 16.4,
|
||
"mu": 16.2,
|
||
"forecasts": {"Open-Meteo": 15.8, "ECMWF": 16.1},
|
||
},
|
||
)
|
||
|
||
response = client.get("/api/history/ankara")
|
||
|
||
assert response.status_code == 200
|
||
payload = response.json()
|
||
assert payload["history"]
|
||
row = next(item for item in payload["history"] if item["date"] == "2026-04-02")
|
||
assert row["actual"] == 16.0
|
||
assert row["deb"] == 16.4
|
||
assert row["mu"] == 16.2
|
||
assert row["forecasts"]["Open-Meteo"] == 15.8
|
||
assert row["settlement_station_code"] == "LTAC"
|
||
assert row["truth_version"] == "v1"
|