12b34929d7
- new energy_irpc collector parsing IRPC performance-highlights table (net profit/EBITDA/ROE margins, latest period 3M26: +10.27%) - factor energy_irpc_net_margin (sign +1) wired into refining_energy/ exploration/utilities, extending the energy theme beyond TOP - scheduler job + dashboard fetch + sources table row (now 9 sources) - tests: parse (incl paren-negatives), value-key resolution, direction; suite 368 OK. Independent review passed: true - Phase B feasibility: REIC/EPPO/NBTC/PTTEP are JS-rendered or anti-bot (recorded deferred in plan); IRPC was the clean server-rendered win
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
"""Tests for the real multi-theme dashboard assembly."""
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from __future__ import annotations
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import unittest
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from unittest.mock import patch
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from app.dashboard import RealDashboard, _auto_read, _zscore
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class _FakeCache:
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"""Minimal cache that invokes the fetcher each call."""
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def __init__(self, data: dict):
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self.data = data
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def fetch_or_stale(self, key, fetcher):
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val = self.data.get(key)
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if val is not None:
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return val
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return fetcher()
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class DashboardTest(unittest.TestCase):
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def test_zscore_centers(self):
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self.assertAlmostEqual(_zscore(1.0, 1.0, 1.0), 0.0)
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self.assertAlmostEqual(_zscore(2.0, 1.0, 1.0), 1.0)
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def test_auto_read_multisource(self):
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auto = {"new_car_sales_yoy": 20.07, "total_vehicle_sales": 59000,
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"vehicle_production": 120000, "auto_exports": 80000}
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npl = {"pct_of_npls": 3.95, "npl_amount": 20602}
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read = _auto_read(auto, npl, _FakeCache({}))
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self.assertEqual(read["new_car_sales_yoy"], 20.07)
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self.assertEqual(read["auto_npl_pct"], 3.95)
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self.assertIn("thesis", read)
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@patch("app.auto_credit.fetch_auto_credit")
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@patch("app.auto_npl.fetch_auto_npl")
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@patch("app.energy_thai.fetch_energy_thai")
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@patch("app.energy_irpc.fetch_energy_irpc")
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@patch("app.macro_thai.fetch_macro_thai")
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@patch("app.bank_npl.fetch_bank_npl")
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def test_build_returns_structure(self, bnpl, macro, energy, eirpc, npl, auto):
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class _Factory:
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def __init__(self, data): self._data = data
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def to_dict(self): return self._data
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macro.return_value = _Factory({
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"private_consumption_yoy": 4.9, "headline_inflation_yoy": 1.95, "periods": {}})
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energy.return_value = _Factory({
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"quarterly": {"Q2/2026": {"net_profit": 8000.0, "ebitda": 9000.0}}})
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eirpc.return_value = _Factory({"net_margin_pct": 10.27, "period": "3M26"})
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auto.return_value = _Factory({
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"new_car_sales_yoy": 20.07, "total_vehicle_sales": 59000})
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npl.return_value = _Factory({
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"pct_of_npls": 3.95, "npl_amount": 20602, "period": "Q2/2568"})
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bnpl.return_value = _Factory({
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"pct_of_npls": 1.07, "npl_amount": 5598, "period": ""})
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cache = _FakeCache({})
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dash = RealDashboard([], cache).build()
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self.assertEqual(len(dash["themes"]), 13) # all SET50 themes
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self.assertEqual(len(dash["sources"]), 9) # auto-derived from FACTORS (energy_irpc added)
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self.assertIn("macro", dash)
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self.assertIn("board", dash)
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def test_auto_read_npl_piece(self):
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read = _auto_read({"new_car_sales_yoy": -3.0, "total_vehicle_sales": 20000},
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{"pct_of_npls": 6.0, "npl_amount": 90000}, _FakeCache({}))
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self.assertEqual(read["new_car_sales_yoy"], -3.0)
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self.assertEqual(read["auto_npl_pct"], 6.0)
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if __name__ == "__main__":
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unittest.main()
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