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Steady-state usage of the Open-Meteo API is removed; it remains only as a dormant fallback. Geocoding -> local GeoNames + Nominatim; wind gusts -> Meteostat; history -> NASA POWER (curated cells seeded with keyless ERA5); recent+forecast -> NASA range + MET Norway. Plus a drift-check tool comparing NASA vs Open-Meteo. All keyless, no new infra. Backend suite green in-image (gate).
96 lines
4 KiB
Python
96 lines
4 KiB
Python
"""Unit tests for the Meteostat gust supplier — all hermetic (no network): the
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station index and per-station daily fetch are monkeypatched, and the pure parse
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helpers are fed literal payloads."""
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import datetime
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import json
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import numpy as np
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import polars as pl
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import pytest
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from data import meteostat
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def _frame(gust=(None, None, None), wind=(10.0, 20.0, None)):
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return pl.DataFrame(
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{
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"date": [datetime.date(2020, 1, 1), datetime.date(2020, 1, 2),
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datetime.date(2020, 1, 3)],
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"wind": list(wind),
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"gust": pl.Series(list(gust), dtype=pl.Float64),
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}
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)
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def test_parse_daily_csv_converts_kmh_to_mph_and_skips_blanks():
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csv = "\n".join([
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"2020-01-01,,,,,,,,50.0,,", # wpgt (col 8) = 50 km/h
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"2020-01-02,,,,,,,,,,", # no gust -> skipped
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"2020-01-03,,,,,,,,80.0,,", # wpgt = 80 km/h
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"bad-date,,,,,,,,50.0,,", # unparseable date -> skipped
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"x,y", # too few columns -> skipped
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])
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out = meteostat._parse_daily_csv(csv)
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assert set(out) == {datetime.date(2020, 1, 1), datetime.date(2020, 1, 3)}
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assert out[datetime.date(2020, 1, 1)] == pytest.approx(50.0 * meteostat.KMH_TO_MPH)
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assert out[datetime.date(2020, 1, 3)] == pytest.approx(80.0 * meteostat.KMH_TO_MPH)
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def test_parse_stations_skips_entries_without_id_or_coords():
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raw = json.dumps([
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{"id": "A", "location": {"latitude": 47.6, "longitude": -122.3}},
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{"id": "B", "location": {"latitude": 51.5, "longitude": -0.1}},
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{"id": "C", "location": {}}, # no coords -> skip
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{"location": {"latitude": 1, "longitude": 2}}, # no id -> skip
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]).encode()
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ids, lats, lons = meteostat._parse_stations(raw)
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assert ids == ["A", "B"]
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assert lats.tolist() == [47.6, 51.5]
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assert lons.tolist() == [-122.3, -0.1]
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def test_nearest_station_picks_closest_within_range(monkeypatch):
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monkeypatch.setattr(meteostat, "_STATIONS",
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(["seattle", "london"],
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np.array([47.6, 51.5]), np.array([-122.3, -0.1])))
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assert meteostat.nearest_station(47.61, -122.31) == "seattle"
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# Middle of the Pacific — both stations are far beyond MAX_STATION_KM.
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assert meteostat.nearest_station(0.0, -160.0) is None
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def test_fill_gusts_prefers_measured_then_estimates(monkeypatch):
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monkeypatch.setattr(meteostat, "nearest_station", lambda lat, lon: "s1")
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monkeypatch.setattr(meteostat, "daily_gusts",
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lambda sid: {datetime.date(2020, 1, 1): 33.0})
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out = meteostat.fill_gusts(47.6, -122.3, _frame())
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gust = out["gust"].to_list()
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assert gust[0] == pytest.approx(33.0) # measured wins
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assert gust[1] == pytest.approx(20.0 * meteostat.GUST_FACTOR) # estimated from wind
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assert gust[2] is None # no wind -> no estimate
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def test_fill_gusts_estimates_everywhere_with_no_station(monkeypatch):
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monkeypatch.setattr(meteostat, "nearest_station", lambda lat, lon: None)
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out = meteostat.fill_gusts(0.0, 0.0, _frame())
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gust = out["gust"].to_list()
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assert gust[0] == pytest.approx(10.0 * meteostat.GUST_FACTOR)
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assert gust[1] == pytest.approx(20.0 * meteostat.GUST_FACTOR)
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assert gust[2] is None
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def test_fill_gusts_degrades_on_fetch_error(monkeypatch):
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def _boom(lat, lon):
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raise RuntimeError("meteostat down")
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monkeypatch.setattr(meteostat, "nearest_station", _boom)
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# Must not raise — falls back to pure estimation.
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out = meteostat.fill_gusts(47.6, -122.3, _frame())
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assert out["gust"].to_list()[1] == pytest.approx(20.0 * meteostat.GUST_FACTOR)
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def test_fill_gusts_noop_when_source_has_gusts(monkeypatch):
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def _fail(*a, **k):
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raise AssertionError("must not consult Meteostat when gusts already present")
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monkeypatch.setattr(meteostat, "nearest_station", _fail)
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df = _frame(gust=(40.0, 41.0, 42.0))
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out = meteostat.fill_gusts(47.6, -122.3, df)
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assert out["gust"].to_list() == [40.0, 41.0, 42.0]
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