30153dbc64
The live history path now tries NASA POWER first (keyless, independent of Open-Meteo) and falls back to the Open-Meteo archive only when NASA is unavailable — the reverse of before. Gusts NASA lacks are filled from Meteostat inside _fetch_history_nasa (added in the prior change). Both sources must still return a plausibly-full span (MIN_ARCHIVE_DAYS) before being cached as complete; a short/partial response is rejected and the other source is tried. _fetch_history_nasa now accepts a start/end range so it also serves the recent tail top-up: _topup_tail fetches NASA-first via the new _fetch_history_tail helper (Open-Meteo range as fallback, skipped during its rate-limit cooldown), removing the last per-active-cell Open-Meteo call from the history path. The Open-Meteo cooldown no longer gates the top-up, since NASA is not subject to it. Open-Meteo remains wired as the fallback (deletion is a later step). Tests updated to the new source order plus tail-fetch coverage.
369 lines
16 KiB
Python
369 lines
16 KiB
Python
"""The source→frame mappings and cache-write path (pure parts of climate.py —
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no network)."""
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import datetime
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import polars as pl
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import pytest
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from data import climate
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def _om_daily(n=3):
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"""A minimal Open-Meteo daily response."""
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return {
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"time": [f"2026-06-{d:02d}" for d in range(1, n + 1)],
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"temperature_2m_max": [80.0, 90.0, None],
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"temperature_2m_min": [60.0, 70.0, 55.0],
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"precipitation_sum": [0.0, 0.25, 0.1],
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"wind_speed_10m_max": [10.0, 20.0, 5.0],
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"wind_gusts_10m_max": [15.0, 30.0, 8.0],
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"apparent_temperature_max": [82.0, 95.0, 70.0],
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"apparent_temperature_min": [58.0, 68.0, 50.0],
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"relative_humidity_2m_mean": [50.0, 60.0, 70.0],
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# Sunshine vs daylight seconds -> `sun` fraction (full sun, half, quarter).
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"sunshine_duration": [43200.0, 21600.0, 10800.0],
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"daylight_duration": [43200.0, 43200.0, 43200.0],
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}
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def test_to_frame_schema_and_day_filter():
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df = climate._to_frame(_om_daily())
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# The None tmax day is dropped: a usable climate day needs a real high/low.
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assert len(df) == 2
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assert df["doy"].dtype == pl.Int16
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assert set(df.columns) == {"date", "tmax", "tmin", "precip", "wind", "gust",
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"humid", "fmax", "fmin", "feels", "sun", "doy"}
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def test_to_frame_derives_sun_fraction():
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df = climate._to_frame(_om_daily())
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# sun = sunshine_duration / daylight_duration, clamped to [0, 1].
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assert df["sun"].to_list() == [1.0, 0.5]
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def test_to_frame_sun_null_without_sunshine():
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daily = _om_daily()
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del daily["sunshine_duration"], daily["daylight_duration"]
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df = climate._to_frame(daily)
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assert "sun" in df.columns and df["sun"].is_null().all()
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def test_to_frame_tolerates_missing_series():
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daily = _om_daily()
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del daily["wind_gusts_10m_max"], daily["relative_humidity_2m_mean"]
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df = climate._to_frame(daily)
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assert df["gust"].is_null().all() and df["humid"].is_null().all()
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assert df["tmax"].is_not_null().all() # required series unaffected
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def test_finalize_frame_backfills_absent_optional_columns():
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# A source frame carrying only the required base columns is finalized into the
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# full schema, every absent optional metric added as an all-null column.
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bare = pl.DataFrame({
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"date": [datetime.date(2026, 6, 1), datetime.date(2026, 6, 2)],
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"tmax": [80.0, 82.0], "tmin": [60.0, 61.0], "precip": [0.0, 0.1],
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})
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df = climate._finalize_frame(bare)
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for c in climate.OPTIONAL_COLS:
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assert c in df.columns and df[c].is_null().all()
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assert all(c in df.columns for c in climate.NEW_COLS) # incl. derived `feels`
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def test_combined_feels_picks_the_extreme_side():
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df = climate._to_frame(_om_daily())
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# Day 2: fmax 95 is 30 past the 65°F comfort point vs fmin 68 only 3 below —
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# the hot side wins; both days here are hot-side days.
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assert df.filter(pl.col("date") == datetime.date(2026, 6, 2))["feels"].item() == 95.0
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def test_combined_feels_falls_back_to_the_present_side():
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"""When one apparent-temperature side is missing, `feels` uses whichever side
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is present (the null-vs-value coalesce must reproduce the old NaN fallback)."""
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daily = _om_daily()
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daily["apparent_temperature_max"] = [None, None, None] # hot side absent
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df = climate._to_frame(daily)
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row = df.filter(pl.col("date") == datetime.date(2026, 6, 1)).row(0, named=True)
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assert row["feels"] == 58.0 # falls back to apparent low
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def test_nasa_to_frame_converts_units_and_fills():
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param = {
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"T2M_MAX": {"20260601": 30.0, "20260602": -999.0}, # °C; -999 = missing
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"T2M_MIN": {"20260601": 20.0, "20260602": 15.0},
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"PRECTOTCORR": {"20260601": 25.4, "20260602": 0.0}, # mm
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"WS10M_MAX": {"20260601": 10.0, "20260602": 5.0}, # m/s
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"RH2M": {"20260601": 50.0, "20260602": 60.0},
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}
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df = climate._nasa_to_frame(param)
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assert len(df) == 1 # the missing-tmax day dropped
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row = df.row(0, named=True)
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assert row["tmax"] == 86.0 # 30°C
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assert row["precip"] == 1.0 # 25.4mm = 1in
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assert round(row["wind"], 1) == 22.4 # 10 m/s in mph
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assert row["gust"] is None # POWER has no gusts (missing -> null)
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assert row["fmax"] > row["tmax"] # ≥80°F: the NWS heat index applies
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def _metno_step(time, t, rh, wind, p1=None, p6=None):
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"""One MET Norway timeseries step (instant details + optional precip blocks)."""
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data = {"instant": {"details": {
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"air_temperature": t, "relative_humidity": rh, "wind_speed": wind}}}
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if p1 is not None:
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data["next_1_hours"] = {"details": {"precipitation_amount": p1}}
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if p6 is not None:
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data["next_6_hours"] = {"details": {"precipitation_amount": p6}}
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return {"time": time, "data": data}
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def test_metno_to_frame_aggregates_daily_and_converts_units():
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props = {"timeseries": [
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# Day 1: two hourly steps. The first also carries a next_6_hours block, which
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# must be IGNORED (next_1_hours wins) so precip isn't double-counted.
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_metno_step("2026-07-16T00:00:00Z", 20.0, 50.0, 2.0, p1=0.5, p6=3.0),
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_metno_step("2026-07-16T01:00:00Z", 25.0, 60.0, 4.0, p1=0.5),
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# Day 2: a 6-hourly step (only next_6_hours) + an instant-only tail step.
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_metno_step("2026-07-17T00:00:00Z", 10.0, 80.0, 10.0, p6=6.0),
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_metno_step("2026-07-17T06:00:00Z", 12.0, 70.0, 8.0),
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]}
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df = climate._metno_to_frame(props)
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assert len(df) == 2
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d1 = df.filter(pl.col("date") == datetime.date(2026, 7, 16)).row(0, named=True)
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assert d1["tmax"] == 77.0 and d1["tmin"] == 68.0 # 25°C / 20°C -> °F
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assert round(d1["precip"], 4) == round(1.0 / 25.4, 4) # 0.5+0.5 mm (not +3.0) -> in
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assert round(d1["wind"], 1) == 8.9 # max 4 m/s -> mph
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assert d1["gust"] is None # MET Norway has no gusts
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assert d1["feels"] is not None
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d2 = df.filter(pl.col("date") == datetime.date(2026, 7, 17)).row(0, named=True)
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assert round(d2["precip"], 4) == round(6.0 / 25.4, 4) # only the 6-hour block
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def test_recent_forecast_falls_back_to_metno(monkeypatch, tmp_path):
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"""When Open-Meteo's forecast API fails, the MET Norway backup serves the frame."""
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monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
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cell = {"id": "fallback_cell", "center_lat": 47.6062, "center_lon": -122.3321}
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met = {"timeseries": [
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_metno_step("2026-07-16T00:00:00Z", 18.0, 55.0, 3.0, p1=0.0),
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_metno_step("2026-07-17T00:00:00Z", 22.0, 45.0, 5.0, p6=1.0),
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]}
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class Resp:
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def json(self): return {"properties": met}
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def fake_request(url, params, timeout, *, phase, headers=None, attempts=climate.MAX_ATTEMPTS):
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if url == climate.FORECAST_URL:
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raise RuntimeError("open-meteo forecast outage")
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assert url == climate.METNO_URL
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assert headers and headers.get("User-Agent"), "MET Norway needs a User-Agent"
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return Resp()
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monkeypatch.setattr(climate, "_request", fake_request)
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df = climate._load_recent_forecast(cell)
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assert len(df) == 2 and df["date"].max() == datetime.date(2026, 7, 17)
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def test_recent_forecast_serves_stale_cache_when_all_sources_fail(monkeypatch, tmp_path):
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"""With Open-Meteo AND MET Norway both down, an existing (stale) cache is served
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rather than failing — and it is NOT rewritten, so its mtime stays old and the
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next request still retries upstream first."""
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import os
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import time
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monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
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cell = {"id": "stale_cell", "center_lat": 47.6, "center_lon": -122.3}
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# Seed an rf cache, then age it well past the TTL so it counts as stale.
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stale = climate._to_frame(_om_daily())
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path = climate._rf_cache_path(cell["id"])
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climate._write_cache(stale, path)
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old = time.time() - (climate.FORECAST_TTL_HOURS + 5) * 3600
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os.utime(path, (old, old))
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def all_down(url, params, timeout, *, phase, headers=None, attempts=climate.MAX_ATTEMPTS):
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raise RuntimeError(f"{phase} down")
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monkeypatch.setattr(climate, "_request", all_down)
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df = climate._load_recent_forecast(cell)
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assert df.height == stale.height # served the stale cache
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assert abs(os.path.getmtime(path) - old) < 2 # not rewritten (mtime unchanged)
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def test_om_daily_params_carries_the_window():
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cell = {"center_lat": 47.6, "center_lon": -122.3}
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p = climate._om_daily_params(cell, start_date="2026-01-01", end_date="2026-02-01")
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assert p["latitude"] == 47.6 and p["daily"] == climate.DAILY_VARS
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assert p["temperature_unit"] == "fahrenheit"
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assert p["start_date"] == "2026-01-01" and p["end_date"] == "2026-02-01"
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p2 = climate._om_daily_params(cell, past_days=25, forecast_days=8)
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assert p2["past_days"] == 25 and "start_date" not in p2
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def test_write_cache_strips_the_derived_doy(tmp_path):
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df = climate._to_frame(_om_daily())
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path = str(tmp_path / "cell.parquet")
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climate._write_cache(df, path)
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stored = pl.read_parquet(path)
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assert "doy" not in stored.columns
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assert len(stored) == len(df)
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def _wb_frame(tmax, humid, tmin=None):
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"""Frame with raw RH (`humid`) for the wet-bulb / humidity derivations."""
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n = len(tmax)
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return pl.DataFrame({
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"date": [datetime.date(2026, 7, 1) + datetime.timedelta(days=i) for i in range(n)],
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"tmax": [float(x) for x in tmax],
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"tmin": [float(t) for t in (tmin or [x - 15 for x in tmax])],
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"humid": [float(h) for h in humid],
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})
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def test_wetbulb_stull_spot_values():
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# Stull (2011): 20 °C / 50% -> ~13.7 °C; 30 °C / 80% -> ~27.2 °C. In °F here.
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df = climate._derive_wetbulb(_wb_frame([68.0, 86.0], [50.0, 80.0]))
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wb = df["wetbulb"].to_list()
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assert wb[0] == pytest.approx((13.7 * 9 / 5) + 32, abs=0.6)
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assert wb[1] == pytest.approx((27.2 * 9 / 5) + 32, abs=0.6)
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def test_wetbulb_never_exceeds_dry_bulb():
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df = climate._derive_wetbulb(_wb_frame([40.0, 60.0, 85.0, 100.0], [20.0, 55.0, 70.0, 95.0]))
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for tmax, wb in zip(df["tmax"], df["wetbulb"]):
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assert wb is not None and wb <= tmax + 0.05
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def test_wetbulb_null_outside_validity_range():
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# RH 3% (< 5) and a scorching 130 °F (> 50 °C) both fall outside Stull's fit.
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df = climate._derive_wetbulb(_wb_frame([70.0, 130.0], [3.0, 40.0]))
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assert df["wetbulb"].to_list() == [None, None]
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def test_wetbulb_noop_without_humidity():
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df = pl.DataFrame({"tmax": [70.0], "tmin": [55.0]})
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assert "wetbulb" not in climate._derive_wetbulb(df).columns
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def test_derive_metrics_adds_wetbulb_and_absolute_humidity():
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out = climate._derive_metrics(_wb_frame([68.0], [50.0]))
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assert "wetbulb" in out.columns
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# _derive_humidity replaces raw RH (50) with absolute humidity (g/m³, ~8-9).
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assert out["humid"][0] < 30 and out["humid"][0] != 50.0
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def test_history_fetch_targets_archive_url_with_seamless_model(monkeypatch):
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"""Historical fetches hit ARCHIVE_URL (env-overridable for self-hosting) and pin
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the ERA5 seamless model, so a self-hosted Open-Meteo serves the same 0.1° blend."""
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seen = {}
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class Resp:
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def json(self): return {"daily": _om_daily(3)}
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def fake_request(url, params, timeout, *, phase, headers=None, attempts=climate.MAX_ATTEMPTS):
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seen["url"], seen["params"], seen["phase"] = url, dict(params), phase
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return Resp()
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monkeypatch.setattr(climate, "_request", fake_request)
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cell = {"center_lat": 47.6, "center_lon": -122.3}
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climate._fetch_history(cell)
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assert seen["url"] == climate.ARCHIVE_URL
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assert seen["params"]["models"] == "era5_seamless"
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assert seen["phase"] == "history_fetch"
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climate._fetch_history_range(cell, "2026-06-01", "2026-06-10") # tail top-up
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assert seen["url"] == climate.ARCHIVE_URL
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assert seen["params"]["models"] == "era5_seamless"
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def test_daily_params_carry_no_model_by_default():
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# The shared param builder (also used by the forecast path) stays model-free;
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# only the archive fetches add models=era5_seamless.
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p = climate._om_daily_params({"center_lat": 1.0, "center_lon": 2.0}, past_days=7)
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assert "models" not in p
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assert climate.ARCHIVE_URL.endswith("/v1/archive") # default (no env override in tests)
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def _full_history_frame(n=4000):
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"""A plausibly-full archive frame (>= MIN_ARCHIVE_DAYS rows), standard columns."""
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start = datetime.date(1990, 1, 1)
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dates = [start + datetime.timedelta(days=i) for i in range(n)]
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return climate._finalize_frame(pl.DataFrame({
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"date": dates,
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"tmax": [70.0] * n, "tmin": [50.0] * n, "precip": [0.0] * n,
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"wind": [5.0] * n, "gust": [8.0] * n, "humid": [60.0] * n,
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"fmax": [70.0] * n, "fmin": [50.0] * n,
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}))
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def test_nasa_is_primary_history_source(monkeypatch, tmp_path):
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"""NASA POWER is the primary history source: a full NASA frame is accepted and
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Open-Meteo is never consulted."""
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monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
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monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
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cell = {"id": "nasa_primary", "center_lat": 47.6, "center_lon": -122.3}
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full = _full_history_frame()
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monkeypatch.setattr(climate, "_fetch_history_nasa", lambda c: full)
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def _om_should_not_run(c): raise AssertionError("Open-Meteo should not be called")
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monkeypatch.setattr(climate, "_fetch_history", _om_should_not_run)
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df, meta = climate._load_history(cell)
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assert meta["source"] == "nasa-power"
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assert df.height == full.height
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def test_falls_back_to_open_meteo_when_nasa_unavailable(monkeypatch, tmp_path):
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"""When NASA POWER fails, the load falls back to the Open-Meteo archive."""
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monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
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monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
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cell = {"id": "om_fallback", "center_lat": 47.6, "center_lon": -122.3}
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full = _full_history_frame()
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def _nasa_down(c): raise RuntimeError("NASA POWER outage")
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monkeypatch.setattr(climate, "_fetch_history_nasa", _nasa_down)
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monkeypatch.setattr(climate, "_fetch_history", lambda c: full)
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df, meta = climate._load_history(cell)
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assert meta["source"] == "open-meteo"
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assert df.height == full.height
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def test_short_nasa_is_rejected_and_falls_back_to_open_meteo(monkeypatch, tmp_path):
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"""A too-short NASA response (a partial/unavailable point) is rejected rather than
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cached as a complete record, and Open-Meteo serves instead."""
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monkeypatch.setattr(climate, "CACHE_DIR", str(tmp_path))
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monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
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cell = {"id": "short_nasa", "center_lat": 47.6, "center_lon": -122.3}
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short = climate._to_frame(_om_daily(3)) # 2 usable days « threshold
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full = _full_history_frame() # >= MIN_ARCHIVE_DAYS
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assert short.height < climate.MIN_ARCHIVE_DAYS <= full.height
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monkeypatch.setattr(climate, "_fetch_history_nasa", lambda c: short)
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monkeypatch.setattr(climate, "_fetch_history", lambda c: full)
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df, meta = climate._load_history(cell)
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assert meta["source"] == "open-meteo" # rejected the short NASA response
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assert df.height == full.height
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def test_history_tail_prefers_nasa(monkeypatch):
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"""The tail top-up fetches NASA POWER first; Open-Meteo's range fetch is not
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touched when NASA succeeds."""
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cell = {"center_lat": 1.0, "center_lon": 2.0}
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sentinel = object()
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monkeypatch.setattr(climate, "_fetch_history_nasa",
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lambda c, start=None, end=None: sentinel)
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def _om_should_not_run(c, s, e): raise AssertionError("Open-Meteo tail should not run")
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monkeypatch.setattr(climate, "_fetch_history_range", _om_should_not_run)
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assert climate._fetch_history_tail(cell, "2026-06-01", "2026-06-10") is sentinel
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def test_history_tail_falls_back_to_open_meteo(monkeypatch):
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"""When NASA fails and Open-Meteo isn't cooling down, the tail falls back to the
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Open-Meteo archive range."""
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monkeypatch.setattr(climate, "_archive_cooldown_until", 0.0)
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cell = {"center_lat": 1.0, "center_lon": 2.0}
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def _nasa_down(c, start=None, end=None): raise RuntimeError("NASA down")
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monkeypatch.setattr(climate, "_fetch_history_nasa", _nasa_down)
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sentinel = object()
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monkeypatch.setattr(climate, "_fetch_history_range", lambda c, s, e: sentinel)
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assert climate._fetch_history_tail(cell, "2026-06-01", "2026-06-10") is sentinel
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