af57b89e4b
Rain intensity gains a Severe tier and Dry becomes strictly no-rain: - The top half of Very Heavy (95th–99th rain-day percentile) becomes a new "Severe" tier; Very Heavy keeps the 90–95 band. Eight rain tiers now — Trace / Light / Brisk / Typical / Heavy / Very Heavy / Severe / Extreme — which refill the wet-2..wet-9 colour ramp contiguously (no gap), so Heavy/Very Heavy shift one shade lighter and Severe takes the second-darkest. - A day is Dry only when it didn't rain at all; any measurable rain, however slight, is at least Trace. _grade_precip splits on > 0 rather than the 0.01" threshold (which still governs the separate dry-streak metric). - The distribution strip drops the range under the Dry column — every dry day is zero, so a "0–0" span was noise. _precip_ladder derives its percentile marks from RAIN_BANDS now, so adding or splitting a tier can't leave a hard-coded list behind (that was the bug the 95th mark would have hit). The detail-view ladder derives from the band table as before. Claude-Session: https://claude.ai/code/session_013dRZmX9D3JEntfMKWMTWZ8
166 lines
6.9 KiB
Python
166 lines
6.9 KiB
Python
import datetime
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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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import grading
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# ---- empirical percentile ----------------------------------------------------
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def test_percentile_mid_rank_handles_ties():
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samples = np.array([1.0, 2.0, 2.0, 3.0])
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# less=1, equal=2 -> (1 + 0.5*2) / 4 = 50%
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assert grading.empirical_percentile(samples, 2.0) == 50.0
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def test_percentile_extremes_and_empties():
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samples = np.array([1.0, 2.0, 3.0])
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assert grading.empirical_percentile(samples, 0.0) == 0.0
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assert grading.empirical_percentile(samples, 4.0) == 100.0
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assert grading.empirical_percentile(np.array([]), 1.0) is None
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assert grading.empirical_percentile(samples, None) is None
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assert grading.empirical_percentile(samples, float("nan")) is None
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# ---- tier bands ---------------------------------------------------------------
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@pytest.mark.parametrize("pct,label,css", [
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(99.5, "Near Record", "rec-hot"), # top tier is strict: > 99 only
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(99.0, "Very High", "very-hot"), # p99 exactly is NOT near-record
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(90.0, "Very High", "very-hot"),
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(75.0, "High", "hot"),
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(60.0, "Above Normal", "warm"),
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(59.9, "Normal", "normal"),
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(40.0, "Normal", "normal"),
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(39.9, "Below Normal", "cool"),
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(10.0, "Low", "cold"),
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(1.0, "Very Low", "very-cold"),
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(0.5, "Near Record", "rec-cold"), # strictly below the 1st percentile
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])
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def test_temp_band_boundaries(pct, label, css):
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assert grading._band(pct, grading.TEMP_BANDS) == (label, css)
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def test_ladders_stay_aligned_with_bands():
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"""The detail-view ladders re-encode the band tables by hand; catch drift."""
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for bands, ladder in [(grading.TEMP_BANDS, grading._TEMP_LADDER),
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(grading.RAIN_BANDS, grading._RAIN_LADDER)]:
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assert len(bands) == len(ladder)
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for (_, label, css), (lcss, llabel, *_rest) in zip(bands, ladder):
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assert (label, css) == (llabel, lcss)
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# ---- seasonal window ----------------------------------------------------------
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def test_window_mask_wraps_across_year_end():
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doys = np.array([1, 180, 360, 366])
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mask = grading.window_mask(doys, target_doy=1, half=7)
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assert mask.tolist() == [True, False, True, True]
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# ---- precip grading -----------------------------------------------------------
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def test_only_zero_precip_is_dry():
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# Dry means no rain at all; any measurable rain, however slight, is at least a
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# Trace day (not Dry).
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dry = grading._grade_precip(np.array([0.0, 0.5, 1.0]), 0.0)
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assert dry["class"] == "dry" and dry["percentile"] is None and dry["grade"] == "Dry"
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trace = grading._grade_precip(np.array([0.0, 0.5, 1.0]), 0.005)
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assert trace["class"] != "dry" and trace["grade"] == "Trace"
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def test_rain_percentile_ranks_among_rain_days_only():
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# 7 dry days + rain days [0.1, 0.2, 0.4]; 0.2 ranks among the 3 rain days:
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# less=1, equal=1 -> (1 + 0.5) / 3 = 50% -> Typical, unaffected by the dry mass.
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samples = np.array([0.0] * 7 + [0.1, 0.2, 0.4])
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g = grading._grade_precip(samples, 0.2)
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assert g["percentile"] == 50.0
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assert g["grade"] == "Typical"
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def test_rain_with_no_historical_rain_days_is_extreme():
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g = grading._grade_precip(np.zeros(10), 0.3)
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assert g["percentile"] == 100.0 and g["class"] == "wet-9"
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def test_rain_scale_eight_tiers_with_severe():
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# Eight tiers filling the wet-2..wet-9 ramp: Trace / Light / Brisk / Typical /
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# Heavy / Very Heavy / Severe / Extreme. Very Heavy was split, its top half
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# becoming Severe (95–99).
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assert [b[1] for b in grading.RAIN_BANDS] == \
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["Extreme", "Severe", "Very Heavy", "Heavy", "Typical", "Brisk", "Light", "Trace"]
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assert [b[2] for b in grading.RAIN_BANDS] == \
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["wet-9", "wet-8", "wet-7", "wet-6", "wet-5", "wet-4", "wet-3", "wet-2"]
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rain = np.arange(1, 201, dtype=float) / 100.0 # 200 rain days: 0.01 .. 2.00
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samples = np.concatenate([np.zeros(20), rain])
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def grade(v):
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g = grading._grade_precip(samples, v)
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return g["grade"], g["class"]
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assert grade(0.01) == ("Trace", "wet-2") # <1st pct -> the bottom tier
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assert grade(1.35) == ("Heavy", "wet-6") # ~67th pct
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assert grade(1.85) == ("Very Heavy", "wet-7") # ~92nd pct (lower half)
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assert grade(1.96) == ("Severe", "wet-8") # ~98th pct (top half -> Severe)
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assert grade(2.00) == ("Extreme", "wet-9") # >99th pct
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# ---- dry streaks ---------------------------------------------------------------
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def test_dry_streaks_walk():
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dates = [datetime.date(2024, 1, 1) + datetime.timedelta(days=i) for i in range(5)]
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precips = [0.5, 0.0, float("nan"), 0.02, 0.005]
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out = grading.dry_streaks(dates, precips)
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assert list(out.values()) == [0, 1, 2, 0, 1] # NaN counts as dry
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# ---- range + day grading over a synthetic record --------------------------------
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def test_grade_range_compact_shape(history):
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end = history["date"].max()
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start = end - datetime.timedelta(days=30)
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days = grading.grade_range(history, start, end)
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assert len(days) == 31
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first = days[0]
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assert set(first) == {"date", "dsr", *grading.TEMP_METRICS, "precip"}
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assert first["dsr"] >= 0
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g = first["tmax"]
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assert set(g) == {"v", "pct", "c", "g"}
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for rec in days: # dry days carry no rain percentile, wet days always do
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if rec["precip"]["c"] == "dry":
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assert rec["precip"]["pct"] is None
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else:
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assert rec["precip"]["pct"] is not None
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def test_grade_day_normals_and_departure(history):
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target = history["date"].max()
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row = history.filter(pl.col("date") == target).row(0, named=True)
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result = grading.grade_day(history, target, {"tmax": row["tmax"], "tmin": row["tmin"],
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"precip": row["precip"]})
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assert set(result["normals"]["tmax"]) == {"p1", "p10", "p25", "p40", "p50", "p60",
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"p75", "p90", "p99"}
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expected = max(abs(result["tmax"]["percentile"] - 50), abs(result["tmin"]["percentile"] - 50))
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assert result["departure"] == round(expected, 1)
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def test_day_detail_with_and_without_observation(history):
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target = history["date"].max()
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detail = grading.day_detail(history, target, {"tmax": 60.0, "precip": 0.0})
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assert detail["metrics"]["tmax"]["obs"]["value"] == 60.0
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assert detail["metrics"]["tmax"]["ladder"]["tiers"][0]["c"] == "rec-hot"
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assert detail["metrics"]["precip"]["ladder"]["tiers"][-1]["c"] == "dry"
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bare = grading.day_detail(history, target, None)
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assert bare["metrics"]["tmax"]["obs"] is None
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assert bare["metrics"]["tmax"]["ladder"] is not None
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def test_climatology_summary(history):
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climo = grading.climatology(history, 180)
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# ±7-day window over ~20 years -> ~15 samples per year.
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assert climo["n_samples"] >= 15 * 19
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assert climo["tmax"]["p40"] <= climo["tmax"]["p50"] <= climo["tmax"]["p60"]
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assert climo["feels"] is None # column absent from this record
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