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https://github.com/jpawlowski/hass.tibber_prices.git
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13b9870f45
_calculate_metadata()'s inline calc_stats() computed the median as a naive sorted(data)[len(data) // 2], which returns the upper-middle value for even-length datasets instead of averaging the two middle values. A full day is always an even interval count (96 quarter-hours or 24 hours), so this silently overstated "median" and "median_position" in nearly every price_stats block of the get_chartdata response. The rest of the codebase already has a correct calculate_median() in utils/average.py; this was an isolated inline duplicate with the bug. Impact: get_chartdata's price_stats.combined/dayN.median and median_position now report the statistically correct median for chart annotations and metadata-driven dashboards.
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
"""Tests for get_chartdata metadata statistics calculation."""
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from __future__ import annotations
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from datetime import UTC, datetime, timedelta
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from custom_components.tibber_prices.services.get_chartdata import _calculate_metadata
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def _make_chart_data(prices: list[float], start: datetime | None = None) -> list[dict]:
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"""Build minimal chart_data entries (start_time + price) for metadata calc."""
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base = start or datetime(2026, 1, 1, 0, 0, tzinfo=UTC)
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return [
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{
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"start_time": (base + timedelta(minutes=15 * i)).isoformat(),
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"price_per_kwh": price,
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}
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for i, price in enumerate(prices)
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]
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class TestCalculateMetadataMedian:
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"""Regression coverage: median must use proper statistics.median, not naive indexing.
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A naive `sorted(data)[len(data)//2]` returns the upper-middle value for
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even-length datasets instead of averaging the two middle values. Since a
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full day always has an even interval count (96 quarter-hours, 24 hours),
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this bug silently affected nearly every "combined"/per-day median in the
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chartdata metadata response.
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"""
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def test_median_for_even_length_dataset_is_averaged(self) -> None:
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"""4 known prices: naive impl would report 30.0, correct median is 25.0."""
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chart_data = _make_chart_data([10.0, 20.0, 30.0, 40.0])
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metadata = _calculate_metadata(
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chart_data=chart_data,
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price_field="price_per_kwh",
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start_time_field="start_time",
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currency="EUR",
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resolution="interval",
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subunit_currency=False,
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)
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assert metadata["price_stats"]["combined"]["median"] == 25.0
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def test_median_for_odd_length_dataset_is_middle_value(self) -> None:
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"""3 known prices: median is simply the middle value."""
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chart_data = _make_chart_data([10.0, 20.0, 30.0])
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metadata = _calculate_metadata(
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chart_data=chart_data,
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price_field="price_per_kwh",
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start_time_field="start_time",
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currency="EUR",
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resolution="interval",
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subunit_currency=False,
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)
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assert metadata["price_stats"]["combined"]["median"] == 20.0
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def test_median_position_reflects_corrected_median(self) -> None:
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"""median_position must be derived from the corrected median, not the naive one."""
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chart_data = _make_chart_data([10.0, 20.0, 30.0, 40.0])
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metadata = _calculate_metadata(
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chart_data=chart_data,
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price_field="price_per_kwh",
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start_time_field="start_time",
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currency="EUR",
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resolution="interval",
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subunit_currency=False,
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)
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combined = metadata["price_stats"]["combined"]
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# median=25.0, min=10.0, max=40.0 -> position = (25-10)/(40-10) = 0.5
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assert combined["median_position"] == 0.5
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