112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
"""Cross-check the ApsisRS rain coefficients against ITU-R P.838-3.
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Reads the values written by ``cargo run -p apsis-rs --features rf --example
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dump_rain`` and recomputes each one with the ``itur`` package, which
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implements the recommendation directly.
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The script needs no network access.
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Usage:
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pixi run python validation/python/atmosphere/check_rain.py [path]
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Exit status is zero when every value agrees within tolerance and one
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otherwise.
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用 ITU-R P.838-3 交叉核对 ApsisRS 的降雨系数。
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读取由 ``cargo run -p apsis-rs --features rf --example dump_rain`` 写出的
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值，并用 ``itur`` 包逐个重算，后者直接实现该建议书。
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本脚本不需要网络访问。
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用法：
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pixi run python validation/python/atmosphere/check_rain.py [path]
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每个值都在容差内一致时退出状态为零，否则为一。
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"""
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from __future__ import annotations
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import csv
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import sys
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from pathlib import Path
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from itur.models.itu838 import _ITU838_3_
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# The implementation reproduces the reference to about twelve significant
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# digits. The Rust output is written with twelve decimal places, which limits
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# the comparison for the smallest coefficients, so the tolerance is set to
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# one part in ten to the eighth.
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# 该实现把参考值复现到约十二位有效数字。Rust 输出以十二位小数写出，这限制
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# 了最小系数的比较，故容差设为十的八次方分之一。
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RELATIVE_TOLERANCE = 1e-8
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DEFAULT_INPUT = Path("validation/data/expected/rust_rain.csv")
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def read_rows(path: Path) -> list[dict[str, str]]:
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"""Reads the CSV rows.
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读取 CSV 各行。
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"""
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with path.open(newline="") as handle:
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reader = csv.DictReader(handle)
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return [row for row in reader if row.get("frequency_ghz")]
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def main(argv: list[str]) -> int:
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path = Path(argv[1]) if len(argv) > 1 else DEFAULT_INPUT
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if not path.exists():
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print(f"missing input file: {path}", file=sys.stderr)
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print(
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"generate it with: cargo run -p apsis-rs --features rf "
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"--example dump_rain > " + str(path),
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file=sys.stderr,
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)
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return 2
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rows = read_rows(path)
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if not rows:
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print("input file has no data rows", file=sys.stderr)
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return 2
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model = _ITU838_3_()
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worst_k = 0.0
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worst_alpha = 0.0
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worst_at = None
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for row in rows:
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frequency = float(row["frequency_ghz"])
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elevation = float(row["elevation_deg"])
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polarisation = float(row["polarisation_deg"])
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k, alpha = model.rain_specific_attenuation_coefficients(
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frequency, elevation, polarisation
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)
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k = float(k)
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alpha = float(alpha)
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k_error = abs(float(row["k"]) - k) / k
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alpha_error = abs(float(row["alpha"]) - alpha) / abs(alpha)
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if k_error > worst_k or alpha_error > worst_alpha:
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worst_at = (frequency, elevation, polarisation)
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worst_k = max(worst_k, k_error)
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worst_alpha = max(worst_alpha, alpha_error)
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print(f"combinations compared: {len(rows)}")
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print(f"worst relative k: {worst_k:.3e}")
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print(f"worst relative alpha: {worst_alpha:.3e}")
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print(f"worst combination: {worst_at}")
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print(f"tolerance: {RELATIVE_TOLERANCE:.3e} relative")
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if worst_k <= RELATIVE_TOLERANCE and worst_alpha <= RELATIVE_TOLERANCE:
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print("result: pass")
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return 0
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print("result: FAIL")
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return 1
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if __name__ == "__main__":
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raise SystemExit(main(sys.argv))
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