153 lines
6.0 KiB
Python
153 lines
6.0 KiB
Python
"""Download CSV and validate the nine PRBS identification parameters."""
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import csv
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import io
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import math
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REQUIRED_FIELDS = {
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"q_in_val", "dt", "n_order", "t_c", "levels",
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"dead_area", "xa_full", "V_val", "repeat",
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}
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def validate_identification_config(config) -> dict:
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"""Validate a parsed config mapping and normalize numeric values."""
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if not isinstance(config, dict):
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raise ValueError("辨识配置必须是参数映射")
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actual = set(config)
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if actual != REQUIRED_FIELDS:
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missing = sorted(REQUIRED_FIELDS - actual)
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extra = sorted(actual - REQUIRED_FIELDS)
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raise ValueError(f"辨识配置字段错误，缺少={missing}，多余={extra}")
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scalar_fields = {
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"q_in_val", "dt", "t_c", "dead_area", "xa_full", "V_val"
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}
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for field in scalar_fields:
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value = config[field]
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if (isinstance(value, bool) or not isinstance(value, (int, float))
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or not math.isfinite(float(value))):
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raise ValueError(f"辨识参数 {field} 必须是有限数字")
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for field in ("n_order", "repeat"):
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value = config[field]
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if isinstance(value, bool) or not isinstance(value, int):
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raise ValueError(f"辨识参数 {field} 必须是整数")
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levels = config["levels"]
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if not isinstance(levels, list) or len(levels) < 2:
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raise ValueError("levels 必须是至少包含 2 项的数组")
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if len(levels) & (len(levels) - 1):
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raise ValueError("levels 长度必须是 2 的整数次幂")
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normalized_levels = []
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for value in levels:
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if (isinstance(value, bool) or not isinstance(value, (int, float))
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or not math.isfinite(float(value)) or not 0 <= value <= 100):
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raise ValueError("levels 中的开度必须是 0 到 100 的有限数字")
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normalized_levels.append(float(value))
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if config["q_in_val"] < 0:
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raise ValueError("q_in_val 不能小于 0")
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if config["dt"] <= 0 or config["t_c"] <= 0:
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raise ValueError("dt 和 t_c 必须大于 0")
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if config["t_c"] < config["dt"]:
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raise ValueError("t_c 必须大于等于 dt，确保每个码元至少采样一次")
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if config["n_order"] < 2:
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raise ValueError("n_order 必须大于等于 2")
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if config["repeat"] <= 0:
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raise ValueError("repeat 必须是正整数")
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if config["dead_area"] < 0 or config["xa_full"] <= config["dead_area"]:
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raise ValueError("必须满足 0 <= dead_area < xa_full")
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if config["xa_full"] < 1000:
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raise ValueError("xa_full 不能小于前置行程扫描上限 1000")
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if config["V_val"] <= 0:
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raise ValueError("V_val 必须大于 0")
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return {
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"q_in_val": float(config["q_in_val"]),
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"dt": float(config["dt"]),
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"n_order": config["n_order"],
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"t_c": float(config["t_c"]),
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"levels": normalized_levels,
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"dead_area": float(config["dead_area"]),
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"xa_full": float(config["xa_full"]),
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"V_val": float(config["V_val"]),
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"repeat": config["repeat"],
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}
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def parse_identification_config_csv(csv_text: str) -> dict:
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"""Parse a two-column CSV into the validated identification config.
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The CSV must use ``parameter,value`` as its header. ``levels`` is one
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quoted comma-separated value, for example ``"10,20,30,40"``.
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"""
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try:
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reader = csv.DictReader(io.StringIO(csv_text))
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fieldnames = [name.strip() for name in (reader.fieldnames or [])]
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if fieldnames != ["parameter", "value"]:
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raise ValueError("CSV 表头必须为 parameter,value")
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raw = {}
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for row in reader:
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if None in row:
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raise ValueError("CSV 每行只能包含 parameter 和 value 两列")
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parameter = (row.get("parameter") or "").strip()
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value = (row.get("value") or "").strip()
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if not parameter:
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raise ValueError("CSV 存在空参数名")
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if parameter in raw:
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raise ValueError(f"CSV 参数重复: {parameter}")
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raw[parameter] = value
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except csv.Error as exc:
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raise ValueError(f"辨识配置 CSV 格式错误: {exc}") from exc
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actual = set(raw)
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if actual != REQUIRED_FIELDS:
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missing = sorted(REQUIRED_FIELDS - actual)
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extra = sorted(actual - REQUIRED_FIELDS)
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raise ValueError(f"辨识配置字段错误，缺少={missing}，多余={extra}")
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try:
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levels = [float(value.strip()) for value in raw["levels"].split(",")]
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config = {
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"q_in_val": float(raw["q_in_val"]),
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"dt": float(raw["dt"]),
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"n_order": int(raw["n_order"]),
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"t_c": float(raw["t_c"]),
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"levels": levels,
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"dead_area": float(raw["dead_area"]),
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"xa_full": float(raw["xa_full"]),
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"V_val": float(raw["V_val"]),
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"repeat": int(raw["repeat"]),
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}
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except (TypeError, ValueError) as exc:
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raise ValueError(f"辨识配置 CSV 参数值无效: {exc}") from exc
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return validate_identification_config(config)
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def download_identification_config(timeout=20) -> dict:
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"""Download the current customer's CSV config through the cloud server."""
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import requests
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from api import data_record_url, the_folder
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try:
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response = requests.post(data_record_url, json={
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"type": "getIdentificationConfig",
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"deviceId": the_folder,
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}, timeout=timeout)
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response.raise_for_status()
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result = response.json()
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except Exception as exc:
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raise ValueError(f"连接云服务器辨识配置服务失败: {exc}") from exc
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if not result.get("success"):
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raise ValueError(result.get("errMsg", "云服务器未返回辨识配置"))
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try:
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config_response = requests.get(result["url"], timeout=timeout)
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config_response.raise_for_status()
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return parse_identification_config_csv(config_response.text)
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except (KeyError, ValueError, requests.RequestException) as exc:
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raise ValueError(f"下载或解析辨识配置 CSV 失败: {exc}") from exc
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