justin
d893ff383a
Gauges: - Optional per-metric warning zones (warn_hi/redline_hi/warn_lo/redline_lo) in the profile schema; gauges draw colored redline/warn bands and color the needle + readout by zone. Default neutral when unset (no false redline). - Removed the value progress-arc fill (it dominated the dial / looked wrong) -> clean tach face: bezel, ticks, numeric scale, needle, redline band, readout. - Auto-derivation rejected: bad direction/threshold vary per metric, so zones are config (with a sensible neutral default). Units: - New Units menu: Temperature C / F. Converts gauges, graph, table, and PID browser (values, scale, zones, unit labels) at display time; data stays C. Ford 6.0 profile: zones for ICP (red<500), FICM (red<40/amber40-48/green48+), ECT/EOT (high redline), RPM (redline 3800), boost, battery; tightened FICM (38-52) and battery (9-15) ranges so redline bands land sensibly. Docs: profiles/PROFILE_SPEC.md -- canonical, AI-agent-ready profile spec (schema, formula language, zones, confidence, rules); README points to it. Validated headless: zones parse/classify, F conversion (112C->233.6F, zones converted), gauges render; obdcore + diagnostics tests pass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016yT89n4zR4qbrySoSiEyZs
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
"""PID + DTC data model and registry, backed by a vehicle Profile.
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The actual PID numbers, scaling formulas, and DTC meanings live in JSON
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vehicle profiles under profiles/ (data, not code) so the app is vehicle-
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agnostic and others can contribute profiles. This module is the in-memory
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model + lookups; profile.py loads/saves the JSON.
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"""
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from dataclasses import dataclass, field
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from typing import Callable, Tuple
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@dataclass
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class Pid:
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key: str
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name: str
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mode: str = "22" # "01" | "22" | "atrv" | "derived"
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pid: str = "" # hex: "1446" (m22) or "0C" (m01)
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nbytes: int = 2
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formula: str = "" # scaling expr in A/B/... (raw) or dep keys (derived)
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decode: Callable = None # built from formula by profile loader
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unit: str = ""
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group: str = "misc" # fuel | ficm | air | engine | driveline | power | misc
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vmin: float = 0.0
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vmax: float = 100.0
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confidence: str = "verified" # verified | doc | tentative
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round: int = None # display rounding (None=raw float, 0=int)
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deps: Tuple[str, ...] = ()
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notes: str = ""
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# optional gauge warning zones (all None = neutral, no redline drawn).
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# high-side: value >= threshold -> warn/redline. low-side: value <= -> warn/redline.
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warn_hi: float = None
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redline_hi: float = None
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warn_lo: float = None
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redline_lo: float = None
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def zone(self, v):
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"""Classify a value as 'crit' (red), 'warn' (amber), or 'ok' (green)."""
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if v is None:
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return "ok"
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if self.redline_hi is not None and v >= self.redline_hi:
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return "crit"
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if self.redline_lo is not None and v <= self.redline_lo:
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return "crit"
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if self.warn_hi is not None and v >= self.warn_hi:
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return "warn"
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if self.warn_lo is not None and v <= self.warn_lo:
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return "warn"
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return "ok"
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@dataclass
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class Dtc:
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code: str
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desc: str
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system: str = "powertrain"
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no_start: bool = False
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causes: str = ""
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class PidRegistry:
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"""In-memory PID set + presets for the active vehicle profile."""
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def __init__(self, profile):
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self.profile = profile
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self._by_key = {p.key: p for p in profile.pids}
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self.presets = dict(profile.presets)
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def get(self, key):
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return self._by_key.get(key)
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def all(self):
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return list(self._by_key.values())
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def group(self, g):
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return [p for p in self._by_key.values() if p.group == g]
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def preset(self, name):
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return [self._by_key[k] for k in self.presets.get(name, []) if k in self._by_key]
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def preset_names(self):
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return list(self.presets.keys())
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class DtcDatabase:
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def __init__(self, profile):
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self._db = {d.code: d for d in profile.dtcs}
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def get(self, code):
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return self._db.get(code) or Dtc(code, "(unknown - look up this code)")
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def all(self):
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return list(self._db.values())
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