mirror of
https://github.com/firestar5683/StarPilot.git
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firestar5683
2605 lines
109 KiB
Python
2605 lines
109 KiB
Python
#!/usr/bin/env python3
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from __future__ import annotations
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import json
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import math
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import os
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import shutil
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import signal
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import subprocess
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import sys
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import threading
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import time
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from dataclasses import asdict, dataclass
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from pathlib import Path
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from typing import Any
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import numpy as np
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from cereal import car
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from opendbc.car.hyundai.values import HyundaiFlags
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from openpilot.common.params import Params
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from openpilot.selfdrive.controls.lib.latcontrol_torque import KP
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from openpilot.selfdrive.controls.lib.latcontrol_vehicle_tunes import (
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FLM_FRICTION_SPEED_KNOTS,
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get_flm_capabilities,
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get_flm_rich_profile_key,
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get_flm_supported_vehicle_knobs,
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get_gm_base_friction_threshold,
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get_hkg_canfd_base_friction_threshold,
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get_standard_friction_threshold,
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normalize_flm_overrides,
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)
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from openpilot.starpilot.common.lateral_delay import full_lateral_delay
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from openpilot.system.hardware import PC
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from openpilot.system.hardware.hw import Paths
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from openpilot.tools.lib.logreader import LogReader
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from openpilot.starpilot.system.the_galaxy import utilities
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FLM_STATUS_PATH = Path("/tmp/galaxy_flm_status.json")
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FLM_LOG_PATH = Path("/tmp/galaxy_flm.log")
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FLM_STATUS_MAX_AGE_SECONDS = 3600.0
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FLM_ANALYZER_ROUTE_LIMIT = 8
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FLM_ANALYZER_PROCESS = None
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FLM_ANALYZER_LOCK = threading.Lock()
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TRIAL_PARAM_SPECS = {
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"AdvancedLateralTune": "bool",
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"ForceAutoTune": "bool",
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"ForceAutoTuneOff": "bool",
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"UseAutoSteerDelay": "bool",
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"SteerDelay": "float",
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"SteerFriction": "float",
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"SteerKP": "float",
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"SteerLatAccel": "float",
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"SteerRatio": "float",
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"FLMActiveProfileId": "string",
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"FLMActiveOverrides": "json",
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"FLMTrialApplied": "bool",
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}
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FLM_ADVANCED_LATERAL_PARAM_KEYS = {
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"AdvancedLateralTune",
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"ForceAutoTune",
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"ForceAutoTuneOff",
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"UseAutoSteerDelay",
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"SteerDelay",
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"SteerFriction",
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"SteerKP",
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"SteerLatAccel",
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"SteerRatio",
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}
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FLM_TRIAL_BASELINE_PARAM = "FLMTrialBaseline"
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GENERIC_PARAM_METADATA = {
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"SteerDelay": {"min": 0.01, "max": 1.0, "precision": 0.001, "deltaType": "absolute", "safeLiveTrial": True},
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"SteerFriction": {"min": 0.0, "max": 1.0, "precision": 0.001, "deltaType": "absolute", "safeLiveTrial": True},
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"SteerKP": {"min": 0.1, "max": 1.5, "precision": 0.001, "deltaType": "absolute", "safeLiveTrial": True},
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"SteerLatAccel": {"min": 0.5, "max": 5.0, "precision": 0.001, "deltaType": "absolute", "safeLiveTrial": True},
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"SteerRatio": {"min": 5.0, "max": 25.0, "precision": 0.001, "deltaType": "absolute", "safeLiveTrial": True},
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}
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FLM_REFERENCE_MODEL = {
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"version": 1,
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"families": {
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"turn_in_boost": {
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"reason": "Used when the car waits too long to initiate torque even though desired lateral accel is already rising.",
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"too_low": "Turn starts late or feels unwilling.",
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"too_high": "Car dives into the curve too early or spikes past the plan on entry.",
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},
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"unwind_taper": {
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"reason": "Used when the car holds steering too long or drops it too quickly on exit.",
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"too_low": "Unwind drags and the car keeps steering past the intended release.",
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"too_high": "Unwind snaps back and the wheel releases too aggressively.",
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},
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"friction_threshold_curve": {
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"reason": "Used to calm chatter or wake up low-speed response without pretending the whole torque map is wrong.",
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"too_low": "Tiny errors create twitch and correction chatter.",
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"too_high": "Controller feels reluctant near center and can hesitate at low speed.",
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},
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"center_taper": {
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"reason": "Used only when the problem is calm-road or near-center nibbling, not general curve response.",
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"too_low": "Straight-road wheel activity stays busy.",
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"too_high": "Car feels lazy around center and can miss light corrections.",
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},
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},
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}
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FLM_PATH_SPECS = {
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"baseline_fix": {
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"title": "Baseline Fix",
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"description": "Use the broad knobs first to get the car into the right zip code before touching narrower cleanup layers.",
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"whenToUse": "Use this when the car is broadly wrong: repeated line riding, multi-band under/oversteer, saturation, or obvious whole-car mismatch.",
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"alternateHint": "If the car is already mostly good and only one band is bothering you, switch to Cleanup Pass instead.",
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},
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"cleanup_pass": {
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"title": "Cleanup Pass",
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"description": "Use the narrow band-specific knobs first so you can clean up one behavior without disturbing the rest of the tune.",
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"whenToUse": "Use this when the car is already mostly in the right zip code and the misses are localized to one phase or speed band.",
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"alternateHint": "If the car is still broadly wrong after this, step back and run Baseline Fix first.",
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},
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}
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FLM_DRIVER_OVERRIDE_PRE_BUFFER_S = 0.35
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FLM_DRIVER_OVERRIDE_POST_BUFFER_S = 1.0
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@dataclass
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class RouteSource:
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route: str
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footage_path: str
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segment: str
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segment_num: int
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log_path: str
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used_qlog: bool
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@dataclass
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class FLMSample:
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route: str
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segment: int
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t: float
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v_ego: float
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lat_active: bool
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steering_pressed: bool
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saturated: bool
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actual_la: float
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desired_la: float
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desired_jerk: float
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error: float
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error_rate: float
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p: float
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i: float
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d: float
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f: float
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output: float
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steering_angle_deg: float
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steering_torque: float
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cmd_torque: float
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out_torque: float
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roll_deg: float
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def _get_galaxy_dir() -> Path:
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return Path(Paths.comma_home()) / "starpilot" / "data" / "galaxy" if PC else Path("/data/galaxy")
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def get_flm_workspace_root() -> Path:
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return _get_galaxy_dir() / "flm"
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def _legacy_workspace_root() -> Path:
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return _get_galaxy_dir() / "".join(("f", "t", "m"))
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def _migrate_legacy_payload(value):
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legacy_upper = "".join(("F", "T", "M"))
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legacy_lower = legacy_upper.lower()
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if isinstance(value, dict):
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migrated = {}
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for key, item in value.items():
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migrated_key = str(key)
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if migrated_key.startswith(legacy_upper):
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migrated_key = f"FLM{migrated_key[len(legacy_upper):]}"
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elif migrated_key.startswith(legacy_lower):
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migrated_key = f"flm{migrated_key[len(legacy_lower):]}"
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migrated[migrated_key] = _migrate_legacy_payload(item)
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return migrated
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if isinstance(value, list):
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return [_migrate_legacy_payload(item) for item in value]
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if isinstance(value, str):
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legacy_method_name = "Firestar " + "Tuning Method"
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return value.replace(legacy_method_name, "Firestar Lateral Method").replace(legacy_upper, "FLM")
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return value
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def _migrate_legacy_workspace(root: Path) -> None:
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marker = root / ".flm_rebrand_v1"
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if marker.exists():
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return
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legacy_root = _legacy_workspace_root()
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if legacy_root.is_dir() and legacy_root != root:
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root.parent.mkdir(parents=True, exist_ok=True)
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if root.exists():
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shutil.copytree(legacy_root, root, dirs_exist_ok=True)
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shutil.rmtree(legacy_root)
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else:
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legacy_root.replace(root)
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if not root.exists():
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return
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legacy_lower = "".join(("f", "t", "m"))
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legacy_reference = root / "reference" / f"{legacy_lower}_reference.json"
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current_reference = root / "reference" / "flm_reference.json"
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if legacy_reference.is_file() and not current_reference.exists():
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legacy_reference.replace(current_reference)
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for path in root.rglob("*.json"):
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try:
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payload = json.loads(path.read_text(encoding="utf-8"))
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migrated = _migrate_legacy_payload(payload)
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if migrated != payload:
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path.write_text(json.dumps(migrated, indent=2, sort_keys=True), encoding="utf-8")
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except Exception:
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continue
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legacy_upper = legacy_lower.upper()
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for path in root.rglob("*.html"):
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try:
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content = path.read_text(encoding="utf-8")
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content = content.replace(legacy_upper, "FLM").replace(legacy_lower, "flm")
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path.write_text(content, encoding="utf-8")
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except Exception:
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continue
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marker.touch()
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def _workspace_paths() -> dict[str, Path]:
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root = get_flm_workspace_root()
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return {
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"root": root,
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"reports": root / "reports",
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"profiles": root / "profiles",
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"feedback": root / "feedback",
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"snapshots": root / "snapshots",
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"reference": root / "reference",
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}
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def ensure_flm_workspace() -> dict[str, Path]:
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_migrate_legacy_workspace(get_flm_workspace_root())
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paths = _workspace_paths()
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for key, path in paths.items():
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if key != "root":
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path.mkdir(parents=True, exist_ok=True)
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reference_path = paths["reference"] / "flm_reference.json"
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if not reference_path.exists():
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reference_path.write_text(json.dumps(FLM_REFERENCE_MODEL, indent=2, sort_keys=True), encoding="utf-8")
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return paths
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def _read_json(path: Path, default):
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try:
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return json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return default
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def _write_json(path: Path, payload) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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tmp_path = path.with_suffix(path.suffix + ".tmp")
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tmp_path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
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tmp_path.replace(path)
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def _worker_env(repo_root: Path) -> dict[str, str]:
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env = os.environ.copy()
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pythonpath = [
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"/usr/local/venv/lib/python3.12/site-packages",
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str(repo_root / "starpilot" / "third_party"),
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str(repo_root),
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]
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if env.get("PYTHONPATH"):
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pythonpath.append(env["PYTHONPATH"])
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env["PYTHONPATH"] = os.pathsep.join(pythonpath)
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env.setdefault("OPENBLAS_NUM_THREADS", "1")
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env.setdefault("OMP_NUM_THREADS", "1")
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env.setdefault("MKL_NUM_THREADS", "1")
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env.setdefault("NUMEXPR_NUM_THREADS", "1")
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return env
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def read_flm_status() -> dict[str, Any]:
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data = _read_json(FLM_STATUS_PATH, {})
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return data if isinstance(data, dict) else {}
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def _write_flm_status(payload: dict[str, Any]) -> None:
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payload = dict(payload)
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payload["updatedAt"] = time.time()
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tmp_path = FLM_STATUS_PATH.with_suffix(".tmp")
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tmp_path.write_text(json.dumps(payload, separators=(",", ":")), encoding="utf-8")
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tmp_path.replace(FLM_STATUS_PATH)
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def clear_flm_status() -> None:
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try:
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FLM_STATUS_PATH.unlink()
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except FileNotFoundError:
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pass
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except OSError:
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pass
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def flm_analyzer_running() -> bool:
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process = FLM_ANALYZER_PROCESS
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if process is not None and process.poll() is None:
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return True
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status = read_flm_status()
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pid = int(status.get("pid") or 0)
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started_at = float(status.get("startedAt") or 0.0)
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if pid <= 0 or started_at <= 0:
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return False
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if (time.time() - started_at) > FLM_STATUS_MAX_AGE_SECONDS:
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clear_flm_status()
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return False
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try:
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os.kill(pid, 0)
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except ProcessLookupError:
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clear_flm_status()
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return False
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except PermissionError:
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return True
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except OSError:
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return False
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return True
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def stop_flm_background_analysis() -> bool:
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global FLM_ANALYZER_PROCESS
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with FLM_ANALYZER_LOCK:
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process = FLM_ANALYZER_PROCESS
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status = read_flm_status()
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pid = int(status.get("pid") or 0)
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if process is not None and process.poll() is None:
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process.terminate()
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try:
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process.wait(timeout=2.0)
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except subprocess.TimeoutExpired:
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process.kill()
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FLM_ANALYZER_PROCESS = None
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clear_flm_status()
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return True
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if pid > 0:
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try:
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os.kill(pid, signal.SIGTERM)
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except ProcessLookupError:
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pass
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except OSError:
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return False
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clear_flm_status()
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return True
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return False
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def start_flm_background_analysis(route_names: list[str], footage_paths: list[str]) -> bool:
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global FLM_ANALYZER_PROCESS
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route_names = [str(route) for route in route_names if str(route).strip()]
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if not route_names:
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return False
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ensure_flm_workspace()
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with FLM_ANALYZER_LOCK:
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if flm_analyzer_running():
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return True
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repo_root = Path(__file__).resolve().parents[3]
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command = [
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"nice",
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"-n",
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"19",
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sys.executable or "python3",
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str(Path(__file__).resolve()),
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"worker",
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json.dumps({
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"routes": route_names[:FLM_ANALYZER_ROUTE_LIMIT],
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"footagePaths": [str(path) for path in footage_paths],
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}),
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]
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log_file = None
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try:
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log_file = open(FLM_LOG_PATH, "ab")
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FLM_ANALYZER_PROCESS = subprocess.Popen(
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command,
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cwd=str(repo_root),
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env=_worker_env(repo_root),
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stdout=log_file,
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stderr=log_file,
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start_new_session=True,
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)
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_write_flm_status({
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"pid": FLM_ANALYZER_PROCESS.pid,
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"startedAt": time.time(),
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"running": True,
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"state": "queued",
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"routes": route_names[:FLM_ANALYZER_ROUTE_LIMIT],
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"progress": 0,
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"total": len(route_names[:FLM_ANALYZER_ROUTE_LIMIT]),
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})
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except Exception:
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FLM_ANALYZER_PROCESS = None
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return False
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finally:
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if log_file is not None:
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log_file.close()
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return flm_analyzer_running()
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def _parse_segment_num(segment_name: str) -> int:
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try:
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return int(str(segment_name).rsplit("--", 1)[1])
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except Exception:
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return 0
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def resolve_route_sources(route_names: list[str], footage_paths: list[str]) -> tuple[list[RouteSource], list[str]]:
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sources: list[RouteSource] = []
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warnings: list[str] = []
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for route in route_names:
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route_added = False
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for footage_path in footage_paths:
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segments = utilities.get_segments_in_route(route, footage_path)
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if not segments:
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continue
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for segment in segments:
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segment_path = Path(footage_path) / segment
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rlog_path = None
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qlog_path = None
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for candidate in ("rlog.zst", "rlog.bz2", "rlog"):
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candidate_path = segment_path / candidate
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if candidate_path.exists():
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rlog_path = candidate_path
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break
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for candidate in ("qlog.zst", "qlog.bz2", "qlog"):
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candidate_path = segment_path / candidate
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if candidate_path.exists():
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qlog_path = candidate_path
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break
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log_path = rlog_path or qlog_path
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if log_path is None:
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continue
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if rlog_path is None and qlog_path is not None:
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warnings.append(f"{route} segment {segment} fell back to qlog.")
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sources.append(RouteSource(
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route=route,
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footage_path=str(footage_path),
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segment=segment,
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segment_num=_parse_segment_num(segment),
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log_path=str(log_path),
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used_qlog=rlog_path is None,
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))
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route_added = True
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if route_added:
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break
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if not route_added:
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warnings.append(f"{route} could not be resolved to a local route with logs.")
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sources.sort(key=lambda source: (source.route, source.segment_num))
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return sources, warnings
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def _speed_band_label(v_ego: float) -> str:
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if v_ego < 6.0:
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return "low"
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if v_ego < 15.0:
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return "mid"
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if v_ego < 25.0:
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return "fast"
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return "highway"
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def _route_label(route: str, segment: int) -> str:
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return f"{route}/{segment}"
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|
|
def _event_direction(samples: list[FLMSample]) -> str:
|
|
mean_desired = float(np.mean([sample.desired_la for sample in samples]))
|
|
if mean_desired > 0.02:
|
|
return "left"
|
|
if mean_desired < -0.02:
|
|
return "right"
|
|
return "center"
|
|
|
|
|
|
def _group_masked_events(samples: list[FLMSample], mask: list[bool], score_series: list[float], min_points: int = 5) -> list[dict[str, Any]]:
|
|
events: list[dict[str, Any]] = []
|
|
start_idx = None
|
|
for idx, active in enumerate(mask + [False]):
|
|
if active and start_idx is None:
|
|
start_idx = idx
|
|
continue
|
|
if active:
|
|
continue
|
|
if start_idx is None:
|
|
continue
|
|
end_idx = idx - 1
|
|
event_samples = samples[start_idx:end_idx + 1]
|
|
if len(event_samples) >= min_points:
|
|
event_scores = score_series[start_idx:end_idx + 1]
|
|
peak_offset = int(np.argmax(event_scores))
|
|
peak_idx = start_idx + peak_offset
|
|
peak_sample = samples[peak_idx]
|
|
direction = _event_direction(event_samples)
|
|
events.append({
|
|
"startIdx": start_idx,
|
|
"endIdx": end_idx,
|
|
"peakIdx": peak_idx,
|
|
"peakScore": float(event_scores[peak_offset]),
|
|
"route": peak_sample.route,
|
|
"segment": peak_sample.segment,
|
|
"speedBand": _speed_band_label(float(np.mean([sample.v_ego for sample in event_samples]))),
|
|
"direction": direction,
|
|
"supportCount": len(event_samples),
|
|
})
|
|
start_idx = None
|
|
return events
|
|
|
|
|
|
def _analysis_eligibility_mask(samples: list[FLMSample]) -> list[bool]:
|
|
eligible = [bool(sample.lat_active) for sample in samples]
|
|
group_start = 0
|
|
while group_start < len(samples):
|
|
group_key = (samples[group_start].route, samples[group_start].segment)
|
|
group_end = group_start + 1
|
|
while group_end < len(samples) and (samples[group_end].route, samples[group_end].segment) == group_key:
|
|
group_end += 1
|
|
|
|
last_override = -math.inf
|
|
for idx in range(group_start, group_end):
|
|
sample = samples[idx]
|
|
if sample.steering_pressed:
|
|
last_override = sample.t
|
|
if sample.steering_pressed or (sample.t - last_override) <= FLM_DRIVER_OVERRIDE_POST_BUFFER_S:
|
|
eligible[idx] = False
|
|
|
|
next_override = math.inf
|
|
for idx in range(group_end - 1, group_start - 1, -1):
|
|
sample = samples[idx]
|
|
if sample.steering_pressed:
|
|
next_override = sample.t
|
|
if sample.steering_pressed or (next_override - sample.t) <= FLM_DRIVER_OVERRIDE_PRE_BUFFER_S:
|
|
eligible[idx] = False
|
|
|
|
# Force an event boundary between route segments even when lateral control stays active.
|
|
eligible[group_start] = False
|
|
eligible[group_end - 1] = False
|
|
group_start = group_end
|
|
|
|
return eligible
|
|
|
|
|
|
def _build_plot_data(samples: list[FLMSample], event: dict[str, Any], eligibility: list[bool] | None = None) -> dict[str, Any]:
|
|
start_idx = int(event["startIdx"])
|
|
end_idx = int(event["endIdx"])
|
|
event_route = samples[start_idx].route
|
|
event_segment = samples[start_idx].segment
|
|
|
|
# Add a small amount of context without crossing an intervention buffer or
|
|
# segment boundary. The highlighted region remains the classified event.
|
|
for _ in range(12):
|
|
candidate = start_idx - 1
|
|
if candidate < 0 or (eligibility is not None and not eligibility[candidate]):
|
|
break
|
|
if samples[candidate].route != event_route or samples[candidate].segment != event_segment:
|
|
break
|
|
start_idx = candidate
|
|
for _ in range(12):
|
|
candidate = end_idx + 1
|
|
if candidate >= len(samples) or (eligibility is not None and not eligibility[candidate]):
|
|
break
|
|
if samples[candidate].route != event_route or samples[candidate].segment != event_segment:
|
|
break
|
|
end_idx = candidate
|
|
|
|
window = samples[start_idx:end_idx + 1]
|
|
if len(window) < 2:
|
|
return {}
|
|
|
|
# Keep reports lightweight on unusually long windows while preserving both ends.
|
|
if len(window) > 160:
|
|
indices = np.linspace(0, len(window) - 1, 160, dtype=int)
|
|
window = [window[int(idx)] for idx in indices]
|
|
|
|
times = np.array([sample.t for sample in window], dtype=float)
|
|
desired = np.array([sample.desired_la for sample in window], dtype=float)
|
|
actual = np.array([sample.actual_la for sample in window], dtype=float)
|
|
relative_times = times - float(times[0])
|
|
event_start_time = max(float(samples[int(event["startIdx"])].t - times[0]), 0.0)
|
|
event_end_time = max(float(samples[int(event["endIdx"])].t - times[0]), event_start_time)
|
|
|
|
return {
|
|
"times": [round(float(value), 3) for value in relative_times],
|
|
"desired": [round(float(value), 4) for value in desired],
|
|
"actual": [round(float(value), 4) for value in actual],
|
|
"windowDurationSec": round(float(relative_times[-1]), 2),
|
|
"eventStartSec": round(event_start_time, 2),
|
|
"eventEndSec": round(event_end_time, 2),
|
|
"eventDurationSec": round(max(event_end_time - event_start_time, 0.0), 2),
|
|
"meanSpeedMph": round(float(np.mean([sample.v_ego for sample in window])) * 2.236936, 1),
|
|
"route": event_route,
|
|
"segment": event_segment,
|
|
"segmentLabel": _route_label(event_route, event_segment),
|
|
"direction": str(event.get("direction", "center")),
|
|
"speedBand": str(event.get("speedBand", "mixed")),
|
|
"driverOverrideFree": bool(eligibility is None or all(eligibility[start_idx:end_idx + 1])),
|
|
}
|
|
|
|
|
|
def _build_plot_svg(plot_data: dict[str, Any]) -> str:
|
|
times = np.array(plot_data.get("times", []), dtype=float)
|
|
desired = np.array(plot_data.get("desired", []), dtype=float)
|
|
actual = np.array(plot_data.get("actual", []), dtype=float)
|
|
if len(times) < 2 or len(desired) != len(times) or len(actual) != len(times):
|
|
return ""
|
|
|
|
time_span = max(float(times.max()), 1e-3)
|
|
y_min = float(min(np.min(desired), np.min(actual)))
|
|
y_max = float(max(np.max(desired), np.max(actual)))
|
|
y_pad = max((y_max - y_min) * 0.10, 0.1)
|
|
y_min -= y_pad
|
|
y_max += y_pad
|
|
y_span = max(y_max - y_min, 1e-3)
|
|
|
|
def _points(series):
|
|
coords = []
|
|
for t_val, y_val in zip(times, series, strict=True):
|
|
x = (float(t_val) / time_span) * 380.0
|
|
y = 120.0 - (((float(y_val) - y_min) / y_span) * 120.0)
|
|
coords.append(f"{x:.1f},{y:.1f}")
|
|
return " ".join(coords)
|
|
|
|
return (
|
|
"<svg viewBox='0 0 380 140' class='flm-plot' preserveAspectRatio='none'>"
|
|
"<rect x='0' y='0' width='380' height='140' rx='8' ry='8' fill='#0f172a'/>"
|
|
"<line x1='0' y1='120' x2='380' y2='120' stroke='#334155' stroke-width='1'/>"
|
|
f"<polyline fill='none' stroke='#ef4444' stroke-width='2' points='{_points(desired)}'/>"
|
|
f"<polyline fill='none' stroke='#38bdf8' stroke-width='2' points='{_points(actual)}'/>"
|
|
"</svg>"
|
|
)
|
|
|
|
|
|
def _segment_samples(segment_source: RouteSource) -> tuple[list[FLMSample], car.CarParams | None, dict[str, str]]:
|
|
samples: list[FLMSample] = []
|
|
car_params = None
|
|
init_data: dict[str, str] = {}
|
|
latest: dict[str, Any] = {}
|
|
|
|
for msg in LogReader(segment_source.log_path, sort_by_time=True):
|
|
which = msg.which()
|
|
if which == "carParams" and car_params is None:
|
|
car_params = msg.carParams
|
|
continue
|
|
if which == "initData":
|
|
init = msg.initData
|
|
init_data = {
|
|
"gitCommit": str(getattr(init, "gitCommit", "") or ""),
|
|
"gitBranch": str(getattr(init, "gitBranch", "") or ""),
|
|
}
|
|
continue
|
|
if which == "carState":
|
|
latest["carState"] = msg.carState
|
|
continue
|
|
if which == "carControl":
|
|
latest["carControl"] = msg.carControl
|
|
continue
|
|
if which == "carOutput":
|
|
latest["carOutput"] = msg.carOutput
|
|
continue
|
|
if which == "liveParameters":
|
|
latest["liveParameters"] = msg.liveParameters
|
|
continue
|
|
if which != "controlsState" or "carState" not in latest or "carControl" not in latest:
|
|
continue
|
|
|
|
controls_state = msg.controlsState
|
|
lateral_state = controls_state.lateralControlState
|
|
if lateral_state.which() != "torqueState":
|
|
continue
|
|
|
|
torque_state = lateral_state.torqueState
|
|
car_state = latest["carState"]
|
|
car_control = latest["carControl"]
|
|
live_parameters = latest.get("liveParameters")
|
|
car_output = latest.get("carOutput")
|
|
roll_deg = math.degrees(float(getattr(live_parameters, "roll", 0.0) or 0.0)) if live_parameters is not None else 0.0
|
|
out_torque = float(getattr(getattr(car_output, "actuatorsOutput", None), "torque", 0.0) or 0.0) if car_output is not None else 0.0
|
|
|
|
samples.append(FLMSample(
|
|
route=segment_source.route,
|
|
segment=segment_source.segment_num,
|
|
t=float(msg.logMonoTime) / 1e9,
|
|
v_ego=float(getattr(car_state, "vEgo", 0.0) or 0.0),
|
|
lat_active=bool(getattr(car_control, "latActive", False)),
|
|
steering_pressed=bool(getattr(car_state, "steeringPressed", False)),
|
|
saturated=bool(getattr(torque_state, "saturated", False)),
|
|
actual_la=float(getattr(torque_state, "actualLateralAccel", 0.0) or 0.0),
|
|
desired_la=float(getattr(torque_state, "desiredLateralAccel", 0.0) or 0.0),
|
|
desired_jerk=float(getattr(torque_state, "desiredLateralJerk", 0.0) or 0.0),
|
|
error=float(getattr(torque_state, "error", 0.0) or 0.0),
|
|
error_rate=float(getattr(torque_state, "errorRate", 0.0) or 0.0),
|
|
p=float(getattr(torque_state, "p", 0.0) or 0.0),
|
|
i=float(getattr(torque_state, "i", 0.0) or 0.0),
|
|
d=float(getattr(torque_state, "d", 0.0) or 0.0),
|
|
f=float(getattr(torque_state, "f", 0.0) or 0.0),
|
|
output=float(getattr(torque_state, "output", 0.0) or 0.0),
|
|
steering_angle_deg=float(getattr(car_state, "steeringAngleDeg", 0.0) or 0.0),
|
|
steering_torque=float(getattr(car_state, "steeringTorque", 0.0) or 0.0),
|
|
cmd_torque=float(getattr(getattr(car_control, "actuators", None), "torque", 0.0) or 0.0),
|
|
out_torque=out_torque,
|
|
roll_deg=roll_deg,
|
|
))
|
|
|
|
return samples, car_params, init_data
|
|
|
|
|
|
def _current_param_state(CP, params: Params) -> dict[str, Any]:
|
|
advanced_enabled = params.get_bool("AdvancedLateralTune")
|
|
torque_tune = CP.lateralTuning.torque if CP.lateralTuning.which() == "torque" else None
|
|
stock_delay = full_lateral_delay(float(getattr(CP, "steerActuatorDelay", 0.0) or 0.0))
|
|
stock_ratio = float(getattr(CP, "steerRatio", 0.0) or 0.0)
|
|
stock_friction = float(getattr(torque_tune, "friction", 0.0) or 0.0) if torque_tune is not None else 0.0
|
|
stock_lat_accel = float(getattr(torque_tune, "latAccelFactor", 0.0) or 0.0) if torque_tune is not None else 0.0
|
|
return {
|
|
"AdvancedLateralTune": advanced_enabled,
|
|
"ForceAutoTune": params.get_bool("ForceAutoTune"),
|
|
"ForceAutoTuneOff": params.get_bool("ForceAutoTuneOff"),
|
|
"UseAutoSteerDelay": params.get_bool("UseAutoSteerDelay"),
|
|
"SteerDelay": params.get_float("SteerDelay", return_default=True, default=stock_delay) if advanced_enabled else stock_delay,
|
|
"SteerFriction": params.get_float("SteerFriction", return_default=True, default=stock_friction) if advanced_enabled else stock_friction,
|
|
"SteerKP": params.get_float("SteerKP", return_default=True, default=KP) if advanced_enabled else KP,
|
|
"SteerLatAccel": params.get_float("SteerLatAccel", return_default=True, default=stock_lat_accel) if advanced_enabled else stock_lat_accel,
|
|
"SteerRatio": params.get_float("SteerRatio", return_default=True, default=stock_ratio) if advanced_enabled else stock_ratio,
|
|
"FLMActiveProfileId": params.get("FLMActiveProfileId", encoding="utf-8") or "",
|
|
"FLMActiveOverrides": normalize_flm_overrides(params.get("FLMActiveOverrides", encoding="utf-8") or "{}"),
|
|
"FLMTrialApplied": params.get_bool("FLMTrialApplied"),
|
|
}
|
|
|
|
|
|
def _stock_param_state(CP, capabilities: dict[str, Any]) -> dict[str, Any]:
|
|
torque_tune = CP.lateralTuning.torque if CP.lateralTuning.which() == "torque" else None
|
|
friction_family = str(capabilities.get("frictionFamily", "standard"))
|
|
rich_profile = capabilities.get("richProfileKey")
|
|
rich_knobs = {
|
|
symbol: float(meta["defaultValue"])
|
|
for symbol, meta in get_flm_supported_vehicle_knobs().items()
|
|
if rich_profile and meta.get("profile") == rich_profile
|
|
}
|
|
return {
|
|
"UseAutoSteerDelay": True,
|
|
"SteerDelay": full_lateral_delay(float(getattr(CP, "steerActuatorDelay", 0.0) or 0.0)),
|
|
"SteerFriction": float(getattr(torque_tune, "friction", 0.0) or 0.0) if torque_tune is not None else 0.0,
|
|
"SteerKP": float(KP),
|
|
"SteerLatAccel": float(getattr(torque_tune, "latAccelFactor", 0.0) or 0.0) if torque_tune is not None else 0.0,
|
|
"SteerRatio": float(getattr(CP, "steerRatio", 0.0) or 0.0),
|
|
"FLMBaseFrictionThresholds": {
|
|
friction_family: {
|
|
"speedKnots": list(FLM_FRICTION_SPEED_KNOTS),
|
|
"values": _baseline_family_curve(friction_family),
|
|
},
|
|
} if torque_tune is not None else {},
|
|
"FLMVehicleKnobs": rich_knobs,
|
|
}
|
|
|
|
|
|
def _nonlinear_torque_map(CP) -> dict[str, Any]:
|
|
if str(getattr(CP, "brand", "") or "") != "gm":
|
|
return {}
|
|
|
|
try:
|
|
from opendbc.car.gm.interface import NON_LINEAR_TORQUE_PARAMS
|
|
except (ImportError, AttributeError):
|
|
return {}
|
|
|
|
raw_params = NON_LINEAR_TORQUE_PARAMS.get(CP.carFingerprint)
|
|
if raw_params is None:
|
|
return {}
|
|
|
|
if isinstance(raw_params, dict):
|
|
left = [float(value) for value in raw_params.get("left", [])]
|
|
right = [float(value) for value in raw_params.get("right", [])]
|
|
else:
|
|
left = [float(value) for value in raw_params]
|
|
right = list(left)
|
|
|
|
if len(left) != 4 or len(right) != 4:
|
|
return {}
|
|
|
|
return {
|
|
"type": "siglin",
|
|
"left": left,
|
|
"right": right,
|
|
"asymmetric": any(not math.isclose(left[idx], right[idx], abs_tol=1e-9) for idx in range(4)),
|
|
"learnedByLiveTorque": False,
|
|
}
|
|
|
|
|
|
def _baseline_family_curve(family: str) -> list[float]:
|
|
getter = {
|
|
"gm": get_gm_base_friction_threshold,
|
|
"standard": get_standard_friction_threshold,
|
|
"hkg_canfd": get_hkg_canfd_base_friction_threshold,
|
|
}.get(family, get_standard_friction_threshold)
|
|
return [round(float(getter(knot)), 4) for knot in FLM_FRICTION_SPEED_KNOTS]
|
|
|
|
|
|
def _current_family_curve(family: str, current: dict[str, Any]) -> list[float]:
|
|
active_overrides = current.get("FLMActiveOverrides", {}) if isinstance(current, dict) else {}
|
|
payload = active_overrides.get("baseFrictionThresholds", {}).get(family, {}) if isinstance(active_overrides, dict) else {}
|
|
values = payload.get("values", []) if isinstance(payload, dict) else []
|
|
if isinstance(values, list) and len(values) == len(FLM_FRICTION_SPEED_KNOTS):
|
|
try:
|
|
return [round(float(value), 4) for value in values]
|
|
except Exception:
|
|
pass
|
|
return _baseline_family_curve(family)
|
|
|
|
|
|
def _clamp(value: float, lower: float, upper: float) -> float:
|
|
return min(max(float(value), lower), upper)
|
|
|
|
|
|
def _round_to_precision(value: float, precision: float) -> float:
|
|
if precision <= 0:
|
|
return float(value)
|
|
steps = round(float(value) / precision)
|
|
return round(steps * precision, 6)
|
|
|
|
|
|
def _current_vehicle_knob_value(symbol: str, current: dict[str, Any]) -> float | None:
|
|
knob = get_flm_supported_vehicle_knobs().get(symbol)
|
|
if knob is None:
|
|
return None
|
|
|
|
active_overrides = current.get("FLMActiveOverrides", {}) if isinstance(current, dict) else {}
|
|
vehicle_knobs = active_overrides.get("vehicleKnobs", {}) if isinstance(active_overrides, dict) else {}
|
|
try:
|
|
return float(vehicle_knobs.get(symbol, knob["defaultValue"]))
|
|
except Exception:
|
|
return float(knob["defaultValue"])
|
|
|
|
|
|
def _vehicle_knob_adjustment(symbol: str, delta: float, current: dict[str, Any] | None = None) -> dict[str, Any] | None:
|
|
knob = get_flm_supported_vehicle_knobs().get(symbol)
|
|
if knob is None:
|
|
return None
|
|
current_value = _current_vehicle_knob_value(symbol, current or {})
|
|
if current_value is None:
|
|
return None
|
|
suggested_value = _round_to_precision(_clamp(current_value + delta, knob["min"], knob["max"]), knob["precision"])
|
|
if math.isclose(current_value, suggested_value, abs_tol=max(float(knob["precision"]) / 2.0, 1e-6)):
|
|
return None
|
|
return {
|
|
"type": "vehicle_knob",
|
|
"symbol": symbol,
|
|
"current": current_value,
|
|
"suggested": suggested_value,
|
|
"delta": round(suggested_value - current_value, 4),
|
|
}
|
|
|
|
|
|
def _rich_profile_supports_knob(capabilities: dict[str, Any], suffix: str) -> bool:
|
|
rich_profile = capabilities.get("richProfileKey")
|
|
if not rich_profile:
|
|
return False
|
|
return f"{rich_profile}.{suffix}" in get_flm_supported_vehicle_knobs()
|
|
|
|
|
|
def _build_event_summaries(samples: list[FLMSample]) -> tuple[list[dict[str, Any]], dict[str, Any]]:
|
|
eligibility = _analysis_eligibility_mask(samples)
|
|
active_samples = [sample for sample, allowed in zip(samples, eligibility, strict=True) if allowed]
|
|
if not active_samples:
|
|
return [], {"sampleCount": 0}
|
|
|
|
over_error = [abs(sample.actual_la) - abs(sample.desired_la) for sample in samples]
|
|
desired = [sample.desired_la for sample in samples]
|
|
jerk = [sample.desired_jerk for sample in samples]
|
|
angle = [sample.steering_angle_deg for sample in active_samples]
|
|
output = [sample.output for sample in active_samples]
|
|
|
|
entry_phase = [(abs(d) > 0.30 and abs(j) > 0.25 and d * j > 0.0) for d, j in zip(desired, jerk, strict=True)]
|
|
unwind_phase = [(abs(d) > 0.25 and abs(j) > 0.20 and d * j < 0.0) for d, j in zip(desired, jerk, strict=True)]
|
|
steady_curve_phase = [(abs(d) > 0.35 and abs(j) < 0.18) for d, j in zip(desired, jerk, strict=True)]
|
|
saturation_phase = [
|
|
bool(allowed and sample.saturated and abs(sample.desired_la) > 0.30 and (abs(sample.desired_jerk) > 0.16 or abs(sample.actual_la) > 0.45))
|
|
for sample, allowed in zip(samples, eligibility, strict=True)
|
|
]
|
|
|
|
base_masks = {
|
|
"understeer": [(allowed and phase and (ov < -0.20)) for allowed, phase, ov in zip(eligibility, steady_curve_phase, over_error, strict=True)],
|
|
"oversteer": [(allowed and phase and (ov > 0.20)) for allowed, phase, ov in zip(eligibility, steady_curve_phase, over_error, strict=True)],
|
|
"late_turn_in": [(allowed and phase and ov < -0.16) for allowed, phase, ov in zip(eligibility, entry_phase, over_error, strict=True)],
|
|
"early_turn_in": [(allowed and phase and ov > 0.16) for allowed, phase, ov in zip(eligibility, entry_phase, over_error, strict=True)],
|
|
"unwind_too_slow": [(allowed and phase and ov > 0.14) for allowed, phase, ov in zip(eligibility, unwind_phase, over_error, strict=True)],
|
|
"unwind_too_fast": [(allowed and phase and ov < -0.14) for allowed, phase, ov in zip(eligibility, unwind_phase, over_error, strict=True)],
|
|
"low_speed_unwillingness": [
|
|
bool(allowed and sample.v_ego < 6.0 and abs(sample.desired_la) > 0.30 and abs(sample.desired_jerk) > 0.18 and
|
|
(abs(sample.actual_la) + 0.18) < abs(sample.desired_la))
|
|
for sample, allowed in zip(samples, eligibility, strict=True)
|
|
],
|
|
"saturation_limited": saturation_phase,
|
|
}
|
|
score_map = {
|
|
"understeer": [max((-ov), 0.0) for ov in over_error],
|
|
"oversteer": [max(ov, 0.0) for ov in over_error],
|
|
"late_turn_in": [max((-ov), 0.0) + abs(j) * 0.1 for ov, j in zip(over_error, jerk, strict=True)],
|
|
"early_turn_in": [max(ov, 0.0) + abs(j) * 0.1 for ov, j in zip(over_error, jerk, strict=True)],
|
|
"unwind_too_slow": [max(ov, 0.0) for ov in over_error],
|
|
"unwind_too_fast": [max((-ov), 0.0) for ov in over_error],
|
|
"low_speed_unwillingness": [max(abs(d) - abs(sample.actual_la), 0.0) for d, sample in zip(desired, samples, strict=True)],
|
|
"saturation_limited": [1.0 if sample.saturated else 0.0 for sample in samples],
|
|
}
|
|
|
|
# Straight-road chatter detection uses a simple 4-second window.
|
|
straight_windows = []
|
|
for start_idx in range(0, max(len(samples) - 20, 1), 10):
|
|
window = samples[start_idx:start_idx + 40]
|
|
if len(window) < 20:
|
|
continue
|
|
if not all(eligibility[start_idx:start_idx + len(window)]):
|
|
continue
|
|
if float(np.mean([sample.v_ego for sample in window])) < 20.0:
|
|
continue
|
|
if float(np.mean([abs(sample.desired_la) for sample in window])) > 0.12:
|
|
continue
|
|
centered_angles = np.array([sample.steering_angle_deg for sample in window]) - float(np.mean([sample.steering_angle_deg for sample in window]))
|
|
sign_changes = int(np.sum(np.sign(centered_angles[1:]) != np.sign(centered_angles[:-1])))
|
|
amplitude = float(np.max(centered_angles) - np.min(centered_angles))
|
|
chatter_score = (amplitude * 0.25) + (sign_changes * 0.04)
|
|
if amplitude > 0.45 and sign_changes >= 6:
|
|
straight_windows.append({
|
|
"startIdx": start_idx,
|
|
"endIdx": start_idx + len(window) - 1,
|
|
"peakIdx": start_idx + int(len(window) / 2),
|
|
"peakScore": chatter_score,
|
|
"route": window[0].route,
|
|
"segment": window[0].segment,
|
|
"speedBand": "highway",
|
|
"direction": "center",
|
|
"supportCount": len(window),
|
|
})
|
|
|
|
curve_windows = []
|
|
for start_idx in range(0, max(len(samples) - 20, 1), 8):
|
|
window = samples[start_idx:start_idx + 36]
|
|
if len(window) < 18:
|
|
continue
|
|
if not all(eligibility[start_idx:start_idx + len(window)]):
|
|
continue
|
|
if float(np.mean([sample.v_ego for sample in window])) < 15.0:
|
|
continue
|
|
desired_sign = float(np.mean([sample.desired_la for sample in window]))
|
|
if abs(desired_sign) < 0.35:
|
|
continue
|
|
if any((sample.desired_la * desired_sign) < 0.0 for sample in window):
|
|
continue
|
|
error_series = np.array([sample.actual_la - sample.desired_la for sample in window])
|
|
sign_changes = int(np.sum(np.sign(error_series[1:]) != np.sign(error_series[:-1])))
|
|
amplitude = float(np.max(error_series) - np.min(error_series))
|
|
if amplitude > 0.22 and sign_changes >= 4:
|
|
curve_windows.append({
|
|
"startIdx": start_idx,
|
|
"endIdx": start_idx + len(window) - 1,
|
|
"peakIdx": start_idx + int(np.argmax(np.abs(error_series))),
|
|
"peakScore": amplitude + sign_changes * 0.03,
|
|
"route": window[0].route,
|
|
"segment": window[0].segment,
|
|
"speedBand": _speed_band_label(float(np.mean([sample.v_ego for sample in window]))),
|
|
"direction": "left" if desired_sign > 0.0 else "right",
|
|
"supportCount": len(window),
|
|
})
|
|
|
|
summaries: list[dict[str, Any]] = []
|
|
for bucket, mask in base_masks.items():
|
|
events = _group_masked_events(samples, mask, score_map[bucket])
|
|
if events:
|
|
summaries.extend(_summaries_from_events(bucket, samples, events, eligibility))
|
|
if straight_windows:
|
|
summaries.extend(_summaries_from_events("center_chatter", samples, straight_windows, eligibility))
|
|
if curve_windows:
|
|
summaries.extend(_summaries_from_events("notchy_mid_curve", samples, curve_windows, eligibility))
|
|
|
|
left_errors = [abs(sample.actual_la) - abs(sample.desired_la) for sample in active_samples if sample.desired_la > 0.25]
|
|
right_errors = [abs(sample.actual_la) - abs(sample.desired_la) for sample in active_samples if sample.desired_la < -0.25]
|
|
summary_stats = {
|
|
"sampleCount": len(active_samples),
|
|
"excludedDriverOverrideSamples": sum(1 for sample, allowed in zip(samples, eligibility, strict=True) if sample.lat_active and not allowed),
|
|
"qlogFallback": False,
|
|
"meanDesiredAbs": round(float(np.mean(np.abs([sample.desired_la for sample in active_samples]))), 4),
|
|
"meanErrorAbs": round(float(np.mean(np.abs([sample.actual_la - sample.desired_la for sample in active_samples]))), 4),
|
|
"leftBias": round(float(np.mean(left_errors)), 4) if left_errors else 0.0,
|
|
"rightBias": round(float(np.mean(right_errors)), 4) if right_errors else 0.0,
|
|
"highwayStraightAngleP2P": round(float(np.percentile(np.abs(angle), 95) - np.percentile(np.abs(angle), 5)), 4) if angle else 0.0,
|
|
"meanOutputAbs": round(float(np.mean(np.abs(output))), 4) if output else 0.0,
|
|
}
|
|
|
|
if summary_stats["meanErrorAbs"] < 0.08 and not any(summary["severity"] > 0.65 for summary in summaries):
|
|
summaries.append({
|
|
"bucket": "model_limited",
|
|
"dimensionId": "model_limited:overall",
|
|
"direction": "center",
|
|
"speedBand": "mixed",
|
|
"count": 1,
|
|
"severity": 0.25,
|
|
"evidence": {
|
|
"speedBand": "mixed",
|
|
"directionBias": "center",
|
|
"eventCount": 1,
|
|
"segments": [],
|
|
},
|
|
"events": [],
|
|
"plotSvg": "",
|
|
"plotData": {},
|
|
})
|
|
|
|
return sorted(summaries, key=lambda item: item["severity"], reverse=True), summary_stats
|
|
|
|
|
|
def _summaries_from_events(bucket: str, samples: list[FLMSample], events: list[dict[str, Any]],
|
|
eligibility: list[bool] | None = None) -> list[dict[str, Any]]:
|
|
grouped: dict[tuple[str, str], list[dict[str, Any]]] = {}
|
|
for event in events:
|
|
key = (bucket, event["direction"])
|
|
grouped.setdefault(key, []).append(event)
|
|
|
|
summaries = []
|
|
for (bucket_name, direction), grouped_events in grouped.items():
|
|
grouped_events.sort(key=lambda item: item["peakScore"], reverse=True)
|
|
strongest = grouped_events[:3]
|
|
strongest_labels = [
|
|
{
|
|
"route": event["route"],
|
|
"segment": event["segment"],
|
|
"label": _route_label(event["route"], event["segment"]),
|
|
"score": round(float(event["peakScore"]), 3),
|
|
}
|
|
for event in strongest
|
|
]
|
|
top_event = strongest[0]
|
|
top_speed_band = top_event["speedBand"]
|
|
plot_data = _build_plot_data(samples, top_event, eligibility)
|
|
summaries.append({
|
|
"bucket": bucket_name,
|
|
"dimensionId": f"{bucket_name}:{direction}:{top_speed_band}",
|
|
"direction": direction,
|
|
"speedBand": top_speed_band,
|
|
"count": len(grouped_events),
|
|
"severity": round(float(min(1.5, np.mean([event["peakScore"] for event in strongest]))), 3),
|
|
"evidence": {
|
|
"speedBand": top_speed_band,
|
|
"directionBias": direction,
|
|
"eventCount": len(grouped_events),
|
|
"segments": strongest_labels,
|
|
},
|
|
"events": grouped_events,
|
|
"plotSvg": _build_plot_svg(plot_data),
|
|
"plotData": plot_data,
|
|
})
|
|
return summaries
|
|
|
|
|
|
def classify_torque_samples(samples: list[FLMSample]) -> tuple[list[dict[str, Any]], dict[str, Any]]:
|
|
return _build_event_summaries(samples)
|
|
|
|
|
|
def _primary_delta_from_summary(summary: dict[str, Any], capabilities: dict[str, Any], current: dict[str, Any],
|
|
strategy: str = "cleanup") -> dict[str, Any] | None:
|
|
bucket = summary["bucket"]
|
|
direction = summary["direction"]
|
|
severity = max(float(summary["severity"]), 0.2)
|
|
speed_band = str(summary.get("speedBand", "mixed"))
|
|
rich_profile = capabilities.get("richProfileKey")
|
|
family = capabilities.get("frictionFamily", "standard")
|
|
side = "left" if direction != "right" else "right"
|
|
curvy_band = speed_band in ("mid", "fast")
|
|
supports_low_speed_assist = _rich_profile_supports_knob(capabilities, "low_speed_angle_assist_max_torque")
|
|
supports_crawl_turn_in = _rich_profile_supports_knob(capabilities, f"crawl_turn_in_ff_boost_{side}")
|
|
supports_turn_in_boost = _rich_profile_supports_knob(capabilities, f"turn_in_boost_{side}")
|
|
supports_unwind_taper = _rich_profile_supports_knob(capabilities, f"unwind_taper_{side}")
|
|
supports_curvy_turn_in_trim = _rich_profile_supports_knob(capabilities, f"curvy_turn_in_trim_{side}")
|
|
supports_curvy_turn_in_speed = _rich_profile_supports_knob(capabilities, "curvy_turn_in_trim_speed_max")
|
|
supports_curvy_speed_max = _rich_profile_supports_knob(capabilities, "curvy_speed_max")
|
|
supports_curvy_unwind_extra = _rich_profile_supports_knob(capabilities, f"curvy_unwind_extra_reduction_{side}")
|
|
supports_curvy_unwind_floor = _rich_profile_supports_knob(capabilities, f"curvy_unwind_floor_relief_{side}")
|
|
supports_ff_gain = _rich_profile_supports_knob(capabilities, f"ff_gain_{side}")
|
|
nonlinear_map = capabilities.get("nonlinearTorqueMap", {})
|
|
asymmetric_nonlinear_map = bool(isinstance(nonlinear_map, dict) and nonlinear_map.get("asymmetric"))
|
|
|
|
if bucket == "model_limited":
|
|
return None
|
|
|
|
if strategy == "baseline":
|
|
if bucket in ("center_chatter", "notchy_mid_curve"):
|
|
current_curve = _current_family_curve(family, current)
|
|
deltas = [0.0, 0.01, 0.02, 0.025, 0.03] if bucket == "center_chatter" else [0.0, 0.0, 0.015, 0.02, 0.02]
|
|
scale = min(max(severity, 0.4), 1.2)
|
|
suggested = [round(current_curve[idx] + (delta * scale), 4) for idx, delta in enumerate(deltas)]
|
|
return {
|
|
"type": "friction_curve",
|
|
"symbol": f"base_friction_threshold.{family}",
|
|
"family": family,
|
|
"current": current_curve,
|
|
"suggested": suggested,
|
|
"delta": [round(suggested[idx] - current_curve[idx], 4) for idx in range(len(current_curve))],
|
|
}
|
|
|
|
if bucket == "low_speed_unwillingness":
|
|
current_curve = _current_family_curve(family, current)
|
|
deltas = [-0.03, -0.025, -0.015, -0.005, 0.0]
|
|
scale = min(max(severity, 0.5), 1.2)
|
|
suggested = [round(max(0.05, current_curve[idx] + (delta * scale)), 4) for idx, delta in enumerate(deltas)]
|
|
return {
|
|
"type": "friction_curve",
|
|
"symbol": f"base_friction_threshold.{family}",
|
|
"family": family,
|
|
"current": current_curve,
|
|
"suggested": suggested,
|
|
"delta": [round(suggested[idx] - current_curve[idx], 4) for idx in range(len(current_curve))],
|
|
}
|
|
|
|
if bucket in ("understeer", "late_turn_in", "saturation_limited"):
|
|
if asymmetric_nonlinear_map and direction in ("left", "right") and supports_ff_gain:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.ff_gain_{side}", 0.025 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
current_value = float(current["SteerLatAccel"])
|
|
scale = 0.04 if bucket == "saturation_limited" else 0.03
|
|
suggested_value = round(_clamp(current_value + max(scale, current_value * scale * severity), 0.5, 5.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerLatAccel", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
if bucket in ("oversteer", "early_turn_in"):
|
|
if asymmetric_nonlinear_map and direction in ("left", "right") and supports_ff_gain:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.ff_gain_{side}", -0.025 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
current_value = float(current["SteerLatAccel"])
|
|
suggested_value = round(_clamp(current_value - max(0.03, current_value * 0.03 * severity), 0.5, 5.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerLatAccel", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
if bucket in ("unwind_too_slow", "unwind_too_fast"):
|
|
current_value = float(current["SteerFriction"])
|
|
direction_mult = -1.0 if bucket == "unwind_too_slow" else 1.0
|
|
suggested_value = round(_clamp(current_value + (0.015 * severity * direction_mult), 0.0, 1.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerFriction", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
if bucket in ("center_chatter", "notchy_mid_curve"):
|
|
current_curve = _current_family_curve(family, current)
|
|
if bucket == "center_chatter":
|
|
deltas = [0.0, 0.01, 0.02, 0.025, 0.03]
|
|
else:
|
|
deltas = [0.0, 0.0, 0.015, 0.02, 0.02]
|
|
scale = min(max(severity, 0.4), 1.2)
|
|
suggested = [round(current_curve[idx] + (delta * scale), 4) for idx, delta in enumerate(deltas)]
|
|
return {
|
|
"type": "friction_curve",
|
|
"symbol": f"base_friction_threshold.{family}",
|
|
"family": family,
|
|
"current": current_curve,
|
|
"suggested": suggested,
|
|
"delta": [round(suggested[idx] - current_curve[idx], 4) for idx in range(len(current_curve))],
|
|
}
|
|
|
|
if bucket == "low_speed_unwillingness":
|
|
current_curve = _current_family_curve(family, current)
|
|
deltas = [-0.03, -0.025, -0.015, -0.005, 0.0]
|
|
scale = min(max(severity, 0.5), 1.2)
|
|
if supports_low_speed_assist:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.low_speed_angle_assist_max_torque", 0.04 * scale, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
if supports_crawl_turn_in:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.crawl_turn_in_ff_boost_{side}", 0.03 * scale, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
if rich_profile and supports_turn_in_boost:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.turn_in_boost_{side}", 0.025 * scale, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
suggested = [round(max(0.05, current_curve[idx] + (delta * scale)), 4) for idx, delta in enumerate(deltas)]
|
|
return {
|
|
"type": "friction_curve",
|
|
"symbol": f"base_friction_threshold.{family}",
|
|
"family": family,
|
|
"current": current_curve,
|
|
"suggested": suggested,
|
|
"delta": [round(suggested[idx] - current_curve[idx], 4) for idx in range(len(current_curve))],
|
|
}
|
|
|
|
if bucket in ("understeer", "late_turn_in"):
|
|
if curvy_band and speed_band == "fast" and bucket == "late_turn_in" and supports_curvy_turn_in_speed:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.curvy_turn_in_trim_speed_max", 1.6 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
if curvy_band and supports_curvy_turn_in_trim:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.curvy_turn_in_trim_{side}", -0.018 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
if rich_profile and supports_turn_in_boost:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.turn_in_boost_{side}", 0.02 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
current_value = float(current["SteerLatAccel"])
|
|
suggested_value = round(_clamp(current_value + max(0.03, current_value * 0.03 * severity), 0.5, 5.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerLatAccel", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
if bucket in ("oversteer", "early_turn_in"):
|
|
if curvy_band and supports_curvy_turn_in_trim:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.curvy_turn_in_trim_{side}", 0.018 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
if rich_profile and supports_turn_in_boost:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.turn_in_boost_{side}", -0.02 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
current_value = float(current["SteerLatAccel"])
|
|
suggested_value = round(_clamp(current_value - max(0.03, current_value * 0.03 * severity), 0.5, 5.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerLatAccel", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
if bucket in ("unwind_too_slow", "unwind_too_fast"):
|
|
if curvy_band and supports_curvy_unwind_extra:
|
|
if bucket == "unwind_too_slow" and speed_band == "fast" and supports_curvy_speed_max:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.curvy_speed_max", 1.8 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
direction_mult = 1.0 if bucket == "unwind_too_slow" else -1.0
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.curvy_unwind_extra_reduction_{side}", 0.03 * severity * direction_mult, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
if rich_profile and supports_unwind_taper:
|
|
direction_mult = 1.0 if bucket == "unwind_too_slow" else -1.0
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.unwind_taper_{side}", 0.08 * severity * direction_mult, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
current_value = float(current["SteerFriction"])
|
|
direction_mult = -1.0 if bucket == "unwind_too_slow" else 1.0
|
|
suggested_value = round(_clamp(current_value + (0.015 * severity * direction_mult), 0.0, 1.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerFriction", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
if bucket == "saturation_limited":
|
|
if curvy_band and supports_curvy_unwind_floor:
|
|
adjustment = _vehicle_knob_adjustment(f"{rich_profile}.curvy_unwind_floor_relief_{side}", 0.04 * severity, current)
|
|
if adjustment is not None:
|
|
return adjustment
|
|
current_value = float(current["SteerLatAccel"])
|
|
suggested_value = round(_clamp(current_value + max(0.04, current_value * 0.04 * severity), 0.5, 5.0), 4)
|
|
return {"type": "generic_param", "paramKey": "SteerLatAccel", "current": current_value, "suggested": suggested_value, "delta": round(suggested_value - current_value, 4)}
|
|
|
|
return None
|
|
|
|
|
|
def _observed_behavior(summary: dict[str, Any]) -> str:
|
|
bucket = summary["bucket"]
|
|
direction = summary["direction"]
|
|
speed_band = summary["speedBand"]
|
|
direction_text = "" if direction == "center" else f" on {direction} {speed_band} inputs"
|
|
mapping = {
|
|
"understeer": f"The car is not matching requested lateral accel{direction_text}; it stays wider than plan before recovery.",
|
|
"oversteer": f"The car is exceeding requested lateral accel{direction_text}; it is stepping past plan before correcting back.",
|
|
"late_turn_in": f"Turn-in is late{direction_text}; desired lateral accel is already building while actual response lags.",
|
|
"early_turn_in": f"Turn-in is too eager{direction_text}; actual response jumps ahead of the plan during entry.",
|
|
"unwind_too_slow": f"Unwind is hanging on too long{direction_text}; the car keeps steering after the plan starts releasing.",
|
|
"unwind_too_fast": f"Unwind is releasing too quickly{direction_text}; the wheel gives back steering sooner than the plan wants.",
|
|
"center_chatter": "The car is doing repeated micro-corrections on straights or very light highway arcs.",
|
|
"notchy_mid_curve": "Mid-curve tracking is correcting in steps instead of flowing through the same steering band cleanly.",
|
|
"low_speed_unwillingness": "At low speed the controller is slow to wake up even though the turn request is already there.",
|
|
"saturation_limited": "The controller is spending meaningful time at or near its steering authority ceiling.",
|
|
"model_limited": "The controller is largely matching the commanded path; this sample does not show a strong tuning mismatch.",
|
|
}
|
|
return mapping.get(bucket, "The controller is showing a repeatable mismatch against the requested path.")
|
|
|
|
|
|
def _likely_interpretation(summary: dict[str, Any], adjustment: dict[str, Any]) -> str:
|
|
bucket = summary["bucket"]
|
|
if adjustment["type"] == "friction_curve":
|
|
if bucket == "low_speed_unwillingness":
|
|
return "The near-center friction threshold is too high in the crawl-speed band, so small requests are being muted."
|
|
return "This looks more like a friction-threshold problem than a whole-tune problem; the controller is busy around center and needs a calmer deadzone slope."
|
|
if adjustment["type"] == "vehicle_knob":
|
|
symbol = adjustment["symbol"]
|
|
if "ff_gain_" in symbol:
|
|
return "This car has a directional nonlinear torque map, and the mismatch is concentrated on one side. Correct that side's feedforward layer before moving global authority."
|
|
if "low_speed_angle_assist_max_torque" in symbol:
|
|
return "The main torque path is waking up too late below about 8 mph, so the low-speed assist layer needs a little more authority."
|
|
if "crawl_turn_in_ff_boost" in symbol:
|
|
return "The crawl-speed turn-in band is still too lazy, even before the broader tune needs to move."
|
|
if "curvy_speed_max" in symbol:
|
|
return "The curvy unwind band is dropping out too early at higher speed, so the curve-specific release cleanup is not staying active long enough."
|
|
if "curvy_turn_in_trim_speed_max" in symbol:
|
|
return "The fast-curve entry trim band is fading out too early, so the controller is falling back to the base path before the curve is done."
|
|
if "curvy_turn_in_trim" in symbol:
|
|
return "This is a curve-band entry problem, not a whole-car turn-in problem; the mid-speed trim needs to move without touching the rest of the tune."
|
|
if "curvy_unwind" in symbol:
|
|
return "This is a curve-band release problem, not a global unwind problem; the mid-speed unwind cleanup needs to move on its own."
|
|
if bucket in ("understeer", "late_turn_in", "low_speed_unwillingness", "saturation_limited"):
|
|
return "Primary turn-in authority is too low for the way this car is reacting in that band."
|
|
if bucket in ("oversteer", "early_turn_in"):
|
|
return "Entry authority is too aggressive for the speed band being hit here."
|
|
if bucket == "unwind_too_slow":
|
|
return "Exit steering is being held too long after the plan starts backing out of the curve."
|
|
if bucket == "unwind_too_fast":
|
|
return "Exit steering is tapering away too quickly once unwind starts."
|
|
return "The mismatch is consistent enough to justify a direct tuning pass."
|
|
|
|
|
|
def _why_this_knob(adjustment: dict[str, Any]) -> str:
|
|
if adjustment["type"] == "friction_curve":
|
|
return "This changes the threshold that maps small lateral-accel error into friction compensation without pretending the whole torque slope is wrong."
|
|
if adjustment["type"] == "vehicle_knob":
|
|
symbol = adjustment["symbol"]
|
|
if "ff_gain_" in symbol:
|
|
return "This compensates the affected side without flattening the car's separate left/right nonlinear torque response into one global value."
|
|
if "low_speed_angle_assist_max_torque" in symbol:
|
|
return "This directly raises the crawl-speed assist ceiling that fills the gap before the normal torque path wakes up."
|
|
if "crawl_turn_in_ff_boost" in symbol:
|
|
return "This only touches the crawl-speed turn-in band instead of disturbing normal-speed behavior."
|
|
if "curvy_speed_max" in symbol:
|
|
return "This keeps the dedicated curvy unwind helper alive deeper into faster curves instead of globally changing the whole unwind map."
|
|
if "curvy_turn_in_trim_speed_max" in symbol:
|
|
return "This extends the fast-curve trim band instead of making the whole car more eager to turn everywhere."
|
|
if "curvy_turn_in_trim" in symbol:
|
|
return "This trims entry only in the dedicated curvy speed band instead of flattening turn-in everywhere."
|
|
if "curvy_unwind" in symbol:
|
|
return "This cleans up release only in the dedicated curvy speed band instead of changing global unwind behavior."
|
|
if "turn_in_boost" in symbol:
|
|
return "This targets entry behavior directly instead of disturbing the whole tune."
|
|
if "unwind_taper" in symbol:
|
|
return "This targets release behavior directly instead of flattening the whole response."
|
|
if "threshold" in symbol:
|
|
return "This adjusts the transition deadzone for the specific phase that is misbehaving."
|
|
return "This is the closest car-specific knob to the symptom being shown."
|
|
return "This is the smallest generic user-facing change that moves the car in the right direction without inventing a new code path."
|
|
|
|
|
|
def _render_adjustment_line(adjustment: dict[str, Any]) -> str:
|
|
if adjustment["type"] == "friction_curve":
|
|
curve = ", ".join(f"{value:.3f}" for value in adjustment["suggested"])
|
|
return f"Adjust {adjustment['family']} friction threshold curve at {FLM_FRICTION_SPEED_KNOTS} m/s to [{curve}]."
|
|
if adjustment["type"] == "vehicle_knob":
|
|
return f"Move `{adjustment['symbol']}` from {adjustment['current']:.3f} to {adjustment['suggested']:.3f}."
|
|
return f"Move `{adjustment['paramKey']}` from {adjustment['current']:.3f} to {adjustment['suggested']:.3f}."
|
|
|
|
|
|
def _what_not_to_touch_yet(summary: dict[str, Any], adjustment: dict[str, Any] | None, strategy: str) -> str:
|
|
if strategy == "baseline":
|
|
if adjustment and adjustment.get("type") in ("generic_param", "friction_curve"):
|
|
return "Do not jump straight into phase-specific cleanup knobs yet. Get the broad authority and friction behavior into the right zip code first."
|
|
return "Do not start layering narrow cleanup knobs onto a car that is still broadly wrong."
|
|
if adjustment and adjustment.get("type") == "generic_param":
|
|
return "Do not widen this into a whole-car ratio or delay change first. This symptom can usually be cleaned up without global geometry edits."
|
|
return "Do not change unrelated center-taper or steer-ratio behavior first. This symptom has a narrower cause than that."
|
|
|
|
|
|
def _if_that_was_wrong(summary: dict[str, Any], adjustment: dict[str, Any], strategy: str) -> str:
|
|
if strategy == "baseline":
|
|
return f"If this gets the car broadly closer but leaves one specific phase ugly, stop here and switch to Cleanup Pass for that band. {_why_this_knob(adjustment)}"
|
|
return f"If this cleans up the main symptom but introduces the opposite behavior, keep half the change and move to the next phase-specific knob. {_why_this_knob(adjustment)}"
|
|
|
|
|
|
def build_suggestions(summaries: list[dict[str, Any]], capabilities: dict[str, Any], current: dict[str, Any],
|
|
strategy: str = "cleanup") -> list[dict[str, Any]]:
|
|
suggestions = []
|
|
for summary in summaries:
|
|
adjustment = _primary_delta_from_summary(summary, capabilities, current, strategy=strategy)
|
|
evidence = summary.get("evidence", {})
|
|
if adjustment is None:
|
|
suggestions.append({
|
|
"dimensionId": summary["dimensionId"],
|
|
"bucket": summary["bucket"],
|
|
"severity": float(summary.get("severity", 0.0)),
|
|
"evidence": evidence,
|
|
"currentVsSuggested": None,
|
|
"observedBehavior": _observed_behavior(summary),
|
|
"likelyInterpretation": _likely_interpretation(summary, {"type": "generic_param", "paramKey": "none"}),
|
|
"primaryAdjustment": "Do not change the tune yet.",
|
|
"whatNotToTouchYet": "Do not start cutting or adding turn-in. This sample does not show a clean controller-side miss.",
|
|
"ifThatWasWrong": "If a stronger sample later shows actual lateral accel lagging or overshooting the plan, revisit with that route.",
|
|
"strategy": strategy,
|
|
"plotSvg": summary.get("plotSvg", ""),
|
|
"plotData": summary.get("plotData", {}),
|
|
})
|
|
continue
|
|
|
|
if adjustment["type"] == "friction_curve":
|
|
current_vs_suggested = {
|
|
"type": "friction_curve",
|
|
"family": adjustment["family"],
|
|
"current": adjustment["current"],
|
|
"suggested": adjustment["suggested"],
|
|
}
|
|
elif adjustment["type"] == "vehicle_knob":
|
|
current_vs_suggested = {
|
|
"type": "vehicle_knob",
|
|
"symbol": adjustment["symbol"],
|
|
"current": adjustment["current"],
|
|
"suggested": adjustment["suggested"],
|
|
}
|
|
else:
|
|
current_vs_suggested = {
|
|
"type": "generic_param",
|
|
"paramKey": adjustment["paramKey"],
|
|
"current": adjustment["current"],
|
|
"suggested": adjustment["suggested"],
|
|
}
|
|
|
|
suggestions.append({
|
|
"dimensionId": summary["dimensionId"],
|
|
"bucket": summary["bucket"],
|
|
"severity": float(summary.get("severity", 0.0)),
|
|
"evidence": evidence,
|
|
"currentVsSuggested": current_vs_suggested,
|
|
"primaryAdjustmentRaw": adjustment,
|
|
"strategy": strategy,
|
|
"observedBehavior": _observed_behavior(summary),
|
|
"likelyInterpretation": _likely_interpretation(summary, adjustment),
|
|
"primaryAdjustment": _render_adjustment_line(adjustment),
|
|
"whatNotToTouchYet": _what_not_to_touch_yet(summary, adjustment, strategy),
|
|
"ifThatWasWrong": _if_that_was_wrong(summary, adjustment, strategy),
|
|
"driverFeel": _observed_behavior(summary),
|
|
"logSupport": f"Matched in {evidence.get('eventCount', 0)} event(s); strongest samples: {', '.join(item['label'] for item in evidence.get('segments', [])[:3]) or 'none'}",
|
|
"whyThisKnob": _why_this_knob(adjustment),
|
|
"plotSvg": summary.get("plotSvg", ""),
|
|
"plotData": summary.get("plotData", {}),
|
|
})
|
|
return suggestions
|
|
|
|
|
|
def _clamp_generic_param(param_key: str, value: float) -> float:
|
|
meta = GENERIC_PARAM_METADATA[param_key]
|
|
return _round_to_precision(_clamp(value, meta["min"], meta["max"]), meta["precision"])
|
|
|
|
|
|
def _merge_primary_adjustments(suggestions: list[dict[str, Any]], multiplier: float) -> tuple[dict[str, Any], dict[str, Any], bool]:
|
|
params_delta: dict[str, Any] = {"AdvancedLateralTune": True}
|
|
requires_force_auto_tune_off = False
|
|
generic_targets: dict[str, dict[str, Any]] = {}
|
|
vehicle_targets: dict[str, dict[str, Any]] = {}
|
|
friction_targets: dict[str, dict[str, Any]] = {}
|
|
|
|
for suggestion in suggestions:
|
|
adjustment = suggestion.get("primaryAdjustmentRaw")
|
|
if not isinstance(adjustment, dict):
|
|
continue
|
|
weight = max(float(suggestion.get("severity", 0.0)), 0.25)
|
|
if adjustment["type"] == "generic_param":
|
|
param_key = adjustment["paramKey"]
|
|
bucket = generic_targets.setdefault(param_key, {
|
|
"current": float(adjustment["current"]),
|
|
"weightedDelta": 0.0,
|
|
"weight": 0.0,
|
|
})
|
|
bucket["weightedDelta"] += float(adjustment["delta"]) * weight
|
|
bucket["weight"] += weight
|
|
if param_key in ("SteerFriction", "SteerLatAccel", "SteerKP", "SteerDelay", "SteerRatio"):
|
|
requires_force_auto_tune_off = True
|
|
elif adjustment["type"] == "vehicle_knob":
|
|
symbol = adjustment["symbol"]
|
|
bucket = vehicle_targets.setdefault(symbol, {
|
|
"current": float(adjustment["current"]),
|
|
"weightedDelta": 0.0,
|
|
"weight": 0.0,
|
|
})
|
|
bucket["weightedDelta"] += float(adjustment["delta"]) * weight
|
|
bucket["weight"] += weight
|
|
requires_force_auto_tune_off = True
|
|
elif adjustment["type"] == "friction_curve":
|
|
family = adjustment["family"]
|
|
delta_curve = [float(value) for value in adjustment["delta"]]
|
|
bucket = friction_targets.setdefault(family, {
|
|
"current": [float(value) for value in adjustment["current"]],
|
|
"weightedDelta": [0.0] * len(delta_curve),
|
|
"weight": 0.0,
|
|
})
|
|
for idx, value in enumerate(delta_curve):
|
|
bucket["weightedDelta"][idx] += value * weight
|
|
bucket["weight"] += weight
|
|
requires_force_auto_tune_off = True
|
|
|
|
overrides: dict[str, Any] = {"schemaVersion": 1, "baseFrictionThresholds": {}, "vehicleKnobs": {}}
|
|
for param_key, bucket in generic_targets.items():
|
|
avg_delta = (bucket["weightedDelta"] / bucket["weight"]) * multiplier if bucket["weight"] > 0 else 0.0
|
|
next_value = _clamp_generic_param(param_key, float(bucket["current"]) + avg_delta)
|
|
precision = float(GENERIC_PARAM_METADATA[param_key]["precision"])
|
|
if not math.isclose(float(bucket["current"]), next_value, abs_tol=max(precision / 2.0, 1e-6)):
|
|
params_delta[param_key] = next_value
|
|
|
|
if "SteerDelay" in params_delta:
|
|
params_delta["UseAutoSteerDelay"] = False
|
|
|
|
supported_knobs = get_flm_supported_vehicle_knobs()
|
|
for symbol, bucket in vehicle_targets.items():
|
|
meta = supported_knobs.get(symbol)
|
|
if meta is None or bucket["weight"] <= 0:
|
|
continue
|
|
avg_delta = (bucket["weightedDelta"] / bucket["weight"]) * multiplier
|
|
next_value = _round_to_precision(_clamp(float(bucket["current"]) + avg_delta, meta["min"], meta["max"]), meta["precision"])
|
|
if not math.isclose(float(bucket["current"]), next_value, abs_tol=max(float(meta["precision"]) / 2.0, 1e-6)):
|
|
overrides["vehicleKnobs"][symbol] = next_value
|
|
|
|
for family, bucket in friction_targets.items():
|
|
if bucket["weight"] <= 0:
|
|
continue
|
|
avg_delta_curve = [value / bucket["weight"] for value in bucket["weightedDelta"]]
|
|
values = [
|
|
round(max(0.05, float(bucket["current"][idx]) + (avg_delta_curve[idx] * multiplier)), 4)
|
|
for idx in range(len(bucket["current"]))
|
|
]
|
|
if any(not math.isclose(float(bucket["current"][idx]), values[idx], abs_tol=1e-6) for idx in range(len(values))):
|
|
overrides["baseFrictionThresholds"][family] = {"speedKnots": list(FLM_FRICTION_SPEED_KNOTS), "values": values}
|
|
|
|
overrides = normalize_flm_overrides(overrides)
|
|
return params_delta, overrides, requires_force_auto_tune_off
|
|
|
|
|
|
def _resolve_conflicting_actionable_suggestions(suggestions: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
|
families = {
|
|
"understeer": ("turn_in", "more"),
|
|
"late_turn_in": ("turn_in", "more"),
|
|
"oversteer": ("turn_in", "less"),
|
|
"early_turn_in": ("turn_in", "less"),
|
|
"unwind_too_slow": ("unwind", "more"),
|
|
"unwind_too_fast": ("unwind", "less"),
|
|
}
|
|
grouped: dict[tuple[str, str, str], dict[str, list[dict[str, Any]]]] = {}
|
|
passthrough: list[dict[str, Any]] = []
|
|
|
|
for suggestion in suggestions:
|
|
bucket = str(suggestion.get("bucket", ""))
|
|
family_info = families.get(bucket)
|
|
if family_info is None:
|
|
passthrough.append(suggestion)
|
|
continue
|
|
evidence = suggestion.get("evidence", {})
|
|
key = (
|
|
family_info[0],
|
|
str(evidence.get("directionBias", "center")),
|
|
str(evidence.get("speedBand", "mixed")),
|
|
)
|
|
grouped.setdefault(key, {"more": [], "less": []})[family_info[1]].append(suggestion)
|
|
|
|
resolved = list(passthrough)
|
|
for polarities in grouped.values():
|
|
more = polarities["more"]
|
|
less = polarities["less"]
|
|
if not more or not less:
|
|
resolved.extend(more or less)
|
|
continue
|
|
|
|
def score(items: list[dict[str, Any]]) -> float:
|
|
total = 0.0
|
|
for item in items:
|
|
severity = max(float(item.get("severity", 0.0)), 0.25)
|
|
event_count = max(int(item.get("evidence", {}).get("eventCount", 0)), 1)
|
|
total += severity * math.log1p(event_count)
|
|
return total
|
|
|
|
more_score = score(more)
|
|
less_score = score(less)
|
|
if more_score >= less_score * 1.2:
|
|
resolved.extend(more)
|
|
elif less_score >= more_score * 1.2:
|
|
resolved.extend(less)
|
|
|
|
return sorted(resolved, key=lambda item: float(item.get("severity", 0.0)), reverse=True)
|
|
|
|
|
|
def _bucket_tuning_family(bucket: str) -> str:
|
|
if bucket in ("understeer", "late_turn_in", "oversteer", "early_turn_in", "saturation_limited", "low_speed_unwillingness"):
|
|
return "authority"
|
|
if bucket in ("unwind_too_slow", "unwind_too_fast"):
|
|
return "release"
|
|
if bucket in ("center_chatter", "notchy_mid_curve"):
|
|
return "stability"
|
|
return "other"
|
|
|
|
|
|
def select_primary_tuning_path(summaries: list[dict[str, Any]], summary_stats: dict[str, Any]) -> dict[str, Any]:
|
|
actionable = [
|
|
summary for summary in summaries
|
|
if summary.get("bucket") not in ("model_limited", "angle_control_diagnostic")
|
|
and float(summary.get("severity", 0.0)) >= 0.4
|
|
]
|
|
if not actionable:
|
|
return {
|
|
"primaryPathKey": "cleanup_pass",
|
|
"alternatePathKey": "baseline_fix",
|
|
"reason": "This sample does not show a broad controller-side miss. Start with the narrower cleanup path if you test anything.",
|
|
"baselineScore": 0,
|
|
}
|
|
|
|
mean_error = float(summary_stats.get("meanErrorAbs", 0.0) or 0.0)
|
|
families = {_bucket_tuning_family(str(summary.get("bucket", ""))) for summary in actionable}
|
|
major_events = [summary for summary in actionable if float(summary.get("severity", 0.0)) >= 0.8]
|
|
severe_global = [
|
|
summary for summary in actionable
|
|
if summary.get("bucket") in ("understeer", "oversteer", "late_turn_in", "early_turn_in", "saturation_limited")
|
|
and float(summary.get("severity", 0.0)) >= 0.85
|
|
]
|
|
severe_global_bands = {
|
|
(str(summary.get("direction", "center")), str(summary.get("speedBand", "mixed")))
|
|
for summary in severe_global
|
|
}
|
|
severe_global_segments = {
|
|
str(segment.get("label", ""))
|
|
for summary in severe_global
|
|
for segment in summary.get("evidence", {}).get("segments", [])
|
|
if segment.get("label")
|
|
}
|
|
severe_saturation = any(
|
|
summary.get("bucket") == "saturation_limited" and float(summary.get("severity", 0.0)) >= 0.85
|
|
for summary in actionable
|
|
)
|
|
|
|
if mean_error < 0.08 and not severe_saturation and not (
|
|
len(severe_global_bands) >= 2 and len(severe_global_segments) >= 2
|
|
):
|
|
return {
|
|
"primaryPathKey": "cleanup_pass",
|
|
"alternatePathKey": "baseline_fix",
|
|
"reason": "Overall lateral-accel tracking is already strong. The remaining misses are isolated enough that changing the base tune would disturb more good behavior than it fixes.",
|
|
"baselineScore": 0,
|
|
}
|
|
|
|
baseline_score = 0
|
|
if mean_error >= 0.14:
|
|
baseline_score += 2
|
|
elif mean_error >= 0.11:
|
|
baseline_score += 1
|
|
if len(actionable) >= 4:
|
|
baseline_score += 1
|
|
if len(families - {"other"}) >= 3:
|
|
baseline_score += 1
|
|
if len(major_events) >= 2:
|
|
baseline_score += 1
|
|
if severe_global:
|
|
baseline_score += 1
|
|
|
|
if baseline_score >= 3:
|
|
return {
|
|
"primaryPathKey": "baseline_fix",
|
|
"alternatePathKey": "cleanup_pass",
|
|
"reason": "This route looks broadly wrong across enough bands that the right first move is to fix base authority and friction behavior before touching narrower cleanup layers.",
|
|
"baselineScore": baseline_score,
|
|
}
|
|
|
|
return {
|
|
"primaryPathKey": "cleanup_pass",
|
|
"alternatePathKey": "baseline_fix",
|
|
"reason": "This route is already close enough overall that the better first move is a narrow cleanup pass instead of a broad whole-car reset.",
|
|
"baselineScore": baseline_score,
|
|
}
|
|
|
|
|
|
def build_trial_profiles(report_id: str, suggestions: list[dict[str, Any]], feedback: dict[str, Any], capabilities: dict[str, Any],
|
|
path_key: str = "cleanup_pass", path_label: str = "Cleanup Pass") -> list[dict[str, Any]]:
|
|
ignored = set(str(item) for item in feedback.get("ignoredDimensions", []))
|
|
accepted = set(str(item) for item in feedback.get("acceptedDimensions", []))
|
|
has_feedback_decisions = bool(ignored or accepted)
|
|
|
|
considered = [
|
|
suggestion for suggestion in suggestions
|
|
if suggestion.get("dimensionId") not in ignored and (
|
|
not accepted or suggestion.get("dimensionId") in accepted
|
|
)
|
|
]
|
|
if not considered and not has_feedback_decisions:
|
|
considered = [suggestion for suggestion in suggestions if suggestion.get("primaryAdjustmentRaw")]
|
|
actionable = [
|
|
suggestion for suggestion in considered
|
|
if suggestion.get("primaryAdjustmentRaw")
|
|
]
|
|
actionable = _resolve_conflicting_actionable_suggestions(actionable)
|
|
|
|
profiles = []
|
|
profile_defs = [
|
|
("conservative", "Conservative", 0.6),
|
|
("recommended", "Recommended", 1.0),
|
|
("assertive", "Assertive", 1.35),
|
|
]
|
|
for suffix, label, multiplier in profile_defs:
|
|
params_delta, overrides, force_auto_tune_off = _merge_primary_adjustments(actionable, multiplier)
|
|
if not overrides and len(params_delta) <= 1:
|
|
continue
|
|
profile = {
|
|
"id": f"{report_id}:{path_key}:{suffix}",
|
|
"reportId": report_id,
|
|
"label": label,
|
|
"pathKey": path_key,
|
|
"pathLabel": path_label,
|
|
"description": f"{label} {path_label.lower()} trial generated from {len(actionable)} confirmed symptom dimension(s).",
|
|
"genericParams": params_delta,
|
|
"flmOverrides": overrides,
|
|
"requiresForceAutoTuneOff": bool(force_auto_tune_off),
|
|
"capabilities": capabilities,
|
|
}
|
|
if force_auto_tune_off:
|
|
profile["genericParams"]["ForceAutoTuneOff"] = True
|
|
profile["genericParams"]["ForceAutoTune"] = False
|
|
profiles.append(profile)
|
|
|
|
return profiles[:3]
|
|
|
|
|
|
def _add_parameters_start_here(capabilities: dict[str, Any], suggestions: list[dict[str, Any]], primary_path_key: str) -> list[str]:
|
|
lines = ["Turn on Advanced Lateral Tune before trying any suggested profile."]
|
|
if primary_path_key == "baseline_fix":
|
|
lines.append("This route looks broadly wrong enough that the first move should be a baseline fix, not a surgical cleanup pass.")
|
|
lines.append("Start with the broad knobs this report suggests. Once the car is in the right zip code, re-run analysis and switch to Cleanup Pass for the leftovers.")
|
|
else:
|
|
lines.append("This route is already mostly in the right zip code, so start with cleanup changes before reaching for broader whole-car adjustments.")
|
|
|
|
if any(suggestion.get("primaryAdjustmentRaw", {}).get("type") == "generic_param" for suggestion in suggestions if suggestion.get("primaryAdjustmentRaw")):
|
|
lines.append("Generic advanced lateral params are in play on this pass, so apply those first before deciding you need deeper code-level changes.")
|
|
if any(suggestion.get("primaryAdjustmentRaw", {}).get("type") == "friction_curve" for suggestion in suggestions if suggestion.get("primaryAdjustmentRaw")):
|
|
lines.append("Friction-threshold changes are active in this pass because the logs point to small-signal steering behavior, not just whole-tune authority.")
|
|
if not capabilities.get("richProfileKey"):
|
|
lines.append("This car does not expose richer live FLM knobs yet, so generic advanced params and friction-threshold trials are the first code-level moves to test.")
|
|
return lines
|
|
|
|
|
|
def build_recommendation_paths(report_id: str, summaries: list[dict[str, Any]], summary_stats: dict[str, Any],
|
|
capabilities: dict[str, Any], current: dict[str, Any],
|
|
feedback: dict[str, Any]) -> tuple[list[dict[str, Any]], dict[str, Any]]:
|
|
decision = select_primary_tuning_path(summaries, summary_stats)
|
|
all_suggestions = {
|
|
"baseline_fix": build_suggestions(summaries, capabilities, current, strategy="baseline"),
|
|
"cleanup_pass": build_suggestions(summaries, capabilities, current, strategy="cleanup"),
|
|
}
|
|
|
|
ordered_keys = [decision["primaryPathKey"], decision["alternatePathKey"]]
|
|
paths = []
|
|
for path_key in ordered_keys:
|
|
spec = FLM_PATH_SPECS[path_key]
|
|
suggestions = all_suggestions[path_key]
|
|
profiles = build_trial_profiles(report_id, suggestions, feedback, capabilities, path_key=path_key, path_label=spec["title"])
|
|
paths.append({
|
|
"key": path_key,
|
|
"title": spec["title"],
|
|
"description": spec["description"],
|
|
"whenToUse": spec["whenToUse"],
|
|
"alternateHint": spec["alternateHint"],
|
|
"isPrimary": path_key == decision["primaryPathKey"],
|
|
"whySelected": decision["reason"] if path_key == decision["primaryPathKey"] else spec["alternateHint"],
|
|
"suggestions": suggestions,
|
|
"profiles": profiles,
|
|
})
|
|
return paths, decision
|
|
|
|
|
|
def _render_report_html(report: dict[str, Any]) -> str:
|
|
report_paths = [path for path in report.get("paths", []) if isinstance(path, dict)]
|
|
selected_path_key = str(report.get("selectedPathKey") or report.get("primaryPathKey") or "")
|
|
primary_path = next((path for path in report_paths if path.get("key") == selected_path_key), None)
|
|
if primary_path is None:
|
|
primary_path = next((path for path in report_paths if path.get("isPrimary")), report_paths[0] if report_paths else {})
|
|
findings_html = []
|
|
for suggestion in report.get("suggestions", []):
|
|
evidence = suggestion.get("evidence", {})
|
|
current_vs_suggested = suggestion.get("currentVsSuggested")
|
|
if current_vs_suggested is None:
|
|
delta_html = "<p class='flm-muted'>No trial adjustment suggested for this dimension.</p>"
|
|
elif current_vs_suggested["type"] == "friction_curve":
|
|
delta_html = (
|
|
f"<p><strong>Current:</strong> {current_vs_suggested['current']}</p>"
|
|
f"<p><strong>Suggested:</strong> {current_vs_suggested['suggested']}</p>"
|
|
)
|
|
else:
|
|
label = current_vs_suggested.get("paramKey") or current_vs_suggested.get("symbol")
|
|
delta_html = (
|
|
f"<p><strong>{label}</strong>: {float(current_vs_suggested['current']):.3f} -> {float(current_vs_suggested['suggested']):.3f}</p>"
|
|
)
|
|
findings_html.append(
|
|
"<section class='flm-card'>"
|
|
f"<h3>{suggestion['bucket'].replace('_', ' ').title()}</h3>"
|
|
f"<p><strong>Observed behavior:</strong> {suggestion['observedBehavior']}</p>"
|
|
f"<p><strong>Likely interpretation:</strong> {suggestion['likelyInterpretation']}</p>"
|
|
f"<p><strong>Primary adjustment:</strong> {suggestion['primaryAdjustment']}</p>"
|
|
f"<p><strong>What not to touch yet:</strong> {suggestion['whatNotToTouchYet']}</p>"
|
|
f"<p><strong>If that was wrong, next thing to try:</strong> {suggestion['ifThatWasWrong']}</p>"
|
|
f"<p><strong>Evidence:</strong> speed={evidence.get('speedBand', 'mixed')}, direction={evidence.get('directionBias', 'center')}, events={evidence.get('eventCount', 0)}</p>"
|
|
f"<p><strong>Strongest segments:</strong> {', '.join(item['label'] for item in evidence.get('segments', [])[:3]) or 'none'}</p>"
|
|
f"{delta_html}"
|
|
f"{suggestion.get('plotSvg', '')}"
|
|
"</section>"
|
|
)
|
|
|
|
path_html = []
|
|
for path in report_paths:
|
|
badges = []
|
|
if path.get("isPrimary"):
|
|
badges.append("Analyzer recommended")
|
|
if path.get("key") == selected_path_key:
|
|
badges.append("Active")
|
|
badge = " / ".join(badges) or "Alternate"
|
|
path_html.append(
|
|
"<section class='flm-card'>"
|
|
f"<h3>{path.get('title', 'Path')}</h3>"
|
|
f"<p><strong>{badge}:</strong> {path.get('description', '')}</p>"
|
|
f"<p><strong>Why this path:</strong> {path.get('whySelected', '')}</p>"
|
|
f"<p><strong>When to use it:</strong> {path.get('whenToUse', '')}</p>"
|
|
"</section>"
|
|
)
|
|
|
|
profile_html = []
|
|
for path in report_paths:
|
|
path_profiles = path.get("profiles", [])
|
|
cards = []
|
|
for profile in path_profiles:
|
|
generic_lines = []
|
|
for key, value in profile.get("genericParams", {}).items():
|
|
if key == "AdvancedLateralTune":
|
|
continue
|
|
generic_lines.append(f"<li><code>{key}</code>: {value}</li>")
|
|
override_lines = []
|
|
for family, payload in profile.get("flmOverrides", {}).get("baseFrictionThresholds", {}).items():
|
|
override_lines.append(f"<li><code>{family}</code> curve: {payload.get('values', [])}</li>")
|
|
for key, value in profile.get("flmOverrides", {}).get("vehicleKnobs", {}).items():
|
|
override_lines.append(f"<li><code>{key}</code>: {value}</li>")
|
|
cards.append(
|
|
"<section class='flm-card'>"
|
|
f"<h3>{profile['label']}</h3>"
|
|
f"<p>{profile['description']}</p>"
|
|
f"<p><strong>Generic params:</strong></p><ul>{''.join(generic_lines) or '<li>None</li>'}</ul>"
|
|
f"<p><strong>FLM overrides:</strong></p><ul>{''.join(override_lines) or '<li>None</li>'}</ul>"
|
|
"</section>"
|
|
)
|
|
path_profiles_html = "".join(cards) or "<p class='flm-muted'>No trial profiles generated for this path.</p>"
|
|
profile_html.append(
|
|
f"<h3>{path.get('title', 'Path')} Profiles</h3>"
|
|
f"{path_profiles_html}"
|
|
)
|
|
|
|
start_here_lines = "".join(f"<li>{line}</li>" for line in report.get("addTheseParametersAndStartHere", []))
|
|
start_here_html = f"<section class='flm-card'><h3>Add These Parameters And Start Here</h3><ul>{start_here_lines}</ul></section>" if start_here_lines else ""
|
|
findings_block = "".join(findings_html) or "<p class='flm-muted'>No strong findings.</p>"
|
|
profiles_block = "".join(profile_html) or "<p class='flm-muted'>No trial profiles generated.</p>"
|
|
return (
|
|
"<!doctype html><html><head><meta charset='utf-8'>"
|
|
"<title>FLM Tuning Report</title>"
|
|
"<style>"
|
|
"body{font-family:system-ui,sans-serif;background:#020617;color:#e2e8f0;margin:0;padding:24px;}"
|
|
".flm-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(280px,1fr));gap:16px;}"
|
|
".flm-card{background:#0f172a;border:1px solid #1e293b;border-radius:12px;padding:16px;margin-bottom:16px;}"
|
|
".flm-muted{color:#94a3b8;}"
|
|
"code{background:#111827;padding:2px 6px;border-radius:6px;}"
|
|
".flm-plot{width:100%;height:140px;margin-top:12px;}"
|
|
"</style></head><body>"
|
|
f"<h1>FLM Tuning Report</h1>"
|
|
f"<p>{report['car']['carFingerprint']} | {report['car'].get('gitBranch', '')} {report['car'].get('gitCommit', '')}</p>"
|
|
f"<div class='flm-grid'><section class='flm-card'><h3>Routes</h3><p>{', '.join(report.get('routeNames', []))}</p></section>"
|
|
f"<section class='flm-card'><h3>Control Path</h3><p>{report['car'].get('controlPath', 'unknown')}</p></section>"
|
|
f"<section class='flm-card'><h3>Friction Family</h3><p>{report['capabilities'].get('frictionFamily', 'standard')}</p></section>"
|
|
f"<section class='flm-card'><h3>Nonlinear Torque Map</h3><p>{'Asymmetric left/right siglin' if report['capabilities'].get('nonlinearTorqueMap', {}).get('asymmetric') else ('Symmetric siglin' if report['capabilities'].get('nonlinearTorqueMap') else 'Not detected')}</p></section></div>"
|
|
f"{''.join(path_html)}"
|
|
f"{start_here_html}"
|
|
f"<h2>Active Findings: {primary_path.get('title', 'Recommendations')}</h2>"
|
|
f"{findings_block}"
|
|
"<h2>Trial Profiles</h2>"
|
|
f"{profiles_block}"
|
|
"</body></html>"
|
|
)
|
|
|
|
|
|
def analyze_routes(route_names: list[str], footage_paths: list[str], feedback: dict[str, Any] | None = None, report_id: str | None = None) -> dict[str, Any]:
|
|
ensure_flm_workspace()
|
|
params = Params(return_defaults=True)
|
|
report_id = report_id or f"flm-{int(time.time())}"
|
|
feedback = feedback or {}
|
|
|
|
sources, warnings = resolve_route_sources(route_names, footage_paths)
|
|
if not sources:
|
|
raise RuntimeError("No local routes with qlogs or rlogs were found for the selected routes.")
|
|
|
|
all_samples: list[FLMSample] = []
|
|
car_params = None
|
|
init_data: dict[str, str] = {}
|
|
used_qlog = False
|
|
processed_segments = 0
|
|
for idx, source in enumerate(sources, start=1):
|
|
_write_flm_status({
|
|
"pid": os.getpid(),
|
|
"startedAt": time.time(),
|
|
"running": True,
|
|
"state": "analyzing",
|
|
"routes": route_names,
|
|
"progress": idx - 1,
|
|
"total": len(sources),
|
|
"currentSegment": source.segment,
|
|
})
|
|
segment_samples, segment_car_params, segment_init = _segment_samples(source)
|
|
if car_params is None and segment_car_params is not None:
|
|
car_params = segment_car_params
|
|
if segment_init and not init_data:
|
|
init_data = segment_init
|
|
if source.used_qlog:
|
|
used_qlog = True
|
|
all_samples.extend(segment_samples)
|
|
processed_segments += 1
|
|
|
|
if car_params is None:
|
|
raise RuntimeError("No carParams were found in the selected routes.")
|
|
|
|
torque_control = car_params.lateralTuning.which() == "torque"
|
|
hyundai_canfd = bool(getattr(car_params, "flags", 0) & HyundaiFlags.CANFD)
|
|
capabilities = get_flm_capabilities(
|
|
car_params.carFingerprint,
|
|
brand=str(getattr(car_params, "brand", "") or ""),
|
|
hyundai_canfd=hyundai_canfd,
|
|
torque_control=torque_control,
|
|
)
|
|
capabilities = dict(capabilities)
|
|
capabilities["nonlinearTorqueMap"] = _nonlinear_torque_map(car_params)
|
|
current_params = _current_param_state(car_params, params)
|
|
stock_params = _stock_param_state(car_params, capabilities)
|
|
|
|
if torque_control:
|
|
raw_summaries, summary_stats = classify_torque_samples(all_samples)
|
|
summaries = _resolve_conflicting_actionable_suggestions(raw_summaries)
|
|
paths_payload, path_decision = build_recommendation_paths(report_id, summaries, summary_stats, capabilities, current_params, feedback)
|
|
primary_path = next((path for path in paths_payload if path.get("isPrimary")), paths_payload[0] if paths_payload else {})
|
|
suggestions = list(primary_path.get("suggestions", []))
|
|
profiles = [profile for path in paths_payload for profile in path.get("profiles", [])]
|
|
else:
|
|
summary_stats = {"sampleCount": len(all_samples), "qlogFallback": used_qlog}
|
|
summaries = [{
|
|
"bucket": "angle_control_diagnostic",
|
|
"dimensionId": "angle_control_diagnostic:overall",
|
|
"direction": "center",
|
|
"speedBand": "mixed",
|
|
"count": 1,
|
|
"severity": 0.0,
|
|
"evidence": {"speedBand": "mixed", "directionBias": "center", "eventCount": 1, "segments": []},
|
|
"events": [],
|
|
"plotSvg": "",
|
|
"plotData": {},
|
|
}]
|
|
suggestions = [{
|
|
"dimensionId": "angle_control_diagnostic:overall",
|
|
"bucket": "angle_control_diagnostic",
|
|
"evidence": summaries[0]["evidence"],
|
|
"currentVsSuggested": None,
|
|
"observedBehavior": "This route is using an angle-control path, so the torque-specific FLM trial system stays in diagnostic mode.",
|
|
"likelyInterpretation": "You can still inspect lane behavior here, but torque-controller trial profiles do not apply.",
|
|
"primaryAdjustment": "Do not apply an FLM torque profile to this car.",
|
|
"whatNotToTouchYet": "Do not write torque-controller override blobs for an angle-control path.",
|
|
"ifThatWasWrong": "If the car later moves to torque control, re-run FLM on a fresh route.",
|
|
"plotSvg": "",
|
|
"plotData": {},
|
|
}]
|
|
path_decision = {
|
|
"primaryPathKey": "cleanup_pass",
|
|
"alternatePathKey": "baseline_fix",
|
|
"reason": "Angle-control diagnostic mode does not participate in the torque trial workflow.",
|
|
"baselineScore": 0,
|
|
}
|
|
paths_payload = [{
|
|
"key": "cleanup_pass",
|
|
"title": "Diagnostic Only",
|
|
"description": "This route is using an angle-control path, so torque-controller trial profiles do not apply.",
|
|
"whenToUse": "Use this report only for diagnostic review.",
|
|
"alternateHint": "",
|
|
"isPrimary": True,
|
|
"whySelected": path_decision["reason"],
|
|
"suggestions": suggestions,
|
|
"profiles": [],
|
|
}]
|
|
profiles = []
|
|
|
|
report = {
|
|
"reportId": report_id,
|
|
"createdAt": time.time(),
|
|
"routeNames": route_names,
|
|
"warnings": warnings,
|
|
"feedback": feedback,
|
|
"car": {
|
|
"carFingerprint": str(car_params.carFingerprint),
|
|
"brand": str(getattr(car_params, "brand", "") or ""),
|
|
"controlPath": "torque" if torque_control else "angle",
|
|
"gitBranch": init_data.get("gitBranch", ""),
|
|
"gitCommit": init_data.get("gitCommit", ""),
|
|
"steerControlType": str(getattr(car_params, "steerControlType", car.CarParams.SteerControlType.torque)),
|
|
},
|
|
"capabilities": capabilities,
|
|
"stockParams": stock_params,
|
|
"currentParams": current_params,
|
|
"summary": {
|
|
**summary_stats,
|
|
"processedSegments": processed_segments,
|
|
"usedQlogFallback": used_qlog,
|
|
},
|
|
"primaryPathKey": path_decision["primaryPathKey"],
|
|
"selectedPathKey": path_decision["primaryPathKey"],
|
|
"pathSelectionSource": "auto",
|
|
"pathDecision": path_decision,
|
|
"paths": paths_payload,
|
|
"findings": summaries,
|
|
"rawFindings": raw_summaries if torque_control else summaries,
|
|
"suggestions": suggestions,
|
|
"profiles": profiles,
|
|
"addTheseParametersAndStartHere": _add_parameters_start_here(capabilities, suggestions, path_decision["primaryPathKey"]),
|
|
}
|
|
|
|
paths = ensure_flm_workspace()
|
|
html = _render_report_html(report)
|
|
report["htmlPath"] = str(paths["reports"] / f"{report_id}.html")
|
|
report["jsonPath"] = str(paths["reports"] / f"{report_id}.json")
|
|
(paths["reports"] / f"{report_id}.html").write_text(html, encoding="utf-8")
|
|
_write_json(paths["reports"] / f"{report_id}.json", report)
|
|
_write_json(paths["profiles"] / f"{report_id}.json", profiles)
|
|
_write_flm_status({
|
|
"pid": os.getpid(),
|
|
"startedAt": time.time(),
|
|
"running": False,
|
|
"state": "complete",
|
|
"routes": route_names,
|
|
"progress": processed_segments,
|
|
"total": processed_segments,
|
|
"reportId": report_id,
|
|
})
|
|
return report
|
|
|
|
|
|
def load_report(report_id: str) -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
report_path = paths["reports"] / f"{report_id}.json"
|
|
report = _read_json(report_path, {})
|
|
if not isinstance(report, dict) or not report:
|
|
raise FileNotFoundError(report_id)
|
|
html_path = paths["reports"] / f"{report_id}.html"
|
|
report["html"] = html_path.read_text(encoding="utf-8") if html_path.exists() else ""
|
|
return report
|
|
|
|
|
|
def select_report_path(report_id: str, path_key: str) -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
report = load_report(report_id)
|
|
report_paths = [path for path in report.get("paths", []) if isinstance(path, dict)]
|
|
selected_path = next((path for path in report_paths if path.get("key") == path_key), None)
|
|
if selected_path is None:
|
|
raise ValueError(f"Unknown FLM path: {path_key}")
|
|
|
|
report["selectedPathKey"] = path_key
|
|
report["pathSelectionSource"] = "manual"
|
|
report["suggestions"] = list(selected_path.get("suggestions", []))
|
|
report["addTheseParametersAndStartHere"] = _add_parameters_start_here(
|
|
report.get("capabilities", {}),
|
|
report["suggestions"],
|
|
path_key,
|
|
)
|
|
report.pop("html", None)
|
|
(paths["reports"] / f"{report_id}.html").write_text(_render_report_html(report), encoding="utf-8")
|
|
_write_json(paths["reports"] / f"{report_id}.json", report)
|
|
return {
|
|
"message": f"Using {selected_path.get('title', path_key)} for this report.",
|
|
"report": load_report(report_id),
|
|
}
|
|
|
|
|
|
def _active_trial_display_state(paths: dict[str, Path], snapshot: Any) -> dict[str, Any] | None:
|
|
if not isinstance(snapshot, dict) or not snapshot:
|
|
return None
|
|
if "appliedGenericParams" in snapshot:
|
|
return snapshot
|
|
|
|
report_id = str(snapshot.get("reportId", "") or "")
|
|
profile_id = str(snapshot.get("profileId", "") or "")
|
|
profiles = _read_json(paths["profiles"] / f"{report_id}.json", []) if report_id else []
|
|
profile = next((
|
|
item for item in profiles
|
|
if isinstance(item, dict) and item.get("id") == profile_id
|
|
), None) if isinstance(profiles, list) else None
|
|
if profile is None:
|
|
return snapshot
|
|
|
|
generic_params = dict(profile.get("genericParams", {}))
|
|
flm_overrides = normalize_flm_overrides(profile.get("flmOverrides", {}))
|
|
return {
|
|
**snapshot,
|
|
"profileLabel": str(profile.get("label", "FLM") or "FLM"),
|
|
"pathKey": str(profile.get("pathKey", "") or ""),
|
|
"pathLabel": str(profile.get("pathLabel", "") or ""),
|
|
"appliedGenericParams": {
|
|
key: value for key, value in generic_params.items()
|
|
if key in FLM_ADVANCED_LATERAL_PARAM_KEYS
|
|
},
|
|
"appliedFrictionThresholds": flm_overrides.get("baseFrictionThresholds", {}),
|
|
"appliedVehicleKnobs": flm_overrides.get("vehicleKnobs", {}),
|
|
}
|
|
|
|
|
|
def list_workspace() -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
reports = []
|
|
for path in sorted(paths["reports"].glob("*.json"), reverse=True):
|
|
payload = _read_json(path, {})
|
|
if not isinstance(payload, dict) or not payload:
|
|
continue
|
|
reports.append({
|
|
"reportId": payload.get("reportId", path.stem),
|
|
"createdAt": payload.get("createdAt", path.stat().st_mtime),
|
|
"carFingerprint": payload.get("car", {}).get("carFingerprint", ""),
|
|
"routeNames": payload.get("routeNames", []),
|
|
"controlPath": payload.get("car", {}).get("controlPath", ""),
|
|
})
|
|
feedback_files = sorted(paths["feedback"].glob("*.json"), reverse=True)
|
|
params = Params(return_defaults=True)
|
|
current_profile_id = params.get("FLMActiveProfileId", encoding="utf-8") or ""
|
|
raw_active_snapshot = _read_json(paths["snapshots"] / "active.json", {})
|
|
if params.get_bool("FLMTrialApplied"):
|
|
active_payload = raw_active_snapshot if isinstance(raw_active_snapshot, dict) else {}
|
|
baseline_snapshot = _find_revert_snapshot(paths, raw_active_snapshot, current_profile_id, params)
|
|
if baseline_snapshot is not None:
|
|
raw_active_snapshot = {
|
|
**active_payload,
|
|
"params": baseline_snapshot["params"],
|
|
"profileId": current_profile_id or active_payload.get("profileId", ""),
|
|
"recoveryNeeded": baseline_snapshot is not raw_active_snapshot,
|
|
"rollbackAvailable": True,
|
|
}
|
|
else:
|
|
raw_active_snapshot = {
|
|
**active_payload,
|
|
"profileId": current_profile_id or active_payload.get("profileId", ""),
|
|
"recoveryNeeded": True,
|
|
"rollbackAvailable": False,
|
|
}
|
|
active_snapshot = _active_trial_display_state(paths, raw_active_snapshot)
|
|
return {
|
|
"reports": reports[:20],
|
|
"feedbackCount": len(feedback_files),
|
|
"activeTrial": active_snapshot,
|
|
"status": read_flm_status(),
|
|
}
|
|
|
|
|
|
def delete_report(report_id: str) -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
params = Params(return_defaults=True)
|
|
if params.get_bool("FLMTrialApplied"):
|
|
raise RuntimeError("Revert or keep the active FLM trial before deleting tuning reports.")
|
|
active_snapshot = _read_json(paths["snapshots"] / "active.json", {})
|
|
if isinstance(active_snapshot, dict) and active_snapshot.get("reportId") == report_id:
|
|
raise RuntimeError("Revert the active FLM trial before deleting its source report.")
|
|
|
|
removed = []
|
|
direct_paths = [
|
|
paths["reports"] / f"{report_id}.json",
|
|
paths["reports"] / f"{report_id}.html",
|
|
paths["profiles"] / f"{report_id}.json",
|
|
paths["feedback"] / f"{report_id}.json",
|
|
]
|
|
for path in direct_paths:
|
|
if path.exists():
|
|
path.unlink()
|
|
removed.append(str(path))
|
|
|
|
for path in paths["snapshots"].glob(f"{report_id}-*.json"):
|
|
path.unlink()
|
|
removed.append(str(path))
|
|
|
|
if not removed:
|
|
raise FileNotFoundError(report_id)
|
|
|
|
status = read_flm_status()
|
|
if not status.get("running") and status.get("reportId") == report_id:
|
|
_clear_flm_status()
|
|
|
|
return {
|
|
"message": f"Deleted tuning report {report_id}.",
|
|
"removed": removed,
|
|
"workspace": list_workspace(),
|
|
}
|
|
|
|
|
|
def clear_workspace() -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
status = read_flm_status()
|
|
if status.get("running"):
|
|
raise RuntimeError("Stop the active FLM analysis before clearing the workspace.")
|
|
|
|
params = Params(return_defaults=True)
|
|
active_snapshot = _read_json(paths["snapshots"] / "active.json", {})
|
|
if params.get_bool("FLMTrialApplied") or (isinstance(active_snapshot, dict) and active_snapshot.get("params")):
|
|
raise RuntimeError("Revert or keep the active FLM trial before clearing the workspace.")
|
|
|
|
removed = []
|
|
for key in ("reports", "profiles", "feedback", "snapshots"):
|
|
for path in paths[key].glob("*"):
|
|
if path.is_file():
|
|
path.unlink()
|
|
removed.append(str(path))
|
|
|
|
_clear_persistent_trial_baseline(params)
|
|
_clear_flm_status()
|
|
|
|
return {
|
|
"message": "Cleared saved tuning reports, feedback, profiles, and snapshots.",
|
|
"removedCount": len(removed),
|
|
"workspace": list_workspace(),
|
|
}
|
|
|
|
|
|
def _snapshot_current_trial_state(params: Params) -> dict[str, Any]:
|
|
snapshot = {}
|
|
for key, kind in TRIAL_PARAM_SPECS.items():
|
|
if kind == "bool":
|
|
snapshot[key] = params.get_bool(key)
|
|
elif kind == "float":
|
|
snapshot[key] = params.get_float(key, return_default=True)
|
|
elif kind == "json":
|
|
snapshot[key] = normalize_flm_overrides(params.get(key, encoding="utf-8") or "{}")
|
|
else:
|
|
snapshot[key] = params.get(key, encoding="utf-8") or ""
|
|
return snapshot
|
|
|
|
|
|
def _read_persistent_trial_baseline(params: Params) -> dict[str, Any] | None:
|
|
raw = params.get(FLM_TRIAL_BASELINE_PARAM, encoding="utf-8") or {}
|
|
if isinstance(raw, str):
|
|
try:
|
|
raw = json.loads(raw)
|
|
except (TypeError, ValueError):
|
|
return None
|
|
if not isinstance(raw, dict) or not isinstance(raw.get("params"), dict):
|
|
return None
|
|
if raw["params"].get("FLMTrialApplied", False):
|
|
return None
|
|
return raw
|
|
|
|
|
|
def _persist_trial_baseline(params: Params, snapshot: dict[str, Any]) -> None:
|
|
if isinstance(snapshot.get("params"), dict) and not snapshot["params"].get("FLMTrialApplied", False):
|
|
params.put(FLM_TRIAL_BASELINE_PARAM, snapshot)
|
|
|
|
|
|
def _clear_persistent_trial_baseline(params: Params) -> None:
|
|
params.remove(FLM_TRIAL_BASELINE_PARAM)
|
|
|
|
|
|
def _profile_report_id(profile_id: str) -> str:
|
|
return str(profile_id or "").split(":", 1)[0]
|
|
|
|
|
|
def _recover_report_baseline(paths: dict[str, Path], profile_id: str,
|
|
visited_profiles: set[str] | None = None) -> dict[str, Any] | None:
|
|
profile_id = str(profile_id or "")
|
|
if not profile_id:
|
|
return None
|
|
|
|
visited_profiles = set(visited_profiles or set())
|
|
if profile_id in visited_profiles:
|
|
return None
|
|
visited_profiles.add(profile_id)
|
|
|
|
report_id = _profile_report_id(profile_id)
|
|
report = _read_json(paths["reports"] / f"{report_id}.json", {})
|
|
report_params = report.get("currentParams") if isinstance(report, dict) else None
|
|
if not isinstance(report_params, dict) or not report_params:
|
|
return None
|
|
|
|
baseline_params = {
|
|
key: value for key, value in report_params.items()
|
|
if key in TRIAL_PARAM_SPECS
|
|
}
|
|
if not baseline_params:
|
|
return None
|
|
if not baseline_params.get("FLMTrialApplied", False):
|
|
baseline_params["FLMTrialApplied"] = False
|
|
baseline_params.setdefault("FLMActiveProfileId", "")
|
|
return {
|
|
"reportId": report_id,
|
|
"profileId": profile_id,
|
|
"capturedAt": float(report.get("createdAt", 0.0) or 0.0),
|
|
"params": baseline_params,
|
|
"recoverySource": "report",
|
|
}
|
|
|
|
previous_profile_id = str(baseline_params.get("FLMActiveProfileId", "") or "")
|
|
if previous_profile_id and previous_profile_id != profile_id:
|
|
return _recover_report_baseline(paths, previous_profile_id, visited_profiles)
|
|
return None
|
|
|
|
|
|
def _apply_param_bundle(params: Params, bundle: dict[str, Any]) -> None:
|
|
for key, value in bundle.items():
|
|
kind = TRIAL_PARAM_SPECS.get(key)
|
|
if kind == "bool":
|
|
params.put_bool(key, bool(value))
|
|
elif kind == "float":
|
|
params.put_float(key, float(value))
|
|
elif kind == "json":
|
|
params.put(key, normalize_flm_overrides(value))
|
|
elif kind == "string":
|
|
params.put(key, str(value or ""))
|
|
|
|
|
|
def _merge_flm_override_state(base: dict[str, Any], delta: dict[str, Any]) -> dict[str, Any]:
|
|
base = normalize_flm_overrides(base)
|
|
delta = normalize_flm_overrides(delta)
|
|
merged = {
|
|
"schemaVersion": 1,
|
|
"baseFrictionThresholds": {
|
|
**base.get("baseFrictionThresholds", {}),
|
|
**delta.get("baseFrictionThresholds", {}),
|
|
},
|
|
"vehicleKnobs": {
|
|
**base.get("vehicleKnobs", {}),
|
|
**delta.get("vehicleKnobs", {}),
|
|
},
|
|
}
|
|
return normalize_flm_overrides(merged)
|
|
|
|
|
|
def _find_revert_snapshot(paths: dict[str, Path], active_snapshot: dict[str, Any],
|
|
current_profile_id: str = "", params: Params | None = None) -> dict[str, Any] | None:
|
|
if isinstance(active_snapshot, dict) and isinstance(active_snapshot.get("params"), dict):
|
|
if not active_snapshot["params"].get("FLMTrialApplied", False):
|
|
return active_snapshot
|
|
|
|
if params is not None:
|
|
persistent_baseline = _read_persistent_trial_baseline(params)
|
|
if persistent_baseline is not None:
|
|
return persistent_baseline
|
|
|
|
cutoff = float(active_snapshot.get("capturedAt", math.inf) or math.inf) if isinstance(active_snapshot, dict) else math.inf
|
|
candidates = []
|
|
for path in paths["snapshots"].glob("*.json"):
|
|
if path.name == "active.json":
|
|
continue
|
|
candidate = _read_json(path, {})
|
|
candidate_params = candidate.get("params", {}) if isinstance(candidate, dict) else {}
|
|
if not isinstance(candidate_params, dict) or candidate_params.get("FLMTrialApplied", False):
|
|
continue
|
|
captured_at = float(candidate.get("capturedAt", 0.0) or 0.0)
|
|
if captured_at > cutoff:
|
|
continue
|
|
candidates.append(candidate)
|
|
|
|
if candidates:
|
|
matching = [candidate for candidate in candidates if current_profile_id and candidate.get("profileId") == current_profile_id]
|
|
pool = matching or candidates
|
|
return max(pool, key=lambda candidate: float(candidate.get("capturedAt", 0.0) or 0.0))
|
|
|
|
return _recover_report_baseline(paths, current_profile_id)
|
|
|
|
|
|
def apply_trial_profile(report_id: str, profile_id: str) -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
params = Params(return_defaults=True)
|
|
profiles = _read_json(paths["profiles"] / f"{report_id}.json", [])
|
|
if not isinstance(profiles, list):
|
|
raise FileNotFoundError(profile_id)
|
|
profile = next((item for item in profiles if isinstance(item, dict) and item.get("id") == profile_id), None)
|
|
if profile is None:
|
|
raise FileNotFoundError(profile_id)
|
|
|
|
generic_params = dict(profile.get("genericParams", {}))
|
|
flm_overrides = normalize_flm_overrides(profile.get("flmOverrides", {}))
|
|
current_state = _snapshot_current_trial_state(params)
|
|
raw_active_snapshot = _read_json(paths["snapshots"] / "active.json", {})
|
|
previous_display_state = _active_trial_display_state(paths, raw_active_snapshot) or {}
|
|
trial_already_active = bool(current_state.get("FLMTrialApplied", False))
|
|
|
|
if trial_already_active:
|
|
baseline_snapshot = _find_revert_snapshot(
|
|
paths,
|
|
raw_active_snapshot,
|
|
str(current_state.get("FLMActiveProfileId", "") or ""),
|
|
params,
|
|
)
|
|
if baseline_snapshot is None:
|
|
raise RuntimeError("The active FLM trial has no recoverable rollback baseline. Keep the current tune as the new baseline before applying another profile.")
|
|
baseline_params = baseline_snapshot["params"]
|
|
session_started_at = float(baseline_snapshot.get("sessionStartedAt", baseline_snapshot.get("capturedAt", time.time())) or time.time())
|
|
else:
|
|
baseline_params = current_state
|
|
session_started_at = time.time()
|
|
|
|
previous_generic_params = dict(previous_display_state.get("appliedGenericParams", {}))
|
|
for key in FLM_ADVANCED_LATERAL_PARAM_KEYS:
|
|
if key in current_state and current_state.get(key) != baseline_params.get(key):
|
|
previous_generic_params[key] = current_state[key]
|
|
|
|
baseline_overrides = normalize_flm_overrides(baseline_params.get("FLMActiveOverrides", {}))
|
|
current_overrides = normalize_flm_overrides(current_state.get("FLMActiveOverrides", {}))
|
|
previous_friction_thresholds = dict(previous_display_state.get("appliedFrictionThresholds", {}))
|
|
for family, payload in current_overrides.get("baseFrictionThresholds", {}).items():
|
|
if payload != baseline_overrides.get("baseFrictionThresholds", {}).get(family):
|
|
previous_friction_thresholds[family] = payload
|
|
previous_vehicle_knobs = dict(previous_display_state.get("appliedVehicleKnobs", {}))
|
|
for symbol, value in current_overrides.get("vehicleKnobs", {}).items():
|
|
if value != baseline_overrides.get("vehicleKnobs", {}).get(symbol):
|
|
previous_vehicle_knobs[symbol] = value
|
|
|
|
applied_generic_params = {
|
|
**previous_generic_params,
|
|
**{
|
|
key: value for key, value in generic_params.items()
|
|
if key in FLM_ADVANCED_LATERAL_PARAM_KEYS
|
|
},
|
|
}
|
|
applied_friction_thresholds = {
|
|
**previous_friction_thresholds,
|
|
**flm_overrides.get("baseFrictionThresholds", {}),
|
|
}
|
|
applied_vehicle_knobs = {
|
|
**previous_vehicle_knobs,
|
|
**flm_overrides.get("vehicleKnobs", {}),
|
|
}
|
|
now = time.time()
|
|
snapshot = {
|
|
"reportId": report_id,
|
|
"profileId": profile_id,
|
|
"profileLabel": str(profile.get("label", "FLM") or "FLM"),
|
|
"pathKey": str(profile.get("pathKey", "") or ""),
|
|
"pathLabel": str(profile.get("pathLabel", "") or ""),
|
|
"capturedAt": session_started_at,
|
|
"updatedAt": now,
|
|
"sessionStartedAt": session_started_at,
|
|
"revisionCount": int(previous_display_state.get("revisionCount", 0) or 0) + 1,
|
|
"params": baseline_params,
|
|
"appliedGenericParams": applied_generic_params,
|
|
"appliedFrictionThresholds": applied_friction_thresholds,
|
|
"appliedVehicleKnobs": applied_vehicle_knobs,
|
|
}
|
|
_write_json(paths["snapshots"] / "active.json", snapshot)
|
|
_write_json(paths["snapshots"] / f"{report_id}-{profile_id.replace(':', '_')}.json", snapshot)
|
|
_persist_trial_baseline(params, snapshot)
|
|
|
|
bundle = generic_params
|
|
bundle["FLMActiveProfileId"] = profile_id
|
|
bundle["FLMActiveOverrides"] = _merge_flm_override_state(
|
|
current_state.get("FLMActiveOverrides", {}),
|
|
flm_overrides,
|
|
)
|
|
bundle["FLMTrialApplied"] = True
|
|
_apply_param_bundle(params, bundle)
|
|
return {
|
|
"message": f"Applied {profile.get('label', 'FLM')} profile.",
|
|
"profile": profile,
|
|
}
|
|
|
|
|
|
def revert_trial_profile() -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
snapshot_path = paths["snapshots"] / "active.json"
|
|
snapshot = _read_json(snapshot_path, {})
|
|
params = Params(return_defaults=True)
|
|
current_profile_id = params.get("FLMActiveProfileId", encoding="utf-8") or ""
|
|
revert_snapshot = _find_revert_snapshot(paths, snapshot if isinstance(snapshot, dict) else {}, current_profile_id, params)
|
|
if revert_snapshot is None:
|
|
raise FileNotFoundError("active trial snapshot")
|
|
_apply_param_bundle(params, revert_snapshot["params"])
|
|
_clear_persistent_trial_baseline(params)
|
|
try:
|
|
snapshot_path.unlink()
|
|
except FileNotFoundError:
|
|
pass
|
|
return {
|
|
"message": "Reverted the complete FLM trial session to its original baseline.",
|
|
"snapshot": {
|
|
**(snapshot if isinstance(snapshot, dict) else {}),
|
|
"params": revert_snapshot["params"],
|
|
"recoveredBaseline": revert_snapshot is not snapshot,
|
|
},
|
|
}
|
|
|
|
|
|
def accept_trial_as_baseline() -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
params = Params(return_defaults=True)
|
|
active_snapshot = _read_json(paths["snapshots"] / "active.json", {})
|
|
if not params.get_bool("FLMTrialApplied") and not (isinstance(active_snapshot, dict) and active_snapshot):
|
|
raise FileNotFoundError("active trial")
|
|
|
|
params.put_bool("FLMTrialApplied", False)
|
|
params.put("FLMActiveProfileId", "")
|
|
_clear_persistent_trial_baseline(params)
|
|
for path in paths["snapshots"].glob("*.json"):
|
|
path.unlink()
|
|
|
|
return {
|
|
"message": "Kept the current tuning values and made them the new FLM baseline.",
|
|
"workspace": list_workspace(),
|
|
}
|
|
|
|
|
|
def record_feedback(report_id: str, feedback: dict[str, Any]) -> dict[str, Any]:
|
|
paths = ensure_flm_workspace()
|
|
normalized = {
|
|
"acceptedDimensions": [str(item) for item in feedback.get("acceptedDimensions", [])],
|
|
"ignoredDimensions": [str(item) for item in feedback.get("ignoredDimensions", [])],
|
|
"notes": str(feedback.get("notes", "") or "").strip(),
|
|
"updatedAt": time.time(),
|
|
}
|
|
_write_json(paths["feedback"] / f"{report_id}.json", normalized)
|
|
report = load_report(report_id)
|
|
report["feedback"] = normalized
|
|
if isinstance(report.get("paths"), list) and report.get("paths"):
|
|
selected_path_key = str(report.get("selectedPathKey") or report.get("primaryPathKey") or "")
|
|
flattened_profiles = []
|
|
for path in report["paths"]:
|
|
if not isinstance(path, dict):
|
|
continue
|
|
profiles = build_trial_profiles(
|
|
report_id,
|
|
path.get("suggestions", []),
|
|
normalized,
|
|
report.get("capabilities", {}),
|
|
path_key=str(path.get("key", "cleanup_pass")),
|
|
path_label=str(path.get("title", "Cleanup Pass")),
|
|
)
|
|
path["profiles"] = profiles
|
|
flattened_profiles.extend(profiles)
|
|
if path.get("key") == selected_path_key:
|
|
report["suggestions"] = list(path.get("suggestions", []))
|
|
report["profiles"] = flattened_profiles
|
|
else:
|
|
report["profiles"] = build_trial_profiles(report_id, report.get("suggestions", []), normalized, report.get("capabilities", {}))
|
|
(paths["reports"] / f"{report_id}.html").write_text(_render_report_html(report), encoding="utf-8")
|
|
report.pop("html", None)
|
|
_write_json(paths["reports"] / f"{report_id}.json", report)
|
|
_write_json(paths["profiles"] / f"{report_id}.json", report["profiles"])
|
|
return {
|
|
"message": "Saved FLM feedback.",
|
|
"feedback": normalized,
|
|
"profiles": report["profiles"],
|
|
"report": load_report(report_id),
|
|
}
|
|
|
|
|
|
def run_worker(payload_json: str) -> None:
|
|
payload = json.loads(payload_json)
|
|
routes = [str(route) for route in payload.get("routes", [])]
|
|
footage_paths = [str(path) for path in payload.get("footagePaths", [])]
|
|
ensure_flm_workspace()
|
|
_write_flm_status({
|
|
"pid": os.getpid(),
|
|
"startedAt": time.time(),
|
|
"running": True,
|
|
"state": "starting",
|
|
"routes": routes,
|
|
"progress": 0,
|
|
"total": len(routes),
|
|
})
|
|
try:
|
|
report = analyze_routes(routes, footage_paths)
|
|
_write_flm_status({
|
|
"pid": os.getpid(),
|
|
"startedAt": time.time(),
|
|
"running": False,
|
|
"state": "complete",
|
|
"routes": routes,
|
|
"progress": len(routes),
|
|
"total": len(routes),
|
|
"reportId": report["reportId"],
|
|
})
|
|
except Exception as error:
|
|
_write_flm_status({
|
|
"pid": os.getpid(),
|
|
"startedAt": time.time(),
|
|
"running": False,
|
|
"state": "failed",
|
|
"routes": routes,
|
|
"progress": 0,
|
|
"total": len(routes),
|
|
"error": str(error),
|
|
})
|
|
raise
|
|
|
|
|
|
def main() -> None:
|
|
if len(sys.argv) >= 3 and sys.argv[1] == "worker":
|
|
run_worker(sys.argv[2])
|
|
return
|
|
if len(sys.argv) >= 2 and sys.argv[1] == "analyze":
|
|
routes = sys.argv[2:]
|
|
footage_paths = [str(Paths.log_root(HD=True, raw=True)), str(Paths.log_root(konik=True, raw=True)), str(Paths.log_root(raw=True))]
|
|
report = analyze_routes(routes, footage_paths)
|
|
print(json.dumps({"reportId": report["reportId"], "htmlPath": report["htmlPath"], "jsonPath": report["jsonPath"]}, indent=2))
|
|
return
|
|
print("Usage: flm_workspace.py analyze <route> [<route>...]")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|