新增单链力滤波引擎（中值 + 因果低通），为"显示=记录"做准备 - What: shared/src/orisys_app_shared/force/filters.py 新增 FilterSettings（中值开关/窗口、低通开关/截止/阶数、采样率、show_unfiltered，窗口强制奇数、<3 自动关闭、空设置直通）与 ForceFilterChain：6 通道（3 原始计数 + 3 牛顿值）、先中值后低通、两级均因果且逐样本、每通道独立 IIR 状态，reset(first) 用稳态初值启动以避免会话开头的假瞬态；settings_from_mapping 支持从 prefs/metadata 构造，describe() 给出一行摘要写进录制元数据；force/__init__ 导出这三个名字。新增 shared/tests/test_force_filters.py（7 例：直通、中值去单点尖峰但不削持续变化、窗口奇数化与下限、阶跃无起始瞬态、通道状态独立、reset 清状态、prefs 往返），并加入 drive_shared 的模块表。 - Why: 用户问"滤波在哪里调"，查清现状是两处固化 10 Hz 低通（SDK 侧影响记录、显示侧再用 filtfilt 二次滤波导致显示≠记录）且 median 完全不存在；用户要求把采集滤波与显示滤波统一成一条链、显示值必须等于记录值、并保留滤波前数据（另开一个 buffer，且写进 forces.csv）。本轮先落地这条链并覆盖测试。 - Verification: drive_shared.py 141 项 0 失败（含新增 7 项）、ui_check.py 554 项 0 失败、test_ui_smoke.py 71 项 0 失败、locale_studio.py 262 键齐全。 - Not in this change: 采集路径未改动（SDK 低通仍是 10 Hz、forces.csv 仍是 11 列）——把带状态的滤波器半途接进记录会让数据只滤一半，因此下一步一次性完成：关 SDK 侧滤波、worker 应用链并让 packet 同时携带滤波前后两套值、forces.csv 追加 6 列未滤波读数（11→17、RECORD_FORMAT_VERSION+1）、SessionStore 第二 buffer、去掉显示端 filtfilt、曲线表头新增齿轮按钮并扩展曲线设置对话框（中值/低通/显示未滤波，运行中禁用滤波控件）。完整清单见 .repro/filter_plan.md。
This commit is contained in:
ykcheungab
@@ -13,6 +13,11 @@ from orisys_app_shared.force.calibration_recorder import (
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CalibrationPoint,
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CalibrationRecorder,
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)
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from orisys_app_shared.force.filters import (
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ForceFilterChain,
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FilterSettings,
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settings_from_mapping,
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)
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from orisys_app_shared.force.history import MetricHistory, PlotState, lowpass_filter
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from orisys_app_shared.force.session import (
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NA,
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@@ -33,6 +38,8 @@ __all__ = [
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"CalibrationPoint",
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"CalibrationRecorder",
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"DisplayForces",
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"FilterSettings",
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"ForceFilterChain",
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"MetricHistory",
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"PlotState",
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"SelectionStats",
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@@ -44,5 +51,6 @@ __all__ = [
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"lowpass_filter",
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"meta_from_sensor_json",
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"select_display_forces",
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"settings_from_mapping",
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"summarise",
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]
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@@ -0,0 +1,205 @@
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"""One causal filter chain for the force readings, and the values it replaced.
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The chain runs **once, in the acquisition loop**, so the numbers on the screen
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and the numbers in ``forces.csv`` are the same filtered values — there is no
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second, display-only smoothing that would make the chart disagree with the
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record. The unfiltered readings are kept beside the filtered ones, so a filter
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can be judged instead of trusted.
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Both stages are causal and per-sample, because acquisition cannot look ahead:
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* **median** — a window of ``median_window`` samples (odd, ≥3), applied per
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channel; it removes single-sample spikes without smearing a step;
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* **low-pass** — a Butterworth IIR with persistent state (``sosfilt`` with
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``zi``), the same shape as the SDK's own ``LowpassFilter``.
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Order is median then low-pass: the median protects the IIR from a spike that
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would otherwise ring for several samples.
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Iterable, Optional
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import numpy as np
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#: Channels of the chain: three raw counts followed by the three newton values.
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CHANNELS = 6
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def _odd(value: Any, *, minimum: int = 3, maximum: int = 31) -> int:
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try:
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number = int(value)
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except (TypeError, ValueError):
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return 0
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if number < minimum:
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return 0
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if number % 2 == 0:
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number += 1
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return min(number, maximum)
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@dataclass(frozen=True)
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class FilterSettings:
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"""What the operator chose. ``enabled`` flags decide what actually runs."""
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median_enabled: bool = False
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median_window: int = 5
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lowpass_enabled: bool = True
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lowpass_hz: float = 10.0
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lowpass_order: int = 2
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sample_rate_hz: float = 30.0
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#: Show the unfiltered readings next to the filtered ones on the cards.
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show_unfiltered: bool = False
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def normalised(self) -> "FilterSettings":
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window = _odd(self.median_window)
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return FilterSettings(
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median_enabled=bool(self.median_enabled) and window >= 3,
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median_window=window or 5,
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lowpass_enabled=bool(self.lowpass_enabled) and float(self.lowpass_hz) > 0,
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lowpass_hz=max(0.0, float(self.lowpass_hz)),
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lowpass_order=max(1, min(6, int(self.lowpass_order))),
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sample_rate_hz=max(1.0, float(self.sample_rate_hz)),
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show_unfiltered=bool(self.show_unfiltered),
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)
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def active(self) -> bool:
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"""Whether any stage actually filters (a disabled chain is a pass-through)."""
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settings = self.normalised()
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return bool(settings.median_enabled or settings.lowpass_enabled)
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def as_dict(self) -> dict:
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settings = self.normalised()
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return {
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"median_enabled": settings.median_enabled,
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"median_window": settings.median_window,
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"lowpass_enabled": settings.lowpass_enabled,
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"lowpass_hz": settings.lowpass_hz,
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"lowpass_order": settings.lowpass_order,
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"sample_rate_hz": settings.sample_rate_hz,
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"show_unfiltered": settings.show_unfiltered,
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}
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def describe(self) -> str:
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"""A short human line for the recording metadata."""
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settings = self.normalised()
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parts = []
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if settings.median_enabled:
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parts.append(f"median({settings.median_window})")
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if settings.lowpass_enabled:
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parts.append(
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f"lowpass({settings.lowpass_hz:g}Hz,order{settings.lowpass_order})"
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)
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return " + ".join(parts) if parts else "none"
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def settings_from_mapping(data: Optional[dict]) -> FilterSettings:
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"""Build settings from a preferences/metadata mapping, with defaults."""
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source = dict(data or {})
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defaults = FilterSettings()
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return FilterSettings(
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median_enabled=source.get("median_enabled", defaults.median_enabled),
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median_window=source.get("median_window", defaults.median_window),
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lowpass_enabled=source.get("lowpass_enabled", defaults.lowpass_enabled),
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lowpass_hz=source.get("lowpass_hz", defaults.lowpass_hz),
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lowpass_order=source.get("lowpass_order", defaults.lowpass_order),
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sample_rate_hz=source.get("sample_rate_hz", defaults.sample_rate_hz),
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show_unfiltered=source.get("show_unfiltered", defaults.show_unfiltered),
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).normalised()
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class ForceFilterChain:
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"""The median + low-pass chain, one instance per acquisition session."""
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def __init__(self, settings: Optional[FilterSettings] = None) -> None:
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self._settings = (settings or FilterSettings()).normalised()
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self._median: list[list[float]] = [[] for _ in range(CHANNELS)]
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self._sos = None
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#: One IIR state per channel: the six readings are independent signals.
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self._zi: Optional[list[np.ndarray]] = None
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self._build()
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# ------------------------------------------------------------------ setup
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def _build(self) -> None:
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self._sos = None
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self._zi = None
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settings = self._settings
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if not settings.lowpass_enabled:
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return
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from scipy.signal import butter
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nyquist = 0.5 * settings.sample_rate_hz
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cutoff = min(float(settings.lowpass_hz), nyquist * 0.95)
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if cutoff <= 0:
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return
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self._sos = butter(
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settings.lowpass_order,
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cutoff / nyquist,
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btype="low",
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output="sos",
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)
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self._zi = None
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def configure(self, settings: FilterSettings) -> None:
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"""Re-tune the chain; the caller resets the stream when it does."""
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self._settings = settings.normalised()
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self._median = [[] for _ in range(CHANNELS)]
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self._build()
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def settings(self) -> FilterSettings:
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return self._settings
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@property
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def active(self) -> bool:
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return self._settings.active()
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def reset(self, first: Optional[Iterable[float]] = None) -> None:
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"""Drop the filter memory (a new session must not inherit the old one)."""
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self._median = [[] for _ in range(CHANNELS)]
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self._zi = None
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if first is not None and self._sos is not None:
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# Start each IIR at its first sample's steady state instead of at
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# zero: a step from 0 to a real reading otherwise rings for a few
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# samples, which would show up as a transient at session start.
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from scipy.signal import sosfilt_zi
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values = np.asarray(list(first), dtype=np.float64).reshape(-1)
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if values.size == CHANNELS:
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steady = sosfilt_zi(self._sos)
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self._zi = [steady * float(value) for value in values]
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# ----------------------------------------------------------------- filter
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def process(self, values) -> np.ndarray:
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"""Filter one 6-channel sample: three raw counts then three newtons."""
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sample = np.asarray(list(values), dtype=np.float64).reshape(-1)
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if sample.size != CHANNELS:
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raise ValueError(f"expected {CHANNELS} channels, got {sample.size}")
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if not self.active:
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return sample.copy()
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settings = self._settings
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out = sample.copy()
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if settings.median_enabled:
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window = int(settings.median_window)
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for index in range(CHANNELS):
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series = self._median[index]
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series.append(float(sample[index]))
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if len(series) > window:
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del series[0]
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out[index] = float(np.median(series))
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if self._sos is not None:
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from scipy.signal import sosfilt
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if self._zi is None:
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self.reset(out)
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states = self._zi if self._zi is not None else [None] * CHANNELS
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for index in range(CHANNELS):
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filtered, states[index] = sosfilt(
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self._sos, np.array([out[index]]), zi=states[index]
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)
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out[index] = float(filtered[0])
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self._zi = states
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return out
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