增加多点标定输出三个合力平均值

2026-10-15 18:31:46 +08:00
parent 2a154307fd
commit a3aeca1d12
2 changed files with 233 additions and 0 deletions
@@ -20,6 +20,7 @@ import csv
 import json
 import math
 import re
+import statistics
 import time
 from dataclasses import dataclass
 from datetime import date, datetime, timezone
@@ -124,6 +125,126 @@ class SiteSpec:
        return {"x": self.x, "y": self.y, "z": self.z}


+#: Columns tried, in order, for the three separate force means. The first set
+#: whose three values are all present is the one averaged. The combined
+#: magnitude is not one of them.
+_COMPONENT_SETS = (
+    ("N", ("FN_output_N", "FX_output_N", "FY_output_N")),
+    ("N", ("FN_N", "FX_N", "FY_N")),
+    ("raw", ("FN_raw", "FX_raw", "FY_raw")),
+)
+STABLE_MEANS_HEADER = (
+    "site_id",
+    "repeat_index",
+    "step_index",
+    "branch",
+    "target_load_n",
+    "samples",
+    "FN_mean",
+    "FX_mean",
+    "FY_mean",
+    "unit",
+)
+
+
+def _cell(row: dict, key: str) -> float | None:
+    text = str(row.get(key) or "").strip()
+    if not text:
+        return None
+    try:
+        value = float(text)
+    except ValueError:
+        return None
+    return value if math.isfinite(value) else None
+
+
+def _components(row: dict) -> tuple[str, tuple[float, float, float]] | None:
+    """Fn, Fx, Fy from one sample. A combined magnitude is never returned."""
+    if row.get("valid") not in (None, "", "1"):
+        return None
+    for unit, keys in _COMPONENT_SETS:
+        values = tuple(_cell(row, key) for key in keys)
+        if all(value is not None for value in values):
+            return unit, values  # type: ignore[return-value]
+    return None
+
+
+#: A point this many robust standard deviations from the median is an outlier.
+_OUTLIER_SIGMAS = 3.0
+_MAD_TO_SIGMA = 1.4826
+
+
+def _without_outliers(values: list[float]) -> list[float]:
+    """Drop values far from the median. Fewer than three samples are left as they are."""
+    if len(values) < 3:
+        return values
+    centre = statistics.median(values)
+    mad = statistics.median([abs(value - centre) for value in values])
+    if mad == 0.0:
+        kept = [value for value in values if value == centre]
+        return kept or values
+    limit = _OUTLIER_SIGMAS * _MAD_TO_SIGMA * mad
+    kept = [value for value in values if abs(value - centre) <= limit]
+    return kept or [centre]
+
+
+def stable_component_rows(repeat_dir: Path, *, site_id: str, repeat_index: int) -> list[dict]:
+    """Mean Fn, Fx and Fy over the latter half of each completed hold.
+
+    The first half of a hold is the settling transient. Each component then
+    drops its own outliers before the mean. The file this feeds does not
+    contain a combined force.
+    """
+    reference_path = repeat_dir / "reference.csv"
+    if not reference_path.is_file():
+        return []
+    with reference_path.open(encoding="utf-8-sig", newline="") as handle:
+        holds = list(csv.DictReader(handle))
+    stamps: dict[int, int] = {}
+    frames_path = repeat_dir / "capture" / "frames.csv"
+    if frames_path.is_file():
+        with frames_path.open(encoding="utf-8-sig", newline="") as handle:
+            for row in csv.DictReader(handle):
+                if row.get("dropped") not in (None, "", "0") or not row.get("t_host_mono_ns"):
+                    continue
+                stamps[int(row["frame_id"])] = int(row["t_host_mono_ns"])
+    samples: list[tuple[int, str, tuple[float, float, float]]] = []
+    forces_path = repeat_dir / "capture" / "forces.csv"
+    if forces_path.is_file():
+        with forces_path.open(encoding="utf-8-sig", newline="") as handle:
+            for row in csv.DictReader(handle):
+                components = _components(row)
+                stamp = stamps.get(int(row["frame_id"])) if row.get("frame_id") else None
+                if components is None or stamp is None:
+                    continue
+                samples.append((stamp, components[0], components[1]))
+    rows = []
+    for hold in holds:
+        if hold.get("hold_complete") not in (None, "", "1"):
+            continue
+        start = int(hold["t_start_host_mono_ns"])
+        end = int(hold["t_end_host_mono_ns"])
+        stable = start + (end - start) // 2
+        chosen = [item for item in samples if stable <= item[0] < end]
+        unit = chosen[0][1] if chosen else ""
+        used = [item[2] for item in chosen if item[1] == unit]
+        cleaned = [_without_outliers(list(axis)) for axis in zip(*used)] if used else []
+        means = tuple(sum(axis) / len(axis) for axis in cleaned) if cleaned else ("", "", "")
+        rows.append({
+            "site_id": site_id,
+            "repeat_index": repeat_index,
+            "step_index": hold.get("step_index", ""),
+            "branch": hold.get("branch", ""),
+            "target_load_n": hold.get("target_load_n", ""),
+            "samples": len(used),
+            "FN_mean": "" if means[0] == "" else f"{means[0]:.6g}",
+            "FX_mean": "" if means[1] == "" else f"{means[1]:.6g}",
+            "FY_mean": "" if means[2] == "" else f"{means[2]:.6g}",
+            "unit": unit,
+        })
+    return rows
+
+
 def load_steps(
    levels: list[float],
    max_force_n: float,
@@ -813,7 +934,25 @@ class Campaign:
        self._document["status"] = status
        self._document["reason"] = self.reason
        self._document["ended_at"] = datetime.now(timezone.utc).isoformat()
+        self._write_stable_means()
        self._save()
+
+    def _write_stable_means(self) -> None:
+        """Write Fn, Fx and Fy means. No combined force."""
+        if self.directory is None or self._document is None:
+            return
+        rows = []
+        for trial in self._document.get("trials") or []:
+            rows.extend(stable_component_rows(
+                self.directory / trial["directory"],
+                site_id=str(trial.get("site_id", "")),
+                repeat_index=int(trial.get("repeat_index") or 0),
+            ))
+        path = self.directory / "stable_means.csv"
+        with path.open("w", newline="", encoding="utf-8") as handle:
+            writer = csv.DictWriter(handle, fieldnames=list(STABLE_MEANS_HEADER))
+            writer.writeheader()
+            writer.writerows(rows)
        if self._session_events_file is not None:
            self._session_events_file.close()
            self._session_events_file = None