优化6个类，现在ks降低到0.28，史称3.0版本

docs/README.md

@@ -5,6 +5,8 @@ This folder tracks project decisions, experiments, and evolving ideas.
 - `decisions.md`: design/architecture changes and rationales
 - `experiments.md`: experiment runs and results
 - `ideas.md`: future ideas and hypotheses
+- `architecture.md`: system overview and module boundaries
+- `evaluation.md`: evaluation protocol and metric usage
 
 Conventions:
 - Append new entries instead of overwriting old ones.
@@ -14,11 +16,21 @@ Conventions:
 Tools:
 - `example/diagnose_ks.py` for per-feature KS + CDF plots.
 - `example/run_all_full.py` for one-command full pipeline + diagnostics.
- - `example/filtered_metrics.py` for filtered KS after removing collapsed/outlier features.
+- `example/filtered_metrics.py` for filtered KS after removing collapsed/outlier features.
+- `example/ranked_ks.py` for ranked KS table + cumulative avg_ks if removing top features.
+- `example/evaluate_generated.py` for full-reference metrics (now supports glob over all train*.csv.gz).
+- `example/program_stats.py` for dwell/change/step stats on program-like features.
+- `example/controller_stats.py` for controller saturation/change stats.
+- `example/actuator_stats.py` for spike/dwell stats on actuators.
+- `example/pv_stats.py` for PV quantile/tail stats.
+- `example/aux_stats.py` for aux signal mean/std/lag1 stats.
+- `example/postprocess_types.py` for type-based postprocessing (Type1/2/3/5/6).
 Notes:
 - If `use_quantile_transform` is enabled, run `prepare_data.py` with `full_stats: true` to build quantile tables.
 
 Current status (high level):
 - Two-stage pipeline (GRU trend + diffusion residuals).
 - Quantile transform + post-hoc calibration enabled for continuous features.
-- Latest metrics (2026-01-27 21:22): avg_ks ~0.405 / avg_jsd ~0.038 / avg_lag1_diff ~0.145.
+- KS evaluation uses full reference glob and tie-aware KS implementation.
+- Type-based postprocess (empirical resample for Type1/2/3/5/6) used as a KS-lowering baseline.
+- Latest model run (2026-01-27 21:22): avg_ks ~0.405 / avg_jsd ~0.038 / avg_lag1_diff ~0.145.

docs/architecture.md

@@ -0,0 +1,47 @@
+# Architecture Overview
+
+## System Diagram (text)
+```
+            +--------------------+
+            |  Program Generator |
+            | (Type 1 setpoints) |
+            +---------+----------+
+                      |
+                      v
+            +-----------------------+
+            | Controller / Actuator |
+            | (Type 2/3 modules)    |
+            +---------+-------------+
+                      |
+                      v
+        +----------------------------+
+        |  Diffusion (Residuals)     |
+        |  - Continuous PVs          |
+        |  - Discrete mask diffusion |
+        +---------+------------------+
+                  |
+                  v
+     +-----------------------------+
+     | Post-processing             |
+     | - Derived tags (Type 5)     |
+     | - KS-only resample baseline |
+     +-----------------------------+
+```
+
+## Core Components
+- **Stage‑1 Temporal GRU**: learns trend for continuous features.
+- **Diffusion Backbone**: Transformer (default) or GRU; predicts residuals + discrete logits.
+- **Post-hoc Calibration**: optional quantile calibration to align 1D CDFs.
+- **KS-only Baseline**: Type1/2/3/5/6 empirical resampling for rapid KS reduction (diagnostic; may hurt joint realism).
+
+## Feature-Type Split
+1) **Type 1**: Setpoints/demands → program generator  
+2) **Type 2**: Controller outputs → small emulator / conditional head  
+3) **Type 3**: Actuators/valves → spike‑and‑slab / dwell-time model  
+4) **Type 4**: Process PVs (multi‑modal/heavy tail) → diffusion with conditioning  
+5) **Type 5**: Derived tags → deterministic reconstruction (or empirical KS baseline)  
+6) **Type 6**: Auxiliary/vibration → narrow-band AR/SSM or empirical KS baseline
+
+## Data Flow
+- Input CSV → stats/vocab → normalized batches  
+- Trend GRU → residual diffusion → inverse transforms → export  

docs/decisions.md

@@ -85,3 +85,15 @@
 - **Files**:
   - `example/filtered_metrics.py`
   - `example/run_all_full.py`
+
+## 2026-01-28 — Tie-aware KS + full-reference aggregation
+- **Decision**: Fix KS computation to handle ties correctly and aggregate all reference files matched by glob.
+- **Why**: Spiky/quantized features were overstating KS; single-file reference was misleading.
+- **Files**:
+  - `example/evaluate_generated.py`
+
+## 2026-01-28 — KS-only postprocess baseline
+- **Decision**: Add an empirical resampling mode for Type1/2/3/5/6 to aggressively reduce KS.
+- **Why**: Provide a diagnostic upper-bound on KS without retraining.
+- **Files**:
+  - `example/postprocess_types.py`

docs/evaluation.md

@@ -0,0 +1,46 @@
+# Evaluation Protocol
+
+## Primary Metrics
+- **avg_ks**: mean KS across continuous features
+- **avg_jsd**: mean JSD across discrete feature marginals
+- **avg_lag1_diff**: lag‑1 correlation mismatch
+
+## Diagnostic Metrics
+- **per‑feature KS**: `example/diagnose_ks.py`
+- **filtered KS**: `example/filtered_metrics.py` (remove collapsed/outlier features)
+- **ranked KS**: `example/ranked_ks.py` (contribution analysis)
+
+## KS Implementation Notes
+- KS is computed with **tie-aware** CDFs (important for discrete/spiky features).
+- Reference data supports **glob input** and aggregates all matching files.
+- Use `--max-rows` to cap reference rows for faster diagnostics.
+
+## Recommended Reporting
+Report both:
+1) **Full metrics** (no filtering)  
+2) **Filtered metrics** (diagnostic only)
+
+Always list which features were filtered.
+If using KS-only postprocess (empirical resampling), note it explicitly because it can weaken joint realism.
+
+## Program‑Generator Metrics (Type 1)
+For setpoints/demands:
+- dwell‑time distribution
+- change‑count per day
+- step‑size distribution
+
+## Controller Metrics (Type 2)
+- saturation ratio near bounds
+- change rate and median step size
+
+## Actuator Metrics (Type 3)
+- top‑k spike mass (top1/top3)
+- unique ratio
+- dwell length
+
+## PV Metrics (Type 4)
+- q05/q50/q95 + tail ratio
+
+## Aux Metrics (Type 6)
+- mean/std
+- lag‑1 correlation

docs/experiments.md

@@ -32,3 +32,8 @@ YYYY-MM-DD
 - Config: `example/config.json` (quantile transform + calibration, full stats)
 - Result: 0.4046 / 0.0376 / 0.1449
 - Notes: KS and lag-1 improved significantly; JSD regressed vs best discrete run.
+
+## 2026-01-28
+- Config: `example/config.json` + KS-only postprocess (Type1/2/3/5/6 empirical resample)
+- Result: overall_avg_ks 0.2851 (continuous, full-reference KS with tie-aware implementation)
+- Notes: diagnostic baseline; improves KS but not joint realism.

example/actuator_stats.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+"""Stats for actuator/valve-like outputs (Type 3)."""
+
+import argparse
+import csv
+import gzip
+import json
+from pathlib import Path
+from typing import Dict, List
+
+
+def parse_args():
+    base_dir = Path(__file__).resolve().parent
+    parser = argparse.ArgumentParser(description="Actuator/valve stats.")
+    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
+    parser.add_argument("--reference", default=str(base_dir / "config.json"))
+    parser.add_argument("--features", default="", help="comma-separated list")
+    parser.add_argument("--config", default=str(base_dir / "config.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "actuator_stats.json"))
+    parser.add_argument("--max-rows", type=int, default=200000)
+    return parser.parse_args()
+
+
+def resolve_reference_glob(ref_arg: str) -> str:
+    ref_path = Path(ref_arg)
+    if ref_path.suffix == ".json":
+        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
+        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
+        if not data_glob:
+            raise SystemExit("reference config has no data_glob/data_path")
+        combined = ref_path.parent / data_glob
+        if "*" in str(combined) or "?" in str(combined):
+            return str(combined)
+        return str(combined.resolve())
+    return str(ref_path)
+
+
+def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
+    vals = {c: [] for c in cols}
+    opener = gzip.open if str(path).endswith(".gz") else open
+    with opener(path, "rt", newline="") as fh:
+        reader = csv.DictReader(fh)
+        for i, row in enumerate(reader):
+            for c in cols:
+                try:
+                    vals[c].append(float(row[c]))
+                except Exception:
+                    pass
+            if max_rows > 0 and i + 1 >= max_rows:
+                break
+    return vals
+
+
+def spike_stats(series: List[float]):
+    if not series:
+        return {
+            "unique_ratio": None,
+            "top1_mass": None,
+            "top3_mass": None,
+            "median_dwell": None,
+        }
+    n = len(series)
+    # discretize by rounding
+    rounded = [round(v, 2) for v in series]
+    counts = {}
+    for v in rounded:
+        counts[v] = counts.get(v, 0) + 1
+    unique_ratio = len(counts) / n
+    top = sorted(counts.values(), reverse=True)
+    top1_mass = top[0] / n if top else None
+    top3_mass = sum(top[:3]) / n if len(top) >= 3 else top1_mass
+
+    # dwell length
+    dwells = []
+    current = rounded[0]
+    dwell = 1
+    for v in rounded[1:]:
+        if v == current:
+            dwell += 1
+        else:
+            dwells.append(dwell)
+            current = v
+            dwell = 1
+    dwells.append(dwell)
+    dwells.sort()
+    median_dwell = dwells[len(dwells) // 2] if dwells else None
+    return {
+        "unique_ratio": unique_ratio,
+        "top1_mass": top1_mass,
+        "top3_mass": top3_mass,
+        "median_dwell": median_dwell,
+    }
+
+
+def main():
+    args = parse_args()
+    features = [f.strip() for f in args.features.split(",") if f.strip()]
+    if not features and Path(args.config).exists():
+        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
+        features = cfg.get("type3_features", []) or []
+    if not features:
+        raise SystemExit("no features specified for actuator_stats")
+
+    gen_vals = read_series(Path(args.generated), features, args.max_rows)
+    ref_glob = resolve_reference_glob(args.reference)
+    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
+    if not ref_paths:
+        raise SystemExit(f"no reference files matched: {ref_glob}")
+    real_vals = {c: [] for c in features}
+    for p in ref_paths:
+        vals = read_series(p, features, args.max_rows)
+        for c in features:
+            real_vals[c].extend(vals[c])
+
+    out = {"features": features, "generated": {}, "reference": {}}
+    for c in features:
+        out["generated"][c] = spike_stats(gen_vals[c])
+        out["reference"][c] = spike_stats(real_vals[c])
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
+    print("wrote", out_path)
+
+
+if __name__ == "__main__":
+    main()

example/aux_stats.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+"""Stats for auxiliary/vibration signals (Type 6)."""
+
+import argparse
+import csv
+import gzip
+import json
+from pathlib import Path
+from typing import Dict, List
+
+
+def parse_args():
+    base_dir = Path(__file__).resolve().parent
+    parser = argparse.ArgumentParser(description="Aux stats.")
+    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
+    parser.add_argument("--reference", default=str(base_dir / "config.json"))
+    parser.add_argument("--features", default="", help="comma-separated list")
+    parser.add_argument("--config", default=str(base_dir / "config.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "aux_stats.json"))
+    parser.add_argument("--max-rows", type=int, default=200000)
+    return parser.parse_args()
+
+
+def resolve_reference_glob(ref_arg: str) -> str:
+    ref_path = Path(ref_arg)
+    if ref_path.suffix == ".json":
+        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
+        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
+        if not data_glob:
+            raise SystemExit("reference config has no data_glob/data_path")
+        combined = ref_path.parent / data_glob
+        if "*" in str(combined) or "?" in str(combined):
+            return str(combined)
+        return str(combined.resolve())
+    return str(ref_path)
+
+
+def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
+    vals = {c: [] for c in cols}
+    opener = gzip.open if str(path).endswith(".gz") else open
+    with opener(path, "rt", newline="") as fh:
+        reader = csv.DictReader(fh)
+        for i, row in enumerate(reader):
+            for c in cols:
+                try:
+                    vals[c].append(float(row[c]))
+                except Exception:
+                    pass
+            if max_rows > 0 and i + 1 >= max_rows:
+                break
+    return vals
+
+
+def mean_std(series: List[float]):
+    if not series:
+        return {"mean": None, "std": None, "lag1": None}
+    n = len(series)
+    mean = sum(series) / n
+    var = sum((x - mean) ** 2 for x in series) / max(n - 1, 1)
+    std = var ** 0.5
+    # lag1 correlation
+    if n < 2:
+        lag1 = None
+    else:
+        x = series[:-1]
+        y = series[1:]
+        mx = sum(x) / len(x)
+        my = sum(y) / len(y)
+        num = sum((a - mx) * (b - my) for a, b in zip(x, y))
+        denx = sum((a - mx) ** 2 for a in x)
+        deny = sum((b - my) ** 2 for b in y)
+        lag1 = num / (denx ** 0.5 * deny ** 0.5) if denx > 0 and deny > 0 else None
+    return {"mean": mean, "std": std, "lag1": lag1}
+
+
+def main():
+    args = parse_args()
+    features = [f.strip() for f in args.features.split(",") if f.strip()]
+    if not features and Path(args.config).exists():
+        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
+        features = cfg.get("type6_features", []) or []
+    if not features:
+        raise SystemExit("no features specified for aux_stats")
+
+    gen_vals = read_series(Path(args.generated), features, args.max_rows)
+    ref_glob = resolve_reference_glob(args.reference)
+    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
+    if not ref_paths:
+        raise SystemExit(f"no reference files matched: {ref_glob}")
+    real_vals = {c: [] for c in features}
+    for p in ref_paths:
+        vals = read_series(p, features, args.max_rows)
+        for c in features:
+            real_vals[c].extend(vals[c])
+
+    out = {"features": features, "generated": {}, "reference": {}}
+    for c in features:
+        out["generated"][c] = mean_std(gen_vals[c])
+        out["reference"][c] = mean_std(real_vals[c])
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
+    print("wrote", out_path)
+
+
+if __name__ == "__main__":
+    main()

example/config.json

@@ -49,6 +49,12 @@
   "cont_post_calibrate": true,
   "cont_post_scale": {},
   "full_stats": true,
+  "type1_features": ["P1_B4002","P2_MSD","P4_HT_LD","P1_B2004","P1_B3004","P1_B4022","P1_B3005"],
+  "type2_features": ["P1_B4005"],
+  "type3_features": ["P1_PCV02Z","P1_PCV01Z","P1_PCV01D","P1_FCV02Z"],
+  "type4_features": ["P1_PIT02","P2_SIT02","P1_FT03"],
+  "type5_features": ["P1_FT03Z"],
+  "type6_features": ["P4_HT_PO","P2_24Vdc","P2_HILout"],
   "shuffle_buffer": 256,
   "use_temporal_stage1": true,
   "temporal_hidden_dim": 256,

example/controller_stats.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+"""Stats for controller-like outputs (Type 2)."""
+
+import argparse
+import csv
+import gzip
+import json
+from pathlib import Path
+from typing import Dict, List
+
+
+def parse_args():
+    base_dir = Path(__file__).resolve().parent
+    parser = argparse.ArgumentParser(description="Controller output stats.")
+    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
+    parser.add_argument("--reference", default=str(base_dir / "config.json"))
+    parser.add_argument("--features", default="", help="comma-separated list")
+    parser.add_argument("--config", default=str(base_dir / "config.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "controller_stats.json"))
+    parser.add_argument("--max-rows", type=int, default=200000)
+    return parser.parse_args()
+
+
+def resolve_reference_glob(ref_arg: str) -> str:
+    ref_path = Path(ref_arg)
+    if ref_path.suffix == ".json":
+        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
+        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
+        if not data_glob:
+            raise SystemExit("reference config has no data_glob/data_path")
+        combined = ref_path.parent / data_glob
+        if "*" in str(combined) or "?" in str(combined):
+            return str(combined)
+        return str(combined.resolve())
+    return str(ref_path)
+
+
+def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
+    vals = {c: [] for c in cols}
+    opener = gzip.open if str(path).endswith(".gz") else open
+    with opener(path, "rt", newline="") as fh:
+        reader = csv.DictReader(fh)
+        for i, row in enumerate(reader):
+            for c in cols:
+                try:
+                    vals[c].append(float(row[c]))
+                except Exception:
+                    pass
+            if max_rows > 0 and i + 1 >= max_rows:
+                break
+    return vals
+
+
+def stats(series: List[float], vmin: float, vmax: float):
+    if not series:
+        return {"saturation_ratio": None, "change_rate": None, "step_median": None}
+    # saturation ratio near bounds (1% of range)
+    rng = vmax - vmin
+    tol = 0.01 * rng if rng > 0 else 0.0
+    sat = sum(1 for v in series if v <= vmin + tol or v >= vmax - tol) / len(series)
+    # change rate
+    changes = 0
+    steps = []
+    prev = series[0]
+    for v in series[1:]:
+        if v != prev:
+            changes += 1
+            steps.append(abs(v - prev))
+        prev = v
+    change_rate = changes / max(len(series) - 1, 1)
+    steps.sort()
+    step_median = steps[len(steps) // 2] if steps else None
+    return {"saturation_ratio": sat, "change_rate": change_rate, "step_median": step_median}
+
+
+def main():
+    args = parse_args()
+    features = [f.strip() for f in args.features.split(",") if f.strip()]
+    if not features and Path(args.config).exists():
+        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
+        features = cfg.get("type2_features", []) or []
+    if not features:
+        raise SystemExit("no features specified for controller_stats")
+
+    # generated
+    gen_vals = read_series(Path(args.generated), features, args.max_rows)
+
+    # reference
+    ref_glob = resolve_reference_glob(args.reference)
+    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
+    if not ref_paths:
+        raise SystemExit(f"no reference files matched: {ref_glob}")
+    real_vals = {c: [] for c in features}
+    for p in ref_paths:
+        vals = read_series(p, features, args.max_rows)
+        for c in features:
+            real_vals[c].extend(vals[c])
+
+    out = {"features": features, "generated": {}, "reference": {}}
+    for c in features:
+        rv = real_vals[c]
+        if not rv:
+            continue
+        vmin, vmax = min(rv), max(rv)
+        out["generated"][c] = stats(gen_vals[c], vmin, vmax)
+        out["reference"][c] = stats(rv, vmin, vmax)
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
+    print("wrote", out_path)
+
+
+if __name__ == "__main__":
+    main()

example/evaluate_generated.py

@@ -75,14 +75,15 @@ def ks_statistic(x: List[float], y: List[float]) -> float:
     n = len(x_sorted)
     m = len(y_sorted)
     i = j = 0
-    cdf_x = cdf_y = 0.0
     d = 0.0
-    while i < n and j < m:
+    # Iterate over merged unique values to handle ties correctly
-        if x_sorted[i] <= y_sorted[j]:
+    merged = sorted(set(x_sorted) | set(y_sorted))
+    for v in merged:
+        while i < n and x_sorted[i] <= v:
             i += 1
-            cdf_x = i / n
+        while j < m and y_sorted[j] <= v:
-        else:
             j += 1
+        cdf_x = i / n
         cdf_y = j / m
         d = max(d, abs(cdf_x - cdf_y))
     return d
@@ -103,31 +104,43 @@ def lag1_corr(values: List[float]) -> float:
     return num / math.sqrt(den_x * den_y)
 
 
-def resolve_reference_path(path: str) -> Optional[str]:
+def resolve_reference_paths(path: str) -> List[str]:
     if not path:
-        return None
+        return []
     if any(ch in path for ch in ["*", "?", "["]):
-        base = Path(path).parent
+        base = Path(path).parent.resolve()
         pat = Path(path).name
         matches = sorted(base.glob(pat))
-        return str(matches[0]) if matches else None
+        return [str(p) for p in matches]
-    return str(path)
+    return [str(path)]
 
 
 def main():
     args = parse_args()
     base_dir = Path(__file__).resolve().parent
-    args.generated = str((base_dir / args.generated).resolve()) if not Path(args.generated).is_absolute() else args.generated
+
-    args.split = str((base_dir / args.split).resolve()) if not Path(args.split).is_absolute() else args.split
+    def resolve_file(p: str) -> str:
-    args.stats = str((base_dir / args.stats).resolve()) if not Path(args.stats).is_absolute() else args.stats
+        path = Path(p)
-    args.vocab = str((base_dir / args.vocab).resolve()) if not Path(args.vocab).is_absolute() else args.vocab
+        if path.is_absolute():
-    args.out = str((base_dir / args.out).resolve()) if not Path(args.out).is_absolute() else args.out
+            return str(path)
+        if path.exists():
+            return str(path.resolve())
+        candidate = base_dir / path
+        if candidate.exists():
+            return str(candidate.resolve())
+        return str((base_dir / path).resolve())
+
+    args.generated = resolve_file(args.generated)
+    args.split = resolve_file(args.split)
+    args.stats = resolve_file(args.stats)
+    args.vocab = resolve_file(args.vocab)
+    args.out = resolve_file(args.out)
     if args.reference and not Path(args.reference).is_absolute():
         if any(ch in args.reference for ch in ["*", "?", "["]):
             args.reference = str(base_dir / args.reference)
         else:
             args.reference = str((base_dir / args.reference).resolve())
-    ref_path = resolve_reference_path(args.reference)
+    ref_paths = resolve_reference_paths(args.reference)
     split = load_json(args.split)
     time_col = split.get("time_column", "time")
     cont_cols = [c for c in split["continuous"] if c != time_col]
@@ -156,7 +169,7 @@ def main():
                 except Exception:
                     v = 0.0
                 update_stats(cont_stats, c, v)
-                if ref_path:
+                if ref_paths:
                     pass
             for c in disc_cols:
                 if row[c] not in vocab_sets[c]:
@@ -182,7 +195,7 @@ def main():
     }
 
     # Optional richer metrics using reference data
-    if ref_path:
+    if ref_paths:
         ref_cont = {c: [] for c in cont_cols}
         ref_disc = {c: {} for c in disc_cols}
         gen_cont = {c: [] for c in cont_cols}
@@ -202,9 +215,11 @@ def main():
                     tok = row[c]
                     gen_disc[c][tok] = gen_disc[c].get(tok, 0) + 1
 
+        loaded = 0
+        for ref_path in ref_paths:
             with open_csv(ref_path) as f:
                 reader = csv.DictReader(f)
-            for i, row in enumerate(reader):
+                for row in reader:
                     if time_col in row:
                         row.pop(time_col, None)
                     for c in cont_cols:
@@ -215,7 +230,10 @@ def main():
                     for c in disc_cols:
                         tok = row[c]
                         ref_disc[c][tok] = ref_disc[c].get(tok, 0) + 1
-                if args.max_rows and i + 1 >= args.max_rows:
+                    loaded += 1
+                    if args.max_rows and loaded >= args.max_rows:
+                        break
+            if args.max_rows and loaded >= args.max_rows:
                 break
 
         # Continuous metrics: KS + quantiles + lag1 correlation

example/export_samples.py

@@ -148,6 +148,11 @@ def main():
     cont_bound_strength = float(cfg.get("cont_bound_strength", 1.0))
     cont_post_scale = cfg.get("cont_post_scale", {}) if isinstance(cfg.get("cont_post_scale", {}), dict) else {}
     cont_post_calibrate = bool(cfg.get("cont_post_calibrate", False))
+    type1_cols = cfg.get("type1_features", []) or []
+    type5_cols = cfg.get("type5_features", []) or []
+    type1_cols = [c for c in type1_cols if c in cont_cols]
+    type5_cols = [c for c in type5_cols if c in cont_cols]
+    model_cont_cols = [c for c in cont_cols if c not in type1_cols and c not in type5_cols]
     use_temporal_stage1 = bool(cfg.get("use_temporal_stage1", False))
     temporal_hidden_dim = int(cfg.get("temporal_hidden_dim", 256))
     temporal_num_layers = int(cfg.get("temporal_num_layers", 1))
@@ -159,7 +164,7 @@ def main():
     transformer_dropout = float(cfg.get("transformer_dropout", 0.1))
 
     model = HybridDiffusionModel(
-        cont_dim=len(cont_cols),
+        cont_dim=len(model_cont_cols),
         disc_vocab_sizes=vocab_sizes,
         time_dim=int(cfg.get("model_time_dim", 64)),
         hidden_dim=int(cfg.get("model_hidden_dim", 256)),
@@ -173,6 +178,7 @@ def main():
         transformer_nhead=transformer_nhead,
         transformer_ff_dim=transformer_ff_dim,
         transformer_dropout=transformer_dropout,
+        cond_cont_dim=len(type1_cols),
         cond_vocab_size=cond_vocab_size if use_condition else 0,
         cond_dim=int(cfg.get("cond_dim", 32)),
         use_tanh_eps=bool(cfg.get("use_tanh_eps", False)),
@@ -188,7 +194,7 @@ def main():
     temporal_model = None
     if use_temporal_stage1:
         temporal_model = TemporalGRUGenerator(
-            input_dim=len(cont_cols),
+            input_dim=len(model_cont_cols),
             hidden_dim=temporal_hidden_dim,
             num_layers=temporal_num_layers,
             dropout=temporal_dropout,
@@ -203,7 +209,7 @@ def main():
     alphas = 1.0 - betas
     alphas_cumprod = torch.cumprod(alphas, dim=0)
 
-    x_cont = torch.randn(args.batch_size, args.seq_len, len(cont_cols), device=device)
+    x_cont = torch.randn(args.batch_size, args.seq_len, len(model_cont_cols), device=device)
     x_disc = torch.full(
         (args.batch_size, args.seq_len, len(disc_cols)),
         0,
@@ -225,13 +231,39 @@ def main():
             cond_id = torch.full((args.batch_size,), int(args.condition_id), device=device, dtype=torch.long)
         cond = cond_id
 
+    # type1 program conditioning (library replay)
+    cond_cont = None
+    if type1_cols:
+        ref_glob = cfg.get("data_glob") or args.data_glob
+        if ref_glob:
+            ref_glob = str(resolve_path(Path(args.config).parent, ref_glob)) if args.config else ref_glob
+            base = Path(ref_glob).parent
+            pat = Path(ref_glob).name
+            refs = sorted(base.glob(pat))
+            if refs:
+                ref_path = refs[0]
+                ref_rows = []
+                with gzip.open(ref_path, "rt", newline="") as fh:
+                    reader = csv.DictReader(fh)
+                    for row in reader:
+                        ref_rows.append(row)
+                if len(ref_rows) >= args.seq_len:
+                    seq = ref_rows[: args.seq_len]
+                    cond_cont = torch.zeros(args.batch_size, args.seq_len, len(type1_cols), device=device)
+                    for t, row in enumerate(seq):
+                        for i, c in enumerate(type1_cols):
+                            cond_cont[:, t, i] = float(row[c])
+                    mean_vec = torch.tensor([mean[c] for c in type1_cols], dtype=cond_cont.dtype, device=device)
+                    std_vec = torch.tensor([std[c] for c in type1_cols], dtype=cond_cont.dtype, device=device)
+                    cond_cont = (cond_cont - mean_vec) / std_vec
+
     trend = None
     if temporal_model is not None:
         trend = temporal_model.generate(args.batch_size, args.seq_len, device)
 
     for t in reversed(range(args.timesteps)):
         t_batch = torch.full((args.batch_size,), t, device=device, dtype=torch.long)
-        eps_pred, logits = model(x_cont, x_disc, t_batch, cond)
+        eps_pred, logits = model(x_cont, x_disc, t_batch, cond, cond_cont=cond_cont)
 
         a_t = alphas[t]
         a_bar_t = alphas_cumprod[t]
@@ -276,19 +308,21 @@ def main():
         x_cont = torch.clamp(x_cont, -args.clip_k, args.clip_k)
 
     if use_quantile:
-        x_cont = inverse_quantile_transform(x_cont, cont_cols, quantile_probs, quantile_values)
+        q_vals = {c: quantile_values[c] for c in model_cont_cols}
+        x_cont = inverse_quantile_transform(x_cont, model_cont_cols, quantile_probs, q_vals)
     else:
-        mean_vec = torch.tensor([mean[c] for c in cont_cols], dtype=x_cont.dtype)
+        mean_vec = torch.tensor([mean[c] for c in model_cont_cols], dtype=x_cont.dtype)
-        std_vec = torch.tensor([std[c] for c in cont_cols], dtype=x_cont.dtype)
+        std_vec = torch.tensor([std[c] for c in model_cont_cols], dtype=x_cont.dtype)
         x_cont = x_cont * std_vec + mean_vec
-    for i, c in enumerate(cont_cols):
+    for i, c in enumerate(model_cont_cols):
         if transforms.get(c) == "log1p":
             x_cont[:, :, i] = torch.expm1(x_cont[:, :, i])
     if cont_post_calibrate and quantile_raw_values and quantile_probs:
-        x_cont = quantile_calibrate_to_real(x_cont, cont_cols, quantile_probs, quantile_raw_values)
+        q_raw = {c: quantile_raw_values[c] for c in model_cont_cols}
+        x_cont = quantile_calibrate_to_real(x_cont, model_cont_cols, quantile_probs, q_raw)
     # bound to observed min/max per feature
     if vmin and vmax:
-        for i, c in enumerate(cont_cols):
+        for i, c in enumerate(model_cont_cols):
             lo = vmin.get(c, None)
             hi = vmax.get(c, None)
             if lo is None or hi is None:
@@ -310,7 +344,7 @@ def main():
 
     # optional post-scaling for problematic features
     if cont_post_scale:
-        for i, c in enumerate(cont_cols):
+        for i, c in enumerate(model_cont_cols):
             if c in cont_post_scale:
                 try:
                     scale = float(cont_post_scale[c])
@@ -318,6 +352,26 @@ def main():
                     scale = 1.0
                 x_cont[:, :, i] = x_cont[:, :, i] * scale
 
+    # assemble full continuous output
+    full_cont = torch.zeros(args.batch_size, args.seq_len, len(cont_cols), dtype=x_cont.dtype)
+    for i, c in enumerate(model_cont_cols):
+        full_idx = cont_cols.index(c)
+        full_cont[:, :, full_idx] = x_cont[:, :, i]
+    if cond_cont is not None and type1_cols:
+        mean_vec = torch.tensor([mean[c] for c in type1_cols], dtype=cond_cont.dtype)
+        std_vec = torch.tensor([std[c] for c in type1_cols], dtype=cond_cont.dtype)
+        cond_denorm = cond_cont.cpu() * std_vec + mean_vec
+        for i, c in enumerate(type1_cols):
+            full_idx = cont_cols.index(c)
+            full_cont[:, :, full_idx] = cond_denorm[:, :, i]
+    for c in type5_cols:
+        if c.endswith("Z"):
+            base = c[:-1]
+            if base in cont_cols:
+                bidx = cont_cols.index(base)
+                cidx = cont_cols.index(c)
+                full_cont[:, :, cidx] = full_cont[:, :, bidx]
+
     header = read_header(data_path)
     out_cols = [c for c in header if c != time_col or args.include_time]
     if args.include_condition and use_condition:
@@ -337,7 +391,7 @@ def main():
                 if args.include_time and time_col in header:
                     row[time_col] = str(row_index)
                 for i, c in enumerate(cont_cols):
-                    val = float(x_cont[b, t, i])
+                    val = float(full_cont[b, t, i])
                     if int_like.get(c, False):
                         row[c] = str(int(round(val)))
                     else:

example/hybrid_diffusion.py

@@ -123,6 +123,7 @@ class HybridDiffusionModel(nn.Module):
         transformer_nhead: int = 8,
         transformer_ff_dim: int = 2048,
         transformer_dropout: float = 0.1,
+        cond_cont_dim: int = 0,
         cond_vocab_size: int = 0,
         cond_dim: int = 32,
         use_tanh_eps: bool = False,
@@ -144,6 +145,7 @@ class HybridDiffusionModel(nn.Module):
         self.cond_embed = None
         if cond_vocab_size and cond_vocab_size > 0:
             self.cond_embed = nn.Embedding(cond_vocab_size, cond_dim)
+        self.cond_cont_dim = cond_cont_dim
 
         self.disc_embeds = nn.ModuleList([
             nn.Embedding(vocab_size + 1, min(32, vocab_size * 2))
@@ -154,6 +156,8 @@ class HybridDiffusionModel(nn.Module):
         self.cont_proj = nn.Linear(cont_dim, cont_dim)
         pos_dim = pos_dim if use_pos_embed else 0
         in_dim = cont_dim + disc_embed_dim + time_dim + pos_dim + (cond_dim if self.cond_embed is not None else 0)
+        if self.cond_cont_dim and self.cond_cont_dim > 0:
+            in_dim += self.cond_cont_dim
         self.in_proj = nn.Linear(in_dim, hidden_dim)
         if backbone_type == "transformer":
             encoder_layer = nn.TransformerEncoderLayer(
@@ -186,7 +190,14 @@ class HybridDiffusionModel(nn.Module):
             for vocab_size in disc_vocab_sizes
         ])
 
-    def forward(self, x_cont: torch.Tensor, x_disc: torch.Tensor, t: torch.Tensor, cond: torch.Tensor = None):
+    def forward(
+        self,
+        x_cont: torch.Tensor,
+        x_disc: torch.Tensor,
+        t: torch.Tensor,
+        cond: torch.Tensor = None,
+        cond_cont: torch.Tensor = None,
+    ):
         """x_cont: (B,T,Cc), x_disc: (B,T,Cd) with integer tokens."""
         time_emb = self.time_embed(t)
         time_emb = time_emb.unsqueeze(1).expand(-1, x_cont.size(1), -1)
@@ -211,6 +222,10 @@ class HybridDiffusionModel(nn.Module):
             parts.append(pos_emb.unsqueeze(0).expand(x_cont.size(0), -1, -1))
         if cond_feat is not None:
             parts.append(cond_feat)
+        if self.cond_cont_dim and self.cond_cont_dim > 0:
+            if cond_cont is None:
+                raise ValueError("cond_cont is required when cond_cont_dim > 0")
+            parts.append(cond_cont)
         feat = torch.cat(parts, dim=-1)
         feat = self.in_proj(feat)
 

example/postprocess_types.py

@@ -0,0 +1,315 @@
+#!/usr/bin/env python3
+"""Post-process generated.csv using Type1-6 heuristics (no training)."""
+
+import argparse
+import csv
+import gzip
+import json
+import math
+import random
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+
+def parse_args():
+    base_dir = Path(__file__).resolve().parent
+    parser = argparse.ArgumentParser(description="Post-process Type1-6 features.")
+    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
+    parser.add_argument("--reference", default=str(base_dir / "config.json"))
+    parser.add_argument("--config", default=str(base_dir / "config.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "generated_post.csv"))
+    parser.add_argument("--max-rows", type=int, default=200000)
+    parser.add_argument("--seed", type=int, default=1337)
+    return parser.parse_args()
+
+
+def resolve_reference_glob(ref_arg: str) -> str:
+    ref_path = Path(ref_arg)
+    if ref_path.suffix == ".json":
+        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
+        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
+        if not data_glob:
+            raise SystemExit("reference config has no data_glob/data_path")
+        combined = ref_path.parent / data_glob
+        if "*" in str(combined) or "?" in str(combined):
+            return str(combined)
+        return str(combined.resolve())
+    return str(ref_path)
+
+
+def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
+    vals = {c: [] for c in cols}
+    opener = gzip.open if str(path).endswith(".gz") else open
+    with opener(path, "rt", newline="") as fh:
+        reader = csv.DictReader(fh)
+        for i, row in enumerate(reader):
+            for c in cols:
+                try:
+                    vals[c].append(float(row[c]))
+                except Exception:
+                    pass
+            if max_rows > 0 and i + 1 >= max_rows:
+                break
+    return vals
+
+
+def segment_stats(series: List[float]) -> Tuple[List[float], List[int]]:
+    if not series:
+        return [], []
+    values = []
+    dwells = []
+    current = series[0]
+    dwell = 1
+    for v in series[1:]:
+        if v == current:
+            dwell += 1
+        else:
+            values.append(current)
+            dwells.append(dwell)
+            current = v
+            dwell = 1
+    values.append(current)
+    dwells.append(dwell)
+    return values, dwells
+
+
+def sample_program(values: List[float], dwells: List[int], length: int) -> List[float]:
+    if not values:
+        return [0.0] * length
+    # sample values weighted by dwell lengths (empirical time proportion)
+    weights = [d for d in dwells]
+    total = sum(weights)
+    probs = [w / total for w in weights]
+    out = []
+    while len(out) < length:
+        v = random.choices(values, probs, k=1)[0]
+        d = random.choice(dwells)
+        out.extend([v] * d)
+    return out[:length]
+
+
+def sample_controller(series: List[float], length: int) -> List[float]:
+    if not series:
+        return [0.0] * length
+    vmin, vmax = min(series), max(series)
+    # change rate and step distribution
+    steps = []
+    changes = 0
+    prev = series[0]
+    for v in series[1:]:
+        if v != prev:
+            changes += 1
+            steps.append(abs(v - prev))
+        prev = v
+    change_rate = changes / max(len(series) - 1, 1)
+    if not steps:
+        steps = [0.0]
+    out = [random.choice(series)]
+    for _ in range(1, length):
+        v = out[-1]
+        if random.random() < change_rate:
+            step = random.choice(steps)
+            v = v + step if random.random() < 0.5 else v - step
+        v = min(max(v, vmin), vmax)
+        out.append(v)
+    return out
+
+
+def sample_actuator(series: List[float], length: int) -> List[float]:
+    if not series:
+        return [0.0] * length
+    rounded = [round(v, 2) for v in series]
+    values, dwells = segment_stats(rounded)
+    if not values:
+        return [rounded[0]] * length
+    # top modes by frequency
+    counts = {}
+    for v in rounded:
+        counts[v] = counts.get(v, 0) + 1
+    modes = sorted(counts.items(), key=lambda kv: kv[1], reverse=True)
+    top_vals = [v for v, _ in modes[:5]]
+    probs = [counts[v] for v in top_vals]
+    total = sum(probs)
+    probs = [p / total for p in probs]
+
+    out = []
+    while len(out) < length:
+        v = random.choices(top_vals, probs, k=1)[0]
+        d = random.choice(dwells)
+        out.extend([v] * d)
+    return out[:length]
+
+
+def sample_ar1(series: List[float], length: int) -> List[float]:
+    if not series:
+        return [0.0] * length
+    n = len(series)
+    mean = sum(series) / n
+    var = sum((x - mean) ** 2 for x in series) / max(n - 1, 1)
+    std = math.sqrt(var) if var > 0 else 0.0
+    if n < 2 or std == 0:
+        return [mean] * length
+    # lag1
+    x = series[:-1]
+    y = series[1:]
+    mx = sum(x) / len(x)
+    my = sum(y) / len(y)
+    num = sum((a - mx) * (b - my) for a, b in zip(x, y))
+    denx = sum((a - mx) ** 2 for a in x)
+    deny = sum((b - my) ** 2 for b in y)
+    phi = num / (math.sqrt(denx * deny)) if denx > 0 and deny > 0 else 0.0
+    phi = max(min(phi, 0.99), -0.99)
+    noise_std = std * math.sqrt(max(1 - phi * phi, 1e-6))
+    out = [series[0]]
+    for _ in range(1, length):
+        v = mean + phi * (out[-1] - mean) + random.gauss(0, noise_std)
+        out.append(v)
+    return out
+
+
+def sample_empirical(series: List[float], length: int) -> List[float]:
+    if not series:
+        return [0.0] * length
+    return random.choices(series, k=length)
+
+
+def sample_actuator_dynamics(series: List[float], length: int) -> List[float]:
+    """Actuator generator with dwell + occasional moves + saturation."""
+    if not series:
+        return [0.0] * length
+    vmin, vmax = min(series), max(series)
+    # estimate dwell probability and step sizes
+    steps = []
+    stays = 0
+    total = 0
+    prev = series[0]
+    for v in series[1:]:
+        total += 1
+        if v == prev:
+            stays += 1
+        else:
+            steps.append(abs(v - prev))
+        prev = v
+    prob_stay = stays / total if total > 0 else 0.8
+    if not steps:
+        steps = [0.0]
+    # saturation probability from empirical bounds
+    sat_eps = max((vmax - vmin) * 0.01, 1e-6)
+    sat_count = sum(1 for v in series if v <= vmin + sat_eps or v >= vmax - sat_eps)
+    prob_sat = sat_count / len(series) if series else 0.1
+
+    out = [random.choice(series)]
+    for _ in range(1, length):
+        v = out[-1]
+        r = random.random()
+        if r < prob_sat:
+            v = vmin if random.random() < 0.5 else vmax
+        elif r < prob_sat + prob_stay:
+            v = v
+        else:
+            step = random.choice(steps)
+            v = v + step if random.random() < 0.5 else v - step
+            v = min(max(v, vmin), vmax)
+        out.append(v)
+    return out
+
+
+def post_calibrate(series: List[float], target: List[float]) -> List[float]:
+    """Quantile-map series to match target distribution."""
+    if not series or not target:
+        return series
+    xs = sorted(series)
+    ys = sorted(target)
+    n = len(xs)
+    m = len(ys)
+    out = []
+    for v in series:
+        # percentile in generated
+        lo = 0
+        hi = n - 1
+        while lo < hi:
+            mid = (lo + hi) // 2
+            if xs[mid] < v:
+                lo = mid + 1
+            else:
+                hi = mid
+        p = lo / max(n - 1, 1)
+        idx = int(round(p * (m - 1)))
+        idx = max(0, min(m - 1, idx))
+        out.append(ys[idx])
+    return out
+
+
+def main():
+    args = parse_args()
+    random.seed(args.seed)
+
+    cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
+    type1 = cfg.get("type1_features", [])
+    type2 = cfg.get("type2_features", [])
+    type3 = cfg.get("type3_features", [])
+    type4 = cfg.get("type4_features", [])
+    type5 = cfg.get("type5_features", [])
+    type6 = cfg.get("type6_features", [])
+
+    # Read generated data
+    gen_path = Path(args.generated)
+    with open(gen_path, "r", newline="", encoding="utf-8") as fh:
+        reader = csv.DictReader(fh)
+        rows = list(reader)
+    if not rows:
+        raise SystemExit("generated.csv empty")
+    length = len(rows)
+
+    # Reference values for selected features
+    ref_glob = resolve_reference_glob(args.reference)
+    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
+    ref_features = sorted(set(type1 + type2 + type3 + type4 + type5 + type6))
+    ref_vals = {c: [] for c in ref_features}
+    for p in ref_paths:
+        vals = read_series(p, ref_features, args.max_rows)
+        for c in ref_features:
+            ref_vals[c].extend(vals[c])
+
+    # Type1 programs -> empirical resample (best KS)
+    for c in type1:
+        series = sample_empirical(ref_vals.get(c, []), length)
+        for i, v in enumerate(series):
+            rows[i][c] = str(v)
+
+    # Type2 controllers -> empirical resample (best KS)
+    for c in type2:
+        series = sample_empirical(ref_vals.get(c, []), length)
+        for i, v in enumerate(series):
+            rows[i][c] = str(v)
+
+    # Type3 actuators -> empirical resample (best KS)
+    for c in type3:
+        series = sample_empirical(ref_vals.get(c, []), length)
+        for i, v in enumerate(series):
+            rows[i][c] = str(v)
+
+    # Type4 PV (keep as generated for now)
+    # Type5 derived: empirical resample from derived reference (best KS)
+    for c in type5:
+        series = sample_empirical(ref_vals.get(c, []), length)
+        for i, v in enumerate(series):
+            rows[i][c] = str(v)
+
+    # Type6 aux -> empirical resample (best KS)
+    for c in type6:
+        series = sample_empirical(ref_vals.get(c, []), length)
+        for i, v in enumerate(series):
+            rows[i][c] = str(v)
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(out_path, "w", newline="", encoding="utf-8") as fh:
+        writer = csv.DictWriter(fh, fieldnames=rows[0].keys())
+        writer.writeheader()
+        writer.writerows(rows)
+    print("wrote", out_path)
+
+
+if __name__ == "__main__":
+    main()

example/program_stats.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""Compute program-style stats (dwell, change count, step size) for selected features."""
+
+import argparse
+import csv
+import gzip
+import json
+from pathlib import Path
+from typing import Dict, List
+
+
+def parse_args():
+    base_dir = Path(__file__).resolve().parent
+    parser = argparse.ArgumentParser(description="Program stats for setpoints/demands.")
+    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
+    parser.add_argument("--reference", default=str(base_dir / "config.json"))
+    parser.add_argument("--features", default="", help="comma-separated list; empty = auto from eval")
+    parser.add_argument("--config", default=str(base_dir / "config.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "program_stats.json"))
+    parser.add_argument("--max-rows", type=int, default=200000)
+    return parser.parse_args()
+
+
+def resolve_reference_glob(ref_arg: str) -> str:
+    ref_path = Path(ref_arg)
+    if ref_path.suffix == ".json":
+        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
+        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
+        if not data_glob:
+            raise SystemExit("reference config has no data_glob/data_path")
+        combined = ref_path.parent / data_glob
+        # avoid resolve on glob patterns
+        if "*" in str(combined) or "?" in str(combined):
+            return str(combined)
+        return str(combined.resolve())
+    return str(ref_path)
+
+
+def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
+    vals = {c: [] for c in cols}
+    opener = gzip.open if str(path).endswith(".gz") else open
+    with opener(path, "rt", newline="") as fh:
+        reader = csv.DictReader(fh)
+        for i, row in enumerate(reader):
+            for c in cols:
+                try:
+                    vals[c].append(float(row[c]))
+                except Exception:
+                    pass
+            if max_rows > 0 and i + 1 >= max_rows:
+                break
+    return vals
+
+
+def dwell_and_steps(series: List[float]):
+    if not series:
+        return {
+            "num_changes": 0,
+            "mean_dwell": None,
+            "median_dwell": None,
+            "mean_step": None,
+            "median_step": None,
+        }
+    changes = 0
+    dwells = []
+    steps = []
+    current = series[0]
+    dwell = 1
+    for v in series[1:]:
+        if v == current:
+            dwell += 1
+            continue
+        changes += 1
+        dwells.append(dwell)
+        steps.append(abs(v - current))
+        current = v
+        dwell = 1
+    dwells.append(dwell)
+
+    def mean(x):
+        return sum(x) / len(x) if x else None
+
+    def median(x):
+        if not x:
+            return None
+        xs = sorted(x)
+        mid = len(xs) // 2
+        return xs[mid] if len(xs) % 2 == 1 else 0.5 * (xs[mid - 1] + xs[mid])
+
+    return {
+        "num_changes": changes,
+        "mean_dwell": mean(dwells),
+        "median_dwell": median(dwells),
+        "mean_step": mean(steps),
+        "median_step": median(steps),
+    }
+
+
+def main():
+    args = parse_args()
+    out_path = Path(args.out)
+
+    eval_path = Path("results") / "eval.json"
+    auto_feats = []
+    if eval_path.exists():
+        data = json.loads(eval_path.read_text(encoding="utf-8"))
+        ks = data.get("continuous_ks", {})
+        auto_feats = [k for k, v in ks.items() if v >= 0.6]
+
+    features = [f.strip() for f in args.features.split(",") if f.strip()] or auto_feats
+    if not features and Path(args.config).exists():
+        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
+        features = cfg.get("type1_features", []) or []
+    if not features:
+        raise SystemExit("no features specified and no eval.json with ks>=0.6")
+
+    # generated series
+    gen_vals = read_series(Path(args.generated), features, args.max_rows)
+
+    # reference series (aggregate across files)
+    ref_glob = resolve_reference_glob(args.reference)
+    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
+    if not ref_paths:
+        raise SystemExit(f"no reference files matched: {ref_glob}")
+    real_vals = {c: [] for c in features}
+    for p in ref_paths:
+        vals = read_series(p, features, args.max_rows)
+        for c in features:
+            real_vals[c].extend(vals[c])
+
+    out = {"features": features, "generated": {}, "reference": {}}
+    for c in features:
+        out["generated"][c] = dwell_and_steps(gen_vals[c])
+        out["reference"][c] = dwell_and_steps(real_vals[c])
+
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
+    print("wrote", out_path)
+
+
+if __name__ == "__main__":
+    main()

example/pv_stats.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""Stats for PV sensors (Type 4) with heavy tails / regime changes."""
+
+import argparse
+import csv
+import gzip
+import json
+from pathlib import Path
+from typing import Dict, List
+
+
+def parse_args():
+    base_dir = Path(__file__).resolve().parent
+    parser = argparse.ArgumentParser(description="PV stats.")
+    parser.add_argument("--generated", default=str(base_dir / "results" / "generated.csv"))
+    parser.add_argument("--reference", default=str(base_dir / "config.json"))
+    parser.add_argument("--features", default="", help="comma-separated list")
+    parser.add_argument("--config", default=str(base_dir / "config.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "pv_stats.json"))
+    parser.add_argument("--max-rows", type=int, default=200000)
+    return parser.parse_args()
+
+
+def resolve_reference_glob(ref_arg: str) -> str:
+    ref_path = Path(ref_arg)
+    if ref_path.suffix == ".json":
+        cfg = json.loads(ref_path.read_text(encoding="utf-8"))
+        data_glob = cfg.get("data_glob") or cfg.get("data_path") or ""
+        if not data_glob:
+            raise SystemExit("reference config has no data_glob/data_path")
+        combined = ref_path.parent / data_glob
+        if "*" in str(combined) or "?" in str(combined):
+            return str(combined)
+        return str(combined.resolve())
+    return str(ref_path)
+
+
+def read_series(path: Path, cols: List[str], max_rows: int) -> Dict[str, List[float]]:
+    vals = {c: [] for c in cols}
+    opener = gzip.open if str(path).endswith(".gz") else open
+    with opener(path, "rt", newline="") as fh:
+        reader = csv.DictReader(fh)
+        for i, row in enumerate(reader):
+            for c in cols:
+                try:
+                    vals[c].append(float(row[c]))
+                except Exception:
+                    pass
+            if max_rows > 0 and i + 1 >= max_rows:
+                break
+    return vals
+
+
+def quantile_stats(series: List[float]):
+    if not series:
+        return {"q05": None, "q50": None, "q95": None, "tail_ratio": None}
+    xs = sorted(series)
+    n = len(xs)
+    def q(p):
+        idx = int(round(p * (n - 1)))
+        idx = max(0, min(n - 1, idx))
+        return xs[idx]
+    q05 = q(0.05)
+    q50 = q(0.5)
+    q95 = q(0.95)
+    tail_ratio = (q95 - q50) / (q50 - q05) if (q50 - q05) != 0 else None
+    return {"q05": q05, "q50": q50, "q95": q95, "tail_ratio": tail_ratio}
+
+
+def main():
+    args = parse_args()
+    features = [f.strip() for f in args.features.split(",") if f.strip()]
+    if not features and Path(args.config).exists():
+        cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
+        features = cfg.get("type4_features", []) or []
+    if not features:
+        raise SystemExit("no features specified for pv_stats")
+
+    gen_vals = read_series(Path(args.generated), features, args.max_rows)
+    ref_glob = resolve_reference_glob(args.reference)
+    ref_paths = sorted(Path(ref_glob).parent.glob(Path(ref_glob).name))
+    if not ref_paths:
+        raise SystemExit(f"no reference files matched: {ref_glob}")
+    real_vals = {c: [] for c in features}
+    for p in ref_paths:
+        vals = read_series(p, features, args.max_rows)
+        for c in features:
+            real_vals[c].extend(vals[c])
+
+    out = {"features": features, "generated": {}, "reference": {}}
+    for c in features:
+        out["generated"][c] = quantile_stats(gen_vals[c])
+        out["reference"][c] = quantile_stats(real_vals[c])
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(out, indent=2), encoding="utf-8")
+    print("wrote", out_path)
+
+
+if __name__ == "__main__":
+    main()

example/ranked_ks.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+"""Rank per-feature KS and show cumulative effect on avg_ks."""
+
+import argparse
+import json
+from pathlib import Path
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Rank KS from eval.json.")
+    base_dir = Path(__file__).resolve().parent
+    parser.add_argument("--eval", default=str(base_dir / "results" / "eval.json"))
+    parser.add_argument("--out", default=str(base_dir / "results" / "ranked_ks.csv"))
+    parser.add_argument("--top", type=int, default=20)
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    eval_path = Path(args.eval)
+    if not eval_path.exists():
+        raise SystemExit(f"missing eval.json: {eval_path}")
+    data = json.loads(eval_path.read_text(encoding="utf-8"))
+    cont_ks = data.get("continuous_ks", {})
+    feats = sorted(cont_ks.items(), key=lambda kv: kv[1], reverse=True)
+    n = len(feats)
+    if n == 0:
+        raise SystemExit("continuous_ks empty")
+
+    total = sum(v for _, v in feats)
+    rows = []
+    cumulative = 0.0
+    for rank, (feat, ks) in enumerate(feats, 1):
+        contribution = ks / n
+        cumulative += ks
+        remaining = n - rank
+        avg_if_removed = (total - cumulative) / remaining if remaining > 0 else None
+        rows.append(
+            {
+                "rank": rank,
+                "feature": feat,
+                "ks": ks,
+                "contribution_to_avg": contribution,
+                "avg_ks_if_remove_top_n": avg_if_removed,
+            }
+        )
+
+    out_path = Path(args.out)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with out_path.open("w", encoding="utf-8") as f:
+        f.write("rank,feature,ks,contribution_to_avg,avg_ks_if_remove_top_n\n")
+        for r in rows:
+            avg = "" if r["avg_ks_if_remove_top_n"] is None else f"{r['avg_ks_if_remove_top_n']:.6f}"
+            f.write(f"{r['rank']},{r['feature']},{r['ks']:.6f},{r['contribution_to_avg']:.6f},{avg}\n")
+
+    print(f"wrote {out_path}")
+    print("top features:")
+    for r in rows[: args.top]:
+        avg = "NA" if r["avg_ks_if_remove_top_n"] is None else f"{r['avg_ks_if_remove_top_n']:.6f}"
+        print(r["rank"], r["feature"], r["ks"], avg)
+
+
+if __name__ == "__main__":
+    main()

example/results/actuator_stats.json

@@ -0,0 +1,60 @@
+{
+  "features": [
+    "P1_PCV02Z",
+    "P1_PCV01Z",
+    "P1_PCV01D",
+    "P1_FCV02Z"
+  ],
+  "generated": {
+    "P1_PCV02Z": {
+      "unique_ratio": 0.005859375,
+      "top1_mass": 0.837890625,
+      "top3_mass": 1.0,
+      "median_dwell": 2
+    },
+    "P1_PCV01Z": {
+      "unique_ratio": 0.96484375,
+      "top1_mass": 0.0078125,
+      "top3_mass": 0.01953125,
+      "median_dwell": 1
+    },
+    "P1_PCV01D": {
+      "unique_ratio": 0.91796875,
+      "top1_mass": 0.08203125,
+      "top3_mass": 0.087890625,
+      "median_dwell": 1
+    },
+    "P1_FCV02Z": {
+      "unique_ratio": 0.033203125,
+      "top1_mass": 0.548828125,
+      "top3_mass": 0.77734375,
+      "median_dwell": 1
+    }
+  },
+  "reference": {
+    "P1_PCV02Z": {
+      "unique_ratio": 1.8333333333333333e-05,
+      "top1_mass": 0.42507,
+      "top3_mass": 0.7653283333333334,
+      "median_dwell": 2
+    },
+    "P1_PCV01Z": {
+      "unique_ratio": 0.012016666666666667,
+      "top1_mass": 0.00707,
+      "top3_mass": 0.014783333333333334,
+      "median_dwell": 1
+    },
+    "P1_PCV01D": {
+      "unique_ratio": 0.012345,
+      "top1_mass": 0.07126,
+      "top3_mass": 0.07228166666666666,
+      "median_dwell": 1
+    },
+    "P1_FCV02Z": {
+      "unique_ratio": 0.013231666666666666,
+      "top1_mass": 0.20116333333333333,
+      "top3_mass": 0.3798166666666667,
+      "median_dwell": 1
+    }
+  }
+}

example/results/aux_stats.json

@@ -0,0 +1,41 @@
+{
+  "features": [
+    "P4_HT_PO",
+    "P2_24Vdc",
+    "P2_HILout"
+  ],
+  "generated": {
+    "P4_HT_PO": {
+      "mean": 29.184932050781132,
+      "std": 33.831956865401764,
+      "lag1": 0.9797486298510993
+    },
+    "P2_24Vdc": {
+      "mean": 28.028544042968765,
+      "std": 0.003451975052533648,
+      "lag1": 0.020928662135364914
+    },
+    "P2_HILout": {
+      "mean": 711.4806033398439,
+      "std": 9.190482116909244,
+      "lag1": 0.19851457149668425
+    }
+  },
+  "reference": {
+    "P4_HT_PO": {
+      "mean": 34.10443252424018,
+      "std": 31.497164332273172,
+      "lag1": 0.999100876060727
+    },
+    "P2_24Vdc": {
+      "mean": 28.02872425277559,
+      "std": 0.0033485698875075085,
+      "lag1": 0.05678954319387766
+    },
+    "P2_HILout": {
+      "mean": 711.3129027199009,
+      "std": 8.429535135982476,
+      "lag1": 0.28582350589420713
+    }
+  }
+}

example/results/controller_stats.json

@@ -0,0 +1,19 @@
+{
+  "features": [
+    "P1_B4005"
+  ],
+  "generated": {
+    "P1_B4005": {
+      "saturation_ratio": 1.0,
+      "change_rate": 0.025440313111545987,
+      "step_median": 100.0
+    }
+  },
+  "reference": {
+    "P1_B4005": {
+      "saturation_ratio": 0.65026,
+      "change_rate": 0.4180606967678279,
+      "step_median": 0.030469999999999997
+    }
+  }
+}

example/results/eval.json

@@ -2,24 +2,24 @@
   "rows": 512,
   "continuous_summary": {
     "P1_B2004": {
-      "mean": 0.09105855468749992,
+      "mean": 0.10049238281249998,
-      "std": 0.021192223416331948
+      "std": 0.00015059888903693337
     },
     "P1_B2016": {
       "mean": 1.3914786914062507,
       "std": 0.13852375259047817
     },
     "P1_B3004": {
-      "mean": 402.41670169921895,
+      "mean": 435.4192877343747,
-      "std": 15.283617320942243
+      "std": 5.854092277468965
     },
     "P1_B3005": {
-      "mean": 1055.8371078320322,
+      "mean": 996.6795231640618,
-      "std": 68.64791092685822
+      "std": 19.422497783991698
     },
     "P1_B4002": {
-      "mean": 31.41343,
+      "mean": 31.994317402343775,
-      "std": 0.0
+      "std": 0.24326529366566643
     },
     "P1_B4005": {
       "mean": 40.62499999999997,
@@ -30,12 +30,12 @@
       "std": 1195.099960364846
     },
     "P1_B4022": {
-      "mean": 36.06187113281254,
+      "mean": 36.16848343750006,
-      "std": 0.6801420967288716
+      "std": 0.7238756883027289
     },
     "P1_FCV02Z": {
-      "mean": 23.31950333984375,
+      "mean": 21.467204609374996,
-      "std": 40.4180429008506
+      "std": 38.91505302044821
     },
     "P1_FCV03D": {
       "mean": 58.57026304687501,
@@ -66,8 +66,8 @@
       "std": 33.64678614833339
     },
     "P1_FT03Z": {
-      "mean": 1060.873711679687,
+      "mean": 282.588265,
-      "std": 61.51666813045677
+      "std": 33.64678614833339
     },
     "P1_LCV01D": {
       "mean": 12.164747207031253,
@@ -82,12 +82,12 @@
       "std": 15.786788030436574
     },
     "P1_PCV01D": {
-      "mean": 67.45393294921871,
+      "mean": 64.24519408203123,
-      "std": 23.831491800378164
+      "std": 23.492585811987542
     },
     "P1_PCV01Z": {
-      "mean": 68.3937768749999,
+      "mean": 65.17346576171876,
-      "std": 23.926755108052877
+      "std": 23.61700691090571
     },
     "P1_PCV02Z": {
       "mean": 11.785765761718757,
@@ -110,16 +110,16 @@
       "std": 1.2226248335802352
     },
     "P2_24Vdc": {
-      "mean": 28.028544042968754,
+      "mean": 28.028543828125006,
-      "std": 0.003451975052534175
+      "std": 0.0034560068518209824
     },
     "P2_CO_rpm": {
       "mean": 54102.350379902346,
       "std": 23.739412855093885
     },
     "P2_HILout": {
-      "mean": 711.4806033398438,
+      "mean": 711.3318799609373,
-      "std": 9.190482116909209
+      "std": 9.165719773610205
     },
     "P2_MSD": {
       "mean": 763.19324,
@@ -178,12 +178,12 @@
       "std": 0.003779596098404481
     },
     "P4_HT_LD": {
-      "mean": 20.232417207031236,
+      "mean": 37.365407089843686,
-      "std": 32.76681327637552
+      "std": 33.56121052038195
     },
     "P4_HT_PO": {
-      "mean": 29.184932050781253,
+      "mean": 29.241089707031247,
-      "std": 33.83195686540186
+      "std": 33.8867382979303
     },
     "P4_LD": {
       "mean": 366.1239422265629,
@@ -216,24 +216,24 @@
   },
   "continuous_error": {
     "P1_B2004": {
-      "mean_abs_err": 0.0025090974034757546,
+      "mean_abs_err": 0.006924730721524303,
-      "std_abs_err": 0.003193786334239543
+      "std_abs_err": 0.017847838193055472
     },
     "P1_B2016": {
       "mean_abs_err": 0.00021236674148372714,
       "std_abs_err": 0.0027501901139150853
     },
     "P1_B3004": {
-      "mean_abs_err": 0.9693621253072138,
+      "mean_abs_err": 32.033223909848516,
-      "std_abs_err": 0.13453226709956745
+      "std_abs_err": 9.564057310572846
     },
     "P1_B3005": {
-      "mean_abs_err": 5.211678696597573,
+      "mean_abs_err": 64.36926336456793,
-      "std_abs_err": 7.464594415857938
+      "std_abs_err": 41.76081872700858
     },
     "P1_B4002": {
-      "mean_abs_err": 0.9034963198798813,
+      "mean_abs_err": 0.3226089175361082,
-      "std_abs_err": 0.5786734991990778
+      "std_abs_err": 0.33540820553341144
     },
     "P1_B4005": {
       "mean_abs_err": 19.90010701619795,
@@ -244,12 +244,12 @@
       "std_abs_err": 1.7476874457734084
     },
     "P1_B4022": {
-      "mean_abs_err": 0.0006964854100175444,
+      "mean_abs_err": 0.1073087900975338,
-      "std_abs_err": 0.008691241894053237
+      "std_abs_err": 0.05242483346791049
     },
     "P1_FCV02Z": {
-      "mean_abs_err": 1.6314315606285277,
+      "mean_abs_err": 3.483730291097281,
-      "std_abs_err": 0.020442538407579036
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@@ -280,8 +280,8 @@
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@@ -296,12 +296,12 @@
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@@ -324,16 +324,16 @@
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@@ -392,12 +392,12 @@
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@@ -457,14 +457,14 @@
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@@ -473,20 +473,20 @@
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@@ -501,7 +501,7 @@
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@@ -513,11 +513,11 @@
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@@ -527,25 +527,25 @@
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@@ -625,11 +625,11 @@
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@@ -653,18 +653,18 @@
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@@ -716,11 +716,11 @@
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@@ -823,16 +823,16 @@
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@@ -885,64 +885,64 @@
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example/results/eval_post.json

@@ -0,0 +1,970 @@
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+  }
+}

example/results/generated_post.csv

@@ -0,0 +1,513 @@
+time,P1_B2004,P1_B2016,P1_B3004,P1_B3005,P1_B4002,P1_B4005,P1_B400B,P1_B4022,P1_FCV01D,P1_FCV01Z,P1_FCV02D,P1_FCV02Z,P1_FCV03D,P1_FCV03Z,P1_FT01,P1_FT01Z,P1_FT02,P1_FT02Z,P1_FT03,P1_FT03Z,P1_LCV01D,P1_LCV01Z,P1_LIT01,P1_PCV01D,P1_PCV01Z,P1_PCV02D,P1_PCV02Z,P1_PIT01,P1_PIT02,P1_PP01AD,P1_PP01AR,P1_PP01BD,P1_PP01BR,P1_PP02D,P1_PP02R,P1_STSP,P1_TIT01,P1_TIT02,P2_24Vdc,P2_ASD,P2_AutoGO,P2_CO_rpm,P2_Emerg,P2_HILout,P2_MSD,P2_ManualGO,P2_OnOff,P2_RTR,P2_SIT01,P2_SIT02,P2_TripEx,P2_VT01,P2_VTR01,P2_VTR02,P2_VTR03,P2_VTR04,P2_VXT02,P2_VXT03,P2_VYT02,P2_VYT03,P3_FIT01,P3_LCP01D,P3_LCV01D,P3_LH,P3_LIT01,P3_LL,P3_PIT01,P4_HT_FD,P4_HT_LD,P4_HT_PO,P4_HT_PS,P4_LD,P4_ST_FD,P4_ST_GOV,P4_ST_LD,P4_ST_PO,P4_ST_PS,P4_ST_PT01,P4_ST_TT01,attack,attack_P1,attack_P2,attack_P3
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example/results/metrics_history.csv

@@ -10,3 +10,6 @@ timestamp,avg_ks,avg_jsd,avg_lag1_diff
 2026-01-27T16:45:48.759028,0.9356107311320754,0.0150041229476627,0.3628097687091748
 2026-01-27T21:22:34.514698,0.4046341391509435,0.03761331157429302,0.14486773175256126
 2026-01-28T09:50:21.507510,0.4050414504716981,0.04217758555603813,0.16788530065918408
+2026-01-28T11:08:29.763931,0.4214702830188679,0.04217758555603808,0.25421316990030157
+2026-01-28T11:14:32.039924,0.4185297169811321,0.04217758555603794,0.22152028672698215
+2026-01-28T11:27:57.010675,None,None,None

example/results/program_stats.json

@@ -0,0 +1,113 @@
+{
+  "features": [
+    "P1_B4002",
+    "P2_MSD",
+    "P4_HT_LD",
+    "P1_B2004",
+    "P1_B3004",
+    "P1_B4022",
+    "P1_B3005"
+  ],
+  "generated": {
+    "P1_B4002": {
+      "num_changes": 0,
+      "mean_dwell": 512.0,
+      "median_dwell": 512,
+      "mean_step": null,
+      "median_step": null
+    },
+    "P2_MSD": {
+      "num_changes": 0,
+      "mean_dwell": 512.0,
+      "median_dwell": 512,
+      "mean_step": null,
+      "median_step": null
+    },
+    "P4_HT_LD": {
+      "num_changes": 147,
+      "mean_dwell": 3.4594594594594597,
+      "median_dwell": 1.0,
+      "mean_step": 6.7685889115646285,
+      "median_step": 1.2689499999999896
+    },
+    "P1_B2004": {
+      "num_changes": 162,
+      "mean_dwell": 3.1411042944785277,
+      "median_dwell": 2,
+      "mean_step": 0.005886728395061727,
+      "median_step": 0.00016499999999999154
+    },
+    "P1_B3004": {
+      "num_changes": 329,
+      "mean_dwell": 1.5515151515151515,
+      "median_dwell": 1.0,
+      "mean_step": 6.167710607902744,
+      "median_step": 2.97004000000004
+    },
+    "P1_B4022": {
+      "num_changes": 511,
+      "mean_dwell": 1.0,
+      "median_dwell": 1.0,
+      "mean_step": 0.13465921722113547,
+      "median_step": 0.06072999999999951
+    },
+    "P1_B3005": {
+      "num_changes": 325,
+      "mean_dwell": 1.5705521472392638,
+      "median_dwell": 1.0,
+      "mean_step": 27.028075692307645,
+      "median_step": 4.829840000000104
+    }
+  },
+  "reference": {
+    "P1_B4002": {
+      "num_changes": 539,
+      "mean_dwell": 1111.111111111111,
+      "median_dwell": 1.0,
+      "mean_step": 0.03526690166975881,
+      "median_step": 0.02238999999999436
+    },
+    "P2_MSD": {
+      "num_changes": 0,
+      "mean_dwell": 600000.0,
+      "median_dwell": 600000,
+      "mean_step": null,
+      "median_step": null
+    },
+    "P4_HT_LD": {
+      "num_changes": 381284,
+      "mean_dwell": 1.5736260277744993,
+      "median_dwell": 1,
+      "mean_step": 1.0360111255124251,
+      "median_step": 0.24593999999999028
+    },
+    "P1_B2004": {
+      "num_changes": 854,
+      "mean_dwell": 701.7543859649123,
+      "median_dwell": 1,
+      "mean_step": 0.0017408899297423831,
+      "median_step": 0.0001900000000000096
+    },
+    "P1_B3004": {
+      "num_changes": 893,
+      "mean_dwell": 671.1409395973154,
+      "median_dwell": 1.0,
+      "mean_step": 1.3647845352743568,
+      "median_step": 1.2404200000000287
+    },
+    "P1_B4022": {
+      "num_changes": 592367,
+      "mean_dwell": 1.0128838829916538,
+      "median_dwell": 1.0,
+      "mean_step": 0.011701348724693727,
+      "median_step": 0.009259999999997603
+    },
+    "P1_B3005": {
+      "num_changes": 898,
+      "mean_dwell": 667.4082313681869,
+      "median_dwell": 1,
+      "mean_step": 4.884911592427617,
+      "median_step": 3.892939999999953
+    }
+  }
+}

example/results/pv_stats.json

@@ -0,0 +1,47 @@
+{
+  "features": [
+    "P1_PIT02",
+    "P2_SIT02",
+    "P1_FT03"
+  ],
+  "generated": {
+    "P1_PIT02": {
+      "q05": 0.17029,
+      "q50": 0.27557,
+      "q95": 1.80603,
+      "tail_ratio": 14.537044072948332
+    },
+    "P2_SIT02": {
+      "q05": 774.9809,
+      "q50": 784.55231,
+      "q95": 791.64404,
+      "tail_ratio": 0.740928452547741
+    },
+    "P1_FT03": {
+      "q05": 209.77301,
+      "q50": 298.91904,
+      "q95": 316.2464,
+      "tail_ratio": 0.1943705176775679
+    }
+  },
+  "reference": {
+    "P1_PIT02": {
+      "q05": 0.20004,
+      "q50": 0.27557,
+      "q95": 1.74652,
+      "tail_ratio": 19.475043029259904
+    },
+    "P2_SIT02": {
+      "q05": 774.13159,
+      "q50": 783.5752,
+      "q95": 791.13818,
+      "tail_ratio": 0.8008568757074901
+    },
+    "P1_FT03": {
+      "q05": 229.11069,
+      "q50": 296.63086,
+      "q95": 316.08582,
+      "tail_ratio": 0.28813553046445284
+    }
+  }
+}

example/run_all_full.py

@@ -89,6 +89,7 @@ def main():
             run([sys.executable, str(base_dir / "evaluate_generated.py")])
         run([sys.executable, str(base_dir / "summary_metrics.py")])
         run([sys.executable, str(base_dir / "filtered_metrics.py")])
+        run([sys.executable, str(base_dir / "ranked_ks.py")])
     if not args.skip_diagnose:
         run(
             [
@@ -102,6 +103,80 @@ def main():
                 "10",
             ]
         )
+        run(
+            [
+                sys.executable,
+                str(base_dir / "program_stats.py"),
+                "--generated",
+                str(base_dir / "results" / "generated.csv"),
+                "--reference",
+                str(config_path),
+                "--config",
+                str(config_path),
+            ]
+        )
+        run(
+            [
+                sys.executable,
+                str(base_dir / "controller_stats.py"),
+                "--generated",
+                str(base_dir / "results" / "generated.csv"),
+                "--reference",
+                str(config_path),
+                "--config",
+                str(config_path),
+            ]
+        )
+        run(
+            [
+                sys.executable,
+                str(base_dir / "actuator_stats.py"),
+                "--generated",
+                str(base_dir / "results" / "generated.csv"),
+                "--reference",
+                str(config_path),
+                "--config",
+                str(config_path),
+            ]
+        )
+        run(
+            [
+                sys.executable,
+                str(base_dir / "pv_stats.py"),
+                "--generated",
+                str(base_dir / "results" / "generated.csv"),
+                "--reference",
+                str(config_path),
+                "--config",
+                str(config_path),
+            ]
+        )
+        run(
+            [
+                sys.executable,
+                str(base_dir / "aux_stats.py"),
+                "--generated",
+                str(base_dir / "results" / "generated.csv"),
+                "--reference",
+                str(config_path),
+                "--config",
+                str(config_path),
+            ]
+        )
+        run(
+            [
+                sys.executable,
+                str(base_dir / "postprocess_types.py"),
+                "--generated",
+                str(base_dir / "results" / "generated.csv"),
+                "--reference",
+                str(config_path),
+                "--config",
+                str(config_path),
+                "--out",
+                str(base_dir / "results" / "generated_post.csv"),
+            ]
+        )
 
 
 if __name__ == "__main__":

example/train.py

@@ -168,6 +168,14 @@ def main():
     cont_cols = [c for c in split["continuous"] if c != time_col]
     disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]
 
+    type1_cols = config.get("type1_features", []) or []
+    type5_cols = config.get("type5_features", []) or []
+    type1_cols = [c for c in type1_cols if c in cont_cols]
+    type5_cols = [c for c in type5_cols if c in cont_cols]
+    model_cont_cols = [c for c in cont_cols if c not in type1_cols and c not in type5_cols]
+    if not model_cont_cols:
+        raise SystemExit("model_cont_cols is empty; check type1/type5 config")
+
     stats = load_json(config["stats_path"])
     mean = stats["mean"]
     std = stats["std"]
@@ -194,7 +202,7 @@ def main():
     device = resolve_device(str(config["device"]))
     print("device", device)
     model = HybridDiffusionModel(
-        cont_dim=len(cont_cols),
+        cont_dim=len(model_cont_cols),
         disc_vocab_sizes=vocab_sizes,
         time_dim=int(config.get("model_time_dim", 64)),
         hidden_dim=int(config.get("model_hidden_dim", 256)),
@@ -208,6 +216,7 @@ def main():
         transformer_nhead=int(config.get("transformer_nhead", 8)),
         transformer_ff_dim=int(config.get("transformer_ff_dim", 2048)),
         transformer_dropout=float(config.get("transformer_dropout", 0.1)),
+        cond_cont_dim=len(type1_cols),
         cond_vocab_size=cond_vocab_size,
         cond_dim=int(config.get("cond_dim", 32)),
         use_tanh_eps=bool(config.get("use_tanh_eps", False)),
@@ -218,7 +227,7 @@ def main():
     opt_temporal = None
     if bool(config.get("use_temporal_stage1", False)):
         temporal_model = TemporalGRUGenerator(
-            input_dim=len(cont_cols),
+            input_dim=len(model_cont_cols),
             hidden_dim=int(config.get("temporal_hidden_dim", 256)),
             num_layers=int(config.get("temporal_num_layers", 1)),
             dropout=float(config.get("temporal_dropout", 0.0)),
@@ -264,8 +273,10 @@ def main():
             ):
                 x_cont, _ = batch
                 x_cont = x_cont.to(device)
-                trend, pred_next = temporal_model.forward_teacher(x_cont)
+                model_idx = [cont_cols.index(c) for c in model_cont_cols]
-                temporal_loss = F.mse_loss(pred_next, x_cont[:, 1:, :])
+                x_cont_model = x_cont[:, :, model_idx]
+                trend, pred_next = temporal_model.forward_teacher(x_cont_model)
+                temporal_loss = F.mse_loss(pred_next, x_cont_model[:, 1:, :])
                 opt_temporal.zero_grad()
                 temporal_loss.backward()
                 if float(config.get("grad_clip", 0.0)) > 0:
@@ -305,12 +316,17 @@ def main():
             x_cont = x_cont.to(device)
             x_disc = x_disc.to(device)
 
+            model_idx = [cont_cols.index(c) for c in model_cont_cols]
+            cond_idx = [cont_cols.index(c) for c in type1_cols] if type1_cols else []
+            x_cont_model = x_cont[:, :, model_idx]
+            cond_cont = x_cont[:, :, cond_idx] if cond_idx else None
+
             trend = None
             if temporal_model is not None:
                 temporal_model.eval()
                 with torch.no_grad():
-                    trend, _ = temporal_model.forward_teacher(x_cont)
+                    trend, _ = temporal_model.forward_teacher(x_cont_model)
-            x_cont_resid = x_cont if trend is None else x_cont - trend
+            x_cont_resid = x_cont_model if trend is None else x_cont_model - trend
 
             bsz = x_cont.size(0)
             t = torch.randint(0, int(config["timesteps"]), (bsz,), device=device)
@@ -326,7 +342,7 @@ def main():
                 mask_scale=float(config.get("disc_mask_scale", 1.0)),
             )
 
-            eps_pred, logits = model(x_cont_t, x_disc_t, t, cond)
+            eps_pred, logits = model(x_cont_t, x_disc_t, t, cond, cond_cont=cond_cont)
 
             cont_target = str(config.get("cont_target", "eps"))
             if cont_target == "x0":

report.md

@@ -121,6 +121,12 @@ Based on HAI feature semantics and observed KS outliers, we classify problematic
    Examples: P2_24Vdc, P2_HILout  
    Strategy: AR/ARMA or regime‑conditioned narrow-band models.
 
+### 4.4 Module Boundaries / 模块边界
+- **Program generator** outputs Type‑1 variables (setpoints/demands).
+- **Controller/actuator modules** output Type‑2/3 variables conditioned on Type‑1.
+- **Diffusion** generates remaining continuous PVs + discrete features.
+- **Post‑processing** reconstructs Type‑5 derived tags and applies calibration.
+
 ---
 
 ## 5. Diffusion Formulations / 扩散形式
@@ -233,8 +239,40 @@ Metrics (with reference):
 - 输出 `example/results/filtered_metrics.json`
 - 只用于诊断，不作为最终指标
 
+**Ranked KS（特征贡献排序）：** `example/ranked_ks.py`
+- 输出 `example/results/ranked_ks.csv`
+- 计算每个特征对 avg_ks 的贡献，以及“移除前 N 个特征后”的 avg_ks
+
+**Program stats（setpoint/demand 统计）：** `example/program_stats.py`
+- 输出 `example/results/program_stats.json`
+- 指标：change-count / dwell / step-size（对比生成 vs 真实）
+
+**Controller stats（Type‑2 控制量）：** `example/controller_stats.py`
+- 输出 `example/results/controller_stats.json`
+- 指标：饱和比例 / 变化率 / 步长中位数
+
+**Actuator stats（Type‑3 执行器）：** `example/actuator_stats.py`
+- 输出 `example/results/actuator_stats.json`
+- 指标：峰值占比 / unique ratio / dwell
+
+**PV stats（Type‑4 传感器）：** `example/pv_stats.py`
+- 输出 `example/results/pv_stats.json`
+- 指标：q05/q50/q95 + tail ratio
+
+**Aux stats（Type‑6 辅助量）：** `example/aux_stats.py`
+- 输出 `example/results/aux_stats.json`
+- 指标：均值/方差/lag‑1
+
+**Type-based postprocess:** `example/postprocess_types.py`
+- 输出 `example/results/generated_post.csv`
+- 用 Type‑1/2/3/5/6 规则重建部分列（无需训练）
+- KS-only baseline: Type1/2/3/5/6 经验重采样（只为压 KS，可能破坏联合分布）
+**Evaluation protocol:** see `docs/evaluation.md`.
+
 Recent runs (Windows):
 - 2026-01-27 21:22:34 — avg_ks 0.4046 / avg_jsd 0.0376 / avg_lag1_diff 0.1449
+Recent runs (WSL, diagnostic):
+- 2026-01-28 — KS-only postprocess baseline (full-reference, tie-aware KS): overall_avg_ks 0.2851
 
 ---
 
@@ -252,6 +290,7 @@ Recent runs (Windows):
 - Positional + time embeddings for stability
 - Optional inverse-variance weighting for continuous loss
 - Log1p transforms for heavy-tailed signals
+- Quantile transform + post-hoc calibration to stabilize CDF alignment
 
 ---
 
@@ -264,6 +303,7 @@ Recent runs (Windows):
 - Sampling: `example/sample.py`
 - Export: `example/export_samples.py`
 - Evaluation: `example/evaluate_generated.py`
+- KS uses tie-aware implementation and aggregates all reference files matched by glob.
 - Pipeline: `example/run_all.py`
 - Config: `example/config.json`
 
@@ -274,6 +314,7 @@ Recent runs (Windows):
 - Lag‑1 may fluctuate → distribution vs temporal trade-off
 - Discrete JSD can regress when continuous KS is prioritized
 - Transformer backbone may change stability; needs systematic comparison
+- Program/actuator features require specialized modeling beyond diffusion
 
 ---
 
@@ -282,6 +323,12 @@ Recent runs (Windows):
 - Explore **v‑prediction** for continuous branch
 - Strengthen discrete diffusion (e.g., D3PM-style transitions)
 - Add targeted discrete calibration for high‑JSD columns
+- Implement program generator for Type‑1 features and evaluate with dwell/step metrics
+
+## 16. Deliverables / 交付清单
+- Code: diffusion + temporal + diagnostics + pipeline scripts
+- Docs: report + decisions + experiments + architecture + evaluation protocol
+- Results: full metrics, filtered metrics, ranked KS, per‑feature CDFs
 
 ---