diff --git a/README.md b/README.md
index ec2714c..0be2322 100644
--- a/README.md
+++ b/README.md
@@ -292,6 +292,7 @@ The normal camera entrypoints are:
 
 ```bash
 ./run_camera_sync_check.sh [flags]
+./run_camera_sync_sweep.sh --manifest runs/<sync-timing-sweep>_manifest.csv
 ./run_dmd_pair_capture.sh [flags]
 python debug_scripts/camera_probe.py [flags]
 ```
@@ -304,6 +305,12 @@ Current default camera behavior is intended to match the original modern `dv_pro
 - No post-trigger grace reads are used unless `--camera-post-trigger-event-batches N` is passed. The default is `0`.
 - No USB reset, stream rearm, shutdown, or power-cycle command runs unless the corresponding flag/env option is passed.
 
+For timing sweeps, prefer `notebooks/01_generate_timing_sweep_commands.ipynb` plus the generated
+`run_camera_sync_sweep.sh --manifest ...` launcher. That command opens the DVXplorer once, reuses the same capture
+handle for every manifest row, and closes it only after the sweep finishes. After a physical replug, run the persistent
+sweep directly; do not run `python -m dmdcontrol camera status`, `camera_probe.py`, or a one-row sync check first if the
+goal is to preserve the first good camera open for the sweep.
+
 The extra camera flags are diagnostic switches, not normal-run requirements:
 
 | Flag | Default | Use |
diff --git a/debug_scripts/camera_capture_test.py b/debug_scripts/camera_capture_test.py
new file mode 100644
index 0000000..e7a472f
--- /dev/null
+++ b/debug_scripts/camera_capture_test.py
@@ -0,0 +1,432 @@
+#!/usr/bin/env python3
+"""
+Minimal DVXplorer reopen repro.
+
+Modes:
+  same-handle:
+    open camera once, drain once, capture multiple windows from same handle
+
+  reopen:
+    for each window, open camera, drain, capture one window, close handle
+
+Writes per window:
+  - .aedat4 raw event stream
+  - _activity.png / _activity.pgm
+  - _signed.png / _signed.pgm
+  - summary.csv
+
+Important:
+  Do NOT gate capture windows on camera.isRunning().
+  Some environments report false even when getNextEventBatch can still be used.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import gc
+import json
+import math
+import platform
+import subprocess
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+
+import dv_processing as dv
+import numpy as np
+from PIL import Image
+
+
+def event_value(event, name: str):
+    attr = getattr(event, name)
+    return attr() if callable(attr) else attr
+
+
+def run_cmd(cmd: list[str]) -> str:
+    try:
+        return subprocess.run(
+            cmd,
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            timeout=5,
+            check=False,
+        ).stdout.strip()
+    except Exception as exc:
+        return f"failed: {exc}"
+
+
+def apply_threshold(camera, threshold: int) -> None:
+    for name in ("setContrastThresholdOn", "setContrastThresholdOff"):
+        method = getattr(camera, name, None)
+        if callable(method):
+            method(threshold)
+
+
+def drain_camera(camera, seconds: float) -> dict[str, int]:
+    deadline = time.monotonic() + seconds
+    batches = 0
+    events = 0
+
+    while time.monotonic() < deadline:
+        batch = camera.getNextEventBatch()
+        if batch is None:
+            time.sleep(0.001)
+            continue
+
+        batches += 1
+        try:
+            events += len(batch)
+        except TypeError:
+            pass
+
+    return {"drained_batches": batches, "drained_events": events}
+
+
+def open_first_camera(threshold: int | None, drain_seconds: float):
+    descriptors = dv.io.camera.discover()
+    if not descriptors:
+        raise RuntimeError("No camera discovered.")
+
+    camera = dv.io.camera.open(descriptors[0])
+    camera_name = camera.getCameraName()
+    width, height = map(int, camera.getEventResolution())
+
+    if threshold is not None:
+        apply_threshold(camera, threshold)
+
+    drain_stats = drain_camera(camera, drain_seconds)
+
+    return camera, camera_name, width, height, drain_stats
+
+
+def make_writer(path: Path, camera_name: str, width: int, height: int):
+    config = dv.io.MonoCameraWriter.EventOnlyConfig(camera_name, (width, height))
+    return dv.io.MonoCameraWriter(str(path), config)
+
+
+def gamma_encode(norm: np.ndarray, gamma: float) -> np.ndarray:
+    norm = np.clip(norm.astype(np.float64), 0.0, 1.0)
+    return np.power(norm, 1.0 / gamma)
+
+
+def make_activity_image(on_counts: np.ndarray, off_counts: np.ndarray, gamma: float):
+    activity = on_counts + off_counts
+    max_activity = int(activity.max()) if activity.size else 0
+
+    if max_activity == 0:
+        return np.zeros(activity.shape, dtype=np.uint8), 0
+
+    norm = np.log1p(activity.astype(np.float64)) / math.log1p(max_activity)
+    img = np.rint(gamma_encode(norm, gamma) * 255.0).astype(np.uint8)
+    return img, max_activity
+
+
+def make_signed_image(on_counts: np.ndarray, off_counts: np.ndarray, gamma: float):
+    signed = on_counts.astype(np.int64) - off_counts.astype(np.int64)
+    max_abs = int(np.max(np.abs(signed))) if signed.size else 0
+
+    if max_abs == 0:
+        return np.full(signed.shape, 127, dtype=np.uint8), 0
+
+    mag = np.log1p(np.abs(signed).astype(np.float64)) / math.log1p(max_abs)
+    mag = gamma_encode(mag, gamma)
+
+    signed_norm = 0.5 + 0.5 * np.sign(signed) * mag
+    img = np.rint(np.clip(signed_norm, 0.0, 1.0) * 255.0).astype(np.uint8)
+    return img, max_abs
+
+
+def save_gray(path: Path, img: np.ndarray) -> None:
+    Image.fromarray(img, mode="L").save(path)
+
+
+def batch_time_range_us(batch):
+    try:
+        return int(batch.getLowestTime()), int(batch.getHighestTime())
+    except Exception:
+        return None
+
+
+def capture_window(
+    camera,
+    label: str,
+    out_dir: Path,
+    *,
+    seconds: float,
+    camera_name: str,
+    width: int,
+    height: int,
+    gamma: float,
+) -> dict:
+    print(f"\n=== {label} ===")
+    is_running = getattr(camera, "isRunning", lambda: "unknown")()
+    print(f"isRunning diagnostic before window: {is_running}")
+
+    aedat4_path = out_dir / f"{label}.aedat4"
+    activity_png = out_dir / f"{label}_activity.png"
+    activity_pgm = out_dir / f"{label}_activity.pgm"
+    signed_png = out_dir / f"{label}_signed.png"
+    signed_pgm = out_dir / f"{label}_signed.pgm"
+
+    on_counts = np.zeros((height, width), dtype=np.uint64)
+    off_counts = np.zeros((height, width), dtype=np.uint64)
+
+    stats = {
+        "events": 0,
+        "on": 0,
+        "off": 0,
+        "batches": 0,
+        "none_count": 0,
+        "out_of_bounds": 0,
+        "first_ts": None,
+        "last_ts": None,
+    }
+
+    writer = make_writer(aedat4_path, camera_name, width, height)
+
+    start = time.monotonic()
+    deadline = start + seconds
+
+    try:
+        # Critical: do NOT gate on camera.isRunning().
+        while time.monotonic() < deadline:
+            batch = camera.getNextEventBatch()
+
+            if batch is None:
+                stats["none_count"] += 1
+                time.sleep(0.001)
+                continue
+
+            stats["batches"] += 1
+            writer.writeEvents(batch)
+
+            tr = batch_time_range_us(batch)
+            if tr is not None:
+                lo, hi = tr
+                stats["first_ts"] = lo if stats["first_ts"] is None else min(stats["first_ts"], lo)
+                stats["last_ts"] = hi if stats["last_ts"] is None else max(stats["last_ts"], hi)
+
+            for event in batch:
+                try:
+                    x = int(event_value(event, "x"))
+                    y = int(event_value(event, "y"))
+                    polarity = bool(event_value(event, "polarity"))
+                except Exception:
+                    continue
+
+                stats["events"] += 1
+
+                if polarity:
+                    stats["on"] += 1
+                else:
+                    stats["off"] += 1
+
+                if not (0 <= x < width and 0 <= y < height):
+                    stats["out_of_bounds"] += 1
+                    continue
+
+                if polarity:
+                    on_counts[y, x] += 1
+                else:
+                    off_counts[y, x] += 1
+
+    finally:
+        writer = None
+        gc.collect()
+
+    elapsed = time.monotonic() - start
+
+    activity = on_counts + off_counts
+    signed = on_counts.astype(np.int64) - off_counts.astype(np.int64)
+
+    activity_img, max_activity = make_activity_image(on_counts, off_counts, gamma)
+    signed_img, max_abs_signed = make_signed_image(on_counts, off_counts, gamma)
+
+    save_gray(activity_png, activity_img)
+    save_gray(activity_pgm, activity_img)
+    save_gray(signed_png, signed_img)
+    save_gray(signed_pgm, signed_img)
+
+    row = {
+        "label": label,
+        "seconds": seconds,
+        "elapsed_s": f"{elapsed:.3f}",
+        "camera_name": camera_name,
+        "resolution": f"{width}x{height}",
+        "events": int(stats["events"]),
+        "events_per_s": f"{stats['events'] / elapsed:.2f}" if elapsed > 0 else "",
+        "on": int(stats["on"]),
+        "off": int(stats["off"]),
+        "on_fraction": float(stats["on"] / stats["events"]) if stats["events"] else None,
+        "batches": int(stats["batches"]),
+        "none_count": int(stats["none_count"]),
+        "out_of_bounds": int(stats["out_of_bounds"]),
+        "nonzero_activity_pixels": int(np.count_nonzero(activity)),
+        "nonzero_signed_pixels": int(np.count_nonzero(signed)),
+        "max_activity_pixel_events": int(max_activity),
+        "max_abs_signed_pixel_events": int(max_abs_signed),
+        "first_ts": stats["first_ts"],
+        "last_ts": stats["last_ts"],
+        "aedat4": str(aedat4_path),
+        "activity_png": str(activity_png),
+        "activity_pgm": str(activity_pgm),
+        "signed_png": str(signed_png),
+        "signed_pgm": str(signed_pgm),
+    }
+
+    print(json.dumps(row, indent=2))
+    return row
+
+
+def write_environment(out_dir: Path, args) -> None:
+    path = out_dir / "environment.txt"
+
+    with path.open("w", encoding="utf-8") as f:
+        f.write(f"timestamp: {datetime.now().isoformat(timespec='seconds')}\n")
+        f.write(f"argv: {' '.join(sys.argv)}\n")
+        f.write(f"python: {sys.version.replace(chr(10), ' ')}\n")
+        f.write(f"platform: {platform.platform()}\n")
+        f.write(f"dv_processing_version: {getattr(dv, '__version__', 'unknown')}\n")
+        f.write(f"dv_processing_file: {getattr(dv, '__file__', 'unknown')}\n")
+        f.write(f"mode: {args.mode}\n")
+        f.write(f"duration: {args.duration}\n")
+        f.write(f"cycles: {args.cycles}\n")
+        f.write(f"pause: {args.pause}\n")
+        f.write(f"drain: {args.drain}\n")
+        f.write(f"threshold: {args.threshold}\n")
+        f.write(f"gamma: {args.gamma}\n")
+
+        f.write("\n--- dv.io.camera.discover() ---\n")
+        try:
+            f.write(str(dv.io.camera.discover()) + "\n")
+        except Exception as exc:
+            f.write(f"discover failed: {exc}\n")
+
+        f.write("\n--- dv-list-devices ---\n")
+        f.write(run_cmd(["dv-list-devices"]) + "\n")
+
+
+def write_summary_csv(out_dir: Path, rows: list[dict]) -> Path:
+    path = out_dir / "summary.csv"
+
+    if not rows:
+        return path
+
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(rows)
+
+    return path
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["same-handle", "reopen"], required=True)
+    parser.add_argument("--duration", type=float, default=8.0)
+    parser.add_argument("--cycles", type=int, default=3)
+    parser.add_argument("--pause", type=float, default=1.0)
+    parser.add_argument("--drain", type=float, default=0.5)
+    parser.add_argument("--threshold", type=int, default=9)
+    parser.add_argument("--skip-threshold", action="store_true")
+    parser.add_argument("--gamma", type=float, default=2.2)
+    parser.add_argument("--out", type=Path, default=Path("runs/camera_reopen_repro"))
+    return parser
+
+
+def main() -> int:
+    args = build_parser().parse_args()
+
+    if args.duration <= 0:
+        raise ValueError("--duration must be positive")
+    if args.cycles <= 0:
+        raise ValueError("--cycles must be positive")
+    if args.pause < 0:
+        raise ValueError("--pause must be nonnegative")
+    if args.drain < 0:
+        raise ValueError("--drain must be nonnegative")
+    if args.gamma <= 0:
+        raise ValueError("--gamma must be positive")
+
+    out_dir = args.out / f"{args.mode}_{time.strftime('%Y%m%d-%H%M%S')}"
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    write_environment(out_dir, args)
+
+    rows = []
+    threshold = None if args.skip_threshold else args.threshold
+
+    if args.mode == "same-handle":
+        camera = None
+        try:
+            camera, camera_name, width, height, drain_stats = open_first_camera(
+                threshold=threshold,
+                drain_seconds=args.drain,
+            )
+            print(f"Opened once: {camera_name}, resolution={width}x{height}")
+            print("drain:", json.dumps(drain_stats))
+
+            for i in range(1, args.cycles + 1):
+                rows.append(
+                    capture_window(
+                        camera,
+                        f"same_handle_{i:02d}",
+                        out_dir,
+                        seconds=args.duration,
+                        camera_name=camera_name,
+                        width=width,
+                        height=height,
+                        gamma=args.gamma,
+                    )
+                )
+
+                if i < args.cycles and args.pause > 0:
+                    time.sleep(args.pause)
+
+        finally:
+            camera = None
+            gc.collect()
+
+    else:
+        for i in range(1, args.cycles + 1):
+            camera = None
+            try:
+                camera, camera_name, width, height, drain_stats = open_first_camera(
+                    threshold=threshold,
+                    drain_seconds=args.drain,
+                )
+                print(f"Opened cycle {i}: {camera_name}, resolution={width}x{height}")
+                print("drain:", json.dumps(drain_stats))
+
+                rows.append(
+                    capture_window(
+                        camera,
+                        f"reopen_{i:02d}",
+                        out_dir,
+                        seconds=args.duration,
+                        camera_name=camera_name,
+                        width=width,
+                        height=height,
+                        gamma=args.gamma,
+                    )
+                )
+
+            finally:
+                camera = None
+                gc.collect()
+
+            if i < args.cycles and args.pause > 0:
+                time.sleep(args.pause)
+
+    summary_path = write_summary_csv(out_dir, rows)
+
+    print("\nOUT:", out_dir)
+    print("summary:", summary_path)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
\ No newline at end of file
diff --git a/debug_scripts/camera_probe.py b/debug_scripts/camera_probe.py
index 5f86432..27144d3 100644
--- a/debug_scripts/camera_probe.py
+++ b/debug_scripts/camera_probe.py
@@ -49,8 +49,7 @@ def positive_int(value: str) -> int:
 
 def build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
-        description="Camera-only DVXplorer positive-event accumulation probe"
-    )
+        description="Camera-only DVXplorer positive-event accumulation probe")
     parser.add_argument(
         "--duration-seconds",
         type=positive_float,
@@ -72,19 +71,28 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--camera-open-method",
         default="modern",
-        choices=["modern", "legacy"],
+        choices=["modern",
+                 "legacy"],
         help="Camera API used to open the device. Use legacy to mirror mentor CameraCapture code.",
     )
     parser.add_argument(
         "--bias-sensitivity",
         default="default",
-        choices=["default", "verylow", "low", "high", "veryhigh"],
+        choices=["default",
+                 "verylow",
+                 "low",
+                 "high",
+                 "veryhigh"],
         help="Optional camera bias/sensitivity preset.",
     )
     parser.add_argument(
         "--efps",
         default="default",
-        choices=["default", "variable", "variable_5000", "constant_1000", "constant_100"],
+        choices=["default",
+                 "variable",
+                 "variable_5000",
+                 "constant_1000",
+                 "constant_100"],
         help="Optional DVXplorer readout/eFPS preset.",
     )
     parser.add_argument(
@@ -127,7 +135,8 @@ def build_parser() -> argparse.ArgumentParser:
         dest="usb_reset",
         action="store_true",
         default=False,
-        help="Diagnostic: run a Linux USB device reset before opening the camera. Disabled by default.",
+        help=
+        "Diagnostic: run a Linux USB device reset before opening the camera. Disabled by default.",
     )
     parser.add_argument(
         "--no-usb-reset",
@@ -141,8 +150,7 @@ def build_parser() -> argparse.ArgumentParser:
         help=(
             "Optional command run before camera open to power-cycle the USB port, "
             "for example: \"uhubctl -l 1-2 -p 3 -a cycle -d 2\". "
-            "Defaults to DMD_CAMERA_POWER_CYCLE_COMMAND when set."
-        ),
+            "Defaults to DMD_CAMERA_POWER_CYCLE_COMMAND when set."),
     )
     parser.add_argument(
         "--stream-rearm",
@@ -170,23 +178,26 @@ def write_png_gray8(path: Path, image_u8: np.ndarray) -> None:
 
     height, width = image_u8.shape
 
-    raw = b"".join(
-        b"\x00" + image_u8[y, :].tobytes()
-        for y in range(height)
-    )
+    raw = b"".join(b"\x00" + image_u8[y, :].tobytes() for y in range(height))
 
     def chunk(tag: bytes, data: bytes) -> bytes:
         return (
-            struct.pack(">I", len(data))
-            + tag
-            + data
-            + struct.pack(">I", zlib.crc32(tag + data) & 0xFFFFFFFF)
-        )
+            struct.pack(">I",
+                        len(data)) + tag + data +
+            struct.pack(">I",
+                        zlib.crc32(tag + data) & 0xFFFFFFFF))
 
     png = b"\x89PNG\r\n\x1a\n"
     png += chunk(
         b"IHDR",
-        struct.pack(">IIBBBBB", width, height, 8, 0, 0, 0, 0),
+        struct.pack(">IIBBBBB",
+                    width,
+                    height,
+                    8,
+                    0,
+                    0,
+                    0,
+                    0),
     )
     png += chunk(b"IDAT", zlib.compress(raw, level=6))
     png += chunk(b"IEND", b"")
@@ -271,7 +282,8 @@ def run() -> int:
 
     print("[probe] dv_processing:", dv)
     print("[probe] dv_processing version:", getattr(dv, "__version__", "unknown"))
-    power_cycle_command = args.power_cycle_command or os.environ.get("DMD_CAMERA_POWER_CYCLE_COMMAND")
+    power_cycle_command = args.power_cycle_command or os.environ.get(
+        "DMD_CAMERA_POWER_CYCLE_COMMAND")
     print("[probe] power cycle:", run_power_cycle_command(power_cycle_command))
     print("[probe] usb reset:", reset_camera_usb(dv, enabled=args.usb_reset))
 
@@ -282,8 +294,7 @@ def run() -> int:
             f"  [{i}] "
             f"model={getattr(d, 'cameraModel', None)} "
             f"serial={getattr(d, 'serialNumber', None)} "
-            f"devAddress={getattr(d, 'devAddress', None)}"
-        )
+            f"devAddress={getattr(d, 'devAddress', None)}")
 
     if not descs and args.camera_open_method == "modern":
         raise RuntimeError("No camera discovered")
@@ -296,9 +307,15 @@ def run() -> int:
     )
 
     print("[probe] capture type:", type(capture))
-    print("[probe] name:", capture.getCameraName() if hasattr(capture, "getCameraName") else "unknown")
+    print(
+        "[probe] name:",
+        capture.getCameraName() if hasattr(capture,
+                                           "getCameraName") else "unknown")
     print("[probe] event stream:", capture.isEventStreamAvailable())
-    print("[probe] trigger stream:", capture.isTriggerStreamAvailable() if hasattr(capture, "isTriggerStreamAvailable") else "unknown")
+    print(
+        "[probe] trigger stream:",
+        capture.isTriggerStreamAvailable() if hasattr(capture,
+                                                      "isTriggerStreamAvailable") else "unknown")
     print("[probe] running:", capture.isRunning())
 
     if not capture.isEventStreamAvailable():
@@ -439,35 +456,60 @@ def run() -> int:
         write_png_gray8(linear_png, image_linear)
 
     stats = {
-        "duration_seconds_requested": args.duration_seconds,
-        "duration_seconds_elapsed": elapsed,
-        "resolution": [width, height],
-        "camera_open_method": args.camera_open_method,
-        "threshold": args.threshold,
-        "threshold_applied": not args.skip_threshold,
-        "bias_sensitivity": args.bias_sensitivity,
-        "efps": args.efps,
-        "total_events": int(total_events),
-        "positive_events": int(positive_events),
-        "negative_events": int(negative_events),
-        "positive_fraction": float(positive_events / total_events) if total_events else None,
-        "mean_total_event_rate_eps": float(total_events / elapsed) if elapsed > 0 else None,
-        "mean_positive_event_rate_eps": float(positive_events / elapsed) if elapsed > 0 else None,
-        "batch_count": int(batch_count),
-        "none_count": int(none_count),
-        "out_of_bounds_positive_events": int(out_of_bounds),
-        "nonzero_pixels": int(np.count_nonzero(accum_on)),
-        "max_pixel_count": int(accum_on.max()) if accum_on.size else 0,
-        "sum_accumulated_positive_events": int(accum_on.sum()),
-        "first_camera_ts_us": int(first_ts) if first_ts is not None else None,
-        "last_camera_ts_us": int(last_ts) if last_ts is not None else None,
-        "camera_ts_span_s": float((last_ts - first_ts) / 1e6)
-        if first_ts is not None and last_ts is not None
-        else None,
-        "monotonic_bad_batches": int(monotonic_bad_batches),
-        "output_log_png": str(log_png),
-        "output_counts_npy": str(npy_path),
-        "output_linear_png": str(linear_png) if linear_png else None,
+        "duration_seconds_requested":
+        args.duration_seconds,
+        "duration_seconds_elapsed":
+        elapsed,
+        "resolution": [width,
+                       height],
+        "camera_open_method":
+        args.camera_open_method,
+        "threshold":
+        args.threshold,
+        "threshold_applied":
+        not args.skip_threshold,
+        "bias_sensitivity":
+        args.bias_sensitivity,
+        "efps":
+        args.efps,
+        "total_events":
+        int(total_events),
+        "positive_events":
+        int(positive_events),
+        "negative_events":
+        int(negative_events),
+        "positive_fraction":
+        float(positive_events / total_events) if total_events else None,
+        "mean_total_event_rate_eps":
+        float(total_events / elapsed) if elapsed > 0 else None,
+        "mean_positive_event_rate_eps":
+        float(positive_events / elapsed) if elapsed > 0 else None,
+        "batch_count":
+        int(batch_count),
+        "none_count":
+        int(none_count),
+        "out_of_bounds_positive_events":
+        int(out_of_bounds),
+        "nonzero_pixels":
+        int(np.count_nonzero(accum_on)),
+        "max_pixel_count":
+        int(accum_on.max()) if accum_on.size else 0,
+        "sum_accumulated_positive_events":
+        int(accum_on.sum()),
+        "first_camera_ts_us":
+        int(first_ts) if first_ts is not None else None,
+        "last_camera_ts_us":
+        int(last_ts) if last_ts is not None else None,
+        "camera_ts_span_s":
+        float((last_ts - first_ts) / 1e6) if first_ts is not None and last_ts is not None else None,
+        "monotonic_bad_batches":
+        int(monotonic_bad_batches),
+        "output_log_png":
+        str(log_png),
+        "output_counts_npy":
+        str(npy_path),
+        "output_linear_png":
+        str(linear_png) if linear_png else None,
     }
 
     json_path.write_text(json.dumps(stats, indent=2))
diff --git a/debug_scripts/debug_numbered_regions.py b/debug_scripts/debug_numbered_regions.py
index e713f4a..59eb436 100644
--- a/debug_scripts/debug_numbered_regions.py
+++ b/debug_scripts/debug_numbered_regions.py
@@ -28,8 +28,13 @@ def generate_crop_visualization(width, height, crop_x, crop_y, crop_w, crop_h):
 
     # Draw the crop region in bright color
     draw.rectangle(
-        [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h],
-        fill=(200, 200, 0),
+        [crop_x,
+         crop_y,
+         crop_x + crop_w,
+         crop_y + crop_h],
+        fill=(200,
+              200,
+              0),
         outline="red",
         width=5,
     )
@@ -37,12 +42,8 @@ def generate_crop_visualization(width, height, crop_x, crop_y, crop_w, crop_h):
     # Add crosshairs at center
     center_x = crop_x + crop_w // 2
     center_y = crop_y + crop_h // 2
-    draw.line(
-        [(center_x - 50, center_y), (center_x + 50, center_y)], fill="red", width=3
-    )
-    draw.line(
-        [(center_x, center_y - 50), (center_x, center_y + 50)], fill="red", width=3
-    )
+    draw.line([(center_x - 50, center_y), (center_x + 50, center_y)], fill="red", width=3)
+    draw.line([(center_x, center_y - 50), (center_x, center_y + 50)], fill="red", width=3)
 
     # Add text labels
     try:
@@ -66,9 +67,7 @@ def save_numbered_diagnostics():
 
     # Full frame with 6x4 grid (24 numbered regions)
     print("  - Creating 6x4 numbered grid...")
-    numbered_pattern = generate_numbered_regions(
-        width, height, grid_cols=6, grid_rows=4
-    )
+    numbered_pattern = generate_numbered_regions(width, height, grid_cols=6, grid_rows=4)
     img_full = Image.fromarray(numbered_pattern)
     full_path = os.path.join(out_dir, "diagnostic_numbered_full.png")
     img_full.save(full_path)
@@ -77,9 +76,7 @@ def save_numbered_diagnostics():
     # Show crop region
     crop_x, crop_y, crop_w, crop_h = 704, 284, 512, 512
     print(f"  - Creating crop visualization ({crop_w}x{crop_h} @ {crop_x},{crop_y})...")
-    crop_viz = generate_crop_visualization(
-        width, height, crop_x, crop_y, crop_w, crop_h
-    )
+    crop_viz = generate_crop_visualization(width, height, crop_x, crop_y, crop_w, crop_h)
     img_crop_viz = Image.fromarray(crop_viz)
     crop_viz_path = os.path.join(out_dir, "diagnostic_crop_visualization.png")
     img_crop_viz.save(crop_viz_path)
@@ -87,9 +84,7 @@ def save_numbered_diagnostics():
 
     # Extract actual crop for comparison
     print("  - Extracting crop region from numbered pattern...")
-    cropped_numbered = numbered_pattern[
-        crop_y: crop_y + crop_h, crop_x: crop_x + crop_w
-    ]
+    cropped_numbered = numbered_pattern[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w]
     img_cropped = Image.fromarray(cropped_numbered)
     crop_path = os.path.join(out_dir, "diagnostic_numbered_crop.png")
     img_cropped.save(crop_path)
@@ -97,9 +92,7 @@ def save_numbered_diagnostics():
 
     print("\nDiagnostic images generated:")
     print("  1. diagnostic_numbered_full.png - Full 1920x1080 with numbered regions")
-    print(
-        "  2. diagnostic_crop_visualization.png - Shows where hardware crop is positioned"
-    )
+    print("  2. diagnostic_crop_visualization.png - Shows where hardware crop is positioned")
     print("  3. diagnostic_numbered_crop.png - What the 512x512 crop should extract")
 
 
diff --git a/debug_scripts/dvx_min_raw_liveness.py b/debug_scripts/dvx_min_raw_liveness.py
new file mode 100644
index 0000000..51b3c31
--- /dev/null
+++ b/debug_scripts/dvx_min_raw_liveness.py
@@ -0,0 +1,275 @@
+#!/usr/bin/env python3
+"""
+Minimal DVXplorer raw-batch liveness probe.
+
+Purpose:
+- Open one DVXplorer through dv_processing.
+- Optionally apply the DV-view-style settings block.
+- Poll getNextEventBatch() across repeated same-handle windows.
+- Save only JSON summaries.
+
+No image writing. No AEDAT4. No accumulator. No trigger logic.
+"""
+
+from __future__ import annotations
+
+import argparse
+import gc
+import json
+import math
+import os
+import platform
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+
+import dv_processing as dv
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--windows", type=int, default=3)
+    parser.add_argument("--duration", type=float, default=8.0)
+    parser.add_argument("--gap", type=float, default=1.0)
+    parser.add_argument("--dv-view-defaults", action="store_true")
+    parser.add_argument("--out", type=Path, default=Path("runs/dvx_min_raw_liveness"))
+    return parser
+
+
+def call0(obj, name: str):
+    method = getattr(obj, name, None)
+    if not callable(method):
+        return None
+    try:
+        return method()
+    except Exception as exc:
+        return f"ERROR: {exc!r}"
+
+
+def call_setting(camera, name: str, *args):
+    method = getattr(camera, name, None)
+    if not callable(method):
+        return {"method": name, "ok": False, "reason": "unavailable"}
+
+    try:
+        method(*args)
+        return {"method": name, "ok": True, "args": [repr(arg) for arg in args]}
+    except Exception as exc:
+        return {"method": name, "ok": False, "reason": repr(exc)}
+
+
+def resolution_tuple(camera):
+    try:
+        resolution = camera.getEventResolution()
+    except Exception as exc:
+        return f"ERROR: {exc!r}"
+
+    if hasattr(resolution, "width") and hasattr(resolution, "height"):
+        return [int(resolution.width), int(resolution.height)]
+
+    try:
+        width, height = resolution
+        return [int(width), int(height)]
+    except Exception:
+        return repr(resolution)
+
+
+def camera_info(camera):
+    return {
+        "type": f"{type(camera).__module__}.{type(camera).__name__}",
+        "camera_name": call0(camera, "getCameraName"),
+        "isConnected": call0(camera, "isConnected"),
+        "isRunning": call0(camera, "isRunning"),
+        "event_stream_available": call0(camera, "isEventStreamAvailable"),
+        "event_resolution": resolution_tuple(camera),
+    }
+
+
+def discover_descriptors():
+    try:
+        return list(dv.io.camera.discover())
+    except Exception:
+        return []
+
+
+def open_first_camera():
+    descriptors = discover_descriptors()
+
+    if descriptors:
+        camera = dv.io.camera.open(descriptors[0])
+    else:
+        camera = dv.io.camera.open()
+
+    return camera, descriptors
+
+
+def apply_dv_view_defaults(camera):
+    """
+    Name is intentional: these are the settings copied from DV-view/C++-style testing,
+    not mentor/legacy settings.
+    """
+    results = [
+        call_setting(camera, "setContrastThresholdOn", 9),
+        call_setting(camera, "setContrastThresholdOff", 9),
+    ]
+
+    dvxplorer = getattr(dv.io.camera, "DVXplorer", None)
+    readout_enum = getattr(dvxplorer, "ReadoutFPS", None)
+    variable_5000 = getattr(readout_enum, "VARIABLE_5000", None) if readout_enum else None
+
+    if variable_5000 is None:
+        results.append({
+            "method": "setReadoutFPS",
+            "ok": False,
+            "reason": "DVXplorer.ReadoutFPS.VARIABLE_5000 unavailable",
+        })
+    else:
+        results.append(call_setting(camera, "setReadoutFPS", variable_5000))
+
+    results.append(call_setting(camera, "setGlobalHold", True))
+    results.append(call_setting(camera, "setGlobalReset", False))
+
+    return results
+
+
+def batch_len(batch) -> int:
+    try:
+        return int(len(batch))
+    except TypeError:
+        return sum(1 for _ in batch)
+
+
+def batch_time_range_us(batch):
+    try:
+        return int(batch.getLowestTime()), int(batch.getHighestTime())
+    except Exception:
+        return None
+
+
+def capture_window(camera, label: str, duration: float):
+    wall_bins = [0 for _ in range(max(1, math.ceil(duration)))]
+
+    stats = {
+        "label": label,
+        "duration_s": duration,
+        "events": 0,
+        "batches": 0,
+        "none_count": 0,
+        "wall_second_event_bins": wall_bins,
+        "first_ts": None,
+        "last_ts": None,
+        "camera_span_s": None,
+        "isRunning_before": call0(camera, "isRunning"),
+        "isRunning_after": None,
+    }
+
+    start = time.monotonic()
+    deadline = start + duration
+
+    while time.monotonic() < deadline:
+        batch_wall_time = time.monotonic()
+        batch = camera.getNextEventBatch()
+
+        if batch is None:
+            stats["none_count"] += 1
+            time.sleep(0.001)
+            continue
+
+        n_events = batch_len(batch)
+        stats["events"] += n_events
+        stats["batches"] += 1
+
+        bin_index = min(int(batch_wall_time - start), len(wall_bins) - 1)
+        wall_bins[bin_index] += n_events
+
+        time_range = batch_time_range_us(batch)
+        if time_range is not None:
+            lo, hi = time_range
+            stats["first_ts"] = lo if stats["first_ts"] is None else min(stats["first_ts"], lo)
+            stats["last_ts"] = hi if stats["last_ts"] is None else max(stats["last_ts"], hi)
+
+    elapsed = time.monotonic() - start
+    stats["elapsed_s"] = elapsed
+    stats["events_per_s"] = stats["events"] / elapsed if elapsed > 0 else None
+    stats["isRunning_after"] = call0(camera, "isRunning")
+
+    if stats["first_ts"] is not None and stats["last_ts"] is not None:
+        stats["camera_span_s"] = (stats["last_ts"] - stats["first_ts"]) / 1_000_000.0
+
+    print(json.dumps(stats, indent=2))
+    return stats
+
+
+def write_json(path: Path, payload) -> None:
+    path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+
+
+def main() -> int:
+    args = build_parser().parse_args()
+
+    if args.windows <= 0:
+        raise SystemExit("--windows must be positive")
+    if args.duration <= 0:
+        raise SystemExit("--duration must be positive")
+    if args.gap < 0:
+        raise SystemExit("--gap must be nonnegative")
+
+    run_dir = args.out / datetime.now().strftime("%Y%m%d-%H%M%S")
+    run_dir.mkdir(parents=True, exist_ok=True)
+
+    camera = None
+
+    try:
+        camera, descriptors = open_first_camera()
+
+        metadata = {
+            "timestamp": datetime.now().isoformat(timespec="seconds"),
+            "argv": sys.argv,
+            "prestate": os.environ.get("DVX_PRESTATE", ""),
+            "python": sys.version.replace("\n", " "),
+            "platform": platform.platform(),
+            "dv_processing_version": getattr(dv, "__version__", "unknown"),
+            "dv_processing_file": getattr(dv, "__file__", None),
+            "dv_view_defaults": bool(args.dv_view_defaults),
+            "descriptors": [repr(desc) for desc in descriptors],
+            "camera_before_settings": camera_info(camera),
+        }
+
+        settings_results = []
+        if args.dv_view_defaults:
+            settings_results = apply_dv_view_defaults(camera)
+
+        metadata["settings_results"] = settings_results
+        metadata["camera_after_settings"] = camera_info(camera)
+
+        print("RUN_DIR:", run_dir.resolve())
+        print(json.dumps(metadata, indent=2))
+        write_json(run_dir / "metadata.json", metadata)
+
+        results = []
+        for index in range(1, args.windows + 1):
+            label = f"window_{index:02d}"
+            print(f"\n=== {label} ===")
+            results.append(capture_window(camera, label, args.duration))
+
+            if index < args.windows and args.gap > 0:
+                time.sleep(args.gap)
+
+        summary = {
+            "run_dir": str(run_dir),
+            "metadata": metadata,
+            "results": results,
+        }
+
+        write_json(run_dir / "summary.json", summary)
+        print("\nSUMMARY_JSON:", (run_dir / "summary.json").resolve())
+        return 0
+
+    finally:
+        camera = None
+        gc.collect()
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/debug_scripts/persistent_camera_liveness.py b/debug_scripts/persistent_camera_liveness.py
new file mode 100644
index 0000000..8e8973a
--- /dev/null
+++ b/debug_scripts/persistent_camera_liveness.py
@@ -0,0 +1,366 @@
+#!/usr/bin/env python3
+"""
+DVXplorer same-handle liveness probe.
+
+Purpose:
+- No DMD.
+- No triggers.
+- Open the camera once.
+- Capture several event windows from the same camera handle.
+
+Use this after a physical camera replug when testing whether close/reopen is
+what poisons useful optical response.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from dmdcontrol.camera.runs import _write_grayscale_png
+
+
+def positive_float(value: str) -> float:
+    parsed = float(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return parsed
+
+
+def positive_int(value: str) -> int:
+    parsed = int(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return parsed
+
+
+def nonnegative_float(value: str) -> float:
+    parsed = float(value)
+    if parsed < 0:
+        raise argparse.ArgumentTypeError("value must be non-negative")
+    return parsed
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Open a DVXplorer once and capture multiple same-handle liveness windows.")
+    parser.add_argument(
+        "--prestate",
+        action="append",
+        default=[],
+        metavar="KEY=VALUE",
+        help="Pre-run state metadata to include in stats.json; repeat for multiple fields.",
+    )
+    parser.add_argument(
+        "--output-root",
+        type=Path,
+        default=Path("runs/camera"),
+        help="Parent output directory.",
+    )
+    parser.add_argument(
+        "--name",
+        default=None,
+        help="Output directory name. Default uses persistent_liveness_<timestamp>.",
+    )
+    parser.add_argument(
+        "--windows",
+        type=positive_int,
+        default=5,
+        help="Number of same-handle capture windows.",
+    )
+    parser.add_argument(
+        "--duration-seconds",
+        type=positive_float,
+        default=2.0,
+        help="Duration of each capture window.",
+    )
+    parser.add_argument(
+        "--gap-seconds",
+        type=nonnegative_float,
+        default=1.0,
+        help="Delay between capture windows.",
+    )
+    parser.add_argument(
+        "--drain-seconds",
+        type=nonnegative_float,
+        default=0.5,
+        help="Initial stale-batch drain time after opening the camera.",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=int,
+        default=9,
+        help="Contrast threshold for ON/OFF, usually 0-17.",
+    )
+    parser.add_argument(
+        "--skip-threshold",
+        action="store_true",
+        default=False,
+        help="Do not call setContrastThresholdOn/Off.",
+    )
+    parser.add_argument(
+        "--percentile",
+        type=float,
+        default=99.5,
+        help="Percentile used for PGM normalization.",
+    )
+    return parser
+
+
+def _monotonic_seconds() -> float:
+    return time.time()
+
+
+def event_value(event, name: str):
+    attr = getattr(event, name)
+    return attr() if callable(attr) else attr
+
+
+def _batch_time_range_us(batch):
+    try:
+        return int(batch.getLowestTime()), int(batch.getHighestTime())
+    except Exception:
+        return None
+
+
+def save_pgm(path: Path, accum: np.ndarray, *, percentile: float) -> None:
+    img = np.log1p(accum.astype(np.float32))
+    nonzero = img[img > 0]
+    if nonzero.size:
+        vmax = float(np.percentile(nonzero, percentile))
+        if not math.isfinite(vmax) or vmax <= 0:
+            vmax = float(nonzero.max())
+        if vmax > 0:
+            img = np.clip(img / vmax, 0, 1)
+    img = (img * 255).astype(np.uint8)
+
+    height, width = img.shape
+    with path.open("wb") as handle:
+        handle.write(f"P5\n{width} {height}\n255\n".encode("ascii"))
+        handle.write(img.tobytes())
+
+
+def drain_camera(camera, seconds: float) -> dict[str, int]:
+    deadline = _monotonic_seconds() + seconds
+    batches = 0
+    events = 0
+    while _monotonic_seconds() < deadline:
+        batch = camera.getNextEventBatch()
+        if batch is None:
+            time.sleep(0.001)
+            continue
+        batches += 1
+        try:
+            events += len(batch)
+        except TypeError:
+            pass
+    return {"drained_batches": batches, "drained_events": events}
+
+
+def capture_window(
+    camera,
+    label: str,
+    out_dir: Path,
+    *,
+    seconds: float,
+    percentile: float,
+) -> dict:
+    width, height = map(int, camera.getEventResolution())
+    accum = np.zeros((height, width), dtype=np.uint32)
+
+    total = 0
+    on = 0
+    off = 0
+    batches = 0
+    none_count = 0
+    first_ts = None
+    last_ts = None
+    wall_bins = [0 for _ in range(max(1, math.ceil(seconds)))]
+
+    print(f"\n=== {label} ===")
+    print("Move laser/flashlight/diffuse spot during this window.")
+
+    window_start = _monotonic_seconds()
+    deadline = window_start + seconds
+    while True:
+        now = _monotonic_seconds()
+        if now >= deadline:
+            break
+
+        batch = camera.getNextEventBatch()
+        if batch is None:
+            none_count += 1
+            time.sleep(0.001)
+            continue
+
+        batches += 1
+        batch_len = None
+        try:
+            batch_len = len(batch)
+        except TypeError:
+            pass
+        if batch_len is not None:
+            total += batch_len
+            bin_index = min(int(now - window_start), len(wall_bins) - 1)
+            wall_bins[bin_index] += batch_len
+
+        time_range = _batch_time_range_us(batch)
+        if time_range is not None:
+            lo, hi = time_range
+            first_ts = lo if first_ts is None else min(first_ts, lo)
+            last_ts = hi if last_ts is None else max(last_ts, hi)
+
+        for event in batch:
+            try:
+                x = int(event_value(event, "x"))
+                y = int(event_value(event, "y"))
+                polarity = bool(event_value(event, "polarity"))
+            except Exception:
+                continue
+
+            if polarity:
+                on += 1
+            else:
+                off += 1
+
+            if 0 <= x < width and 0 <= y < height:
+                accum[y, x] += 1
+
+    camera_span_s = None
+    if first_ts is not None and last_ts is not None:
+        camera_span_s = (last_ts - first_ts) / 1_000_000.0
+
+    pgm_path = out_dir / f"{label}.pgm"
+    png_path = out_dir / f"{label}.png"
+    npy_path = out_dir / f"{label}.npy"
+    save_pgm(pgm_path, accum, percentile=percentile)
+    _write_grayscale_png(png_path, accum)
+    np.save(npy_path, accum)
+
+    stats = {
+        "label": label,
+        "seconds": seconds,
+        "events": int(total),
+        "on": int(on),
+        "off": int(off),
+        "on_fraction": float(on / total) if total else None,
+        "batches": int(batches),
+        "none_count": int(none_count),
+        "nonzero_pixels": int(np.count_nonzero(accum)),
+        "max_pixel": int(accum.max()) if accum.size else 0,
+        "wall_second_event_bins": [int(value) for value in wall_bins],
+        "first_ts": first_ts,
+        "last_ts": last_ts,
+        "camera_span_s": camera_span_s,
+        "pgm": str(pgm_path),
+        "png": str(png_path),
+        "npy": str(npy_path),
+    }
+    print(json.dumps(stats, indent=2))
+    return stats
+
+
+def _apply_threshold(camera, threshold: int) -> None:
+    for name in ("setContrastThresholdOn", "setContrastThresholdOff"):
+        method = getattr(camera, name, None)
+        if callable(method):
+            method(threshold)
+
+
+def parse_prestate(values: list[str]) -> dict:
+    fields = {}
+    notes = []
+    for value in values:
+        if "=" not in value:
+            notes.append(value)
+            continue
+        key, parsed = value.split("=", 1)
+        key = key.strip()
+        if key:
+            fields[key] = parsed.strip()
+        else:
+            notes.append(value)
+    return {"fields": fields, "notes": notes}
+
+
+def run(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    import dv_processing as dv
+
+    output_name = args.name or f"persistent_liveness_{time.strftime('%Y%m%d-%H%M%S')}"
+    out_dir = args.output_root / output_name
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    print("dv:", getattr(dv, "__version__", "unknown"), getattr(dv, "__file__", None))
+    descriptors = dv.io.camera.discover()
+    print("discovered:", len(descriptors))
+    if not descriptors:
+        raise SystemExit("No camera discovered")
+
+    open_count = 0
+    camera = dv.io.camera.open(descriptors[0])
+    open_count += 1
+    try:
+        print("opened:", camera.getCameraName() if hasattr(camera, "getCameraName") else type(camera))
+        print("resolution:", tuple(camera.getEventResolution()))
+
+        if not args.skip_threshold:
+            _apply_threshold(camera, args.threshold)
+
+        drain_stats = drain_camera(camera, args.drain_seconds)
+        print("drain:", json.dumps(drain_stats))
+
+        windows = []
+        for index in range(1, args.windows + 1):
+            label = f"window_{index:02d}_same_handle"
+            windows.append(
+                capture_window(
+                    camera,
+                    label,
+                    out_dir,
+                    seconds=args.duration_seconds,
+                    percentile=args.percentile,
+                ))
+            if index < args.windows and args.gap_seconds > 0:
+                time.sleep(args.gap_seconds)
+
+        stats = {
+            "summary": {
+                "prestate": parse_prestate(args.prestate),
+                "camera_opens": open_count,
+                "output_dir": str(out_dir),
+                "windows": args.windows,
+                "duration_seconds": args.duration_seconds,
+                "gap_seconds": args.gap_seconds,
+                "drain_seconds": args.drain_seconds,
+                "threshold": args.threshold,
+                "threshold_applied": not args.skip_threshold,
+                "drain": drain_stats,
+            },
+            "windows": windows,
+        }
+        stats_path = out_dir / "stats.json"
+        stats_path.write_text(json.dumps(stats, indent=2), encoding="utf-8")
+
+        print("\nOUT:", out_dir)
+        print("stats:", stats_path)
+        return 0
+    finally:
+        try:
+            del camera
+        except Exception:
+            pass
+        import gc
+        gc.collect()
+
+
+if __name__ == "__main__":
+    raise SystemExit(run())
diff --git a/debug_scripts/persistent_camera_liveness_official.py b/debug_scripts/persistent_camera_liveness_official.py
new file mode 100644
index 0000000..5a14627
--- /dev/null
+++ b/debug_scripts/persistent_camera_liveness_official.py
@@ -0,0 +1,440 @@
+#!/usr/bin/env python3
+"""
+Official-style DVXplorer accumulator same-handle probe.
+
+This intentionally follows the dv-processing accumulator sample structure:
+open the camera, feed event batches through dv.EventStreamSlicer, call
+dv.Accumulator.accept() on slices, then save generated frames as PNG files.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import struct
+import sys
+import time
+import zlib
+from datetime import timedelta
+from pathlib import Path
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+
+def positive_float(value: str) -> float:
+    parsed = float(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return parsed
+
+
+def positive_int(value: str) -> int:
+    parsed = int(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return parsed
+
+
+def nonnegative_float(value: str) -> float:
+    parsed = float(value)
+    if parsed < 0:
+        raise argparse.ArgumentTypeError("value must be non-negative")
+    return parsed
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Open a DVXplorer once and run official-style accumulator windows.")
+    parser.add_argument(
+        "--prestate",
+        action="append",
+        default=[],
+        metavar="KEY=VALUE",
+        help="Pre-run state metadata to include in stats.json; repeat for multiple fields.",
+    )
+    parser.add_argument(
+        "--output-root",
+        type=Path,
+        default=Path("runs/camera"),
+        help="Parent output directory.",
+    )
+    parser.add_argument(
+        "--name",
+        default=None,
+        help="Output directory name. Default uses official_accumulator_<timestamp>.",
+    )
+    parser.add_argument("--windows", type=positive_int, default=5)
+    parser.add_argument("--duration-seconds", type=positive_float, default=2.0)
+    parser.add_argument("--gap-seconds", type=nonnegative_float, default=1.0)
+    parser.add_argument(
+        "--slice-ms",
+        type=positive_float,
+        default=33.0,
+        help="EventStreamSlicer interval in milliseconds; official sample uses 33.",
+    )
+    parser.add_argument(
+        "--camera-open-method",
+        choices=["open",
+                 "dvxplorer"],
+        default="open",
+        help="Use dv.io.camera.open() or dv.io.camera.DVXplorer().",
+    )
+    parser.add_argument(
+        "--set-dvxplorer-defaults",
+        action="store_true",
+        default=False,
+        help=
+        "Set threshold 9/9, VARIABLE_5000, global hold true, and global reset false when available.",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=int,
+        default=9,
+        help="Threshold used with --set-dvxplorer-defaults.",
+    )
+    parser.add_argument("--event-contribution", type=float, default=0.25)
+    parser.add_argument("--neutral-potential", type=float, default=0.5)
+    parser.add_argument("--min-potential", type=float, default=0.0)
+    parser.add_argument("--max-potential", type=float, default=1.0)
+    parser.add_argument(
+        "--decay-function",
+        choices=["EXPONENTIAL",
+                 "LINEAR",
+                 "STEP",
+                 "NONE"],
+        default="LINEAR",
+        help="Accumulator decay function; official sample uses LINEAR.",
+    )
+    parser.add_argument(
+        "--decay-param",
+        type=float,
+        default=1e-6,
+        help="Accumulator decay parameter; official sample uses 1e-6.",
+    )
+    parser.add_argument(
+        "--synchronous-decay",
+        action="store_true",
+        default=False,
+        help="Enable synchronous decay. Official sample leaves this false.",
+    )
+    parser.add_argument(
+        "--ignore-polarity",
+        action="store_true",
+        default=False,
+        help="Make all events contribute in the same direction.",
+    )
+    parser.add_argument(
+        "--save-initial-frames",
+        type=positive_int,
+        default=5,
+        help="Save the first N slicer-generated frames per window.",
+    )
+    parser.add_argument(
+        "--save-every-n-frames",
+        type=positive_int,
+        default=10,
+        help="After initial frames, save every Nth generated frame.",
+    )
+    return parser
+
+
+def _monotonic_seconds() -> float:
+    return time.time()
+
+
+def _batch_time_range_us(batch):
+    try:
+        return int(batch.getLowestTime()), int(batch.getHighestTime())
+    except Exception:
+        return None
+
+
+def _camera_running(camera) -> bool:
+    method = getattr(camera, "isRunning", None)
+    return bool(method()) if callable(method) else True
+
+
+def _call_if_available(obj, name: str, *args):
+    method = getattr(obj, name, None)
+    if not callable(method):
+        print(f"[official_accu] {name} unavailable")
+        return None
+    result = method(*args)
+    print(f"[official_accu] {name}{args} -> OK")
+    return result
+
+
+def _open_camera(dv, method: str):
+    if method == "open":
+        print("[official_accu] opening with dv.io.camera.open()")
+        return dv.io.camera.open()
+    constructor = getattr(dv.io.camera, "DVXplorer", None)
+    if not callable(constructor):
+        raise SystemExit("dv.io.camera.DVXplorer is not available in this environment")
+    print("[official_accu] opening with dv.io.camera.DVXplorer()")
+    return constructor()
+
+
+def _set_dvxplorer_defaults(dv, camera, threshold: int) -> None:
+    _call_if_available(camera, "setContrastThresholdOn", threshold)
+    _call_if_available(camera, "setContrastThresholdOff", threshold)
+    dvxplorer = getattr(dv.io.camera, "DVXplorer", None)
+    readout_enum = getattr(dvxplorer, "ReadoutFPS", None)
+    if readout_enum is not None and hasattr(readout_enum, "VARIABLE_5000"):
+        _call_if_available(camera, "setReadoutFPS", readout_enum.VARIABLE_5000)
+    _call_if_available(camera, "setGlobalHold", True)
+    _call_if_available(camera, "setGlobalReset", False)
+
+
+def parse_prestate(values: list[str]) -> dict:
+    fields = {}
+    notes = []
+    for value in values:
+        if "=" not in value:
+            notes.append(value)
+            continue
+        key, parsed = value.split("=", 1)
+        key = key.strip()
+        if key:
+            fields[key] = parsed.strip()
+        else:
+            notes.append(value)
+    return {"fields": fields, "notes": notes}
+
+
+def _configure_accumulator(dv, camera, args):
+    accumulator = dv.Accumulator(camera.getEventResolution())
+    accumulator.setEventContribution(args.event_contribution)
+    accumulator.setNeutralPotential(args.neutral_potential)
+    accumulator.setMinPotential(args.min_potential)
+    accumulator.setMaxPotential(args.max_potential)
+    accumulator.setDecayFunction(getattr(dv.Accumulator.Decay, args.decay_function))
+    accumulator.setDecayParam(args.decay_param)
+    accumulator.setSynchronousDecay(args.synchronous_decay)
+    accumulator.setIgnorePolarity(args.ignore_polarity)
+    return accumulator
+
+
+def _to_u8_image(image) -> np.ndarray:
+    array = np.asarray(image)
+    if array.dtype == np.uint8:
+        return np.ascontiguousarray(array)
+
+    array = array.astype(np.float32)
+    if array.size == 0:
+        return np.zeros((1, 1), dtype=np.uint8)
+
+    minimum = float(np.min(array))
+    maximum = float(np.max(array))
+    if math.isfinite(minimum) and math.isfinite(maximum) and minimum >= 0.0 and maximum <= 1.0:
+        return np.ascontiguousarray(np.clip(array * 255.0, 0, 255).astype(np.uint8))
+    if maximum <= minimum:
+        return np.zeros(array.shape, dtype=np.uint8)
+    scaled = (array - minimum) * (255.0 / (maximum - minimum))
+    return np.ascontiguousarray(np.clip(scaled, 0, 255).astype(np.uint8))
+
+
+def _write_png(path: Path, image) -> None:
+    image_u8 = _to_u8_image(image)
+    if image_u8.ndim == 2:
+        color_type = 0
+        rows = [b"\x00" + image_u8[row].tobytes() for row in range(image_u8.shape[0])]
+    elif image_u8.ndim == 3 and image_u8.shape[2] in (3, 4):
+        color_type = 2 if image_u8.shape[2] == 3 else 6
+        rows = [b"\x00" + image_u8[row].tobytes() for row in range(image_u8.shape[0])]
+    else:
+        raise ValueError(f"unsupported image shape for PNG: {image_u8.shape}")
+
+    height, width = image_u8.shape[:2]
+    payload = b"\x89PNG\r\n\x1a\n"
+    payload += _png_chunk(b"IHDR", struct.pack(">IIBBBBB", width, height, 8, color_type, 0, 0, 0))
+    payload += _png_chunk(b"IDAT", zlib.compress(b"".join(rows)))
+    payload += _png_chunk(b"IEND", b"")
+    path.write_bytes(payload)
+
+
+def _png_chunk(kind, data):
+    return (
+        struct.pack(">I",
+                    len(data)) + kind + data +
+        struct.pack(">I",
+                    zlib.crc32(kind + data) & 0xFFFFFFFF))
+
+
+def capture_official_window(dv, camera, label: str, out_dir: Path, args) -> dict:
+    accumulator = _configure_accumulator(dv, camera, args)
+    slicer = dv.EventStreamSlicer()
+
+    frame_count = 0
+    saved_pngs = []
+    last_image = None
+
+    def accumulate_events(event_slice):
+        nonlocal frame_count, last_image
+        accumulator.accept(event_slice)
+        frame = accumulator.generateFrame()
+        last_image = np.array(frame.image, copy=True)
+        frame_count += 1
+        if frame_count <= args.save_initial_frames or frame_count % args.save_every_n_frames == 0:
+            png_path = out_dir / f"{label}_frame_{frame_count:04d}.png"
+            _write_png(png_path, last_image)
+            saved_pngs.append(str(png_path))
+
+    slicer.doEveryTimeInterval(timedelta(milliseconds=args.slice_ms), accumulate_events)
+
+    total_events = 0
+    batch_count = 0
+    none_count = 0
+    first_ts = None
+    last_ts = None
+    wall_bins = [0 for _ in range(max(1, math.ceil(args.duration_seconds)))]
+
+    print(f"\n=== {label} ===")
+    print("Move laser/flashlight/diffuse spot during this official accumulator window.")
+
+    window_start = _monotonic_seconds()
+    deadline = window_start + args.duration_seconds
+    while True:
+        now = _monotonic_seconds()
+        if now >= deadline:
+            break
+
+        events = camera.getNextEventBatch()
+        if events is None:
+            none_count += 1
+            time.sleep(0.001)
+            continue
+
+        batch_count += 1
+        batch_len = None
+        try:
+            batch_len = len(events)
+        except TypeError:
+            pass
+        if batch_len is not None:
+            total_events += batch_len
+            bin_index = min(int(now - window_start), len(wall_bins) - 1)
+            wall_bins[bin_index] += batch_len
+
+        time_range = _batch_time_range_us(events)
+        if time_range is not None:
+            lo, hi = time_range
+            first_ts = lo if first_ts is None else min(first_ts, lo)
+            last_ts = hi if last_ts is None else max(last_ts, hi)
+
+        slicer.accept(events)
+
+    if last_image is None:
+        last_image = np.array(accumulator.generateFrame().image, copy=True)
+
+    final_png = out_dir / f"{label}_final.png"
+    _write_png(final_png, last_image)
+    if str(final_png) not in saved_pngs:
+        saved_pngs.append(str(final_png))
+
+    camera_span_s = None
+    if first_ts is not None and last_ts is not None:
+        camera_span_s = (last_ts - first_ts) / 1_000_000.0
+
+    stats = {
+        "label": label,
+        "seconds": args.duration_seconds,
+        "events": int(total_events),
+        "batches": int(batch_count),
+        "none_count": int(none_count),
+        "frame_count": int(frame_count),
+        "wall_second_event_bins": [int(value) for value in wall_bins],
+        "first_ts": first_ts,
+        "last_ts": last_ts,
+        "camera_span_s": camera_span_s,
+        "final_png": str(final_png),
+        "saved_pngs": saved_pngs,
+    }
+    print(json.dumps(stats, indent=2))
+    return stats
+
+
+def run(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    import dv_processing as dv
+
+    output_name = args.name or f"official_accumulator_{time.strftime('%Y%m%d-%H%M%S')}"
+    out_dir = args.output_root / output_name
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    print("[official_accu] dv_processing version:", getattr(dv, "__version__", "unknown"))
+    print("[official_accu] dv_processing file:", getattr(dv, "__file__", None))
+
+    camera = _open_camera(dv, args.camera_open_method)
+    camera_opens = 1
+    try:
+        print("[official_accu] capture type:", type(camera))
+        print(
+            "[official_accu] camera name:",
+            camera.getCameraName() if hasattr(camera,
+                                              "getCameraName") else "unknown",
+        )
+        print("[official_accu] isRunning:", _camera_running(camera))
+        print(
+            "[official_accu] event stream:",
+            camera.isEventStreamAvailable() if hasattr(camera,
+                                                       "isEventStreamAvailable") else "unknown",
+        )
+
+        if hasattr(camera, "isEventStreamAvailable") and not camera.isEventStreamAvailable():
+            raise RuntimeError("Input camera does not provide an event stream.")
+
+        print("[official_accu] resolution:", tuple(camera.getEventResolution()))
+        if args.set_dvxplorer_defaults:
+            _set_dvxplorer_defaults(dv, camera, args.threshold)
+
+        windows = []
+        for index in range(1, args.windows + 1):
+            label = f"window_{index:02d}_official_accumulator"
+            windows.append(capture_official_window(dv, camera, label, out_dir, args))
+            if index < args.windows and args.gap_seconds > 0:
+                time.sleep(args.gap_seconds)
+
+        stats = {
+            "summary": {
+                "prestate": parse_prestate(args.prestate),
+                "camera_opens": camera_opens,
+                "output_dir": str(out_dir),
+                "camera_open_method": args.camera_open_method,
+                "set_dvxplorer_defaults": args.set_dvxplorer_defaults,
+                "threshold": args.threshold,
+                "windows": args.windows,
+                "duration_seconds": args.duration_seconds,
+                "gap_seconds": args.gap_seconds,
+                "slice_ms": args.slice_ms,
+                "event_contribution": args.event_contribution,
+                "neutral_potential": args.neutral_potential,
+                "min_potential": args.min_potential,
+                "max_potential": args.max_potential,
+                "decay_function": args.decay_function,
+                "decay_param": args.decay_param,
+                "synchronous_decay": args.synchronous_decay,
+                "ignore_polarity": args.ignore_polarity,
+            },
+            "windows": windows,
+        }
+        stats_path = out_dir / "stats.json"
+        stats_path.write_text(json.dumps(stats, indent=2), encoding="utf-8")
+
+        print("\n[official_accu] OUT:", out_dir)
+        print("[official_accu] stats:", stats_path)
+        return 0
+    finally:
+        try:
+            del camera
+        except Exception:
+            pass
+        import gc
+        gc.collect()
+
+
+if __name__ == "__main__":
+    raise SystemExit(run())
diff --git a/debug_scripts/raw_batch_probe.py b/debug_scripts/raw_batch_probe.py
new file mode 100644
index 0000000..fa9cd92
--- /dev/null
+++ b/debug_scripts/raw_batch_probe.py
@@ -0,0 +1,272 @@
+#!/usr/bin/env python3
+"""
+Minimal DVXplorer raw event-batch liveness probe.
+
+Purpose:
+- No DMD.
+- No triggers.
+- No AEDAT4.
+- No PNG/image generation.
+- No accumulator.
+- Open the camera once and report raw getNextEventBatch() delivery by wall-time bin.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import time
+
+
+def positive_float(value: str) -> float:
+    parsed = float(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return parsed
+
+
+def positive_int(value: str) -> int:
+    parsed = int(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return parsed
+
+
+def nonnegative_float(value: str) -> float:
+    parsed = float(value)
+    if parsed < 0:
+        raise argparse.ArgumentTypeError("value must be non-negative")
+    return parsed
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Open a DVXplorer once and report raw getNextEventBatch() liveness.")
+    parser.add_argument(
+        "--prestate",
+        action="append",
+        default=[],
+        metavar="KEY=VALUE",
+        help="Pre-run state metadata to echo into logs; repeat for multiple fields.",
+    )
+    parser.add_argument("--windows", type=positive_int, default=3)
+    parser.add_argument(
+        "--duration",
+        "--duration-seconds",
+        dest="duration_seconds",
+        type=positive_float,
+        default=8.0,
+        help="Duration of each capture window.",
+    )
+    parser.add_argument(
+        "--gap",
+        "--gap-seconds",
+        dest="gap_seconds",
+        type=nonnegative_float,
+        default=1.0,
+        help="Delay between capture windows.",
+    )
+    parser.add_argument(
+        "--defaults",
+        "--set-dvxplorer-defaults",
+        dest="set_dvxplorer_defaults",
+        action="store_true",
+        default=False,
+        help="Set threshold 9/9, VARIABLE_5000, global hold true, and global reset false when available.",
+    )
+    parser.add_argument(
+        "--camera-open-method",
+        choices=["open", "dvxplorer"],
+        default="open",
+        help="Use dv.io.camera.open() or dv.io.camera.DVXplorer().",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=int,
+        default=9,
+        help="Threshold used with --defaults.",
+    )
+    return parser
+
+
+def _monotonic_seconds() -> float:
+    return time.monotonic()
+
+
+def event_count(batch) -> int:
+    try:
+        return len(batch)
+    except TypeError:
+        return sum(1 for _ in batch)
+
+
+def ts_range(batch):
+    try:
+        return int(batch.getLowestTime()), int(batch.getHighestTime())
+    except Exception:
+        return None, None
+
+
+def call_if_available(obj, name: str, *args) -> None:
+    method = getattr(obj, name, None)
+    if callable(method):
+        method(*args)
+        print(f"{name}{args}: OK")
+    else:
+        print(f"{name}: unavailable")
+
+
+def apply_defaults(dv, camera, threshold: int) -> None:
+    call_if_available(camera, "setContrastThresholdOn", threshold)
+    call_if_available(camera, "setContrastThresholdOff", threshold)
+
+    dvxplorer = getattr(dv.io.camera, "DVXplorer", None)
+    readout = getattr(dvxplorer, "ReadoutFPS", None)
+    if readout is not None and hasattr(readout, "VARIABLE_5000"):
+        call_if_available(camera, "setReadoutFPS", readout.VARIABLE_5000)
+    else:
+        print("setReadoutFPS: unavailable")
+
+    call_if_available(camera, "setGlobalHold", True)
+    call_if_available(camera, "setGlobalReset", False)
+
+
+def open_camera(dv, method: str):
+    if method == "open":
+        print("open method: dv.io.camera.open()")
+        return dv.io.camera.open()
+
+    constructor = getattr(dv.io.camera, "DVXplorer", None)
+    if not callable(constructor):
+        raise SystemExit("dv.io.camera.DVXplorer is not available in this environment")
+    print("open method: dv.io.camera.DVXplorer()")
+    return constructor()
+
+
+def parse_prestate(values: list[str]) -> dict:
+    fields = {}
+    notes = []
+    for value in values:
+        if "=" not in value:
+            notes.append(value)
+            continue
+        key, parsed = value.split("=", 1)
+        key = key.strip()
+        if key:
+            fields[key] = parsed.strip()
+        else:
+            notes.append(value)
+    return {"fields": fields, "notes": notes}
+
+
+def capture_window(camera, label: str, seconds: float) -> dict:
+    wall_bins = [0 for _ in range(max(1, math.ceil(seconds)))]
+    batches = 0
+    none_count = 0
+    total = 0
+    first_ts = None
+    last_ts = None
+
+    start = _monotonic_seconds()
+    deadline = start + seconds
+
+    while True:
+        now = _monotonic_seconds()
+        if now >= deadline:
+            break
+
+        batch = camera.getNextEventBatch()
+        if batch is None:
+            none_count += 1
+            time.sleep(0.001)
+            continue
+
+        batch_len = event_count(batch)
+        batches += 1
+        total += batch_len
+
+        bin_index = min(int(now - start), len(wall_bins) - 1)
+        wall_bins[bin_index] += batch_len
+
+        lo, hi = ts_range(batch)
+        if lo is not None:
+            first_ts = lo if first_ts is None else min(first_ts, lo)
+            last_ts = hi if last_ts is None else max(last_ts, hi)
+
+    camera_span_s = None
+    if first_ts is not None and last_ts is not None:
+        camera_span_s = (last_ts - first_ts) / 1_000_000.0
+
+    result = {
+        "label": label,
+        "wall_seconds": seconds,
+        "events": total,
+        "batches": batches,
+        "none_count": none_count,
+        "wall_second_event_bins": wall_bins,
+        "first_ts": first_ts,
+        "last_ts": last_ts,
+        "camera_span_s": camera_span_s,
+    }
+
+    print(json.dumps(result, indent=2))
+    return result
+
+
+def run(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    import dv_processing as dv
+
+    run_metadata = {
+        "prestate": parse_prestate(args.prestate),
+        "camera_open_method": args.camera_open_method,
+        "set_dvxplorer_defaults": args.set_dvxplorer_defaults,
+        "threshold": args.threshold,
+        "windows": args.windows,
+        "duration_seconds": args.duration_seconds,
+        "gap_seconds": args.gap_seconds,
+    }
+    print("RUN_METADATA")
+    print(json.dumps(run_metadata, indent=2))
+
+    print("dv_processing:", getattr(dv, "__version__", "unknown"))
+    print("dv_processing file:", getattr(dv, "__file__", None))
+    print("discover:", dv.io.camera.discover())
+
+    camera = open_camera(dv, args.camera_open_method)
+    try:
+        print("camera:", camera.getCameraName() if hasattr(camera, "getCameraName") else type(camera))
+        print("type:", type(camera))
+        print("resolution:", tuple(camera.getEventResolution()))
+        print("isRunning:", camera.isRunning() if hasattr(camera, "isRunning") else "unknown")
+        print(
+            "event stream:",
+            camera.isEventStreamAvailable() if hasattr(camera, "isEventStreamAvailable") else "unknown",
+        )
+
+        if args.set_dvxplorer_defaults:
+            apply_defaults(dv, camera, args.threshold)
+
+        results = []
+        for index in range(1, args.windows + 1):
+            print(f"\n=== window {index} ===")
+            print("Move flashlight/laser/stimulus continuously now.")
+            results.append(capture_window(camera, f"window_{index:02d}", args.duration_seconds))
+            if index < args.windows and args.gap_seconds > 0:
+                time.sleep(args.gap_seconds)
+
+        print("\nSUMMARY")
+        print(json.dumps(results, indent=2))
+        return 0
+    finally:
+        try:
+            del camera
+        except Exception:
+            pass
+        import gc
+        gc.collect()
+
+
+if __name__ == "__main__":
+    raise SystemExit(run())
diff --git a/debug_scripts/writer_mono_dvs.py b/debug_scripts/writer_mono_dvs.py
new file mode 100644
index 0000000..bff95e8
--- /dev/null
+++ b/debug_scripts/writer_mono_dvs.py
@@ -0,0 +1,66 @@
+import argparse
+
+import dv_processing as dv
+
+parser = argparse.ArgumentParser(description='Record data from a single iniVation camera to a file.')
+parser.add_argument("-c",
+                    "--camera_name",
+                    dest='camera_name',
+                    default="",
+                    type=str,
+                    help="Camera name (e.g. DVXplorer_DXA00093). The application will open any supported camera "
+                    "if no camera name is provided.")
+parser.add_argument("-o",
+                    "--output_path",
+                    dest='output_path',
+                    type=str,
+                    required=True,
+                    help="Path to an output aedat4 file for writing.")
+args = parser.parse_args()
+
+# Open any camera that is discovered in the system
+camera = dv.io.camera.open(args.camera_name)
+
+# Check whether frames are available
+eventsAvailable = camera.isEventStreamAvailable()
+framesAvailable = camera.isFrameStreamAvailable()
+imuAvailable = camera.isImuStreamAvailable()
+triggersAvailable = camera.isTriggerStreamAvailable()
+
+try:
+    # Open a file to write, will allocate streams for all available data types
+    writer = dv.io.MonoCameraWriter(args.output_path, camera)
+
+    print("Start recording")
+    while camera.isRunning():
+        if eventsAvailable:
+            # Get Events
+            events = camera.getNextEventBatch()
+            # Write Events
+            if events is not None:
+                writer.writeEvents(events, streamName='events')
+
+        if framesAvailable:
+            # Get Frame
+            frame = camera.getNextFrame()
+            # Write Frame
+            if frame is not None:
+                writer.writeFrame(frame, streamName='frames')
+
+        if imuAvailable:
+            # Get IMU data
+            imus = camera.getNextImuBatch()
+            # Write IMU data
+            if imus is not None:
+                writer.writeImuPacket(imus, streamName='imu')
+
+        if triggersAvailable:
+            # Get trigger data
+            triggers = camera.getNextTriggerBatch()
+            # Write trigger data
+            if triggers is not None:
+                writer.writeTriggerPacket(triggers, streamName='triggers')
+
+except KeyboardInterrupt:
+    print("Ending recording")
+    pass
diff --git a/dmdcontrol/camera/accumulation.py b/dmdcontrol/camera/accumulation.py
index f84c199..84b93b8 100644
--- a/dmdcontrol/camera/accumulation.py
+++ b/dmdcontrol/camera/accumulation.py
@@ -4,6 +4,8 @@
 
 import numpy as np
 
+_MISSING = object()
+
 
 @dataclass(frozen=True)
 class TriggerRecord:
@@ -19,20 +21,29 @@ class EventRecord:
     polarity: bool
 
 
+@dataclass(frozen=True)
+class _EventArrays:
+    timestamp: np.ndarray
+    x: np.ndarray
+    y: np.ndarray
+    polarity: np.ndarray
+
+
 def filter_rising_triggers(triggers):
     return [trigger for trigger in triggers if _trigger_edge(trigger) == "rising"]
 
 
-# becoming suspect of this function now
 def accumulate_events_for_triggers(
     events,
     triggers,
     resolution,
     window_us,
     polarity_mode,
+    window_start_offset_us=0,
 ):
     if window_us < 0:
         raise ValueError("window_us must be non-negative")
+    window_start_offset_us = int(window_start_offset_us)
     width, height = resolution
     if width <= 0 or height <= 0:
         raise ValueError("resolution must contain positive width and height")
@@ -42,23 +53,14 @@ def accumulate_events_for_triggers(
     rising_triggers = filter_rising_triggers(triggers)
     frames = np.zeros((len(rising_triggers), height, width), dtype=np.float32)
 
-    if not events:
+    event_arrays = _event_arrays(events)
+    ev_t = event_arrays.timestamp
+    if len(ev_t) == 0:
         return frames
 
-    if isinstance(events[0], np.ndarray):
-        ev = np.concatenate(events)
-    else:
-        ev = np.zeros(len(events), dtype=[('timestamp', np.int64), ('x', np.int16), ('y', np.int16), ('polarity', np.bool_)])
-        for i, e in enumerate(events):
-            ev['timestamp'][i] = _timestamp(e)
-            ev['x'][i] = _field(e, 'x')
-            ev['y'][i] = _field(e, 'y')
-            ev['polarity'][i] = _field(e, 'polarity')
-
-    ev_t = ev['timestamp']
-    ev_x = ev['x'].astype(np.int64)
-    ev_y = ev['y'].astype(np.int64)
-    ev_p = ev['polarity']
+    ev_x = event_arrays.x
+    ev_y = event_arrays.y
+    ev_p = event_arrays.polarity
 
     if polarity_mode == "positive":
         ev_v = ev_p.astype(np.float32)
@@ -70,7 +72,7 @@ def accumulate_events_for_triggers(
     valid = (ev_x >= 0) & (ev_x < width) & (ev_y >= 0) & (ev_y < height)
     if polarity_mode == "positive":
         valid = valid & ev_p
-    
+
     ev_t = ev_t[valid]
     ev_x = ev_x[valid]
     ev_y = ev_y[valid]
@@ -85,12 +87,12 @@ def accumulate_events_for_triggers(
         ev_v = ev_v[sort_idx]
 
     for trigger_index, trigger in enumerate(rising_triggers):
-        start_us = _timestamp(trigger)
+        start_us = _timestamp(trigger) + window_start_offset_us
         end_us = start_us + window_us
-        
-        start_idx = np.searchsorted(ev_t, start_us, side='left')
-        end_idx = np.searchsorted(ev_t, end_us, side='left')
-        
+
+        start_idx = np.searchsorted(ev_t, start_us, side="left")
+        end_idx = np.searchsorted(ev_t, end_us, side="left")
+
         if start_idx < end_idx:
             idx_x = ev_x[start_idx:end_idx]
             idx_y = ev_y[start_idx:end_idx]
@@ -100,29 +102,113 @@ def accumulate_events_for_triggers(
     return frames
 
 
-def _event_increment(event, polarity_mode):
-    polarity = bool(_field(event, "polarity"))
-    if polarity_mode == "positive":
-        return 1.0 if polarity else 0.0
-    if polarity_mode == "signed":
-        return 1.0 if polarity else -1.0
-    return 1.0
+def _event_arrays(events):
+    empty = _EventArrays(
+        timestamp=np.array([],
+                           dtype=np.int64),
+        x=np.array([],
+                   dtype=np.int64),
+        y=np.array([],
+                   dtype=np.int64),
+        polarity=np.array([],
+                          dtype=np.bool_),
+    )
+    if events is None:
+        return empty
+    event_sequence = [events] if isinstance(events, np.ndarray) else list(events)
+    if not event_sequence:
+        return empty
+
+    first = event_sequence[0]
+    if isinstance(first, np.ndarray):
+        batches = [np.asarray(batch) for batch in event_sequence if len(batch)]
+        if not batches:
+            return empty
+        event_array = np.concatenate(batches) if len(batches) > 1 else batches[0]
+        return _EventArrays(
+            timestamp=structured_field(event_array,
+                                       "timestamp",
+                                       "t").astype(np.int64,
+                                                   copy=False),
+            x=structured_field(event_array,
+                               "x").astype(np.int64,
+                                           copy=False),
+            y=structured_field(event_array,
+                               "y").astype(np.int64,
+                                           copy=False),
+            polarity=structured_field(event_array,
+                                      "polarity",
+                                      "p").astype(np.bool_,
+                                                  copy=False),
+        )
+
+    return _EventArrays(
+        timestamp=np.array([_timestamp(event) for event in event_sequence],
+                           dtype=np.int64),
+        x=np.array([_field(event,
+                           "x") for event in event_sequence],
+                   dtype=np.int64),
+        y=np.array([_field(event,
+                           "y") for event in event_sequence],
+                   dtype=np.int64),
+        polarity=np.array(
+            [_field_any(event,
+                        ("polarity",
+                         "p")) for event in event_sequence],
+            dtype=np.bool_,
+        ),
+    )
+
+
+def structured_field(event_array, *names):
+    field_names = event_array.dtype.names or ()
+    for name in names:
+        if name in field_names:
+            return event_array[name]
+    raise ValueError(f"event array missing required field: one of {names!r}")
 
 
 def _trigger_edge(trigger):
-    return str(_field(trigger, "edge", default="rising")).lower()
+    edge = _field(trigger, "edge", default=None)
+    if edge is not None:
+        return str(edge).lower()
+    trigger_type = _field(trigger, "type", default=None)
+    if trigger_type is None:
+        return "rising"
+    trigger_type_text = str(trigger_type).lower()
+    if "rising" in trigger_type_text:
+        return "rising"
+    if "falling" in trigger_type_text:
+        return "falling"
+    return trigger_type_text
 
 
 def _timestamp(record):
-    return int(_field(record, "timestamp"))
+    return int(_field_any(record, ("timestamp", "t")))
+
+
+def _field_any(record, names, default=_MISSING):
+    for name in names:
+        try:
+            return _field(record, name)
+        except AttributeError:
+            pass
+    if default is not _MISSING:
+        return default
+    raise AttributeError(f"{record!r} has no field in {names!r}")
 
 
-def _field(record, name, default=None):
+def _field(record, name, default=_MISSING):
     if hasattr(record, name):
         value = getattr(record, name)
         return value() if callable(value) else value
     if isinstance(record, dict):
-        return record.get(name, default)
-    if default is not None:
+        if name in record:
+            return record[name]
+        if default is not _MISSING:
+            return default
+    if isinstance(record, np.void) and record.dtype.names and name in record.dtype.names:
+        return record[name]
+    if default is not _MISSING:
         return default
     raise AttributeError(f"{record!r} has no {name!r} field")
diff --git a/dmdcontrol/camera/capture.py b/dmdcontrol/camera/capture.py
index d987df4..d466a27 100644
--- a/dmdcontrol/camera/capture.py
+++ b/dmdcontrol/camera/capture.py
@@ -7,12 +7,17 @@
 
 import numpy as np
 
+DEFAULT_STOP_DRAIN_READS = 8
+DEFAULT_STOP_DRAIN_IDLE_READS = 2
+
 
 @dataclass(frozen=True)
 class CameraReadyState:
     event_stream_available: bool
     trigger_stream_available: bool
     event_resolution: tuple[int, int]
+    trigger_configuration: dict | None = None
+    initial_flush: dict | None = None
     stream_rearm: dict | None = None
     usb_reset: dict | None = None
     power_cycle: dict | None = None
@@ -31,17 +36,21 @@ class CaptureResult:
 
 
 class AsyncCapture:
+
     def __init__(
-            self,
-            capture,
-            writer,
-            expected_trigger_count,
-            timeout_s,
-            idle_sleep_s=0.001,
-            on_events=None,
-            on_triggers=None,
-            record_fn=None,
-            post_trigger_event_batches=0,
+        self,
+        capture,
+        writer,
+        expected_trigger_count,
+        timeout_s,
+        idle_sleep_s=0.001,
+        on_events=None,
+        on_triggers=None,
+        record_fn=None,
+        post_trigger_event_batches=0,
+        post_trigger_event_time_us=0,
+        stop_drain_reads=DEFAULT_STOP_DRAIN_READS,
+        stop_drain_idle_reads=DEFAULT_STOP_DRAIN_IDLE_READS,
     ):
         self.capture = capture
         self.writer = writer
@@ -52,6 +61,9 @@ def __init__(
         self.on_triggers = on_triggers
         self.record_fn = record_fn or record_until_trigger_count
         self.post_trigger_event_batches = int(post_trigger_event_batches or 0)
+        self.post_trigger_event_time_us = int(post_trigger_event_time_us or 0)
+        self.stop_drain_reads = int(stop_drain_reads or 0)
+        self.stop_drain_idle_reads = int(stop_drain_idle_reads or 0)
         self.result = None
         self.error = None
         self._stop_event = Event()
@@ -81,34 +93,77 @@ def _record(self):
                 on_triggers=self.on_triggers,
                 stop_event=self._stop_event,
                 post_trigger_event_batches=self.post_trigger_event_batches,
+                post_trigger_event_time_us=self.post_trigger_event_time_us,
+                stop_drain_reads=self.stop_drain_reads,
+                stop_drain_idle_reads=self.stop_drain_idle_reads,
             )
         except BaseException as exc:
             self.error = exc
 
 
-def validate_camera_ready(capture, stream_rearm=None, usb_reset=None, power_cycle=None) -> CameraReadyState:
+def validate_camera_ready(
+    capture,
+    stream_rearm=None,
+    usb_reset=None,
+    power_cycle=None,
+    trigger_configuration=None,
+) -> CameraReadyState:
     event_available = bool(capture.isEventStreamAvailable())
     trigger_available = bool(capture.isTriggerStreamAvailable())
     if not event_available:
         raise RuntimeError("Camera event stream is not available.")
     if not trigger_available:
         raise RuntimeError("Camera trigger stream is not available.")
+    trigger_errors = (
+        trigger_configuration.get("errors") if isinstance(trigger_configuration,
+                                                          dict) else None)
+    if trigger_errors:
+        raise RuntimeError(
+            "Camera trigger detector setup failed: " +
+            "; ".join(str(error) for error in trigger_errors))
     return CameraReadyState(
         event_stream_available=event_available,
         trigger_stream_available=trigger_available,
         event_resolution=tuple(capture.getEventResolution()),
+        trigger_configuration=trigger_configuration,
         stream_rearm=stream_rearm,
         usb_reset=usb_reset,
         power_cycle=power_cycle,
     )
 
 
-def flush_stale_batches(capture, reads=3) -> None:
-    for _ in range(reads):
+def flush_stale_batches(capture, reads=3, include_triggers=True) -> dict:
+    requested_reads = max(0, int(reads))
+    stats = {
+        "requested_reads": requested_reads,
+        "event_reads": 0,
+        "trigger_reads": 0,
+        "event_batches_discarded": 0,
+        "trigger_batches_discarded": 0,
+        "event_count_discarded": 0,
+        "trigger_count_discarded": 0,
+        "stopped_early": False,
+    }
+    for _ in range(requested_reads):
+        did_work = False
         if hasattr(capture, "getNextEventBatch"):
-            capture.getNextEventBatch()
-        if hasattr(capture, "getNextTriggerBatch"):
-            capture.getNextTriggerBatch()
+            stats["event_reads"] += 1
+            events = capture.getNextEventBatch()
+            if events is not None:
+                stats["event_batches_discarded"] += 1
+                stats["event_count_discarded"] += _batch_len(events)
+                did_work = True
+        if include_triggers and hasattr(capture, "getNextTriggerBatch"):
+            stats["trigger_reads"] += 1
+            triggers = capture.getNextTriggerBatch()
+            if triggers is not None:
+                stats["trigger_batches_discarded"] += 1
+                stats["trigger_count_discarded"] += _batch_len(triggers)
+                did_work = True
+        if not did_work:
+            stats["stopped_early"] = True
+            break
+    return stats
 
 
 def append_batch_records(destination, batch, as_numpy=False) -> None:
@@ -131,7 +186,7 @@ def _batch_len(batch) -> int:
         return 0
 
 
-def _merge_time_range(current, update):
+def merge_time_range(current, update):
     if update is None:
         return current
     if current is None:
@@ -174,27 +229,37 @@ def _record_timestamp_or_none(record):
             return int(value() if callable(value) else value)
         if isinstance(record, dict) and name in record:
             return int(record[name])
-        if isinstance(record, np.void) and record.dtype.names and name in record.dtype.names:
+        if (isinstance(record, np.void) and record.dtype.names and name in record.dtype.names):
             return int(record[name])
     return None
 
 
+def _post_trigger_event_window_reached(
+        event_time_range_us,
+        trigger_time_range_us,
+        post_trigger_event_time_us):
+    if post_trigger_event_time_us <= 0 or trigger_time_range_us is None:
+        return True
+    if event_time_range_us is None:
+        return False
+    return (event_time_range_us[1] >= trigger_time_range_us[1] + post_trigger_event_time_us)
+
+
 def record_until_trigger_count(
-        capture,
-        writer,
-        expected_trigger_count,
-        timeout_s,
-        idle_sleep_s=0.001,
-        on_events=None,
-        on_triggers=None,
-        stop_event=None,
-        post_trigger_event_batches=0,
+    capture,
+    writer,
+    expected_trigger_count,
+    timeout_s,
+    idle_sleep_s=0.001,
+    on_events=None,
+    on_triggers=None,
+    stop_event=None,
+    post_trigger_event_batches=0,
+    post_trigger_event_time_us=0,
+    stop_drain_reads=DEFAULT_STOP_DRAIN_READS,
+    stop_drain_idle_reads=DEFAULT_STOP_DRAIN_IDLE_READS,
 ) -> CaptureResult:
-    deadline = (
-        time.time() + timeout_s
-        if timeout_s is not None and timeout_s > 0
-        else None
-    )
+    deadline = (time.time() + timeout_s if timeout_s is not None and timeout_s > 0 else None)
     trigger_count = 0
     event_count = 0
     event_batch_count = 0
@@ -204,6 +269,62 @@ def record_until_trigger_count(
     timed_out = False
     stopped = False
 
+    def read_event_batch():
+        nonlocal event_count, event_batch_count, event_time_range_us
+        events = (capture.getNextEventBatch() if hasattr(capture, "getNextEventBatch") else None)
+        if events is None:
+            return False
+        writer.writeEvents(events, streamName="events")
+        if on_events is not None:
+            on_events(events)
+        event_count += _batch_len(events)
+        event_time_range_us = merge_time_range(
+            event_time_range_us,
+            _batch_time_range_us(events),
+        )
+        event_batch_count += 1
+        return True
+
+    def read_trigger_batch():
+        nonlocal trigger_count, trigger_batch_count, trigger_time_range_us
+        triggers = (
+            capture.getNextTriggerBatch() if hasattr(capture,
+                                                     "getNextTriggerBatch") else None)
+        if triggers is None:
+            return False
+        writer.writeTriggerPacket(triggers, streamName="triggers")
+        if on_triggers is not None:
+            on_triggers(triggers)
+        trigger_batch_count += 1
+        trigger_count += _batch_len(triggers)
+        trigger_time_range_us = merge_time_range(
+            trigger_time_range_us,
+            _batch_time_range_us(triggers),
+        )
+        return True
+
+    def read_available_batches():
+        event_work = read_event_batch()
+        trigger_work = read_trigger_batch()
+        return event_work or trigger_work
+
+    def drain_after_stop():
+        drain_reads = max(0, int(stop_drain_reads or 0))
+        idle_limit = max(1, int(stop_drain_idle_reads or 0))
+        idle_reads = 0
+        for _ in range(drain_reads):
+            if hasattr(capture, "isRunning") and not capture.isRunning():
+                break
+            did_work = read_available_batches()
+            if did_work:
+                idle_reads = 0
+                continue
+            idle_reads += 1
+            if idle_reads >= idle_limit:
+                break
+            if idle_sleep_s > 0:
+                time.sleep(idle_sleep_s)
+
     while True:
         if stop_event is not None and stop_event.is_set():
             stopped = True
@@ -214,44 +335,16 @@ def record_until_trigger_count(
             timed_out = True
             break
 
-        did_work = False
-        events = (
-            capture.getNextEventBatch()
-            if hasattr(capture, "getNextEventBatch")
-            else None
-        )
-        if events is not None:
-            writer.writeEvents(events, streamName="events")
-            if on_events is not None:
-                on_events(events)
-            event_count += _batch_len(events)
-            event_time_range_us = _merge_time_range(
-                event_time_range_us,
-                _batch_time_range_us(events),
-            )
-            event_batch_count += 1
-            did_work = True
+        did_work = read_available_batches()
 
-        triggers = (
-            capture.getNextTriggerBatch()
-            if hasattr(capture, "getNextTriggerBatch")
-            else None
-        )
-        if triggers is not None:
-            writer.writeTriggerPacket(triggers, streamName="triggers")
-            if on_triggers is not None:
-                on_triggers(triggers)
-            trigger_batch_count += 1
-            trigger_count += _batch_len(triggers)
-            trigger_time_range_us = _merge_time_range(
-                trigger_time_range_us,
-                _batch_time_range_us(triggers),
-            )
-            did_work = True
-
-        if expected_trigger_count is not None and trigger_count >= expected_trigger_count:
+        if (expected_trigger_count is not None and trigger_count >= expected_trigger_count):
             post_batches_remaining = max(0, int(post_trigger_event_batches or 0))
-            while post_batches_remaining > 0:
+            post_trigger_event_time_us = max(0, int(post_trigger_event_time_us or 0))
+            while post_batches_remaining > 0 or not _post_trigger_event_window_reached(
+                    event_time_range_us,
+                    trigger_time_range_us,
+                    post_trigger_event_time_us,
+            ):
                 if stop_event is not None and stop_event.is_set():
                     stopped = True
                     break
@@ -261,30 +354,20 @@ def record_until_trigger_count(
                     timed_out = True
                     break
 
-                events = (
-                    capture.getNextEventBatch()
-                    if hasattr(capture, "getNextEventBatch")
-                    else None
-                )
-                if events is None:
+                if not read_event_batch():
                     if idle_sleep_s > 0:
                         time.sleep(idle_sleep_s)
                     continue
 
-                writer.writeEvents(events, streamName="events")
-                if on_events is not None:
-                    on_events(events)
-                event_count += _batch_len(events)
-                event_time_range_us = _merge_time_range(
-                    event_time_range_us,
-                    _batch_time_range_us(events),
-                )
-                event_batch_count += 1
-                post_batches_remaining -= 1
+                if post_batches_remaining > 0:
+                    post_batches_remaining -= 1
             break
         if not did_work and idle_sleep_s > 0:
             time.sleep(idle_sleep_s)
 
+    if stopped:
+        drain_after_stop()
+
     return CaptureResult(
         trigger_count=trigger_count,
         event_batch_count=event_batch_count,
diff --git a/dmdcontrol/camera/discovery.py b/dmdcontrol/camera/discovery.py
index 2994172..29837c5 100644
--- a/dmdcontrol/camera/discovery.py
+++ b/dmdcontrol/camera/discovery.py
@@ -7,25 +7,33 @@
     DVXPLORER_READOUT_FPS_NAMES,
 )
 
+
 def descriptor_to_dict(index, descriptor):
     return {
         "index": index,
         "repr": repr(descriptor),
-        "devAddress": getattr(descriptor, "devAddress", None),
-        "deviceType": str(getattr(descriptor, "deviceType", None)),
-        "cameraModel": str(getattr(descriptor, "cameraModel", None)),
-        "firmwareVersion": str(getattr(descriptor, "firmwareVersion", None)),
-        "serialNumber": str(getattr(descriptor, "serialNumber", None)),
+        "devAddress": getattr(descriptor,
+                              "devAddress",
+                              None),
+        "deviceType": str(getattr(descriptor,
+                                  "deviceType",
+                                  None)),
+        "cameraModel": str(getattr(descriptor,
+                                   "cameraModel",
+                                   None)),
+        "firmwareVersion": str(getattr(descriptor,
+                                       "firmwareVersion",
+                                       None)),
+        "serialNumber": str(getattr(descriptor,
+                                    "serialNumber",
+                                    None)),
     }
 
 
 def discover_cameras():
     import dv_processing as dv
     cameras = dv.io.camera.discover()
-    return [
-        descriptor_to_dict(index, descriptor)
-        for index, descriptor in enumerate(cameras)
-    ]
+    return [descriptor_to_dict(index, descriptor) for index, descriptor in enumerate(cameras)]
 
 
 def open_camera_capture(dv, *, method="modern", descriptor=None):
@@ -34,8 +42,7 @@ def open_camera_capture(dv, *, method="modern", descriptor=None):
         if camera_capture is None:
             raise RuntimeError(
                 "dv.io.CameraCapture is not available in this dv_processing build; "
-                "use --camera-open-method modern or run in an environment with the legacy API."
-            )
+                "use --camera-open-method modern or run in an environment with the legacy API.")
         return camera_capture()
     if method != "modern":
         raise ValueError(f"Unsupported camera open method: {method}")
@@ -95,22 +102,67 @@ def _stop(method_name):
     }
 
 
+def _detector_call_key(method_name, value):
+    value_name = "true" if value is True else "false" if value is False else str(value)
+    return f"{method_name}_{value_name}"
+
+
 def configure_rising_edge_triggers(capture):
-    if hasattr(capture, "setDetectorRisingEdges"):
-        capture.setDetectorRisingEdges(True)
-    if hasattr(capture, "setDetectorFallingEdges"):
-        capture.setDetectorFallingEdges(False)
-    if hasattr(capture, "setDetectorRunning"):
-        capture.setDetectorRunning(True)
+    calls = [
+        ("setDetectorRunning",
+         False),
+        ("setDetectorRisingEdges",
+         True),
+        ("setDetectorFallingEdges",
+         False),
+        ("setDetectorRunning",
+         True),
+    ]
+    result = {
+        "has_setDetectorRisingEdges": callable(getattr(capture,
+                                                       "setDetectorRisingEdges",
+                                                       None)),
+        "has_setDetectorFallingEdges": callable(getattr(capture,
+                                                        "setDetectorFallingEdges",
+                                                        None)),
+        "has_setDetectorRunning": callable(getattr(capture,
+                                                   "setDetectorRunning",
+                                                   None)),
+        "call_order": [],
+        "errors": [],
+    }
+    for method_name, value in calls:
+        key = _detector_call_key(method_name, value)
+        result[f"{key}_result"] = None
+        result[f"{key}_error"] = None
+
+    for method_name, value in calls:
+        method = getattr(capture, method_name, None)
+        if not callable(method):
+            continue
+        key = _detector_call_key(method_name, value)
+        call_label = f"{method_name}({value})"
+        result["call_order"].append(call_label)
+        try:
+            call_result = method(value)
+            result[f"{key}_result"] = (
+                call_result
+                if call_result is None or isinstance(call_result, (bool, int, float, str))
+                else repr(call_result))
+        except Exception as exc:
+            error = repr(exc)
+            result[f"{key}_error"] = error
+            result["errors"].append(f"{call_label}: {error}")
+    return result
 
 
 def configure_camera_performance(capture, bias_sensitivity=None, efps=None, prefer_legacy=False):
     if bias_sensitivity is not None and bias_sensitivity != "default":
         configured = (
-            _configure_legacy_dvs_bias_sensitivity(capture, bias_sensitivity)
-            if prefer_legacy
-            else _configure_dvxplorer_contrast_thresholds(capture, bias_sensitivity)
-        )
+            _configure_legacy_dvs_bias_sensitivity(capture,
+                                                   bias_sensitivity) if prefer_legacy else
+            _configure_dvxplorer_contrast_thresholds(capture,
+                                                     bias_sensitivity))
         if not configured:
             if prefer_legacy:
                 _configure_dvxplorer_contrast_thresholds(capture, bias_sensitivity)
@@ -119,10 +171,10 @@ def configure_camera_performance(capture, bias_sensitivity=None, efps=None, pref
 
     if efps is not None and efps != "default":
         configured = (
-            _configure_legacy_dvxplorer_efps(capture, efps)
-            if prefer_legacy
-            else _configure_dvxplorer_readout_fps(capture, efps)
-        )
+            _configure_legacy_dvxplorer_efps(capture,
+                                             efps)
+            if prefer_legacy else _configure_dvxplorer_readout_fps(capture,
+                                                                   efps))
         if not configured:
             if prefer_legacy:
                 _configure_dvxplorer_readout_fps(capture, efps)
@@ -134,10 +186,9 @@ def _configure_dvxplorer_contrast_thresholds(capture, bias_sensitivity):
     threshold = DVXPLORER_CONTRAST_THRESHOLDS.get(bias_sensitivity.lower())
     if threshold is None:
         return False
-    if not (
-            hasattr(capture, "setContrastThresholdOn")
-            and hasattr(capture, "setContrastThresholdOff")
-    ):
+    if not (hasattr(capture,
+                    "setContrastThresholdOn") and hasattr(capture,
+                                                          "setContrastThresholdOff")):
         return False
     capture.setContrastThresholdOn(threshold)
     capture.setContrastThresholdOff(threshold)
@@ -165,10 +216,18 @@ def _configure_legacy_dvs_bias_sensitivity(capture, bias_sensitivity):
     import dv_processing as dv
     bias = getattr(getattr(dv.io, "CameraCapture", None), "BiasSensitivity", None)
     mapping = {
-        "verylow": getattr(bias, "VeryLow", None),
-        "low": getattr(bias, "Low", None),
-        "high": getattr(bias, "High", None),
-        "veryhigh": getattr(bias, "VeryHigh", None),
+        "verylow": getattr(bias,
+                           "VeryLow",
+                           None),
+        "low": getattr(bias,
+                       "Low",
+                       None),
+        "high": getattr(bias,
+                        "High",
+                        None),
+        "veryhigh": getattr(bias,
+                            "VeryHigh",
+                            None),
     }
     value = mapping.get(bias_sensitivity.lower())
     if value is None:
@@ -183,10 +242,18 @@ def _configure_legacy_dvxplorer_efps(capture, efps):
     import dv_processing as dv
     efps_enum = getattr(getattr(dv.io, "CameraCapture", None), "DVXeFPS", None)
     mapping = {
-        "variable": getattr(efps_enum, "EFPS_VARIABLE", None),
-        "variable_5000": getattr(efps_enum, "EFPS_VARIABLE_5000", None),
-        "constant_1000": getattr(efps_enum, "EFPS_CONSTANT_1000", None),
-        "constant_100": getattr(efps_enum, "EFPS_CONSTANT_100", None),
+        "variable": getattr(efps_enum,
+                            "EFPS_VARIABLE",
+                            None),
+        "variable_5000": getattr(efps_enum,
+                                 "EFPS_VARIABLE_5000",
+                                 None),
+        "constant_1000": getattr(efps_enum,
+                                 "EFPS_CONSTANT_1000",
+                                 None),
+        "constant_100": getattr(efps_enum,
+                                "EFPS_CONSTANT_100",
+                                None),
     }
     value = mapping.get(efps.lower())
     if value is None:
@@ -196,20 +263,17 @@ def _configure_legacy_dvxplorer_efps(capture, efps):
 
 
 def capability_dict(capture):
-    payload = {
-        "name": capture.getCameraName() if hasattr(capture, "getCameraName") else None
-    }
+    payload = {"name": capture.getCameraName() if hasattr(capture, "getCameraName") else None}
     for key, method_name in (
-            ("event_stream", "isEventStreamAvailable"),
-            ("frame_stream", "isFrameStreamAvailable"),
-            ("imu_stream", "isImuStreamAvailable"),
-            ("trigger_stream", "isTriggerStreamAvailable"),
+        ("event_stream", "isEventStreamAvailable"),
+        ("frame_stream", "isFrameStreamAvailable"),
+        ("imu_stream", "isImuStreamAvailable"),
+        ("trigger_stream", "isTriggerStreamAvailable"),
     ):
         payload[key] = (
-            bool(getattr(capture, method_name)())
-            if hasattr(capture, method_name)
-            else None
-        )
+            bool(getattr(capture,
+                         method_name)()) if hasattr(capture,
+                                                    method_name) else None)
     if hasattr(capture, "getEventResolution") and payload["event_stream"]:
         payload["event_resolution"] = tuple(capture.getEventResolution())
     if hasattr(capture, "getFrameResolution") and payload["frame_stream"]:
diff --git a/dmdcontrol/camera/local_support_filter.py b/dmdcontrol/camera/local_support_filter.py
index 78e1605..978b387 100644
--- a/dmdcontrol/camera/local_support_filter.py
+++ b/dmdcontrol/camera/local_support_filter.py
@@ -6,6 +6,7 @@
 
 import numpy as np
 
+from dmdcontrol.support.argparse_types import nonnegative_int, positive_int
 from dmdcontrol.support.constants import (
     DEFAULT_FILTER_DELTA_T_US,
     DEFAULT_FILTER_POLARITY,
@@ -41,8 +42,7 @@ def to_metadata(self) -> dict:
         out["algorithm"] = "centered-local-support"
         out["note"] = (
             "Practical notebook-validated local support filter; "
-            "not a source-faithful DV Runtime YNoise implementation."
-        )
+            "not a source-faithful DV Runtime YNoise implementation.")
         return out
 
 
@@ -65,7 +65,8 @@ def centered_local_support_mask(
     y: np.ndarray,
     t: np.ndarray,
     p: np.ndarray,
-    resolution: tuple[int, int],
+    resolution: tuple[int,
+                      int],
     config: LocalSupportFilterConfig,
 ) -> np.ndarray:
     """Return boolean keep mask for centered causal local-support event filtering.
@@ -144,9 +145,13 @@ def apply_local_support_filter_arrays(
     y: np.ndarray,
     t: np.ndarray,
     p: np.ndarray,
-    resolution: tuple[int, int],
+    resolution: tuple[int,
+                      int],
     config: LocalSupportFilterConfig,
-) -> tuple[dict[str, np.ndarray], np.ndarray, LocalSupportFilterStats]:
+) -> tuple[dict[str,
+                np.ndarray],
+           np.ndarray,
+           LocalSupportFilterStats]:
     """Filter event arrays and return filtered arrays, mask, and stats."""
     config.validate()
 
@@ -187,10 +192,23 @@ def apply_local_support_filter_arrays(
 
 def add_event_noise_filter_arguments(parser: argparse.ArgumentParser) -> None:
     parser.add_argument("--event-noise-filter", default="none", choices=["none", "local-support"])
-    parser.add_argument("--event-filter-delta-us", type=_positive_int, default=DEFAULT_FILTER_DELTA_T_US)
-    parser.add_argument("--event-filter-window-px", type=_positive_int, default=DEFAULT_FILTER_WINDOW_PX)
-    parser.add_argument("--event-filter-threshold", type=_non_negative_int, default=DEFAULT_FILTER_THRESHOLD)
-    parser.add_argument("--event-filter-polarity", default=DEFAULT_FILTER_POLARITY, choices=["same", "any"])
+    parser.add_argument(
+        "--event-filter-delta-us",
+        type=positive_int,
+        default=DEFAULT_FILTER_DELTA_T_US)
+    parser.add_argument(
+        "--event-filter-window-px",
+        type=positive_int,
+        default=DEFAULT_FILTER_WINDOW_PX)
+    parser.add_argument(
+        "--event-filter-threshold",
+        type=nonnegative_int,
+        default=DEFAULT_FILTER_THRESHOLD)
+    parser.add_argument(
+        "--event-filter-polarity",
+        default=DEFAULT_FILTER_POLARITY,
+        choices=["same",
+                 "any"])
     parser.add_argument("--save-filtered-events", action="store_true", default=False)
 
 
@@ -215,28 +233,9 @@ def event_noise_filter_metadata(
         metadata["algorithm"] = "none"
         metadata["note"] = (
             "Event noise filtering disabled. Local-support parameters are resolved "
-            "but were not applied."
-        )
+            "but were not applied.")
     if stats is not None:
         metadata.update(stats.to_metadata())
     return metadata
 
 
-def _positive_int(value: str) -> int:
-    try:
-        number = int(value)
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("value must be positive") from exc
-    if number <= 0:
-        raise argparse.ArgumentTypeError("value must be positive")
-    return number
-
-
-def _non_negative_int(value: str) -> int:
-    try:
-        number = int(value)
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("value must be >= 0") from exc
-    if number < 0:
-        raise argparse.ArgumentTypeError("value must be >= 0")
-    return number
diff --git a/dmdcontrol/camera/pair_capture.py b/dmdcontrol/camera/pair_capture.py
index 785ad34..8c4626d 100644
--- a/dmdcontrol/camera/pair_capture.py
+++ b/dmdcontrol/camera/pair_capture.py
@@ -2,8 +2,6 @@
 
 import argparse
 import sys
-from dataclasses import asdict, is_dataclass
-from importlib import import_module
 
 import numpy as np
 
@@ -19,6 +17,8 @@
 )
 from dmdcontrol.camera.runs import (
     create_run_directory,
+    final_capture_artifacts,
+    metadata_dict,
     write_capture_artifacts,
     write_json,
     write_run_metadata,
@@ -27,26 +27,7 @@
     close_camera_resources,
     open_ready_camera as _open_ready_camera,
 )
-
-
-def positive_int(value: str) -> int:
-    try:
-        number = int(value)
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("value must be positive") from exc
-    if number <= 0:
-        raise argparse.ArgumentTypeError("value must be positive")
-    return number
-
-
-def nonnegative_int(value: str) -> int:
-    try:
-        number = int(value)
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("value must be non-negative") from exc
-    if number < 0:
-        raise argparse.ArgumentTypeError("value must be non-negative")
-    return number
+from dmdcontrol.support.argparse_types import nonnegative_int, positive_int
 
 
 def build_parser() -> argparse.ArgumentParser:
@@ -74,14 +55,34 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--trigger-out-2-delay-fraction", type=float, default=0.00)
     parser.add_argument("--dmd-config", default=None)
     parser.add_argument("--hz", type=positive_int, default=None)
-    parser.add_argument("--bias-sensitivity", default="default", choices=["default", "verylow", "low", "high", "veryhigh"])
-    parser.add_argument("--efps", default="default", choices=["default", "variable", "variable_5000", "constant_1000", "constant_100"])
-    parser.add_argument("--polarity-mode", default="positive", choices=["positive", "signed", "ignore"])
+    parser.add_argument(
+        "--bias-sensitivity",
+        default="default",
+        choices=["default",
+                 "verylow",
+                 "low",
+                 "high",
+                 "veryhigh"])
+    parser.add_argument(
+        "--efps",
+        default="default",
+        choices=["default",
+                 "variable",
+                 "variable_5000",
+                 "constant_1000",
+                 "constant_100"])
+    parser.add_argument(
+        "--polarity-mode",
+        default="positive",
+        choices=["positive",
+                 "signed",
+                 "ignore"])
     parser.add_argument("--dark-time-us", type=int, default=None)
     parser.add_argument(
         "--camera-open-method",
         default="modern",
-        choices=["modern", "legacy"],
+        choices=["modern",
+                 "legacy"],
         help="Camera API used to open the device. Use legacy to mirror mentor CameraCapture code.",
     )
     parser.add_argument(
@@ -89,7 +90,8 @@ def build_parser() -> argparse.ArgumentParser:
         dest="camera_usb_reset",
         action="store_true",
         default=False,
-        help="Diagnostic: run a Linux USB device reset before opening the camera. Disabled by default.",
+        help=
+        "Diagnostic: run a Linux USB device reset before opening the camera. Disabled by default.",
     )
     parser.add_argument(
         "--no-camera-usb-reset",
@@ -103,8 +105,7 @@ def build_parser() -> argparse.ArgumentParser:
         help=(
             "Optional command run before camera open to power-cycle the USB port, "
             "for example: \"uhubctl -l 1-2 -p 3 -a cycle -d 2\". "
-            "Defaults to DMD_CAMERA_POWER_CYCLE_COMMAND when set."
-        ),
+            "Defaults to DMD_CAMERA_POWER_CYCLE_COMMAND when set."),
     )
     parser.add_argument(
         "--camera-flush-reads",
@@ -128,13 +129,15 @@ def build_parser() -> argparse.ArgumentParser:
         "--camera-shutdown-streams",
         action="store_true",
         default=False,
-        help="Diagnostic: stop camera streams before releasing the capture object. Disabled by default.",
+        help=
+        "Diagnostic: stop camera streams before releasing the capture object. Disabled by default.",
     )
     parser.add_argument(
         "--max-accumulation-triggers",
         type=positive_int,
         default=512,
-        help="Maximum rising triggers used for derived accumulation artifacts. Raw AEDAT recording is unchanged.",
+        help=
+        "Maximum rising triggers used for derived accumulation artifacts. Raw AEDAT recording is unchanged.",
     )
     add_event_noise_filter_arguments(parser)
     parser.add_argument("-v", "--verbose", action="count", default=0)
@@ -223,7 +226,10 @@ def dry_run(args: argparse.Namespace):
     write_run_metadata(
         run,
         metadata,
-        artifacts=["metadata.json", "timing.json", "command.txt", "run.log"],
+        artifacts=["metadata.json",
+                   "timing.json",
+                   "command.txt",
+                   "run.log"],
     )
     run.command_path.write_text(
         "python -m dmdcontrol camera pair-capture --dry-run-timing\n",
@@ -233,12 +239,6 @@ def dry_run(args: argparse.Namespace):
     return run
 
 
-def _asdict(value):
-    if is_dataclass(value):
-        return asdict(value)
-    return dict(getattr(value, "__dict__", {}))
-
-
 def _to_pair_runtime_args(args: argparse.Namespace) -> list[str]:
     pair_args = [
         "--test",
@@ -271,10 +271,6 @@ def _to_pair_runtime_args(args: argparse.Namespace) -> list[str]:
     return pair_args
 
 
-def _expected_trigger_count(args: argparse.Namespace) -> int | None:
-    return None
-
-
 def _accumulation_window_us(args: argparse.Namespace) -> int:
     if args.kernel_exposure_us is not None:
         return args.kernel_exposure_us
@@ -282,6 +278,7 @@ def _accumulation_window_us(args: argparse.Namespace) -> int:
 
 
 class _BoundedArtifactBuffer:
+
     def __init__(self, max_rising_triggers: int | None, window_us: int):
         self.max_rising_triggers = max_rising_triggers
         self.window_us = max(0, int(window_us))
@@ -314,11 +311,8 @@ def append_triggers(self, batch) -> None:
                 self.truncated = True
                 continue
             self.triggers.append(trigger)
-            if (
-                    is_rising
-                    and self.max_rising_triggers is not None
-                    and self.raw_rising_triggers == self.max_rising_triggers
-            ):
+            if (is_rising and self.max_rising_triggers is not None
+                    and self.raw_rising_triggers == self.max_rising_triggers):
                 self.cutoff_us = _record_timestamp(trigger) + self.window_us
                 self._prune_events_to_cutoff()
 
@@ -408,7 +402,8 @@ def _record_field(record, name, default=None):
 
 
 def _run_pair_with_callback(pair_args, before_start):
-    pair_module = import_module("dmdcontrol.runtime.pair")
+    from dmdcontrol.runtime import pair as pair_module
+
     return pair_module.run_with_before_start_callback(pair_args, before_start)
 
 
@@ -456,18 +451,20 @@ def live(args: argparse.Namespace) -> int:
 
         def before_start(context):
             nonlocal recording
-            metadata.update({
-                "camera_ready": _asdict(ready),
-                "dmd_ready": True,
-                "timing_a": context["state_a"]["timing"],
-                "timing_b": context["state_b"]["timing"],
-            })
+            metadata.update(
+                {
+                    "camera_ready": metadata_dict(ready),
+                    "dmd_ready": True,
+                    "timing_a": context["state_a"]["timing"],
+                    "timing_b": context["state_b"]["timing"],
+                })
             if recording is None:
                 recording = AsyncCapture(
                     capture,
                     writer,
-                    expected_trigger_count=_expected_trigger_count(args),
-                    timeout_s=max(1, args.runtime_seconds),
+                    expected_trigger_count=None,
+                    timeout_s=max(1,
+                                  args.runtime_seconds),
                     on_events=artifact_buffer.append_events,
                     on_triggers=artifact_buffer.append_triggers,
                     record_fn=record_until_trigger_count,
@@ -477,7 +474,8 @@ def before_start(context):
             write_run_metadata(
                 run,
                 metadata,
-                artifacts=["raw.aedat4", "metadata.json"],
+                artifacts=["raw.aedat4",
+                           "metadata.json"],
             )
 
         _run_pair_with_callback(_to_pair_runtime_args(args), before_start)
@@ -508,31 +506,11 @@ def before_start(context):
             except BaseException as exc:
                 metadata["capture_error"] = repr(exc)
         if capture_result is not None:
-            metadata["capture"] = _asdict(capture_result)
-            artifacts = [
-                "raw.aedat4",
-                "metadata.json",
-                "command.txt",
-                "run.log",
-                "timing.json",
-            ]
-            if artifact_summary is not None:
-                artifacts.extend([
-                    "triggers.csv",
-                    "accumulated.npy",
-                    "contact_sheet.png",
-                    "summary.json",
-                ])
-                artifacts.extend(artifact_summary.get("frame_artifacts", []))
-                artifacts.extend(artifact_summary.get("filtered_frame_artifacts", []))
-                if artifact_summary.get("filtered_contact_sheet_artifact"):
-                    artifacts.append(artifact_summary["filtered_contact_sheet_artifact"])
-                if artifact_summary.get("filtered_events_artifact"):
-                    artifacts.append(artifact_summary["filtered_events_artifact"])
+            metadata["capture"] = metadata_dict(capture_result)
             write_run_metadata(
                 run,
                 metadata,
-                artifacts=artifacts,
+                artifacts=final_capture_artifacts(artifact_summary),
             )
         if recording is not None:
             recording = None
diff --git a/dmdcontrol/camera/reprocess_aedat4.py b/dmdcontrol/camera/reprocess_aedat4.py
new file mode 100644
index 0000000..fc4179a
--- /dev/null
+++ b/dmdcontrol/camera/reprocess_aedat4.py
@@ -0,0 +1,304 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+from dmdcontrol.camera.accumulation import TriggerRecord
+from dmdcontrol.camera.capture import merge_time_range
+from dmdcontrol.camera.runs import (
+    CameraRunDirectory,
+    write_capture_artifacts,
+    write_json,
+    write_run_metadata,
+)
+from dmdcontrol.support.argparse_types import nonnegative_int, positive_int
+
+
+@dataclass(frozen=True)
+class Aedat4RecordingData:
+    events: list[np.ndarray]
+    triggers: list[TriggerRecord]
+    resolution: tuple[int, int]
+    stats: dict[str, object]
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        prog="python -m dmdcontrol camera reprocess-aedat4",
+        description="Regenerate accumulation artifacts from an existing raw.aedat4 recording.",
+    )
+    parser.add_argument("run_dir", help="Run directory containing raw.aedat4 and metadata.json.")
+    parser.add_argument("--aedat4", default=None, help="Override the AEDAT4 file path.")
+    parser.add_argument(
+        "--output-dir",
+        default=None,
+        help=
+        "Directory for derived artifacts. Defaults to RUN_DIR/analysis_outputs/reprocessed_aedat4.",
+    )
+    parser.add_argument("--window-us", type=nonnegative_int, default=None)
+    parser.add_argument("--accumulation-start-offset-us", type=int, default=0)
+    parser.add_argument("--polarity-mode", choices=("positive", "signed", "ignore"), default=None)
+    parser.add_argument("--trigger-cycle-length", type=positive_int, default=None)
+    parser.add_argument("--accumulation-cycles", type=positive_int, default=None)
+    parser.add_argument("--max-accumulation-triggers", type=positive_int, default=None)
+    parser.add_argument("--contact-sheet-columns", type=positive_int, default=None)
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    run_dir = Path(args.run_dir).expanduser()
+    if not run_dir.is_absolute():
+        run_dir = Path.cwd() / run_dir
+    metadata_path = run_dir / "metadata.json"
+    source_metadata = json.loads(metadata_path.read_text(encoding="utf-8")) if metadata_path.exists() else {}
+    options = _resolve_options(args, source_metadata)
+    aedat4_path = (Path(args.aedat4).expanduser() if args.aedat4 else run_dir / "raw.aedat4")
+    if not aedat4_path.is_absolute():
+        aedat4_path = Path.cwd() / aedat4_path
+    output_dir = (
+        Path(args.output_dir).expanduser() if args.output_dir else run_dir / "analysis_outputs" /
+        "reprocessed_aedat4")
+    if not output_dir.is_absolute():
+        output_dir = Path.cwd() / output_dir
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    recording = read_aedat4_recording(aedat4_path)
+    derived_run = CameraRunDirectory(
+        path=output_dir,
+        raw_recording_path=aedat4_path,
+        metadata_path=output_dir / "metadata.json",
+        command_path=output_dir / "command.txt",
+        log_path=output_dir / "run.log",
+        triggers_path=output_dir / "triggers.csv",
+        accumulated_path=output_dir / "accumulated.npy",
+        timing_path=output_dir / "timing.json",
+        contact_sheet_path=output_dir / "contact_sheet.png",
+        summary_path=output_dir / "summary.json",
+    )
+    derived_run.command_path.write_text(" ".join(sys.argv) + "\n", encoding="utf-8")
+    derived_run.log_path.write_text("aedat4 reprocess\n", encoding="utf-8")
+
+    summary = write_capture_artifacts(
+        derived_run,
+        events=recording.events,
+        triggers=recording.triggers,
+        resolution=recording.resolution,
+        window_us=options["window_us"],
+        polarity_mode=options["polarity_mode"],
+        window_start_offset_us=options["accumulation_start_offset_us"],
+        max_accumulation_triggers=options["max_accumulation_triggers"],
+        trigger_cycle_length=options["trigger_cycle_length"],
+        accumulation_cycles=options["accumulation_cycles"],
+        contact_sheet_columns=options["contact_sheet_columns"],
+    )
+    metadata = {
+        "mode": "aedat4-reprocess",
+        "source_run_directory": str(run_dir),
+        "source_aedat4": str(aedat4_path),
+        "source_metadata_path": str(metadata_path),
+        "options": options,
+        "aedat4": recording.stats,
+    }
+    artifacts = [
+        "metadata.json",
+        "command.txt",
+        "run.log",
+        "triggers.csv",
+        "accumulated.npy",
+        "contact_sheet.png",
+        "summary.json",
+        *summary.get("frame_artifacts",
+                     []),
+    ]
+    write_run_metadata(derived_run, metadata, artifacts=artifacts)
+    print(
+        json.dumps(
+            {
+                "output_dir": str(output_dir),
+                "actual_trigger_count": summary["actual_trigger_count"],
+                "raw_rising_trigger_count": summary["raw_rising_trigger_count"],
+                "window_us": summary["window_us"],
+                "window_start_offset_us": summary["window_start_offset_us"],
+            },
+            indent=2,
+            sort_keys=True,
+        ))
+    return 0
+
+
+def read_aedat4_recording(path: str | Path) -> Aedat4RecordingData:
+    import dv_processing as dv
+
+    recording_path = Path(path)
+    if not recording_path.exists():
+        raise FileNotFoundError(recording_path)
+    recording = dv.io.MonoCameraRecording(str(recording_path))
+    if not recording.isEventStreamAvailable():
+        raise RuntimeError(f"{recording_path} has no event stream")
+    if not recording.isTriggerStreamAvailable():
+        raise RuntimeError(f"{recording_path} has no trigger stream")
+
+    events, event_stats = _read_event_batches(recording)
+    recording.resetSequentialRead()
+    triggers, trigger_stats = _read_trigger_batches(recording)
+    resolution = tuple(int(value) for value in recording.getEventResolution())
+    stats = {
+        "event_batches": event_stats["batches"],
+        "event_count": event_stats["count"],
+        "event_time_range_us": event_stats["time_range_us"],
+        "trigger_batches": trigger_stats["batches"],
+        "trigger_count": trigger_stats["count"],
+        "trigger_time_range_us": trigger_stats["time_range_us"],
+        "trigger_edges": trigger_stats["edges"],
+        "resolution": list(resolution),
+    }
+    return Aedat4RecordingData(
+        events=events,
+        triggers=triggers,
+        resolution=resolution,
+        stats=stats,
+    )
+
+
+def _read_event_batches(recording) -> tuple[list[np.ndarray], dict[str, object]]:
+    batches = []
+    count = 0
+    time_range = None
+    while True:
+        batch = recording.getNextEventBatch()
+        if batch is None:
+            break
+        if len(batch) == 0:
+            continue
+        array = np.asarray(batch.numpy()).copy()
+        batches.append(array)
+        count += len(array)
+        time_range = merge_time_range(time_range, _array_time_range(array))
+    return batches, {
+        "batches": len(batches),
+        "count": count,
+        "time_range_us": _json_time_range(time_range), }
+
+
+def _read_trigger_batches(recording) -> tuple[list[TriggerRecord], dict[str, object]]:
+    triggers = []
+    batch_count = 0
+    time_range = None
+    edge_counts = {}
+    while True:
+        batch = recording.getNextTriggerBatch()
+        if batch is None:
+            break
+        if len(batch) == 0:
+            continue
+        batch_count += 1
+        for trigger in batch:
+            record = _trigger_record(trigger)
+            triggers.append(record)
+            edge_counts[record.edge] = edge_counts.get(record.edge, 0) + 1
+            time_range = merge_time_range(time_range, (record.timestamp, record.timestamp))
+    return triggers, {
+        "batches": batch_count,
+        "count": len(triggers),
+        "time_range_us": _json_time_range(time_range),
+        "edges": edge_counts, }
+
+
+def _trigger_record(trigger) -> TriggerRecord:
+    timestamp = int(_record_value(trigger, "timestamp"))
+    trigger_type = _record_value(trigger, "type", default=None)
+    return TriggerRecord(timestamp=timestamp, edge=_trigger_edge_name(trigger_type))
+
+
+def _trigger_edge_name(trigger_type) -> str:
+    value = str(trigger_type).lower()
+    if "rising" in value:
+        return "rising"
+    if "falling" in value:
+        return "falling"
+    return value or "unknown"
+
+
+def _record_value(record, name, default=None):
+    if hasattr(record, name):
+        value = getattr(record, name)
+        return value() if callable(value) else value
+    if isinstance(record, dict) and name in record:
+        return record[name]
+    if default is not None:
+        return default
+    raise AttributeError(f"{record!r} has no {name!r} field")
+
+
+def _array_time_range(array) -> tuple[int, int] | None:
+    field_names = array.dtype.names or ()
+    timestamp_field = (
+        "timestamp" if "timestamp" in field_names else "t" if "t" in field_names else None)
+    if timestamp_field is None or len(array) == 0:
+        return None
+    timestamps = array[timestamp_field]
+    return int(np.min(timestamps)), int(np.max(timestamps))
+
+
+def _json_time_range(time_range):
+    if time_range is None:
+        return None
+    return [int(time_range[0]), int(time_range[1])]
+
+
+def _resolve_options(args, metadata: dict[str, object]) -> dict[str, object]:
+    trigger_cycle_length = _first_not_none(
+        args.trigger_cycle_length,
+        metadata.get("expected_trigger_count"),
+        len(metadata.get("number_sequence",
+                         [])) or None,
+    )
+    return {
+        "window_us":
+        int(
+            _first_not_none(
+                args.window_us,
+                metadata.get("accumulation_window_us"),
+                metadata.get("numbers_exposure_us"),
+                metadata.get("count_exposure_us"),
+                0,
+            )),
+        "accumulation_start_offset_us":
+        int(args.accumulation_start_offset_us),
+        "polarity_mode":
+        str(_first_not_none(args.polarity_mode,
+                            metadata.get("polarity_mode"),
+                            "positive")),
+        "trigger_cycle_length":
+        int(trigger_cycle_length) if trigger_cycle_length is not None else None,
+        "accumulation_cycles": (
+            int(args.accumulation_cycles)
+            if args.accumulation_cycles is not None else
+            int(metadata["accumulation_cycles"]) if metadata.get("accumulation_cycles") is not None else None),
+        "max_accumulation_triggers": (
+            int(args.max_accumulation_triggers)
+            if args.max_accumulation_triggers is not None else None),
+        "contact_sheet_columns":
+        int(_first_not_none(
+            args.contact_sheet_columns,
+            trigger_cycle_length,
+            1,
+        )),
+    }
+
+
+def _first_not_none(*values):
+    for value in values:
+        if value is not None:
+            return value
+    return None
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/dmdcontrol/camera/runs.py b/dmdcontrol/camera/runs.py
index b6116bc..5ce4bc9 100644
--- a/dmdcontrol/camera/runs.py
+++ b/dmdcontrol/camera/runs.py
@@ -2,15 +2,18 @@
 
 import json
 import math
-import struct
-import zlib
-from dataclasses import dataclass
+from dataclasses import asdict, dataclass, is_dataclass
 from datetime import datetime
 from pathlib import Path
 
 import numpy as np
+from PIL import Image
 
-from dmdcontrol.camera.accumulation import accumulate_events_for_triggers, filter_rising_triggers
+from dmdcontrol.camera.accumulation import (
+    accumulate_events_for_triggers,
+    filter_rising_triggers,
+    structured_field,
+)
 from dmdcontrol.camera.local_support_filter import (
     LocalSupportFilterConfig,
     apply_local_support_filter_arrays,
@@ -32,6 +35,42 @@ class CameraRunDirectory:
     summary_path: Path
 
 
+@dataclass(frozen=True)
+class _AccumulationTriggerStages:
+    raw: list
+    aligned: list
+    selected: list
+    final: list
+    alignment_metadata: dict
+    cycle_limit_metadata: dict
+
+
+def final_capture_artifacts(artifact_summary):
+    artifacts = [
+        "raw.aedat4",
+        "metadata.json",
+        "command.txt",
+        "run.log",
+        "timing.json",
+    ]
+    if artifact_summary is None:
+        return artifacts
+
+    artifacts.extend([
+        "triggers.csv",
+        "accumulated.npy",
+        "contact_sheet.png",
+        "summary.json",
+    ])
+    artifacts.extend(artifact_summary.get("frame_artifacts", []))
+    artifacts.extend(artifact_summary.get("filtered_frame_artifacts", []))
+    if artifact_summary.get("filtered_contact_sheet_artifact"):
+        artifacts.append(artifact_summary["filtered_contact_sheet_artifact"])
+    if artifact_summary.get("filtered_events_artifact"):
+        artifacts.append(artifact_summary["filtered_events_artifact"])
+    return artifacts
+
+
 def default_timestamp() -> str:
     return datetime.now().strftime("%Y%m%d-%H%M%S")
 
@@ -57,12 +96,20 @@ def create_run_directory(mode, output_root=None, timestamp=None):
 def write_json(path, payload):
     output_path = Path(path)
     output_path.write_text(
-        json.dumps(payload, indent=2, sort_keys=True) + "\n",
+        json.dumps(payload,
+                   indent=2,
+                   sort_keys=True) + "\n",
         encoding="utf-8",
     )
     return payload
 
 
+def metadata_dict(value):
+    if is_dataclass(value):
+        return asdict(value)
+    return dict(getattr(value, "__dict__", {}))
+
+
 def write_run_metadata(run_directory, metadata, artifacts=None):
     payload = dict(metadata)
     payload["run_directory"] = str(run_directory.path)
@@ -73,18 +120,25 @@ def write_run_metadata(run_directory, metadata, artifacts=None):
 
 
 def write_capture_artifacts(
-        run_directory,
-        events,
-        triggers,
-        resolution,
-        window_us,
-        polarity_mode="positive",
-        event_noise_filter=None,
-        save_filtered_events=False,
-        max_accumulation_triggers=None,
+    run_directory,
+    events,
+    triggers,
+    resolution,
+    window_us,
+    polarity_mode="positive",
+    window_start_offset_us=0,
+    event_noise_filter=None,
+    save_filtered_events=False,
+    max_accumulation_triggers=None,
+    trigger_cycle_length=None,
+    accumulation_cycles=None,
+    contact_sheet_columns=None,
 ):
-    if max_accumulation_triggers is not None and max_accumulation_triggers <= 0:
-        raise ValueError("max_accumulation_triggers must be positive")
+    _validate_capture_artifact_options(
+        max_accumulation_triggers=max_accumulation_triggers,
+        trigger_cycle_length=trigger_cycle_length,
+        accumulation_cycles=accumulation_cycles,
+    )
     filter_config = event_noise_filter or LocalSupportFilterConfig(enabled=False)
     filter_config.validate()
     event_sequence = _event_sequence(events)
@@ -98,17 +152,19 @@ def write_capture_artifacts(
         config=filter_config,
     )
     accumulation_events = (
-        _arrays_to_event_batches(filtered_arrays)
-        if filter_config.enabled
-        else event_sequence
-    )
+        _arrays_to_event_batches(filtered_arrays) if filter_config.enabled else event_sequence)
 
-    raw_rising_triggers = filter_rising_triggers(triggers)
-    rising_triggers = (
-        raw_rising_triggers[:max_accumulation_triggers]
-        if max_accumulation_triggers is not None
-        else raw_rising_triggers
+    accumulation_arrays = filtered_arrays if filter_config.enabled else event_arrays
+    trigger_stages = _process_accumulation_triggers(
+        triggers,
+        accumulation_arrays["t"],
+        window_us=int(window_us),
+        window_start_offset_us=int(window_start_offset_us),
+        max_accumulation_triggers=max_accumulation_triggers,
+        trigger_cycle_length=trigger_cycle_length,
+        accumulation_cycles=accumulation_cycles,
     )
+    rising_triggers = trigger_stages.final
     _write_triggers_csv(run_directory.triggers_path, rising_triggers)
     accumulated = accumulate_events_for_triggers(
         accumulation_events,
@@ -116,87 +172,60 @@ def write_capture_artifacts(
         resolution=resolution,
         window_us=window_us,
         polarity_mode=polarity_mode,
+        window_start_offset_us=window_start_offset_us,
     )
     np.save(run_directory.accumulated_path, accumulated)
-    accumulation_arrays = filtered_arrays if filter_config.enabled else event_arrays
+    raw_rising_trigger_timestamps = _trigger_timestamps(trigger_stages.raw)
     rising_trigger_timestamps = _trigger_timestamps(rising_triggers)
 
-    frame_artifacts = []
-    filtered_frame_artifacts = []
-    for index, frame in enumerate(accumulated, start=1):
-        frame_path = run_directory.path / f"accumulated_{index:03d}.png"
-        _write_grayscale_png(frame_path, frame)
-        frame_artifacts.append(frame_path.name)
-        if filter_config.enabled:
-            filtered_frame_path = run_directory.path / f"filtered_accumulated_{index:03d}.png"
-            _write_grayscale_png(filtered_frame_path, frame)
-            filtered_frame_artifacts.append(filtered_frame_path.name)
-    contact_sheet = _contact_sheet(accumulated)
-    _write_grayscale_png(run_directory.contact_sheet_path, contact_sheet)
-
-    filtered_contact_sheet_artifact = None
-    if filter_config.enabled:
-        filtered_contact_sheet_artifact = "filtered_contact_sheet.png"
-        _write_grayscale_png(run_directory.path / filtered_contact_sheet_artifact, contact_sheet)
-
-    filtered_events_artifact = None
-    if save_filtered_events:
-        filtered_events_artifact = "filtered_events.npz"
-        _write_event_npz(run_directory.path / filtered_events_artifact, filtered_arrays)
+    artifact_files = _write_accumulation_image_artifacts(
+        run_directory,
+        accumulated,
+        include_filtered=filter_config.enabled,
+        contact_sheet_columns=contact_sheet_columns,
+    )
+    filtered_events_artifact = _write_filtered_events_artifact(
+        run_directory,
+        filtered_arrays,
+        save_filtered_events=save_filtered_events,
+    )
 
-    summary = {
-        "actual_trigger_count": len(rising_triggers),
-        "raw_rising_trigger_count": len(raw_rising_triggers),
-        "max_accumulation_triggers": max_accumulation_triggers,
-        "accumulation_trigger_limited": len(rising_triggers) < len(raw_rising_triggers),
-        "event_count": filter_stats.raw_events,
-        "accumulation_event_count": (
-            filter_stats.filtered_events if filter_config.enabled else filter_stats.raw_events
-        ),
-        "accumulation_event_source": "filtered" if filter_config.enabled else "raw",
-        "event_noise_filter": event_noise_filter_metadata(filter_config, filter_stats),
-        "window_us": int(window_us),
-        "polarity_mode": polarity_mode,
-        "resolution": [int(resolution[0]), int(resolution[1])],
-        "accumulated_shape": list(accumulated.shape),
-        "event_time_range_us": _time_range(event_arrays["t"]),
-        "accumulation_event_time_range_us": _time_range(accumulation_arrays["t"]),
-        "rising_trigger_time_range_us": _time_range(rising_trigger_timestamps),
-        "events_per_accumulation_window": _window_counts(
-            accumulation_arrays["t"],
-            rising_trigger_timestamps,
-            int(window_us),
-        ),
-        "events_per_pre_trigger_window": _window_counts(
-            accumulation_arrays["t"],
-            rising_trigger_timestamps - int(window_us),
-            int(window_us),
-        ),
-        "events_per_post_window": _window_counts(
-            accumulation_arrays["t"],
-            rising_trigger_timestamps + int(window_us),
-            int(window_us),
-        ),
-        "accumulated_nonzero_pixels": [
-            int(np.count_nonzero(frame))
-            for frame in accumulated
-        ],
-        "accumulated_abs_sums": [
-            float(np.sum(np.abs(frame)))
-            for frame in accumulated
-        ],
-        "frame_artifacts": frame_artifacts,
-    }
-    if filtered_events_artifact is not None:
-        summary["filtered_events_artifact"] = filtered_events_artifact
-    if filtered_frame_artifacts:
-        summary["filtered_frame_artifacts"] = filtered_frame_artifacts
-    if filtered_contact_sheet_artifact is not None:
-        summary["filtered_contact_sheet_artifact"] = filtered_contact_sheet_artifact
+    summary = _capture_summary(
+        accumulated=accumulated,
+        accumulation_arrays=accumulation_arrays,
+        artifact_files=artifact_files,
+        event_arrays=event_arrays,
+        filter_config=filter_config,
+        filter_stats=filter_stats,
+        filtered_events_artifact=filtered_events_artifact,
+        max_accumulation_triggers=max_accumulation_triggers,
+        polarity_mode=polarity_mode,
+        window_start_offset_us=window_start_offset_us,
+        raw_rising_trigger_timestamps=raw_rising_trigger_timestamps,
+        resolution=resolution,
+        rising_trigger_timestamps=rising_trigger_timestamps,
+        rising_triggers=rising_triggers,
+        trigger_stages=trigger_stages,
+        window_us=window_us,
+    )
     write_json(run_directory.summary_path, summary)
     return summary
 
 
+def _validate_capture_artifact_options(
+    *,
+    max_accumulation_triggers,
+    trigger_cycle_length,
+    accumulation_cycles,
+):
+    if max_accumulation_triggers is not None and max_accumulation_triggers <= 0:
+        raise ValueError("max_accumulation_triggers must be positive")
+    if trigger_cycle_length is not None and trigger_cycle_length <= 0:
+        raise ValueError("trigger_cycle_length must be positive")
+    if accumulation_cycles is not None and accumulation_cycles <= 0:
+        raise ValueError("accumulation_cycles must be positive")
+
+
 def _event_sequence(events):
     if events is None:
         return []
@@ -207,10 +236,14 @@ def _event_sequence(events):
 
 def _events_to_arrays(events):
     empty = {
-        "x": np.array([], dtype=np.int64),
-        "y": np.array([], dtype=np.int64),
-        "t": np.array([], dtype=np.int64),
-        "p": np.array([], dtype=np.bool_),
+        "x": np.array([],
+                      dtype=np.int64),
+        "y": np.array([],
+                      dtype=np.int64),
+        "t": np.array([],
+                      dtype=np.int64),
+        "p": np.array([],
+                      dtype=np.bool_),
     }
     if not events:
         return empty
@@ -221,35 +254,49 @@ def _events_to_arrays(events):
             return empty
         event_array = np.concatenate(batches) if len(batches) > 1 else batches[0]
         return {
-            "x": _structured_field(event_array, "x").astype(np.int64, copy=False),
-            "y": _structured_field(event_array, "y").astype(np.int64, copy=False),
-            "t": _structured_field(event_array, "timestamp", "t").astype(np.int64, copy=False),
-            "p": _structured_field(event_array, "polarity", "p").astype(np.bool_, copy=False),
+            "x": structured_field(event_array,
+                                  "x").astype(np.int64,
+                                              copy=False),
+            "y": structured_field(event_array,
+                                  "y").astype(np.int64,
+                                              copy=False),
+            "t": structured_field(event_array,
+                                  "timestamp",
+                                  "t").astype(np.int64,
+                                              copy=False),
+            "p": structured_field(event_array,
+                                  "polarity",
+                                  "p").astype(np.bool_,
+                                              copy=False),
         }
     return {
-        "x": np.array([_record_field(event, "x") for event in events], dtype=np.int64),
-        "y": np.array([_record_field(event, "y") for event in events], dtype=np.int64),
-        "t": np.array([_record_field(event, "timestamp") for event in events], dtype=np.int64),
-        "p": np.array([_record_field(event, "polarity") for event in events], dtype=np.bool_),
+        "x": np.array([_record_field(event,
+                                     "x") for event in events],
+                      dtype=np.int64),
+        "y": np.array([_record_field(event,
+                                     "y") for event in events],
+                      dtype=np.int64),
+        "t": np.array([_record_field(event,
+                                     "timestamp") for event in events],
+                      dtype=np.int64),
+        "p": np.array([_record_field(event,
+                                     "polarity") for event in events],
+                      dtype=np.bool_),
     }
 
 
-def _structured_field(event_array, *names):
-    field_names = event_array.dtype.names or ()
-    for name in names:
-        if name in field_names:
-            return event_array[name]
-    raise ValueError(f"event array missing required field: one of {names!r}")
-
-
 def _arrays_to_event_batches(arrays):
     event_array = np.zeros(
         len(arrays["t"]),
         dtype=[
-            ("timestamp", np.int64),
-            ("x", np.int16),
-            ("y", np.int16),
-            ("polarity", np.bool_),
+            ("timestamp",
+             np.int64),
+            ("x",
+             np.int16),
+            ("y",
+             np.int16),
+            ("polarity",
+             np.bool_),
         ],
     )
     event_array["timestamp"] = arrays["t"]
@@ -278,9 +325,156 @@ def _write_triggers_csv(path, triggers):
     Path(path).write_text("\n".join(lines) + "\n", encoding="utf-8")
 
 
+def _write_accumulation_image_artifacts(
+    run_directory,
+    accumulated,
+    *,
+    include_filtered,
+    contact_sheet_columns=None,
+):
+    frame_artifacts = []
+    filtered_frame_artifacts = []
+    scale_max = _grayscale_scale_max(accumulated)
+    for index, frame in enumerate(accumulated, start=1):
+        frame_path = run_directory.path / f"accumulated_{index:03d}.png"
+        _write_grayscale_png(frame_path, frame, scale_max=scale_max)
+        frame_artifacts.append(frame_path.name)
+        if include_filtered:
+            filtered_frame_path = (run_directory.path / f"filtered_accumulated_{index:03d}.png")
+            _write_grayscale_png(filtered_frame_path, frame, scale_max=scale_max)
+            filtered_frame_artifacts.append(filtered_frame_path.name)
+
+    contact_sheet = _contact_sheet(
+        accumulated,
+        scale_max=scale_max,
+        cols=contact_sheet_columns,
+    )
+    _write_grayscale_png(run_directory.contact_sheet_path, contact_sheet, scale_max=255)
+
+    filtered_contact_sheet_artifact = None
+    if include_filtered:
+        filtered_contact_sheet_artifact = "filtered_contact_sheet.png"
+        _write_grayscale_png(
+            run_directory.path / filtered_contact_sheet_artifact,
+            contact_sheet,
+            scale_max=255,
+        )
+
+    return {
+        "frame_artifacts": frame_artifacts,
+        "filtered_frame_artifacts": filtered_frame_artifacts,
+        "filtered_contact_sheet_artifact": filtered_contact_sheet_artifact,
+    }
+
+
+def _write_filtered_events_artifact(run_directory, filtered_arrays, *, save_filtered_events):
+    if not save_filtered_events:
+        return None
+    artifact = "filtered_events.npz"
+    _write_event_npz(run_directory.path / artifact, filtered_arrays)
+    return artifact
+
+
+def _capture_summary(
+    *,
+    accumulated,
+    accumulation_arrays,
+    artifact_files,
+    event_arrays,
+    filter_config,
+    filter_stats,
+    filtered_events_artifact,
+    max_accumulation_triggers,
+    polarity_mode,
+    window_start_offset_us,
+    raw_rising_trigger_timestamps,
+    resolution,
+    rising_trigger_timestamps,
+    rising_triggers,
+    trigger_stages,
+    window_us,
+):
+    summary = {
+        "actual_trigger_count":
+        len(rising_triggers),
+        "aligned_rising_trigger_count":
+        len(trigger_stages.aligned),
+        "selected_rising_trigger_count":
+        len(trigger_stages.selected),
+        "raw_rising_trigger_count":
+        len(trigger_stages.raw),
+        "max_accumulation_triggers":
+        max_accumulation_triggers,
+        "accumulation_trigger_limited":
+        len(rising_triggers) < len(trigger_stages.raw),
+        "trigger_alignment":
+        trigger_stages.alignment_metadata,
+        "trigger_cycle_limit":
+        trigger_stages.cycle_limit_metadata,
+        "event_count":
+        filter_stats.raw_events,
+        "accumulation_event_count":
+        (filter_stats.filtered_events if filter_config.enabled else filter_stats.raw_events),
+        "accumulation_event_source":
+        "filtered" if filter_config.enabled else "raw",
+        "event_noise_filter":
+        event_noise_filter_metadata(filter_config,
+                                    filter_stats),
+        "window_us":
+        int(window_us),
+        "window_start_offset_us":
+        int(window_start_offset_us),
+        "polarity_mode":
+        polarity_mode,
+        "resolution": [int(resolution[0]),
+                       int(resolution[1])],
+        "accumulated_shape":
+        list(accumulated.shape),
+        "event_time_range_us":
+        _time_range(event_arrays["t"]),
+        "accumulation_event_time_range_us":
+        _time_range(accumulation_arrays["t"]),
+        "raw_rising_trigger_time_range_us":
+        _time_range(raw_rising_trigger_timestamps),
+        "rising_trigger_time_range_us":
+        _time_range(rising_trigger_timestamps),
+        "events_per_accumulation_window":
+        _window_counts(
+            accumulation_arrays["t"],
+            rising_trigger_timestamps + int(window_start_offset_us),
+            int(window_us),
+        ),
+        "events_per_pre_trigger_window":
+        _window_counts(
+            accumulation_arrays["t"],
+            rising_trigger_timestamps + int(window_start_offset_us) - int(window_us),
+            int(window_us),
+        ),
+        "events_per_post_window":
+        _window_counts(
+            accumulation_arrays["t"],
+            rising_trigger_timestamps + int(window_start_offset_us) + int(window_us),
+            int(window_us),
+        ),
+        "accumulated_nonzero_pixels": [int(np.count_nonzero(frame)) for frame in accumulated],
+        "accumulated_abs_sums": [float(np.sum(np.abs(frame))) for frame in accumulated],
+        "frame_artifacts":
+        artifact_files["frame_artifacts"],
+    }
+    if filtered_events_artifact is not None:
+        summary["filtered_events_artifact"] = filtered_events_artifact
+    if artifact_files["filtered_frame_artifacts"]:
+        summary["filtered_frame_artifacts"] = artifact_files["filtered_frame_artifacts"]
+    if artifact_files["filtered_contact_sheet_artifact"] is not None:
+        summary["filtered_contact_sheet_artifact"] = artifact_files[
+            "filtered_contact_sheet_artifact"]
+    return summary
+
+
 def _trigger_timestamps(triggers):
     return np.array(
-        [int(_record_field(trigger, "timestamp")) for trigger in triggers],
+        [int(_record_field(trigger,
+                           "timestamp")) for trigger in triggers],
         dtype=np.int64,
     )
 
@@ -292,6 +486,111 @@ def _time_range(values):
     return [int(np.min(array)), int(np.max(array))]
 
 
+def _process_accumulation_triggers(
+    triggers,
+    event_timestamps,
+    *,
+    window_us,
+    window_start_offset_us,
+    max_accumulation_triggers,
+    trigger_cycle_length,
+    accumulation_cycles,
+):
+    raw = filter_rising_triggers(triggers)
+    aligned, alignment_metadata = _align_triggers_to_event_range(
+        raw,
+        event_timestamps,
+        int(window_us),
+        int(window_start_offset_us),
+    )
+    selected, cycle_limit_metadata = _limit_trigger_cycles(
+        aligned,
+        trigger_cycle_length=trigger_cycle_length,
+        accumulation_cycles=accumulation_cycles,
+    )
+    final = (
+        selected[:max_accumulation_triggers] if max_accumulation_triggers is not None else selected)
+    return _AccumulationTriggerStages(
+        raw=raw,
+        aligned=aligned,
+        selected=selected,
+        final=final,
+        alignment_metadata=alignment_metadata,
+        cycle_limit_metadata=cycle_limit_metadata,
+    )
+
+
+def _align_triggers_to_event_range(triggers, event_timestamps, window_us, window_start_offset_us=0):
+    trigger_timestamps = _trigger_timestamps(triggers)
+    event_time_range = _time_range(event_timestamps)
+    metadata = {
+        "mode": "event_overlap",
+        "event_time_range_us": event_time_range,
+        "window_us": int(window_us),
+        "window_start_offset_us": int(window_start_offset_us),
+        "input_trigger_count": len(triggers),
+        "aligned_trigger_count": len(triggers),
+        "dropped_before_event_count": 0,
+        "dropped_after_event_count": 0,
+    }
+    if len(triggers) == 0 or event_time_range is None or window_us <= 0:
+        return list(triggers), metadata
+
+    event_start, event_end = event_time_range
+    window_us = int(window_us)
+    window_starts = trigger_timestamps + int(window_start_offset_us)
+    keep = (window_starts + window_us > event_start) & (window_starts <= event_end)
+    dropped_before = window_starts + window_us <= event_start
+    dropped_after = window_starts > event_end
+    aligned = [
+        trigger for trigger, should_keep in zip(triggers, keep, strict=False) if bool(should_keep)]
+    metadata.update(
+        {
+            "aligned_trigger_count": len(aligned),
+            "dropped_before_event_count": int(np.count_nonzero(dropped_before)),
+            "dropped_after_event_count": int(np.count_nonzero(dropped_after)),
+        })
+    return aligned, metadata
+
+
+def _limit_trigger_cycles(
+    triggers,
+    *,
+    trigger_cycle_length,
+    accumulation_cycles,
+):
+    available_full_cycles = (
+        len(triggers) // int(trigger_cycle_length) if trigger_cycle_length is not None else 0)
+    metadata = {
+        "cycle_length": trigger_cycle_length,
+        "requested_cycles": accumulation_cycles,
+        "available_full_cycles": available_full_cycles,
+        "selected_cycle_indices": [],
+        "selected_trigger_count": len(triggers),
+    }
+    if accumulation_cycles is None or trigger_cycle_length is None:
+        return list(triggers), metadata
+    if len(triggers) == 0 or available_full_cycles == 0:
+        metadata["selected_trigger_count"] = 0
+        return [], metadata
+
+    cycle_length = int(trigger_cycle_length)
+    requested_cycles = min(int(accumulation_cycles), available_full_cycles)
+    selected_cycle_indices = list(range(requested_cycles))
+
+    selected = []
+    for cycle_index in selected_cycle_indices:
+        start = cycle_index * cycle_length
+        selected.extend(triggers[start:start + cycle_length])
+
+    metadata.update(
+        {
+            "selected_cycle_indices": selected_cycle_indices,
+            "selected_trigger_count": len(selected),
+        })
+    return selected, metadata
+
+
 def _window_counts(timestamps, starts, window_us):
     start_array = np.asarray(starts, dtype=np.int64)
     if len(start_array) == 0:
@@ -318,12 +617,15 @@ def _record_field(record, name, default=None):
     raise AttributeError(f"{record!r} has no {name!r} field")
 
 
-def _contact_sheet(frames):
+def _contact_sheet(frames, *, scale_max=None, cols=None):
     if len(frames) == 0:
         return np.zeros((1, 1), dtype=np.uint8)
-    normalized = [_normalize_grayscale(frame) for frame in frames]
+    normalized = [_normalize_grayscale(frame, scale_max=scale_max) for frame in frames]
     frame_h, frame_w = normalized[0].shape
-    cols = max(1, math.ceil(math.sqrt(len(normalized))))
+    cols = (
+        max(1,
+            int(cols)) if cols is not None else max(1,
+                                                    math.ceil(math.sqrt(len(normalized)))))
     rows = math.ceil(len(normalized) / cols)
     sheet = np.zeros((rows * frame_h, cols * frame_w), dtype=np.uint8)
     for index, frame in enumerate(normalized):
@@ -335,35 +637,27 @@ def _contact_sheet(frames):
     return sheet
 
 
-def _normalize_grayscale(frame):
+def _grayscale_scale_max(frames):
+    array = np.asarray(frames, dtype=np.float32)
+    if array.size == 0:
+        return 0.0
+    return float(np.max(np.abs(array)))
+
+
+def _normalize_grayscale(frame, *, scale_max=None):
     array = np.asarray(frame, dtype=np.float32)
     if array.ndim != 2:
         raise ValueError("frame must be a 2D array")
     if array.size == 0:
         return np.zeros((1, 1), dtype=np.uint8)
-    minimum = float(np.min(array))
-    maximum = float(np.max(array))
-    if maximum <= minimum:
+    magnitude = np.abs(array)
+    maximum = float(scale_max) if scale_max is not None else float(np.max(magnitude))
+    if maximum <= 0:
         return np.zeros(array.shape, dtype=np.uint8)
-    scaled = (array - minimum) * (255.0 / (maximum - minimum))
-    return np.clip(scaled, 0, 255).astype(np.uint8)
-
-
-def _write_grayscale_png(path, frame):
-    image = _normalize_grayscale(frame)
-    height, width = image.shape
-    raw = b"".join(b"\x00" + image[row].tobytes() for row in range(height))
-    payload = b"\x89PNG\r\n\x1a\n"
-    payload += _png_chunk(b"IHDR", struct.pack(">IIBBBBB", width, height, 8, 0, 0, 0, 0))
-    payload += _png_chunk(b"IDAT", zlib.compress(raw))
-    payload += _png_chunk(b"IEND", b"")
-    Path(path).write_bytes(payload)
-
-
-def _png_chunk(kind, data):
-    return (
-        struct.pack(">I", len(data))
-        + kind
-        + data
-        + struct.pack(">I", zlib.crc32(kind + data) & 0xFFFFFFFF)
-    )
+    scaled = np.log1p(magnitude) * (255.0 / np.log1p(maximum))
+    return np.rint(np.clip(scaled, 0, 255)).astype(np.uint8)
+
+
+def _write_grayscale_png(path, frame, *, scale_max=None):
+    image = _normalize_grayscale(frame, scale_max=scale_max)
+    Image.fromarray(np.ascontiguousarray(image)).save(Path(path), format="PNG")
diff --git a/dmdcontrol/camera/session.py b/dmdcontrol/camera/session.py
index 3efc1d8..b4ff0be 100644
--- a/dmdcontrol/camera/session.py
+++ b/dmdcontrol/camera/session.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from dataclasses import is_dataclass, replace
 import gc
 import os
 
@@ -14,10 +15,21 @@
 from dmdcontrol.camera.usb_reset import reset_camera_usb, run_power_cycle_command
 
 
-def open_ready_camera(run, args):
+def _ready_with_initial_flush(ready, initial_flush):
+    if is_dataclass(ready):
+        return replace(ready, initial_flush=initial_flush)
+    try:
+        setattr(ready, "initial_flush", initial_flush)
+    except Exception:
+        pass
+    return ready
+
+
+def _open_configured_camera_capture(args):
     import dv_processing as dv
 
-    power_cycle_command = args.camera_power_cycle_command or os.environ.get("DMD_CAMERA_POWER_CYCLE_COMMAND")
+    power_cycle_command = args.camera_power_cycle_command or os.environ.get(
+        "DMD_CAMERA_POWER_CYCLE_COMMAND")
     power_cycle_info = run_power_cycle_command(power_cycle_command)
     usb_reset_info = reset_camera_usb(dv, enabled=args.camera_usb_reset)
     camera_open_method = getattr(args, "camera_open_method", "modern")
@@ -25,33 +37,71 @@ def open_ready_camera(run, args):
     writer = None
     try:
         rearm_info = (
-            rearm_camera_streams(capture)
-            if getattr(args, "camera_stream_rearm", False)
-            else None
-        )
+            rearm_camera_streams(capture) if getattr(args,
+                                                     "camera_stream_rearm",
+                                                     False) else None)
         configure_camera_performance(
             capture,
             bias_sensitivity=args.bias_sensitivity,
             efps=args.efps,
             prefer_legacy=(camera_open_method == "legacy"),
         )
-        configure_rising_edge_triggers(capture)
+        trigger_configuration = configure_rising_edge_triggers(capture)
         ready = validate_camera_ready(
             capture,
             stream_rearm=rearm_info,
             usb_reset=usb_reset_info,
             power_cycle=power_cycle_info,
+            trigger_configuration=trigger_configuration,
         )
-        writer = dv.io.MonoCameraWriter(str(run.raw_recording_path), capture)
-        flush_stale_batches(capture, reads=args.camera_flush_reads)
     except Exception:
-        if writer is not None:
-            del writer
         if getattr(args, "camera_shutdown_streams", False):
             shutdown_camera_streams(capture)
         del capture
         gc.collect()
         raise
+    return capture, ready
+
+
+def open_ready_camera_capture(args):
+    capture = None
+    try:
+        capture, ready = _open_configured_camera_capture(args)
+        initial_flush = flush_stale_batches(capture, reads=args.camera_flush_reads)
+        ready = _ready_with_initial_flush(ready, initial_flush)
+    except Exception:
+        if capture is not None:
+            if getattr(args, "camera_shutdown_streams", False):
+                shutdown_camera_streams(capture)
+            del capture
+        gc.collect()
+        raise
+    return capture, ready
+
+
+def open_camera_writer(run, capture):
+    import dv_processing as dv
+
+    return dv.io.MonoCameraWriter(str(run.raw_recording_path), capture)
+
+
+def open_ready_camera(run, args):
+    capture = None
+    writer = None
+    try:
+        capture, ready = _open_configured_camera_capture(args)
+        writer = open_camera_writer(run, capture)
+        initial_flush = flush_stale_batches(capture, reads=args.camera_flush_reads)
+        ready = _ready_with_initial_flush(ready, initial_flush)
+    except Exception:
+        if writer is not None:
+            del writer
+        if capture is not None:
+            if getattr(args, "camera_shutdown_streams", False):
+                shutdown_camera_streams(capture)
+            del capture
+        gc.collect()
+        raise
     return capture, writer, ready
 
 
diff --git a/dmdcontrol/camera/sync_check.py b/dmdcontrol/camera/sync_check.py
index f28e827..122d096 100644
--- a/dmdcontrol/camera/sync_check.py
+++ b/dmdcontrol/camera/sync_check.py
@@ -3,8 +3,6 @@
 import argparse
 import math
 import sys
-from dataclasses import asdict, is_dataclass
-from importlib import import_module
 
 from dmdcontrol.camera.capture import (
     AsyncCapture,
@@ -19,6 +17,8 @@
 )
 from dmdcontrol.camera.runs import (
     create_run_directory,
+    final_capture_artifacts,
+    metadata_dict,
     write_capture_artifacts,
     write_json,
     write_run_metadata,
@@ -27,6 +27,28 @@
     close_camera_resources,
     open_ready_camera as _open_ready_camera,
 )
+from dmdcontrol.patterns.paired import MAX_COUNT_SEQUENCE_FRAMES
+from dmdcontrol.support.argparse_types import (
+    nonnegative_int,
+    numbers_bitplane_order,
+    positive_int,
+)
+from dmdcontrol.support.constants import BITPLANES
+
+A_COUNT_B_STATIC_TEST = "a-count-b-static"
+
+
+class SyncCheckArgumentParser(argparse.ArgumentParser):
+
+    def parse_args(self, args=None, namespace=None):
+        parsed = super().parse_args(args, namespace)
+        try:
+            _validate_count_mode_args(parsed)
+            _validate_numbers_mode_args(parsed)
+        except ValueError as exc:
+            self.error(str(exc))
+        parsed.requested_accumulation_cycles = _requested_accumulation_cycles(parsed)
+        return parsed
 
 
 def parse_numbers(value: str) -> list[int]:
@@ -43,28 +65,42 @@ def parse_numbers(value: str) -> list[int]:
     return numbers
 
 
-def positive_int(value: str) -> int:
-    try:
-        number = int(value)
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("value must be positive") from exc
-    if number <= 0:
-        raise argparse.ArgumentTypeError("value must be positive")
-    return number
+def _requested_accumulation_cycles(args: argparse.Namespace) -> int | None:
+    if args.accumulation_cycles is not None:
+        return args.accumulation_cycles
+    if args.test == A_COUNT_B_STATIC_TEST:
+        return None
+    return 1
 
 
-def nonnegative_int(value: str) -> int:
-    try:
-        number = int(value)
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("value must be non-negative") from exc
-    if number < 0:
-        raise argparse.ArgumentTypeError("value must be non-negative")
-    return number
+def _validate_count_mode_args(args: argparse.Namespace) -> None:
+    if args.test != A_COUNT_B_STATIC_TEST:
+        return
+    if args.count_start > args.count_end:
+        raise ValueError("--count-start must be <= --count-end")
+    if args.count_slots_per_frame <= 0 or args.count_slots_per_frame > BITPLANES:
+        raise ValueError(f"--count-slots-per-frame must be in the range 1..{BITPLANES}")
+    count_total = args.count_end - args.count_start + 1
+    if count_total % args.count_slots_per_frame != 0:
+        raise ValueError("count range length must be divisible by --count-slots-per-frame")
+    frame_count = count_total // args.count_slots_per_frame
+    if frame_count > MAX_COUNT_SEQUENCE_FRAMES:
+        raise ValueError(
+            f"a-count-b-static can span at most {MAX_COUNT_SEQUENCE_FRAMES} VSYNC frames")
+
+
+def _validate_numbers_mode_args(args: argparse.Namespace) -> None:
+    if args.test == A_COUNT_B_STATIC_TEST or args.numbers_bitplane_order is None:
+        return
+    if len(args.numbers_bitplane_order) != len(args.numbers):
+        raise ValueError("--numbers-bitplane-order length must match --numbers length")
+    if sorted(args.numbers_bitplane_order) != list(range(len(args.numbers))):
+        raise ValueError(
+            "--numbers-bitplane-order must be a zero-based permutation of --numbers slots")
 
 
 def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
+    parser = SyncCheckArgumentParser(
         prog="python -m dmdcontrol camera sync-check",
         description="Paired DMD + DVXplorer sync check.",
     )
@@ -79,12 +115,29 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--timestamp", default=None, help=argparse.SUPPRESS)
     parser.add_argument("--number-size-px", type=positive_int, default=100)
     parser.add_argument("--numbers", type=parse_numbers, default=parse_numbers("1,2,3,4,5"))
+    parser.add_argument(
+        "--numbers-bitplane-order",
+        type=numbers_bitplane_order,
+        default=None,
+        help=(
+            "Zero-based bitplane indexes in chronological display order for numbers mode. "
+            "Use 1,2,3,4,0 if --numbers 1,2,3,4,5 captures visually as 2,3,4,5,1."),
+    )
     parser.add_argument(
         "--numbers-exposure-us",
         type=positive_int,
         default=None,
         help="Optional per-bitplane LUT exposure override in microseconds. "
-             "Omit for the maximum safe exposure at the configured VSYNC.",
+        "Omit for the maximum safe exposure at the configured VSYNC.",
+    )
+    parser.add_argument("--count-start", type=positive_int, default=1)
+    parser.add_argument("--count-end", type=positive_int, default=100)
+    parser.add_argument("--count-slots-per-frame", type=positive_int, default=2)
+    parser.add_argument(
+        "--count-exposure-us",
+        type=positive_int,
+        default=None,
+        help="Optional per-count LUT exposure override in microseconds.",
     )
     parser.add_argument("--trigger-out-2-delay-fraction", type=float, default=0.00)
     parser.add_argument("--runtime-seconds", type=int, default=0)
@@ -93,7 +146,7 @@ def build_parser() -> argparse.ArgumentParser:
         type=float,
         default=None,
         help="Optional paired-runtime LUT budget utilization override. "
-             "Use 1.0 only when intentionally using nearly the full VSYNC budget.",
+        "Use 1.0 only when intentionally using nearly the full VSYNC budget.",
     )
     parser.add_argument("--dmd-config", default=None)
     parser.add_argument("--hz", type=int, default=None)
@@ -102,14 +155,40 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--b-dot-x", type=int, default=960)
     parser.add_argument("--b-dot-y", type=int, default=540)
     parser.add_argument("--b-dot-radius", type=positive_int, default=20)
-    parser.add_argument("--bias-sensitivity", default="default", choices=["default", "verylow", "low", "high", "veryhigh"])
-    parser.add_argument("--efps", default="default", choices=["default", "variable", "variable_5000", "constant_1000", "constant_100"])
-    parser.add_argument("--polarity-mode", default="positive", choices=["positive", "signed", "ignore"])
+    parser.add_argument(
+        "--bias-sensitivity",
+        default="default",
+        choices=["default",
+                 "verylow",
+                 "low",
+                 "high",
+                 "veryhigh"])
+    parser.add_argument(
+        "--efps",
+        default="default",
+        choices=["default",
+                 "variable",
+                 "variable_5000",
+                 "constant_1000",
+                 "constant_100"])
+    parser.add_argument(
+        "--polarity-mode",
+        default="positive",
+        choices=["positive",
+                 "signed",
+                 "ignore"])
+    parser.add_argument(
+        "--accumulation-start-offset-us",
+        type=int,
+        default=0,
+        help="Shift each accumulation window relative to its trigger timestamp.",
+    )
     parser.add_argument("--dark-time-us", type=int, default=None)
     parser.add_argument(
         "--camera-open-method",
         default="modern",
-        choices=["modern", "legacy"],
+        choices=["modern",
+                 "legacy"],
         help="Camera API used to open the device. Use legacy to mirror mentor CameraCapture code.",
     )
     parser.add_argument(
@@ -117,7 +196,8 @@ def build_parser() -> argparse.ArgumentParser:
         dest="camera_usb_reset",
         action="store_true",
         default=False,
-        help="Diagnostic: run a Linux USB device reset before opening the camera. Disabled by default.",
+        help=
+        "Diagnostic: run a Linux USB device reset before opening the camera. Disabled by default.",
     )
     parser.add_argument(
         "--no-camera-usb-reset",
@@ -131,14 +211,13 @@ def build_parser() -> argparse.ArgumentParser:
         help=(
             "Optional command run before camera open to power-cycle the USB port, "
             "for example: \"uhubctl -l 1-2 -p 3 -a cycle -d 2\". "
-            "Defaults to DMD_CAMERA_POWER_CYCLE_COMMAND when set."
-        ),
+            "Defaults to DMD_CAMERA_POWER_CYCLE_COMMAND when set."),
     )
     parser.add_argument(
         "--camera-flush-reads",
         type=nonnegative_int,
-        default=1,
-        help="Number of stale event/trigger batch reads to discard after opening the camera.",
+        default=32,
+        help="Maximum stale event/trigger batch reads to discard before capture.",
     )
     parser.add_argument(
         "--camera-post-trigger-event-batches",
@@ -156,7 +235,16 @@ def build_parser() -> argparse.ArgumentParser:
         "--camera-shutdown-streams",
         action="store_true",
         default=False,
-        help="Diagnostic: stop camera streams before releasing the capture object. Disabled by default.",
+        help=
+        "Diagnostic: stop camera streams before releasing the capture object. Disabled by default.",
+    )
+    parser.add_argument(
+        "--accumulation-cycles",
+        type=positive_int,
+        default=None,
+        help=(
+            "Number of complete trigger cycles to use for derived accumulation artifacts. "
+            "Numbers mode defaults to 1; count mode defaults to unlimited."),
     )
     add_event_noise_filter_arguments(parser)
     parser.add_argument("-v", "--verbose", action="count", default=0)
@@ -164,25 +252,115 @@ def build_parser() -> argparse.ArgumentParser:
 
 
 def expected_trigger_count(args: argparse.Namespace) -> int:
+    if args.test == A_COUNT_B_STATIC_TEST:
+        return args.count_end - args.count_start + 1
     return len(args.numbers)
 
 
-def _asdict(value):
-    if is_dataclass(value):
-        return asdict(value)
-    return dict(getattr(value, "__dict__", {}))
-
-
 def _pair_runtime_seconds(args: argparse.Namespace) -> int:
     runtime_seconds = args.runtime_seconds
     if runtime_seconds <= 0:
-        if args.numbers_exposure_us is None:
+        if args.test == A_COUNT_B_STATIC_TEST:
+            exposure_us = args.count_exposure_us
+            if exposure_us is None:
+                runtime_seconds = 1
+            else:
+                per_count_us = exposure_us + max(0, args.dark_time_us or 0)
+                sequence_seconds = expected_trigger_count(args) * per_count_us / 1_000_000.0
+                runtime_seconds = max(1, math.ceil(sequence_seconds))
+        elif args.numbers_exposure_us is None:
             runtime_seconds = 1
         else:
             sequence_seconds = len(args.numbers) * args.numbers_exposure_us / 1_000_000.0
             runtime_seconds = max(1, math.ceil(sequence_seconds))
     return runtime_seconds
 
+
+def _requested_accumulation_window_us(args: argparse.Namespace) -> int | None:
+    if args.test == A_COUNT_B_STATIC_TEST:
+        return args.count_exposure_us
+    return args.numbers_exposure_us
+
+
+def _trigger_policy(args: argparse.Namespace) -> dict[str, object]:
+    return {
+        "channel": "TRIG_OUT_2",
+        "edge": "rising",
+        "delay_fraction": args.trigger_out_2_delay_fraction,
+    }
+
+
+def _sync_check_test_metadata(args: argparse.Namespace, *, dry_run: bool) -> dict[str, object]:
+    if args.test == A_COUNT_B_STATIC_TEST:
+        metadata = {
+            "count_start": args.count_start,
+            "count_end": args.count_end,
+            "count_slots_per_frame": args.count_slots_per_frame,
+            "count_exposure_us": args.count_exposure_us,
+        }
+        if dry_run:
+            metadata.update(
+                {
+                    "accumulation_window_us": _requested_accumulation_window_us(args),
+                    "bitplane_count": args.count_slots_per_frame,
+                })
+        return metadata
+
+    metadata = {
+        "number_sequence":
+        list(args.numbers),
+        "numbers_bitplane_order":
+        (list(args.numbers_bitplane_order) if args.numbers_bitplane_order is not None else None),
+        "numbers_exposure_us":
+        args.numbers_exposure_us,
+    }
+    if dry_run:
+        metadata["bitplane_count"] = len(args.numbers)
+    return metadata
+
+
+def _sync_check_metadata(
+    args: argparse.Namespace,
+    event_filter,
+    *,
+    dry_run: bool,
+    command: list[str],
+) -> dict[str,
+          object]:
+    metadata = {
+        "mode": "sync-check",
+        "dry_run": dry_run,
+        "test": args.test,
+        "command": command,
+        "number_size_px": args.number_size_px,
+        "b_dot_x": args.b_dot_x,
+        "b_dot_y": args.b_dot_y,
+        "b_dot_radius": args.b_dot_radius,
+        "expected_trigger_count": expected_trigger_count(args),
+        "seq_utilization": args.seq_utilization,
+        "trigger_policy": _trigger_policy(args),
+        "bias_sensitivity": args.bias_sensitivity,
+        "efps": args.efps,
+        "polarity_mode": args.polarity_mode,
+        "dark_time_us": args.dark_time_us,
+        "camera_usb_reset": args.camera_usb_reset,
+        "camera_power_cycle_command": args.camera_power_cycle_command,
+        "camera_open_method": args.camera_open_method,
+        "camera_flush_reads": args.camera_flush_reads,
+        "camera_post_trigger_event_batches": args.camera_post_trigger_event_batches,
+        "camera_stream_rearm": args.camera_stream_rearm,
+        "camera_shutdown_streams": args.camera_shutdown_streams,
+        "accumulation_cycles": args.requested_accumulation_cycles,
+        "accumulation_start_offset_us": args.accumulation_start_offset_us,
+        "event_noise_filter": event_noise_filter_metadata(event_filter),
+        "save_filtered_events": args.save_filtered_events,
+    }
+    if dry_run:
+        metadata["trigger_mode"] = "per_bitplane"
+    metadata.update(_sync_check_test_metadata(args, dry_run=dry_run))
+    return metadata
+
+
 # special run system for sync check
 def _to_pair_runtime_args(args: argparse.Namespace) -> list[str]:
     runtime_seconds = _pair_runtime_seconds(args)
@@ -197,16 +375,41 @@ def _to_pair_runtime_args(args: argparse.Namespace) -> list[str]:
         str(args.b_dot_y),
         "--b-dot-radius",
         str(args.b_dot_radius),
-        "--numbers",
-        ",".join(str(number) for number in args.numbers),
-        "--numbers-size-px",
-        str(args.number_size_px),
         "--runtime-seconds",
         str(runtime_seconds),
         "--trigger-out-2-delay-fraction",
         str(args.trigger_out_2_delay_fraction),
     ]
-    if args.numbers_exposure_us is not None:
+
+    if args.test == A_COUNT_B_STATIC_TEST:
+        pair_args.extend(
+            [
+                "--count-start",
+                str(args.count_start),
+                "--count-end",
+                str(args.count_end),
+                "--count-slots-per-frame",
+                str(args.count_slots_per_frame),
+                "--numbers-size-px",
+                str(args.number_size_px),
+            ])
+        if args.count_exposure_us is not None:
+            pair_args.extend(["--count-exposure-us", str(args.count_exposure_us)])
+    else:
+        pair_args.extend(
+            [
+                "--numbers",
+                ",".join(str(number) for number in args.numbers),
+                "--numbers-size-px",
+                str(args.number_size_px),
+            ])
+        if args.numbers_bitplane_order is not None:
+            pair_args.extend(
+                [
+                    "--numbers-bitplane-order",
+                    ",".join(str(index) for index in args.numbers_bitplane_order),
+                ])
+    if args.test != A_COUNT_B_STATIC_TEST and args.numbers_exposure_us is not None:
         pair_args.extend(["--numbers-exposure-us", str(args.numbers_exposure_us)])
     if args.seq_utilization is not None:
         pair_args.extend(["--seq-utilization", str(args.seq_utilization)])
@@ -222,51 +425,100 @@ def _to_pair_runtime_args(args: argparse.Namespace) -> list[str]:
 
 
 def _run_pair_with_callback(pair_args, before_start):
-    pair_module = import_module("dmdcontrol.runtime.pair")
+    from dmdcontrol.runtime import pair as pair_module
+
     return pair_module.run_with_before_start_callback(pair_args, before_start)
 
+
+def _copy_sweep_metadata(args: argparse.Namespace, metadata: dict[str, object]) -> None:
+    for source_name, metadata_name in (
+        ("sweep_id", "sweep_id"),
+        ("sweep_index", "sweep_index"),
+        ("sweep_repeat", "sweep_repeat"),
+        ("sweep_manifest", "sweep_manifest"),
+    ):
+        if hasattr(args, source_name):
+            metadata[metadata_name] = getattr(args, source_name)
+
+
+def _start_recording(
+    args,
+    capture,
+    writer,
+    event_records,
+    trigger_records,
+    accumulation_window_us,
+):
+    recording = AsyncCapture(
+        capture,
+        writer,
+        expected_trigger_count=None,
+        timeout_s=max(1,
+                      _pair_runtime_seconds(args)),
+        on_events=lambda batch: append_batch_records(event_records, batch, as_numpy=True),
+        on_triggers=lambda batch: append_batch_records(trigger_records, batch),
+        record_fn=record_until_trigger_count,
+        post_trigger_event_batches=args.camera_post_trigger_event_batches,
+        post_trigger_event_time_us=accumulation_window_us or 0,
+    )
+    recording.start()
+    return recording
+
+
+def _update_before_start_metadata(metadata, ready, context, accumulation_window_us):
+    metadata.update(
+        {
+            "camera_ready": metadata_dict(ready),
+            "dmd_ready": True,
+            "timing_a": context.get("state_a",
+                                    {}).get("timing"),
+            "timing_b": context.get("state_b",
+                                    {}).get("timing"),
+        })
+    if accumulation_window_us["value"] is None:
+        timing = context.get("state_a", {}).get("timing") or {}
+        accumulation_window_us["value"] = timing.get("exposure_us")
+    metadata["accumulation_window_us"] = accumulation_window_us["value"]
+
+
+def _write_capture_artifacts_for_sync_check(
+    args,
+    run,
+    ready,
+    event_records,
+    trigger_records,
+    event_filter,
+    accumulation_window_us,
+):
+    return write_capture_artifacts(
+        run,
+        events=event_records,
+        triggers=trigger_records,
+        resolution=tuple(ready.event_resolution),
+        window_us=accumulation_window_us or 0,
+        polarity_mode=args.polarity_mode,
+        event_noise_filter=event_filter,
+        save_filtered_events=args.save_filtered_events,
+        trigger_cycle_length=expected_trigger_count(args),
+        accumulation_cycles=args.requested_accumulation_cycles,
+        window_start_offset_us=args.accumulation_start_offset_us,
+        contact_sheet_columns=expected_trigger_count(args),
+    )
+
+
 def dry_run(args: argparse.Namespace):
     run = create_run_directory("sync-check", args.output_root, timestamp=args.timestamp)
     event_filter = event_noise_filter_config_from_args(args)
-    trigger_policy = {
-        "channel": "TRIG_OUT_2",
-        "edge": "rising",
-        "delay_fraction": args.trigger_out_2_delay_fraction,
-    }
-    metadata = {
-        "mode": "sync-check",
-        "dry_run": True,
-        "command": sys.argv,
-        "number_sequence": list(args.numbers),
-        "number_size_px": args.number_size_px,
-        "numbers_exposure_us": args.numbers_exposure_us,
-        "b_dot_x": args.b_dot_x,
-        "b_dot_y": args.b_dot_y,
-        "b_dot_radius": args.b_dot_radius,
-        "expected_trigger_count": expected_trigger_count(args),
-        "trigger_mode": "per_bitplane",
-        "bitplane_count": len(args.numbers),
-        "seq_utilization": args.seq_utilization,
-        "trigger_policy": trigger_policy,
-        "bias_sensitivity": args.bias_sensitivity,
-        "efps": args.efps,
-        "polarity_mode": args.polarity_mode,
-        "dark_time_us": args.dark_time_us,
-        "camera_usb_reset": args.camera_usb_reset,
-        "camera_power_cycle_command": args.camera_power_cycle_command,
-        "camera_open_method": args.camera_open_method,
-        "camera_flush_reads": args.camera_flush_reads,
-        "camera_post_trigger_event_batches": args.camera_post_trigger_event_batches,
-        "camera_stream_rearm": args.camera_stream_rearm,
-        "camera_shutdown_streams": args.camera_shutdown_streams,
-        "event_noise_filter": event_noise_filter_metadata(event_filter),
-        "save_filtered_events": args.save_filtered_events,
-    }
+    trigger_policy = _trigger_policy(args)
+    metadata = _sync_check_metadata(args, event_filter, dry_run=True, command=sys.argv)
     write_json(run.timing_path, trigger_policy)
     write_run_metadata(
         run,
         metadata,
-        artifacts=["metadata.json", "timing.json", "command.txt", "run.log"],
+        artifacts=["metadata.json",
+                   "timing.json",
+                   "command.txt",
+                   "run.log"],
     )
     run.command_path.write_text(
         "python -m dmdcontrol camera sync-check --dry-run\n",
@@ -276,101 +528,74 @@ def dry_run(args: argparse.Namespace):
     return run
 
 
-def live(args: argparse.Namespace) -> int:
-    run = create_run_directory("sync-check", args.output_root, timestamp=args.timestamp)
+def live_capture(
+    args: argparse.Namespace,
+    run,
+    capture,
+    writer,
+    ready,
+    command_argv: list[str] | None = None,
+) -> int:
     event_filter = event_noise_filter_config_from_args(args)
-    capture = None
-    writer = None
     recording = None
     capture_result = None
     artifact_summary = None
     event_records = []
     trigger_records = []
-    accumulation_window_us = {"value": args.numbers_exposure_us}
-    trigger_policy = {
-        "channel": "TRIG_OUT_2",
-        "edge": "rising",
-        "delay_fraction": args.trigger_out_2_delay_fraction,
-    }
-    metadata = {
-        "mode": "sync-check",
-        "dry_run": False,
-        "command": sys.argv,
-        "number_sequence": list(args.numbers),
-        "number_size_px": args.number_size_px,
-        "numbers_exposure_us": args.numbers_exposure_us,
-        "b_dot_x": args.b_dot_x,
-        "b_dot_y": args.b_dot_y,
-        "b_dot_radius": args.b_dot_radius,
-        "expected_trigger_count": expected_trigger_count(args),
-        "seq_utilization": args.seq_utilization,
-        "trigger_policy": trigger_policy,
-        "bias_sensitivity": args.bias_sensitivity,
-        "efps": args.efps,
-        "polarity_mode": args.polarity_mode,
-        "dark_time_us": args.dark_time_us,
-        
-        "camera_usb_reset": args.camera_usb_reset,
-        "camera_power_cycle_command": args.camera_power_cycle_command,
-        "camera_open_method": args.camera_open_method,
-        "camera_flush_reads": args.camera_flush_reads,
-        "camera_post_trigger_event_batches": args.camera_post_trigger_event_batches,
-        "camera_stream_rearm": args.camera_stream_rearm,
-        "camera_shutdown_streams": args.camera_shutdown_streams,
-        
-        "event_noise_filter": event_noise_filter_metadata(event_filter),
-        "save_filtered_events": args.save_filtered_events,
-    }
+    accumulation_window_us = {"value": _requested_accumulation_window_us(args)}
+    trigger_policy = _trigger_policy(args)
+    metadata = _sync_check_metadata(
+        args,
+        event_filter,
+        dry_run=False,
+        command=command_argv or sys.argv,
+    )
+    _copy_sweep_metadata(args, metadata)
 
     try:
-        capture, writer, ready = _open_ready_camera(run, args)
         write_json(run.timing_path, trigger_policy)
-        run.command_path.write_text(" ".join(sys.argv) + "\n", encoding="utf-8")
+        run.command_path.write_text(
+            " ".join(command_argv or sys.argv) + "\n",
+            encoding="utf-8",
+        )
         run.log_path.write_text("live\n", encoding="utf-8")
 
         def before_start(context):
             nonlocal recording
-            metadata.update({
-                "camera_ready": _asdict(ready),
-                "dmd_ready": True,
-                "timing_a": context.get("state_a", {}).get("timing"),
-                "timing_b": context.get("state_b", {}).get("timing"),
-            })
-            if accumulation_window_us["value"] is None:
-                timing = context.get("state_a", {}).get("timing") or {}
-                accumulation_window_us["value"] = timing.get("exposure_us")
-            metadata["accumulation_window_us"] = accumulation_window_us["value"]
+            _update_before_start_metadata(metadata, ready, context, accumulation_window_us)
+            metadata["camera_pre_capture_flush"] = flush_stale_batches(
+                capture,
+                reads=args.camera_flush_reads,
+                include_triggers=True,
+            )
             if recording is None:
-                recording = AsyncCapture(
+                recording = _start_recording(
+                    args,
                     capture,
                     writer,
-                    expected_trigger_count=expected_trigger_count(args),
-                    timeout_s=max(1, _pair_runtime_seconds(args)),
-                    on_events=lambda batch: append_batch_records(event_records, batch, as_numpy=True),
-                    on_triggers=lambda batch: append_batch_records(trigger_records, batch),
-                    record_fn=record_until_trigger_count,
-                    post_trigger_event_batches=args.camera_post_trigger_event_batches,
+                    event_records,
+                    trigger_records,
+                    accumulation_window_us["value"],
                 )
-                recording.start()
             write_run_metadata(
                 run,
                 metadata,
-                artifacts=["raw.aedat4", "metadata.json"],
+                artifacts=["raw.aedat4",
+                           "metadata.json"],
             )
 
         _run_pair_with_callback(_to_pair_runtime_args(args), before_start)
         if recording is not None:
             recording.stop()
             capture_result = recording.join()
-            artifact_summary = write_capture_artifacts(
+            artifact_summary = _write_capture_artifacts_for_sync_check(
+                args,
                 run,
-                events=event_records,
-                triggers=trigger_records,
-                resolution=tuple(ready.event_resolution),
-                window_us=accumulation_window_us["value"] or 0,
-                polarity_mode=args.polarity_mode,
-                event_noise_filter=event_filter,
-                save_filtered_events=args.save_filtered_events,
+                ready,
+                event_records,
+                trigger_records,
+                event_filter,
+                accumulation_window_us["value"],
             )
             metadata["artifact_summary"] = artifact_summary
             if "event_noise_filter" in artifact_summary:
@@ -384,37 +609,25 @@ def before_start(context):
             except BaseException as exc:
                 metadata["capture_error"] = repr(exc)
         if capture_result is not None:
-            metadata["capture"] = _asdict(capture_result)
-            artifacts = [
-                "raw.aedat4",
-                "metadata.json",
-                "command.txt",
-                "run.log",
-                "timing.json",
-            ]
-            if artifact_summary is not None:
-                artifacts.extend([
-                    "triggers.csv",
-                    "accumulated.npy",
-                    "contact_sheet.png",
-                    "summary.json",
-                ])
-                artifacts.extend(artifact_summary.get("frame_artifacts", []))
-                artifacts.extend(artifact_summary.get("filtered_frame_artifacts", []))
-                if artifact_summary.get("filtered_contact_sheet_artifact"):
-                    artifacts.append(artifact_summary["filtered_contact_sheet_artifact"])
-                if artifact_summary.get("filtered_events_artifact"):
-                    artifacts.append(artifact_summary["filtered_events_artifact"])
+            metadata["capture"] = metadata_dict(capture_result)
             write_run_metadata(
                 run,
                 metadata,
-                artifacts=artifacts,
+                artifacts=final_capture_artifacts(artifact_summary),
             )
         if recording is not None:
             recording = None
+
+
+def live(args: argparse.Namespace) -> int:
+    run = create_run_directory("sync-check", args.output_root, timestamp=args.timestamp)
+    capture = None
+    writer = None
+    try:
+        capture, writer, ready = _open_ready_camera(run, args)
+        return live_capture(args, run, capture, writer, ready)
+    finally:
         resources = {"writer": writer, "capture": capture}
-        writer = None
-        capture = None
         close_camera_resources(
             resources,
             shutdown_streams=args.camera_shutdown_streams,
diff --git a/dmdcontrol/camera/sync_sweep.py b/dmdcontrol/camera/sync_sweep.py
new file mode 100644
index 0000000..d92e6f2
--- /dev/null
+++ b/dmdcontrol/camera/sync_sweep.py
@@ -0,0 +1,231 @@
+from __future__ import annotations
+
+import argparse
+import csv
+import shlex
+from pathlib import Path
+
+from dmdcontrol.camera import sync_check
+from dmdcontrol.camera.capture import flush_stale_batches
+from dmdcontrol.camera.runs import create_run_directory
+from dmdcontrol.camera.session import (
+    close_camera_resources,
+    open_camera_writer,
+    open_ready_camera_capture,
+)
+
+ROW_OPTION_MAP = [
+    ("test",
+     "--test"),
+    ("test_b",
+     "--test-b"),
+    ("numbers",
+     "--numbers"),
+    ("number_size_px",
+     "--number-size-px"),
+    ("b_dot_x",
+     "--b-dot-x"),
+    ("b_dot_y",
+     "--b-dot-y"),
+    ("b_dot_radius",
+     "--b-dot-radius"),
+    ("numbers_exposure_us",
+     "--numbers-exposure-us"),
+    ("dark_time_us",
+     "--dark-time-us"),
+    ("trigger_delay_fraction",
+     "--trigger-out-2-delay-fraction"),
+    ("runtime_seconds",
+     "--runtime-seconds"),
+    ("seq_utilization",
+     "--seq-utilization"),
+    ("dmd_config",
+     "--dmd-config"),
+    ("hz",
+     "--hz"),
+    ("polarity_mode",
+     "--polarity-mode"),
+    ("event_noise_filter",
+     "--event-noise-filter"),
+    ("event_filter_delta_us",
+     "--event-filter-delta-us"),
+    ("event_filter_window_px",
+     "--event-filter-window-px"),
+    ("event_filter_threshold",
+     "--event-filter-threshold"),
+    ("event_filter_polarity",
+     "--event-filter-polarity"),
+    ("output_root",
+     "--output-root"),
+    ("timestamp",
+     "--timestamp"),
+    ("camera_open_method",
+     "--camera-open-method"),
+    ("camera_power_cycle_command",
+     "--camera-power-cycle-command"),
+    ("camera_flush_reads",
+     "--camera-flush-reads"),
+    ("camera_post_trigger_event_batches",
+     "--camera-post-trigger-event-batches"),
+    ("accumulation_cycles",
+     "--accumulation-cycles"),
+    ("bias_sensitivity",
+     "--bias-sensitivity"),
+    ("efps",
+     "--efps"),
+]
+
+ROW_FLAG_MAP = [
+    ("save_filtered_events",
+     "--save-filtered-events"),
+    ("camera_usb_reset",
+     "--camera-usb-reset"),
+    ("camera_stream_rearm",
+     "--camera-stream-rearm"),
+    ("camera_shutdown_streams",
+     "--camera-shutdown-streams"),
+]
+
+PERSISTENT_CAMERA_FIELDS = [
+    "camera_open_method",
+    "camera_usb_reset",
+    "camera_power_cycle_command",
+    "camera_stream_rearm",
+    "camera_shutdown_streams",
+    "camera_flush_reads",
+    "bias_sensitivity",
+    "efps",
+]
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        prog="python -m dmdcontrol camera sync-sweep",
+        description="Run a sync-check manifest in one process with one camera open.",
+    )
+    parser.add_argument("--manifest", required=True, help="CSV manifest generated by notebook 01.")
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Create run artifacts without opening hardware.")
+    return parser
+
+
+def _has_value(value) -> bool:
+    return value is not None and str(value).strip() not in {"", "None", "none", "nan"}
+
+
+def _row_sync_check_argv(row: dict[str, str]) -> list[str]:
+    explicit = row.get("sync_check_argv", "")
+    if explicit.strip():
+        return shlex.split(explicit)
+
+    command = row.get("command", "")
+    if command.strip():
+        tokens = shlex.split(command)
+        if tokens and tokens[0].endswith("run_camera_sync_check.sh"):
+            return tokens[1:]
+        if "sync-check" in tokens:
+            return tokens[tokens.index("sync-check") + 1:]
+        return tokens
+
+    argv = []
+    for key, flag in ROW_OPTION_MAP:
+        value = row.get(key)
+        if _has_value(value):
+            argv.extend([flag, str(value)])
+    for key, flag in ROW_FLAG_MAP:
+        if str(row.get(key, "")).strip().lower() in {"1", "true", "yes", "y", "on"}:
+            argv.append(flag)
+    verbose = row.get("verbose")
+    if _has_value(verbose):
+        count = int(float(verbose))
+        if count > 0:
+            argv.append("-" + ("v" * count))
+    return argv
+
+
+def _load_manifest(path: Path) -> list[tuple[dict[str, str], list[str], argparse.Namespace]]:
+    with path.open(newline="", encoding="utf-8") as handle:
+        rows = list(csv.DictReader(handle))
+    if not rows:
+        raise SystemExit(f"Manifest has no rows: {path}")
+
+    parsed = []
+    parser = sync_check.build_parser()
+    for index, row in enumerate(rows):
+        argv = _row_sync_check_argv(row)
+        args = parser.parse_args(argv)
+        args.sweep_manifest = str(path)
+        args.sweep_index = int(row["index"]) if _has_value(row.get("index")) else index
+        if _has_value(row.get("sweep_id")):
+            args.sweep_id = row["sweep_id"]
+        if _has_value(row.get("repeat")):
+            args.sweep_repeat = int(row["repeat"])
+        parsed.append((row, argv, args))
+    return parsed
+
+
+def _validate_persistent_camera_args(parsed_rows) -> None:
+    first_args = parsed_rows[0][2]
+    first = {name: getattr(first_args, name, None) for name in PERSISTENT_CAMERA_FIELDS}
+    for row_number, (_, _, args) in enumerate(parsed_rows[1:], start=2):
+        for name, expected in first.items():
+            actual = getattr(args, name, None)
+            if actual != expected:
+                raise SystemExit(
+                    f"Manifest row {row_number} changes --{name.replace('_', '-')} "
+                    f"from {expected!r} to {actual!r}; persistent camera settings must not vary.")
+
+
+def dry_run(parsed_rows) -> int:
+    for _, _, args in parsed_rows:
+        args.dry_run = True
+        sync_check.dry_run(args)
+    return 0
+
+
+def live(parsed_rows) -> int:
+    _validate_persistent_camera_args(parsed_rows)
+    first_args = parsed_rows[0][2]
+    capture = None
+    try:
+        capture, ready = open_ready_camera_capture(first_args)
+        for _, argv, args in parsed_rows:
+            run = create_run_directory("sync-check", args.output_root, timestamp=args.timestamp)
+            writer = None
+            try:
+                flush_stale_batches(
+                    capture,
+                    reads=args.camera_flush_reads,
+                )
+                writer = open_camera_writer(run, capture)
+                command_argv = ["python", "-m", "dmdcontrol", "camera", "sync-check", *argv]
+                sync_check.live_capture(
+                    args,
+                    run,
+                    capture,
+                    writer,
+                    ready,
+                    command_argv=command_argv)
+            finally:
+                resources = {"writer": writer}
+                writer = None
+                close_camera_resources(resources, shutdown_streams=False)
+        return 0
+    finally:
+        resources = {"capture": capture}
+        capture = None
+        close_camera_resources(
+            resources,
+            shutdown_streams=first_args.camera_shutdown_streams if parsed_rows else False,
+        )
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = build_parser().parse_args(argv)
+    manifest_path = Path(args.manifest)
+    parsed_rows = _load_manifest(manifest_path)
+    if args.dry_run:
+        return dry_run(parsed_rows)
+    return live(parsed_rows)
diff --git a/dmdcontrol/camera/usb_reset.py b/dmdcontrol/camera/usb_reset.py
index e8005ed..7faa4a9 100644
--- a/dmdcontrol/camera/usb_reset.py
+++ b/dmdcontrol/camera/usb_reset.py
@@ -9,19 +9,18 @@
 from dataclasses import asdict, dataclass
 from pathlib import Path
 
-# remove completely later
-
 try:
     import fcntl
 except ImportError:
+
     class _MissingFcntl:
+
         @staticmethod
         def ioctl(*_args):
             raise OSError("fcntl is unavailable on this platform")
 
     fcntl = _MissingFcntl()
 
-
 USBDEVFS_RESET = (ord("U") << 8) | 20
 
 
@@ -76,9 +75,13 @@ def reset_camera_usb(
         dev_bus_usb_root=Path("/dev/bus/usb"),
 ):
     if not enabled:
-        return UsbResetResult(attempted=False, success=False, errors=("disabled",)).to_dict()
+        return UsbResetResult(attempted=False, success=False, errors=("disabled", )).to_dict()
     if sys.platform != "linux":
-        return UsbResetResult(attempted=False, success=False, errors=(f"unsupported platform: {sys.platform}",)).to_dict()
+        return UsbResetResult(
+            attempted=False,
+            success=False,
+            errors=(f"unsupported platform: {sys.platform}",
+                    )).to_dict()
     if method not in {"auto", "ioctl", "authorized"}:
         raise ValueError("method must be one of: auto, ioctl, authorized")
 
@@ -89,7 +92,8 @@ def reset_camera_usb(
         return UsbResetResult(
             attempted=True,
             success=False,
-            errors=("no matching USB camera device found in sysfs",),
+            errors=("no matching USB camera device found in sysfs",
+                    ),
         ).to_dict()
 
     errors = []
@@ -198,8 +202,7 @@ def _list_usb_devices(sysfs_root):
                 manufacturer=_read_text(path / "manufacturer"),
                 product=_read_text(path / "product"),
                 serial=_read_text(path / "serial"),
-            )
-        )
+            ))
     return devices
 
 
@@ -219,9 +222,7 @@ def _select_usb_device(devices, descriptor):
             return address_match
 
     camera_matches = [
-        device for device in devices
-        if _looks_like_event_camera(device, descriptor_model)
-    ]
+        device for device in devices if _looks_like_event_camera(device, descriptor_model)]
     if len(camera_matches) == 1:
         return camera_matches[0]
 
@@ -233,10 +234,7 @@ def _match_by_address(devices, descriptor_address):
     if len(numbers) < 2:
         return None
     bus, dev = numbers[-2], numbers[-1]
-    matches = [
-        device for device in devices
-        if device.busnum == bus and device.devnum == dev
-    ]
+    matches = [device for device in devices if device.busnum == bus and device.devnum == dev]
     return matches[0] if len(matches) == 1 else None
 
 
@@ -244,8 +242,7 @@ def _looks_like_event_camera(device, descriptor_model):
     haystack = " ".join(
         value.lower()
         for value in (device.manufacturer, device.product, device.serial, descriptor_model)
-        if value
-    )
+        if value)
     return any(token in haystack for token in ("inivation", "dvxplorer", "davis", "dvs"))
 
 
@@ -266,7 +263,8 @@ def _reset_with_ioctl(device, dev_bus_usb_root, settle_s):
             "ioctl",
             device_path=device_path,
             success=False,
-            errors=(repr(exc),),
+            errors=(repr(exc),
+                    ),
         )
 
 
@@ -280,7 +278,7 @@ def _reset_with_authorized(device, settle_s):
             time.sleep(settle_s)
         return _result(device, "authorized", success=True)
     except Exception as exc:
-        return _result(device, "authorized", success=False, errors=(repr(exc),))
+        return _result(device, "authorized", success=False, errors=(repr(exc), ))
 
 
 def _result(device, method, *, success, device_path=None, errors=()):
diff --git a/dmdcontrol/cli/camera.py b/dmdcontrol/cli/camera.py
index 02b53d3..4eb087e 100644
--- a/dmdcontrol/cli/camera.py
+++ b/dmdcontrol/cli/camera.py
@@ -1,19 +1,12 @@
 from __future__ import annotations
 
 import json
-from importlib import import_module
 
 
 def _discovery_module():
-    return import_module("dmdcontrol.camera.discovery")
+    from dmdcontrol.camera import discovery
 
-
-def _sync_check_module():
-    return import_module("dmdcontrol.camera.sync_check")
-
-
-def _pair_capture_module():
-    return import_module("dmdcontrol.camera.pair_capture")
+    return discovery
 
 
 def discover(argv):
@@ -29,8 +22,24 @@ def status(argv):
 
 
 def sync_check(argv):
-    return _sync_check_module().main(argv)
+    from dmdcontrol.camera import sync_check as sync_check_module
+
+    return sync_check_module.main(argv)
+
+
+def sync_sweep(argv):
+    from dmdcontrol.camera import sync_sweep as sync_sweep_module
+
+    return sync_sweep_module.main(argv)
 
 
 def pair_capture(argv):
-    return _pair_capture_module().main(argv)
+    from dmdcontrol.camera import pair_capture as pair_capture_module
+
+    return pair_capture_module.main(argv)
+
+
+def reprocess_aedat4(argv):
+    from dmdcontrol.camera import reprocess_aedat4 as reprocess_aedat4_module
+
+    return reprocess_aedat4_module.main(argv)
diff --git a/dmdcontrol/cli/config.py b/dmdcontrol/cli/config.py
index 4b4f92f..e013564 100644
--- a/dmdcontrol/cli/config.py
+++ b/dmdcontrol/cli/config.py
@@ -2,7 +2,6 @@
 
 import argparse
 import json
-from dataclasses import asdict, is_dataclass
 from types import ModuleType
 
 FIELDS = (
@@ -29,16 +28,10 @@ def _build_parser() -> argparse.ArgumentParser:
     return parser
 
 
-def _mapping_dict(mapping) -> dict:
-    if is_dataclass(mapping):
-        return asdict(mapping)
-    return {field: getattr(mapping, field) for field in FIELDS}
-
-
 def show(argv: list[str]) -> int:
     args = _build_parser().parse_args(argv)
     mapping = _mapping_module().resolve_dmd_mapping(args.dmd, args.config)
-    values = _mapping_dict(mapping)
+    values = {field: getattr(mapping, field) for field in FIELDS}
     if args.field:
         value = values[args.field]
         print("" if value is None else value)
diff --git a/dmdcontrol/cli/main.py b/dmdcontrol/cli/main.py
index 6a9238b..fe36eba 100644
--- a/dmdcontrol/cli/main.py
+++ b/dmdcontrol/cli/main.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import argparse
-from importlib import import_module
 
 
 def _build_parser() -> argparse.ArgumentParser:
@@ -39,7 +38,9 @@ def _build_parser() -> argparse.ArgumentParser:
     camera_actions.add_parser("discover", add_help=False)
     camera_actions.add_parser("status", add_help=False)
     camera_actions.add_parser("sync-check", add_help=False)
+    camera_actions.add_parser("sync-sweep", add_help=False)
     camera_actions.add_parser("pair-capture", add_help=False)
+    camera_actions.add_parser("reprocess-aedat4", add_help=False)
 
     return parser
 
@@ -47,39 +48,48 @@ def _build_parser() -> argparse.ArgumentParser:
 def main(argv: list[str] | None = None) -> int | None:
     args, passthrough = _build_parser().parse_known_args(argv)
     if args.area == "single" and args.command == "run":
-        single = import_module("dmdcontrol.cli.single")
+        from dmdcontrol.cli import single
+
         return single.run(passthrough)
     if args.area == "pair" and args.command == "run":
-        pair = import_module("dmdcontrol.cli.pair")
+        from dmdcontrol.cli import pair
+
         return pair.run(passthrough)
     if args.area == "pair" and args.command == "calibrate":
-        pair = import_module("dmdcontrol.cli.pair")
+        from dmdcontrol.cli import pair
+
         return pair.calibrate(passthrough)
     if args.area == "preview" and args.command == "serve":
-        preview = import_module("dmdcontrol.cli.preview")
+        from dmdcontrol.cli import preview
+
         return preview.serve(passthrough)
     if args.area == "usb" and args.command == "discover":
-        usb = import_module("dmdcontrol.cli.usb")
+        from dmdcontrol.cli import usb
+
         return usb.discover(passthrough)
     if args.area == "usb" and args.command == "wake":
-        usb = import_module("dmdcontrol.cli.usb")
+        from dmdcontrol.cli import usb
+
         return usb.wake(passthrough)
     if args.area == "flood" and args.command == "run":
-        flood = import_module("dmdcontrol.cli.flood")
+        from dmdcontrol.cli import flood
+
         return flood.run(passthrough)
     if args.area == "config" and args.command == "show":
-        config = import_module("dmdcontrol.cli.config")
+        from dmdcontrol.cli import config
+
         return config.show(passthrough)
-    if args.area == "camera" and args.command == "discover":
-        camera = import_module("dmdcontrol.cli.camera")
-        return camera.discover(passthrough)
-    if args.area == "camera" and args.command == "status":
-        camera = import_module("dmdcontrol.cli.camera")
-        return camera.status(passthrough)
-    if args.area == "camera" and args.command == "sync-check":
-        camera = import_module("dmdcontrol.cli.camera")
-        return camera.sync_check(passthrough)
-    if args.area == "camera" and args.command == "pair-capture":
-        camera = import_module("dmdcontrol.cli.camera")
-        return camera.pair_capture(passthrough)
+    if args.area == "camera":
+        from dmdcontrol.cli import camera
+
+        handlers = {
+            "discover": camera.discover,
+            "status": camera.status,
+            "sync-check": camera.sync_check,
+            "sync-sweep": camera.sync_sweep,
+            "pair-capture": camera.pair_capture,
+            "reprocess-aedat4": camera.reprocess_aedat4,
+        }
+        if args.command in handlers:
+            return handlers[args.command](passthrough)
     raise SystemExit(f"Unsupported command: {args.area} {args.command}")
diff --git a/dmdcontrol/cli/pair.py b/dmdcontrol/cli/pair.py
index 464a9ab..f45f792 100644
--- a/dmdcontrol/cli/pair.py
+++ b/dmdcontrol/cli/pair.py
@@ -27,17 +27,13 @@ def _translate_run_args(argv: list[str]) -> list[str]:
     return translated
 
 
-def _has_runtime_seconds(argv: list[str]) -> bool:
-    return any(arg == "--runtime-seconds" or arg.startswith("--runtime-seconds=") for arg in argv)
-
-
 def run(argv: list[str]) -> int | None:
     return _pair_runtime().main(_translate_run_args(argv))
 
 
 def calibrate(argv: list[str]) -> int | None:
     translated = ["--test", CALIBRATION_TEST]
-    if not _has_runtime_seconds(argv):
+    if not any(arg == "--runtime-seconds" or arg.startswith("--runtime-seconds=") for arg in argv):
         translated.extend(["--runtime-seconds", "0"])
     translated.extend(argv)
     return _pair_runtime().main(translated)
diff --git a/dmdcontrol/cli/preview.py b/dmdcontrol/cli/preview.py
index f33a910..20a47e1 100644
--- a/dmdcontrol/cli/preview.py
+++ b/dmdcontrol/cli/preview.py
@@ -1,13 +1,7 @@
 from __future__ import annotations
 
-from types import ModuleType
 
-
-def _preview_server() -> ModuleType:
+def serve(argv: list[str]) -> int | None:
     from dmdcontrol.preview import server
 
-    return server
-
-
-def serve(argv: list[str]) -> int | None:
-    return _preview_server().main(argv)
+    return server.main(argv)
diff --git a/dmdcontrol/hardware/dlpc900.py b/dmdcontrol/hardware/dlpc900.py
index 7be54ca..5f20c26 100644
--- a/dmdcontrol/hardware/dlpc900.py
+++ b/dmdcontrol/hardware/dlpc900.py
@@ -26,6 +26,8 @@ class DLPC900:
     PID = DLPC900_PID
 
     def __init__(self, usb_id_path=None, usb_devpath_contains=None):
+        self._hid_intf = None
+        self._closed = False
         try:
             if usb_id_path:
                 from dmdcontrol.hardware.usb import select_pyusb_device_for_mapping
@@ -36,8 +38,7 @@ def __init__(self, usb_id_path=None, usb_devpath_contains=None):
                 )
                 logger.info(
                     f"[DLPC900] Selected USB mapping id_path={usb_id_path} "
-                    f"devpath_contains={usb_devpath_contains or '<not required>'}"
-                )
+                    f"devpath_contains={usb_devpath_contains or '<not required>'}")
             else:
                 found = usb.core.find(idVendor=self.VID, idProduct=self.PID)
         except Exception as e:
@@ -75,6 +76,21 @@ def __init__(self, usb_id_path=None, usb_devpath_contains=None):
         usb.util.claim_interface(self.dev, hid_intf)
         self._seq = 0
 
+    def close(self):
+        if self._closed:
+            return
+        self._closed = True
+        hid_intf = self._hid_intf
+        if hid_intf is not None:
+            try:
+                usb.util.release_interface(self.dev, hid_intf)
+            except Exception as exc:
+                logger.debug(f"[DLPC900] USB release_interface warning: {exc}")
+        try:
+            usb.util.dispose_resources(self.dev)
+        except Exception as exc:
+            logger.debug(f"[DLPC900] USB dispose_resources warning: {exc}")
+
     def _seq_next(self):
         v = self._seq & 0xFF
         self._seq = (self._seq + 1) & 0xFF
@@ -87,16 +103,19 @@ def _command(self, read: bool, cmd_id: int, data: bytes = b""):
         plen = 2 + len(data)
 
         # Build full packet: header + data, then split into 64-byte frames.
-        buf = bytearray([
-            flag, seq,
-            plen & 0xFF, (plen >> 8) & 0xFF,
-            cmd_id & 0xFF,  # LSB → byte 4
-            (cmd_id >> 8) & 0xFF,  # MSB → byte 5
-        ])
+        buf = bytearray(
+            [
+                flag,
+                seq,
+                plen & 0xFF,
+                (plen >> 8) & 0xFF,
+                cmd_id & 0xFF,  # LSB → byte 4
+                (cmd_id >> 8) & 0xFF,  # MSB → byte 5
+            ])
         buf.extend(data)
 
         for off in range(0, max(len(buf), 1), 64):
-            chunk = bytes(buf[off: off + 64]).ljust(64, b"\x00")
+            chunk = bytes(buf[off:off + 64]).ljust(64, b"\x00")
             try:
                 self.dev.write(0x01, chunk, timeout=2000)
             except usb.core.USBError:
@@ -122,8 +141,7 @@ def _command(self, read: bool, cmd_id: int, data: bytes = b""):
         if resp is not None and (resp[0] & 0x20):
             logger.warning(
                 f"[NACK] Firmware rejected cmd 0x{cmd_id:04X} (flags=0x{resp[0]:02X}). "
-                "Check command validity for current display mode."
-            )
+                "Check command validity for current display mode.")
         return resp if resp is not None else last_resp
 
     def _write(self, cmd_id: int, data: bytes = b""):
@@ -145,10 +163,10 @@ def _payload(self, resp, min_len: int = 1):
         if plen == 0:
             return b""
         if len(resp) >= 7 and plen >= 2:
-            d = resp[6: 6 + (plen - 2)]
+            d = resp[6:6 + (plen - 2)]
             if len(d) >= min_len:
                 return d
-        d = resp[4: 4 + plen]
+        d = resp[4:4 + plen]
         return d if len(d) >= min_len else None
 
     # ---- status / diagnostic -----------------------------------------
@@ -235,7 +253,8 @@ def get_channel_swap(self):
             "raw": hex(v),
             "port": f"P{port + 1}",
             "swap_code": swap,
-            "swap_label": labels.get(swap, f"Unknown({swap})"),
+            "swap_label": labels.get(swap,
+                                     f"Unknown({swap})"),
         }
 
     def get_display_dimensions(self):
@@ -243,14 +262,30 @@ def get_display_dimensions(self):
         p = self._payload(resp, min_len=18)
         if p and len(p) >= 18:
             return {
-                "total_pixels_per_line": struct.unpack_from("<H", p, 0)[0],
-                "total_lines_per_frame": struct.unpack_from("<H", p, 2)[0],
-                "active_pixels_per_line": struct.unpack_from("<H", p, 4)[0],
-                "active_lines_per_frame": struct.unpack_from("<H", p, 6)[0],
-                "first_active_pixel": struct.unpack_from("<H", p, 8)[0],
-                "first_active_line": struct.unpack_from("<H", p, 10)[0],
-                "bottom_field_first_line": struct.unpack_from("<H", p, 12)[0],
-                "pixel_clock_khz": struct.unpack_from("<I", p, 14)[0],
+                "total_pixels_per_line": struct.unpack_from("<H",
+                                                            p,
+                                                            0)[0],
+                "total_lines_per_frame": struct.unpack_from("<H",
+                                                            p,
+                                                            2)[0],
+                "active_pixels_per_line": struct.unpack_from("<H",
+                                                             p,
+                                                             4)[0],
+                "active_lines_per_frame": struct.unpack_from("<H",
+                                                             p,
+                                                             6)[0],
+                "first_active_pixel": struct.unpack_from("<H",
+                                                         p,
+                                                         8)[0],
+                "first_active_line": struct.unpack_from("<H",
+                                                        p,
+                                                        10)[0],
+                "bottom_field_first_line": struct.unpack_from("<H",
+                                                              p,
+                                                              12)[0],
+                "pixel_clock_khz": struct.unpack_from("<I",
+                                                      p,
+                                                      14)[0],
             }
         return None
 
@@ -259,10 +294,14 @@ def get_port_config(self):
         p = self._payload(resp)
         if p:
             v = p[0]
-            modes = {0: "Single Pixel Port 1", 1: "Single Pixel Port 2",
-                     2: "Dual Pixel P1-P2", 3: "Dual Pixel P2-P1"}
+            modes = {
+                0: "Single Pixel Port 1",
+                1: "Single Pixel Port 2",
+                2: "Dual Pixel P1-P2",
+                3: "Dual Pixel P2-P1"}
             return {
-                "pixel_mode": modes.get(v & 0x03, f"Unknown({v & 0x03})"),
+                "pixel_mode": modes.get(v & 0x03,
+                                        f"Unknown({v & 0x03})"),
                 "pixel_clock": f"Clock {((v >> 2) & 0x03) + 1}",
                 "data_enable": f"DE {((v >> 4) & 0x01) + 1}",
                 "sync_select": f"P{((v >> 5) & 0x01) + 1} VSync/HSync",
@@ -276,8 +315,12 @@ def get_trigger_out_1(self):
         if p and len(p) >= 5:
             return {
                 "polarity": "Inverted" if p[0] & 0x01 else "Non-inverted",
-                "rising_delay_us": struct.unpack_from("<h", p, 1)[0],
-                "falling_delay_us": struct.unpack_from("<h", p, 3)[0],
+                "rising_delay_us": struct.unpack_from("<h",
+                                                      p,
+                                                      1)[0],
+                "falling_delay_us": struct.unpack_from("<h",
+                                                       p,
+                                                       3)[0],
                 "raw": p.hex(),
             }
         return {"raw": p.hex() if p else "NO_RESPONSE"}
@@ -288,8 +331,12 @@ def get_trigger_out_2(self):
         if p and len(p) >= 5:
             return {
                 "polarity": "Inverted" if p[0] & 0x01 else "Non-inverted",
-                "rising_delay_us": struct.unpack_from("<h", p, 1)[0],
-                "falling_delay_us": struct.unpack_from("<h", p, 3)[0],
+                "rising_delay_us": struct.unpack_from("<h",
+                                                      p,
+                                                      1)[0],
+                "falling_delay_us": struct.unpack_from("<h",
+                                                       p,
+                                                       3)[0],
                 "raw": p.hex(),
             }
         return {"raw": p.hex() if p else "NO_RESPONSE"}
@@ -305,26 +352,44 @@ def set_input_source(self, source=0, bit_depth_sel=1):
 
     def set_port_config(self, pixel_mode=2, pixel_clock=0, data_enable=0, sync_select=0):
         val = (
-                (pixel_mode & 0x03)
-                | ((pixel_clock & 0x03) << 2)
-                | ((data_enable & 0x01) << 4)
-                | ((sync_select & 0x01) << 5)
-        )
+            (pixel_mode & 0x03)
+            | ((pixel_clock & 0x03) << 2)
+            | ((data_enable & 0x01) << 4)
+            | ((sync_select & 0x01) << 5))
         self._write(0x1A03, struct.pack("<B", val))
 
     def toggle_dual_pixel_mode(self, enable):
-        self.set_port_config(pixel_mode=(2 if enable else 0),
-                             pixel_clock=0, data_enable=0, sync_select=0)
-
-    def set_input_display_resolution(self, in_x, in_y, in_w, in_h,
-                                     out_x=None, out_y=None, out_w=None, out_h=None):
+        self.set_port_config(
+            pixel_mode=(2 if enable else 0),
+            pixel_clock=0,
+            data_enable=0,
+            sync_select=0)
+
+    def set_input_display_resolution(
+            self,
+            in_x,
+            in_y,
+            in_w,
+            in_h,
+            out_x=None,
+            out_y=None,
+            out_w=None,
+            out_h=None):
         out_x = in_x if out_x is None else out_x
         out_y = in_y if out_y is None else out_y
         out_w = in_w if out_w is None else out_w
         out_h = in_h if out_h is None else out_h
-        self._write(0x1000, struct.pack("<HHHHHHHH",
-                                        in_x, in_y, in_w, in_h,
-                                        out_x, out_y, out_w, out_h))
+        self._write(
+            0x1000,
+            struct.pack("<HHHHHHHH",
+                        in_x,
+                        in_y,
+                        in_w,
+                        in_h,
+                        out_x,
+                        out_y,
+                        out_w,
+                        out_h))
 
     # ---- LEDs --------------------------------------------------------
 
@@ -351,13 +416,11 @@ def apply_block_lock_workaround(self):
 
     # ---- triggers ----------------------------------------------------
 
-    def configure_trigger_out_1(self, polarity_high=True,
-                                rising_delay_us=0, falling_delay_us=20):
+    def configure_trigger_out_1(self, polarity_high=True, rising_delay_us=0, falling_delay_us=20):
         b0 = 0 if polarity_high else 1
         self._write(0x1A1D, struct.pack("<Bhh", b0, rising_delay_us, falling_delay_us))
 
-    def configure_trigger_out_2(self, polarity_high=True,
-                                rising_delay_us=0, falling_delay_us=20):
+    def configure_trigger_out_2(self, polarity_high=True, rising_delay_us=0, falling_delay_us=20):
         b0 = 0 if polarity_high else 1
         self._write(0x1A1E, struct.pack("<Bhh", b0, rising_delay_us, falling_delay_us))
 
@@ -372,20 +435,6 @@ def set_pattern_lut_config(self, num_entries, repeat=True):
         num_to_display = 0 if repeat else num_entries
         self._write(0x1A31, struct.pack("<HI", num_entries, num_to_display))
 
-    def set_pattern_lut_reorder(self, order, repeat=True):
-        """0x1A32: Reorder LUT playback sequence for Pattern Mode."""
-        order_list = [int(idx) for idx in order]
-        if not order_list:
-            raise ValueError("Pattern LUT reorder list cannot be empty")
-        for idx in order_list:
-            if not (0 <= idx <= 399):
-                raise ValueError(f"Reorder index out of range: {idx}")
-        nr = len(order_list)
-        np_ = 0 if repeat else nr
-        payload = struct.pack("<HI", nr, np_)
-        payload += b"".join(struct.pack("<H", idx) for idx in order_list)
-        self._write(0x1A32, payload)
-
     def set_pattern_lut_definition(self, entries):
         """0x1A34: Define pattern LUT entries (DLPU018J Table 2-143).
 
@@ -418,22 +467,36 @@ def set_pattern_lut_definition(self, entries):
             b1011 = (image_index & 0x07FF) | ((bit_pos & 0x1F) << 11)
 
             entry_payload = (
-                    struct.pack("<H", idx)
-                    + exp3
-                    + struct.pack("<B", b5)
-                    + dark3
-                    + struct.pack("<B", b9)
-                    + struct.pack("<H", b1011)
-            )
+                struct.pack("<H",
+                            idx) + exp3 + struct.pack("<B",
+                                                      b5) + dark3 + struct.pack("<B",
+                                                                                b9) +
+                struct.pack("<H",
+                            b1011))
             self._write(0x1A34, entry_payload)
 
     # ---- misc --------------------------------------------------------
 
+    def wake_displayport_receiver(self):
+        self._write(0x1A01, bytes([2]))
+
+    def set_input_pixel_format(self, pixel_format):
+        self._write(0x1A02, struct.pack("<B", pixel_format))
+
+    def set_internal_test_pattern_color(self, foreground, background=None):
+        background = foreground if background is None else background
+        self._write(
+            0x1204,
+            struct.pack(
+                "<HHHHHH",
+                foreground,
+                foreground,
+                foreground,
+                background,
+                background,
+                background,
+            ),
+        )
+
     def set_internal_test_pattern(self, pattern):
         self._write(0x1203, struct.pack("<B", pattern))
-
-    def send_packet(self, cmd_id: int, data: bytes = b"", read: bool = False):
-        """Backward-compat shim: routes to _read or _write."""
-        if read:
-            return self._read(cmd_id)
-        self._write(cmd_id, data)
diff --git a/dmdcontrol/hardware/flood.py b/dmdcontrol/hardware/flood.py
index ac14f2b..cf0968a 100644
--- a/dmdcontrol/hardware/flood.py
+++ b/dmdcontrol/hardware/flood.py
@@ -22,7 +22,6 @@
 """
 
 import argparse
-import struct
 import sys
 import time
 
@@ -33,8 +32,8 @@
 DLPC900_VID = 0x0451
 DLPC900_PID = 0xC900
 
-COLOR_WHITE_10BIT = 0x03FF
-COLOR_BLACK_10BIT = 0x0000
+INTERNAL_PATTERN_WHITE_LEVEL = 0x03FF
+INTERNAL_PATTERN_BLACK_LEVEL = 0x0000
 
 DISPLAY_MODE_VIDEO = 0
 INPUT_SOURCE_INTERNAL_TEST_PATTERN = 1
@@ -52,60 +51,14 @@ def _count_dlpc900_devices() -> int:
     return sum(1 for _ in devices)
 
 
-def _require_byte_range(name: str, value: int) -> int:
-    if not (0 <= value <= 255):
-        raise ValueError(f"{name} must be in range 0..255, got {value}")
-    return value
-
-
-def _effective_color(requested: str, invert: bool) -> str:
-    if requested not in ("white", "black"):
-        raise ValueError(f"Unsupported color: {requested}")
-    if not invert:
-        return requested
-    return "black" if requested == "white" else "white"
-
-
-def _color_value_10bit(color: str) -> int:
-    if color == "white":
-        return COLOR_WHITE_10BIT
-    if color == "black":
-        return COLOR_BLACK_10BIT
-    raise ValueError(f"Unsupported color: {color}")
-
-
-def _write_pixel_format_rgb(dlpc: DLPC900) -> None:
-    # DLPC900 command 0x1A02: Input Pixel Data Format.
-    # 0 = RGB.
-    dlpc.send_packet(0x1A02, struct.pack("<B", PIXEL_FORMAT_RGB))
-
-
-def _write_internal_test_pattern_color(dlpc: DLPC900, color: str) -> None:
-    """
-    DLPC900 command 0x1204: Internal Test Patterns Color.
-
-    Payload is six 10-bit color intensities stored as little-endian uint16:
-      red foreground
-      green foreground
-      blue foreground
-      red background
-      green background
-      blue background
-
-    For solid field, foreground is the one that matters. Background is set to
-    the same value anyway so other simple patterns do not surprise you if the
-    test pattern state changes.
-    """
-    v = _color_value_10bit(color)
-    payload = struct.pack("<HHHHHH", v, v, v, v, v, v)
-    dlpc.send_packet(0x1204, payload)
-
-
 def configure_solid_flood(
-        color: str,
-        led_current: int,
-        leave_leds_alone: bool,
+    color: str,
+    led_current: int,
+    leave_leds_alone: bool,
 ) -> None:
+    if color not in ("white", "black"):
+        raise ValueError(f"Unsupported color: {color}")
+
     device_count = _count_dlpc900_devices()
     if device_count == 0:
         raise RuntimeError("No DLPC900 USB device found.")
@@ -113,8 +66,7 @@ def configure_solid_flood(
         raise RuntimeError(
             f"Found {device_count} DLPC900 USB devices. "
             "Unplug the other controller or pass --allow-multiple if you accept "
-            "that usb.core.find() will use the first matching device."
-        )
+            "that usb.core.find() will use the first matching device.")
 
     dlpc = DLPC900()
 
@@ -126,7 +78,8 @@ def configure_solid_flood(
         pass
 
     if not leave_leds_alone:
-        led_current = _require_byte_range("led_current", led_current)
+        if not (0 <= led_current <= 255):
+            raise ValueError(f"led_current must be in range 0..255, got {led_current}")
         dlpc.set_led_current(led_current, led_current, led_current)
         dlpc.set_led_enables(r=True, g=True, b=True, sequencer=True)
         time.sleep(0.1)
@@ -141,10 +94,11 @@ def configure_solid_flood(
     )
     time.sleep(0.1)
 
-    _write_pixel_format_rgb(dlpc)
+    dlpc.set_input_pixel_format(PIXEL_FORMAT_RGB)
     time.sleep(0.05)
 
-    _write_internal_test_pattern_color(dlpc, color)
+    dlpc.set_internal_test_pattern_color(
+        INTERNAL_PATTERN_WHITE_LEVEL if color == "white" else INTERNAL_PATTERN_BLACK_LEVEL)
     time.sleep(0.05)
 
     dlpc.set_internal_test_pattern(INTERNAL_TEST_PATTERN_SOLID_FIELD)
@@ -165,13 +119,13 @@ def configure_solid_flood(
 
 def build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
-        description="USB-only DLPC900 solid white/black flood using internal test pattern mode."
-    )
+        description="USB-only DLPC900 solid white/black flood using internal test pattern mode.")
 
     color_group = parser.add_mutually_exclusive_group()
     color_group.add_argument(
         "--color",
-        choices=("white", "black"),
+        choices=("white",
+                 "black"),
         default=None,
         help="Requested flood color. Default: white.",
     )
@@ -225,7 +179,7 @@ def main(argv=None) -> int:
     args = parser.parse_args(argv)
 
     requested = args.color_flag or args.color or "white"
-    output = _effective_color(requested, args.invert)
+    output = ("black" if requested == "white" else "white") if args.invert else requested
 
     if not args.yes:
         print("This will configure the connected DLPC900 over USB.")
diff --git a/dmdcontrol/hardware/mapping.py b/dmdcontrol/hardware/mapping.py
index 192b458..e7d6820 100644
--- a/dmdcontrol/hardware/mapping.py
+++ b/dmdcontrol/hardware/mapping.py
@@ -72,11 +72,6 @@ def resolve_dmd_mapping(name, config_path=None):
     )
 
 
-def _field_value(mapping, field):
-    value = getattr(mapping, field)
-    return "" if value is None else str(value)
-
-
 def _build_parser():
     parser = argparse.ArgumentParser(description="Resolve configured DMD mapping values")
     parser.add_argument("--dmd", required=True, help="Configured DMD name, for example A or B")
@@ -100,7 +95,8 @@ def _build_parser():
 def main(argv=None):
     args = _build_parser().parse_args(argv)
     mapping = resolve_dmd_mapping(args.dmd, args.config)
-    print(_field_value(mapping, args.field))
+    value = getattr(mapping, args.field)
+    print("" if value is None else str(value))
     return 0
 
 
diff --git a/dmdcontrol/hardware/usb.py b/dmdcontrol/hardware/usb.py
index bc11d4a..47a1611 100644
--- a/dmdcontrol/hardware/usb.py
+++ b/dmdcontrol/hardware/usb.py
@@ -114,7 +114,10 @@ def candidate_from_udev_properties(props, hidraw=None, sys_root="/sys"):
 
 def _udevadm_properties_for_hidraw(hidraw):
     result = subprocess.run(
-        ["udevadm", "info", "--query=property", f"--name={hidraw}"],
+        ["udevadm",
+         "info",
+         "--query=property",
+         f"--name={hidraw}"],
         check=False,
         text=True,
         stdout=subprocess.PIPE,
@@ -130,10 +133,7 @@ def discover_dlpc900_usb(dev_dir="/dev", sys_root="/sys"):
     sys_hidraw_path = Path(sys_root) / "class" / "hidraw"
     hidraw_nodes = sorted(dev_path.glob("hidraw*"))
     if not hidraw_nodes and sys_hidraw_path.exists():
-        hidraw_nodes = [
-            dev_path / path.name
-            for path in sorted(sys_hidraw_path.glob("hidraw*"))
-        ]
+        hidraw_nodes = [dev_path / path.name for path in sorted(sys_hidraw_path.glob("hidraw*"))]
     candidates = []
     for hidraw_path in hidraw_nodes:
         hidraw = str(hidraw_path)
@@ -160,23 +160,16 @@ def resolve_usb_candidate(usb_id_path, usb_devpath_contains=None, candidates=Non
         candidates = discover_dlpc900_usb()
     coerced = [_coerce_candidate(candidate) for candidate in candidates]
     matches = [
-        candidate
-        for candidate in coerced
-        if candidate.id_path == usb_id_path
-           and (
-                   not usb_devpath_contains
-                   or (candidate.devpath and usb_devpath_contains in candidate.devpath)
-           )
-    ]
+        candidate for candidate in coerced if candidate.id_path == usb_id_path and (
+            not usb_devpath_contains or
+            (candidate.devpath and usb_devpath_contains in candidate.devpath))]
     if not matches:
         discovered = ", ".join(
             f"{c.id_path or '<no ID_PATH>'} ({c.physical_path or '<no physical path>'})"
-            for c in coerced
-        )
+            for c in coerced)
         raise RuntimeError(
             f"Expected DLPC900 USB mapping not present: id_path={usb_id_path!r}, "
-            f"devpath_contains={usb_devpath_contains!r}. Discovered: {discovered or '<none>'}"
-        )
+            f"devpath_contains={usb_devpath_contains!r}. Discovered: {discovered or '<none>'}")
     if len(matches) > 1:
         raise RuntimeError(f"Ambiguous DLPC900 USB mapping for id_path={usb_id_path!r}")
     return matches[0]
@@ -187,10 +180,7 @@ def _load_pyusb_devices(pyusb_devices=None):
         return list(pyusb_devices)
     import usb.core
 
-    return list(
-        usb.core.find(find_all=True, idVendor=DLPC900_VID, idProduct=DLPC900_PID)
-        or []
-    )
+    return list(usb.core.find(find_all=True, idVendor=DLPC900_VID, idProduct=DLPC900_PID) or [])
 
 
 def _select_pyusb_device_by_physical_path(devices, physical_path):
@@ -206,28 +196,28 @@ def _select_pyusb_device_by_physical_path(devices, physical_path):
         if ports is not None and tuple(ports) == expected_ports:
             matches.append(device)
     if len(matches) > 1:
-        raise RuntimeError(
-            f"Ambiguous DLPC900 PyUSB devices for physical path {physical_path!r}"
-        )
+        raise RuntimeError(f"Ambiguous DLPC900 PyUSB devices for physical path {physical_path!r}")
     return matches[0] if matches else None
 
 
 def _select_pyusb_device_by_candidate(devices, candidate):
     if candidate.bus is not None and candidate.address is not None:
         for device in devices:
-            if (
-                    getattr(device, "bus", None) == candidate.bus
-                    and getattr(device, "address", None) == candidate.address
-            ):
+            if (getattr(device,
+                        "bus",
+                        None) == candidate.bus and getattr(device,
+                                                           "address",
+                                                           None) == candidate.address):
                 return device
     return _select_pyusb_device_by_physical_path(devices, candidate.physical_path)
 
 
 def select_pyusb_device_for_mapping(
-        usb_id_path,
-        usb_devpath_contains=None,
-        candidates=None,
-        pyusb_devices=None, ):
+    usb_id_path,
+    usb_devpath_contains=None,
+    candidates=None,
+    pyusb_devices=None,
+):
     devices = _load_pyusb_devices(pyusb_devices)
     try:
         candidate = resolve_usb_candidate(
@@ -247,8 +237,7 @@ def select_pyusb_device_for_mapping(
         return device
 
     raise RuntimeError(
-        f"Found hidraw mapping {candidate.id_path}, but could not match it to a PyUSB device."
-    )
+        f"Found hidraw mapping {candidate.id_path}, but could not match it to a PyUSB device.")
 
 
 def _format_int(value, width=3):
@@ -272,14 +261,13 @@ def format_usb_candidates(candidates):
                 f"  devpath: {candidate.devpath or 'unknown'}",
                 f"  physical_path: {candidate.physical_path or 'unknown'}",
                 f"  suggested_config_key: {candidate.id_path or 'unknown'}",
-            ]
-        )
+            ])
     return "\n".join(lines)
 
 
 def _build_parser():
     parser = argparse.ArgumentParser(description="Discover DLPC900 USB HID devices")
-    parser.add_argument("command", nargs="?", default="discover", choices=("discover",))
+    parser.add_argument("command", nargs="?", default="discover", choices=("discover", ))
     return parser
 
 
diff --git a/dmdcontrol/hardware/wake.py b/dmdcontrol/hardware/wake.py
index d3860e7..430c04f 100644
--- a/dmdcontrol/hardware/wake.py
+++ b/dmdcontrol/hardware/wake.py
@@ -21,8 +21,7 @@ def main(argv=None):
     if mapping:
         logger.info(
             f"[+] Waking DMD {mapping.name}: USB id_path={mapping.usb_id_path}, "
-            f"expected devpath fragment={mapping.usb_devpath_contains or '<not required>'}"
-        )
+            f"expected devpath fragment={mapping.usb_devpath_contains or '<not required>'}")
 
     dlpc = DLPC900(
         usb_id_path=mapping.usb_id_path if mapping else None,
@@ -30,7 +29,7 @@ def main(argv=None):
     )
     logger.info("[+] Waking up DisplayPort receiver...")
 
-    dlpc.send_packet(0x1A01, bytes([2]))
+    dlpc.wake_displayport_receiver()
     time.sleep(1)
 
     dlpc.set_input_source(0, 1)
diff --git a/dmdcontrol/patterns/bitplanes.py b/dmdcontrol/patterns/bitplanes.py
new file mode 100644
index 0000000..b757682
--- /dev/null
+++ b/dmdcontrol/patterns/bitplanes.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import numpy as np
+
+from dmdcontrol.support.constants import BITPLANES
+
+
+def pack_bitplanes_rgb(binary_images, width, height):
+    if len(binary_images) != BITPLANES:
+        raise ValueError(f"expected {BITPLANES} binary images, got {len(binary_images)}")
+    red = np.zeros((height, width), dtype=np.uint8)
+    green = np.zeros((height, width), dtype=np.uint8)
+    blue = np.zeros((height, width), dtype=np.uint8)
+    for bit in range(8):
+        green |= np.asarray(binary_images[bit], dtype=np.uint8) << bit
+        red |= np.asarray(binary_images[bit + 8], dtype=np.uint8) << bit
+        blue |= np.asarray(binary_images[bit + 16], dtype=np.uint8) << bit
+    return np.ascontiguousarray(np.stack([red, green, blue], axis=-1))
+
+
+def unpack_rgb_bitplanes(rgb_array, width, height):
+    if rgb_array.shape[:2] != (height, width):
+        raise ValueError(f"RGB array must be {height}x{width}, got {rgb_array.shape[:2]}")
+    patterns = []
+    for bit in range(8):
+        patterns.append(((rgb_array[:, :, 1] >> bit) & 1).astype(np.uint8))
+    for bit in range(8):
+        patterns.append(((rgb_array[:, :, 0] >> bit) & 1).astype(np.uint8))
+    for bit in range(8):
+        patterns.append(((rgb_array[:, :, 2] >> bit) & 1).astype(np.uint8))
+    return patterns
diff --git a/dmdcontrol/patterns/calibration_square.py b/dmdcontrol/patterns/calibration_square.py
index 7196fbf..c6e3d99 100644
--- a/dmdcontrol/patterns/calibration_square.py
+++ b/dmdcontrol/patterns/calibration_square.py
@@ -37,8 +37,7 @@ def format_calibration_square_state(state, width, height):
     return (
         f"center=({state.x:.0f},{state.y:.0f}) px, "
         f"bounds=({left},{top})..({right},{bottom}) px, "
-        f"size={state.size:.0f}px, angle={state.angle_deg:.1f}deg"
-    )
+        f"size={state.size:.0f}px, angle={state.angle_deg:.1f}deg")
 
 
 def read_calibration_square_control_file(path, offset):
@@ -55,36 +54,43 @@ def read_calibration_square_control_file(path, offset):
     except OSError as exc:
         logger.warning(f"[CALIBRATION] Cannot read control file {path}: {exc}")
         return "", offset
-    commands = "".join(
-        ch.lower() for ch in data if ch.lower() in VALID_CALIBRATION_COMMANDS
-    )
+    commands = "".join(ch.lower() for ch in data if ch.lower() in VALID_CALIBRATION_COMMANDS)
     return commands, offset
 
 
 def calibration_square_key_commands(glfw):
     return (
-        (glfw.KEY_W, "w"),
-        (glfw.KEY_A, "a"),
-        (glfw.KEY_S, "s"),
-        (glfw.KEY_D, "d"),
-        (glfw.KEY_Q, "q"),
-        (glfw.KEY_E, "e"),
-        (glfw.KEY_R, "r"),
-        (glfw.KEY_F, "f"),
+        (glfw.KEY_W,
+         "w"),
+        (glfw.KEY_A,
+         "a"),
+        (glfw.KEY_S,
+         "s"),
+        (glfw.KEY_D,
+         "d"),
+        (glfw.KEY_Q,
+         "q"),
+        (glfw.KEY_E,
+         "e"),
+        (glfw.KEY_R,
+         "r"),
+        (glfw.KEY_F,
+         "f"),
     )
 
 
 def make_calibration_square_frame_provider(
-        engine,
-        initial_frame,
-        control_file=None,
-        initial_state=None, ):
+    engine,
+    initial_frame,
+    control_file=None,
+    initial_state=None,
+):
     import glfw
 
     key_commands = calibration_square_key_commands(glfw)
     state = {
-        "square": initial_state
-                  or default_calibration_square_state(engine.width, engine.height),
+        "square": initial_state or default_calibration_square_state(engine.width,
+                                                                    engine.height),
         "frame": initial_frame,
         "control_offset": 0,
         "last_log": 0.0,
@@ -96,10 +102,8 @@ def _provider_calibration_square():
             state["control_offset"],
         )
         keyboard_commands = "".join(
-            command
-            for key, command in key_commands
-            if glfw.get_key(engine.window, key) == glfw.PRESS
-        )
+            command for key, command in key_commands
+            if glfw.get_key(engine.window, key) == glfw.PRESS)
         commands = file_commands + keyboard_commands
         if "x" in commands:
             logger.info("[CALIBRATION] Exit requested from calibration control input.")
diff --git a/dmdcontrol/patterns/engine.py b/dmdcontrol/patterns/engine.py
index a3e8bf8..a27c303 100644
--- a/dmdcontrol/patterns/engine.py
+++ b/dmdcontrol/patterns/engine.py
@@ -4,11 +4,13 @@
 import numpy as np
 from OpenGL.GL import *
 
+from dmdcontrol.patterns.bitplanes import pack_bitplanes_rgb, unpack_rgb_bitplanes
 from dmdcontrol.support.constants import DMD_HEIGHT, DMD_WIDTH, TARGET_HZ
 from dmdcontrol.support.logging import logger
 
 
 class PatternEngine:
+
     def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT, monitor_index=0, fps=TARGET_HZ):
         self.width = width
         self.height = height
@@ -32,25 +34,20 @@ def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT, monitor_index=0, fps=TARG
 
         if not monitors:
             raise TimeoutError(
-                "GLFW found no monitors after 5 seconds. Is the display connected and X11 running?"
-            )
+                "GLFW found no monitors after 5 seconds. Is the display connected and X11 running?")
 
         if len(monitors) > monitor_index:
             monitor = monitors[monitor_index]
         else:
             monitor = monitors[0]
-            logger.warning(
-                f"[WARNING] Monitor {monitor_index} not found, using primary."
-            )
+            logger.warning(f"[WARNING] Monitor {monitor_index} not found, using primary.")
 
         glfw.window_hint(glfw.DECORATED, glfw.FALSE)
         glfw.window_hint(glfw.RESIZABLE, glfw.FALSE)
         glfw.window_hint(glfw.AUTO_ICONIFY, glfw.FALSE)
         glfw.window_hint(glfw.REFRESH_RATE, self.fps)
 
-        self.window = glfw.create_window(
-            width, height, "DLPC900 Pattern Engine", monitor, None
-        )
+        self.window = glfw.create_window(width, height, "DLPC900 Pattern Engine", monitor, None)
 
         if not self.window:
             glfw.terminate()
@@ -63,8 +60,7 @@ def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT, monitor_index=0, fps=TARG
         if mode is not None:
             logger.info(
                 f"[+] GLFW video mode: {mode.size.width}x{mode.size.height} @ {mode.refresh_rate}Hz "
-                f"(requested {width}x{height} @ {self.fps}Hz)"
-            )
+                f"(requested {width}x{height} @ {self.fps}Hz)")
 
         # Verify 1:1 Framebuffer scaling
         fb_w, fb_h = glfw.get_framebuffer_size(self.window)
@@ -90,35 +86,7 @@ def pack_patterns(self, binary_images):
         Packs 24 independent binary masks into a single 24-bit RGB frame.
         WARNING: This requires RGB 4:4:4 video without YCbCr chroma subsampling. make sure nvidia or opensource drivers support it
         """
-        r = np.zeros((self.height, self.width), dtype=np.uint8)
-        g = np.zeros((self.height, self.width), dtype=np.uint8)
-        b = np.zeros((self.height, self.width), dtype=np.uint8)
-        for i in range(8):
-            g |= binary_images[i] << i
-            r |= binary_images[i + 8] << i
-            b |= binary_images[i + 16] << i
-        return np.ascontiguousarray(np.stack([r, g, b], axis=-1))
-
-    def pack_patterns_safe_8bit(self, binary_images):
-        """
-        Future-proof wrapper that packs up to 8 independent binary masks into a pure grayscale frame. 
-        can be bad, lose alot of detail Because R=G=B, this perfectly bypasses Linux YCbCr chroma subsampling 
-        and dithering natively. The 8 bitplanes are duplicated across Green, Red, and Blue channels for a total
-        of 24 planes.
-
-        Args:
-            binary_images: List of up to 8 binary numpy arrays (0 or 1)
-        """
-        if len(binary_images) > 8:
-            logger.warning(
-                "[WARNING] pack_patterns_safe_8bit received more than 8 patterns. Only the first 8 will be used.")
-
-        gray = np.zeros((self.height, self.width), dtype=np.uint8)
-        for i in range(min(8, len(binary_images))):
-            gray |= binary_images[i] << i
-
-        # Duplicate the gray channel into R, G, and B
-        return np.ascontiguousarray(np.stack([gray, gray, gray], axis=-1))
+        return pack_bitplanes_rgb(binary_images, self.width, self.height)
 
     def rgb_to_binary_patterns(self, rgb_array):
         """
@@ -131,23 +99,7 @@ def rgb_to_binary_patterns(self, rgb_array):
             List of 24 binary numpy arrays (values 0 or 1)
             Order: G0-G7, R0-R7, B0-B7 (matches DLPC900 bit-plane extraction)
         """
-        if rgb_array.shape[:2] != (self.height, self.width):
-            raise ValueError(
-                f"RGB array must be {self.height}x{self.width}, got {rgb_array.shape[:2]}"
-            )
-
-        patterns = []
-        # Green channel: bits 0-7
-        for bit in range(8):
-            patterns.append((rgb_array[:, :, 1] >> bit) & 1)
-        # Red channel: bits 8-15
-        for bit in range(8):
-            patterns.append((rgb_array[:, :, 0] >> bit) & 1)
-        # Blue channel: bits 16-23
-        for bit in range(8):
-            patterns.append((rgb_array[:, :, 2] >> bit) & 1)
-
-        return patterns
+        return unpack_rgb_bitplanes(rgb_array, self.width, self.height)
 
     def display_frame(self, frame_array):
         glBindTexture(GL_TEXTURE_2D, self.tex_id)
@@ -227,8 +179,15 @@ def generate_snake_frame(self, grid_w=24, grid_h=13):
         import random
         # Initialize snake state if it doesn't exist
         if not hasattr(self, 'snake_pos'):
-            self.snake_pos = [(grid_w // 2, grid_h // 2), (grid_w // 2 - 1, grid_h // 2),
-                              (grid_w // 2 - 2, grid_h // 2), (grid_w // 2 - 3, grid_h // 2)]
+            self.snake_pos = [
+                (grid_w // 2,
+                 grid_h // 2),
+                (grid_w // 2 - 1,
+                 grid_h // 2),
+                (grid_w // 2 - 2,
+                 grid_h // 2),
+                (grid_w // 2 - 3,
+                 grid_h // 2)]
             self.snake_dir = (1, 0)
 
         # Move snake
@@ -262,7 +221,8 @@ def generate_snake_frame(self, grid_w=24, grid_h=13):
         if frame_2d.shape != (self.height, self.width):
             padded = np.zeros((self.height, self.width), dtype=np.uint8)
             h, w = frame_2d.shape
-            padded[:min(h, self.height), :min(w, self.width)] = frame_2d[:min(h, self.height), :min(w, self.width)]
+            padded[:min(h, self.height), :min(w, self.width)] = frame_2d[:min(h, self.height), :min(
+                w, self.width)]
             frame_2d = padded
 
         # Return directly packed RGB frame (pure Grayscale for chroma bypass)
@@ -292,7 +252,16 @@ def generate_clock_frame(self):
         text_x = (self.width - text_size[0]) // 2
         text_y = (self.height + text_size[1]) // 2
 
-        cv2.putText(canvas, time_str, (text_x, text_y), font, font_scale, color, thickness, cv2.LINE_AA)
+        cv2.putText(
+            canvas,
+            time_str,
+            (text_x,
+             text_y),
+            font,
+            font_scale,
+            color,
+            thickness,
+            cv2.LINE_AA)
 
         # flip the image Y axis
         # canvas = np.flip(canvas, axis=0)
@@ -331,81 +300,14 @@ def generate_ordering_diagnostic_patterns(self, sub_width=512, sub_height=512):
             bx_start = x_start + (i * block_w)
             bx_end = min(x_start + sub_width, bx_start + block_w)
 
-            img[y_start: y_start + sub_height, bx_start:bx_end] = 1
-            patterns.append(img)
-        return patterns
-
-    def generate_subscale_patterns(self, sub_width=512, sub_height=512):
-        patterns = []
-        for i in range(24):
-            img = np.zeros((self.height, self.width), dtype=np.uint8)
-            y_start = (self.height - sub_height) // 2
-            x_start = (self.width - sub_width) // 2
-            img[y_start: y_start + sub_height, x_start: x_start + sub_width] = (
-                    np.random.rand(sub_height, sub_width) > 0.5
-            ).astype(np.uint8)
+            img[y_start:y_start + sub_height, bx_start:bx_end] = 1
             patterns.append(img)
         return patterns
 
-    def generate_kernel_masks(self, kernel_px):
-        """Generate 512 binary masks, one per 3x3 binary kernel variation.
-
-        Each mask is a (height, width) uint8 array of 0/1 with a centered
-        kernel_px x kernel_px square divided into a 3x3 grid of cells.
-        Bit b of the kernel index k (0..511) drives the cell at
-        (row=b//3, col=b%3). bit 0 = top-left, bit 8 = bottom-right.
-        """
-        if kernel_px % 3 != 0:
-            raise ValueError(f"kernel_px ({kernel_px}) must be a multiple of 3")
-        if kernel_px > min(self.width, self.height):
-            raise ValueError(
-                f"kernel_px ({kernel_px}) exceeds frame {self.width}x{self.height}"
-            )
-        cell = kernel_px // 3
-        x0 = (self.width - kernel_px) // 2
-        y0 = (self.height - kernel_px) // 2
-        masks = []
-        for k in range(512):
-            m = np.zeros((self.height, self.width), dtype=np.uint8)
-            for bit in range(9):
-                if k & (1 << bit):
-                    row, col = bit // 3, bit % 3
-                    yy, xx = y0 + row * cell, x0 + col * cell
-                    m[yy: yy + cell, xx: xx + cell] = 1
-            masks.append(m)
-        return masks
-
-    def pack_kernel_frames(self, masks, slots_per_frame=24, blank_end_frame=False):
-        """Pack a list of kernel masks into VSYNC RGB frames.
-
-        slots_per_frame: number of masks consumed by the LUT per VSYNC (1..24).
-        Each RGB frame still carries 24 bit-positions; unused positions (those
-        not referenced by the LUT) are zero-padded and ignored by the sequencer.
-
-        Returns ceil(len(masks)/slots_per_frame) RGB frames. The last group
-        is zero-padded if shorter than slots_per_frame.
-        If blank_end_frame=True, appends one fully-black RGB frame as a
-        per-cycle sync marker.
-        """
-        if slots_per_frame < 1 or slots_per_frame > 24:
-            raise ValueError(f"slots_per_frame ({slots_per_frame}) must be in [1, 24].")
-        pad = (-len(masks)) % slots_per_frame
-        black_mask = np.zeros((self.height, self.width), dtype=np.uint8)
-        padded = list(masks) + [black_mask] * pad
-        unused = [black_mask] * (24 - slots_per_frame)
-        frames = [
-            self.pack_patterns(padded[i: i + slots_per_frame] + unused)
-            for i in range(0, len(padded), slots_per_frame)
-        ]
-        if blank_end_frame:
-            frames.append(self.pack_patterns([black_mask] * 24))
-        return frames
-
     def should_close(self):
         return (
-                glfw.window_should_close(self.window)
-                or glfw.get_key(self.window, glfw.KEY_ESCAPE) == glfw.PRESS
-        )
+            glfw.window_should_close(self.window) or glfw.get_key(self.window,
+                                                                  glfw.KEY_ESCAPE) == glfw.PRESS)
 
     def cleanup(self):
         glfw.terminate()
diff --git a/dmdcontrol/patterns/kernel.py b/dmdcontrol/patterns/kernel.py
index 5467802..e9b344e 100644
--- a/dmdcontrol/patterns/kernel.py
+++ b/dmdcontrol/patterns/kernel.py
@@ -14,11 +14,11 @@
 
 
 def compute_kernel_lut_override(
-        enabled,
-        kernel_exposure_us,
-        target_hz,
-        sequence_utilization,
-        dark_time_us=None,
+    enabled,
+    kernel_exposure_us,
+    target_hz,
+    sequence_utilization,
+    dark_time_us=None,
 ):
     if not enabled or kernel_exposure_us is None:
         return None, None
@@ -47,7 +47,7 @@ def generate_kernel_masks(width=DMD_WIDTH, height=DMD_HEIGHT, kernel_px=30):
             if kernel_index & (1 << bit):
                 row, col = bit // 3, bit % 3
                 yy, xx = y0 + row * cell, x0 + col * cell
-                mask[yy: yy + cell, xx: xx + cell] = 1
+                mask[yy:yy + cell, xx:xx + cell] = 1
         masks.append(mask)
     return masks
 
@@ -55,35 +55,33 @@ def generate_kernel_masks(width=DMD_WIDTH, height=DMD_HEIGHT, kernel_px=30):
 def pack_kernel_frames(engine, masks, slots_per_frame=BITPLANES, blank_end_frame=False):
     """Pack kernel masks into DisplayPort RGB frames consumed by the LUT."""
     if slots_per_frame < 1 or slots_per_frame > BITPLANES:
-        raise ValueError(
-            f"slots_per_frame ({slots_per_frame}) must be in [1, {BITPLANES}]."
-        )
+        raise ValueError(f"slots_per_frame ({slots_per_frame}) must be in [1, {BITPLANES}].")
     black_mask = np.zeros((engine.height, engine.width), dtype=np.uint8)
     pad = (-len(masks)) % slots_per_frame
     padded = list(masks) + [black_mask] * pad
     unused = [black_mask] * (BITPLANES - slots_per_frame)
     frames = [
-        engine.pack_patterns(padded[i: i + slots_per_frame] + unused)
-        for i in range(0, len(padded), slots_per_frame)
-    ]
+        engine.pack_patterns(padded[i:i + slots_per_frame] + unused)
+        for i in range(0, len(padded), slots_per_frame)]
     if blank_end_frame:
         frames.append(engine.pack_patterns([black_mask] * BITPLANES))
     return frames
 
 
 def build_kernel_frames(
-        engine,
-        kernel_px,
-        slots_per_frame=BITPLANES,
-        leader_frames=3,
-        blank_end_frame=True,
+    engine,
+    kernel_px,
+    slots_per_frame=BITPLANES,
+    leader_frames=3,
+    blank_end_frame=True,
 ):
     if leader_frames < 0:
         raise ValueError("leader_frames must be non-negative")
     kernel_masks = generate_kernel_masks(engine.width, engine.height, kernel_px)
     black_frame = engine.pack_patterns(
-        [np.zeros((engine.height, engine.width), dtype=np.uint8)] * BITPLANES
-    )
+        [np.zeros((engine.height,
+                   engine.width),
+                  dtype=np.uint8)] * BITPLANES)
     payload_frames = pack_kernel_frames(
         engine,
         kernel_masks,
@@ -92,21 +90,24 @@ def build_kernel_frames(
     )
     frames = [black_frame] * leader_frames + payload_frames
     metadata = {
-        "leader_frames": leader_frames,
-        "payload_vsyncs": len(payload_frames),
-        "blank_slot_count": (slots_per_frame - (512 % slots_per_frame))
-                            % slots_per_frame,
-        "cycle_vsyncs": len(frames),
-        "cycle_fires": (leader_frames * slots_per_frame)
-                       + 512
-                       + ((slots_per_frame - (512 % slots_per_frame)) % slots_per_frame)
-                       + (slots_per_frame if blank_end_frame else 0),
-        "black_frame": black_frame,
+        "leader_frames":
+        leader_frames,
+        "payload_vsyncs":
+        len(payload_frames),
+        "blank_slot_count": (slots_per_frame - (512 % slots_per_frame)) % slots_per_frame,
+        "cycle_vsyncs":
+        len(frames),
+        "cycle_fires": (leader_frames * slots_per_frame) + 512 +
+        ((slots_per_frame -
+          (512 % slots_per_frame)) % slots_per_frame) + (slots_per_frame if blank_end_frame else 0),
+        "black_frame":
+        black_frame,
     }
     return frames, metadata
 
 
 class KernelFrameProvider:
+
     def __init__(self, frames, black_frame, single_shot=False):
         if not frames:
             raise ValueError("frames must not be empty")
diff --git a/dmdcontrol/patterns/modes.py b/dmdcontrol/patterns/modes.py
index 21ad9e8..ee1f36f 100644
--- a/dmdcontrol/patterns/modes.py
+++ b/dmdcontrol/patterns/modes.py
@@ -13,7 +13,6 @@
 from dmdcontrol.patterns.visual import generate_coarse_grid_rgb, generate_coarse_lines_rgb
 from dmdcontrol.support.constants import (
     DEFAULT_CALIBRATION_SQUARE_FRACTION,
-    DEFAULT_NUMBERS_EXPOSURE_US,
     DMD_HEIGHT,
     DMD_WIDTH,
     MIN_CALIBRATION_SQUARE_PX,
@@ -21,15 +20,59 @@
 )
 
 _DIGIT_SEGMENTS = {
-    1: ("b", "c"),
-    2: ("a", "b", "g", "e", "d"),
-    3: ("a", "b", "g", "c", "d"),
-    4: ("f", "g", "b", "c"),
-    5: ("a", "f", "g", "c", "d"),
-    6: ("a", "f", "g", "e", "c", "d"),
-    7: ("a", "b", "c"),
-    8: ("a", "b", "c", "d", "e", "f", "g"),
-    9: ("a", "b", "c", "d", "f", "g"),
+    1: ("b",
+        "c"),
+    2: ("a",
+        "b",
+        "g",
+        "e",
+        "d"),
+    3: ("a",
+        "b",
+        "g",
+        "c",
+        "d"),
+    4: ("f",
+        "g",
+        "b",
+        "c"),
+    5: ("a",
+        "f",
+        "g",
+        "c",
+        "d"),
+    6: ("a",
+        "f",
+        "g",
+        "e",
+        "c",
+        "d"),
+    7: ("a",
+        "b",
+        "c"),
+    8: ("a",
+        "b",
+        "c",
+        "d",
+        "e",
+        "f",
+        "g"),
+    9: ("a",
+        "b",
+        "c",
+        "d",
+        "f",
+        "g"),
+}
+
+_DECIMAL_DIGIT_SEGMENTS = {
+    0: ("a",
+        "b",
+        "c",
+        "d",
+        "e",
+        "f"),
+    **_DIGIT_SEGMENTS,
 }
 
 
@@ -48,7 +91,8 @@ def _clamp(value, lo, hi):
 def default_calibration_square_state(width=DMD_WIDTH, height=DMD_HEIGHT):
     size = max(
         MIN_CALIBRATION_SQUARE_PX,
-        min(width, height) * DEFAULT_CALIBRATION_SQUARE_FRACTION,
+        min(width,
+            height) * DEFAULT_CALIBRATION_SQUARE_FRACTION,
     )
     return CalibrationSquareState(
         x=width / 2.0,
@@ -61,9 +105,17 @@ def default_calibration_square_state(width=DMD_WIDTH, height=DMD_HEIGHT):
 def clamp_calibration_square_state(state, width=DMD_WIDTH, height=DMD_HEIGHT):
     max_size = max(MIN_CALIBRATION_SQUARE_PX, min(width, height))
     return CalibrationSquareState(
-        x=float(_clamp(state.x, 0.0, max(0.0, width - 1.0))),
-        y=float(_clamp(state.y, 0.0, max(0.0, height - 1.0))),
-        size=float(_clamp(state.size, MIN_CALIBRATION_SQUARE_PX, max_size)),
+        x=float(_clamp(state.x,
+                       0.0,
+                       max(0.0,
+                           width - 1.0))),
+        y=float(_clamp(state.y,
+                       0.0,
+                       max(0.0,
+                           height - 1.0))),
+        size=float(_clamp(state.size,
+                          MIN_CALIBRATION_SQUARE_PX,
+                          max_size)),
         angle_deg=float(state.angle_deg % 360.0),
     )
 
@@ -76,29 +128,41 @@ def calibration_square_bounds(state, width=DMD_WIDTH, height=DMD_HEIGHT):
     xs = []
     ys = []
     for local_x, local_y in (
-            (-half, -half),
-            (half, -half),
-            (half, half),
-            (-half, half),
+        (-half, -half),
+        (half, -half),
+        (half, half),
+        (-half, half),
     ):
         xs.append(state.x + cos_a * local_x - sin_a * local_y)
         ys.append(state.y + sin_a * local_x + cos_a * local_y)
     return (
-        int(_clamp(np.floor(min(xs)), 0, max(0, width - 1))),
-        int(_clamp(np.floor(min(ys)), 0, max(0, height - 1))),
-        int(_clamp(np.ceil(max(xs)), 0, max(0, width - 1))),
-        int(_clamp(np.ceil(max(ys)), 0, max(0, height - 1))),
+        int(_clamp(np.floor(min(xs)),
+                   0,
+                   max(0,
+                       width - 1))),
+        int(_clamp(np.floor(min(ys)),
+                   0,
+                   max(0,
+                       height - 1))),
+        int(_clamp(np.ceil(max(xs)),
+                   0,
+                   max(0,
+                       width - 1))),
+        int(_clamp(np.ceil(max(ys)),
+                   0,
+                   max(0,
+                       height - 1))),
     )
 
 
 def apply_calibration_square_commands(
-        state,
-        commands,
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        move_px=10,
-        rotation_deg=2,
-        size_step_px=10,
+    state,
+    commands,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    move_px=10,
+    rotation_deg=2,
+    size_step_px=10,
 ):
     x = state.x
     y = state.y
@@ -123,19 +187,22 @@ def apply_calibration_square_commands(
         elif command == "f":
             size -= size_step_px
     return clamp_calibration_square_state(
-        CalibrationSquareState(x=x, y=y, size=size, angle_deg=angle),
+        CalibrationSquareState(x=x,
+                               y=y,
+                               size=size,
+                               angle_deg=angle),
         width=width,
         height=height,
     )
 
 
 def generate_calibration_square_mask(
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        center_x=None,
-        center_y=None,
-        size_px=None,
-        angle_deg=0.0,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    center_x=None,
+    center_y=None,
+    size_px=None,
+    angle_deg=0.0,
 ):
     if width <= 0 or height <= 0:
         raise ValueError("width and height must be positive")
@@ -196,6 +263,47 @@ def _fill_rect(img, x0, y0, x1, y1):
         img[y0:y1, x0:x1, :] = 255
 
 
+def _draw_digit_segments(img, segments, x0, y0, digit_w, digit_h, min_stroke_px=1):
+    x1 = x0 + digit_w
+    y1 = y0 + digit_h
+    mid = (y0 + y1) // 2
+    thickness = max(min_stroke_px, int(min(digit_w, digit_h) * 0.16))
+    half_t = max(max(1, min_stroke_px // 2), thickness // 2)
+
+    boxes = {
+        "a": (x0 + thickness,
+              y0,
+              x1 - thickness,
+              y0 + thickness),
+        "b": (x1 - thickness,
+              y0 + thickness,
+              x1,
+              mid),
+        "c": (x1 - thickness,
+              mid,
+              x1,
+              y1 - thickness),
+        "d": (x0 + thickness,
+              y1 - thickness,
+              x1 - thickness,
+              y1),
+        "e": (x0,
+              mid,
+              x0 + thickness,
+              y1 - thickness),
+        "f": (x0,
+              y0 + thickness,
+              x0 + thickness,
+              mid),
+        "g": (x0 + thickness,
+              mid - half_t,
+              x1 - thickness,
+              mid + half_t),
+    }
+    for segment in segments:
+        _fill_rect(img, *boxes[segment])
+
+
 def generate_number_rgb(number, width=DMD_WIDTH, height=DMD_HEIGHT, size_px=None):
     """Generate a binary RGB seven-segment digit frame for number mode."""
     if number not in _DIGIT_SEGMENTS:
@@ -210,25 +318,56 @@ def generate_number_rgb(number, width=DMD_WIDTH, height=DMD_HEIGHT, size_px=None
     else:
         digit_h = max(24, int(height * 0.78))
         digit_w = min(max(16, int(width * 0.46)), max(16, int(digit_h * 0.62)))
-    thickness = max(4, int(min(digit_w, digit_h) * 0.16))
     x0 = (width - digit_w) // 2
-    x1 = x0 + digit_w
     y0 = (height - digit_h) // 2
-    y1 = y0 + digit_h
-    mid = (y0 + y1) // 2
-    half_t = max(2, thickness // 2)
+    _draw_digit_segments(
+        img,
+        _DIGIT_SEGMENTS[number],
+        x0,
+        y0,
+        digit_w,
+        digit_h,
+        min_stroke_px=4,
+    )
+    return img
 
-    boxes = {
-        "a": (x0 + thickness, y0, x1 - thickness, y0 + thickness),
-        "b": (x1 - thickness, y0 + thickness, x1, mid),
-        "c": (x1 - thickness, mid, x1, y1 - thickness),
-        "d": (x0 + thickness, y1 - thickness, x1 - thickness, y1),
-        "e": (x0, mid, x0 + thickness, y1 - thickness),
-        "f": (x0, y0 + thickness, x0 + thickness, mid),
-        "g": (x0 + thickness, mid - half_t, x1 - thickness, mid + half_t),
-    }
-    for segment in _DIGIT_SEGMENTS[number]:
-        _fill_rect(img, *boxes[segment])
+
+def generate_decimal_number_rgb(number, width=DMD_WIDTH, height=DMD_HEIGHT, size_px=None):
+    """Generate a binary RGB seven-segment decimal label frame."""
+    if number < 0:
+        raise ValueError("number must be non-negative")
+
+    img = np.zeros((height, width, 3), dtype=np.uint8)
+    if size_px is not None:
+        if size_px <= 0:
+            raise ValueError("size_px must be positive")
+        digit_h = min(int(size_px), height)
+    else:
+        digit_h = max(24, int(height * 0.78))
+
+    digits = [int(char) for char in str(int(number))]
+    digit_w = max(1, int(digit_h * 0.62))
+    gap = max(1, int(digit_w * 0.18)) if len(digits) > 1 else 0
+    group_w = len(digits) * digit_w + (len(digits) - 1) * gap
+    if group_w > width:
+        scale = width / max(1, group_w)
+        digit_w = max(1, int(digit_w * scale))
+        digit_h = max(1, min(height, int(digit_h * scale)))
+        gap = max(1, int(gap * scale)) if len(digits) > 1 else 0
+        group_w = len(digits) * digit_w + (len(digits) - 1) * gap
+
+    x = max(0, (width - group_w) // 2)
+    y = max(0, (height - digit_h) // 2)
+    for digit in digits:
+        _draw_digit_segments(
+            img,
+            _DECIMAL_DIGIT_SEGMENTS[digit],
+            x,
+            y,
+            digit_w,
+            digit_h,
+        )
+        x += digit_w + gap
     return img
 
 
@@ -250,16 +389,24 @@ def _coarse_lines(engine):
 PATTERN_MODES = {
     #                 label                                   pattern generator          dynamic or not
     "checkerboard": ("Static Checkerboard", lambda e: (e.generate_checkerboard(), None)),
-    "ordering": ("Bit Ordering Sweep", lambda e: (e.generate_ordering_diagnostic_patterns(DMD_WIDTH, DMD_HEIGHT), None)),
-    "numbered": ("Numbered Regions (6x4 grid)", _numbered),
+    "ordering": (
+        "Bit Ordering Sweep", lambda e:
+        (e.generate_ordering_diagnostic_patterns(DMD_WIDTH, DMD_HEIGHT), None)),
+    "numbered": ("Numbered Regions (6x4 grid)",
+                 _numbered),
     "single-pixel": ("1x1 Single Pixel", lambda e: (e.generate_checkerboard(block_size=1), None)),
     "2x2": ("2x2 Checkerboard", lambda e: (e.generate_checkerboard(block_size=2), None)),
     "lines": ("1-pixel Lines", lambda e: (e.generate_lines(), None)),
-    "colors": ("Color Channels (R/G/B)", lambda e: (e.rgb_to_binary_patterns(_solid_color(0)), "colors")),
-    "coarse-grid": ("Human-Visible Coarse Grid", _coarse_grid),
-    "grid": ("Human-Visible Coarse Grid", _coarse_grid),
-    "coarse-lines": ("Human-Visible Coarse Lines", _coarse_lines),
-    "bands": ("Human-Visible Coarse Lines", _coarse_lines),
+    "colors":
+    ("Color Channels (R/G/B)", lambda e: (e.rgb_to_binary_patterns(_solid_color(0)), "colors")),
+    "coarse-grid": ("Human-Visible Coarse Grid",
+                    _coarse_grid),
+    "grid": ("Human-Visible Coarse Grid",
+             _coarse_grid),
+    "coarse-lines": ("Human-Visible Coarse Lines",
+                     _coarse_lines),
+    "bands": ("Human-Visible Coarse Lines",
+              _coarse_lines),
     "numbers": ("Sequential Numbers (1-9)", lambda e: (None, "numbers")),
     "calibr-square": ("Interactive Calibration Square", lambda e: (None, "calibr-square")),
     "snake": ("60FPS Snake", lambda e: (None, "snake")),
diff --git a/dmdcontrol/patterns/numbered_regions.py b/dmdcontrol/patterns/numbered_regions.py
index 8d46f4e..5a1fa97 100644
--- a/dmdcontrol/patterns/numbered_regions.py
+++ b/dmdcontrol/patterns/numbered_regions.py
@@ -19,12 +19,24 @@ def generate_numbered_regions(width, height, grid_cols=6, grid_rows=4):
     cell_width = width // grid_cols
     cell_height = height // grid_rows
     colors = [
-        (255, 255, 255),
-        (128, 128, 128),
-        (64, 64, 64),
-        (192, 192, 192),
-        (32, 32, 32),
-        (96, 96, 96),
+        (255,
+         255,
+         255),
+        (128,
+         128,
+         128),
+        (64,
+         64,
+         64),
+        (192,
+         192,
+         192),
+        (32,
+         32,
+         32),
+        (96,
+         96,
+         96),
     ]
 
     region_num = 1
@@ -48,7 +60,8 @@ def generate_numbered_regions(width, height, grid_cols=6, grid_rows=4):
             for offset_x in (-2, 0, 2):
                 for offset_y in (-2, 0, 2):
                     draw.text(
-                        (text_x + offset_x, text_y + offset_y),
+                        (text_x + offset_x,
+                         text_y + offset_y),
                         text,
                         fill="black",
                         font=font,
diff --git a/dmdcontrol/patterns/paired.py b/dmdcontrol/patterns/paired.py
index 5365e95..ed86077 100644
--- a/dmdcontrol/patterns/paired.py
+++ b/dmdcontrol/patterns/paired.py
@@ -2,13 +2,17 @@
 
 from __future__ import annotations
 
-import importlib
 import time
 from dataclasses import dataclass
 
 import numpy as np
 
-from dmdcontrol.patterns.modes import NUMBER_SEQUENCE, generate_number_rgb
+from dmdcontrol.patterns.bitplanes import pack_bitplanes_rgb
+from dmdcontrol.patterns.modes import (
+    NUMBER_SEQUENCE,
+    generate_decimal_number_rgb,
+    generate_number_rgb,
+)
 from dmdcontrol.patterns.visual import (
     DEFAULT_COARSE_GRID_SPACING,
     DEFAULT_COARSE_LINE_SPACING,
@@ -28,11 +32,13 @@
 STATIC_PAIR_TESTS = ("checkerboard", "lines", "colors", "dot") + HUMAN_VISIBLE_PAIR_TESTS
 NUMBER_PAIR_TEST = "numbers"
 A_NUMBERS_B_STATIC_PAIR_TEST = "a-numbers-b-static"
+A_COUNT_B_STATIC_PAIR_TEST = "a-count-b-static"
 DYNAMIC_PAIR_TESTS = ("gradient", "snake", NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST)
 CALIBRATION_DOT_PAIR_TEST = "a-calibr-square-b-dot"
 KERNEL_STATIC_PAIR_TEST = "a-kernel-b-static"
-RECIPE_PAIR_TESTS = (CALIBRATION_DOT_PAIR_TEST, KERNEL_STATIC_PAIR_TEST)
+RECIPE_PAIR_TESTS = (CALIBRATION_DOT_PAIR_TEST, KERNEL_STATIC_PAIR_TEST, A_COUNT_B_STATIC_PAIR_TEST)
 PAIR_TESTS = STATIC_PAIR_TESTS + DYNAMIC_PAIR_TESTS + RECIPE_PAIR_TESTS
+MAX_COUNT_SEQUENCE_FRAMES = 64
 
 
 def _validate_rgb_frame(frame, label):
@@ -73,12 +79,6 @@ def _lines(width, height):
     return img
 
 
-def _colors(width, height, channel=0):
-    img = np.zeros((height, width, 3), dtype=np.uint8)
-    img[:, :, channel] = 255
-    return img
-
-
 def _fill_rect_rgb(frame, x0, y0, x1, y1, value=255):
     height, width = frame.shape[:2]
     x0 = max(0, min(width, int(x0)))
@@ -92,19 +92,49 @@ def _fill_rect_rgb(frame, x0, y0, x1, y1, value=255):
 def _draw_block_letter(frame, label, x0, y0, cell):
     if label == "A":
         rects = (
-            (0, 1, 1, 7),
-            (4, 1, 5, 7),
-            (1, 0, 4, 1),
-            (1, 3, 4, 4),
+            (0,
+             1,
+             1,
+             7),
+            (4,
+             1,
+             5,
+             7),
+            (1,
+             0,
+             4,
+             1),
+            (1,
+             3,
+             4,
+             4),
         )
     else:
         rects = (
-            (0, 0, 1, 7),
-            (1, 0, 4, 1),
-            (1, 3, 4, 4),
-            (1, 6, 4, 7),
-            (4, 1, 5, 3),
-            (4, 4, 5, 6),
+            (0,
+             0,
+             1,
+             7),
+            (1,
+             0,
+             4,
+             1),
+            (1,
+             3,
+             4,
+             4),
+            (1,
+             6,
+             4,
+             7),
+            (4,
+             1,
+             5,
+             3),
+            (4,
+             4,
+             5,
+             6),
         )
     for rx0, ry0, rx1, ry1 in rects:
         _fill_rect_rgb(
@@ -117,13 +147,13 @@ def _draw_block_letter(frame, label, x0, y0, cell):
 
 
 def generate_dot_frame(
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        x=None,
-        y=None,
-        radius=40,
-        shape="circle",
-        invert=False,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    x=None,
+    y=None,
+    radius=40,
+    shape="circle",
+    invert=False,
 ):
     """Generate a static RGB dot mask/aperture frame."""
     if width <= 0 or height <= 0:
@@ -139,7 +169,7 @@ def generate_dot_frame(
 
     yy, xx = np.ogrid[:height, :width]
     if shape == "circle":
-        mask = (xx - x) ** 2 + (yy - y) ** 2 <= radius ** 2
+        mask = (xx - x)**2 + (yy - y)**2 <= radius**2
     else:
         mask = (np.abs(xx - x) <= radius) & (np.abs(yy - y) <= radius)
 
@@ -173,22 +203,23 @@ def _route_mark(frame, label):
 
 
 def generate_static_frame(
-        mode,
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        route_label="A",
-        dot_x=None,
-        dot_y=None,
-        dot_radius=40,
-        dot_shape="circle",
-        dot_invert=False,
+    mode,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    route_label="A",
+    dot_x=None,
+    dot_y=None,
+    dot_radius=40,
+    dot_shape="circle",
+    dot_invert=False,
 ):
     if mode == "checkerboard":
         frame = _checkerboard(width, height)
     elif mode == "lines":
         frame = _lines(width, height)
     elif mode == "colors":
-        frame = _colors(width, height, channel=0 if route_label == "A" else 1)
+        frame = np.zeros((height, width, 3), dtype=np.uint8)
+        frame[:, :, 0 if route_label == "A" else 1] = 255
     elif mode == "dot":
         return generate_dot_frame(
             width=width,
@@ -222,11 +253,18 @@ def generate_static_frame(
     return _route_mark(frame, route_label)
 
 
-def _static_frame(mode, width, height, route_label):
-    return generate_static_frame(mode, width, height, route_label)
+def _static_frame(mode, width, height, route_label, dot_radius=40):
+    return generate_static_frame(
+        mode,
+        width,
+        height,
+        route_label,
+        dot_radius=dot_radius,
+    )
 
 
 class PairFrameProvider:
+
     def initial_pair(self):
         raise NotImplementedError
 
@@ -243,14 +281,7 @@ def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT, window=None):
         self.window = window
 
     def pack_patterns(self, binary_images):
-        r = np.zeros((self.height, self.width), dtype=np.uint8)
-        g = np.zeros((self.height, self.width), dtype=np.uint8)
-        b = np.zeros((self.height, self.width), dtype=np.uint8)
-        for i in range(8):
-            g |= binary_images[i] << i
-            r |= binary_images[i + 8] << i
-            b |= binary_images[i + 16] << i
-        return np.ascontiguousarray(np.stack([r, g, b], axis=-1))
+        return pack_bitplanes_rgb(binary_images, self.width, self.height)
 
 
 @dataclass
@@ -259,10 +290,23 @@ class StaticPairFrameProvider(PairFrameProvider):
     mode_b: str = "checkerboard"
     width: int = DMD_WIDTH
     height: int = DMD_HEIGHT
+    dot_radius: int = 40
 
     def __post_init__(self):
-        self._frame_a = _static_frame(self.mode_a, self.width, self.height, "A")
-        self._frame_b = _static_frame(self.mode_b, self.width, self.height, "B")
+        self._frame_a = _static_frame(
+            self.mode_a,
+            self.width,
+            self.height,
+            "A",
+            dot_radius=self.dot_radius,
+        )
+        self._frame_b = _static_frame(
+            self.mode_b,
+            self.width,
+            self.height,
+            "B",
+            dot_radius=self.dot_radius,
+        )
 
     def initial_pair(self):
         return self._frame_a, self._frame_b
@@ -272,6 +316,7 @@ def next_pair(self):
 
 
 class DynamicAStaticBPairFrameProvider(PairFrameProvider):
+
     def __init__(self, frame_provider_a, frame_b, initial_frame_a=None):
         _validate_rgb_frame(frame_b, "frame_b")
         if initial_frame_a is not None:
@@ -303,13 +348,13 @@ def next_pair(self):
 
 
 class CalibrationSquareDotPairFrameProvider(DynamicAStaticBPairFrameProvider):
+
     def __init__(self, frame_provider_a, frame_b, initial_frame_a=None, flicker_a=False):
         super().__init__(frame_provider_a, frame_b, initial_frame_a=initial_frame_a)
         self.flicker_a = flicker_a
         self.frame_index = 0
         self._black_frame_a = (
-            np.zeros_like(initial_frame_a) if initial_frame_a is not None else None
-        )
+            np.zeros_like(initial_frame_a) if initial_frame_a is not None else None)
 
     def _remember_black_frame(self, frame_a):
         if self._black_frame_a is None:
@@ -330,6 +375,7 @@ def next_pair(self):
 
 
 class DynamicGradientPairFrameProvider(PairFrameProvider):
+
     def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT):
         self.width = width
         self.height = height
@@ -356,6 +402,7 @@ def next_pair(self):
 
 
 class DynamicSnakePairFrameProvider(PairFrameProvider):
+
     def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT, cells_x=24, cells_y=13):
         self.width = width
         self.height = height
@@ -379,7 +426,7 @@ def _frame_for_index(self, index):
                 x0 = col * cell_w
                 y0 = row * cell_h
                 level = max(64, 255 - segment * 32)
-                frame[y0: y0 + cell_h, x0: x0 + cell_w, :] = level
+                frame[y0:y0 + cell_h, x0:x0 + cell_w, :] = level
         return _route_mark(frame_a, "A"), _route_mark(frame_b, "B")
 
     def initial_pair(self):
@@ -391,12 +438,14 @@ def next_pair(self):
 
 
 class NumberSequencePairFrameProvider(PairFrameProvider):
+
     def __init__(
-            self,
-            numbers=NUMBER_SEQUENCE,
-            width=DMD_WIDTH,
-            height=DMD_HEIGHT,
-            size_px=None,
+        self,
+        numbers=NUMBER_SEQUENCE,
+        width=DMD_WIDTH,
+        height=DMD_HEIGHT,
+        size_px=None,
+        numbers_bitplane_order=None,
     ):
         if not numbers:
             raise ValueError("numbers must not be empty")
@@ -412,6 +461,7 @@ def __init__(
             width=width,
             height=height,
             size_px=size_px,
+            bitplane_order=numbers_bitplane_order,
         )
 
     def initial_pair(self):
@@ -422,18 +472,20 @@ def next_pair(self):
 
 
 class NumberSequenceAStaticBPairFrameProvider(PairFrameProvider):
+
     def __init__(
-            self,
-            mode_b="dot",
-            numbers=NUMBER_SEQUENCE,
-            width=DMD_WIDTH,
-            height=DMD_HEIGHT,
-            size_px=None,
-            b_dot_x=None,
-            b_dot_y=None,
-            b_dot_radius=40,
-            b_dot_shape="circle",
-            b_dot_invert=False,
+        self,
+        mode_b="dot",
+        numbers=NUMBER_SEQUENCE,
+        width=DMD_WIDTH,
+        height=DMD_HEIGHT,
+        size_px=None,
+        b_dot_x=None,
+        b_dot_y=None,
+        b_dot_radius=40,
+        b_dot_shape="circle",
+        b_dot_invert=False,
+        numbers_bitplane_order=None,
     ):
         if not numbers:
             raise ValueError("numbers must not be empty")
@@ -446,6 +498,7 @@ def __init__(
             width=width,
             height=height,
             size_px=size_px,
+            bitplane_order=numbers_bitplane_order,
         )
         self._frame_b = generate_static_frame(
             mode_b,
@@ -466,22 +519,141 @@ def next_pair(self):
         return self._frame_a, self._frame_b
 
 
-def _pack_number_sequence_bitplanes(numbers, width, height, size_px=None):
-    masks = [
+class CountSequenceAStaticBPairFrameProvider(PairFrameProvider):
+
+    def __init__(
+        self,
+        mode_b="dot",
+        count_start=1,
+        count_end=100,
+        count_slots_per_frame=2,
+        width=DMD_WIDTH,
+        height=DMD_HEIGHT,
+        size_px=None,
+        b_dot_x=None,
+        b_dot_y=None,
+        b_dot_radius=40,
+        b_dot_shape="circle",
+        b_dot_invert=False,
+        numbers_bitplane_order=None,
+    ):
+        _validate_count_sequence_args(count_start, count_end, count_slots_per_frame)
+        self.width = width
+        self.height = height
+        self.frame_index = 0
+        self._frames_a = _pack_count_sequence_frames(
+            count_start,
+            count_end,
+            count_slots_per_frame,
+            width=width,
+            height=height,
+            size_px=size_px,
+        )
+        self._frame_b = generate_static_frame(
+            mode_b,
+            width=width,
+            height=height,
+            route_label="B",
+            dot_x=b_dot_x,
+            dot_y=b_dot_y,
+            dot_radius=b_dot_radius,
+            dot_shape=b_dot_shape,
+            dot_invert=b_dot_invert,
+        )
+
+    def initial_pair(self):
+        return self._frames_a[self.frame_index], self._frame_b
+
+    def next_pair(self):
+        self.frame_index = (self.frame_index + 1) % len(self._frames_a)
+        return self._frames_a[self.frame_index], self._frame_b
+
+
+def _pack_number_sequence_bitplanes(numbers, width, height, size_px=None, bitplane_order=None):
+    masks = _number_bitplane_masks(numbers, width=width, height=height, size_px=size_px)
+    if bitplane_order is not None:
+        masks = _reorder_masks_for_bitplane_display(masks, bitplane_order)
+    return _pack_binary_masks_bitplanes(masks, width, height)
+
+
+def _reorder_masks_for_bitplane_display(masks, bitplane_order):
+    masks = list(masks)
+    order = tuple(bitplane_order)
+    if len(order) != len(masks):
+        raise ValueError("numbers_bitplane_order length must match numbers length")
+    if sorted(order) != list(range(len(masks))):
+        raise ValueError("numbers_bitplane_order must be a zero-based permutation of numbers slots")
+
+    ordered = [None] * len(masks)
+    for display_slot, bitplane_index in enumerate(order):
+        ordered[bitplane_index] = masks[display_slot]
+    return ordered
+
+
+def _pack_count_sequence_frames(
+        count_start,
+        count_end,
+        count_slots_per_frame,
+        width,
+        height,
+        size_px=None):
+    _validate_count_sequence_args(count_start, count_end, count_slots_per_frame)
+    frames = []
+    counts = tuple(range(count_start, count_end + 1))
+    for offset in range(0, len(counts), count_slots_per_frame):
+        chunk = counts[offset:offset + count_slots_per_frame]
+        masks = _decimal_number_bitplane_masks(chunk, width=width, height=height, size_px=size_px)
+        frames.append(_pack_binary_masks_bitplanes(masks, width, height))
+    return tuple(frames)
+
+
+def _validate_count_sequence_args(count_start, count_end, count_slots_per_frame):
+    if count_start <= 0 or count_end <= 0:
+        raise ValueError("count range values must be positive")
+    if count_start > count_end:
+        raise ValueError("count_start must be <= count_end")
+    if count_slots_per_frame <= 0 or count_slots_per_frame > BITPLANES:
+        raise ValueError(f"count_slots_per_frame must be in the range 1..{BITPLANES}")
+    count_total = count_end - count_start + 1
+    if count_total % count_slots_per_frame != 0:
+        raise ValueError("count range length must be divisible by count_slots_per_frame")
+    frame_count = count_total // count_slots_per_frame
+    if frame_count > MAX_COUNT_SEQUENCE_FRAMES:
+        raise ValueError(
+            f"count sequence can span at most {MAX_COUNT_SEQUENCE_FRAMES} VSYNC frames")
+
+
+def count_sequence_frame_count(count_start, count_end, count_slots_per_frame):
+    _validate_count_sequence_args(count_start, count_end, count_slots_per_frame)
+    return (count_end - count_start + 1) // count_slots_per_frame
+
+
+def _number_bitplane_masks(numbers, *, width, height, size_px=None):
+    return [
+        (generate_number_rgb(
+            number,
+            width=width,
+            height=height,
+            size_px=size_px,
+        )[:, :, 0] > 0).astype(np.uint8) for number in numbers]
+
+
+def _decimal_number_bitplane_masks(numbers, *, width, height, size_px=None):
+    return [
         (
-            generate_number_rgb(
+            generate_decimal_number_rgb(
                 number,
                 width=width,
                 height=height,
                 size_px=size_px,
-            )[:, :, 0] > 0
-        ).astype(np.uint8)
-        for number in numbers
-    ]
-    masks.extend(
-        np.zeros((height, width), dtype=np.uint8)
-        for _ in range(BITPLANES - len(masks))
-    )
+            )[:, :, 0] > 0).astype(np.uint8) for number in numbers]
+
+
+def _pack_binary_masks_bitplanes(masks, width, height):
+    masks = list(masks)
+    if len(masks) > BITPLANES:
+        raise ValueError(f"masks can contain at most {BITPLANES} entries")
+    masks.extend(np.zeros((height, width), dtype=np.uint8) for _ in range(BITPLANES - len(masks)))
 
     r = np.zeros((height, width), dtype=np.uint8)
     g = np.zeros((height, width), dtype=np.uint8)
@@ -494,19 +666,24 @@ def _pack_number_sequence_bitplanes(numbers, width, height, size_px=None):
 
 
 def make_pair_frame_provider(
-        test,
-        test_a=None,
-        test_b=None,
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        numbers=None,
-        numbers_size_px=None,
-        numbers_exposure_us=None,
-        b_dot_x=None,
-        b_dot_y=None,
-        b_dot_radius=40,
-        b_dot_shape="circle",
-        b_dot_invert=False,
+    test,
+    test_a=None,
+    test_b=None,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    numbers=None,
+    numbers_size_px=None,
+    numbers_bitplane_order=None,
+    numbers_exposure_us=None,
+    count_start=1,
+    count_end=100,
+    count_slots_per_frame=2,
+    dot_radius=40,
+    b_dot_x=None,
+    b_dot_y=None,
+    b_dot_radius=40,
+    b_dot_shape="circle",
+    b_dot_invert=False,
 ):
     if test in STATIC_PAIR_TESTS:
         return StaticPairFrameProvider(
@@ -514,6 +691,7 @@ def make_pair_frame_provider(
             mode_b=test_b or test,
             width=width,
             height=height,
+            dot_radius=dot_radius,
         )
     if test == "gradient":
         return DynamicGradientPairFrameProvider(width=width, height=height)
@@ -525,6 +703,7 @@ def make_pair_frame_provider(
             width=width,
             height=height,
             size_px=numbers_size_px,
+            numbers_bitplane_order=numbers_bitplane_order,
         )
     if test == A_NUMBERS_B_STATIC_PAIR_TEST:
         return NumberSequenceAStaticBPairFrameProvider(
@@ -539,16 +718,34 @@ def make_pair_frame_provider(
             b_dot_shape=b_dot_shape,
             b_dot_invert=b_dot_invert,
         )
+    if test == A_COUNT_B_STATIC_PAIR_TEST:
+        return CountSequenceAStaticBPairFrameProvider(
+            mode_b=test_b or "dot",
+            count_start=count_start,
+            count_end=count_end,
+            count_slots_per_frame=count_slots_per_frame,
+            width=width,
+            height=height,
+            size_px=numbers_size_px,
+            numbers_bitplane_order=numbers_bitplane_order,
+            b_dot_x=b_dot_x,
+            b_dot_y=b_dot_y,
+            b_dot_radius=b_dot_radius,
+            b_dot_shape=b_dot_shape,
+            b_dot_invert=b_dot_invert,
+        )
     raise ValueError(f"Unsupported paired test mode: {test}")
 
 
 def _load_gl_modules():
-    glfw = importlib.import_module("glfw")
-    gl = importlib.import_module("OpenGL.GL")
+    import glfw
+    import OpenGL.GL as gl
+
     return glfw, gl
 
 
 class PairedPatternEngine:
+
     def __init__(self, width=PAIR_WIDTH, height=PAIR_HEIGHT, fps=TARGET_HZ, x=0, y=0):
         self.width = width
         self.height = height
@@ -569,8 +766,11 @@ def __init__(self, width=PAIR_WIDTH, height=PAIR_HEIGHT, fps=TARGET_HZ, x=0, y=0
         self._glfw.window_hint(self._glfw.REFRESH_RATE, self.fps)
 
         self.window = self._glfw.create_window(
-            width, height, "DLPC900 Paired Pattern Engine", None, None
-        )
+            width,
+            height,
+            "DLPC900 Paired Pattern Engine",
+            None,
+            None)
         if not self.window:
             self._glfw.terminate()
             raise RuntimeError("Could not create paired GLFW window")
@@ -582,14 +782,12 @@ def __init__(self, width=PAIR_WIDTH, height=PAIR_HEIGHT, fps=TARGET_HZ, x=0, y=0
         fb_w, fb_h = self._glfw.get_framebuffer_size(self.window)
         logger.info(
             f"[+] Paired framebuffer: {fb_w}x{fb_h} "
-            f"(requested {width}x{height} @ {self.fps}Hz)"
-        )
+            f"(requested {width}x{height} @ {self.fps}Hz)")
         if fb_w != width or fb_h != height:
             self.cleanup()
             raise RuntimeError(
                 f"Paired framebuffer is {fb_w}x{fb_h}, expected {width}x{height}. "
-                "Refusing paired run because output halves would not map 1:1 to the DMDs."
-            )
+                "Refusing paired run because output halves would not map 1:1 to the DMDs.")
 
         gl = self._gl
         gl.glViewport(0, 0, fb_w, fb_h)
@@ -618,8 +816,7 @@ def display_frame(self, frame_array):
         if frame_array.shape[:2] != (self.height, self.width):
             raise ValueError(
                 f"Paired frame shape {frame_array.shape} does not match "
-                f"{self.height}x{self.width}x3"
-            )
+                f"{self.height}x{self.width}x3")
 
         now = time.perf_counter()
         if self.last_frame_time > 0:
@@ -629,8 +826,7 @@ def display_frame(self, frame_array):
                 if now - self.last_stutter_log > 2.0:
                     logger.warning(
                         f"[WARNING] Paired render stutter: dt={dt * 1000:.2f}ms, "
-                        f"dropped_frames={self.dropped_frames}"
-                    )
+                        f"dropped_frames={self.dropped_frames}")
                     self.last_stutter_log = now
         self.last_frame_time = now
 
@@ -664,8 +860,8 @@ def display_frame(self, frame_array):
 
     def should_close(self):
         return self._glfw.window_should_close(self.window) or (
-                self._glfw.get_key(self.window, self._glfw.KEY_ESCAPE) == self._glfw.PRESS
-        )
+            self._glfw.get_key(self.window,
+                               self._glfw.KEY_ESCAPE) == self._glfw.PRESS)
 
     def cleanup(self):
         try:
diff --git a/dmdcontrol/patterns/visual.py b/dmdcontrol/patterns/visual.py
index 89171ec..857380d 100644
--- a/dmdcontrol/patterns/visual.py
+++ b/dmdcontrol/patterns/visual.py
@@ -32,7 +32,7 @@ def _paint_vertical_bands(frame, spacing, thickness, offset):
     width = frame.shape[1]
     x = offset % spacing
     while x < width:
-        frame[:, x: min(width, x + thickness), :] = 255
+        frame[:, x:min(width, x + thickness), :] = 255
         x += spacing
 
 
@@ -40,17 +40,17 @@ def _paint_horizontal_bands(frame, spacing, thickness, offset):
     height = frame.shape[0]
     y = offset % spacing
     while y < height:
-        frame[y: min(height, y + thickness), :, :] = 255
+        frame[y:min(height, y + thickness), :, :] = 255
         y += spacing
 
 
 def generate_coarse_grid_rgb(
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        spacing=DEFAULT_COARSE_GRID_SPACING,
-        thickness=DEFAULT_COARSE_GRID_THICKNESS,
-        offset_x=0,
-        offset_y=0,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    spacing=DEFAULT_COARSE_GRID_SPACING,
+    thickness=DEFAULT_COARSE_GRID_THICKNESS,
+    offset_x=0,
+    offset_y=0,
 ):
     """Return a binary RGB grid with human-scale spacing and thick strokes."""
     _validate_spacing(spacing, thickness)
@@ -61,12 +61,12 @@ def generate_coarse_grid_rgb(
 
 
 def generate_coarse_lines_rgb(
-        width=DMD_WIDTH,
-        height=DMD_HEIGHT,
-        spacing=DEFAULT_COARSE_LINE_SPACING,
-        thickness=DEFAULT_COARSE_LINE_THICKNESS,
-        orientation="vertical",
-        offset=0,
+    width=DMD_WIDTH,
+    height=DMD_HEIGHT,
+    spacing=DEFAULT_COARSE_LINE_SPACING,
+    thickness=DEFAULT_COARSE_LINE_THICKNESS,
+    orientation="vertical",
+    offset=0,
 ):
     """Return thick vertical or horizontal bands instead of one-pixel lines."""
     _validate_spacing(spacing, thickness)
diff --git a/dmdcontrol/preview/render.py b/dmdcontrol/preview/render.py
index 1841ec3..0465011 100644
--- a/dmdcontrol/preview/render.py
+++ b/dmdcontrol/preview/render.py
@@ -11,6 +11,7 @@
 import numpy as np
 from PIL import Image
 
+from dmdcontrol.patterns.bitplanes import pack_bitplanes_rgb, unpack_rgb_bitplanes
 from dmdcontrol.patterns.calibration_square import build_calibration_square_frame
 from dmdcontrol.patterns.kernel import build_kernel_frames
 from dmdcontrol.patterns.modes import (
@@ -39,12 +40,9 @@
 from dmdcontrol.support.constants import BITPLANES
 
 BITPLANE_LABELS = tuple(
-    [f"G{i}" for i in range(8)]
-    + [f"R{i}" for i in range(8)]
-    + [f"B{i}" for i in range(8)]
-)
+    [f"G{i}" for i in range(8)] + [f"R{i}" for i in range(8)] + [f"B{i}" for i in range(8)])
 # GRB
-_BITPLANE_CHANNELS = (1,) * 8 + (0,) * 8 + (2,) * 8
+_BITPLANE_CHANNELS = (1, ) * 8 + (0, ) * 8 + (2, ) * 8
 _BITPLANE_BITS = tuple(range(8)) * 3
 
 
@@ -62,12 +60,6 @@ def _json_safe_value(value):
     return value
 
 
-def _bitplane_label(plane_index):
-    if 0 <= plane_index < len(BITPLANE_LABELS):
-        return BITPLANE_LABELS[plane_index]
-    return f"P{plane_index}"
-
-
 def build_lut_preview_metadata(entries, timing=None):
     """Describe a DLPC900 video-pattern LUT in preview-friendly JSON data."""
 
@@ -82,7 +74,8 @@ def build_lut_preview_metadata(entries, timing=None):
         dark_us = int(entry[5]) if len(entry) > 5 else 0
         trig2_disabled = bool(entry[6]) if len(entry) > 6 else False
         image_index = int(entry[7]) if len(entry) > 7 else plane_index
-        label = _bitplane_label(plane_index)
+        label = BITPLANE_LABELS[plane_index] if 0 <= plane_index < len(
+            BITPLANE_LABELS) else f"P{plane_index}"
         preview_entries.append(
             {
                 "index": int(index),
@@ -100,8 +93,7 @@ def build_lut_preview_metadata(entries, timing=None):
                 "led_select": led_select,
                 "trig2_disabled": trig2_disabled,
                 "image_index": image_index,
-            }
-        )
+            })
         cursor_us += exposure_us + dark_us
 
     return {
@@ -119,30 +111,10 @@ def __init__(self, width=DMD_WIDTH, height=DMD_HEIGHT):
         self.height = height
 
     def pack_patterns(self, binary_images):
-        if len(binary_images) != BITPLANES:
-            raise ValueError(f"expected {BITPLANES} binary images, got {len(binary_images)}")
-        r = np.zeros((self.height, self.width), dtype=np.uint8)
-        g = np.zeros((self.height, self.width), dtype=np.uint8)
-        b = np.zeros((self.height, self.width), dtype=np.uint8)
-        for i in range(8):
-            g |= np.asarray(binary_images[i], dtype=np.uint8) << i
-            r |= np.asarray(binary_images[i + 8], dtype=np.uint8) << i
-            b |= np.asarray(binary_images[i + 16], dtype=np.uint8) << i
-        return np.ascontiguousarray(np.stack([r, g, b], axis=-1))
+        return pack_bitplanes_rgb(binary_images, self.width, self.height)
 
     def rgb_to_binary_patterns(self, rgb_array):
-        if rgb_array.shape[:2] != (self.height, self.width):
-            raise ValueError(
-                f"RGB array must be {self.height}x{self.width}, got {rgb_array.shape[:2]}"
-            )
-        patterns = []
-        for bit in range(8):
-            patterns.append(((rgb_array[:, :, 1] >> bit) & 1).astype(np.uint8))
-        for bit in range(8):
-            patterns.append(((rgb_array[:, :, 0] >> bit) & 1).astype(np.uint8))
-        for bit in range(8):
-            patterns.append(((rgb_array[:, :, 2] >> bit) & 1).astype(np.uint8))
-        return patterns
+        return unpack_rgb_bitplanes(rgb_array, self.width, self.height)
 
     def generate_checkerboard(self, block_size=32):
         y, x = np.indices((self.height, self.width))
@@ -179,7 +151,7 @@ def generate_ordering_diagnostic_patterns(self, sub_width=512, sub_height=512):
             img = np.zeros((self.height, self.width), dtype=np.uint8)
             bx_start = x_start + (i * block_w)
             bx_end = min(x_start + sub_width, bx_start + block_w)
-            img[y_start: y_start + sub_height, bx_start:bx_end] = 1
+            img[y_start:y_start + sub_height, bx_start:bx_end] = 1
             patterns.append(img)
         return patterns
 
@@ -197,26 +169,11 @@ def generate_snake_frame(self, frame_index=0, grid_w=24, grid_h=13):
         frame_2d = np.repeat(np.repeat(grid, block_h, axis=0), block_w, axis=1)
         padded = np.zeros((self.height, self.width), dtype=np.uint8)
         h, w = frame_2d.shape
-        padded[: min(h, self.height), : min(w, self.width)] = frame_2d[
-            : min(h, self.height), : min(w, self.width)
-        ]
+        padded[:min(h, self.height), :min(w, self.width)] = frame_2d[:min(h, self.height
+                                                                          ), :min(w, self.width)]
         return np.ascontiguousarray(np.stack([padded, padded, padded], axis=-1))
 
 
-def _solid_rgb(channel, width=DMD_WIDTH, height=DMD_HEIGHT):
-    frame = np.zeros((height, width, 3), dtype=np.uint8)
-    frame[:, :, channel] = 255
-    return np.ascontiguousarray(frame)
-
-
-def _clock_preview_frame(frame_index, width=DMD_WIDTH, height=DMD_HEIGHT):
-    frame = np.zeros((height, width, 3), dtype=np.uint8)
-    stripe = max(1, width // 16)
-    x0 = (frame_index % 16) * stripe
-    frame[:, x0: min(width, x0 + stripe), :] = 255
-    return np.ascontiguousarray(frame)
-
-
 def _kernel_preview_frame(engine, frame_index):
     frames, _metadata = build_kernel_frames(
         engine,
@@ -234,7 +191,9 @@ def render_single_frame(test="coarse-grid", frame_index=0, width=DMD_WIDTH, heig
     engine = PreviewEngine(width=width, height=height)
 
     if test == "colors":
-        return _solid_rgb(frame_index % 3, width=width, height=height)
+        frame = np.zeros((height, width, 3), dtype=np.uint8)
+        frame[:, :, frame_index % 3] = 255
+        return np.ascontiguousarray(frame)
     if test == "numbers":
         number = NUMBER_SEQUENCE[frame_index % len(NUMBER_SEQUENCE)]
         return generate_number_rgb(number, width=width, height=height)
@@ -244,7 +203,11 @@ def render_single_frame(test="coarse-grid", frame_index=0, width=DMD_WIDTH, heig
     if test == "snake":
         return engine.generate_snake_frame(frame_index=frame_index)
     if test == "clock":
-        return _clock_preview_frame(frame_index, width=width, height=height)
+        frame = np.zeros((height, width, 3), dtype=np.uint8)
+        stripe = max(1, width // 16)
+        x0 = (frame_index % 16) * stripe
+        frame[:, x0:min(width, x0 + stripe), :] = 255
+        return np.ascontiguousarray(frame)
     if test == "kernel":
         return _kernel_preview_frame(engine, frame_index)
 
@@ -289,11 +252,11 @@ def render_pair_frame(test="coarse-grid", test_a=None, test_b=None, frame_index=
 
 
 def render_offline_frame(
-        layout="pair",
-        test="coarse-grid",
-        test_a=None,
-        test_b=None,
-        frame_index=0,
+    layout="pair",
+    test="coarse-grid",
+    test_a=None,
+    test_b=None,
+    frame_index=0,
 ):
     if layout == "pair":
         return render_pair_frame(test=test, test_a=test_a, test_b=test_b, frame_index=frame_index)
@@ -329,13 +292,13 @@ def render_png_bytes(image_array):
 
 
 def render_preview_png(
-        layout="pair",
-        test="coarse-grid",
-        test_a=None,
-        test_b=None,
-        frame_index=0,
-        view="packed",
-        plane=0,
+    layout="pair",
+    test="coarse-grid",
+    test_a=None,
+    test_b=None,
+    frame_index=0,
+    view="packed",
+    plane=0,
 ):
     packed = render_offline_frame(
         layout=layout,
@@ -348,6 +311,7 @@ def render_preview_png(
 
 
 class LiveFrameStore:
+
     def __init__(self):
         self._lock = threading.Lock()
         self._frame = None
@@ -378,6 +342,7 @@ def has_frame(self):
 
 
 class LivePreviewPoster:
+
     def __init__(self, url, fps=1.0):
         if fps <= 0:
             raise ValueError("fps must be positive")
@@ -404,7 +369,8 @@ def maybe_post(self, packed_frame, metadata=None, force=False):
             metadata_copy = dict(metadata or {})
             self._active_thread = threading.Thread(
                 target=self._post_frame,
-                args=(frame_copy, metadata_copy),
+                args=(frame_copy,
+                      metadata_copy),
                 daemon=True,
             )
             self._active_thread.start()
@@ -413,7 +379,8 @@ def _post_frame(self, packed_frame, metadata):
         body = render_png_bytes(packed_frame)
         headers = {
             "Content-Type": "image/png",
-            "X-DMD-Metadata": json.dumps(metadata, sort_keys=True),
+            "X-DMD-Metadata": json.dumps(metadata,
+                                         sort_keys=True),
         }
         req = request.Request(self.url, data=body, headers=headers, method="POST")
         try:
diff --git a/dmdcontrol/preview/server.py b/dmdcontrol/preview/server.py
index 6892f11..35111cd 100644
--- a/dmdcontrol/preview/server.py
+++ b/dmdcontrol/preview/server.py
@@ -82,7 +82,7 @@ def _send_offline_frame(self, params):
         test = _query_value(params, "test", "coarse-grid")
         test_a = _query_value(params, "test_a", None)
         test_b = _query_value(params, "test_b", None)
-        frame_index = _query_int(params, "frame", 0)
+        frame_index = int(_query_value(params, "frame", 0))
         view = _query_value(params, "view", "packed")
         plane = _query_plane(params, "plane", 0)
         png = render_preview_png(
@@ -119,6 +119,7 @@ def _send_bytes(self, body, content_type):
 
 
 class DmdPreviewServer(ThreadingHTTPServer):
+
     def __init__(self, server_address):
         super().__init__(server_address, DmdPreviewHandler)
         self.live_store = LiveFrameStore()
@@ -131,13 +132,6 @@ def _query_value(params, name, default):
     return values[0] or default
 
 
-def _query_int(params, name, default):
-    value = _query_value(params, name, None)
-    if value is None:
-        return default
-    return int(value)
-
-
 def _query_plane(params, name, default):
     value = _query_value(params, name, None)
     if value is None:
diff --git a/dmdcontrol/runtime/lifecycle.py b/dmdcontrol/runtime/lifecycle.py
index a7ce03d..72405e6 100644
--- a/dmdcontrol/runtime/lifecycle.py
+++ b/dmdcontrol/runtime/lifecycle.py
@@ -74,14 +74,12 @@ def log_board_snapshot(dlpc, tag):
     if dd:
         logger.debug("Display Dimensions:")
         logger.debug(
-            f"  Total:              {dd['total_pixels_per_line']} x {dd['total_lines_per_frame']}"
-        )
+            f"  Total:              {dd['total_pixels_per_line']} x {dd['total_lines_per_frame']}")
         logger.debug(
             f"  Active:             {dd['active_pixels_per_line']} x {dd['active_lines_per_frame']}"
         )
         logger.debug(
-            f"  First pixel:        ({dd['first_active_pixel']}, {dd['first_active_line']})"
-        )
+            f"  First pixel:        ({dd['first_active_pixel']}, {dd['first_active_line']})")
         logger.debug(f"  Pixel clock:        {dd['pixel_clock_khz']} kHz")
 
     err = dlpc.get_last_error()
@@ -96,8 +94,7 @@ def log_board_snapshot(dlpc, tag):
     if fw:
         logger.debug(
             f"Firmware Version:     app={fw['app']['str']} api={fw['api']['str']} "
-            f"sw_cfg={fw['sw_cfg']['str']} seq_cfg={fw['seq_cfg']['str']}"
-        )
+            f"sw_cfg={fw['sw_cfg']['str']} seq_cfg={fw['seq_cfg']['str']}")
         if fw["app"]["major"] <= 5 and fw["app"]["minor"] == 0:
             logger.warning(
                 "Firmware <= 5.0.x: DLPT028 block-lock workaround required (park/unpark after mode change)."
@@ -110,8 +107,7 @@ def log_board_snapshot(dlpc, tag):
     cs = dlpc.get_channel_swap()
     if cs:
         logger.debug(
-            f"Channel Swap:         {cs['swap_label']} (port {cs['port']}, raw {cs['raw']})"
-        )
+            f"Channel Swap:         {cs['swap_label']} (port {cs['port']}, raw {cs['raw']})")
         if cs["swap_label"] != "ABC":
             logger.debug(
                 f"  Note: non-default channel swap '{cs['swap_label']}' active. Affects RGB->bitplane pin mapping."
@@ -121,13 +117,13 @@ def log_board_snapshot(dlpc, tag):
 
 
 def build_lut_entries(
-        dlpc,
-        target_hz,
-        sequence_utilization=DEFAULT_SEQUENCE_UTILIZATION,
-        trig2_frame_zero=False,
-        entries_count=None,
-        per_entry_exposure_us=None,
-        dark_time_us=None,
+    dlpc,
+    target_hz,
+    sequence_utilization=DEFAULT_SEQUENCE_UTILIZATION,
+    trig2_frame_zero=False,
+    entries_count=None,
+    per_entry_exposure_us=None,
+    dark_time_us=None,
 ):
     if target_hz <= 0:
         raise ValueError("target_hz must be positive")
@@ -142,12 +138,8 @@ def build_lut_entries(
 
     measured_frame_hz = None
     dd = dlpc.get_display_dimensions()
-    if (
-            dd
-            and dd.get("pixel_clock_khz")
-            and dd.get("total_pixels_per_line")
-            and dd.get("total_lines_per_frame")
-    ):
+    if (dd and dd.get("pixel_clock_khz") and dd.get("total_pixels_per_line")
+            and dd.get("total_lines_per_frame")):
         total_pixels = int(dd["total_pixels_per_line"]) * int(dd["total_lines_per_frame"])
         pixel_clock_hz = int(dd["pixel_clock_khz"]) * 1000
         if total_pixels > 0 and pixel_clock_hz > 0:
@@ -183,15 +175,13 @@ def build_lut_entries(
     if requested_binary_rate_hz > MAX_BINARY_RATE_HZ_DLP6500:
         raise ValueError(
             f"Requested binary rate {requested_binary_rate_hz:.1f} Hz exceeds "
-            f"DLP6500 1-bit limit (~{MAX_BINARY_RATE_HZ_DLP6500} Hz)."
-        )
+            f"DLP6500 1-bit limit (~{MAX_BINARY_RATE_HZ_DLP6500} Hz).")
 
     effective_binary_rate_hz = effective_frame_hz * entries_count
     if effective_binary_rate_hz > MAX_BINARY_RATE_HZ_DLP6500:
         raise ValueError(
             f"Measured source binary rate {effective_binary_rate_hz:.1f} Hz exceeds "
-            f"DLP6500 1-bit limit (~{MAX_BINARY_RATE_HZ_DLP6500} Hz)."
-        )
+            f"DLP6500 1-bit limit (~{MAX_BINARY_RATE_HZ_DLP6500} Hz).")
 
     min_segment_us = MIN_EXPOSURE_US + actual_dark_us
     usable_frame_period_us = safe_frame_period_us * sequence_utilization
@@ -200,39 +190,33 @@ def build_lut_entries(
         if per_entry_exposure_us < MIN_EXPOSURE_US:
             raise ValueError(
                 f"per_entry_exposure_us ({per_entry_exposure_us}) is below MIN_EXPOSURE_US "
-                f"({MIN_EXPOSURE_US})."
-            )
+                f"({MIN_EXPOSURE_US}).")
         total_needed_us = (per_entry_exposure_us + actual_dark_us) * entries_count
         if total_needed_us > usable_frame_period_us:
             raise ValueError(
                 f"{entries_count} LUT entries at {per_entry_exposure_us} us exposure need "
                 f"{total_needed_us:.1f} us per VSYNC but only {usable_frame_period_us:.1f} us is "
                 f"usable (frame_period {frame_period_us:.1f} us, margin {SAFE_MARGIN_US} us, "
-                f"utilization {sequence_utilization})."
-            )
+                f"utilization {sequence_utilization}).")
         exposure_us = int(per_entry_exposure_us)
     else:
         segment_budget_us = usable_frame_period_us / entries_count
         if segment_budget_us < min_segment_us:
             max_safe_hz = 1_000_000.0 / (
-                    (entries_count * min_segment_us / sequence_utilization) + SAFE_MARGIN_US
-            )
+                (entries_count * min_segment_us / sequence_utilization) + SAFE_MARGIN_US)
             raise ValueError(
                 f"Requested sequence exceeds VSYNC budget: each pattern has {segment_budget_us:.2f} us "
                 f"but needs >= {min_segment_us} us (exposure {MIN_EXPOSURE_US} us + dark {actual_dark_us} us). "
-                f"Reduce source frame rate to <= {max_safe_hz:.2f} Hz."
-            )
+                f"Reduce source frame rate to <= {max_safe_hz:.2f} Hz.")
 
         segment_us = int(usable_frame_period_us / entries_count)
         exposure_us = segment_us - actual_dark_us
         if exposure_us < MIN_EXPOSURE_US:
             max_safe_hz = 1_000_000.0 / (
-                    (entries_count * min_segment_us / sequence_utilization) + SAFE_MARGIN_US
-            )
+                (entries_count * min_segment_us / sequence_utilization) + SAFE_MARGIN_US)
             raise ValueError(
                 f"Computed exposure {exposure_us} us is below minimum {MIN_EXPOSURE_US} us. "
-                f"Reduce source frame rate to <= {max_safe_hz:.2f} Hz."
-            )
+                f"Reduce source frame rate to <= {max_safe_hz:.2f} Hz.")
 
     total_sequence_us = (exposure_us + actual_dark_us) * entries_count
     idle_headroom_us = frame_period_us - total_sequence_us
@@ -251,8 +235,7 @@ def build_lut_entries(
                 actual_dark_us,
                 trig2_disable,
                 bit_pos,
-            )
-        )
+            ))
 
     timing = {
         "timing_source": timing_source,
@@ -276,10 +259,10 @@ def build_lut_entries(
 
 
 def compute_trigger_out_2_timing(
-        exposure_us,
-        delay_fraction=0.00,
-        min_pulse_width_us=20,
-        delay_basis="exposure_us",
+    exposure_us,
+    delay_fraction=0.00,
+    min_pulse_width_us=20,
+    delay_basis="exposure_us",
 ):
     if exposure_us <= 0:
         raise ValueError("exposure_us must be positive")
@@ -295,13 +278,11 @@ def compute_trigger_out_2_timing(
     if not (0 <= rising_delay_us <= SIGNED_INT16_MAX):
         raise ValueError(
             f"rising_delay_us ({rising_delay_us}) must fit signed int16 trigger delay range "
-            f"[{SIGNED_INT16_MIN}, {SIGNED_INT16_MAX}]"
-        )
+            f"[{SIGNED_INT16_MIN}, {SIGNED_INT16_MAX}]")
     if not (0 <= falling_delay_us <= SIGNED_INT16_MAX):
         raise ValueError(
             f"falling_delay_us ({falling_delay_us}) must fit signed int16 trigger delay range "
-            f"[{SIGNED_INT16_MIN}, {SIGNED_INT16_MAX}]"
-        )
+            f"[{SIGNED_INT16_MIN}, {SIGNED_INT16_MAX}]")
     return {
         "channel": "TRIG_OUT_2",
         "edge": "rising",
@@ -342,11 +323,8 @@ def _bit6_is_cosmetic(dlpc, hw):
     ms = dlpc.get_main_status() or {}
     mode, _ = dlpc.get_display_mode()
     return bool(
-        mode == 2
-        and ms.get("sequencer_running")
-        and ms.get("external_source_locked")
-        and ms.get("port1_syncs_valid")
-    )
+        mode == 2 and ms.get("sequencer_running") and ms.get("external_source_locked")
+        and ms.get("port1_syncs_valid"))
 
 
 def ensure_video_pattern_mode(dlpc, retries=3, poll_timeout_s=1.2):
@@ -356,8 +334,7 @@ def ensure_video_pattern_mode(dlpc, retries=3, poll_timeout_s=1.2):
 
     for attempt in range(1, retries + 1):
         logger.warning(
-            f"Mode readback shows {mode}, not 2! Retrying mode transition ({attempt}/{retries})..."
-        )
+            f"Mode readback shows {mode}, not 2! Retrying mode transition ({attempt}/{retries})...")
         dlpc.set_display_mode(0x02)
 
         time.sleep(0.35)
@@ -396,10 +373,10 @@ def start_loaded_pattern_sequence(dlpc, post_start_delay_s=0.2):
 
 
 def start_loaded_pattern_sequences(
-        dlpc_a,
-        dlpc_b,
-        post_start_delay_s=0.2,
-        verify=False,
+    dlpc_a,
+    dlpc_b,
+    post_start_delay_s=0.2,
+    verify=False,
 ):
     barrier = threading.Barrier(3)
     errors = []
@@ -412,8 +389,14 @@ def _start_one(label, dlpc):
             errors.append((label, exc))
 
     threads = [
-        threading.Thread(target=_start_one, args=("A", dlpc_a), daemon=True),
-        threading.Thread(target=_start_one, args=("B", dlpc_b), daemon=True),
+        threading.Thread(target=_start_one,
+                         args=("A",
+                               dlpc_a),
+                         daemon=True),
+        threading.Thread(target=_start_one,
+                         args=("B",
+                               dlpc_b),
+                         daemon=True),
     ]
     for thread in threads:
         thread.start()
@@ -440,28 +423,24 @@ def verify_started_pattern_sequence(dlpc, label="DLPC900"):
         ms = dlpc.get_main_status() or {}
         raise RuntimeError(
             f"{label} dropped out of Video Pattern Mode after sequencer start. "
-            f"Mode readback: {mode}, main status: {ms}."
-        )
+            f"Mode readback: {mode}, main status: {ms}.")
 
     if not wait_for_sequencer_running(dlpc, timeout_s=1.5):
         ms = dlpc.get_main_status() or {}
         hw = dlpc.get_hardware_status()
         raise RuntimeError(
             f"{label} pattern sequencer did not report running after start command. "
-            f"Main status: {ms}, hardware status: {hw}."
-        )
+            f"Main status: {ms}, hardware status: {hw}.")
 
     hw = dlpc.get_hardware_status()
     if hw is not None and (hw & 0x80):
         logger.warning(
             f"{label} hardware status sequence-error bit set (hw=0x{hw:02X}). "
-            "Sequencer has reported a runtime error condition."
-        )
+            "Sequencer has reported a runtime error condition.")
     elif hw is not None and (hw & 0x40):
         logger.debug(
             f"  {label} post-config hw=0x{hw:02X}. "
-            "Bit 6 latched (cosmetic, set by Pattern Stop)."
-        )
+            "Bit 6 latched (cosmetic, set by Pattern Stop).")
     return hw
 
 
@@ -486,8 +465,7 @@ def apply_pattern_sequence(dlpc, entries, frame_pump=None):
         if _bit6_is_cosmetic(dlpc, hw):
             logger.debug(
                 f"  [arm] bit-6 latched but health checks are good "
-                f"(hw={_format_hw(hw)}); skipping retry churn."
-            )
+                f"(hw={_format_hw(hw)}); skipping retry churn.")
             break
 
         err_code = dlpc.get_last_error()
@@ -495,8 +473,7 @@ def apply_pattern_sequence(dlpc, entries, frame_pump=None):
         logger.debug(
             f"  [arm] bit-6 latched hw={_format_hw(hw)} after start attempt {attempt}. "
             f"last_err={err_code!r} desc={err_desc!r}. "
-            f"Stop -> park/unpark -> {_RETRY_DELAYS[attempt - 1]:.2f}s -> resend LUT -> restart."
-        )
+            f"Stop -> park/unpark -> {_RETRY_DELAYS[attempt - 1]:.2f}s -> resend LUT -> restart.")
         dlpc.start_pattern_display(0)
         time.sleep(0.1)
         hw_after_stop = dlpc.get_hardware_status()
@@ -526,21 +503,20 @@ def apply_pattern_sequence(dlpc, entries, frame_pump=None):
 
 
 def prepare_dlpc900_for_video_pattern(
-        dlpc,
-        target_hz=DEFAULT_HZ,
-        dual_pixel=False,
-        sequence_utilization=DEFAULT_SEQUENCE_UTILIZATION,
-        trig2_frame_zero=False,
-        entries_count=None,
-        per_entry_exposure_us=None,
-        trigger_out_2_delay_fraction=0.00,
-        dark_time_us=None,
+    dlpc,
+    target_hz=DEFAULT_HZ,
+    dual_pixel=False,
+    sequence_utilization=DEFAULT_SEQUENCE_UTILIZATION,
+    trig2_frame_zero=False,
+    entries_count=None,
+    per_entry_exposure_us=None,
+    trigger_out_2_delay_fraction=0.00,
+    dark_time_us=None,
 ):
     actual_entries = entries_count if entries_count is not None else BITPLANES
     logger.info(
         f"[+] Configuring DLPC900 for {DMD_WIDTH}x{DMD_HEIGHT} @ {target_hz}Hz Video Pattern Mode "
-        f"({actual_entries} LUT entr{'y' if actual_entries == 1 else 'ies'} per VSYNC)..."
-    )
+        f"({actual_entries} LUT entr{'y' if actual_entries == 1 else 'ies'} per VSYNC)...")
     logger.debug("Following TI documentation sequence (DLPU018J Section 5.1)...")
 
     # First-touch hw status. NOTE: hw bit 6 ("ABORT" per DLPU018J Table 2-21)
@@ -553,19 +529,20 @@ def prepare_dlpc900_for_video_pattern(
     err0_desc = dlpc.get_error_description()
     logger.info(
         f"[+] DLPC900 first-touch status: hw={_format_hw(hw_first)} "
-        f"last_err={err0_code!r} desc={err0_desc!r}"
-    )
+        f"last_err={err0_code!r} desc={err0_desc!r}")
 
     # Stop pattern display ONLY if currently in Pattern Mode (2). At boot the
     # display mode defaults to 0 (Video Mode) and Pattern Stop is firmware-NACKed,
     # producing harmless but noisy WARNING. Skip the unconditional stop.
     current_mode, _ = dlpc.get_display_mode()
     if current_mode == 2:
-        logger.debug(f"  - Pre-config stop: display already in mode {current_mode}, sending Pattern Stop...")
+        logger.debug(
+            f"  - Pre-config stop: display already in mode {current_mode}, sending Pattern Stop...")
         dlpc.start_pattern_display(0)
         time.sleep(0.2)
     else:
-        logger.debug(f"  - Pre-config stop skipped (display mode={current_mode}, no pattern running).")
+        logger.debug(
+            f"  - Pre-config stop skipped (display mode={current_mode}, no pattern running).")
 
     dlpc.set_led_current(255, 255, 255)
     dlpc.set_led_enables(True, True, True, sequencer=True)
@@ -575,9 +552,7 @@ def prepare_dlpc900_for_video_pattern(
     dlpc.set_display_mode(0x00)
     dlpc.set_input_source(0, 1)
     dlpc.toggle_dual_pixel_mode(bool(dual_pixel))
-    logger.info(
-        f"[+] Parallel input pixel mode: {'Dual P1-P2' if dual_pixel else 'Single P1'}"
-    )
+    logger.info(f"[+] Parallel input pixel mode: {'Dual P1-P2' if dual_pixel else 'Single P1'}")
 
     # Force full active area — otherwise DLPC900 may use a stale Flash-resident crop.
     logger.debug(f"  - Forcing Input Display Resolution to {DMD_WIDTH}x{DMD_HEIGHT}...")
@@ -622,9 +597,11 @@ def prepare_dlpc900_for_video_pattern(
     # Mode transition + park/unpark can reset DP receiver. Re-lock before arming sequencer.
     logger.info("[+] Waiting for external source lock in Video Pattern Mode...")
     if not wait_for_external_lock(dlpc, timeout_s=6.0):
-        logger.warning("External lock not re-acquired in mode 2. Proceeding — triggers may be unreliable.")
+        logger.warning(
+            "External lock not re-acquired in mode 2. Proceeding — triggers may be unreliable.")
     else:
-        logger.info("[+] External lock confirmed in mode 2. Waiting 2s for DP pipeline to stabilize...")
+        logger.info(
+            "[+] External lock confirmed in mode 2. Waiting 2s for DP pipeline to stabilize...")
         time.sleep(2.0)
 
     # DLPU018J Table 2-118/2-120: byte 0 bit 0 = polarity. No enable bit.
@@ -665,32 +642,31 @@ def prepare_dlpc900_for_video_pattern(
     if timing["measured_frame_hz"] and abs(timing["measured_frame_hz"] - target_hz) > 0.5:
         logger.warning(
             f"Source VSYNC is {timing['measured_frame_hz']:.3f} Hz while --hz is {target_hz} Hz. "
-            f"Sequencer timing follows source VSYNC ({timing['effective_frame_hz']:.3f} Hz)."
-        )
+            f"Sequencer timing follows source VSYNC ({timing['effective_frame_hz']:.3f} Hz).")
     logger.info(
         f"[+] LUT: {timing['entries_count']} entries, exposure={timing['exposure_us']}us, "
         f"dark={timing['dark_us']}us, sequence={timing['total_sequence_us']:.1f}/{timing['usable_frame_period_us']:.1f}us "
         f"(utilization {timing['sequence_utilization']:.2f}, reserved margin {timing['safe_margin_us']:.1f}us, "
         f"idle headroom {timing['idle_headroom_us']:.1f}us from {timing['frame_period_us']:.1f}us VSYNC), "
         f"binary rate req={timing['requested_binary_rate_hz']:.1f}Hz, "
-        f"effective={timing['effective_binary_rate_hz']:.1f}Hz"
-    )
+        f"effective={timing['effective_binary_rate_hz']:.1f}Hz")
     if timing["trig2_mode"] == "frame_zero":
         logger.info(
             f"[SCOPE] Expected TRIG_OUT_2: rising delay={trigger_out_2_timing['rising_delay_us']}us, "
-            f"falling={trigger_out_2_timing['falling_delay_us']}us, triggered only on bitplane 0."
+            f"falling={trigger_out_2_timing['falling_delay_us']}us, triggered only on bitplane 0.")
+        logger.info(
+            f"[SCOPE] TRIG_OUT_2 mode: frame_zero anchor (~{timing['effective_frame_hz']:.3f} pulses/s)."
         )
-        logger.info(f"[SCOPE] TRIG_OUT_2 mode: frame_zero anchor (~{timing['effective_frame_hz']:.3f} pulses/s).")
     else:
         logger.info(
             f"[SCOPE] Expected TRIG_OUT_2: rising delay={trigger_out_2_timing['rising_delay_us']}us, "
-            f"falling={trigger_out_2_timing['falling_delay_us']}us, active at each bitplane start."
+            f"falling={trigger_out_2_timing['falling_delay_us']}us, active at each bitplane start.")
+        logger.info(
+            f"[SCOPE] TRIG_OUT_2 mode: per_bitplane (~{timing['effective_binary_rate_hz']:.1f} pulses/s)."
         )
-        logger.info(f"[SCOPE] TRIG_OUT_2 mode: per_bitplane (~{timing['effective_binary_rate_hz']:.1f} pulses/s).")
     logger.info(
         f"[SCOPE] Expected TRIG_OUT_1: ~{timing['effective_frame_hz']:.3f} pulses/s. "
-        "With dark=0us, pulse may appear as a wide frame-level gate."
-    )
+        "With dark=0us, pulse may appear as a wide frame-level gate.")
 
     # Empirically: arming before DLPC900 processes several VSYNCs in mode 2 -> forced-swap (hw 0x08) -> abort (0x40).
     logger.debug("  - Final VSYNC settling wait (1s)...")
@@ -700,17 +676,17 @@ def prepare_dlpc900_for_video_pattern(
 
 
 def configure_dlpc900_for_video_pattern(
-        dlpc,
-        target_hz=DEFAULT_HZ,
-        dual_pixel=False,
-        sequence_utilization=DEFAULT_SEQUENCE_UTILIZATION,
-        trig2_frame_zero=False,
-        pre_arm_callback=None,
-        frame_pump=None,
-        entries_count=None,
-        per_entry_exposure_us=None,
-        trigger_out_2_delay_fraction=0.00,
-        dark_time_us=None,
+    dlpc,
+    target_hz=DEFAULT_HZ,
+    dual_pixel=False,
+    sequence_utilization=DEFAULT_SEQUENCE_UTILIZATION,
+    trig2_frame_zero=False,
+    pre_arm_callback=None,
+    frame_pump=None,
+    entries_count=None,
+    per_entry_exposure_us=None,
+    trigger_out_2_delay_fraction=0.00,
+    dark_time_us=None,
 ):
     sequence_state = prepare_dlpc900_for_video_pattern(
         dlpc,
@@ -748,13 +724,16 @@ def verify_runtime_state(dlpc):
 
     hard_checks = {
         "display_mode_is_video_pattern": mode == 2,
-        "sequencer_running": bool(ms.get("sequencer_running", False)),
+        "sequencer_running": bool(ms.get("sequencer_running",
+                                         False)),
         "forced_swap_clear": not forced_swap,
         "seq_error_clear": not seq_error,
     }
     advisory_checks = {
-        "external_source_locked": bool(ms.get("external_source_locked", False)),
-        "port1_syncs_valid": bool(ms.get("port1_syncs_valid", False)),
+        "external_source_locked": bool(ms.get("external_source_locked",
+                                              False)),
+        "port1_syncs_valid": bool(ms.get("port1_syncs_valid",
+                                         False)),
     }
 
     logger.debug("Verification:")
@@ -768,26 +747,20 @@ def verify_runtime_state(dlpc):
     if not hard_ok:
         logger.warning("Runtime verification hard checks failed!")
         logger.warning(
-            "           Video Pattern Mode (2), sequencer running, forced-swap clear, or SEQ_ERR clear failed.")
+            "           Video Pattern Mode (2), sequencer running, forced-swap clear, or SEQ_ERR clear failed."
+        )
         if hw is not None:
             logger.warning(f"           Hardware status raw: 0x{hw:02X}")
     else:
         if not advisory_ok:
             logger.warning(
                 "Runtime verification advisory checks did not all pass; continuing because "
-                "mode/sequencer/hardware error bits are healthy. Confirm TRIG_OUT_2 on scope."
-            )
+                "mode/sequencer/hardware error bits are healthy. Confirm TRIG_OUT_2 on scope.")
             logger.warning(
                 f"           external_source_locked={advisory_checks['external_source_locked']} "
-                f"port1_syncs_valid={advisory_checks['port1_syncs_valid']}"
-            )
+                f"port1_syncs_valid={advisory_checks['port1_syncs_valid']}")
         if seq_abort:
             # bit 6 = state-machine flag latched after every Pattern Stop; cosmetic
-            logger.debug(
-                f"  Runtime hw=0x{hw:02X}. Bit 6 latched (cosmetic, set by Pattern Stop)."
-            )
+            logger.debug(f"  Runtime hw=0x{hw:02X}. Bit 6 latched (cosmetic, set by Pattern Stop).")
         logger.info("[OK] Runtime verification passed (mode=VideoPattern, sequencer running).")
     return hard_ok
-
-
-
diff --git a/dmdcontrol/runtime/loop.py b/dmdcontrol/runtime/loop.py
index 18fcf0c..bea14fc 100644
--- a/dmdcontrol/runtime/loop.py
+++ b/dmdcontrol/runtime/loop.py
@@ -6,15 +6,6 @@
 from dmdcontrol.support.logging import logger
 
 
-def _mode_description(mode):
-    descriptions = {
-        0: "Video Mode",
-        1: "Pre-stored Pattern Mode",
-        2: "Video Pattern Mode",
-    }
-    return descriptions.get(mode, "unknown")
-
-
 def _format_bool_state(value, ok_when_true=True):
     state = bool(value)
     ok = state if ok_when_true else not state
@@ -60,9 +51,14 @@ def _format_watchdog_status(mode, ms, hw):
     video_frozen = bool(ms.get("video_frozen", False))
     dmd_parked = bool(ms.get("dmd_parked", False))
     mode_ok = mode == 2
+    mode_description = {
+        0: "Video Mode",
+        1: "Pre-stored Pattern Mode",
+        2: "Video Pattern Mode",
+    }.get(mode, "unknown")
 
     return (
-        f"[WATCHDOG] mode={mode}({_mode_description(mode)}, {'OK' if mode_ok else 'WARN: expected Video Pattern Mode=2'}); "
+        f"[WATCHDOG] mode={mode}({mode_description}, {'OK' if mode_ok else 'WARN: expected Video Pattern Mode=2'}); "
         f"sequencer_running={_format_bool_state(sequencer_running)} required for triggers; "
         f"external_source_locked={_format_bool_state(external_locked)} advisory DP sync bit; "
         f"port1_syncs_valid={_format_bool_state(port1_sync_valid)} advisory P1 sync bit; "
@@ -87,7 +83,8 @@ def _maybe_recover_abort(dlpc, sequence_state, args, now_monotonic, last_abort_r
         dlpc.start_pattern_display(0)
         time.sleep(0.05)
         if not ensure_video_pattern_mode(dlpc, retries=2, poll_timeout_s=1.0):
-            logger.warning("[WATCHDOG] Auto-recover failed to latch Video Pattern Mode before re-arm.")
+            logger.warning(
+                "[WATCHDOG] Auto-recover failed to latch Video Pattern Mode before re-arm.")
         else:
             apply_pattern_sequence(dlpc, sequence_state["entries"])
             logger.warning("[WATCHDOG] Auto-recover sequence re-arm issued.")
@@ -96,7 +93,14 @@ def _maybe_recover_abort(dlpc, sequence_state, args, now_monotonic, last_abort_r
     return now_monotonic
 
 
-def run_render_loop(dlpc, engine, frame_provider, args, sequence_state, video_writer=None, cv2_module=None):
+def run_render_loop(
+        dlpc,
+        engine,
+        frame_provider,
+        args,
+        sequence_state,
+        video_writer=None,
+        cv2_module=None):
     """Run the main render loop.
 
     frame_provider: callable() -> packed frame (np.ndarray).
@@ -125,8 +129,13 @@ def run_render_loop(dlpc, engine, frame_provider, args, sequence_state, video_wr
                 hw = dlpc.get_hardware_status()
                 logger.debug(_format_watchdog_status(mode, ms, hw))
                 last_abort_recover_at = _maybe_recover_abort(
-                    dlpc, sequence_state, args, now_monotonic, last_abort_recover_at, hw, ms
-                )
+                    dlpc,
+                    sequence_state,
+                    args,
+                    now_monotonic,
+                    last_abort_recover_at,
+                    hw,
+                    ms)
                 watchdog_last = now_monotonic
 
         if video_writer is not None:
diff --git a/dmdcontrol/runtime/pair.py b/dmdcontrol/runtime/pair.py
index dbd63ed..78e63b2 100644
--- a/dmdcontrol/runtime/pair.py
+++ b/dmdcontrol/runtime/pair.py
@@ -20,11 +20,13 @@
 )
 from dmdcontrol.patterns.modes import default_calibration_square_state
 from dmdcontrol.patterns.paired import (
+    A_COUNT_B_STATIC_PAIR_TEST,
     A_NUMBERS_B_STATIC_PAIR_TEST,
     CALIBRATION_DOT_PAIR_TEST,
     CalibrationSquareDotPairFrameProvider,
     DynamicAStaticBPairFrameProvider,
     KERNEL_STATIC_PAIR_TEST,
+    MAX_COUNT_SEQUENCE_FRAMES,
     NUMBER_PAIR_TEST,
     OFFSET_A,
     OFFSET_B,
@@ -33,6 +35,7 @@
     PAIR_WIDTH,
     STATIC_PAIR_TESTS,
     SingleDmdFrameAdapter,
+    count_sequence_frame_count,
     generate_dot_frame,
     generate_static_frame,
     make_pair_frame_provider,
@@ -52,6 +55,11 @@
     DMD_HEIGHT,
     DMD_WIDTH,
 )
+from dmdcontrol.support.argparse_types import (
+    nonnegative_int,
+    numbers_bitplane_order,
+    positive_int,
+)
 from dmdcontrol.support.logging import logger, setup_logger
 
 
@@ -67,24 +75,11 @@ class PairConfig:
 
 
 class _DryRunDLPC:
+
     def get_display_dimensions(self):
         return None
 
 
-def _positive_int(value):
-    parsed = int(value)
-    if parsed <= 0:
-        raise argparse.ArgumentTypeError("must be positive")
-    return parsed
-
-
-def _nonnegative_int(value):
-    parsed = int(value)
-    if parsed < 0:
-        raise argparse.ArgumentTypeError("must be non-negative")
-    return parsed
-
-
 def _parse_numbers(value):
     try:
         numbers = [int(part.strip()) for part in value.split(",") if part.strip()]
@@ -102,30 +97,24 @@ def resolve_pair_config(config_path=None, target_hz=None):
     dmd_b = resolve_dmd_mapping("B", config_path)
     for mapping in (dmd_a, dmd_b):
         if not mapping.xrandr_output:
-            raise ValueError(
-                f"DMD {mapping.name} must define xrandr_output for paired runs."
-            )
+            raise ValueError(f"DMD {mapping.name} must define xrandr_output for paired runs.")
         if not mapping.usb_id_path:
-            raise ValueError(
-                f"DMD {mapping.name} must define usb_id_path for paired runs."
-            )
+            raise ValueError(f"DMD {mapping.name} must define usb_id_path for paired runs.")
 
-    configured_hz = {
-        hz for hz in (dmd_a.target_hz, dmd_b.target_hz) if hz is not None
-    }
+    configured_hz = {hz for hz in (dmd_a.target_hz, dmd_b.target_hz) if hz is not None}
     if len(configured_hz) > 1:
-        raise ValueError(
-            f"Paired DMD target_hz values must match, got {sorted(configured_hz)}"
-        )
+        raise ValueError(f"Paired DMD target_hz values must match, got {sorted(configured_hz)}")
     resolved_hz = int(target_hz or next(iter(configured_hz), DEFAULT_HZ))
     return PairConfig(dmd_a=dmd_a, dmd_b=dmd_b, target_hz=resolved_hz)
 
 
 def _build_parser():
-    parser = argparse.ArgumentParser(
-        description="Dual DLPC900 paired Video Pattern Mode runtime"
-    )
-    parser.add_argument("--hz", type=int, default=None, help=f"Target Hz, default from dmd_devices.json or {DEFAULT_HZ}")
+    parser = argparse.ArgumentParser(description="Dual DLPC900 paired Video Pattern Mode runtime")
+    parser.add_argument(
+        "--hz",
+        type=int,
+        default=None,
+        help=f"Target Hz, default from dmd_devices.json or {DEFAULT_HZ}")
     parser.add_argument("--dmd-config", default=None, help="Path to DMD mapping config")
     parser.add_argument("--test", choices=PAIR_TESTS, default="checkerboard")
     parser.add_argument(
@@ -148,22 +137,29 @@ def _build_parser():
     )
     parser.add_argument("--b-dot-x", type=int, default=DMD_WIDTH // 2)
     parser.add_argument("--b-dot-y", type=int, default=DMD_HEIGHT // 2)
-    parser.add_argument("--b-dot-radius", type=_positive_int, default=40)
+    parser.add_argument("--b-dot-radius", type=positive_int, default=40)
+    parser.add_argument(
+        "--dot-radius",
+        type=positive_int,
+        default=40,
+        help="Radius for generic static dot frames, for example --test dot",
+    )
     parser.add_argument(
         "--b-dot-shape",
-        choices=("circle", "square"),
+        choices=("circle",
+                 "square"),
         default="circle",
     )
     parser.add_argument("--b-dot-invert", action="store_true")
     parser.add_argument(
         "--kernel-px",
-        type=_positive_int,
+        type=positive_int,
         default=30,
         help="A-kernel paired recipe: total 3x3 kernel side length in pixels",
     )
     parser.add_argument(
         "--kernel-exposure-us",
-        type=_positive_int,
+        type=positive_int,
         default=None,
         help="A-kernel paired recipe: uniform exposure per kernel bitplane",
     )
@@ -187,7 +183,7 @@ def _build_parser():
     )
     parser.add_argument(
         "--kernel-leader-frames",
-        type=_nonnegative_int,
+        type=nonnegative_int,
         default=3,
         help="A-kernel paired recipe: all-black VSYNC frames prepended to each cycle",
     )
@@ -199,17 +195,53 @@ def _build_parser():
     )
     parser.add_argument(
         "--numbers-exposure-us",
-        type=_positive_int,
+        type=positive_int,
         default=None,
         help="Numbers paired recipe: optional per-bitplane LUT exposure override",
     )
     parser.add_argument(
         "--numbers-size-px",
-        type=_positive_int,
+        type=positive_int,
         default=None,
         help="Numbers paired recipe: seven-segment digit height in pixels",
     )
-    parser.add_argument("--wake-dp", action="store_true", help="Wake both DP receivers before runtime")
+    parser.add_argument(
+        "--numbers-bitplane-order",
+        type=numbers_bitplane_order,
+        default=None,
+        help=(
+            "Numbers paired recipe: zero-based bitplane indexes in chronological "
+            "display order. Use 1,2,3,4,0 if the first five captured triggers "
+            "visually show 2,3,4,5,1 for --numbers 1,2,3,4,5."),
+    )
+    parser.add_argument(
+        "--count-start",
+        type=positive_int,
+        default=1,
+        help="A-count paired recipe: first integer label to display",
+    )
+    parser.add_argument(
+        "--count-end",
+        type=positive_int,
+        default=100,
+        help="A-count paired recipe: final integer label to display, inclusive",
+    )
+    parser.add_argument(
+        "--count-slots-per-frame",
+        type=int,
+        default=2,
+        help="A-count paired recipe: count labels packed into bitplanes per VSYNC frame",
+    )
+    parser.add_argument(
+        "--count-exposure-us",
+        type=positive_int,
+        default=None,
+        help="A-count paired recipe: optional per-count LUT exposure override",
+    )
+    parser.add_argument(
+        "--wake-dp",
+        action="store_true",
+        help="Wake both DP receivers before runtime")
     parser.add_argument(
         "--dual-pixel",
         action="store_true",
@@ -269,33 +301,71 @@ def _validate_pair_args(args):
         if args.test_a:
             raise SystemExit("--test-a is not valid for a-kernel-b-static; A is the kernel stream")
         return
-    if args.test in (NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST):
+    if _is_count_recipe(args.test):
+        _validate_count_recipe_args(args)
+        return
+    if _is_numbers_recipe(args.test):
         if args.test == A_NUMBERS_B_STATIC_PAIR_TEST and args.test_a:
-            raise SystemExit("--test-a is not valid for a-numbers-b-static; A is the numbers stream")
+            raise SystemExit(
+                "--test-a is not valid for a-numbers-b-static; A is the numbers stream")
         if len(args.numbers) > BITPLANES:
             raise SystemExit(f"--numbers can contain at most {BITPLANES} entries")
+        if args.numbers_bitplane_order is not None:
+            if len(args.numbers_bitplane_order) != len(args.numbers):
+                raise SystemExit("--numbers-bitplane-order length must match --numbers length")
+            if sorted(args.numbers_bitplane_order) != list(range(len(args.numbers))):
+                raise SystemExit(
+                    "--numbers-bitplane-order must be a zero-based permutation of --numbers slots")
         return
     if args.test not in STATIC_PAIR_TESTS and (args.test_a or args.test_b):
         raise SystemExit("--test-a/--test-b are only valid for static paired tests")
 
 
+def _is_numbers_recipe(test):
+    return test in (NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST)
+
+
+def _is_count_recipe(test):
+    return test == A_COUNT_B_STATIC_PAIR_TEST
+
+
+def _validate_count_recipe_args(args):
+    if args.test_a:
+        raise SystemExit("--test-a is not valid for a-count-b-static; A is the count stream")
+    if args.count_start > args.count_end:
+        raise SystemExit("--count-start must be <= --count-end")
+    if args.count_slots_per_frame <= 0 or args.count_slots_per_frame > BITPLANES:
+        raise SystemExit(f"--count-slots-per-frame must be in the range 1..{BITPLANES}")
+    count_total = args.count_end - args.count_start + 1
+    if count_total % args.count_slots_per_frame != 0:
+        raise SystemExit("count range length must be divisible by --count-slots-per-frame")
+    frame_count = count_total // args.count_slots_per_frame
+    if frame_count > MAX_COUNT_SEQUENCE_FRAMES:
+        raise SystemExit(
+            f"a-count-b-static can span at most {MAX_COUNT_SEQUENCE_FRAMES} VSYNC frames")
+
+
 def _kernel_lut_override(args, target_hz):
     return compute_kernel_lut_override(
         enabled=args.test == KERNEL_STATIC_PAIR_TEST,
         kernel_exposure_us=args.kernel_exposure_us,
         target_hz=target_hz,
         sequence_utilization=args.seq_utilization,
-        dark_time_us=getattr(args, "dark_time_us", None),
+        dark_time_us=getattr(args,
+                             "dark_time_us",
+                             None),
     )
 
 
 def _lut_override(args, target_hz):
-    if args.test in (NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST):
+    if _is_numbers_recipe(args.test):
         # The numbers recipe packs only the requested digits into the first N
         # video bitplanes. The LUT must expose exactly those N bitplanes, not
         # all 24 possible RGB bitplanes. Forcing BITPLANES makes long exposures
         # impossible at 60 Hz; e.g. 24 * 3000 us.
         return len(args.numbers), args.numbers_exposure_us
+    if _is_count_recipe(args.test):
+        return args.count_slots_per_frame, args.count_exposure_us
     return _kernel_lut_override(args, target_hz)
 
 
@@ -348,8 +418,7 @@ def _make_runtime_pair_frame_provider(args, engine, target_hz):
         logger.info(
             f"[+] A-kernel frames ready: {metadata['cycle_vsyncs']} VSYNC frames per cycle "
             f"({metadata['leader_frames']} leader + {metadata['payload_vsyncs']} payload/end-marker), "
-            f"{metadata['cycle_fires']} bitplane fires."
-        )
+            f"{metadata['cycle_fires']} bitplane fires.")
         frame_provider_a = KernelFrameProvider(
             kernel_frames,
             black_frame=metadata["black_frame"],
@@ -379,13 +448,15 @@ def _make_runtime_pair_frame_provider(args, engine, target_hz):
             test_b=args.test_b,
             width=DMD_WIDTH,
             height=DMD_HEIGHT,
+            dot_radius=args.dot_radius,
         )
-    if args.test in (NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST):
+    if _is_numbers_recipe(args.test):
         return make_pair_frame_provider(
             args.test,
             test_b=args.test_b,
             numbers=args.numbers,
             numbers_size_px=args.numbers_size_px,
+            numbers_bitplane_order=getattr(args, "numbers_bitplane_order", None),
             numbers_exposure_us=args.numbers_exposure_us,
             b_dot_x=args.b_dot_x,
             b_dot_y=args.b_dot_y,
@@ -395,6 +466,22 @@ def _make_runtime_pair_frame_provider(args, engine, target_hz):
             width=DMD_WIDTH,
             height=DMD_HEIGHT,
         )
+    if _is_count_recipe(args.test):
+        return make_pair_frame_provider(
+            args.test,
+            test_b=args.test_b,
+            count_start=args.count_start,
+            count_end=args.count_end,
+            count_slots_per_frame=args.count_slots_per_frame,
+            numbers_size_px=args.numbers_size_px,
+            b_dot_x=args.b_dot_x,
+            b_dot_y=args.b_dot_y,
+            b_dot_radius=args.b_dot_radius,
+            b_dot_shape=args.b_dot_shape,
+            b_dot_invert=args.b_dot_invert,
+            width=DMD_WIDTH,
+            height=DMD_HEIGHT,
+        )
     return make_pair_frame_provider(args.test, width=DMD_WIDTH, height=DMD_HEIGHT)
 
 
@@ -417,16 +504,14 @@ def _dry_run_timing(args, pair_config):
     )
     logger.info(
         f"[DRY RUN] USB: A id_path={pair_config.dmd_a.usb_id_path}, "
-        f"B id_path={pair_config.dmd_b.usb_id_path}."
-    )
+        f"B id_path={pair_config.dmd_b.usb_id_path}.")
     if args.test == CALIBRATION_DOT_PAIR_TEST:
         logger.info(
             f"[DRY RUN] Pair content: A=calibr-square control_file="
             f"{args.a_calibr_square_control_file or '(none)'}, "
             f"flicker=every-other-frame, "
             f"B=dot x={args.b_dot_x}, y={args.b_dot_y}, radius={args.b_dot_radius}, "
-            f"shape={args.b_dot_shape}, invert={args.b_dot_invert}."
-        )
+            f"shape={args.b_dot_shape}, invert={args.b_dot_invert}.")
     elif args.test == KERNEL_STATIC_PAIR_TEST:
         slots = timing["entries_count"]
         pad = (slots - (512 % slots)) % slots
@@ -436,34 +521,40 @@ def _dry_run_timing(args, pair_config):
         logger.info(
             f"[DRY RUN] Pair content: A=kernel kernel_px={args.kernel_px}, "
             f"leader_frames={args.kernel_leader_frames}, blank_end_frame={args.kernel_blank_end_frame}, "
-            f"single_shot={args.kernel_single_shot}; B={args.test_b or 'checkerboard'} static."
-        )
+            f"single_shot={args.kernel_single_shot}; B={args.test_b or 'checkerboard'} static.")
         logger.info(
             f"[DRY RUN] A-kernel cycle: {cycle_vsyncs} VSYNC frames, "
             f"{args.kernel_leader_frames * slots} leader fires, 512 kernels, {pad} pad fires"
-            f"{', ' + str(slots) + ' end-marker fires' if args.kernel_blank_end_frame else ''}."
-        )
-    elif args.test in (NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST):
+            f"{', ' + str(slots) + ' end-marker fires' if args.kernel_blank_end_frame else ''}.")
+    elif _is_numbers_recipe(args.test):
         logger.info(
             f"[DRY RUN] Pair content: numbers={','.join(str(n) for n in args.numbers)}, "
+            f"bitplane_order={','.join(str(i) for i in args.numbers_bitplane_order) if args.numbers_bitplane_order is not None else 'default'}, "
             f"per-bitplane exposure={args.numbers_exposure_us or timing['exposure_us']}us, "
-            f"size_px={args.numbers_size_px or 'default'} on both DMDs."
+            f"size_px={args.numbers_size_px or 'default'} on both DMDs.")
+    elif _is_count_recipe(args.test):
+        payload_vsyncs = count_sequence_frame_count(
+            args.count_start,
+            args.count_end,
+            args.count_slots_per_frame,
         )
+        logger.info(
+            f"[DRY RUN] Pair content: A=count {args.count_start}..{args.count_end}, "
+            f"slots_per_frame={args.count_slots_per_frame}, "
+            f"per-count exposure={args.count_exposure_us or timing['exposure_us']}us, "
+            f"payload_vsyncs={payload_vsyncs}; B={args.test_b or 'dot'} static.")
     elif args.test in STATIC_PAIR_TESTS:
         logger.info(
             f"[DRY RUN] Pair content: test={args.test}, test_a={args.test_a or args.test}, "
-            f"test_b={args.test_b or args.test}."
-        )
+            f"test_b={args.test_b or args.test}, dot_radius={args.dot_radius}.")
     else:
         logger.info(
-            f"[DRY RUN] Pair content: dynamic test={args.test}, one shared frame index/timebase."
-        )
+            f"[DRY RUN] Pair content: dynamic test={args.test}, one shared frame index/timebase.")
     logger.info(
         f"[DRY RUN] Pattern LUT: {len(entries)} entries, exposure={timing['exposure_us']}us, "
         f"dark={timing['dark_us']}us, sequence={timing['total_sequence_us']:.1f}/"
         f"{timing['usable_frame_period_us']:.1f}us usable, "
-        f"effective VSYNC={timing['effective_frame_hz']:.3f}Hz."
-    )
+        f"effective VSYNC={timing['effective_frame_hz']:.3f}Hz.")
     logger.info(
         f"[DRY RUN] TRIG_OUT_2 mode: {timing['trig2_mode']}; expected pulses/s="
         f"{timing['effective_frame_hz'] if args.trig2_frame_zero else timing['effective_binary_rate_hz']:.1f}."
@@ -476,8 +567,7 @@ def _dry_run_timing(args, pair_config):
     logger.info(
         f"[DRY RUN] TRIG_OUT_2 rising edge delay={trigger_timing['rising_delay_us']}us, "
         f"falling={trigger_timing['falling_delay_us']}us "
-        f"({trigger_timing['delay_fraction']:.3f} of {trigger_timing['delay_basis']})."
-    )
+        f"({trigger_timing['delay_fraction']:.3f} of {trigger_timing['delay_basis']}).")
 
 
 def _live_preview_metadata_for_frame(base_metadata, provider):
@@ -509,12 +599,19 @@ def _build_live_preview_metadata(args, pair_config, state_a, state_b):
         },
         "target_hz": pair_config.target_hz,
     }
-    if args.test in (NUMBER_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST):
+    if _is_numbers_recipe(args.test):
         metadata["numbers"] = {
             "sequence": list(args.numbers),
             "exposure_us": args.numbers_exposure_us,
             "size_px": args.numbers_size_px,
         }
+    if _is_count_recipe(args.test):
+        metadata["count"] = {
+            "start": args.count_start,
+            "end": args.count_end,
+            "slots_per_frame": args.count_slots_per_frame,
+            "exposure_us": args.count_exposure_us,
+        }
     if lut_state:
         metadata["lut"] = build_lut_preview_metadata(lut_state["entries"], lut_state["timing"])
         metadata["lut_applies_to"] = ["A", "B"]
@@ -522,13 +619,13 @@ def _build_live_preview_metadata(args, pair_config, state_a, state_b):
 
 
 def _run_pair_render_loop(
-        dlpc_a,
-        dlpc_b,
-        engine,
-        provider,
-        args,
-        preview_poster=None,
-        preview_metadata=None,
+    dlpc_a,
+    dlpc_b,
+    engine,
+    provider,
+    args,
+    preview_poster=None,
+    preview_metadata=None,
 ):
     end_t = None if args.runtime_seconds <= 0 else time.time() + args.runtime_seconds
     while (end_t is None or time.time() < end_t) and not engine.should_close():
@@ -538,7 +635,8 @@ def _run_pair_render_loop(
             preview_poster.maybe_post_pair(
                 frame_a,
                 frame_b,
-                metadata=_live_preview_metadata_for_frame(preview_metadata, provider),
+                metadata=_live_preview_metadata_for_frame(preview_metadata,
+                                                          provider),
             )
 
 
@@ -584,8 +682,7 @@ def _run_prepared_pair(args, pair_config, before_sequencer_start=None):
 
     logger.info(
         f"[+] Paired DMD layout: B {pair_config.dmd_b.xrandr_output} left +0+0, "
-        f"A {pair_config.dmd_a.xrandr_output} right +{DMD_WIDTH}+0"
-    )
+        f"A {pair_config.dmd_a.xrandr_output} right +{DMD_WIDTH}+0")
     engine = None
     dlpc_a = None
     dlpc_b = None
@@ -609,7 +706,7 @@ def _run_prepared_pair(args, pair_config, before_sequencer_start=None):
         if args.wake_dp:
             for label, dlpc in (("A", dlpc_a), ("B", dlpc_b)):
                 logger.info(f"[+] Waking DisplayPort receiver for DMD {label}...")
-                dlpc.send_packet(0x1A01, bytes([2]))
+                dlpc.wake_displayport_receiver()
             time.sleep(1.0)
 
         logger.info("[+] Starting paired continuous GL pump before DLPC preparation...")
@@ -646,13 +743,14 @@ def _run_prepared_pair(args, pair_config, before_sequencer_start=None):
         live_preview_metadata = _build_live_preview_metadata(args, pair_config, state_a, state_b)
 
         if before_sequencer_start is not None:
-            before_sequencer_start({
-                "args": args,
-                "pair_config": pair_config,
-                "state_a": state_a,
-                "state_b": state_b,
-                "preview_metadata": live_preview_metadata,
-            })
+            before_sequencer_start(
+                {
+                    "args": args,
+                    "pair_config": pair_config,
+                    "state_a": state_a,
+                    "state_b": state_b,
+                    "preview_metadata": live_preview_metadata,
+                })
 
         logger.info("[+] Starting both DLPC900 sequencers from paired software barrier...")
         start_loaded_pattern_sequences(dlpc_a, dlpc_b, post_start_delay_s=0.0, verify=True)
@@ -668,7 +766,8 @@ def _run_prepared_pair(args, pair_config, before_sequencer_start=None):
             preview_poster.maybe_post_pair(
                 first_a,
                 first_b,
-                metadata=_live_preview_metadata_for_frame(live_preview_metadata, provider),
+                metadata=_live_preview_metadata_for_frame(live_preview_metadata,
+                                                          provider),
                 force=True,
             )
         _run_pair_render_loop(
@@ -696,11 +795,17 @@ def _run_prepared_pair(args, pair_config, before_sequencer_start=None):
                 dlpc.apply_block_lock_workaround()
             except Exception as cleanup_exc:
                 logger.warning(f"DMD {label} cleanup warning: {cleanup_exc}")
+            close = getattr(dlpc, "close", None)
+            if callable(close):
+                try:
+                    close()
+                except Exception as close_exc:
+                    logger.warning(f"DMD {label} close warning: {close_exc}")
         if engine is not None:
             engine.cleanup()
 
 
-def run_with_before_start_callback(argv, before_start):
+def _run(argv, before_start=None):
     args = _build_parser().parse_args(argv)
     setup_logger(verbosity=args.verbose)
     pair_config = resolve_pair_config(args.dmd_config, target_hz=args.hz)
@@ -711,17 +816,12 @@ def run_with_before_start_callback(argv, before_start):
     return _run_prepared_pair(args, pair_config, before_sequencer_start=before_start)
 
 
-def main(argv=None):
-    args = _build_parser().parse_args(argv)
-    setup_logger(verbosity=args.verbose)
-    pair_config = resolve_pair_config(args.dmd_config, target_hz=args.hz)
-    _validate_pair_args(args)
+def run_with_before_start_callback(argv, before_start):
+    return _run(argv, before_start)
 
-    if args.dry_run_timing:
-        _dry_run_timing(args, pair_config)
-        return 0
 
-    return _run_prepared_pair(args, pair_config)
+def main(argv=None):
+    return _run(argv)
 
 
 if __name__ == "__main__":
diff --git a/dmdcontrol/runtime/single.py b/dmdcontrol/runtime/single.py
index a026898..b5dc755 100644
--- a/dmdcontrol/runtime/single.py
+++ b/dmdcontrol/runtime/single.py
@@ -23,6 +23,7 @@
     format_calibration_square_state,
     make_calibration_square_frame_provider,
 )
+from dmdcontrol.patterns.kernel import build_kernel_frames
 from dmdcontrol.runtime.lifecycle import (
     build_lut_entries,
     compute_trigger_out_2_timing,
@@ -44,77 +45,144 @@
 
 def _build_parser():
     parser = argparse.ArgumentParser(description="DLPC900 1080p Video Pattern Runtime")
-    parser.add_argument("--hz", type=int, default=DEFAULT_HZ, help="Target Hz (60 or 120, experimental)")
+    parser.add_argument(
+        "--hz",
+        type=int,
+        default=DEFAULT_HZ,
+        help="Target Hz (60 or 120, experimental)")
     parser.add_argument("--monitor", type=int, default=None, help="GLFW monitor index")
-    parser.add_argument("--dmd", default=None,
-                        help="Configured DMD name from dmd_devices.json, for example A or B.")
-    parser.add_argument("--dmd-config", default=None,
-                        help="Path to DMD mapping config. Defaults to repository dmd_devices.json.")
-    parser.add_argument("--test", choices=PATTERN_NAMES, default="checkerboard",
-                        help=f"Diagnostic pattern mode. Choices: {', '.join(PATTERN_NAMES)}.")
+    parser.add_argument(
+        "--dmd",
+        default=None,
+        help="Configured DMD name from dmd_devices.json, for example A or B.")
+    parser.add_argument(
+        "--dmd-config",
+        default=None,
+        help="Path to DMD mapping config. Defaults to repository dmd_devices.json.")
+    parser.add_argument(
+        "--test",
+        choices=PATTERN_NAMES,
+        default="checkerboard",
+        help=f"Diagnostic pattern mode. Choices: {', '.join(PATTERN_NAMES)}.")
     parser.add_argument("--trigger", action="store_true", help="Software Trigger Mode (Approach A)")
-    parser.add_argument("--runtime-seconds", type=int, default=60,
-                        help="Runtime for diagnostic patterns. Use 0 to run until ESC/window close.")
+    parser.add_argument(
+        "--runtime-seconds",
+        type=int,
+        default=60,
+        help="Runtime for diagnostic patterns. Use 0 to run until ESC/window close.")
     parser.add_argument("--wake-dp", action="store_true", help="Wake DP receiver before runtime")
-    parser.add_argument("--dual-pixel", action="store_true",
-                        help="Force dual-pixel P1-P2 mode for DLPC900 parallel input (default: single-pixel P1)")
-    parser.add_argument("--seq-utilization", type=float, default=DEFAULT_SEQUENCE_UTILIZATION,
-                        help="Fraction of safe frame budget allocated to LUT exposure timing (0<value<=1). "
-                             "Lower values increase idle headroom and improve robustness.")
-    parser.add_argument("--trig2-frame-zero", action="store_true",
-                        help="Emit TRIG_OUT_2 only on LUT bitplane 0 (single frame anchor). "
-                             "Default mode emits TRIG_OUT_2 on every bitplane.")
+    parser.add_argument(
+        "--dual-pixel",
+        action="store_true",
+        help="Force dual-pixel P1-P2 mode for DLPC900 parallel input (default: single-pixel P1)")
+    parser.add_argument(
+        "--seq-utilization",
+        type=float,
+        default=DEFAULT_SEQUENCE_UTILIZATION,
+        help="Fraction of safe frame budget allocated to LUT exposure timing (0<value<=1). "
+        "Lower values increase idle headroom and improve robustness.")
+    parser.add_argument(
+        "--trig2-frame-zero",
+        action="store_true",
+        help="Emit TRIG_OUT_2 only on LUT bitplane 0 (single frame anchor). "
+        "Default mode emits TRIG_OUT_2 on every bitplane.")
     parser.add_argument(
         "--trigger-out-2-delay-fraction",
         type=float,
         default=0.00,
         help="Fraction of LUT exposure used as TRIG_OUT_2 rising-edge delay. Default: 0.",
     )
-    parser.add_argument("--abort-recover-cooldown", type=float, default=8.0,
-                        help="Seconds between automatic abort recovery attempts while watchdog detects sequencer abort.")
-    parser.add_argument("--no-auto-recover-abort", action="store_true",
-                        help="Disable automatic sequencer re-arm attempts when abort bit is detected during runtime.")
-    parser.add_argument("--capture", type=str, help="Save the generated packed frames to an mp4 video (e.g. test.mp4)")
-    parser.add_argument("--invert-dmd", action="store_true",
-                        help="Invert the final packed DMD output: every pixel in every displayed bitplane. "
-                             "This also inverts leader, pad, blank-end, and trigger black frames.")
-    parser.add_argument("--kernel-px", type=int, default=30,
-                        help="Total kernel side length in pixels for --test kernel (must be a multiple of 3). "
-                             "Default 30 (3x3 cells of 10px). Use 999 for naked-eye visibility (3x3 cells of 333px).")
-    parser.add_argument("--kernel-single-shot", action="store_true",
-                        help="Play the kernel cycle once then hold the idle marker frame. Default: loop continuously.")
-    parser.add_argument("--kernel-blank-end-frame", dest="kernel_blank_end_frame", action="store_true", default=True,
-                        help="Append one all-black VSYNC frame (24 black bitplanes) at the end of each kernel cycle "
-                             "as a sync marker for downstream DAQ. This is on by default.")
-    parser.add_argument("--no-kernel-blank-end-frame", dest="kernel_blank_end_frame", action="store_false",
-                        help="Disable the all-black VSYNC frame normally appended to each kernel cycle.")
-    parser.add_argument("--kernel-leader-frames", type=int, default=3,
-                        help="Number of all-black VSYNC frames prepended to each kernel cycle as an acquisition "
-                             "leader marker. DAQ should ignore these initial trigger pulses. Default: 3.")
-    parser.add_argument("--kernel-exposure-us", type=int, default=None,
-                        help="Uniform exposure time in microseconds for every kernel (kernel mode only). "
-                             "Default: use full 24-entry LUT (~615 us/kernel at 60 Hz with 0.90 utilization, "
-                             "1440 Hz binary rate). Larger values reduce kernels per VSYNC and lengthen the "
-                             "512-kernel cycle. "
-                             "Ceiling = one VSYNC period (~16670 us at 60 Hz).")
-    parser.add_argument("--numbers-exposure-us", type=int, default=None,
-                        help="Wall-clock display time in microseconds for each digit in --test numbers. "
-                             f"Default: {DEFAULT_NUMBERS_EXPOSURE_US} us.")
-    parser.add_argument("--numbers-size-px", type=int, default=None,
-                        help="Seven-segment digit height in pixels for --test numbers. "
-                             "Default: preserve existing height-scaled rendering.")
-    parser.add_argument("--calibr-square-control-file", default=None,
-                        help="Calibration-square only: read single-character controls from this file. "
-                             "Used by run_calibr_square.sh; normal run_dmd.sh behavior is unchanged.")
-    parser.add_argument("--dry-run-timing", action="store_true",
-                        help="Print LUT, trigger, and kernel-cycle timing without opening OpenGL or USB hardware.")
-    parser.add_argument("-v", "--verbose", action="count", default=0,
-                        help="Increase logging verbosity. Use -v for DEBUG logs and watchdog; -vv adds source paths "
-                             "and full board snapshots.")
+    parser.add_argument(
+        "--abort-recover-cooldown",
+        type=float,
+        default=8.0,
+        help=
+        "Seconds between automatic abort recovery attempts while watchdog detects sequencer abort.")
+    parser.add_argument(
+        "--no-auto-recover-abort",
+        action="store_true",
+        help="Disable automatic sequencer re-arm attempts when abort bit is detected during runtime."
+    )
+    parser.add_argument(
+        "--capture",
+        type=str,
+        help="Save the generated packed frames to an mp4 video (e.g. test.mp4)")
+    parser.add_argument(
+        "--invert-dmd",
+        action="store_true",
+        help="Invert the final packed DMD output: every pixel in every displayed bitplane. "
+        "This also inverts leader, pad, blank-end, and trigger black frames.")
+    parser.add_argument(
+        "--kernel-px",
+        type=int,
+        default=30,
+        help="Total kernel side length in pixels for --test kernel (must be a multiple of 3). "
+        "Default 30 (3x3 cells of 10px). Use 999 for naked-eye visibility (3x3 cells of 333px).")
+    parser.add_argument(
+        "--kernel-single-shot",
+        action="store_true",
+        help=
+        "Play the kernel cycle once then hold the idle marker frame. Default: loop continuously.")
+    parser.add_argument(
+        "--kernel-blank-end-frame",
+        dest="kernel_blank_end_frame",
+        action="store_true",
+        default=True,
+        help="Append one all-black VSYNC frame (24 black bitplanes) at the end of each kernel cycle "
+        "as a sync marker for downstream DAQ. This is on by default.")
+    parser.add_argument(
+        "--no-kernel-blank-end-frame",
+        dest="kernel_blank_end_frame",
+        action="store_false",
+        help="Disable the all-black VSYNC frame normally appended to each kernel cycle.")
+    parser.add_argument(
+        "--kernel-leader-frames",
+        type=int,
+        default=3,
+        help="Number of all-black VSYNC frames prepended to each kernel cycle as an acquisition "
+        "leader marker. DAQ should ignore these initial trigger pulses. Default: 3.")
+    parser.add_argument(
+        "--kernel-exposure-us",
+        type=int,
+        default=None,
+        help="Uniform exposure time in microseconds for every kernel (kernel mode only). "
+        "Default: use full 24-entry LUT (~615 us/kernel at 60 Hz with 0.90 utilization, "
+        "1440 Hz binary rate). Larger values reduce kernels per VSYNC and lengthen the "
+        "512-kernel cycle. "
+        "Ceiling = one VSYNC period (~16670 us at 60 Hz).")
+    parser.add_argument(
+        "--numbers-exposure-us",
+        type=int,
+        default=None,
+        help="Wall-clock display time in microseconds for each digit in --test numbers. "
+        f"Default: {DEFAULT_NUMBERS_EXPOSURE_US} us.")
+    parser.add_argument(
+        "--numbers-size-px",
+        type=int,
+        default=None,
+        help="Seven-segment digit height in pixels for --test numbers. "
+        "Default: preserve existing height-scaled rendering.")
+    parser.add_argument(
+        "--calibr-square-control-file",
+        default=None,
+        help="Calibration-square only: read single-character controls from this file. "
+        "Used by run_calibr_square.sh; normal run_dmd.sh behavior is unchanged.")
+    parser.add_argument(
+        "--dry-run-timing",
+        action="store_true",
+        help="Print LUT, trigger, and kernel-cycle timing without opening OpenGL or USB hardware.")
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="count",
+        default=0,
+        help="Increase logging verbosity. Use -v for DEBUG logs and watchdog; -vv adds source paths "
+        "and full board snapshots.")
     return parser
 
 
 class _DryRunDLPC:
+
     def get_display_dimensions(self):
         return None
 
@@ -160,47 +228,41 @@ def _log_kernel_timing_summary(args, timing, prefix="[TIMING]"):
     logger.info(
         f"{prefix} Kernel LUT: {slots} slots/VSYNC, exposure={timing['exposure_us']}us, "
         f"dark={timing['dark_us']}us, effective VSYNC={timing['effective_frame_hz']:.3f}Hz, "
-        f"bitplane rate={timing['effective_binary_rate_hz']:.1f}Hz."
-    )
+        f"bitplane rate={timing['effective_binary_rate_hz']:.1f}Hz.")
     cycle_period_ms = cycle_vsyncs * 1000.0 / timing["effective_frame_hz"]
     logger.info(
         f"{prefix} Kernel cycle: {cycle_vsyncs} VSYNC frames, {total_fires} bitplane fires, "
-        f"{trig2_pulses} TRIG_OUT_2 pulses, period ~{cycle_period_ms:.1f}ms."
-    )
+        f"{trig2_pulses} TRIG_OUT_2 pulses, period ~{cycle_period_ms:.1f}ms.")
     logger.info(
         f"{prefix} Cycle structure: {leader_vsyncs} leader marker VSYNCs "
         f"({leader_fires} bitplane fires), {payload_vsyncs} payload VSYNCs "
         f"(512 kernels + {payload_pad} pad blanks)"
-        f"{', 1 end-marker VSYNC' if args.kernel_blank_end_frame else ''}."
-    )
+        f"{', 1 end-marker VSYNC' if args.kernel_blank_end_frame else ''}.")
     logger.info(
         f"{prefix} DMD output polarity: "
-        f"{'inverted after packing; black markers output white' if args.invert_dmd else 'normal'}."
-    )
+        f"{'inverted after packing; black markers output white' if args.invert_dmd else 'normal'}.")
     if args.trig2_frame_zero:
         marker_level = "white" if args.invert_dmd else "black"
         logger.warning(
             f"{prefix} --trig2-frame-zero is active: TRIG_OUT_2 marks VSYNC frames, not individual kernels. "
-            f"Each payload trigger covers up to {slots} kernels."
-        )
+            f"Each payload trigger covers up to {slots} kernels.")
         logger.info(
             f"{prefix} DAQ: ignore first {leader_vsyncs} TRIG_OUT_2 pulses "
-            f"for the {marker_level} leader."
-        )
+            f"for the {marker_level} leader.")
     else:
         marker_level = "white" if args.invert_dmd else "black"
         logger.info(
             f"{prefix} Trigger map is relative to kernel-cycle start; "
-            f"startup pulses emitted during DLPC arm or post-arm video-prime are outside this map."
-        )
+            f"startup pulses emitted during DLPC arm or post-arm video-prime are outside this map.")
         logger.info(
             f"{prefix} DAQ: ignore first {leader_fires} TRIG_OUT_2 pulses "
-            f"for the {marker_level} leader."
-        )
+            f"for the {marker_level} leader.")
         kernel_range = _format_range(leader_fires, 512)
         pad_range = _format_range(leader_fires + 512, payload_pad)
         end_range = _format_range(leader_fires + payload_fires, end_marker_fires)
-        logger.info(f"{prefix} Trigger map: pulses {kernel_range} -> kernel_index = pulse - {leader_fires}.")
+        logger.info(
+            f"{prefix} Trigger map: pulses {kernel_range} -> kernel_index = pulse - {leader_fires}."
+        )
         if pad_range:
             logger.info(f"{prefix} Trigger map: pulses {pad_range} -> pad {marker_level}.")
         if end_range:
@@ -213,54 +275,38 @@ def _log_numbers_timing_summary(args, timing, prefix="[TIMING]"):
     cycle_s = exposure_s * len(NUMBER_SEQUENCE)
     pulse_rate_hz = (
         timing["effective_frame_hz"]
-        if args.trig2_frame_zero
-        else timing["effective_binary_rate_hz"]
-    )
+        if args.trig2_frame_zero else timing["effective_binary_rate_hz"])
     pulses_per_number = exposure_s * pulse_rate_hz
     trig2_mode = (
-        "one pulse per VSYNC frame"
-        if args.trig2_frame_zero
-        else "one pulse per LUT bitplane"
-    )
+        "one pulse per VSYNC frame" if args.trig2_frame_zero else "one pulse per LUT bitplane")
     logger.info(
         f"{prefix} Numbers mode: digits 1..9, exposure={exposure_us}us "
-        f"({exposure_us / 1000.0:.3f}ms) per number, full cycle ~{cycle_s:.3f}s."
-    )
+        f"({exposure_us / 1000.0:.3f}ms) per number, full cycle ~{cycle_s:.3f}s.")
     logger.info(
         f"{prefix} Numbers mode uses dynamic DisplayPort frames, not a custom packed LUT; "
-        "existing Video Pattern Mode LUT timing remains unchanged."
-    )
+        "existing Video Pattern Mode LUT timing remains unchanged.")
     logger.info(
         f"{prefix} TRIG_OUT_2 is the acquisition/index signal for numbers mode "
         f"({trig2_mode}); expect ~{pulses_per_number:.1f} pulses per displayed number. "
-        "TRIG_OUT_1 is advisory only."
-    )
+        "TRIG_OUT_1 is advisory only.")
 
 
 def _log_calibration_square_summary(args, timing, prefix="[TIMING]"):
     pulse_rate_hz = (
         timing["effective_frame_hz"]
-        if args.trig2_frame_zero
-        else timing["effective_binary_rate_hz"]
-    )
+        if args.trig2_frame_zero else timing["effective_binary_rate_hz"])
     trig2_mode = (
-        "one pulse per VSYNC frame"
-        if args.trig2_frame_zero
-        else "one pulse per LUT bitplane"
-    )
+        "one pulse per VSYNC frame" if args.trig2_frame_zero else "one pulse per LUT bitplane")
     logger.info(
         f"{prefix} Calibration square controls: W/A/S/D move, Q/E rotate, R/F resize, ESC or X exits. "
-        "Use run_calibr_square.sh for terminal controls and pixel-bound feedback."
-    )
+        "Use run_calibr_square.sh for terminal controls and pixel-bound feedback.")
     logger.info(
         f"{prefix} Calibration square uses dynamic DisplayPort frames, not a custom packed LUT; "
-        "existing Video Pattern Mode LUT timing remains unchanged."
-    )
+        "existing Video Pattern Mode LUT timing remains unchanged.")
     logger.info(
         f"{prefix} TRIG_OUT_2 is the acquisition/index signal for calibration square mode "
         f"({trig2_mode}, ~{pulse_rate_hz:.1f} pulses/s). It marks the running Video Pattern "
-        "Mode sequence, not keyboard edits or square edges. TRIG_OUT_1 is advisory only."
-    )
+        "Mode sequence, not keyboard edits or square edges. TRIG_OUT_1 is advisory only.")
 
 
 def _dry_run_timing(args):
@@ -273,7 +319,8 @@ def _dry_run_timing(args):
         entries_count=entries_count,
         per_entry_exposure_us=exposure_us,
     )
-    logger.info("[DRY RUN] Hardware was not opened. Timing uses target Hz, not measured DLPC900 timing.")
+    logger.info(
+        "[DRY RUN] Hardware was not opened. Timing uses target Hz, not measured DLPC900 timing.")
     logger.info(
         f"[DRY RUN] Pattern LUT: {len(entries)} entries, exposure={timing['exposure_us']}us, "
         f"dark={timing['dark_us']}us, sequence={timing['total_sequence_us']:.1f}/"
@@ -291,8 +338,7 @@ def _dry_run_timing(args):
     logger.info(
         f"[DRY RUN] TRIG_OUT_2 rising edge delay={trigger_timing['rising_delay_us']}us, "
         f"falling={trigger_timing['falling_delay_us']}us "
-        f"({trigger_timing['delay_fraction']:.3f} of {trigger_timing['delay_basis']})."
-    )
+        f"({trigger_timing['delay_fraction']:.3f} of {trigger_timing['delay_basis']}).")
     if args.test == "kernel":
         _log_kernel_timing_summary(args, timing, prefix="[DRY RUN]")
     elif args.test == "numbers":
@@ -330,31 +376,15 @@ def _select_post_arm_prime_frame(initial_frame, dynamic_kind, kernel_frames, ker
     return initial_frame
 
 
-def _build_numbers_frames(engine, size_px=None):
-    return tuple(
-        engine.pack_patterns(
-            engine.rgb_to_binary_patterns(
-                generate_number_rgb(
-                    number,
-                    width=engine.width,
-                    height=engine.height,
-                    size_px=size_px,
-                )
-            )
-        )
-        for number in NUMBER_SEQUENCE
-    )
-
-
 def _make_frame_provider(
-        engine,
-        initial_frame,
-        dynamic_kind,
-        args=None,
-        kernel_frames=None,
-        numbers_frames=None,
-        calibration_square_state=None,
-        invert_dmd=False,
+    engine,
+    initial_frame,
+    dynamic_kind,
+    args=None,
+    kernel_frames=None,
+    numbers_frames=None,
+    calibration_square_state=None,
+    invert_dmd=False,
 ):
     """Returns callable() -> frame. Hides per-mode frame regeneration from loop."""
 
@@ -409,16 +439,18 @@ def _provider_numbers():
             make_calibration_square_frame_provider(
                 engine,
                 initial_frame,
-                control_file=getattr(args, "calibr_square_control_file", None),
+                control_file=getattr(args,
+                                     "calibr_square_control_file",
+                                     None),
                 initial_state=calibration_square_state,
-            )
-        )
+            ))
     if dynamic_kind == "kernel":
         frames = kernel_frames
         n = len(frames)
         black = engine.pack_patterns(engine.generate_solid(0))
         state = {"i": 0}
         if args is not None and args.kernel_single_shot:
+
             def _provider_once():
                 i = state["i"]
                 if i < n:
@@ -473,14 +505,9 @@ def main(argv=None):
     target_hz = args.hz
     dmd_mapping = resolve_dmd_mapping(args.dmd, args.dmd_config) if args.dmd else None
     monitor_index = (
-        args.monitor
-        if args.monitor is not None
-        else (
+        args.monitor if args.monitor is not None else (
             dmd_mapping.glfw_monitor_index
-            if dmd_mapping and dmd_mapping.glfw_monitor_index is not None
-            else 0
-        )
-    )
+            if dmd_mapping and dmd_mapping.glfw_monitor_index is not None else 0))
     if dmd_mapping:
         if not dmd_mapping.xrandr_output:
             raise SystemExit(
@@ -491,8 +518,7 @@ def main(argv=None):
             f"[+] DMD {dmd_mapping.name}: USB id_path={dmd_mapping.usb_id_path}, "
             f"expected devpath fragment={dmd_mapping.usb_devpath_contains or '<not required>'}, "
             f"xrandr_output={dmd_mapping.xrandr_output or '<not configured>'}, "
-            f"GLFW monitor={monitor_index}"
-        )
+            f"GLFW monitor={monitor_index}")
     dlpc = None
     engine = None
     try:
@@ -509,7 +535,7 @@ def main(argv=None):
 
         if args.wake_dp:
             logger.info("[+] Waking up DisplayPort receiver...")
-            dlpc.send_packet(0x1A01, bytes([2]))
+            dlpc.wake_displayport_receiver()
             time.sleep(1.0)
 
         black_frame = engine.pack_patterns(engine.generate_solid(0))
@@ -518,7 +544,8 @@ def main(argv=None):
         logger.info(f"[+] Preparing Diagnostic Mode: {label}...")
 
         if args.trigger and patterns is None:
-            logger.warning(f"Trigger mode does not support dynamic '{args.test}'; using checkerboard.")
+            logger.warning(
+                f"Trigger mode does not support dynamic '{args.test}'; using checkerboard.")
             patterns = engine.generate_checkerboard()
             dynamic_kind = None
             label = "Static Checkerboard"
@@ -526,7 +553,8 @@ def main(argv=None):
         lut_entries_count = None
         lut_per_entry_exposure_us = None
         if dynamic_kind == "kernel":
-            lut_entries_count, lut_per_entry_exposure_us = _compute_kernel_lut_override(args, target_hz)
+            lut_entries_count, lut_per_entry_exposure_us = _compute_kernel_lut_override(
+                args, target_hz)
         if dynamic_kind == "kernel" and lut_per_entry_exposure_us is not None:
             logger.info(
                 f"[+] Kernel exposure override: {lut_per_entry_exposure_us} us uniformly per kernel -> "
@@ -554,35 +582,38 @@ def main(argv=None):
                 f"(kernel_px={args.kernel_px}, slots_per_vsync={slots}, "
                 f"invert_dmd={args.invert_dmd}, "
                 f"leader_frames={args.kernel_leader_frames}, "
-                f"blank_end_frame={args.kernel_blank_end_frame})..."
-            )
-            kernel_masks = engine.generate_kernel_masks(args.kernel_px)
-            kernel_payload_frames = engine.pack_kernel_frames(
-                kernel_masks,
+                f"blank_end_frame={args.kernel_blank_end_frame})...")
+            kernel_frames, kernel_metadata = build_kernel_frames(
+                engine,
+                args.kernel_px,
                 slots_per_frame=slots,
+                leader_frames=args.kernel_leader_frames,
                 blank_end_frame=args.kernel_blank_end_frame,
             )
-            kernel_payload_vsyncs = len(kernel_payload_frames)
-            kernel_frames = [black_frame] * args.kernel_leader_frames + kernel_payload_frames
-            kernel_cycle_vsyncs = len(kernel_frames)
-            kernel_blank_slot_count = (slots - (512 % slots)) % slots
+            kernel_payload_vsyncs = kernel_metadata["payload_vsyncs"]
+            kernel_cycle_vsyncs = kernel_metadata["cycle_vsyncs"]
+            kernel_blank_slot_count = kernel_metadata["blank_slot_count"]
             kernel_leader_fires = args.kernel_leader_frames * slots
-            kernel_cycle_kernels = 512 + kernel_blank_slot_count + (
-                slots if args.kernel_blank_end_frame else 0
-            )
+            kernel_cycle_kernels = kernel_metadata["cycle_fires"] - kernel_leader_fires
             logger.info(
                 f"[+] Kernel frames ready: {kernel_cycle_vsyncs} VSYNC frames per cycle "
                 f"({args.kernel_leader_frames} leader + {kernel_payload_vsyncs} payload/end-marker) "
-                f"covering {kernel_leader_fires + kernel_cycle_kernels} bitplane fires."
-            )
+                f"covering {kernel_leader_fires + kernel_cycle_kernels} bitplane fires.")
         if dynamic_kind == "numbers":
             number_exposure_us = _numbers_exposure_us(args)
             logger.info(
                 f"[+] Prebuilding {len(NUMBER_SEQUENCE)} number frames "
                 f"(digits 1..9, exposure={number_exposure_us} us per number, "
-                f"size_px={args.numbers_size_px or 'default'})..."
-            )
-            numbers_frames = _build_numbers_frames(engine, size_px=args.numbers_size_px)
+                f"size_px={args.numbers_size_px or 'default'})...")
+            numbers_frames = tuple(
+                engine.pack_patterns(
+                    engine.rgb_to_binary_patterns(
+                        generate_number_rgb(
+                            number,
+                            width=engine.width,
+                            height=engine.height,
+                            size_px=args.numbers_size_px,
+                        ))) for number in NUMBER_SEQUENCE)
             logger.info("[+] Number frames ready as full packed DisplayPort frames.")
         if dynamic_kind == "calibr-square":
             calibration_square_state = default_calibration_square_state(engine.width, engine.height)
@@ -592,7 +623,8 @@ def main(argv=None):
             )
             logger.info("[+] Controls: W/A/S/D move, Q/E rotate, R/F resize, ESC exits.")
             if args.calibr_square_control_file:
-                logger.info(f"[+] Reading calibration controls from {args.calibr_square_control_file}")
+                logger.info(
+                    f"[+] Reading calibration controls from {args.calibr_square_control_file}")
 
         if patterns is not None:
             frame = engine.pack_patterns(patterns)
@@ -625,8 +657,7 @@ def main(argv=None):
             # kernel/numbers/calibration provider during DLPC arm. The real
             # loop starts at frame 0 after configuration completes.
             prearm_frame_provider = lambda: _maybe_invert_frame(
-                black_frame if args.trigger else frame, args.invert_dmd
-            )
+                black_frame if args.trigger else frame, args.invert_dmd)
         else:
             prearm_frame_provider = frame_provider
 
@@ -668,19 +699,15 @@ def _prime_video_buffer():
             # before the USB control thread starts issuing LUT writes + start command.
             time.sleep(0.1)
 
-        def _frame_pump():
-            # No-op: the background pump is already pushing frames continuously,
-            # no synchronous pump is needed at this point.
-            pass
-
         try:
             sequence_state = configure_dlpc900_for_video_pattern(
-                dlpc, target_hz,
+                dlpc,
+                target_hz,
                 dual_pixel=args.dual_pixel,
                 sequence_utilization=args.seq_utilization,
                 trig2_frame_zero=args.trig2_frame_zero,
                 pre_arm_callback=_prime_video_buffer,
-                frame_pump=_frame_pump,
+                frame_pump=lambda: None,
                 entries_count=lut_entries_count,
                 per_entry_exposure_us=lut_per_entry_exposure_us,
                 trigger_out_2_delay_fraction=args.trigger_out_2_delay_fraction,
@@ -706,21 +733,13 @@ def _frame_pump():
             )
             prime_label = (
                 "first kernel payload frame"
-                if dynamic_kind == "kernel" and post_arm_prime_frame is not frame
-                else "number 1 frame"
-                if dynamic_kind == "numbers"
-                else "calibration square frame"
-                if dynamic_kind == "calibr-square"
-                else "initial pattern frame"
-            )
+                if dynamic_kind == "kernel" and post_arm_prime_frame is not frame else
+                "number 1 frame" if dynamic_kind == "numbers" else "calibration square frame"
+                if dynamic_kind == "calibr-square" else "initial pattern frame")
         logger.info(f"[+] Priming DP output after sequencer arm with {prime_label}...")
         engine.display_frame(_maybe_invert_frame(post_arm_prime_frame, args.invert_dmd))
-        if (
-                dynamic_kind == "kernel"
-                and kernel_frames is not None
-                and len(kernel_frames) > 0
-                and post_arm_prime_frame is not kernel_frames[0]
-        ):
+        if (dynamic_kind == "kernel" and kernel_frames is not None and len(kernel_frames) > 0
+                and post_arm_prime_frame is not kernel_frames[0]):
             logger.info("[+] Returning DP output to kernel leader frame before runtime cycle...")
             engine.display_frame(_maybe_invert_frame(kernel_frames[0], args.invert_dmd))
 
@@ -729,8 +748,7 @@ def _frame_pump():
         if not verify_runtime_state(dlpc):
             raise RuntimeError(
                 "Runtime state check failed after post-arm DP prime. "
-                "Triggers are likely unavailable because mode 2/sequencer/lock is not valid."
-            )
+                "Triggers are likely unavailable because mode 2/sequencer/lock is not valid.")
 
         logger.info(f"[+] Holding output for {args.runtime_seconds} seconds...")
         logger.info(f"[+] Starting Diagnostic Mode: {label}...")
@@ -743,8 +761,7 @@ def _frame_pump():
                 f"{' + ' + str(sequence_state['timing']['entries_count']) + ' end-marker blanks' if args.kernel_blank_end_frame else ''}); "
                 f"cycle period ~{kernel_cycle_vsyncs * 1000.0 / sequence_state['timing']['effective_frame_hz']:.1f} ms; "
                 f"uniform exposure ~{sequence_state['timing']['exposure_us']} us per kernel; "
-                f"DMD output polarity={'inverted' if args.invert_dmd else 'normal'}."
-            )
+                f"DMD output polarity={'inverted' if args.invert_dmd else 'normal'}.")
             _log_kernel_timing_summary(args, sequence_state["timing"])
         elif dynamic_kind == "numbers":
             _log_numbers_timing_summary(args, sequence_state["timing"])
@@ -753,20 +770,28 @@ def _frame_pump():
 
         if args.trigger:
             logger.info("[+] Software Trigger Mode (Approach A) Active.")
-            logger.info("    Press spacebar to trigger 1 frame of pattern sequence, or ESC to exit.")
+            logger.info(
+                "    Press spacebar to trigger 1 frame of pattern sequence, or ESC to exit.")
             trig_frame = engine.pack_patterns(patterns)
             run_trigger_loop(
                 engine,
-                _maybe_invert_frame(black_frame, args.invert_dmd),
-                _maybe_invert_frame(trig_frame, args.invert_dmd),
+                _maybe_invert_frame(black_frame,
+                                    args.invert_dmd),
+                _maybe_invert_frame(trig_frame,
+                                    args.invert_dmd),
                 args.runtime_seconds,
             )
             return 0
         video_writer = _open_video_writer(args.capture, target_hz) if args.capture else None
         try:
             run_render_loop(
-                dlpc, engine, frame_provider, args, sequence_state,
-                video_writer=video_writer, cv2_module=cv2,
+                dlpc,
+                engine,
+                frame_provider,
+                args,
+                sequence_state,
+                video_writer=video_writer,
+                cv2_module=cv2,
             )
         finally:
             if video_writer is not None:
diff --git a/dmdcontrol/support/argparse_types.py b/dmdcontrol/support/argparse_types.py
new file mode 100644
index 0000000..e696744
--- /dev/null
+++ b/dmdcontrol/support/argparse_types.py
@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+import argparse
+
+
+def positive_int(value: str) -> int:
+    try:
+        number = int(value)
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError("value must be positive") from exc
+    if number <= 0:
+        raise argparse.ArgumentTypeError("value must be positive")
+    return number
+
+
+def nonnegative_int(value: str) -> int:
+    try:
+        number = int(value)
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError("value must be non-negative") from exc
+    if number < 0:
+        raise argparse.ArgumentTypeError("value must be non-negative")
+    return number
+
+
+def numbers_bitplane_order(value: str) -> list[int]:
+    try:
+        order = [int(part.strip()) for part in value.split(",") if part.strip()]
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError("numbers bitplane order must be decimal indexes") from exc
+    if not order:
+        raise argparse.ArgumentTypeError("numbers bitplane order must not be empty")
+    if any(index < 0 for index in order):
+        raise argparse.ArgumentTypeError("numbers bitplane order indexes must be non-negative")
+    return order
diff --git a/dmdcontrol/support/logging.py b/dmdcontrol/support/logging.py
index 358bd2d..7a9a1b9 100644
--- a/dmdcontrol/support/logging.py
+++ b/dmdcontrol/support/logging.py
@@ -24,9 +24,9 @@ def setup_logger(verbosity=0, verbose=None):
         level=level,
         format="%(message)s",
         datefmt="[%X]",
-        handlers=[
-            RichHandler(rich_tracebacks=True, show_path=verbosity >= 2, console=console)
-        ],
+        handlers=[RichHandler(rich_tracebacks=True,
+                              show_path=verbosity >= 2,
+                              console=console)],
         force=True,
     )
 
diff --git a/format.py b/format.py
new file mode 100644
index 0000000..e7589c5
--- /dev/null
+++ b/format.py
@@ -0,0 +1,61 @@
+# /// script
+# requires-python = ">=3.13"
+# dependencies = ["yapf==0.43.0"]
+# ///
+from __future__ import annotations
+
+import subprocess
+import sys
+from pathlib import Path
+
+
+FORMAT_ROOTS = (
+    "dmdcontrol",
+    "tests",
+    "compat",
+    "debug_scripts",
+    "hannah_cam_code",
+)
+
+EXCLUDED_PARTS = {
+    ".venv",
+    "__pycache__",
+    ".pytest_cache",
+    ".ruff_cache",
+}
+
+
+def _python_files() -> list[Path]:
+    files: list[Path] = []
+    for root_name in FORMAT_ROOTS:
+        root = Path(root_name)
+        if not root.exists():
+            continue
+        files.extend(
+            path for path in root.rglob("*.py")
+            if not any(part in EXCLUDED_PARTS for part in path.parts)
+        )
+    return sorted(files)
+
+
+def main() -> int:
+    files = _python_files()
+    for path in files:
+        subprocess.run(
+            [
+                sys.executable,
+                "-m",
+                "yapf",
+                "--in-place",
+                "--style",
+                "pyproject.toml",
+                str(path),
+            ],
+            check=True,
+        )
+    print(f"Formatted {len(files)} Python files.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/hannah_cam_code/camera.py b/hannah_cam_code/camera.py
index c95534e..4ce44ba 100644
--- a/hannah_cam_code/camera.py
+++ b/hannah_cam_code/camera.py
@@ -9,6 +9,7 @@
 
 
 class Camera:
+
     def __init__(self):
         # get cofiguration
         with open('config/config.yaml', 'r') as f:
@@ -20,10 +21,8 @@ def __init__(self):
         if 'output_folder' not in config:
             raise ValueError('output_folder not specified in config.yaml')
         self.filename = (
-            WORKSPACE
-            / config['output_folder']
-            / datetime.now().strftime("%Y-%m-%d_%H-%M-%S") / 'recording.aedat4'
-        )
+            WORKSPACE / config['output_folder'] / datetime.now().strftime("%Y-%m-%d_%H-%M-%S") /
+            'recording.aedat4')
         os.makedirs(self.filename.parent, exist_ok=True)
 
         # open camera
diff --git a/hannah_cam_code/capture_accumulate.py b/hannah_cam_code/capture_accumulate.py
index 061d4f3..b1eb54c 100644
--- a/hannah_cam_code/capture_accumulate.py
+++ b/hannah_cam_code/capture_accumulate.py
@@ -131,11 +131,19 @@ def write_grayscale_png(path, pixels, width, height):
         scanlines.extend(image_bytes[start:start + width])
 
     png = (
-        b"\x89PNG\r\n\x1a\n"
-        + _png_chunk(b"IHDR", struct.pack("!IIBBBBB", width, height, 8, 0, 0, 0, 0))
-        + _png_chunk(b"IDAT", zlib.compress(bytes(scanlines)))
-        + _png_chunk(b"IEND", b"")
-    )
+        b"\x89PNG\r\n\x1a\n" +
+        _png_chunk(b"IHDR",
+                   struct.pack("!IIBBBBB",
+                               width,
+                               height,
+                               8,
+                               0,
+                               0,
+                               0,
+                               0)) + _png_chunk(b"IDAT",
+                                                zlib.compress(bytes(scanlines))) +
+        _png_chunk(b"IEND",
+                   b""))
     path.write_bytes(png)
 
 
@@ -182,7 +190,8 @@ def run_capture(seconds, output_path=None):
         recording_path,
         fallback_resolution=fallback_resolution,
     )
-    png_path = Path(output_path) if output_path else recording_path.with_name("accumulated_events.png")
+    png_path = Path(output_path) if output_path else recording_path.with_name(
+        "accumulated_events.png")
     if not png_path.is_absolute():
         png_path = script_dir / png_path
     write_grayscale_png(png_path, pixels, width, height)
@@ -199,8 +208,13 @@ def run_capture(seconds, output_path=None):
 
 
 def main(argv=None):
-    parser = argparse.ArgumentParser(description="Briefly record DAVIS events and save an accumulated PNG.")
-    parser.add_argument("--seconds", type=float, default=0.5, help="Capture duration, clamped to 0.05-5.0 seconds.")
+    parser = argparse.ArgumentParser(
+        description="Briefly record DAVIS events and save an accumulated PNG.")
+    parser.add_argument(
+        "--seconds",
+        type=float,
+        default=0.5,
+        help="Capture duration, clamped to 0.05-5.0 seconds.")
     parser.add_argument("--output", type=Path, help="Optional PNG output path.")
     args = parser.parse_args(argv)
 
@@ -213,8 +227,7 @@ def main(argv=None):
         f"{summary['total_events']} events from {summary['batch_count']} batches, "
         f"{summary['width']}x{summary['height']} image, "
         f"{summary['seconds']:.2f}s capture, "
-        f"recording={summary['recording_path']}"
-    )
+        f"recording={summary['recording_path']}")
 
 
 if __name__ == "__main__":
diff --git a/hannah_cam_code/utils/env.py b/hannah_cam_code/utils/env.py
index 11210c1..5addd7e 100644
--- a/hannah_cam_code/utils/env.py
+++ b/hannah_cam_code/utils/env.py
@@ -1,4 +1,3 @@
 from pathlib import Path
 
-
 WORKSPACE = Path(__file__).resolve().parents[1]
diff --git a/notebooks/01_generate_timing_sweep_commands.ipynb b/notebooks/01_generate_timing_sweep_commands.ipynb
new file mode 100644
index 0000000..6930679
--- /dev/null
+++ b/notebooks/01_generate_timing_sweep_commands.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate Numbered Sync-Check Timing Sweep Commands\n",
+    "\n",
+    "This notebook builds repeatable commands for `run_camera_sync_check.sh`. The sync-check workflow is the right timing diagnostic because DMD A shows a known number sequence and DMD B holds a small dot. Each trigger/frame can then be compared against the expected number.\n",
+    "\n",
+    "Defaults here use numbers on A at `100 px` and a B dot radius of `20 px`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import csv\n",
+    "import itertools\n",
+    "import shlex\n",
+    "from datetime import datetime\n",
+    "\n",
+    "REPO_ROOT = Path.cwd().resolve()\n",
+    "OUTPUT_ROOT = Path(\"runs/camera\")\n",
+    "SWEEP_ID = datetime.now().strftime(\"sync-timing-sweep-%Y%m%d-%H%M%S\")\n",
+    "SWEEP_MANIFEST_PATH = REPO_ROOT / \"runs\" / f\"{SWEEP_ID}_manifest.csv\"\n",
+    "SWEEP_COMMAND_PATH = REPO_ROOT / \"runs\" / f\"{SWEEP_ID}_command.sh\"\n",
+    "\n",
+    "REPO_ROOT, SWEEP_MANIFEST_PATH, SWEEP_COMMAND_PATH"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sweep Parameters\n",
+    "\n",
+    "Keep the first sweep small. At 60 Hz, the requested numbers are packed into one LUT cycle, so `len(numbers) * (numbers_exposure_us + dark_time_us)` must fit inside the usable frame budget. The defaults below stay conservative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3, 4, 5]\n",
+    "number_size_px = 100\n",
+    "b_dot_radius = 20\n",
+    "b_dot_x = 960\n",
+    "b_dot_y = 540\n",
+    "\n",
+    "numbers_exposure_us_values = [600, 1000, 1500, 2000]\n",
+    "dark_time_us_values = [0, 100, 250, 500]\n",
+    "trigger_delay_fraction_values = [0.00, 0.05, 0.10, 0.20]\n",
+    "repeats_per_setting = 1\n",
+    "\n",
+    "base_capture = {\n",
+    "    \"test\": \"a-numbers-b-static\",\n",
+    "    \"test_b\": \"dot\",\n",
+    "    \"numbers\": \",\".join(str(n) for n in numbers),\n",
+    "    \"number_size_px\": number_size_px,\n",
+    "    \"b_dot_x\": b_dot_x,\n",
+    "    \"b_dot_y\": b_dot_y,\n",
+    "    \"b_dot_radius\": b_dot_radius,\n",
+    "    \"runtime_seconds\": 0,\n",
+    "    \"polarity_mode\": \"ignore\",\n",
+    "    \"event_noise_filter\": \"none\",\n",
+    "    \"save_filtered_events\": True,\n",
+    "    \"output_root\": str(OUTPUT_ROOT),\n",
+    "    \"verbose\": 1,\n",
+    "}\n",
+    "\n",
+    "len(numbers_exposure_us_values) * len(dark_time_us_values) * len(trigger_delay_fraction_values) * repeats_per_setting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def delay_tag(value):\n",
+    "    return str(value).replace(\".\", \"p\")\n",
+    "\n",
+    "\n",
+    "def build_sync_check_argv(row):\n",
+    "    cmd = [\n",
+    "        \"--test\", row[\"test\"],\n",
+    "        \"--test-b\", row[\"test_b\"],\n",
+    "        \"--numbers\", row[\"numbers\"],\n",
+    "        \"--number-size-px\", str(row[\"number_size_px\"]),\n",
+    "        \"--b-dot-x\", str(row[\"b_dot_x\"]),\n",
+    "        \"--b-dot-y\", str(row[\"b_dot_y\"]),\n",
+    "        \"--b-dot-radius\", str(row[\"b_dot_radius\"]),\n",
+    "        \"--numbers-exposure-us\", str(row[\"numbers_exposure_us\"]),\n",
+    "        \"--dark-time-us\", str(row[\"dark_time_us\"]),\n",
+    "        \"--trigger-out-2-delay-fraction\", str(row[\"trigger_delay_fraction\"]),\n",
+    "        \"--runtime-seconds\", str(row[\"runtime_seconds\"]),\n",
+    "        \"--polarity-mode\", row[\"polarity_mode\"],\n",
+    "        \"--event-noise-filter\", row[\"event_noise_filter\"],\n",
+    "        \"--output-root\", row[\"output_root\"],\n",
+    "        \"--timestamp\", row[\"timestamp\"],\n",
+    "    ]\n",
+    "    if row[\"save_filtered_events\"]:\n",
+    "        cmd.append(\"--save-filtered-events\")\n",
+    "    if row[\"verbose\"]:\n",
+    "        cmd.extend([\"-\" + \"v\" * int(row[\"verbose\"])])\n",
+    "    return cmd\n",
+    "\n",
+    "\n",
+    "rows = []\n",
+    "index = 0\n",
+    "for repeat, numbers_exposure_us, dark_time_us, trigger_delay_fraction in itertools.product(\n",
+    "    range(repeats_per_setting),\n",
+    "    numbers_exposure_us_values,\n",
+    "    dark_time_us_values,\n",
+    "    trigger_delay_fraction_values,\n",
+    "):\n",
+    "    timestamp = (\n",
+    "        f\"{SWEEP_ID}-{index:03d}\"\n",
+    "        f\"-numexp{numbers_exposure_us}us\"\n",
+    "        f\"-dark{dark_time_us}us\"\n",
+    "        f\"-delay{delay_tag(trigger_delay_fraction)}\"\n",
+    "        f\"-r{repeat}\"\n",
+    "    )\n",
+    "    row = dict(base_capture)\n",
+    "    row.update(\n",
+    "        index=index,\n",
+    "        sweep_id=SWEEP_ID,\n",
+    "        repeat=repeat,\n",
+    "        timestamp=timestamp,\n",
+    "        numbers_exposure_us=numbers_exposure_us,\n",
+    "        dark_time_us=dark_time_us,\n",
+    "        trigger_delay_fraction=trigger_delay_fraction,\n",
+    "    )\n",
+    "    row[\"sync_check_argv\"] = shlex.join(build_sync_check_argv(row))\n",
+    "    rows.append(row)\n",
+    "    index += 1\n",
+    "\n",
+    "rows[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SWEEP_MANIFEST_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
+    "fieldnames = list(rows[0].keys())\n",
+    "with SWEEP_MANIFEST_PATH.open(\"w\", newline=\"\", encoding=\"utf-8\") as handle:\n",
+    "    writer = csv.DictWriter(handle, fieldnames=fieldnames)\n",
+    "    writer.writeheader()\n",
+    "    writer.writerows(rows)\n",
+    "\n",
+    "sweep_command = [\"./run_camera_sync_sweep.sh\", \"--manifest\", str(SWEEP_MANIFEST_PATH)]\n",
+    "\n",
+    "with SWEEP_COMMAND_PATH.open(\"w\", encoding=\"utf-8\") as handle:\n",
+    "    handle.write(\"#!/usr/bin/env bash\\n\")\n",
+    "    handle.write(\"set -euo pipefail\\n\\n\")\n",
+    "    handle.write(shlex.join(sweep_command) + \"\\n\")\n",
+    "\n",
+    "print(f\"Wrote {len(rows)} sync-check manifest rows\")\n",
+    "print(SWEEP_MANIFEST_PATH)\n",
+    "print(SWEEP_COMMAND_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run Plan\n",
+    "\n",
+    "Run the generated shell file on the DMD machine from the repo root. It launches one persistent `camera sync-sweep` process, opens the camera once, and reuses that handle for every timing row in the manifest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Persistent sweep command:\")\n",
+    "print(shlex.join(sweep_command))\n",
+    "\n",
+    "print(\"\\nFirst manifest rows:\")\n",
+    "for row in rows[:10]:\n",
+    "    print(row[\"sync_check_argv\"])\n",
+    "    print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/02_analyze_timing_sweep_results.ipynb b/notebooks/02_analyze_timing_sweep_results.ipynb
new file mode 100644
index 0000000..25cc86c
--- /dev/null
+++ b/notebooks/02_analyze_timing_sweep_results.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyze Numbered Sync-Check Timing Sweep Results\n",
+    "\n",
+    "This notebook scans `runs/camera/**/summary.json`, `metadata.json`, `timing.json`, and `accumulated.npy` from `run_camera_sync_check.sh`. It ranks timing settings and also builds per-trigger rows with the `expected_number`, so you can inspect whether frame `i` looks like the number that should have been visible for trigger `i`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "RUN_ROOT = Path(\"runs/camera\")\n",
+    "RUN_ROOT.resolve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_json(path):\n",
+    "    if not path.exists():\n",
+    "        return {}\n",
+    "    with path.open(encoding=\"utf-8\") as handle:\n",
+    "        return json.load(handle)\n",
+    "\n",
+    "\n",
+    "def mean_or_nan(values):\n",
+    "    if values is None or len(values) == 0:\n",
+    "        return np.nan\n",
+    "    return float(np.mean(values))\n",
+    "\n",
+    "\n",
+    "def sum_or_nan(values):\n",
+    "    if values is None or len(values) == 0:\n",
+    "        return np.nan\n",
+    "    return float(np.sum(values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_rows = []\n",
+    "frame_rows = []\n",
+    "for summary_path in sorted(RUN_ROOT.rglob(\"summary.json\")):\n",
+    "    run_dir = summary_path.parent\n",
+    "    summary = load_json(summary_path)\n",
+    "    metadata = load_json(run_dir / \"metadata.json\")\n",
+    "    if metadata.get(\"mode\") != \"sync-check\":\n",
+    "        continue\n",
+    "    timing = load_json(run_dir / \"timing.json\")\n",
+    "    number_sequence = metadata.get(\"number_sequence\", [])\n",
+    "    trigger_policy = metadata.get(\"trigger_policy\", timing)\n",
+    "\n",
+    "    in_counts = summary.get(\"events_per_accumulation_window\", [])\n",
+    "    pre_counts = summary.get(\"events_per_pre_trigger_window\", [])\n",
+    "    post_counts = summary.get(\"events_per_post_window\", [])\n",
+    "    abs_sums = summary.get(\"accumulated_abs_sums\", [])\n",
+    "    nonzero = summary.get(\"accumulated_nonzero_pixels\", [])\n",
+    "    frame_count = max(len(in_counts), len(abs_sums), len(nonzero))\n",
+    "\n",
+    "    for i in range(frame_count):\n",
+    "        expected_number = number_sequence[i % len(number_sequence)] if number_sequence else None\n",
+    "        in_count = in_counts[i] if i < len(in_counts) else np.nan\n",
+    "        pre_count = pre_counts[i] if i < len(pre_counts) else np.nan\n",
+    "        post_count = post_counts[i] if i < len(post_counts) else np.nan\n",
+    "        abs_sum = abs_sums[i] if i < len(abs_sums) else np.nan\n",
+    "        crossover_score = (float(pre_count or 0) + float(post_count or 0)) / (float(in_count or 0) + 1.0)\n",
+    "        frame_rows.append({\n",
+    "            \"run_dir\": str(run_dir),\n",
+    "            \"run_name\": run_dir.name,\n",
+    "            \"frame_index\": i,\n",
+    "            \"expected_number\": expected_number,\n",
+    "            \"in_window_events\": in_count,\n",
+    "            \"pre_window_events\": pre_count,\n",
+    "            \"post_window_events\": post_count,\n",
+    "            \"accumulated_abs_sum\": abs_sum,\n",
+    "            \"crossover_score\": crossover_score,\n",
+    "        })\n",
+    "\n",
+    "    run_rows.append({\n",
+    "        \"run_dir\": str(run_dir),\n",
+    "        \"run_name\": run_dir.name,\n",
+    "        \"number_sequence\": \",\".join(str(n) for n in number_sequence),\n",
+    "        \"number_size_px\": metadata.get(\"number_size_px\"),\n",
+    "        \"b_dot_radius\": metadata.get(\"b_dot_radius\"),\n",
+    "        \"numbers_exposure_us\": metadata.get(\"numbers_exposure_us\"),\n",
+    "        \"dark_time_us\": metadata.get(\"dark_time_us\"),\n",
+    "        \"trigger_delay_fraction\": trigger_policy.get(\"delay_fraction\"),\n",
+    "        \"window_us\": summary.get(\"window_us\"),\n",
+    "        \"actual_trigger_count\": summary.get(\"actual_trigger_count\"),\n",
+    "        \"expected_trigger_count\": metadata.get(\"expected_trigger_count\"),\n",
+    "        \"event_count\": summary.get(\"event_count\"),\n",
+    "        \"in_window_mean\": mean_or_nan(in_counts),\n",
+    "        \"pre_window_mean\": mean_or_nan(pre_counts),\n",
+    "        \"post_window_mean\": mean_or_nan(post_counts),\n",
+    "        \"in_window_sum\": sum_or_nan(in_counts),\n",
+    "        \"pre_window_sum\": sum_or_nan(pre_counts),\n",
+    "        \"post_window_sum\": sum_or_nan(post_counts),\n",
+    "        \"accumulated_abs_sum_mean\": mean_or_nan(abs_sums),\n",
+    "        \"accumulated_nonzero_mean\": mean_or_nan(nonzero),\n",
+    "        \"summary_path\": str(summary_path),\n",
+    "    })\n",
+    "\n",
+    "df = pd.DataFrame(run_rows)\n",
+    "frames = pd.DataFrame(frame_rows)\n",
+    "print(f\"Loaded {len(df)} sync-check runs and {len(frames)} frame rows from {RUN_ROOT}\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if df.empty:\n",
+    "    raise RuntimeError(\"No sync-check summaries found. Run the commands from 01_generate_timing_sweep_commands.ipynb first.\")\n",
+    "\n",
+    "eps = 1.0\n",
+    "df[\"pre_ratio\"] = df[\"pre_window_sum\"].fillna(0) / (df[\"in_window_sum\"].fillna(0) + eps)\n",
+    "df[\"post_ratio\"] = df[\"post_window_sum\"].fillna(0) / (df[\"in_window_sum\"].fillna(0) + eps)\n",
+    "df[\"crossover_score\"] = df[\"pre_ratio\"] + df[\"post_ratio\"]\n",
+    "df[\"window_signal\"] = np.log1p(df[\"in_window_sum\"].fillna(0))\n",
+    "df[\"image_signal\"] = np.log1p(df[\"accumulated_abs_sum_mean\"].fillna(0))\n",
+    "df[\"trigger_completeness\"] = df[\"actual_trigger_count\"].fillna(0) / df[\"expected_trigger_count\"].replace(0, np.nan)\n",
+    "\n",
+    "# timing_score rewards complete in-window signal and penalizes nearby leakage/crossover.\n",
+    "df[\"timing_score\"] = (\n",
+    "    df[\"window_signal\"]\n",
+    "    + 0.35 * df[\"image_signal\"]\n",
+    "    + 1.0 * df[\"trigger_completeness\"].fillna(0)\n",
+    "    - 1.50 * df[\"pre_ratio\"].fillna(0)\n",
+    "    - 1.00 * df[\"post_ratio\"].fillna(0)\n",
+    ")\n",
+    "\n",
+    "ranked_runs = df.sort_values(\"timing_score\", ascending=False).reset_index(drop=True)\n",
+    "ranked_runs[[\n",
+    "    \"run_name\", \"timing_score\", \"crossover_score\", \"numbers_exposure_us\", \"dark_time_us\",\n",
+    "    \"trigger_delay_fraction\", \"number_sequence\", \"number_size_px\", \"b_dot_radius\",\n",
+    "    \"actual_trigger_count\", \"expected_trigger_count\", \"in_window_mean\", \"pre_window_mean\", \"post_window_mean\",\n",
+    "]].head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Per-Number / Per-Frame Inspection\n",
+    "\n",
+    "The frame table maps each trigger index to the `expected_number`. Use this to spot number crossover: high pre/post counts around a frame suggest the camera accumulation window is catching events from a neighboring numbered bitplane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not frames.empty:\n",
+    "    display(frames.sort_values([\"run_name\", \"frame_index\"]).head(30))\n",
+    "    display(\n",
+    "        frames.groupby(\"expected_number\", dropna=False)[[\n",
+    "            \"in_window_events\", \"pre_window_events\", \"post_window_events\", \"crossover_score\"\n",
+    "        ]].mean()\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for exposure, group in ranked_runs.groupby(\"numbers_exposure_us\", dropna=False):\n",
+    "    pivot = group.pivot_table(\n",
+    "        index=\"dark_time_us\",\n",
+    "        columns=\"trigger_delay_fraction\",\n",
+    "        values=\"timing_score\",\n",
+    "        aggfunc=\"mean\",\n",
+    "    )\n",
+    "    if pivot.empty:\n",
+    "        continue\n",
+    "    fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "    image = ax.imshow(pivot.values, aspect=\"auto\", origin=\"lower\")\n",
+    "    ax.set_title(f\"sync-check timing_score, numbers_exposure_us={exposure}\")\n",
+    "    ax.set_xlabel(\"trigger_delay_fraction\")\n",
+    "    ax.set_ylabel(\"dark_time_us\")\n",
+    "    ax.set_xticks(range(len(pivot.columns)))\n",
+    "    ax.set_xticklabels(pivot.columns)\n",
+    "    ax.set_yticks(range(len(pivot.index)))\n",
+    "    ax.set_yticklabels(pivot.index)\n",
+    "    fig.colorbar(image, ax=ax)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = [\n",
+    "    \"run_name\", \"timing_score\", \"crossover_score\", \"numbers_exposure_us\", \"dark_time_us\",\n",
+    "    \"trigger_delay_fraction\", \"number_sequence\", \"number_size_px\", \"b_dot_radius\",\n",
+    "    \"window_us\", \"actual_trigger_count\", \"expected_trigger_count\", \"run_dir\",\n",
+    "]\n",
+    "Path(\"runs\").mkdir(exist_ok=True)\n",
+    "ranked_runs[columns].to_csv(\"runs/sync_timing_sweep_ranked_runs.csv\", index=False)\n",
+    "frames.to_csv(\"runs/sync_timing_sweep_frame_rows.csv\", index=False)\n",
+    "print(\"Wrote runs/sync_timing_sweep_ranked_runs.csv\")\n",
+    "print(\"Wrote runs/sync_timing_sweep_frame_rows.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/03_accumulation_offset_rescan.ipynb b/notebooks/03_accumulation_offset_rescan.ipynb
new file mode 100644
index 0000000..3c03bd5
--- /dev/null
+++ b/notebooks/03_accumulation_offset_rescan.ipynb
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accumulation Offset Rescan\n",
+    "\n",
+    "This notebook tests hypothetical software accumulation offsets for numbered `sync-check` runs without rerunning hardware. It needs saved event arrays plus `triggers.csv`. The sync sweep notebook enables `--save-filtered-events`, which writes `filtered_events.npz` even when `--event-noise-filter none` is selected.\n",
+    "\n",
+    "If a run has only `raw.aedat4`, this notebook will skip it. Parsing AEDAT directly is intentionally not included here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import csv\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "RUN_ROOT = Path(\"runs/camera\")\n",
+    "offset_us_values = list(range(-3000, 3001, 100))\n",
+    "RUN_ROOT.resolve(), offset_us_values[:5], offset_us_values[-5:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_json(path):\n",
+    "    if not path.exists():\n",
+    "        return {}\n",
+    "    with path.open(encoding=\"utf-8\") as handle:\n",
+    "        return json.load(handle)\n",
+    "\n",
+    "\n",
+    "def load_triggers(path):\n",
+    "    rows = []\n",
+    "    if not path.exists():\n",
+    "        return pd.DataFrame(columns=[\"index\", \"timestamp\", \"edge\"])\n",
+    "    with path.open(encoding=\"utf-8\") as handle:\n",
+    "        reader = csv.DictReader(handle)\n",
+    "        for row in reader:\n",
+    "            rows.append({\n",
+    "                \"index\": int(row[\"index\"]),\n",
+    "                \"timestamp\": int(row[\"timestamp\"]),\n",
+    "                \"edge\": row.get(\"edge\", \"rising\"),\n",
+    "            })\n",
+    "    return pd.DataFrame(rows)\n",
+    "\n",
+    "\n",
+    "def load_event_npz(run_dir):\n",
+    "    for name in (\"filtered_events.npz\", \"events.npz\"):\n",
+    "        path = run_dir / name\n",
+    "        if path.exists():\n",
+    "            data = np.load(path)\n",
+    "            return path, {\n",
+    "                \"x\": np.asarray(data[\"x\"]),\n",
+    "                \"y\": np.asarray(data[\"y\"]),\n",
+    "                \"t\": np.asarray(data[\"t\"], dtype=np.int64),\n",
+    "                \"p\": np.asarray(data[\"p\"]),\n",
+    "            }\n",
+    "    return None, None\n",
+    "\n",
+    "\n",
+    "def window_counts(timestamps, starts, window_us):\n",
+    "    timestamps = np.sort(np.asarray(timestamps, dtype=np.int64))\n",
+    "    starts = np.asarray(starts, dtype=np.int64)\n",
+    "    if len(starts) == 0 or len(timestamps) == 0 or window_us <= 0:\n",
+    "        return np.zeros(len(starts), dtype=np.int64)\n",
+    "    ends = starts + int(window_us)\n",
+    "    left = np.searchsorted(timestamps, starts, side=\"left\")\n",
+    "    right = np.searchsorted(timestamps, ends, side=\"left\")\n",
+    "    return right - left"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescan_offsets(run_dir, offset_us_values, window_us=None):\n",
+    "    run_dir = Path(run_dir)\n",
+    "    summary = load_json(run_dir / \"summary.json\")\n",
+    "    metadata = load_json(run_dir / \"metadata.json\")\n",
+    "    triggers = load_triggers(run_dir / \"triggers.csv\")\n",
+    "    event_path, events = load_event_npz(run_dir)\n",
+    "    if events is None or triggers.empty:\n",
+    "        return pd.DataFrame()\n",
+    "\n",
+    "    resolved_window_us = int(window_us or summary.get(\"window_us\") or metadata.get(\"accumulation_window_us\") or 0)\n",
+    "    rising = triggers[triggers[\"edge\"].str.lower().eq(\"rising\")].copy()\n",
+    "    trigger_timestamps = rising[\"timestamp\"].to_numpy(dtype=np.int64)\n",
+    "    event_timestamps = events[\"t\"]\n",
+    "\n",
+    "    rows = []\n",
+    "    for offset_us in offset_us_values:\n",
+    "        starts = trigger_timestamps + int(offset_us)\n",
+    "        counts = window_counts(event_timestamps, starts, resolved_window_us)\n",
+    "        pre_counts = window_counts(event_timestamps, starts - resolved_window_us, resolved_window_us)\n",
+    "        post_counts = window_counts(event_timestamps, starts + resolved_window_us, resolved_window_us)\n",
+    "        in_sum = float(np.sum(counts))\n",
+    "        pre_sum = float(np.sum(pre_counts))\n",
+    "        post_sum = float(np.sum(post_counts))\n",
+    "        score = np.log1p(in_sum) - 1.25 * (pre_sum / (in_sum + 1.0)) - 0.75 * (post_sum / (in_sum + 1.0))\n",
+    "        rows.append({\n",
+    "            \"run_dir\": str(run_dir),\n",
+    "            \"run_name\": run_dir.name,\n",
+    "            \"event_file\": str(event_path),\n",
+    "            \"window_us\": resolved_window_us,\n",
+    "            \"offset_us\": int(offset_us),\n",
+    "            \"trigger_count\": int(len(trigger_timestamps)),\n",
+    "            \"in_window_sum\": in_sum,\n",
+    "            \"pre_window_sum\": pre_sum,\n",
+    "            \"post_window_sum\": post_sum,\n",
+    "            \"in_window_mean\": float(np.mean(counts)) if len(counts) else 0.0,\n",
+    "            \"score\": float(score),\n",
+    "        })\n",
+    "    return pd.DataFrame(rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_scans = []\n",
+    "skipped = []\n",
+    "for summary_path in sorted(RUN_ROOT.rglob(\"summary.json\")):\n",
+    "    run_dir = summary_path.parent\n",
+    "    scan = rescan_offsets(run_dir, offset_us_values)\n",
+    "    if scan.empty:\n",
+    "        skipped.append(str(run_dir))\n",
+    "    else:\n",
+    "        all_scans.append(scan)\n",
+    "\n",
+    "if all_scans:\n",
+    "    offset_scan_df = pd.concat(all_scans, ignore_index=True)\n",
+    "else:\n",
+    "    offset_scan_df = pd.DataFrame()\n",
+    "\n",
+    "print(f\"Scanned {len(all_scans)} runs; skipped {len(skipped)} runs without filtered_events.npz/events.npz\")\n",
+    "offset_scan_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if offset_scan_df.empty:\n",
+    "    print(\"No event NPZ files found. Re-run a small capture with --event-noise-filter local-support --save-filtered-events, then rerun this notebook.\")\n",
+    "else:\n",
+    "    best_offset_by_run = (\n",
+    "        offset_scan_df.sort_values(\"score\", ascending=False)\n",
+    "        .groupby(\"run_name\", as_index=False)\n",
+    "        .first()\n",
+    "        .sort_values(\"score\", ascending=False)\n",
+    "    )\n",
+    "    display(best_offset_by_run.head(20))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not offset_scan_df.empty:\n",
+    "    for run_name, group in offset_scan_df.groupby(\"run_name\"):\n",
+    "        fig, ax = plt.subplots(figsize=(8, 3))\n",
+    "        ax.plot(group[\"offset_us\"], group[\"score\"], label=\"score\")\n",
+    "        ax.plot(group[\"offset_us\"], np.log1p(group[\"in_window_sum\"]), label=\"log in-window\")\n",
+    "        ax.set_title(run_name)\n",
+    "        ax.set_xlabel(\"offset_us\")\n",
+    "        ax.set_ylabel(\"score\")\n",
+    "        ax.legend()\n",
+    "        plt.show()\n",
+    "        if len(offset_scan_df[\"run_name\"].unique()) > 8:\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not offset_scan_df.empty:\n",
+    "    Path(\"runs\").mkdir(exist_ok=True)\n",
+    "    offset_scan_df.to_csv(\"runs/timing_offset_rescan_all.csv\", index=False)\n",
+    "    best_offset_by_run.to_csv(\"runs/timing_offset_rescan_best.csv\", index=False)\n",
+    "    print(\"Wrote runs/timing_offset_rescan_all.csv\")\n",
+    "    print(\"Wrote runs/timing_offset_rescan_best.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/04_analyze_selected_cycle_run.ipynb b/notebooks/04_analyze_selected_cycle_run.ipynb
new file mode 100644
index 0000000..ce81f2a
--- /dev/null
+++ b/notebooks/04_analyze_selected_cycle_run.ipynb
@@ -0,0 +1,897 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Selected Cycle Run Diagnostics\n",
+    "\n",
+    "This notebook inspects one `sync-check` run and separates three failure modes:\n",
+    "\n",
+    "- trigger/cycle selection problems\n",
+    "- accumulation-window timing problems\n",
+    "- PNG display-scaling problems\n",
+    "\n",
+    "The default run is `runs/camera/laser_selected_cycle_40px_dot123-sync-check`."
+   ],
+   "id": "54e6c43478033798"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:02.811774100Z",
+     "start_time": "2026-06-04T21:26:02.722996200Z"
+    }
+   },
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "import csv\n",
+    "import json\n",
+    "import math\n",
+    "import statistics\n",
+    "\n",
+    "import numpy as np\n",
+    "from PIL import Image, ImageDraw\n",
+    "\n",
+    "try:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "except Exception as exc:\n",
+    "    plt = None\n",
+    "    print(f\"matplotlib unavailable: {exc}\")\n",
+    "\n",
+    "try:\n",
+    "    from IPython.display import display\n",
+    "except Exception:\n",
+    "    display = None\n",
+    "\n",
+    "PROJECT_ROOT = Path.cwd()\n",
+    "if not (PROJECT_ROOT / \"dmdcontrol\").exists() and (PROJECT_ROOT.parent / \"dmdcontrol\").exists():\n",
+    "    PROJECT_ROOT = PROJECT_ROOT.parent\n",
+    "\n",
+    "DEFAULT_RUN_DIR = PROJECT_ROOT / \"runs\" / \"camera\" / \"laser_selected_cycle_40px_dot123-sync-check\"\n",
+    "RUN_DIR = Path(os.environ.get(\"DMD_SYNC_RUN_DIR\", DEFAULT_RUN_DIR)).expanduser()\n",
+    "if not RUN_DIR.is_absolute():\n",
+    "    RUN_DIR = PROJECT_ROOT / RUN_DIR\n",
+    "OUT_DIR = RUN_DIR / \"analysis_outputs\"\n",
+    "OUT_DIR.mkdir(exist_ok=True)\n",
+    "\n",
+    "print(f\"PROJECT_ROOT = {PROJECT_ROOT}\")\n",
+    "print(f\"RUN_DIR      = {RUN_DIR}\")\n",
+    "print(f\"OUT_DIR      = {OUT_DIR}\")\n",
+    "assert RUN_DIR.exists(), RUN_DIR"
+   ],
+   "id": "c2c245f7d924beec",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROJECT_ROOT = C:\\dev\\dmdcontrol\n",
+      "RUN_DIR      = C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123-sync-check\n",
+      "OUT_DIR      = C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123-sync-check\\analysis_outputs\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:03.009592200Z",
+     "start_time": "2026-06-04T21:26:02.863718Z"
+    }
+   },
+   "source": [
+    "def load_json(name):\n",
+    "    with (RUN_DIR / name).open(\"r\", encoding=\"utf-8\") as handle:\n",
+    "        return json.load(handle)\n",
+    "\n",
+    "metadata = load_json(\"metadata.json\")\n",
+    "summary = load_json(\"summary.json\")\n",
+    "accumulated = np.load(RUN_DIR / \"accumulated.npy\")\n",
+    "events_npz = np.load(RUN_DIR / \"filtered_events.npz\")\n",
+    "\n",
+    "with (RUN_DIR / \"triggers.csv\").open(\"r\", newline=\"\", encoding=\"utf-8\") as handle:\n",
+    "    triggers = list(csv.DictReader(handle))\n",
+    "trigger_ts = np.array([int(row[\"timestamp\"]) for row in triggers], dtype=np.int64)\n",
+    "trigger_order = np.argsort(trigger_ts, kind=\"stable\")\n",
+    "ordered_trigger_ts = trigger_ts[trigger_order]\n",
+    "\n",
+    "number_sequence = metadata.get(\"number_sequence\") or [1, 2, 3, 4, 5]\n",
+    "cycle_info = summary.get(\"trigger_cycle_limit\") or summary.get(\"trigger_cycle_selection\") or {}\n",
+    "cycle_len = int(cycle_info.get(\"cycle_length\") or len(number_sequence))\n",
+    "selected_cycle_indices = cycle_info.get(\"selected_cycle_indices\") or []\n",
+    "window_us = int(metadata.get(\"accumulation_window_us\") or summary.get(\"accumulation_window_us\") or metadata.get(\"numbers_exposure_us\") or 1500)\n",
+    "resolution = tuple(summary.get(\"resolution\") or metadata.get(\"resolution\") or (accumulated.shape[2], accumulated.shape[1]))\n",
+    "\n",
+    "print(f\"accumulated shape: {accumulated.shape}, dtype={accumulated.dtype}, min={accumulated.min()}, max={accumulated.max()}\")\n",
+    "print(f\"events keys: {list(events_npz.keys())}\")\n",
+    "print(f\"event count: {len(events_npz['t']):,}\")\n",
+    "print(f\"selected triggers: {len(trigger_ts)}\")\n",
+    "print(f\"triggers.csv already timestamp-sorted: {bool(np.all(trigger_order == np.arange(len(trigger_order))))}\")\n",
+    "print(f\"cycle_len: {cycle_len}; selected_cycle_indices: {selected_cycle_indices}\")\n",
+    "print(f\"window_us: {window_us}; resolution: {resolution}\")"
+   ],
+   "id": "480431dfe5675968",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accumulated shape: (50, 240, 320), dtype=float32, min=-3.0, max=3.0\n",
+      "events keys: ['x', 'y', 't', 'p']\n",
+      "event count: 564,149\n",
+      "selected triggers: 50\n",
+      "triggers.csv already timestamp-sorted: True\n",
+      "cycle_len: 5; selected_cycle_indices: [9, 10, 11, 12, 13, 14, 15, 16, 18, 19]\n",
+      "window_us: 1500; resolution: (320, 240)\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run facts\n",
+    "\n",
+    "Check this first. New runs select the first complete trigger cycles after the pre-capture flush. Older runs may still contain legacy cycle-selection metadata."
+   ],
+   "id": "3bd7abe4b02ece03"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:03.082199200Z",
+     "start_time": "2026-06-04T21:26:03.037600900Z"
+    }
+   },
+   "source": [
+    "for key in [\n",
+    "    \"test\", \"test_b\", \"number_size_px\", \"b_dot_radius\", \"numbers_exposure_us\",\n",
+    "    \"dark_time_us\", \"runtime_seconds\", \"polarity_mode\", \"event_noise_filter\",\n",
+    "    \"accumulation_cycles\", \"accumulation_start_offset_us\",\n",
+    "]:\n",
+    "    print(f\"metadata[{key!r}] = {metadata.get(key)}\")\n",
+    "\n",
+    "print(\"\\nsummary counts\")\n",
+    "for key in [\n",
+    "    \"raw_rising_trigger_count\", \"aligned_rising_trigger_count\",\n",
+    "    \"selected_rising_trigger_count\", \"actual_trigger_count\", \"event_count\",\n",
+    "]:\n",
+    "    print(f\"summary[{key!r}] = {summary.get(key)}\")\n",
+    "\n",
+    "print(\"\\ntrigger_cycle_limit\")\n",
+    "for key, value in cycle_info.items():\n",
+    "    if key == \"cycle_event_counts\":\n",
+    "        print(f\"{key}: {len(value)} counts; first 20 = {value[:20]}\")\n",
+    "    else:\n",
+    "        print(f\"{key}: {value}\")"
+   ],
+   "id": "d91e57bfabee4eca",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "metadata['test'] = a-numbers-b-static\n",
+      "metadata['test_b'] = None\n",
+      "metadata['number_size_px'] = 400\n",
+      "metadata['b_dot_radius'] = 40\n",
+      "metadata['numbers_exposure_us'] = 1500\n",
+      "metadata['dark_time_us'] = 250\n",
+      "metadata['runtime_seconds'] = None\n",
+      "metadata['polarity_mode'] = signed\n",
+      "metadata['event_noise_filter'] = {'algorithm': 'none', 'delta_t_us': 50000, 'enabled': False, 'filtered_events': 564149, 'filtered_off': 240449, 'filtered_on': 323700, 'kept_fraction': 1.0, 'note': 'Event noise filtering disabled. Local-support parameters are resolved but were not applied.', 'polarity': 'same', 'raw_events': 564149, 'raw_off': 240449, 'raw_on': 323700, 'threshold': 2, 'window_px': 3}\n",
+      "metadata['accumulation_cycles'] = 10\n",
+      "metadata['cycle_selection'] = strongest\n",
+      "metadata['trigger_cluster_us'] = 250\n",
+      "\n",
+      "summary counts\n",
+      "summary['raw_rising_trigger_count'] = 5666\n",
+      "summary['aligned_rising_trigger_count'] = 5662\n",
+      "summary['clustered_rising_trigger_count'] = 2831\n",
+      "summary['selected_rising_trigger_count'] = 50\n",
+      "summary['actual_trigger_count'] = 50\n",
+      "summary['event_count'] = 564149\n",
+      "\n",
+      "trigger_cycle_selection\n",
+      "available_full_cycles: 566\n",
+      "cycle_event_counts: 566 counts; first 20 = [567, 1263, 1515, 1681, 1582, 1894, 1877, 1944, 2014, 2057, 2145, 2124, 2122, 2086, 2102, 2122, 2025, 1991, 2064, 2050]\n",
+      "cycle_length: 5\n",
+      "mode: strongest\n",
+      "requested_cycles: 10\n",
+      "selected_cycle_indices: [9, 10, 11, 12, 13, 14, 15, 16, 18, 19]\n",
+      "selected_trigger_count: 50\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trigger timing by 1..5 group\n",
+    "\n",
+    "Each group below is one displayed `1,2,3,4,5` set in the selected artifacts. Large `gap_to_next_us` values mean `strongest` skipped physical cycles between selected groups."
+   ],
+   "id": "7666389fc576d743"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:03.212351400Z",
+     "start_time": "2026-06-04T21:26:03.121259800Z"
+    }
+   },
+   "source": [
+    "event_counts = summary.get(\"events_per_accumulation_window\") or [None] * len(trigger_ts)\n",
+    "dt = np.diff(trigger_ts) if len(trigger_ts) > 1 else np.array([], dtype=np.int64)\n",
+    "inner_dt = []\n",
+    "for group_start in range(0, len(trigger_ts), cycle_len):\n",
+    "    group = trigger_ts[group_start:group_start + cycle_len]\n",
+    "    if len(group) > 1:\n",
+    "        inner_dt.extend(np.diff(group).tolist())\n",
+    "typical_dt = int(statistics.median(inner_dt)) if inner_dt else None\n",
+    "print(f\"typical inner trigger dt: {typical_dt} us\")\n",
+    "print(f\"all selected-trigger dt: min={dt.min() if len(dt) else None}, max={dt.max() if len(dt) else None}, mean={dt.mean() if len(dt) else None}\")\n",
+    "\n",
+    "for row, group_start in enumerate(range(0, len(trigger_ts), cycle_len), start=1):\n",
+    "    group = trigger_ts[group_start:group_start + cycle_len]\n",
+    "    counts = event_counts[group_start:group_start + cycle_len]\n",
+    "    physical_cycle = selected_cycle_indices[row - 1] if row - 1 < len(selected_cycle_indices) else None\n",
+    "    group_inner_dt = np.diff(group).tolist() if len(group) > 1 else []\n",
+    "    next_index = group_start + cycle_len\n",
+    "    gap_to_next = int(trigger_ts[next_index] - group[-1]) if next_index < len(trigger_ts) else None\n",
+    "    skipped_hint = \"\"\n",
+    "    if typical_dt and gap_to_next and gap_to_next > 2 * typical_dt:\n",
+    "        skipped_hint = \"  <-- skipped physical cycle(s)\"\n",
+    "    print(\n",
+    "        f\"row {row:02d}: physical_cycle={physical_cycle}, \"\n",
+    "        f\"start={int(group[0])}, inner_dt={group_inner_dt}, \"\n",
+    "        f\"gap_to_next_us={gap_to_next}, events={counts}{skipped_hint}\"\n",
+    "    )"
+   ],
+   "id": "73e5fa57f2829bab",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "typical inner trigger dt: 1750 us\n",
+      "all selected-trigger dt: min=1721, max=10471, mean=1927.1020408163265\n",
+      "row 01: physical_cycle=9, start=1780606395564537, inner_dt=[1750, 1750, 1750, 1750], gap_to_next_us=1750, events=[416, 407, 402, 469, 363]\n",
+      "row 02: physical_cycle=10, start=1780606395573287, inner_dt=[1750, 1721, 1750, 1750], gap_to_next_us=1750, events=[465, 433, 477, 490, 280]\n",
+      "row 03: physical_cycle=11, start=1780606395582008, inner_dt=[1750, 1750, 1750, 1751], gap_to_next_us=1750, events=[434, 515, 423, 445, 307]\n",
+      "row 04: physical_cycle=12, start=1780606395590759, inner_dt=[1750, 1756, 1750, 1750], gap_to_next_us=1750, events=[468, 493, 393, 445, 323]\n",
+      "row 05: physical_cycle=13, start=1780606395599515, inner_dt=[1750, 1750, 1750, 1750], gap_to_next_us=1750, events=[467, 412, 426, 486, 295]\n",
+      "row 06: physical_cycle=14, start=1780606395608265, inner_dt=[1722, 1750, 1750, 1750], gap_to_next_us=1750, events=[406, 518, 398, 435, 345]\n",
+      "row 07: physical_cycle=15, start=1780606395616987, inner_dt=[1750, 1750, 1750, 1750], gap_to_next_us=1750, events=[449, 449, 387, 436, 401]\n",
+      "row 08: physical_cycle=16, start=1780606395625737, inner_dt=[1756, 1750, 1750, 1751], gap_to_next_us=10471, events=[435, 398, 389, 480, 323]  <-- skipped physical cycle(s)\n",
+      "row 09: physical_cycle=18, start=1780606395643215, inner_dt=[1750, 1750, 1750, 1750], gap_to_next_us=1750, events=[458, 516, 349, 405, 336]\n",
+      "row 10: physical_cycle=19, start=1780606395651965, inner_dt=[1750, 1750, 1750, 1750], gap_to_next_us=None, events=[429, 415, 353, 450, 403]\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saved PNG scaling check\n",
+    "\n",
+    "`accumulated.npy` has a true zero background. The existing `accumulated_###.png` files are min/max-normalized per frame. With `--polarity-mode signed`, zero can map to gray instead of black, and that gray jumps frame to frame."
+   ],
+   "id": "bdba6ae670036fa8"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:03.288985200Z",
+     "start_time": "2026-06-04T21:26:03.220350900Z"
+    }
+   },
+   "source": [
+    "print(\"idx  frame_min frame_max  saved_min saved_max  saved_zero_bg_median  saved_zero_bg_unique_head\")\n",
+    "for i in range(min(15, accumulated.shape[0])):\n",
+    "    png_path = RUN_DIR / f\"accumulated_{i + 1:03d}.png\"\n",
+    "    saved = np.array(Image.open(png_path))\n",
+    "    zero_mask = accumulated[i] == 0\n",
+    "    bg = saved[zero_mask]\n",
+    "    unique_head = np.unique(bg)[:8].tolist() if bg.size else []\n",
+    "    print(\n",
+    "        f\"{i + 1:03d}  {accumulated[i].min():8.1f} {accumulated[i].max():8.1f}\"\n",
+    "        f\"  {saved.min():9d} {saved.max():9d}  {float(np.median(bg)) if bg.size else math.nan:20.1f}  {unique_head}\"\n",
+    "    )"
+   ],
+   "id": "b2d03fc37621612d",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "idx  frame_min frame_max  saved_min saved_max  saved_zero_bg_median  saved_zero_bg_unique_head\n",
+      "001      -2.0      1.0          0       255                 170.0  [170]\n",
+      "002      -2.0      2.0          0       255                 127.0  [127]\n",
+      "003      -2.0      3.0          0       255                 102.0  [102]\n",
+      "004      -1.0      2.0          0       255                  85.0  [85]\n",
+      "005      -2.0      2.0          0       255                 127.0  [127]\n",
+      "006      -2.0      2.0          0       255                 127.0  [127]\n",
+      "007      -1.0      2.0          0       255                  85.0  [85]\n",
+      "008      -1.0      2.0          0       255                  85.0  [85]\n",
+      "009      -1.0      2.0          0       255                  85.0  [85]\n",
+      "010      -1.0      1.0          0       255                 127.0  [127]\n",
+      "011      -2.0      2.0          0       255                 127.0  [127]\n",
+      "012      -1.0      1.0          0       255                 127.0  [127]\n",
+      "013      -1.0      1.0          0       255                 127.0  [127]\n",
+      "014      -2.0      2.0          0       255                 127.0  [127]\n",
+      "015      -2.0      2.0          0       255                 127.0  [127]\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Black-background renderers\n",
+    "\n",
+    "These render from `accumulated.npy`, not from the saved PNGs.\n",
+    "\n",
+    "- `positive`: show ON events only, zero background stays black.\n",
+    "- `absolute`: show ON and OFF event magnitude, zero background stays black.\n",
+    "- `signed_rgb`: positive is green, negative is magenta, zero background stays black."
+   ],
+   "id": "9444fe0b88a2a3ba"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:05.098577600Z",
+     "start_time": "2026-06-04T21:26:03.304442200Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "DISPLAY_TONE_CURVE = \"gamma\"  # \"linear\", \"log\", or \"gamma\".\n",
+    "DISPLAY_GAMMA = 0.5\n",
+    "DISPLAY_PERCENTILE = None  # None uses the full nonzero max. Use 99.5 to intentionally clip outliers.\n",
+    "\n",
+    "def _scale_label(percentile=DISPLAY_PERCENTILE):\n",
+    "    if percentile is None:\n",
+    "        return \"maxscale\"\n",
+    "    return f\"p{str(percentile).replace('.', 'p')}\"\n",
+    "\n",
+    "def _scale_to_uint8(values, vmax=None, percentile=DISPLAY_PERCENTILE, tone_curve=DISPLAY_TONE_CURVE, gamma=DISPLAY_GAMMA):\n",
+    "    arr = np.asarray(values, dtype=np.float32)\n",
+    "    if vmax is None:\n",
+    "        nonzero = arr[arr > 0]\n",
+    "        if not nonzero.size:\n",
+    "            vmax = 1.0\n",
+    "        elif percentile is None:\n",
+    "            vmax = float(nonzero.max())\n",
+    "        else:\n",
+    "            vmax = float(np.percentile(nonzero, percentile))\n",
+    "    vmax = max(float(vmax), 1e-6)\n",
+    "    if tone_curve == \"linear\":\n",
+    "        scaled = arr / vmax\n",
+    "    elif tone_curve == \"log\":\n",
+    "        scaled = np.log1p(arr) / np.log1p(vmax)\n",
+    "    elif tone_curve == \"gamma\":\n",
+    "        scaled = np.power(np.clip(arr / vmax, 0, None), gamma)\n",
+    "    else:\n",
+    "        raise ValueError(f\"unknown tone curve: {tone_curve}\")\n",
+    "    return np.rint(np.clip(scaled * 255.0, 0, 255)).astype(np.uint8), vmax\n",
+    "\n",
+    "def render_frame(frame, mode=\"positive\", vmax=None):\n",
+    "    frame = np.asarray(frame, dtype=np.float32)\n",
+    "    if mode == \"positive\":\n",
+    "        image, used_vmax = _scale_to_uint8(np.maximum(frame, 0), vmax=vmax)\n",
+    "        return Image.fromarray(image), used_vmax\n",
+    "    if mode == \"absolute\":\n",
+    "        image, used_vmax = _scale_to_uint8(np.abs(frame), vmax=vmax)\n",
+    "        return Image.fromarray(image), used_vmax\n",
+    "    if mode == \"signed_rgb\":\n",
+    "        pos, used_vmax = _scale_to_uint8(np.maximum(frame, 0), vmax=vmax)\n",
+    "        neg, _ = _scale_to_uint8(np.maximum(-frame, 0), vmax=used_vmax)\n",
+    "        rgb = np.zeros((frame.shape[0], frame.shape[1], 3), dtype=np.uint8)\n",
+    "        rgb[..., 1] = pos\n",
+    "        rgb[..., 0] = neg\n",
+    "        rgb[..., 2] = neg\n",
+    "        return Image.fromarray(rgb), used_vmax\n",
+    "    raise ValueError(mode)\n",
+    "\n",
+    "DISPLAY_FLIP_X = False  # Primary notebook display orientation. True mirrors each tile for easier digit reading.\n",
+    "DISPLAY_SAVE_ALTERNATE_FLIP_X = True\n",
+    "\n",
+    "def _orientation_suffix(flip_x):\n",
+    "    return \"_flip_x\" if flip_x else \"\"\n",
+    "\n",
+    "def _orientation_variants():\n",
+    "    variants = [(DISPLAY_FLIP_X, _orientation_suffix(DISPLAY_FLIP_X))]\n",
+    "    alternate = not DISPLAY_FLIP_X\n",
+    "    if DISPLAY_SAVE_ALTERNATE_FLIP_X:\n",
+    "        variants.append((alternate, _orientation_suffix(alternate)))\n",
+    "    return variants\n",
+    "\n",
+    "def make_contact(frames, *, mode=\"positive\", cols=None, labels=None, scale=2, pad=10, flip_x=False):\n",
+    "    frames = np.asarray(frames)\n",
+    "    if cols is None:\n",
+    "        cols = cycle_len\n",
+    "    rows = int(math.ceil(len(frames) / cols))\n",
+    "    h, w = frames.shape[1:3]\n",
+    "    if mode == \"positive\":\n",
+    "        _, vmax = _scale_to_uint8(np.maximum(frames, 0))\n",
+    "    elif mode == \"absolute\":\n",
+    "        _, vmax = _scale_to_uint8(np.abs(frames))\n",
+    "    else:\n",
+    "        _, vmax = _scale_to_uint8(np.maximum(np.abs(frames), 0))\n",
+    "    tile_w, tile_h = w * scale, h * scale\n",
+    "    sheet_mode = \"RGB\" if mode == \"signed_rgb\" else \"L\"\n",
+    "    bg = (0, 0, 0) if sheet_mode == \"RGB\" else 0\n",
+    "    sheet = Image.new(sheet_mode, (cols * tile_w + (cols + 1) * pad, rows * tile_h + (rows + 1) * pad), bg)\n",
+    "    draw = ImageDraw.Draw(sheet)\n",
+    "    for i, frame in enumerate(frames):\n",
+    "        row, col = divmod(i, cols)\n",
+    "        image, _ = render_frame(frame, mode=mode, vmax=vmax)\n",
+    "        if flip_x:\n",
+    "            image = image.transpose(Image.Transpose.FLIP_LEFT_RIGHT)\n",
+    "        image = image.resize((tile_w, tile_h), Image.Resampling.NEAREST)\n",
+    "        x = pad + col * (tile_w + pad)\n",
+    "        y = pad + row * (tile_h + pad)\n",
+    "        sheet.paste(image, (x, y))\n",
+    "        if labels:\n",
+    "            text = labels[i]\n",
+    "            bbox = draw.textbbox((0, 0), text)\n",
+    "            label_w = min(tile_w, max(40, bbox[2] - bbox[0] + 6))\n",
+    "            label_h = min(tile_h, max(15, bbox[3] - bbox[1] + 5))\n",
+    "            if sheet_mode == \"RGB\":\n",
+    "                draw.rectangle((x, y, x + label_w, y + label_h), fill=(0, 0, 0))\n",
+    "                draw.text((x + 2, y + 2), text, fill=(255, 255, 255))\n",
+    "            else:\n",
+    "                draw.rectangle((x, y, x + label_w, y + label_h), fill=0)\n",
+    "                draw.text((x + 2, y + 2), text, fill=255)\n",
+    "    return sheet\n",
+    "\n",
+    "ordered_accumulated = accumulated[trigger_order]\n",
+    "trigger_labels = []\n",
+    "t0 = int(ordered_trigger_ts[0]) if len(ordered_trigger_ts) else 0\n",
+    "for rank, original_index in enumerate(trigger_order, start=1):\n",
+    "    delta_ms = (int(trigger_ts[original_index]) - t0) / 1000.0\n",
+    "    trigger_labels.append(f\"T{rank:02d} +{delta_ms:.3f}ms\")\n",
+    "\n",
+    "print(\"Timestamp order from triggers.csv:\")\n",
+    "for row_start in range(0, len(trigger_labels), cycle_len):\n",
+    "    print(\"  \" + \" | \".join(trigger_labels[row_start:row_start + cycle_len]))\n",
+    "\n",
+    "orientation_suffix = _orientation_suffix(DISPLAY_FLIP_X)\n",
+    "tone_suffix = f\"_{DISPLAY_TONE_CURVE}_{_scale_label()}\"\n",
+    "for flip_x, suffix in _orientation_variants():\n",
+    "    for mode in [\"positive\", \"absolute\", \"signed_rgb\"]:\n",
+    "        sheet = make_contact(ordered_accumulated, mode=mode, labels=trigger_labels, scale=2, flip_x=flip_x)\n",
+    "        path = OUT_DIR / f\"timestamp_ordered_{mode}_black_background{tone_suffix}{suffix}.png\"\n",
+    "        sheet.save(path)\n",
+    "        print(path)\n",
+    "        if display is not None and flip_x == DISPLAY_FLIP_X:\n",
+    "            display(sheet)\n"
+   ],
+   "id": "be1a62486179197e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Timestamp order from triggers.csv:\n",
+      "  T01 +0.000ms | T02 +1.750ms | T03 +3.500ms | T04 +5.250ms | T05 +7.000ms\n",
+      "  T06 +8.750ms | T07 +10.500ms | T08 +12.221ms | T09 +13.971ms | T10 +15.721ms\n",
+      "  T11 +17.471ms | T12 +19.221ms | T13 +20.971ms | T14 +22.721ms | T15 +24.472ms\n",
+      "  T16 +26.222ms | T17 +27.972ms | T18 +29.728ms | T19 +31.478ms | T20 +33.228ms\n",
+      "  T21 +34.978ms | T22 +36.728ms | T23 +38.478ms | T24 +40.228ms | T25 +41.978ms\n",
+      "  T26 +43.728ms | T27 +45.450ms | T28 +47.200ms | T29 +48.950ms | T30 +50.700ms\n",
+      "  T31 +52.450ms | T32 +54.200ms | T33 +55.950ms | T34 +57.700ms | T35 +59.450ms\n",
+      "  T36 +61.200ms | T37 +62.956ms | T38 +64.706ms | T39 +66.456ms | T40 +68.207ms\n",
+      "  T41 +78.678ms | T42 +80.428ms | T43 +82.178ms | T44 +83.928ms | T45 +85.678ms\n",
+      "  T46 +87.428ms | T47 +89.178ms | T48 +90.928ms | T49 +92.678ms | T50 +94.428ms\n",
+      "C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123-sync-check\\analysis_outputs\\timestamp_ordered_positive_black_background_gamma.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<PIL.Image.Image image mode=L size=3260x4910>"
+      ],
+      "image/png": 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",
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123-sync-check\\analysis_outputs\\timestamp_ordered_absolute_black_background_gamma.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<PIL.Image.Image image mode=L size=3260x4910>"
+      ],
+      "image/png": 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",
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123-sync-check\\analysis_outputs\\timestamp_ordered_signed_rgb_black_background_gamma.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=3260x4910>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Event depth check\n",
+    "\n",
+    "This shows whether tone curves have enough count depth to work with. If percentile scaling reports `vmax=1`, every nonzero pixel will render white regardless of the selected curve.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "magnitudes = np.abs(ordered_accumulated.astype(np.float32))\n",
+    "nonzero = magnitudes[magnitudes > 0]\n",
+    "print(f\"nonzero pixels: {nonzero.size} / {magnitudes.size} ({(nonzero.size / magnitudes.size * 100) if magnitudes.size else 0:.4f}%)\")\n",
+    "if nonzero.size:\n",
+    "    values, counts = np.unique(nonzero, return_counts=True)\n",
+    "    print(\"nonzero magnitude histogram:\")\n",
+    "    for value, count in zip(values, counts):\n",
+    "        print(f\"  {value:g}: {count} ({count / nonzero.size * 100:.3f}%)\")\n",
+    "    for percentile in [99.0, 99.5, 100.0]:\n",
+    "        vmax = float(np.percentile(nonzero, percentile))\n",
+    "        print(f\"vmax at p{percentile:g}: {vmax:g}\")\n",
+    "    vmax = float(nonzero.max())\n",
+    "    print(\"tone mapping with max-scale:\")\n",
+    "    for value in range(1, int(math.ceil(vmax)) + 1):\n",
+    "        linear = round(value / vmax * 255)\n",
+    "        log = round(np.log1p(value) / np.log1p(vmax) * 255)\n",
+    "        gamma = round((value / vmax) ** DISPLAY_GAMMA * 255)\n",
+    "        print(f\"  count {value}: linear={linear}, log={log}, gamma={gamma}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeat-summed slot views\n",
+    "\n",
+    "These sum timestamp-ordered frames by trigger position: slot 1 uses frames `1,6,11,...`, slot 2 uses `2,7,12,...`, and so on. This should make the ten repeats per expected number much easier to inspect without changing the raw data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _expected_number_for_slot(slot):\n",
+    "    return number_sequence[slot] if slot < len(number_sequence) else slot + 1\n",
+    "\n",
+    "def sum_by_slot(frames, cycle_len):\n",
+    "    slot_frames = []\n",
+    "    labels = []\n",
+    "    for slot in range(cycle_len):\n",
+    "        indices = list(range(slot, len(frames), cycle_len))\n",
+    "        if not indices:\n",
+    "            continue\n",
+    "        selected = frames[indices]\n",
+    "        summed = selected.sum(axis=0)\n",
+    "        slot_frames.append(summed)\n",
+    "        labels.append(f\"slot {slot + 1} exp {_expected_number_for_slot(slot)} n={len(indices)}\")\n",
+    "        positive_sums = [float(np.maximum(frame, 0).sum()) for frame in selected]\n",
+    "        print(\n",
+    "            f\"slot {slot + 1} expected {_expected_number_for_slot(slot)}: \"\n",
+    "            f\"trigger_ranks={[i + 1 for i in indices]}, \"\n",
+    "            f\"positive_total={sum(positive_sums):.0f}, \"\n",
+    "            f\"per_trigger_min_mean_max={min(positive_sums):.0f}/{statistics.mean(positive_sums):.1f}/{max(positive_sums):.0f}\"\n",
+    "        )\n",
+    "    return np.stack(slot_frames), labels\n",
+    "\n",
+    "slot_sums, slot_sum_labels = sum_by_slot(ordered_accumulated, cycle_len)\n",
+    "slot_mag = np.abs(slot_sums.astype(np.float32))\n",
+    "slot_nonzero = slot_mag[slot_mag > 0]\n",
+    "if slot_nonzero.size:\n",
+    "    print(f\"slot-summed nonzero max: {float(slot_nonzero.max()):g}\")\n",
+    "    print(f\"slot-summed p99.5: {float(np.percentile(slot_nonzero, 99.5)):g}\")\n",
+    "\n",
+    "for flip_x, suffix in _orientation_variants():\n",
+    "    for mode in [\"positive\", \"absolute\", \"signed_rgb\"]:\n",
+    "        sheet = make_contact(slot_sums, mode=mode, cols=len(slot_sums), labels=slot_sum_labels, scale=3, flip_x=flip_x)\n",
+    "        path = OUT_DIR / f\"timestamp_ordered_slot_sums_{mode}_black_background{tone_suffix}{suffix}.png\"\n",
+    "        sheet.save(path)\n",
+    "        print(path)\n",
+    "        if display is not None and flip_x == DISPLAY_FLIP_X:\n",
+    "            display(sheet)\n",
+    "\n",
+    "cycle_sums = []\n",
+    "cycle_labels = []\n",
+    "for start in range(0, len(ordered_accumulated), cycle_len):\n",
+    "    selected = ordered_accumulated[start:start + cycle_len]\n",
+    "    if len(selected) != cycle_len:\n",
+    "        continue\n",
+    "    cycle_sums.append(selected.sum(axis=0))\n",
+    "    cycle_labels.append(f\"cycle {len(cycle_labels) + 1}\")\n",
+    "\n",
+    "if cycle_sums:\n",
+    "    cycle_sums = np.stack(cycle_sums)\n",
+    "    for flip_x, suffix in _orientation_variants():\n",
+    "        sheet = make_contact(cycle_sums, mode=\"positive\", cols=min(5, len(cycle_sums)), labels=cycle_labels, scale=2, flip_x=flip_x)\n",
+    "        path = OUT_DIR / f\"timestamp_ordered_cycle_sums_positive_black_background{tone_suffix}{suffix}.png\"\n",
+    "        sheet.save(path)\n",
+    "        print(path)\n",
+    "        if display is not None and flip_x == DISPLAY_FLIP_X:\n",
+    "            display(sheet)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Slot consistency check\n",
+    "\n",
+    "If the selected frames are ordered correctly, all slot-1 images should look more like each other than like slot-2, slot-3, etc. This is a quick numerical check for jumbling."
+   ],
+   "id": "3b305f74095b6350"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:05.268633Z",
+     "start_time": "2026-06-04T21:26:05.120168300Z"
+    }
+   },
+   "source": [
+    "features = np.maximum(ordered_accumulated, 0).reshape(ordered_accumulated.shape[0], -1)\n",
+    "norm = np.linalg.norm(features, axis=1, keepdims=True)\n",
+    "features_norm = np.divide(features, norm, out=np.zeros_like(features), where=norm > 0)\n",
+    "corr = features_norm @ features_norm.T\n",
+    "\n",
+    "same_slot = []\n",
+    "different_slot = []\n",
+    "for i in range(len(corr)):\n",
+    "    for j in range(i + 1, len(corr)):\n",
+    "        if i % cycle_len == j % cycle_len:\n",
+    "            same_slot.append(float(corr[i, j]))\n",
+    "        else:\n",
+    "            different_slot.append(float(corr[i, j]))\n",
+    "\n",
+    "print(f\"mean same-slot correlation:      {statistics.mean(same_slot):.4f}\")\n",
+    "print(f\"mean different-slot correlation: {statistics.mean(different_slot):.4f}\")\n",
+    "\n",
+    "for slot in range(cycle_len):\n",
+    "    indices = list(range(slot, ordered_accumulated.shape[0], cycle_len))\n",
+    "    sums = [float(np.maximum(ordered_accumulated[i], 0).sum()) for i in indices]\n",
+    "    print(f\"slot {slot + 1} number {number_sequence[slot] if slot < len(number_sequence) else slot + 1}: frames={[i + 1 for i in indices]}, positive_sums={[round(v, 1) for v in sums]}\")\n",
+    "\n",
+    "if plt is not None:\n",
+    "    plt.figure(figsize=(7, 6))\n",
+    "    plt.imshow(corr, vmin=0, vmax=1, cmap=\"viridis\")\n",
+    "    plt.colorbar(label=\"cosine similarity\")\n",
+    "    plt.title(\"Positive-event frame similarity\")\n",
+    "    plt.xlabel(\"frame index\")\n",
+    "    plt.ylabel(\"frame index\")\n",
+    "    for boundary in range(cycle_len, ordered_accumulated.shape[0], cycle_len):\n",
+    "        plt.axhline(boundary - 0.5, color=\"white\", linewidth=0.5)\n",
+    "        plt.axvline(boundary - 0.5, color=\"white\", linewidth=0.5)\n",
+    "    plt.show()"
+   ],
+   "id": "1d30f06351ed1da5",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean same-slot correlation:      0.0473\n",
+      "mean different-slot correlation: 0.0323\n",
+      "slot 1 number 1: frames=[1, 6, 11, 16, 21, 26, 31, 36, 41, 46], positive_sums=[402.0, 446.0, 408.0, 451.0, 449.0, 386.0, 428.0, 420.0, 438.0, 400.0]\n",
+      "slot 2 number 2: frames=[2, 7, 12, 17, 22, 27, 32, 37, 42, 47], positive_sums=[379.0, 416.0, 487.0, 473.0, 392.0, 503.0, 434.0, 378.0, 489.0, 393.0]\n",
+      "slot 3 number 3: frames=[3, 8, 13, 18, 23, 28, 33, 38, 43, 48], positive_sums=[375.0, 459.0, 405.0, 383.0, 410.0, 382.0, 372.0, 377.0, 333.0, 339.0]\n",
+      "slot 4 number 4: frames=[4, 9, 14, 19, 24, 29, 34, 39, 44, 49], positive_sums=[449.0, 474.0, 425.0, 420.0, 469.0, 426.0, 418.0, 462.0, 389.0, 426.0]\n",
+      "slot 5 number 5: frames=[5, 10, 15, 20, 25, 30, 35, 40, 45, 50], positive_sums=[336.0, 265.0, 289.0, 306.0, 273.0, 325.0, 381.0, 313.0, 316.0, 393.0]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Re-accumulate selected triggers with timing offsets\n",
+    "\n",
+    "This uses `filtered_events.npz` plus the selected `triggers.csv` timestamps. It cannot recover cycles that were not selected by `strongest`, but it can test whether each selected window should start earlier or later than the trigger timestamp."
+   ],
+   "id": "ec93fe36a558116c"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:05.560848800Z",
+     "start_time": "2026-06-04T21:26:05.271583200Z"
+    }
+   },
+   "source": [
+    "events_t = events_npz[\"t\"].astype(np.int64)\n",
+    "events_x = events_npz[\"x\"].astype(np.int64)\n",
+    "events_y = events_npz[\"y\"].astype(np.int64)\n",
+    "events_p = events_npz[\"p\"].astype(bool)\n",
+    "\n",
+    "def accumulate_selected(offset_us=0, window_us=window_us, polarity_mode=\"positive\"):\n",
+    "    width, height = int(resolution[0]), int(resolution[1])\n",
+    "    frames = np.zeros((len(trigger_ts), height, width), dtype=np.float32)\n",
+    "    if polarity_mode == \"positive\":\n",
+    "        values = events_p.astype(np.float32)\n",
+    "        valid_polarity = events_p\n",
+    "    elif polarity_mode == \"signed\":\n",
+    "        values = np.where(events_p, 1.0, -1.0).astype(np.float32)\n",
+    "        valid_polarity = np.ones_like(events_p, dtype=bool)\n",
+    "    elif polarity_mode == \"ignore\":\n",
+    "        values = np.ones_like(events_p, dtype=np.float32)\n",
+    "        valid_polarity = np.ones_like(events_p, dtype=bool)\n",
+    "    else:\n",
+    "        raise ValueError(polarity_mode)\n",
+    "    valid_xy = (events_x >= 0) & (events_x < width) & (events_y >= 0) & (events_y < height) & valid_polarity\n",
+    "    t = events_t[valid_xy]\n",
+    "    x = events_x[valid_xy]\n",
+    "    y = events_y[valid_xy]\n",
+    "    v = values[valid_xy]\n",
+    "    for i, trigger_time in enumerate(trigger_ts):\n",
+    "        start = int(trigger_time) + int(offset_us)\n",
+    "        end = start + int(window_us)\n",
+    "        left = np.searchsorted(t, start, side=\"left\")\n",
+    "        right = np.searchsorted(t, end, side=\"left\")\n",
+    "        if left < right:\n",
+    "            np.add.at(frames[i], (y[left:right], x[left:right]), v[left:right])\n",
+    "    return frames\n",
+    "\n",
+    "offsets = [-1000, -500, -250, 0, 250, 500, 1000]\n",
+    "print(f\"window_us={window_us}\")\n",
+    "for offset in offsets:\n",
+    "    frames = accumulate_selected(offset_us=offset, polarity_mode=\"positive\")\n",
+    "    counts = [int(np.maximum(frame, 0).sum()) for frame in frames]\n",
+    "    grouped = [counts[i:i + cycle_len] for i in range(0, len(counts), cycle_len)]\n",
+    "    total = sum(counts)\n",
+    "    per_frame_nonzero = int(sum(np.count_nonzero(frame) for frame in frames))\n",
+    "    print(f\"offset {offset:+5d} us: total_positive_events={total:6d}, nonzero_pixels={per_frame_nonzero:6d}, first3_groups={grouped[:3]}\")"
+   ],
+   "id": "50283fec3369fbe2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "window_us=1500\n",
+      "offset -1000 us: total_positive_events= 20133, nonzero_pixels= 20068, first3_groups=[[312, 409, 394, 403, 477], [345, 448, 459, 446, 441], [270, 470, 472, 363, 406]]\n",
+      "offset  -500 us: total_positive_events= 19633, nonzero_pixels= 19577, first3_groups=[[326, 352, 433, 445, 374], [368, 498, 394, 378, 392], [347, 422, 362, 445, 385]]\n",
+      "offset  -250 us: total_positive_events= 19487, nonzero_pixels= 19429, first3_groups=[[387, 345, 382, 455, 364], [368, 442, 448, 403, 305], [392, 471, 354, 407, 369]]\n",
+      "offset    +0 us: total_positive_events= 20081, nonzero_pixels= 20020, first3_groups=[[404, 381, 379, 453, 339], [448, 417, 460, 476, 267], [409, 491, 408, 428, 292]]\n",
+      "offset  +250 us: total_positive_events= 20914, nonzero_pixels= 20845, first3_groups=[[431, 418, 432, 486, 317], [505, 447, 434, 514, 293], [433, 473, 425, 505, 290]]\n",
+      "offset  +500 us: total_positive_events= 20418, nonzero_pixels= 20358, first3_groups=[[408, 394, 427, 487, 303], [467, 460, 414, 455, 292], [460, 428, 367, 495, 312]]\n",
+      "offset +1000 us: total_positive_events= 19966, nonzero_pixels= 19900, first3_groups=[[404, 425, 396, 400, 366], [458, 391, 427, 428, 283], [423, 441, 438, 365, 294]]\n"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-04T21:26:06.054094800Z",
+     "start_time": "2026-06-04T21:26:05.563818300Z"
+    }
+   },
+   "source": [
+    "CHOSEN_OFFSET_US = 0\n",
+    "frames = accumulate_selected(offset_us=CHOSEN_OFFSET_US, polarity_mode=\"positive\")\n",
+    "for flip_x, suffix in _orientation_variants():\n",
+    "    sheet = make_contact(frames[trigger_order], mode=\"positive\", labels=trigger_labels, scale=2, flip_x=flip_x)\n",
+    "    path = OUT_DIR / f\"timestamp_ordered_reaccumulated_positive_offset_{CHOSEN_OFFSET_US:+d}us{tone_suffix}{suffix}.png\"\n",
+    "    sheet.save(path)\n",
+    "    print(path)\n",
+    "    if display is not None and flip_x == DISPLAY_FLIP_X:\n",
+    "        display(sheet)\n"
+   ],
+   "id": "4c572e6c6ac1f7ab",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123-sync-check\\analysis_outputs\\timestamp_ordered_reaccumulated_positive_offset_+0us_gamma.png\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<PIL.Image.Image image mode=L size=3260x4910>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpretation for this run\n",
+    "\n",
+    "- New runs select the first complete cycles after pre-capture trigger flushing. If an older run used legacy strongest-cycle selection, use this notebook's trigger-gap table before assuming the rows are adjacent physical cycles.\n",
+    "- The saved PNG gray/white background is a rendering artifact from signed min/max normalization. Trust `accumulated.npy` and the black-background outputs in `analysis_outputs` for visual inspection.\n",
+    "- For a consecutive `1,2,3,4,5` x10 capture after optics are stable, rerun with `--accumulation-cycles 10`."
+   ],
+   "id": "50964e224793b082"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/05_accumulation_start_offset_slider.ipynb b/notebooks/05_accumulation_start_offset_slider.ipynb
new file mode 100644
index 0000000..95622c9
--- /dev/null
+++ b/notebooks/05_accumulation_start_offset_slider.ipynb
@@ -0,0 +1,474 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accumulation Start Offset Slider\n",
+    "\n",
+    "Use this notebook to re-accumulate the saved AEDAT4 recording from `runs/camera/laser_selected_cycle_40px_dot12-sync-check` with different `accumulation_start_offset_us` values. It keeps trigger order intact: with five numbers and ten cycles, the grid is 10 rows by 5 columns, not repeat-summed by slot."
+   ],
+   "id": "23486e99b76efdf5"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T02:40:01.125012900Z",
+     "start_time": "2026-06-05T02:40:00.322775700Z"
+    }
+   },
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "from functools import lru_cache\n",
+    "from math import ceil\n",
+    "from pathlib import Path\n",
+    "import json\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from IPython.display import clear_output, display\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    for candidate in [start, *start.parents]:\n",
+    "        if (candidate / \"dmdcontrol\").is_dir() and (candidate / \"runs\").is_dir():\n",
+    "            return candidate\n",
+    "    raise RuntimeError(\"Could not find repository root from the current notebook directory.\")\n",
+    "\n",
+    "\n",
+    "REPO_ROOT = find_repo_root(Path.cwd().resolve())\n",
+    "if str(REPO_ROOT) not in sys.path:\n",
+    "    sys.path.insert(0, str(REPO_ROOT))\n",
+    "\n",
+    "RUN_DIR = REPO_ROOT / \"runs\" / \"camera\" / \"laser_selected_cycle_40px_dot12-sync-check\"\n",
+    "AEDAT4_PATH = RUN_DIR / \"raw.aedat4\"\n",
+    "METADATA_PATH = RUN_DIR / \"metadata.json\"\n",
+    "SUMMARY_PATH = RUN_DIR / \"summary.json\"\n",
+    "\n",
+    "print(f\"repo root: {REPO_ROOT}\")\n",
+    "print(f\"run dir:   {RUN_DIR}\")\n",
+    "print(f\"aedat4:    {AEDAT4_PATH.exists()} {AEDAT4_PATH}\")"
+   ],
+   "id": "d074efa2db74b595",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "repo root: C:\\dev\\dmdcontrol\n",
+      "run dir:   C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot12-sync-check\n",
+      "aedat4:    True C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot12-sync-check\\raw.aedat4\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T02:40:01.567891500Z",
+     "start_time": "2026-06-05T02:40:01.286843700Z"
+    }
+   },
+   "source": [
+    "from dmdcontrol.camera.accumulation import accumulate_events_for_triggers\n",
+    "from dmdcontrol.camera.reprocess_aedat4 import read_aedat4_recording\n",
+    "from dmdcontrol.camera.runs import _events_to_arrays, _process_accumulation_triggers, _trigger_timestamps\n",
+    "\n",
+    "\n",
+    "def load_json(path: Path) -> dict:\n",
+    "    if not path.exists():\n",
+    "        return {}\n",
+    "    return json.loads(path.read_text(encoding=\"utf-8\"))\n",
+    "\n",
+    "\n",
+    "metadata = load_json(METADATA_PATH)\n",
+    "summary = load_json(SUMMARY_PATH)\n",
+    "number_sequence = list(metadata.get(\"number_sequence\") or [])\n",
+    "cycle_length = len(number_sequence) or int(metadata.get(\"expected_trigger_count\") or 5)\n",
+    "default_cycles = int(metadata.get(\"accumulation_cycles\") or 1)\n",
+    "default_window_us = int(\n",
+    "    metadata.get(\"accumulation_window_us\")\n",
+    "    or metadata.get(\"numbers_exposure_us\")\n",
+    "    or summary.get(\"window_us\")\n",
+    "    or 1500\n",
+    ")\n",
+    "default_polarity_mode = str(metadata.get(\"polarity_mode\") or summary.get(\"polarity_mode\") or \"signed\")\n",
+    "default_offset_us = int(metadata.get(\"accumulation_start_offset_us\") or summary.get(\"window_start_offset_us\") or -250)\n",
+    "\n",
+    "print(\"loading AEDAT4 once; slider redraws reuse this in-memory recording\")\n",
+    "recording = read_aedat4_recording(AEDAT4_PATH)\n",
+    "event_arrays = _events_to_arrays(recording.events)\n",
+    "event_timestamps = event_arrays[\"t\"]\n",
+    "width, height = recording.resolution\n",
+    "\n",
+    "print(f\"resolution: {width} x {height}\")\n",
+    "print(f\"event batches: {len(recording.events):,}; events: {recording.stats['event_count']:,}\")\n",
+    "print(f\"triggers: {recording.stats['trigger_count']:,}; trigger edges: {recording.stats['trigger_edges']}\")\n",
+    "print(f\"cycle length: {cycle_length}; default cycles: {default_cycles}; default window: {default_window_us} us\")"
+   ],
+   "id": "ab66521b7db65ee8",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading AEDAT4 once; slider redraws reuse this in-memory recording\n",
+      "resolution: 320 x 240\n",
+      "event batches: 756; events: 528,257\n",
+      "triggers: 5,676; trigger edges: {'rising': 2838, 'triggertype.external_signal_pulse': 2838}\n",
+      "cycle length: 5; default cycles: 10; default window: 1500 us\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T02:40:01.639959600Z",
+     "start_time": "2026-06-05T02:40:01.594461Z"
+    }
+   },
+   "source": [
+    "def _stages_for(offset_us: int, cycles: int, window_us: int):\n",
+    "    return _process_accumulation_triggers(\n",
+    "        recording.triggers,\n",
+    "        event_timestamps,\n",
+    "        window_us=int(window_us),\n",
+    "        window_start_offset_us=int(offset_us),\n",
+    "        max_accumulation_triggers=None,\n",
+    "        trigger_cycle_length=cycle_length,\n",
+    "        accumulation_cycles=int(cycles),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "@lru_cache(maxsize=16)\n",
+    "def accumulate_for_offset(offset_us: int, cycles: int, window_us: int, polarity_mode: str):\n",
+    "    stages = _stages_for(offset_us, cycles, window_us)\n",
+    "    frames = accumulate_events_for_triggers(\n",
+    "        recording.events,\n",
+    "        stages.final,\n",
+    "        resolution=recording.resolution,\n",
+    "        window_us=int(window_us),\n",
+    "        polarity_mode=polarity_mode,\n",
+    "        window_start_offset_us=int(offset_us),\n",
+    "    )\n",
+    "    trigger_ts = _trigger_timestamps(stages.final)\n",
+    "    return frames, trigger_ts, stages.alignment_metadata, stages.cycle_limit_metadata\n",
+    "\n",
+    "\n",
+    "def display_values(frames: np.ndarray, polarity_view: str, tone_curve: str, gamma: float) -> tuple[np.ndarray, str]:\n",
+    "    frames = frames.astype(np.float32, copy=False)\n",
+    "    if polarity_view == \"signed\":\n",
+    "        return frames, \"signed\"\n",
+    "    if polarity_view == \"positive\":\n",
+    "        values = np.maximum(frames, 0)\n",
+    "    elif polarity_view == \"negative\":\n",
+    "        values = np.maximum(-frames, 0)\n",
+    "    else:\n",
+    "        values = np.abs(frames)\n",
+    "\n",
+    "    if tone_curve == \"log\":\n",
+    "        values = np.log1p(values)\n",
+    "    elif tone_curve == \"gamma\":\n",
+    "        values = np.power(values, float(gamma))\n",
+    "    return values, \"gray\"\n",
+    "\n",
+    "\n",
+    "def robust_limit(values: np.ndarray, percentile: float, signed: bool) -> float:\n",
+    "    source = np.abs(values) if signed else values\n",
+    "    nonzero = source[source > 0]\n",
+    "    if nonzero.size == 0:\n",
+    "        return 1.0\n",
+    "    return max(float(np.percentile(nonzero, percentile)), 1e-6)\n",
+    "\n",
+    "\n",
+    "def render_offset(\n",
+    "    offset_us: int = default_offset_us,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    polarity_mode: str = default_polarity_mode,\n",
+    "    polarity_view: str = \"magnitude\",\n",
+    "    tone_curve: str = \"log\",\n",
+    "    gamma: float = 0.5,\n",
+    "    vmax_percentile: float = 99.5,\n",
+    "    flip_x: bool = True,\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_for_offset(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    if flip_x:\n",
+    "        frames = frames[:, :, ::-1]\n",
+    "\n",
+    "    values, color_kind = display_values(frames, polarity_view, tone_curve, gamma)\n",
+    "    signed = color_kind == \"signed\"\n",
+    "    limit = robust_limit(values, vmax_percentile, signed=signed)\n",
+    "    cmap = \"coolwarm\" if signed else \"gray\"\n",
+    "    vmin, vmax = (-limit, limit) if signed else (0, limit)\n",
+    "\n",
+    "    frame_count = values.shape[0]\n",
+    "    cols = max(1, int(cycle_length))\n",
+    "    rows = max(1, ceil(frame_count / cols))\n",
+    "    fig, axes = plt.subplots(rows, cols, figsize=(2.35 * cols, 2.35 * rows), squeeze=False)\n",
+    "\n",
+    "    first_trigger = int(trigger_ts[0]) if len(trigger_ts) else 0\n",
+    "    counts = np.count_nonzero(np.abs(frames) > 0, axis=(1, 2)) if frame_count else np.array([])\n",
+    "\n",
+    "    for index, ax in enumerate(axes.ravel()):\n",
+    "        ax.axis(\"off\")\n",
+    "        if index >= frame_count:\n",
+    "            continue\n",
+    "        ax.imshow(values[index], cmap=cmap, vmin=vmin, vmax=vmax, interpolation=\"nearest\")\n",
+    "        slot = index % cols\n",
+    "        cycle = index // cols + 1\n",
+    "        label = number_sequence[slot] if slot < len(number_sequence) else slot + 1\n",
+    "        dt = int(trigger_ts[index] - first_trigger) if len(trigger_ts) else 0\n",
+    "        ax.set_title(f\"{index + 1}: {label}  c{cycle}\\n+{dt} us\", fontsize=8)\n",
+    "\n",
+    "    plt.suptitle(\n",
+    "        f\"offset {int(offset_us)} us | window {int(window_us)} us | \"\n",
+    "        f\"{frame_count} ordered frames | view={polarity_view}/{tone_curve}\",\n",
+    "        y=1.0,\n",
+    "        fontsize=12,\n",
+    "    )\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "    print(\n",
+    "        \"raw rising:\", alignment.get(\"input_trigger_count\"),\n",
+    "        \"aligned:\", alignment.get(\"aligned_trigger_count\"),\n",
+    "        \"selected:\", cycle_info.get(\"selected_trigger_count\"),\n",
+    "        \"cycles:\", cycle_info.get(\"selected_cycle_indices\"),\n",
+    "    )\n",
+    "    if counts.size:\n",
+    "        print(\"nonzero pixels per frame: min\", int(counts.min()), \"median\", int(np.median(counts)), \"max\", int(counts.max()))\n",
+    "    return frames"
+   ],
+   "id": "fd8801eda5e0c9c",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive Offset Sweep\n",
+    "\n",
+    "Move the offset slider to shift each accumulation window relative to its trigger. Negative values start before the trigger. The redraw keeps frames in trigger order, so frame 1 through frame 50 should remain the actual acquisition sequence."
+   ],
+   "id": "6d7ddcabd91ace21"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T02:40:04.387773500Z",
+     "start_time": "2026-06-05T02:40:01.642950200Z"
+    }
+   },
+   "source": [
+    "try:\n",
+    "    import ipywidgets as widgets\n",
+    "    HAVE_WIDGETS = True\n",
+    "except ModuleNotFoundError:\n",
+    "    HAVE_WIDGETS = False\n",
+    "    print(\"ipywidgets is not installed in this kernel. For the slider, run `%pip install ipywidgets`, restart the kernel, then rerun this notebook.\")\n",
+    "\n",
+    "\n",
+    "if HAVE_WIDGETS:\n",
+    "    available_full_cycles = max(1, recording.stats[\"trigger_edges\"].get(\"rising\", 0) // max(1, cycle_length))\n",
+    "    offset_slider = widgets.IntSlider(\n",
+    "        value=default_offset_us,\n",
+    "        min=-1500,\n",
+    "        max=750,\n",
+    "        step=25,\n",
+    "        description=\"offset us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"520px\"),\n",
+    "    )\n",
+    "    cycles_slider = widgets.IntSlider(\n",
+    "        value=min(default_cycles, available_full_cycles),\n",
+    "        min=1,\n",
+    "        max=max(1, min(25, available_full_cycles)),\n",
+    "        step=1,\n",
+    "        description=\"cycles\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"520px\"),\n",
+    "    )\n",
+    "    window_slider = widgets.IntSlider(\n",
+    "        value=default_window_us,\n",
+    "        min=100,\n",
+    "        max=max(default_window_us * 2, 2000),\n",
+    "        step=25,\n",
+    "        description=\"window us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"520px\"),\n",
+    "    )\n",
+    "    polarity_mode_dropdown = widgets.Dropdown(\n",
+    "        value=default_polarity_mode if default_polarity_mode in {\"positive\", \"signed\", \"ignore\"} else \"signed\",\n",
+    "        options=[\"signed\", \"positive\", \"ignore\"],\n",
+    "        description=\"accumulate\",\n",
+    "    )\n",
+    "    polarity_view_dropdown = widgets.Dropdown(\n",
+    "        value=\"magnitude\",\n",
+    "        options=[\"magnitude\", \"positive\", \"negative\", \"signed\"],\n",
+    "        description=\"view\",\n",
+    "    )\n",
+    "    tone_dropdown = widgets.Dropdown(value=\"log\", options=[\"log\", \"gamma\", \"linear\"], description=\"tone\")\n",
+    "    gamma_slider = widgets.FloatSlider(\n",
+    "        value=0.5,\n",
+    "        min=0.2,\n",
+    "        max=1.5,\n",
+    "        step=0.05,\n",
+    "        description=\"gamma\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"520px\"),\n",
+    "    )\n",
+    "    percentile_slider = widgets.FloatSlider(\n",
+    "        value=99.5,\n",
+    "        min=90.0,\n",
+    "        max=100.0,\n",
+    "        step=0.1,\n",
+    "        description=\"vmax %\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"520px\"),\n",
+    "    )\n",
+    "    flip_checkbox = widgets.Checkbox(value=True, description=\"flip x for viewing\")\n",
+    "    out = widgets.Output()\n",
+    "\n",
+    "    def redraw(_=None):\n",
+    "        with out:\n",
+    "            clear_output(wait=True)\n",
+    "            render_offset(\n",
+    "                offset_us=offset_slider.value,\n",
+    "                cycles=cycles_slider.value,\n",
+    "                window_us=window_slider.value,\n",
+    "                polarity_mode=polarity_mode_dropdown.value,\n",
+    "                polarity_view=polarity_view_dropdown.value,\n",
+    "                tone_curve=tone_dropdown.value,\n",
+    "                gamma=gamma_slider.value,\n",
+    "                vmax_percentile=percentile_slider.value,\n",
+    "                flip_x=flip_checkbox.value,\n",
+    "            )\n",
+    "\n",
+    "    for widget in [\n",
+    "        offset_slider,\n",
+    "        cycles_slider,\n",
+    "        window_slider,\n",
+    "        polarity_mode_dropdown,\n",
+    "        polarity_view_dropdown,\n",
+    "        tone_dropdown,\n",
+    "        gamma_slider,\n",
+    "        percentile_slider,\n",
+    "        flip_checkbox,\n",
+    "    ]:\n",
+    "        widget.observe(redraw, names=\"value\")\n",
+    "\n",
+    "    display(widgets.VBox([\n",
+    "        offset_slider,\n",
+    "        cycles_slider,\n",
+    "        window_slider,\n",
+    "        widgets.HBox([polarity_mode_dropdown, polarity_view_dropdown, tone_dropdown]),\n",
+    "        gamma_slider,\n",
+    "        percentile_slider,\n",
+    "        flip_checkbox,\n",
+    "    ]))\n",
+    "    display(out)\n",
+    "    redraw()\n",
+    "else:\n",
+    "    MANUAL_OFFSET_US = default_offset_us\n",
+    "    frames = render_offset(offset_us=MANUAL_OFFSET_US, cycles=default_cycles, window_us=default_window_us)"
+   ],
+   "id": "ccb0de887e035e68",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VBox(children=(IntSlider(value=-250, continuous_update=False, description='offset us', layout=Layout(width='52…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "9198c2713e9d4bc88a22d3259fe0985f"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6ce41698cc874054bee9e72ed73636b3"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Manual Calls\n",
+    "\n",
+    "If you want to compare exact offsets without the widget, run calls like these. Each call reuses the already loaded AEDAT4 object."
+   ],
+   "id": "f371545e942ab5d6"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T02:40:15.880861100Z",
+     "start_time": "2026-06-05T02:40:06.403185300Z"
+    }
+   },
+   "source": [
+    "# Example manual checks:\n",
+    "# frames_m250 = render_offset(offset_us=-250, cycles=10, window_us=1500)\n",
+    "# frames_0 = render_offset(offset_us=0, cycles=10, window_us=1500)\n",
+    "# frames_p250 = render_offset(offset_us=250, cycles=10, window_us=1500)\n",
+    "\n",
+    "# The returned array is shaped: (ordered_trigger_frames, height, width).\n",
+    "# Nothing is written back to the run directory unless you explicitly add a save call."
+   ],
+   "id": "24213e39950cf838",
+   "outputs": [],
+   "execution_count": 5
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/06_fast_accumulation_offset_explorer.ipynb b/notebooks/06_fast_accumulation_offset_explorer.ipynb
new file mode 100644
index 0000000..affd7ae
--- /dev/null
+++ b/notebooks/06_fast_accumulation_offset_explorer.ipynb
@@ -0,0 +1,1319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fast Accumulation Offset Explorer\n",
+    "\n",
+    "This notebook is for quickly tuning `accumulation_start_offset_us` on `runs/camera/laser_selected_cycle_40px_dot12-sync-check`. It avoids the slow 50-subplot matplotlib redraw by rendering one cached PNG contact sheet with ROI zoom, optional event dilation, and per-frame contrast.\n",
+    "\n",
+    "Trigger order is preserved. For this run, 10 cycles x 5 numbers = 50 ordered frames."
+   ],
+   "id": "67f4afa5eee1ec45"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:37:45.011009200Z",
+     "start_time": "2026-06-05T17:37:44.894844700Z"
+    }
+   },
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "from functools import lru_cache\n",
+    "from io import BytesIO\n",
+    "from math import ceil\n",
+    "from pathlib import Path\n",
+    "import csv\n",
+    "import json\n",
+    "import sys\n",
+    "import time\n",
+    "\n",
+    "import numpy as np\n",
+    "try:\n",
+    "    from IPython.display import Image as DisplayImage, clear_output, display\n",
+    "except ModuleNotFoundError:\n",
+    "    class DisplayImage:\n",
+    "        def __init__(self, data=None, **kwargs):\n",
+    "            self.data = data\n",
+    "\n",
+    "    def clear_output(*args, **kwargs):\n",
+    "        return None\n",
+    "\n",
+    "    def display(obj):\n",
+    "        print(type(obj).__name__)\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "\n",
+    "try:\n",
+    "    import cv2\n",
+    "except ModuleNotFoundError:\n",
+    "    cv2 = None\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    for candidate in [start, *start.parents]:\n",
+    "        if (candidate / \"dmdcontrol\").is_dir() and (candidate / \"runs\").is_dir():\n",
+    "            return candidate\n",
+    "    raise RuntimeError(\"Could not find repository root from the current notebook directory.\")\n",
+    "\n",
+    "\n",
+    "REPO_ROOT = find_repo_root(Path.cwd().resolve())\n",
+    "if str(REPO_ROOT) not in sys.path:\n",
+    "    sys.path.insert(0, str(REPO_ROOT))\n",
+    "\n",
+    "RUN_DIR = REPO_ROOT / \"runs\" / \"camera\" / \"laser_selected_cycle_40px_dot12-sync-check\"\n",
+    "AEDAT4_PATH = RUN_DIR / \"raw.aedat4\"\n",
+    "METADATA_PATH = RUN_DIR / \"metadata.json\"\n",
+    "SUMMARY_PATH = RUN_DIR / \"summary.json\"\n",
+    "TRIGGERS_CSV_PATH = RUN_DIR / \"triggers.csv\"\n",
+    "\n",
+    "print(f\"run dir: {RUN_DIR}\")\n",
+    "print(f\"aedat4 exists: {AEDAT4_PATH.exists()}\")"
+   ],
+   "id": "d826c536d3ded738",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "run dir: C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot12-sync-check\n",
+      "aedat4 exists: True\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:37:45.363489700Z",
+     "start_time": "2026-06-05T17:37:45.040853800Z"
+    }
+   },
+   "source": [
+    "from dmdcontrol.camera.accumulation import TriggerRecord, accumulate_events_for_triggers\n",
+    "from dmdcontrol.camera.reprocess_aedat4 import read_aedat4_recording\n",
+    "from dmdcontrol.camera.runs import _events_to_arrays, _process_accumulation_triggers, _trigger_timestamps\n",
+    "\n",
+    "\n",
+    "def load_json(path: Path) -> dict:\n",
+    "    return json.loads(path.read_text(encoding=\"utf-8\")) if path.exists() else {}\n",
+    "\n",
+    "\n",
+    "def load_trigger_csv(path: Path) -> list[TriggerRecord]:\n",
+    "    if not path.exists():\n",
+    "        return []\n",
+    "    triggers = []\n",
+    "    with path.open(newline=\"\", encoding=\"utf-8\") as handle:\n",
+    "        for row in csv.DictReader(handle):\n",
+    "            triggers.append(TriggerRecord(timestamp=int(row[\"timestamp\"]), edge=row.get(\"edge\") or \"rising\"))\n",
+    "    return triggers\n",
+    "\n",
+    "\n",
+    "metadata = load_json(METADATA_PATH)\n",
+    "summary = load_json(SUMMARY_PATH)\n",
+    "number_sequence = list(metadata.get(\"number_sequence\") or [])\n",
+    "cycle_length = len(number_sequence) or int(metadata.get(\"expected_trigger_count\") or 5)\n",
+    "default_cycles = int(metadata.get(\"accumulation_cycles\") or 10)\n",
+    "default_window_us = int(\n",
+    "    metadata.get(\"accumulation_window_us\")\n",
+    "    or metadata.get(\"numbers_exposure_us\")\n",
+    "    or summary.get(\"window_us\")\n",
+    "    or 1500\n",
+    ")\n",
+    "\n",
+    "t0 = time.perf_counter()\n",
+    "recording = read_aedat4_recording(AEDAT4_PATH)\n",
+    "event_arrays = _events_to_arrays(recording.events)\n",
+    "event_timestamps = event_arrays[\"t\"]\n",
+    "csv_triggers = load_trigger_csv(TRIGGERS_CSV_PATH)\n",
+    "trigger_source = \"triggers.csv\" if csv_triggers else \"raw AEDAT4 rising triggers\"\n",
+    "available_cycles = (\n",
+    "    len(csv_triggers) // max(1, cycle_length)\n",
+    "    if csv_triggers\n",
+    "    else int(recording.stats[\"trigger_edges\"].get(\"rising\", 0)) // max(1, cycle_length)\n",
+    ")\n",
+    "default_cycles = max(1, min(default_cycles, max(1, available_cycles)))\n",
+    "load_s = time.perf_counter() - t0\n",
+    "\n",
+    "width, height = recording.resolution\n",
+    "print(f\"loaded AEDAT4 in {load_s:.2f}s\")\n",
+    "print(f\"resolution: {width} x {height}\")\n",
+    "print(f\"events: {recording.stats['event_count']:,}; triggers: {recording.stats['trigger_count']:,}\")\n",
+    "print(f\"trigger edges: {recording.stats['trigger_edges']}\")\n",
+    "print(f\"trigger source for accumulation: {trigger_source}; available cycles: {available_cycles}\")\n",
+    "print(f\"cycle length: {cycle_length}; default cycles: {default_cycles}; default window: {default_window_us} us\")\n"
+   ],
+   "id": "30d1f0c7762d0f74",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded AEDAT4 in 0.29s\n",
+      "resolution: 320 x 240\n",
+      "events: 528,257; triggers: 5,676\n",
+      "trigger edges: {'rising': 2838, 'triggertype.external_signal_pulse': 2838}\n",
+      "trigger source for accumulation: triggers.csv; available cycles: 10\n",
+      "cycle length: 5; default cycles: 10; default window: 1500 us\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:37:45.411917700Z",
+     "start_time": "2026-06-05T17:37:45.382542400Z"
+    }
+   },
+   "source": [
+    "@lru_cache(maxsize=8)\n",
+    "def accumulate_cached(offset_us: int, cycles: int, window_us: int, polarity_mode: str):\n",
+    "    requested_cycles = int(cycles)\n",
+    "    requested_count = requested_cycles * int(cycle_length)\n",
+    "    if csv_triggers:\n",
+    "        selected = csv_triggers[:requested_count]\n",
+    "        cycle_info = {\n",
+    "            \"mode\": \"trigger_csv\",\n",
+    "            \"cycle_length\": int(cycle_length),\n",
+    "            \"requested_cycles\": requested_cycles,\n",
+    "            \"available_full_cycles\": int(available_cycles),\n",
+    "            \"selected_cycle_indices\": list(range(min(requested_cycles, int(available_cycles)))),\n",
+    "            \"selected_trigger_count\": len(selected),\n",
+    "        }\n",
+    "        alignment = {\n",
+    "            \"mode\": \"trigger_csv\",\n",
+    "            \"source\": str(TRIGGERS_CSV_PATH),\n",
+    "            \"selected_trigger_count\": len(selected),\n",
+    "        }\n",
+    "    else:\n",
+    "        stages = _process_accumulation_triggers(\n",
+    "            recording.triggers,\n",
+    "            event_timestamps,\n",
+    "            window_us=int(window_us),\n",
+    "            window_start_offset_us=int(offset_us),\n",
+    "            max_accumulation_triggers=None,\n",
+    "            trigger_cycle_length=cycle_length,\n",
+    "            accumulation_cycles=requested_cycles,\n",
+    "        )\n",
+    "        selected = stages.final\n",
+    "        alignment = stages.alignment_metadata\n",
+    "        cycle_info = stages.cycle_limit_metadata\n",
+    "    frames = accumulate_events_for_triggers(\n",
+    "        recording.events,\n",
+    "        selected,\n",
+    "        resolution=recording.resolution,\n",
+    "        window_us=int(window_us),\n",
+    "        polarity_mode=str(polarity_mode),\n",
+    "        window_start_offset_us=int(offset_us),\n",
+    "    )\n",
+    "    return frames, _trigger_timestamps(selected), alignment, cycle_info\n",
+    "\n",
+    "\n",
+    "def frame_values(frames: np.ndarray, view: str, tone: str, gamma: float) -> tuple[np.ndarray, bool]:\n",
+    "    source = frames.astype(np.float32, copy=False)\n",
+    "    if view == \"signed\":\n",
+    "        return source, True\n",
+    "    if view == \"positive\":\n",
+    "        values = np.maximum(source, 0)\n",
+    "    elif view == \"negative\":\n",
+    "        values = np.maximum(-source, 0)\n",
+    "    else:\n",
+    "        values = np.abs(source)\n",
+    "    if tone == \"log\":\n",
+    "        values = np.log1p(values)\n",
+    "    elif tone == \"gamma\":\n",
+    "        values = np.power(values, float(gamma))\n",
+    "    return values, False\n",
+    "\n",
+    "\n",
+    "def auto_roi(frames: np.ndarray, pad: int, flip_x: bool, crop_mode: str) -> tuple[slice, slice]:\n",
+    "    if crop_mode == \"full\":\n",
+    "        return slice(0, frames.shape[1]), slice(0, frames.shape[2])\n",
+    "    source = frames[:, :, ::-1] if flip_x else frames\n",
+    "    mask = np.any(np.abs(source) > 0, axis=0)\n",
+    "    if not np.any(mask):\n",
+    "        return slice(0, frames.shape[1]), slice(0, frames.shape[2])\n",
+    "    ys, xs = np.where(mask)\n",
+    "    y0 = max(0, int(ys.min()) - int(pad))\n",
+    "    y1 = min(frames.shape[1], int(ys.max()) + int(pad) + 1)\n",
+    "    x0 = max(0, int(xs.min()) - int(pad))\n",
+    "    x1 = min(frames.shape[2], int(xs.max()) + int(pad) + 1)\n",
+    "    if crop_mode == \"square\":\n",
+    "        side = max(y1 - y0, x1 - x0)\n",
+    "        cy = (y0 + y1) // 2\n",
+    "        cx = (x0 + x1) // 2\n",
+    "        y0 = max(0, min(frames.shape[1] - side, cy - side // 2))\n",
+    "        x0 = max(0, min(frames.shape[2] - side, cx - side // 2))\n",
+    "        y1 = min(frames.shape[1], y0 + side)\n",
+    "        x1 = min(frames.shape[2], x0 + side)\n",
+    "    return slice(y0, y1), slice(x0, x1)\n",
+    "\n",
+    "\n",
+    "def dilate_plane(plane: np.ndarray, dot_size: int) -> np.ndarray:\n",
+    "    dot_size = int(dot_size)\n",
+    "    if dot_size <= 1:\n",
+    "        return plane\n",
+    "    if cv2 is not None:\n",
+    "        kernel = np.ones((dot_size, dot_size), dtype=np.uint8)\n",
+    "        return cv2.dilate(plane.astype(np.float32, copy=False), kernel)\n",
+    "    padded = np.pad(plane, dot_size // 2, mode=\"edge\")\n",
+    "    out = np.zeros_like(plane)\n",
+    "    for dy in range(dot_size):\n",
+    "        for dx in range(dot_size):\n",
+    "            out = np.maximum(out, padded[dy:dy + plane.shape[0], dx:dx + plane.shape[1]])\n",
+    "    return out\n",
+    "\n",
+    "\n",
+    "def scale_limit(values: np.ndarray, signed: bool, percentile: float) -> float:\n",
+    "    source = np.abs(values) if signed else values\n",
+    "    nonzero = source[source > 0]\n",
+    "    if nonzero.size == 0:\n",
+    "        return 1.0\n",
+    "    return max(float(np.percentile(nonzero, float(percentile))), 1e-6)\n",
+    "\n",
+    "\n",
+    "def frame_rgb(values: np.ndarray, signed: bool, limit: float, dot_size: int) -> np.ndarray:\n",
+    "    if signed:\n",
+    "        pos = dilate_plane(np.maximum(values, 0), dot_size)\n",
+    "        neg = dilate_plane(np.maximum(-values, 0), dot_size)\n",
+    "        pos8 = np.clip(pos / limit * 255, 0, 255).astype(np.uint8)\n",
+    "        neg8 = np.clip(neg / limit * 255, 0, 255).astype(np.uint8)\n",
+    "        rgb = np.zeros((*values.shape, 3), dtype=np.uint8)\n",
+    "        rgb[..., 0] = pos8\n",
+    "        rgb[..., 1] = neg8\n",
+    "        rgb[..., 2] = neg8\n",
+    "        return rgb\n",
+    "    plane = dilate_plane(values, dot_size)\n",
+    "    gray = np.clip(plane / limit * 255, 0, 255).astype(np.uint8)\n",
+    "    return np.repeat(gray[..., None], 3, axis=2)\n",
+    "\n",
+    "\n",
+    "def resize_nearest(rgb: np.ndarray, scale: int) -> Image.Image:\n",
+    "    image = Image.fromarray(rgb, mode=\"RGB\")\n",
+    "    if int(scale) <= 1:\n",
+    "        return image\n",
+    "    return image.resize((image.width * int(scale), image.height * int(scale)), Image.Resampling.NEAREST)\n",
+    "\n",
+    "\n",
+    "def png_bytes(image: Image.Image) -> bytes:\n",
+    "    buffer = BytesIO()\n",
+    "    image.save(buffer, format=\"PNG\", optimize=False)\n",
+    "    return buffer.getvalue()"
+   ],
+   "id": "25d019acc1fa9368",
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:37:46.467626700Z",
+     "start_time": "2026-06-05T17:37:45.415099300Z"
+    }
+   },
+   "source": [
+    "FONT = ImageFont.load_default()\n",
+    "\n",
+    "\n",
+    "def render_fast_sheet(\n",
+    "    offset_us: int = -250,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "    view: str = \"magnitude\",\n",
+    "    tone: str = \"log\",\n",
+    "    gamma: float = 0.65,\n",
+    "    contrast: str = \"per-frame\",\n",
+    "    vmax_percentile: float = 99.0,\n",
+    "    crop_mode: str = \"square\",\n",
+    "    crop_pad: int = 28,\n",
+    "    dot_size: int = 2,\n",
+    "    tile_scale: int = 2,\n",
+    "    focus_frame: int = 1,\n",
+    "    focus_scale: int = 4,\n",
+    "    flip_x: bool = True,\n",
+    "):\n",
+    "    start = time.perf_counter()\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    frames_for_view = frames[:, :, ::-1] if flip_x else frames\n",
+    "    y_slice, x_slice = auto_roi(frames, crop_pad, flip_x=flip_x, crop_mode=crop_mode)\n",
+    "    cropped = frames_for_view[:, y_slice, x_slice]\n",
+    "    values, signed = frame_values(cropped, view=view, tone=tone, gamma=gamma)\n",
+    "    frame_count = values.shape[0]\n",
+    "    cols = max(1, int(cycle_length))\n",
+    "    rows = max(1, ceil(frame_count / cols))\n",
+    "\n",
+    "    if contrast == \"global\":\n",
+    "        global_limit = scale_limit(values, signed=signed, percentile=vmax_percentile)\n",
+    "    else:\n",
+    "        global_limit = None\n",
+    "\n",
+    "    tile_images = []\n",
+    "    for index in range(frame_count):\n",
+    "        limit = global_limit or scale_limit(values[index], signed=signed, percentile=vmax_percentile)\n",
+    "        tile_images.append(resize_nearest(frame_rgb(values[index], signed, limit, dot_size), tile_scale))\n",
+    "\n",
+    "    tile_w = tile_images[0].width if tile_images else 1\n",
+    "    tile_h = tile_images[0].height if tile_images else 1\n",
+    "    label_h = 26\n",
+    "    header_h = 58\n",
+    "    gap = 8\n",
+    "    focus_index = max(0, min(frame_count - 1, int(focus_frame) - 1)) if frame_count else 0\n",
+    "    focus_limit = global_limit or scale_limit(values[focus_index], signed=signed, percentile=vmax_percentile)\n",
+    "    focus_image = resize_nearest(frame_rgb(values[focus_index], signed, focus_limit, max(1, int(dot_size))), focus_scale)\n",
+    "\n",
+    "    sheet_w = cols * tile_w + (cols - 1) * gap\n",
+    "    sheet_h = rows * (tile_h + label_h) + (rows - 1) * gap\n",
+    "    canvas_w = max(sheet_w, focus_image.width) + 20\n",
+    "    canvas_h = header_h + focus_image.height + 22 + sheet_h + 24\n",
+    "    canvas = Image.new(\"RGB\", (canvas_w, canvas_h), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "\n",
+    "    counts = np.count_nonzero(np.abs(frames) > 0, axis=(1, 2)) if frame_count else np.array([], dtype=int)\n",
+    "    roi_text = f\"roi y={y_slice.start}:{y_slice.stop} x={x_slice.start}:{x_slice.stop}\"\n",
+    "    title = (\n",
+    "        f\"offset {int(offset_us)} us | window {int(window_us)} us | {frame_count} frames | \"\n",
+    "        f\"{polarity_mode}/{view}/{tone} | contrast={contrast} | dot={int(dot_size)}\"\n",
+    "    )\n",
+    "    draw.text((10, 8), title, fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((10, 28), roi_text, fill=(180, 180, 180), font=FONT)\n",
+    "    if counts.size:\n",
+    "        draw.text(\n",
+    "            (10, 44),\n",
+    "            f\"nonzero pixels: first={int(counts[0])}, min={int(counts.min())}, median={int(np.median(counts))}, max={int(counts.max())}\",\n",
+    "            fill=(180, 180, 180),\n",
+    "            font=FONT,\n",
+    "        )\n",
+    "\n",
+    "    focus_x = (canvas_w - focus_image.width) // 2\n",
+    "    focus_y = header_h\n",
+    "    canvas.paste(focus_image, (focus_x, focus_y))\n",
+    "    focus_slot = focus_index % cols\n",
+    "    focus_cycle = focus_index // cols + 1\n",
+    "    focus_label = number_sequence[focus_slot] if focus_slot < len(number_sequence) else focus_slot + 1\n",
+    "    draw.text(\n",
+    "        (10, focus_y + focus_image.height + 4),\n",
+    "        f\"focus frame {focus_index + 1}: expected {focus_label}, cycle {focus_cycle}, nonzero={int(counts[focus_index]) if counts.size else 0}\",\n",
+    "        fill=(235, 235, 235),\n",
+    "        font=FONT,\n",
+    "    )\n",
+    "\n",
+    "    first_trigger = int(trigger_ts[0]) if len(trigger_ts) else 0\n",
+    "    grid_y = focus_y + focus_image.height + 26\n",
+    "    for index, image in enumerate(tile_images):\n",
+    "        row = index // cols\n",
+    "        col = index % cols\n",
+    "        x = 10 + col * (tile_w + gap)\n",
+    "        y = grid_y + row * (tile_h + label_h + gap)\n",
+    "        canvas.paste(image, (x, y + label_h))\n",
+    "        slot = index % cols\n",
+    "        cycle = index // cols + 1\n",
+    "        label = number_sequence[slot] if slot < len(number_sequence) else slot + 1\n",
+    "        dt = int(trigger_ts[index] - first_trigger) if len(trigger_ts) else 0\n",
+    "        color = (255, 245, 170) if index == focus_index else (220, 220, 220)\n",
+    "        draw.text((x, y), f\"{index + 1}: {label} c{cycle} +{dt}us\", fill=color, font=FONT)\n",
+    "        if counts.size:\n",
+    "            draw.text((x, y + 12), f\"nz={int(counts[index])}\", fill=(170, 170, 170), font=FONT)\n",
+    "\n",
+    "    elapsed_ms = (time.perf_counter() - start) * 1000\n",
+    "    draw.text((canvas_w - 115, 8), f\"{elapsed_ms:.0f} ms\", fill=(120, 210, 120), font=FONT)\n",
+    "    return canvas, frames, counts, alignment, cycle_info\n",
+    "\n",
+    "\n",
+    "image, frames, counts, alignment, cycle_info = render_fast_sheet(offset_us=-250)\n",
+    "display(DisplayImage(data=png_bytes(image)))"
+   ],
+   "id": "52d8840a82e058d4",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trigger Timeline and Bleed Diagnostics\n",
+    "\n",
+    "These views answer different questions than the frame grid. The timeline shows trigger positions, accumulation windows, and event density over time. The relative activity graph shows whether events are arriving before, during, or after each trigger. The bleed matrix estimates how much spatial activity carries from one ordered frame into the next."
+   ],
+   "id": "70d6cb5646bcec9a"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:38:32.693581200Z",
+     "start_time": "2026-06-05T17:38:29.962118400Z"
+    }
+   },
+   "source": [
+    "SLOT_COLORS = [\n",
+    "    (95, 190, 255),\n",
+    "    (255, 175, 80),\n",
+    "    (140, 220, 120),\n",
+    "    (235, 105, 130),\n",
+    "    (190, 150, 255),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def slot_color(slot: int) -> tuple[int, int, int]:\n",
+    "    return SLOT_COLORS[int(slot) % len(SLOT_COLORS)]\n",
+    "\n",
+    "\n",
+    "def render_trigger_timeline(\n",
+    "    offset_us: int = -250,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    bin_us: int = 100,\n",
+    "    margin_us: int = 500,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    trigger_ts = np.asarray(trigger_ts, dtype=np.int64)\n",
+    "    if trigger_ts.size == 0:\n",
+    "        return Image.new(\"RGB\", (900, 120), (14, 14, 14))\n",
+    "\n",
+    "    # Keep the absolute-time axis anchored to triggers, not to shifted windows.\n",
+    "    # Otherwise changing offset re-centers the graph and makes trigger lines look like they move backward.\n",
+    "    axis_offset_min_us = -100\n",
+    "    axis_offset_max_us = 750\n",
+    "    start = int(np.min(trigger_ts) + min(axis_offset_min_us, int(offset_us), 0) - int(margin_us))\n",
+    "    end = int(np.max(trigger_ts) + max(axis_offset_max_us, int(offset_us), 0) + int(window_us) + int(margin_us))\n",
+    "    bins = np.arange(start, end + int(bin_us), int(bin_us), dtype=np.int64)\n",
+    "    if bins.size < 2:\n",
+    "        bins = np.array([start, end + 1], dtype=np.int64)\n",
+    "    event_counts, _ = np.histogram(event_timestamps, bins=bins)\n",
+    "\n",
+    "    plot_w = 1160\n",
+    "    left = 58\n",
+    "    right = 16\n",
+    "    top_h = 128\n",
+    "    row_h = 30\n",
+    "    footer_h = 42\n",
+    "    rows = max(1, ceil(len(trigger_ts) / cycle_length))\n",
+    "    canvas = Image.new(\"RGB\", (plot_w + left + right, top_h + rows * row_h + footer_h), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "\n",
+    "    def x_of(t_us: int | float) -> int:\n",
+    "        return int(left + (float(t_us) - start) / max(1, end - start) * plot_w)\n",
+    "\n",
+    "    draw.text((10, 8), f\"trigger/window timeline | offset {int(offset_us)} us | window {int(window_us)} us | bin {int(bin_us)} us\", fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((10, 26), \"top graph = all event timestamps binned over time; rows = ordered cycles\", fill=(175, 175, 175), font=FONT)\n",
+    "\n",
+    "    hist_top = 54\n",
+    "    hist_h = 60\n",
+    "    max_count = max(1, int(event_counts.max()) if event_counts.size else 1)\n",
+    "    for idx, count in enumerate(event_counts):\n",
+    "        x0 = x_of(int(bins[idx]))\n",
+    "        x1 = max(x0 + 1, x_of(int(bins[idx + 1])))\n",
+    "        h = int((int(count) / max_count) * hist_h)\n",
+    "        draw.rectangle((x0, hist_top + hist_h - h, x1, hist_top + hist_h), fill=(95, 95, 95))\n",
+    "    draw.rectangle((left, hist_top, left + plot_w, hist_top + hist_h), outline=(90, 90, 90))\n",
+    "\n",
+    "    for index, trigger in enumerate(trigger_ts):\n",
+    "        slot = index % cycle_length\n",
+    "        color = slot_color(slot)\n",
+    "        x = x_of(int(trigger))\n",
+    "        draw.line((x, hist_top, x, hist_top + hist_h), fill=color)\n",
+    "\n",
+    "    for cycle in range(rows):\n",
+    "        y = top_h + cycle * row_h\n",
+    "        draw.text((10, y + 7), f\"c{cycle + 1}\", fill=(210, 210, 210), font=FONT)\n",
+    "        draw.line((left, y + row_h // 2, left + plot_w, y + row_h // 2), fill=(45, 45, 45))\n",
+    "        for slot in range(cycle_length):\n",
+    "            index = cycle * cycle_length + slot\n",
+    "            if index >= len(trigger_ts):\n",
+    "                break\n",
+    "            trigger = int(trigger_ts[index])\n",
+    "            win_start = trigger + int(offset_us)\n",
+    "            win_end = win_start + int(window_us)\n",
+    "            x0 = x_of(win_start)\n",
+    "            x1 = x_of(win_end)\n",
+    "            xt = x_of(trigger)\n",
+    "            color = slot_color(slot)\n",
+    "            fill = tuple(max(25, c // 3) for c in color)\n",
+    "            draw.rectangle((x0, y + 5, x1, y + row_h - 6), fill=fill, outline=color)\n",
+    "            draw.line((xt, y + 2, xt, y + row_h - 2), fill=(255, 255, 255))\n",
+    "            if index + 1 < len(trigger_ts) and win_end > int(trigger_ts[index + 1]):\n",
+    "                draw.line((x1, y + 3, x1, y + row_h - 3), fill=(255, 80, 80), width=2)\n",
+    "\n",
+    "    dt = np.diff(trigger_ts)\n",
+    "    footer_y = top_h + rows * row_h + 8\n",
+    "    if dt.size:\n",
+    "        min_gap_after_window = int(np.min(dt - int(window_us) - int(offset_us)))\n",
+    "        draw.text(\n",
+    "            (10, footer_y),\n",
+    "            f\"trigger spacing us: min={int(dt.min())}, median={float(np.median(dt)):.0f}, max={int(dt.max())}; min next-trigger gap after window={min_gap_after_window} us\",\n",
+    "            fill=(210, 210, 210),\n",
+    "            font=FONT,\n",
+    "        )\n",
+    "    draw.text((10, footer_y + 16), \"colored blocks = accumulation windows; white line = trigger; red marker = window overlaps next trigger\", fill=(175, 175, 175), font=FONT)\n",
+    "    return canvas\n",
+    "\n",
+    "\n",
+    "def render_relative_activity(\n",
+    "    offset_us: int = -250,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    pre_us: int = 1000,\n",
+    "    post_us: int = 3000,\n",
+    "    bin_us: int = 50,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    trigger_ts = np.asarray(trigger_ts, dtype=np.int64)\n",
+    "    rel_edges = np.arange(-int(pre_us), int(post_us) + int(bin_us), int(bin_us), dtype=np.int64)\n",
+    "    rel_centers = (rel_edges[:-1] + rel_edges[1:]) / 2.0\n",
+    "    slot_counts = np.zeros((cycle_length, len(rel_centers)), dtype=np.float32)\n",
+    "    slot_seen = np.zeros(cycle_length, dtype=np.int64)\n",
+    "    for index, trigger in enumerate(trigger_ts):\n",
+    "        slot = index % cycle_length\n",
+    "        hist, _ = np.histogram(event_timestamps, bins=trigger + rel_edges)\n",
+    "        slot_counts[slot] += hist.astype(np.float32)\n",
+    "        slot_seen[slot] += 1\n",
+    "    for slot in range(cycle_length):\n",
+    "        if slot_seen[slot] > 0:\n",
+    "            slot_counts[slot] /= slot_seen[slot]\n",
+    "\n",
+    "    W, H = 1000, 360\n",
+    "    left, right, top, bottom = 68, 18, 40, 52\n",
+    "    plot_w = W - left - right\n",
+    "    plot_h = H - top - bottom\n",
+    "    canvas = Image.new(\"RGB\", (W, H), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "\n",
+    "    def x_of(rel_us: int | float) -> int:\n",
+    "        return int(left + (float(rel_us) + int(pre_us)) / max(1, int(pre_us) + int(post_us)) * plot_w)\n",
+    "\n",
+    "    max_y = max(1.0, float(slot_counts.max()))\n",
+    "\n",
+    "    def y_of(count: int | float) -> int:\n",
+    "        return int(top + plot_h - (float(count) / max_y) * plot_h)\n",
+    "\n",
+    "    win_x0 = x_of(int(offset_us))\n",
+    "    win_x1 = x_of(int(offset_us) + int(window_us))\n",
+    "    draw.rectangle((win_x0, top, win_x1, top + plot_h), fill=(32, 42, 32), outline=(90, 130, 90))\n",
+    "    draw.line((x_of(0), top, x_of(0), top + plot_h), fill=(230, 230, 230))\n",
+    "    draw.rectangle((left, top, left + plot_w, top + plot_h), outline=(95, 95, 95))\n",
+    "    draw.text((10, 8), f\"event activity relative to trigger | offset {int(offset_us)} us | window {int(window_us)} us\", fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((left, H - 34), \"relative time from trigger (us)\", fill=(190, 190, 190), font=FONT)\n",
+    "    draw.text((8, top + 4), \"avg events/bin\", fill=(190, 190, 190), font=FONT)\n",
+    "    for tick in range(-int(pre_us), int(post_us) + 1, 500):\n",
+    "        x = x_of(tick)\n",
+    "        draw.line((x, top + plot_h, x, top + plot_h + 5), fill=(140, 140, 140))\n",
+    "        draw.text((x - 16, top + plot_h + 8), str(tick), fill=(160, 160, 160), font=FONT)\n",
+    "\n",
+    "    for slot in range(cycle_length):\n",
+    "        points = [(x_of(x), y_of(y)) for x, y in zip(rel_centers, slot_counts[slot], strict=False)]\n",
+    "        if len(points) >= 2:\n",
+    "            draw.line(points, fill=slot_color(slot), width=2)\n",
+    "        label = number_sequence[slot] if slot < len(number_sequence) else slot + 1\n",
+    "        draw.text((W - 138, 44 + slot * 16), f\"{label}: avg peak {float(slot_counts[slot].max()):.1f}\", fill=slot_color(slot), font=FONT)\n",
+    "    draw.text((win_x0 + 3, top + 4), \"accum window\", fill=(170, 220, 170), font=FONT)\n",
+    "    return canvas\n",
+    "\n",
+    "\n",
+    "def render_bleed_matrix(\n",
+    "    offset_us: int = -250,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "    mask_dilate: int = 3,\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    masks = np.abs(frames) > 0\n",
+    "    if int(mask_dilate) > 1:\n",
+    "        masks = np.stack([dilate_plane(mask.astype(np.float32), int(mask_dilate)) > 0 for mask in masks], axis=0)\n",
+    "    active = masks.reshape(masks.shape[0], -1).sum(axis=1) if masks.size else np.array([], dtype=np.int64)\n",
+    "    matrix_values = [[[] for _ in range(cycle_length)] for _ in range(cycle_length)]\n",
+    "    adjacent_scores = []\n",
+    "    for index in range(max(0, len(masks) - 1)):\n",
+    "        a = index % cycle_length\n",
+    "        b = (index + 1) % cycle_length\n",
+    "        inter = int(np.logical_and(masks[index], masks[index + 1]).sum())\n",
+    "        denom = max(1, int(min(active[index], active[index + 1])))\n",
+    "        score = inter / denom\n",
+    "        matrix_values[a][b].append(score)\n",
+    "        adjacent_scores.append(score)\n",
+    "\n",
+    "    matrix = np.zeros((cycle_length, cycle_length), dtype=np.float32)\n",
+    "    for r in range(cycle_length):\n",
+    "        for c in range(cycle_length):\n",
+    "            if matrix_values[r][c]:\n",
+    "                matrix[r, c] = float(np.mean(matrix_values[r][c]))\n",
+    "\n",
+    "    cell = 74\n",
+    "    left, top = 116, 72\n",
+    "    W = left + cycle_length * cell + 260\n",
+    "    H = top + cycle_length * cell + 86\n",
+    "    canvas = Image.new(\"RGB\", (W, H), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "    draw.text((10, 8), f\"consecutive-frame spatial overlap | offset {int(offset_us)} us | mask dilate {int(mask_dilate)}\", fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((10, 27), \"cell value = mean intersection / smaller active-pixel count for ordered frame transitions\", fill=(175, 175, 175), font=FONT)\n",
+    "    draw.text((10, 47), \"high off-diagonal values can mean bleed, but shared strokes in the same position also contribute\", fill=(175, 175, 175), font=FONT)\n",
+    "\n",
+    "    for slot in range(cycle_length):\n",
+    "        label = str(number_sequence[slot] if slot < len(number_sequence) else slot + 1)\n",
+    "        draw.text((left + slot * cell + 28, top - 22), label, fill=slot_color(slot), font=FONT)\n",
+    "        draw.text((left - 34, top + slot * cell + 28), label, fill=slot_color(slot), font=FONT)\n",
+    "    draw.text((left + 95, top - 42), \"next frame\", fill=(210, 210, 210), font=FONT)\n",
+    "    draw.text((22, top + 145), \"current\", fill=(210, 210, 210), font=FONT)\n",
+    "\n",
+    "    max_value = max(0.01, float(matrix.max()))\n",
+    "    for r in range(cycle_length):\n",
+    "        for c in range(cycle_length):\n",
+    "            value = float(matrix[r, c])\n",
+    "            intensity = int(np.clip(value / max_value * 255, 0, 255))\n",
+    "            fill = (intensity, max(18, intensity // 3), 28)\n",
+    "            x0 = left + c * cell\n",
+    "            y0 = top + r * cell\n",
+    "            draw.rectangle((x0, y0, x0 + cell - 4, y0 + cell - 4), fill=fill, outline=(85, 85, 85))\n",
+    "            draw.text((x0 + 13, y0 + 27), f\"{value * 100:.1f}%\", fill=(245, 245, 245), font=FONT)\n",
+    "\n",
+    "    stats_x = left + cycle_length * cell + 32\n",
+    "    if active.size:\n",
+    "        draw.text((stats_x, top), f\"active px/frame\", fill=(230, 230, 230), font=FONT)\n",
+    "        draw.text((stats_x, top + 18), f\"first: {int(active[0])}\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 34), f\"min:   {int(active.min())}\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 50), f\"med:   {int(np.median(active))}\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 66), f\"max:   {int(active.max())}\", fill=(190, 190, 190), font=FONT)\n",
+    "    if adjacent_scores:\n",
+    "        draw.text((stats_x, top + 102), f\"adjacent overlap\", fill=(230, 230, 230), font=FONT)\n",
+    "        draw.text((stats_x, top + 120), f\"mean: {float(np.mean(adjacent_scores)) * 100:.1f}%\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 136), f\"max:  {float(np.max(adjacent_scores)) * 100:.1f}%\", fill=(190, 190, 190), font=FONT)\n",
+    "    return canvas\n",
+    "\n",
+    "\n",
+    "timeline_image = render_trigger_timeline(offset_us=-250)\n",
+    "activity_image = render_relative_activity(offset_us=-250)\n",
+    "bleed_image = render_bleed_matrix(offset_us=-250)\n",
+    "display(DisplayImage(data=png_bytes(timeline_image)))\n",
+    "display(DisplayImage(data=png_bytes(activity_image)))\n",
+    "display(DisplayImage(data=png_bytes(bleed_image)))"
+   ],
+   "id": "50f5e2de87c4a00e",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive Controls\n",
+    "\n",
+    "Defaults are tuned to make sparse event frames visible: ROI crop, per-frame contrast, log tone, and dot dilation. Switch contrast to `global` when you want brightness comparisons to be physically meaningful across frames."
+   ],
+   "id": "ab941b0099e65e31"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:38:37.370388Z",
+     "start_time": "2026-06-05T17:38:34.705910500Z"
+    }
+   },
+   "source": [
+    "try:\n",
+    "    import ipywidgets as widgets\n",
+    "    HAVE_WIDGETS = True\n",
+    "except ModuleNotFoundError:\n",
+    "    HAVE_WIDGETS = False\n",
+    "    print(\"ipywidgets is not installed in this kernel. Run `%pip install ipywidgets`, restart the kernel, then rerun this notebook.\")\n",
+    "\n",
+    "\n",
+    "if HAVE_WIDGETS:\n",
+    "    offset_slider = widgets.IntSlider(\n",
+    "        value=-180,\n",
+    "        min=-1500,\n",
+    "        max=750,\n",
+    "        step=25,\n",
+    "        description=\"offset us\",\n",
+    "        continuous_update=True,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    cycles_slider = widgets.IntSlider(\n",
+    "        value=default_cycles,\n",
+    "        min=1,\n",
+    "        max=max(1, min(20, int(available_cycles))),\n",
+    "        step=1,\n",
+    "        description=\"cycles\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    window_slider = widgets.IntSlider(\n",
+    "        value=1750,\n",
+    "        min=100,\n",
+    "        max=max(2500, default_window_us * 2),\n",
+    "        step=25,\n",
+    "        description=\"window us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    focus_slider = widgets.IntSlider(\n",
+    "        value=1,\n",
+    "        min=1,\n",
+    "        max=max(1, default_cycles * cycle_length),\n",
+    "        step=1,\n",
+    "        description=\"focus\",\n",
+    "        continuous_update=True,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    polarity_dropdown = widgets.Dropdown(value=\"ignore\", options=[\"ignore\", \"signed\", \"positive\"], description=\"accum\")\n",
+    "    view_dropdown = widgets.Dropdown(value=\"magnitude\", options=[\"magnitude\", \"positive\", \"negative\", \"signed\"], description=\"view\")\n",
+    "    tone_dropdown = widgets.Dropdown(value=\"log\", options=[\"log\", \"gamma\", \"linear\"], description=\"tone\")\n",
+    "    contrast_dropdown = widgets.Dropdown(value=\"per-frame\", options=[\"per-frame\", \"global\"], description=\"contrast\")\n",
+    "    crop_dropdown = widgets.Dropdown(value=\"square\", options=[\"square\", \"auto\", \"full\"], description=\"crop\")\n",
+    "    gamma_slider = widgets.FloatSlider(value=0.65, min=0.2, max=1.5, step=0.05, description=\"gamma\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    vmax_slider = widgets.FloatSlider(value=99.0, min=90.0, max=100.0, step=0.1, description=\"vmax %\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    pad_slider = widgets.IntSlider(value=28, min=0, max=80, step=2, description=\"crop pad\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    dot_slider = widgets.IntSlider(value=2, min=1, max=6, step=1, description=\"dot size\", continuous_update=True, layout=widgets.Layout(width=\"560px\"))\n",
+    "    tile_scale_slider = widgets.IntSlider(value=2, min=1, max=4, step=1, description=\"tile scale\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    focus_scale_slider = widgets.IntSlider(value=4, min=2, max=8, step=1, description=\"focus scale\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    flip_checkbox = widgets.Checkbox(value=True, description=\"flip x for viewing\")\n",
+    "    out = widgets.Output()\n",
+    "\n",
+    "    def sync_focus_max(*_):\n",
+    "        focus_slider.max = max(1, cycles_slider.value * cycle_length)\n",
+    "        if focus_slider.value > focus_slider.max:\n",
+    "            focus_slider.value = focus_slider.max\n",
+    "\n",
+    "    def redraw(_=None):\n",
+    "        sync_focus_max()\n",
+    "        with out:\n",
+    "            clear_output(wait=True)\n",
+    "            image, frames, counts, alignment, cycle_info = render_fast_sheet(\n",
+    "                offset_us=offset_slider.value,\n",
+    "                cycles=cycles_slider.value,\n",
+    "                window_us=window_slider.value,\n",
+    "                polarity_mode=polarity_dropdown.value,\n",
+    "                view=view_dropdown.value,\n",
+    "                tone=tone_dropdown.value,\n",
+    "                gamma=gamma_slider.value,\n",
+    "                contrast=contrast_dropdown.value,\n",
+    "                vmax_percentile=vmax_slider.value,\n",
+    "                crop_mode=crop_dropdown.value,\n",
+    "                crop_pad=pad_slider.value,\n",
+    "                dot_size=dot_slider.value,\n",
+    "                tile_scale=tile_scale_slider.value,\n",
+    "                focus_frame=focus_slider.value,\n",
+    "                focus_scale=focus_scale_slider.value,\n",
+    "                flip_x=flip_checkbox.value,\n",
+    "            )\n",
+    "            display(DisplayImage(data=png_bytes(image)))\n",
+    "\n",
+    "    for widget in [\n",
+    "        offset_slider,\n",
+    "        cycles_slider,\n",
+    "        window_slider,\n",
+    "        focus_slider,\n",
+    "        polarity_dropdown,\n",
+    "        view_dropdown,\n",
+    "        tone_dropdown,\n",
+    "        contrast_dropdown,\n",
+    "        crop_dropdown,\n",
+    "        gamma_slider,\n",
+    "        vmax_slider,\n",
+    "        pad_slider,\n",
+    "        dot_slider,\n",
+    "        tile_scale_slider,\n",
+    "        focus_scale_slider,\n",
+    "        flip_checkbox,\n",
+    "    ]:\n",
+    "        widget.observe(redraw, names=\"value\")\n",
+    "\n",
+    "    controls = widgets.VBox([\n",
+    "        offset_slider,\n",
+    "        cycles_slider,\n",
+    "        window_slider,\n",
+    "        focus_slider,\n",
+    "        widgets.HBox([polarity_dropdown, view_dropdown, tone_dropdown, contrast_dropdown, crop_dropdown]),\n",
+    "        gamma_slider,\n",
+    "        vmax_slider,\n",
+    "        pad_slider,\n",
+    "        widgets.HBox([dot_slider, tile_scale_slider, focus_scale_slider, flip_checkbox]),\n",
+    "    ])\n",
+    "    display(controls)\n",
+    "    display(out)\n",
+    "    redraw()\n",
+    "else:\n",
+    "    image, frames, counts, alignment, cycle_info = render_fast_sheet(offset_us=-250)\n",
+    "    display(DisplayImage(data=png_bytes(image)))"
+   ],
+   "id": "55dcce48ae090aad",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VBox(children=(IntSlider(value=-180, description='offset us', layout=Layout(width='560px'), max=750, min=-1500…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2c46662545b7453ba9f2ecce5c61de5c"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "45d1621433c34bf9a57aa70e45bf7f8e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive Timeline and Bleed Controls\n",
+    "\n",
+    "Use this when the frame grid looks bad and you want to separate timing problems from image-rendering problems. These controls update on release instead of continuously, which keeps the notebook responsive."
+   ],
+   "id": "c56bff8d6cf7818"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:38:41.858833700Z",
+     "start_time": "2026-06-05T17:38:39.381493100Z"
+    }
+   },
+   "source": [
+    "try:\n",
+    "    import ipywidgets as widgets\n",
+    "    HAVE_WIDGETS = True\n",
+    "except ModuleNotFoundError:\n",
+    "    HAVE_WIDGETS = False\n",
+    "\n",
+    "\n",
+    "if HAVE_WIDGETS:\n",
+    "    diag_offset_slider = widgets.IntSlider(\n",
+    "        value=-250,\n",
+    "        min=-1500,\n",
+    "        max=750,\n",
+    "        step=25,\n",
+    "        description=\"offset us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    diag_window_slider = widgets.IntSlider(\n",
+    "        value=default_window_us,\n",
+    "        min=100,\n",
+    "        max=max(2500, default_window_us * 2),\n",
+    "        step=25,\n",
+    "        description=\"window us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    diag_cycles_slider = widgets.IntSlider(\n",
+    "        value=default_cycles,\n",
+    "        min=1,\n",
+    "        max=max(1, min(20, recording.stats[\"trigger_edges\"].get(\"rising\", 0) // max(1, cycle_length))),\n",
+    "        step=1,\n",
+    "        description=\"cycles\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    diag_bin_slider = widgets.IntSlider(value=100, min=25, max=500, step=25, description=\"time bin\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_pre_slider = widgets.IntSlider(value=1000, min=0, max=3000, step=100, description=\"pre us\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_post_slider = widgets.IntSlider(value=3000, min=500, max=6000, step=100, description=\"post us\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_mask_slider = widgets.IntSlider(value=3, min=1, max=9, step=1, description=\"mask dilate\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_polarity_dropdown = widgets.Dropdown(value=\"ignore\", options=[\"ignore\", \"signed\", \"positive\"], description=\"accum\")\n",
+    "    diag_out = widgets.Output()\n",
+    "\n",
+    "    def redraw_diagnostics(_=None):\n",
+    "        with diag_out:\n",
+    "            clear_output(wait=True)\n",
+    "            timeline_image = render_trigger_timeline(\n",
+    "                offset_us=diag_offset_slider.value,\n",
+    "                cycles=diag_cycles_slider.value,\n",
+    "                window_us=diag_window_slider.value,\n",
+    "                bin_us=diag_bin_slider.value,\n",
+    "                polarity_mode=diag_polarity_dropdown.value,\n",
+    "            )\n",
+    "            activity_image = render_relative_activity(\n",
+    "                offset_us=diag_offset_slider.value,\n",
+    "                cycles=diag_cycles_slider.value,\n",
+    "                window_us=diag_window_slider.value,\n",
+    "                pre_us=diag_pre_slider.value,\n",
+    "                post_us=diag_post_slider.value,\n",
+    "                bin_us=max(25, diag_bin_slider.value // 2),\n",
+    "                polarity_mode=diag_polarity_dropdown.value,\n",
+    "            )\n",
+    "            bleed_image = render_bleed_matrix(\n",
+    "                offset_us=diag_offset_slider.value,\n",
+    "                cycles=diag_cycles_slider.value,\n",
+    "                window_us=diag_window_slider.value,\n",
+    "                polarity_mode=diag_polarity_dropdown.value,\n",
+    "                mask_dilate=diag_mask_slider.value,\n",
+    "            )\n",
+    "            display(DisplayImage(data=png_bytes(timeline_image)))\n",
+    "            display(DisplayImage(data=png_bytes(activity_image)))\n",
+    "            display(DisplayImage(data=png_bytes(bleed_image)))\n",
+    "\n",
+    "    for widget in [\n",
+    "        diag_offset_slider,\n",
+    "        diag_window_slider,\n",
+    "        diag_cycles_slider,\n",
+    "        diag_bin_slider,\n",
+    "        diag_pre_slider,\n",
+    "        diag_post_slider,\n",
+    "        diag_mask_slider,\n",
+    "        diag_polarity_dropdown,\n",
+    "    ]:\n",
+    "        widget.observe(redraw_diagnostics, names=\"value\")\n",
+    "\n",
+    "    display(widgets.VBox([\n",
+    "        diag_offset_slider,\n",
+    "        diag_window_slider,\n",
+    "        diag_cycles_slider,\n",
+    "        widgets.HBox([diag_bin_slider, diag_mask_slider, diag_polarity_dropdown]),\n",
+    "        widgets.HBox([diag_pre_slider, diag_post_slider]),\n",
+    "    ]))\n",
+    "    display(diag_out)\n",
+    "    redraw_diagnostics()\n",
+    "else:\n",
+    "    print(\"ipywidgets is not installed in this kernel. Static diagnostics were rendered above.\")"
+   ],
+   "id": "83065a9934309a9b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VBox(children=(IntSlider(value=-250, continuous_update=False, description='offset us', layout=Layout(width='56…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "1380f1a7b2d645a3b7fad91e233da8e2"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "8b59c251d427434486e392d3921e4ce7"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Offset Score Table\n",
+    "\n",
+    "This is a quick numerical scan. It does not pick a cycle or reorder frames; it only reports how many nonzero pixels each offset produces in the first `N x 5` ordered frames."
+   ],
+   "id": "e43aa313b750612a"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:38:46.178211Z",
+     "start_time": "2026-06-05T17:38:43.866364800Z"
+    }
+   },
+   "source": [
+    "def offset_score_table(offsets=range(100, 501, 50), cycles=default_cycles, window_us=1750, polarity_mode=\"ignore\"):\n",
+    "    rows = []\n",
+    "    for offset in offsets:\n",
+    "        frames, trigger_ts, alignment, cycle_info = accumulate_cached(int(offset), int(cycles), int(window_us), str(polarity_mode))\n",
+    "        counts = np.count_nonzero(np.abs(frames) > 0, axis=(1, 2))\n",
+    "        first_cycle = counts[:cycle_length] if counts.size else np.array([], dtype=int)\n",
+    "        rows.append({\n",
+    "            \"offset_us\": int(offset),\n",
+    "            \"frame1\": int(counts[0]) if counts.size else 0,\n",
+    "            \"first_cycle_min\": int(first_cycle.min()) if first_cycle.size else 0,\n",
+    "            \"first_cycle_median\": int(np.median(first_cycle)) if first_cycle.size else 0,\n",
+    "            \"all_min\": int(counts.min()) if counts.size else 0,\n",
+    "            \"all_median\": int(np.median(counts)) if counts.size else 0,\n",
+    "            \"all_max\": int(counts.max()) if counts.size else 0,\n",
+    "        })\n",
+    "    return sorted(rows, key=lambda row: (row[\"first_cycle_min\"], row[\"frame1\"], row[\"all_median\"]), reverse=True)\n",
+    "\n",
+    "\n",
+    "scores = offset_score_table()\n",
+    "for row in scores[:12]:\n",
+    "    print(row)"
+   ],
+   "id": "72d2c1268013fdae",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'offset_us': 100, 'frame1': 434, 'first_cycle_min': 348, 'first_cycle_median': 527, 'all_min': 340, 'all_median': 486, 'all_max': 589}\n",
+      "{'offset_us': 200, 'frame1': 438, 'first_cycle_min': 344, 'first_cycle_median': 515, 'all_min': 344, 'all_median': 496, 'all_max': 586}\n",
+      "{'offset_us': 250, 'frame1': 441, 'first_cycle_min': 342, 'first_cycle_median': 512, 'all_min': 316, 'all_median': 494, 'all_max': 585}\n",
+      "{'offset_us': 300, 'frame1': 433, 'first_cycle_min': 339, 'first_cycle_median': 530, 'all_min': 339, 'all_median': 490, 'all_max': 581}\n",
+      "{'offset_us': 450, 'frame1': 454, 'first_cycle_min': 335, 'first_cycle_median': 518, 'all_min': 305, 'all_median': 497, 'all_max': 598}\n",
+      "{'offset_us': 400, 'frame1': 454, 'first_cycle_min': 332, 'first_cycle_median': 499, 'all_min': 332, 'all_median': 496, 'all_max': 597}\n",
+      "{'offset_us': 500, 'frame1': 444, 'first_cycle_min': 329, 'first_cycle_median': 505, 'all_min': 329, 'all_median': 494, 'all_max': 587}\n",
+      "{'offset_us': 150, 'frame1': 414, 'first_cycle_min': 327, 'first_cycle_median': 533, 'all_min': 326, 'all_median': 491, 'all_max': 566}\n",
+      "{'offset_us': 350, 'frame1': 415, 'first_cycle_min': 312, 'first_cycle_median': 528, 'all_min': 312, 'all_median': 487, 'all_max': 570}\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic Offset/Window Scan\n",
+    "\n",
+    "This ranks `(accumulation_start_offset_us, window_us)` pairs without reordering triggers. The balanced score is a starting point; compare it with the signal-heavy and low-bleed lists before choosing a value for a real run."
+   ],
+   "id": "1d9899ecd03e684d"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T17:38:27.945058100Z",
+     "start_time": "2026-06-05T17:38:06.695118800Z"
+    }
+   },
+   "source": [
+    "def _dilate_mask(mask: np.ndarray, size: int = 3) -> np.ndarray:\n",
+    "    if int(size) <= 1:\n",
+    "        return mask.astype(bool, copy=False)\n",
+    "    return dilate_plane(mask.astype(np.float32), int(size)) > 0\n",
+    "\n",
+    "\n",
+    "def timing_candidate_metrics(offset_us: int, window_us: int, cycles: int = default_cycles, polarity_mode: str = \"ignore\") -> dict:\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    masks = np.abs(frames) > 0\n",
+    "    counts = masks.sum(axis=(1, 2)).astype(np.float32)\n",
+    "    trigger_ts = np.asarray(trigger_ts, dtype=np.int64)\n",
+    "    if counts.size == 0:\n",
+    "        return {\"valid\": False, \"offset_us\": int(offset_us), \"window_us\": int(window_us)}\n",
+    "\n",
+    "    first_cycle = counts[:cycle_length]\n",
+    "    slot_counts = counts[: (counts.size // cycle_length) * cycle_length].reshape((-1, cycle_length))\n",
+    "    slot_cv = np.std(slot_counts, axis=0) / np.maximum(1.0, np.mean(slot_counts, axis=0))\n",
+    "    dilated = np.stack([_dilate_mask(mask, 3) for mask in masks], axis=0)\n",
+    "    overlaps = []\n",
+    "    for index in range(len(dilated) - 1):\n",
+    "        intersection = np.logical_and(dilated[index], dilated[index + 1]).sum()\n",
+    "        denominator = max(1, min(dilated[index].sum(), dilated[index + 1].sum()))\n",
+    "        overlaps.append(float(intersection / denominator))\n",
+    "    overlaps = np.asarray(overlaps, dtype=np.float32)\n",
+    "    spacing = np.diff(trigger_ts).astype(np.float32)\n",
+    "    min_gap_after_window = float(np.min(spacing - int(offset_us) - int(window_us))) if spacing.size else 9999.0\n",
+    "\n",
+    "    signal_score = (\n",
+    "        np.log1p(float(np.median(counts)))\n",
+    "        + 0.75 * np.log1p(float(counts[0]))\n",
+    "        + 0.50 * np.log1p(float(np.min(first_cycle)))\n",
+    "    )\n",
+    "    consistency_penalty = 1.5 * float(np.mean(slot_cv))\n",
+    "    bleed_penalty = (3.0 * float(np.mean(overlaps)) + 1.0 * float(np.max(overlaps))) if overlaps.size else 0.0\n",
+    "    gap_penalty = max(0.0, -min_gap_after_window / 250.0) + max(0.0, (100.0 - min_gap_after_window) / 1000.0)\n",
+    "\n",
+    "    return {\n",
+    "        \"valid\": True,\n",
+    "        \"offset_us\": int(offset_us),\n",
+    "        \"window_us\": int(window_us),\n",
+    "        \"balanced_score\": float(signal_score - consistency_penalty - bleed_penalty - gap_penalty),\n",
+    "        \"signal_heavy_score\": float(signal_score - 0.5 * consistency_penalty - 0.4 * bleed_penalty - gap_penalty),\n",
+    "        \"low_bleed_score\": float(signal_score - consistency_penalty - 2.5 * bleed_penalty - 1.5 * gap_penalty),\n",
+    "        \"frame1_active\": int(counts[0]),\n",
+    "        \"first_cycle_min_active\": int(np.min(first_cycle)),\n",
+    "        \"median_active\": int(np.median(counts)),\n",
+    "        \"adjacent_overlap_mean_pct\": float(np.mean(overlaps) * 100.0) if overlaps.size else 0.0,\n",
+    "        \"adjacent_overlap_max_pct\": float(np.max(overlaps) * 100.0) if overlaps.size else 0.0,\n",
+    "        \"min_gap_after_window_us\": int(round(min_gap_after_window)),\n",
+    "        \"slot_cv_mean\": float(np.mean(slot_cv)),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def run_automatic_timing_scan(offsets=range(-500, 101, 25), windows=range(1200, 1801, 50), cycles=default_cycles, polarity_mode=\"ignore\"):\n",
+    "    started = time.perf_counter()\n",
+    "    rows = []\n",
+    "    for window_us in windows:\n",
+    "        for offset_us in offsets:\n",
+    "            row = timing_candidate_metrics(offset_us, window_us, cycles=cycles, polarity_mode=polarity_mode)\n",
+    "            if row.get(\"valid\"):\n",
+    "                rows.append(row)\n",
+    "    print(f\"scanned {len(rows)} candidates in {time.perf_counter() - started:.1f}s\")\n",
+    "    return rows\n",
+    "\n",
+    "\n",
+    "def print_ranked(rows, key: str, title: str, limit: int = 10):\n",
+    "    print(\"\\n\" + title)\n",
+    "    print(\"offset window score  f1 first med overlap_mean overlap_max min_gap cv\")\n",
+    "    for row in sorted(rows, key=lambda item: item[key], reverse=True)[:limit]:\n",
+    "        print(\n",
+    "            f\"{row['offset_us']:>6} {row['window_us']:>6} {row[key]:>6.3f} \"\n",
+    "            f\"{row['frame1_active']:>3} {row['first_cycle_min_active']:>5} {row['median_active']:>3} \"\n",
+    "            f\"{row['adjacent_overlap_mean_pct']:>11.1f}% {row['adjacent_overlap_max_pct']:>10.1f}% \"\n",
+    "            f\"{row['min_gap_after_window_us']:>7} {row['slot_cv_mean']:.2f}\"\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "auto_timing_rows = run_automatic_timing_scan()\n",
+    "print_ranked(auto_timing_rows, \"balanced_score\", \"Balanced candidates\")\n",
+    "print_ranked(auto_timing_rows, \"signal_heavy_score\", \"Signal-heavy candidates\")\n",
+    "print_ranked(auto_timing_rows, \"low_bleed_score\", \"Low-bleed candidates\")"
+   ],
+   "id": "88e1a7255b28f785",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scanned 325 candidates in 13.8s\n",
+      "\n",
+      "Balanced candidates\n",
+      "offset window score  f1 first med overlap_mean overlap_max min_gap cv\n",
+      "  -175   1750 12.388 427   390 485        27.3%       40.7%     146 0.07\n",
+      "  -150   1750 12.366 428   391 484        27.4%       41.3%     121 0.08\n",
+      "  -200   1750 12.363 426   389 483        27.3%       42.7%     171 0.07\n",
+      "  -225   1750 12.358 426   383 485        27.3%       43.1%     196 0.07\n",
+      "  -175   1700 12.349 424   382 463        26.5%       40.8%     196 0.07\n",
+      "  -150   1700 12.339 426   388 459        26.5%       40.9%     171 0.07\n",
+      "  -125   1750 12.332 428   389 483        27.6%       43.5%      96 0.08\n",
+      "  -125   1700 12.331 426   387 466        26.8%       41.9%     146 0.08\n",
+      "  -375   1750 12.329 398   398 479        27.5%       40.9%     346 0.06\n",
+      "  -250   1800 12.329 429   392 498        29.9%       43.0%     171 0.06\n",
+      "\n",
+      "Signal-heavy candidates\n",
+      "offset window score  f1 first med overlap_mean overlap_max min_gap cv\n",
+      "  -175   1750 13.174 427   390 485        27.3%       40.7%     146 0.07\n",
+      "  -250   1800 13.171 429   392 498        29.9%       43.0%     171 0.06\n",
+      "  -225   1800 13.170 429   391 499        29.8%       43.3%     146 0.06\n",
+      "  -275   1800 13.170 430   391 497        29.8%       43.7%     196 0.06\n",
+      "  -200   1800 13.167 428   392 500        29.8%       44.8%     121 0.06\n",
+      "  -300   1800 13.165 413   410 499        29.8%       44.3%     221 0.06\n",
+      "  -150   1750 13.165 428   391 484        27.4%       41.3%     121 0.08\n",
+      "  -200   1750 13.161 426   389 483        27.3%       42.7%     171 0.07\n",
+      "  -225   1750 13.156 426   383 485        27.3%       43.1%     196 0.07\n",
+      "  -175   1800 13.153 429   392 501        29.6%       48.1%      96 0.06\n",
+      "\n",
+      "Low-bleed candidates\n",
+      "offset window score  f1 first med overlap_mean overlap_max min_gap cv\n",
+      "  -175   1750 10.550 427   390 485        27.3%       40.7%     146 0.07\n",
+      "  -175   1700 10.544 424   382 463        26.5%       40.8%     196 0.07\n",
+      "  -125   1500 10.538 371   349 409        24.2%       36.4%     346 0.08\n",
+      "  -125   1450 10.537 362   339 388        23.4%       36.1%     396 0.07\n",
+      "  -175   1500 10.535 363   342 403        23.9%       36.4%     396 0.07\n",
+      "  -150   1700 10.531 426   388 459        26.5%       40.9%     171 0.07\n",
+      "  -175   1550 10.530 372   352 419        24.6%       37.4%     346 0.07\n",
+      "  -200   1550 10.522 370   351 424        24.8%       37.7%     371 0.07\n",
+      "  -125   1650 10.519 423   379 451        26.0%       42.1%     196 0.07\n",
+      "  -325   1650 10.517 388   388 448        26.3%       39.1%     396 0.06\n"
+     ]
+    }
+   ],
+   "execution_count": 18
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/07_fast_accumulation_offset_explorer_laser3.ipynb b/notebooks/07_fast_accumulation_offset_explorer_laser3.ipynb
new file mode 100644
index 0000000..4bdf009
--- /dev/null
+++ b/notebooks/07_fast_accumulation_offset_explorer_laser3.ipynb
@@ -0,0 +1,1331 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fast Accumulation Offset Explorer: Laser 3\n",
+    "\n",
+    "This notebook is for quickly tuning `accumulation_start_offset_us` on `runs/camera/laser_selected_cycle_40px_dot123_test1_laser_3-sync-check`. It avoids the slow 30-subplot matplotlib redraw by rendering one cached PNG contact sheet with ROI zoom, optional event dilation, and per-frame contrast.\n",
+    "\n",
+    "Trigger order is preserved. For this run, 10 cycles x 3 numbers = 30 ordered frames. Display labels default to the observed no-bitplane-remap order `2,3,1`; change `display_sequence` if the hardware order changes."
+   ],
+   "id": "67f4afa5eee1ec45"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:05.708646500Z",
+     "start_time": "2026-06-05T18:09:05.620227Z"
+    }
+   },
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "from functools import lru_cache\n",
+    "from io import BytesIO\n",
+    "from math import ceil\n",
+    "from pathlib import Path\n",
+    "import csv\n",
+    "import json\n",
+    "import sys\n",
+    "import time\n",
+    "\n",
+    "import numpy as np\n",
+    "try:\n",
+    "    from IPython.display import Image as DisplayImage, clear_output, display\n",
+    "except ModuleNotFoundError:\n",
+    "    class DisplayImage:\n",
+    "        def __init__(self, data=None, **kwargs):\n",
+    "            self.data = data\n",
+    "\n",
+    "    def clear_output(*args, **kwargs):\n",
+    "        return None\n",
+    "\n",
+    "    def display(obj):\n",
+    "        print(type(obj).__name__)\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "\n",
+    "try:\n",
+    "    import cv2\n",
+    "except ModuleNotFoundError:\n",
+    "    cv2 = None\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    for candidate in [start, *start.parents]:\n",
+    "        if (candidate / \"dmdcontrol\").is_dir() and (candidate / \"runs\").is_dir():\n",
+    "            return candidate\n",
+    "    raise RuntimeError(\"Could not find repository root from the current notebook directory.\")\n",
+    "\n",
+    "\n",
+    "REPO_ROOT = find_repo_root(Path.cwd().resolve())\n",
+    "if str(REPO_ROOT) not in sys.path:\n",
+    "    sys.path.insert(0, str(REPO_ROOT))\n",
+    "\n",
+    "RUN_DIR = REPO_ROOT / \"runs\" / \"camera\" / \"laser_selected_cycle_40px_dot123_test1_laser_3-sync-check\"\n",
+    "AEDAT4_PATH = RUN_DIR / \"raw.aedat4\"\n",
+    "METADATA_PATH = RUN_DIR / \"metadata.json\"\n",
+    "SUMMARY_PATH = RUN_DIR / \"summary.json\"\n",
+    "TRIGGERS_CSV_PATH = RUN_DIR / \"triggers.csv\"\n",
+    "\n",
+    "print(f\"run dir: {RUN_DIR}\")\n",
+    "print(f\"aedat4 exists: {AEDAT4_PATH.exists()}\")"
+   ],
+   "id": "d826c536d3ded738",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "run dir: C:\\dev\\dmdcontrol\\runs\\camera\\laser_selected_cycle_40px_dot123_test1_laser_3-sync-check\n",
+      "aedat4 exists: True\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:05.863786300Z",
+     "start_time": "2026-06-05T18:09:05.710593100Z"
+    }
+   },
+   "source": [
+    "from dmdcontrol.camera.accumulation import TriggerRecord, accumulate_events_for_triggers\n",
+    "from dmdcontrol.camera.reprocess_aedat4 import read_aedat4_recording\n",
+    "from dmdcontrol.camera.runs import _events_to_arrays, _process_accumulation_triggers, _trigger_timestamps\n",
+    "\n",
+    "\n",
+    "def load_json(path: Path) -> dict:\n",
+    "    return json.loads(path.read_text(encoding=\"utf-8\")) if path.exists() else {}\n",
+    "\n",
+    "\n",
+    "def load_trigger_csv(path: Path) -> list[TriggerRecord]:\n",
+    "    if not path.exists():\n",
+    "        return []\n",
+    "    triggers = []\n",
+    "    with path.open(newline=\"\", encoding=\"utf-8\") as handle:\n",
+    "        for row in csv.DictReader(handle):\n",
+    "            triggers.append(TriggerRecord(timestamp=int(row[\"timestamp\"]), edge=row.get(\"edge\") or \"rising\"))\n",
+    "    return triggers\n",
+    "\n",
+    "\n",
+    "metadata = load_json(METADATA_PATH)\n",
+    "summary = load_json(SUMMARY_PATH)\n",
+    "number_sequence = list(metadata.get(\"number_sequence\") or [])\n",
+    "cycle_length = len(number_sequence) or int(metadata.get(\"expected_trigger_count\") or 5)\n",
+    "default_cycles = int(metadata.get(\"accumulation_cycles\") or 10)\n",
+    "default_window_us = int(\n",
+    "    metadata.get(\"accumulation_window_us\")\n",
+    "    or metadata.get(\"numbers_exposure_us\")\n",
+    "    or summary.get(\"window_us\")\n",
+    "    or 1500\n",
+    ")\n",
+    "default_offset_us = int(metadata.get(\"accumulation_start_offset_us\") or summary.get(\"window_start_offset_us\") or 0)\n",
+    "default_dark_time_us = int(metadata.get(\"dark_time_us\") or 1000)\n",
+    "offset_min_us = -int(default_window_us)\n",
+    "offset_max_us = max(1000, int(default_dark_time_us))\n",
+    "# This command did not use --numbers-bitplane-order. From the earlier observed DMD chronology,\n",
+    "# the trigger order for [1,2,3] displays as [2,3,1]. Frames are not reordered; only labels change.\n",
+    "display_sequence = [2, 3, 1] if number_sequence == [1, 2, 3] and metadata.get(\"numbers_bitplane_order\") is None else list(number_sequence)\n",
+    "\n",
+    "t0 = time.perf_counter()\n",
+    "recording = read_aedat4_recording(AEDAT4_PATH)\n",
+    "event_arrays = _events_to_arrays(recording.events)\n",
+    "event_timestamps = event_arrays[\"t\"]\n",
+    "csv_triggers = load_trigger_csv(TRIGGERS_CSV_PATH)\n",
+    "trigger_source = \"triggers.csv\" if csv_triggers else \"raw AEDAT4 rising triggers\"\n",
+    "available_cycles = (\n",
+    "    len(csv_triggers) // max(1, cycle_length)\n",
+    "    if csv_triggers\n",
+    "    else int(recording.stats[\"trigger_edges\"].get(\"rising\", 0)) // max(1, cycle_length)\n",
+    ")\n",
+    "default_cycles = max(1, min(default_cycles, max(1, available_cycles)))\n",
+    "load_s = time.perf_counter() - t0\n",
+    "\n",
+    "width, height = recording.resolution\n",
+    "print(f\"loaded AEDAT4 in {load_s:.2f}s\")\n",
+    "print(f\"resolution: {width} x {height}\")\n",
+    "print(f\"events: {recording.stats['event_count']:,}; triggers: {recording.stats['trigger_count']:,}\")\n",
+    "print(f\"trigger edges: {recording.stats['trigger_edges']}\")\n",
+    "print(f\"trigger source for accumulation: {trigger_source}; available cycles: {available_cycles}\")\n",
+    "print(f\"cycle length: {cycle_length}; default cycles: {default_cycles}; default window: {default_window_us} us\")\n",
+    "print(f\"default offset: {default_offset_us} us; offset slider range: {offset_min_us}..{offset_max_us} us\")\n",
+    "print(f\"metadata number sequence: {number_sequence}; display labels: {display_sequence}\")\n"
+   ],
+   "id": "30d1f0c7762d0f74",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded AEDAT4 in 0.10s\n",
+      "resolution: 320 x 240\n",
+      "events: 694,987; triggers: 3,188\n",
+      "trigger edges: {'rising': 1594, 'triggertype.external_signal_pulse': 1594}\n",
+      "trigger source for accumulation: triggers.csv; available cycles: 10\n",
+      "cycle length: 3; default cycles: 10; default window: 4000 us\n",
+      "default offset: 0 us; offset slider range: -4000..1000 us\n",
+      "metadata number sequence: [1, 2, 3]; display labels: [2, 3, 1]\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:05.887227300Z",
+     "start_time": "2026-06-05T18:09:05.866930100Z"
+    }
+   },
+   "source": [
+    "@lru_cache(maxsize=8)\n",
+    "def accumulate_cached(offset_us: int, cycles: int, window_us: int, polarity_mode: str):\n",
+    "    requested_cycles = int(cycles)\n",
+    "    requested_count = requested_cycles * int(cycle_length)\n",
+    "    if csv_triggers:\n",
+    "        selected = csv_triggers[:requested_count]\n",
+    "        cycle_info = {\n",
+    "            \"mode\": \"trigger_csv\",\n",
+    "            \"cycle_length\": int(cycle_length),\n",
+    "            \"requested_cycles\": requested_cycles,\n",
+    "            \"available_full_cycles\": int(available_cycles),\n",
+    "            \"selected_cycle_indices\": list(range(min(requested_cycles, int(available_cycles)))),\n",
+    "            \"selected_trigger_count\": len(selected),\n",
+    "        }\n",
+    "        alignment = {\n",
+    "            \"mode\": \"trigger_csv\",\n",
+    "            \"source\": str(TRIGGERS_CSV_PATH),\n",
+    "            \"selected_trigger_count\": len(selected),\n",
+    "        }\n",
+    "    else:\n",
+    "        stages = _process_accumulation_triggers(\n",
+    "            recording.triggers,\n",
+    "            event_timestamps,\n",
+    "            window_us=int(window_us),\n",
+    "            window_start_offset_us=int(offset_us),\n",
+    "            max_accumulation_triggers=None,\n",
+    "            trigger_cycle_length=cycle_length,\n",
+    "            accumulation_cycles=requested_cycles,\n",
+    "        )\n",
+    "        selected = stages.final\n",
+    "        alignment = stages.alignment_metadata\n",
+    "        cycle_info = stages.cycle_limit_metadata\n",
+    "    frames = accumulate_events_for_triggers(\n",
+    "        recording.events,\n",
+    "        selected,\n",
+    "        resolution=recording.resolution,\n",
+    "        window_us=int(window_us),\n",
+    "        polarity_mode=str(polarity_mode),\n",
+    "        window_start_offset_us=int(offset_us),\n",
+    "    )\n",
+    "    return frames, _trigger_timestamps(selected), alignment, cycle_info\n",
+    "\n",
+    "\n",
+    "def frame_values(frames: np.ndarray, view: str, tone: str, gamma: float) -> tuple[np.ndarray, bool]:\n",
+    "    source = frames.astype(np.float32, copy=False)\n",
+    "    if view == \"signed\":\n",
+    "        return source, True\n",
+    "    if view == \"positive\":\n",
+    "        values = np.maximum(source, 0)\n",
+    "    elif view == \"negative\":\n",
+    "        values = np.maximum(-source, 0)\n",
+    "    else:\n",
+    "        values = np.abs(source)\n",
+    "    if tone == \"log\":\n",
+    "        values = np.log1p(values)\n",
+    "    elif tone == \"gamma\":\n",
+    "        values = np.power(values, float(gamma))\n",
+    "    return values, False\n",
+    "\n",
+    "\n",
+    "def auto_roi(frames: np.ndarray, pad: int, flip_x: bool, crop_mode: str) -> tuple[slice, slice]:\n",
+    "    if crop_mode == \"full\":\n",
+    "        return slice(0, frames.shape[1]), slice(0, frames.shape[2])\n",
+    "    source = frames[:, :, ::-1] if flip_x else frames\n",
+    "    mask = np.any(np.abs(source) > 0, axis=0)\n",
+    "    if not np.any(mask):\n",
+    "        return slice(0, frames.shape[1]), slice(0, frames.shape[2])\n",
+    "    ys, xs = np.where(mask)\n",
+    "    y0 = max(0, int(ys.min()) - int(pad))\n",
+    "    y1 = min(frames.shape[1], int(ys.max()) + int(pad) + 1)\n",
+    "    x0 = max(0, int(xs.min()) - int(pad))\n",
+    "    x1 = min(frames.shape[2], int(xs.max()) + int(pad) + 1)\n",
+    "    if crop_mode == \"square\":\n",
+    "        side = max(y1 - y0, x1 - x0)\n",
+    "        cy = (y0 + y1) // 2\n",
+    "        cx = (x0 + x1) // 2\n",
+    "        y0 = max(0, min(frames.shape[1] - side, cy - side // 2))\n",
+    "        x0 = max(0, min(frames.shape[2] - side, cx - side // 2))\n",
+    "        y1 = min(frames.shape[1], y0 + side)\n",
+    "        x1 = min(frames.shape[2], x0 + side)\n",
+    "    return slice(y0, y1), slice(x0, x1)\n",
+    "\n",
+    "\n",
+    "def dilate_plane(plane: np.ndarray, dot_size: int) -> np.ndarray:\n",
+    "    dot_size = int(dot_size)\n",
+    "    if dot_size <= 1:\n",
+    "        return plane\n",
+    "    if cv2 is not None:\n",
+    "        kernel = np.ones((dot_size, dot_size), dtype=np.uint8)\n",
+    "        return cv2.dilate(plane.astype(np.float32, copy=False), kernel)\n",
+    "    padded = np.pad(plane, dot_size // 2, mode=\"edge\")\n",
+    "    out = np.zeros_like(plane)\n",
+    "    for dy in range(dot_size):\n",
+    "        for dx in range(dot_size):\n",
+    "            out = np.maximum(out, padded[dy:dy + plane.shape[0], dx:dx + plane.shape[1]])\n",
+    "    return out\n",
+    "\n",
+    "\n",
+    "def scale_limit(values: np.ndarray, signed: bool, percentile: float) -> float:\n",
+    "    source = np.abs(values) if signed else values\n",
+    "    nonzero = source[source > 0]\n",
+    "    if nonzero.size == 0:\n",
+    "        return 1.0\n",
+    "    return max(float(np.percentile(nonzero, float(percentile))), 1e-6)\n",
+    "\n",
+    "\n",
+    "def frame_rgb(values: np.ndarray, signed: bool, limit: float, dot_size: int) -> np.ndarray:\n",
+    "    if signed:\n",
+    "        pos = dilate_plane(np.maximum(values, 0), dot_size)\n",
+    "        neg = dilate_plane(np.maximum(-values, 0), dot_size)\n",
+    "        pos8 = np.clip(pos / limit * 255, 0, 255).astype(np.uint8)\n",
+    "        neg8 = np.clip(neg / limit * 255, 0, 255).astype(np.uint8)\n",
+    "        rgb = np.zeros((*values.shape, 3), dtype=np.uint8)\n",
+    "        rgb[..., 0] = pos8\n",
+    "        rgb[..., 1] = neg8\n",
+    "        rgb[..., 2] = neg8\n",
+    "        return rgb\n",
+    "    plane = dilate_plane(values, dot_size)\n",
+    "    gray = np.clip(plane / limit * 255, 0, 255).astype(np.uint8)\n",
+    "    return np.repeat(gray[..., None], 3, axis=2)\n",
+    "\n",
+    "\n",
+    "def resize_nearest(rgb: np.ndarray, scale: int) -> Image.Image:\n",
+    "    image = Image.fromarray(rgb, mode=\"RGB\")\n",
+    "    if int(scale) <= 1:\n",
+    "        return image\n",
+    "    return image.resize((image.width * int(scale), image.height * int(scale)), Image.Resampling.NEAREST)\n",
+    "\n",
+    "\n",
+    "def png_bytes(image: Image.Image) -> bytes:\n",
+    "    buffer = BytesIO()\n",
+    "    image.save(buffer, format=\"PNG\", optimize=False)\n",
+    "    return buffer.getvalue()"
+   ],
+   "id": "25d019acc1fa9368",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:06.231182700Z",
+     "start_time": "2026-06-05T18:09:05.891309200Z"
+    }
+   },
+   "source": [
+    "FONT = ImageFont.load_default()\n",
+    "\n",
+    "\n",
+    "def render_fast_sheet(\n",
+    "    offset_us: int = default_offset_us,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "    view: str = \"magnitude\",\n",
+    "    tone: str = \"log\",\n",
+    "    gamma: float = 0.65,\n",
+    "    contrast: str = \"per-frame\",\n",
+    "    vmax_percentile: float = 99.0,\n",
+    "    crop_mode: str = \"square\",\n",
+    "    crop_pad: int = 28,\n",
+    "    dot_size: int = 2,\n",
+    "    tile_scale: int = 2,\n",
+    "    focus_frame: int = 1,\n",
+    "    focus_scale: int = 4,\n",
+    "    flip_x: bool = True,\n",
+    "):\n",
+    "    start = time.perf_counter()\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    frames_for_view = frames[:, :, ::-1] if flip_x else frames\n",
+    "    y_slice, x_slice = auto_roi(frames, crop_pad, flip_x=flip_x, crop_mode=crop_mode)\n",
+    "    cropped = frames_for_view[:, y_slice, x_slice]\n",
+    "    values, signed = frame_values(cropped, view=view, tone=tone, gamma=gamma)\n",
+    "    frame_count = values.shape[0]\n",
+    "    cols = max(1, int(cycle_length))\n",
+    "    rows = max(1, ceil(frame_count / cols))\n",
+    "\n",
+    "    if contrast == \"global\":\n",
+    "        global_limit = scale_limit(values, signed=signed, percentile=vmax_percentile)\n",
+    "    else:\n",
+    "        global_limit = None\n",
+    "\n",
+    "    tile_images = []\n",
+    "    for index in range(frame_count):\n",
+    "        limit = global_limit or scale_limit(values[index], signed=signed, percentile=vmax_percentile)\n",
+    "        tile_images.append(resize_nearest(frame_rgb(values[index], signed, limit, dot_size), tile_scale))\n",
+    "\n",
+    "    tile_w = tile_images[0].width if tile_images else 1\n",
+    "    tile_h = tile_images[0].height if tile_images else 1\n",
+    "    label_h = 26\n",
+    "    header_h = 58\n",
+    "    gap = 8\n",
+    "    focus_index = max(0, min(frame_count - 1, int(focus_frame) - 1)) if frame_count else 0\n",
+    "    focus_limit = global_limit or scale_limit(values[focus_index], signed=signed, percentile=vmax_percentile)\n",
+    "    focus_image = resize_nearest(frame_rgb(values[focus_index], signed, focus_limit, max(1, int(dot_size))), focus_scale)\n",
+    "\n",
+    "    sheet_w = cols * tile_w + (cols - 1) * gap\n",
+    "    sheet_h = rows * (tile_h + label_h) + (rows - 1) * gap\n",
+    "    canvas_w = max(sheet_w, focus_image.width) + 20\n",
+    "    canvas_h = header_h + focus_image.height + 22 + sheet_h + 24\n",
+    "    canvas = Image.new(\"RGB\", (canvas_w, canvas_h), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "\n",
+    "    counts = np.count_nonzero(np.abs(frames) > 0, axis=(1, 2)) if frame_count else np.array([], dtype=int)\n",
+    "    roi_text = f\"roi y={y_slice.start}:{y_slice.stop} x={x_slice.start}:{x_slice.stop}\"\n",
+    "    title = (\n",
+    "        f\"offset {int(offset_us)} us | window {int(window_us)} us | {frame_count} frames | \"\n",
+    "        f\"{polarity_mode}/{view}/{tone} | contrast={contrast} | dot={int(dot_size)}\"\n",
+    "    )\n",
+    "    draw.text((10, 8), title, fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((10, 28), roi_text, fill=(180, 180, 180), font=FONT)\n",
+    "    if counts.size:\n",
+    "        draw.text(\n",
+    "            (10, 44),\n",
+    "            f\"nonzero pixels: first={int(counts[0])}, min={int(counts.min())}, median={int(np.median(counts))}, max={int(counts.max())}\",\n",
+    "            fill=(180, 180, 180),\n",
+    "            font=FONT,\n",
+    "        )\n",
+    "\n",
+    "    focus_x = (canvas_w - focus_image.width) // 2\n",
+    "    focus_y = header_h\n",
+    "    canvas.paste(focus_image, (focus_x, focus_y))\n",
+    "    focus_slot = focus_index % cols\n",
+    "    focus_cycle = focus_index // cols + 1\n",
+    "    focus_label = display_sequence[focus_slot] if focus_slot < len(display_sequence) else focus_slot + 1\n",
+    "    draw.text(\n",
+    "        (10, focus_y + focus_image.height + 4),\n",
+    "        f\"focus frame {focus_index + 1}: expected {focus_label}, cycle {focus_cycle}, nonzero={int(counts[focus_index]) if counts.size else 0}\",\n",
+    "        fill=(235, 235, 235),\n",
+    "        font=FONT,\n",
+    "    )\n",
+    "\n",
+    "    first_trigger = int(trigger_ts[0]) if len(trigger_ts) else 0\n",
+    "    grid_y = focus_y + focus_image.height + 26\n",
+    "    for index, image in enumerate(tile_images):\n",
+    "        row = index // cols\n",
+    "        col = index % cols\n",
+    "        x = 10 + col * (tile_w + gap)\n",
+    "        y = grid_y + row * (tile_h + label_h + gap)\n",
+    "        canvas.paste(image, (x, y + label_h))\n",
+    "        slot = index % cols\n",
+    "        cycle = index // cols + 1\n",
+    "        label = display_sequence[slot] if slot < len(display_sequence) else slot + 1\n",
+    "        dt = int(trigger_ts[index] - first_trigger) if len(trigger_ts) else 0\n",
+    "        color = (255, 245, 170) if index == focus_index else (220, 220, 220)\n",
+    "        draw.text((x, y), f\"{index + 1}: {label} c{cycle} +{dt}us\", fill=color, font=FONT)\n",
+    "        if counts.size:\n",
+    "            draw.text((x, y + 12), f\"nz={int(counts[index])}\", fill=(170, 170, 170), font=FONT)\n",
+    "\n",
+    "    elapsed_ms = (time.perf_counter() - start) * 1000\n",
+    "    draw.text((canvas_w - 115, 8), f\"{elapsed_ms:.0f} ms\", fill=(120, 210, 120), font=FONT)\n",
+    "    return canvas, frames, counts, alignment, cycle_info\n",
+    "\n",
+    "\n",
+    "image, frames, counts, alignment, cycle_info = render_fast_sheet(offset_us=default_offset_us)\n",
+    "display(DisplayImage(data=png_bytes(image)))"
+   ],
+   "id": "52d8840a82e058d4",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trigger Timeline and Bleed Diagnostics\n",
+    "\n",
+    "These views answer different questions than the frame grid. The timeline shows trigger positions, accumulation windows, and event density over time. The relative activity graph shows whether events are arriving before, during, or after each trigger. The bleed matrix estimates how much spatial activity carries from one ordered frame into the next."
+   ],
+   "id": "70d6cb5646bcec9a"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:06.636839500Z",
+     "start_time": "2026-06-05T18:09:06.234215700Z"
+    }
+   },
+   "source": [
+    "SLOT_COLORS = [\n",
+    "    (95, 190, 255),\n",
+    "    (255, 175, 80),\n",
+    "    (140, 220, 120),\n",
+    "    (235, 105, 130),\n",
+    "    (190, 150, 255),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def slot_color(slot: int) -> tuple[int, int, int]:\n",
+    "    return SLOT_COLORS[int(slot) % len(SLOT_COLORS)]\n",
+    "\n",
+    "\n",
+    "def render_trigger_timeline(\n",
+    "    offset_us: int = default_offset_us,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    bin_us: int = 100,\n",
+    "    margin_us: int = 500,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    trigger_ts = np.asarray(trigger_ts, dtype=np.int64)\n",
+    "    if trigger_ts.size == 0:\n",
+    "        return Image.new(\"RGB\", (900, 120), (14, 14, 14))\n",
+    "\n",
+    "    # Keep the absolute-time axis anchored to triggers, not to shifted windows.\n",
+    "    # Otherwise changing offset re-centers the graph and makes trigger lines look like they move backward.\n",
+    "    axis_offset_min_us = int(offset_min_us)\n",
+    "    axis_offset_max_us = int(offset_max_us)\n",
+    "    start = int(np.min(trigger_ts) + min(axis_offset_min_us, int(offset_us), 0) - int(margin_us))\n",
+    "    end = int(np.max(trigger_ts) + max(axis_offset_max_us, int(offset_us), 0) + int(window_us) + int(margin_us))\n",
+    "    bins = np.arange(start, end + int(bin_us), int(bin_us), dtype=np.int64)\n",
+    "    if bins.size < 2:\n",
+    "        bins = np.array([start, end + 1], dtype=np.int64)\n",
+    "    event_counts, _ = np.histogram(event_timestamps, bins=bins)\n",
+    "\n",
+    "    plot_w = 1160\n",
+    "    left = 58\n",
+    "    right = 16\n",
+    "    top_h = 128\n",
+    "    row_h = 30\n",
+    "    footer_h = 42\n",
+    "    rows = max(1, ceil(len(trigger_ts) / cycle_length))\n",
+    "    canvas = Image.new(\"RGB\", (plot_w + left + right, top_h + rows * row_h + footer_h), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "\n",
+    "    def x_of(t_us: int | float) -> int:\n",
+    "        return int(left + (float(t_us) - start) / max(1, end - start) * plot_w)\n",
+    "\n",
+    "    draw.text((10, 8), f\"trigger/window timeline | offset {int(offset_us)} us | window {int(window_us)} us | bin {int(bin_us)} us\", fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((10, 26), \"top graph = all event timestamps binned over time; rows = ordered cycles\", fill=(175, 175, 175), font=FONT)\n",
+    "\n",
+    "    hist_top = 54\n",
+    "    hist_h = 60\n",
+    "    max_count = max(1, int(event_counts.max()) if event_counts.size else 1)\n",
+    "    for idx, count in enumerate(event_counts):\n",
+    "        x0 = x_of(int(bins[idx]))\n",
+    "        x1 = max(x0 + 1, x_of(int(bins[idx + 1])))\n",
+    "        h = int((int(count) / max_count) * hist_h)\n",
+    "        draw.rectangle((x0, hist_top + hist_h - h, x1, hist_top + hist_h), fill=(95, 95, 95))\n",
+    "    draw.rectangle((left, hist_top, left + plot_w, hist_top + hist_h), outline=(90, 90, 90))\n",
+    "\n",
+    "    for index, trigger in enumerate(trigger_ts):\n",
+    "        slot = index % cycle_length\n",
+    "        color = slot_color(slot)\n",
+    "        x = x_of(int(trigger))\n",
+    "        draw.line((x, hist_top, x, hist_top + hist_h), fill=color)\n",
+    "\n",
+    "    for cycle in range(rows):\n",
+    "        y = top_h + cycle * row_h\n",
+    "        draw.text((10, y + 7), f\"c{cycle + 1}\", fill=(210, 210, 210), font=FONT)\n",
+    "        draw.line((left, y + row_h // 2, left + plot_w, y + row_h // 2), fill=(45, 45, 45))\n",
+    "        for slot in range(cycle_length):\n",
+    "            index = cycle * cycle_length + slot\n",
+    "            if index >= len(trigger_ts):\n",
+    "                break\n",
+    "            trigger = int(trigger_ts[index])\n",
+    "            win_start = trigger + int(offset_us)\n",
+    "            win_end = win_start + int(window_us)\n",
+    "            x0 = x_of(win_start)\n",
+    "            x1 = x_of(win_end)\n",
+    "            xt = x_of(trigger)\n",
+    "            color = slot_color(slot)\n",
+    "            fill = tuple(max(25, c // 3) for c in color)\n",
+    "            draw.rectangle((x0, y + 5, x1, y + row_h - 6), fill=fill, outline=color)\n",
+    "            draw.line((xt, y + 2, xt, y + row_h - 2), fill=(255, 255, 255))\n",
+    "            if index + 1 < len(trigger_ts) and win_end > int(trigger_ts[index + 1]):\n",
+    "                draw.line((x1, y + 3, x1, y + row_h - 3), fill=(255, 80, 80), width=2)\n",
+    "\n",
+    "    dt = np.diff(trigger_ts)\n",
+    "    footer_y = top_h + rows * row_h + 8\n",
+    "    if dt.size:\n",
+    "        min_gap_after_window = int(np.min(dt - int(window_us) - int(offset_us)))\n",
+    "        draw.text(\n",
+    "            (10, footer_y),\n",
+    "            f\"trigger spacing us: min={int(dt.min())}, median={float(np.median(dt)):.0f}, max={int(dt.max())}; min next-trigger gap after window={min_gap_after_window} us\",\n",
+    "            fill=(210, 210, 210),\n",
+    "            font=FONT,\n",
+    "        )\n",
+    "    draw.text((10, footer_y + 16), \"colored blocks = accumulation windows; white line = trigger; red marker = window overlaps next trigger\", fill=(175, 175, 175), font=FONT)\n",
+    "    return canvas\n",
+    "\n",
+    "\n",
+    "def render_relative_activity(\n",
+    "    offset_us: int = default_offset_us,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    pre_us: int = 1000,\n",
+    "    post_us: int = max(3000, default_window_us + 1000),\n",
+    "    bin_us: int = 50,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    trigger_ts = np.asarray(trigger_ts, dtype=np.int64)\n",
+    "    rel_edges = np.arange(-int(pre_us), int(post_us) + int(bin_us), int(bin_us), dtype=np.int64)\n",
+    "    rel_centers = (rel_edges[:-1] + rel_edges[1:]) / 2.0\n",
+    "    slot_counts = np.zeros((cycle_length, len(rel_centers)), dtype=np.float32)\n",
+    "    slot_seen = np.zeros(cycle_length, dtype=np.int64)\n",
+    "    for index, trigger in enumerate(trigger_ts):\n",
+    "        slot = index % cycle_length\n",
+    "        hist, _ = np.histogram(event_timestamps, bins=trigger + rel_edges)\n",
+    "        slot_counts[slot] += hist.astype(np.float32)\n",
+    "        slot_seen[slot] += 1\n",
+    "    for slot in range(cycle_length):\n",
+    "        if slot_seen[slot] > 0:\n",
+    "            slot_counts[slot] /= slot_seen[slot]\n",
+    "\n",
+    "    W, H = 1000, 360\n",
+    "    left, right, top, bottom = 68, 18, 40, 52\n",
+    "    plot_w = W - left - right\n",
+    "    plot_h = H - top - bottom\n",
+    "    canvas = Image.new(\"RGB\", (W, H), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "\n",
+    "    def x_of(rel_us: int | float) -> int:\n",
+    "        return int(left + (float(rel_us) + int(pre_us)) / max(1, int(pre_us) + int(post_us)) * plot_w)\n",
+    "\n",
+    "    max_y = max(1.0, float(slot_counts.max()))\n",
+    "\n",
+    "    def y_of(count: int | float) -> int:\n",
+    "        return int(top + plot_h - (float(count) / max_y) * plot_h)\n",
+    "\n",
+    "    win_x0 = x_of(int(offset_us))\n",
+    "    win_x1 = x_of(int(offset_us) + int(window_us))\n",
+    "    draw.rectangle((win_x0, top, win_x1, top + plot_h), fill=(32, 42, 32), outline=(90, 130, 90))\n",
+    "    draw.line((x_of(0), top, x_of(0), top + plot_h), fill=(230, 230, 230))\n",
+    "    draw.rectangle((left, top, left + plot_w, top + plot_h), outline=(95, 95, 95))\n",
+    "    draw.text((10, 8), f\"event activity relative to trigger | offset {int(offset_us)} us | window {int(window_us)} us\", fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((left, H - 34), \"relative time from trigger (us)\", fill=(190, 190, 190), font=FONT)\n",
+    "    draw.text((8, top + 4), \"avg events/bin\", fill=(190, 190, 190), font=FONT)\n",
+    "    for tick in range(-int(pre_us), int(post_us) + 1, 500):\n",
+    "        x = x_of(tick)\n",
+    "        draw.line((x, top + plot_h, x, top + plot_h + 5), fill=(140, 140, 140))\n",
+    "        draw.text((x - 16, top + plot_h + 8), str(tick), fill=(160, 160, 160), font=FONT)\n",
+    "\n",
+    "    for slot in range(cycle_length):\n",
+    "        points = [(x_of(x), y_of(y)) for x, y in zip(rel_centers, slot_counts[slot], strict=False)]\n",
+    "        if len(points) >= 2:\n",
+    "            draw.line(points, fill=slot_color(slot), width=2)\n",
+    "        label = display_sequence[slot] if slot < len(display_sequence) else slot + 1\n",
+    "        draw.text((W - 138, 44 + slot * 16), f\"{label}: avg peak {float(slot_counts[slot].max()):.1f}\", fill=slot_color(slot), font=FONT)\n",
+    "    draw.text((win_x0 + 3, top + 4), \"accum window\", fill=(170, 220, 170), font=FONT)\n",
+    "    return canvas\n",
+    "\n",
+    "\n",
+    "def render_bleed_matrix(\n",
+    "    offset_us: int = default_offset_us,\n",
+    "    cycles: int = default_cycles,\n",
+    "    window_us: int = default_window_us,\n",
+    "    polarity_mode: str = \"ignore\",\n",
+    "    mask_dilate: int = 3,\n",
+    "):\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    masks = np.abs(frames) > 0\n",
+    "    if int(mask_dilate) > 1:\n",
+    "        masks = np.stack([dilate_plane(mask.astype(np.float32), int(mask_dilate)) > 0 for mask in masks], axis=0)\n",
+    "    active = masks.reshape(masks.shape[0], -1).sum(axis=1) if masks.size else np.array([], dtype=np.int64)\n",
+    "    matrix_values = [[[] for _ in range(cycle_length)] for _ in range(cycle_length)]\n",
+    "    adjacent_scores = []\n",
+    "    for index in range(max(0, len(masks) - 1)):\n",
+    "        a = index % cycle_length\n",
+    "        b = (index + 1) % cycle_length\n",
+    "        inter = int(np.logical_and(masks[index], masks[index + 1]).sum())\n",
+    "        denom = max(1, int(min(active[index], active[index + 1])))\n",
+    "        score = inter / denom\n",
+    "        matrix_values[a][b].append(score)\n",
+    "        adjacent_scores.append(score)\n",
+    "\n",
+    "    matrix = np.zeros((cycle_length, cycle_length), dtype=np.float32)\n",
+    "    for r in range(cycle_length):\n",
+    "        for c in range(cycle_length):\n",
+    "            if matrix_values[r][c]:\n",
+    "                matrix[r, c] = float(np.mean(matrix_values[r][c]))\n",
+    "\n",
+    "    cell = 74\n",
+    "    left, top = 116, 72\n",
+    "    W = left + cycle_length * cell + 260\n",
+    "    H = top + cycle_length * cell + 86\n",
+    "    canvas = Image.new(\"RGB\", (W, H), (14, 14, 14))\n",
+    "    draw = ImageDraw.Draw(canvas)\n",
+    "    draw.text((10, 8), f\"consecutive-frame spatial overlap | offset {int(offset_us)} us | mask dilate {int(mask_dilate)}\", fill=(235, 235, 235), font=FONT)\n",
+    "    draw.text((10, 27), \"cell value = mean intersection / smaller active-pixel count for ordered frame transitions\", fill=(175, 175, 175), font=FONT)\n",
+    "    draw.text((10, 47), \"high off-diagonal values can mean bleed, but shared strokes in the same position also contribute\", fill=(175, 175, 175), font=FONT)\n",
+    "\n",
+    "    for slot in range(cycle_length):\n",
+    "        label = str(display_sequence[slot] if slot < len(display_sequence) else slot + 1)\n",
+    "        draw.text((left + slot * cell + 28, top - 22), label, fill=slot_color(slot), font=FONT)\n",
+    "        draw.text((left - 34, top + slot * cell + 28), label, fill=slot_color(slot), font=FONT)\n",
+    "    draw.text((left + 95, top - 42), \"next frame\", fill=(210, 210, 210), font=FONT)\n",
+    "    draw.text((22, top + 145), \"current\", fill=(210, 210, 210), font=FONT)\n",
+    "\n",
+    "    max_value = max(0.01, float(matrix.max()))\n",
+    "    for r in range(cycle_length):\n",
+    "        for c in range(cycle_length):\n",
+    "            value = float(matrix[r, c])\n",
+    "            intensity = int(np.clip(value / max_value * 255, 0, 255))\n",
+    "            fill = (intensity, max(18, intensity // 3), 28)\n",
+    "            x0 = left + c * cell\n",
+    "            y0 = top + r * cell\n",
+    "            draw.rectangle((x0, y0, x0 + cell - 4, y0 + cell - 4), fill=fill, outline=(85, 85, 85))\n",
+    "            draw.text((x0 + 13, y0 + 27), f\"{value * 100:.1f}%\", fill=(245, 245, 245), font=FONT)\n",
+    "\n",
+    "    stats_x = left + cycle_length * cell + 32\n",
+    "    if active.size:\n",
+    "        draw.text((stats_x, top), f\"active px/frame\", fill=(230, 230, 230), font=FONT)\n",
+    "        draw.text((stats_x, top + 18), f\"first: {int(active[0])}\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 34), f\"min:   {int(active.min())}\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 50), f\"med:   {int(np.median(active))}\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 66), f\"max:   {int(active.max())}\", fill=(190, 190, 190), font=FONT)\n",
+    "    if adjacent_scores:\n",
+    "        draw.text((stats_x, top + 102), f\"adjacent overlap\", fill=(230, 230, 230), font=FONT)\n",
+    "        draw.text((stats_x, top + 120), f\"mean: {float(np.mean(adjacent_scores)) * 100:.1f}%\", fill=(190, 190, 190), font=FONT)\n",
+    "        draw.text((stats_x, top + 136), f\"max:  {float(np.max(adjacent_scores)) * 100:.1f}%\", fill=(190, 190, 190), font=FONT)\n",
+    "    return canvas\n",
+    "\n",
+    "\n",
+    "timeline_image = render_trigger_timeline(offset_us=default_offset_us)\n",
+    "activity_image = render_relative_activity(offset_us=default_offset_us)\n",
+    "bleed_image = render_bleed_matrix(offset_us=default_offset_us)\n",
+    "display(DisplayImage(data=png_bytes(timeline_image)))\n",
+    "display(DisplayImage(data=png_bytes(activity_image)))\n",
+    "display(DisplayImage(data=png_bytes(bleed_image)))"
+   ],
+   "id": "50f5e2de87c4a00e",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive Controls\n",
+    "\n",
+    "Defaults are tuned to make sparse event frames visible: ROI crop, per-frame contrast, log tone, and dot dilation. Switch contrast to `global` when you want brightness comparisons to be physically meaningful across frames."
+   ],
+   "id": "ab941b0099e65e31"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:07.131109Z",
+     "start_time": "2026-06-05T18:09:06.653927400Z"
+    }
+   },
+   "source": [
+    "try:\n",
+    "    import ipywidgets as widgets\n",
+    "    HAVE_WIDGETS = True\n",
+    "except ModuleNotFoundError:\n",
+    "    HAVE_WIDGETS = False\n",
+    "    print(\"ipywidgets is not installed in this kernel. Run `%pip install ipywidgets`, restart the kernel, then rerun this notebook.\")\n",
+    "\n",
+    "\n",
+    "if HAVE_WIDGETS:\n",
+    "    offset_slider = widgets.IntSlider(\n",
+    "        value=default_offset_us,\n",
+    "        min=offset_min_us,\n",
+    "        max=offset_max_us,\n",
+    "        step=25,\n",
+    "        description=\"offset us\",\n",
+    "        continuous_update=True,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    cycles_slider = widgets.IntSlider(\n",
+    "        value=default_cycles,\n",
+    "        min=1,\n",
+    "        max=max(1, min(20, int(available_cycles))),\n",
+    "        step=1,\n",
+    "        description=\"cycles\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    window_slider = widgets.IntSlider(\n",
+    "        value=default_window_us,\n",
+    "        min=100,\n",
+    "        max=max(2500, default_window_us * 2),\n",
+    "        step=25,\n",
+    "        description=\"window us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    focus_slider = widgets.IntSlider(\n",
+    "        value=1,\n",
+    "        min=1,\n",
+    "        max=max(1, default_cycles * cycle_length),\n",
+    "        step=1,\n",
+    "        description=\"focus\",\n",
+    "        continuous_update=True,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    polarity_dropdown = widgets.Dropdown(value=\"ignore\", options=[\"ignore\", \"signed\", \"positive\"], description=\"accum\")\n",
+    "    view_dropdown = widgets.Dropdown(value=\"magnitude\", options=[\"magnitude\", \"positive\", \"negative\", \"signed\"], description=\"view\")\n",
+    "    tone_dropdown = widgets.Dropdown(value=\"log\", options=[\"log\", \"gamma\", \"linear\"], description=\"tone\")\n",
+    "    contrast_dropdown = widgets.Dropdown(value=\"per-frame\", options=[\"per-frame\", \"global\"], description=\"contrast\")\n",
+    "    crop_dropdown = widgets.Dropdown(value=\"square\", options=[\"square\", \"auto\", \"full\"], description=\"crop\")\n",
+    "    gamma_slider = widgets.FloatSlider(value=0.65, min=0.2, max=1.5, step=0.05, description=\"gamma\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    vmax_slider = widgets.FloatSlider(value=99.0, min=90.0, max=100.0, step=0.1, description=\"vmax %\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    pad_slider = widgets.IntSlider(value=28, min=0, max=80, step=2, description=\"crop pad\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    dot_slider = widgets.IntSlider(value=2, min=1, max=6, step=1, description=\"dot size\", continuous_update=True, layout=widgets.Layout(width=\"560px\"))\n",
+    "    tile_scale_slider = widgets.IntSlider(value=2, min=1, max=4, step=1, description=\"tile scale\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    focus_scale_slider = widgets.IntSlider(value=4, min=2, max=8, step=1, description=\"focus scale\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    flip_checkbox = widgets.Checkbox(value=True, description=\"flip x for viewing\")\n",
+    "    out = widgets.Output()\n",
+    "\n",
+    "    def sync_focus_max(*_):\n",
+    "        focus_slider.max = max(1, cycles_slider.value * cycle_length)\n",
+    "        if focus_slider.value > focus_slider.max:\n",
+    "            focus_slider.value = focus_slider.max\n",
+    "\n",
+    "    def redraw(_=None):\n",
+    "        sync_focus_max()\n",
+    "        with out:\n",
+    "            clear_output(wait=True)\n",
+    "            image, frames, counts, alignment, cycle_info = render_fast_sheet(\n",
+    "                offset_us=offset_slider.value,\n",
+    "                cycles=cycles_slider.value,\n",
+    "                window_us=window_slider.value,\n",
+    "                polarity_mode=polarity_dropdown.value,\n",
+    "                view=view_dropdown.value,\n",
+    "                tone=tone_dropdown.value,\n",
+    "                gamma=gamma_slider.value,\n",
+    "                contrast=contrast_dropdown.value,\n",
+    "                vmax_percentile=vmax_slider.value,\n",
+    "                crop_mode=crop_dropdown.value,\n",
+    "                crop_pad=pad_slider.value,\n",
+    "                dot_size=dot_slider.value,\n",
+    "                tile_scale=tile_scale_slider.value,\n",
+    "                focus_frame=focus_slider.value,\n",
+    "                focus_scale=focus_scale_slider.value,\n",
+    "                flip_x=flip_checkbox.value,\n",
+    "            )\n",
+    "            display(DisplayImage(data=png_bytes(image)))\n",
+    "\n",
+    "    for widget in [\n",
+    "        offset_slider,\n",
+    "        cycles_slider,\n",
+    "        window_slider,\n",
+    "        focus_slider,\n",
+    "        polarity_dropdown,\n",
+    "        view_dropdown,\n",
+    "        tone_dropdown,\n",
+    "        contrast_dropdown,\n",
+    "        crop_dropdown,\n",
+    "        gamma_slider,\n",
+    "        vmax_slider,\n",
+    "        pad_slider,\n",
+    "        dot_slider,\n",
+    "        tile_scale_slider,\n",
+    "        focus_scale_slider,\n",
+    "        flip_checkbox,\n",
+    "    ]:\n",
+    "        widget.observe(redraw, names=\"value\")\n",
+    "\n",
+    "    controls = widgets.VBox([\n",
+    "        offset_slider,\n",
+    "        cycles_slider,\n",
+    "        window_slider,\n",
+    "        focus_slider,\n",
+    "        widgets.HBox([polarity_dropdown, view_dropdown, tone_dropdown, contrast_dropdown, crop_dropdown]),\n",
+    "        gamma_slider,\n",
+    "        vmax_slider,\n",
+    "        pad_slider,\n",
+    "        widgets.HBox([dot_slider, tile_scale_slider, focus_scale_slider, flip_checkbox]),\n",
+    "    ])\n",
+    "    display(controls)\n",
+    "    display(out)\n",
+    "    redraw()\n",
+    "else:\n",
+    "    image, frames, counts, alignment, cycle_info = render_fast_sheet(offset_us=default_offset_us)\n",
+    "    display(DisplayImage(data=png_bytes(image)))"
+   ],
+   "id": "55dcce48ae090aad",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VBox(children=(IntSlider(value=0, description='offset us', layout=Layout(width='560px'), max=1000, min=-4000, …"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "f77a1bca0d4148119c1aa5a521d7e0b1"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "88f70bfea9d845e583da50bb01be428e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive Timeline and Bleed Controls\n",
+    "\n",
+    "Use this when the frame grid looks bad and you want to separate timing problems from image-rendering problems. These controls update on release instead of continuously, which keeps the notebook responsive."
+   ],
+   "id": "c56bff8d6cf7818"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:07.541863300Z",
+     "start_time": "2026-06-05T18:09:07.148922Z"
+    }
+   },
+   "source": [
+    "try:\n",
+    "    import ipywidgets as widgets\n",
+    "    HAVE_WIDGETS = True\n",
+    "except ModuleNotFoundError:\n",
+    "    HAVE_WIDGETS = False\n",
+    "\n",
+    "\n",
+    "if HAVE_WIDGETS:\n",
+    "    diag_offset_slider = widgets.IntSlider(\n",
+    "        value=default_offset_us,\n",
+    "        min=offset_min_us,\n",
+    "        max=offset_max_us,\n",
+    "        step=25,\n",
+    "        description=\"offset us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    diag_window_slider = widgets.IntSlider(\n",
+    "        value=default_window_us,\n",
+    "        min=100,\n",
+    "        max=max(2500, default_window_us * 2),\n",
+    "        step=25,\n",
+    "        description=\"window us\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    diag_cycles_slider = widgets.IntSlider(\n",
+    "        value=default_cycles,\n",
+    "        min=1,\n",
+    "        max=max(1, min(20, recording.stats[\"trigger_edges\"].get(\"rising\", 0) // max(1, cycle_length))),\n",
+    "        step=1,\n",
+    "        description=\"cycles\",\n",
+    "        continuous_update=False,\n",
+    "        layout=widgets.Layout(width=\"560px\"),\n",
+    "    )\n",
+    "    diag_bin_slider = widgets.IntSlider(value=100, min=25, max=500, step=25, description=\"time bin\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_pre_slider = widgets.IntSlider(value=1000, min=0, max=3000, step=100, description=\"pre us\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_post_default_us = max(3000, default_window_us + 1000)\n",
+    "    diag_post_slider = widgets.IntSlider(value=diag_post_default_us, min=500, max=max(6000, default_window_us * 2), step=100, description=\"post us\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_mask_slider = widgets.IntSlider(value=3, min=1, max=9, step=1, description=\"mask dilate\", continuous_update=False, layout=widgets.Layout(width=\"560px\"))\n",
+    "    diag_polarity_dropdown = widgets.Dropdown(value=\"ignore\", options=[\"ignore\", \"signed\", \"positive\"], description=\"accum\")\n",
+    "    diag_out = widgets.Output()\n",
+    "\n",
+    "    def redraw_diagnostics(_=None):\n",
+    "        with diag_out:\n",
+    "            clear_output(wait=True)\n",
+    "            timeline_image = render_trigger_timeline(\n",
+    "                offset_us=diag_offset_slider.value,\n",
+    "                cycles=diag_cycles_slider.value,\n",
+    "                window_us=diag_window_slider.value,\n",
+    "                bin_us=diag_bin_slider.value,\n",
+    "                polarity_mode=diag_polarity_dropdown.value,\n",
+    "            )\n",
+    "            activity_image = render_relative_activity(\n",
+    "                offset_us=diag_offset_slider.value,\n",
+    "                cycles=diag_cycles_slider.value,\n",
+    "                window_us=diag_window_slider.value,\n",
+    "                pre_us=diag_pre_slider.value,\n",
+    "                post_us=diag_post_slider.value,\n",
+    "                bin_us=max(25, diag_bin_slider.value // 2),\n",
+    "                polarity_mode=diag_polarity_dropdown.value,\n",
+    "            )\n",
+    "            bleed_image = render_bleed_matrix(\n",
+    "                offset_us=diag_offset_slider.value,\n",
+    "                cycles=diag_cycles_slider.value,\n",
+    "                window_us=diag_window_slider.value,\n",
+    "                polarity_mode=diag_polarity_dropdown.value,\n",
+    "                mask_dilate=diag_mask_slider.value,\n",
+    "            )\n",
+    "            display(DisplayImage(data=png_bytes(timeline_image)))\n",
+    "            display(DisplayImage(data=png_bytes(activity_image)))\n",
+    "            display(DisplayImage(data=png_bytes(bleed_image)))\n",
+    "\n",
+    "    for widget in [\n",
+    "        diag_offset_slider,\n",
+    "        diag_window_slider,\n",
+    "        diag_cycles_slider,\n",
+    "        diag_bin_slider,\n",
+    "        diag_pre_slider,\n",
+    "        diag_post_slider,\n",
+    "        diag_mask_slider,\n",
+    "        diag_polarity_dropdown,\n",
+    "    ]:\n",
+    "        widget.observe(redraw_diagnostics, names=\"value\")\n",
+    "\n",
+    "    display(widgets.VBox([\n",
+    "        diag_offset_slider,\n",
+    "        diag_window_slider,\n",
+    "        diag_cycles_slider,\n",
+    "        widgets.HBox([diag_bin_slider, diag_mask_slider, diag_polarity_dropdown]),\n",
+    "        widgets.HBox([diag_pre_slider, diag_post_slider]),\n",
+    "    ]))\n",
+    "    display(diag_out)\n",
+    "    redraw_diagnostics()\n",
+    "else:\n",
+    "    print(\"ipywidgets is not installed in this kernel. Static diagnostics were rendered above.\")"
+   ],
+   "id": "83065a9934309a9b",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VBox(children=(IntSlider(value=0, continuous_update=False, description='offset us', layout=Layout(width='560px…"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "031e36d070184e8a9e2cbc830b183a93"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "e470285e413f44dabfe01319be37d8bc"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Offset Score Table\n",
+    "\n",
+    "This is a quick numerical scan. It does not pick a cycle or reorder frames; it only reports how many nonzero pixels each offset produces in the first `N x 5` ordered frames."
+   ],
+   "id": "e43aa313b750612a"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:07.912086900Z",
+     "start_time": "2026-06-05T18:09:07.563234Z"
+    }
+   },
+   "source": [
+    "def offset_score_table(offsets=range(100, 501, 50), cycles=default_cycles, window_us=1750, polarity_mode=\"ignore\"):\n",
+    "    rows = []\n",
+    "    for offset in offsets:\n",
+    "        frames, trigger_ts, alignment, cycle_info = accumulate_cached(int(offset), int(cycles), int(window_us), str(polarity_mode))\n",
+    "        counts = np.count_nonzero(np.abs(frames) > 0, axis=(1, 2))\n",
+    "        first_cycle = counts[:cycle_length] if counts.size else np.array([], dtype=int)\n",
+    "        rows.append({\n",
+    "            \"offset_us\": int(offset),\n",
+    "            \"frame1\": int(counts[0]) if counts.size else 0,\n",
+    "            \"first_cycle_min\": int(first_cycle.min()) if first_cycle.size else 0,\n",
+    "            \"first_cycle_median\": int(np.median(first_cycle)) if first_cycle.size else 0,\n",
+    "            \"all_min\": int(counts.min()) if counts.size else 0,\n",
+    "            \"all_median\": int(np.median(counts)) if counts.size else 0,\n",
+    "            \"all_max\": int(counts.max()) if counts.size else 0,\n",
+    "        })\n",
+    "    return sorted(rows, key=lambda row: (row[\"first_cycle_min\"], row[\"frame1\"], row[\"all_median\"]), reverse=True)\n",
+    "\n",
+    "\n",
+    "scores = offset_score_table()\n",
+    "for row in scores[:12]:\n",
+    "    print(row)"
+   ],
+   "id": "72d2c1268013fdae",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'offset_us': 500, 'frame1': 342, 'first_cycle_min': 342, 'first_cycle_median': 505, 'all_min': 329, 'all_median': 477, 'all_max': 555}\n",
+      "{'offset_us': 350, 'frame1': 335, 'first_cycle_min': 335, 'first_cycle_median': 473, 'all_min': 318, 'all_median': 442, 'all_max': 553}\n",
+      "{'offset_us': 400, 'frame1': 335, 'first_cycle_min': 335, 'first_cycle_median': 473, 'all_min': 335, 'all_median': 437, 'all_max': 552}\n",
+      "{'offset_us': 300, 'frame1': 329, 'first_cycle_min': 329, 'first_cycle_median': 467, 'all_min': 327, 'all_median': 441, 'all_max': 525}\n",
+      "{'offset_us': 450, 'frame1': 315, 'first_cycle_min': 315, 'first_cycle_median': 455, 'all_min': 315, 'all_median': 455, 'all_max': 550}\n",
+      "{'offset_us': 150, 'frame1': 311, 'first_cycle_min': 311, 'first_cycle_median': 422, 'all_min': 311, 'all_median': 422, 'all_max': 515}\n",
+      "{'offset_us': 200, 'frame1': 309, 'first_cycle_min': 309, 'first_cycle_median': 420, 'all_min': 309, 'all_median': 415, 'all_max': 516}\n",
+      "{'offset_us': 100, 'frame1': 306, 'first_cycle_min': 306, 'first_cycle_median': 413, 'all_min': 306, 'all_median': 410, 'all_max': 486}\n",
+      "{'offset_us': 250, 'frame1': 299, 'first_cycle_min': 299, 'first_cycle_median': 412, 'all_min': 299, 'all_median': 431, 'all_max': 513}\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic Offset/Window Scan\n",
+    "\n",
+    "This ranks `(accumulation_start_offset_us, window_us)` pairs without reordering triggers. The balanced score is a starting point; compare it with the signal-heavy and low-bleed lists before choosing a value for a real run."
+   ],
+   "id": "1d9899ecd03e684d"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-05T18:09:18.758625800Z",
+     "start_time": "2026-06-05T18:09:07.915532900Z"
+    }
+   },
+   "source": [
+    "def _dilate_mask(mask: np.ndarray, size: int = 3) -> np.ndarray:\n",
+    "    if int(size) <= 1:\n",
+    "        return mask.astype(bool, copy=False)\n",
+    "    return dilate_plane(mask.astype(np.float32), int(size)) > 0\n",
+    "\n",
+    "\n",
+    "def timing_candidate_metrics(offset_us: int, window_us: int, cycles: int = default_cycles, polarity_mode: str = \"ignore\") -> dict:\n",
+    "    frames, trigger_ts, alignment, cycle_info = accumulate_cached(\n",
+    "        int(offset_us), int(cycles), int(window_us), str(polarity_mode)\n",
+    "    )\n",
+    "    masks = np.abs(frames) > 0\n",
+    "    counts = masks.sum(axis=(1, 2)).astype(np.float32)\n",
+    "    trigger_ts = np.asarray(trigger_ts, dtype=np.int64)\n",
+    "    if counts.size == 0:\n",
+    "        return {\"valid\": False, \"offset_us\": int(offset_us), \"window_us\": int(window_us)}\n",
+    "\n",
+    "    first_cycle = counts[:cycle_length]\n",
+    "    slot_counts = counts[: (counts.size // cycle_length) * cycle_length].reshape((-1, cycle_length))\n",
+    "    slot_cv = np.std(slot_counts, axis=0) / np.maximum(1.0, np.mean(slot_counts, axis=0))\n",
+    "    dilated = np.stack([_dilate_mask(mask, 3) for mask in masks], axis=0)\n",
+    "    overlaps = []\n",
+    "    for index in range(len(dilated) - 1):\n",
+    "        intersection = np.logical_and(dilated[index], dilated[index + 1]).sum()\n",
+    "        denominator = max(1, min(dilated[index].sum(), dilated[index + 1].sum()))\n",
+    "        overlaps.append(float(intersection / denominator))\n",
+    "    overlaps = np.asarray(overlaps, dtype=np.float32)\n",
+    "    spacing = np.diff(trigger_ts).astype(np.float32)\n",
+    "    min_gap_after_window = float(np.min(spacing - int(offset_us) - int(window_us))) if spacing.size else 9999.0\n",
+    "\n",
+    "    signal_score = (\n",
+    "        np.log1p(float(np.median(counts)))\n",
+    "        + 0.75 * np.log1p(float(counts[0]))\n",
+    "        + 0.50 * np.log1p(float(np.min(first_cycle)))\n",
+    "    )\n",
+    "    consistency_penalty = 1.5 * float(np.mean(slot_cv))\n",
+    "    bleed_penalty = (3.0 * float(np.mean(overlaps)) + 1.0 * float(np.max(overlaps))) if overlaps.size else 0.0\n",
+    "    gap_penalty = max(0.0, -min_gap_after_window / 250.0) + max(0.0, (100.0 - min_gap_after_window) / 1000.0)\n",
+    "\n",
+    "    return {\n",
+    "        \"valid\": True,\n",
+    "        \"offset_us\": int(offset_us),\n",
+    "        \"window_us\": int(window_us),\n",
+    "        \"balanced_score\": float(signal_score - consistency_penalty - bleed_penalty - gap_penalty),\n",
+    "        \"signal_heavy_score\": float(signal_score - 0.5 * consistency_penalty - 0.4 * bleed_penalty - gap_penalty),\n",
+    "        \"low_bleed_score\": float(signal_score - consistency_penalty - 2.5 * bleed_penalty - 1.5 * gap_penalty),\n",
+    "        \"frame1_active\": int(counts[0]),\n",
+    "        \"first_cycle_min_active\": int(np.min(first_cycle)),\n",
+    "        \"median_active\": int(np.median(counts)),\n",
+    "        \"adjacent_overlap_mean_pct\": float(np.mean(overlaps) * 100.0) if overlaps.size else 0.0,\n",
+    "        \"adjacent_overlap_max_pct\": float(np.max(overlaps) * 100.0) if overlaps.size else 0.0,\n",
+    "        \"min_gap_after_window_us\": int(round(min_gap_after_window)),\n",
+    "        \"slot_cv_mean\": float(np.mean(slot_cv)),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def run_automatic_timing_scan(offsets=range(-500, 101, 25), windows=range(1200, 1801, 50), cycles=default_cycles, polarity_mode=\"ignore\"):\n",
+    "    started = time.perf_counter()\n",
+    "    rows = []\n",
+    "    for window_us in windows:\n",
+    "        for offset_us in offsets:\n",
+    "            row = timing_candidate_metrics(offset_us, window_us, cycles=cycles, polarity_mode=polarity_mode)\n",
+    "            if row.get(\"valid\"):\n",
+    "                rows.append(row)\n",
+    "    print(f\"scanned {len(rows)} candidates in {time.perf_counter() - started:.1f}s\")\n",
+    "    return rows\n",
+    "\n",
+    "\n",
+    "def print_ranked(rows, key: str, title: str, limit: int = 10):\n",
+    "    print(\"\\n\" + title)\n",
+    "    print(\"offset window score  f1 first med overlap_mean overlap_max min_gap cv\")\n",
+    "    for row in sorted(rows, key=lambda item: item[key], reverse=True)[:limit]:\n",
+    "        print(\n",
+    "            f\"{row['offset_us']:>6} {row['window_us']:>6} {row[key]:>6.3f} \"\n",
+    "            f\"{row['frame1_active']:>3} {row['first_cycle_min_active']:>5} {row['median_active']:>3} \"\n",
+    "            f\"{row['adjacent_overlap_mean_pct']:>11.1f}% {row['adjacent_overlap_max_pct']:>10.1f}% \"\n",
+    "            f\"{row['min_gap_after_window_us']:>7} {row['slot_cv_mean']:.2f}\"\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "auto_timing_rows = run_automatic_timing_scan()\n",
+    "print_ranked(auto_timing_rows, \"balanced_score\", \"Balanced candidates\")\n",
+    "print_ranked(auto_timing_rows, \"signal_heavy_score\", \"Signal-heavy candidates\")\n",
+    "print_ranked(auto_timing_rows, \"low_bleed_score\", \"Low-bleed candidates\")"
+   ],
+   "id": "88e1a7255b28f785",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scanned 325 candidates in 10.8s\n",
+      "\n",
+      "Balanced candidates\n",
+      "offset window score  f1 first med overlap_mean overlap_max min_gap cv\n",
+      "    75   1800 12.099 313   313 425        20.7%       41.7%    3056 0.07\n",
+      "    50   1800 12.089 309   309 417        20.5%       40.1%    3081 0.07\n",
+      "   100   1800 12.078 313   313 425        21.0%       41.9%    3031 0.08\n",
+      "   100   1750 12.055 306   306 410        20.4%       40.3%    3081 0.07\n",
+      "     0   1800 12.029 290   290 411        20.1%       37.6%    3131 0.07\n",
+      "    25   1800 12.026 291   291 409        20.2%       37.6%    3106 0.07\n",
+      "  -150   1800 12.023 288   288 393        19.3%       36.0%    3281 0.07\n",
+      "  -125   1800 12.018 292   292 394        19.6%       37.3%    3256 0.07\n",
+      "   -50   1800 12.018 290   290 403        20.0%       37.4%    3181 0.07\n",
+      "   -25   1800 12.018 289   289 406        20.0%       37.4%    3156 0.07\n",
+      "\n",
+      "Signal-heavy candidates\n",
+      "offset window score  f1 first med overlap_mean overlap_max min_gap cv\n",
+      "    75   1800 12.775 313   313 425        20.7%       41.7%    3056 0.07\n",
+      "   100   1800 12.765 313   313 425        21.0%       41.9%    3031 0.08\n",
+      "    50   1800 12.749 309   309 417        20.5%       40.1%    3081 0.07\n",
+      "   100   1750 12.718 306   306 410        20.4%       40.3%    3081 0.07\n",
+      "     0   1800 12.669 290   290 411        20.1%       37.6%    3131 0.07\n",
+      "    25   1800 12.667 291   291 409        20.2%       37.6%    3106 0.07\n",
+      "   -25   1800 12.655 289   289 406        20.0%       37.4%    3156 0.07\n",
+      "   -50   1800 12.653 290   290 403        20.0%       37.4%    3181 0.07\n",
+      "  -100   1800 12.648 291   291 398        19.8%       37.4%    3231 0.07\n",
+      "  -125   1800 12.645 292   292 394        19.6%       37.3%    3256 0.07\n",
+      "\n",
+      "Low-bleed candidates\n",
+      "offset window score  f1 first med overlap_mean overlap_max min_gap cv\n",
+      "  -500   1750 10.684 257   257 340        16.3%       29.5%    3681 0.09\n",
+      "  -475   1750 10.661 266   266 342        16.7%       31.4%    3656 0.08\n",
+      "  -450   1700 10.652 256   256 331        16.2%       29.7%    3681 0.09\n",
+      "  -400   1650 10.647 255   255 321        15.9%       29.8%    3681 0.08\n",
+      "  -500   1800 10.645 266   266 348        17.2%       31.5%    3631 0.08\n",
+      "  -350   1800 10.642 269   269 372        18.2%       32.0%    3481 0.07\n",
+      "  -425   1700 10.630 264   264 331        16.5%       31.6%    3656 0.09\n",
+      "  -175   1800 10.630 278   278 392        19.2%       33.9%    3306 0.07\n",
+      "  -450   1750 10.629 265   265 344        17.1%       31.7%    3631 0.08\n",
+      "  -300   1750 10.628 265   265 370        18.0%       32.3%    3481 0.07\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index b84d355..cb206bb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,3 +28,11 @@ dev = [
 [tool.uv]
 package = false
 default-groups = ["dev"]
+
+[tool.yapf]
+based_on_style = "pep8"
+column_limit = 100
+dedent_closing_brackets = false
+split_before_closing_bracket = false
+split_before_first_argument = true
+split_all_comma_separated_values = true
diff --git a/run_camera_sync_sweep.sh b/run_camera_sync_sweep.sh
new file mode 100644
index 0000000..eff058d
--- /dev/null
+++ b/run_camera_sync_sweep.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+# run_camera_sync_sweep.sh
+# Paired DLPC900 runner for a persistent-camera DVXplorer sync-check sweep.
+set -e
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+source "$SCRIPT_DIR/scripts/lib/dmd_shell_common.sh"
+
+dmd_parse_dmd_config_arg "$@"
+
+if dmd_has_flag --dry-run "$@"; then
+    echo "=== Camera sync-sweep dry-run (no DP wake, no X, no sudo) ==="
+    dmd_exec_python_module "$SCRIPT_DIR" dmdcontrol camera sync-sweep "$@"
+    exit 0
+fi
+
+echo "=== Paired DLPC900 DP Wake for camera sync-sweep ==="
+dmd_wake_configured_dmd "$SCRIPT_DIR" A "${DMD_CONFIG_ARGS[@]}"
+dmd_wake_configured_dmd "$SCRIPT_DIR" B "${DMD_CONFIG_ARGS[@]}"
+
+dmd_wait_for_hotplug "Xorg and GPU to detect both DP hotplug events"
+
+echo "=== Launching Camera Sync Sweep (via scripts/xinit/xinitrc_camera_sync_sweep.sh wrapper) ==="
+dmd_run_xinit "$SCRIPT_DIR" "$SCRIPT_DIR/scripts/xinit/xinitrc_camera_sync_sweep.sh" "$@"
diff --git a/scripts/xinit/xinitrc_camera_sync_sweep.sh b/scripts/xinit/xinitrc_camera_sync_sweep.sh
new file mode 100644
index 0000000..22fe135
--- /dev/null
+++ b/scripts/xinit/xinitrc_camera_sync_sweep.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+# xinitrc_camera_sync_sweep.sh
+set -e
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)"
+source "$REPO_ROOT/scripts/lib/dmd_shell_common.sh"
+source "$REPO_ROOT/scripts/lib/dmd_x11_common.sh"
+
+echo "=== xinitrc_camera_sync_sweep: Configuring one spanning X screen ==="
+sleep 1
+
+dmd_parse_dmd_config_arg "$@"
+dmd_parse_hz_arg "" "$@"
+
+A_OUTPUT="$(dmd_config_field "$REPO_ROOT" A xrandr_output "${DMD_CONFIG_FIELD_ARGS[@]}")"
+B_OUTPUT="$(dmd_config_field "$REPO_ROOT" B xrandr_output "${DMD_CONFIG_FIELD_ARGS[@]}")"
+A_HZ="$(dmd_config_field "$REPO_ROOT" A target_hz "${DMD_CONFIG_FIELD_ARGS[@]}")"
+B_HZ="$(dmd_config_field "$REPO_ROOT" B target_hz "${DMD_CONFIG_FIELD_ARGS[@]}")"
+
+if [ -z "$A_OUTPUT" ] || [ -z "$B_OUTPUT" ]; then
+    echo "[ERROR] DMD A and B must both define xrandr_output in dmd_devices.json."
+    exit 1
+fi
+TARGET_HZ="$DMD_TARGET_HZ"
+if [ -z "$TARGET_HZ" ]; then
+    if [ -n "$A_HZ" ] && [ -n "$B_HZ" ] && [ "$A_HZ" != "$B_HZ" ]; then
+        echo "[ERROR] DMD A and B target_hz values differ ($A_HZ vs $B_HZ). Pass --hz explicitly or fix dmd_devices.json."
+        exit 1
+    fi
+    TARGET_HZ="${A_HZ:-${B_HZ:-60}}"
+fi
+
+dmd_x11_require_connected "$B_OUTPUT" "B"
+dmd_x11_require_connected "$A_OUTPUT" "A"
+
+echo "Pair outputs: B=$B_OUTPUT at +0+0, A=$A_OUTPUT at +1920+0"
+
+dmd_x11_define_raw_modes "$B_OUTPUT" "$A_OUTPUT"
+TARGET_MODE="$(dmd_x11_target_mode_for_hz "$TARGET_HZ")"
+
+echo "Applying paired custom modeline: $TARGET_MODE"
+dmd_x11_apply_pair_mode "$B_OUTPUT" "$A_OUTPUT" "$TARGET_MODE"
+
+echo "--- xrandr verification ---"
+xrandr --query
+dmd_x11_verify_pair_layout "$B_OUTPUT" "$A_OUTPUT"
+
+echo "=== Launching dmdcontrol camera sync-sweep ==="
+dmd_exec_python_module "$REPO_ROOT" dmdcontrol camera sync-sweep "$@"
diff --git a/tests/test_calibration_square_mode.py b/tests/test_calibration_square_mode.py
index d9f7bd5..3045612 100644
--- a/tests/test_calibration_square_mode.py
+++ b/tests/test_calibration_square_mode.py
@@ -11,6 +11,7 @@
 
 
 class CalibrationSquareModeTests(unittest.TestCase):
+
     def test_default_state_is_centered_and_sized_from_surface(self):
         state = default_calibration_square_state(width=100, height=80)
 
diff --git a/tests/test_calibration_square_runtime.py b/tests/test_calibration_square_runtime.py
index ad8a597..2ea935a 100644
--- a/tests/test_calibration_square_runtime.py
+++ b/tests/test_calibration_square_runtime.py
@@ -25,6 +25,7 @@ def pack_patterns(self, patterns):
 
 
 class CalibrationSquareRuntimeTests(unittest.TestCase):
+
     def test_build_frame_repeats_square_mask_for_all_bitplanes(self):
         engine = _Engine()
         state = default_calibration_square_state(engine.width, engine.height)
diff --git a/tests/test_camera_accumulation.py b/tests/test_camera_accumulation.py
index a9fdd18..97018eb 100644
--- a/tests/test_camera_accumulation.py
+++ b/tests/test_camera_accumulation.py
@@ -10,30 +10,72 @@
 
 def test_filter_rising_triggers_keeps_rising_only():
     triggers = [
-        TriggerRecord(timestamp=10, edge="rising"),
-        TriggerRecord(timestamp=20, edge="falling"),
-        TriggerRecord(timestamp=30, edge="rising"),
+        TriggerRecord(timestamp=10,
+                      edge="rising"),
+        TriggerRecord(timestamp=20,
+                      edge="falling"),
+        TriggerRecord(timestamp=30,
+                      edge="rising"),
     ]
 
     assert [t.timestamp for t in filter_rising_triggers(triggers)] == [10, 30]
 
 
+def test_filter_rising_triggers_understands_dv_trigger_type():
+
+    class Trigger:
+
+        def __init__(self, timestamp, trigger_type):
+            self._timestamp = timestamp
+            self._trigger_type = trigger_type
+
+        def timestamp(self):
+            return self._timestamp
+
+        def type(self):
+            return self._trigger_type
+
+    triggers = [
+        Trigger(10,
+                "TriggerType.EXTERNAL_SIGNAL_RISING_EDGE"),
+        Trigger(20,
+                "TriggerType.EXTERNAL_SIGNAL_FALLING_EDGE"),
+    ]
+
+    assert [trigger.timestamp() for trigger in filter_rising_triggers(triggers)] == [10]
+
+
 def test_linear_accumulation_counts_positive_events_by_trigger_window():
     triggers = [
-        TriggerRecord(timestamp=100, edge="rising"),
-        TriggerRecord(timestamp=200, edge="rising"),
+        TriggerRecord(timestamp=100,
+                      edge="rising"),
+        TriggerRecord(timestamp=200,
+                      edge="rising"),
     ]
     events = [
-        EventRecord(timestamp=105, x=1, y=2, polarity=True),
-        EventRecord(timestamp=110, x=1, y=2, polarity=True),
-        EventRecord(timestamp=115, x=3, y=4, polarity=False),
-        EventRecord(timestamp=205, x=0, y=0, polarity=True),
+        EventRecord(timestamp=105,
+                    x=1,
+                    y=2,
+                    polarity=True),
+        EventRecord(timestamp=110,
+                    x=1,
+                    y=2,
+                    polarity=True),
+        EventRecord(timestamp=115,
+                    x=3,
+                    y=4,
+                    polarity=False),
+        EventRecord(timestamp=205,
+                    x=0,
+                    y=0,
+                    polarity=True),
     ]
 
     frames = accumulate_events_for_triggers(
         events,
         triggers,
-        resolution=(5, 6),
+        resolution=(5,
+                    6),
         window_us=50,
         polarity_mode="positive",
     )
@@ -48,22 +90,33 @@ def test_linear_accumulation_counts_positive_events_by_trigger_window():
 def test_linear_accumulation_supports_signed_and_ignore_modes():
     triggers = [TriggerRecord(timestamp=100, edge="rising")]
     events = [
-        EventRecord(timestamp=100, x=1, y=1, polarity=True),
-        EventRecord(timestamp=101, x=1, y=1, polarity=False),
-        EventRecord(timestamp=102, x=1, y=1, polarity=False),
+        EventRecord(timestamp=100,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=101,
+                    x=1,
+                    y=1,
+                    polarity=False),
+        EventRecord(timestamp=102,
+                    x=1,
+                    y=1,
+                    polarity=False),
     ]
 
     signed = accumulate_events_for_triggers(
         events,
         triggers,
-        resolution=(3, 3),
+        resolution=(3,
+                    3),
         window_us=10,
         polarity_mode="signed",
     )
     ignored = accumulate_events_for_triggers(
         events,
         triggers,
-        resolution=(3, 3),
+        resolution=(3,
+                    3),
         window_us=10,
         polarity_mode="ignore",
     )
@@ -76,8 +129,12 @@ def test_window_boundary_left_inclusive():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [EventRecord(timestamp=1000, x=1, y=1, polarity=True)]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
     assert frames[0, 1, 1] == 1.0
 
 
@@ -85,8 +142,12 @@ def test_window_boundary_right_exclusive():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [EventRecord(timestamp=1100, x=1, y=1, polarity=True)]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
     assert frames[0, 1, 1] == 0.0
 
 
@@ -94,8 +155,12 @@ def test_window_boundary_one_tick_inside():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [EventRecord(timestamp=1099, x=1, y=1, polarity=True)]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
     assert frames[0, 1, 1] == 1.0
 
 
@@ -103,24 +168,70 @@ def test_window_boundary_one_tick_before_start():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [EventRecord(timestamp=999, x=1, y=1, polarity=True)]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
+    assert frames[0, 1, 1] == 0.0
+
+
+def test_accumulation_window_can_start_after_trigger():
+    triggers = [TriggerRecord(timestamp=1000, edge="rising")]
+    events = [
+        EventRecord(timestamp=1005,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1025,
+                    x=2,
+                    y=1,
+                    polarity=True),
+    ]
+
+    frames = accumulate_events_for_triggers(
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=20,
+        polarity_mode="positive",
+        window_start_offset_us=20,
     )
+
     assert frames[0, 1, 1] == 0.0
+    assert frames[0, 1, 2] == 1.0
 
 
 def test_events_between_triggers_are_discarded():
     triggers = [
-        TriggerRecord(timestamp=1000, edge="rising"),
-        TriggerRecord(timestamp=2000, edge="rising"),
+        TriggerRecord(timestamp=1000,
+                      edge="rising"),
+        TriggerRecord(timestamp=2000,
+                      edge="rising"),
     ]
     events = [
-        EventRecord(timestamp=1200, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1500, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1900, x=1, y=1, polarity=True),
+        EventRecord(timestamp=1200,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1500,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1900,
+                    x=1,
+                    y=1,
+                    polarity=True),
     ]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
     assert frames[0, 1, 1] == 0.0
     assert frames[1, 1, 1] == 0.0
 
@@ -128,64 +239,135 @@ def test_events_between_triggers_are_discarded():
 def test_polarity_positive_ignores_negative_events():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [
-        EventRecord(timestamp=1010, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1020, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1030, x=1, y=1, polarity=False),
+        EventRecord(timestamp=1010,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1020,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1030,
+                    x=1,
+                    y=1,
+                    polarity=False),
     ]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
     assert frames[0, 1, 1] == 2.0
 
 
 def test_polarity_signed_subtracts_negative_events():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events_1 = [
-        EventRecord(timestamp=1010, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1020, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1030, x=1, y=1, polarity=False),
+        EventRecord(timestamp=1010,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1020,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1030,
+                    x=1,
+                    y=1,
+                    polarity=False),
     ]
     frames_1 = accumulate_events_for_triggers(
-        events_1, triggers, resolution=(3, 3), window_us=100, polarity_mode="signed"
-    )
+        events_1,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="signed")
     assert frames_1[0, 1, 1] == 1.0
 
     events_2 = [
-        EventRecord(timestamp=1010, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1020, x=1, y=1, polarity=False),
-        EventRecord(timestamp=1030, x=1, y=1, polarity=False),
+        EventRecord(timestamp=1010,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1020,
+                    x=1,
+                    y=1,
+                    polarity=False),
+        EventRecord(timestamp=1030,
+                    x=1,
+                    y=1,
+                    polarity=False),
     ]
     frames_2 = accumulate_events_for_triggers(
-        events_2, triggers, resolution=(3, 3), window_us=100, polarity_mode="signed"
-    )
+        events_2,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="signed")
     assert frames_2[0, 1, 1] == -1.0
 
 
 def test_polarity_ignore_counts_all_events_equally():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [
-        EventRecord(timestamp=1010, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1020, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1030, x=1, y=1, polarity=False),
+        EventRecord(timestamp=1010,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1020,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1030,
+                    x=1,
+                    y=1,
+                    polarity=False),
     ]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="ignore"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="ignore")
     assert frames[0, 1, 1] == 3.0
 
 
 def test_out_of_bounds_events_are_silently_discarded():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events = [
-        EventRecord(timestamp=1010, x=10, y=5, polarity=True),
-        EventRecord(timestamp=1020, x=-1, y=5, polarity=True),
-        EventRecord(timestamp=1030, x=5, y=10, polarity=True),
-        EventRecord(timestamp=1040, x=5, y=-1, polarity=True),
-        EventRecord(timestamp=1050, x=5, y=5, polarity=True),
+        EventRecord(timestamp=1010,
+                    x=10,
+                    y=5,
+                    polarity=True),
+        EventRecord(timestamp=1020,
+                    x=-1,
+                    y=5,
+                    polarity=True),
+        EventRecord(timestamp=1030,
+                    x=5,
+                    y=10,
+                    polarity=True),
+        EventRecord(timestamp=1040,
+                    x=5,
+                    y=-1,
+                    polarity=True),
+        EventRecord(timestamp=1050,
+                    x=5,
+                    y=5,
+                    polarity=True),
     ]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(10, 10), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(10,
+                    10),
+        window_us=100,
+        polarity_mode="positive")
     assert frames.shape == (1, 10, 10)
     assert np.sum(frames) == 1.0
     assert frames[0, 5, 5] == 1.0
@@ -194,8 +376,12 @@ def test_out_of_bounds_events_are_silently_discarded():
 def test_empty_events_returns_zero_frames():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     frames = accumulate_events_for_triggers(
-        [], triggers, resolution=(5, 5), window_us=100, polarity_mode="positive"
-    )
+        [],
+        triggers,
+        resolution=(5,
+                    5),
+        window_us=100,
+        polarity_mode="positive")
     assert frames.shape == (1, 5, 5)
     assert np.sum(frames) == 0.0
 
@@ -203,23 +389,127 @@ def test_empty_events_returns_zero_frames():
 def test_accumulation_supports_native_numpy_structured_arrays():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     events_arr = np.array(
-        [(1000, 1, 1, True), (1050, 1, 1, False)],
-        dtype=[("timestamp", np.int64), ("x", np.int16), ("y", np.int16), ("polarity", np.bool_)]
-    )
+        [(1000,
+          1,
+          1,
+          True),
+         (1050,
+          1,
+          1,
+          False)],
+        dtype=[("timestamp",
+                np.int64),
+               ("x",
+                np.int16),
+               ("y",
+                np.int16),
+               ("polarity",
+                np.bool_)])
     frames = accumulate_events_for_triggers(
-        [events_arr], triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        [events_arr],
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
+    assert frames[0, 1, 1] == 1.0
+
+
+def test_accumulation_supports_direct_numpy_structured_array():
+    triggers = [TriggerRecord(timestamp=1000, edge="rising")]
+    events_arr = np.array(
+        [(1000,
+          1,
+          1,
+          True),
+         (1050,
+          1,
+          1,
+          False)],
+        dtype=[("timestamp",
+                np.int64),
+               ("x",
+                np.int16),
+               ("y",
+                np.int16),
+               ("polarity",
+                np.bool_)])
+
+    frames = accumulate_events_for_triggers(
+        events_arr,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
+
     assert frames[0, 1, 1] == 1.0
 
 
+def test_accumulation_supports_numpy_t_and_p_fields():
+    triggers = [TriggerRecord(timestamp=1000, edge="rising")]
+    events_arr = np.array(
+        [(1000,
+          1,
+          1,
+          True),
+         (1050,
+          1,
+          1,
+          False)],
+        dtype=[("t",
+                np.int64),
+               ("x",
+                np.int16),
+               ("y",
+                np.int16),
+               ("p",
+                np.bool_)])
+
+    frames = accumulate_events_for_triggers(
+        [events_arr],
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="signed")
+
+    assert frames[0, 1, 1] == 0.0
+
+
+def test_accumulation_preserves_large_record_coordinates():
+    triggers = [TriggerRecord(timestamp=1000, edge="rising")]
+    events = [EventRecord(timestamp=1000, x=40000, y=2, polarity=True)]
+
+    frames = accumulate_events_for_triggers(
+        events,
+        triggers,
+        resolution=(40001,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
+
+    assert frames[0, 2, 40000] == 1.0
+
+
 def test_accumulation_sorts_unsorted_events_correctly():
     triggers = [TriggerRecord(timestamp=1000, edge="rising")]
     # Insert out of order
     events = [
-        EventRecord(timestamp=1050, x=1, y=1, polarity=True),
-        EventRecord(timestamp=1010, x=1, y=1, polarity=True),
+        EventRecord(timestamp=1050,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1010,
+                    x=1,
+                    y=1,
+                    polarity=True),
     ]
     frames = accumulate_events_for_triggers(
-        events, triggers, resolution=(3, 3), window_us=100, polarity_mode="positive"
-    )
+        events,
+        triggers,
+        resolution=(3,
+                    3),
+        window_us=100,
+        polarity_mode="positive")
     assert frames[0, 1, 1] == 2.0
diff --git a/tests/test_camera_capture.py b/tests/test_camera_capture.py
index d57ff61..a4c333d 100644
--- a/tests/test_camera_capture.py
+++ b/tests/test_camera_capture.py
@@ -1,6 +1,7 @@
 from threading import Event
 
 import numpy as np
+import pytest
 
 from dmdcontrol.camera.capture import (
     CameraReadyState,
@@ -13,6 +14,7 @@
 
 
 class FakeCapture:
+
     def __init__(self):
         self.event_reads = 0
         self.trigger_reads = 0
@@ -36,24 +38,109 @@ def getNextTriggerBatch(self):
 
 
 def test_validate_camera_ready_requires_event_and_trigger_streams():
-    state = validate_camera_ready(FakeCapture())
+    trigger_configuration = {"configured": True}
+
+    state = validate_camera_ready(FakeCapture(), trigger_configuration=trigger_configuration)
 
     assert isinstance(state, CameraReadyState)
     assert state.event_stream_available is True
     assert state.trigger_stream_available is True
     assert state.event_resolution == (320, 240)
+    assert state.trigger_configuration == trigger_configuration
+
+
+def test_validate_camera_ready_rejects_trigger_setup_errors():
+    trigger_configuration = {"errors": ["setDetectorRunning(True): RuntimeError('failed')"]}
+
+    with pytest.raises(RuntimeError, match="trigger detector setup failed"):
+        validate_camera_ready(FakeCapture(), trigger_configuration=trigger_configuration)
 
 
 def test_flush_stale_batches_reads_events_and_triggers():
     capture = FakeCapture()
 
-    flush_stale_batches(capture, reads=2)
+    result = flush_stale_batches(capture, reads=2)
 
-    assert capture.event_reads == 2
-    assert capture.trigger_reads == 2
+    assert capture.event_reads == 1
+    assert capture.trigger_reads == 1
+    assert result == {
+        "requested_reads": 2,
+        "event_reads": 1,
+        "trigger_reads": 1,
+        "event_batches_discarded": 0,
+        "trigger_batches_discarded": 0,
+        "event_count_discarded": 0,
+        "trigger_count_discarded": 0,
+        "stopped_early": True,
+    }
+
+
+def test_flush_stale_batches_reports_discarded_trigger_batches():
+
+    class StaleCapture:
+
+        def __init__(self):
+            self.events = [[{"timestamp": 1}], None]
+            self.triggers = [[{"timestamp": 2}, {"timestamp": 3}], None]
+
+        def getNextEventBatch(self):
+            return self.events.pop(0)
+
+        def getNextTriggerBatch(self):
+            return self.triggers.pop(0)
+
+    result = flush_stale_batches(StaleCapture(), reads=4)
+
+    assert result == {
+        "requested_reads": 4,
+        "event_reads": 2,
+        "trigger_reads": 2,
+        "event_batches_discarded": 1,
+        "trigger_batches_discarded": 1,
+        "event_count_discarded": 1,
+        "trigger_count_discarded": 2,
+        "stopped_early": True,
+    }
+
+
+def test_flush_stale_batches_can_preserve_trigger_batches():
+
+    class StaleCapture:
+
+        def __init__(self):
+            self.event_reads = 0
+            self.trigger_reads = 0
+            self.events = [[{"timestamp": 1}], None]
+            self.triggers = [[{"timestamp": 2}, {"timestamp": 3}]]
+
+        def getNextEventBatch(self):
+            self.event_reads += 1
+            return self.events.pop(0)
+
+        def getNextTriggerBatch(self):
+            self.trigger_reads += 1
+            return self.triggers.pop(0)
+
+    capture = StaleCapture()
+
+    result = flush_stale_batches(capture, reads=4, include_triggers=False)
+
+    assert result == {
+        "requested_reads": 4,
+        "event_reads": 2,
+        "trigger_reads": 0,
+        "event_batches_discarded": 1,
+        "trigger_batches_discarded": 0,
+        "event_count_discarded": 1,
+        "trigger_count_discarded": 0,
+        "stopped_early": True,
+    }
+    assert capture.trigger_reads == 0
+    assert capture.triggers == [[{"timestamp": 2}, {"timestamp": 3}]]
 
 
 class FakeNumpyBatch:
+
     def __init__(self, array):
         self.array = array
 
@@ -63,12 +150,19 @@ def numpy(self):
 
 def test_append_batch_records_snapshots_numpy_batches():
     source = np.array(
-        [(100, 2, 1, True)],
+        [(100,
+          2,
+          1,
+          True)],
         dtype=[
-            ("timestamp", np.int64),
-            ("x", np.int16),
-            ("y", np.int16),
-            ("polarity", np.bool_),
+            ("timestamp",
+             np.int64),
+            ("x",
+             np.int16),
+            ("y",
+             np.int16),
+            ("polarity",
+             np.bool_),
         ],
     )
     destination = []
@@ -83,6 +177,7 @@ def test_append_batch_records_snapshots_numpy_batches():
 
 
 class FakeWriter:
+
     def __init__(self):
         self.events = []
         self.triggers = []
@@ -95,6 +190,7 @@ def writeTriggerPacket(self, triggers, streamName="triggers"):
 
 
 class FakeLiveCapture(FakeCapture):
+
     def __init__(self):
         super().__init__()
         self.event_batches = [["e1"], None, ["e2"], None]
@@ -134,6 +230,21 @@ def test_record_until_trigger_count_writes_events_and_triggers():
     assert len(writer.triggers) == 2
 
 
+def test_flush_stale_batches_drains_until_idle():
+    capture = FakeLiveCapture()
+    capture.event_batches = [["stale-events"], None, ["not-read"]]
+    capture.trigger_batches = [["stale-triggers"], None, ["not-read"]]
+
+    from dmdcontrol.camera.capture import flush_stale_batches
+
+    flush_stale_batches(capture, reads=10)
+
+    assert capture.event_reads == 2
+    assert capture.trigger_reads == 2
+    assert capture.event_batches == [["not-read"]]
+    assert capture.trigger_batches == [["not-read"]]
+
+
 def test_record_until_trigger_count_reads_post_trigger_event_batches():
     capture = FakeLiveCapture()
     capture.event_batches = [None, ["tail1"], ["tail2"]]
@@ -156,20 +267,77 @@ def test_record_until_trigger_count_reads_post_trigger_event_batches():
     assert writer.events[1][1] == ["tail2"]
 
 
+def test_record_until_trigger_count_waits_until_events_reach_trigger_window():
+    capture = FakeLiveCapture()
+    capture.event_batches = [
+        [{
+            "timestamp": 100,
+            "x": 1,
+            "y": 1,
+            "polarity": True}],
+        [{
+            "timestamp": 160,
+            "x": 2,
+            "y": 1,
+            "polarity": True}],
+        [{
+            "timestamp": 260,
+            "x": 3,
+            "y": 1,
+            "polarity": True}],
+    ]
+    capture.trigger_batches = [
+        [{
+            "timestamp": 200,
+            "edge": "rising"}],
+    ]
+    writer = FakeWriter()
+
+    result = record_until_trigger_count(
+        capture,
+        writer,
+        expected_trigger_count=1,
+        timeout_s=1.0,
+        idle_sleep_s=0.0,
+        post_trigger_event_time_us=50,
+    )
+
+    assert result.trigger_count == 1
+    assert result.event_count == 3
+    assert result.event_batch_count == 3
+    assert result.event_time_range_us == (100, 260)
+
+
 def test_record_until_trigger_count_reports_capture_time_ranges():
     capture = FakeLiveCapture()
     capture.event_batches = [
         [
-            {"timestamp": 100, "x": 1, "y": 1, "polarity": True},
-            {"timestamp": 120, "x": 2, "y": 1, "polarity": True},
+            {
+                "timestamp": 100,
+                "x": 1,
+                "y": 1,
+                "polarity": True},
+            {
+                "timestamp": 120,
+                "x": 2,
+                "y": 1,
+                "polarity": True},
         ],
         [
-            {"timestamp": 250, "x": 3, "y": 1, "polarity": False},
+            {
+                "timestamp": 250,
+                "x": 3,
+                "y": 1,
+                "polarity": False},
         ],
     ]
     capture.trigger_batches = [
-        [{"timestamp": 110, "edge": "rising"}],
-        [{"timestamp": 260, "edge": "rising"}],
+        [{
+            "timestamp": 110,
+            "edge": "rising"}],
+        [{
+            "timestamp": 260,
+            "edge": "rising"}],
     ]
     writer = FakeWriter()
 
@@ -211,3 +379,43 @@ def stop_after_first_idle_read():
     assert result.trigger_count == 0
     assert result.timed_out is False
     assert result.stopped is True
+
+
+def test_record_until_trigger_count_drains_tail_batches_after_stop_event():
+    stop_event = Event()
+
+    class DelayedTailCapture(FakeCapture):
+
+        def isRunning(self):
+            return True
+
+        def getNextEventBatch(self):
+            self.event_reads += 1
+            if stop_event.is_set() and self.event_reads == 2:
+                return [{"timestamp": 300, "x": 1, "y": 1, "polarity": True}]
+            return None
+
+        def getNextTriggerBatch(self):
+            self.trigger_reads += 1
+            if stop_event.is_set() and self.trigger_reads == 2:
+                return [{"timestamp": 250, "edge": "rising"}]
+            return None
+
+    capture = DelayedTailCapture()
+    writer = FakeWriter()
+    stop_event.set()
+
+    result = record_until_trigger_count(
+        capture,
+        writer,
+        expected_trigger_count=None,
+        timeout_s=10.0,
+        idle_sleep_s=0.0,
+        stop_event=stop_event,
+    )
+
+    assert result.stopped is True
+    assert result.event_count == 1
+    assert result.trigger_count == 1
+    assert writer.events[0][1][0]["timestamp"] == 300
+    assert writer.triggers[0][1][0]["timestamp"] == 250
diff --git a/tests/test_camera_cli.py b/tests/test_camera_cli.py
index bac4947..23f251e 100644
--- a/tests/test_camera_cli.py
+++ b/tests/test_camera_cli.py
@@ -25,7 +25,7 @@ def test_camera_help_import_does_not_load_dv_processing():
     assert "dv_processing" not in sys.modules
 
 
-@pytest.mark.parametrize("command", ["sync-check", "pair-capture"])
+@pytest.mark.parametrize("command", ["sync-check", "pair-capture", "reprocess-aedat4"])
 def test_camera_subcommand_help_exits_zero_without_dv_processing(command):
     sys.modules.pop("dv_processing", None)
 
diff --git a/tests/test_camera_discovery.py b/tests/test_camera_discovery.py
index 4233ff1..d806a00 100644
--- a/tests/test_camera_discovery.py
+++ b/tests/test_camera_discovery.py
@@ -6,6 +6,7 @@
 
 
 class ModernDVXplorerCapture:
+
     def __init__(self):
         self.thresholds = []
         self.readout_fps = []
@@ -21,6 +22,7 @@ def setReadoutFPS(self, value):
 
 
 class LegacyDVXplorerCapture:
+
     def __init__(self):
         self.bias = []
         self.efps = []
@@ -33,6 +35,7 @@ def setDVXplorerEFPS(self, value):
 
 
 class ResettableCapture:
+
     def __init__(self):
         self.calls = []
         self.event_batches = ["stale-event", None]
@@ -70,14 +73,22 @@ def test_rearm_camera_streams_cycles_events_and_drains_stale_batches(monkeypatch
         "drain_reads": 2,
     }
     assert capture.calls == [
-        ("events", False),
-        ("detector", False),
-        ("generator", False),
-        ("events", True),
-        ("read-events", None),
-        ("read-triggers", None),
-        ("read-events", None),
-        ("read-triggers", None),
+        ("events",
+         False),
+        ("detector",
+         False),
+        ("generator",
+         False),
+        ("events",
+         True),
+        ("read-events",
+         None),
+        ("read-triggers",
+         None),
+        ("read-events",
+         None),
+        ("read-triggers",
+         None),
     ]
 
 
@@ -93,14 +104,19 @@ def test_shutdown_camera_streams_stops_available_streams():
         "errors": [],
     }
     assert capture.calls[:3] == [
-        ("events", False),
-        ("detector", False),
-        ("generator", False),
+        ("events",
+         False),
+        ("detector",
+         False),
+        ("generator",
+         False),
     ]
 
 
 def test_shutdown_camera_streams_reports_stop_errors_without_raising():
+
     class Capture:
+
         def setEventsRunning(self, value):
             raise RuntimeError("camera gone")
 
@@ -110,14 +126,82 @@ def setEventsRunning(self, value):
     assert "camera gone" in result["errors"][0]
 
 
+def test_configure_rising_edge_triggers_restarts_detector_and_reports_calls():
+
+    class Capture:
+
+        def __init__(self):
+            self.calls = []
+
+        def setDetectorRunning(self, value):
+            self.calls.append(("running", value))
+
+        def setDetectorRisingEdges(self, value):
+            self.calls.append(("rising", value))
+
+        def setDetectorFallingEdges(self, value):
+            self.calls.append(("falling", value))
+
+    capture = Capture()
+
+    result = discovery.configure_rising_edge_triggers(capture)
+
+    assert capture.calls == [
+        ("running",
+         False),
+        ("rising",
+         True),
+        ("falling",
+         False),
+        ("running",
+         True),
+    ]
+    assert result["has_setDetectorRunning"] is True
+    assert result["has_setDetectorRisingEdges"] is True
+    assert result["has_setDetectorFallingEdges"] is True
+    assert result["call_order"] == [
+        "setDetectorRunning(False)",
+        "setDetectorRisingEdges(True)",
+        "setDetectorFallingEdges(False)",
+        "setDetectorRunning(True)",
+    ]
+    assert result["setDetectorRunning_false_error"] is None
+    assert result["setDetectorRunning_true_error"] is None
+    assert result["errors"] == []
+
+
+def test_configure_rising_edge_triggers_records_errors_without_raising():
+
+    class Capture:
+
+        def setDetectorRunning(self, value):
+            if value is True:
+                raise RuntimeError("detector would not start")
+
+        def setDetectorRisingEdges(self, value):
+            return None
+
+    result = discovery.configure_rising_edge_triggers(Capture())
+
+    assert result["has_setDetectorRunning"] is True
+    assert result["has_setDetectorRisingEdges"] is True
+    assert result["has_setDetectorFallingEdges"] is False
+    assert result["setDetectorRunning_true_result"] is None
+    assert "detector would not start" in result["setDetectorRunning_true_error"]
+    assert result["errors"] == [
+        "setDetectorRunning(True): RuntimeError('detector would not start')"]
+
+
 def test_configure_camera_performance_uses_dvxplorer_contrast_thresholds(monkeypatch):
     capture = ModernDVXplorerCapture()
     import builtins
     original_import = builtins.__import__
+
     def mock_import(name, *args, **kwargs):
         if name == "dv_processing":
             raise AssertionError("dv import not needed for thresholds")
         return original_import(name, *args, **kwargs)
+
     monkeypatch.setattr(builtins, "__import__", mock_import)
 
     discovery.configure_camera_performance(capture, bias_sensitivity="veryhigh")
@@ -131,9 +215,8 @@ def test_configure_camera_performance_uses_dvxplorer_readout_fps(monkeypatch):
     readout = SimpleNamespace(VARIABLE_5000="variable-5000")
     dv = SimpleNamespace(
         io=SimpleNamespace(
-            camera=SimpleNamespace(
-                DVXplorer=SimpleNamespace(ReadoutFPS=readout),
-            ),
+            camera=SimpleNamespace(DVXplorer=SimpleNamespace(ReadoutFPS=readout),
+                                   ),
         ),
     )
     monkeypatch.setitem(sys.modules, "dv_processing", dv)
@@ -149,12 +232,11 @@ def test_configure_camera_performance_can_prefer_legacy_setters(monkeypatch):
     bias = SimpleNamespace(Low="legacy-low")
     efps = SimpleNamespace(EFPS_VARIABLE_5000="legacy-variable-5000")
     dv = SimpleNamespace(
-        io=SimpleNamespace(
-            CameraCapture=SimpleNamespace(
-                BiasSensitivity=bias,
-                DVXeFPS=efps,
-            ),
+        io=SimpleNamespace(CameraCapture=SimpleNamespace(
+            BiasSensitivity=bias,
+            DVXeFPS=efps,
         ),
+                           ),
     )
     monkeypatch.setitem(sys.modules, "dv_processing", dv)
 
@@ -171,11 +253,7 @@ def test_configure_camera_performance_can_prefer_legacy_setters(monkeypatch):
 
 def test_open_camera_capture_supports_legacy_camera_capture_api():
     opened = object()
-    dv = SimpleNamespace(
-        io=SimpleNamespace(
-            CameraCapture=lambda: opened,
-        ),
-    )
+    dv = SimpleNamespace(io=SimpleNamespace(CameraCapture=lambda: opened, ), )
 
     assert discovery.open_camera_capture(dv, method="legacy") is opened
 
diff --git a/tests/test_camera_live_plumbing.py b/tests/test_camera_live_plumbing.py
index f3768e0..426e04d 100644
--- a/tests/test_camera_live_plumbing.py
+++ b/tests/test_camera_live_plumbing.py
@@ -27,7 +27,8 @@ def test_pair_capture_live_records_while_dmd_runtime_is_active(monkeypatch, tmp_
     fake_capture = Mock()
     fake_writer = Mock()
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -68,12 +69,13 @@ def fake_run(pair_args, before_start):
 
     monkeypatch.setattr(pair_capture, "_run_pair_with_callback", fake_run)
 
-    args = pair_capture.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-    ])
+    args = pair_capture.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+        ])
 
     assert pair_capture.live(args) == 0
     assert events.index("record_start") < events.index("dmd_return")
@@ -87,7 +89,8 @@ def test_pair_capture_live_records_until_dmd_runtime_finishes(monkeypatch, tmp_p
     runtime_can_finish = Event()
     record_kwargs = {}
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -123,14 +126,15 @@ def fake_run(pair_args, before_start):
 
     monkeypatch.setattr(pair_capture, "_run_pair_with_callback", fake_run)
 
-    args = pair_capture.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--test",
-        "a-kernel-b-static",
-        "--runtime-seconds",
-        "1",
-    ])
+    args = pair_capture.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--test",
+            "a-kernel-b-static",
+            "--runtime-seconds",
+            "1",
+        ])
 
     assert pair_capture.live(args) == 0
     assert record_kwargs["expected_trigger_count"] is None
@@ -143,7 +147,8 @@ def test_pair_capture_live_writes_capture_artifacts_with_event_filter(monkeypatc
     runtime_can_finish = Event()
     artifact_call = {}
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -189,24 +194,25 @@ def fake_write_capture_artifacts(run, **kwargs):
 
     monkeypatch.setattr(pair_capture, "write_capture_artifacts", fake_write_capture_artifacts)
 
-    args = pair_capture.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-        "--kernel-exposure-us",
-        "600",
-        "--event-noise-filter",
-        "local-support",
-        "--event-filter-delta-us",
-        "50000",
-        "--event-filter-window-px",
-        "3",
-        "--event-filter-threshold",
-        "2",
-        "--event-filter-polarity",
-        "same",
-    ])
+    args = pair_capture.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+            "--kernel-exposure-us",
+            "600",
+            "--event-noise-filter",
+            "local-support",
+            "--event-filter-delta-us",
+            "50000",
+            "--event-filter-window-px",
+            "3",
+            "--event-filter-threshold",
+            "2",
+            "--event-filter-polarity",
+            "same",
+        ])
 
     assert pair_capture.live(args) == 0
     assert artifact_call["events"] == [{"timestamp": 105, "x": 2, "y": 1, "polarity": True}]
@@ -227,7 +233,8 @@ def test_pair_capture_live_limits_in_memory_artifact_batches(monkeypatch, tmp_pa
     runtime_can_finish = Event()
     artifact_call = {}
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -244,20 +251,45 @@ def test_pair_capture_live_limits_in_memory_artifact_batches(monkeypatch, tmp_pa
     )
 
     def fake_record(*args, **kwargs):
-        kwargs["on_events"]([
-            {"timestamp": 101, "x": 1, "y": 1, "polarity": True},
-            {"timestamp": 112, "x": 9, "y": 9, "polarity": True},
-        ])
-        kwargs["on_triggers"]([
-            {"timestamp": 100, "edge": "rising"},
-            {"timestamp": 200, "edge": "rising"},
-        ])
-        kwargs["on_events"]([
-            {"timestamp": 104, "x": 2, "y": 2, "polarity": True},
-            {"timestamp": 120, "x": 3, "y": 3, "polarity": True},
-        ])
+        kwargs["on_events"](
+            [
+                {
+                    "timestamp": 101,
+                    "x": 1,
+                    "y": 1,
+                    "polarity": True},
+                {
+                    "timestamp": 112,
+                    "x": 9,
+                    "y": 9,
+                    "polarity": True},
+            ])
+        kwargs["on_triggers"](
+            [
+                {
+                    "timestamp": 100,
+                    "edge": "rising"},
+                {
+                    "timestamp": 200,
+                    "edge": "rising"},
+            ])
+        kwargs["on_events"](
+            [
+                {
+                    "timestamp": 104,
+                    "x": 2,
+                    "y": 2,
+                    "polarity": True},
+                {
+                    "timestamp": 120,
+                    "x": 3,
+                    "y": 3,
+                    "polarity": True},
+            ])
         kwargs["on_triggers"]([
-            {"timestamp": 300, "edge": "rising"},
+            {
+                "timestamp": 300,
+                "edge": "rising"},
         ])
         record_started.set()
         assert runtime_can_finish.wait(timeout=1.0)
@@ -286,23 +318,32 @@ def fake_write_capture_artifacts(run, **kwargs):
 
     monkeypatch.setattr(pair_capture, "write_capture_artifacts", fake_write_capture_artifacts)
 
-    args = pair_capture.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-        "--kernel-exposure-us",
-        "10",
-        "--max-accumulation-triggers",
-        "1",
-    ])
+    args = pair_capture.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+            "--kernel-exposure-us",
+            "10",
+            "--max-accumulation-triggers",
+            "1",
+        ])
 
     assert pair_capture.live(args) == 0
     assert artifact_call["max_accumulation_triggers"] == 1
     assert artifact_call["triggers"] == [{"timestamp": 100, "edge": "rising"}]
     assert artifact_call["events"] == [
-        {"timestamp": 101, "x": 1, "y": 1, "polarity": True},
-        {"timestamp": 104, "x": 2, "y": 2, "polarity": True},
+        {
+            "timestamp": 101,
+            "x": 1,
+            "y": 1,
+            "polarity": True},
+        {
+            "timestamp": 104,
+            "x": 2,
+            "y": 2,
+            "polarity": True},
     ]
 
 
@@ -313,7 +354,8 @@ def test_sync_check_live_records_while_dmd_runtime_is_active(monkeypatch, tmp_pa
     record_started = Event()
     runtime_can_finish = Event()
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -354,12 +396,13 @@ def fake_record(*args, **kwargs):
 
     monkeypatch.setattr(sync_check, "record_until_trigger_count", fake_record)
 
-    args = sync_check.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-    ])
+    args = sync_check.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+        ])
 
     assert sync_check.live(args) == 0
     assert events.index("record_start") < events.index("dmd_return")
@@ -372,7 +415,8 @@ def test_live_stops_recording_when_dmd_runtime_raises(monkeypatch, tmp_path):
     events = []
     record_started = Event()
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -418,12 +462,13 @@ def fake_run(pair_args, before_start):
 
     monkeypatch.setattr(pair_capture, "_run_pair_with_callback", fake_run)
 
-    args = pair_capture.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-    ])
+    args = pair_capture.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+        ])
 
     try:
         pair_capture.live(args)
@@ -441,7 +486,8 @@ def test_sync_check_live_stops_recording_when_dmd_runtime_raises(monkeypatch, tm
     events = []
     record_started = Event()
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -487,12 +533,13 @@ def fake_run(pair_args, before_start):
 
     monkeypatch.setattr(sync_check, "_run_pair_with_callback", fake_run)
 
-    args = sync_check.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-    ])
+    args = sync_check.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+        ])
 
     try:
         sync_check.live(args)
@@ -508,8 +555,10 @@ def test_sync_check_live_writes_capture_artifacts_from_recorded_batches(monkeypa
     from dmdcontrol.camera import sync_check
 
     artifact_call = {}
+    record_kwargs = {}
     ready = SimpleNamespace(
-        event_resolution=(320, 240),
+        event_resolution=(320,
+                          240),
         event_stream_available=True,
         trigger_stream_available=True,
     )
@@ -531,6 +580,7 @@ def fake_run(pair_args, before_start):
     monkeypatch.setattr(sync_check, "_run_pair_with_callback", fake_run)
 
     def fake_record(*args, **kwargs):
+        record_kwargs.update(kwargs)
         kwargs["on_events"]([{"timestamp": 105, "x": 2, "y": 1, "polarity": True}])
         kwargs["on_triggers"]([{"timestamp": 100, "edge": "rising"}])
         return SimpleNamespace(
@@ -551,26 +601,28 @@ def fake_write_capture_artifacts(run, **kwargs):
 
     monkeypatch.setattr(sync_check, "write_capture_artifacts", fake_write_capture_artifacts)
 
-    args = sync_check.build_parser().parse_args([
-        "--output-root",
-        str(tmp_path),
-        "--runtime-seconds",
-        "1",
-        "--numbers-exposure-us",
-        "600",
-        "--event-noise-filter",
-        "local-support",
-        "--event-filter-delta-us",
-        "50000",
-        "--event-filter-window-px",
-        "3",
-        "--event-filter-threshold",
-        "2",
-        "--event-filter-polarity",
-        "same",
-    ])
+    args = sync_check.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+            "--numbers-exposure-us",
+            "600",
+            "--event-noise-filter",
+            "local-support",
+            "--event-filter-delta-us",
+            "50000",
+            "--event-filter-window-px",
+            "3",
+            "--event-filter-threshold",
+            "2",
+            "--event-filter-polarity",
+            "same",
+        ])
 
     assert sync_check.live(args) == 0
+    assert record_kwargs["expected_trigger_count"] is None
     assert artifact_call["events"] == [{"timestamp": 105, "x": 2, "y": 1, "polarity": True}]
     assert artifact_call["triggers"] == [{"timestamp": 100, "edge": "rising"}]
     assert artifact_call["resolution"] == (320, 240)
@@ -580,3 +632,89 @@ def fake_write_capture_artifacts(run, **kwargs):
     assert artifact_call["event_noise_filter"].window_px == 3
     assert artifact_call["event_noise_filter"].threshold == 2
     assert artifact_call["event_noise_filter"].polarity == "same"
+
+
+def test_sync_check_live_capture_flushes_stale_batches_immediately_before_recording(
+        monkeypatch,
+        tmp_path):
+    from dmdcontrol.camera import sync_check
+
+    events = []
+    record_started = Event()
+    ready = SimpleNamespace(
+        event_resolution=(320,
+                          240),
+        event_stream_available=True,
+        trigger_stream_available=True,
+    )
+    capture = Mock()
+    writer = Mock()
+
+    def fake_flush(opened_capture, reads, include_triggers=True):
+        assert opened_capture is capture
+        events.append(("flush", reads, include_triggers))
+        return {
+            "requested_reads": reads,
+            "event_reads": 1,
+            "trigger_reads": 0,
+            "event_batches_discarded": 0,
+            "trigger_batches_discarded": 0,
+            "event_count_discarded": 0,
+            "trigger_count_discarded": 0,
+            "stopped_early": True,
+        }
+
+    monkeypatch.setattr(sync_check, "flush_stale_batches", fake_flush)
+
+    def fake_record(*args, **kwargs):
+        events.append("record_start")
+        record_started.set()
+        return SimpleNamespace(
+            trigger_count=0,
+            event_batch_count=0,
+            trigger_batch_count=0,
+            timed_out=False,
+        )
+
+    monkeypatch.setattr(sync_check, "record_until_trigger_count", fake_record)
+
+    def fake_run(pair_args, before_start):
+        events.append("before_start")
+        before_start({"state_a": {"timing": {}}, "state_b": {"timing": {}}})
+        assert record_started.wait(timeout=1.0)
+        events.append("sequencer_start_continued")
+
+    monkeypatch.setattr(sync_check, "_run_pair_with_callback", fake_run)
+    monkeypatch.setattr(sync_check, "write_capture_artifacts", lambda *args, **kwargs: {})
+
+    args = sync_check.build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--runtime-seconds",
+            "1",
+            "--camera-flush-reads",
+            "7",
+        ])
+    run = _fake_run_directory(tmp_path)
+
+    assert sync_check.live_capture(args, run, capture, writer, ready) == 0
+    assert events == [
+        "before_start",
+        ("flush",
+         7,
+         True),
+        "record_start",
+        "sequencer_start_continued",
+    ]
+    metadata = __import__("json").loads(run.metadata_path.read_text(encoding="utf-8"))
+    assert metadata["camera_pre_capture_flush"] == {
+        "requested_reads": 7,
+        "event_reads": 1,
+        "trigger_reads": 0,
+        "event_batches_discarded": 0,
+        "trigger_batches_discarded": 0,
+        "event_count_discarded": 0,
+        "trigger_count_discarded": 0,
+        "stopped_early": True,
+    }
diff --git a/tests/test_camera_local_support_filter.py b/tests/test_camera_local_support_filter.py
index f0d0a6a..3b72a61 100644
--- a/tests/test_camera_local_support_filter.py
+++ b/tests/test_camera_local_support_filter.py
@@ -15,11 +15,21 @@ def test_config_validate_accepts_default_values():
 @pytest.mark.parametrize(
     "kwargs, message",
     [
-        ({"delta_t_us": 0}, "delta_t_us must be positive"),
-        ({"window_px": 0}, "window_px must be >= 1"),
-        ({"window_px": 2}, "window_px must be odd"),
-        ({"threshold": -1}, "threshold must be >= 0"),
-        ({"polarity": "bad"}, "polarity must be 'same' or 'any'"),
+        ({
+            "delta_t_us": 0},
+         "delta_t_us must be positive"),
+        ({
+            "window_px": 0},
+         "window_px must be >= 1"),
+        ({
+            "window_px": 2},
+         "window_px must be odd"),
+        ({
+            "threshold": -1},
+         "threshold must be >= 0"),
+        ({
+            "polarity": "bad"},
+         "polarity must be 'same' or 'any'"),
     ],
 )
 def test_config_validate_rejects_invalid_values(kwargs, message):
@@ -42,7 +52,8 @@ def test_isolated_single_event_drops_when_enabled():
         y=np.array([1]),
         t=np.array([100]),
         p=np.array([True]),
-        resolution=(4, 4),
+        resolution=(4,
+                    4),
         config=LocalSupportFilterConfig(
             enabled=True,
             delta_t_us=50,
@@ -66,16 +77,24 @@ def test_nearby_same_polarity_events_can_pass_thresholds():
         y,
         t,
         p,
-        resolution=(4, 4),
-        config=LocalSupportFilterConfig(enabled=True, delta_t_us=50, window_px=3, threshold=1),
+        resolution=(4,
+                    4),
+        config=LocalSupportFilterConfig(enabled=True,
+                                        delta_t_us=50,
+                                        window_px=3,
+                                        threshold=1),
     )
     threshold_two = centered_local_support_mask(
         x,
         y,
         t,
         p,
-        resolution=(4, 4),
-        config=LocalSupportFilterConfig(enabled=True, delta_t_us=50, window_px=3, threshold=2),
+        resolution=(4,
+                    4),
+        config=LocalSupportFilterConfig(enabled=True,
+                                        delta_t_us=50,
+                                        window_px=3,
+                                        threshold=2),
     )
 
     assert threshold_one.tolist() == [False, True, True]
@@ -84,12 +103,20 @@ def test_nearby_same_polarity_events_can_pass_thresholds():
 
 def test_event_outside_delta_window_does_not_support():
     mask = centered_local_support_mask(
-        x=np.array([1, 1]),
-        y=np.array([1, 2]),
-        t=np.array([100, 200]),
-        p=np.array([True, True]),
-        resolution=(4, 4),
-        config=LocalSupportFilterConfig(enabled=True, delta_t_us=50, window_px=3, threshold=1),
+        x=np.array([1,
+                    1]),
+        y=np.array([1,
+                    2]),
+        t=np.array([100,
+                    200]),
+        p=np.array([True,
+                    True]),
+        resolution=(4,
+                    4),
+        config=LocalSupportFilterConfig(enabled=True,
+                                        delta_t_us=50,
+                                        window_px=3,
+                                        threshold=1),
     )
 
     assert mask.tolist() == [False, False]
@@ -97,11 +124,16 @@ def test_event_outside_delta_window_does_not_support():
 
 def test_opposite_polarity_does_not_support_when_same_required():
     mask = centered_local_support_mask(
-        x=np.array([1, 1]),
-        y=np.array([1, 2]),
-        t=np.array([100, 110]),
-        p=np.array([True, False]),
-        resolution=(4, 4),
+        x=np.array([1,
+                    1]),
+        y=np.array([1,
+                    2]),
+        t=np.array([100,
+                    110]),
+        p=np.array([True,
+                    False]),
+        resolution=(4,
+                    4),
         config=LocalSupportFilterConfig(
             enabled=True,
             delta_t_us=50,
@@ -116,11 +148,16 @@ def test_opposite_polarity_does_not_support_when_same_required():
 
 def test_opposite_polarity_supports_when_any_polarity_allowed():
     mask = centered_local_support_mask(
-        x=np.array([1, 1]),
-        y=np.array([1, 2]),
-        t=np.array([100, 110]),
-        p=np.array([True, False]),
-        resolution=(4, 4),
+        x=np.array([1,
+                    1]),
+        y=np.array([1,
+                    2]),
+        t=np.array([100,
+                    110]),
+        p=np.array([True,
+                    False]),
+        resolution=(4,
+                    4),
         config=LocalSupportFilterConfig(
             enabled=True,
             delta_t_us=50,
@@ -139,8 +176,12 @@ def test_current_event_does_not_support_itself():
         y=np.array([1]),
         t=np.array([100]),
         p=np.array([True]),
-        resolution=(4, 4),
-        config=LocalSupportFilterConfig(enabled=True, delta_t_us=50, window_px=1, threshold=1),
+        resolution=(4,
+                    4),
+        config=LocalSupportFilterConfig(enabled=True,
+                                        delta_t_us=50,
+                                        window_px=1,
+                                        threshold=1),
     )
 
     assert mask.tolist() == [False]
@@ -148,12 +189,24 @@ def test_current_event_does_not_support_itself():
 
 def test_invalid_coordinates_do_not_crash_or_update_support_memory():
     mask = centered_local_support_mask(
-        x=np.array([9, 1, 1]),
-        y=np.array([9, 1, 2]),
-        t=np.array([100, 110, 120]),
-        p=np.array([True, True, True]),
-        resolution=(4, 4),
-        config=LocalSupportFilterConfig(enabled=True, delta_t_us=50, window_px=3, threshold=1),
+        x=np.array([9,
+                    1,
+                    1]),
+        y=np.array([9,
+                    1,
+                    2]),
+        t=np.array([100,
+                    110,
+                    120]),
+        p=np.array([True,
+                    True,
+                    True]),
+        resolution=(4,
+                    4),
+        config=LocalSupportFilterConfig(enabled=True,
+                                        delta_t_us=50,
+                                        window_px=3,
+                                        threshold=1),
     )
 
     assert mask.tolist() == [False, False, True]
diff --git a/tests/test_camera_pair_capture.py b/tests/test_camera_pair_capture.py
index ac6826c..7d5cba3 100644
--- a/tests/test_camera_pair_capture.py
+++ b/tests/test_camera_pair_capture.py
@@ -6,6 +6,7 @@
 
 
 class FakeNumpyBatch:
+
     def __init__(self, array):
         self.array = array
 
@@ -15,12 +16,19 @@ def numpy(self):
 
 def test_bounded_artifact_buffer_snapshots_numpy_event_batches():
     source = np.array(
-        [(100, 2, 1, True)],
+        [(100,
+          2,
+          1,
+          True)],
         dtype=[
-            ("timestamp", np.int64),
-            ("x", np.int16),
-            ("y", np.int16),
-            ("polarity", np.bool_),
+            ("timestamp",
+             np.int64),
+            ("x",
+             np.int16),
+            ("y",
+             np.int16),
+            ("polarity",
+             np.bool_),
         ],
     )
     buffer = _BoundedArtifactBuffer(max_rising_triggers=None, window_us=10)
@@ -35,25 +43,26 @@ def test_bounded_artifact_buffer_snapshots_numpy_event_batches():
 
 
 def test_pair_capture_parser_accepts_requested_command_shape():
-    args = build_parser().parse_args([
-        "--dry-run-timing",
-        "--test",
-        "a-kernel-b-static",
-        "--test-b",
-        "dot",
-        "--b-dot-x",
-        "960",
-        "--b-dot-y",
-        "540",
-        "--b-dot-radius",
-        "40",
-        "--kernel-px",
-        "1080",
-        "--kernel-exposure-us",
-        "3000",
-        "--runtime-seconds",
-        "999",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--test",
+            "a-kernel-b-static",
+            "--test-b",
+            "dot",
+            "--b-dot-x",
+            "960",
+            "--b-dot-y",
+            "540",
+            "--b-dot-radius",
+            "40",
+            "--kernel-px",
+            "1080",
+            "--kernel-exposure-us",
+            "3000",
+            "--runtime-seconds",
+            "999",
+        ])
 
     assert args.dry_run_timing is True
     assert args.test == "a-kernel-b-static"
@@ -67,20 +76,21 @@ def test_pair_capture_parser_accepts_requested_command_shape():
 
 
 def test_pair_capture_parser_accepts_event_noise_filter_options():
-    args = build_parser().parse_args([
-        "--dry-run-timing",
-        "--event-noise-filter",
-        "local-support",
-        "--event-filter-delta-us",
-        "50000",
-        "--event-filter-window-px",
-        "3",
-        "--event-filter-threshold",
-        "2",
-        "--event-filter-polarity",
-        "same",
-        "--save-filtered-events",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--event-noise-filter",
+            "local-support",
+            "--event-filter-delta-us",
+            "50000",
+            "--event-filter-window-px",
+            "3",
+            "--event-filter-threshold",
+            "2",
+            "--event-filter-polarity",
+            "same",
+            "--save-filtered-events",
+        ])
 
     assert args.event_noise_filter == "local-support"
     assert args.event_filter_delta_us == 50000
@@ -122,11 +132,12 @@ def test_pair_capture_parser_uses_mentor_style_camera_lifecycle_by_default():
 
 
 def test_pair_capture_parser_accepts_power_cycle_command():
-    args = build_parser().parse_args([
-        "--dry-run-timing",
-        "--camera-power-cycle-command",
-        "uhubctl -l 1-2 -p 3 -a cycle -d 2",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--camera-power-cycle-command",
+            "uhubctl -l 1-2 -p 3 -a cycle -d 2",
+        ])
 
     assert args.camera_power_cycle_command == "uhubctl -l 1-2 -p 3 -a cycle -d 2"
 
@@ -142,36 +153,37 @@ def test_pair_capture_parser_accepts_name_override_alias():
 
 
 def test_pair_capture_dry_run_creates_run_artifacts(tmp_path):
-    args = build_parser().parse_args([
-        "--dry-run-timing",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120104",
-        "--test",
-        "a-kernel-b-static",
-        "--test-b",
-        "dot",
-        "--b-dot-x",
-        "960",
-        "--b-dot-y",
-        "540",
-        "--b-dot-radius",
-        "40",
-        "--kernel-px",
-        "1080",
-        "--kernel-exposure-us",
-        "3000",
-        "--runtime-seconds",
-        "999",
-        "--trigger-out-2-delay-fraction",
-        "0.05",
-        "--dmd-config",
-        "dmd_devices.json",
-        "--hz",
-        "60",
-        "-vv",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120104",
+            "--test",
+            "a-kernel-b-static",
+            "--test-b",
+            "dot",
+            "--b-dot-x",
+            "960",
+            "--b-dot-y",
+            "540",
+            "--b-dot-radius",
+            "40",
+            "--kernel-px",
+            "1080",
+            "--kernel-exposure-us",
+            "3000",
+            "--runtime-seconds",
+            "999",
+            "--trigger-out-2-delay-fraction",
+            "0.05",
+            "--dmd-config",
+            "dmd_devices.json",
+            "--hz",
+            "60",
+            "-vv",
+        ])
 
     run = dry_run(args)
 
@@ -229,15 +241,16 @@ def test_pair_capture_dry_run_creates_run_artifacts(tmp_path):
 
 
 def test_pair_capture_dry_run_records_accumulation_trigger_limit(tmp_path):
-    args = build_parser().parse_args([
-        "--dry-run-timing",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120109",
-        "--max-accumulation-triggers",
-        "64",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120109",
+            "--max-accumulation-triggers",
+            "64",
+        ])
 
     run = dry_run(args)
 
@@ -246,54 +259,62 @@ def test_pair_capture_dry_run_records_accumulation_trigger_limit(tmp_path):
 
 
 def test_pair_capture_dry_run_records_event_filter_config(tmp_path):
-    args = build_parser().parse_args([
-        "--dry-run-timing",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120108",
-        "--event-noise-filter",
-        "local-support",
-        "--event-filter-delta-us",
-        "50000",
-        "--event-filter-window-px",
-        "3",
-        "--event-filter-threshold",
-        "2",
-        "--event-filter-polarity",
-        "same",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120108",
+            "--event-noise-filter",
+            "local-support",
+            "--event-filter-delta-us",
+            "50000",
+            "--event-filter-window-px",
+            "3",
+            "--event-filter-threshold",
+            "2",
+            "--event-filter-polarity",
+            "same",
+        ])
 
     run = dry_run(args)
 
     metadata = json.loads(run.metadata_path.read_text(encoding="utf-8"))
     assert metadata["event_noise_filter"] == {
-        "algorithm": "centered-local-support",
-        "delta_t_us": 50000,
-        "enabled": True,
+        "algorithm":
+        "centered-local-support",
+        "delta_t_us":
+        50000,
+        "enabled":
+        True,
         "note": (
             "Practical notebook-validated local support filter; "
-            "not a source-faithful DV Runtime YNoise implementation."
-        ),
-        "polarity": "same",
-        "threshold": 2,
-        "window_px": 3,
+            "not a source-faithful DV Runtime YNoise implementation."),
+        "polarity":
+        "same",
+        "threshold":
+        2,
+        "window_px":
+        3,
     }
 
 
 def test_camera_pair_capture_cli_dry_run_creates_artifacts(tmp_path):
     from dmdcontrol.cli.main import main
 
-    assert main([
-        "camera",
-        "pair-capture",
-        "--dry-run-timing",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120105",
-    ]) == 0
-
-    metadata = json.loads((tmp_path / "20260527-120105-pair-capture" / "metadata.json").read_text(encoding="utf-8"))
+    assert main(
+        [
+            "camera",
+            "pair-capture",
+            "--dry-run-timing",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120105",
+        ]) == 0
+
+    metadata = json.loads(
+        (tmp_path / "20260527-120105-pair-capture" / "metadata.json").read_text(encoding="utf-8"))
     assert metadata["mode"] == "pair-capture"
     assert metadata["expected_shape"]["kernel_count"] == 512
diff --git a/tests/test_camera_probe.py b/tests/test_camera_probe.py
index face9f9..517fdf1 100644
--- a/tests/test_camera_probe.py
+++ b/tests/test_camera_probe.py
@@ -4,7 +4,6 @@
 
 from debug_scripts import camera_probe
 
-
 ROOT = Path(__file__).resolve().parents[1]
 
 
@@ -25,10 +24,11 @@ def test_camera_probe_does_not_reset_usb_by_default():
 
 
 def test_camera_probe_accepts_power_cycle_command():
-    args = camera_probe.build_parser().parse_args([
-        "--power-cycle-command",
-        "uhubctl -l 1-2 -p 3 -a cycle -d 2",
-    ])
+    args = camera_probe.build_parser().parse_args(
+        [
+            "--power-cycle-command",
+            "uhubctl -l 1-2 -p 3 -a cycle -d 2",
+        ])
 
     assert args.power_cycle_command == "uhubctl -l 1-2 -p 3 -a cycle -d 2"
 
@@ -52,6 +52,7 @@ def test_camera_probe_rearms_event_stream_before_capture(monkeypatch):
     calls = []
 
     class Capture:
+
         def setEventsRunning(self, value):
             calls.append(("events", value))
 
@@ -64,16 +65,22 @@ def getNextEventBatch(self):
     camera_probe.rearm_event_stream(Capture(), settle_s=0.01, drain_reads=2)
 
     assert calls == [
-        ("events", False),
-        ("events", True),
-        ("read-events", None),
-        ("read-events", None),
+        ("events",
+         False),
+        ("events",
+         True),
+        ("read-events",
+         None),
+        ("read-events",
+         None),
     ]
 
 
 def test_camera_probe_help_works_when_run_as_script():
     result = subprocess.run(
-        [sys.executable, "debug_scripts/camera_probe.py", "--help"],
+        [sys.executable,
+         "debug_scripts/camera_probe.py",
+         "--help"],
         cwd=ROOT,
         stdout=subprocess.PIPE,
         stderr=subprocess.PIPE,
diff --git a/tests/test_camera_reprocess_aedat4.py b/tests/test_camera_reprocess_aedat4.py
new file mode 100644
index 0000000..b5e4da7
--- /dev/null
+++ b/tests/test_camera_reprocess_aedat4.py
@@ -0,0 +1,113 @@
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from dmdcontrol.camera.accumulation import TriggerRecord
+from dmdcontrol.camera.reprocess_aedat4 import (
+    Aedat4RecordingData,
+    build_parser,
+    _trigger_edge_name,
+    _trigger_record,
+    main,
+)
+
+
+def test_trigger_edge_name_maps_dv_trigger_enum_text():
+    assert _trigger_edge_name("TriggerType.EXTERNAL_SIGNAL_RISING_EDGE") == "rising"
+    assert _trigger_edge_name("TriggerType.EXTERNAL_SIGNAL_FALLING_EDGE") == "falling"
+
+
+def test_trigger_record_converts_dv_trigger_object():
+    trigger = SimpleNamespace(
+        timestamp=lambda: 123,
+        type=lambda: "TriggerType.EXTERNAL_SIGNAL_RISING_EDGE",
+    )
+
+    assert _trigger_record(trigger) == TriggerRecord(timestamp=123, edge="rising")
+
+
+def test_reprocess_main_writes_derived_artifacts_from_reader(monkeypatch, tmp_path):
+    run_dir = tmp_path / "source-sync-check"
+    run_dir.mkdir()
+    (run_dir / "metadata.json").write_text(
+        json.dumps(
+            {
+                "accumulation_window_us": 20,
+                "polarity_mode": "positive",
+                "expected_trigger_count": 2,
+                "accumulation_cycles": 1,
+            }),
+        encoding="utf-8",
+    )
+    raw_path = run_dir / "raw.aedat4"
+    raw_path.write_bytes(b"fake")
+
+    events = np.array(
+        [
+            (105,
+             1,
+             1,
+             True),
+            (125,
+             2,
+             1,
+             True),
+        ],
+        dtype=[("timestamp",
+                np.int64),
+               ("x",
+                np.int16),
+               ("y",
+                np.int16),
+               ("polarity",
+                np.bool_)],
+    )
+
+    def fake_read(path):
+        assert Path(path) == raw_path
+        return Aedat4RecordingData(
+            events=[events],
+            triggers=[
+                TriggerRecord(timestamp=100,
+                              edge="rising"),
+                TriggerRecord(timestamp=120,
+                              edge="rising"),
+            ],
+            resolution=(4,
+                        3),
+            stats={
+                "event_count": 2,
+                "trigger_count": 2},
+        )
+
+    monkeypatch.setattr("dmdcontrol.camera.reprocess_aedat4.read_aedat4_recording", fake_read)
+    output_dir = tmp_path / "derived"
+
+    assert main(
+        [
+            str(run_dir),
+            "--output-dir",
+            str(output_dir),
+            "--accumulation-start-offset-us",
+            "5",
+        ]) == 0
+
+    accumulated = np.load(output_dir / "accumulated.npy")
+    summary = json.loads((output_dir / "summary.json").read_text(encoding="utf-8"))
+    metadata = json.loads((output_dir / "metadata.json").read_text(encoding="utf-8"))
+
+    assert accumulated.shape == (2, 3, 4)
+    assert accumulated[0, 1, 1] == 1
+    assert accumulated[1, 1, 2] == 1
+    assert summary["window_start_offset_us"] == 5
+    assert metadata["mode"] == "aedat4-reprocess"
+    assert metadata["source_aedat4"] == str(raw_path)
+
+
+@pytest.mark.parametrize("flag", ["--trigger-cluster-us", "--cycle-selection"])
+def test_reprocess_parser_rejects_removed_trigger_selection_options(flag):
+    with pytest.raises(SystemExit):
+        build_parser().parse_args(["source-run", flag, "1"])
diff --git a/tests/test_camera_runs.py b/tests/test_camera_runs.py
index d1bae36..fc12c70 100644
--- a/tests/test_camera_runs.py
+++ b/tests/test_camera_runs.py
@@ -1,6 +1,7 @@
 import json
 
 import numpy as np
+from PIL import Image
 
 from dmdcontrol.camera.accumulation import EventRecord, TriggerRecord
 from dmdcontrol.camera.local_support_filter import LocalSupportFilterConfig
@@ -46,20 +47,32 @@ def test_write_capture_artifacts_saves_rising_trigger_accumulations(tmp_path):
         timestamp="20260527-120106",
     )
     events = [
-        EventRecord(timestamp=105, x=2, y=1, polarity=True),
-        EventRecord(timestamp=110, x=2, y=1, polarity=True),
-        EventRecord(timestamp=210, x=0, y=0, polarity=True),
+        EventRecord(timestamp=105,
+                    x=2,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=110,
+                    x=2,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=210,
+                    x=0,
+                    y=0,
+                    polarity=True),
     ]
     triggers = [
-        TriggerRecord(timestamp=100, edge="rising"),
-        TriggerRecord(timestamp=200, edge="falling"),
+        TriggerRecord(timestamp=100,
+                      edge="rising"),
+        TriggerRecord(timestamp=200,
+                      edge="falling"),
     ]
 
     summary = write_capture_artifacts(
         run,
         events=events,
         triggers=triggers,
-        resolution=(4, 3),
+        resolution=(4,
+                    3),
         window_us=50,
         polarity_mode="positive",
     )
@@ -73,6 +86,12 @@ def test_write_capture_artifacts_saves_rising_trigger_accumulations(tmp_path):
     assert accumulated[0, 1, 2] == 2
     assert (run.path / "accumulated_001.png").exists()
     assert run.contact_sheet_path.exists()
+    with Image.open(run.path / "accumulated_001.png") as image:
+        assert image.mode == "L"
+        assert image.size == (4, 3)
+    with Image.open(run.contact_sheet_path) as image:
+        assert image.mode == "L"
+        assert image.size == (4, 3)
     assert summary_json == summary
     assert summary["actual_trigger_count"] == 1
     assert summary["accumulated_shape"] == [1, 3, 4]
@@ -86,6 +105,143 @@ def test_write_capture_artifacts_saves_rising_trigger_accumulations(tmp_path):
     assert summary["accumulated_abs_sums"] == [2.0]
 
 
+def test_write_capture_artifacts_keeps_signed_png_background_black(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260604-120113",
+    )
+    events = [
+        EventRecord(timestamp=101,
+                    x=0,
+                    y=0,
+                    polarity=True),
+        EventRecord(timestamp=102,
+                    x=1,
+                    y=0,
+                    polarity=False),
+    ]
+    triggers = [TriggerRecord(timestamp=100, edge="rising")]
+
+    write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(3,
+                    2),
+        window_us=10,
+        polarity_mode="signed",
+    )
+
+    accumulated = np.load(run.accumulated_path)
+    with Image.open(run.path / "accumulated_001.png") as image:
+        pixels = np.array(image)
+
+    assert accumulated[0, 0, 0] == 1
+    assert accumulated[0, 0, 1] == -1
+    assert accumulated[0, 1, 2] == 0
+    assert pixels[0, 0] == 255
+    assert pixels[0, 1] == 255
+    assert pixels[1, 2] == 0
+
+
+def test_write_capture_artifacts_applies_window_start_offset(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260604-120115",
+    )
+    events = [
+        EventRecord(timestamp=105,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=125,
+                    x=2,
+                    y=1,
+                    polarity=True),
+    ]
+    triggers = [TriggerRecord(timestamp=100, edge="rising")]
+
+    summary = write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(4,
+                    3),
+        window_us=20,
+        polarity_mode="positive",
+        window_start_offset_us=20,
+    )
+
+    accumulated = np.load(run.accumulated_path)
+    assert accumulated[0, 1, 1] == 0
+    assert accumulated[0, 1, 2] == 1
+    assert summary["window_start_offset_us"] == 20
+    assert summary["events_per_accumulation_window"] == [1]
+    assert summary["events_per_pre_trigger_window"] == [1]
+
+
+def test_write_capture_artifacts_can_use_cycle_columns_for_contact_sheet(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260604-120116",
+    )
+    triggers = [TriggerRecord(timestamp=100 + index * 10, edge="rising") for index in range(6)]
+
+    write_capture_artifacts(
+        run,
+        events=[],
+        triggers=triggers,
+        resolution=(4,
+                    3),
+        window_us=5,
+        polarity_mode="positive",
+        contact_sheet_columns=3,
+    )
+
+    with Image.open(run.contact_sheet_path) as image:
+        assert image.size == (12, 6)
+
+
+def test_write_capture_artifacts_uses_log_grayscale_for_weak_events(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260604-120114",
+    )
+    events = [EventRecord(timestamp=101, x=0, y=0, polarity=True)]
+    events.extend(
+        EventRecord(timestamp=101 + offset,
+                    x=1,
+                    y=0,
+                    polarity=True) for offset in range(100))
+    triggers = [TriggerRecord(timestamp=100, edge="rising")]
+
+    write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(3,
+                    2),
+        window_us=200,
+        polarity_mode="positive",
+    )
+
+    accumulated = np.load(run.accumulated_path)
+    with Image.open(run.path / "accumulated_001.png") as image:
+        pixels = np.array(image)
+
+    assert accumulated[0, 0, 0] == 1
+    assert accumulated[0, 0, 1] == 100
+    assert accumulated[0, 1, 2] == 0
+    assert pixels[0, 0] > 20
+    assert pixels[0, 0] < pixels[0, 1]
+    assert pixels[0, 1] == 255
+    assert pixels[1, 2] == 0
+
+
 def test_write_capture_artifacts_filters_events_before_accumulation(tmp_path):
     run = create_run_directory(
         "sync-check",
@@ -93,20 +249,32 @@ def test_write_capture_artifacts_filters_events_before_accumulation(tmp_path):
         timestamp="20260527-120107",
     )
     events = [
-        EventRecord(timestamp=101, x=1, y=1, polarity=True),
-        EventRecord(timestamp=102, x=1, y=2, polarity=True),
-        EventRecord(timestamp=103, x=2, y=2, polarity=False),
+        EventRecord(timestamp=101,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=102,
+                    x=1,
+                    y=2,
+                    polarity=True),
+        EventRecord(timestamp=103,
+                    x=2,
+                    y=2,
+                    polarity=False),
     ]
     triggers = [
-        TriggerRecord(timestamp=100, edge="rising"),
-        TriggerRecord(timestamp=150, edge="falling"),
+        TriggerRecord(timestamp=100,
+                      edge="rising"),
+        TriggerRecord(timestamp=150,
+                      edge="falling"),
     ]
 
     summary = write_capture_artifacts(
         run,
         events=events,
         triggers=triggers,
-        resolution=(4, 4),
+        resolution=(4,
+                    4),
         window_us=10,
         polarity_mode="ignore",
         event_noise_filter=LocalSupportFilterConfig(
@@ -150,21 +318,34 @@ def test_write_capture_artifacts_can_limit_accumulation_triggers(tmp_path):
         timestamp="20260527-120108",
     )
     events = [
-        EventRecord(timestamp=101, x=1, y=1, polarity=True),
-        EventRecord(timestamp=201, x=2, y=2, polarity=True),
-        EventRecord(timestamp=301, x=3, y=3, polarity=True),
+        EventRecord(timestamp=101,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=201,
+                    x=2,
+                    y=2,
+                    polarity=True),
+        EventRecord(timestamp=301,
+                    x=3,
+                    y=3,
+                    polarity=True),
     ]
     triggers = [
-        TriggerRecord(timestamp=100, edge="rising"),
-        TriggerRecord(timestamp=200, edge="rising"),
-        TriggerRecord(timestamp=300, edge="rising"),
+        TriggerRecord(timestamp=100,
+                      edge="rising"),
+        TriggerRecord(timestamp=200,
+                      edge="rising"),
+        TriggerRecord(timestamp=300,
+                      edge="rising"),
     ]
 
     summary = write_capture_artifacts(
         run,
         events=events,
         triggers=triggers,
-        resolution=(5, 5),
+        resolution=(5,
+                    5),
         window_us=10,
         polarity_mode="positive",
         max_accumulation_triggers=2,
@@ -185,3 +366,243 @@ def test_write_capture_artifacts_can_limit_accumulation_triggers(tmp_path):
     assert summary["raw_rising_trigger_count"] == 3
     assert summary["max_accumulation_triggers"] == 2
     assert summary["accumulation_trigger_limited"] is True
+
+
+def test_write_capture_artifacts_aligns_accumulation_triggers_to_event_range(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260602-120109",
+    )
+    events = [
+        EventRecord(timestamp=1000,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1010,
+                    x=2,
+                    y=2,
+                    polarity=True),
+        EventRecord(timestamp=1110,
+                    x=3,
+                    y=3,
+                    polarity=True),
+    ]
+    triggers = [
+        TriggerRecord(timestamp=100,
+                      edge="rising"),
+        TriggerRecord(timestamp=200,
+                      edge="rising"),
+        TriggerRecord(timestamp=1000,
+                      edge="rising"),
+        TriggerRecord(timestamp=1100,
+                      edge="rising"),
+    ]
+
+    summary = write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(5,
+                    5),
+        window_us=50,
+        polarity_mode="positive",
+    )
+
+    trigger_lines = run.triggers_path.read_text(encoding="utf-8").splitlines()
+    accumulated = np.load(run.accumulated_path)
+
+    assert trigger_lines == [
+        "index,timestamp,edge",
+        "0,1000,rising",
+        "1,1100,rising",
+    ]
+    assert accumulated.shape == (2, 5, 5)
+    assert accumulated[0, 1, 1] == 1
+    assert accumulated[0, 2, 2] == 1
+    assert accumulated[1, 3, 3] == 1
+    assert summary["raw_rising_trigger_count"] == 4
+    assert summary["actual_trigger_count"] == 2
+    assert summary["raw_rising_trigger_time_range_us"] == [100, 1100]
+    assert summary["rising_trigger_time_range_us"] == [1000, 1100]
+    assert summary["trigger_alignment"]["mode"] == "event_overlap"
+    assert summary["trigger_alignment"]["dropped_before_event_count"] == 2
+    assert summary["trigger_alignment"]["dropped_after_event_count"] == 0
+
+
+def test_write_capture_artifacts_keeps_close_triggers_as_separate_frames(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260603-120110",
+    )
+    events = [
+        EventRecord(timestamp=1001,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1009,
+                    x=2,
+                    y=2,
+                    polarity=True),
+    ]
+    triggers = [
+        TriggerRecord(timestamp=1000,
+                      edge="rising"),
+        TriggerRecord(timestamp=1008,
+                      edge="rising"),
+    ]
+
+    summary = write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(5,
+                    5),
+        window_us=5,
+        polarity_mode="positive",
+    )
+
+    trigger_lines = run.triggers_path.read_text(encoding="utf-8").splitlines()
+    accumulated = np.load(run.accumulated_path)
+
+    assert trigger_lines == [
+        "index,timestamp,edge",
+        "0,1000,rising",
+        "1,1008,rising",
+    ]
+    assert accumulated.shape == (2, 5, 5)
+    assert accumulated[0, 1, 1] == 1
+    assert accumulated[1, 2, 2] == 1
+    assert summary["raw_rising_trigger_count"] == 2
+    assert summary["aligned_rising_trigger_count"] == 2
+    assert summary["selected_rising_trigger_count"] == 2
+    assert "trigger_clustering" not in summary
+    assert "clustered_rising_trigger_count" not in summary
+
+
+def test_write_capture_artifacts_selects_first_full_trigger_cycle(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260603-120111",
+    )
+    events = [
+        EventRecord(timestamp=1001,
+                    x=1,
+                    y=1,
+                    polarity=True),
+        EventRecord(timestamp=1101,
+                    x=2,
+                    y=2,
+                    polarity=True),
+        EventRecord(timestamp=1201,
+                    x=3,
+                    y=3,
+                    polarity=True),
+        EventRecord(timestamp=1301,
+                    x=4,
+                    y=4,
+                    polarity=True),
+        EventRecord(timestamp=1401,
+                    x=0,
+                    y=0,
+                    polarity=True),
+        EventRecord(timestamp=1501,
+                    x=1,
+                    y=0,
+                    polarity=True),
+    ]
+    triggers = [
+        TriggerRecord(timestamp=1000,
+                      edge="rising"),
+        TriggerRecord(timestamp=1100,
+                      edge="rising"),
+        TriggerRecord(timestamp=1200,
+                      edge="rising"),
+        TriggerRecord(timestamp=1300,
+                      edge="rising"),
+        TriggerRecord(timestamp=1400,
+                      edge="rising"),
+        TriggerRecord(timestamp=1500,
+                      edge="rising"),
+    ]
+
+    summary = write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(5,
+                    5),
+        window_us=20,
+        polarity_mode="positive",
+        trigger_cycle_length=3,
+        accumulation_cycles=1,
+    )
+
+    trigger_lines = run.triggers_path.read_text(encoding="utf-8").splitlines()
+    accumulated = np.load(run.accumulated_path)
+
+    assert trigger_lines == [
+        "index,timestamp,edge",
+        "0,1000,rising",
+        "1,1100,rising",
+        "2,1200,rising",
+    ]
+    assert accumulated.shape == (3, 5, 5)
+    assert summary["actual_trigger_count"] == 3
+    assert summary["selected_rising_trigger_count"] == 3
+    assert summary["trigger_cycle_limit"] == {
+        "cycle_length": 3,
+        "requested_cycles": 1,
+        "available_full_cycles": 2,
+        "selected_cycle_indices": [0],
+        "selected_trigger_count": 3,
+    }
+    assert summary["frame_artifacts"] == [
+        "accumulated_001.png",
+        "accumulated_002.png",
+        "accumulated_003.png",
+    ]
+    assert not (run.path / "accumulated_004.png").exists()
+
+
+def test_write_capture_artifacts_selects_first_cycle_block(tmp_path):
+    run = create_run_directory(
+        "sync-check",
+        output_root=tmp_path,
+        timestamp="20260604-120114",
+    )
+    triggers = [TriggerRecord(timestamp=1000 + index * 100, edge="rising") for index in range(15)]
+    events = []
+    cycle_counts = [90, 3, 60, 60, 60]
+    for cycle_index, cycle_total in enumerate(cycle_counts):
+        event_count_per_trigger = cycle_total // 3
+        for slot in range(3):
+            trigger_index = cycle_index * 3 + slot
+            for offset in range(event_count_per_trigger):
+                events.append(
+                    EventRecord(
+                        timestamp=1000 + trigger_index * 100 + offset,
+                        x=offset % 5,
+                        y=cycle_index,
+                        polarity=True,
+                    ))
+
+    summary = write_capture_artifacts(
+        run,
+        events=events,
+        triggers=triggers,
+        resolution=(5,
+                    5),
+        window_us=90,
+        polarity_mode="positive",
+        trigger_cycle_length=3,
+        accumulation_cycles=3,
+    )
+
+    trigger_lines = run.triggers_path.read_text(encoding="utf-8").splitlines()
+
+    assert summary["trigger_cycle_limit"]["selected_cycle_indices"] == [0, 1, 2]
+    assert trigger_lines[1] == "0,1000,rising"
+    assert trigger_lines[-1] == "8,1800,rising"
diff --git a/tests/test_camera_session.py b/tests/test_camera_session.py
index c96f274..59cf57c 100644
--- a/tests/test_camera_session.py
+++ b/tests/test_camera_session.py
@@ -3,6 +3,7 @@
 
 
 class FakeCapture:
+
     def __init__(self):
         self.event_reads = 0
         self.trigger_reads = 0
@@ -26,6 +27,7 @@ def getNextTriggerBatch(self):
 
 
 class FakeWriter:
+
     def __init__(self, path, capture):
         self.path = path
         self.capture = capture
@@ -55,8 +57,7 @@ def test_open_ready_camera_applies_shared_lifecycle(monkeypatch, tmp_path):
         io=SimpleNamespace(
             camera=SimpleNamespace(open=lambda: calls.append("open") or capture),
             MonoCameraWriter=FakeWriter,
-        )
-    )
+        ))
     run = SimpleNamespace(raw_recording_path=tmp_path / "raw.aedat4")
     ready = SimpleNamespace(event_resolution=(320, 240))
 
@@ -75,32 +76,33 @@ def test_open_ready_camera_applies_shared_lifecycle(monkeypatch, tmp_path):
     monkeypatch.setattr(
         session,
         "rearm_camera_streams",
-        lambda opened_capture: calls.append(("rearm", opened_capture is capture)) or {"rearmed": True},
+        lambda opened_capture: calls.append(
+            ("rearm", opened_capture is capture)) or {"rearmed": True},
     )
     monkeypatch.setattr(
         session,
         "configure_camera_performance",
         lambda opened_capture, bias_sensitivity, efps, prefer_legacy: calls.append(
-            ("performance", opened_capture is capture, bias_sensitivity, efps, prefer_legacy)
-        ),
+            ("performance", opened_capture is capture, bias_sensitivity, efps, prefer_legacy)),
     )
     monkeypatch.setattr(
         session,
         "configure_rising_edge_triggers",
-        lambda opened_capture: calls.append(("triggers", opened_capture is capture)),
+        lambda opened_capture: calls.append(
+            ("triggers", opened_capture is capture)) or {"configured": True},
     )
     monkeypatch.setattr(
         session,
         "validate_camera_ready",
-        lambda opened_capture, stream_rearm, usb_reset, power_cycle: calls.append(
+        lambda opened_capture, stream_rearm, usb_reset, power_cycle, trigger_configuration: calls.
+        append(
             (
                 "ready",
                 opened_capture is capture,
                 stream_rearm,
                 usb_reset,
                 power_cycle,
-            )
-        ) or ready,
+                trigger_configuration, )) or ready,
     )
 
     opened_capture, writer, opened_ready = session.open_ready_camera(
@@ -119,21 +121,35 @@ def test_open_ready_camera_applies_shared_lifecycle(monkeypatch, tmp_path):
     assert writer.path == str(run.raw_recording_path)
     assert writer.capture is capture
     assert opened_ready is ready
-    assert capture.event_reads == 2
-    assert capture.trigger_reads == 2
+    assert capture.event_reads == 1
+    assert capture.trigger_reads == 1
     assert calls == [
-        ("power", "cycle-camera"),
-        ("reset", True, True),
+        ("power",
+         "cycle-camera"),
+        ("reset",
+         True,
+         True),
         "open",
-        ("rearm", True),
-        ("performance", True, "low", "variable_5000", False),
-        ("triggers", True),
+        ("rearm",
+         True),
+        ("performance",
+         True,
+         "low",
+         "variable_5000",
+         False),
+        ("triggers",
+         True),
         (
             "ready",
             True,
-            {"rearmed": True},
-            {"enabled": True},
-            {"command": "cycle-camera"},
+            {
+                "rearmed": True},
+            {
+                "enabled": True},
+            {
+                "command": "cycle-camera"},
+            {
+                "configured": True},
         ),
     ]
 
@@ -146,6 +162,7 @@ def test_open_ready_camera_cleans_up_when_flush_fails(monkeypatch, tmp_path):
     writers = []
 
     class RecordingWriter(FakeWriter):
+
         def __init__(self, path, capture):
             super().__init__(path, capture)
             writers.append(self)
@@ -154,8 +171,7 @@ def __init__(self, path, capture):
         io=SimpleNamespace(
             camera=SimpleNamespace(open=lambda: capture),
             MonoCameraWriter=RecordingWriter,
-        )
-    )
+        ))
     run = SimpleNamespace(raw_recording_path=tmp_path / "raw.aedat4")
 
     monkeypatch.setitem(sys.modules, "dv_processing", fake_dv)
@@ -259,8 +275,7 @@ def test_open_ready_camera_can_use_legacy_camera_open_method(monkeypatch, tmp_pa
         session,
         "configure_camera_performance",
         lambda opened_capture, bias_sensitivity, efps, prefer_legacy: calls.append(
-            ("performance", opened_capture is capture, bias_sensitivity, efps, prefer_legacy)
-        ),
+            ("performance", opened_capture is capture, bias_sensitivity, efps, prefer_legacy)),
     )
     monkeypatch.setattr(session, "configure_rising_edge_triggers", lambda *args, **kwargs: None)
     monkeypatch.setattr(session, "validate_camera_ready", lambda *args, **kwargs: SimpleNamespace())
@@ -275,6 +290,12 @@ def test_open_ready_camera_can_use_legacy_camera_open_method(monkeypatch, tmp_pa
     )
 
     assert calls == [
-        ("open", True, "legacy"),
-        ("performance", True, "low", "variable_5000", True),
+        ("open",
+         True,
+         "legacy"),
+        ("performance",
+         True,
+         "low",
+         "variable_5000",
+         True),
     ]
diff --git a/tests/test_camera_sync_check.py b/tests/test_camera_sync_check.py
index d5e7cdd..82c0fec 100644
--- a/tests/test_camera_sync_check.py
+++ b/tests/test_camera_sync_check.py
@@ -6,6 +6,7 @@
     _to_pair_runtime_args,
     build_parser,
     dry_run,
+    expected_trigger_count,
     parse_numbers,
 )
 
@@ -18,6 +19,45 @@ def test_sync_check_parser_defaults_to_digits_one_through_five():
     assert args.test_b == "dot"
     assert args.number_size_px == 100
     assert args.b_dot_radius == 20
+    assert args.accumulation_cycles is None
+    assert args.accumulation_start_offset_us == 0
+
+
+def test_sync_check_numbers_mode_defaults_to_one_accumulation_cycle():
+    args = build_parser().parse_args(["--dry-run"])
+
+    assert args.requested_accumulation_cycles == 1
+
+
+def test_sync_check_count_mode_does_not_limit_accumulation_cycles_by_default():
+    args = build_parser().parse_args([
+        "--dry-run",
+        "--test",
+        "a-count-b-static",
+    ])
+
+    assert args.requested_accumulation_cycles is None
+
+
+def test_sync_check_parser_accepts_accumulation_cycle_options():
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--accumulation-cycles",
+            "2",
+            "--accumulation-start-offset-us",
+            "-50",
+        ])
+
+    assert args.accumulation_cycles == 2
+    assert args.requested_accumulation_cycles == 2
+    assert args.accumulation_start_offset_us == -50
+
+
+@pytest.mark.parametrize("flag", ["--trigger-cluster-us", "--cycle-selection"])
+def test_sync_check_parser_rejects_removed_trigger_selection_options(flag):
+    with pytest.raises(SystemExit):
+        build_parser().parse_args(["--dry-run", flag, "1"])
 
 
 @pytest.mark.parametrize("value", ["", ",", "0", "1,10", "-1", "x"])
@@ -37,14 +77,15 @@ def test_parse_numbers_accepts_comma_separated_digits():
 
 
 def test_sync_check_runtime_args_use_requested_number_sequence():
-    args = build_parser().parse_args([
-        "--numbers",
-        "2,4,6",
-        "--number-size-px",
-        "123",
-        "--runtime-seconds",
-        "7",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--numbers",
+            "2,4,6",
+            "--number-size-px",
+            "123",
+            "--runtime-seconds",
+            "7",
+        ])
 
     pair_args = _to_pair_runtime_args(args)
 
@@ -57,19 +98,46 @@ def test_sync_check_runtime_args_use_requested_number_sequence():
     assert "--numbers-exposure-us" not in pair_args
 
 
+def test_sync_check_runtime_args_forward_numbers_bitplane_order():
+    args = build_parser().parse_args(
+        [
+            "--numbers",
+            "1,2,3,4,5",
+            "--numbers-bitplane-order",
+            "1,2,3,4,0",
+        ])
+
+    pair_args = _to_pair_runtime_args(args)
+
+    assert args.numbers_bitplane_order == [1, 2, 3, 4, 0]
+    assert pair_args[pair_args.index("--numbers-bitplane-order") + 1] == "1,2,3,4,0"
+
+
+@pytest.mark.parametrize("value", ["4,2,3,1", "4,2,2,1,0", "5,2,3,1,0"])
+def test_sync_check_numbers_bitplane_order_must_match_numbers_slots(value):
+    with pytest.raises(SystemExit):
+        build_parser().parse_args([
+            "--numbers",
+            "1,2,3,4,5",
+            "--numbers-bitplane-order",
+            value,
+        ])
+
+
 def test_sync_check_runtime_args_pass_b_dot_geometry():
-    args = build_parser().parse_args([
-        "--test",
-        "a-numbers-b-static",
-        "--test-b",
-        "dot",
-        "--b-dot-x",
-        "955",
-        "--b-dot-y",
-        "535",
-        "--b-dot-radius",
-        "12",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--test",
+            "a-numbers-b-static",
+            "--test-b",
+            "dot",
+            "--b-dot-x",
+            "955",
+            "--b-dot-y",
+            "535",
+            "--b-dot-radius",
+            "12",
+        ])
 
     pair_args = _to_pair_runtime_args(args)
 
@@ -89,21 +157,126 @@ def test_sync_check_runtime_args_allow_explicit_bitplane_exposure_override():
     assert pair_args[pair_args.index("--numbers-exposure-us") + 1] == "600"
 
 
+def test_sync_check_parser_accepts_count_mode_options():
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--test",
+            "a-count-b-static",
+            "--count-start",
+            "1",
+            "--count-end",
+            "100",
+            "--count-slots-per-frame",
+            "2",
+            "--count-exposure-us",
+            "7000",
+        ])
+
+    assert args.count_start == 1
+    assert args.count_end == 100
+    assert args.count_slots_per_frame == 2
+    assert args.count_exposure_us == 7000
+
+
+@pytest.mark.parametrize(
+    "argv",
+    [
+        ["--test",
+         "a-count-b-static",
+         "--count-start",
+         "5",
+         "--count-end",
+         "4"],
+        [
+            "--test",
+            "a-count-b-static",
+            "--count-start",
+            "1",
+            "--count-end",
+            "5",
+            "--count-slots-per-frame",
+            "2"],
+        ["--test",
+         "a-count-b-static",
+         "--count-end",
+         "130",
+         "--count-slots-per-frame",
+         "2"],
+        ["--test",
+         "a-count-b-static",
+         "--count-slots-per-frame",
+         "25"],
+    ],
+)
+def test_sync_check_parser_rejects_invalid_count_mode_options(argv):
+    with pytest.raises(SystemExit):
+        build_parser().parse_args(["--dry-run", *argv])
+
+
+def test_sync_check_count_mode_expects_one_trigger_per_count():
+    args = build_parser().parse_args(
+        [
+            "--test",
+            "a-count-b-static",
+            "--count-start",
+            "1",
+            "--count-end",
+            "100",
+        ])
+
+    assert expected_trigger_count(args) == 100
+
+
+def test_sync_check_runtime_args_forward_count_options_without_numbers():
+    args = build_parser().parse_args(
+        [
+            "--test",
+            "a-count-b-static",
+            "--test-b",
+            "dot",
+            "--count-start",
+            "1",
+            "--count-end",
+            "100",
+            "--count-slots-per-frame",
+            "2",
+            "--count-exposure-us",
+            "7000",
+            "--number-size-px",
+            "123",
+            "--runtime-seconds",
+            "2",
+        ])
+
+    pair_args = _to_pair_runtime_args(args)
+
+    assert pair_args[:4] == ["--test", "a-count-b-static", "--test-b", "dot"]
+    assert "--numbers" not in pair_args
+    assert "--numbers-exposure-us" not in pair_args
+    assert pair_args[pair_args.index("--count-start") + 1] == "1"
+    assert pair_args[pair_args.index("--count-end") + 1] == "100"
+    assert pair_args[pair_args.index("--count-slots-per-frame") + 1] == "2"
+    assert pair_args[pair_args.index("--count-exposure-us") + 1] == "7000"
+    assert pair_args[pair_args.index("--numbers-size-px") + 1] == "123"
+
+
 def test_sync_check_parser_accepts_event_noise_filter_options():
-    args = build_parser().parse_args([
-        "--dry-run",
-        "--event-noise-filter",
-        "local-support",
-        "--event-filter-delta-us",
-        "50000",
-        "--event-filter-window-px",
-        "3",
-        "--event-filter-threshold",
-        "2",
-        "--event-filter-polarity",
-        "same",
-        "--save-filtered-events",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--event-noise-filter",
+            "local-support",
+            "--event-filter-delta-us",
+            "50000",
+            "--event-filter-window-px",
+            "3",
+            "--event-filter-threshold",
+            "2",
+            "--event-filter-polarity",
+            "same",
+            "--save-filtered-events",
+        ])
 
     assert args.event_noise_filter == "local-support"
     assert args.event_filter_delta_us == 50000
@@ -128,16 +301,17 @@ def test_sync_check_parser_uses_mentor_style_camera_lifecycle_by_default():
 
     assert args.camera_stream_rearm is False
     assert args.camera_shutdown_streams is False
-    assert args.camera_flush_reads == 1
+    assert args.camera_flush_reads == 32
     assert args.camera_post_trigger_event_batches == 0
 
 
 def test_sync_check_parser_accepts_power_cycle_command():
-    args = build_parser().parse_args([
-        "--dry-run",
-        "--camera-power-cycle-command",
-        "uhubctl -l 1-2 -p 3 -a cycle -d 2",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--camera-power-cycle-command",
+            "uhubctl -l 1-2 -p 3 -a cycle -d 2",
+        ])
 
     assert args.camera_power_cycle_command == "uhubctl -l 1-2 -p 3 -a cycle -d 2"
 
@@ -153,19 +327,20 @@ def test_sync_check_parser_accepts_name_override_alias():
 
 
 def test_sync_check_dry_run_creates_run_artifacts(tmp_path):
-    args = build_parser().parse_args([
-        "--dry-run",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120102",
-        "--number-size-px",
-        "420",
-        "--numbers",
-        "1,2,3,4,5",
-        "--trigger-out-2-delay-fraction",
-        "0.05",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120102",
+            "--number-size-px",
+            "420",
+            "--numbers",
+            "1,2,3,4,5",
+            "--trigger-out-2-delay-fraction",
+            "0.05",
+        ])
 
     run = dry_run(args)
 
@@ -194,53 +369,162 @@ def test_sync_check_dry_run_creates_run_artifacts(tmp_path):
 
 
 def test_sync_check_dry_run_records_event_filter_config(tmp_path):
-    args = build_parser().parse_args([
-        "--dry-run",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120107",
-        "--event-noise-filter",
-        "local-support",
-        "--event-filter-delta-us",
-        "50000",
-        "--event-filter-window-px",
-        "3",
-        "--event-filter-threshold",
-        "2",
-        "--event-filter-polarity",
-        "same",
-    ])
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120107",
+            "--event-noise-filter",
+            "local-support",
+            "--event-filter-delta-us",
+            "50000",
+            "--event-filter-window-px",
+            "3",
+            "--event-filter-threshold",
+            "2",
+            "--event-filter-polarity",
+            "same",
+        ])
 
     run = dry_run(args)
 
     metadata = json.loads(run.metadata_path.read_text(encoding="utf-8"))
     assert metadata["event_noise_filter"] == {
-        "algorithm": "centered-local-support",
-        "delta_t_us": 50000,
-        "enabled": True,
+        "algorithm":
+        "centered-local-support",
+        "delta_t_us":
+        50000,
+        "enabled":
+        True,
         "note": (
             "Practical notebook-validated local support filter; "
-            "not a source-faithful DV Runtime YNoise implementation."
-        ),
-        "polarity": "same",
-        "threshold": 2,
-        "window_px": 3,
+            "not a source-faithful DV Runtime YNoise implementation."),
+        "polarity":
+        "same",
+        "threshold":
+        2,
+        "window_px":
+        3,
     }
 
 
+def test_sync_check_count_mode_dry_run_records_count_metadata(tmp_path):
+    args = build_parser().parse_args(
+        [
+            "--dry-run",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260602-120100",
+            "--test",
+            "a-count-b-static",
+            "--count-start",
+            "1",
+            "--count-end",
+            "100",
+            "--count-slots-per-frame",
+            "2",
+            "--count-exposure-us",
+            "7000",
+        ])
+
+    run = dry_run(args)
+
+    metadata = json.loads(run.metadata_path.read_text(encoding="utf-8"))
+    assert metadata["test"] == "a-count-b-static"
+    assert metadata["count_start"] == 1
+    assert metadata["count_end"] == 100
+    assert metadata["count_slots_per_frame"] == 2
+    assert metadata["count_exposure_us"] == 7000
+    assert metadata["expected_trigger_count"] == 100
+    assert metadata["accumulation_window_us"] == 7000
+
+
 def test_camera_sync_check_cli_dry_run_creates_artifacts(tmp_path):
     from dmdcontrol.cli.main import main
 
-    assert main([
-        "camera",
-        "sync-check",
-        "--dry-run",
-        "--output-root",
-        str(tmp_path),
-        "--timestamp",
-        "20260527-120103",
-    ]) == 0
-
-    metadata = json.loads((tmp_path / "20260527-120103-sync-check" / "metadata.json").read_text(encoding="utf-8"))
+    assert main(
+        [
+            "camera",
+            "sync-check",
+            "--dry-run",
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260527-120103",
+        ]) == 0
+
+    metadata = json.loads(
+        (tmp_path / "20260527-120103-sync-check" / "metadata.json").read_text(encoding="utf-8"))
     assert metadata["number_sequence"] == [1, 2, 3, 4, 5]
+
+
+def test_live_capture_flushes_queued_triggers_before_recording(monkeypatch, tmp_path):
+    from types import SimpleNamespace
+
+    from dmdcontrol.camera.capture import CaptureResult
+    from dmdcontrol.camera.runs import create_run_directory
+    from dmdcontrol.camera import sync_check
+
+    args = build_parser().parse_args(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--timestamp",
+            "20260604-120116",
+            "--camera-flush-reads",
+            "3",
+        ])
+    run = create_run_directory("sync-check", tmp_path, timestamp="20260604-120116")
+    flush_calls = []
+
+    monkeypatch.setattr(
+        sync_check,
+        "flush_stale_batches",
+        lambda capture, reads, include_triggers=True: flush_calls.
+        append({
+            "reads": reads, "include_triggers": include_triggers}) or {"requested_reads": reads},
+    )
+
+    def fake_run_pair(_pair_args, before_start):
+        before_start(
+            {
+                "state_a": {
+                    "timing": {
+                        "exposure_us": 1500}},
+                "state_b": {
+                    "timing": {
+                        "exposure_us": 1500}},
+            })
+        return 0
+
+    class FakeRecording:
+
+        def stop(self):
+            pass
+
+        def join(self):
+            return CaptureResult(
+                trigger_count=0,
+                event_batch_count=0,
+                trigger_batch_count=0,
+                timed_out=False,
+            )
+
+    monkeypatch.setattr(sync_check, "_run_pair_with_callback", fake_run_pair)
+    monkeypatch.setattr(sync_check, "_start_recording", lambda *args, **kwargs: FakeRecording())
+    monkeypatch.setattr(
+        sync_check,
+        "_write_capture_artifacts_for_sync_check", lambda *args, **kwargs: {
+            "frame_artifacts": [],
+            "filtered_frame_artifacts": [],
+            "filtered_contact_sheet_artifact": None,
+            "event_noise_filter": {
+                "enabled": False}, })
+
+    ready = SimpleNamespace(event_resolution=(320, 240))
+
+    assert sync_check.live_capture(args, run, object(), object(), ready) == 0
+    assert flush_calls == [{"reads": 3, "include_triggers": True}]
diff --git a/tests/test_camera_sync_sweep.py b/tests/test_camera_sync_sweep.py
new file mode 100644
index 0000000..8479868
--- /dev/null
+++ b/tests/test_camera_sync_sweep.py
@@ -0,0 +1,174 @@
+import csv
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+from dmdcontrol.camera import sync_sweep
+
+
+def _write_manifest(path, rows):
+    fieldnames = sorted({key for row in rows for key in row})
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def _row(tmp_path, timestamp, exposure, delay):
+    return {
+        "output_root": str(tmp_path),
+        "timestamp": timestamp,
+        "test": "a-numbers-b-static",
+        "test_b": "dot",
+        "numbers": "1,2,3,4,5",
+        "number_size_px": "100",
+        "b_dot_x": "960",
+        "b_dot_y": "540",
+        "b_dot_radius": "20",
+        "numbers_exposure_us": str(exposure),
+        "dark_time_us": "100",
+        "trigger_delay_fraction": str(delay),
+        "runtime_seconds": "1",
+        "polarity_mode": "ignore",
+        "event_noise_filter": "none",
+        "save_filtered_events": "True",
+        "verbose": "1",
+    }
+
+
+def test_sync_sweep_dry_run_creates_one_run_per_manifest_row(tmp_path):
+    manifest = tmp_path / "sweep.csv"
+    _write_manifest(
+        manifest,
+        [
+            _row(tmp_path,
+                 "sweep-000",
+                 600,
+                 0.0),
+            _row(tmp_path,
+                 "sweep-001",
+                 1000,
+                 0.05),
+        ],
+    )
+
+    assert sync_sweep.main(["--dry-run", "--manifest", str(manifest)]) == 0
+
+    first = json.loads(
+        (tmp_path / "sweep-000-sync-check" / "metadata.json").read_text(encoding="utf-8"))
+    second = json.loads(
+        (tmp_path / "sweep-001-sync-check" / "metadata.json").read_text(encoding="utf-8"))
+    assert first["dry_run"] is True
+    assert first["numbers_exposure_us"] == 600
+    assert first["trigger_policy"]["delay_fraction"] == 0.0
+    assert second["numbers_exposure_us"] == 1000
+    assert second["trigger_policy"]["delay_fraction"] == 0.05
+
+
+def test_camera_sync_sweep_cli_dry_run_creates_artifacts(tmp_path):
+    from dmdcontrol.cli.main import main
+
+    manifest = tmp_path / "sweep.csv"
+    _write_manifest(manifest, [_row(tmp_path, "sweep-000", 600, 0.0)])
+
+    assert main([
+        "camera",
+        "sync-sweep",
+        "--dry-run",
+        "--manifest",
+        str(manifest),
+    ]) == 0
+
+    metadata = json.loads(
+        (tmp_path / "sweep-000-sync-check" / "metadata.json").read_text(encoding="utf-8"))
+    assert metadata["number_sequence"] == [1, 2, 3, 4, 5]
+    assert metadata["numbers_exposure_us"] == 600
+
+
+def test_sync_sweep_forwards_accumulation_cycle_options_from_manifest(tmp_path):
+    argv = sync_sweep._row_sync_check_argv(
+        {
+            **_row(tmp_path,
+                   "sweep-000",
+                   600,
+                   0.0),
+            "accumulation_cycles": "2",
+            "trigger_cluster_us": "0",
+            "cycle_selection": "strongest",
+        })
+
+    assert argv[argv.index("--accumulation-cycles") + 1] == "2"
+    assert "--trigger-cluster-us" not in argv
+    assert "--cycle-selection" not in argv
+
+
+def test_sync_sweep_live_opens_camera_once_for_all_rows(tmp_path, monkeypatch):
+    manifest = tmp_path / "sweep.csv"
+    _write_manifest(
+        manifest,
+        [
+            _row(tmp_path,
+                 "sweep-000",
+                 600,
+                 0.0),
+            _row(tmp_path,
+                 "sweep-001",
+                 1000,
+                 0.05),
+        ],
+    )
+    calls = {
+        "open": 0,
+        "runs": [],
+        "writers": [],
+        "closed_writers": 0,
+        "closed_captures": 0,
+        "flushes": [],
+    }
+    capture = object()
+    ready = SimpleNamespace(event_resolution=(346, 260))
+
+    def fake_open_ready_camera_capture(args):
+        calls["open"] += 1
+        return capture, ready
+
+    def fake_open_camera_writer(run, opened_capture):
+        assert opened_capture is capture
+        writer = object()
+        calls["writers"].append((run.path.name, writer))
+        return writer
+
+    def fake_live_capture(args, run, opened_capture, writer, opened_ready, command_argv=None):
+        assert opened_capture is capture
+        assert opened_ready is ready
+        calls["runs"].append((args.timestamp, run.path.name, writer, command_argv))
+        return 0
+
+    def fake_close_camera_resources(resources, *, shutdown_streams):
+        if resources.get("writer") is not None:
+            calls["closed_writers"] += 1
+        if resources.get("capture") is not None:
+            calls["closed_captures"] += 1
+
+    def fake_flush_stale_batches(opened_capture, *, reads, include_triggers=True):
+        assert opened_capture is capture
+        calls["flushes"].append((reads, include_triggers))
+        return {}
+
+    monkeypatch.setattr(sync_sweep, "open_ready_camera_capture", fake_open_ready_camera_capture)
+    monkeypatch.setattr(sync_sweep, "open_camera_writer", fake_open_camera_writer)
+    monkeypatch.setattr(sync_sweep, "flush_stale_batches", fake_flush_stale_batches)
+    monkeypatch.setattr(sync_sweep.sync_check, "live_capture", fake_live_capture)
+    monkeypatch.setattr(sync_sweep, "close_camera_resources", fake_close_camera_resources)
+
+    assert sync_sweep.main(["--manifest", str(manifest)]) == 0
+
+    assert calls["open"] == 1
+    assert [run[0] for run in calls["runs"]] == ["sweep-000", "sweep-001"]
+    assert [writer[0] for writer in calls["writers"]] == [
+        "sweep-000-sync-check",
+        "sweep-001-sync-check",
+    ]
+    assert calls["closed_writers"] == 2
+    assert calls["closed_captures"] == 1
+    assert calls["flushes"] == [(32, True), (32, True)]
diff --git a/tests/test_camera_usb_reset.py b/tests/test_camera_usb_reset.py
index 512ae77..c03c5d1 100644
--- a/tests/test_camera_usb_reset.py
+++ b/tests/test_camera_usb_reset.py
@@ -23,9 +23,8 @@ def _dv_with_descriptor(serial="CAM123", dev_address=None):
     if dev_address is not None:
         descriptor.devAddress = dev_address
     return SimpleNamespace(
-        io=SimpleNamespace(
-            camera=SimpleNamespace(discover=lambda: [descriptor]),
-        ),
+        io=SimpleNamespace(camera=SimpleNamespace(discover=lambda: [descriptor]),
+                           ),
     )
 
 
@@ -39,7 +38,9 @@ def test_reset_camera_usb_uses_usbdevfs_reset_ioctl(monkeypatch, tmp_path):
     closed = []
     monkeypatch.setattr(usb_reset.sys, "platform", "linux")
     monkeypatch.setattr(usb_reset.os, "open", lambda path, flags: opened.append((path, flags)) or 9)
-    monkeypatch.setattr(usb_reset.fcntl, "ioctl", lambda fd, request, arg: ioctl_calls.append((fd, request, arg)))
+    monkeypatch.setattr(
+        usb_reset.fcntl,
+        "ioctl", lambda fd, request, arg: ioctl_calls.append((fd, request, arg)))
     monkeypatch.setattr(usb_reset.os, "close", lambda fd: closed.append(fd))
     monkeypatch.setattr(usb_reset.time, "sleep", lambda seconds: None)
 
@@ -62,7 +63,9 @@ def test_reset_camera_usb_falls_back_to_authorized_toggle(monkeypatch, tmp_path)
     device = _write_device(sysfs_root, "1-2")
 
     monkeypatch.setattr(usb_reset.sys, "platform", "linux")
-    monkeypatch.setattr(usb_reset.os, "open", lambda path, flags: (_ for _ in ()).throw(PermissionError("no access")))
+    monkeypatch.setattr(
+        usb_reset.os,
+        "open", lambda path, flags: (_ for _ in ()).throw(PermissionError("no access")))
     monkeypatch.setattr(usb_reset.time, "sleep", lambda seconds: None)
 
     result = usb_reset.reset_camera_usb(
diff --git a/tests/test_dmd_preview_package.py b/tests/test_dmd_preview_package.py
index d52e713..c315b6c 100644
--- a/tests/test_dmd_preview_package.py
+++ b/tests/test_dmd_preview_package.py
@@ -4,6 +4,7 @@
 
 
 class DmdPreviewPackageTests(unittest.TestCase):
+
     def test_package_server_exports_preview_api_without_hardware_imports(self):
         for module_name in ("glfw", "OpenGL.GL", "dlpc900_hid"):
             sys.modules.pop(module_name, None)
diff --git a/tests/test_dmd_preview_render.py b/tests/test_dmd_preview_render.py
index 23d677f..32dc6b1 100644
--- a/tests/test_dmd_preview_render.py
+++ b/tests/test_dmd_preview_render.py
@@ -7,6 +7,7 @@
 
 
 class DmdPreviewRenderTests(unittest.TestCase):
+
     def test_import_does_not_load_hardware_modules(self):
         for module_name in ("glfw", "OpenGL.GL", "dlpc900_hid"):
             sys.modules.pop(module_name, None)
@@ -74,9 +75,30 @@ def test_lut_preview_metadata_labels_entries_and_timing_windows(self):
         from dmdcontrol.preview.render import build_lut_preview_metadata
 
         entries = [
-            (0, 600, False, 1, 7, 10, False, 0),
-            (8, 600, False, 1, 7, 10, False, 8),
-            (16, 600, True, 1, 7, 10, True, 16),
+            (0,
+             600,
+             False,
+             1,
+             7,
+             10,
+             False,
+             0),
+            (8,
+             600,
+             False,
+             1,
+             7,
+             10,
+             False,
+             8),
+            (16,
+             600,
+             True,
+             1,
+             7,
+             10,
+             True,
+             16),
         ]
         timing = {
             "entries_count": 3,
diff --git a/tests/test_dmd_preview_server.py b/tests/test_dmd_preview_server.py
index 72a38ee..0906fa6 100644
--- a/tests/test_dmd_preview_server.py
+++ b/tests/test_dmd_preview_server.py
@@ -10,6 +10,7 @@
 
 
 class DmdPreviewServerTests(unittest.TestCase):
+
     def setUp(self):
         from dmdcontrol.preview.server import create_server
 
@@ -138,9 +139,10 @@ def test_live_frame_returns_404_before_post_then_serves_posted_png(self):
                         "start_us": 0,
                         "end_us": 600,
                         "segment_end_us": 600,
-                    }
-                ],
-                "timing": {"effective_frame_hz": 60.0, "entries_count": 1},
+                    }],
+                "timing": {
+                    "effective_frame_hz": 60.0,
+                    "entries_count": 1},
             },
         }
         req = request.Request(
diff --git a/tests/test_dmd_usb_mapping.py b/tests/test_dmd_usb_mapping.py
index 733f13f..649f66d 100644
--- a/tests/test_dmd_usb_mapping.py
+++ b/tests/test_dmd_usb_mapping.py
@@ -24,6 +24,7 @@
 
 
 class _FakeUsbDevice:
+
     def __init__(self, bus, address, port_numbers):
         self.bus = bus
         self.address = address
@@ -31,6 +32,7 @@ def __init__(self, bus, address, port_numbers):
 
 
 class DmdUsbMappingTests(unittest.TestCase):
+
     def test_parse_udev_properties_and_physical_path(self):
         props = parse_udevadm_properties(UDEV_HIDRAW0)
 
@@ -46,12 +48,11 @@ def test_usb_ids_parse_from_standard_and_hid_id_properties(self):
         self.assertEqual(usb_ids_from_properties(props), (0x0451, 0xC900))
 
         hid_props = parse_udevadm_properties(
-            "HID_ID=0003:00000451:0000C900\nDEVNAME=/dev/hidraw1\n"
-        )
+            "HID_ID=0003:00000451:0000C900\nDEVNAME=/dev/hidraw1\n")
         self.assertEqual(usb_ids_from_properties(hid_props), (0x0451, 0xC900))
 
     def test_physical_usb_path_parses_port_topology(self):
-        self.assertEqual(parse_physical_usb_path("usb1/1-8"), (1, (8,)))
+        self.assertEqual(parse_physical_usb_path("usb1/1-8"), (1, (8, )))
         self.assertEqual(parse_physical_usb_path("usb2/2-4.3"), (2, (4, 3)))
 
     def test_resolve_dmd_mapping_from_json(self):
@@ -67,10 +68,7 @@ def test_resolve_dmd_mapping_from_json(self):
                                 "xrandr_output": "DP-2",
                                 "glfw_monitor_index": 0,
                                 "target_hz": 60,
-                            }
-                        }
-                    }
-                ),
+                            }}}),
                 encoding="utf-8",
             )
 
@@ -97,9 +95,7 @@ def test_format_usb_candidates_matches_operator_shape(self):
                     "id_path": props["ID_PATH"],
                     "devpath": props["DEVPATH"],
                     "physical_path": physical_path_from_devpath(props["DEVPATH"]),
-                }
-            ]
-        )
+                }])
 
         self.assertIn("Found 1 DLPC900 USB device", text)
         self.assertIn("vidpid: 0451:c900", text)
@@ -109,14 +105,15 @@ def test_format_usb_candidates_matches_operator_shape(self):
         self.assertIn("suggested_config_key: pci-0000:03:00.0-usb-0:1:1.0", text)
 
     def test_select_pyusb_device_falls_back_to_configured_physical_port(self):
-        expected = _FakeUsbDevice(bus=1, address=18, port_numbers=(1,))
-        other = _FakeUsbDevice(bus=1, address=19, port_numbers=(8,))
+        expected = _FakeUsbDevice(bus=1, address=18, port_numbers=(1, ))
+        other = _FakeUsbDevice(bus=1, address=19, port_numbers=(8, ))
 
         selected = select_pyusb_device_for_mapping(
             "pci-0000:03:00.0-usb-0:1:1.0",
             usb_devpath_contains="/usb1/1-1/1-1:1.0/",
             candidates=[],
-            pyusb_devices=[other, expected],
+            pyusb_devices=[other,
+                           expected],
         )
 
         self.assertIs(selected, expected)
diff --git a/tests/test_dmdcontrol_cli.py b/tests/test_dmdcontrol_cli.py
index 3d92e74..51811f7 100644
--- a/tests/test_dmdcontrol_cli.py
+++ b/tests/test_dmdcontrol_cli.py
@@ -1,8 +1,10 @@
+import ast
 import importlib
 import json
 import subprocess
 import sys
 from dataclasses import dataclass
+from pathlib import Path
 from types import SimpleNamespace
 from unittest.mock import Mock
 
@@ -38,6 +40,24 @@ def test_cli_main_import_does_not_load_hardware_modules():
     assert "dmdcontrol.hardware.dlpc900" not in sys.modules
 
 
+def test_production_code_uses_navigable_lazy_imports():
+    root = Path(__file__).resolve().parents[1]
+    offenders = []
+    for path in sorted((root / "dmdcontrol").rglob("*.py")):
+        tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ImportFrom) and node.module == "importlib":
+                if any(alias.name == "import_module" for alias in node.names):
+                    offenders.append(path.relative_to(root).as_posix())
+            if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute):
+                if (isinstance(node.func.value,
+                               ast.Name) and node.func.value.id == "importlib"
+                        and node.func.attr == "import_module"):
+                    offenders.append(path.relative_to(root).as_posix())
+
+    assert offenders == []
+
+
 def test_single_run_delegates_passthrough_args(monkeypatch):
     runtime = Mock(return_value=7)
     monkeypatch.setattr(
@@ -67,8 +87,7 @@ def test_pair_run_translates_preferred_flags(monkeypatch):
             "--mode=checkerboard",
             "--b-test",
             "black",
-        ]
-    ) == 0
+        ]) == 0
 
     runtime.assert_called_once_with(
         [
@@ -78,8 +97,7 @@ def test_pair_run_translates_preferred_flags(monkeypatch):
             "--test=checkerboard",
             "--test-b",
             "black",
-        ]
-    )
+        ])
 
 
 def test_pair_calibrate_injects_test_and_default_zero_runtime(monkeypatch):
@@ -99,8 +117,7 @@ def test_pair_calibrate_injects_test_and_default_zero_runtime(monkeypatch):
             "0",
             "--b-dot-x",
             "10",
-        ]
-    )
+        ])
 
 
 def test_pair_calibrate_preserves_essential_preview_dot_args(monkeypatch):
@@ -111,24 +128,21 @@ def test_pair_calibrate_preserves_essential_preview_dot_args(monkeypatch):
     )
 
     assert (
-            run_cli(
-                [
-                    "pair",
-                    "calibrate",
-                    "--b-dot-x",
-                    "960",
-                    "--b-dot-y",
-                    "540",
-                    "--b-dot-radius",
-                    "40",
-                    "--preview-url",
-                    "http://127.0.0.1:8080/api/live-frame",
-                    "--preview-fps",
-                    "1",
-                ]
-            )
-            == 0
-    )
+        run_cli(
+            [
+                "pair",
+                "calibrate",
+                "--b-dot-x",
+                "960",
+                "--b-dot-y",
+                "540",
+                "--b-dot-radius",
+                "40",
+                "--preview-url",
+                "http://127.0.0.1:8080/api/live-frame",
+                "--preview-fps",
+                "1",
+            ]) == 0)
 
     runtime.assert_called_once_with(
         [
@@ -146,8 +160,7 @@ def test_pair_calibrate_preserves_essential_preview_dot_args(monkeypatch):
             "http://127.0.0.1:8080/api/live-frame",
             "--preview-fps",
             "1",
-        ]
-    )
+        ])
 
 
 def test_pair_calibrate_preserves_user_runtime_seconds(monkeypatch):
@@ -159,9 +172,7 @@ def test_pair_calibrate_preserves_user_runtime_seconds(monkeypatch):
 
     assert run_cli(["pair", "calibrate", "--runtime-seconds=5"]) == 0
 
-    runtime.assert_called_once_with(
-        ["--test", "a-calibr-square-b-dot", "--runtime-seconds=5"]
-    )
+    runtime.assert_called_once_with(["--test", "a-calibr-square-b-dot", "--runtime-seconds=5"])
 
 
 def test_preview_serve_help_exits_zero(capsys):
@@ -239,7 +250,12 @@ def test_config_show_prints_one_field(monkeypatch, capsys):
 
 def test_module_preview_help_subprocess_exits_zero():
     result = subprocess.run(
-        [sys.executable, "-m", "dmdcontrol", "preview", "serve", "--help"],
+        [sys.executable,
+         "-m",
+         "dmdcontrol",
+         "preview",
+         "serve",
+         "--help"],
         check=False,
         stdout=subprocess.PIPE,
         stderr=subprocess.PIPE,
diff --git a/tests/test_kernel_runtime.py b/tests/test_kernel_runtime.py
index 6880760..420327e 100644
--- a/tests/test_kernel_runtime.py
+++ b/tests/test_kernel_runtime.py
@@ -11,6 +11,7 @@
 
 
 class _Engine:
+
     def __init__(self, width=12, height=12):
         self.width = width
         self.height = height
@@ -27,6 +28,7 @@ def pack_patterns(self, binary_images):
 
 
 class KernelRuntimeTests(unittest.TestCase):
+
     def test_compute_kernel_lut_override_clamps_to_bitplane_count(self):
         entries, exposure_us = compute_kernel_lut_override(
             enabled=True,
@@ -58,7 +60,8 @@ def test_compute_kernel_lut_override_returns_none_when_disabled(self):
                 target_hz=60,
                 sequence_utilization=0.9,
             ),
-            (None, None),
+            (None,
+             None),
         )
 
     def test_generate_kernel_masks_builds_512_centered_masks(self):
@@ -90,8 +93,16 @@ def test_build_kernel_frames_includes_leaders_and_optional_blank(self):
 
     def test_kernel_frame_provider_loops_or_holds_black_after_single_shot(self):
         frames = [
-            np.full((2, 2, 3), 10, dtype=np.uint8),
-            np.full((2, 2, 3), 20, dtype=np.uint8),
+            np.full((2,
+                     2,
+                     3),
+                    10,
+                    dtype=np.uint8),
+            np.full((2,
+                     2,
+                     3),
+                    20,
+                    dtype=np.uint8),
         ]
         black = np.zeros((2, 2, 3), dtype=np.uint8)
 
diff --git a/tests/test_main_calibration_square_control.py b/tests/test_main_calibration_square_control.py
index bc73c1b..d848c0b 100644
--- a/tests/test_main_calibration_square_control.py
+++ b/tests/test_main_calibration_square_control.py
@@ -6,6 +6,7 @@
 
 
 class CalibrationSquareControlFileTests(unittest.TestCase):
+
     def test_reads_only_new_valid_commands(self):
         with tempfile.NamedTemporaryFile("w+", delete=False) as f:
             path = f.name
diff --git a/tests/test_main_pair_config.py b/tests/test_main_pair_config.py
index 5aad390..c9beb9d 100644
--- a/tests/test_main_pair_config.py
+++ b/tests/test_main_pair_config.py
@@ -12,6 +12,7 @@
 
 
 class MainPairConfigTests(unittest.TestCase):
+
     def test_resolve_pair_config_maps_b_left_and_a_right(self):
         with tempfile.TemporaryDirectory() as tmp:
             config_path = Path(tmp) / "dmd_devices.json"
@@ -33,9 +34,7 @@ def test_resolve_pair_config_maps_b_left_and_a_right(self):
                                 "glfw_monitor_index": 0,
                                 "target_hz": 60,
                             },
-                        }
-                    }
-                ),
+                        }}),
                 encoding="utf-8",
             )
 
@@ -56,11 +55,13 @@ def test_resolve_pair_config_rejects_missing_display_mapping(self):
                 json.dumps(
                     {
                         "dmds": {
-                            "A": {"usb_id_path": "pci-a", "xrandr_output": ""},
-                            "B": {"usb_id_path": "pci-b", "xrandr_output": "DP-0"},
-                        }
-                    }
-                ),
+                            "A": {
+                                "usb_id_path": "pci-a",
+                                "xrandr_output": ""},
+                            "B": {
+                                "usb_id_path": "pci-b",
+                                "xrandr_output": "DP-0"},
+                        }}),
                 encoding="utf-8",
             )
 
@@ -88,8 +89,7 @@ def test_dry_run_accepts_essential_calibration_dot_command_without_hardware_impo
                 "1",
                 "--runtime-seconds",
                 "0",
-            ]
-        )
+            ])
 
         self.assertEqual(rc, 0)
         self.assertFalse({"glfw", "OpenGL.GL", "dlpc900_hid"} & set(sys.modules))
@@ -109,8 +109,7 @@ def test_calibration_dot_recipe_rejects_static_pair_overrides(self):
                     "540",
                     "--b-dot-radius",
                     "40",
-                ]
-            )
+                ])
 
     def test_dry_run_accepts_kernel_static_recipe_without_hardware_imports(self):
         for module_name in ("glfw", "OpenGL.GL", "dlpc900_hid"):
@@ -130,8 +129,7 @@ def test_dry_run_accepts_kernel_static_recipe_without_hardware_imports(self):
                 "--kernel-leader-frames",
                 "0",
                 "--no-kernel-blank-end-frame",
-            ]
-        )
+            ])
 
         self.assertEqual(rc, 0)
         self.assertFalse({"glfw", "OpenGL.GL", "dlpc900_hid"} & set(sys.modules))
@@ -154,8 +152,7 @@ def test_dry_run_accepts_essential_kernel_static_dot_command(self):
                 "201",
                 "--runtime-seconds",
                 "999",
-            ]
-        )
+            ])
 
         self.assertEqual(rc, 0)
 
@@ -168,8 +165,7 @@ def test_kernel_static_recipe_rejects_test_a_override(self):
                     "a-kernel-b-static",
                     "--test-a",
                     "lines",
-                ]
-            )
+                ])
 
     def test_dry_run_accepts_human_visible_pair_modes_without_hardware_imports(self):
         for module_name in ("glfw", "OpenGL.GL", "dlpc900_hid"):
@@ -195,8 +191,7 @@ def test_dry_run_accepts_preview_args_without_hardware_imports(self):
                 "http://127.0.0.1:8080/api/live-frame",
                 "--preview-fps",
                 "1",
-            ]
-        )
+            ])
 
         self.assertEqual(rc, 0)
         self.assertFalse({"glfw", "OpenGL.GL", "dlpc900_hid"} & set(sys.modules))
@@ -212,8 +207,7 @@ def test_calibration_dot_recipe_flickers_a_square_only(self):
                 "1",
                 "--b-dot-radius",
                 "1",
-            ]
-        )
+            ])
         engine = SimpleNamespace(window=object())
         visible_a = np.full((main_pair.DMD_HEIGHT, main_pair.DMD_WIDTH, 3), 77, dtype=np.uint8)
         original_build = main_pair.build_calibration_square_frame
@@ -221,8 +215,7 @@ def test_calibration_dot_recipe_flickers_a_square_only(self):
         try:
             main_pair.build_calibration_square_frame = lambda _engine, _state: visible_a
             main_pair.make_calibration_square_frame_provider = (
-                lambda _engine, _initial_frame, **_kwargs: lambda: visible_a
-            )
+                lambda _engine, _initial_frame, **_kwargs: lambda: visible_a)
 
             provider = main_pair._make_runtime_pair_frame_provider(args, engine, 60)
             first_a, first_b = provider.initial_pair()
@@ -241,13 +234,35 @@ def test_calibration_dot_recipe_flickers_a_square_only(self):
     def test_live_preview_metadata_includes_lut_timing(self):
         args = main_pair._build_parser().parse_args(["--test", "snake"])
         pair_config = main_pair.PairConfig(
-            dmd_a=main_pair.DmdMapping(name="A", usb_id_path="pci-a", xrandr_output="DP-2"),
-            dmd_b=main_pair.DmdMapping(name="B", usb_id_path="pci-b", xrandr_output="DP-0"),
+            dmd_a=main_pair.DmdMapping(name="A",
+                                       usb_id_path="pci-a",
+                                       xrandr_output="DP-2"),
+            dmd_b=main_pair.DmdMapping(name="B",
+                                       usb_id_path="pci-b",
+                                       xrandr_output="DP-0"),
             target_hz=60,
         )
         state = {
-            "entries": [(0, 600, False, 1, 7, 0, False, 0), (8, 600, True, 1, 7, 0, False, 8)],
-            "timing": {"entries_count": 2, "effective_frame_hz": 60.0, "exposure_us": 600},
+            "entries": [(0,
+                         600,
+                         False,
+                         1,
+                         7,
+                         0,
+                         False,
+                         0),
+                        (8,
+                         600,
+                         True,
+                         1,
+                         7,
+                         0,
+                         False,
+                         8)],
+            "timing": {
+                "entries_count": 2,
+                "effective_frame_hz": 60.0,
+                "exposure_us": 600},
         }
 
         metadata = main_pair._build_live_preview_metadata(args, pair_config, state, state)
diff --git a/tests/test_main_post_arm_prime.py b/tests/test_main_post_arm_prime.py
index cf7f3cb..12f8c04 100644
--- a/tests/test_main_post_arm_prime.py
+++ b/tests/test_main_post_arm_prime.py
@@ -6,6 +6,7 @@
 
 
 class PostArmPrimeFrameTests(unittest.TestCase):
+
     def test_kernel_prime_skips_black_leader_frames(self):
         leader0 = np.zeros((1, 1, 3), dtype=np.uint8)
         leader1 = np.zeros((1, 1, 3), dtype=np.uint8)
diff --git a/tests/test_numbers_mode.py b/tests/test_numbers_mode.py
index 93badb2..18b58a6 100644
--- a/tests/test_numbers_mode.py
+++ b/tests/test_numbers_mode.py
@@ -18,8 +18,10 @@
 def _old_default_number_nonzero_count(number, width, height):
     digit_h = max(24, int(height * 0.78))
     digit_w = min(
-        max(16, int(width * 0.46)),
-        max(16, int(digit_h * 0.62)),
+        max(16,
+            int(width * 0.46)),
+        max(16,
+            int(digit_h * 0.62)),
     )
     thickness = max(4, int(min(digit_w, digit_h) * 0.16))
     x0 = (width - digit_w) // 2
@@ -29,16 +31,43 @@ def _old_default_number_nonzero_count(number, width, height):
     mid = (y0 + y1) // 2
     half_t = max(2, thickness // 2)
     segments = {
-        8: ("a", "b", "c", "d", "e", "f", "g"),
+        8: ("a",
+            "b",
+            "c",
+            "d",
+            "e",
+            "f",
+            "g"),
     }
     boxes = {
-        "a": (x0 + thickness, y0, x1 - thickness, y0 + thickness),
-        "b": (x1 - thickness, y0 + thickness, x1, mid),
-        "c": (x1 - thickness, mid, x1, y1 - thickness),
-        "d": (x0 + thickness, y1 - thickness, x1 - thickness, y1),
-        "e": (x0, mid, x0 + thickness, y1 - thickness),
-        "f": (x0, y0 + thickness, x0 + thickness, mid),
-        "g": (x0 + thickness, mid - half_t, x1 - thickness, mid + half_t),
+        "a": (x0 + thickness,
+              y0,
+              x1 - thickness,
+              y0 + thickness),
+        "b": (x1 - thickness,
+              y0 + thickness,
+              x1,
+              mid),
+        "c": (x1 - thickness,
+              mid,
+              x1,
+              y1 - thickness),
+        "d": (x0 + thickness,
+              y1 - thickness,
+              x1 - thickness,
+              y1),
+        "e": (x0,
+              mid,
+              x0 + thickness,
+              y1 - thickness),
+        "f": (x0,
+              y0 + thickness,
+              x0 + thickness,
+              mid),
+        "g": (x0 + thickness,
+              mid - half_t,
+              x1 - thickness,
+              mid + half_t),
     }
     mask = np.zeros((height, width), dtype=np.uint8)
     for segment in segments[number]:
@@ -48,6 +77,7 @@ def _old_default_number_nonzero_count(number, width, height):
 
 
 class NumbersModeTests(unittest.TestCase):
+
     def test_number_index_advances_by_exposure_and_wraps(self):
         exposure_s = 0.25
 
@@ -73,7 +103,9 @@ def test_default_number_geometry_matches_legacy_width_cap(self):
 
         self.assertEqual(
             np.count_nonzero(eight),
-            _old_default_number_nonzero_count(8, width=120, height=160),
+            _old_default_number_nonzero_count(8,
+                                              width=120,
+                                              height=160),
         )
 
     def test_generated_number_frame_respects_requested_size(self):
@@ -121,8 +153,11 @@ def reject_debug_scripts(name, *args, **kwargs):
 
     def test_dry_run_warns_when_number_size_is_ignored(self):
         args = single._build_parser().parse_args(
-            ["--dry-run-timing", "--test", "checkerboard", "--numbers-size-px", "80"]
-        )
+            ["--dry-run-timing",
+             "--test",
+             "checkerboard",
+             "--numbers-size-px",
+             "80"])
 
         with mock.patch.object(single.logger, "warning") as warning:
             single._dry_run_timing(args)
@@ -140,21 +175,26 @@ def test_live_run_warns_when_number_size_is_ignored(self):
         fake_dlpc_module = types.SimpleNamespace(DLPC900=mock.Mock(return_value=dlpc))
 
         with (
-            mock.patch.dict(
-                sys.modules,
-                {
-                    "glfw": mock.Mock(),
-                    "dmdcontrol.patterns.engine": fake_engine_module,
-                    "dmdcontrol.hardware.dlpc900": fake_dlpc_module,
-                },
-            ),
-            mock.patch.object(single, "build_patterns", return_value=("Static Checkerboard", "patterns", None)),
-            mock.patch.object(
-                single,
-                "configure_dlpc900_for_video_pattern",
-                side_effect=RuntimeError("stop after warning"),
-            ),
-            mock.patch.object(single.logger, "warning") as warning,
+                mock.patch.dict(
+                    sys.modules,
+                    {
+                        "glfw": mock.Mock(),
+                        "dmdcontrol.patterns.engine": fake_engine_module,
+                        "dmdcontrol.hardware.dlpc900": fake_dlpc_module,
+                    },
+                ),
+                mock.patch.object(single,
+                                  "build_patterns",
+                                  return_value=("Static Checkerboard",
+                                                "patterns",
+                                                None)),
+                mock.patch.object(
+                    single,
+                    "configure_dlpc900_for_video_pattern",
+                    side_effect=RuntimeError("stop after warning"),
+                ),
+                mock.patch.object(single.logger,
+                                  "warning") as warning,
         ):
             result = single.main(["--test", "checkerboard", "--numbers-size-px", "80"])
 
diff --git a/tests/test_pair_lut_override.py b/tests/test_pair_lut_override.py
index 50e9b52..c77177b 100644
--- a/tests/test_pair_lut_override.py
+++ b/tests/test_pair_lut_override.py
@@ -1,8 +1,9 @@
 import types
 
+import numpy as np
 import pytest
 
-from dmdcontrol.patterns.paired import A_NUMBERS_B_STATIC_PAIR_TEST
+from dmdcontrol.patterns.paired import A_COUNT_B_STATIC_PAIR_TEST, A_NUMBERS_B_STATIC_PAIR_TEST
 from dmdcontrol.runtime.pair import _lut_override, main
 
 
@@ -18,7 +19,11 @@ def test_lut_override_a_numbers_b_static_returns_digit_count():
     # Test with explicitly provided exposure
     args_1 = types.SimpleNamespace(
         test=A_NUMBERS_B_STATIC_PAIR_TEST,
-        numbers=[1, 2, 3, 4, 5],
+        numbers=[1,
+                 2,
+                 3,
+                 4,
+                 5],
         numbers_exposure_us=3000,
     )
     entries_count_1, exposure_1 = _lut_override(args_1, target_hz=60)
@@ -28,7 +33,9 @@ def test_lut_override_a_numbers_b_static_returns_digit_count():
     # Test with 3 numbers and auto-computed exposure (None)
     args_2 = types.SimpleNamespace(
         test=A_NUMBERS_B_STATIC_PAIR_TEST,
-        numbers=[1, 2, 3],
+        numbers=[1,
+                 2,
+                 3],
         numbers_exposure_us=None,
     )
     entries_count_2, exposure_2 = _lut_override(args_2, target_hz=60)
@@ -36,10 +43,27 @@ def test_lut_override_a_numbers_b_static_returns_digit_count():
     assert exposure_2 is None
 
 
+def test_lut_override_a_count_b_static_returns_count_slots():
+    args = types.SimpleNamespace(
+        test=A_COUNT_B_STATIC_PAIR_TEST,
+        count_slots_per_frame=2,
+        count_exposure_us=7000,
+    )
+
+    entries_count, exposure = _lut_override(args, target_hz=60)
+
+    assert entries_count == 2
+    assert exposure == 7000
+
+
 def test_lut_override_non_numbers_test_delegates_to_kernel():
     args = types.SimpleNamespace(
         test="some-other-test",
-        numbers=[1, 2, 3, 4, 5],
+        numbers=[1,
+                 2,
+                 3,
+                 4,
+                 5],
         numbers_exposure_us=3000,
         b_exposure_us=None,
         kernel_pairs=5,
@@ -52,6 +76,135 @@ def test_lut_override_non_numbers_test_delegates_to_kernel():
     assert exposure is None
 
 
+def test_pair_runtime_parser_accepts_count_mode_options():
+    from dmdcontrol.runtime import pair
+
+    args = pair._build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--test",
+            A_COUNT_B_STATIC_PAIR_TEST,
+            "--test-b",
+            "dot",
+            "--count-start",
+            "1",
+            "--count-end",
+            "100",
+            "--count-slots-per-frame",
+            "2",
+            "--count-exposure-us",
+            "7000",
+        ])
+
+    assert args.count_start == 1
+    assert args.count_end == 100
+    assert args.count_slots_per_frame == 2
+    assert args.count_exposure_us == 7000
+
+
+def test_pair_runtime_parser_accepts_numbers_bitplane_order():
+    from dmdcontrol.runtime import pair
+
+    args = pair._build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--test",
+            A_NUMBERS_B_STATIC_PAIR_TEST,
+            "--numbers",
+            "1,2,3,4,5",
+            "--numbers-bitplane-order",
+            "1,2,3,4,0",
+        ])
+
+    assert args.numbers == [1, 2, 3, 4, 5]
+    assert args.numbers_bitplane_order == [1, 2, 3, 4, 0]
+
+
+def test_pair_runtime_parser_accepts_static_dot_radius():
+    from dmdcontrol.runtime import pair
+
+    args = pair._build_parser().parse_args(
+        [
+            "--dry-run-timing",
+            "--test",
+            "dot",
+            "--dot-radius",
+            "17",
+        ])
+
+    assert args.dot_radius == 17
+
+
+def test_static_dot_radius_applies_to_both_dmds():
+    from dmdcontrol.patterns.paired import make_pair_frame_provider
+
+    provider = make_pair_frame_provider("dot", width=21, height=21, dot_radius=3)
+
+    frame_a, frame_b = provider.initial_pair()
+
+    assert frame_a[10, 10, 0] == 255
+    assert frame_a[10, 13, 0] == 255
+    assert frame_a[10, 14, 0] == 0
+    assert np.array_equal(frame_a, frame_b)
+
+
+@pytest.mark.parametrize(
+    ("argv",
+     "message"),
+    [
+        (
+            ["--test",
+             A_COUNT_B_STATIC_PAIR_TEST,
+             "--count-start",
+             "5",
+             "--count-end",
+             "4"],
+            "--count-start must be <= --count-end"),
+        (
+            ["--test",
+             A_COUNT_B_STATIC_PAIR_TEST,
+             "--count-slots-per-frame",
+             "0"],
+            "--count-slots-per-frame must be in the range"),
+        (
+            ["--test",
+             A_COUNT_B_STATIC_PAIR_TEST,
+             "--test-a",
+             "dot"],
+            "--test-a is not valid for a-count-b-static"),
+        (
+            [
+                "--test",
+                A_COUNT_B_STATIC_PAIR_TEST,
+                "--count-start",
+                "1",
+                "--count-end",
+                "5",
+                "--count-slots-per-frame",
+                "2"],
+            "divisible by --count-slots-per-frame",
+        ),
+        (
+            [
+                "--test",
+                A_COUNT_B_STATIC_PAIR_TEST,
+                "--count-end",
+                "130",
+                "--count-slots-per-frame",
+                "2"],
+            "at most 64 VSYNC frames",
+        ),
+    ],
+)
+def test_pair_runtime_validates_count_mode_options(argv, message):
+    from dmdcontrol.runtime import pair
+
+    args = pair._build_parser().parse_args(argv)
+
+    with pytest.raises(SystemExit, match=message):
+        pair._validate_pair_args(args)
+
+
 def test_a_numbers_b_static_runtime_forwards_b_static_geometry(monkeypatch):
     from dmdcontrol.runtime import pair
 
@@ -68,7 +221,9 @@ def fake_make_pair_frame_provider(*args, **kwargs):
     args = types.SimpleNamespace(
         test=A_NUMBERS_B_STATIC_PAIR_TEST,
         test_b="dot",
-        numbers=[1, 2, 3],
+        numbers=[1,
+                 2,
+                 3],
         numbers_size_px=123,
         numbers_exposure_us=600,
         b_dot_x=955,
@@ -78,10 +233,13 @@ def fake_make_pair_frame_provider(*args, **kwargs):
         b_dot_invert=False,
     )
 
-    result = pair._make_runtime_pair_frame_provider(args, engine=types.SimpleNamespace(window=None), target_hz=60)
+    result = pair._make_runtime_pair_frame_provider(
+        args,
+        engine=types.SimpleNamespace(window=None),
+        target_hz=60)
 
     assert result is provider
-    assert captured["args"] == (A_NUMBERS_B_STATIC_PAIR_TEST,)
+    assert captured["args"] == (A_NUMBERS_B_STATIC_PAIR_TEST, )
     assert captured["kwargs"]["test_b"] == "dot"
     assert captured["kwargs"]["numbers"] == [1, 2, 3]
     assert captured["kwargs"]["numbers_size_px"] == 123
@@ -92,19 +250,98 @@ def fake_make_pair_frame_provider(*args, **kwargs):
     assert captured["kwargs"]["b_dot_invert"] is False
 
 
+def test_a_count_b_static_runtime_forwards_count_geometry(monkeypatch):
+    from dmdcontrol.runtime import pair
+
+    captured = {}
+    provider = object()
+
+    def fake_make_pair_frame_provider(*args, **kwargs):
+        captured["args"] = args
+        captured["kwargs"] = kwargs
+        return provider
+
+    monkeypatch.setattr(pair, "make_pair_frame_provider", fake_make_pair_frame_provider)
+
+    args = types.SimpleNamespace(
+        test=A_COUNT_B_STATIC_PAIR_TEST,
+        test_b="dot",
+        count_start=1,
+        count_end=100,
+        count_slots_per_frame=2,
+        numbers_size_px=123,
+        b_dot_x=955,
+        b_dot_y=535,
+        b_dot_radius=12,
+        b_dot_shape="circle",
+        b_dot_invert=False,
+    )
+
+    result = pair._make_runtime_pair_frame_provider(
+        args,
+        engine=types.SimpleNamespace(window=None),
+        target_hz=60)
+
+    assert result is provider
+    assert captured["args"] == (A_COUNT_B_STATIC_PAIR_TEST, )
+    assert captured["kwargs"]["test_b"] == "dot"
+    assert captured["kwargs"]["count_start"] == 1
+    assert captured["kwargs"]["count_end"] == 100
+    assert captured["kwargs"]["count_slots_per_frame"] == 2
+    assert captured["kwargs"]["numbers_size_px"] == 123
+    assert captured["kwargs"]["b_dot_x"] == 955
+    assert captured["kwargs"]["b_dot_y"] == 535
+    assert captured["kwargs"]["b_dot_radius"] == 12
+    assert captured["kwargs"]["b_dot_shape"] == "circle"
+    assert captured["kwargs"]["b_dot_invert"] is False
+
+
+def test_pair_live_preview_metadata_includes_count_mode():
+    from dmdcontrol.runtime import pair
+
+    args = types.SimpleNamespace(
+        test=A_COUNT_B_STATIC_PAIR_TEST,
+        test_a=None,
+        test_b="dot",
+        count_start=1,
+        count_end=100,
+        count_slots_per_frame=2,
+        count_exposure_us=7000,
+    )
+    pair_config = types.SimpleNamespace(
+        dmd_a=types.SimpleNamespace(xrandr_output="DP-2"),
+        dmd_b=types.SimpleNamespace(xrandr_output="DP-0"),
+        offset_a=(1920,
+                  0),
+        offset_b=(0,
+                  0),
+        target_hz=60,
+    )
+
+    metadata = pair._build_live_preview_metadata(args, pair_config, state_a=None, state_b=None)
+
+    assert metadata["count"] == {
+        "start": 1,
+        "end": 100,
+        "slots_per_frame": 2,
+        "exposure_us": 7000,
+    }
+
+
 def test_pair_dry_run_timing_rejects_numbers_sequence_when_dark_time_exceeds_budget():
     with pytest.raises(ValueError, match="need .* usable"):
-        main([
-            "--dry-run-timing",
-            "--test",
-            A_NUMBERS_B_STATIC_PAIR_TEST,
-            "--numbers",
-            "1,2,3,4,5",
-            "--numbers-exposure-us",
-            "2900",
-            "--dark-time-us",
-            "500",
-        ])
+        main(
+            [
+                "--dry-run-timing",
+                "--test",
+                A_NUMBERS_B_STATIC_PAIR_TEST,
+                "--numbers",
+                "1,2,3,4,5",
+                "--numbers-exposure-us",
+                "2900",
+                "--dark-time-us",
+                "500",
+            ])
 
 
 def test_run_prepared_pair_starts_rendering_without_post_start_sleep(monkeypatch):
@@ -113,10 +350,13 @@ def test_run_prepared_pair_starts_rendering_without_post_start_sleep(monkeypatch
     import dmdcontrol.patterns.paired as paired_module
 
     calls = {}
+    dlpcs = []
 
     class FakeDLPC:
+
         def __init__(self, **kwargs):
-            pass
+            self.closed = False
+            dlpcs.append(self)
 
         def start_pattern_display(self, value):
             pass
@@ -127,7 +367,11 @@ def set_display_mode(self, value):
         def apply_block_lock_workaround(self):
             pass
 
+        def close(self):
+            self.closed = True
+
     class FakeEngine:
+
         def __init__(self, fps):
             pass
 
@@ -138,19 +382,23 @@ def cleanup(self):
             pass
 
     class FakeProvider:
+
         def initial_pair(self):
             return object(), object()
 
     monkeypatch.setattr(dlpc_module, "DLPC900", FakeDLPC)
     monkeypatch.setattr(paired_module, "PairedPatternEngine", FakeEngine)
     monkeypatch.setattr(pair, "_lut_override", lambda args, target_hz: (None, None))
-    monkeypatch.setattr(pair, "_make_runtime_pair_frame_provider", lambda args, engine, target_hz: FakeProvider())
+    monkeypatch.setattr(
+        pair,
+        "_make_runtime_pair_frame_provider", lambda args, engine, target_hz: FakeProvider())
     monkeypatch.setattr(pair, "_start_pair_pump", lambda engine, provider: (object(), object()))
     monkeypatch.setattr(pair, "_stop_pair_pump", lambda engine, pump_event, pump_thread: None)
     monkeypatch.setattr(
         pair,
         "prepare_dlpc900_for_video_pattern",
-        lambda *args, **kwargs: {"entries": [], "timing": {}},
+        lambda *args, **kwargs: {
+            "entries": [], "timing": {}},
     )
     monkeypatch.setattr(pair, "load_pattern_sequence", lambda dlpc, entries: None)
     monkeypatch.setattr(pair, "_build_live_preview_metadata", lambda *args, **kwargs: {})
@@ -186,3 +434,4 @@ def fake_start_loaded_pattern_sequences(dlpc_a, dlpc_b, post_start_delay_s=0.2,
 
     assert pair._run_prepared_pair(args, pair_config) == 0
     assert calls == {"post_start_delay_s": 0.0, "verify": True}
+    assert [dlpc.closed for dlpc in dlpcs] == [True, True]
diff --git a/tests/test_pair_wrappers.py b/tests/test_pair_wrappers.py
index 7bf1f3f..2365014 100644
--- a/tests/test_pair_wrappers.py
+++ b/tests/test_pair_wrappers.py
@@ -1,7 +1,6 @@
 import unittest
 from pathlib import Path
 
-
 ROOT = Path(__file__).resolve().parents[1]
 SCRIPTS = ROOT / "scripts"
 SHELL_HELPER = SCRIPTS / "lib" / "dmd_shell_common.sh"
@@ -10,6 +9,7 @@
 
 
 class PairWrapperTests(unittest.TestCase):
+
     def test_run_pair_dry_run_bypasses_hardware_wake_and_xinit(self):
         script = (ROOT / "run_dmd_pair.sh").read_text(encoding="utf-8")
 
@@ -28,7 +28,9 @@ def test_common_shell_helper_execs_python_modules_with_repo_pythonpath(self):
         self.assertIn("dmd_python_module() {", helper)
         self.assertIn("dmd_pythonpath() {", helper)
         self.assertIn('local repo_root="$script_dir"', helper)
-        self.assertIn('local pythonpath="$repo_root:/home/main/.local/lib/python3.14/site-packages"', helper)
+        self.assertIn(
+            'local pythonpath="$repo_root:/home/main/.local/lib/python3.14/site-packages"',
+            helper)
         self.assertIn('exec env PYTHONPATH="$(dmd_pythonpath "$script_dir")"', helper)
         self.assertIn('/usr/bin/python3 -m "$module" "$@"', helper)
 
@@ -71,10 +73,10 @@ def test_xinit_pair_uses_configured_target_hz_when_hz_omitted(self):
 
     def test_runners_source_common_shell_helpers(self):
         for name in (
-            "run_dmd.sh",
-            "run_calibr_square.sh",
-            "run_dmd_pair.sh",
-            "run_dmd_pair_calibr_square.sh",
+                "run_dmd.sh",
+                "run_calibr_square.sh",
+                "run_dmd_pair.sh",
+                "run_dmd_pair_calibr_square.sh",
         ):
             script = (ROOT / name).read_text(encoding="utf-8")
             self.assertIn('source "$SCRIPT_DIR/scripts/lib/dmd_shell_common.sh"', script)
@@ -96,7 +98,9 @@ def test_pair_calibration_runner_wires_recipe_and_control_file(self):
         self.assertIn("dmd_start_calibr_square_control_reader", script)
         self.assertIn('dmd_wake_configured_dmd "$SCRIPT_DIR" A', script)
         self.assertIn('dmd_wake_configured_dmd "$SCRIPT_DIR" B', script)
-        self.assertIn('dmd_run_xinit "$SCRIPT_DIR" "$SCRIPT_DIR/scripts/xinit/xinitrc_dmd_pair.sh"', script)
+        self.assertIn(
+            'dmd_run_xinit "$SCRIPT_DIR" "$SCRIPT_DIR/scripts/xinit/xinitrc_dmd_pair.sh"',
+            script)
         self.assertIn("--test a-calibr-square-b-dot", script)
         self.assertIn('--a-calibr-square-control-file "$CONTROL_FILE"', script)
         self.assertIn("--runtime-seconds 0", script)
@@ -128,15 +132,14 @@ def test_camera_sync_check_runner_routes_dry_run_without_xinit(self):
         self.assertIn('dmd_wake_configured_dmd "$SCRIPT_DIR" B', script)
 
         dry_run_command_idx = script.index(
-            'dmd_exec_python_module "$SCRIPT_DIR" dmdcontrol camera sync-check "$@"'
-        )
-        dry_run_guarded_by_exit = (
-            script.find("exit 0", dry_run_command_idx, wake_idx) != -1
-        )
+            'dmd_exec_python_module "$SCRIPT_DIR" dmdcontrol camera sync-check "$@"')
+        dry_run_guarded_by_exit = (script.find("exit 0", dry_run_command_idx, wake_idx) != -1)
         dry_run_guarded_by_else = (
-            script.find("else", dry_run_command_idx, wake_idx) != -1
-            and script.find("fi", wake_idx, xinit_idx) != -1
-        )
+            script.find("else",
+                        dry_run_command_idx,
+                        wake_idx) != -1 and script.find("fi",
+                                                        wake_idx,
+                                                        xinit_idx) != -1)
         self.assertTrue(
             dry_run_guarded_by_exit or dry_run_guarded_by_else,
             "sync-check dry-run must exit or use else before DP wake/xinit",
@@ -149,13 +152,32 @@ def test_camera_sync_check_xinit_runs_camera_module(self):
         self.assertIn("dmd_x11_verify_pair_layout", script)
         self.assertIn('source "$REPO_ROOT/scripts/lib/dmd_x11_common.sh"', script)
 
+    def test_camera_sync_sweep_runner_routes_dry_run_without_xinit(self):
+        script = (ROOT / "run_camera_sync_sweep.sh").read_text(encoding="utf-8")
+
+        dry_run_idx = script.index("Camera sync-sweep dry-run")
+        wake_idx = script.index("dmd_wake_configured_dmd")
+        xinit_idx = script.index("dmd_run_xinit")
+
+        self.assertLess(dry_run_idx, wake_idx)
+        self.assertLess(dry_run_idx, xinit_idx)
+        self.assertIn("camera sync-sweep", script)
+        self.assertIn("dmd_has_flag --dry-run", script)
+        self.assertIn("xinitrc_camera_sync_sweep.sh", script)
+
+    def test_camera_sync_sweep_xinit_runs_camera_module(self):
+        script = (XINIT / "xinitrc_camera_sync_sweep.sh").read_text(encoding="utf-8")
+
+        self.assertIn("dmdcontrol camera sync-sweep", script)
+        self.assertIn("dmd_x11_verify_pair_layout", script)
+        self.assertIn('source "$REPO_ROOT/scripts/lib/dmd_x11_common.sh"', script)
+
     def test_pair_capture_runner_routes_dry_run_without_xinit(self):
         script = (ROOT / "run_dmd_pair_capture.sh").read_text(encoding="utf-8")
 
         dry_run_idx = script.index("Paired camera capture dry-run timing")
         dry_run_command_idx = script.index(
-            'dmd_exec_python_module "$SCRIPT_DIR" dmdcontrol camera pair-capture "$@"'
-        )
+            'dmd_exec_python_module "$SCRIPT_DIR" dmdcontrol camera pair-capture "$@"')
         wake_idx = script.index("dmd_wake_configured_dmd")
         xinit_idx = script.index("dmd_run_xinit")
 
@@ -167,13 +189,13 @@ def test_pair_capture_runner_routes_dry_run_without_xinit(self):
         self.assertIn('dmd_wake_configured_dmd "$SCRIPT_DIR" A', script)
         self.assertIn('dmd_wake_configured_dmd "$SCRIPT_DIR" B', script)
 
-        dry_run_guarded_by_exit = (
-            script.find("exit 0", dry_run_command_idx, wake_idx) != -1
-        )
+        dry_run_guarded_by_exit = (script.find("exit 0", dry_run_command_idx, wake_idx) != -1)
         dry_run_guarded_by_else = (
-            script.find("else", dry_run_command_idx, wake_idx) != -1
-            and script.find("fi", wake_idx, xinit_idx) != -1
-        )
+            script.find("else",
+                        dry_run_command_idx,
+                        wake_idx) != -1 and script.find("fi",
+                                                        wake_idx,
+                                                        xinit_idx) != -1)
         self.assertTrue(
             dry_run_guarded_by_exit or dry_run_guarded_by_else,
             "pair-capture dry-run must exit or use else before DP wake/xinit",
diff --git a/tests/test_paired_lifecycle.py b/tests/test_paired_lifecycle.py
index b3d0652..8c38b43 100644
--- a/tests/test_paired_lifecycle.py
+++ b/tests/test_paired_lifecycle.py
@@ -4,12 +4,13 @@
 
 
 class _FakeDlpc:
+
     def __init__(self, name):
         self.name = name
         self.calls = []
 
     def get_hardware_status(self):
-        self.calls.append(("get_hardware_status",))
+        self.calls.append(("get_hardware_status", ))
         return 0
 
     def set_pattern_lut_definition(self, entries):
@@ -33,6 +34,7 @@ def get_main_status(self):
 
 
 class PairedLifecycleTests(unittest.TestCase):
+
     def test_load_pattern_sequence_does_not_start_sequencer(self):
         dlpc = _FakeDlpc("A")
 
diff --git a/tests/test_paired_pattern_engine.py b/tests/test_paired_pattern_engine.py
index 62f4d3a..3c3ea0b 100644
--- a/tests/test_paired_pattern_engine.py
+++ b/tests/test_paired_pattern_engine.py
@@ -33,6 +33,7 @@ def _extract_packed_bitplane(frame, plane):
 
 
 class PairedPatternEngineTests(unittest.TestCase):
+
     def test_compose_pair_frame_places_b_left_and_a_right(self):
         frame_a = np.full((2, 3, 3), 11, dtype=np.uint8)
         frame_b = np.full((2, 3, 3), 22, dtype=np.uint8)
@@ -91,7 +92,9 @@ def test_dynamic_snake_provider_uses_grayscale_on_both_routes(self):
 
     def test_number_sequence_provider_packs_requested_digits_into_bitplanes(self):
         provider = NumberSequencePairFrameProvider(
-            numbers=(1, 2, 3),
+            numbers=(1,
+                     2,
+                     3),
             width=120,
             height=160,
             size_px=80,
@@ -114,13 +117,140 @@ def test_number_sequence_provider_packs_requested_digits_into_bitplanes(self):
         )
         self.assertEqual(int(np.count_nonzero(_extract_packed_bitplane(frame_a, 3))), 0)
 
+    def test_number_sequence_provider_can_pack_digits_for_observed_bitplane_order(self):
+        provider = NumberSequencePairFrameProvider(
+            numbers=(1,
+                     2,
+                     3,
+                     4,
+                     5),
+            numbers_bitplane_order=(1,
+                                    2,
+                                    3,
+                                    4,
+                                    0),
+            width=120,
+            height=160,
+            size_px=80,
+        )
+
+        frame_a, frame_b = provider.initial_pair()
+
+        np.testing.assert_array_equal(frame_a, frame_b)
+        expected_by_bitplane = {
+            0: 5,
+            1: 1,
+            2: 2,
+            3: 3,
+            4: 4,
+        }
+        for bitplane, number in expected_by_bitplane.items():
+            np.testing.assert_array_equal(
+                _extract_packed_bitplane(frame_a, bitplane),
+                generate_number_rgb(number, width=120, height=160, size_px=80)[:, :, 0],
+            )
+        self.assertEqual(int(np.count_nonzero(_extract_packed_bitplane(frame_a, 5))), 0)
+
+    def test_decimal_number_renderer_supports_multi_digit_labels(self):
+        from dmdcontrol.patterns.modes import generate_decimal_number_rgb
+
+        for number in (1, 10, 100):
+            with self.subTest(number=number):
+                frame = generate_decimal_number_rgb(number, width=160, height=160, size_px=90)
+
+                self.assertEqual(frame.shape, (160, 160, 3))
+                self.assertEqual(frame.dtype, np.uint8)
+                self.assertGreater(int(np.count_nonzero(frame[:, :, 0])), 0)
+                np.testing.assert_array_equal(frame[:, :, 0], frame[:, :, 1])
+                np.testing.assert_array_equal(frame[:, :, 1], frame[:, :, 2])
+
+        with self.assertRaises(ValueError):
+            generate_number_rgb(10, width=160, height=160, size_px=90)
+
+    def test_generate_number_rgb_keeps_legacy_minimum_stroke_width(self):
+        frame = generate_number_rgb(1, width=20, height=20, size_px=10)
+
+        self.assertEqual(int(frame[9, 13, 0]), 0)
+        np.testing.assert_array_equal(frame[9, 14:18, 0], np.full(4, 255, dtype=np.uint8))
+
+    def test_count_a_static_b_provider_packs_counts_across_vsync_frames(self):
+        from dmdcontrol.patterns.modes import generate_decimal_number_rgb
+        from dmdcontrol.patterns.paired import A_COUNT_B_STATIC_PAIR_TEST, make_pair_frame_provider
+
+        provider = make_pair_frame_provider(
+            A_COUNT_B_STATIC_PAIR_TEST,
+            test_b="dot",
+            count_start=1,
+            count_end=4,
+            count_slots_per_frame=2,
+            width=120,
+            height=160,
+            numbers_size_px=80,
+            b_dot_x=60,
+            b_dot_y=80,
+            b_dot_radius=3,
+        )
+
+        frame0_a, frame0_b = provider.initial_pair()
+        frame1_a, frame1_b = provider.next_pair()
+        frame0_again_a, frame0_again_b = provider.next_pair()
+
+        np.testing.assert_array_equal(
+            _extract_packed_bitplane(frame0_a, 0),
+            generate_decimal_number_rgb(1, width=120, height=160, size_px=80)[:, :, 0],
+        )
+        np.testing.assert_array_equal(
+            _extract_packed_bitplane(frame0_a, 1),
+            generate_decimal_number_rgb(2, width=120, height=160, size_px=80)[:, :, 0],
+        )
+        np.testing.assert_array_equal(
+            _extract_packed_bitplane(frame1_a, 0),
+            generate_decimal_number_rgb(3, width=120, height=160, size_px=80)[:, :, 0],
+        )
+        np.testing.assert_array_equal(
+            _extract_packed_bitplane(frame1_a, 1),
+            generate_decimal_number_rgb(4, width=120, height=160, size_px=80)[:, :, 0],
+        )
+        self.assertEqual(int(np.count_nonzero(_extract_packed_bitplane(frame0_a, 2))), 0)
+        self.assertEqual(int(np.count_nonzero(_extract_packed_bitplane(frame1_a, 2))), 0)
+        np.testing.assert_array_equal(frame0_a, frame0_again_a)
+        np.testing.assert_array_equal(frame0_b, frame1_b)
+        np.testing.assert_array_equal(frame0_b, frame0_again_b)
+
+    def test_count_a_static_b_provider_rejects_partial_final_vsync(self):
+        from dmdcontrol.patterns.paired import A_COUNT_B_STATIC_PAIR_TEST, make_pair_frame_provider
+
+        with self.assertRaisesRegex(ValueError, "divisible by count_slots_per_frame"):
+            make_pair_frame_provider(
+                A_COUNT_B_STATIC_PAIR_TEST,
+                count_start=1,
+                count_end=5,
+                count_slots_per_frame=2,
+                width=120,
+                height=160,
+            )
+
+    def test_count_a_static_b_provider_rejects_excessive_frame_count(self):
+        from dmdcontrol.patterns.paired import A_COUNT_B_STATIC_PAIR_TEST, make_pair_frame_provider
+
+        with self.assertRaisesRegex(ValueError, "at most 64 VSYNC frames"):
+            make_pair_frame_provider(
+                A_COUNT_B_STATIC_PAIR_TEST,
+                count_start=1,
+                count_end=130,
+                count_slots_per_frame=2,
+                width=120,
+                height=160,
+            )
+
     def test_a_numbers_b_static_provider_applies_requested_b_dot_geometry(self):
         from dmdcontrol.patterns.paired import A_NUMBERS_B_STATIC_PAIR_TEST, make_pair_frame_provider
 
         provider = make_pair_frame_provider(
             A_NUMBERS_B_STATIC_PAIR_TEST,
             test_b="dot",
-            numbers=(1,),
+            numbers=(1,
+                     ),
             width=9,
             height=9,
             numbers_size_px=5,
@@ -187,8 +317,16 @@ def test_generate_dot_frame_draws_square_and_inverts(self):
 
     def test_calibration_dot_provider_keeps_b_static_while_a_changes(self):
         frames_a = [
-            np.full((3, 4, 3), 11, dtype=np.uint8),
-            np.full((3, 4, 3), 33, dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    11,
+                    dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    33,
+                    dtype=np.uint8),
         ]
         calls = {"count": 0}
 
@@ -210,9 +348,21 @@ def next_a():
 
     def test_calibration_dot_provider_can_flicker_a_every_other_frame(self):
         frames_a = [
-            np.full((3, 4, 3), 11, dtype=np.uint8),
-            np.full((3, 4, 3), 33, dtype=np.uint8),
-            np.full((3, 4, 3), 55, dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    11,
+                    dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    33,
+                    dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    55,
+                    dtype=np.uint8),
         ]
         calls = {"count": 0}
 
@@ -245,8 +395,16 @@ def next_a():
     def test_dynamic_a_static_b_provider_does_not_consume_a_for_initial_pair(self):
         initial_a = np.full((3, 4, 3), 7, dtype=np.uint8)
         frames_a = [
-            np.full((3, 4, 3), 11, dtype=np.uint8),
-            np.full((3, 4, 3), 33, dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    11,
+                    dtype=np.uint8),
+            np.full((3,
+                     4,
+                     3),
+                    33,
+                    dtype=np.uint8),
         ]
         calls = {"count": 0}
 
@@ -295,7 +453,11 @@ def test_generate_static_frame_exposes_dot_frame(self):
         self.assertEqual(frame[0, 0, 0], 0)
         np.testing.assert_array_equal(
             frame,
-            generate_dot_frame(width=7, height=7, x=3, y=3, radius=1),
+            generate_dot_frame(width=7,
+                               height=7,
+                               x=3,
+                               y=3,
+                               radius=1),
         )
 
     def test_coarse_visual_modes_are_static_pair_choices(self):
diff --git a/tests/test_persistent_camera_liveness.py b/tests/test_persistent_camera_liveness.py
new file mode 100644
index 0000000..b0214ba
--- /dev/null
+++ b/tests/test_persistent_camera_liveness.py
@@ -0,0 +1,159 @@
+import json
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+from debug_scripts import persistent_camera_liveness
+
+
+class FakeEvent:
+
+    def __init__(self, x, y, polarity, timestamp):
+        self.x = x
+        self.y = y
+        self.polarity = polarity
+        self.timestamp = timestamp
+
+
+class FakeBatch(list):
+
+    def getLowestTime(self):
+        return min(event.timestamp for event in self)
+
+    def getHighestTime(self):
+        return max(event.timestamp for event in self)
+
+
+class FakeCamera:
+
+    def __init__(self):
+        self.calls = []
+        self.opened = True
+        self.threshold_on = None
+        self.threshold_off = None
+        self.batches = [
+            None,
+            FakeBatch([FakeEvent(1,
+                                 1,
+                                 True,
+                                 100),
+                       FakeEvent(2,
+                                 2,
+                                 False,
+                                 101)]),
+            FakeBatch([FakeEvent(3,
+                                 3,
+                                 True,
+                                 200)]),
+            None,
+            FakeBatch([FakeEvent(4,
+                                 4,
+                                 True,
+                                 300)]),
+        ]
+
+    def getCameraName(self):
+        return "FakeDVXplorer"
+
+    def getEventResolution(self):
+        return (8, 6)
+
+    def getNextEventBatch(self):
+        self.calls.append("read")
+        if self.batches:
+            return self.batches.pop(0)
+        return None
+
+    def setContrastThresholdOn(self, value):
+        self.threshold_on = value
+
+    def setContrastThresholdOff(self, value):
+        self.threshold_off = value
+
+
+def test_parser_defaults_to_multiple_same_handle_windows():
+    args = persistent_camera_liveness.build_parser().parse_args([])
+
+    assert args.windows == 5
+    assert args.duration_seconds == 2.0
+    assert args.gap_seconds == 1.0
+    assert args.threshold == 9
+
+
+def test_persistent_liveness_opens_once_and_writes_per_window_stats(tmp_path, monkeypatch):
+    camera = FakeCamera()
+    opens = []
+
+    fake_dv = SimpleNamespace(
+        __version__="fake",
+        __file__="fake-dv.py",
+        io=SimpleNamespace(
+            camera=SimpleNamespace(
+                discover=lambda: [SimpleNamespace(serialNumber="DXBFAKE")],
+                open=lambda descriptor: opens.append(descriptor) or camera,
+            )),
+    )
+
+    monkeypatch.setitem(sys.modules, "dv_processing", fake_dv)
+    monkeypatch.setattr(persistent_camera_liveness.time, "sleep", lambda seconds: None)
+    monkeypatch.setattr(
+        persistent_camera_liveness,
+        "_monotonic_seconds",
+        _fake_clock([0.0,
+                     0.2,
+                     0.5,
+                     0.6,
+                     0.9,
+                     1.2,
+                     1.3,
+                     1.6,
+                     1.9,
+                     2.2]),
+    )
+
+    result = persistent_camera_liveness.run(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--name",
+            "same_handle",
+            "--windows",
+            "2",
+            "--duration-seconds",
+            "0.5",
+            "--gap-seconds",
+            "0.1",
+        ])
+
+    assert result == 0
+    assert len(opens) == 1
+    assert camera.threshold_on == 9
+    assert camera.threshold_off == 9
+
+    out_dir = tmp_path / "same_handle"
+    stats = json.loads((out_dir / "stats.json").read_text(encoding="utf-8"))
+    assert [row["label"] for row in stats["windows"]] == [
+        "window_01_same_handle",
+        "window_02_same_handle",
+    ]
+    assert stats["summary"]["camera_opens"] == 1
+    assert stats["windows"][0]["events"] == 2
+    assert stats["windows"][1]["events"] == 1
+    assert (out_dir / "window_01_same_handle.pgm").exists()
+    png_path = out_dir / "window_01_same_handle.png"
+    assert png_path.exists()
+    assert png_path.read_bytes().startswith(b"\x89PNG\r\n\x1a\n")
+    assert stats["windows"][0]["png"] == str(png_path)
+    assert (out_dir / "window_02_same_handle.npy").exists()
+
+
+def _fake_clock(values):
+    values = list(values)
+    last = values[-1]
+
+    def now():
+        if values:
+            return values.pop(0)
+        return last
+
+    return now
diff --git a/tests/test_persistent_camera_liveness_official.py b/tests/test_persistent_camera_liveness_official.py
new file mode 100644
index 0000000..3dae60b
--- /dev/null
+++ b/tests/test_persistent_camera_liveness_official.py
@@ -0,0 +1,212 @@
+import json
+import sys
+from types import SimpleNamespace
+
+import numpy as np
+
+from debug_scripts import persistent_camera_liveness_official
+
+
+class FakeBatch(list):
+
+    def __init__(self, size, lo, hi):
+        super().__init__(range(size))
+        self.lo = lo
+        self.hi = hi
+
+    def getLowestTime(self):
+        return self.lo
+
+    def getHighestTime(self):
+        return self.hi
+
+
+class FakeCamera:
+
+    def __init__(self):
+        self.batches = [
+            FakeBatch(2,
+                      100,
+                      120),
+            FakeBatch(3,
+                      200,
+                      230),
+            FakeBatch(4,
+                      400,
+                      450),
+        ]
+        self.threshold_on = None
+        self.threshold_off = None
+
+    def getCameraName(self):
+        return "FakeDVXplorer"
+
+    def getEventResolution(self):
+        return (8, 6)
+
+    def getNextEventBatch(self):
+        if self.batches:
+            return self.batches.pop(0)
+        return None
+
+    def isRunning(self):
+        return True
+
+    def isEventStreamAvailable(self):
+        return True
+
+    def setContrastThresholdOn(self, value):
+        self.threshold_on = value
+
+    def setContrastThresholdOff(self, value):
+        self.threshold_off = value
+
+
+class FakeAccumulator:
+    created = []
+
+    class Decay:
+        EXPONENTIAL = "EXPONENTIAL"
+        LINEAR = "LINEAR"
+        STEP = "STEP"
+        NONE = "NONE"
+
+    def __init__(self, resolution):
+        self.resolution = tuple(resolution)
+        self.accepted_events = 0
+        self.settings = []
+        FakeAccumulator.created.append(self)
+
+    def setEventContribution(self, value):
+        self.settings.append(("event_contribution", value))
+
+    def setNeutralPotential(self, value):
+        self.settings.append(("neutral_potential", value))
+
+    def setMinPotential(self, value):
+        self.settings.append(("min_potential", value))
+
+    def setMaxPotential(self, value):
+        self.settings.append(("max_potential", value))
+
+    def setDecayFunction(self, value):
+        self.settings.append(("decay_function", value))
+
+    def setDecayParam(self, value):
+        self.settings.append(("decay_param", value))
+
+    def setSynchronousDecay(self, value):
+        self.settings.append(("synchronous_decay", value))
+
+    def setIgnorePolarity(self, value):
+        self.settings.append(("ignore_polarity", value))
+
+    def accept(self, events):
+        self.accepted_events += len(events)
+
+    def generateFrame(self):
+        width, height = self.resolution
+        image = np.zeros((height, width), dtype=np.uint8)
+        image[0, 0] = min(255, self.accepted_events * 20)
+        return SimpleNamespace(image=image)
+
+
+class FakeSlicer:
+
+    def doEveryTimeInterval(self, interval, callback):
+        self.interval = interval
+        self.callback = callback
+
+    def accept(self, events):
+        self.callback(events)
+
+
+def test_official_parser_defaults_match_reference_style():
+    args = persistent_camera_liveness_official.build_parser().parse_args([])
+
+    assert args.windows == 5
+    assert args.duration_seconds == 2.0
+    assert args.slice_ms == 33.0
+    assert args.event_contribution == 0.25
+    assert args.neutral_potential == 0.5
+    assert args.decay_function == "LINEAR"
+    assert args.ignore_polarity is False
+
+
+def test_official_liveness_uses_one_open_and_writes_accumulator_pngs(tmp_path, monkeypatch):
+    camera = FakeCamera()
+    opens = []
+    FakeAccumulator.created = []
+
+    fake_dv = SimpleNamespace(
+        __version__="fake",
+        __file__="fake-dv.py",
+        Accumulator=FakeAccumulator,
+        EventStreamSlicer=FakeSlicer,
+        io=SimpleNamespace(camera=SimpleNamespace(open=lambda: opens.append("open") or camera,
+                                                  )),
+    )
+
+    monkeypatch.setitem(sys.modules, "dv_processing", fake_dv)
+    monkeypatch.setattr(persistent_camera_liveness_official.time, "sleep", lambda seconds: None)
+    monkeypatch.setattr(
+        persistent_camera_liveness_official,
+        "_monotonic_seconds",
+        _fake_clock([0.0,
+                     0.1,
+                     0.2,
+                     0.6,
+                     0.7,
+                     0.8,
+                     1.3]),
+    )
+
+    result = persistent_camera_liveness_official.run(
+        [
+            "--output-root",
+            str(tmp_path),
+            "--name",
+            "official_same_handle",
+            "--windows",
+            "2",
+            "--duration-seconds",
+            "0.5",
+            "--gap-seconds",
+            "0.1",
+            "--set-dvxplorer-defaults",
+            "--ignore-polarity",
+        ])
+
+    assert result == 0
+    assert opens == ["open"]
+    assert camera.threshold_on == 9
+    assert camera.threshold_off == 9
+    assert len(FakeAccumulator.created) == 2
+    assert ("ignore_polarity", True) in FakeAccumulator.created[0].settings
+
+    out_dir = tmp_path / "official_same_handle"
+    stats = json.loads((out_dir / "stats.json").read_text(encoding="utf-8"))
+    assert stats["summary"]["camera_opens"] == 1
+    assert [row["label"] for row in stats["windows"]] == [
+        "window_01_official_accumulator",
+        "window_02_official_accumulator",
+    ]
+    assert stats["windows"][0]["events"] == 5
+    assert stats["windows"][1]["events"] == 4
+
+    final_png = out_dir / "window_01_official_accumulator_final.png"
+    assert final_png.exists()
+    assert final_png.read_bytes().startswith(b"\x89PNG\r\n\x1a\n")
+    assert stats["windows"][0]["final_png"] == str(final_png)
+
+
+def _fake_clock(values):
+    values = list(values)
+    last = values[-1]
+
+    def now():
+        if values:
+            return values.pop(0)
+        return last
+
+    return now
diff --git a/tests/test_raw_batch_probe.py b/tests/test_raw_batch_probe.py
new file mode 100644
index 0000000..8c511d1
--- /dev/null
+++ b/tests/test_raw_batch_probe.py
@@ -0,0 +1,142 @@
+import sys
+from types import SimpleNamespace
+
+from debug_scripts import raw_batch_probe
+
+
+class FakeBatch(list):
+
+    def __init__(self, size, lo, hi):
+        super().__init__(range(size))
+        self.lo = lo
+        self.hi = hi
+
+    def getLowestTime(self):
+        return self.lo
+
+    def getHighestTime(self):
+        return self.hi
+
+
+class FakeCamera:
+
+    def __init__(self):
+        self.batches = [
+            None,
+            FakeBatch(2, 100, 120),
+            FakeBatch(3, 200, 230),
+        ]
+        self.threshold_on = None
+        self.threshold_off = None
+        self.global_hold = None
+        self.global_reset = None
+
+    def getCameraName(self):
+        return "FakeDVXplorer"
+
+    def getEventResolution(self):
+        return (8, 6)
+
+    def getNextEventBatch(self):
+        if self.batches:
+            return self.batches.pop(0)
+        return None
+
+    def isRunning(self):
+        return True
+
+    def isEventStreamAvailable(self):
+        return True
+
+    def setContrastThresholdOn(self, value):
+        self.threshold_on = value
+
+    def setContrastThresholdOff(self, value):
+        self.threshold_off = value
+
+    def setGlobalHold(self, value):
+        self.global_hold = value
+
+    def setGlobalReset(self, value):
+        self.global_reset = value
+
+
+def test_raw_batch_probe_parser_aliases():
+    args = raw_batch_probe.build_parser().parse_args(
+        [
+            "--duration-seconds",
+            "4",
+            "--gap-seconds",
+            "0.5",
+            "--set-dvxplorer-defaults",
+            "--prestate",
+            "physical_replug=yes",
+        ])
+
+    assert args.duration_seconds == 4
+    assert args.gap_seconds == 0.5
+    assert args.set_dvxplorer_defaults is True
+    assert raw_batch_probe.parse_prestate(args.prestate) == {
+        "fields": {
+            "physical_replug": "yes",
+        },
+        "notes": [],
+    }
+
+
+def test_raw_batch_probe_reports_wall_bins_and_timestamp_span(monkeypatch):
+    camera = FakeCamera()
+
+    monkeypatch.setattr(raw_batch_probe.time, "sleep", lambda seconds: None)
+    monkeypatch.setattr(raw_batch_probe, "_monotonic_seconds", _fake_clock([0.0, 0.1, 0.2, 0.4, 0.6]))
+
+    result = raw_batch_probe.capture_window(camera, "window_01", 0.5)
+
+    assert result["events"] == 5
+    assert result["batches"] == 2
+    assert result["none_count"] == 1
+    assert result["wall_second_event_bins"] == [5]
+    assert result["first_ts"] == 100
+    assert result["last_ts"] == 230
+    assert result["camera_span_s"] == 0.00013
+
+
+def test_raw_batch_probe_run_opens_once_and_applies_defaults(monkeypatch):
+    camera = FakeCamera()
+    opens = []
+
+    fake_dv = SimpleNamespace(
+        __version__="fake",
+        __file__="fake-dv.py",
+        io=SimpleNamespace(
+            camera=SimpleNamespace(
+                discover=lambda: [SimpleNamespace(serialNumber="DXBFAKE")],
+                open=lambda: opens.append("open") or camera,
+                DVXplorer=SimpleNamespace(ReadoutFPS=SimpleNamespace(VARIABLE_5000="VARIABLE_5000")),
+            )),
+    )
+
+    monkeypatch.setitem(sys.modules, "dv_processing", fake_dv)
+    monkeypatch.setattr(raw_batch_probe.time, "sleep", lambda seconds: None)
+    monkeypatch.setattr(raw_batch_probe, "_monotonic_seconds", _fake_clock([0.0, 0.1, 0.2]))
+
+    result = raw_batch_probe.run(["--windows", "1", "--duration", "0.15", "--defaults"])
+
+    assert result == 0
+    assert opens == ["open"]
+    assert camera.threshold_on == 9
+    assert camera.threshold_off == 9
+    assert camera.global_hold is True
+    assert camera.global_reset is False
+
+
+def _fake_clock(values):
+    values = list(values)
+    last = values[-1]
+
+    def now():
+        if values:
+            return values.pop(0)
+        return last
+
+    return now
diff --git a/tests/test_runtime_loop_until_close.py b/tests/test_runtime_loop_until_close.py
index ed9eb60..cefe011 100644
--- a/tests/test_runtime_loop_until_close.py
+++ b/tests/test_runtime_loop_until_close.py
@@ -5,6 +5,7 @@
 
 
 class _Engine:
+
     def __init__(self):
         self.displayed = 0
 
@@ -16,6 +17,7 @@ def display_frame(self, frame):
 
 
 class RuntimeLoopUntilCloseTests(unittest.TestCase):
+
     def test_runtime_seconds_zero_runs_until_window_close(self):
         engine = _Engine()
         args = types.SimpleNamespace(runtime_seconds=0, verbose=0)
diff --git a/tests/test_timing_notebooks.py b/tests/test_timing_notebooks.py
new file mode 100644
index 0000000..fd5eaee
--- /dev/null
+++ b/tests/test_timing_notebooks.py
@@ -0,0 +1,61 @@
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+NOTEBOOKS = ROOT / "notebooks"
+
+
+def _load_notebook(name):
+    path = NOTEBOOKS / name
+    assert path.exists(), f"missing notebook: {path}"
+    with path.open(encoding="utf-8") as handle:
+        notebook = json.load(handle)
+    assert notebook["nbformat"] == 4
+    assert notebook["cells"]
+    return notebook
+
+
+def _joined_source(notebook):
+    return "\n".join("".join(cell.get("source", [])) for cell in notebook["cells"])
+
+
+def test_timing_sweep_notebooks_exist_and_are_parseable():
+    expected = {
+        "01_generate_timing_sweep_commands.ipynb": [
+            "SWEEP_MANIFEST_PATH",
+            "SWEEP_COMMAND_PATH",
+            "dark_time_us_values",
+            "trigger_delay_fraction_values",
+            "run_camera_sync_sweep.sh",
+            "number_size_px",
+            "b_dot_radius",
+            "numbers_exposure_us_values",
+            "sync_check_argv",
+        ],
+        "02_analyze_timing_sweep_results.ipynb": [
+            "summary.json",
+            "events_per_accumulation_window",
+            "number_sequence",
+            "expected_number",
+            "crossover_score",
+            "timing_score",
+            "ranked_runs",
+        ],
+        "03_accumulation_offset_rescan.ipynb": [
+            "filtered_events.npz",
+            "offset_us_values",
+            "rescan_offsets",
+            "best_offset_by_run",
+        ],
+    }
+
+    for name, required_terms in expected.items():
+        notebook = _load_notebook(name)
+        source = _joined_source(notebook)
+        for term in required_terms:
+            assert term in source, f"{term!r} missing from {name}"
+
+    generator_source = _joined_source(_load_notebook("01_generate_timing_sweep_commands.ipynb"))
+    assert "./run_camera_sync_check.sh" not in generator_source
+    assert "run_dmd_pair_capture.sh" not in generator_source
+    assert "kernel_exposure_us" not in generator_source
diff --git a/tests/test_trigger_timing.py b/tests/test_trigger_timing.py
index d5d59a2..ea4c867 100644
--- a/tests/test_trigger_timing.py
+++ b/tests/test_trigger_timing.py
@@ -5,11 +5,13 @@
 
 
 class DryRunDLPC:
+
     def get_display_dimensions(self):
         return None
 
 
 class TriggerTimingTests(unittest.TestCase):
+
     def test_default_delay_is_zero_percent_of_exposure(self):
         timing = compute_trigger_out_2_timing(exposure_us=3000)
 
diff --git a/tests/test_visual_patterns.py b/tests/test_visual_patterns.py
index c7d53f1..6bba2a7 100644
--- a/tests/test_visual_patterns.py
+++ b/tests/test_visual_patterns.py
@@ -14,6 +14,7 @@
 
 
 class VisualPatternTests(unittest.TestCase):
+
     def test_coarse_grid_uses_human_scale_spacing_and_thick_lines(self):
         frame = generate_coarse_grid_rgb(width=240, height=180)
 
@@ -28,8 +29,8 @@ def test_coarse_grid_uses_human_scale_spacing_and_thick_lines(self):
         t = DEFAULT_COARSE_GRID_THICKNESS
         s = DEFAULT_COARSE_GRID_SPACING
         self.assertTrue(np.all(frame[:, 0:t, :] == 255))
-        self.assertTrue(np.all(frame[:, s: s + t, :] == 255))
-        self.assertTrue(np.all(frame[s: s + t, :, :] == 255))
+        self.assertTrue(np.all(frame[:, s:s + t, :] == 255))
+        self.assertTrue(np.all(frame[s:s + t, :, :] == 255))
         self.assertEqual(frame[t + 4, t + 4, 0], 0)
 
     def test_coarse_grid_can_shift_position_for_pair_distinction(self):
@@ -52,9 +53,9 @@ def test_coarse_lines_are_thick_bands_not_single_pixel_lines(self):
         t = DEFAULT_COARSE_LINE_THICKNESS
         s = DEFAULT_COARSE_LINE_SPACING
         self.assertTrue(np.all(vertical[:, 0:t, :] == 255))
-        self.assertTrue(np.all(vertical[:, s: s + t, :] == 255))
+        self.assertTrue(np.all(vertical[:, s:s + t, :] == 255))
         self.assertTrue(np.all(horizontal[0:t, :, :] == 255))
-        self.assertTrue(np.all(horizontal[s: s + t, :, :] == 255))
+        self.assertTrue(np.all(horizontal[s:s + t, :, :] == 255))
         self.assertFalse(np.array_equal(vertical, horizontal))
 
     def test_single_dmd_visual_modes_are_registered(self):