Merge branch 'main' of ssh://ssh.pstruebi.xyz:222/auracaster/latency_test_suit

frequency_quality.py

@@ -0,0 +1,251 @@
+#!/usr/bin/env python3
+import argparse
+import threading
+import numpy as np
+import sounddevice as sd
+import matplotlib
+matplotlib.use("TkAgg")
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Button, Slider
+
+
+def generate_log_sweep(f_start: float, f_end: float, dur_s: float, fs: int, volume: float,
+                       pre_silence: float = 0.10, post_silence: float = 0.20) -> tuple[np.ndarray, np.ndarray, int, int]:
+    n_pre = int(pre_silence * fs)
+    n_tone = int(dur_s * fs)
+    n_post = int(post_silence * fs)
+    t = np.arange(n_tone, dtype=np.float64) / fs
+    r = float(f_end) / float(f_start)
+    k = np.log(r)
+    phase = 2.0 * np.pi * f_start * (np.exp((t / dur_s) * k) - 1.0) * (dur_s / k)
+    sweep = np.sin(phase).astype(np.float32)
+    fade_n = max(1, int(0.005 * fs))
+    if fade_n > 0 and fade_n * 2 < n_tone:
+        w = np.ones_like(sweep)
+        w[:fade_n] *= np.linspace(0, 1, fade_n, endpoint=False, dtype=np.float32)
+        w[-fade_n:] *= np.linspace(1, 0, fade_n, endpoint=False, dtype=np.float32)
+        sweep *= w
+    sweep *= float(volume)
+    out = np.concatenate([
+        np.zeros(n_pre, dtype=np.float32),
+        sweep,
+        np.zeros(n_post, dtype=np.float32)
+    ])
+    return out, sweep, n_pre, n_pre + n_tone
+
+
+def map_time_to_freq_log(t: np.ndarray, f_start: float, f_end: float, dur_s: float) -> np.ndarray:
+    r = float(f_end) / float(f_start)
+    return f_start * np.exp((t / dur_s) * np.log(r))
+
+
+def analyze_rms_vs_freq(rec: np.ndarray, fs: int, start_idx: int, end_idx: int,
+                        dur_s: float, f_start: float, f_end: float,
+                        frame: int = 2048, hop: int = 1024,
+                        eps: float = 1e-12) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    x = rec.astype(np.float32)
+    x = x[start_idx:end_idx]
+    if x.size < frame:
+        return np.array([], dtype=np.float32), np.array([], dtype=np.float32)
+    n = x.size
+    centers = []
+    vals = []
+    confs = []
+    i = 0
+    while i + frame <= n:
+        seg = x[i:i+frame]
+        rms = float(np.sqrt(np.mean(seg.astype(np.float64) ** 2)))
+        vals.append(20.0 * np.log10(max(rms, eps)))
+        c = (i + frame * 0.5) / fs
+        centers.append(c)
+        # Confidence via spectral peak prominence near expected frequency
+        win = np.hanning(len(seg)).astype(np.float64)
+        segw = seg.astype(np.float64) * win
+        # next power of two for speed
+        nfft = 1 << (int(len(segw) - 1).bit_length())
+        spec = np.fft.rfft(segw, n=nfft)
+        mag = np.abs(spec)
+        # Expected bin
+        t_here = c
+        f_exp = float(map_time_to_freq_log(np.array([t_here]), f_start, f_end, dur_s)[0])
+        bin_exp = int(np.clip(np.round(f_exp / fs * nfft), 1, (nfft // 2)))
+        b0 = max(1, bin_exp - 2)
+        b1 = min(nfft // 2, bin_exp + 2)
+        peak = float(np.max(mag[b0:b1+1])) if b1 >= b0 else float(mag[bin_exp])
+        noise = float(np.median(mag[1:])) if mag.size > 1 else peak
+        snr_db = 20.0 * np.log10((peak + eps) / (noise + eps))
+        conf = float(np.clip((snr_db - 6.0) / 24.0, 0.0, 1.0))  # 6..30 dB -> 0..1
+        confs.append(conf)
+        i += hop
+    t = np.array(centers, dtype=np.float64)
+    f = map_time_to_freq_log(t, f_start, f_end, dur_s).astype(np.float32)
+    db = np.array(vals, dtype=np.float32)
+    confa = np.array(confs, dtype=np.float32)
+    return f, db, confa
+
+
+def run_gui(fs: int, dur: float, vol: float, indev: int | None, outdev: int | None,
+            f_start: float = 20.0, f_end: float = 20000.0,
+            frame: int = 2048, hop: int = 1024,
+            blocksize: int | None = None, iolatency: float | str | None = "high"):
+    freqs: list[float] = []
+    dbfs: list[float] = []
+    confs: list[float] = []
+
+    fig = plt.figure(figsize=(10, 6))
+    plt.tight_layout(rect=[0, 0.16, 1, 1])
+
+    ax_sc = fig.add_axes([0.08, 0.22, 0.84, 0.72])
+    ax_sc.set_title("Sweep capture: dBFS vs frequency", loc="left")
+    ax_sc.set_xlabel("frequency [Hz]")
+    ax_sc.set_ylabel("level [dBFS]")
+    ax_sc.set_xscale("log")
+    ax_sc.grid(True, which="both", axis="both", alpha=0.25)
+    sc = ax_sc.scatter([], [], c=[], cmap="viridis", vmin=0.0, vmax=1.0, s=18, alpha=0.9)
+    cbar = fig.colorbar(sc, ax=ax_sc, pad=0.02)
+    cbar.set_label("confidence")
+
+    busy = threading.Event()
+    latest_changed = threading.Event()
+    lock = threading.Lock()
+
+    current_vol = [float(vol)]
+
+    def update_plot():
+        with lock:
+            if len(freqs) == 0:
+                ax_sc.set_xlim(f_start, f_end)
+                ax_sc.set_ylim(-100.0, 0.0)
+                sc.set_offsets(np.empty((0, 2)))
+                sc.set_array(np.array([]))
+            else:
+                f = np.array(freqs, dtype=float)
+                y = np.array(dbfs, dtype=float)
+                xy = np.column_stack([f, y])
+                sc.set_offsets(xy)
+                sc.set_array(np.array(confs, dtype=float))
+                ax_sc.set_xlim(max(10.0, np.nanmin(f)), min(24000.0, np.nanmax(f)))
+                ymin = np.nanpercentile(y, 2) if np.isfinite(y).any() else -100.0
+                ymax = np.nanpercentile(y, 98) if np.isfinite(y).any() else 0.0
+                if not np.isfinite(ymin):
+                    ymin = -100.0
+                if not np.isfinite(ymax):
+                    ymax = 0.0
+                if ymax - ymin < 10.0:
+                    ymin = min(ymin, -100.0)
+                    ymax = max(ymax, 0.0)
+                pad = 0.05 * (ymax - ymin)
+                ax_sc.set_ylim(ymin - pad, ymax + pad)
+        fig.canvas.draw_idle()
+
+    def run_one_sweep():
+        play_buf, ref, start_idx, end_idx = generate_log_sweep(
+            f_start, f_end, dur, fs, current_vol[0], pre_silence=0.10, post_silence=0.20
+        )
+        record_buf = []
+        written = 0
+
+        def cb(indata, outdata, frames, time_info, status):
+            nonlocal written
+            if status:
+                print(status, flush=True)
+            chunk = play_buf[written:written+frames]
+            out = np.zeros((frames,), dtype=np.float32)
+            if len(chunk) > 0:
+                out[:len(chunk)] = chunk
+            outdata[:] = out.reshape(-1, 1)
+            record_buf.append(indata.copy().reshape(-1))
+            written += frames
+
+        stream_kwargs = dict(samplerate=fs, dtype="float32", channels=1)
+        if indev is not None or outdev is not None:
+            stream_kwargs["device"] = (indev, outdev)
+        if blocksize is not None:
+            stream_kwargs["blocksize"] = int(blocksize)
+        if iolatency is not None:
+            stream_kwargs["latency"] = iolatency
+
+        with sd.Stream(callback=cb, **stream_kwargs):
+            sd.sleep(int(1000 * (len(play_buf) / fs)))
+            sd.sleep(100)
+
+        if not record_buf:
+            return np.array([], dtype=np.float32), np.array([], dtype=np.float32), np.array([], dtype=np.float32)
+        rec = np.concatenate(record_buf).astype(np.float32)
+        f, y, c = analyze_rms_vs_freq(rec, fs, start_idx, end_idx, dur, f_start, f_end, frame=frame, hop=hop)
+        return f, y, c
+    def on_sweep(event):
+        if busy.is_set():
+            return
+        busy.set()
+        btn_sweep.label.set_text("Running…")
+        def task():
+            try:
+                f, y, c = run_one_sweep()
+                with lock:
+                    if f.size > 0:
+                        freqs.extend(list(f))
+                        dbfs.extend(list(y))
+                        confs.extend(list(c))
+                        latest_changed.set()
+            finally:
+                busy.clear()
+                btn_sweep.label.set_text("Sweep")
+        threading.Thread(target=task, daemon=True).start()
+
+    sweep_ax = fig.add_axes([0.79, 0.05, 0.15, 0.07])
+    btn_sweep = Button(sweep_ax, "Sweep")
+    btn_sweep.on_clicked(on_sweep)
+
+    vol_ax = fig.add_axes([0.08, 0.05, 0.45, 0.07])
+    vol_slider = Slider(vol_ax, 'volume', 0.0, 1.0, valinit=current_vol[0], valstep=0.01)
+
+    def on_vol(val):
+        current_vol[0] = float(val)
+    vol_slider.on_changed(on_vol)
+
+    timer = fig.canvas.new_timer(interval=250)
+    def on_timer():
+        if latest_changed.is_set():
+            latest_changed.clear()
+            update_plot()
+    timer.add_callback(on_timer)
+    timer.start()
+
+    update_plot()
+    plt.show()
+
+
+def main():
+    ap = argparse.ArgumentParser(description="Frequency sweep capture: plot recorded dBFS vs frequency")
+    ap.add_argument("--samplerate", "-r", type=int, default=48_000, help="Sample rate")
+    ap.add_argument("--time", "-t", type=float, default=8.0, help="Sweep duration (s)")
+    ap.add_argument("--volume", "-v", type=float, default=0.5, help="Playback volume 0..1")
+    ap.add_argument("--indev", type=int, default=None, help="Input device index")
+    ap.add_argument("--outdev", type=int, default=None, help="Output device index")
+    ap.add_argument("--f-start", type=float, default=20.0, help="Sweep start frequency (Hz)")
+    ap.add_argument("--f-end", type=float, default=20000.0, help="Sweep end frequency (Hz)")
+    ap.add_argument("--frame", type=int, default=2048, help="RMS window size (samples)")
+    ap.add_argument("--hop", type=int, default=1024, help="RMS hop size (samples)")
+    ap.add_argument("--blocksize", type=int, default=None, help="Audio blocksize (frames)")
+    ap.add_argument("--iolatency", type=str, default="high", help="Audio I/O latency preset or seconds")
+    args = ap.parse_args()
+
+    run_gui(
+        fs=args.samplerate,
+        dur=args.time,
+        vol=args.volume,
+        indev=args.indev,
+        outdev=args.outdev,
+        f_start=args.f_start,
+        f_end=args.f_end,
+        frame=args.frame,
+        hop=args.hop,
+        blocksize=args.blocksize,
+        iolatency=args.iolatency,
+    )
+
+
+if __name__ == "__main__":
+    main()
+

latency_440.py

@@ -7,8 +7,12 @@ import sounddevice as sd
 import matplotlib
 matplotlib.use("TkAgg")  # GUI-Ausgabe für interaktives Fenster
 import matplotlib.pyplot as plt
-from matplotlib.widgets import Button
+import matplotlib.ticker as mticker
+from matplotlib.widgets import Button, Slider, CheckButtons
+import matplotlib.patches as mpatches
 import threading
+import time
+from datetime import datetime
 
 # ---------- Audio/Signal-Helfer ----------
 @dataclass
@@ -32,7 +36,7 @@ def generate_tone(f_hz: float, dur_s: float, fs: int, volume: float,
     out = np.concatenate([np.zeros(n_pre, dtype=np.float32), ref, np.zeros(n_post, dtype=np.float32)])
     return out, ref, n_pre
 
-def detect_onset_xcorr(signal: np.ndarray, ref: np.ndarray):
+def detect_onset_xcorr(signal: np.ndarray, ref: np.ndarray, pre_len: int | None = None):
     """Normierte Kreuzkorrelation; liefert Onset-Index und Confidence."""
     x = signal.astype(np.float64)
     r = ref.astype(np.float64)
@@ -50,14 +54,60 @@ def detect_onset_xcorr(signal: np.ndarray, ref: np.ndarray):
     nrm = np.sqrt(E_x * E_r) + 1e-20
     nxc = corr / nrm
     k = int(np.argmax(nxc))
-    conf = float(nxc[k])
+    peak = float(nxc[k])
+    # Robust confidence: compare peak to pre-silence baseline distribution
+    if pre_len is None or pre_len <= M:
+        base_end = max(1, int(len(nxc) * 0.2))
+    else:
+        base_end = max(1, min(len(nxc), int(pre_len - M + 1)))
+    base = nxc[:base_end]
+    if base.size <= 1:
+        conf = peak
+    else:
+        med = float(np.median(base))
+        mad = float(np.median(np.abs(base - med)))
+        scale = 1.4826 * mad + 1e-6
+        z = (peak - med) / scale
+        conf = float(1.0 / (1.0 + np.exp(-0.5 * z)))
     return k, nxc, conf
 
+# Simple biquad band-pass (RBJ cookbook) and direct-form I filter
+def design_biquad_bandpass(fs: float, f0: float, Q: float) -> tuple[np.ndarray, np.ndarray]:
+    w0 = 2.0 * np.pi * (f0 / fs)
+    alpha = np.sin(w0) / (2.0 * Q)
+    b0 =   Q * alpha
+    b1 =   0.0
+    b2 =  -Q * alpha
+    a0 =   1.0 + alpha
+    a1 =  -2.0 * np.cos(w0)
+    a2 =   1.0 - alpha
+    # Normalize
+    b = np.array([b0/a0, b1/a0, b2/a0], dtype=np.float64)
+    a = np.array([1.0, a1/a0, a2/a0], dtype=np.float64)
+    return b, a
+
+def lfilter_biquad(b: np.ndarray, a: np.ndarray, x: np.ndarray) -> np.ndarray:
+    y = np.zeros_like(x, dtype=np.float64)
+    x1 = x2 = y1 = y2 = 0.0
+    b0, b1, b2 = float(b[0]), float(b[1]), float(b[2])
+    a1, a2 = float(a[1]), float(a[2])
+    for i in range(len(x)):
+        xi = float(x[i])
+        yi = b0*xi + b1*x1 + b2*x2 - a1*y1 - a2*y2
+        y[i] = yi
+        x2, x1 = x1, xi
+        y2, y1 = y1, yi
+    return y.astype(np.float32)
+
 def measure_latency_once(freq_hz: float, fs: int, dur_s: float, volume: float,
                          indev: int | None, outdev: int | None,
-                         pre_silence: float = 0.20, post_silence: float = 0.40):
+                         pre_silence: float = 0.20, post_silence: float = 0.40,
+                         blocksize: int | None = None, iolatency: float | str | None = None,
+                         estimator: str = "xcorr", xrun_counter: dict | None = None,
+                         bandpass: bool = True, rms_info: dict | None = None,
+                         io_info: dict | None = None, diag: dict | None = None):
     """Spielt einen Ton, nimmt parallel auf, schätzt Latenz in ms, gibt Confidence zurück."""
-    play_buf, ref, _ = generate_tone(freq_hz, dur_s, fs, volume, pre_silence, post_silence)
+    play_buf, ref, n_pre = generate_tone(freq_hz, dur_s, fs, volume, pre_silence, post_silence)
     record_buf = []
     written = 0
     times = Times()
@@ -67,6 +117,27 @@ def measure_latency_once(freq_hz: float, fs: int, dur_s: float, volume: float,
         if status:
             # Ausgabe nur informativ; Xruns etc. beeinflussen Latenz
             print(status, flush=True)
+            if xrun_counter is not None:
+                try:
+                    xrun_counter["count"] = int(xrun_counter.get("count", 0)) + 1
+                except Exception:
+                    pass
+        # Input RMS/clip
+        if rms_info is not None:
+            try:
+                rms = float(np.sqrt(np.mean(np.square(indata.astype(np.float64)))))
+                peak = float(np.max(np.abs(indata)))
+                rms_db = -120.0 if rms <= 1e-9 else 20.0 * np.log10(rms)
+                rms_info["rms_dbfs"] = rms_db
+                rms_info["clip"] = bool(peak >= 0.999)
+            except Exception:
+                pass
+        # Report actual frames-per-buffer used by the stream
+        if io_info is not None:
+            try:
+                io_info["blocksize_actual"] = int(frames)
+            except Exception:
+                pass
         if times.adc_first_time is None:
             times.adc_first_time = time_info.inputBufferAdcTime
 
@@ -86,19 +157,68 @@ def measure_latency_once(freq_hz: float, fs: int, dur_s: float, volume: float,
     stream_kwargs = dict(samplerate=fs, dtype="float32", channels=1)
     if indev is not None or outdev is not None:
         stream_kwargs["device"] = (indev, outdev)
+    if blocksize is not None:
+        stream_kwargs["blocksize"] = int(blocksize)
+    if iolatency is not None:
+        stream_kwargs["latency"] = iolatency
 
     with sd.Stream(callback=cb, **stream_kwargs):
         sd.sleep(int(1000 * (len(play_buf) / fs)))
         sd.sleep(200)
 
-    if times.adc_first_time is None or times.dac_first_time is None or not record_buf:
+    if not record_buf:
         return np.nan, 0.0
 
     rec = np.concatenate(record_buf).astype(np.float32)
-    onset_idx, _, conf = detect_onset_xcorr(rec, ref)
-    adc_detect_time = times.adc_first_time + onset_idx / fs
-    latency_ms = (adc_detect_time - times.dac_first_time) * 1000.0
-    return float(latency_ms), conf
+    # Optional band-pass around the test tone to increase SNR
+    if bandpass:
+        try:
+            b, a = design_biquad_bandpass(fs=float(fs), f0=float(freq_hz), Q=8.0)
+            rec = lfilter_biquad(b, a, rec)
+        except Exception:
+            pass
+    onset_idx, nxc, conf = detect_onset_xcorr(rec, ref, n_pre)
+
+    # Simple RMS gate: require window RMS to exceed pre-silence RMS
+    try:
+        M = len(ref)
+        base_rms = float(np.sqrt(np.mean(np.square(rec[:max(1, n_pre)])))) + 1e-12
+        w0 = int(max(0, onset_idx))
+        w1 = int(min(len(rec), w0 + M))
+        win_rms = float(np.sqrt(np.mean(np.square(rec[w0:w1])))) if w1 > w0 else 0.0
+        snr_lin = win_rms / max(base_rms, 1e-12)
+        if snr_lin < 2.0:
+            conf = float(min(conf, 0.2))
+    except Exception:
+        pass
+
+    # Fill diagnostics for visualization if requested
+    if diag is not None:
+        try:
+            diag.clear()
+            diag.update({
+                "fs": int(fs),
+                "play_buf": play_buf.copy(),
+                "rec": rec.copy(),
+                "ref": ref.copy(),
+                "n_pre": int(n_pre),
+                "onset_idx": int(onset_idx),
+                "nxc": nxc.copy(),
+                "bandpass": bool(bandpass)
+            })
+        except Exception:
+            pass
+
+    if estimator == "timeinfo":
+        if times.adc_first_time is None or times.dac_first_time is None:
+            return np.nan, conf
+        adc_detect_time = times.adc_first_time + onset_idx / fs
+        latency_ms = (adc_detect_time - times.dac_first_time) * 1000.0
+        return float(latency_ms), conf
+    else:  # "xcorr" (default)
+        latency_samples = max(0, int(onset_idx) - int(n_pre))
+        latency_ms = (latency_samples / fs) * 1000.0
+        return float(latency_ms), conf
 
 # ---------- 440-Hz-Runner & Einzel-Balken-Plot ----------
 def run_440(repeats, fs, dur, vol, indev, outdev, conf_min):
@@ -141,89 +261,401 @@ def plot_single_bar(latencies: list[float]):
     plt.tight_layout()
     plt.show()
 
-def run_gui(fs: int, dur: float, vol: float, indev: int | None, outdev: int | None, conf_min: float):
+def run_gui(fs: int, dur: float, vol: float, indev: int | None, outdev: int | None,
+            conf_min: float, blocksize: int | None = None, iolatency: float | str | None = None,
+            estimator: str = "xcorr", pre_silence: float = 0.20, post_silence: float = 0.40,
+            bandpass: bool = True):
     latencies: list[float] = []
     confidences: list[float] = []
 
-    fig, ax = plt.subplots(figsize=(5, 6))
-    ax.set_title("Latenz bei 440 Hz")
-    bar = ax.bar([0], [0.0], color="#4C78A8", width=0.6, label="Mittelwert")
-    err = ax.errorbar([0], [0.0], yerr=[[0.0], [0.0]], fmt="none", ecolor="#333333", capsize=6, label="Std")
-    vline = ax.vlines(0, 0.0, 0.0, colors="#E45756", linewidth=3, label="Min–Max")
-    ax.set_xticks([0])
-    ax.set_xticklabels(["440 Hz"])
-    ax.set_ylim(0.0, 1.0)
-    ax.set_ylabel("Latenz [ms]")
-    ax.grid(True, axis="y", alpha=0.3)
-    ax.legend(loc="best")
-    txt = ax.text(0.02, 0.96, "Confidence: -", transform=ax.transAxes, ha="left", va="top")
-    txt_mean = ax.text(0.02, 0.90, "Mean: - ms", transform=ax.transAxes, ha="left", va="top")
-    plt.tight_layout(rect=[0, 0.1, 1, 1])
+    fig = plt.figure(figsize=(11, 6))
+    # Leave more space at bottom for a two-row control area
+    plt.tight_layout(rect=[0, 0.20, 1, 1])
+
+    # Scatterplot of latency vs sample index (top-left)
+    ax_sc = fig.add_axes([0.05, 0.55, 0.62, 0.42])
+    ax_sc.set_title("Latency over samples", loc="left")
+    ax_sc.set_xlabel("sample index")
+    ax_sc.set_ylabel("latency [ms]")
+    ax_sc.grid(True, axis="both", alpha=0.25)
+    # Zero reference line
+    zero_line = ax_sc.axhline(0.0, color="#999999", linewidth=1, alpha=0.6, zorder=0)
+    # Two series (legacy, cleared each update) and one confidence-graded scatter
+    sc_valid, = ax_sc.plot([], [], 'o', color="#4C78A8", markersize=6, label="valid")
+    sc_low,   = ax_sc.plot([], [], 'o', markerfacecolor='none', markeredgecolor="#E45756", markersize=6, label="low/invalid")
+    sc_conf = ax_sc.scatter([], [], c=[], s=24, cmap='viridis_r', vmin=0.0, vmax=1.0, edgecolors='none', alpha=0.9)
+    # Legend cleanup: show only rolling mean and last sample
+    leg = ax_sc.legend([ ], [ ], loc="upper right")
+    SCATTER_WINDOW = [50]  # fixed default: number of last points to display
+    # Rolling mean and std band (initialized empty)
+    line_mean, = ax_sc.plot([], [], '-', color="#1f77b4", linewidth=1.5, alpha=0.9, label="rolling mean")
+    band_poly = [None]
+    # Latest sample highlight
+    sc_last, = ax_sc.plot([], [], 'o', color="#2ca02c", markersize=7, label="last")
+    ann_last = ax_sc.text(0, 0, "", va="bottom", ha="left", fontsize=8, color="#2ca02c")
+    # Add colorbar for confidence
+    try:
+        cbar = fig.colorbar(sc_conf, ax=ax_sc, fraction=0.046, pad=0.04)
+        cbar.set_label('confidence')
+    except Exception:
+        pass
+
+    # Duplex waveform panel (below scatter, left)
+    ax_duplex = fig.add_axes([0.05, 0.25, 0.62, 0.25])
+    ax_duplex.set_title("Duplex stream (time-domain)", loc="left", fontsize=9)
+    ax_duplex.set_xlabel("time [ms]")
+    ax_duplex.set_ylabel("amplitude")
+    ax_duplex.grid(True, axis="both", alpha=0.25)
+    line_play, = ax_duplex.plot([], [], '-', color="#4C78A8", linewidth=1.0, label="playout")
+    line_rec,  = ax_duplex.plot([], [], '-', color="#E45756", linewidth=1.0, alpha=0.9, label="record")
+    v_on = ax_duplex.axvline(0.0, color="#2ca02c", linestyle="--", linewidth=1.0, label="onset")
+    v_t0 = ax_duplex.axvline(0.0, color="#999999", linestyle=":", linewidth=1.0, label="tone start")
+    ax_duplex.legend(loc="upper right", fontsize=8)
+    # Visual box background
+    ax_duplex.set_facecolor("#fcfcff")
+    try:
+        ax_duplex.add_patch(mpatches.FancyBboxPatch((0, 0), 1, 1, transform=ax_duplex.transAxes,
+                                                   boxstyle="round,pad=0.01", facecolor="#f7f9ff",
+                                                   edgecolor="#c6d3f5", linewidth=0.8, zorder=-1, clip_on=False))
+    except Exception:
+        pass
+
+    # Terminal-style readout panel (right)
+    ax_log = fig.add_axes([0.73, 0.30, 0.23, 0.60])
+    ax_log.set_title("Measurements", loc="center", fontsize=10)
+    ax_log.axis("off")
+    log_text = ax_log.text(0.0, 1.0, "", va="top", ha="left", family="monospace", fontsize=8)
+    LOG_WINDOW = 10  # show last 10 lines; start scrolling after 10
+    # Visual box
+    try:
+        ax_log.add_patch(mpatches.FancyBboxPatch((0, 0), 1, 1, transform=ax_log.transAxes,
+                                                boxstyle="round,pad=0.01", facecolor="#fbfbfb",
+                                                edgecolor="#dddddd", linewidth=0.8, zorder=-1, clip_on=False))
+    except Exception:
+        pass
+
+    # Stats panel (move higher and slightly to the right)
+    ax_stats = fig.add_axes([0.73, 0.60, 0.18, 0.04])
+    ax_stats.axis("off")
+    ax_stats.set_title("Stats", loc="left", fontsize=9)
+    stats_text = ax_stats.text(0.0, 1.0, "", va="top", ha="left", family="monospace", fontsize=8)
+    try:
+        ax_stats.add_patch(mpatches.FancyBboxPatch((0, 0), 1, 1, transform=ax_stats.transAxes,
+                                                  boxstyle="round,pad=0.01", facecolor="#fbfbff",
+                                                  edgecolor="#dfe6ff", linewidth=0.8, zorder=-1, clip_on=False))
+    except Exception:
+        pass
+
+    # Hardware/Status panel (just below the moved stats)
+    ax_hw = fig.add_axes([0.73, 0.46, 0.18, 0.04])
+    ax_hw.axis("off")
+    ax_hw.set_title("Hardware", loc="left", fontsize=9)
+    hw_text = ax_hw.text(0.0, 1.0, "", va="top", ha="left", family="monospace", fontsize=8)
+    try:
+        ax_hw.add_patch(mpatches.FancyBboxPatch((0, 0), 1, 1, transform=ax_hw.transAxes,
+                                               boxstyle="round,pad=0.01", facecolor="#fbfffb",
+                                               edgecolor="#d8f0d8", linewidth=0.8, zorder=-1, clip_on=False))
+    except Exception:
+        pass
+
+    # Information box (explains the measurement method). Placed above controls.
+    ax_info = fig.add_axes([0.2, 0.5, 0.5, 0.20])
+    ax_info.axis("off")
+    ax_info.set_title("Method", loc="left", fontsize=9)
+    # runtime I/O info (used below in info text; updated by stream callback later)
+    io_info = {"blocksize_actual": None}
+    info_text_str = (
+        f"Method details:\n"
+        f"- Signal: 440 Hz sine, dur={dur:.3f}s, pre={pre_silence:.2f}s, post={post_silence:.2f}s, vol={vol:.2f}; 5 ms fade-in/out.\n"
+        f"- I/O: full-duplex sd.Stream(fs={fs}, ch=1, dtype=float32, blocksize={io_info['blocksize_actual'] if io_info['blocksize_actual'] is not None else (blocksize if blocksize is not None else 'auto')}, latency={iolatency}).\n"
+        f"- Band-pass (optional): RBJ biquad centered 440 Hz, Q=8; direct-form I.\n"
+        f"- Pre-whitening: apply x[n]-0.97*x[n-1] on ref and recording.\n"
+        f"- Normalized xcorr: corr / sqrt(E_x*E_r) over valid lags; take peak index k and value.\n"
+        f"- Baseline/confidence: median/MAD of pre-silence nxc; z=(peak-med)/(1.4826*MAD+eps); conf=sigmoid(0.5*z).\n"
+        f"- SNR gate: window RMS vs pre-silence RMS; if <2x then cap conf≤0.2.\n"
+        f"- Latency (xcorr): ((k - n_pre)/fs)*1000 ms.\n"
+        f"- Latency (timeinfo): uses PortAudio DAC/ADC timestamps around onset.\n"
+        f"- Negatives are invalid → shown as NaN; conf_min and 'include low' control filtering.\n"
+        f"- Display: zero_offset subtracts current mean; rolling mean/std shown over window."
+    )
+    info_box = ax_info.text(
+        0.0, 1.0, info_text_str,
+        va="top", ha="left", fontsize=14, wrap=True,
+        bbox=dict(boxstyle="round", facecolor="#f0f6ff", edgecolor="#4C78A8", alpha=0.9)
+    )
 
     running = threading.Event()
     latest_changed = threading.Event()
     lock = threading.Lock()
     latest_conf = {"value": float("nan")}
+    last_diag = {}
+    info_visible = [True]
+
+    current_conf_min = [float(conf_min)]
+    include_low = [False]
+    zero_offset = [0.0]
+    # status/xrun counter shared with stream callback
+    xrun_counter = {"count": 0}
+    # input RMS meter shared
+    rms_info = {"rms_dbfs": float('nan'), "clip": False}
+
+    # Resolve device names for display
+    try:
+        dev_in_name = sd.query_devices(indev)["name"] if indev is not None else sd.query_devices(sd.default.device[0])["name"]
+    except Exception:
+        dev_in_name = str(indev)
+    try:
+        dev_out_name = sd.query_devices(outdev)["name"] if outdev is not None else sd.query_devices(sd.default.device[1])["name"]
+    except Exception:
+        dev_out_name = str(outdev)
 
     def compute_stats():
         data = np.array(latencies, dtype=float)
-        mean = float(np.nanmean(data)) if data.size else 0.0
-        std = float(np.nanstd(data, ddof=1)) if data.size > 1 else 0.0
-        vmin = float(np.nanmin(data)) if data.size else 0.0
-        vmax = float(np.nanmax(data)) if data.size else 0.0
-        return mean, std, vmin, vmax
+        conf = np.array(confidences, dtype=float)
+        if data.size == 0:
+            return float('nan'), 0.0, 0.0, 0.0, 0
+        # When include_low is ON, include all finite samples (even negative latencies)
+        if include_low[0]:
+            mask = np.isfinite(data)
+        else:
+            mask = np.isfinite(data) & (data >= 0.0)
+            if conf.size == data.size:
+                mask &= np.isfinite(conf) & (conf >= current_conf_min[0])
+        valid = data[mask]
+        if valid.size == 0:
+            return float('nan'), 0.0, 0.0, 0.0, 0
+        mean = float(np.nanmean(valid))
+        std = float(np.nanstd(valid, ddof=1)) if valid.size > 1 else 0.0
+        vmin = float(np.nanmin(valid))
+        vmax = float(np.nanmax(valid))
+        return mean, std, vmin, vmax, int(valid.size)
 
-    def remove_errorbar(container):
-        if container is None:
-            return
-        # container has: lines (list[Line2D] or []), caplines (list[Line2D]), barlinecols (list[LineCollection])
-        for artist in list(getattr(container, 'lines', []) or []):
-            try:
-                artist.remove()
-            except Exception:
-                pass
-        for artist in list(getattr(container, 'caplines', []) or []):
-            try:
-                artist.remove()
-            except Exception:
-                pass
-        for artist in list(getattr(container, 'barlinecols', []) or []):
-            try:
-                artist.remove()
-            except Exception:
-                pass
+    def compute_stats_all():
+        data = np.array(latencies, dtype=float)
+        if data.size == 0:
+            return float('nan'), 0
+        # 'All' means all finite samples, including negatives
+        mask = np.isfinite(data)
+        allv = data[mask]
+        if allv.size == 0:
+            return float('nan'), 0
+        return float(np.nanmean(allv)), int(allv.size)
+
+    # (removed old remove_errorbar helper; no longer needed)
 
     def update_plot():
         with lock:
-            mean, std, vmin, vmax = compute_stats()
-            bar.patches[0].set_height(mean)
-            # remove previous errorbar artists and create new ones
-            prev = nonlocal_err[0]
-            remove_errorbar(prev)
-            err_lines = ax.errorbar([0], [mean], yerr=[[std], [std]], fmt="none", ecolor="#333333", capsize=6)
-            nonlocal_err[0] = err_lines
-            vline.set_segments([[(0, vmin), (0, vmax)]])
-            c = latest_conf["value"]
-            if np.isfinite(c):
-                txt.set_text(f"Confidence: {c:.3f}")
+            # Update scatterplot with last N points
+            n = len(latencies)
+            if n > 0:
+                start = max(0, n - SCATTER_WINDOW[0])
+                idx = np.arange(start, n)
+                y = np.array(latencies[start:n], dtype=float)
+                # Apply display zero-offset
+                y = y - zero_offset[0]
+                c = np.array(confidences[start:n], dtype=float)
+                finite = np.isfinite(y)
+                thr = current_conf_min[0]
+                is_valid = finite & (y >= 0.0) & np.isfinite(c) & (c >= thr)
+                is_low = finite & ~is_valid
+                y_plot = y.copy()
+                y_plot[~finite] = np.nan
+                # No artificial floor; allow negatives when zero-offset is applied
+                # Build display mask respecting include_low and conf_min
+                if include_low[0]:
+                    disp_mask = (np.isfinite(y_plot))
+                else:
+                    disp_mask = is_valid
+                # Update confidence-graded scatter
+                x_disp = idx[disp_mask]
+                y_disp = y_plot[disp_mask]
+                c_disp = c[disp_mask]
+                if c_disp.size > 0:
+                    offs = np.column_stack([x_disp, y_disp])
+                    sc_conf.set_offsets(offs)
+                    sc_conf.set_array(c_disp.astype(float))
+                    # Marker size scaled by confidence
+                    sizes = 16.0 + 36.0 * np.clip(c_disp.astype(float), 0.0, 1.0)
+                    sc_conf.set_sizes(sizes)
+                else:
+                    sc_conf.set_offsets(np.empty((0, 2)))
+                    sc_conf.set_array(np.array([], dtype=float))
+                    sc_conf.set_sizes(np.array([], dtype=float))
+                # Clear legacy series to avoid double plotting
+                sc_valid.set_data([], [])
+                sc_low.set_data([], [])
+                # Y-axis: include zero and any negatives (when offset applied)
+                # Guard all-NaN window: if no finite data, show default axes and clear rolling overlays
+                if not np.any(np.isfinite(y_plot)):
+                    ax_sc.set_ylim(0.0, 1.0)
+                    ax_sc.set_xlim(max(0, n - SCATTER_WINDOW[0]), max(SCATTER_WINDOW[0], n))
+                    line_mean.set_data([], [])
+                    if band_poly[0] is not None:
+                        try:
+                            band_poly[0].remove()
+                        except Exception:
+                            pass
+                        band_poly[0] = None
+                    sc_last.set_data([], [])
+                    ann_last.set_text("")
+                else:
+                    y_max = float(np.nanmax(y_plot))
+                    y_min = float(np.nanmin(y_plot))
+                    y_low = min(0.0, y_min)
+                    y_high = max(1.0, y_max)
+                    pad = 0.05 * (y_high - y_low)
+                    ax_sc.set_ylim(y_low, y_high + pad)
+                # Use nice ticks and a readable formatter
+                ax_sc.yaxis.set_major_locator(mticker.MaxNLocator(nbins=6))
+                ax_sc.yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f'))
+                ax_sc.set_xlim(max(0, n - SCATTER_WINDOW[0]), max(SCATTER_WINDOW[0], n))
+
+                # Rolling mean/std band on displayed window (only if some finite data)
+                if np.any(np.isfinite(y_plot)):
+                    win = max(3, min(50, int(max(10, SCATTER_WINDOW[0] * 0.05))))
+                    y_roll = y_plot.copy()
+                    m = np.full_like(y_roll, np.nan, dtype=float)
+                    s = np.full_like(y_roll, np.nan, dtype=float)
+                    for k in range(len(y_roll)):
+                        a = max(0, k - win + 1)
+                        seg = y_roll[a:k+1]
+                        seg = seg[np.isfinite(seg)]
+                        if seg.size >= 2:
+                            m[k] = float(np.nanmean(seg))
+                            s[k] = float(np.nanstd(seg, ddof=1))
+                        elif seg.size == 1:
+                            m[k] = float(seg[0])
+                            s[k] = 0.0
+                    line_mean.set_data(idx, m)
+                    if band_poly[0] is not None:
+                        try:
+                            band_poly[0].remove()
+                        except Exception:
+                            pass
+                    upper = m + s
+                    lower = m - s
+                    band_poly[0] = ax_sc.fill_between(idx, lower, upper, color="#1f77b4", alpha=0.15, linewidth=0)
+
+                    # Latest sample highlight
+                    last_x = idx[-1]
+                    last_y = y_plot[-1] if np.isfinite(y_plot[-1]) else np.nan
+                    if np.isfinite(last_y):
+                        sc_last.set_data([last_x], [last_y])
+                        ann_last.set_position((last_x, last_y))
+                        ann_last.set_text(f"{last_y:.2f} ms")
+                    else:
+                        sc_last.set_data([], [])
+                        ann_last.set_text("")
+
+            # Update duplex waveform (left-bottom)
+            if last_diag:
+                try:
+                    fs_d = int(last_diag.get("fs", fs))
+                    rec = np.asarray(last_diag.get("rec", []), dtype=float)
+                    play = np.asarray(last_diag.get("play_buf", []), dtype=float)
+                    n_pre = int(last_diag.get("n_pre", 0))
+                    onset_idx = int(last_diag.get("onset_idx", 0))
+                    M = len(last_diag.get("ref", []))
+                    # Choose a window around onset
+                    w_before = max(M // 2, int(0.03 * fs_d))
+                    w_after = max(int(1.5 * M), int(0.06 * fs_d))
+                    s0 = max(0, onset_idx - w_before)
+                    s1 = min(len(rec), onset_idx + w_after)
+                    if s1 > s0:
+                        t_ms = (np.arange(s0, s1) - onset_idx) * 1000.0 / fs_d
+                        y_rec = rec[s0:s1]
+                        y_play = play[s0:s1] if s1 <= len(play) else play[s0:min(s1, len(play))]
+                        # Ensure same length for plotting
+                        if y_play.shape[0] != (s1 - s0):
+                            y_play = np.pad(y_play, (0, (s1 - s0) - y_play.shape[0]), mode='constant')
+                        line_rec.set_data(t_ms, y_rec)
+                        line_play.set_data(t_ms, y_play)
+                        v_on.set_xdata([0.0, 0.0])
+                        t0_ms = (n_pre - onset_idx) * 1000.0 / fs_d
+                        v_t0.set_xdata([t0_ms, t0_ms])
+                        # Y limits with padding
+                        y_min = float(np.nanmin([np.min(y_rec), np.min(y_play)]) if y_rec.size and y_play.size else -1.0)
+                        y_max = float(np.nanmax([np.max(y_rec), np.max(y_play)]) if y_rec.size and y_play.size else 1.0)
+                        if not np.isfinite(y_min) or not np.isfinite(y_max) or y_min == y_max:
+                            y_min, y_max = -1.0, 1.0
+                        pad = 0.05 * (y_max - y_min)
+                        ax_duplex.set_xlim(t_ms[0], t_ms[-1])
+                        ax_duplex.set_ylim(y_min - pad, y_max + pad)
+                except Exception:
+                    # If anything goes wrong, clear the duplex plot gracefully
+                    line_rec.set_data([], [])
+                    line_play.set_data([], [])
+
+            # Update rolling terminal (right)
+            lines = []
+            thr = current_conf_min[0]
+            for i, (lat, conf) in enumerate(zip(latencies, confidences)):
+                lat_ok = np.isfinite(lat)
+                conf_ok = np.isfinite(conf) and (conf >= thr)
+                flag = "OK " if (lat_ok and lat >= 0.0 and conf_ok) else ("LOW" if np.isfinite(conf) else "NA ")
+                lat_str = f"{lat:8.2f}" if np.isfinite(lat) else "     NaN"
+                conf_str = f"{conf:5.3f}" if np.isfinite(conf) else " NaN"
+                lines.append(f"{i:04d} | {lat_str} ms | conf={conf_str} | {flag}")
+            log_text.set_text("\n".join(lines[-LOG_WINDOW:]))
+
+            # Update stats panel (respect filters like the scatter). Stats use zero-offset adjusted values.
+            data_all = np.array(latencies, dtype=float)
+            conf_all = np.array(confidences, dtype=float)
+            thr = current_conf_min[0]
+            if include_low[0]:
+                msk = np.isfinite(data_all)
             else:
-                txt.set_text("Confidence: -")
-            if np.isfinite(mean):
-                txt_mean.set_text(f"Mean: {mean:.2f} ms")
+                msk = np.isfinite(data_all) & (data_all >= 0.0)
+                if conf_all.size == data_all.size:
+                    msk &= np.isfinite(conf_all) & (conf_all >= thr)
+            n_sel = int(np.sum(msk))
+            if n_sel >= 1:
+                adj = data_all - zero_offset[0]
+                mean_val = float(np.nanmean(adj[msk]))
+                std_val = float(np.nanstd(adj[msk], ddof=1)) if n_sel >= 2 else 0.0
+                stats_text.set_text(f"N={n_sel}  mean={mean_val:.2f} ms  std={std_val:.2f} ms")
             else:
-                txt_mean.set_text("Mean: - ms")
-            ymax = max(1.0, vmax)
-            ax.set_ylim(0.0, ymax * 1.1)
+                stats_text.set_text("N=0  mean=--  std=--")
+
+            # Update hardware/status panel
+            hw_lines = [
+                f"fs={fs} Hz, win={SCATTER_WINDOW[0]}",
+                f"conf_min={current_conf_min[0]:.2f}, include_low={'on' if include_low[0] else 'off'}",
+                f"estimator={estimator}",
+                f"indev={indev} ({dev_in_name})",
+                f"outdev={outdev} ({dev_out_name})",
+                f"blocksize={io_info['blocksize_actual'] if io_info['blocksize_actual'] is not None else (blocksize if blocksize is not None else 'auto')}",
+                f"iolatency={iolatency if iolatency is not None else 'default'}",
+                f"pre={pre_silence:.2f}s, post={post_silence:.2f}s",
+                f"xruns={xrun_counter['count']}",
+                f"inRMS={rms_info['rms_dbfs']:.1f} dBFS, clip={'YES' if rms_info['clip'] else 'no'}",
+                f"bandpass={'on' if bandpass else 'off'}",
+                f"zero_offset={zero_offset[0]:.2f} ms"
+            ]
+            hw_text.set_text("\n".join(hw_lines))
         fig.canvas.draw_idle()
 
     def worker():
         f = 440.0
         while running.is_set():
-            lat_ms, conf = measure_latency_once(f, fs, dur, vol, indev, outdev)
+            local_diag = {}
+            lat_ms, conf = measure_latency_once(
+                f, fs, dur, vol, indev, outdev,
+                pre_silence=pre_silence, post_silence=post_silence,
+                blocksize=blocksize, iolatency=iolatency,
+                estimator=estimator, xrun_counter=xrun_counter,
+                bandpass=bandpass, rms_info=rms_info, io_info=io_info, diag=local_diag
+            )
             with lock:
-                latencies.append(lat_ms)
+                # Negative latencies are physically impossible -> mark as invalid (NaN)
+                if np.isfinite(lat_ms) and lat_ms < 0.0:
+                    latencies.append(np.nan)
+                else:
+                    latencies.append(lat_ms)
                 confidences.append(conf)
                 latest_conf["value"] = conf
+                last_diag.clear()
+                last_diag.update(local_diag)
                 latest_changed.set()
 
     def on_start(event):
@@ -237,14 +669,103 @@ def run_gui(fs: int, dur: float, vol: float, indev: int | None, outdev: int | No
     def on_stop(event):
         running.clear()
 
-    start_ax = fig.add_axes([0.58, 0.02, 0.15, 0.06])
-    stop_ax = fig.add_axes([0.77, 0.02, 0.15, 0.06])
+    # (removed middle window slider control)
+
+    # Controls, two rows (no overlap)
+    slider_ax = fig.add_axes([0.08, 0.02, 0.46, 0.06])
+    cbox_ax   = fig.add_axes([0.6, 0.02, 0.12, 0.06])
+    info_ax   = fig.add_axes([0.75, 0.02, 0.08, 0.06])
+    start_ax  = fig.add_axes([0.08, 0.10, 0.10, 0.08])
+    stop_ax   = fig.add_axes([0.20, 0.10, 0.10, 0.08])
+    reset_ax  = fig.add_axes([0.32, 0.10, 0.08, 0.08])
+    save_ax   = fig.add_axes([0.42, 0.10, 0.08, 0.08])
+    zero_ax   = fig.add_axes([0.52, 0.10, 0.10, 0.08])
+    zero_clr_ax = fig.add_axes([0.64, 0.10, 0.12, 0.08])
+    btn_info = Button(info_ax, "Info")
     btn_start = Button(start_ax, "Start")
     btn_stop = Button(stop_ax, "Stop")
+    btn_reset = Button(reset_ax, "Clr")
+    btn_save = Button(save_ax, "Save")
+    btn_zero = Button(zero_ax, "Zero")
+    btn_zero_clr = Button(zero_clr_ax, "ZeroClr")
     btn_start.on_clicked(on_start)
     btn_stop.on_clicked(on_stop)
+    def on_info(event):
+        info_visible[0] = not info_visible[0]
+        ax_info.set_visible(info_visible[0])
+        fig.canvas.draw_idle()
+    btn_info.on_clicked(on_info)
 
-    nonlocal_err = [err]
+    # Now create Slider and CheckButtons after their axes exist
+    slider = Slider(slider_ax, 'conf_min', 0.0, 1.0, valinit=current_conf_min[0], valstep=0.01)
+
+    def on_slider(val):
+        current_conf_min[0] = float(val)
+        update_plot()
+    slider.on_changed(on_slider)
+
+    cbox = CheckButtons(cbox_ax, ["include low"], [include_low[0]])
+    def on_cbox(label):
+        include_low[0] = not include_low[0]
+        update_plot()
+    cbox.on_clicked(on_cbox)
+    def on_reset(event):
+        with lock:
+            latencies.clear()
+            confidences.clear()
+            latest_conf["value"] = float("nan")
+            zero_offset[0] = 0.0
+        update_plot()
+    btn_reset.on_clicked(on_reset)
+
+    def on_zero(event):
+        # Set zero_offset to current mean of selected (masked) samples
+        data_all = np.array(latencies, dtype=float)
+        conf_all = np.array(confidences, dtype=float)
+        thr = current_conf_min[0]
+        if include_low[0]:
+            msk = np.isfinite(data_all)
+        else:
+            msk = np.isfinite(data_all) & (data_all >= 0.0)
+            if conf_all.size == data_all.size:
+                msk &= np.isfinite(conf_all) & (conf_all >= thr)
+        if np.any(msk):
+            zero_offset[0] = float(np.nanmean(data_all[msk]))
+        else:
+            zero_offset[0] = 0.0
+        update_plot()
+    btn_zero.on_clicked(on_zero)
+
+    def on_zero_clr(event):
+        zero_offset[0] = 0.0
+        update_plot()
+    btn_zero_clr.on_clicked(on_zero_clr)
+
+    def on_save(event):
+        # Save current measurements to CSV with timestamped filename
+        ts = datetime.now().strftime('%Y%m%d_%H%M%S')
+        fname = f"latency_{ts}.csv"
+        try:
+            with open(fname, 'w', encoding='utf-8') as fcsv:
+                fcsv.write("# latency_440 export\n")
+                fcsv.write(f"# fs,{fs}\n")
+                fcsv.write(f"# blocksize,{blocksize}\n")
+                fcsv.write(f"# iolatency,{iolatency}\n")
+                fcsv.write(f"# estimator,{estimator}\n")
+                fcsv.write(f"# pre_silence,{pre_silence}\n")
+                fcsv.write(f"# post_silence,{post_silence}\n")
+                fcsv.write(f"# bandpass,{bandpass}\n")
+                fcsv.write("index,latency_ms,confidence\n")
+                with lock:
+                    for i, (lat, conf) in enumerate(zip(latencies, confidences)):
+                        lv = '' if not np.isfinite(lat) else f"{lat:.6f}"
+                        cv = '' if not np.isfinite(conf) else f"{conf:.6f}"
+                        fcsv.write(f"{i},{lv},{cv}\n")
+        except Exception as e:
+            print(f"Save failed: {e}")
+    btn_save.on_clicked(on_save)
+
+    # (no old errorbar state to keep)
 
     timer = fig.canvas.new_timer(interval=200)
     def on_timer():
@@ -264,10 +785,20 @@ def main():
     ap.add_argument("-v", "--volume", type=float, default=0.6, help="Lautstärke 0..1")
     ap.add_argument("--indev", type=int, default=None, help="Input-Geräteindex")
     ap.add_argument("--outdev", type=int, default=None, help="Output-Geräteindex")
-    ap.add_argument("--conf-min", type=float, default=0.3, help="Warnschwelle für Confidence")
+    ap.add_argument("--conf-min", type=float, default=0.9, help="Warnschwelle für Confidence")
+    ap.add_argument("--blocksize", type=int, default=None, help="Audio blocksize (frames), e.g. 1024/2048")
+    ap.add_argument("--iolatency", type=str, default="high", help="Audio I/O latency (seconds or preset: 'low','high')")
+    ap.add_argument("--estimator", type=str, choices=["xcorr","timeinfo"], default="xcorr", help="Latency estimator: 'xcorr' (default, robust) or 'timeinfo' (host timestamps)")
+    ap.add_argument("--pre-silence", type=float, default=0.30, help="Pre-silence before tone (s)")
+    ap.add_argument("--post-silence", type=float, default=0.60, help="Post-silence after tone (s)")
+    ap.add_argument("--bandpass", action='store_true', default=True, help="Apply 440 Hz band-pass before correlation")
+    ap.add_argument("--no-bandpass", dest='bandpass', action='store_false', help="Disable band-pass prefilter")
     args = ap.parse_args()
 
-    run_gui(args.samplerate, args.time, args.volume, args.indev, args.outdev, args.conf_min)
+    run_gui(args.samplerate, args.time, args.volume, args.indev, args.outdev,
+            args.conf_min, blocksize=args.blocksize, iolatency=args.iolatency,
+            estimator=args.estimator, pre_silence=args.pre_silence,
+            post_silence=args.post_silence, bandpass=args.bandpass)
 
 if __name__ == "__main__":
     main()