Adds null test to artifact.

ai_stuff/prompts.md

@@ -52,3 +52,72 @@ Again input it into the audio interface and measure both loopback and radio path
 
 
 
+
+============
+
+Implement Matrix test
+
+
+Test:
+
+    Fast / Robust
+
+    16k / 24k / 48k
+
+    Mono / Stereo
+
+    Presentation Delay 10 / 20 / 40 / 80
+
+
+For each combination test:
+
+    Latency
+
+    Latency buildup yes/no
+
+    Maybe: Audio quality BUT this way test gets really long.
+
+
+
+Plot a table with the results, also compare to 'baseline' measurement.
+
+Use the existing tests as a guideline how to save the results.
+
+For setting the parameters for the tests use the API:
+http://beacon29.local:5000/init
+curl -X 'POST' \  'http://beacon29.local:5000/init' \  -H 'accept: application/json' \  -H 'Content-Type: application/json' \  -d '{  "qos_config": {    "iso_int_multiple_10ms": 1,    "number_of_retransmissions": 2,    "max_transport_latency_ms": 23  },  "debug": false,  "device_name": "Auracaster",  "transport": "",  "auracast_device_address": "F0:F1:F2:F3:F4:F5",  "auracast_sampling_rate_hz": 16000,  "octets_per_frame": 160,  "frame_duration_us": 10000,  "presentation_delay_us": 10000,  "manufacturer_data": [    null,    null  ],  "immediate_rendering": false,  "assisted_listening_stream": false,  "bigs": [    {      "id": 12,      "random_address": "F1:F1:F2:F3:F4:F5",      "language": "deu",      "name": "Broadcast0",      "program_info": "Vorlesung DE",      "audio_source": "device:ch1",      "input_format": "auto",      "loop": true,      "precode_wav": false,      "iso_que_len": 1,      "num_bis": 1,      "input_gain_db": 0    }  ],  "analog_gain": 50 }'
+
+It has to have the name Broadcast0.
+qos fast is "number_of_retransmissions": 2,    "max_transport_latency_ms": 23
+qos robust is "number_of_retransmissions": 4,    "max_transport_latency_ms": 43
+Mono is "num_bis": 1
+Stereo is "num_bis": 2
+16k is "auracast_sampling_rate_hz": 16000,  "octets_per_frame": 40
+24k is "auracast_sampling_rate_hz": 24000,  "octets_per_frame": 60
+48k is "auracast_sampling_rate_hz": 48000,  "octets_per_frame": 120
+
+The results shall be plotted as a table:
+Presentation delay 10 / 20 / 40 /80
+Mono Stereo Mono Stereo Mono Stereo ...
+x
+Fast 16k
+Fast 24k
+Fast 48k
+Robust 16k
+Robust 24k
+Robust 48k
+
+For each combination you have to run the latency test. If the test fails print fail. Else print the ms value.
+Optional: Also run the build up test for 20 secs. As a result just print if there is a buildup or not.
+Optional: Also run the quality test for 3 min per combination and display the err/min.
+
+The result shall be saved as a yaml (like in all the other scripts).
+Important to save the API call aswell.
+
+And create an image with the table.
+
+There should be a feature to compare this measurement to a 'baseline' measurement.
+Failed tests should be colored red.
+Tests significantly worse than the baseline in orange.
+And better values in green.
+No change should be just white.

config.yaml

@@ -31,13 +31,38 @@ artifact_detection:
       threshold_db: -60  # Detect unexpected frequencies above noise floor + this threshold (more negative = less sensitive)
     amplitude_spikes:
       enabled: true
-      threshold_factor: 5.0  # MAD-based outlier detection on envelope (detects clicks, pops, dropouts). Lower = more sensitive.
+      threshold_factor: 10.0  # MAD-based outlier detection on envelope (detects clicks, pops, dropouts). Lower = more sensitive.
     zero_crossing:
       enabled: false
       threshold_factor: 2.0  # Number of standard deviations for zero-crossing anomalies (detects distortion)
     energy_variation:
       enabled: true
       threshold_db: 6.0  # Energy change threshold in dB between consecutive windows (detects level changes)
+    null_test:
+      enabled: true
+      # Align Ch2 (DUT) to Ch1 (Loopback), subtract, detect bursts in residual
+      max_lag_ms: 500.0          # Maximum expected delay between channels for alignment search
+      window_ms: 5.0             # Short-time RMS window length for burst detection
+      threshold_factor: 6.0      # Flag windows where residual RMS exceeds baseline × this factor
+      min_burst_ms: 0.5          # Minimum burst duration to report (filters out single-sample spikes)
+      sample_slip_detection: true
+      sample_slip_window_ms: 50.0  # Correlation window for xcorr-based lag tracking (fallback only)
+      # Sync marker: one chirp burst at the start and one at the end of the played signal.
+      # Marker-based alignment is immune to periodic-signal ambiguity (e.g. pure sine).
+      # Sample slip detection compares the lag at the start marker vs. the end marker.
+      marker_duration_sec: 0.05      # Length of each chirp marker burst
+      marker_first_offset_sec: 0.5   # Offset from signal start (and from signal end) for markers
+      marker_f0: 200.0           # Marker chirp start frequency (Hz)
+      marker_f1: 16000.0         # Marker chirp end frequency (Hz) — wider BW = sharper correlation peak
+      marker_amplitude: 0.7      # Marker amplitude (mixed on top of test tone)
+      # Sample slip threshold: only report if inter-marker lag deviates from median by >= this many samples.
+      # Rule of thumb: peak timing precision ≈ 1 / (marker_f1 - marker_f0) * sample_rate
+      # With 200-16000 Hz BW at 44100 Hz: precision ≈ 3 samples → min_slip_samples = 5 gives good margin.
+      min_slip_samples: 5
+
+latency:
+  max_std_dev_ms: 1.0  # Maximum allowed std deviation; test fails if exceeded
+  min_avg_ms: 1.0      # Minimum expected average latency; near-zero indicates bad loopback
 
 latency_buildup:
   measurement_interval: 10  # seconds between latency measurements

plot_alsa_status.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""Parse ALSA status log file and plot avail value over time."""
+
+import sys
+import re
+import os
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+
+
+TIMESTAMP_RE = re.compile(r"^===== (\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\.\d+) =====")
+AVAIL_RE = re.compile(r"^avail\s*:\s*(\d+)")
+
+
+def parse_log(log_path):
+    timestamps = []
+    avail_values = []
+
+    with open(log_path, "r") as f:
+        current_timestamp = None
+        for line in f:
+            line = line.strip()
+            
+            # Check for timestamp line
+            ts_match = TIMESTAMP_RE.match(line)
+            if ts_match:
+                current_timestamp = datetime.strptime(ts_match.group(1), "%Y-%m-%d %H:%M:%S.%f")
+                continue
+            
+            # Check for avail line (only if we have a timestamp)
+            if current_timestamp:
+                avail_match = AVAIL_RE.match(line)
+                if avail_match:
+                    timestamps.append(current_timestamp)
+                    avail_values.append(int(avail_match.group(1)))
+                    current_timestamp = None  # Reset until next timestamp
+
+    if not timestamps:
+        print("No valid timestamp/avail pairs found in the log file.", file=sys.stderr)
+        sys.exit(1)
+
+    # Convert to relative seconds from first timestamp
+    t0 = timestamps[0]
+    seconds = [(t - t0).total_seconds() for t in timestamps]
+    return seconds, avail_values
+
+
+def plot(seconds, avail_values, out_path):
+    plt.figure(figsize=(12, 6))
+    plt.plot(seconds, avail_values, label="avail", linewidth=1, alpha=0.7)
+    
+    # Add moving average (windowed mean)
+    if len(avail_values) >= 10:  # Only if we have enough data points
+        window_size = min(50, len(avail_values) // 10)  # Adaptive window size
+        import numpy as np
+        moving_avg = np.convolve(avail_values, np.ones(window_size)/window_size, mode='valid')
+        # Adjust timestamps for the moving average (they align with window centers)
+        ma_seconds = seconds[window_size-1:]
+        plt.plot(ma_seconds, moving_avg, label=f"moving mean (window={window_size})", linewidth=2)
+    
+    plt.xlabel("Time (s)")
+    plt.ylabel("Available samples")
+    plt.title("ALSA Available Samples Over Time")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    print(f"Plot saved to {out_path}")
+
+
+def main():
+    if len(sys.argv) != 2:
+        print(f"Usage: {sys.argv[0]} <path_to_alsa_status_log>", file=sys.stderr)
+        sys.exit(1)
+
+    log_path = sys.argv[1]
+    if not os.path.isfile(log_path):
+        print(f"File not found: {log_path}", file=sys.stderr)
+        sys.exit(1)
+
+    seconds, avail_values = parse_log(log_path)
+
+    log_dir = os.path.dirname(os.path.abspath(log_path))
+    log_base = os.path.splitext(os.path.basename(log_path))[0]
+    out_path = os.path.join(log_dir, f"{log_base}_avail_plot.png")
+
+    plot(seconds, avail_values, out_path)
+
+
+if __name__ == "__main__":
+    main()

plot_combined.py

@@ -0,0 +1,302 @@
+#!/usr/bin/env python3
+"""Combine ALSA avail, perf metrics, and latency plots into one figure."""
+
+import sys
+import re
+import os
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Regex patterns
+TIMESTAMP_RE = re.compile(r"^===== (\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\.\d+) =====")
+AVAIL_RE = re.compile(r"^avail\s*:\s*(\d+)")
+PERF_RE = re.compile(
+    r"^(\w+ \d+ \d+:\d+:\d+) .* Perf\(.*?\):"
+    r".*?sample mean=([\d.]+)ms"
+    r".*?write mean=([\d.]+)ms"
+    r".*?loop mean=([\d.]+)ms"
+)
+LATENCY_RE = re.compile(r"^(\w+ \d+ \d+:\d+:\d+).*latency.*?(\d+(?:\.\d+)?)ms")
+PYALSA_AVAIL_BEFORE_RE = re.compile(r"^(\w+ \d+ \d+:\d+:\d+).*PyALSA: avail before read: (\d+)")
+PYALSA_AVAIL_AFTER_RE = re.compile(r"^(\w+ \d+ \d+:\d+:\d+).*PyALSA: .* avail=(\d+)")
+
+
+def parse_alsa_status(log_path):
+    timestamps = []
+    avail_values = []
+
+    with open(log_path, "r") as f:
+        current_timestamp = None
+        for line in f:
+            line = line.strip()
+            
+            ts_match = TIMESTAMP_RE.match(line)
+            if ts_match:
+                current_timestamp = datetime.strptime(ts_match.group(1), "%Y-%m-%d %H:%M:%S.%f")
+                continue
+            
+            if current_timestamp:
+                avail_match = AVAIL_RE.match(line)
+                if avail_match:
+                    timestamps.append(current_timestamp)
+                    avail_values.append(int(avail_match.group(1)))
+                    current_timestamp = None
+
+    if not timestamps:
+        return [], []
+
+    t0 = timestamps[0]
+    seconds = [(t - t0).total_seconds() for t in timestamps]
+    return seconds, avail_values
+
+
+def parse_perf_log(log_path):
+    timestamps = []
+    sample_means = []
+    write_means = []
+    loop_means = []
+
+    with open(log_path, "r") as f:
+        for line in f:
+            m = PERF_RE.search(line)
+            if m:
+                ts_str, sample, write, loop = m.groups()
+                ts = datetime.strptime(ts_str, "%b %d %H:%M:%S")
+                timestamps.append(ts)
+                sample_means.append(float(sample))
+                write_means.append(float(write))
+                loop_means.append(float(loop))
+
+    if not timestamps:
+        return [], [], [], []
+
+    t0 = timestamps[0]
+    seconds = [(t - t0).total_seconds() for t in timestamps]
+    return seconds, sample_means, write_means, loop_means
+
+
+def parse_pyalsa_avail(perf_file):
+    """Parse PyALSA avail before/after read from the perf log file."""
+    before_timestamps = []
+    before_values = []
+    after_timestamps = []
+    after_values = []
+    
+    with open(perf_file, "r") as f:
+        for line in f:
+            line = line.strip()
+            
+            # Check for "avail before read"
+            before_match = PYALSA_AVAIL_BEFORE_RE.match(line)
+            if before_match:
+                ts_str, avail = before_match.groups()
+                current_year = datetime.now().year
+                ts_with_year = f"{current_year} {ts_str}"
+                ts = datetime.strptime(ts_with_year, "%Y %b %d %H:%M:%S")
+                before_timestamps.append(ts)
+                before_values.append(int(avail))
+                continue
+            
+            # Check for "avail=" (after read)
+            after_match = PYALSA_AVAIL_AFTER_RE.match(line)
+            if after_match:
+                ts_str, avail = after_match.groups()
+                current_year = datetime.now().year
+                ts_with_year = f"{current_year} {ts_str}"
+                ts = datetime.strptime(ts_with_year, "%Y %b %d %H:%M:%S")
+                after_timestamps.append(ts)
+                after_values.append(int(avail))
+    
+    return before_timestamps, before_values, after_timestamps, after_values
+
+
+def parse_latency_yaml(yaml_path):
+    import yaml
+    
+    with open(yaml_path, 'r') as f:
+        data = yaml.safe_load(f)
+    
+    latency_measurements = data.get('latency_buildup_result', {}).get('latency_measurements', [])
+    
+    timestamps = []
+    latencies = []
+    
+    for measurement in latency_measurements:
+        ts_str = measurement['timestamp']
+        latency = measurement['latency_ms']
+        
+        # Parse ISO format timestamp
+        ts = datetime.fromisoformat(ts_str)
+        timestamps.append(ts)
+        latencies.append(float(latency))
+    
+    if not timestamps:
+        return [], []
+    
+    t0 = timestamps[0]
+    seconds = [(t - t0).total_seconds() for t in timestamps]
+    return seconds, latencies
+
+
+def plot_combined(alsa_file, perf_file, latency_file, out_path):
+    # Parse all logs
+    alsa_seconds, avail_values = parse_alsa_status(alsa_file)
+    perf_seconds, sample_means, write_means, loop_means = parse_perf_log(perf_file)
+    latency_seconds, latencies = parse_latency_yaml(latency_file)
+    
+    # Parse PyALSA avail data
+    before_timestamps, before_values, after_timestamps, after_values = parse_pyalsa_avail(perf_file)
+
+    # Get absolute timestamps for proper alignment
+    alsa_timestamps = []
+    perf_timestamps = []
+    latency_timestamps = []
+    
+    # Re-parse to get absolute timestamps for alignment
+    with open(alsa_file, "r") as f:
+        current_timestamp = None
+        for line in f:
+            line = line.strip()
+            ts_match = TIMESTAMP_RE.match(line)
+            if ts_match:
+                current_timestamp = datetime.strptime(ts_match.group(1), "%Y-%m-%d %H:%M:%S.%f")
+                continue
+            if current_timestamp:
+                avail_match = AVAIL_RE.match(line)
+                if avail_match:
+                    alsa_timestamps.append(current_timestamp)
+                    current_timestamp = None
+    
+    with open(perf_file, "r") as f:
+        for line in f:
+            m = PERF_RE.search(line)
+            if m:
+                ts_str = m.group(1)
+                # Add current year to the timestamp since it doesn't include year
+                current_year = datetime.now().year
+                ts_with_year = f"{current_year} {ts_str}"
+                ts = datetime.strptime(ts_with_year, "%Y %b %d %H:%M:%S")
+                perf_timestamps.append(ts)
+    
+    import yaml
+    with open(latency_file, 'r') as f:
+        data = yaml.safe_load(f)
+    latency_measurements = data.get('latency_buildup_result', {}).get('latency_measurements', [])
+    for measurement in latency_measurements:
+        ts_str = measurement['timestamp']
+        ts = datetime.fromisoformat(ts_str)
+        latency_timestamps.append(ts)
+    
+    # Find earliest timestamp
+    all_abs_timestamps = []
+    if alsa_timestamps:
+        all_abs_timestamps.extend(alsa_timestamps)
+    if perf_timestamps:
+        all_abs_timestamps.extend(perf_timestamps)
+    if latency_timestamps:
+        all_abs_timestamps.extend(latency_timestamps)
+    if before_timestamps:
+        all_abs_timestamps.extend(before_timestamps)
+    if after_timestamps:
+        all_abs_timestamps.extend(after_timestamps)
+    
+    t0_absolute = min(all_abs_timestamps)
+    
+    # Convert all times to seconds from earliest timestamp
+    alsa_aligned = [(ts - t0_absolute).total_seconds() for ts in alsa_timestamps] if alsa_timestamps else []
+    perf_aligned = [(ts - t0_absolute).total_seconds() for ts in perf_timestamps] if perf_timestamps else []
+    latency_aligned = [(ts - t0_absolute).total_seconds() for ts in latency_timestamps] if latency_timestamps else []
+    before_aligned = [(ts - t0_absolute).total_seconds() for ts in before_timestamps] if before_timestamps else []
+    after_aligned = [(ts - t0_absolute).total_seconds() for ts in after_timestamps] if after_timestamps else []
+
+    # Create figure with 4 subplots sharing x-axis
+    fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(14, 12), sharex=True)
+    fig.suptitle("Combined Audio Performance Metrics", fontsize=16)
+
+    # Plot 1: ALSA avail
+    if alsa_aligned and avail_values:
+        ax1.plot(alsa_aligned, avail_values, label="avail", linewidth=1, alpha=0.7, color='blue')
+        if len(avail_values) >= 10:
+            window_size = min(50, len(avail_values) // 10)
+            moving_avg = np.convolve(avail_values, np.ones(window_size)/window_size, mode='valid')
+            ma_seconds = alsa_aligned[window_size-1:]
+            ax1.plot(ma_seconds, moving_avg, label=f"moving mean (window={window_size})", 
+                    linewidth=2, color='darkblue')
+        ax1.set_ylabel("Available samples")
+        ax1.set_title("ALSA Available Samples")
+        ax1.legend()
+        ax1.grid(True, alpha=0.3)
+
+    # Plot 2: Perf metrics
+    if perf_aligned:
+        ax2.plot(perf_aligned, sample_means, label="sample mean", linewidth=1, alpha=0.8, color='green')
+        ax2.plot(perf_aligned, write_means, label="write mean", linewidth=1, alpha=0.8, color='orange')
+        ax2.plot(perf_aligned, loop_means, label="loop mean", linewidth=1, alpha=0.8, color='red')
+        
+        # Add moving average for loop mean
+        if len(loop_means) >= 10:
+            window_size = min(50, len(loop_means) // 10)
+            moving_avg = np.convolve(loop_means, np.ones(window_size)/window_size, mode='valid')
+            ma_seconds = perf_aligned[window_size-1:]
+            ax2.plot(ma_seconds, moving_avg, label=f"loop mean moving avg (window={window_size})", 
+                    linewidth=2, color='darkred', alpha=0.9)
+        
+        ax2.set_ylabel("Duration (ms)")
+        ax2.set_title("Performance Metrics")
+        ax2.legend()
+        ax2.grid(True, alpha=0.3)
+
+    # Plot 3: Latency
+    if latency_aligned:
+        ax3.plot(latency_aligned, latencies, label="latency", linewidth=1, color='purple')
+        ax3.set_ylabel("Latency (ms)")
+        ax3.set_title("Latency Buildup")
+        ax3.legend()
+        ax3.grid(True, alpha=0.3)
+
+    # Plot 4: PyALSA avail before/after read
+    if before_aligned and before_values:
+        ax4.plot(before_aligned, before_values, label="avail before read", linewidth=1, alpha=0.7, color='cyan')
+    if after_aligned and after_values:
+        ax4.plot(after_aligned, after_values, label="avail after read", linewidth=1, alpha=0.7, color='magenta')
+    ax4.set_xlabel("Time (s)")
+    ax4.set_ylabel("Available samples")
+    ax4.set_title("PyALSA Available Samples (Before/After Read)")
+    if before_aligned or after_aligned:
+        ax4.legend()
+    ax4.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150, bbox_inches='tight')
+    print(f"Combined plot saved to {out_path}")
+    
+    # Show interactive plot
+    plt.show()
+
+
+def main():
+    if len(sys.argv) != 4:
+        print(f"Usage: {sys.argv[0]} <alsa_status.log> <perf_log.log> <latency_results.yaml>", file=sys.stderr)
+        sys.exit(1)
+
+    alsa_file = sys.argv[1]
+    perf_file = sys.argv[2]
+    latency_file = sys.argv[3]
+
+    for file_path in [alsa_file, perf_file, latency_file]:
+        if not os.path.isfile(file_path):
+            print(f"File not found: {file_path}", file=sys.stderr)
+            sys.exit(1)
+
+    # Determine output path (same directory as first file)
+    log_dir = os.path.dirname(os.path.abspath(alsa_file))
+    out_path = os.path.join(log_dir, "combined_audio_plot.png")
+
+    plot_combined(alsa_file, perf_file, latency_file, out_path)
+
+
+if __name__ == "__main__":
+    main()

plot_matrix.py

@@ -0,0 +1,437 @@
+#!/usr/bin/env python3
+"""
+Plot a results table image from a matrix test YAML file.
+
+Usage:
+    python plot_matrix.py <results.yaml>
+    python plot_matrix.py <results.yaml> --baseline <baseline.yaml>
+    python plot_matrix.py <results.yaml> --baseline <baseline.yaml> --output table.png
+"""
+import argparse
+import sys
+from typing import Optional
+import yaml
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from pathlib import Path
+from datetime import datetime
+
+# ---------------------------------------------------------------------------
+# Matrix layout constants
+# ---------------------------------------------------------------------------
+
+QOS_RATES = [
+    ('fast',   '16k'),
+    ('fast',   '24k'),
+    ('fast',   '48k'),
+    ('robust', '16k'),
+    ('robust', '24k'),
+    ('robust', '48k'),
+]
+
+CHANNELS = ['mono', 'stereo']
+PRESENTATION_DELAYS_MS = [10, 20, 40, 80]
+
+
+# ---------------------------------------------------------------------------
+# Colour helpers
+# ---------------------------------------------------------------------------
+
+COLOR_FAIL     = '#FF4444'   # red
+COLOR_WORSE    = '#FFA500'   # orange
+COLOR_BETTER   = '#66BB6A'   # green
+COLOR_NEUTRAL  = '#FFFFFF'   # white
+COLOR_MISSING  = '#DDDDDD'   # light grey – not run / no data
+COLOR_HEADER   = '#263238'   # dark blue-grey header
+COLOR_SUBHDR   = '#455A64'   # secondary header
+COLOR_ROW_EVEN = '#FAFAFA'
+COLOR_ROW_ODD  = '#F0F4F8'
+COLOR_HEADER_TEXT = '#FFFFFF'
+
+
+def _latency_ok(lat: Optional[dict]) -> bool:
+    if lat is None:
+        return False
+    if lat.get('error'):
+        return False
+    if lat.get('valid') is False:
+        return False
+    return lat.get('avg') is not None
+
+
+def _cell_color(result: dict, baseline_result: Optional[dict],
+                worse_threshold_pct: float = 10.0,
+                better_threshold_pct: float = 5.0) -> str:
+    """Return a hex colour for the cell."""
+    lat = result.get('latency')
+
+    if not _latency_ok(lat):
+        return COLOR_FAIL
+
+    if baseline_result is None:
+        return COLOR_NEUTRAL
+
+    base_lat = baseline_result.get('latency')
+    if not _latency_ok(base_lat):
+        return COLOR_NEUTRAL
+
+    current_avg = lat['avg']
+    base_avg = base_lat['avg']
+
+    if base_avg == 0:
+        return COLOR_NEUTRAL
+
+    diff_pct = (current_avg - base_avg) / base_avg * 100.0
+
+    if diff_pct > worse_threshold_pct:
+        return COLOR_WORSE
+    if diff_pct < -better_threshold_pct:
+        return COLOR_BETTER
+    return COLOR_NEUTRAL
+
+
+def _cell_text(result: dict, show_buildup: bool, show_quality: bool) -> list:
+    """Return list of text lines for a cell."""
+    lat = result.get('latency')
+    lines = []
+
+    if not _latency_ok(lat):
+        err = lat.get('error', 'FAIL') if lat else 'NO DATA'
+        short = err[:20] if len(err) > 20 else err
+        lines.append('FAIL')
+        if short and short != 'FAIL':
+            lines.append(short)
+        return lines
+
+    lines.append(f"{lat['avg']:.1f} ms")
+
+    if show_buildup:
+        bd = result.get('buildup')
+        if bd is not None:
+            detected = bd.get('buildup_detected')
+            if detected is True:
+                lines.append('buildup: YES')
+            elif detected is False:
+                lines.append('buildup: no')
+            else:
+                lines.append('buildup: n/a')
+
+    if show_quality:
+        q = result.get('quality')
+        if q is not None:
+            apm = q.get('artifacts_per_min')
+            if apm is not None:
+                lines.append(f"{apm:.1f} art/min")
+            else:
+                lines.append('quality: err')
+
+    return lines
+
+
+# ---------------------------------------------------------------------------
+# Core table builder
+# ---------------------------------------------------------------------------
+
+def build_table(
+    matrix_results: dict,
+    baseline_results: Optional[dict],
+    metadata: dict,
+    baseline_metadata: Optional[dict],
+    show_buildup: bool,
+    show_quality: bool,
+    worse_threshold_pct: float = 10.0,
+    better_threshold_pct: float = 5.0,
+) -> plt.Figure:
+    """
+    Build and return a matplotlib Figure containing the results table.
+    """
+    n_rows = len(QOS_RATES)         # 6
+    n_pd   = len(PRESENTATION_DELAYS_MS)  # 4
+    n_ch   = len(CHANNELS)          # 2
+    n_cols = n_pd * n_ch            # 8
+
+    # Determine cell height based on content rows per cell
+    lines_per_cell = 1
+    if show_buildup:
+        lines_per_cell += 1
+    if show_quality:
+        lines_per_cell += 1
+
+    cell_h = 0.5 + 0.22 * lines_per_cell   # inches
+    cell_w = 1.45                            # inches
+    row_label_w = 1.4                        # inches for row labels
+
+    hdr_h = 0.55   # top presentation-delay header row
+    sub_h = 0.38   # mono/stereo sub-header row
+
+    total_w = row_label_w + n_cols * cell_w + 0.3
+    total_h = hdr_h + sub_h + n_rows * cell_h + 1.6   # extra for title & legend
+
+    fig, ax = plt.subplots(figsize=(total_w, total_h))
+    ax.set_xlim(0, total_w)
+    ax.set_ylim(0, total_h)
+    ax.axis('off')
+
+    # coordinate helpers (y grows upward in matplotlib, so we flip)
+    def x_col(col_idx: int) -> float:
+        return row_label_w + col_idx * cell_w
+
+    def y_row(row_idx: int) -> float:
+        # row 0 = topmost data row
+        return total_h - 1.4 - hdr_h - sub_h - (row_idx + 1) * cell_h
+
+    def add_rect(x, y, w, h, facecolor, edgecolor='#90A4AE', lw=0.6, zorder=1):
+        rect = mpatches.FancyBboxPatch(
+            (x, y), w, h,
+            boxstyle='square,pad=0',
+            facecolor=facecolor, edgecolor=edgecolor, linewidth=lw, zorder=zorder)
+        ax.add_patch(rect)
+
+    def add_text(x, y, text, fontsize=8, color='black', ha='center', va='center',
+                 bold=False, wrap_lines=None):
+        weight = 'bold' if bold else 'normal'
+        if wrap_lines:
+            for i, line in enumerate(wrap_lines):
+                offset = (len(wrap_lines) - 1) / 2.0 - i
+                ax.text(x, y + offset * (fontsize * 0.014),
+                        line, fontsize=fontsize, color=color,
+                        ha=ha, va='center', fontweight=weight,
+                        clip_on=True)
+        else:
+            ax.text(x, y, text, fontsize=fontsize, color=color,
+                    ha=ha, va='center', fontweight=weight, clip_on=True)
+
+    # -----------------------------------------------------------------------
+    # Title
+    # -----------------------------------------------------------------------
+    ts = metadata.get('timestamp', '')
+    try:
+        ts_fmt = datetime.fromisoformat(ts).strftime('%Y-%m-%d %H:%M')
+    except Exception:
+        ts_fmt = ts
+
+    title_lines = [
+        f"Matrix Test Results  —  {metadata.get('test_id', '')}",
+        f"SN: {metadata.get('serial_number', 'n/a')}   SW: {metadata.get('software_version', 'n/a')}   {ts_fmt}",
+    ]
+    if metadata.get('comment'):
+        title_lines.append(f"Comment: {metadata['comment']}")
+    if baseline_metadata:
+        title_lines.append(
+            f"Baseline: {baseline_metadata.get('test_id', 'n/a')}  "
+            f"({baseline_metadata.get('timestamp', '')[:10]})"
+        )
+
+    title_y = total_h - 0.25
+    for i, line in enumerate(title_lines):
+        ax.text(total_w / 2, title_y - i * 0.28, line,
+                fontsize=9 if i == 0 else 7.5,
+                fontweight='bold' if i == 0 else 'normal',
+                ha='center', va='top', color='#1A237E')
+
+    # -----------------------------------------------------------------------
+    # Row label column header (top-left corner block)
+    # -----------------------------------------------------------------------
+    hdr_top = total_h - 1.4
+    # Spans presentation-delay header + mono/stereo sub-header
+    add_rect(0, hdr_top - hdr_h - sub_h, row_label_w, hdr_h + sub_h,
+             facecolor=COLOR_HEADER)
+    add_text(row_label_w / 2, hdr_top - (hdr_h + sub_h) / 2,
+             'QoS / Rate', fontsize=8, color=COLOR_HEADER_TEXT, bold=True)
+
+    # -----------------------------------------------------------------------
+    # Presentation-delay group headers
+    # -----------------------------------------------------------------------
+    for pd_idx, pd_ms in enumerate(PRESENTATION_DELAYS_MS):
+        col_start = pd_idx * n_ch
+        x = x_col(col_start)
+        w = cell_w * n_ch
+        add_rect(x, hdr_top - hdr_h, w, hdr_h, facecolor=COLOR_HEADER)
+        add_text(x + w / 2, hdr_top - hdr_h / 2,
+                 f'PD {pd_ms} ms', fontsize=8.5, color=COLOR_HEADER_TEXT, bold=True)
+
+    # -----------------------------------------------------------------------
+    # Mono / Stereo sub-headers
+    # -----------------------------------------------------------------------
+    sub_top = hdr_top - hdr_h
+    for col in range(n_cols):
+        ch = CHANNELS[col % n_ch]
+        x = x_col(col)
+        add_rect(x, sub_top - sub_h, cell_w, sub_h, facecolor=COLOR_SUBHDR)
+        add_text(x + cell_w / 2, sub_top - sub_h / 2,
+                 ch.capitalize(), fontsize=7.5, color=COLOR_HEADER_TEXT, bold=True)
+
+    # -----------------------------------------------------------------------
+    # Data rows
+    # -----------------------------------------------------------------------
+    for row_idx, (qos, rate) in enumerate(QOS_RATES):
+        row_bg = COLOR_ROW_EVEN if row_idx % 2 == 0 else COLOR_ROW_ODD
+
+        # Row label
+        y = y_row(row_idx)
+        add_rect(0, y, row_label_w, cell_h, facecolor=COLOR_SUBHDR if row_idx < 3 else '#37474F')
+        label = f"{'Fast' if qos == 'fast' else 'Robust'}  {rate}"
+        add_text(row_label_w / 2, y + cell_h / 2,
+                 label, fontsize=8, color=COLOR_HEADER_TEXT, bold=True)
+
+        for col_idx, (pd_ms, ch) in enumerate(
+                [(pd, ch)
+                 for pd in PRESENTATION_DELAYS_MS
+                 for ch in CHANNELS]):
+            key = f"{qos}_{rate}_{ch}_{pd_ms}ms"
+            result = matrix_results.get(key)
+            baseline_result = baseline_results.get(key) if baseline_results else None
+
+            x = x_col(col_idx)
+
+            if result is None:
+                add_rect(x, y, cell_w, cell_h, facecolor=COLOR_MISSING)
+                add_text(x + cell_w / 2, y + cell_h / 2, '—', fontsize=8)
+                continue
+
+            color = _cell_color(result, baseline_result,
+                                 worse_threshold_pct, better_threshold_pct)
+            add_rect(x, y, cell_w, cell_h, facecolor=color)
+
+            lines = _cell_text(result, show_buildup, show_quality)
+            # font size depends on how many lines
+            fs = 8.5 if len(lines) == 1 else 7.5
+            is_fail = color == COLOR_FAIL
+            txt_color = '#FFFFFF' if is_fail else '#1A1A2E'
+
+            # centre vertically
+            n = len(lines)
+            line_gap = cell_h / (n + 1)
+            for li, line in enumerate(lines):
+                line_y = y + cell_h - line_gap * (li + 1)
+                bold_line = li == 0  # first line (latency) is bold
+                ax.text(x + cell_w / 2, line_y, line,
+                        fontsize=fs if li == 0 else fs - 0.5,
+                        color=txt_color,
+                        ha='center', va='center',
+                        fontweight='bold' if bold_line else 'normal',
+                        clip_on=True)
+
+    # -----------------------------------------------------------------------
+    # Outer border for the full table
+    # -----------------------------------------------------------------------
+    table_x = 0
+    table_y = y_row(n_rows - 1)
+    table_w = row_label_w + n_cols * cell_w
+    table_h_total = hdr_top - table_y
+    rect = mpatches.Rectangle((table_x, table_y), table_w, table_h_total,
+                               fill=False, edgecolor='#37474F', linewidth=1.5)
+    ax.add_patch(rect)
+
+    # -----------------------------------------------------------------------
+    # Legend
+    # -----------------------------------------------------------------------
+    legend_y = y_row(n_rows - 1) - 0.55
+    legend_items = [
+        (COLOR_FAIL,    'FAIL / error'),
+        (COLOR_WORSE,   f'>{worse_threshold_pct:.0f}% worse than baseline'),
+        (COLOR_NEUTRAL, 'Within threshold'),
+        (COLOR_BETTER,  f'>{better_threshold_pct:.0f}% better than baseline'),
+        (COLOR_MISSING, 'Not measured'),
+    ]
+    lx = 0.2
+    for color, label in legend_items:
+        add_rect(lx, legend_y - 0.18, 0.28, 0.25, facecolor=color,
+                 edgecolor='#90A4AE', lw=0.8)
+        ax.text(lx + 0.35, legend_y - 0.055, label, fontsize=7, va='center')
+        lx += 2.2
+
+    plt.tight_layout(pad=0.1)
+    return fig
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+def load_matrix_results(path: Path) -> tuple:
+    """Load a matrix results YAML and return (matrix_results, metadata)."""
+    with open(path, 'r') as f:
+        data = yaml.safe_load(f)
+    return data.get('matrix_results', {}), data.get('metadata', {})
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Plot matrix test results as a table image')
+    parser.add_argument('results', help='Path to matrix results YAML file')
+    parser.add_argument('--baseline', default=None,
+                        help='Path to baseline matrix results YAML for comparison')
+    parser.add_argument('--output', default=None,
+                        help='Output image path (default: <results_stem>_table.png)')
+    parser.add_argument('--worse-threshold', type=float, default=10.0,
+                        help='Percent worse than baseline to colour orange (default: 10)')
+    parser.add_argument('--better-threshold', type=float, default=5.0,
+                        help='Percent better than baseline to colour green (default: 5)')
+    parser.add_argument('--dpi', type=int, default=150,
+                        help='Output image DPI (default: 150)')
+    args = parser.parse_args()
+
+    results_path = Path(args.results)
+    if not results_path.exists():
+        print(f"ERROR: Results file not found: {results_path}", file=sys.stderr)
+        sys.exit(1)
+
+    matrix_results, metadata = load_matrix_results(results_path)
+
+    baseline_results = None
+    baseline_metadata = None
+    if args.baseline:
+        baseline_path = Path(args.baseline)
+        if not baseline_path.exists():
+            print(f"ERROR: Baseline file not found: {baseline_path}", file=sys.stderr)
+            sys.exit(1)
+        baseline_results, baseline_metadata = load_matrix_results(baseline_path)
+        print(f"Comparing against baseline: {baseline_path.name}")
+
+    # Detect which optional columns are present
+    show_buildup = any(
+        r.get('buildup') is not None
+        for r in matrix_results.values()
+    )
+    show_quality = any(
+        r.get('quality') is not None
+        for r in matrix_results.values()
+    )
+
+    print(f"Results: {len(matrix_results)} combinations")
+    print(f"Show buildup column: {show_buildup}")
+    print(f"Show quality column: {show_quality}")
+
+    fig = build_table(
+        matrix_results=matrix_results,
+        baseline_results=baseline_results,
+        metadata=metadata,
+        baseline_metadata=baseline_metadata,
+        show_buildup=show_buildup,
+        show_quality=show_quality,
+        worse_threshold_pct=args.worse_threshold,
+        better_threshold_pct=args.better_threshold,
+    )
+
+    # Always save next to the results YAML
+    folder_copy = results_path.parent / f"{results_path.stem}_table.png"
+    fig.savefig(folder_copy, dpi=args.dpi, bbox_inches='tight',
+                facecolor='white', edgecolor='none')
+    print(f"Table saved to: {folder_copy}")
+
+    # If a custom --output path was given (and differs), save there too
+    if args.output:
+        output_path = Path(args.output)
+        if output_path.resolve() != folder_copy.resolve():
+            fig.savefig(output_path, dpi=args.dpi, bbox_inches='tight',
+                        facecolor='white', edgecolor='none')
+            print(f"Table also saved to: {output_path}")
+
+    plt.close(fig)
+
+
+if __name__ == '__main__':
+    main()

plot_perf_log.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+"""Parse Perf lines from a log file and plot sample mean, write mean, and loop mean over time."""
+
+import sys
+import re
+import os
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+
+
+PERF_RE = re.compile(
+    r"^(\w+ \d+ \d+:\d+:\d+) .* Perf\(.*?\):"
+    r".*?sample mean=([\d.]+)ms"
+    r".*?write mean=([\d.]+)ms"
+    r".*?loop mean=([\d.]+)ms"
+)
+
+
+def parse_log(log_path):
+    timestamps = []
+    sample_means = []
+    write_means = []
+    loop_means = []
+
+    with open(log_path, "r") as f:
+        for line in f:
+            m = PERF_RE.search(line)
+            if m:
+                ts_str, sample, write, loop = m.groups()
+                ts = datetime.strptime(ts_str, "%b %d %H:%M:%S")
+                timestamps.append(ts)
+                sample_means.append(float(sample))
+                write_means.append(float(write))
+                loop_means.append(float(loop))
+
+    if not timestamps:
+        print("No Perf lines found in the log file.", file=sys.stderr)
+        sys.exit(1)
+
+    t0 = timestamps[0]
+    seconds = [(t - t0).total_seconds() for t in timestamps]
+    return seconds, sample_means, write_means, loop_means
+
+
+def plot(seconds, sample_means, write_means, loop_means, out_path):
+    plt.figure(figsize=(12, 6))
+    plt.plot(seconds, sample_means, label="sample mean (ms)")
+    plt.plot(seconds, write_means, label="write mean (ms)")
+    plt.plot(seconds, loop_means, label="loop mean (ms)")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Duration (ms)")
+    plt.title("Perf Metrics Over Time")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    print(f"Plot saved to {out_path}")
+
+
+def main():
+    if len(sys.argv) != 2:
+        print(f"Usage: {sys.argv[0]} <path_to_log_file>", file=sys.stderr)
+        sys.exit(1)
+
+    log_path = sys.argv[1]
+    if not os.path.isfile(log_path):
+        print(f"File not found: {log_path}", file=sys.stderr)
+        sys.exit(1)
+
+    seconds, sample_means, write_means, loop_means = parse_log(log_path)
+
+    log_dir = os.path.dirname(os.path.abspath(log_path))
+    log_base = os.path.splitext(os.path.basename(log_path))[0]
+    out_path = os.path.join(log_dir, f"{log_base}_perf_plot.png")
+
+    plot(seconds, sample_means, write_means, loop_means, out_path)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -3,3 +3,4 @@ scipy>=1.10.0
 sounddevice>=0.4.6
 PyYAML>=6.0
 matplotlib>=3.7.0
+requests>=2.28.0

src/audio_tests.py

@@ -1,33 +1,34 @@
+import time
 import numpy as np
 import sounddevice as sd
 from scipy import signal
-from typing import Tuple, Dict, List
+from scipy.io import wavfile
+from typing import Tuple, Dict, List, Optional
 import matplotlib.pyplot as plt
 from pathlib import Path
 
 
 def find_audio_device(device_name: str = "Scarlett") -> tuple:
     devices = sd.query_devices()
-    
     for idx, device in enumerate(devices):
         if device_name.lower() in device['name'].lower():
             if device['max_input_channels'] >= 2 and device['max_output_channels'] >= 2:
                 return (idx, idx)
-    
+
     default_device = sd.default.device
     if hasattr(default_device, '__getitem__'):
         input_dev = int(default_device[0]) if default_device[0] is not None else 0
         output_dev = int(default_device[1]) if default_device[1] is not None else 0
     else:
         input_dev = output_dev = int(default_device) if default_device is not None else 0
-    
+
     input_info = devices[input_dev]
     output_info = devices[output_dev]
-    
+
     if input_info['max_input_channels'] >= 2 and output_info['max_output_channels'] >= 2:
         print(f"Using default device - Input: {input_info['name']}, Output: {output_info['name']}")
         return (input_dev, output_dev)
-    
+
     raise RuntimeError(f"No suitable audio device found with 2+ input/output channels")
 
 
@@ -45,11 +46,18 @@ def generate_chirp(duration: float, sample_rate: int, f0: float = 100, f1: float
 
 def play_and_record(tone: np.ndarray, sample_rate: int, device_id: tuple, channels: int = 2) -> np.ndarray:
     output_signal = np.column_stack([tone, tone])
-    
     input_dev, output_dev = device_id
-    recording = sd.playrec(output_signal, samplerate=sample_rate, 
+
-                          channels=channels, device=(input_dev, output_dev), blocking=True)
+    sd.stop()
-    
+    recording = sd.playrec(output_signal, samplerate=sample_rate,
+                           channels=channels, device=(input_dev, output_dev),
+                           latency='high', blocking=True)
+    sd.stop()
+
+    if not np.isfinite(recording).all():
+        raise RuntimeError("Recording contains NaN/Inf — ALSA stream corrupted. "
+                           "Try replugging the audio interface.")
+
     return recording
 
 
@@ -213,9 +221,15 @@ def run_latency_test(config: Dict, num_measurements: int = 5, save_plots: bool =
     channels = config['audio']['channels']
     
     device_ids = find_audio_device(device_name)
-    
+
     chirp_signal = generate_chirp(duration, sample_rate, amplitude=amplitude)
-    
+
+    # Discard one warm-up recording to flush stale ALSA ring buffer data
+    try:
+        play_and_record(chirp_signal, sample_rate, device_ids, channels)
+    except Exception:
+        pass
+
     latencies = []
     last_recording = None
     last_correlation = None
@@ -235,11 +249,22 @@ def run_latency_test(config: Dict, num_measurements: int = 5, save_plots: bool =
             last_correlation = correlation
             last_lags = lags
     
+    avg = float(np.mean(latencies))
+    std_dev = float(np.std(latencies))
+    latency_cfg = config.get('latency', {})
+    max_std_dev_ms = latency_cfg.get('max_std_dev_ms', None)
+    min_avg_ms = latency_cfg.get('min_avg_ms', None)
+    valid = True
+    if max_std_dev_ms is not None and std_dev > max_std_dev_ms:
+        valid = False
+    if min_avg_ms is not None and avg < min_avg_ms:
+        valid = False
     latency_stats = {
-        'avg': float(np.mean(latencies)),
+        'avg': avg,
         'min': float(np.min(latencies)),
         'max': float(np.max(latencies)),
-        'std': float(np.std(latencies))
+        'std': std_dev,
+        'valid': valid
     }
     
     if save_plots and output_dir and last_recording is not None:
@@ -509,6 +534,312 @@ def detect_artifacts_energy_variation(signal_data: np.ndarray, sample_rate: int,
     return artifacts
 
 
+def generate_sync_marker(sample_rate: int, duration_sec: float = 0.05,
+                          f0: float = 200.0, f1: float = 4000.0,
+                          amplitude: float = 0.7) -> np.ndarray:
+    t = np.linspace(0, duration_sec, int(sample_rate * duration_sec), endpoint=False)
+    win = np.hanning(len(t))
+    return amplitude * win * signal.chirp(t, f0, duration_sec, f1, method='linear')
+
+
+def embed_markers(base_signal: np.ndarray, marker: np.ndarray,
+                   sample_rate: int, interval_sec: float = 5.0,
+                   first_offset_sec: float = 0.5) -> Tuple[np.ndarray, List[int]]:
+    out = base_signal.copy()
+    marker_len = len(marker)
+    positions: List[int] = []
+    t = first_offset_sec
+    total_sec = len(base_signal) / sample_rate
+    while t + marker_len / sample_rate + 0.1 <= total_sec:
+        pos = int(t * sample_rate)
+        end = pos + marker_len
+        out[pos:end] = np.clip(out[pos:end] + marker, -1.0, 1.0)
+        positions.append(pos)
+        t += interval_sec
+    return out, positions
+
+
+def find_all_marker_positions(channel: np.ndarray, marker_template: np.ndarray,
+                               expected_positions: List[int], sample_rate: int,
+                               search_radius_ms: float = 500.0) -> List[Optional[int]]:
+    corr = np.abs(signal.correlate(channel, marker_template, mode='valid'))
+    search_radius = int(search_radius_ms / 1000.0 * sample_rate)
+    found: List[Optional[int]] = []
+    for expected in expected_positions:
+        lo = max(0, expected - search_radius)
+        hi = min(len(corr), expected + search_radius + 1)
+        if lo >= hi:
+            found.append(None)
+            continue
+        local_idx = int(np.argmax(corr[lo:hi]))
+        found.append(lo + local_idx)
+    return found
+
+
+def align_by_markers(reference: np.ndarray, dut: np.ndarray,
+                      marker_template: np.ndarray, expected_positions: List[int],
+                      sample_rate: int, max_lag_ms: float = 500.0) -> Tuple[np.ndarray, np.ndarray, int]:
+    ref_pos = find_all_marker_positions(reference, marker_template, expected_positions,
+                                        sample_rate, max_lag_ms)
+    dut_pos = find_all_marker_positions(dut, marker_template, expected_positions,
+                                        sample_rate, max_lag_ms)
+    lags = [dp - rp for rp, dp in zip(ref_pos, dut_pos)
+            if rp is not None and dp is not None]
+    if not lags:
+        return align_channels(reference, dut, sample_rate, max_lag_ms)
+    lag_samples = int(round(float(np.median(lags))))
+    if lag_samples >= 0:
+        ref_aligned = reference[:len(reference) - lag_samples]
+        dut_aligned = dut[lag_samples:]
+    else:
+        ref_aligned = reference[-lag_samples:]
+        dut_aligned = dut[:len(dut) + lag_samples]
+    min_len = min(len(ref_aligned), len(dut_aligned))
+    return ref_aligned[:min_len], dut_aligned[:min_len], lag_samples
+
+
+def detect_sample_slips_by_markers(reference: np.ndarray, dut: np.ndarray,
+                                    marker_template: np.ndarray,
+                                    expected_ref_positions: List[int],
+                                    sample_rate: int, lag_samples: int,
+                                    min_slip_samples: int = 3) -> List[Dict]:
+    artifacts: List[Dict] = []
+    total_duration = len(reference) / sample_rate
+    expected_dut_positions = [p + lag_samples for p in expected_ref_positions]
+    ref_found = find_all_marker_positions(reference, marker_template, expected_ref_positions,
+                                           sample_rate, search_radius_ms=200.0)
+    dut_found = find_all_marker_positions(dut, marker_template, expected_dut_positions,
+                                           sample_rate, search_radius_ms=200.0)
+    interval_lags: List[Optional[int]] = [
+        (dp - rp) if rp is not None and dp is not None else None
+        for rp, dp in zip(ref_found, dut_found)
+    ]
+    valid_lags = [v for v in interval_lags if v is not None]
+    baseline_lag = int(round(float(np.median(valid_lags)))) if valid_lags else 0
+    for i in range(len(interval_lags)):
+        if interval_lags[i] is None:
+            continue
+        delta = int(interval_lags[i]) - baseline_lag
+        if abs(delta) >= min_slip_samples:
+            time_sec = float(expected_ref_positions[i] / sample_rate)
+            if time_sec < 1.0 or time_sec > total_duration - 1.0:
+                continue
+            artifacts.append({
+                'type': 'sample_slip',
+                'time_sec': round(time_sec, 4),
+                'lag_change_samples': int(delta),
+                'lag_baseline': baseline_lag,
+                'lag_at_marker': int(interval_lags[i]),
+            })
+    return artifacts
+
+
+def align_channels(reference: np.ndarray, dut: np.ndarray, sample_rate: int,
+                   max_lag_ms: float = 500.0) -> Tuple[np.ndarray, np.ndarray, int]:
+    max_lag_samples = int(max_lag_ms / 1000.0 * sample_rate)
+
+    # Use a representative middle segment (up to 4 s) for robust correlation
+    seg_len = min(len(reference), len(dut), int(sample_rate * 4))
+    mid = min(len(reference), len(dut)) // 2
+    ref_seg = reference[mid - seg_len // 2: mid + seg_len // 2]
+    dut_seg = dut[mid - seg_len // 2: mid + seg_len // 2]
+
+    correlation = signal.correlate(dut_seg, ref_seg, mode='full')
+    lags = np.arange(-(len(ref_seg) - 1), len(ref_seg))
+    valid_mask = np.abs(lags) <= max_lag_samples
+    corr_masked = np.where(valid_mask, np.abs(correlation), 0)
+    lag_samples = int(lags[np.argmax(corr_masked)])
+
+    if lag_samples >= 0:
+        ref_aligned = reference[:len(reference) - lag_samples]
+        dut_aligned = dut[lag_samples:]
+    else:
+        ref_aligned = reference[-lag_samples:]
+        dut_aligned = dut[:len(dut) + lag_samples]
+
+    min_len = min(len(ref_aligned), len(dut_aligned))
+    return ref_aligned[:min_len], dut_aligned[:min_len], lag_samples
+
+
+def compute_residual(ref_aligned: np.ndarray, dut_aligned: np.ndarray) -> Tuple[np.ndarray, float]:
+    ref_rms = np.sqrt(np.mean(ref_aligned ** 2))
+    if ref_rms < 1e-10:
+        return dut_aligned.copy(), 1.0
+    gain = float(np.sqrt(np.mean(dut_aligned ** 2)) / ref_rms)
+    residual = dut_aligned - gain * ref_aligned
+    return residual, gain
+
+
+def detect_glitches_short_time_energy(residual: np.ndarray, sample_rate: int,
+                                       window_ms: float = 5.0,
+                                       threshold_factor: float = 6.0,
+                                       min_burst_ms: float = 0.5) -> List[Dict]:
+    from scipy.ndimage import uniform_filter1d
+    artifacts = []
+    window_samples = max(4, int(window_ms / 1000.0 * sample_rate))
+    min_burst_samples = max(1, int(min_burst_ms / 1000.0 * sample_rate))
+    total_duration = len(residual) / sample_rate
+
+    rms_envelope = np.sqrt(np.maximum(
+        uniform_filter1d(residual ** 2, size=window_samples, mode='reflect'), 0.0))
+
+    baseline_rms = np.median(rms_envelope)
+    if baseline_rms < 1e-12:
+        return artifacts
+
+    exceed = rms_envelope > threshold_factor * baseline_rms
+    changes = np.diff(exceed.astype(np.int8), prepend=0, append=0)
+    burst_starts = np.where(changes == 1)[0]
+    burst_ends = np.where(changes == -1)[0]
+
+    for bstart, bend in zip(burst_starts, burst_ends):
+        burst_len = int(bend - bstart)
+        if burst_len < min_burst_samples:
+            continue
+        time_sec = float(bstart / sample_rate)
+        if time_sec < 1.0 or time_sec > total_duration - 1.0:
+            continue
+        peak_rms = float(np.max(rms_envelope[bstart:bend]))
+        artifacts.append({
+            'type': 'null_test_glitch',
+            'time_sec': round(time_sec, 4),
+            'duration_ms': round(burst_len / sample_rate * 1000.0, 2),
+            'peak_residual_rms': round(peak_rms, 8),
+            'baseline_rms': round(float(baseline_rms), 8),
+            'deviation_factor': round(peak_rms / baseline_rms, 2)
+        })
+
+    return artifacts
+
+
+def detect_sample_slips(reference: np.ndarray, dut: np.ndarray, sample_rate: int,
+                         window_ms: float = 50.0, step_ms: float = 100.0) -> List[Dict]:
+    artifacts = []
+    window_samples = int(window_ms / 1000.0 * sample_rate)
+    step_samples = int(step_ms / 1000.0 * sample_rate)
+    max_search = min(window_samples // 4, int(0.010 * sample_rate))  # ±10 ms
+
+    min_len = min(len(reference), len(dut))
+    total_duration = min_len / sample_rate
+    if min_len < window_samples * 2:
+        return artifacts
+
+    lags_over_time = []
+    times = []
+
+    for start in range(0, min_len - window_samples, step_samples):
+        ref_seg = reference[start: start + window_samples]
+        dut_seg = dut[start: start + window_samples]
+        corr = signal.correlate(dut_seg, ref_seg, mode='full')
+        lags = np.arange(-(len(ref_seg) - 1), len(ref_seg))
+        valid = np.abs(lags) <= max_search
+        best_lag = int(lags[np.argmax(np.where(valid, np.abs(corr), 0))])
+        lags_over_time.append(best_lag)
+        times.append(float(start / sample_rate))
+
+    if len(lags_over_time) < 3:
+        return artifacts
+
+    lags_arr = np.array(lags_over_time)
+    for i in range(1, len(lags_arr)):
+        delta = int(lags_arr[i]) - int(lags_arr[i - 1])
+        if abs(delta) >= 1:
+            time_sec = times[i]
+            if time_sec < 1.0 or time_sec > total_duration - 1.0:
+                continue
+            artifacts.append({
+                'type': 'sample_slip',
+                'time_sec': round(time_sec, 4),
+                'lag_change_samples': int(delta),
+                'lag_before': int(lags_arr[i - 1]),
+                'lag_after': int(lags_arr[i])
+            })
+
+    return artifacts
+
+
+def detect_artifacts_null_test(reference: np.ndarray, dut: np.ndarray,
+                                sample_rate: int, null_test_config: Dict,
+                                marker_template: Optional[np.ndarray] = None,
+                                marker_positions: Optional[List[int]] = None) -> Dict:
+    result = {
+        'enabled': null_test_config.get('enabled', True),
+        'lag_samples': 0,
+        'lag_ms': 0.0,
+        'gain_factor': 1.0,
+        'residual_rms': 0.0,
+        'residual_peak': 0.0,
+        'total_count': 0,
+        'by_type': {},
+        'artifacts': [],
+        '_ref_aligned': None,
+        '_dut_aligned': None,
+        '_residual': None,
+    }
+
+    if not result['enabled']:
+        return result
+
+    # Guard: skip if either channel is silent/corrupted (e.g. ALSA underrun)
+    min_signal_level = 1e-4
+    if np.max(np.abs(reference)) < min_signal_level or np.max(np.abs(dut)) < min_signal_level:
+        result['error'] = 'recording_too_quiet_or_corrupted'
+        print("  ⚠  Null test skipped: one or both channels are silent (possible ALSA underrun).")
+        return result
+
+    max_lag_ms = float(null_test_config.get('max_lag_ms', 500.0))
+
+    if marker_template is not None and marker_positions:
+        ref_aligned, dut_aligned, lag_samples = align_by_markers(
+            reference, dut, marker_template, marker_positions, sample_rate, max_lag_ms)
+        alignment_method = 'marker'
+    else:
+        ref_aligned, dut_aligned, lag_samples = align_channels(
+            reference, dut, sample_rate, max_lag_ms)
+        alignment_method = 'xcorr'
+
+    result['lag_samples'] = lag_samples
+    result['lag_ms'] = round(float(lag_samples) / sample_rate * 1000.0, 3)
+    result['alignment_method'] = alignment_method
+
+    residual, gain = compute_residual(ref_aligned, dut_aligned)
+    result['gain_factor'] = round(gain, 6)
+    result['residual_rms'] = round(float(np.sqrt(np.mean(residual ** 2))), 8)
+    result['residual_peak'] = round(float(np.max(np.abs(residual))), 6)
+
+    result['_ref_aligned'] = ref_aligned
+    result['_dut_aligned'] = dut_aligned
+    result['_residual'] = residual
+
+    all_artifacts = []
+
+    window_ms = float(null_test_config.get('window_ms', 5.0))
+    threshold_factor = float(null_test_config.get('threshold_factor', 6.0))
+    min_burst_ms = float(null_test_config.get('min_burst_ms', 0.5))
+    glitches = detect_glitches_short_time_energy(
+        residual, sample_rate, window_ms, threshold_factor, min_burst_ms)
+    all_artifacts.extend(glitches)
+
+    if null_test_config.get('sample_slip_detection', True):
+        if marker_template is not None and marker_positions and len(marker_positions) >= 2:
+            min_slip_samples = int(null_test_config.get('min_slip_samples', 3))
+            slips = detect_sample_slips_by_markers(
+                reference, dut, marker_template, marker_positions, sample_rate, lag_samples,
+                min_slip_samples=min_slip_samples)
+        else:
+            slip_window_ms = float(null_test_config.get('sample_slip_window_ms', 50.0))
+            slips = detect_sample_slips(ref_aligned, dut_aligned, sample_rate, slip_window_ms)
+        all_artifacts.extend(slips)
+
+    result['total_count'] = len(all_artifacts)
+    result['artifacts'] = all_artifacts
+    for a in all_artifacts:
+        t = a['type']
+        result['by_type'][t] = result['by_type'].get(t, 0) + 1
+
+    return result
+
+
 def measure_frequency_accuracy(signal_data: np.ndarray, sample_rate: int, 
                                 expected_freq: float) -> Dict:
     """
@@ -698,6 +1029,147 @@ def plot_artifact_detection(channel_1: np.ndarray, channel_2: np.ndarray,
     plt.close()
 
 
+def plot_deviation_histogram(artifacts_ch1: Dict, artifacts_ch2: Dict, output_dir: Path):
+    def get_deviations(artifacts_dict):
+        values = []
+        for a in artifacts_dict.get('artifacts', []):
+            if 'deviation_factor' in a:
+                values.append(a['deviation_factor'])
+        return values
+
+    dev_ch1 = get_deviations(artifacts_ch1)
+    dev_ch2 = get_deviations(artifacts_ch2)
+
+    all_devs = dev_ch1 + dev_ch2
+    if not all_devs:
+        return
+
+    raw_min = min(all_devs)
+    raw_max = max(all_devs)
+    MAX_BINS = 50
+
+    if raw_max / max(raw_min, 1e-9) > 20 or (raw_max - raw_min) > MAX_BINS:
+        bins = np.logspace(np.log10(max(raw_min, 0.1)), np.log10(raw_max + 1), MAX_BINS + 1)
+        bin_labels = [f"{bins[i]:.1f}-{bins[i+1]:.1f}" for i in range(len(bins) - 1)]
+    else:
+        bin_min = int(np.floor(raw_min))
+        bin_max = int(np.ceil(raw_max)) + 1
+        bins = np.arange(bin_min, bin_max + 1)
+        if len(bins) > MAX_BINS + 1:
+            bins = np.linspace(bin_min, bin_max, MAX_BINS + 1)
+        bin_labels = [f"{bins[i]:.1f}-{bins[i+1]:.1f}" for i in range(len(bins) - 1)]
+
+    counts_ch1, _ = np.histogram(dev_ch1 if dev_ch1 else [0], bins=bins)
+    counts_ch2, _ = np.histogram(dev_ch2 if dev_ch2 else [0], bins=bins)
+
+    x = np.arange(len(bin_labels))
+    width = 0.4
+
+    fig_width = min(24, max(10, len(bin_labels) * 0.5))
+    fig, ax = plt.subplots(figsize=(fig_width, 6))
+    bars1 = ax.bar(x - width / 2, counts_ch1, width, label='Ch1 Loopback', color='steelblue', alpha=0.85)
+    bars2 = ax.bar(x + width / 2, counts_ch2, width, label='Ch2 DUT/Radio', color='tomato', alpha=0.85)
+
+    ax.set_xlabel('Deviation Factor (σ)')
+    ax.set_ylabel('Count')
+    ax.set_title('Artifact Deviation Factor Distribution')
+    ax.set_xticks(x)
+    ax.set_xticklabels(bin_labels, rotation=45, ha='right')
+    ax.yaxis.get_major_locator().set_params(integer=True)
+    ax.legend()
+    ax.grid(True, axis='y', alpha=0.3)
+
+    for bar in bars1:
+        if bar.get_height() > 0:
+            ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.05,
+                    str(int(bar.get_height())), ha='center', va='bottom', fontsize=8)
+    for bar in bars2:
+        if bar.get_height() > 0:
+            ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.05,
+                    str(int(bar.get_height())), ha='center', va='bottom', fontsize=8)
+
+    plt.tight_layout()
+    plot_file = output_dir / 'artifact_deviation_histogram.png'
+    plt.savefig(plot_file, dpi=150, bbox_inches='tight')
+    plt.close()
+
+
+def plot_null_test(null_test_result: Dict, sample_rate: int, output_dir: Path,
+                   marker_positions: Optional[List[int]] = None,
+                   marker_len_samples: int = 0):
+    from scipy.ndimage import uniform_filter1d
+
+    ref_aligned = null_test_result['_ref_aligned']
+    dut_aligned = null_test_result['_dut_aligned']
+    residual = null_test_result['_residual']
+
+    if ref_aligned is None or residual is None:
+        return
+
+    total_duration = len(ref_aligned) / sample_rate
+    fig, axes = plt.subplots(3, 1, figsize=(16, 12))
+    time = np.arange(len(ref_aligned)) / sample_rate
+
+    axes[0].plot(time, ref_aligned, alpha=0.6, linewidth=0.5, label='Ch1 Loopback (aligned)')
+    axes[0].plot(time, dut_aligned, alpha=0.6, linewidth=0.5, label='Ch2 DUT (aligned)')
+    axes[0].set_ylabel('Amplitude')
+    axes[0].set_title(
+        f'Null Test — Aligned Channels  '
+        f'(lag={null_test_result["lag_ms"]:.2f} ms, '
+        f'gain={null_test_result["gain_factor"]:.4f})')
+    axes[0].legend(loc='upper right', fontsize=8)
+    axes[0].grid(True, alpha=0.3)
+
+    axes[1].plot(time, residual, alpha=0.8, linewidth=0.4, color='purple')
+    axes[1].set_ylabel('Amplitude')
+    axes[1].set_title(
+        f'Residual  e[n] = DUT − gain·Ref  '
+        f'(RMS={null_test_result["residual_rms"]:.2e}, '
+        f'peak={null_test_result["residual_peak"]:.4f})')
+    for a in null_test_result['artifacts']:
+        color = 'red' if a['type'] == 'null_test_glitch' else 'orange'
+        axes[1].axvline(x=a['time_sec'], color=color, alpha=0.6, linewidth=1.0)
+    axes[1].grid(True, alpha=0.3)
+
+    window_samples = max(4, int(5.0 / 1000.0 * sample_rate))
+    rms_envelope = np.sqrt(np.maximum(
+        uniform_filter1d(residual ** 2, size=window_samples, mode='reflect'), 0.0))
+
+    axes[2].plot(time, rms_envelope, linewidth=0.6, color='darkgreen', label='Short-time RMS (5 ms)')
+    axes[2].set_xlabel('Time (s)')
+    axes[2].set_ylabel('Short-time RMS')
+    axes[2].set_title('Short-time RMS of Residual')
+    axes[2].grid(True, alpha=0.3)
+
+    # Determine x-window for lower two axes: exclude marker regions at start and end
+    margin_sec = 0.5
+    if marker_positions and len(marker_positions) >= 2 and marker_len_samples > 0:
+        x_min = (marker_positions[0] + marker_len_samples) / sample_rate + margin_sec
+        x_max = marker_positions[-1] / sample_rate - margin_sec
+    else:
+        x_min = margin_sec
+        x_max = total_duration - margin_sec
+
+    if x_min < x_max:
+        axes[1].set_xlim(x_min, x_max)
+        axes[2].set_xlim(x_min, x_max)
+
+        # Recompute baseline from the visible region only (unaffected by marker bursts)
+        i_min = max(0, int(x_min * sample_rate))
+        i_max = min(len(rms_envelope), int(x_max * sample_rate))
+        baseline = float(np.median(rms_envelope[i_min:i_max]))
+    else:
+        baseline = float(np.median(rms_envelope))
+
+    axes[2].axhline(y=baseline, color='steelblue', linestyle='--', linewidth=1,
+                    label=f'Baseline ({baseline:.2e})')
+    axes[2].legend(fontsize=8)
+
+    plt.tight_layout()
+    plt.savefig(output_dir / 'null_test_residual.png', dpi=150, bbox_inches='tight')
+    plt.close()
+
+
 def run_artifact_detection_test(config: Dict, save_plots: bool = False, output_dir: Path = None) -> Dict:
     import time
     
@@ -708,6 +1180,7 @@ def run_artifact_detection_test(config: Dict, save_plots: bool = False, output_d
     device_name = config['audio']['device_name']
     channels = config['audio']['channels']
     detector_config = config['artifact_detection']['detectors']
+    null_test_config = detector_config.get('null_test', {})
     startup_delay = config['artifact_detection'].get('startup_delay', 10)
     signal_type = config['artifact_detection'].get('signal_type', 'sine')
     
@@ -718,6 +1191,10 @@ def run_artifact_detection_test(config: Dict, save_plots: bool = False, output_d
         time.sleep(startup_delay)
         print("Starting recording...")
     
+    use_null_test = signal_type != 'silent' and null_test_config.get('enabled', True)
+    marker_template: Optional[np.ndarray] = None
+    marker_positions: Optional[List[int]] = []
+
     if signal_type == 'chirp':
         f0 = config['artifact_detection'].get('chirp_f0', 100)
         f1 = config['artifact_detection'].get('chirp_f1', 8000)
@@ -726,21 +1203,67 @@ def run_artifact_detection_test(config: Dict, save_plots: bool = False, output_d
         recording = play_and_record(tone, sample_rate, device_ids, channels)
     elif signal_type == 'silent':
         frequency = 1000
-        recording = sd.rec(int(duration * sample_rate), samplerate=sample_rate, 
+        recording = sd.rec(int(duration * sample_rate), samplerate=sample_rate,
                           channels=channels, device=device_ids[0], blocking=True)
     else:
         tone = generate_test_tone(frequency, duration, sample_rate, amplitude)
+        if use_null_test:
+            marker_duration_sec = float(null_test_config.get('marker_duration_sec', 0.05))
+            marker_offset_sec = float(null_test_config.get('marker_first_offset_sec', 0.5))
+            marker_template = generate_sync_marker(
+                sample_rate,
+                duration_sec=marker_duration_sec,
+                f0=float(null_test_config.get('marker_f0', 200.0)),
+                f1=float(null_test_config.get('marker_f1', 16000.0)),
+                amplitude=float(null_test_config.get('marker_amplitude', 0.7)),
+            )
+            marker_len = len(marker_template)
+            pos_start = int(marker_offset_sec * sample_rate)
+            pos_end = len(tone) - int(marker_offset_sec * sample_rate) - marker_len
+            marker_positions = [pos_start, pos_end]
+            tone = tone.copy()
+            for pos in marker_positions:
+                tone[pos:pos + marker_len] = np.clip(
+                    tone[pos:pos + marker_len] + marker_template, -1.0, 1.0)
+            print(f"  Embedded sync markers at start ({marker_offset_sec:.1f}s) and end "
+                  f"({pos_end/sample_rate:.1f}s)  "
+                  f"(chirp 200→16000 Hz, {marker_duration_sec*1000:.0f} ms)")
         recording = play_and_record(tone, sample_rate, device_ids, channels)
-    
+
     channel_1 = recording[:, 0]
     channel_2 = recording[:, 1]
-    
+
+    if output_dir:
+        wavfile.write(str(output_dir / 'channel_1_loopback_recording.wav'), sample_rate, channel_1.astype(np.float32))
+        wavfile.write(str(output_dir / 'channel_2_dut_recording.wav'), sample_rate, channel_2.astype(np.float32))
+
     artifacts_ch1 = detect_artifacts_combined(channel_1, sample_rate, frequency, detector_config)
     artifacts_ch2 = detect_artifacts_combined(channel_2, sample_rate, frequency, detector_config)
-    
+
+    null_test_result = None
+    if use_null_test:
+        print("Running null test (align → subtract → residual analysis)...")
+        null_test_result = detect_artifacts_null_test(
+            channel_1, channel_2, sample_rate, null_test_config,
+            marker_template=marker_template,
+            marker_positions=marker_positions if marker_positions else None,
+        )
+        if 'error' not in null_test_result:
+            print(f"  Lag: {null_test_result['lag_ms']:.2f} ms  "
+                  f"[{null_test_result.get('alignment_method', '?')}]  |  "
+                  f"Gain: {null_test_result['gain_factor']:.4f}  |  "
+                  f"Residual RMS: {null_test_result['residual_rms']:.2e}  |  "
+                  f"Glitches: {null_test_result['total_count']}")
+
     if save_plots and output_dir:
         plot_artifact_detection(channel_1, channel_2, artifacts_ch1, artifacts_ch2, 
                                frequency, sample_rate, output_dir)
+        plot_deviation_histogram(artifacts_ch1, artifacts_ch2, output_dir)
+
+        if null_test_result is not None and null_test_result['_residual'] is not None:
+            plot_null_test(null_test_result, sample_rate, output_dir,
+                           marker_positions=marker_positions,
+                           marker_len_samples=len(marker_template) if marker_template is not None else 0)
         
         anomalies_dir = output_dir / 'individual_anomalies'
         anomalies_dir.mkdir(exist_ok=True)
@@ -758,7 +1281,13 @@ def run_artifact_detection_test(config: Dict, save_plots: bool = False, output_d
         total_anomaly_plots = len(artifacts_ch1['artifacts']) + len(artifacts_ch2['artifacts'])
         if total_anomaly_plots > 0:
             print(f"✓ Generated {total_anomaly_plots} individual anomaly plots")
-    
+
+    null_test_serializable = None
+    if null_test_result is not None:
+        null_test_serializable = {
+            k: v for k, v in null_test_result.items() if not k.startswith('_')
+        }
+
     result = {
         'signal_type': signal_type,
         'duration_sec': float(duration),
@@ -774,6 +1303,7 @@ def run_artifact_detection_test(config: Dict, save_plots: bool = False, output_d
             'artifact_rate_per_minute': float(artifacts_ch2['total_count'] / duration * 60),
             'frequency_accuracy': artifacts_ch2['frequency_accuracy']
         },
+        'null_test': null_test_serializable,
         'detector_config': detector_config
     }
     

test_artifact_detection.py

@@ -36,10 +36,9 @@ def main():
     test_id = timestamp.strftime('%Y%m%d_%H%M%S')
     
     results_dir = Path(config['output']['results_dir'])
-    results_dir.mkdir(exist_ok=True)
     
-    test_output_dir = results_dir / f"{test_id}_artifact_detection"
+    test_output_dir = results_dir / timestamp.strftime('%Y') / timestamp.strftime('%m') / timestamp.strftime('%d') / f"{test_id}_artifact_detection"
-    test_output_dir.mkdir(exist_ok=True)
+    test_output_dir.mkdir(parents=True, exist_ok=True)
     
     save_plots = config['output'].get('save_plots', False)
     
@@ -134,13 +133,42 @@ def main():
             print(f"      Measured: {freq_acc['measured_freq_hz']:.2f} Hz")
             print(f"      Error: {freq_acc['error_hz']:+.2f} Hz ({freq_acc['error_percent']:+.3f}%)")
         
+        nt = result.get('null_test')
+        if nt and nt.get('enabled'):
+            print("\n🔬 NULL TEST (Ch2 DUT vs Ch1 Loopback reference):")
+            print(f"   Alignment lag:   {nt['lag_ms']:.2f} ms ({nt['lag_samples']} samples)")
+            print(f"   Gain factor:     {nt['gain_factor']:.4f}")
+            print(f"   Residual RMS:    {nt['residual_rms']:.2e}")
+            print(f"   Residual peak:   {nt['residual_peak']:.4f}")
+            print(f"   Glitches found:  {nt['total_count']}")
+            if nt['by_type']:
+                print("   By type:")
+                for artifact_type, count in nt['by_type'].items():
+                    print(f"      - {artifact_type}: {count}")
+            if nt['artifacts']:
+                print("   Glitch timestamps:")
+                for a in nt['artifacts'][:20]:
+                    if a['type'] == 'null_test_glitch':
+                        print(f"      {a['time_sec']:.3f}s  dur={a['duration_ms']:.1f}ms  "
+                              f"dev={a['deviation_factor']:.1f}×baseline")
+                    elif a['type'] == 'sample_slip':
+                        baseline = a.get('lag_baseline', a.get('lag_before', '?'))
+                        at = a.get('lag_at_marker', a.get('lag_after', '?'))
+                        print(f"      {a['time_sec']:.3f}s  sample_slip  "
+                              f"Δ={a['lag_change_samples']:+d} samples  "
+                              f"(baseline={baseline}, at_marker={at})")
+                if len(nt['artifacts']) > 20:
+                    print(f"      ... and {len(nt['artifacts']) - 20} more (see YAML)")
+
         ch1_count = result['channel_1_loopback']['total_artifacts']
         ch2_count = result['channel_2_dut']['total_artifacts']
         
-        if ch2_count > ch1_count:
+        if nt and nt.get('enabled') and nt['total_count'] > 0:
+            print(f"\n⚠️  NULL TEST: {nt['total_count']} glitch(es) detected in DUT path residual")
+        elif ch2_count > ch1_count:
             delta = ch2_count - ch1_count
             print(f"\n⚠️  DEGRADATION DETECTED: {delta} more artifacts in radio path vs loopback")
-        elif ch1_count == ch2_count == 0:
+        elif ch1_count == ch2_count == 0 and (not nt or nt['total_count'] == 0):
             print("\n✅ EXCELLENT: No artifacts detected in either path!")
         else:
             print(f"\nℹ️  Loopback baseline: {ch1_count} artifacts")

test_latency.py

@@ -26,10 +26,9 @@ def main():
     test_id = timestamp.strftime('%Y%m%d_%H%M%S')
     
     results_dir = Path(config['output']['results_dir'])
-    results_dir.mkdir(exist_ok=True)
     
-    test_output_dir = results_dir / f"{test_id}_latency"
+    test_output_dir = results_dir / timestamp.strftime('%Y') / timestamp.strftime('%m') / timestamp.strftime('%d') / f"{test_id}_latency"
-    test_output_dir.mkdir(exist_ok=True)
+    test_output_dir.mkdir(parents=True, exist_ok=True)
     
     save_plots = config['output'].get('save_plots', False)
     
@@ -47,7 +46,9 @@ def main():
     try:
         latency_stats = run_latency_test(config, num_measurements=args.measurements, 
                                          save_plots=save_plots, output_dir=test_output_dir)
-        print(f"✓ Latency: avg={latency_stats['avg']:.3f}ms, "
+        valid = latency_stats.get('valid', True)
+        status = "PASS" if valid else "FAIL"
+        print(f"{'✓' if valid else '✗'} Latency [{status}]: avg={latency_stats['avg']:.3f}ms, "
               f"min={latency_stats['min']:.3f}ms, max={latency_stats['max']:.3f}ms, "
               f"std={latency_stats['std']:.3f}ms")
     except Exception as e:

test_latency_buildup.py

@@ -263,10 +263,9 @@ def main():
     test_id = timestamp.strftime('%Y%m%d_%H%M%S')
     
     results_dir = Path(config['output']['results_dir'])
-    results_dir.mkdir(exist_ok=True)
     
-    test_output_dir = results_dir / f"{test_id}_latency_buildup"
+    test_output_dir = results_dir / timestamp.strftime('%Y') / timestamp.strftime('%m') / timestamp.strftime('%d') / f"{test_id}_latency_buildup"
-    test_output_dir.mkdir(exist_ok=True)
+    test_output_dir.mkdir(parents=True, exist_ok=True)
     
     save_plots = config['output'].get('save_plots', False)
     

test_matrix.py

@@ -0,0 +1,527 @@
+#!/usr/bin/env python3
+import argparse
+import copy
+import sys
+import time
+import yaml
+import requests
+import numpy as np
+from datetime import datetime
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+from src.audio_tests import run_latency_test, run_artifact_detection_test
+
+
+# ---------------------------------------------------------------------------
+# Parameter definitions
+# ---------------------------------------------------------------------------
+
+QOS_PROFILES = {
+    'fast':   {'number_of_retransmissions': 2, 'max_transport_latency_ms': 22},
+    'robust': {'number_of_retransmissions': 4, 'max_transport_latency_ms': 43},
+}
+
+SAMPLE_RATES = {
+    '16k': {'auracast_sampling_rate_hz': 16000, 'octets_per_frame': 40},
+    '24k': {'auracast_sampling_rate_hz': 24000, 'octets_per_frame': 60},
+    '48k': {'auracast_sampling_rate_hz': 48000, 'octets_per_frame': 120},
+}
+
+CHANNELS = {
+    'mono':   {'num_bis': 1},
+    'stereo': {'num_bis': 2},
+}
+
+# PRESENTATION_DELAYS_MS = [10, 20, 40, 80]
+PRESENTATION_DELAYS_MS = [10]
+
+API_URL = 'http://beacon29.local:5000/init'
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def build_api_payload(qos_name: str, rate_name: str, channel_name: str, pd_ms: int) -> dict:
+    qos = QOS_PROFILES[qos_name]
+    rate = SAMPLE_RATES[rate_name]
+    ch = CHANNELS[channel_name]
+    return {
+        'qos_config': {
+            'iso_int_multiple_10ms': 1,
+            'number_of_retransmissions': qos['number_of_retransmissions'],
+            'max_transport_latency_ms': qos['max_transport_latency_ms'],
+        },
+        'debug': False,
+        'device_name': 'Auracaster',
+        'transport': '',
+        'auracast_device_address': 'F0:F1:F2:F3:F4:F5',
+        'auracast_sampling_rate_hz': rate['auracast_sampling_rate_hz'],
+        'octets_per_frame': rate['octets_per_frame'],
+        'frame_duration_us': 10000,
+        'presentation_delay_us': pd_ms * 1000,
+        'manufacturer_data': [None, None],
+        'immediate_rendering': False,
+        'assisted_listening_stream': False,
+        'bigs': [{
+            'id': 12,
+            'random_address': 'F1:F1:F2:F3:F4:F5',
+            'language': 'deu',
+            'name': 'Broadcast0',
+            'program_info': 'Vorlesung DE',
+            'audio_source': 'device:ch1',
+            'input_format': 'auto',
+            'loop': True,
+            'precode_wav': False,
+            'iso_que_len': 1,
+            'num_bis': ch['num_bis'],
+            'input_gain_db': 0,
+        }],
+        'analog_gain': 50,
+    }
+
+
+STOP_URL = 'http://beacon29.local:5000/stop_audio'
+
+
+def stop_device(timeout: int = 10) -> None:
+    """POST to stop_audio before reconfiguring. Errors are non-fatal."""
+    try:
+        requests.post(STOP_URL, timeout=timeout,
+                      headers={'accept': 'application/json'})
+    except Exception as e:
+        print(f"  stop_audio warning: {e}")
+
+
+def configure_device(payload: dict, timeout: int = 15) -> tuple:
+    """POST the init payload to the device API. Returns (success, response_or_error)."""
+    try:
+        resp = requests.post(API_URL, json=payload, timeout=timeout,
+                             headers={'accept': 'application/json',
+                                      'Content-Type': 'application/json'})
+        resp.raise_for_status()
+        try:
+            return True, resp.json()
+        except Exception:
+            return True, resp.text
+    except Exception as e:
+        return False, str(e)
+
+
+def run_buildup_check(config: dict, duration_sec: int = 20, interval_sec: int = 1) -> dict:
+    """
+    Lightweight buildup check: take latency measurements over duration_sec seconds,
+    return analysis dict with 'buildup_detected' bool and stats.
+    """
+    measurements = []
+    t_end = time.time() + duration_sec
+
+    while time.time() < t_end:
+        try:
+            stats = run_latency_test(config, num_measurements=1, save_plots=False)
+            measurements.append(float(stats['avg']))
+        except Exception as e:
+            print(f"    buildup measurement error: {e}")
+        remaining = t_end - time.time()
+        if remaining <= 0:
+            break
+        time.sleep(min(interval_sec, remaining))
+
+    if len(measurements) < 2:
+        return {'buildup_detected': None, 'measurements': measurements,
+                'note': 'insufficient_data'}
+
+    start_l = measurements[0]
+    end_l = measurements[-1]
+    change_ms = end_l - start_l
+    change_pct = (change_ms / start_l * 100.0) if start_l > 0 else 0.0
+    buildup_detected = abs(change_pct) > 5.0
+
+    x = np.arange(len(measurements))
+    y = np.array(measurements)
+    slope = float(np.polyfit(x, y, 1)[0]) if len(measurements) >= 3 else 0.0
+    if slope > 0.01:
+        trend = 'increasing'
+    elif slope < -0.01:
+        trend = 'decreasing'
+    else:
+        trend = 'stable'
+
+    return {
+        'buildup_detected': buildup_detected,
+        'start_latency_ms': round(start_l, 3),
+        'end_latency_ms': round(end_l, 3),
+        'change_ms': round(change_ms, 3),
+        'change_percent': round(change_pct, 2),
+        'trend': trend,
+        'measurements': [round(m, 3) for m in measurements],
+    }
+
+
+def run_quality_check(config: dict, duration_sec: int = 180,
+                      output_dir: Path = None) -> dict:
+    """
+    Run artifact detection for duration_sec seconds.
+    Returns dict with artifacts_per_min and total_artifacts.
+    """
+    cfg = copy.deepcopy(config)
+    cfg['artifact_detection']['duration'] = float(duration_sec)
+    cfg['artifact_detection']['startup_delay'] = 0
+
+    try:
+        result = run_artifact_detection_test(
+            cfg,
+            save_plots=output_dir is not None,
+            output_dir=output_dir,
+        )
+        dut = result['channel_2_dut']
+        return {
+            'artifacts_per_min': round(float(dut['artifact_rate_per_minute']), 2),
+            'total_artifacts': int(dut['total_artifacts']),
+            'duration_sec': duration_sec,
+            'artifacts_by_type': dut['artifacts_by_type'],
+        }
+    except Exception as e:
+        return {'error': str(e), 'artifacts_per_min': None}
+
+
+# ---------------------------------------------------------------------------
+# USB recovery helper
+# ---------------------------------------------------------------------------
+
+def _try_usb_audio_reset(config: dict) -> None:
+    """
+    Try to recover the audio device after an ALSA xrun.
+
+    Strategy:
+      1. Reinitialize PortAudio (Pa_Terminate + Pa_Initialize) — no root needed,
+         closes all ALSA handles and reopens them cleanly.
+      2. If that fails, attempt a USB-level reset via USBDEVFS_RESET ioctl.
+         Requires either root or membership in the 'plugdev' group:
+           sudo usermod -aG plugdev $USER  (then re-login)
+      3. Always finish with a 3 s settle sleep.
+    """
+    import fcntl
+    import os
+    import re
+    import sounddevice as _sd
+
+    USBDEVFS_RESET = 0x5514
+
+    # Stop any active sounddevice stream first
+    try:
+        _sd.stop()
+    except Exception:
+        pass
+
+    # USB-level reset via ioctl (equivalent to replug)
+    device_name = config['audio'].get('device_name', 'Scarlett')
+    try:
+        with open('/proc/asound/cards') as f:
+            cards_text = f.read()
+
+        card_num = None
+        for line in cards_text.splitlines():
+            if device_name.lower() in line.lower():
+                m = re.match(r'\s*(\d+)', line)
+                if m:
+                    card_num = m.group(1)
+                    break
+
+        if card_num is not None:
+            card_sysfs = f'/sys/class/sound/card{card_num}'
+            real_path = Path(os.path.realpath(card_sysfs))
+            usb_dev_path = None
+            for parent in real_path.parents:
+                if (parent / 'idVendor').exists():
+                    usb_dev_path = parent
+                    break
+
+            if usb_dev_path is not None:
+                bus_num = int((usb_dev_path / 'busnum').read_text().strip())
+                dev_num = int((usb_dev_path / 'devnum').read_text().strip())
+                dev_file = f'/dev/bus/usb/{bus_num:03d}/{dev_num:03d}'
+                with open(dev_file, 'wb') as f:
+                    fcntl.ioctl(f, USBDEVFS_RESET, 0)
+                print(f"  Recovery: USB reset of {dev_file} OK")
+
+    except PermissionError as e:
+        print(f"  Recovery: USB reset skipped (permission denied — "
+              f"add yourself to plugdev: sudo usermod -aG plugdev $USER)")
+    except Exception as e:
+        print(f"  Recovery: USB reset skipped ({e})")
+
+    time.sleep(3)
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Run matrix test across all QoS/rate/channel/delay combinations')
+    parser.add_argument('--serial-number', required=True,
+                        help='Serial number (e.g. SN001234)')
+    parser.add_argument('--software-version', required=True,
+                        help='Software version / git commit hash')
+    parser.add_argument('--comment', default='',
+                        help='Free-text comment for this test run')
+    parser.add_argument('--config', default='config.yaml',
+                        help='Path to config file')
+    parser.add_argument('--measurements', type=int, default=5,
+                        help='Latency measurements per combination (default: 5)')
+    parser.add_argument('--settle-time', type=int, default=5,
+                        help='Seconds to wait after API call before measuring (default: 15)')
+    parser.add_argument('--buildup', action='store_true',
+                        help='Run 20 s buildup test per combination')
+    parser.add_argument('--quality', action='store_true',
+                        help='Run 3 min quality/artifact test per combination')
+    parser.add_argument('--quality-duration', type=int, default=180,
+                        help='Quality test duration in seconds (default: 180)')
+    parser.add_argument('--dry-run', action='store_true',
+                        help='Skip API calls and audio measurements (for testing the script)')
+    args = parser.parse_args()
+
+    with open(args.config, 'r') as f:
+        config = yaml.safe_load(f)
+
+    timestamp = datetime.now()
+    test_id = timestamp.strftime('%Y%m%d_%H%M%S')
+
+    results_dir = Path(config['output']['results_dir'])
+    test_output_dir = (results_dir
+                       / timestamp.strftime('%Y')
+                       / timestamp.strftime('%m')
+                       / timestamp.strftime('%d')
+                       / f"{test_id}_matrix")
+    test_output_dir.mkdir(parents=True, exist_ok=True)
+
+    # All combinations in the specified order
+    combos = [
+        (qos, rate, ch, pd)
+        for qos in ['fast', 'robust']
+        for rate in ['16k', '24k', '48k']
+        for ch in ['mono', 'stereo']
+        for pd in PRESENTATION_DELAYS_MS
+    ]
+
+    total = len(combos)
+    print("=" * 70)
+    print("MATRIX TEST")
+    print("=" * 70)
+    print(f"Test ID:          {test_id}")
+    print(f"Serial Number:    {args.serial_number}")
+    print(f"Software:         {args.software_version}")
+    if args.comment:
+        print(f"Comment:          {args.comment}")
+    print(f"Combinations:     {total}")
+    print(f"Measurements/combo: {args.measurements}")
+    print(f"Settle time:      {args.settle_time} s")
+    print(f"Buildup test:     {'yes (20 s)' if args.buildup else 'no'}")
+    print(f"Quality test:     {'yes (' + str(args.quality_duration) + ' s)' if args.quality else 'no'}")
+    if args.dry_run:
+        print("DRY RUN MODE - no API calls or audio measurements")
+    print("=" * 70)
+
+    def run_combo(qos, rate, ch, pd):
+        """Run a single combination and return its result dict."""
+        payload = build_api_payload(qos, rate, ch, pd)
+        result = {
+            'qos': qos,
+            'sample_rate': rate,
+            'channels': ch,
+            'presentation_delay_ms': pd,
+            'api_payload': payload,
+            'api_success': None,
+            'latency': None,
+            'buildup': None,
+            'quality': None,
+        }
+
+        if not args.dry_run:
+            stop_device()
+            ok, api_resp = configure_device(payload)
+            result['api_success'] = ok
+            result['api_response'] = api_resp if not ok else str(api_resp)
+
+            if not ok:
+                print(f"  API FAILED: {api_resp}")
+                result['latency'] = {'error': f'API failed: {api_resp}', 'valid': False,
+                                     'avg': None}
+                return result
+
+            print(f"  API OK  ->  settling {args.settle_time} s...")
+            time.sleep(args.settle_time)
+        else:
+            result['api_success'] = True
+
+        if not args.dry_run:
+            try:
+                lat = run_latency_test(config, num_measurements=args.measurements,
+                                       save_plots=False)
+                result['latency'] = {
+                    'avg': round(float(lat['avg']), 3),
+                    'min': round(float(lat['min']), 3),
+                    'max': round(float(lat['max']), 3),
+                    'std': round(float(lat['std']), 3),
+                    'valid': bool(lat.get('valid', True)),
+                }
+                status = "PASS" if result['latency']['valid'] else "FAIL"
+                print(f"  Latency [{status}]: avg={lat['avg']:.1f} ms  "
+                      f"std={lat['std']:.2f} ms")
+            except Exception as e:
+                result['latency'] = {'error': str(e), 'valid': False, 'avg': None}
+                print(f"  Latency ERROR: {e}")
+
+            if not result['latency'].get('valid', False):
+                print("  Latency invalid — attempting USB recovery, skipping buildup/quality.")
+                result['latency']['alsa_error'] = True
+                _try_usb_audio_reset(config)
+                return result
+        else:
+            import random
+            avg = pd + random.uniform(-1, 1)
+            result['latency'] = {'avg': round(avg, 3), 'min': round(avg - 0.5, 3),
+                                 'max': round(avg + 0.5, 3), 'std': 0.2, 'valid': True}
+
+        if args.buildup:
+            if not args.dry_run:
+                print(f"  Buildup check (20 s)...")
+                buildup = run_buildup_check(config, duration_sec=20, interval_sec=1)
+                result['buildup'] = buildup
+                bd = buildup.get('buildup_detected')
+                print(f"  Buildup: {'YES ⚠' if bd else ('NO' if bd is False else 'N/A')}")
+            else:
+                result['buildup'] = {'buildup_detected': False, 'note': 'dry_run'}
+
+        if args.quality:
+            if not args.dry_run:
+                print(f"  Quality test ({args.quality_duration} s)...")
+                combo_plot_dir = test_output_dir / f"{qos}_{rate}_{ch}_{pd}ms"
+                combo_plot_dir.mkdir(parents=True, exist_ok=True)
+                quality = run_quality_check(config, duration_sec=args.quality_duration,
+                                            output_dir=combo_plot_dir)
+                result['quality'] = quality
+                apm = quality.get('artifacts_per_min')
+                print(f"  Quality: {f'{apm:.1f} artifacts/min' if apm is not None else 'ERROR'}")
+            else:
+                result['quality'] = {'artifacts_per_min': 0.5, 'total_artifacts': 1,
+                                     'note': 'dry_run'}
+
+        return result
+
+    matrix_results = {}
+
+    for idx, (qos, rate, ch, pd) in enumerate(combos, 1):
+        key = f"{qos}_{rate}_{ch}_{pd}ms"
+        print(f"\n[{idx:2d}/{total}] {qos:6s} {rate:3s} {ch:6s} PD={pd:2d}ms")
+        matrix_results[key] = run_combo(qos, rate, ch, pd)
+
+    # --- Retry failed combinations ---
+    # ALSA/hardware failures always retry (up to 3 times) regardless of threshold.
+    # Other failures retry only if the failure rate is <= 10%.
+    def _is_failed(r):
+        lat = r.get('latency')
+        return lat is None or lat.get('valid') is False
+
+    def _is_alsa_failure(r):
+        lat = r.get('latency') or {}
+        return lat.get('alsa_error', False)
+
+    MAX_RETRIES = 3
+    for retry_round in range(1, MAX_RETRIES + 1):
+        failed_keys = [k for k, r in matrix_results.items() if _is_failed(r)]
+        if not failed_keys:
+            break
+
+        alsa_keys = [k for k in failed_keys if _is_alsa_failure(matrix_results[k])]
+        other_keys = [k for k in failed_keys if k not in alsa_keys]
+        retry_threshold = total * 0.10
+
+        keys_to_retry = list(alsa_keys)
+        if 0 < len(other_keys) <= retry_threshold:
+            keys_to_retry += other_keys
+        elif len(other_keys) > retry_threshold:
+            print(f"\n{len(other_keys)}/{total} non-hardware failures "
+                  f"({len(other_keys)/total*100:.0f}%) — above 10% threshold, skipping retry.")
+
+        if not keys_to_retry:
+            break
+
+        n_other_retrying = len(keys_to_retry) - len(alsa_keys)
+        print(f"\n{'=' * 70}")
+        print(f"RETRY ROUND {retry_round}/{MAX_RETRIES} — "
+              f"{len(keys_to_retry)} combo(s)  "
+              f"[{len(alsa_keys)} hw-error, {n_other_retrying} other]")
+        print(f"{'=' * 70}")
+        for retry_idx, key in enumerate(keys_to_retry, 1):
+            r = matrix_results[key]
+            qos, rate, ch, pd = r['qos'], r['sample_rate'], r['channels'], r['presentation_delay_ms']
+            print(f"\n[retry {retry_round}.{retry_idx}/{len(keys_to_retry)}] {qos:6s} {rate:3s} {ch:6s} PD={pd:2d}ms")
+            matrix_results[key] = run_combo(qos, rate, ch, pd)
+            matrix_results[key]['retried'] = True
+
+    # --- Save results ---
+    output_data = {
+        'metadata': {
+            'test_id': test_id,
+            'timestamp': timestamp.isoformat(),
+            'serial_number': args.serial_number,
+            'software_version': args.software_version,
+            'comment': args.comment,
+            'options': {
+                'measurements_per_combo': args.measurements,
+                'settle_time_sec': args.settle_time,
+                'buildup_enabled': args.buildup,
+                'quality_enabled': args.quality,
+                'quality_duration_sec': args.quality_duration if args.quality else None,
+            },
+        },
+        'matrix_results': matrix_results,
+    }
+
+    output_file = test_output_dir / f"{test_id}_matrix_results.yaml"
+    with open(output_file, 'w') as f:
+        yaml.dump(output_data, f, default_flow_style=False, sort_keys=False)
+
+    # --- Auto-generate table image ---
+    try:
+        from plot_matrix import build_table
+        import matplotlib.pyplot as plt
+        show_buildup = any(r.get('buildup') is not None for r in matrix_results.values())
+        show_quality = any(r.get('quality') is not None for r in matrix_results.values())
+        fig = build_table(
+            matrix_results=matrix_results,
+            baseline_results=None,
+            metadata=output_data['metadata'],
+            baseline_metadata=None,
+            show_buildup=show_buildup,
+            show_quality=show_quality,
+        )
+        plot_file = test_output_dir / f"{test_id}_matrix_results_table.png"
+        fig.savefig(plot_file, dpi=150, bbox_inches='tight',
+                    facecolor='white', edgecolor='none')
+        plt.close(fig)
+        plot_file_path = plot_file
+        print(f"Table image saved to: {plot_file}")
+    except Exception as e:
+        plot_file_path = None
+        print(f"Warning: could not auto-generate table image: {e}")
+
+    # --- Summary ---
+    passed = sum(1 for r in matrix_results.values()
+                 if r.get('latency') and r['latency'].get('valid', False))
+    failed = total - passed
+    print("\n" + "=" * 70)
+    print(f"MATRIX TEST COMPLETE  |  PASS: {passed}  FAIL: {failed}  Total: {total}")
+    print(f"Results: {output_file}")
+    if plot_file_path:
+        print(f"Table:   {plot_file_path.resolve()}")
+    print(f"Re-plot: python plot_matrix.py {output_file}")
+    print("=" * 70)
+
+
+if __name__ == '__main__':
+    main()

view_results.py

@@ -88,7 +88,7 @@ def display_results(yaml_file: Path):
 
 
 def list_all_results(results_dir: Path):
-    yaml_files = sorted(results_dir.glob("*_results.yaml"))
+    yaml_files = sorted(results_dir.rglob("*_results.yaml"))
     
     if not yaml_files:
         print("No test results found.")