Refactoring to sort results in year/month/day forlder.

config.yaml

@@ -38,3 +38,12 @@ artifact_detection:
     energy_variation:
       enabled: true
       threshold_db: 6.0  # Energy change threshold in dB between consecutive windows (detects level changes)
+
+latency:
+  max_std_dev_ms: 0.5  # 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
+  max_duration: null  # maximum test duration in seconds (null = run until canceled)
+  buildup_threshold_percent: 5.0  # percentage change threshold for buildup detection

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_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()

src/audio_tests.py

@@ -235,11 +235,22 @@ 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:

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)
     

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

@@ -0,0 +1,361 @@
+#!/usr/bin/env python3
+import argparse
+import yaml
+import time
+import signal
+import sys
+from datetime import datetime
+from pathlib import Path
+import numpy as np
+import matplotlib.pyplot as plt
+
+sys.path.insert(0, str(Path(__file__).parent))
+from src.audio_tests import run_latency_test, find_audio_device, generate_chirp, play_and_record, calculate_latency
+
+
+class LatencyBuildupTest:
+    def __init__(self, config, measurement_interval=30, max_duration=None):
+        self.config = config
+        self.measurement_interval = measurement_interval
+        self.max_duration = max_duration
+        self.running = False
+        self.measurements = []
+        self.start_time = None
+        
+    def signal_handler(self, signum, frame):
+        print(f"\n\n{'='*70}")
+        print("TEST STOPPED - Generating final results...")
+        print(f"{'='*70}")
+        self.running = False
+        
+    def run_single_latency_measurement(self):
+        """Run a single latency measurement and return the result"""
+        try:
+            # Use existing latency test function with 1 measurement for speed
+            latency_stats = run_latency_test(self.config, num_measurements=1, 
+                                           save_plots=False, output_dir=None)
+            return latency_stats['avg']
+        except Exception as e:
+            print(f"❌ Error in latency measurement: {e}")
+            return None
+    
+    def analyze_buildup(self, latencies, timestamps):
+        """Analyze latency build-up and return analysis results"""
+        if len(latencies) < 2:
+            return {
+                'buildup_detected': False,
+                'start_latency': 0,
+                'end_latency': 0,
+                'change_ms': 0,
+                'change_percent': 0,
+                'trend': 'insufficient_data'
+            }
+        
+        start_latency = latencies[0]
+        end_latency = latencies[-1]
+        change_ms = end_latency - start_latency
+        change_percent = (change_ms / start_latency) * 100 if start_latency > 0 else 0
+        
+        # Determine if buildup occurred (±5% threshold)
+        buildup_detected = abs(change_percent) > 5.0
+        
+        # Calculate trend using linear regression
+        if len(latencies) >= 3:
+            x = np.arange(len(latencies))
+            y = np.array(latencies)
+            slope = np.polyfit(x, y, 1)[0]
+            
+            if slope > 0.01:  # Positive trend
+                trend = 'increasing'
+            elif slope < -0.01:  # Negative trend
+                trend = 'decreasing'
+            else:
+                trend = 'stable'
+        else:
+            trend = 'insufficient_data'
+        
+        return {
+            'buildup_detected': buildup_detected,
+            'start_latency': start_latency,
+            'end_latency': end_latency,
+            'change_ms': change_ms,
+            'change_percent': change_percent,
+            'trend': trend
+        }
+    
+    def plot_latency_buildup(self, timestamps, latencies, output_dir):
+        """Create and save latency over time plot"""
+        fig, ax = plt.subplots(1, 1, figsize=(12, 6))
+        
+        # Convert timestamps to relative time in seconds
+        relative_times = [(t - timestamps[0]).total_seconds() for t in timestamps]
+        
+        # Plot latency measurements
+        ax.plot(relative_times, latencies, 'b-o', markersize=4, linewidth=2, label='Latency Measurements')
+        
+        # Add trend line if we have enough data
+        if len(latencies) >= 3:
+            x = np.array(relative_times)
+            y = np.array(latencies)
+            z = np.polyfit(x, y, 1)
+            p = np.poly1d(z)
+            ax.plot(x, p(x), "r--", alpha=0.8, linewidth=2, label=f'Trend: {z[0]:.4f} ms/s')
+        
+        # Add reference line for start latency
+        ax.axhline(y=latencies[0], color='g', linestyle=':', alpha=0.7, 
+                  label=f'Start: {latencies[0]:.3f} ms')
+        
+        ax.set_xlabel('Time (seconds)', fontsize=12)
+        ax.set_ylabel('Latency (ms)', fontsize=12)
+        ax.set_title('Latency Build-up Over Time', fontsize=14, fontweight='bold')
+        ax.grid(True, alpha=0.3)
+        ax.legend()
+        
+        # Format y-axis to show reasonable precision
+        ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{x:.3f}'))
+        
+        plt.tight_layout()
+        plot_file = output_dir / 'latency_buildup_graph.png'
+        plt.savefig(plot_file, dpi=150, bbox_inches='tight')
+        plt.close()
+        
+        return plot_file
+    
+    def run_test(self):
+        """Run the latency build-up test"""
+        self.running = True
+        self.start_time = datetime.now()
+        
+        print(f"Starting latency build-up test at {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
+        print(f"Measurement interval: {self.measurement_interval} seconds")
+        if self.max_duration:
+            print(f"Maximum duration: {self.max_duration} seconds")
+        print("Press Ctrl+C to stop the test early")
+        print("=" * 70)
+        
+        # Set up signal handler for graceful shutdown
+        signal.signal(signal.SIGINT, self.signal_handler)
+        signal.signal(signal.SIGTERM, self.signal_handler)
+        
+        measurement_count = 0
+        
+        while self.running:
+            current_time = datetime.now()
+            measurement_count += 1
+            
+            print(f"\n[{current_time.strftime('%H:%M:%S')}] Measurement #{measurement_count}")
+            
+            # Perform latency measurement
+            latency = self.run_single_latency_measurement()
+            
+            if latency is not None:
+                self.measurements.append((current_time, latency))
+                timestamps, latencies = zip(*self.measurements)
+                
+                # Calculate current statistics
+                avg_latency = np.mean(latencies)
+                min_latency = np.min(latencies)
+                max_latency = np.max(latencies)
+                std_latency = np.std(latencies)
+                
+                print(f"  Current latency: {latency:.3f} ms")
+                print(f"  Average so far: {avg_latency:.3f} ms")
+                print(f"  Range: {min_latency:.3f} - {max_latency:.3f} ms")
+                print(f"  Std deviation: {std_latency:.3f} ms")
+                
+                # Analyze for buildup
+                analysis = self.analyze_buildup(latencies, timestamps)
+                if analysis['buildup_detected']:
+                    print(f"  ⚠️  BUILDUP DETECTED: {analysis['change_percent']:+.2f}% "
+                          f"({analysis['change_ms']:+.3f} ms)")
+                else:
+                    print(f"  ✅ No significant buildup: {analysis['change_percent']:+.2f}%")
+                
+                print(f"  Trend: {analysis['trend']}")
+            else:
+                print(f"  ❌ Measurement failed")
+            
+            # Check if we should continue
+            if self.max_duration:
+                elapsed = (current_time - self.start_time).total_seconds()
+                if elapsed >= self.max_duration:
+                    print(f"\nMaximum duration of {self.max_duration} seconds reached")
+                    break
+            
+            if not self.running:
+                break
+                
+            # Wait for next measurement with interruptible sleep
+            if self.running:
+                print(f"  Waiting {self.measurement_interval} seconds...")
+                # Sleep in smaller chunks to allow quick interruption
+                sleep_chunk = 1.0  # Check every second
+                time_slept = 0
+                while self.running and time_slept < self.measurement_interval:
+                    time.sleep(min(sleep_chunk, self.measurement_interval - time_slept))
+                    time_slept += sleep_chunk
+        
+        return self.generate_results()
+    
+    def generate_results(self):
+        """Generate final results and analysis"""
+        if not self.measurements:
+            return {'error': 'No measurements completed'}
+        
+        timestamps, latencies = zip(*self.measurements)
+        end_time = datetime.now()
+        total_duration = (end_time - self.start_time).total_seconds()
+        
+        # Final analysis
+        analysis = self.analyze_buildup(latencies, timestamps)
+        
+        # Statistics
+        stats = {
+            'count': len(latencies),
+            'avg_ms': float(np.mean(latencies)),
+            'min_ms': float(np.min(latencies)),
+            'max_ms': float(np.max(latencies)),
+            'std_ms': float(np.std(latencies)),
+            'range_ms': float(np.max(latencies) - np.min(latencies))
+        }
+        
+        results = {
+            'test_metadata': {
+                'start_time': self.start_time.isoformat(),
+                'end_time': end_time.isoformat(),
+                'total_duration_sec': total_duration,
+                'measurement_interval_sec': self.measurement_interval,
+                'total_measurements': len(latencies)
+            },
+            'latency_measurements': [
+                {
+                    'timestamp': t.isoformat(),
+                    'latency_ms': float(l)
+                }
+                for t, l in self.measurements
+            ],
+            'statistics': stats,
+            'buildup_analysis': analysis
+        }
+        
+        return results
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Run latency build-up test over time')
+    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='Comments about this test')
+    parser.add_argument('--config', default='config.yaml', help='Path to config file')
+    parser.add_argument('--interval', type=int, help='Measurement interval in seconds (default from config)')
+    parser.add_argument('--duration', type=int, help='Maximum test duration in seconds (default: run until canceled)')
+    
+    args = parser.parse_args()
+    
+    with open(args.config, 'r') as f:
+        config = yaml.safe_load(f)
+    
+    # Use config values as defaults if not overridden by command line
+    measurement_interval = args.interval if args.interval else config['latency_buildup']['measurement_interval']
+    max_duration = args.duration if args.duration else config['latency_buildup'].get('max_duration')
+    
+    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}_latency_buildup"
+    test_output_dir.mkdir(parents=True, exist_ok=True)
+    
+    save_plots = config['output'].get('save_plots', False)
+    
+    print("=" * 70)
+    print("LATENCY BUILD-UP 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"Measurement Interval: {measurement_interval} seconds")
+    if max_duration:
+        print(f"Maximum Duration: {max_duration} seconds")
+    else:
+        print("Duration: Run until canceled (Ctrl+C)")
+    if save_plots:
+        print(f"Plots will be saved to: {test_output_dir}")
+    print("-" * 70)
+    
+    # Create and run test
+    test = LatencyBuildupTest(config, measurement_interval=measurement_interval, max_duration=max_duration)
+    results = test.run_test()
+    
+    # Display final results
+    print("\n" + "=" * 70)
+    print("TEST COMPLETE - FINAL RESULTS")
+    print("=" * 70)
+    
+    if 'error' in results:
+        print(f"❌ Test failed: {results['error']}")
+    else:
+        metadata = results['test_metadata']
+        stats = results['statistics']
+        analysis = results['buildup_analysis']
+        
+        print(f"\n📊 Test Summary:")
+        print(f"   Duration: {metadata['total_duration_sec']:.1f} seconds")
+        print(f"   Measurements: {metadata['total_measurements']}")
+        print(f"   Interval: {metadata['measurement_interval_sec']} seconds")
+        
+        print(f"\n⏱️  Latency Statistics:")
+        print(f"   Average: {stats['avg_ms']:.3f} ms")
+        print(f"   Range: {stats['min_ms']:.3f} - {stats['max_ms']:.3f} ms")
+        print(f"   Std Dev: {stats['std_ms']:.3f} ms")
+        
+        print(f"\n📈 Build-up Analysis:")
+        print(f"   Start Latency: {analysis['start_latency']:.3f} ms")
+        print(f"   End Latency: {analysis['end_latency']:.3f} ms")
+        print(f"   Change: {analysis['change_ms']:+.3f} ms ({analysis['change_percent']:+.2f}%)")
+        print(f"   Trend: {analysis['trend']}")
+        
+        if analysis['buildup_detected']:
+            print(f"\n⚠️  BUILDUP DETECTED!")
+            print(f"   Latency changed by {abs(analysis['change_percent']):.2f}% (threshold: ±5%)")
+        else:
+            print(f"\n✅ No significant buildup detected")
+            print(f"   Latency change within acceptable range (±5%)")
+        
+        # Generate and save plot
+        if save_plots and len(results['latency_measurements']) > 1:
+            timestamps = [datetime.fromisoformat(m['timestamp']) for m in results['latency_measurements']]
+            latencies = [m['latency_ms'] for m in results['latency_measurements']]
+            
+            plot_file = test.plot_latency_buildup(timestamps, latencies, test_output_dir)
+            print(f"\n📊 Latency graph saved to: {plot_file}")
+    
+    # Save results to file
+    output_data = {
+        'metadata': {
+            'test_id': test_id,
+            'timestamp': timestamp.isoformat(),
+            'serial_number': args.serial_number,
+            'software_version': args.software_version,
+            'comment': args.comment
+        },
+        'latency_buildup_result': results
+    }
+    
+    output_file = test_output_dir / f"{test_id}_latency_buildup_results.yaml"
+    with open(output_file, 'w') as f:
+        yaml.dump(output_data, f, default_flow_style=False, sort_keys=False)
+    
+    print("\n" + "=" * 70)
+    print("✅ Results saved to:")
+    print(f"   YAML: {output_file}")
+    if save_plots and len(results.get('latency_measurements', [])) > 1:
+        print(f"   Graph: {test_output_dir}/latency_buildup_graph.png")
+    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.")