Refactoring and minor improvents.

ARTIFACT_DETECTION_README.md

@@ -0,0 +1,193 @@
+# Artifact Detection Test
+
+## Overview
+This test plays a 1kHz sine wave for a configurable duration (default 60 seconds) and records both channels simultaneously:
+- **Channel 1**: Loopback path (direct audio interface connection)
+- **Channel 2**: DUT/Radio path (through beacon and radio transmission)
+
+The test detects buzzing, clicks, dropouts, and other audio artifacts using multiple configurable algorithms.
+
+## Quick Start
+
+```bash
+python test_artifact_detection.py \
+  --serial-number SN001234 \
+  --software-version abc123 \
+  --comment "Testing new firmware"
+```
+
+## Detection Algorithms
+
+The test uses four configurable detection algorithms (spectral_anomaly is **disabled by default** due to false positives):
+
+### 1. Spectral Anomaly Detection (DISABLED BY DEFAULT)
+- **Status**: ⚠️ Currently generates too many false positives - disabled by default
+- **What it detects**: Unexpected frequencies that aren't harmonics of the fundamental tone
+- **Use case**: Buzzing, interference, crosstalk
+- **Configuration**: `threshold_db` - how far below fundamental to search (-60 dB default)
+
+### 2. Amplitude Spike Detection (WORKING)
+- **What it detects**: Sudden changes in signal amplitude (RMS)
+- **Use case**: Clicks, pops, dropouts
+- **Configuration**: `threshold_factor` - number of standard deviations (3.0 default)
+
+### 3. Zero-Crossing Anomaly Detection (WORKING)
+- **What it detects**: Irregular zero-crossing patterns
+- **Use case**: Distortion, clipping, non-linear artifacts
+- **Configuration**: `threshold_factor` - number of standard deviations (2.0 default)
+
+### 4. Energy Variation Detection (WORKING)
+- **What it detects**: Rapid energy changes between time windows
+- **Use case**: Dropouts, level fluctuations, intermittent issues
+- **Configuration**: `threshold_db` - energy change threshold (6.0 dB default)
+
+## Configuration
+
+Edit `config.yaml` to customize the test:
+
+```yaml
+artifact_detection:
+  test_frequency: 1000  # Hz
+  duration: 60.0        # seconds
+  amplitude: 0.5        # 0.0 to 1.0
+  detectors:
+    spectral_anomaly:
+      enabled: true
+      threshold_db: -40
+    amplitude_spikes:
+      enabled: true
+      threshold_factor: 3.0
+    zero_crossing:
+      enabled: true
+      threshold_factor: 2.0
+    energy_variation:
+      enabled: true
+      threshold_db: 6.0
+```
+
+## Command Line Options
+
+- `--serial-number`: Serial number (required)
+- `--software-version`: Git commit hash or version (required)
+- `--comment`: Optional comments about the test
+- `--config`: Path to config file (default: config.yaml)
+- `--duration`: Override duration in seconds
+- `--frequency`: Override test frequency in Hz
+
+## Example: Quick 10-second Test
+
+```bash
+python test_artifact_detection.py \
+  --serial-number SN001234 \
+  --software-version abc123 \
+  --duration 10
+```
+
+## Example: Custom Frequency
+
+```bash
+python test_artifact_detection.py \
+  --serial-number SN001234 \
+  --software-version abc123 \
+  --frequency 440
+```
+
+## Tuning Detection Algorithms
+
+### More Sensitive Detection
+To catch more subtle artifacts, make thresholds stricter:
+
+```yaml
+detectors:
+  spectral_anomaly:
+    threshold_db: -50      # Lower = more sensitive
+  amplitude_spikes:
+    threshold_factor: 2.0  # Lower = more sensitive
+  zero_crossing:
+    threshold_factor: 1.5  # Lower = more sensitive
+  energy_variation:
+    threshold_db: 3.0      # Lower = more sensitive
+```
+
+### Less Sensitive Detection
+To reduce false positives in noisy environments:
+
+```yaml
+detectors:
+  spectral_anomaly:
+    threshold_db: -30      # Higher = less sensitive
+  amplitude_spikes:
+    threshold_factor: 4.0  # Higher = less sensitive
+  zero_crossing:
+    threshold_factor: 3.0  # Higher = less sensitive
+  energy_variation:
+    threshold_db: 10.0     # Higher = less sensitive
+```
+
+### Disable Specific Detectors
+```yaml
+detectors:
+  spectral_anomaly:
+    enabled: false  # Turn off this detector
+```
+
+## Output
+
+The test generates:
+1. **YAML results file**: `test_results/{timestamp}_artifact_detection_results.yaml`
+2. **JSON results file**: `test_results/{timestamp}_artifact_detection_results.json`
+3. **Summary plots** (if enabled): `test_results/{timestamp}_artifact_detection/`
+   - Time domain waveforms with artifact markers
+   - Frequency spectrum analysis
+4. **Individual anomaly plots**: `test_results/{timestamp}_artifact_detection/individual_anomalies/`
+   - Each anomaly plotted individually with ~20 periods of context
+   - Detailed view showing exactly what the anomaly looks like
+   - Named by channel, type, and timestamp for easy identification
+
+### Results Structure
+
+```yaml
+metadata:
+  test_id: "20260317_140530"
+  timestamp: "2026-03-17T14:05:30.123456"
+  test_type: "artifact_detection"
+  pcb_version: "v2.1"
+  pcb_revision: "A"
+  software_version: "abc123"
+
+artifact_detection_result:
+  test_frequency_hz: 1000
+  duration_sec: 60.0
+  channel_1_loopback:
+    total_artifacts: 5
+    artifact_rate_per_minute: 5.0
+    artifacts_by_type:
+      spectral_anomaly: 2
+      amplitude_spike: 3
+  channel_2_dut:
+    total_artifacts: 23
+    artifact_rate_per_minute: 23.0
+    artifacts_by_type:
+      spectral_anomaly: 8
+      amplitude_spike: 10
+      energy_variation: 5
+  detector_config: {...}
+```
+
+## Interpreting Results
+
+- **Zero artifacts in both channels**: Excellent signal quality
+- **Same artifacts in both channels**: Likely environmental interference or audio interface issue
+- **More artifacts in Channel 2 (radio path)**: Radio transmission degradation detected
+- **High spectral_anomaly count**: Interference or crosstalk
+- **High amplitude_spike count**: Clicks, pops, or dropouts
+- **High energy_variation count**: Level instability or dropouts
+
+## Comparison with Loopback Baseline
+
+The loopback path (Channel 1) serves as a baseline reference. Any additional artifacts in the radio path (Channel 2) indicate degradation introduced by the radio transmission system.
+
+Expected behavior:
+- Loopback should have minimal artifacts (ideally zero)
+- Radio path may have some artifacts due to transmission
+- Large difference indicates issues in radio hardware/firmware

QUICKSTART.md

@@ -12,19 +12,18 @@ pip install -r requirements.txt
 ### 1. Run Your First Test
 
 ```bash
-python run_test.py \
-  --pcb-version "v1.0" \
-  --pcb-revision "A" \
+python test_latency.py \
+  --serial-number "SN001234" \
   --software-version "initial" \
-  --notes "First test run"
+  --comment "First test run"
 ```
 
 **What happens:**
 - Auto-detects your Scarlett audio interface
-- Plays test tones at 7 frequencies (100 Hz to 8 kHz)
+- Plays chirp signal and measures latency (5 measurements by default)
 - Records input/output on both channels
-- Calculates latency, THD, and SNR
-- Saves results to `test_results/YYYYMMDD_HHMMSS_results.yaml`
+- Calculates average, min, max, and standard deviation of latency
+- Saves results to `test_results/YYYYMMDD_HHMMSS_latency/YYYYMMDD_HHMMSS_latency_results.yaml`
 
 ### 2. View Results
 
@@ -39,35 +38,33 @@ python view_results.py test_results/20260226_123456_results.yaml
 python view_results.py example_test_result.yaml
 ```
 
-### 3. Compare Different PCB Versions
+### 3. Compare Different Units
 
 Run multiple tests with different metadata:
 
 ```bash
-# Test PCB v1.0
-python run_test.py --pcb-version "v1.0" --pcb-revision "A" --software-version "abc123"
+# Test unit SN001234
+python test_latency.py --serial-number "SN001234" --software-version "abc123"
 
-# Test PCB v2.0  
-python run_test.py --pcb-version "v2.0" --pcb-revision "A" --software-version "abc123"
+# Test unit SN001235 with more measurements
+python test_latency.py --serial-number "SN001235" --software-version "abc123" --measurements 10
 
 # Compare by viewing both YAML files
-python view_results.py test_results/20260226_120000_results.yaml
-python view_results.py test_results/20260226_130000_results.yaml
+python view_results.py test_results/20260226_120000_latency/20260226_120000_latency_results.yaml
+python view_results.py test_results/20260226_130000_latency/20260226_130000_latency_results.yaml
 ```
 
 ## Understanding the Output
 
-Each test produces metrics at 7 frequencies:
+Each latency test produces:
 
-- **Latency (ms)**: Delay between channels (should be near 0 for loopback)
-- **THD Input (%)**: Distortion in channel 1 (lower is better)
-- **THD Output (%)**: Distortion in channel 2 (lower is better)
-- **SNR Input (dB)**: Signal quality in channel 1 (higher is better)
-- **SNR Output (dB)**: Signal quality in channel 2 (higher is better)
+- **Average Latency (ms)**: Mean delay across all measurements
+- **Min/Max Latency (ms)**: Range of measured values
+- **Standard Deviation (ms)**: Consistency of measurements (lower is better)
 
 **Good values:**
-- THD: < 0.1% (< 0.01% is excellent)
-- SNR: > 80 dB (> 90 dB is excellent)
+- Latency: Depends on your system (audio interface typically < 10ms)
+- Standard Deviation: < 1ms (consistent measurements)
 - Latency: < 5 ms for loopback
 
 ## Configuration
@@ -75,11 +72,21 @@ Each test produces metrics at 7 frequencies:
 Edit `config.yaml` to customize test parameters:
 
 ```yaml
-test_tones:
-  frequencies: [1000]  # Test only 1 kHz
-  duration: 3.0        # Shorter test (3 seconds)
+audio:
+  sample_rate: 44100
+  channels: 2
+  device_name: "Scarlett"
+
+output:
+  results_dir: "test_results"
+  save_plots: true
 ```
 
+```bash
+python -c "import sounddevice as sd; print(sd.query_devices())"
+```
+Update `device_name` in `config.yaml` to match your device.
+
 ## Troubleshooting
 
 **Audio device not found:**

README.md

@@ -4,8 +4,8 @@ Simple Python-based testing system for PCB audio hardware validation.
 
 ## Features
 
-- **Automated Testing**: Latency, THD, and SNR measurements across multiple frequencies
-- **Metadata Tracking**: PCB version, revision, software version, timestamps, notes
+- **Automated Testing**: Latency measurements with configurable iterations
+- **Metadata Tracking**: Serial number, software version, timestamps, comments
 - **YAML Output**: Human-readable structured results
 - **Simple Workflow**: Run tests, view results, compare versions
 
@@ -19,12 +19,20 @@ pip install -r requirements.txt
 
 ### 2. Run a Test
 
+**Latency Test:**
 ```bash
-python run_test.py \
-  --pcb-version "v2.1" \
-  --pcb-revision "A" \
+python test_latency.py \
+  --serial-number "SN001234" \
   --software-version "a3f2b1c" \
-  --notes "Replaced capacitor C5"
+  --comment "Replaced capacitor C5"
+```
+
+**Artifact Detection Test:**
+```bash
+python test_artifact_detection.py \
+  --serial-number "SN001234" \
+  --software-version "a3f2b1c" \
+  --comment "Baseline test"
 ```
 
 ### 3. View Results
@@ -43,10 +51,8 @@ python view_results.py test_results/*.yaml | tail -1
 ## Test Metrics
 
 - **Latency**: Round-trip delay between input and output channels (ms)
-- **THD**: Total Harmonic Distortion for input and output (%)
-- **SNR**: Signal-to-Noise Ratio for input and output (dB)
-
-Tests run at multiple frequencies: 100 Hz, 250 Hz, 500 Hz, 1 kHz, 2 kHz, 4 kHz, 8 kHz
+  - Average, minimum, maximum, and standard deviation across measurements
+  - Uses chirp signal for accurate cross-correlation measurement
 
 ## Output Format
 
@@ -56,27 +62,35 @@ Results are saved as YAML files in `test_results/`:
 metadata:
   test_id: 20260226_123456
   timestamp: '2026-02-26T12:34:56.789012'
-  pcb_version: v2.1
-  pcb_revision: A
+  serial_number: SN001234
   software_version: a3f2b1c
-  notes: Replaced capacitor C5
-test_results:
-  - frequency_hz: 1000
-    latency_ms: 2.345
-    thd_input_percent: 0.012
-    thd_output_percent: 0.034
-    snr_input_db: 92.5
-    snr_output_db: 89.2
+  comment: Replaced capacitor C5
+latency_test:
+  avg: 2.345
+  min: 2.201
+  max: 2.489
+  std: 0.087
 ```
 
 ## Configuration
 
 Edit `config.yaml` to customize:
 - Audio device settings
-- Test frequencies
-- Test duration
 - Output options
 
+```yaml
+audio:
+  sample_rate: 44100
+  channels: 2
+  device_name: "Scarlett"
+
+output:
+  results_dir: "test_results"
+  save_plots: true
+```
+
+The system auto-detects Focusrite Scarlett audio interfaces.
+
 ## Hardware Setup
 
 ```
@@ -84,19 +98,19 @@ Laptop <-> Audio Interface (Scarlett) <-> DUT <-> Audio Interface (Scarlett) <->
          Output Channels 1&2         Input Channels 1&2
 ```
 
-The system auto-detects Focusrite Scarlett audio interfaces.
-
 ## File Structure
 
 ```
 closed_loop_audio_test_suite/
 ├── config.yaml              # Test configuration
-├── run_test.py              # Main test runner
+├── test_latency.py          # Latency test runner
+├── test_artifact_detection.py  # Artifact detection test
 ├── view_results.py          # Results viewer
 ├── src/
 │   └── audio_tests.py       # Core test functions
 └── test_results/            # YAML output files
-    └── YYYYMMDD_HHMMSS_results.yaml
+    ├── YYYYMMDD_HHMMSS_latency/
+    └── YYYYMMDD_HHMMSS_artifact_detection/
 ```
 
 ## Tips

ai_stuff/prompts.md

@@ -35,4 +35,20 @@ Play a sine in different frequencies, and for every frequency 5 sec long and do
 
 Dont do fourier yet.
 
-Do a simple project.
+Do a simple project.
+
+
+
+
+---- 
+
+I want you to write a new test:
+Put a 1khz sine into the system and record both channels for x seconds e.g. 60. 
+I want you to detect buzzing and other artifacts in the recording.
+Give me a number how many artifacts you found.
+Make the detection algorithm configurable, so we can try different approaches.
+
+Again input it into the audio interface and measure both loopback and radio path like in the other test.
+
+
+

config.yaml

@@ -7,7 +7,7 @@ audio:
   
 test_tones:
   frequencies: [100, 250, 500, 1000, 2000, 4000, 8000]  # Hz
-  duration: 5.0  # seconds per frequency
+  duration: 10.0  # seconds per frequency
   amplitude: 0.5  # 0.0 to 1.0
   latency_runs: 5  # Number of latency measurements to average
 
@@ -15,3 +15,26 @@ output:
   results_dir: "test_results"
   save_plots: true
   save_raw_audio: false
+
+artifact_detection:
+  test_frequency: 1000  # Hz - Test tone frequency (for sine wave mode)
+  duration: 60.0  # seconds - Recording duration
+  amplitude: 0.5  # 0.0 to 1.0
+  startup_delay: 0  # seconds - Wait before starting recording to let system settle
+  # Chirp signal parameters (used when --signal-type chirp is specified)
+  chirp_f0: 100  # Hz - Chirp start frequency
+  chirp_f1: 8000  # Hz - Chirp end frequency
+  # NOTE: All detectors skip the first and last 1 second of recording to avoid startup/shutdown transients
+  detectors:
+    spectral_anomaly:
+      enabled: false  # DISABLED - generates too many false positives, needs better algorithm
+      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.
+    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)

example_test_result.yaml

@@ -1,10 +1,9 @@
 metadata:
   test_id: 20260226_123456
   timestamp: '2026-02-26T12:34:56.789012'
-  pcb_version: v2.1
-  pcb_revision: A
+  serial_number: SN001234
   software_version: a3f2b1c8d9e
-  notes: Baseline test with new capacitor values
+  comment: Baseline test with new capacitor values
 test_results:
   - frequency_hz: 100
     latency_ms: 2.341

src/audio_tests.py

@@ -279,3 +279,512 @@ def plot_latency_test(channel_1: np.ndarray, channel_2: np.ndarray, correlation:
     plot_file = output_dir / 'latency_chirp_analysis.png'
     plt.savefig(plot_file, dpi=150, bbox_inches='tight')
     plt.close()
+
+
+def detect_artifacts_spectral_anomaly(signal_data: np.ndarray, sample_rate: int, 
+                                       fundamental_freq: float, threshold_db: float = -60) -> List[Dict]:
+    artifacts = []
+    window_size = int(sample_rate * 0.5)
+    hop_size = int(sample_rate * 0.25)
+    
+    for i in range(0, len(signal_data) - window_size, hop_size):
+        segment = signal_data[i:i+window_size]
+        fft = np.fft.rfft(segment)
+        freqs = np.fft.rfftfreq(len(segment), 1/sample_rate)
+        power_spectrum_db = 20 * np.log10(np.abs(fft) + 1e-10)
+        
+        fundamental_idx = np.argmin(np.abs(freqs - fundamental_freq))
+        fundamental_power_db = power_spectrum_db[fundamental_idx]
+        
+        expected_harmonics = set()
+        harmonic_tolerance_bins = 3
+        for n in range(1, 11):
+            harmonic_freq = n * fundamental_freq
+            if harmonic_freq < sample_rate / 2:
+                harmonic_idx = np.argmin(np.abs(freqs - harmonic_freq))
+                for offset in range(-harmonic_tolerance_bins, harmonic_tolerance_bins + 1):
+                    if 0 <= harmonic_idx + offset < len(freqs):
+                        expected_harmonics.add(harmonic_idx + offset)
+        
+        noise_floor_db = np.percentile(power_spectrum_db[10:], 10)
+        
+        unexpected_peaks = []
+        for idx in range(10, len(power_spectrum_db)):
+            if idx not in expected_harmonics:
+                if power_spectrum_db[idx] > noise_floor_db + abs(threshold_db):
+                    unexpected_peaks.append((freqs[idx], power_spectrum_db[idx]))
+        
+        if len(unexpected_peaks) >= 5:
+            artifacts.append({
+                'type': 'spectral_anomaly',
+                'time_sec': i / sample_rate,
+                'unexpected_frequencies': unexpected_peaks[:10],
+                'count': len(unexpected_peaks)
+            })
+    
+    return artifacts
+
+
+def detect_artifacts_amplitude_spikes(signal_data: np.ndarray, sample_rate: int, 
+                                        threshold_factor: float = 3.0) -> List[Dict]:
+    artifacts = []
+    
+    skip_samples = int(sample_rate * 1.0)
+    if len(signal_data) <= 2 * skip_samples:
+        return artifacts
+    
+    envelope = np.abs(signal_data)
+    
+    window_size = int(sample_rate * 0.01)
+    if window_size % 2 == 0:
+        window_size += 1
+    
+    from scipy.ndimage import uniform_filter1d
+    envelope_smooth = uniform_filter1d(envelope, size=window_size, mode='reflect')
+    
+    median_env = np.median(envelope_smooth)
+    mad = np.median(np.abs(envelope_smooth - median_env))
+    
+    if mad == 0:
+        return artifacts
+    
+    threshold_high = median_env + threshold_factor * mad * 1.4826
+    threshold_low = median_env - threshold_factor * mad * 1.4826
+    
+    # Detect spikes (too high)
+    spike_indices = np.where(envelope_smooth > threshold_high)[0]
+    
+    # Detect dropouts (too low)
+    dropout_indices = np.where(envelope_smooth < threshold_low)[0]
+    
+    total_duration = len(signal_data) / sample_rate
+    
+    # Process spikes
+    if len(spike_indices) > 0:
+        groups = []
+        current_group = [spike_indices[0]]
+        
+        for idx in spike_indices[1:]:
+            if idx - current_group[-1] <= int(sample_rate * 0.05):
+                current_group.append(idx)
+            else:
+                groups.append(current_group)
+                current_group = [idx]
+        groups.append(current_group)
+        
+        for group in groups:
+            peak_idx = group[np.argmax(envelope_smooth[group])]
+            time_sec = peak_idx / sample_rate
+            peak_value = envelope_smooth[peak_idx]
+            
+            # Skip artifacts in first and last second
+            if time_sec < 1.0 or time_sec > (total_duration - 1.0):
+                continue
+            
+            artifacts.append({
+                'type': 'amplitude_spike',
+                'time_sec': float(time_sec),
+                'peak_amplitude': float(peak_value),
+                'median_amplitude': float(median_env),
+                'deviation_factor': float((peak_value - median_env) / (mad * 1.4826)) if mad > 0 else 0
+            })
+    
+    # Process dropouts
+    if len(dropout_indices) > 0:
+        groups = []
+        current_group = [dropout_indices[0]]
+        
+        for idx in dropout_indices[1:]:
+            if idx - current_group[-1] <= int(sample_rate * 0.05):
+                current_group.append(idx)
+            else:
+                groups.append(current_group)
+                current_group = [idx]
+        groups.append(current_group)
+        
+        for group in groups:
+            dropout_idx = group[np.argmin(envelope_smooth[group])]
+            time_sec = dropout_idx / sample_rate
+            dropout_value = envelope_smooth[dropout_idx]
+            
+            # Skip artifacts in first and last second
+            if time_sec < 1.0 or time_sec > (total_duration - 1.0):
+                continue
+            
+            artifacts.append({
+                'type': 'amplitude_dropout',
+                'time_sec': float(time_sec),
+                'dropout_amplitude': float(dropout_value),
+                'median_amplitude': float(median_env),
+                'deviation_factor': float((median_env - dropout_value) / (mad * 1.4826)) if mad > 0 else 0
+            })
+    
+    return artifacts
+
+
+def detect_artifacts_zero_crossing(signal_data: np.ndarray, sample_rate: int, 
+                                     threshold_factor: float = 2.0) -> List[Dict]:
+    artifacts = []
+    
+    if len(signal_data) <= int(sample_rate * 2.0):
+        return artifacts
+    
+    window_size = int(sample_rate * 0.1)
+    hop_size = int(sample_rate * 0.05)
+    
+    zcr_values = []
+    for i in range(0, len(signal_data) - window_size, hop_size):
+        segment = signal_data[i:i+window_size]
+        zero_crossings = np.sum(np.abs(np.diff(np.sign(segment)))) / 2
+        zcr = zero_crossings / len(segment)
+        zcr_values.append((i, zcr))
+    
+    if not zcr_values:
+        return artifacts
+    
+    zcr_array = np.array([z[1] for z in zcr_values])
+    median_zcr = np.median(zcr_array)
+    std_zcr = np.std(zcr_array)
+    
+    total_duration = len(signal_data) / sample_rate
+    
+    for i, zcr in zcr_values:
+        time_sec = i / sample_rate
+        
+        # Skip artifacts in first and last second
+        if time_sec < 1.0 or time_sec > (total_duration - 1.0):
+            continue
+        
+        if std_zcr > 0 and abs(zcr - median_zcr) > threshold_factor * std_zcr:
+            artifacts.append({
+                'type': 'zero_crossing_anomaly',
+                'time_sec': float(time_sec),
+                'zcr_value': float(zcr),
+                'median_zcr': float(median_zcr),
+                'deviation_factor': float(abs(zcr - median_zcr) / std_zcr)
+            })
+    
+    return artifacts
+
+
+def detect_artifacts_energy_variation(signal_data: np.ndarray, sample_rate: int, 
+                                        threshold_db: float = 6.0) -> List[Dict]:
+    artifacts = []
+    
+    if len(signal_data) <= int(sample_rate * 2.0):
+        return artifacts
+    
+    window_size = int(sample_rate * 0.1)
+    hop_size = int(sample_rate * 0.05)
+    
+    energy_values = []
+    for i in range(0, len(signal_data) - window_size, hop_size):
+        segment = signal_data[i:i+window_size]
+        energy = np.sum(segment**2)
+        energy_values.append((i, energy))
+    
+    total_duration = len(signal_data) / sample_rate
+    
+    for idx in range(1, len(energy_values)):
+        prev_energy = energy_values[idx-1][1]
+        curr_energy = energy_values[idx][1]
+        
+        if prev_energy > 0 and curr_energy > 0:
+            energy_change_db = 10 * np.log10(curr_energy / prev_energy)
+            
+            if abs(energy_change_db) > threshold_db:
+                time_sec = energy_values[idx][0] / sample_rate
+                
+                # Skip artifacts in first and last second
+                if time_sec < 1.0 or time_sec > (total_duration - 1.0):
+                    continue
+                
+                artifacts.append({
+                    'type': 'energy_variation',
+                    'time_sec': float(time_sec),
+                    'energy_change_db': float(energy_change_db),
+                    'threshold_db': float(threshold_db)
+                })
+    
+    return artifacts
+
+
+def measure_frequency_accuracy(signal_data: np.ndarray, sample_rate: int, 
+                                expected_freq: float) -> Dict:
+    """
+    Measure the actual dominant frequency in the signal and compare to expected.
+    Uses FFT on the full signal (skipping first and last second).
+    """
+    # Skip first and last second
+    skip_samples = int(sample_rate * 1.0)
+    if len(signal_data) <= 2 * skip_samples:
+        return {
+            'expected_freq_hz': float(expected_freq),
+            'measured_freq_hz': 0.0,
+            'error_hz': 0.0,
+            'error_percent': 0.0
+        }
+    
+    signal_trimmed = signal_data[skip_samples:-skip_samples]
+    
+    # Perform FFT
+    fft = np.fft.rfft(signal_trimmed)
+    freqs = np.fft.rfftfreq(len(signal_trimmed), 1/sample_rate)
+    
+    # Find the peak frequency
+    magnitude = np.abs(fft)
+    peak_idx = np.argmax(magnitude)
+    measured_freq = freqs[peak_idx]
+    
+    # Calculate error
+    error_hz = measured_freq - expected_freq
+    error_percent = (error_hz / expected_freq) * 100.0 if expected_freq > 0 else 0.0
+    
+    return {
+        'expected_freq_hz': float(expected_freq),
+        'measured_freq_hz': float(measured_freq),
+        'error_hz': float(error_hz),
+        'error_percent': float(error_percent)
+    }
+
+
+def detect_artifacts_combined(signal_data: np.ndarray, sample_rate: int, fundamental_freq: float,
+                                detector_config: Dict) -> Dict:
+    all_artifacts = []
+    
+    if detector_config.get('spectral_anomaly', {}).get('enabled', True):
+        threshold = detector_config.get('spectral_anomaly', {}).get('threshold_db', -60)
+        artifacts = detect_artifacts_spectral_anomaly(signal_data, sample_rate, fundamental_freq, threshold)
+        all_artifacts.extend(artifacts)
+    
+    if detector_config.get('amplitude_spikes', {}).get('enabled', True):
+        threshold = detector_config.get('amplitude_spikes', {}).get('threshold_factor', 3.0)
+        artifacts = detect_artifacts_amplitude_spikes(signal_data, sample_rate, threshold)
+        all_artifacts.extend(artifacts)
+    
+    if detector_config.get('zero_crossing', {}).get('enabled', True):
+        threshold = detector_config.get('zero_crossing', {}).get('threshold_factor', 2.0)
+        artifacts = detect_artifacts_zero_crossing(signal_data, sample_rate, threshold)
+        all_artifacts.extend(artifacts)
+    
+    if detector_config.get('energy_variation', {}).get('enabled', True):
+        threshold = detector_config.get('energy_variation', {}).get('threshold_db', 6.0)
+        artifacts = detect_artifacts_energy_variation(signal_data, sample_rate, threshold)
+        all_artifacts.extend(artifacts)
+    
+    # Measure frequency accuracy
+    freq_accuracy = measure_frequency_accuracy(signal_data, sample_rate, fundamental_freq)
+    
+    artifact_summary = {
+        'total_count': len(all_artifacts),
+        'by_type': {},
+        'artifacts': all_artifacts,
+        'frequency_accuracy': freq_accuracy
+    }
+    
+    for artifact in all_artifacts:
+        artifact_type = artifact['type']
+        if artifact_type not in artifact_summary['by_type']:
+            artifact_summary['by_type'][artifact_type] = 0
+        artifact_summary['by_type'][artifact_type] += 1
+    
+    return artifact_summary
+
+
+def plot_individual_anomaly(signal_data: np.ndarray, artifact: Dict, artifact_idx: int,
+                            channel_name: str, frequency: float, sample_rate: int, 
+                            output_dir: Path):
+    periods_to_show = 20
+    period_samples = int(sample_rate / frequency)
+    total_samples = periods_to_show * period_samples
+    
+    artifact_time = artifact['time_sec']
+    artifact_sample = int(artifact_time * sample_rate)
+    
+    start_sample = max(0, artifact_sample - total_samples // 2)
+    end_sample = min(len(signal_data), artifact_sample + total_samples // 2)
+    
+    if end_sample - start_sample < total_samples:
+        if start_sample == 0:
+            end_sample = min(len(signal_data), start_sample + total_samples)
+        else:
+            start_sample = max(0, end_sample - total_samples)
+    
+    segment = signal_data[start_sample:end_sample]
+    time_ms = (np.arange(len(segment)) + start_sample) / sample_rate * 1000
+    
+    fig, ax = plt.subplots(1, 1, figsize=(14, 6))
+    
+    ax.plot(time_ms, segment, linewidth=1.0, color='blue', alpha=0.8)
+    ax.axvline(x=artifact_time * 1000, color='red', linestyle='--', linewidth=2, 
+               label=f'Anomaly at {artifact_time:.3f}s', alpha=0.7)
+    
+    ax.set_xlabel('Time (ms)', fontsize=11)
+    ax.set_ylabel('Amplitude', fontsize=11)
+    
+    artifact_type = artifact['type'].replace('_', ' ').title()
+    ax.set_title(f'{channel_name} - {artifact_type} #{artifact_idx+1} (~{periods_to_show} periods @ {frequency}Hz)', 
+                 fontsize=12, fontweight='bold')
+    
+    info_text = f"Type: {artifact_type}\nTime: {artifact_time:.3f}s"
+    if 'deviation_factor' in artifact:
+        info_text += f"\nDeviation: {artifact['deviation_factor']:.2f}σ"
+    if 'energy_change_db' in artifact:
+        info_text += f"\nEnergy Change: {artifact['energy_change_db']:.2f} dB"
+    if 'count' in artifact and artifact['type'] == 'spectral_anomaly':
+        info_text += f"\nUnexpected Peaks: {artifact['count']}"
+    
+    ax.text(0.02, 0.98, info_text, transform=ax.transAxes, 
+            fontsize=9, verticalalignment='top',
+            bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+    
+    ax.legend(loc='upper right')
+    ax.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    
+    safe_type = artifact['type'].replace('_', '-')
+    plot_file = output_dir / f'{channel_name.lower().replace(" ", "_")}_anomaly_{artifact_idx+1:04d}_{safe_type}_{artifact_time:.3f}s.png'
+    plt.savefig(plot_file, dpi=150, bbox_inches='tight')
+    plt.close()
+
+
+def plot_artifact_detection(channel_1: np.ndarray, channel_2: np.ndarray, 
+                            artifacts_ch1: Dict, artifacts_ch2: Dict,
+                            frequency: float, sample_rate: int, output_dir: Path):
+    fig, axes = plt.subplots(2, 2, figsize=(16, 10))
+    
+    time = np.arange(len(channel_1)) / sample_rate
+    
+    axes[0, 0].plot(time, channel_1, alpha=0.7, linewidth=0.5)
+    axes[0, 0].set_xlabel('Time (s)')
+    axes[0, 0].set_ylabel('Amplitude')
+    axes[0, 0].set_title(f'Channel 1 (Loopback) - {artifacts_ch1["total_count"]} artifacts')
+    axes[0, 0].grid(True, alpha=0.3)
+    
+    for artifact in artifacts_ch1['artifacts']:
+        axes[0, 0].axvline(x=artifact['time_sec'], color='r', alpha=0.3, linewidth=0.5)
+    
+    axes[1, 0].plot(time, channel_2, alpha=0.7, linewidth=0.5)
+    axes[1, 0].set_xlabel('Time (s)')
+    axes[1, 0].set_ylabel('Amplitude')
+    axes[1, 0].set_title(f'Channel 2 (DUT/Radio) - {artifacts_ch2["total_count"]} artifacts')
+    axes[1, 0].grid(True, alpha=0.3)
+    
+    for artifact in artifacts_ch2['artifacts']:
+        axes[1, 0].axvline(x=artifact['time_sec'], color='r', alpha=0.3, linewidth=0.5)
+    
+    fft_ch1 = np.fft.rfft(channel_1)
+    fft_ch2 = np.fft.rfft(channel_2)
+    freqs = np.fft.rfftfreq(len(channel_1), 1/sample_rate)
+    
+    axes[0, 1].plot(freqs, 20*np.log10(np.abs(fft_ch1) + 1e-10), linewidth=0.5)
+    axes[0, 1].set_xlabel('Frequency (Hz)')
+    axes[0, 1].set_ylabel('Magnitude (dB)')
+    axes[0, 1].set_title('Channel 1 Spectrum')
+    axes[0, 1].set_xlim(0, min(10000, sample_rate/2))
+    axes[0, 1].grid(True, alpha=0.3)
+    
+    axes[1, 1].plot(freqs, 20*np.log10(np.abs(fft_ch2) + 1e-10), linewidth=0.5)
+    axes[1, 1].set_xlabel('Frequency (Hz)')
+    axes[1, 1].set_ylabel('Magnitude (dB)')
+    axes[1, 1].set_title('Channel 2 Spectrum')
+    axes[1, 1].set_xlim(0, min(10000, sample_rate/2))
+    axes[1, 1].grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plot_file = output_dir / f'artifact_detection_{frequency}Hz.png'
+    plt.savefig(plot_file, 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
+    
+    sample_rate = config['audio']['sample_rate']
+    duration = config['artifact_detection']['duration']
+    frequency = config['artifact_detection']['test_frequency']
+    amplitude = config['artifact_detection']['amplitude']
+    device_name = config['audio']['device_name']
+    channels = config['audio']['channels']
+    detector_config = config['artifact_detection']['detectors']
+    startup_delay = config['artifact_detection'].get('startup_delay', 10)
+    signal_type = config['artifact_detection'].get('signal_type', 'sine')
+    
+    device_ids = find_audio_device(device_name)
+    
+    if startup_delay > 0:
+        print(f"Waiting {startup_delay} seconds for system to settle...")
+        time.sleep(startup_delay)
+        print("Starting recording...")
+    
+    if signal_type == 'chirp':
+        f0 = config['artifact_detection'].get('chirp_f0', 100)
+        f1 = config['artifact_detection'].get('chirp_f1', 8000)
+        tone = generate_chirp(duration, sample_rate, f0=f0, f1=f1, amplitude=amplitude)
+        frequency = (f0 + f1) / 2
+        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, 
+                          channels=channels, device=device_ids[0], blocking=True)
+    else:
+        tone = generate_test_tone(frequency, duration, sample_rate, amplitude)
+        recording = play_and_record(tone, sample_rate, device_ids, channels)
+    
+    channel_1 = recording[:, 0]
+    channel_2 = recording[:, 1]
+    
+    artifacts_ch1 = detect_artifacts_combined(channel_1, sample_rate, frequency, detector_config)
+    artifacts_ch2 = detect_artifacts_combined(channel_2, sample_rate, frequency, detector_config)
+    
+    if save_plots and output_dir:
+        plot_artifact_detection(channel_1, channel_2, artifacts_ch1, artifacts_ch2, 
+                               frequency, sample_rate, output_dir)
+        
+        anomalies_dir = output_dir / 'individual_anomalies'
+        anomalies_dir.mkdir(exist_ok=True)
+        
+        print(f"\nPlotting individual anomalies to: {anomalies_dir}")
+        
+        for idx, artifact in enumerate(artifacts_ch1['artifacts']):
+            plot_individual_anomaly(channel_1, artifact, idx, 'Channel 1 Loopback', 
+                                   frequency, sample_rate, anomalies_dir)
+        
+        for idx, artifact in enumerate(artifacts_ch2['artifacts']):
+            plot_individual_anomaly(channel_2, artifact, idx, 'Channel 2 DUT', 
+                                   frequency, sample_rate, anomalies_dir)
+        
+        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")
+    
+    result = {
+        'signal_type': signal_type,
+        'duration_sec': float(duration),
+        'channel_1_loopback': {
+            'total_artifacts': artifacts_ch1['total_count'],
+            'artifacts_by_type': artifacts_ch1['by_type'],
+            'artifact_rate_per_minute': float(artifacts_ch1['total_count'] / duration * 60),
+            'frequency_accuracy': artifacts_ch1['frequency_accuracy']
+        },
+        'channel_2_dut': {
+            'total_artifacts': artifacts_ch2['total_count'],
+            'artifacts_by_type': artifacts_ch2['by_type'],
+            'artifact_rate_per_minute': float(artifacts_ch2['total_count'] / duration * 60),
+            'frequency_accuracy': artifacts_ch2['frequency_accuracy']
+        },
+        'detector_config': detector_config
+    }
+    
+    if signal_type == 'chirp':
+        f0 = config['artifact_detection'].get('chirp_f0', 100)
+        f1 = config['artifact_detection'].get('chirp_f1', 8000)
+        result['chirp_f0_hz'] = int(f0)
+        result['chirp_f1_hz'] = int(f1)
+    elif signal_type == 'silent':
+        result['note'] = 'Silent mode - no playback, noise floor measurement'
+    else:
+        result['test_frequency_hz'] = int(frequency)
+    
+    return result

test_artifact_detection.py

@@ -0,0 +1,183 @@
+#!/usr/bin/env python3
+import argparse
+import yaml
+from datetime import datetime
+from pathlib import Path
+import sys
+
+sys.path.insert(0, str(Path(__file__).parent))
+from src.audio_tests import run_artifact_detection_test
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Run artifact detection test on audio loopback and radio path')
+    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('--duration', type=float, help='Override recording duration in seconds (default from config)')
+    parser.add_argument('--frequency', type=float, help='Override test frequency in Hz (default from config)')
+    parser.add_argument('--signal-type', choices=['sine', 'chirp', 'silent'], default='sine', 
+                        help='Signal type: sine (single frequency), chirp (frequency sweep), or silent (no signal)')
+    
+    args = parser.parse_args()
+    
+    with open(args.config, 'r') as f:
+        config = yaml.safe_load(f)
+    
+    if args.duration:
+        config['artifact_detection']['duration'] = args.duration
+    if args.frequency:
+        config['artifact_detection']['test_frequency'] = args.frequency
+    
+    config['artifact_detection']['signal_type'] = args.signal_type
+    
+    timestamp = datetime.now()
+    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.mkdir(exist_ok=True)
+    
+    save_plots = config['output'].get('save_plots', False)
+    
+    print("=" * 70)
+    print("ARTIFACT DETECTION 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"Duration: {config['artifact_detection']['duration']} seconds")
+    signal_type = config['artifact_detection'].get('signal_type', 'sine')
+    if signal_type == 'sine':
+        print(f"Signal Type: Sine wave @ {config['artifact_detection']['test_frequency']} Hz")
+    elif signal_type == 'chirp':
+        print(f"Signal Type: Chirp (100 Hz - 8000 Hz)")
+    else:
+        print(f"Signal Type: Silent (no playback - noise floor measurement)")
+    if save_plots:
+        print(f"Plots will be saved to: {test_output_dir}")
+    print("-" * 70)
+    
+    print("\nDetection Algorithms:")
+    for detector_name, detector_settings in config['artifact_detection']['detectors'].items():
+        status = "ENABLED" if detector_settings.get('enabled', False) else "DISABLED"
+        print(f"  - {detector_name}: {status}")
+        if detector_settings.get('enabled', False):
+            for param, value in detector_settings.items():
+                if param != 'enabled':
+                    print(f"      {param}: {value}")
+    
+    print("\n" + "=" * 70)
+    signal_type = config['artifact_detection'].get('signal_type', 'sine')
+    if signal_type == 'sine':
+        freq = config['artifact_detection']['test_frequency']
+        print(f"STARTING TEST - Playing {freq}Hz sine wave and recording both channels...")
+    elif signal_type == 'chirp':
+        print("STARTING TEST - Playing chirp signal (100-8000Hz) and recording both channels...")
+    else:
+        print("STARTING TEST - Recording silence (no playback)...")
+    print("=" * 70)
+    print("\nChannel 1: Loopback path (direct audio interface loopback)")
+    print("Channel 2: DUT/Radio path (through beacon and radio transmission)")
+    print()
+    
+    try:
+        result = run_artifact_detection_test(config, save_plots=save_plots, output_dir=test_output_dir)
+        
+        print("\n" + "=" * 70)
+        print("TEST COMPLETE - RESULTS")
+        print("=" * 70)
+        
+        signal_type = result.get('signal_type', 'sine')
+        if signal_type == 'chirp':
+            print(f"\n📊 Signal: Chirp {result['chirp_f0_hz']} Hz → {result['chirp_f1_hz']} Hz")
+        elif signal_type == 'silent':
+            print(f"\n📊 Signal: Silent (no playback - noise floor measurement)")
+        else:
+            print(f"\n📊 Test Frequency: {result['test_frequency_hz']} Hz")
+        print(f"⏱️  Duration: {result['duration_sec']} seconds")
+        
+        print("\n🔊 CHANNEL 1 (LOOPBACK PATH):")
+        print(f"   Total Artifacts: {result['channel_1_loopback']['total_artifacts']}")
+        print(f"   Artifact Rate: {result['channel_1_loopback']['artifact_rate_per_minute']:.2f} per minute")
+        if result['channel_1_loopback']['artifacts_by_type']:
+            print("   By Type:")
+            for artifact_type, count in result['channel_1_loopback']['artifacts_by_type'].items():
+                print(f"      - {artifact_type}: {count}")
+        
+        # Display frequency accuracy for channel 1
+        if 'frequency_accuracy' in result['channel_1_loopback']:
+            freq_acc = result['channel_1_loopback']['frequency_accuracy']
+            print(f"   Frequency Accuracy:")
+            print(f"      Expected: {freq_acc['expected_freq_hz']:.1f} Hz")
+            print(f"      Measured: {freq_acc['measured_freq_hz']:.2f} Hz")
+            print(f"      Error: {freq_acc['error_hz']:+.2f} Hz ({freq_acc['error_percent']:+.3f}%)")
+        
+        print("\n📻 CHANNEL 2 (DUT/RADIO PATH):")
+        print(f"   Total Artifacts: {result['channel_2_dut']['total_artifacts']}")
+        print(f"   Artifact Rate: {result['channel_2_dut']['artifact_rate_per_minute']:.2f} per minute")
+        if result['channel_2_dut']['artifacts_by_type']:
+            print("   By Type:")
+            for artifact_type, count in result['channel_2_dut']['artifacts_by_type'].items():
+                print(f"      - {artifact_type}: {count}")
+        
+        # Display frequency accuracy for channel 2
+        if 'frequency_accuracy' in result['channel_2_dut']:
+            freq_acc = result['channel_2_dut']['frequency_accuracy']
+            print(f"   Frequency Accuracy:")
+            print(f"      Expected: {freq_acc['expected_freq_hz']:.1f} Hz")
+            print(f"      Measured: {freq_acc['measured_freq_hz']:.2f} Hz")
+            print(f"      Error: {freq_acc['error_hz']:+.2f} Hz ({freq_acc['error_percent']:+.3f}%)")
+        
+        ch1_count = result['channel_1_loopback']['total_artifacts']
+        ch2_count = result['channel_2_dut']['total_artifacts']
+        
+        if 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:
+            print("\n✅ EXCELLENT: No artifacts detected in either path!")
+        else:
+            print(f"\nℹ️  Loopback baseline: {ch1_count} artifacts")
+        
+    except Exception as e:
+        print(f"\n❌ ERROR: {e}")
+        import traceback
+        traceback.print_exc()
+        result = {
+            'error': str(e),
+            'test_frequency_hz': config['artifact_detection']['test_frequency'],
+            'duration_sec': config['artifact_detection']['duration']
+        }
+    
+    output_data = {
+        'metadata': {
+            'test_id': test_id,
+            'timestamp': timestamp.isoformat(),
+            'serial_number': args.serial_number,
+            'software_version': args.software_version,
+            'comment': args.comment
+        },
+        'artifact_detection_result': result
+    }
+    
+    output_file = test_output_dir / f"{test_id}_artifact_detection_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:
+        print(f"   Summary plots: {test_output_dir}/")
+        print(f"   Individual anomaly plots: {test_output_dir}/individual_anomalies/")
+    print("=" * 70)
+
+
+if __name__ == '__main__':
+    main()

test_latency.py

@@ -6,16 +6,16 @@ from pathlib import Path
 import sys
 
 sys.path.insert(0, str(Path(__file__).parent))
-from src.audio_tests import run_single_test, run_latency_test
+from src.audio_tests import run_latency_test
 
 
 def main():
-    parser = argparse.ArgumentParser(description='Run PCB hardware audio tests')
-    parser.add_argument('--pcb-version', required=True, help='PCB version (e.g., v2.1)')
-    parser.add_argument('--pcb-revision', required=True, help='PCB revision (e.g., A, B, C)')
+    parser = argparse.ArgumentParser(description='Run latency test on audio loopback and radio path')
+    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('--notes', default='', help='Adjustments or comments about this test')
+    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('--measurements', type=int, default=5, help='Number of latency measurements (default: 5)')
     
     args = parser.parse_args()
     
@@ -28,59 +28,44 @@ def main():
     results_dir = Path(config['output']['results_dir'])
     results_dir.mkdir(exist_ok=True)
     
-    test_output_dir = results_dir / test_id
+    test_output_dir = results_dir / f"{test_id}_latency"
     test_output_dir.mkdir(exist_ok=True)
     
     save_plots = config['output'].get('save_plots', False)
     
-    print(f"Starting audio test run: {test_id}")
-    print(f"PCB: {args.pcb_version} Rev {args.pcb_revision}")
+    print(f"Starting latency test: {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"Measurements: {args.measurements}")
     if save_plots:
         print(f"Plots will be saved to: {test_output_dir}")
     print("-" * 60)
     
-    print("\n[1/2] Running chirp-based latency test (5 measurements)...")
+    print(f"\nRunning chirp-based latency test ({args.measurements} measurements)...")
     try:
-        latency_stats = run_latency_test(config, num_measurements=5, 
+        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, "
-              f"min={latency_stats['min']:.3f}ms, max={latency_stats['max']:.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:
         print(f"✗ Error: {e}")
         latency_stats = {'avg': 0.0, 'min': 0.0, 'max': 0.0, 'std': 0.0, 'error': str(e)}
     
-    print("\n[2/2] Running frequency sweep tests...")
-    test_results = []
-    frequencies = config['test_tones']['frequencies']
-    
-    for i, freq in enumerate(frequencies, 1):
-        print(f"Testing frequency {i}/{len(frequencies)}: {freq} Hz...", end=' ', flush=True)
-        try:
-            result = run_single_test(freq, config, save_plots=save_plots, output_dir=test_output_dir)
-            test_results.append(result)
-            print("✓")
-        except Exception as e:
-            print(f"✗ Error: {e}")
-            test_results.append({
-                'frequency_hz': freq,
-                'error': str(e)
-            })
-    
     output_data = {
         'metadata': {
             'test_id': test_id,
             'timestamp': timestamp.isoformat(),
-            'pcb_version': args.pcb_version,
-            'pcb_revision': args.pcb_revision,
+            'serial_number': args.serial_number,
             'software_version': args.software_version,
-            'notes': args.notes
+            'comment': args.comment
         },
-        'latency_test': latency_stats,
-        'test_results': test_results
+        'latency_test': latency_stats
     }
     
-    output_file = results_dir / f"{test_id}_results.yaml"
+    output_file = test_output_dir / f"{test_id}_latency_results.yaml"
     with open(output_file, 'w') as f:
         yaml.dump(output_data, f, default_flow_style=False, sort_keys=False)