🧲 Remarkable Magnetic Field Monitor

An advanced monitoring system for DRV425 magnetic field sensors (or similar) connected to Raspberry Pi via SPI ADC.

✨ Features

• Real-time monitoring with live ASCII visualization
• Anomaly detection using statistical analysis (2.5σ threshold)
• Data logging to CSV with timestamps
• Post-analysis visualization with matplotlib
• Live plotting capability
• Baseline calibration for drift detection

🔌 Hardware Requirements

• Raspberry Pi (Zero or newer)
• MCP3008 ADC (or compatible 10-bit ADC)
• DRV425 magnetic field sensor (or similar analog sensor)
• SPI connection enabled on Pi

📦 Installation

# Install Python dependencies
sudo apt-get update
sudo apt-get install python3-spidev python3-pandas python3-matplotlib

# Or using pip
pip3 install spidev pandas matplotlib

# Enable SPI on Raspberry Pi
sudo raspi-config
# Navigate to: Interface Options -> SPI -> Enable

🚀 Usage

1. Run the Monitor

# Make executable
chmod +x magnetic_field_monitor.py

# Run monitoring (Ctrl+C to stop)
python3 magnetic_field_monitor.py

Output example:

╔═══════════════════════════════════════════════════════════════╗
â•‘        REMARKABLE MAGNETIC FIELD MONITOR - DRV425             â•‘
╚═══════════════════════════════════════════════════════════════╝

📊 Logging to: mag_log_20260202_143022.csv
🔍 Calibrating baseline...

✓ Baseline established: 1.650V

Press Ctrl+C to stop monitoring

──────────────────────────────────────────────────────────────────
⏱️  14:30:45 | Raw:  512 | V: 1.653V | ΔV: +0.003V | ████████████████████████████░░░░░░░░░░

2. Visualize Logged Data

# Static analysis (generates PNG and displays plots)
python3 visualize_data.py mag_log_20260202_143022.csv

# Live updating plot (while monitoring is running)
python3 visualize_data.py mag_log_20260202_143022.csv live

📊 Understanding the Output

Monitor Display

• ⏱️ Timestamp: Current time
• Raw: ADC raw value (0-1023)
• V: Actual voltage reading
• ΔV: Change from baseline
• Bar graph: Visual representation of voltage range
• ⚠️ ANOMALY: Flagged when reading exceeds 2.5σ from mean

Analysis Plots

1. Voltage over Time: Shows temporal variation with anomalies marked in red
2. Deviation from Baseline: Displays positive/negative changes
3. Voltage Distribution: Histogram showing statistical distribution

⚙️ Configuration

Edit these variables in magnetic_field_monitor.py:

SAMPLE_RATE = 0.1           # Seconds between readings
V_REF = 3.3                 # Reference voltage (Pi Zero = 3.3V)
ADC_MAX = 1023.0            # 10-bit ADC max value
HISTORY_SIZE = 100          # Rolling window size
ANOMALY_THRESHOLD = 2.5     # Std deviations for anomaly

🔧 Hardware Connections

MCP3008 to Raspberry Pi (SPI)

MCP3008    -> Raspberry Pi
VDD        -> 3.3V (Pin 1)
VREF       -> 3.3V (Pin 1)
AGND       -> GND (Pin 6)
DGND       -> GND (Pin 6)
CLK        -> SCLK (Pin 23 / GPIO 11)
DOUT       -> MISO (Pin 21 / GPIO 9)
DIN        -> MOSI (Pin 19 / GPIO 10)
CS/SHDN    -> CE0 (Pin 24 / GPIO 8)

DRV425 to MCP3008

DRV425     -> MCP3008
VOUT       -> CH0 (Pin 1)
GND        -> AGND

🎯 Use Cases

• Metal detection: Detect ferromagnetic objects
• Current sensing: Monitor AC/DC current via magnetic field
• Position sensing: Track magnetic position/proximity
• Quality control: Detect anomalies in manufacturing
• Research: Scientific magnetic field measurements
• Security: Magnetic intrusion detection

📈 Example Applications

Detect Metal Objects

Watch for sudden voltage spikes when metal passes near the sensor.

Monitor Electric Current

DRV425 can measure current flow by detecting the magnetic field around a wire.

Create a Compass

Map Earth's magnetic field variations.

🐛 Troubleshooting

No readings / stuck at 0:

• Check SPI is enabled: lsmod | grep spi
• Verify wiring connections
• Check power supply to ADC and sensor

Noisy readings:

• Add decoupling capacitors (0.1µF) near power pins
• Increase SAMPLE_RATE for averaging
• Check for electromagnetic interference

Permission errors:

• Add user to SPI group: sudo usermod -a -G spi $USER
• Reboot after adding to group

📄 CSV Log Format

timestamp,raw_value,voltage,delta_v,anomaly
2026-02-02T14:30:22.123456,512,1.650000,0.000000,False
2026-02-02T14:30:22.223456,515,1.660000,0.010000,False
2026-02-02T14:30:22.323456,580,1.870000,0.220000,True

🚀 Advanced Features

Custom Anomaly Detection

Modify the detect_anomaly() method to implement your own detection algorithm.

Data Export

CSV files can be imported into Excel, MATLAB, or Python for further analysis.

Remote Monitoring

Add network logging or webhook notifications when anomalies are detected.

📚 References

• DRV425 Datasheet
• MCP3008 Datasheet
• Raspberry Pi SPI Documentation

🎉 That's Remarkable!

You now have a professional-grade magnetic field monitoring system. Go detect some magnetic phenomena! 🧲✨