Python Integration

This page provides practical Python examples for working with the FGA Logger — reading live serial data, saving to CSV, and loading data for analysis.

Dependencies:

• pyserial — serial port communication
• pandas — data analysis (optional, for analysis examples)

Install with:

pip install pyserial pandas

---

Reading Live Serial Data

This example connects to the FGA Logger over USB and prints each incoming CSV row:

import serial

# Change to your port:
#   Windows:   'COM3', 'COM4', etc.
#   Linux:     '/dev/ttyUSB0'
#   macOS:     '/dev/tty.usbserial-XXXX'
PORT = 'COM3'
BAUD = 115200  # Adjust to match your FGA Logger serial settings

def read_logger(port, baud):
    with serial.Serial(port, baud, timeout=2) as ser:
        print(f"Connected to {port} at {baud} baud")
        print("Waiting for data...\n")

        while True:
            line = ser.readline().decode('utf-8', errors='replace').strip()
            if line:
                print(line)

if __name__ == '__main__':
    read_logger(PORT, BAUD)

---

Saving Serial Data to CSV

This example reads from the FGA Logger and saves all incoming rows to a local CSV file. The first received header line is written once; all subsequent data rows follow.

import serial
import csv
from datetime import datetime

PORT    = 'COM3'
BAUD    = 115200
OUTFILE = f"fga_log_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"

# Expected CSV header from FGA Logger
EXPECTED_HEADER = [
    'Timestamp_ms', 'B1x_nT', 'B1y_nT', 'B1z_nT', 'B1v_nT',
    'B2x_nT', 'B2y_nT', 'B2z_nT', 'B2v_nT',
    'Lat_deg', 'Lon_deg', 'Alt_m', 'SIV', 'Fix', 'HDOP_m'
]

def save_to_csv(port, baud, outfile):
    header_written = False

    with serial.Serial(port, baud, timeout=5) as ser, \
         open(outfile, 'w', newline='') as f:

        writer = csv.writer(f)
        print(f"Logging to {outfile}")

        try:
            while True:
                line = ser.readline().decode('utf-8', errors='replace').strip()
                if not line:
                    continue

                fields = line.split(',')

                # Write header once (either from device or our expected header)
                if not header_written:
                    if fields[0] == 'Timestamp_ms':
                        writer.writerow(fields)   # Use device header
                    else:
                        writer.writerow(EXPECTED_HEADER)  # Use expected header
                        writer.writerow(fields)   # Write this row as data
                    header_written = True
                    f.flush()
                    continue

                # Write data rows
                writer.writerow(fields)
                f.flush()
                print(f"  {line[:80]}")  # Print first 80 chars for monitoring

        except KeyboardInterrupt:
            print(f"\nLogging stopped. File saved: {outfile}")

if __name__ == '__main__':
    save_to_csv(PORT, BAUD, OUTFILE)

---

Loading a CSV File with pandas

Once you have a CSV file — either from the SD card or saved via serial — load it with pandas for analysis:

import pandas as pd

CSV_FILE = 'fga_log_20240315_143022.csv'

# Load the CSV
df = pd.read_csv(CSV_FILE)

print("Shape:", df.shape)
print("\nFirst rows:")
print(df.head())

print("\nColumn types:")
print(df.dtypes)

print("\nBasic statistics:")
print(df[['B1x_nT', 'B1y_nT', 'B1z_nT', 'B1v_nT']].describe())

---

Computing the Gradient (Gradiometer Mode)

In gradiometer configurations with two sensor assemblies, compute the gradient per axis:

import pandas as pd

df = pd.read_csv('fga_log.csv')

# Compute gradient (sensor 1 minus sensor 2) per axis
df['Grad_x_nT'] = df['B1x_nT'] - df['B2x_nT']
df['Grad_y_nT'] = df['B1y_nT'] - df['B2y_nT']
df['Grad_z_nT'] = df['B1z_nT'] - df['B2z_nT']

# Total gradient magnitude
df['Grad_v_nT'] = (
    df['Grad_x_nT']**2 +
    df['Grad_y_nT']**2 +
    df['Grad_z_nT']**2
) ** 0.5

print(df[['Timestamp_ms', 'Grad_x_nT', 'Grad_y_nT', 'Grad_z_nT', 'Grad_v_nT']].head(10))

---

Filtering by GPS Quality

Filter out rows with no GPS fix or poor accuracy before analysis:

import pandas as pd

df = pd.read_csv('fga_log.csv')

# Keep only rows with 3D GPS fix and HDOP below 2.0
df_clean = df[
    (df['Fix'] == 3) &
    (df['HDOP_m'] < 2.0) &
    (df['SIV'] >= 4)
].copy()

print(f"Total rows:  {len(df)}")
print(f"Clean rows:  {len(df_clean)}")
print(f"Removed:     {len(df) - len(df_clean)}")

---

Plotting the Total Field

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('fga_log.csv')

plt.figure(figsize=(12, 4))
plt.plot(df['Timestamp_ms'] / 1000, df['B1v_nT'], linewidth=0.8)
plt.xlabel('Time (s)')
plt.ylabel('Total Field B1 (nT)')
plt.title('FGA Logger — Total Magnetic Field')
plt.tight_layout()
plt.savefig('field_plot.png', dpi=150)
plt.show()

---

Exporting GPS Track to GeoJSON

Export the GPS track for use in QGIS or other GIS tools:

import pandas as pd
import json

df = pd.read_csv('fga_log.csv')

# Keep only rows with valid GPS fix
df_gps = df[df['Fix'] >= 2].dropna(subset=['Lat_deg', 'Lon_deg'])

# Build GeoJSON FeatureCollection
features = []
for _, row in df_gps.iterrows():
    feature = {
        "type": "Feature",
        "geometry": {
            "type": "Point",
            "coordinates": [row['Lon_deg'], row['Lat_deg'], row['Alt_m']]
        },
        "properties": {
            "timestamp_ms": row['Timestamp_ms'],
            "B1v_nT": row['B1v_nT'],
            "B2v_nT": row['B2v_nT'],
        }
    }
    features.append(feature)

geojson = {"type": "FeatureCollection", "features": features}

with open('track.geojson', 'w') as f:
    json.dump(geojson, f, indent=2)

print(f"Exported {len(features)} points to track.geojson")