🌡️ Lab Weather Tracker

Environmental Monitoring System for Raspberry Pi

A professional Python-based weather monitoring system for tracking lab environmental conditions using the Pimoroni Enviro HAT

Features • Installation • Usage • Dashboard • Configuration

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📋 Table of Contents

• Features
• Hardware Requirements
• Installation
• Usage
• Web Dashboard
• Temperature Compensation
• Data Storage
• Running as a Service
• Configuration
• Troubleshooting
• Future Enhancements
• License

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✨ Features

📊 Data Collection 🌡️ Temperature Monitoring with CPU heat compensation 💧 Humidity Tracking (relative humidity %) 🔽 Barometric Pressure (hPa) 💡 Ambient Light levels (lux) 🎨 Color Detection (RGB values)

🎯 Core Features ⏱️ 5-minute sampling interval (configurable) 📁 CSV logging (one file per day) 📈 Real-time console display 🌐 Web dashboard with live charts 🔄 Auto-refresh every 10 seconds 📊 Historical data visualization

🔥 Temperature Compensation

This system implements intelligent CPU temperature compensation to ensure accurate ambient temperature readings:

• 🧮 Compensates for Raspberry Pi CPU heat affecting the BME280 sensor
• 📉 Uses rolling average of 5 CPU temperature samples to reduce jitter
• 🎯 Applies scientifically-proven compensation formula
• 📊 Logs both raw and compensated temperatures for analysis
• ⚙️ Configurable compensation factor (default: 2.25)

Formula: compensated_temp = raw_temp - ((avg_cpu_temp - raw_temp) / factor)

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🔧 Hardware Requirements

Component	Description
Raspberry Pi	Any model with 40-pin GPIO header (3/4/Zero recommended)
Pimoroni Enviro HAT	Environmental monitoring HAT with BME280 & LTR559 sensors
Power Supply	Official Raspberry Pi power supply (5V, 2.5A+)
microSD Card	8GB+ with Raspberry Pi OS installed

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📥 Installation

1️⃣ Install System Dependencies

sudo apt update && apt upgrade
sudo apt install -y python3-pip python3-dev git i2c-tools

2️⃣ Clone the Repository

git clone https://github.com/yourusername/binglab-enviro.git

3️⃣ Create Python virtual environment and install Dependencies

python3 -m venv enviro
source enviro/bin/activate
cd binglab-enviro
pip3 install -r requirements.txt

4️⃣ Enable I2C Interface

The Enviro HAT requires I2C to be enabled:

sudo raspi-config

Navigate to: Interfacing Options → I2C → Enable

Then reboot:

sudo reboot

5️⃣ Verify Installation

i2cdetect -y 1

You should see device addresses 76 (BME280) and 23 (LTR559).

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🚀 Usage

Console Mode (Data Logger)

Start the weather tracker to log data every 5 minutes:

python3 weather_tracker.py

Output:

Lab Weather Tracker Starting...
Logging interval: 300 seconds (5.0 minutes)
✓ BME280 sensor initialized
✓ LTR559 sensor initialized

==================================================
Lab Weather Report - 2025-12-03T14:23:45.123456
==================================================
Temperature (raw):        24.32°C
Temperature (compensated): 21.85°C
Pressure:                 1013.25 hPa
Humidity:                 45.60%
Light Level:              234.50 lux
Proximity:                12.00
==================================================

✓ Data logged to data/weather_log_2025-12-03.csv
Next reading in 300 seconds...

Keyboard Shortcuts:

• Ctrl + C - Gracefully stop the tracker

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🌐 Web Dashboard

Starting the Dashboard

python3 web_dashboard.py

Then open your browser:

• Local: http://localhost:5000
• Network: http://<raspberry-pi-ip>:5000

Dashboard Features

📊 Current Readings Card Live temperature (compensated + raw) Real-time pressure Current humidity Light levels Color swatch visualization

📈 Analytics Min/Max/Average statistics Historical trend charts Last 100 data points Auto-refresh every 60 seconds Responsive mobile design

Dashboard Preview

╔════════════════════════════════════════════╗
║   MBTECH Binglab Envoro Dashboard         ║
╠════════════════════════════════════════════╣
║                                            ║
║   🌡️ Temperature    🔽 Pressure           ║
║      21.85°C          1013.25 hPa         ║
║      Raw: 24.32°C                          ║
║                                            ║
║   💡 Light          🎨 Color               ║
║      234.5 lux        [■■■]               ║
║                                            ║
╠════════════════════════════════════════════╣
║   📊 Today's Statistics                    ║
║   Temperature: 18.2°C - 22.5°C (avg 20.3)  ║
║   Pressure: 1010-1015 hPa (avg 1012.5)     ║
║                                            ║
╠════════════════════════════════════════════╣
║   📈 Historical Trends (24 hours)          ║
║   [Temperature Chart]                      ║
║   [Pressure Chart]                         ║
║   [Light Chart]                            ║
╚════════════════════════════════════════════╝

💡 Tip: Run both the tracker and dashboard simultaneously! The tracker logs data, while the dashboard displays it.

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🌡️ Temperature Compensation

Why Temperature Compensation?

The BME280 sensor is located near the Raspberry Pi CPU, which generates heat. This causes raw temperature readings to be 2-5°C higher than actual ambient temperature.

How It Works

1. Read CPU Temperature from /sys/class/thermal/thermal_zone0/temp
2. Smooth with Rolling Average of last 5 readings to reduce jitter
3. Apply Compensation Formula:

   compensated_temp = raw_temp - ((avg_cpu_temp - raw_temp) / 2.25)
   

4. Log Both Values for comparison and analysis

Adjusting the Compensation Factor

Edit the TEMP_COMP_FACTOR in both files:

# weather_tracker.py (line 27)
TEMP_COMP_FACTOR = 2.25  # Decrease for more compensation, increase for less

# web_dashboard.py (line 40)
TEMP_COMP_FACTOR = 2.25  # Keep values synchronized

Tuning Guide:

• Too Hot? Decrease factor → 2.0 (more aggressive compensation)
• Too Cold? Increase factor → 2.5 (less aggressive compensation)

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💾 Data Storage

Data is automatically saved to the data/ directory:

data/
├── weather_log_2025-12-01.csv
├── weather_log_2025-12-02.csv
└── weather_log_2025-12-03.csv

CSV Format

Column	Description	Unit
timestamp	ISO 8601 timestamp	-
temperature_raw_c	Raw BME280 reading	°C
temperature_c	CPU-compensated temperature	°C
pressure_hpa	Barometric pressure	hPa
humidity_percent	Relative humidity	%
light_lux	Ambient light level	lux
proximity	Proximity sensor value	-

Example:

timestamp,temperature_raw_c,temperature_c,pressure_hpa,humidity_percent,light_lux,proximity
2025-12-03T14:23:45.123456,24.32,21.85,1013.25,45.60,234.50,12.00

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⚙️ Configuration

weather_tracker.py

# Line 25-27
DATA_DIR = "data"                    # Change data storage location
LOG_INTERVAL = 300                   # Seconds between readings (300 = 5 min)
TEMP_COMP_FACTOR = 2.25              # Temperature compensation tuning

web_dashboard.py

# Line 38-40
DATA_DIR = "data"                    # Must match weather_tracker.py
MAX_HISTORY_POINTS = 100             # Number of points on charts
TEMP_COMP_FACTOR = 2.25              # Temperature compensation tuning

Common Configurations:

Interval	Seconds	Use Case
1 minute	60	High-frequency monitoring
5 minutes	300	Default - Balanced
15 minutes	900	Low power consumption
1 hour	3600	Long-term trends

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🔍 Troubleshooting

❌ ImportError: No module named 'bme280'

Solution:

pip3 install pimoroni-bme280
pip3 install smbus2

❌ I2C Errors / Sensor Not Detected

Solution:

# 1. Enable I2C
sudo raspi-config  # Interface Options → I2C → Enable

# 2. Check I2C devices
i2cdetect -y 1

# 3. Add user to i2c group
sudo usermod -a -G i2c $USER
newgrp i2c  # Or logout and login again

# 4. Verify connections
# BME280 should show at 0x76
# LTR559 should show at 0x23

❌ Permission Denied Errors

Solution:

sudo usermod -a -G i2c,gpio $USER
sudo chmod 644 /sys/class/thermal/thermal_zone0/temp

❌ Web Dashboard Not Accessible from Network

Solution:

# 1. Check firewall
sudo ufw allow 5000/tcp

# 2. Find Raspberry Pi IP address
hostname -I

# 3. Verify Flask is running on all interfaces
# web_dashboard.py line 176 should be:
# app.run(host='0.0.0.0', port=5000, ...)

❌ Temperature Readings Too High/Low

Solution: Adjust the TEMP_COMP_FACTOR:

• Too high? Decrease factor (e.g., 2.0 for more compensation)
• Too low? Increase factor (e.g., 2.5 for less compensation)

Test with a calibrated thermometer and adjust accordingly.

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🎯 Future Enhancements

Planned Features

• 📧 Email/SMS alerts for threshold breaches
• 🏠 Home Assistant integration
• 🗄️ Database storage (SQLite/InfluxDB)
• 📥 CSV export from web dashboard
• 📊 Multi-day comparison views
• 🔔 Push notifications
• 📱 Mobile app
• ☁️ Cloud sync capabilities
• 🤖 AI-powered anomaly detection
• 📈 Predictive analytics

Contributing

Contributions are welcome! Feel free to:

• 🐛 Report bugs
• 💡 Suggest features
• 🔧 Submit pull requests

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📄 License

MIT License

Copyright (c) 2025 MBTECH

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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Made with ❤️ by MBTECH

MBTECH Binglab Environmental Monitoring System

Keeping your lab conditions perfect, one reading at a time.