IP Address Visualization

This project visualizes given list of IP addresses on an interactive geographical map, showing the distribution and concentration of IP addresses across different regions. It also includes utilities for generating random IP addresses and looking up IP address.

Screenshot

Prerequisites

• Python 3.12 or higher
• MaxMind GeoLite2 City database (for IP geolocation)
• uv (https://astral.sh/uv)

Setup

1. Install the required Python packages:

   uv sync
   
   # Or pip ≥ 23.1
   pip install .

2. Download the MaxMind GeoLite2 City database:

   • Go to https://dev.maxmind.com/geoip/geolite2-free-geolocation-data
   • Sign up for a free account
   • Download the GeoLite2 City database (GeoLite2-City.mmdb)
   • Place the downloaded file in the project root directory

3. Install the CLI:

   uv pip install -e .

Usage

1. Run the visualization script:

   ip-visualizer --help

2. Open the generated ip_heatmap.html file in a web browser to view the interactive map

The demo application visualize 100 random IP addresses to show the distribution of IP addresses across different regions. You can put a CSV file which contains IP addresses to visualize the IP addresses on the map (use the load_ip_data_from_csv function defined in ip_visualizer/core/visualizer.py)).

Output

The script will generate an interactive HTML map (ip_heatmap.html) that shows:

• A heatmap visualization of IP address distribution
• Higher concentrations of IPs are shown in warmer colors
• You can zoom and pan the map to explore different regions

Testing

Run the test suite with coverage report:

pytest --cov=./ --cov-report=term-missing --cov-report=html

This will:

• Run all tests
• Show test coverage in the terminal
• Generate an HTML coverage report in the htmlcov directory

Open htmlcov/index.html in a browser to view the detailed coverage report.

Notes

• The visualization uses a heatmap to show IP address density
• Areas with more IP addresses will appear in warmer colors
• The map is interactive and can be zoomed and panned

Code Quality

This project maintains a minimum test coverage of 80%. The test coverage report is generated automatically when running tests and can be viewed in the htmlcov directory.