A simulated real-time typhoon visualization system that scrapes and animates tropical storms worldwide.
See releases for app installation
- Supports 5 Different Regions: Northern Atlantic, Eastern & Western Pacific, Northern & Southern Indian
- Real-time Typhoon Tracking: Visualizes typhoon paths with smooth animations
- Dynamic Color Coding: Changes typhoon colors based on intensity categories
- Landfall Detection: Automatically detects and marks typhoon landfall points
- Detailed Information Display: Shows typhoon names, wind speeds, and pressure data
- Interactive Timeline: Includes a time display that matches the simulation accurately
- UI Elements: Play / Pause, Skip 1 Week, and Return to Main Menu
- Data Caching: Stores previously webscraped data to your computer for faster retrieval
- Creates high-resolution maps of tropcial storm basins
- Utilizes Cartopy for accurate geographical projections
- Supports both detailed and simplified map versions
- Features include land masses, ocean, country borders, and basic elevation data
- Scrapes typhoon data from Digital Typhoon database
- Extracts detailed track information including:
- Position (latitude/longitude)
- Wind speeds
- Pressure data
- Timestamps
- Caches data as a
.JSONtoroot/datafolder
- Built with Pygame for smooth real-time animations
- Features include:
- Rotating typhoon symbols
- Color-coded intensity levels
- Dynamic fade in/out effects
- Landfall detection and marking
- Time scaling for visualization
- Pause / Play
- Skip 1 Week
- Symbol: Animated rotating spiral with center dot
- Color Coding:
category_colors = { 0: (135, 206, 235), # Light Blue 1: (100, 238, 100), # Light Green 2: (225, 225, 0), # Yellow 3: (255, 130, 0), # Orange 4: (255, 0, 0), # Red 5: (255, 0, 255) # Purple }
- Information Display: Shows name, wind speed (kt), and pressure (hPa)
- Smooth fade-in/fade-out transitions
- Constant speed movement between track points
- Rotating blade animation
- Dynamic landfall markers with zoom effects
pip install -r requirements.txtRequired packages:
- pygame
- requests
- beautifulsoup4
- cartopy
- matplotlib
- numpy
-
Generate the map (first run only):
python scripts/map_maker.py
-
Start the visualization:
python scripts/stormchaser.py
- Click the "Play" button to start the animation
- Click the "Pause" button to pause the animation
- Click the "Skip 1 Week" button to jump 1 week forward into the timeline
- Click the "Return to Menu" button to regenerate an animation
- Close window to exit
{
"name": str, # Typhoon name
"path": [{
"time": str, # Format: "YYYY-MM-DD HH:MM"
"lat": float, # Latitude
"long": float, # Longitude
"class": int, # Intensity category (0-5)
"speed": str, # Wind speed in km/h
"pressure": int # Pressure in hPa
}],
"start_time": int # Animation start time offset
}Key parameters that can be adjusted:
# Time scaling
TIME_SCALE_FACTOR = 1 / (12 * 60 * 60) # 1 second = 12 hours
# Screen dimensions
SCREEN_WIDTH, SCREEN_HEIGHT = 1200, 900
# Animation parameters
fade_in_duration = 1
fade_out_duration = 0.5
fps_target = 120- Install
pyinstaller
pip install pyinstaller- Navigate to the root folder (example):
cd C:\Users\User\Documents\GitHub\Project Stormchaser- Run the following command:
pyinstaller --add-data "resources/*;resources" --add-data "data/*;data" --noconsole --icon=resources\_stormchaser.ico scripts/stormchaser.py-
View the output dist folder in file explorer and go inside
_internals -
Move both the
dataandresourcesfolder outside, so the structure will look like this:
─dist
│ └───stormchaser
│ ├───data
│ ├───resources
│ └───_internal
│ ├───cartopy
│ ├───certifi
│ ├───...other
-
Launch the executable located inside
stormchaser, and ensure it is working correctly. -
You can now package
stormchaseras a ZIP, or use software installer setups like Inno Setup Compiler
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Data source: IBTRACS (http://agora.ex.nii.ac.jp/digital-typhoon/)
- Map data: Natural Earth via Cartopy
- Compilation: Hispano @zk12-dev