SensorView

A Flask/Dash-based web application for sensor data visualization and analysis with advanced caching and interactive features.

Screenshots

Features

Core Visualization Modes

3D Visualization and Filtering

• Interactive 3D scatter plots for data visualization
• Advanced filtering and color mapping options
• Decay effects for temporal data analysis

2D Visualization and Filtering

• Dual-panel 2D scatter plots (left and right views)
• Synchronized data filtering across multiple views
• Interactive data exploration tools

Statistical Visualization

• Heatmap visualization for correlation analysis
• Histogram analysis with customizable binning
• Parallel categories (parcats) for categorical data
• Violin plots for distribution analysis

Advanced Features

• Efficient Data Processing: WebWorker-based caching system for smooth performance
• Multiple File Support: Load and compare multiple datasets simultaneously
• Session Management: Isolated data sessions for concurrent users
• Interactive Controls: Frame navigation, playback controls, and data updates
• Dark/Light Mode: Theme switching for different viewing preferences
• Data Buffering: IndexedDB-based client-side caching for large datasets
• Configuration Persistence: JSON-based configuration management
• Test Case Management: Organized data file and test case selection

Architecture

Server Components

• Flask/Dash Server: Main application server with REST API endpoints
• WebWorker Integration: Client-side data management and processing
• Cache Management: Multi-level caching (server + client-side IndexedDB)
• Session Isolation: Independent data sessions for multiple users

Client Components

• Interactive UI: Modal dialogs for configuration and file selection
• Dynamic Updates: Automatic data refresh and visualization updates
• IndexedDB Storage: Browser-based data persistence and buffering

Dependencies

Python Modules

See requirements.txt for complete list:

• Flask & Dash: Web framework and interactive components
• plotly: Advanced visualization library
• pandas & numpy: Data manipulation and analysis
• flaskwebgui: Desktop application wrapper

Installation

1. Clone the repository:

git clone https://github.com/rookiepeng/sensorview.git
cd sensorview

2. Install Python dependencies:

pip install -r requirements.txt

Usage

Data Preparation

1. Data Format: Save data as .pkl (pickle) or .csv files
2. Directory Structure: Organize data under ./data directory
3. Configuration: Create info.json in each test case directory

Directory Structure Example

./data/
├── Example/
│   ├── info.json          # Required: Column specifications
│   ├── sensor_data.csv    # Data files
│   ├── test_results.pkl   # Alternative format
│   └── subfolder/         # Nested data organization
│       └── more_data.csv
└── Another_Test/
    ├── info.json
    └── dataset.pkl

Configuration File (info.json)

Specify data column mappings, visualization settings, and metadata:

{
  "slider": "Frame",
  "x_3d": "Latitude",
  "y_3d": "Longitude",
  "z_3d": "Height",
  "x_ref": "Host_Latitude",
  "y_ref": "Host_Longitude",
  "z_ref": "Height",
  "keys": {
    "Height": {
      "description": "Height (m)",
      "decimal": 2,
      "type": "numerical"
    },
    "Longitude": {
      "description": "Longitude (m)",
      "decimal": 2,
      "type": "numerical"
    },
    "Latitude": {
      "description": "Latitude (m)",
      "decimal": 2,
      "type": "numerical"
    },
    "Time": {
      "description": "Time (s)",
      "decimal": 2,
      "type": "numerical"
    },
    "Sensor": {
      "description": "Sensor",
      "type": "categorical"
    },
    "Frame": {
      "description": "Frame",
      "decimal": 0,
      "type": "numerical"
    },
    "Host_Latitude": {
      "description": "Ref Latitude (m)",
      "decimal": 2,
      "type": "numerical"
    },
    "Host_Longitude": {
      "description": "Ref Longitude (m)",
      "decimal": 2,
      "type": "numerical"
    }
  }
}

Configuration Parameters:

• slider: Column to use for frame navigation/time slider
• x_3d, y_3d, z_3d: Default columns for 3D visualization axes
• x_ref, y_ref, z_ref: Reference point columns for 3D visualization
• keys: Column definitions with metadata:
  • description: Human-readable column description
  • decimal: Number of decimal places for numerical display
  • type: Data type ("numerical" or "categorical")

Running the Application

Development Mode

python app.py

Set DEBUG = True in app.py for development with hot reload.

Production Mode (Desktop App)

python app.py

The application will launch as a desktop application using FlaskWebGUI.

Server Mode

Uncomment the Waitress server lines in app.py for production deployment:

from waitress import serve
serve(app.server, listen="*:8000")

Application Features

Configuration Modal

• Data Path Selection: Choose root directory for data files
• Test Case Selection: Pick from available test cases
• File Selection: Select specific data files to analyze
• Settings Persistence: Automatic configuration saving

Visualization Controls

• Frame Navigation: Manual frame selection or automatic playback
• View Customization: Toggle between different visualization modes
• Color Mapping: Customizable color schemes and mappings
• Filtering: Interactive data filtering and selection
• Multi-file Comparison: Load multiple files for comparative analysis

Data Management

• Automatic Caching: Server and client-side data buffering
• Session Management: Isolated data sessions
• Dynamic Updates: Automatic data refresh and synchronization
• Performance Optimization: IndexedDB storage for large datasets

Development

Callback Architecture

The application uses a modular callback system with separate modules for different views:

• test_case_view: Test case management
• control_view: Playback and navigation controls
• scatter_3d_view: 3D visualization callbacks
• scatter_2d_left_view & scatter_2d_right_view: 2D visualization panels
• heatmap_view: Statistical heatmap visualization
• histogram_view: Distribution analysis
• parcats_view: Parallel categories visualization
• violin_view: Violin plot analysis

Client-side Functions

The application includes several client-side callback functions for:

• WebWorker initialization and management
• IndexedDB data storage and retrieval
• Dynamic figure updates and buffering
• Performance optimization

License

GPL-3.0 License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.