Fast, Modern CSV Signal Analysis for Oscilloscope Data & Beyond.
Stop fighting sluggish spreadsheets and manual data extraction. Pico Signal Processor (PSP) is a high-performance, cross-platform desktop application designed to batch-process millions of signal data points in milliseconds.
Processing 10,000+ signal points or hundreds of files manually is slow. PSP uses Kotlin Coroutines to scale analysis across your entire CPU (from 2 to 16+ threads), turning minutes of manual work into seconds of automated precision.
- PicoScope Native Support: Optimized for standard oscilloscope CSV exports with automatic header handling.
- Blazing Performance: Multi-threaded CSV parsing ensures no lag, even with massive datasets.
- Modern "Control Center" UX: Drag and Drop folder selection, real-time progress bars, and a clean Material 3 interface.
- Smart Summaries: Automatic MAX/MIN value extraction and configurable frequency distribution reports.
- Dark Mode Support: A sleek interface that adapts to your system theme.
| Feature | Microsoft Excel | Pico Signal Processor |
|---|---|---|
| Load Speed | Slow for large CSVs (>1M rows) | Blazing fast (Stream-based) |
| Batching | Manual per file | Automatic folder-wide processing |
| CPU Usage | Single-threaded | Multi-threaded scalability |
| Focus | Generic spreadsheet | Specialized Signal Analysis |
PSP is designed for flexibility. While it excels at PicoScope data, it works with any CSV signal data that follows this simple schema:
- Header Lines: The app automatically skips the first 2 lines (metadata/headers).
- Data Columns: Expects
[Index/Time, Value]. - File Scanning: Recursively finds every
.csvfile in your selected input directory.
- JDK 17+ or higher must be installed on your system.
Clone the repository and run the application directly from your terminal:
# macOS / Linux
./gradlew :composeApp:run
# Windows
.\gradlew.bat :composeApp:runContributions are welcome! If you have ideas for new signal processing operations or UI improvements, please open an issue or submit a PR.
Built with ❤️ using Kotlin Multiplatform.