Oscilloscope Waveform Viewer

A Python-based desktop application for visualizing and analyzing oscilloscope waveform data from CSV files. Built using PySide6 and pyqtgraph for high-performance interactive visualization.

Features

• Fast loading and visualization of large CSV files (10MB+)
• Smart data decimation for smooth performance with large datasets
• Interactive waveform plotting with zoom and pan
• Movable cursors with intuitive mouse interaction:
  • Vertical cursors (red) with horizontal movement indicators
  • Horizontal cursors (green) with vertical movement indicators
• Real-time measurements:
  • Delta time (ΔT) between vertical cursors
  • Frequency calculation (1/ΔT)
  • Voltage difference (ΔV) between horizontal cursors
• Adjustable display resolution (points shown)
• Drag and drop file loading

Installation

1. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

Usage

1. Run the application:

python oscilloscope_viewer.py

2. Use the interface:
   • Click "Load CSV" to open your oscilloscope data file
   • Add cursors using the buttons
   • Drag cursors by hovering over them (cursor will change to indicate movability)
   • Adjust the "Max Points" value to balance between performance and detail
   • Use mouse wheel to zoom and right-click drag to pan

Supported File Formats

The application supports the following file formats:

Oscilloscope Formats

• Batronix CSV

  • Standard format with time and voltage data
  • Identified by header containing 'time difference to trigger in s'

• Batronix Display Data CSV

  • Contains min/max envelopes
  • Identified by headers containing 'time in s' with 'minimum' and 'maximum' columns

• Rigol Standard CSV

  • Basic CSV format with Time(s) and channel data
  • Identified by header starting with 'Time(s),CH1V'

• Rigol Arb CSV

  • Advanced format with metadata
  • Identified by header containing 'RIGOL:CSV DATA FILE'

• Siglent CSV

  • Includes detailed metadata
  • Identified by headers containing 'Record Length' and 'Model Number'

General Purpose Formats

• PyQtGraph CSV

  • Simple two-column format (time, value)
  • Compatible with PyQtGraph's CSV export
  • Automatically detected by header format (x0000,y0000)

• Binary Files

  • Raw binary data import with configurable settings
  • Supports various data types and endianness
  • Accessible through the "Import Binary..." option

The application will automatically detect the file format and parse it accordingly. For binary files, you'll be prompted to specify the data format.