epicsdev 3.1.1

Helper module for creating EPICS PVAccess servers using p4p

epicsdev

Helper module for building EPICS PVAccess servers using p4p.

epicsdev is designed for:

• Rapid PVAccess server development
• High-rate data simulation and stress testing
• GUI-based monitoring and control
• AI-assisted automatic device support generation

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Installation

python -m pip install epicsdev

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Quick Demo

Start the demo PVAccess server:

python -m epicsdev.epicsdev

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Control & Visualization

Install optional GUI and plotting tools:

python -m pip install pypeto pvplot

Launch the control interface:

python -m pypeto -c config -f epicsdev

This provides:

• Device control panel
• Live waveform plots
• Real-time parameter monitoring

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Multi-Channel Waveform Generator

epicsdev.multiadc generates high-throughput synthetic data for stress-testing EPICS systems.

Example

Generate:

• 10,000 noisy waveforms per second
• 100 points per waveform
• 40,000 scalar parameters per second

python -m epicsdev.multiadc -s 0.1 -c 10000 -n 100

Monitoring GUI

python -m pypeto -c config -f multiadc

The GUI includes:

• Control page
• Real-time waveform plots
  The screenshots can be seen here: control page, plots.

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Phoebus Display

An example Phoebus display is provided:

config/epicsdev.bob<br>

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Screenshot

AI-Assisted Device Support Development

epicsdev is structured to enable automated server generation using AI tools such as GitHub Copilot.

Workflow Example

1. Create a new GitHub repository.

2. Provide an AI prompt such as:

   Build device support for Tektronix MSO oscilloscopes 
   using epicsdev_rigol_scope as a template and the 
   programming manual available at <PDF link>.
   

3. Within ~20–40 minutes, the AI can generate a pull request.

4. Review, test, make minor corrections if needed, then merge.

Real-World Example

Using this method, a server implementation for a Tektronix MSO oscilloscope was:

• ~99% correct on first generation
• Required only minor adjustments

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Use Cases

• EPICS PVAccess server prototyping
• High-rate data simulation
• Control system stress testing
• Rapid instrument integration
• AI-driven device support generation

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Requirements

• Python 3.8+
• p4p 4.2.2+

Optional:

• pypeto
• pvplot
• Phoebus (for .bob display files)

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