JMV-DAS Infrastructure Secure

JMV-DAS is a Qt-based acquisition and visualization application for ExploreX-based DAS systems.

It provides:

• Real-time data acquisition via vendor C API (ctypes wrapper)
• Multi-line block handling
• Waterfall visualization (grayscale)
• Basic acquisition parameter control
• Linux deployment support

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1. Project Structure

JMV-DAS/
├── app/                 # Qt GUI application
│   └── main.py
├── backend/             # Hardware interface and acquisition logic
│   ├── acquisition.py
│   ├── pyexplorex.py    # ctypes wrapper for vendor C library
│   └── linux/           # Vendor shared libraries (.so)
├── scripts/             # Deployment helpers
├── requirements.txt
└── README.md

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2. Requirements

OS

Ubuntu 22.04 (recommended)

Python

Python 3.10+

System Dependencies (required for Qt)

sudo apt update
sudo apt install -y \
  python3.10-venv \
  python3-pip \
  libxcb-cursor0 \
  libxcb1 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 \
  libxcb-randr0 libxcb-render-util0 libxcb-shape0 \
  libxcb-xinerama0 libxkbcommon-x11-0 \
  libxrender1 libxi6 libxext6 libx11-6 \
  libgl1 \
  libtbb12

libtbb12 is required by the vendor library.

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3. Installation

Clone repository:

git clone https://github.com/Enlitech/JMV-DAS.git
cd JMV-DAS

Create virtual environment:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

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4. Run

./scripts/run.sh

or manually:

source .venv/bin/activate
python -m app.main

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5. Features

Acquisition

• Uses ExploreX vendor C API (libexplorex_c.so)
• Float32 block-based callback
• Multi-line block handling
• Thread-safe queue between C callback and UI thread

Visualization

• Grayscale waterfall rendering
• Real-time scrolling
• Robust percentile-based scaling
• 30 FPS UI refresh limit

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6. Current Status

✔ Hardware connection validated ✔ Real data acquisition confirmed ✔ Multi-line block reshape implemented ✔ Vendor dependency resolved (libtbb12) ✔ Remote deployment via GitHub verified

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7. Notes

• The application currently prioritizes stability and real-time visualization.
• Advanced DSP, filtering, and event detection are not yet implemented.
• The GUI parameters must match the device configuration.
• Large block sizes may produce high CPU usage depending on scan rate.

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8. Known Vendor Library Dependencies

Use:

ldd backend/linux/libexplorex_c.so

to verify runtime dependencies.

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9. Development Workflow

Local Development

git add .
git commit -m "message"
git push

Remote Machine

git pull
./scripts/run.sh

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10. Next Steps (Planned)

• Parameter binding between UI and device
• Data decimation for UI performance optimization
• Log-scale visualization option
• Event detection overlay
• Packaging (AppImage or PyInstaller)

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© JMV-DAS – Infrastructure Secure Platform