Dspx-Monitor

Cryogenic Dilution Refrigerator Monitoring Dashboard built with Streamlit and Plotly.

Features

Temperature Monitoring

• Displays full range, still, and Platine 4K thermometer readings (K)
• Interactive charts with zoom, pan, and crosshairs
• Optional logarithmic Y-axis scale

Pressure Monitoring

• Shows P1, P2, P3 pressure values (mbar)
• Log scale enabled by default for pressure data
• Interactive charts with full zoom capabilities

Turbo Pump Speed

• Monitors main turbo pump speed (%)
• Real-time status display

Resistance Readings

• Displays R MMR1 1, R MMR1 2, R MMR1 3 (Ω)
• Optional logarithmic Y-axis scale

Mixture Percentage (P/T)

• Shows mixture percentage over time

OVC Turbo Status (Turbo AUX)

• Displays On/Off status with visual indicators
• Timeline chart showing state changes

Pulse Tube Status (PT)

• Displays On/Off status with visual indicators
• Timeline chart showing state changes

Valve Status

• Visual grid display of all 39 valves (VE1-VE39)
• 🟢 Open | 🔴 Closed
• Interactive timeline chart (VE1 shown by default)
• Click legend to show/hide individual valves

Slack Integration

• Configure webhook URL for notifications
• Send daily reports with min/max temps and rate of change

Interactive Charts (Plotly)

• Zoom: Click and drag to zoom in on any area (X and Y axes)
• Pan: Hold Shift and drag to pan
• Reset: Double-click to reset zoom
• Crosshairs: Hover to see values at cursor position
• Unified Hover: Shows all trace values at cursor X position

Requirements

• Python 3.8.13
• Windows 7 SP1 32-bit compatible
• Edge 109 or compatible browser

Installation

1. Create Conda Environment

conda create -n dspx_mon python=3.8.13
conda activate dspx_mon

2. Install Dependencies

Option A: Using setup.py (recommended)

pip install -e .

Option B: Manual installation

pip install streamlit==1.22.0 pandas requests plotly

Usage

Windows (Recommended)

1. Open Command Prompt
2. Navigate to the Dspx-Monitor directory
3. Activate the conda environment:

   conda activate dspx_mon

4. Run the startup script:

   run.bat

Linux/Mac

conda activate dspx_mon
streamlit run app.py --server.port 8501

The dashboard will be available at http://localhost:8501

Date Range Selection

• Use the calendar date pickers in the sidebar to select a date range
• The app will load and combine data from all files within the selected range
• Data is cached for 5 minutes for faster subsequent loads

Slack Integration

1. Create a Slack Incoming Webhook at https://api.slack.com/messaging/webhooks
2. Enter the webhook URL in the sidebar under "Slack Notifications"
3. Click "Send Daily Report" to send a summary to Slack

Data Format

The app reads TSV files from the data/ directory with the naming format MMDDYY.txt.

Expected columns include:

• date, heures (time)
• full range, still, Platine 4K (temperatures in K)
• P1, P2, P3 (pressures in mbar)
• Pumping turbo speed (turbo pump %)
• R MMR1 1, R MMR1 2, R MMR1 3 (resistances in Ω)
• P/T (mixture percentage)
• Turbo AUX (OVC turbo status 0/1)
• PT (pulse tube status 0/1)
• VE1 through VE39 (valve states 0/1)

File Structure

Dspx-Monitor/
├── app.py              # Main Streamlit dashboard
├── setup.py            # Package installation script
├── run.bat             # Windows startup script
├── config.json         # Slack webhook URL storage
├── README.md           # This file
└── data/
    └── *.txt           # TSV data files (MMDDYY.txt format)

Troubleshooting

Charts not rendering

• Ensure Plotly is installed: pip install plotly
• Try refreshing the page (F5)

Slow loading with large date ranges

• Data is downsampled automatically for performance
• First load may take time; subsequent loads are cached

Memory issues

• Reduce the date range selection
• Close other browser tabs