PPMS_Toolkit 0.2.2

Toolkit for PPMS data processing

PPMS Toolkit

⚠️ Development Notice

This project is currently under active development and primarily developed and tested on macOS. It is a pure Python application built with Qt (PySide6), so it should be cross-platform in principle. However, minor GUI display issues may occur on Windows or Linux systems.

A Python toolkit for PPMS (Physical Property Measurement System) data analysis

Features • Installation • Quick Start • Documentation

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📖 Overview

PPMS Toolkit is a modern, user-friendly application designed for researchers working with Quantum Design's Physical Property Measurement System (PPMS). It provides both a powerful GUI application and a flexible Python library for:

• 📂 Data Management: Import, organize, and manage PPMS .dat files with SQLite database
• 📊 Interactive Plotting: Visualize VSM and Heat Capacity measurements with Matplotlib
• 🔬 Advanced Analysis: Curie temperature fitting, background subtraction, susceptibility analysis
• 💾 Efficient Storage: Parquet-based file format for fast data loading and minimal disk usage
• 🎨 Cross-Platform: Built with PySide6 (Qt6) for cross-platform compatibility

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✨ Features

🖥️ GUI Application

• Sample Management

  • Create and edit sample metadata (name, mass, orientation, chemical formula)
  • Track multiple measurements per sample
  • Delete samples and associated data files

• Measurement Management

  • Batch import of .dat files from PPMS
  • Support for VSM (Vibrating Sample Magnetometer) measurements:
    • MH mode: Magnetization vs. Field
    • MT mode: Magnetization vs. Temperature
  • Support for Heat Capacity measurements
  • Automatic deduplication based on file content hash

• Interactive Plotting

  • Plot multiple measurements with customizable legends
  • Click-to-hide curves for easy comparison
  • Switch between susceptibility (χ) and moment views
  • Zoom, pan, and export plots

• Data Filtering

  • Multi-column filtering in measurement tables
  • Filter by sample, mode, field, temperature, or condition

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🚀 Installation

Option 1: Conda Environment (Recommended)

# Clone the repository
git clone https://github.com/AlbertRyu/PPMS_ToolKit.git
cd PPMS_ToolKit

# Create and activate conda environment
conda env create -f environment.yml
conda activate ppms_toolkit

# Install the package in development mode
pip install -e ".[gui]"

# Launch the GUI
ppms-toolkit

Option 2: pip + venv

# Clone the repository
git clone https://github.com/AlbertRyu/PPMS_ToolKit.git
cd PPMS_ToolKit

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

# Install with GUI support
pip install -e ".[gui]"

# Launch the GUI
ppms-toolkit

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🎯 Quick Start

GUI Workflow

1. Launch Application

   ppms-toolkit
   

2. Select/Create Project

   • Choose an existing project folder or create a new one
   • All data will be stored in this folder

3. Add Samples

   • Navigate to "Samples" tab
   • Click "Add Sample" and enter metadata (name, mass, orientation)

4. Import Measurements

   • Go to "Plots" tab
   • Click "Add Measurement"
   • Select multiple .dat files (batch import supported)
   • Choose the sample and measurement mode (MH/MT)

   📁 File Naming for MT Mode

   The toolkit automatically detects measurement conditions from filenames:

   Filename Contains	Detected Condition
   ZFC (case-insensitive)	Zero Field Cooling
   FC (case-insensitive)	Field Cooling
   Neither	Unknown Condition

   Examples:

   ✅ sample_ZFC_100Oe.dat  → Condition: ZFC
   ✅ data_FC.dat           → Condition: FC
   ⚠️ measurement.dat       → Condition: Unknown
   

5. Visualize Data

   • Select measurements from the table
   • Click "Plot" to visualize
   • Use legend to toggle curves
   • Toggle χ/Moment view with checkbox

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📊 Supported Measurement Types

VSM (Vibrating Sample Magnetometer)

Mode	Description	Analysis Tools
MH	Magnetization vs. Field	fit_MH() - Coercivity extraction～～
MT	Magnetization vs. Temperature	fit_MT() - Curie temperature fitting

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🗂️ Project Structure

PPMS_ToolKit/
├── src/
│   ├── ppms_toolkit/              # Core library
│   │   ├── sample.py              # Sample class
│   │   └── measurement/
│   │       ├── base.py            # Base Measurement class
│   │       ├── vsm.py             # VSM analysis
│   │       └── heat_capacity.py   # Heat Capacity analysis
│   │
│   ├── ppms_toolkit_gui/          # GUI application
│   │   ├── app.py                 # Entry point
│   │   ├── main_window.py         # Main window
│   │   ├── controller/            # MVC controllers
│   │   ├── widgets/               # Qt widgets
│   │   └── dialogs/               # Dialog windows
│   │
│   └── infrastructure/
│       └── db/
│           └── db.py              # SQLite database wrapper
│
├── examples/
│   └── code_example.ipynb         # Jupyter notebook examples
│
├── pyproject.toml                 # Project metadata & dependencies
├── environment.yml                # Conda environment specification
└── README.md                      # This file

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🔧 Dependencies

Core Dependencies

• numpy ≥ 1.21 - Numerical computing
• pandas ≥ 1.3 - Data manipulation
• scipy ≥ 1.7 - Scientific computing
• matplotlib ≥ 3.4 - Plotting

GUI Dependencies (Optional)

• PySide6 ≥ 6.4 - Qt6 GUI framework
• pyarrow ≥ 10.0 - Parquet file I/O

Development Dependencies

• ipykernel - Jupyter notebook support
• IPython - Enhanced interactive shell

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📚 Documentation

Database Structure

The toolkit uses SQLite for data management with two main tables:

samples

• id, name, mass, chemical, orientation, created_at, notes

measurements

• id, sample_id (FK), measurement_type, mode
• const_field, const_temperature
• original_filepath, data_filepath, processed_data_filepath
• content_hash (for deduplication)
• extra_parameters (JSON), comment, created_at

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📄 License

This project is licensed under the MIT License.

You are free to use, modify, and distribute this software, provided that:

• You include the original copyright notice and license text in any copies
• You do not hold the author liable for any damages

See LICENSE file for full details.

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🙏 Acknowledgments

• Built with PySide6 (Qt for Python)
• Data storage powered by Apache Arrow (Parquet format)
• Scientific computing with NumPy, SciPy, and pandas

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