17T_Plots - Superconductor Critical Temperature Analysis

This project provides a comprehensive Python script for analyzing temperature-dependent transport measurements of superconducting samples. The script specializes in plotting measurement data and calculating critical temperatures using the tangent intersection method.

🎯 Project Overview

The 17T_plots.py script is designed to process experimental data from superconducting samples measured at various temperatures and magnetic fields. It automatically handles:

• Temperature-dependent voltage measurements
• Critical temperature (Tc) calculations using tangent intersection method
• Publication-quality plotting with focused transition region views
• Automatic temperature correction for measurements taken before sensor calibration
• Support for multiple sample conditions (pristine, irradiated, annealed)

🚀 Features

Data Processing

• Automatic Temperature Correction: Applies +0.45K correction to measurements taken before April 25, 2025
• Multiple File Format Support: Handles tab-separated .dat files with temperature, voltage, and timestamp data
• Flexible Data Input: Supports both custom folder paths and predefined sample selections

Analysis Capabilities

• Critical Temperature Calculation: Uses tangent intersection method to determine Tc
• Savitzky-Golay Filtering: Applies sophisticated smoothing for derivative calculations
• Transition Region Detection: Automatically identifies superconducting transition regions
• Dual Tc Values: Calculates both Tc_high (intersection) and Tc_low (x-intercept)

Visualization

• Publication-Quality Plots: Generates high-resolution PDF outputs
• Focused View: Automatically centers plots around transition regions
• Color-Coded Analysis: Different colors for raw data, fits, and critical temperatures
• Comprehensive Legends: Clear labeling of all plot elements

Sample Management

• Condition Detection: Automatically identifies sample states from folder structure
• Batch Processing: Can process multiple measurement files simultaneously
• Flexible Naming: Supports custom sample names and automatic name extraction

📋 Requirements

Install the required dependencies using pip:

pip install -r requirements.txt

Key Dependencies

• matplotlib (3.9.4): For plotting and visualization
• numpy (2.0.2): Numerical computations and array operations
• pandas (2.2.3): Data manipulation and analysis
• scipy (1.13.1): Scientific computing, especially Savitzky-Golay filtering
• python-dateutil (2.9.0.post0): Timestamp parsing for temperature correction

🛠️ Installation

1. Clone this repository:

git clone <repository-url>
cd 17T_Plots

2. Install dependencies:

pip install -r requirements.txt

3. Ensure your data follows the expected directory structure (see Data Format section)

📖 Usage

Basic Usage

# Analyze a specific sample with critical temperature calculation
python 17T_plots.py --ISet_SPSCS09_020 --calculate-tc

# Plot a specific measurement file
python 17T_plots.py --ISet_SPSCS09_021 --file 1 --calculate-tc

# Use a custom folder path
python 17T_plots.py --folder "ISet_SPSCS09_021/IRR1/il" --sample "Sample 021 (irradiated)" --calculate-tc

Command Line Options

Option	Description	Example
--folder PATH	Custom data folder path	--folder "custom/path/il"
--ISet_SPSCS09_XXX	Use predefined sample (018-025, 027, 028)	--ISet_SPSCS09_020
--file NUMBER/all	Plot specific file(s): 1, 2, 3, or "all"	--file 1
--sample NAME	Custom sample name for plot title	--sample "My Sample"
--calculate-tc	Calculate and display critical temperature	--calculate-tc

Supported Samples

The script includes predefined paths for the following samples:

• ISet_SPSCS09_018 through ISet_SPSCS09_025
• ISet_SPSCS09_027
• ISet_SPSCS09_028

📁 Data Format

Expected File Structure

your_project/
├── ISet_SPSCS09_XXX/
│   ├── 0e22/              # Pristine samples
│   │   └── il/
│   ├── 0e22-ann/          # Pristine & annealed samples
│   │   └── il/
│   ├── IRR1/              # Irradiated samples
│   │   └── il/
│   └── IRR1-ann/          # Irradiated & annealed samples
│       └── il/
└── plots/                 # Output directory (auto-created)

Data File Format

The script expects .dat files with tab-separated columns:

1. Temperature [K]: Measurement temperature
2. Voltage [V]: Measured voltage
3. Timestamp [ISO8601]: Measurement timestamp for temperature correction

Example data file (0.000000e+00T_1.dat):

# Temperature	Voltage	Timestamp
4.2000	1.234e-06	2025-03-15T10:30:00+01:00
4.1500	1.189e-06	2025-03-15T10:31:00+01:00
...

📊 Output Files

Plot Files

• Location: plots/ directory (automatically created)
• Format: High-resolution PDF files
• Naming: {sample}_{condition}_0T_{file}_with_tc.pdf

Critical Temperature Results

• Location: plots/ directory
• Format: Text files with detailed Tc calculations
• Naming: {sample}_{condition}_tc_results.txt

Example Output

Critical Temperature Results for SPSCS09 020 (pristine)
==================================================
File: 0.000000e+00T_1.dat
Tc_high: 4.157 K
Tc_low: 4.142 K
------------------------------

🔬 Scientific Methodology

Critical Temperature Calculation

The script implements a robust tangent intersection method:

1. Data Smoothing: Applies Savitzky-Golay filter to reduce noise
2. Derivative Calculation: Computes first derivative to identify transition steepness
3. Region Identification:
   • Transition region: gradient > 60% of maximum
   • Resistive region: gradient < 30% of maximum
4. Linear Fitting: Fits straight lines to both regions
5. Tc Determination:
   • Tc_high: Intersection of transition and resistive fits
   • Tc_low: X-intercept of transition fit

Temperature Correction

For measurements taken before April 25, 2025, the script automatically applies a +0.45K correction to account for sensor calibration changes.

🤖 AI-Assisted Development

This project was developed with significant assistance from AI (GitHub Copilot and Claude), which helped with:

• Algorithm Implementation: Critical temperature calculation using tangent intersection method
• Data Processing Logic: Robust file parsing and timestamp handling
• Code Structure: Modular design with clear separation of concerns
• Error Handling: Comprehensive exception handling and data validation
• Documentation: Detailed docstrings and comments throughout the code
• Best Practices: Following Python coding standards and scientific computing conventions

The AI assistance was particularly valuable for:

• Implementing the Savitzky-Golay filtering and derivative calculations
• Developing the robust timestamp parsing with timezone awareness
• Creating the flexible command-line interface
• Optimizing the plotting aesthetics for publication-quality figures
• Ensuring proper handling of edge cases in data processing

Human oversight ensured the scientific accuracy of the methods and validation against experimental requirements.

🛠️ Development Notes

Code Organization

• Main Script: 17T_plots.py - Contains all functionality
• Configuration: Command-line arguments for flexible usage
• Output Management: Automatic directory creation and file naming

Key Functions

• calculate_Tc(): Core critical temperature calculation
• plot_cryostat_data(): Data visualization and plotting
• parse_args(): Command-line argument handling

Error Handling

The script includes comprehensive error handling for:

• Missing data files
• Malformed data
• Timestamp parsing failures
• Insufficient data for fitting

📝 License

This project is developed for scientific research purposes. Please cite appropriately if used in publications.

🤝 Contributing

Contributions are welcome! Please consider:

• Adding support for additional file formats
• Implementing alternative Tc calculation methods
• Improving visualization options
• Enhancing error handling

📞 Support

For questions about the scientific methodology or data format requirements, please refer to the detailed docstrings in the code or examine the example usage patterns provided.

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This README was prepared with AI assistance to ensure comprehensive documentation of the project's capabilities and usage.