oect-transfer-analysis 1.0.0

Advanced analysis tools for OECT transfer curves - time series analysis, visualization, and animation

OECT Transfer Analysis

Advanced analysis tools for Organic Electrochemical Transistor (OECT) transfer curves, providing comprehensive time series analysis, visualization, and animation capabilities.

🚀 Key Features

• 📁 Batch Data Loading: Automatically load and process multiple transfer curve CSV files
• 📈 Time Series Analysis: Extract parameter evolution over time with drift detection
• 🎨 Advanced Visualization: Create publication-ready plots with custom color schemes
• 🎬 Animation Generation: Generate high-quality videos showing device evolution
• 📊 Statistical Analysis: Comprehensive statistical summaries and stability analysis
• ⚡ Performance Optimized: Parallel processing for fast animation generation

📦 Installation

Basic Installation

pip install oect-transfer-analysis

With Animation Support

pip install oect-transfer-analysis[animation]

Development Installation

git clone https://github.com/Durian-leader/oect_transfer_analyse.git
cd oect-transfer-analysis
pip install -e .[dev]

🔧 Quick Start

Basic Usage

from oect_transfer_analysis import DataLoader, TimeSeriesAnalyzer, Visualizer

# 1. Load transfer curve data
loader = DataLoader("path/to/csv/files")
transfer_objects = loader.load_all_files(device_type="N")

# 2. Time series analysis
analyzer = TimeSeriesAnalyzer(transfer_objects)
time_series = analyzer.extract_time_series()

# 3. Create visualizations
viz = Visualizer()

# Evolution plot with black-to-red colormap
fig, ax = viz.plot_evolution(transfer_objects, colormap="black_to_red")

# Compare specific time points
fig, ax = viz.plot_comparison(
    transfer_objects, 
    indices=[0, 25, 50],
    labels=["Initial", "Middle", "Final"]
)

# 4. Statistical analysis
stats = analyzer.get_summary_statistics()
print(stats)

# Drift detection
drift = analyzer.detect_drift("gm_max_raw", threshold=0.05)
print(f"Drift detected: {drift['drift_detected']}")

Animation Generation

# Generate animation (requires animation dependencies)
if viz.ANIMATION_AVAILABLE:
    viz.generate_animation(
        transfer_objects,
        "device_evolution.mp4",
        fps=30,
        dpi=150
    )

📊 Data Format

Your CSV files should contain voltage and current columns. The package automatically detects common column names:

Voltage columns: vg, v_g, gate, vgs, v_gs Current columns: id, i_d, drain, ids, i_ds, current

Example CSV structure:

Vg,Id
-0.6,-1.23e-11
-0.59,-1.45e-11
...

🎨 Visualization Examples

Evolution Plot with Custom Colormap

# Black to red gradient showing time evolution
viz.plot_evolution(
    transfer_objects,
    label="Device A",
    colormap="black_to_red",
    y_scale="log",
    save_path="evolution_plot.png"
)

Multi-point Comparison

# Compare initial, degraded, and recovered states
viz.plot_comparison(
    transfer_objects,
    indices=[0, 50, 100],
    labels=["Fresh", "Degraded", "Recovered"],
    colormap="viridis"
)

Time Series Analysis

# Plot parameter evolution over time
analyzer.plot_time_series(
    parameters=['gm_max_raw', 'Von_raw', 'I_max_raw'],
    save_path="time_series.png"
)

📈 Advanced Analysis

Stability Analysis

# Comprehensive stability analysis
stability_results = analyzer.analyze_stability(threshold=0.05)
print(stability_results)

# Custom drift detection
for param in ['gm_max_raw', 'Von_raw', 'I_max_raw']:
    drift = analyzer.detect_drift(param, threshold=0.03)
    print(f"{param}: {drift['drift_direction']} by {drift['final_drift_percent']:.2f}%")

Export Results

# Export to pandas DataFrame
df = analyzer.to_dataframe()
df.to_csv("analysis_results.csv", index=False)

# Get statistical summary
stats = analyzer.get_summary_statistics()
stats.to_csv("statistics_summary.csv")

🎬 Animation Features

Standard Animation

from oect_transfer_analysis import generate_transfer_animation

generate_transfer_animation(
    transfer_objects,
    output_path="device_evolution.mp4",
    fps=30,
    dpi=150,
    figsize=(12, 5)
)

Memory-Optimized Animation

# For large datasets
generator = AnimationGenerator()
generator.generate_memory_optimized(
    transfer_objects,
    "large_dataset_evolution.mp4",
    batch_size=50
)

Custom Animation Parameters

generate_transfer_animation(
    transfer_objects,
    "custom_animation.mp4",
    fps=60,
    dpi=200,
    xlim=(-0.6, 0.6),
    ylim_log=(1e-12, 1e-6),
    codec='H264'
)

🔍 API Reference

Core Classes

DataLoader

DataLoader(folder_path)

• load_all_files(device_type, file_pattern, sort_numerically): Load transfer files
• analyze_batch(): Get summary of loaded files
• get_metadata(): Get loading metadata

TimeSeriesAnalyzer

TimeSeriesAnalyzer(transfer_objects)

• extract_time_series(): Extract time series data
• detect_drift(parameter, threshold): Detect parameter drift
• get_summary_statistics(): Statistical summary
• analyze_stability(): Multi-parameter stability analysis

Visualizer

Visualizer()

• plot_evolution(): Plot transfer curve evolution
• plot_comparison(): Compare curves at specific indices
• generate_animation(): Create evolution animation

Utility Functions

from oect_transfer_analysis import (
    load_transfer_files,
    plot_transfer_evolution,
    plot_transfer_comparison,
    check_dependencies
)

📋 Requirements

Core Dependencies

• oect-transfer>=0.4.2
• numpy>=1.20.0
• pandas>=1.3.0
• matplotlib>=3.5.0

Optional Dependencies (for animation)

• opencv-python>=4.5.0
• Pillow>=8.0.0

🏗️ Architecture

The package is built on top of the oect-transfer library and provides:

oect-transfer-analysis/
├── DataLoader        # Batch file loading and validation
├── TimeSeriesAnalyzer # Parameter extraction and drift analysis  
├── Visualizer        # Advanced plotting with custom colormaps
├── AnimationGenerator # Video generation with parallel processing
└── Utilities         # Helper functions and system checks

🎯 Use Cases

• Device Degradation Studies: Track parameter changes over operational cycles
• Environmental Testing: Analyze stability under different conditions
• Quality Control: Automated analysis of production batches
• Research Publications: Generate publication-ready figures and animations
• Real-time Monitoring: Process data streams from measurement setups

⚡ Performance Tips

For Large Datasets

• Use generate_memory_optimized() for animations with >1000 frames
• Set lower DPI (50-100) for faster processing
• Use batch processing for very large file sets

For High Quality Output

• Use DPI 200-300 for publication figures
• Enable higher frame rates (60+ fps) for smooth animations
• Use 'H264' codec for better compression

Parallel Processing

# Automatically uses all CPU cores
generate_transfer_animation(transfer_objects, n_workers=None)

# Limit workers for memory constraints
generate_transfer_animation(transfer_objects, n_workers=4)

🐛 Troubleshooting

Common Issues

Import Error:

pip install oect-transfer oect-transfer-analysis

Animation Dependencies Missing:

pip install oect-transfer-analysis[animation]

Memory Issues with Large Datasets:

# Use memory-optimized animation
generator.generate_memory_optimized(data, batch_size=25)

Column Detection Issues:

# Specify columns explicitly
loader.load_all_files(vg_column="VGate", id_column="IDrain")

Check System Status

from oect_transfer_analysis import check_dependencies, print_system_info

check_dependencies()  # Check what's available
print_system_info()   # Detailed system information

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines.

Development Setup

git clone https://github.com/yourusername/oect-transfer-analysis.git
cd oect-transfer-analysis
pip install -e .[dev]

Code Style

We use black for code formatting and flake8 for linting:

black src/
flake8 src/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Authors

• lidonghao - Lead Developer - [e-mail]

🙏 Acknowledgments

• Built on the excellent oect-transfer library
• Thanks to the OECT research community for feedback and testing
• Matplotlib and OpenCV teams for visualization and video capabilities

📞 Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues

🗺️ Roadmap

• Real-time data streaming support
• Interactive web dashboard
• Machine learning-based anomaly detection
• Integration with measurement equipment APIs
• Advanced statistical models for degradation prediction

---

Keywords: OECT, Organic Electrochemical Transistor, Transfer Curve, Time Series Analysis, Device Characterization, Python, Visualization, Animation