CASTA is a Python package for analyzing spatial transient patterns in tracking data using Hidden Markov Models (HMM). It provides tools for processing and plotting trajectory data.

pip install casta

git clone https://github.com/NanoSignalingLab/photochromic-reversion.git
cd photochromic-reversion
pip install -e .

import casta

casta.calculate_sta(
    dir="/path/to/data/directory",
    out_dir="/path/to/output/directory",
    min_track_length=25,
    dt=0.05,
    plot=True,
    image_format="svg"
)

# Basic usage
python -m casta /path/to/your/track/data

# With parameters
python -m casta /path/to/data --dt 0.05 --min-track-length 25

CASTA includes an example file.

import os
import casta

example_df, path = casta.example.load_example_data()

current_dir = os.getcwd()

casta.calculate_sta(path, out_dir=current_dir)

The analysis generates:

• Excel files with detailed results (*_CASTA_results.xlsx)
• Visualization plots (optional, in specified format)

• Python 3.10.18
• NumPy 1.26.4
• Pandas 2.2.3
• Matplotlib 3.10.0
• SciPy 1.15.0
• Scikit-learn 1.6.1
• Seaborn 0.13.2
• hmm-learn 0.3.3
• Shapely 2.0.6
• xlsxwriter 3.2.3

Contributions are welcome! Please feel free to submit a Pull Request.

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

If you use CASTA in your research, please cite:

Photochromic reversion enables long-term tracking of single molecules in living plants
Michelle von Arx, Kaltra Xhelilaj, Philip Schulz, Sven zur Oven-Krockhaus, Julien Gronnier
bioRxiv 2024.04.10.585335; doi: https://doi.org/10.1101/2024.04.10.585335

For questions and support, please open an issue on the GitHub repository.