casta 0.1.0

Calculate spatial transient arrests

CASTA - Computational Analysis of Spatial Transient Arrests

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.

Installation

From PyPI (recommended)

pip install casta

For development

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

Quick Start

Command Line Usage

# 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

Jupyter Notebook Usage

import casta

# Run analysis in notebook
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"
)

Command Line Options

Option	Type	Default	Description
dir	str	required	Path to directory containing input track data
--out_dir	str	None	Path to output directory to save results, defaults to input directory
--dt	float	0.05	Time step for analysis
--min-track-length	int	25	Minimum track length for analysis
--plot	bool	False	Enable additional plotting
--image-format	str	svg	Image format (svg, tiff)

Input Data Format

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)

Output

The analysis generates:

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

Requirements

• 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

Contributing

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

License

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

Citation

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

Support

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