PyTrAns (Trajectories Analysis) is python programs for trajectories analysis. The package has several modules, which one can use independently from each other for
- generating or loading trajectories
loading_trajectories.py - statistical analysis of trajectories (including scaling exponent estimation)
convex_hull.py - classification and post-analysis of trajectories based on analysis modules
There are several types of trajectories we generate and analyse:
from random generating processes (generated by stochastic system of equations)
from real trajectories (from observations) see description and credits in generating_trajectories file
- Data for testing package can be generated (see generating or loading_trajectories files).
- Data examples can be found on website in challanges such as https://competitions.codalab.org/ and https://github.com/AnDiChallenge/ANDI_datasets
Please follow the notebooks to see how all functions are working. The main functions are: generating_trajectories.py, loadng_trajectories and modules for calculation of distributions etc. We recommend you to read more about stochastic processes analysis in the papers e.g. here https://sites.google.com/view/fellowshipresultsliubov/research-projects/random-walks-analysis-and-applications?authuser=0
The package is under development, for using it you need to use import PROGRAM_NAME e.g. import convex_hull_analysis as *
Here we propose investigation of properties of random walks, such as gyration radius, msd, number of sites visited by random walk and some other properties.
(work in progress)
There are multiple ways to contribute to netrd (borrowed description of contribution from netrd).
To report a bug in the package, open an issue at https://github.com/Liyubov/pyTrAns/issues
Please include in your bug report:
Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.
Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it. Implement Features or New Methods
While preparing this software some other open packages were used, which are mentioned in notebooks and code (with the MIT license). This is work in progress.