Trajectory analysis is a challenging task and fundamental for understanding the movement of living organisms in various scales.
We propose TrajPy as an easy pythonic solution to be applied in studies that demand trajectory analysis. With a friendly graphic user interface (GUI) it requires little knowledge of computing and physics to be used by nonspecialists.
TrajPy is composed of three main units of code:
- Basic usage:
- The GUI: it is where you interact with trajpy and the only thing you need to know to start using it
- Advanced
- trajpy.py: it's the heart of trajpy, it computes the Features for characterizing the trajectories
- traj_generator.py: a trajectory generator that can be used to build a dataset for trajectory classification
Our dataset and Machine Learning (ML) model are available for use, as well the generator for building your own database.
We have the package hosted at PyPi, for installing use the command line:
pip3 install trajpyIf you want to test the development version, clone the repository at your local directory from your terminal:
git clone https://github.com/ocbe-uio/trajpyThen run the setup.py for installing
python setup.py --installOpen a terminal and execute the line bellow
python3 -m trajpy.gui1 - You can open one file at time clicking on Open file... or process several files in the same director with Open directory...
2 - Select the features to be computed by ticking the boxes
3 - Click on Compute
4 - Select the directory and file name where the results will be stored
The processing is ready when the following message appears in the text box located at the bottom of the GUI:
Results saved to /path/to/results/output.csv
Currently trajpy support CSV files organized in 4 columns: time t and 3 spatial coordinates x, y, z:
| t | x | y | z |
|---|---|---|---|
| 1.00 | 10.00 | 50.00 | 50.00 |
| 2.00 | 11.00 | 50.00 | 50.00 |
| 3.00 | 11.00 | 50.00 | 50.00 |
| 4.00 | 12.00 | 50.00 | 50.00 |
| 5.00 | 12.00 | 50.00 | 50.00 |
| 6.00 | 13.00 | 50.00 | 50.00 |
See the sample file provided in this repository as example.
LAMMPS YAML files are defined with the following structure:
---
time: 0.0
natoms: 100
keywords: [id, type, x, y, z, vx, vy, vz, fx, fy, fz]
data:
- [1, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -nan, -nan, -nan]
- [2, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -nan, -nan, -nan]
- [3, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -nan, -nan, -nan]
...We provide support for parsing this type of data files with the function parse_lammps_dump_yaml().
First we import the package
import trajpy.trajpy as tjThen we load the data sample provided in this repository, we pass the arguments skip_header=1
to skip the first line of the file and delimiter=',' to specify the file format
filename = 'data/samples/sample.csv'
r = tj.Trajectory(filename,
skip_header=1,
delimiter=',')Finally, for computing a set of features for trajectory analysis we can simple run the function r.compute_features()
r.compute_features()The features will be stored in the object r, for instance:
>>> r.asymmetry
>>> 0.5782095322093505
>>> r.fractal_dimension
>>> 1.04
>>> r.efficiency
>>> 0.29363293632936327
>>> r.gyration_radius
>>> array([[30.40512689, 5.82735002, 0.96782673],
>>> [ 5.82735002, 2.18625318, 0.27296851],
>>> [ 0.96782673, 0.27296851, 2.41663589]])For more examples please consult the extended documentation: https://trajpy.readthedocs.io/
- numpy >= 1.14.3
- scipy >= 1.7.1
- ttkthemes >= 2.4.0
- Pillow >= 8.1.0
- PyYAML >= 5.3.1
If using TrajPy for academic work, please cite our methodological paper and Software DOI:
@article{10.1093/bioadv/vbae026,
author = {Moreira-Soares, Maurício and Mossmann, Eduardo and Travasso, Rui D M and Bordin, José Rafael},
title = "{TrajPy: empowering feature engineering for trajectory analysis across domains}",
journal = {Bioinformatics Advances},
volume = {4},
number = {1},
pages = {vbae026},
year = {2024},
month = {02},
issn = {2635-0041},
doi = {10.1093/bioadv/vbae026},
url = {https://doi.org/10.1093/bioadv/vbae026},
eprint = {https://academic.oup.com/bioinformaticsadvances/article-pdf/4/1/vbae026/56926570/vbae026.pdf},
}
@software{mauricio_moreira_2020_3978699,
author = {Mauricio Moreira and Eduardo Mossmann},
title = {phydev/trajpy: TrajPy 1.3.1},
month = aug,
year = 2020,
publisher = {Zenodo},
version = {1.3.1},
doi = {10.5281/zenodo.3978699},
url = {https://doi.org/10.5281/zenodo.3978699}
}This is an open source project, and all contributions are welcome. Feel free to open an Issue, a Pull Request, or to e-mail us.
Moreira-Soares M., Mossmann E., Travasso R. D. M, Bordin J. R., TrajPy: empowering feature engineering for trajectory analysis across domains, Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae026, doi:10.1093/bioadv/vbae026
Eduardo Henrique Mossmann. A physics based feature engineering framework for trajectory analysis. MSc dissertation. Federal University of Pelotas 2022, Brazil.
Simões, RF, Pino, R, Moreira-Soares, M, et al. Quantitative Analysis of Neuronal Mitochondrial Movement Reveals Patterns Resulting from Neurotoxicity of Rotenone and 6-Hydroxydopamine. FASEB J. 2021; 35:e22024. doi:10.1096/fj.202100899R
Moreira-Soares, M., Pinto-Cunha, S., Bordin, J. R., Travasso, R. D. M. Adhesion modulates cell morphology and migration within dense fibrous networks. https://doi.org/10.1088/1361-648X/ab7c17
Arkin, H. and Janke, W. 2013. Gyration tensor based analysis of the shapes of polymer chains in an attractive spherical cage. J Chem Phys 138, 054904.
Wagner, T., Kroll, A., Haramagatti, C.R., Lipinski, H.G. and Wiemann, M. 2017. Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments. PLoS One 12, e0170165.
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