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https://github.com/filipinascimento/bl-timeseries2network
Calculates a similarity matrix (such as correlation, covariance, etc) from time series and convert it to a network datatype (JGFZ) so it can be used in the network pipeline.
https://github.com/filipinascimento/bl-timeseries2network
network
Last synced: 11 days ago
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Calculates a similarity matrix (such as correlation, covariance, etc) from time series and convert it to a network datatype (JGFZ) so it can be used in the network pipeline.
- Host: GitHub
- URL: https://github.com/filipinascimento/bl-timeseries2network
- Owner: filipinascimento
- License: mit
- Created: 2021-06-03T05:24:44.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-06-03T23:38:29.000Z (over 3 years ago)
- Last Synced: 2023-03-11T19:05:58.535Z (over 1 year ago)
- Topics: network
- Language: Python
- Homepage:
- Size: 1.51 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://github.com/brain-life/abcd-spec)
[](https://doi.org/10.25663/brainlife.app.393)
# Timeseries 2 Network
This simple app calculates a similarity matrix from time series and convert it to a network datatype (JGFZ) so it can be used in the network pipeline.
### Authors
- [Filipi N. Silva](https://filipinascimento.github.io)
### Contributors
- [Franco Pestilli](https://liberalarts.utexas.edu/psychology/faculty/fp4834)
- [Josh Faskowitz](https://faskowit.github.io/)
### Funding Acknowledgement
brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your publications and code reusing this code.
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1734853)
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1636893)
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1916518)
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1912270)
[](https://grantome.com/grant/NIH/R01-EB029272-01)
### Citations
1. Avesani, P., McPherson, B., Hayashi, S. et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). [https://doi.org/10.1038/s41597-019-0073-y](https://doi.org/10.1038/s41597-019-0073-y)
2. Bassett, Danielle S., and Olaf Sporns. "Network neuroscience." Nature neuroscience 20, no. 3 (2017): 353. [https://doi.org/10.1038/nn.4502](https://doi.org/10.1038/nn.4502)
## Running the App
### On Brainlife.io
You can submit this App online at [https://doi.org/10.25663/brainlife.app.393](https://doi.org/10.25663/brainlife.app.393) via the "Execute" tab.
### Running Locally (on your machine)
Singularity is required to run the package locally.
1. git clone this repo.
```bash
git clone
cd
```
2. Inside the cloned directory, edit `config-sample.json` with your data or use the provided data.
3. Rename `config-sample.json` to `config.json` .
```bash
mv config-sample.json config.json
```
4. Launch the App by executing `main`
```bash
./main
```
### Sample Datasets
A sample dataset is provided in folder `data` and `config-sample.json`
## Output
The output is a network file containing all the properties from the conmat.
### Dependencies
This App only requires [singularity](https://www.sylabs.io/singularity/) to run. If you don't have singularity, you will need to install the python packages defined in `environment.yml`, then you can run the code directly from python using:
```bash
./main.py config.json
```