elephant-data/unittest/causality/granger/pairwise_spectral_granger

Spectral Granger causality test data

This folder contains the data used for the spectral Granger causality (GC) unit tests. Specifically, it contains the time series data generated using an autoregressive model (ARM) as well as the R grangers [1] parametric GC spectrum.

Description

authors -- list of authors and contributors
LICENSE.md -- summary of scripts/ and data/ licenses

scripts/

• LICENSE (BSD 3-clause)
• generate_test_data.py -- Python script that generates time series data using parameters from [2].
• grangers_estimate.R -- R script that loads the generated time_series_small.npy and estimates the directional causalities using Granger.unconditional (see [3] for details).
• requirements.txt -- requirements containing the exact package versions used in generate_test_data.py to generate time_series.npy and time_series_small.npy in data/ folder. The requirements file was generated using pipreqs-0.4.11 package.

data/

• LICENSE (CC-BY)
• weights.npy -- weight parameter values for ARM used to generate the time series taken from [2].
• noise_covariance.npy -- noise covariance parameter values for ARM used to generate the time series taken from [2].
• time_series.npy -- artificial time series data (2, 300000) generated using ARM.
• time_series_small.npy -- artificial time series data (2, 30000) generated using ARM.
• gc_matrix.npy -- Granger causality estimate resulting from the grangers_estimate.R.

Running the scripts

generate_test_data.py

• Create and activate a conda environment:
  conda create -n test_data_env python
conda activate test_data_env
• Navigate to the scripts/ folder
• Install required packages:
  pip install -r requirements.txt
• Run the script:
  python generate_test_data.py

grangers_estimate.R

• Download and install R and RStudio
• Install the required packages within the RStudio console:
  install.packages(c("RcppCNPy", "grangers"))
• Navigate to the scripts/ folder
• Run grangers_estimate.R interactively

Copyright

:copyright: 2023 by the development team.

References and links

1. https://cran.r-project.org/web/packages/grangers/index.html
2. Ding, M., Chen, Y., & Bressler, S. L. (2006) "Granger Causality: Basic Theory and Application to Neuroscience." arXiv preprint q-bio/0608035.
3. https://cran.r-project.org/web/packages/grangers/grangers.pdf