frites.simulations.sim_local_cc_ss#
- frites.simulations.sim_local_cc_ss(n_epochs=10, n_times=100, n_roi=1, cl_index=[40, 60], cl_cov=[0.8], cl_sgn=1, random_state=None)[source]#
Single-subject simulations for computing local MI (CC).
This function can be used for simulating local representations of mutual information between two continuous variables (CC) for a single subject.
- Parameters
- n_epochs
python:int| 30 Number of trials
- n_times
python:int| 100 Number of time points
- n_roi
python:int| 1 Number of ROI
- cl_indexnumpy:array_like | [40, 60]
Sample indices where the clusters are located. Should be an array of shape (n_clusters, 2)
- cl_covnumpy:array_like | [.8]
Covariance level between the data and the regressor variable. Should be an array of shape (n_clusters,)
- cl_sgn{-1, 1}
Sign of the correlation. Use -1 for anti-correlated variables and 1 for correlated variables
- random_state
python:int|python:None Random state (use it for reproducibility)
- n_epochs
- Returns
- xnumpy:array_like
Data array of shape (n_epochs, n_channels, n_times)
- ynumpy:array_like
Regressor array of shape (n_epochs,)
- roinumpy:array_like
Array of ROI names of shape (n_roi,)
- timesnumpy:array_like
Time vector of shape (n_times,)