frites.simulations.StimSpecAR#

class frites.simulations.StimSpecAR(verbose=None)[source]#

Stimulus-specific autoregressive (AR) Model.

This class can be used to simulate several networks where the information sent between node is stimulus specific inside a temporal region.

Attributes

ar: Output data generated by the selected model.
gc: Granger causality.
mi: Mutual-information between the granger causality and stimulus.

Methods

`compute_covgc`(ar[, dt, lag, step, method, ...])	Compute the Covariance-based Granger Causality.
`fit`([ar_type, sf, n_times, n_epochs, dt, ...])	Get the data generated by the selected model.
`plot`([psd, cmap, colorbar])	Plot the generated data.
`plot_covgc`([gc, plot_mi])	Plot the Granger Causality.
`plot_model`()	Plot the model of the network.

property ar#: Output data generated by the selected model.

compute_covgc(ar, dt=50, lag=5, step=1, method='gc', conditional=False)[source]#

Compute the Covariance-based Granger Causality.

In addition of computing the Granger Causality, the mutual-information between the Granger causalitity and the stimulus is also computed.

Parameters

dtpython:int: Duration of the time window for covariance correlation in samples
lagpython:int: Number of samples for the lag within each trial
steppython:int | 1: Number of samples stepping between each sliding window onset
method{‘gauss’, ‘gc’}: Method for the estimation of the covgc. Use either ‘gauss’ which assumes that the time-points are normally distributed or ‘gc’ in order to use the gaussian-copula.

Returns

gcnumpy:array_like

Granger Causality arranged as (n_epochs, n_pairs, n_windows, 3) where the last dimension means :

0 : pairs[:, 0] -> pairs[:, 1] (x->y)

1 : pairs[:, 1] -> pairs[:, 0] (y->x)

2 : instantaneous (x.y)

fit(ar_type='hga', sf=200, n_times=300, n_epochs=100, dt=50, n_stim=3, n_std=3, stim_onset=100, random_state=None)[source]#

Get the data generated by the selected model.

Parameters

ar_type{‘hga’, ‘osc_20’, ‘osc_40’, ‘ding_2’, ‘ding_3’, ‘ding_5’}

Autoregressive model type. Choose either :

‘hga’ : for evoked high-gamma activity

‘osc_20’ / ‘osc_40’ : for oscillations respectively around 20Hz and 40Hz

‘osc_40_3’ : oscillations at 40hz for 3 nodes. This model simulates X->Y, X->Z and instantaneous Y.Z

‘ding_2’ / ‘ding_3_direct’ / ‘ding_3_indirect’ / ‘ding_5’ : respectively the models with 2, 3 or 5 nodes described by Ding et al. [7]

sfpython:float | 200

The sampling frequency

n_timespython:int | 300

Number of time points

n_epochspython:int | 100

Number of epochs

dtpython:int | 50

Width of the time-varying Gaussian stimulus

n_stimpython:int | 3

Number of stimulus to use

n_stdpython:float, python:int | 3

Number of standard deviations the stimulus exceed the random noise. Should be an integer striclty over 1. Note that this concerns the first stimulus. For example, if n_std=3, the first stimulus is going to have a deviation 3 times larger than the noise, the second stimulus 6 times the noise, the third stimulus 9 times.

stim_onsetpython:int | 100

Index where the time-varying Gaussian stimulus should start

random_statepython:int | python:None

Fix the random state of the machine for reproducibility

Returns

dataxarray.DataArray: DataArray of shape (n_epochs * n_stim, n_roi, n_times)

Examples using fit:

Statistical analysis of a stimulus-specific network

FIT: Feature specific information transfer

PID: Decomposing the information carried by pairs of brain regions

AR : pairwise illustration

AR : simulate common driving input

AR : conditional covariance based Granger Causality

property gc#: Granger causality.

property mi#: Mutual-information between the granger causality and stimulus.

plot(psd=False, cmap='plasma', colorbar=False, **kwargs)[source]#

Plot the generated data.

Parameters

psdbool | python:False: If False (default), the raw data are plotted. If True, the power spectrum density (PSD) is plotted instead
cmap‘string’ | ‘plasma’: Colormap to use
colorbarbool | python:False: Display or not the colorbar
kwargspython:dict | {}: Additional inputs are sent to the plt.imshow function

Examples using plot:

FIT: Feature specific information transfer

PID: Decomposing the information carried by pairs of brain regions

AR : pairwise illustration

AR : simulate common driving input

plot_covgc(gc=None, plot_mi=False)[source]#

Plot the Granger Causality.

Note that before plotting the Granger causality, the method StimSpecAR.compute_covgc have to be launched before.

Parameters

gcDataArray | python:None: Granger Causality output of the function frites.conn.conn_covgc()
plot_mibool | python:False: If False (default) the Granger causality is plotted. If True, it is the information shared between the Granger causality and the stimulus that is plotted.