macro_eeg_model.simulation.stationary_model_developer

macro_eeg_model.simulation.stationary_model_developer#

Classes#

StationaryModelDeveloper

A class to develop a stationary vector autoregression (VAR) model from given parameters.

Module Contents#

class macro_eeg_model.simulation.stationary_model_developer.StationaryModelDeveloper(nr_lags, nr_nodes, nodes, distances, connectivity_weights, sample_rate, delay_calculator)[source]#

A class to develop a stationary vector autoregression (VAR) model from given parameters.

_nr_lags#

The number of lags (p) in the VAR(p) model.

Type:

int

_nr_nodes#

The number of nodes in the model.

Type:

int

_nodes#

The list of node names.

Type:

list[str]

_distances#

A matrix containing the distances between nodes.

Type:

numpy.ndarray

_connectivity_weights#

The initial connectivity weights between nodes.

Type:

numpy.ndarray

_sample_rate#

The sample rate used for the model.

Type:

int

_delay_calculator#

An instance of the src.simulation.delay_calculator.DelayCalculator class used to calculate delay distributions.

Type:

DelayCalculator

_tempx#

The array of lag indices.

Type:

numpy.ndarray

_delays_x#

The array of delay values based on the sample rate.

Type:

numpy.ndarray

__init__(nr_lags, nr_nodes, nodes, distances, connectivity_weights, sample_rate, delay_calculator)[source]#

Initializes the StationaryModelDeveloper with the provided parameters.

Parameters:

  • nr_lags (int) – The number of lags (p) in the VAR(p) model.

  • nr_nodes (int) – The number of nodes in the model.

  • nodes (list[str]) – The list of node names.

  • distances (numpy.ndarray) – A matrix containing the distances between nodes.

  • connectivity_weights (numpy.ndarray) – The initial connectivity weights between nodes.

  • sample_rate (int) – The sample rate used for the model.

  • delay_calculator (DelayCalculator) – An instance of the src.simulation.delay_calculator.DelayCalculator class used to calculate delay distributions.

develop(verbose=False)[source]#

Develops a stationary VAR(p) model.

It calculates the lag connectivity weights using _calculate_lag_connectivity_weights(), and adjusts the overall connectivity weights using _adjust_connectivity_weights() until the model becomes stationary (check with _is_stationary()).

Parameters:

verbose (bool, optional) – If True, displays progress information during the model development (default is False).

Returns:

The lag connectivity weights matrix for the stationary model.

Return type:

numpy.ndarray

_adjust_connectivity_weights()[source]#

Adjusts the connectivity weights by scaling them down (preserving the relative weights).

_is_stationary(lag_connectivity_weights)[source]#

Determines whether the model is stationary.

It constructs an augmented matrix from the lag connectivity weights and checks if all eigenvalues are within the unit circle.

Parameters:

lag_connectivity_weights (numpy.ndarray) – The matrix of lag connectivity weights.

Returns:

True if the model is stationary (i.e., all eigenvalues are within the unit circle), False otherwise.

Return type:

bool

_calculate_lag_connectivity_weights()[source]#

Computes the connectivity weights for each lag between all pairs of nodes using _get_lag_distribution().

Returns:

The matrix of lag connectivity weights.

Return type:

numpy.ndarray

_get_lag_distribution(node1, node2)[source]#

Calculates the lag distribution (using :py:attr:_delay_calculator and :py:meth:src.simulation.delay_calculator.DelayCalculator.get_delays_distribution) between two nodes based on their delays and connectivity weights. If the nodes are the same, the distribution is set to zero.

Parameters:

  • node1 (int) – The index of the first node.

  • node2 (int) – The index of the second node.

Returns:

The lag distribution values, or 0 if the nodes are the same.

Return type:

numpy.ndarray or int

plot_connectivity(lag_connectivity_weights, plots_dir)[source]#

Visualizes the lag connectivity weights between nodes as a line plot, showing the relative strength of connections over different delays.

Parameters:

  • lag_connectivity_weights (numpy.ndarray) – The matrix of lag connectivity weights to be plotted.

  • plots_dir (pathlib.Path) – The directory where the plots are saved.

Raises:

AssertionError – If the plots directory does not exist.

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