macro_eeg_model.simulation.simulation_info

macro_eeg_model.simulation.simulation_info#

Classes#

SimulationInfo

A class responsible for storing and retrieving information about a simulation.

Module Contents#

class macro_eeg_model.simulation.simulation_info.SimulationInfo(output_dir, nodes=None, distances=None, connectivity_weights=None, sample_rate=None, lag_connectivity_weights=None, simulation_data=None, frequencies=None, power=None)[source]#

A class responsible for storing and retrieving information about a simulation. It handles saving and loading the data related to a simulation, such as nodes, distances, connectivity weights, and results.

nodes#

The array of nodes used in the simulation.

Type:

numpy.ndarray

distances#

The distance matrix between nodes used in the simulation.

Type:

numpy.ndarray

connectivity_weights#

The connectivity weights matrix between nodes.

Type:

numpy.ndarray

sample_rate#

The sample rate of the simulation in Hz.

Type:

int

lag_connectivity_weights#

The lagged connectivity weights matrix used in the VAR model.

Type:

numpy.ndarray

simulation_data#

The simulated EEG data.

Type:

numpy.ndarray

frequencies#

The array of frequencies corresponding to the power spectrum.

Type:

numpy.ndarray

power#

The power spectrum calculated from the simulation data.

Type:

numpy.ndarray

_output_dir#

The directory path where simulation results are saved.

Type:

pathlib.Path

__init__(output_dir, nodes=None, distances=None, connectivity_weights=None, sample_rate=None, lag_connectivity_weights=None, simulation_data=None, frequencies=None, power=None)[source]#

Initializes the SimulationInfo class with the provided simulation parameters and data.

Parameters:

  • output_dir (pathlib.Path) – The path to the output directory where simulation results are saved.

  • nodes (numpy.ndarray, optional) – The array of nodes used in the simulation.

  • distances (numpy.ndarray, optional) – The distance matrix between nodes used in the simulation.

  • connectivity_weights (numpy.ndarray, optional) – The connectivity weights matrix between nodes.

  • sample_rate (int, optional) – The sample rate of the simulation in Hz.

  • lag_connectivity_weights (numpy.ndarray, optional) – The lagged connectivity weights matrix used in the VAR model.

  • simulation_data (numpy.ndarray, optional) – The simulated EEG data.

  • frequencies (numpy.ndarray, optional) – The array of frequencies corresponding to the power spectrum.

  • power (numpy.ndarray, optional) – The power spectrum calculated from the simulation data.

Raises:

AssertionError – If the output directory does not exist.

save_simulation_info()[source]#

Saves the simulation data to the output directory as .npy files. The data includes nodes, distances, connectivity weights, sample rate, lag connectivity weights, simulation data, frequencies, and power spectrum.

load_simulation_info()[source]#

Loads all the relevant data of the simulation from the output directory and assigns them to the corresponding attributes of the class.

Raises:

FileNotFoundError – If any of the required files are not found in the output directory.

Contents