osl_dynamics.simulation.mvn#

Multivariate normal observation model.

Module Contents#

Classes#

MVN

Class that generates data from a multivariate normal distribution.

MDyn_MVN

Class that generates data from a multivariate normal distribution.

MSess_MVN

Class that generates data from a multivariate normal distribution for
class osl_dynamics.simulation.mvn.MVN(means, covariances, n_modes=None, n_channels=None, n_covariances_act=1, observation_error=0.0)[source]#

Class that generates data from a multivariate normal distribution.

Parameters:

  • means (np.ndarray or str) – Mean vector for each mode, shape should be (n_modes, n_channels). Either a numpy array or 'zero' or 'random'.

  • covariances (np.ndarray or str) – Covariance matrix for each mode, shape should be (n_modes, n_channels, n_channels). Either a numpy array or 'random'.

  • n_modes (int, optional) – Number of modes.

  • n_channels (int, optional) – Number of channels.

  • n_covariances_act (int, optional) – Number of iterations to add activations to covariance matrices.

  • observation_error (float, optional) – Standard deviation of the error added to the generated data.

create_means(option, mu=0, sigma=0.2)[source]#
create_covariances(option, activation_strength=1, eps=1e-06)[source]#
simulate_data(state_time_course)[source]#

Simulate time series data.

Parameters:

state_time_course (np.ndarray) – 2D array containing state activations. Shape must be (n_samples, n_states).

Returns:

data – Time series data. Shape is (n_samples, n_channels).

Return type:

np.ndarray

get_instantaneous_covariances(state_time_course)[source]#

Get the ground truth covariance at each time point.

Parameters:

state_time_course (np.ndarray) – 2D array containing state activations. Shape must be (n_samples, n_states).

Returns:

inst_covs – Instantaneous covariances. Shape is (n_samples, n_channels, n_channels).

Return type:

np.ndarray

class osl_dynamics.simulation.mvn.MDyn_MVN(means, covariances, n_modes=None, n_channels=None, n_covariances_act=1, observation_error=0.0)[source]#

Bases: MVN

Class that generates data from a multivariate normal distribution.

Multi-time-scale version of MVN.

Parameters:

  • means (np.ndarray or str) – Mean vector for each mode, shape should be (n_modes, n_channels). Either a numpy array or 'zero' or 'random'.

  • covariances (np.ndarray or str) – Covariance matrix for each mode, shape should be (n_modes, n_channels, n_channels). Either a numpy array or 'random'.

  • n_modes (int, optional) – Number of modes.

  • n_channels (int, optional) – Number of channels.

  • n_covariances_act (int, optional) – Number of iterations to add activations to covariance matrices.

  • observation_error (float, optional) – Standard deviation of the error added to the generated data.

simulate_data(state_time_courses)[source]#

Simulates data.

Parameters:

state_time_courses (np.ndarray) – Should contain two time courses: one for the mean and standard deviations and another for functional connectiivty. Shape is (2, n_samples, n_modes).

Returns:

data – Simulated data. Shape is (n_samples, n_channels).

Return type:

np.ndarray

get_instantaneous_covariances(state_time_courses)[source]#

Get the ground truth covariance at each time point.

Parameters:

state_time_courses (np.ndarray) – Should contain two time courses: one for the mean and standard deviations and another for functional connectiivty. Shape is (2, n_samples, n_modes).

Returns:

inst_covs – Instantaneous covariances. Shape is (n_samples, n_channels, n_channels).

Return type:

np.ndarray

class osl_dynamics.simulation.mvn.MSess_MVN(session_means, session_covariances, n_modes=None, n_channels=None, n_covariances_act=1, embedding_vectors=None, n_sessions=None, embeddings_dim=None, spatial_embeddings_dim=None, embeddings_scale=None, n_groups=None, between_group_scale=None, observation_error=0.0)[source]#

Bases: MVN

Class that generates data from a multivariate normal distribution for multiple sessions.

Parameters:

  • session_means (np.ndarray or str) – Mean vector for each mode for each session, shape should be (n_sessions, n_modes, n_channels). Either a numpy array or 'zero' or 'random'.

  • session_covariances (np.ndarray or str) – Covariance matrix for each mode for each session, shape should be (n_sessions, n_modes, n_channels, n_channels). Either a numpy array or 'random'.

  • n_modes (int, optional) – Number of modes.

  • n_channels (int, optional) – Number of channels.

  • n_covariances_act (int, optional) – Number of iterations to add activations to covariance matrices.

  • embedding_vectors (np.ndarray, optional) – Embedding vectors for each session, shape should be (n_sessions, embeddings_dim).

  • n_sessions (int, optional) – Number of sessions.

  • embeddings_dim (int, optional) – Dimension of embeddings.

  • spatial_embeddings_dim (int, optional) – Dimension of spatial embeddings.

  • embeddings_scale (float, optional) – Standard deviation when generating embeddings with a normal distribution.

  • n_groups (int, optional) – Number of groups when generating embeddings.

  • between_group_scale (float, optional) – Standard deviation when generating centroids of groups of embeddings.

  • observation_error (float, optional) – Standard deviation of the error added to the generated data.

validate_embedding_parameters(embedding_vectors)[source]#
create_embeddings(embedding_vectors)[source]#
create_linear_transform(input_dim, output_dim, scale=0.1)[source]#
create_session_means_deviations()[source]#
create_session_covariances_deviations()[source]#
create_session_means(option)[source]#
create_session_covariances(eps=1e-06)[source]#
simulate_session_data(session, mode_time_course)[source]#

Simulate single session data.

Parameters:

  • session (int) – Session number.

  • mode_time_course (np.ndarray) – Mode time course. Shape is (n_samples, n_modes).

Returns:

data – Simulated data. Shape is (n_samples, n_channels).

Return type:

np.ndarray

get_session_instantaneous_covariances(session, mode_time_course)[source]#

Get ground truth covariances at each time point for a particular session.

Parameters:

  • session (int) – Session number.

  • mode_time_course (np.ndarray) – Mode time course. Shape is (n_samples, n_modes).

Returns:

inst_covs – Instantaneous covariances for an session. Shape is (n_samples, n_channels, n_channels).

Return type:

np.ndarray

get_instantaneous_covariances(mode_time_courses)[source]#

Get ground truth covariance at each time point for each session.

Parameters:

mode_time_courses (np.ndarray) – Mode time courses. Shape is (n_sessions, n_samples, n_modes).

Returns:

inst_covs – Instantaneous covariances. Shape is (n_sessions, n_samples, n_channels, n_channels).

Return type:

np.ndarray

simulate_multi_session_data(mode_time_courses)[source]#

Simulates data.

Parameters:

mode_time_courses (np.ndarray) – It contains n_sessions time courses. Shape is (n_sessions, n_samples, n_modes).

Returns:

data – Simulated data for sessions. Shape is (n_sessions, n_samples, n_channels).

Return type:

np.ndarray