`osl_dynamics.config_api.wrappers`#

Wrapper functions for use in the config API.

All of the functions in this module can be listed in the config passed to osl_dynamics.run_pipeline.

All wrapper functions have the structure:

func(data, output_dir, **kwargs)

where:

data is an osl_dynamics.data.Data object.
output_dir is the path to save output to.
kwargs are keyword arguments for function specific options.

Module Contents#

Functions#

`load_data`(inputs[, kwargs, prepare])	Load and prepare data.
`train_hmm`(data, output_dir, config_kwargs[, ...])	Train a Hidden Markov Model.
`train_dynemo`(data, output_dir, config_kwargs[, ...])	Train DyNeMo.
`train_hive`(data, output_dir, config_kwargs[, ...])	Train a HIVE Model.
`get_inf_params`(data, output_dir[, observation_model_only])	Get inferred alphas.
`plot_power_maps_from_covariances`(data, output_dir[, ...])	Plot power maps calculated directly from the inferred covariances.
`plot_tde_covariances`(data, output_dir)	Plot inferred covariance of the time-delay embedded data.
`plot_state_psds`(data, output_dir)	Plot state PSDs.
`dual_estimation`(data, output_dir[, n_jobs])	Dual estimation for session-specific observation model parameters.
`multitaper_spectra`(data, output_dir, kwargs[, ...])	Calculate multitaper spectra.
`nnmf`(data, output_dir, n_components)	Calculate non-negative matrix factorization (NNMF).
`regression_spectra`(data, output_dir, kwargs)	Calculate regression spectra.
`plot_group_ae_networks`(data, output_dir[, mask_file, ...])	Plot group-level amplitude envelope networks.
`plot_group_tde_hmm_networks`(data, output_dir[, ...])	Plot group-level TDE-HMM networks for a specified frequency band.
`plot_group_nnmf_tde_hmm_networks`(data, output_dir, ...)	Plot group-level TDE-HMM networks using a NNMF component to integrate
`plot_group_tde_dynemo_networks`(data, output_dir[, ...])	Plot group-level TDE-DyNeMo networks for a specified frequency band.
`plot_alpha`(data, output_dir[, session, normalize, ...])	Plot inferred alphas.
`calc_gmm_alpha`(data, output_dir[, kwargs])	Binarize inferred alphas using a two-component GMM.
`plot_hmm_network_summary_stats`(data, output_dir[, ...])	Plot HMM summary statistics for networks as violin plots.
`plot_dynemo_network_summary_stats`(data, output_dir)	Plot DyNeMo summary statistics for networks as violin plots.
`compare_groups_hmm_summary_stats`(data, output_dir, ...)	Compare HMM summary statistics between two groups.
`plot_burst_summary_stats`(data, output_dir[, ...])	Plot burst summary statistics as violin plots.

Attributes#

_logger

osl_dynamics.config_api.wrappers._logger[source]#

osl_dynamics.config_api.wrappers.load_data(inputs, kwargs=None, prepare=None)[source]#

Load and prepare data.

Parameters:

inputs (str) – Path to directory containing npy files.
kwargs (dict, optional) – Keyword arguments to pass to the Data class. Useful keyword arguments to pass are sampling_frequency, mask_file and parcellation_file.
prepare (dict, optional) –
Methods dict to pass to the prepare method. See docstring for Data.prepare.

Returns:

data – Data object.

Return type:

osl_dynamics.data.Data

osl_dynamics.config_api.wrappers.train_hmm(data, output_dir, config_kwargs, init_kwargs=None, fit_kwargs=None, save_inf_params=True)[source]#

Train a Hidden Markov Model.

This function will:

Build an hmm.Model object.
Initialize the parameters of the model using Model.random_state_time_course_initialization.
Perform full training.
Save the inferred parameters (state probabilities, means and covariances) if save_inf_params=True.

This function will create two directories:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters. This directory is only created if save_inf_params=True.

Parameters:

data (osl_dynamics.data.Data) – Data object for training the model.
output_dir (str) – Path to output directory.

config_kwargs (dict) –

Keyword arguments to pass to hmm.Config. Defaults to:

{'sequence_length': 2000,
 'batch_size': 32,
 'learning_rate': 0.01,
 'n_epochs': 20}.

init_kwargs (dict, optional) –
Keyword arguments to pass to Model.random_state_time_course_initialization. Defaults to:
```
{'n_init': 3, 'n_epochs': 1}.
```
fit_kwargs (dict, optional) – Keyword arguments to pass to the Model.fit. No defaults.
save_inf_params (bool, optional) – Should we save the inferred parameters?

osl_dynamics.config_api.wrappers.train_dynemo(data, output_dir, config_kwargs, init_kwargs=None, fit_kwargs=None, save_inf_params=True)[source]#

Train DyNeMo.

This function will:

Build a dynemo.Model object.
Initialize the parameters of the model using Model.random_subset_initialization.
Perform full training.
Save the inferred parameters (mode mixing coefficients, means and covariances) if save_inf_params=True.

This function will create two directories:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.

Parameters:

data (osl_dynamics.data.Data) – Data object for training the model.
output_dir (str) – Path to output directory.

config_kwargs (dict) –

Keyword arguments to pass to dynemo.Config. Defaults to:

{'n_channels': data.n_channels.
 'sequence_length': 200,
 'inference_n_units': 64,
 'inference_normalization': 'layer',
 'model_n_units': 64,
 'model_normalization': 'layer',
 'learn_alpha_temperature': True,
 'initial_alpha_temperature': 1.0,
 'do_kl_annealing': True,
 'kl_annealing_curve': 'tanh',
 'kl_annealing_sharpness': 10,
 'n_kl_annealing_epochs': 20,
 'batch_size': 128,
 'learning_rate': 0.01,
 'lr_decay': 0.1,
 'n_epochs': 40}

init_kwargs (dict, optional) –
Keyword arguments to pass to Model.random_subset_initialization. Defaults to:
```
{'n_init': 5, 'n_epochs': 2, 'take': 1}.
```
fit_kwargs (dict, optional) – Keyword arguments to pass to the Model.fit.
save_inf_params (bool, optional) – Should we save the inferred parameters?

osl_dynamics.config_api.wrappers.train_hive(data, output_dir, config_kwargs, init_kwargs=None, fit_kwargs=None, save_inf_params=True)[source]#

Train a HIVE Model.

This function will:

Build an hive.Model object.
Initialize the parameters of the HIVE model using
Model.random_state_time_course_initialization.
Perform full training.
Save the inferred parameters (state probabilities, means,
covariances and embeddings) if save_inf_params=True.

This function will create two directories:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.
This directory is only created if save_inf_params=True.

Parameters:

data (osl_dynamics.data.Data) – Data object for training the model.
output_dir (str) – Path to output directory.

config_kwargs (dict) –

Keyword arguments to pass to hive.Config. Defaults to:

{
    'sequence_length': 200,
    'spatial_embeddings_dim': 2,
    'dev_n_layers': 5,
    'dev_n_units': 32,
    'dev_activation': 'tanh',
    'dev_normalization': 'layer',
    'dev_regularizer': 'l1',
    'dev_regularizer_factor': 10,
    'batch_size': 128,
    'learning_rate': 0.005,
    'lr_decay': 0.1,
    'n_epochs': 30,
    'do_kl_annealing': True,
    'kl_annealing_curve': 'tanh',
    'kl_annealing_sharpness': 10,
    'n_kl_annealing_epochs': 15,
}.

init_kwargs (dict, optional) –
Keyword arguments to pass to Model.random_state_time_course_initialization. Defaults to:
```
{'n_init': 10, 'n_epochs': 2}.
```
fit_kwargs (dict, optional) – Keyword arguments to pass to the Model.fit. No defaults.
save_inf_params (bool, optional) – Should we save the inferred parameters?

osl_dynamics.config_api.wrappers.get_inf_params(data, output_dir, observation_model_only=False)[source]#

Get inferred alphas.

This function expects a model has already been trained and the following directory to exist:

<output_dir>/model, which contains the trained model.

This function will create the following directory:

<output_dir>/inf_params, which contains the inferred parameters.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
observation_model_only (bool, optional) – We we only want to get the observation model parameters?

osl_dynamics.config_api.wrappers.plot_power_maps_from_covariances(data, output_dir, mask_file=None, parcellation_file=None, power_save_kwargs=None)[source]#

Plot power maps calculated directly from the inferred covariances.

This function expects a model has already been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will output files called covs_.png which contain plots of the power map of each state/mode taken directly from the inferred covariance matrices. The files will be saved to <output_dir>/inf_params.

This function also expects the data to be prepared in the same script that this wrapper is called from.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
mask_file (str, optional) – Mask file used to preprocess the training data. If None, we use data.mask_file.
parcellation_file (str, optional) – Parcellation file used to parcellate the training data. If None, we use data.parcellation_file.

power_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.power.save. Defaults to:

{'filename': '<inf_params_dir>/covs_.png',
 'mask_file': data.mask_file,
 'parcellation_file': data.parcellation_file,
 'plot_kwargs': {'symmetric_cbar': True}}

osl_dynamics.config_api.wrappers.plot_tde_covariances(data, output_dir)[source]#

Plot inferred covariance of the time-delay embedded data.

This function expects a model has already been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will output a tde_covs.png file containing a plot of the covariances in the <output_dir>/inf_params directory.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.

osl_dynamics.config_api.wrappers.plot_state_psds(data, output_dir)[source]#

Plot state PSDs.

This function expects multitaper spectra to have already been calculated and are in:

<output_dir>/spectra.

This function will output a file called psds.png which contains a plot of each state PSD.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.

osl_dynamics.config_api.wrappers.dual_estimation(data, output_dir, n_jobs=1)[source]#

Dual estimation for session-specific observation model parameters.

This function expects a model has already been trained and the following directories to exist:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.

This function will create the following directory:

<output_dir>/dual_estimates, which contains the session-specific means and covariances.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
n_jobs (int, optional) – Number of jobs to run in parallel.

osl_dynamics.config_api.wrappers.multitaper_spectra(data, output_dir, kwargs, nnmf_components=None)[source]#

Calculate multitaper spectra.

This function expects a model has already been trained and the following directories exist:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.

This function will create the following directory:

<output_dir>/spectra, which contains the post-hoc spectra.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
kwargs (dict) –
Keyword arguments to pass to analysis.spectral.multitaper_spectra. Defaults to:
```
{'sampling_frequency': data.sampling_frequency,
 'keepdims': True}
```
nnmf_components (int, optional) – Number of non-negative matrix factorization (NNMF) components to fit to the stacked session-specific coherence spectra.

osl_dynamics.config_api.wrappers.nnmf(data, output_dir, n_components)[source]#

Calculate non-negative matrix factorization (NNMF).

This function expects spectra have already been calculated and are in:

<output_dir>/spectra, which contains multitaper spectra.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
n_components (int) – Number of components to fit.

osl_dynamics.config_api.wrappers.regression_spectra(data, output_dir, kwargs)[source]#

Calculate regression spectra.

This function expects a model has already been trained and the following directories exist:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.

This function will create the following directory:

<output_dir>/spectra, which contains the post-hoc spectra.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.

kwargs (dict) –

Keyword arguments to pass to analysis.spectral.regress_spectra. Defaults to:

{'sampling_frequency': data.sampling_frequency,
 'window_length': 4 * sampling_frequency,
 'step_size': 20,
 'n_sub_windows': 8,
 'return_coef_int': True,
 'keepdims': True}

osl_dynamics.config_api.wrappers.plot_group_ae_networks(data, output_dir, mask_file=None, parcellation_file=None, aec_abs=True, power_save_kwargs=None, conn_save_kwargs=None)[source]#

Plot group-level amplitude envelope networks.

This function expects a model has been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will create:

<output_dir>/networks, which contains plots of the networks.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
mask_file (str, optional) – Mask file used to preprocess the training data. If None, we use data.mask_file.
parcellation_file (str, optional) – Parcellation file used to parcellate the training data. If None, we use data.parcellation_file.
aec_abs (bool, optional) – Should we take the absolute value of the amplitude envelope correlations?

power_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.power.save. Defaults to:

{'filename': '<output_dir>/networks/mean_.png',
 'mask_file': data.mask_file,
 'parcellation_file': data.parcellation_file,
 'plot_kwargs': {'symmetric_cbar': True}}

conn_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.connectivity.save. Defaults to:

{'parcellation_file': parcellation_file,
 'filename': '<output_dir>/networks/aec_.png',
 'threshold': 0.97}

osl_dynamics.config_api.wrappers.plot_group_tde_hmm_networks(data, output_dir, mask_file=None, parcellation_file=None, frequency_range=None, percentile=97, power_save_kwargs=None, conn_save_kwargs=None)[source]#

Plot group-level TDE-HMM networks for a specified frequency band.

This function will:

Plot state PSDs.
Plot the power maps.
Plot coherence networks.

This function expects spectra have already been calculated and are in:

<output_dir>/spectra, which contains multitaper spectra.

This function will create:

<output_dir>/networks, which contains plots of the networks.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
mask_file (str, optional) – Mask file used to preprocess the training data. If None, we use data.mask_file.
parcellation_file (str, optional) – Parcellation file used to parcellate the training data. If None, we use data.parcellation_file.
frequency_range (list, optional) – List of length 2 containing the minimum and maximum frequency to integrate spectra over. Defaults to the full frequency range.
percentile (float, optional) – Percentile for thresholding the coherence networks. Default is 97, which corresponds to the top 3% of edges (relative to the mean across states).

power_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.power.save. Defaults to:

{'mask_file': mask_file,
 'parcellation_file': parcellation_file,
 'filename': '<output_dir>/networks/pow_.png',
 'subtract_mean': True,
 'plot_kwargs': {'symmetric_cbar': True}}

conn_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.connectivity.save. Defaults to:

{'parcellation_file': parcellation_file,
 'filename': '<output_dir>/networks/coh_.png',
 'plot_kwargs': {'edge_cmap': 'Reds'}}

osl_dynamics.config_api.wrappers.plot_group_nnmf_tde_hmm_networks(data, output_dir, nnmf_file, mask_file=None, parcellation_file=None, component=0, percentile=97, power_save_kwargs=None, conn_save_kwargs=None)[source]#

Plot group-level TDE-HMM networks using a NNMF component to integrate the spectra.

This function will:

Plot state PSDs.
Plot the power maps.
Plot coherence networks.

This function expects spectra have already been calculated and are in:

<output_dir>/spectra, which contains multitaper spectra.

This function will create:

<output_dir>/networks, which contains plots of the networks.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
nnmf_file (str) – Path relative to output_dir for a npy file (with the output of analysis.spectral.decompose_spectra) containing the NNMF components.
mask_file (str, optional) – Mask file used to preprocess the training data. If None, we use data.mask_file.
parcellation_file (str, optional) – Parcellation file used to parcellate the training data. If None, we use data.parcellation_file.
component (int, optional) – NNMF component to plot. Defaults to the first component.
percentile (float, optional) – Percentile for thresholding the coherence networks. Default is 97, which corresponds to the top 3% of edges (relative to the mean across states).

power_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.power.save. Defaults to:

{'mask_file': mask_file,
 'parcellation_file': parcellation_file,
 'component': component,
 'filename': '<output_dir>/networks/pow_.png',
 'subtract_mean': True,
 'plot_kwargs': {'symmetric_cbar': True}}

conn_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.connectivity.save. Defaults to:

{'parcellation_file': parcellation_file,
 'component': component,
 'filename': '<output_dir>/networks/coh_.png',
 'plot_kwargs': {'edge_cmap': 'Reds'}}

osl_dynamics.config_api.wrappers.plot_group_tde_dynemo_networks(data, output_dir, mask_file=None, parcellation_file=None, frequency_range=None, percentile=97, power_save_kwargs=None, conn_save_kwargs=None)[source]#

Plot group-level TDE-DyNeMo networks for a specified frequency band.

This function will:

Plot mode PSDs.
Plot the power maps.
Plot coherence networks.

This function expects spectra have already been calculated and are in:

<output_dir>/spectra, which contains regression spectra.

This function will create:

<output_dir>/networks, which contains plots of the networks.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
mask_file (str, optional) – Mask file used to preprocess the training data. If None, we use data.mask_file.
parcellation_file (str, optional) – Parcellation file used to parcellate the training data. If None, we use data.parcellation_file.
frequency_range (list, optional) – List of length 2 containing the minimum and maximum frequency to integrate spectra over. Defaults to the full frequency range.
percentile (float, optional) – Percentile for thresholding the coherence networks. Default is 97, which corresponds to the top 3% of edges (relative to the mean across states).

plot_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.power.save. Defaults to:

{'mask_file': mask_file,
 'parcellation_file': parcellation_file,
 'filename': '<output_dir>/networks/pow_.png',
 'subtract_mean': True,
 'plot_kwargs': {'symmetric_cbar': True}}

conn_save_kwargs (dict, optional) –

Keyword arguments to pass to analysis.connectivity.save. Defaults to:

{'parcellation_file': parcellation_file,
 'filename': '<output_dir>/networks/coh_.png',
 'plot_kwargs': {'edge_cmap': 'Reds'}}

osl_dynamics.config_api.wrappers.plot_alpha(data, output_dir, session=0, normalize=False, sampling_frequency=None, kwargs=None)[source]#

Plot inferred alphas.

This is a wrapper for utils.plotting.plot_alpha.

This function expects a model has been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will create:

<output_dir>/alphas, which contains plots of the inferred alphas.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
session (int, optional) – Index for session to plot. If ‘all’ is passed we create a separate plot for each session.
normalize (bool, optional) – Should we also plot the alphas normalized using the trace of the inferred covariance matrices? Useful if we are plotting the inferred alphas from DyNeMo.
sampling_frequency (float, optional) – Sampling frequency in Hz. If None, we see if it is present in data.sampling_frequency.

kwargs (dict, optional) –

Keyword arguments to pass to utils.plotting.plot_alpha. Defaults to:

{'sampling_frequency': data.sampling_frequency,
 'filename': '<output_dir>/alphas/alpha_*.png'}

osl_dynamics.config_api.wrappers.calc_gmm_alpha(data, output_dir, kwargs=None)[source]#

Binarize inferred alphas using a two-component GMM.

This function expects a model has been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will create the following file:

<output_dir>/inf_params/gmm_alp.pkl, which contains the binarized alphas.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
kwargs (dict, optional) – Keyword arguments to pass to inference.modes.gmm_time_courses.

osl_dynamics.config_api.wrappers.plot_hmm_network_summary_stats(data, output_dir, use_gmm_alpha=False, sampling_frequency=None, sns_kwargs=None)[source]#

Plot HMM summary statistics for networks as violin plots.

This function will plot the distribution over sessions for the following summary statistics:

Fractional occupancy.
Mean lifetime (s).
Mean interval (s).
Switching rate (Hz).

This function expects a model has been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will create:

<output_dir>/summary_stats, which contains plots of the summary statistics.

The <output_dir>/summary_stats directory will also contain numpy files with the summary statistics.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
use_gmm_alpha (bool, optional) – Should we use alphas binarised using a Gaussian mixture model? This function assumes calc_gmm_alpha has been called and the file <output_dir>/inf_params/gmm_alp.pkl exists.
sampling_frequency (float, optional) – Sampling frequency in Hz. If None, we use data.sampling_frequency.
sns_kwargs (dict, optional) – Arguments to pass to sns.violinplot().

osl_dynamics.config_api.wrappers.plot_dynemo_network_summary_stats(data, output_dir)[source]#

Plot DyNeMo summary statistics for networks as violin plots.

This function will plot the distribution over sessions for the following summary statistics:

Mean (renormalised) mixing coefficients.
Standard deviation of (renormalised) mixing coefficients.

This function expects a model has been trained and the following directories to exist:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.

This function will create:

<output_dir>/summary_stats, which contains plots of the summary statistics.

The <output_dir>/summary_stats directory will also contain numpy files with the summary statistics.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.

osl_dynamics.config_api.wrappers.compare_groups_hmm_summary_stats(data, output_dir, group2_indices, separate_tests=False, covariates=None, n_perm=1000, n_jobs=1, sampling_frequency=None)[source]#

Compare HMM summary statistics between two groups.

This function expects a model has been trained and the following directory to exist:

<output_dir>/inf_params, which contains the inferred parameters.

This function will create:

<output_dir>/group_diff, which contains the summary statistics and plots.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
group2_indices (np.ndarray or list) – Indices indicating which sessions belong to the second group.
separate_tests (bool, optional) – Should we perform a maximum statistic permutation test for each summary statistic separately?
covariates (str, optional) –
Path to a pickle file containing a dict with covariances. Each item in the dict must be the covariate name and value for each session. The covariates will be loaded with:
```
from osl_dynamics.utils.misc import load
covariates = load("/path/to/file.pkl")
```
Example covariates:
```
covariates = {"age": [...], "sex": [...]}
```
n_perm (int, optional) – Number of permutations.
n_jobs (int, optional) – Number of jobs for parallel processing.
sampling_frequency (float, optional) – Sampling frequency in Hz. If None, we use data.sampling_frequency.

osl_dynamics.config_api.wrappers.plot_burst_summary_stats(data, output_dir, sampling_frequency=None)[source]#

Plot burst summary statistics as violin plots.

This function will plot the distribution over sessions for the following summary statistics:

Mean lifetime (s).
Mean interval (s).
Burst count (Hz).
Mean amplitude (a.u.).

This function expects a model has been trained and the following directories to exist:

<output_dir>/model, which contains the trained model.
<output_dir>/inf_params, which contains the inferred parameters.

This function will create:

<output_dir>/summary_stats, which contains plots of the summary statistics.

The <output_dir>/summary_stats directory will also contain numpy files with the summary statistics.

Parameters:

data (osl_dynamics.data.Data) – Data object.
output_dir (str) – Path to output directory.
sampling_frequency (float, optional) – Sampling frequency in Hz. If None, we use data.sampling_frequency.

osl_dynamics.config_api.wrappers#

Module Contents#

Functions#

Attributes#

`osl_dynamics.config_api.wrappers`#