This module provides an API to load parameters for the model functions in order to produce surrogate networks that have statistics similar to the original temporal networks SocioPatterns HT09 and one week of DTU data.

tacoma.load_model_parameters.load_dtu_ZSBB_params()[source]

Load the standard parameters to generate surrogate networks for one week of DTU data using the ZSBB model.

Returns: The kwargs to pass to tacoma.ZSBB_model(). dict
tacoma.load_model_parameters.load_dtu_dyn_RGG_params()[source]

Load the standard parameters to generate surrogate networks for one week of DTU data using the dynamic RGG model.

Returns: The kwargs to pass to tacoma.dynamic_RGG(). dict
tacoma.load_model_parameters.load_dtu_flockwork_params(scaled=False)[source]

Load the standard parameters to generate surrogate networks for one week of DTU data using the Flockwork-P model with varying rates.

Parameters: scaled (bool, optional) – If this is True, load the rewiring rate gamma(t) and proba- bility to reconnect P(t) rescaled with a corrective factor. This factor emerges because in the original network the mean degree is overestimated due to binning of edges. This overestimation typically leads to an underestimation of gamma(t) and an overestimation of P(t). The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_dtu_flockwork_scaled_params()[source]

Load the standard parameters to generate surrogate networks for one week of DTU data using the Flockwork-P model with varying rates, corrected for overestimation of the mean degree in the original data.

Returns: The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_dtu_flockwork_unscaled_params()[source]

Load the standard parameters to generate surrogate networks for one week of DTU data using the Flockwork-P model with varying rates, not corrected for overestimation of the mean degree in the original data.

Returns: The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_hs13_ZSBB_params()[source]

Load the standard parameters to generate SocioPatterns HS13 surrogate networks using the ZSBB model.

Returns: The kwargs to pass to tacoma.ZSBB_model(). dict
tacoma.load_model_parameters.load_hs13_dyn_RGG_params()[source]

Load the standard parameters to generate SocioPatterns HS13 surrogate networks using the dynamic RGG model.

Returns: The kwargs to pass to tacoma.dynamic_RGG(). dict
tacoma.load_model_parameters.load_hs13_flockwork_params(scaled=False)[source]

Load the standard parameters to generate SocioPatterns HS13 surrogate networks using the Flockwork-P model with varying rates.

Parameters: scaled (bool, optional) – If this is True, load the rewiring rate gamma(t) and proba- bility to reconnect P(t) rescaled with a corrective factor. This factor emerges because in the original network the mean degree is overestimated due to binning of edges. This overestimation typically leads to an underestimation of gamma(t) and an overestimation of P(t). The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_hs13_flockwork_scaled_params()[source]

Load the standard parameters to generate SocioPatterns HS13 surrogate networks using the Flockwork-P model with varying rates, corrected for overestimation of the mean degree in the original data.

Returns: The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_hs13_flockwork_unscaled_params()[source]

Load the standard parameters to generate SocioPatterns HS13 surrogate networks using the Flockwork-P model with varying rates, not corrected for overestimation of the mean degree in the original data.

Returns: The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_ht09_ZSBB_params()[source]

Load the standard parameters to generate SocioPatterns HT09 surrogate networks using the ZSBB model.

Returns: The kwargs to pass to tacoma.ZSBB_model(). dict
tacoma.load_model_parameters.load_ht09_dyn_RGG_params()[source]

Load the standard parameters to generate SocioPatterns HT09 surrogate networks using the dynamic RGG model.

Returns: The kwargs to pass to tacoma.dynamic_RGG(). dict
tacoma.load_model_parameters.load_ht09_flockwork_params(scaled=False)[source]

Load the standard parameters to generate SocioPatterns HT09 surrogate networks using the Flockwork-P model with varying rates.

Parameters: scaled (bool, optional) – If this is True, load the rewiring rate gamma(t) and proba- bility to reconnect P(t) rescaled with a corrective factor. This factor emerges because in the original network the mean degree is overestimated due to binning of edges. This overestimation typically leads to an underestimation of gamma(t) and an overestimation of P(t). The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_ht09_flockwork_scaled_params()[source]

Load the standard parameters to generate SocioPatterns HT09 surrogate networks using the Flockwork-P model with varying rates, corrected for overestimation of the mean degree in the original data.

Returns: The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict
tacoma.load_model_parameters.load_ht09_flockwork_unscaled_params()[source]

Load the standard parameters to generate SocioPatterns HT09 surrogate networks using the Flockwork-P model with varying rates, not corrected for overestimation of the mean degree in the original data.

Returns: The kwargs to pass to tacoma.flockwork_P_varying_rates(). dict