PostProcessing

This module collects some useful post-processing functions that can be used in the Dataset class.

mppi.Datasets.PostProcessing.pw_get_energy(dataset)

Extract the total energy from the results dictionary of the dataset.

Parameters:

dataset (Dataset) – the instance of Dataset

Returns:

dictionary with the energy (in Hartree) for all the (computed) runs

of the dataset

Return type:

dict

mppi.Datasets.PostProcessing.pw_get_gap(dataset)

Extract the value of the gap from the results dictionary of the dataset.

Parameters:

dataset (Dataset) – the instance of Dataset

Returns:

dictionary with the gap (in eV) for all the (computed) runs

of the dataset. Information on the nature of the gap (direct or indirect) are written on terminal

Return type:

dict

mppi.Datasets.PostProcessing.pw_parse_data(dataset)

Apply the PwParser to the elements of the results dictionary of the dataset.

Parameters:

dataset (Dataset) – the instance of Dataset

Returns:

dictionary with the parsed data for all the (computed) runs

of the dataset

Return type:

dict

mppi.Datasets.PostProcessing.yambo_get_gap(dataset)

Extract the value of the gap from the results dictionary of the dataset. The function requires that the parameters k_full and band_full are defined as kwargs in the dataset, and the optional paremeters k_empty and band_empty can be provided in the same way.

Parameters:

dataset (Dataset) – the instance of Dataset

Returns:

dictionary with the gap (in eV) for all the (computed) runs

of the dataset. Information on the gap are written on terminal if the verbose option of the Dataset is True

Return type:

dict

mppi.Datasets.PostProcessing.yambo_parse_data(dataset)

Apply the YamboParser to the elements of the results dictionary of the dataset.

Parameters:

dataset (Dataset) – the instance of Dataset

Returns:

dictionary with the parsed data for all the (computed) runs

of the dataset

Return type:

dict