calib_params.py

get_emod_calib_params(exp, scen_df)

Retrieves and prepares the calibration parameters for EMOD.

This function loads calibration parameters from a pre-defined CSV file and uses them to create a list of partial functions that will set specific model parameters.

Parameters:
  • exp (Experiment) –

    An object containing experiment configuration, including the paths to input files, the number of seeds to use, and the step of the simulation (e.g., ‘burnin’).

  • scen_df (DataFrame) –

    A DataFrame containing scenario-specific information, including a pointer to the row in the calibration configuration CSV file.
Returns:
  • list –

    A list of lists, where each inner list contains partial functions (from functools.partial) that, when called, will set the corresponding model parameters for the EMOD simulation.
Source code in EMOD\functions\calib_params.py 
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def get_emod_calib_params(exp, scen_df):
    """
    Retrieves and prepares the calibration parameters for EMOD.

    This function loads calibration parameters from a pre-defined CSV file and uses them to create a list of partial
    functions that will set specific model parameters.

    Args:
        exp (Experiment): An object containing experiment configuration, including the paths to input files,
                           the number of seeds to use, and the step of the simulation (e.g., 'burnin').
        scen_df (pandas.DataFrame): A DataFrame containing scenario-specific information, including a pointer
                                     to the row in the calibration configuration CSV file.

    Returns:
        list: A list of lists, where each inner list contains partial functions (from `functools.partial`) that, when called,
              will set the corresponding model parameters for the EMOD simulation.
    """

    calib_config_df = pd.read_csv(os.path.join(exp.emod_input_path, 'emod_best.csv'))
    if exp.emod_step == 'burnin':
        seeds = exp.num_seeds_burnin
    else:
        seeds = exp.num_seeds
    calib_params = [[
        partial(set_param, param='Antigen_Switch_Rate', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Antigen_Switch_Rate'])),
        partial(set_param, param='Base_Gametocyte_Fraction_Male', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Base_Gametocyte_Fraction_Male'])),
        partial(set_param, param='Base_Gametocyte_Mosquito_Survival_Rate', value = float  (calib_config_df.loc[int(row.calib_config_pointer), 'Base_Gametocyte_Mosquito_Survival_Rate'])),
        partial(set_param, param='Base_Gametocyte_Production_Rate', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Base_Gametocyte_Production_Rate'])),
        partial(set_param, param='Falciparum_MSP_Variants', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Falciparum_MSP_Variants'])),
        partial(set_param, param='Falciparum_Nonspecific_Types', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Falciparum_Nonspecific_Types'])),
        partial(set_param, param='Falciparum_PfEMP1_Variants', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Falciparum_PfEMP1_Variants'])),
        partial(set_param, param='Fever_IRBC_Kill_Rate', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Fever_IRBC_Kill_Rate'])),
        partial(set_param, param='Gametocyte_Stage_Survival_Rate', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Gametocyte_Stage_Survival_Rate'])),
        partial(set_param, param='MSP1_Merozoite_Kill_Fraction', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'MSP1_Merozoite_Kill_Fraction'])),
        partial(set_param, param='Nonspecific_Antibody_Growth_Rate_Factor', value = float  (calib_config_df.loc[int(row.calib_config_pointer), 'Nonspecific_Antibody_Growth_Rate_Factor'])),
        partial(set_param, param='Nonspecific_Antigenicity_Factor', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Nonspecific_Antigenicity_Factor'])),
        partial(set_param, param='Pyrogenic_Threshold', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Pyrogenic_Threshold'])),
        partial(set_param, param='Max_Individual_Infections', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Max_Individual_Infections'])),
        partial(set_param, param='Erythropoiesis_Anemia_Effect', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Erythropoiesis_Anemia_Effect'])),
        partial(set_param, param='RBC_Destruction_Multiplier', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'RBC_Destruction_Multiplier'])),
        partial(set_param, param='Max_Individual_Infections', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Max_Individual_Infections'])),
        partial(set_param, param='Cytokine_Gametocyte_Inactivation', value = float(calib_config_df.loc[int(row.calib_config_pointer), 'Cytokine_Gametocyte_Inactivation'])),
        partial(set_param, param='Innate_Immune_Variation_Type', value = str(calib_config_df.loc[int(row.calib_config_pointer), 'Innate_Immune_Variation_Type']))]
        for i, row in scen_df.iterrows()
        for s in range(seeds)]
    return calib_params