utility_modules

Submodules

utility_modules.av_module

utility_modules.av_module.oop_av(av_from, av_to, oop_from)[source]
parameters:

av_from: Baseline Actuarial Value av_to: Actuarial Value to change to oop_from: Baseline Out of Pocket

output:

Out of Pocket to update to

utility_modules.av_module.prem_av(av_from, av_to, prem_from)[source]
parameters:

av_from: Baseline Actuarial Value av_to: Actuarial Value to change to prem_from: Baseline Premium

output:

Premium to update to

utility_modules.calibrate_exante_latents

class utility_modules.calibrate_exante_latents.calibrate_exante_latents(calibrate_ex_ante_variables, Latent_vars, output_calib, person_id)[source]

Bases: object

create_ex_ante_latent()[source]

utility_modules.check_mcaid_subsidy

utility_modules.check_mcaid_subsidy.main()[source]
utility_modules.check_mcaid_subsidy.mcaid_elig_check(long_table_path, hieu_path)[source]

utility_modules.checksum

utility_modules.checksum.buffer_file(f_name, block_size=1024)[source]
utility_modules.checksum.md5_checksum(f_name)[source]

utility_modules.dotMap

class utility_modules.dotMap.DotMap[source]

Bases: dict

utility_modules.firm_calibration

SETS THE FIRM LATENT VARIABLES TO MAINTAIN THE EX-ANTE FIRM STATE. THE LATENT VALUE GIVES CHOICE 0 THE LOWEST COST STATE BY THE AMOUNT OF maintain_offer_delta, maintain_nonoffer_delta, aintain_offerPT_delta

class utility_modules.firm_calibration.Firmcal(offer_threshold, noffoer_threshold, ptoffer_threshold, firm_choice)[source]

Bases: object

create_firm_latent()[source]

utility_modules.function_dict

utility_modules.function_dict.assert_type(type_)[source]
utility_modules.function_dict.curr_av(choices, loc, esi)[source]
utility_modules.function_dict.curr_contr(choices, loc, esi, type_)[source]
utility_modules.function_dict.curr_prems(choices, loc, esi, type_)[source]
utility_modules.function_dict.determine_n_kids(choices, loc)[source]
utility_modules.function_dict.family_taxes(choices, loc, hieu, family, FedTax)[source]
utility_modules.function_dict.firm_ids(choices, loc)[source]
utility_modules.function_dict.firm_taxes(choices, loc, firm)[source]
utility_modules.function_dict.historical_prems(choices, loc, esi0, type_)[source]
utility_modules.function_dict.min_value(x, y)[source]
utility_modules.function_dict.plan_ids(choices, loc)[source]
utility_modules.function_dict.populate_fun_dict()[source]
utility_modules.function_dict.rtn_ex_prems(choices, loc, type_, col='silver_premium_')[source]

utility_modules.get_parameters

utility_modules.get_parameters.get_params(f_name, f_type='csv')[source]
parameters:

f_name: name of file f_type: format of file

output:

pandas dataframe workbook

utility_modules.get_parameters.get_parse_params(f_name)[source]

utility_modules.import_zip

utility_modules.import_zip.unzip_to_df(fn)[source]
utility_modules.import_zip.zipdir(path, ziph)[source]

utility_modules.input

class utility_modules.input.Input(config_dict=None, data_path=None)[source]

Bases: object

utility_modules.insurance_choice

utility_modules.insurance_choice.cut_eligs(curr_enum_fam, curr_fam_strct, fam_rltn)[source]
utility_modules.insurance_choice.fam_struct_create()[source]
utility_modules.insurance_choice.insur_choice(input)[source]
utility_modules.insurance_choice.insur_choice_enumerate(curr_fam_strct, elig_lst)[source]
utility_modules.insurance_choice.make_choices(input, names, fam_rltn, elig_lst)[source]
utility_modules.insurance_choice.match_fam_type(choices, names)[source]

utility_modules.math_functions

utility_modules.math_functions.expand_grid(*args, **kwargs)[source]

functionally equivlant as R expand.grid fucntion http://stackoverflow.com/questions/12130883/r-expand-grid-function-in-python

utility_modules.multi_merge

utility_modules.multi_merge.multi_merge(x, y, diff, common, keep_cols=False, cols=None, ncols=None)[source]

x and y: two pandas data frames to merge diff: the columns that change common: the column that to be preserved keep_cols: controls if previous cols are dropped (default = False) cols: specific cols to use in x (default uses all cols in x) ncols: base cols to use; must be in same order and length as cols

utility_modules.multi_merge.new_base_col(col, *args)[source]
utility_modules.multi_merge.new_col(col1, col2)[source]

utility_modules.output

class utility_modules.output.Output(out_path, git_info, general_dict, params_dict, cal_levers_dict, policy_levers_dict, system_dict, is_regional)[source]

Bases: object

build_out_tables(firm_threshold)[source]
force_output(table_type, table_name, table)[source]
initialize_raw_data(raw_data)[source]
Available Data from CalSim.py (self refers to CalSim):

‘perm_ind_choices’ : self.perm_ind_choices, ‘long_output’ : long_output, ‘long_calib_table’ : output_calib, ‘firm_choices_all’ : self.firm_choices_all, ‘Hieu’ : self.Hieu.get_hieu_table(), ‘firm_table’ : self.Firm, ‘fam_table’ : self.Family, ‘worker_table’ : self.Worker, ‘esi_table’ : self.Esi, ‘exp_table’ : self.Expenditures, ‘weights’ : (self.person_count, self.weight_total), ‘dynamic_version’ : self.dynamic_version, ‘dynamic_exchange_statewide_premiums’ : self.statewide_premiums, ‘dynamic_exchange_regional_premiums’ : self.regional_premiums, ‘hieu_table_paired_down’ : self.hieu_table_paired_down, ‘dropped_firms’ : self.dropped_firms, ‘dropped_PT_firms’ : self.dropped_PT_firms, ‘calibrateRun’ : self.calibrateRun, ‘calibrate_ex_ante_latents’ : self.calibrate_ex_ante_latents, ‘Latent_vars’ : self.Latent_vars

initiate_out_table(table_type, table_name, table, index=False)[source]
effect:

adds table to the output list

arguments:
table_type - an integer as follows:

0 option is ‘other’, 1 option is ‘individual_data’, 2 option is ‘tables’, 3 option is ‘aggregate_results’

table_name - desired name of the table table - the dataframe itself

output_tables()[source]

effect: output all tables in list of output tables to the correct path

set_year(year)[source]
write_config()[source]

utility_modules.shuffle

utility_modules.shuffle.shuffle_df(DataFrame)[source]

Module contents