Source code for policy_modules.premium_calculate

#!/usr/bin/env python
import re

from utility_modules.get_parameters import get_params
import pandas as pd
import numpy as np
import pdb


[docs]class CalculatePremium():
    def __init__(self, policy_params, year):
        gid_subsidy = policy_params['prem_sub']
        self.prem_sub = get_params(gid_subsidy)

        #subset to only values in designated year
        self.premium_sub = self.prem_sub[(self.prem_sub.year==year) & (self.prem_sub.metal_tier=='silver')]
        self.premium_sub= self.premium_sub.reset_index(drop=True)

        #subset from subset table
        self.fpl_bracket = np.ndarray((2,  self.premium_sub.fpl_lower.shape[0]))
        self.fpl_bracket[0,:]= self.premium_sub.fpl_lower
        self.fpl_bracket[1,:]= self.premium_sub.fpl_upper
        
        self.beta_0= self.premium_sub.beta0
        self.beta_1= self.premium_sub.beta1



[docs]    def get_fpl_bracket(self):
        return self.fpl_bracket



[docs]    def get_beta_0(self):
        return self.beta_0



[docs]    def get_beta_1(self):
        return self.beta_1



[docs]    def get_year(self):
        return self.year



[docs]    def calculate_premium(self, fpl, pop_df):

        fpl_check = [fpl-self.fpl_bracket[1, i] for i in range(len(self.fpl_bracket[0,]))]
        fpl_index = sum(x>0 for x in fpl_check)

        #If income is below 100% FPL, they are treated as if they have income at 100% FPL
        if(fpl<100):
            fpl=100
        prem_sub_percent= fpl*self.beta_1[fpl_index]+ self.beta_0[fpl_index]

        return prem_sub_percent


##########################
#                        #
#      Qiyang's code     #
#                        #
##########################


[docs]    def calculate_premium_h(self, fpl_base, incomeArr):
        xp = self.premium_sub.fpl_lower/100.0 * fpl_base
        fp = self.premium_sub.p_lower/100.0 * fpl_base
        xp[0] = xp[1] - 0.1 # when fpl is less than 133%, it is in medicaid region
        premArr = np.interp(incomeArr, xp, fp)
        
        return premArr


    # famMemberLst[0:'age', 1:'undoc', 2:'prem cap', 3:'plan_id', 4:'firm_id', 5:'Pr1')]

[docs]    def prem_cap_sum(self, famMemLst, esi_drop):
        ppsum_undoc = 0
        ppsum_doc = 0
        for m in famMemLst :
            #if esi_drop and self.planIsInFirm(m[3], m[4]): continue
            if m[2] == None: continue
            if m[1] :
                ppsum_undoc += m[2]
            else :
                ppsum_doc += m[2]
        return (ppsum_doc, ppsum_undoc)



[docs]    def calculate_premium_h_df(self, incomeArr, df, esi_drop):

        x = df['income'].shape[0]
        premArr = np.empty((x, 1), dtype=float)
        for ind, tr in df.iterrows():
            income          = incomeArr[int(ind)]
            fpl_base        = tr['fpl_base']
            famMemLst       = tr['fam_members']

            prem_cap        = self.prem_cap_sum(famMemLst, esi_drop)

            xp = self.premium_sub.fpl_lower/100.0 * fpl_base
            fp = self.premium_sub.p_lower/100.0 * income

            xp[0] = xp[1] - 0.1 # when fpl is less than 133%, it is in medicaid region
            premArr[int(ind)] = min(prem_cap[0], np.interp(income, xp.astype(float), fp.astype(float))) + prem_cap[1]

        return premArr