Source code for policy_modules.premium_parameters

#!/usr/bin/env python
from utility_modules.get_parameters import get_params
import numpy as np


class PremiumParams():
    def __init__(self, policy_dict):
        prem_gid = policy_dict['prem_ratio']
        admin_gid = policy_dict['admin_loading']
        prem_equ = policy_dict['prem_equiv']

        self.prem_df = get_params(prem_gid)
        self.admin_df = get_params(admin_gid)
        self.eq_df = get_params(prem_equ)

    ### administrative loading, depending on firm size
    def admin_load(self, f_size):
        # idx stores the row number
        idx = (self.admin_df.min_firm_size < f_size).sum() - 1
        return self.admin_df.loading[idx]
        
    def prem_ratio(self, premiums):
        av = premiums[['av_value']].values
        max_thresh = np.matrix(self.prem_df['max_av']).T
        ones_mtx = np.ones([self.prem_df.shape[0], av.shape[0]]) 
        max_mtx = np.multiply(max_thresh, ones_mtx).T
        idx = (av >= max_mtx).sum(axis=1)
        idx = np.array(idx.T)[0]

        rtn_df = self.prem_df[['self', 'sp', 'ch', 'fam']].iloc[idx]
        rtn_df['plan_id'] = premiums['plan_id'].values
        return rtn_df

    ### Converting other premiums to the ratio of self premiums
    ### for the purpose of aggregating all the people? 
    def reference_prem(self, premium):
        my_ratio = self.prem_ratio(premium)
#        ones_mtx = np.ones([max_av.shape[0], input_av.shape[0]])

 #       print "ones_mtx:"
  #      print ones_mtx
        
   #     max_mtx = np.multiply(max_av, ones_mtx)
    #    max_mtx = max_mtx.T
        
      #  print "max_mtx"
     #   print max_mtx

       # input_mtx = np.multiply(np.matrix(input_av).T, ones_mtx.T)

        ##print "input_mtx"
        #print input_mtx
        
        #idx_mtx = (input_mtx >= max_mtx).sum(axis = 1)
        #print "idx_mtx is"
        #print idx_mtx
        #return (np.array(self.eq_df.alpha.values[idx_mtx]).T)[0]
        return my_ratio