Source code for policy_modules.fpl

#!/usr/bin/env python
from utility_modules.get_parameters import get_params
from policy_modules.calculate_regression import FplRegression

import pandas as pd
import numpy as np

[docs]class Fpl:

    # Borg singleton config object
    __shared_state = {}

    def __init__(self, policy_params = None, sys_params = None):

        # implement the borg pattern
        self.__dict__ = self.__shared_state

        if policy_params != None:
            #read in data
            gid_mcaid = policy_params['mcaid_elig']
            gid_fpl = policy_params['fpl']

            max_year = int(sys_params['max_year'])

            #read in fpl data
            self.fpl = get_params(gid_fpl)
            self.regression_model= FplRegression(policy_params)

            #make a dataframe for prediction
            if (max(self.fpl.year) < max_year):
                future = pd.DataFrame(range(max(self.fpl.year)+1, max_year+1))
                future.columns = ['year']
                #predict new years and return data
                future['base'] = self.regression_model.calculate_regression_line(self.fpl.year, self.fpl.base, future[['year']])
                future['inc']  = self.regression_model.calculate_regression_line(self.fpl.year, self.fpl.inc, future[['year']])
                self.fpl = self.fpl.append(future)


[docs]    def set_year(self, curr_year):
        """
        return slice of pertinant year
        allows this to be reset at begining of each simulation year
        """

        if curr_year not in self.fpl.year.values:
            return
        else:
            self.base = self.fpl[self.fpl.year == curr_year][['base']].values
            self.inc =  self.fpl[self.fpl.year == curr_year][['inc']].values



[docs]    def calculate_fpl(self, df):
        """
        calculate fpl based off of income
        note: data specified from set_year method

        curr_data     pd.DataFrame of current year fpl data
        income:       pd.series of income
        family_size:  pd.series of family_size
        """

        income = df['fam_income']
        family_size = df['family_size']
        base = np.repeat(self.base, df.shape[0])
        inc = np.repeat(self.inc, df.shape[0])
        fpl = income/(base + (family_size-1)*inc) * 100

        return(fpl)



[docs]    def calculate_ind_fpl(self, df):
        """
        calculate individual_fpl based on individual_Income
        note: data specified from set_year method

        curr_data     pd.DataFrame of current year fpl data
        ind_income:       pd.series of ind_income
        family_size:  always 1
        """

        income = df['ind_income']
        family_size = 1
        base = np.repeat(self.base, df.shape[0])
        inc = np.repeat(self.inc, df.shape[0])
        ind_fpl = income / (base + (family_size - 1) * inc) * 100

        return (ind_fpl)

    # from Ben -- calculating the income threshold for a FPL value

[docs]    def calculate_income(self, fpl, family_size, df):
        """
        calculate income based off of fpl
        note: data specified from set_year method

        curr_data     pd.DataFrame of current year fpl data
        fpl:       pd.series of fpl
        family_size:  pd.series of family_size
        """
        base = np.repeat(self.base, df.shape[0])
        inc = np.repeat(self.inc, df.shape[0])
        income = fpl*((base + (family_size-1)*inc) / 100)
        return(income)