import numpy as np
import matplotlib.pyplot as plt
from multipolyfit import multipolyfit as mpf


class ConsumerNotEligibleError(Exception):
    def __init__(self, reason, *args):
        super().__init__(args)
        self.reason = reason

    def __str__(self):
        return f'Consumer is ineligible due to {self.reason}'


def fico_default_risk_modifier_by_lend_percent(credit_score, loan_percent):
    # The initial data set is organized as [{credit_score}, {loan_percent}]
    initial_data = [[620, 1],
                    [620, 70],
                    [620, 85],
                    [620, 90],
                    [620, 95],
                    [660, 1],
                    [660, 70],
                    [660, 85],
                    [660, 90],
                    [660, 95],
                    [700, 1],
                    [700, 70],
                    [700, 85],
                    [700, 90],
                    [700, 95],
                    [740, 1],
                    [740, 70],
                    [740, 85],
                    [740, 90],
                    [740, 95]]
    # the default rates to use
    def_rate_620 = 3.03
    def_rate_660 = 2.01
    def_rate_700 = 1.50
    def_rate_740 = 0.94
    # the risk factor multiples to use
    risk_factor_620 = [0.20, 0.61, 1.22, 1.48, 1.80]
    risk_factor_660 = [0.20, 0.62, 1.22, 1.48, 1.82]
    risk_factor_700 = [0.20, 0.62, 1.22, 1.49, 1.83]
    risk_factor_740 = [0.20, 0.63, 1.21, 1.47, 1.81]

    net_def_risks_620 = np.multiply(def_rate_620, risk_factor_620)
    net_def_risks_660 = np.multiply(def_rate_660, risk_factor_660)
    net_def_risks_700 = np.multiply(def_rate_700, risk_factor_700)
    net_def_risks_740 = np.multiply(def_rate_740, risk_factor_740)

    initial_risk_mults = [r for r in net_def_risks_620] + [r for r in net_def_risks_660] + [r for r in net_def_risks_700] + [r for r in net_def_risks_740]

    x, y = zip(*initial_data)
    ax = plt.axes(projection='3d')
    ax.scatter(x, y, initial_risk_mults)

    coefficients = mpf(initial_data, initial_risk_mults, 1)
    result = np.multiply(coefficients[1], x) + np.multiply(coefficients[2], y) + coefficients[0]
    ax.plot3D(x, y, result)
    ax.set_title('Credit Score/Loan % Line of Best Fit')
    plt.savefig('graph.png')

    return coefficients[0] + coefficients[1] * credit_score + coefficients[2] * loan_percent


def get_loss_severity():
    return 0.19


def get_recovery_rate():
    return 0.9


def get_risk_pool_allocation_percent():
    return 0.01


def get_gross_rent_yield():
    return 3.686


def compute_mre(home_value, down_payment_percent, consumer_fico):
    if not 0 <= down_payment_percent < 100:
        raise ValueError('Down payment percent must be contained in [0, 100)')

    if consumer_fico < 620:
        raise ConsumerNotEligibleError('Credit score too low (must be 620 or Greater)')

    monthly_payment = home_value * get_gross_rent_yield()/100/12

    income_interruption_home_percentage = 100 * (monthly_payment * 4 / home_value)
    risk_pool_percent = get_risk_pool_allocation_percent()

    loss_severity = get_loss_severity()
    recovery_rate = get_recovery_rate()
    default_rate = fico_default_risk_modifier_by_lend_percent(consumer_fico, 100 - down_payment_percent)

    at_risk_value = home_value * loss_severity

    def_rate_mult = 100/default_rate
    risk_pool_allocation = risk_pool_percent * home_value
    denom = recovery_rate
    num = at_risk_value - (def_rate_mult * risk_pool_allocation)
    result = num/denom
    mre = result/home_value + income_interruption_home_percentage
    mre += income_interruption_home_percentage

    return mre


if __name__ == '__main__':
    print(compute_mre(742500, 2.5, 680))
    # print(fico_default_risk_modifier_by_lend_percent(620, 85))