from otree.api import Currency as c, currency_range
from . import pages
from ._builtin import Bot
from otree.api import Submission
from .models import Constants
import random
import numpy as np

decision_type_arr = ["Baseline1", "Extreme1","Control", "EI_only", "EP_only", "Inbetween", "ShiftedBaseline"]

p1_Baseline1 = np.array([3.0, 2.5, 2.0, 1.5, 1.0, 0.5, 0.0])
p2_Baseline1 = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])

p1_Baseline2 = 2*p1_Baseline1
p2_Baseline2 = 2*p2_Baseline1

p1_Extreme1 = np.array([7.5, 7.0, 6.5, 6.0, 5.5, 5.0, 4.5])/2
p2_Extreme1 = np.array([1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5])/2

p1_Extreme2 = p1_Extreme1/2
p2_Extreme2 = p2_Extreme1/2

p1_Control = p1_Baseline1
p2_Control = p2_Baseline1

p1_EI_Extreme = np.array([6.0, 5.5, 5.0, 4.5, 4.0, 3.5, 3.0])
p2_EI_Extreme = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])

p1_EP_Extreme = np.array([5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0])
p2_EP_Extreme = np.array([1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0])

p1_EI_only = np.array([4.0, 3.75, 3.5, 3.25, 3.0, 2.75, 2.5])
p2_EI_only = np.array([2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5])

p1_EP_only = np.array([5.5, 5.25, 5.0, 4.75, 4.5, 4.25, 4.0])
p2_EP_only = np.array([0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0])

p1_Inbetween = 0.5*np.array([9.5, 9.0, 8.5, 8.0, 7.5, 7.0, 6.5])
p2_Inbetween = 0.5*np.array([2.5, 3.0, 3.5, 4.0, 3.5, 5.0, 5.5])

p1_EI_only = np.array([4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.0])
p2_EI_only = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])

p1_EP_only = np.array([5.6, 5.0, 4.4, 3.8, 3.2, 2.6, 2.0])
p2_EP_only = np.array([0.4, 1.0, 1.6, 2.2, 2.8, 3.4, 4.0])

p1_ShiftedBaseline = np.array([3.0, 2.5, 2.0, 1.5, 1.0, 0.5, 0.0])+0.5
p2_ShiftedBaseline = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])+0.25


p1_dict = {"Baseline1": p1_Baseline1, "Baseline2": p1_Baseline2, "Extreme1": p1_Extreme1, "Extreme2": p1_Extreme2,
        "Control": p1_Control, "EI_Extreme": p1_EI_Extreme, "EP_Extreme": p1_EP_Extreme, "EI_only": p1_EI_only,
        "EP_only": p1_EP_only, "Inbetween": p1_Inbetween, "ShiftedBaseline" : p1_ShiftedBaseline}

p2_dict = {"Baseline1": p2_Baseline1, "Baseline2": p2_Baseline2, "Extreme1": p2_Extreme1, "Extreme2": p2_Extreme2,
        "Control": p2_Control, "EI_Extreme": p2_EI_Extreme, "EP_Extreme": p2_EP_Extreme, "EI_only": p2_EI_only,
        "EP_only": p2_EP_only, "Inbetween": p2_Inbetween, "ShiftedBaseline" : p2_ShiftedBaseline}

p1_prize_dict = {"Baseline1": 5.0, "Baseline2": 5.0, "Extreme1": 5.0, "Extreme2": 5.0,
        "Control": 3.0, "EI_Extreme": 5.0, "EP_Extreme": 5.0, "EI_only": 5.0,
        "EP_only": 5.0, "Inbetween": 5.0, "ShiftedBaseline": 5.0}

p2_prize_dict = {"Baseline1": 1.0, "Baseline2": 1.0, "Extreme1": 1.0, "Extreme2": 1.0,
        "Control": 3.0, "EI_Extreme": 1.0, "EP_Extreme": 1.0, "EI_only": 1.0,
        "EP_only": 1.0, "Inbetween": 1.0, "ShiftedBaseline": 1.0}

rel_answer_dict = {"Baseline1": 2, "Baseline2": 2, "Extreme1": 1, "Extreme2": 1,
        "Control": 4, "EI_Extreme": 3, "EP_Extreme": 1, "EI_only": 2,
        "EP_only": 2, "Inbetween": 1, "ShiftedBaseline": 2}

abs_answer_dict = {"Baseline1": 4, "Baseline2": 4, "Extreme1": 7, "Extreme2": 7,
        "Control": 4, "EI_Extreme": 7, "EP_Extreme": 5, "EI_only": 5,
        "EP_only": 5, "Inbetween": 7, "ShiftedBaseline": 4}


class PlayerBot(Bot):
    def play_round(self):

        if self.player.round_number == 1:
            yield pages.Welcome, dict(name="name", email="a@b.de", iban="DE22120300001060942669", bic="BYLADEM1001")
            yield pages.Intro
            yield pages.Introduction_Quiz
            yield pages.Comprehension_Quiz, dict(comprehension_tries_quiz_1=0, comprehension_tries_quiz_2=0)
            yield pages.Ready
            yield pages.Quiz, dict(score=np.random.randint(0, 30))
            yield pages.Introduction_Decision
            yield pages.Comprehension,dict (comprehension_tries_1=0, comprehension_tries_2=0, comprehension_tries_3=0, comprehension_tries_4=0,)
            yield pages.Before_Decision




        if self.player.round_number in [1,2,3,4,5,6,7]:

            type = self.player.decision_type


            if self.player.in_round(1).debug_follows_type == 0:
                answer = random.choice([1,2,3,4,5,6,7])
            if self.player.in_round(1).debug_follows_type == 1:
                if self.player.treatment == "Relative":

                    if np.random.binomial(n=1, p=0.5) == 1:
                        answer = rel_answer_dict[type]
                    else:
                        answer = random.choice([1,2,3,4,5,6,7])

                if self.player.treatment == "Absolute":

                    if np.random.binomial(n=1, p=0.5) == 1:
                        answer = abs_answer_dict[type]
                    else:
                        answer = random.choice([1,2,3,4,5,6,7])

            p1_w = p1_prize_dict[type]+p1_dict[type][answer-1]
            p2_w = p2_prize_dict[type]+p2_dict[type][answer-1]

            yield pages.Decision_slim, dict (p1_wealth = p1_w, p2_wealth = p2_w, option_chosen = answer)

        if self.player.round_number == 7:
            yield pages.Motive, dict(motive="BLA")
            yield pages.Advantage, dict(advantage_answer="winner")
            yield pages.Meritocracy, dict(meritocracy_answer = random.choice([1,0]))
            yield pages.Guess_2, dict(p1_guess= 6.5, p2_guess=2.5)

            yield pages.Introduction_Calibration
            yield pages.Comprehension_Calibration, dict(comprehension_tries_calibration_1=0)

        if self.player.round_number == 8:
            yield pages.Calibration_1, dict(calibration_1_p1_wealth = 6.5, calibration_1_p2_wealth= 2.5)
            yield pages.Calibration_2, dict(calibration_2_p1_wealth = 7.5, calibration_2_p2_wealth= 1.5)
            yield pages.Final_Questions, dict(knew_difference = 1, would_change=0)