from otree.api import (
    models,
    widgets,
    BaseConstants,
    BaseSubsession,
    BaseGroup,
    BasePlayer,
    Currency as c,
    currency_range,
)
import numpy as np
import pandas as pd
from random import sample

author = 'Putu Sanjiwacika Wibisana'

doc = """
Adaptation of Preference Discovery by Delaney, Jacobson and Moenig (2018) for risk preference discovery.
"""


class Constants(BaseConstants):
    name_in_url = 'preference_discovery_v2'
    players_per_group = None
    num_rounds = 20
    with open('preference_discovery/Lottery.csv', encoding="utf-8") as file:
        prospects = pd.read_csv(file)


class Subsession(BaseSubsession):
    pass


class Group(BaseGroup):
    pass


class Player(BasePlayer):

    def set_player_param(self):
        # round settings
        self.training_round = 1 if self.round_number <= self.session.config["training_rounds"] else 0
        if self.round_number == 1:
            self.participant.vars["prospect_table"] = Constants.prospects
            self.endowment = self.session.config["endowment"]
            self.participant.vars["payoff_vector"] = list()
        elif self.round_number == self.session.config["training_rounds"] + 1:
            self.participant.vars["prospect_table"] = Constants.prospects
        #randomizer
        rand = sample(list(range(0,20)),4)
        rand.append(20)
        self.participant.vars["random_indexes"] = rand
        self.participant.vars["displayed_lotteries"] = list(self.participant.vars["prospect_table"].loc[self.participant.vars["random_indexes"],"Index"])
        self.participant.vars["displayed_prospects"] = self.participant.vars["prospect_table"].loc[self.participant.vars["random_indexes"],:]
        self.displayed_lotteries = str(list(self.participant.vars["displayed_lotteries"]))

    def payoff_realizer(self):
        df = self.participant.vars["displayed_prospects"]
        df[["Allocation"]] = [self.Lotere_A, self.Lotere_B, self.Lotere_C, self.Lotere_D, self.Lotere_E]                ### df[["Allocation"]] = [0,0,2,1,2]
        df[["payoff"]] = [0, 0, 0, 0, 0]
        for i in self.participant.vars["random_indexes"]:
            df.loc[i,"A_or_B"] = np.random.choice(["A","B"], p=[df.loc[i,"p1"],df.loc[i,"p2"]])
            df.loc[i,"payoff"] = df.loc[i,"x1"] * df.loc[i,"Allocation"] if df.loc[i,"A_or_B"] == "A" else df.loc[i,"x2"] * df.loc[i,"Allocation"]
        self.payoff_thisround = int(df[["payoff"]].sum())
        if self.training_round == False:
            self.participant.vars["payoff_vector"].append(self.payoff_thisround)
        self.participant.vars["prospect_table"].update(df)
        for i in range(0,len(self.participant.vars["prospect_table"])):
            if self.participant.vars["prospect_table"].loc[i,"A_or_B"] != "X":
                if self.participant.vars["prospect_table"].loc[i,"A_or_B"] == "A":
                    self.participant.vars["prospect_table"].loc[i, "p1"] = 1
                    self.participant.vars["prospect_table"].loc[i, "p2"] = 0
                elif self.participant.vars["prospect_table"].loc[i,"A_or_B"] == "B":
                    self.participant.vars["prospect_table"].loc[i, "p1"] = 0
                    self.participant.vars["prospect_table"].loc[i, "p2"] = 1
            else:
                pass
        self.participant.vars["displayed_prospects"] = df

    endowment = models.IntegerField()
    payoff_thisround = models.IntegerField()
    displayed_lotteries = models.StringField()
    training_round = models.BooleanField()

    Lotere_A = models.IntegerField(min=0, max=10, initial=0)
    Lotere_B = models.IntegerField(min=0, max=10, initial=0)
    Lotere_C = models.IntegerField(min=0, max=10, initial=0)
    Lotere_D = models.IntegerField(min=0, max=10, initial=0)
    Lotere_E = models.IntegerField(min=0, max=10, initial=0)

    ## Vars for questionnaire

    Name = models.StringField(label="Nama Lengkap Anda:")
    Age = models.IntegerField(label="Usia:", min=14, max=35)
    Gender = models.StringField(label="Gender:", choices=["Pria", "Wanita"])