from otree.api import (
    models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer,
    Currency as c, currency_range
)
import csv
import xlrd # for reading XLS-files

author = 'Dr. Jana Freundt & Leander Kopp'

doc = """
This app reads 2 options from a CSV-file and exports the decision.
A prototype_Part2 app that reads its questions from a spreadsheet
(see lotteries.csv in this directory).
There is 1 decision per page; the number of pages in the game
is determined by the number of decisions in the CSV.
"""


class Constants(BaseConstants):
    name_in_url = 'Part1_IE_DOSE'
    players_per_group = None
    ############ LANGUAGE SETTINGS
    # 1: english, 2:german, 3:french.....
    language_code=1
    ############ DEFINE CURRENCY, XRs AND LOTTERYSCALING ###
    # define displayed currency
    currency='points'
    #currency='CHF'
    currency_real='£'
    # define exchange rate 1 ECU = xxx $ = xxx CHF
    XR = 1200
    # define scaling of lottery
    scale = 1
    FixedPointsType1 = 2000
    ########################################################

    ############ DEFINE SHOWUPFEE
    showup_fee =2

    #read & store all lotteries
    with open('Part1_IE_DOSE/distributions_20increments.csv') as questions_file:
        questions = list(csv.DictReader(questions_file))

    with open('Part1_IE_DOSE/IE_decisiontree_20increments.csv') as decisiontree_file:
        decisiontree = list(csv.DictReader(decisiontree_file))

    #tree = ("Part1_risk_DOSE/decisiontree.xlsx")
    #tree = ("Part1_risk_DOSE/decisiontree_spicy.xlsx")
    #tree = ("Part1_risk_DOSE/decisiontree_spicy_9rounds.xlsx")
   # tree = ("Part1_risk_DOSE/decisiontree_spicy_prolific.xlsx")
    #tree = ("Part1_risk_DOSE/decisiontree_10rounds.xlsx")
    #tree = ("Part1_risk_DOSE/decisiontree_spicy_10rounds.xlsx")
    #tree = ("Part1_risk_DOSE/decisiontree_spicy_10rounds_mu120.xlsx")
    tree = ("Part1_IE_DOSE/IE_tree_20increments.xlsx")

    # To open Workbook
    wb = xlrd.open_workbook(tree)
    sheet = wb.sheet_by_index(0)

    # Get for every stage all optimal lotteries (row,column)
    num_rounds = 10
    #num_rounds = 7 # 10 rounds for DOSE
    # prepare numbers for along the binary decision tree
    shift=[256,128,64,32,16,8,4,2,1]


class Subsession(BaseSubsession):

    def creating_session(self):
        if self.round_number == 1:
            self.session.vars['questions'] = Constants.questions.copy()


class Group(BaseGroup):
    pass


class Player(BasePlayer):
    price_round_selected=models.IntegerField()
    #### IE parameter
    IE_parameter = models.FloatField()
    B_low_indifferent=models.FloatField()
    #### Choice consistency
    choice_consistency = models.FloatField()
    #### payoffs variables
    # Fixed Points for type 1 participant to perform part 1
    FixedPointsType1 = models.IntegerField(initial=2000)
    Payoff_Participant_1_selected_round  = models.FloatField()
    Payoff_Participant_2_selected_round  = models.FloatField()
    #### selected round for payoff (including high and low payoffs of this lottery)
    payoffrelevant_round = models.IntegerField()


    optimal_option = models.IntegerField()
    #tree_row = models.IntegerField(initial=128) # set the initial row of the optimal lottery from the binary tree
    #tree_row = models.IntegerField(initial=256) # set the initial row of the optimal lottery from the binary tree
    tree_row = models.IntegerField(initial=512) # set the initial row of the optimal lottery from the binary tree
    ############ DEFINE LOTTERY VARIABLES ###
    # Define B-Option variables
    OptionB_high = models.FloatField()
    OptionB_low = models.FloatField()

    # Define A-Option variables
    OptionA_high = models.FloatField()
    OptionA_low = models.FloatField()

    #########################################
    # Define Choice variable: This variable will store whether lottery A or B was chosen (in each round)
    optionchoice = models.IntegerField()
    ########### DEFINE PRACTICE ROUND VARIABLES ###


    ########## DEFINE CONTROL QUESTION VARIABLES ###
    ControlQuestion1 = models.IntegerField()
    ControlQuestion2 = models.IntegerField()
    ControlQuestion3 = models.IntegerField()
    ControlQuestion4 = models.IntegerField()
    # counter for control questions
    counterWrong = models.IntegerField(initial=0)
    counterWrong_page1 = models.IntegerField(initial=0)
    counterWrong_page2 = models.IntegerField(initial=0)
    ################################################
    prolificID = models.StringField()

    # tab switches & focus-/unfocus-time
    tab_consent = models.IntegerField()
    tab_understand = models.IntegerField()
    tab_instructions_general_part1 = models.IntegerField()
    tab_intro_title_part1 = models.IntegerField()
    tab_desc_lotteries_part1 = models.IntegerField()
    tab_practice_part1 = models.IntegerField()
    tab_desc_rlim_part1 = models.IntegerField()
    tab_pre_question_part1 = models.IntegerField()
    tab_question_part1 = models.IntegerField()
    tab_end_part1 = models.IntegerField()

    # tab switches & focus-/unfocus-time
    focus_consent = models.FloatField()
    focus_understand = models.FloatField()
    focus_instructions_general_part1 = models.FloatField()
    focus_intro_title_part1 = models.FloatField()
    focus_desc_lotteries_part1 = models.FloatField()
    focus_practice_part1 = models.FloatField()
    focus_desc_rlim_part1 = models.FloatField()
    focus_pre_question_part1 = models.FloatField()
    focus_question_part1 = models.FloatField()
    focus_end_part1 = models.FloatField()

    # tab switches & focus-/unfocus-time
    totaltime_consent = models.FloatField()
    totaltime_understand = models.FloatField()
    totaltime_instructions_general_part1 = models.FloatField()
    totaltime_intro_title_part1 = models.FloatField()
    totaltime_desc_lotteries_part1 = models.FloatField()
    totaltime_practice_part1 = models.FloatField()
    totaltime_desc_rlim_part1 = models.FloatField()
    totaltime_pre_question_part1 = models.FloatField()
    totaltime_question_part1 = models.FloatField()
    totaltime_end_part1 = models.FloatField()