from otree.api import *

c = cu

doc = 'The same procedure for all participants'


class C(BaseConstants):
    NAME_IN_URL = 'choice_task_1'
    PLAYERS_PER_GROUP = None
    NUM_ROUNDS = 1
    NUM_GRAPH = 20
    CHOICE_BONUS = 0.5
    INSTRUCTIONS_TEMPLATE = 'choice_task_1/instructions.html'


class Subsession(BaseSubsession):
    pass


def creating_session(subsession: Subsession):
    session = subsession.session
    if subsession.round_number == 1:
        ps = subsession.get_players()
        ps_choice_first = [p for p in ps if p.participant.first_task == 'choice']
        ps_ambiguity_first = [p for p in ps if p.participant.first_task == 'ambiguity']

        group_matrix = [ps_choice_first, ps_ambiguity_first]

        for s in subsession.in_rounds(1, C.NUM_ROUNDS):
            s.set_group_matrix(group_matrix)

class Group(BaseGroup):
    pass


class Player(BasePlayer):
    q1 = models.StringField(choices=[['€13', '€13'], ['€6.5', '€6.5'], ['I do not know', 'I do not know']],
                            widget=widgets.RadioSelect)
    q2 = models.StringField(choices=[['€5', '€5'], ['€4.5', '€4.5'], ['I do not know', 'I do not know']],
                            widget=widgets.RadioSelect)
    num_errors = models.IntegerField(initial=0)
    choice = models.IntegerField()

    choice = models.IntegerField(choices=[[1, 'Option A: Predictions made by the human analyst. The human analyst is an equity analyst employed by a brokerage house or investment bank.'],
                                          [0, 'Option B: Predictions made by the algorithmic analyst. The algorithmic analyst is a machine learning algorithm trained by historical data to predict stock prices.'],
                                          [2, 'I am indifferent between the two types of analysts.']],
                                 label='', widget=widgets.RadioSelect)


def set_payoff(player: Player):
    participant = player.participant
    print(participant.task_to_pay)
    if participant.task_to_pay == 'choice':
        participant.vars['choice_player_to_pay'] = player.choice
        print(participant.vars['choice_player_to_pay'])
        records = participant.vars['records']

        human_accuracy = sum([1 if r[-3] == 'TRUE' else 0 for r in records])
        algorithmic__accuracy = sum([1 if r[-1] == 'TRUE' else 0 for r in records])

        # Option A: Predictions made by the human analyst
        if player.choice == 1:
            accuracy = human_accuracy
        # Option B: Predictions made by the algorithmic analyst
        elif player.choice == 0:
            accuracy = algorithmic__accuracy
        # Remaining choice is Option C: I am indifferent between the two types of analyst
        else:   # the remaining option is player.choice == 2
            accuracy = min(human_accuracy, algorithmic__accuracy)
        participant.payoff = C.CHOICE_BONUS * accuracy


class WaitingTask(WaitPage):
    @staticmethod
    def is_displayed(player: Player):
        return player.round_number == 1


class Instruction(Page):
    form_model = 'player'
    form_fields = ['q1', 'q2']

    @staticmethod
    def error_message(player: Player, values):
        if values["q1"] != "€6.5" or values["q2"] != "€4.5":
            player.num_errors += 1
            return "There is at least one mistake in your responses. Please try again."


class CorrectAnswer(Page):
    form_model = 'player'


class Decision(Page):
    form_model = 'player'
    form_fields = ['choice']

    @staticmethod
    def vars_for_template(player: Player):
        participant = player.participant
        return dict(
            graphs=[f'stock/record_id_{n}.png' for n in participant.graphs]
        )

    @staticmethod
    def before_next_page(player: Player, timeout_happened):
        set_payoff(player)



page_sequence = [Instruction, CorrectAnswer, Decision]