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

doc = ''
class Constants(BaseConstants):
    name_in_url = 'FramingInequality'
    players_per_group = None
    num_rounds = 9

class Subsession(BaseSubsession):
    def vars_for_admin_report(self):
        redist_choices = []
        player_ids = []
        treatments = []
        player_list = list(self.get_players())

        for p in player_list:
            for r in [1,2,3,4,5,6]:
                player_ids.append(str(p.id_in_group))
                treatments.append(p.treatment)
                redist_choices.append(p.in_round(r).redist_choice)


        df = pd.DataFrame()
        df["id"] = player_ids
        df["treatment"] = treatments
        df["absolute"] = np.array((np.array(treatments) == "Absolute"), dtype="float")
        df["redist"] = redist_choices

        df.dropna(inplace=True)

        if len(df) > 5:

            relative_mean = np.mean(df[df["treatment"]=="Relative"]["redist"])
            absolute_mean = np.mean(df[df["treatment"]=="Absolute"]["redist"])

            y = df["redist"]

            x = df["absolute"]

            reg = sm.OLS(y,sm.add_constant(x)).fit(cov_type='cluster', cov_kwds={'groups': df["id"]})
            robust_p_val = reg.pvalues[1]
            diff = reg.params[1]
            summary = reg.summary()

            return {'relative_mean': relative_mean, 'absolute_mean': absolute_mean, 'robust_p_val': robust_p_val, 'difference': diff, "summary": summary}

        else:
            return {'relative_mean': np.nan, 'absolute_mean': np.nan, 'robust_p_val': np.nan, 'difference': np.nan, "summary": np.nan}

class Group(BaseGroup):

    pass

class Player(BasePlayer):

    #treatment
    treatment = models.StringField()


    #score in math quiz
    score = models.IntegerField()

    #Variables for redistribution choices
    p1_prize = models.CurrencyField()
    p2_prize = models.CurrencyField()

    #which decisions are asked?
    decisions=models.StringField()

    #RelativeDefault
    p1_wealth_RelativeDefault = models.CurrencyField()
    p2_wealth_RelativeDefault = models.CurrencyField()
    p1_income_RelativeDefault = models.CurrencyField()
    p2_income_RelativeDefault = models.CurrencyField()
    p1_factor_RelativeDefault = models.FloatField()
    p2_factor_RelativeDefault = models.FloatField()

    #AbsoluteDefault
    p1_wealth_AbsoluteDefault = models.CurrencyField()
    p2_wealth_AbsoluteDefault = models.CurrencyField()
    p1_income_AbsoluteDefault = models.CurrencyField()
    p2_income_AbsoluteDefault = models.CurrencyField()
    p1_factor_AbsoluteDefault = models.FloatField()
    p2_factor_AbsoluteDefault = models.FloatField()

    #AdvantageRich
    p1_wealth_AdvantageRich = models.CurrencyField()
    p2_wealth_AdvantageRich = models.CurrencyField()
    p1_income_AdvantageRich = models.CurrencyField()
    p2_income_AdvantageRich = models.CurrencyField()
    p1_factor_AdvantageRich = models.FloatField()
    p2_factor_AdvantageRich = models.FloatField()

    #AdvantagePoor
    p1_wealth_AdvantagePoor = models.CurrencyField()
    p2_wealth_AdvantagePoor = models.CurrencyField()
    p1_income_AdvantagePoor = models.CurrencyField()
    p2_income_AdvantagePoor = models.CurrencyField()
    p1_factor_AdvantagePoor = models.FloatField()
    p2_factor_AdvantagePoor = models.FloatField()

    #Intermediate
    p1_wealth_Intermediate = models.CurrencyField()
    p2_wealth_Intermediate = models.CurrencyField()
    p1_income_Intermediate = models.CurrencyField()
    p2_income_Intermediate = models.CurrencyField()
    p1_factor_Intermediate = models.FloatField()
    p2_factor_Intermediate = models.FloatField()


    #EqualWealth
    p1_wealth_EqualWealth = models.CurrencyField()
    p2_wealth_EqualWealth = models.CurrencyField()
    p1_income_EqualWealth = models.CurrencyField()
    p2_income_EqualWealth = models.CurrencyField()
    p1_factor_EqualWealth = models.FloatField()
    p2_factor_EqualWealth = models.FloatField()


    #chosen values
    p1_wealth = models.CurrencyField()
    p2_wealth = models.CurrencyField()
    p1_income = models.CurrencyField()
    p2_income = models.CurrencyField()

    additional_sum = models.CurrencyField()



    choice = models.StringField()

    absolute_fairness_answer = models.IntegerField(choices=[[1, 'sehr unfair'], [2, 'eher unfair'], [3, 'neutral'], [4, 'eher fair'], [5, 'sehr fair']], widget=widgets.RadioSelectHorizontal, label="Wie fair finden Sie diesen Vorschlag?")
    relative_fairness_answer = models.IntegerField(choices=[[1, 'sehr unfair'], [2, 'eher unfair'], [3, 'neutral'], [4, 'eher fair'], [5, 'sehr fair']], widget=widgets.RadioSelectHorizontal, label="Wie fair finden Sie diesen Vorschlag?")

    attention_check_answer = models.FloatField(min=-100, max=100)
    attention_check_passed = models.BooleanField()


    comprehension_tries_calibration_1 = models.IntegerField()

    comprehension_tries_quiz_1 = models.IntegerField()
    comprehension_tries_quiz_2 = models.IntegerField()

    comprehension_tries_1 = models.IntegerField()
    comprehension_tries_2 = models.IntegerField()
    comprehension_tries_3 = models.IntegerField()
    comprehension_tries_4 = models.IntegerField()


    #calibration sliders
    calibration_choice_1 = models.StringField()
    calibration_choice_2 = models.StringField()
    calibration_choice_3 = models.StringField()


    #payment stuff
    name = models.StringField()
    email = models.StringField()
    iban = models.StringField()
    bic = models.StringField()

    #progress stuff
    progress = models.IntegerField()