from otree.api import (
    models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer,
    Currency as c, currency_range
)

doc = ''
class Constants(BaseConstants):
    name_in_url = 'NewsVendorFI'
    players_per_group = None
    num_rounds = 30
    mean = 100
    sd = 50
    price = 12
    costhigh = 9
    salvage = 0
    costlow = 3
    truncLower = 0
    truncHigher = 200
    breakRound = 15
    indextemplate_template = 'NewsVendorFI/indextemplate.html'
class Subsession(BaseSubsession):
    Demand = models.IntegerField(initial=0)
    fractile = models.FloatField()
    optimalOrder = models.IntegerField(initial=0)
    def DemandInRound(self):
        pass
    def CalculateOptimal(self):
        from scipy.stats import truncnorm
        if self.round_number == 1:
            self.fractile = (Constants.price-Constants.costhigh)/(Constants.price-Constants.salvage);
            self.optimalOrder = int(Constants.mean + Constants.sd*truncnorm.ppf(self.fractile,(Constants.truncLower - Constants.mean) / Constants.sd, (Constants.truncHigher - Constants.mean) / Constants.sd))
        else:
            self.fractile = self.in_round(1).fractile;
            self.optimalOrder = self.in_round(1).optimalOrder;
        
class Group(BaseGroup):
    pass
class Player(BasePlayer):
    name = models.StringField(label='Your name:')
    OrderQty = models.IntegerField(label='Your order for the round:')
    roundDemand = models.IntegerField(initial=0)
    profit = models.FloatField(initial=0)
    cumulativeProfit = models.FloatField(initial=0)
    optimalCumulativeProfitInRound = models.FloatField(initial=0)
    rImprove = models.StringField(choices=[['Yes', 'Yes'], ['No', 'No']], initial='Yes', label='Do you think you could improve your cumulative profit if you change your strategy of ordering?', widget=widgets.RadioSelect)
    rStrategy = models.LongStringField(choices=[["Ordering close to the previous round's demand", "Ordering close to the previous round's demand"], ['Ordering close to the mean demand', 'Ordering close to the mean demand'], ['Ordering close to the optimal value', 'Ordering close to the optimal value'], ['Ordering between mean demand and optimal value', 'Ordering between mean demand and optimal value'], ['None of these', 'None of these']], label='In your opinion, what pattern of ordering did you follow in the last 10 rounds?', widget=widgets.RadioSelect)
    rForecast = models.StringField(choices=[['Yes', 'Yes'], ['No', 'No']], label='Were you trying to guess the demand while placing the order?', widget=widgets.RadioSelect)
    rFutureStrategy = models.StringField(choices=[['Try to guess the demand at every round', 'Try to guess the demand at every round'], ['Place the optimal order every round', 'Place the optimal order every round'], ['Place order quantity around mean demand every round', 'Place order quantity around mean demand every round'], ['Follow the last round demand', 'Follow the last round demand'], ['No strategy', 'No strategy']], label='What do you (now) think is the best strategy over the next 20 rounds?', widget=widgets.RadioSelect)
    treatment = models.IntegerField(choices=[[1, '1'], [2, '2'], [3, '3']], initial=1, label='Group', widget=widgets.RadioSelectHorizontal)
    optimalProfit = models.FloatField(initial=0)
    optimalCumulativeProfit = models.FloatField(initial=0)
    rChasingMean = models.StringField(choices=[['Yes', 'Yes'], ['No', 'No']], label="Did you often revert to ordering close to the mean demand (100) whenever you noticed the previous round's demand exceeded 100?", widget=widgets.RadioSelect)