from datetime import timedelta, datetime
from src.agent import Client
from plotly.offline import plot
from  scipy import stats
import plotly.graph_objs as go
import os

class Singleton(object):
    _instance = None

    def __new__(class_, *args, **kwargs):
        if not isinstance(class_._instance, class_):
            class_._instance = object.__new__(class_, *args, **kwargs)
        return class_._instance

class Environment(Singleton):
    def __init__(self):

        self.global_time = 0
        self.week = 0
        self.day = 0
        self.shop_list = []
        self.client_list = []
        self.cumulative_shops = {}
        self.avg_prices = {'auchan': 624.0,
             'magnit': 283.0,
             'pyaterochka': 359.0,
             'lenta': 957.0,
             'dixy': 263.0,
             'okey': 911.0,
             'perekrestok': 376.0,
             'spar': 326.0,
             'semishagoff': 294.0,
             'prisma': 457.0,
             'semya': 227.0,
             'real': 254.0}

    def register_shop(self, shop):
        self.shop_list.append(shop)

    def register_client(self, client):
        self.client_list.append(client)

    def iteration(self, iterat):
        self.iterat = iterat
        self.cumulative_shops[iterat] = {shop.name: [0, 0] for shop in self.shop_list}
        for client in self.client_list:
            client.go_shop(self)

        if self.global_time <= 22:
            self.global_time += 1
        else:
            self.global_time = 0
            if self.day <= 5:
                self.day += 1
            else:
                self.day = 0
                self.week += 1

    def populate_user(self,user, data, first_week):
        transactions = sorted(data, key=lambda x: datetime.strptime(x['TRANS_DATE'], '%d.%m.%Y'))
        transactions = [x for x in transactions if
                         datetime.strptime(x['TRANS_DATE'], '%d.%m.%Y') <= (first_week)]

        if len(transactions) > 0:
            c = Client(self)
            res = True
            c.initial(['name'], [user])

            for trans in transactions:
                point = {'date': datetime.strptime(trans['TRANS_DATE'] + ' ' + trans['TRANS_TIME'], '%d.%m.%Y %H:%M:%S'),
                         'h': int(datetime.strptime(trans['TRANS_TIME'], '%H:%M:%S').hour), #+(float(datetime.strptime(trans['TRANS_TIME'], '%H:%M:%S').minute) / 60),
                         'w': trans['AMOUNT_EQ'],
                         'shop': trans['shop']}
                c.identify(point)
                if point['date'].hour == 0 and point['date'].minute == 0:
                    res = False

            c.identify_shop_probs()
            return res

    def populate(self, user_data, first_week):
        self.first_week = first_week

        bad = set()
        for user in user_data:
            flag = self.populate_user(user,user_data[user],self.first_week)
            if not flag:
                bad.add(user)
        print('Users initialised:', len(self.client_list))
        print('Users without transactions for selected period:', len(user_data) - len(self.client_list))
        #print('List of users with bad time:', bad)

    def plot_3d(self):
        x, y, z, text = [], [], [], []
        for client in self.client_list:
            x.append(client.day_begin)
            y.append(client.lunch)
            z.append(client.day_end)
            text.append(client.name)
        plot([go.Scatter3d(x=x, y=y, z=z, text=text, opacity=0.7, mode='markers')], filename='plots/3d.html')