import pandas as pd
from imports.schedules.helpers.schedules_helper import SchedulesHelper
import parseidf



class IDFParser:
    _OCCUPANCY_SCHEDULE = ("BLDG_OCC_SCH_wo_SB", "Occupancy")
    _Activity_Schedule = "ACTIVITY_SCH"
    _Electrical_Equipent_Schedule = "BLDG_EQUIP_SCH"
    _Lighting_Schedule = "BLDG_LIGHT_SCH"
    _Infiltration_Ventilation_Schedule = "INFIL_QUARTER_ON_SCH"

    def __init__(self, city, base_path):
        self._city = city
        self._idf_schedules_path = base_path / 'ASHRAE901_OfficeSmall_STD2019_Buffalo.idf'
        with open(self._idf_schedules_path, 'r') as f:
            idf = parseidf.parse(f.read())
        print(idf.keys())  # lists the object types in the idf file
        print(idf['SCHEDULE:COMPACT'])  # lists all the Output:Variable objects in the idf file
        Holiday = []
        Winterdesignday = []
        Summerdesignday = []
        Customday1 = []
        Customday2 = []
        Weekday = []
        Weekend = []
        dayType = []
        compactKeywords = ['Weekdays', 'Weekends', 'Alldays', 'AllOtherDays', 'Sunday', 'Monday', 'Tuesday', 'Wednesday',
                           'Thursday', 'Friday', 'Saturday', 'Holiday', 'Winterdesignday', 'Summerdesignday', 'Customday1',
                           'Customday2']
        for val in idf['SCHEDULE:COMPACT']:
            if val[1] == 'BLDG_LIGHT_SCH':
                count = 0
                for words in val:
                    dayType.append(words.title().split('For: ')[-1])
                    index = []
                    for count, word_dayType in enumerate(dayType):
                        if word_dayType in compactKeywords:
                            index.append(count)
                index.append(len(dayType))
                for i in range(len(index) - 1):
                    numberOfDaySch = list(dayType[index[i] + 1:index[i + 1]])
                    hourlyValues = list(range(24))
                    startHour = 0
                    for num in range(int(len(numberOfDaySch) / 2)):
                        untilTime = list(map(int, (numberOfDaySch[2 * num].split('Until: ')[-1]).split(":")[0:]))
                        endHour = int(untilTime[0] + untilTime[1] / 60)
                        value = float(numberOfDaySch[2 * num + 1])
                        for hour in range(startHour, endHour):
                            hourlyValues[hour] = value
                            print(hour, value)
                        startHour = endHour
                    if dayType[index[i]] == 'Weekdays':
                        Weekday.append(hourlyValues)
                    elif dayType[index[i]] == 'Weekends':
                        Weekend.append(hourlyValues)
                    elif dayType[index[i]] == 'Holiday':
                        Holiday.append(hourlyValues)
                    elif dayType[index[i]] == 'Winterdesignday':
                        Winterdesignday.append(hourlyValues)
                    elif dayType[index[i]] == 'Summerdesignday':
                        Summerdesignday.append(hourlyValues)
                    elif dayType[index[i]] == 'Customday1':
                        Customday1.append(hourlyValues)
                    elif dayType[index[i]] == 'Customday2':
                        Customday2.append(hourlyValues)
                    else:
                        print('its none of them')
        idf_schedules = pd.DataFrame(
            {'week_day': Weekday,
             'Weekends': Weekend,
             'Holiday': Holiday,
             'Winterdesignday': Winterdesignday,
             'Summerdesignday': Summerdesignday,
             'Customday1': Customday1,
             'Customday2': Customday2,
             })
        print(idf_schedules)

        for building in city.buildings:
          schedules = dict()
          for usage_zone in building.usage_zones:
            usage_schedules = idf_schedules

          # todo: should we save the data type? How?
            number_of_schedule_types = 5
            schedules_per_schedule_type = 6
            idf_day_types = dict({'week_day': 0, 'Weekends': 1, 'Holiday': 2, 'Winterdesignday': 3, 'Summerdesignday': 4, 'Customday1': 5, 'Customday2': 6})
            for schedule_types in range(0, number_of_schedule_types):
              data = pd.DataFrame()
              columns_names = []
              name = ''
              for schedule_day in range(0, len(idf_schedules)):
                row_cells = usage_schedules.iloc[schedules_per_schedule_type*schedule_types + schedule_day]
                if schedule_day == idf_day_types['week_day']:
                  name = row_cells[0]
                columns_names.append(row_cells[2])
                data1 = row_cells[schedules_per_schedule_type:]
                data = pd.concat([data, data1],  axis=1)
              data.columns = columns_names
              schedules[name] = data
            usage_zone.schedules = schedules