"""
MyClass module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project
"""


import pandas as pd
import parseidf


class DoeIdf:
    """
    This is a import factory to add Idf schedules into the data model
    """
    idf_schedule_to_commet_schedule = {'BLDG_LIGHT_SCH': 'Lights',
                                       'BLDG_OCC_SCH_wo_SB': 'Occupancy',
                                       'BLDG_EQUIP_SCH': 'Equipment',
                                       'ACTIVITY_SCH': 'Activity',
                                       'INFIL_QUARTER_ON_SCH': 'Infiltration'}

    _SCHEDULE_COMPACT_TYPE = 'SCHEDULE:COMPACT'
    _SCHEDULE_TYPE_NAME = 1

    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())
        self._load_schedule(idf, 'small_office')

    def _load_schedule(self, idf, building_usage):
        holiday = []
        winter_design_day = []
        summer_design_day = []
        custom_day_1 = []
        custom_day_2 = []
        weekday = []
        weekend = []
        day_types = []
        schedules_day = {'None':[]}
        compact_keywords = ['Weekdays', 'Weekends', 'Alldays', 'AllOtherDays', 'Sunday', 'Monday', 'Tuesday',
                            'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Holiday', 'Winterdesignday',
                            'Summerdesignday', 'Customday1', 'Customday2']
        j = 0
        for compact_schedule in idf[self._SCHEDULE_COMPACT_TYPE]:
            j = j +1
            if self._SCHEDULE_TYPE_NAME in self.idf_schedule_to_commet_schedule:
                schedule_type = self.idf_schedule_to_commet_schedule[compact_schedule[self._SCHEDULE_TYPE_NAME]]
            else:
                schedule_type = 'Unknown'
            # todo: check this piece of code logic
            days_index = []
            for position, elements in enumerate(compact_schedule):
                element_title = elements.title().replace('For: ', '')
                if elements.title() != element_title:
                    days_index.append(position)
                day_types.append(element_title)
            days_index.append(len(day_types))
            print(day_types)
            print(days_index)
            '''
            for count, word_day_type in enumerate(day_type):
            if word_day_type in compact_keywords:
                index.append(count)
            index.append(len(day_type))    
            '''

            for i, day_index in enumerate(days_index):
                if day_index == len(day_types):
                    break
                schedules_day[f'{day_types[day_index]}'] = []
                print(f'{day_types[day_index]}')
                for hours_values in range(day_index+1, days_index[i + 1]-1, 2):

                    print(f'{day_types[hours_values]}# {day_types[hours_values+1]} ')

            if j == 2:
                return

            '''
            for i in range(len(index) - 1):
                number_of_day_schedule = list(day_types[index[i] + 1:index[i + 1]])
                hourly_values = list(range(24))
                start_hour = 0
                for num in range(int(len(number_of_day_schedule) / 2)):
                    until_time = list(map(int, (number_of_day_schedule[2*num].split('Until: ')[-1]).split(":")[0:]))
                    end_hour = int(until_time[0] + until_time[1] / 60)
                    value = float(number_of_day_schedule[2 * num + 1])
                    for hour in range(start_hour, end_hour):
                        hourly_values[hour] = value
                    start_hour = end_hour
                    print(f'{compact_schedule}')
                    print(f'{building_usage} {schedule_type} {hourly_values}')
                if day_type[index[i]] == 'Weekdays':
                    weekday.append(hourly_values)
                elif day_type[index[i]] == 'Weekends':
                    weekend.append(hourly_values)
                elif day_type[index[i]] == 'Holiday':
                    holiday.append(hourly_values)
                elif day_type[index[i]] == 'Winterdesignday':
                    winter_design_day.append(hourly_values)
                elif day_type[index[i]] == 'Summerdesignday':
                    summer_design_day.append(hourly_values)
                elif day_type[index[i]] == 'Customday1':
                    custom_day_1.append(hourly_values)
                elif day_type[index[i]] == 'Customday2':
                    custom_day_2.append(hourly_values)
                else:
                    raise ValueError('Unknown schedule day type')
'''
        idf_schedules = pd.DataFrame(
            {'week_day': weekday,
             'Weekends': weekend,
             'Holiday': holiday,
             'Winterdesignday': winter_design_day,
             'Summerdesignday': summer_design_day,
             'Customday1': custom_day_1,
             'Customday2': custom_day_2,
             })
        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
          '''