<commit_before>"""
This script creates schedules per building in CEA
"""
from __future__ import division
from __future__ import print_function

import os

import numpy as np
import pandas as pd

import cea
import cea.config
import cea.inputlocator
from cea.datamanagement.databases_verification import COLUMNS_ZONE_OCCUPANCY
from cea.demand.constants import VARIABLE_CEA_SCHEDULE_RELATION
from cea.utilities.schedule_reader import read_cea_schedule, save_cea_schedule

__author__ = "<NAME>"
__copyright__ = "Copyright 2018, Architecture and Building Systems - ETH Zurich"
__credits__ = ["<NAME>"]
__license__ = "MIT"
__version__ = "0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Production"

COLUMN_NAMES_CEA_SCHEDULE = ['DAY',
                             'HOUR',
                             'OCCUPANCY',
                             'APPLIANCES',
                             'LIGHTING',
                             'WATER',
                             'HEATING',
                             'COOLING',
                             'PROCESSES',
                             'SERVERS']


def calc_mixed_schedule(locator,
                        building_occupancy_df,
                        buildings,
                        model_schedule):

    metadata = model_schedule
    schedules_DB = locator.get_database_standard_schedules(model_schedule)
    schedule_data_all_uses = ScheduleData(locator, schedules_DB)
    building_occupancy_df = building_occupancy_df.set_index('Name')
    building_occupancy_df = building_occupancy_df.loc[buildings]

    # get list of uses only with a valid value in building_occupancy_df
    list_uses = get_short_list_of_uses_in_case_study(building_occupancy_df)

    internal_DB = pd.read_excel(locator.get_archetypes_properties(), 'INTERNAL_LOADS')
    internal_DB = internal_DB.set_index('Code')

    occupant_densities = {}
    for use in list_uses:
        if internal_DB.loc[use, 'Occ_m2pax'] > 0.0:
            occupant_densities[use] = 1 / internal_DB.loc[use, 'Occ_m2pax']
        else:
            occupant_densities[use] = 0.0

    for building in buildings:
        schedule_new_data = {}
        main_use_this_building = building_occupancy_df['mainuse'][building]
        monthly_multiplier = np.sum([np.array(
            schedule_data_all_uses.schedule_complementary_data[use]['MONTHLY_MULTIPLIER']) * building_occupancy_df.loc[
                                         building, use] for use in list_uses], axis=0)
        for schedule_type in VARIABLE_CEA_SCHEDULE_RELATION.values():
            current_schedule = np.zeros(len(schedule_data_all_uses.schedule_data['HOTEL'][schedule_type]))
            normalizing_value = 0.0
            if schedule_type in ['HEATING', 'COOLING']:
                schedule_new_data[schedule_type] = schedule_data_all_uses.schedule_data[main_use_this_building][
                    schedule_type]
            else:<commit_msg>change inconsistent variable naming<commit_after>"""
This script creates schedules per building in CEA
"""
from __future__ import division
from __future__ import print_function

import os

import numpy as np
import pandas as pd

import cea
import cea.config
import cea.inputlocator
from cea.datamanagement.databases_verification import COLUMNS_ZONE_OCCUPANCY
from cea.demand.constants import VARIABLE_CEA_SCHEDULE_RELATION
from cea.utilities.schedule_reader import read_cea_schedule, save_cea_schedule

__author__ = "<NAME>"
__copyright__ = "Copyright 2018, Architecture and Building Systems - ETH Zurich"
__credits__ = ["<NAME>"]
__license__ = "MIT"
__version__ = "0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Production"

COLUMN_NAMES_CEA_SCHEDULE = ['DAY',
                             'HOUR',
                             'OCCUPANCY',
                             'APPLIANCES',
                             'LIGHTING',
                             'WATER',
                             'HEATING',
                             'COOLING',
                             'PROCESSES',
                             'SERVERS']


def calc_mixed_schedule(locator,
                        building_occupancy_df,
                        buildings,
                        schedule_model):

    metadata = schedule_model
    schedules_DB = locator.get_database_standard_schedules(schedule_model)
    schedule_data_all_uses = ScheduleData(locator, schedules_DB)
    building_occupancy_df = building_occupancy_df.set_index('Name')
    building_occupancy_df = building_occupancy_df.loc[buildings]

    # get list of uses only with a valid value in building_occupancy_df
    list_uses = get_short_list_of_uses_in_case_study(building_occupancy_df)

    internal_DB = pd.read_excel(locator.get_archetypes_properties(), 'INTERNAL_LOADS')
    internal_DB = internal_DB.set_index('Code')

    occupant_densities = {}
    for use in list_uses:
        if internal_DB.loc[use, 'Occ_m2pax'] > 0.0:
            occupant_densities[use] = 1 / internal_DB.loc[use, 'Occ_m2pax']
        else:
            occupant_densities[use] = 0.0

    for building in buildings:
        schedule_new_data = {}
        main_use_this_building = building_occupancy_df['mainuse'][building]
        monthly_multiplier = np.sum([np.array(
            schedule_data_all_uses.schedule_complementary_data[use]['MONTHLY_MULTIPLIER']) * building_occupancy_df.loc[
                                         building, use] for use in list_uses], axis=0)
        for schedule_type in VARIABLE_CEA_SCHEDULE_RELATION.values():
            current_schedule = np.zeros(len(schedule_data_all_uses.schedule_data['HOTEL'][schedule_type]))
            normalizing_value = 0.0
            if schedule_type in ['HEATING', 'COOLING']:
                schedule_new_data[schedule_type] = schedule_data_all_uses.schedule_data[main_use_this_building][
                    schedule_type]
            else:
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