Source code for honeybee_energy.load.people

# coding=utf-8
"""Complete definition of people in a simulation, including schedule and load."""
from __future__ import division

from honeybee._lockable import lockable
from honeybee.typing import float_in_range, float_positive, clean_and_id_ep_string
from honeybee.altnumber import autocalculate

from ._base import _LoadBase
from ..schedule.ruleset import ScheduleRuleset
from ..schedule.fixedinterval import ScheduleFixedInterval
from ..reader import parse_idf_string
from ..writer import generate_idf_string
from ..properties.extension import PeopleProperties

import honeybee_energy.lib.schedules as _sched_lib


[docs]@lockable class People(_LoadBase): """A complete definition of people, including schedules and load. Args: identifier: Text string for a unique People ID. Must be < 100 characters and not contain any EnergyPlus special characters. This will be used to identify the object across a model and in the exported IDF. people_per_area: A numerical value for the number of people per square meter of floor area. occupancy_schedule: A ScheduleRuleset or ScheduleFixedInterval for the occupancy over the course of the year. The type of this schedule should be Fractional and the fractional values will get multiplied by the people_per_area to yield a complete occupancy profile. activity_schedule: A ScheduleRuleset or ScheduleFixedInterval for the activity of the occupants over the course of the year. The type of this schedule should be ActivityLevel and the values of the schedule equal to the number of Watts given off by an individual person in the room. If None, a default constant schedule with 120 Watts per person will be used, which is typical of awake, adult humans who are seated. radiant_fraction: A number between 0 and 1 for the fraction of the sensible heat given off by people that is radiant (as opposed to convective). (Default: 0.3). latent_fraction: A number between 0 and 1 for the fraction of the heat given off by people that is latent (as opposed to sensible). This input can also be an Autocalculate object, which will automatically estimate the latent fraction based on the occupant's activity level. (Default: autocalculate). Properties: * identifier * display_name * people_per_area * area_per_person * occupancy_schedule * activity_schedule * radiant_fraction * latent_fraction * user_data """ __slots__ = ('_people_per_area', '_occupancy_schedule', '_activity_schedule', '_radiant_fraction', '_latent_fraction') def __init__(self, identifier, people_per_area, occupancy_schedule, activity_schedule=None, radiant_fraction=0.3, latent_fraction=autocalculate): """Initialize People.""" _LoadBase.__init__(self, identifier) self.people_per_area = people_per_area self.occupancy_schedule = occupancy_schedule self.activity_schedule = activity_schedule self.radiant_fraction = radiant_fraction self.latent_fraction = latent_fraction self._properties = PeopleProperties(self) @property def people_per_area(self): """Get or set the number of people per square meter of floor area.""" return self._people_per_area @people_per_area.setter def people_per_area(self, value): self._people_per_area = float_positive(value, 'people per area') @property def area_per_person(self): """Get or set the number of square meters of floor area per person.""" return 1 / self._people_per_area if self._people_per_area != 0 else 0 @area_per_person.setter def area_per_person(self, value): if float(value) != 0: self._people_per_area = 1 / float_positive(value, 'area per person') else: self._people_per_area = 0 @property def occupancy_schedule(self): """Get or set a ScheduleRuleset or ScheduleFixedInterval for the occupancy.""" return self._occupancy_schedule @occupancy_schedule.setter def occupancy_schedule(self, value): assert isinstance(value, (ScheduleRuleset, ScheduleFixedInterval)), \ 'Expected ScheduleRuleset or ScheduleFixedInterval for People ' \ 'occupancy_schedule. Got {}.'.format(type(value)) self._check_fractional_schedule_type(value, 'Occupancy') value.lock() # lock editing in case schedule has multiple references self._occupancy_schedule = value @property def activity_schedule(self): """Get or set a ScheduleRuleset or ScheduleFixedInterval for the occupancy.""" return self._activity_schedule @activity_schedule.setter def activity_schedule(self, value): if value is not None: assert isinstance(value, (ScheduleRuleset, ScheduleFixedInterval)), \ 'Expected ScheduleRuleset or ScheduleFixedInterval for People' \ ' activity_schedule. Got {}.'.format(type(value)) self._check_activity_schedule_type(value) value.lock() # lock editing in case schedule has multiple references self._activity_schedule = value else: self._activity_schedule = _sched_lib.seated_activity @property def radiant_fraction(self): """Get or set the radiant fraction of sensible heat given off by people.""" return self._radiant_fraction @radiant_fraction.setter def radiant_fraction(self, value): self._radiant_fraction = float_in_range( value, 0.0, 1.0, 'people radiant fraction') @property def latent_fraction(self): """Get or set the fraction of the heat given off by people that is latent.""" return self._latent_fraction @latent_fraction.setter def latent_fraction(self, value): if value == autocalculate: self._latent_fraction = autocalculate else: self._latent_fraction = float_in_range( value, 0.0, 1.0, 'people latent fraction')
[docs] def diversify(self, count, occupancy_stdev=20, schedule_offset=1, timestep=1, schedule_indices=None): """Get an array of diversified People derived from this "average" one. Approximately 2/3 of the schedules in the output objects will be offset from the mean by the input schedule_offset (1/3 ahead and another 1/3 behind). Args: count: An positive integer for the number of diversified objects to generate from this mean object. occupancy_stdev: A number between 0 and 100 for the percent of the occupancy people_per_area representing one standard deviation of diversification from the mean. (Default 20 percent). schedule_offset: A positive integer for the number of timesteps at which the occupancy schedule of the resulting objects will be shifted - roughly 1/3 of the objects ahead and another 1/3 behind. (Default: 1). timestep: An integer for the number of timesteps per hour at which the shifting is occurring. This must be a value between 1 and 60, which is evenly divisible by 60. 1 indicates that each step is an hour while 60 indicates that each step is a minute. (Default: 1). schedule_indices: An optional list of integers from 0 to 2 with a length equal to the input count, which will be used to set whether a given schedule is behind (0), ahead (2), or the same (1). This can be used to coordinate schedules across diversified programs. If None a random list of integers will be generated. (Default: None). """ # generate shifted schedules and a gaussian distribution of people_per_area occ_schs = self._shift_schedule( self.occupancy_schedule, schedule_offset, timestep) stdev = self.people_per_area * (occupancy_stdev / 100) new_loads, sch_ints = self._gaussian_values(count, self.people_per_area, stdev) sch_ints = sch_ints if schedule_indices is None else schedule_indices # generate the new objects and return them new_objects = [] for load_val, sch_int in zip(new_loads, sch_ints): new_obj = self.duplicate() new_obj.identifier = clean_and_id_ep_string(self.identifier) new_obj.people_per_area = load_val new_obj.occupancy_schedule = occ_schs[sch_int] new_objects.append(new_obj) return new_objects
[docs] @classmethod def from_idf(cls, idf_string, schedule_dict): """Create an People object from an EnergyPlus IDF text string. Note that the People idf_string must use the 'people per zone floor area' method in order to be successfully imported. Args: idf_string: A text string fully describing an EnergyPlus people definition. schedule_dict: A dictionary with schedule identifiers as keys and honeybee schedule objects as values (either ScheduleRuleset or ScheduleFixedInterval). These will be used to assign the schedules to the People object. Returns: A tuple with four elements - people: A People object loaded from the idf_string. - zone_identifier: The identifier of the zone to which the People object should be assigned. """ # check the inputs ep_strs = parse_idf_string(idf_string, 'People,') assert ep_strs[3].lower() == 'people/area', \ 'People must use People/Area method to be loaded from IDF to honeybee.' # extract the properties from the string lat_fract = autocalculate if ep_strs[8] == '' or \ ep_strs[8].lower() == 'autocalculate' else 1 - float(ep_strs[8]) rad_fract = ep_strs[7] if ep_strs[7] != '' else 0.3 # extract the schedules from the string occ_sched, activity_sched = cls._get_occ_act_schedules_from_dict( schedule_dict, ep_strs[2], ep_strs[9]) # return the people object and the zone id for the people object obj_id = ep_strs[0].split('..')[0] zone_id = ep_strs[1] people = cls(obj_id, ep_strs[5], occ_sched, activity_sched, rad_fract, lat_fract) return people, zone_id
[docs] @classmethod def from_dict(cls, data): """Create a People object from a dictionary. Note that the dictionary must be a non-abridged version for this classmethod to work. Args: data: A People dictionary in following the format below. .. code-block:: python { "type": 'People', "identifier": 'Open_Office_People_005_03_02', "display_name": 'Office People', "people_per_area": 0.05, # number of people per square meter of floor area "occupancy_schedule": {}, # ScheduleRuleset/ScheduleFixedInterval dictionary "activity_schedule": {}, # ScheduleRuleset/ScheduleFixedInterval dictionary "radiant_fraction": 0.3, # fraction of sensible heat that is radiant "latent_fraction": 0.2 # fraction of total heat that is latent } """ assert data['type'] == 'People', \ 'Expected People dictionary. Got {}.'.format(data['type']) occ_sched = cls._get_schedule_from_dict(data['occupancy_schedule']) act_sched = cls._get_schedule_from_dict(data['activity_schedule']) if \ 'activity_schedule' in data and data['activity_schedule'] is not None \ else None rad_fract, lat_fract = cls._optional_dict_keys(data) new_obj = cls(data['identifier'], data['people_per_area'], occ_sched, act_sched, rad_fract, lat_fract) if 'display_name' in data and data['display_name'] is not None: new_obj.display_name = data['display_name'] if 'user_data' in data and data['user_data'] is not None: new_obj.user_data = data['user_data'] if 'properties' in data and data['properties'] is not None: new_obj.properties._load_extension_attr_from_dict(data['properties']) return new_obj
[docs] @classmethod def from_dict_abridged(cls, data, schedule_dict): """Create a People object from an abridged dictionary. Args: data: A PeopleAbridged dictionary in following the format below. schedule_dict: A dictionary with schedule identifiers as keys and honeybee schedule objects as values (either ScheduleRuleset or ScheduleFixedInterval). These will be used to assign the schedules to the People object. .. code-block:: python { "type": "PeopleAbridged", "identifier": 'Open_Office_People_005_03_02', "display_name": 'Office People', "people_per_area": 0.05, # number of people per square meter of floor area "occupancy_schedule": "Office Occupancy", # Schedule identifier "activity_schedule": "Office Activity", # Schedule identifier "radiant_fraction": 0.3, # fraction of sensible heat that is radiant "latent_fraction": 0.2 # fraction of total heat that is latent } """ assert data['type'] == 'PeopleAbridged', \ 'Expected PeopleAbridged dictionary. Got {}.'.format(data['type']) act_sch_id = data['activity_schedule'] if 'activity_schedule' in data and \ data['activity_schedule'] is not None else '' occ_sched, activity_sched = cls._get_occ_act_schedules_from_dict( schedule_dict, data['occupancy_schedule'], act_sch_id) rad_fract, lat_fract = cls._optional_dict_keys(data) new_obj = cls(data['identifier'], data['people_per_area'], occ_sched, activity_sched, rad_fract, lat_fract) if 'display_name' in data and data['display_name'] is not None: new_obj.display_name = data['display_name'] if 'user_data' in data and data['user_data'] is not None: new_obj.user_data = data['user_data'] if 'properties' in data and data['properties'] is not None: new_obj.properties._load_extension_attr_from_dict(data['properties']) return new_obj
[docs] def to_idf(self, zone_identifier): """IDF string representation of People object. Note that this method only outputs a single string for the People object and, to write everything needed to describe the object into an IDF, this object's occupancy_schedule and activity_schedule must also be written. This is done to give more control over the export process since you typically want to check whether these schedules are used by multiple People objects and write the schedule into the IDF only once. Args: zone_identifier: Text for the zone identifier that the People object is assigned to. """ sens_fract = 'autocalculate' if self.latent_fraction == autocalculate else \ 1 - float(self.latent_fraction) values = ('{}..{}'.format(self.identifier, zone_identifier), zone_identifier, self.occupancy_schedule.identifier, 'People/Area', '', self.people_per_area, '', self.radiant_fraction, sens_fract, self.activity_schedule.identifier) comments = ('name', 'zone name', 'occupancy schedule name', 'occupancy method', 'number of people {ppl}', 'people per floor area {ppl/m2}', 'floor area per person {m2/ppl}', 'radiant fraction', 'sensible heat fraction', 'activity schedule name') return generate_idf_string('People', values, comments)
[docs] def to_dict(self, abridged=False): """People dictionary representation. Args: abridged: Boolean to note whether the full dictionary describing the object should be returned (False) or just an abridged version (True), which only specifies the identifiers of schedules. Default: False. """ base = {'type': 'People'} if not abridged else {'type': 'PeopleAbridged'} base['identifier'] = self.identifier base['people_per_area'] = self.people_per_area base['radiant_fraction'] = self.radiant_fraction base['latent_fraction'] = self.latent_fraction if \ isinstance(self.latent_fraction, float) else self.latent_fraction.to_dict() if not abridged: base['occupancy_schedule'] = self.occupancy_schedule.to_dict() base['activity_schedule'] = self.activity_schedule.to_dict() else: base['occupancy_schedule'] = self.occupancy_schedule.identifier base['activity_schedule'] = self.activity_schedule.identifier if self._display_name is not None: base['display_name'] = self.display_name if self._user_data is not None: base['user_data'] = self.user_data prop_dict = self.properties.to_dict() if prop_dict is not None: base['properties'] = prop_dict return base
[docs] @staticmethod def average(identifier, peoples, weights=None, timestep_resolution=1): """Get a People object that's a weighted average between other People objects. Args: identifier: Text string for a unique ID for the new averaged People. Must be < 100 characters and not contain any EnergyPlus special characters. This will be used to identify the object across a model and in the exported IDF. peoples: A list of People objects that will be averaged together to make a new People. weights: An optional list of fractional numbers with the same length as the input peoples. These will be used to weight each of the People objects in the resulting average. Note that these weights can sum to less than 1 in which case the average people_per_area will assume 0 for the unaccounted fraction of the weights. If None, the objects will be weighted equally. Default: None. timestep_resolution: An optional integer for the timestep resolution at which the schedules will be averaged. Any schedule details smaller than this timestep will be lost in the averaging process. Default: 1. """ weights, u_weights = People._check_avg_weights(peoples, weights, 'People') # calculate the average values ppl_area = sum([ppl.people_per_area * w for ppl, w in zip(peoples, weights)]) rad_fract = sum([ppl.radiant_fraction * w for ppl, w in zip(peoples, u_weights)]) lat_fracts = [] for i, ppl in enumerate(peoples): if ppl.latent_fraction == autocalculate: lat_fract = autocalculate break lat_fracts.append(ppl.latent_fraction * u_weights[i]) else: lat_fract = sum(lat_fracts) # calculate the average schedules occ_sched = People._average_schedule( '{}_Occ Schedule'.format(identifier), [ppl.occupancy_schedule for ppl in peoples], u_weights, timestep_resolution) act_sched = People._average_schedule( '{}_Act Schedule'.format(identifier), [ppl.activity_schedule for ppl in peoples], u_weights, timestep_resolution) # return the averaged people object return People(identifier, ppl_area, occ_sched, act_sched, rad_fract, lat_fract)
def _check_activity_schedule_type(self, schedule): """Check that the type limit of an input schedule is fractional.""" if schedule.schedule_type_limit is not None: t_lim = schedule.schedule_type_limit assert t_lim.unit_type == 'ActivityLevel', 'Activity schedule must have a ' \ 'unit type of ActivityLevel. Got a schedule' \ ' of unit type [{}].'.format(t_lim.unit_type) assert t_lim.lower_limit == 0, 'Activity schedule should have either ' \ 'no type limit or a lower limit of 0. Got a schedule type with ' \ 'lower limit [{}].'.format(t_lim.lower_limit) @staticmethod def _optional_dict_keys(data): """Get the optional keys from a People dictionary.""" rad_fract = data['radiant_fraction'] if 'radiant_fraction' in data else 0.3 lat_fract = autocalculate if 'latent_fraction' not in data or \ data['latent_fraction'] == autocalculate.to_dict() \ else data['latent_fraction'] return rad_fract, lat_fract @staticmethod def _get_occ_act_schedules_from_dict(schedule_dict, occ_sch_id, act_sch_id): """Get schedule objects from a dictionary.""" try: occ_sched = schedule_dict[occ_sch_id] except KeyError as e: raise ValueError('Failed to find {} in the schedule_dict.'.format(e)) if act_sch_id == '' or act_sch_id.lower() == 'seated adult activity': activity_sched = None else: try: activity_sched = schedule_dict[act_sch_id] except KeyError as e: raise ValueError( 'Failed to find {} in the People schedule_dict.'.format(e) ) return occ_sched, activity_sched def __key(self): """A tuple based on the object properties, useful for hashing.""" return (self.identifier, self.people_per_area, hash(self.occupancy_schedule), hash(self.activity_schedule), self.radiant_fraction, str(self.latent_fraction)) def __hash__(self): return hash(self.__key()) def __eq__(self, other): return isinstance(other, People) and self.__key() == other.__key() def __ne__(self, other): return not self.__eq__(other) def __copy__(self): new_obj = People( self.identifier, self.people_per_area, self.occupancy_schedule, self.activity_schedule, self.radiant_fraction, self.latent_fraction) new_obj._display_name = self._display_name new_obj._user_data = None if self._user_data is None else self._user_data.copy() new_obj._properties._duplicate_extension_attr(self._properties) return new_obj def __repr__(self): return 'People: {} [{} people/m2] [schedule: {}]'.format( self.display_name, round(self.people_per_area, 3), self.occupancy_schedule.display_name)