350 lines
17 KiB
Python
350 lines
17 KiB
Python
import dataclasses
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import html
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import logging
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import typing
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from caimira import models
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from . import model_generator
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minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
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LOG = logging.getLogger(__name__)
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# Used to declare when an attribute of a class must have a value provided, and
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# there should be no default value used.
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_NO_DEFAULT = object()
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@dataclasses.dataclass
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class CO2FormData:
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CO2_data: dict
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specific_breaks: dict
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exposed_coffee_break_option: str
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exposed_coffee_duration: int
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exposed_finish: minutes_since_midnight
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exposed_lunch_finish: minutes_since_midnight
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exposed_lunch_option: bool
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exposed_lunch_start: minutes_since_midnight
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exposed_start: minutes_since_midnight
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infected_coffee_break_option: str #Used if infected_dont_have_breaks_with_exposed
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infected_coffee_duration: int #Used if infected_dont_have_breaks_with_exposed
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infected_dont_have_breaks_with_exposed: bool
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infected_finish: minutes_since_midnight
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infected_lunch_finish: minutes_since_midnight #Used if infected_dont_have_breaks_with_exposed
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infected_lunch_option: bool #Used if infected_dont_have_breaks_with_exposed
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infected_lunch_start: minutes_since_midnight #Used if infected_dont_have_breaks_with_exposed
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infected_people: int
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infected_start: minutes_since_midnight
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room_volume: float
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total_people: int
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ventilation_type: str
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windows_duration: float
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windows_frequency: float
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window_opening_regime: str
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#: The default values for undefined fields. Note that the defaults here
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#: and the defaults in the html form must not be contradictory.
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_DEFAULTS: typing.ClassVar[typing.Dict[str, typing.Any]] = {
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'CO2_data': '{}',
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'specific_breaks': '{}',
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'exposed_coffee_break_option': 'coffee_break_0',
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'exposed_coffee_duration': 5,
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'exposed_finish': '17:30',
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'exposed_lunch_finish': '13:30',
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'exposed_lunch_option': True,
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'exposed_lunch_start': '12:30',
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'exposed_start': '08:30',
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'infected_coffee_break_option': 'coffee_break_0',
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'infected_coffee_duration': 5,
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'infected_dont_have_breaks_with_exposed': False,
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'infected_finish': '17:30',
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'infected_lunch_finish': '13:30',
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'infected_lunch_option': True,
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'infected_lunch_start': '12:30',
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'infected_people': 1,
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'infected_start': '08:30',
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'room_volume': _NO_DEFAULT,
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'total_people': _NO_DEFAULT,
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'ventilation_type': 'no_ventilation',
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'windows_duration': 10.,
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'windows_frequency': 60.,
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'window_opening_regime': 'windows_open_permanently',
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}
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@classmethod
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def from_dict(cls, form_data: typing.Dict) -> "CO2FormData":
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# Take a copy of the form data so that we can mutate it.
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form_data = form_data.copy()
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form_data.pop('_xsrf', None)
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# Don't let arbitrary unescaped HTML through the net.
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for key, value in form_data.items():
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if isinstance(value, str):
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form_data[key] = html.escape(value)
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for key, default_value in cls._DEFAULTS.items():
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if form_data.get(key, '') == '':
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if default_value is _NO_DEFAULT:
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raise ValueError(f"{key} must be specified")
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form_data[key] = default_value
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for key, value in form_data.items():
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if key in _CAST_RULES_FORM_ARG_TO_NATIVE:
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form_data[key] = _CAST_RULES_FORM_ARG_TO_NATIVE[key](value)
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if key not in cls._DEFAULTS:
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raise ValueError(f'Invalid argument "{html.escape(key)}" given')
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instance = cls(**form_data)
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# instance.validate()
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return instance
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def build_model(self) -> models.CO2Data:
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population_presence=self.population_present_interval()
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last_time_present = population_presence.boundaries()[-1][-1]
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last_present_time_index = next((index for index, time in enumerate(self.CO2_data['times'])
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if time > last_time_present), len(self.CO2_data['times']))
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return models.CO2Data(
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room_volume=self.room_volume,
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number=self.total_people,
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presence=population_presence,
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ventilation_transition_times=self.ventilation_transition_times(last_time_present),
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times=self.CO2_data['times'][:last_present_time_index],
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CO2_concentrations=self.CO2_data['CO2'][:last_present_time_index],
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)
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def _compute_breaks_in_interval(self, start, finish, n_breaks, duration) -> models.BoundarySequence_t:
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break_delay = ((finish - start) - (n_breaks * duration)) // (n_breaks+1)
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break_times = []
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end = start
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for n in range(n_breaks):
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begin = end + break_delay
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end = begin + duration
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break_times.append((begin, end))
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return tuple(break_times)
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def exposed_lunch_break_times(self) -> models.BoundarySequence_t:
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result = []
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if self.exposed_lunch_option:
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result.append((self.exposed_lunch_start, self.exposed_lunch_finish))
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return tuple(result)
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def infected_lunch_break_times(self) -> models.BoundarySequence_t:
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if self.infected_dont_have_breaks_with_exposed:
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result = []
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if self.infected_lunch_option:
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result.append((self.infected_lunch_start, self.infected_lunch_finish))
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return tuple(result)
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else:
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return self.exposed_lunch_break_times()
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def exposed_number_of_coffee_breaks(self) -> int:
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return model_generator.COFFEE_OPTIONS_INT[self.exposed_coffee_break_option]
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def infected_number_of_coffee_breaks(self) -> int:
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return model_generator.COFFEE_OPTIONS_INT[self.infected_coffee_break_option]
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def _coffee_break_times(self, activity_start, activity_finish, coffee_breaks, coffee_duration, lunch_start, lunch_finish) -> models.BoundarySequence_t:
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time_before_lunch = lunch_start - activity_start
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time_after_lunch = activity_finish - lunch_finish
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before_lunch_frac = time_before_lunch / (time_before_lunch + time_after_lunch)
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n_morning_breaks = round(coffee_breaks * before_lunch_frac)
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breaks = (
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self._compute_breaks_in_interval(
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activity_start, lunch_start, n_morning_breaks, coffee_duration
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)
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+ self._compute_breaks_in_interval(
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lunch_finish, activity_finish, coffee_breaks - n_morning_breaks, coffee_duration
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)
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)
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return breaks
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def exposed_coffee_break_times(self) -> models.BoundarySequence_t:
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exposed_coffee_breaks = self.exposed_number_of_coffee_breaks()
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if exposed_coffee_breaks == 0:
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return ()
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if self.exposed_lunch_option:
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breaks = self._coffee_break_times(self.exposed_start, self.exposed_finish, exposed_coffee_breaks, self.exposed_coffee_duration, self.exposed_lunch_start, self.exposed_lunch_finish)
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else:
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breaks = self._compute_breaks_in_interval(self.exposed_start, self.exposed_finish, exposed_coffee_breaks, self.exposed_coffee_duration)
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return breaks
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def infected_coffee_break_times(self) -> models.BoundarySequence_t:
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if self.infected_dont_have_breaks_with_exposed:
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infected_coffee_breaks = self.infected_number_of_coffee_breaks()
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if infected_coffee_breaks == 0:
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return ()
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if self.infected_lunch_option:
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breaks = self._coffee_break_times(self.infected_start, self.infected_finish, infected_coffee_breaks, self.infected_coffee_duration, self.infected_lunch_start, self.infected_lunch_finish)
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else:
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breaks = self._compute_breaks_in_interval(self.infected_start, self.infected_finish, infected_coffee_breaks, self.infected_coffee_duration)
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return breaks
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else:
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return self.exposed_coffee_break_times()
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def generate_specific_break_times(self, population_breaks) -> models.BoundarySequence_t:
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break_times = []
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for n in population_breaks:
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# Parse break times.
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begin = model_generator.time_string_to_minutes(n["start_time"])
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end = model_generator.time_string_to_minutes(n["finish_time"])
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for time in [begin, end]:
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# For a specific break, the infected and exposed presence is the same.
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if not getattr(self, 'infected_start') < time < getattr(self, 'infected_finish'):
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raise ValueError(f'All breaks should be within the simulation time. Got {model_generator.time_minutes_to_string(time)}.')
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break_times.append((begin, end))
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return tuple(break_times)
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def present_interval(
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self,
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start: int,
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finish: int,
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breaks: typing.Optional[models.BoundarySequence_t] = None,
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) -> models.Interval:
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"""
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Calculate the presence interval given the start and end times (in minutes), and
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a number of monotonic, non-overlapping, but potentially unsorted, breaks (also in minutes).
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"""
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if not breaks:
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# If there are no breaks, the interval is the start and end.
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return models.SpecificInterval(((start/60, finish/60),))
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# Order the breaks by their start-time, and ensure that they are monotonic
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# and that the start of one break happens after the end of another.
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break_boundaries: models.BoundarySequence_t = tuple(sorted(breaks, key=lambda break_pair: break_pair[0]))
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for break_start, break_end in break_boundaries:
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if break_start >= break_end:
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raise ValueError("Break ends before it begins.")
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prev_break_end = break_boundaries[0][1]
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for break_start, break_end in break_boundaries[1:]:
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if prev_break_end >= break_start:
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raise ValueError(f"A break starts before another ends ({break_start}, {break_end}, {prev_break_end}).")
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prev_break_end = break_end
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present_intervals = []
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current_time = start
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LOG.debug(f"starting time march at {model_generator._hours2timestring(current_time/60)} to {model_generator._hours2timestring(finish/60)}")
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# As we step through the breaks. For each break there are 6 important cases
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# we must cover. Let S=start; E=end; Bs=Break start; Be=Break end:
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# 1. The interval is entirely before the break. S < E <= Bs < Be
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# 2. The interval straddles the start of the break. S < Bs < E <= Be
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# 3. The break is entirely inside the interval. S < Bs < Be <= E
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# 4. The interval is entirely inside the break. Bs <= S < E <= Be
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# 5. The interval straddles the end of the break. Bs <= S < Be <= E
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# 6. The interval is entirely after the break. Bs < Be <= S < E
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for current_break in break_boundaries:
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if current_time >= finish:
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break
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LOG.debug(f"handling break {model_generator._hours2timestring(current_break[0]/60)}-{model_generator._hours2timestring(current_break[1]/60)} "
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f" (current time: {model_generator._hours2timestring(current_time/60)})")
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break_s, break_e = current_break
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case1 = finish <= break_s
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case2 = current_time < break_s < finish < break_e
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case3 = current_time < break_s < break_e <= finish
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case4 = break_s <= current_time < finish <= break_e
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case5 = break_s <= current_time < break_e < finish
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case6 = break_e <= current_time
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if case1:
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LOG.debug(f"case 1: interval entirely before break")
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present_intervals.append((current_time / 60, finish / 60))
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LOG.debug(f" + added interval {model_generator._hours2timestring(present_intervals[-1][0])} "
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f"- {model_generator._hours2timestring(present_intervals[-1][1])}")
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current_time = finish
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elif case2:
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LOG.debug(f"case 2: interval straddles start of break")
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present_intervals.append((current_time / 60, break_s / 60))
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LOG.debug(f" + added interval {model_generator._hours2timestring(present_intervals[-1][0])} "
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f"- {model_generator._hours2timestring(present_intervals[-1][1])}")
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current_time = break_e
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elif case3:
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LOG.debug(f"case 3: break entirely inside interval")
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# We add the bit before the break, but not the bit afterwards,
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# as it may hit another break.
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present_intervals.append((current_time / 60, break_s / 60))
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LOG.debug(f" + added interval {model_generator._hours2timestring(present_intervals[-1][0])} "
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f"- {model_generator._hours2timestring(present_intervals[-1][1])}")
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current_time = break_e
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elif case4:
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LOG.debug(f"case 4: interval entirely inside break")
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current_time = finish
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elif case5:
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LOG.debug(f"case 5: interval straddles end of break")
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current_time = break_e
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elif case6:
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LOG.debug(f"case 6: interval entirely after the break")
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if current_time < finish:
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LOG.debug("trailing interval")
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present_intervals.append((current_time / 60, finish / 60))
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return models.SpecificInterval(tuple(present_intervals))
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def infected_present_interval(self) -> models.Interval:
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if self.specific_breaks != {}: # It means the breaks are specific and not predefined
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breaks = self.generate_specific_break_times(self.specific_breaks['infected_breaks'])
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else:
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breaks = self.infected_lunch_break_times() + self.infected_coffee_break_times()
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return self.present_interval(
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self.infected_start, self.infected_finish,
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breaks=breaks,
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)
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def population_present_interval(self) -> models.Interval:
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state_change_times = set(self.infected_present_interval().transition_times())
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state_change_times.update(self.exposed_present_interval().transition_times())
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all_state_changes = sorted(state_change_times)
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return models.SpecificInterval(tuple(zip(all_state_changes[:-1], all_state_changes[1:])))
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def exposed_present_interval(self) -> models.Interval:
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if self.specific_breaks != {}: # It means the breaks are specific and not predefined
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breaks = self.generate_specific_break_times(self.specific_breaks['exposed_breaks'])
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else:
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breaks = self.exposed_lunch_break_times() + self.exposed_coffee_break_times()
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return self.present_interval(
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self.exposed_start, self.exposed_finish,
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breaks=breaks,
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)
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def ventilation_transition_times(self, last_present_time) -> typing.Tuple[float, ...]:
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if self.ventilation_type == 'from_fitting' and self.window_opening_regime == 'windows_open_periodically':
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transition_times = sorted(models.PeriodicInterval(self.windows_frequency,
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self.windows_duration, min(self.infected_start, self.exposed_start)/60).transition_times())
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return tuple(filter(lambda x: x <= last_present_time, transition_times))
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else:
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return tuple((min(self.infected_start, self.exposed_start)/60, max(self.infected_finish, self.exposed_finish)/60), ) # all day long
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#: Mapping of field name to a callable which can convert values from form
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#: input (URL encoded arguments / string) into the correct type.
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_CAST_RULES_FORM_ARG_TO_NATIVE: typing.Dict[str, typing.Callable] = {}
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#: Mapping of field name to callable which can convert native type to values
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#: that can be encoded to URL arguments.
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_CAST_RULES_NATIVE_TO_FORM_ARG: typing.Dict[str, typing.Callable] = {}
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for _field in dataclasses.fields(CO2FormData):
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if _field.type is minutes_since_midnight:
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_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = model_generator.time_string_to_minutes
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_CAST_RULES_NATIVE_TO_FORM_ARG[_field.name] = model_generator.time_minutes_to_string
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elif _field.type is int:
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_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = model_generator._safe_int_cast
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elif _field.type is float:
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_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = float
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elif _field.type is bool:
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_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = lambda v: v == '1'
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_CAST_RULES_NATIVE_TO_FORM_ARG[_field.name] = int
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elif _field.type is list:
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_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = model_generator.string_to_list
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_CAST_RULES_NATIVE_TO_FORM_ARG[_field.name] = model_generator.list_to_string
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elif _field.type is dict:
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_CAST_RULES_FORM_ARG_TO_NATIVE[_field.name] = model_generator.string_to_dict
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_CAST_RULES_NATIVE_TO_FORM_ARG[_field.name] = model_generator.dict_to_string
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