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adapted model_generator validation references

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This commit is contained in:
Luis Aleixo 2023-04-20 16:39:29 +02:00
parent 98f6e3446b
commit 14a6fb73a7
1 changed files with 18 additions and 172 deletions
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190
caimira/apps/calculator/model_generator.py
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@ -16,16 +16,12 @@ import caimira.monte_carlo as mc
from .. import calculator
from caimira.monte_carlo.data import activity_distributions, virus_distributions, mask_distributions, short_range_distances
from caimira.monte_carlo.data import expiration_distribution, expiration_BLO_factors, expiration_distributions, short_range_expiration_distributions
from .DEFAULT_DATA import *
LOG = logging.getLogger(__name__)
minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
# Used to declare when an attribute of a class must have a value provided, and
# there should be no default value used.
_NO_DEFAULT = object()
_DEFAULT_MC_SAMPLE_SIZE = 250000
@dataclasses.dataclass
class FormData:
@ -94,74 +90,7 @@ class FormData:
short_range_option: str
short_range_interactions: list
#: The default values for undefined fields. Note that the defaults here
#: and the defaults in the html form must not be contradictory.
_DEFAULTS: typing.ClassVar[typing.Dict[str, typing.Any]] = {
'activity_type': 'office',
'air_changes': 0.,
'air_supply': 0.,
'arve_sensors_option': False,
'specific_breaks': '{}',
'precise_activity': '{}',
'calculator_version': _NO_DEFAULT,
'ceiling_height': 0.,
'conditional_probability_plot': False,
'exposed_coffee_break_option': 'coffee_break_0',
'exposed_coffee_duration': 5,
'exposed_finish': '17:30',
'exposed_lunch_finish': '13:30',
'exposed_lunch_option': True,
'exposed_lunch_start': '12:30',
'exposed_start': '08:30',
'event_month': 'January',
'floor_area': 0.,
'hepa_amount': 0.,
'hepa_option': False,
'humidity': '',
'infected_coffee_break_option': 'coffee_break_0',
'infected_coffee_duration': 5,
'infected_dont_have_breaks_with_exposed': False,
'infected_finish': '17:30',
'infected_lunch_finish': '13:30',
'infected_lunch_option': True,
'infected_lunch_start': '12:30',
'infected_people': 1,
'infected_start': '08:30',
'inside_temp': _NO_DEFAULT,
'location_latitude': _NO_DEFAULT,
'location_longitude': _NO_DEFAULT,
'location_name': _NO_DEFAULT,
'geographic_population': 0,
'geographic_cases': 0,
'ascertainment_bias': 'confidence_low',
'exposure_option': 'p_deterministic_exposure',
'mask_type': 'Type I',
'mask_wearing_option': 'mask_off',
'mechanical_ventilation_type': 'not-applicable',
'opening_distance': 0.,
'room_heating_option': False,
'room_number': _NO_DEFAULT,
'room_volume': 0.,
'simulation_name': _NO_DEFAULT,
'total_people': _NO_DEFAULT,
'vaccine_option': False,
'vaccine_booster_option': False,
'vaccine_type': 'AZD1222_(AstraZeneca)',
'vaccine_booster_type': 'AZD1222_(AstraZeneca)',
'ventilation_type': 'no_ventilation',
'virus_type': 'SARS_CoV_2',
'volume_type': _NO_DEFAULT,
'window_type': 'window_sliding',
'window_height': 0.,
'window_width': 0.,
'windows_duration': 10.,
'windows_frequency': 60.,
'windows_number': 0,
'window_opening_regime': 'windows_open_permanently',
'sensor_in_use': '',
'short_range_option': 'short_range_no',
'short_range_interactions': '[]',
}
_DEFAULTS: typing.ClassVar[typing.Dict[str, typing.Any]] = DEFAULTS
@classmethod
def from_dict(cls, form_data: typing.Dict) -> "FormData":
@ -176,7 +105,7 @@ class FormData:
for key, default_value in cls._DEFAULTS.items():
if form_data.get(key, '') == '':
if default_value is _NO_DEFAULT:
if default_value is NO_DEFAULT:
raise ValueError(f"{key} must be specified")
form_data[key] = default_value
@ -206,8 +135,8 @@ class FormData:
del form_dict['calculator_version']
for attr, value in list(form_dict.items()):
default = cls._DEFAULTS.get(attr, _NO_DEFAULT)
if default is not _NO_DEFAULT and value in [default, 'not-applicable']:
default = cls._DEFAULTS.get(attr, NO_DEFAULT)
if default is not NO_DEFAULT and value in [default, 'not-applicable']:
form_dict.pop(attr)
return form_dict
@ -273,7 +202,7 @@ class FormData:
f"Length of breaks >= Length of {population} presence."
)
validation_tuples = [('activity_type', ACTIVITY_TYPES),
validation_tuples = [('activity_type', ACTIVITY_TYPES),
('exposed_coffee_break_option', COFFEE_OPTIONS_INT),
('infected_coffee_break_option', COFFEE_OPTIONS_INT),
('mechanical_ventilation_type', MECHANICAL_VENTILATION_TYPES),
@ -422,7 +351,7 @@ class FormData:
),
)
def build_model(self, sample_size=_DEFAULT_MC_SAMPLE_SIZE) -> models.ExposureModel:
def build_model(self, sample_size=DEFAULT_MC_SAMPLE_SIZE) -> models.ExposureModel:
return self.build_mc_model().build_model(size=sample_size)
def tz_name_and_utc_offset(self) -> typing.Tuple[str, float]:
@ -453,7 +382,7 @@ class FormData:
month = MONTH_NAMES.index(self.event_month) + 1
wx_station = self.nearest_weather_station()
temp_profile = caimira.data.weather.mean_hourly_temperatures(wx_station[0], month)
temp_profile = caimira.data.weather.mean_hourly_temperatures(wx_station = wx_station[0], month = MONTH_NAMES.index(self.event_month) + 1)
_, utc_offset = self.tz_name_and_utc_offset()
@ -548,74 +477,16 @@ class FormData:
# Initializes the virus
virus = virus_distributions[self.virus_type]
scenario_activity_and_expiration = {
'office': (
'Seated',
# Mostly silent in the office, but 1/3rd of time speaking.
{'Speaking': 1, 'Breathing': 2}
),
'controlroom-day': (
'Seated',
# Daytime control room shift, 50% speaking.
{'Speaking': 1, 'Breathing': 1}
),
'controlroom-night': (
'Seated',
# Nightshift control room, 10% speaking.
{'Speaking': 1, 'Breathing': 9}
),
'smallmeeting': (
'Seated',
# Conversation of N people is approximately 1/N% of the time speaking.
{'Speaking': 1, 'Breathing': self.total_people - 1}
),
'largemeeting': (
'Standing',
# each infected person spends 1/3 of time speaking.
{'Speaking': 1, 'Breathing': 2}
),
'callcentre': ('Seated', 'Speaking'),
'library': ('Seated', 'Breathing'),
'training': ('Standing', 'Speaking'),
'training_attendee': ('Seated', 'Breathing'),
'lab': (
'Light activity',
#Model 1/2 of time spent speaking in a lab.
{'Speaking': 1, 'Breathing': 1}),
'workshop': (
'Moderate activity',
#Model 1/2 of time spent speaking in a workshop.
{'Speaking': 1, 'Breathing': 1}),
'gym':('Heavy exercise', 'Breathing'),
# Other activity types
'household-day': (
'Light activity',
{'Breathing': 5, 'Speaking': 5}
),
'household-night': (
'Seated',
{'Breathing': 7, 'Speaking': 3}
),
'primary-school': (
'Light activity',
{'Breathing': 5, 'Speaking': 5}
),
'secondary-school': (
'Light activity',
{'Breathing': 7, 'Speaking': 3}
),
'university': (
'Seated',
{'Breathing': 9, 'Speaking': 1}
),
'restaurant': (
'Seated',
{'Breathing': 1, 'Speaking': 9}
),
'precise': self.generate_precise_activity_expiration(),
}
[activity_defn, expiration_defn] = scenario_activity_and_expiration[self.activity_type]
activity_index = ACTIVITY_TYPES.index(self.activity_type)
activity_defn = ACTIVITIES[activity_index]['activity']
expiration_defn = ACTIVITIES[activity_index]['expiration']
if (self.activity_type == 'smallmeeting'):
# Conversation of N people is approximately 1/N% of the time speaking.
expiration_defn = {'Speaking': 1, 'Breathing': self.total_people - 1}
elif (self.activity_type == 'precise'):
activity_defn, expiration_defn = self.generate_precise_activity_expiration()
activity = activity_distributions[activity_defn]
expiration = build_expiration(expiration_defn)
@ -960,31 +831,6 @@ def baseline_raw_form_data() -> typing.Dict[str, typing.Union[str, float]]:
}
ACTIVITY_TYPES = {
'office', 'smallmeeting', 'largemeeting', 'callcentre', 'controlroom-day', 'controlroom-night', 'library', 'lab', 'workshop', 'training',
'training_attendee', 'gym', 'household-day', 'household-night', 'primary-school', 'secondary-school', 'university', 'restaurant', 'precise',
}
MECHANICAL_VENTILATION_TYPES = {'mech_type_air_changes', 'mech_type_air_supply', 'not-applicable'}
MASK_TYPES = {'Type I', 'FFP2', 'Cloth'}
MASK_WEARING_OPTIONS = {'mask_on', 'mask_off'}
VENTILATION_TYPES = {'natural_ventilation', 'mechanical_ventilation', 'no_ventilation'}
VIRUS_TYPES = {'SARS_CoV_2', 'SARS_CoV_2_ALPHA', 'SARS_CoV_2_BETA','SARS_CoV_2_GAMMA', 'SARS_CoV_2_DELTA', 'SARS_CoV_2_OMICRON'}
VOLUME_TYPES = {'room_volume_explicit', 'room_volume_from_dimensions'}
WINDOWS_OPENING_REGIMES = {'windows_open_permanently', 'windows_open_periodically', 'not-applicable'}
WINDOWS_TYPES = {'window_sliding', 'window_hinged', 'not-applicable'}
COFFEE_OPTIONS_INT = {'coffee_break_0': 0, 'coffee_break_1': 1, 'coffee_break_2': 2, 'coffee_break_4': 4}
CONFIDENCE_LEVEL_OPTIONS = {'confidence_low': 10, 'confidence_medium': 5, 'confidence_high': 2}
MONTH_NAMES = [
'January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December',
]
VACCINE_TYPE = ['Ad26.COV2.S_(Janssen)', 'Any_mRNA_-_heterologous', 'AZD1222_(AstraZeneca)', 'AZD1222_(AstraZeneca)_and_any_mRNA_-_heterologous', 'AZD1222_(AstraZeneca)_and_BNT162b2_(Pfizer)',
'BBIBP-CorV_(Beijing_CNBG)', 'BNT162b2_(Pfizer)', 'BNT162b2_(Pfizer)_and_mRNA-1273_(Moderna)', 'CoronaVac_(Sinovac)', 'CoronaVac_(Sinovac)_and_AZD1222_(AstraZeneca)', 'Covishield',
'mRNA-1273_(Moderna)', 'Sputnik_V_(Gamaleya)', 'CoronaVac_(Sinovac)_and_BNT162b2_(Pfizer)']
VACCINE_BOOSTER_TYPE = ['AZD1222_(AstraZeneca)', 'Ad26.COV2.S_(Janssen)', 'BNT162b2_(Pfizer)', 'BNT162b2_(Pfizer)_(4th_dose)', 'BNT162b2_(Pfizer)_and_mRNA-1273_(Moderna)',
'BNT162b2_(Pfizer)_or_mRNA-1273_(Moderna)', 'BNT162b2_(Pfizer)_or_mRNA-1273_(Moderna)_(4th_dose)', 'CoronaVac_(Sinovac)', 'Coronavac_(Sinovac)', 'Sinopharm',
'mRNA-1273_(Moderna)', 'mRNA-1273_(Moderna)_(4th_dose)', 'Other']
def _hours2timestring(hours: float):
# Convert times like 14.5 to strings, like "14:30"
return f"{int(np.floor(hours)):02d}:{int(np.round((hours % 1) * 60)):02d}"