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Merge branch 'feature/default_data' into 'master'

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Default data

See merge request caimira/caimira!440
This commit is contained in:
Luis Aleixo 2023-05-26 11:53:14 +02:00
commit 3c139fb62d
4 changed files with 173 additions and 181 deletions
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150
caimira/apps/calculator/defaults.py Normal file
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@ -0,0 +1,150 @@
import typing
# ------------------ Default form values ----------------------
# 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 = 250_000
#: The default values for undefined fields. Note that the defaults here
#: and the defaults in the html form must not be contradictory.
DEFAULTS = {
'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': '[]',
}
# ------------------ Activities ----------------------
ACTIVITIES: typing.List[typing.Dict[str, typing.Any]] = [
# Mostly silent in the office, but 1/3rd of time speaking.
{'name': 'office', 'activity': 'Seated',
'expiration': {'Speaking': 1, 'Breathing': 2}},
{'name': 'smallmeeting', 'activity': 'Seated',
'expiration': {'Speaking': 1, 'Breathing': None}},
# Each infected person spends 1/3 of time speaking.
{'name': 'largemeeting', 'activity': 'Standing',
'expiration': {'Speaking': 1, 'Breathing': 2}},
{'name': 'callcentre', 'activity': 'Seated', 'expiration': 'Speaking'},
# Daytime control room shift, 50% speaking.
{'name': 'controlroom-day', 'activity': 'Seated',
'expiration': {'Speaking': 1, 'Breathing': 1}},
# Nightshift control room, 10% speaking.
{'name': 'controlroom-night', 'activity': 'Seated',
'expiration': {'Speaking': 1, 'Breathing': 9}},
{'name': 'library', 'activity': 'Seated', 'expiration': 'Breathing'},
# Model 1/2 of time spent speaking in a lab.
{'name': 'lab', 'activity': 'Light activity',
'expiration': {'Speaking': 1, 'Breathing': 1}},
# Model 1/2 of time spent speaking in a workshop.
{'name': 'workshop', 'activity': 'Moderate activity',
'expiration': {'Speaking': 1, 'Breathing': 1}},
{'name': 'training', 'activity': 'Standing', 'expiration': 'Speaking'},
{'name': 'training_attendee', 'activity': 'Seated', 'expiration': 'Breathing'},
{'name': 'gym', 'activity': 'Heavy exercise', 'expiration': 'Breathing'},
{'name': 'household-day', 'activity': 'Light activity',
'expiration': {'Breathing': 5, 'Speaking': 5}},
{'name': 'household-night', 'activity': 'Seated',
'expiration': {'Breathing': 7, 'Speaking': 3}},
{'name': 'primary-school', 'activity': 'Light activity',
'expiration': {'Breathing': 5, 'Speaking': 5}},
{'name': 'secondary-school', 'activity': 'Light activity',
'expiration': {'Breathing': 7, 'Speaking': 3}},
{'name': 'university', 'activity': 'Seated',
'expiration': {'Breathing': 9, 'Speaking': 1}},
{'name': 'restaurant', 'activity': 'Seated',
'expiration': {'Breathing': 1, 'Speaking': 9}},
{'name': 'precise', 'activity': None, 'expiration': None},
]
# ------------------ Validation ----------------------
ACTIVITY_TYPES = [activity['name'] for activity in ACTIVITIES]
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}
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'}
MONTH_NAMES = [
'January', 'February', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December',
]
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']
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)']
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'}

192
caimira/apps/calculator/model_generator.py
View file

@ -16,16 +16,14 @@ 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 .defaults import (NO_DEFAULT, DEFAULT_MC_SAMPLE_SIZE, DEFAULTS, ACTIVITIES, ACTIVITY_TYPES, COFFEE_OPTIONS_INT, CONFIDENCE_LEVEL_OPTIONS,
MECHANICAL_VENTILATION_TYPES, MASK_TYPES, MASK_WEARING_OPTIONS, MONTH_NAMES, VACCINE_BOOSTER_TYPE, VACCINE_TYPE,
VENTILATION_TYPES, VIRUS_TYPES, VOLUME_TYPES, WINDOWS_OPENING_REGIMES, WINDOWS_TYPES)
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 +92,7 @@ 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 +107,7 @@ 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 +137,8 @@ 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 +204,7 @@ 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 +353,7 @@ 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 +384,7 @@ 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 +479,16 @@ 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 +833,6 @@ 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}"

10
caimira/apps/calculator/report_generator.py
View file

@ -15,7 +15,7 @@ import matplotlib.pyplot as plt
from caimira import models
from caimira.apps.calculator import markdown_tools
from ... import monte_carlo as mc
from .model_generator import FormData, _DEFAULT_MC_SAMPLE_SIZE
from .model_generator import FormData, DEFAULT_MC_SAMPLE_SIZE
from ... import dataclass_utils
@ -123,7 +123,6 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
for time in times
]
lower_concentrations = concentrations_with_sr_breathing(form, model, times, short_range_intervals)
highest_const = max(concentrations)
cumulative_doses = np.cumsum([
np.array(model.deposited_exposure_between_bounds(float(time1), float(time2))).mean()
@ -137,8 +136,6 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
prob = np.array(model.infection_probability())
prob_dist_count, prob_dist_bins = np.histogram(prob/100, bins=100, density=True)
prob_probabilistic_exposure = np.array(model.total_probability_rule()).mean()
er = np.array(model.concentration_model.infected.emission_rate_per_person_when_present()).mean()
exposed_occupants = model.exposed.number
expected_new_cases = np.array(model.expected_new_cases()).mean()
uncertainties_plot_src = img2base64(_figure2bytes(uncertainties_plot(model))) if form.conditional_probability_plot else None
exposed_presence_intervals = [list(interval) for interval in model.exposed.presence_interval().boundaries()]
@ -151,7 +148,6 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
"short_range_expirations": short_range_expirations,
"concentrations": concentrations,
"concentrations_zoomed": lower_concentrations,
"highest_const": highest_const,
"cumulative_doses": list(cumulative_doses),
"long_range_cumulative_doses": list(long_range_cumulative_doses),
"prob_inf": prob.mean(),
@ -160,8 +156,6 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
"prob_hist_count": list(prob_dist_count),
"prob_hist_bins": list(prob_dist_bins),
"prob_probabilistic_exposure": prob_probabilistic_exposure,
"emission_rate_per_person": er,
"exposed_occupants": exposed_occupants,
"expected_new_cases": expected_new_cases,
"uncertainties_plot_src": uncertainties_plot_src,
}
@ -362,7 +356,7 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp
def scenario_statistics(mc_model: mc.ExposureModel, sample_times: typing.List[float], compute_prob_exposure: bool):
model = mc_model.build_model(size=_DEFAULT_MC_SAMPLE_SIZE)
model = mc_model.build_model(size=DEFAULT_MC_SAMPLE_SIZE)
if (compute_prob_exposure):
# It means we have data to calculate the total_probability_rule
prob_probabilistic_exposure = model.total_probability_rule()

2
caimira/tests/apps/calculator/test_model_generator.py
View file

@ -540,7 +540,7 @@ def test_default_types():
# Handle typing.NewType definitions.
field_type = field_type.__supertype__
if value is model_generator._NO_DEFAULT:
if value is model_generator.NO_DEFAULT:
continue
if field in model_generator._CAST_RULES_FORM_ARG_TO_NATIVE: