cd3df90c4c
- Correction of typos - Changes do model to accommodate group of exposure models - Handled virus validator to accommodate group of exposure models - Modifications to accomodate generation of results for exposure model group - Added data registry attribute to exposuremodelgroups class - Fixed test for dynamic models - Defined ExposureModelGroup with required methods - Adapted virus report data to generate results for groups of exposed population - Build same distributions for different models - Fixed bug with defaults - Handled definition of a single ExposureModel root obj when only one group is defined - Added short-range expirations per group - Added full validation on short_range interactions with dynamic exposure model - Added full set of tests - Updated docstrings - Added support for number of people in exposed population, which should be identical within each group - Added type checks - Added UI adjustments for expected new cases - Adapted exposed population data format to accommodate constant total_people
95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
import pytest
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from caimira.calculator.models import models
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import caimira.calculator.models.data
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import caimira.calculator.models.dataclass_utils
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from caimira.calculator.validators.virus import virus_validator
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from caimira.calculator.store.data_registry import DataRegistry
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@pytest.fixture
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def baseline_form_data():
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return virus_validator.baseline_raw_form_data()
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@pytest.fixture
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def baseline_form(baseline_form_data, data_registry):
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return virus_validator.VirusFormData.from_dict(baseline_form_data, data_registry)
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@pytest.fixture
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def data_registry():
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return DataRegistry()
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@pytest.fixture
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def baseline_concentration_model(data_registry):
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model = models.ConcentrationModel(
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data_registry=data_registry,
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room=models.Room(
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volume=75, inside_temp=models.PiecewiseConstant((0., 24.), (293,))),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0., 24.), )),
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air_exch=30.,
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),
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infected=models.EmittingPopulation(
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data_registry=data_registry,
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number=1,
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virus=models.Virus.types['SARS_CoV_2'],
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presence=models.SpecificInterval(((0., 4.), (5., 8.))),
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Light activity'],
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known_individual_emission_rate=970 * 50,
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host_immunity=0.,
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# Superspreading event, where ejection factor is fixed based
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# on Miller et al. (2020) - 50 represents the infectious dose.
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),
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evaporation_factor=0.3,
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)
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return model
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@pytest.fixture
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def baseline_sr_model():
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return ()
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@pytest.fixture
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def baseline_exposure_model(data_registry, baseline_concentration_model, baseline_sr_model):
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return models.ExposureModel(
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data_registry=data_registry,
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concentration_model=baseline_concentration_model,
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short_range=baseline_sr_model,
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exposed=models.Population(
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number=1000,
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presence=baseline_concentration_model.infected.presence,
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activity=baseline_concentration_model.infected.activity,
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mask=baseline_concentration_model.infected.mask,
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host_immunity=0.,
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),
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geographical_data=models.Cases(),
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)
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@pytest.fixture
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def exposure_model_w_outside_temp_changes(data_registry, baseline_exposure_model: models.ExposureModel):
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exp_model = caimira.calculator.models.dataclass_utils.nested_replace(
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baseline_exposure_model, {
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'concentration_model.ventilation': models.SlidingWindow(
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data_registry=data_registry,
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active=models.PeriodicInterval(2.2 * 60, 1.8 * 60),
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outside_temp=caimira.calculator.models.data.GenevaTemperatures['Jan'],
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window_height=1.6,
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opening_length=0.6,
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)
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})
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return exp_model
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@pytest.fixture
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def baseline_form_with_sr(baseline_form_data, data_registry):
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form_data_sr = baseline_form_data
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form_data_sr['short_range_option'] = 'short_range_yes'
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form_data_sr['short_range_interactions'] = '{"group_1": [{"expiration": "Shouting", "start_time": "10:30", "duration": 30}]}'
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form_data_sr['short_range_occupants'] = 5
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return virus_validator.VirusFormData.from_dict(form_data_sr, data_registry)
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