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
233 lines
9.5 KiB
Python
233 lines
9.5 KiB
Python
import re
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import numpy as np
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import numpy.testing as npt
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import pytest
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from caimira.calculator.models import models
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from caimira.calculator.models import dataclass_utils as dc_utils
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@pytest.fixture
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def full_exposure_model(data_registry):
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return models.ExposureModel(
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data_registry=data_registry,
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concentration_model=models.ConcentrationModel(
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data_registry=data_registry,
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room=models.Room(volume=100),
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ventilation=models.AirChange(
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active=models.PeriodicInterval(120, 120), air_exch=0.25),
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infected=models.InfectedPopulation(
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data_registry=data_registry,
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number=1,
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presence=models.SpecificInterval(((8, 12), (13, 17), )),
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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expiration=models.Expiration.types['Breathing'],
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virus=models.Virus.types['SARS_CoV_2'],
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host_immunity=0.
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),
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evaporation_factor=0.3,
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),
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short_range=(),
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exposed=models.Population(
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number=10,
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presence=models.SpecificInterval(((8, 12), (13, 17), )),
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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host_immunity=0.
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),
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geographical_data=(),
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)
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@pytest.fixture
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def baseline_infected_population(data_registry):
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return models.InfectedPopulation(
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data_registry=data_registry,
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number=models.IntPiecewiseConstant(
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(8, 12, 13, 17), (1, 0, 1)),
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presence=None,
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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virus=models.Virus.types['SARS_CoV_2'],
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expiration=models.Expiration.types['Breathing'],
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host_immunity=0.,
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)
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@pytest.fixture
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def dynamic_infected_single_exposure_model(full_exposure_model, baseline_infected_population):
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return dc_utils.nested_replace(full_exposure_model,
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{'concentration_model.infected': baseline_infected_population, })
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@pytest.fixture
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def dynamic_population_exposure_model(full_exposure_model, baseline_infected_population):
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return dc_utils.nested_replace(full_exposure_model, {
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'concentration_model.infected': baseline_infected_population,
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})
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@pytest.mark.parametrize(
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"time",
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[4., 8., 10., 12., 13., 14., 16., 20., 24.],
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)
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def test_population_number(full_exposure_model: models.ExposureModel,
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baseline_infected_population: models.InfectedPopulation, time: float):
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int_population_number: models.InfectedPopulation = full_exposure_model.concentration_model.infected
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piecewise_population_number: models.InfectedPopulation = baseline_infected_population
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with pytest.raises(
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TypeError,
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match=f'The presence argument must be an "Interval". Got {type(None)}'
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):
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dc_utils.nested_replace(
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int_population_number, {'presence': None}
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)
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with pytest.raises(
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TypeError,
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match="The presence argument must be None for a IntPiecewiseConstant number"
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):
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dc_utils.nested_replace(
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piecewise_population_number, {'presence': models.SpecificInterval(((8, 12), ))}
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)
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assert int_population_number.person_present(time) == piecewise_population_number.person_present(time)
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assert int_population_number.people_present(time) == piecewise_population_number.people_present(time)
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@pytest.mark.parametrize(
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"time",
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[4., 8., 10., 12., 13., 14., 16., 20., 24.],
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)
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def test_concentration_model_dynamic_population(full_exposure_model: models.ExposureModel,
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dynamic_infected_single_exposure_model: models.ExposureModel,
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time: float):
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assert full_exposure_model.concentration(time) == dynamic_infected_single_exposure_model.concentration(time)
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@pytest.mark.parametrize("number_of_infected",[1, 2, 3, 4, 5])
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@pytest.mark.parametrize("time",[9., 12.5, 16.])
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def test_linearity_with_number_of_infected(full_exposure_model: models.ExposureModel,
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dynamic_infected_single_exposure_model: models.ExposureModel,
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time: float,
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number_of_infected: int):
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static_multiple_exposure_model: models.ExposureModel = dc_utils.nested_replace(
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full_exposure_model,
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{
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'concentration_model.infected.number': number_of_infected,
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}
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)
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npt.assert_almost_equal(static_multiple_exposure_model.concentration(time), dynamic_infected_single_exposure_model.concentration(time) * number_of_infected)
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npt.assert_almost_equal(static_multiple_exposure_model.deposited_exposure(), dynamic_infected_single_exposure_model.deposited_exposure() * number_of_infected)
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@pytest.mark.parametrize(
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"time", (8., 9., 10., 11., 12., 13., 14.),
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)
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def test_dynamic_dose(data_registry, full_exposure_model: models.ExposureModel, time: float):
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dynamic_infected: models.ExposureModel = dc_utils.nested_replace(
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full_exposure_model,
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{
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'concentration_model.infected': models.InfectedPopulation(
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data_registry=data_registry,
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number=models.IntPiecewiseConstant(
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(8, 10, 12, 13, 17), (1, 2, 0, 3)),
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presence=None,
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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virus=models.Virus.types['SARS_CoV_2'],
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expiration=models.Expiration.types['Breathing'],
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host_immunity=0.,
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),
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}
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)
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single_infected: models.ExposureModel = dc_utils.nested_replace(
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full_exposure_model,
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{
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'concentration_model.infected.number': 1,
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'concentration_model.infected.presence': models.SpecificInterval(((8, 10), )),
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}
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)
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two_infected: models.ExposureModel = dc_utils.nested_replace(
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full_exposure_model,
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{
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'concentration_model.infected.number': 2,
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'concentration_model.infected.presence': models.SpecificInterval(((10, 12), )),
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}
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)
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three_infected: models.ExposureModel = dc_utils.nested_replace(
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full_exposure_model,
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{
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'concentration_model.infected.number': 3,
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'concentration_model.infected.presence': models.SpecificInterval(((13, 17), )),
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}
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)
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dynamic_concentration = dynamic_infected.concentration(time)
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dynamic_exposure = dynamic_infected.deposited_exposure()
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static_concentration, static_exposure = zip(*[(model.concentration(time), model.deposited_exposure())
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for model in (single_infected, two_infected, three_infected)])
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npt.assert_almost_equal(dynamic_concentration, np.sum(static_concentration))
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npt.assert_almost_equal(dynamic_exposure, np.sum(static_exposure))
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def test_infection_probability(
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full_exposure_model: models.ExposureModel,
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dynamic_infected_single_exposure_model: models.ExposureModel,
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dynamic_population_exposure_model: models.ExposureModel):
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base_infection_probability = full_exposure_model.infection_probability()
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npt.assert_almost_equal(base_infection_probability, dynamic_infected_single_exposure_model.infection_probability())
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npt.assert_almost_equal(base_infection_probability, dynamic_population_exposure_model.infection_probability())
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@pytest.mark.skip
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def test_dynamic_total_probability_rule(
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dynamic_infected_single_exposure_model: models.ExposureModel,
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dynamic_population_exposure_model: models.ExposureModel):
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with pytest.raises(NotImplementedError, match=re.escape("Cannot compute total probability "
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"(including incidence rate) with dynamic occupancy")):
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dynamic_infected_single_exposure_model.total_probability_rule()
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with pytest.raises(NotImplementedError, match=re.escape("Cannot compute total probability "
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"(including incidence rate) with dynamic occupancy")):
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dynamic_population_exposure_model.total_probability_rule()
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@pytest.mark.skip
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def test_dynamic_expected_new_cases(
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dynamic_infected_single_exposure_model: models.ExposureModel,
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dynamic_population_exposure_model: models.ExposureModel):
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with pytest.raises(NotImplementedError, match=re.escape("Cannot compute expected new cases "
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"with dynamic occupancy")):
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dynamic_infected_single_exposure_model.expected_new_cases()
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with pytest.raises(NotImplementedError, match=re.escape("Cannot compute expected new cases "
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"with dynamic occupancy")):
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dynamic_population_exposure_model.expected_new_cases()
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@pytest.mark.skip
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def test_dynamic_reproduction_number(
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dynamic_infected_single_exposure_model: models.ExposureModel,
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dynamic_population_exposure_model: models.ExposureModel):
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with pytest.raises(NotImplementedError, match=re.escape("Cannot compute reproduction number "
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"with dynamic occupancy")):
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dynamic_infected_single_exposure_model.reproduction_number()
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with pytest.raises(NotImplementedError, match=re.escape("Cannot compute reproduction number "
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"with dynamic occupancy")):
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dynamic_population_exposure_model.reproduction_number()
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