20b0467f89
- extract, isolate and package it in a completely independent Python module, versioned and in a way that allows releases on PyPI.org - fixed error in placeholder for secondary school (data registry defaults) - added restriction in pytest version to install - expected number of new cases fix - data registry update (schema v2.1.1) - github update - deprecate ExpertApplication and CO2Application - changes to reflect schema update 2.0.2 - version update - Fixed error with f_inf (short-range) - new folder layout - Conditional probability data update - General fixes - Fitting results in L/S/person - CO2 fitting algorithm refinement
102 lines
4.4 KiB
Python
102 lines
4.4 KiB
Python
import dataclasses
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import numpy as np
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import pytest
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import caimira.calculator.models
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import caimira.calculator.models.models
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import caimira.calculator.models.monte_carlo.sampleable
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MODEL_CLASSES = [
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cls for cls in vars(caimira.calculator.models).values()
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if dataclasses.is_dataclass(cls)
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]
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def test_type_annotations():
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# Check that there are appropriate type annotations for all of the model
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# classes in caimira.models. Note that these must be statically defined in
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# caimira.monte_carlo, rather than being dynamically generated, in order to
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# allow the type system to be able to see their definition without needing
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# runtime execution.
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missing = []
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for cls in MODEL_CLASSES:
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if not hasattr(caimira.calculator.models.monte_carlo, cls.__name__):
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missing.append(cls.__name__)
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continue
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mc_cls = getattr(caimira.calculator.models.monte_carlo, cls.__name__)
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assert issubclass(mc_cls, caimira.calculator.models.monte_carlo.MCModelBase)
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if missing:
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msg = (
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'There are missing model implementations in caimira.monte_carlo. '
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'The following definitions are needed:\n ' +
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'\n '.join([f'{model} = build_mc_model(caimira.models.{model})' for model in missing])
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)
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pytest.fail(msg)
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@pytest.fixture
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def baseline_mc_concentration_model(data_registry) -> caimira.calculator.models.monte_carlo.ConcentrationModel:
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mc_model = caimira.calculator.models.monte_carlo.ConcentrationModel(
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data_registry=data_registry,
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room=caimira.calculator.models.monte_carlo.Room(volume=caimira.calculator.models.monte_carlo.sampleable.Normal(75, 20),
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inside_temp=caimira.calculator.models.models.PiecewiseConstant((0., 24.), (293,))),
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ventilation=caimira.calculator.models.monte_carlo.SlidingWindow(
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data_registry=data_registry,
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active=caimira.calculator.models.models.PeriodicInterval(period=120, duration=120),
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outside_temp=caimira.calculator.models.models.PiecewiseConstant((0., 24.), (283,)),
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window_height=1.6, opening_length=0.6,
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),
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infected=caimira.calculator.models.models.InfectedPopulation(
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data_registry=data_registry,
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number=1,
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virus=caimira.calculator.models.models.Virus.types['SARS_CoV_2'],
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presence=caimira.calculator.models.models.SpecificInterval(((0., 4.), (5., 8.))),
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mask=caimira.calculator.models.models.Mask.types['No mask'],
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activity=caimira.calculator.models.models.Activity.types['Light activity'],
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expiration=caimira.calculator.models.models.Expiration.types['Breathing'],
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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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return mc_model
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@pytest.fixture
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def baseline_mc_sr_model() -> caimira.calculator.models.monte_carlo.ShortRangeModel:
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return ()
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@pytest.fixture
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def baseline_mc_exposure_model(data_registry, baseline_mc_concentration_model, baseline_mc_sr_model) -> caimira.calculator.models.monte_carlo.ExposureModel:
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return caimira.calculator.models.monte_carlo.ExposureModel(
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data_registry,
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baseline_mc_concentration_model,
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baseline_mc_sr_model,
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exposed=caimira.calculator.models.models.Population(
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number=10,
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presence=baseline_mc_concentration_model.infected.presence,
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activity=baseline_mc_concentration_model.infected.activity,
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mask=baseline_mc_concentration_model.infected.mask,
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host_immunity=0.,
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),
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geographical_data=caimira.calculator.models.models.Cases(),
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)
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def test_build_concentration_model(baseline_mc_concentration_model: caimira.calculator.models.monte_carlo.ConcentrationModel):
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model = baseline_mc_concentration_model.build_model(7)
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assert isinstance(model, caimira.calculator.models.models.ConcentrationModel)
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assert isinstance(model.concentration(time=0.), float)
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conc = model.concentration(time=1.)
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assert isinstance(conc, np.ndarray)
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assert conc.shape == (7, )
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def test_build_exposure_model(baseline_mc_exposure_model: caimira.calculator.models.monte_carlo.ExposureModel):
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model = baseline_mc_exposure_model.build_model(7)
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assert isinstance(model, caimira.calculator.models.models.ExposureModel)
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prob = model.deposited_exposure()
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assert isinstance(prob, np.ndarray)
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assert prob.shape == (7, )
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