generate initial CO2 plot
This commit is contained in:
Luis Aleixo
parent
2faeabf05c
commit
e75846a3b2
5 changed files with 35 additions and 40 deletions
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@ -325,11 +325,8 @@ class FormData:
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humidity = float(self.humidity)
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inside_temp = self.inside_temp
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return models.Room(volume=volume, inside_temp=models.PiecewiseConstant((0, 24), (inside_temp,)), humidity=humidity)
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def build_mc_model(self) -> mc.ExposureModel:
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room = self.initialize_room()
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room = models.Room(volume=volume, inside_temp=models.PiecewiseConstant((0, 24), (inside_temp,)), humidity=humidity)
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ventilation: models._VentilationBase = self.ventilation()
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infected_population = self.infected_population()
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short_range = []
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@ -342,11 +339,10 @@ class FormData:
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distance=short_range_distances,
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))
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# Initializes and returns a model with the attributes defined above
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return mc.ExposureModel(
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concentration_model=mc.ConcentrationModel(
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room=room,
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ventilation=self.ventilation(),
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ventilation=ventilation,
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infected=infected_population,
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evaporation_factor=0.3,
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),
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@ -357,6 +353,14 @@ class FormData:
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geographic_cases=self.geographic_cases,
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ascertainment_bias=CONFIDENCE_LEVEL_OPTIONS[self.ascertainment_bias],
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),
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CO2_profile=models.CO2Data(
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room_volume=self.room_volume,
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number=self.total_people,
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presence=self.population_present_interval(),
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ventilation_transition_times=tuple(ventilation.transition_times(room=room)),
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times=self.CO2_data['times'],
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CO2_concentrations=self.CO2_data['CO2']
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) if self.CO2_data_option else (),
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)
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def build_model(self, sample_size=DEFAULT_MC_SAMPLE_SIZE) -> models.ExposureModel:
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@ -754,6 +758,12 @@ class FormData:
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self.infected_start, self.infected_finish,
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breaks=breaks,
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)
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def population_present_interval(self) -> models.Interval:
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state_change_times = set(self.infected_present_interval().transition_times())
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state_change_times.update(self.exposed_present_interval().transition_times())
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all_state_changes = sorted(state_change_times)
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return models.SpecificInterval(tuple(zip(all_state_changes[:-1], all_state_changes[1:])))
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def short_range_interval(self, interaction) -> models.SpecificInterval:
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start_time = time_string_to_minutes(interaction['start_time'])
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@ -150,6 +150,11 @@ def calculate_report_data(form: FormData, model: models.ExposureModel) -> typing
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zip(('viral_loads', 'pi_means', 'lower_percentiles', 'upper_percentiles'),
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manufacture_conditional_probability_data(model, prob))}
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CO2_times = list(form.CO2_data['times']) if form.CO2_data_option else []
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CO2_values = list(form.CO2_data['CO2']) if form.CO2_data_option else []
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ex, airs = model.CO2_profile.CO2_fit_params()
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co2_plot = img2base64(_figure2bytes(CO2_plot(CO2_times, CO2_values))) if form.CO2_data_option else None
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return {
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"model_repr": repr(model),
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"times": list(times),
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@ -181,6 +181,9 @@
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let short_range_expirations = {{ short_range_expirations | JSONify }};
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draw_plot("concentration_plot");
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</script>
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<img src= "{{ CO2_plot }}" />
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<p>Emission rate = {{ ex }}</p>
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<p>Ventilation = {{ airs }}</p>
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</p>
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</div>
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</div>
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@ -1584,6 +1584,9 @@ class ExposureModel:
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c_model.ventilation.air_exchange(c_model.room, time)) for time in c_model.state_change_times()))):
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raise ValueError("If the diameter is an array, none of the ventilation parameters "
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"or virus decay constant can be arrays at the same time.")
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if not isinstance(self.exposed.number, int):
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raise NotImplementedError("Cannot use dynamic occupancy for"
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" the exposed population")
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@method_cache
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def population_state_change_times(self) -> typing.List[float]:
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@ -27,7 +27,7 @@ def full_exposure_model():
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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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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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@ -51,37 +51,11 @@ def baseline_infected_population_number():
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@pytest.fixture
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def baseline_exposed_population_number():
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return models.Population(
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number=models.IntPiecewiseConstant(
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(8, 12, 13, 17), (10, 0, 10)),
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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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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_number):
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def dynamic_single_exposure_model(full_exposure_model, baseline_infected_population_number):
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return dc_utils.nested_replace(full_exposure_model,
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{'concentration_model.infected': baseline_infected_population_number, })
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@pytest.fixture
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def dynamic_exposed_single_exposure_model(full_exposure_model, baseline_exposed_population_number):
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return dc_utils.nested_replace(full_exposure_model,
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{'exposed': baseline_exposed_population_number, })
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@pytest.fixture
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def dynamic_population_exposure_model(full_exposure_model, baseline_infected_population_number ,baseline_exposed_population_number):
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return dc_utils.nested_replace(full_exposure_model, {
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'concentration_model.infected': baseline_infected_population_number,
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'exposed': baseline_exposed_population_number,
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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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@ -117,16 +91,16 @@ def test_population_number(full_exposure_model: models.ExposureModel,
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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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dynamic_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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assert full_exposure_model.concentration(time) == dynamic_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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dynamic_single_exposure_model: models.ExposureModel,
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time: float,
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number_of_infected: int):
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@ -138,8 +112,8 @@ def test_linearity_with_number_of_infected(full_exposure_model: models.ExposureM
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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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npt.assert_almost_equal(static_multiple_exposure_model.concentration(time), dynamic_single_exposure_model.concentration(time) * number_of_infected)
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npt.assert_almost_equal(static_multiple_exposure_model.deposited_exposure(), dynamic_single_exposure_model.deposited_exposure() * number_of_infected)
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@pytest.mark.parametrize(
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