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other variable names

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This commit is contained in:
Luis Aleixo 2021-08-03 15:33:40 +02:00
parent 4b63281f02
commit 9a43db9c5a
6 changed files with 12 additions and 10 deletions
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4
cara/apps/calculator/report_generator.py
View file

@ -116,7 +116,7 @@ def plot(times, concentrations, model: models.ExposureModel):
ax.set_xlabel('Time of day')
ax.set_ylabel('Mean concentration ($q/m^3$)')
ax.set_title('Mean concentration of infectious quanta')
ax.set_title('Mean concentration of infectious virus')
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M"))
# Plot presence of exposed person
@ -237,7 +237,7 @@ def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: np.ndarray)
ax.set_xlabel('Time of day')
ax.set_ylabel('Mean concentration ($q/m^3$)')
ax.set_title('Mean concentration of infectious quanta')
ax.set_title('Mean concentration of infectious virus')
return fig

2
cara/apps/calculator/templates/base/calculator.report.html.j2
View file

@ -247,7 +247,7 @@
<p class="data_text"> <strong> Notes for alternative scenarios: </strong><br>
<ol>
<li>This graph shows the concentration of infectious quanta in the air. The filtration of Type I and FFP2 masks, if worn, applies not only to the emission rate but also to the individual exposure (i.e. inhalation).
<li>This graph shows the concentration of infectious virus in the air. The filtration of Type I and FFP2 masks, if worn, applies not only to the emission rate but also to the individual exposure (i.e. inhalation).
For this reason, scenarios with different types of mask will show the same concentration on the graph but have different absorbed doses and infection probabilities.</li>
<li>If you have selected more sophisticated options, such as HEPA filtration or FFP2 masks, alternatives will be indicated in the plot as the "base scenario with/without...", representing a variation on the inputs inserted in the form.<br>
The other alternative scenarios shown for comparison will not include either HEPA filtration or FFP2 masks.</li>

2
cara/apps/calculator/templates/userguide.html.j2
View file

@ -188,7 +188,7 @@ It is determined by:</p>
<ul>
<li>The presence of the infected person, who emits airborne viruses in the volume.</li>
<li>The emission rate is related to the type of activity of the infected person (sitting, light exercise), their level of vocalisation (breathing, talking or shouting).</li>
<li>The accumulation of infectious quanta in the volume, which is driven, among other factors, by ventilation (if applicable).<ul>
<li>The accumulation of infectious virus in the volume, which is driven, among other factors, by ventilation (if applicable).<ul>
<li>In a mechanical ventilation scenario, the removal rate is constant, based on fresh airflow supply in and out of the simulated space.</li>
<li>Under natural ventilation conditions, the effectiveness of ventilation relies upon the hourly temperature difference between the inside and outside air temperature.</li>
<li>A HEPA filter removes infectious virus from the air at a constant rate and is modelled in the same way as mechanical ventilation, however air passed through a HEPA filter is recycled (i.e. it is not fresh air).</li>

6
cara/apps/expert.py
View file

@ -141,7 +141,7 @@ class ExposureModelResult(View):
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Concentration ($q/m^3$)')
ax.set_title('Concentration of infectious quanta')
ax.set_title('Concentration of infectious virus')
else:
self.ax.ignore_existing_data_limits = True
self.line.set_data(ts, concentration)
@ -154,7 +154,7 @@ class ExposureModelResult(View):
def update_textual_result(self, model: models.ExposureModel):
lines = []
P = model.infection_probability()
lines.append(f'Emission rate (quanta/hr): {np.round(model.concentration_model.infected.emission_rate_when_present(),0)}')
lines.append(f'Emission rate (virus/hr): {np.round(model.concentration_model.infected.emission_rate_when_present(),0)}')
lines.append(f'Probability of infection: {np.round(P, 0)}%')
lines.append(f'Number of exposed: {model.exposed.number}')
@ -186,7 +186,7 @@ class ExposureComparissonResult(View):
ax.spines['top'].set_visible(False)
ax.set_xlabel('Time (hours)')
ax.set_ylabel('Concentration ($q/m^3$)')
ax.set_title('Concentration of infectious quanta')
ax.set_title('Concentration of infectious virus')
return ax
def scenarios_updated(self, scenarios: typing.Sequence[ScenarioType], _):

4
cara/models.py
View file

@ -803,7 +803,7 @@ class ConcentrationModel:
def concentration(self, time: float) -> _VectorisedFloat:
"""
Virus quanta concentration, as a function of time.
Virus exposure concentration, as a function of time.
The formulas used here assume that all parameters (ventilation,
emission rate) are constant between two state changes - only
the value of these parameters at the next state change, are used.
@ -867,7 +867,7 @@ class ExposureModel:
fraction_deposited: _VectorisedFloat = 0.6
def exposure(self) -> _VectorisedFloat:
"""The number of virus quanta per meter^3."""
"""The number of virus per meter^3."""
exposure = 0.0
for start, stop in self.exposed.presence.boundaries():

4
cara/tests/test_infected_population.py
View file

@ -7,12 +7,14 @@ import cara.models
@pytest.mark.parametrize(
"override_params", [
{'viral_load_in_sputum': np.array([5e8, 1e9])},
{'infectious_dose': np.array([50, 20])},
{'exhalation_rate': np.array([0.75, 0.81])},
]
)
def test_infected_population_vectorisation(override_params):
defaults = {
'viral_load_in_sputum': 1e9,
'infectious_dose': 50,
'exhalation_rate': 0.75,
}
defaults.update(override_params)
@ -31,7 +33,7 @@ def test_infected_population_vectorisation(override_params):
),
virus=cara.models.Virus(
viral_load_in_sputum=defaults['viral_load_in_sputum'],
infectious_dose=defaults['quantum_infectious_dose'],
infectious_dose=defaults['infectious_dose'],
),
expiration=cara.models.Expiration((1., 0., 0.)),
)