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Merge branch 'feature/concentration_cumulative_dose' into 'master'

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Concentration profile plot with cumulative dose

Closes #164

See merge request cara/cara!227
This commit is contained in:
Andre Henriques 2021-11-03 16:30:22 +01:00
commit fdffee20b5
5 changed files with 201 additions and 98 deletions
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11
cara/apps/calculator/report_generator.py
View file

@ -108,10 +108,15 @@ def calculate_report_data(model: models.ExposureModel):
er = np.array(model.concentration_model.infected.emission_rate_when_present()).mean() er = np.array(model.concentration_model.infected.emission_rate_when_present()).mean()
exposed_occupants = model.exposed.number exposed_occupants = model.exposed.number
expected_new_cases = np.array(model.expected_new_cases()).mean() expected_new_cases = np.array(model.expected_new_cases()).mean()
cumulative_doses = np.cumsum([
np.array(model.exposure_between_bounds(float(time1), float(time2))).mean()
for time1, time2 in zip(times[:-1], times[1:])
])
return { return {
"times": list(times), "times": list(times),
"exposed_presence_intervals": [list(interval) for interval in model.exposed.presence.boundaries()], "exposed_presence_intervals": [list(interval) for interval in model.exposed.presence.boundaries()],
"cumulative_doses": list(cumulative_doses),
"concentrations": concentrations, "concentrations": concentrations,
"highest_const": highest_const, "highest_const": highest_const,
"prob_inf": prob, "prob_inf": prob,
@ -303,11 +308,11 @@ class ReportGenerator:
context['permalink'] = generate_permalink(base_url, self.calculator_prefix, form) context['permalink'] = generate_permalink(base_url, self.calculator_prefix, form)
context['calculator_prefix'] = self.calculator_prefix context['calculator_prefix'] = self.calculator_prefix
context['scale_warning'] = { context['scale_warning'] = {
'level': 'yellow-2', 'level': 'yellow-2',
'incidence_rate': 'lower than 25 new cases per 100 000 inhabitants', 'incidence_rate': 'lower than 25 new cases per 100 000 inhabitants',
'onsite_access': 'of about 8000', 'onsite_access': 'of about 8000',
'threshold': '' 'threshold': ''
} }
return context return context
def _template_environment(self) -> jinja2.Environment: def _template_environment(self) -> jinja2.Environment:

231
cara/apps/calculator/static/js/report.js
View file

@ -1,34 +1,40 @@
/* Generate the concentration plot using d3 library. */ /* Generate the concentration plot using d3 library. */
function draw_concentration_plot(svg_id, times, concentrations, exposed_presence_intervals) { function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses, exposed_presence_intervals) {
var visBoundingBox = d3.select(svg_id) var visBoundingBox = d3.select(svg_id)
.node() .node()
.getBoundingClientRect(); .getBoundingClientRect();
var time_format = d3.timeFormat('%H:%M'); var time_format = d3.timeFormat('%H:%M');
var data = [] var data_for_graphs = {
times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index] })) 'concentrations': [],
'cumulative_doses': [],
}
times.map((time, index) => data_for_graphs.concentrations.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index]}));
times.map((time, index) => data_for_graphs.cumulative_doses.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': cumulative_doses[index]}));
var vis = d3.select(svg_id), var vis = d3.select(svg_id),
width = visBoundingBox.width - 300, width = visBoundingBox.width - 400,
height = visBoundingBox.height, height = visBoundingBox.height,
margins = { top: 30, right: 20, bottom: 50, left: 50 }, margins = { top: 30, right: 20, bottom: 50, left: 50 },
// H:M time format for x axis. // H:M time format for x axis.
xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([data[0].hour, data[data.length - 1].hour]), xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([data_for_graphs.concentrations[0].hour, data_for_graphs.concentrations[data_for_graphs.concentrations.length - 1].hour]),
xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([data[0].time, data[data.length - 1].time]), xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([data_for_graphs.concentrations[0].time, data_for_graphs.concentrations[data_for_graphs.concentrations.length - 1].time]),
bisecHour = d3.bisector((d) => { return d.hour; }).left, bisecHour = d3.bisector((d) => { return d.hour; }).left,
yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]), yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]),
yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_doses)*1.1]),
xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)),
yAxis = d3.axisLeft(yRange); yAxis = d3.axisLeft(yRange).ticks(4),
yCumulatedAxis = d3.axisRight(yCumulatedRange).ticks(4);
// Plot tittle.
plot_title(vis, width, margins.top, 'Mean concentration of virions');
// Line representing the mean concentration. // Line representing the mean concentration.
plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4'); plot_scenario_data(vis, data_for_graphs.concentrations, xTimeRange, yRange, '#1f77b4');
// Line representing the cumulative concentration.
plot_cumulative_data(vis, data_for_graphs.cumulative_doses, xTimeRange, yCumulatedRange, '#1f77b4');
// X axis. // X axis.
plot_x_axis(vis, height, width, margins, xAxis, 'Time of day'); plot_x_axis(vis, height, width, margins, xAxis, 'Time of day');
@ -36,6 +42,24 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
// Y axis // Y axis
plot_y_axis(vis, height, width, margins, yAxis, 'Mean concentration (virions/m³)') plot_y_axis(vis, height, width, margins, yAxis, 'Mean concentration (virions/m³)')
// Y cumulative concentration axis declaration.
vis.append('svg:g')
.attr('class', 'y axis')
.style('font-size', 14)
.style("stroke-dasharray", "5 5")
.attr('transform', 'translate(' + (width - margins.right) + ',0)')
.call(yCumulatedAxis);
// Y cumulated concentration axis label.
vis.append('svg:text')
.attr('class', 'y label')
.attr('fill', 'black')
.attr('transform', 'rotate(-90, 0,' + height + ')')
.attr('text-anchor', 'middle')
.attr('x', (height + margins.bottom) / 2)
.attr('y', 1.71 * width)
.text('Mean cumulative dose (virions)');
// Area representing the presence of exposed person(s). // Area representing the presence of exposed person(s).
exposed_presence_intervals.forEach(b => { exposed_presence_intervals.forEach(b => {
var curveFunc = d3.area() var curveFunc = d3.area()
@ -44,7 +68,7 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
.y1(d => yRange(d.concentration)); .y1(d => yRange(d.concentration));
vis.append('svg:path') vis.append('svg:path')
.attr('d', curveFunc(data.filter(d => { .attr('d', curveFunc(data_for_graphs.concentrations.filter(d => {
return d.time >= b[0] && d.time <= b[1] return d.time >= b[0] && d.time <= b[1]
}))) })))
.attr('fill', '#1f77b4') .attr('fill', '#1f77b4')
@ -54,39 +78,56 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
// Legend for the plot elements - line and area. // Legend for the plot elements - line and area.
var size = 20 var size = 20
vis.append('rect') vis.append('rect')
.attr('x', width + size) .attr('x', width + size + 50)
.attr('y', margins.top + size) .attr('y', margins.top + size)
.attr('width', 20) .attr('width', 20)
.attr('height', 3) .attr('height', 3)
.style('fill', '#1f77b4'); .style('fill', '#1f77b4');
vis.append('line')
.attr("x1", width + size + 50)
.attr("x2", width + 2 * size + 52)
.attr("y1", 3.5 * size)
.attr("y2", 3.5 * size)
.style("stroke-dasharray", "5 5") //dashed array for line
.attr('stroke-width', '2')
.style("stroke", '#1f77b4');
vis.append('rect') vis.append('rect')
.attr('x', width + size) .attr('x', width + size + 50)
.attr('y', 3 * size) .attr('y', 4 * size)
.attr('width', 20) .attr('width', 20)
.attr('height', 20) .attr('height', 20)
.attr('fill', '#1f77b4') .attr('fill', '#1f77b4')
.attr('fill-opacity', '0.1'); .attr('fill-opacity', '0.1');
vis.append('text') vis.append('text')
.attr('x', width + 3 * size) .attr('x', width + 3 * size + 50)
.attr('y', margins.top + size) .attr('y', margins.top + size)
.text('Mean concentration') .text('Viral concentration')
.style('font-size', '15px') .style('font-size', '15px')
.attr('alignment-baseline', 'central'); .attr('alignment-baseline', 'central');
vis.append('text') vis.append('text')
.attr('x', width + 3 * size) .attr('x', width + 3 * size + 50)
.attr('y', margins.top + 2 * size) .attr('y', margins.top + 2 * size)
.text('Cumulative dose')
.style('font-size', '15px')
.attr('alignment-baseline', 'central');
vis.append('text')
.attr('x', width + 3 * size + 50)
.attr('y', margins.top + 3 * size)
.text('Presence of exposed person(s)') .text('Presence of exposed person(s)')
.style('font-size', '15px') .style('font-size', '15px')
.attr('alignment-baseline', 'central'); .attr('alignment-baseline', 'central');
// Legend bounding box. // Legend bounding box.
vis.append('rect') vis.append('rect')
.attr('width', 275) .attr('width', 270)
.attr('height', 50) .attr('height', 70)
.attr('x', width * 1.005) .attr('x', width * 1.1)
.attr('y', margins.top + 5) .attr('y', margins.top + 5)
.attr('stroke', 'lightgrey') .attr('stroke', 'lightgrey')
.attr('stroke-width', '2') .attr('stroke-width', '2')
@ -96,50 +137,80 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
.attr('fill', 'none'); .attr('fill', 'none');
// Tooltip. // Tooltip.
var focus = vis.append('svg:g') var focus = {}, tooltip_rect = {}, tooltip_time = {}, tooltip_concentration = {}, toolBox = {};
.style('display', 'none'); for (const [concentration, data] of Object.entries(data_for_graphs)) {
focus.append('circle') focus[concentration] = vis.append('svg:g')
.attr('r', 3); .style('display', 'none');
focus.append('rect') focus[concentration].append('circle')
.attr('fill', 'white') .attr('r', 3);
.attr('stroke', '#000')
.attr('width', 80)
.attr('height', 50)
.attr('x', 10)
.attr('y', -22)
.attr('rx', 4)
.attr('ry', 4);
focus.append('text') tooltip_rect[concentration] = focus[concentration].append('rect')
.attr('id', 'tooltip-time') .attr('fill', 'white')
.attr('x', 18) .attr('stroke', '#000')
.attr('y', -2); .attr('width', 85)
.attr('height', 50)
.attr('x', 10)
.attr('y', -22)
.attr('rx', 4)
.attr('ry', 4);
focus.append('text') tooltip_time[concentration] = focus[concentration].append('text')
.attr('id', 'tooltip-concentration') .attr('id', 'tooltip-time')
.attr('x', 18) .attr('x', 18)
.attr('y', 18); .attr('y', -2);
vis.append('rect') tooltip_concentration[concentration] = focus[concentration].append('text')
.attr('fill', 'none') .attr('id', 'tooltip-concentration')
.attr('pointer-events', 'all') .attr('x', 18)
.attr('width', width - margins.right) .attr('y', 18);
.attr('height', height)
.on('mouseover', () => { focus.style('display', null); }) toolBox[concentration] = vis.append('rect')
.on('mouseout', () => { focus.style('display', 'none'); }) .attr('fill', 'none')
.on('mousemove', mousemove); .attr('pointer-events', 'all')
.attr('width', width - margins.right)
.attr('height', height)
.on('mouseover', () => { for (const [concentration, data] of Object.entries(focus)) focus[concentration].style('display', null); })
.on('mouseout', () => { for (const [concentration, data] of Object.entries(focus)) focus[concentration].style('display', 'none'); })
.on('mousemove', mousemove);
}
function mousemove() { function mousemove() {
for (const [scenario, data] of Object.entries(data_for_graphs)) {
if (d3.pointer(event)[0] < width / 2) {
tooltip_rect[scenario].attr('x', 10)
tooltip_time[scenario].attr('x', 18)
tooltip_concentration[scenario].attr('x', 18);
}
else {
tooltip_rect[scenario].attr('x', -90)
tooltip_time[scenario].attr('x', -82)
tooltip_concentration[scenario].attr('x', -82)
}
}
// Concentration line
var x0 = xRange.invert(d3.pointer(event, this)[0]), var x0 = xRange.invert(d3.pointer(event, this)[0]),
i = bisecHour(data, x0, 1), i = bisecHour(data_for_graphs.concentrations, x0, 1),
d0 = data[i - 1], d0 = data_for_graphs.concentrations[i - 1],
d1 = data[i], d1 = data_for_graphs.concentrations[i];
d = x0 - d0.hour > d1.hour - x0 ? d1 : d0; if (d1) {
focus.attr('transform', 'translate(' + xRange(d.hour) + ',' + yRange(d.concentration) + ')'); var d = x0 - d0.hour > d1.hour - x0 ? d1 : d0;
focus.select('#tooltip-time').text('x = ' + time_format(d.hour)); focus.concentrations.attr('transform', 'translate(' + xRange(d.hour) + ',' + yRange(d.concentration) + ')');
focus.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2)); focus.concentrations.select('#tooltip-time').text('x = ' + time_format(d.hour));
focus.concentrations.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2));
}
// Cumulative line
var x0 = xRange.invert(d3.pointer(event, this)[0]),
i = bisecHour(data_for_graphs.cumulative_doses, x0, 1),
d0 = data_for_graphs.cumulative_doses[i - 1],
d1 = data_for_graphs.cumulative_doses[i];
if (d1 && d1.concentration) {
var d = x0 - d0.hour > d1.hour - x0 ? d1 : d0;
focus.cumulative_doses.attr('transform', 'translate(' + xRange(d.hour) + ',' + yCumulatedRange(d.concentration) + ')');
focus.cumulative_doses.select('#tooltip-time').text('x = ' + time_format(d.hour));
focus.cumulative_doses.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2));
}
} }
} }
@ -155,7 +226,7 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
// Variable for the highest concentration for all the scenarios // Variable for the highest concentration for all the scenarios
var highest_concentration = 0. var highest_concentration = 0.
var data_for_scenarios = {} var data_for_graphs = {}
for (scenario in alternative_scenarios) { for (scenario in alternative_scenarios) {
scenario_concentrations = alternative_scenarios[scenario].concentrations scenario_concentrations = alternative_scenarios[scenario].concentrations
@ -165,11 +236,11 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': scenario_concentrations[index] })) times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': scenario_concentrations[index] }))
// Add data into lines dictionary // Add data into lines dictionary
data_for_scenarios[scenario] = data data_for_graphs[scenario] = data
} }
// We need one scenario to get the time range // We need one scenario to get the time range
var first_scenario = Object.values(data_for_scenarios)[0] var first_scenario = Object.values(data_for_graphs)[0]
var vis = d3.select(svg_id), var vis = d3.select(svg_id),
width = width, width = width,
@ -185,12 +256,9 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)), xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)),
yAxis = d3.axisLeft(yRange); yAxis = d3.axisLeft(yRange);
// Plot title.
plot_title(vis, width, margins.top, 'Mean concentration of virions');
// Line representing the mean concentration for each scenario. // Line representing the mean concentration for each scenario.
for (const [scenario_name, data] of Object.entries(data_for_scenarios)) { for (const [scenario_name, data] of Object.entries(data_for_graphs)) {
var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name) var scenario_index = Object.keys(data_for_graphs).indexOf(scenario_name)
// Line representing the mean concentration. // Line representing the mean concentration.
plot_scenario_data(vis, data, xTimeRange, yRange, colors[scenario_index]) plot_scenario_data(vis, data, xTimeRange, yRange, colors[scenario_index])
@ -235,7 +303,7 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
// Legend bounding box. // Legend bounding box.
vis.append('rect') vis.append('rect')
.attr('width', 275) .attr('width', 275)
.attr('height', 25 * (Object.keys(data_for_scenarios).length)) .attr('height', 25 * (Object.keys(data_for_graphs).length))
.attr('x', width * 1.005) .attr('x', width * 1.005)
.attr('y', margins.top + 5) .attr('y', margins.top + 5)
.attr('stroke', 'lightgrey') .attr('stroke', 'lightgrey')
@ -249,22 +317,11 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
// Functions used to build the plots' components // Functions used to build the plots' components
function plot_title(vis, width, margin_top, title) {
vis.append('svg:foreignObject')
.attr('width', width)
.attr('height', margin_top)
.attr('fill', 'none')
.append('xhtml:div')
.style('text-align', 'center')
.html(title);
return vis;
}
function plot_x_axis(vis, height, width, margins, xAxis, label) { function plot_x_axis(vis, height, width, margins, xAxis, label) {
// X axis declaration // X axis declaration
vis.append('svg:g') vis.append('svg:g')
.attr('class', 'x axis') .attr('class', 'x axis')
.style('font-size', 14)
.attr('transform', 'translate(0,' + (height - margins.bottom) + ')') .attr('transform', 'translate(0,' + (height - margins.bottom) + ')')
.call(xAxis); .call(xAxis);
@ -284,6 +341,7 @@ function plot_y_axis(vis, height, width, margins, yAxis, label) {
// Y axis declaration. // Y axis declaration.
vis.append('svg:g') vis.append('svg:g')
.attr('class', 'y axis') .attr('class', 'y axis')
.style('font-size', 14)
.attr('transform', 'translate(' + margins.left + ',0)') .attr('transform', 'translate(' + margins.left + ',0)')
.call(yAxis); .call(yAxis);
@ -314,5 +372,22 @@ function plot_scenario_data(vis, data, xTimeRange, yRange, line_color) {
.attr('stroke-width', 2) .attr('stroke-width', 2)
.attr('fill', 'none'); .attr('fill', 'none');
return vis;
}
function plot_cumulative_data(vis, data, xTimeRange, yCumulativeRange, line_color) {
var lineCumulativeFunc = d3.line()
.defined(d => !isNaN(d.concentration))
.x(d => xTimeRange(d.time))
.y(d => yCumulativeRange(d.concentration))
.curve(d3.curveBasis);
vis.append('svg:path')
.attr('d', lineCumulativeFunc(data))
.attr('stroke', line_color)
.attr('stroke-width', 2)
.style("stroke-dasharray", "5 5")
.attr('fill', 'none');
return vis; return vis;
} }

5
cara/apps/calculator/templates/base/calculator.report.html.j2
View file

@ -85,12 +85,13 @@
{% endblock report_summary_footnote %} {% endblock report_summary_footnote %}
</div> </div>
<p id="section1">* The results are based on the parameters and assumptions published in the CERN Open Report <a href="https://cds.cern.ch/record/2756083"> CERN-OPEN-2021-004</a>.</p> <p id="section1">* The results are based on the parameters and assumptions published in the CERN Open Report <a href="https://cds.cern.ch/record/2756083"> CERN-OPEN-2021-004</a>.</p>
<svg id="result_plot" width="900" height="400"></svg> <svg id="result_plot" width="1000" height="400"></svg>
<script type="application/javascript"> <script type="application/javascript">
var times = {{ times | JSONify }} var times = {{ times | JSONify }}
var concentrations = {{ concentrations | JSONify }} var concentrations = {{ concentrations | JSONify }}
var cumulative_doses = {{ cumulative_doses | JSONify }}
var exposed_presence_intervals = {{ exposed_presence_intervals | JSONify }} var exposed_presence_intervals = {{ exposed_presence_intervals | JSONify }}
draw_concentration_plot("#result_plot", times, concentrations, exposed_presence_intervals); draw_concentration_plot("#result_plot", times, concentrations, cumulative_doses, exposed_presence_intervals);
</script> </script>
</p> </p>
</div> </div>

29
cara/models.py
View file

@ -102,7 +102,6 @@ class Interval:
return True return True
return False return False
@dataclass(frozen=True) @dataclass(frozen=True)
class SpecificInterval(Interval): class SpecificInterval(Interval):
#: A sequence of times (start, stop), in hours, that the infected person #: A sequence of times (start, stop), in hours, that the infected person
@ -922,9 +921,33 @@ class ExposureModel:
#: The fraction of viruses actually deposited in the respiratory tract #: The fraction of viruses actually deposited in the respiratory tract
fraction_deposited: _VectorisedFloat = 0.6 fraction_deposited: _VectorisedFloat = 0.6
def _normed_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""The number of virions per meter^3 between any two times, normalized
by the emission rate of the infected population"""
exposure = 0.
for start, stop in self.exposed.presence.boundaries():
if stop < time1:
continue
elif start > time2:
break
elif start <= time1 and time2<= stop:
exposure += self.concentration_model.normed_integrated_concentration(time1, time2)
elif start <= time1 and stop < time2:
exposure += self.concentration_model.normed_integrated_concentration(time1, stop)
elif time1 < start and time2 <= stop:
exposure += self.concentration_model.normed_integrated_concentration(start, time2)
elif time1 <= start and stop < time2:
exposure += self.concentration_model.normed_integrated_concentration(start, stop)
return exposure
def exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""The number of virions per meter^3 between any two times."""
return (self._normed_exposure_between_bounds(time1, time2) *
self.concentration_model.infected.emission_rate_when_present())
def _normed_exposure(self) -> _VectorisedFloat: def _normed_exposure(self) -> _VectorisedFloat:
""" """
The number of virus per meter^3, normalized by the emission rate The number of virions per meter^3, normalized by the emission rate
of the infected population. of the infected population.
""" """
normed_exposure = 0.0 normed_exposure = 0.0
@ -935,7 +958,7 @@ class ExposureModel:
return normed_exposure * self.repeats return normed_exposure * self.repeats
def exposure(self) -> _VectorisedFloat: def exposure(self) -> _VectorisedFloat:
"""The number of virus per meter^3.""" """The number of virions per meter^3."""
return (self._normed_exposure() * return (self._normed_exposure() *
self.concentration_model.infected.emission_rate_when_present()) self.concentration_model.infected.emission_rate_when_present())

23
cara/tests/models/test_exposure_model.py
View file

@ -55,7 +55,6 @@ populations = [
), ),
] ]
def known_concentrations(func): def known_concentrations(func):
dummy_room = models.Room(50, 0.5) dummy_room = models.Room(50, 0.5)
dummy_ventilation = models._VentilationBase() dummy_ventilation = models._VentilationBase()
@ -73,21 +72,21 @@ def known_concentrations(func):
@pytest.mark.parametrize( @pytest.mark.parametrize(
"population, cm, f_dep, expected_exposure, expected_probability", [ "population, cm, f_dep, expected_exposure, expected_probability",[
[populations[1], known_concentrations(lambda t: 36.), 1., [populations[1], known_concentrations(lambda t: 36.), 1.,
np.array([432, 432]), np.array([99.6803184113, 99.5181053773])], np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
[populations[2], known_concentrations(lambda t: 36.), 1., [populations[2], known_concentrations(lambda t: 36.), 1.,
np.array([432, 432]), np.array([97.4574432074, 98.3493482895])], np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
[populations[0], known_concentrations(lambda t: np.array([36., 72.])), 1., [populations[0], known_concentrations(lambda t: np.array([36., 72.])), 1.,
np.array([432, 864]), np.array([98.3493482895, 99.9727534893])], np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
[populations[1], known_concentrations(lambda t: np.array([36., 72.])), 1., [populations[1], known_concentrations(lambda t: np.array([36., 72.])), 1.,
np.array([432, 864]), np.array([99.6803184113, 99.9976777757])], np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
[populations[0], known_concentrations(lambda t: 72.), np.array([0.5, 1.]), [populations[0], known_concentrations(lambda t: 72.), np.array([0.5, 1.]),
864, np.array([98.3493482895, 99.9727534893])], 864, np.array([98.3493482895, 99.9727534893])],
]) ])
def test_exposure_model_ndarray(population, cm, f_dep, def test_exposure_model_ndarray(population, cm, f_dep,
expected_exposure, expected_probability): expected_exposure, expected_probability):