Merge branch 'feature/concentration_cumulative_dose' into 'master'
Concentration profile plot with cumulative dose Closes #164 See merge request cara/cara!227
This commit is contained in:
Andre Henriques
commit
fdffee20b5
5 changed files with 201 additions and 98 deletions
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@ -108,10 +108,15 @@ def calculate_report_data(model: models.ExposureModel):
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er = np.array(model.concentration_model.infected.emission_rate_when_present()).mean()
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exposed_occupants = model.exposed.number
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expected_new_cases = np.array(model.expected_new_cases()).mean()
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cumulative_doses = np.cumsum([
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np.array(model.exposure_between_bounds(float(time1), float(time2))).mean()
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for time1, time2 in zip(times[:-1], times[1:])
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])
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return {
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"times": list(times),
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"exposed_presence_intervals": [list(interval) for interval in model.exposed.presence.boundaries()],
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"cumulative_doses": list(cumulative_doses),
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"concentrations": concentrations,
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"highest_const": highest_const,
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"prob_inf": prob,
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@ -303,11 +308,11 @@ class ReportGenerator:
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context['permalink'] = generate_permalink(base_url, self.calculator_prefix, form)
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context['calculator_prefix'] = self.calculator_prefix
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context['scale_warning'] = {
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'level': 'yellow-2',
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'level': 'yellow-2',
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'incidence_rate': 'lower than 25 new cases per 100 000 inhabitants',
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'onsite_access': 'of about 8000',
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'onsite_access': 'of about 8000',
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'threshold': ''
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}
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}
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return context
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def _template_environment(self) -> jinja2.Environment:
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@ -1,34 +1,40 @@
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/* Generate the concentration plot using d3 library. */
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function draw_concentration_plot(svg_id, times, concentrations, exposed_presence_intervals) {
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function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses, exposed_presence_intervals) {
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var visBoundingBox = d3.select(svg_id)
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.node()
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.getBoundingClientRect();
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var time_format = d3.timeFormat('%H:%M');
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var data = []
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times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': concentrations[index] }))
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var data_for_graphs = {
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'concentrations': [],
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'cumulative_doses': [],
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}
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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]}));
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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]}));
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var vis = d3.select(svg_id),
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width = visBoundingBox.width - 300,
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width = visBoundingBox.width - 400,
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height = visBoundingBox.height,
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margins = { top: 30, right: 20, bottom: 50, left: 50 },
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// H:M time format for x axis.
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xRange = d3.scaleTime().range([margins.left, width - margins.right]).domain([data[0].hour, data[data.length - 1].hour]),
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xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([data[0].time, data[data.length - 1].time]),
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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]),
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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]),
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bisecHour = d3.bisector((d) => { return d.hour; }).left,
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yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...concentrations)]),
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yCumulatedRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., Math.max(...cumulative_doses)*1.1]),
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xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)),
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yAxis = d3.axisLeft(yRange);
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// Plot tittle.
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plot_title(vis, width, margins.top, 'Mean concentration of virions');
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yAxis = d3.axisLeft(yRange).ticks(4),
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yCumulatedAxis = d3.axisRight(yCumulatedRange).ticks(4);
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// Line representing the mean concentration.
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plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4');
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plot_scenario_data(vis, data_for_graphs.concentrations, xTimeRange, yRange, '#1f77b4');
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// Line representing the cumulative concentration.
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plot_cumulative_data(vis, data_for_graphs.cumulative_doses, xTimeRange, yCumulatedRange, '#1f77b4');
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// X axis.
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plot_x_axis(vis, height, width, margins, xAxis, 'Time of day');
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@ -36,6 +42,24 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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// Y axis
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plot_y_axis(vis, height, width, margins, yAxis, 'Mean concentration (virions/m³)')
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// Y cumulative concentration axis declaration.
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vis.append('svg:g')
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.attr('class', 'y axis')
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.style('font-size', 14)
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.style("stroke-dasharray", "5 5")
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.attr('transform', 'translate(' + (width - margins.right) + ',0)')
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.call(yCumulatedAxis);
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// Y cumulated concentration axis label.
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vis.append('svg:text')
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.attr('class', 'y label')
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.attr('fill', 'black')
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.attr('transform', 'rotate(-90, 0,' + height + ')')
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.attr('text-anchor', 'middle')
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.attr('x', (height + margins.bottom) / 2)
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.attr('y', 1.71 * width)
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.text('Mean cumulative dose (virions)');
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// Area representing the presence of exposed person(s).
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exposed_presence_intervals.forEach(b => {
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var curveFunc = d3.area()
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@ -44,7 +68,7 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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.y1(d => yRange(d.concentration));
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vis.append('svg:path')
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.attr('d', curveFunc(data.filter(d => {
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.attr('d', curveFunc(data_for_graphs.concentrations.filter(d => {
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return d.time >= b[0] && d.time <= b[1]
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})))
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.attr('fill', '#1f77b4')
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@ -54,39 +78,56 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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// Legend for the plot elements - line and area.
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var size = 20
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vis.append('rect')
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.attr('x', width + size)
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.attr('x', width + size + 50)
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.attr('y', margins.top + size)
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.attr('width', 20)
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.attr('height', 3)
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.style('fill', '#1f77b4');
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vis.append('line')
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.attr("x1", width + size + 50)
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.attr("x2", width + 2 * size + 52)
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.attr("y1", 3.5 * size)
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.attr("y2", 3.5 * size)
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.style("stroke-dasharray", "5 5") //dashed array for line
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.attr('stroke-width', '2')
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.style("stroke", '#1f77b4');
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vis.append('rect')
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.attr('x', width + size)
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.attr('y', 3 * size)
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.attr('x', width + size + 50)
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.attr('y', 4 * size)
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.attr('width', 20)
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.attr('height', 20)
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.attr('fill', '#1f77b4')
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.attr('fill-opacity', '0.1');
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vis.append('text')
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.attr('x', width + 3 * size)
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.attr('x', width + 3 * size + 50)
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.attr('y', margins.top + size)
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.text('Mean concentration')
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.text('Viral concentration')
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.style('font-size', '15px')
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.attr('alignment-baseline', 'central');
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vis.append('text')
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.attr('x', width + 3 * size)
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.attr('x', width + 3 * size + 50)
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.attr('y', margins.top + 2 * size)
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.text('Cumulative dose')
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.style('font-size', '15px')
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.attr('alignment-baseline', 'central');
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vis.append('text')
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.attr('x', width + 3 * size + 50)
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.attr('y', margins.top + 3 * size)
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.text('Presence of exposed person(s)')
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.style('font-size', '15px')
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.attr('alignment-baseline', 'central');
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// Legend bounding box.
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vis.append('rect')
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.attr('width', 275)
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.attr('height', 50)
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.attr('x', width * 1.005)
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.attr('width', 270)
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.attr('height', 70)
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.attr('x', width * 1.1)
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.attr('y', margins.top + 5)
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.attr('stroke', 'lightgrey')
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.attr('stroke-width', '2')
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@ -96,50 +137,80 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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.attr('fill', 'none');
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// Tooltip.
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var focus = vis.append('svg:g')
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.style('display', 'none');
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var focus = {}, tooltip_rect = {}, tooltip_time = {}, tooltip_concentration = {}, toolBox = {};
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for (const [concentration, data] of Object.entries(data_for_graphs)) {
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focus.append('circle')
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.attr('r', 3);
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focus[concentration] = vis.append('svg:g')
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.style('display', 'none');
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focus.append('rect')
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.attr('fill', 'white')
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.attr('stroke', '#000')
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.attr('width', 80)
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.attr('height', 50)
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.attr('x', 10)
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.attr('y', -22)
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.attr('rx', 4)
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.attr('ry', 4);
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focus[concentration].append('circle')
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.attr('r', 3);
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focus.append('text')
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.attr('id', 'tooltip-time')
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.attr('x', 18)
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.attr('y', -2);
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tooltip_rect[concentration] = focus[concentration].append('rect')
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.attr('fill', 'white')
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.attr('stroke', '#000')
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.attr('width', 85)
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.attr('height', 50)
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.attr('x', 10)
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.attr('y', -22)
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.attr('rx', 4)
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.attr('ry', 4);
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focus.append('text')
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.attr('id', 'tooltip-concentration')
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.attr('x', 18)
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.attr('y', 18);
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tooltip_time[concentration] = focus[concentration].append('text')
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.attr('id', 'tooltip-time')
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.attr('x', 18)
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.attr('y', -2);
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vis.append('rect')
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.attr('fill', 'none')
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.attr('pointer-events', 'all')
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.attr('width', width - margins.right)
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.attr('height', height)
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.on('mouseover', () => { focus.style('display', null); })
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.on('mouseout', () => { focus.style('display', 'none'); })
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.on('mousemove', mousemove);
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tooltip_concentration[concentration] = focus[concentration].append('text')
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.attr('id', 'tooltip-concentration')
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.attr('x', 18)
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.attr('y', 18);
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toolBox[concentration] = vis.append('rect')
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.attr('fill', 'none')
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.attr('pointer-events', 'all')
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.attr('width', width - margins.right)
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.attr('height', height)
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.on('mouseover', () => { for (const [concentration, data] of Object.entries(focus)) focus[concentration].style('display', null); })
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.on('mouseout', () => { for (const [concentration, data] of Object.entries(focus)) focus[concentration].style('display', 'none'); })
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.on('mousemove', mousemove);
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}
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function mousemove() {
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for (const [scenario, data] of Object.entries(data_for_graphs)) {
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if (d3.pointer(event)[0] < width / 2) {
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tooltip_rect[scenario].attr('x', 10)
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tooltip_time[scenario].attr('x', 18)
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tooltip_concentration[scenario].attr('x', 18);
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}
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else {
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tooltip_rect[scenario].attr('x', -90)
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tooltip_time[scenario].attr('x', -82)
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tooltip_concentration[scenario].attr('x', -82)
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}
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}
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// Concentration line
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var x0 = xRange.invert(d3.pointer(event, this)[0]),
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i = bisecHour(data, x0, 1),
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d0 = data[i - 1],
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d1 = data[i],
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d = x0 - d0.hour > d1.hour - x0 ? d1 : d0;
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focus.attr('transform', 'translate(' + xRange(d.hour) + ',' + yRange(d.concentration) + ')');
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focus.select('#tooltip-time').text('x = ' + time_format(d.hour));
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focus.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2));
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i = bisecHour(data_for_graphs.concentrations, x0, 1),
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d0 = data_for_graphs.concentrations[i - 1],
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d1 = data_for_graphs.concentrations[i];
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if (d1) {
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var d = x0 - d0.hour > d1.hour - x0 ? d1 : d0;
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focus.concentrations.attr('transform', 'translate(' + xRange(d.hour) + ',' + yRange(d.concentration) + ')');
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focus.concentrations.select('#tooltip-time').text('x = ' + time_format(d.hour));
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focus.concentrations.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2));
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}
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// Cumulative line
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var x0 = xRange.invert(d3.pointer(event, this)[0]),
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i = bisecHour(data_for_graphs.cumulative_doses, x0, 1),
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d0 = data_for_graphs.cumulative_doses[i - 1],
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d1 = data_for_graphs.cumulative_doses[i];
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if (d1 && d1.concentration) {
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var d = x0 - d0.hour > d1.hour - x0 ? d1 : d0;
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focus.cumulative_doses.attr('transform', 'translate(' + xRange(d.hour) + ',' + yCumulatedRange(d.concentration) + ')');
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focus.cumulative_doses.select('#tooltip-time').text('x = ' + time_format(d.hour));
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focus.cumulative_doses.select('#tooltip-concentration').text('y = ' + d.concentration.toFixed(2));
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}
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}
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}
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@ -155,7 +226,7 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
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// Variable for the highest concentration for all the scenarios
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var highest_concentration = 0.
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var data_for_scenarios = {}
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var data_for_graphs = {}
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for (scenario in alternative_scenarios) {
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scenario_concentrations = alternative_scenarios[scenario].concentrations
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@ -165,11 +236,11 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
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times.map((time, index) => data.push({ 'time': time, 'hour': new Date().setHours(Math.trunc(time), (time - Math.trunc(time)) * 60), 'concentration': scenario_concentrations[index] }))
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// Add data into lines dictionary
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data_for_scenarios[scenario] = data
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data_for_graphs[scenario] = data
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}
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// We need one scenario to get the time range
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var first_scenario = Object.values(data_for_scenarios)[0]
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var first_scenario = Object.values(data_for_graphs)[0]
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var vis = d3.select(svg_id),
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width = width,
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@ -185,12 +256,9 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
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xAxis = d3.axisBottom(xRange).tickFormat(d => time_format(d)),
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yAxis = d3.axisLeft(yRange);
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// Plot title.
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plot_title(vis, width, margins.top, 'Mean concentration of virions');
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// Line representing the mean concentration for each scenario.
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for (const [scenario_name, data] of Object.entries(data_for_scenarios)) {
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var scenario_index = Object.keys(data_for_scenarios).indexOf(scenario_name)
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for (const [scenario_name, data] of Object.entries(data_for_graphs)) {
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var scenario_index = Object.keys(data_for_graphs).indexOf(scenario_name)
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// Line representing the mean concentration.
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plot_scenario_data(vis, data, xTimeRange, yRange, colors[scenario_index])
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@ -235,7 +303,7 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
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// Legend bounding box.
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vis.append('rect')
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.attr('width', 275)
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.attr('height', 25 * (Object.keys(data_for_scenarios).length))
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.attr('height', 25 * (Object.keys(data_for_graphs).length))
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.attr('x', width * 1.005)
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.attr('y', margins.top + 5)
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.attr('stroke', 'lightgrey')
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@ -249,22 +317,11 @@ function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scen
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// Functions used to build the plots' components
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function plot_title(vis, width, margin_top, title) {
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vis.append('svg:foreignObject')
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.attr('width', width)
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.attr('height', margin_top)
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.attr('fill', 'none')
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.append('xhtml:div')
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.style('text-align', 'center')
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.html(title);
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return vis;
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}
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function plot_x_axis(vis, height, width, margins, xAxis, label) {
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// X axis declaration
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vis.append('svg:g')
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.attr('class', 'x axis')
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.style('font-size', 14)
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.attr('transform', 'translate(0,' + (height - margins.bottom) + ')')
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.call(xAxis);
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@ -284,6 +341,7 @@ function plot_y_axis(vis, height, width, margins, yAxis, label) {
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// Y axis declaration.
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vis.append('svg:g')
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.attr('class', 'y axis')
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.style('font-size', 14)
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.attr('transform', 'translate(' + margins.left + ',0)')
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.call(yAxis);
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@ -314,5 +372,22 @@ function plot_scenario_data(vis, data, xTimeRange, yRange, line_color) {
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.attr('stroke-width', 2)
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.attr('fill', 'none');
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return vis;
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}
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|
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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;
|
||||
}
|
||||
|
|
@ -85,12 +85,13 @@
|
|||
{% endblock report_summary_footnote %}
|
||||
</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>
|
||||
<svg id="result_plot" width="900" height="400"></svg>
|
||||
<svg id="result_plot" width="1000" height="400"></svg>
|
||||
<script type="application/javascript">
|
||||
var times = {{ times | JSONify }}
|
||||
var concentrations = {{ concentrations | JSONify }}
|
||||
var cumulative_doses = {{ cumulative_doses | 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>
|
||||
</p>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -102,7 +102,6 @@ class Interval:
|
|||
return True
|
||||
return False
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class SpecificInterval(Interval):
|
||||
#: 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
|
||||
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:
|
||||
"""
|
||||
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.
|
||||
"""
|
||||
normed_exposure = 0.0
|
||||
|
|
@ -935,7 +958,7 @@ class ExposureModel:
|
|||
return normed_exposure * self.repeats
|
||||
|
||||
def exposure(self) -> _VectorisedFloat:
|
||||
"""The number of virus per meter^3."""
|
||||
"""The number of virions per meter^3."""
|
||||
return (self._normed_exposure() *
|
||||
self.concentration_model.infected.emission_rate_when_present())
|
||||
|
||||
|
|
|
|||
|
|
@ -55,7 +55,6 @@ populations = [
|
|||
),
|
||||
]
|
||||
|
||||
|
||||
def known_concentrations(func):
|
||||
dummy_room = models.Room(50, 0.5)
|
||||
dummy_ventilation = models._VentilationBase()
|
||||
|
|
@ -73,21 +72,21 @@ def known_concentrations(func):
|
|||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"population, cm, f_dep, expected_exposure, expected_probability", [
|
||||
[populations[1], known_concentrations(lambda t: 36.), 1.,
|
||||
np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
|
||||
"population, cm, f_dep, expected_exposure, expected_probability",[
|
||||
[populations[1], known_concentrations(lambda t: 36.), 1.,
|
||||
np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
|
||||
|
||||
[populations[2], known_concentrations(lambda t: 36.), 1.,
|
||||
np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
|
||||
[populations[2], known_concentrations(lambda t: 36.), 1.,
|
||||
np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
|
||||
|
||||
[populations[0], known_concentrations(lambda t: np.array([36., 72.])), 1.,
|
||||
np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
|
||||
[populations[0], known_concentrations(lambda t: np.array([36., 72.])), 1.,
|
||||
np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
|
||||
|
||||
[populations[1], known_concentrations(lambda t: np.array([36., 72.])), 1.,
|
||||
np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
|
||||
[populations[1], known_concentrations(lambda t: np.array([36., 72.])), 1.,
|
||||
np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
|
||||
|
||||
[populations[0], known_concentrations(lambda t: 72.), np.array([0.5, 1.]),
|
||||
864, np.array([98.3493482895, 99.9727534893])],
|
||||
[populations[0], known_concentrations(lambda t: 72.), np.array([0.5, 1.]),
|
||||
864, np.array([98.3493482895, 99.9727534893])],
|
||||
])
|
||||
def test_exposure_model_ndarray(population, cm, f_dep,
|
||||
expected_exposure, expected_probability):
|
||||
|
|
|
|||
Loading…
Reference in a new issue