Merge branch 'master' into feature/virions_plot
This commit is contained in:
Luis Aleixo
commit
b2d794316a
8 changed files with 245 additions and 35 deletions
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@ -26,7 +26,7 @@ Each event modelled is unique, and the results generated therein are only as acc
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## Authors
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CARA was developed by following members of CERN - European Council for Nuclear Research (visit https://home.cern/):
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Andre Henriques<sup>1</sup>, Marco Andreini<sup>1</sup>, Gabriella Azzopardi<sup>2</sup>, James Devine<sup>3</sup>, Philip Elson<sup>4</sup>, Nicolas Mounet<sup>2</sup>, Markus Kongstein Rognlien<sup>2,6</sup>, Nicola Tarocco<sup>5</sup>
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Andre Henriques<sup>1</sup>, Luis Aleixo<sup>1</sup>, Marco Andreini<sup>1</sup>, Gabriella Azzopardi<sup>2</sup>, James Devine<sup>3</sup>, Philip Elson<sup>4</sup>, Nicolas Mounet<sup>2</sup>, Markus Kongstein Rognlien<sup>2,6</sup>, Nicola Tarocco<sup>5</sup>
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<sup>1</sup>HSE Unit, Occupational Health & Safety Group, CERN<br>
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<sup>2</sup>Beams Department, Accelerators and Beam Physics Group, CERN<br>
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@ -44,13 +44,13 @@ def calculate_report_data(model: models.ExposureModel):
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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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"concentrations": concentrations,
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"highest_const": highest_const,
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"prob_inf": prob,
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"emission_rate": er,
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"exposed_occupants": exposed_occupants,
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"expected_new_cases": expected_new_cases,
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"scenario_plot_src": img2base64(_figure2bytes(plot(times, concentrations, model))),
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}
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@ -133,7 +133,7 @@ def plot(times, concentrations, model: models.ExposureModel):
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ax.set_ylim(0)
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return fig
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def minutes_to_time(minutes: int) -> str:
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minute_string = str(minutes % 60)
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190
cara/apps/calculator/static/js/report.js
Normal file
190
cara/apps/calculator/static/js/report.js
Normal file
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@ -0,0 +1,190 @@
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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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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 vis = d3.select(svg_id),
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width = visBoundingBox.width - 300,
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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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bisecHour = d3.bisector((d) => { return d.hour; }).left,
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yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([data[0].concentration, data[data.length - 1].concentration]),
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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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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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vis.append('svg:foreignObject')
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.attr('width', width)
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.attr('height', margins.top)
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.append('xhtml:body')
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.style('text-align', 'center')
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.html('Mean concentration of infectious quanta');
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// Line representing the mean concentration.
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var lineFunc = d3.line()
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.defined(d => !isNaN(d.concentration))
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.x(d => xTimeRange(d.time))
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.y(d => yRange(d.concentration))
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.curve(d3.curveBasis);
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vis.append('svg:path')
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.attr('d', lineFunc(data))
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.attr('stroke', '#1f77b4')
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.attr('stroke-width', 2)
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.attr('fill', 'none');
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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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.attr('transform', 'translate(0,' + (height - margins.bottom) + ')')
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.call(xAxis);
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// X axis label.
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vis.append('text')
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.attr('class', 'x label')
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.attr('fill', 'black')
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.attr('text-anchor', 'middle')
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.attr('x', (width + margins.right) / 2)
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.attr('y', height * 0.97)
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.text('Time of day')
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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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.attr('transform', 'translate(' + margins.left + ',0)')
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.call(yAxis);
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// Y 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', (height + margins.left) * 0.92)
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.text('Mean concentration (q/m^3)');
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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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vis.append('svg:path')
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.attr('d', lineFunc(data.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', 'none');
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var curveFunc = d3.area()
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.x(d => xTimeRange(d.time))
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.y0(height - margins.bottom)
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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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return d.time >= b[0] && d.time <= b[1]
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})))
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.attr('fill', '#1f77b4')
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.attr('fill-opacity', '0.1');
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})
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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('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('rect')
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.attr('x', width + size)
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.attr('y', 3 * 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('y', margins.top + size)
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.text('Mean 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('y', margins.top + 2 * 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('y', margins.top + 5)
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.attr('stroke', 'lightgrey')
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.attr('stroke-width', '2')
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.attr('rx', '5px')
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.attr('ry', '5px')
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.attr('stroke-linejoin', 'round')
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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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focus.append('circle')
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.attr('r', 3);
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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.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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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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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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function mousemove() {
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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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}
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}
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@ -8,6 +8,10 @@
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<link rel="stylesheet" type="text/css" href="{{ calculator_prefix }}/static/css/report.css">
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<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
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<script src="https://d3js.org/d3.v7.min.js"></script>
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<script src="{{ calculator_prefix }}/static/js/report.js" type="application/javascript"></script>
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</head>
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<body id="body">
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@ -216,7 +220,13 @@
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<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>
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</p>
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<img id="scenario_concentration_plot" src="{{ scenario_plot_src }}">
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<svg id="result_plot" width="900" height="400"></svg>
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<script type="application/javascript">
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var times = {{times}}
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var concentrations = {{concentrations}}
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var exposed_presence_intervals = {{exposed_presence_intervals}}
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draw_concentration_plot("#result_plot", times, concentrations, exposed_presence_intervals);
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</script>
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<p class="data_title">Alternative scenarios:</p>
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<p class="data_text">
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@ -43,7 +43,7 @@
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<h2>Authors</h2>
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<div class="text-component-text cern_full_html" >
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<p>
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<h4>Andre Henriques<sup>1</sup>, Marco Andreini<sup>1</sup>, Gabriella Azzopardi<sup>2</sup>, James Devine<sup>3</sup>, Philip Elson<sup>4</sup>, Nicolas Mounet<sup>2</sup>, Markus Kongstein Rognlien<sup>2,6</sup>, Nicola Tarocco<sup>5</sup></h4><br>
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<h4>Andre Henriques<sup>1</sup>, Luis Aleixo<sup>1</sup>, Marco Andreini<sup>1</sup>, Gabriella Azzopardi<sup>2</sup>, James Devine<sup>3</sup>, Philip Elson<sup>4</sup>, Nicolas Mounet<sup>2</sup>, Markus Kongstein Rognlien<sup>2,6</sup>, Nicola Tarocco<sup>5</sup></h4><br>
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<sup>1</sup>HSE Unit, Occupational Health & Safety Group, CERN<br>
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<sup>2</sup>Beams Department, Accelerators and Beam Physics Group, CERN<br>
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|
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@ -420,7 +420,7 @@ class Virus:
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#: RNA copies / mL
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viral_load_in_sputum: _VectorisedFloat
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#: RNA-copies
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#: Dose to initiate infection, in RNA copies
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infectious_dose: _VectorisedFloat
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#: Pre-populated examples of Viruses.
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@ -50,12 +50,12 @@ populations = [
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# A population with some array component for inhalation_rate.
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models.Population(
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10, halftime, models.Mask.types['Type I'],
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models.Activity(np.array([0.51,0.57]), 0.57),
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models.Activity(np.array([0.51, 0.57]), 0.57),
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),
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]
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dummyRoom = models.Room(50, 0.5)
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dummyVentilation = models._VentilationBase()
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dummyInfPopulation = models.InfectedPopulation(
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dummy_room = models.Room(50, 0.5)
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dummy_ventilation = models._VentilationBase()
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dummy_infected_population = models.InfectedPopulation(
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number=1,
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presence=halftime,
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mask=models.Mask.types['Type I'],
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@ -64,30 +64,31 @@ dummyInfPopulation = models.InfectedPopulation(
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expiration=models.Expiration.types['Talking']
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)
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def known_concentrations(func):
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return KnownConcentrations(dummyRoom, dummyVentilation, dummyInfPopulation, func)
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return KnownConcentrations(dummy_room, dummy_ventilation, dummy_infected_population, func)
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@pytest.mark.parametrize(
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"population, cm, f_dep, expected_exposure, expected_probability",[
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[populations[1], known_concentrations(lambda t: 36), 1.,
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np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
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"population, cm, f_dep, expected_exposure, expected_probability", [
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[populations[1], known_concentrations(lambda t: 36), 1.,
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np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
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[populations[2], known_concentrations(lambda t: 36), 1.,
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np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
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[populations[2], known_concentrations(lambda t: 36), 1.,
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np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
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[populations[0], known_concentrations(lambda t: np.array([36, 72])), 1.,
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np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
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[populations[0], known_concentrations(lambda t: np.array([36, 72])), 1.,
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np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
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[populations[1], known_concentrations(lambda t: np.array([36, 72])), 1.,
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np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
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[populations[1], known_concentrations(lambda t: np.array([36, 72])), 1.,
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np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
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[populations[0], known_concentrations(lambda t: 72), np.array([0.5, 1.]),
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864, np.array([98.3493482895, 99.9727534893])],
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[populations[0], known_concentrations(lambda t: 72), np.array([0.5, 1.]),
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864, np.array([98.3493482895, 99.9727534893])],
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])
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def test_exposure_model_ndarray(population, cm, f_dep,
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expected_exposure, expected_probability):
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model = ExposureModel(cm, population, fraction_deposited = f_dep)
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model = ExposureModel(cm, population, fraction_deposited=f_dep)
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np.testing.assert_almost_equal(
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model.exposure(), expected_exposure
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)
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@ -103,7 +104,8 @@ def test_exposure_model_ndarray(population, cm, f_dep,
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@pytest.mark.parametrize("population", populations)
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def test_exposure_model_ndarray_and_float_mix(population):
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cm = known_concentrations(lambda t: 0 if np.floor(t) % 2 else np.array([1.2, 1.2]))
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cm = known_concentrations(lambda t: 0 if np.floor(t) %
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2 else np.array([1.2, 1.2]))
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model = ExposureModel(cm, population)
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expected_exposure = np.array([14.4, 14.4])
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@ -132,7 +134,8 @@ def test_exposure_model_compare_scalar_vector(population):
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@pytest.fixture
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def conc_model():
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interesting_times = models.SpecificInterval(([0, 1], [1.01, 1.02], [12, 24]))
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interesting_times = models.SpecificInterval(
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([0, 1], [1.01, 1.02], [12, 24]))
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always = models.SpecificInterval(((0, 24),))
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return models.ConcentrationModel(
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models.Room(25),
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@ -149,14 +152,16 @@ def conc_model():
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# expected exposure were computed with a trapezoidal integration, using
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# a mesh of 10'000 pts per exposed presence interval.
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@pytest.mark.parametrize("exposed_time_interval, expected_exposure", [
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[(0, 1), 266.67176],
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[(1, 1.01), 3.0879539],
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[(1.01, 1.02), 3.00082435],
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[(12, 12.01), 0.095063235],
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[(12, 24), 3775.65025],
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[(0, 24), 4097.8494],
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]
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[(0, 1), 266.67176],
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||||
[(1, 1.01), 3.0879539],
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||||
[(1.01, 1.02), 3.00082435],
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||||
[(12, 12.01), 0.095063235],
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||||
[(12, 24), 3775.65025],
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[(0, 24), 4097.8494],
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]
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)
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def test_exposure_model_integral_accuracy(exposed_time_interval,
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expected_exposure, conc_model):
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|
@ -168,19 +173,21 @@ def test_exposure_model_integral_accuracy(exposed_time_interval,
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model = ExposureModel(conc_model, population, fraction_deposited=1.)
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np.testing.assert_allclose(model.exposure(), expected_exposure)
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def test_infectious_dose_vectorisation():
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infPopulation = models.InfectedPopulation(
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infected_population = models.InfectedPopulation(
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number=1,
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presence=halftime,
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mask=models.Mask.types['Type I'],
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||||
activity=models.Activity.types['Standing'],
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||||
virus = models.SARSCoV2(
|
||||
virus=models.SARSCoV2(
|
||||
viral_load_in_sputum=1e9,
|
||||
infectious_dose=np.array([50, 20, 30]),
|
||||
),
|
||||
expiration=models.Expiration.types['Talking']
|
||||
)
|
||||
cm = KnownConcentrations(dummyRoom, dummyVentilation, infPopulation, lambda t: 1.2)
|
||||
cm = KnownConcentrations(
|
||||
dummy_room, dummy_ventilation, infected_population, lambda t: 1.2)
|
||||
|
||||
presence_interval = models.SpecificInterval(((0, 1),))
|
||||
population = models.Population(
|
||||
|
|
|
|||
|
|
@ -25,3 +25,6 @@ ignore_missing_imports = True
|
|||
|
||||
[mypy-scipy.*]
|
||||
ignore_missing_imports = True
|
||||
|
||||
[mypy-tqdm.*]
|
||||
ignore_missing_imports = True
|
||||
|
|
|
|||
Loading…
Reference in a new issue