Merge branch 'master' into remotes/origin/feature/website
This commit is contained in:
Luis Aleixo
commit
b28064d06d
9 changed files with 210 additions and 122 deletions
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@ -35,7 +35,7 @@ Andre Henriques<sup>1</sup>, Luis Aleixo<sup>1</sup>, Marco Andreini<sup>1</sup>
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<sup>5</sup>Information Technology Department, Collaboration, Devices & Applications Group, CERN<br>
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<sup>6</sup>Norwegian University of Science and Technology (NTNU)<br>
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### citation
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### Citation
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A. Henriques, M. Andreini, G. Azzopardi, J. Devine, P. Elson, N. Mounet, M. Kongstein, N. Tarocco. CARA - COVID Airborne Risk Assessment tools. CERN (2021).
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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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@ -1,22 +1,32 @@
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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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console.log(cumulative_doses)
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var time_format = d3.timeFormat('%H:%M');
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// H:M time format for x axis.
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var data = []
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// Prepare 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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// Add main SVG element
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var plot_div = document.getElementById(svg_id);
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var vis = d3.select(plot_div).append('svg');
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var xRange = d3.scaleTime().domain([data[0].hour, data[data.length - 1].hour]);
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var xTimeRange = d3.scaleLinear().domain([data[0].time, data[data.length - 1].time]);
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var bisecHour = d3.bisector((d) => { return d.hour; }).left;
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var yRange = d3.scaleLinear().domain([0., Math.max(...concentrations)]);
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// H:M time format for x axis.
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xRange = d3.scaleTime().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().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().domain([0., Math.max(...concentrations)]),
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yCumulativeRange = d3.scaleLinear().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).ticks(4),
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yCumulativeAxis = d3.axisRight(yCumulativeRange).ticks(4);
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// Line representing the mean concentration.
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var lineFunc = d3.line();
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@ -25,6 +35,13 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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.attr('stroke-width', 2)
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.attr('fill', 'none');
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var lineCumulative = d3.line();
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var draw_cumulative_line = vis.append('svg:path')
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.attr('stroke', '#1f77b4')
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.attr('stroke-width', 2)
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.style("stroke-dasharray", "5 5")
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.attr('fill', 'none');
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// Area representing the presence of exposed person(s).
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var exposedArea = {};
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var drawArea = {};
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@ -64,6 +81,19 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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.attr('text-anchor', 'middle')
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.text('Mean concentration (virions/m³)');
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// Y cumulative concentration axis declaration.
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var yAxisCumEl = 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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// Y cumulated concentration axis label.
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var yAxisCumLabelEl = vis.append('svg:text')
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.attr('class', 'y label')
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.attr('fill', 'black')
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.attr('text-anchor', 'middle')
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.text('Mean cumulative dose (virions)');
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// Legend for the plot elements - line and area.
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var legendLineIcon = vis.append('rect')
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.attr('width', 20)
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@ -88,7 +118,7 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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// Legend bounding
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var legendBBox = vis.append('rect')
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.attr('width', 275)
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.attr('width', 255)
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.attr('height', 50)
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.attr('stroke', 'lightgrey')
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.attr('stroke-width', '2')
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@ -98,35 +128,42 @@ 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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var tooltip_rect = 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('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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var tooltip_time = focus.append('text')
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.attr('id', 'tooltip-time')
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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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var tooltip_concentration = focus.append('text')
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.attr('id', 'tooltip-concentration')
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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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var toolBox = vis.append('rect')
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.attr('fill', 'none')
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.attr('pointer-events', 'all')
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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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.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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var graph_width;
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var graph_height;
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@ -148,7 +185,7 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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else {
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var margins = { top: 30, right: 20, bottom: 50, left: 40 };
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div_width = div_width * 1.1
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graph_width = div_width * .95;
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graph_width = div_width * .9;
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graph_height = div_height * 0.65; // On mobile screen sizes we want the legend to be on the bottom of the graph.
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const svg_margins = {'margin-left': '-1rem', 'margin-top': '3rem'};
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Object.entries(svg_margins).forEach(([prop,val]) => vis.style(prop,val));
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@ -163,13 +200,20 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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// Axis ranges.
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xRange.range([margins.left, graph_width - margins.right]);
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xTimeRange.range([margins.left, graph_width - margins.right]);
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yRange.range([graph_height - margins.bottom, margins.top])
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yRange.range([graph_height - margins.bottom, margins.top]);
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yCumulativeRange.range([graph_height - margins.bottom, margins.top]);
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// Line.
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lineFunc.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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draw_line.attr("d", lineFunc(data));
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draw_line.attr("d", lineFunc(data_for_graphs.concentrations));
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// Cumulative line
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lineCumulative.defined(d => !isNaN(d.concentration))
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.x(d => xTimeRange(d.time))
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.y(d => yCumulativeRange(d.concentration));
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draw_cumulative_line.attr("d", lineCumulative(data_for_graphs.cumulative_doses));
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// Area.
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exposed_presence_intervals.forEach((b, index) => {
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@ -177,7 +221,7 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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.y0(graph_height - margins.bottom)
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.y1(d => yRange(d.concentration));
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drawArea[index].attr('d', exposedArea[index](data.filter(d => {
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drawArea[index].attr('d', exposedArea[index](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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});
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@ -199,18 +243,33 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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.attr('y', (graph_height + margins.left) * 0.9)
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.attr('transform', 'rotate(-90, 0,' + graph_height + ')');
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yAxisCumEl.attr('transform', 'translate(' + (graph_width - margins.right) + ',0)').call(yCumulativeAxis);
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yAxisCumLabelEl.attr('transform', 'rotate(-90, 0,' + graph_height + ')')
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.attr('x', (graph_height + margins.bottom) / 2);
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if (plot_div.clientWidth >= 900) {
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yAxisCumLabelEl.attr('transform', 'rotate(-90, 0,' + graph_height + ')')
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.attr('x', (graph_height + margins.bottom) / 2)
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.attr('y', 1.71 * graph_width);
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}
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else {
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yAxisCumLabelEl.attr('transform', 'rotate(-90, 0,' + graph_height + ')')
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.attr('x', (graph_height + margins.bottom * 0.55) / 2)
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.attr('y', graph_width + 290);
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}
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// Legend on right side.
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const size = 20;
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if (plot_div.clientWidth >= 900) {
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legendLineIcon.attr('x', graph_width + size)
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legendLineIcon.attr('x', graph_width + size * 2.5)
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.attr('y', margins.top + size);
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legendLineText.attr('x', graph_width + 3 * size)
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legendLineText.attr('x', graph_width + 4 * size)
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.attr('y', margins.top + size);
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legendAreaIcon.attr('x', graph_width + size)
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legendAreaIcon.attr('x', graph_width + size * 2.5)
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.attr('y', margins.top + 1.5 * size);
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legendAreaText.attr('x', graph_width + 3 * size)
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legendAreaText.attr('x', graph_width + 4 * size)
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.attr('y', margins.top + 2 * size);
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legendBBox.attr('x', graph_width * 1.005)
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legendBBox.attr('x', graph_width * 1.07)
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.attr('y', margins.top * 1.2);
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}
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// Legend on the bottom.
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@ -228,32 +287,50 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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}
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// ToolBox.
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toolBox.attr('width', graph_width - margins.right)
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.attr('height', graph_height);
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for (const [concentration, data] of Object.entries(data_for_graphs)) {
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toolBox[concentration].attr('width', graph_width - margins.right)
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.attr('height', graph_height);
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}
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}
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// Draw for the first time to initialize.
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redraw();
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function mousemove() {
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if (d3.pointer(event)[0] < graph_width / 2) {
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tooltip_rect.attr('x', 10)
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tooltip_time.attr('x', 18)
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tooltip_concentration.attr('x', 18);
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}
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else {
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tooltip_rect.attr('x', -90)
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tooltip_time.attr('x', -82)
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tooltip_concentration.attr('x', -82)
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for (const [scenario, data] of Object.entries(data_for_graphs)) {
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if (d3.pointer(event)[0] < graph_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) + ',' + yCumulativeRange(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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// Redraw based on the new size whenever the browser window is resized.
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|
|
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@ -93,8 +93,9 @@
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<script type="application/javascript">
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var times = {{ times | JSONify }}
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var concentrations = {{ concentrations | JSONify }}
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var cumulative_doses = {{ cumulative_doses | JSONify }}
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var exposed_presence_intervals = {{ exposed_presence_intervals | JSONify }}
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draw_concentration_plot("concentration_plot", times, concentrations, exposed_presence_intervals);
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draw_concentration_plot("concentration_plot", times, concentrations, cumulative_doses, exposed_presence_intervals);
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</script>
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</p>
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</div>
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|
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@ -1,37 +1,20 @@
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import numpy as np
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from cara import models
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from cara.data.weather import wx_data, nearest_wx_station
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# TODO: The values in this module to be removed and instead use the cara.data.weather functionality.
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MONTH_NAMES = [
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'January', 'February', 'March', 'April', 'May', 'June', 'July',
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'August', 'September', 'October', 'November', 'December',
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]
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# Load the weather data (temperature in kelvin) for Geneva.
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coordinates = (46.204391, 6.143158)
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wx_station_id = nearest_wx_station(longitude=coordinates[1], latitude=coordinates[0])[0]
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# average temperature of each month, hour per hour (from midnight to 11 pm)
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Geneva_hourly_temperatures_celsius_per_hour = {
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'Jan': [0.2, -0.3, -0.5, -0.9, -1.1, -1.4, -1.5, -1.5, -1.1, 0.1, 1.5,
|
||||
2.8, 3.8, 4.4, 4.5, 4.4, 4.4, 3.9, 3.1, 2.7, 2.2, 1.7, 1.5, 1.1],
|
||||
'Feb': [0.9, 0.3, 0.0, -0.5, -0.7, -1.1, -1.2, -1.1, -0.7, 0.8, 2.5,
|
||||
4.2, 5.4, 6.2, 6.3, 6.2, 6.1, 5.5, 4.5, 4.1, 3.5, 2.8, 2.5, 2.0],
|
||||
'Mar': [4.2, 3.5, 3.1, 2.5, 2.1, 1.6, 1.5, 1.6, 2.2, 4.0, 6.3, 8.4,
|
||||
10.0, 11.1, 11.2, 11.1, 11.0, 10.2, 8.9, 8.3, 7.5, 6.7, 6.3, 5.6],
|
||||
'Apr': [7.4, 6.7, 6.2, 5.5, 5.2, 4.7, 4.5, 4.6, 5.3, 7.2, 9.6, 11.9,
|
||||
13.7, 14.8, 14.9, 14.8, 14.7, 13.8, 12.4, 11.8, 10.9, 10.1, 9.6, 8.9],
|
||||
'May': [11.8, 11.1, 10.6, 9.9, 9.5, 8.9, 8.8, 8.9, 9.6, 11.6, 14.2, 16.6,
|
||||
18.4, 19.6, 19.7, 19.6, 19.4, 18.6, 17.1, 16.5, 15.6, 14.6, 14.2, 13.4],
|
||||
'Jun': [15.2, 14.4, 13.9, 13.2, 12.7, 12.2, 12.0, 12.1, 12.8, 15.0, 17.7,
|
||||
20.2, 22.1, 23.3, 23.5, 23.4, 23.2, 22.3, 20.8, 20.1, 19.1, 18.2, 17.7, 16.9],
|
||||
'Jul': [17.6, 16.7, 16.1, 15.3, 14.9, 14.3, 14.1, 14.2, 15.0, 17.3, 20.2,
|
||||
23.0, 25.0, 26.3, 26.5, 26.4, 26.2, 25.2, 23.6, 22.8, 21.8, 20.8, 20.2, 19.4],
|
||||
'Aug': [17.1, 16.2, 15.7, 14.9, 14.5, 13.9, 13.7, 13.8, 14.6, 16.9, 19.7,
|
||||
22.4, 24.4, 25.6, 25.8, 25.7, 25.5, 24.5, 22.9, 22.2, 21.2, 20.2, 19.7, 18.9],
|
||||
'Sep': [13.4, 12.7, 12.2, 11.5, 11.2, 10.7, 10.5, 10.6, 11.3, 13.2, 15.6,
|
||||
17.9, 19.6, 20.8, 20.9, 20.8, 20.7, 19.8, 18.4, 17.8, 16.9, 16.1, 15.6, 14.9],
|
||||
'Oct': [9.4, 8.8, 8.5, 7.9, 7.6, 7.2, 7.1, 7.2, 7.7, 9.3, 11.2, 13.0,
|
||||
14.4, 15.3, 15.4, 15.3, 15.2, 14.5, 13.4, 12.9, 12.2, 11.6, 11.2, 10.6],
|
||||
'Nov': [4.0, 3.6, 3.3, 2.9, 2.6, 2.3, 2.2, 2.2, 2.7, 3.9, 5.5, 6.9, 8.0,
|
||||
8.7, 8.8, 8.7, 8.7, 8.1, 7.2, 6.8, 6.3, 5.7, 5.5, 5.0],
|
||||
'Dec': [1.4, 1.0, 0.8, 0.4, 0.2, -0.0, -0.1, -0.1, 0.3, 1.3, 2.6, 3.8,
|
||||
4.7, 5.2, 5.3, 5.2, 5.2, 4.7, 4.0, 3.7, 3.2, 2.8, 2.6, 2.2]
|
||||
}
|
||||
|
||||
|
||||
local_hourly_temperatures_celsius_per_hour = {month.replace(month, MONTH_NAMES[i][:3]):
|
||||
[t - 273.15 for t in temp] for i, (month, temp)
|
||||
in enumerate(wx_data()[wx_station_id].items())}
|
||||
|
||||
# Geneva hourly temperatures as piecewise constant function (in Kelvin).
|
||||
GenevaTemperatures_hourly = {
|
||||
month: models.PiecewiseConstant(
|
||||
|
|
@ -40,12 +23,12 @@ GenevaTemperatures_hourly = {
|
|||
tuple(float(time) for time in range(25)),
|
||||
tuple(273.15 + np.array(temperatures)),
|
||||
)
|
||||
for month, temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
|
||||
for month, temperatures in local_hourly_temperatures_celsius_per_hour.items()
|
||||
}
|
||||
|
||||
|
||||
# Same temperatures on a finer temperature mesh (every 6 minutes).
|
||||
GenevaTemperatures = {
|
||||
month: GenevaTemperatures_hourly[month].refine(refine_factor=10)
|
||||
for month, temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
|
||||
for month, temperatures in local_hourly_temperatures_celsius_per_hour.items()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
|
|
|
|||
|
|
@ -291,7 +291,7 @@ def build_hourly_dependent_model_multipleventilation(month, intervals_open=((7.5
|
|||
|
||||
@pytest.mark.parametrize(
|
||||
"month, temperatures",
|
||||
data.Geneva_hourly_temperatures_celsius_per_hour.items(),
|
||||
data.local_hourly_temperatures_celsius_per_hour.items(),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
|
|
@ -306,7 +306,7 @@ def test_concentrations_hourly_dep_temp_vs_constant(month, temperatures, time):
|
|||
|
||||
@pytest.mark.parametrize(
|
||||
"month, temperatures",
|
||||
data.Geneva_hourly_temperatures_celsius_per_hour.items(),
|
||||
data.local_hourly_temperatures_celsius_per_hour.items(),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
|
|
@ -330,7 +330,7 @@ def test_concentrations_hourly_dep_multipleventilation():
|
|||
|
||||
@pytest.mark.parametrize(
|
||||
"month_temp_item",
|
||||
data.Geneva_hourly_temperatures_celsius_per_hour.items(),
|
||||
data.local_hourly_temperatures_celsius_per_hour.items(),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
|
|
@ -378,8 +378,8 @@ def build_exposure_model(concentration_model):
|
|||
@pytest.mark.parametrize(
|
||||
"month, expected_exposure",
|
||||
[
|
||||
['Jan', 496.5427],
|
||||
['Jun', 1898.1354],
|
||||
['Jan', 503.254087759],
|
||||
['Jun', 2294.71115639],
|
||||
],
|
||||
)
|
||||
def test_exposure_hourly_dep(month,expected_exposure):
|
||||
|
|
@ -399,8 +399,8 @@ def test_exposure_hourly_dep(month,expected_exposure):
|
|||
@pytest.mark.parametrize(
|
||||
"month, expected_exposure",
|
||||
[
|
||||
['Jan', 499.6921],
|
||||
['Jun', 2007.59925],
|
||||
['Jan', 511.118941481],
|
||||
['Jun', 2398.90129579],
|
||||
],
|
||||
)
|
||||
def test_exposure_hourly_dep_refined(month,expected_exposure):
|
||||
|
|
|
|||
2
setup.py
2
setup.py
|
|
@ -20,7 +20,7 @@ REQUIREMENTS: dict = {
|
|||
'core': [
|
||||
'dataclasses; python_version < "3.7"',
|
||||
'ipykernel',
|
||||
'ipympl != 0.8.0',
|
||||
'ipympl != 0.8.0, != 0.8.1',
|
||||
'ipywidgets',
|
||||
'Jinja2',
|
||||
'loky',
|
||||
|
|
|
|||
Loading…
Reference in a new issue