Mark overlap between exposed and infected presence
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gaazzopa
parent
87cba1c6c3
commit
3f12a69168
1 changed files with 16 additions and 99 deletions
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@ -3,7 +3,6 @@ import dataclasses
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from datetime import datetime
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import io
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from pathlib import Path
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import typing
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import jinja2
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import matplotlib
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@ -22,18 +21,14 @@ class RepeatEvents:
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expected_new_cases: float
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def model_start_end(model: models.ExposureModel):
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def calculate_report_data(model: models.ExposureModel):
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resolution = 600
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t_start = min(model.exposed.presence.boundaries()[0][0],
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model.concentration_model.infected.presence.boundaries()[0][0])
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t_end = max(model.exposed.presence.boundaries()[-1][1],
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model.concentration_model.infected.presence.boundaries()[-1][1])
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return t_start, t_end
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def calculate_report_data(model: models.ExposureModel):
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resolution = 600
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t_start, t_end = model_start_end(model)
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times = list(np.linspace(t_start, t_end, resolution))
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concentrations = [model.concentration_model.concentration(time) for time in times]
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highest_const = max(concentrations)
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@ -61,7 +56,7 @@ def calculate_report_data(model: models.ExposureModel):
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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": embed_figure(plot(times, concentrations)),
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"scenario_plot_src": embed_figure(plot(times, concentrations, model)),
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"repeated_events": repeated_events,
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}
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@ -78,7 +73,7 @@ def embed_figure(figure) -> str:
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return f'data:image/png;base64,{pic_hash}'
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def plot(times, concentrations):
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def plot(times, concentrations, model: models.ExposureModel):
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax.plot(times, concentrations)
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@ -89,6 +84,17 @@ def plot(times, concentrations):
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ax.set_ylabel('Concentration ($q/m^3$)')
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ax.set_title('Concentration of infectious quanta')
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# Plot overlap of exposed and infected
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overlap_start = max(model.exposed.presence.boundaries()[0][0], model.concentration_model.infected.presence.boundaries()[0][0])
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overlap_finish = min(model.exposed.presence.boundaries()[-1][1], model.concentration_model.infected.presence.boundaries()[-1][1])
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ax.plot([overlap_start, overlap_start], [0, model.concentration_model.concentration(overlap_start)], linestyle='--', color="#1f77b4")
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ax.plot([overlap_finish, overlap_finish], [0, model.concentration_model.concentration(overlap_finish)], linestyle='--', color="#1f77b4")
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plt.fill_between(times, concentrations, 0, where=(np.array(times)<overlap_start), color="white")
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plt.fill_between(times, concentrations, 0, where=(np.array(times)>=overlap_start), alpha=0.2)
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plt.fill_between(times, concentrations, 0, where=(np.array(times)>overlap_finish), color="white")
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# top = max([0.75, max(concentrations)])
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# print(max(concentrations))
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# ax.set_ylim(bottom=1e-4, top=top)
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@ -104,93 +110,6 @@ def minutes_to_time(minutes: int) -> str:
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return f"{hour_string}:{minute_string}"
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def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, models.ExposureModel]:
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scenarios = {}
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# Two special option cases - HEPA and/or FFP2 masks.
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FFP2_being_worn = bool(form.mask_wearing == 'continuous' and form.mask_type == 'FFP2')
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if FFP2_being_worn and form.hepa_option:
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scenarios['Base scenario with HEPA and FFP2 masks'] = form.build_model()
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elif FFP2_being_worn:
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scenarios['Base scenario with FFP2 masks'] = form.build_model()
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elif form.hepa_option:
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scenarios['Base scenario with HEPA filter'] = form.build_model()
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# The remaining scenarios are based on Type I masks (possibly not worn)
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# and no HEPA filtration.
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form = dataclasses.replace(form, mask_type='Type I')
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if form.hepa_option:
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form = dataclasses.replace(form, hepa_option=False)
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with_mask = dataclasses.replace(form, mask_wearing='continuous')
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without_mask = dataclasses.replace(form, mask_wearing='removed')
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if form.ventilation_type == 'mechanical':
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scenarios['Mechanical ventilation with Type I masks'] = with_mask.build_model()
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scenarios['Mechanical ventilation without masks'] = without_mask.build_model()
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elif form.ventilation_type == 'natural':
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scenarios['Windows open with Type I masks'] = with_mask.build_model()
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scenarios['Windows open without masks'] = without_mask.build_model()
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# No matter the ventilation scheme, we include scenarios which don't have any ventilation.
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with_mask_no_vent = dataclasses.replace(with_mask, ventilation_type='no-ventilation')
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without_mask_or_vent = dataclasses.replace(without_mask, ventilation_type='no-ventilation')
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scenarios['No ventilation with Type I masks'] = with_mask_no_vent.build_model()
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scenarios['Neither ventilation nor masks'] = without_mask_or_vent.build_model()
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return scenarios
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def comparison_plot(scenarios: typing.Dict[str, models.ExposureModel]):
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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resolution = 350
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times = None
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dash_styled_scenarios = [
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'Base scenario with FFP2 masks',
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'Base scenario with HEPA filter',
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'Base scenario with HEPA and FFP2 masks',
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]
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for name, model in scenarios.items():
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if times is None:
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t_start, t_end = model_start_end(model)
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times = np.linspace(t_start, t_end, resolution)
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concentrations = [model.concentration_model.concentration(time) for time in times]
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if name in dash_styled_scenarios:
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ax.plot(times, concentrations, label=name, linestyle='--')
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else:
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ax.plot(times, concentrations, label=name, linestyle='-', alpha=0.5)
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# Place a legend outside of the axes itself.
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ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.set_xlabel('Time (hour of day)')
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ax.set_ylabel('Concentration ($q/m^3$)')
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ax.set_title('Concentration of infectious quanta')
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return fig
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def comparison_report(scenarios: typing.Dict[str, models.ExposureModel]):
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statistics = {}
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for name, model in scenarios.items():
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statistics[name] = {
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'probability_of_infection': model.infection_probability(),
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'expected_new_cases': model.expected_new_cases(),
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}
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return {
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'plot': embed_figure(comparison_plot(scenarios)),
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'stats': statistics,
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}
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def build_report(model: models.ExposureModel, form: FormData):
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now = datetime.now()
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time = now.strftime("%d/%m/%Y %H:%M:%S")
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@ -204,8 +123,6 @@ def build_report(model: models.ExposureModel, form: FormData):
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}
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context.update(calculate_report_data(model))
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alternative_scenarios = manufacture_alternative_scenarios(form)
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context['alternative_scenarios'] = comparison_report(alternative_scenarios)
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cara_templates = Path(__file__).parent.parent / "templates"
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calculator_templates = Path(__file__).parent / "templates"
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