merge with master
This commit is contained in:
Luis Aleixo
parent
0b83c6491e
commit
c27f567f3a
8 changed files with 264 additions and 159 deletions
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@ -11,7 +11,7 @@ from cara import data
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import cara.data.weather
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import cara.monte_carlo as mc
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from .. import calculator
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from cara.monte_carlo.data import activity_distributions, virus_distributions
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from cara.monte_carlo.data import activity_distributions, virus_distributions, mask_distributions
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LOG = logging.getLogger(__name__)
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@ -359,7 +359,10 @@ class FormData:
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def mask(self) -> models.Mask:
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# Initializes the mask type if mask wearing is "continuous", otherwise instantiates the mask attribute as
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# the "No mask"-mask
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mask = models.Mask.types[self.mask_type if self.mask_wearing_option == "mask_on" else 'No mask']
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if self.mask_wearing_option == 'mask_on':
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mask = mask_distributions[self.mask_type]
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else:
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mask = models.Mask.types['No mask']
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return mask
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def infected_population(self) -> mc.InfectedPopulation:
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@ -1,10 +1,3 @@
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from ... import dataclass_utils
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from .model_generator import FormData, _DEFAULT_MC_SAMPLE_SIZE
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from ... import monte_carlo as mc
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from cara import models
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import qrcode
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import numpy as np
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import matplotlib.pyplot as plt
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import concurrent.futures
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import base64
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import dataclasses
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@ -16,9 +9,14 @@ import zlib
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import loky
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import jinja2
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import matplotlib
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from numpy.lib.function_base import append, quantile
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matplotlib.use('agg')
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import numpy as np
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import qrcode
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import json
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from cara import models
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from ... import monte_carlo as mc
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from .model_generator import FormData, _DEFAULT_MC_SAMPLE_SIZE
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from ... import dataclass_utils
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def model_start_end(model: models.ExposureModel):
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@ -96,8 +94,7 @@ def interesting_times(model: models.ExposureModel, approx_n_pts=100) -> typing.L
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# Expand the times list to ensure that we have a maximum gap size between
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# the key times.
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nice_times = fill_big_gaps(times, gap_size=(
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max(times) - min(times)) / approx_n_pts)
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nice_times = fill_big_gaps(times, gap_size=(max(times) - min(times)) / approx_n_pts)
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return nice_times
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@ -110,8 +107,7 @@ def calculate_report_data(model: models.ExposureModel):
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]
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highest_const = max(concentrations)
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prob = np.array(model.infection_probability()).mean()
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er = np.array(
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model.concentration_model.infected.emission_rate_when_present()).mean()
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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 = [
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@ -169,21 +165,12 @@ def _img2bytes(figure):
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return img_data
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def _figure2bytes(figure):
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# Draw the image
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img_data = io.BytesIO()
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figure.savefig(img_data, format='png',
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bbox_inches="tight", transparent=True)
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return img_data
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def img2base64(img_data) -> str:
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plt.close()
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img_data.seek(0)
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pic_hash = base64.b64encode(img_data.read()).decode('ascii')
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# A src suitable for a tag such as f'<img id="scenario_concentration_plot" src="{result}">.
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return f'data:image/png;base64,{pic_hash}'
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def minutes_to_time(minutes: int) -> str:
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minute_string = str(minutes % 60)
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@ -224,18 +211,14 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp
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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_option ==
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'mask_on' and form.mask_type == 'FFP2')
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FFP2_being_worn = bool(form.mask_wearing_option == 'mask_on' and form.mask_type == 'FFP2')
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if FFP2_being_worn and form.hepa_option:
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FFP2andHEPAalternative = dataclass_utils.replace(
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form, mask_type='Type I')
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FFP2andHEPAalternative = dataclass_utils.replace(form, mask_type='Type I')
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scenarios['Base scenario with HEPA filter and Type I masks'] = FFP2andHEPAalternative.build_mc_model()
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if not FFP2_being_worn and form.hepa_option:
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noHEPAalternative = dataclass_utils.replace(form, mask_type='FFP2')
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noHEPAalternative = dataclass_utils.replace(
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noHEPAalternative, mask_wearing_option='mask_on')
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noHEPAalternative = dataclass_utils.replace(
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noHEPAalternative, hepa_option=False)
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noHEPAalternative = dataclass_utils.replace(form, mask_type = 'FFP2')
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noHEPAalternative = dataclass_utils.replace(noHEPAalternative, mask_wearing_option = 'mask_on')
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noHEPAalternative = dataclass_utils.replace(noHEPAalternative, hepa_option=False)
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scenarios['Base scenario without HEPA filter, with FFP2 masks'] = noHEPAalternative.build_mc_model()
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# The remaining scenarios are based on Type I masks (possibly not worn)
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@ -245,8 +228,7 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp
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form = dataclass_utils.replace(form, hepa_option=False)
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with_mask = dataclass_utils.replace(form, mask_wearing_option='mask_on')
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without_mask = dataclass_utils.replace(
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form, mask_wearing_option='mask_off')
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without_mask = dataclass_utils.replace(form, mask_wearing_option='mask_off')
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if form.ventilation_type == 'mechanical_ventilation':
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#scenarios['Mechanical ventilation with Type I masks'] = with_mask.build_mc_model()
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@ -257,62 +239,17 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp
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scenarios['Windows open without masks'] = without_mask.build_mc_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 = dataclass_utils.replace(
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with_mask, ventilation_type='no_ventilation')
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without_mask_or_vent = dataclass_utils.replace(
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without_mask, ventilation_type='no_ventilation')
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with_mask_no_vent = dataclass_utils.replace(with_mask, ventilation_type='no_ventilation')
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without_mask_or_vent = dataclass_utils.replace(without_mask, ventilation_type='no_ventilation')
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scenarios['No ventilation with Type I masks'] = with_mask_no_vent.build_mc_model()
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scenarios['Neither ventilation nor masks'] = without_mask_or_vent.build_mc_model()
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return scenarios
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def comparison_plot(scenarios: typing.Dict[str, dict], sample_times: typing.List[float]):
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax1 = ax.twinx()
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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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datetimes = [datetime(1970, 1, 1) + timedelta(hours=time)
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for time in sample_times]
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for name, statistics in scenarios.items():
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model = statistics['model']
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concentrations = statistics['concentrations']
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# Plot concentrations
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if name in dash_styled_scenarios:
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ax.plot(datetimes, concentrations, label=name, linestyle='--')
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else:
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ax.plot(datetimes, concentrations,
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label=name, linestyle='-', alpha=0.5)
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# Place a legend outside of the axes itself.
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fig.legend(bbox_to_anchor=(1.05, 0.95), 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 of day', fontsize=14)
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ax.set_ylabel('Mean viral concentration\n(virion m$^{-3}$)', fontsize=14)
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ax.set_title('Concentration profile')
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ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M"))
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ax.set_xlabel('Time of day')
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ax.set_ylabel('Mean concentration ($virions/m^{3}$)')
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ax.set_title('Mean concentration of virions')
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return fig
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def scenario_statistics(mc_model: mc.ExposureModel, sample_times: typing.List[float]):
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def scenario_statistics(mc_model: mc.ExposureModel, sample_times: np.ndarray):
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model = mc_model.build_model(size=_DEFAULT_MC_SAMPLE_SIZE)
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return {
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'model': model,
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'probability_of_infection': np.mean(model.infection_probability()),
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'expected_new_cases': np.mean(model.expected_new_cases()),
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'concentrations': [
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@ -339,7 +276,6 @@ def comparison_report(
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for (name, model), model_stats in zip(scenarios.items(), results):
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statistics[name] = model_stats
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return {
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'plot': img2base64(_figure2bytes(comparison_plot(statistics, sample_times))),
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'stats': statistics,
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}
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@ -356,8 +292,7 @@ class ReportGenerator:
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executor_factory: typing.Callable[[], concurrent.futures.Executor],
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) -> str:
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model = form.build_model()
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context = self.prepare_context(
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base_url, model, form, executor_factory=executor_factory)
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context = self.prepare_context(base_url, model, form, executor_factory=executor_factory)
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return self.render(context)
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def prepare_context(
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@ -383,8 +318,7 @@ class ReportGenerator:
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context['alternative_scenarios'] = comparison_report(
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alternative_scenarios, scenario_sample_times, executor_factory=executor_factory,
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)
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context['qr_code'] = generate_qr_code(
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base_url, self.calculator_prefix, form)
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context['qr_code'] = generate_qr_code(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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@ -404,6 +338,7 @@ class ReportGenerator:
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env.filters['minutes_to_time'] = minutes_to_time
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env.filters['float_format'] = "{0:.2f}".format
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env.filters['int_format'] = "{:0.0f}".format
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env.filters['JSONify'] = json.dumps
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return env
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def render(self, context: dict) -> str:
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@ -27,70 +27,18 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses
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yCumulatedAxis = d3.axisRight(yCumulatedRange);
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// Plot tittle.
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vis.append('svg:foreignObject')
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.attr("background-color", "transparent")
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.attr('width', width)
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.attr('height', margins.top)
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.style('text-align', 'center')
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.html('<b>Mean concentration of virions</b>');
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plot_title(vis, width, margins.top, 'Mean concentration of virions');
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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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plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4');
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// Line representing the cumulative concentration.
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var lineCumulativeFunc = d3.line()
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.defined(d => !isNaN(d.cumulative_doses))
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.x(d => xTimeRange(d.time))
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.y(d => yCumulatedRange(d.cumulative_doses))
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.curve(d3.curveBasis);
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plot_cumulative_data(vis, data, xTimeRange, yCumulatedRange, '#1f77b4');
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vis.append('svg:path')
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.attr('d', lineCumulativeFunc(data))
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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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// X axis.
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plot_x_axis(vis, height, width, margins, xAxis, 'Time of day');
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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 concentration 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 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', (height + margins.left) * 0.92)
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.text('Mean concentration (virions/m³)');
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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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@ -230,4 +178,195 @@ function draw_concentration_plot(svg_id, times, concentrations, cumulative_doses
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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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// Generate the alternative scenarios plot using d3 library.
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// 'alternative_scenarios' is a dictionary with all the alternative scenarios
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// 'times' is a list of times for all the scenarios
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function draw_alternative_scenarios_plot(svg_id, width, height, alternative_scenarios, times) {
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// H:M format
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var time_format = d3.timeFormat('%H:%M');
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// D3 array of ten categorical colors represented as RGB hexadecimal strings.
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var colors = d3.schemeAccent;
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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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for (scenario in alternative_scenarios) {
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scenario_concentrations = alternative_scenarios[scenario].concentrations
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highest_concentration = Math.max(highest_concentration, Math.max(...scenario_concentrations))
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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': 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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}
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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 vis = d3.select(svg_id),
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width = width,
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height = 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([first_scenario[0].hour, first_scenario[first_scenario.length - 1].hour]),
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xTimeRange = d3.scaleLinear().range([margins.left, width - margins.right]).domain([times[0], times[times.length - 1]]),
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yRange = d3.scaleLinear().range([height - margins.bottom, margins.top]).domain([0., highest_concentration]),
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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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// Line representing the mean concentration.
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plot_scenario_data(vis, data, xTimeRange, yRange, colors[scenario_index])
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// Legend for the plot elements - lines.
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var size = 20 * (scenario_index + 1)
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vis.append('rect')
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.attr('x', width + 20)
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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', colors[scenario_index]);
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vis.append('text')
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.attr('x', width + 3 * 20)
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.attr('y', margins.top + size)
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.text(scenario_name)
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.style('font-size', '15px')
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.attr('alignment-baseline', 'central');
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}
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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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// 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 (virions/m³)');
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// Legend bounding box.
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vis.append('rect')
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.attr('width', 275)
|
||||
.attr('height', 25 * (Object.keys(data_for_scenarios).length))
|
||||
.attr('x', width * 1.005)
|
||||
.attr('y', margins.top + 5)
|
||||
.attr('stroke', 'lightgrey')
|
||||
.attr('stroke-width', '2')
|
||||
.attr('rx', '5px')
|
||||
.attr('ry', '5px')
|
||||
.attr('stroke-linejoin', 'round')
|
||||
.attr('fill', 'none');
|
||||
}
|
||||
|
||||
|
||||
// Functions used to build the plots' components
|
||||
|
||||
function plot_title(vis, width, margin_top, title) {
|
||||
vis.append('svg:foreignObject')
|
||||
.attr('width', width)
|
||||
.attr('height', margin_top)
|
||||
.attr('fill', 'none')
|
||||
.append('xhtml:div')
|
||||
.style('text-align', 'center')
|
||||
.html(title);
|
||||
|
||||
return vis;
|
||||
}
|
||||
|
||||
function plot_x_axis(vis, height, width, margins, xAxis, label) {
|
||||
// X axis declaration
|
||||
vis.append('svg:g')
|
||||
.attr('class', 'x axis')
|
||||
.attr('transform', 'translate(0,' + (height - margins.bottom) + ')')
|
||||
.call(xAxis);
|
||||
|
||||
// X axis label.
|
||||
vis.append('text')
|
||||
.attr('class', 'x label')
|
||||
.attr('fill', 'black')
|
||||
.attr('text-anchor', 'middle')
|
||||
.attr('x', (width + margins.right) / 2)
|
||||
.attr('y', height * 0.97)
|
||||
.text(label);
|
||||
|
||||
return vis;
|
||||
}
|
||||
|
||||
function plot_y_axis(vis, height, width, margins, yAxis, label) {
|
||||
// Y axis declaration.
|
||||
vis.append('svg:g')
|
||||
.attr('class', 'y axis')
|
||||
.attr('transform', 'translate(' + margins.left + ',0)')
|
||||
.call(yAxis);
|
||||
|
||||
// Y axis label.
|
||||
vis.append('svg:text')
|
||||
.attr('class', 'y label')
|
||||
.attr('fill', 'black')
|
||||
.attr('transform', 'rotate(-90, 0,' + height + ')')
|
||||
.attr('text-anchor', 'middle')
|
||||
.attr('x', (height + margins.bottom) / 2)
|
||||
.attr('y', (height + margins.left) * 0.92)
|
||||
.text(label);
|
||||
|
||||
return vis;
|
||||
|
||||
}
|
||||
|
||||
function plot_scenario_data(vis, data, xTimeRange, yRange, line_color) {
|
||||
var lineFunc = d3.line()
|
||||
.defined(d => !isNaN(d.concentration))
|
||||
.x(d => xTimeRange(d.time))
|
||||
.y(d => yRange(d.concentration))
|
||||
.curve(d3.curveBasis);
|
||||
|
||||
vis.append('svg:path')
|
||||
.attr('d', lineFunc(data))
|
||||
.attr("stroke", line_color)
|
||||
.attr('stroke-width', 2)
|
||||
.attr('fill', 'none');
|
||||
|
||||
return vis;
|
||||
}
|
||||
|
||||
function plot_cumulative_data(vis, data, xTimeRange, yCumulativeRange, line_color) {
|
||||
var lineCumulativeFunc = d3.line()
|
||||
.defined(d => !isNaN(d.cumulative_doses))
|
||||
.x(d => xTimeRange(d.time))
|
||||
.y(d => yCumulativeRange(d.cumulative_doses))
|
||||
.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;
|
||||
}
|
||||
|
|
@ -86,12 +86,12 @@
|
|||
{% 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="1000" height="400"></svg>
|
||||
<svg id="result_plot" width="900" height="400"></svg>
|
||||
<script type="application/javascript">
|
||||
var times = {{times}}
|
||||
var concentrations = {{concentrations}}
|
||||
var cumulative_doses = {{cumulative_doses}}
|
||||
var exposed_presence_intervals = {{exposed_presence_intervals}}
|
||||
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, cumulative_doses, exposed_presence_intervals);
|
||||
</script>
|
||||
</p>
|
||||
|
|
@ -109,8 +109,12 @@
|
|||
<div class="collapse" id="collapseAlternativeScenarios">
|
||||
<div class="card-body">
|
||||
<div>
|
||||
<img id="scenario_concentration_plot" src="{{ alternative_scenarios.plot }}" />
|
||||
|
||||
<svg id="alternative_scenario_plot" width="900" height="400"></svg>
|
||||
<script type="application/javascript">
|
||||
var alternative_scenarios = {{ alternative_scenarios.stats | JSONify }}
|
||||
var times = {{ times | JSONify }}
|
||||
draw_alternative_scenarios_plot("#alternative_scenario_plot", width=600, height=400, alternative_scenarios, times);
|
||||
</script>
|
||||
{% block report_scenarios_summary_table %}
|
||||
<table class="table w-auto">
|
||||
<thead class="thead-light">
|
||||
|
|
@ -437,4 +441,4 @@
|
|||
<script src="https://cdnjs.cloudflare.com/ajax/libs/html2pdf.js/0.9.2/html2pdf.bundle.js"></script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
</html>
|
||||
|
|
|
|||
|
|
@ -71,3 +71,6 @@ We wish to thank CERN’s HSE Unit, Beams Department, Experimental Physics Depar
|
|||
[54] Leung, N.H.L et al. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat Med (2020). 10.1038/s41591-020-0843-2.<br>
|
||||
[55] Asadi, S., Cappa, C.D., Barreda, S. et al. Efficacy of masks and face coverings in controlling outward aerosol particle emission from expiratory activities. Sci Rep 10, 15665 (2020). https://doi.org/10.1038/s41598-020-72798-7.<br>
|
||||
[56] Endo A, Abbott S et al. Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China [version 3; peer review: 2 approved]. Wellcome Open Res 2020, 5:67. doi:10.12688/wellcomeopenres.15842.3.<br>
|
||||
[57] Jin Pan, Charbel Harb, Weinan Leng & Linsey C. Marr (2021) Inward and outward effectiveness of cloth masks, a surgical mask, and a face shield, Aerosol Science and Technology, 55:6, 718-733, doi: 10.1080/02786826.2021.1890687.<br>
|
||||
[58] C. Makison Booth, M. Clayton, B. Crook, J.M. Gawn, Effectiveness of surgical masks against influenza bioaerosols, Journal of Hospital Infection, Volume 84, Issue 1, 2013, Pages 22-26, https://doi.org/10.1016/j.jhin.2013.02.007.<br>
|
||||
[59] Riediker, M., Monn, C. (2021). Simulation of SARS-CoV-2 Aerosol Emissions in the Infected Population and Resulting Airborne Exposures in Different Indoor Scenarios. Aerosol Air Qual. Res. 21, 200531. https://doi.org/10.4209/aaqr.2020.08.0531.<br>
|
||||
|
|
@ -904,7 +904,6 @@ class ExposureModel:
|
|||
return 0
|
||||
|
||||
def inhaled_exposure_between_bounds(self, time: float) -> _VectorisedFloat:
|
||||
|
||||
exposure = self.exposure_between_bounds(time)
|
||||
|
||||
return (
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import numpy as np
|
||||
|
||||
import cara.monte_carlo as mc
|
||||
from cara.monte_carlo.sampleable import Normal,LogNormal,LogCustomKernel
|
||||
from cara.monte_carlo.sampleable import Normal,LogNormal,LogCustomKernel, Uniform
|
||||
|
||||
|
||||
# From CERN-OPEN-2021-04 and refererences therein
|
||||
|
|
@ -58,3 +58,13 @@ virus_distributions = {
|
|||
infectious_dose=60/1.6,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# From:
|
||||
# https://doi.org/10.1080/02786826.2021.1890687
|
||||
# https://doi.org/10.1016/j.jhin.2013.02.007
|
||||
# https://doi.org/10.4209/aaqr.2020.08.0531
|
||||
mask_distributions = {
|
||||
'Type I': mc.Mask(Uniform(0.25, 0.80)),
|
||||
'FFP2': mc.Mask(Uniform(0.83, 0.91)),
|
||||
}
|
||||
|
|
@ -28,6 +28,18 @@ class Normal(SampleableDistribution):
|
|||
return np.random.normal(self.mean, self.standard_deviation, size=size)
|
||||
|
||||
|
||||
class Uniform(SampleableDistribution):
|
||||
"""
|
||||
Defines a continuous uniform distribution
|
||||
"""
|
||||
def __init__(self, low: float, high: float):
|
||||
self.low = low
|
||||
self.high = high
|
||||
|
||||
def generate_samples(self, size: int) -> float_array_size_n:
|
||||
return np.random.uniform(self.low, self.high, size=size)
|
||||
|
||||
|
||||
class LogNormal(SampleableDistribution):
|
||||
"""
|
||||
Defines a lognormal distribution (i.e. Gaussian distribution vs. the
|
||||
|
|
|
|||
Loading…
Reference in a new issue