Merge branch 'master' into remotes/origin/feature/website
This commit is contained in:
Luis Aleixo
commit
b9f8c93b47
16 changed files with 383 additions and 295 deletions
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@ -33,7 +33,7 @@ from .user import AuthenticatedUser, AnonymousUser
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# calculator version. If the calculator needs to make breaking changes (e.g. change
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# form attributes) then it can also increase its MAJOR version without needing to
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# increase the overall CARA version (found at ``cara.__version__``).
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__version__ = "3.0.0"
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__version__ = "3.0.1"
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class BaseRequestHandler(RequestHandler):
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@ -1,19 +1,15 @@
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import concurrent.futures
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import base64
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import dataclasses
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from datetime import datetime, timedelta
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from datetime import datetime
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import io
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import json
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import typing
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import urllib
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import zlib
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import loky
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import jinja2
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import matplotlib
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matplotlib.use('agg')
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import matplotlib.pyplot as plt
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import numpy as np
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import qrcode
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from cara import models
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from ... import monte_carlo as mc
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@ -125,7 +121,7 @@ def calculate_report_data(model: models.ExposureModel):
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}
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def generate_qr_code(base_url, calculator_prefix, form: FormData):
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def generate_permalink(base_url, calculator_prefix, form: FormData):
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form_dict = FormData.to_dict(form, strip_defaults=True)
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# Generate the calculator URL arguments that would be needed to re-create this
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@ -138,20 +134,9 @@ def generate_qr_code(base_url, calculator_prefix, form: FormData):
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qr_url = f"{base_url}/_c/{compressed_args}"
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url = f"{base_url}{calculator_prefix}?{args}"
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qr = qrcode.QRCode(
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version=1,
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error_correction=qrcode.constants.ERROR_CORRECT_H,
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box_size=10,
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border=4,
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)
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qr.add_data(qr_url)
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qr.make(fit=True)
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img = qr.make_image(fill_color="black", back_color="white").convert('RGB')
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return {
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'image': img2base64(_img2bytes(img)),
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'link': url,
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'qr_url': qr_url,
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'shortened': qr_url,
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}
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@ -162,50 +147,13 @@ 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', 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 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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datetimes = [datetime(1970, 1, 1) + timedelta(hours=time) for time in times]
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ax.plot(datetimes, concentrations, lw=2, color='#1f77b4', label='Mean concentration')
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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')
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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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ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%H:%M"))
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# Plot presence of exposed person
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for i, (presence_start, presence_finish) in enumerate(model.exposed.presence.boundaries()):
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plt.fill_between(
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datetimes, concentrations, 0,
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where=(np.array(times) > presence_start) & (np.array(times) < presence_finish),
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color="#1f77b4", alpha=0.1,
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label="Presence of exposed person(s)" if i == 0 else ""
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)
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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.set_ylim(0)
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return fig
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def minutes_to_time(minutes: int) -> str:
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minute_string = str(minutes % 60)
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minute_string = "0" * (2 - len(minute_string)) + minute_string
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@ -281,39 +229,7 @@ def manufacture_alternative_scenarios(form: FormData) -> typing.Dict[str, mc.Exp
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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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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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sample_dts = [datetime(1970, 1, 1) + timedelta(hours=time) for time in sample_times]
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for name, statistics in scenarios.items():
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concentrations = statistics['concentrations']
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if name in dash_styled_scenarios:
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ax.plot(sample_dts, concentrations, label=name, linestyle='--')
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else:
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ax.plot(sample_dts, 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.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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'probability_of_infection': np.mean(model.infection_probability()),
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@ -342,7 +258,6 @@ 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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@ -385,7 +300,7 @@ 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(base_url, self.calculator_prefix, form)
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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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@ -405,6 +320,7 @@ 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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@ -56,7 +56,7 @@ p.notes {
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margin: 1%
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}
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#pdf-qr-code {
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#pdf_qrcode_aref {
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margin-right: 1%;
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width: 100pt;
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}
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@ -102,11 +102,6 @@ p.notes {
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border-radius: 5px;
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}
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.print-button {
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margin-left: auto;
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margin-right: 1%;
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}
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/* @media (width: 1200px) { */
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@media print {
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/* #body {
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@ -120,7 +115,7 @@ p.notes {
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#link_reproduce_results {
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display: none!important;
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}
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#pdf-qr-code {
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#pdf_qrcode_aref {
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visibility: inherit!important;
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}
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.collapse {
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@ -138,9 +133,6 @@ p.notes {
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.icon_button {
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display: none!important;
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}
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.print-button {
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display: none!important;
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}
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.card {
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page-break-inside: avoid;
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}
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@ -1,29 +0,0 @@
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function generate_pdf_version(qr_link) {
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const pdf_version = this.document.getElementById("body");
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// PDF styling
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var opt = {
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filename: 'myfile.pdf',
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image: { type: 'jpeg', quality: 0.98 },
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html2canvas: { scale: 2, width: 1200, windowWidth: 1200 },
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enableLinks: false,
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jsPDF: {
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unit: 'pt',
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format: 'letter',
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orientation: 'portrait',
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},
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pagebreak: { mode: '', avoid: '.break-avoid' },
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};
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html2pdf().set(opt).from(pdf_version).toPdf().get('pdf').then(function(pdf) {
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var totalPages = pdf.internal.getNumberOfPages();
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pdf.setPage(1);
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pdf.link(530, 25, 60, 60, { url: qr_link }); //Hyperlink to reproduce results
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for (i = 1; i <= totalPages; i++) {
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pdf.setPage(i);
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pdf.setFontSize(10);
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pdf.setTextColor(150);
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pdf.text('Page ' + i + ' of ' + totalPages, (pdf.internal.pageSize.getWidth() / 2.25), (pdf.internal.pageSize.getHeight() - 10));
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}
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}).save();
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};
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@ -25,56 +25,16 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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yAxis = d3.axisLeft(yRange);
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// Plot tittle.
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vis.append('svg:foreignObject')
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.attr("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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plot_scenario_data(vis, data, xTimeRange, yRange, '#1f77b4');
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vis.append('svg:path')
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.attr('d', lineFunc(data))
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.attr('stroke', '#1f77b4')
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.attr('stroke-width', 2)
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.attr('fill', 'none');
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// X axis.
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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 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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// Y axis
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plot_y_axis(vis, height, width, margins, yAxis, 'Mean concentration (virions/m³)')
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// Area representing the presence of exposed person(s).
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exposed_presence_intervals.forEach(b => {
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@ -181,4 +141,178 @@ function draw_concentration_plot(svg_id, times, concentrations, exposed_presence
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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)
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.attr('height', 25 * (Object.keys(data_for_scenarios).length))
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.attr('x', width * 1.005)
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.attr('y', margins.top + 5)
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.attr('stroke', 'lightgrey')
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.attr('stroke-width', '2')
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.attr('rx', '5px')
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.attr('ry', '5px')
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.attr('stroke-linejoin', 'round')
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.attr('fill', 'none');
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}
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// Functions used to build the plots' components
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function plot_title(vis, width, margin_top, title) {
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vis.append('svg:foreignObject')
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.attr('width', width)
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.attr('height', margin_top)
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.attr('fill', 'none')
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.append('xhtml:div')
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.style('text-align', 'center')
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.html(title);
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return vis;
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}
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function plot_x_axis(vis, height, width, margins, xAxis, label) {
|
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// X axis declaration
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vis.append('svg:g')
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.attr('class', 'x axis')
|
||||
.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;
|
||||
}
|
||||
|
|
@ -23,9 +23,8 @@
|
|||
<h2 class="text-component-title mb-0">CARA - CALCULATOR REPORT</h1>
|
||||
<p class="mb-0"> Created {{ creation_date }} using CARA calculator version v{{ form.calculator_version }}</p>
|
||||
</div>
|
||||
<button type="button" class="btn btn-outline-dark align-self-center" style="margin-right: -100pt" id="download-pdf" onclick="print()">Print Report</button>
|
||||
{# To be replaced by "Generate PDF" #}
|
||||
<img id="pdf-qr-code" class="align-self-center invisible" src="{{ qr_code.image }}"/>
|
||||
<button type="button" class="btn btn-outline-dark align-self-center" id="print-button" style="margin-right: -100pt" onclick="print()">Print Report</button>
|
||||
<a href="{{ permalink.link }}" style="float: left;" id="pdf_qrcode_aref" class="align-self-center invisible"><div id="pdf_qrcode"></div></a>
|
||||
</div>
|
||||
|
||||
{% endblock report_header %}
|
||||
|
|
@ -88,9 +87,9 @@
|
|||
<p id="section1">* The results are based on the parameters and assumptions published in the CERN Open Report <a href="https://cds.cern.ch/record/2756083"> CERN-OPEN-2021-004</a>.</p>
|
||||
<svg id="result_plot" width="900" height="400"></svg>
|
||||
<script type="application/javascript">
|
||||
var times = {{times}}
|
||||
var concentrations = {{concentrations}}
|
||||
var exposed_presence_intervals = {{exposed_presence_intervals}}
|
||||
var times = {{ times | JSONify }}
|
||||
var concentrations = {{ concentrations | JSONify }}
|
||||
var exposed_presence_intervals = {{ exposed_presence_intervals | JSONify }}
|
||||
draw_concentration_plot("#result_plot", times, concentrations, exposed_presence_intervals);
|
||||
</script>
|
||||
</p>
|
||||
|
|
@ -108,8 +107,12 @@
|
|||
<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">
|
||||
|
|
@ -158,10 +161,10 @@
|
|||
<div class="collapse show" id="collapseQRcode">
|
||||
<div class="card-body">
|
||||
<div>
|
||||
<a href="{{ qr_code.link }}" style="float: left;"><img style="width:250pt;" id="qr_code" src="{{ qr_code.image }}"/></a>
|
||||
<a href="{{ permalink.link }}" style="float: left;"><div id="qrcode"></div></a>
|
||||
<span style="float: left; min-height: 250pt; line-height: 250pt; vertical-align: middle; display: inline-block;">
|
||||
<p style="display: inline-block; vertical-align: middle; line-height: normal;">
|
||||
Click the QR code to regenerate the report and get a shareable link.<br>Alternatively, scan to regenerate the report.<br> Mobile-friendly app coming soon!
|
||||
Click the QR code to regenerate the report and get a shareable link.<br>Alternatively, scan to regenerate the report.<br>
|
||||
</p>
|
||||
</span>
|
||||
</div>
|
||||
|
|
@ -429,11 +432,26 @@
|
|||
</div>
|
||||
{% endblock disclaimer_container %}
|
||||
|
||||
<script src="{{ calculator_prefix }}/static/js/pdf.js"></script>
|
||||
<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.3/umd/popper.min.js" integrity="sha384-ZMP7rVo3mIykV+2+9J3UJ46jBk0WLaUAdn689aCwoqbBJiSnjAK/l8WvCWPIPm49" crossorigin="anonymous"></script>
|
||||
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/js/bootstrap.min.js" integrity="sha384-ChfqqxuZUCnJSK3+MXmPNIyE6ZbWh2IMqE241rYiqJxyMiZ6OW/JmZQ5stwEULTy" crossorigin="anonymous"></script>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/html2pdf.js/0.9.2/html2pdf.bundle.js"></script>
|
||||
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/qrcodejs/1.0.0/qrcode.min.js" integrity="sha512-CNgIRecGo7nphbeZ04Sc13ka07paqdeTu0WR1IM4kNcpmBAUSHSQX0FslNhTDadL4O5SAGapGt4FodqL8My0mA==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
|
||||
<script type="text/javascript">
|
||||
new QRCode(document.getElementById("qrcode"), {
|
||||
text: "{{ permalink.shortened }}",
|
||||
width: 330,
|
||||
height: 330,
|
||||
correctLevel : QRCode.CorrectLevel.L
|
||||
});
|
||||
new QRCode(document.getElementById("pdf_qrcode"), {
|
||||
text: "{{ permalink.shortened }}",
|
||||
width: 133,
|
||||
height: 133,
|
||||
correctLevel : QRCode.CorrectLevel.L
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
|
|
|||
|
|
@ -57,21 +57,16 @@ v{{ calculator_version }} <span style="float:right; font-weight:bold">Please sen
|
|||
|
||||
<div data-tooltip="Choose the SARS-CoV-2 Variant of Concern (VOC).">
|
||||
<span class="tooltip_text">?</span>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
<br>
|
||||
</div><br>
|
||||
|
||||
<div class="form-group row">
|
||||
<label class="col-sm-2 col-form-label">Variant:</label>
|
||||
<div class="col-sm-8">
|
||||
<select id="Variant" name="virus_type" class="form-control">
|
||||
<option value="SARS_CoV_2">SARS-CoV-2 (nominal strain)</option>
|
||||
<option value="SARS_CoV_2_B117">SARS-CoV-2 (Alpha VOC)</option>
|
||||
<option value="SARS_CoV_2_P1">SARS-CoV-2 (Gamma VOC)</option>
|
||||
<option value="SARS_CoV_2_B16172">SARS-CoV-2 (Delta VOC)</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="row">
|
||||
<label class="col-xl-3 col-lg-4 col-sm-3 col-form-label">Variant:</label>
|
||||
<select id="Variant" name="virus_type" class="col-xl-5 col-lg-7 col-sm-7 col-7">
|
||||
<option value="SARS_CoV_2">SARS-CoV-2 (nominal strain)</option>
|
||||
<option value="SARS_CoV_2_B117">SARS-CoV-2 (Alpha VOC)</option>
|
||||
<option value="SARS_CoV_2_P1">SARS-CoV-2 (Gamma VOC)</option>
|
||||
<option selected value="SARS_CoV_2_B16172">SARS-CoV-2 (Delta VOC)</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<hr width="80%">
|
||||
|
|
@ -456,13 +451,13 @@ v{{ calculator_version }} <span style="float:right; font-weight:bold">Please sen
|
|||
<b>Quick Guide:</b><br>
|
||||
This tool simulates the long range airborne spread SARS-CoV-2 virus in a finite volume and estimates the risk of COVID-19 infection. It is based on current scientific data and can be used to compare the effectiveness of different mitigation measures.<br>
|
||||
<b>Virus data:</b> <br>
|
||||
SARS-CoV-2 covers typical strains of the virus and three variants of concern (VOC):<br>
|
||||
SARS-CoV-2 covers the original "wild type" strain of the virus and three variants of concern (VOC):<br>
|
||||
<ul>
|
||||
<li>Alpha (also known as B.1.1.7, first identified in UK, Dec 2020),</li>
|
||||
<li>Gamma (also known as P.1, first identified in Brazil/Japan, Jan 2021).</li>
|
||||
<li>Delta (also known as B.1.617.2, first identified in India, Oct 2020).</li>
|
||||
</ul>
|
||||
Choose variant according to local area prevalence, e.g. for <a href="https://www.covid19.admin.ch/fr/epidemiologic/virus-variants?detGeo=GE">Geneva</a>
|
||||
Modify the default as necessary, according to local area prevalence e.g. for <a href="https://www.covid19.admin.ch/fr/epidemiologic/virus-variants?detGeo=GE">Geneva</a>
|
||||
or <a href="https://www.santepubliquefrance.fr/dossiers/coronavirus-covid-19/covid-19-cartographie-des-variants-en-france-donnees-par-region-et-par-departement">Ain (France)</a>.<br>
|
||||
<b>Ventilation data:</b> <br>
|
||||
<ul>
|
||||
|
|
|
|||
172
cara/models.py
172
cara/models.py
|
|
@ -620,7 +620,6 @@ _ExpirationBase.types = {
|
|||
'Talking': Expiration((1., 1., 1.)),
|
||||
'Shouting': Expiration((1., 5., 5.)),
|
||||
'Singing': Expiration((1., 5., 5.)),
|
||||
'Superspreading event': Expiration((np.inf, 0., 0.)),
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -669,44 +668,21 @@ class Population:
|
|||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class InfectedPopulation(Population):
|
||||
class _PopulationWithVirus(Population):
|
||||
#: The virus with which the population is infected.
|
||||
virus: Virus
|
||||
|
||||
#: The type of expiration that is being emitted whilst doing the activity.
|
||||
expiration: _ExpirationBase
|
||||
|
||||
@method_cache
|
||||
def emission_rate_when_present(self) -> _VectorisedFloat:
|
||||
"""
|
||||
The emission rate if the infected population is present.
|
||||
|
||||
Note that the rate is not currently time-dependent.
|
||||
|
||||
The emission rate if the infected population is present
|
||||
(in virions / h). It should not be a function of time.
|
||||
"""
|
||||
# Emission Rate (virions / h)
|
||||
# Note on units: exhalation rate is in m^3/h, aerosols in mL/cm^3
|
||||
# and viral load in virus/mL -> 1e6 conversion factor
|
||||
aerosols = self.expiration.aerosols(self.mask)
|
||||
raise NotImplementedError("Subclass must implement")
|
||||
|
||||
ER = (self.virus.viral_load_in_sputum *
|
||||
self.activity.exhalation_rate *
|
||||
10 ** 6 *
|
||||
aerosols)
|
||||
|
||||
# For superspreading event, where ejection_factor is infinite we fix the ER
|
||||
# based on Miller et al. (2020).
|
||||
if isinstance(aerosols, np.ndarray):
|
||||
ER[np.isinf(aerosols)] = 970 * self.virus.infectious_dose
|
||||
elif np.isinf(aerosols):
|
||||
ER = 970 * self.virus.infectious_dose
|
||||
|
||||
return ER
|
||||
|
||||
def individual_emission_rate(self, time) -> _VectorisedFloat:
|
||||
def emission_rate(self, time) -> _VectorisedFloat:
|
||||
"""
|
||||
The emission rate of a single individual in the population.
|
||||
|
||||
The emission rate of the population vs time.
|
||||
"""
|
||||
# Note: The original model avoids time dependence on the emission rate
|
||||
# at the cost of implementing a piecewise (on time) concentration function.
|
||||
|
|
@ -721,19 +697,50 @@ class InfectedPopulation(Population):
|
|||
|
||||
return self.emission_rate_when_present()
|
||||
|
||||
def emission_rate(self, time) -> _VectorisedFloat:
|
||||
"""
|
||||
The emission rate of the entire population.
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class EmittingPopulation(_PopulationWithVirus):
|
||||
#: The emission rate of a single individual, in virions / h.
|
||||
known_individual_emission_rate: float
|
||||
|
||||
@method_cache
|
||||
def emission_rate_when_present(self) -> _VectorisedFloat:
|
||||
"""
|
||||
return self.individual_emission_rate(time) * self.number
|
||||
The emission rate if the infected population is present.
|
||||
"""
|
||||
return self.known_individual_emission_rate * self.number
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class InfectedPopulation(_PopulationWithVirus):
|
||||
#: The type of expiration that is being emitted whilst doing the activity.
|
||||
expiration: _ExpirationBase
|
||||
|
||||
@method_cache
|
||||
def emission_rate_when_present(self) -> _VectorisedFloat:
|
||||
"""
|
||||
The emission rate if the infected population is present.
|
||||
Note that the rate is not currently time-dependent.
|
||||
"""
|
||||
# Emission Rate (virions / h)
|
||||
# Note on units: exhalation rate is in m^3/h, aerosols in mL/cm^3
|
||||
# and viral load in virus/mL -> 1e6 conversion factor
|
||||
|
||||
aerosols = self.expiration.aerosols(self.mask)
|
||||
|
||||
ER = (self.virus.viral_load_in_sputum *
|
||||
self.activity.exhalation_rate *
|
||||
10 ** 6 *
|
||||
aerosols)
|
||||
|
||||
return ER * self.number
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ConcentrationModel:
|
||||
room: Room
|
||||
ventilation: _VentilationBase
|
||||
infected: InfectedPopulation
|
||||
infected: _PopulationWithVirus
|
||||
|
||||
@property
|
||||
def virus(self):
|
||||
|
|
@ -753,16 +760,22 @@ class ConcentrationModel:
|
|||
)
|
||||
|
||||
@method_cache
|
||||
def _concentration_limit(self, time: float) -> _VectorisedFloat:
|
||||
def _normed_concentration_limit(self, time: float) -> _VectorisedFloat:
|
||||
"""
|
||||
Provides a constant that represents the theoretical asymptotic
|
||||
value reached by the concentration when time goes to infinity,
|
||||
if all parameters were to stay time-independent.
|
||||
This is normalized by the emission rate, the latter acting as a
|
||||
multiplicative constant factor for the concentration model that
|
||||
can be put back in front of the concentration after the time
|
||||
dependence has been solved for.
|
||||
"""
|
||||
if not self.infected.person_present(time):
|
||||
return 0.
|
||||
V = self.room.volume
|
||||
IVRR = self.infectious_virus_removal_rate(time)
|
||||
|
||||
return (self.infected.emission_rate(time)) / (IVRR * V)
|
||||
return 1. / (IVRR * V)
|
||||
|
||||
@method_cache
|
||||
def state_change_times(self) -> typing.List[float]:
|
||||
|
|
@ -776,6 +789,14 @@ class ConcentrationModel:
|
|||
state_change_times.update(self.ventilation.transition_times())
|
||||
return sorted(state_change_times)
|
||||
|
||||
@method_cache
|
||||
def _first_presence_time(self) -> float:
|
||||
"""
|
||||
First presence time. Before that, the concentration is zero.
|
||||
|
||||
"""
|
||||
return self.infected.presence.boundaries()[0][0]
|
||||
|
||||
def last_state_change(self, time: float) -> float:
|
||||
"""
|
||||
Find the most recent/previous state change.
|
||||
|
|
@ -807,15 +828,16 @@ class ConcentrationModel:
|
|||
)
|
||||
|
||||
@method_cache
|
||||
def _concentration_cached(self, time: float) -> _VectorisedFloat:
|
||||
# A cached version of the concentration method. Use this method if you
|
||||
# expect that there may be multiple concentration calculations for the
|
||||
# same time (e.g. at state change times).
|
||||
return self.concentration(time)
|
||||
def _normed_concentration_cached(self, time: float) -> _VectorisedFloat:
|
||||
# A cached version of the _normed_concentration method. Use this
|
||||
# method if you expect that there may be multiple concentration
|
||||
# calculations for the same time (e.g. at state change times).
|
||||
return self._normed_concentration(time)
|
||||
|
||||
def concentration(self, time: float) -> _VectorisedFloat:
|
||||
def _normed_concentration(self, time: float) -> _VectorisedFloat:
|
||||
"""
|
||||
Virus exposure concentration, as a function of time.
|
||||
Virus exposure concentration, as a function of time, and
|
||||
normalized by the emission rate.
|
||||
The formulas used here assume that all parameters (ventilation,
|
||||
emission rate) are constant between two state changes - only
|
||||
the value of these parameters at the next state change, are used.
|
||||
|
|
@ -823,27 +845,42 @@ class ConcentrationModel:
|
|||
Note that time is not vectorised. You can only pass a single float
|
||||
to this method.
|
||||
"""
|
||||
if time == 0:
|
||||
# The model always starts at t=0, but we avoid running concentration calculations
|
||||
# before the first presence as an optimisation.
|
||||
if time <= self._first_presence_time():
|
||||
return 0.0
|
||||
next_state_change_time = self._next_state_change(time)
|
||||
IVRR = self.infectious_virus_removal_rate(next_state_change_time)
|
||||
concentration_limit = self._concentration_limit(next_state_change_time)
|
||||
conc_limit = self._normed_concentration_limit(next_state_change_time)
|
||||
|
||||
t_last_state_change = self.last_state_change(time)
|
||||
concentration_at_last_state_change = self._concentration_cached(t_last_state_change)
|
||||
conc_at_last_state_change = self._normed_concentration_cached(t_last_state_change)
|
||||
|
||||
delta_time = time - t_last_state_change
|
||||
fac = np.exp(-IVRR * delta_time)
|
||||
return concentration_limit * (1 - fac) + concentration_at_last_state_change * fac
|
||||
return conc_limit * (1 - fac) + conc_at_last_state_change * fac
|
||||
|
||||
def concentration(self, time: float) -> _VectorisedFloat:
|
||||
"""
|
||||
Virus exposure concentration, as a function of time.
|
||||
|
||||
Note that time is not vectorised. You can only pass a single float
|
||||
to this method.
|
||||
"""
|
||||
return (self._normed_concentration(time) *
|
||||
self.infected.emission_rate_when_present())
|
||||
|
||||
@method_cache
|
||||
def integrated_concentration(self, start: float, stop: float) -> _VectorisedFloat:
|
||||
def normed_integrated_concentration(self, start: float, stop: float) -> _VectorisedFloat:
|
||||
"""
|
||||
Get the integrated concentration dose between the times start and stop.
|
||||
Get the integrated concentration of viruses in the air between the times start and stop,
|
||||
normalized by the emission rate.
|
||||
"""
|
||||
if stop <= self._first_presence_time():
|
||||
return 0.0
|
||||
state_change_times = self.state_change_times()
|
||||
req_start, req_stop = start, stop
|
||||
total_concentration = 0.
|
||||
total_normed_concentration = 0.
|
||||
for interval_start, interval_stop in zip(state_change_times[:-1], state_change_times[1:]):
|
||||
if req_start > interval_stop or req_stop < interval_start:
|
||||
continue
|
||||
|
|
@ -851,17 +888,24 @@ class ConcentrationModel:
|
|||
start = max([interval_start, req_start])
|
||||
stop = min([interval_stop, req_stop])
|
||||
|
||||
conc_start = self._concentration_cached(start)
|
||||
conc_start = self._normed_concentration_cached(start)
|
||||
|
||||
next_conc_state = self._next_state_change(stop)
|
||||
conc_limit = self._concentration_limit(next_conc_state)
|
||||
conc_limit = self._normed_concentration_limit(next_conc_state)
|
||||
IVRR = self.infectious_virus_removal_rate(next_conc_state)
|
||||
delta_time = stop - start
|
||||
total_concentration += (
|
||||
total_normed_concentration += (
|
||||
conc_limit * delta_time +
|
||||
(conc_limit - conc_start) * (np.exp(-IVRR*delta_time)-1) / IVRR
|
||||
)
|
||||
return total_concentration
|
||||
return total_normed_concentration
|
||||
|
||||
def integrated_concentration(self, start: float, stop: float) -> _VectorisedFloat:
|
||||
"""
|
||||
Get the integrated concentration of viruses in the air between the times start and stop.
|
||||
"""
|
||||
return (self.normed_integrated_concentration(start, stop) *
|
||||
self.infected.emission_rate_when_present())
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
|
|
@ -878,14 +922,22 @@ class ExposureModel:
|
|||
#: The fraction of viruses actually deposited in the respiratory tract
|
||||
fraction_deposited: _VectorisedFloat = 0.6
|
||||
|
||||
def exposure(self) -> _VectorisedFloat:
|
||||
"""The number of virus per meter^3."""
|
||||
exposure = 0.0
|
||||
def _normed_exposure(self) -> _VectorisedFloat:
|
||||
"""
|
||||
The number of virus per meter^3, normalized by the emission rate
|
||||
of the infected population.
|
||||
"""
|
||||
normed_exposure = 0.0
|
||||
|
||||
for start, stop in self.exposed.presence.boundaries():
|
||||
exposure += self.concentration_model.integrated_concentration(start, stop)
|
||||
normed_exposure += self.concentration_model.normed_integrated_concentration(start, stop)
|
||||
|
||||
return exposure * self.repeats
|
||||
return normed_exposure * self.repeats
|
||||
|
||||
def exposure(self) -> _VectorisedFloat:
|
||||
"""The number of virus per meter^3."""
|
||||
return (self._normed_exposure() *
|
||||
self.concentration_model.infected.emission_rate_when_present())
|
||||
|
||||
def infection_probability(self) -> _VectorisedFloat:
|
||||
exposure = self.exposure()
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ import pytest
|
|||
import tornado.testing
|
||||
|
||||
import cara.apps.calculator
|
||||
from cara.apps.calculator.report_generator import generate_qr_code
|
||||
from cara.apps.calculator.report_generator import generate_permalink
|
||||
|
||||
_TIMEOUT = 20.
|
||||
|
||||
|
|
@ -97,26 +97,26 @@ class TestOpenApp(tornado.testing.AsyncHTTPTestCase):
|
|||
assert response.code == 404
|
||||
|
||||
|
||||
async def test_qrcode_urls(http_server_client, baseline_form):
|
||||
async def test_permalink_urls(http_server_client, baseline_form):
|
||||
base_url = 'proto://hostname/prefix'
|
||||
qr_data = generate_qr_code(base_url, "/calculator", baseline_form)
|
||||
permalink_data = generate_permalink(base_url, "/calculator", baseline_form)
|
||||
expected = f'{base_url}/calculator?exposed_coffee_break_option={baseline_form.exposed_coffee_break_option}&'
|
||||
assert qr_data['link'].startswith(expected)
|
||||
assert permalink_data['link'].startswith(expected)
|
||||
|
||||
# We should get a 200 for the link.
|
||||
response = await http_server_client.fetch(qr_data['link'].replace(base_url, ''))
|
||||
response = await http_server_client.fetch(permalink_data['link'].replace(base_url, ''))
|
||||
assert response.code == 200
|
||||
|
||||
# And a 302 for the QR url itself. The redirected URL should be the same as
|
||||
# in the link.
|
||||
assert qr_data['qr_url'].startswith(base_url)
|
||||
assert permalink_data['shortened'].startswith(base_url)
|
||||
response = await http_server_client.fetch(
|
||||
qr_data['qr_url'].replace(base_url, ''),
|
||||
permalink_data['shortened'].replace(base_url, ''),
|
||||
max_redirects=0,
|
||||
raise_error=False,
|
||||
)
|
||||
assert response.code == 302
|
||||
assert response.headers['Location'] == qr_data['link'].replace(base_url, '')
|
||||
assert response.headers['Location'] == permalink_data['link'].replace(base_url, '')
|
||||
|
||||
|
||||
async def test_invalid_compressed_url(http_server_client, baseline_form):
|
||||
|
|
|
|||
|
|
@ -13,13 +13,15 @@ def baseline_model():
|
|||
active=models.SpecificInterval(((0., 24.), )),
|
||||
air_exch=30.,
|
||||
),
|
||||
infected=models.InfectedPopulation(
|
||||
infected=models.EmittingPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0., 4.), (5., 8.))),
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=models.Activity.types['Light activity'],
|
||||
expiration=models.Expiration.types['Superspreading event'],
|
||||
known_individual_emission_rate=970 * 50,
|
||||
# superspreading event, where ejection factor is fixed based
|
||||
# on Miller et al. (2020) - 50 represents the infectious dose.
|
||||
),
|
||||
)
|
||||
return model
|
||||
|
|
|
|||
|
|
@ -121,6 +121,10 @@ def test_next_state_change_time_out_of_range(simple_conc_model: models.Concentra
|
|||
simple_conc_model._next_state_change(3.1)
|
||||
|
||||
|
||||
def test_first_presence_time(simple_conc_model):
|
||||
assert simple_conc_model._first_presence_time() == 0.5
|
||||
|
||||
|
||||
def test_integrated_concentration(simple_conc_model):
|
||||
c1 = simple_conc_model.integrated_concentration(0, 2)
|
||||
c2 = simple_conc_model.integrated_concentration(0, 1)
|
||||
|
|
|
|||
|
|
@ -11,20 +11,20 @@ from cara.dataclass_utils import replace
|
|||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class KnownConcentrations(models.ConcentrationModel):
|
||||
class KnownNormedconcentration(models.ConcentrationModel):
|
||||
"""
|
||||
A ConcentrationModel which is based on pre-known exposure concentrations and
|
||||
which therefore doesn't need other components. Useful for testing.
|
||||
|
||||
"""
|
||||
concentration_function: typing.Callable
|
||||
normed_concentration_function: typing.Callable
|
||||
|
||||
def infectious_virus_removal_rate(self, time: float) -> models._VectorisedFloat:
|
||||
# very large decay constant -> same as constant concentration
|
||||
return 1.e50
|
||||
|
||||
def _concentration_limit(self, time: float) -> models._VectorisedFloat:
|
||||
return self.concentration_function(time)
|
||||
def _normed_concentration_limit(self, time: float) -> models._VectorisedFloat:
|
||||
return self.normed_concentration_function(time)
|
||||
|
||||
def state_change_times(self):
|
||||
return [0., 24.]
|
||||
|
|
@ -32,8 +32,8 @@ class KnownConcentrations(models.ConcentrationModel):
|
|||
def _next_state_change(self, time: float):
|
||||
return 24.
|
||||
|
||||
def concentration(self, time: float) -> models._VectorisedFloat: # noqa
|
||||
return self.concentration_function(time)
|
||||
def _normed_concentration(self, time: float) -> models._VectorisedFloat: # noqa
|
||||
return self.normed_concentration_function(time)
|
||||
|
||||
|
||||
halftime = models.PeriodicInterval(120, 60)
|
||||
|
|
@ -67,7 +67,9 @@ def known_concentrations(func):
|
|||
virus=models.Virus.types['SARS_CoV_2_B117'],
|
||||
expiration=models.Expiration.types['Talking']
|
||||
)
|
||||
return KnownConcentrations(dummy_room, dummy_ventilation, dummy_infected_population, func)
|
||||
normed_func = lambda x: func(x) / dummy_infected_population.emission_rate_when_present()
|
||||
return KnownNormedconcentration(dummy_room, dummy_ventilation,
|
||||
dummy_infected_population, normed_func)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -142,13 +144,15 @@ def conc_model():
|
|||
return models.ConcentrationModel(
|
||||
models.Room(25),
|
||||
models.AirChange(always, 5),
|
||||
models.InfectedPopulation(
|
||||
models.EmittingPopulation(
|
||||
number=1,
|
||||
presence=interesting_times,
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=models.Activity.types['Seated'],
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
expiration=models.Expiration.types['Superspreading event'],
|
||||
known_individual_emission_rate=970 * 50,
|
||||
# superspreading event, where ejection factor is fixed based
|
||||
# on Miller et al. (2020) - 50 represents the infectious dose.
|
||||
)
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -66,13 +66,15 @@ def build_model(interval_duration):
|
|||
active=models.PeriodicInterval(period=120, duration=interval_duration),
|
||||
q_air_mech=500.,
|
||||
),
|
||||
infected=models.InfectedPopulation(
|
||||
infected=models.EmittingPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0., 4.), (5., 8.))),
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=models.Activity.types['Light activity'],
|
||||
expiration=models.Expiration.types['Superspreading event'],
|
||||
known_individual_emission_rate=970 * 50,
|
||||
# superspreading event, where ejection factor is fixed based
|
||||
# on Miller et al. (2020) - 50 represents the infectious dose.
|
||||
),
|
||||
)
|
||||
return model
|
||||
|
|
@ -226,13 +228,13 @@ def build_hourly_dependent_model(
|
|||
outside_temp=outside_temp,
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
infected=models.InfectedPopulation(
|
||||
infected=models.EmittingPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(intervals_presence_infected),
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=models.Activity.types['Light activity'],
|
||||
expiration=models.Expiration.types['Superspreading event'],
|
||||
known_individual_emission_rate=970 * 50,
|
||||
),
|
||||
)
|
||||
return model
|
||||
|
|
@ -247,13 +249,13 @@ def build_constant_temp_model(outside_temp, intervals_open=((7.5, 8.5),)):
|
|||
outside_temp=models.PiecewiseConstant((0., 24.), (outside_temp,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
infected=models.InfectedPopulation(
|
||||
infected=models.EmittingPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0., 4.), (5., 7.5))),
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=models.Activity.types['Light activity'],
|
||||
expiration=models.Expiration.types['Superspreading event'],
|
||||
known_individual_emission_rate=970 * 50,
|
||||
),
|
||||
)
|
||||
return model
|
||||
|
|
@ -275,13 +277,13 @@ def build_hourly_dependent_model_multipleventilation(month, intervals_open=((7.5
|
|||
model = models.ConcentrationModel(
|
||||
room=models.Room(volume=75),
|
||||
ventilation=vent,
|
||||
infected=models.InfectedPopulation(
|
||||
infected=models.EmittingPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0., 4.), (5., 7.5))),
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=models.Activity.types['Light activity'],
|
||||
expiration=models.Expiration.types['Superspreading event'],
|
||||
known_individual_emission_rate=970 * 50,
|
||||
),
|
||||
)
|
||||
return model
|
||||
|
|
|
|||
|
|
@ -238,7 +238,7 @@ def skagit_chorale_mc():
|
|||
["shared_office_mc", 10.7, 0.32, 57.24, 654],
|
||||
["classroom_mc", 36.1, 6.85, 780.0, 28464],
|
||||
["ski_cabin_mc", 16.3, 0.49, 35.94, 7404],
|
||||
["gym_mc", 2.25, 0.63, 0.7842, 984],
|
||||
["gym_mc", 2.25, 0.63, 0.7842, 1968],
|
||||
["waiting_room_mc", 9.72, 1.36, 34.26, 3534],
|
||||
["skagit_chorale_mc",29.9, 17.9, 190.0, 141400],
|
||||
]
|
||||
|
|
|
|||
|
|
@ -62,7 +62,6 @@ pyparsing==2.4.7
|
|||
pyrsistent==0.18.0
|
||||
python-dateutil==2.8.2
|
||||
pyzmq==22.1.0
|
||||
qrcode==7.2
|
||||
requests==2.26.0
|
||||
requests-unixsocket==0.2.0
|
||||
scikit-learn==0.24.2
|
||||
|
|
|
|||
3
setup.py
3
setup.py
|
|
@ -20,7 +20,7 @@ REQUIREMENTS: dict = {
|
|||
'core': [
|
||||
'dataclasses; python_version < "3.7"',
|
||||
'ipykernel',
|
||||
'ipympl',
|
||||
'ipympl != 0.8.0',
|
||||
'ipywidgets',
|
||||
'Jinja2',
|
||||
'loky',
|
||||
|
|
@ -30,7 +30,6 @@ REQUIREMENTS: dict = {
|
|||
'numpy',
|
||||
'psutil',
|
||||
'python-dateutil',
|
||||
'qrcode[pil]',
|
||||
'scipy',
|
||||
'sklearn',
|
||||
'timezonefinder',
|
||||
|
|
|
|||
Loading…
Reference in a new issue