From 881669ab0953e69baed0e0bef3b2478a02518f13 Mon Sep 17 00:00:00 2001 From: gaazzopa Date: Thu, 5 Nov 2020 23:35:33 +0100 Subject: [PATCH] Updated report to use form values --- cara/apps/calculator/report_generator.py | 81 +++++++++++++++---- cara/apps/calculator/static/css/report.css | 9 +-- cara/apps/calculator/templates/report.html.j2 | 3 +- 3 files changed, 69 insertions(+), 24 deletions(-) diff --git a/cara/apps/calculator/report_generator.py b/cara/apps/calculator/report_generator.py index 2ce64f03..db6db86b 100644 --- a/cara/apps/calculator/report_generator.py +++ b/cara/apps/calculator/report_generator.py @@ -2,28 +2,77 @@ from datetime import datetime from pathlib import Path import jinja2 +import numpy as np from cara import models from .model_generator import FormData +def calculate_report_data(model: models.Model): + resolution = 600 + times = list(np.linspace(0, 10, resolution)) + concentrations = [model.concentration(time) for time in times] + highest_const = max(concentrations) + prob = model.infection_probability() + er = model.infected.emission_rate(0.1) + exposed_occupants = model.exposed_occupants + r0 = prob * exposed_occupants / 100 + return { + "times": times, + "concentrations": concentrations, + "highest_const": highest_const, + "prob_inf": prob, + "emission_rate": er, + "exposed_occupants": exposed_occupants, + "R0": r0, + } + + def build_report(model: models.Model, form: FormData): now = datetime.now() - time = now.strftime('%d/%m/%Y %H:%M:%S') - request = {'the': 'form', 'request': 'data'} - context = {'model': model, 'request': request, 'creation_date': time, 'model_version': 'Beta v1.0.0', - 'simulation_name': 'SAMPLE', 'room_number': '40/1-02A', 'room_volume': 30, 'ventilation_type': 'natural_ventilation', - 'air_supply': 1, 'air_changes': 2, 'windows_number': 5, 'window_height': 2, 'window_width': 1, - 'opening_distance': 0.05, 'windows_open': '20 minutes every 2 hours', 'hepa_option': 'No', 'total_people': 8, - 'infected_people': 7, 'activity_type': 'Office work – typical scenario with all persons seated, talking', - 'activity_start': '00:00', 'activity_finish': '01:15', 'exposure_start': '00:00', 'exposure_finish': '01:15', - 'event_type' : 'single_event', 'single_event_date': '5th November', 'recurrent_event_month': 'November', - 'lunch_option': 'No', 'lunch_start': '00:00', 'lunch_finish': '01:15', 'coffee_breaks': 4,'coffee_duration': 15, - 'coffee_times': [['00:00','00:00'], ['00:00','00:00'], ['00:00','00:00'], ['00:00','00:00']], 'mask_wearing': 'No', - 'infection_probability': round(model.infection_probability(), 2), 'reproduction_rate': 2} + time = now.strftime("%d/%m/%Y %H:%M:%S") + request = {"the": "form", "request": "data"} + context = { + 'model': model, + 'request': request, + 'creation_date': time, + 'model_version': 'Beta v1.0.0', + 'simulation_name': form.simulation_name, + 'room_number': form.room_number, + 'room_volume': form.room_volume, + 'ventilation_type': form.ventilation_type, + 'air_supply': form.air_supply, + 'air_changes': form.air_changes, + 'windows_number': form.windows_number, + 'window_height': form.window_height, + 'window_width': form.window_width, + 'opening_distance': form.opening_distance, + 'windows_open': form.windows_open, + 'hepa_option': 'No', + 'total_people': form.total_people, + 'infected_people': form.infected_people, + 'activity_type': form.activity_type, + 'activity_start': form.activity_start, + 'activity_finish': form.activity_finish, + 'exposure_start': '00:00', + 'exposure_finish': '01:15', + 'event_type': form.event_type, + 'single_event_date': form.single_event_date, + 'recurrent_event_month': form.recurrent_event_month, + 'lunch_option': form.lunch_option, + 'lunch_start': form.lunch_start, + 'lunch_finish': form.lunch_finish, + 'coffee_breaks': form.coffee_breaks, + 'coffee_duration': form.coffee_duration, + 'coffee_times': [['00:00','00:00'], ['00:00','00:00'], ['00:00','00:00'], ['00:00','00:00']], + 'mask_wearing': form.mask_wearing, + 'infection_probability': round(model.infection_probability(), 2), + 'reproduction_rate': 2 + } - p = Path(__file__).parent / 'templates' - env = jinja2.Environment( - loader=jinja2.FileSystemLoader(Path(p))) - template = env.get_template('report.html.j2') + context.update(calculate_report_data(model)) + + p = Path(__file__).parent / "templates" + env = jinja2.Environment(loader=jinja2.FileSystemLoader(Path(p))) + template = env.get_template("report.html.j2") return template.render(**context) \ No newline at end of file diff --git a/cara/apps/calculator/static/css/report.css b/cara/apps/calculator/static/css/report.css index 1ecf4eaf..0ad87e49 100644 --- a/cara/apps/calculator/static/css/report.css +++ b/cara/apps/calculator/static/css/report.css @@ -6,7 +6,7 @@ padding: 20px; } -h1{ +h1 { text-align: center; } @@ -36,13 +36,10 @@ p.result_title { } .image { - display: flex; - justify-content: center; + text-align: center; font-size: 13pt; } .discalimer { - display: flex; - justify-content: center; - font-size: 10pt; + font-size: 12pt; } \ No newline at end of file diff --git a/cara/apps/calculator/templates/report.html.j2 b/cara/apps/calculator/templates/report.html.j2 index efcdd3a3..9d8586dc 100644 --- a/cara/apps/calculator/templates/report.html.j2 +++ b/cara/apps/calculator/templates/report.html.j2 @@ -102,8 +102,7 @@






-

-

Disclaimer:

+

Disclaimer:

The risk assessment tool simulates the long range airborne spread SARS-CoV-2 virus in a finite volume, assuming a homogenous mixture, and estimates the risk of COVID-19 infection thereto. The results DO NOT include short-range airborne exposure (where the physical distance plays a factor) nor the other know modes of transmission of SARS-CoV-2. Hence, this model implies that proper physical distancing, good hand hygiene and other barrier measures are ensured.

It is based on current scientific data and can be used to measures the effectiveness of different mitigation measures.

Note that this model is based on a deterministic approach, i.e., at least one person is infected and shedding viruses into the volume. Nonetheless, it is also important to understand that the absolute risk of infection is uncertain as it will depend on the probability that someone infected attends the event. The model is mostly useful to compare the impact and effectiveness of mitigation measures such as ventilation, filtration, exposure time, activity and the size of the room on long-range airborne transmission of COVID-19 in indoor settings.