WIP.
This commit is contained in:
Phil Elson
parent
151ab9af54
commit
3c4606d435
15 changed files with 396 additions and 323 deletions
25
README.md
25
README.md
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@ -85,26 +85,20 @@ This will start a local version of CARA, which can be visited at http://localhos
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## Development guide
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### Running the COVID calculator app in development mode
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The CARA repository makes use of Git's Large File Storage (LFS) feature.
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You will need a working installation of git-lfs in order to run CARA in development mode.
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See https://git-lfs.github.com/ for installation instructions.
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Download Git Large File Storage (LFS) - **macOS**:
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### Installing CARA in editable mode
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```
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brew install git-LFS
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```
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Download Git Large File Storage (LFS) - **Linux**:
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```
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apt-get install git-lfs
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```
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Install dependencies:
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```
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git lfs install
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git lfs pull # Fetch the data from LFS
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pip install -e . # At the root of the repository
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```
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### Running the COVID calculator app in development mode
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```
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python -m cara.apps.calculator
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```
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@ -123,7 +117,6 @@ python -m cara.apps.calculator --prefix=/mycalc
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### Running the CARA Expert-App app in development mode
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```
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pip install -e . # At the root of the repository
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voila cara/apps/expert/cara.ipynb --port=8080
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```
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@ -9,6 +9,7 @@ import numpy as np
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from cara import models
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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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@ -20,8 +21,6 @@ LOG = logging.getLogger(__name__)
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minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
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# Used to declare when an attribute of a class must have a value provided, and
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# there should be no default value used.
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_NO_DEFAULT = object()
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@ -53,10 +52,9 @@ class FormData:
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infected_lunch_start: minutes_since_midnight #Used if infected_dont_have_breaks_with_exposed
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infected_people: int
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infected_start: minutes_since_midnight
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location: str
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location_coordinates: str
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weather_station_location: str
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weather_station_ref: str
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location_name: str
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location_latitude: float
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location_longitude: float
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mask_type: str
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mask_wearing_option: str
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mechanical_ventilation_type: str
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@ -107,10 +105,9 @@ class FormData:
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'infected_lunch_start': '12:30',
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'infected_people': _NO_DEFAULT,
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'infected_start': '08:30',
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'location': _NO_DEFAULT,
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'location_coordinates': _NO_DEFAULT,
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'weather_station_location':'GENEVA COINTRIN',
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'weather_station_ref': '067000-99999',
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'location_latitude': 46.20833,
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'location_longitude': 6.14275,
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'location_name': 'Geneva',
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'mask_type': 'Type I',
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'mask_wearing_option': 'mask_off',
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'mechanical_ventilation_type': 'not-applicable',
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@ -269,14 +266,12 @@ class FormData:
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month = datetime_object.month
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# set location
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self.weather_station_location = data.location_to_weather_stn(self.location_coordinates)[1]
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data.local_tempatures = data.location_celcius_per_hour(self.location_coordinates)
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print(data.local_tempatures)
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wx_station = self.nearest_weather_station()
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temp_profile = cara.data.weather.mean_hourly_temperatures(wx_station[0], month)
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inside_temp = models.PiecewiseConstant((0, 24), (293,))
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outside_temp = data.Temperatures[str(month)]
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# Interpolate the temperature to every 6 minutes (60/10).
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outside_temp = cara.data.weather.hourly_to_piecewise(temp_profile).refine(10)
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ventilation: models.Ventilation
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if self.window_type == 'window_sliding':
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@ -318,6 +313,12 @@ class FormData:
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else:
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return models.MultipleVentilation((ventilation, infiltration_ventilation))
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def nearest_weather_station(self) -> cara.data.weather.WxStationType:
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wx_station = cara.data.weather.nearest_wx_station(
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longitude=self.location_longitude, latitude=self.location_latitude
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)
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return wx_station
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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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@ -621,8 +622,6 @@ def baseline_raw_form_data():
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'infected_lunch_start': '12:30',
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'infected_people': '1',
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'infected_start': '09:00',
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'location': 'Geneva',
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'location_coordinates': '46.2044, 6.1432',
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'mask_type': 'Type I',
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'mask_wearing_option': 'mask_off',
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'mechanical_ventilation_type': '',
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@ -566,8 +566,8 @@ $(document).ready(function() {
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id: candidate.address,
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text: candidate.address,
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country: candidate.attributes.country,
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latitude: String(candidate.location.y),
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longitude: String(candidate.location.x),
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latitude: candidate.location.y,
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longitude: candidate.location.x,
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}
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}),
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pagination: {
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@ -602,8 +602,13 @@ function formatlocation(location) {
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}
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function formatLocationSelection(location) {
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if (location.latitude != null && location.latitude != null)
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$(document.getElementById("coordinates_input").value = (location.latitude + ',' + location.longitude));
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console.log(location);
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if (location.latitude != null && location.latitude != null) {
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console.log('setting!');
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console.log($('input[name="location_latitude"]'));
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$('input[name="location_latitude"]').val(location.latitude);
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$('input[name="location_longitude"]').val(location.longitude);
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}
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return location.text;
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}
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@ -53,9 +53,9 @@
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</p></li>
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<li><p class="data_text">Room Volume: {{ model.concentration_model.room.volume }} m³</p></li>
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<li><p class="data_text">Room Central Heating: {{ "On" if form.room_heating_option else "Off" }}</p></li>
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<li><p class="data_text">Geographic Location: {{ form.location }}</p></li>
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{% if form.ventilation_type == "natural_ventilation"%}
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<li><p class="data_text">Nearest weather station: {{ form.weather_station_location }}</p></li>
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<li><p class="data_text">Geographic Location: {{ form.location_name }}</p></li>
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{% if form.ventilation_type == "natural_ventilation" %}
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<li><p class="data_text">Nearest weather station: {{ form.nearest_weather_station()[1].strip().title() }}</p></li>
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{% endif %}
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</ul>
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@ -107,11 +107,11 @@ v{{ calculator_version }} <span style="float:right; font-weight:bold">Please sen
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<div class="row">
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<label class="col-xl-3 col-lg-4 col-sm-3 col-form-label">Location:</label>
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<select id="location_select" class="col-xl-5 col-lg-7 col-sm-7 col-7" name="location" required></select>
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</div>
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<div class="row" hidden>
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<input id="coordinates_input" class="col-xl-5 col-lg-7 col-sm-7 col-7" name="location_coordinates" required></select>
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<select id="location_select" class="col-xl-5 col-lg-7 col-sm-7 col-7" name="location_name" required></select>
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</div>
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<input type="hidden" name="location_latitude" required>
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<input type="hidden" name="location_longitude" required>
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<hr width="80%">
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@ -360,10 +360,10 @@ class ModelWidgets(View):
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def _build_month(self, node) -> WidgetGroup:
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month_choice = widgets.Select(options=list(data.Temperatures.keys()), value='1')
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month_choice = widgets.Select(options=list(data.GenevaTemperatures.keys()), value='Jan')
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def on_month_change(change):
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node.outside_temp = data.Temperatures[change['new']]
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node.outside_temp = data.GenevaTemperatures[change['new']]
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month_choice.observe(on_month_change, names=['value'])
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return WidgetGroup(
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93
cara/data.py
93
cara/data.py
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@ -1,93 +0,0 @@
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import numpy as np
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from cara import models
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import json
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import urllib.request
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from pathlib import Path
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from scipy.spatial import cKDTree
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import os
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import typing
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weather_debug = False
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def location_to_weather_stn(location_loc):
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# expects a tuple (lat, long)
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# returns: weather station ID, weather station name, weather station lat, long
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search_coords = location_loc.split(',')
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lat = []
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long = []
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station_array = []
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fixed_delimits = [0, 12, 13, 44, 51, 60, 69, 90, 91]
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station_file = Path(__file__).parent / 'data' / 'cara_weather_stations.txt'
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for line in station_file.open('rt'):
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start_end_positions = zip(fixed_delimits[:-1], fixed_delimits[1:])
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split_vals = [line[start:end] for start, end in start_end_positions]
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station_location = [split_vals[0],
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split_vals[2], split_vals[3], split_vals[4]]
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station_array.append(station_location)
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lat.append(split_vals[3])
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long.append(split_vals[4])
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tree = cKDTree(np.c_[lat, long])
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dd, ii = tree.query(search_coords, k=[1])
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return (station_array[ii[0]][0], station_array[ii[0]][1], station_array[ii[0]][2], station_array[ii[0]][3])
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def location_celcius_per_hour(location: object) -> typing.Dict[str, typing.List[float]]:
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# expects a tuple (lat, long)
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# returns a json format set of weather data
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w_station = location_to_weather_stn(location)
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with open(Path(__file__).parent / 'data' / 'global_weather_set.json', "r") as json_file:
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weather_dict = json.load(json_file)
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Location_hourly_temperatures_celsius_per_hour = weather_dict[w_station[0]]
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if weather_debug:
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print(location)
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print("weather station name: ", w_station[1])
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print("weather station ref: ", w_station[0])
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print("weather station location: ", w_station[2], " ", w_station[3])
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print(Location_hourly_temperatures_celsius_per_hour)
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return Location_hourly_temperatures_celsius_per_hour
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# initialise with Geneva, change if location is different.
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local_tempatures = {
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'1': [0.2, -0.3, -0.5, -0.9, -1.1, -1.4, -1.5, -1.5, -1.1, 0.1, 1.5,
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2.8, 3.8, 4.4, 4.5, 4.4, 4.4, 3.9, 3.1, 2.7, 2.2, 1.7, 1.5, 1.1],
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'2': [0.9, 0.3, 0.0, -0.5, -0.7, -1.1, -1.2, -1.1, -0.7, 0.8, 2.5,
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4.2, 5.4, 6.2, 6.3, 6.2, 6.1, 5.5, 4.5, 4.1, 3.5, 2.8, 2.5, 2.0],
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'3': [4.2, 3.5, 3.1, 2.5, 2.1, 1.6, 1.5, 1.6, 2.2, 4.0, 6.3, 8.4,
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10.0, 11.1, 11.2, 11.1, 11.0, 10.2, 8.9, 8.3, 7.5, 6.7, 6.3, 5.6],
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'4': [7.4, 6.7, 6.2, 5.5, 5.2, 4.7, 4.5, 4.6, 5.3, 7.2, 9.6, 11.9,
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13.7, 14.8, 14.9, 14.8, 14.7, 13.8, 12.4, 11.8, 10.9, 10.1, 9.6, 8.9],
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'5': [11.8, 11.1, 10.6, 9.9, 9.5, 8.9, 8.8, 8.9, 9.6, 11.6, 14.2, 16.6,
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18.4, 19.6, 19.7, 19.6, 19.4, 18.6, 17.1, 16.5, 15.6, 14.6, 14.2, 13.4],
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'6': [15.2, 14.4, 13.9, 13.2, 12.7, 12.2, 12.0, 12.1, 12.8, 15.0, 17.7,
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20.2, 22.1, 23.3, 23.5, 23.4, 23.2, 22.3, 20.8, 20.1, 19.1, 18.2, 17.7, 16.9],
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'7': [17.6, 16.7, 16.1, 15.3, 14.9, 14.3, 14.1, 14.2, 15.0, 17.3, 20.2,
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23.0, 25.0, 26.3, 26.5, 26.4, 26.2, 25.2, 23.6, 22.8, 21.8, 20.8, 20.2, 19.4],
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'8': [17.1, 16.2, 15.7, 14.9, 14.5, 13.9, 13.7, 13.8, 14.6, 16.9, 19.7,
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22.4, 24.4, 25.6, 25.8, 25.7, 25.5, 24.5, 22.9, 22.2, 21.2, 20.2, 19.7, 18.9],
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'9': [13.4, 12.7, 12.2, 11.5, 11.2, 10.7, 10.5, 10.6, 11.3, 13.2, 15.6,
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17.9, 19.6, 20.8, 20.9, 20.8, 20.7, 19.8, 18.4, 17.8, 16.9, 16.1, 15.6, 14.9],
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'10': [9.4, 8.8, 8.5, 7.9, 7.6, 7.2, 7.1, 7.2, 7.7, 9.3, 11.2, 13.0,
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14.4, 15.3, 15.4, 15.3, 15.2, 14.5, 13.4, 12.9, 12.2, 11.6, 11.2, 10.6],
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'11': [4.0, 3.6, 3.3, 2.9, 2.6, 2.3, 2.2, 2.2, 2.7, 3.9, 5.5, 6.9, 8.0,
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8.7, 8.8, 8.7, 8.7, 8.1, 7.2, 6.8, 6.3, 5.7, 5.5, 5.0],
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'12': [1.4, 1.0, 0.8, 0.4, 0.2, -0.0, -0.1, -0.1, 0.3, 1.3, 2.6, 3.8,
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4.7, 5.2, 5.3, 5.2, 5.2, 4.7, 4.0, 3.7, 3.2, 2.8, 2.6, 2.2]
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}
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# Geneva hourly temperatures as piecewise constant function (in Kelvin)
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Temperatures_hourly = {
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month: models.PiecewiseConstant(tuple(np.arange(25.)),
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tuple(273.15+np.array(temperatures)))
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for month, temperatures in local_tempatures.items()
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}
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# same temperatures on a finer temperature mesh
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Temperatures = {
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month: Temperatures_hourly[month].refine(refine_factor=10)
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for month, temperatures in local_tempatures.items()
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}
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51
cara/data/__init__.py
Normal file
51
cara/data/__init__.py
Normal file
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@ -0,0 +1,51 @@
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import numpy as np
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from cara import models
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# TODO: The values in this module to be removed and instead use the cara.data.weather functionality.
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# average temperature of each month, hour per hour (from midnight to 11 pm)
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Geneva_hourly_temperatures_celsius_per_hour = {
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'Jan': [0.2, -0.3, -0.5, -0.9, -1.1, -1.4, -1.5, -1.5, -1.1, 0.1, 1.5,
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2.8, 3.8, 4.4, 4.5, 4.4, 4.4, 3.9, 3.1, 2.7, 2.2, 1.7, 1.5, 1.1],
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'Feb': [0.9, 0.3, 0.0, -0.5, -0.7, -1.1, -1.2, -1.1, -0.7, 0.8, 2.5,
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4.2, 5.4, 6.2, 6.3, 6.2, 6.1, 5.5, 4.5, 4.1, 3.5, 2.8, 2.5, 2.0],
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'Mar': [4.2, 3.5, 3.1, 2.5, 2.1, 1.6, 1.5, 1.6, 2.2, 4.0, 6.3, 8.4,
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10.0, 11.1, 11.2, 11.1, 11.0, 10.2, 8.9, 8.3, 7.5, 6.7, 6.3, 5.6],
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'Apr': [7.4, 6.7, 6.2, 5.5, 5.2, 4.7, 4.5, 4.6, 5.3, 7.2, 9.6, 11.9,
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13.7, 14.8, 14.9, 14.8, 14.7, 13.8, 12.4, 11.8, 10.9, 10.1, 9.6, 8.9],
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'May': [11.8, 11.1, 10.6, 9.9, 9.5, 8.9, 8.8, 8.9, 9.6, 11.6, 14.2, 16.6,
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18.4, 19.6, 19.7, 19.6, 19.4, 18.6, 17.1, 16.5, 15.6, 14.6, 14.2, 13.4],
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'Jun': [15.2, 14.4, 13.9, 13.2, 12.7, 12.2, 12.0, 12.1, 12.8, 15.0, 17.7,
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20.2, 22.1, 23.3, 23.5, 23.4, 23.2, 22.3, 20.8, 20.1, 19.1, 18.2, 17.7, 16.9],
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'Jul': [17.6, 16.7, 16.1, 15.3, 14.9, 14.3, 14.1, 14.2, 15.0, 17.3, 20.2,
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23.0, 25.0, 26.3, 26.5, 26.4, 26.2, 25.2, 23.6, 22.8, 21.8, 20.8, 20.2, 19.4],
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'Aug': [17.1, 16.2, 15.7, 14.9, 14.5, 13.9, 13.7, 13.8, 14.6, 16.9, 19.7,
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22.4, 24.4, 25.6, 25.8, 25.7, 25.5, 24.5, 22.9, 22.2, 21.2, 20.2, 19.7, 18.9],
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'Sep': [13.4, 12.7, 12.2, 11.5, 11.2, 10.7, 10.5, 10.6, 11.3, 13.2, 15.6,
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17.9, 19.6, 20.8, 20.9, 20.8, 20.7, 19.8, 18.4, 17.8, 16.9, 16.1, 15.6, 14.9],
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'Oct': [9.4, 8.8, 8.5, 7.9, 7.6, 7.2, 7.1, 7.2, 7.7, 9.3, 11.2, 13.0,
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14.4, 15.3, 15.4, 15.3, 15.2, 14.5, 13.4, 12.9, 12.2, 11.6, 11.2, 10.6],
|
||||
'Nov': [4.0, 3.6, 3.3, 2.9, 2.6, 2.3, 2.2, 2.2, 2.7, 3.9, 5.5, 6.9, 8.0,
|
||||
8.7, 8.8, 8.7, 8.7, 8.1, 7.2, 6.8, 6.3, 5.7, 5.5, 5.0],
|
||||
'Dec': [1.4, 1.0, 0.8, 0.4, 0.2, -0.0, -0.1, -0.1, 0.3, 1.3, 2.6, 3.8,
|
||||
4.7, 5.2, 5.3, 5.2, 5.2, 4.7, 4.0, 3.7, 3.2, 2.8, 2.6, 2.2]
|
||||
}
|
||||
|
||||
|
||||
# Geneva hourly temperatures as piecewise constant function (in Kelvin).
|
||||
GenevaTemperatures_hourly = {
|
||||
month: models.PiecewiseConstant(
|
||||
# NOTE: It is important that the time type is float, not np.float, in
|
||||
# order to allow hashability (for caching).
|
||||
tuple(float(time) for time in range(25)),
|
||||
tuple(273.15 + np.array(temperatures)),
|
||||
)
|
||||
for month, temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
|
||||
}
|
||||
|
||||
|
||||
# Same temperatures on a finer temperature mesh (every 6 minutes).
|
||||
GenevaTemperatures = {
|
||||
month: GenevaTemperatures_hourly[month].refine(refine_factor=10)
|
||||
for month, temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
|
||||
}
|
||||
141
cara/data/weather.py
Normal file
141
cara/data/weather.py
Normal file
|
|
@ -0,0 +1,141 @@
|
|||
import functools
|
||||
|
||||
import numpy as np
|
||||
from cara import models
|
||||
import json
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
from scipy.spatial import cKDTree
|
||||
import os
|
||||
import typing
|
||||
|
||||
weather_debug = False
|
||||
|
||||
DATA_LOCATION = Path(__file__).absolute().parent
|
||||
|
||||
|
||||
def location_to_weather_stn(location_loc):
|
||||
# expects a tuple (lat, long)
|
||||
# returns: weather station ID, weather station name, weather station lat, long
|
||||
search_coords = location_loc.split(',')
|
||||
lat = []
|
||||
long = []
|
||||
station_array = []
|
||||
fixed_delimits = [0, 12, 13, 44, 51, 60, 69, 90, 91]
|
||||
station_file = DATA_LOCATION / 'hadisd_station_fullinfo_v311_202001p.txt'
|
||||
|
||||
for line in station_file.open('rt'):
|
||||
start_end_positions = zip(fixed_delimits[:-1], fixed_delimits[1:])
|
||||
split_vals = [line[start:end] for start, end in start_end_positions]
|
||||
station_location = [split_vals[0],
|
||||
split_vals[2], split_vals[3], split_vals[4]]
|
||||
station_array.append(station_location)
|
||||
lat.append(split_vals[3])
|
||||
long.append(split_vals[4])
|
||||
|
||||
tree = cKDTree(np.c_[lat, long])
|
||||
dd, ii = tree.query(search_coords, k=[1])
|
||||
|
||||
return (station_array[ii[0]][0], station_array[ii[0]][1], station_array[ii[0]][2], station_array[ii[0]][3])
|
||||
|
||||
|
||||
WxStationType = MonthType = str
|
||||
# HourlyTempType - 24 temperatures, one for each hour of the day (the average for the given month).
|
||||
HourlyTempType = typing.List[float]
|
||||
|
||||
|
||||
@functools.lru_cache()
|
||||
def wx_data() -> typing.Dict[WxStationType, typing.Dict[MonthType, HourlyTempType]]:
|
||||
"""
|
||||
Load the weather data.
|
||||
|
||||
The data is structured by station location, and for each station location, by month.
|
||||
|
||||
"""
|
||||
with (DATA_LOCATION / 'global_weather_set.json').open("r") as json_file:
|
||||
data = json.load(json_file)
|
||||
|
||||
for station in list(data.keys()):
|
||||
for month in list(data[station].keys()):
|
||||
data[station][month] = tuple(273.15 + np.array(data[station][month]))
|
||||
return data
|
||||
|
||||
|
||||
StationRecordType = typing.Tuple[WxStationType, str, float, float]
|
||||
|
||||
|
||||
@functools.lru_cache()
|
||||
def wx_station_data() -> typing.Dict[WxStationType, StationRecordType]:
|
||||
weather_data = wx_data()
|
||||
station_data = {}
|
||||
fixed_delimits = [0, 12, 13, 44, 51, 60, 69, 90, 91]
|
||||
station_file = DATA_LOCATION / 'hadisd_station_fullinfo_v311_202001p.txt'
|
||||
|
||||
for line in station_file.open('rt'):
|
||||
start_end_positions = zip(fixed_delimits[:-1], fixed_delimits[1:])
|
||||
split_vals = [line[start:end] for start, end in start_end_positions]
|
||||
station_location = (split_vals[0],
|
||||
split_vals[2], split_vals[3], split_vals[4])
|
||||
# We only consider stations with weather data, don't include the rest.
|
||||
if split_vals[0] in weather_data:
|
||||
station_data[split_vals[0]] = station_location
|
||||
return station_data
|
||||
|
||||
|
||||
@functools.lru_cache()
|
||||
def _wx_station_kdtree() -> cKDTree:
|
||||
station_data = wx_station_data().values()
|
||||
coords = np.array([(stn_record[3], stn_record[2]) for stn_record in station_data])
|
||||
return cKDTree(coords)
|
||||
|
||||
|
||||
def mean_hourly_temperatures(wx_station: str, month: int) -> HourlyTempType:
|
||||
"""
|
||||
Return the mean monthly temperature for the given weather station and month.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
temperatures: List[24 floats]
|
||||
A list containing 24 temperature values, one for each hour, in kelvin.
|
||||
Index 0 of the result corresponds to hour 00:00-01:00, and index 23 (the last) to 23:00-00:00.
|
||||
|
||||
"""
|
||||
return wx_data()[wx_station][str(month)]
|
||||
|
||||
|
||||
def hourly_to_piecewise(hourly_data: HourlyTempType) -> models.PiecewiseConstant:
|
||||
"""
|
||||
Transform a list of 24 floats into a :class:`cara.models.PiecewiseConstant`.
|
||||
|
||||
"""
|
||||
pc = models.PiecewiseConstant(
|
||||
# NOTE: It is important that the time type is float, not np.float, in
|
||||
# order to allow hashability (for caching).
|
||||
tuple(float(time) for time in range(25)),
|
||||
tuple(hourly_data),
|
||||
)
|
||||
return pc
|
||||
|
||||
|
||||
def nearest_wx_station(*, longitude: float, latitude: float) -> StationRecordType:
|
||||
ktree = _wx_station_kdtree()
|
||||
station_data = list(wx_station_data().values())
|
||||
dd, ii = ktree.query((longitude, latitude), k=[1])
|
||||
return station_data[ii[0]]
|
||||
|
||||
|
||||
def location_celcius_per_hour(location: object) -> typing.Dict[str, typing.List[float]]:
|
||||
# expects a tuple (lat, long)
|
||||
# returns a json format set of weather data
|
||||
w_station = location_to_weather_stn(location)
|
||||
with (DATA_LOCATION / 'global_weather_set.json').open("r") as json_file:
|
||||
weather_dict = json.load(json_file)
|
||||
Location_hourly_temperatures_celsius_per_hour = weather_dict[w_station[0]]
|
||||
if weather_debug:
|
||||
print(location)
|
||||
print("weather station name: ", w_station[1])
|
||||
print("weather station ref: ", w_station[0])
|
||||
print("weather station location: ", w_station[2], " ", w_station[3])
|
||||
print(Location_hourly_temperatures_celsius_per_hour)
|
||||
return Location_hourly_temperatures_celsius_per_hour
|
||||
|
|
@ -1,16 +1,14 @@
|
|||
import dataclasses
|
||||
|
||||
import pytest
|
||||
import numpy as np
|
||||
import numpy.testing as npt
|
||||
from pathlib import Path
|
||||
import json
|
||||
|
||||
from cara.apps.calculator import model_generator
|
||||
from cara.apps.calculator.model_generator import _hours2timestring
|
||||
from cara.apps.calculator.model_generator import minutes_since_midnight
|
||||
from cara import models
|
||||
from cara import data
|
||||
import numpy as np
|
||||
import numpy.testing as npt
|
||||
|
||||
|
||||
def test_model_from_dict(baseline_form_data):
|
||||
|
|
@ -39,7 +37,7 @@ def test_ventilation_slidingwindow(baseline_form: model_generator.FormData):
|
|||
window = models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=10),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=data.Temperatures['12'],
|
||||
outside_temp=data.GenevaTemperatures['Dec'],
|
||||
window_height=1.6, opening_length=0.6,
|
||||
)
|
||||
baseline_form.ventilation_type = 'natural_ventilation'
|
||||
|
|
@ -61,7 +59,7 @@ def test_ventilation_hingedwindow(baseline_form: model_generator.FormData):
|
|||
window = models.HingedWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=10),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=data.Temperatures['12'],
|
||||
outside_temp=data.GenevaTemperatures['Dec'],
|
||||
window_height=1.6, window_width=1., opening_length=0.6,
|
||||
)
|
||||
baseline_form.ventilation_type = 'natural_ventilation'
|
||||
|
|
@ -110,19 +108,6 @@ def test_ventilation_airchanges(baseline_form: model_generator.FormData):
|
|||
|
||||
|
||||
def test_ventilation_window_hepa(baseline_form: model_generator.FormData):
|
||||
room = models.Room(75)
|
||||
window = models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=10),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=data.Temperatures['12'],
|
||||
window_height=1.6, opening_length=0.6,
|
||||
)
|
||||
hepa = models.HEPAFilter(
|
||||
active=models.PeriodicInterval(period=120, duration=120),
|
||||
q_air_mech=250.,
|
||||
)
|
||||
ventilation = models.MultipleVentilation((window, hepa))
|
||||
|
||||
baseline_form.ventilation_type = 'natural_ventilation'
|
||||
baseline_form.windows_duration = 10
|
||||
baseline_form.windows_frequency = 120
|
||||
|
|
@ -131,10 +116,24 @@ def test_ventilation_window_hepa(baseline_form: model_generator.FormData):
|
|||
baseline_form.window_height = 1.6
|
||||
baseline_form.opening_distance = 0.6
|
||||
baseline_form.hepa_option = True
|
||||
baseline_vent = baseline_form.ventilation()
|
||||
|
||||
ts = np.linspace(9, 17, 100)
|
||||
np.testing.assert_allclose([ventilation.air_exchange(room, t)+0.25 for t in ts],
|
||||
[baseline_form.ventilation().air_exchange(room, t) for t in ts])
|
||||
# Now build the equivalent ventilation instance directly, and compare.
|
||||
room = models.Room(75)
|
||||
window = models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=10),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=baseline_vent.ventilations[0].outside_temp,
|
||||
window_height=1.6, opening_length=0.6,
|
||||
)
|
||||
hepa = models.HEPAFilter(
|
||||
active=models.PeriodicInterval(period=120, duration=120),
|
||||
q_air_mech=250.,
|
||||
)
|
||||
ach = models.AirChange(active=models.PeriodicInterval(period=120, duration=120), air_exch=0.25)
|
||||
ventilation = models.MultipleVentilation((window, hepa, ach))
|
||||
|
||||
assert ventilation == baseline_vent
|
||||
|
||||
|
||||
def present_times(interval: models.Interval) -> models.BoundarySequence_t:
|
||||
|
|
@ -163,8 +162,7 @@ def test_exposed_present_intervals(baseline_form: model_generator.FormData):
|
|||
baseline_form.exposed_finish = minutes_since_midnight(17 * 60)
|
||||
baseline_form.exposed_lunch_start = minutes_since_midnight(12 * 60 + 30)
|
||||
baseline_form.exposed_lunch_finish = minutes_since_midnight(13 * 60 + 30)
|
||||
correct = ((9, 10+37/60), (10+52/60, 12.5),
|
||||
(13.5, 15+7/60), (15+22/60, 17.0))
|
||||
correct = ((9, 10+37/60), (10+52/60, 12.5), (13.5, 15+7/60), (15+22/60, 17.0))
|
||||
assert present_times(baseline_form.exposed_present_interval()) == correct
|
||||
|
||||
|
||||
|
|
@ -172,50 +170,37 @@ def test_present_intervals_common_breaks(baseline_form: model_generator.FormData
|
|||
baseline_form.infected_dont_have_breaks_with_exposed = False
|
||||
baseline_form.infected_coffee_duration = baseline_form.exposed_coffee_duration = 15
|
||||
baseline_form.infected_coffee_break_option = baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
baseline_form.exposed_lunch_start = baseline_form.infected_lunch_start = minutes_since_midnight(
|
||||
12 * 60 + 30)
|
||||
baseline_form.exposed_lunch_finish = baseline_form.infected_lunch_finish = minutes_since_midnight(
|
||||
13 * 60 + 30)
|
||||
baseline_form.exposed_lunch_start = baseline_form.infected_lunch_start = minutes_since_midnight(12 * 60 + 30)
|
||||
baseline_form.exposed_lunch_finish = baseline_form.infected_lunch_finish = minutes_since_midnight(13 * 60 + 30)
|
||||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
baseline_form.exposed_finish = minutes_since_midnight(17 * 60)
|
||||
baseline_form.infected_start = minutes_since_midnight(9 * 60)
|
||||
baseline_form.infected_finish = minutes_since_midnight(16 * 60)
|
||||
correct_exposed = ((9, 10+37/60), (10+52/60, 12.5),
|
||||
(13.5, 15+7/60), (15+22/60, 17.0))
|
||||
correct_infected = ((9, 10+37/60), (10+52/60, 12.5),
|
||||
(13.5, 15+7/60), (15+22/60, 16.0))
|
||||
assert present_times(
|
||||
baseline_form.exposed_present_interval()) == correct_exposed
|
||||
assert present_times(
|
||||
baseline_form.infected_present_interval()) == correct_infected
|
||||
correct_exposed = ((9, 10+37/60), (10+52/60, 12.5), (13.5, 15+7/60), (15+22/60, 17.0))
|
||||
correct_infected = ((9, 10+37/60), (10+52/60, 12.5), (13.5, 15+7/60), (15+22/60, 16.0))
|
||||
assert present_times(baseline_form.exposed_present_interval()) == correct_exposed
|
||||
assert present_times(baseline_form.infected_present_interval()) == correct_infected
|
||||
|
||||
|
||||
def test_present_intervals_split_breaks(baseline_form: model_generator.FormData):
|
||||
baseline_form.infected_dont_have_breaks_with_exposed = True
|
||||
baseline_form.infected_coffee_duration = baseline_form.exposed_coffee_duration = 15
|
||||
baseline_form.infected_coffee_break_option = baseline_form.exposed_coffee_break_option = 'coffee_break_2'
|
||||
baseline_form.infected_lunch_start = baseline_form.exposed_lunch_start = minutes_since_midnight(
|
||||
12 * 60 + 30)
|
||||
baseline_form.infected_lunch_finish = baseline_form.exposed_lunch_finish = minutes_since_midnight(
|
||||
13 * 60 + 30)
|
||||
baseline_form.infected_lunch_start = baseline_form.exposed_lunch_start = minutes_since_midnight(12 * 60 + 30)
|
||||
baseline_form.infected_lunch_finish = baseline_form.exposed_lunch_finish = minutes_since_midnight(13 * 60 + 30)
|
||||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
baseline_form.exposed_finish = minutes_since_midnight(17 * 60)
|
||||
baseline_form.infected_start = minutes_since_midnight(9 * 60)
|
||||
baseline_form.infected_finish = minutes_since_midnight(16 * 60)
|
||||
correct_exposed = ((9, 10+37/60), (10+52/60, 12.5),
|
||||
(13.5, 15+7/60), (15+22/60, 17.0))
|
||||
correct_infected = ((9, 10+37/60), (10+52/60, 12.5),
|
||||
(13.5, 14+37/60), (14+52/60, 16.0))
|
||||
assert present_times(
|
||||
baseline_form.exposed_present_interval()) == correct_exposed
|
||||
assert present_times(
|
||||
baseline_form.infected_present_interval()) == correct_infected
|
||||
correct_exposed = ((9, 10+37/60), (10+52/60, 12.5), (13.5, 15+7/60), (15+22/60, 17.0))
|
||||
correct_infected = ((9, 10+37/60), (10+52/60, 12.5), (13.5, 14+37/60), (14+52/60, 16.0))
|
||||
assert present_times(baseline_form.exposed_present_interval()) == correct_exposed
|
||||
assert present_times(baseline_form.infected_present_interval()) == correct_infected
|
||||
|
||||
|
||||
def test_exposed_present_intervals_starting_with_lunch(baseline_form: model_generator.FormData):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_0'
|
||||
baseline_form.exposed_start = baseline_form.exposed_lunch_start = minutes_since_midnight(
|
||||
13 * 60)
|
||||
baseline_form.exposed_start = baseline_form.exposed_lunch_start = minutes_since_midnight(13 * 60)
|
||||
baseline_form.exposed_finish = minutes_since_midnight(18 * 60)
|
||||
baseline_form.exposed_lunch_finish = minutes_since_midnight(14 * 60)
|
||||
correct = ((14.0, 18.0), )
|
||||
|
|
@ -225,8 +210,7 @@ def test_exposed_present_intervals_starting_with_lunch(baseline_form: model_gene
|
|||
def test_exposed_present_intervals_ending_with_lunch(baseline_form: model_generator.FormData):
|
||||
baseline_form.exposed_coffee_break_option = 'coffee_break_0'
|
||||
baseline_form.exposed_start = minutes_since_midnight(11 * 60)
|
||||
baseline_form.exposed_finish = baseline_form.exposed_lunch_start = minutes_since_midnight(
|
||||
13 * 60)
|
||||
baseline_form.exposed_finish = baseline_form.exposed_lunch_start = minutes_since_midnight(13 * 60)
|
||||
baseline_form.exposed_lunch_finish = minutes_since_midnight(14 * 60)
|
||||
correct = ((11.0, 13.0),)
|
||||
assert present_times(baseline_form.exposed_present_interval()) == correct
|
||||
|
|
@ -308,8 +292,7 @@ def breaks_every_25_mins_for_20_mins(baseline_form: model_generator.FormData):
|
|||
baseline_form.exposed_lunch_finish = time2mins("12:15")
|
||||
baseline_form.exposed_lunch_option = True
|
||||
|
||||
breaks = baseline_form.exposed_coffee_break_times(
|
||||
) + baseline_form.exposed_lunch_break_times()
|
||||
breaks = baseline_form.exposed_coffee_break_times() + baseline_form.exposed_lunch_break_times()
|
||||
interval = baseline_form.present_interval(
|
||||
baseline_form.exposed_start, baseline_form.exposed_finish, breaks=breaks,
|
||||
)
|
||||
|
|
@ -326,8 +309,7 @@ def breaks_every_25_mins_for_20_mins(baseline_form: model_generator.FormData):
|
|||
|
||||
|
||||
def test_present_after_two_breaks_for_small_interval(breaks_every_25_mins_for_20_mins):
|
||||
breaks = breaks_every_25_mins_for_20_mins.exposed_coffee_break_times(
|
||||
) + breaks_every_25_mins_for_20_mins.exposed_lunch_break_times()
|
||||
breaks = breaks_every_25_mins_for_20_mins.exposed_coffee_break_times() + breaks_every_25_mins_for_20_mins.exposed_lunch_break_times()
|
||||
# The first two breaks start at 10:25 and 11:10.
|
||||
interval = breaks_every_25_mins_for_20_mins.present_interval(
|
||||
time2mins("11:35"), time2mins("11:40"), breaks=breaks,
|
||||
|
|
@ -337,8 +319,7 @@ def test_present_after_two_breaks_for_small_interval(breaks_every_25_mins_for_20
|
|||
|
||||
|
||||
def test_present_only_during_second_break(breaks_every_25_mins_for_20_mins):
|
||||
breaks = breaks_every_25_mins_for_20_mins.exposed_coffee_break_times(
|
||||
) + breaks_every_25_mins_for_20_mins.exposed_lunch_break_times()
|
||||
breaks = breaks_every_25_mins_for_20_mins.exposed_coffee_break_times() + breaks_every_25_mins_for_20_mins.exposed_lunch_break_times()
|
||||
# The first two breaks start at 10:25 and 11:10.
|
||||
interval = breaks_every_25_mins_for_20_mins.present_interval(
|
||||
time2mins("11:15"), time2mins("11:20"), breaks=breaks
|
||||
|
|
@ -366,10 +347,8 @@ def test_no_breaks(baseline_form: model_generator.FormData):
|
|||
baseline_form.infected_finish = minutes_since_midnight(15 * 60)
|
||||
exposed_correct = ((9, 17),)
|
||||
infected_correct = ((10, 15),)
|
||||
assert present_times(
|
||||
baseline_form.exposed_present_interval()) == exposed_correct
|
||||
assert present_times(
|
||||
baseline_form.infected_present_interval()) == infected_correct
|
||||
assert present_times(baseline_form.exposed_present_interval()) == exposed_correct
|
||||
assert present_times(baseline_form.infected_present_interval()) == infected_correct
|
||||
|
||||
|
||||
def test_coffee_lunch_breaks(baseline_form: model_generator.FormData):
|
||||
|
|
@ -381,8 +360,7 @@ def test_coffee_lunch_breaks(baseline_form: model_generator.FormData):
|
|||
baseline_form.exposed_lunch_finish = minutes_since_midnight(13 * 60 + 30)
|
||||
correct = ((9, 9+50/60), (10+20/60, 11+10/60), (11+40/60, 12+30/60),
|
||||
(13+30/60, 14+40/60), (15+10/60, 16+20/60), (16+50/60, 18))
|
||||
np.testing.assert_allclose(present_times(
|
||||
baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
np.testing.assert_allclose(present_times(baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
|
||||
|
||||
def test_coffee_lunch_breaks_unbalance(baseline_form: model_generator.FormData):
|
||||
|
|
@ -393,8 +371,7 @@ def test_coffee_lunch_breaks_unbalance(baseline_form: model_generator.FormData):
|
|||
baseline_form.exposed_lunch_start = minutes_since_midnight(12 * 60 + 30)
|
||||
baseline_form.exposed_lunch_finish = minutes_since_midnight(13 * 60 + 30)
|
||||
correct = ((9, 9+50/60), (10+20/60, 11+10/60), (11+40/60, 12+30/60))
|
||||
np.testing.assert_allclose(present_times(
|
||||
baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
np.testing.assert_allclose(present_times(baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
|
||||
|
||||
def test_coffee_breaks(baseline_form: model_generator.FormData):
|
||||
|
|
@ -403,10 +380,8 @@ def test_coffee_breaks(baseline_form: model_generator.FormData):
|
|||
baseline_form.exposed_start = minutes_since_midnight(9 * 60)
|
||||
baseline_form.exposed_finish = minutes_since_midnight(10 * 60)
|
||||
baseline_form.exposed_lunch_option = False
|
||||
correct = ((9, 9+4/60), (9+14/60, 9+18/60), (9+28/60, 9+32/60),
|
||||
(9+42/60, 9+46/60), (9+56/60, 10))
|
||||
np.testing.assert_allclose(present_times(
|
||||
baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
correct = ((9, 9+4/60), (9+14/60, 9+18/60), (9+28/60, 9+32/60), (9+42/60, 9+46/60), (9+56/60, 10))
|
||||
np.testing.assert_allclose(present_times(baseline_form.exposed_present_interval()), correct, rtol=1e-14)
|
||||
|
||||
|
||||
def test_key_validation(baseline_form_data):
|
||||
|
|
@ -439,8 +414,7 @@ def test_key_validation_mech_ventilation_type_na(baseline_form_data):
|
|||
def test_default_types():
|
||||
# Validate that FormData._DEFAULTS are complete and of the correct type.
|
||||
# Validate that we have the right types and matching attributes to the DEFAULTS.
|
||||
fields = {field.name: field for field in dataclasses.fields(
|
||||
model_generator.FormData)}
|
||||
fields = {field.name: field for field in dataclasses.fields(model_generator.FormData)}
|
||||
for field, value in model_generator.FormData._DEFAULTS.items():
|
||||
if field not in fields:
|
||||
raise ValueError(f"Unmatched default {field}")
|
||||
|
|
@ -454,12 +428,10 @@ def test_default_types():
|
|||
continue
|
||||
|
||||
if field in model_generator._CAST_RULES_FORM_ARG_TO_NATIVE:
|
||||
value = model_generator._CAST_RULES_FORM_ARG_TO_NATIVE[field](
|
||||
value)
|
||||
value = model_generator._CAST_RULES_FORM_ARG_TO_NATIVE[field](value)
|
||||
|
||||
if not isinstance(value, field_type):
|
||||
raise TypeError(
|
||||
f'{field} has type {field_type}, got {type(value)}')
|
||||
raise TypeError(f'{field} has type {field_type}, got {type(value)}')
|
||||
|
||||
for field in fields.values():
|
||||
assert field.name in model_generator.FormData._DEFAULTS, f"No default set for field name {field.name}"
|
||||
|
|
@ -471,28 +443,5 @@ def test_form_to_dict(baseline_form):
|
|||
assert 1 < len(stripped) < len(full)
|
||||
assert 'exposed_coffee_break_option' in stripped
|
||||
# If we set the value to the default one, it should no longer turn up in the dictionary.
|
||||
baseline_form.exposed_coffee_break_option = model_generator.FormData._DEFAULTS[
|
||||
'exposed_coffee_break_option']
|
||||
assert 'exposed_coffee_break_option' not in baseline_form.to_dict(
|
||||
baseline_form, strip_defaults=True)
|
||||
|
||||
|
||||
def test_weather_stations():
|
||||
fixed_delimits = [0, 12, 13, 44, 51, 60, 69, 90, 91]
|
||||
|
||||
station_file = Path(__file__).parent.parent.parent.parent / 'data' / \
|
||||
'hadisd_station_fullinfo_v311_202001p.txt'
|
||||
|
||||
with open(Path(__file__).parent.parent.parent.parent / 'data' / 'global_weather_set.json', "r") as json_file:
|
||||
weather_dict = json.load(json_file)
|
||||
|
||||
for line in station_file.open('rt'):
|
||||
start_end_positions = zip(fixed_delimits[:-1], fixed_delimits[1:])
|
||||
split_vals = [line[start:end] for start, end in start_end_positions]
|
||||
|
||||
station_id = split_vals[0]
|
||||
# Check if weather station exists
|
||||
temp_dict = weather_dict[station_id]
|
||||
|
||||
for month in range(1, 13):
|
||||
assert not np.any(np.isnan(temp_dict[str(month)]))
|
||||
baseline_form.exposed_coffee_break_option = model_generator.FormData._DEFAULTS['exposed_coffee_break_option']
|
||||
assert 'exposed_coffee_break_option' not in baseline_form.to_dict(baseline_form, strip_defaults=True)
|
||||
|
|
|
|||
0
cara/tests/data/__init__.py
Normal file
0
cara/tests/data/__init__.py
Normal file
11
cara/tests/data/test_weather.py
Normal file
11
cara/tests/data/test_weather.py
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
import cara.data.weather as wx
|
||||
|
||||
|
||||
def test_nearest_wx_station():
|
||||
melbourne_lat, melbourne_lon = -37.81739, 144.96751
|
||||
station_rec = wx.nearest_wx_station(longitude=melbourne_lon, latitude=melbourne_lat)
|
||||
|
||||
station_name = station_rec[1].strip()
|
||||
# Note: For Melbourne, the nearest station is 'MELBOURNE REGIONAL OFFICE',
|
||||
# but the nearest location with suitable wx data is 'MELBOURNE ESSENDON'
|
||||
assert station_name == 'MELBOURNE ESSENDON'
|
||||
|
|
@ -81,5 +81,5 @@ def test_piecewiseconstant_vs_interval(time):
|
|||
|
||||
|
||||
def test_piecewiseconstant_transition_times():
|
||||
outside_temp = data.Temperatures['1']
|
||||
outside_temp = data.GenevaTemperatures['Jan']
|
||||
assert set(outside_temp.transition_times) == outside_temp.interval().transition_times()
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import cara.data as data
|
|||
def test_no_mask_superspeading_emission_rate(baseline_model):
|
||||
expected_rate = 48500.
|
||||
npt.assert_allclose(
|
||||
[baseline_model.infected.emission_rate(t) for t in [0, 1, 4, 4.5, 5, 8, 9]],
|
||||
[baseline_model.infected.emission_rate(float(t)) for t in [0, 1, 4, 4.5, 5, 8, 9]],
|
||||
[0, expected_rate, expected_rate, 0, 0, expected_rate, 0],
|
||||
rtol=1e-12
|
||||
)
|
||||
|
|
@ -19,8 +19,8 @@ def test_no_mask_superspeading_emission_rate(baseline_model):
|
|||
def baseline_periodic_window():
|
||||
return models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=15),
|
||||
inside_temp=models.PiecewiseConstant((0,24),(293,)),
|
||||
outside_temp=models.PiecewiseConstant((0,24),(283,)),
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=models.PiecewiseConstant((0., 24.), (283,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
)
|
||||
|
||||
|
|
@ -41,7 +41,7 @@ def baseline_periodic_hepa():
|
|||
def test_concentrations(baseline_model):
|
||||
# expected concentrations were computed analytically
|
||||
ts = [0, 4, 5, 7, 10]
|
||||
concentrations = [baseline_model.concentration(t) for t in ts]
|
||||
concentrations = [baseline_model.concentration(float(t)) for t in ts]
|
||||
npt.assert_allclose(
|
||||
concentrations,
|
||||
[0.000000e+00, 20.805628, 6.602814e-13, 20.805628, 2.09545e-26],
|
||||
|
|
@ -55,7 +55,7 @@ def test_smooth_concentrations(baseline_model):
|
|||
dx = 0.002
|
||||
dy_limit = 0.2 # Anything more than this (in relative) is a bit steep.
|
||||
ts = np.arange(0, 10, dx)
|
||||
concentrations = [baseline_model.concentration(t) for t in ts]
|
||||
concentrations = [baseline_model.concentration(float(t)) for t in ts]
|
||||
assert np.abs(np.diff(concentrations)).max()/np.mean(concentrations) < dy_limit
|
||||
|
||||
|
||||
|
|
@ -69,7 +69,7 @@ def build_model(interval_duration):
|
|||
infected=models.InfectedPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0, 4), (5, 8))),
|
||||
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'],
|
||||
|
|
@ -78,7 +78,7 @@ def build_model(interval_duration):
|
|||
return model
|
||||
|
||||
|
||||
def test_concentrations_startup(baseline_model):
|
||||
def test_concentrations_startup():
|
||||
# The concentrations should be the same until the beginning of the
|
||||
# first time that the ventilation is disabled.
|
||||
m1 = build_model(interval_duration=120)
|
||||
|
|
@ -183,28 +183,38 @@ def test_multiple_ventilation_HEPA_HVAC_AirChange(volume, expected_value):
|
|||
],
|
||||
)
|
||||
def test_windowopening(time, expected_value):
|
||||
tempOutside = models.PiecewiseConstant((0,10,24),(273.15,283.15))
|
||||
tempInside = models.PiecewiseConstant((0,24),(293.15,))
|
||||
w = models.SlidingWindow(active=models.SpecificInterval([(0,24)]),
|
||||
inside_temp=tempInside,outside_temp=tempOutside,
|
||||
window_height=1.,opening_length=0.6)
|
||||
npt.assert_allclose(w.air_exchange(models.Room(volume=68),time),
|
||||
expected_value,rtol=1e-5)
|
||||
tempOutside = models.PiecewiseConstant((0., 10., 24.),(273.15, 283.15))
|
||||
tempInside = models.PiecewiseConstant((0., 24.), (293.15,))
|
||||
w = models.SlidingWindow(
|
||||
active=models.SpecificInterval([(0., 24.)]),
|
||||
inside_temp=tempInside,outside_temp=tempOutside,
|
||||
window_height=1., opening_length=0.6,
|
||||
)
|
||||
npt.assert_allclose(
|
||||
w.air_exchange(models.Room(volume=68), time), expected_value, rtol=1e-5
|
||||
)
|
||||
|
||||
|
||||
def build_hourly_dependent_model(month, intervals_open=((7.5, 8.5),),
|
||||
intervals_presence_infected=((0, 4), (5, 7.5)),
|
||||
artificial_refinement=False,
|
||||
temperatures=data.Temperatures_hourly):
|
||||
def build_hourly_dependent_model(
|
||||
month,
|
||||
intervals_open=((7.5, 8.5),),
|
||||
intervals_presence_infected=((0., 4.), (5., 7.5)),
|
||||
artificial_refinement=False,
|
||||
temperatures=data.GenevaTemperatures_hourly
|
||||
):
|
||||
if artificial_refinement:
|
||||
# 5-fold increase of number of times, WITHOUT interpolation
|
||||
# (hence transparent for the results)
|
||||
refine_factor = 2
|
||||
times_refined = tuple(np.linspace(0.,24,
|
||||
refine_factor*len(temperatures[month].values)+1))
|
||||
temperatures_refined = tuple(np.hstack([[v]*refine_factor
|
||||
for v in temperatures[month].values]))
|
||||
outside_temp = models.PiecewiseConstant(times_refined,temperatures_refined)
|
||||
times_refined = tuple(
|
||||
float(t) for t in np.linspace(
|
||||
0., 24, refine_factor * len(temperatures[month].values) + 1
|
||||
)
|
||||
)
|
||||
temperatures_refined = tuple(np.hstack(
|
||||
[[v] * refine_factor for v in temperatures[month].values]
|
||||
))
|
||||
outside_temp = models.PiecewiseConstant(times_refined, temperatures_refined)
|
||||
else:
|
||||
outside_temp = temperatures[month]
|
||||
|
||||
|
|
@ -212,7 +222,7 @@ def build_hourly_dependent_model(month, intervals_open=((7.5, 8.5),),
|
|||
room=models.Room(volume=75),
|
||||
ventilation=models.SlidingWindow(
|
||||
active=models.SpecificInterval(intervals_open),
|
||||
inside_temp=models.PiecewiseConstant((0,24),(293,)),
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293, )),
|
||||
outside_temp=outside_temp,
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
|
|
@ -233,14 +243,14 @@ def build_constant_temp_model(outside_temp, intervals_open=((7.5, 8.5),)):
|
|||
room=models.Room(volume=75),
|
||||
ventilation=models.SlidingWindow(
|
||||
active=models.SpecificInterval(intervals_open),
|
||||
inside_temp=models.PiecewiseConstant((0,24),(293,)),
|
||||
outside_temp=models.PiecewiseConstant((0,24),(outside_temp,)),
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=models.PiecewiseConstant((0., 24.), (outside_temp,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
infected=models.InfectedPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0, 4), (5, 7.5))),
|
||||
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'],
|
||||
|
|
@ -253,21 +263,22 @@ def build_hourly_dependent_model_multipleventilation(month, intervals_open=((7.5
|
|||
vent = models.MultipleVentilation((
|
||||
models.SlidingWindow(
|
||||
active=models.SpecificInterval(intervals_open),
|
||||
inside_temp=models.PiecewiseConstant((0,24),(293,)),
|
||||
outside_temp=data.Temperatures[month],
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=data.GenevaTemperatures[month],
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
models.HEPAFilter(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
q_air_mech=500.,
|
||||
)))
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
q_air_mech=500.,
|
||||
),
|
||||
))
|
||||
model = models.ConcentrationModel(
|
||||
room=models.Room(volume=75),
|
||||
ventilation=vent,
|
||||
infected=models.InfectedPopulation(
|
||||
number=1,
|
||||
virus=models.Virus.types['SARS_CoV_2'],
|
||||
presence=models.SpecificInterval(((0, 4), (5, 7.5))),
|
||||
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'],
|
||||
|
|
@ -278,7 +289,7 @@ def build_hourly_dependent_model_multipleventilation(month, intervals_open=((7.5
|
|||
|
||||
@pytest.mark.parametrize(
|
||||
"month, temperatures",
|
||||
data.local_tempatures.items(),
|
||||
data.Geneva_hourly_temperatures_celsius_per_hour.items(),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
|
|
@ -288,12 +299,12 @@ def test_concentrations_hourly_dep_temp_vs_constant(month, temperatures, time):
|
|||
# The concentrations should be the same up to 8 AM (time when the
|
||||
# temperature changes DURING the window opening).
|
||||
m1 = build_hourly_dependent_model(month)
|
||||
m2 = build_constant_temp_model(temperatures[7]+273.15)
|
||||
m2 = build_constant_temp_model(temperatures[7] + 273.15)
|
||||
npt.assert_allclose(m1.concentration(time), m2.concentration(time), rtol=1e-5)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"month, temperatures",
|
||||
data.local_tempatures.items(),
|
||||
data.Geneva_hourly_temperatures_celsius_per_hour.items(),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
|
|
@ -301,20 +312,23 @@ def test_concentrations_hourly_dep_temp_vs_constant(month, temperatures, time):
|
|||
)
|
||||
def test_concentrations_hourly_dep_temp_startup(month, temperatures, time):
|
||||
# The concentrations should be the zero up to the first presence time
|
||||
# of an infecter person.
|
||||
m = build_hourly_dependent_model(month,((0.,0.5),(1,1.5),(4,4.5),(7.5,8)),
|
||||
((8,12.),))
|
||||
# of an infected person.
|
||||
m = build_hourly_dependent_model(
|
||||
month,
|
||||
((0., 0.5), (1., 1.5), (4., 4.5), (7.5, 8), ),
|
||||
((8., 12.), ),
|
||||
)
|
||||
assert m.concentration(time) == 0.
|
||||
|
||||
|
||||
def test_concentrations_hourly_dep_multipleventilation():
|
||||
m = build_hourly_dependent_model_multipleventilation('1')
|
||||
m = build_hourly_dependent_model_multipleventilation('Jan')
|
||||
m.concentration(12.)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"month_temp_item",
|
||||
data.local_tempatures.items(),
|
||||
data.Geneva_hourly_temperatures_celsius_per_hour.items(),
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
|
|
@ -323,19 +337,22 @@ def test_concentrations_hourly_dep_multipleventilation():
|
|||
def test_concentrations_hourly_dep_adding_artificial_transitions(month_temp_item, time):
|
||||
month, temperatures = month_temp_item
|
||||
# Adding a second opening inside the first one should not change anything
|
||||
m1 = build_hourly_dependent_model(month,intervals_open=((7.5, 8.5),))
|
||||
m2 = build_hourly_dependent_model(month,intervals_open=((7.5, 8.5),(8.,8.1)))
|
||||
m1 = build_hourly_dependent_model(month, intervals_open=((7.5, 8.5), ))
|
||||
m2 = build_hourly_dependent_model(month, intervals_open=((7.5, 8.5), (8., 8.1), ))
|
||||
npt.assert_allclose(m1.concentration(time), m2.concentration(time), rtol=1e-5)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"time",
|
||||
list(np.random.random_sample(10)*24.)+list(np.arange(0,24.5,0.5)),
|
||||
(
|
||||
[float(t) for t in np.random.random_sample(10) * 24.] # type: ignore
|
||||
+ [float(t) for t in np.arange(0, 24.5, 0.5)]
|
||||
),
|
||||
)
|
||||
def test_concentrations_refine_times(time):
|
||||
month = '1'
|
||||
m1 = build_hourly_dependent_model(month,intervals_open=((0, 24),))
|
||||
m2 = build_hourly_dependent_model(month,intervals_open=((0, 24),),
|
||||
month = 'Jan'
|
||||
m1 = build_hourly_dependent_model(month, intervals_open=((0., 24.),))
|
||||
m2 = build_hourly_dependent_model(month, intervals_open=((0., 24.),),
|
||||
artificial_refinement=True)
|
||||
npt.assert_allclose(m1.concentration(time), m2.concentration(time), rtol=1e-8)
|
||||
|
||||
|
|
@ -350,7 +367,7 @@ def build_exposure_model(concentration_model):
|
|||
activity=infected.activity,
|
||||
mask=infected.mask,
|
||||
),
|
||||
fraction_deposited = 1.,
|
||||
fraction_deposited=1.,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -359,16 +376,16 @@ def build_exposure_model(concentration_model):
|
|||
@pytest.mark.parametrize(
|
||||
"month, expected_exposure",
|
||||
[
|
||||
['1', 496.5427],
|
||||
['6', 1898.1354],
|
||||
['Jan', 496.5427],
|
||||
['Jun', 1898.1354],
|
||||
],
|
||||
)
|
||||
def test_exposure_hourly_dep(month,expected_exposure):
|
||||
m = build_exposure_model(
|
||||
build_hourly_dependent_model(
|
||||
month,
|
||||
intervals_open=((0,24),),
|
||||
intervals_presence_infected=((8, 12), (13, 17))
|
||||
intervals_open=((0., 24.), ),
|
||||
intervals_presence_infected=((8., 12.), (13., 17.))
|
||||
)
|
||||
)
|
||||
exposure = m.exposure()
|
||||
|
|
@ -380,17 +397,17 @@ def test_exposure_hourly_dep(month,expected_exposure):
|
|||
@pytest.mark.parametrize(
|
||||
"month, expected_exposure",
|
||||
[
|
||||
['1', 499.6921],
|
||||
['6', 2007.59925],
|
||||
['Jan', 499.6921],
|
||||
['Jun', 2007.59925],
|
||||
],
|
||||
)
|
||||
def test_exposure_hourly_dep_refined(month,expected_exposure):
|
||||
m = build_exposure_model(
|
||||
build_hourly_dependent_model(
|
||||
month,
|
||||
intervals_open=((0, 24),),
|
||||
intervals_presence_infected=((8, 12), (13, 17)),
|
||||
temperatures=data.Temperatures,
|
||||
intervals_open=((0., 24.),),
|
||||
intervals_presence_infected=((8., 12.), (13., 17.)),
|
||||
temperatures=data.GenevaTemperatures,
|
||||
)
|
||||
)
|
||||
exposure = m.exposure()
|
||||
|
|
|
|||
|
|
@ -26,12 +26,12 @@ def shared_office_mc():
|
|||
(
|
||||
models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=10),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=models.PiecewiseConstant((0, 24), (283,)),
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=models.PiecewiseConstant((0., 24.), (283,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
models.AirChange(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
active=models.SpecificInterval(((0., 24.), )),
|
||||
air_exch=0.25,
|
||||
),
|
||||
),
|
||||
|
|
@ -39,7 +39,7 @@ def shared_office_mc():
|
|||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=virus_distributions['SARS_CoV_2_B117'],
|
||||
presence=mc.SpecificInterval(((0, 2), (2.1, 4), (5, 7), (7.1, 9))),
|
||||
presence=mc.SpecificInterval(((0., 2.), (2.1, 4.), (5., 7.), (7.1, 9.))),
|
||||
mask=models.Mask(η_inhale=0.3),
|
||||
activity=activity_distributions['Seated'],
|
||||
expiration=models.MultipleExpiration(
|
||||
|
|
@ -70,12 +70,12 @@ def classroom_mc():
|
|||
(
|
||||
models.SlidingWindow(
|
||||
active=models.PeriodicInterval(period=120, duration=10),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=models.PiecewiseConstant((0, 24), (283,)),
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=models.PiecewiseConstant((0., 24.), (283,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
models.AirChange(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
air_exch=0.25,
|
||||
),
|
||||
),
|
||||
|
|
@ -83,7 +83,7 @@ def classroom_mc():
|
|||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=virus_distributions['SARS_CoV_2_B117'],
|
||||
presence=mc.SpecificInterval(((0, 2), (2.5, 4), (5, 7), (7.5, 9))),
|
||||
presence=mc.SpecificInterval(((0., 2.), (2.5, 4.), (5., 7.), (7.5, 9.))),
|
||||
mask=models.Mask.types['No mask'],
|
||||
activity=activity_distributions['Light activity'],
|
||||
expiration=models.Expiration.types['Talking'],
|
||||
|
|
@ -108,13 +108,13 @@ def ski_cabin_mc():
|
|||
concentration_mc = mc.ConcentrationModel(
|
||||
room=models.Room(volume=10, humidity=0.5),
|
||||
ventilation=models.AirChange(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
air_exch=0,
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=virus_distributions['SARS_CoV_2_B117'],
|
||||
presence=mc.SpecificInterval(((0, 1/3),)),
|
||||
presence=mc.SpecificInterval(((0., 1/3),)),
|
||||
mask=models.Mask(η_inhale=0.3),
|
||||
activity=activity_distributions['Moderate activity'],
|
||||
expiration=models.Expiration.types['Talking'],
|
||||
|
|
@ -141,13 +141,13 @@ def gym_mc():
|
|||
concentration_mc = mc.ConcentrationModel(
|
||||
room=models.Room(volume=300, humidity=0.5),
|
||||
ventilation=models.AirChange(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
air_exch=6,
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=2,
|
||||
virus=virus_distributions['SARS_CoV_2_B117'],
|
||||
presence=mc.SpecificInterval(((0, 1),)),
|
||||
presence=mc.SpecificInterval(((0., 1.),)),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Heavy exercise'],
|
||||
expiration=models.Expiration.types['Breathing'],
|
||||
|
|
@ -173,13 +173,13 @@ def waiting_room_mc():
|
|||
concentration_mc = mc.ConcentrationModel(
|
||||
room=models.Room(volume=100, humidity=0.5),
|
||||
ventilation=models.AirChange(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
air_exch=0.25,
|
||||
),
|
||||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=virus_distributions['SARS_CoV_2_B117'],
|
||||
presence=mc.SpecificInterval(((0, 2),)),
|
||||
presence=mc.SpecificInterval(((0., 2.),)),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Seated'],
|
||||
expiration=models.MultipleExpiration(
|
||||
|
|
@ -215,7 +215,7 @@ def skagit_chorale_mc():
|
|||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=virus_distributions['SARS_CoV_2'],
|
||||
presence=mc.SpecificInterval(((0, 2.5),)),
|
||||
presence=mc.SpecificInterval(((0., 2.5),)),
|
||||
mask=models.Mask.types["No mask"],
|
||||
activity=activity_distributions['Light activity'],
|
||||
expiration=models.Expiration((5., 5., 5.)),
|
||||
|
|
@ -261,10 +261,10 @@ def test_report_models(mc_model, expected_pi, expected_new_cases,
|
|||
@pytest.mark.parametrize(
|
||||
"mask_type, month, expected_pi, expected_dose, expected_ER",
|
||||
[
|
||||
["No mask", "7", 30.0, 405.84, 3894],
|
||||
["Type I", "7", 10.2, 73.38, 702],
|
||||
["FFP2", "7", 4.0, 73.38, 702],
|
||||
["Type I", "2", 4.25, 21.42, 702],
|
||||
["No mask", "Jul", 30.0, 405.84, 3894],
|
||||
["Type I", "Jul", 10.2, 73.38, 702],
|
||||
["FFP2", "Jul", 4.0, 73.38, 702],
|
||||
["Type I", "Feb", 4.25, 21.42, 702],
|
||||
],
|
||||
)
|
||||
def test_small_shared_office_Geneva(mask_type, month, expected_pi,
|
||||
|
|
@ -274,13 +274,13 @@ def test_small_shared_office_Geneva(mask_type, month, expected_pi,
|
|||
ventilation=models.MultipleVentilation(
|
||||
(
|
||||
models.SlidingWindow(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
inside_temp=models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=data.Temperatures[month],
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=data.GenevaTemperatures[month],
|
||||
window_height=1.5, opening_length=0.2,
|
||||
),
|
||||
models.AirChange(
|
||||
active=models.SpecificInterval(((0,24),)),
|
||||
active=models.SpecificInterval(((0., 24.),)),
|
||||
air_exch=0.25,
|
||||
),
|
||||
),
|
||||
|
|
@ -288,7 +288,7 @@ def test_small_shared_office_Geneva(mask_type, month, expected_pi,
|
|||
infected=mc.InfectedPopulation(
|
||||
number=1,
|
||||
virus=virus_distributions['SARS_CoV_2_B117'],
|
||||
presence=mc.SpecificInterval(((9, 10+2/3), (10+5/6, 12.5), (13.5, 15+2/3), (15+5/6, 18))),
|
||||
presence=mc.SpecificInterval(((9., 10+2/3), (10+5/6, 12.5), (13.5, 15+2/3), (15+5/6, 18.))),
|
||||
mask=models.Mask.types[mask_type],
|
||||
activity=activity_distributions['Seated'],
|
||||
expiration=models.MultipleExpiration(
|
||||
|
|
|
|||
Loading…
Reference in a new issue