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Merge branch 'feature/code_cleanout' into 'master'

Browse source 
Code cleanup

Closes #250 and #249

See merge request cara/cara!364
This commit is contained in:
Philip Elson 2022-06-09 09:46:32 +02:00
commit 9231890aa4
25 changed files with 107 additions and 121 deletions
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1
app-config/auth-service/auth_service/__init__.py
View file

@ -13,7 +13,6 @@ from keycloak.aio.realm import KeycloakRealm
from tornado.web import Application, RequestHandler
import tornado.log
LOG = logging.getLogger(__name__)

3
app-config/auth-service/setup.py
View file

@ -8,12 +8,10 @@ https://packaging.python.org/guides/distributing-packages-using-setuptools/
from pathlib import Path
from setuptools import setup, find_packages
HERE = Path(__file__).parent.absolute()
with (HERE / 'README.md').open('rt') as fh:
LONG_DESCRIPTION = fh.read().strip()
REQUIREMENTS: dict = {
'core': [
'aiohttp',
@ -27,7 +25,6 @@ REQUIREMENTS: dict = {
],
}
setup(
name='auth-service',
version="0.0.1",

1
cara/apps/calculator/__init__.py
View file

@ -25,7 +25,6 @@ from . import model_generator
from .report_generator import ReportGenerator, calculate_report_data
from .user import AuthenticatedUser, AnonymousUser
# The calculator version is based on a combination of the model version and the
# semantic version of the calculator itself. The version uses the terms
# "{MAJOR}.{MINOR}.{PATCH}" to describe the 3 distinct numbers constituting a version.

1
cara/apps/calculator/markdown_tools.py
View file

@ -3,7 +3,6 @@ import re
import jinja2
import mistune
HEADER_PATTERN = re.compile(r'\n(#+)(.*)')

7
cara/apps/calculator/model_generator.py
View file

@ -16,13 +16,10 @@ from .. import calculator
from cara.monte_carlo.data import activity_distributions, virus_distributions, mask_distributions, short_range_distances
from cara.monte_carlo.data import expiration_distribution, expiration_BLO_factors, expiration_distributions, short_range_expiration_distributions
LOG = logging.getLogger(__name__)
minutes_since_midnight = typing.NewType('minutes_since_midnight', int)
# Used to declare when an attribute of a class must have a value provided, and
# there should be no default value used.
_NO_DEFAULT = object()
@ -365,7 +362,7 @@ class FormData:
ventilation = models.HVACMechanical(
active=always_on, q_air_mech=self.air_supply)
# this is a minimal, always present source of ventilation, due
# This is a minimal, always present source of ventilation, due
# to the air infiltration from the outside.
# See CERN-OPEN-2021-004, p. 12.
infiltration_ventilation = models.AirChange(active=always_on, air_exch=0.25)
@ -583,8 +580,6 @@ class FormData:
present_intervals = []
# def add_interval(start, end):
current_time = start
LOG.debug(f"starting time march at {_hours2timestring(current_time/60)} to {_hours2timestring(finish/60)}")

2
cara/data/__init__.py
View file

@ -11,7 +11,7 @@ MONTH_NAMES = [
def get_hourly_temperatures_celsius_per_hour(coordinates):
wx_station_id = nearest_wx_station(
longitude=coordinates[1], latitude=coordinates[0])[0]
# average temperature of each month, hour per hour (from midnight to 11 pm)
# Average temperature of each month, hour per hour (from midnight to 11 pm)
return {month.replace(month, MONTH_NAMES[i][:3]):
[t - 273.15 for t in temp] for i, (month, temp)
in enumerate(wx_data()[wx_station_id].items())}

58
cara/models.py
View file

@ -56,7 +56,6 @@ oneoverln2 = 1 / np.log(2)
_VectorisedFloat = typing.Union[float, np.ndarray]
_VectorisedInt = typing.Union[int, np.ndarray]
Time_t = typing.TypeVar('Time_t', float, int)
BoundaryPair_t = typing.Tuple[Time_t, Time_t]
BoundarySequence_t = typing.Union[typing.Tuple[BoundaryPair_t, ...], typing.Tuple]
@ -130,7 +129,7 @@ class PeriodicInterval(Interval):
@dataclass(frozen=True)
class PiecewiseConstant:
# TODO: implement rather a periodic version (24-hour period), where
# TODO: Implement rather a periodic version (24-hour period), where
# transition_times and values have the same length.
#: transition times at which the function changes value (hours).
@ -161,7 +160,7 @@ class PiecewiseConstant:
return value
def interval(self) -> Interval:
# build an Interval object
# Build an Interval object
present_times = []
for t1, t2, value in zip(self.transition_times[:-1],
self.transition_times[1:], self.values):
@ -170,7 +169,7 @@ class PiecewiseConstant:
return SpecificInterval(present_times=tuple(present_times))
def refine(self, refine_factor=10) -> "PiecewiseConstant":
# build a new PiecewiseConstant object with a refined mesh,
# Build a new PiecewiseConstant object with a refined mesh,
# using a linear interpolation in-between the initial mesh points
refined_times = np.linspace(self.transition_times[0], self.transition_times[-1],
(len(self.transition_times)-1) * refine_factor+1)
@ -482,7 +481,7 @@ Virus.types = {
transmissibility_factor=1.0,
),
'SARS_CoV_2_ALPHA': SARSCoV2(
# also called VOC-202012/01
# Also called VOC-202012/01
viral_load_in_sputum=1e9,
infectious_dose=50.,
viable_to_RNA_ratio = 0.5,
@ -630,19 +629,19 @@ class _ExpirationBase:
@property
def particle(self) -> Particle:
"""
the Particle object representing the aerosol - here the default one
The Particle object representing the aerosol - here the default one
"""
return Particle()
def aerosols(self, mask: Mask):
"""
total volume of aerosols expired per volume of air (mL/cm^3).
Total volume of aerosols expired per volume of air (mL/cm^3).
"""
raise NotImplementedError("Subclass must implement")
def jet_origin_concentration(self):
"""
concentration of viruses at the jet origin (mL/m3).
Concentration of viruses at the jet origin (mL/m3).
"""
raise NotImplementedError("Subclass must implement")
@ -657,7 +656,7 @@ class Expiration(_ExpirationBase):
#: diameter of the aerosol in microns
diameter: _VectorisedFloat
#: total concentration of aerosols per unit volume of expired air
#: Total concentration of aerosols per unit volume of expired air
# (in cm^-3), integrated over all aerosol diameters (corresponding
# to c_n,i in Eq. (4) of https://doi.org/10.1101/2021.10.14.21264988)
cn: float = 1.
@ -665,7 +664,7 @@ class Expiration(_ExpirationBase):
@property
def particle(self) -> Particle:
"""
the Particle object representing the aerosol
The Particle object representing the aerosol
"""
return Particle(diameter=self.diameter)
@ -675,7 +674,7 @@ class Expiration(_ExpirationBase):
def volume(d):
return (np.pi * d**3) / 6.
# final result converted from microns^3/cm3 to mL/cm^3
# Final result converted from microns^3/cm3 to mL/cm^3
return self.cn * (volume(self.diameter) *
(1 - mask.exhale_efficiency(self.diameter))) * 1e-12
@ -684,7 +683,7 @@ class Expiration(_ExpirationBase):
def volume(d):
return (np.pi * d**3) / 6.
# final result converted from microns^3/cm3 to mL/m3
# Final result converted from microns^3/cm3 to mL/m3
return self.cn * volume(self.diameter) * 1e-6
@ -792,7 +791,7 @@ class _PopulationWithVirus(Population):
def aerosols(self):
"""
total volume of aerosols expired per volume of air (mL/cm^3).
Total volume of aerosols expired per volume of air (mL/cm^3).
"""
raise NotImplementedError("Subclass must implement")
@ -833,7 +832,7 @@ class _PopulationWithVirus(Population):
@property
def particle(self) -> Particle:
"""
the Particle object representing the aerosol expired by the
The Particle object representing the aerosol expired by the
population - here we take the default Particle object
"""
return Particle()
@ -846,7 +845,7 @@ class EmittingPopulation(_PopulationWithVirus):
def aerosols(self):
"""
total volume of aerosols expired per volume of air (mL/cm^3).
Total volume of aerosols expired per volume of air (mL/cm^3).
Here arbitrarily set to 1 as the full emission rate is known.
"""
return 1.
@ -875,7 +874,7 @@ class InfectedPopulation(_PopulationWithVirus):
def aerosols(self):
"""
total volume of aerosols expired per volume of air (mL/cm^3).
Total volume of aerosols expired per volume of air (mL/cm^3).
"""
return self.expiration.aerosols(self.mask)
@ -897,7 +896,7 @@ class InfectedPopulation(_PopulationWithVirus):
@property
def particle(self) -> Particle:
"""
the Particle object representing the aerosol - here the default one
The Particle object representing the aerosol - here the default one
"""
return self.expiration.particle
@ -924,7 +923,6 @@ class ConcentrationModel:
h = 1.5
# Deposition rate (h^-1)
k = (vg * 3600) / h
#todo: Inside_temp needs to be exposed/added to the room;
return (
k + self.virus.decay_constant(self.room.humidity, self.room.inside_temp.value(time))
+ self.ventilation.air_exchange(self.room, time)
@ -1274,8 +1272,10 @@ class ExposureModel:
self.concentration_model.evaporation_factor)
def _long_range_normed_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""The number of virions per meter^3 between any two times, normalized
by the emission rate of the infected population"""
"""
The number of virions per meter^3 between any two times, normalized
by the emission rate of the infected population
"""
exposure = 0.
for start, stop in self.exposed.presence.boundaries():
if stop < time1:
@ -1315,7 +1315,7 @@ class ExposureModel:
diameter = self.concentration_model.infected.particle.diameter
if not np.isscalar(diameter) and diameter is not None:
# we compute first the mean of all diameter-dependent quantities
# We compute first the mean of all diameter-dependent quantities
# to perform properly the Monte-Carlo integration over
# particle diameters (doing things in another order would
# lead to wrong results for the probability of infection).
@ -1323,11 +1323,11 @@ class ExposureModel:
aerosols *
fdep).mean()
else:
# in the case of a single diameter or no diameter defined,
# In the case of a single diameter or no diameter defined,
# one should not take any mean at this stage.
dep_exposure_integrated = self._long_range_normed_exposure_between_bounds(time1, time2)*aerosols*fdep
# then we multiply by the diameter-independent quantity emission_rate_per_aerosol,
# Then we multiply by the diameter-independent quantity emission_rate_per_aerosol,
# and parameters of the vD equation (i.e. BR_k and n_in).
deposited_exposure += (dep_exposure_integrated * emission_rate_per_aerosol *
self.exposed.activity.inhalation_rate *
@ -1361,7 +1361,7 @@ class ExposureModel:
# Aerosols not considered given the formula for the initial
# concentration at mouth/nose.
if diameter is not None and not np.isscalar(diameter):
# we compute first the mean of all diameter-dependent quantities
# We compute first the mean of all diameter-dependent quantities
# to perform properly the Monte-Carlo integration over
# particle diameters (doing things in another order would
# lead to wrong results for the probability of infection).
@ -1370,24 +1370,24 @@ class ExposureModel:
- np.array(short_range_lr_exposure * fdep).mean()
* self.concentration_model.infected.activity.exhalation_rate)
else:
# in the case of a single diameter or no diameter defined,
# In the case of a single diameter or no diameter defined,
# one should not take any mean at this stage.
this_deposited_exposure = (short_range_jet_exposure * fdep
- short_range_lr_exposure * fdep
* self.concentration_model.infected.activity.exhalation_rate)
# multiply by the (diameter-independent) inhalation rate
# Multiply by the (diameter-independent) inhalation rate
deposited_exposure += (this_deposited_exposure *
interaction.activity.inhalation_rate
/dilution)
# then we multiply by diameter-independent quantities: viral load
# Then we multiply by diameter-independent quantities: viral load
# and fraction of infected virions
f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
deposited_exposure *= (f_inf
* self.concentration_model.virus.viral_load_in_sputum
* (1 - self.exposed.mask.inhale_efficiency()))
# long-range concentration
# Long-range concentration
deposited_exposure += self.long_range_deposited_exposure_between_bounds(time1, time2)
return deposited_exposure
@ -1404,7 +1404,7 @@ class ExposureModel:
return deposited_exposure * self.repeats
def infection_probability(self) -> _VectorisedFloat:
# viral dose (vD)
# Viral dose (vD)
vD = self.deposited_exposure()
# oneoverln2 multiplied by ID_50 corresponds to ID_63.

1
cara/monte_carlo/__init__.pyi
View file

@ -1,4 +1,5 @@
from typing import Any
# For now we disable all type-checking in the monte-carlo submodule.
def __getattr__(name) -> Any: ...

7
cara/monte_carlo/data.py
View file

@ -8,7 +8,6 @@ from scipy.stats import weibull_min
import cara.monte_carlo as mc
from cara.monte_carlo.sampleable import LogCustom, LogNormal,LogCustomKernel,CustomKernel,Uniform, Custom
sqrt2pi = np.sqrt(2.*np.pi)
sqrt2 = np.sqrt(2.)
@ -26,7 +25,7 @@ class BLOmodel:
vol. 42, no. 12, pp. 839 – 851, 2011,
https://doi.org/10.1016/j.jaerosci.2011.07.009).
"""
#: factors assigned to resp. the B, L and O modes. They are
#: Factors assigned to resp. the B, L and O modes. They are
# charateristics of the kind of expiratory activity (e.g. breathing,
# speaking, singing, or shouting). These are applied on top of the
# cn concentrations (see below), and depend on the kind of activity
@ -37,11 +36,11 @@ class BLOmodel:
# total concentration of aerosols for each mode.
cn: typing.Tuple[float, float, float] = (0.06, 0.2, 0.0010008)
# mean of the underlying normal distributions (represents the log of a
# Mean of the underlying normal distributions (represents the log of a
# diameter in microns), for resp. the B, L and O modes.
mu: typing.Tuple[float, float, float] = (0.989541, 1.38629, 4.97673)
# std deviation of the underlying normal distribution, for resp.
# Std deviation of the underlying normal distribution, for resp.
# the B, L and O modes.
sigma: typing.Tuple[float, float, float] = (0.262364, 0.506818, 0.585005)

1
cara/monte_carlo/models.py
View file

@ -7,7 +7,6 @@ import cara.models
from .sampleable import SampleableDistribution, _VectorisedFloatOrSampleable
_ModelType = typing.TypeVar('_ModelType')

1
cara/monte_carlo/sampleable.py
View file

@ -5,7 +5,6 @@ from sklearn.neighbors import KernelDensity # type: ignore
import cara.models
# Declare a float array type of a given size.
# There is no better way to declare this currently, unfortunately.
float_array_size_n = np.ndarray

1
cara/state.py
View file

@ -13,7 +13,6 @@ from contextlib import contextmanager
import dataclasses
import typing
Datamodel_T = typing.TypeVar('Datamodel_T')
dataclass_instance = typing.Any

1
cara/tests/apps/calculator/test_webapp.py
View file

@ -9,6 +9,7 @@ from cara.apps.calculator.report_generator import generate_permalink
_TIMEOUT = 20.
@pytest.fixture
def app():
return cara.apps.calculator.make_app()

2
cara/tests/conftest.py
View file

@ -21,7 +21,7 @@ def baseline_concentration_model():
activity=models.Activity.types['Light activity'],
known_individual_emission_rate=970 * 50,
host_immunity=0.,
# superspreading event, where ejection factor is fixed based
# Superspreading event, where ejection factor is fixed based
# on Miller et al. (2020) - 50 represents the infectious dose.
),
evaporation_factor=0.3,

2
cara/tests/models/test_exposure_model.py
View file

@ -20,7 +20,7 @@ class KnownNormedconcentration(models.ConcentrationModel):
normed_concentration_function: typing.Callable = lambda x: 0
def infectious_virus_removal_rate(self, time: float) -> models._VectorisedFloat:
# very large decay constant -> same as constant concentration
# Very large decay constant -> same as constant concentration
return 1.e50
def _normed_concentration_limit(self, time: float) -> models._VectorisedFloat:

1
cara/tests/models/test_mask.py
View file

@ -4,6 +4,7 @@ import pytest
from cara import models
@pytest.mark.parametrize(
"η_inhale, expected_inhale_efficiency",
[

6
cara/tests/models/test_piecewiseconstant.py
View file

@ -6,12 +6,12 @@ from cara import data
def test_piecewiseconstantfunction_wrongarguments():
# number of values should be 1+number of transition times
# Number of values should be 1+number of transition times
pytest.raises(ValueError, models.PiecewiseConstant, (0, 1), (0, 0))
pytest.raises(ValueError, models.PiecewiseConstant, (0,), (0, 0))
# two transition times cannot be equal
# Two transition times cannot be equal
pytest.raises(ValueError, models.PiecewiseConstant, (0, 2, 2), (0, 0))
# unsorted transition times are not allowed
# Unsorted transition times are not allowed
pytest.raises(ValueError, models.PiecewiseConstant, (2, 0), (0, 0))
# If vectors, must all be same length.

1
cara/tests/models/test_virus.py
View file

@ -4,6 +4,7 @@ import pytest
from cara import models
@pytest.mark.parametrize(
"inside_temp, humidity, expected_halflife, expected_decay_constant",
[

2
cara/tests/test_expiration.py
View file

@ -43,7 +43,7 @@ def test_multiple():
@retry(tries=10)
# expected values obtained from analytical formulas
# Expected values obtained from analytical formulas
@pytest.mark.parametrize(
"BLO_weights, expected_aerosols",
[

101
cara/tests/test_full_algorithm.py
View file

@ -24,6 +24,7 @@ sqrt2pi = np.sqrt(2.*np.pi)
sqrt2 = np.sqrt(2.)
ln2 = np.log(2)
@dataclass(frozen=True)
class SimpleConcentrationModel:
"""
@ -34,54 +35,54 @@ class SimpleConcentrationModel:
times.
"""
#: infected people presence interval
#: Infected people presence interval
infected_presence: Interval
#: viral load (RNA copies / mL)
#: Viral load (RNA copies / mL)
viral_load: _VectorisedFloat
#: breathing rate (m^3/h)
#: Breathing rate (m^3/h)
breathing_rate: _VectorisedFloat
#: room volume (m^3)
#: Room volume (m^3)
room_volume: _VectorisedFloat
#: ventilation rate (air changes per hour) - including HEPA
#: Ventilation rate (air changes per hour) - including HEPA
lambda_ventilation: _VectorisedFloat
#: BLO factors
BLO_factors: typing.Tuple[float, float, float]
#: number of infected people
#: Number of infected people
num_infected: int = 1
#: relative humidity RH
#: Relative humidity RH
humidity: float = 0.3
#: minimum particle diameter considered (microns)
#: Minimum particle diameter considered (microns)
diameter_min: float = 0.1
#: maximum particle diameter considered (microns)
#: Maximum particle diameter considered (microns)
diameter_max: float = 30.
#: evaporation factor
#: Evaporation factor
evaporation: float = 0.3
#: cn (cm^-3) for resp. the B, L and O modes. Corresponds to the
# total concentration of aerosols for each mode.
cn: typing.Tuple[float, float, float] = (0.06, 0.2, 0.0010008)
# mean of the underlying normal distributions (represents the log of a
# Mean of the underlying normal distributions (represents the log of a
# diameter in microns), for resp. the B, L and O modes.
mu: typing.Tuple[float, float, float] = (0.989541, 1.38629, 4.97673)
# std deviation of the underlying normal distribution, for resp.
# Std deviation of the underlying normal distribution, for resp.
# the B, L and O modes.
sigma: typing.Tuple[float, float, float] = (0.262364, 0.506818, 0.585005)
def removal_rate(self) -> _VectorisedFloat:
"""
removal rate lambda in h^-1, excluding the deposition rate.
Removal rate lambda in h^-1, excluding the deposition rate.
"""
hl_calc = ((ln2/((0.16030 + 0.04018*(((293-273.15)-20.615)/10.585)
+0.02176*(((self.humidity*100)-45.235)/28.665)
@ -94,7 +95,7 @@ class SimpleConcentrationModel:
@method_cache
def deposition_removal_coefficient(self) -> float:
"""
coefficient in front of gravitational deposition rate, in h^-1.microns^-2
Coefficient in front of gravitational deposition rate, in h^-1.microns^-2
Note: 0.4512 = 1.88e-4 * 3600 / 1.5
"""
return 0.4512*(self.evaporation/2.5)**2
@ -102,7 +103,7 @@ class SimpleConcentrationModel:
@method_cache
def aerosol_volume(self,diameter: float) -> float:
"""
particle volume in microns^3
Particle volume in microns^3
"""
return 4*np.pi/3. * (diameter/2.)**3
@ -110,7 +111,7 @@ class SimpleConcentrationModel:
def Np(self,diameter: float,
BLO_factors: typing.Tuple[float, float, float]) -> float:
"""
number of emitted particles per unit volume (BLO model)
Number of emitted particles per unit volume (BLO model)
in cm^-3.ln(micron)^-1
"""
result = 0.
@ -122,7 +123,7 @@ class SimpleConcentrationModel:
def vR(self,diameter: float) -> float:
"""
emission rate per unit diameter, in RNA copies / h / micron
Emission rate per unit diameter, in RNA copies / h / micron
"""
return (self.Np(diameter, self.BLO_factors)
* self.aerosol_volume(diameter) * 1e-6)
@ -145,7 +146,7 @@ class SimpleConcentrationModel:
def concentration(self,t: float) -> _VectorisedFloat:
"""
concentration at a given time t
Concentration at a given time t
"""
trans_times = sorted(self.infected_presence.transition_times())
if t==trans_times[0]:
@ -187,28 +188,28 @@ class SimpleShortRangeModel:
This assumes no mask wearing.
"""
#: time intervals in which a short-range interaction occurs
#: Time intervals in which a short-range interaction occurs
interaction_interval: SpecificInterval
#: tuple with interpersonal distanced from infected person (m)
#: Tuple with interpersonal distanced from infected person (m)
distance : _VectorisedFloat = 0.854
#: breathing rate (m^3/h)
#: Breathing rate (m^3/h)
breathing_rate: _VectorisedFloat = 0.51
#: tuple with BLO factors
#: Tuple with BLO factors
BLO_factors: typing.Tuple[float, float, float] = (1,0,0)
#: minimum diameter for integration (short-range only) (microns)
#: Minimum diameter for integration (short-range only) (microns)
diameter_min: float = 0.1
#: maximum diameter for integration (short-range only) (microns)
#: Maximum diameter for integration (short-range only) (microns)
diameter_max: float = 100.
#: mouth opening diameter (m)
#: Mouth opening diameter (m)
D: float = 0.02
#: duration of the expiration (s)
#: Duration of the expiration (s)
tstar: float = 2.
#: Streamwise and radial penetration coefficients
@ -220,27 +221,27 @@ class SimpleShortRangeModel:
@method_cache
def dilution_factor(self) -> _VectorisedFloat:
"""
computes dilution factor at a certain distance x
Computes dilution factor at a certain distance x
based on Wei JIA matlab script.
"""
x = np.array(self.distance)
dilution = np.empty(x.shape, dtype=np.float64)
# expired flow rate during the expiration period, m^3/s
# Expired flow rate during the expiration period, m^3/s
Q0 = np.array(self.breathing_rate/3600)
# the expired flow velocity at the noozle (mouth opening), m/s
# The expired flow velocity at the noozle (mouth opening), m/s
u0 = np.array(Q0/(np.pi/4. * self.D**2))
# parameters in the jet-like stage
# Parameters in the jet-like stage
# position of virtual origin
x01 = self.D/2/self.Cr1
# time of virtual origin
# Time of virtual origin
t01 = (x01/self.Cx1)**2 * (Q0*u0)**(-0.5)
# transition point (in m)
# Transition point (in m)
xstar = np.array(self.Cx1*(Q0*u0)**0.25*(self.tstar + t01)**0.5
- x01)
# dilution factor at the transition point xstar
# Dilution factor at the transition point xstar
Sxstar = np.array(2.*self.Cr1*(xstar+x01)/self.D)
# calculate dilution factor at the short-range distance x
# Calculate dilution factor at the short-range distance x
dilution[x <= xstar] = 2.*self.Cr1*(x[x <= xstar] + x01)/self.D
dilution[x > xstar] = Sxstar[x > xstar]*(1. + self.Cr2*(x[x > xstar]
- xstar[x > xstar])
@ -250,7 +251,7 @@ class SimpleShortRangeModel:
def jet_concentration(self,conc_model: SimpleConcentrationModel) -> _VectorisedFloat:
"""
virion concentration at the origin of the jet (close to
Virion concentration at the origin of the jet (close to
the mouth of the infected person), in m^-3
we perform the integral of Np(d)*V(d) over diameter analytically
"""
@ -269,7 +270,7 @@ class SimpleShortRangeModel:
def concentration(self, conc_model: SimpleConcentrationModel, time: float) -> _VectorisedFloat:
"""
compute the short-range part of the concentration, and add it
Compute the short-range part of the concentration, and add it
to the long-range concentration
"""
if self.interaction_interval.triggered(time):
@ -293,25 +294,25 @@ class SimpleExposureModel(SimpleConcentrationModel):
interaction intervals are within presence intervals of the infected.
"""
#: fraction of infected viruses
#: Fraction of infected viruses
finf: _VectorisedFloat = 0.5
#: host immunity factor (0. for not immune)
#: Host immunity factor (0. for not immune)
HI: _VectorisedFloat = 0.
#: infectious dose (ID50)
#: Infectious dose (ID50)
ID50: _VectorisedFloat = 50.
#: transmissibility factor w.r.t. original strain
#: Transmissibility factor w.r.t. original strain
# (<1 means more transmissible)
transmissibility: _VectorisedFloat = 1.
#: list of short-range interaction models
#: List of short-range interaction models
sr_models: typing.Tuple[SimpleShortRangeModel, ...] = ()
def fdep(self, diameter: float, evaporation: float) -> float:
"""
fraction deposited
Fraction deposited
"""
d = diameter * evaporation
IFrac = 1 - 0.5 * (1 - (1 / (1 + (0.00076*(d**2.8)))))
@ -327,7 +328,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
A general function to compute the main integral over diameters
"""
def integrand(diameter):
# function to return the integrand
# Function to return the integrand
a = self.deposition_removal_coefficient()
a_dsquare = a*diameter**2
return -(self.vR(diameter)*self.fdep(diameter,evaporation)/(
@ -345,7 +346,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
Same as f but with fdep included in the integral.
"""
def integrand(diameter):
# function to return the integrand
# Function to return the integrand
a = self.deposition_removal_coefficient()
a_dsquare = a*diameter**2
return (self.vR(diameter)*self.fdep(diameter,evaporation)
@ -357,7 +358,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
def total_concentration(self, t: float):
"""
total concentration at time t
Total concentration at time t
"""
res = self.concentration(t)
for sr_mod in self.sr_models:
@ -368,7 +369,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
def integrated_longrange_concentration(self,t1: float,t2: float,
evaporation: float) -> _VectorisedFloat:
"""
background (long-range) concentration integrated from t1 to t2
Background (long-range) concentration integrated from t1 to t2
assuming both t1 and t2 are within a single presence interval.
This includes the deposition fraction (fdep).
"""
@ -413,7 +414,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
@method_cache
def integrated_shortrange_concentration(self) -> _VectorisedFloat:
"""
short-range concentration integrated over interaction times and
Short-range concentration integrated over interaction times and
diameters. This includes the deposition fraction (fdep).
"""
result = 0.
@ -436,7 +437,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
def dose(self) -> _VectorisedFloat:
"""
total deposited dose (integrated over time and over particle
Total deposited dose (integrated over time and over particle
diameters), including short and long-range.
"""
result = 0.
@ -450,7 +451,7 @@ class SimpleExposureModel(SimpleConcentrationModel):
def probability_infection(self):
"""
total probability of infection
Total probability of infection
"""
return (1. - np.exp(-self.dose() * ln2 * (1-self.HI)
/(self.ID50 * self.transmissibility) )) * 100.
@ -766,7 +767,7 @@ def test_longrange_exposure_with_distributions(c_model_distr):
)
# tests on the concentration with short-range should be skipped until
# Tests on the concentration with short-range should be skipped until
# one finds a way to avoid the large variability of the concentration
# with short-range 'Speaking' or 'Shouting' interactions
@pytest.mark.skip

10
cara/tests/test_known_quantities.py
View file

@ -38,7 +38,7 @@ def baseline_periodic_hepa():
def test_concentrations(baseline_concentration_model):
# expected concentrations were computed analytically
# Expected concentrations were computed analytically
ts = [0, 4, 5, 7, 10]
concentrations = [baseline_concentration_model.concentration(float(t)) for t in ts]
npt.assert_allclose(
@ -73,7 +73,7 @@ def build_model(interval_duration):
activity=models.Activity.types['Light activity'],
known_individual_emission_rate=970 * 50,
host_immunity=0.,
# superspreading event, where ejection factor is fixed based
# Superspreading event, where ejection factor is fixed based
# on Miller et al. (2020) - 50 represents the infectious dose.
),
evaporation_factor=0.3,
@ -91,7 +91,7 @@ def test_concentrations_startup():
def test_r0(baseline_exposure_model):
# expected r0 was computed with a trapezoidal integration, using
# Expected r0 was computed with a trapezoidal integration, using
# a mesh of 100'000 pts per exposed presence interval.
r0 = baseline_exposure_model.reproduction_number()
npt.assert_allclose(r0, 771.380385)
@ -376,7 +376,7 @@ def build_exposure_model(concentration_model, short_range_model):
)
# expected deposited exposure were computed with a trapezoidal integration, using
# Expected deposited exposure were computed with a trapezoidal integration, using
# a mesh of 100'000 pts per exposed presence interval.
@pytest.mark.parametrize(
"month, expected_deposited_exposure",
@ -396,7 +396,7 @@ def test_exposure_hourly_dep(month,expected_deposited_exposure, baseline_sr_mode
deposited_exposure = m.deposited_exposure()
npt.assert_allclose(deposited_exposure, expected_deposited_exposure)
# expected deposited exposure were computed with a trapezoidal integration, using
# Expected deposited exposure were computed with a trapezoidal integration, using
# a mesh of 100'000 pts per exposed presence interval and 25 pts per hour
# for the temperature discretization.
@pytest.mark.parametrize(

1
cara/tests/test_monte_carlo.py
View file

@ -7,7 +7,6 @@ import cara.models
import cara.monte_carlo.models as mc_models
import cara.monte_carlo.sampleable
MODEL_CLASSES = [
cls for cls in vars(cara.models).values()
if dataclasses.is_dataclass(cls)

4
cara/tests/test_monte_carlo_full_models.py
View file

@ -16,7 +16,6 @@ toronto_coordinates = (43.667, 79.400)
toronto_hourly_temperatures_celsius_per_hour = data.get_hourly_temperatures_celsius_per_hour(
toronto_coordinates)
# Toronto hourly temperatures as piecewise constant function (in Kelvin).
TorontoTemperatures_hourly = {
month: models.PiecewiseConstant(
@ -28,7 +27,6 @@ TorontoTemperatures_hourly = {
for month, temperatures in toronto_hourly_temperatures_celsius_per_hour.items()
}
# Same Toronto temperatures on a finer temperature mesh (every 6 minutes).
TorontoTemperatures = {
month: TorontoTemperatures_hourly[month].refine(refine_factor=10)
@ -36,7 +34,7 @@ TorontoTemperatures = {
}
# references values for infection_probability and expected new cases
# References values for infection_probability and expected new cases
# in the following tests, were obtained from the feature/mc branch
@pytest.fixture
def shared_office_mc():

5
cara/tests/test_predefined_distributions.py
View file

@ -5,8 +5,7 @@ import pytest
from cara.monte_carlo.data import activity_distributions, virus_distributions
# mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
# Mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
# NOTE: a mistake was corrected for the std deviation of the
# "Moderate exercise" case (0.37 in the report, but should be 0.34)
@pytest.mark.parametrize(
@ -28,7 +27,7 @@ def test_activity_distributions(distribution, mean, std):
npt.assert_allclose(activity.inhalation_rate.std(), std, atol=0.01)
# mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
# Mean & std deviations from https://doi.org/10.1101/2021.10.14.21264988 (Table 3)
# - with a refined precision on the values
@pytest.mark.parametrize(
"distribution, mean, std",[

8
cara/tests/test_sampleable_distribution.py
View file

@ -11,7 +11,7 @@ from cara.monte_carlo import sampleable
]
)
def test_normal(mean, std):
# test that the sample has approximately the right mean,
# Test that the sample has approximately the right mean,
# std deviation and distribution function.
sample_size = 2000000
samples = sampleable.Normal(mean, std).generate_samples(sample_size)
@ -32,7 +32,7 @@ def test_normal(mean, std):
]
)
def test_lognormal(mean_gaussian, std_gaussian):
# test that the sample has approximately the right mean,
# Test that the sample has approximately the right mean,
# std deviation and distribution function.
sample_size = 2000000
samples = sampleable.LogNormal(mean_gaussian, std_gaussian
@ -58,7 +58,7 @@ def test_lognormal(mean_gaussian, std_gaussian):
[False, True],
)
def test_custom(use_kernel):
# test that the sample has approximately the right distribution
# Test that the sample has approximately the right distribution
# function, with both Custom and CustomKernel method. The latter
# is less accurate for smooth functions.
# the distribution function is an inverted parabola, with maximum 0.15,
@ -87,7 +87,7 @@ def test_custom(use_kernel):
def test_logcustomkernel():
# test that the sample has approximately the right distribution
# Test that the sample has approximately the right distribution
# function, for the LogCustomKernel.
# the distribution function is an inverted parabola vs. the log of
# the variable (normalized)