Use floats throughout the cara codebase to represent time.
This commit is contained in:
Phil Elson
parent
e1d8b3a2c8
commit
aa269aaf6e
12 changed files with 193 additions and 135 deletions
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@ -550,7 +550,6 @@ class FormData:
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if current_time < finish:
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LOG.debug("trailing interval")
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present_intervals.append((current_time / 60, finish / 60))
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return models.SpecificInterval(tuple(present_intervals))
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def infected_present_interval(self) -> models.Interval:
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@ -34,8 +34,10 @@ def calculate_report_data(model: models.ExposureModel):
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t_start, t_end = model_start_end(model)
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times = np.linspace(t_start, t_end, resolution)
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concentrations = [np.array(model.concentration_model.concentration(time)).mean()
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for time in times]
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concentrations = [
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np.array(model.concentration_model.concentration(float(time))).mean()
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for time in times
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]
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highest_const = max(concentrations)
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prob = np.array(model.infection_probability()).mean()
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er = np.array(model.concentration_model.infected.emission_rate_when_present()).mean()
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@ -488,14 +488,14 @@ baseline_model = models.ExposureModel(
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room=models.Room(volume=75),
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ventilation=models.SlidingWindow(
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active=models.PeriodicInterval(period=120, duration=15),
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inside_temp=models.PiecewiseConstant((0,24),(293.15,)),
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outside_temp=models.PiecewiseConstant((0,24),(283.15,)),
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inside_temp=models.PiecewiseConstant((0., 24.), (293.15,)),
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outside_temp=models.PiecewiseConstant((0., 24.), (283.15,)),
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window_height=1.6, opening_length=0.6,
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),
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infected=models.InfectedPopulation(
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number=1,
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virus=models.Virus.types['SARS_CoV_2'],
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presence=models.SpecificInterval(((8, 12), (13, 17))),
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presence=models.SpecificInterval(((8., 12.), (13., 17.))),
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Seated'],
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expiration=models.Expiration.types['Talking'],
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@ -503,7 +503,7 @@ baseline_model = models.ExposureModel(
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),
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exposed=models.Population(
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number=10,
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presence=models.SpecificInterval(((8, 12), (13, 17))),
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presence=models.SpecificInterval(((8., 12.), (13., 17.))),
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activity=models.Activity.types['Seated'],
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mask=models.Mask.types['No mask'],
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),
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16
cara/data.py
16
cara/data.py
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@ -30,15 +30,19 @@ Geneva_hourly_temperatures_celsius_per_hour = {
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}
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# Geneva hourly temperatures as piecewise constant function (in Kelvin)
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# Geneva hourly temperatures as piecewise constant function (in Kelvin).
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GenevaTemperatures_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 Geneva_hourly_temperatures_celsius_per_hour.items()
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month: models.PiecewiseConstant(
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# NOTE: It is important that the time type is float, not np.float, in
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# order to allow hashability (for caching).
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tuple(float(time) for time in range(25)),
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tuple(273.15 + np.array(temperatures)),
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)
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for month, temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
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}
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# same temperatures on a finer temperature mesh
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# Same temperatures on a finer temperature mesh.
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GenevaTemperatures = {
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month: GenevaTemperatures_hourly[month].refine(refine_factor=4)
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for month,temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
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for month, temperatures in Geneva_hourly_temperatures_celsius_per_hour.items()
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}
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@ -46,6 +46,8 @@ else:
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# by providing a no-op cache decorator when type-checking.
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cached = lambda *cached_args, **cached_kwargs: lambda function: function # noqa
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from .utils import method_cache
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from .dataclass_utils import nested_replace
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@ -62,7 +64,7 @@ class Room:
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volume: _VectorisedFloat
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#: The humidity in the room (from 0 to 1 - e.g. 0.5 is 50% humidity)
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humidity: _VectorisedFloat=0.5
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humidity: _VectorisedFloat = 0.5
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Time_t = typing.TypeVar('Time_t', float, int)
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@ -127,7 +129,9 @@ class PeriodicInterval(Interval):
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return tuple()
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result = []
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for i in np.arange(0, 24, self.period / 60):
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result.append((i, i+self.duration/60))
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# NOTE: It is important that the time type is float, not np.float, in
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# order to allow hashability (for caching).
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result.append((float(i), float(i+self.duration/60)))
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return tuple(result)
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@ -183,7 +187,9 @@ class PiecewiseConstant:
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np.concatenate([self.values, self.values[-1:]], axis=0),
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axis=0)
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return PiecewiseConstant(
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tuple(refined_times),
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# NOTE: It is important that the time type is float, not np.float, in
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# order to allow hashability (for caching).
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tuple(float(time) for time in refined_times),
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tuple(interpolator(refined_times)[:-1]),
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)
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@ -740,8 +746,10 @@ class ConcentrationModel:
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# Deposition rate (h^-1)
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k = (vg * 3600) / h
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return k + self.virus.decay_constant(self.room.humidity
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) + self.ventilation.air_exchange(self.room, time)
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return (
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k + self.virus.decay_constant(self.room.humidity)
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+ self.ventilation.air_exchange(self.room, time)
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)
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def _concentration_limit(self, time: float) -> _VectorisedFloat:
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"""
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@ -754,7 +762,7 @@ class ConcentrationModel:
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return (self.infected.emission_rate(time)) / (IVRR * V)
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def state_change_times(self):
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def state_change_times(self) -> typing.List[float]:
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"""
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All time dependent entities on this model must provide information about
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the times at which their state changes.
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@ -763,10 +771,9 @@ class ConcentrationModel:
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state_change_times = set()
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state_change_times.update(self.infected.presence.transition_times())
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state_change_times.update(self.ventilation.transition_times())
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return sorted(state_change_times)
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def last_state_change(self, time: float):
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def last_state_change(self, time: float) -> float:
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"""
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Find the most recent state change.
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@ -774,9 +781,9 @@ class ConcentrationModel:
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for change_time in self.state_change_times()[::-1]:
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if change_time < time:
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return change_time
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return 0
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return 0.
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def _next_state_change(self, time: float):
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def _next_state_change(self, time: float) -> float:
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"""
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Find the nearest future state change.
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@ -797,7 +804,7 @@ class ConcentrationModel:
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"""
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return (self.last_state_change(stop) <= start)
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@cached()
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@method_cache
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def _concentration_at_state_change(self, time: float) -> _VectorisedFloat:
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return self.concentration(time)
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@ -811,7 +818,6 @@ class ConcentrationModel:
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Note that time is not vectorised. You can only pass a single float
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to this method.
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"""
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if time == 0:
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return 0.0
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next_state_change_time = self._next_state_change(time)
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@ -839,7 +845,7 @@ class ConcentrationModel:
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start = max([interval_start, req_start])
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stop = min([interval_stop, req_stop])
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conc_start = self.concentration(start)
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conc_start = self._concentration_at_state_change(start)
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next_conc_state = self._next_state_change(stop)
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conc_limit = self._concentration_limit(next_conc_state)
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@ -8,13 +8,13 @@ def baseline_model():
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model = models.ConcentrationModel(
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room=models.Room(volume=75),
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ventilation=models.AirChange(
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active=models.SpecificInterval(((0,24),)),
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active=models.SpecificInterval(((0., 24.), )),
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air_exch=30.,
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),
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infected=models.InfectedPopulation(
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number=1,
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virus=models.Virus.types['SARS_CoV_2'],
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presence=models.SpecificInterval(((0, 4), (5, 8))),
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presence=models.SpecificInterval(((0., 4.), (5., 8.))),
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mask=models.Mask.types['No mask'],
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activity=models.Activity.types['Light activity'],
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expiration=models.Expiration.types['Superspreading event'],
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@ -53,7 +53,7 @@ def test_concentration_model_vectorisation(override_params):
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@pytest.fixture
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def simple_conc_model():
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interesting_times = models.SpecificInterval(([0, 1], [1.1, 1.999], [2, 3]), )
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interesting_times = models.SpecificInterval(([0., 1.], [1.1, 1.999], [2., 3.]), )
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return models.ConcentrationModel(
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models.Room(75),
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models.AirChange(interesting_times, 100),
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@ -86,13 +86,13 @@ def test_next_state_change_time(
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time,
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expected_next_state_change,
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):
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assert simple_conc_model._next_state_change(time) == expected_next_state_change
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assert simple_conc_model._next_state_change(float(time)) == expected_next_state_change
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def test_next_state_change_time_out_of_range(simple_conc_model: models.ConcentrationModel):
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with pytest.raises(
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ValueError,
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match=re.escape("The requested time (3.1) is greater than last available state change time (3)")
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match=re.escape("The requested time (3.1) is greater than last available state change time (3.0)")
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):
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simple_conc_model._next_state_change(3.1)
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@ -7,6 +7,7 @@ from dataclasses import dataclass
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from cara import models
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from cara.models import ExposureModel
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from cara.dataclass_utils import replace
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@dataclass(frozen=True)
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@ -26,10 +27,10 @@ class KnownConcentrations(models.ConcentrationModel):
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return self.concentration_function(time)
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def state_change_times(self):
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return [0, 24]
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return [0., 24.]
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def _next_state_change(self, time: float):
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return 24
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return 24.
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def concentration(self, time: float) -> models._VectorisedFloat: # noqa
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return self.concentration_function(time)
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@ -53,37 +54,37 @@ populations = [
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models.Activity(np.array([0.51, 0.57]), 0.57),
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),
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]
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dummy_room = models.Room(50, 0.5)
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dummy_ventilation = models._VentilationBase()
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dummy_infected_population = models.InfectedPopulation(
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number=1,
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presence=halftime,
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mask=models.Mask.types['Type I'],
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activity=models.Activity.types['Standing'],
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virus=models.Virus.types['SARS_CoV_2_B117'],
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expiration=models.Expiration.types['Talking']
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)
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def known_concentrations(func):
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dummy_room = models.Room(50, 0.5)
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dummy_ventilation = models._VentilationBase()
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dummy_infected_population = models.InfectedPopulation(
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number=1,
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presence=halftime,
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mask=models.Mask.types['Type I'],
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activity=models.Activity.types['Standing'],
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virus=models.Virus.types['SARS_CoV_2_B117'],
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expiration=models.Expiration.types['Talking']
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)
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return KnownConcentrations(dummy_room, dummy_ventilation, dummy_infected_population, func)
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@pytest.mark.parametrize(
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"population, cm, f_dep, expected_exposure, expected_probability", [
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[populations[1], known_concentrations(lambda t: 36), 1.,
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[populations[1], known_concentrations(lambda t: 36.), 1.,
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np.array([432, 432]), np.array([99.6803184113, 99.5181053773])],
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[populations[2], known_concentrations(lambda t: 36), 1.,
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[populations[2], known_concentrations(lambda t: 36.), 1.,
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np.array([432, 432]), np.array([97.4574432074, 98.3493482895])],
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[populations[0], known_concentrations(lambda t: np.array([36, 72])), 1.,
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[populations[0], known_concentrations(lambda t: np.array([36., 72.])), 1.,
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np.array([432, 864]), np.array([98.3493482895, 99.9727534893])],
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[populations[1], known_concentrations(lambda t: np.array([36, 72])), 1.,
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[populations[1], known_concentrations(lambda t: np.array([36., 72.])), 1.,
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np.array([432, 864]), np.array([99.6803184113, 99.9976777757])],
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[populations[0], known_concentrations(lambda t: 72), np.array([0.5, 1.]),
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[populations[0], known_concentrations(lambda t: 72.), np.array([0.5, 1.]),
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864, np.array([98.3493482895, 99.9727534893])],
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])
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def test_exposure_model_ndarray(population, cm, f_dep,
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@ -104,8 +105,8 @@ def test_exposure_model_ndarray(population, cm, f_dep,
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@pytest.mark.parametrize("population", populations)
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def test_exposure_model_ndarray_and_float_mix(population):
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cm = known_concentrations(lambda t: 0 if np.floor(t) %
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2 else np.array([1.2, 1.2]))
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cm = known_concentrations(
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lambda t: 0. if np.floor(t) % 2 else np.array([1.2, 1.2]))
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model = ExposureModel(cm, population)
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expected_exposure = np.array([14.4, 14.4])
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@ -135,8 +136,9 @@ def test_exposure_model_compare_scalar_vector(population):
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@pytest.fixture
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def conc_model():
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interesting_times = models.SpecificInterval(
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([0, 1], [1.01, 1.02], [12, 24]))
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always = models.SpecificInterval(((0, 24),))
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([0., 1.], [1.01, 1.02], [12., 24.]),
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)
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always = models.SpecificInterval(((0., 24.), ))
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return models.ConcentrationModel(
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models.Room(25),
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models.AirChange(always, 5),
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@ -150,18 +152,19 @@ def conc_model():
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)
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)
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# expected exposure were computed with a trapezoidal integration, using
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# Expected exposure were computed with a trapezoidal integration, using
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# a mesh of 10'000 pts per exposed presence interval.
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@pytest.mark.parametrize("exposed_time_interval, expected_exposure", [
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[(0, 1), 266.67176],
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[(1, 1.01), 3.0879539],
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[(1.01, 1.02), 3.00082435],
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[(12, 12.01), 0.095063235],
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[(12, 24), 3775.65025],
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[(0, 24), 4097.8494],
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]
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@pytest.mark.parametrize(
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["exposed_time_interval", "expected_exposure"],
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[
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[(0., 1.), 266.67176],
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[(1., 1.01), 3.0879539],
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[(1.01, 1.02), 3.00082435],
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[(12., 12.01), 0.095063235],
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[(12., 24.), 3775.65025],
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[(0., 24.), 4097.8494],
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]
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)
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def test_exposure_model_integral_accuracy(exposed_time_interval,
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expected_exposure, conc_model):
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@ -186,10 +189,10 @@ def test_infectious_dose_vectorisation():
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),
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expiration=models.Expiration.types['Talking']
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)
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cm = KnownConcentrations(
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dummy_room, dummy_ventilation, infected_population, lambda t: 1.2)
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cm = known_concentrations(lambda t: 1.2)
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cm = replace(cm, infected=infected_population)
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presence_interval = models.SpecificInterval(((0, 1),))
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presence_interval = models.SpecificInterval(((0., 1.),))
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population = models.Population(
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10, presence_interval, models.Mask.types['Type I'],
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models.Activity.types['Standing'],
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@ -9,7 +9,7 @@ import cara.data as data
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def test_no_mask_superspeading_emission_rate(baseline_model):
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expected_rate = 48500.
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npt.assert_allclose(
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[baseline_model.infected.emission_rate(t) for t in [0, 1, 4, 4.5, 5, 8, 9]],
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[baseline_model.infected.emission_rate(float(t)) for t in [0, 1, 4, 4.5, 5, 8, 9]],
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[0, expected_rate, expected_rate, 0, 0, expected_rate, 0],
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rtol=1e-12
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)
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@ -19,8 +19,8 @@ def test_no_mask_superspeading_emission_rate(baseline_model):
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def baseline_periodic_window():
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return models.SlidingWindow(
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active=models.PeriodicInterval(period=120, duration=15),
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inside_temp=models.PiecewiseConstant((0,24),(293,)),
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outside_temp=models.PiecewiseConstant((0,24),(283,)),
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inside_temp=models.PiecewiseConstant((0., 24.), (293,)),
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outside_temp=models.PiecewiseConstant((0., 24.), (283,)),
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window_height=1.6, opening_length=0.6,
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)
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@ -41,7 +41,7 @@ def baseline_periodic_hepa():
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def test_concentrations(baseline_model):
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# expected concentrations were computed analytically
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ts = [0, 4, 5, 7, 10]
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concentrations = [baseline_model.concentration(t) for t in ts]
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concentrations = [baseline_model.concentration(float(t)) for t in ts]
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npt.assert_allclose(
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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.GenevaTemperatures_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,)),
|
||||
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'],
|
||||
|
|
@ -288,7 +299,7 @@ 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(
|
||||
|
|
@ -302,8 +313,11 @@ 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.),))
|
||||
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.
|
||||
|
||||
|
||||
|
|
@ -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 = 'Jan'
|
||||
m1 = build_hourly_dependent_model(month,intervals_open=((0, 24),))
|
||||
m2 = build_hourly_dependent_model(month,intervals_open=((0, 24),),
|
||||
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.,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -367,8 +384,8 @@ 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()
|
||||
|
|
@ -388,8 +405,8 @@ 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)),
|
||||
intervals_open=((0., 24.),),
|
||||
intervals_presence_infected=((8., 12.), (13., 17.)),
|
||||
temperatures=data.GenevaTemperatures,
|
||||
)
|
||||
)
|
||||
|
|
|
|||
|
|
@ -43,14 +43,14 @@ def baseline_mc_model() -> cara.monte_carlo.ConcentrationModel:
|
|||
room=cara.monte_carlo.Room(volume=cara.monte_carlo.sampleable.Normal(75, 20)),
|
||||
ventilation=cara.monte_carlo.SlidingWindow(
|
||||
active=cara.models.PeriodicInterval(period=120, duration=120),
|
||||
inside_temp=cara.models.PiecewiseConstant((0, 24), (293,)),
|
||||
outside_temp=cara.models.PiecewiseConstant((0, 24), (283,)),
|
||||
inside_temp=cara.models.PiecewiseConstant((0., 24.), (293,)),
|
||||
outside_temp=cara.models.PiecewiseConstant((0., 24.), (283,)),
|
||||
window_height=1.6, opening_length=0.6,
|
||||
),
|
||||
infected=cara.models.InfectedPopulation(
|
||||
number=1,
|
||||
virus=cara.models.Virus.types['SARS_CoV_2'],
|
||||
presence=cara.models.SpecificInterval(((0, 4), (5, 8))),
|
||||
presence=cara.models.SpecificInterval(((0., 4.), (5., 8.))),
|
||||
mask=cara.models.Mask.types['No mask'],
|
||||
activity=cara.models.Activity.types['Light activity'],
|
||||
expiration=cara.models.Expiration.types['Breathing'],
|
||||
|
|
@ -75,8 +75,8 @@ def baseline_mc_exposure_model(baseline_mc_model) -> cara.monte_carlo.ExposureMo
|
|||
def test_build_concentration_model(baseline_mc_model: cara.monte_carlo.ConcentrationModel):
|
||||
model = baseline_mc_model.build_model(7)
|
||||
assert isinstance(model, cara.models.ConcentrationModel)
|
||||
assert isinstance(model.concentration(time=0), float)
|
||||
conc = model.concentration(time=1)
|
||||
assert isinstance(model.concentration(time=0.), float)
|
||||
conc = model.concentration(time=1.)
|
||||
assert isinstance(conc, np.ndarray)
|
||||
assert conc.shape == (7, )
|
||||
|
||||
|
|
|
|||
|
|
@ -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.)),
|
||||
|
|
@ -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,)),
|
||||
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(
|
||||
|
|
|
|||
27
cara/utils.py
Normal file
27
cara/utils.py
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
import functools
|
||||
|
||||
|
||||
def method_cache(fn):
|
||||
"""
|
||||
A decorator for instance based caching.
|
||||
|
||||
Unlike lru_cache / memoization, this allows us to not have to have the
|
||||
instance itself be hashable - only the arguments must be so.
|
||||
|
||||
The cache is stored as a dictionary in a private attribute on the instance
|
||||
with the name ``_cache_{func_name}``.
|
||||
|
||||
"""
|
||||
cache_name = f'_cache_{fn.__name__}'
|
||||
|
||||
@functools.wraps(fn)
|
||||
def cached_method(self, *args, **kwargs):
|
||||
cache = getattr(self, cache_name, None)
|
||||
if cache is None:
|
||||
cache = {}
|
||||
object.__setattr__(self, cache_name, cache)
|
||||
cache_key = hash(args + tuple(kwargs.items()))
|
||||
if cache_key not in cache:
|
||||
cache[cache_key] = fn(self, *args, **kwargs)
|
||||
return cache[cache_key]
|
||||
return cached_method
|
||||
Loading…
Reference in a new issue