101 lines
No EOL
3.5 KiB
Python
101 lines
No EOL
3.5 KiB
Python
import numpy as np
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import numpy.testing as npt
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import pytest
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from caimira import models
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@pytest.mark.parametrize(
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"activity_type, ventilation_active, air_exch", [
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['Seated', [8, 12, 13, 17], [0.25, 2.45, 0.25]],
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['Standing', [8, 10, 11, 12, 17], [1.25, 3.25, 1.45, 0.25]],
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['Light activity', [8, 12, 17], [1.25, 0.25]],
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['Moderate activity', [8, 13, 15, 16, 17], [2.25, 0.25, 3.45, 0.25]],
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['Heavy exercise', [8, 17], [0.25]],
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]
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)
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def test_fitting_algorithm_custom_ventilation(activity_type, ventilation_active, air_exch):
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conc_model = models.CO2ConcentrationModel(
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room=models.Room(
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volume=75, inside_temp=models.PiecewiseConstant((0., 24.), (293,))),
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ventilation=models.CustomVentilation(models.PiecewiseConstant(
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tuple(ventilation_active), tuple(air_exch))),
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CO2_emitters=models.SimplePopulation(
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number=models.IntPiecewiseConstant(transition_times=tuple(
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[8, 12, 13, 17]), values=tuple([2, 1, 2])),
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presence=None,
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activity=models.Activity.types[activity_type]
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),
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)
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times = np.linspace(8, 17, 100)
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CO2_concentrations = [
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conc_model.concentration(float(time))
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for time in times
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]
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# Generate CO2DataModel
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data_model = models.CO2DataModel(
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room_volume=75,
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number=models.IntPiecewiseConstant(transition_times=tuple(
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[8, 12, 13, 17]), values=tuple([2, 1, 2])),
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presence=None,
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ventilation_transition_times=tuple(ventilation_active),
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times=times,
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CO2_concentrations=CO2_concentrations
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)
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fit_parameters = data_model.CO2_fit_params()
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exhalation_rate = fit_parameters['exhalation_rate']
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npt.assert_almost_equal(
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round(exhalation_rate, 2), conc_model.CO2_emitters.activity.exhalation_rate)
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ventilation_values = fit_parameters['ventilation_values']
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npt.assert_allclose([round(vent, 2) for vent in ventilation_values], air_exch)
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@pytest.mark.parametrize(
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"activity_type, air_exch", [
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['Seated', 0.25],
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['Standing', 2.45],
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]
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)
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def test_fitting_algorithm_fixed_ventilation(activity_type, air_exch):
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conc_model = models.CO2ConcentrationModel(
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room=models.Room(
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volume=75, inside_temp=models.PiecewiseConstant((0., 24.), (293,))),
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ventilation=models.AirChange(active=models.PeriodicInterval(120, 120), air_exch=air_exch),
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CO2_emitters=models.SimplePopulation(
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number=models.IntPiecewiseConstant(transition_times=tuple(
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[8, 12, 13, 17]), values=tuple([2, 1, 2])),
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presence=None,
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activity=models.Activity.types[activity_type]
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),
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)
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times = np.linspace(8, 17, 100)
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CO2_concentrations = [
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conc_model.concentration(float(time))
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for time in times
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]
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# Generate CO2DataModel
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data_model = models.CO2DataModel(
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room_volume=75,
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number=models.IntPiecewiseConstant(transition_times=tuple(
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[8, 12, 13, 17]), values=tuple([2, 1, 2])),
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presence=None,
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ventilation_transition_times=tuple([8, 17]),
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times=times,
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CO2_concentrations=CO2_concentrations
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)
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fit_parameters = data_model.CO2_fit_params()
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exhalation_rate = fit_parameters['exhalation_rate']
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npt.assert_almost_equal(
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round(exhalation_rate, 2), conc_model.CO2_emitters.activity.exhalation_rate)
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ventilation_values = fit_parameters['ventilation_values']
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npt.assert_almost_equal(round(ventilation_values[0], 2), air_exch)
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