20b0467f89
- extract, isolate and package it in a completely independent Python module, versioned and in a way that allows releases on PyPI.org - fixed error in placeholder for secondary school (data registry defaults) - added restriction in pytest version to install - expected number of new cases fix - data registry update (schema v2.1.1) - github update - deprecate ExpertApplication and CO2Application - changes to reflect schema update 2.0.2 - version update - Fixed error with f_inf (short-range) - new folder layout - Conditional probability data update - General fixes - Fitting results in L/S/person - CO2 fitting algorithm refinement
61 lines
No EOL
2.5 KiB
Python
61 lines
No EOL
2.5 KiB
Python
import numpy as np
|
|
import numpy.testing as npt
|
|
import pytest
|
|
|
|
from caimira.calculator.models import models
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"activity_type, ventilation_active, air_exch, flow_rate_lsp", [
|
|
['Seated', [8, 12, 13, 17], [0.25, 2.45, 0.25], [2.604166667, 25.520833335, 2.604166667]],
|
|
['Standing', [8, 10, 11, 12, 17], [1.25, 3.25, 1.45, 0.25], [13.02083333333, 33.8541666667, 15.1041666667, 2.6041666667]],
|
|
['Light activity', [8, 12, 17], [1.25, 0.25], [13.02083333333, 2.6041666667]],
|
|
['Moderate activity', [8, 13, 15, 16, 17], [2.25, 0.25, 3.45, 0.25], [23.4375, 2.6041666667, 35.9375, 2.6041666667]],
|
|
['Heavy exercise', [8, 17], [0.25], [2.6041666667]],
|
|
['Seated', [8, 17], [0.25], [2.6041666667]],
|
|
['Standing', [8, 17], [2.45], [25.5208333333]],
|
|
]
|
|
)
|
|
def test_fitting_algorithm(data_registry, activity_type, ventilation_active, air_exch, flow_rate_lsp):
|
|
conc_model = models.CO2ConcentrationModel(
|
|
data_registry = data_registry,
|
|
room=models.Room(
|
|
volume=75, inside_temp=models.PiecewiseConstant((0., 24.), (293,))),
|
|
ventilation=models.CustomVentilation(models.PiecewiseConstant(
|
|
tuple(ventilation_active), tuple(air_exch))),
|
|
CO2_emitters=models.SimplePopulation(
|
|
number=models.IntPiecewiseConstant(transition_times=tuple(
|
|
[8, 12, 13, 17]), values=tuple([2, 1, 2])),
|
|
presence=None,
|
|
activity=models.Activity.types[activity_type]
|
|
),
|
|
)
|
|
|
|
times = np.linspace(8, 17, 100)
|
|
CO2_concentrations = [
|
|
conc_model.concentration(float(time))
|
|
for time in times
|
|
]
|
|
|
|
# Generate CO2DataModel
|
|
data_model = models.CO2DataModel(
|
|
data_registry=data_registry,
|
|
room=models.Room(volume=75, capacity=2),
|
|
occupancy=models.IntPiecewiseConstant(transition_times=tuple(
|
|
[8, 12, 13, 17]), values=tuple([2, 1, 2])),
|
|
ventilation_transition_times=tuple(ventilation_active),
|
|
times=times,
|
|
CO2_concentrations=CO2_concentrations
|
|
)
|
|
|
|
fit_parameters = data_model.CO2_fit_params()
|
|
exhalation_rate = fit_parameters['exhalation_rate']
|
|
npt.assert_almost_equal(
|
|
exhalation_rate, conc_model.CO2_emitters.activity.exhalation_rate, decimal=2)
|
|
|
|
ventilation_values = fit_parameters['ventilation_values']
|
|
npt.assert_allclose(ventilation_values, air_exch, rtol=1e-2)
|
|
|
|
ventilation_lsp_values = fit_parameters['ventilation_lsp_values']
|
|
npt.assert_allclose(ventilation_lsp_values, flow_rate_lsp, rtol=1e-2)
|
|
|