added test for natural and mechanical ventilation fitting algorithm

2023-11-22 10:15:04 +01:00 · 2023-11-22 10:15:04 +01:00 · eca8e035c4
commit eca8e035c4
parent afdc3cbec8
1 changed files with 101 additions and 0 deletions
--- a/caimira/tests/models/test_fitting_algorithm.py
+++ b/caimira/tests/models/test_fitting_algorithm.py
@ -0,0 +1,101 @@
+import numpy as np
+import numpy.testing as npt
+import pytest
+
+from caimira import models
+
+
+@pytest.mark.parametrize(
+    "activity_type, ventilation_active, air_exch", [
+        ['Seated', [8, 12, 13, 17], [0.25, 2.45, 0.25]],
+        ['Standing', [8, 10, 11, 12, 17], [1.25, 3.25, 1.45, 0.25]],
+        ['Light activity', [8, 12, 17], [1.25, 0.25]],
+        ['Moderate activity', [8, 13, 15, 16, 17], [2.25, 0.25, 3.45, 0.25]],
+        ['Heavy exercise', [8, 17], [0.25]],
+    ]
+)
+def test_fitting_algorithm_custom_ventilation(activity_type, ventilation_active, air_exch):
+    conc_model = models.CO2ConcentrationModel(
+        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(
+        room_volume=75,
+        number=models.IntPiecewiseConstant(transition_times=tuple(
+            [8, 12, 13, 17]), values=tuple([2, 1, 2])),
+        presence=None,
+        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(
+        round(exhalation_rate, 2), conc_model.CO2_emitters.activity.exhalation_rate)
+
+    ventilation_values = fit_parameters['ventilation_values']
+    npt.assert_allclose([round(vent, 2) for vent in ventilation_values], air_exch)
+        
+
+@pytest.mark.parametrize(
+    "activity_type, air_exch", [
+        ['Seated', 0.25],
+        ['Standing', 2.45],
+    ]
+)
+def test_fitting_algorithm_fixed_ventilation(activity_type, air_exch):
+    conc_model = models.CO2ConcentrationModel(
+        room=models.Room(
+            volume=75, inside_temp=models.PiecewiseConstant((0., 24.), (293,))),
+        ventilation=models.AirChange(active=models.PeriodicInterval(120, 120), air_exch=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(
+        room_volume=75,
+        number=models.IntPiecewiseConstant(transition_times=tuple(
+            [8, 12, 13, 17]), values=tuple([2, 1, 2])),
+        presence=None,
+        ventilation_transition_times=tuple([8, 17]),
+        times=times,
+        CO2_concentrations=CO2_concentrations
+    )
+
+    fit_parameters = data_model.CO2_fit_params()
+
+    exhalation_rate = fit_parameters['exhalation_rate']
+    npt.assert_almost_equal(
+        round(exhalation_rate, 2), conc_model.CO2_emitters.activity.exhalation_rate)
+
+    ventilation_values = fit_parameters['ventilation_values']
+    npt.assert_almost_equal(round(ventilation_values[0], 2), air_exch)
+