merged tests (custom and fixed)

CERN · Nov 22, 2023 · 8bd53e5 · 8bd53e5
1 parent 11cbc85
commit 8bd53e5
Showing 1 changed file with 5 additions and 49 deletions.
diff --git a/caimira/tests/models/test_fitting_algorithm.py b/caimira/tests/models/test_fitting_algorithm.py
@@ -12,9 +12,11 @@
         ['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]],
+        ['Seated', [8, 17], [0.25]],
+        ['Standing', [8, 17], [2.45]],
     ]
 )
-def test_fitting_algorithm_custom_ventilation(activity_type, ventilation_active, air_exch):
+def test_fitting_algorithm(activity_type, ventilation_active, air_exch):
     conc_model = models.CO2ConcentrationModel(
         room=models.Room(
             volume=75, inside_temp=models.PiecewiseConstant((0., 24.), (293,))),
@@ -48,54 +50,8 @@ def test_fitting_algorithm_custom_ventilation(activity_type, ventilation_active,
     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)
+        exhalation_rate, conc_model.CO2_emitters.activity.exhalation_rate, decimal=2)
 
     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)
+    npt.assert_allclose(ventilation_values, air_exch, rtol=1e-2)