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
117 lines
No EOL
5 KiB
Python
117 lines
No EOL
5 KiB
Python
import numpy.testing as npt
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import numpy as np
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import typing
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import pytest
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from caimira.calculator.models import models
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from caimira.calculator.validators.co2.co2_validator import CO2FormData
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@pytest.fixture
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def simple_co2_conc_model(data_registry):
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return models.CO2ConcentrationModel(
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data_registry=data_registry,
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room=models.Room(200, models.PiecewiseConstant((0., 24.), (293,))),
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ventilation=models.AirChange(models.PeriodicInterval(period=120, duration=120), 0.25),
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CO2_emitters=models.SimplePopulation(
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number=5,
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presence=models.SpecificInterval((([0., 4.], ))),
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activity=models.Activity.types['Seated'],
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),
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)
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@pytest.mark.parametrize(
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"time, expected_co2_concentration", [
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[0., 440.44],
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[1., 914.2487227],
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[2., 1283.251327],
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[3., 1570.630844],
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[4., 1794.442237],
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]
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)
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def test_co2_concentration(
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simple_co2_conc_model: models.CO2ConcentrationModel,
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time: float,
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expected_co2_concentration: float,
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):
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npt.assert_almost_equal(simple_co2_conc_model.concentration(time), expected_co2_concentration)
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def test_integrated_concentration(simple_co2_conc_model):
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c1 = simple_co2_conc_model.integrated_concentration(0, 2)
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c2 = simple_co2_conc_model.integrated_concentration(0, 1)
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c3 = simple_co2_conc_model.integrated_concentration(1, 2)
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assert c1 != 0
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npt.assert_almost_equal(c1, c2 + c3)
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@pytest.mark.parametrize(
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"scenario_data, room_volume, max_total_people, start, finish, state_changes", [
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["office_scenario_1_sensor_data", 102, 4, "14:00", "17:30", (14.78, 15.1, 15.53, 15.87, 16.52, 16.83)],
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["office_scenario_2_sensor_data", 60, 2, "08:38", "17:30", (10.17, 12.45, 14.5)], # Second state change should actually be 12.87 - that's the real time at which the ventilation was changed in the room.
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["meeting_scenario_1_sensor_data", 83, 3, "09:04", "11:45", (10.37, 11.07)],
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["meeting_scenario_2_sensor_data", 83, 4, "13:40", "16:40", (14.37, 14.72, 15, 15.33, 15.68, 16.03)]
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]
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)
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def test_find_change_points(scenario_data, room_volume, max_total_people, start, finish, state_changes, request):
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'''
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Specific test of the find_change_points method.
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Testing the ventilation state changes only.
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'''
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CO2_form_model: CO2FormData = CO2FormData(
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CO2_data=request.getfixturevalue(scenario_data),
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fitting_ventilation_states=[],
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exposed_start=start,
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exposed_finish=finish,
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total_people=max_total_people,
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room_volume=room_volume,
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)
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find_points = CO2_form_model.find_change_points()
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assert np.allclose(find_points, state_changes, rtol=1e-2)
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@pytest.mark.parametrize(
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"scenario_data, room_volume, occupancy, presence_interval, all_state_changes", [
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["office_scenario_1_sensor_data", 102, (4,), (14, 17.5), (14, 14.25, 14.78, 15.1, 15.53, 15.87, 16.52, 16.83, 17.5)],
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["office_scenario_2_sensor_data", 60, (2, 0, 2), (8.62, 11.93, 12.42, 17.5), (8.62, 10.17, 12.45, 14.5, 17.5, 20.)], # Third state change should actually be 12.87 - that's the real time at which the ventilation was changed in the room.
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["meeting_scenario_1_sensor_data", 83, (2, 3, 2, 3), (9.07, 9.32, 9.75, 10.75, 11.75), (9.07, 10.37, 11.07, 11.75)],
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["meeting_scenario_2_sensor_data", 83, (2, 3, 4), (13.67, 13.75, 15.87, 16.67), (13.67, 14.37, 14.72, 15.00, 15.33, 15.68, 16.03, 16.67)]
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]
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)
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def test_predictive_model_accuracy(data_registry, scenario_data, room_volume, occupancy, presence_interval, all_state_changes, request):
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'''
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Specific test corresponding to the data files of four
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different scenarios (2 in an office and 2 in a meeting room).
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The room volume, number of people and ventilation transition times
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correspond to the actual state change occurrences during the day.
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Note that the last time from the input file is considered as a ventilation
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state change.
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'''
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input_fitting_data = request.getfixturevalue(scenario_data)
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fitting_model: models.CO2DataModel = models.CO2DataModel(
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data_registry=data_registry,
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room=models.Room(volume=room_volume),
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occupancy=models.IntPiecewiseConstant(
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transition_times=presence_interval,
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values=occupancy
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),
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ventilation_transition_times=all_state_changes,
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times=input_fitting_data['times'],
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CO2_concentrations=input_fitting_data['CO2'],
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)
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# Get fitting results
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fitting_results: typing.Dict = fitting_model.CO2_fit_params()
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predictive_CO2: typing.List[float] = fitting_results['predictive_CO2']
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def root_mean_square_error_percentage(actual, predicted) -> float:
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return np.sqrt(np.mean(((actual - predicted) / actual) ** 2)) * 100
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# Calculate RMSEP metric
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rmsep = root_mean_square_error_percentage(np.array(input_fitting_data['CO2']), np.array(predictive_CO2))
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acceptable_rmsep = 10 # Threshold of 10% for the accepted error margin
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assert rmsep <= acceptable_rmsep, f"RMSEP {rmsep} exceeds acceptable threshold {acceptable_rmsep}"
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