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
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
import numpy as np
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import pytest
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from caimira.calculator.models import models
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from caimira.calculator.models import data
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def test_piecewiseconstantfunction_wrongarguments():
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# Number of values should be 1+number of transition times
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pytest.raises(ValueError, models.PiecewiseConstant, (0, 1), (0, 0))
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pytest.raises(ValueError, models.PiecewiseConstant, (0,), (0, 0))
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# Two transition times cannot be equal
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pytest.raises(ValueError, models.PiecewiseConstant, (0, 2, 2), (0, 0))
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# Unsorted transition times are not allowed
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pytest.raises(ValueError, models.PiecewiseConstant, (2, 0), (0, 0))
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# If vectors, must all be same length.
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with pytest.raises(ValueError, match="All values must have the same shape"):
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models.PiecewiseConstant(
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(0, 8, 16), (np.array([5, 7]), np.array([8, 9, 10])),
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)
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@pytest.mark.parametrize(
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"time, expected_value",
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[
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[10, 5],
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[20.5, 8],
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[8, 2],
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[0, 2],
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[24, 8],
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[-1, 2],
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[25, 8],
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],
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)
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def test_piecewiseconstant(time, expected_value):
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transition_times = (0, 8, 16, 24)
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values = (2, 5, 8)
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fun = models.PiecewiseConstant(transition_times, values)
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assert fun.value(time) == expected_value
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def test_piecewiseconstant_interp():
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transition_times = (0, 8, 16, 24)
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values = (2, 5, 8)
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refined_fun = models.PiecewiseConstant(transition_times, values).refine(refine_factor=2)
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assert refined_fun.transition_times == (0, 4, 8, 12, 16, 20, 24)
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assert refined_fun.values == (2, 3.5, 5, 6.5, 8, 8)
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def test_piecewiseconstant_interp_vectorised():
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transition_times = (0, 8, 16, 24)
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values = (np.array([2, 3]), np.array([5, 7]), np.array([8, 9]))
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refined_fun = models.PiecewiseConstant(transition_times, values).refine(refine_factor=2)
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assert refined_fun.transition_times == (0, 4, 8, 12, 16, 20, 24)
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np.testing.assert_almost_equal(
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refined_fun.values, ((2, 3), (3.5, 5), (5, 7), (6.5, 8), (8, 9), (8, 9)),
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)
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def test_constantfunction():
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transition_times = (0, 24)
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values = (20,)
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fun = models.PiecewiseConstant(transition_times, values)
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for t in [0, 1, 8, 10, 16, 20.1, 24]:
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assert (fun.value(t) == 20)
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@pytest.mark.parametrize(
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"time",
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[0, 1, 8, 10, 16, 20.1, 24],
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)
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def test_piecewiseconstant_vs_interval(time):
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transition_times = (0, 8, 16, 24)
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values = (0, 1, 0)
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fun = models.PiecewiseConstant(transition_times, values)
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interval = models.SpecificInterval(present_times=[(8,16)])
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assert interval.transition_times() == fun.interval().transition_times()
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assert fun.interval().triggered(time) == interval.triggered(time)
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def test_piecewiseconstant_transition_times():
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outside_temp = data.GenevaTemperatures['Jan']
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assert set(outside_temp.transition_times) == outside_temp.interval().transition_times()
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