15282160672

Committed 27 May 2025 05:45PM UTC coverage: 47.272%. First build

Build # 15282160672

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Merge pull request #49 from victoriyaforsythe/victoriya_tests

New F1 model, new EDP construction, tests.

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/PyIRI/tests/test_edp_update.py

"""
Unit tests for PyIRI's edp_update module.
"""

import numpy as np
import pytest

import PyIRI
from PyIRI import edp_update
from PyIRI import edp_update as ml


def test_IRI_density_1day_runs():
    """Test that IRI_density_1day runs and returns expected shape."""
    year = 2024
    mth = 6
    day = 21
    aUT = np.array([12.0])
    alon = np.array([0.0])
    alat = np.array([0.0])
    aalt = np.linspace(100, 600, 20)
    F107 = 100.0
    coeff_dir = PyIRI.coeff_dir

    try:
        F2, F1, E, Es, sun, mag, EDP = edp_update.IRI_density_1day(
            year, mth, day, aUT, alon, alat, aalt, F107, coeff_dir)
        assert EDP.shape[1] == len(aalt)
    except Exception as e:
        pytest.fail(f"Function raised an exception: {e}")


def test_Probability_F1_output():
    """Test output of Probability_F1 against expected values."""
    year = 2020
    month = 4
    aUT = np.arange(0, 24, 24)
    alon = np.array([10.])
    alat = np.array([20.])
    expected = np.array([[[0.00210238, 0.00210238]]])

    result = ml.Probability_F1(year, month, aUT, alon, alat)
    assert result.shape == expected.shape, (
        "Shape mismatch in Probability_F1 output")
    assert np.allclose(result, expected, atol=1e-8), (
        "Value mismatch in Probability_F1 output")


def test_drop_up_output():
    """Test drop_up returns expected value."""
    z_E = 230.0
    z_F = 110.0
    z_top = 250.0
    expected = 0.2245381938521092

    result = ml.drop_up(z_E, z_F, z_top, drop_fraction=0.2)
    assert abs(result - expected) < 1e-8, "drop_up output mismatch"


def test_logistic_curve_output():
    """Test logistic_curve returns expected value."""
    z = 240.0
    z0 = 200.0
    scale = 50.0
    expected = 0.6899744811276125

    result = ml.logistic_curve(z, z0, scale)
    assert abs(result - expected) < 1e-8, "logistic_curve output mismatch"


def test_derive_dependent_F1_parameters_output():
    """Test derive_dependent_F1_parameters output accuracy."""
    p_F1 = np.array([0.5])
    NmF2 = np.array([1.0e11])
    hmF2 = np.array([350.])
    B_F2_bot = np.array([60.])
    z_E = np.array([110.])
    expected_NmF1 = np.array([5.54030525e+10])
    expected_B_F1_bot = np.array([2.1137616])
    expected_hmF1 = np.array([254.])
    expected_thickness = np.array([72.])

    NmF1, B_F1_bot, hmF1, thickness = ml.derive_dependent_F1_parameters(
        p_F1, NmF2, hmF2, B_F2_bot, z_E)

    assert np.allclose(NmF1, expected_NmF1, atol=1e-6), "NmF1 mismatch"
    assert np.allclose(B_F1_bot, expected_B_F1_bot,
                       atol=1e-6), "B_F1_bot mismatch"
    assert np.allclose(hmF1, expected_hmF1, atol=1e-6), "hmF1 mismatch"
    assert np.allclose(thickness, expected_thickness,
                       atol=1e-6), "thickness mismatch"

1	"""
2	Unit tests for PyIRI's edp_update module.
3	"""
4
5	import numpy as np	7✔
6	import pytest	7✔
7
8	import PyIRI	7✔
9	from PyIRI import edp_update	7✔
10	from PyIRI import edp_update as ml	7✔
11
12
13	def test_IRI_density_1day_runs():	7✔
14	"""Test that IRI_density_1day runs and returns expected shape."""
15	year = 2024	7✔
16	mth = 6	7✔
17	day = 21	7✔
18	aUT = np.array([12.0])	7✔
19	alon = np.array([0.0])	7✔
20	alat = np.array([0.0])	7✔
21	aalt = np.linspace(100, 600, 20)	7✔
22	F107 = 100.0	7✔
23	coeff_dir = PyIRI.coeff_dir	7✔
24
25	try:	7✔
26	F2, F1, E, Es, sun, mag, EDP = edp_update.IRI_density_1day(	7✔
27	year, mth, day, aUT, alon, alat, aalt, F107, coeff_dir)
28	assert EDP.shape[1] == len(aalt)	7✔
NEW 29	except Exception as e:	×
NEW 30	pytest.fail(f"Function raised an exception: {e}")	×
31
32
33	def test_Probability_F1_output():	7✔
34	"""Test output of Probability_F1 against expected values."""
35	year = 2020	7✔
36	month = 4	7✔
37	aUT = np.arange(0, 24, 24)	7✔
38	alon = np.array([10.])	7✔
39	alat = np.array([20.])	7✔
40	expected = np.array([[[0.00210238, 0.00210238]]])	7✔
41
42	result = ml.Probability_F1(year, month, aUT, alon, alat)	7✔
43	assert result.shape == expected.shape, (	7✔
44	"Shape mismatch in Probability_F1 output")
45	assert np.allclose(result, expected, atol=1e-8), (	7✔
46	"Value mismatch in Probability_F1 output")
47
48
49	def test_drop_up_output():	7✔
50	"""Test drop_up returns expected value."""
51	z_E = 230.0	7✔
52	z_F = 110.0	7✔
53	z_top = 250.0	7✔
54	expected = 0.2245381938521092	7✔
55
56	result = ml.drop_up(z_E, z_F, z_top, drop_fraction=0.2)	7✔
57	assert abs(result - expected) < 1e-8, "drop_up output mismatch"	7✔
58
59
60	def test_logistic_curve_output():	7✔
61	"""Test logistic_curve returns expected value."""
62	z = 240.0	7✔
63	z0 = 200.0	7✔
64	scale = 50.0	7✔
65	expected = 0.6899744811276125	7✔
66
67	result = ml.logistic_curve(z, z0, scale)	7✔
68	assert abs(result - expected) < 1e-8, "logistic_curve output mismatch"	7✔
69
70
71	def test_derive_dependent_F1_parameters_output():	7✔
72	"""Test derive_dependent_F1_parameters output accuracy."""
73	p_F1 = np.array([0.5])	7✔
74	NmF2 = np.array([1.0e11])	7✔
75	hmF2 = np.array([350.])	7✔
76	B_F2_bot = np.array([60.])	7✔
77	z_E = np.array([110.])	7✔
78	expected_NmF1 = np.array([5.54030525e+10])	7✔
79	expected_B_F1_bot = np.array([2.1137616])	7✔
80	expected_hmF1 = np.array([254.])	7✔
81	expected_thickness = np.array([72.])	7✔
82
83	NmF1, B_F1_bot, hmF1, thickness = ml.derive_dependent_F1_parameters(	7✔
84	p_F1, NmF2, hmF2, B_F2_bot, z_E)
85
86	assert np.allclose(NmF1, expected_NmF1, atol=1e-6), "NmF1 mismatch"	7✔
87	assert np.allclose(B_F1_bot, expected_B_F1_bot,	7✔
88	atol=1e-6), "B_F1_bot mismatch"
89	assert np.allclose(hmF1, expected_hmF1, atol=1e-6), "hmF1 mismatch"	7✔
90	assert np.allclose(thickness, expected_thickness,	7✔
91	atol=1e-6), "thickness mismatch"

victoriyaforsythe / PyIRI / 15282160672

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