59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
"""Tests para normality_tests."""
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import numpy as np
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from normality_tests import normality_tests
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def test_normal_large_sample_is_normal():
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rng = np.random.default_rng(42)
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values = rng.normal(loc=10.0, scale=2.0, size=2000).tolist()
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result = normality_tests(values)
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assert result["n"] == 2000
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assert result["shapiro"] is not None
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assert result["jarque_bera"]["normal"] is True
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assert result["dagostino"]["normal"] is True
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assert result["is_normal"] is True
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def test_skewed_sample_is_not_normal():
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rng = np.random.default_rng(7)
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values = rng.exponential(scale=1.0, size=2000).tolist()
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result = normality_tests(values)
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assert result["jarque_bera"]["normal"] is False
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assert result["dagostino"]["normal"] is False
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assert result["is_normal"] is False
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def test_small_sample_returns_note():
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result = normality_tests([1, 2, 3, 4, 5])
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assert result["n"] == 5
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assert result["note"] == "muestra insuficiente"
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assert result["is_normal"] is None
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assert "jarque_bera" not in result
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def test_drops_none_nan_and_non_numeric():
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rng = np.random.default_rng(1)
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base = rng.normal(0.0, 1.0, size=50).tolist()
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dirty = base + [None, float("nan"), "x", float("inf")]
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result = normality_tests(dirty)
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assert result["n"] == 50
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def test_shapiro_skipped_above_5000():
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rng = np.random.default_rng(3)
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values = rng.normal(0.0, 1.0, size=6000).tolist()
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result = normality_tests(values)
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assert result["n"] == 6000
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assert result["shapiro"] is None
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# is_normal still computed from JB + D'Agostino.
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assert result["is_normal"] is True
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def test_normal_below_eight_after_cleaning_is_note():
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result = normality_tests([1.0, 2.0, None, 3.0])
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assert result["n"] == 3
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assert result["note"] == "muestra insuficiente"
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assert result["is_normal"] is None
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