new validation type for NumericArrayLikes and appropriate tests for n…

…ew pdf, cdf, icdf methods in Uniform and Normal
connor-krill · Jun 2, 2023 · Jun 2, 2023 · Jun 2, 2023 · Jun 2, 2023 · Sep 1, 2023
commit 8db235f16aab2e8c4b6e6d2875738bff49790999
diff --git a/src/UQpy/utilities/ValidationTypes.py b/src/UQpy/utilities/ValidationTypes.py
@@ -3,9 +3,11 @@
 import numpy as np
 from beartype.vale import Is
 
+
 RandomStateType = Union[None, int, np.random.RandomState]
 PositiveInteger = Annotated[int, Is[lambda number: number > 0]]
 PositiveFloat = Annotated[float, Is[lambda number: number > 0]]
+NumericArrayLike = Union[int, float, list, tuple, np.ndarray]
 Numpy2DFloatArray = Annotated[
     np.ndarray,
     Is[lambda array: array.ndim == 2 and np.issubdtype(array.dtype, float)],

diff --git a/tests/unit_tests/distributions/test_distribution_methods.py b/tests/unit_tests/distributions/test_distribution_methods.py
@@ -1,5 +1,8 @@
 from UQpy.distributions import *
 import numpy as np
+import pytest
+import scipy
+
 
 # Test all functions for one type of continuous distribution: uniform
 dist_continuous = Uniform(loc=1., scale=2.)
@@ -81,3 +84,66 @@ def test_update_params_copula():
     copula = Gumbel(theta=2.)
     copula.update_parameters(theta=1.)
     assert copula.get_parameters()['theta'] == 1.
+
+
+# Test function for custom implementation of Uniform PDF and CDF
+@pytest.mark.parametrize("value,expected_probability", [
+    (-4, 0),
+    (0, 1),
+    (0.5, 1),
+    (1, 1),
+    (12, 0),
+    ([-1, 0, 0.5, 1, 2], [0, 1, 1, 1, 0]),
+    ((-1, 0, 0.5, 1, 2), (0, 1, 1, 1, 0)),
+    (np.array([-1, 0, 0.5, 1, 2]), np.array([0, 1, 1, 1, 0]))
+])
+def test_uniform_pdf(value, expected_probability):
+    uniform = Uniform()
+    assert all(uniform.pdf(value) == expected_probability)
+
+
+@pytest.mark.parametrize("value,expected_probability", [
+    (-0.1245, 0),
+    (0.4, 0.4),
+    (3.14159, 1),
+    ([-4, 0.2, 0.75, 1, 6], [0, 0.2, 0.75, 1, 1]),
+    ((-4, 0.2, 0.75, 1, 6), (0, 0.2, 0.75, 1, 1)),
+    (np.array([-4, 0.2, 0.75, 1, 6]), np.array([0, 0.2, 0.75, 1, 1])),
+])
+def test_uniform_cdf(value, expected_probability):
+    uniform = Uniform()
+    assert all(uniform.cdf(value) == expected_probability)
+
+
+@pytest.mark.parametrize("probability,expected_value", [
+    (-1, np.nan),
+    (0, 0),
+    (0.123, 0.123),
+    (1, 1),
+    (1.2, np.nan),
+    ((-0.5, 0, 0.5, 1, 1.5), (np.nan, 0, 0.5, 1, np.nan)),
+    ([-0.5, 0, 0.5, 1, 1.5], [np.nan, 0, 0.5, 1, np.nan]),
+    (np.array([-0.5, 0, 0.5, 1, 1.5]), np.array([np.nan, 0, 0.5, 1, np.nan]))
+])
+def test_uniform_icdf(probability, expected_value):
+    uniform = Uniform()
+    assert np.array_equal(uniform.icdf(probability), np.atleast_1d(expected_value), equal_nan=True)
+
+
+@pytest.mark.parametrize("value", [-3, -2.5, -1, 0, 1, 2.415, 6.168, np.array([-8, -4.13, -1, 0, 2])])
+def test_normal_pdf(value):
+    normal = Normal()
+    assert (np.isclose(normal.pdf(value), scipy.stats.norm.pdf(value))).all()
+
+
+@pytest.mark.parametrize("value", [-4, -2.7, -0.4, 0, 0.4321, 2, 8, np.array([-3, -2, 0, 1, 4])])
+def test_normal_cdf(value):
+    normal = Normal()
+    assert (np.isclose(normal.cdf(value), scipy.stats.norm.cdf(value))).all()
+
+
+@pytest.mark.parametrize("probability", [-1, 0, 1e-9, 0.15, 0.6626, 1-1e-9, 1, 2, np.array([-1, 0, 0.321123, 1, 5])])
+def test_normal_icdf(probability):
+    normal = Normal()
+    expected_value = np.atleast_1d(scipy.stats.norm.ppf(probability))
+    assert np.allclose(normal.icdf(probability), expected_value, equal_nan=True)