Changes to correlation distortion

src/UQpy/utilities/Utilities.py

@@ -320,7 +320,7 @@ def correlation_distortion(dist_object, rho):
 
     if rho == 1.0:
         rho = 0.999
-    n = 1024
+    n = 512
     zmax = 8
     zmin = -zmax
     eta, w2d, xi = calculate_gauss_quadrature_2d(n, zmax, zmin)

tests/unit_tests/stochastic_process/test_inverse_translation.py

@@ -1,61 +1,61 @@
-# import numpy as np
-# from UQpy.distributions import Uniform
-# from UQpy.stochastic_process import SpectralRepresentation, Translation, InverseTranslation
-#
-# n_sim = 100  # Num of samples
-# T = 100  # Time(1 / T = dw)
-# nt = 256  # Num.of Discretized Time
-# F = 1 / T * nt / 2  # Frequency.(Hz)
-# nw = 128  # Num of Discretized Freq.
-# dt = T / nt
-# t = np.linspace(0, T - dt, nt)
-# dw = F / nw
-# w = np.linspace(0, F - dw, nw)
-# S = 125 / 4 * w ** 2 * np.exp(-5 * w)
-# SRM_object = SpectralRepresentation(n_sim, S, dt, dw, nt, nw, random_state=128)
-# samples = SRM_object.samples
-#
-#
-# def S_to_R(S, w, t):
-#     dw = w[1] - w[0]
-#     fac = np.ones(len(w))
-#     fac[1: len(w) - 1: 2] = 4
-#     fac[2: len(w) - 2: 2] = 2
-#     fac = fac * dw / 3
-#     R = np.zeros(len(t))
-#     for i in range(len(t)):
-#         R[i] = 2 * np.dot(fac, S * np.cos(w * t[i]))
-#     return R
-#
-#
-# R = S_to_R(S, w, t)
-# distribution = Uniform(0, 1)
-#
-# Translate_object = Translation(distributions=distribution, time_interval=dt, frequency_interval=dw,
-#                                n_time_intervals=nt, n_frequency_intervals=nw, correlation_function_gaussian=R,
-#                                samples_gaussian=samples)
-#
-# samples_ng = Translate_object.samples_non_gaussian
-# R_ng = Translate_object.scaled_correlation_function_non_gaussian
-#
-# InverseTranslate_object = InverseTranslation(distributions=distribution, time_interval=dt, frequency_interval=dw,
-#                                              n_time_intervals=nt, n_frequency_intervals=nw,
-#                                              correlation_function_non_gaussian=R_ng, samples_non_gaussian=samples_ng,
-#                                              percentage_error=5.0)
-# samples_g = InverseTranslate_object.samples_gaussian
-# S_g = InverseTranslate_object.power_spectrum_gaussian
-# R_g = InverseTranslate_object.auto_correlation_function_gaussian
-# r_g = InverseTranslate_object.correlation_function_gaussian
-#
-#
-# def test_samples_shape():
-#     assert samples_g.shape == samples_ng.shape
-#
-#
-# def test_samples_g_value():
-#     assert np.isclose(samples_g[25, 0, 43], 0.2544126816395569)
-#
-#
-# def test_R_g_value():
-#     assert np.isclose(R_g[42], 0.06893298630483506)
-#
+import numpy as np
+from UQpy.distributions import Uniform
+from UQpy.stochastic_process import SpectralRepresentation, Translation, InverseTranslation
+
+n_sim = 100  # Num of samples
+T = 100  # Time(1 / T = dw)
+nt = 256  # Num.of Discretized Time
+F = 1 / T * nt / 2  # Frequency.(Hz)
+nw = 128  # Num of Discretized Freq.
+dt = T / nt
+t = np.linspace(0, T - dt, nt)
+dw = F / nw
+w = np.linspace(0, F - dw, nw)
+S = 125 / 4 * w ** 2 * np.exp(-5 * w)
+SRM_object = SpectralRepresentation(n_sim, S, dt, dw, nt, nw, random_state=128)
+samples = SRM_object.samples
+
+
+def S_to_R(S, w, t):
+    dw = w[1] - w[0]
+    fac = np.ones(len(w))
+    fac[1: len(w) - 1: 2] = 4
+    fac[2: len(w) - 2: 2] = 2
+    fac = fac * dw / 3
+    R = np.zeros(len(t))
+    for i in range(len(t)):
+        R[i] = 2 * np.dot(fac, S * np.cos(w * t[i]))
+    return R
+
+
+R = S_to_R(S, w, t)
+distribution = Uniform(0, 1)
+
+Translate_object = Translation(distributions=distribution, time_interval=dt, frequency_interval=dw,
+                               n_time_intervals=nt, n_frequency_intervals=nw, correlation_function_gaussian=R,
+                               samples_gaussian=samples)
+
+samples_ng = Translate_object.samples_non_gaussian
+R_ng = Translate_object.scaled_correlation_function_non_gaussian
+
+InverseTranslate_object = InverseTranslation(distributions=distribution, time_interval=dt, frequency_interval=dw,
+                                             n_time_intervals=nt, n_frequency_intervals=nw,
+                                             correlation_function_non_gaussian=R_ng, samples_non_gaussian=samples_ng,
+                                             percentage_error=5.0)
+samples_g = InverseTranslate_object.samples_gaussian
+S_g = InverseTranslate_object.power_spectrum_gaussian
+R_g = InverseTranslate_object.auto_correlation_function_gaussian
+r_g = InverseTranslate_object.correlation_function_gaussian
+
+
+def test_samples_shape():
+    assert samples_g.shape == samples_ng.shape
+
+
+def test_samples_g_value():
+    assert np.isclose(samples_g[25, 0, 43], 0.2544126816395569)
+
+
+def test_R_g_value():
+    assert np.isclose(R_g[42], 0.06893298630483506)
+