Fixes minor static code analysis issues

src/UQpy/sensitivity/MorrisSensitivity.py

@@ -211,7 +211,7 @@ def _compute_elementary_effects(self, trajectories_physical_space):
             self.runmodel_object.run(samples=samples, append_samples=False)
             qoi = np.array(self.runmodel_object.qoi_list)
             el_effect = np.zeros((self.dimension,))
-            perms = [np.argwhere(bi != 0.0)[0, 0] for bi in (samples[1:] - samples[:-1])]
+            perms = [np.argwhere(not np.isclose(bi, 0.0))[0, 0] for bi in (samples[1:] - samples[:-1])]
             for count_d, d in enumerate(perms):
                 el_effect[d] = (qoi[count_d + 1] - qoi[count_d]) / self.delta
             elementary_effects.append(el_effect)

src/UQpy/surrogates/gaussian_process/GaussianProcessRegression.py

@@ -297,8 +297,9 @@ def predict(self, points, return_std: bool = False, hyperparameters: list = None
             else:
                 mu1 = np.einsum("ij,jk->ik", fx, self.beta)
 
-        self.kernel.kernel_parameter = kernelparameters[:-1]
-        sigma = kernelparameters[-1]
+        if kernelparameters is not None:
+            self.kernel.kernel_parameter = kernelparameters[:-1]
+            sigma = kernelparameters[-1]
 
         k = sigma**2*self.kernel.calculate_kernel_matrix(x=x_, s=s_)
         y = mu1 + k @ alpha_

src/UQpy/utilities/Utilities.py

@@ -316,7 +316,7 @@ def correlation_distortion(dist_object, rho):
     :rtype: float
     """
 
-    if rho == 1.0:
+    if np.isclose(rho, 1.0):
         rho = 0.999
     n = 512
     zmax = 8

src/UQpy/utilities/kernels/euclidean_kernels/Matern.py

@@ -21,11 +21,11 @@ def __init__(self, kernel_parameter: Union[int, float] = 1, nu=1.5):
     def calculate_kernel_matrix(self, x, s):
         l = self.kernel_parameter
         stack = cdist(x / l, s / l, metric='euclidean')
-        if self.nu == 0.5:
+        if np.isclose(self.nu, 0.5):
             self.kernel_matrix = np.exp(-np.abs(stack))
-        elif self.nu == 1.5:
+        elif np.isclose(self.nu,1.5):
             self.kernel_matrix = (1 + np.sqrt(3) * stack) * np.exp(-np.sqrt(3) * stack)
-        elif self.nu == 2.5:
+        elif np.isclose(self.nu, 2.5):
             self.kernel_matrix = (1 + np.sqrt(5) * stack + 5 * (stack ** 2) / 3) * np.exp(-np.sqrt(5) * stack)
         elif self.nu == np.inf:
             self.kernel_matrix = np.exp(-(stack ** 2) / 2)