New functions for PCE basis

src/UQpy/surrogates/polynomial_chaos/polynomials/baseclass/Polynomials.py

@@ -4,7 +4,7 @@
 import numpy as np
 import scipy.integrate as integrate
 from beartype import beartype
-
+from scipy import stats as stats
 from UQpy.distributions.baseclass import Distribution
 import warnings
 
@@ -25,6 +25,64 @@ def __init__(self, distributions: Union[Distribution, list[Distribution]], degre
         """
         self.distributions = distributions
         self.degree = degree + 1
+
+    @staticmethod
+    def standardize_sample(x,joint_distribution):
+        """
+        Static method: Standardize data based on the joint probability distribution.
+
+        :param x: Input data generated from a joint probability distribution.
+        :param joint_distribution: joint probability distribution from :py:mod:`UQpy` distribution object
+        :return: Standardized data.
+        """
+
+
+        s=np.zeros(x.shape)
+        inputs_number = len(x[0,:])
+        if inputs_number == 1:
+            marginals = [joint_distribution]
+        else:
+            marginals = joint_distribution.marginals
+
+        for i in range (inputs_number):
+            if type(marginals[i]) == Normal:    
+                s[:,i]=Polynomials.standardize_normal(x[:,i],mean=marginals[i].parameters['loc'],std=marginals[i].parameters['scale'])
+
+            if type(marginals[i]) == Uniform:
+                s[:,i]=Polynomials.standardize_uniform(x[:,i], marginals[i])  
+
+            else:
+                raise TypeError("standarize_sample is defined only for Uniform and Gaussian marginal distributions")
+        return s
+
+    @staticmethod
+    def standardize_pdf(x,joint_distribution):
+        """
+        Static method: PDF of standardized distributions associated to Hermite or Legendre polynomials.
+
+        :param x: Input data generated from a joint probability distribution
+        :param joint_distribution: joint probability distribution from :py:mod:`UQpy` distribution object
+        :return: Value of standardized PDF calculated for x
+        """
+
+        inputs_number = len(x[0,:])
+        pdf_val=1
+        s=Polynomials.standardize_sample(x,joint_distribution)
+
+        if inputs_number == 1:
+            marginals = [joint_distribution]
+        else:
+            marginals = joint_distribution.marginals
+
+        for i in range (inputs_number):
+            if type(marginals[i]) == Normal:    
+                pdf_val = pdf_val * (stats.norm.pdf(s[:,i]))
+            if type(marginals[i]) == Uniform:
+                pdf_val = pdf_val * (stats.uniform.pdf(s[:,i], loc=-1, scale=2))
+
+            else:
+                raise TypeError("standardize_pdf is defined only for Uniform and Gaussian marginal distributions")
+        return pdf_val
 
     @staticmethod
     def standardize_normal(tensor: np.ndarray, mean: float, std: float):
@@ -111,4 +169,4 @@ def scale(self):
     def evaluate(self, x: np.ndarray):
         pass
 
-    distribution_to_polynomial = { }
+    distribution_to_polynomial = { }