Development

docs/code/dimension_reduction/grassmann/plot_grassmann_kernel.py

@@ -20,7 +20,7 @@
 from UQpy.dimension_reduction.grassmann_manifold.projections.SVDProjection import SVDProjection
 from UQpy.dimension_reduction import GrassmannOperations
 from UQpy.utilities import GrassmannPoint
-from UQpy.utilities.kernels import Kernel, ProjectionKernel
+from UQpy.utilities.kernels import ProjectionKernel
 import sys
 
 # %% md
@@ -73,10 +73,10 @@
 # %%
 projection_kernel = ProjectionKernel()
 
-projection_kernel.calculate_kernel_matrix(points=manifold_projection.u)
+projection_kernel.calculate_kernel_matrix(x=manifold_projection.u, s=manifold_projection.u)
 kernel_psi = projection_kernel.kernel_matrix
 
-projection_kernel.calculate_kernel_matrix(points=manifold_projection.v)
+projection_kernel.calculate_kernel_matrix(x=manifold_projection.v, s=manifold_projection.v)
 kernel_phi = projection_kernel.kernel_matrix
 
 fig, (ax1, ax2) = plt.subplots(1, 2)
@@ -92,9 +92,9 @@
 
 # %%
 
-projection_kernel.calculate_kernel_matrix(points=[manifold_projection.u[0],
-                                                  manifold_projection.u[1],
-                                                  manifold_projection.u[2]])
+projection_kernel.\
+    calculate_kernel_matrix(x=[manifold_projection.u[0], manifold_projection.u[1], manifold_projection.u[2]],
+                            s=[manifold_projection.u[0], manifold_projection.u[1], manifold_projection.u[2]])
 kernel_01 = projection_kernel.kernel_matrix
 
 fig = plt.figure()
@@ -111,17 +111,18 @@
 
 class UserKernel(GrassmannianKernel):
 
-    def kernel_entry(self, xi: GrassmannPoint, xj: GrassmannPoint):
-        r = np.dot(xi.data.T, xj.data)
+    def element_wise_operation(self, xi_j) -> float:
+        xi, xj = xi_j
+        r = np.dot(xi.T, xj)
         det = np.linalg.det(r)
         return det * det
 
 
 user_kernel = UserKernel()
-user_kernel.calculate_kernel_matrix(points=manifold_projection.u)
+user_kernel.calculate_kernel_matrix(x=manifold_projection.u, s=manifold_projection.u)
 kernel_user_psi = user_kernel.kernel_matrix
 
-user_kernel.calculate_kernel_matrix(points=manifold_projection.v)
+user_kernel.calculate_kernel_matrix(x=manifold_projection.v, s=manifold_projection.v)
 kernel_user_phi = user_kernel.kernel_matrix
 
 fig, (ax1, ax2) = plt.subplots(1, 2)

docs/code/surrogates/gpr/plot_gpr_custom2D.py

@@ -32,13 +32,14 @@
 import matplotlib.pyplot as plt
 from matplotlib import cm
 from matplotlib.ticker import LinearLocator, FormatStrFormatter
-from UQpy.surrogates import GaussianProcessRegression, Matern
+from UQpy.surrogates import GaussianProcessRegression
 
 # %% md
 #
 # Create a distribution object.
 
 # %%
+from UQpy.utilities import Matern
 
 marginals = [Uniform(loc=0., scale=1.), Uniform(loc=0., scale=1.)]
 

docs/code/surrogates/gpr/plot_gpr_no_noise.py

@@ -19,16 +19,17 @@
 
 # %%
 
-import numpy as np
-import matplotlib.pyplot as plt
 import warnings
 
+import matplotlib.pyplot as plt
+import numpy as np
+
 from UQpy.surrogates.gaussian_process.regression_models.LinearRegression import LinearRegression
+from UQpy.utilities import RBF
 
 warnings.filterwarnings('ignore')
 from UQpy.utilities.MinimizeOptimizer import MinimizeOptimizer
-from UQpy.utilities.FminCobyla import FminCobyla
-from UQpy.surrogates import GaussianProcessRegression, NonNegative, RBF
+from UQpy.surrogates import GaussianProcessRegression
 
 
 # %% md

docs/code/surrogates/gpr/plot_gpr_noisy.py

@@ -22,10 +22,13 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import warnings
+
+from UQpy.utilities import RBF
+
 warnings.filterwarnings('ignore')
 from UQpy.utilities.MinimizeOptimizer import MinimizeOptimizer
 from UQpy.surrogates.gaussian_process.regression_models.LinearRegression import LinearRegression
-from UQpy.surrogates import GaussianProcessRegression, RBF
+from UQpy.surrogates import GaussianProcessRegression
 
 
 # %% md

docs/code/surrogates/gpr/plot_gpr_sine.py

@@ -29,13 +29,14 @@
 from UQpy.distributions import Gamma
 import numpy as np
 import matplotlib.pyplot as plt
-from UQpy.surrogates import GaussianProcessRegression, RBF
+from UQpy.surrogates import GaussianProcessRegression
 
 # %% md
 #
 # Create a distribution object.
 
 # %%
+from UQpy.utilities import RBF
 
 marginals = [Gamma(a=2., loc=1., scale=3.)]
 

docs/code/surrogates/pce/plot_pce_camel.py

@@ -9,14 +9,15 @@
 
 .. math:: f(x) = \Big(4-2.1x_1^2 + \frac{x_1^4}{3} \Big)x_1^2 + x_1x_2 + (-4 + 4x_2^2)x_2^2
 
+
 **Description:**  Dimensions: 2
 
 **Input Domain:**  This function is evaluated on the hypercube :math:`x_1 \in [-3, 3], x_2 \in [-2, 2]`.
 
-**Global minimum:** :math:`f(x^*)=-1.0316,` at :math:`x^* = (0.0898, -0.7126)` and
-:math:`(-0.0898, 0.7126)`.
+**Global minimum:** :math:`f(x^*)=-1.0316,` at :math:`x^* = (0.0898, -0.7126)` and :math:`(-0.0898, 0.7126)`.
 
 **Reference:**  Molga, M., & Smutnicki, C. Test functions for optimization needs (2005). Retrieved June 2013, from http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
+
 """
 
 # %% md

docs/requirements.txt

@@ -2,4 +2,5 @@
     sphinx_autodoc_typehints
     sphinx_rtd_theme
     sphinx_gallery
-    sphinxcontrib_bibtex
+    sphinxcontrib_bibtex
+    Sphinx==6.1.3

docs/source/architecture.rst

@@ -129,7 +129,7 @@ Surrogates
 Another module that has extensively restructured in v4 is the surrogates. Apart from the :class:`.SROM` method which
 was retained as an independent algorithm, the previous Kriging functionality was removed. It is now replaced with
 :class:`.GaussianProcessRegression`. The functionality of the Gaussian is constructed using object composition,
-and the specific implementation of :class:`.Regression` and :class:`.Kernel` abstract base classes. An additional
+and the specific implementation of :class:`.Regression` and :class:`Kernel` abstract base classes. An additional
 functionality of constrained surrogates is added by implementing the :class:`.ConstraintsGPR` abstract class. The
 functionality of :class:`.PolynomialChaosExpansion` was rewritten from scratch to address some performance issues of v3.
 The Strategy Design pattern was used here as well, with three abstract base classes :class:`.Polynomials`,

docs/source/bibliography.bib

@@ -829,12 +829,19 @@ @article{saltelli_2002
 keywords = {Sensitivity analysis, Sensitivity measures, Sensitivity indices, Importance measures},
 }
 
-@article{PTMCMC1,
-title = {Parallel Tempering: Theory, Applications, and New Perspectives},
-author = {David J. Earl, Michael W. Deem},
-year = {2005},
-doi = {https://doi.org/10.48550/arXiv.physics/0508111}
-}
+@Article{PTMCMC1,
+author ="Earl, David J. and Deem, Michael W.",
+title  ="Parallel tempering: Theory{,} applications{,} and new perspectives",
+journal  ="Phys. Chem. Chem. Phys.",
+year  ="2005",
+volume  ="7",
+issue  ="23",
+pages  ="3910-3916",
+publisher  ="The Royal Society of Chemistry",
+doi  ="10.1039/B509983H",
+url  ="http://dx.doi.org/10.1039/B509983H",
+abstract  ="We review the history of the parallel tempering simulation method. From its origins in data analysis{,} the parallel tempering method has become a standard workhorse of physicochemical simulations. We discuss the theory behind the method and its various generalizations. We mention a selected set of the many applications that have become possible with the introduction of parallel tempering{,} and we suggest several promising avenues for future research."}
+
 
 @inproceedings{PTMCMC2,
 title = {Using Thermodynamic Integration to Calculate the Posterior Probability in Bayesian Model Selection Problems},
@@ -844,13 +851,22 @@ @inproceedings{PTMCMC2
 author = {Paul M. Goggans and Ying Chi}
 }
 
+
 @article{STMCMC_ChingChen,
+author = {Jianye Ching  and Yi-Chu Chen },
 title = {Transitional Markov Chain Monte Carlo Method for Bayesian Model Updating, Model Class Selection, and Model Averaging},
-author = {Jianye Ching, Yi-Chu Chen},
+journal = {Journal of Engineering Mechanics},
+volume = {133},
+number = {7},
+pages = {816-832},
 year = {2007},
-doi = {https://doi.org/10.1061/(ASCE)0733-9399(2007)133:7(816)}
+doi = {10.1061/(ASCE)0733-9399(2007)133:7(816)},
+URL = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282007%29133%3A7%28816%29},
+eprint = {https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290733-9399%282007%29133%3A7%28816%29}
 }
 
+
+
 @article{Kle2D,
 title = {Simulation of multi-dimensional random fields by Karhunen–Loève expansion},
 journal = {Computer Methods in Applied Mechanics and Engineering},

docs/source/conf.py

@@ -25,7 +25,7 @@
 )
 
 # The full version, including alpha/beta/rc tags
-release = "v4.0.0"
+release = "v4.1.0"
 
 # -- General configuration ---------------------------------------------------
 
@@ -173,15 +173,15 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-language = None
+language = "en"
 pygments_style = None
 
 html_theme = "sphinx_rtd_theme"
 
 html_theme_options = {
     "logo_only": True,
     "style_nav_header_background": "#F0F0F0",
-    "vcs_pageview_mode": "view",
+    # "vcs_pageview_mode": "view",
 }
 
 github_url = "https://github.com/SURGroup/UQpy"

docs/source/sensitivity/sobol.rst

@@ -81,8 +81,8 @@ Attributes
 .. autoattribute:: UQpy.sensitivity.SobolSensitivity.second_order_confidence_interval
 .. autoattribute:: UQpy.sensitivity.SobolSensitivity.total_order_confidence_interval
 .. autoattribute:: UQpy.sensitivity.SobolSensitivity.n_samples
-.. autoattribute:: UQpy.sensitivity.Sobol.n_variables
-.. autoattribute:: UQpy.sensitivity.Sobol.is_multi_output
+.. autoattribute:: UQpy.sensitivity.SobolSensitivity.n_variables
+.. autoattribute:: UQpy.sensitivity.SobolSensitivity.is_multi_output
 
 
 Examples

docs/source/stochastic_process/karhunen_loeve_1d.rst

@@ -29,4 +29,4 @@ Examples
 
 .. toctree::
 
-   Karhunen Loeve Examples <../auto_examples/stochastic_processes/karhunen_loeve/index>
+   Karhunen Loeve Examples <../auto_examples/stochastic_processes/karhunen_loeve_1d/index>

docs/source/stochastic_process/karhunen_loeve_2d.rst

@@ -1,5 +1,5 @@
 Karhunen Loève Expansion for Multi-Dimensional Fields
-----------------------------
+-----------------------------------------------------
 
 The Karhunen Loève Expansion expands the stochastic field as follows:
 
@@ -9,7 +9,7 @@ where :math:`\eta_{nk}(\theta)` are uncorrelated standardized normal random vari
 at :cite:`Kle2D`
 
 KarhunenLoeve2D Class
-^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^
 
 The :class:`.KarhunenLoeve2D` class is imported using the following command:
 
@@ -23,7 +23,6 @@ Methods
 Attributes
 """"""""""
 .. autoattribute:: UQpy.stochastic_process.KarhunenLoeveExpansion2D.samples
-.. autoattribute:: UQpy.stochastic_process.KarhunenLoeveExpansion2D.xi
 
 Examples
 """"""""""

docs/source/surrogates/gpr.rst

@@ -150,16 +150,13 @@ User-Defined Kernel
 """"""""""""""""""""""""""""
 
 Adding a new kernel to the :class:`.GaussianProcessRegression` class is straightforward. This is done by creating a new class
-that extends the :class:`UQpy.surrogates.gaussian_process.kernels.baseclass.Kernel` abstract base class.
+that extends the :class:`.Kernel` abstract base class.
 This new class must have a method ``c(self, x, s, params)`` that takes as input the new points, training points and hyperparameters.
 Notice that the input ``params`` include lengthscales and process standard deviation, not noise standard deviation (even for noisy data).
 
-The :class:`UQpy.surrogates.gaussian_process.kernels.baseclass.Kernel` class is imported using the following command:
+The :class:`.Kernel` class is imported using the following command:
 
->>> from UQpy.surrogates.gaussian_process.kernels.baseclass.Kernel import Kernel
-
-.. autoclass:: UQpy.surrogates.gaussian_process.Kernel
-    :members:
+>>> from UQpy.utilities.kernels.baseclass.Kernel import Kernel
 
 The method should return covariance matrix, i.e. a 2-D array with first dimension
 being the number of new points and second dimension being the number of training points.

docs/source/utilities/kernels/euclidean_kernels.rst

@@ -29,7 +29,7 @@ Methods
 ~~~~~~~~~
 
 .. autoclass:: UQpy.utilities.kernels.GaussianKernel
-    :members: kernel_entry, optimize_parameters
+    :members: optimize_parameters
 
 Attributes
 ~~~~~~~~~~

docs/source/utilities/kernels/index.rst

@@ -20,7 +20,7 @@ The :class:`UQpy.utilities.kernels.baseclass.Kernel` class is imported using the
 >>> from UQpy.utilities.kernels.baseclass.Kernel import Kernel
 
 .. autoclass:: UQpy.utilities.kernels.baseclass.Kernel
-    :members: kernel_entry, optimize_parameters, calculate_kernel_matrix
+    :members: calculate_kernel_matrix
 
 Types of Kernels
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

src/UQpy/dimension_reduction/diffusion_maps/DiffusionMaps.py

@@ -16,7 +16,7 @@
 from typing import Annotated, Union
 from beartype.vale import Is
 from UQpy.utilities.ValidationTypes import Numpy2DFloatArray, NumpyFloatArray
-from UQpy.utilities.kernels.baseclass import Kernel
+from UQpy.utilities.kernels.baseclass.Kernel import Kernel
 
 
 class DiffusionMaps:
@@ -68,7 +68,7 @@ def __init__(
         if kernel_matrix is not None:
             self.kernel_matrix = kernel_matrix
         elif data is not None and kernel is not None:
-            kernel.calculate_kernel_matrix(points=data)
+            kernel.calculate_kernel_matrix(x=data, s=data)
             self.kernel_matrix = kernel.kernel_matrix
         else:
             raise ValueError("Either `kernel_matrix` or both `data` and `kernel` must be provided")

src/UQpy/dimension_reduction/grassmann_manifold/projections/baseclass/GrassmannProjection.py

@@ -1,7 +1,5 @@
 from abc import ABC, abstractmethod
 
-from UQpy.utilities.kernels.baseclass.Kernel import Kernel
-
 
 class GrassmannProjection(ABC):
     """

src/UQpy/sampling/mcmc/tempering_mcmc/baseclass/TemperingMCMC.py

@@ -13,7 +13,7 @@ def __init__(self, pdf_intermediate=None, log_pdf_intermediate=None, args_pdf_in
          least two inputs :code:`x` (ndarray, point(s) at which to evaluate the function), and :code:`temper_param` (float,
          tempering parameter). Eit  her `pdf_intermediate` or `log_pdf_intermediate` must be provided
          (`log_pdf_intermediate` is preferred). Within the code, the `log_pdf_intermediate` is evaluated as:
-             :code:`log_pdf_intermediate(x, temper_param, *args_pdf_intermediate)`
+         :code:`log_pdf_intermediate(x, temper_param, *args_pdf_intermediate)`
          where `args_pdf_intermediate` are additional positional arguments that are provided to the class via its
          `args_pdf_intermediate` input
         :param log_pdf_intermediate: see `pdf_intermediate`