Move tests to the new version of RunModel

docs/code/RunModel/abaqus_example.py

@@ -15,7 +15,7 @@
 import time
 
 from UQpy.distributions import Normal, Uniform
-from UQpy.run_model.RunModel import *
+from UQpy.run_model.RunModel_New import *
 from UQpy.sampling import MonteCarloSampling
 
 calling_directory = os.getcwd()

docs/code/RunModel/ls_dyna_example_multijob.py

@@ -11,7 +11,7 @@
 
 # %%
 from UQpy.distributions import Uniform
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.sampling import MonteCarloSampling
 
 # %% md

docs/code/RunModel/ls_dyna_example_singlejob.py

@@ -11,7 +11,7 @@
 
 # %%
 from UQpy.distributions import Uniform
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.sampling import MonteCarloSampling
 
 # %% md

docs/code/RunModel/matlab_example.py

@@ -63,7 +63,7 @@
 # %%
 
 from UQpy.sampling import MonteCarloSampling
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Normal
 import time
 import numpy as np

docs/code/RunModel/opensees_example.py

@@ -13,7 +13,7 @@
 import numpy as np
 
 from UQpy.distributions import Uniform
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.sampling import MonteCarloSampling
 
 # %% md

docs/code/RunModel/python_example.py

@@ -69,7 +69,7 @@
 # %%
 
 from UQpy.sampling import MonteCarloSampling
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Normal
 import time
 import numpy as np

docs/code/inference/bayes_model_selection/bayes_model_selection.py

@@ -30,7 +30,7 @@
 import matplotlib.pyplot as plt
 from UQpy.sampling.mcmc import MetropolisHastings
 from UQpy.inference import BayesModelSelection, BayesParameterEstimation, ComputationalModel
-from UQpy.run_model.RunModel import RunModel  # required to run the quadratic model
+from UQpy.run_model.RunModel_New import RunModel_New  # required to run the quadratic model
 from UQpy.distributions import Normal, JointIndependent
 from scipy.stats import multivariate_normal, norm
 

docs/code/inference/bayes_parameter_estimation/plot_bayes_parameter_IS_regression.py

@@ -24,7 +24,7 @@
 
 from UQpy.sampling.ImportanceSampling import ImportanceSampling
 from UQpy.inference import BayesParameterEstimation, ComputationalModel
-from UQpy.run_model.RunModel import RunModel  # required to run the quadratic model
+from UQpy.run_model.RunModel_New import RunModel_New  # required to run the quadratic model
 from sklearn.neighbors import KernelDensity  # for the plots
 from UQpy.distributions import JointIndependent, Normal
 

docs/code/inference/bayes_parameter_estimation/plot_bayes_parameter_MCMC_regression.py

@@ -24,7 +24,7 @@
 
 from UQpy.sampling.mcmc.MetropolisHastings import MetropolisHastings
 from UQpy.inference.inference_models.ComputationalModel import ComputationalModel
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.inference import BayesParameterEstimation
 from sklearn.neighbors import KernelDensity  # for the plots
 from UQpy.distributions import JointIndependent, Normal

docs/code/inference/info_model_selection/plot_selection_regression_model.py

@@ -26,7 +26,7 @@
 import shutil
 
 from UQpy.inference import InformationModelSelection, MLE
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 import numpy as np
 from UQpy.inference import BIC
 import matplotlib.pyplot as plt

docs/code/inference/mle/plot_regression_model.py

@@ -25,7 +25,7 @@
 from UQpy.inference import ComputationalModel, MLE
 from UQpy.distributions import Normal
 from UQpy.inference import MinimizeOptimizer
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 
 #%% md
 #

docs/code/reliability/subset_simulation/plot_subset_resonance.py

@@ -30,7 +30,7 @@
 import shutil
 
 from UQpy.reliability import SubsetSimulation
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.sampling import Stretch, ModifiedMetropolisHastings, MonteCarloSampling
 import numpy as np
 import scipy.stats as stats

docs/code/reliability/subset_simulation/plot_subset_rosenbrock.py

@@ -22,7 +22,7 @@
 import numpy as np
 # Import this newly defined Rosenbrock distribution into the Distributions module
 from UQpy.distributions import Normal
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 import scipy.stats as stats
 
 # %% md

docs/code/sampling/adaptive_kriging/plot_adaptive_kriging_branin_hoo.py

@@ -42,7 +42,7 @@
 
 from UQpy.surrogates import GaussianProcessRegression
 from UQpy.sampling import MonteCarloSampling, AdaptiveKriging
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Uniform
 from local_BraninHoo import function
 import time

docs/code/sampling/adaptive_kriging/plot_adaptive_kriging_normal.py

@@ -18,7 +18,7 @@
 
 from UQpy.surrogates.gaussian_process import GaussianProcessRegression
 from UQpy.sampling import MonteCarloSampling, AdaptiveKriging
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Normal
 from local_series import series
 import matplotlib.pyplot as plt

docs/code/sampling/refined_stratified_sampling/refined_stratified_rectangular_gradient.py

@@ -18,7 +18,7 @@
 
 from UQpy.sampling import TrueStratifiedSampling, RefinedStratifiedSampling
 from UQpy.surrogates import GaussianProcessRegression
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Uniform
 import matplotlib.pyplot as plt
 from matplotlib import cm

docs/code/sampling/refined_stratified_sampling/refined_stratified_voronoi_gradient.py

@@ -18,7 +18,7 @@
 
 from UQpy.sampling import TrueStratifiedSampling, RefinedStratifiedSampling
 from UQpy.sampling import VoronoiStrata
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Uniform
 import matplotlib.pyplot as plt
 from matplotlib import cm

docs/code/surrogates/gpr/plot_gpr_custom2D.py

@@ -25,7 +25,7 @@
 from UQpy.surrogates.gaussian_process.regression_models import ConstantRegression
 from UQpy.sampling import RectangularStrata
 from UQpy.sampling import TrueStratifiedSampling
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Uniform
 import numpy as np
 import matplotlib.pyplot as plt

docs/code/surrogates/gpr/plot_gpr_sine.py

@@ -24,7 +24,7 @@
 
 from UQpy.sampling.stratified_sampling.strata import RectangularStrata
 from UQpy.sampling import TrueStratifiedSampling
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions import Gamma
 import numpy as np
 import matplotlib.pyplot as plt

docs/code/surrogates/srom/plot_srom_eigenvalues.py

@@ -21,7 +21,7 @@
 from UQpy.sampling import RectangularStrata
 from UQpy.distributions import Gamma
 from UQpy.surrogates import SROM
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from scipy.stats import gamma
 import numpy as np
 import matplotlib.pyplot as plt

src/UQpy/__init__.py

@@ -10,7 +10,7 @@
 import UQpy.dimension_reduction
 import UQpy.inference
 import UQpy.reliability
-import UQpy.run_model.RunModel
+import UQpy.run_model.RunModel_New
 import UQpy.sampling
 import UQpy.stochastic_process
 import UQpy.surrogates

src/UQpy/inference/inference_models/ComputationalModel.py

@@ -2,7 +2,7 @@
 
 from beartype import beartype
 
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions.baseclass import Distribution
 from UQpy.inference.inference_models.baseclass.InferenceModel import *
 from UQpy.distributions.collection.MultivariateNormal import MultivariateNormal
@@ -16,7 +16,7 @@
 
 class ComputationalModel(InferenceModel):
     @beartype
-    def __init__(self, n_parameters: PositiveInteger, runmodel_object: RunModel,
+    def __init__(self, n_parameters: PositiveInteger, runmodel_object: RunModel_New,
                  error_covariance: Union[np.ndarray, float] = 1.0, name: str = "", prior: Distribution = None,
                  log_likelihood: Callable = None):
         """

src/UQpy/reliability/taylor_series/SORM.py

@@ -5,7 +5,7 @@
 import scipy.stats as stats
 from beartype import beartype
 
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions.baseclass import Distribution
 from UQpy.utilities.ValidationTypes import PositiveInteger
 from UQpy.reliability.taylor_series.FORM import FORM
@@ -46,7 +46,7 @@ def __init__(
     def build_from_first_order(
             cls,
             distributions: Union[None, Distribution, list[Distribution]],
-            runmodel_object: RunModel,
+            runmodel_object: RunModel_New,
             seed_x: Union[list, np.ndarray] = None,
             seed_u: Union[list, np.ndarray] = None,
             df_step: Union[int, float] = 0.01,

src/UQpy/run_model/__init__.py

@@ -1,4 +1,3 @@
-from UQpy.run_model.RunModel import RunModel
 from UQpy.run_model.RunModel_New import RunModel_New
 
 from UQpy.run_model.model_execution import *

src/UQpy/sampling/AdaptiveKriging.py

@@ -2,7 +2,7 @@
 from beartype import beartype
 from sklearn.gaussian_process import GaussianProcessRegressor
 
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.distributions.baseclass import Distribution
 from UQpy.sampling.stratified_sampling.LatinHypercubeSampling import LatinHypercubeSampling
 from UQpy.sampling.adaptive_kriging_functions.baseclass.LearningFunction import (
@@ -23,7 +23,7 @@ class AdaptiveKriging:
     def __init__(
             self,
             distributions: Union[Distribution, list[Distribution]],
-            runmodel_object: RunModel,
+            runmodel_object: RunModel_New,
             surrogate: SurrogateType,
             learning_function: LearningFunction,
             samples: Numpy2DFloatArray = None,

src/UQpy/sampling/stratified_sampling/refinement/GradientEnhancedRefinement.py

@@ -3,7 +3,7 @@
 
 from UQpy.surrogates.baseclass import Surrogate
 from UQpy.utilities.ValidationTypes import *
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 from UQpy.sampling.stratified_sampling.refinement.baseclass.Refinement import *
 from UQpy.utilities.Utilities import gradient
 from UQpy.sampling.stratified_sampling.strata.VoronoiStrata import VoronoiStrata
@@ -17,7 +17,7 @@ class GradientEnhancedRefinement(Refinement):
     def __init__(
         self,
         strata: Strata,
-        runmodel_object: RunModel,
+        runmodel_object: RunModel_New,
         surrogate: Union[Surrogate, GaussianProcessRegressor, CompatibleSurrogate],
         nearest_points_number: int = None,
         qoi_name: str = None,

src/UQpy/utilities/Utilities.py

@@ -2,7 +2,7 @@
 import scipy.stats as stats
 from UQpy.utilities.ValidationTypes import RandomStateType
 
-from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.RunModel_New import RunModel_New
 
 
 def svd(matrix, rank=None, tol=None):
@@ -156,14 +156,12 @@ def gradient(runmodel_object=None, point=None, order="first", df_step=None):
         if len(df_step) == 1:
             df_step = [df_step[0]] * dimension
 
-    if not callable(runmodel_object) and not isinstance(runmodel_object, RunModel):
-        raise RuntimeError(
-            "A RunModel object or callable function must be provided as model."
-        )
+    if not callable(runmodel_object) and not isinstance(runmodel_object, RunModel_New):
+        raise RuntimeError("A RunModel object or callable function must be provided as model.")
 
     def func(m):
         def func_eval(x):
-            if isinstance(m, RunModel):
+            if isinstance(m, RunModel_New):
                 m.run(samples=x, append_samples=False)
                 return np.array(m.qoi_list).flatten()
             else:

tests/unit_tests/dimension_reduction/test_grassman.py

@@ -71,14 +71,11 @@ def test_parsimonious():
     X, X_color = make_s_curve(n, random_state=3, noise=0)
     kernel = GaussianKernel()
 
-    dmaps_object = DiffusionMaps.build_from_data(data=X,
-                                                 alpha=1.0, n_eigenvectors=9,
-                                                 is_sparse=True, n_neighbors=100,
-                                                 optimize_parameters=True,
-                                                 kernel=kernel)
+    dmaps_object = DiffusionMaps(data=X, alpha=1.0, n_eigenvectors=9,
+                                 is_sparse=True, n_neighbors=100,
+                                 kernel=kernel)
 
-    dmaps_object.fit()
-    index, residuals = DiffusionMaps.parsimonious(dmaps_object.eigenvectors, 2)
+    dmaps_object.parsimonious(dim=2)
 
-    assert index[0] == 1
-    assert index[1] == 5
+    assert dmaps_object.parsimonious_indices[0] == 1
+    assert dmaps_object.parsimonious_indices[1] == 5

tests/unit_tests/dimension_reduction/test_kernel.py

@@ -15,7 +15,9 @@ def test_kernel_projection():
     xk = GrassmannPoint(np.array([[-0.69535592, -0.0546034], [-0.34016974, -0.85332868],
                                   [-0.63305978, 0.51850616]]))
     points = [xi, xj, xk]
-    kernel = np.matrix.round(ProjectionKernel().calculate_kernel_matrix(points), 4)
+    kernel = ProjectionKernel()
+    kernel.calculate_kernel_matrix(points)
+    kernel = np.matrix.round(kernel.kernel_matrix, 4)
 
     assert np.allclose(kernel, np.array([[2, 1.0063, 1.2345], [1.0063, 2, 1.0101], [1.2345, 1.0101, 2]]))
 
@@ -26,7 +28,10 @@ def test_kernel_binet_cauchy():
     xj = GrassmannPoint(np.array([[0, np.sqrt(2) / 2], [1, 0], [0, -np.sqrt(2) / 2]]))
     xk = GrassmannPoint(np.array([[-0.69535592, -0.0546034], [-0.34016974, -0.85332868], [-0.63305978, 0.51850616]]))
     points = [xi, xj, xk]
-    kernel = np.matrix.round(BinetCauchyKernel().calculate_kernel_matrix(points), 4)
+
+    kernel = BinetCauchyKernel()
+    kernel.calculate_kernel_matrix(points)
+    kernel = np.matrix.round(kernel.kernel_matrix, 4)
 
     assert np.allclose(kernel, np.array([[1, 0.0063, 0.2345], [0.0063, 1, 0.0101], [0.2345, 0.0101, 1]]))
 
@@ -37,10 +42,10 @@ def test_kernel_gaussian_1d():
     xk = np.array([1, 2, 3, 4])
     points = [xi, xj, xk]
     gaussian = GaussianKernel(epsilon=2.0)
-    kernel = gaussian.calculate_kernel_matrix(points)
+    gaussian.calculate_kernel_matrix(points)
 
-    assert np.allclose(np.matrix.round(kernel, 4), np.array([[1., 0.2645, 1.], [0.2645, 1., 0.2645], [1, 0.2645, 1]]),
-                       atol=1e-04)
+    assert np.allclose(np.matrix.round(gaussian.kernel_matrix, 4),
+                       np.array([[1., 0.2645, 1.], [0.2645, 1., 0.2645], [1, 0.2645, 1]]), atol=1e-04)
     assert np.round(gaussian.epsilon, 4) == 2
 
 
@@ -50,11 +55,11 @@ def test_kernel_gaussian_2d():
     xk = np.array([[-0.69535592, -0.0546034], [-0.34016974, -0.85332868], [-0.63305978, 0.51850616]])
     points = [xi, xj, xk]
     gaussian = GaussianKernel()
-    kernel = gaussian.calculate_kernel_matrix(points)
+    gaussian.calculate_kernel_matrix(points)
 
-    assert np.allclose(np.matrix.round(kernel, 4), np.array([[1., 0.628, 0.3912],
-                                                             [0.628, 1., 0.2534],
-                                                             [0.3912, 0.2534, 1.]]))
+    assert np.allclose(np.matrix.round(gaussian.kernel_matrix, 4), np.array([[1., 0.628, 0.3912],
+                                                                             [0.628, 1., 0.2534],
+                                                                             [0.3912, 0.2534, 1.]]))
     assert np.round(gaussian.epsilon, 4) == 1.0
 
 
@@ -88,22 +93,16 @@ def test_kernel_gaussian_2d():
 
 
 def test_kernel():
-    D1 = 6
-    r0 = 2  # rank sample 0
-    r1 = 3  # rank sample 1
-    r2 = 4  # rank sample 2
-    r3 = 3  # rank sample 2
 
     np.random.seed(1111)  # For reproducibility.
     from numpy.random import RandomState
     rnd = RandomState(0)
 
     # Creating a list of solutions.
     Solutions = [sol0, sol1, sol2, sol3]
-    from UQpy.dimension_reduction.grassmann_manifold.GrassmannOperations import GrassmannOperations
     manifold_projection = SVDProjection(Solutions, p="max")
     kernel = ProjectionKernel()
 
-    kernel = kernel.calculate_kernel_matrix(manifold_projection.u)
+    kernel.calculate_kernel_matrix(manifold_projection.u)
 
-    assert np.round(kernel[0, 1], 8) == 6.0
+    assert np.round(kernel.kernel_matrix[0, 1], 8) == 6.0

tests/unit_tests/inference/test_bayes_model_selection.py

@@ -81,6 +81,3 @@ def test_models():
     assert selection.candidate_models[1].name == 'model_cubic'
     assert selection.candidate_models[2].name == 'model_linear'
 
-    shutil.rmtree(runmodel4.model.model_dir)
-    shutil.rmtree(runmodel5.model.model_dir)
-    shutil.rmtree(runmodel6.model.model_dir)