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SURGroup · Apr 9, 2022 · f1d626c · f1d626c
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diff --git a/docs/code/RunModel/abaqus_example.py b/docs/code/RunModel/abaqus_example.py
@@ -15,7 +15,7 @@
 import time
 
 from UQpy.distributions import Normal, Uniform
-from UQpy.run_model.RunModel_New import *
+from UQpy.run_model.RunModel import *
 from UQpy.sampling import MonteCarloSampling
 
 calling_directory = os.getcwd()

diff --git a/docs/code/RunModel/ls_dyna_example_multijob.py b/docs/code/RunModel/ls_dyna_example_multijob.py
@@ -11,7 +11,7 @@
 
 # %%
 from UQpy.distributions import Uniform
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.sampling import MonteCarloSampling
 
 # %% md

diff --git a/docs/code/RunModel/ls_dyna_example_singlejob.py b/docs/code/RunModel/ls_dyna_example_singlejob.py
@@ -11,7 +11,7 @@
 
 # %%
 from UQpy.distributions import Uniform
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.sampling import MonteCarloSampling
 
 # %% md

diff --git a/docs/code/RunModel/matlab_example.py b/docs/code/RunModel/matlab_example.py
@@ -63,7 +63,7 @@
 # %%
 
 from UQpy.sampling import MonteCarloSampling
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Normal
 import time
 import numpy as np

diff --git a/docs/code/RunModel/opensees_example.py b/docs/code/RunModel/opensees_example.py
@@ -13,7 +13,7 @@
 import numpy as np
 
 from UQpy.distributions import Uniform
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.sampling import MonteCarloSampling
 
 # %% md

diff --git a/docs/code/RunModel/python_example.py b/docs/code/RunModel/python_example.py
@@ -69,7 +69,7 @@
 # %%
 
 from UQpy.sampling import MonteCarloSampling
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Normal
 import time
 import numpy as np

diff --git a/docs/code/inference/bayes_model_selection/bayes_model_selection.py b/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_New import RunModel_New  # required to run the quadratic model
+from UQpy.run_model.RunModel import RunModel  # required to run the quadratic model
 from UQpy.distributions import Normal, JointIndependent
 from scipy.stats import multivariate_normal, norm
 

diff --git a/docs/code/inference/bayes_parameter_estimation/plot_bayes_parameter_IS_regression.py b/docs/code/inference/bayes_parameter_estimation/plot_bayes_parameter_IS_regression.py
@@ -25,7 +25,7 @@
 from UQpy import PythonModel
 from UQpy.sampling.ImportanceSampling import ImportanceSampling
 from UQpy.inference import BayesParameterEstimation, ComputationalModel
-from UQpy.run_model.RunModel_New import RunModel_New  # required to run the quadratic model
+from UQpy.run_model.RunModel import RunModel  # required to run the quadratic model
 from sklearn.neighbors import KernelDensity  # for the plots
 from UQpy.distributions import JointIndependent, Normal
 
@@ -46,7 +46,7 @@ def pdf_from_kde(domain, samples1d):
 
 model = PythonModel(model_script='local_pfn_models.py', model_object_name='model_quadratic', delete_files=True,
                     var_names=['theta_0', 'theta_1'])
-h_func = RunModel_New(model=model)
+h_func = RunModel(model=model)
 h_func.run(samples=param_true)
 
 # Add noise

diff --git a/docs/code/inference/bayes_parameter_estimation/plot_bayes_parameter_MCMC_regression.py b/docs/code/inference/bayes_parameter_estimation/plot_bayes_parameter_MCMC_regression.py
@@ -25,7 +25,7 @@
 from UQpy import PythonModel
 from UQpy.sampling.mcmc.MetropolisHastings import MetropolisHastings
 from UQpy.inference.inference_models.ComputationalModel import ComputationalModel
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.inference import BayesParameterEstimation
 from sklearn.neighbors import KernelDensity  # for the plots
 from UQpy.distributions import JointIndependent, Normal
@@ -48,7 +48,7 @@ def pdf_from_kde(domain, samples1d):
 
 model = PythonModel(model_script='local_pfn_models.py', model_object_name='model_quadratic',
                     var_names=['theta_0', 'theta_1'])
-h_func = RunModel_New(model=model)
+h_func = RunModel(model=model)
 h_func.run(samples=param_true)
 data_clean = np.array(h_func.qoi_list[0])
 print(data_clean.shape)

diff --git a/docs/code/inference/info_model_selection/plot_selection_regression_model.py b/docs/code/inference/info_model_selection/plot_selection_regression_model.py
@@ -27,7 +27,7 @@
 
 from UQpy import PythonModel
 from UQpy.inference import InformationModelSelection, MLE
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 import numpy as np
 from UQpy.inference import BIC
 import matplotlib.pyplot as plt
@@ -45,7 +45,7 @@
 print('Shape of true parameter vector: {}'.format(param_true.shape))
 
 model = PythonModel(model_script='pfn_models.py', model_object_name='model_quadratic', var_names=['theta_0', 'theta_1'])
-h_func = RunModel_New(model=model)
+h_func = RunModel(model=model)
 h_func.run(samples=param_true)
 
 # Add noise
@@ -68,7 +68,7 @@
 for i in range(3):
     model = PythonModel(model_script='pfn_models.py', model_object_name=names[i],
                         var_names=['theta_{}'.format(j) for j in range(i + 1)])
-    h_func = RunModel_New(model=model)
+    h_func = RunModel(model=model)
     M = ComputationalModel(runmodel_object=h_func, n_parameters=i + 1,
                            name=names[i], error_covariance=error_covariance)
     estimators.append(MLE(inference_model=M, data=data_1))

diff --git a/docs/code/inference/mle/plot_regression_model.py b/docs/code/inference/mle/plot_regression_model.py
@@ -27,7 +27,7 @@
 from UQpy.inference import ComputationalModel, MLE
 from UQpy.distributions import Normal
 from UQpy.inference import MinimizeOptimizer
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 
 #%% md
 #
@@ -42,7 +42,7 @@
 
 model = PythonModel(model_script='local_pfn_models.py', model_object_name='model_quadratic', delete_files=True,
                     var_names=['theta_0', 'theta_1'])
-h_func = RunModel_New(model=model)
+h_func = RunModel(model=model)
 h_func.run(samples=param_true)
 
 # Add noise

diff --git a/docs/code/reliability/form/plot_FORM_linear function_2d.py b/docs/code/reliability/form/plot_FORM_linear function_2d.py
@@ -19,7 +19,7 @@
 
 import shutil
 
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.run_model.model_execution.PythonModel import PythonModel
 from UQpy.distributions import Normal
 from UQpy.reliability import FORM
@@ -28,7 +28,7 @@
 dist2 = Normal(loc=0., scale=1.)
 
 model = PythonModel(model_script='pfn.py', model_object_name="example2")
-RunModelObject2 = RunModel_New(model=model)
+RunModelObject2 = RunModel(model=model)
 
 Z = FORM(distributions=[dist1, dist2], runmodel_object=RunModelObject2)
 Z.run()

diff --git a/docs/code/reliability/form/plot_FORM_linear_function_3d.py b/docs/code/reliability/form/plot_FORM_linear_function_3d.py
@@ -25,7 +25,7 @@
 
 # %%
 
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.run_model.model_execution.PythonModel import PythonModel
 from UQpy.distributions import Normal
 from UQpy.reliability import FORM
@@ -35,7 +35,7 @@
 dist3 = Normal(loc=4., scale=0.4)
 
 model = PythonModel(model_script='pfn.py', model_object_name="example3",)
-RunModelObject3 = RunModel_New(model=model)
+RunModelObject3 = RunModel(model=model)
 
 Z0 = FORM(distributions=[dist1, dist2, dist3], runmodel_object=RunModelObject3)
 Z0.run()

diff --git a/docs/code/reliability/form/plot_FORM_structural_reliability.py b/docs/code/reliability/form/plot_FORM_structural_reliability.py
@@ -28,14 +28,14 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.run_model.model_execution.PythonModel import PythonModel
 from UQpy.distributions import Normal
 from UQpy.reliability import FORM
 
 
 model = PythonModel(model_script='pfn.py', model_object_name="example1")
-RunModelObject = RunModel_New(model=model)
+RunModelObject = RunModel(model=model)
 
 dist1 = Normal(loc=200., scale=20.)
 dist2 = Normal(loc=150, scale=10.)

diff --git a/docs/code/reliability/sorm/plot_SORM_nonlinear_function.py b/docs/code/reliability/sorm/plot_SORM_nonlinear_function.py
@@ -21,7 +21,7 @@
 import shutil
 
 import numpy as np
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.run_model.model_execution.PythonModel import PythonModel
 from UQpy.distributions import Normal
 from UQpy.reliability import FORM
@@ -38,7 +38,7 @@
 dist1 = Normal(loc=20., scale=2)
 dist2 = Lognormal(s=s, loc=0.0, scale=scale)
 model = PythonModel(model_script='pfn.py', model_object_name="example4",)
-RunModelObject4 = RunModel_New(model=model)
+RunModelObject4 = RunModel(model=model)
 form = FORM(distributions=[dist1, dist2], runmodel_object=RunModelObject4)
 form.run()
 Q0 = SORM(form_object=form)

diff --git a/docs/code/reliability/subset_simulation/plot_subset_resonance.py b/docs/code/reliability/subset_simulation/plot_subset_resonance.py
@@ -31,7 +31,7 @@
 
 from UQpy import PythonModel
 from UQpy.reliability import SubsetSimulation
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.sampling import Stretch, ModifiedMetropolisHastings, MonteCarloSampling
 import numpy as np
 import scipy.stats as stats
@@ -113,7 +113,7 @@
 plt.show()
 
 m = PythonModel(model_script='local_Resonance_pfn.py', model_object_name="RunPythonModel")
-model = RunModel_New(model=m)
+model = RunModel(model=m)
 
 # %% md
 #
@@ -143,7 +143,7 @@
 
 for i in range(ntrials):
     m1 = PythonModel(model_script='local_Resonance_pfn.py', model_object_name="RunPythonModel")
-    model = RunModel_New(model=m1)
+    model = RunModel(model=m1)
     dist = MultivariateNormal(mean=m, cov=C)
     xx = dist.rvs(nsamples=1000, random_state=123)
     xx1 = dist.rvs(nsamples=100, random_state=123)
@@ -187,7 +187,7 @@
 
 for i in range(ntrials):
     m1 = PythonModel(model_script='local_Resonance_pfn.py', model_object_name="RunPythonModel")
-    model = RunModel_New(model=m1)
+    model = RunModel(model=m1)
     dist = MultivariateNormal(mean=m, cov=C)
     xx = dist.rvs(nsamples=1000, random_state=123)
     xx1 = dist.rvs(nsamples=100, random_state=123)

diff --git a/docs/code/reliability/subset_simulation/plot_subset_rosenbrock.py b/docs/code/reliability/subset_simulation/plot_subset_rosenbrock.py
@@ -20,7 +20,7 @@
 # Import this newly defined Rosenbrock distribution into the Distributions module
 from UQpy.distributions import Normal
 from UQpy.reliability import SubsetSimulation
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.sampling import ModifiedMetropolisHastings, Stretch
 # First import the file that contains the newly defined Rosenbrock distribution
 from local_Rosenbrock import Rosenbrock
@@ -32,7 +32,7 @@
 # %%
 
 m = PythonModel(model_script='local_Rosenbrock_pfn.py', model_object_name="RunPythonModel")
-model = RunModel_New(model=m)
+model = RunModel(model=m)
 dist = Rosenbrock(p=100.)
 dist_prop1 = Normal(loc=0, scale=1)
 dist_prop2 = Normal(loc=0, scale=10)
@@ -69,7 +69,7 @@
 # %%
 
 m = PythonModel(model_script='local_Rosenbrock_pfn.py', model_object_name="RunPythonModel")
-model = RunModel_New(model=m)
+model = RunModel(model=m)
 dist = Rosenbrock(p=100.)
 
 x = stats.norm.rvs(loc=0, scale=1, size=(100, 2), random_state=83276)

diff --git a/docs/code/sampling/adaptive_kriging/plot_adaptive_kriging_branin_hoo.py b/docs/code/sampling/adaptive_kriging/plot_adaptive_kriging_branin_hoo.py
@@ -43,7 +43,7 @@
 from UQpy import PythonModel
 from UQpy.surrogates import GaussianProcessRegression
 from UQpy.sampling import MonteCarloSampling, AdaptiveKriging
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Uniform
 from local_BraninHoo import function
 import time
@@ -66,7 +66,7 @@
 # %%
 
 model = PythonModel(model_script='local_BraninHoo.py', model_object_name='function')
-rmodel = RunModel_New(model=model)
+rmodel = RunModel(model=model)
 
 # %% md
 #

diff --git a/docs/code/sampling/adaptive_kriging/plot_adaptive_kriging_normal.py b/docs/code/sampling/adaptive_kriging/plot_adaptive_kriging_normal.py
@@ -19,7 +19,7 @@
 from UQpy import PythonModel
 from UQpy.surrogates.gaussian_process import GaussianProcessRegression
 from UQpy.sampling import MonteCarloSampling, AdaptiveKriging
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Normal
 from local_series import series
 import matplotlib.pyplot as plt
@@ -44,7 +44,7 @@
 # %%
 
 model = PythonModel(model_script='local_series.py', model_object_name='series')
-rmodel = RunModel_New(model=model)
+rmodel = RunModel(model=model)
 
 
 # %% md
@@ -156,7 +156,7 @@ def evaluate_function(self, distributions, n_add, surrogate, population, qoi=Non
 K1 = GaussianProcessRegression(regression_model=LinearRegression(), kernel=RBF(), optimizer=optimizer,
                                hyperparameters=[1, 1, 0.1], optimizations_number=1)
 model = PythonModel(model_script='local_series.py', model_object_name='series')
-rmodel1 = RunModel_New(model=model)
+rmodel1 = RunModel(model=model)
 
 # %% md
 #
@@ -209,7 +209,7 @@ def evaluate_function(self, distributions, n_add, surrogate, population, qoi=Non
 # Code
 b = MonteCarloSampling(distributions=marginals, nsamples=10 ** 4, random_state=4)
 model = PythonModel(model_script='local_series.py', model_object_name='series')
-r1model = RunModel_New(model=model)
+r1model = RunModel(model=model)
 r1model.run(samples=b.samples)
 
 

diff --git a/docs/code/sampling/refined_stratified_sampling/refined_stratified_rectangular_gradient.py b/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_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Uniform
 import matplotlib.pyplot as plt
 from matplotlib import cm

diff --git a/docs/code/sampling/refined_stratified_sampling/refined_stratified_voronoi_gradient.py b/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_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Uniform
 import matplotlib.pyplot as plt
 from matplotlib import cm

diff --git a/docs/code/sensitivity/morris/plot_12_dimensional_gfunction.py b/docs/code/sensitivity/morris/plot_12_dimensional_gfunction.py
@@ -15,7 +15,7 @@
 # %%
 import shutil
 from UQpy.run_model.model_execution.PythonModel import PythonModel
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Uniform
 from UQpy.sensitivity import MorrisSensitivity
 import matplotlib.pyplot as plt
@@ -31,7 +31,7 @@
 
 model = PythonModel(model_script='local_pfn.py', model_object_name='gfun_sensitivity', delete_files=True,
                     a_values=a_values, var_names=['X{}'.format(i) for i in range(na)])
-runmodel_object = RunModel_New(model=model)
+runmodel_object = RunModel(model=model)
 
 dist_object = [Uniform(), ] * na
 

diff --git a/docs/code/sensitivity/morris/plot_morris_2d_gfunction.py b/docs/code/sensitivity/morris/plot_morris_2d_gfunction.py
@@ -13,7 +13,7 @@
 import shutil
 
 from UQpy.run_model.model_execution.PythonModel import PythonModel
-from UQpy.run_model.RunModel_New import RunModel_New
+from UQpy.run_model.RunModel import RunModel
 from UQpy.distributions import Uniform
 from UQpy.sensitivity import MorrisSensitivity
 import numpy as np
@@ -30,7 +30,7 @@
 
 model = PythonModel(model_script='local_pfn.py', model_object_name='gfun_sensitivity', delete_files=True,
                     a_values=a_values, var_names=['X{}'.format(i) for i in range(na)])
-runmodel_object = RunModel_New(model=model)
+runmodel_object = RunModel(model=model)
 
 dist_object = [Uniform(), ] * na