Merge remote-tracking branch 'origin/Development' into feature/v4/KLE_2D

# Conflicts: # docs/source/bibliography.bib # docs/source/conf.py
SURGroup · Feb 1, 2023 · 8ea79d1 · 8ea79d1
2 parents 8bd92a8 + 55fed0b
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diff --git a/README.rst b/README.rst
@@ -1,7 +1,6 @@
-|AzureDevops| |PyPIdownloads| |PyPI| |CondaSURG| |CondaPlatforms| |GithubRelease| |Binder| |Docs| |bear-ified|
+|AzureDevops| |PyPIdownloads| |PyPI| |CondaPlatforms| |GithubRelease| |Binder| |Docs| |bear-ified|
 
 .. |Docs| image:: https://img.shields.io/readthedocs/uqpy?style=plastic  :alt: Read the Docs
-.. |CondaSURG| image:: https://img.shields.io/conda/vn/SURG_JHU/uqpy?style=plastic   :alt: Conda (channel only)
 .. |CondaPlatforms| image:: https://img.shields.io/conda/pn/SURG_JHU/uqpy?style=plastic   :alt: Conda
 .. |GithubRelease| image:: https://img.shields.io/github/v/release/SURGroup/UQpy?style=plastic   :alt: GitHub release (latest by date)
 .. |AzureDevops| image:: https://img.shields.io/azure-devops/build/UQpy/5ce1851f-e51f-4e18-9eca-91c3ad9f9900/1?style=plastic   :alt: Azure DevOps builds
@@ -35,7 +34,7 @@ Uncertainty Quantification with python (UQpy)
 +                       +                                                                  +
 |                       | Promit Chakroborty, Lukáš Novák, Andrew Solanto                  |
 +-----------------------+------------------------------------------------------------------+
-| **Contributors:**     | Michael Gardner                                                  |
+| **Contributors:**     | Michael Gardner, Prateek Bhustali, Julius Schultz, Ulrich Römer  |
 +-----------------------+------------------------------------------------------------------+
 
 Contact
@@ -61,7 +60,7 @@ Dependencies
 
             * ::
             
-                Python >= 3.6
+                Python >= 3.9
                 Git >= 2.13.1
 
 License
@@ -91,7 +90,6 @@ Using Conda
             * ::
 
                         conda install -c conda-forge uqpy
-                        conda install -c surg_jhu uqpy (latest version)
 
 Clone your fork of the UQpy repo from your GitHub account to your local disk (to get the latest version): 
 

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -152,18 +152,18 @@ jobs:
       displayName: Install Anaconda packages
       condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
 
-    - bash: |
-        source activate myEnvironment
-        conda build . recipe --variants "{'version': ['$(GitVersion.SemVer)']}"
-      displayName: Build Noarch conda packages
-      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
-
-    - bash: |
-        source activate myEnvironment
-        anaconda login --username $(ANACONDAUSER) --password $(ANACONDAPW)
-        anaconda upload /usr/local/miniconda/envs/myEnvironment/conda-bld/noarch/*.tar.bz2
-      displayName: Upload conda packages
-      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
+#    - bash: |
+#        source activate myEnvironment
+#        conda build . recipe --variants "{'version': ['$(GitVersion.SemVer)']}"
+#      displayName: Build Noarch conda packages
+#      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
+#
+#    - bash: |
+#        source activate myEnvironment
+#        anaconda login --username $(ANACONDAUSER) --password $(ANACONDAPW)
+#        anaconda upload /usr/local/miniconda/envs/myEnvironment/conda-bld/noarch/*.tar.bz2
+#      displayName: Upload conda packages
+#      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
 
 - job: "Create_Docker_images"
   dependsOn: Build_UQpy_and_run_tests

diff --git a/docs/code/RunModel/abaqus_example.py b/docs/code/RunModel/abaqus_example.py
@@ -4,18 +4,18 @@
 ==================================
 """
 
-
 # %% md
 #
 # Import the necessary libraries.
 
 # %%
-
+import glob
 import pickle
 import time
-
+import os
 from UQpy.distributions import Normal, Uniform
-from UQpy.run_model.RunModel import *
+from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.model_execution.ThirdPartyModel import ThirdPartyModel
 from UQpy.sampling import MonteCarloSampling
 
 calling_directory = os.getcwd()
@@ -36,12 +36,12 @@
 
 # %%
 
-abaqus_sfe_model = RunModel(model_script='abaqus_fire_analysis.py', input_template='abaqus_input.py',
-                            output_script='extract_abaqus_output.py', var_names=var_names, ntasks=24,
-                            model_dir='SFE_MCS', verbose=True, cores_per_task=1)
+m = ThirdPartyModel(model_script='abaqus_fire_analysis.py', input_template='abaqus_input.py',
+                    output_script='extract_abaqus_output.py', var_names=var_names,
+                    model_dir='SFE_MCS', )
+abaqus_sfe_model = RunModel(cores_per_task=1, ntasks=24, model=m)
 print('Example: Created the model object.')
 
-
 # %% md
 #
 # Towards defining the sampling scheme

diff --git a/docs/code/RunModel/ls_dyna_example_multijob.py b/docs/code/RunModel/ls_dyna_example_multijob.py
@@ -4,14 +4,14 @@
 ==================================
 """
 
-
 # %% md
 #
 # Import the necessary libraries.
 
 # %%
 from UQpy.distributions import Uniform
 from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.model_execution.ThirdPartyModel import ThirdPartyModel
 from UQpy.sampling import MonteCarloSampling
 
 # %% md
@@ -42,13 +42,7 @@
 # Run the model.
 
 # %%
-
-run_ = RunModel(samples=x.samples, ntasks=6, model_script='dyna_script.py', input_template='dyna_input.k',
-                var_names=['x0', 'y0', 'z0', 'R0', 'x1', 'y1', 'z1', 'R1'],  model_dir='dyna_test', cluster=True,
-                verbose=False, fmt='{:>10.4f}', cores_per_task=12)
-
-
-
-
-
-
+m = ThirdPartyModel(model_script='dyna_script.py', input_template='dyna_input.k',
+                    var_names=['x0', 'y0', 'z0', 'R0', 'x1', 'y1', 'z1', 'R1'], model_dir='dyna_test',
+                    fmt='{:>10.4f}')
+run_ = RunModel(samples=x.samples, ntasks=6, cores_per_task=12, model=m)
diff --git a/docs/code/RunModel/ls_dyna_example_singlejob.py b/docs/code/RunModel/ls_dyna_example_singlejob.py
@@ -4,14 +4,14 @@
 ==================================
 """
 
-
 # %% md
 #
 # Import the necessary libraries.
 
 # %%
 from UQpy.distributions import Uniform
 from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.model_execution.ThirdPartyModel import ThirdPartyModel
 from UQpy.sampling import MonteCarloSampling
 
 # %% md
@@ -40,12 +40,6 @@
 # Run the model.
 
 # %%
-run_ = RunModel(samples=x.samples, ntasks=1, model_script='dyna_script.py', input_template='dyna_input.k',
-                var_names=['x0', 'y0', 'z0', 'R0', 'x1', 'y1', 'z1', 'R1'], model_dir='dyna_test', cluster=True,
-                verbose=False, fmt='{:>10.4f}', cores_per_task=48)
-
-
-
-
-
-
+m = ThirdPartyModel(model_script='dyna_script.py', input_template='dyna_input.k',
+                    var_names=['x0', 'y0', 'z0', 'R0', 'x1', 'y1', 'z1', 'R1'], model_dir='dyna_test', fmt='{:>10.4f}')
+run_ = RunModel(samples=x.samples, ntasks=1, cores_per_task=48, model=m)
diff --git a/docs/code/RunModel/matlab_example.py b/docs/code/RunModel/matlab_example.py
@@ -4,7 +4,6 @@
 ==================================
 """
 
-
 # %% md
 #
 # The RunModel class is capable of passing input in different formats into a single computational model. This means that
@@ -64,6 +63,7 @@
 
 from UQpy.sampling import MonteCarloSampling
 from UQpy.run_model.RunModel import RunModel
+from UQpy.run_model.model_execution.ThirdPartyModel import ThirdPartyModel
 from UQpy.distributions import Normal
 import time
 import numpy as np
@@ -133,10 +133,11 @@
 if pick_model == 'scalar' or pick_model == 'all':
     # Call to RunModel - Here we run the model while instantiating the RunModel object.
     t = time.time()
-    m = RunModel(ntasks=1, model_script='matlab_model_sum_scalar.py',
-                 input_template='sum_scalar.m', var_names=names, model_object_name="matlab",
-                 output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model', fmt="{:>10.4f}", verbose=True)
+    model = ThirdPartyModel(model_script='matlab_model_sum_scalar.py',
+                            input_template='sum_scalar.m', var_names=names, model_object_name="matlab",
+                            output_script='process_matlab_output.py', output_object_name='read_output',
+                            model_dir='Matlab_Model', fmt="{:>10.4f}")
+    m = RunModel(ntasks=1, model=model)
     m.run(x_mcs.samples)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
@@ -145,7 +146,6 @@
     print("The values returned from the Matlab simulation:")
     print(m.qoi_list)
 
-
 # %% md
 #
 # 1.2 Samples passed as list, no format specification, parallel execution
@@ -161,18 +161,18 @@
 if pick_model == 'scalar' or pick_model == 'all':
     # Call to RunModel with samples as a list - Again we run the model while instantiating the RunModel object.
     t = time.time()
-    m = RunModel(samples=x_mcs_list, ntasks=2, model_script='matlab_model_sum_scalar.py',
-                 input_template='sum_scalar.m', var_names=names, model_object_name="matlab",
-                 output_script='process_matlab_output.py', output_object_name='read_output', resume=False,
-                 model_dir='Matlab_Model', verbose=True)
+    model = ThirdPartyModel(model_script='matlab_model_sum_scalar.py',
+                            input_template='sum_scalar.m', var_names=names, model_object_name="matlab",
+                            output_script='process_matlab_output.py', output_object_name='read_output',
+                            model_dir='Matlab_Model')
+    m = RunModel(samples=x_mcs_list, ntasks=2, model=model)
     t_par_matlab = time.time() - t
     print("\nTime for parallel execution:")
     print(t_par_matlab)
     print()
     print("The values retured from the Matlab simulation:")
     print(m.qoi_list)
 
-
 # %% md
 #
 # Example 2: Single tri-variate random variable
@@ -228,13 +228,12 @@
 
 if pick_model == 'vector' or pick_model == 'all':
     # Call to RunModel - Here we run the model while instantiating the RunModel object.
-    # Notice that we do not specify var_names. This will default to a single variable with name x0. In this case,
-    # we will read them in by indexing in the input_template.
     t = time.time()
-    m = RunModel(samples=x_mcs_tri, ntasks=1, model_script='matlab_model_sum_vector_indexed.py',
-                 input_template='sum_vector_indexed.m', model_object_name="matlab",
-                 output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model', fmt="{:>10.4f}")
+    model = ThirdPartyModel(model_script='matlab_model_sum_vector_indexed.py',
+                            input_template='sum_vector_indexed.m', model_object_name="matlab",
+                            output_script='process_matlab_output.py', output_object_name='read_output',
+                            model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0'])
+    m = RunModel(samples=x_mcs_tri, ntasks=1, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
@@ -255,13 +254,12 @@
 
 if pick_model == 'vector' or pick_model == 'all':
     # Call to RunModel - Here we run the model while instantiating the RunModel object.
-    # Notice that we do not specify var_names. This will default to a single variable with name x0. In this case,
-    # we will read them in by indexing in the input_template.
     t = time.time()
-    m = RunModel(samples=x_mcs_tri_list, ntasks=2, model_script='matlab_model_sum_vector_indexed.py',
-                 input_template='sum_vector_indexed.m', model_object_name="matlab",
-                 output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model')
+    model = ThirdPartyModel(model_script='matlab_model_sum_vector_indexed.py',
+                            input_template='sum_vector_indexed.m', model_object_name="matlab",
+                            output_script='process_matlab_output.py', output_object_name='read_output',
+                            model_dir='Matlab_Model', var_names=['x0'])
+    m = RunModel(samples=x_mcs_tri_list, ntasks=2, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for parallel execution:")
     print(t_ser_matlab)
@@ -285,18 +283,18 @@
     # Notice that we do not specify var_names. This will default to a single variable with name x0. In this case,
     # we will read them in by indexing in the input_template.
     t = time.time()
-    m = RunModel(samples=x_mcs_tri, ntasks=1, model_script='matlab_model_sum_vector.py',
-                 input_template='sum_vector.m', model_object_name="matlab",
-                 output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model', fmt="{:>10.4f}")
+    model = ThirdPartyModel(model_script='matlab_model_sum_vector.py',
+                            input_template='sum_vector.m', model_object_name="matlab",
+                            output_script='process_matlab_output.py', output_object_name='read_output',
+                            model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0'])
+    m = RunModel(samples=x_mcs_tri, ntasks=1, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
     print()
     print("The values returned from the Matlab simulation:")
     print(m.qoi_list)
 
-
 # %% md
 #
 # Example 3: Passing a scalar and an array to RunModel
@@ -388,10 +386,11 @@
     # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in without indexing in the
     # input_template.
     t = time.time()
-    m = RunModel(samples=x_mixed_array, ntasks=1, model_script='matlab_model_det.py',
-                 input_template='prod_determinant.m', model_object_name="matlab",
-                 output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model', fmt="{:>10.4f}")
+    model = ThirdPartyModel(model_script='matlab_model_det.py',
+                            input_template='prod_determinant.m', model_object_name="matlab", var_names=['x0', 'x1'],
+                            output_script='process_matlab_output.py', output_object_name='read_output',
+                            model_dir='Matlab_Model', fmt="{:>10.4f}")
+    m = RunModel(samples=x_mixed_array, ntasks=1, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
@@ -412,14 +411,13 @@
 
 if pick_model == 'mixed' or pick_model == 'all':
     # Call to RunModel - Here we run the model while instantiating the RunModel object.
-    # Notice that we do not specify var_names. This will default to two variables with names x0 and x1. In this
-    # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in with indexing in the
-    # input_template.
+    # We will read the matrix in with indexing in the input_template.
     t = time.time()
-    m = RunModel(samples=x_mixed_array, ntasks=1, model_script='matlab_model_det_index.py',
+    model = ThirdPartyModel(model_script='matlab_model_det_index.py',
                  input_template='prod_determinant_index.m', model_object_name="matlab",
                  output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model', fmt="{:>10.4f}")
+                 model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0', 'x1'])
+    m = RunModel(samples=x_mixed_array, ntasks=1, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
@@ -442,14 +440,12 @@
 
 if pick_model == 'mixed' or pick_model == 'all':
     # Call to RunModel - Here we run the model while instantiating the RunModel object.
-    # Notice that we do not specify var_names. This will default to two variables with names x0 and x1. In this
-    # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in without indexing in the
-    # input_template.
     t = time.time()
-    m = RunModel(samples=x_mixed, ntasks=2, model_script='matlab_model_det.py',
+    model = ThirdPartyModel(model_script='matlab_model_det.py',
                  input_template='prod_determinant.m', model_object_name="matlab",
                  output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model')
+                model_dir='Matlab_Model', var_names=['x0', 'x1'])
+    m = RunModel(samples=x_mixed, ntasks=2, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
@@ -477,10 +473,11 @@
     # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in with indexing in the
     # input_template.
     t = time.time()
-    m = RunModel(samples=x_mixed, ntasks=2, model_script='matlab_model_det_index.py',
+    model = ThirdPartyModel(model_script='matlab_model_det_index.py',
                  input_template='prod_determinant_index.m', model_object_name="matlab",
                  output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model')
+                 model_dir='Matlab_Model', var_names=['x0', 'x1'])
+    m = RunModel(samples=x_mixed, ntasks=2, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
@@ -508,13 +505,14 @@
     # case, x0 is a scalar and x1 is a 3x3 matrix. We will read the matrix in with indexing in the
     # input_template.
     t = time.time()
-    m = RunModel(samples=x_mixed_array, ntasks=1, model_script='matlab_model_det_partial.py',
+    model = ThirdPartyModel(model_script='matlab_model_det_partial.py',
                  input_template='prod_determinant_partial.m', model_object_name="matlab",
                  output_script='process_matlab_output.py', output_object_name='read_output',
-                 resume=False, model_dir='Matlab_Model', fmt="{:>10.4f}")
+                model_dir='Matlab_Model', fmt="{:>10.4f}", var_names=['x0', 'x1'])
+    m = RunModel(samples=x_mixed_array, ntasks=1, model=model)
     t_ser_matlab = time.time() - t
     print("\nTime for serial execution:")
     print(t_ser_matlab)
     print()
     print("The values returned from the Python simulation:")
-    print(m.qoi_list)
+    print(m.qoi_list)