Example updates

SURGroup · May 8, 2022 · eb64378 · eb64378
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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)
diff --git a/docs/code/RunModel/opensees_example.py b/docs/code/RunModel/opensees_example.py
@@ -4,7 +4,6 @@
 ==================================
 """
 
-
 # %% md
 #
 # Import the necessary libraries.
@@ -14,6 +13,7 @@
 
 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
@@ -41,12 +41,13 @@
 
 # %%
 
-names_ = ['fc1', 'fy1', 'Es1',  'fc2', 'fy2', 'Es2', 'fc3', 'fy3', 'Es3', 'fc4', 'fy4', 'Es4', 'fc5', 'fy5', 'Es5',
+names_ = ['fc1', 'fy1', 'Es1', 'fc2', 'fy2', 'Es2', 'fc3', 'fy3', 'Es3', 'fc4', 'fy4', 'Es4', 'fc5', 'fy5', 'Es5',
           'fc6', 'fy6', 'Es6']
 
-opensees_rc6_model = RunModel(samples=samples, ntasks=5, model_script='opensees_model.py',
-                              input_template='import_variables.tcl', var_names=names_, model_object_name="opensees_run",
-                              output_script='process_opensees_output.py', output_object_name='read_output')
+m = ThirdPartyModel(model_script='opensees_model.py', input_template='import_variables.tcl', var_names=names_,
+                    model_object_name="opensees_run", output_script='process_opensees_output.py',
+                    output_object_name='read_output')
+opensees_rc6_model = RunModel(samples=samples, ntasks=5, model=m)
 
 outputs = opensees_rc6_model.qoi_list
 print(outputs)