Fix issue #391 with database dump.

easyvvuq/db/sql.py

@@ -948,6 +948,15 @@ def dump(self):
         meta = MetaData()
         meta.reflect(bind=self.engine)
         result = {}
-        for table in meta.sorted_tables:
-            result[table.name] = [dict(row) for row in self.engine.execute(table.select())]
+
+        # Create a connection from the engine
+        with self.engine.connect() as connection:
+            for table in meta.sorted_tables:
+                try:
+                    query_result = connection.execute(table.select()).fetchall()
+                    result[table.name] = [dict(row) for row in query_result]
+                except Exception as e:
+                    result[table.name] = str(e)
+
         return json.dumps(result)
+

tutorials/basic_tutorial_qcgpj.py

@@ -3,52 +3,75 @@
 import chaospy as cp
 import matplotlib.pyplot as plt
 
+# Import necessary actions from EasyVVUQ
 from easyvvuq.actions import QCGPJPool
 from easyvvuq.actions import CreateRunDirectory, Encode, Decode, ExecuteLocal, Actions
 
+# Define the parameters for the model
 params = {
-    "F": {"type": "float", "default": 1.0},
-    "L": {"type": "float", "default": 1.5},
-    "a": {"type": "float", "min": 0.7, "max": 1.2, "default": 1.0},
-    "D": {"type": "float", "min": 0.75, "max": 0.85, "default": 0.8},
-    "d": {"type": "float", "default": 0.1},
-    "E": {"type": "float", "default": 200000},
-    "outfile": {"type": "string", "default": "output.json"}
+    "F": {"type": "float", "default": 1.0},  # Force parameter
+    "L": {"type": "float", "default": 1.5},  # Length parameter
+    "a": {"type": "float", "min": 0.7, "max": 1.2, "default": 1.0},  # Variable a with range
+    "D": {"type": "float", "min": 0.75, "max": 0.85, "default": 0.8},  # Variable D with range
+    "d": {"type": "float", "default": 0.1},  # Variable d
+    "E": {"type": "float", "default": 200000},  # Young's modulus (material property)
+    "outfile": {"type": "string", "default": "output.json"}  # Output file name
 }
 
-encoder = uq.encoders.GenericEncoder(template_fname='beam.template', delimiter='$', target_filename='input.json')
-decoder = uq.decoders.JSONDecoder(target_filename='output.json', output_columns=['g1'])
-execute = ExecuteLocal('{}/beam input.json'.format(os.getcwd()))
+# Set up the encoder and decoder to read/write data
+encoder = uq.encoders.GenericEncoder(template_fname='beam.template', delimiter='$', target_filename='input.json')  # Template encoder
+decoder = uq.decoders.JSONDecoder(target_filename='output.json', output_columns=['g1'])  # Decoder to read the output 'g1'
 
-actions = Actions(CreateRunDirectory('/tmp'),
-                  Encode(encoder), execute, Decode(decoder))
+# Execute the local model using the 'beam' input file
+execute = ExecuteLocal('{}/beam input.json'.format(os.getcwd()))  # Path to the model script and input file
 
+# Set up the actions for the campaign
+actions = Actions(
+    CreateRunDirectory('/tmp'),  # Create a directory to store results
+    Encode(encoder),  # Encode the input data
+    execute,  # Run the computational model
+    Decode(decoder)  # Decode the results from the output
+)
 
+# Create the EasyVVUQ campaign using the parameters and actions defined above
 campaign = uq.Campaign(name='beam', params=params, actions=actions)
 
+# Define the uncertainty distributions for the input parameters using Chaospy
 vary = {
-    "F": cp.Normal(1, 0.1),
-    "L": cp.Normal(1.5, 0.01),
-    "a": cp.Uniform(0.7, 1.2),
-    "D": cp.Triangle(0.75, 0.8, 0.85)
+    "F": cp.Normal(1, 0.1),  # Normal distribution for Force with mean 1 and std 0.1
+    "L": cp.Normal(1.5, 0.01),  # Normal distribution for Length with mean 1.5 and std 0.01
+    "a": cp.Uniform(0.7, 1.2),  # Uniform distribution for parameter a
+    "D": cp.Triangle(0.75, 0.8, 0.85)  # Triangular distribution for parameter D
 }
 
+# Set the sampling method to use Polynomial Chaos Expansion (PCE) for sensitivity analysis
 campaign.set_sampler(uq.sampling.PCESampler(vary=vary, polynomial_order=1))
 
-with QCGPJPool(template_params={'venv': '/home/bartek/.virtualenv/qcgpj7'}) as qcgpj:
-#with QCGPJPool() as qcgpj:
+# Run the campaign with a parallel pool using QCGPJ (a parallel job pool)
+with QCGPJPool() as qcgpj:
+    # Execute the campaign with the defined pool and then collate the results
     campaign.execute(pool=qcgpj).collate()
 
+# Get the collation results from the campaign
 campaign.get_collation_result()
 
+# Dump the campaign database as JSON for debugging purposes
 campaign.campaign_db.dump()
 
+# Perform the analysis on the output quantities (in this case, 'g1')
 results = campaign.analyse(qoi_cols=['g1'])
-results.plot_sobols_treemap('g1', figsize=(10, 10))
-plt.axis('off')
-results.sobols_first('g1')
-results.supported_stats()
-results._get_sobols_first('g1', 'F')
-results.sobols_total('g1', 'F')
-
-print("success")
+
+# Plot the Sobol indices as a treemap and save it to a file
+results.plot_sobols_treemap('g1', figsize=(10, 10))  # Create the Sobol indices treemap plot
+plt.axis('off')  # Turn off the axis for a cleaner plot
+plt.savefig('sobols_treemap.png', bbox_inches='tight')  # Save the plot to a file
+plt.show()  # Display the plot
+
+# Perform additional analysis on the Sobol indices and print the results
+results.sobols_first('g1')  # First-order Sobol indices
+results.supported_stats()  # Get the supported statistical measures
+results._get_sobols_first('g1', 'F')  # First-order Sobol index for parameter 'F'
+results.sobols_total('g1', 'F')  # Total Sobol index for parameter 'F'
+
+print("success")  # Print success message after all tasks are complete
+