ViennaTools · kenyastyle · Mar 10, 2026 · Mar 10, 2026 · Mar 10, 2026 · Mar 10, 2026
diff --git a/LICENSE b/LICENSE
diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 # ViennaFit
 
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](LICENSE)
 [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
 
 A Python package for parameter optimization and calibration of ViennaPS process simulation models. ViennaFit automates the fitting of simulation parameters to match experimental data through various optimization algorithms and distance metrics.
@@ -138,7 +138,7 @@ If you use ViennaFit in your research, please cite:
 
 ## License
 
-ViennaFit is released under the MIT License. See [LICENSE](LICENSE) for details.
+ViennaFit is released under the GNU General Public License v3. See [LICENSE](LICENSE) for details.
 
 ## Support
 

diff --git a/docs/getting-started/quick-start.md b/docs/getting-started/quick-start.md
@@ -196,7 +196,7 @@ Execute the optimization:
 print("Starting optimization...")
 opt.apply(
     numEvaluations=50,        # Try 50 parameter combinations
-    saveVisualization=True     # Save domain visualizations
+    saveComparison=True     # Save domain visualizations
 )
 print("Optimization complete!")
 ```
@@ -261,7 +261,7 @@ for param, value in results['bestParameters'].items():
 
 ### Visualize Convergence
 
-If `saveVisualization=True`, convergence plots are in the `plots/` directory:
+If `saveComparison=True`, convergence plots are in the `plots/` directory:
 
 - `convergence_plot.png` - Objective value vs evaluation number
 - `parameter_evolution.png` - How parameters changed during optimization
@@ -334,7 +334,7 @@ opt.setVariableParameters({
 })
 opt.setDistanceMetrics(primaryMetric="CA")
 opt.setName("run1")
-opt.apply(numEvaluations=50, saveVisualization=True)
+opt.apply(numEvaluations=50, saveComparison=True)
 
 # View results
 results_dir = os.path.join(project.projectPath, "optimizationRuns", "run1")

diff --git a/docs/tutorials/tutorial-1-basic-optimization.md b/docs/tutorials/tutorial-1-basic-optimization.md
@@ -348,7 +348,7 @@ print("="*60)
 
 opt.apply(
     numEvaluations=100,         # Try 100 parameter combinations
-    saveVisualization=True      # Save intermediate results
+    saveComparison=True      # Save intermediate results
 )
 
 print("\n" + "="*60)
@@ -551,7 +551,7 @@ opt2 = fit.Optimization(project)
 # ... configure same as before ...
 opt.setName("run2_continued")
 # Use run1 results as starting point
-opt.apply(numEvaluations=50, saveVisualization=True)
+opt.apply(numEvaluations=50, saveComparison=True)
 ```
 
 **Option 2: Narrow Bounds**
@@ -619,7 +619,7 @@ opt.setDistanceMetrics(primaryMetric="CCH", additionalMetrics=["CA"])
 opt.setName("run1_initialCalibration")
 
 # Run
-opt.apply(numEvaluations=100, saveVisualization=True)
+opt.apply(numEvaluations=100, saveComparison=True)
 
 # Analyze
 results_dir = os.path.join(project.projectPath, "optimizationRuns", "run1_initialCalibration")

diff --git a/docs/tutorials/tutorial-2-custom-evaluation.md b/docs/tutorials/tutorial-2-custom-evaluation.md
@@ -118,7 +118,7 @@ print(f"\nGrid defined: 5 × 5 = 25 evaluations")
 # Execute grid evaluation
 results = evaluator.apply(
     evaluationName="parameterGrid_ionAndEtchant",
-    saveVisualization=False  # Don't save all 25 VTP files
+    saveComparison=False  # Don't save all 25 VTP files
 )
 
 print(f"\nGrid evaluation complete!")
@@ -223,7 +223,7 @@ evaluator.setDistanceMetric("CCH")
 # Run evaluation
 specific_results = evaluator.apply(
     evaluationName="specificCombinations",
-    saveVisualization=True  # Save these for detailed analysis
+    saveComparison=True  # Save these for detailed analysis
 )
 
 # Analyze
@@ -264,7 +264,7 @@ evaluator.setDistanceMetric("CCH")
 # Execute
 repeat_results = evaluator.apply(
     evaluationName="repeatabilityTest",
-    saveVisualization=False
+    saveComparison=False
 )
 
 print(f"\nRepeatability test complete: {len(repeat_results)} runs")

diff --git a/docs/tutorials/tutorial-4-multi-domain.md b/docs/tutorials/tutorial-4-multi-domain.md
@@ -389,7 +389,7 @@ print("Note: Twice as many simulations per evaluation!")
 
 opt.apply(
     numEvaluations=100,
-    saveVisualization=True
+    saveComparison=True
 )
 
 print("\n" + "="*60)
@@ -591,7 +591,7 @@ opt.setDistanceMetrics(primaryMetric="CCH")
 opt.setName("run1_multiDomain")
 
 # Run
-opt.apply(numEvaluations=100, saveVisualization=True)
+opt.apply(numEvaluations=100, saveComparison=True)
 ```
 
 ## Key Takeaways

diff --git a/examples/2-optimization/basicOptimization.py b/examples/2-optimization/basicOptimization.py
@@ -103,4 +103,4 @@ def rateFunction(fluxes, material):
 
 opt1.setNotes("Basic optimization of the multiparticle process for deposition.")
 
-opt1.apply(numEvaluations=100, saveVisualization=True)
+opt1.apply(numEvaluations=100, saveComparison=True)
diff --git a/examples/2-optimization/customEvaluatorExample.py b/examples/2-optimization/customEvaluatorExample.py
@@ -39,7 +39,7 @@
 # Run the grid evaluation
 # This will evaluate all combinations of the variable parameters
 # while keeping other parameters at their optimal values
-results = evaluator.apply(evaluationName="parameterSweep1", saveVisualization=True)
+results = evaluator.apply(evaluationName="parameterSweep1", saveComparison=True)
 
 # Get and display the best result from the grid
 bestResult = evaluator.getBestResult()

diff --git a/examples/3-custom-evaluations/grid-customEvaluator.py b/examples/3-custom-evaluations/grid-customEvaluator.py
@@ -41,7 +41,7 @@
 # Run the grid evaluation
 # This will evaluate all combinations of the variable parameters
 # while keeping other parameters at their optimal values
-results = evaluator.apply(evaluationName="parameterSweep1", saveVisualization=True)
+results = evaluator.apply(evaluationName="parameterSweep1", saveComparison=True)
 
 # Get and display the best result from the grid
 bestResult = evaluator.getBestResult()

diff --git a/examples/3-custom-evaluations/repeated-customEvaluator.py b/examples/3-custom-evaluations/repeated-customEvaluator.py
@@ -33,4 +33,4 @@
 
 # Run the repeated evaluation
 # Uses the same process sequence as the loaded optimization run
-results = evaluator.apply(evaluationName="repeatedEvaluation", saveVisualization=True)
+results = evaluator.apply(evaluationName="repeatedEvaluation", saveComparison=True)
diff --git a/examples/sensitivityAnalysis/globalSensitivityStudy.py b/examples/sensitivityAnalysis/globalSensitivityStudy.py
@@ -42,4 +42,4 @@
 gss.setSamplingOptions(numSamples=1000, secondOrder=True)
 
 # Run the sensitivity analysis
-gss.apply(saveVisualization=False)
+gss.apply(saveComparison=False)
diff --git a/examples/sensitivityAnalysis/parameterStudies.py b/examples/sensitivityAnalysis/parameterStudies.py
@@ -24,9 +24,7 @@
 }
 
 evaluator.setVariableValues(singleParamValues)
-results = evaluator.apply(
-    evaluationName="singleParameterSweep", saveVisualization=False
-)
+results = evaluator.apply(evaluationName="singleParameterSweep", saveComparison=False)
 
 print(f"Evaluated {len(results)} parameter combinations")
 bestResult = evaluator.getBestResult()
@@ -47,7 +45,7 @@
 }
 
 evaluator2.setVariableValues(twoParamValues)
-results2 = evaluator2.apply(evaluationName="twoParameterStudy", saveVisualization=False)
+results2 = evaluator2.apply(evaluationName="twoParameterStudy", saveComparison=False)
 
 print(f"Evaluated {len(results2)} parameter combinations")
 bestResult2 = evaluator2.getBestResult()

diff --git a/src/viennafit/fitDistanceMetrics.py b/src/viennafit/fitDistanceMetrics.py
@@ -441,15 +441,15 @@ def _compareChamfer(
         the RMS Chamfer distance which provides a robust measure of shape similarity.
 
         Args:
-            domain1: Target domain (reference)
-            domain2: Sample domain (to compare)
+            domain1: Sample domain (result to compare)
+            domain2: Target domain (reference)
             saveComparison: Whether to save visualization files
             writePath: Path for saving visualization files
 
         Returns:
             RMS Chamfer distance between the two surfaces
         """
-        cch = vls.CompareChamfer(domain1, domain2)
+        cch = vls.CompareChamfer(domain2, domain1)
 
         if saveComparison:
             targetMesh = vls.Mesh()
Original file line number	Diff line number	Diff line change
Expand Up		@@ -103,4 +103,4 @@ def rateFunction(fluxes, material):

		opt1.setNotes("Basic optimization of the multiparticle process for deposition.")

		opt1.apply(numEvaluations=100, saveVisualization=True)
		opt1.apply(numEvaluations=100, saveComparison=True)