/// <summary>
/// Use the low-level macro code generator and optimizer to generate low-level assignments
/// </summary>
/// <returns>The optimized low-level assignments output</returns>
public GMacCodeBlock CreateOptimizedCodeBlock()
{
//Create the low-level generator object
var gen = new LlGenerator(BaseMacro.AssociatedMacro);
//Define bindings for low-level primitive macro input and output parameters
foreach (var binding in Bindings)
{
if (binding.IsConstant)
{
gen.DefineParameter(
binding.ValueAccess.AssociatedValueAccess,
binding.ConstantValue.AssociatedScalarValue
);
}
else
{
gen.DefineParameter(binding.ValueAccess.AssociatedValueAccess);
}
}
//Generate un-optimized low-level macro computations from the base macro and its parameters bindings
gen.GenerateLowLevelItems();
//Optimize low-level macro computations
return(TcbOptimizer.Process(gen, FixOutputComputationsOrder, Progress));
}
public string TestGenerationByConstantInputs()
{
_randomSource = new Random(DateTime.Now.Millisecond);
var gen1 = new LlGenerator(BaseMacro);
//Fill input parameters by random values
foreach (var param in BaseMacro.Parameters)
{
if (param.DirectionIn)
{
var valueAccessList = param.ExpandAll();
foreach (var valueAccess in valueAccessList)
{
var assignedValue = GetRandomValue(valueAccess.ExpressionType);
gen1.DataTable.DefineConstantInputParameter(valueAccess, assignedValue);
}
}
else
{
gen1.DataTable.DefineOutputParameter(param);
}
}
//Evaluate (generate) the outputs using the constant inputs
gen1.GenerateLowLevelItems();
Log.AppendLine();
Log.AppendLineAtNewLine("First Generator Result");
Log.AppendAtNewLine(gen1.ToString());
Log.AppendLine();
Log.AppendAtNewLine("Begin full low level symbolic generation ... ");
_timer.Reset();
_timer.Start();
//Use another generator with all input parameters set as variable
var gen2 = new LlGenerator(BaseMacro);
gen2.DefineAllParameters();
//Generate the symbolic expressions without optimizations
gen2.GenerateLowLevelItems();
//Optimize the generated symbolic expressions
var genOpt = TcbOptimizer.Process(gen2);
//genOpt.OptimizeLowLevelItems();
_timer.Stop();
Log.AppendLine(" Finished after " + _timer.Elapsed);
Log.AppendLine();
Log.AppendLine();
Log.AppendLineAtNewLine("Second Generator Result");
Log.AppendAtNewLine(genOpt.ToString());
//Evaluate all symbolic expressions in order using the constant values for inputs from the first generator
var valuesDict =
new Dictionary <string, ValuePrimitive <MathematicaScalar> >();
foreach (var inputItem in genOpt.InputVariables)
{
var inputItemName = inputItem.LowLevelName;
var inputItemValue = gen1.DataTable.GetItemByName(inputItem.LowLevelName).AssignedRhsValue;
valuesDict.Add(inputItemName, inputItemValue);
Log.AppendAtNewLine("Input ");
Log.Append(inputItem.ValueAccessName);
Log.Append(" = ");
Log.AppendLine(inputItemValue.ToString());
}
foreach (var item in genOpt.ComputedVariables)
{
ExecuteAssignment(item, valuesDict);
}
//Report the differences between the output parameters of the two methods of computation
foreach (var outputItem1 in gen1.DataTable.Outputs)
{
var outputItem1Value = outputItem1.AssignedRhsValue;
var outputItem2Value = valuesDict[outputItem1.ItemName];
Log.AppendAtNewLine("Input ");
Log.Append(outputItem1.HlItemName);
Log.AppendLine(" = ");
Log.IncreaseIndentation();
Log.AppendLineAtNewLine(outputItem1Value.ToString());
Log.AppendLineAtNewLine(outputItem2Value.ToString());
Log.DecreaseIndentation();
}
return(Log.ToString());
}
