The VectorPostprocessors (VPP) System is designed to compute multiple related values each time it is executed. It can be thought of as a Postprocessor that outputs multiple values. For example, if you'd like to sample a solution field across a line, a VPP is a good choice (See PointValueSampler). In addition to outputting the values along the line a VPP can actually output multiple vectors simultaneously. Each vector is given a name, which is the column name. Together, all of the vectors a VPP outputs are output in one CSV file (usually per-timestep).
The VPP system builds off from MOOSE's UserObject system. Each VPP contains the full interface of a UserObject but is also expected to declare one or more vectors that will be populated and output by each VPP. There are no restrictions on the lengths of these vectors and the state of these vectors is managed by MOOSE and is automatically "restartable".
Vectors are declared with the
VectorPostprocessorValue & declareVector(const std::string & vector_name);
This method returns a writable reference to a VectorPostprocessorValue that must be captured and stored in the object.
typedef Real ScatterVectorPostprocessorValue;
Developers are responsible for sizing these vectors as needed.
VPP data can vary depending on the type of data being output. Again, the the "sample over line" example mentioned in the introduction, a complete set of values will be generated each time the VPP is executed. The VPP system handles this scenario by creating seperate output files for each invocation. The form of the output is as follows:
<filebase>_<vector name>_<serial number>.csv # filebase - the normal output file base # vector name - the name of the vector postprocessor (normally the input block name for that VPP) # serial number - a fixed-width (normally four digit) number starting from 0000 and counting up.
In some cases however, a VPP may be accumulating information in time. For example, a user may wish to track values at several locations over time. The output might consist of the coordinates of those positions and the sampled value. In such a scenario, the default seperate file output may be cumbersome as each file would effectively have a single line so a script to aggregate the information from all of the seperate output files may need to be used. Instead, MOOSE supports an option, which may be of use in these cases:
contains_complete_history = true
By setting this value, you are telling MOOSE that the values in all of the vectors of the given VPP are cummulative. MOOSE will take advantage of this information in multiple ways. First, it will turn off writing the output to seperate files and will drop the serial number from the output filename format altogether. Secondly, it will ignore any changed values in the vectors only outputting the newest rows in each vector postprocessor.
VectorPostprocessors are required to create their complete vectors on processor zero (rank 0). They should use the
_communicator to reduce their values to processor zero. Objects that use VPPs must specify how they need the data by calling the
getScatterVectorPostprocessorValue() functions with the correct arguments.
If the object needing VPP values only needs those values on processor zero it should call:
getVectorPostprocessorValue('the_vpp_parameter_name', 'the_vector_name', false)
false in that call tells MOOSE that this object does NOT need the vector to be "broadcast" (i.e. "replicated).
If this object does indeed need the VPP data to be broadcast (replicated on all processors) it should make this call:
getVectorPostprocessorValue('the_vpp_parameter_name', 'the_vector_name', true)
In the very special case that a VPP is producing vectors that are
num_procs length an object can ask for the value of a VPP to be "scattered" - which means that each processor will receive only the value that corresponds to it's rank (i.e.
_values[rank]). This is accomplished by calling:
getScatterVectorPostprocessorValue() returns a
const ScatterVectorPostprocessorValue &... which is a single scalar value that you don't index into. Each process receives the "correct" value and can just directly use it.
If the data in a VPP is naturally replicated on all processors a VectorPostprocessor should set
_is_broadcast = true in its
validParams() like so:
params.set<bool>("_is_broadcast") = true;
This tells MOOSE that the data is already replicated and there is no need to broadcast it if another object is asking for it to be broadcast.
- Moose App
- CSVReaderConverts columns of a CSV file into vectors of a VectorPostprocessor.
- ElementValueSamplerSamples values of elemental variable(s).
- ElementsAlongLineOutputs the IDs of every element intersected by a user-defined line
- ElementsAlongPlaneOutputs the IDs of every element intersected by a user-defined plane
- HistogramVectorPostprocessorCompute a histogram for each column of a VectorPostprocessor
- LeastSquaresFitPerforms a polynomial least squares fit on the data contained in another VectorPostprocessor
- LeastSquaresFitHistoryPerforms a polynomial least squares fit on the data contained in another VectorPostprocessor and stores the full time history of the coefficients
- NodalValueSamplerSamples values of nodal variable(s).
- StatisticsVectorPostprocessorCompute statistical values of a given VectorPostprocessor. The statistics are computed for each column.
- VectorMemoryUsageGet memory stats for all ranks in the simulation
- VectorOfPostprocessorsOutputs the values of an arbitrary user-specified set of postprocessors as a vector in the order specified by the user
- WorkBalanceComputes several metrics for workload balance per processor
- Solid Mechanics App
- Tensor Mechanics App
- CrackDataSamplerOutputs the values of a set of domain integral postprocessors as a vector, along with their positions along the crack front.
- LineMaterialRankTwoSamplerAccess a component of a RankTwoTensor
- LineMaterialRankTwoScalarSamplerCompute a scalar property of a RankTwoTensor
- Stochastic Tools App
- SamplerDataTool for extracting Sampler object data and storing in VectorPostprocessor vectors.
- StochasticResultsStorage container for stochastic simulation results coming from a Postprocessor.
- Phase Field App
- EulerAngleUpdaterCheckProvide updated euler angles after rigid body rotation of the grains.
- FeatureVolumeVectorPostprocessorThis object is designed to pull information from the data structures of a "FeatureFloodCount" or derived object (e.g. individual feature volumes)
- GrainForcesPostprocessorOutputs the values from GrainForcesPostprocessor
- GrainTextureVectorPostprocessorGives out info on the grain boundary properties
- Moose App