CoarsenedPiecewiseLinear

Perform a point reduction of the tabulated data upon initialization, then evaluate using a linear interpolation.

Description

The CoarsenedPiecewiseLinear performs preprocessing and linear interpolation on an x/y data set. The object acts like PiecewiseLinear except that it reduces the number of function point at the start of the simulation. It uses the Ramer-Douglas-Peucker algorithm for data reduction.

Example Input Syntax

[Functions]
  [./ic_function]
    type = PiecewiseLinear
    data_file = piecewise_linear_columns.csv #Will generate error because data is expected in rows
    scale_factor = 1.0
  [../]
[]

Input Parameters

epsilonSignificant distance in the function below which points are considered removable noise
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Significant distance in the function below which points are considered removable noise

Required Parameters

axisThe axis used (x, y, or z) if this is to be a function of position
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:x, y, z
Controllable:No
Description:The axis used (x, y, or z) if this is to be a function of position
scale_factor1Scale factor to be applied to the ordinate values
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:Yes
Description:Scale factor to be applied to the ordinate values
x_scale1Scaling factor to apply to the function nodes for the purpose of computing distances in the Douglas-Peucker point reduction algorithm. This permits shifting the weight between x and y-direction distances.
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Scaling factor to apply to the function nodes for the purpose of computing distances in the Douglas-Peucker point reduction algorithm. This permits shifting the weight between x and y-direction distances.
y_scale1Scaling factor to apply to the function nodes for the purpose of computing distances in the Douglas-Peucker point reduction algorithm. This permits shifting the weight between x and y-direction distances.
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Scaling factor to apply to the function nodes for the purpose of computing distances in the Douglas-Peucker point reduction algorithm. This permits shifting the weight between x and y-direction distances.

Optional Parameters

control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Set the enabled status of the MooseObject.

Advanced Parameters

data_fileFile holding CSV data
C++ Type:FileName
Unit:(no unit assumed)
Controllable:No
Description:File holding CSV data
formatrowsFormat of csv data file that is in either in columns or rows
Default:rows
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:columns, rows
Controllable:No
Description:Format of csv data file that is in either in columns or rows
x_index_in_file0The abscissa index in the data file
Default:0
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The abscissa index in the data file
x_titleThe title of the column/row containing the x data in the data file
C++ Type:std::string
Unit:(no unit assumed)
Controllable:No
Description:The title of the column/row containing the x data in the data file
xy_in_file_onlyTrueIf the data file only contains abscissa and ordinate data
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:If the data file only contains abscissa and ordinate data
y_index_in_file1The ordinate index in the data file
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The ordinate index in the data file
y_titleThe title of the column/row containing the y data in the data file
C++ Type:std::string
Unit:(no unit assumed)
Controllable:No
Description:The title of the column/row containing the y data in the data file

Data From Csv File Parameters

json_uoJSONFileReader holding the data
C++ Type:UserObjectName
Unit:(no unit assumed)
Controllable:No
Description:JSONFileReader holding the data
x_keysOrdered vector of keys in the JSON tree to obtain the abscissa
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Ordered vector of keys in the JSON tree to obtain the abscissa
y_keysOrdered vector of keys in the JSON tree to obtain the ordinate
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Ordered vector of keys in the JSON tree to obtain the ordinate

Data From Json Parameters

xThe abscissa values
C++ Type:std::vector<double>
Unit:(no unit assumed)
Controllable:No
Description:The abscissa values
xy_dataAll function data, supplied in abscissa, ordinate pairs
C++ Type:std::vector<double>
Unit:(no unit assumed)
Controllable:No
Description:All function data, supplied in abscissa, ordinate pairs
yThe ordinate values
C++ Type:std::vector<double>
Unit:(no unit assumed)
Controllable:No
Description:The ordinate values

Data From Input File Parameters

(moose/test/tests/misc/check_error/function_file_test1.i)

# Test for usage of missing function
[Mesh]
  [./square]
    type = GeneratedMeshGenerator
    nx = 2
    ny = 2
    dim = 2
  [../]
[]

[Variables]
  active = 'u'

  [./u]
    order = FIRST
    family = LAGRANGE
    [./InitialCondition]
      type = FunctionIC
      function = ic_function
    [../]
  [../]

[]

[Functions]
  [./ic_function]
    type = PiecewiseLinear
    data_file = piecewise_linear_columns.csv #Will generate error because data is expected in rows
    scale_factor = 1.0
  [../]
[]

[Kernels]
  active = 'diff'

  [./diff]
    type = Diffusion
    variable = u
  [../]
[]

[BCs]
  active = 'left right'

  [./left]
    type = DirichletBC
    variable = u
    boundary = 3
    value = 0
  [../]

  [./right]
    type = DirichletBC
    variable = u
    boundary = 1
    value = 1
  [../]
[]

[Executioner]
  type = Steady

  solve_type = 'PJFNK'
[]

[Outputs]
  file_base = out
[]

Description
Example Input Syntax
Input Parameters