Resolve issues with polynomial order truncation in LeastSquaresFit cl…

…oses idaholab#13498

Add a 'truncate_order' option to LeastSquaresFit and LeastSquaresFitHistory

Correct logic for determining required points for a given polynomial
order in PolynomialFit.C

Add tests for fits of polynomials with order 0, 1, and 2

Add test for insufficient order error

framework/doc/content/source/vectorpostprocessors/LeastSquaresFit.md

@@ -10,7 +10,9 @@ This VectorPostprocessor is closely related to the [LeastSquaresFitHistory](/Lea
 
 The vectors of values of the independent ($x$) and dependent ($y$) variables on which the least squares fit is performed are provided through another VectorPostprocessor, which must provide two equally-sized vectors of data upon which to operate.  The name of this VectorPostprocesor is provided using the `vectorpostprocessor` parameter, and the names of the data vectors are provided with the `x_name` and `y_name` parameters. The vectors of data can be shifted and/or scaled through the use of optional parameters.
 
-The user must define whether the output should be in the form of polynomial coefficients or samples using the `output` parameter. If the option to output polynommial coefficients is used, they are stored in a vector named `coefficients`. If samples are requested, the nam es of the sample vectors are the same as those of the data specified by `x_name` and `y_name`.
+By default, if an insufficient number of points is provided in these data vectors, the order of the polynomial will be truncated to one less than the number of points. If the `truncate_order   parameter is set to `false`, an error will be generated in this case.
+
+The user must define whether the output should be in the form of polynomial coefficients or samples using the `output` parameter. If the option to output polynommial coefficients is used, they are stored in a vector named `coefficients`. If samples are requested, the names of the sample vectors are the same as those of the data specified by `x_name` and `y_name`.
 
 !syntax parameters /VectorPostprocessors/LeastSquaresFit
 

framework/doc/content/source/vectorpostprocessors/LeastSquaresFitHistory.md

@@ -12,6 +12,8 @@ The polynomial coefficients are stored in a set of vectors named `coef_0` throug
 
 The vectors of values of the independent ($x$) and dependent ($y$) variables on which the least squares fit is performed are provided through another VectorPostprocessor, which must provide two equally-sized vectors of data upon which to operate.  The name of this VectorPostprocesor is provided using the `vectorpostprocessor` parameter, and the names of the data vectors are provided with the `x_name` and `y_name` parameters. The vectors of data can be shifted and/or scaled through the use of optional parameters.
 
+By default, if an insufficient number of points is provided in these data vectors, the order of the polynomial will be truncated to one less than the number of points. If the `truncate_order   parameter is set to `false`, an error will be generated in this case.
+
 !syntax parameters /VectorPostprocessors/LeastSquaresFitHistory
 
 !syntax inputs /VectorPostprocessors/LeastSquaresFitHistory

framework/include/vectorpostprocessors/LeastSquaresFit.h

@@ -26,9 +26,9 @@ class LeastSquaresFit : public GeneralVectorPostprocessor
 {
 public:
   /**
-    * Class constructor
-    * @param parameters The input parameters
-    */
+   * Class constructor
+   * @param parameters The input parameters
+   */
   LeastSquaresFit(const InputParameters & parameters);
 
   /**
@@ -46,15 +46,19 @@ class LeastSquaresFit : public GeneralVectorPostprocessor
   VectorPostprocessorName _vpp_name;
 
   /// The order of the polynomial fit to be performed
-  unsigned int _order;
+  const unsigned int _order;
+
+  /// Whether to truncate the polynomial order if an insufficient number of points is provided
+  const bool _truncate_order;
 
   /// The name of the variables storing the x, y data
   const std::string _x_name;
   const std::string _y_name;
 
-  /// The variables with the x, y data to be fit
+  ///@{ The variables with the x, y data to be fit
   const VectorPostprocessorValue & _x_values;
   const VectorPostprocessorValue & _y_values;
+  ///@}
 
   /// The type of output
   const MooseEnum _output_type;
@@ -73,13 +77,15 @@ class LeastSquaresFit : public GeneralVectorPostprocessor
   bool _have_sample_x_min;
   bool _have_sample_x_max;
 
-  /// The min and max x values for sampling
+  ///@{ The min and max x values for sampling
   Real _sample_x_min;
   Real _sample_x_max;
+  ///@}
 
-  /// The variables used to write out samples of the least squares fit
+  ///@{ The variables used to write out samples of the least squares fit
   VectorPostprocessorValue * _sample_x;
   VectorPostprocessorValue * _sample_y;
+  ///@}
 
   /// The variable used to write out the coefficients of the fit
   VectorPostprocessorValue * _coeffs;

framework/include/vectorpostprocessors/LeastSquaresFitHistory.h

@@ -47,15 +47,19 @@ class LeastSquaresFitHistory : public GeneralVectorPostprocessor
   VectorPostprocessorName _vpp_name;
 
   /// The order of the polynomial fit to be performed
-  unsigned int _order;
+  const unsigned int _order;
+
+  /// Whether to truncate the polynomial order if an insufficient number of points is provided
+  const bool _truncate_order;
 
   /// The name of the variables storing the x, y data
   const std::string _x_name;
   const std::string _y_name;
 
-  /// The variables with the x, y data to be fit
+  ///@{ The variables with the x, y data to be fit
   const VectorPostprocessorValue & _x_values;
   const VectorPostprocessorValue & _y_values;
+  ///@}
 
   ///@{ Values used to scale and or shift x and y data
   const Real _x_scale;

framework/src/utils/PolynomialFit.C

@@ -9,8 +9,11 @@
 
 #include "PolynomialFit.h"
 
+#include "MooseError.h"
+
 // C++ includes
 #include <fstream>
+#include <algorithm>
 
 int PolynomialFit::_file_number = 0;
 
@@ -20,21 +23,15 @@ PolynomialFit::PolynomialFit(const std::vector<Real> & x,
                              bool truncate_order)
   : LeastSquaresFitBase(x, y), _order(order), _truncate_order(truncate_order)
 {
-  if (_truncate_order) // && (_x.size() / 10) < _order)
+  if (_x.size() == 0)
+    mooseError("PolynomialFit does not allow empty input vectors");
+  if (_truncate_order)
   {
-    if (_x.size() == 1)
-      _order = 0;
-    else
-    {
-      _order = (_x.size() / 10) + 1;
-
-      if (_order > order)
-        _order = order;
-    }
+    if (_x.size() <= _order)
+      _order = _x.size() - 1;
   }
-  else if (_x.size() < order)
-    throw std::domain_error(
-        "Polynomial Fit requires an order less than the size of the input vector");
+  else if (_x.size() <= order)
+    mooseError("PolynomialFit requires an order less than the size of the input vector");
 
   _num_coeff = _order + 1;
 }

framework/src/vectorpostprocessors/LeastSquaresFit.C

@@ -25,6 +25,11 @@ validParams<LeastSquaresFit>()
   params.addRequiredParam<std::string>("x_name", "The name of the independent variable");
   params.addRequiredParam<std::string>("y_name", "The name of the dependent variable");
   params.addRequiredParam<unsigned int>("order", "The order of the polynomial fit");
+  params.addParam<bool>(
+      "truncate_order",
+      true,
+      "Truncate the order of the fitted polynomial if an insufficient number of data points are "
+      "provided. If this is set to false, an error will be generated in that case.");
   params.addParam<unsigned int>("num_samples", "The number of samples to be output");
   params.addParam<Real>(
       "x_scale", 1.0, "Value used to scale x values (scaling is done after shifting)");
@@ -49,6 +54,7 @@ LeastSquaresFit::LeastSquaresFit(const InputParameters & parameters)
   : GeneralVectorPostprocessor(parameters),
     _vpp_name(getParam<VectorPostprocessorName>("vectorpostprocessor")),
     _order(parameters.get<unsigned int>("order")),
+    _truncate_order(parameters.get<bool>("truncate_order")),
     _x_name(getParam<std::string>("x_name")),
     _y_name(getParam<std::string>("y_name")),
     _x_values(getVectorPostprocessorValue("vectorpostprocessor", _x_name)),
@@ -126,7 +132,7 @@ LeastSquaresFit::execute()
     y_values[i] = (y_values[i] + _y_shift) * _y_scale;
   }
 
-  PolynomialFit pf(x_values, y_values, _order, true);
+  PolynomialFit pf(x_values, y_values, _order, _truncate_order);
   pf.generate();
 
   if (_output_type == "Samples")

framework/src/vectorpostprocessors/LeastSquaresFitHistory.C

@@ -26,6 +26,11 @@ validParams<LeastSquaresFitHistory>()
   params.addRequiredParam<std::string>("x_name", "The name of the independent variable");
   params.addRequiredParam<std::string>("y_name", "The name of the dependent variable");
   params.addRequiredParam<unsigned int>("order", "The order of the polynomial fit");
+  params.addParam<bool>(
+      "truncate_order",
+      true,
+      "Truncate the order of the fitted polynomial if an insufficient number of data points are "
+      "provided. If this is set to false, an error will be generated in that case.");
   params.addParam<Real>(
       "x_scale", 1.0, "Value used to scale x values (scaling is done after shifting)");
   params.addParam<Real>(
@@ -48,6 +53,7 @@ LeastSquaresFitHistory::LeastSquaresFitHistory(const InputParameters & parameter
   : GeneralVectorPostprocessor(parameters),
     _vpp_name(getParam<VectorPostprocessorName>("vectorpostprocessor")),
     _order(parameters.get<unsigned int>("order")),
+    _truncate_order(parameters.get<bool>("truncate_order")),
     _x_name(getParam<std::string>("x_name")),
     _y_name(getParam<std::string>("y_name")),
     _x_values(getVectorPostprocessorValue("vectorpostprocessor", _x_name)),
@@ -86,7 +92,7 @@ LeastSquaresFitHistory::execute()
     y_values[i] = (y_values[i] + _y_shift) * _y_scale;
   }
 
-  PolynomialFit pf(x_values, y_values, _order, true);
+  PolynomialFit pf(x_values, y_values, _order, _truncate_order);
   pf.generate();
 
   std::vector<Real> coeffs = pf.getCoefficients();

test/tests/vectorpostprocessors/least_squares_fit/fit_data_0.csv

@@ -0,0 +1,2 @@
+id,u
+0.0,1.0

test/tests/vectorpostprocessors/least_squares_fit/fit_data_1.csv

@@ -0,0 +1,3 @@
+id,u
+0.0,1.0
+1.0,2.0

test/tests/vectorpostprocessors/least_squares_fit/fit_data_2.csv

@@ -0,0 +1,4 @@
+id,u
+0.0,1.0
+1.0,3.0
+2.0,7.0

test/tests/vectorpostprocessors/least_squares_fit/gold/csv0_least_squares_fit_coeffs_0000.csv

@@ -0,0 +1,2 @@
+coefficients
+1

test/tests/vectorpostprocessors/least_squares_fit/gold/csv1_least_squares_fit_coeffs_0000.csv

@@ -0,0 +1,3 @@
+coefficients
+1
+1

test/tests/vectorpostprocessors/least_squares_fit/gold/csv2_least_squares_fit_coeffs_0000.csv

@@ -0,0 +1,4 @@
+coefficients
+1
+1
+1

test/tests/vectorpostprocessors/least_squares_fit/least_squares_fit_csv_data.i

@@ -0,0 +1,40 @@
+[Problem]
+  solve = false
+[]
+
+[Mesh]
+  type = GeneratedMesh
+  dim = 1
+  nx = 1
+[]
+
+[Executioner]
+  type = Transient
+  start_time = 0.0
+  end_time = 0.0
+[]
+
+[VectorPostprocessors]
+  [./csv_data]
+    type = CSVReader
+    csv_file = fit_data_0.csv
+    header = true
+    outputs = none
+  [../]
+  [./least_squares_fit_coeffs]
+    type = LeastSquaresFit
+    vectorpostprocessor = csv_data
+    x_name = 'id'
+    y_name = 'u'
+    order = 0
+    output = coefficients
+    truncate_order = false
+    execute_on = initial
+  [../]
+[]
+
+[Outputs]
+  file_base = csv0
+  execute_on = initial
+  csv = true
+[]

test/tests/vectorpostprocessors/least_squares_fit/tests

@@ -1,7 +1,45 @@
 [Tests]
   [./least_squares]
+    issues = '#7907 #4464'
+    requirement = 'The LeastSquaresFit vectorpostprocessor shall compute a least squares fit of a first-order polynomial sampled from a solution field using LineValueSampler with shifting and scaling parameters specified'
+    design = 'LeastSquaresFit.md LineValueSampler.md'
     type = 'CSVDiff'
     input = 'least_squares_fit.i'
     csvdiff = 'out_least_squares_fit_coeffs_0001.csv out_least_squares_fit_sample_0001.csv out_shift_and_scale_y_least_squares_fit_sample_0001.csv out_shift_and_scale_x_least_squares_fit_coeffs_0001.csv out_shift_and_scale_x_least_squares_fit_sample_0001.csv out_shift_and_scale_y_least_squares_fit_coeffs_0001.csv'
   [../]
+  [./least_squares_csv0]
+    issues = '#13498'
+    requirement = 'The LeastSquaresFit vectorpostprocessor shall compute a least squares fit of a zeroth-rder polynomial with data provided by a CSVReader'
+    design = 'LeastSquaresFit.md CSVReader.md'
+    type = 'CSVDiff'
+    input = 'least_squares_fit_csv_data.i'
+    csvdiff = 'csv0_least_squares_fit_coeffs_0000.csv'
+  [../]
+  [./least_squares_csv1]
+    issues = '#13498'
+    requirement = 'The LeastSquaresFit vectorpostprocessor shall compute a least squares fit of a first-rder polynomial with data provided by a CSVReader'
+    design = 'LeastSquaresFit.md CSVReader.md'
+    type = 'CSVDiff'
+    input = 'least_squares_fit_csv_data.i'
+    cli_args = 'VectorPostprocessors/csv_data/csv_file=fit_data_1.csv VectorPostprocessors/least_squares_fit_coeffs/order=1 Outputs/file_base=csv1'
+    csvdiff = 'csv1_least_squares_fit_coeffs_0000.csv'
+  [../]
+  [./least_squares_csv2]
+    issues = '#13498'
+    requirement = 'The LeastSquaresFit vectorpostprocessor shall compute a least squares fit of a second-rder polynomial with data provided by a CSVReader'
+    design = 'LeastSquaresFit.md CSVReader.md'
+    type = 'CSVDiff'
+    input = 'least_squares_fit_csv_data.i'
+    cli_args = 'VectorPostprocessors/csv_data/csv_file=fit_data_2.csv VectorPostprocessors/least_squares_fit_coeffs/order=2 Outputs/file_base=csv2'
+    csvdiff = 'csv2_least_squares_fit_coeffs_0000.csv'
+  [../]
+  [./least_squares_csv3_order_err]
+    issues = '#13498'
+    requirement = 'The LeastSquaresFit vectorpostprocessor shall generate an error if a fit for a third-order polynomial is requested and only three data points are provided'
+    design = 'LeastSquaresFit.md CSVReader.md'
+    type = 'RunException'
+    expect_err = 'PolynomialFit requires an order less than the size of the input vector'
+    input = 'least_squares_fit_csv_data.i'
+    cli_args = 'VectorPostprocessors/csv_data/csv_file=fit_data_2.csv VectorPostprocessors/least_squares_fit_coeffs/order=3 Outputs/file_base=csv2_order_err'
+  [../]
 []