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|   | TransformationModelInterpolated (const DataPoints &data, const Param ¶ms) | 
|   | Constructor.  More...
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|   | TransformationModelInterpolated (const std::vector< std::pair< double, double >> &data, const Param ¶ms, bool preprocess) | 
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|   | ~TransformationModelInterpolated () override | 
|   | Destructor.  More...
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| double  | evaluate (double value) const override | 
|   | Evaluate the interpolation model at the given value.  More...
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|   | TransformationModel () | 
|   | Constructor.  More...
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|   | TransformationModel (const TransformationModel::DataPoints &, const Param &) | 
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| virtual  | ~TransformationModel () | 
|   | Destructor.  More...
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| virtual void  | weightData (DataPoints &data) | 
|   | Weight the data by the given weight function.  More...
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| virtual void  | unWeightData (DataPoints &data) | 
|   | Unweight the data by the given weight function.  More...
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| bool  | checkValidWeight (const String &weight, const std::vector< String > &valid_weights) const | 
|   | Check for a valid weighting function string.  More...
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| double  | checkDatumRange (const double &datum, const double &datum_min, const double &datum_max) | 
|   | Check that the datum is within the valid min and max bounds.  More...
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| double  | weightDatum (const double &datum, const String &weight) const | 
|   | Weight the data according to the weighting function.  More...
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| double  | unWeightDatum (const double &datum, const String &weight) const | 
|   | Apply the reverse of the weighting function to the data.  More...
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| const Param &  | getParameters () const | 
|   | Gets the (actual) parameters.  More...
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| std::vector< String >  | getValidXWeights () const | 
|   | Returns a list of valid x weight function strings.  More...
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| std::vector< String >  | getValidYWeights () const | 
|   | Returns a list of valid y weight function strings.  More...
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Interpolation model for transformations. 
Between the data points, the interpolation uses the neighboring points to interpolate. The following interpolation methods are available:
- linear: Linearly interpolate between neighboring points
 
- cspline: Use a cubic spline to interpolate between neighboring points
 
- akima: Use an akima spline to interpolate between neighboring points (less affected by outliers)
 
Outside the range spanned by the points, we extrapolate using one of the following methods:
- two-point-linear: Uses a line through the first and last point to extrapolate
 
- four-point-linear: Uses a line through the first and second point to extrapolate in front and and a line through the last and second-to-last point in the end. If the data is non-linear, this may yield better approximations for extrapolation.
 
- global-linear: Uses a linear regression to fit a line through all data points and use it for extrapolation. Note that global-linear extrapolation may not be continuous with the interpolation at the border.