Returns the Gaussian fit of a data set using a specific fitting method.
Initial guesses of the amplitude, center, standard deviation, and offset for use in the iterative algorithm.
If initial amplitude, initial center, initial standard deviation, or offset is NaN, this node calculates the initial guess automatically.
Initial guess of the amplitude.
Default: NaN
Initial guess of the center.
Default: NaN
Initial guess of the standard deviation.
Default: NaN
Initial guess of the offset.
Default: NaN
An array of dependent values representing the y-values of the data set.
This input changes to signal when the data type is a waveform or an array of waveforms.
An array of independent values representing the x-values of the data set.
This input is available only if you wire an array of double-precision floating-point numbers to y or signal.
An array of weights for the data set.
Value that determines when to stop the iterative adjustment of the amplitude, center, standard deviation, and offset.
If tolerance is less than or equal to 0, this node sets tolerance to 0.0001.
How tolerance Affects the Outputs with Different Fitting Methods
For the Least Square and Least Absolute Residual methods, if the relative difference between residue in two successive iterations is less than tolerance, this node returns the resulting residue. For the Bisquare method, if any relative difference between amplitude, center, standard deviation, and offset in two successive iterations is less than tolerance, this node returns the resulting amplitude, center, standard deviation, and offset.
Default: 0.0001
Error conditions that occur before this node runs.
The node responds to this input according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Default: No error
Upper and lower constraints for the amplitude, center, standard deviation, and offset.
Lower bound for the amplitude.
Default: -Infinity, which means no lower bound is imposed on the amplitude.
Upper bound for the amplitude.
Default: Infinity, which means no upper bound is imposed on the amplitude.
Lower bound for the center.
Default: -Infinity, which means no lower bound is imposed on the center.
Upper bound for the center.
Default: Infinity, which means no upper bound is imposed on the center.
Lower bound for the standard deviation.
Default: -Infinity, which means no lower bound is imposed on the standard deviation.
Upper bound for the standard deviation.
Default: Infinity, which means no upper bound is imposed on the standard deviation.
Lower bound for the offset.
Default: 0
Upper bound for the offset.
Default: 0
The fitting method.
Name | Value | Description |
---|---|---|
Least Square | 0 | Uses the least square method. |
Least Absolute Residual | 1 | Uses the least absolute residual method. |
Bisquare | 2 | Uses the bisquare method. |
Algorithm for the Least Square Method
The least square method of fitting finds the amplitude, center, standard deviation, and offset of the Gaussian model by minimizing the residue according to the following equation:
where
Algorithm for the Least Absolute Residual Method
The least absolute residual method finds the amplitude, center, standard deviation, and offset of the Gaussian model by minimizing the residue according to the following equation:
where
Algorithm for the Bisquare Method
The bisquare method of fitting finds the amplitude, center, standard deviation, and offset using an iterative process, as shown in the following illustration.
The node calculates residue according to the following equation:
where
Default: Least Square
Offset of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
Amplitude of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
Center of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
Standard deviation of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
Error information.
The node produces this output according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Weighted mean error of the fitted model.
This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.
This node uses the iterative general least square method and the Levenberg-Marquardt method to fit data to a Gaussian curve of the general form described by the following equation:
where
This node finds the values of a, $\mu $, $\sigma $, and c that best fit the observations (x, y).
The following equation specifically describes the Gaussian curve resulting from the Gaussian fit algorithm:
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported
Web Server: Not supported in VIs that run in a web application