2.8.10 interp1
Menu Information
Analysis: Mathematics: Interpolate/Extrapolate Y from X
Brief Information
Interpolate or extrapolate XY data at a given set of X values
Command Line Usage
1. interp1 ix:=Col(3) iy:=(Col(1), Col(2));
2. interp1 ix:=Col(3) iy:=Col(2) ox:=Col(4);
3. interp1 ix:=Col(3) iy:= Col(2) method:= bspline coef:=Col(4);
4. interp1 ix:=Col(3) iy:=(Col(1), Col(2)) method:=spline;
5. interp1 ix:=Col(3) iy:=Col(2) method:=spline ox:=<new> coef:=<new>;
X-Function Execution Options
Please refer to the page for additional option switches when accessing the x-function from script
Variables
Display
Name
|
Variable
Name
|
I/O
and
Type
|
Default
Value
|
Description
|
| X Values to Interpolate
|
ix
|
Input
vector
|
<unassigned>
|
The vector to interpolate on.
|
| Input
|
iy
|
Input
XYRange
|
<active>
|
The reference XY dataset(s) for interpolation. Multiple XY ranges are supported. If multi-ranges are selected, corresponding sets of interpolated Y vectors and coefficients will be output.
|
| Method
|
method
|
Input
int
|
linear
|
Interpolation methods
Option list:
- method:=0, Linear interpolation is a fast method of estimating a data point by constructing a line between two neighboring data points. The resulting point may not be an accurate estimation of the missing data.
- method:=1, This method splits the input data into a given number of pieces, and fits each segment with a cubic polynomial. The second derivative of each cubic function is set equal to zero. With these boundary conditions met, an entire function can be constructed in a piece-wise manner.
- bspline:Cubic B-Spline{2}
- method:=2, This method also splits the input data into pieces, each segment is fitted with discrete Bezier splines.
- method:=3, This method is based on a piecewise function composed of a set of polynomials. The akima interpolation is stable to outliers.
You could refer to the algorithm of each interpolation methods.
|
| Extrapolate Option
|
option
|
Input
int
|
0
|
When parts of the data range specified by ix is outside that of the X range specified by iy, these range parts will be considered as the extrapolated range, because the resulted Y values for these parts will be computed from extrapolation. This option can then be used to specify how to extrapolate the corresponding Y values.
Option list:
- extrap:Extrapolate{0}
- Extrapolate Y using the last two points
- miss:Set missing{1}
- Set all Y values in the extrapolated range to be missing values.
- repeat:Repeat the last value{2}
- Use the Y value of the closest input X value for all values in the extrapolated range.
|
| Boundary
|
boundary
|
Input
int
|
notaknot
|
Boundary condition only available in cubic spline method
Option list:
- 2nd derivatives are 0 on both end
- 3rd derivatives are continuous on the second and last-second point
|
| Smoothing Factor
|
sf
|
Input
double
|
0
|
A non-negative parameter that specifies the smoothness of the interpolated curve in Cubic B-Spline interpolation. The factor helps user control the balance between the smoothness and fidelity to actual data. Larger values produce smoother curves.
|
| Result of interpolation
|
ox
|
Output
Range
|
<new>
|
The Y range(s) to output interpolated Y values.
|
| Coefficients
|
coef
|
Output
Range
|
<optional>
|
Spline coefficients when using spline or B-spline method.
|
Examples
1. Import Interpolation.dat on \Samples\Mathematics folder.
2. Highlight column B and click Analysis: Mathematics: Interpolate/Extrapolate Y from X on the menu to bring up the dialog.
3. Now the Input branch have filled with proper data range, click the drop-down list beside X Values to Interpolate edit box, and select Col(C).
4. Select Cubic B-spline interpolate method.
5. Click OK to do interpolation.
More Information
Please refer to this page in the User Guide for more information:
- Description
- Algorithm
- Reference.
Related X-Functions
interp1xy, interp1q, spline, bspline, minterp2
Keywords:interpolation, find y from x
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