# 3.5.8.20 Table

## Description

This function searches a numeric value d in dataset vd and returns the value in vref whose index number is the same as d in vd. Interpolation can be employed during this process. This function is usually used to find out the fitted X or Y values after performing the linear or nonlinear fitting, depending on the order of the dataset arguments (See the two examples below).

## Syntax

//If vref contains numeric values,
double Table(vector vd, vector vref, double d[, int option])

//If vref contains strings,
string Table(vector vd, vector<string> vref, double d[, int option])

## Parameters

vd

is the dataset in which value d is looked up. The values in vd must be placed in ascending order. Otherwise, the function might not return the correct value.

vref

is the reference vector whose nth value will be return. n is the index number of d within vd.

d

is the numeric value to find in vd.

option

[optional] integer that specifies how to find out value d. The default value -1 means the function does linear interpolation on vd vs vref and returns the interpolated value. When option = 0, the function searches in vd and finds a value that is equal to d or closest from left. If return value is string, 0 will be used as default since linear interpolation is meaningless to sting. When option = 1, the function finds a value that is equal to d or closest from right. When option = 2, the function finds a value that is equal to d or closest from both sides.

## Return

Returns the value in vref whose index number is the same as d in vd.

## Example

Example1

The following script returns new Y values from a fit curve:

linearfit_Ynew = Table(linearfit_a, linearfit_data1b, linearfit_b)

where linearfit_a is the X fit dataset, linearfit_data1b is the Y fit dataset, and linearfit_b holds the X values for predicting Y values.

Example2

The following script returns new X values from a fit curve:

linearfit_Xnew = Table(linearfit_data1b, linearfit_a, linearfit_b)

where linearfit_data1b is the Y fit dataset, linearfit_a is the X fit dataset, and linearfit_b holds the Y values for predicting X values.

Example3

The following example shows you how the return value is determined by parameter option

col(a) = data(1,20);
col(b) = data(2,20);
a1 = table(col(a),col(b),4.3,1); //search 4.3 from right
a1 = ; //should return a1=6
a2 = table(col(a),col(b),4.3,0); //search 4.3 from left
a2 = ; //should return a2=5
a3 = table(col(a),col(b),4.3,2); //search 4.3 from both sides
a3 = ; //should return a3=5
a4 = table(col(a),col(b),4.3,-1); //perform linear interpolation on column A and B
a4 = ; //should return a4=5.3