## org.apache.commons.math3.transform Class FastSineTransformer

```java.lang.Object org.apache.commons.math3.transform.FastSineTransformer
```
All Implemented Interfaces:
Serializable, RealTransformer

`public class FastSineTransformerextends Objectimplements RealTransformer, Serializable`

Implements the Fast Sine Transform for transformation of one-dimensional real data sets. For reference, see James S. Walker, Fast Fourier Transforms, chapter 3 (ISBN 0849371635).

There are several variants of the discrete sine transform. The present implementation corresponds to DST-I, with various normalization conventions, which are specified by the parameter `DstNormalization`. It should be noted that regardless to the convention, the first element of the dataset to be transformed must be zero.

DST-I is equivalent to DFT of an odd extension of the data series. More precisely, if x0, …, xN-1 is the data set to be sine transformed, the extended data set x0#, …, x2N-1# is defined as follows

• x0# = x0 = 0,
• xk# = xk if 1 ≤ k < N,
• xN# = 0,
• xk# = -x2N-k if N + 1 ≤ k < 2N.

Then, the standard DST-I y0, …, yN-1 of the real data set x0, …, xN-1 is equal to half of i (the pure imaginary number) times the N first elements of the DFT of the extended data set x0#, …, x2N-1#
yn = (i / 2) ∑k=02N-1 xk# exp[-2πi nk / (2N)]     k = 0, …, N-1.

The present implementation of the discrete sine transform as a fast sine transform requires the length of the data to be a power of two. Besides, it implicitly assumes that the sampled function is odd. In particular, the first element of the data set must be 0, which is enforced in `transform(UnivariateFunction, double, double, int, TransformType)`, after sampling.

Since:
1.2
Version:
\$Id: FastSineTransformer.java 1385310 2012-09-16 16:32:10Z tn \$
Serialized Form

Constructor Summary
`FastSineTransformer(DstNormalization normalization)`
Creates a new instance of this class, with various normalization conventions.

Method Summary
`protected  double[]` `fst(double[] f)`
Perform the FST algorithm (including inverse).
` double[]` ```transform(double[] f, TransformType type)```
Returns the (forward, inverse) transform of the specified real data set.
` double[]` ```transform(UnivariateFunction f, double min, double max, int n, TransformType type)```
Returns the (forward, inverse) transform of the specified real function, sampled on the specified interval.

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### FastSineTransformer

`public FastSineTransformer(DstNormalization normalization)`
Creates a new instance of this class, with various normalization conventions.

Parameters:
`normalization` - the type of normalization to be applied to the transformed data
Method Detail

### transform

```public double[] transform(double[] f,
TransformType type)```
Returns the (forward, inverse) transform of the specified real data set. The first element of the specified data set is required to be `0`.

Specified by:
`transform` in interface `RealTransformer`
Parameters:
`f` - the real data array to be transformed (signal)
`type` - the type of transform (forward, inverse) to be performed
Returns:
the real transformed array (spectrum)
Throws:
`MathIllegalArgumentException` - if the length of the data array is not a power of two, or the first element of the data array is not zero

### transform

```public double[] transform(UnivariateFunction f,
double min,
double max,
int n,
TransformType type)```
Returns the (forward, inverse) transform of the specified real function, sampled on the specified interval. This implementation enforces `f(x) = 0.0` at `x = 0.0`.

Specified by:
`transform` in interface `RealTransformer`
Parameters:
`f` - the function to be sampled and transformed
`min` - the (inclusive) lower bound for the interval
`max` - the (exclusive) upper bound for the interval
`n` - the number of sample points
`type` - the type of transform (forward, inverse) to be performed
Returns:
the real transformed array
Throws:
`NonMonotonicSequenceException` - if the lower bound is greater than, or equal to the upper bound
`NotStrictlyPositiveException` - if the number of sample points is negative
`MathIllegalArgumentException` - if the number of sample points is not a power of two

### fst

```protected double[] fst(double[] f)
throws MathIllegalArgumentException```
Perform the FST algorithm (including inverse). The first element of the data set is required to be `0`.

Parameters:
`f` - the real data array to be transformed
Returns:
the real transformed array
Throws:
`MathIllegalArgumentException` - if the length of the data array is not a power of two, or the first element of the data array is not zero