Understanding the Discrete Cosine Transform in Java
July 11, 2006
Java Programming Notes # 2444
Preface
This lesson is one in a series designed to teach you about the inner
workings of data and image compression. The first lesson in the series was
Understanding
the LempelZiv Data Compression Algorithm in Java. The previous lesson
was Understanding the Huffman Data Compression
Algorithm in Java.
JPEG image compression
One of the objectives of the series is to teach you about the inner workings
of JPEG image compression. According to
Wikipedia,
"... JPEG (pronounced jaypeg) is a commonly used standard
method of
lossy compression for photographic images. ... The name stands for
Joint Photographic Experts Group. JPEG itself specifies only how an
image is transformed into a stream of
bytes, but not
how those bytes are encapsulated in any particular storage medium. A further
standard, created by the Independent JPEG Group, called JFIF
(JPEG File Interchange Format) specifies how to produce a file suitable for
computer storage and transmission (such as over the
Internet)
from a JPEG stream. ... JPEG/JFIF is the most common format used for storing
and transmitting photographs on the
World Wide Web."
Entropy encoding
Without getting into the technical details at this point, let me tell you
that one of the central components of JPEG compression is
entropy encoding. Huffman encoding,
which was the primary topic of the
previous lesson
is a common form of entropy encoding.
The Discrete Cosine Transform
Another central component of JPEG compression is the
Discrete Cosine Transform, which is the primary topic of this lesson. Again,
according to
Wikipedia,
"The discrete cosine transform (DCT) is a
Fourierrelated transform similar to the
discrete Fourier transform (DFT), but using only
real
numbers. It is equivalent to a DFT of roughly twice the length,
operating on real data with
even symmetry ..."
In order to understand JPEG ...
In order to understand JPEG image compression, you must understand Huffman
encoding, the Discrete Cosine Transform, and some other topics as well, such as
spectral requantization. I
plan to teach you about the different components of JPEG in separate
lessons, and then to teach you how they work together to produce "the
most common format used for storing and transmitting photographs on the
World Wide Web"
Viewing tip
You may find it useful to open another copy of this lesson in a
separate browser window. That will make it easier for you to
scroll back
and forth among the different listings and figures while you are
reading
about them.
Supplementary material
I recommend that you also study the other lessons in my extensive
collection of online Java tutorials. You will find those lessons
published
at Gamelan.com.
However, as of the date of this writing, Gamelan doesn't maintain a
consolidated index of my Java tutorial lessons, and sometimes they are
difficult to locate there. You will find a consolidated index at www.DickBaldwin.com.
In preparation for understanding the material in this lesson, I also
recommend that you also study the lessons referred to in the
References section.
Although the Discrete Cosine Transform (DCT) may be
"similar to the
discrete Fourier transform", it is not a Discrete Fourier Transform
(DFT) as described in my earlier lesson entitled
Fun with Java,
How and Why Spectral Analysis Works. There are some major differences
between DFT and DCT. Nonetheless, the DCT is very interesting from a technical
viewpoint and is a central component of JPEG image compression.
The DFT and symmetrical real data
To get started, I'm going to show you the results of applying the DFT to
symmetrical real data. In the earlier lesson entitled
Spectrum
Analysis using Java, Forward and Inverse Transforms, Filtering in the Frequency
Domain, I introduced you to a program named Dsp035, which:
"... illustrates the reversible nature of the Fourier transform. This
program transforms a real time series into a complex spectrum, and then
reproduces the real time series by performing an inverse Fourier transform
on the complex spectrum. This is accomplished using a DFT algorithm."
The program named Dsp043
Listing 11 contains a complete listing of a program named Dsp043,
which is designed to demonstrate that the imaginary part of the Fourier transform of a
real symmetrical time series is all zeros if the origin is properly located.
(The code in this program is very similar to the code in the program
named Dsp035 that I explained
earlier,
so I won't repeat that explanation in this lesson. Rather, I will just
explain the results of executing the program named Dsp043.)
The program output
Figure 1 shows the results of executing the program
named Dsp043 shown in Listing 11.
The first graph at the top of Figure 1 shows a 300sample symmetrical time
series. Note that this time series is symmetrical about its center point.
The spectral results
The DFT was applied to this time series. The next three graphs going
down the page in Figure 1 show the
spectral results of applying the DFT to the time series. The three
spectral graphs are plotted from a
frequency of zero to the sampling frequency.
(Recall that the
Nyquist folding frequency occurs half way between zero and the sampling
frequency, and that the spectrum shown above the folding frequency is the
mirror image of the spectrum below the folding frequency. Therefore,
we are usually interested only in the spectral results below the folding
frequency. Therefore, you can ignore the right half of the spectral graphs.)
What do the five graphs show?
The five graphs in Figure 1 show (in order from top to bottom):
 The 300sample symmetrical real time series to which the DFT was applied.
 The real part of the output from the DFT.
 The imaginary part of the output from the DFT.
 The magnitude of the output from the DFT.
 The result of applying an inverse DFT to the spectral data in order to
reconstruct the original time series from the spectral data.
The important points
The most important points illustrated by Figure 1 are:
 The time series is purely real.
 The time series is symmetrical about its center point.
 The imaginary part of the spectrum has all zero values.
Therefore, Figure 1 demonstrates that the imaginary part of the Fourier
transform of a real symmetrical time series is all zeros if the origin is
properly located. This is important because I will use that fact to develop the rationale for
the Discrete Cosine Transform, and why it works the way that it does.
Equations for the DFT
Referring back to the equations in my
earlier lesson, you can see that the real part of the output from the DFT
results from a sum of products involving a cosine term, and that the imaginary part
of the output results from a sum of products involving a sine term.
Can sometimes avoid the sine computation
When
computing the DFT, if we already know that the input time series is symmetrical
and that the imaginary part of the output will be zero, we can simply forego the
computation of the imaginary part that involves the sine term, thereby reducing
the computational requirements.
Can make the cosine computation less burdensome
In addition, if we know that the input time series is symmetrical, we can
reformulate the computation of the real part of the transform involving the cosine
term wherein we perform the computation on onehalf of the time series only and
then double the result. Thus for a symmetrical time series having a length
of 2N+1 samples, we can reduce the number of realpart computations to N+1.
How does the DCT work?
Basically the DCT works by implicitly doubling the length of the input time series by
concatenating it to a mirror image of itself. The concatenation of the
original time series to the mirror image results in a symmetrical time series.
(You don't actually see the doubling of the time series. Rather,
the doubling is implicit in the formulation of the equations for the DCT.)
Because the new time series is symmetrical, it is known in advance that if we were to perform a DFT on the new
doublelength time series, the imaginary part would be zero. Thus, it is
also known in advance that we can forego the computation of the imaginary part
that uses the sine term in the DFT.
No need to double the number of cosine computations
In addition, because the new doublelength time series is symmetrical, we
don't need to double the number of realpart computations involving the cosine
term (but we will have to reformulate the realpart computation relative to a
straight DFT computation).
The definition of the DCT
The
definition of the DCT is very similar to the definition of the DFT but the
computation of the imaginary part using the sine term simply isn't part of the
definition.
In addition to eliminating the computation of the imaginary part, the
definition of the DCT also reformulates the DFT to take advantage of the
symmetry of the time series relative to the computation of the real part using
the cosine term.
Let's see some equations
Rather than to deal with the somewhat difficult task of producing equations
in this HTML document, I have provided three separate references
that contain the equations for the DCT. The equations in these three
references are essentially the same (to within a scale factor).
I elected to formulate my DCT program for this lesson using the
onedimensional form of the DCT equations that you will find at
National Taiwan University  DSP Group  Discrete Cosine Transform.
(The next lesson in this series will deal with the twodimensional
formulation of the DCT.)
No sine term
If you examine those equations, you will find that there is no sine term in
either the forward or the inverse DCT. Only the cosine term is
included, and it is included in such a way as to take advantage of the symmetry
of the doublelength time series.
(While you are at the site mentioned
above, also note that the author provides an
explanation of the doubling of the length of the time series in order to
produce a new symmetrical time series for which the Fourier Transform is
guaranteed to have a zero imaginary part.)
The forward Discrete Cosine Transform (DCT)
code
Listing 12 provides a class named ForwardDCT01, which is an implementation of
the forward DCT equation discussed
above.
The code for the forward DCT is amazingly simple, considering what it is
capable of accomplishing.
(Perhaps the simplicity of the code had something to do with why the
DCT was selected to be a standard part of JPEG image compression.)
The transform method
The static method named transform belonging to the class named ForwardDCT01
performs a forward Discreet Cosine Transform (DCT) on an incoming
time series and returns the DCT spectrum.
Input and output
The incoming parameters are:
 double[] x  incoming real data
 double[] y  outgoing real data
Thus, the input time series is provided by way of a double[] array
object referred to by x. The transform method populates an output
double[] array object referred to by y.
(Insofar as practical, the variable names and terms used in the
transform method
match the terms used in the equations discussed
earlier.)
Knowing the equation that the method is designed to implement, you should
find the code in Listing 12 to be straightforward.
The inverse Discrete Cosine Transform (DCT)
code
Listing 13 provides the code for a class named InverseDCT01, which is
an implementation of the inverse DCT equation discussed
earlier.
The static method named transform of the InverseDCT01 class
performs an inverse Discreet Cosine Transform (DCT) on an incoming DCT
spectrum and returns the DCT time series.
(As before, insofar as practical, the variable names and terms used in the method
match the terms used in the equations discussed
earlier.)
Input and output
Incoming parameters to the method are:
 double[] y  incoming real data
 double[] x  outgoing real data
Thus, the DCT spectrum to be transformed is provided by the user in an array
object of type double[] referred to by y. The method named
transform populates an array object of type double[] referred to
by x with the timeseries resulting from the transform.
As before, you should find the code in Listing 13
to be straightforward.
The program named Dsp042
The program named Dsp042 illustrates the application of the forward
and inverse Discrete Cosine Transform (DCT) to three different waveforms,
all concatenated into a single time series.
This program is very similar to Dsp035, which was explained in the
earlier lesson entitled
Spectrum
Analysis using Java, Forward and Inverse Transforms, Filtering in the Frequency
Domain.
The program named Dsp035
The program named Dsp035 applies the full forward and inverse Discrete Fourier
Transform (DFT) to the same three waveforms producing the output shown in
Figure 2.
Figure 2 does not contain new material. You saw a figure similar to this one in
the earlier
lesson.
What do the five graphs show?
The five graphs in Figure 2 show the following information, in order from top
to bottom:
 A 256sample input time series consisting of three waveforms in sequence
with values of zero between the waveforms. (Note that even though
the display is 300 points wide, the actual data being plotted on each of the
five graphs ends at 256 samples.)
 The real part of the frequency spectrum produced by the forward DFT,
plotted from a frequency of zero on the left to the sampling frequency
(twice the
Nyquist folding frequency) on the right.
 The imaginary part of the frequency spectrum on the same scale as the
real part of the transform.
 The magnitude of the frequency spectrum, also on the same scale.
 The result of applying the inverse DFT to the spectral data in order to
reconstruct the original time series from the spectral data.
The most important thing to note in Figure 2 is that the imaginary part of
the DFT output is clearly not zero for this time series input. The time
series output in the bottom graph can only be produced by performing a complex
transform on the real and imaginary parts of the frequency spectrum.
Back to the program named Dsp042
The material shown in Figure 3, which is the output
produced by the program named Dsp042 is new to this lesson.
The three graphs in Figure 3 show:
 A 256sample input time series consisting of the same three waveforms
shown in Figure 2. (As with Figure 2, even though
the display is 300 points wide, the actual data being plotted on each of the
three graphs ends at 256 samples.)
 The real frequency spectrum produced by the forward DCT.
In this case, the spectrum is plotted from a frequency of zero on the left
to the
Nyquist folding frequency on the right.
 The result of applying the inverse DCT to the spectral data in order to
reconstruct the original time series from the spectral data.
Where is the imaginary part of the spectrum?
There is no imaginary part of the spectrum plotted in Figure 3, simply
because the DCT doesn't produce an imaginary part. Since there is no
imaginary part, there is also no need for a plot of the magnitude spectrum for
the DCT. (It would look exactly like the real part of the spectrum,
with all negative values converted to positive values, if
it were computed and plotted.)
If you compare Figure 3 with
Figure 2, you will see that the DFT and the DCT both appeared to do an
equally good job of
reproducing the original time series when the inverse transform was applied to the spectral
data. The big difference between Figure 2 and
Figure 3 is that this was accomplished with the somewhat
more economical DCT in Figure 3.
Water to ice and back to water
There is one point that I would like to make here, because it will become
very important in a future lesson that deals with the inner workings of JPEG.
The second and third graphs in Figure 2 represent the
same information as the first graph in Figure 2.
Similarly, the second graph in Figure 3 represents the
same information as the first graph in Figure 3.
In other words, the spectral data represents the same information as the
timeseries data. They simply represent that information in different
forms.
As an analogy, if we lower the temperature of a container of water to less than 32 degrees
Fahrenheit, the form of the water will change from a liquid to a solid. If we warm
it back up, the form will change back to a liquid. This is roughly
analogous to transforming a time series into the frequency domain and then
transforming the frequency spectrum back into the time domain. The same
information is represented in both cases. That information is simply
represented in different forms.
Subdividing the input
When performing spectral analysis, it is common practice to perform a DFT
(or perhaps a DCT) on the entire time series as was the case in
Figure 1,
Figure 2, and
Figure 3. However, in a future lesson we will
learn that this is not the case for JPEG image compression. Instead, the
JPEG procedure subdivides the image into a set of small images where each small
image consists of an 8x8 block of 64 pixels.
Then the DCT is performed on
each individual block of 64 pixels. Several additional processing steps are performed
on the spectra produced for the set of 8x8 blocks to produce the compressed
image. Later on, when the
image is reconstructed, the 8x8 blocks are individually reconstructed and are
then assembled into a larger image that approximates the original image.
The program named Dsp044
The program named Dsp044 is designed to
investigate the impact of subdividing the time series into eightsample
segments and processing those segments individually in order to be more consistent with the 8x8pixel block concept in JPEG.
As it turns out, there appears to be no noticeable impact. As you can see
in Figure 4, the reconstructed output shown in the second
graph is a very good replica of the input shown in the first graph.
(Figure 4 shows only the input and output time series. In order
to display the spectral results, it would have been necessary to display 32
individual spectra, one computed for each 8sample segment of the input time
series. That would have been fairly impractical.)
Testing
Both programs were tested using J2SE 5.0 under WinXP. (Both programs
require J2SE 5.0 or later due to the use of
static import of Math class.)
Discussion
and Sample Code
The program named Dsp042
The class
definition for Dsp042 begins in Listing 1 by declaring
and initializing some instance variables.
class Dsp042 implements GraphIntfc01{
int len = 256;
double[] timeDataIn = new double[len];
double[] realSpect = new double[len];
double[] timeDataOut = new double[len];
int zero = 0;
Listing 1

Note that the class named Dsp042 implements the interface named GraphIntfc01.
The constructor for Dsp042
The constructor begins in Listing 2.
public Dsp042(){//constructor
//Create the raw data pulses
timeDataIn[0] = 0;
timeDataIn[1] = 50;
//code deleted for brevity
timeDataIn[20] = 2;
timeDataIn[21] = 1;
timeDataIn[240] = 80;
timeDataIn[241] = 80;
//code deleted for brevity
timeDataIn[254] = 80;
timeDataIn[255] = 80;
Listing 2

The code in Listing 2 creates the first and last waveforms shown in the first
graph in Figure 3. Note that I deleted quite a lot
of code from Listing 2 for brevity. You can view the code that I deleted
in Listing 14.
Create the sinusoidal waveform
Listing 3 creates the truncated sinusoidal waveform shown near the center of
the first graph
in Figure 3.
for(int x = len/3;x < 3*len/4;x++){
timeDataIn[x] = 80.0 * Math.sin(2*PI*(x)*1.0/20.0);
}//end for loop
Listing 3

Perform the forward Discrete Cosine Transform
Listing 4 invokes the static transform method of the ForwardDCT01
class to compute the forward DCT of the time data and to save the results in the
array referred to by realSpect, which was created
in Listing 1.
ForwardDCT01.transform(timeDataIn,realSpect);
Listing 4

Perform the inverse Discrete Cosine Transform
Listing 5 invokes the static transform method of the InverseDCT01
class to compute the inverse DCT of the spectral data and to save the results in
the array referred to by timeDataOut, which was created
in
Listing 1.
InverseDCT01.transform(realSpect,timeDataOut);
}//end constructor
Listing 5

Listing 5 also signals the end of the constructor for the class named
Dsp042.
The remaining code
The remaining code in the class named Dsp042 consists of six methods,
which are required by the interface named GraphIntfc01. The purpose
of these methods is simply to plot the data contained in three arrays, producing the
three graphs shown in Figure 3. I explained those
six methods in the earlier lesson entitled
Plotting Engineering and Scientific Data using Java and won't repeat that
explanation here. You can view the methods in
Listing 14.
The class named Dsp044
This class is a modification of the class named Dsp042 designed to investigate the impact of
subdividing the input time series into eightsample segments in order to be consistent with the 8x8 blocks in JPEG.
Listing 6 shows the beginning of the class and the beginning of the
constructor.
class Dsp044 implements GraphIntfc01{
int len = 256;
double[] timeDataIn = new double[len];
double[] timeDataOut = new double[len];
int zero = 0;
public Dsp044(){//constructor
//Create the raw data pulses
timeDataIn[0] = 0;
timeDataIn[1] = 50;
//code deleted for brevity
timeDataIn[20] = 2;
timeDataIn[21] = 1;
timeDataIn[240] = 80;
timeDataIn[241] = 80;
//code deleted for brevity
timeDataIn[254] = 80;
timeDataIn[255] = 80;
//Create raw data sinusoid
for(int x = len/3;x < 3*len/4;x++){
timeDataIn[x] = 80.0 * Math.sin(2*PI*(x)*1.0/20.0);
}//end for loop
Listing 6

Once again, note that the class named Dsp044 implements the interface
named GraphIntfc01.
As before, I deleted some of the code from Listing 6 for brevity. The
code in Listing 6 is very similar to the code that I explained earlier with
respect to the class named Dsp042.
Eightelement array objects
The real difference between Dsp042 and Dsp044 begins in Listing 7 where I
create some eightelement array objects to handle the eightsample
segments.
double[] workingArrayIn = new double[8];
double[] workingArrayOut = new double[8];
double[] realSpect = new double[8];
Listing 7

Process eight samples at a time
Listing 8 shows the beginning of a while loop, which computes a forward
and an inverse DCT on each successive eightsample segment of the input time
series. Code inside
the while loop also concatenates the output segments from the inverse DCT
to produce the output signal, which is shown by the bottom graph
in
Figure
4.
int segmentCnt = 0;
while((segmentCnt + 8) <= len){
System.arraycopy(timeDataIn,
segmentCnt,
workingArrayIn,
0,
8);
Listing 8

During each iteration of the while loop, the code in Listing 8 copies the
next eight samples from the input time series into an eightelement working
array.
Compute the forward and the inverse transforms
Listing 9 performs a forward DCT on the contents of the eightelement working
array. Then it performs an inverse DCT on the eightsamples of spectral
data produced by the forward transform.
ForwardDCT01.transform(workingArrayIn,realSpect);
InverseDCT01.transform(realSpect,workingArrayOut);
Listing 9

Concatenate the output timeseries segments
Listing 10 copies the eight samples of output timeseries data produced by
the inverse transform into the next eight samples of the array designated to
hold the final output. This concatenates the eightsample segments into
the time series shown in the bottom graph in Figure 4.
System.arraycopy(workingArrayOut,
0,
timeDataOut,
segmentCnt,
8);
segmentCnt += 8;
}//end while
}//end constructor
Listing 10

Then Listing 10 increments the segment counter by 8 and control is transferred
back to the top of the while loop shown in Listing 8.
Listing 10 also signals the end of the constructor.
The remaining code
As before, the remaining code in the class named Dsp044 consists of six methods, which are required by the interface named GraphIntfc01. The
purpose of these methods is to plot the data contained in two arrays, producing
the graphs shown in Figure 4. You can view the
methods in Listing 15.
Run the Programs
I encourage you to copy, compile, and execute the code from the listings in
the section entitled Complete Program
Listings. Experiment with the code, making
changes and observing the results of your changes. For example, as one
experiment you can see what
happens if you make changes to the computed argument for the cosine function in
either the forward or the inverse transform.
Run under control of Graph03
Dsp042, Dsp043, and Dsp044 must all be run under the control of the program named
Graph03.
(The program named Graph03 is a plotting program. See the
earlier lesson entitled
Spectrum
Analysis using Java, Sampling Frequency, Folding Frequency, and the FFT
Algorithm, which was the first lesson in which the plotting program
named Graph03 was used. Also see Graph01, which was a predecessor of
Graph03, in the lesson
entitled
Plotting Engineering and Scientific Data using Java.)
The
source code for Graph03 is provided in Listing 16.
The source code
for the interface named GraphIntfc01, which is required by these
programs,
is provided in Listing 17.
Running the programs
To run these programs, first compile the programs and then enter one of the following statements at the command prompt.
java Graph03 Dsp042
java Graph03 Dsp042
java Graph03 Dsp044 Support classes
You will need some support classes in order to run these programs. In
those cases where the source code for a required support class is not included in this lesson, you should be
able to find the source code in the lessons referred to in the
References section.
You can also find the source code by searching for it on
Google. For example, searching
for the following keywords on Google will identify the earlier lesson entitled
Spectrum Analysis
using Java, Sampling Frequency, Folding Frequency, and the FFT Algorithm
containing the source code for the class named ForwardRealToComplex01.
java Baldwin "ForwardRealToComplex01.java"
I introduced you to the basics of the Discrete Cosine
Transform (DCT) by:
 Explaining some of the underlying theory behind the transform.
 Demonstrating the use of the transform in two onedimensional cases.
The next lesson will explain the use of the twodimensional Discrete Cosine
Transform and will illustrate its use to transform images into the wavenumber
domain and back into the space or image domain.
Future lessons in this series will explain the inner workings behind several
data and image compression schemes, including the following:
 Runlength data encoding
 GIF image compression
 JPEG image compression
General
2440 Understanding the LempelZiv Data Compression Algorithm in Java
2442 Understanding the Huffman Data Compression Algorithm in Java
1468
Plotting Engineering and Scientific Data using Java
1478 Fun
with Java, How and Why Spectral Analysis Works
1482
Spectrum Analysis using Java, Sampling Frequency, Folding Frequency, and the FFT
Algorithm
1483
Spectrum Analysis using Java, Frequency Resolution versus Data Length
1484
Spectrum Analysis using Java, Complex Spectrum and Phase Angle
1485
Spectrum Analysis using Java, Forward and Inverse Transforms, Filtering in the
Frequency Domain
1486 Fun
with Java, Understanding the Fast Fourier Transform (FFT) Algorithm
1489
Plotting 3D Surfaces using Java
1490 2D
Fourier Transforms using Java
1491 2D
Fourier Transforms using Java, Part 2
Discrete Cosine Transform equations
Discrete
cosine transform  Wikipedia, the free encyclopedia
The Data Analysis Briefbook 
Discrete Cosine
Transform
National Taiwan University 
DSP Group 
Discrete Cosine Transform
Complete Program Listings
Complete listings of the programs discussed in this lesson are provided in the
following listings:Listing 11
/* File Dsp043.java
Copyright 2006, R.G.Baldwin
Revised 01/05/06
The purpose of this program is to demonstrate that the
imaginary part of the Fourier transform of a symmetrical
time series is all zeros if the origin is properly located.
Illustrates forward and inverse Fourier transforms on a
symmetrical time series using DFT algorithms.
Passes resulting real and complex parts to inverse Fourier
transform program to reconstruct the original time series.
Run with Graph03. Enter the following to run the program:
java Graph03 Dsp043
Exection of this program requires access to the following
class files:
Dsp043.class
ForwardRealToComplex01.class
Graph01.class
GraphIntfc01.class
GUI$MyCanvas.class
GUI.class
InverseComplexToReal01.class
Tested using J2SE 5.0 under WinXP.
**********************************************************/
import java.util.*;
class Dsp043 implements GraphIntfc01{
final double pi = Math.PI;
int len = 300;
double[] timeDataIn = new double[len];
double[] realSpect = new double[len];
double[] imagSpect = new double[len];
double[] angle = new double[len];//unused
double[] magnitude = new double[len];
double[] timeDataOut = new double[len];
int zero = 0;
public Dsp043(){//constructor
//Create raw waveform consisting of mirror image
// sinusoidal segments with a sample having a value
// of 0 in the center.
//Set shift to a nonzero value to cause the imaginary
// part of the transform to be non zero.
for(int x = 0;x < len/4;x++){
timeDataIn[len/2 + x + 1] =
80.0 * Math.sin(2*pi*(x)*1.0/20.0);
timeDataIn[len/2  x  1] =
timeDataIn[len/2 + x + 1];
}//end for loop
//Compute DFT of the time data and save it in
// the output arrays.
ForwardRealToComplex01.transform(timeDataIn,
realSpect,
imagSpect,
angle,
magnitude,
zero,
0.0,
1.0);
//Compute inverse DFT of the spectal data and
// save output time data in output array
InverseComplexToReal01.inverseTransform(realSpect,
imagSpect,
timeDataOut);
}//end constructor
////
//The following six methods are required by the interface
// named GraphIntfc01.
public int getNmbr(){
//Return number of curves to plot. Must not exceed 5.
return 5;
}//end getNmbr
////
public double f1(double x){
int index = (int)Math.round(x);
if(index < 0  index > timeDataIn.length1){
return 0;
}else{
return timeDataIn[index];
}//end else
}//end function
////
public double f2(double x){
int index = (int)Math.round(x);
if(index < 0  index > realSpect.length1){
return 0;
}else{
//scale for convenient viewing
return 5*realSpect[index];
}//end else
}//end function
////
public double f3(double x){
int index = (int)Math.round(x);
if(index < 0  index > imagSpect.length1){
return 0;
}else{
//scale for convenient viewing
return 5*imagSpect[index];
}//end else
}//end function
////
public double f4(double x){
int index = (int)Math.round(x);
if(index < 0  index > magnitude.length1){
return 0;
}else{
//scale for convenient viewing
return 5*magnitude[index];
}//end else
}//end function
////
public double f5(double x){
int index = (int)Math.round(x);
if(index < 0 
index > timeDataOut.length1){
return 0;
}else{
return timeDataOut[index];
}//end else
}//end function
}//end sample class Dsp043
Listing 11

Listing 12
/*File ForwardDCT01.java
Copyright 2006, R.G.Baldwin
Rev 01/03/06
The static method named transform performs a forward
Discreet Cosine Transform (DCT) on an incoming time series
and returns the DCT spectrum.
See http://en.wikipedia.org/wiki/Discrete_cosine_transform
#DCTII and http://rkb.home.cern.ch/rkb/AN16pp/node61.html
for background on the DCT.
This formulation is from
http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/
coding/transform/dct.html
Incoming parameters are:
double[] x  incoming real data
double[] y  outgoing real data
Tested using J2SE 5.0 under WinXP. Requires J2SE 5.0 or
later due to the use of static import of Math class.
**********************************************************/
import static java.lang.Math.*;
public class ForwardDCT01{
public static void transform(double[] x,
double[] y){
int N = x.length;
//Outer loop interates on frequency values.
for(int k=0; k < N;k++){
double sum = 0.0;
//Inner loop iterates on timeseries points.
for(int n=0; n < N; n++){
double arg = PI*k*(2.0*n+1)/(2*N);
double cosine = cos(arg);
double product = x[n]*cosine;
sum += product;
}//end inner loop
double alpha;
if(k == 0){
alpha = 1.0/sqrt(2);
}else{
alpha = 1;
}//end else
y[k] = sum*alpha*sqrt(2.0/N);
}//end outer loop
}//end transform method
////
}//end class ForwardDCT01
Listing 12

Listing 13
/*File InverseDCT01.java
Copyright 2006, R.G.Baldwin
Rev 01/03/06
The static method named transform performs an inverse
Discreet Cosine Transform (DCT) on an incoming DCT
spectrum and returns the DCT time series.
See http://en.wikipedia.org/wiki/Discrete_cosine_transform
#DCTII and http://rkb.home.cern.ch/rkb/AN16pp/node61.html
for background on the DCT.
This formulation is from
http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/
coding/transform/dct.html
Incoming parameters are:
double[] y  incoming real data
double[] x  outgoing real data
Tested using J2SE 5.0 under WinXP. Requires J2SE 5.0 or
later due to the use of static import of Math class.
**********************************************************/
import static java.lang.Math.*;
public class InverseDCT01{
public static void transform(double[] y,
double[] x){
int N = y.length;
//Outer loop interates on time values.
for(int n=0; n < N;n++){
double sum = 0.0;
//Inner loop iterates on frequency values
for(int k=0; k < N; k++){
double arg = PI*k*(2.0*n+1)/(2*N);
double cosine = cos(arg);
double product = y[k]*cosine;
double alpha;
if(k == 0){
alpha = 1.0/sqrt(2);
}else{
alpha = 1;
}//end else
sum += alpha * product;
}//end inner loop
x[n] = sum * sqrt(2.0/N);
}//end outer loop
}//end transform method
////
}//end class InverseDCT01
Listing 13

Listing 14
/* File Dsp042.java
Copyright 2006, R.G.Baldwin
Revised 01/05/06
Note: Dsp044 will investigate the impact of breaking the
time series into eightsample segments to be consistent
with the 8x8 blocks in JPEG.
Illustrates the application of forward and inverse Discrete
Cosine Transform (DCT) to three different waveforms. Very
similar to Dsp035, which applies full forward and inverse
Fourier transforms to the same three waveforms
See http://en.wikipedia.org/wiki/Discrete_cosine_transform
#DCTII, http://rkb.home.cern.ch/rkb/AN16pp/node61.html,
and http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/
coding/transform/dct.html fortechnical background
information on the Discrete Cosine Transform.
Shows a plot of the input time series, the DCT spectrum,
and the output time series resulting from the inverse DCT.
Run with Graph03. Enter the following to run the program:
java Graph03 Dsp042
Execution of this program requires access to the following
class files:
Dsp042.class
ForwardDCT01.class
Graph01.class
GraphIntfc01.class
GUI$MyCanvas.class
GUI.class
InverseDCT01.class
Tested using J2SE 5.0 under WinXP. Requires J2SE 5.0 or
later due to the use of static import of Math class.
**********************************************************/
import java.util.*;
import static java.lang.Math.*;
class Dsp042 implements GraphIntfc01{
int len = 256;
double[] timeDataIn = new double[len];
double[] realSpect = new double[len];
double[] timeDataOut = new double[len];
int zero = 0;
public Dsp042(){//constructor
//Create the raw data pulses
timeDataIn[0] = 0;
timeDataIn[1] = 50;
timeDataIn[2] = 75;
timeDataIn[3] = 80;
timeDataIn[4] = 75;
timeDataIn[5] = 50;
timeDataIn[6] = 25;
timeDataIn[7] = 0;
timeDataIn[8] = 25;
timeDataIn[9] = 50;
timeDataIn[10] = 75;
timeDataIn[11] = 80;
timeDataIn[12] = 60;
timeDataIn[13] = 40;
timeDataIn[14] = 26;
timeDataIn[15] = 17;
timeDataIn[16] = 11;
timeDataIn[17] = 8;
timeDataIn[18] = 5;
timeDataIn[19] = 3;
timeDataIn[20] = 2;
timeDataIn[21] = 1;
timeDataIn[240] = 80;
timeDataIn[241] = 80;
timeDataIn[242] = 80;
timeDataIn[243] = 80;
timeDataIn[244] = 80;
timeDataIn[245] = 80;
timeDataIn[246] = 80;
timeDataIn[247] = 80;
timeDataIn[248] = 80;
timeDataIn[249] = 80;
timeDataIn[250] = 80;
timeDataIn[251] = 80;
timeDataIn[252] = 80;
timeDataIn[253] = 80;
timeDataIn[254] = 80;
timeDataIn[255] = 80;
//Create raw data sinusoid
for(int x = len/3;x < 3*len/4;x++){
timeDataIn[x] = 80.0 * Math.sin(
2*PI*(x)*1.0/20.0);
}//end for loop
//Compute forward DCT of the time data and save it in
// the output array.
ForwardDCT01.transform(timeDataIn,realSpect);
//Compute inverse DCT of the time data and save it in
// the output array.
InverseDCT01.transform(realSpect,timeDataOut);
}//end constructor
////
//The following six methods are required by the interface
// named GraphIntfc01.
public int getNmbr(){
//Return number of curves to plot. Must not exceed 5.
return 3;
}//end getNmbr
////
public double f1(double x){
int index = (int)round(x);
if(index < 0  index > timeDataIn.length1){
return 0;
}else{
return timeDataIn[index];
}//end else
}//end function
////
public double f2(double x){
int index = (int)round(x);
if(index < 0  index > realSpect.length1){
return 0;
}else{
//Scale for convenient viewing
return 0.22*realSpect[index];
}//end else
}//end function
////
public double f3(double x){
int index = (int)round(x);
if(index < 0  index > timeDataOut.length1){
return 0;
}else{
return timeDataOut[index];
}//end else
}//end function
////
public double f4(double x){
return 0;
}//end function
////
public double f5(double x){
return 0;
}//end function
////
}//end sample class Dsp042
Listing 14

Listing 15
/* File Dsp044.java
Copyright 2006, R.G.Baldwin
Revised 01/03/06
Update of Dsp042 to investigate the impact of breaking the
time series into eightsample segments in order to be
consistent with the 8x8 blocks in JPEG. There appears to
be no impact. The output is a very good replica of the
input.
Illustrates the application of forward and inverse Discrete
Cosine Transform (DCT) to three different waveforms. Very
similar to Dsp035, which applies full forward and inverse
Fourier transforms to the same three waveforms
See http://en.wikipedia.org/wiki/Discrete_cosine_transform
#DCTII, http://rkb.home.cern.ch/rkb/AN16pp/node61.html,
and http://www.cmlab.csie.ntu.edu.tw/cml/dsp/training/
coding/transform/dct.html fortechnical background
information on the Discrete Cosine Transform.
Shows a plot of the input time series, and the output
time series resulting from the inverse DCT. Can't show
the spectrum because a new spectrum is produced every
eight samples.
Run with Graph03. Enter the following to run the program:
java Graph03 Dsp044
Execution of this program requires access to the following
class files:
Dsp044.class
ForwardDCT01.class
Graph01.class
GraphIntfc01.class
GUI$MyCanvas.class
GUI.class
InverseDCT01.class
Tested using J2SE 5.0 under WinXP. Requires J2SE 5.0 or
later due to the use of static import of Math class.
**********************************************************/
import java.util.*;
import static java.lang.Math.*;
class Dsp044 implements GraphIntfc01{
int len = 256;
double[] timeDataIn = new double[len];
double[] timeDataOut = new double[len];
int zero = 0;
public Dsp044(){//constructor
//Create the raw data pulses
timeDataIn[0] = 0;
timeDataIn[1] = 50;
timeDataIn[2] = 75;
timeDataIn[3] = 80;
timeDataIn[4] = 75;
timeDataIn[5] = 50;
timeDataIn[6] = 25;
timeDataIn[7] = 0;
timeDataIn[8] = 25;
timeDataIn[9] = 50;
timeDataIn[10] = 75;
timeDataIn[11] = 80;
timeDataIn[12] = 60;
timeDataIn[13] = 40;
timeDataIn[14] = 26;
timeDataIn[15] = 17;
timeDataIn[16] = 11;
timeDataIn[17] = 8;
timeDataIn[18] = 5;
timeDataIn[19] = 3;
timeDataIn[20] = 2;
timeDataIn[21] = 1;
timeDataIn[240] = 80;
timeDataIn[241] = 80;
timeDataIn[242] = 80;
timeDataIn[243] = 80;
timeDataIn[244] = 80;
timeDataIn[245] = 80;
timeDataIn[246] = 80;
timeDataIn[247] = 80;
timeDataIn[248] = 80;
timeDataIn[249] = 80;
timeDataIn[250] = 80;
timeDataIn[251] = 80;
timeDataIn[252] = 80;
timeDataIn[253] = 80;
timeDataIn[254] = 80;
timeDataIn[255] = 80;
//Create raw data sinusoid
for(int x = len/3;x < 3*len/4;x++){
timeDataIn[x] = 80.0 * Math.sin(
2*PI*(x)*1.0/20.0);
}//end for loop
double[] workingArrayIn = new double[8];
double[] workingArrayOut = new double[8];
double[] realSpect = new double[8];
int segmentCnt = 0;
//Compute forward and inverse DCTs on the input signal,
// eight samples at a time. Concatenate the output
// segments from the DCT to represent the output
// signal.
while((segmentCnt + 8) <= len){
System.arraycopy(timeDataIn,
segmentCnt,
workingArrayIn,
0,
8);
//Compute forward DCT of the time data and save it in
// the output array.
ForwardDCT01.transform(workingArrayIn,realSpect);
//Compute inverse DCT of the time data and save it in
// the output array.
InverseDCT01.transform(realSpect,workingArrayOut);
//Concatenate the new output with the old output.
System.arraycopy(workingArrayOut,
0,
timeDataOut,
segmentCnt,
8);
segmentCnt += 8;
}//end while
}//end constructor
////
//The following six methods are required by the interface
// named GraphIntfc01.
public int getNmbr(){
//Return number of curves to plot. Must not exceed 5.
return 2;
}//end getNmbr
////
public double f1(double x){
int index = (int)round(x);
if(index < 0  index > timeDataIn.length1){
return 0;
}else{
return timeDataIn[index];
}//end else
}//end function
////
public double f2(double x){
int index = (int)round(x);
if(index < 0  index > timeDataOut.length1){
return 0;
}else{
return timeDataOut[index];
}//end else
}//end function
////
public double f3(double x){
return 0;
}//end function
////
public double f4(double x){
return 0;
}//end function
////
public double f5(double x){
return 0;
}//end function
////
}//end class Dsp044
Listing 15

Listing 16
/* File Graph03.java
Copyright 2002, R.G.Baldwin
This program is very similar to Graph01
except that it has been modified to
allow the user to manually resize and
replot the frame.
Note: This program requires access to
the interface named GraphIntfc01.
This is a plotting program. It is
designed to access a class file, which
implements GraphIntfc01, and to plot up
to five functions defined in that class
file. The plotting surface is divided
into the required number of equally
sized plotting areas, and one function
is plotted on cartesian coordinates in
each area.
The methods corresponding to the
functions are named f1, f2, f3, f4,
and f5.
The class containing the functions must
also define a method named
getNmbr(), which takes no parameters
and returns the number of functions to
be plotted. If this method returns a
value greater than 5, a
NoSuchMethodException will be thrown.
Note that the constructor for the class
that implements GraphIntfc01 must not
require any parameters due to the
use of the newInstance method of the
Class class to instantiate an object
of that class.
If the number of functions is less
than 5, then the absent method names
must begin with f5 and work down toward
f1. For example, if the number of
functions is 3, then the program will
expect to call methods named f1, f2,
and f3. It is OK for the absent
methods to be defined in the class.
They simply won't be invoked.
The plotting areas have alternating
white and gray backgrounds to make them
easy to separate visually.
All curves are plotted in black. A
cartesian coordinate system with axes,
tic marks, and labels is drawn in red
in each plotting area.
The cartesian coordinate system in each
plotting area has the same horizontal
and vertical scale, as well as the
same tic marks and labels on the axes.
The labels displayed on the axes,
correspond to the values of the extreme
edges of the plotting area.
The program also compiles a sample
class named junk, which contains five
methods and the method named getNmbr.
This makes it easy to compile and test
this program in a standalone mode.
At runtime, the name of the class that
implements the GraphIntfc01 interface
must be provided as a commandline
parameter. If this parameter is
missing, the program instantiates an
object from the internal class named
junk and plots the data provided by
that class. Thus, you can test the
program by running it with no
commandline parameter.
This program provides the following
text fields for user input, along with
a button labeled Graph. This allows
the user to adjust the parameters and
replot the graph as many times with as
many plotting scales as needed:
xMin = minimum xaxis value
xMax = maximum xaxis value
yMin = minimum yaxis value
yMax = maximum yaxis value
xTicInt = tic interval on xaxis
yTicInt = tic interval on yaxis
xCalcInc = calculation interval
The user can modify any of these
parameters and then click the Graph
button to cause the five functions
to be replotted according to the
new parameters.
Whenever the Graph button is clicked,
the event handler instantiates a new
object of the class that implements
the GraphIntfc01 interface. Depending
on the nature of that class, this may
be redundant in some cases. However,
it is useful in those cases where it
is necessary to refresh the values of
instance variables defined in the
class (such as a counter, for example).
Tested using JDK 1.4.0 under Win 2000.
This program uses constants that were
first defined in the Color class of
v1.4.0. Therefore, the program
requires v1.4.0 or later to compile and
run correctly.
**************************************/
import java.awt.*;
import java.awt.event.*;
import java.awt.geom.*;
import javax.swing.*;
import javax.swing.border.*;
class Graph03{
public static void main(
String[] args)
throws NoSuchMethodException,
ClassNotFoundException,
InstantiationException,
IllegalAccessException{
if(args.length == 1){
//pass commandline paramater
new GUI(args[0]);
}else{
//no commandline parameter given
new GUI(null);
}//end else
}// end main
}//end class Graph03 definition
//===================================//
class GUI extends JFrame
implements ActionListener{
//Define plotting parameters and
// their default values.
double xMin = 0.0;
double xMax = 400.0;
double yMin = 100.0;
double yMax = 100.0;
//Tic mark intervals
double xTicInt = 20.0;
double yTicInt = 20.0;
//Tic mark lengths. If too small
// on xaxis, a default value is
// used later.
double xTicLen = (yMaxyMin)/50;
double yTicLen = (xMaxxMin)/50;
//Calculation interval along xaxis
double xCalcInc = 1.0;
//Text fields for plotting parameters
JTextField xMinTxt =
new JTextField("" + xMin);
JTextField xMaxTxt =
new JTextField("" + xMax);
JTextField yMinTxt =
new JTextField("" + yMin);
JTextField yMaxTxt =
new JTextField("" + yMax);
JTextField xTicIntTxt =
new JTextField("" + xTicInt);
JTextField yTicIntTxt =
new JTextField("" + yTicInt);
JTextField xCalcIncTxt =
new JTextField("" + xCalcInc);
//Panels to contain a label and a
// text field
JPanel pan0 = new JPanel();
JPanel pan1 = new JPanel();
JPanel pan2 = new JPanel();
JPanel pan3 = new JPanel();
JPanel pan4 = new JPanel();
JPanel pan5 = new JPanel();
JPanel pan6 = new JPanel();
//Misc instance variables
int frmWidth = 408;
int frmHeight = 430;
int width;
int height;
int number;
GraphIntfc01 data;
String args = null;
//Plots are drawn on the canvases
// in this array.
Canvas[] canvases;
//Constructor
GUI(String args)throws
NoSuchMethodException,
ClassNotFoundException,
InstantiationException,
IllegalAccessException{
if(args != null){
//Save for use later in the
// ActionEvent handler
this.args = args;
//Instantiate an object of the
// target class using the String
// name of the class.
data = (GraphIntfc01)
Class.forName(args).
newInstance();
}else{
//Instantiate an object of the
// test class named junk.
data = new junk();
}//end else
//Create array to hold correct
// number of Canvas objects.
canvases =
new Canvas[data.getNmbr()];
//Throw exception if number of
// functions is greater than 5.
number = data.getNmbr();
if(number > 5){
throw new NoSuchMethodException(
"Too many functions. "
+ "Only 5 allowed.");
}//end if
//Create the control panel and
// give it a border for cosmetics.
JPanel ctlPnl = new JPanel();
ctlPnl.setLayout(//?rows x 4 cols
new GridLayout(0,4));
ctlPnl.setBorder(
new EtchedBorder());
//Button for replotting the graph
JButton graphBtn =
new JButton("Graph");
graphBtn.addActionListener(this);
//Populate each panel with a label
// and a text field. Will place
// these panels in a grid on the
// control panel later.
pan0.add(new JLabel("xMin"));
pan0.add(xMinTxt);
pan1.add(new JLabel("xMax"));
pan1.add(xMaxTxt);
pan2.add(new JLabel("yMin"));
pan2.add(yMinTxt);
pan3.add(new JLabel("yMax"));
pan3.add(yMaxTxt);
pan4.add(new JLabel("xTicInt"));
pan4.add(xTicIntTxt);
pan5.add(new JLabel("yTicInt"));
pan5.add(yTicIntTxt);
pan6.add(new JLabel("xCalcInc"));
pan6.add(xCalcIncTxt);
//Add the populated panels and the
// button to the control panel with
// a grid layout.
ctlPnl.add(pan0);
ctlPnl.add(pan1);
ctlPnl.add(pan2);
ctlPnl.add(pan3);
ctlPnl.add(pan4);
ctlPnl.add(pan5);
ctlPnl.add(pan6);
ctlPnl.add(graphBtn);
//Create a panel to contain the
// Canvas objects. They will be
// displayed in a onecolumn grid.
JPanel canvasPanel = new JPanel();
canvasPanel.setLayout(//?rows,1 col
new GridLayout(0,1));
//Create a custom Canvas object for
// each function to be plotted and
// add them to the onecolumn grid.
// Make background colors alternate
// between white and gray.
for(int cnt = 0;
cnt < number; cnt++){
switch(cnt){
case 0 :
canvases[cnt] =
new MyCanvas(cnt);
canvases[cnt].setBackground(
Color.WHITE);
break;
case 1 :
canvases[cnt] =
new MyCanvas(cnt);
canvases[cnt].setBackground(
Color.LIGHT_GRAY);
break;
case 2 :
canvases[cnt] =
new MyCanvas(cnt);
canvases[cnt].setBackground(
Color.WHITE);
break;
case 3 :
canvases[cnt] =
new MyCanvas(cnt);
canvases[cnt].setBackground(
Color.LIGHT_GRAY);
break;
case 4 :
canvases[cnt] =
new MyCanvas(cnt);
canvases[cnt].
setBackground(Color.WHITE);
}//end switch
//Add the object to the grid.
canvasPanel.add(canvases[cnt]);
}//end for loop
//Add the subassemblies to the
// frame. Set its location, size,
// and title, and make it visible.
getContentPane().
add(ctlPnl,"South");
getContentPane().
add(canvasPanel,"Center");
setBounds(0,0,frmWidth,frmHeight);
if(args == null){
setTitle("Graph03, " +
"Copyright 2002, " +
"Richard G. Baldwin");
}else{
setTitle("Graph03/" + args +
" Copyright 2002, " +
"R. G. Baldwin");
}//end else
setVisible(true);
//Set to exit on Xbutton click
setDefaultCloseOperation(
EXIT_ON_CLOSE);
//Guarantee a repaint on startup.
for(int cnt = 0;
cnt < number; cnt++){
canvases[cnt].repaint();
}//end for loop
}//end constructor
////
//This event handler is registered
// on the JButton to cause the
// functions to be replotted.
public void actionPerformed(
ActionEvent evt){
//Reinstantiate the object that
// provides the data
try{
if(args != null){
data = (GraphIntfc01)Class.
forName(args).newInstance();
}else{
data = new junk();
}//end else
}catch(Exception e){
//Known to be safe at this point.
// Otherwise would have aborted
// earlier.
}//end catch
//Set plotting parameters using
// data from the text fields.
xMin = Double.parseDouble(
xMinTxt.getText());
xMax = Double.parseDouble(
xMaxTxt.getText());
yMin = Double.parseDouble(
yMinTxt.getText());
yMax = Double.parseDouble(
yMaxTxt.getText());
xTicInt = Double.parseDouble(
xTicIntTxt.getText());
yTicInt = Double.parseDouble(
yTicIntTxt.getText());
xCalcInc = Double.parseDouble(
xCalcIncTxt.getText());
//Calculate new values for the
// length of the tic marks on the
// axes. If too small on xaxis,
// a default value is used later.
xTicLen = (yMaxyMin)/50;
yTicLen = (xMaxxMin)/50;
//Repaint the plotting areas
for(int cnt = 0;
cnt < number; cnt++){
canvases[cnt].repaint();
}//end for loop
}//end actionPerformed
////
//This is an inner class, which is used
// to override the paint method on the
// plotting surface.
class MyCanvas extends Canvas{
int cnt;//object number
//Factors to convert from double
// values to integer pixel locations.
double xScale;
double yScale;
MyCanvas(int cnt){//save obj number
this.cnt = cnt;
}//end constructor
//Override the paint method
public void paint(Graphics g){
//Get and save the size of the
// plotting surface
width = canvases[0].getWidth();
height = canvases[0].getHeight();
//Calculate the scale factors
xScale = width/(xMaxxMin);
yScale = height/(yMaxyMin);
//Set the origin based on the
// minimum values in x and y
g.translate((int)((0xMin)*xScale),
(int)((0yMin)*yScale));
drawAxes(g);//Draw the axes
g.setColor(Color.BLACK);
//Get initial data values
double xVal = xMin;
int oldX = getTheX(xVal);
int oldY = 0;
//Use the Canvas obj number to
// determine which method to
// invoke to get the value for y.
switch(cnt){
case 0 :
oldY = getTheY(data.f1(xVal));
break;
case 1 :
oldY = getTheY(data.f2(xVal));
break;
case 2 :
oldY = getTheY(data.f3(xVal));
break;
case 3 :
oldY = getTheY(data.f4(xVal));
break;
case 4 :
oldY = getTheY(data.f5(xVal));
}//end switch
//Now loop and plot the points
while(xVal < xMax){
int yVal = 0;
//Get next data value. Use the
// Canvas obj number to
// determine which method to
// invoke to get the value for y.
switch(cnt){
case 0 :
yVal =
getTheY(data.f1(xVal));
break;
case 1 :
yVal =
getTheY(data.f2(xVal));
break;
case 2 :
yVal =
getTheY(data.f3(xVal));
break;
case 3 :
yVal =
getTheY(data.f4(xVal));
break;
case 4 :
yVal =
getTheY(data.f5(xVal));
}//end switch1
//Convert the xvalue to an int
// and draw the next line segment
int x = getTheX(xVal);
g.drawLine(oldX,oldY,x,yVal);
//Increment along the xaxis
xVal += xCalcInc;
//Save end point to use as start
// point for next line segment.
oldX = x;
oldY = yVal;
}//end while loop
}//end overridden paint method
////
//Method to draw axes with tic marks
// and labels in the color RED
void drawAxes(Graphics g){
g.setColor(Color.RED);
//Lable left xaxis and bottom
// yaxis. These are the easy
// ones. Separate the labels from
// the ends of the tic marks by
// two pixels.
g.drawString("" + (int)xMin,
getTheX(xMin),
getTheY(xTicLen/2)2);
g.drawString("" + (int)yMin,
getTheX(yTicLen/2)+2,
getTheY(yMin));
//Label the right xaxis and the
// top yaxis. These are the hard
// ones because the position must
// be adjusted by the font size and
// the number of characters.
//Get the width of the string for
// right end of xaxis and the
// height of the string for top of
// yaxis
//Create a string that is an
// integer representation of the
// label for the right end of the
// xaxis. Then get a character
// array that represents the
// string.
int xMaxInt = (int)xMax;
String xMaxStr = "" + xMaxInt;
char[] array = xMaxStr.
toCharArray();
//Get a FontMetrics object that can
// be used to get the size of the
// string in pixels.
FontMetrics fontMetrics =
g.getFontMetrics();
//Get a bounding rectangle for the
// string
Rectangle2D r2d =
fontMetrics.getStringBounds(
array,0,array.length,g);
//Get the width and the height of
// the bounding rectangle. The
// width is the width of the label
// at the right end of the
// xaxis. The height applies to
// all the labels, but is needed
// specifically for the label at
// the top end of the yaxis.
int labWidth =
(int)(r2d.getWidth());
int labHeight =
(int)(r2d.getHeight());
//Label the positive xaxis and the
// positive yaxis using the width
// and height from above to
// position the labels. These
// labels apply to the very ends of
// the axes at the edge of the
// plotting surface.
g.drawString("" + (int)xMax,
getTheX(xMax)labWidth,
getTheY(xTicLen/2)2);
g.drawString("" + (int)yMax,
getTheX(yTicLen/2)+2,
getTheY(yMax)+labHeight);
//Draw the axes
g.drawLine(getTheX(xMin),
getTheY(0.0),
getTheX(xMax),
getTheY(0.0));
g.drawLine(getTheX(0.0),
getTheY(yMin),
getTheX(0.0),
getTheY(yMax));
//Draw the tic marks on axes
xTics(g);
yTics(g);
}//end drawAxes
////
//Method to draw tic marks on xaxis
void xTics(Graphics g){
double xDoub = 0;
int x = 0;
//Get the ends of the tic marks.
int topEnd = getTheY(xTicLen/2);
int bottomEnd =
getTheY(xTicLen/2);
//If the vertical size of the
// plotting area is small, the
// calculated tic size may be too
// small. In that case, set it to
// 10 pixels.
if(topEnd < 5){
topEnd = 5;
bottomEnd = 5;
}//end if
//Loop and draw a series of short
// lines to serve as tic marks.
// Begin with the positive xaxis
// moving to the right from zero.
while(xDoub < xMax){
x = getTheX(xDoub);
g.drawLine(x,topEnd,x,bottomEnd);
xDoub += xTicInt;
}//end while
//Now do the negative xaxis moving
// to the left from zero
xDoub = 0;
while(xDoub > xMin){
x = getTheX(xDoub);
g.drawLine(x,topEnd,x,bottomEnd);
xDoub = xTicInt;
}//end while
}//end xTics
////
//Method to draw tic marks on yaxis
void yTics(Graphics g){
double yDoub = 0;
int y = 0;
int rightEnd = getTheX(yTicLen/2);
int leftEnd = getTheX(yTicLen/2);
//Loop and draw a series of short
// lines to serve as tic marks.
// Begin with the positive yaxis
// moving up from zero.
while(yDoub < yMax){
y = getTheY(yDoub);
g.drawLine(rightEnd,y,leftEnd,y);
yDoub += yTicInt;
}//end while
//Now do the negative yaxis moving
// down from zero.
yDoub = 0;
while(yDoub > yMin){
y = getTheY(yDoub);
g.drawLine(rightEnd,y,leftEnd,y);
yDoub = yTicInt;
}//end while
}//end yTics
////
//This method translates and scales
// a double y value to plot properly
// in the integer coordinate system.
// In addition to scaling, it causes
// the positive direction of the
// yaxis to be from bottom to top.
int getTheY(double y){
double yDoub = (yMax+yMin)y;
int yInt = (int)(yDoub*yScale);
return yInt;
}//end getTheY
////
//This method scales a double x value
// to plot properly in the integer
// coordinate system.
int getTheX(double x){
return (int)(x*xScale);
}//end getTheX
////
}//end inner class MyCanvas
//===================================//
}//end class GUI
//===================================//
//Sample test class. Required for
// compilation and standalone
// testing.
class junk implements GraphIntfc01{
public int getNmbr(){
//Return number of functions to
// process. Must not exceed 5.
return 4;
}//end getNmbr
public double f1(double x){
return (x*x*x)/200.0;
}//end f1
public double f2(double x){
return (x*x*x)/200.0;
}//end f2
public double f3(double x){
return (x*x)/200.0;
}//end f3
public double f4(double x){
return 50*Math.cos(x/10.0);
}//end f4
public double f5(double x){
return 100*Math.sin(x/20.0);
}//end f5
}//end sample class junk
Listing 16

Listing 17
/* File GraphIntfc01.java
Copyright 2004, R.G.Baldwin
Rev 5/14/04
This interface must be implemented by classes
whose objects produce data to be plotted by
programs such as Graph03 and Graph06.
Tested using SDK 1.4.2 under WinXP.
************************************************/
public interface GraphIntfc01{
public int getNmbr();
public double f1(double x);
public double f2(double x);
public double f3(double x);
public double f4(double x);
public double f5(double x);
}//end GraphIntfc01
Listing 17

Copyright 2006, Richard G. Baldwin. Reproduction in whole or in part in any
form or medium without express written permission from Richard Baldwin is
prohibited.
About the author
Richard Baldwin is a
college professor (at Austin Community College in Austin, TX) and private
consultant whose primary focus is a combination of Java, C#, and XML. In
addition to the many platform and/or language independent benefits of Java and
C# applications, he believes that a combination of Java, C#, and XML will become
the primary driving force in the delivery of structured information on the Web.
Richard has participated in numerous consulting projects and he
frequently provides onsite training at the hightech companies located in and
around Austin, Texas. He is the author of Baldwin's Programming
Tutorials, which have gained a
worldwide following among experienced and aspiring programmers. He has also
published articles in JavaPro magazine.
In addition to his programming expertise, Richard has many years of
practical experience in Digital Signal Processing (DSP). His first job after he
earned his Bachelor's degree was doing DSP in the Seismic Research Department of
Texas Instruments. (TI is still a world leader in DSP.) In the following
years, he applied his programming and DSP expertise to other interesting areas
including sonar and underwater acoustics.
Richard holds an MSEE degree from Southern Methodist University and has
many years of experience in the application of computer technology to realworld
problems.
Baldwin@DickBaldwin.com
