Java Programming, Notes # 1482
- Preface
- Preview
- Discussion and Sample Code
- Run the Programs
- Summary
- What’s Next?
- Complete Program Listings
Preface
The how and the why of spectral analysis
A previous lesson entitled
Fun with Java,
How and Why Spectral Analysis Works explained how and why spectral
analysis works. An understanding of that lesson is a prerequisite to
understanding this lesson.
Programs to perform spectral analysis
In this lesson I will provide and explain different programs used for performing spectral analysis. The first program
is a very general program that implements a Discrete Fourier Transform (DFT)
algorithm. I will explain this program in detail.
The second program is a less general, but much faster
program that implements a Fast Fourier Transform (FFT) algorithm. I
will defer an explanation of this program until a future lesson. I am
providing it here so that you can use it and compare it with the DFT program in
terms of speed and flexibility.
Fundamental aspects of spectral analysis
I will use the DFT program to illustrate several fundamental aspects of
spectral analysis that center around the sampling frequency and the Nyquist
folding frequency.
Visual illustration of sampling
I will also provide and explain a program that produces a visual illustration
of the impact of the sampling frequency and the Nyquist folding frequency.
Plotting programs
Finally, I will provide, but will not explain two different programs used for
display purposes. These are newer versions of graphics display programs
that I explained in the earlier lesson entitled
Plotting Engineering and Scientific Data using Java.
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 figures and listings 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.
Preview
Before I get into the technical details, here is a preview of the programs
and their purposes that I will present and explain in this lesson:
- Dsp029 – Provides a visual illustration of the impact of the
sampling frequency and the Nyquist folding frequency. - Dsp028 – Driver program for doing spectral analysis using a DFT
algorithm. - ForwardRealToComplex01 – Class that implements the DFT algorithm.
- Dsp030 – Driver program for doing spectral analysis using an FFT
algorithm. - ForwardRealToComplexFFT01 – Class that implements the FFT
algorithm (will defer explanation until a future lesson). - Graph03 – Used to display results of spectral analysis.
(The concepts were explained in an earlier lesson.) - Graph06 – Used to display the impact of sampling frequency and the Nyquist
folding frequency. Also used to display the results of spectral
analysis. (The concepts were explained in an earlier lesson).
Discussion and Sample
Code
This will be a long lesson involving lots of code and lots of explanations,
so fill your cup with java and let’s get started.
Sampling
Frequency and the Nyquist Folding Frequency
I will begin the discussion with the program named Dsp029,
which provides a visual illustration of the impact of the
sampling frequency and the Nyquist folding frequency. A complete listing of
this program is shown in Listing 16 near the end of the lesson.
Display sinusoids
This program generates and displays up to five sinusoids having the same
sampling frequency but having different sinusoidal frequencies and amplitudes.
The program provides a visual illustration of the way in which frequencies above one-half the sampling frequency fold back into the area bounded by zero and
one-half the sampling frequency.
(The frequency at one-half the sampling frequency is known as the Nyquist folding frequency.)
Input parameters
The program gets its input parameters from a file named Dsp029.txt. If that file doesn’t exist in the current directory, the program uses a set of default parameters.
Each parameter value must be stored as characters on a separate line in the file named
Dsp029.txt. The required parameters are shown in Figure 1.
Data length as type int Number of sinusoids as type int. Max value is 5. List of sinusoid frequency values as type double. List of sinusoid amplitude values as type double. Figure 1 |
The length of the two lists
The number of values in each of the lists must
match the value for the number of sinusoids. Also, you must not allow blank
lines at the end of the data in the file.
Frequency value specifications
Each frequency value is specified as a type double value representing a fractional part of the
sampling frequency.
(For example, a double value of 0.5 specifies
one-half the sampling frequency, or the Nyquist folding frequency. A
double value of 2.0 specifies a frequency that is twice the sampling
frequency.)
Figure 2 shows the contents of the file named Dsp029.txt that I used to produce the output
shown in Figure 3. I will discuss that output later.
50.0 5 0.03125 0.0625 0.125 0.25 0.5 90 90 90 90 90 Figure 2 |
The plotting program named Graph06
The plotting program that is used to plot the
output data from this program requires that the
program implement GraphIntfc01. I discussed that interface in the
earlier lesson entitled
Plotting Engineering and Scientific Data using Java. For example,
the plotting program named Graph06 can be used
to plot the data produced by this program. When it is used, the program is
executed and its output is plotted by entering the following at the command
line prompt:
java Graph06 Dsp029
Program output
Figure 3 shows the output produced by running the program named Dsp029
with the parameters shown in Figure 2.
Figure 3 Program output for five sinusoids |
Five horizontal plots
Each of the five horizontal plots in Figure 3 shows a sampled sinusoid.
Each of the vertical bars represents one sample value for a given sinusoid.
If you examine the frequency values in Figure 2 carefully, you will see that
they represent the sampling frequency divided by the factors 32, 16, 8, 4, and
2. Thus, the last frequency value is the Nyquist folding frequency and the
first four frequency values are related to that frequency by multiples of two.
The horizontal plot at the top of Figure 3 is a reasonably well defined
cosine wave. The horizontal plot at the bottom of Figure 3 shows the
result of having exactly two samples per cycle of the sinusoid. Using this
plotting scheme, the sampled sinusoid is represented as a square wave.
Using another plotting
scheme (such as that used in the program named Graph03) the
sampled sinusoid at the bottom would be represented as a triangular wave.
An upper frequency limit
Regardless
of the plotting scheme used, it should be obvious that a set of uniform samples
cannot possibly represent frequencies higher than one-half the sampling frequency
because then there would be less than one sample per cycle of the sinusoid.
The Nyquist folding frequency
Now I will show you why the frequency at one-half the sampling frequency is referred to as the
folding frequency using the new set of frequency values shown in Figure
4.
(Figure 4 shows the values read from the file named Dsp029.txt
and displayed in an improved format.)
In this case, the third
frequency in the list is one-half the sampling frequency, which is the folding
frequency. The two frequencies on either side of that one have values that
are symmetrical about the folding frequency.
Data length: 50 Number sinusoids: 5 Frequencies 0.125 0.25 0.5 0.75 0.875 Amplitudes 90.0 90.0 90.0 90.0 90.0 Figure 4 |
The program output
The five horizontal plots in Figure 5 show the result of running the program
named Dsp029 with the frequencies shown in Figure 4.
Figure 5 Program output for five sinusoids |
The center plot in Figure 5 is the sampled representation of the folding
frequency.
The top two plots in Figure 5 are obviously the sampled
representations of the two lower frequencies specified by the first two
frequencies in Figure 4.
Not so obvious …
However, it is not so obvious that the bottom two plots in Figure 5 are the sampled representations of the two higher
frequencies specified by the last two frequencies in Figure 4. They look
exactly like the top two plots but in reverse order.
Unable to distinguish …
Frequencies above one-half the sampling frequency are not distinguishable by
viewing the sampled data.
In fact, they are converted to lower frequencies by sampling process. The
new lower frequencies fold around a point in
the frequency spectrum given by one-half the sampling frequency. That is
why it is called the folding frequency. (In 1933, this frequency was named after
scientist Harry
Nyquist.)
One more example
Let’s look at one more example of plotted sinusoids. Consider the frequency values shown in
Figure 6. The second and third frequencies are symmetrical about the
sampling frequency. The fourth and fifth frequencies are symmetrical about
twice the sampling frequency. The first frequency value is
the same distance from zero as the other four frequencies are from the sampling
frequency and twice the sampling frequency.
Data length: 50 Number sinusoids: 5 Frequencies 0.1 0.9 1.1 1.9 2.1 Amplitudes 90.0 90.0 90.0 90.0 90.0 Figure 6 |
The program output
Figure 7 shows the output produced by running the program named Dsp029
with the frequency
parameters specified by Figure 6.
Figure 7 Program output for five sinusoids |
The sinusoids are indistinguishable
Although the actual frequencies of the five cosine functions are
significantly different, once they are sampled, they are indistinguishable.
The sampling process converts the actual frequencies to new frequencies
that not only fold around one-half the sampling frequency, they also fold around
all multiples of one-half the sampling frequency.
Enough talk, let’s see some code
The program named Dsp029 is provided in Listing 16 near the end of the
lesson. I will discuss the program in fragments, beginning with the fragment shown in Listing 1.
class Dsp029 implements GraphIntfc01{ final double pi = Math.PI;//for simplification //Begin default parameters int len = 400;//data length int numberSinusoids = 5; //Frequencies of the sinusoids double[] freq = {0.1,0.25,0.5,0.75,0.9}; //Amplitudes of the sinusoids double[] amp = {75,75,75,75,75}; //End default parameters Listing 1 |
The code in Listing 1 defines a convenience constant representing the value
of pi and then defines the set of default parameters that will be used by the
program in the event that the file named Dsp029.txt does not exist in the
current directory.
Create array objects to hold sinusoidal data
The code in Listing 2 creates five array objects that will be populated with
sinusoidal data.
double[] data1 = new double[len]; double[] data2 = new double[len]; double[] data3 = new double[len]; double[] data4 = new double[len]; double[] data5 = new double[len]; Listing 2 |
Get the parameters
The constructor begins in Listing 3. The code in this fragment invokes
the method named getParameters to read the parameters from the file named
Dsp029.txt.
public Dsp029(){//constructor if(new File("Dsp029.txt").exists()){ getParameters(); }//end if Listing 3 |
Before invoking the getParameters method, however, the program invokes
the exists method of the File class to confirm that the file
actually exists. If the file doesn’t exist, the call to getParameters is skipped,
causing the default parameters defined in Listing 1 to be used instead.
The getParameters method
The getParameters method is straightforward, so I won’t discuss it in
detail. You can view it in Listing 16. Suffice it to say that the
method reads the input parameters from the disk file and writes their values into
the variables declared in Listing 1, overwriting the default values stored in
those variables.
In addition, the getParameters method displays the values read from the disk file in the
format shown in Figure 4 and Figure 6.
Create the sinusoidal data
For simplicity, this program always generates five sinusoids, even if fewer than five were requested as the input parameter
value for numberSinusoids. In that case, the extra sinusoids are generated using default values and
are simply ignored when the sinusoids are plotted.
The code fragment in Listing 4 creates the sinusoidal data for each of the
five specified frequencies and saves that data in the array objects that were
created in Listing 2.
for(int n = 0;n < len;n++){ data1[n] = amp[0]*Math.cos(2*pi*n*freq[0]); data2[n] = amp[1]*Math.cos(2*pi*n*freq[1]); data3[n] = amp[2]*Math.cos(2*pi*n*freq[2]); data4[n] = amp[3]*Math.cos(2*pi*n*freq[3]); data5[n] = amp[4]*Math.cos(2*pi*n*freq[4]); }//end for loop }//end constructor Listing 4 |
The end of the constructor
Listing 4 also signals the end of the constructor. When the constructor
terminates, an object of the Dsp029 class has been instantiated. The
five arrays shown in Listing 4 have been populated with sinusoidal data
according to the parameters read from the file named Dsp029.txt or
according to the
default values of the parameters shown in Listing 1.
Plotting the sinusoidal data
In order to better understand what is going on in the plotting process, it
would be helpful for you to read the previous lesson entitled
Plotting Engineering and Scientific Data using Java. However, assuming that
you don’t have the time to do that, I will provide a very brief explanation as to how the
plotting programs work.
Using Graph06 to plot the sinusoidal data
To use the plotting program named Graph06 to plot the sinusoidal data, I needed to do the following:
- Define and compile the program named Dsp029, implementing the interface named GraphIntfc01.
The definition of that interface is shown in Listing 5 - Start the plotting program named Graph06 running by entering
java Graph06 Dsp029 at the command line prompt.
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 5 |
What does this do?
When executed in this manner, the program named Graph06 instantiates
an object of the class named Dsp029 and then invokes the interface
methods on that object to obtain the data to be plotted.
In this case, the constructor for the Dsp029 class populates the five
array objects with sinusoidal data. The subsequent invocation of the
interface methods by the program named Graph06 causes that sinusoidal
data to be retrieved and plotted by Graph06.
The GraphIntfc01 interface methods
A brief description of each of the interface methods is provided in the
following sections.
The getNmbr method
Plotting programs based on GraphIntfc01 can be used to plot any number
of functions from one to five.
The method named getNmbr must return an integer value between 1 and 5
that specifies the number of functions to be plotted. The plotting program uses
that value to divide the total plotting surface into the specified number of
plotting areas, and plots each of the functions named f1 through fn
in one of those plotting areas.
The methods named f1, f2, f3, f4, and f5
As you can see in Listing 5, each of these methods receives a double
value as an incoming parameter and returns a double value. In essence,
each of these methods receives a value for the horizontal coordinate x
and returns the corresponding value for the vertical coordinate y.
One plotting area per method
Each of these methods provides the data to be plotted in one plotting area.
The method named f1 provides the data for the top plotting area; the
method named f2 provides the data for the first plotting area down from
the top, and so forth.
(For example, if the getNmbr method returns a value of 4, the
method named f5 will never be invoked. If getNmbr returns 5,
the method named f5 will be invoked to provide the data for the
bottom plotting area.)
How does it work?
Each plotting area contains a horizontal axis. The plotting program moves
across the horizontal axis in each plotting area one step at a time (moving
in incremental steps equal to the plotting parameter named xCalcInc, which you
will find if you examine the code for Graph06).
At each step along the way, the plotting program invokes the method
associated with that plotting area, (f1, f2, etc.), passing the
horizontal position as a parameter to the method.
The value returned by the method is assumed to be the vertical value
associated with that horizontal position, and that is the vertical value that is
plotted for that horizontal position.
Doesn’t know and doesn’t care
The plotting program doesn’t know, and doesn’t care how the interface method decides on
the value to return for each value that it receives as an incoming parameter.
The plotting program simply invokes the methods to get the data, and then plots
the returned values.
Computed "on the fly"
For example, the returned values could be computed and returned "on the
fly," as was the case in the example program named Graph01Demo, which
I explained in the earlier lesson entitled
Plotting Engineering and Scientific Data using Java.
Returned from an array
On the other hand, the values could have been computed earlier and saved in
an array. That is the case with all the programs that I will explain in
this lesson.
From a disk file, a database, the Internet, etc.
The returned values could be read from a disk file, obtained from a database
on another computer, or obtained from any other source such as another computer
on the Internet.
All that matters is that when the plotting program invokes one of the five
methods named f1 through f5, passing a double value as a
parameter, it expects to receive a double value as a return value, and it
will plot the value that it receives.
The getNmbr method
The getNmbr method for the class named Dsp029 is shown in
Listing 6.
public int getNmbr(){ //Return number of functions to // process. Must not exceed 5. return numberSinusoids; }//end getNmbr Listing 6 |
This is a very simple method. It returns the value stored in the
variable named numberSinusoids. This variable may contain the
default value established by Listing 1, or may contain the value read from the
file named Dsp029.txt by the method named getParameters.
The method named f1
The code for the method named f1 is shown in Listing 7.
public double f1(double x){ int index = (int)Math.round(x); if(index < 0 || index > data1.length-1){ return 0; }else{ return data1[index]; }//end else }//end function Listing 7 |
Note that there is not a one-to-one correspondence between horizontal
coordinate values and pixels on the screen. For example, it may sometimes
be necessary to plot 90 values across an area of the screen containing 110
pixels. The plotting program must interpolate properly to deal
with that issue. Therefore, the plotting program deals with horizontal
coordinates at type double and then converts those coordinate values to
integer pixel values when the time comes to actually draw the material on the
screen.
Round from double to int
The method named f1 receives an incoming horizontal coordinate value
as type double. It rounds that value to the nearest value of type
long and casts it to type int to determine the index value to use
when retrieving the corresponding vertical value from the array object.
If the index value is outside the bounds of the array, the method simply
returns a value of zero. Otherwise, it uses the index value to return the
value stored in the array object at that index.
The remaining interface methods
The remaining four interface methods are identical to the method named f1,
except that each method returns data values stored in a different array object.
Therefore, I won’t discuss those methods.
That’s it for Dsp029
That’s about it for the program named Dsp029. If you understand
this program, you are well ahead of the game. The overall structure for
the programs named Dsp028 and Dsp030 are very similar to the
structure for Dsp029. The big difference is the manner in which
they populate the array objects with the data that is to be plotted.
Instead of simply plotting sinusoids, they perform spectral analysis on
sinusoids and provide the results of the spectral analysis to be plotted.
Using the interface named GraphIntfc01
As you learned earlier, this is a very simple interface. However,
because the class named Dsp029 implements the interface, the interface
definition file must be in the same directory as the source file for Dsp029
in order to successfully compile Dsp029. Therefore, I have provided
a complete listing of GraphIntfc01 in Listing 17 near the end of the
lesson.
The program named Graph06
A complete listing of the program named Graph06 is provided in Listing
18 near the end of the lesson. This is simply a newer version of graphics
display programs that I explained in the earlier lesson entitled
Plotting Engineering and Scientific Data using Java. Therefore, I
won’t repeat that explanation here. The comments at the beginning and
spread throughout the program provide considerable information about it.
Operational aspects of Graph06
However, an explanation of the operational aspects of the program will be
useful here. You can use this program to display the output produced by
Dsp029 by entering the following at the command line prompt:
java Graph06 Dsp029
As you saw in Figure
3 and other previous figures, this program provides the following text fields for user input, along with a button labeled
Graph:
- xMin = minimum x-axis value
- xMax = maximum x-axis value
- yMin = minimum y-axis value
- yMax = maximum y-axis value
- xTicInt = tic interval on x-axis
- yTicInt = tic interval on y-axis
- xCalcInc = calculation interval
These text fields make it possible for you to adjust the plotting parameters and to re-plot the graphs
as many times as needed.
You can modify any of these parameters and then click the Graph button to cause the five functions to be re-plotted according to the new
plotting parameters.
Spectral Analysis using a
DFT Algorithm
Now that you have a good idea where we are heading, it’s time to start doing
some spectral analysis.
Let’s begin by looking at some output obtained by performing a spectral
analysis on the same five sinusoids shown in Figure 3. The parameters used
to perform this spectral analysis are shown in Figure 8. (I will
explain each of these parameters as we go along.)
Data length: 400 Sample for zero time: 0 Lower frequency bound: 0.0 Upper frequency bound: 1.0 Number spectra: 5 Frequencies 0.03125 0.0625 0.125 0.25 0.5 Amplitudes 90.0 90.0 90.0 90.0 90.0 Figure 8 |
Although more parameters are required to perform spectral analysis than are
required to simply generate and plot the sinusoids, the number of sinusoids, the
frequencies of the sinusoids, and the amplitudes of the sinusoids in Figure 8
are the same as in Figure 2.
The spectral analysis output
The output produced by performing spectral analysis on these five sinusoids
is shown in Figure 9.
Figure 9 Spectral analysis of five sinusoids. |
The format explained
Five separate spectral analyses were performed and the results of those five
spectral analyses are shown in Figure 9. Each of the horizontal lines in
Figure 9 is the horizontal axis used to display the result of performing a
spectral analysis on a different sinusoid. In other words, Figure 9
contains five separate graphs moving from the top to the bottom of the display.
The individual graphs have alternating white and gray backgrounds to make them
easier to separate visually.
The top graph in Figure 9 shows the result of performing a spectral analysis
on the top sinusoid in Figure 3. Moving down the page, each graph in
Figure 9 shows the result of performing a spectral analysis on the
corresponding sinusoid in Figure 3.
The frequency axes
The horizontal axes in Figure 9 represent the frequency range from zero to
the sampling frequency.
(The frequency range covered is specified by the Lower frequency
bound and the Upper frequency bound parameters in Figure 8.)
The horizontal units
The horizontal units in Figure 9 don’t represent frequency in an absolute
sense of cycles per second or Hertz. Rather, the horizontal units in
Figure 9 represent the frequency bins for which spectral energy was computed.
In this case, the spectral energy for each sinusoid was computed in 400 equally
spaced bins distributed between zero and the sampling frequency.
(The number of frequency bins for each individual spectrum computed by
this program is equal to the Data length parameter in Figure 8.
Those frequency bins are distributed uniformly between the Lower
frequency bound and the Upper frequency bound parameters in
Figure 8.)
Location of the folding frequency
Because the right-most end of each horizontal axis in Figure 9 represents the
sampling frequency, the center of each horizontal axis represents one-half the
sampling frequency, or the Nyquist folding frequency. Thus, the frequency
represented by the center of each horizontal axis represents the frequency
specified by a value of 0.5 in Figure 8.
A peak at the folding frequency
You can see a large peak in energy at the folding frequency of the bottom
graph in Figure 9. That peak corresponds to the frequency of the fifth
sinusoid specified in the parameters shown in Figure 8. (This also corresponds to the spectrum of the bottom graph in
Figure 3.)
Knowing that, you should be able to correlate each of the peaks to the left
of center in Figure 9 with the frequencies of the sinusoids specified in Figure
8 and with the individual sinusoids plotted in Figure 3.
The frequency folding effect
Figure 9 clearly shows the frequency folding effect of the sampling process
illustrated earlier. As you can see, the peaks in the various graphs to
the right of the folding frequency are mirror images of the peaks to the left of
the folding frequency. In other words, given a set of samples of a
sinusoid, the spectral analysis process is unable to determine whether the peak
is above or below the folding frequency, so the energy is equally distributed
between two peaks on opposite sides of the folding frequency.
Size of the peak at the folding frequency
Note that the peak in the bottom graph is approximately twice the height of
the peaks in the other graphs. This is because the peak at the folding
frequency has no mirror-image partner, and all the energy is concentrated in
that single peak.
(Another interpretation is that two mirror-image peaks converge at the
folding frequency causing the resulting peak to be twice as large as either
mirror-image peak. I will illustrate this effect with another example
later.)
A short fat peak at the top
You may also have noticed that the peaks in the top graph are shorter and wider
than the peaks in the other graphs. This may be because the actual
frequency of the sinusoid for the top graph is about half way between the values
of the twelfth and thirteenth bins for which spectral energy was computed.
Thus, the energy in the sinusoid was spread between the bins on either side of
the actual frequency.
This frequency spreading effect can be minimized by increasing the data
length to 800 samples. This causes the frequency bins to be only half as
wide and the peak in the top graph becomes tall and narrow just like the peaks
in the other graphs. You should try this and observe the result when you
run the program later.
It is also instructive to plot these spectra with a data length of 400 using
the program named Graph06. This will show you how the energy is
distributed between the frequency bins. This is most effective when the
graph is expanded as described in the next section.
Mapping the peaks to pixels
The broadening of the peak in the top graph may also have to do with the
requirement to map the peaks in the spectrum to the locations of the actual
pixels on the screen. If the location of the peak falls between the
positions of two pixels, the plotting program must interpolate the energy in the
peak so as to display that energy in actual pixel locations.
This effect can be minimized by plotting the same number of spectral values
across a wider area of the screen. When you run this program later, click
the maximize button on the Frame to cause the display to occupy the entire screen. That
will give you a much better look at the actual shape of each of the peaks.
Do this using both Graph03 and Graph06 to plot the results.
(Note: When switching between the plotting programs, you may
need to delete the class files from the old program and compile the new
program to avoid having class files with the same names from the two
programs becoming intermingled in the same directory.)
Another DFT example
This next example is designed to illustrate the following features of the DFT
algorithm which don’t generally apply to an FFT algorithm:
- Ability to do spectral analysis on data of arbitrary lengths.
(With most FFT algorithms, the data length must be a power of two.) - Ability to zero in on an arbitrary range of frequencies and to ignore
all other frequencies. (Most FFT algorithms always compute the
spectrum at uniform frequency increments from zero to one unit less than the sampling
frequency.)
As mentioned earlier, the DFT algorithm is much more flexible while the FFT
algorithm is much faster, particularly for large data lengths.
Peaks merge at the folding frequency
In addition to illustrating these fundamental aspects of the DFT algorithm,
this example also illustrates how the mirror image peaks on either side of the
folding frequency merge into a single larger peak as the data frequency
approaches the folding frequency.
The input parameters
The input parameters are shown in Figure 10. Note in particular the
values for the following parameters:
- Data length: 200
- Sample for zero time: 0
- Lower frequency bound: 0.4
- Upper frequency bound: 0.6
Data length: 200 Sample for zero time: 0 Lower frequency bound: 0.4 Upper frequency bound: 0.6 Number spectra: 5 Frequencies 0.492 0.494 0.496 0.498 0.5 Amplitudes 90.0 90.0 90.0 90.0 90.0 Figure 10 |
A different data length
As you can see, the data length for this experiment is different from the
data length of 400 used earlier. In addition, neither data length is a
power of two.
Computational frequency bounds
As you can also see, the lower and upper frequency bounds are not 0.0 and 1.0
as in the earlier cases. In this case, the frequency bounds describe a
much narrower range centered on the folding frequency.
Frequencies are close to the folding frequency
Finally, the frequencies of each of the five sinusoids specified in Figure 10
is progressively closer to the folding frequency with the frequency of the fifth
sinusoid being equal to the folding frequency.
The spectral analysis output
The output from the spectral analysis for each of the five sinusoids is shown
in Figure 11. (Another interesting view of the same results is shown
later in Figure 14.)
Figure 11 Spectral analysis of five sinusoids. |
Peaks are symmetrical to the folding frequency
The spectral peaks shown in Figure 11 are symmetrical to the folding frequency,
which in turn is centered horizontally in each of the graphs. As you
already know, the peaks are always symmetrical to the folding frequency due to the
frequency folding at that frequency.
The folding frequency is centered horizontally due to the way that I defined
the lower and upper frequency bounds, and the way that I adjusted the plotting
parameters.
Peaks are well defined and wider than before
The peaks are well defined because I computed the spectral energy at 200
points across the specified frequency range from 0.4 to 0.6. Thus, the
frequency bins at which I computer spectral energy were much narrower than
before.
The peaks are wider because I displayed a much smaller slice of the entire
frequency spectrum in the same physical screen space.
Peaks merge at the folding frequency
As the frequency of each sinusoid approaches the folding frequency, the two
mirror-image peaks corresponding to that sinusoid merge into a single peak with twice the height at the folding
frequency. This agrees with what you saw in Figure 9, but on a much more
detailed basis.
It would be difficult to perform this experiment using an FFT algorithm
because of the inherent limitations built into the algorithm. The FFT
algorithm sacrifices flexibility for speed.
Implementing the DFT algorithm
At this point, I will present and explain two different programs:
- Dsp028 – Driver program for doing spectral analysis using a DFT
algorithm. - ForwardRealToComplex01 – Class that implements the DFT algorithm.
In addition, I will present, but will not explain the plotting program named
Graph03.
The Dsp028 program
This driver program is similar in many respects to the program named
Dsp029 that I explained earlier. It differs mainly in how it populates
the array objects containing the data that is plotted by the plotting program.
The program named Dsp029 simply populates those array objects with
five sinusoidal functions. The program named Dsp028 also creates
five sinusoidal functions. However, it passes those functions to a static method
named transform belonging to the ForwardRealToComplex01 class to
perform spectral analysis on those functions. The results of the spectral
analysis are used to populate the five array objects whose contents are plotted
by the plotting program.
Because of the similarity of the two programs, my discussion of Dsp028
will be much more brief than was my discussion of Dsp029.
Computes and displays the magnitude spectrum
The program named Dsp028 computes and displays the magnitude of the spectral content for up to five sinusoids having different frequencies and amplitudes.
(Future lessons will discuss other aspects of spectral analysis such
as the complex spectrum, zero time, and the phase angle.)
Input parameters
The program gets input parameters from a file named Dsp028.txt. If that file doesn’t exist in the current directory, the program uses a set of default parameters.
As with the program named Dsp029, each parameter value must be stored as characters on a separate line in the file named
Dsp028.txt. The required parameters are shown in Figure mm.
Data length as type int Sample number representing zero time as type int Lower frequency bound as type double Upper frequency bound as type double Number of spectra as type int. Max value is 5. List of sinusoid frequency values as type double. List of sinusoid amplitude values as type double. Figure mm Required input parameters for Dsp028. |
Don’t allow blank lines at the end of the data in the file.
The number of values in each of the lists must
match the value for the number of spectra.
Specification of sinusoidal frequencies
As before, each frequency value is specified as a double value representing a fractional part of the
sampling frequency. For example, a value of 0.5
specifies a frequency that is one-half the sampling
frequency.
Example contents for Dsp028.txt
Figure 12 shows the contents of the file named Dsp028.txt that represent
the parameters shown in Figure 10.
200 0 0.4 0.6 5 0.492 0.494 0.496 0.498 0.5 90 90 90 90 90 Figure 12 Contents of Dsp028.txt file |
Performing the spectral analysis
A static method named transform belonging to the
class named ForwardRealToComplex01 is used to
perform the actual spectral analysis. The
method named transform does not implement an FFT algorithm. Rather,
it is more general than, but much slower than an FFT algorithm.
Will discuss the code in fragments
As usual, I will discuss the code in fragments. A complete listing of
the program is presented in Listing 19 near the end of the lesson. Because
of the similarity of Dsp028 with Dsp029 discussed earlier, the fragments for Dsp028 will be much larger and will be explained in much
less detail.
The class definition begins in Listing 8. For reasons that you already
understand, this class implements the interface named GraphIntfc01.
class Dsp028 implements GraphIntfc01{ final double pi = Math.PI;//for simplification //Begin default parameters int len = 400;//data length //Sample that represents zero time. int zeroTime = 0; //Low and high frequency limits for the // spectral analysis. double lowF = 0.0; double highF = 1.0; int numberSpectra = 5; //Frequencies of the sinusoids double[] freq = {0.1,0.2,0.3,0.4,0.5}; //Amplitudes of the sinusoids double[] amp = {60,70,80,90,100}; //End default parameters Listing 8 |
The code in Listing 8 defines a set of default parameter values that are used
in the event that a file named Dsp028.txt does not exist in the current
directory.
Declare array variables
The code in Listing 9 declares several array variables that will be used to
point to array objects whose purposes are explained in the comments.
//Following arrays will contain data that is // input to the spectral analysis process. double[] data1; double[] data2; double[] data3; double[] data4; double[] data5; //Following arrays receive information back // from the spectral analysis that is not used // in this program. double[] real; double[] imag; double[] angle; //Following arrays receive the magnitude // spectral information back from the spectral // analysis process. double[] magnitude1; double[] magnitude2; double[] magnitude3; double[] magnitude4; double[] magnitude5; Listing 9 |
The constructor
The constructor for the class begins in Listing 10. The constructor
begins by getting the parameters from a file named Dsp028.txt. If
that file doesn’t exist in the current directory, default parameters are used.
public Dsp028(){//constructor if(new File("Dsp028.txt").exists()){ getParameters(); }//end if Listing 10 |
Always processes five sinusoids
For simplicity, this program always processes
five sinusoids, even if fewer than five
were requested as the input parameter
for numberSpectra. In that case, the
extra sinusoids are processed using default values
and simply ignored when the results are
plotted.
Create the raw sinusoidal data
The code in Listing 11 instantiates array objects and creates the sinusoidal
data upon which spectral analysis will be performed.
//First create empty array objects. double[] data1 = new double[len]; double[] data2 = new double[len]; double[] data3 = new double[len]; double[] data4 = new double[len]; double[] data5 = new double[len]; //Now populate the array objects for(int n = 0;n < len;n++){ data1[n] = amp[0]*Math.cos(2*pi*n*freq[0]); data2[n] = amp[1]*Math.cos(2*pi*n*freq[1]); data3[n] = amp[2]*Math.cos(2*pi*n*freq[2]); data4[n] = amp[3]*Math.cos(2*pi*n*freq[3]); data5[n] = amp[4]*Math.cos(2*pi*n*freq[4]); }//end for loop Listing 11 |
Perform the spectral analysis
The code in Listing 12 creates array objects to receive the results and
invokes the static transform method of the forwardRealToComplex01
class five times in succession to perform the spectral analysis on each of the
five sinusoids.
(I will explain the transform method that performs the spectral
analysis shortly.)
Only the magnitude data is displayed by this program. Therefore, the arrays that
receive the other spectral analysis results from the transform method are
discarded each time a new spectral analysis is performed.
magnitude1 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data1,real, imag,angle,magnitude1,zeroTime,lowF,highF); magnitude2 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data2,real, imag,angle,magnitude2,zeroTime,lowF,highF); magnitude3 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data3,real, imag,angle,magnitude3,zeroTime,lowF,highF); magnitude4 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data4,real, imag,angle,magnitude4,zeroTime,lowF,highF); magnitude5 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data5,real, imag,angle,magnitude5,zeroTime,lowF,highF); }//end constructor Listing 12 |
The spectral magnitude results
Note that the magnitude results are saved in the array objects referred to by
magnitude1, magnitude2, etc. This will be important later
when I discuss the interface methods defined by Dsp028.
The end of the constructor
Listing 12 also signals the end of the constructor. When the
constructor terminates, the object has been instantiated and populated with
spectral analysis results for five sinusoids using the parameters specified by
the file named Dsp028.txt.
The getParameters method
The getParameters method used in this program is the same as that used in
Dsp029, so I won’t discuss it further.
The interface methods
The Dsp028 class must define the same six interface methods as the Dsp029 class discussed earlier. The only difference
in the interface methods is the identification of the array objects from which the methods return data
when the methods are invoked.
The code in Listing 13 is typical of the code for methods f1 through
f5. As you can see, these methods return the data stored in the
magnitude arrays. These are the spectral analysis results that are plotted
in Figure 9, Figure 11, and later in Figure 14.
public double f1(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude1.length-1){ return 0; }else{ return magnitude1[index]; }//end else }//end function Listing 13 |
The program named Graph03
The plots in Figure 9 and Figure 11 were produced by entering the following
at the command line prompt:
java Graph03 Dsp028
The program named Graph03 is very similar to the program named
Graph06 discussed earlier. In fact, the program named Graph06
can be used to produce very similar plots where the sample values are
represented by vertical bars instead of being represented by connected dots.
This results in the very interesting display shown in Figure 14. Each of
the vertical bars in Figure 14 represents a computational frequency bin.
(Compare Figure 14 with Figure 11.)
Figure 14 Spectral analysis of five sinusoids. |
In any event, Graph03 is so similar to Graph06 that I’m not
going to discuss it further. A complete listing of the program named
Graph03 is provided in Listing 20 near the end of the lesson.
The transform method of the ForwardRealToComplex01
class
That brings us to the heart of this lesson, which is the method that actually
implements the DFT algorithm and performs the spectral analysis. This is a
method named transform, which is a static method of the class named
ForwardRealToComplex01. You saw this method being invoked five times
in the code in Listing 12.
Will discuss in fragments
As usual, I will discuss this method in fragments. A complete listing
of the class is presented in Listing 21 near the end of the lesson.
The transform method is a rather straightforward implementation of the
concepts that I explained in the earlier lesson entitled
Fun with Java,
How and Why Spectral Analysis Works. If you
have not done so already, I strongly urge you go to back and study that lesson
at this time. You need to understand those concepts in order for the code
in the transform method to make sense.
A brief description
For those of you who don’t have the time to go back and study that lesson in
detail, a brief description of the DFT algorithm follows.
Using a notation that I described in the earlier lesson, the expressions that
you must evaluate to determine the frequency spectral content of a target time
series at a frequency F are shown in Figure 15.
Real(F) = S(n=0,N-1)[x(n)*cos(2Pi*F*n)] Imag(F) = S(n=0,N-1)[x(n)*sin(2Pi*F*n)] ComplexAmplitude(F) = Real(F) - j*Imag(F) Power(F) = Real(F)*Real(F) + Imag(F)*Imag(F) Amplitude(F) = SqRt(Power(F)) Figure 15 |
What does this really mean?
Before you panic, let me explain what this means in layman’s terms.
Given a time series, x(n), you can determine if that time series
contains a cosine component or a sine component at a given frequency, F,
by doing the following:
- Create one new time series, cos(n), which is a cosine function
with the frequency F. - Create another new time series, sin(n), which is a sine
function with the frequency F. - Multiply x(n) by cos(n) and compute the sum of the
products. Save this value, calling it Real(F). This
is an estimate of the amplitude, if any, of the cosine component with the
matching frequency contained in the time series x(n). - Multiply x(n) by sin(n) and compute the sum of the
products. Save this value, calling it Imag(f). This
is an estimate of the amplitude, if any, of the sine component with the matching
frequency contained in the time series x(n). - Consider the values for Real(F) and Imag(F) to be the
real and imaginary parts of a complex number. - Consider the sum of the squares of the real and imaginary parts to
represent the power at that frequency in the time series. - Consider the square root of the power to be the amplitude at that
frequency in the time series. (This is the value that is plotted in
Figure 9, Figure 11, and Figure 14.)
Compute the complex energy at each frequency
That is all there is to it. For each frequency of interest, you
can use this process to compute a complex number, Real(F)-jImag(F),
which represents the complex energy corresponding to that frequency in the target time series.
Similarly, you can compute the sum of the squares of the real and imaginary
parts and consider that to be a measure of the power at that frequency in
the time series. The square root of the power is the amplitude of the
energy at that frequency.
Nested for loops
Normally we are interested in more than one frequency, so we would repeat
the above procedure once for each frequency of interest. This suggests
the use of nested for loops in the algorithm. The outer loop
specifies the frequency of interest. The inner loop computes the sum of
the products at a particular frequency.
Description of the transform method
The static method named transform performs a real
to complex Fourier transform. The method does not implement the FFT algorithm.
Rather, it implements
a straightforward sampled data version of the
continuous Fourier transform defined using
integral calculus. (See ForwardRealToComplexFFT01
for an FFT algorithm.)
The return values
The method returns the following:
- Real part of the spectral analysis result
- Imaginary part of the spectral analysis result
- Magnitude of the spectral analysis result
- Phase angle of the spectral analysis result in degrees
The transform method parameters
The method parameters are:
- double[] data – incoming real data
- double[] realOut – outgoing real data
- double[] imagOut – outgoing imaginary data
- double[] angleOut – outgoing phase angle in degrees
- double[] magnitude – outgoing amplitude spectrum
- int zero – the index of the incoming data
sample that represents zero time - double lowF – low frequency limit for computation as a fraction of
sampling frequency - double highF – high frequency limit for computation as a fraction of
sampling frequency
Frequency increment, magnitude spectrum, and number
of returned values
The computational frequency increment is the difference between the
high and low limits divided by the length of
the magnitude array.
The magnitude or amplitude is computed as the square root of
the sum of the squares of the real and imaginary
parts. This value is divided by the incoming
data length, which is given by data.length.
The method returns a number of points in the frequency
domain equal to the incoming data length
regardless of the high and low frequency
limits.
The beginning of the transform method
The class and the transform method begin in Listing 14. The code
in Listing 14 is described above.
public class ForwardRealToComplex01{ public static void transform( double[] data, double[] realOut, double[] imagOut, double[] angleOut, double[] magnitude, int zero, double lowF, double highF){ double pi = Math.PI;//for convenience int dataLen = data.length; double delF = (highF-lowF)/data.length; Listing 14 |
The remainder of the method and the class
The nested for loops discussed above are included in the code shown in
Listing 15. As suggested above, the outer loop iterates on frequency while
the inner loop iterates on the values that make up the incoming samples.
The code in the inner loop computes the sum of the product of the time series
and the reference cosine and sine functions.
//Outer loop iterates on frequency // values. for(int i=0; i < dataLen;i++){ double freq = lowF + i*delF; double real = 0.0; double imag = 0.0; double ang = 0.0; //Inner loop iterates on time- // series points. for(int j=0; j < dataLen; j++){ real += data[j]*Math.cos( 2*pi*freq*(j-zero)); imag += data[j]*Math.sin( 2*pi*freq*(j-zero)); }//end inner loop realOut[i] = real/dataLen; imagOut[i] = imag/dataLen; magnitude[i] = (Math.sqrt( real*real + imag*imag))/dataLen; //Calculate and return the phase // angle in degrees. if(imag == 0.0 && real == 0.0){ang = 0.0;} else{ang = Math.atan(imag/real)*180.0/pi;} if(real < 0.0 && imag == 0.0){ang = 180.0;} else if(real < 0.0 && imag == -0.0){ ang = -180.0;} else if(real < 0.0 && imag > 0.0){ ang += 180.0;} else if(real < 0.0 && imag < 0.0){ ang += -180.0;} angleOut[i] = ang; }//end outer loop }//end transform method }//end class ForwardRealToComplex01 Listing 15 |
Store results in output array objects
At the end of each iteration of the inner loop, code in the outer loop
deposits the real, imaginary, magnitude, and phase angle results in the output
array objects. To accomplish this, the code:
- Computes the magnitude or amplitude as the square root of the sum of the
squares of the real and imaginary parts. - Performs some trigonometry operations to determine the phase angle in
degrees based on the values of the real and imaginary parts.
Now you know about the DFT algorithm
Now you know about the DFT algorithm. You also know about some of the
fundamental aspects of spectral analysis involving the sampling frequency and the
folding frequency.
Future lessons will discuss other aspects of spectral analysis including:
- Frequency resolution versus data length.
- The relationship between the phase angle and delays in the time domain.
- The reversible nature of the Fourier transform involving both forward
and inverse Fourier transforms.
Spectral Analysis using
an FFT Algorithm
At this point, I will present a similar spectral
analysis program that uses an FFT algorithm. I will present this program
with very little discussion. I am providing it in this lesson for two primary purposes:
- To allow you to experiment and appreciate the flexibility of the DFT as compared to the
FFT. - To allow you to experiment and appreciate the speed of the FFT as compared to the DFT.
The program named Dsp030
The program named Dsp030 is very similar to Dsp028. The major differences
are:
- Because it uses an FFT algorithm, Dsp030 is much less flexible
than Dsp028, particularly with respect to data length and selection
of the frequencies of interest. - Because it uses an FFT algorithm, Dsp030 is much faster than
Dsp028, particularly when used to perform spectral analysis on long data
lengths.
A complete listing of Dsp030 is provided in Listing 22.
Description of the program named Dsp030
This program uses an FFT algorithm to compute and display the
magnitude of the spectral content for up to five
sinusoids having different frequencies and
amplitudes. (See the program named Dsp028 for a program that
does not use an FFT algorithm.)
The input parameters
The program gets input parameters from a file named Dsp030.txt. If that file doesn’t exist in the
current directory, the program uses a set of
default parameters.
Each parameter value must be stored as characters
on a separate line in the file named Dsp030.txt.
The required input parameters are shown in Figure 16. (Contrast this
with the required input parameters for Dsp028 shown in Figure mm.)
Data length as type int Number of spectra as type int. Max value is 5. List of sinusoid frequency values as type double. List of sinusoid amplitude values as type double. Figure 16 Required input parameters for Dsp030. |
Note in contrast with Figure mm, the required input parameters for Dsp030
do not include the sample number representing zero time, the lower frequency
bound for computation of the spectra, and the upper frequency bound for
computation of the spectra.
(The computational frequency range cannot be
specified for the FFT algorithm. It always computes the spectra from zero
to one unit less than the sampling frequency.)
Restrictions on the data length
Note also that the data length must always be a power of two. Otherwise, the
FFT algorithm will fail to run properly.
(This restriction is an important contributor to the speed achieved by the
FFT algorithm.)
The sinusoidal frequency values
As with Dsp028, the number of values in each of the lists must
match the value for the number of spectra.
All frequency values are specified as a double representing a
fractional part of the sampling frequency.
Figure 17 shows the parameters used to produce the spectral analysis plots
shown later in Figure 18.
(Note that the data length is a power of two as
required by the FFT.)
256 5 0.1 0.2 0.3 0.5 0.005 90 90 90 90 90 Figure 17 |
The plotting program
The plotting program that is used to plot the
output data from this program requires that the
program implement GraphIntfc01. For example,
the plotting program named Graph03 can be used
to plot the data produced by this program. This requires that you enter the
following at the command line prompt:
java Graph03 Dsp030
The plotting program named Graph06 can also be used to plot the data produced
by this program, requiring that you enter the following at the command line
prompt:
java Graph06 Dsp030
The transform method
A static method named transform belonging to the
class named ForwardRealToComplexFFT01 is used to
perform the actual spectral analysis. The method
named transform implements an FFT algorithm. The
FFT algorithm requires that the data length be
a power of two. This method will be discussed very briefly later.
A sample FFT spectral analysis
The output produced by running Dsp030 using the input parameters shown in
Figure 17 is shown in Figure 18.
Figure 18 FFT of five sinusoids. |
Nothing special
here
There is nothing special about this
particular spectral analysis. I presented it here to illustrate the use of
the FFT algorithm for spectral analysis. You should be able to produce the same results
using the same program and the same parameters.
A matching DFT spectral analysis
Figure 19 shows the parameters required for the program named Dsp028
to perform a DFT spectral analysis producing the same results as those produced
by the FFT analysis shown in Figure 18. Note that the data length has been
set to 256 and the computational frequency range extends from zero to the
sampling frequency in Figure 19.
Data length: 256 Sample for zero time: 0 Lower frequency bound: 0.0 Upper frequency bound: 1.0 Number spectra: 5 Frequencies 0.1 0.2 0.3 0.5 0.0050 Amplitudes 90.0 90.0 90.0 90.0 90.0 Figure 19 |
The matching DFT
output
The DFT output produced by running Dsp028 with the parameters shown in
Figure 19 is shown in Figure 20.
Figure 20 DFT of five sinusoids. |
Hopefully you noticed that Figure 20
looks almost exactly like Figure 18. This is how it should be. The DFT
algorithm and the FFT algorithm are simply two different algorithms for
computing the same results. However, the DFT algorithm is much more
flexible than the FFT algorithm
while the FFT algorithm is much faster than the DFT algorithm.
Repeat these two experiments
I recommend that you repeat these two experiments several times increasing
the data length to a higher power of two each time you run the experiments.
On my machine, the DFT algorithm used by Dsp028 becomes noticeably
slow by the time the data length reaches 2048 samples. However, the FFT
algorithm used by Dsp030 is still reasonably responsive at a data length
of 131,072 samples.
(Performing the DFT on five input samples each having a data length of
131,072 samples would require an intolerably long time on my machine.)
If what you need is speed for long data lengths, the FFT is your best
approach. On the other hand, if you need more flexibility than the FFT
provides and the data length is not too long, then the DFT may be your best
approach.
The ForwardRealToComplexFFT01 class
The ForwardRealToComplexFFT01 class containing the method that
implements the FFT algorithm is provided in Listing 23 near the end of this
lesson.
The FFT algorithm is based on some very complicated signal processing
concepts. I’m not going to explain how this algorithm works in this lesson
because I haven’t given you the proper background for understanding it. I
plan to explain additional signal processing concepts in future lessons that
will prepare you to understand how the FFT algorithm works.
Fortunately, you don’t have to understand how the FFT algorithm works to be able to use it.
Run the Programs
I encourage you to copy, compile, and run the programs provided in this lesson.
Experiment with them, making changes and observing the results of your changes.
I suggest that you begin by compiling and running the following files to
confirm that everything is working correctly on your machine before attempting to compile and
run the spectral analysis programs:
- Dsp029.java
- GraphIntfc01.java
- Graph06.java
Make sure that you create an appropriate file named Dsp029.txt, as
described in Figure 2. You should be able to reproduce my results if
everything is working correctly.
Once you confirm that things are working correctly, copy, compile, and run
the spectral analysis programs. Experiment with the parameters and try to
understand the result of making changes to the parameters. Confirm the
flexibility of the DFT algorithm and the speed of the FFT algorithm.
Summary
In this lesson I have provided and explained programs that illustrate the
impact of sampling and the Nyquist folding frequency.
I have also provided and explained several different programs used for
performing spectral analysis. The first program was a very general program that
implements a Discrete Fourier Transform (DFT) algorithm. I
explained this program in detail.
The second program was a less general, but much faster program that
implements a Fast Fourier Transform (FFT) algorithm. I will defer an
explanation of this program until a future lesson. I provided it in this lesson
so that you can use it and compare it with the DFT program in terms of speed and
flexibility.
What’s Next?
Future lessons will discuss other aspects of spectral analysis including:
- Frequency resolution versus data length.
- The relationship between the phase angle and delays in the time domain.
- The reversible nature of the Fourier transform involving both forward
and inverse Fourier transforms. - Additional material aimed towards an understanding of the
signal processing concepts behind the FFT algorithm.
Complete Program Listings
Complete listings of all the programs discussed in this lesson follow.
/* File Dsp029.java Copyright 2004, R.G.Baldwin Rev 5/6/04 Generates and displays up to five sinusoids having different frequencies and amplitudes. Very useful for providing a visual illustration of the way in which frequencies above half the sampling frequency fold back down into the area bounded by zero and half the sampling frequency (the Nyquist folding frequency). Gets input parameters from a file named Dsp029.txt. If that file doesn't exist in the current directory, the program uses a set of default parameters. Each parameter value must be stored as characters on a separate line in the file named Dsp029.txt. The required parameters are as follows: Data length as type int Number of sinusoids as type int. Max value is 5. List of sinusoid frequency values as type double. List of sinusoid amplitude values as type double. The number of values in each of the lists must match the value for the number of spectra. Note: All frequency values are specified as a double representing a fractional part of the sampling frequency. Here is a set of sample parameter values. Don't allow blank lines at the end of the data in the file. 400.0 5 0.1 0.9 1.1 1.9 2.1 90 90 90 90 90 The plotting program that is used to plot the output data from this program requires that the program implement GraphIntfc01. For example, the plotting program named Graph06 can be used to plot the data produced by this program. When it is used, the usage information is: java Graph06 Dsp029 Tested using SDK 1.4.2 under WinXP. ************************************************/ import java.util.*; import java.io.*; class Dsp029 implements GraphIntfc01{ final double pi = Math.PI;//for simplification //Begin default parameters int len = 400;//data length int numberSinusoids = 5; //Frequencies of the sinusoids double[] freq = {0.1,0.25,0.5,0.75,0.9}; //Amplitudes of the sinusoids double[] amp = {75,75,75,75,75}; //End default parameters //Following arrays will be populated with // sinusoidal data to be plotted double[] data1 = new double[len]; double[] data2 = new double[len]; double[] data3 = new double[len]; double[] data4 = new double[len]; double[] data5 = new double[len]; public Dsp029(){//constructor //Get the parameters from a file named // Dsp029.txt. Use the default parameters // if the file doesn't exist in the current // directory. if(new File("Dsp029.txt").exists()){ getParameters(); }//end if //Note that this program always generates // five sinusoids, even if fewer than five // were requested as the input parameter // for numberSinusoids. In that case, the // extras are generated using default values // and simply ignored when the results are // plotted. //Create the raw data. Note that the // argument for a sinusoid at half the // sampling frequency would be (2*pi*x*0.5). // This would represent one half cycle or pi // radians per sample. for(int n = 0;n < len;n++){ data1[n] = amp[0]*Math.cos(2*pi*n*freq[0]); data2[n] = amp[1]*Math.cos(2*pi*n*freq[1]); data3[n] = amp[2]*Math.cos(2*pi*n*freq[2]); data4[n] = amp[3]*Math.cos(2*pi*n*freq[3]); data5[n] = amp[4]*Math.cos(2*pi*n*freq[4]); }//end for loop }//end constructor //-------------------------------------------// //This method gets processing parameters from // a file named Dsp029.txt and stores those // parameters in instance variables belonging // to the object of type Dsp029. void getParameters(){ int cnt = 0; //Temporary holding area for strings. Allow // space for a few blank lines at the end // of the data in the file. String[] data = new String[20]; try{ //Open an input stream. BufferedReader inData = new BufferedReader(new FileReader( "Dsp029.txt")); //Read and save the strings from each of // the lines in the file. Be careful to // avoid having blank lines at the end, // which may cause an ArrayIndexOutOfBounds // exception to be thrown. while((data[cnt] = inData.readLine()) != null){ cnt++; }//end while inData.close(); }catch(IOException e){} //Move the parameter values from the // temporary holding array into the instance // variables, converting from characters to // numeric values in the process. cnt = 0; len = (int)Double.parseDouble(data[cnt++]); numberSinusoids = (int)Double.parseDouble( data[cnt++]); for(int fCnt = 0;fCnt < numberSinusoids; fCnt++){ freq[fCnt] = Double.parseDouble( data[cnt++]); }//end for loop for(int aCnt = 0;aCnt < numberSinusoids; aCnt++){ amp[aCnt] = Double.parseDouble( data[cnt++]); }//end for loop //Print parameter values. System.out.println(); System.out.println("Data length: " + len); System.out.println( "Number sinusoids: " + numberSinusoids); System.out.println("Frequencies"); for(cnt = 0;cnt < numberSinusoids;cnt++){ System.out.println(freq[cnt]); }//end for loop System.out.println("Amplitudes"); for(cnt = 0;cnt < numberSinusoids;cnt++){ System.out.println(amp[cnt]); }//end for loop }//end getParameters //-------------------------------------------// //The following six methods are required by the // interface named GraphIntfc01. The plotting // program pulls the data values to be plotted // by invoking these methods. public int getNmbr(){ //Return number of functions to // process. Must not exceed 5. return numberSinusoids; }//end getNmbr //-------------------------------------------// public double f1(double x){ int index = (int)Math.round(x); if(index < 0 || index > data1.length-1){ return 0; }else{ return data1[index]; }//end else }//end function //-------------------------------------------// public double f2(double x){ int index = (int)Math.round(x); if(index < 0 || index > data2.length-1){ return 0; }else{ return data2[index]; }//end else }//end function //-------------------------------------------// public double f3(double x){ int index = (int)Math.round(x); if(index < 0 || index > data3.length-1){ return 0; }else{ return data3[index]; }//end else }//end function //-------------------------------------------// public double f4(double x){ int index = (int)Math.round(x); if(index < 0 || index > data4.length-1){ return 0; }else{ return data4[index]; }//end else }//end function //-------------------------------------------// public double f5(double x){ int index = (int)Math.round(x); if(index < 0 || index > data5.length-1){ return 0; }else{ return data5[index]; }//end else }//end function //-------------------------------------------// }//end class Dsp029 Listing 16 |
/* 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 |
/* File Graph06.java Copyright 2002, R.G.Baldwin Revised 5/15/04 Very similar to Graph03, except that each point is displayed as a rectangle, centered on the sample. Can be used to explain integration through summation of the sample values. 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 stand-alone mode. At runtime, the name of the class that implements the GraphIntfc01 interface must be provided as a command-line 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 command-line 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 x-axis value xMax = maximum x-axis value yMin = minimum y-axis value yMax = maximum y-axis value xTicInt = tic interval on x-axis yTicInt = tic interval on y-axis xCalcInc = calculation interval The user can modify any of these parameters and then click the Graph button to cause the five functions to be re-plotted 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 Graph06{ public static void main( String[] args) throws NoSuchMethodException, ClassNotFoundException, InstantiationException, IllegalAccessException{ if(args.length == 1){ //pass command-line paramater new GUI(args[0]); }else{ //no command-line parameter given new GUI(null); }//end else }// end main }//end class Graph06 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 x-axis, a default value is // used later. double xTicLen = (yMax-yMin)/50; double yTicLen = (xMax-xMin)/50; //Calculation interval along x-axis 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 one-column 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 one-column 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 sub-assemblies 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("Graph06, " + "Copyright 2002, " + "Richard G. Baldwin"); }else{ setTitle("Graph06/" + args + " Copyright 2002, " + "R. G. Baldwin"); }//end else setVisible(true); //Set to exit on X-button 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){ //Re-instantiate 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 x-axis, // a default value is used later. xTicLen = (yMax-yMin)/50; yTicLen = (xMax-xMin)/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/(xMax-xMin); yScale = height/(yMax-yMin); //Set the origin based on the // minimum values in x and y g.translate((int)((0-xMin)*xScale), (int)((0-yMin)*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 x-value to an int // and draw the next horizontal // line segment int x = getTheX(xVal+xCalcInc/2); g.drawLine(oldX,yVal,x,yVal); //Draw a vertical line at the // old x-value int yZero = getTheY(0); g.drawLine(oldX,yZero,oldX,yVal); //Draw a vertical line at the // new y-value g.drawLine(x,yZero,x,yVal); //Increment along the x-axis 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 x-axis and bottom // y-axis. 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 x-axis and the // top y-axis. 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 x-axis and the // height of the string for top of // y-axis //Create a string that is an // integer representation of the // label for the right end of the // x-axis. 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 // x-axis. The height applies to // all the labels, but is needed // specifically for the label at // the top end of the y-axis. int labWidth = (int)(r2d.getWidth()); int labHeight = (int)(r2d.getHeight()); //Label the positive x-axis and the // positive y-axis 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 x-axis 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 x-axis // 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 x-axis 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 y-axis 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 y-axis // moving up from zero. while(yDoub < yMax){ y = getTheY(yDoub); g.drawLine(rightEnd,y,leftEnd,y); yDoub += yTicInt; }//end while //Now do the negative y-axis 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 // y-axis 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 stand-alone // 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 18 |
/* File Dsp028.java Copyright 2004, R.G.Baldwin Rev 5/14/04 Computes and displays the magnitude of the spectral content for up to five sinusoids having different frequencies and amplitudes. Gets input parameters from a file named Dsp028.txt. If that file doesn't exist in the current directory, the program uses a set of default parameters. Each parameter value must be stored as characters on a separate line in the file named Dsp028.txt. The required parameters are as follows: Data length as type int Sample number representing zero time as type int Lower frequency bound as type double (See note) Upper frequency bound as type double Number of spectra as type int. Max value is 5. List of sinusoid frequency values as type double. List of sinusoid amplitude values as type double. The number of values in each of the lists must match the value for the number of spectra. Note: All frequency values are specified as a double representing a fractional part of the sampling frequency. For example, a value of 0.5 specifies a frequency that is half the sampling frequency. Here is a set of sample parameter values. Don't allow blank lines at the end of the data in the file. 400 0 0.0 1.0 5 0.1 0.2 0.3 0.4 0.45 60 70 80 90 100 The plotting program that is used to plot the output data from this program requires that the program implement GraphIntfc01. For example, the plotting program named Graph03 can be used to plot the data produced by this program. When it is used, the usage information is: java Graph03 Dsp028 A static method named transform belonging to the class named ForwardRealToComplex01 is used to perform the actual spectral analysis. The method named transform does not implement an FFT algorithm. Rather, it is more general than, but much slower than an FFT algorithm. (See the program named Dsp030 for the use of an FFT algorithm.) Tested using SDK 1.4.2 under WinXP. ************************************************/ import java.util.*; import java.io.*; class Dsp028 implements GraphIntfc01{ final double pi = Math.PI;//for simplification //Begin default parameters int len = 400;//data length //Sample that represents zero time. int zeroTime = 0; //Low and high frequency limits for the // spectral analysis. double lowF = 0.0; double highF = 1.0; int numberSpectra = 5; //Frequencies of the sinusoids double[] freq = {0.1,0.2,0.3,0.4,0.5}; //Amplitudes of the sinusoids double[] amp = {60,70,80,90,100}; //End default parameters //Following arrays will contain data that is // input to the spectral analysis process. double[] data1; double[] data2; double[] data3; double[] data4; double[] data5; //Following arrays receive information back // from the spectral analysis that is not used // in this program. double[] real; double[] imag; double[] angle; //Following arrays receive the magnitude // spectral information back from the spectral // analysis process. double[] magnitude1; double[] magnitude2; double[] magnitude3; double[] magnitude4; double[] magnitude5; public Dsp028(){//constructor //Get the parameters from a file named // Dsp028.txt. Use the default parameters // if the file doesn't exist in the current // directory. if(new File("Dsp028.txt").exists()){ getParameters(); }//end if //Note that this program always processes // five sinusoids, even if fewer than five // were requested as the input parameter // for numberSpectra. In that case, the // extras are processed using default values // and simply ignored when the results are // plotted. //Create the raw data. Note that the // argument for a sinusoid at half the // sampling frequency would be (2*pi*x*0.5). // This would represent one half cycle or pi // radians per sample. //First create empty array objects. double[] data1 = new double[len]; double[] data2 = new double[len]; double[] data3 = new double[len]; double[] data4 = new double[len]; double[] data5 = new double[len]; //Now populate the array objects for(int n = 0;n < len;n++){ data1[n] = amp[0]*Math.cos(2*pi*n*freq[0]); data2[n] = amp[1]*Math.cos(2*pi*n*freq[1]); data3[n] = amp[2]*Math.cos(2*pi*n*freq[2]); data4[n] = amp[3]*Math.cos(2*pi*n*freq[3]); data5[n] = amp[4]*Math.cos(2*pi*n*freq[4]); }//end for loop //Compute magnitude spectra of the raw data // and save it in output arrays. Note that // the real, imag, and angle arrays are not // used later, so they are discarded each // time a new spectral analysis is performed. magnitude1 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data1,real, imag,angle,magnitude1,zeroTime,lowF,highF); magnitude2 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data2,real, imag,angle,magnitude2,zeroTime,lowF,highF); magnitude3 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data3,real, imag,angle,magnitude3,zeroTime,lowF,highF); magnitude4 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data4,real, imag,angle,magnitude4,zeroTime,lowF,highF); magnitude5 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplex01.transform(data5,real, imag,angle,magnitude5,zeroTime,lowF,highF); }//end constructor //-------------------------------------------// //This method gets processing parameters from // a file named Dsp028.txt and stores those // parameters in instance variables belonging // to the object of type Dsp028. void getParameters(){ int cnt = 0; //Temporary holding area for strings. Allow // space for a few blank lines at the end // of the data in the file. String[] data = new String[20]; try{ //Open an input stream. BufferedReader inData = new BufferedReader(new FileReader( "Dsp028.txt")); //Read and save the strings from each of // the lines in the file. Be careful to // avoid having blank lines at the end, // which may cause an ArrayIndexOutOfBounds // exception to be thrown. while((data[cnt] = inData.readLine()) != null){ cnt++; }//end while inData.close(); }catch(IOException e){} //Move the parameter values from the // temporary holding array into the instance // variables, converting from characters to // numeric values in the process. cnt = 0; len = (int)Double.parseDouble(data[cnt++]); zeroTime = (int)Double.parseDouble( data[cnt++]); lowF = Double.parseDouble(data[cnt++]); highF = Double.parseDouble(data[cnt++]); numberSpectra = (int)Double.parseDouble( data[cnt++]); for(int fCnt = 0;fCnt < numberSpectra; fCnt++){ freq[fCnt] = Double.parseDouble( data[cnt++]); }//end for loop for(int aCnt = 0;aCnt < numberSpectra; aCnt++){ amp[aCnt] = Double.parseDouble( data[cnt++]); }//end for loop //Print parameter values. System.out.println(); System.out.println("Data length: " + len); System.out.println( "Sample for zero time: " + zeroTime); System.out.println( "Lower frequency bound: " + lowF); System.out.println( "Upper frequency bound: " + highF); System.out.println( "Number spectra: " + numberSpectra); System.out.println("Frequencies"); for(cnt = 0;cnt < numberSpectra;cnt++){ System.out.println(freq[cnt]); }//end for loop System.out.println("Amplitudes"); for(cnt = 0;cnt < numberSpectra;cnt++){ System.out.println(amp[cnt]); }//end for loop }//end getParameters //-------------------------------------------// //The following six methods are required by the // interface named GraphIntfc01. The plotting // program pulls the data values to be plotted // by invoking these methods. public int getNmbr(){ //Return number of functions to // process. Must not exceed 5. return numberSpectra; }//end getNmbr //-------------------------------------------// public double f1(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude1.length-1){ return 0; }else{ return magnitude1[index]; }//end else }//end function //-------------------------------------------// public double f2(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude2.length-1){ return 0; }else{ return magnitude2[index]; }//end else }//end function //-------------------------------------------// public double f3(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude3.length-1){ return 0; }else{ return magnitude3[index]; }//end else }//end function //-------------------------------------------// public double f4(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude4.length-1){ return 0; }else{ return magnitude4[index]; }//end else }//end function //-------------------------------------------// public double f5(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude5.length-1){ return 0; }else{ return magnitude5[index]; }//end else }//end function //-------------------------------------------// }//end class Dsp028 Listing 19 |
/* 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 stand-alone mode. At runtime, the name of the class that implements the GraphIntfc01 interface must be provided as a command-line 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 command-line 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 x-axis value xMax = maximum x-axis value yMin = minimum y-axis value yMax = maximum y-axis value xTicInt = tic interval on x-axis yTicInt = tic interval on y-axis xCalcInc = calculation interval The user can modify any of these parameters and then click the Graph button to cause the five functions to be re-plotted 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 command-line paramater new GUI(args[0]); }else{ //no command-line 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 x-axis, a default value is // used later. double xTicLen = (yMax-yMin)/50; double yTicLen = (xMax-xMin)/50; //Calculation interval along x-axis 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 one-column 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 one-column 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 sub-assemblies 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 X-button 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){ //Re-instantiate 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 x-axis, // a default value is used later. xTicLen = (yMax-yMin)/50; yTicLen = (xMax-xMin)/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/(xMax-xMin); yScale = height/(yMax-yMin); //Set the origin based on the // minimum values in x and y g.translate((int)((0-xMin)*xScale), (int)((0-yMin)*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 x-value to an int // and draw the next line segment int x = getTheX(xVal); g.drawLine(oldX,oldY,x,yVal); //Increment along the x-axis 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 x-axis and bottom // y-axis. 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 x-axis and the // top y-axis. 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 x-axis and the // height of the string for top of // y-axis //Create a string that is an // integer representation of the // label for the right end of the // x-axis. 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 // x-axis. The height applies to // all the labels, but is needed // specifically for the label at // the top end of the y-axis. int labWidth = (int)(r2d.getWidth()); int labHeight = (int)(r2d.getHeight()); //Label the positive x-axis and the // positive y-axis 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 x-axis 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 x-axis // 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 x-axis 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 y-axis 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 y-axis // moving up from zero. while(yDoub < yMax){ y = getTheY(yDoub); g.drawLine(rightEnd,y,leftEnd,y); yDoub += yTicInt; }//end while //Now do the negative y-axis 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 // y-axis 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 stand-alone // 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 20 |
/*File ForwardRealToComplex01.java Copyright 2004, R.G.Baldwin Rev 5/14/04 The static method named transform performs a real to complex Fourier transform. Does not implement the FFT algorithm. Implements a straight-forward sampled-data version of the continuous Fourier transform defined using integral calculus. See ForwardRealToComplexFFT01 for an FFT algorithm. Returns real, imag, magnitude, and phase angle in degrees. Incoming parameters are: double[] data - incoming real data double[] realOut - outgoing real data double[] imagOut - outgoing imaginary data double[] angleOut - outgoing phase angle in degrees double[] magnitude - outgoing amplitude spectrum int zero - the index of the incoming data sample that represents zero time double lowF - Low freq limit as fraction of sampling frequency double highF - High freq limit as fraction of sampling frequency The frequency increment is the difference between high and low limits divided by the length of the magnitude array The magnitude is computed as the square root of the sum of the squares of the real and imaginary parts. This value is divided by the incoming data length, which is given by data.length. Returns a number of points in the frequency domain equal to the incoming data length regardless of the high and low frequency limits. ************************************************/ public class ForwardRealToComplex01{ public static void transform( double[] data, double[] realOut, double[] imagOut, double[] angleOut, double[] magnitude, int zero, double lowF, double highF){ double pi = Math.PI;//for convenience int dataLen = data.length; double delF = (highF-lowF)/data.length; //Outer loop iterates on frequency // values. for(int i=0; i < dataLen;i++){ double freq = lowF + i*delF; double real = 0.0; double imag = 0.0; double ang = 0.0; //Inner loop iterates on time- // series points. for(int j=0; j < dataLen; j++){ real += data[j]*Math.cos( 2*pi*freq*(j-zero)); imag += data[j]*Math.sin( 2*pi*freq*(j-zero)); }//end inner loop realOut[i] = real/dataLen; imagOut[i] = imag/dataLen; magnitude[i] = (Math.sqrt( real*real + imag*imag))/dataLen; //Calculate and return the phase // angle in degrees. if(imag == 0.0 && real == 0.0){ang = 0.0;} else{ang = Math.atan(imag/real)*180.0/pi;} if(real < 0.0 && imag == 0.0){ang = 180.0;} else if(real < 0.0 && imag == -0.0){ ang = -180.0;} else if(real < 0.0 && imag > 0.0){ ang += 180.0;} else if(real < 0.0 && imag < 0.0){ ang += -180.0;} angleOut[i] = ang; }//end outer loop }//end transform method }//end class ForwardRealToComplex01 Listing 21 |
/* File Dsp030.java Copyright 2004, R.G.Baldwin Rev 5/14/04 Uses an FFT algorithm to compute and display the magnitude of the spectral content for up to five sinusoids having different frequencies and amplitudes. See the program named Dsp028 for a program that does not use an FFT algorithm. Gets input parameters from a file named Dsp030.txt. If that file doesn't exist in the current directory, the program uses a set of default parameters. Each parameter value must be stored as characters on a separate line in the file named Dsp030.txt. The required parameters are as follows: Data length as type int Number of spectra as type int. Max value is 5. List of sinusoid frequency values as type double. List of sinusoid amplitude values as type double. CAUTION: THE DATA LENGTH MUST BE A POWER OF TWO. OTHERWISE, THIS PROGRAM WILL FAIL TO RUN PROPERLY. The number of values in each of the lists must match the value for the number of spectra. Note: All frequency values are specified as a double representing a fractional part of the sampling frequency. For example, a value of 0.5 specifies a frequency that is half the sampling frequency. Here is a set of sample parameter values. Don't allow blank lines at the end of the data in the file. 128.0 5 0.1 0.2 0.3 0.4 0.45 60 70 80 90 100 The plotting program that is used to plot the output data from this program requires that the program implement GraphIntfc01. For example, the plotting program named Graph03 can be used to plot the data produced by this program. When it is used, the usage information is: java Graph03 Dsp030 A static method named transform belonging to the class named ForwardRealToComplexFFT01 is used to perform the actual spectral analysis. The method named transform implements an FFT algorithm. The FFT algorithm requires that the data length be a power of two. Tested using SDK 1.4.2 under WinXP. ************************************************/ import java.util.*; import java.io.*; class Dsp030 implements GraphIntfc01{ final double pi = Math.PI;//for simplification //Begin default parameters int len = 128;//data length int numberSpectra = 5; //Frequencies of the sinusoids double[] freq = {0.1,0.2,0.3,0.4,0.5}; //Amplitudes of the sinusoids double[] amp = {60,70,80,90,100}; //End default parameters //Following arrays will contain data that is // input to the spectral analysis process. double[] data1; double[] data2; double[] data3; double[] data4; double[] data5; //Following arrays receive information back // from the spectral analysis that is not used // in this program. double[] real; double[] imag; double[] angle; //Following arrays receive the magnitude // spectral information back from the spectral // analysis process. double[] magnitude1; double[] magnitude2; double[] magnitude3; double[] magnitude4; double[] magnitude5; public Dsp030(){//constructor //Get the parameters from a file named // Dsp030.txt. Use the default parameters // if the file doesn't exist in the current // directory. if(new File("Dsp030.txt").exists()){ getParameters(); }//end if //Note that this program always processes // five sinusoids, even if fewer than five // were requested as the input parameter // for numberSpectra. In that case, the // extras are processed using default values // and simply ignored when the results are // plotted. //Create the raw data. Note that the // argument for a sinusoid at half the // sampling frequency would be (2*pi*x*0.5). // This would represent one half cycle or pi // radians per sample. //First create empty array objects. double[] data1 = new double[len]; double[] data2 = new double[len]; double[] data3 = new double[len]; double[] data4 = new double[len]; double[] data5 = new double[len]; //Now populate the array objects for(int n = 0;n < len;n++){ data1[n] = amp[0]*Math.cos(2*pi*n*freq[0]); data2[n] = amp[1]*Math.cos(2*pi*n*freq[1]); data3[n] = amp[2]*Math.cos(2*pi*n*freq[2]); data4[n] = amp[3]*Math.cos(2*pi*n*freq[3]); data5[n] = amp[4]*Math.cos(2*pi*n*freq[4]); }//end for loop //Compute magnitude spectra of the raw data // and save it in output arrays. Note that // the real, imag, and angle arrays are not // used later, so they are discarded each // time a new spectral analysis is performed. magnitude1 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplexFFT01.transform(data1, real,imag,angle,magnitude1); magnitude2 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplexFFT01.transform(data2, real,imag,angle,magnitude2); magnitude3 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplexFFT01.transform(data3, real,imag,angle,magnitude3); magnitude4 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplexFFT01.transform(data4, real,imag,angle,magnitude4); magnitude5 = new double[len]; real = new double[len]; imag = new double[len]; angle = new double[len]; ForwardRealToComplexFFT01.transform(data5, real,imag,angle,magnitude5); }//end constructor //-------------------------------------------// //This method gets processing parameters from // a file named Dsp030.txt and stores those // parameters in instance variables belonging // to the object of type Dsp030. void getParameters(){ int cnt = 0; //Temporary holding area for strings. Allow // space for a few blank lines at the end // of the data in the file. String[] data = new String[20]; try{ //Open an input stream. BufferedReader inData = new BufferedReader(new FileReader( "Dsp030.txt")); //Read and save the strings from each of // the lines in the file. Be careful to // avoid having blank lines at the end, // which may cause an ArrayIndexOutOfBounds // exception to be thrown. while((data[cnt] = inData.readLine()) != null){ cnt++; }//end while inData.close(); }catch(IOException e){} //Move the parameter values from the // temporary holding array into the instance // variables, converting from characters to // numeric values in the process. cnt = 0; len = (int)Double.parseDouble(data[cnt++]); numberSpectra = (int)Double.parseDouble( data[cnt++]); for(int fCnt = 0;fCnt < numberSpectra; fCnt++){ freq[fCnt] = Double.parseDouble( data[cnt++]); }//end for loop for(int aCnt = 0;aCnt < numberSpectra; aCnt++){ amp[aCnt] = Double.parseDouble( data[cnt++]); }//end for loop //Print parameter values. System.out.println(); System.out.println("Data length: " + len); System.out.println( "Number spectra: " + numberSpectra); System.out.println("Frequencies"); for(cnt = 0;cnt < numberSpectra;cnt++){ System.out.println(freq[cnt]); }//end for loop System.out.println("Amplitudes"); for(cnt = 0;cnt < numberSpectra;cnt++){ System.out.println(amp[cnt]); }//end for loop }//end getParameters //-------------------------------------------// //The following six methods are required by the // interface named GraphIntfc01. The plotting // program pulls the data values to be plotted // by invoking these methods. public int getNmbr(){ //Return number of functions to // process. Must not exceed 5. return numberSpectra; }//end getNmbr //-------------------------------------------// public double f1(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude1.length-1){ return 0; }else{ return magnitude1[index]; }//end else }//end function //-------------------------------------------// public double f2(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude2.length-1){ return 0; }else{ return magnitude2[index]; }//end else }//end function //-------------------------------------------// public double f3(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude3.length-1){ return 0; }else{ return magnitude3[index]; }//end else }//end function //-------------------------------------------// public double f4(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude4.length-1){ return 0; }else{ return magnitude4[index]; }//end else }//end function //-------------------------------------------// public double f5(double x){ int index = (int)Math.round(x); if(index < 0 || index > magnitude5.length-1){ return 0; }else{ return magnitude5[index]; }//end else }//end function //-------------------------------------------// }//end class Dsp030 Listing 22 |
/*File ForwardRealToComplexFFT01.java Copyright 2004, R.G.Baldwin Rev 5/14/04 The static method named transform performs a real to complex Fourier transform using a crippled complex-to-complex FFT algorithm. It is crippled in the sense that it is not being used to its full potential as a complex-to-complex forward or inverse FFT algorithm. See ForwardRealToComplex01 for a slower but more general approach that does not use an FFT algorithm. Returns real, imag, magnitude, and phase angle in degrees. Incoming parameters are: double[] data - incoming real data double[] realOut - outgoing real data double[] imagOut - outgoing imaginary data double[] angleOut - outgoing phase angle in degrees double[] magnitude - outgoing amplitude spectrum The magnitude is computed as the square root of the sum of the squares of the real and imaginary parts. This value is divided by the incoming data length, which is given by data.length. CAUTION: THE INCOMING DATA LENGTH MUST BE A POWER OF TWO. OTHERWISE, THIS PROGRAM WILL FAIL TO RUN PROPERLY. Returns a number of points in the frequency domain equal to the incoming data length. Those points are uniformly distributed between zero and one less than the sampling frequency. ************************************************/ public class ForwardRealToComplexFFT01{ public static void transform( double[] data, double[] realOut, double[] imagOut, double[] angleOut, double[] magnitude){ double pi = Math.PI;//for convenience int dataLen = data.length; //The complexToComplex FFT method does an // in-place transform causing the output // complex data to be stored in the arrays // containing the input complex data. // Therefore, it is necessary to copy the // input data to this method into the real // part of the complex data passed to the // complexToComplex method. System.arraycopy(data,0,realOut,0,dataLen); //Perform the spectral analysis. The results // are stored in realOut and imagOut. The +1 // causes it to be a forward transform. A -1 // would cause it to be an inverse transform. complexToComplex(1,dataLen,realOut,imagOut); //Compute the magnitude and the phase angle // in degrees. for(int cnt = 0;cnt < dataLen;cnt++){ magnitude[cnt] = (Math.sqrt(realOut[cnt]*realOut[cnt] + imagOut[cnt]*imagOut[cnt]))/dataLen; if(imagOut[cnt] == 0.0 && realOut[cnt] == 0.0){ angleOut[cnt] = 0.0; }//end if else{ angleOut[cnt] = Math.atan( imagOut[cnt]/realOut[cnt])*180.0/pi; }//end else if(realOut[cnt] < 0.0 && imagOut[cnt] == 0.0){ angleOut[cnt] = 180.0; }else if(realOut[cnt] < 0.0 && imagOut[cnt] == -0.0){ angleOut[cnt] = -180.0; }else if(realOut[cnt] < 0.0 && imagOut[cnt] > 0.0){ angleOut[cnt] += 180.0; }else if(realOut[cnt] < 0.0 && imagOut[cnt] < 0.0){ angleOut[cnt] += -180.0; }//end else }//end for loop }//end transform method //-------------------------------------------// //This method computes a complex-to-complex // FFT. The value of sign must be 1 for a // forward FFT. public static void complexToComplex( int sign, int len, double real[], double imag[]){ double scale = 1.0; //Reorder the input data into reverse binary // order. int i,j; for (i=j=0; i < len; ++i) { if (j>=i) { double tempr = real[j]*scale; double tempi = imag[j]*scale; real[j] = real[i]*scale; imag[j] = imag[i]*scale; real[i] = tempr; imag[i] = tempi; }//end if int m = len/2; while (m>=1 && j>=m) { j -= m; m /= 2; }//end while loop j += m; }//end for loop //Input data has been reordered. int stage = 0; int maxSpectraForStage,stepSize; //Loop once for each stage in the spectral // recombination process. for(maxSpectraForStage = 1, stepSize = 2*maxSpectraForStage; maxSpectraForStage < len; maxSpectraForStage = stepSize, stepSize = 2*maxSpectraForStage){ double deltaAngle = sign*Math.PI/maxSpectraForStage; //Loop once for each individual spectra for (int spectraCnt = 0; spectraCnt < maxSpectraForStage; ++spectraCnt){ double angle = spectraCnt*deltaAngle; double realCorrection = Math.cos(angle); double imagCorrection = Math.sin(angle); int right = 0; for (int left = spectraCnt; left < len;left += stepSize){ right = left + maxSpectraForStage; double tempReal = realCorrection*real[right] - imagCorrection*imag[right]; double tempImag = realCorrection*imag[right] + imagCorrection*real[right]; real[right] = real[left]-tempReal; imag[right] = imag[left]-tempImag; real[left] += tempReal; imag[left] += tempImag; }//end for loop }//end for loop for individual spectra maxSpectraForStage = stepSize; }//end for loop for stages }//end complexToComplex method }//end class ForwardRealToComplexFFT01 Listing 23 |
Copyright 2004, 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 high-tech companies located in and around
Austin, Texas. He is the author of Baldwin’s Programming Tutorials,
which has 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 real-world problems.
-end-