DMelt:Math/1 Functions
Mathematical functions
Function is a relation between inputs values and a set of outputs values such that each input is related to exactly one output.
DataMelt can be used to evaluate, plot, simplify functions. There are several approaches to work with parametric or non-parametric functions using DataMelt Java API:
- User 2D/3D function plotter from DataMelt IDE (see the "Tools" menu)
- Simplify and plot functions using ether Octave or Matlab syntax. Plotting functions in such approach is a bit too simplistic and there is no good integration with Java API and data structures
- Use the full-blown DataMelt Java libraries to create parametric, non-parametric, special functions as they are provided by Java API. One can also integrate, differentiate and simplify such functions. In this case one can use the Java language, or any Java scripting languages (such as Jython or BeanShell). There is complete integration with data structures API and functions can be plotted in 2D/3D using several powerful interactive canvases.
In this section we will discuss the third option as it is the most flexible for integration with Java libraries. We will start with simple analytical functions with one variable ("x") and then will consider more complicated cases with several variables ("y", "z"). Finally, we will show how to make function of arbitrary complexity with any number of variables
Functions with one variable
Functions can be defined using either strings or programically. First we will discuss how to show functions using strings and then we will show other, more flexible methods.
We will start this tutorial with a simple example of how to create and display a function with one and only one variable ("x"). First, let us create an object representing a function:
[math]\displaystyle{ f(x)=2*exp(-x*x/50)+sin(pi*x)/x }[/math]
Such functions are represented by the 
jhplot.F1D Java class (1D means one-dimensional function).
from jhplot import *
f1 = F1D("2*exp(-x*x/50)+sin(pi*x)/x")
Here the input function is given by java.lang.String. One can evaluate this function at a certain point, say at 10, as:
print f1.eval(10) 0.270670566473
By default, all trigonometric functions are implemented using the fast calculations with e-14 accuracy. Note, you can also use exact calculations using </javadoc>java.lang.Math</javadoc> using additional parameter:
from jhplot import *
HParam.setExactMath()
f1 = F1D("2*exp(-x*x/50)+sin(pi*x)/x")
The method "HParam.setExactMath()" tells that full precision of Java Math package is not required. In this case, calculations of trigonometric
functions is a factor 2-3 slower. Look at the class jhplot.HParam.
The code shown below benchmark calculations of F1D function containing trigonometric functions with and without the fast math enabled.
from jhplot import *
import time
HParam.setFastMath() # apply fast calculations for functions
# comment out this code for full precision (java Math)
print "Fast calculations are set to ", HParam.isFastMath(), " working .."
a=F1D("acos(x)+asin(x)+atan(2*x)+cos(x)")
s=0;
start = time.time()
for i in range (10000000):
s=s+a.eval(0.99-0.00000001*i)
end = time.time()
print "fast calculation result=",s, " time used (sec)",end - start
HParam.setFastMath(False)
print "Fast calculations are set to ", HParam.isFastMath(), " working .."
s=0;
start = time.time()
for i in range (10000000):
s=s+a.eval(0.99-0.00000001*i)
end = time.time()
print "fast calculation result=",s, " time used (sec)",end - startTo draw the function, one should specify limits during the function initialization (or later using the
methods of the Java object jhplot.F1D. Then one should build a canvas and use the method "draw" to plot the function.
In the example below, we define the limits for this function (-2,5) and then we plot it:
from jhplot import *
f1 = F1D("2*exp(-x*x/50)+sin(pi*x)/x", 1.0, 10.0)
c1 = HPlot("Example of function")
c1.visible()
c1.setAutoRange()
c1.draw(f1)Here we drop the Python invitation ">>>" since we assume that the this code snippet is executed as a file. Insert these lines to the DataMelt editor and hit the key [F8]. You will see a pop-up window with the output as shown below:
To learn about the method of DataMelt or any Jython or Java objects (like f1 and c1 in the above example):
- Hit <Ctrl>+<Space> after the dot when using the Jython shell prompt (bottom part of the DataMelt IDE).
- When using the DataMelt editor, use the key [F4] (after the dot) which brings up a table with all available Java methods.
- Use Eclipse or NetBean IDE when developing the code using Java. They have their own help system.
- Read the Java API of these classes.
 | F1D code examples |
Information about the syntax
Here is a short description about how to define functions. The following table shows the main mathematical operations:
() parenthesis + plus - minus * times / divide ^ raise to a power ** raise to a power
Functions:
| String | Definition |
|---|---|
| abs | absolute value |
| log(x) | Natural Logarithm |
| log10(x) | Logarithm base 10 |
| sqrt(x) | <m>sqrt{x}</m> |
| exp(x) | Exp |
| ceil | nearest upper integer |
| floor | nearest lower integer |
| cbrt | cubic root |
| sin(x) | Sine |
| cos(x) | Cosine |
| tan(x) | Tangent |
| asin(x) | ArcSine |
| acos(x) | ArcCosine |
| atan(x) | ArcTangent |
| sinh(x) | Hyperbolic Sine |
| cosh(x) | Hyperbolic Cosine |
| tanh(x) | Hyperbolic Tangent |
One can also use some predefined constants, such as pi or Pi (the pi value, 3.14..). It is possible to use Scientific notation for numbers. The number is split into a significant / mantissa (y) and exponent (x) of the form 'yEx' which is evaluated as 'y * 10^x'. Let us give the examples:
from jhplot import *
f1=F1D("1+x+(x^2)-(x^3)") # correct answer -1
print f1.eval(2)
f1=F1D("1+x+(x*x)-(x*x*x)") # correct answer -1
print f1.eval(2)
f1=F1D("1+x+x^2-x^3") # correct answer -1
print f1.eval(2)
f1=F1D("1+x+x**2-x**3") # correct answer -1
print f1.eval(2)
Parameters
Functions can have parameters. If you have parameters, you should avoid "parsing" functions, and apply parsing only after finals substitution. This example shows how to work with such functions:
f1=F1D("(3*alpha)/alpha+10+x",0) # 0 means False
f1.simplify() # simplify the function
print f1.getName()
f1.setPar("alpha",100) # substitute alpha=100
f1.parse() # parse it for evaluation, plotting etc.
print f1.getName()
print f1.eval([1,2,3])
Please also consider the method f1.toString() that prints more information about jhplot.F1D.
Function initialization
Functions can be initialized with and without ranges.
When using this syntax:
from jhplot import *
f1=F1D("x^2")
function does not have limits used for plotting and evaluation. For such definition, plot ranges are entirely determined by the plotting canvases (i.e. by the setRange() methods.
When using this syntax:
from jhplot import *
f1=F1D("x^2",-10,10) # limit to [-10,10]
The function includes limits for numeric calculation and plotting. This means you can set auto-range for plotting canvases, since the function includes the X-range in the definition, thus it will be shown in the range [-10,10], as in this example.
Non-parametric functions
The most flexible way to draw functions is to use codding with objects and calling third-party Java libraries directly, instead of using strings with function definitions. This topic will be discussed in Section Non-parametric functions
Symbolic manipulations
Symbolic manipulation with functions will be discussed in more details in Section Introduction. Still, one should note that symbolic manipulations can also be done with the F1D functions using Java (or Jython) coding without using any other specialized language (as Matlab or Octave). One can simplify and expand F1D functions. Functions can also be rewritten in terms of elementary functions (log, sqrt, exp). Finally, one can perform some elementary substitutions before attempting to plot a F1D function.
In this example we simplify a few elementary functions:
from jhplot import * s="sqrt(17/12)-(1/6*sqrt(51))" f1 = F1D(s) f1.simplify() print "Simplified "+s+" = ",f1.getName() s= "(x^2-1)/(x-1)" f1 = F1D(s) f1.simplify() print "Simplified "+s+" = ",f1.getName()
It is often useful to rewrite an expression in terms of elementary functions (log, exp, frac, sqrt, implicit roots), using the "elementary()" method, before simplifying it.
from jhplot import * s="cos(x)^2+sin(x)^2" f1 = F1D(s) f1.elementary() print "In ementary functions "+s+" = ",f1.getName() f1.simplify() print "Simplified "+s+" = ",f1.getName()
Integration and differentiation
Functions can be numerically integrated. The program supports 5 methods of integration, which vary in evaluation time and accuracy. Below we will integrate the function "cos(x*x)+sin(1/x)*x^2)"
from jhplot import *
import time
f1=F1D('cos(x*x)+sin(1.0/x)*x^2')
methods=['trapezium','gauss4', 'gauss8', 'richardson', 'simpson']
for m in methods:
start = time.clock()
d=f1.integral(m,5000,10,100)
t = time.clock()-start
print m,' =',d,' time (ms)=',t*1000This code integrates the function between 10 and 100 using 5000 integration point (the large the number is, the more accurate the results are). The code also performs some benchmarking which gives you ideas about the accuracy of calculations:
trapezium = 4949.64217622 time (ms)= 31.654937 gauss4 = 4949.64028115 time (ms)= 43.84014 gauss8 = 4949.64028111 time (ms)= 65.27855 richardson = 4949.64028393 time (ms)= 56.030938 simpson = 4949.64014049 time (ms)= 17.634015
Displaying functions
Here is a more detailed example showing how to plot several functions using different colors:
from java.awt import Color
from jhplot import *
# first function
f1 = F1D("2*exp(-x*x/50)+sin(pi*x)/x", 1.0, 10.0)
f1.setPenDash(4)
# second function
f2 = F1D("exp(-x*x/50)+pi*x", 1.0, 10.0)
f2.setColor(Color.green)
f2.setPenWidth(1)
# build a canvas with X and Y titles
c1 = HPlot("Canvas")
c1.setGTitle("2 functions", Color.red)
c1.setMarginLeft(90)
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()
c1.draw(f1)
c1.draw(f2)
Note that we have imported the Java class java.awt.Color. The output of this script is shown here
You can also plot objects on different pads as shown in the Section Plotting canvases.
Exporting functions
As any Java object, F1D functions can be saved into files. Read Input and Output for detailed discussion. In addition, one can convert a function in MathML or Java formats, or just display it as a table with values after evaluation of a function.
>>> from jhplot import F1D
>>> f=F1D("10*sin(x)")
>>> print f.toMathML() # convert to MathML
>>> print f.toJava() # convert to Java code
>>> f.toTable() # show a table with X-Y values (the function should be plotted or evaluated before)
2D Functions
Functions in 2 dimensions can be build analogously using the
Java class jhplot.F2D.
The example below shows how to construct and evaluate a function 2D:
>>> from jhplot import *
>>> f1 = F2D("2*exp(-x*y/50)+sin(pi*x)/y")
>>> print f1.eval(10,20)
0.0366312777775
The code prints the answer: 0.270670566473.
Such functions can be displayed as will be shown later. Also, look at the Sections 2D Plots and 3D Plots
3D Functions
Functions in 2 dimensions can be build by analogy using the
Java jhplot.F3D class.
The example below shows how to construct and evaluate a function 3D:
>>> from jhplot import *
>>> f1 = F3D("2*exp(-x*y/50)+sin(pi*x)/z")
>>> print f1.eval(10,20,30)
0.0366312777775
The code prints the answer: 0.0366312777775.
Converting functions to histograms
Histograms can be created from 1D and 2D functions. In the example above, we created a 2D function and then used it to generate 2D histogram. Functions can be converted to Histograms with arbitrary number of bins. This often can be used for show selected regions in different color.
Consider the example in which a function is used to create a histogram with fine bins. We use this histogram to highlight a range of our original function.
from java.awt import Color
from jhplot import *
c1 = HPlot("Canvas",700,400,2, 1)
c1.setAutoRange()
c1.setGTitle("Converting to histogram", Color.red) #put title
c1.setNameX("X")
c1.setNameY("Y")
c1.visible(1)
c1.cd(1,1)
c1.setAutoRange()
f1 = F1D("2*exp(-x*x/50)+sin(pi*x)/x", -2.0, 5.0)
c1.draw(f1)
c1.cd(2,1)
c1.setAutoRange()
c1.setNameX("X")
c1.setNameY("Y")
h=f1.getH1D()
h.setColor(Color.red)
h.setFill(1)
h.setFillColor(Color.red)
h.setErrX(0)
h.setErrY(0)
c1.draw(h)
# export to some image (png,eps,pdf,jpeg...)
# c1.export(Editor.DocMaster
Histograms can have arbitrary number of bins, but if the bin number is close to 500 (a typical evaluation step for functions), the difference between function and a histogram will be almost impossible to see.
Plotting in 3D
F2D functions can be shown using 3D canvases. They can be overlayed and shown on a single plot. Here is a small example of showing 2 functions in 3D:
from java.awt import Color
from jhplot import *
c1 = HPlot3D('Canvas',600,600)
c1.setNameX('X')
c1.setNameY('Y')
c1.visible()
f1=F2D('x^2+y^2', -2.0, 2.0, -2.0, 2.0)
f2=F2D('4*x*y', -2.0, 2.0, -2.0, 2.0)
c1.draw(f1,f2)The output of this code is:
One can also overlay several different objects. Let us show an example of how to show functions and histograms in 3D:
The code used to generate this image is given below.
from jhplot import HPlot3D,H2D,F2D
from java.util import Random
c1 = HPlot3D("Canvas",600,400)
c1.setGTitle("F2D and H2D objects")
c1.setTextBottom("Global X")
c1.setTextLeft("Global Y")
c1.setNameX("X axis")
c1.setNameY("Y axis")
c1.setColorMode(4)
c1.visible(1)
h1 = H2D("My 2D Test 1",30,-3.0, 3.0, 30, -3.0, 3.0)
f1 = F2D("8*(x*x+y*y)", -3.0, 3.0, -3.0, 5.0)
rand = Random()
for i in range(10000):
h1.fill(0.4*rand.nextGaussian(),rand.nextGaussian())
c1.draw(h1,f1)
# export to some image (png,eps,pdf,jpeg...)
# c1.export(Editor.DocMasterName()+".png")
You can plot an arbitrary function in 3D using jhplot.HJavaView by defining function programmatically, and rebuilding vertexes for surface plots.
Here is how you can do it:
# Show how to visualise a 2D function using Python code
# @author Sergei Chekanov
from jv.geom import PgElementSet
from jhplot import HJavaView
from java.lang import Math
from math import *
geom = PgElementSet(3)
geom.setName("2D function")
# Set number of lines in x and y directions.
numXLines,numYLines = 50,50
def func(X,Y): # define some function in 2D using Python syntax
return 10*sin(sqrt(X*X+Y*Y))/sqrt(X*X+Y*Y);
# ranges in X and Y
xMin,xMax=-10.,10
yMin,yMax=-10.,10
zMin,zMax=0,10
geom.setNumVertices(numXLines*numYLines) # Allocate space for vertices
ind = 0 # Compute the vertices
x=xMin;
for i in xrange(numXLines):
x = xMin + i*(xMax-xMin)/(numXLines-1)
for j in xrange(numYLines):
y = yMin + j*(yMax-yMin)/(numYLines-1)
geom.setVertex(ind, x, y, func(x,y))
ind=ind+1
# Compute connectivity of a rectangular mesh.
geom.makeQuadrConn(numXLines, numYLines)
geom.makeElementColorsFromXYZ() # add color and transparancy
geom.showElementColors(True)
geom.setTransparency(0.5);
geom.showTransparency(True);
# show everythin
c1= HJavaView()
c1.draw(geom)
view=c1.getView()
disp=view.getDisplay()
disp.showAxes(True)
disp.showBndBox(True);
c1.visible()The output of this code is:
You can adjust all visual characteristics of this plot using JavaView GUI.
Multidimensional functions
So far we have learned how to build functions in 1D (F1D class), 2D (F2D class) and 3D (F3D class). In addition to these "fixed dimension" classes, there is a
jhplot.FND which can be used to build a function with arbitrary parameters and variables. To build such function is very easy and similar to the above styles, only now one should be more explicit about the names of variables. As example, below we make a function with 4 variables, called xx, yy, bb, kk:
from jhplot import *
f=FND("xx*sqrt(yy)*bb*+kk",'xx,yy,bb,kk')
ans=f.eval('xx=10,yy=1,bb=0,kk=20')
print ans
Such functions can be evaluated at any variable values and plotted.
Integration
Numeric integration of functions is discussed in section Integration. Symbolic integrations will be discussed in dedicated sections.
Let us give a small example showing how to integrate [math]\displaystyle{ cos(x)^3 }[/math] using a trapezium rule. We will integrate this function between 1 and 10 using 10k iterations.
from jhplot import F1D
f1=F1D('cos(x)^3')
print f1.integral(10000,1,10)
Differentiation
For differentiation, use the "diff" method. As argument, you must specify variable used for differentiation.
>>> from jhplot import *
>>> fn=FND('x^4+x^2+x^4+y+z+h+10+20','x,y,z,h')
>>> fn.diff('x') # differentiate using x
>>> print 'Differentiate=',fn.toString()
Diffirentiate= 4.0*x^3.0+2.0*x+4.0*x^3.0
Simplification
F1D functions can be simplified using the method "simplify". The example below simplifies
>>> from jhplot import *
>>> fn=FND('x^4+x^2+x^4+y+z+h+10+20','x,y,z,h')
>>> fn.simplify()
>>> print 'Simplify=',fn.toString()
The output is:
Simplify= 30.0+x^4.0+x^2.0+x^4.0+y+z+h
Minimization
Please read the section Minimization
Expression Builder
Multidimensional functions can also be built using the package jhplot.math.exp4j.
In the example below we build a function using jhplot.math.exp4j.ExpressionBuilder:
from java.awt import*
from jhplot import *
from jhplot.math.exp4j import *
e=ExpressionBuilder("3 * sin(x) - 2 / (x - 2)")
e.variables("x")
ff=F1D("Test", e.build() ,10,100)
print ff.eval(10) # check at x=10
c1 = HPlot()
c1.visible(1)
c1.setAutoRange()
c1.draw(ff)
You can build an arbitrary function using Python and plot it, mixing Java and Jython libraries in one code. You can put an arbitrary complex expressions and "if" statements. In the example below we make a custom function using a Jython class and build an "ExpressionBuilder" object. then we evaluate this function for array of "X" values and plot it.
from java.awt import*
from jhplot import *
from jhplot.math.exp4j import *
from jhplot.math.exp4j.function import *
class cf2(Function):
def apply(self,val):
if (val[0]<100):
return val[0]*val[0]
if (val[0]>=100):
return val[0]
e=ExpressionBuilder("cf2(x)")
func=cf2("cf2",1);
e.function(func)
e.variables("x")
fff=e.build()
#fff.setVariable("x", 10);
#print fff.evaluate()
c1 = HPlot()
c1.visible(1)
c1.setAutoRange()
f=F1D(e.build(), 10,1000)
c1.draw(f)Now we can show how to create a custom function in 3D. Again, we will use a python code to do this.
from java.awt import*
from jhplot import *
from jhplot.math.exp4j import *
from jhplot.math.exp4j.function import *
class cf2d(Function):
def apply(self,val):
if (val[0]<50 or val[1]<50):
return val[1]*val[0]
if (val[0]>=50 and val[1]>=50):
return val[0]*val[0]+val[1]*val[1]
e=ExpressionBuilder("cf2d(x,y)")
func=cf2d("cf2d",2);
e.function(func)
e.variables("x")
e.variables("y")
fff=e.build()
#fff.setVariable("x", 10);
#print fff.evaluate()
c1 = HPlot3D()
c1.visible(1)
c1.setAutoRange()
f=F2D(e.build(), 0,200, 0,200)
c1.draw(f)Fast Math
The implementation of trigonometric functions is a 3-4 times slower performance compare to C/C++. This is especially true for atan2(), acos(), asin() functions. By default, DMelt uses fast calculations for all F1D, F2D etc. functions. The table below shows the performance of FastMath compared to the standard Java implementation blog. The times are in mean nanoseconds.
<html>
<thead> </thead> <tbody> </tbody>| Function | Math Mean | FastMath Mean | Times Faster |
|---|---|---|---|
| acos | 58 | 16 | 3.6 |
| asin | 57 | 15 | 3.8 |
| atan | 94 | 15 | 6.2 |
| atan2 | 145 | 23 | 6.3 |
| cbrt | 112 | 18 | 6.2 |
| ceil | 10 | 11 | 0.9 |
| cos | 74 | 13 | 5.7 |
| cosh | 141 | 28 | 5.0 |
| exp | 73 | 16 | 4.6 |
| expm1 | 113 | 17 | 6.6 |
| floor | 11 | 12 | 0.9 |
| hypot | 435 | 23 | 18.9 |
| log | 31 | 16 | 1.9 |
| log10 | 27 | 13 | 2.1 |
| log1p | 97 | 15 | 6.5 |
| nextAfter | 11 | 10 | 1.1 |
| nextUp | 10 | 8 | 1.3 |
| pow | 101 | 38 | 2.7 |
| round | 11 | 10 | 1.1 |
| sin | 72 | 16 | 4.5 |
| sinh | 126 | 23 | 5.5 |
| sqrt | 7 | 15 | 0.5 |
| tan | 52 | 14 | 3.7 |
| tanh | 167 | 26 | 6.4 |
| toDegrees | 7 | 8 | 0.9 |
| toRadians | 7 | 7 | 1.0 |
</html>
DMelt also include less precise methods for fast evaluation basic functions using jhplot.math.MathUtilsd. They are faster than the functions shown in above table, but their accuracy is roughly 1E-05.
Using special functions
You can integrate special functions from third-party Java libraries into your code and show them in 2D and 3D.
Parametric functions
Please read the section Parametric functions.
