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File “hello.py”
Working with complex numbers
Complex transcendental functions
Java list example
Sorting Java lists
Sorting and removing duplicates
File “Func.py”
A thread example
Benchmarking Jython arrays
Writing a CSV file
Reading a CSV file
Converting CSV file
Reading a CSV file using sniffer
Writing data using the java.io package
Reading data using the java.io package
File “walker.py”
File collector
Building a file list
File list splitter
Swing GUI using Jython
Creating a F1D function
Initiation of a F1D function without
parsing
Integration of a function
A function with parameters
Plotting two functions on the same canvas
Plotting two functions using
java.awt.Color
Using 2 pads
Using HPlot canvas
Plotting 2D functions
Contour plot
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2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
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G. Schaub and T. Turek, Energy Flows, Material Cycles
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Drawing a FND function
Drawing a FND function
Creating a non-continuous function using
the Java class FNon
Creating 1 − er f (x) function using the
Apache library
Building a function using Expression
Builder
Building a custom function using
Expression Builder
Showing a 2D function using 3D canvas
File “p2.py”
Building a custom function
Setting parameters
Setting parameters
Calling Java libraries. Module
“special.py”
Calling special functions
Conversion to F1D
Conversion to F1D
Drawing parametric functions
Numerical minimization of a 2D function
Minimization of a 2D function
Minimization of a 2D function n Java
Using Migrad for a 1D function
Using Migrad for a 2D function
Serialization of functions
Transforming an array using the P0D class
Plotting an one-dimensional array using
the P0D Java class
Creating an one-dimensional array using
the F1D function
Serialized I/O
Java serialized I/O for the P0D class
Writing multiple 1D arrays
Reading multiple 1D arrays
Statistical characteristics of X and Y
values
Adding data with correlations
Errors from two input P1D arrays
P1D serialization
Serialization of multiple containers
Serialization using dictionaries
Henon attractor
“measurement.py” module
Adding data
module “average.py”
A weighted average
Drawing P2D data in 3D
Showing P2D data in 3D
Showing objects in 3D using the P3D
class
Showing PND data as a histogram
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5.20
5.21
5.22
5.23
5.24
5.25
5.26
6.1
6.2
6.3
6.4
6.5
6.6
6.7
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5.5
5.6
5.7
5.8
Visualizing a Lorenz vector
3D array using the Python approach
4D arrays using Nd4j Java library
Solving a linear system of equations
Computing eigenvectors of a real
symmetric matrix
Working with complex matrices
Creating vectors in La4J
Creating matrices in La4J
Operations on La4J matricies
User-defined transformations
File output and input using the
MatrixMarket format
Operations on EJML matricies
File input and output
Matrix visualization using a 2D density
plot
Types of matrices of the Colt library
Working with Colt matrices
Matrix operations using multithreading
JBLAS matrix operations
Apache matrix operations
Vector operations
Operations on matrices using the Python
approach
Creating a tensor using Redberry
Quadratic equation
Solving a cubic equation
Solving a polynomial equation
Solving a linear system of equations using
La4J
Solving a linear system of equations using
substitutions
Solving a linear system of equations using
EJML
Solving a system of linear equations using
Apache Math
Converting to elementary functions
Numeric conversions
Simplifying expression
Reducing to elementary functions
Symbolic substitution rules
Integration and simplification
Integration of symbolic calculations with
DMelt Java classes
Series expansion
SymPy symbolic calculations
Differentiation using SymPy
Plotting histograms
Histogram operations
Histogram created from a function
Filling a 2D histogram
Showing several 2D histograms in 3D
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5.2
3
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9.2
9.3
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9.5
9.6
9.7
9.8
9.9
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8.16
8.17
8.18
8.19
8.20
8.21
8.22
8.23
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7.13
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
8.9
8.10
8.11
8.12
8.13
8.14
2D contour histogram
Profile histogram
Working with a 2D profile histogram
Saving multiple histograms in a file
Reading histograms from a file
Writing and reading histograms without
graphical attributes
Writing 10 arrays to files
Reading histograms
Text label example
Keys and their descriptions
Showing special symbols
Using mathematical symbols
A graph with interactive objects
Chart examples
A pie chart using JFreeChart
SPlot example
Creating a Henon attractor
Drawing “gluon” and “fermion” lines
Building blocks of the DMelt program
Drawing X-Y data using HPlotXY
HPlotXY example
Different types of data showed using
HPlotJas
Visualizing 2D functions
Showing data on the HPlot2D canvas
Showing a parametric function in 3D
Drawing a Kuen Surface in 3D
Drawing a half-torus in 3D
Showing real-time data using HPlot
Real-time data showed in SPlot canvas
Showing real-time line traces
Combining plotting area with swing
components
Writing objects to a file
Reading objects from a file
Using Java object browser for files
Writing event records with objects
Reading random events from a file
Benchmarking I/O operations
Writing DMelt objects in PFile files
Reading DMelt objects from PFile files
Reading objects using their names
Browsing data in PFile files
Writing an XML file with numeric data
Reading data from an XML HBook file
Reading XML HBook using a data
browser
Writing ASCII data
Reading ASCII data
Reading ASCII data in Python
File “table.csv”
Reading the file “table.csv”
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7.8
7.9
7.10
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9.41
9.42
9.43
9.44
9.45
9.46
9.47
9.48
9.49
9.50
9.51
9.52
9.53
9.54
9.55
9.56
9.57
9.58
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Showing “table.csv” file in a spreadsheet
Writing “out.csv” file
Writing lists to EDN files
Reading data from an EDN file
Reading files in the DIF format
Reading a ROOT file
Reading AIDA files
Data prototype. File “experiment.proto”
Generating a code based on Prototype
Buffer
Writing data into a file. “WriteData.java”
file
Compiling code with Prototype Buffer
Running WriteData program
Reading data. “ReadData.java” file
Running ReadData program
Writing data into a file. “WriteData.py”
file
Reading data into a file. “ReadData.py”
file
Adding new records. “AddData.py” file
Generating C++ code
Writing data into a file. “write_test.cc” file
Compiling the source codes
Reading data from a file. “read_test.cc”
file
Generating C++ code
Creating a simple excel spreadsheet
Creating a large excel file with random
numbers
Reading an excel spreadsheet
Creating a excel with styles
A simple object database with addresses
An object database: event records
Creating a file-based map with objects
Reading the file map with objects
Creating a MapDB database with numeric
objects
Reading MapDB database
Creating a NeoDatis database with data
Reading objects from a NeoDatis database
Common module: “openDB.py”
Creating a SQL database
Reading a SQL database
Compacting a SQL database to a jar file
A module to work with binary data
Writing a histogram and 2D array to a
database
Reading a histogram and 2D array from
the database
Creating and filling a HSQLDB database
Creating a simple SQLite database
Reading a SQLite database
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9.21
9.22
9.23
9.24
9.25
9.26
9.27
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10.13
10.14
10.15
10.16
10.17
10.18
10.19
10.20
10.21
10.22
10.23
10.24
10.25
10.26
10.27
10.28
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10.8
Example of a configuration file
Reading configuration file
Reading a configuration file
Descriptive statistics with Colt
Showing statistics of a histogram
Statistical analysis of lists
Calculation of correlation coefficients
Creating random numbers in Python loops
Random numbers using high-level DMelt
methods
Colt random numbers
An empirical distribution of random
numbers
Creating random density distributions in
Colt
Convoluting Gamma with Poisson
random numbers
Checking random numbers using a
histogram
Random arrays with arbitrary PDF
Statistical summary of the binomial PDF
Creating random numbers using the
Gamma function
Calculating the p-value for the Normal
distribution
Numeric calculation of the p-value
Calculating the number events for p-value
of 0.05
Calculating the p-value for the Poisson
statistics
p-value for the Poisson statistics with
uncertainty
Converting the p-value 3 × 10−7 to Z
Statistical significances for 3 and 5 σ
95% confidence interval using the
t-distribution
Calculation of p-value for the Poisson
statistics
Calculation of C L b using a Monte Carlo
method
95% confidence interval in the
Cousins–Highland method
Calculation of a χ 2 test for X–Y data
Calculation of the χ 2 test
Comparing two histograms using the χ 2
test
Creating fake data for limit settings
Calculating exclusion limit using
histograms
Creating fake “Higgs” data using random
numbers
Calculating 5σ significance for “Higgs
discovery”
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9.65
10.1
10.2
10.3
10.4
10.5
10.6
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11.10
11.11
11.12
11.13
11.14
11.15
11.16
11.17
11.18
11.19
12.1
12.2
12.3
12.4
12.5
12.6
12.7
12.8
12.9
12.10
12.11
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12.15
12.16
12.17
12.18
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EC
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10.35
Fitting data with a signal plus background
function
Propogation of statistical uncertainties
Propogation of statistical uncertainties for
arrays
Uncertainty propagation using a Monte
Carlo method
Uncertainty propagation using random
numbers
Creating data for the linear regression
analysis
Linear regression analysis
Performing a fit using a Gaussian function
Preparing histograms for curve fitting
Signal + background fit of data
Creating a histogram with multiple peaks
Fitting multiple peaks
Fitting a 2D histogram using an analytic
function
Fitting x−y data with a second-order
polynomial
An interactive fit of x−y data
Performing a polynomial regression
Data preparation for fitting
Creating a fit function
Fitting data using the MIGRAD algorithm
Visualization of the fit results
Step 1: Data preparation
Step 2: Fit results
Performing a symbolic regression using
input data
Drawing results of symbolic regression
Reading a CSV file from the web
Downloading a CSV file showing it in a
spreadsheet
Transforming data records
Skimming data records
Slimming data records
Sorting records
A custom sorting function
Module “unique.py”
Testing duplicate removal module
Removing duplicate entries
Sorting and removing duplicates using
java.util
Creating a file with multiple events
Analyzing complex data
Creating metadata records
Data analysis using metadata
Making an external file with metadata
Using external metadata records
Creating data files with arrays
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13.4
13.5
13.6
13.7
13.8
13.9
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13.11
13.12
13.13
13.14
13.15
13.16
13.17
13.18
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12.31
12.32
12.33
12.34
12.35
12.36
12.37
12.38
13.1
13.2
EC
12.28
12.29
RR
12.25
12.26
12.27
Module “readthread.py”
Reading data in two threads
Data writer. Module “pwrite.py”
Module “readthread2.py”
Data reader using 1 thread
Reading data in parallel using 3
independent threads
Module “functhread.py”
Evaluation of four functions in parallel
Creating a data set using the Rutherford
formula
Analyzing a particle distribution
Creating data with independent particle
production
Modelling distances between particles
Modelling correlations between particles
Plotting multiplicity distributions
Studies of fluctuations
Getting data from the Web
“reader.py” module to read a CSV file
Analyzing galaxies
Reading a data file
Analyzing elementary particles
Creating a CSV file with input and output
Creating a neural net and train using a
CSV file
Restoring the neural network
Programming a neural network using
Encog
Solving the XOR problem with the
Neuroph package
Training backpropagation neural network
using words
Backpropagation neural network using
words
Creating a data set for neural network
studies
Standardizing input data for neural
networks
Rescaling the data for neural networks
Preparing data for neural network studies
Building a feedforward neural network
Training a neural net
Neural network verification
Using the neural network for predictions
Building a simple Bayesian network (I)
Working with Bayesian networks (II)
Building a Bayesian network for hospital
visitors
Preparing data and graphical canvas for a
Kohonen SOFM
Building a Kohonen SOFM with
self-organizing neurons
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12.20
12.21
12.22
12.23
12.24
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14.12
14.13
14.14
14.15
14.16
15.1
15.2
15.3
15.4
15.5
15.6
15.7
15.8
15.9
15.10
15.11
15.12
15.13
UN
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15.15
15.16
15.17
15.18
15.19
15.20
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13.26
13.27
14.1
14.2
14.3
14.4
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13.23
Preparing data for a Kohonen SOFM in
3D
Running a Kohonen SOFM in 3D
Illustrating a Bayesian self-organizing
map (BSOM)
Running a non interactive BSOM
A Python module “NNpython.py”
Creating a data set
Neural network training and predictions
Creating data for a clustering analysis
Visualizing data for used for clustring
Running the K-means algorithm
Running the C-means algorithm with
visualization
Running an interactive clustering analysis
Building a list of particles
Clustering particles into jets
Creating a histogram for smoothing
Applying the Lowess smoothing to a
histogram
Interpolating scattered data
Smoothing using the Cubic smoothing
spline
Example of a peak finder for arrays
Running a peak finder using a histogram
Eccentricity analysis in the X −Y plane
Running a PCA analysis in 2D space
Creating a simple decision tree
Extracting data from images
Table filled with numeric values
Conversion from kg to lb
Calculations with units and uncertainties
Calculation of the velocity of a falling
object
Loading an image using the ImageJ
package
Openning an image in ImageJ
Extracting the pixel array from an image
Statistical analysis of image colors
Building the histogram with image colors
Image creation after changing the pixel
array
Edge detection of images
Discrete Fourier transforms for random
matrices
Compound interest calculations
Creating a time series file
Exporting time series into external files
Exporting time series data in external files
Accessing time ranges
Plotting time series
Time series with a moving average
Creating a candlestick chart
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17.4
17.5
17.6
17.7
17.8
17.9
17.10
17.11
17.12
17.13
17.14
17.15
17.16
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16.4
16.5
16.6
16.7
16.8
Jython scripting with operator overloading
Showing histograms using
“Example.java”
Compilation script “compile.sh”
file “Histogram.java”
PHP script “applet.php”
PHP script “list_files.php”
Showing histograms using “Example.bsh”
Showing histograms using
“Example.groovy”
Creating a chart using JFreeChart and
Groovy
Showing histograms using “Example.rb”
Creating custom functions in jMathLab
scripts
Finding roots of the polynomial
3x 4 + 2x 3 + 5x 2 + 7x + 4
Benchmarking Octave calculations in
jMathLab
Benchmarking Jython calculations
Numeric integration in jMathLab
Plotting a function and its indefinite
integral
Example of simplifying an expression
Showing data and a function on the same
plot
Drawing two functions using the plot2d
canvas
Showing a surface plot of the function
x 2 + y 2 in 3D
Showing data in 3D suing the same canvas
Solving a system of linear equations
Solving a system of equations
symbolically
Non linear regression of x−y data array
Plotting a histogram in Octave
Integrating an Octave calculation with
Java
Integrating an Octave calculation with
Python/Jython
RR
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16.2
Index of Code Examples
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