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Scientific Data Analysis using Jython Scripting and Java 2010 [Kõva köide]

  • Formaat: Hardback, 440 pages, kõrgus x laius: 235x155 mm, kaal: 1820 g, XXIV, 440 p., 1 Hardback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 20-Aug-2010
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1849962863
  • ISBN-13: 9781849962865
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Scientific Data Analysis using Jython Scripting and Java 2010Suurem pilt
  • Formaat: Hardback, 440 pages, kõrgus x laius: 235x155 mm, kaal: 1820 g, XXIV, 440 p., 1 Hardback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 20-Aug-2010
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1849962863
  • ISBN-13: 9781849962865
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781849962865.html
Märksõnad:
Information systems and intelligent knowledge processing are playing an increasing role in business, science and technology. Recently, advanced information systems have evolved to facilitate the co-evolution of human and information networks within communities. These advanced information systems use various paradigms including artificial intelligence, knowledge management, and bioinformatics, as well as conventional information processing paradigms.

This research-oriented series publishes books on new designs and applications of advanced information and knowledge processing concepts. Books in the series have a strong focus on information processing combined with, or extended by, new results from adjacent sciences.

Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Jave scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and historgrams to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.

Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation.

This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multiplatform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.
Introduction 1(1)
Introduction to Data Analysis and Why This Book Is Special 1(1)
Who Is This Book for 2(1)
1 Jython, Java and jHep Work
3(24)
1.1 Introduction
3(4)
1.1.1 Books You May Read Before
4(1)
1.1.2 Yes, It Is Pure Java
4(1)
1.1.3 Some Warnings
5(2)
1.1.4 Errors
7(1)
1.2 Introduction to Scientific Computing
7(6)
1.2.1 Book Examples and the Power of Jython
7(1)
1.2.2 The History of jHepWork
8(1)
1.2.3 Why Jython?
9(1)
1.2.4 Differences with Other Data-analysis Packages
10(1)
1.2.5 How Fast It Is?
11(1)
1.2.6 Jython and CPython Versions
12(1)
1.3 Installation
13(1)
1.4 Introduction to the jHepWork IDE
14(9)
1.4.1 Source Code Editor
15(1)
1.4.2 jHepWork Java Libraries and Python Packages
15(2)
1.4.3 Jython and Bean Shell Consoles
17(2)
1.4.4 Accessing Methods of Instances
19(1)
1.4.5 Editing Jython Scripts
19(1)
1.4.6 Running Jython Scripts
19(1)
1.4.7 Running a BeanShell Scripts
20(1)
1.4.8 Compiling and Running Java Code
20(1)
1.4.9 Working with Command-line Scripts
21(1)
1.4.10 jHepWork Code Assist
21(1)
1.4.11 Other Features
22(1)
1.5 Third-party Packages and the License
23(4)
1.5.1 Contributions and Third-party Packages
23(2)
1.5.2 Disclaimer of Warranty
25(1)
1.5.3 jHepWork License
25(1)
References
26(1)
2 Introduction to Jython
27(58)
2.1 Code Structure and Commentary
27(1)
2.2 Quick Introduction to Jython Objects
28(6)
2.2.1 Numbers as Objects
31(1)
2.2.2 Formatted Output
32(1)
2.2.3 Mathematical Functions
33(1)
2.2.4 Complex Numbers
34(1)
2.3 Strings as Objects
34(1)
2.4 Import Statements
35(2)
2.4.1 Executing Native Applications
36(1)
2.5 Comparison Tests and Loops
37(3)
2.5.1 The `if-else' Statement
37(1)
2.5.2 Loops. The "for" Statement
38(1)
2.5.3 The `continue' and `break' Statements
39(1)
2.5.4 Loops. The `while' Statement
39(1)
2.6 Collections
40(10)
2.6.1 Lists
40(1)
2.6.2 List Creation
41(1)
2.6.3 Iteration over Elements
42(1)
2.6.4 Removal of Duplicates
43(2)
2.6.5 Tuples
45(1)
2.6.6 Functional Programming. Operations with Lists
46(2)
2.6.7 Dictionaries
48(2)
2.7 Java Collections in Jython
50(7)
2.7.1 List. An Ordered Collection
50(3)
2.7.2 Set. A Collection Without Duplicate Elements
53(1)
2.7.3 SortedSet. Sorted Unique Elements
54(1)
2.7.4 Map. Mapping Keys to Values
55(1)
2.7.5 Java Map with Sorted Elements
55(1)
2.7.6 Real Life Example: Sorting and Removing Duplicates
56(1)
2.8 Random Numbers
57(1)
2.9 Time Module
58(2)
2.9.1 Benchmarking
59(1)
2.10 Python Functions and Modules
60(3)
2.11 Python Classes
63(4)
2.11.1 Initializing a Class
65(1)
2.11.2 Classes Inherited from Other Classes
66(1)
2.11.3 Java Classes in Jython
66(1)
2.11.4 Topics Not Covered
67(1)
2.12 Used Memory
67(1)
2.13 Parallel Computing and Threads
67(1)
2.14 Arrays in Jython
68(3)
2.14.1 Array Conversion and Transformations
70(1)
2.14.2 Performance Issues
70(1)
2.15 Exceptions in Python
71(1)
2.16 Input and Output
72(8)
2.16.1 User Interaction
72(1)
2.16.2 Reading and Writing Files
72(2)
2.16.3 Input and Output for Arrays
74(1)
2.16.4 Working with CSV Python Module
75(2)
2.16.5 Saving Objects in a Serialized File
77(1)
2.16.6 Storing Multiple Objects
77(1)
2.16.7 Using Java for I/O
78(1)
2.16.8 Reading Data from the Network
79(1)
2.17 Real-life Example. Collecting Data Files
80(3)
2.18 Using Java for GUI Programming
83(1)
2.19 Concluding Remarks
84(1)
References
84(1)
3 Mathematical Functions
85(36)
3.1 Jython Functions
85(2)
3.2 1D Functions in jHepWork
87(4)
3.2.1 Details of Java Implementation
89(1)
3.2.2 Integration and Differentiation
90(1)
3.3 Plotting 1D Functions
91(9)
3.3.1 Building a Graphical Canvas
92(3)
3.3.2 Drawing 1D Functions
95(2)
3.3.3 Plotting 1D Functions on Different Pads
97(1)
3.3.4 Short Summary of HPlot Methods
98(1)
3.3.5 Examples
98(2)
3.4 2D Functions
100(5)
3.4.1 Functions in Two Dimensions
100(1)
3.4.2 Displaying 2D Functions on a Lego Plot
101(3)
3.4.3 Using a Contour Plot
104(1)
3.5 3D Functions
105(1)
3.5.1 Functions in Three Dimensions
105(1)
3.6 Functions in Many Dimensions
105(2)
3.6.1 FND Functions
105(1)
3.6.2 Drawing FND Functions
106(1)
3.7 Custom Functions Defined by Jython Scripts
107(6)
3.7.1 Custom Functions and Their Methods
107(3)
3.7.2 Using External Libraries
110(1)
3.7.3 Plotting Custom Functions
111(2)
3.8 Parametric Surfaces in 3D
113(3)
3.8.1 FPR Functions
113(3)
3.8.2 3D Mathematical Objects
116(1)
3.9 Symbolic Calculations
116(3)
3.10 File Input and Output
119(2)
References
120(1)
4 One-dimensional Data
121(14)
4.1 One Dimensional Arrays
121(1)
4.2 POD Data Container
122(8)
4.2.1 POD Transformations
125(1)
4.2.2 Analyzing POD and Summary Statistics
126(2)
4.2.3 Displaying POD Data
128(2)
4.3 Reading and Writing POD Files
130(5)
4.3.1 Serialization
131(1)
4.3.2 XML Format
131(2)
4.3.3 Dealing with Object Collections
133(2)
5 Two-dimensional Data
135(26)
5.1 Two Dimensional Data Structures
135(1)
5.2 Two Dimensional Data with Errors
136(9)
5.2.1 Viewing P1D Data
140(2)
5.2.2 Plotting P1D Data
142(2)
5.2.3 Contour Plots
144(1)
5.3 Manipulations with P1D Data
145(6)
5.3.1 Advanced P1D Operations
146(2)
5.3.2 Weighted Average and Systematical Uncertainties
148(3)
5.4 Reading and Writing P1D Data
151(3)
5.4.1 Dealing with a Single P1D Container
151(2)
5.4.2 Reading and Writing Collections
153(1)
5.5 Real-life Example I: Henon Attractor
154(1)
5.6 Real-life Example II. Weighted Average
155(6)
References
159(2)
6 Multi-dimensional Data
161(10)
6.1 P2D Data Container
161(3)
6.1.1 Drawing P2D and HP1ot3D Canvas
161(3)
6.2 P3D Data Container
164(2)
6.3 PND Data Container
166(3)
6.3.1 Operations with PND Data
167(2)
6.4 Input and Output
169(2)
7 Arrays, Matrices and Linear Algebra
171(22)
7.1 Jaida Data Containers
171(3)
7.1.1 Jaida Clouds
172(2)
7.2 jMathTools Arrays and Operations
174(3)
7.2.1 1D Arrays and Operations
174(2)
7.2.2 2D Arrays
176(1)
7.3 Colt Data Containers
177(1)
7.4 Statistical Analysis Using Jython
178(3)
7.5 Matrix Packages
181(8)
7.5.1 Basic Matrix Arithmetic
183(1)
7.5.2 Elements of Linear Algebra
184(1)
7.5.3 Jampack Matrix Computations and Complex Matrices
185(1)
7.5.4 Jython Vector and Matrix Operations
186(2)
7.5.5 Matrix Operations in SymPy
188(1)
7.6 Lorentz Vector and Particle Representations
189(4)
7.6.1 Three-vector and Lorentz Vector
189(2)
7.6.2 Classes Representing Particles
191(1)
References
192(1)
8 Histograms
193(30)
8.1 One-dimensional Histogram
193(12)
8.1.1 Probability Distribution and Probability Density
198(1)
8.1.2 Histogram Characteristics
198(1)
8.1.3 Histogram Initialization and Filling Methods
199(2)
8.1.4 Accessing Histogram Values
201(1)
8.1.5 Integration
201(2)
8.1.6 Histogram Operations
203(1)
8.1.7 Accessing Low-level Jaida Classes
204(1)
8.1.8 Graphical Attributes
205(1)
8.2 Histogram in 2D
205(7)
8.2.1 Histogram Operations
207(2)
8.2.2 Graphical Representation
209(3)
8.3 Histograms in Jaida
212(2)
8.4 Histogram in 3D
214(1)
8.5 Profile Histograms
214(3)
8.5.1 Profile Histograms in 1D
215(1)
8.5.2 Profile Histograms in 2D
215(2)
8.6 Histogram Input and Output
217(3)
8.6.1 External Programs for Histograms
218(2)
8.7 Real-life Example. Analyzing Histograms from Multiple Files
220(3)
References
221(2)
9 Random Numbers and Statistical Samples
223(12)
9.1 Random Numbers in Jython
223(2)
9.2 Random Numbers in Java
225(1)
9.3 Random Numbers from the Colt Package
226(1)
9.4 Random Numbers from the jhplot.math Package
227(2)
9.4.1 Apache Common Math Package
229(1)
9.5 Random Sampling
229(4)
9.5.1 Methods for 1D Arrays from jhplot.math
230(2)
9.5.2 Methods for 2D Arrays from jhplot.math
232(1)
9.6 Sampling Using the Colt Package
233(2)
References
233(2)
10 Graphical Canvases
235(38)
10.1 HPlot Canvas
236(2)
10.2 Working with the HPlot Canvas
238(6)
10.2.1 Find USER or NDC Coordinators
238(1)
10.2.2 Zoom in to a Certain Region
238(1)
10.2.3 How to Change Titles, Legends and Labels
238(1)
10.2.4 Edit Style of Data Presentation
239(1)
10.2.5 How to Modify the Global Margins
239(1)
10.2.6 Saving Plots in XML Files
240(1)
10.2.7 Reading Data
240(1)
10.2.8 Cleaning the HPlot Canvas from Graphics
241(1)
10.2.9 Axes
241(1)
10.2.10 Summary of the HPlot Methods
242(1)
10.2.11 Saving Drawings in an Image File
242(2)
10.3 Labels and Keys
244(4)
10.3.1 Simple Text Labels
244(1)
10.3.2 Interactive Labels
245(1)
10.3.3 Interactive Text Labels with Keys
246(2)
10.4 Geometrical Primitives
248(1)
10.5 Text Strings and Symbols
249(1)
10.6 SHPlot Class. HPlot Canvas as a Singleton
249(2)
10.7 Visualizing Interconnected Objects
251(2)
10.8 Showing Charts
253(1)
10.9 SPlot Class. A Simple Canvas
254(3)
10.9.1 Henon Attractor Again
256(1)
10.10 Canvas for Interactive Drawing
257(3)
10.10.1 Drawing Diagrams
258(1)
10.10.2 SHPlotJa Class
259(1)
10.11 HPlot2D Canvas
260(2)
10.12 3D Canvas
262(1)
10.13 HPlot3D Canvas
263(4)
10.13.1 HPlot3DP Canvas
263(3)
10.13.2 3D Geometry Package
266(1)
10.14 Combining Graphs with Java Swing GUI Components
267(3)
10.15 Showing Streams of Data in Real Time
270(3)
References
271(2)
11 Input and Output
273(40)
11.1 Non-persistent Data. Memory-based Data
273(1)
11.2 Serialization of Objects
274(2)
11.3 Storing Data Persistently
276(7)
11.3.1 Sequential Input and Output
276(2)
11.3.2 GUI Browser for Serialized Objects
278(1)
11.3.3 Saving Event Records Persistently
279(1)
11.3.4 Buffer Size for I/O Intensive Operations
280(1)
11.3.5 Input and Output to XML Files
281(1)
11.3.6 Non-sequential Input and Output
282(1)
11.4 Compressed PFile Format
283(4)
11.4.1 Browser Dialog for PFile Files
286(1)
11.5 Reading ROOT and AIDA Files
287(4)
11.5.1 Reading ROOT Histograms
287(1)
11.5.2 Reading ROOT Trees
288(2)
11.5.3 Plotting ROOT or AIDA Objects Using jHepWork IDE
290(1)
11.6 Working with Relational SQL Databases
291(4)
11.6.1 Creating a SQL Database
292(1)
11.6.2 Working with a Database
293(1)
11.6.3 Creating a Read-only Compact Database
294(1)
11.7 Reading and Writing CSV Files
295(2)
11.7.1 Reading CSV Files
295(1)
11.7.2 Writing CSV File
296(1)
11.8 Google's Protocol Buffer Format
297(9)
11.8.1 Prototyping Data Records
298(1)
11.8.2 Dealing with Data Using Java
299(3)
11.8.3 Switching to Jython
302(1)
11.8.4 Adding New Data Records
303(1)
11.8.5 Using C++ for I/O in the Protocol Buffers Format
303(3)
11.8.6 Some Remarks
306(1)
11.9 EFile Data Output
306(3)
11.10 Reading DIF Files
309(1)
11.11 Summary
310(3)
11.11.1 Dealing with Single Objects
310(1)
11.11.2 Dealing with Collections of Objects
310(1)
References
311(2)
12 Miscellaneous Analysis Issues Using jHepWork
313(22)
12.1 Accessing Third-party Libraries
313(2)
12.1.1 Extracting Data Points from a Figure
313(1)
12.1.2 Cellular Automaton
314(1)
12.2 Downloading Files from the Web
315(1)
12.3 Macro Files for jHepWork Editor
315(1)
12.4 Data Output to Tables and Spreadsheets
316(2)
12.4.1 Showing Data in a Sortable Table
316(2)
12.4.2 Spreadsheet Support
318(1)
12.5 Accessing External Java and Jython Libraries
318(1)
12.6 Working with a jHepWork Project
319(2)
12.6.1 Pure Jython Project
319(1)
12.6.2 Pure Java Project
320(1)
12.6.3 Mixing Jython with Java
320(1)
12.7 Working with Images
321(1)
12.7.1 Saving Plots in External Image File
321(1)
12.7.2 View an Image. IView Class
321(1)
12.7.3 Analyzing and Editing Images
321(1)
12.8 Complex Numbers
322(1)
12.9 Building List of Files
322(1)
12.10 Reading Configuration Files
323(3)
12.10.1 Configuration Files Using Jython
323(2)
12.10.2 Reading Configuration Files Using Java
325(1)
12.11 Jython Scripting with jHepWork
326(3)
12.11.1 Jython Operations with Data Holders
328(1)
12.12 Unwrapping Jython Code. Back to Java
329(2)
12.13 Embedding jHepWork in Applets
331(4)
References
334(1)
13 Data Clustering
335(8)
13.1 Data Clustering. Real-life Example
335(8)
13.1.1 Preparing a Data Sample
336(2)
13.1.2 Clustering Analysis
338(4)
13.1.3 Interactive Clustering with JMinHEP
342(1)
References
342(1)
14 Linear Regression and Curve Fitting
343(24)
14.1 Linear Regression
343(3)
14.1.1 Data Set
343(1)
14.1.2 Analyzing the Data Set
344(2)
14.2 Curve Fitting of Data
346(8)
14.2.1 Preparing a Fit
348(1)
14.2.2 Creating a Fit Function
349(5)
14.3 Displaying a Fit Function
354(3)
14.3.1 Making a Fit
354(3)
14.4 Real-life Example. Signal Plus Background
357(6)
14.4.1 Preparing a Data Sample
357(1)
14.4.2 Performing Curve Fitting
357(2)
14.4.3 Fitting Multiple Peaks
359(3)
14.4.4 Fitting Histograms in 3D
362(1)
14.5 Interactive Fit
363(4)
References
365(2)
15 Neural Networks
367(16)
15.1 Introduction
367(8)
15.1.1 Generating a Data Sample
368(1)
15.1.2 Data Preparation
369(2)
15.1.3 Building a Neural Net
371(2)
15.1.4 Training and Verifying
373(2)
15.2 Bayesian Networks
375(1)
15.3 Self-organizing Map
376(3)
15.3.1 Non-interactive BSOM
378(1)
15.4 Neural Network Using Python Libraries
379(4)
References
382(1)
16 Steps in Data Analysis
383(24)
16.1 Major Analysis Steps
383(2)
16.2 Real Life Example. Analyzing a Gene Catalog
385(6)
16.2.1 Data Transformation
386(1)
16.2.2 Data Skimming
386(1)
16.2.3 Data Slimming
387(1)
16.2.4 Data Sorting
387(2)
16.2.5 Removing Duplicate Records
389(1)
16.2.6 Sorting and Removing Duplicate Records Using Java
390(1)
16.3 Using Metadata for Data Mining
391(5)
16.3.1 Analyzing Data Using Built-in Metadata File
392(3)
16.3.2 Using an External Metadata File
395(1)
16.4 Multiprocessor Programming
396(7)
16.4.1 Reading Data in Parallel
397(2)
16.4.2 Processing a Single Input File in Parallel
399(2)
16.4.3 Numerical Computations Using Multiple Cores
401(2)
16.5 Data Consistency and Security. MD5 Class
403(4)
16.5.1 MD5 Fingerprint at Runtime
403(1)
16.5.2 Fingerprinting Files
404(1)
References
405(2)
17 Real-life Examples
407(28)
17.1 Measuring Single-particle Densities
407(4)
17.1.1 Preparing a Data Sample
408(1)
17.1.2 Analyzing Data
409(2)
17.2 Many-particle Densities, Fluctuations and Correlations
411(9)
17.2.1 Building a Data Sample for Analysis
411(4)
17.2.2 Analyzing the Data
415(1)
17.2.3 Reading the Data and Plotting Multiplicities
416(4)
17.3 Analyzing Macro Data: Nearby Galaxies
420(3)
17.4 Analyzing Micro Data: Elementary Particles
423(2)
17.5 A Monte Carlo Simulation of Particle Decays
425(3)
17.6 Measuring the Speed of Light Using the Internet
428(7)
17.6.1 Getting Host Names in Each Continent
429(1)
17.6.2 Checking Response from Servers
430(1)
17.6.3 Creating Histograms with the Response Time
430(1)
17.6.4 Final Measurements
431(2)
References
433(2)
Index of Examples 435(2)
Index 437
S.V.Chekanov was born in Minsk (USSR) and received his Ph.D. in experimental physics (University of Nijmegen, The Netherlands). He has more than twenty years of experience in high-energy fields including advanced programming and analysis of large data volumes collected by high-energy experiments operated by major international collaborations. He has written over a hundred physics articles, many of them are based on analysis of experimental data from large-scale international experiments, LEP (CERN, European Organization for Nuclear Research), HERA (DESY, German Electron Synchrotron) and LHC, the Large Hadron Collider experiment at CERN. Over the past decade he has divided his time between physics analysis, developing analysis tools and providing software support for the Midwest data analysis center (USA) of the LHC experiment. In 2005 he began the creation of the jHepWork data analysis framework which is now the world's leading open-source Java software for scientists. Presently, he works at the Argonne National Laboratory (Chicago, USA).