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| Line 54: |
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| The commercial licenses cannot apply to source code that was imported or contributed<ref>{{cite web|url=http://jwork.org/wiki/DMelt:Dev/Contributions|title=Contributed Packages (DataMelt Manual)}}</ref> to DataMelt from other authors. | | The commercial licenses cannot apply to source code that was imported or contributed<ref>{{cite web|url=http://jwork.org/wiki/DMelt:Dev/Contributions|title=Contributed Packages (DataMelt Manual)}}</ref> to DataMelt from other authors. |
|
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|
| ==Examples==
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|
| |
|
| ===Jython scripts===
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|
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| Here is an example of how to show 2D bar graphs by reading a CVS file downloaded from the [[World Bank]] web site.
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|
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| <syntaxhighlight lang="python">
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| from jhplot.io.csv import *
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| from java.io import *
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| from jhplot import *
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|
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| d = {}
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| reader = CSVReader(FileReader("ny.gdp.pcap.cd_Indicator_en_csv_v2.csv"));
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| while True:
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| nextLine = reader.readNext()
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| if nextLine is None:
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| break
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| xlen = len(nextLine)
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| if xlen < 50:
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| continue
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| d[nextLine[0]] = float(nextLine[xlen-2]) # key=country, value=DGP
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|
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| c1 = HChart("2013",800,400)
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| #c1.setGTitle("2013 Gross domestic product per capita")
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| c1.visible()
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| c1.setChartBar()
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| c1.setNameY("current US $")
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| c1.setNameX("")
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| c1.setName("2013 Gross domestic product per capita")
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|
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| name1 = "Data Source: World Development Indicators"
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|
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| set_value = lambda name: c1.valueBar(d[name], name, name1)
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|
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| set_value(name="Russia")
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| set_value(name="Poland")
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| set_value(name="Romania")
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| set_value(name="Bulgaria")
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| set_value(name="Belarus")
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| set_value(name="Ukraine")
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| c1.update()
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| </syntaxhighlight>
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|
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| The execution of this script plots a bar chart in a separate window. The image can be saved in a number of formats.
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|
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| Here is another simple example which illustrates how to fill a 2D histogram and display it on a canvas. The script also creates a figure in the [[Portable Document Format|PDF]] format.
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| This script illustrates how to glue and mix the native JAVA classes (from the package java.util) and DataMelt classes (the package jhplot) inside a script written using the Python syntax.
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|
| |
| <syntaxhighlight lang="python">
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| from java.util import Random
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| from jhplot import *
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|
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| c1 = HPlot3D("Canvas") # create an interactive canvas
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| c1.setGTitle("Global title")
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| c1.setNameX("X")
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| c1.setNameY("Y")
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| c1.visible()
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| c1.setAutoRange()
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|
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| h1 = H2D("2D histogram", 25, -3.0, 3.0, 25, -3.0, 3.0)
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| rand = Random()
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| for i in range(200):
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| h1.fill(rand.nextGaussian(), rand.nextGaussian())
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| c1.draw(h1)
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| c1.export("jhplot3d.eps") # export to EPS Vector Graphics
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| </syntaxhighlight>
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|
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| This script can be run either using DataMelt IDE or using a stand-alone Jython after specifying classpath to DataMelt libraries. The output is shown below:
| |
|
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| [[File:Histograms_in_3D_showing_random_Gaussian_data.png|thumb|center|222px|3D histogram]]
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|
| |
| ===Groovy scripts===
| |
| The same example can also be coded using the [[Groovy (programming language)|Groovy]] programming language which is supported by DataMelt.
| |
|
| |
| <syntaxhighlight lang="groovy">
| |
| import java.util.Random
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| import jhplot.*
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|
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| c1 = new HPlot3D("Canvas") // create an interactive canvas
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| c1.setGTitle("Global title")
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| c1.setNameX("X")
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| c1.setNameY("Y")
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| c1.visible()
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| c1.setAutoRange()
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|
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| h1 = new H2D("2D histogram",25,-3.0, 3.0,25,-3.0, 3.0)
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| rand = Random()
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| (1..200).each{ // or (0..<200).each{
| |
| // or Java: for (i=0; i<200; i++){
| |
| // if argument is required, you cann access it through "it" inside the loop:
| |
| // (0..<200).each{ println "step: ${it+1}" }
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| h1.fill(rand.nextGaussian(),rand.nextGaussian())
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| }
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| c1.draw(h1);
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| c1.export("jhplot3d.eps") // export to EPS Vector Graphics
| |
| </syntaxhighlight>
| |
|
| |
| [[Groovy (programming language)|Groovy]] is better integrated with Java and can be a factor three
| |
| faster for long loops over primitives compared to Jython.
| |
|
| |
| ==Reviews==
| |
|
| |
| DataMelt and its earlier versions, SCaVis (2013-2015) and JHepWork (2005-2013), which are still available from [http://jwork.org/dmelt/index.php?id=previous DataMelt archive repository], are described in these articles:
| |
| <ref>Data Analysis and Data Mining Using Java, Jython and jHepWork Blog. 2010. Oracle.com. [https://community.oracle.com/docs/DOC-982931]
| |
| </ref>
| |
| <ref>
| |
| DataMelt – Werkbank für technisch-wissenschaftliche Berechnungen und Visualisierungen mit Java und Jython. by Rohe Klaus. Java SPEKTRUM. (in German) volume 5 (2013) 26-28 [https://www.sigs-datacom.de/fachzeitschriften/javaspektrum/archiv/artikelansicht/artikel-titel/integrationsspektrum-scavis-werkbank-fuer-technisch-wissenschaftliche-berechnungen-und-visualis.html]
| |
| </ref>
| |
| <ref>HEP data analysis using jHepWork and Java. Proceedings of the HERA-LHC workshops (2007-2008), DESY-CERN [https://arxiv.org/abs/0809.0840]
| |
| </ref>
| |
| <ref>Suitability analysis of data mining tools and methods. [https://is.muni.cz/th/255695/fi_b/]. S.Kovac, Bachelor's thesis (in English), jHepWork is reviewed on page 39-42, Masaryk University.
| |
| </ref>
| |
| The program was compared with other similar frameworks in these resources
| |
| <ref>
| |
| A Review: Comparative Study of Diverse Collection of Data Mining Tools. By S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila. International Journal of Computer, Control, Quantum and Information Engineering. 2014; 8(6). 7.
| |
| </ref>
| |
| <ref>jHepWork – full-featured multiplatform data-analysis framework. [http://blog.dreamcss.com/frameworks/jhepwork-multiplatform-data-analysis-framework/]
| |
| </ref>
| |
| <ref>Java Applications: Weka, Svnkit, Jhepwork, Fiji, Memoranda, Livegraph, Dirsync Pro, Moneydance, Rachota Timetracker, Data Mapping Engine. de LLC Books (2010) (Redactor) [https://www.amazon.es/Java-Applications-Memoranda-Moneydance-Timetracker/dp/1156999391]
| |
| </ref>
| |
| <ref>A Study of Tools, Techniques, and Trends for Big Data Analytics. By R.Shireesha et al. (2016) International Journal of Advance Computing Technique and Applications (IJACTA), ISSN : 2321-4546, Vol 4, Issue 1 [http://www.ijacta.com/index.php/ojs/article/download/41/33]
| |
| </ref>
| |
| <ref>Comparison of Various Tools for Data Mining. By P.Kaur etc. IJERT ISSN: 2278-0181 Vol. 3 Issue 10 (2010) [[https://www.ijert.org/download/11405/comparison-of-various-tools-for-data-mining]]
| |
| </ref>
| |
| <ref>Advanced Web and Network Technologies, and Applications. By Heng Tao Shen et al. Springer Science & Business Media - 2006-01-09
| |
| </ref>
| |
| <ref>SCaVis 2.1. Review by Pete Daniel (2014) [https://www.download3k.com/Home-Education/Science/Download-SCaVis.html]
| |
| </ref>
| |
| .
| |
|
| |
| The DataMelt (2015-), a new development of the JHepWork and SCaVis programs.
| |
| Comparisons of DataMelt with other popular packages for statistical and numeric analysis are given in these resources
| |
| <ref>Comparative Analysis of Information Extraction Techniques for Data Mining, by Amit Verma et al. Indian Journal of Science and Technology, Vol 9, March 2016 [http://www.indjst.org/index.php/indjst/article/download/80464/67992]
| |
| </ref>
| |
| <ref>Evaluation and comparison of open source software suites for data mining and knowledge discovery. A.H. Altalhi et al. Wiley Online Library (2017) [https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1204]
| |
| </ref>
| |
| <ref>Brief Review of Educational Applications Using Data Mining and Machine Learning, [https://redie.uabc.mx/redie/article/viewFile/1305/1586], by A. Berenice Urbina Nájera, Jorgede la Calleja Mora, Redie ISSN 1607-4041. Revista Electrónica de Investigación Educativa, 19(4), 84-96
| |
| </ref>
| |
| <ref>Analysis of Data Using Data Mining tool Orange. Maqsud S.Kukasvadiya et. al. [https://www.ijedr.org/papers/IJEDR1702288.pdf] (2017) IJEDR, Volume 5, Issue 2, ISSN: 2321-9939
| |
| </ref>
| |
| <ref>Big Data - A Survey of Big Data Technologies. By P.Dhavalchandra, M.Jignasu, R.Amit. International Journal of Science and Technology. Volume 2, p45-50 (2016) [http://www.ijsrt.us/vol2issue1/pdf%209.pdf]
| |
| </ref>
| |
| .
| |
|
| |
| ==Popularity==
| |
|
| |
| jHepWork, SCaVis/DatMelt are part of the software library of National Institutes of Health Library
| |
| <ref>Data Sciences Workstation: SCaVis. By Lisa Federer. National Institutes of Health Library [http://nihlibrary.campusguides.com/c.php?g=102650&p=672758]</ref>,
| |
| Mathematical support of Institute for Nuclear Research of Russian academy of Sciences<ref>The DataForge, Sector for Mathematical Support of Institute for Nuclear Research of Russian academy of Sciences [http://www.inr.ru/~nozik/dataforge/misc.html]</ref> and others.
| |
| On a commercial site, DataMelt is provided as a service on Amazon EC2 clouds by the Miri Infotech IT Solution Provider company
| |
| <ref>Miri Infotech. A Complete IT Solution Provider. [http://www.miritech.com/products/aws/datamelt.aspx DataMelt deployment]</ref>.
| |
|
| |
| It is difficult to judge how many users use DataMelt since download information from the main resource [http://jwork.org/dmelt] is not available.
| |
| [[Sourceforge|Sourceforge]], which provides an alternative download option, quotes 300 monthly downloads [https://sourceforge.net/projects/dmelt/] (May 2018).
| |
|
| |
| One estimate can be done by looking at the popularity of the book
| |
| <ref>Numeric Computation and Statistical Data Analysis on the Java Platform (Book). S.V.Chekanov, Springer, (2016)
| |
| ISBN 978-3-319-28531-3, 700 pages, [https://www.springer.com/gp/book/9783319285290]</ref>
| |
| which is an introduction to the DataMelt program. According to the
| |
| Springer International, this book is top 25% most downloadable books in 2016 and 2017 in the category "Advanced Information and Knowledge Processing"
| |
| <ref>Springer Book Performance Report [http://jwork.org/dmelt/data_dm/reports/BookPerformanceReport2017.pdf]</ref>.
| |
| Since the publication of the book, Springer detects 26k chapter downloads until May 2018<ref>Springer download Statistics of the book "Numeric Computation and Statistical Data Analysis on the Java Platform" 2016 [http://www.bookmetrix.com/detail/book/1b56c407-cc52-49ee-9547-5ec300e18498#downloads]</ref>, about 1500 per chapter.
| |
| The previous book describing jHepWork had a similar popularity <ref>Springer download Statistics of the book "Scientific Data Analysis using Jython Scripting and Java" [http://www.bookmetrix.com/detail/book/0d0c46b5-edee-4145-b112-4167b6739c11#downloads]</ref>.
| |
| Bookmetrix estimates 140 readers of the DataMelt book.
| |
|
| |
|
| |
| == References ==
| |
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|
