DMelt:General/WikiForWiki

DataMelt article from Wikipedia

DataMelt

Example DataMelt histogram and function
Initial release	2005 (initial name JHepWork)
Stable release	2.2 / April 2018
Written in	Java
Type	Data analysis
License	Open source (LGPL, GPL and similar)
Website	datamelt.org

DataMelt (or, in short, DMelt) a computation and visualization environment is an interactive framework for scientific computation, data analysis and data visualization designed for scientists, engineers and students. DataMelt is multiplatform since it is written in Java, thus it runs on any operating system where the Java virtual machine can be installed.

The program is designed for statistical data analysis, curve fitting, data-mining algorithms, numeric computations and interactive scientific plotting in 2D and 3D. DataMelt uses high-level programming languages, such as Jython, Apache Groovy, JRuby, but Java coding can also be used to call DataMelt numerical and graphical libraries.

DataMelt is an attempt to create a data-analysis environment using open-source packages with a coherent user interface and tools competitive to commercial programs. The idea behind the project is to incorporate open-source mathematical and numerical software packages with GUI-type user interfaces into a coherent program in which the main user interface is based on short-named Java/Python classes. This was required to build an analysis environment using Java scripting concept. A typical example will be shown below.

Scripts and Java code (in case of the Java programming) can be run either in a GUI editor of DataMelt or as batch programs. The graphical libraries of DataMelt can be used to create applets. All charts (or "Canvases") used for data representation can be embedded into web browser.

DataMelt can be used for analysis of large numerical data volumes, data mining, statistical data analysis and mathematics are essential. The program can be used in natural sciences, engineering, modeling and analysis of financial markets.

Data-analysis features

The package supports several mathematical, data-analysis and data mining features:

• 2D and 3D interactive visualization of data, functions, histograms, charts.
• analytic calculations using Matlab or Octave syntax
• histograms in 2D and 3D
• random numbers and statistical samples
• functions, including parametric equations in 3D
• contour plots, scatter plots
• neural networks
• linear regression and curve fitting using several minimization techniques
• Cluster analysis (K-means clustering analysis (single and multi pass), Fuzzy (C-means) algorithm, agglomerative hierarchical clustering)
• Input and Output (IO) for all data objects (arrays, functions, histograms) are based on Java serialization. There is also a support for I/O from/to C++ and other languages using the Google's Protocol Buffers format. Several databases are supported (Java-object databases and SQL-based)
• Cellular automaton
• output to high-quality Vector graphics. Support for PostScript, EPS, PDF and raster formats
• VisAD data visualization integrated with scripting languages.

Input and output

DataMelt includes the native Java and Python methods for file input and outputs. In addition, it allows to write data in the following formats:

• The HFile format based on Java serialization. Optionally, compression and XML serialization are supported. Data can be written sequentially or using the key-value maps.
• The PFile format based on the Protocol Buffers engine for multiplatform input output
• The HBook format, which is a simplified XML format to write large data structures without XML tags
• Arbitrary data structure can be written into object databases with file system as back-end. This allows writing large data collections to files which normally do not fit into the computer memory.
• Several SQL database engines are included as external packages
• AIDA (computing) file format (read only)
• ROOT file format (read only)

Data stored in external files can be viewed using browsers for convenient visualization.

History

DataMelt has its roots in particle physics where data mining is a primary task. It was created as jHepWork project in 2005 and it was initially written for data analysis for particle physics at the DESY laboratory in Germany. Later at was improved at the Argonne National Laboratory for studies in particle physics using the Java software concept for International Linear Collider project developed at SLAC. Later versions of jHepWork were modified for general public use (for scientists, engineers, students for educational purpose) since the International Linear Collider project has stalled. In 2013, jHepWork was renamed to DataMelt and become a general-purpose community-supported project. The main source of the reference is the book "Scientific Data analysis using Jython Scripting and Java" which discusses data-analysis methods using Java and Jython scripting. Later it was also discussed in the German Java SPEKTRUM journal . The string "HEP" in the project name "jHepWork" abbreviates "High-Energy Physics". But due to a wide popularity outside this area of physics, it was renamed to SCaViS (Scientific Computation and Visualization Environment). This project existed for 3 years before it was renamed to DataMelt (or, in short, DMelt).

DataMelt is hosted by the jWork.ORG portal

Supported platforms

DataMelt runs on Windows, Linux, Mac and the Android platforms. The package for the Android is called AWork.

Documentation

In 2018, the DataMelt web page of this project contained about 600 examples written in Jython, Java, Groovy, JRuby, covering a number of fields, from general mathematics to data mining and data visualization. The Java API documentation includes the description of more than 40,000 Java classes. In addition, there is a wiki documentation. The documentation includes certain restrictions for general public due to the proprietorial nature of the documentation project.

License terms

The core source code of the numerical and graphical libraries is licensed by the GNU General Public License. The interactive development environment (IDE) used by DataMelt has some restrictions for commercial usage since language files, documentation files, examples, installer, code-assist databases, interactive help are licensed by the creative-common license. Full members of the DataMelt project have several benefits, such as: the license for a commercial usage, access to the source repository, an extended help system, a user script repository and an access to the complete documentation.

Examples

Jython scripts

Here is an example of how to show 2D bar graphs by reading a CVS file downloaded from the World Bank web site.

from jhplot.io.csv import *
from java.io import *
from jhplot import *

d = {}
reader = CSVReader(FileReader("ny.gdp.pcap.cd_Indicator_en_csv_v2.csv"));
while True:
    nextLine = reader.readNext()
    if nextLine is None:
        break
    xlen = len(nextLine)
    if xlen < 50:
        continue
    d[nextLine[0]] = float(nextLine[xlen-2]) # key=country, value=DGP

c1 = HChart("2013",800,400)
#c1.setGTitle("2013 Gross domestic product  per capita")
c1.visible()
c1.setChartBar()
c1.setNameY("current US $")
c1.setNameX("")
c1.setName("2013 Gross domestic product  per capita")

name1 = "Data Source: World Development Indicators"

set_value = lambda name: c1.valueBar(d[name], name, name1)

set_value(name="Russia")
set_value(name="Poland")
set_value(name="Romania")
set_value(name="Bulgaria")
set_value(name="Belarus")
set_value(name="Ukraine")
c1.update()

The execution of this script plots a bar chart in a separate window. The image can be saved in a number of formats.

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 PDF format. 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.

from java.util import Random
from jhplot import *

c1 = HPlot3D("Canvas") # create an interactive canvas
c1.setGTitle("Global title")
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()

h1 = H2D("2D histogram", 25, -3.0, 3.0, 25, -3.0, 3.0)
rand = Random()
for i in range(200):
    h1.fill(rand.nextGaussian(), rand.nextGaussian())
c1.draw(h1)
c1.export("jhplot3d.eps") # export to EPS Vector Graphics

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:

3D histogram

Groovy scripts

The same example can also be coded using the Groovy programming language which is supported by DataMelt.

import java.util.Random
import jhplot.*

c1 = new HPlot3D("Canvas")  //  create an interactive canvas
c1.setGTitle("Global title")
c1.setNameX("X")
c1.setNameY("Y")
c1.visible()
c1.setAutoRange()

h1 = new H2D("2D histogram",25,-3.0, 3.0,25,-3.0, 3.0)
rand = Random()
(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}" }
     h1.fill(rand.nextGaussian(),rand.nextGaussian())
}
c1.draw(h1);
c1.export("jhplot3d.eps") // export to EPS Vector Graphics

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 DataMelt archive repository, are described in these articles: The program was compared with other similar frameworks in these resources .

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 .

Popularity

jHepWork, SCaVis/DatMelt are part of the software library of National Institutes of Health Library , Mathematical support of Institute for Nuclear Research of Russian academy of Sciences and others. On a commercial site, DataMelt is provided as a service on Amazon EC2 clouds by the Miri Infotech IT Solution Provider company .

It is difficult to judge how many users use DataMelt since download information from the main resource [1] is not available. Sourceforge, which provides an alternative download option, quotes 300 monthly downloads [2] (May 2018). One estimate can be done by looking at the popularity of the book 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". Since the publication of the book, Springer detects 26k chapter downloads until May 2018, about 1500 per chapter. The previous book describing jHepWork had a similar popularity . Bookmetrix estimates 140 readers of the DataMelt book.