[Savannah-hackers] submission of ABE, A bioassay analysis program written in Python - savannah.gnu.org

[gordonwebster]

A package was submitted to savannah.gnu.org
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Gordon Webster <address@hidden> described the package as follows:
License: gpl
Other License: 
Package: ABE, A bioassay analysis program written in Python
System name: abe
Type: GNU

Description:
ABE Version 1.0 (stable), released today on sourceforge.net, is the first 
public release of ABE, a bioassay analysis program for cell biologists and 
molecular biologists working in biotechnology companies or academic 
laboratories. 

The ABE source code and docs are available at:
http://sourceforge.net/projects/abe-module

ABE visualizes and analyzes the data from biossay experiments such as cell 
proliferation assays, and allows the user to model the experimental data using 
either polynomials, or a more specific sigmoidal dose-response model. 

ABE reads the experimental data in XML format, constructs mathematical models 
of the data and allows the user to graphically compare the observed and modeled 
data. A complete record of the analysis is kept in a comprehensive activity log 
and any generated graphs can be saved in PostScript (TM) format, allowing the 
user to include detailed and accurate records of the analysis as part of 
his/her laboratory notes. 

Since many excellent data-graphing packages already exist, the graph options in 
ABE were deliberately kept to a minimum to keep the code simple and to focus on 
the mathematical part of the analysis.ABE does however allow the user to export 
a data table containing a summary of the analysis into programs such as 
Microsoft Excel (TM) for further analysis, visualization and archiving.

ABE is written in Python 2.2 and uses the Tkinter graphic library to generate a 
friendly graphical user interface (GUI) for manipulating and visualizing the 
data. As a result, the program should be capable of being run on any platform 
with a standard Python 2.2 (or higher) distribution.

For the data modeling, ABE uses the nonlinear regression routines from the 
Scientific Python (SciPy) and Numeric libraries as well as the polyfit function 
from Raymond Hettinger\'s Matfunc module(Public Domain), for computing the 
fitted polynomial coefficients from the supplied data. Functions for computing 
the derivatives of the fitted polynomials, solving polynomial roots and 
estimating the initial parameters for the nonlinear regression were added by 
the author and are included in the body of the main Abe module. In addition to 
the libraries of the standard Python distribution therefore, only the SciPy, 
Numeric and Matfunc modules need to be included in the Python path for ABE to 
be run. 

Using the \'py2exe\' extension of the Python \'distutils\' library, convenient, 
pre-compiled and standalone ABE executables can be generated, that can be run 
without a Python installation.




Other Software Required:
To work with the source code, a user of ABE would require the following Python 
distributions/packages (all freely available):

Python 2.2 or higher
The Numeric Python package (http://www.pfdubois.com/numpy/)
The Scientific Python package (http://www.scipy.org)
Raymond Hettinger\'s \'Matfunc\' Python module (address@hidden)


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