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pybrn-0.4.3.zip 2014-04-10 72.5 kB 11 weekly downloads
pybrn-0.4.3.tar.gz 2014-04-10 57.2 kB 22 weekly downloads
pybrn-0.4.2.tar.gz 2013-11-16 57.1 kB 11 weekly downloads
pybrn-0.4.2.zip 2013-11-16 72.3 kB 22 weekly downloads
README.txt 2013-06-21 3.0 kB 11 weekly downloads
pybrn-0.4.1.zip 2013-06-21 72.1 kB 11 weekly downloads
pybrn-0.4.1.tar.gz 2013-06-21 56.9 kB 11 weekly downloads
pybrn-0.4.zip 2012-10-15 70.3 kB 11 weekly downloads
pybrn-0.4.tar.gz 2012-10-15 55.4 kB 11 weekly downloads
pybrn-0.3.zip 2011-07-14 61.0 kB 11 weekly downloads
pybrn-0.3.tar.gz 2011-07-14 47.0 kB 11 weekly downloads
pybrn-0.2.3.zip 2011-04-26 56.4 kB 11 weekly downloads
pybrn-0.2.3.tar.gz 2011-04-26 44.6 kB 11 weekly downloads
pybrn-0.2.2.zip 2011-02-12 54.2 kB 11 weekly downloads
pybrn-0.2.2.tar.gz 2011-02-12 42.4 kB 11 weekly downloads
pybrn-0.2.1.zip 2011-02-10 54.1 kB 11 weekly downloads
pybrn-0.2.1.tar.gz 2011-02-10 42.3 kB 11 weekly downloads
pybrn-0.2.zip 2011-02-07 53.0 kB 11 weekly downloads
pybrn-0.2.tar.gz 2011-02-07 41.3 kB 11 weekly downloads
===== pybrn ===== pybrn is a Python package for the analysis of biochemical reaction networks. It is mainly meant as a basic library for researchers developing their own model analysis routines in Python. pybrn currently features: - basic model creation, data handling and evaluation - import of SBML files into pybrn's data structures - analysis of network conservation relations - computation of steady states and steady state branches - integration of the network's differential equation The following interactive Python session shows some of the basic features:: >>> import brn >>> net = brn.fromSBML("doc/examples/simplenet.xml") >>> print net Reactions: v1: -> 1*A; rate: 1 v2: 1*A -> 1*B; rate: k2 * A v3: 1*B -> ; rate: k3 * B <BLANKLINE> Species initial conditions: A = 0.0 B = 0.0 <BLANKLINE> Parameter values: k2 = 1.0 k3 = 1.0 default_compartment = 1.0 >>> net.steadystate() array([ 1., 1.]) >>> t,x = net.simulate(list(xrange(11))) Found integrator vode >>> print t[-1], x[-1] 10.0 [ 0.9999546 0.9995006] Installation ============ Software requirements --------------------- Required software: - Python version 2.x with x >= 5. - NumPy: http://www.numpy.org/ Recommended software: - libSBML with Python bindings version 3.4.1 or later: http://sbml.org/Software/libSBML required for importing SBML models - SciPy: http://www.scipy.org required for simulation and steady state computation - sympy: http://code.google.com/p/sympy/ required for Jacobian computation and for using the Jacobian in numerical computations Optional software: - Cython 0.14 or later: http://www.cython.org can be used for efficient network evaluation - SUNDIALS library 2.3.0 with headers: https://computation.llnl.gov/casc/sundials/main.html can be used in combination with Cython for efficient network simulation Installation instructions ------------------------- pybrn is installed via the Python setuptools. For standard installation, simply run the following command in the package's root directory:: python setup.py install Depending on your system configuration, you may need administrator rights to do the installation. Testing the installation ------------------------ In order to test whether everything works, e.g. all required and recommended packages are available, you can run the following test command before installing the package:: python test/test_all.py To test the minimal functionality without the need for any of the recommended or optional packages, use the command:: python test/test_minimal.py Usage ===== The basic usage of pybrn is described in the ``TUTORIAL.txt`` file in the ``docs`` subdirectory. Most of the functions are also documented within the Python online help system, which can for example be accessed by the command ``help(brn)``. Copyright ========= pybrn is (C) 2010-2013 by Steffen Waldherr, steffen.waldherr@ovgu.de The package is licensed under the GPL v3, see the file LICENSE.txt for more information.
Source: README.txt, updated 2013-06-21