We are presenting the Python framework “Pyphant” for the creation and application of information flow models. The central idea of this approach is to encapsulate each data processing step in one unit which we call a worker. A worker receives input via sockets and provides the results of its data processing via plugs. These can be connected to other workers' sockets. The resulting directed graph is called a recipe. Classes for these objects comprise the Pyphant core. To implement the actual processing steps, Pyphant relies on third-party plug-ins which extend the basic worker class and can be distributed as Python eggs. On top of the core, Pyphant offers an information exchange layer which facilitates the interoperability of the workers, using Numpy objects. A third layer comprises textual and graphical user interfaces. The former allows for the batch processing of data and the latter allows for the interactive construction of recipes.
[1]
M. Bohnert,et al.
A Monte Carlo-based model for steady-state diffuse reflectance spectrometry in human skin: estimation of carbon monoxide concentration in livor mortis
,
2005,
International Journal of Legal Medicine.
[2]
Jürgen Weese,et al.
A generalized regularization method for nonlinear ill-posed problems enhanced for nonlinear regularization terms
,
2001
.
[3]
J. Weese,et al.
A nonlinear regularization method for the calculation of relaxation spectra
,
1993
.
[4]
Michel F. Sanner,et al.
Integrating Computation and Visualization for Biomolecular Analysis: An Example Using Python and AVS
,
1998,
Pacific Symposium on Biocomputing.
[5]
Michel F. Sanner,et al.
ViPEr , a Visual programming Environment for Python
,
2001
.