ViPEr, a Visual Programming Environment for Python

In this paper we describe a Pythonand Tkinter-based visual-programming environment called ViPEr. This tool enables non-programmers to build computational and visualization networks interactively. Computational nodes can be placed onto a canvas and their input and output ports can be connected using the mouse. The connections between the nodes define a directed graph that will be used to propagate data and trigger the execution of nodes that have new input data. ViPEr is, in appearance, similar to programs such as AVS [Upson et al. 89] from Advanced Visual Simulations Inc, or OpenDX [DX 93] from IBM, but presents some fundamental differences which will be pointed out throughout this paper. Several examples of applications will be used to illustrate ViPEr’s design and current range of capabilities. I Background and significance The focus of our laboratory is the modelisation of molecular interactions. We are working on several aspects of this problem, including molecular visualization, protein-ligand docking, proteinprotein docking, molecular surfaces, phenomenological potentials, etc. The methods we use in our models come from fields as diverse as computational chemistry and biology, computational geometry and augmented reality. We have been using Python as a platform to develop re-usable and interoperable components dealing with different aspects of structural bioinformatics [Sanner 99a, Coon et al. 01, Python at TSRI]. These components are the basic building blocks from which several domain specific applications have been developed. These include a generic molecule visualization program (PMV [Sanner 99a, Coon et al. 01]), a viewer for volumetric data (PVV) and a graphical user interface to our molecular docking program AutoDock [Coon et al. 01]. These applications have demonstrated a great deal of flexibility, extensibility and code re-use and have been distributed to over 200 laboratories around the world. They all expose the underlying Python interpreter, providing a fully-fledged programming language to operate, both over the data structures in the application, and the application itself. However, we have found that this capability is seldomly used, because it requires learning the Python programming language and becoming familiar with the data-structures used in the applications. This is often beyond the level of investment a typical user is willing to make. On the other hand, we realize that it is impossible to anticipate all uses and combinations of the components that we have developed, or to implement all possible commands users are ever going to want. To address this problem we developed a visual programming tool that enables nonprogrammers to intuitively and interactively build networks describing new computational streams and novel visualizations of their data, without having to write code or understand the details of the application’s data structures. This development was partly motivated by our previous experience with the data-flow environment AVS [Upson et al. 89] that has been used successfully in our laboratory for over 10 years [Macke et al. 98]. AVS provides an environment in which components (also called modules) are connected to create a computational pipeline. It is comprised of a large number of processing modules for a wide variety of operations. Custom modules can be added to extend the environment with new computational methods. The modular nature of AVS provides a good level of code re-use as modules, once written, can be shared with other AVS users. Moreover, its modular nature fosters the compartmentalization of computational tasks. However, we also experienced some serious limitations, which prompted us to gradually develop our own Python-based set of components to deal with our visualization needs.