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Enhancing User Interfaces through Dynamic Visual Scripting: Insights from Pyre and Python Development

This workshop, featuring Patrick Hung and Jiao Lin from the California Institute of Technology, explored innovative user interface design using Pyre and Python. Key topics included the development of prototypes for dynamic visual scripting, integration methods, and algorithms for function fitting. Attendees learned about client-side HTML/Javascript and server-side CGI scripting, with discussions on maintaining flexibility in rapidly evolving frameworks. The session also highlighted practical use cases, effects of algorithms, and the intersection of scripting with effective data handling.

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Enhancing User Interfaces through Dynamic Visual Scripting: Insights from Pyre and Python Development

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  1. Pyre User Interfaces, etc. Patrick Hung, Jiao Lin California Institute of Technology Danse Developers Workshop January 22-23, 2007

  2. Homotopy and you.

  3. User interfaces prototypes • Pyre command line • Integrator.py --func=myfunc --technique=trapezoidal --lower=0 --upper=1 --trapezoidal.n=1000 • Integrator.py --func=stringfunc --stringfunc.str=‘sin(x) * cos(x)^2’ --technique=romberg --romberg.maxlevels=10 • Interpreter level / shell level • First prototypes (wx, text, etc.) (with Jiao Lin) • Demands nothing • Provides … a little • Demo later • Web prototype • Client-side: HTML/Javascript • Server-side: cgi scripting with python • Self-hosting • Many alternatives exist, rapidly evolving field.

  4. Some (UI related) Use Cases • Dynamic / visual scripting • Visual programming environment • Labview, Explorer, Simulink, Amira, etc. • Python universe: vision, vistrails • Framework level changes required. • Effects of algorithms • Changing integration algorithm ? • Using another renderer ? • No problems. Static wiring, static but open “slots” for components. • Batch processing • of images, etc ? • Scripts ! • Database access, staging, authentication, sessions, etc. ? • SNS Portal, Pyre, Middleware …

  5. Estimating functional dependencies • Example interpretation: function fitting • Given experimental data, find a best fit polynomial • Picking the order • Picking the coefficients • Given empirical observations • and a set of functions indexed by parameter alpha • Minimize the functional (aka expected risk) • A common loss function

  6. Parametric fitting: density function • Typically Ill-posed • Occam’s razor, Kolmogorov complexity, minimum description length, …

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