DATA LAB: Parametric Design + Data Structures with Grasshopper | 2012 September 29-30
DATA LAB is a two-day workshop on Advanced Topics and Data Structures in Grasshopper for Rhinoceros. In a fast-paced and hands-on learning environment, we will cover the Fundamental Concepts of Data Structures as well as strategies for working with Lists, Sequences, and Data Trees in the newly released version of Grasshoppper 0.9. We will engage a series of design problems which highlight the limitations of standard Parametric Design workflows and serve as catalysts for discussions related to Best Practices, Linear versus Non-Linear Design Processes, and the Re-Use of Files. Each design problem will require either the specific use and manipulation of Data Structures or the extension of Grasshopper through Add-Ons.
Lab participants will learn how to create, manipulate, and manage Data Structures ranging from simple Lists to dense Data Trees. Participants will gain in-depth experience using objects such as Weave, Repeat Data, Path Mapper, Relative Item(s), Replace Branches, and more. The Lab curriculum will include a series of instructional lectures, lab exercises, and open work sessions. Instructional lectures will serve to both introduce key concepts and spark discussion amongst the group. Modeled after carefully selected case-studies, lab exercises are designed to make abstract technical concepts concrete and applicable to creative professionals. Open work sessions will offer participants time to develop individual design studies with one-on-one assistance by the Lab instructors. In the DATA LAB, participants will also be introduced to a collection of Add-Ons, including Kangaroo, Weaverbird, and Python Scripting as well as topics concerning the Coordination of Data and Geometry into and out of Grasshopper. Participants from all creative fields and backgrounds are encouraged to attend and lend their perspective to the Lab. As part of a larger online infrastructure, modeLab, this workshop provides participants with continued support and knowledge to draw upon for future learning.