Rise and Shine
Part 1


Here I created an arc-shaped wall surface and used LunchBox to divide it into a regular grid of adaptive panels. The goal was to treat the wall as a low-resolution image screen, where each panel receives one sampled color from an image file. I used a tree image as a placeholder because it had clear contrast between sky, foliage, trunk, and ground, which made it easier to check whether the image mapping was working. The main challenge was controlling the wall proportions. Since the image samples are mapped directly to the panel grid, the image distorts when the wall aspect ratio does not match the source image. I adjusted the wall radius, arc angle, height, and panel size until the tree image became readable on the curved surface. This made the relationship between geometry and data mapping more obvious. My dynamic parameters for Part 1 include: arc radius, start angle, end angle, wall height, and panel size.
Part 2



Next, I created a serpentine wall surface using a sine curve, then panelized it into a grid of rectangular adaptive panels. I used image data to control the panels through Color.Hue, which converted the sampled image colors into values that could drive the panel height parameter. This created a relief effect across the wall, where changes in image color produced changes in panel depth. I first tested the workflow with a Mexican flag image. The mapping worked, although the height variation was subtle because the flag has large flat color regions. I then tested a kaleidoscope image with stronger color variation, which made the thickness changes easier to see across the wall. The denser image variation produced a more legible three-dimensional surface. The main challenge was setting up the sine curve correctly and keeping the panel grid aligned with the image samples. My dynamic parameters for Part 2 include wall length, wall height, number of waves, wave amplitude, and panel size. These parameters control the overall wall form, while the image hue controls the individual panel depth.