Note: Click on images to see larger versions
|
July 14th 2004 |
|
|
Viewing posslamellae.pdb using Pymol |
|
|
May 17th 2004 |
|
|
VIZ Using PyMOL - Nano data is converted into a PDB format and loaded into PyMol. The advantage of using PDB format is user can provide connectivity information about atoms. These are snapshots of somethings that could be readily done in PyMOL. View
a movie clip generated using PyMOL PyMOL is a Python-enhanced molecular graphics program. It excels at 3D visualization of proteins, small molecules, density, surfaces, and trajectories. It also includes molecular editing, ray tracing, and movies. Open Source PyMOL is free to everyone and is available for all platforms. Download
the zipped pdb and pml file for x_t5030000.dat dataset
Click on Images to enlarge them |
|
![]() |
|
![]() Raytraced |
|
|
May 11th 2004 |
|
|
Custom scripting in Maya Pros:Great Renderings Cons: Long learning curve /development time, Proprietary |
|
Dec 11h, 2003, Thursday |
|
|
Test run on cylindrical phase of a diblock copolymer melt |
|
Dec 8th, 2003, Monday |
|
|
Initial rendering of dataset using mental ray. Although the distinction is not good between different types this requires more work. Once satisfied and other issues I'll look into this lastly. Shading and texturing will be performed after discussion with research groups Rendering Method 3: Mental ray rendering able to handle this dataset Pros: Fast Cons: n/a |
|
Dec 5th, 2003, Friday |
|
|
Rendering Method 2: Software rendering in layers. Pros: Scalable can handle virtually infinite particles Cons: Compositing is difficult. No tool available for free. Recommendation: Write a compositer for layered renderings in C++ |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
|
![]() |
![]() |
![]() |
![]() |
|
|
Dec 2nd, 2003, Tuesday |
|
|
Just a Screen Shot (not actual rendering) |
|
Dec 1st, 2003, Monday |
|
|
Test image created from dataset (42,000 particles Rendering Method 1: Hardware rendering Features Automated creation of different particles based on file description (every different category is assigned a different shape, color,etc) Rendering this huge dataset in one pass is a bottleneck. Currently devicing & implementing a schema based on divide and render (conquer) paradigm. This would be multipass batch rendering. In a nut shell render few particles at a time and composite the image when every particle has been rendered. |