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NANO Viz Progress log


July 14th 2004

Click to enlarge

Viewing posslamellae.pdb using Pymol

Download script

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
(Encoded using divx 5.11codec.
You can download divx player and codec from here)

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
(unzip and load the pml file in PyMol)

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.

Curently 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.