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purpose

This is a moon-shot standalone prototype for fluidly learning AxI imagery science in combination with other sources.

Ultimately, this should make use of the capability of modern multiplatform OpenGL (including WebGL at some point) to provide full-resolution imagery manipulation, animation, and combination in a classroom setting.

what we hope to learn

A lot of capability of modern computers resides in the video subsystem. Efforts such as vispy show that a lot of heavy lifting for time-critical or data-intensive rendering can be done using well-standardized OpenGL capabilities across languages, operating systems, and even in the web browser using WebGL now.

By smartly buffering into OpenGL the right subset tiles of a huge (up to 22000x22000 x16bands x12frames) dataset, we hope to achieve a fluid and resource-efficient user experience for data analysis.

Later experiments will include using GLSL (shading language) to allow real-time adjustments, enhancements and combinations of bands to be rendered in realtime.

Once we have a stable and useful system, we can look at porting the engine to other target systems, e.g. Web/mobile/tablet.

dev environment setup and startup

conda create -n cspov python=3.4 anaconda
source activate cspov
conda install netCDF4 h5py pyopengl vispy gdal
export PYTHONPATH=/path/to/CSPOV/py
python -m cspov