cog

We are not yet postcog... but this is at least better than precog.

This package is intended to serve as a lightweight Python wrapper for Precognition that facilitates common Laue data reduction routines. cog provides a Python module that can be loaded in other Python scripts or IPython/Jupyter notebooks, as well as a convenience function, cog, that can be called from the commandline.

Installation Instructions

This is a pure python library that can be installed as:

pip install git+https://github.com/Hekstra-Lab/cog.git

Note that this repo is private, so you'll need to enter your github credentials (unless they're already cached, which is fairly likely).

Development install

If you'll be actively developing the cog source code, you should clone the repo and install in "editable" (-e) mode:

git clone https://github.com/Hekstra-Lab/cog.git
cd cog
pip install -e .

Note about hard-coding

The cog library is intended to be used for processing BioCARS Laue data using precognition, so I see it most useful as a private repo for use in our group. This way we can avoid providing "general" support/features, and can tailor things more to our specific usage and context. Specifically, there are Odyssey paths/features hardcoded in to make things easy to use on the Harvard cluster.

This package can be installed locally so that you can read Experiment .pkl files for plotting, analysis, etc. However, you won't be able to run precognition data processing locally.