Based solely on peak posistions:

• find good-fit sets of lattice parameters
• index the diffraction peaks

The raw data should be a matlab *.fig file which can be parsed directly with scipy.io. After the experimental peak positions are identified, a simulated annealing process is used to find the good-fit solutions. Notice the solution space may be further reduced based on unit cell transformation (thanks to Dr. Sean Parkin).

The theoretical positions calculation is a python translation of DPC Toolkit developed by Anna Hailey from Lynn Loo's group.

The simulated annealing part is an implementation of Matthew Perry's simanneal project.

scipy

numpy

cv2

scikit-image

The peak finding parameters are set in __init__ of Mathcer.py. Always make a peak finding plot by:

python PeakPlt.py

to make sure these parameters are resonable.

The updating parameters are set as global vars in GIXDSimAnneal.py. To find a plasusible set of annealing parameters, uncomment the folloing section in gindex_anneal.py.

# # auto find anneal param
# print (tsp.auto(minutes=50))

After setting up all annealing parameters, one can do the annealing by:

python gindex_anneal.py

and the solution will be written to anneal.out. Use dlx3anneal.js as job submission file if you are on dlx.

Once you have some solutions written in anneal.out, run

python solred.py anneal.out

to find the unique solutons.

1. When the crystallography plane // to substrate is set to (00L), the diffraction pattern does not change even after a cell transformation involving changes in c axis. Same thing happened when using Anna's code. Not sure this is physical
2. The algo for reducing solution space can be further optimized.