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Methods defined here:
- __init__(self, unitcell=None, gv=None, cosine_tol=0.002, minpks=10, hkl_tol=0.01, ring_1=1, ring_2=2, ds_tol=0.0050000000000000001, wavelength=-1, uniqueness=0.5, eta_range=0.0, max_grains=100)
- Unitcell would be a unitcell object for generating hkls peaks
gv would be a 3*n array of points in reciprocal space
The rest of the arguments are parameters.
- assigntorings(self)
- Assign the g-vectors to hkl rings
- coverage(self)
- Compute the expected coverage of reciprocal space
use the min/max obs values of xp/yp/omega to work out what was measured in the scan?
No lambda or
- find(self)
- Dig out the potential hits
- friedelpairs(self, filename)
- Attempt to identify Freidel pairs
Peaks must be assigned to the same powder ring
Peaks will be the closest thing to being 180 degrees apart
- getind(self, UBI, tol=None)
- Returns the indices of peaks in self.gv indexed by matrix UBI
- histogram_drlv_fit(self, UBI=None, bins=None)
- Generate a histogram of |drlv| for a ubi matrix
For use in validation of grains
- loadpars(self, filename=None)
- out_of_eta_range(self, e)
- decide if an eta is going to be kept
- readgvfile(self, filename, quiet=False)
- refine(self, UBI)
- Refine an orientation matrix and rescore it.
From Paciorek et al Acta A55 543 (1999)
UB = R H-1
where:
R = sum_n r_n h_n^t
H = sum_n h_n h_n^t
r = g-vectors
h = hkl indices
- saveindexing(self, filename, tol=None)
- Save orientation matrices
FIXME : refactor this into something to do
grain by grain }
peak by peak }
- savepars(self, filename=None)
- saveubis(self, filename)
- Save the generated ubi matrices into a text file
- score(self, UBI, tol=None)
- Decide which are the best orientation matrices
- scorethem(self)
- updateparameters(self)
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