|
Methods defined here:
- __init__(self, tolerance=0.01, intensity_tth_range=(8.4000000000000004, 9.0))
- applyargs(self, args)
- assignlabels(self)
- Fill out the appropriate labels for the spots
- compute_gv(self, grainname, scanname)
- Makes self.gv refer be g-vectors computed for this grain in this scan
- estimate_steps(self, gof, guess, steps)
- fit(self, maxiters=100)
- Fit the global parameters
- generate_grains(self)
- getgrains(self)
- gof(self, args)
- <drlv> for all of the grains in all of the scans
- loadfiltered(self, filename)
- Read in file containing filtered and merged peak positions
- loadparameters(self, filename)
- makeuniq(self, symmetry)
- Flip orientations to a particular choice
Be aware that if the unit cell is distorted and you do this,
you might have problems...
- printresult(self, arg)
- readubis(self, filename)
- Read ubi matrices from a text file
- refine(self, ubi, quiet=True)
- Fit the matrix without changing the peak assignments
- refinepositions(self, quiet=True, maxiters=100)
- refineubis(self, quiet=True, scoreonly=False)
- reset_labels(self, scanname)
- savegrains(self, filename, sort_npks=True)
- Save the refined grains
- saveparameters(self, filename)
- set_translation(self, gr, sc)
Data and other attributes defined here:
- pars = {'cell__a': 4.1569162000000004, 'cell__b': 4.1569162000000004, 'cell__c': 4.1569162000000004, 'cell_alpha': 90.0, 'cell_beta': 90.0, 'cell_gamma': 90.0, 'cell_lattice_[P,A,B,C,I,F,R]': 'P', 'chi': 0.0, 'distance': 7367.8452301999996, 'fit_tolerance': 0.5, ...}
- stepsizes = {'chi': 0.0017453292519943296, 'distance': 0.20000000000000001, 't_x': 0.20000000000000001, 't_y': 0.20000000000000001, 't_z': 0.20000000000000001, 'tilt_x': 0.0017453292519943296, 'tilt_y': 0.0017453292519943296, 'tilt_z': 0.0017453292519943296, 'wavelength': 0.001, 'wedge': 0.0017453292519943296, ...}
|