fitga.refine(far)
far.residual = []
far.volume = []
far.mean_ia = []
for i in range(far.no_grains):
far.residual.append([])
far.volume.append([])
far.mean_ia.append([])
for j in range(far.nrefl[i]):
far.residual[i].append(1)
far.volume[i].append(1)
far.mean_ia[i].append(1)
from FitAllB import reject
reject.residual(far, far.fit['rej_resmedian'])
reject.mean_ia(far, far.fit['rej_ia'] * float(k + 2) / float(k + 1))
reject.intensity(far)
from FitAllB import write_output
write_output.write_rej(far, message='globals%i' % k)
for i in range(far.no_grains):
if far.nrefl[i] < far.fit['min_refl'] and i + 1 not in far.fit['skip']:
far.fit['skip'].append(i + 1)
far.fit['skip'].sort()
# optional nearfield info
if far.files['near_flt_file'] != None:
assert far.files[
'near_par_file'] != None, 'near_par_file parameter file for near-field detector is missing'
near = deepcopy(far)
# take special care of near-field keywords (eg copy near_dety_size to dety_size)
for key in list(near.fit.keys()):
def run(options):
if options.filename == None:
#parser.print_help()
print "\nNo input file supplied [-i filename]\n"
sys.exit()
#Read and check input
far = check_input.parse_input(
input_file=options.filename) # Make instance of parse_input class
far.read() # read input file
far.check() # check validity of input
if far.missing == True: # if problem exit
logging.info('MISSING ITEMS')
sys.exit()
far.initialize() # if ok initialize
# mandatory farfield info
far.read_par(far.files['par_file']) # read detector.par file
far.read_res(
) # read paramters file to resume refinement NB! optional
far.read_flt(far.files['flt_file']) # read peaks_t##.flt file
if far.files['res_file'] == None:
far.read_log() # read grainspotter.log file
far.read_rej(
far.files['rej_file']
) # read file containing rejected peaks to resume refinement NB! optional
far.set_start() # set values and errors for refinement start
check_input.set_globals(far)
#for key in far.param.keys():
# print key,far.param[key]
# Farfield outlier rejection
if far.files['res_file'] != None and far.labels == None:
from FitAllB import near_field
near_field.find_refl(far)
near_field.match(far)
far.read_rej(
far.files['rej_file']
) # read file containing rejected peaks to resume refinement NB! optional
from FitAllB import error
error.vars(far)
from FitAllB import build_fcn
build_fcn.FCN(far)
far.reject()
far.write_rej()
# farfield refinement
for k in range(far.fit['cycle']):
#while len(far.fit['newreject_grain']) > 0:
# refine grain paramters
far.fit['reforder'] = ['start%s' % k, 'rotpos%s' % k, 'end']
far.fit['goon'] = far.fit['reforder'][0]
if k == 0: #start with globals in stead of grain refinement
far.fit['reforder'] = ['start%s' % k, 'end']
far.fit['newreject_grain'] = range(far.no_grains + 1)
from FitAllB import fit
fit.refine(far)
# refine globals
far.fit['reforder'] = ['globals%s' % k, 'end']
far.fit['goon'] = far.fit['reforder'][0]
from FitAllB import fitga
fitga.refine(far)
far.residual = []
far.volume = []
far.mean_ia = []
for i in range(far.no_grains):
far.residual.append([])
far.volume.append([])
far.mean_ia.append([])
for j in range(far.nrefl[i]):
far.residual[i].append(1)
far.volume[i].append(1)
far.mean_ia[i].append(1)
from FitAllB import reject
reject.residual(far, far.fit['rej_resmedian'])
reject.mean_ia(far, far.fit['rej_ia'] * float(k + 2) / float(k + 1))
reject.intensity(far)
from FitAllB import write_output
write_output.write_rej(far, message='globals%i' % k)
for i in range(far.no_grains):
if far.nrefl[i] < far.fit['min_refl'] and i + 1 not in far.fit[
'skip']:
far.fit['skip'].append(i + 1)
far.fit['skip'].sort()
# optional nearfield info
if far.files['near_flt_file'] != None:
assert far.files[
'near_par_file'] != None, 'near_par_file parameter file for near-field detector is missing'
near = deepcopy(far)
# take special care of near-field keywords (eg copy near_dety_size to dety_size)
for key in near.fit.keys():
if key[0:5] == 'near_':
near.fit[key[5:len(key)]] = near.fit[key]
near.fit['stem'] = far.fit['stem'] + '_near'
near.read_par(near.files['near_par_file'])
near.read_flt(near.files['near_flt_file'])
keys = [
'cell__a', 'cell__b', 'cell__c', 'cell_alpha', 'cell_beta',
'cell_gamma', 'cell_lattice_[P,A,B,C,I,F,R]', 'chi', 'omegasign',
'wavelength'
]
for key in keys:
assert near.param[key] == far.param[
key], '%s is different for near- and far-field detectors' % key
# in case of different wedge use farfield value and refine
if near.param['wedge'] != far.param['wedge']:
near.param['wedge'] = far.param['wedge']
near.values = far.values
near.errors = far.errors
near.fit['skip'] = far.fit['skip']
check_input.set_globals(near)
# match peaks on nearfield detector and reject outliers
from FitAllB import near_field
near_field.find_refl(near)
near_field.match(near)
from FitAllB import error
error.vars(near)
from FitAllB import build_fcn
build_fcn.FCN(near)
near.reject()
near.write_rej()
# nearfield refinement
for k in range(near.fit['cycle']):
# refine grain paramters
near.fit['reforder'] = ['start%s' % k, 'rotpos%s' % k, 'end']
near.fit['goon'] = near.fit['reforder'][0]
near.fit['newreject_grain'] = range(near.no_grains + 1)
from FitAllB import fit
fit.refine(near)
# refine globals
near.fit['reforder'] = ['globals%s' % k, 'end']
near.fit['goon'] = near.fit['reforder'][0]
from FitAllB import fitga
fitga.refine(near)
near.residual = []
near.volume = []
near.mean_ia = []
for i in range(near.no_grains):
near.residual.append([])
near.volume.append([])
near.mean_ia.append([])
for j in range(near.nrefl[i]):
near.residual[i].append(1)
near.volume[i].append(1)
near.mean_ia[i].append(1)
from FitAllB import reject
reject.residual(near, near.fit['rej_resmedian'])
reject.mean_ia(near,
near.fit['rej_ia'] * float(k + 2) / float(k + 1))
reject.intensity(near)
from FitAllB import write_output
write_output.write_rej(near, message='globals%i' % k)
for i in range(near.no_grains):
if near.nrefl[i] < near.fit[
'min_refl'] and i + 1 not in near.fit['skip']:
near.fit['skip'].append(i + 1)
near.fit['skip'].sort()
# program ends here after deleting fcn.py and fcn.pyc
print '\nNormal termination of FitGlobAll'
os.remove('%s/fcn.py' % far.fit['direc'])
os.remove('%s/fcn.pyc' % far.fit['direc'])