def refine(inp,killfile=None):
check_input.interrupt(killfile)
while inp.fit['goon'] != 'end':
check_input.set_globals(inp)
# build functions to minimise
from FitAllB import build_fcn
build_fcn.FCN(inp)
import fcn
reload(fcn)
# minuit fitting
from FitAllB import fit
lsqr = fit.fit_minuit(inp)
lsqr.refine()
check_input.copy_globals(inp)
def mean_ia(inp,limit,only=None):
"""
Calculate the internal angle for each peak and store in inp.mena_ia[inp.no_grains][inp.nrefl[i]]
Jette Oddershede Januar 2009
"""
import build_fcn
build_fcn.FCN(inp)
import fcn
reload(fcn)
delete = 0
for i in range(inp.no_grains):
if i+1 in inp.fit['skip']:
pass
else:
rod = n.array([inp.rod[i][0]+inp.values['rodx%s' %i],inp.rod[i][1]+inp.values['rody%s' %i],inp.rod[i][2]+inp.values['rodz%s' %i]])
for j in range(inp.nrefl[i]-1,-1,-1):
Omega = tools.form_omega_mat_general(inp.w[inp.id[i][j]]*n.pi/180,inp.values['wx']*n.pi/180,inp.values['wy']*n.pi/180)
gexp = fcn.gexp(inp.w[inp.id[i][j]],inp.dety[inp.id[i][j]],inp.detz[inp.id[i][j]],
inp.values['wx'],inp.values['wy'],inp.values['tx'],inp.values['ty'],inp.values['tz'],
inp.values['py'],inp.values['pz'],inp.values['cy'],inp.values['cz'],inp.values['L'],
inp.values['x%s' %i],inp.values['y%s' %i],inp.values['z%s' %i])
gcalc = fcn.gcalc(inp.values['a'],inp.values['b'],inp.values['c'],inp.values['alpha'],inp.values['beta'],inp.values['gamma'],
inp.h[i][j],inp.k[i][j],inp.l[i][j],
rod[0],rod[1],rod[2],
inp.values['epsaa%s' %i],inp.values['epsab%s' %i],inp.values['epsac%s' %i],
inp.values['epsbb%s' %i],inp.values['epsbc%s' %i],inp.values['epscc%s' %i])
# gexp = n.dot(Omega,gexp)
# gcalc = n.dot(Omega,gcalc)
inp.mean_ia[i][j] = IA(n.transpose(gexp)[0],n.transpose(gcalc)[0])
# print i+1,inp.mean_ia[i][j]
if inp.mean_ia[i][j] > limit:
delete = delete + 1
reject(inp,i,j,'ia')
if only != []:
print 'Rejected', delete, 'reflection based on internal angles'
insignificant(inp)
def residual(inp,limit,only=None):
"""
Reject outliers peaks based on residuals until mean<limit*median
Jette Oddershede, Risoe DTU, May 15 2008
"""
# must update inp.vars because the order here is [i][j] in stead of [id[i][j]], the latter doesn't change when peaks are rejected, the former does.
# calculate experimental errors using the present values
import error
error.vars(inp)
# build functions to minimise
import build_fcn
build_fcn.FCN(inp)
#refinement update
import fcn
reload(fcn)
for i in range(inp.no_grains):
if i+1 in inp.fit['skip']:
pass
else:
for j in range(inp.nrefl[i]):
inp.residual[i][j] = fcn.peak(inp.values['a'],inp.values['b'],inp.values['c'],inp.values['alpha'],inp.values['beta'],inp.values['gamma'],
inp.h[i][j],inp.k[i][j],inp.l[i][j],
inp.w[inp.id[i][j]],inp.dety[inp.id[i][j]],inp.detz[inp.id[i][j]],
#n.array([inp.Syy[inp.id[i][j]],inp.Szz[inp.id[i][j]],inp.Sww[inp.id[i][j]]]),
inp.vars[i][j],
inp.values['wx'],inp.values['wy'],
inp.values['tx'],inp.values['ty'],inp.values['tz'],
inp.values['py'],inp.values['pz'],
inp.values['cy'],inp.values['cz'],
inp.values['L'],
inp.values['x%s' %i],inp.values['y%s' %i],inp.values['z%s' %i],
inp.rod[i][0]+inp.values['rodx%s' %i],
inp.rod[i][1]+inp.values['rody%s' %i],
inp.rod[i][2]+inp.values['rodz%s' %i],
inp.values['epsaa%s' %i],inp.values['epsab%s' %i],inp.values['epsac%s' %i],
inp.values['epsbb%s' %i],inp.values['epsbc%s' %i],inp.values['epscc%s' %i])
data = deepcopy(inp.residual)
maxres = [0]*inp.no_grains
for i in range(inp.no_grains):
data[i].sort()
if i+1 in inp.fit['skip']:
pass
else:
mean = int(n.sum(data[i])/len(data[i]))
medi = median(data[i])
# print i, len(data[i]), medi, mean,'\n',data[i]
while mean > limit*medi:
data[i].pop()
mean = int(n.sum(data[i])/inp.nrefl[i])
medi = median(data[i])
maxres[i] = max(data[i])
# print i, len(data[i]),medi,mean,'\n',data[i],'\n'
delete = 0
if only==None:
only = range(1,1+inp.no_grains)
for i in range(inp.no_grains):
if i+1 in inp.fit['skip'] or i+1 not in only:
pass
else:
for j in range(inp.nrefl[i]-1,-1,-1): # loop backwards to make pop work
if inp.residual[i][j] > maxres[i]:
delete = delete + 1
reject(inp,i,j,'residual')
if only != []:
print 'Rejected', delete, 'reflection based on residuals'
insignificant(inp)
def mean_ia_old(inp,limit,only=None):
"""
Calculate the internal angle for each peak and store in inp.mena_ia[inp.no_grains][inp.nrefl[i]]
Jette Oddershede Januar 2009
"""
import build_fcn
build_fcn.FCN(inp)
import fcn
reload(fcn)
for i in range(inp.no_grains):
if i+1 in inp.fit['skip']:
pass
else:
rod = n.array([inp.rod[i][0]+inp.values['rodx%s' %i],inp.rod[i][1]+inp.values['rody%s' %i],inp.rod[i][2]+inp.values['rodz%s' %i]])
for j in range(inp.nrefl[i]):
Omega = tools.form_omega_mat_general(inp.w[inp.id[i][j]]*n.pi/180,inp.values['wx']*n.pi/180,inp.values['wy']*n.pi/180)
gexp = fcn.gexp(inp.w[inp.id[i][j]],inp.dety[inp.id[i][j]],inp.detz[inp.id[i][j]],
inp.values['wx'],inp.values['wy'],inp.values['tx'],inp.values['ty'],inp.values['tz'],
inp.values['py'],inp.values['pz'],inp.values['cy'],inp.values['cz'],inp.values['L'],
inp.values['x%s' %i],inp.values['y%s' %i],inp.values['z%s' %i])
gcalc = fcn.gcalc(inp.values['a'],inp.values['b'],inp.values['c'],inp.values['alpha'],inp.values['beta'],inp.values['gamma'],
inp.h[i][j],inp.k[i][j],inp.l[i][j],
rod[0],rod[1],rod[2],
inp.values['epsaa%s' %i],inp.values['epsab%s' %i],inp.values['epsac%s' %i],
inp.values['epsbb%s' %i],inp.values['epsbc%s' %i],inp.values['epscc%s' %i])
gexp = n.dot(Omega,gexp)
gcalc = n.dot(Omega,gcalc)
# print int(inp.h[i][j]), int(inp.k[i][j]), int(inp.l[i][j]),inp.w[inp.id[i][j]],inp.dety[inp.id[i][j]],inp.detz[inp.id[i][j]],'gexp', 2*n.pi*n.transpose(gexp)[0]/inp.param['wavelength']
# print int(inp.h[i][j]), int(inp.k[i][j]), int(inp.l[i][j]),inp.w[inp.id[i][j]],inp.dety[inp.id[i][j]],inp.detz[inp.id[i][j]],'gcalc', 2*n.pi*n.transpose(gcalc)[0]/inp.param['wavelength']
inp.mean_ia[i][j] = IA(n.transpose(gexp)[0],n.transpose(gcalc)[0])
# inp.mean_ia[i][j] = IAforrod(n.transpose(gexp)[0],n.transpose(gcalc)[0],rod)
# print inp.h[i][j], inp.k[i][j], inp.l[i][j], inp.id[i][j], inp.mean_ia[i][j]
data = deepcopy(inp.mean_ia)
maxia = [0]*inp.no_grains
for i in range(inp.no_grains):
data[i].sort()
if i+1 in inp.fit['skip']:
pass
else:
mean = n.sum(data[i])/len(data[i])
medi = median(data[i])
# print i, len(data[i]), medi, mean,'\n',data[i]
while mean > limit*medi:
data[i].pop()
mean = n.sum(data[i])/inp.nrefl[i]
medi = median(data[i])
maxia[i] = max(data[i])
# print i, len(data[i]),medi,mean,'\n',data[i],'\n'
delete = 0
if only==None:
only = range(1,1+inp.no_grains)
for i in range(inp.no_grains):
if i+1 in inp.fit['skip'] or i+1 not in only:
pass
else:
for j in range(inp.nrefl[i]-1,-1,-1): # loop backwards to make pop work
if inp.mean_ia[i][j] > maxia[i]:
delete = delete + 1
reject(inp,i,j,'ia')
if only != []:
print 'Rejected', delete, 'reflection based on internal angles'
insignificant(inp)
def grain_values(lsqr):
# """
# Calculate the contributions from each grain
# For extreme contributions print a warning (*****)
#
# Jette Oddershede, Risoe DTU, May 15 2008
# """
# rebuild function and load
import build_fcn
build_fcn.FCN(lsqr.inp)
import fcn
reload(fcn)
# save values before making a new lsqr of minuit
# temp1 = deepcopy(lsqr.m.values)
# temp2 = deepcopy(lsqr.m.errors)
# temp3 = deepcopy(lsqr.m.fixed)
# temp4 = deepcopy(lsqr.mg.tol)
g = n.zeros((lsqr.inp.no_grains))
lsqr.inp.fit['poor'] = []
lsqr.poor_value = []
lsqr.poor_nrefl = []
for i in range(lsqr.inp.no_grains):
if i+1 in lsqr.inp.fit['skip']:
pass
else:
for j in range(lsqr.inp.nrefl[i]):
g[i] = g[i] + fcn.peak(lsqr.m.values['a'],lsqr.m.values['b'],lsqr.m.values['c'],lsqr.m.values['alpha'],lsqr.m.values['beta'],lsqr.m.values['gamma'],
lsqr.inp.h[i][j],lsqr.inp.k[i][j],lsqr.inp.l[i][j],
lsqr.inp.w[lsqr.inp.id[i][j]],lsqr.inp.dety[lsqr.inp.id[i][j]],lsqr.inp.detz[lsqr.inp.id[i][j]],
lsqr.inp.vars[i][j],
lsqr.m.values['wx'],lsqr.m.values['wy'],
lsqr.m.values['tx'],lsqr.m.values['ty'],lsqr.m.values['tz'],
lsqr.m.values['py'],lsqr.m.values['pz'],
lsqr.m.values['cy'],lsqr.m.values['cz'],
lsqr.m.values['L'],
lsqr.m.values['x%s' %i],lsqr.m.values['y%s' %i],lsqr.m.values['z%s' %i],
lsqr.inp.rod[i][0]+lsqr.m.values['rodx%s' %i],
lsqr.inp.rod[i][1]+lsqr.m.values['rody%s' %i],
lsqr.inp.rod[i][2]+lsqr.m.values['rodz%s' %i],
lsqr.m.values['epsaa%s' %i],lsqr.m.values['epsab%s' %i],lsqr.m.values['epsac%s' %i],
lsqr.m.values['epsbb%s' %i],lsqr.m.values['epsbc%s' %i],lsqr.m.values['epscc%s' %i])
# for i in range(lsqr.inp.no_grains):
# if i+1 not in lsqr.inp.fit['skip']:
# # make new lsqr of minuit
# lsqr.mg = Minuit(fcn.FCNgrain)
# lsqr.mg.values = temp1
# lsqr.mg.values['i'] = i
# lsqr.mg.scan(("L",1,lsqr.mg.values['L']-1,lsqr.mg.values['L']+1)) # scan to set lsqr.m.fval, function starting value
# g[i] = lsqr.mg.fval
data = []
poor = []
for i in range(lsqr.inp.no_grains):
if i+1 not in lsqr.inp.fit['skip']:
data.append(g[i]/lsqr.inp.nrefl[i])
reject.mad(data,poor,lsqr.inp.fit['rej_vol']**2)
for i in range(lsqr.inp.no_grains):
if i+1 not in lsqr.inp.fit['skip']:
print 'Grain %i %i: %e %f' %(i+1,lsqr.inp.nrefl[i],g[i],g[i]/lsqr.inp.nrefl[i])
# give back old values
# lsqr.m.errors = temp2
# lsqr.m.fixed = temp3
# lsqr.mg.tol = temp4
return g