def RefineCore(Controls,Histograms,Phases,restraintDict,rigidbodyDict,parmDict,varyList,
calcControls,pawleyLookup,ifSeq,printFile,dlg,refPlotUpdate=None):
'''Core optimization routines, shared between SeqRefine and Refine
:returns: 5-tuple of ifOk (bool), Rvals (dict), result, covMatrix, sig
'''
# print 'current',varyList
# for item in parmDict: print item,parmDict[item] ######### show dict just before refinement
G2mv.Map2Dict(parmDict,varyList)
ifPrint = True
if ifSeq:
ifPrint = False
Rvals = {}
while True:
begin = time.time()
values = np.array(G2stMth.Dict2Values(parmDict, varyList))
if np.any(np.isnan(values)):
raise G2obj.G2Exception('ERROR - nan found in LS parameters - use Calculate/View LS parms to locate')
# test code to compute GOF and save for external repeat
#args = ([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg)
#print '*** before fit chi**2',np.sum(G2stMth.errRefine(values,*args)**2)
#fl = open('beforeFit.cpickle','wb')
#cPickle.dump(values,fl,1)
#cPickle.dump(args[:-1],fl,1)
#fl.close()
Ftol = Controls['min dM/M']
Xtol = Controls['SVDtol']
Factor = Controls['shift factor']
if 'Jacobian' in Controls['deriv type']:
result = so.leastsq(G2stMth.errRefine,values,Dfun=G2stMth.dervRefine,full_output=True,
ftol=Ftol,col_deriv=True,factor=Factor,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg))
ncyc = int(result[2]['nfev']/2)
if refPlotUpdate is not None: refPlotUpdate(Histograms) # update plot after completion
elif 'analytic Hessian' in Controls['deriv type']:
Lamda = Controls.get('Marquardt',-3)
maxCyc = Controls['max cyc']
result = G2mth.HessianLSQ(G2stMth.errRefine,values,Hess=G2stMth.HessRefine,ftol=Ftol,xtol=Xtol,maxcyc=maxCyc,Print=ifPrint,lamda=Lamda,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg),
refPlotUpdate=refPlotUpdate)
ncyc = result[2]['num cyc']+1
Rvals['lamMax'] = result[2]['lamMax']
Controls['Marquardt'] = -3 #reset to default
elif 'Hessian SVD' in Controls['deriv type']:
maxCyc = Controls['max cyc']
result = G2mth.HessianSVD(G2stMth.errRefine,values,Hess=G2stMth.HessRefine,ftol=Ftol,xtol=Xtol,maxcyc=maxCyc,Print=ifPrint,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg),
refPlotUpdate=refPlotUpdate)
if result[1] is None:
IfOK = False
covMatrix = []
sig = len(varyList)*[None,]
break
ncyc = result[2]['num cyc']+1
else: #'numeric'
result = so.leastsq(G2stMth.errRefine,values,full_output=True,ftol=Ftol,epsfcn=1.e-8,factor=Factor,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg))
ncyc = 1
if len(varyList):
ncyc = int(result[2]['nfev']/len(varyList))
if refPlotUpdate is not None: refPlotUpdate(Histograms) # update plot
# table = dict(zip(varyList,zip(values,result[0],(result[0]-values))))
# for item in table: print item,table[item] #useful debug - are things shifting?
runtime = time.time()-begin
Rvals['SVD0'] = result[2].get('SVD0',0)
Rvals['converged'] = result[2].get('Converged')
Rvals['DelChi2'] = result[2].get('DelChi2',-1.)
Rvals['chisq'] = np.sum(result[2]['fvec']**2)
G2stMth.Values2Dict(parmDict, varyList, result[0])
G2mv.Dict2Map(parmDict,varyList)
Rvals['Nobs'] = Histograms['Nobs']
Rvals['Rwp'] = np.sqrt(Rvals['chisq']/Histograms['sumwYo'])*100. #to %
Rvals['GOF'] = np.sqrt(Rvals['chisq']/(Histograms['Nobs']-len(varyList)))
printFile.write(' Number of function calls: %d No. of observations: %d No. of parameters: %d User rejected: %d Sp. gp. extinct: %d\n'% \
(result[2]['nfev'],Histograms['Nobs'],len(varyList),Histograms['Nrej'],Histograms['Next']))
if ncyc:
printFile.write(' Refinement time = %8.3fs, %8.3fs/cycle, for %d cycles\n'%(runtime,runtime/ncyc,ncyc))
printFile.write(' wR = %7.2f%%, chi**2 = %12.6g, GOF = %6.2f\n'%(Rvals['Rwp'],Rvals['chisq'],Rvals['GOF']))
sig = len(varyList)*[None,]
if 'None' in str(type(result[1])) and ifSeq: #this bails out of a sequential refinement on singular matrix
IfOK = False
covMatrix = []
G2fil.G2Print ('Warning: **** Refinement failed - singular matrix ****')
if 'Hessian' in Controls['deriv type']:
num = len(varyList)-1
for i,val in enumerate(np.flipud(result[2]['psing'])):
if val:
G2fil.G2Print('Bad parameter: '+varyList[num-i],mode='warn')
else:
Ipvt = result[2]['ipvt']
for i,ipvt in enumerate(Ipvt):
if not np.sum(result[2]['fjac'],axis=1)[i]:
G2fil.G2Print('Bad parameter: '+varyList[ipvt-1],mode='warn')
break
IfOK = True
try:
covMatrix = result[1]*Rvals['GOF']**2
sig = np.sqrt(np.diag(covMatrix))
if np.any(np.isnan(sig)) or not sig.shape:
G2fil.G2Print ('*** Least squares aborted - some invalid esds possible ***',mode='error')
# table = dict(zip(varyList,zip(values,result[0],(result[0]-values)/sig)))
# for item in table: print item,table[item] #useful debug - are things shifting?
break #refinement succeeded - finish up!
except TypeError: #result[1] is None on singular matrix or LinAlgError
IfOK = False
if not len(varyList):
covMatrix = []
break
G2fil.G2Print ('**** Refinement failed - singular matrix ****',mode='error')
if 'Hessian' in Controls['deriv type']:
if result[1] is None:
IfOK = False
covMatrix = []
sig = len(varyList)*[None,]
break
num = len(varyList)-1
for i,val in enumerate(np.flipud(result[2]['psing'])):
if val:
G2fil.G2Print ('Removing parameter: '+varyList[num-i])
del(varyList[num-i])
else:
Ipvt = result[2]['ipvt']
for i,ipvt in enumerate(Ipvt):
if not np.sum(result[2]['fjac'],axis=1)[i]:
G2fil.G2Print ('Removing parameter: '+varyList[ipvt-1])
del(varyList[ipvt-1])
break
if IfOK:
G2stMth.GetFobsSq(Histograms,Phases,parmDict,calcControls)
return IfOK,Rvals,result,covMatrix,sig
def RefineCore(Controls,Histograms,Phases,restraintDict,rigidbodyDict,parmDict,varyList,
calcControls,pawleyLookup,ifPrint,printFile,dlg):
'Core optimization routines, shared between SeqRefine and Refine'
# print 'current',varyList
# for item in parmDict: print item,parmDict[item] ######### show dict just before refinement
G2mv.Map2Dict(parmDict,varyList)
Rvals = {}
while True:
begin = time.time()
values = np.array(G2stMth.Dict2Values(parmDict, varyList))
# test code to compute GOF and save for external repeat
#args = ([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg)
#print '*** before fit chi**2',np.sum(G2stMth.errRefine(values,*args)**2)
#fl = open('beforeFit.cpickle','wb')
#import cPickle
#cPickle.dump(values,fl,1)
#cPickle.dump(args[:-1],fl,1)
#fl.close()
Ftol = Controls['min dM/M']
Factor = Controls['shift factor']
if 'Jacobian' in Controls['deriv type']:
result = so.leastsq(G2stMth.errRefine,values,Dfun=G2stMth.dervRefine,full_output=True,
ftol=Ftol,col_deriv=True,factor=Factor,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg))
ncyc = int(result[2]['nfev']/2)
elif 'Hessian' in Controls['deriv type']:
maxCyc = Controls['max cyc']
result = G2mth.HessianLSQ(G2stMth.errRefine,values,Hess=G2stMth.HessRefine,ftol=Ftol,maxcyc=maxCyc,Print=ifPrint,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg))
ncyc = result[2]['num cyc']+1
Rvals['lamMax'] = result[2]['lamMax']
else: #'numeric'
result = so.leastsq(G2stMth.errRefine,values,full_output=True,ftol=Ftol,epsfcn=1.e-8,factor=Factor,
args=([Histograms,Phases,restraintDict,rigidbodyDict],parmDict,varyList,calcControls,pawleyLookup,dlg))
ncyc = 1
if len(varyList):
ncyc = int(result[2]['nfev']/len(varyList))
# table = dict(zip(varyList,zip(values,result[0],(result[0]-values))))
# for item in table: print item,table[item] #useful debug - are things shifting?
runtime = time.time()-begin
Rvals['converged'] = result[2].get('Converged')
Rvals['DelChi2'] = result[2].get('DelChi2',-1.)
Rvals['chisq'] = np.sum(result[2]['fvec']**2)
G2stMth.Values2Dict(parmDict, varyList, result[0])
G2mv.Dict2Map(parmDict,varyList)
Rvals['Nobs'] = Histograms['Nobs']
Rvals['Rwp'] = np.sqrt(Rvals['chisq']/Histograms['sumwYo'])*100. #to %
Rvals['GOF'] = np.sqrt(Rvals['chisq']/(Histograms['Nobs']-len(varyList)))
print >>printFile,' Number of function calls:',result[2]['nfev'], \
' No. of observations: ',Histograms['Nobs'],' No. of parameters: ',len(varyList), \
' User rejected: ',Histograms['Nrej'],' Sp. gp. extinct: ',Histograms['Next']
print >>printFile,' Refinement time = %8.3fs, %8.3fs/cycle, for %d cycles'%(runtime,runtime/ncyc,ncyc)
print >>printFile,' wR = %7.2f%%, chi**2 = %12.6g, GOF = %6.2f'%(Rvals['Rwp'],Rvals['chisq'],Rvals['GOF'])
IfOK = True
try:
covMatrix = result[1]*Rvals['GOF']**2
sig = np.sqrt(np.diag(covMatrix))
if np.any(np.isnan(sig)):
print '*** Least squares aborted - some invalid esds possible ***'
# table = dict(zip(varyList,zip(values,result[0],(result[0]-values)/sig)))
# for item in table: print item,table[item] #useful debug - are things shifting?
break #refinement succeeded - finish up!
except TypeError,FloatingPointError: #result[1] is None on singular matrix
IfOK = False
if not len(varyList):
covMatrix = []
sig = []
break
print '**** Refinement failed - singular matrix ****'
if 'Hessian' in Controls['deriv type']:
num = len(varyList)-1
for i,val in enumerate(np.flipud(result[2]['psing'])):
if val:
print 'Removing parameter: ',varyList[num-i]
del(varyList[num-i])
else:
Ipvt = result[2]['ipvt']
for i,ipvt in enumerate(Ipvt):
if not np.sum(result[2]['fjac'],axis=1)[i]:
print 'Removing parameter: ',varyList[ipvt-1]
del(varyList[ipvt-1])
break