'Kr': Kr,
'Lr': Lr,
'd': d,
'q': q,
'alphastar': alphastar,
'astar': astar,
'lattice': lattice,
'Hh': Hh,
'Kh': Kh,
'Lh': Lh
}
lowerm = [0, 0]
upperm = [100, pi / 2]
p0,jmin=anneal(chisq_an,p0,args=(Hr,Kr,Lr,d,q,alphastar,astar,lattice,Hh,Kh,Lh,y,yerr),\
schedule='simple',lower=lowerm,upper=upperm,\
maxeval=None, maxaccept=None,dwell=500,maxiter=6000,T0=None)
dof = len(y) - len(p0)
fake_dof = len(y)
chimin = (cost_func(p0, Hr, Kr, Lr, d, q, alphastar, astar, lattice, Hh,
Kh, Lh, y, yerr)**2).sum()
chimin = chimin / dof if dof > 0 else chimin / fake_dof
print 'p0_anneal', p0[0], N.degrees(p0[1])
print 'chi_anneal', chimin
m = mpfit(myfunctlin, p0, parinfo=parinfo, functkw=fa)
print 'status = ', m.status
print 'params = ', m.params
p1 = m.params
covariance = m.covar
'Hr':Hr
,'Kr':Kr
,'Lr':Lr
,'d':d
,'q':q
,'alphastar':alphastar,
'astar':astar,
'lattice':lattice,
'Hh':Hh,
'Kh':Kh,
'Lh':Lh}
lowerm=[0,0,0,0,0]
upperm=[100,pi,pi/2,2*pi,pi]
p0,jmin=anneal(chisq_an,p0,args=(Hr,Kr,Lr,d,q,alphastar,astar,lattice,Hh,Kh,Lh,y,yerr),\
schedule='simple',lower=lowerm,upper=upperm,\
maxeval=None, maxaccept=None,dwell=50,maxiter=60,T0=None)
dof=len(y)-len(p0)
fake_dof=len(y)
chimin=(cost_func(p0,Hr,Kr,Lr,d,q,alphastar,astar,lattice,Hh,Kh,Lh,y,yerr)**2).sum()
chimin=chimin/dof if dof>0 else chimin/fake_dof
print 'p0_anneal',p0[0],N.degrees(p0[1:])
print 'chi_anneal', chimin
m = mpfit(myfunctlin, p0, parinfo=parinfo,functkw=fa)
print 'status = ', m.status
print 'params = ', m.params
p1=m.params
#scale by nuclear factor
y = y / A
yerr = yerr / A
if 1:
print 'annealing'
#myschedule='fast'
myschedule = 'simple'
lowerm = 1e-4 * N.ones(len(pfit))
#lowerm[0:3]=[-1,-1,-1]
#upperm=N.ones(len(pfit))
myargs = (Qlist, y, yerr, corrections)
upperm = np.array(
[1.0, 2.0, pi, pi, pi, pi, 2 * pi, 2 * pi, 2 * pi, 2 * pi])
pout,jmin=anneal(chisq_an,pfit,args=myargs,\
schedule=myschedule,lower=lowerm,upper=upperm,\
maxeval=1000, maxaccept=None,dwell=2000,maxiter=120,feps=1e-2,full_output = 0)
print 'annealed', pout
pfit = pout
parbase = {
'value': 0.,
'fixed': 0,
'limited': [0, 0],
'limits': [0., 0.]
}
parinfo = []
for i in range(len(pfit)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(pfit)):
print 'fm',fm
#scale by nuclear factor
y=y/A
yerr=yerr/A
if 0:
print 'annealing'
#myschedule='fast'
myschedule='simple'
lowerm=1e-4*N.ones(len(pfit))
#lowerm[0:3]=[-1,-1,-1]
#upperm=N.ones(len(pfit))
myargs=(Qlist,y,yerr,corrections)
upperm=np.array([1000.0])
pout,jmin=anneal(chisq_an,pfit,args=myargs,\
schedule=myschedule,lower=lowerm,upper=upperm,\
maxeval=1000, maxaccept=None,dwell=2000,maxiter=120,feps=1e-2,full_output = 0)
print 'annealed',pout
pfit=pout
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(pfit)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(pfit)):
parinfo[i]['value']=pfit[i]
fa = {'x':Qlist, 'y':y, 'err':yerr, 'correction':corrections}
m = mpfit(myfunctlin, pfit, parinfo=parinfo,functkw=fa)
if 0:
scipy.optimize.optimize.fmin_l_bfgs_b(max_wrap, p0, fprime = None, args=h_args, approx_grad = 0, \
bounds = None, m = 10, factr = 10000000.0, \
pgtol = 1.0000000000000001e-05, epsilon = 1e-08, iprint = -Const(1), maxfun = 15)
if 0:
print 'fmin'
pout=scipy.optimize.optimize.fmin(max_wrap,p0,maxiter = 5, maxfun = 100,disp=1,args=h_args)
if 0:
print 'annealing'
myschedule='fast'
#myschedule='simple'
pout,jmin=anneal(max_wrap,p0,args=h_args,\
schedule=myschedule,lower=lowerm,upper=upperm,\
maxeval=100, maxaccept=None,dwell=20,maxiter=20,feps=1e-1,full_output = 0)
if 0:
multipliers=pout[0:3]
print 'multipliers',multipliers
pout=pout[3::]
if 0:
myfilestr=r'c:\structfactors_density.dat'
x2,y2,z2=N.loadtxt(myfilestr).T
P2=z2.reshape((Mx,Mz))
#P2=P2/P2.max()
fq_density=calc_model(P2,h,k,l,fq,fqerr,x,z,cosmat_list,coslist,flist)
X3,Z3,P3=plotdensity(h,k,l,fq)
def getmatrix(sx,sy,sz,mytol=1e-45,maxiter=2):
print "(sx, sy, sz) = ", (sx, sy, sz)
p0=N.array([0,1,0,1],'d')
p0=N.array([0,0,0,1],'d')
#p0=N.array([0,1,0,1],'d')
lowerm=[-2,-2,-2,-2]
upperm=[2,2,2,2]
lowerm=[-1,-1,-1,-1]
upperm=[1,1,1,1]
flag=True
smat=N.empty((3,1),'float64')
smat=N.matrix(smat)
smat[0]=0
smat[1]=0
smat[2]=1
iter=0
pbest=p0
currbest=chisq_an(p0,sx,sy,sz)
while flag:
print 'iter',iter
p0,jmin=anneal(chisq_an,p0,args=(sx,sy,sz),\
schedule='simple',lower=lowerm,upper=upperm,\
maxeval=None, maxaccept=None,dwell=10,maxiter=600,T0=10000)
print 'p0',p0
p = NLSP(chisq, p0,xtol=1e-37)
#p = NLP(chisq_an, p0,xtol=1e-17)
p.args.f=(sx,sy,sz)
p.iprint = 10
p.ftol=1e-30
p.maxFunEvals = 1e10
#p.checkdf()
r = p.solve('nssolve')
#r = p.solve('scipy_fsolve')
#r = p.solve('nlp:ralg')
#r = p.solve('ralg')
#r = p.solve('scipy_cobyla')
print 'solution:', r.xf
a,b,c,s=r.xf
if 1:
p0=[a,b,c,s]
p=scipy.optimize.minpack.fsolve(chisq_f,p0,args=(sx,sy,sz),xtol=1e-37)
a,b,c,s=p
print '2nd soln',p
amat=genmat(a,b,c,s)
if 1:
array_err=1e-2
for i in range(3):
for j in range(3):
if N.absolute(amat[i,j])<array_err:
amat[i,j]=0
sout=amat*smat
curr_score=chisq_an(p0,sx,sy,sz)
if curr_score<currbest:
currbest=curr_score
pbest=p
if (N.abs(sout[0]-sx)<mytol and N.abs(sout[1]-sy)<mytol and N.abs(sout[2]-sz)<mytol) or iter>maxiter:
flag=False
iter=iter+1
a,b,c,s=pbest
amat=genmat(a,b,c,s)
if 1:
array_err=1e-5
for i in range(3):
for j in range(3):
if N.absolute(amat[i,j])<array_err:
amat[i,j]=0
#print 'amat',amat
print "AMAT\n\n\nAMAT: \n", amat, "\nSMAT\n" , smat, "\nAMAT * SMAT\n\n", amat*smat, "\nsx, sy, sz\n", (sx,sy,sz)
return amat
pgtol = 1.0000000000000001e-05, epsilon = 1e-08, iprint = -Const(1), maxfun = 15)
if 0:
print 'fmin'
pout = scipy.optimize.optimize.fmin(max_wrap,
p0,
maxiter=5,
maxfun=100,
disp=1,
args=h_args)
if 0:
print 'annealing'
myschedule = 'fast'
#myschedule='simple'
pout,jmin=anneal(max_wrap,p0,args=h_args,\
schedule=myschedule,lower=lowerm,upper=upperm,\
maxeval=100, maxaccept=None,dwell=20,maxiter=20,feps=1e-1,full_output = 0)
if 0:
multipliers = pout[0:3]
print 'multipliers', multipliers
pout = pout[3::]
if 0:
myfilestr = r'c:\structfactors_density.dat'
x2, y2, z2 = N.loadtxt(myfilestr).T
P2 = z2.reshape((Mx, Mz))
#P2=P2/P2.max()
fq_density = calc_model(P2, h, k, l, fq, fqerr, x, z, cosmat_list,
coslist, flist)
def getmatrix(sx, sy, sz, mytol=1e-45, maxiter=2):
print "(sx, sy, sz) = ", (sx, sy, sz)
p0 = N.array([0, 1, 0, 1], 'd')
p0 = N.array([0, 0, 0, 1], 'd')
#p0=N.array([0,1,0,1],'d')
lowerm = [-2, -2, -2, -2]
upperm = [2, 2, 2, 2]
lowerm = [-1, -1, -1, -1]
upperm = [1, 1, 1, 1]
flag = True
smat = N.empty((3, 1), 'float64')
smat = N.matrix(smat)
smat[0] = 0
smat[1] = 0
smat[2] = 1
iter = 0
pbest = p0
currbest = chisq_an(p0, sx, sy, sz)
while flag:
print 'iter', iter
p0,jmin=anneal(chisq_an,p0,args=(sx,sy,sz),\
schedule='simple',lower=lowerm,upper=upperm,\
maxeval=None, maxaccept=None,dwell=10,maxiter=600,T0=10000)
print 'p0', p0
p = NLSP(chisq, p0, xtol=1e-37)
#p = NLP(chisq_an, p0,xtol=1e-17)
p.args.f = (sx, sy, sz)
p.iprint = 10
p.ftol = 1e-30
p.maxFunEvals = 1e10
#p.checkdf()
r = p.solve('nssolve')
#r = p.solve('scipy_fsolve')
#r = p.solve('nlp:ralg')
#r = p.solve('ralg')
#r = p.solve('scipy_cobyla')
print 'solution:', r.xf
a, b, c, s = r.xf
if 1:
p0 = [a, b, c, s]
p = scipy.optimize.minpack.fsolve(chisq_f,
p0,
args=(sx, sy, sz),
xtol=1e-37)
a, b, c, s = p
print '2nd soln', p
amat = genmat(a, b, c, s)
if 1:
array_err = 1e-2
for i in range(3):
for j in range(3):
if N.absolute(amat[i, j]) < array_err:
amat[i, j] = 0
sout = amat * smat
curr_score = chisq_an(p0, sx, sy, sz)
if curr_score < currbest:
currbest = curr_score
pbest = p
if (N.abs(sout[0] - sx) < mytol and N.abs(sout[1] - sy) < mytol
and N.abs(sout[2] - sz) < mytol) or iter > maxiter:
flag = False
iter = iter + 1
a, b, c, s = pbest
amat = genmat(a, b, c, s)
if 1:
array_err = 1e-5
for i in range(3):
for j in range(3):
if N.absolute(amat[i, j]) < array_err:
amat[i, j] = 0
#print 'amat',amat
print "AMAT\n\n\nAMAT: \n", amat, "\nSMAT\n", smat, "\nAMAT * SMAT\n\n", amat * smat, "\nsx, sy, sz\n", (
sx, sy, sz)
return amat
