def fit_node(self,index):
qnode=self.qlist[index]
print qnode.q
th=qnode.th_condensed['a3']
counts=qnode.th_condensed['counts']
counts_err=qnode.th_condensed['counts_err']
print qnode.th_condensed['counts'].std()
print qnode.th_condensed['counts'].mean()
maxval=qnode.th_condensed['counts'].max()
minval=qnode.th_condensed['counts'].min()
diff=qnode.th_condensed['counts'].max()-qnode.th_condensed['counts'].min()\
-qnode.th_condensed['counts'].mean()
sig=qnode.th_condensed['counts'].std()
if diff-2*sig>0:
#the difference between the high and low point and
#the mean is greater than 3 sigma so we have a signal
p0=findpeak(th,counts,1)
print 'p0',p0
#Area center width Bak
center=p0[0]
width=p0[1]
sigma=width/2/N.sqrt(2*N.log(2))
Imax=maxval-minval
area=Imax*(N.sqrt(2*pi)*sigma)
print 'Imax',Imax
pin=[area,center,width,0]
if 1:
p = NLP(chisq, pin, maxIter = 1e3, maxFunEvals = 1e5)
#p.lb=lowerm
#p.ub=upperm
p.args.f=(th,counts,counts_err)
p.plot = 0
p.iprint = 1
p.contol = 1e-5#3 # required constraints tolerance, default for NLP is 1e-6
# for ALGENCAN solver gradtol is the only one stop criterium connected to openopt
# (except maxfun, maxiter)
# Note that in ALGENCAN gradtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
p.gradtol = 1e-5#5 # gradient stop criterium (default for NLP is 1e-6)
#print 'maxiter', p.maxiter
#print 'maxfun', p.maxfun
p.maxIter=50
# p.maxfun=100
#p.df_iter = 50
p.maxTime = 4000
#r=p.solve('scipy_cobyla')
#r=p.solve('scipy_lbfgsb')
#r = p.solve('algencan')
print 'ralg'
r = p.solve('ralg')
print 'done'
pfit=r.xf
print 'pfit openopt',pfit
print 'r dict', r.__dict__
if 0:
print 'curvefit'
print sys.executable
pfit,popt=curve_fit(gauss2, th, counts, p0=pfit, sigma=counts_err)
print 'p,popt', pfit,popt
perror=N.sqrt(N.diag(popt))
print 'perror',perror
chisqr=chisq(pfit,th,counts,counts_err)
dof=len(th)-len(pfit)
print 'chisq',chisqr
if 0:
oparam=scipy.odr.Model(gauss)
mydatao=scipy.odr.RealData(th,counts,sx=None,sy=counts_err)
myodr = scipy.odr.ODR(mydatao, oparam, beta0=pfit)
myoutput=myodr.run()
myoutput.pprint()
pfit=myoutput.beta
if 1:
print 'mpfit'
p0=pfit
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
fa = {'x':th, 'y':counts, 'err':counts_err}
#parinfo[1]['fixed']=1
#parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params=m.params
pfit=params
perror=m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr=chisq(pfit,th,counts,counts_err)
dof=m.dof
#Icalc=gauss(pfit,th)
#print 'mpfit chisqr', chisqr
if 0:
width_x=N.linspace(p0[0]-p0[1],p0[0]+p0[1],100)
width_y=N.ones(width_x.shape)*(maxval-minval)/2
pos_y=N.linspace(minval,maxval,100)
pos_x=N.ones(pos_y.shape)*p0[0]
if 1:
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(width_x,width_y)
pylab.plot(pos_x,pos_y)
pylab.plot(th,Icalc)
pylab.show()
else:
#fix center
#fix width
print 'no peak'
#Area center width Bak
area=0
center=th[len(th)/2]
width=(th.max()-th.min())/5.0 #rather arbitrary, but we don't know if it's the first....
Bak=qnode.th_condensed['counts'].mean()
p0=N.array([area,center,width,Bak],dtype='float64') #initial conditions
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
fa = {'x':th, 'y':counts, 'err':counts_err}
parinfo[1]['fixed']=1
parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params=m.params
pfit=params
perror=m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr=chisq(pfit,th,counts,counts_err)
dof=m.dof
Icalc=gauss(pfit,th)
#print 'perror',perror
if 0:
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(th,Icalc)
pylab.show()
print 'final answer'
print 'perror', 'perror'
#If the fit is unweighted (i.e. no errors were given, or the weights
# were uniformly set to unity), then .perror will probably not represent
#the true parameter uncertainties.
# *If* you can assume that the true reduced chi-squared value is unity --
# meaning that the fit is implicitly assumed to be of good quality --
# then the estimated parameter uncertainties can be computed by scaling
# .perror by the measured chi-squared value.
# dof = len(x) - len(mpfit.params) # deg of freedom
# # scaled uncertainties
# pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof)
print 'params', pfit
print 'chisqr', chisqr #note that chisqr already is scaled by dof
pcerror=perror*N.sqrt(m.fnorm / m.dof)#chisqr
print 'pcerror', pcerror
self.qlist[index].th_integrated_intensity=N.abs(pfit[0])
self.qlist[index].th_integrated_intensity_err=N.abs(pcerror[0])
Icalc=gauss(pfit,th)
print 'perror',perror
if 0:
pylab.figure()
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(th,Icalc)
qstr=str(qnode.q['h_center'])+','+str(qnode.q['k_center'])+','+str(qnode.q['l_center'])
pylab.title(qstr)
#pylab.show()
return
def fit_node(self,index):
qnode=self.qlist[index]
print qnode.q
th=qnode.th_condensed['a3']
counts=qnode.th_condensed['counts']
counts_err=qnode.th_condensed['counts_err']
print qnode.th_condensed['counts'].std()
print qnode.th_condensed['counts'].mean()
maxval=qnode.th_condensed['counts'].max()
minval=qnode.th_condensed['counts'].min()
diff=qnode.th_condensed['counts'].max()-qnode.th_condensed['counts'].min()\
-qnode.th_condensed['counts'].mean()
sig=qnode.th_condensed['counts'].std()
if diff-2*sig>0:
#the difference between the high and low point and
#the mean is greater than 3 sigma so we have a signal
p0=findpeak(th,counts,1)
print 'p0',p0
#Area center width Bak
center=p0[0]
width=p0[1]
sigma=width/2/N.sqrt(2*N.log(2))
Imax=maxval-minval
area=Imax*(N.sqrt(2*pi)*sigma)
print 'Imax',Imax
pin=[area,center,width,0]
if 1:
p = NLP(chisq, pin, maxIter = 1e3, maxFunEvals = 1e5)
#p.lb=lowerm
#p.ub=upperm
p.args.f=(th,counts,counts_err)
p.plot = 0
p.iprint = 1
p.contol = 1e-5#3 # required constraints tolerance, default for NLP is 1e-6
# for ALGENCAN solver gradtol is the only one stop criterium connected to openopt
# (except maxfun, maxiter)
# Note that in ALGENCAN gradtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
p.gradtol = 1e-5#5 # gradient stop criterium (default for NLP is 1e-6)
#print 'maxiter', p.maxiter
#print 'maxfun', p.maxfun
p.maxIter=50
# p.maxfun=100
#p.df_iter = 50
p.maxTime = 4000
#r=p.solve('scipy_cobyla')
#r=p.solve('scipy_lbfgsb')
#r = p.solve('algencan')
print 'ralg'
r = p.solve('ralg')
print 'done'
pfit=r.xf
print 'pfit openopt',pfit
print 'r dict', r.__dict__
if 0:
print 'curvefit'
print sys.executable
pfit,popt=curve_fit(gauss2, th, counts, p0=pfit, sigma=counts_err)
print 'p,popt', pfit,popt
perror=N.sqrt(N.diag(popt))
print 'perror',perror
chisqr=chisq(pfit,th,counts,counts_err)
dof=len(th)-len(pfit)
print 'chisq',chisqr
if 0:
oparam=scipy.odr.Model(gauss)
mydatao=scipy.odr.RealData(th,counts,sx=None,sy=counts_err)
myodr = scipy.odr.ODR(mydatao, oparam, beta0=pfit)
myoutput=myodr.run()
myoutput.pprint()
pfit=myoutput.beta
if 1:
print 'mpfit'
p0=pfit
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
fa = {'x':th, 'y':counts, 'err':counts_err}
#parinfo[1]['fixed']=1
#parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params=m.params
pfit=params
perror=m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr=chisq(pfit,th,counts,counts_err)
dof=m.dof
#Icalc=gauss(pfit,th)
#print 'mpfit chisqr', chisqr
if 0:
width_x=N.linspace(p0[0]-p0[1],p0[0]+p0[1],100)
width_y=N.ones(width_x.shape)*(maxval-minval)/2
pos_y=N.linspace(minval,maxval,100)
pos_x=N.ones(pos_y.shape)*p0[0]
if 1:
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(width_x,width_y)
pylab.plot(pos_x,pos_y)
pylab.plot(th,Icalc)
pylab.show()
else:
#fix center
#fix width
print 'no peak'
#Area center width Bak
area=0
center=th[len(th)/2]
width=(th.max()-th.min())/5.0 #rather arbitrary, but we don't know if it's the first....
Bak=qnode.th_condensed['counts'].mean()
p0=N.array([area,center,width,Bak],dtype='float64') #initial conditions
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
fa = {'x':th, 'y':counts, 'err':counts_err}
parinfo[1]['fixed']=1
parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params=m.params
pfit=params
perror=m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr=chisq(pfit,th,counts,counts_err)
dof=m.dof
Icalc=gauss(pfit,th)
#print 'perror',perror
if 0:
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(th,Icalc)
pylab.show()
print 'final answer'
print 'perror', 'perror'
#If the fit is unweighted (i.e. no errors were given, or the weights
# were uniformly set to unity), then .perror will probably not represent
#the true parameter uncertainties.
# *If* you can assume that the true reduced chi-squared value is unity --
# meaning that the fit is implicitly assumed to be of good quality --
# then the estimated parameter uncertainties can be computed by scaling
# .perror by the measured chi-squared value.
# dof = len(x) - len(mpfit.params) # deg of freedom
# # scaled uncertainties
# pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof)
print 'params', pfit
print 'chisqr', chisqr #note that chisqr already is scaled by dof
pcerror=perror*N.sqrt(m.fnorm / m.dof)#chisqr
print 'pcerror', pcerror
self.qlist[index].th_integrated_intensity=N.abs(pfit[0])
self.qlist[index].th_integrated_intensity_err=N.abs(pcerror[0])
Icalc=gauss(pfit,th)
print 'perror',perror
if 0:
pylab.figure()
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(th,Icalc)
qstr=str(qnode.q['h_center'])+','+str(qnode.q['k_center'])+','+str(qnode.q['l_center'])
pylab.title(qstr)
#pylab.show()
return
def fitpeak(x,y,yerr):
maxval=x.max()
minval=x.min()
diff=y.max()-y.min()-y.mean()
sig=y.std()
print 'diff',diff,'std',sig
if diff-1*sig>0:
#the difference between the high and low point and
#the mean is greater than 3 sigma so we have a signal
p0=findpeak(x,y,2)
print 'p0',p0
#Area center width Bak area2 center2 width2
center1=p0[0]
width1=p0[1]
center2=p0[2]
width2=p0[3]
sigma=width/2/N.sqrt(2*N.log(2))
ymax=maxval-minval
area=ymax*(N.sqrt(2*pi)*sigma)
print 'ymax',ymax
pin=[area,center1,width1,0,area,center2,width2]
if 1:
p = NLP(chisq, pin, maxIter = 1e3, maxFunEvals = 1e5)
#p.lb=lowerm
#p.ub=upperm
p.args.f=(x,y,yerr)
p.plot = 0
p.iprint = 1
p.contol = 1e-5#3 # required constraints tolerance, default for NLP is 1e-6
# for ALGENCAN solver gradtol is the only one stop criterium connected to openopt
# (except maxfun, maxiter)
# Note that in ALGENCAN gradtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
p.gradtol = 1e-5#5 # gradient stop criterium (default for NLP is 1e-6)
#print 'maxiter', p.maxiter
#print 'maxfun', p.maxfun
p.maxIter=50
# p.maxfun=100
#p.df_iter = 50
p.maxTime = 4000
#r=p.solve('scipy_cobyla')
#r=p.solve('scipy_lbfgsb')
#r = p.solve('algencan')
print 'ralg'
r = p.solve('ralg')
print 'done'
pfit=r.xf
print 'pfit openopt',pfit
print 'r dict', r.__dict__
if 1:
print 'mpfit'
p0=pfit
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
fa = {'x':x, 'y':y, 'err':yerr}
#parinfo[1]['fixed']=1
#parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params=m.params
pfit=params
perror=m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr=chisq(pfit,x,y,yerr)
dof=m.dof
#Icalc=gauss(pfit,th)
#print 'mpfit chisqr', chisqr
ycalc=gauss(pfit,x)
if 1:
width_x=N.linspace(p0[0]-p0[1],p0[0]+p0[1],100)
width_y=N.ones(width_x.shape)*(maxval-minval)/2
pos_y=N.linspace(minval,maxval,100)
pos_x=N.ones(pos_y.shape)*p0[0]
if 0:
pylab.errorbar(th,counts,counts_err,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(width_x,width_y)
pylab.plot(pos_x,pos_y)
pylab.plot(x,ycalc)
pylab.show()
else:
#fix center
#fix width
print 'no peak'
#Area center width Bak
area=0
center=x[len(x)/2]
width=(x.max()-x.min())/5.0 #rather arbitrary, but we don't know if it's the first.... #better to use resolution
Bak=y.mean()
p0=N.array([area,center,width,Bak],dtype='float64') #initial conditions
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
fa = {'x':x, 'y':y, 'err':yerr}
parinfo[1]['fixed']=1
parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params=m.params
pfit=params
perror=m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr=chisq(pfit,x,y,yerr)
dof=m.dof
ycalc=gauss(pfit,x)
#print 'perror',perror
if 0:
pylab.errorbar(x,y,yerr,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(x,ycalc)
pylab.show()
print 'final answer'
print 'perror', 'perror'
#If the fit is unweighted (i.e. no errors were given, or the weights
# were uniformly set to unity), then .perror will probably not represent
#the true parameter uncertainties.
# *If* you can assume that the true reduced chi-squared value is unity --
# meaning that the fit is implicitly assumed to be of good quality --
# then the estimated parameter uncertainties can be computed by scaling
# .perror by the measured chi-squared value.
# dof = len(x) - len(mpfit.params) # deg of freedom
# # scaled uncertainties
# pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof)
print 'params', pfit
print 'chisqr', chisqr #note that chisqr already is scaled by dof
pcerror=perror*N.sqrt(m.fnorm / m.dof)#chisqr
print 'pcerror', pcerror
integrated_intensity=N.abs(pfit[0])
integrated_intensity_err=N.abs(pcerror[0])
ycalc=gauss(pfit,x)
print 'perror',perror
if 1:
pylab.figure()
pylab.errorbar(x,y,yerr,marker='s',linestyle='None',mfc='black',mec='black',ecolor='black')
pylab.plot(x,ycalc)
#qstr=str(qnode.q['h_center'])+','+str(qnode.q['k_center'])+','+str(qnode.q['l_center'])
#pylab.title(qstr)
pylab.show()
return pfit,perror,pcerror,chisq
def fitpeak(x, y, yerr):
maxval = x.max()
minval = x.min()
diff = y.max() - y.min() - y.mean()
sig = y.std()
print 'diff', diff, 'std', sig
if diff - 1 * sig > 0:
#the difference between the high and low point and
#the mean is greater than 3 sigma so we have a signal
p0 = findpeak(x, y, 2)
print 'p0', p0
#Area center width Bak area2 center2 width2
center1 = p0[0]
width1 = p0[1]
center2 = p0[2]
width2 = p0[3]
sigma = width / 2 / N.sqrt(2 * N.log(2))
ymax = maxval - minval
area = ymax * (N.sqrt(2 * pi) * sigma)
print 'ymax', ymax
pin = [area, center1, width1, 0, area, center2, width2]
if 1:
p = NLP(chisq, pin, maxIter=1e3, maxFunEvals=1e5)
#p.lb=lowerm
#p.ub=upperm
p.args.f = (x, y, yerr)
p.plot = 0
p.iprint = 1
p.contol = 1e-5 #3 # required constraints tolerance, default for NLP is 1e-6
# for ALGENCAN solver gradtol is the only one stop criterium connected to openopt
# (except maxfun, maxiter)
# Note that in ALGENCAN gradtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
p.gradtol = 1e-5 #5 # gradient stop criterium (default for NLP is 1e-6)
#print 'maxiter', p.maxiter
#print 'maxfun', p.maxfun
p.maxIter = 50
# p.maxfun=100
#p.df_iter = 50
p.maxTime = 4000
#r=p.solve('scipy_cobyla')
#r=p.solve('scipy_lbfgsb')
#r = p.solve('algencan')
print 'ralg'
r = p.solve('ralg')
print 'done'
pfit = r.xf
print 'pfit openopt', pfit
print 'r dict', r.__dict__
if 1:
print 'mpfit'
p0 = pfit
parbase = {
'value': 0.,
'fixed': 0,
'limited': [0, 0],
'limits': [0., 0.]
}
parinfo = []
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value'] = p0[i]
fa = {'x': x, 'y': y, 'err': yerr}
#parinfo[1]['fixed']=1
#parinfo[2]['fixed']=1
m = mpfit(myfunct_res, p0, parinfo=parinfo, functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params = m.params
pfit = params
perror = m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr = chisq(pfit, x, y, yerr)
dof = m.dof
#Icalc=gauss(pfit,th)
#print 'mpfit chisqr', chisqr
ycalc = gauss(pfit, x)
if 1:
width_x = N.linspace(p0[0] - p0[1], p0[0] + p0[1], 100)
width_y = N.ones(width_x.shape) * (maxval - minval) / 2
pos_y = N.linspace(minval, maxval, 100)
pos_x = N.ones(pos_y.shape) * p0[0]
if 0:
pylab.errorbar(th,
counts,
counts_err,
marker='s',
linestyle='None',
mfc='black',
mec='black',
ecolor='black')
pylab.plot(width_x, width_y)
pylab.plot(pos_x, pos_y)
pylab.plot(x, ycalc)
pylab.show()
else:
#fix center
#fix width
print 'no peak'
#Area center width Bak
area = 0
center = x[len(x) / 2]
width = (
x.max() - x.min()
) / 5.0 #rather arbitrary, but we don't know if it's the first.... #better to use resolution
Bak = y.mean()
p0 = N.array([area, center, width, Bak],
dtype='float64') #initial conditions
parbase = {
'value': 0.,
'fixed': 0,
'limited': [0, 0],
'limits': [0., 0.]
}
parinfo = []
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value'] = p0[i]
fa = {'x': x, 'y': y, 'err': yerr}
parinfo[1]['fixed'] = 1
parinfo[2]['fixed'] = 1
m = mpfit(myfunct_res, p0, parinfo=parinfo, functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
params = m.params
pfit = params
perror = m.perror
#chisqr=(myfunct_res(m.params, x=th, y=counts, err=counts_err)[1]**2).sum()
chisqr = chisq(pfit, x, y, yerr)
dof = m.dof
ycalc = gauss(pfit, x)
#print 'perror',perror
if 0:
pylab.errorbar(x,
y,
yerr,
marker='s',
linestyle='None',
mfc='black',
mec='black',
ecolor='black')
pylab.plot(x, ycalc)
pylab.show()
print 'final answer'
print 'perror', 'perror'
#If the fit is unweighted (i.e. no errors were given, or the weights
# were uniformly set to unity), then .perror will probably not represent
#the true parameter uncertainties.
# *If* you can assume that the true reduced chi-squared value is unity --
# meaning that the fit is implicitly assumed to be of good quality --
# then the estimated parameter uncertainties can be computed by scaling
# .perror by the measured chi-squared value.
# dof = len(x) - len(mpfit.params) # deg of freedom
# # scaled uncertainties
# pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof)
print 'params', pfit
print 'chisqr', chisqr #note that chisqr already is scaled by dof
pcerror = perror * N.sqrt(m.fnorm / m.dof) #chisqr
print 'pcerror', pcerror
integrated_intensity = N.abs(pfit[0])
integrated_intensity_err = N.abs(pcerror[0])
ycalc = gauss(pfit, x)
print 'perror', perror
if 1:
pylab.figure()
pylab.errorbar(x,
y,
yerr,
marker='s',
linestyle='None',
mfc='black',
mec='black',
ecolor='black')
pylab.plot(x, ycalc)
#qstr=str(qnode.q['h_center'])+','+str(qnode.q['k_center'])+','+str(qnode.q['l_center'])
#pylab.title(qstr)
pylab.show()
return pfit, perror, pcerror, chisq
