def fit_peak(plotdict):
x = plotdict['data']['x']
y = plotdict['data']['y']
yerr = plotdict['data']['yerr']
kernel = findpeak.find_kernel(y)
npeaks, nlist, plist = findpeak.find_npeaks(x, y, yerr, kernel, nmax=2)
results = findpeak.findpeak(x, y, npeaks, kernel=kernel)
fwhm = findpeak.findwidths(x, y, npeaks, results['xpeaks'],
results['indices'])
sigma = fwhm / 2.354
p0 = [0, 0]
pb = N.concatenate((results['xpeaks'], fwhm,
results['heights'] * N.sqrt(2 * pi * sigma**2)))
pb = N.array(pb).flatten()
p0 = N.concatenate((p0, pb)).flatten()
print 'p0', p0
#
fresults = scipy.optimize.leastsq(findpeak.cost_func,
p0,
args=(x, y, yerr),
full_output=1)
p1 = fresults[0]
covariance = fresults[1]
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]
parinfo[1]['fixed'] = 1 #fix slope
fa = {'x': x, 'y': y, 'err': yerr}
m = mpfit(findpeak.myfunctlin, p0, parinfo=parinfo, functkw=fa)
print 'status = ', m.status
print 'params = ', m.params
p1 = m.params
covariance = m.covar
dof = len(y) - len(p1)
fake_dof = len(y)
chimin = (findpeak.cost_func(p1, x, y, yerr)**2).sum()
chimin = chimin / dof if dof > 0 else chimin / fake_dof
covariance = covariance * chimin #assume our model is good
area = N.array(N.abs(p1[2 + 2 * npeaks::]))
area_sig = covariance.diagonal()[2 + 2 * npeaks::]
fwhm = N.array(N.abs(p1[2 + npeaks:2 + 2 * npeaks]))
ycalc = findpeak.gen_function(p1, x)
fitdict = {}
fitdict['x'] = x
fitdict['y'] = ycalc
fitdict['area'] = area.sum()
fitdict['chi'] = chimin
fitdict['area_err'] = N.sqrt(area_sig.sum())
print 'area', fitdict['area']
#next add the fit results
print 'chi', chimin
return fitdict
def fit_peak(plotdict):
x=plotdict['data']['x']
y=plotdict['data']['y']
yerr=plotdict['data']['yerr']
kernel=findpeak.find_kernel(y)
npeaks,nlist,plist=findpeak.find_npeaks(x,y,yerr,kernel,nmax=2)
results=findpeak.findpeak(x,y,npeaks,kernel=kernel)
fwhm=findpeak.findwidths(x,y,npeaks,results['xpeaks'],results['indices'])
sigma=fwhm/2.354
p0=[0,0]
pb=N.concatenate((results['xpeaks'], fwhm, results['heights']*N.sqrt(2*pi*sigma**2)))
pb=N.array(pb).flatten()
p0=N.concatenate((p0,pb)).flatten()
print 'p0',p0
#
fresults= scipy.optimize.leastsq(findpeak.cost_func, p0, args=(x,y,yerr),full_output=1)
p1=fresults[0]
covariance=fresults[1]
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]
parinfo[1]['fixed']=1 #fix slope
fa = {'x':x, 'y':y, 'err':yerr}
m = mpfit(findpeak.myfunctlin, p0, parinfo=parinfo,functkw=fa)
print 'status = ', m.status
print 'params = ', m.params
p1=m.params
covariance=m.covar
dof=len(y)-len(p1)
fake_dof=len(y)
chimin=(findpeak.cost_func(p1,x,y,yerr)**2).sum()
chimin=chimin/dof if dof>0 else chimin/fake_dof
covariance=covariance*chimin #assume our model is good
area=N.array(N.abs(p1[2+2*npeaks::]))
area_sig=covariance.diagonal()[2+2*npeaks::]
fwhm=N.array(N.abs(p1[2+npeaks:2+2*npeaks]))
ycalc=findpeak.gen_function(p1,x)
fitdict={}
fitdict['x']=x
fitdict['y']=ycalc
fitdict['area']=area.sum()
fitdict['chi']=chimin
fitdict['area_err']=N.sqrt(area_sig.sum())
print 'area',fitdict['area']
#next add the fit results
print 'chi',chimin
return fitdict
def fit_peak(plotdict):
x = plotdict["data"]["x"]
y = plotdict["data"]["y"]
yerr = plotdict["data"]["yerr"]
kernel = findpeak.find_kernel(y)
npeaks, nlist, plist = findpeak.find_npeaks(x, y, yerr, kernel, nmax=2)
results = findpeak.findpeak(x, y, npeaks, kernel=kernel)
fwhm = findpeak.findwidths(x, y, npeaks, results["xpeaks"], results["indices"])
sigma = fwhm / 2.354
p0 = [0, 0]
pb = N.concatenate((results["xpeaks"], fwhm, results["heights"] * N.sqrt(2 * pi * sigma ** 2)))
pb = N.array(pb).flatten()
p0 = N.concatenate((p0, pb)).flatten()
print "p0", p0
#
fresults = scipy.optimize.leastsq(findpeak.cost_func, p0, args=(x, y, yerr), full_output=1)
p1 = fresults[0]
covariance = fresults[1]
parbase = {"value": 0.0, "fixed": 0, "limited": [0, 0], "limits": [0.0, 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]
parinfo[1]["fixed"] = 1 # fix slope
fa = {"x": x, "y": y, "err": yerr}
m = mpfit(findpeak.myfunctlin, p0, parinfo=parinfo, functkw=fa)
print "status = ", m.status
print "params = ", m.params
p1 = m.params
covariance = m.covar
dof = len(y) - len(p1)
fake_dof = len(y)
chimin = (findpeak.cost_func(p1, x, y, yerr) ** 2).sum()
chimin = chimin / dof if dof > 0 else chimin / fake_dof
covariance = covariance * chimin # assume our model is good
area = N.array(N.abs(p1[2 + 2 * npeaks : :]))
area_sig = covariance.diagonal()[2 + 2 * npeaks : :]
fwhm = N.array(N.abs(p1[2 + npeaks : 2 + 2 * npeaks]))
ycalc = findpeak.gen_function(p1, x)
fitdict = {}
fitdict["x"] = x
fitdict["y"] = ycalc
fitdict["area"] = area.sum()
fitdict["chi"] = chimin
fitdict["area_err"] = N.sqrt(area_sig.sum())
print "area", fitdict["area"]
# next add the fit results
print "chi", chimin
return fitdict
}
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
dof = len(y) - len(p1)
fake_dof = len(y)
chimin = (cost_func(p1, 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
ycalc = calc_struct(p1, Hr, Kr, Lr, d, q, alphastar, astar, lattice, Hh,
Kh, Lh)
print 'chimin', chimin
print 'p1', p1
covariance = covariance * chimin #assume our model is good
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
covariance=m.covar
dof=len(y)-len(p1)
fake_dof=len(y)
chimin=(cost_func(p1,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
ycalc=calc_struct(p1,Hr,Kr,Lr,d,q,alphastar,astar,lattice,Hh,Kh,Lh)
print 'chimin',chimin
print 'p1',p1
covariance=covariance*chimin #assume our model is good
scale=N.abs(p1[0])
scale_sig=N.sqrt(covariance.diagonal()[0])
def driver(x,y):
kernel=31
if 1:
kern,DW=optimize_DW(y) #choose the right window size
#pylab.plot(kern,DW,'s')
#pylab.show()
kernel=min(N.abs(kern-2))
npeaks=2
nlist=[]
plist=[]
for npeaks in range(1,10):
print 'npeaks',npeaks
results=findpeak(x,y,npeaks,kernel=31)
fwhm=findwidths(x,y,npeaks,results['xpeaks'],results['indices'])
print 'res',results['xpeaks']
print 'fwhm',fwhm
print 'heights',results['heights']
p0=[0,0]
#results['heights']=[1000,500,1000]
#fwhm=[.1,.2,.4]
sigma=fwhm/2.354
pb=N.concatenate((results['xpeaks'], fwhm, results['heights']*N.sqrt(2*pi*sigma**2)))
pb=N.array(pb).flatten()
p0=N.concatenate((p0,pb)).flatten()
print 'p0',p0
ycalc=gen_function(p0,x)
if 1:
results= scipy.optimize.leastsq(cost_func, p0, args=(x,y,yerr),full_output=1)
p1=results[0]
covariance=results[1]
print 'p1',p1
if 0:
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}
m = mpfit(myfunctlin, p0, parinfo=parinfo,functkw=fa)
print 'status = ', m.status
print 'params = ', m.params
p1=m.params
covariance=m.covar
if 1:
area=max(N.abs(p1[-3::]))
sigma=p1[-6:-3]/2.354
amax=max(area/N.sqrt(2*pi*sigma**2))
chimin=(cost_func(p1,x,y,yerr)**2).sum()
rangex=max(x)-min(x)
prob=calc_prob(npeaks,amax,covariance,chimin,rangex)
print 'prob',prob
nlist.append(npeaks)
plist.append(prob)
#sys.exit()
pylab.semilogy(nlist,plist,'s')
pylab.show()
if 0:
pylab.plot(x,y,'s')
pylab.axis([0,2,0,1.4e4])
for i in range(npeaks):
pylab.axvline(x=results['xpeaks'][i])
xcen=results['xpeaks'][i]
half_height=y[results['indices'][i]]/2
pylab.plot([(xcen-fwhm[i]/2),(xcen+fwhm[i]/2)],[half_height,half_height])
ycalc=gen_function(p1,x)
pylab.plot(x,ycalc)
pylab.axis([0,2,0,1.4e4])
pylab.show()
def driver(x, y):
kernel = 31
if 1:
kern, DW = optimize_DW(y) #choose the right window size
#pylab.plot(kern,DW,'s')
#pylab.show()
kernel = min(N.abs(kern - 2))
npeaks = 2
nlist = []
plist = []
for npeaks in range(1, 10):
print 'npeaks', npeaks
results = findpeak(x, y, npeaks, kernel=31)
fwhm = findwidths(x, y, npeaks, results['xpeaks'], results['indices'])
print 'res', results['xpeaks']
print 'fwhm', fwhm
print 'heights', results['heights']
p0 = [0, 0]
#results['heights']=[1000,500,1000]
#fwhm=[.1,.2,.4]
sigma = fwhm / 2.354
pb = N.concatenate((results['xpeaks'], fwhm,
results['heights'] * N.sqrt(2 * pi * sigma**2)))
pb = N.array(pb).flatten()
p0 = N.concatenate((p0, pb)).flatten()
print 'p0', p0
ycalc = gen_function(p0, x)
if 1:
results = scipy.optimize.leastsq(cost_func,
p0,
args=(x, y, yerr),
full_output=1)
p1 = results[0]
covariance = results[1]
print 'p1', p1
if 0:
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}
m = mpfit(myfunctlin, p0, parinfo=parinfo, functkw=fa)
print 'status = ', m.status
print 'params = ', m.params
p1 = m.params
covariance = m.covar
if 1:
area = max(N.abs(p1[-3::]))
sigma = p1[-6:-3] / 2.354
amax = max(area / N.sqrt(2 * pi * sigma**2))
chimin = (cost_func(p1, x, y, yerr)**2).sum()
rangex = max(x) - min(x)
prob = calc_prob(npeaks, amax, covariance, chimin, rangex)
print 'prob', prob
nlist.append(npeaks)
plist.append(prob)
#sys.exit()
pylab.semilogy(nlist, plist, 's')
pylab.show()
if 0:
pylab.plot(x, y, 's')
pylab.axis([0, 2, 0, 1.4e4])
for i in range(npeaks):
pylab.axvline(x=results['xpeaks'][i])
xcen = results['xpeaks'][i]
half_height = y[results['indices'][i]] / 2
pylab.plot([(xcen - fwhm[i] / 2), (xcen + fwhm[i] / 2)],
[half_height, half_height])
ycalc = gen_function(p1, x)
pylab.plot(x, ycalc)
pylab.axis([0, 2, 0, 1.4e4])
pylab.show()