def guess_fwhm(self, **kw):
if 'y' in kw:
y=kw['y']
else:
return self.config['FwhmPoints']
if 'x' in kw:
x=kw['x']
else:
x=numpy.arange(len(y))*1.0
#set at least a default value for the fwhm
fwhm=4
zz=SpecfitFuns.subac(y,1.000,1000)
yfit=y-zz
#now I should do some sort of peak search ...
maximum = max(yfit)
idx = numpy.nonzero(yfit == maximum)[0]
pos = numpy.take(x,idx)[-1]
posindex = idx[-1]
height = yfit[posindex]
imin = posindex
while ((yfit[imin] > 0.5*height) & (imin >0)):
imin=imin - 1
imax=posindex
while ((yfit[imax] > 0.5*height) & (imax <(len(yfit)-1))):
imax=imax + 1
fwhm=max(imax-imin-1,fwhm)
return fwhm
def bkg_internal(self,pars,x):
"""
Internal Background
"""
#fast
#return self.zz
#slow: recalculate the background as function of the parameters
#yy=SpecfitFuns.subac(self.ydata*self.fitconfig['Yscaling'],
# pars[0],pars[1])
yy=SpecfitFuns.subac(self.ydata*1.0,
pars[0],pars[1])
nrx=shape(x)[0]
nry=shape(yy)[0]
if nrx == nry:
return SpecfitFuns.subac(yy,pars[0],pars[1])
else:
return SpecfitFuns.subac(numpy.take(yy,numpy.arange(0,nry,2)),pars[0],pars[1])
def estimate_gauss(self,xx,yy,zzz,xscaling=1.0,yscaling=None):
if yscaling == None:
try:
yscaling=self.config['Yscaling']
except:
yscaling=1.0
if yscaling == 0:
yscaling=1.0
fittedpar=[]
zz=SpecfitFuns.subac(yy,1.000,10000)
npoints = len(zz)
if self.config['AutoFwhm']:
search_fwhm=self.guess_fwhm(x=xx,y=yy)
else:
search_fwhm=int(float(self.config['FwhmPoints']))
search_sens=float(self.config['Sensitivity'])
search_mca=int(float(self.config['McaMode']))
if search_fwhm < 3:
search_fwhm = 3
self.config['FwhmPoints']=3
if search_sens < 1:
search_sens = 1
self.config['Sensitivity']=1
if npoints > 1.5*search_fwhm:
peaks=self.seek(yy,fwhm=search_fwhm,
sensitivity=search_sens,
yscaling=yscaling,
mca=search_mca)
#print "estimate peaks = ",peaks
#peaks=self.seek(yy-zz,fwhm=search_fwhm,
# sensitivity=search_sens,
# yscaling=yscaling,
# mca=search_mca)
else:
peaks = []
if not len(peaks):
mca = int(float(self.config.get('McaMode', 0)))
forcePeak = int(float(self.config.get('ForcePeakPresence', 0)))
if (not mca) and forcePeak:
delta = yy - zz
peaks = [int(numpy.nonzero(delta == delta.max())[0])]
#print "peaks = ",peaks
#print "peaks subac = ",self.seek(yy-zz,fwhm=search_fwhm,
# sensitivity=search_sens,
# yscaling=yscaling,
# mca=search_mca)
largest_index = 0
if len(peaks) > 0:
j = 0
for i in peaks:
if j == 0:
sig=5*abs(xx[npoints-1]-xx[0])/npoints
peakpos = xx[int(i)]
if abs(peakpos) < 1.0e-16:
peakpos = 0.0
param = numpy.array([yy[int(i)] - zz[int(i)], peakpos ,sig])
largest = param
else:
param2 = numpy.array([yy[int(i)] - zz [int(i)], xx[int(i)] ,sig])
param = numpy.concatenate((param,param2),1)
if (param2[0] > largest[0]):
largest = param2
largest_index = j
j = j + 1
xw = numpy.resize(xx,(npoints,1))
if (0):
sy = numpy.sqrt(abs(yy))
yw = numpy.resize(yy-zz,(npoints,1))
sy = numpy.resize(sy,(npoints,1))
datawork = numpy.concatenate((xw,yw,sy),1)
else:
yw = numpy.resize(yy-zz,(npoints,1))
datawork = numpy.concatenate((xw,yw),1)
cons = numpy.zeros((3,len(param)),numpy.float)
cons [0] [0:len(param):3] = CPOSITIVE
#force peaks to stay around their position
if (1):
cons [0] [1:len(param):3] = CQUOTED
#This does not work!!!!
#FWHM should be given in terms of X not of points!
#cons [1] [1:len(param):3] = param[1:len(param):3]-0.5*search_fwhm
#cons [2] [1:len(param):3] = param[1:len(param):3]+0.5*search_fwhm
if len(xw) > search_fwhm:
fwhmx = numpy.fabs(xw[int(search_fwhm)]-xw[0])
cons [1] [1:len(param):3] = param[1:len(param):3]-0.5*fwhmx
cons [2] [1:len(param):3] = param[1:len(param):3]+0.5*fwhmx
else:
cons [1] [1:len(param):3] = numpy.ones(shape(param[1:len(param):3]),numpy.float)*min(xw)
cons [2] [1:len(param):3] = numpy.ones(shape(param[1:len(param):3]),numpy.float)*max(xw)
if 0:
cons [0] [2:len(param):3] = CFACTOR
cons [1] [2:len(param):3] = 2
cons [2] [2:len(param):3] = 1.0
cons [0] [2] = CPOSITIVE
cons [1] [2] = 0
cons [2] [2] = 0
else:
cons [0] [2:len(param):3] = CPOSITIVE
fittedpar, chisq, sigmapar = LeastSquaresFit(SpecfitFuns.gauss,param,
datawork,
weightflag=self.config['WeightFlag'],
maxiter=4,constrains=cons.tolist())
#I already have the estimation
#yw=yy-zz-SpecfitFuns.gauss(fittedpar,xx)
#if DEBUG:
# SimplePlot.plot([xx,yw])
#print self.seek(yw,\
# fwhm=search_fwhm,\
# sensitivity=search_sens,\
# yscaling=yscaling)
#else:
# #Use SPEC estimate ...
# peakpos,height,myidx = SpecArithmetic.search_peak(xx,yy-zz)
# fwhm,cfwhm = SpecArithmetic.search_fwhm(xx,yy-zz,
# peak=peakpos,index=myidx)
# xx = numpy.array(xx)
# if npoints > 2:
# #forget SPEC estimation of fwhm
# fwhm=5*abs(xx[npoints-1]-xx[0])/npoints
# fittedpar=[height,peakpos,fwhm]
# largest=numpy.array([height,peakpos,fwhm])
# peaks=[peakpos]
cons = numpy.zeros((3,len(fittedpar)),numpy.float)
j=0
for i in range(len(peaks)):
#Setup height area constrains
if self.config['NoConstrainsFlag'] == 0:
if self.config['HeightAreaFlag']:
#POSITIVE = 1
cons[0] [j] = 1
cons[1] [j] = 0
cons[2] [j] = 0
j=j+1
#Setup position constrains
if self.config['NoConstrainsFlag'] == 0:
if self.config['PositionFlag']:
#QUOTED = 2
cons[0][j]=2
cons[1][j]=min(xx)
cons[2][j]=max(xx)
j=j+1
#Setup positive FWHM constrains
if self.config['NoConstrainsFlag'] == 0:
if self.config['PosFwhmFlag']:
#POSITIVE=1
cons[0][j]=1
cons[1][j]=0
cons[2][j]=0
if self.config['SameFwhmFlag']:
if (i != largest_index):
#FACTOR=4
cons[0][j]=4
cons[1][j]=3*largest_index+2
cons[2][j]=1.0
j=j+1
return fittedpar,cons
def estimate_periodic_gauss(self,xx,yy,zzz,xscaling=1.0,yscaling=None):
if yscaling == None:
try:
yscaling=self.config['Yscaling']
except:
yscaling=1.0
if yscaling == 0:
yscaling=1.0
fittedpar=[]
zz=SpecfitFuns.subac(yy,1.000,10000)
npoints = len(zz)
if self.config['AutoFwhm']:
search_fwhm=self.guess_fwhm(x=xx,y=yy)
else:
search_fwhm=int(float(self.config['FwhmPoints']))
search_sens=float(self.config['Sensitivity'])
search_mca=int(float(self.config['McaMode']))
if search_fwhm < 3:
search_fwhm = 3
self.config['FwhmPoints']=3
if search_sens < 1:
search_sens = 1
self.config['Sensitivity']=1
if npoints > 1.5*search_fwhm:
peaks=self.seek(yy,fwhm=search_fwhm,
sensitivity=search_sens,
yscaling=yscaling,
mca=search_mca)
else:
peaks = []
npeaks = len(peaks)
if not npeaks:
fittedpar = []
cons = numpy.zeros((3,len(fittedpar)), numpy.float)
return fittedpar, cons
fittedpar = [0.0, 0.0, 0.0, 0.0, 0.0]
#The number of peaks
fittedpar[0] = npeaks
#The separation between peaks in x units
delta = 0.0
height = 0.0
for i in range(npeaks):
height += yy[int(peaks[i])] - zz [int(peaks[i])]
if i != ((npeaks)-1):
delta += (xx[int(peaks[i+1])] - xx[int(peaks[i])])
#delta between peaks
if npeaks > 1:
fittedpar[1] = delta/(npeaks-1)
#starting height
fittedpar[2] = height/npeaks
#position of the first peak
fittedpar[3] = xx[int(peaks[0])]
#Estimate the fwhm
fittedpar[4] = search_fwhm
#setup constraints
cons = numpy.zeros((3, 5), numpy.float)
cons [0] [0] = CFIXED #the number of gaussians
if npeaks == 1:
cons[0][1] = CFIXED #the delta between peaks
else:
cons[0][1] = CFREE #the delta between peaks
j = 2
#Setup height area constrains
if self.config['NoConstrainsFlag'] == 0:
if self.config['HeightAreaFlag']:
#POSITIVE = 1
cons[0] [j] = 1
cons[1] [j] = 0
cons[2] [j] = 0
j=j+1
#Setup position constrains
if self.config['NoConstrainsFlag'] == 0:
if self.config['PositionFlag']:
#QUOTED = 2
cons[0][j]=2
cons[1][j]=min(xx)
cons[2][j]=max(xx)
j=j+1
#Setup positive FWHM constrains
if self.config['NoConstrainsFlag'] == 0:
if self.config['PosFwhmFlag']:
#POSITIVE=1
cons[0][j]=1
cons[1][j]=0
cons[2][j]=0
j=j+1
return fittedpar,cons
def update(self):
if self._y is None:
return
pars = self.getParameters()
#smoothed data
y = numpy.ravel(numpy.array(self._y)).astype(numpy.float)
ysmooth = SpecfitFuns.SavitskyGolay(y, pars['stripfilterwidth'])
f = [0.25, 0.5, 0.25]
ysmooth[1:-1] = numpy.convolve(ysmooth, f, mode=0)
ysmooth[0] = 0.5 * (ysmooth[0] + ysmooth[1])
ysmooth[-1] = 0.5 * (ysmooth[-1] + ysmooth[-2])
#loop for anchors
x = self._x
niter = pars['stripiterations']
anchorslist = []
if pars['stripanchorsflag']:
if pars['stripanchorslist'] is not None:
ravelled = x
for channel in pars['stripanchorslist']:
if channel <= ravelled[0]: continue
index = numpy.nonzero(ravelled >= channel)[0]
if len(index):
index = min(index)
if index > 0:
anchorslist.append(index)
if niter > 1000:
stripBackground = SpecfitFuns.subac(ysmooth, pars['stripconstant'],
niter, pars['stripwidth'],
anchorslist)
#final smoothing
stripBackground = SpecfitFuns.subac(stripBackground,
pars['stripconstant'], 500, 1,
anchorslist)
elif niter > 0:
stripBackground = SpecfitFuns.subac(ysmooth, pars['stripconstant'],
niter, pars['stripwidth'],
anchorslist)
else:
stripBackground = 0.0 * ysmooth + ysmooth.min()
if len(anchorslist) == 0:
anchorslist = [0, len(ysmooth) - 1]
anchorslist.sort()
snipBackground = 0.0 * ysmooth
lastAnchor = 0
width = pars['snipwidth']
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < len(ysmooth)):
snipBackground[lastAnchor:anchor] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:anchor], width, 0)
lastAnchor = anchor
if lastAnchor < len(ysmooth):
snipBackground[lastAnchor:] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:], width, 0)
self.graphWidget.addCurve(x, y, \
legend='Input Data',\
replace=True,
replot=False)
self.graphWidget.addCurve(x, stripBackground,\
legend='Strip Background',\
replot=False)
self.graphWidget.addCurve(x, snipBackground,\
legend='SNIP Background',
replot=True)
def _getStripBackground(self, x=None, y=None):
#this makes the assumption x are equally spaced
#and I should build a spline if that is not the case
#but I do not want to put a dependency on SciPy
if y is not None:
ywork = y
else:
ywork = self._y
if x is not None:
xwork = x
else:
xwork = self._x
n=len(xwork)
#loop for anchors
anchorslist = []
if self._fitConfiguration['fit']['stripanchorsflag']:
if self._fitConfiguration['fit']['stripanchorslist'] is not None:
oldShape = xwork.shape
ravelled = xwork
ravelled.shape = -1
for channel in self._fitConfiguration['fit']['stripanchorslist']:
if channel <= ravelled[0]:continue
index = numpy.nonzero(ravelled >= channel)
if len(index):
index = min(index)
if index > 0:
anchorslist.append(index)
ravelled.shape = oldShape
#work with smoothed data
ysmooth = self._getSmooth(xwork, ywork)
#SNIP algorithm
if self._fitConfiguration['fit']['stripalgorithm'] in ["SNIP", 1]:
if DEBUG:
print("CALCULATING SNIP")
if len(anchorslist) == 0:
anchorslist = [0, len(ysmooth)-1]
anchorslist.sort()
result = 0.0 * ysmooth
lastAnchor = 0
width = self._fitConfiguration['fit']['snipwidth']
for anchor in anchorslist:
if (anchor > lastAnchor) and (anchor < len(ysmooth)):
result[lastAnchor:anchor] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:anchor], width, 0)
lastAnchor = anchor
if lastAnchor < len(ysmooth):
result[lastAnchor:] =\
SpecfitFuns.snip1d(ysmooth[lastAnchor:], width, 0)
return result
#strip background
niter = self._fitConfiguration['fit']['stripiterations']
if niter > 0:
if DEBUG:
print("CALCULATING STRIP")
print("iterations = ", niter)
print("constant = ",
self._fitConfiguration['fit']['stripconstant'])
print("width = ",
self._fitConfiguration['fit']['stripwidth'])
print("anchors = ", anchorslist)
result=SpecfitFuns.subac(ysmooth,
self._fitConfiguration['fit']['stripconstant'],
niter,
self._fitConfiguration['fit']['stripwidth'],
anchorslist)
if niter > 1000:
#make sure to get something smooth
result = SpecfitFuns.subac(result,
self._fitConfiguration['fit']['stripconstant'],
500,1,
anchorslist)
else:
#make sure to get something smooth but with less than
#500 iterations
result = SpecfitFuns.subac(result,
self._fitConfiguration['fit']['stripconstant'],
int(self._fitConfiguration['fit']['stripwidth']*2),
1,
anchorslist)
else:
if DEBUG:
print("NO STRIP, NO SNIP")
result = numpy.zeros(ysmooth.shape, numpy.float) + min(ysmooth)
return result