def save(self, collectionNumber=None):
voltages=[]
electrometers=[]
if collectionNumber == None:
filename = self.filename+"_"+self.getName()+".dat"
print "concatenating data points to produce P-E data..."
for each in self.voltages:
voltages += each
voltages += []
for each in self.electrometers:
electrometers += each
electrometers += []
else:
filename = self.filename+"_"+self.getName()+"_"+str(collectionNumber)+".dat"
print "summing data points to produce P-E data..."
voltages = sum_datasets(self.voltages)
electrometers = sum_datasets(self.electrometers)
print "%s: saving data to %s" % (self.getName(), filename)
if len(voltages) != len(electrometers):
print "***Warning: voltage nord: %d, electrometer nord: %d" % (len(voltages),len(electrometers))
self.file=open(filename, "w")
for voltage, electrometer in zip(voltages, electrometers):
self.file.write("%f\t%f\n"%(voltage, electrometer))
self.file.close()
print "%s: save data to %s completed." % (self.getName(), filename)
voltagearray=scisoftpy.array(voltages)
electrometerarray=scisoftpy.array(electrometers)
vds=scisoftpy.toDS(voltagearray)
eds=scisoftpy.toDS(electrometerarray)
print "plotting PE-loop in 'DataPlot' panel..."
if self.firstData:
Plotter.plot("DataPlot", vds, eds)
self.firstData=False
else:
Plotter.plotOver("DataPlot", vds, eds)
def plotPEdata(self, *args, **kwargs):
xarray=scisoftpy.array(kwargs[self.hv][args[0]])
yrarray=scisoftpy.array(kwargs[self.el][args[0]])
vds=scisoftpy.toDS(xarray)
eds=scisoftpy.toDS(yrarray)
print "plotting PE-loop in 'DataPlot' panel..."
if self.firstData:
Plotter.plot("DataPlot", vds, eds)
self.firstData=False
else:
Plotter.plotOver("DataPlot", vds, eds)
def _process(self,xDataSet, yDataSet):
if yDataSet.max()-yDataSet.min() == 0:
raise ValueError("There is no peak")
xDataSet = np.Sciwrap(xDataSet)
yDataSet = np.Sciwrap(yDataSet) * self.scale
funcs = [ holefunc ]
initparams = [ xDataSet.mean(), (xDataSet.max() - xDataSet.min())* 0.90, yDataSet.min(), yDataSet.max()]
fr = np.fit.fit(funcs, xDataSet, yDataSet, initparams, ptol=1e-4, optimizer='local')
if self.plotPanel != None:
fr.plot(self.plotPanel)
initparams = [ p for p in fr.func.getParameterValues() ]
start = initparams[0] - 0.5 * initparams[1]
length = initparams[1]
truncx = []
truncy = []
for i in range(xDataSet.size):
val = xDataSet[i]
if val >= start and val <= start+length:
truncx.append(val)
truncy.append(yDataSet[i])
lala = Generic1DFitter.fitPeaks(np.toDS(np.array(truncx)), np.toDS(np.array(truncy)), Gaussian(1,1,1,1), NelderMead(self.accuracy), 2, self.gaussians, 0.05, True, False)
print lala
if lala == None or len(lala) == 0:
print "no peaks found"
return [0, []]
positions = []
cff = np.fit.fitcore.cfitfunc()
for f in lala:
print f.getParameter(1), f.getParameter(2)
if f.getParameter(1).getValue() <= self.maxfwhm and f.getParameter(2).getValue() >= self.minarea:
positions.append(f.getParameter(0).getValue())
cff.addFunction(f)
else:
print "removed one"
if len(positions) == 0:
print "too bad nothing found within threshold"
else:
print "plotting remaining found positions"
if self.plotPanel != None:
fire = np.fit.fitcore.fitresult(cff, [np.toDS(np.array(truncx))], np.array(truncy))
fire.plot(self.plotPanel)
return [len(positions), positions]
def _process(self, xDataSet, yDataSet):
if yDataSet.max() - yDataSet.min() == 0:
raise ValueError("There is no peak")
xDataSet = np.Sciwrap(xDataSet)
yDataSet = np.Sciwrap(yDataSet) * self.scale
funcs = [holefunc]
initparams = [
xDataSet.mean(), (xDataSet.max() - xDataSet.min()) * 0.90,
yDataSet.min(),
yDataSet.max()
]
fr = np.fit.fit(funcs,
xDataSet,
yDataSet,
initparams,
ptol=1e-4,
optimizer='local')
if self.plotPanel != None:
fr.plot(self.plotPanel)
initparams = [p for p in fr.func.getParameterValues()]
start = initparams[0] - 0.5 * initparams[1]
length = initparams[1]
truncx = []
truncy = []
for i in range(xDataSet.size):
val = xDataSet[i]
if val >= start and val <= start + length:
truncx.append(val)
truncy.append(yDataSet[i])
lala = Generic1DFitter.fitPeaks(np.toDS(np.array(truncx)),
np.toDS(np.array(truncy)),
Gaussian(1, 1, 1, 1),
NelderMead(self.accuracy), 2,
self.gaussians, 0.05, True, False)
print lala
if lala == None or len(lala) == 0:
print "no peaks found"
return [0, []]
positions = []
cff = np.fit.fitcore.cfitfunc()
for f in lala:
print f.getParameter(1), f.getParameter(2)
if f.getParameter(1).getValue() <= self.maxfwhm and f.getParameter(
2).getValue() >= self.minarea:
positions.append(f.getParameter(0).getValue())
cff.addFunction(f)
else:
print "removed one"
if len(positions) == 0:
print "too bad nothing found within threshold"
else:
print "plotting remaining found positions"
if self.plotPanel != None:
fire = np.fit.fitcore.fitresult(cff,
[np.toDS(np.array(truncx))],
np.array(truncy))
fire.plot(self.plotPanel)
return [len(positions), positions]
