def display(self):
"""Plot data, fitted function for FWHM and optionally flux.
"""
# plot them through ds9 in parallel to the fit
ds9DisplayThread = None
if self.Ds9Display and self.xdisplay:
# start thread
ds9DisplayThread = Ds9DisplayThread(debug=self.debug,
dataSxtr=self.dataSxtr,
logger=self.logger)
ds9DisplayThread.start()
elif self.Ds9Display and not self.xdisplay:
self.logger.warn('analyze: OOOOOOOOPS, no ds9 display available')
if self.dataSxtr[0].assocFn is not None:
ft = FitDisplay(date=self.date,
comment='ASSOC',
logger=self.logger)
else:
ft = FitDisplay(date=self.date, logger=self.logger)
if self.i_flux is None:
ft.fitDisplay(dataFit=self.dataFitFwhm,
resultFit=self.resultFitFwhm,
display=self.FitDisplay,
xdisplay=self.xdisplay)
else:
# plot FWHM but don't show
ft.fitDisplay(dataFit=self.dataFitFwhm,
resultFit=self.resultFitFwhm,
show=False,
display=self.FitDisplay,
xdisplay=self.xdisplay)
ft.fitDisplay(dataFit=self.dataFitFlux,
resultFit=self.resultFitFlux,
display=self.FitDisplay,
xdisplay=self.xdisplay)
# very important (otherwise all plots show up in next show())
ft.ax1 = None
# http://stackoverflow.com/questions/741877/how-do-i-tell-matplotlib-that-i-am-done-with-a-plot
ft.fig.clf()
ft.fig = None
ft = None
# stop ds9 display thread
if self.Ds9Display and self.xdisplay:
ds9DisplayThread.join(timeout=1.)
def test_FluxfitDisplay(self):
logger.info('== {} =='.format(self._testMethodName))
ft = FitFunction(dataFit=self.dataFitFwhm, logger=logger)
min_focpos_fwhm, val_fwhm, par, flag = ft.fitData()
if flag != None:
resultFitFwhm = ResultFit(ambientTemp='20.1',
ftName='UNK',
extrFitPos=min_focpos_fwhm,
extrFitVal=val_fwhm,
fitPar=par,
fitFlag=flag,
color='blue',
ylabel='FWHM [px]: blue',
titleResult='fwhm:{0:5d}'.format(
int(min_focpos_fwhm)))
fd = FitDisplay(date=self.date, comment='unittest', logger=logger)
fd.fitDisplay(dataFit=self.dataFitFwhm,
resultFit=resultFitFwhm,
xdisplay=False)
x = np.array([p for p in self.dataFitFlux.pos])
y = np.array([v for v in self.dataFitFlux.val])
wmean = np.average(a=x, weights=y)
xy = zip(x, y)
wstd = np.std(a=xy)
# scale the values [a.u.]
sv = [
max(self.dataFitFwhm.val) / max(self.dataFitFlux.val) * z
for z in self.dataFitFlux.val
]
self.dataFitFlux.val = sv
# gaussian
self.parFlux = np.array([10., wmean, wstd / 4., 2.])
ft = FitFunction(dataFit=self.dataFitFlux, logger=logger)
max_focpos_flux, val_flux, par, flag = ft.fitData()
if flag != None:
resultFitFlux = ResultFit(
ambientTemp='20.1',
ftName='UNK',
extrFitPos=max_focpos_flux,
extrFitVal=val_flux,
fitPar=par,
fitFlag=flag,
color='red',
ylabel='FWHM [px]: blue, Flux [a.u.]:red',
titleResult='fwhm:{0:5d}, flux: {1:5d}'.format(
int(resultFitFwhm.extrFitPos), int(max_focpos_flux)))
fd.fitDisplay(dataFit=self.dataFitFlux,
resultFit=resultFitFlux,
xdisplay=False)
def test_FwhmfitDisplay(self):
logger.info('== {} =='.format(self._testMethodName))
ft = FitFunction(dataFit=self.dataFitFwhm, logger=logger)
min_focpos_fwhm, val_fwhm, par, flag = ft.fitData()
resultFitFwhm = ResultFit(ambientTemp='20.1',
ftName='UNK',
extrFitPos=min_focpos_fwhm,
extrFitVal=val_fwhm,
fitPar=par,
fitFlag=flag,
color='blue',
ylabel='FWHM [px]: blue',
titleResult='fwhm:{0:5d}'.format(
int(min_focpos_fwhm)))
self.fd = FitDisplay(date=self.date, comment='unittest', logger=logger)
self.fd.fitDisplay(dataFit=self.dataFitFwhm,
resultFit=resultFitFwhm,
xdisplay=False)
