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)
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)
class TestFitDisplay(unittest.TestCase):
def tearDown(self):
try:
os.unlink(self.plotFnIn)
except:
pass
def setUp(self):
from rts2saf.data import DataFitFwhm, DataFitFlux, DataSxtr
pos = np.asarray([ 2000., 2100., 2200., 2300., 2400., 2500., 2600., 2700., 2800., 2900., 3000.])
fwhm = np.asarray([ 40., 30., 20., 15., 10., 5., 10., 15., 20., 30., 40.])
stdFocPos = np.asarray([ 20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20.])
stdFwhm = np.asarray([ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])
flux = np.asarray([ 2.2, 3.1, 4.8, 7.9, 10.1, 11.2, 11.1, 8.2, 5.4, 3.2, 2.2])
stdFlux = np.asarray([ 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
dataSxtr=list()
for i in range( 0, len(pos)-1):
dataSxtr.append(
DataSxtr(
focPos=pos[i],
stdFocPos=stdFocPos[i],
fwhm=fwhm[i],
stdFwhm=stdFwhm[i],
flux=flux[i],
stdFlux=stdFlux[i],
catalog=[1, 2, 3]
)
)
self.plotFnIn='./test-plot.png'
self.date = '2013-09-08T09:30:09'
self.dataFitFwhm=DataFitFwhm(
dataSxtr=dataSxtr,
ambientTemp='21.3',
plotFn= self.plotFnIn)
self.dataFitFlux=DataFitFlux(
dataSxtr=dataSxtr,
dataFitFwhm=self.dataFitFwhm,
ambientTemp='21.3',
plotFn= self.plotFnIn)
#@unittest.skip('feature not yet implemented')
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)
#@unittest.skip('feature not yet implemented')
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 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.)
class TestFitDisplay(unittest.TestCase):
def tearDown(self):
try:
os.unlink(self.plotFnIn)
except:
pass
def setUp(self):
from rts2saf.data import DataFitFwhm, DataFitFlux, DataSxtr
pos = np.asarray([
2000., 2100., 2200., 2300., 2400., 2500., 2600., 2700., 2800.,
2900., 3000.
])
fwhm = np.asarray(
[40., 30., 20., 15., 10., 5., 10., 15., 20., 30., 40.])
stdFocPos = np.asarray(
[20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20.])
stdFwhm = np.asarray([2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])
flux = np.asarray(
[2.2, 3.1, 4.8, 7.9, 10.1, 11.2, 11.1, 8.2, 5.4, 3.2, 2.2])
stdFlux = np.asarray(
[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
dataSxtr = list()
for i in range(0, len(pos) - 1):
dataSxtr.append(
DataSxtr(focPos=pos[i],
stdFocPos=stdFocPos[i],
fwhm=fwhm[i],
stdFwhm=stdFwhm[i],
flux=flux[i],
stdFlux=stdFlux[i],
catalog=[1, 2, 3]))
self.plotFnIn = './test-plot.png'
self.date = '2013-09-08T09:30:09'
self.dataFitFwhm = DataFitFwhm(dataSxtr=dataSxtr,
ambientTemp='21.3',
plotFn=self.plotFnIn)
self.dataFitFlux = DataFitFlux(dataSxtr=dataSxtr,
dataFitFwhm=self.dataFitFwhm,
ambientTemp='21.3',
plotFn=self.plotFnIn)
#@unittest.skip('feature not yet implemented')
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)
#@unittest.skip('feature not yet implemented')
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)
