def __replot__(self):
xlim = pyplot.xlim()
ylim = pyplot.ylim()
pyplot.clf()
# -- spectrum
pyplot.plot(self.wlTable, self.spectrum, 'k')
# -- continuum
pyplot.plot(self.wlTable[self.whereCont],
self.spectrum[self.whereCont],
'.', color='orange', alpha=0.5)
# -- model
#print self.model
pyplot.plot(self.wlTable,
dpfit.meta(self.wlTable, self.model),
color='red', linewidth=3, alpha=0.5)
pyplot.xlim(xlim[0], xlim[1])
pyplot.ylim(ylim[0], ylim[1])
return
def __handler__(self, event):
"""
React to a click of the mouse: will fit a line (Lorentzian) on
the line. Private method, not to be called by the user.
"""
if event.name=='button_press_event' and \
event.button==1:
# -- set first limit for fit
self.wlMIN = event.xdata
if event.name=='button_release_event' and \
event.button==1:
# -- set second limit
self.wlMAX = event.xdata
# -- invert min and max if needed
try:
if self.wlMAX<self.wlMIN:
self.wlMIN, self.wlMAX = self.wlMAX, self.wlMIN
except:
pass
if event.key==keyCont:
# -- add range to continuum
self.whereCont.extend(np.where((self.wlTable<=self.wlMAX)*
(self.wlTable>=self.wlMIN))[0])
self.__fitContinuum__()
self.__replot__()
elif event.key==keyGauss:
# --
g = set(filter(lambda k: k.split(';')[0]=='gaussian',
self.model.keys()))
N = str(len(g)/3+1)
if self.wlMIN == self.wlMAX:
cont = dpfit.meta(event.xdata, self.model)
self.model['gaussian;'+N+':MU']=event.xdata
self.model['gaussian;'+N+':AMP']=event.ydata-cont
self.model['gaussian;'+N+':SIGMA']=1e-3*event.xdata
else:
w = np.where((self.wlTable>=self.wlMIN)*
(self.wlTable<=self.wlMAX))
cont = dpfit.meta(self.wlTable[w].mean(), self.model)
self.model['gaussian;'+N+':MU']=self.wlTable[w].mean()
self.model['gaussian;'+N+':AMP']=self.spectrum[w].min()-cont
self.model['gaussian;'+N+':SIGMA']=(self.wlMAX-self.wlMIN)/2
self.__fitAll__()
self.__replot__()
elif event.key==keyLorent:
# --
g = set(filter(lambda k: k.split(';')[0]=='lorentzian',
self.model.keys()))
N = str(len(g)/3+1)
if self.wlMIN == self.wlMAX:
cont = dpfit.meta(event.xdata, self.model)
self.model['lorentzian;'+N+':MU']=event.xdata
self.model['lorentzian;'+N+':AMP']=event.ydata-cont
self.model['lorentzian;'+N+':GAMMA']=1e-3*event.xdata
else:
w = np.where((self.wlTable>=self.wlMIN)*
(self.wlTable<=self.wlMAX))
cont = dpfit.meta(self.wlTable[w].mean(), self.model)
self.model['lorentzian;'+N+':MU']=self.wlTable[w].mean()
self.model['lorentzian;'+N+':AMP']=self.spectrum[w].min()-cont
self.model['lorentzian;'+N+':GAMMA']=(self.wlMAX-self.wlMIN)/2
self.__fitAll__()
self.__replot__()
return
