def do_fit(self):
print 'Fitting the template with unbinned likelihood...'
template = LCTemplate(self.primitives,norms=self.norms)
fitter = LCFitter(template,self.phases,weights=self.weights)
fitter.fit(estimate_errors=self.errors)
print 'Fitting finished!'
print fitter
print 'Overlaying fitted template...'
self.fig = pl.figure(self.fignum)
self.ax = pl.gca()
light_curve(self.phases,weights=self.weights,nbins=self.nbins,axes=self.ax,template=template)
pl.show()
self.fitter = fitter
def __init__(self,phases,**kwargs):
self.init()
self.__dict__.update(**kwargs)
self.phases = phases
self.primitives = []
self.norms = []
self.dom = np.linspace(0,1,100)
self.welcome()
pl.close(self.fignum)
self.fig = pl.figure(self.fignum)
self.ax = pl.gca()
self.connect()
light_curve(self.phases,weights=self.weights,nbins=self.nbins,axes=self.ax)
pl.show()
def on_release(self,event):
x1 = event.xdata
y1 = event.ydata
fwhm = x1 - self.x0
peak = (y1 + self.y0)/2.
phase = (x1 + self.x0)/2.
# just Gaussian for now
sigma = fwhm/(8 * np.log(2))**0.5
ampl = peak * sigma * (2*np.pi)**0.5
self.primitives.append(LCGaussian(p=[sigma,phase]))
self.norms.append(ampl)
template = LCTemplate(self.primitives,norms=self.norms)
self.ax.clear()
light_curve(self.phases,weights=self.weights,nbins=self.nbins,axes=self.ax,template=template)
pl.draw()