def bootstrap(self,fraction=0.68,niter=1000,xmax=None):
nedge = len(self._non)
hon = Histogram.createHistModel(self._axis.edges,self._non[1:])
hoff = Histogram.createHistModel(self._axis.edges,self._noff[1:])
non = np.random.poisson(np.concatenate(([0],hon.counts)),
(niter,nedge))
noff = np.random.poisson(np.concatenate(([0],hoff.counts)),
(niter,nedge))
xq = []
for i in range(niter):
xq.append(self._quantile(non[i],noff[i],fraction))
xq_mean = np.mean(np.array(xq))
xq_rms = np.std(np.array(xq))
return xq_mean, xq_rms
def bootstrap(self,fraction=0.68,niter=100,xmax=None):
nedge = len(self._ncounts[self._xedge<=xmax])
xedge = self._xedge[:nedge]
h = Histogram.createHistModel(xedge,self._ncounts[1:nedge])
nbkg = np.random.poisson(self._nbkg,niter)
ncounts = np.random.poisson(np.concatenate(([0],h.counts)),
(niter,nedge))
xq = []
for i in range(niter):
xq.append(self._quantile(nbkg[i],self._xedge[:nedge],
ncounts[i],fraction))
xq_mean = np.mean(np.array(xq))
xq_rms = np.std(np.array(xq))
return xq_mean, xq_rms
