def computeCore(self, data, tRange):
[obsP, p] = Bs.getP(data, tRange)
bs = np.nan*np.zeros(len(p), 'float')
meanObs = np.mean(obsP)
for i in range(0, len(self._edges)-1):
I = np.where((p >= self._edges[i]) & (p < self._edges[i+1]))[0]
if(len(I) > 0):
meanObsI = np.mean(obsP[I])
bs[I] = (meanObsI - meanObs)**2
return Common.nanmean(bs)
def computeCore(self, data, tRange):
[obsP,p] = Bs.getP(data, tRange)
# Break p into bins, and comute reliability
bs = np.nan*np.zeros(len(p), 'float')
for i in range(0, len(self._edges)-1):
I = np.where((p >= self._edges[i]) & (p < self._edges[i+1]))[0]
if(len(I) > 0):
meanObsI = np.mean(obsP[I])
bs[I] = (np.mean(p[I]) - meanObsI)**2
return Common.nanmean(bs)
def computeCore(self, data, tRange):
[obsP,p] = Bs.getP(data, tRange)
bs = np.nan*np.zeros(len(p), 'float')
for i in range(0, len(self._edges)-1):
I = np.where((p >= self._edges[i]) & (p < self._edges[i+1]))[0]
if(len(I) > 0):
bs[I] = (np.mean(p[I]) - obsP[I])**2
bs = Common.nanmean(bs)
bsunc = np.mean(obsP)*(1-np.mean(obsP))
if(bsunc == 0):
bss = np.nan
else:
bss = (bsunc - bs)/bsunc
return bss
def computeCore(self, data, tRange):
# Compute probabilities based on thresholds
p0 = 0
p1 = 1
if(tRange[0] != -np.inf and tRange[1] != np.inf):
var0 = data.getPvar(tRange[0])
var1 = data.getPvar(tRange[1])
[obs, p0, p1] = data.getScores(["obs", var0, var1])
elif(tRange[0] != -np.inf):
var0 = data.getPvar(tRange[0])
[obs, p0] = data.getScores(["obs", var0])
elif(tRange[1] != np.inf):
var1 = data.getPvar(tRange[1])
[obs, p1] = data.getScores(["obs", var1])
obsP = self.within(obs, tRange)
p = p1 - p0 # Prob of obs within range
bs = np.nan*np.zeros(len(p), 'float')
# Split into bins and compute Brier score on each bin
for i in range(0, len(self._edges)-1):
I = np.where((p >= self._edges[i]) & (p < self._edges[i+1]))[0]
if(len(I) > 0):
bs[I] = (np.mean(p[I]) - obsP[I])**2
return Common.nanmean(bs)
