def entropy_sharp(self,mu,h0):
b = 1.0;
mmu = np.mean(mu);
if mmu+entropy(mu) > 1+h0:
b = findroot((lambda a: mmu+entropy(np.power(mu,a))-(1+h0)));
if b < 0.0:
b = 1.0;
return np.power(mu,b);
def multiplier(eta):
x = findroot(lambda l: m.u(m.u_inverse((l + eta *
(1 - m.sigma)) ** (-1)),
m.u_prime_of_n_inverse(
- (l + eta * (1 + m.gamma)) **
(-1))) - udev,
0.001, 10)
return x
def Rci(cl,xref,xdat,res=1e3):
if not xref:
xref = max(xdat)
Rbnds = [min(xdat),xref]
else:
Rbnds = [xref,max(xdat)]
kde = gaussian_kde(xdat)
def cumprob(xbnd):
xgrid = np.linspace(xref,xbnd,res)
ydat = kde(xgrid)
return abs(0.5*cl-abs(prob(ydat,xgrid)))
rootR = findroot(cumprob,bounds=tuple(Rbnds),method='bounded',options={'xatol':1e-3})
xbndR = rootR.x
return xbndR
def ypoint(self, y):
root = findroot(lambda y2: self._BezierFunction(y2)[1]-y, 0, 1)
return self._BezierFunction(root)
def xpoint(self, x):
root = findroot(lambda x2: self._BezierFunction(x2)[0]-x, 0, 1)
return self._BezierFunction(root)
def ypoint(self, y):
root = findroot(lambda y2: self._BezierFunction(y2)[1] - y, 0, 1)
return self._BezierFunction(root)
def xpoint(self, x):
root = findroot(lambda x2: self._BezierFunction(x2)[0] - x, 0, 1)
return self._BezierFunction(root)