def _correctorSteps(self,y0,b):
err = 10
count = 0
while err>self.epsCorr and count<1e2:
F = self._PC_F(y0)
dF = self._PC_DF(y0)
A = np.append(dF,b,axis=0)
B = A.dot(y0) - np.append(F,[[0]],axis=0)
ynew = linalg.solve(A,B)
err = linalg.norm(F,2)
y0 = ynew
count = count + 1
print "Correction err:", err
return (y0,err)
def _JacLinearHomotopy(self, x, t):
# dH(x,t) = [dH/dx dH/dt]
# = [I-t*dF(x) x0-F(x) ]
return np.append(self.Id - t * self.DF(x),
self.xinit - self.F(x),
axis=1)
def runSolver(self):
(y, i_iter) = self._solverPC(np.append(self.xinit, [[0]], axis=0))
errCode = 0
if (i_iter > self.maxSteps):
errCode |= 0b001
if ~self.isValidEquilibrium(1e-2):
errCode |= 0b010
self.yeq = y
self.tfinal = y[-1]
return (y, errCode)
def dfun(x):
y = np.array([x]).T
dF = self._PC_DF(y)
return np.append(dF,b,axis=0)
def fun(x):
y = np.array([x]).T
F = self._PC_F(y)
return np.append(F,b.dot(y-y0),axis=0)[:,0]
def dfun(x):
y = np.append(np.array([x]).T, [1])
return self._PC_DF(np.array([y]).T)[:, :-1]
def fun(x):
y = np.append(np.array([x]).T, [1])
return self._PC_F(np.array([y]).T)[:, 0]