def transformDerivU(self, u, m):
self.setModel(m)
alpha = self.alpha
I = self.I
n = self.n
Ks = self.Ks
m = 1.0 - 1.0 / n
g = I * alpha * n * np.exp(Ks) * abs(alpha * u) ** (n - 1.0) * np.sign(alpha * u) * (1.0 / n - 1.0) * (
(abs(alpha * u) ** n + 1) ** (1.0 / n - 1)
) ** (I - 1) * (
(1 - 1.0 / ((abs(alpha * u) ** n + 1) ** (1.0 / n - 1)) ** (1.0 / (1.0 / n - 1))) ** (1 - 1.0 / n) - 1
) ** 2 * (
abs(alpha * u) ** n + 1
) ** (
1.0 / n - 2
) - (
2
* alpha
* n
* np.exp(Ks)
* abs(alpha * u) ** (n - 1)
* np.sign(alpha * u)
* (1.0 / n - 1)
* ((abs(alpha * u) ** n + 1) ** (1.0 / n - 1)) ** I
* ((1 - 1.0 / ((abs(alpha * u) ** n + 1) ** (1.0 / n - 1)) ** (1.0 / (1.0 / n - 1))) ** (1 - 1.0 / n) - 1)
* (abs(alpha * u) ** n + 1) ** (1.0 / n - 2)
) / (
((abs(alpha * u) ** n + 1) ** (1.0 / n - 1)) ** (1.0 / (1.0 / n - 1) + 1)
* (1 - 1.0 / ((abs(alpha * u) ** n + 1) ** (1.0 / n - 1)) ** (1.0 / (1.0 / n - 1))) ** (1.0 / n)
)
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
self.setModel(m)
alpha = self.alpha
I = self.I
n = self.n
Ks = self.Ks
m = 1.0 - 1.0 / n
g = I * alpha * n * np.exp(Ks) * abs(alpha * u)**(n - 1.0) * np.sign(
alpha * u) * (1.0 / n - 1.0) * (
(abs(alpha * u)**n + 1)**(1.0 / n - 1))**(I - 1) * (
(1 - 1.0 / ((abs(alpha * u)**n + 1)**(1.0 / n - 1))**
(1.0 / (1.0 / n - 1)))**(1 - 1.0 / n) -
1)**2 * (abs(alpha * u)**n + 1)**(1.0 / n - 2) - (
2 * alpha * n * np.exp(Ks) * abs(alpha * u)**
(n - 1) * np.sign(alpha * u) * (1.0 / n - 1) *
((abs(alpha * u)**n + 1)**(1.0 / n - 1))**I *
((1 - 1.0 / ((abs(alpha * u)**n + 1)**(1.0 / n - 1))**
(1.0 / (1.0 / n - 1)))**(1 - 1.0 / n) - 1) *
(abs(alpha * u)**n + 1)**(1.0 / n - 2)) / (
((abs(alpha * u)**n + 1)**
(1.0 / n - 1))**(1.0 / (1.0 / n - 1) + 1) *
(1 - 1.0 /
((abs(alpha * u)**n + 1)**(1.0 / n - 1))**
(1.0 / (1.0 / n - 1)))**(1.0 / n))
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
self.setModel(m)
g = (self.alpha*((self.theta_s - self.theta_r)/
(self.alpha + abs(u)**self.beta)**2)
*(-self.beta*abs(u)**(self.beta-1)*np.sign(u)))
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
self.setModel(m)
g = (self.alpha*((self.theta_s - self.theta_r)/
(self.alpha + abs(u)**self.beta)**2)
*(-self.beta*abs(u)**(self.beta-1)*np.sign(u)))
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
self.setModel(m)
g = -(np.exp(self.Ks) * self.A * self.gamma * abs(u) ** (self.gamma - 1) * np.sign(u)) / (
(self.A + abs(u) ** self.gamma) ** 2
)
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
g = -self.alpha * self.n * abs(self.alpha * u)**(self.n - 1) * np.sign(
self.alpha * u) * (1. / self.n - 1) * (
self.theta_r - self.theta_s) * (abs(self.alpha * u)**self.n +
1)**(1. / self.n - 2)
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
self.setModel(m)
g = -(np.exp(self.Ks) * self.A * self.gamma * abs(u)**
(self.gamma - 1) * np.sign(u)) / (
(self.A + abs(u)**self.gamma)**2)
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
def transformDerivU(self, u, m):
g = (
-self.alpha
* self.n
* abs(self.alpha * u) ** (self.n - 1)
* np.sign(self.alpha * u)
* (1.0 / self.n - 1)
* (self.theta_r - self.theta_s)
* (abs(self.alpha * u) ** self.n + 1) ** (1.0 / self.n - 2)
)
g[u >= 0] = 0
g = Utils.sdiag(g)
return g
