def test_estimated_degree():
cell = ufl.tetrahedron
mesh = ufl.Mesh(ufl.VectorElement('P', cell, 1))
V = ufl.FunctionSpace(mesh, ufl.FiniteElement('P', cell, 1))
f = ufl.Coefficient(V)
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
a = u * v * ufl.tanh(ufl.sqrt(ufl.sinh(f) / ufl.sin(f**f))) * ufl.dx
handler = MockHandler()
logger.addHandler(handler)
with pytest.raises(RuntimeError):
compile_form(a)
logger.removeHandler(handler)
def _I(self, v, s, time):
"""
Original gotran transmembrane current dV/dt
"""
time = time if time else Constant(0.0)
# Assign states
V = v
assert (len(s) == 2)
v, w = s
# Assign parameters
u_c = self._parameters["u_c"]
g_fi_max = self._parameters["g_fi_max"]
tau_0 = self._parameters["tau_0"]
tau_r = self._parameters["tau_r"]
k = self._parameters["k"]
tau_si = self._parameters["tau_si"]
u_csi = self._parameters["u_csi"]
Cm = self._parameters["Cm"]
V_0 = self._parameters["V_0"]
V_fi = self._parameters["V_fi"]
# Init return args
current = [ufl.zero()] * 1
# Expressions for the p component
p = ufl.conditional(ufl.lt((-V_0 + V) / (V_fi - V_0), u_c), 0, 1)
# Expressions for the Fast inward current component
tau_d = Cm / g_fi_max
J_fi = -(1 - (-V_0 + V)/(V_fi - V_0))*(-u_c + (-V_0 + V)/(V_fi -\
V_0))*p*v/tau_d
# Expressions for the Slow outward current component
J_so = p / tau_r + (1 - p) * (-V_0 + V) / (tau_0 * (V_fi - V_0))
# Expressions for the Slow inward current component
J_si = -(1 + ufl.tanh(k*(-u_csi + (-V_0 + V)/(V_fi -\
V_0))))*w/(2*tau_si)
# Expressions for the Stimulus protocol component
J_stim = 0
# Expressions for the Membrane component
current[0] = (V_0 - V_fi) * (J_stim + J_fi + J_si + J_so)
# Return results
return current[0]
def Vwitch(rho, params={}):
tanh = ufl.tanh((rho - params['rhomax']) / params['cushion'])
return (params['maxscale'] * params['sigma']**2 / 2 * (tanh + 1) *
(rho / params['rhomax']))
def Vtophat(rho, params={}):
tanh = ufl.tanh((rho - params['rhomax']) / params['cushion'])
return params['maxscale'] * params['sigma']**2 / 2 * (tanh + 1)
def hs_Cinf():
if type(x) == np.ndarray:
return 0.5*np.tanh(2.5*(x-float(x0))/eps) + 0.5
return 0.5*tanh(2.5*(x-x0)/eps) + 0.5