def eqtn_fparams_vars(x0_var,
slope_var,
Iext1_var,
Iext2_var,
K_var,
x0,
slope,
Iext1,
Iext2,
K,
tau1,
tau0,
pmode="const",
z=None,
g=None):
fx0 = eqtn_fx0(x0_var, x0, tau1)
from tvb_epilepsy.tvb_api.epileptor_models import EpileptorDPrealistic
slope_eq, Iext2_eq = EpileptorDPrealistic.fun_slope_Iext2(
z, g, pmode, slope, Iext2)
fslope = eqtn_fslope(slope_var, slope_eq, tau1)
fIext1 = eqtn_fIext1(Iext1_var, Iext1, tau1, tau0)
fIext2 = eqtn_fIext2(Iext2_var, Iext2_eq, tau1)
fK = eqtn_fK(K_var, K, tau1, tau0)
return fx0, fslope, fIext1, fIext2, fK
def build_ep_11sv_model(model_configuration, zmode=numpy.array("lin"), pmode=numpy.array("z")):
# We use the opposite sign for K with respect to all epileptor models
K = -model_configuration.K
model = EpileptorDPrealistic(x0=model_configuration.x0, Iext1=model_configuration.Iext1,
Iext2=model_configuration.Iext2, K=K, yc=model_configuration.yc,
a=model_configuration.a, b=model_configuration.b, d=model_configuration.d,
s=model_configuration.s, gamma=model_configuration.gamma, zmode=zmode, pmode=pmode)
return model
def build_ep_11sv_model(model_configuration, zmode=numpy.array("lin")):
x0_transformed = calc_rescaled_x0(model_configuration.x0_values,
model_configuration.yc,
model_configuration.Iext1)
model = EpileptorDPrealistic(x0=x0_transformed,
Iext1=model_configuration.Iext1,
K=model_configuration.K,
yc=model_configuration.yc,
zmode=zmode)
return model
def symbol_eqtn_fIext2(n, pmode=array("const"), shape=None):
Iext2_var, z, g, Iext2, tau1, vars_dict = symbol_vars(n, ["Iext2_var", "z", "g", "Iext2", "tau1"], shape=shape)
from tvb_epilepsy.tvb_api.epileptor_models import EpileptorDPrealistic
Iext2_eq = EpileptorDPrealistic.fun_slope_Iext2(z, g, pmode, 0.0, Iext2)[1]
fIext2 = Array(eqtn_fIext2(Iext2_var, Iext2_eq, tau1))
vars_dict["pmode"] = pmode
if pmode=="z":
fIext2_lambda = lambdify([Iext2_var, z, tau1], fIext2, "numpy")
elif pmode == "g":
fIext2_lambda = lambdify([Iext2_var, g, tau1], fIext2, "numpy")
elif pmode == "z*g":
fIext2_lambda = lambdify([Iext2_var, z, g, tau1], fIext2, "numpy")
else:
fIext2_lambda = lambdify([Iext2_var, Iext2, tau1], fIext2, "numpy")
return fIext2_lambda, fIext2, vars_dict
def symbol_eqtn_fslope(n, pmode=array("const"), shape=None):
slope_var, z, g, slope, tau1, vars_dict = symbol_vars(n, ["slope_var", "z", "g", "slope", "tau1"], shape=shape)
from tvb_epilepsy.tvb_api.epileptor_models import EpileptorDPrealistic
slope_eq = EpileptorDPrealistic.fun_slope_Iext2(z, g, pmode, slope, 0.0)[0]
fslope = Array(eqtn_fslope(slope_var, slope_eq, tau1))
vars_dict["pmode"] = pmode
if pmode=="z":
fslope_lambda = lambdify([slope_var, z, tau1], fslope, "numpy")
elif pmode == "g":
fslope_lambda = lambdify([slope_var, g, tau1], fslope, "numpy"),
elif pmode == "z*g":
fslope_lambda = lambdify([slope_var, z, g, tau1], fslope, "numpy")
else:
fslope_lambda = lambdify([slope_var, slope, tau1], fslope, "numpy")
return fslope_lambda, fslope, vars_dict