from tvb.simulator.models.base import Model, ModelNumbaDfun
import numpy
from numpy import *
from numba import guvectorize, float64
from tvb.basic.neotraits.api import NArray, Final, List, Range
[docs]class EpileptorT(ModelNumbaDfun):
a = NArray(
label=":math:`a`",
default=numpy.array([1.0]),
doc=""""""
)
b = NArray(
label=":math:`b`",
default=numpy.array([3.0]),
doc=""""""
)
c = NArray(
label=":math:`c`",
default=numpy.array([1.0]),
doc=""""""
)
d = NArray(
label=":math:`d`",
default=numpy.array([5.0]),
doc=""""""
)
r = NArray(
label=":math:`r`",
default=numpy.array([0.00035]),
domain=Range(lo=0.0, hi=0.001, step=0.00005),
doc=""""""
)
s = NArray(
label=":math:`s`",
default=numpy.array([4.0]),
doc=""""""
)
x0 = NArray(
label=":math:`x0`",
default=numpy.array([-1.6]),
domain=Range(lo=-3.0, hi=-1.0, step=0.1),
doc=""""""
)
Iext = NArray(
label=":math:`Iext`",
default=numpy.array([3.1]),
domain=Range(lo=1.5, hi=5.0, step=0.1),
doc=""""""
)
slope = NArray(
label=":math:`slope`",
default=numpy.array([0.]),
domain=Range(lo=-16.0, hi=6.0, step=0.1),
doc=""""""
)
Iext2 = NArray(
label=":math:`Iext2`",
default=numpy.array([0.45]),
domain=Range(lo=0.0, hi=1.0, step=0.05),
doc=""""""
)
tau = NArray(
label=":math:`tau`",
default=numpy.array([10.0]),
doc=""""""
)
aa = NArray(
label=":math:`aa`",
default=numpy.array([6.0]),
doc=""""""
)
bb = NArray(
label=":math:`bb`",
default=numpy.array([2.0]),
doc=""""""
)
Kvf = NArray(
label=":math:`Kvf`",
default=numpy.array([0.0]),
domain=Range(lo=0.0, hi=4.0, step=0.5),
doc=""""""
)
Kf = NArray(
label=":math:`Kf`",
default=numpy.array([0.0]),
domain=Range(lo=0.0, hi=4.0, step=0.5),
doc=""""""
)
Ks = NArray(
label=":math:`Ks`",
default=numpy.array([0.0]),
domain=Range(lo=-4.0, hi=4.0, step=0.1),
doc=""""""
)
tt = NArray(
label=":math:`tt`",
default=numpy.array([1.0]),
domain=Range(lo=0.001, hi=10.0, step=0.001),
doc=""""""
)
modification = NArray(
label=":math:`modification`",
default=numpy.array([0]),
doc=""""""
)
state_variable_range = Final(
label="State Variable ranges [lo, hi]",
default={"x1": numpy.array([-2., 1.]),
"y1": numpy.array([-20., 2.]),
"z": numpy.array([2.0, 5.0]),
"x2": numpy.array([-2., 0.]),
"y2": numpy.array([0., 2.]),
"g": numpy.array([-1., 1.])},
doc="""state variables"""
)
variables_of_interest = List(
of=str,
label="Variables or quantities available to Monitors",
choices=('x1', 'x2', ),
default=('x1', 'x2', ),
doc="Variables to monitor"
)
state_variables = ['x1', 'y1', 'z', 'x2', 'y2', 'g']
_nvar = 6
cvar = numpy.array([0,1,2,3,4,5,], dtype = numpy.int32)
[docs] def dfun(self, vw, c, local_coupling=0.0):
vw_ = vw.reshape(vw.shape[:-1]).T
c_ = c.reshape(c.shape[:-1]).T
deriv = _numba_dfun_EpileptorT(vw_, c_, self.a, self.b, self.c, self.d, self.r, self.s, self.x0, self.Iext, self.slope, self.Iext2, self.tau, self.aa, self.bb, self.Kvf, self.Kf, self.Ks, self.tt, self.modification, local_coupling)
return deriv.T[..., numpy.newaxis]
@guvectorize([(float64[:], float64[:], float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64[:])], '(n),(m)' + ',()'*19 + '->(n)', nopython=True)
def _numba_dfun_EpileptorT(vw, coupling, a, b, c, d, r, s, x0, Iext, slope, Iext2, tau, aa, bb, Kvf, Kf, Ks, tt, modification, local_coupling, dx):
"Gufunc for EpileptorT model equations."
# long-range coupling
c_pop0 = coupling[0]
c_pop1 = coupling[1]
c_pop2 = coupling[2]
c_pop3 = coupling[3]
c_pop4 = coupling[4]
c_pop5 = coupling[5]
x1 = vw[0]
y1 = vw[1]
z = vw[2]
x2 = vw[3]
y2 = vw[4]
g = vw[5]
# derived variables
ztmp = z-4
# Conditional variables
if x1 < 0.0:
ydot0 = -a * x1**2 + b * x1
else:
ydot0 = slope - x2 + 0.6 * ztmp**2
if z < 0.0:
ydot2 = - 0.1 * z**7
else:
ydot2 = 0
if modification:
h = x0 + 3. / (1. + exp(-(x1 + 0.5) / 0.1))
else:
h = 4 * (x1 - x0) + ydot2
if x2 < -0.25:
ydot4 = 0.0
else:
ydot4 = aa * (x2 + 0.25)
dx[0] = tt * (y1 - z + Iext + Kvf * c_pop0 + ydot0 )
dx[1] = tt * (c - d * x1**2 - y1)
dx[2] = tt * (r * (h - z + Ks * c_pop0))
dx[3] = tt * (-y2 + x2 - x2**3 + Iext2 + bb * g - 0.3 * (z - 3.5) + Kf * c_pop1)
dx[4] = tt * (-y2 + ydot4) / tau
dx[5] = tt * (-0.01 * (g - 0.1 * x1) )
