Source code for tvb.simulator.models.linear

# -*- coding: utf-8 -*-
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# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
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"""
Generic linear model.
"""

import numpy
from .base import Model
from tvb.basic.neotraits.api import NArray, Final, List, Range


class Linear(Model):
    gamma = NArray(
        label=r":math:`\gamma`",
        default=numpy.array([-10.0]),
        domain=Range(lo=-100.0, hi=0.0, step=1.0),
        doc="The damping coefficient specifies how quickly the node's activity relaxes, must be larger"
            " than the node's in-degree in order to remain stable.")

    state_variable_range = Final(
        label="State Variable ranges [lo, hi]",
        default={"x": numpy.array([-1, 1])},
        doc="Range used for state variable initialization and visualization.")

    variables_of_interest = List(
        of=str,
        label="Variables watched by Monitors",
        choices=("x",),
        default=("x",), )

    coupling_terms = Final(
        label="Coupling terms",
        # how to unpack coupling array
        default=["c"]
    )

    state_variable_dfuns = Final(
        label="Drift functions",
        default={
            "x": "gamma * x + c",
        }
    )

    parameter_names = List(
        of=str,
        label="List of parameters for this model",
        default=tuple('gamma'.split()))

    state_variables = ('x',)
    _nvar = 1
    cvar = numpy.array([0], dtype=numpy.int32)

    def dfun(self, state, coupling, local_coupling=0.0):
        """
        .. math::
            x = a{\gamma} + b
        """
        x, = state
        c, = coupling
        dx = self.gamma * x + c + local_coupling * x
        return numpy.array([dx])