I am done

rl/Lib/site-packages/sympy/physics/biomechanics/__init__.py

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+"""Biomechanics extension for SymPy.
+
+Includes biomechanics-related constructs which allows users to extend multibody
+models created using `sympy.physics.mechanics` into biomechanical or
+musculoskeletal models involding musculotendons and activation dynamics.
+
+"""
+
+from .activation import (
+   ActivationBase,
+   FirstOrderActivationDeGroote2016,
+   ZerothOrderActivation,
+)
+from .curve import (
+   CharacteristicCurveCollection,
+   CharacteristicCurveFunction,
+   FiberForceLengthActiveDeGroote2016,
+   FiberForceLengthPassiveDeGroote2016,
+   FiberForceLengthPassiveInverseDeGroote2016,
+   FiberForceVelocityDeGroote2016,
+   FiberForceVelocityInverseDeGroote2016,
+   TendonForceLengthDeGroote2016,
+   TendonForceLengthInverseDeGroote2016,
+)
+from .musculotendon import (
+   MusculotendonBase,
+   MusculotendonDeGroote2016,
+   MusculotendonFormulation,
+)
+
+
+__all__ = [
+   # Musculotendon characteristic curve functions
+   'CharacteristicCurveCollection',
+   'CharacteristicCurveFunction',
+   'FiberForceLengthActiveDeGroote2016',
+   'FiberForceLengthPassiveDeGroote2016',
+   'FiberForceLengthPassiveInverseDeGroote2016',
+   'FiberForceVelocityDeGroote2016',
+   'FiberForceVelocityInverseDeGroote2016',
+   'TendonForceLengthDeGroote2016',
+   'TendonForceLengthInverseDeGroote2016',
+
+   # Activation dynamics classes
+   'ActivationBase',
+   'FirstOrderActivationDeGroote2016',
+   'ZerothOrderActivation',
+
+   # Musculotendon classes
+   'MusculotendonBase',
+   'MusculotendonDeGroote2016',
+   'MusculotendonFormulation',
+]

rl/Lib/site-packages/sympy/physics/biomechanics/_mixin.py

@@ -0,0 +1,53 @@
+"""Mixin classes for sharing functionality between unrelated classes.
+
+This module is named with a leading underscore to signify to users that it's
+"private" and only intended for internal use by the biomechanics module.
+
+"""
+
+
+__all__ = ['_NamedMixin']
+
+
+class _NamedMixin:
+    """Mixin class for adding `name` properties.
+
+    Valid names, as will typically be used by subclasses as a suffix when
+    naming automatically-instantiated symbol attributes, must be nonzero length
+    strings.
+
+    Attributes
+    ==========
+
+    name : str
+        The name identifier associated with the instance. Must be a string of
+        length at least 1.
+
+    """
+
+    @property
+    def name(self) -> str:
+        """The name associated with the class instance."""
+        return self._name
+
+    @name.setter
+    def name(self, name: str) -> None:
+        if hasattr(self, '_name'):
+            msg = (
+                f'Can\'t set attribute `name` to {repr(name)} as it is '
+                f'immutable.'
+            )
+            raise AttributeError(msg)
+        if not isinstance(name, str):
+            msg = (
+                f'Name {repr(name)} passed to `name` was of type '
+                f'{type(name)}, must be {str}.'
+            )
+            raise TypeError(msg)
+        if name in {''}:
+            msg = (
+                f'Name {repr(name)} is invalid, must be a nonzero length '
+                f'{type(str)}.'
+            )
+            raise ValueError(msg)
+        self._name = name

rl/Lib/site-packages/sympy/physics/biomechanics/activation.py

@@ -0,0 +1,869 @@
+r"""Activation dynamics for musclotendon models.
+
+Musculotendon models are able to produce active force when they are activated,
+which is when a chemical process has taken place within the muscle fibers
+causing them to voluntarily contract. Biologically this chemical process (the
+diffusion of :math:`\textrm{Ca}^{2+}` ions) is not the input in the system,
+electrical signals from the nervous system are. These are termed excitations.
+Activation dynamics, which relates the normalized excitation level to the
+normalized activation level, can be modeled by the models present in this
+module.
+
+"""
+
+from abc import ABC, abstractmethod
+from functools import cached_property
+
+from sympy.core.symbol import Symbol
+from sympy.core.numbers import Float, Integer, Rational
+from sympy.functions.elementary.hyperbolic import tanh
+from sympy.matrices.dense import MutableDenseMatrix as Matrix, zeros
+from sympy.physics.biomechanics._mixin import _NamedMixin
+from sympy.physics.mechanics import dynamicsymbols
+
+
+__all__ = [
+    'ActivationBase',
+    'FirstOrderActivationDeGroote2016',
+    'ZerothOrderActivation',
+]
+
+
+class ActivationBase(ABC, _NamedMixin):
+    """Abstract base class for all activation dynamics classes to inherit from.
+
+    Notes
+    =====
+
+    Instances of this class cannot be directly instantiated by users. However,
+    it can be used to created custom activation dynamics types through
+    subclassing.
+
+    """
+
+    def __init__(self, name):
+        """Initializer for ``ActivationBase``."""
+        self.name = str(name)
+
+        # Symbols
+        self._e = dynamicsymbols(f"e_{name}")
+        self._a = dynamicsymbols(f"a_{name}")
+
+    @classmethod
+    @abstractmethod
+    def with_defaults(cls, name):
+        """Alternate constructor that provides recommended defaults for
+        constants."""
+        pass
+
+    @property
+    def excitation(self):
+        """Dynamic symbol representing excitation.
+
+        Explanation
+        ===========
+
+        The alias ``e`` can also be used to access the same attribute.
+
+        """
+        return self._e
+
+    @property
+    def e(self):
+        """Dynamic symbol representing excitation.
+
+        Explanation
+        ===========
+
+        The alias ``excitation`` can also be used to access the same attribute.
+
+        """
+        return self._e
+
+    @property
+    def activation(self):
+        """Dynamic symbol representing activation.
+
+        Explanation
+        ===========
+
+        The alias ``a`` can also be used to access the same attribute.
+
+        """
+        return self._a
+
+    @property
+    def a(self):
+        """Dynamic symbol representing activation.
+
+        Explanation
+        ===========
+
+        The alias ``activation`` can also be used to access the same attribute.
+
+        """
+        return self._a
+
+    @property
+    @abstractmethod
+    def order(self):
+        """Order of the (differential) equation governing activation."""
+        pass
+
+    @property
+    @abstractmethod
+    def state_vars(self):
+        """Ordered column matrix of functions of time that represent the state
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``x`` can also be used to access the same attribute.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def x(self):
+        """Ordered column matrix of functions of time that represent the state
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``state_vars`` can also be used to access the same attribute.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def input_vars(self):
+        """Ordered column matrix of functions of time that represent the input
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``r`` can also be used to access the same attribute.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def r(self):
+        """Ordered column matrix of functions of time that represent the input
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``input_vars`` can also be used to access the same attribute.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def constants(self):
+        """Ordered column matrix of non-time varying symbols present in ``M``
+        and ``F``.
+
+        Only symbolic constants are returned. If a numeric type (e.g. ``Float``)
+        has been used instead of ``Symbol`` for a constant then that attribute
+        will not be included in the matrix returned by this property. This is
+        because the primary use of this property attribute is to provide an
+        ordered sequence of the still-free symbols that require numeric values
+        during code generation.
+
+        Explanation
+        ===========
+
+        The alias ``p`` can also be used to access the same attribute.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def p(self):
+        """Ordered column matrix of non-time varying symbols present in ``M``
+        and ``F``.
+
+        Only symbolic constants are returned. If a numeric type (e.g. ``Float``)
+        has been used instead of ``Symbol`` for a constant then that attribute
+        will not be included in the matrix returned by this property. This is
+        because the primary use of this property attribute is to provide an
+        ordered sequence of the still-free symbols that require numeric values
+        during code generation.
+
+        Explanation
+        ===========
+
+        The alias ``constants`` can also be used to access the same attribute.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def M(self):
+        """Ordered square matrix of coefficients on the LHS of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The square matrix that forms part of the LHS of the linear system of
+        ordinary differential equations governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def F(self):
+        """Ordered column matrix of equations on the RHS of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The column matrix that forms the RHS of the linear system of ordinary
+        differential equations governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        """
+        pass
+
+    @abstractmethod
+    def rhs(self):
+        """
+
+        Explanation
+        ===========
+
+        The solution to the linear system of ordinary differential equations
+        governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        """
+        pass
+
+    def __eq__(self, other):
+        """Equality check for activation dynamics."""
+        if type(self) != type(other):
+            return False
+        if self.name != other.name:
+            return False
+        return True
+
+    def __repr__(self):
+        """Default representation of activation dynamics."""
+        return f'{self.__class__.__name__}({self.name!r})'
+
+
+class ZerothOrderActivation(ActivationBase):
+    """Simple zeroth-order activation dynamics mapping excitation to
+    activation.
+
+    Explanation
+    ===========
+
+    Zeroth-order activation dynamics are useful in instances where you want to
+    reduce the complexity of your musculotendon dynamics as they simple map
+    exictation to activation. As a result, no additional state equations are
+    introduced to your system. They also remove a potential source of delay
+    between the input and dynamics of your system as no (ordinary) differential
+    equations are involed.
+
+    """
+
+    def __init__(self, name):
+        """Initializer for ``ZerothOrderActivation``.
+
+        Parameters
+        ==========
+
+        name : str
+            The name identifier associated with the instance. Must be a string
+            of length at least 1.
+
+        """
+        super().__init__(name)
+
+        # Zeroth-order activation dynamics has activation equal excitation so
+        # overwrite the symbol for activation with the excitation symbol.
+        self._a = self._e
+
+    @classmethod
+    def with_defaults(cls, name):
+        """Alternate constructor that provides recommended defaults for
+        constants.
+
+        Explanation
+        ===========
+
+        As this concrete class doesn't implement any constants associated with
+        its dynamics, this ``classmethod`` simply creates a standard instance
+        of ``ZerothOrderActivation``. An implementation is provided to ensure
+        a consistent interface between all ``ActivationBase`` concrete classes.
+
+        """
+        return cls(name)
+
+    @property
+    def order(self):
+        """Order of the (differential) equation governing activation."""
+        return 0
+
+    @property
+    def state_vars(self):
+        """Ordered column matrix of functions of time that represent the state
+        variables.
+
+        Explanation
+        ===========
+
+        As zeroth-order activation dynamics simply maps excitation to
+        activation, this class has no associated state variables and so this
+        property return an empty column ``Matrix`` with shape (0, 1).
+
+        The alias ``x`` can also be used to access the same attribute.
+
+        """
+        return zeros(0, 1)
+
+    @property
+    def x(self):
+        """Ordered column matrix of functions of time that represent the state
+        variables.
+
+        Explanation
+        ===========
+
+        As zeroth-order activation dynamics simply maps excitation to
+        activation, this class has no associated state variables and so this
+        property return an empty column ``Matrix`` with shape (0, 1).
+
+        The alias ``state_vars`` can also be used to access the same attribute.
+
+        """
+        return zeros(0, 1)
+
+    @property
+    def input_vars(self):
+        """Ordered column matrix of functions of time that represent the input
+        variables.
+
+        Explanation
+        ===========
+
+        Excitation is the only input in zeroth-order activation dynamics and so
+        this property returns a column ``Matrix`` with one entry, ``e``, and
+        shape (1, 1).
+
+        The alias ``r`` can also be used to access the same attribute.
+
+        """
+        return Matrix([self._e])
+
+    @property
+    def r(self):
+        """Ordered column matrix of functions of time that represent the input
+        variables.
+
+        Explanation
+        ===========
+
+        Excitation is the only input in zeroth-order activation dynamics and so
+        this property returns a column ``Matrix`` with one entry, ``e``, and
+        shape (1, 1).
+
+        The alias ``input_vars`` can also be used to access the same attribute.
+
+        """
+        return Matrix([self._e])
+
+    @property
+    def constants(self):
+        """Ordered column matrix of non-time varying symbols present in ``M``
+        and ``F``.
+
+        Only symbolic constants are returned. If a numeric type (e.g. ``Float``)
+        has been used instead of ``Symbol`` for a constant then that attribute
+        will not be included in the matrix returned by this property. This is
+        because the primary use of this property attribute is to provide an
+        ordered sequence of the still-free symbols that require numeric values
+        during code generation.
+
+        Explanation
+        ===========
+
+        As zeroth-order activation dynamics simply maps excitation to
+        activation, this class has no associated constants and so this property
+        return an empty column ``Matrix`` with shape (0, 1).
+
+        The alias ``p`` can also be used to access the same attribute.
+
+        """
+        return zeros(0, 1)
+
+    @property
+    def p(self):
+        """Ordered column matrix of non-time varying symbols present in ``M``
+        and ``F``.
+
+        Only symbolic constants are returned. If a numeric type (e.g. ``Float``)
+        has been used instead of ``Symbol`` for a constant then that attribute
+        will not be included in the matrix returned by this property. This is
+        because the primary use of this property attribute is to provide an
+        ordered sequence of the still-free symbols that require numeric values
+        during code generation.
+
+        Explanation
+        ===========
+
+        As zeroth-order activation dynamics simply maps excitation to
+        activation, this class has no associated constants and so this property
+        return an empty column ``Matrix`` with shape (0, 1).
+
+        The alias ``constants`` can also be used to access the same attribute.
+
+        """
+        return zeros(0, 1)
+
+    @property
+    def M(self):
+        """Ordered square matrix of coefficients on the LHS of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The square matrix that forms part of the LHS of the linear system of
+        ordinary differential equations governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        As zeroth-order activation dynamics have no state variables, this
+        linear system has dimension 0 and therefore ``M`` is an empty square
+        ``Matrix`` with shape (0, 0).
+
+        """
+        return Matrix([])
+
+    @property
+    def F(self):
+        """Ordered column matrix of equations on the RHS of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The column matrix that forms the RHS of the linear system of ordinary
+        differential equations governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        As zeroth-order activation dynamics have no state variables, this
+        linear system has dimension 0 and therefore ``F`` is an empty column
+        ``Matrix`` with shape (0, 1).
+
+        """
+        return zeros(0, 1)
+
+    def rhs(self):
+        """Ordered column matrix of equations for the solution of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The solution to the linear system of ordinary differential equations
+        governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        As zeroth-order activation dynamics have no state variables, this
+        linear has dimension 0 and therefore this method returns an empty
+        column ``Matrix`` with shape (0, 1).
+
+        """
+        return zeros(0, 1)
+
+
+class FirstOrderActivationDeGroote2016(ActivationBase):
+    r"""First-order activation dynamics based on De Groote et al., 2016 [1]_.
+
+    Explanation
+    ===========
+
+    Gives the first-order activation dynamics equation for the rate of change
+    of activation with respect to time as a function of excitation and
+    activation.
+
+    The function is defined by the equation:
+
+    .. math::
+
+        \frac{da}{dt} = \left(\frac{\frac{1}{2} + a0}{\tau_a \left(\frac{1}{2}
+            + \frac{3a}{2}\right)} + \frac{\left(\frac{1}{2}
+            + \frac{3a}{2}\right) \left(\frac{1}{2} - a0\right)}{\tau_d}\right)
+            \left(e - a\right)
+
+    where
+
+    .. math::
+
+        a0 = \frac{\tanh{\left(b \left(e - a\right) \right)}}{2}
+
+    with constant values of :math:`tau_a = 0.015`, :math:`tau_d = 0.060`, and
+    :math:`b = 10`.
+
+    References
+    ==========
+
+    .. [1] De Groote, F., Kinney, A. L., Rao, A. V., & Fregly, B. J., Evaluation
+           of direct collocation optimal control problem formulations for
+           solving the muscle redundancy problem, Annals of biomedical
+           engineering, 44(10), (2016) pp. 2922-2936
+
+    """
+
+    def __init__(self,
+        name,
+        activation_time_constant=None,
+        deactivation_time_constant=None,
+        smoothing_rate=None,
+    ):
+        """Initializer for ``FirstOrderActivationDeGroote2016``.
+
+        Parameters
+        ==========
+        activation time constant : Symbol | Number | None
+            The value of the activation time constant governing the delay
+            between excitation and activation when excitation exceeds
+            activation.
+        deactivation time constant : Symbol | Number | None
+            The value of the deactivation time constant governing the delay
+            between excitation and activation when activation exceeds
+            excitation.
+        smoothing_rate : Symbol | Number | None
+            The slope of the hyperbolic tangent function used to smooth between
+            the switching of the equations where excitation exceed activation
+            and where activation exceeds excitation. The recommended value to
+            use is ``10``, but values between ``0.1`` and ``100`` can be used.
+
+        """
+        super().__init__(name)
+
+        # Symbols
+        self.activation_time_constant = activation_time_constant
+        self.deactivation_time_constant = deactivation_time_constant
+        self.smoothing_rate = smoothing_rate
+
+    @classmethod
+    def with_defaults(cls, name):
+        r"""Alternate constructor that will use the published constants.
+
+        Explanation
+        ===========
+
+        Returns an instance of ``FirstOrderActivationDeGroote2016`` using the
+        three constant values specified in the original publication.
+
+        These have the values:
+
+        :math:`tau_a = 0.015`
+        :math:`tau_d = 0.060`
+        :math:`b = 10`
+
+        """
+        tau_a = Float('0.015')
+        tau_d = Float('0.060')
+        b = Float('10.0')
+        return cls(name, tau_a, tau_d, b)
+
+    @property
+    def activation_time_constant(self):
+        """Delay constant for activation.
+
+        Explanation
+        ===========
+
+        The alias ```tau_a`` can also be used to access the same attribute.
+
+        """
+        return self._tau_a
+
+    @activation_time_constant.setter
+    def activation_time_constant(self, tau_a):
+        if hasattr(self, '_tau_a'):
+            msg = (
+                f'Can\'t set attribute `activation_time_constant` to '
+                f'{repr(tau_a)} as it is immutable and already has value '
+                f'{self._tau_a}.'
+            )
+            raise AttributeError(msg)
+        self._tau_a = Symbol(f'tau_a_{self.name}') if tau_a is None else tau_a
+
+    @property
+    def tau_a(self):
+        """Delay constant for activation.
+
+        Explanation
+        ===========
+
+        The alias ``activation_time_constant`` can also be used to access the
+        same attribute.
+
+        """
+        return self._tau_a
+
+    @property
+    def deactivation_time_constant(self):
+        """Delay constant for deactivation.
+
+        Explanation
+        ===========
+
+        The alias ``tau_d`` can also be used to access the same attribute.
+
+        """
+        return self._tau_d
+
+    @deactivation_time_constant.setter
+    def deactivation_time_constant(self, tau_d):
+        if hasattr(self, '_tau_d'):
+            msg = (
+                f'Can\'t set attribute `deactivation_time_constant` to '
+                f'{repr(tau_d)} as it is immutable and already has value '
+                f'{self._tau_d}.'
+            )
+            raise AttributeError(msg)
+        self._tau_d = Symbol(f'tau_d_{self.name}') if tau_d is None else tau_d
+
+    @property
+    def tau_d(self):
+        """Delay constant for deactivation.
+
+        Explanation
+        ===========
+
+        The alias ``deactivation_time_constant`` can also be used to access the
+        same attribute.
+
+        """
+        return self._tau_d
+
+    @property
+    def smoothing_rate(self):
+        """Smoothing constant for the hyperbolic tangent term.
+
+        Explanation
+        ===========
+
+        The alias ``b`` can also be used to access the same attribute.
+
+        """
+        return self._b
+
+    @smoothing_rate.setter
+    def smoothing_rate(self, b):
+        if hasattr(self, '_b'):
+            msg = (
+                f'Can\'t set attribute `smoothing_rate` to {b!r} as it is '
+                f'immutable and already has value {self._b!r}.'
+            )
+            raise AttributeError(msg)
+        self._b = Symbol(f'b_{self.name}') if b is None else b
+
+    @property
+    def b(self):
+        """Smoothing constant for the hyperbolic tangent term.
+
+        Explanation
+        ===========
+
+        The alias ``smoothing_rate`` can also be used to access the same
+        attribute.
+
+        """
+        return self._b
+
+    @property
+    def order(self):
+        """Order of the (differential) equation governing activation."""
+        return 1
+
+    @property
+    def state_vars(self):
+        """Ordered column matrix of functions of time that represent the state
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``x`` can also be used to access the same attribute.
+
+        """
+        return Matrix([self._a])
+
+    @property
+    def x(self):
+        """Ordered column matrix of functions of time that represent the state
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``state_vars`` can also be used to access the same attribute.
+
+        """
+        return Matrix([self._a])
+
+    @property
+    def input_vars(self):
+        """Ordered column matrix of functions of time that represent the input
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``r`` can also be used to access the same attribute.
+
+        """
+        return Matrix([self._e])
+
+    @property
+    def r(self):
+        """Ordered column matrix of functions of time that represent the input
+        variables.
+
+        Explanation
+        ===========
+
+        The alias ``input_vars`` can also be used to access the same attribute.
+
+        """
+        return Matrix([self._e])
+
+    @property
+    def constants(self):
+        """Ordered column matrix of non-time varying symbols present in ``M``
+        and ``F``.
+
+        Only symbolic constants are returned. If a numeric type (e.g. ``Float``)
+        has been used instead of ``Symbol`` for a constant then that attribute
+        will not be included in the matrix returned by this property. This is
+        because the primary use of this property attribute is to provide an
+        ordered sequence of the still-free symbols that require numeric values
+        during code generation.
+
+        Explanation
+        ===========
+
+        The alias ``p`` can also be used to access the same attribute.
+
+        """
+        constants = [self._tau_a, self._tau_d, self._b]
+        symbolic_constants = [c for c in constants if not c.is_number]
+        return Matrix(symbolic_constants) if symbolic_constants else zeros(0, 1)
+
+    @property
+    def p(self):
+        """Ordered column matrix of non-time varying symbols present in ``M``
+        and ``F``.
+
+        Explanation
+        ===========
+
+        Only symbolic constants are returned. If a numeric type (e.g. ``Float``)
+        has been used instead of ``Symbol`` for a constant then that attribute
+        will not be included in the matrix returned by this property. This is
+        because the primary use of this property attribute is to provide an
+        ordered sequence of the still-free symbols that require numeric values
+        during code generation.
+
+        The alias ``constants`` can also be used to access the same attribute.
+
+        """
+        constants = [self._tau_a, self._tau_d, self._b]
+        symbolic_constants = [c for c in constants if not c.is_number]
+        return Matrix(symbolic_constants) if symbolic_constants else zeros(0, 1)
+
+    @property
+    def M(self):
+        """Ordered square matrix of coefficients on the LHS of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The square matrix that forms part of the LHS of the linear system of
+        ordinary differential equations governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        """
+        return Matrix([Integer(1)])
+
+    @property
+    def F(self):
+        """Ordered column matrix of equations on the RHS of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The column matrix that forms the RHS of the linear system of ordinary
+        differential equations governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        """
+        return Matrix([self._da_eqn])
+
+    def rhs(self):
+        """Ordered column matrix of equations for the solution of ``M x' = F``.
+
+        Explanation
+        ===========
+
+        The solution to the linear system of ordinary differential equations
+        governing the activation dynamics:
+
+        ``M(x, r, t, p) x' = F(x, r, t, p)``.
+
+        """
+        return Matrix([self._da_eqn])
+
+    @cached_property
+    def _da_eqn(self):
+        HALF = Rational(1, 2)
+        a0 = HALF * tanh(self._b * (self._e - self._a))
+        a1 = (HALF + Rational(3, 2) * self._a)
+        a2 = (HALF + a0) / (self._tau_a * a1)
+        a3 = a1 * (HALF - a0) / self._tau_d
+        activation_dynamics_equation = (a2 + a3) * (self._e - self._a)
+        return activation_dynamics_equation
+
+    def __eq__(self, other):
+        """Equality check for ``FirstOrderActivationDeGroote2016``."""
+        if type(self) != type(other):
+            return False
+        self_attrs = (self.name, self.tau_a, self.tau_d, self.b)
+        other_attrs = (other.name, other.tau_a, other.tau_d, other.b)
+        if self_attrs == other_attrs:
+            return True
+        return False
+
+    def __repr__(self):
+        """Representation of ``FirstOrderActivationDeGroote2016``."""
+        return (
+            f'{self.__class__.__name__}({self.name!r}, '
+            f'activation_time_constant={self.tau_a!r}, '
+            f'deactivation_time_constant={self.tau_d!r}, '
+            f'smoothing_rate={self.b!r})'
+        )

rl/Lib/site-packages/sympy/physics/biomechanics/tests/test_activation.py

@@ -0,0 +1,348 @@
+"""Tests for the ``sympy.physics.biomechanics.activation.py`` module."""
+
+import pytest
+
+from sympy import Symbol
+from sympy.core.numbers import Float, Integer, Rational
+from sympy.functions.elementary.hyperbolic import tanh
+from sympy.matrices import Matrix
+from sympy.matrices.dense import zeros
+from sympy.physics.mechanics import dynamicsymbols
+from sympy.physics.biomechanics import (
+    ActivationBase,
+    FirstOrderActivationDeGroote2016,
+    ZerothOrderActivation,
+)
+from sympy.physics.biomechanics._mixin import _NamedMixin
+from sympy.simplify.simplify import simplify
+
+
+class TestZerothOrderActivation:
+
+    @staticmethod
+    def test_class():
+        assert issubclass(ZerothOrderActivation, ActivationBase)
+        assert issubclass(ZerothOrderActivation, _NamedMixin)
+        assert ZerothOrderActivation.__name__ == 'ZerothOrderActivation'
+
+    @pytest.fixture(autouse=True)
+    def _zeroth_order_activation_fixture(self):
+        self.name = 'name'
+        self.e = dynamicsymbols('e_name')
+        self.instance = ZerothOrderActivation(self.name)
+
+    def test_instance(self):
+        instance = ZerothOrderActivation(self.name)
+        assert isinstance(instance, ZerothOrderActivation)
+
+    def test_with_defaults(self):
+        instance = ZerothOrderActivation.with_defaults(self.name)
+        assert isinstance(instance, ZerothOrderActivation)
+        assert instance == ZerothOrderActivation(self.name)
+
+    def test_name(self):
+        assert hasattr(self.instance, 'name')
+        assert self.instance.name == self.name
+
+    def test_order(self):
+        assert hasattr(self.instance, 'order')
+        assert self.instance.order == 0
+
+    def test_excitation_attribute(self):
+        assert hasattr(self.instance, 'e')
+        assert hasattr(self.instance, 'excitation')
+        e_expected = dynamicsymbols('e_name')
+        assert self.instance.e == e_expected
+        assert self.instance.excitation == e_expected
+        assert self.instance.e is self.instance.excitation
+
+    def test_activation_attribute(self):
+        assert hasattr(self.instance, 'a')
+        assert hasattr(self.instance, 'activation')
+        a_expected = dynamicsymbols('e_name')
+        assert self.instance.a == a_expected
+        assert self.instance.activation == a_expected
+        assert self.instance.a is self.instance.activation is self.instance.e
+
+    def test_state_vars_attribute(self):
+        assert hasattr(self.instance, 'x')
+        assert hasattr(self.instance, 'state_vars')
+        assert self.instance.x == self.instance.state_vars
+        x_expected = zeros(0, 1)
+        assert self.instance.x == x_expected
+        assert self.instance.state_vars == x_expected
+        assert isinstance(self.instance.x, Matrix)
+        assert isinstance(self.instance.state_vars, Matrix)
+        assert self.instance.x.shape == (0, 1)
+        assert self.instance.state_vars.shape == (0, 1)
+
+    def test_input_vars_attribute(self):
+        assert hasattr(self.instance, 'r')
+        assert hasattr(self.instance, 'input_vars')
+        assert self.instance.r == self.instance.input_vars
+        r_expected = Matrix([self.e])
+        assert self.instance.r == r_expected
+        assert self.instance.input_vars == r_expected
+        assert isinstance(self.instance.r, Matrix)
+        assert isinstance(self.instance.input_vars, Matrix)
+        assert self.instance.r.shape == (1, 1)
+        assert self.instance.input_vars.shape == (1, 1)
+
+    def test_constants_attribute(self):
+        assert hasattr(self.instance, 'p')
+        assert hasattr(self.instance, 'constants')
+        assert self.instance.p == self.instance.constants
+        p_expected = zeros(0, 1)
+        assert self.instance.p == p_expected
+        assert self.instance.constants == p_expected
+        assert isinstance(self.instance.p, Matrix)
+        assert isinstance(self.instance.constants, Matrix)
+        assert self.instance.p.shape == (0, 1)
+        assert self.instance.constants.shape == (0, 1)
+
+    def test_M_attribute(self):
+        assert hasattr(self.instance, 'M')
+        M_expected = Matrix([])
+        assert self.instance.M == M_expected
+        assert isinstance(self.instance.M, Matrix)
+        assert self.instance.M.shape == (0, 0)
+
+    def test_F(self):
+        assert hasattr(self.instance, 'F')
+        F_expected = zeros(0, 1)
+        assert self.instance.F == F_expected
+        assert isinstance(self.instance.F, Matrix)
+        assert self.instance.F.shape == (0, 1)
+
+    def test_rhs(self):
+        assert hasattr(self.instance, 'rhs')
+        rhs_expected = zeros(0, 1)
+        rhs = self.instance.rhs()
+        assert rhs == rhs_expected
+        assert isinstance(rhs, Matrix)
+        assert rhs.shape == (0, 1)
+
+    def test_repr(self):
+        expected = 'ZerothOrderActivation(\'name\')'
+        assert repr(self.instance) == expected
+
+
+class TestFirstOrderActivationDeGroote2016:
+
+    @staticmethod
+    def test_class():
+        assert issubclass(FirstOrderActivationDeGroote2016, ActivationBase)
+        assert issubclass(FirstOrderActivationDeGroote2016, _NamedMixin)
+        assert FirstOrderActivationDeGroote2016.__name__ == 'FirstOrderActivationDeGroote2016'
+
+    @pytest.fixture(autouse=True)
+    def _first_order_activation_de_groote_2016_fixture(self):
+        self.name = 'name'
+        self.e = dynamicsymbols('e_name')
+        self.a = dynamicsymbols('a_name')
+        self.tau_a = Symbol('tau_a')
+        self.tau_d = Symbol('tau_d')
+        self.b = Symbol('b')
+        self.instance = FirstOrderActivationDeGroote2016(
+            self.name,
+            self.tau_a,
+            self.tau_d,
+            self.b,
+        )
+
+    def test_instance(self):
+        instance = FirstOrderActivationDeGroote2016(self.name)
+        assert isinstance(instance, FirstOrderActivationDeGroote2016)
+
+    def test_with_defaults(self):
+        instance = FirstOrderActivationDeGroote2016.with_defaults(self.name)
+        assert isinstance(instance, FirstOrderActivationDeGroote2016)
+        assert instance.tau_a == Float('0.015')
+        assert instance.activation_time_constant == Float('0.015')
+        assert instance.tau_d == Float('0.060')
+        assert instance.deactivation_time_constant == Float('0.060')
+        assert instance.b == Float('10.0')
+        assert instance.smoothing_rate == Float('10.0')
+
+    def test_name(self):
+        assert hasattr(self.instance, 'name')
+        assert self.instance.name == self.name
+
+    def test_order(self):
+        assert hasattr(self.instance, 'order')
+        assert self.instance.order == 1
+
+    def test_excitation(self):
+        assert hasattr(self.instance, 'e')
+        assert hasattr(self.instance, 'excitation')
+        e_expected = dynamicsymbols('e_name')
+        assert self.instance.e == e_expected
+        assert self.instance.excitation == e_expected
+        assert self.instance.e is self.instance.excitation
+
+    def test_excitation_is_immutable(self):
+        with pytest.raises(AttributeError):
+            self.instance.e = None
+        with pytest.raises(AttributeError):
+            self.instance.excitation = None
+
+    def test_activation(self):
+        assert hasattr(self.instance, 'a')
+        assert hasattr(self.instance, 'activation')
+        a_expected = dynamicsymbols('a_name')
+        assert self.instance.a == a_expected
+        assert self.instance.activation == a_expected
+
+    def test_activation_is_immutable(self):
+        with pytest.raises(AttributeError):
+            self.instance.a = None
+        with pytest.raises(AttributeError):
+            self.instance.activation = None
+
+    @pytest.mark.parametrize(
+        'tau_a, expected',
+        [
+            (None, Symbol('tau_a_name')),
+            (Symbol('tau_a'), Symbol('tau_a')),
+            (Float('0.015'), Float('0.015')),
+        ]
+    )
+    def test_activation_time_constant(self, tau_a, expected):
+        instance = FirstOrderActivationDeGroote2016(
+            'name', activation_time_constant=tau_a,
+        )
+        assert instance.tau_a == expected
+        assert instance.activation_time_constant == expected
+        assert instance.tau_a is instance.activation_time_constant
+
+    def test_activation_time_constant_is_immutable(self):
+        with pytest.raises(AttributeError):
+            self.instance.tau_a = None
+        with pytest.raises(AttributeError):
+            self.instance.activation_time_constant = None
+
+    @pytest.mark.parametrize(
+        'tau_d, expected',
+        [
+            (None, Symbol('tau_d_name')),
+            (Symbol('tau_d'), Symbol('tau_d')),
+            (Float('0.060'), Float('0.060')),
+        ]
+    )
+    def test_deactivation_time_constant(self, tau_d, expected):
+        instance = FirstOrderActivationDeGroote2016(
+            'name', deactivation_time_constant=tau_d,
+        )
+        assert instance.tau_d == expected
+        assert instance.deactivation_time_constant == expected
+        assert instance.tau_d is instance.deactivation_time_constant
+
+    def test_deactivation_time_constant_is_immutable(self):
+        with pytest.raises(AttributeError):
+            self.instance.tau_d = None
+        with pytest.raises(AttributeError):
+            self.instance.deactivation_time_constant = None
+
+    @pytest.mark.parametrize(
+        'b, expected',
+        [
+            (None, Symbol('b_name')),
+            (Symbol('b'), Symbol('b')),
+            (Integer('10'), Integer('10')),
+        ]
+    )
+    def test_smoothing_rate(self, b, expected):
+        instance = FirstOrderActivationDeGroote2016(
+            'name', smoothing_rate=b,
+        )
+        assert instance.b == expected
+        assert instance.smoothing_rate == expected
+        assert instance.b is instance.smoothing_rate
+
+    def test_smoothing_rate_is_immutable(self):
+        with pytest.raises(AttributeError):
+            self.instance.b = None
+        with pytest.raises(AttributeError):
+            self.instance.smoothing_rate = None
+
+    def test_state_vars(self):
+        assert hasattr(self.instance, 'x')
+        assert hasattr(self.instance, 'state_vars')
+        assert self.instance.x == self.instance.state_vars
+        x_expected = Matrix([self.a])
+        assert self.instance.x == x_expected
+        assert self.instance.state_vars == x_expected
+        assert isinstance(self.instance.x, Matrix)
+        assert isinstance(self.instance.state_vars, Matrix)
+        assert self.instance.x.shape == (1, 1)
+        assert self.instance.state_vars.shape == (1, 1)
+
+    def test_input_vars(self):
+        assert hasattr(self.instance, 'r')
+        assert hasattr(self.instance, 'input_vars')
+        assert self.instance.r == self.instance.input_vars
+        r_expected = Matrix([self.e])
+        assert self.instance.r == r_expected
+        assert self.instance.input_vars == r_expected
+        assert isinstance(self.instance.r, Matrix)
+        assert isinstance(self.instance.input_vars, Matrix)
+        assert self.instance.r.shape == (1, 1)
+        assert self.instance.input_vars.shape == (1, 1)
+
+    def test_constants(self):
+        assert hasattr(self.instance, 'p')
+        assert hasattr(self.instance, 'constants')
+        assert self.instance.p == self.instance.constants
+        p_expected = Matrix([self.tau_a, self.tau_d, self.b])
+        assert self.instance.p == p_expected
+        assert self.instance.constants == p_expected
+        assert isinstance(self.instance.p, Matrix)
+        assert isinstance(self.instance.constants, Matrix)
+        assert self.instance.p.shape == (3, 1)
+        assert self.instance.constants.shape == (3, 1)
+
+    def test_M(self):
+        assert hasattr(self.instance, 'M')
+        M_expected = Matrix([1])
+        assert self.instance.M == M_expected
+        assert isinstance(self.instance.M, Matrix)
+        assert self.instance.M.shape == (1, 1)
+
+    def test_F(self):
+        assert hasattr(self.instance, 'F')
+        da_expr = (
+            ((1/(self.tau_a*(Rational(1, 2) + Rational(3, 2)*self.a)))
+            *(Rational(1, 2) + Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+            + ((Rational(1, 2) + Rational(3, 2)*self.a)/self.tau_d)
+            *(Rational(1, 2) - Rational(1, 2)*tanh(self.b*(self.e - self.a))))
+            *(self.e - self.a)
+        )
+        F_expected = Matrix([da_expr])
+        assert self.instance.F == F_expected
+        assert isinstance(self.instance.F, Matrix)
+        assert self.instance.F.shape == (1, 1)
+
+    def test_rhs(self):
+        assert hasattr(self.instance, 'rhs')
+        da_expr = (
+            ((1/(self.tau_a*(Rational(1, 2) + Rational(3, 2)*self.a)))
+            *(Rational(1, 2) + Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+            + ((Rational(1, 2) + Rational(3, 2)*self.a)/self.tau_d)
+            *(Rational(1, 2) - Rational(1, 2)*tanh(self.b*(self.e - self.a))))
+            *(self.e - self.a)
+        )
+        rhs_expected = Matrix([da_expr])
+        rhs = self.instance.rhs()
+        assert rhs == rhs_expected
+        assert isinstance(rhs, Matrix)
+        assert rhs.shape == (1, 1)
+        assert simplify(self.instance.M.solve(self.instance.F) - rhs) == zeros(1)
+
+    def test_repr(self):
+        expected = (
+            'FirstOrderActivationDeGroote2016(\'name\', '
+            'activation_time_constant=tau_a, '
+            'deactivation_time_constant=tau_d, '
+            'smoothing_rate=b)'
+        )
+        assert repr(self.instance) == expected

rl/Lib/site-packages/sympy/physics/biomechanics/tests/test_mixin.py

@@ -0,0 +1,48 @@
+"""Tests for the ``sympy.physics.biomechanics._mixin.py`` module."""
+
+import pytest
+
+from sympy.physics.biomechanics._mixin import _NamedMixin
+
+
+class TestNamedMixin:
+
+    @staticmethod
+    def test_subclass():
+
+        class Subclass(_NamedMixin):
+
+            def __init__(self, name):
+                self.name = name
+
+        instance = Subclass('name')
+        assert instance.name == 'name'
+
+    @pytest.fixture(autouse=True)
+    def _named_mixin_fixture(self):
+
+        class Subclass(_NamedMixin):
+
+            def __init__(self, name):
+                self.name = name
+
+        self.Subclass = Subclass
+
+    @pytest.mark.parametrize('name', ['a', 'name', 'long_name'])
+    def test_valid_name_argument(self, name):
+        instance = self.Subclass(name)
+        assert instance.name == name
+
+    @pytest.mark.parametrize('invalid_name', [0, 0.0, None, False])
+    def test_invalid_name_argument_not_str(self, invalid_name):
+        with pytest.raises(TypeError):
+            _ = self.Subclass(invalid_name)
+
+    def test_invalid_name_argument_zero_length_str(self):
+        with pytest.raises(ValueError):
+            _ = self.Subclass('')
+
+    def test_name_attribute_is_immutable(self):
+        instance = self.Subclass('name')
+        with pytest.raises(AttributeError):
+            instance.name = 'new_name'

rl/Lib/site-packages/sympy/physics/biomechanics/tests/test_musculotendon.py

@@ -0,0 +1,837 @@
+"""Tests for the ``sympy.physics.biomechanics.musculotendon.py`` module."""
+
+import abc
+
+import pytest
+
+from sympy.core.expr import UnevaluatedExpr
+from sympy.core.numbers import Float, Integer, Rational
+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.exponential import exp
+from sympy.functions.elementary.hyperbolic import tanh
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import sin
+from sympy.matrices.dense import MutableDenseMatrix as Matrix, eye, zeros
+from sympy.physics.biomechanics.activation import (
+    FirstOrderActivationDeGroote2016
+)
+from sympy.physics.biomechanics.curve import (
+    CharacteristicCurveCollection,
+    FiberForceLengthActiveDeGroote2016,
+    FiberForceLengthPassiveDeGroote2016,
+    FiberForceLengthPassiveInverseDeGroote2016,
+    FiberForceVelocityDeGroote2016,
+    FiberForceVelocityInverseDeGroote2016,
+    TendonForceLengthDeGroote2016,
+    TendonForceLengthInverseDeGroote2016,
+)
+from sympy.physics.biomechanics.musculotendon import (
+    MusculotendonBase,
+    MusculotendonDeGroote2016,
+    MusculotendonFormulation,
+)
+from sympy.physics.biomechanics._mixin import _NamedMixin
+from sympy.physics.mechanics.actuator import ForceActuator
+from sympy.physics.mechanics.pathway import LinearPathway
+from sympy.physics.vector.frame import ReferenceFrame
+from sympy.physics.vector.functions import dynamicsymbols
+from sympy.physics.vector.point import Point
+from sympy.simplify.simplify import simplify
+
+
+class TestMusculotendonFormulation:
+    @staticmethod
+    def test_rigid_tendon_member():
+        assert MusculotendonFormulation(0) == 0
+        assert MusculotendonFormulation.RIGID_TENDON == 0
+
+    @staticmethod
+    def test_fiber_length_explicit_member():
+        assert MusculotendonFormulation(1) == 1
+        assert MusculotendonFormulation.FIBER_LENGTH_EXPLICIT == 1
+
+    @staticmethod
+    def test_tendon_force_explicit_member():
+        assert MusculotendonFormulation(2) == 2
+        assert MusculotendonFormulation.TENDON_FORCE_EXPLICIT == 2
+
+    @staticmethod
+    def test_fiber_length_implicit_member():
+        assert MusculotendonFormulation(3) == 3
+        assert MusculotendonFormulation.FIBER_LENGTH_IMPLICIT == 3
+
+    @staticmethod
+    def test_tendon_force_implicit_member():
+        assert MusculotendonFormulation(4) == 4
+        assert MusculotendonFormulation.TENDON_FORCE_IMPLICIT == 4
+
+
+class TestMusculotendonBase:
+
+    @staticmethod
+    def test_is_abstract_base_class():
+        assert issubclass(MusculotendonBase, abc.ABC)
+
+    @staticmethod
+    def test_class():
+        assert issubclass(MusculotendonBase, ForceActuator)
+        assert issubclass(MusculotendonBase, _NamedMixin)
+        assert MusculotendonBase.__name__ == 'MusculotendonBase'
+
+    @staticmethod
+    def test_cannot_instantiate_directly():
+        with pytest.raises(TypeError):
+            _ = MusculotendonBase()
+
+
+@pytest.mark.parametrize('musculotendon_concrete', [MusculotendonDeGroote2016])
+class TestMusculotendonRigidTendon:
+
+    @pytest.fixture(autouse=True)
+    def _musculotendon_rigid_tendon_fixture(self, musculotendon_concrete):
+        self.name = 'name'
+        self.N = ReferenceFrame('N')
+        self.q = dynamicsymbols('q')
+        self.origin = Point('pO')
+        self.insertion = Point('pI')
+        self.insertion.set_pos(self.origin, self.q*self.N.x)
+        self.pathway = LinearPathway(self.origin, self.insertion)
+        self.activation = FirstOrderActivationDeGroote2016(self.name)
+        self.e = self.activation.excitation
+        self.a = self.activation.activation
+        self.tau_a = self.activation.activation_time_constant
+        self.tau_d = self.activation.deactivation_time_constant
+        self.b = self.activation.smoothing_rate
+        self.formulation = MusculotendonFormulation.RIGID_TENDON
+        self.l_T_slack = Symbol('l_T_slack')
+        self.F_M_max = Symbol('F_M_max')
+        self.l_M_opt = Symbol('l_M_opt')
+        self.v_M_max = Symbol('v_M_max')
+        self.alpha_opt = Symbol('alpha_opt')
+        self.beta = Symbol('beta')
+        self.instance = musculotendon_concrete(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=self.formulation,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        self.da_expr = (
+            (1/(self.tau_a*(Rational(1, 2) + Rational(3, 2)*self.a)))
+            *(Rational(1, 2) + Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+            + ((Rational(1, 2) + Rational(3, 2)*self.a)/self.tau_d)
+            *(Rational(1, 2) - Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+        )*(self.e - self.a)
+
+    def test_state_vars(self):
+        assert hasattr(self.instance, 'x')
+        assert hasattr(self.instance, 'state_vars')
+        assert self.instance.x == self.instance.state_vars
+        x_expected = Matrix([self.a])
+        assert self.instance.x == x_expected
+        assert self.instance.state_vars == x_expected
+        assert isinstance(self.instance.x, Matrix)
+        assert isinstance(self.instance.state_vars, Matrix)
+        assert self.instance.x.shape == (1, 1)
+        assert self.instance.state_vars.shape == (1, 1)
+
+    def test_input_vars(self):
+        assert hasattr(self.instance, 'r')
+        assert hasattr(self.instance, 'input_vars')
+        assert self.instance.r == self.instance.input_vars
+        r_expected = Matrix([self.e])
+        assert self.instance.r == r_expected
+        assert self.instance.input_vars == r_expected
+        assert isinstance(self.instance.r, Matrix)
+        assert isinstance(self.instance.input_vars, Matrix)
+        assert self.instance.r.shape == (1, 1)
+        assert self.instance.input_vars.shape == (1, 1)
+
+    def test_constants(self):
+        assert hasattr(self.instance, 'p')
+        assert hasattr(self.instance, 'constants')
+        assert self.instance.p == self.instance.constants
+        p_expected = Matrix(
+            [
+                self.l_T_slack,
+                self.F_M_max,
+                self.l_M_opt,
+                self.v_M_max,
+                self.alpha_opt,
+                self.beta,
+                self.tau_a,
+                self.tau_d,
+                self.b,
+                Symbol('c_0_fl_T_name'),
+                Symbol('c_1_fl_T_name'),
+                Symbol('c_2_fl_T_name'),
+                Symbol('c_3_fl_T_name'),
+                Symbol('c_0_fl_M_pas_name'),
+                Symbol('c_1_fl_M_pas_name'),
+                Symbol('c_0_fl_M_act_name'),
+                Symbol('c_1_fl_M_act_name'),
+                Symbol('c_2_fl_M_act_name'),
+                Symbol('c_3_fl_M_act_name'),
+                Symbol('c_4_fl_M_act_name'),
+                Symbol('c_5_fl_M_act_name'),
+                Symbol('c_6_fl_M_act_name'),
+                Symbol('c_7_fl_M_act_name'),
+                Symbol('c_8_fl_M_act_name'),
+                Symbol('c_9_fl_M_act_name'),
+                Symbol('c_10_fl_M_act_name'),
+                Symbol('c_11_fl_M_act_name'),
+                Symbol('c_0_fv_M_name'),
+                Symbol('c_1_fv_M_name'),
+                Symbol('c_2_fv_M_name'),
+                Symbol('c_3_fv_M_name'),
+            ]
+        )
+        assert self.instance.p == p_expected
+        assert self.instance.constants == p_expected
+        assert isinstance(self.instance.p, Matrix)
+        assert isinstance(self.instance.constants, Matrix)
+        assert self.instance.p.shape == (31, 1)
+        assert self.instance.constants.shape == (31, 1)
+
+    def test_M(self):
+        assert hasattr(self.instance, 'M')
+        M_expected = Matrix([1])
+        assert self.instance.M == M_expected
+        assert isinstance(self.instance.M, Matrix)
+        assert self.instance.M.shape == (1, 1)
+
+    def test_F(self):
+        assert hasattr(self.instance, 'F')
+        F_expected = Matrix([self.da_expr])
+        assert self.instance.F == F_expected
+        assert isinstance(self.instance.F, Matrix)
+        assert self.instance.F.shape == (1, 1)
+
+    def test_rhs(self):
+        assert hasattr(self.instance, 'rhs')
+        rhs_expected = Matrix([self.da_expr])
+        rhs = self.instance.rhs()
+        assert isinstance(rhs, Matrix)
+        assert rhs.shape == (1, 1)
+        assert simplify(rhs - rhs_expected) == zeros(1)
+
+
+@pytest.mark.parametrize(
+    'musculotendon_concrete, curve',
+    [
+        (
+            MusculotendonDeGroote2016,
+            CharacteristicCurveCollection(
+                tendon_force_length=TendonForceLengthDeGroote2016,
+                tendon_force_length_inverse=TendonForceLengthInverseDeGroote2016,
+                fiber_force_length_passive=FiberForceLengthPassiveDeGroote2016,
+                fiber_force_length_passive_inverse=FiberForceLengthPassiveInverseDeGroote2016,
+                fiber_force_length_active=FiberForceLengthActiveDeGroote2016,
+                fiber_force_velocity=FiberForceVelocityDeGroote2016,
+                fiber_force_velocity_inverse=FiberForceVelocityInverseDeGroote2016,
+            ),
+        )
+    ],
+)
+class TestFiberLengthExplicit:
+
+    @pytest.fixture(autouse=True)
+    def _musculotendon_fiber_length_explicit_fixture(
+        self,
+        musculotendon_concrete,
+        curve,
+    ):
+        self.name = 'name'
+        self.N = ReferenceFrame('N')
+        self.q = dynamicsymbols('q')
+        self.origin = Point('pO')
+        self.insertion = Point('pI')
+        self.insertion.set_pos(self.origin, self.q*self.N.x)
+        self.pathway = LinearPathway(self.origin, self.insertion)
+        self.activation = FirstOrderActivationDeGroote2016(self.name)
+        self.e = self.activation.excitation
+        self.a = self.activation.activation
+        self.tau_a = self.activation.activation_time_constant
+        self.tau_d = self.activation.deactivation_time_constant
+        self.b = self.activation.smoothing_rate
+        self.formulation = MusculotendonFormulation.FIBER_LENGTH_EXPLICIT
+        self.l_T_slack = Symbol('l_T_slack')
+        self.F_M_max = Symbol('F_M_max')
+        self.l_M_opt = Symbol('l_M_opt')
+        self.v_M_max = Symbol('v_M_max')
+        self.alpha_opt = Symbol('alpha_opt')
+        self.beta = Symbol('beta')
+        self.instance = musculotendon_concrete(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=self.formulation,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+            with_defaults=True,
+        )
+        self.l_M_tilde = dynamicsymbols('l_M_tilde_name')
+        l_MT = self.pathway.length
+        l_M = self.l_M_tilde*self.l_M_opt
+        l_T = l_MT - sqrt(l_M**2 - (self.l_M_opt*sin(self.alpha_opt))**2)
+        fl_T = curve.tendon_force_length.with_defaults(l_T/self.l_T_slack)
+        fl_M_pas = curve.fiber_force_length_passive.with_defaults(self.l_M_tilde)
+        fl_M_act = curve.fiber_force_length_active.with_defaults(self.l_M_tilde)
+        v_M_tilde = curve.fiber_force_velocity_inverse.with_defaults(
+            ((((fl_T*self.F_M_max)/((l_MT - l_T)/l_M))/self.F_M_max) - fl_M_pas)
+            /(self.a*fl_M_act)
+        )
+        self.dl_M_tilde_expr = (self.v_M_max/self.l_M_opt)*v_M_tilde
+        self.da_expr = (
+            (1/(self.tau_a*(Rational(1, 2) + Rational(3, 2)*self.a)))
+            *(Rational(1, 2) + Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+            + ((Rational(1, 2) + Rational(3, 2)*self.a)/self.tau_d)
+            *(Rational(1, 2) - Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+        )*(self.e - self.a)
+
+    def test_state_vars(self):
+        assert hasattr(self.instance, 'x')
+        assert hasattr(self.instance, 'state_vars')
+        assert self.instance.x == self.instance.state_vars
+        x_expected = Matrix([self.l_M_tilde, self.a])
+        assert self.instance.x == x_expected
+        assert self.instance.state_vars == x_expected
+        assert isinstance(self.instance.x, Matrix)
+        assert isinstance(self.instance.state_vars, Matrix)
+        assert self.instance.x.shape == (2, 1)
+        assert self.instance.state_vars.shape == (2, 1)
+
+    def test_input_vars(self):
+        assert hasattr(self.instance, 'r')
+        assert hasattr(self.instance, 'input_vars')
+        assert self.instance.r == self.instance.input_vars
+        r_expected = Matrix([self.e])
+        assert self.instance.r == r_expected
+        assert self.instance.input_vars == r_expected
+        assert isinstance(self.instance.r, Matrix)
+        assert isinstance(self.instance.input_vars, Matrix)
+        assert self.instance.r.shape == (1, 1)
+        assert self.instance.input_vars.shape == (1, 1)
+
+    def test_constants(self):
+        assert hasattr(self.instance, 'p')
+        assert hasattr(self.instance, 'constants')
+        assert self.instance.p == self.instance.constants
+        p_expected = Matrix(
+            [
+                self.l_T_slack,
+                self.F_M_max,
+                self.l_M_opt,
+                self.v_M_max,
+                self.alpha_opt,
+                self.beta,
+                self.tau_a,
+                self.tau_d,
+                self.b,
+            ]
+        )
+        assert self.instance.p == p_expected
+        assert self.instance.constants == p_expected
+        assert isinstance(self.instance.p, Matrix)
+        assert isinstance(self.instance.constants, Matrix)
+        assert self.instance.p.shape == (9, 1)
+        assert self.instance.constants.shape == (9, 1)
+
+    def test_M(self):
+        assert hasattr(self.instance, 'M')
+        M_expected = eye(2)
+        assert self.instance.M == M_expected
+        assert isinstance(self.instance.M, Matrix)
+        assert self.instance.M.shape == (2, 2)
+
+    def test_F(self):
+        assert hasattr(self.instance, 'F')
+        F_expected = Matrix([self.dl_M_tilde_expr, self.da_expr])
+        assert self.instance.F == F_expected
+        assert isinstance(self.instance.F, Matrix)
+        assert self.instance.F.shape == (2, 1)
+
+    def test_rhs(self):
+        assert hasattr(self.instance, 'rhs')
+        rhs_expected = Matrix([self.dl_M_tilde_expr, self.da_expr])
+        rhs = self.instance.rhs()
+        assert isinstance(rhs, Matrix)
+        assert rhs.shape == (2, 1)
+        assert simplify(rhs - rhs_expected) == zeros(2, 1)
+
+
+@pytest.mark.parametrize(
+    'musculotendon_concrete, curve',
+    [
+        (
+            MusculotendonDeGroote2016,
+            CharacteristicCurveCollection(
+                tendon_force_length=TendonForceLengthDeGroote2016,
+                tendon_force_length_inverse=TendonForceLengthInverseDeGroote2016,
+                fiber_force_length_passive=FiberForceLengthPassiveDeGroote2016,
+                fiber_force_length_passive_inverse=FiberForceLengthPassiveInverseDeGroote2016,
+                fiber_force_length_active=FiberForceLengthActiveDeGroote2016,
+                fiber_force_velocity=FiberForceVelocityDeGroote2016,
+                fiber_force_velocity_inverse=FiberForceVelocityInverseDeGroote2016,
+            ),
+        )
+    ],
+)
+class TestTendonForceExplicit:
+
+    @pytest.fixture(autouse=True)
+    def _musculotendon_tendon_force_explicit_fixture(
+        self,
+        musculotendon_concrete,
+        curve,
+    ):
+        self.name = 'name'
+        self.N = ReferenceFrame('N')
+        self.q = dynamicsymbols('q')
+        self.origin = Point('pO')
+        self.insertion = Point('pI')
+        self.insertion.set_pos(self.origin, self.q*self.N.x)
+        self.pathway = LinearPathway(self.origin, self.insertion)
+        self.activation = FirstOrderActivationDeGroote2016(self.name)
+        self.e = self.activation.excitation
+        self.a = self.activation.activation
+        self.tau_a = self.activation.activation_time_constant
+        self.tau_d = self.activation.deactivation_time_constant
+        self.b = self.activation.smoothing_rate
+        self.formulation = MusculotendonFormulation.TENDON_FORCE_EXPLICIT
+        self.l_T_slack = Symbol('l_T_slack')
+        self.F_M_max = Symbol('F_M_max')
+        self.l_M_opt = Symbol('l_M_opt')
+        self.v_M_max = Symbol('v_M_max')
+        self.alpha_opt = Symbol('alpha_opt')
+        self.beta = Symbol('beta')
+        self.instance = musculotendon_concrete(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=self.formulation,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+            with_defaults=True,
+        )
+        self.F_T_tilde = dynamicsymbols('F_T_tilde_name')
+        l_T_tilde = curve.tendon_force_length_inverse.with_defaults(self.F_T_tilde)
+        l_MT = self.pathway.length
+        v_MT = self.pathway.extension_velocity
+        l_T = l_T_tilde*self.l_T_slack
+        l_M = sqrt((l_MT - l_T)**2 + (self.l_M_opt*sin(self.alpha_opt))**2)
+        l_M_tilde = l_M/self.l_M_opt
+        cos_alpha = (l_MT - l_T)/l_M
+        F_T = self.F_T_tilde*self.F_M_max
+        F_M = F_T/cos_alpha
+        F_M_tilde = F_M/self.F_M_max
+        fl_M_pas = curve.fiber_force_length_passive.with_defaults(l_M_tilde)
+        fl_M_act = curve.fiber_force_length_active.with_defaults(l_M_tilde)
+        fv_M = (F_M_tilde - fl_M_pas)/(self.a*fl_M_act)
+        v_M_tilde = curve.fiber_force_velocity_inverse.with_defaults(fv_M)
+        v_M = v_M_tilde*self.v_M_max
+        v_T = v_MT - v_M/cos_alpha
+        v_T_tilde = v_T/self.l_T_slack
+        self.dF_T_tilde_expr = (
+            Float('0.2')*Float('33.93669377311689')*exp(
+                Float('33.93669377311689')*UnevaluatedExpr(l_T_tilde - Float('0.995'))
+            )*v_T_tilde
+        )
+        self.da_expr = (
+            (1/(self.tau_a*(Rational(1, 2) + Rational(3, 2)*self.a)))
+            *(Rational(1, 2) + Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+            + ((Rational(1, 2) + Rational(3, 2)*self.a)/self.tau_d)
+            *(Rational(1, 2) - Rational(1, 2)*tanh(self.b*(self.e - self.a)))
+        )*(self.e - self.a)
+
+    def test_state_vars(self):
+        assert hasattr(self.instance, 'x')
+        assert hasattr(self.instance, 'state_vars')
+        assert self.instance.x == self.instance.state_vars
+        x_expected = Matrix([self.F_T_tilde, self.a])
+        assert self.instance.x == x_expected
+        assert self.instance.state_vars == x_expected
+        assert isinstance(self.instance.x, Matrix)
+        assert isinstance(self.instance.state_vars, Matrix)
+        assert self.instance.x.shape == (2, 1)
+        assert self.instance.state_vars.shape == (2, 1)
+
+    def test_input_vars(self):
+        assert hasattr(self.instance, 'r')
+        assert hasattr(self.instance, 'input_vars')
+        assert self.instance.r == self.instance.input_vars
+        r_expected = Matrix([self.e])
+        assert self.instance.r == r_expected
+        assert self.instance.input_vars == r_expected
+        assert isinstance(self.instance.r, Matrix)
+        assert isinstance(self.instance.input_vars, Matrix)
+        assert self.instance.r.shape == (1, 1)
+        assert self.instance.input_vars.shape == (1, 1)
+
+    def test_constants(self):
+        assert hasattr(self.instance, 'p')
+        assert hasattr(self.instance, 'constants')
+        assert self.instance.p == self.instance.constants
+        p_expected = Matrix(
+            [
+                self.l_T_slack,
+                self.F_M_max,
+                self.l_M_opt,
+                self.v_M_max,
+                self.alpha_opt,
+                self.beta,
+                self.tau_a,
+                self.tau_d,
+                self.b,
+            ]
+        )
+        assert self.instance.p == p_expected
+        assert self.instance.constants == p_expected
+        assert isinstance(self.instance.p, Matrix)
+        assert isinstance(self.instance.constants, Matrix)
+        assert self.instance.p.shape == (9, 1)
+        assert self.instance.constants.shape == (9, 1)
+
+    def test_M(self):
+        assert hasattr(self.instance, 'M')
+        M_expected = eye(2)
+        assert self.instance.M == M_expected
+        assert isinstance(self.instance.M, Matrix)
+        assert self.instance.M.shape == (2, 2)
+
+    def test_F(self):
+        assert hasattr(self.instance, 'F')
+        F_expected = Matrix([self.dF_T_tilde_expr, self.da_expr])
+        assert self.instance.F == F_expected
+        assert isinstance(self.instance.F, Matrix)
+        assert self.instance.F.shape == (2, 1)
+
+    def test_rhs(self):
+        assert hasattr(self.instance, 'rhs')
+        rhs_expected = Matrix([self.dF_T_tilde_expr, self.da_expr])
+        rhs = self.instance.rhs()
+        assert isinstance(rhs, Matrix)
+        assert rhs.shape == (2, 1)
+        assert simplify(rhs - rhs_expected) == zeros(2, 1)
+
+
+class TestMusculotendonDeGroote2016:
+
+    @staticmethod
+    def test_class():
+        assert issubclass(MusculotendonDeGroote2016, ForceActuator)
+        assert issubclass(MusculotendonDeGroote2016, _NamedMixin)
+        assert MusculotendonDeGroote2016.__name__ == 'MusculotendonDeGroote2016'
+
+    @staticmethod
+    def test_instance():
+        origin = Point('pO')
+        insertion = Point('pI')
+        insertion.set_pos(origin, dynamicsymbols('q')*ReferenceFrame('N').x)
+        pathway = LinearPathway(origin, insertion)
+        activation = FirstOrderActivationDeGroote2016('name')
+        l_T_slack = Symbol('l_T_slack')
+        F_M_max = Symbol('F_M_max')
+        l_M_opt = Symbol('l_M_opt')
+        v_M_max = Symbol('v_M_max')
+        alpha_opt = Symbol('alpha_opt')
+        beta = Symbol('beta')
+        instance = MusculotendonDeGroote2016(
+            'name',
+            pathway,
+            activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=l_T_slack,
+            peak_isometric_force=F_M_max,
+            optimal_fiber_length=l_M_opt,
+            maximal_fiber_velocity=v_M_max,
+            optimal_pennation_angle=alpha_opt,
+            fiber_damping_coefficient=beta,
+        )
+        assert isinstance(instance, MusculotendonDeGroote2016)
+
+    @pytest.fixture(autouse=True)
+    def _musculotendon_fixture(self):
+        self.name = 'name'
+        self.N = ReferenceFrame('N')
+        self.q = dynamicsymbols('q')
+        self.origin = Point('pO')
+        self.insertion = Point('pI')
+        self.insertion.set_pos(self.origin, self.q*self.N.x)
+        self.pathway = LinearPathway(self.origin, self.insertion)
+        self.activation = FirstOrderActivationDeGroote2016(self.name)
+        self.l_T_slack = Symbol('l_T_slack')
+        self.F_M_max = Symbol('F_M_max')
+        self.l_M_opt = Symbol('l_M_opt')
+        self.v_M_max = Symbol('v_M_max')
+        self.alpha_opt = Symbol('alpha_opt')
+        self.beta = Symbol('beta')
+
+    def test_with_defaults(self):
+        origin = Point('pO')
+        insertion = Point('pI')
+        insertion.set_pos(origin, dynamicsymbols('q')*ReferenceFrame('N').x)
+        pathway = LinearPathway(origin, insertion)
+        activation = FirstOrderActivationDeGroote2016('name')
+        l_T_slack = Symbol('l_T_slack')
+        F_M_max = Symbol('F_M_max')
+        l_M_opt = Symbol('l_M_opt')
+        v_M_max = Float('10.0')
+        alpha_opt = Float('0.0')
+        beta = Float('0.1')
+        instance = MusculotendonDeGroote2016.with_defaults(
+            'name',
+            pathway,
+            activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=l_T_slack,
+            peak_isometric_force=F_M_max,
+            optimal_fiber_length=l_M_opt,
+        )
+        assert instance.tendon_slack_length == l_T_slack
+        assert instance.peak_isometric_force == F_M_max
+        assert instance.optimal_fiber_length == l_M_opt
+        assert instance.maximal_fiber_velocity == v_M_max
+        assert instance.optimal_pennation_angle == alpha_opt
+        assert instance.fiber_damping_coefficient == beta
+
+    @pytest.mark.parametrize(
+        'l_T_slack, expected',
+        [
+            (None, Symbol('l_T_slack_name')),
+            (Symbol('l_T_slack'), Symbol('l_T_slack')),
+            (Rational(1, 2), Rational(1, 2)),
+            (Float('0.5'), Float('0.5')),
+        ],
+    )
+    def test_tendon_slack_length(self, l_T_slack, expected):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        assert instance.l_T_slack == expected
+        assert instance.tendon_slack_length == expected
+
+    @pytest.mark.parametrize(
+        'F_M_max, expected',
+        [
+            (None, Symbol('F_M_max_name')),
+            (Symbol('F_M_max'), Symbol('F_M_max')),
+            (Integer(1000), Integer(1000)),
+            (Float('1000.0'), Float('1000.0')),
+        ],
+    )
+    def test_peak_isometric_force(self, F_M_max, expected):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        assert instance.F_M_max == expected
+        assert instance.peak_isometric_force == expected
+
+    @pytest.mark.parametrize(
+        'l_M_opt, expected',
+        [
+            (None, Symbol('l_M_opt_name')),
+            (Symbol('l_M_opt'), Symbol('l_M_opt')),
+            (Rational(1, 2), Rational(1, 2)),
+            (Float('0.5'), Float('0.5')),
+        ],
+    )
+    def test_optimal_fiber_length(self, l_M_opt, expected):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        assert instance.l_M_opt == expected
+        assert instance.optimal_fiber_length == expected
+
+    @pytest.mark.parametrize(
+        'v_M_max, expected',
+        [
+            (None, Symbol('v_M_max_name')),
+            (Symbol('v_M_max'), Symbol('v_M_max')),
+            (Integer(10), Integer(10)),
+            (Float('10.0'), Float('10.0')),
+        ],
+    )
+    def test_maximal_fiber_velocity(self, v_M_max, expected):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        assert instance.v_M_max == expected
+        assert instance.maximal_fiber_velocity == expected
+
+    @pytest.mark.parametrize(
+        'alpha_opt, expected',
+        [
+            (None, Symbol('alpha_opt_name')),
+            (Symbol('alpha_opt'), Symbol('alpha_opt')),
+            (Integer(0), Integer(0)),
+            (Float('0.1'), Float('0.1')),
+        ],
+    )
+    def test_optimal_pennation_angle(self, alpha_opt, expected):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        assert instance.alpha_opt == expected
+        assert instance.optimal_pennation_angle == expected
+
+    @pytest.mark.parametrize(
+        'beta, expected',
+        [
+            (None, Symbol('beta_name')),
+            (Symbol('beta'), Symbol('beta')),
+            (Integer(0), Integer(0)),
+            (Rational(1, 10), Rational(1, 10)),
+            (Float('0.1'), Float('0.1')),
+        ],
+    )
+    def test_fiber_damping_coefficient(self, beta, expected):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=beta,
+        )
+        assert instance.beta == expected
+        assert instance.fiber_damping_coefficient == expected
+
+    def test_excitation(self):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+        )
+        assert hasattr(instance, 'e')
+        assert hasattr(instance, 'excitation')
+        e_expected = dynamicsymbols('e_name')
+        assert instance.e == e_expected
+        assert instance.excitation == e_expected
+        assert instance.e is instance.excitation
+
+    def test_excitation_is_immutable(self):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+        )
+        with pytest.raises(AttributeError):
+            instance.e = None
+        with pytest.raises(AttributeError):
+            instance.excitation = None
+
+    def test_activation(self):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+        )
+        assert hasattr(instance, 'a')
+        assert hasattr(instance, 'activation')
+        a_expected = dynamicsymbols('a_name')
+        assert instance.a == a_expected
+        assert instance.activation == a_expected
+
+    def test_activation_is_immutable(self):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+        )
+        with pytest.raises(AttributeError):
+            instance.a = None
+        with pytest.raises(AttributeError):
+            instance.activation = None
+
+    def test_repr(self):
+        instance = MusculotendonDeGroote2016(
+            self.name,
+            self.pathway,
+            self.activation,
+            musculotendon_dynamics=MusculotendonFormulation.RIGID_TENDON,
+            tendon_slack_length=self.l_T_slack,
+            peak_isometric_force=self.F_M_max,
+            optimal_fiber_length=self.l_M_opt,
+            maximal_fiber_velocity=self.v_M_max,
+            optimal_pennation_angle=self.alpha_opt,
+            fiber_damping_coefficient=self.beta,
+        )
+        expected = (
+            'MusculotendonDeGroote2016(\'name\', '
+            'pathway=LinearPathway(pO, pI), '
+            'activation_dynamics=FirstOrderActivationDeGroote2016(\'name\', '
+            'activation_time_constant=tau_a_name, '
+            'deactivation_time_constant=tau_d_name, '
+            'smoothing_rate=b_name), '
+            'musculotendon_dynamics=0, '
+            'tendon_slack_length=l_T_slack, '
+            'peak_isometric_force=F_M_max, '
+            'optimal_fiber_length=l_M_opt, '
+            'maximal_fiber_velocity=v_M_max, '
+            'optimal_pennation_angle=alpha_opt, '
+            'fiber_damping_coefficient=beta)'
+        )
+        assert repr(instance) == expected