Types

types

Type definitions for MyoGen with Beartype validation.

This module provides type aliases for physical quantities, neural signals, and data structures used throughout MyoGen simulations. All types include runtime validation constraints using Beartype's vale system to ensure data integrity and dimensional correctness.

Type Categories

• Physical Quantities: Time, angles, electrical properties, lengths, velocities
• Signal Types: Neo-based analog signals and blocks for neural data
• Array Types: NumPy arrays for matrices and multi-dimensional data structures

Quantity__s module-attribute

Quantity__s: TypeAlias = __make_quantity_type(pq.s)

Physical quantity type for time in seconds.

Quantity__ms module-attribute

Quantity__ms: TypeAlias = __make_quantity_type(pq.ms)

Physical quantity type for time in milliseconds.

Quantity__rad module-attribute

Quantity__rad: TypeAlias = __make_quantity_type(pq.rad)

Physical quantity type for angles in radians.

Quantity__deg module-attribute

Quantity__deg: TypeAlias = __make_quantity_type(pq.deg)

Physical quantity type for angles in degrees.

Quantity__mV module-attribute

Quantity__mV: TypeAlias = __make_quantity_type(pq.mV)

Physical quantity type for electrical potential in millivolts.

Quantity__uV module-attribute

Quantity__uV: TypeAlias = __make_quantity_type(pq.uV)

Physical quantity type for electrical potential in microvolts.

Quantity__nA module-attribute

Quantity__nA: TypeAlias = __make_quantity_type(pq.nA)

Physical quantity type for electrical current in nanoamperes.

Quantity__uS module-attribute

Quantity__uS: TypeAlias = __make_quantity_type(pq.uS)

Physical quantity type for electrical conductance in microsiemens.

Quantity__S_per_m module-attribute

Quantity__S_per_m: TypeAlias = __make_quantity_type(pq.S / pq.m)

Physical quantity type for conductivity in siemens per meter.

Quantity__Hz module-attribute

Quantity__Hz: TypeAlias = __make_quantity_type(pq.Hz)

Physical quantity type for frequency in hertz.

Quantity__pps module-attribute

Quantity__pps: TypeAlias = __make_quantity_type(pps)

Physical quantity type for firing rate in pulses per second.

Quantity__mm module-attribute

Quantity__mm: TypeAlias = __make_quantity_type(pq.mm)

Physical quantity type for length in millimeters.

Quantity__m module-attribute

Quantity__m: TypeAlias = __make_quantity_type(pq.m)

Physical quantity type for length in meters.

Quantity__mm2 module-attribute

Quantity__mm2: TypeAlias = __make_quantity_type(pq.mm ** 2)

Physical quantity type for area in square millimeters.

Quantity__per_mm2 module-attribute

Quantity__per_mm2: TypeAlias = __make_quantity_type(pq.mm ** -2)

Physical quantity type for density per square millimeter.

Quantity__m_per_s module-attribute

Quantity__m_per_s: TypeAlias = __make_quantity_type(pq.m / pq.s)

Physical quantity type for velocity in meters per second.

Quantity__mm_per_s module-attribute

Quantity__mm_per_s: TypeAlias = __make_quantity_type(pq.mm / pq.s)

Physical quantity type for velocity in millimeters per second.

CURRENT__AnalogSignal module-attribute

CURRENT__AnalogSignal = Annotated[AnalogSignal, Is[lambda x: x.units == pq.nA and x.sampling_period.units == pq.s]]

Neo AnalogSignal for input currents in nanoamperes with time in seconds. Shape: (time_points, n_channels)

FORCE__AnalogSignal module-attribute

FORCE__AnalogSignal = Annotated[AnalogSignal, Is[lambda x: x.units == pq.dimensionless or x.units == pq.N]]

Neo AnalogSignal for force measurements in newtons or dimensionless units. Shape: (time_points, n_channels)

SPIKE_TRAIN__Block module-attribute

SPIKE_TRAIN__Block = Annotated[Block, Is[lambda x: isinstance(x, Block) and len(x.segments) > 0 and all((hasattr(seg, 'spiketrains')) for seg in (x.segments)) and all((len(seg.spiketrains) > 0) for seg in (x.segments))]]

Neo Block containing spike train data organized by motor unit pools. Structure: segments (motor pools) → spiketrains (individual neurons)

SURFACE_MUAP__Block module-attribute

SURFACE_MUAP__Block = Annotated[Block, Is[lambda x: isinstance(x, Block) and len(x.groups) > 0 and all(('ElectrodeArray_' in grp.name) for grp in (x.groups)) and all((hasattr(grp, 'segments')) for grp in (x.groups)) and all((len(grp.segments) > 0) for grp in (x.groups)) and all(('MUAP_' in seg.name) for grp in (x.groups) for seg in (grp.segments)) and all((hasattr(seg, 'analogsignals') and len(seg.analogsignals) > 0 and all((hasattr(signal, 'shape')) for signal in (seg.analogsignals)) and all((len(signal.shape) == 2) for signal in (seg.analogsignals))) for grp in (x.groups) for seg in (grp.segments))]]

Neo Block containing surface motor unit action potentials (MUAPs). Structure: groups (electrode arrays) → segments (MUAP indices) → analogsignals (samples × n_electrodes) Grid shape stored in signal annotations['grid_shape'].

SURFACE_EMG__Block module-attribute

SURFACE_EMG__Block = Annotated[Block, Is[lambda x: isinstance(x, Block) and len(x.groups) > 0 and all((hasattr(grp, 'segments')) for grp in (x.groups)) and all((len(grp.segments) > 0) for grp in (x.groups)) and all((hasattr(seg, 'analogsignals') and len(seg.analogsignals) > 0 and all((hasattr(signal, 'shape')) for signal in (seg.analogsignals)) and all((len(signal.shape) == 2) for signal in (seg.analogsignals))) for grp in (x.groups) for seg in (grp.segments))]]

Neo Block containing surface EMG signals. Structure: groups (electrode arrays) → segments (motor pools) → analogsignals (time × n_electrodes) Grid shape stored in signal annotations['grid_shape'].

INTRAMUSCULAR_MUAP__Block module-attribute

INTRAMUSCULAR_MUAP__Block = Annotated[Block, Is[lambda x: isinstance(x, Block) and all(('MUAP_' in seg.name) for seg in (x.segments)) and all((hasattr(seg, 'analogsignals') and len(seg.analogsignals) > 0 and all((hasattr(signal, 'shape')) for signal in (seg.analogsignals)) and all((len(signal.shape) == 2) for signal in (seg.analogsignals))) for seg in (x.segments))]]

Neo Block containing intramuscular motor unit action potentials (MUAPs). Structure: segments (MUAP indices) → analogsignals (samples × electrodes)

INTRAMUSCULAR_EMG__Block module-attribute

INTRAMUSCULAR_EMG__Block = Annotated[Block, Is[lambda x: isinstance(x, Block) and all(('Pool_' in seg.name) for seg in (x.segments)) and all((hasattr(seg, 'analogsignals') and len(seg.analogsignals) > 0 and all((hasattr(signal, 'shape')) for signal in (seg.analogsignals)) and all((len(signal.shape) == 2) for signal in (seg.analogsignals))) for seg in (x.segments))]]

Neo Block containing intramuscular EMG signals. Structure: segments (motor pools) → analogsignals (time × electrodes)

CORTICAL_INPUT__MATRIX module-attribute

CORTICAL_INPUT__MATRIX = Annotated[npt.NDArray[np.floating], Is[lambda x: x.ndim == 2]]

2D floating-point array for cortical input patterns. Shape: (n_motor_units, n_timesteps)

RECRUITMENT_THRESHOLDS__ARRAY module-attribute

RECRUITMENT_THRESHOLDS__ARRAY = Annotated[npt.NDArray[np.floating], Is[lambda x: x.ndim == 1]]

1D array of recruitment threshold values for motor units. Shape: (n_motor_units,)

JOINT_ANGLE__ARRAY module-attribute

JOINT_ANGLE__ARRAY = Annotated[npt.NDArray[np.floating], Is[lambda x: x.ndim == 1]]

1D array representing joint angle trajectory over time. Shape: (n_timesteps,)

MOMENT_ARM__MATRIX module-attribute

MOMENT_ARM__MATRIX = Annotated[npt.NDArray[np.floating], Is[lambda x: x.ndim == 2]]

2D array of moment arms as a function of joint angle. Shape: (n_angle_samples, n_muscles)