Source code for spynnaker.pyNN.models.neuron.implementations.abstract_standard_neuron_component
# Copyright (c) 2017 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Iterable, List, Union
import numpy
from numpy import floating
from numpy.typing import NDArray
from typing_extensions import TypeAlias
from pyNN.random import RandomDistribution
from spinn_utilities.abstract_base import AbstractBase, abstractmethod
from spinn_utilities.ranged import RangeDictionary, RangedList
from spynnaker.pyNN.utilities.ranged import SpynnakerRangedList
from spynnaker.pyNN.utilities.struct import Struct
#: The type of parameters to a neuron model.
ModelParameter: TypeAlias = Union[
float, Iterable[float], RandomDistribution, NDArray[floating]]
class AbstractStandardNeuronComponent(object, metaclass=AbstractBase):
"""
Represents a component of a standard neural model.
"""
__slots__ = (
"__structs",
"__units")
def __init__(self, structs: List[Struct], units: Dict[str, str]):
"""
:param list(Struct) structs: The structures of the component
:param dict units: The units to use for each parameter
"""
self.__structs = structs
self.__units = units
@property
def structs(self) -> List[Struct]:
"""
The structures of the component. If there are multiple structures,
the order is how they will appear in memory; where there are
structures that repeat per neuron the repeats will appear adjacent
e.g. for non-repeating structure `g`, followed by repeating structures
`s1` and `s2` with 3 neurons the layout will be:
``[g, s1, s1, s1, s2, s2, s2]``.
:rtype: list(~spynnaker.pyNN.utilities.struct.Struct)
"""
return self.__structs
[docs]
@abstractmethod
def add_parameters(self, parameters: RangeDictionary[float]):
"""
Add the initial values of the parameters to the parameter holder.
:param ~spinn_utilities.ranged.RangeDictionary parameters:
A holder of the parameters
"""
raise NotImplementedError
[docs]
@abstractmethod
def add_state_variables(self, state_variables: RangeDictionary[float]):
"""
Add the initial values of the state variables to the state
variables holder.
:param ~spinn_utilities.ranged.RangeDictionary state_variables:
A holder of the state variables
"""
raise NotImplementedError
[docs]
def has_variable(self, variable: str) -> bool:
"""
Determine if this component has a variable by the given name.
:param str variable: The name of the variable
:rtype: bool
"""
return variable in self.__units
[docs]
def get_units(self, variable: str) -> str:
"""
Get the units of the given variable.
:param str variable: The name of the variable
"""
return self.__units[variable]
@staticmethod
def _convert(value: ModelParameter) -> \
Union[float, RangedList[float], RandomDistribution]:
"""
Converts a model parameter into a form that can be ingested by a
RangeDictionary.
"""
if isinstance(value, (float, int, numpy.integer, numpy.floating)):
return float(value)
if isinstance(value, RandomDistribution):
return value
# TODO: Is this correct? Without this, things will only handle floats
return SpynnakerRangedList(None, value)