# 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 __future__ import annotations
from typing import Any, Dict, List, Optional, TYPE_CHECKING, Union, Tuple
from spinn_utilities.overrides import overrides
from spynnaker.pyNN.models.neuron import PopulationVertex
from spynnaker.pyNN.models.abstract_pynn_model import AbstractPyNNModel
from spynnaker.pyNN.utilities.constants import POP_TABLE_MAX_ROW_LENGTH
if TYPE_CHECKING:
from spynnaker.pyNN.models.neuron.implementations import AbstractNeuronImpl
from spynnaker.pyNN.extra_algorithms.splitter_components import (
SplitterPopulationVertex)
# The maximum atoms per core is the master population table row length to
# make it easier when all-to-all-connector is used
DEFAULT_MAX_ATOMS_PER_CORE = POP_TABLE_MAX_ROW_LENGTH
_population_parameters: Dict[str, Any] = {
"spikes_per_second": None, "ring_buffer_sigma": None,
"max_expected_summed_weight": None,
"incoming_spike_buffer_size": None, "drop_late_spikes": None,
"splitter": None, "seed": None, "n_colour_bits": None,
"n_synapse_cores": None, "allow_delay_extensions": None,
"neurons_per_core": None,
}
class AbstractPyNNNeuronModel(AbstractPyNNModel):
"""
API for a PyNN Neuron Model
"""
__slots__ = ("__model", )
# The number of synapse cores for PyNN models that use PopulationVertex
# or None to determine based on time-step
_n_synapse_cores: Dict[type, Optional[int]] = {}
# Whether to allow delay extensions when using PyNN models that use
# PopulationVertex
_allow_delay_extensions: Dict[type, bool] = {}
#: Population parameters for neuron models.
default_population_parameters = _population_parameters
[docs]
@classmethod
def set_model_n_synapse_cores(cls, n_synapse_cores: Optional[int]) -> None:
"""
Set the number of synapse cores for a model.
:param n_synapse_cores:
The number of synapse cores; 0 to force combined cores, and None to
allow the system to choose
"""
cls.verify_may_set(param="n_synapse_cores")
cls._n_synapse_cores[cls] = n_synapse_cores
[docs]
@classmethod
def get_model_n_synapse_cores(cls) -> Optional[int]:
"""
Get the number of synapse cores for the model.
"""
return cls._n_synapse_cores.get(cls, None)
[docs]
@classmethod
def set_model_allow_delay_extensions(cls, allow: bool) -> None:
"""
Set whether to allow delay extensions for a model.
:param allow: Whether to allow delay extensions
"""
cls.verify_may_set(param="allow_delay_extensions")
cls._allow_delay_extensions[cls] = allow
[docs]
@classmethod
def get_model_allow_delay_extensions(cls) -> bool:
"""
Get whether to allow delay extensions for the model.
"""
return cls._allow_delay_extensions.get(cls, True)
[docs]
@classmethod
@overrides(AbstractPyNNModel.reset_all)
def reset_all(cls) -> None:
super().reset_all()
AbstractPyNNNeuronModel._n_synapse_cores.clear()
AbstractPyNNNeuronModel._allow_delay_extensions.clear()
def __init__(self, model: AbstractNeuronImpl):
"""
:param AbstractNeuronImpl model: The model implementation
"""
self.__model = model
@property
def _model(self) -> AbstractNeuronImpl:
return self.__model
[docs]
@overrides(AbstractPyNNModel.create_vertex,
additional_arguments=_population_parameters.keys())
def create_vertex(
self, n_neurons: int, label: str, *,
spikes_per_second: Optional[float] = None,
ring_buffer_sigma: Optional[float] = None,
max_expected_summed_weight: Optional[List[float]] = None,
incoming_spike_buffer_size: Optional[int] = None,
drop_late_spikes: Optional[bool] = None,
splitter: Optional[SplitterPopulationVertex] = None,
seed: Optional[int] = None,
n_colour_bits: Optional[int] = None,
neurons_per_core: Optional[Union[int, Tuple[int, ...]]] = None,
n_synapse_cores: Optional[int] = None,
allow_delay_extensions: Optional[bool] = None) -> PopulationVertex:
"""
:param float spikes_per_second:
:param float ring_buffer_sigma:
:param int incoming_spike_buffer_size:
:param bool drop_late_spikes:
:param splitter:
:type splitter: SplitterPopulationVertex or None
:param int seed:
:param int n_colour_bits:
"""
# pylint: disable=arguments-differ
if neurons_per_core is None:
neurons_per_core = \
self.get_model_max_atoms_per_dimension_per_core()
if n_synapse_cores is None:
n_synapse_cores = self.get_model_n_synapse_cores()
if allow_delay_extensions is None:
allow_delay_extensions = self.get_model_allow_delay_extensions()
return PopulationVertex(
n_neurons=n_neurons, label=label,
max_atoms_per_core=neurons_per_core,
n_synapse_cores=n_synapse_cores,
allow_delay_extensions=allow_delay_extensions,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
max_expected_summed_weight=max_expected_summed_weight,
incoming_spike_buffer_size=incoming_spike_buffer_size,
neuron_impl=self.__model, pynn_model=self,
drop_late_spikes=drop_late_spikes or False,
splitter=splitter, seed=seed, n_colour_bits=n_colour_bits)
@property
@overrides(AbstractPyNNModel.name)
def name(self) -> str:
return self.__model.model_name