# Copyright (c) 2015 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 List, Tuple, Union
from numpy import integer, uint32
from numpy.typing import NDArray
from spinn_utilities.abstract_base import AbstractBase, abstractmethod
from spynnaker.pyNN.models.neuron.synapse_dynamics.types import (
ConnectionsArray)
from .abstract_sdram_synapse_dynamics import AbstractSDRAMSynapseDynamics
class AbstractPlasticSynapseDynamics(
AbstractSDRAMSynapseDynamics, metaclass=AbstractBase):
"""
Synapses which change over time.
"""
# pylint: disable=too-many-arguments
__slots__ = ()
[docs]
@abstractmethod
def get_n_words_for_plastic_connections(self, n_connections: int) -> int:
"""
Get the number of 32-bit words for `n_connections` in a single row.
:param int n_connections:
:rtype: int
"""
raise NotImplementedError
[docs]
@abstractmethod
def get_plastic_synaptic_data(
self, connections: ConnectionsArray,
connection_row_indices: NDArray[integer], n_rows: int,
n_synapse_types: int,
max_n_synapses: int, max_atoms_per_core: int) -> Union[
Tuple[NDArray[uint32], NDArray[uint32],
NDArray[uint32], NDArray[uint32]],
Tuple[List[NDArray[uint32]], List[NDArray[uint32]],
NDArray[uint32], NDArray[uint32]]]:
"""
Get the fixed-plastic data, and plastic-plastic data for each row, and
lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for
each row, for each of the fixed-plastic and plastic-plastic data
regions. The row into which connection should go is given by
`connection_row_indices`, and the total number of rows is given by
`n_rows`.
Lengths are returned as an array made up of an integer for each row,
for each of the fixed-plastic and plastic-plastic regions.
:param ~numpy.ndarray connections: The connections to get data for
:param ~numpy.ndarray connection_row_indices:
The row into which each connection should go
:param int n_rows: The total number of rows
:param int n_synapse_types: The number of synapse types
:param int max_n_synapses: The maximum number of synapses to generate
:param int max_atoms_per_core: The maximum number of atoms on a core
:return: (fp_data (2D), pp_data (2D), fp_size (1D), pp_size (1D))
:rtype:
tuple(~numpy.ndarray, ~numpy.ndarray, ~numpy.ndarray,
~numpy.ndarray)
"""
raise NotImplementedError
[docs]
@abstractmethod
def get_n_plastic_plastic_words_per_row(
self, pp_size: NDArray[uint32]) -> NDArray[integer]:
"""
Get the number of plastic plastic words to be read from each row.
:param ~numpy.ndarray pp_size:
"""
raise NotImplementedError
[docs]
@abstractmethod
def get_n_fixed_plastic_words_per_row(
self, fp_size: NDArray[uint32]) -> NDArray[integer]:
"""
Get the number of fixed plastic words to be read from each row.
:param ~numpy.ndarray fp_size:
"""
raise NotImplementedError
[docs]
@abstractmethod
def get_n_synapses_in_rows(
self, pp_size: NDArray[uint32],
fp_size: NDArray[uint32]) -> NDArray[integer]:
"""
Get the number of synapses in each of the rows with plastic sizes
`pp_size` and `fp_size`.
:param ~numpy.ndarray pp_size:
:param ~numpy.ndarray fp_size:
"""
raise NotImplementedError
[docs]
@abstractmethod
def read_plastic_synaptic_data(
self, n_synapse_types: int,
pp_size: NDArray[uint32], pp_data: List[NDArray[uint32]],
fp_size: NDArray[uint32], fp_data: List[NDArray[uint32]],
max_atoms_per_core: int) -> ConnectionsArray:
"""
Read the connections indicated in the connection indices from the
data in `pp_data` and `fp_data`.
:param int n_synapse_types:
:param ~numpy.ndarray pp_size: 1D
:param ~numpy.ndarray pp_data: 2D
:param ~numpy.ndarray fp_size: 1D
:param ~numpy.ndarray fp_data: 2D
:param int max_atoms_per_core:
:return:
array with columns ``source``, ``target``, ``weight``, ``delay``
:rtype: ~numpy.ndarray
"""
raise NotImplementedError