Source code for spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics

# Copyright (c) 2017-2019 The University of Manchester
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <>.

import math
import numpy
from spinn_utilities.abstract_base import (
    AbstractBase, abstractmethod, abstractproperty)

[docs]class AbstractSynapseDynamics(object, metaclass=AbstractBase): """ How do the dynamics of a synapse interact with the rest of the model. """ __slots__ = () #: Type model of the basic configuration data of a connector NUMPY_CONNECTORS_DTYPE = [("source", "uint32"), ("target", "uint32"), ("weight", "float64"), ("delay", "float64")]
[docs] @abstractmethod def merge(self, synapse_dynamics): """ Merge with the given synapse_dynamics and return the result, or\ error if merge is not possible :param AbstractSynapseDynamics synapse_dynamics: :rtype: AbstractSynapseDynamics """
[docs] @abstractmethod def is_same_as(self, synapse_dynamics): """ Determines if this synapse dynamics is the same as another :param AbstractSynapseDynamics synapse_dynamics: :rtype: bool """
[docs] @abstractmethod def are_weights_signed(self): """ Determines if the weights are signed values :rtype: bool """
[docs] @abstractmethod def get_vertex_executable_suffix(self): """ Get the executable suffix for a vertex for this dynamics :rtype: str """
[docs] @abstractmethod def get_parameters_sdram_usage_in_bytes(self, n_neurons, n_synapse_types): """ Get the SDRAM usage of the synapse dynamics parameters in bytes :param int n_neurons: :param int n_synapse_types: :rtype: int """
[docs] @abstractmethod def write_parameters(self, spec, region, weight_scales): """ Write the synapse parameters to the spec :param ~data_specification.DataSpecificationGenerator spec: :param int region: region ID :param list(float) weight_scales: """
[docs] @abstractmethod def get_parameter_names(self): """ Get the parameter names available from the synapse \ dynamics components :rtype: iterable(str) """
[docs] @abstractmethod def get_max_synapses(self, n_words): """ Get the maximum number of synapses that can be held in the given\ number of words :param int n_words: The number of words the synapses must fit in :rtype: int """
@abstractproperty def changes_during_run(self): """ Determine if the synapses change during a run :rtype: bool """ @abstractproperty def weight(self): """ The weight of connections """ @abstractproperty def delay(self): """ The delay of connections """
[docs] @abstractmethod def set_delay(self, delay): """ Set the delay """
@abstractproperty def pad_to_length(self): """ The amount each row should pad to, or None if not specified """
[docs] def get_provenance_data(self, pre_population_label, post_population_label): """ Get the provenance data from this synapse dynamics object :param str pre_population_label: :param str post_population_label: :rtype: iterable(~spinn_front_end_common.utilities.utility_objs.ProvenanceDataItem) """ # pylint: disable=unused-argument return []
[docs] def get_delay_maximum(self, connector, synapse_info): """ Get the maximum delay for the synapses :param AbstractConnector connector: :param ~numpy.ndarray delays: """ return connector.get_delay_maximum(synapse_info)
[docs] def get_delay_minimum(self, connector, synapse_info): """ Get the minimum delay for the synapses. \ This will support the filtering of the undelayed edge\ from the graph, but requires fixes in the synaptic manager to \ happen first before this can be utilised fully. :param AbstractConnector connector: connector :param ~numpy.ndarray synapse_info: synapse info """ return connector.get_delay_minimum(synapse_info)
[docs] def get_delay_variance(self, connector, delays, synapse_info): """ Get the variance in delay for the synapses :param AbstractConnector connector: :param ~numpy.ndarray delays: """ # pylint: disable=too-many-arguments return connector.get_delay_variance(delays, synapse_info)
[docs] def get_weight_mean(self, connector, synapse_info): """ Get the mean weight for the synapses :param AbstractConnector connector: :param ~numpy.ndarray weights: """ # pylint: disable=too-many-arguments return connector.get_weight_mean(synapse_info.weights, synapse_info)
[docs] def get_weight_maximum(self, connector, synapse_info): """ Get the maximum weight for the synapses :param AbstractConnector connector: :param ~numpy.ndarray weights: """ # pylint: disable=too-many-arguments return connector.get_weight_maximum(synapse_info)
[docs] def get_weight_variance(self, connector, weights, synapse_info): """ Get the variance in weight for the synapses :param AbstractConnector connector: :param ~numpy.ndarray weights: """ # pylint: disable=too-many-arguments return connector.get_weight_variance(weights, synapse_info)
[docs] def convert_per_connection_data_to_rows( self, connection_row_indices, n_rows, data, max_n_synapses): """ Converts per-connection data generated from connections into\ row-based data to be returned from get_synaptic_data :param ~numpy.ndarray connection_row_indices: The index of the row that each item should go into :param int n_rows: The number of rows :param ~numpy.ndarray data: The non-row-based data :param int max_n_synapses: The maximum number of synapses to generate in each row :rtype: list(~numpy.ndarray) """ return [ data[connection_row_indices == i][:max_n_synapses].reshape(-1) for i in range(n_rows)]
[docs] def get_n_items(self, rows, item_size): """ Get the number of items in each row as 4-byte values, given the\ item size :param ~numpy.ndarray rows: :param int item_size: :rtype: ~numpy.ndarray """ return numpy.array([ int(math.ceil(float(row.size) / float(item_size))) for row in rows], dtype="uint32").reshape((-1, 1))
[docs] def get_words(self, rows): """ Convert the row data to words :param ~numpy.ndarray rows: :rtype: ~numpy.ndarray """ words = [numpy.pad( row, (0, (4 - (row.size % 4)) & 0x3), mode="constant", constant_values=0).view("uint32") for row in rows] return words