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

# Copyright (c) 2021 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import math
import numpy
from spinn_utilities.abstract_base import abstractmethod, abstractproperty
from .abstract_synapse_dynamics import AbstractSynapseDynamics

class AbstractSDRAMSynapseDynamics(AbstractSynapseDynamics):
    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 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 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, global_weight_scale, synapse_weight_scales): """ Write the synapse parameters to the spec. :param ~data_specification.DataSpecificationGenerator spec: The specification to write to :param int region: region ID to write to :param float global_weight_scale: The weight scale applied globally :param list(float) synapse_weight_scales: The total weight scale applied to each synapse including the global weight scale """
[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 pad_to_length(self): """ The amount each row should pad to, or `None` if not specified. """
[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