spynnaker.pyNN.extra_algorithms package

Subpackages

Module contents

class spynnaker.pyNN.extra_algorithms.SpYNNakerConnectionHolderGenerator

Bases: object

Sets up connection holders for reports to use.

__call__(application_graph)[source]
Parameters:

application_graph (ApplicationGraph) – application graph

Returns:

the set of connection holders for after data specification generation

Return type:

dict(tuple(ProjectionApplicationEdge, SynapseInformation), ConnectionHolder)

class spynnaker.pyNN.extra_algorithms.SpYNNakerSynapticMatrixReport

Bases: object

Generate the synaptic matrices for reporting purposes.

__call__(connection_holder)[source]

Convert synaptic matrix for every application edge.

Parameters:

connection_holder (dict(tuple(ProjectionApplicationEdge, SynapseInformation), ConnectionHolder)) – where the synaptic matrices are stored (possibly after retrieval from the machine)

spynnaker.pyNN.extra_algorithms.delay_support_adder()

Adds the delay extensions to the application graph, now that all the splitter objects have been set.

spynnaker.pyNN.extra_algorithms.finish_connection_holders()

Finishes the connection holders after data has been generated within them, allowing any waiting callbacks to be called.

Parameters:

application_graph (ApplicationGraph) –

spynnaker.pyNN.extra_algorithms.neuron_expander()

Run the neuron expander.

Note

Needs to be done after data has been loaded.

spynnaker.pyNN.extra_algorithms.redundant_packet_count_report()
spynnaker.pyNN.extra_algorithms.spynnaker_machine_bitField_pair_router_compressor()

Perform routing table compression using pairs with bit fields.

spynnaker.pyNN.extra_algorithms.spynnaker_machine_bitfield_ordered_covering_compressor()

Perform routing table compression using ordered coverings with bit fields.

spynnaker.pyNN.extra_algorithms.spynnaker_neuron_graph_network_specification_report()

Produces a report describing the graph created from the neural populations and projections.

Parameters:

report_folder (str) – the report folder to put figure into

spynnaker.pyNN.extra_algorithms.synapse_expander()

Run the synapse expander.

Note

Needs to be done after data has been loaded.