def get_v(self, label, buffer_manager, region, state_region, placements,
graph_mapper, partitionable_vertex):
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
ms_per_tick = self._machine_time_step / 1000.0
data = list()
missing_str = ""
progress_bar = \
ProgressBar(len(subvertices),
"Getting membrane voltage for {}".format(label))
for subvertex in subvertices:
vertex_slice = graph_mapper.get_subvertex_slice(subvertex)
placement = placements.get_placement_of_subvertex(subvertex)
x = placement.x
y = placement.y
p = placement.p
# for buffering output info is taken form the buffer manager
neuron_param_region_data_pointer, missing_data =\
buffer_manager.get_data_for_vertex(
placement, region, state_region)
if missing_data:
missing_str += "({}, {}, {}); ".format(x, y, p)
record_raw = neuron_param_region_data_pointer.read_all()
record_length = len(record_raw)
n_rows = record_length / ((vertex_slice.n_atoms + 1) * 4)
record = (numpy.asarray(record_raw, dtype="uint8").
view(dtype="<i4")).reshape((n_rows,
(vertex_slice.n_atoms + 1)))
split_record = numpy.array_split(record, [1, 1], 1)
record_time = numpy.repeat(
split_record[0] * float(ms_per_tick), vertex_slice.n_atoms, 1)
record_ids = numpy.tile(
numpy.arange(vertex_slice.lo_atom, vertex_slice.hi_atom + 1),
len(record_time)).reshape((-1, vertex_slice.n_atoms))
record_membrane_potential = split_record[2] / 32767.0
part_data = numpy.dstack(
[record_ids, record_time, record_membrane_potential])
part_data = numpy.reshape(part_data, [-1, 3])
data.append(part_data)
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing membrane voltage data in region {}"
" from the following cores: {}".format(
label, region, missing_str))
data = numpy.vstack(data)
order = numpy.lexsort((data[:, 1], data[:, 0]))
result = data[order]
return result
def __call__(self, partitioned_graph, placements, buffer_manager,
ran_token):
if not ran_token:
raise exceptions.ConfigurationException(
"The ran token has not been set")
# Count the regions to be read
n_regions_to_read = 0
for vertex in partitioned_graph.subvertices:
if isinstance(vertex, AbstractReceiveBuffersToHost):
n_regions_to_read += len(vertex.get_buffered_regions())
progress_bar = ProgressBar(n_regions_to_read,
"Extracting buffers from the last run")
# Read back the regions
for vertex in partitioned_graph.subvertices:
if isinstance(vertex, AbstractReceiveBuffersToHost):
placement = placements.get_placement_of_subvertex(vertex)
state_region = vertex.get_buffered_state_region()
for region in vertex.get_buffered_regions():
buffer_manager.get_data_for_vertex(placement, region,
state_region)
progress_bar.update()
progress_bar.end()
def get_v(self, label, buffer_manager, region, state_region, placements,
graph_mapper, partitionable_vertex):
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
ms_per_tick = self._machine_time_step / 1000.0
data = list()
missing_str = ""
progress_bar = \
ProgressBar(len(subvertices),
"Getting membrane voltage for {}".format(label))
for subvertex in subvertices:
vertex_slice = graph_mapper.get_subvertex_slice(subvertex)
placement = placements.get_placement_of_subvertex(subvertex)
x = placement.x
y = placement.y
p = placement.p
# for buffering output info is taken form the buffer manager
neuron_param_region_data_pointer, missing_data =\
buffer_manager.get_data_for_vertex(
placement, region, state_region)
if missing_data:
missing_str += "({}, {}, {}); ".format(x, y, p)
record_raw = neuron_param_region_data_pointer.read_all()
record_length = len(record_raw)
n_rows = record_length / ((vertex_slice.n_atoms + 1) * 4)
record = (numpy.asarray(record_raw,
dtype="uint8").view(dtype="<i4")).reshape(
(n_rows, (vertex_slice.n_atoms + 1)))
split_record = numpy.array_split(record, [1, 1], 1)
record_time = numpy.repeat(split_record[0] * float(ms_per_tick),
vertex_slice.n_atoms, 1)
record_ids = numpy.tile(
numpy.arange(vertex_slice.lo_atom, vertex_slice.hi_atom + 1),
len(record_time)).reshape((-1, vertex_slice.n_atoms))
record_membrane_potential = split_record[2] / 32767.0
part_data = numpy.dstack(
[record_ids, record_time, record_membrane_potential])
part_data = numpy.reshape(part_data, [-1, 3])
data.append(part_data)
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing membrane voltage data in region {}"
" from the following cores: {}".format(label, region,
missing_str))
data = numpy.vstack(data)
order = numpy.lexsort((data[:, 1], data[:, 0]))
result = data[order]
return result
def __call__(self,
machine,
partitionable_graph=None,
partitioned_graph=None):
"""
:param partitionable_graph:
:param partitioned_graph:
:param machine:
:return:
"""
if partitionable_graph is not None:
# Go through the groups and allocate keys
progress_bar = ProgressBar(
(len(partitionable_graph.vertices) + len(list(machine.chips))),
"Allocating virtual identifiers")
elif partitioned_graph is not None:
# Go through the groups and allocate keys
progress_bar = ProgressBar((len(partitioned_graph.subvertices) +
len(list(machine.chips))),
"Allocating virtual identifiers")
else:
progress_bar = ProgressBar(len(list(machine.chips)),
"Allocating virtual identifiers")
# allocate standard ids for real chips
for chip in machine.chips:
expected_chip_id = (chip.x << 8) + chip.y
self._allocate_elements(expected_chip_id, 1)
progress_bar.update()
if partitionable_graph is not None:
# allocate ids for virtual chips
for vertex in partitionable_graph.vertices:
if isinstance(vertex, AbstractVirtualVertex):
link = vertex.spinnaker_link_id
virtual_x, virtual_y, real_x, real_y, real_link = \
self._assign_virtual_chip_info(machine, link)
vertex.set_virtual_chip_coordinates(
virtual_x, virtual_y, real_x, real_y, real_link)
progress_bar.update()
progress_bar.end()
elif partitioned_graph is not None:
# allocate ids for virtual chips
for vertex in partitioned_graph.subvertices:
if isinstance(vertex, VirtualPartitionedVertex):
link = vertex.spinnaker_link_id
virtual_x, virtual_y, real_x, real_y, real_link = \
self._assign_virtual_chip_info(machine, link)
vertex.set_virtual_chip_coordinates(
virtual_x, virtual_y, real_x, real_y, real_link)
progress_bar.update()
progress_bar.end()
return {"machine": machine}
def __call__(self, report_folder, connection_holder, dsg_targets):
""" converts synaptic matrix for every application edge.
"""
# Update the print options to display everything
import numpy
print_opts = numpy.get_printoptions()
numpy.set_printoptions(threshold=numpy.nan)
if dsg_targets is None:
raise SynapticConfigurationException(
"dsg targets should not be none, used as a check for "
"connection holder data to be generated")
# generate folder for synaptic reports
top_level_folder = os.path.join(report_folder,
"synaptic_matrix_reports")
if not os.path.exists(top_level_folder):
os.mkdir(top_level_folder)
# create progress bar
progress = ProgressBar(len(connection_holder.keys()),
"Generating synaptic matrix reports")
# for each application edge, write matrix in new file
for application_edge, _ in connection_holder.keys():
# only write matrix's for edges which have matrix's
if isinstance(application_edge, ProjectionApplicationEdge):
# figure new file name
file_name = os.path.join(
top_level_folder,
"synaptic_matrix_for_application_edge_{}".format(
application_edge.label))
# open writer
output = None
try:
output = open(file_name, "w")
except IOError:
logger.error("Generate_placement_reports: Can't open file"
" {} for writing.".format(file_name))
# write all data for all synapse_information's in same file
for info in application_edge.synapse_information:
this_connection_holder = connection_holder[(
application_edge, info)]
output.write("{}".format(this_connection_holder))
output.flush()
output.close()
progress.update()
progress.end()
# Reset the print options
numpy.set_printoptions(**print_opts)
def get_spikes(self, label, buffer_manager, region, state_region,
placements, graph_mapper, partitionable_vertex,
base_key_function):
results = list()
missing_str = ""
ms_per_tick = self._machine_time_step / 1000.0
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
progress_bar = ProgressBar(len(subvertices),
"Getting spikes for {}".format(label))
for subvertex in subvertices:
placement = placements.get_placement_of_subvertex(subvertex)
subvertex_slice = graph_mapper.get_subvertex_slice(subvertex)
x = placement.x
y = placement.y
p = placement.p
# Read the spikes
raw_spike_data, data_missing = \
buffer_manager.get_data_for_vertex(
placement, region, state_region)
if data_missing:
missing_str += "({}, {}, {}); ".format(x, y, p)
spike_data = str(raw_spike_data.read_all())
number_of_bytes_written = len(spike_data)
offset = 0
while offset < number_of_bytes_written:
eieio_header = EIEIODataHeader.from_bytestring(
spike_data, offset)
offset += eieio_header.size
timestamp = eieio_header.payload_base * ms_per_tick
timestamps = numpy.repeat([timestamp], eieio_header.count)
keys = numpy.frombuffer(
spike_data, dtype="<u4", count=eieio_header.count,
offset=offset)
neuron_ids = ((keys - base_key_function(subvertex)) +
subvertex_slice.lo_atom)
offset += eieio_header.count * 4
results.append(numpy.dstack((neuron_ids, timestamps))[0])
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing spike data in region {} from the"
" following cores: {}".format(label, region, missing_str))
if len(results) != 0:
result = numpy.vstack(results)
result = result[numpy.lexsort((result[:, 1], result[:, 0]))]
else:
result = []
return result
def __call__(
self, partitioned_graph, user_create_database, tags,
runtime, machine, time_scale_factor, machine_time_step,
placements, routing_infos, router_tables, database_directory,
create_atom_to_event_id_mapping=False, partitionable_graph=None,
graph_mapper=None):
self._writer = DatabaseWriter(database_directory)
self._user_create_database = user_create_database
# add database generation if requested
self._needs_database = \
self._writer.auto_detect_database(partitioned_graph)
if ((self._user_create_database == "None" and self._needs_database) or
self._user_create_database == "True"):
if (partitionable_graph is not None and
len(partitionable_graph.vertices) != 0):
database_progress = ProgressBar(11, "Creating database")
else:
database_progress = ProgressBar(10, "Creating database")
self._writer.add_system_params(
time_scale_factor, machine_time_step, runtime)
database_progress.update()
self._writer.add_machine_objects(machine)
database_progress.update()
if (partitionable_graph is not None and
len(partitionable_graph.vertices) != 0):
self._writer.add_partitionable_vertices(partitionable_graph)
database_progress.update()
self._writer.add_partitioned_vertices(
partitioned_graph, graph_mapper, partitionable_graph)
database_progress.update()
self._writer.add_placements(placements, partitioned_graph)
database_progress.update()
self._writer.add_routing_infos(
routing_infos, partitioned_graph)
database_progress.update()
self._writer.add_routing_tables(router_tables)
database_progress.update()
self._writer.add_tags(partitioned_graph, tags)
database_progress.update()
if (graph_mapper is not None and
partitionable_graph is not None and
create_atom_to_event_id_mapping):
self._writer.create_atom_to_event_id_mapping(
graph_mapper=graph_mapper,
partitionable_graph=partitionable_graph,
partitioned_graph=partitioned_graph,
routing_infos=routing_infos)
database_progress.update()
database_progress.update()
database_progress.end()
return {"database_interface": self,
"database_file_path": self.database_file_path}
def get_spikes(self, label, buffer_manager, region, placements,
graph_mapper, application_vertex, base_key_function,
machine_time_step):
results = list()
missing_str = ""
ms_per_tick = machine_time_step / 1000.0
vertices = \
graph_mapper.get_machine_vertices(application_vertex)
progress_bar = ProgressBar(len(vertices),
"Getting spikes for {}".format(label))
for vertex in vertices:
placement = placements.get_placement_of_vertex(vertex)
vertex_slice = graph_mapper.get_slice(vertex)
x = placement.x
y = placement.y
p = placement.p
# Read the spikes
raw_spike_data, data_missing = \
buffer_manager.get_data_for_vertex(placement, region)
if data_missing:
missing_str += "({}, {}, {}); ".format(x, y, p)
spike_data = str(raw_spike_data.read_all())
number_of_bytes_written = len(spike_data)
offset = 0
while offset < number_of_bytes_written:
eieio_header = EIEIODataHeader.from_bytestring(
spike_data, offset)
offset += eieio_header.size
timestamp = eieio_header.payload_base * ms_per_tick
timestamps = numpy.repeat([timestamp], eieio_header.count)
keys = numpy.frombuffer(spike_data,
dtype="<u4",
count=eieio_header.count,
offset=offset)
neuron_ids = ((keys - base_key_function(vertex)) +
vertex_slice.lo_atom)
offset += eieio_header.count * 4
results.append(numpy.dstack((neuron_ids, timestamps))[0])
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing spike data in region {} from the"
" following cores: {}".format(label, region, missing_str))
if len(results) != 0:
result = numpy.vstack(results)
result = result[numpy.lexsort((result[:, 1], result[:, 0]))]
else:
result = []
return result
def __call__(self, report_folder, application_graph):
"""
:param report_folder: the report folder to put figure into
:param application_graph: the app graph
:rtype: None
"""
try:
import graphviz
except:
raise SpynnakerException(
"graphviz is required to use this report. Please install"
" graphviz if you want to use this report.")
# create holders for data
vertex_holders = dict()
dot_diagram = graphviz.Digraph(
comment="The graph of the network in graphical form")
# build progress bar for the vertices, edges, and rendering
progress_bar = ProgressBar(
application_graph.n_vertices +
application_graph.n_outgoing_edge_partitions + 1,
"generating the graphical representation of the neural network")
# write vertices into dot diagram
vertex_counter = 0
for vertex in application_graph.vertices:
dot_diagram.node(
"{}".format(vertex_counter),
"{} ({} neurons)".format(vertex.label, vertex.n_atoms))
vertex_holders[vertex] = vertex_counter
vertex_counter += 1
progress_bar.update()
# write edges into dot diagram
for partition in application_graph.outgoing_edge_partitions:
for edge in partition.edges:
source_vertex_id = vertex_holders[edge.pre_vertex]
dest_vertex_id = vertex_holders[edge.post_vertex]
if isinstance(edge, ProjectionApplicationEdge):
for synapse_info in edge.synapse_information:
dot_diagram.edge(
"{}".format(source_vertex_id),
"{}".format(dest_vertex_id),
"{}".format(synapse_info.connector))
else:
dot_diagram.edge(
"{}".format(source_vertex_id),
"{}".format(dest_vertex_id))
progress_bar.update()
# write dot file and generate pdf
file_to_output = os.path.join(report_folder, "network_graph.gv")
dot_diagram.render(file_to_output, view=False)
progress_bar.update()
progress_bar.end()
def router_report_from_paths(
report_folder, routing_tables, routing_infos, hostname,
partitioned_graph, placements, machine):
""" Generates a text file of routing paths
:param routing_tables:
:param report_folder:
:param hostname:
:param routing_infos:
:param partitioned_graph:
:param placements:
:param machine:
:return:
"""
file_name = os.path.join(report_folder, "edge_routing_info.rpt")
f_routing = None
try:
f_routing = open(file_name, "w")
except IOError:
logger.error("Generate_routing_reports: Can't open file {} for "
"writing.".format(file_name))
f_routing.write(" Edge Routing Report\n")
f_routing.write(" ===================\n\n")
time_date_string = time.strftime("%c")
f_routing.write("Generated: {}".format(time_date_string))
f_routing.write(" for target machine '{}'".format(hostname))
f_routing.write("\n\n")
progress_bar = ProgressBar(len(partitioned_graph.subedges),
"Generating Routing path report")
for edge in partitioned_graph.subedges:
source_placement = placements.get_placement_of_subvertex(
edge.pre_subvertex)
destination_placement = placements.get_placement_of_subvertex(
edge.post_subvertex)
partition = partitioned_graph.get_partition_of_subedge(edge)
key_and_mask = routing_infos.get_keys_and_masks_from_partition(
partition)[0]
path, number_of_entries = _search_route(
source_placement, destination_placement, key_and_mask,
routing_tables, machine)
text = "**** SubEdge '{}', from vertex: '{}' to vertex: '{}'".format(
edge.label, edge.pre_subvertex.label, edge.post_subvertex.label)
text += " Takes path \n {}".format(path)
f_routing.write(text)
f_routing.write("\n")
text = "Route length: {}\n".format(number_of_entries)
f_routing.write(text)
# End one entry:
f_routing.write("\n")
progress_bar.update()
f_routing.flush()
f_routing.close()
progress_bar.end()
def _run_for_core_subsets(self, core_subsets, transceiver):
progress_bar = ProgressBar(len(core_subsets), "Extracting IOBUF")
error_entries = list()
warn_entries = list()
io_buffers = list(transceiver.get_iobuf(core_subsets))
for io_buffer in io_buffers:
self._check_iobuf_for_error(io_buffer, error_entries, warn_entries)
progress_bar.update()
progress_bar.end()
return io_buffers, error_entries, warn_entries
def partitioner_report(report_folder, hostname, graph, graph_mapper):
""" Generate report on the placement of sub-vertices onto cores.
"""
# Cycle through all vertices, and for each cycle through its sub-vertices.
# For each sub-vertex, describe its core mapping.
file_name = os.path.join(report_folder, "partitioned_by_vertex.rpt")
f_place_by_vertex = None
try:
f_place_by_vertex = open(file_name, "w")
except IOError:
logger.error("Generate_placement_reports: Can't open file {} for"
" writing.".format(file_name))
f_place_by_vertex.write(
" Placement Information by Vertex\n")
f_place_by_vertex.write(" ===============================\n\n")
time_date_string = time.strftime("%c")
f_place_by_vertex.write("Generated: {}".format(time_date_string))
f_place_by_vertex.write(" for target machine '{}'".format(hostname))
f_place_by_vertex.write("\n\n")
vertices = sorted(graph.vertices, key=lambda x: x.label)
progress_bar = ProgressBar(len(vertices),
"Generating partitioner report")
for v in vertices:
vertex_name = v.label
vertex_model = v.model_name
num_atoms = v.n_atoms
f_place_by_vertex.write(
"**** Vertex: '{}'\n".format(vertex_name))
f_place_by_vertex.write("Model: {}\n".format(vertex_model))
f_place_by_vertex.write("Pop size: {}\n".format(num_atoms))
f_place_by_vertex.write("Sub-vertices: \n")
partitioned_vertices = \
sorted(graph_mapper.get_subvertices_from_vertex(v),
key=lambda x: x.label)
partitioned_vertices = \
sorted(partitioned_vertices,
key=lambda x: graph_mapper.get_subvertex_slice(x).lo_atom)
for sv in partitioned_vertices:
lo_atom = graph_mapper.get_subvertex_slice(sv).lo_atom
hi_atom = graph_mapper.get_subvertex_slice(sv).hi_atom
num_atoms = hi_atom - lo_atom + 1
my_string = " Slice {}:{} ({} atoms) \n"\
.format(lo_atom, hi_atom, num_atoms)
f_place_by_vertex.write(my_string)
f_place_by_vertex.flush()
f_place_by_vertex.write("\n")
progress_bar.update()
# Close file:
f_place_by_vertex.close()
progress_bar.end()
def __call__(self, subgraph, graph_mapper):
"""
:param subgraph: the subgraph whose edges are to be filtered
:param graph_mapper: the graph mapper between partitionable and \
partitioned graphs.
:return: a new graph mapper and partitioned graph
"""
new_sub_graph = PartitionedGraph(label=subgraph.label)
new_graph_mapper = GraphMapper(graph_mapper.first_graph_label,
subgraph.label)
# create progress bar
progress_bar = ProgressBar(
len(subgraph.subvertices) + len(subgraph.subedges),
"Filtering edges")
# add the subverts directly, as they wont be pruned.
for subvert in subgraph.subvertices:
new_sub_graph.add_subvertex(subvert)
associated_vertex = graph_mapper.get_vertex_from_subvertex(subvert)
vertex_slice = graph_mapper.get_subvertex_slice(subvert)
new_graph_mapper.add_subvertex(subvertex=subvert,
vertex_slice=vertex_slice,
vertex=associated_vertex)
progress_bar.update()
# start checking subedges to decide which ones need pruning....
for subvert in subgraph.subvertices:
out_going_partitions = \
subgraph.outgoing_edges_partitions_from_vertex(subvert)
for partitioner_identifier in out_going_partitions:
for subedge in \
out_going_partitions[partitioner_identifier].edges:
if not self._is_filterable(subedge, graph_mapper):
logger.debug(
"this subedge was not pruned {}".format(subedge))
new_sub_graph.add_subedge(subedge,
partitioner_identifier)
associated_edge = graph_mapper.\
get_partitionable_edge_from_partitioned_edge(
subedge)
new_graph_mapper.add_partitioned_edge(
subedge, associated_edge)
else:
logger.debug(
"this subedge was pruned {}".format(subedge))
progress_bar.update()
progress_bar.end()
# returned the pruned partitioned_graph and graph_mapper
return {
'new_sub_graph': new_sub_graph,
'new_graph_mapper': new_graph_mapper
}
def __call__(self, executable_targets, app_id, transceiver,
loaded_application_data_token):
""" Go through the executable targets and load each binary to \
everywhere and then send a start request to the cores that \
actually use it
"""
if not loaded_application_data_token:
raise exceptions.ConfigurationException(
"The token for having loaded the application data token is set"
" to false and therefore I cannot run. Please fix and try "
"again")
progress_bar = ProgressBar(executable_targets.total_processors,
"Loading executables onto the machine")
for executable_target_key in executable_targets.binaries:
file_reader = FileDataReader(executable_target_key)
core_subset = executable_targets.get_cores_for_binary(
executable_target_key)
statinfo = os.stat(executable_target_key)
size = statinfo.st_size
# TODO there is a need to parse the binary and see if its
# ITCM and DTCM requirements are within acceptable params for
# operating on spinnaker. Currently there just a few safety
# checks which may not be accurate enough.
if size > constants.MAX_SAFE_BINARY_SIZE:
logger.warn(
"The size of {} is large enough that its"
" possible that the binary may be larger than what is"
" supported by spinnaker currently. Please reduce the"
" binary size if it starts to behave strangely, or goes"
" into the WDOG state before starting.".format(
executable_target_key))
if size > constants.MAX_POSSIBLE_BINARY_SIZE:
raise exceptions.ConfigurationException(
"The size of {} is too large and therefore"
" will very likely cause a WDOG state. Until a more"
" precise measurement of ITCM and DTCM can be produced"
" this is deemed as an error state. Please reduce the"
" size of your binary or circumvent this error check.".
format(executable_target_key))
transceiver.execute_flood(core_subset, file_reader, app_id, size)
acutal_cores_loaded = 0
for chip_based in core_subset.core_subsets:
for _ in chip_based.processor_ids:
acutal_cores_loaded += 1
progress_bar.update(amount_to_add=acutal_cores_loaded)
progress_bar.end()
return {"LoadBinariesToken": True}
def get_synaptic_list_from_machine(self, graph_mapper, partitioned_graph,
placements, transceiver, routing_infos):
""" Get synaptic data for all connections in this Projection from the\
machine.
"""
if self._stored_synaptic_data_from_machine is None:
timer = None
if conf.config.getboolean("Reports", "display_algorithm_timings"):
timer = Timer()
timer.start_timing()
subedges = \
graph_mapper.get_partitioned_edges_from_partitionable_edge(
self)
if subedges is None:
subedges = list()
synaptic_list = copy.copy(self._synapse_list)
synaptic_list_rows = synaptic_list.get_rows()
progress_bar = ProgressBar(
len(subedges),
"Reading back synaptic matrix for edge between"
" {} and {}".format(self._pre_vertex.label,
self._post_vertex.label))
for subedge in subedges:
n_rows = subedge.get_n_rows(graph_mapper)
pre_vertex_slice = \
graph_mapper.get_subvertex_slice(subedge.pre_subvertex)
post_vertex_slice = \
graph_mapper.get_subvertex_slice(subedge.post_subvertex)
sub_edge_post_vertex = \
graph_mapper.get_vertex_from_subvertex(
subedge.post_subvertex)
rows = sub_edge_post_vertex.get_synaptic_list_from_machine(
placements, transceiver, subedge.pre_subvertex, n_rows,
subedge.post_subvertex,
self._synapse_row_io, partitioned_graph,
routing_infos, subedge.weight_scales).get_rows()
for i in range(len(rows)):
synaptic_list_rows[
i + pre_vertex_slice.lo_atom].set_slice_values(
rows[i], vertex_slice=post_vertex_slice)
progress_bar.update()
progress_bar.end()
self._stored_synaptic_data_from_machine = synaptic_list
if conf.config.getboolean("Reports", "display_algorithm_timings"):
logger.info("Time to read matrix: {}".format(
timer.take_sample()))
return self._stored_synaptic_data_from_machine
def _do_allocation(self, ordered_subverts, placements, machine):
# Iterate over subvertices and generate placements
progress_bar = ProgressBar(len(ordered_subverts),
"Placing graph vertices")
resource_tracker = ResourceTracker(
machine, self._generate_radial_chips(machine))
# iterate over subverts
for subvertex_list in ordered_subverts:
# if too many one to ones to fit on a chip, allocate individually
if len(subvertex_list) > self.MAX_CORES_PER_CHIP_TO_CONSIDER:
for subvertex in subvertex_list:
self._allocate_individual(subvertex, placements,
progress_bar, resource_tracker)
else: # can allocate in one block
# merge constraints
placement_constraint, ip_tag_constraints, \
reverse_ip_tag_constraints = \
self._merge_constraints(subvertex_list)
# locate most cores on a chip
max_size_on_a_chip = resource_tracker.\
max_available_cores_on_chips_that_satisfy(
placement_constraint, ip_tag_constraints,
reverse_ip_tag_constraints)
# if size fits block allocate, otherwise allocate individually
if max_size_on_a_chip < len(subvertex_list):
# collect resource requirement
resources = list()
for subvert in subvertex_list:
resources.append(subvert.resources_required)
# get cores
cores = resource_tracker.allocate_group(
resources, placement_constraint, ip_tag_constraints,
reverse_ip_tag_constraints)
# allocate cores to subverts
for subvert, (x, y, p, _, _) in zip(subvertex_list, cores):
placement = Placement(subvert, x, y, p)
placements.add_placement(placement)
progress_bar.update()
else:
for subvertex in subvertex_list:
self._allocate_individual(subvertex, placements,
progress_bar,
resource_tracker)
progress_bar.end()
def _run_for_placements(self, placements, transceiver):
io_buffers = list()
error_entries = list()
warn_entries = list()
progress_bar = ProgressBar(len(placements), "Extracting IOBUF")
for placement in placements:
iobuf = transceiver.get_iobuf_from_core(placement.x, placement.y,
placement.p)
io_buffers.append(iobuf)
self._check_iobuf_for_error(iobuf, error_entries, warn_entries)
progress_bar.update()
progress_bar.end()
return io_buffers, error_entries, warn_entries
def _handle_external_algorithm(self, algorithm):
""" Creates the input files for the algorithm
:param algorithm: the algorithm
:return: None
"""
input_params = self._create_input_commands(algorithm)
inputs = \
[a.format(**input_params) for a in algorithm.command_line_args]
# output debug info in case things go wrong
logger.debug(
"The inputs to the external mapping function are {}".format(
inputs))
# create progress bar for external algorithm
algorithm_progress_bar = ProgressBar(
1, "Running external algorithm {}".format(algorithm.algorithm_id))
timer = None
if self._do_timing:
timer = Timer()
timer.start_timing()
# execute other command
child = subprocess.Popen(inputs,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE)
child.wait()
algorithm_progress_bar.end()
if self._do_timing:
self._update_timings(timer, algorithm)
# check the return code for a successful execution
if child.returncode != 0:
stdout, stderr = child.communicate()
raise exceptions.\
PacmanExternalAlgorithmFailedToCompleteException(
"Algorithm {} returned a non-zero error code {}\n"
" Inputs: {}\n"
" Output: {}\n"
" Error: {}\n".format(
algorithm.algorithm_id, child.returncode,
inputs, stdout, stderr))
outputs = self._sort_out_external_algorithm_outputs(algorithm)
self._map_output_parameters(outputs, algorithm)
def __call__(self, partitioned_graph, partitionable_graph=None,
graph_mapper=None):
# Generate an n_keys map for the graph and add constraints
n_keys_map = DictBasedPartitionedPartitionNKeysMap()
# generate progress bar
progress_bar = ProgressBar(
len(partitioned_graph.subvertices),
"Deducing edge to number of keys map")
# contains a partitionable vertex
if partitionable_graph is not None and graph_mapper is not None:
# iterate over each partition in the partitioned graph
for vertex in partitioned_graph.subvertices:
partitions = \
partitioned_graph.outgoing_edges_partitions_from_vertex(
vertex)
for partition_id in partitions:
partition = partitions[partition_id]
added_constraints = False
constraints = self._process_partitionable_partition(
partition, n_keys_map, partition_id, graph_mapper,
partitionable_graph)
if not added_constraints:
partition.add_constraints(constraints)
else:
self._check_constraints_equal(
constraints, partition.constraints)
progress_bar.update()
progress_bar.end()
else:
for vertex in partitioned_graph.subvertices:
partitions = \
partitioned_graph.outgoing_edges_partitions_from_vertex(
vertex)
for partition_id in partitions:
partition = partitions[partition_id]
added_constraints = False
constraints = self._process_partitioned_partition(
partition, n_keys_map, partition_id, partitioned_graph)
if not added_constraints:
partition.add_constraints(constraints)
else:
self._check_constraints_equal(
constraints, partition.constraints)
progress_bar.update()
progress_bar.end()
return {'n_keys_map': n_keys_map}
def load_initial_buffers(self):
""" Load the initial buffers for the senders using mem writes
"""
total_data = 0
for vertex in self._sender_vertices:
for region in vertex.get_regions():
total_data += vertex.get_region_buffer_size(region)
progress_bar = ProgressBar(
total_data, "Loading buffers ({} bytes)".format(total_data))
for vertex in self._sender_vertices:
for region in vertex.get_regions():
self._send_initial_messages(vertex, region, progress_bar)
progress_bar.end()
def _get_synaptic_data(self, as_list, data_to_get):
post_vertex = self._projection_edge.post_vertex
pre_vertex = self._projection_edge.pre_vertex
# If in virtual board mode, the connection data should be set
if self._virtual_connection_list is not None:
post_vertex = self._projection_edge.post_vertex
pre_vertex = self._projection_edge.pre_vertex
return ConnectionHolder(data_to_get, as_list, pre_vertex.n_atoms,
post_vertex.n_atoms,
self._virtual_connection_list)
connection_holder = ConnectionHolder(data_to_get, as_list,
pre_vertex.n_atoms,
post_vertex.n_atoms)
# If we haven't run, add the holder to get connections, and return it
if not self._spinnaker.has_ran:
post_vertex.add_pre_run_connection_holder(
connection_holder, self._projection_edge,
self._synapse_information)
return connection_holder
# Otherwise, get the connections now
graph_mapper = self._spinnaker.graph_mapper
placements = self._spinnaker.placements
transceiver = self._spinnaker.transceiver
routing_infos = self._spinnaker.routing_infos
partitioned_graph = self._spinnaker.partitioned_graph
subedges = graph_mapper.get_partitioned_edges_from_partitionable_edge(
self._projection_edge)
progress = ProgressBar(
len(subedges),
"Getting {}s for projection between {} and {}".format(
data_to_get, pre_vertex.label, post_vertex.label))
for subedge in subedges:
placement = placements.get_placement_of_subvertex(
subedge.post_subvertex)
connections = post_vertex.get_connections_from_machine(
transceiver, placement, subedge, graph_mapper, routing_infos,
self._synapse_information, partitioned_graph)
if connections is not None:
connection_holder.add_connections(connections)
progress.update()
progress.end()
connection_holder.finish()
return connection_holder
def __call__(self, machine_graph, graph_mapper):
"""
:param machine_graph: the machine_graph whose edges are to be filtered
:param graph_mapper: the graph mapper between graphs
:return: a new graph mapper and machine graph
"""
new_machine_graph = MachineGraph(label=machine_graph.label)
new_graph_mapper = GraphMapper()
# create progress bar
progress_bar = ProgressBar(
machine_graph.n_vertices +
machine_graph.n_outgoing_edge_partitions, "Filtering edges")
# add the vertices directly, as they wont be pruned.
for vertex in machine_graph.vertices:
new_machine_graph.add_vertex(vertex)
associated_vertex = graph_mapper.get_application_vertex(vertex)
vertex_slice = graph_mapper.get_slice(vertex)
new_graph_mapper.add_vertex_mapping(
machine_vertex=vertex,
vertex_slice=vertex_slice,
application_vertex=associated_vertex)
progress_bar.update()
# start checking edges to decide which ones need pruning....
for partition in machine_graph.outgoing_edge_partitions:
for edge in partition.edges:
if not self._is_filterable(edge, graph_mapper):
logger.debug("this edge was not pruned {}".format(edge))
new_machine_graph.add_edge(edge, partition.identifier)
app_edge = graph_mapper.get_application_edge(edge)
new_graph_mapper.add_edge_mapping(edge, app_edge)
# add partition constraints from the original graph to
# the new graph
# add constraints from the application partition
new_machine_graph_partition = new_machine_graph.\
get_outgoing_edge_partition_starting_at_vertex(
edge.pre_vertex, partition.identifier)
new_machine_graph_partition.add_constraints(
partition.constraints)
else:
logger.debug("this edge was pruned {}".format(edge))
progress_bar.update()
progress_bar.end()
# returned the pruned graph and graph_mapper
return new_machine_graph, new_graph_mapper
def _write_router_provenance_data(self, router_tables, machine, txrx):
""" Writes the provenance data of the router diagnostics
:param router_tables: the routing tables generated by pacman
:param machine: the spinnMachine object
:param txrx: the transceiver object
:return: None
"""
progress = ProgressBar(machine.n_chips, "Getting Router Provenance")
# acquire diagnostic data
items = list()
seen_chips = set()
for router_table in sorted(
router_tables.routing_tables,
key=lambda table: (table.x, table.y)):
x = router_table.x
y = router_table.y
if not machine.get_chip_at(x, y).virtual:
router_diagnostic = txrx.get_router_diagnostics(x, y)
seen_chips.add((x, y))
reinjector_status = txrx.get_reinjection_status(x, y)
items.extend(self._write_router_diagnostics(
x, y, router_diagnostic, reinjector_status, True))
self._add_totals(router_diagnostic, reinjector_status)
progress.update()
for chip in sorted(machine.chips, key=lambda c: (c.x, c.y)):
if not chip.virtual and (chip.x, chip.y) not in seen_chips:
try:
diagnostic = txrx.get_router_diagnostics(chip.x, chip.y)
if (diagnostic.n_dropped_multicast_packets != 0 or
diagnostic.n_local_multicast_packets != 0 or
diagnostic.n_external_multicast_packets != 0):
reinjector_status = txrx.get_reinjection_status(
chip.x, chip.y)
items.extend(self._write_router_diagnostics(
chip.x, chip.y, diagnostic, reinjector_status,
False))
self._add_totals(diagnostic, reinjector_status)
progress.update()
except Exception:
# There could be issues with unused chips - don't worry!
pass
progress.end()
return items
def _get_synaptic_data(self, as_list, data_to_get):
post_vertex = self._projection_edge.post_vertex
pre_vertex = self._projection_edge.pre_vertex
# If in virtual board mode, the connection data should be set
if self._virtual_connection_list is not None:
post_vertex = self._projection_edge.post_vertex
pre_vertex = self._projection_edge.pre_vertex
return ConnectionHolder(
data_to_get, as_list, pre_vertex.n_atoms, post_vertex.n_atoms,
self._virtual_connection_list)
connection_holder = ConnectionHolder(
data_to_get, as_list, pre_vertex.n_atoms, post_vertex.n_atoms)
# If we haven't run, add the holder to get connections, and return it
if not self._spinnaker.has_ran:
post_vertex.add_pre_run_connection_holder(
connection_holder, self._projection_edge,
self._synapse_information)
return connection_holder
# Otherwise, get the connections now
graph_mapper = self._spinnaker.graph_mapper
placements = self._spinnaker.placements
transceiver = self._spinnaker.transceiver
routing_infos = self._spinnaker.routing_infos
partitioned_graph = self._spinnaker.partitioned_graph
subedges = graph_mapper.get_partitioned_edges_from_partitionable_edge(
self._projection_edge)
progress = ProgressBar(
len(subedges),
"Getting {}s for projection between {} and {}".format(
data_to_get, pre_vertex.label, post_vertex.label))
for subedge in subedges:
placement = placements.get_placement_of_subvertex(
subedge.post_subvertex)
connections = post_vertex.get_connections_from_machine(
transceiver, placement, subedge, graph_mapper, routing_infos,
self._synapse_information, partitioned_graph)
if connections is not None:
connection_holder.add_connections(connections)
progress.update()
progress.end()
connection_holder.finish()
return connection_holder
def __call__(self, partitioned_graph, machine):
progress_bar = ProgressBar(9, "Placing and Routing")
vertices_resources, nets, net_names = \
rig_converters.convert_to_rig_partitioned_graph(
partitioned_graph)
progress_bar.update()
rig_machine = rig_converters.convert_to_rig_machine(machine)
progress_bar.update()
rig_constraints = rig_converters.create_rig_machine_constraints(
machine)
progress_bar.update()
rig_constraints.extend(
rig_converters.create_rig_partitioned_graph_constraints(
partitioned_graph, rig_machine))
progress_bar.update()
rig_placements = place(vertices_resources, nets, rig_machine,
rig_constraints)
progress_bar.update()
rig_allocations = allocate(vertices_resources, nets, rig_machine,
rig_constraints, rig_placements)
progress_bar.update()
rig_routes = route(vertices_resources, nets, rig_machine,
rig_constraints, rig_placements, rig_allocations,
"cores")
rig_routes = {
name: rig_routes[net]
for net, name in net_names.iteritems()
}
progress_bar.update()
placements = rig_converters.convert_from_rig_placements(
rig_placements, rig_allocations, partitioned_graph)
progress_bar.update()
routes = rig_converters.convert_from_rig_routes(
rig_routes, partitioned_graph)
progress_bar.update()
progress_bar.end()
return {"placements": placements, "routing_paths": routes}
def __call__(
self, placements, graph_mapper, tags, executable_finder,
partitioned_graph, partitionable_graph, routing_infos, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder):
# Keep the results
executable_targets = ExecutableTargets()
dsg_targets = dict()
# Keep delay extensions until the end
delay_extension_placements = list()
# create a progress bar for end users
progress_bar = ProgressBar(len(list(placements.placements)),
"Generating sPyNNaker data specifications")
for placement in placements.placements:
associated_vertex = graph_mapper.get_vertex_from_subvertex(
placement.subvertex)
if isinstance(associated_vertex, DelayExtensionVertex):
delay_extension_placements.append(
(placement, associated_vertex))
else:
self._generate_data_spec_for_subvertices(
placement, associated_vertex, executable_targets,
dsg_targets, graph_mapper, tags, executable_finder,
partitioned_graph, partitionable_graph, routing_infos,
hostname, report_default_directory, write_text_specs,
app_data_runtime_folder)
progress_bar.update()
for placement, associated_vertex in delay_extension_placements:
self._generate_data_spec_for_subvertices(
placement, associated_vertex, executable_targets,
dsg_targets, graph_mapper, tags, executable_finder,
partitioned_graph, partitionable_graph, routing_infos,
hostname, report_default_directory, write_text_specs,
app_data_runtime_folder)
progress_bar.update()
# finish the progress bar
progress_bar.end()
return {'executable_targets': executable_targets,
'dsg_targets': dsg_targets}
def __call__(self,
placements,
graph,
hostname,
report_default_directory,
write_text_specs,
app_data_runtime_folder,
machine,
graph_mapper=None):
# Keep the results
dsg_targets = dict()
# Keep delay extensions until the end
delay_extension_placements = list()
# create a progress bar for end users
progress_bar = ProgressBar(len(list(placements.placements)),
"Generating sPyNNaker data specifications")
for placement in placements.placements:
associated_vertex = graph_mapper.get_application_vertex(
placement.vertex)
if isinstance(associated_vertex, DelayExtensionVertex):
delay_extension_placements.append(
(placement, associated_vertex))
else:
self._generate_data_spec_for_vertices(
placement, associated_vertex, dsg_targets, hostname,
report_default_directory, write_text_specs,
app_data_runtime_folder, machine)
progress_bar.update()
for placement, associated_vertex in delay_extension_placements:
self._generate_data_spec_for_vertices(placement, associated_vertex,
dsg_targets, hostname,
report_default_directory,
write_text_specs,
app_data_runtime_folder,
machine)
progress_bar.update()
# finish the progress bar
progress_bar.end()
return dsg_targets
def generate_sub_edges(subgraph, graph_to_subgraph_mapper, graph):
""" Generate the sub edges for the subvertices in the graph
:param subgraph: the partitioned graph to work with
:type subgraph:\
:py:class:`pacman.model.partitioned_graph.partitioned_graph.PartitionedGraph`
:param graph_to_subgraph_mapper: the mapper between the \
partitionable graph and the partitioned graph
:type graph_to_subgraph_mapper:\
:py:class:`pacman.model.graph_mapper.GraphMapper`
:param graph: the partitionable graph to work with
:type graph:\
:py:class:`pacman.model.graph.partitionable_graph.PartitionableGraph`
"""
# start progress bar
progress_bar = ProgressBar(len(subgraph.subvertices),
"Partitioning graph edges")
# Partition edges according to vertex partitioning
for src_sv in subgraph.subvertices:
# For each out edge of the parent vertex...
vertex = graph_to_subgraph_mapper.get_vertex_from_subvertex(src_sv)
outgoing_partitions = \
graph.outgoing_edges_partitions_from_vertex(vertex)
for outgoing_partition_identifer in outgoing_partitions:
partition = outgoing_partitions[outgoing_partition_identifer]
out_edges = partition.edges
partition_constraints = partition.constraints
for edge in out_edges:
# and create and store a new subedge for each post-subvertex
post_vertex = edge.post_vertex
post_subverts = (graph_to_subgraph_mapper
.get_subvertices_from_vertex(post_vertex))
for dst_sv in post_subverts:
subedge = edge.create_subedge(src_sv, dst_sv)
subgraph.add_subedge(subedge,
outgoing_partition_identifer,
partition_constraints)
graph_to_subgraph_mapper.add_partitioned_edge(
subedge, edge)
progress_bar.update()
progress_bar.end()
def __call__(self,
partitioned_graph,
partitionable_graph=None,
provenance_data_objects=None):
"""
:param partitioned_graph: The partitioned graph to inspect
:param partitionable_graph: The optional partitionable graph
:param provenance_data_objects: Any existing objects to append to
"""
if provenance_data_objects is not None:
prov_items = provenance_data_objects
else:
prov_items = list()
progress = ProgressBar(
len(partitioned_graph.subvertices) +
len(partitioned_graph.subedges),
"Getting provenance data from partitioned graph")
for subvertex in partitioned_graph.subvertices:
if isinstance(subvertex, AbstractProvidesLocalProvenanceData):
prov_items.extend(subvertex.get_local_provenance_data())
progress.update()
for subedge in partitioned_graph.subedges:
if isinstance(subedge, AbstractProvidesLocalProvenanceData):
prov_items.extend(subedge.get_local_provenance_data())
progress.update()
progress.end()
if partitionable_graph is not None:
progress = ProgressBar(
len(partitionable_graph.vertices) +
len(partitionable_graph.edges),
"Getting provenance data from partitionable graph")
for vertex in partitionable_graph.vertices:
if isinstance(vertex, AbstractProvidesLocalProvenanceData):
prov_items.extend(vertex.get_local_provenance_data())
progress.update()
for edge in partitionable_graph.edges:
if isinstance(edge, AbstractProvidesLocalProvenanceData):
prov_items.extend(edge.get_local_provenance_data())
progress.end()
return {'prov_items': prov_items}
def generate_comparison_router_report(
report_folder, routing_tables, compressed_routing_tables):
""" Make a report on comparison of the compressed and uncompressed \
routing tables
:param report_folder: the folder to store the resulting report
:param routing_tables: the original routing tables
:param compressed_routing_tables: the compressed routing tables
:return: None
"""
file_name = os.path.join(
report_folder, "comparison_of_compressed_uncompressed_routing_tables")
output = None
try:
output = open(file_name, "w")
except IOError:
logger.error("Generate_router_comparison_reports: Can't open file"
" {} for writing.".format(file_name))
progress_bar = ProgressBar(len(routing_tables.routing_tables),
"Generating comparison of router table report")
for uncompressed_table in routing_tables.routing_tables:
x = uncompressed_table.x
y = uncompressed_table.y
compressed_table = compressed_routing_tables.\
get_routing_table_for_chip(x, y)
n_entries_un_compressed = uncompressed_table.number_of_entries
n_entries_compressed = compressed_table.number_of_entries
percentage = ((float(n_entries_un_compressed - n_entries_compressed)) /
float(n_entries_un_compressed)) * 100
output.write(
"Uncompressed table at {}:{} has {} entries whereas compressed "
"table has {} entries. This is a decrease of {} %\n".format(
x, y, n_entries_un_compressed, n_entries_compressed,
percentage))
progress_bar.update()
progress_bar.end()
output.flush()
output.close()
def router_report_from_router_tables(report_folder, routing_tables):
"""
:param report_folder:
:param routing_tables:
:return:
"""
top_level_folder = os.path.join(report_folder, "routing_tables_generated")
if not os.path.exists(top_level_folder):
os.mkdir(top_level_folder)
progress_bar = ProgressBar(len(routing_tables.routing_tables),
"Generating Router table report")
for routing_table in routing_tables.routing_tables:
if routing_table.number_of_entries > 0:
_generate_routing_table(routing_table, top_level_folder)
progress_bar.update()
progress_bar.end()
def __call__(self, machine_graph, graph_mapper):
"""
:param machine_graph: the machine_graph whose edges are to be updated
:param graph_mapper: the graph mapper between graphs
"""
# create progress bar
progress_bar = ProgressBar(machine_graph.n_outgoing_edge_partitions,
"Updating edge weights")
# start checking edges to decide which ones need pruning....
for partition in machine_graph.outgoing_edge_partitions:
for edge in partition.edges:
if isinstance(edge, AbstractWeightUpdatable):
edge.update_weight(graph_mapper)
progress_bar.update()
progress_bar.end()
# return nothing
return machine_graph
def __call__(self, subgraph, graph_mapper):
"""
:param subgraph: the subgraph whose edges are to be updated
:param graph_mapper: the graph mapper between partitionable and \
partitioned graphs.
"""
# create progress bar
progress_bar = ProgressBar(
len(subgraph.subedges), "Updating edge weights")
# start checking subedges to decide which ones need pruning....
for subedge in subgraph.subedges:
if isinstance(subedge, AbstractWeightUpdatable):
subedge.update_weight(graph_mapper)
progress_bar.update()
progress_bar.end()
# return nothing
return {'subgraph': subgraph}
def get_spikes(self, label, buffer_manager, region, state_region,
placements, graph_mapper, partitionable_vertex):
spike_times = list()
spike_ids = list()
ms_per_tick = self._machine_time_step / 1000.0
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
missing_str = ""
progress_bar = ProgressBar(len(subvertices),
"Getting spikes for {}".format(label))
for subvertex in subvertices:
placement = placements.get_placement_of_subvertex(subvertex)
subvertex_slice = graph_mapper.get_subvertex_slice(subvertex)
x = placement.x
y = placement.y
p = placement.p
lo_atom = subvertex_slice.lo_atom
# Read the spikes
n_words = int(math.ceil(subvertex_slice.n_atoms / 32.0))
n_bytes = n_words * 4
n_words_with_timestamp = n_words + 1
# for buffering output info is taken form the buffer manager
neuron_param_region_data_pointer, data_missing = \
buffer_manager.get_data_for_vertex(
placement, region, state_region)
if data_missing:
missing_str += "({}, {}, {}); ".format(x, y, p)
record_raw = neuron_param_region_data_pointer.read_all()
raw_data = (numpy.asarray(record_raw, dtype="uint8").
view(dtype="<i4")).reshape(
[-1, n_words_with_timestamp])
split_record = numpy.array_split(raw_data, [1, 1], 1)
record_time = split_record[0] * float(ms_per_tick)
spikes = split_record[2].byteswap().view("uint8")
bits = numpy.fliplr(numpy.unpackbits(spikes).reshape(
(-1, 32))).reshape((-1, n_bytes * 8))
time_indices, indices = numpy.where(bits == 1)
times = record_time[time_indices].reshape((-1))
indices = indices + lo_atom
spike_ids.append(indices)
spike_times.append(times)
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing spike data in region {} from the"
" following cores: {}".format(label, region, missing_str))
spike_ids = numpy.hstack(spike_ids)
spike_times = numpy.hstack(spike_times)
result = numpy.dstack((spike_ids, spike_times))[0]
return result[numpy.lexsort((spike_times, spike_ids))]
def get_spikes(self, label, buffer_manager, region, state_region,
placements, graph_mapper, partitionable_vertex):
spike_times = list()
spike_ids = list()
ms_per_tick = self._machine_time_step / 1000.0
subvertices = \
graph_mapper.get_subvertices_from_vertex(partitionable_vertex)
missing_str = ""
progress_bar = ProgressBar(len(subvertices),
"Getting spikes for {}".format(label))
for subvertex in subvertices:
placement = placements.get_placement_of_subvertex(subvertex)
subvertex_slice = graph_mapper.get_subvertex_slice(subvertex)
x = placement.x
y = placement.y
p = placement.p
lo_atom = subvertex_slice.lo_atom
# Read the spikes
n_words = int(math.ceil(subvertex_slice.n_atoms / 32.0))
n_bytes_per_block = n_words * 4
# for buffering output info is taken form the buffer manager
neuron_param_region_data_pointer, data_missing = \
buffer_manager.get_data_for_vertex(
placement, region, state_region)
if data_missing:
missing_str += "({}, {}, {}); ".format(x, y, p)
raw_data = neuron_param_region_data_pointer.read_all()
offset = 0
while offset < len(raw_data):
((time, n_blocks), offset) = (
struct.unpack_from("<II", raw_data, offset), offset + 8)
(spike_data, offset) = (numpy.frombuffer(
raw_data, dtype="uint8",
count=n_bytes_per_block * n_blocks, offset=offset),
offset + (n_bytes_per_block * n_blocks))
spikes = spike_data.view("<i4").byteswap().view("uint8")
bits = numpy.fliplr(numpy.unpackbits(spikes).reshape(
(-1, 32))).reshape((-1, n_bytes_per_block * 8))
indices = numpy.nonzero(bits)[1]
times = numpy.repeat([time * ms_per_tick], len(indices))
indices = indices + lo_atom
spike_ids.append(indices)
spike_times.append(times)
progress_bar.update()
progress_bar.end()
if len(missing_str) > 0:
logger.warn(
"Population {} is missing spike data in region {} from the"
" following cores: {}".format(label, region, missing_str))
spike_ids = numpy.hstack(spike_ids)
spike_times = numpy.hstack(spike_times)
result = numpy.dstack((spike_ids, spike_times))[0]
return result[numpy.lexsort((spike_times, spike_ids))]
