spynnaker.pyNN.utilities package

Subpackages

• spynnaker.pyNN.utilities.random_stats package
  • Module contents
• spynnaker.pyNN.utilities.ranged package
  • Module contents

Submodules

spynnaker.pyNN.utilities.bit_field_utilities module

spynnaker.pyNN.utilities.bit_field_utilities.ELEMENTS_USED_IN_BIT_FIELD_HEADER = 2

n_filters, pointer for array

spynnaker.pyNN.utilities.bit_field_utilities.ELEMENTS_USED_IN_EACH_BIT_FIELD = 3

number of elements

spynnaker.pyNN.utilities.bit_field_utilities.N_ELEMENTS_IN_EACH_KEY_N_ATOM_MAP = 2

n elements in each key to n atoms map for bitfield (key, n atoms)

spynnaker.pyNN.utilities.bit_field_utilities.N_KEYS_DATA_SET_IN_WORDS = 1

n key to n neurons maps size in words

spynnaker.pyNN.utilities.bit_field_utilities.N_REGIONS_ADDRESSES = 6

the regions addresses needed ( pop table, synaptic matrix, direct matrix, bit_field, bit field builder, bit_field_key, structural region)

spynnaker.pyNN.utilities.bit_field_utilities.exact_sdram_for_bit_field_builder_region()

Gets the SDRAM requirement for the builder region

Returns:the SDRAM requirement for the builder region Return type:int

spynnaker.pyNN.utilities.bit_field_utilities.get_estimated_sdram_for_bit_field_region(app_graph, vertex)

estimates the SDRAM for the bit field region

Parameters:

• app_graph (ApplicationGraph) – the app graph
• vertex (ApplicationVertex) – app vertex

Returns:

the estimated number of bytes used by the bit field region

Return type:

int

spynnaker.pyNN.utilities.bit_field_utilities.get_estimated_sdram_for_key_region(app_graph, vertex)

gets an estimate of the bitfield builder region

Parameters:

• app_graph (ApplicationGraph) – the app graph
• vertex (ApplicationVertex) – app vertex

Returns:

SDRAM needed

Return type:

int

spynnaker.pyNN.utilities.bit_field_utilities.reserve_bit_field_regions(spec, machine_graph, n_key_map, vertex, bit_field_builder_region, bit_filter_region, bit_field_key_region)

reserves the regions for the bitfields

Parameters:

• spec (DataSpecificationGenerator) – dsg spec writer
• machine_graph (MachineGraph) – machine graph
• n_key_map (AbstractMachinePartitionNKeysMap) – map between partitions and n keys
• vertex (MachineVertex) – machine vertex
• bit_field_builder_region (int) – region id for the builder region
• bit_filter_region (int) – region id for the bitfield region
• bit_field_key_region (int) – region id for the key map

spynnaker.pyNN.utilities.bit_field_utilities.write_bitfield_init_data(spec, machine_vertex, machine_graph, routing_info, n_key_map, bit_field_builder_region, master_pop_region_id, synaptic_matrix_region_id, direct_matrix_region_id, bit_field_region_id, bit_field_key_map_region_id, structural_dynamics_region_id, has_structural_dynamics_region)

writes the init data needed for the bitfield generator

Parameters:

• spec (DataSpecificationGenerator) – data spec writer
• machine_vertex (MachineVertex) – machine vertex
• machine_graph (MachineGraph) – machine graph
• routing_info (RoutingInfo) – keys
• n_key_map (AbstractMachinePartitionNKeysMap) – map for edge to n keys
• bit_field_builder_region (int) – the region id for the bitfield builder
• master_pop_region_id (int) – the region id for the master pop table
• synaptic_matrix_region_id (int) – the region id for the synaptic matrix
• direct_matrix_region_id (int) – the region id for the direct matrix
• bit_field_region_id (int) – the region id for the bit-fields
• bit_field_key_map_region_id (int) – the region id for the key map
• structural_dynamics_region_id (int) – the region id for the structural
• has_structural_dynamics_region (bool) – whether the core has a structural_dynamics region

spynnaker.pyNN.utilities.constants module

spynnaker.pyNN.utilities.constants.LIVE_POISSON_CONTROL_PARTITION_ID = 'CONTROL'

The partition ID used for Poisson live control data

spynnaker.pyNN.utilities.constants.MIN_SUPPORTED_DELAY = 1

the minimum supported delay slot between two neurons

spynnaker.pyNN.utilities.constants.OUT_SPIKE_BYTES = 32

The number of bytes for each spike line

spynnaker.pyNN.utilities.constants.OUT_SPIKE_SIZE = 8

The size of each output spike line

class spynnaker.pyNN.utilities.constants.POPULATION_BASED_REGIONS

Bases: enum.Enum

Regions for populations.

BIT_FIELD_BUILDER = 13

BIT_FIELD_FILTER = 12

BIT_FIELD_KEY_MAP = 14

CONNECTOR_BUILDER = 10

DIRECT_MATRIX = 11

NEURON_PARAMS = 1

NEURON_RECORDING = 7

POPULATION_TABLE = 3

PROFILING = 9

PROVENANCE_DATA = 8

STRUCTURAL_DYNAMICS = 6

SYNAPSE_DYNAMICS = 5

SYNAPSE_PARAMS = 2

SYNAPTIC_MATRIX = 4

SYSTEM = 0

spynnaker.pyNN.utilities.constants.POP_TABLE_MAX_ROW_LENGTH = 256

The maximum row length of the master population table

spynnaker.pyNN.utilities.constants.SPIKE_PARTITION_ID = 'SPIKE'

The partition ID used for spike data

spynnaker.pyNN.utilities.constants.SYNAPTIC_ROW_HEADER_WORDS = 3

Words: 2 for row length and number of rows and 1 for plastic region size (which might be 0)

spynnaker.pyNN.utilities.data_cache module

class spynnaker.pyNN.utilities.data_cache.DataCache(label, description, segment_number, recording_start_time, t)

Bases: object

Storage object to hold all the data to (re)create a Neo Segment

Note

Required because deep-copy does not work on neo Objects

Stores the Data shared by all variable types at the top level and holds a cache for the variable specific data.

Parameters:

• label (str) – cache label
• description (str or dict) – cache description
• segment_number (int) – cache segment number
• recording_start_time (float) – when this cache was started in recording space.
• t (float) – time

description

get_data(variable)

Get the variable cache for the named variable

Parameters:variable (str) – name of variable to get cache for Returns:The cache data, IDs, indexes and units Return type:VariableCache

has_data(variable)

Checks if data for a variable has been cached

Parameters:variable (str) – Name of variable Returns:True if there is cached data Return type:bool

label

rec_datetime

recording_start_time

save_data(variable, data, indexes, n_neurons, units, sampling_interval)

Saves the data for one variable in this segment

Parameters:

• variable (str) – name of variable data applies to
• data (ndarray) – raw data in sPyNNaker format
• indexes (ndarray) – population indexes for which data should be returned
• n_neurons (int) – Number of neurons in the population, regardless of if they where recording or not.
• units (str) – the units in which the data is
• sampling_interval (float or int) – The number of milliseconds between samples.

segment_number

t

variables

Provides a list of which variables data has been cached for

Return type:Iterator (str)

spynnaker.pyNN.utilities.extracted_data module

class spynnaker.pyNN.utilities.extracted_data.ExtractedData

Bases: object

Data holder for all synaptic data being extracted in parallel.

get(projection, attribute)

Allow getting data from a given projection and attribute

Parameters:

• projection (Projection) – the projection data was extracted from
• attribute (list(int) or tuple(int) or None) – the attribute to retrieve

Returns:

the attribute data in a connection holder

Return type:

ConnectionHolder

set(projection, attribute, data)

Allow the addition of data from a projection and attribute.

Parameters:

• projection (Projection) – the projection data was extracted from
• attribute (list(int) or tuple(int) or None) – the attribute to store
• data (ConnectionHolder) – attribute data in a connection holder

Return type:

None

spynnaker.pyNN.utilities.fake_HBP_Portal_machine_provider module

class spynnaker.pyNN.utilities.fake_HBP_Portal_machine_provider.FakeHBPPortalMachineProvider(n_boards, config)

Bases: object

create()

destroy()

get_machine_info()

wait_till_not_ready()

wait_until_ready()

spynnaker.pyNN.utilities.running_stats module

class spynnaker.pyNN.utilities.running_stats.RunningStats

Bases: object

Keeps running statistics. From: http://www.johndcook.com/blog/skewness_kurtosis/

add_item(x)

Adds an item to the running statistics.

Parameters:x (int or float) – The item to add

add_items(mean, variance, n_items)

Add a bunch of items (via their statistics).

Parameters:

• mean (float) – The mean of the items to add.
• variance (float) – The variance of the items to add.
• n_items (int) – The number of items represented.

mean

The mean of the items seen.

Return type:float

n_items

The number of items seen.

Return type:int

standard_deviation

The population standard deviation of the items seen.

Return type:float

variance

The variance of the items seen.

Return type:float

spynnaker.pyNN.utilities.struct module

class spynnaker.pyNN.utilities.struct.Struct(field_types)

Bases: object

Represents a C code structure.

Parameters:field_types (list(DataType)) – The types of the fields, ordered as they appear in the struct.

field_types

The types of the fields, ordered as they appear in the struct.

Return type:list(DataType)

get_data(values, offset=0, array_size=1)

Get a numpy array of uint32 of data for the given values

Parameters:

• values (list(int or float or list(int) or list(float) or RangedList)) – A list of values with length the same size as the number of fields returned by field_types
• offset (int) – The offset into each of the values where to start
• array_size (int) – The number of structs to generate

Return type:

ndarray(dtype=”uint32”)

get_size_in_whole_words(array_size=1)

Get the size of the struct in whole words in an array of given size (default 1 item)

Parameters:array_size (int) – The number of elements in an array of structs Return type:int

numpy_dtype

The numpy data type of the struct

Return type:dtype

read_data(data, offset=0, array_size=1)

Read a bytearray of data and convert to struct values

Parameters:

• data (bytes or bytearray) – The data to be read
• offset (int) – Index of the byte at the start of the valid data
• array_size (int) – The number of struct elements to read

Returns:

a list of lists of data values, one list for each struct element

Return type:

list(float)

spynnaker.pyNN.utilities.utility_calls module

utility class containing simple helper methods

spynnaker.pyNN.utilities.utility_calls.check_directory_exists_and_create_if_not(filename)

Create a parent directory for a file if it doesn’t exist

Parameters:filename (str) – The file whose parent directory is to be created

spynnaker.pyNN.utilities.utility_calls.convert_param_to_numpy(param, no_atoms)

Convert parameters into numpy arrays.

Parameters:

• param (NumpyRNG or int or float or list(int) or list(float) or ndarray) – the param to convert
• no_atoms (int) – the number of atoms available for conversion of param

Returns:

the converted param as an array of floats

Return type:

ndarray(float)

spynnaker.pyNN.utilities.utility_calls.convert_to(value, data_type)

Convert a value to a given data type

Parameters:

• value – The value to convert
• data_type (DataType) – The data type to convert to

Returns:

The converted data as a numpy data type

Return type:

ndarray(int32)

spynnaker.pyNN.utilities.utility_calls.create_mars_kiss_seeds(rng, seed=None)

generates and checks that the seed values generated by the given random number generator or seed to a random number generator are suitable for use as a mars 64 kiss seed.

Parameters:

• rng (None or RandomState) – the random number generator.
• seed (int or None) – the seed to create a random number generator if not handed.

Returns:

a list of 4 ints which are used by the mars64 kiss random number generator for seeds.

Return type:

list(int)

spynnaker.pyNN.utilities.utility_calls.get_maximum_probable_value(dist, n_items, chance=0.01)

Get the likely maximum value of a RandomDistribution given a number of draws

spynnaker.pyNN.utilities.utility_calls.get_mean(dist)

Get the mean of a RandomDistribution

spynnaker.pyNN.utilities.utility_calls.get_minimum_probable_value(dist, n_items, chance=0.01)

Get the likely minimum value of a RandomDistribution given a number of draws

spynnaker.pyNN.utilities.utility_calls.get_n_bits(n_values)

Determine how many bits are required for the given number of values

Parameters:n_values (int) – the number of values (starting at 0) Returns:the number of bits required to express that many values Return type:int

spynnaker.pyNN.utilities.utility_calls.get_probability_within_range(dist, lower, upper)

Get the probability that a value will fall within the given range for a given RandomDistribution

spynnaker.pyNN.utilities.utility_calls.get_probable_maximum_selected(n_total_trials, n_trials, selection_prob, chance=0.01)

Get the likely maximum number of items that will be selected from a set of n_trials from a total set of n_total_trials with a probability of selection of selection_prob

spynnaker.pyNN.utilities.utility_calls.get_probable_minimum_selected(n_total_trials, n_trials, selection_prob, chance=0.01)

Get the likely minimum number of items that will be selected from a set of n_trials from a total set of n_total_trials with a probability of selection of selection_prob

spynnaker.pyNN.utilities.utility_calls.get_standard_deviation(dist)

Get the standard deviation of a RandomDistribution

spynnaker.pyNN.utilities.utility_calls.get_variance(dist)

Get the variance of a RandomDistribution

spynnaker.pyNN.utilities.utility_calls.high(dist)

Gets the high or max boundary value for this distribution

Could return None

spynnaker.pyNN.utilities.utility_calls.low(dist)

Gets the high or min boundary value for this distribution

Could return None

spynnaker.pyNN.utilities.utility_calls.moved_in_v6(old_location, new_location)

Warns the users that they are using an old import.

In version 7 this will ne upgraded to a exception and then later removed

Parameters:

• old_location (str) – old import
• new_location (str) – new import

Raise:

an exception if in CONTINUOUS_INTEGRATION

spynnaker.pyNN.utilities.utility_calls.read_in_data_from_file(file_path, min_atom, max_atom, min_time, max_time, extra=False)

Read in a file of data values where the values are in a format of:

<time> <atom ID> <data value>

Parameters:

• file_path (str) – absolute path to a file containing the data
• min_atom (int) – min neuron ID to which neurons to read in
• max_atom (int) – max neuron ID to which neurons to read in
• extra –
• min_time (float or int) – min time slot to read neurons values of.
• max_time (float or int) – max time slot to read neurons values of.

Returns:

a numpy array of (time stamp, atom ID, data value)

Return type:

ndarray(tuple(float, int, float))

spynnaker.pyNN.utilities.utility_calls.read_spikes_from_file(file_path, min_atom=0, max_atom=inf, min_time=0, max_time=inf, split_value='\t')

Read spikes from a file formatted as:

<time> <neuron ID>

Parameters:

• file_path (str) – absolute path to a file containing spike values
• min_atom (int or float) – min neuron ID to which neurons to read in
• max_atom (int or float) – max neuron ID to which neurons to read in
• min_time (float or int) – min time slot to read neurons values of.
• max_time (float or int) – max time slot to read neurons values of.
• split_value (str) – the pattern to split by

Returns:

a numpy array with max_atom elements each of which is a list of spike times.

Return type:

numpy.ndarray(int, int)

spynnaker.pyNN.utilities.variable_cache module

class spynnaker.pyNN.utilities.variable_cache.VariableCache(data, indexes, n_neurons, units, sampling_interval)

Bases: object

Simple holder method to keep data, IDs, indexes and units together

Typically used to recreate the Neo object for one type of variable for one segment.

Parameters:

• data (ndarray) – raw data in sPyNNaker format
• indexes (list(int)) – Population indexes for which data was collected
• n_neurons (int) – Number of neurons in the population, regardless of whether they were recording or not.
• units (str) – the units in which the data is
• sampling_interval (float or int) – The number of milliseconds between samples.

data

Return type:ndarray

indexes

Return type:list(int)

n_neurons

Return type:int

sampling_interval

Return type:float or int

units

Return type:str

Module contents