def __init__(self,
size,
cellclass,
cellparams,
structure=None,
label=None):
"""
Instantiates a :py:object:`Population`.
"""
global controller, multi_cast_vertex
# Raise an exception if the Pop. attempts to employ spatial structure
if structure:
raise Exception(
"Spatial structure is unsupported for Populations.")
# Create a graph vertex for the population and add it to PACMAN
self.vertex = cellclass(size, label=label, **cellparams)
#check if the vertex is a cmd sender, if so store for future
if self.vertex.requires_multi_cast_source():
if multi_cast_vertex is None:
multi_cast_vertex = MultiCastSource()
controller.add_vertex(multi_cast_vertex)
edge = graph.Edge(multi_cast_vertex, self.vertex)
controller.add_edge(edge)
self.parameters = PyNNParametersSurrogate(self.vertex)
controller.add_vertex(self.vertex)
#add any dependant edges and verts if needed
dependant_verts, dependant_edges = \
self.vertex.get_dependant_vertexes_edges()
if dependant_verts is not None:
for dependant_vert in dependant_verts:
controller.add_vertex(dependant_vert)
if dependant_edges is not None:
for dependant_edge in dependant_edges:
controller.add_edge(dependant_edge)
#initlise common stuff
self.size = size
self.recordSpikeFile = None
self.recordVFile = None
self.recordGSynFile = None
def __init__(self, size, cellclass, cellparams, structure=None, label=None):
"""
Instantiates a :py:object:`Population`.
"""
global controller, multi_cast_vertex
# Raise an exception if the Pop. attempts to employ spatial structure
if structure:
raise Exception("Spatial structure is unsupported for Populations.")
# Create a graph vertex for the population and add it to PACMAN
self.vertex = cellclass(size, label=label, **cellparams)
#check if the vertex is a cmd sender, if so store for future
if self.vertex.requires_multi_cast_source():
if multi_cast_vertex is None:
multi_cast_vertex = MultiCastSource()
controller.add_vertex(multi_cast_vertex)
edge = graph.Edge(multi_cast_vertex, self.vertex)
controller.add_edge(edge)
self.parameters = PyNNParametersSurrogate(self.vertex)
controller.add_vertex(self.vertex)
#add any dependant edges and verts if needed
dependant_verts, dependant_edges = \
self.vertex.get_dependant_vertexes_edges()
if dependant_verts is not None:
for dependant_vert in dependant_verts:
controller.add_vertex(dependant_vert)
if dependant_edges is not None:
for dependant_edge in dependant_edges:
controller.add_edge(dependant_edge)
#initlise common stuff
self.size = size
self.recordSpikeFile = None
self.recordVFile = None
self.recordGSynFile = None
class Population(object):
"""
A collection neuron of the same types. It encapsulates a type of :class:`pacman103.lib.graph.Vertex`
used with Spiking Neural Networks, comprising n cells (atoms)
of the same :py:mod:`pacman103.front.pynn.models` type.
:param int size:
size (number of cells) of the Population.
:param `pacman103.front.pynn.models` cellclass:
specifies the neural model to use for the Population
:param dict cellparams:
a dictionary containing model specific parameters and values
:param `pyNN.space` structure:
a spatial structure - not supported
:param string label:
a label identifying the Population
"""
def __init__(self,
size,
cellclass,
cellparams,
structure=None,
label=None):
"""
Instantiates a :py:object:`Population`.
"""
global controller, multi_cast_vertex
# Raise an exception if the Pop. attempts to employ spatial structure
if structure:
raise Exception(
"Spatial structure is unsupported for Populations.")
# Create a graph vertex for the population and add it to PACMAN
self.vertex = cellclass(size, label=label, **cellparams)
#check if the vertex is a cmd sender, if so store for future
if self.vertex.requires_multi_cast_source():
if multi_cast_vertex is None:
multi_cast_vertex = MultiCastSource()
controller.add_vertex(multi_cast_vertex)
edge = graph.Edge(multi_cast_vertex, self.vertex)
controller.add_edge(edge)
self.parameters = PyNNParametersSurrogate(self.vertex)
controller.add_vertex(self.vertex)
#add any dependant edges and verts if needed
dependant_verts, dependant_edges = \
self.vertex.get_dependant_vertexes_edges()
if dependant_verts is not None:
for dependant_vert in dependant_verts:
controller.add_vertex(dependant_vert)
if dependant_edges is not None:
for dependant_edge in dependant_edges:
controller.add_edge(dependant_edge)
#initlise common stuff
self.size = size
self.recordSpikeFile = None
self.recordVFile = None
self.recordGSynFile = None
def __add__(self, other):
'''
merges populations
'''
raise NotImplementedError
def _add_recorder(self, variable):
"""Create a new Recorder for the supplied variable."""
raise NotImplementedError
def all(self):
'''
Iterator over cell ids on all nodes.
'''
raise NotImplementedError
@property
def conductance_based(self):
'''
returns a boolean based on if the population is a conductance based pop
'''
raise NotImplementedError
def describe(self, template='population_default.txt', engine='default'):
"""
Returns a human-readable description of the population.
The output may be customized by specifying a different template
togther with an associated template engine (see ``pyNN.descriptions``).
If template is None, then a dictionary containing the template context
will be returned.
"""
raise NotImplementedError
@property
def grandparent(self):
raise NotImplementedError
def get(self, paramter_name, gather=False):
'''
Get the values of a parameter for every local cell in the population.
'''
raise NotImplementedError
def _get_cell_position(self, id):
'''
returns the position of a cell (no idea what a cell is)
'''
raise NotImplementedError
def _get_cell_initial_value(self, id, variable):
'''
set a given cells intial value
'''
raise NotImplementedError
def getSpikes(self, compatible_output=False, gather=True):
"""
Return a 2-column numpy array containing cell ids and spike times for
recorded cells. This is read directly from the memory for the board.
"""
global controller
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
spikes = self.vertex.getSpikes(controller, controller.dao.run_time,
compatible_output)
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer.take_sample()
return spikes
def get_spike_counts(self, gather=True):
"""
Returns the number of spikes for each neuron.
"""
raise NotImplementedError
def get_gsyn(self, gather=True, compatible_output=False):
"""
Return a 3-column numpy array containing cell ids and synaptic conductances for recorded cells.
"""
global controller
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
gsyn = self.vertex.get_gsyn(controller,
gather=gather,
compatible_output=compatible_output)
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer.take_sample()
return gsyn
def get_v(self, gather=True, compatible_output=False):
"""
Return a 3-column numpy array containing cell ids, time, and Vm for recorded cells.
:param bool gather:
not used - inserted to match PyNN specs
:param bool compatible_output:
not used - inserted to match PyNN specs
"""
global controller
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
v = self.vertex.get_v(controller,
gather=gather,
compatible_output=compatible_output)
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer.take_sample()
return v
def id_to_index(self, id):
"""
Given the ID(s) of cell(s) in the Population, return its (their) index
(order in the Population).
"""
raise NotImplementedError
def id_to_local_index(self, id):
"""
Given the ID(s) of cell(s) in the Population, return its (their) index
(order in the Population), counting only cells on the local MPI node.
"""
raise NotImplementedError
def initialize(self, variable, value):
"""
Set the initial value of one of the state variables of the neurons in this population.
"""
initialize_attr = getattr(self.vertex, "initialize_%s" % variable,
None)
if ((initialize_attr == None) or not callable(initialize_attr)):
raise Exception(
"Vertex does not support initialization of parameter %s" %
variable)
initialize_attr(value)
def is_local(self, id):
'''
Determine whether the cell with the given ID exists on the local MPI node.
'''
raise NotImplementedError
def can_record(self, variable):
"""Determine whether `variable` can be recorded from this population."""
raise NotImplementedError
def inject(self, current_source):
"""
Connect a current source to all cells in the Population.
"""
raise NotImplementedError
def __iter__(self):
'''
suppose to iterate over local cells
'''
raise NotImplementedError
def __len__(self):
"""
Returns the total number of cells in the population.
"""
return self.size
@property
def local_size(self):
'''
returns the number of local cells ???
'''
raise NotImplementedError
def meanSpikeCount(self, gather=True):
"""
Returns the mean number of spikes per neuron.
"""
raise NotImplementedError
def nearest(self, position):
'''
return the neuron closest to the specificed position
'''
raise NotImplementedError
@property
def position_generator(self):
'''
returns a position generator
'''
raise NotImplementedError
def randomInit(self, distribution):
"""
Set initial membrane potentials for all the cells in the population to random values.
:param `pyNN.random.RandomDistribution` distribution:
the distribution used to draw random values.
"""
newEntryForVInit = {'v_init': distribution}
self.parameters.update(newEntryForVInit)
def stream(self):
# If the monitor is enabled, add an edge to the monitor
global appMonitorVertex
self.vertex.setup_visualizer(visualiser_mode=visualiser_modes.RASTER)
if appMonitorVertex != None:
controller.add_edge(graph.Edge(self.vertex, appMonitorVertex))
def record(self,
to_file=None,
focus=None,
visualiser_mode=visualiser_modes.RASTER,
visualiser_2d_dimension=None,
visualiser_raster_seperate=None,
visualiser_no_colours=None,
visualiser_average_period_tics=None,
visualiser_longer_period_tics=None,
visualiser_update_screen_in_tics=None,
visualiser_reset_counters=None,
visualiser_reset_counter_period=None,
stream=True):
"""
Record spikes from all cells in the Population.
A flag is set for this population that is passed to the simulation,
triggering spike time recording.
"""
record_attr = getattr(self.vertex, "record", None)
if ((record_attr == None) or not callable(record_attr)):
raise Exception("Vertex does not support recording of spikes")
# Tell the vertex to record spikes
self.vertex.record()
self.recordSpikeFile = to_file
if stream:
self.vertex.setup_visualizer(
focus=focus,
visualiser_mode=visualiser_mode,
visualiser_2d_dimension=visualiser_2d_dimension,
visualiser_raster_seperate=visualiser_raster_seperate,
visualiser_no_colours=visualiser_no_colours,
visualiser_average_period_tics=visualiser_average_period_tics,
visualiser_longer_period_tics=visualiser_longer_period_tics,
visualiser_update_screen_in_tics=
visualiser_update_screen_in_tics,
visualiser_reset_counters=visualiser_reset_counters,
visualiser_reset_counter_period=visualiser_reset_counter_period
)
# If the monitor is enabled, add an edge to the monitor
global appMonitorVertex
if appMonitorVertex != None:
controller.add_edge(graph.Edge(self.vertex, appMonitorVertex))
def record_gsyn(self, to_file=None):
"""
Record the synaptic conductance for all cells in the Population.
A flag is set for this population that is passed to the simulation,
triggering gsyn value recording.
"""
if (not hasattr(self.vertex, "record_gsyn")
or not callable(self.vertex.record_gsyn)):
raise Exception("Vertex does not support recording of gsyn")
self.vertex.record_gsyn()
self.recordGSynFile = to_file
def record_v(self, to_file=None):
"""
Record the membrane potential for all cells in the Population.
A flag is set for this population that is passed to the simulation,
triggering potential recording.
"""
if (not hasattr(self.vertex, "record_v")
or not callable(self.vertex.record_v)):
raise Exception("Vertex does not support recording of potential")
self.vertex.record_v()
self.recordVFile = to_file
@property
def positions(self):
raise NotImplementedError
def printSpikes(self, filename, gather=True):
"""
Write spike time information from the population to a given file.
"""
spikes = self.getSpikes(compatible_output=True)
if spikes != None:
first_id = 0
num_neurons = self.vertex.atoms
dimensions = self.vertex.atoms
last_id = self.vertex.atoms - 1
utility_calls.check_directory_exists(filename)
spikeFile = open(filename, "w")
spikeFile.write("# first_id = %d\n" % first_id)
spikeFile.write("# n = %d\n" % num_neurons)
spikeFile.write("# dimensions = [%d]\n" % dimensions)
spikeFile.write("# last_id = %d\n" % last_id)
for (neuronId, time) in spikes:
spikeFile.write("%d\t%d\n" % (time, neuronId))
spikeFile.close()
def print_gsyn(self, filename, gather=True):
"""
Write conductance information from the population to a given file.
"""
global controller
timeStep = (controller.dao.machineTimeStep * 1.0) / 1000.0
gsyn = self.get_gsyn(gather, compatible_output=True)
first_id = 0
num_neurons = self.vertex.atoms
dimensions = self.vertex.atoms
fileHandle = open(filename, "w")
fileHandle.write("# first_id = %d\n" % first_id)
fileHandle.write("# n = %d\n" % num_neurons)
fileHandle.write("# dt = %f\n" % timeStep)
fileHandle.write("# dimensions = [%d]\n" % dimensions)
fileHandle.write("# last_id = %d\n" % num_neurons - 1)
utility_calls.check_directory_exists(filename)
fileHandle = open(filename, "w")
for (neuronId, time, value) in gsyn:
fileHandle.write("%f\t%d\t%f\n" % (time, neuronId, value))
fileHandle.close()
def print_v(self, filename, gather=True):
"""
Write membrane potential information from the population to a given file.
"""
global controller
timeStep = (controller.dao.machineTimeStep * 1.0) / 1000.0
v = self.get_v(gather, compatible_output=True)
utility_calls.check_directory_exists(filename)
fileHandle = open(filename, "w")
first_id = 0
num_neurons = self.vertex.atoms
dimensions = self.vertex.atoms
fileHandle.write("# first_id = %d\n" % first_id)
fileHandle.write("# n = %d\n" % num_neurons)
fileHandle.write("# dt = %f\n" % timeStep)
fileHandle.write("# dimensions = [%d]\n" % dimensions)
fileHandle.write("# last_id = %d\n" % (num_neurons - 1))
for (neuronId, time, value) in v:
fileHandle.write("%f\t%d\n" % (value, neuronId))
fileHandle.close()
def rset(self, parametername, rand_distr):
"""
'Random' set. Set the value of parametername to a value taken from
rand_distr, which should be a RandomDistribution object.
"""
raise NotImplementedError
def sample(self, n, rng=None):
'''
returns a random selection fo neurons from a population in the form
of a population view
'''
raise NotImplementedError
def save_positions(self, file):
'''
save positions to file
'''
raise NotImplementedError
def set(self, *args, **kargs):
"""
converts key value pairs in key args into a collection of string
parameter and value entries used with old fashion set.
Assumes parameters_surrogate will throw error when entries not
avilable for a vertex is given
"""
if len(args) == 0:
for key in kargs.keys():
self._set_string_value_pair(key, kargs[key])
else:
for element in range(0, len(args), 2):
self._set_string_value_pair(args[element], args[element + 1])
def _set_cell_initial_value(self, id, variable, value):
'''
set a given cells intial value
'''
raise NotImplementedError
def _set_cell_position(self, id, pos):
'''
sets a cell to a given position
'''
raise NotImplementedError
def _set_string_value_pair(self, parameter, value=None):
"""
Set one or more parameters for every cell in the population.
param can be a dict, in which case val should not be supplied, or a string
giving the parameter name, in which case val is the parameter value.
val can be a numeric value, or list of such (e.g. for setting spike times)::
p.set("tau_m", 20.0).
p.set({'tau_m':20, 'v_rest':-65})
"""
if type(parameter) is str:
if value == None:
raise Exception(
"Error: No value given in set() function for population parameter. Exiting."
)
self.parameters[parameter] = value
return
if type(parameter) is not dict:
raise Exception(
"Error: invalid parameter type for set() function for population parameter. Exiting."
)
# Add a dictionary-structured set of new parameters to the current set:
self.parameters.update(parameter)
def set_mapping_constraint(self, constraint):
"""
Apply a constraint to a population that restricts the processor
onto which its sub-populations will be placed.
"""
placementConstraint = lib_map.VertexConstraints()
if 'x' in constraint:
placementConstraint.x = constraint['x']
if 'y' in constraint:
placementConstraint.y = constraint['y']
if 'p' in constraint:
placementConstraint.p = constraint['p']
self.vertex.constraints = placementConstraint
@property
def structure(self):
raise NotImplementedError
def tset(self, parametername, value_array):
"""
'Topographic' set. Set the value of parametername to the values in
value_array, which must have the same dimensions as the Population.
"""
raise NotImplementedError
class Population( object ):
"""
A collection neuron of the same types. It encapsulates a type of :class:`pacman103.lib.graph.Vertex`
used with Spiking Neural Networks, comprising n cells (atoms)
of the same :py:mod:`pacman103.front.pynn.models` type.
:param int size:
size (number of cells) of the Population.
:param `pacman103.front.pynn.models` cellclass:
specifies the neural model to use for the Population
:param dict cellparams:
a dictionary containing model specific parameters and values
:param `pyNN.space` structure:
a spatial structure - not supported
:param string label:
a label identifying the Population
"""
def __init__(self, size, cellclass, cellparams, structure=None, label=None):
"""
Instantiates a :py:object:`Population`.
"""
global controller, multi_cast_vertex
# Raise an exception if the Pop. attempts to employ spatial structure
if structure:
raise Exception("Spatial structure is unsupported for Populations.")
# Create a graph vertex for the population and add it to PACMAN
self.vertex = cellclass(size, label=label, **cellparams)
#check if the vertex is a cmd sender, if so store for future
if self.vertex.requires_multi_cast_source():
if multi_cast_vertex is None:
multi_cast_vertex = MultiCastSource()
controller.add_vertex(multi_cast_vertex)
edge = graph.Edge(multi_cast_vertex, self.vertex)
controller.add_edge(edge)
self.parameters = PyNNParametersSurrogate(self.vertex)
controller.add_vertex(self.vertex)
#add any dependant edges and verts if needed
dependant_verts, dependant_edges = \
self.vertex.get_dependant_vertexes_edges()
if dependant_verts is not None:
for dependant_vert in dependant_verts:
controller.add_vertex(dependant_vert)
if dependant_edges is not None:
for dependant_edge in dependant_edges:
controller.add_edge(dependant_edge)
#initlise common stuff
self.size = size
self.recordSpikeFile = None
self.recordVFile = None
self.recordGSynFile = None
def __add__(self, other):
'''
merges populations
'''
raise NotImplementedError
def _add_recorder(self, variable):
"""Create a new Recorder for the supplied variable."""
raise NotImplementedError
def all(self):
'''
Iterator over cell ids on all nodes.
'''
raise NotImplementedError
@property
def conductance_based(self):
'''
returns a boolean based on if the population is a conductance based pop
'''
raise NotImplementedError
def describe(self, template='population_default.txt', engine='default'):
"""
Returns a human-readable description of the population.
The output may be customized by specifying a different template
togther with an associated template engine (see ``pyNN.descriptions``).
If template is None, then a dictionary containing the template context
will be returned.
"""
raise NotImplementedError
@property
def grandparent(self):
raise NotImplementedError
def get(self, paramter_name, gather=False):
'''
Get the values of a parameter for every local cell in the population.
'''
raise NotImplementedError
def _get_cell_position(self, id):
'''
returns the position of a cell (no idea what a cell is)
'''
raise NotImplementedError
def _get_cell_initial_value(self, id, variable):
'''
set a given cells intial value
'''
raise NotImplementedError
def getSpikes(self, compatible_output=False, gather=True):
"""
Return a 2-column numpy array containing cell ids and spike times for
recorded cells. This is read directly from the memory for the board.
"""
global controller
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
spikes = self.vertex.getSpikes(controller, controller.dao.run_time, compatible_output)
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer.take_sample()
return spikes
def get_spike_counts(self, gather=True):
"""
Returns the number of spikes for each neuron.
"""
raise NotImplementedError
def get_gsyn(self, gather=True, compatible_output=False):
"""
Return a 3-column numpy array containing cell ids and synaptic conductances for recorded cells.
"""
global controller
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
gsyn = self.vertex.get_gsyn(controller, gather=gather, compatible_output=compatible_output)
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer.take_sample()
return gsyn
def get_v(self, gather=True, compatible_output=False):
"""
Return a 3-column numpy array containing cell ids, time, and Vm for recorded cells.
:param bool gather:
not used - inserted to match PyNN specs
:param bool compatible_output:
not used - inserted to match PyNN specs
"""
global controller
timer = None
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer = Timer()
timer.start_timing()
v = self.vertex.get_v(controller, gather=gather, compatible_output=compatible_output)
if conf.config.getboolean("Reports", "outputTimesForSections"):
timer.take_sample()
return v
def id_to_index(self, id):
"""
Given the ID(s) of cell(s) in the Population, return its (their) index
(order in the Population).
"""
raise NotImplementedError
def id_to_local_index(self, id):
"""
Given the ID(s) of cell(s) in the Population, return its (their) index
(order in the Population), counting only cells on the local MPI node.
"""
raise NotImplementedError
def initialize(self, variable, value):
"""
Set the initial value of one of the state variables of the neurons in this population.
"""
initialize_attr = getattr(self.vertex, "initialize_%s" % variable, None)
if ((initialize_attr == None) or not callable(initialize_attr)):
raise Exception("Vertex does not support initialization of parameter %s" % variable)
initialize_attr(value)
def is_local(self, id):
'''
Determine whether the cell with the given ID exists on the local MPI node.
'''
raise NotImplementedError
def can_record(self, variable):
"""Determine whether `variable` can be recorded from this population."""
raise NotImplementedError
def inject(self, current_source):
"""
Connect a current source to all cells in the Population.
"""
raise NotImplementedError
def __iter__(self):
'''
suppose to iterate over local cells
'''
raise NotImplementedError
def __len__(self):
"""
Returns the total number of cells in the population.
"""
return self.size
@property
def local_size(self):
'''
returns the number of local cells ???
'''
raise NotImplementedError
def meanSpikeCount(self, gather=True):
"""
Returns the mean number of spikes per neuron.
"""
raise NotImplementedError
def nearest(self, position):
'''
return the neuron closest to the specificed position
'''
raise NotImplementedError
@property
def position_generator(self):
'''
returns a position generator
'''
raise NotImplementedError
def randomInit(self, distribution):
"""
Set initial membrane potentials for all the cells in the population to random values.
:param `pyNN.random.RandomDistribution` distribution:
the distribution used to draw random values.
"""
newEntryForVInit = {'v_init': distribution}
self.parameters.update(newEntryForVInit)
def stream(self):
# If the monitor is enabled, add an edge to the monitor
global appMonitorVertex
self.vertex.setup_visualizer(visualiser_mode=visualiser_modes.RASTER)
if appMonitorVertex != None:
controller.add_edge(graph.Edge(self.vertex, appMonitorVertex))
def record(self, to_file=None,
focus=None, visualiser_mode=visualiser_modes.RASTER,
visualiser_2d_dimension=None, visualiser_raster_seperate=None,
visualiser_no_colours=None, visualiser_average_period_tics=None,
visualiser_longer_period_tics=None,
visualiser_update_screen_in_tics=None,
visualiser_reset_counters=None,
visualiser_reset_counter_period=None,
stream=True):
"""
Record spikes from all cells in the Population.
A flag is set for this population that is passed to the simulation,
triggering spike time recording.
"""
record_attr = getattr(self.vertex, "record", None)
if ((record_attr == None) or not callable(record_attr)):
raise Exception("Vertex does not support recording of spikes")
# Tell the vertex to record spikes
self.vertex.record()
self.recordSpikeFile = to_file
if stream:
self.vertex.setup_visualizer(focus=focus, visualiser_mode=visualiser_mode,
visualiser_2d_dimension=visualiser_2d_dimension,
visualiser_raster_seperate=visualiser_raster_seperate,
visualiser_no_colours=visualiser_no_colours,
visualiser_average_period_tics=visualiser_average_period_tics,
visualiser_longer_period_tics=visualiser_longer_period_tics,
visualiser_update_screen_in_tics=visualiser_update_screen_in_tics,
visualiser_reset_counters=visualiser_reset_counters,
visualiser_reset_counter_period=visualiser_reset_counter_period)
# If the monitor is enabled, add an edge to the monitor
global appMonitorVertex
if appMonitorVertex != None:
controller.add_edge(graph.Edge(self.vertex, appMonitorVertex))
def record_gsyn(self, to_file=None):
"""
Record the synaptic conductance for all cells in the Population.
A flag is set for this population that is passed to the simulation,
triggering gsyn value recording.
"""
if (not hasattr(self.vertex, "record_gsyn")
or not callable(self.vertex.record_gsyn)):
raise Exception("Vertex does not support recording of gsyn")
self.vertex.record_gsyn()
self.recordGSynFile = to_file
def record_v(self, to_file=None):
"""
Record the membrane potential for all cells in the Population.
A flag is set for this population that is passed to the simulation,
triggering potential recording.
"""
if (not hasattr(self.vertex, "record_v")
or not callable(self.vertex.record_v)):
raise Exception("Vertex does not support recording of potential")
self.vertex.record_v()
self.recordVFile = to_file
@property
def positions(self):
raise NotImplementedError
def printSpikes(self, filename, gather=True):
"""
Write spike time information from the population to a given file.
"""
spikes = self.getSpikes(compatible_output=True)
if spikes != None:
first_id = 0
num_neurons = self.vertex.atoms
dimensions = self.vertex.atoms
last_id = self.vertex.atoms - 1
utility_calls.check_directory_exists(filename)
spikeFile = open(filename, "w")
spikeFile.write("# first_id = %d\n" % first_id)
spikeFile.write("# n = %d\n" % num_neurons)
spikeFile.write("# dimensions = [%d]\n" % dimensions)
spikeFile.write("# last_id = %d\n" % last_id)
for (neuronId, time) in spikes:
spikeFile.write("%d\t%d\n" % (time, neuronId))
spikeFile.close()
def print_gsyn(self, filename, gather=True):
"""
Write conductance information from the population to a given file.
"""
global controller
timeStep = (controller.dao.machineTimeStep*1.0)/1000.0
gsyn = self.get_gsyn(gather, compatible_output=True)
first_id = 0
num_neurons = self.vertex.atoms
dimensions = self.vertex.atoms
fileHandle = open(filename, "w")
fileHandle.write("# first_id = %d\n" % first_id)
fileHandle.write("# n = %d\n" % num_neurons)
fileHandle.write("# dt = %f\n" % timeStep)
fileHandle.write("# dimensions = [%d]\n" % dimensions)
fileHandle.write("# last_id = %d\n" % num_neurons-1)
utility_calls.check_directory_exists(filename)
fileHandle = open(filename, "w")
for (neuronId, time, value) in gsyn:
fileHandle.write("%f\t%d\t%f\n" % (time, neuronId, value))
fileHandle.close()
def print_v(self, filename, gather=True):
"""
Write membrane potential information from the population to a given file.
"""
global controller
timeStep = (controller.dao.machineTimeStep*1.0)/1000.0
v = self.get_v(gather, compatible_output=True)
utility_calls.check_directory_exists(filename)
fileHandle = open(filename, "w")
first_id = 0
num_neurons = self.vertex.atoms
dimensions = self.vertex.atoms
fileHandle.write("# first_id = %d\n" % first_id)
fileHandle.write("# n = %d\n" % num_neurons)
fileHandle.write("# dt = %f\n" % timeStep)
fileHandle.write("# dimensions = [%d]\n" % dimensions)
fileHandle.write("# last_id = %d\n" % (num_neurons-1))
for (neuronId, time, value) in v:
fileHandle.write("%f\t%d\n" % (value, neuronId))
fileHandle.close()
def rset(self, parametername, rand_distr):
"""
'Random' set. Set the value of parametername to a value taken from
rand_distr, which should be a RandomDistribution object.
"""
raise NotImplementedError
def sample(self, n, rng=None):
'''
returns a random selection fo neurons from a population in the form
of a population view
'''
raise NotImplementedError
def save_positions(self, file):
'''
save positions to file
'''
raise NotImplementedError
def set(self, *args, **kargs):
"""
converts key value pairs in key args into a collection of string
parameter and value entries used with old fashion set.
Assumes parameters_surrogate will throw error when entries not
avilable for a vertex is given
"""
if len(args) == 0:
for key in kargs.keys():
self._set_string_value_pair(key, kargs[key])
else:
for element in range(0, len(args), 2):
self._set_string_value_pair(args[element], args[element+1])
def _set_cell_initial_value(self, id, variable, value):
'''
set a given cells intial value
'''
raise NotImplementedError
def _set_cell_position(self, id, pos):
'''
sets a cell to a given position
'''
raise NotImplementedError
def _set_string_value_pair(self, parameter, value=None):
"""
Set one or more parameters for every cell in the population.
param can be a dict, in which case val should not be supplied, or a string
giving the parameter name, in which case val is the parameter value.
val can be a numeric value, or list of such (e.g. for setting spike times)::
p.set("tau_m", 20.0).
p.set({'tau_m':20, 'v_rest':-65})
"""
if type(parameter) is str:
if value==None:
raise Exception("Error: No value given in set() function for population parameter. Exiting.")
self.parameters[parameter] = value
return
if type(parameter) is not dict:
raise Exception("Error: invalid parameter type for set() function for population parameter. Exiting.")
# Add a dictionary-structured set of new parameters to the current set:
self.parameters.update(parameter)
def set_mapping_constraint(self, constraint):
"""
Apply a constraint to a population that restricts the processor
onto which its sub-populations will be placed.
"""
placementConstraint = lib_map.VertexConstraints()
if 'x' in constraint:
placementConstraint.x = constraint['x']
if 'y' in constraint:
placementConstraint.y = constraint['y']
if 'p' in constraint:
placementConstraint.p = constraint['p']
self.vertex.constraints = placementConstraint
@property
def structure(self):
raise NotImplementedError
def tset(self, parametername, value_array):
"""
'Topographic' set. Set the value of parametername to the values in
value_array, which must have the same dimensions as the Population.
"""
raise NotImplementedError