def __init__(self, logger):
self.commited = False
self.logger = logger
self.output_struct = VisualisableNetworkStructure()
self.pynn_units_it = []
# individual units list
self.vis_units = []
# ordered list of PyNN maps
self.maps = []
# dict of (pyNN population, previous (latest) list of
# population units' spikes counts):
self.spikes = dict()
# individual connections list
self.units_connections = []
self.maps_connections = []
self.num_units = 0
# Aliases for names of populations {PyNN_name: alias, ...}
self.aliases = {}
def __init__(self, logger):
self.commited = False
self.logger = logger
self.output_struct = VisualisableNetworkStructure()
self.pynn_units_it = []
# individual units list
self.vis_units = []
# ordered list of PyNN maps
self.maps = []
# dict of (pyNN population, previous (latest) list of
# population units' spikes counts):
self.spikes = dict()
# individual connections list
self.units_connections = []
self.maps_connections = []
self.num_units = 0
# Aliases for names of populations {PyNN_name: alias, ...}
self.aliases = {}
class PynnToVisuAdapter(object):
def __init__(self, logger):
self.commited = False
self.logger = logger
self.output_struct = VisualisableNetworkStructure()
self.pynn_units_it = []
# individual units list
self.vis_units = []
# ordered list of PyNN maps
self.maps = []
# dict of (pyNN population, previous (latest) list of
# population units' spikes counts):
self.spikes = dict()
# individual connections list
self.units_connections = []
self.maps_connections = []
self.num_units = 0
# Aliases for names of populations {PyNN_name: alias, ...}
self.aliases = {}
def check_open(self):
"""Returns if the network structure is open for changes"""
return not self.commited
def assert_open(self):
"""Checks if the network structure is still open for changes,
raising an exceptio if not"""
if not self.check_open():
raise AdapterLockedError()
def commit_structure(self):
"""Adds all units to the output network structure, sorted by
PyNN ID, and closes the adapter to prevent further structural
change."""
self.assert_open()
self.vis_units = sorted(self.vis_units,
key=lambda v_u_t: v_u_t[1].real)
for u in self.vis_units:
self.output_struct.add_unit(u[0], u[2])
self.output_struct.connect_units_list(self.units_connections)
for c in self.maps_connections:
self.output_struct.connect_maps(c[0], c[1])
self.commited = True
def reopen(self):
"""TODO: to be able to reopen, we should keep track of units
already commited to the output struct."""
raise NotImplementedError
def add_pynn_population(self, p, alias=None):
""" Adds all units of the pyNN population p to the list of
visualizable units"""
self.assert_open()
if alias == None:
alias = p.label
self.pynn_units_it = itertools.chain(self.pynn_units_it, p)
three_d = "dz" in p.structure.parameter_names
for pynn_u in p:
u = None
if not three_d:
u = Unit(int(pynn_u), pynn_u.position[0], pynn_u.position[1])
else:
u = Unit(int(pynn_u), pynn_u.position[0], pynn_u.position[1],
pynn_u.position[2])
self.vis_units.append((u, pynn_u, alias))
# We add the unit when commiting the structure, because we
# need to add them to the output_struct in global id order
self.num_units += p.size
self.maps.append(p)
self.spikes[p] = [0] * p.size
self.aliases[p.label] = alias
def add_pynn_projection(self, sending_population, receiving_population,
projection):
"""Records a projection from sending_population to
receiving_population. The projection parameter expects a
pyNN.common.Projection."""
self.assert_open()
w = projection.get("weight", "array")
n_send, n_recv = w.shape
for i in range(0, n_send):
pynn_u = sending_population[i]
# list of tuples containing the sending unit's global
# index (same for each tuple), the global indices of the
# receiving cells in existing connections from neuron i,
# and the weights
connections = [(pynn_u.real, receiving_population[a].real,
self.convert_weight(b)) for a, b in enumerate(w[i])
if not math.isnan(b)]
self.units_connections.extend(connections)
self.maps_connections.append(
(sending_population.label, receiving_population.label))
def convert_weight(self, w):
"""Converts pyNN weights by normalizing them from their
natural range to [-1, 1]."""
# TODO: this is a dummy method. it should prompt pyNN for the
# range and use a sigmoidal adjustment.
if w > 1 or w < -1:
w = min(max(w, -1), 1)
self.logger.info("convert_weights made a dummy adjustment.")
return w
def make_activity_update_message(self):
newly_fired = dict()
max_current_activity_list = list()
for m in self.maps:
spikes = m.get_spike_counts().values()
newly_fired[m] = map(operator.sub, spikes, self.spikes[m])
self.spikes[m] = spikes
max_current_activity_list.append(max(newly_fired[m]))
self.logger.debug(("A map with %s units and a max activity of "
"%s passes by."), len(newly_fired[m]),
max(newly_fired[m]))
max_current_activity = max(max_current_activity_list)
activity = list()
if max_current_activity == 0:
activity = [0] * self.num_units
self.logger.debug(("There is no overall activity, making an"
"update list of %s zeros."), self.num_units)
else:
for m in self.maps:
activity = activity + \
map(lambda x: min(1.,
x / float(max_current_activity)),
newly_fired[m])
self.logger.debug(("The update list contains %s values. It "
"looks like that: %s"), len(activity), activity)
return ActivityUpdateMessage(activity)
class PynnToVisuAdapter(object):
def __init__(self, logger):
self.commited = False
self.logger = logger
self.output_struct = VisualisableNetworkStructure()
self.pynn_units_it = []
# individual units list
self.vis_units = []
# ordered list of PyNN maps
self.maps = []
# dict of (pyNN population, previous (latest) list of
# population units' spikes counts):
self.spikes = dict()
# individual connections list
self.units_connections = []
self.maps_connections = []
self.num_units = 0
# Aliases for names of populations {PyNN_name: alias, ...}
self.aliases = {}
def check_open(self):
"""Returns if the network structure is open for changes"""
return not self.commited
def assert_open(self):
"""Checks if the network structure is still open for changes,
raising an exceptio if not"""
if not self.check_open():
raise AdapterLockedError()
def commit_structure(self):
"""Adds all units to the output network structure, sorted by
PyNN ID, and closes the adapter to prevent further structural
change."""
self.assert_open()
self.vis_units = sorted(
self.vis_units, key = lambda v_u_t: v_u_t[1].real)
for u in self.vis_units:
self.output_struct.add_unit(u[0], u[2])
self.output_struct.connect_units_list(self.units_connections)
for c in self.maps_connections:
self.output_struct.connect_maps(c[0], c[1])
self.commited = True
def reopen(self):
"""TODO: to be able to reopen, we should keep track of units
already commited to the output struct."""
raise NotImplementedError
def add_pynn_population(self, p, alias = None):
""" Adds all units of the pyNN population p to the list of
visualizable units"""
self.assert_open()
if alias == None:
alias = p.label
self.pynn_units_it = itertools.chain(self.pynn_units_it, p)
three_d = "dz" in p.structure.parameter_names
for pynn_u in p:
u = None
if not three_d:
u = Unit(
int(pynn_u),
pynn_u.position[0], pynn_u.position[1])
else:
u = Unit(
int(pynn_u),
pynn_u.position[0], pynn_u.position[1],
pynn_u.position[2])
self.vis_units.append((u, pynn_u, alias))
# We add the unit when commiting the structure, because we
# need to add them to the output_struct in global id order
self.num_units += p.size
self.maps.append(p)
self.spikes[p] = [0]*p.size
self.aliases[p.label] = alias
def add_pynn_projection(self, sending_population,
receiving_population,
projection):
"""Records a projection from sending_population to
receiving_population. The projection parameter expects a
pyNN.common.Projection."""
self.assert_open()
w = projection.get("weight", "array")
n_send, n_recv = w.shape
for i in range(0, n_send):
pynn_u = sending_population[i]
# list of tuples containing the sending unit's global
# index (same for each tuple), the global indices of the
# receiving cells in existing connections from neuron i,
# and the weights
connections = [
(pynn_u.real,
receiving_population[a].real,
self.convert_weight(b))
for a, b in enumerate(w[i]) if not math.isnan(b)]
self.units_connections.extend(connections)
self.maps_connections.append((sending_population.label,
receiving_population.label))
def convert_weight(self, w):
"""Converts pyNN weights by normalizing them from their
natural range to [-1, 1]."""
# TODO: this is a dummy method. it should prompt pyNN for the
# range and use a sigmoidal adjustment.
if w > 1 or w < -1:
w = min(max(w, -1), 1)
self.logger.info("convert_weights made a dummy adjustment.")
return w
def make_activity_update_message(self):
newly_fired = dict()
max_current_activity_list = list()
for m in self.maps:
spikes = m.get_spike_counts().values()
newly_fired[m] = map(operator.sub, spikes, self.spikes[m])
self.spikes[m] = spikes
max_current_activity_list.append(max(newly_fired[m]))
self.logger.debug(("A map with %s units and a max activity of "
"%s passes by."), len(newly_fired[m]),
max(newly_fired[m]))
max_current_activity = max(max_current_activity_list)
activity = list()
if max_current_activity == 0:
activity = [0] * self.num_units
self.logger.debug(("There is no overall activity, making an"
"update list of %s zeros."), self.num_units)
else:
for m in self.maps:
activity = activity + \
map(lambda x: min(1.,
x / float(max_current_activity)),
newly_fired[m])
self.logger.debug(("The update list contains %s values. It "
"looks like that: %s"), len(activity),
activity)
return ActivityUpdateMessage(activity)
