def __init__(
self,
sim,
synapses,
synapse_pop_name=None,
user_tags=None,
):
# Some functions return lists of synapses; so we
# reduce the input down to a flat list:
self.synapses = []
for s in synapses:
if s is None:
continue
elif is_iterable(s):
self.synapses.extend(s)
else:
print 'Not Iterable:', s
self.synapses.append(s)
for s in self.synapses:
print s, type(s)
assert s.population is None
s.population = self
self.sim = sim
self.synapse_pop_name=synapse_pop_name if synapse_pop_name is not None else ObjectLabeller.get_next_unamed_object_name(SynapsePopulation, prefix="SynPop")
user_tags = user_tags or []
def __init__(self, sim, neuron_functor, n, pop_name=None, name_tmpl_str=None, user_tags=None):
user_tags = user_tags or []
if pop_name:
user_tags.extend(pop_name.split("_"))
if pop_name is None:
pop_name = ObjectLabeller.get_next_unamed_object_name(
NeuronPopulation, prefix="NrnPop", num_fmt_string="%d"
)
self.pop_name = pop_name
if name_tmpl_str is None:
name_tmpl_str = "%s_$i" % self.pop_name
name_tmpl = string.Template(name_tmpl_str)
self.sim = sim
# Create the neurons:
self._nrns = []
for i in range(n):
cell_name = name_tmpl.substitute({"i": i})
cell_tags = user_tags + ["Index%d" % i]
n = neuron_functor(sim=sim, name=cell_name, cell_tags=cell_tags)
n.population = self
self._nrns.append(n)
self._cell_to_index_lut = self._build_cell_to_index_lut()
def __init__(self, region_number_to_name_bidict=None, name=None,
metadata=None):
self.region_number_to_name_bidict = region_number_to_name_bidict
self._name = name or ObjectLabeller.get_next_unamed_object_name(MorphologyBase)
check_cstyle_varname(self._name)
self.metadata = (metadata if metadata else {})
def __init__(self,
region_number_to_name_bidict=None,
name=None,
metadata=None):
self.region_number_to_name_bidict = region_number_to_name_bidict
self._name = name or ObjectLabeller.get_next_unamed_object_name(
MorphologyBase)
check_cstyle_varname(self._name)
self.metadata = (metadata if metadata else {})
def __init__(
self,
sim,
neuron_functor,
n,
pop_name=None,
name_tmpl_str=None,
user_tags=None,
):
user_tags = user_tags or []
if pop_name:
user_tags.extend(pop_name.split('_'))
if pop_name is None:
pop_name = ObjectLabeller.get_next_unamed_object_name(NeuronPopulation, prefix='NrnPop', num_fmt_string='%d')
self.pop_name = pop_name
if name_tmpl_str is None:
name_tmpl_str = '%s_$i' % self.pop_name
name_tmpl = string.Template(name_tmpl_str)
self.sim = sim
# Create the neurons:
self._nrns = []
for i in range(n):
cell_name = name_tmpl.substitute({'i': i})
# print cell_name
# assert False
cell_tags = user_tags + ['Index%d' % i]
n = neuron_functor(sim=sim, name=cell_name,
cell_tags=cell_tags)
n.population = self
self._nrns.append(n)
self._cell_to_index_lut = self._build_cell_to_index_lut()
def __init__(
self,
sim,
neuron_functor,
n,
pop_name=None,
name_tmpl_str=None,
user_tags=None,
):
user_tags = user_tags or []
if pop_name:
user_tags.extend(pop_name.split('_'))
if pop_name is None:
pop_name = ObjectLabeller.get_next_unamed_object_name(
NeuronPopulation, prefix='NrnPop', num_fmt_string='%d')
self.pop_name = pop_name
if name_tmpl_str is None:
name_tmpl_str = '%s_$i' % self.pop_name
name_tmpl = string.Template(name_tmpl_str)
self.sim = sim
# Create the neurons:
self._nrns = []
for i in range(n):
cell_name = name_tmpl.substitute({'i': i})
# print cell_name
# assert False
cell_tags = user_tags + ['Index%d' % i]
n = neuron_functor(sim=sim, name=cell_name, cell_tags=cell_tags)
n.population = self
self._nrns.append(n)
self._cell_to_index_lut = self._build_cell_to_index_lut()
def __init__(
self,
sim,
synapses,
synapse_pop_name=None,
user_tags=None,
):
# Some functions return lists of synapses; so we
# reduce the input down to a flat list:
self.synapses = []
for s in synapses:
if s is None:
continue
elif is_iterable(s):
self.synapses.extend(s)
else:
print 'Not Iterable:', s
self.synapses.append(s)
for s in self.synapses:
print s, type(s)
assert s.population is None
s.population = self
self.sim = sim
self.synapse_pop_name = synapse_pop_name if synapse_pop_name is not None else ObjectLabeller.get_next_unamed_object_name(
SynapsePopulation, prefix="SynPop")
user_tags = user_tags or []
