def __init__(self,
target,
target_var,
N,
rate,
weight,
when='synapses',
order=0):
if target_var not in target.variables:
raise KeyError('%s is not a variable of %s' %
(target_var, target.name))
self._weight = weight
self._target_var = target_var
if isinstance(weight, str):
weight = '(%s)' % weight
else:
weight_dims = get_dimensions(weight)
target_dims = target.variables[target_var].dim
# This will be checked automatically in the abstract code as well
# but doing an explicit check here allows for a clearer error
# message
if not have_same_dimensions(weight_dims, target_dims):
raise DimensionMismatchError(
('The provided weight does not '
'have the same unit as the '
'target variable "%s"') % target_var, weight_dims,
target_dims)
weight = repr(weight)
self._N = N
self._rate = rate
binomial_sampling = BinomialFunction(N,
rate * target.clock.dt,
name='poissoninput_binomial*')
code = '{targetvar} += {binomial}()*{weight}'.format(
targetvar=target_var,
binomial=binomial_sampling.name,
weight=weight)
self._stored_dt = target.dt_[:] # make a copy
# FIXME: we need an explicit reference here for on-the-fly subgroups
# For example: PoissonInput(group[:N], ...)
self._group = target
CodeRunner.__init__(self,
group=target,
template='stateupdate',
code=code,
user_code='',
when=when,
order=order,
name='poissoninput*',
clock=target.clock)
self.variables = Variables(self)
self.variables._add_variable(binomial_sampling.name, binomial_sampling)
def __init__(self,
source,
name='ratemonitor*',
codeobj_class=None,
dtype=np.float64):
#: The group we are recording from
self.source = source
self.codeobj_class = codeobj_class
CodeRunner.__init__(self,
group=self,
code='',
template='ratemonitor',
clock=source.clock,
when='end',
order=0,
name=name)
self.add_dependency(source)
self.variables = Variables(self)
# Handle subgroups correctly
start = getattr(source, 'start', 0)
stop = getattr(source, 'stop', len(source))
self.variables.add_constant('_source_start', start)
self.variables.add_constant('_source_stop', stop)
self.variables.add_reference('_spikespace', source)
self.variables.add_dynamic_array('rate',
size=0,
dimensions=hertz.dim,
read_only=True,
dtype=dtype)
self.variables.add_dynamic_array(
't',
size=0,
dimensions=second.dim,
read_only=True,
dtype=self._clock.variables['t'].dtype)
self.variables.add_reference('_num_source_neurons', source, 'N')
self.variables.add_array('N',
dtype=np.int32,
size=1,
scalar=True,
read_only=True)
self.variables.create_clock_variables(self._clock, prefix='_clock_')
self._enable_group_attributes()
def __init__(self, group, method, clock, order=0):
# group is the neuron (a group of compartments)
self.method_choice = method
self.group = weakref.proxy(group)
compartments = group.flat_morphology.n
sections = group.flat_morphology.sections
CodeRunner.__init__(self, group,
'spatialstateupdate',
code='''_gtot = gtot__private
_I0 = I0__private''',
clock=clock,
when='groups',
order=order,
name=group.name + '_spatialstateupdater*',
check_units=False,
template_kwds={'number_sections': sections})
self.variables = Variables(self, default_index='_section_idx')
self.variables.add_reference('N', group)
# One value per compartment
self.variables.add_arange('_compartment_idx', size=compartments)
self.variables.add_array('_invr', dimensions=siemens.dim,
size=compartments, constant=True,
index='_compartment_idx')
# one value per section
self.variables.add_arange('_section_idx', size=sections)
self.variables.add_array('_P_parent', size=sections,
constant=True) # elements below diagonal
self.variables.add_arrays(['_morph_idxchild', '_morph_parent_i',
'_starts', '_ends'], size=sections,
dtype=np.int32, constant=True)
self.variables.add_arrays(['_invr0', '_invrn'], dimensions=siemens.dim,
size=sections, constant=True)
# one value per section + 1 value for the root
self.variables.add_arange('_section_root_idx', size=sections+1)
self.variables.add_array('_P_diag', size=sections+1,
constant=True, index='_section_root_idx')
self.variables.add_array('_B', size=sections+1,
constant=True, index='_section_root_idx')
self.variables.add_array('_morph_children_num',
size=sections+1, dtype=np.int32,
constant=True, index='_section_root_idx')
# 2D matrices of size (sections + 1) x max children per section
self.variables.add_arange('_morph_children_idx',
size=len(group.flat_morphology.morph_children))
self.variables.add_array('_P_children',
size=len(group.flat_morphology.morph_children),
index='_morph_children_idx',
constant=True) # elements above diagonal
self.variables.add_array('_morph_children',
size=len(group.flat_morphology.morph_children),
dtype=np.int32, constant=True,
index='_morph_children_idx')
self._enable_group_attributes()
self._morph_parent_i = group.flat_morphology.morph_parent_i
self._morph_children_num = group.flat_morphology.morph_children_num
self._morph_children = group.flat_morphology.morph_children
self._morph_idxchild = group.flat_morphology.morph_idxchild
self._starts = group.flat_morphology.starts
self._ends = group.flat_morphology.ends
def __init__(self, N, indices, times, dt=None, clock=None,
period=0*second, when='thresholds', order=0, sorted=False,
name='spikegeneratorgroup*', codeobj_class=None):
Group.__init__(self, dt=dt, clock=clock, when=when, order=order, name=name)
# We store the indices and times also directly in the Python object,
# this way we can use them for checks in `before_run` even in standalone
# TODO: Remove this when the checks in `before_run` have been moved to the template
#: Array of spiking neuron indices.
self._neuron_index = None
#: Array of spiking neuron times.
self._spike_time = None
#: "Dirty flag" that will be set when spikes are changed after the
#: `before_run` check
self._spikes_changed = True
# Let other objects know that we emit spikes events
self.events = {'spike': None}
self.codeobj_class = codeobj_class
if N < 1 or int(N) != N:
raise TypeError('N has to be an integer >=1.')
N = int(N)
self.start = 0
self.stop = N
self.variables = Variables(self)
self.variables.create_clock_variables(self._clock)
indices, times = self._check_args(indices, times, period, N, sorted,
self._clock.dt)
self.variables.add_constant('N', value=N)
self.variables.add_array('period', dimensions=second.dim, size=1,
constant=True, read_only=True, scalar=True,
dtype=self._clock.variables['t'].dtype)
self.variables.add_arange('i', N)
self.variables.add_dynamic_array('spike_number',
values=np.arange(len(indices)),
size=len(indices),
dtype=np.int32, read_only=True,
constant=True, index='spike_number',
unique=True)
self.variables.add_dynamic_array('neuron_index', values=indices,
size=len(indices),
dtype=np.int32, index='spike_number',
read_only=True, constant=True)
self.variables.add_dynamic_array('spike_time', values=times, size=len(times),
dimensions=second.dim, index='spike_number',
read_only=True, constant=True,
dtype=self._clock.variables['t'].dtype)
self.variables.add_dynamic_array('_timebins', size=len(times),
index='spike_number',
read_only=True, constant=True,
dtype=np.int32)
self.variables.add_array('_period_bins', size=1, constant=True,
read_only=True, scalar=True,
dtype=np.int32)
self.variables.add_array('_spikespace', size=N+1, dtype=np.int32)
self.variables.add_array('_lastindex', size=1, values=0, dtype=np.int32,
read_only=True, scalar=True)
#: Remember the dt we used the last time when we checked the spike bins
#: to not repeat the work for multiple runs with the same dt
self._previous_dt = None
CodeRunner.__init__(self, self,
code='',
template='spikegenerator',
clock=self._clock,
when=when,
order=order,
name=None)
# Activate name attribute access
self._enable_group_attributes()
self.variables['period'].set_value(period)
def __init__(self,
source,
variables,
record,
dt=None,
clock=None,
when='start',
order=0,
name='statemonitor*',
codeobj_class=None):
self.source = source
# Make the monitor use the explicitly defined namespace of its source
# group (if it exists)
self.namespace = getattr(source, 'namespace', None)
self.codeobj_class = codeobj_class
# run by default on source clock at the end
if dt is None and clock is None:
clock = source.clock
# variables should always be a list of strings
if variables is True:
variables = source.equations.names
elif isinstance(variables, str):
variables = [variables]
#: The variables to record
self.record_variables = variables
# record should always be an array of ints
self.record_all = False
if hasattr(record, '_indices'):
# The ._indices method always returns absolute indices
# If the source is already a subgroup of another group, we therefore
# have to shift the indices to become relative to the subgroup
record = record._indices() - getattr(source, '_offset', 0)
if record is True:
self.record_all = True
try:
record = np.arange(len(source), dtype=np.int32)
except NotImplementedError:
# In standalone mode, this is not possible for synaptic
# variables because the number of synapses is not defined yet
raise NotImplementedError(
('Cannot determine the actual '
'indices to record for record=True. '
'This can occur for example in '
'standalone mode when trying to '
'record a synaptic variable. '
'Consider providing an explicit '
'array of indices for the record '
'argument.'))
elif record is False:
record = np.array([], dtype=np.int32)
elif isinstance(record, numbers.Number):
record = np.array([record], dtype=np.int32)
else:
record = np.asarray(record, dtype=np.int32)
#: The array of recorded indices
self.record = record
self.n_indices = len(record)
# Some dummy code so that code generation takes care of the indexing
# and subexpressions
code = ['_to_record_%s = _source_%s' % (v, v) for v in variables]
code = '\n'.join(code)
CodeRunner.__init__(self,
group=self,
template='statemonitor',
code=code,
name=name,
clock=clock,
dt=dt,
when=when,
order=order,
check_units=False)
self.add_dependency(source)
# Setup variables
self.variables = Variables(self)
self.variables.add_dynamic_array(
't',
size=0,
dimensions=second.dim,
constant=False,
dtype=self._clock.variables['t'].dtype)
self.variables.add_array('N',
dtype=np.int32,
size=1,
scalar=True,
read_only=True)
self.variables.add_array('_indices',
size=len(self.record),
dtype=self.record.dtype,
constant=True,
read_only=True,
values=self.record)
self.variables.create_clock_variables(self._clock, prefix='_clock_')
for varname in variables:
var = source.variables[varname]
if var.scalar and len(self.record) > 1:
logger.warn(('Variable %s is a shared variable but it will be '
'recorded once for every target.' % varname),
once=True)
index = source.variables.indices[varname]
self.variables.add_reference('_source_%s' % varname,
source,
varname,
index=index)
if not index in ('_idx', '0') and index not in variables:
self.variables.add_reference(index, source)
self.variables.add_dynamic_array(varname,
size=(0, len(self.record)),
resize_along_first=True,
dimensions=var.dim,
dtype=var.dtype,
constant=False,
read_only=True)
for varname in variables:
var = self.source.variables[varname]
self.variables.add_auxiliary_variable('_to_record_' + varname,
dimensions=var.dim,
dtype=var.dtype,
scalar=var.scalar)
self.recorded_variables = dict([(varname, self.variables[varname])
for varname in variables])
recorded_names = [varname for varname in variables]
self.needed_variables = recorded_names
self.template_kwds = {'_recorded_variables': self.recorded_variables}
self.written_readonly_vars = {
self.variables[varname]
for varname in self.record_variables
}
self._enable_group_attributes()
def __init__(self,
source,
event,
variables=None,
record=True,
when=None,
order=None,
name='eventmonitor*',
codeobj_class=None):
if not isinstance(source, SpikeSource):
raise TypeError(
('%s can only monitor groups producing spikes '
'(such as NeuronGroup), but the given argument '
'is of type %s.') % (self.__class__.__name__, type(source)))
#: The source we are recording from
self.source = source
#: Whether to record times and indices of events
self.record = record
#: The array of event counts (length = size of target group)
self.count = None
del self.count # this is handled by the Variable mechanism
if when is None:
if order is not None:
raise ValueError(
'Cannot specify order if when is not specified.')
if hasattr(source, 'thresholder'):
parent_obj = source.thresholder[event]
else:
parent_obj = source
when = parent_obj.when
order = parent_obj.order + 1
elif order is None:
order = 0
#: The event that we are listening to
self.event = event
if variables is None:
variables = {}
elif isinstance(variables, str):
variables = {variables}
#: The additional variables that will be recorded
self.record_variables = set(variables)
for variable in variables:
if variable not in source.variables:
raise ValueError(("'%s' is not a variable of the recorded "
"group" % variable))
if self.record:
self.record_variables |= {'i', 't'}
# Some dummy code so that code generation takes care of the indexing
# and subexpressions
code = [
'_to_record_%s = _source_%s' % (v, v)
for v in self.record_variables
]
code = '\n'.join(code)
self.codeobj_class = codeobj_class
# Since this now works for general events not only spikes, we have to
# pass the information about which variable to use to the template,
# it can not longer simply refer to "_spikespace"
eventspace_name = '_{}space'.format(event)
# Handle subgroups correctly
start = getattr(source, 'start', 0)
stop = getattr(source, 'stop', len(source))
source_N = getattr(source, '_source_N', len(source))
Nameable.__init__(self, name=name)
self.variables = Variables(self)
self.variables.add_reference(eventspace_name, source)
for variable in self.record_variables:
source_var = source.variables[variable]
self.variables.add_reference('_source_%s' % variable, source,
variable)
self.variables.add_auxiliary_variable('_to_record_%s' % variable,
dimensions=source_var.dim,
dtype=source_var.dtype)
self.variables.add_dynamic_array(variable,
size=0,
dimensions=source_var.dim,
dtype=source_var.dtype,
read_only=True)
self.variables.add_arange('_source_idx', size=len(source))
self.variables.add_array('count',
size=len(source),
dtype=np.int32,
read_only=True,
index='_source_idx')
self.variables.add_constant('_source_start', start)
self.variables.add_constant('_source_stop', stop)
self.variables.add_constant('_source_N', source_N)
self.variables.add_array('N',
size=1,
dtype=np.int32,
read_only=True,
scalar=True)
record_variables = {
varname: self.variables[varname]
for varname in self.record_variables
}
template_kwds = {
'eventspace_variable': source.variables[eventspace_name],
'record_variables': record_variables,
'record': self.record
}
needed_variables = {eventspace_name} | self.record_variables
CodeRunner.__init__(
self,
group=self,
code=code,
template='spikemonitor',
name=None, # The name has already been initialized
clock=source.clock,
when=when,
order=order,
needed_variables=needed_variables,
template_kwds=template_kwds)
self.variables.create_clock_variables(self._clock, prefix='_clock_')
self.add_dependency(source)
self.written_readonly_vars = {
self.variables[varname]
for varname in self.record_variables
}
self._enable_group_attributes()