def __init__(self,
default_variable=None,
mode: tc.optional(
tc.enum(MAX_VAL, MAX_ABS_VAL, MAX_INDICATOR,
MAX_ABS_INDICATOR, MIN_VAL, MIN_ABS_VAL,
MIN_INDICATOR, MIN_ABS_INDICATOR, PROB,
PROB_INDICATOR)) = None,
seed=None,
params=None,
owner=None,
prefs: tc.optional(is_pref_set) = None):
reset_variable_shape_flexibility = False
if mode in {PROB, PROB_INDICATOR} and default_variable is None:
default_variable = [[0], [0]]
reset_variable_shape_flexibility = True
super().__init__(
default_variable=default_variable,
mode=mode,
seed=seed,
params=params,
owner=owner,
prefs=prefs,
)
if reset_variable_shape_flexibility:
self._variable_shape_flexibility = DefaultsFlexibility.FLEXIBLE
def __init__(self,
default_variable=None,
mode: tc.enum(MAX_VAL, MAX_ABS_VAL, MAX_INDICATOR,
MAX_ABS_INDICATOR, MIN_VAL, MIN_ABS_VAL,
MIN_INDICATOR, MIN_ABS_INDICATOR, PROB,
PROB_INDICATOR) = MAX_VAL,
seed=None,
params=None,
owner=None,
prefs: is_pref_set = None):
if seed is None:
seed = get_global_seed()
random_state = np.random.RandomState([seed])
if not hasattr(self, "stateful_attributes"):
self.stateful_attributes = ["random_state"]
reset_default_variable_flexibility = False
if mode in {PROB, PROB_INDICATOR} and default_variable is None:
default_variable = [[0], [0]]
reset_default_variable_flexibility = True
super().__init__(
default_variable=default_variable,
mode=mode,
random_state=random_state,
params=params,
owner=owner,
prefs=prefs,
)
if reset_default_variable_flexibility:
self._default_variable_flexibility = DefaultsFlexibility.FLEXIBLE
def __init__(
self,
default_variable=None,
matrix=HOLLOW_MATRIX,
# metric:is_distance_metric=ENERGY,
metric: tc.any(tc.enum(ENERGY, ENTROPY),
is_distance_metric) = ENERGY,
transfer_fct: tc.optional(tc.any(function_type,
method_type)) = None,
normalize: bool = False,
params=None,
owner=None,
prefs: is_pref_set = None):
# Assign args to params and functionParams dicts
params = self._assign_args_to_param_dicts(matrix=matrix,
metric=metric,
transfer_fct=transfer_fct,
normalize=normalize,
params=params)
super().__init__(default_variable=default_variable,
params=params,
owner=owner,
prefs=prefs,
context=ContextFlags.CONSTRUCTOR)
def __init__(self,
sender=None,
receiver=None,
matrix=DEFAULT_MATRIX,
mask: tc.optional(
tc.any(int, float, list, np.ndarray, np.matrix)) = None,
mask_operation: tc.enum(ADD, MULTIPLY,
EXPONENTIATE) = MULTIPLY,
function=None,
params=None,
name=None,
prefs: is_pref_set = None):
params = self._assign_args_to_param_dicts(
mask=mask,
mask_operation=mask_operation,
function_params={MATRIX: matrix},
params=params)
super().__init__(sender=sender,
receiver=receiver,
matrix=matrix,
function=function,
params=params,
name=name,
prefs=prefs)
def _is_pathway_entry_spec(entry, desired_type: tc.enum(NODE, PROJECTION,
ANY)):
"""Test whether pathway entry is specified type (NODE or PROJECTION)"""
from psyneulink.core.components.projections.projection import _is_projection_spec
node_specs = (Mechanism, Composition)
is_node = is_proj = False
if desired_type in {NODE, ANY}:
is_node = (isinstance(entry, node_specs)
or (isinstance(entry, tuple)
and isinstance(entry[0], node_specs) and
(isinstance(entry[1], NodeRole) or
(isinstance(entry[1], list)
and all(isinstance(nr, NodeRole)
for nr in entry[1])))))
if desired_type in {PROJECTION, ANY}:
is_proj = (_is_projection_spec(entry) or
(isinstance(entry, tuple) and _is_projection_spec(entry[0])
and entry[1] in {True, FEEDBACK, False, MAYBE}))
if is_node or is_proj:
return True
else:
return False
def test_enum5():
pred = tc.enum(1, 2.0, "three", [1]*4)
assert pred(2*1.0)
assert pred("thr"+2*"e")
assert pred([1,1,1,1])
assert pred(1.0)
assert not pred("thr")
assert not pred([1,1])
def test_enum5():
pred = tc.enum(1, 2.0, "three", [1] * 4)
assert pred(2 * 1.0)
assert pred("thr" + 2 * "e")
assert pred([1, 1, 1, 1])
assert pred(1.0)
assert not pred("thr")
assert not pred([1, 1])
def _get_context_string(cls, condition_flags,
fields:tc.any(tc.enum(INITIALIZATION_STATUS,
EXECUTION_PHASE,
SOURCE), set, list)={INITIALIZATION_STATUS,
EXECUTION_PHASE,
SOURCE},
string:tc.optional(str)=None):
"""Return string with the names of flags that are set in **condition_flags**
If **fields** is specified, then only the names of the flag(s) in the specified field(s) are returned.
The fields argument must be the name of a field (*INITIALIZATION_STATUS*, *EXECUTION_PHASE*, or *SOURCE*)
or a set or list of them.
If **string** is specified, the string returned is prepended by **string**.
"""
if string:
string += ": "
else:
string = ""
if isinstance(fields, str):
fields = {fields}
flagged_items = []
# If OFF or ALL_FLAGS, just return that
if condition_flags == ContextFlags.ALL_FLAGS:
return ContextFlags.ALL_FLAGS.name
if condition_flags == ContextFlags.UNSET:
return ContextFlags.UNSET.name
# Otherwise, append each flag's name to the string
# for c in (INITIALIZATION_STATUS_FLAGS | EXECUTION_PHASE_FLAGS | SOURCE_FLAGS):
# if c & condition_flags:
# flagged_items.append(c.name)
if INITIALIZATION_STATUS in fields:
for c in INITIALIZATION_STATUS_FLAGS:
if not condition_flags & ContextFlags.INITIALIZATION_MASK:
flagged_items.append(ContextFlags.UNINITIALIZED.name)
break
if c & condition_flags:
flagged_items.append(c.name)
if EXECUTION_PHASE in fields:
for c in EXECUTION_PHASE_FLAGS:
if not condition_flags & ContextFlags.EXECUTION_PHASE_MASK:
flagged_items.append(ContextFlags.IDLE.name)
break
if c & condition_flags:
flagged_items.append(c.name)
if SOURCE in fields:
for c in SOURCE_FLAGS:
if not condition_flags & ContextFlags.SOURCE_MASK:
flagged_items.append(ContextFlags.NONE.name)
break
if c & condition_flags:
flagged_items.append(c.name)
string += ", ".join(flagged_items)
return string
def test_enum5():
namespace = None
pred = tc.enum(1, 2.0, "three", [1] * 4)
assert pred(2 * 1.0, namespace)
assert pred("thr" + 2 * "e", namespace)
assert pred([1, 1, 1, 1], namespace)
assert pred(1.0, namespace)
assert not pred("thr", namespace)
assert not pred([1, 1], namespace)
def test_enum5():
namespace = None
pred = tc.enum(1, 2.0, "three", [1] * 4)
assert pred(2 * 1.0, namespace)
assert pred("thr" + 2 * "e", namespace)
assert pred([1, 1, 1, 1], namespace)
assert pred(1.0, namespace)
assert not pred("thr", namespace)
assert not pred([1, 1], namespace)
def __init__(
self,
sender: tc.optional(tc.any(LearningSignal,
LearningMechanism)) = None,
receiver: tc.optional(tc.any(ParameterPort,
MappingProjection)) = None,
error_function: tc.optional(is_function_type) = LinearCombination(
weights=[[-1], [1]]),
learning_function: tc.optional(is_function_type) = BackPropagation,
# FIX: 10/3/17 - TEST IF THIS OK AND REINSTATE IF SO
# learning_signal_params:tc.optional(dict)=None,
learning_rate: tc.optional(tc.any(parameter_spec)) = None,
learning_enabled: tc.optional(tc.any(bool, tc.enum(ONLINE,
AFTER))) = None,
weight=None,
exponent=None,
params: tc.optional(dict) = None,
name=None,
prefs: is_pref_set = None,
**kwargs):
# IMPLEMENTATION NOTE:
# the error_function and learning_function arguments are implemented to preserve the ability to pass
# error function and learning function specifications from the specification of a LearningProjection (used
# to implement learning for a MappingProjection, e.g., in a tuple) to the LearningMechanism responsible
# for implementing the function; and for specifying the default LearningProjection for a Process.
# If receiver has not been assigned, defer init to Port.instantiate_projection_to_state()
if sender is None or receiver is None:
# Flag for deferred initialization
self.initialization_status = ContextFlags.DEFERRED_INIT
# parameters should be passed through methods like
# instantiate_sender instead of grabbed from attributes like this
self._learning_function = learning_function
self._learning_rate = learning_rate
self._error_function = error_function
# replaces similar code in _instantiate_sender
try:
if sender.owner.learning_rate is not None:
learning_rate = sender.owner.learning_rate
except AttributeError:
pass
super().__init__(sender=sender,
receiver=receiver,
weight=weight,
exponent=exponent,
params=params,
name=name,
prefs=prefs,
error_function=error_function,
learning_function=learning_function,
learning_rate=learning_rate,
learning_enabled=learning_enabled,
**kwargs)
def __init__(
self,
sender: tc.optional(tc.any(LearningSignal,
LearningMechanism)) = None,
receiver: tc.optional(tc.any(ParameterPort,
MappingProjection)) = None,
error_function: tc.optional(is_function_type) = LinearCombination(
weights=[[-1], [1]]),
learning_function: tc.optional(is_function_type) = BackPropagation,
# FIX: 10/3/17 - TEST IF THIS OK AND REINSTATE IF SO
# learning_signal_params:tc.optional(dict)=None,
learning_rate: tc.optional(tc.any(parameter_spec)) = None,
learning_enabled: tc.optional(tc.any(bool, tc.enum(ONLINE,
AFTER))) = None,
weight=None,
exponent=None,
params: tc.optional(dict) = None,
name=None,
prefs: is_pref_set = None,
**kwargs):
# IMPLEMENTATION NOTE:
# the error_function and learning_function arguments are implemented to preserve the ability to pass
# error function and learning function specifications from the specification of a LearningProjection (used
# to implement learning for a MappingProjection, e.g., in a tuple) to the LearningMechanism responsible
# for implementing the function; and for specifying the default LearningProjection for a Process.
# Assign args to params and functionParams dicts
params = self._assign_args_to_param_dicts(
error_function=error_function,
learning_function=learning_function,
learning_rate=learning_rate,
# FIX: 10/3/17 - TEST IF THIS OK AND REINSTATE IF SO
# learning_signal_params=learning_signal_params,
learning_enabled=learning_enabled,
weight=weight,
exponent=exponent,
params=params)
# If receiver has not been assigned, defer init to Port.instantiate_projection_to_state()
if sender is None or receiver is None:
# Flag for deferred initialization
self.initialization_status = ContextFlags.DEFERRED_INIT
super().__init__(sender=sender,
receiver=receiver,
weight=weight,
exponent=exponent,
params=params,
name=name,
prefs=prefs,
**kwargs)
def __init__(
self,
default_variable=None,
size=None,
matrix=HOLLOW_MATRIX,
# metric:is_distance_metric=ENERGY,
metric: tc.any(tc.enum(ENERGY, ENTROPY),
is_distance_metric) = ENERGY,
transfer_fct: tc.optional(tc.any(function_type,
method_type)) = None,
normalize: bool = False,
params=None,
owner=None,
prefs: is_pref_set = None):
if size:
if default_variable is None:
default_variable = np.zeros(size)
elif size != len(default_variable):
raise FunctionError(
f"Both {repr(DEFAULT_VARIABLE)} ({default_variable}) and {repr(SIZE)} ({size}) "
f"are specified for {self.name} but are {SIZE}!=len({DEFAULT_VARIABLE})."
)
# Assign args to params and functionParams dicts
params = self._assign_args_to_param_dicts(matrix=matrix,
metric=metric,
transfer_fct=transfer_fct,
normalize=normalize,
params=params)
super().__init__(
default_variable=default_variable,
params=params,
owner=owner,
prefs=prefs,
)
# MODIFIED 6/12/19 NEW: [JDC]
self._default_variable_flexibility = DefaultsFlexibility.FLEXIBLE
def __init__(
self,
default_variable=None,
size=None,
matrix=None,
# metric:is_distance_metric=None,
metric: tc.optional(
tc.any(tc.enum(ENERGY, ENTROPY), is_distance_metric)) = None,
transfer_fct: tc.optional(
tc.optional(tc.any(types.FunctionType,
types.MethodType))) = None,
normalize: tc.optional(bool) = None,
params=None,
owner=None,
prefs: tc.optional(is_pref_set) = None):
if size:
if default_variable is None:
default_variable = np.zeros(size)
elif size != len(default_variable):
raise FunctionError(
f"Both {repr(DEFAULT_VARIABLE)} ({default_variable}) and {repr(SIZE)} ({size}) "
f"are specified for {self.name} but are {SIZE}!=len({DEFAULT_VARIABLE})."
)
super().__init__(
default_variable=default_variable,
matrix=matrix,
metric=metric,
transfer_fct=transfer_fct,
normalize=normalize,
params=params,
owner=owner,
prefs=prefs,
)
# MODIFIED 6/12/19 NEW: [JDC]
self._variable_shape_flexibility = DefaultsFlexibility.FLEXIBLE
def __init__(self,
sender=None,
receiver=None,
matrix=DEFAULT_MATRIX,
mask: tc.optional(
tc.any(int, float, list, np.ndarray, np.matrix)) = None,
mask_operation: tc.enum(ADD, MULTIPLY,
EXPONENTIATE) = MULTIPLY,
function=None,
params=None,
name=None,
prefs: is_pref_set = None,
**kwargs):
super().__init__(sender=sender,
receiver=receiver,
mask=mask,
mask_operation=mask_operation,
matrix=matrix,
function=function,
params=params,
name=name,
prefs=prefs,
**kwargs)
def foo(x: tc.optional(tc.enum(1, 2)) = 2):
return x
def foo(x: tc.enum(1) = 2):
pass
def bar(*, x: tc.enum(None)) -> tc.enum():
return x
def foo(x: tc.enum(int, 1)) -> tc.enum(1, int):
return x
def foo(x: tc.optional(tc.enum(1, 2)) = 2):
return x
def run(object,
inputs,
num_trials: tc.optional(int) = None,
reset_clock: bool = True,
initialize: bool = False,
initial_values: tc.optional(tc.any(list, dict, np.ndarray)) = None,
targets: tc.optional(tc.any(list, dict, np.ndarray,
function_type)) = None,
learning: tc.optional(bool) = None,
call_before_trial: tc.optional(callable) = None,
call_after_trial: tc.optional(callable) = None,
call_before_time_step: tc.optional(callable) = None,
call_after_time_step: tc.optional(callable) = None,
clock=CentralClock,
time_scale: tc.optional(tc.enum(TimeScale.TRIAL,
TimeScale.TIME_STEP)) = None,
termination_processing=None,
termination_learning=None,
context=None):
"""run( \
inputs, \
num_trials=None, \
reset_clock=True, \
initialize=False, \
intial_values=None, \
targets=None, \
learning=None, \
call_before_trial=None, \
call_after_trial=None, \
call_before_time_step=None, \
call_after_time_step=None, \
clock=CentralClock, \
time_scale=None)
Run a sequence of executions for a `Process` or `System`.
COMMENT:
First, validate inputs (and targets, if learning is enabled). Then, for each `TRIAL`:
* call call_before_trial if specified;
* for each time_step in the trial:
* call call_before_time_step if specified;
* call ``object.execute`` with inputs, and append result to ``object.results``;
* call call_after_time_step if specified;
* call call_after_trial if specified.
Return ``object.results``.
The inputs argument must be a list or an np.ndarray array of the appropriate dimensionality:
* the inner-most dimension must equal the length of object.instance_defaults.variable (i.e., the input to the object);
* for Mechanism format, the length of the value of all entries must be equal (== number of executions);
* the outer-most dimension is the number of input sets (num_input_sets) specified (one per execution)
Note: num_input_sets need not equal num_trials (the number of executions to actually run)
if num_trials > num_input_sets:
executions will cycle through input_sets, with the final one being only a partial cycle
if num_trials < num_input_sets:
the executions will only partially sample the input sets
COMMENT
Arguments
---------
inputs : List[input] or ndarray(input) : default default_variable for a single `TRIAL`
the input for each `TRIAL` in a sequence (see `Run_Inputs` for detailed description of formatting
requirements and options).
num_trials : int : default None
the number of `TRIAL` \\s to run. If it is `None` (the default), then a number of `TRIAL` \\s run will be equal
equal to the number of items specified in the **inputs** argument. If **num_trials** exceeds the number of
inputs, then the inputs will be cycled until the number of `TRIAL` \\s specified have been run.
reset_clock : bool : default True
if `True`, resets `CentralClock` to 0 before a sequence of `TRIAL` \\s.
initialize : bool default False
calls the `initialize <System.initialize>` method of the System prior to the first `TRIAL`.
initial_values : Dict[Mechanism:List[input]], List[input] or np.ndarray(input) : default None
the initial values assigned to Mechanisms designated as `INITIALIZE_CYCLE`.
targets : List[input] or np.ndarray(input) : default None
the target values assigned to the `ComparatorMechanism` for each `TRIAL` (used for learning).
The length must be equal to **inputs**.
learning : bool : default None
enables or disables learning during execution for a `Process <Process_Execution_Learning>` or
`System <System_Execution_Learning>`. If it is not specified, the current state of learning is left intact.
If it is `True`, learning is forced on; if it is `False`, learning is forced off.
call_before_trial : Function : default= `None`
called before each `TRIAL` in the sequence is run.
call_after_trial : Function : default= `None`
called after each `TRIAL` in the sequence is run.
call_before_time_step : Function : default= ``None`
called before each `TIME_STEP` is executed.
call_after_time_step : Function : default= `None`
called after each `TIME_STEP` is executed.
Returns
-------
<object>.results : List[OutputState.value]
list of the values, for each `TRIAL`, of the OutputStates for a Mechanism run directly,
or of the OutputStates of the `TERMINAL` Mechanisms for the Process or System run.
"""
# small version of 'sequence' format in the once case where it was still working (single origin mechanism)
if isinstance(inputs, (list, np.ndarray)):
if len(object.origin_mechanisms) == 1:
inputs = {object.origin_mechanisms[0]: inputs}
else:
raise RunError(
"Inputs to {} must be specified in a dictionary with a key for each of its {} origin "
"mechanisms.".format(object.name,
len(object.origin_mechanisms)))
elif not isinstance(inputs, dict):
if len(object.origin_mechanisms) == 1:
raise RunError(
"Inputs to {} must be specified in a list or in a dictionary with the origin mechanism({}) "
"as its only key".format(object.name,
object.origin_mechanisms[0].name))
else:
raise RunError(
"Inputs to {} must be specified in a dictionary with a key for each of its {} origin "
"mechanisms.".format(object.name,
len(object.origin_mechanisms)))
inputs, num_inputs_sets = _adjust_stimulus_dict(object, inputs)
num_trials = num_trials or num_inputs_sets # num_trials may be provided by user, otherwise = # of input sets
if targets:
if isinstance(targets, dict):
targets = _adjust_target_dict(object, targets)
elif not isinstance(targets, function_type):
raise RunError(
"Targets for {} must be a dictionary or function.".format(
object.name))
_validate_targets(object, targets, num_inputs_sets, context=context)
object_type = _get_object_type(object)
object.targets = targets
time_scale = time_scale or TimeScale.TRIAL
# SET LEARNING (if relevant)
# FIX: THIS NEEDS TO BE DONE FOR EACH PROCESS IF THIS CALL TO run() IS FOR SYSTEM
# IMPLEMENT learning_enabled FOR SYSTEM, WHICH FORCES LEARNING OF PROCESSES WHEN SYSTEM EXECUTES?
# OR MAKE LEARNING A PARAM THAT IS PASSED IN execute
# If learning is specified, buffer current state and set to specified state
if learning is not None:
try:
learning_state_buffer = object._learning_enabled
except AttributeError:
if object.verbosePref:
warnings.warn("WARNING: learning not enabled for {}".format(
object.name))
else:
if learning is True:
object._learning_enabled = True
elif learning is False:
object._learning_enabled = False
# SET LEARNING_RATE, if specified, for all learningProjections in process or system
if object.learning_rate is not None:
from psyneulink.components.projections.modulatory.learningprojection import LearningProjection
for learning_mech in object.learning_mechanisms.mechanisms:
for projection in learning_mech.output_state.efferents:
if isinstance(projection, LearningProjection):
projection.function_object.learning_rate = object.learning_rate
# Class-specific validation:
context = context or RUN + "validating " + object.name
# INITIALIZATION
if reset_clock:
clock.trial = 0
clock.time_step = 0
if initialize:
object.initialize()
# SET UP TIMING
if object_type == MECHANISM:
time_steps = 1
else:
time_steps = object.numPhases
# EXECUTE
execution_inputs = {}
for execution in range(num_trials):
execution_id = _get_unique_id()
if call_before_trial:
call_before_trial()
for time_step in range(time_steps):
if call_before_time_step:
call_before_time_step()
input_num = execution % num_inputs_sets
for mech in inputs:
execution_inputs[mech] = inputs[mech][input_num]
if object_type == SYSTEM:
object.inputs = execution_inputs
# Assign targets:
if targets is not None:
if isinstance(targets, function_type):
object.target = targets
# IMPLEMENTATION NOTE: USE input_num since # of inputs must equal # targets,
# whereas targets can be assigned a function (so can't be used to generated #)
elif object_type == PROCESS:
# object.target = targets[input_num][time_step]
object.target = targets[input_num][time_step]
elif object_type == SYSTEM:
object.current_targets = targets[input_num]
# MODIFIED 3/16/17 END
if RUN in context and not EVC_SIMULATION in context:
context = RUN + ": EXECUTING " + object_type.upper(
) + " " + object.name
object.execution_status = ExecutionStatus.EXECUTING
result = object.execute(
input=execution_inputs,
execution_id=execution_id,
clock=clock,
time_scale=time_scale,
termination_processing=termination_processing,
termination_learning=termination_learning,
context=context)
if call_after_time_step:
call_after_time_step()
clock.time_step += 1
# object.results.append(result)
if isinstance(result, Iterable):
result_copy = result.copy()
else:
result_copy = result
object.results.append(result_copy)
if call_after_trial:
call_after_trial()
clock.trial += 1
# Restore learning state
try:
learning_state_buffer
except UnboundLocalError:
pass
else:
object._learning_enabled = learning_state_buffer
return object.results
def foo(x: tc.enum(int, 1)) -> tc.enum(1, int):
return x
def bar(*, x: tc.enum(None)) -> tc.enum():
return x
def bar(x: tc.any((int, float), tc.re("^foo$"), tc.enum(b"X", b"Y"))):
pass
def bar(x: tc.any((int, float), tc.re("^foo$"), tc.enum(b"X", b"Y"))):
pass
def __init__(
self,
default_variable=None,
size=None,
input_states: tc.optional(tc.any(list, dict)) = None,
function: tc.optional(
tc.enum(TIME_AVERAGE_INPUT, AVERAGE_INPUTS,
INPUT_SEQUENCE)) = TIME_AVERAGE_INPUT,
initial_value=None,
# rate:tc.optional(tc.any(int, float))=1.0,
# noise:tc.optional(tc.any(int, float, callable))=0.0,
rate: tc.any(int, float) = 1.0,
noise: tc.any(int, float, callable) = 0.0,
window_size=1,
filter_function: tc.optional(callable) = None,
params=None,
name=None,
prefs: is_pref_set = None,
context=None):
if not context in {
ContextFlags.COMPONENT, ContextFlags.COMPOSITION,
ContextFlags.COMMAND_LINE
}:
warnings.warn(
"PredictionMechanism should not be constructed on its own. If you insist,"
"set context=ContextFlags.COMMAND_LINE, but proceed at your peril!"
)
return
if params and FUNCTION in params:
function = params[FUNCTION]
input_type = None
if function in input_types:
input_type = function
params = self._assign_args_to_param_dicts(
window_size=window_size,
input_type=input_type,
filter_function=filter_function,
params=params)
if function in input_types:
if function is TIME_AVERAGE_INPUT:
# Use default for IntegratorMechanism: AdaptiveIntegrator
function = self.ClassDefaults.function
elif function in {AVERAGE_INPUTS, INPUT_SEQUENCE}:
# Maintain the preceding sequence of inputs (of length window_size), and use those for each simulation
function = Buffer(default_variable=[[0]],
initializer=initial_value,
rate=rate,
noise=noise,
history=self.window_size)
params.update({FUNCTION_PARAMS: {RATE: rate, NOISE: noise}})
super().__init__(default_variable=default_variable,
size=size,
input_states=input_states,
function=function,
params=params,
name=name,
prefs=prefs)
def foo(x: tc.enum(1) = 2):
pass