def test_filter_discrete_types(self):
# Signature: name(lst, acceptedtypes)
# Creates a dictionary mapping types to objects of that type.
#
# Starting with a list of object, and a list of types, this returns a
# dictionary mapping each type to a list of objects of that type.
#
# For example::
#
# >>> import types
# >>> filter_discrete_types( ['hello',1,2,'world'], ( basestring, types.IntType) ) #doctest: +NORMALIZE_WHITESPACE
# {<type 'basestring'>: ['hello', 'world'], <type 'int'>: [1, 2]}
#
#
# The function checks that each object is mapped to exactly one type
# Good Case:
data = ['hello', 'world', 1, 2, 3]
types_ = [basestring, numbers.Number, types.BooleanType]
filtered = filter_discrete_types(data, types_)
self.assertEqual(filtered[basestring], ['hello', 'world'])
self.assertEqual(filtered[numbers.Number], [1, 2, 3])
# Not all objects covered by listed classes:
self.assertRaises(
NineMLRuntimeError,
filter_discrete_types, data, [basestring]
)
def test_filter_discrete_types(self):
# Signature: name(lst, acceptedtypes)
# Creates a dictionary mapping types to objects of that type.
#
# Starting with a list of object, and a list of types, this returns a
# dictionary mapping each type to a list of objects of that type.
#
# For example::
#
# >>> import types
# >>> filter_discrete_types( ['hello',1,2,'world'], ( basestring, types.IntType) ) #doctest: +NORMALIZE_WHITESPACE
# {<type 'basestring'>: ['hello', 'world'], <type 'int'>: [1, 2]}
#
#
# The function checks that each object is mapped to exactly one type
# Good Case:
data = ['hello', 'world', 1, 2, 3]
types_ = [basestring, numbers.Number, types.BooleanType]
filtered = filter_discrete_types(data, types_)
self.assertEqual(filtered[basestring], ['hello', 'world'])
self.assertEqual(filtered[numbers.Number], [1, 2, 3])
# Not all objects covered by listed classes:
self.assertRaises(NineMLRuntimeError, filter_discrete_types, data,
[basestring])
def do_to_assignments_and_events(doList):
if not doList:
return [], []
# 'doList' is a list of strings, OutputEvents, and StateAssignments.
do_type_list = (OutputEvent, basestring, StateAssignment)
do_types = filter_discrete_types(doList, do_type_list)
# Convert strings to StateAssignments:
sa_from_strs = [StateAssignment.from_str(s)
for s in do_types[basestring]]
return do_types[StateAssignment] + sa_from_strs, do_types[OutputEvent]
def __init__(self,
state_assignments=None,
event_outputs=None,
target_regime_name=None):
"""Abstract class representing a transition from one |Regime| to
another.
|Transition| objects are not created directly, but via the subclasses
|OnEvent| and |OnCondition|.
:param state_assignments: A list of the state-assignments performed
when this transition occurs. Objects in this list are either
`string` (e.g A = A+13) or |StateAssignment| objects.
:param event_outputs: A list of |OutputEvent| objects emitted when
this transition occurs.
:param target_regime_name: The name of the regime to go into after this
transition. ``None`` implies staying in the same regime. This has
to be specified as a string, not the object, because in general the
|Regime| object is not yet constructed. This is automatically
resolved by the |DynamicsClass| in
``_ResolveTransitionRegimeNames()`` during construction.
.. todo::
For more information about what happens at a regime transition, see
here: XXXXXXX
"""
if target_regime_name:
assert isinstance(target_regime_name, basestring)
# Load state-assignment objects as strings or StateAssignment objects
state_assignments = state_assignments or []
sa_types = (basestring, StateAssignment)
sa_type_dict = filter_discrete_types(state_assignments, sa_types)
sa_from_str = [
StateAssignment.from_str(o) for o in sa_type_dict[basestring]
]
self._state_assignments = sa_type_dict[StateAssignment] + sa_from_str
self._event_outputs = event_outputs or []
self._target_regime_name = target_regime_name
self._source_regime_name = None
# Set later, once attached to a regime:
self._target_regime = None
self._source_regime = None
def __init__(self, name, parameters, main_block):
ensure_valid_identifier(name)
BaseALObject.__init__(self)
self._name = name
self._main_block = main_block
# Turn any strings in the parameter list into Parameters:
if parameters is None:
parameters = []
else:
param_types = (basestring, Parameter)
param_td = filter_discrete_types(parameters, param_types)
params_from_strings = [Parameter(s) for s in param_td[basestring]]
parameters = param_td[Parameter] + params_from_strings
self._parameters = dict((p.name, p) for p in parameters)
def __init__(self, name, parameters, main_block):
ensure_valid_identifier(name)
BaseALObject.__init__(self)
self._name = name
self._main_block = main_block
# Turn any strings in the parameter list into Parameters:
if parameters is None:
parameters = []
else:
param_types = (basestring, Parameter)
param_td = filter_discrete_types(parameters, param_types)
params_from_strings = [Parameter(s) for s in param_td[basestring]]
parameters = param_td[Parameter] + params_from_strings
self._parameters = dict((p.name, p) for p in parameters)
def __init__(self, regimes=None, aliases=None, state_variables=None):
"""DynamicsBlock object constructor
:param aliases: A list of aliases, which must be either |Alias|
objects or ``string``s.
:param regimes: A list containing at least one |Regime| object.
:param state_variables: An optional list of the state variables,
which can either be |StateVariable| objects or `string` s. If
provided, it must match the inferred state-variables from the
regimes; if it is not provided it will be inferred
automatically.
"""
aliases = normalise_parameter_as_list(aliases)
regimes = normalise_parameter_as_list(regimes)
state_variables = normalise_parameter_as_list(state_variables)
# Load the aliases as objects or strings:
alias_td = filter_discrete_types(aliases, (basestring, Alias))
aliases_from_strs = [Alias.from_str(o) for o in alias_td[basestring]]
aliases = alias_td[Alias] + aliases_from_strs
# Load the state variables as objects or strings:
sv_types = (basestring, StateVariable)
sv_td = filter_discrete_types(state_variables, sv_types)
sv_from_strings = [
StateVariable(o, dimension=None) for o in sv_td[basestring]
]
state_variables = sv_td[StateVariable] + sv_from_strings
assert_no_duplicates(r.name for r in regimes)
assert_no_duplicates(a.lhs for a in aliases)
assert_no_duplicates(s.name for s in state_variables)
self._regimes = dict((r.name, r) for r in regimes)
self._aliases = dict((a.lhs, a) for a in aliases)
self._state_variables = dict((s.name, s) for s in state_variables)
def __init__(self, regimes=None, aliases=None, state_variables=None):
"""DynamicsBlock object constructor
:param aliases: A list of aliases, which must be either |Alias|
objects or ``string``s.
:param regimes: A list containing at least one |Regime| object.
:param state_variables: An optional list of the state variables,
which can either be |StateVariable| objects or `string` s. If
provided, it must match the inferred state-variables from the
regimes; if it is not provided it will be inferred
automatically.
"""
aliases = normalise_parameter_as_list(aliases)
regimes = normalise_parameter_as_list(regimes)
state_variables = normalise_parameter_as_list(state_variables)
# Load the aliases as objects or strings:
alias_td = filter_discrete_types(aliases, (basestring, Alias))
aliases_from_strs = [Alias.from_str(o) for o in alias_td[basestring]]
aliases = alias_td[Alias] + aliases_from_strs
# Load the state variables as objects or strings:
sv_types = (basestring, StateVariable)
sv_td = filter_discrete_types(state_variables, sv_types)
sv_from_strings = [StateVariable(o, dimension=None)
for o in sv_td[basestring]]
state_variables = sv_td[StateVariable] + sv_from_strings
assert_no_duplicates(r.name for r in regimes)
assert_no_duplicates(a.lhs for a in aliases)
assert_no_duplicates(s.name for s in state_variables)
self._regimes = dict((r.name, r) for r in regimes)
self._aliases = dict((a.lhs, a) for a in aliases)
self._state_variables = dict((s.name, s) for s in state_variables)
def __init__(self, state_assignments=None, event_outputs=None, target_regime_name=None):
"""Abstract class representing a transition from one |Regime| to
another.
|Transition| objects are not created directly, but via the subclasses
|OnEvent| and |OnCondition|.
:param state_assignments: A list of the state-assignments performed
when this transition occurs. Objects in this list are either
`string` (e.g A = A+13) or |StateAssignment| objects.
:param event_outputs: A list of |OutputEvent| objects emitted when
this transition occurs.
:param target_regime_name: The name of the regime to go into after this
transition. ``None`` implies staying in the same regime. This has
to be specified as a string, not the object, because in general the
|Regime| object is not yet constructed. This is automatically
resolved by the |DynamicsClass| in
``_ResolveTransitionRegimeNames()`` during construction.
.. todo::
For more information about what happens at a regime transition, see
here: XXXXXXX
"""
if target_regime_name:
assert isinstance(target_regime_name, basestring)
# Load state-assignment objects as strings or StateAssignment objects
state_assignments = state_assignments or []
sa_types = (basestring, StateAssignment)
sa_type_dict = filter_discrete_types(state_assignments, sa_types)
sa_from_str = [StateAssignment.from_str(o) for o in sa_type_dict[basestring]]
self._state_assignments = sa_type_dict[StateAssignment] + sa_from_str
self._event_outputs = event_outputs or []
self._target_regime_name = target_regime_name
self._source_regime_name = None
# Set later, once attached to a regime:
self._target_regime = None
self._source_regime = None
def __init__(self, *args, **kwargs):
"""Regime constructor
:param name: The name of the constructor. If none, then a name will
be automatically generated.
:param time_derivatives: A list of time derivatives, as
either ``string``s (e.g 'dg/dt = g/gtau') or as
|TimeDerivative| objects.
:param transitions: A list containing either |OnEvent| or
|OnCondition| objects, which will automatically be sorted into
the appropriate classes automatically.
:param *args: Any non-keyword arguments will be treated as
time_derivatives.
"""
valid_kwargs = ('name', 'transitions', 'time_derivatives')
for arg in kwargs:
if arg not in valid_kwargs:
err = 'Unexpected Arg: %s' % arg
raise NineMLRuntimeError(err)
transitions = kwargs.get('transitions', None)
name = kwargs.get('name', None)
kw_tds = normalise_parameter_as_list(kwargs.get('time_derivatives',
None))
time_derivatives = list(args) + kw_tds
self._name = name
if self.name is not None:
self._name = self._name.strip()
ensure_valid_identifier(self._name)
# Un-named arguments are time_derivatives:
time_derivatives = normalise_parameter_as_list(time_derivatives)
# time_derivatives.extend( args )
td_types = (basestring, TimeDerivative)
td_type_dict = filter_discrete_types(time_derivatives, td_types)
td_from_str = [TimeDerivative.from_str(o)
for o in td_type_dict[basestring]]
time_derivatives = td_type_dict[TimeDerivative] + td_from_str
# Check for double definitions:
td_dep_vars = [td.dependent_variable for td in time_derivatives]
assert_no_duplicates(td_dep_vars)
# Store as a dictionary
self._time_derivatives = dict((td.dependent_variable, td)
for td in time_derivatives)
# We support passing in 'transitions', which is a list of both OnEvents
# and OnConditions. So, lets filter this by type and add them
# appropriately:
transitions = normalise_parameter_as_list(transitions)
f_dict = filter_discrete_types(transitions, (OnEvent, OnCondition))
self._on_events = []
self._on_conditions = []
# Add all the OnEvents and OnConditions:
for event in f_dict[OnEvent]:
self.add_on_event(event)
for condition in f_dict[OnCondition]:
self.add_on_condition(condition)
# Sort for equality checking
self._on_events = sorted(self._on_events,
key=lambda x: x.src_port_name)
self._on_conditions = sorted(self._on_conditions,
key=lambda x: x.trigger)
