def test_flatten_first_level(self):
# Signature: name(nested_list)
# Flattens the first level of an iterable, ie
#
# >>> flatten_first_level( [ ['This','is'],['a','short'],['phrase'] ] ) #doctest: +NORMALIZE_WHITESPACE
# ['This', 'is', 'a', 'short', 'phrase']
self.assertEqual(
flatten_first_level([[1, 2], [3, 4, 5], [6]]),
[1, 2, 3, 4, 5, 6]
)
self.assertEqual(
flatten_first_level(((1, 2), (3, 4, 5), (6,))),
[1, 2, 3, 4, 5, 6]
)
# Check nesting is not flattened:
self.assertEqual(
flatten_first_level([[1, ['a', 'b'], 2], [3, 4, ['c', 'd'], 5], [6]]),
[1, ['a', 'b'], 2, 3, 4, ['c', 'd'], 5, 6]
)
self.assertRaises(NineMLRuntimeError, flatten_first_level, [None])
self.assertRaises(NineMLRuntimeError, flatten_first_level, ['abbn'])
self.assertRaises(NineMLRuntimeError, flatten_first_level, [True])
self.assertRaises(NineMLRuntimeError, flatten_first_level, [None, None])
def __init__(self, componentclass, componentname=None):
assert isinstance(componentclass, DynamicsClass)
# Is our componentclass already flat??
if componentclass.is_flat():
self.reducedcomponent = DynamicsClonerVisitor().visit(componentclass)
if componentclass.was_flattened():
self.reducedcomponent.set_flattener(componentclass.flattener)
return
# New components name
self.componentname = componentname if componentname else componentclass.name
# Make a clone of the componentclass; in which all hierachical components
# have their internal symbols prefixed:
cloned_comp = DynamicsClonerVisitorPrefixNamespace().visit(componentclass)
# Make a list of all components, and those components with regimes:
self.all_components = list(cloned_comp.query.recurse_all_components)
self.componentswithregimes = [
m for m in self.all_components if list(m.regimes)]
# This will get filled in build_new_regime_space(): (It maps {
# (Regime,Regime,...,Regime) : Regime, (Regime,Regime,...,Regime) :
# Regime,} Where the key tuple represents the regimes in the
# hierachical componentclass, corresponding to self.componentswithregimes.
# And the values are the regimes in the new componentclass.
self.old_regime_tuple_to_new_regime_map = None
# OK, Heavy-lifting Code:
# ===================== #
self.build_new_regime_space()
# Build Our New Component
self.reducedcomponent = DynamicsClass(
name=self.componentname,
aliases=flatten_first_level(
[m.aliases for m in self.all_components]),
state_variables=flatten_first_level(
[m.state_variables for m in self.all_components]),
regimes=self.old_regime_tuple_to_new_regime_map.values(),
analog_ports=flatten_first_level(
[comp.analog_ports for comp in self.all_components]),
event_ports=flatten_first_level(
[comp.event_ports for comp in self.all_components]),
parameters=flatten_first_level([m.parameters
for m in self.all_components]))
self.remap_analog_ports()
# Attach this flattening information to the componentclass:
self.reducedcomponent.set_flattener(self)
def remap_analog_ports(self):
new_analog_ports = flatten_first_level(
[comp.analog_ports for comp in self.all_components])
new_analog_ports = dict([(p.name, p) for p in new_analog_ports])
# Handle port mappings:
# portconnections = [ (NS -> NS), (NS -> NS ), (NS -> NS) ]
portconnections = [
model.portconnections for model in self.all_components]
portconnections = list(itertools.chain(* portconnections))
# ONLY ANALOG PORTS
portconnections = [pc for pc in portconnections if pc[
0].get_local_name() in new_analog_ports]
# A. Handle Receive Ports:
for src_addr, dst_addr in portconnections[:]:
srcport = new_analog_ports[src_addr.get_local_name()]
dstport = new_analog_ports[dst_addr.get_local_name()]
if dstport.mode == 'recv':
DynamicsExpandPortDefinition(
originalname=dstport.name, targetname=srcport.name).visit(
self.reducedcomponent)
del new_analog_ports[dst_addr.get_local_name()]
del self.reducedcomponent._analog_receive_ports[
dst_addr.get_local_name()]
# expect_single(
# [p for p in self.reducedcomponent.analog_receive_ports
# if p.name == ]))
portconnections.remove((src_addr, dst_addr))
# B. Handle Reduce Ports:
# 1/ Make a map { reduce_port -> [send_port1, send_port2, send_port3],
# ...}
reduce_connections = defaultdict(list)
for src, dst in portconnections:
dstport = new_analog_ports[dst.get_local_name()]
srcport = new_analog_ports[src.get_local_name()]
if dstport.mode == 'reduce':
reduce_connections[dstport].append(srcport)
# 2/ Substitute each reduce port in turn:
for dstport, srcport_list in reduce_connections.iteritems():
src_subs = [s.name for s in srcport_list]
terms = [dstport.name] + src_subs
reduce_expr = dstport.reduce_op.join(terms)
# globalRemapPort( dstport.name, reduce_expr )
DynamicsExpandPortDefinition(
originalname=dstport.name, targetname=reduce_expr).visit(
self.reducedcomponent)
def create_compound_regime(cls, regimetuple, name):
# Copy accross all the odes from each regime.
# We don't worry about transitions yet, we deal with them later.
# We need to clone the time_derivatives:
time_derivs = flatten_first_level(
[r.time_derivatives for r in regimetuple])
time_derivs = [DynamicsClonerVisitor().visit(td) for td in time_derivs]
return Regime(name=name, time_derivatives=time_derivs)
def test_flatten_first_level(self):
# Signature: name(nested_list)
# Flattens the first level of an iterable, ie
#
# >>> flatten_first_level( [ ['This','is'],['a','short'],['phrase'] ] ) #doctest: +NORMALIZE_WHITESPACE
# ['This', 'is', 'a', 'short', 'phrase']
self.assertEqual(flatten_first_level([[1, 2], [3, 4, 5], [6]]),
[1, 2, 3, 4, 5, 6])
self.assertEqual(flatten_first_level(((1, 2), (3, 4, 5), (6, ))),
[1, 2, 3, 4, 5, 6])
# Check nesting is not flattened:
self.assertEqual(
flatten_first_level([[1, ['a', 'b'], 2], [3, 4, ['c', 'd'], 5],
[6]]),
[1, ['a', 'b'], 2, 3, 4, ['c', 'd'], 5, 6])
self.assertRaises(NineMLRuntimeError, flatten_first_level, [None])
self.assertRaises(NineMLRuntimeError, flatten_first_level, ['abbn'])
self.assertRaises(NineMLRuntimeError, flatten_first_level, [True])
self.assertRaises(NineMLRuntimeError, flatten_first_level,
[None, None])
def concat(cls, *args):
"""Concatenates all the Namespace Addresses.
This method take all the arguments supplied, converts each one into a
namespace object, then, produces a new namespace object which is the
concatenation of all the arguments' namespaces.
For example:
>>> NamespaceAddress.concat('first.second','third.forth','fifth.sixth')
NameSpaceAddress: '/first/second/third/forth/fifth/sixth'
"""
# Turn all the arguments into NamespaceAddress Objects:
args = [NamespaceAddress(a) for a in args]
# Combine all the location tuples in each argument
# into one long list.
loc = flatten_first_level([list(a.loctuple) for a in args])
# Create a namespace out of this long new tuple:
return NamespaceAddress(loc=tuple(loc))
def concat(cls, *args):
"""Concatenates all the Namespace Addresses.
This method take all the arguments supplied, converts each one into a
namespace object, then, produces a new namespace object which is the
concatenation of all the arguments' namespaces.
For example:
>>> NamespaceAddress.concat('first.second','third.forth','fifth.sixth')
NameSpaceAddress: '/first/second/third/forth/fifth/sixth'
"""
# Turn all the arguments into NamespaceAddress Objects:
args = [NamespaceAddress(a) for a in args]
# Combine all the location tuples in each argument
# into one long list.
loc = flatten_first_level([list(a.loctuple) for a in args])
# Create a namespace out of this long new tuple:
return NamespaceAddress(loc=tuple(loc))