def transition_directions_test():
"""
unit test for correctness of abstracted transition directions, with:
- uni-directional control authority
- no disturbance
"""
modes = []
modes.append(('normal', 'fly'))
modes.append(('refuel', 'fly'))
env_modes, sys_modes = zip(*modes)
cont_state_space = pc.box2poly([[0., 3.], [0., 2.]])
pwa_sys = dict()
pwa_sys[('normal', 'fly')] = hybrid.PwaSysDyn([subsys0()],
cont_state_space)
pwa_sys[('refuel', 'fly')] = hybrid.PwaSysDyn([subsys1()],
cont_state_space)
switched_dynamics = hybrid.SwitchedSysDyn(
disc_domain_size=(len(env_modes), len(sys_modes)),
dynamics=pwa_sys,
env_labels=env_modes,
disc_sys_labels=sys_modes,
cts_ss=cont_state_space)
cont_props = {}
cont_props['home'] = pc.box2poly([[0., 1.], [0., 1.]])
cont_props['lot'] = pc.box2poly([[2., 3.], [1., 2.]])
ppp = abstract.prop2part(cont_state_space, cont_props)
ppp, new2old = abstract.part2convex(ppp)
N = 8
trans_len = 1
disc_params = {}
for mode in modes:
disc_params[mode] = {'N': N, 'trans_length': trans_len}
swab = abstract.discretize_switched(ppp,
switched_dynamics,
disc_params,
plot=True,
show_ts=True,
only_adjacent=False)
ts = swab.modes[('normal', 'fly')].ts
edges = {(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (1, 2), (1, 4),
(1, 5), (2, 3), (2, 5), (2, 0), (3, 0), (4, 5), (5, 0)}
logger.debug(set(ts.edges()).symmetric_difference(edges))
assert (set(ts.edges()) == edges)
ts = swab.ts
assert (set(ts.edges()) == edges)
for i, j in edges:
assert (ts[i][j][0]['env_actions'] == 'normal')
assert (ts[i][j][0]['sys_actions'] == 'fly')
def define_partition(dom):
p = dict()
p['a'] = pc.box2poly([[0.0, 10.0], [15.0, 18.0]])
p['b'] = pc.box2poly([[0.0, 1.0], [0.0, 20.0]])
ppp = abstract.prop2part(dom, p)
ppp, new2old_reg = abstract.part2convex(ppp)
return ppp
def cont_predicates():
p = dict()
p['safe'] = pc.box2poly([[0.5, 3.5], [0.5, 2.5]])
ppp = abstract.prop2part(dom, p)
ppp, new2old_reg = abstract.part2convex(ppp)
ppp.plot()
return ppp
def define_partition(dom):
p = dict()
p["a"] = pc.box2poly([[0.0, 10.0], [15.0, 18.0]])
p["b"] = pc.box2poly([[0.0, 1.0], [0.0, 20.0]])
ppp = abstract.prop2part(dom, p)
ppp, new2old_reg = abstract.part2convex(ppp)
return ppp
def cont_predicates():
p = dict()
p["safe"] = pc.box2poly([[0.5, 3.5], [0.5, 2.5]])
ppp = abstract.prop2part(dom, p)
ppp, new2old_reg = abstract.part2convex(ppp)
ppp.plot()
return ppp
def transition_directions_test():
"""Unit test for correctness of abstracted transition directions, with:
- uni-directional control authority
- no disturbance
"""
modes = list()
modes.append(('normal', 'fly'))
modes.append(('refuel', 'fly'))
env_modes, sys_modes = zip(*modes)
# dynamics
cont_state_space = pc.box2poly([[0.0, 3.0], [0.0, 2.0]])
pwa_sys = dict()
pwa_sys[('normal', 'fly')] = hybrid.PwaSysDyn([subsys0()],
cont_state_space)
pwa_sys[('refuel', 'fly')] = hybrid.PwaSysDyn([subsys1()],
cont_state_space)
switched_dynamics = hybrid.SwitchedSysDyn(
disc_domain_size=(len(env_modes), len(sys_modes)),
dynamics=pwa_sys,
env_labels=env_modes,
disc_sys_labels=sys_modes,
cts_ss=cont_state_space)
# propositions
cont_props = dict()
cont_props['home'] = pc.box2poly([[0.0, 1.0], [0.0, 1.0]])
cont_props['lot'] = pc.box2poly([[2.0, 3.0], [1.0, 2.0]])
# partition
ppp = abstract.prop2part(cont_state_space, cont_props)
ppp, new2old = abstract.part2convex(ppp)
# configure discretization
N = 8
trans_len = 1
disc_params = dict()
for mode in modes:
disc_params[mode] = dict(N=N, trans_length=trans_len)
# discretize
swab = abstract.discretize_switched(ppp,
switched_dynamics,
disc_params,
plot=True,
show_ts=True,
only_adjacent=False)
# assertions
ts = swab.modes[('normal', 'fly')].ts
edges = {(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (1, 2), (1, 4),
(1, 5), (2, 3), (2, 5), (2, 0), (3, 0), (4, 5), (5, 0)}
h = nx.MultiDiGraph()
h.add_edges_from(edges)
assert nx.is_isomorphic(ts, h)
ts = swab.ts
assert nx.is_isomorphic(ts, h)
for _, _, d in ts.edges(data=True):
assert d['env_actions'] == 'normal'
assert d['sys_actions'] == 'fly'
pwa_normal = hybrid.PwaSysDyn([cont_dyn_normal], domain=cont_ss)
pwa_refuel = hybrid.PwaSysDyn([cont_dyn_refuel], domain=cont_ss)
dynamics_dict = {('normal', 'fly'): pwa_normal, ('refuel', 'fly'): pwa_refuel}
switched_dynamics = hybrid.SwitchedSysDyn(cts_ss=cont_ss,
disc_domain_size=(len(env_modes),
len(sys_modes)),
dynamics=dynamics_dict,
env_labels=env_modes,
disc_sys_labels=sys_modes)
## Create convex proposition preserving partition
ppp = abstract.prop2part(cont_ss, cont_props)
ppp, new2old = abstract.part2convex(ppp)
ax = ppp.plot_props()
ax.figure.savefig(imgpath + 'cprops.pdf')
ax = ppp.plot()
ax.figure.savefig(imgpath + 'ppp.pdf')
## Discretize to establish transitions
if os.name == "posix":
start = os.times()[2]
logger.info('start time: ' + str(start))
else:
logger.info(
'Timing currently only available for POSIX platforms (not Windows)')
msg = 'Found:\n'
msg += '\t Environment modes: ' + str(env_modes)
msg += '\t System modes: ' + str(sys_modes)
switched_dynamics = hybrid.SwitchedSysDyn(
disc_domain_size=(len(env_modes), len(sys_modes)),
dynamics=sys_dyn,
env_labels=env_modes,
disc_sys_labels=sys_modes,
cts_ss=cont_state_space
)
print(switched_dynamics)
ppp = abstract.prop2part(cont_state_space, cont_props)
ppp, new2old = abstract.part2convex(ppp)
"""Discretize to establish transitions"""
start = time.time()
N = 8
trans_len=1
disc_params = {}
for mode in modes:
disc_params[mode] = {'N':N, 'trans_length':trans_len}
swab = abstract.multiproc_discretize_switched(
ppp, switched_dynamics, disc_params,
plot=True, show_ts=True
)
def transition_directions_test():
"""
unit test for correctness of abstracted transition directions, with:
- uni-directional control authority
- no disturbance
"""
modes = []
modes.append(("normal", "fly"))
modes.append(("refuel", "fly"))
env_modes, sys_modes = zip(*modes)
cont_state_space = pc.box2poly([[0.0, 3.0], [0.0, 2.0]])
pwa_sys = dict()
pwa_sys[("normal", "fly")] = hybrid.PwaSysDyn([subsys0()], cont_state_space)
pwa_sys[("refuel", "fly")] = hybrid.PwaSysDyn([subsys1()], cont_state_space)
switched_dynamics = hybrid.SwitchedSysDyn(
disc_domain_size=(len(env_modes), len(sys_modes)),
dynamics=pwa_sys,
env_labels=env_modes,
disc_sys_labels=sys_modes,
cts_ss=cont_state_space,
)
cont_props = {}
cont_props["home"] = pc.box2poly([[0.0, 1.0], [0.0, 1.0]])
cont_props["lot"] = pc.box2poly([[2.0, 3.0], [1.0, 2.0]])
ppp = abstract.prop2part(cont_state_space, cont_props)
ppp, new2old = abstract.part2convex(ppp)
N = 8
trans_len = 1
disc_params = {}
for mode in modes:
disc_params[mode] = {"N": N, "trans_length": trans_len}
swab = abstract.discretize_switched(
ppp, switched_dynamics, disc_params, plot=True, show_ts=True, only_adjacent=False
)
ts = swab.modes[("normal", "fly")].ts
edges = {
(0, 0),
(1, 1),
(2, 2),
(3, 3),
(4, 4),
(5, 5),
(1, 2),
(1, 4),
(1, 5),
(2, 3),
(2, 5),
(2, 0),
(3, 0),
(4, 5),
(5, 0),
}
logger.debug(set(ts.edges()).symmetric_difference(edges))
assert set(ts.edges()) == edges
ts = swab.ts
assert set(ts.edges()) == edges
for i, j in edges:
assert ts[i][j][0]["env_actions"] == "normal"
assert ts[i][j][0]["sys_actions"] == "fly"
