def init(self):
id_discdds = self.config.id_discdds
dp_config = get_dp_config()
dp_config.load(self.config.config_dir)
self.discdds = dp_config.discdds.instance(id_discdds)
plan = self.config.plan
self.action = self.discdds.plan2action(plan)
def init_diffeo(self, shape):
id_symdiffeo = self.config.id_symdiffeo
symdiffeo = get_dp_config().symdiffeos.instance(id_symdiffeo)
label = 'tmp'
original_cmd = [np.zeros(1)]
self.info('creating diffeo_action')
contracts.disable_all()
self.diffeo_action = \
diffeo_action_from_symdiffeo(symdiffeo, shape,
label, original_cmd)
self.info('..done')
def init_diffeo(self, shape):
id_symdiffeo = self.config.id_symdiffeo
symdiffeo = get_dp_config().symdiffeos.instance(id_symdiffeo)
label = 'tmp'
original_cmd = [np.zeros(1)]
self.info('creating diffeo_action')
contracts.disable_all()
self.diffeo_action = \
diffeo_action_from_symdiffeo(symdiffeo, shape,
label, original_cmd)
self.info('..done')
def init(self):
contracts.disable_all()
id_discdds = self.config.id_discdds
dp_config = get_dp_config()
dp_config.load(self.config.config_dir)
self.discdds = dp_config.discdds.instance(id_discdds)
N = self.config.nsteps
m = make_matrix(nsteps=N, mult=self.config.mult)
cmd_to_action = {'a': 3, 'b': 2, 'c': 1, 'd': 0}
cmd_inv = {'a': 'c', 'b': 'd'}
mult = self.config.mult
sequence = {0: 0, 1: 1 * mult, 2: 2 * mult, 3: 3 * mult}
@contract(ins='tuple(int,str)', returns='list[int]')
def normalize_instruction(ins):
num, cmd = ins
if num < 0:
cmd = cmd_inv[cmd]
num = -num
assert num >= 0
k = cmd_to_action[cmd]
n = sequence[num]
return [k] * n
@contract(splan='list[P](tuple(int,str))', returns='list(int)')
def normalize_splan(splan):
plan = []
for ins in splan:
plan.extend(normalize_instruction(ins))
return plan
M = 2 * N + 1
self.actions = []
for i, j in itertools.product(range(M), range(M)):
m[i][j] = normalize_splan(m[i][j])
action = self.discdds.plan2action(m[i][j])
self.actions.append(action)
self.info('shape: %s' % str(self.discdds.get_shape()))
self.M = M
def init(self):
contracts.disable_all()
id_discdds = self.config.id_discdds
dp_config = get_dp_config()
dp_config.load(self.config.config_dir)
self.discdds = dp_config.discdds.instance(id_discdds)
N = self.config.nsteps
m = make_matrix(nsteps=N, mult=self.config.mult)
cmd_to_action = {'a': 3, 'b': 2, 'c': 1, 'd': 0}
cmd_inv = {'a': 'c', 'b': 'd'}
mult = self.config.mult
sequence = {0: 0, 1: 1 * mult, 2: 2 * mult, 3: 3 * mult}
@contract(ins='tuple(int,str)', returns='list[int]')
def normalize_instruction(ins):
num, cmd = ins
if num < 0:
cmd = cmd_inv[cmd]
num = -num
assert num >= 0
k = cmd_to_action[cmd]
n = sequence[num]
return [k] * n
@contract(splan='list[P](tuple(int,str))', returns='list(int)')
def normalize_splan(splan):
plan = []
for ins in splan:
plan.extend(normalize_instruction(ins))
return plan
M = 2 * N + 1
self.actions = []
for i, j in itertools.product(range(M), range(M)):
m[i][j] = normalize_splan(m[i][j])
action = self.discdds.plan2action(m[i][j])
self.actions.append(action)
self.info('shape: %s' % str(self.discdds.get_shape()))
self.M = M
def from_yaml(discdds, servo):
dp_config = get_dp_config()
_, i = dp_config.discdds.instance_smarter(discdds)
return DiffeoPlanAgent(i, servo)
def __init__(self, plans_generator, reduce_tolerance, distance):
self.reduce_tolerance = reduce_tolerance
self.plans_generator = instantiate_spec(plans_generator)
_, self.distance = get_dp_config().distances.instance_smarter(distance)
def from_yaml(discdds, servo):
dp_config = get_dp_config()
_, i = dp_config.discdds.instance_smarter(discdds)
return DiffeoPlanAgent(i, servo)
