def test_uniform_rule(self):
mean = random.random()
min = mean - 1
max = mean + 1
rule = create_rule('uniform', mean=mean, min=min, max=max)
self.assertIsNotNone(rule)
self.assertEqual(rule.mean, mean)
self.assertEqual(rule.min, min),
self.assertEqual(rule.max, max)
for tag in rule.dofs:
self.assertFalse(rule.dofs[tag])
for tag in DOF_TAGS:
dofs = dict()
for t in DOF_TAGS:
dofs[t] = t == tag
rule.dofs = dofs
pose = rule.get_pose()
for t in DOF_TAGS:
if t == tag:
self.assertGreaterEqual(getattr(pose, t), min)
self.assertLessEqual(getattr(pose, t), max)
else:
if t in ['x', 'y', 'z']:
self.assertEqual(getattr(pose, t), 0)
def test_random_rule(self):
scaling_factor = random.random()
offset = random.random()
rule = create_rule('random',
scaling_factor=scaling_factor,
offset=offset)
self.assertIsNotNone(rule)
self.assertEqual(rule.scaling_factor, scaling_factor)
self.assertEqual(rule.offset, offset)
for tag in rule.dofs:
self.assertFalse(rule.dofs[tag])
for tag in DOF_TAGS:
dofs = dict()
for t in DOF_TAGS:
dofs[t] = t == tag
rule.dofs = dofs
pose = rule.get_pose()
for t in DOF_TAGS:
if t == tag:
self.assertNotEqual(getattr(pose, t), 0)
else:
if t in ['x', 'y', 'z']:
self.assertEqual(getattr(pose, t), 0)
def test_from_set_rule(self):
values = [random.random() for _ in range(5)]
rule = create_rule('from_set', values=values)
self.assertIsNotNone(rule)
self.assertEqual(rule.values, values)
for tag in rule.dofs:
self.assertFalse(rule.dofs[tag])
for tag in DOF_TAGS:
dofs = dict()
for t in DOF_TAGS:
dofs[t] = t == tag
rule.dofs = dofs
pose = rule.get_pose()
for t in DOF_TAGS:
if t == tag:
found_value = False
for v in values:
if np.isclose(v, getattr(pose, t)):
found_value = True
break
self.assertTrue(found_value)
else:
if t in ['x', 'y', 'z']:
self.assertEqual(getattr(pose, t), 0)
def test_deprecated_rules_args(self):
DOFS = dict(x=True, y=True, z=True)
rule = create_rule(policy=dict(name='workspace', args='my_workspace'),
dofs=DOFS)
self.assertIsNotNone(rule)
self.assertEqual(rule.name, 'workspace')
self.assertEqual(rule._workspace_tag, 'my_workspace')
rule = create_rule(policy=dict(name='uniform',
args=dict(min=-1, max=1)),
dofs=DOFS)
self.assertIsNotNone(rule)
self.assertEqual(rule.name, 'uniform')
self.assertEqual(rule._min, -1)
self.assertEqual(rule._max, 1)
self.assertEqual(rule._mean, 0)
rule = create_rule(policy=dict(name='uniform',
args=dict(min=3, max=5, mean=4)),
dofs=DOFS)
self.assertIsNotNone(rule)
self.assertEqual(rule.name, 'uniform')
self.assertEqual(rule._min, 3)
self.assertEqual(rule._max, 5)
self.assertEqual(rule._mean, 4)
rule = create_rule(policy=dict(name='choice',
args=dict(values=[1, 2, 3])),
dofs=DOFS)
self.assertIsNotNone(rule)
self.assertEqual(rule.name, 'from_set')
self.assertEqual(rule._values, [1, 2, 3])
rule = create_rule(policy=dict(name='value', args=10), dofs=DOFS)
self.assertIsNotNone(rule)
self.assertEqual(rule.name, 'value')
self.assertEqual(rule._value, 10)
def test_within_workspace_rule(self):
cm = ConstraintsManager.get_instance()
self.assertTrue(cm.add(**WORKSPACE_CONSTRAINT))
self.assertIn(WORKSPACE_CONSTRAINT['name'], cm.tags)
rule = create_rule('workspace', workspace=WORKSPACE_CONSTRAINT['name'])
self.assertIsNotNone(rule)
pose = rule.get_pose()
constraint = cm.get(WORKSPACE_CONSTRAINT['name'])
self.assertTrue(constraint.contains_point([pose.x, pose.y]))
rule.dofs = dict(x=True, y=True, z=True)
pose = rule.get_pose()
constraint = cm.get(WORKSPACE_CONSTRAINT['name'])
self.assertTrue(constraint.contains_point([pose.x, pose.y]))
def test_fixed_value_rule(self):
value = random.random()
rule = create_rule('value', value=value)
self.assertIsNotNone(rule)
self.assertEqual(rule.value, value)
for tag in rule.dofs:
self.assertFalse(rule.dofs[tag])
for tag in DOF_TAGS:
dofs = dict()
for t in DOF_TAGS:
dofs[t] = t == tag
rule.dofs = dofs
pose = rule.get_pose()
for t in DOF_TAGS:
if t == tag:
self.assertTrue(np.isclose(getattr(pose, t), value))
else:
if t in ['x', 'y', 'z']:
self.assertEqual(getattr(pose, t), 0)