def test_tf_delete(self, mock_cleanup):
nrp.start_new_tf_manager()
brain = MockBrainCommunicationAdapter()
robot = MockRobotCommunicationAdapter()
config.active_node.brain_adapter = brain
nrp.set_nest_adapter(brain)
nrp.set_robot_adapter(robot)
@nrp.MapCSVRecorder("recorder1", filename = "1", headers=["Name", "time", "Position"])
@nrp.MapSpikeSink("neuron0", nrp.brain.actors[slice(0, 2, 1)], nrp.leaky_integrator_alpha,
v_rest=1.0, updates=[(1.0, 0.3)])
@nrp.Neuron2Robot(Husky.RightArm.pose)
def right_arm(t, neuron0, recorder1):
return neuron0.voltage * 1.345
@nrp.MapCSVRecorder("recorder2", filename = "2", headers=["Name", "time", "Position"])
@nrp.MapSpikeSink("neuron1", nrp.brain.actors[slice(2, 4, 1)], nrp.leaky_integrator_alpha,
updates=[(1.0, 0.4)], v_rest=1.0)
@nrp.MapSpikeSink("neuron2", nrp.brain.actors[slice(4, 6, 1)], nrp.leaky_integrator_alpha,
updates=[(1.0, 0.4)], v_rest=1.0)
@nrp.Neuron2Robot(Husky.LeftArm.twist)
def left_arm_tw(t, neuron1, neuron2, recorder2):
if neuron1.voltage < 0.678:
if neuron2.voltage > 0.345:
return 0.756
else:
return 1.123
else:
if neuron2.voltage < 0.789:
return 0.632
else:
return 0.256
nrp.initialize("MyTransferFunctions")
# Delete an existing transfer function
self.assertEqual(2, len(nrp.get_transfer_functions()))
self.assertEqual(True, nrp.delete_transfer_function("left_arm_tw"))
self.assertEqual(1, len(nrp.get_transfer_functions()))
self.assertEqual(mock_cleanup.call_count, 1)
# Try to delete it again
self.assertEqual(False, nrp.delete_transfer_function("left_arm_tw"))
self.assertEqual(1, len(nrp.get_transfer_functions()))
# Delete another existing transfer function
self.assertEqual(True, nrp.delete_transfer_function("right_arm"))
self.assertEqual(0, len(nrp.get_transfer_functions()))
self.assertEqual(0, len(config.active_node.brain_adapter.detector_devices))
self.assertEqual(mock_cleanup.call_count, 2)
def __get_transfer_function_sources_and_activation(self,
request,
send_errors=True):
"""
Return the source code of the transfer functions and its activity state
in a tuple of parallel lists.
:param request: The mandatory rospy request parameter
:param send_errors: when available, send error messages on the cle_error topic
:return A tuple (tf_sources, tf_activation_state)
"""
tfs = tf_framework.get_transfer_functions()
tf_arr = []
arr_active_mask = []
for tf in tfs:
tf_arr.append(tf.source.encode('UTF-8'))
arr_active_mask.append(tf.active)
if send_errors and hasattr(
tf, 'error'): # tf may not have an error member
self._publish_error_from_exception(tf.error, tf.name)
return numpy.array(tf_arr), numpy.array(arr_active_mask)
def __get_structured_transfer_functions(request):
"""
Return the source code of the transfer functions
:param request: The mandatory rospy request parameter
"""
tfs = tf_framework.get_transfer_functions(flawed=False)
return srv.GetStructuredTransferFunctionsResponse([
tf for tf in map(StructuredTransferFunction.extract_structure, tfs)
if tf is not None
])
def __check_set_brain(self, data_type, brain_populations,
change_population):
"""
Checks whether the given brain change request is valid
:param data_type: The data type
:param brain_populations: The brain populations
:param change_population: A flag indicating whether populations should be changed
:return: None in case everything is alright, otherwise an error tuple
"""
if data_type != "text" and data_type != "base64":
return ["Data type {0} is invalid".format(data_type), 0, 0, 0]
new_populations = [
str(item) for item in json.loads(brain_populations).keys()
]
old_populations = [
str(item) for item in tf_framework.get_brain_populations().keys()
]
old_changed = find_changed_strings(old_populations, new_populations)
new_added = find_changed_strings(new_populations, old_populations)
if len(new_added) == len(old_changed) >= 1:
transfer_functions = tf_framework.get_transfer_functions(
flawed=False)
check_var_name_re = re.compile(r'^([a-zA-Z_]+\w*)$')
for item in new_added:
if not check_var_name_re.match(item):
return [
"Provided name \"" + item +
"\" is not a valid population name", 0, 0, 0
]
return self.change_transfer_functions(change_population,
old_changed, new_added,
transfer_functions)
elif old_changed and len(new_added) > len(old_changed):
return [
"Renamed populations must be applied separately. Please apply the renaming "
+ "before proceeding with other changes.", -2, -2, 0
]
return None
def test_tf_source(self):
nrp.start_new_tf_manager()
brain = MockBrainCommunicationAdapter()
robot = MockRobotCommunicationAdapter()
@nrp.MapSpikeSink("neuron0", nrp.brain.actors[slice(0, 2, 1)], nrp.leaky_integrator_alpha,
v_rest=1.0, updates=[(1.0, 0.3)])
@nrp.Neuron2Robot(Husky.RightArm.pose)
def right_arm(t, neuron0):
return neuron0.voltage * 1.345
@nrp.Robot2Neuron()
def transform_camera(t, camera, camera_device):
if camera.changed:
camera_device.inner.amplitude = 42.0
expected_source_n2r = """@nrp.MapSpikeSink("neuron0", nrp.brain.actors[slice(0, 2, 1)], nrp.leaky_integrator_alpha,
v_rest=1.0, updates=[(1.0, 0.3)])
@nrp.Neuron2Robot(Husky.RightArm.pose)
def right_arm(t, neuron0):
return neuron0.voltage * 1.345
"""
expected_source_r2n = """@nrp.Robot2Neuron()
def transform_camera(t, camera, camera_device):
if camera.changed:
camera_device.inner.amplitude = 42.0
"""
loaded_source_n2r = config.active_node.n2r[0].source
self.assertEqual(loaded_source_n2r, expected_source_n2r)
loaded_source_r2n = config.active_node.r2n[0].source
self.assertEqual(loaded_source_r2n, expected_source_r2n)
loaded_source_n2r_and_r2n = [tf.source for tf in nrp.get_transfer_functions()]
self.assertIn(expected_source_n2r, loaded_source_n2r_and_r2n)
self.assertIn(expected_source_r2n, loaded_source_n2r_and_r2n)
self.assertEqual(2, len(loaded_source_n2r_and_r2n))
def test_tf_set(self):
nrp.start_new_tf_manager()
brain = MockBrainCommunicationAdapter()
robot = MockRobotCommunicationAdapter()
config.active_node.brain_adapter = brain
config.active_node.robot_adapter = robot
config.active_node.initialize_tf = MagicMock(return_value=None)
@nrp.MapSpikeSink("neuron0", nrp.brain.actors[slice(0, 2, 1)], nrp.leaky_integrator_alpha,
v_rest=1.0, updates=[(1.0, 0.3)]
)
@nrp.Neuron2Robot(Husky.RightArm.pose)
def right_arm(t, neuron0):
return neuron0.voltage * 1.345
@nrp.Robot2Neuron()
def transform_camera(t, camera, camera_device):
if camera.changed:
camera_device.inner.amplitude = 42.0
tf_n2r = """@nrp.MapSpikeSink("neuron1", nrp.brain.actors[slice(0, 2, 1)], nrp.leaky_integrator_alpha,
v_rest=1.0, updates=[(3.0, 0.1)])
@nrp.Neuron2Robot()
def right_arm(t, neuron1):
return neuron1.voltage * 2.345
"""
tf_r2n = """@nrp.MapRobotSubscriber("camera", Topic('/husky/camera', sensor_msgs.msg.Image))
@nrp.Robot2Neuron()
def transform_camera(t, camera):
if camera.changed:
pass
"""
nrp.delete_transfer_function('right_arm')
nrp.delete_transfer_function('transform_camera')
nrp.set_transfer_function(tf_n2r, tf_n2r, 'right_arm')
nrp.set_transfer_function(tf_r2n, tf_r2n, 'transform_camera')
self.assertEqual(config.active_node.initialize_tf.call_count, 2)
loaded_source_n2r_and_r2n = [tf.source for tf in nrp.get_transfer_functions()]
self.assertIn(tf_n2r, loaded_source_n2r_and_r2n)
self.assertIn(tf_r2n, loaded_source_n2r_and_r2n)
number_of_tf = len(loaded_source_n2r_and_r2n)
self.assertEqual(2, number_of_tf)
source_update_flags = [tf.updated for tf in nrp.get_transfer_functions()]
self.assertEqual([True] * number_of_tf, source_update_flags)
fancy_tf = "def it_wont_work():\n return None"
self.assertRaises(TFLoadingException, nrp.set_transfer_function, fancy_tf, fancy_tf, "it_wont_work")
try:
nrp.set_transfer_function(fancy_tf, fancy_tf, 'it_wont_work')
except TFLoadingException as e:
self.assertIn('no decorator', e.message)
self.assertEqual('it_wont_work', e.tf_name)
funny_tf = """@nrp.Robot2Neuron()
def transform_camera(t, camera, camera_device):
if camera.changed:
return Nothing!
"""
self.assertRaises(TFLoadingException, nrp.set_transfer_function, funny_tf, funny_tf, "it_cant_work")
try:
nrp.set_transfer_function(funny_tf, funny_tf, 'it_cant_work')
except TFLoadingException as e:
self.assertIn('invalid syntax', e.message)
self.assertIn('line 4', e.message)
self.assertEqual('it_cant_work', e.tf_name)