class SimCLDAControlMultiDispl2D_PPF(SimTime, Autostart, WindowDispl2D,
SimCosineTunedPointProc,
SimPPFDecoderCursorShuffled,
cursor_clda_tasks.CLDAControlPPFContAdapt
):
win_res = (250, 140)
tau = traits.Float(2.7, desc="Magic parameter for speed of OFC.")
param_noise_scale = traits.Float(1.0, desc="Stuff")
half_life = (0, 0)
half_life_time = 1
def __init__(self, *args, **kwargs):
from riglib.bmi.state_space_models import StateSpaceEndptVel2D
ssm = StateSpaceEndptVel2D()
A, B, W = ssm.get_ssm_matrices(update_rate=1. / 180)
Q = np.mat(np.diag([1., 1, 1, 0, 0, 0, 0]))
R = 100 * np.mat(np.diag([1., 1., 1.]))
self.fb_ctrl = LQRController(A, B, Q, R)
self.ssm = ssm
super(SimCLDAControlMultiDispl2D_PPF, self).__init__(*args, **kwargs)
self.batch_time = 1. / 10 #60. # TODO 10 Hz running seems to be hardcoded somewhere
self.assist_level = 0., 0.
self.assist_level_time = 120.
self.last_get_spike_counts_time = -1. / 60
self.learn_flag = True
class ButtonTask(LogExperiment):
side = traits.String("left",
desc='Use "left" for one side, "right" for the other')
reward_time = traits.Float(5, desc='Amount of reward (in seconds)')
penalty_time = traits.Float(5, desc='Amount of penalty (in seconds)')
status = dict(
left=dict(left_correct="reward", left_incorrect="penalty", stop=None),
right=dict(right_correct="reward",
right_incorrect="penalty",
stop=None),
reward=dict(post_reward="picktrial"),
penalty=dict(post_penalty="picktrial"),
)
state = "picktrial"
def __init__(self, **kwargs):
from riglib import button
super(ButtonTask, self).__init__(**kwargs)
self.button = button.Button()
def _start_picktrial(self):
self.set_state(self.side)
def _get_event(self):
if self.button is not None:
return self.button.pressed()
return None
def _while_left(self):
self.event = self._get_event()
def _while_right(self):
self.event = self._get_event()
def _test_left_correct(self, ts):
return self.event is not None and self.event in [1, 2]
def _test_left_incorrect(self, ts):
return self.event is not None and self.event in [8, 4]
def _test_right_correct(self, ts):
return self.event is not None and self.event in [8, 4]
def _test_right_incorrect(self, ts):
return self.event is not None and self.event in [1, 2]
def _test_post_reward(self, ts):
return ts > self.reward_time
def _test_post_penalty(self, ts):
return ts > self.penalty_time
def _test_both_correct(self, ts):
return self.event is not None
def _start_None(self):
pass
class FixationStart(CalibratedEyeData):
'''Triggers the start_trial event whenever fixation exceeds *fixation_length*'''
fixation_length = traits.Float(
2., desc="Length of fixation required to start the task")
fixation_dist = traits.Float(
50., desc="Distance from center that is considered a broken fixation")
def __init__(self, *args, **kwargs):
'''
Docstring
Parameters
----------
Returns
-------
'''
super(FixationStart, self).__init__(*args, **kwargs)
self.status['wait']['fixation_break'] = "wait"
self.log_exclude.add(("wait", "fixation_break"))
def _start_wait(self):
'''
Docstring
Parameters
----------
Returns
-------
'''
self.eyedata.get()
super(FixationStart, self)._start_wait()
def _test_fixation_break(self, ts):
'''
Docstring
Parameters
----------
Returns
-------
'''
return (np.sqrt(
(self.eyedata.get()**2).sum(1)) > self.fixation_dist).any()
def _test_start_trial(self, ts):
'''
Docstring
Parameters
----------
Returns
-------
'''
return ts > self.fixation_length
class Conditions(Sequence):
status = dict(
wait = dict(start_trial="trial"),
trial = dict(end_trial="wait", stoppable=False, end_state=True),
)
wait_time = traits.Float(5.0, desc="Inter-trial interval (s)")
trial_time = traits.Float(1.0, desc="Trial duration (s)")
sequence_generators = ['null_sequence']
def init(self):
self.trial_dtype = np.dtype([('trial', 'u4'), ('index', 'u4')])
super().init()
def _parse_next_trial(self):
self.trial_index = self.next_trial
# Send record of trial to sinks
self.trial_record['trial'] = self.calc_trial_num()
self.trial_record['index'] = self.trial_index
self.sinks.send("trials", self.trial_record)
def _test_start_trial(self, ts):
return ts > self.wait_time and not self.pause
def _test_end_trial(self, ts):
return ts > self.trial_time
def _start_trial(self):
self.sync_event('TRIAL_START', self.trial_index)
def _end_trial(self):
self.sync_event('TRIAL_END')
@staticmethod
def gen_random_conditions(nreps, *args, replace=False):
''' Generate random sequence of all combinations of the given arguments'''
unique = list(itertools.product(*args))
conds = np.random.choice(nreps*len(unique), nreps*len(unique), replace=replace)
seq = [[i % len(unique)] + list(unique[i % len(unique)]) for i in conds] # list of [index, arg1, arg2, ..., argn]
return tuple(zip(*seq))
@staticmethod
def gen_conditions(nreps, *args, ascend=True):
''' Generate a sequential sequence of all combinations of the given arguments'''
unique = list(itertools.product(*args))
conds = np.tile(range(len(unique)), nreps)
if not ascend: # descending
conds = np.flipud(conds)
seq = [[i % len(unique)] + list(unique[i % len(unique)]) for i in conds] # list of [index, arg1, arg2, ..., argn]
return tuple(zip(*seq))
@staticmethod
def null_sequence(ntrials=100):
return [0 for _ in range(ntrials)]
class BMIControlCursorElip(BMIControlMulti):
w_aspect_ratio = traits.Float(2., desc="aspect ratio for W matrix")
w_zero_scale = traits.Float(0.5, desc="aspect ratio for W matrix")
v_max = traits.Float(20, desc="aspect ratio for W matrix")
def init(self):
super(BMIControlCursorElip, self).init()
self.decoder.filt.w_aspect_ratio = self.w_aspect_ratio
self.decoder.filt.w_zero_scale = self.w_zero_scale
self.decoder.filt.v_max = self.v_max
class BMIControlCursorHold(BMIControlMulti):
'''
A cursor BMI task where reward is proportional to length of hold
'''
_target_color = (0, 0, 1, .5)
status = dict(wait=dict(start_trial="target", stop=None),
target=dict(enter_target="hold",
timeout="timeout_penalty",
stop=None),
hold=dict(hold_complete="targ_transition"),
targ_transition=dict(trial_complete="reward",
trial_abort="wait",
trial_incomplete="target"),
timeout_penalty=dict(timeout_penalty_end="targ_transition"),
reward=dict(reward_end="wait"))
reward_mult_factor = traits.Float(
1.0, desc="reward time = reward_mult_factor * hold_time")
max_hold = traits.Float(2.0, desc="max time for rewarded hold")
def __init__(self, *args, **kwargs):
super(BMIControlCursorHold, self).__init__(*args, **kwargs)
self.hold_start_time = np.nan
def init(self):
self.add_dtype('reward_time', 'f8', (1, ))
super(BMIControlCursorHold, self).init()
def _cycle(self):
self.task_data['reward_time'] = self.reward_time
super(BMIControlCursorHold, self)._cycle()
def _start_hold(self):
self.hold_start_time = self.get_time()
def _test_hold_complete(self, ts):
cursor_pos = self.plant.get_endpoint_pos()
d = np.linalg.norm(cursor_pos - self.target_location)
rad = self.target_radius - self.cursor_radius
outside_targ = d > rad
compl = (ts > self.max_hold) or outside_targ
if compl:
self.reward_time += ts #(ts - self.hold_start_time)
return compl
def _test_trial_abort(self, ts):
abort = super(BMIControlCursorHold, self)._test_trial_abort(ts)
if abort:
self.reward_time = 0
return abort
def _start_wait(self):
self.reward_time = 0
super(BMIControlCursorHold, self)._start_wait()
class JoystickDrivenCursorOPSBiased(JoystickDrivenCursorOPS):
bias_angle = traits.Float(0,
desc="Angle to bias cursor velocity, in degrees")
bias_gain = traits.Float(
0, desc="Gain of directional velocity bias in cm/sec")
def load_decoder(self):
super(JoystickDrivenCursorOPSBiased, self).load_decoder()
assert isinstance(self.decoder, kfdecoder.KFDecoder)
self.decoder.filt.A[3, -1] += self.bias_gain * np.cos(
self.bias_angle * np.pi / 180)
self.decoder.filt.A[5, -1] += self.bias_gain * np.sin(
self.bias_angle * np.pi / 180)
print self.decoder.filt.A
class CLDAControlPPFContAdapt(CLDAControlMulti):
tau = traits.Float(2.7, desc="Magic parameter for speed of OFC.")
param_noise_scale = traits.Float(1.0, desc="Stuff")
exclude_parent_traits = ['half_life', 'half_life_decay_time', 'batch_time']
ordered_traits = [
'session_length', 'assist_level', 'assist_time', 'tau',
'param_noise_scale'
]
def create_learner(self):
self.learn_flag = True
kwargs = dict()
dt = kwargs.pop('dt', 1. / 180)
use_tau_unNat = self.tau
self.tau = use_tau_unNat
print "learner cost fn param: %g" % use_tau_unNat
tau_scale = 28 * use_tau_unNat / 1000
bin_num_ms = (dt / 0.001)
w_r = 3 * tau_scale**2 / 2 * (bin_num_ms)**2 * 26.61
I = np.eye(3)
zero_col = np.zeros([3, 1])
zero_row = np.zeros([1, 3])
zero = np.zeros([1, 1])
one = np.ones([1, 1])
A = np.bmat([[I, dt * I, zero_col], [0 * I, 0 * I, zero_col],
[zero_row, zero_row, one]])
B = np.bmat([[0 * I], [dt / 1e-3 * I], [zero_row]])
Q = np.mat(np.diag([1., 1, 1, 0, 0, 0, 0]))
R = np.mat(np.diag([w_r, w_r, w_r]))
F = feedback_controllers.LQRController.dlqr(A, B, Q, R)
F_dict = dict(target=F, hold=F)
fb_ctrl = feedback_controllers.MultiModalLFC(A=A, B=B, F_dict=F_dict)
batch_size = 1
self.learner = clda.OFCLearner(batch_size, A, B, F_dict)
# Tell BMISystem that this learner wants the most recent output
# of the decoder rather than the second most recent, to match MATLAB
self.learner.input_state_index = 0
def create_updater(self):
self.updater = clda.PPFContinuousBayesianUpdater(
self.decoder, param_noise_scale=self.param_noise_scale)
class BMIControlManipulatedFB(bmimultitasks.BMIControlMulti):
feedback_rate = traits.Float(60, desc="Rate in hz that cursor position is updated on screen (best if factor of 60)")
task_update_rate = traits.Float(60, desc="Rate in hz that decoded cursor position is updated within task (best if factor of 60)")
ordered_traits = ['session_length', 'assist_level', 'assist_time', 'feedback_rate', 'task_update_rate']
def __init__(self, *args, **kwargs):
super(BMIControlManipulatedFB, self).__init__(*args, **kwargs)
self.visible_cursor = Sphere(radius=self.cursor_radius, color=(1,1,1,1))
self.add_model(self.visible_cursor)
self.cursor_visible = True
def init(self):
self.dtype.append(('visible_cursor','f8',3))
super(BMIControlManipulatedFB, self).init()
self.feedback_num = int(60.0/self.feedback_rate)
self.task_update_num = int(60.0/self.task_update_rate)
self.loopcount = 0
def update_cursor(self):
''' Update the cursor's location and visibility status.'''
pt = self.get_cursor_location()
prev = self.cursor_visible
self.cursor_visible = False
if prev != self.cursor_visible:
self.show_object(self.cursor, show=False) #self.cursor.detach()
self.requeue()
#update the "real" cursor location only according to specified task update rate
if self.loopcount%self.task_update_num==0:
if pt is not None:
self.move_cursor(pt)
#update the visible cursor location only according to specified feedback rate
if self.loopcount%self.feedback_num==0:
loc = self.cursor.xfm.move
self.visible_cursor.translate(*loc,reset=True)
def _cycle(self):
''' Overwriting parent methods since this one works differently'''
self.update_assist_level()
self.task_data['assist_level'] = self.current_assist_level
self.update_cursor()
self.task_data['cursor'] = self.cursor.xfm.move.copy()
self.task_data['target'] = self.target_location.copy()
self.task_data['target_index'] = self.target_index
self.task_data['visible_cursor'] = self.visible_cursor.xfm.move.copy()
self.loopcount += 1
#write to screen
self.draw_world()
class FixationTraining(Window):
status = dict(wait=dict(start_trial="reward", stop=None),
reward=dict(reward_end="wait"))
#initial state
state = "wait"
#settable traits
reward_time = traits.Float(.5, desc="Length of juice reward")
#initialize and create fixation point object
def __init__(self, **kwargs):
super(FixationTraining, self).__init__(**kwargs)
self.fixation_point = Sphere(radius=.1, color=(1, 0, 0, 1))
#keep fixation point hidden for now
#self.add_model(self.fixation_point)
def _get_renderer(self):
return stereo.MirrorDisplay(self.window_size, self.fov, 1, 1024,
self.screen_dist, self.iod)
def _test_reward_end(self, ts):
return ts > self.reward_time
def _while_wait(self):
self.draw_world()
def _while_reward(self):
self.draw_world()
def test_norm_trait(self):
from db.tracker import json_param
from riglib.experiment import traits
t = traits.Float(1, descr='test trait')
t1 = json_param.norm_trait(t, 1.0)
self.assertEqual(t1, 1.0)
class CLDAControlMulti(BMIControlMulti, LinearlyDecreasingHalfLife):
'''
BMI task that periodically refits the decoder parameters based on intended
movements toward the targets. Inherits directly from BMIControl. Can be made
to automatically linearly decrease assist level over set time period, or
to provide constant assistance by setting assist_level and assist_min equal.
'''
batch_time = traits.Float(80.0, desc='The length of the batch in seconds')
decoder_sequence = traits.String(
'test', desc='signifier to group together sequences of decoders')
ordered_traits = [
'session_length', 'assist_level', 'assist_level_time', 'batch_time',
'half_life', 'half_life_time'
]
def __init__(self, *args, **kwargs):
super(CLDAControlMulti, self).__init__(*args, **kwargs)
self.learn_flag = True
def create_learner(self):
self.batch_size = int(self.batch_time / self.decoder.binlen)
self.learner = CursorGoalLearner2(self.batch_size)
self.learn_flag = True
def create_updater(self):
half_life_start, half_life_end = self.half_life
self.updater = clda.KFSmoothbatch(self.batch_time, half_life_start)
def call_decoder(self, *args, **kwargs):
kwargs['half_life'] = self.current_half_life
return super(CLDAControlMulti, self).call_decoder(*args, **kwargs)
class RewardSystem(traits.HasTraits):
'''
Feature for the Crist solenoid reward system
'''
trials_per_reward = traits.Float(
1, desc='Number of successful trials before solenoid is opened')
def __init__(self, *args, **kwargs):
from riglib import reward
super(RewardSystem, self).__init__(*args, **kwargs)
self.reward = reward.open()
def _start_reward(self):
self.reward_start = self.get_time()
if self.reward is not None:
self.reportstats['Reward #'] += 1
if self.reportstats['Reward #'] % self.trials_per_reward == 0:
self.reward.reward(self.reward_time * 1000.)
super(RewardSystem, self)._start_reward()
def _test_reward_end(self, ts):
if self.reportstats['Reward #'] % self.trials_per_reward == 0:
return ts > self.reward_time
else:
return True
class CLDATentacleRL(CLDAControlTentacle):
n_trial_ofc_learner = traits.Float(
16.0,
desc=
'Number of rewards before switching from continuous adaptation to trial-based adaptation'
)
batch_size = 0.1
def create_learner(self):
A, B, _ = self.decoder.ssm.get_ssm_matrices()
kin_chain = self.plant.kin_chain
Q = np.mat(np.diag(np.hstack([kin_chain.link_lengths, np.zeros(5)])))
R = 100000 * np.mat(np.eye(B.shape[1]))
self.ofc_learner = OFCLearnerTentacle(
1,
A,
B,
Q,
R,
done_states=['reward', 'hold_penalty'],
reset_states=['timeout_penalty'])
self.rl_learner = TentacleValueLearner(
np.inf,
done_states=['reward', 'hold_penalty'],
reset_states=['timeout_penalty'],
kin_chain=self.plant.kin_chain)
self.learner = self.ofc_learner
def _cycle(self):
super(CLDATentacleRL, self)._cycle()
if (self.calc_state_occurrences('reward') > self.n_trial_ofc_learner
) and self.state not in ['reward', 'hold_penalty']:
# switch learner to the trial-based learner
self.bmi_system.learner = self.rl_learner
class LinearlyDecreasingN(LinearlyDecreasingAttribute):
memory_decay_rate = traits.Tuple((-1., 0.5), desc="")
memory_decay_rate_time = traits.Float(300, desc="")
def __init__(self, *args, **kwargs):
super(LinearlyDecreasingAssist, self).__init__(*args, **kwargs)
if 'memory_decay_rate' not in self.attrs:
self.attrs.append('memory_decay_rate')
class CLDACursorAdaptRscale(CLDAControlMulti):
r_scale = traits.Float('20')
def init(self):
self.decoder.filt.r_scale = self.r_scale
super(CLDACursorAdaptRscale, self).init()
def create_updater(self):
self.updater = RScaleUpdater(self.batch_time, self.half_life[0])
class KinarmFreeChoice(manualcontrolmultitasks.JoystickSpeedFreeChoice):
sequence_generators = manualcontrolmultitasks.JoystickSpeedFreeChoice.sequence_generators
pre_choise_pause_time = traits.Float(
3., desc='time before allowed to make a choice')
status = dict(wait=dict(start_trial="targ_transition", stop=None),
pre_choice_orig=dict(enter_orig='choice_target',
timeout='timeout_penalty',
stop=None),
choice_target=dict(enter_choice_target='targ_transition',
timeout='timeout_penalty',
stop=None),
target=dict(enter_target="hold",
timeout="timeout_penalty",
stop=None),
hold=dict(leave_early="hold_penalty",
hold_complete="targ_transition"),
targ_transition=dict(trial_complete="reward",
trial_abort="wait",
trial_incomplete="target",
make_choice='pre_choice_orig'),
timeout_penalty=dict(timeout_penalty_end="targ_transition"),
hold_penalty=dict(hold_penalty_end="targ_transition"),
reward=dict(reward_end="wait"))
def move_effector(self):
self = kinarm_move_effector(self)
self.current_pt = self.current_pt + (
np.array([np.random.rand() - 0.5, 0.,
np.random.rand() - 0.5]) * self.joystick_speed)
self.plant.set_endpoint_pos(self.current_pt)
self.last_pt = self.current_pt.copy()
def _test_enter_choice_target(self, ts):
cursor_pos = self.plant.get_endpoint_pos()
enter_targ = 0
for ic, c in enumerate(self.choice_locs):
d = np.linalg.norm(cursor_pos - c)
if d <= self.choice_target_rad: #NOTE, gets in if CENTER of cursor is in target (not entire cursor)
enter_targ += 1
#Set chosen as new input:
self.chosen_input_ix = ic
self.joystick_speed = self.input_type_dict[ic]
print 'trial: ', self.choice_instructed, self.joystick_speed
#Declare that choice has been made:
self.choice_made = 1
#Change color of cursor:
sph = self.plant.graphics_models[0]
sph.color = self.input_type_dict[ic, 'color']
if ts > self.pre_choise_pause_time:
return enter_targ > 0
else:
return False
class LinearlyDecreasingReachAngle(LinearlyDecreasingAssist):
'''
For the reach direction task, decrease the maximum reach angle linearly
'''
reach_angle_time = traits.Float(
600, desc="Number of seconds to go from initial to final reach angle")
def __init__(self, *args, **kwargs):
super(LinearlyDecreasingHalfLife, self).__init__(*args, **kwargs)
if 'half_life' not in self.attrs:
self.attrs.append('half_life')
class Dots(TrialTypes, Pygame):
trial_types = ["flat", "depth"]
saturation = traits.Float(1.)
def init(self):
super(Dots, self).init()
self.width, self.height = self.surf.get_size()
mask = squaremask()
mid = self.height / 2 - mask.shape[0] / 2
lc = self.width / 4 - mask.shape[1] / 2
rc = 3 * self.width / 4 - mask.shape[1] / 2
self.mask = mask
self.coords = (lc, mid), (rc, mid)
def _start_depth(self):
c = 255 * (1 - self.saturation)
left, right, flat = generate(self.mask)
sleft = pygame.surfarray.make_surface(left.T)
sright = pygame.surfarray.make_surface(right.T)
sleft.set_palette([(0, 0, 0, 255), (c, c, 255, 255), (c, 255, c, 255)])
sright.set_palette([(0, 0, 0, 255), (c, c, 255, 255),
(c, 255, c, 255)])
self.sleft, self.sright = sleft, sright
def _start_flat(self):
left, right, flat = generate(self.mask)
sflat = pygame.surfarray.make_surface(flat.T)
sflat.set_palette([(0, 0, 0, 255), (255, 255, 255, 255)])
self.sflat = sflat
def _while_depth(self):
self.surf.blit(self.sleft, self.coords[0])
self.surf.blit(self.sright, self.coords[1])
self.flip_wait()
def _while_flat(self):
self.surf.blit(self.sflat, self.coords[0])
self.surf.blit(self.sflat, self.coords[1])
self.flip_wait()
def _test_flat_correct(self, ts):
return self.event in [1, 2]
def _test_flat_incorrect(self, ts):
return self.event in [4, 8]
def _test_depth_correct(self, ts):
return self.event in [4, 8]
def _test_depth_incorrect(self, ts):
return self.event in [1, 2]
class VisualFeedbackMulti(manualcontrolmultitasks.ManualControlMulti):
'''
Displays task to subject but cursor moves automatically to targets with some
noise added. Subject still gets reward when cursor hits targets.
'''
background = (.5, .5, .5, 1) # Set the screen background color to grey
noise_level = traits.Float(
0.5, desc="Percent noise to add to straight line movements.")
smoothparam = 10 # number of frames to move in one direction before switching
smoothcounter = 9
velnoise = np.array([0, 0, 0])
ordered_traits = ['session_length']
exclude_parent_traits = ['marker_count', 'marker_num']
def __init__(self, *args, **kwargs):
self.cursor_visible = True
super(VisualFeedbackMulti, self).__init__(*args, **kwargs)
self.prev_cursor = self.plant.get_endpoint_pos()
def move_effector(self):
'''
Returns the 3D coordinates of the cursor.
'''
# calculate straight line x and z velocities
targetvec = self.target_location - self.prev_cursor
vecmag = np.sqrt(targetvec[0]**2 + targetvec[1]**2 + targetvec[2]**2)
if vecmag < .05:
velideal = np.array([0, 0, 0])
else:
direction = targetvec / vecmag
velideal = direction * .1 # constant velocity for now, maybe change to bell shaped curve later??
if self.smoothcounter == (self.smoothparam - 1):
# create random noise x and z velocities
self.velnoise = np.array(
[np.random.randn(1)[0], 0,
np.random.randn(1)[0]]) * .1
self.smoothcounter = 0
else:
self.smoothcounter += 1
# combine ideal velocity with noise
vel = velideal * (1 -
self.noise_level) + self.velnoise * self.noise_level
# calculate new cursor position
#self.set_arm_endpoint(self.prev_cursor + vel, time_limit=0.012)
self.plant.set_endpoint_pos(self.prev_cursor + vel)
def update_cursor(self):
''' Update the cursor's location and visibility status.'''
pt = self.get_cursor_location()
self.move_cursor(pt)
self.prev_cursor = pt.copy()
class CLDA_BMIResettingObstacles(BMIResettingObstacles,
LinearlyDecreasingHalfLife):
sequence_generators = [
'centerout_2D_discrete', 'centerout_2D_discrete_w_obstacle'
]
batch_time = traits.Float(0.1, desc='The length of the batch in seconds')
decoder_sequence = traits.String(
'test', desc='signifier to group together sequences of decoders')
memory_decay_rate = traits.Float(0.5, desc="")
ordered_traits = [
'session_length', 'assist_level', 'assist_level_time', 'batch_time',
'half_life', 'half_life_time'
]
def __init__(self, *args, **kwargs):
super(CLDA_BMIResettingObstacles, self).__init__(*args, **kwargs)
self.learn_flag = True
def call_decoder(self, *args, **kwargs):
kwargs['half_life'] = self.current_half_life
return super(CLDA_BMIResettingObstacles,
self).call_decoder(*args, **kwargs)
def create_updater(self):
self.updater = clda.KFRML(self.batch_time, self.half_life[0])
self.updater.default_gain = self.memory_decay_rate
def create_learner(self):
# self.batch_size = int(self.batch_time/self.decoder.binlen)
# self.learner = ObstacleLearner(self.batch_size)
# self.learn_flag = True
self.batch_size = int(self.batch_time / self.decoder.binlen)
A, B, _ = self.decoder.ssm.get_ssm_matrices()
Q = np.mat(np.diag([10, 10, 10, 5, 5, 5, 0]))
R = 10**6 * np.mat(np.eye(B.shape[1]))
from tentaclebmitasks import OFCLearnerTentacle
self.learner = OFCLearnerTentacle(self.batch_size, A, B, Q, R)
self.learn_flag = True
class LinearlyDecreasingHalfLife(LinearlyDecreasingAttribute):
'''
Specific case of LinearlyDecreasingAttribute for a linearly decreasing CLDA half-life
'''
half_life = traits.Tuple((450., 450.),
desc="Initial and final half life for CLDA")
half_life_time = traits.Float(
600, desc="Number of seconds to go from initial to final half life")
def __init__(self, *args, **kwargs):
super(LinearlyDecreasingHalfLife, self).__init__(*args, **kwargs)
if 'half_life' not in self.attrs:
self.attrs.append('half_life')
class CLDAControlTentaclePPF(CLDAControlTentacle):
param_noise_scale = traits.Float(1.0, desc="Stuff")
def create_updater(self):
vel_gain = 1e-8
# vel_gain *= self.param_noise_scale
const_var = 1e-4 * 0.06 / 50
vel_var = vel_gain * 0.13
param_noise_variances = np.array([
vel_var / 225, vel_var / 225, vel_var / 5, vel_var / 5, const_var
])
self.updater = clda.PPFContinuousBayesianUpdater(
self.decoder, param_noise_variances=param_noise_variances)
class CLDAControlPPFContAdapt2(CLDAControlMulti):
exclude_parent_traits = ['half_life', 'half_life_decay_time', 'batch_time']
param_noise_scale = traits.Float(1.0, desc="Stuff")
cost_fn_scale = traits.Float(10000, desc="Stuff")
ordered_traits = [
'session_length', 'assist_level', 'assist_time', 'param_noise_scale'
]
def create_learner(self):
self.batch_size = 1
A, B, _ = self.decoder.ssm.get_ssm_matrices(
update_rate=self.decoder.binlen)
Q = np.mat(np.diag([1., 1, 1, 0, 0, 0, 0]))
R = self.cost_fn_scale * np.mat(np.eye(B.shape[1]))
from tasks.tentaclebmitasks import OFCLearnerTentacle
self.learner = OFCLearnerTentacle(self.batch_size, A, B, Q, R)
self.learn_flag = True
def create_updater(self):
self.updater = clda.PPFContinuousBayesianUpdater(
self.decoder, param_noise_scale=self.param_noise_scale)
class EndpointManualControl(ExoBase, PositionerTaskController):
status = dict(
go_to_origin = dict(microcontroller_done='wait', stop=None),
wait = dict(start_trial='move_target', stop=None),
move_target = dict(microcontroller_done='reach'),
reach = dict(force_applied='reward', new_target_set_remotely='move_target', skip='wait', stop=None),
reward = dict(time_expired='wait', stop=None),
)
trial_end_states = ['reward']
min_force_on_target = traits.Float(1., desc='Force that needs to be applied, in Newtons')
reward_time = traits.Float(3., desc='reward time for solenoid')
def move_plant(self):
self.plant._get_sensor_data()
def _test_force_applied(self, *args, **kwargs):
return self.plant.force_N > self.min_force_on_target
def _end_move_target(self):
# send command to kill motors
steps_actuated = self.pos_uctrl_iface.end_continuous_move(stiff=True)
self._integrate_steps(steps_actuated, self.pos_uctrl_iface.motor_dir)
def _cycle(self):
# print "_cycle"
super(EndpointManualControl, self)._cycle()
def init(self):
import pygame
pygame.init()
super(EndpointManualControl, self).init()
def _test_skip(self, *args, **kwargs):
import pygame
keys = pygame.key.get_pressed()
return keys[pygame.K_RIGHT]
class LinearlyDecreasingAssist(LinearlyDecreasingAttribute):
'''
Specific case of LinearlyDecreasingAttribute for a linearly decreasing assist parameter
'''
assist_level = traits.Tuple((0.0, 0.0),
desc="Level of assist to apply to BMI output")
assist_level_time = traits.Float(
600,
desc="Number of seconds to go from initial to minimum assist level")
def __init__(self, *args, **kwargs):
super(LinearlyDecreasingAssist, self).__init__(*args, **kwargs)
if 'assist_level' not in self.attrs:
self.attrs.append('assist_level')
class LinearlyDecreasingAngAssist(LinearlyDecreasingAttribute):
'''
linearly decreasing angular assist -- for psi and ReHand
'''
rh_assist_level = traits.Tuple(
(0.0, 0.0), desc='level of assist to apply to ang output')
rh_assist_level_time = traits.Float(
600,
desc="Number of seconds to go from initial to minimum assist level")
def __init__(self, *args, **kwargs):
super(LinearlyDecreasingAngAssist, self).__init__(*args, **kwargs)
if 'rh_assist_level' not in self.attrs:
self.attrs.append('rh_assist_level')
class CLDAControlKFCGRML(CLDAControlMulti):
memory_decay_rate = traits.Float(
0.45, desc='Shape parameter for the impulse response of the KF')
ordered_traits = [
'session_length', 'assist_level', 'assist_time', 'batch_time',
'half_life', 'half_life_decay_time', 'memory_decay_rate'
]
def create_learner(self):
self.batch_size = int(self.batch_time / self.decoder.binlen)
self.learner = CursorGoalLearner2(self.batch_size,
int_speed_type='decoded_speed')
self.learn_flag = True
def create_updater(self):
self.updater = clda.KFRML(self.batch_time, self.half_life[0])
class CLDAControlExoEndpt(BMIControlExoEndpt, LinearlyDecreasingHalfLife):
batch_time = traits.Float(0.1, desc='The length of the batch in seconds')
decoder_sequence = traits.String('exo', desc='signifier to group together sequences of decoders')
def create_updater(self):
self.updater = clda.KFRML(self.batch_time, self.half_life[0])
def init(self):
super(CLDAControlExoEndpt, self).init()
self.batch_time = self.decoder.binlen
self.updater.init(self.decoder)
def create_learner(self):
self.batch_size = int(self.batch_time/self.decoder.binlen)
self.learner = clda.FeedbackControllerLearner(self.batch_size, joint_vel_fb_ctrl, reset_states=['go_to_origin', 'wait', 'init_exo', 'move_target', 'pause', 'reward' ])
self.learn_flag = True
class BMICursorKinematicCurlField(BMIResetting):
rot_factor = traits.Float(
10., desc='scaling factor from speed to rotation angle in degrees')
def load_decoder(self):
super(BMICursorKinematicCurlField, self).load_decoder()
# Conver the KF to a curl-field generating KF
dec = self.decoder
filt = CurlFieldKalmanFilter(A=self.decoder.filt.A,
W=self.decoder.filt.W,
C=dec.filt.C,
Q=dec.filt.Q,
is_stochastic=dec.filt.is_stochastic)
filt.C_xpose_Q_inv = dec.filt.C_xpose_Q_inv
filt.C_xpose_Q_inv_C = dec.filt.C_xpose_Q_inv_C
filt.rot_factor = self.rot_factor
self.decoder.filt = filt
