def next_trial(self):
# First trial exception
if self.trial_num == 0:
self.trial_num += 1
# Send next trial info to Bonsai
bonsai.send_current_trial_info(self)
return
self.data_file = str(self.data_file)
# update + next contrast: update buffers/counters + get next contrast
# This has to happen before self.contrast is pointing to next trials
self.contrast.next_trial(self.position) # still prev_position
# Increment trial number
self.trial_num += 1
# Update quiescent period
self.quiescent_period = self.quiescent_period_base + misc.texp()
# Update stimulus phase
self.stim_phase = random.uniform(0, math.pi)
# Update contrast
self._next_contrast()
# Update position
self.position, self.stim_probability_left = self._next_position()
# Update signed_contrast and buffer (AFTER position update)
self.signed_contrast = self.contrast.value * np.sign(self.position)
self.signed_contrast_buffer.append(self.signed_contrast)
# Update state machine events
self.event_error = self.threshold_events_dict[self.position]
self.event_reward = self.threshold_events_dict[-self.position]
# Reset outcome variables for next trial
self.trial_correct = None
# Open the data file to append the next trial
self.data_file = open(self.data_file_path, 'a')
# Send next trial info to Bonsai
bonsai.send_current_trial_info(self)
def make_ephysCW_pcqs(pc):
qperiod_base = 0.2 # + x, where x~exp(0.35), t ∈ 0.2 <= R <= 0.5
sphase = []
qperiod = []
for i in pc:
sphase.append(np.random.uniform(0, math.pi))
qperiod.append(qperiod_base +
misc.texp(factor=0.35, min_=0.2, max_=0.5))
qs = np.array([qperiod, sphase]).T
pcqs = np.append(pc, qs, axis=1)
return pcqs
def make_ephysCW_pcqs(pc):
qperiod_base = 0.2 # + x, where x~exp(0.35), t ∈ 0.2 <= R <= 0.5
sphase = []
qperiod = []
for i in pc:
sphase.append(np.random.uniform(0, 2 * math.pi))
qperiod.append(qperiod_base +
misc.texp(factor=0.35, min_=0.2, max_=0.5))
qs = np.array([qperiod, sphase]).T
pcqs = np.append(pc, qs, axis=1)
perm = [0, 1, 3, 4, 2]
idx = np.empty_like(perm)
idx[perm] = np.arange(len(perm))
pcqs[:, idx]
pcqs[:] = pcqs[:, idx]
return pcqs
def next_trial(self):
# First trial exception
if self.trial_num == 0:
self.trial_num += 1
self.block_num += 1
self.block_trial_num += 1
# Send next trial info to Bonsai
bonsai.send_current_trial_info(self)
return
self.data_file = str(self.data_file)
# Increment trial number
self.trial_num += 1
# Update quiescent period
self.quiescent_period = self.quiescent_period_base + misc.texp()
# Update stimulus phase
self.stim_phase = random.uniform(0, 2 * math.pi)
# Update block
self = update_block_params(self)
# Update stim probability left + buffer
self.stim_probability_left = update_probability_left(self)
self.stim_probability_left_buffer.append(self.stim_probability_left)
# Update position + buffer
self.position = draw_position(self.position_set,
self.stim_probability_left)
self.position_buffer.append(self.position)
# Update reward
self.left_reward, self.right_reward = draw_reward(
self.stim_probability_left)
# Update contrast + buffer
self.contrast = misc.draw_contrast(
self.contrast_set, prob_type=self.contrast_set_probability_type)
self.contrast_buffer.append(self.contrast)
# Update signed_contrast + buffer (AFTER position update)
self.signed_contrast = self.contrast * np.sign(self.position)
self.signed_contrast_buffer.append(self.signed_contrast)
# Update state machine events
self.event_error = self.threshold_events_dict[self.position]
self.event_reward = self.threshold_events_dict[-self.position]
# Reset outcome variables for next trial
self.trial_correct = None
# Open the data file to append the next trial
self.data_file = open(self.data_file_path, "a")
# Send next trial info to Bonsai
bonsai.send_current_trial_info(self)
def get_block_len(factor, min_, max_):
return int(misc.texp(factor=factor, min_=min_, max_=max_))
def __init__(self, sph):
# Constants from settings
self.session_start_delay_sec = sph.SESSION_START_DELAY_SEC
self.init_datetime = parser.parse(
sph.PYBPOD_SESSION) + datetime.timedelta(
0, self.session_start_delay_sec)
self.task_protocol = sph.PYBPOD_PROTOCOL
self.data_file_path = sph.DATA_FILE_PATH
self.data_file = open(self.data_file_path, "a")
self.position_set = sph.STIM_POSITIONS
self.contrast_set = sph.CONTRAST_SET
self.contrast_set_probability_type = sph.CONTRAST_SET_PROBABILITY_TYPE
self.repeat_on_error = sph.REPEAT_ON_ERROR
self.threshold_events_dict = sph.ROTARY_ENCODER.THRESHOLD_EVENTS
self.quiescent_period_base = sph.QUIESCENT_PERIOD
self.quiescent_period = self.quiescent_period_base + misc.texp()
self.response_window = sph.RESPONSE_WINDOW
self.interactive_delay = sph.INTERACTIVE_DELAY
self.iti_error = sph.ITI_ERROR
self.iti_correct_target = sph.ITI_CORRECT
self.osc_client = sph.OSC_CLIENT
self.stim_freq = sph.STIM_FREQ
self.stim_angle = sph.STIM_ANGLE
self.stim_gain = sph.STIM_GAIN
self.stim_sigma = sph.STIM_SIGMA
self.out_tone = sph.OUT_TONE
self.out_noise = sph.OUT_NOISE
self.out_stop_sound = sph.OUT_STOP_SOUND
self.poop_count = sph.POOP_COUNT
self.save_ambient_data = sph.RECORD_AMBIENT_SENSOR_DATA
self.as_data = {
"Temperature_C": -1,
"AirPressure_mb": -1,
"RelativeHumidity": -1,
}
# Reward amount
self.reward_amount = sph.REWARD_AMOUNT
self.reward_valve_time = sph.REWARD_VALVE_TIME
self.iti_correct = self.iti_correct_target - self.reward_valve_time
# Initialize parameters that may change every trial
self.trial_num = 0
self.stim_phase = 0.0
self.opto_block_prob = sph.OPTO_BLOCK_PROBABILITY
self.opto_block = np.random.choice(
2, p=[1 - self.opto_block_prob, self.opto_block_prob])
self.block_num = 0
self.block_trial_num = 0
self.opto_block_trial_num = 0
self.block_len_factor = sph.BLOCK_LEN_FACTOR
self.block_len_min = sph.BLOCK_LEN_MIN
self.block_len_max = sph.BLOCK_LEN_MAX
self.block_probability_set = sph.BLOCK_PROBABILITY_SET
self.block_init_5050 = sph.BLOCK_INIT_5050
self.block_len = init_block_len(self)
self.opto_block_len = get_block_len(factor=30, min_=1, max_=60)
# Position
self.stim_probability_left = init_probability_left(self)
self.stim_probability_left_buffer = [self.stim_probability_left]
self.position = draw_position(self.position_set,
self.stim_probability_left)
self.position_buffer = [self.position]
# Reward
self.left_reward, self.right_reward = draw_reward(
self.stim_probability_left)
self.penalties = 0
# Contrast
self.contrast = misc.draw_contrast(self.contrast_set)
self.contrast_buffer = [self.contrast]
self.signed_contrast = self.contrast * np.sign(self.position)
self.signed_contrast_buffer = [self.signed_contrast]
# RE event names
self.event_error = self.threshold_events_dict[self.position]
self.event_reward = self.threshold_events_dict[-self.position]
self.movement_left = self.threshold_events_dict[
sph.QUIESCENCE_THRESHOLDS[0]]
self.movement_right = self.threshold_events_dict[
sph.QUIESCENCE_THRESHOLDS[1]]
self.choice_left = self.threshold_events_dict[sph.CHOICE_THRESHOLDS[1]]
self.choice_right = self.threshold_events_dict[
sph.CHOICE_THRESHOLDS[0]]
# Trial Completed params
self.elapsed_time = 0
self.behavior_data = []
self.response_time = None
self.response_time_buffer = []
self.response_side_buffer = []
self.trial_correct = None
self.trial_correct_buffer = []
self.ntrials_correct = 0
self.water_delivered = 0
def __init__(self, sph):
# Constants from settings
self.init_datetime = parser.parse(sph.PYBPOD_SESSION)
self.task_protocol = sph.PYBPOD_PROTOCOL
self.data_file_path = sph.DATA_FILE_PATH
self.data_file = open(self.data_file_path, 'a')
self.position_set = sph.STIM_POSITIONS
self.repeat_on_error = sph.REPEAT_ON_ERROR
self.repeat_contrasts = sph.REPEAT_CONTRASTS
self.threshold_events_dict = sph.ROTARY_ENCODER.THRESHOLD_EVENTS
self.quiescent_period_base = sph.QUIESCENT_PERIOD
self.quiescent_period = self.quiescent_period_base + misc.texp()
self.response_window = sph.RESPONSE_WINDOW
self.interactive_delay = sph.INTERACTIVE_DELAY
self.iti_error = sph.ITI_ERROR
self.iti_correct_target = sph.ITI_CORRECT
self.osc_client = sph.OSC_CLIENT
self.stim_freq = sph.STIM_FREQ
self.stim_angle = sph.STIM_ANGLE
self.stim_gain = sph.STIM_GAIN
self.stim_sigma = sph.STIM_SIGMA
self.out_tone = sph.OUT_TONE
self.out_noise = sph.OUT_NOISE
self.out_stop_sound = sph.OUT_STOP_SOUND
self.poop_count = sph.POOP_COUNT
self.save_ambient_data = sph.RECORD_AMBIENT_SENSOR_DATA
self.as_data = {
'Temperature_C': -1,
'AirPressure_mb': -1,
'RelativeHumidity': -1
}
# Reward amount
self.reward_amount = sph.REWARD_AMOUNT
self.reward_valve_time = sph.REWARD_VALVE_TIME
self.iti_correct = self.iti_correct_target - self.reward_valve_time
# Init trial type objects
self.ac = AdaptiveContrast(sph)
self.rc = RepeatContrast()
# Initialize parameters that may change every trial
self.contrast_set = sph.CONTRAST_SET
self.trial_num = 0
self.position = random.choice(sph.STIM_POSITIONS)
self.stim_probability_left = sph.STIM_PROBABILITY_LEFT
self.stim_phase = 0.
self.event_error = self.threshold_events_dict[self.position]
self.event_reward = self.threshold_events_dict[-self.position]
self.movement_left = (
self.threshold_events_dict[sph.QUIESCENCE_THRESHOLDS[0]])
self.movement_right = (
self.threshold_events_dict[sph.QUIESCENCE_THRESHOLDS[1]])
# Outcome related parmeters
self.contrast = self.ac
self.current_contrast = self.contrast.value
self.signed_contrast = self.contrast.value * np.sign(self.position)
self.signed_contrast_buffer = [self.signed_contrast]
# Trial Completed params
self.elapsed_time = 0
self.behavior_data = []
self.response_time = None
self.response_time_buffer = []
self.response_buffer = [0] * sph.RESPONSE_BUFFER_LENGTH
self.response_side_buffer = []
self.trial_correct = None
self.ntrials_correct = 0
self.water_delivered = 0
self.non_rc_ntrials = self.trial_num - self.rc.ntrials
