def next_trial(self):
# First trial exception
if self.trial_num == 0:
self.trial_num += 1
self.block_num += 1
self.block_rew_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, math.pi)
# Update block
self = blocks.update_block_params(self)
# Update stim probability left
self.stim_probability_left = blocks.update_probability_left(self)
# Update position
self.position = blocks.draw_position(self.position_set,
self.stim_probability_left)
# Update contrast
self.contrast = misc.draw_contrast(
self.contrast_set, prob_type=self.contrast_set_probability_type)
self.signed_contrast = self.contrast * np.sign(self.position)
# Update state machine events
self.event_error = self.threshold_events_dict[self.position]
self.event_correct = self.threshold_events_dict[
-self.position] # need to check this
self.rewarded = blocks_rew.draw_reward(
self.position, self.rew_set, self.rew_probability_left
) #### @alejandro This determines whether an event is rewarded
# Reset outcome variables for next trial
self.trial_correct_rewarded = None ####
self.trial_correct_unrewarded = 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)
#Update opto_variables
self.opto_trial = optobpod.opto_switch(self.opto_on, self.opto_freq)
def make_pc():
contrasts = [1., 0.25, 0.125, 0.0625, 0.0]
len_block = [90]
pos = [-35] * int(len_block[0] / 2) + [35] * int(len_block[0] / 2)
cont = np.sort(contrasts * 10)[::-1][:-5].tolist()
pc = np.array([pos, cont + cont]).T
np.random.shuffle(pc) # only shuffles on the first dimension
prob_left = 0.8 if blocks.draw_position([-35, 35], 0.5) < 0 else 0.2
while len(pc) < 2001:
len_block.append(blocks.get_block_len(60, min_=20, max_=100))
for x in range(len_block[-1]):
p = blocks.draw_position([-35, 35], prob_left)
c = misc.draw_contrast(contrasts)
pc = np.append(pc, np.array([[p, c]]), axis=0)
# do this in PC space
prob_left = np.round(np.abs(1 - prob_left), 1)
return pc, len_block
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, math.pi)
# Update block
self = blocks.update_block_params(self)
# Update stim probability left + buffer
self.stim_probability_left = blocks.update_probability_left(self)
self.stim_probability_left_buffer.append(self.stim_probability_left)
# Update position + buffer
self.position = blocks.draw_position(self.position_set,
self.stim_probability_left)
self.position_buffer.append(self.position)
# 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 __init__(self, sph):
# Constants from settings
self.init_datetime = parser.parse(sph.PYBPOD_SESSION)
self.task_protocol = sph.PYBPOD_PROTOCOL
self.elapsed_time = 0
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.poop_count = sph.POOP_COUNT
#######################################################################
##########################Opto Settings################################
#######################################################################
self.opto_on = sph.OPTO_ON
self.opto_freq = sph.OPTO_FREQ
self.opto_pulse_length = sph.OPTO_PULSE_LENGTH
self.opto_pulse_freq = sph.OPTO_PULSE_FREQ
self.opto_trial = optobpod.opto_switch(
self.opto_on,
self.opto_freq) #Determines whether this trial is an opto trial
#######################################################################
#######################################################################
# 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.
self.block_num = 0
self.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_len = blocks.init_block_len(self)
##########################Rewward blocks #############################
self.rew_set = [1, 0] #### @alejandro
self.rew_prob_set = sph.BLOCK_REW_PROBABILITY_SET #### @alejandro
self.block_rew_num = 0
self.block_rew_len_factor = sph.BLOCK_REW_LEN_FACTOR
self.block_rew_len_min = sph.BLOCK_REW_LEN_MIN
self.block_reW_len_max = sph.BLOCK_REW_LEN_MAX
self.block_rew_probability_set = sph.BLOCK_REW_PROBABILITY_SET
self.block_len = blocks_rew.init_block_len(self)
#######################################################################
# Position
self.stim_probability_left = blocks.init_probability_left(self)
self.position = blocks.draw_position(self.position_set,
self.stim_probability_left)
# Contrast
self.contrast = misc.draw_contrast(self.contrast_set)
self.signed_contrast = self.contrast * np.sign(self.position)
# RE event names
self.event_error = self.threshold_events_dict[self.position]
self.event_correct = self.threshold_events_dict[
-self.position] #need to change this
self.movement_left = (
self.threshold_events_dict[sph.QUIESCENCE_THRESHOLDS[0]])
self.movement_right = (
self.threshold_events_dict[sph.QUIESCENCE_THRESHOLDS[1]])
self.response_time_buffer = []
# Rewarded
self.rew_probability_left = blocks_rew.init_rew_probability_left(
self) #### @alejandro
self.rewarded = blocks_rew.draw_reward(self.position, self.rew_set,
self.rew_probability_left) ####
#@alejandro This determines whether an event is rewarded
# Outcome related parmeters
self.trial_correct_rewarded = None #### @alejandro
self.trial_correct_unrewarded = None #### @alejandro
self.ntrials_correct_unrewarded = 0 #### @alejandro
self.ntrials_correct_rewarded = 0 #### @alejandro
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.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.poop_count = sph.POOP_COUNT
self.save_ambient_data = sph.RECORD_AMBIENT_SENSOR_DATA
self.as_data = {
'Temperature_C': 0,
'AirPressure_mb': 0,
'RelativeHumidity': 0
}
# 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.
self.block_num = 0
self.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 = blocks.init_block_len(self)
# Position
self.stim_probability_left = blocks.init_probability_left(self)
self.stim_probability_left_buffer = [self.stim_probability_left]
self.position = blocks.draw_position(self.position_set,
self.stim_probability_left)
self.position_buffer = [self.position]
# 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]])
# 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