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)
# Increment trial number
self.trial_num += 1
# Update contrast
self.contrast = random.choice(self.contrast_set)
# Update stimulus phase
self.stim_phase = random.uniform(0, math.pi)
# Update position
self.position = random.choice(self.position_set)
# Update delay to stimulus center
self.delay_to_stim_center = np.random.normal(
self.delay_to_stim_center_mean, 2)
# Update water delivered
self.water_delivered += self.reward_amount
# 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 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 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 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_buffer[self.trial_num -
1]
# Update stimulus phase
self.stim_phase = self.stim_phase_buffer[self.trial_num - 1]
# Update block
self.block_trial_num += 1
if self.block_trial_num > self.block_len:
self.block_num += 1
self.block_trial_num = 1
self.block_len = self.len_blocks_buffer[self.block_num - 1]
# Update stim probability left + buffer
self.stim_probability_left = blocks.calc_probability_left(self)
self.stim_probability_left_buffer.append(self.stim_probability_left)
# Update position + buffer
self.position = int(self.position_buffer[self.trial_num - 1])
# Update contrast + buffer
self.contrast = self.contrast_buffer[self.trial_num - 1]
# 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 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)