pyoperant-refactor-example.py

## The subject object. Importantly, the subject object manages DATA & the
## history of the subject's experiences
subj = Subect(name='B9999',
              store='json', # csv, postgres
              path='/home/bird/opdat/B9999'
              )

## Panel objects are defined in ~/.pyoperant/panel_config.py
## Panels have methods for manipulating panels. The "availability" of a panel
## (that is, light schedules and trial schedules) are governed by the panel
## object
panel = get_panel(id=7,schedule='sun',light_schedule='sun')


## OK, let's construct a block.
##
## blocks are built from a list of dictionaries which contain the possible trial
## conditions.
condition_list = [
    {'stimulus': 'a.wav', 'class': 'left'},
    {'stimulus': 'b.wav', 'class': 'right'},
]
condition_list *= 5
condition_list += {'stimulus': 'c.wav', 'class': 'probe'} # probe
condition_list *= 10

# trial logic (that is, which trial comes next) is managed by the TrialQueue.
# All trial queues require access to the trial history (via the subject 
# instance)

queue = RandomQueue(
    condition_list=condition_list,
    correction_trials=True,
    )

CORRECT = {'feed': 2.0,'flash': 1.0,'timeout': False}
NOFEED = {'feed': False,'flash': 1.0,'timeout': False}
WRONG = {'feed': False,'flash': False,'timeout': 10.0}


# each consequence can get it's own reinforcement schedule. we are only going to
# implement a schedule for feeds
reinforcement = VariableRatio(ratio=3)


# Trial objects maintain necessary information about how to run a trial.
# for each consequence (feed, flash, timeout, etc) it knows what to do on a panel

trial_builder = TwoAltChoiceBuilder(
    subject=subject,
    panel=panel,
    queue=queue, # optional
    reinforcement=reinforcement,
    consequate=consequate,
    correction=correction_consequate,
    )

for trial_parameters in queue:
    if trial_parameters['class']=='probe':
        on_left = random.choice([CORRECT,WRONG])
        on_right = on_left
    elif trial_parameters['class']=='left':
        on_left = CORRECT if reinforcement.consequate(correct=True) else NOFEED
        on_right = WRONG
    elif trial_parameters['class']=='right':
        on_left = WRONG
        on_right = CORRECT if reinforcement.consequate(correct=True) else NOFEED

    trial = Trial(panel=panel,
                  stimulus=trial_parameters['stimulus'],
                  on_left=on_left,
                  on_right=on_right,
                  )
    trial.run()
    subj.save(trial)
    if trial_parameters['type']=='normal' and trial_parameters['class']!='probe':
        reinforcement.update(trial.correct) 
    time.sleep(intertrial_min)
    
    if correction_trials==True:
        while trial.correct==False:
            if trial_parameters['type']=='correction':
                if trial_parameters['class']=='left':
                    on_left = NOFEED
                    on_right = WRONG
                elif trial_parameters['class']=='right':
                    on_left = WRONG
                    on_right = NOFEED
            trial = Trial(panel=panel,
                          stimulus=trial_parameters['stimulus'],
                          on_left=on_left,
                          on_right=on_right,
                          )
            trial.run()
            subj.save(trial)