Experiment class refactor #183
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| """ | ||
| Initial run of the Experiment Class Refactor base class | ||
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| Derived classes have to set a few things in major: | ||
| 1. load_stimulus function : returns an array of stimuli | ||
| 2. present_stimulus function : presents the stimuli and pushes eeg data back and forth as needed | ||
| Additional parameters can be set from the derived class as per the initializer | ||
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| """ | ||
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| class Experiment: | ||
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| def __init_(self, exp_name): | ||
| """ Anything that must be passed as a minimum for the experiment should be initialized here """ | ||
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| """ Dk if this overwrites the class variable or is worth doing | ||
| if we just assume they will overwrite """ | ||
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| self.exp_name= exp_name | ||
| self.instruction_text = """\nWelcome to the {} experiment!\nStay still, focus on the centre of the screen, and try not to blink. \nThis block will run for %s seconds.\n | ||
| Press spacebar to continue. \n""".format(exp_name) | ||
| self.duration=120 | ||
| self.eeg:EEG=None | ||
| self.save_fn=None | ||
| self.n_trials=2010 | ||
| self.iti = 0.4 | ||
| self.soa = 0.3 | ||
| self.jitter = 0.2 | ||
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| def setup(self): | ||
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| self.record_duration = np.float32(self.duration) | ||
| self.markernames = [1, 2] | ||
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| # Setup Trial list -> Common in most (csv in Unicorn) | ||
| self.parameter = np.random.binomial(1, 0.5, n_trials) | ||
| self.trials = DataFrame(dict(parameter=parameter, timestamp=np.zeros(n_trials))) | ||
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| # Setup Graphics | ||
| mywin = visual.Window([1600, 900], monitor="testMonitor", units="deg", fullscr=True) | ||
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| # Needs to be overwritten by specific experiment | ||
| self.stim = self.load_stimulus() | ||
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| # Show Instruction Screen | ||
| self.show_instructions(duration=duration) | ||
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| # Establish save function | ||
| if self.save_fn is None: # If no save_fn passed, generate a new unnamed save file | ||
| random_id = random.randint(1000,10000) | ||
| self.save_fn = generate_save_fn(eeg.device_name, experiement_id, random_id, random_id, "unnamed") | ||
| print( | ||
| f"No path for a save file was passed to the experiment. Saving data to {save_fn}" | ||
| ) | ||
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| def present(self): | ||
| """ Do the present operation for a bunch of experiments """ | ||
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| # Start EEG Stream, wait for signal to settle, and then pull timestamp for start point | ||
| if eeg: | ||
| eeg.start(self.save_fn, duration=self.record_duration + 5) | ||
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| start = time() | ||
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| # Iterate through the events | ||
| for ii, trial in self.trials.iterrows(): | ||
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| # Intertrial interval | ||
| core.wait(self.iti + np.random.rand() * self.jitter) | ||
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| # Some form of presenting the stimulus - sometimes order changed in lower files like ssvep | ||
| self.present_stimulus(self.trials, ii, self.eeg, self.markernames) | ||
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| # Offset | ||
| mywin.flip() | ||
| if len(event.getKeys()) > 0 or (time() - start) > self.record_duration: | ||
| break | ||
| event.clearEvents() | ||
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| # Close the EEG stream | ||
| if eeg: | ||
| eeg.stop() | ||
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| def show_instructions(self): | ||
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| self.instruction_text = self.instruction_text % duration | ||
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| # graphics | ||
| mywin = visual.Window([1600, 900], monitor="testMonitor", units="deg", fullscr=True) | ||
| mywin.mouseVisible = False | ||
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| # Instructions | ||
| text = visual.TextStim(win=mywin, text=self.instruction_text, color=[-1, -1, -1]) | ||
| text.draw() | ||
| mywin.flip() | ||
| event.waitKeys(keyList="space") | ||
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| mywin.mouseVisible = True | ||
| mywin.close() | ||
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| Looking for a general implementation structure where base class implements and passes the following functions, | ||
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| def load_stimulus() -> stim (some form of dd array) | ||
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| def present_stimulus() -> given trial details does specific thing for experiment | ||
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| ** Slight issue is that a lot of parameters will have to be passed which is not the best in practice | ||
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| Stuff that can be overwritten in general ... | ||
| instruction_text | ||
| parameter/trial |
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@@ -17,107 +17,40 @@ | |
| from eegnb import generate_save_fn | ||
| from eegnb.devices.eeg import EEG | ||
| from eegnb.stimuli import FACE_HOUSE | ||
| from Experiment import Experiment | ||
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| def load_stimulus(): | ||
| load_image = lambda fn: visual.ImageStim(win=mywin, image=fn) | ||
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| faces = list(map(load_image, glob(os.path.join(FACE_HOUSE, "faces", "*_3.jpg")))) | ||
| houses = list(map(load_image, glob(os.path.join(FACE_HOUSE, "houses", "*.3.jpg")))) | ||
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| return [houses, faces] | ||
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| def present_stimulus(trials, ii, eeg, markernames): | ||
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| label = trials["image_type"].iloc[ii] | ||
| image = choice(faces if label == 1 else houses) | ||
| image.draw() | ||
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| # Push sample | ||
| if eeg: | ||
| timestamp = time() | ||
| if eeg.backend == "muselsl": | ||
| marker = [markernames[label]] | ||
| else: | ||
| marker = markernames[label] | ||
| eeg.push_sample(marker=marker, timestamp=timestamp) | ||
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There was a problem hiding this comment. @JohnGriffiths This muselsl-edgecase should prob be handled directly in There was a problem hiding this comment. Also the whole push sample part should be inside the experiment base class; in the majority of cases just comes immediately after stim presentation and doesn't need to be repeated |
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| if __name__ == "__main__": | ||
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| test = Experiment("Visual N170") | ||
| test.instruction_text = instruction_text | ||
| test.load_stimulus = load_stimulus | ||
| test.present_stimulus = present_stimulus | ||
| test.setup() | ||
| test.present() | ||
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There was a problem hiding this comment. Why do it like this? I assumed you'd create a subclass of Experiment? There was a problem hiding this comment. I think this works better because I don't need to care about redefining a bunch of stuff and thinking about defining the functions in the base class etc. This way I can use the same class everywhere and just initialize it differently for the different experiments. And since I am seperating the present_stimulus and load_stimulus functions, it does not make much of a difference There was a problem hiding this comment. I agree with Erik I think the natural way of doing this is with an experiment base class that is subclassed for every experiment. The functions that you are passing here would then be (potentially decorated) methods in the subclasses. You're right that you're saving some overhead by using the same class everywhere and not needing placeholder function definitions in the base class like def present_stimulus(self):
pass...but that's pretty standard idiomatic python and isn't a huge deal. I'm still going through this thoroughly but I'd like a bit more spelling out as to why this function passing approach is a better than subclassing from a general code base structure perspective. There was a problem hiding this comment. Yeah I've had a look around and I think it makes more sense to have subclasses cause it can also provide readymade access to the variables from outside. Gonna convert it to that type now. There was a problem hiding this comment. Just a note/tidbit/lesson on your existing approach, that if you do: class A:
def b(self):
print(self)
def c(self):
print(self)
a = A()
print(a.b) # <bound method A.b of <__main__.A object at 0x7f645b76c4c0>>
a.b() # <__main__.A object at 0x7f6459ecd9c0>
a.c = c
print(a.c) # <function c at 0x7f645c04c790>
a.c() # TypeError: c() missing 1 required positional argument: 'self'In other words, |
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@JohnGriffiths Always wondered about these confusing variable names. Are
itiandsoaabbreviations or something?In any case, they should probably be given more descriptive names (and note that the values are different in different experiments, so they need to be passed too!)
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yeah so they are being initialized to default values and then if the experiment needs different ones it can change it after the init (otherwise I could force it to initialize too) - see visual ssvep
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They are standard technical acronyms in psych / cog neuro:
ITI = Inter-trial interval
SOA = Stimulus onset asynchrony
( A related term that we don't have here is ISI (inter-stimulus interval) )
These absolutely should be more clearly defined and consistently used.