Counterbalancing across different factors

Coding
2

Create lists which cycle at the required rates to get balancing across their respective entries:

SOAs = [1.0, 2.0] * 2                 # entries cycle quickly
[1, 2, 1, 2]

colours = ['red'] * 2 + ['blue'] * 2  # entries cycle slower
['red', 'red', 'blue', 'blue']

Zip them together in a single list:

condition_pairs = list(zip(colours, SOAs))

[('red', 1), ('red', 2), ('blue', 1), ('blue', 2)]

More usefully you might want the trial conditions in a list of dictionaries so you can refer to attributes by name, so do something like this:

trials = [{'colour':c[0], 'SOA':c[1]} for c in zip(colours, SOAs)]

[{'colour': 'red', 'SOA': 1},
 {'colour': 'red', 'SOA': 2},
 {'colour': 'blue', 'SOA': 1},
 {'colour': 'blue', 'SOA': 2}]

To randomise:

from numpy.random import shuffle

shuffle(trials)

e.g.

for trial in trials:
    some_stimulus.colour = trial['colour']
    some_stimulus.draw()
    win.flip()
    core.wait(trial['SOA'])