def factor_variables_for_trial(self, f: Factor, t: int) -> List[int]:
if not f.applies_to_trial(t):
raise ValueError('Factor does not apply to trial #' + str(t) +
' f=' + str(f))
previous_trials = sum(
map(lambda trial: 1 if f.applies_to_trial(trial + 1) else 0,
range(t))) - 1
initial_sequence = list(
map(lambda l: self.first_variable_for_level(f, l),
list(filter(lambda l: (f, l) not in self.exclude, f.levels))))
offset = 0
if f.has_complex_window():
offset = len(f.levels) * previous_trials
else:
offset = self.variables_per_trial() * previous_trials
return list(map(lambda n: n + offset + 1, initial_sequence))
def __trials_required_for_crossing(self, f: Factor,
crossing_size: int) -> int:
trial = 0
counter = 0
while counter != crossing_size:
trial += 1
if f.applies_to_trial(trial):
counter += 1
return trial
def factor_variables_for_trial(self, f: Factor, t: int) -> List[int]:
"""Given a factor and a trial number (1-based) this function will
return a list of the variables representing the levels of the given
factor for that trial. The variable list is also 1-based.
"""
if not f.applies_to_trial(t):
raise ValueError('Factor does not apply to trial #' + str(t) + ' f=' + str(f))
previous_trials = sum(map(lambda trial: 1 if f.applies_to_trial(trial + 1) else 0, range(t))) - 1
initial_sequence = list(map(lambda l: self.first_variable_for_level(f, l),
list(filter(lambda l: (f, l) not in self.exclude,
f.levels))))
offset = 0
if f.has_complex_window:
offset = len(f.levels) * previous_trials
else:
offset = self.variables_per_trial() * previous_trials
return list(map(lambda n: n + offset + 1, initial_sequence))
def test_factor_applies_to_trial():
assert color.applies_to_trial(1) == True
assert color.applies_to_trial(2) == True
assert color.applies_to_trial(3) == True
assert color.applies_to_trial(4) == True
with pytest.raises(ValueError):
color.applies_to_trial(0)
assert color_repeats_factor.applies_to_trial(1) == False
assert color_repeats_factor.applies_to_trial(2) == True
assert color_repeats_factor.applies_to_trial(3) == True
assert color_repeats_factor.applies_to_trial(4) == True
f = Factor('f', [DerivedLevel('l', Window(op.eq, [color], 2, 2))])
assert f.applies_to_trial(1) == False
assert f.applies_to_trial(2) == True
assert f.applies_to_trial(3) == False
assert f.applies_to_trial(4) == True
assert color3.applies_to_trial(1) == True
def __trials_required_for_crossing(self, f: Factor, crossing_size: int) -> int:
"""Given a factor ``f``, and a crossing size, this function will
compute the number of trials required to fully cross ``f`` with the
other factors.
For example, if ``f`` is a transition, it doesn't apply to trial 1. So
when the ``crossing_size`` is ``4``, we'd actually need 5 trials to
fully cross with ``f``.
This is a helper for :class:`.MultipleCrossBlock.trials_per_sample`.
"""
trial = 0
counter = 0
while counter != crossing_size:
trial += 1
if f.applies_to_trial(trial):
counter += 1
return trial
def variables_for_factor(self, f: Factor) -> int:
trial_list = range(1, self.trials_per_sample() + 1)
return reduce(
lambda sum, t: sum + len(f.levels)
if f.applies_to_trial(t) else sum, trial_list, 0)
def variables_for_factor(self, f: Factor) -> int:
"""Indicates the number of variables needed to encode this factor."""
trial_list = range(1, self.trials_per_sample() + 1)
return reduce(
lambda sum, t: sum + len(f.levels)
if f.applies_to_trial(t) else sum, trial_list, 0)
