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 first_variable_for_level(self, factor: Factor, level: Any) -> int:
if (type(level) is not SimpleLevel
and type(level) is not DerivedLevel):
print("Attempt to find first variable for a non-level object " +
str(level))
if factor.has_complex_window():
offset = 0
complex_factors = filter(lambda f: f.has_complex_window(),
self.design)
for f in complex_factors:
if f == factor:
offset += f.levels.index(level)
break
else:
offset += self.variables_for_factor(f)
return self.grid_variables() + offset
else:
simple_factors = list(
filter(lambda f: not f.has_complex_window(), self.design))
simple_levels = get_all_levels(simple_factors)
return simple_levels.index((factor, level))