def test_generate_derivations_within_trial():
assert DerivationProcessor.generate_derivations(blk) == [
Derivation(4, [[0, 2], [1, 3]], con_factor),
Derivation(5, [[0, 3], [1, 2]], con_factor)
]
integer = Factor("integer", ["1", "2"])
numeral = Factor("numeral", ["I", "II"])
text = Factor("text", ["one", "two"])
twoConLevel = DerivedLevel("twoCon",
WithinTrial(two_con, [integer, numeral, text]))
twoNotConLevel = DerivedLevel(
"twoNotCon", WithinTrial(two_not_con, [integer, numeral, text]))
two_con_factor = Factor("twoCon?", [twoConLevel, twoNotConLevel])
one_two_design = [integer, numeral, text, two_con_factor]
one_two_crossing = [integer, numeral, text]
assert DerivationProcessor.generate_derivations(
fully_cross_block(one_two_design, one_two_crossing, [])) == [
Derivation(6,
[[0, 2, 5], [0, 3, 4], [0, 3, 5], [1, 2, 4], [1, 2, 5],
[1, 3, 4]], two_con_factor),
Derivation(7, [[0, 2, 4], [1, 3, 5]], two_con_factor)
]
def test_generate_derivations_when_derived_factor_precedes_dependencies():
block = fully_cross_block([congruency, motion, color, task],
[color, motion, task], [])
derivations = DerivationProcessor.generate_derivations(block)
assert Derivation(0, [[4, 2], [5, 3]], congruency) in derivations
assert Derivation(1, [[4, 3], [5, 2]], congruency) in derivations
def test_generate_derivations_with_window():
block = fully_cross_block([color, text, congruent_bookend], [color, text], [])
assert DerivationProcessor.generate_derivations(block) == [
Derivation(16, [[0, 2], [1, 3]], congruent_bookend),
Derivation(17, [[0, 3], [1, 2]], congruent_bookend)
]
def test_generate_derivations_transition(design):
block = fully_cross_block(design, [color, text], [])
assert DerivationProcessor.generate_derivations(block) == [
Derivation(16, [[0, 4], [1, 5]], color_repeats_factor),
Derivation(17, [[0, 5], [1, 4]], color_repeats_factor)
]
def fully_cross_block(design: List[Factor],
crossing: List[Factor],
constraints: List[Constraint],
cnf_fn=to_cnf_tseitin) -> Block:
all_constraints = cast(List[Constraint],
[FullyCross, Consistency]) + constraints
block = FullyCrossBlock(design, crossing, all_constraints, cnf_fn)
block.constraints += DerivationProcessor.generate_derivations(block)
return block
def test_generate_derivations_with_transition_that_depends_on_derived_levels():
block = fully_cross_block(
[color, motion, task, response, response_transition],
[color, motion, task], [])
derivations = DerivationProcessor.generate_derivations(block)
assert Derivation(64, [[6, 14], [7, 15]],
response_transition) in derivations
assert Derivation(65, [[6, 15], [7, 14]],
response_transition) in derivations
def fully_cross_block(design: List[Factor],
crossing: List[Factor],
constraints: List[Constraint],
require_complete_crossing=True,
cnf_fn=to_cnf_tseitin) -> Block:
"""Returns a fully crossed :class:`.Block` meant to be used in experiment
synthesis. This is the preferred mechanism for describing an experiment.
:param design:
A :class:`list` of all the :class:`Factors <.Factor>` in the design.
When a sequence of trials is generated, each trial will have one level
from each factor in ``design``.
:param crossing:
A :class:`list` of :class:`Factors <.Factor>` used to produce
crossings. The number of trials in each run of the experiment is
determined as the product of the number of levels of factors in
``crossing``.
If ``require_complete_crossing`` is ``False``, the ``constraints`` can
reduce the total number of trials.
Different trial sequences of the experiment will have different
combinations of levels in different orders. The factors in ``crossing``
supply an implicit constraint that every combination of levels in the
cross should appear once. Derived factors impose additional
constraints: only combinations of levels that are consistent with
derivations can appear as a trial. Additional constraints can be
manually imposed via the ``constraints`` parameter.
:param constraints:
A :class:`list` of :class:`Constraints <.Constraint>` that restrict the
generated trials.
:param require_complete_crossing:
Whether every combination in ``crossing`` must appear in a block of
trials. ``True`` by default. A ``False`` value is appropriate if
combinations are excluded through an :class:`.Exclude`
:class:`.Constraint`.
:param cnf_fn:
A CNF conversion function. Default is :func:`.to_cnf_tseitin`.
"""
all_constraints = cast(List[Constraint],
[FullyCross(), Consistency()]) + constraints
all_constraints = __desugar_constraints(
all_constraints) #expand the constraints into a form we can process.
block = FullyCrossBlock(design, [crossing], all_constraints,
require_complete_crossing, cnf_fn)
block.constraints += DerivationProcessor.generate_derivations(block)
if not constraints and not list(filter(
lambda f: f.is_derived(), crossing)) and not list(
filter(lambda f: f.has_complex_window, design)):
block.complex_factors_or_constraints = False
return block
def test_generate_derivations_with_multiple_transitions(design):
block = fully_cross_block([color, text, color_repeats_factor, text_repeats_factor],
[color, text],
[])
assert DerivationProcessor.generate_derivations(block) == [
Derivation(16, [[0, 4], [1, 5]], color_repeats_factor),
Derivation(17, [[0, 5], [1, 4]], color_repeats_factor),
Derivation(22, [[2, 6], [3, 7]], text_repeats_factor),
Derivation(23, [[2, 7], [3, 6]], text_repeats_factor)
]
def test_generate_derivations():
assert DerivationProcessor.generate_derivations(block) == [
Derivation(16,
[[0, 4, 2, 7], [0, 4, 3, 6], [0, 5, 2, 6], [0, 5, 3, 7],
[1, 4, 2, 6], [1, 4, 3, 7], [1, 5, 2, 7], [1, 5, 3, 6]],
change),
Derivation(17,
[[0, 4, 2, 6], [0, 4, 3, 7], [0, 5, 2, 7], [0, 5, 3, 6],
[1, 4, 2, 7], [1, 4, 3, 6], [1, 5, 2, 6], [1, 5, 3, 7]],
change)
]
def multiple_cross_block(design: List[Factor],
crossings: List[List[Factor]],
constraints: List[Constraint],
require_complete_crossing=True,
cnf_fn=to_cnf_tseitin) -> Block:
"""Returns a :class:`.Block` with multiple crossings, meant to be used in
experiment synthesis. Similar to :func:`fully_cross_block`, except it can
be configured with multiple crossings.
:param design:
A :class:`list` of all the :class:`Factors <.Factor>` in the design.
When a sequence of trials is generated, each trial will have one level
from each factor in ``design``.
:param crossings:
A :class:`list` of :class:`lists <list>` of :class:`Factors <.Factor>`
representing crossings. The number of trials in each run of the
experiment is determined by the *maximum* product among the number of
levels in the crossings.
Every combination of levels in each individual crossing in
``crossings`` appears at least once. Different crossings can refer to
the same factors, which constrains how factor levels are chosen across
crossings.
:param constraints:
A :class:`list` of :class:`Constraints <.Constraint>` that restrict the
generated trials.
:param require_complete_crossing:
Whether every combination in ``crossings`` must appear in a block of
trials. ``True`` by default. A ``False`` value is appropriate if
combinations are excluded through an :class:`.Exclude`
:class:`.Constraint.`
:param cnf_fn:
A CNF conversion function. Default is :func:`.to_cnf_tseitin`.
"""
all_constraints = cast(List[Constraint],
[MultipleCross(), Consistency()]) + constraints
all_constraints = __desugar_constraints(
all_constraints) #expand the constraints into a form we can process.
block = MultipleCrossBlock(design, crossings, all_constraints,
require_complete_crossing, cnf_fn)
block.constraints += DerivationProcessor.generate_derivations(block)
return block