def test_ordering(): for n in range(1, 6, 2): for conditions in (CONDITIONS_3, CONDITIONS_6, CONDITIONS_2_3, CONDITIONS_2_3_2): o = Ordering(n) o.first_pass(conditions) yield check_ordering, o, len(conditions)
def make_deterministic_exp(): Experiment.basic(('participant', 'block', 'trial'), { 'block': { 'b': [0, 1, 2] }, 'trial': { 'a': [False, True] } }, ordering_by_level={ 'trial': Ordering(4), 'block': Ordering(4), 'participant': Ordering() }, filename='test.yaml').save()
def make_heterogeneous_tree(): main_structure = [ ('participant', Design(ivs={ 'a': [1, 2], 'b': [1, 2] }, ordering=Shuffle(3))), ('session', Design(ivs={'design': ['practice', 'test']}, design_matrix=[[0], [1], [1]])), ] other_structures = { 'practice': [ ('block', Design()), ('trial', Design(ivs={'difficulty': [1, 2]}, ordering=Shuffle(20))), ], 'test': [ ('block', Design(ordering=Ordering(2))), ('trial', Design(ivs={'difficulty': [1, 3, 5, 7]}, ordering=Shuffle(5))), ], } return DesignTree.new(main_structure, **other_structures)
def make_tree(levels, data): designs = [[Design([('a', range(len(levels))), ('b', [False, True])], extra_data=data, ordering=Ordering())] for _ in levels] return DesignTree.new(list(zip(levels, designs)))
def test_schema_string(): assert OrderSchema.from_any('Ordering') == Ordering()
def test_reprs(): for ord in (CompleteCounterbalance(), Shuffle(), LatinSquare(), Ordering(), Sorted()): yield check_repr, ord
def test_ordering_generator_condition(): n = 3 o = Ordering(n) o.first_pass(c for c in CONDITIONS_3) yield check_ordering, o, len(CONDITIONS_3)
def test_design_tree(): trial_matrix = np.array( [[-1, -1], [1, -1], [-1, 1], [1, 1], [0, 0], [0, 0], [0, 0], [0, 0], [-1.41421356, 0.1], [1.41421356, 0.1], [0, -1.41421356], [0, 1.41421356], [0, 0], [0, 0], [0, 0], [0, 0]] ) # pyDOE.ccdesign(2) with two 0.1 elements to prevent symmetry in the IVs. trial_iv_names = ['a', 'b'] trial_iv_values = [None, None] block_ivs = {'block': [1, 2]} participant_iv_names = ('A', 'B') participant_iv_values = [[1, 2], [1, 2, 3]] trial_design = Design(ivs=zip(trial_iv_names, trial_iv_values), ordering=Shuffle(3), design_matrix=trial_matrix) block_design = Design(block_ivs, ordering=CompleteCounterbalance()) practice_block_design = Design() participant_design = Design(dict( zip(participant_iv_names, participant_iv_values)), ordering=Ordering(10)) levels_and_designs = OrderedDict([('participant', participant_design), ('block', [practice_block_design, block_design]), ('trial', trial_design)]) tree = DesignTree.new(levels_and_designs) tree.add_base_level() levels, designs = zip(*tree.levels_and_designs) yield check_identity, designs[1][0], participant_design yield check_identity, designs[2][0], practice_block_design yield check_identity, designs[2][1], block_design yield check_identity, designs[3][0], trial_design yield check_equality, len(tree), 4 yield check_equality, tree[3], ('trial', [trial_design]) yield check_equality, next(tree).levels_and_designs, next( tree).levels_and_designs, tree.levels_and_designs[1:] tree_with_participant_base = next(tree) tree_with_block_base = next(tree_with_participant_base) yield (check_equality, tree_with_block_base.levels_and_designs, next(next(tree)).levels_and_designs, [('block', [practice_block_design, block_design]), ('trial', [trial_design])]) tree_with_trial_base = next(tree_with_block_base) yield (check_equality, tree_with_trial_base.levels_and_designs, [ ('trial', [trial_design]) ], next(tree_with_block_base).levels_and_designs) with pytest.raises(StopIteration): next(tree_with_trial_base) for design, iv_names, iv_values, n, data, matrix in zip( [designs[1][0], designs[2][0], designs[2][1], designs[3][0]], [['A', 'B', CompleteCounterbalance.iv_name], [], ['block'], ['a', 'b'] ], [[[1, 2], [1, 2, 3], [0, 1]], [], [[1, 2]], [None, None]], [10 * 2 * 2 * 3, 1, 2, 3 * len(trial_matrix)], [{}, {}, { CompleteCounterbalance.iv_name: 0 }, {}], [None, None, None, trial_matrix]): yield check_design, design, iv_names, iv_values, n, data, matrix yield check_design_matrix, designs[3][0].get_order(), ['a', 'b' ], [None, None ], trial_matrix