def test_distance_rank_window():
"""Test distance rank window analysis."""
distances = np.array([
[0, 3, 1, 1, 2, 2, 2, 2],
[3, 0, 1, 1, 2, 2, 2, 2],
[1, 1, 0, 4, 2, 2, 2, 2],
[1, 1, 4, 0, 2, 2, 2, 2],
[2, 2, 2, 2, 0, 5, 1, 1],
[2, 2, 2, 2, 5, 0, 1, 1],
[2, 2, 2, 2, 1, 1, 0, 6],
[2, 2, 2, 2, 1, 1, 6, 0],
])
subjects = [1]
study = [[
'absence', 'hollow', 'pupil', 'fountain', 'piano', 'pillow', 'cat',
'tree'
]]
recall = [[
'fountain', 'hollow', 'absence', 'cat', 'piano', 'pupil', 'fountain'
]]
item_index = ([[0, 1, 2, 3, 4, 5, 6, 7]], [[3, 1, 0, 6, 4, 2, 3]])
raw = fr.table_from_lists(subjects, study, recall, item_index=item_index)
data = fr.merge_free_recall(raw, list_keys=['item_index'])
stat = fr.distance_rank_window(data, 'item_index', distances, [-1, 0, 1])
expected = np.array([[0.875, 0.875, 0.375], [0, 1, 1], [0, 0, 0]])
np.testing.assert_allclose(np.mean(expected, 0), stat['rank'].to_numpy())
def test_distance_rank_shifted():
"""Test shifted distance rank analysis."""
distances = np.array([
[0, 1, 1, 1, 2, 2, 2, 2],
[1, 0, 1, 4, 2, 2, 2, 2],
[1, 1, 0, 1, 2, 2, 2, 2],
[1, 4, 1, 0, 2, 2, 2, 2],
[2, 2, 2, 2, 0, 3, 3, 3],
[2, 2, 2, 2, 3, 0, 3, 3],
[2, 2, 2, 2, 3, 3, 0, 3],
[2, 2, 2, 2, 3, 3, 3, 0],
])
subjects = [1]
study = [[
'absence', 'hollow', 'pupil', 'fountain', 'piano', 'pillow', 'cat',
'tree'
]]
recall = [[
'piano', 'fountain', 'hollow', 'tree', 'fountain', 'absence', 'cat',
'pupil'
]]
item_index = ([[0, 1, 2, 3, 4, 5, 6, 7]], [[4, 3, 1, 7, 3, 0, 6, 2]])
raw = fr.table_from_lists(subjects, study, recall, item_index=item_index)
data = fr.merge_free_recall(raw, list_keys=['item_index'])
stat = fr.distance_rank_shifted(data, 'item_index', distances, 2)
expected = np.array([0.683333, 0.416667])
np.testing.assert_allclose(expected,
stat['rank'].to_numpy(),
atol=0.000001)
def test_category_clustering():
"""Test category clustering statistics."""
subject = [1] * 2
# category of study and list items (two cases from category
# clustering tests)
study_category = [list('abcd') * 4] * 2
recall_str = ['aaabbbcccddd', 'aabbcdcd']
recall_category = [list(s) for s in recall_str]
# unique item codes (needed for merging study and recall events;
# not actually needed for the stats)
study_item = [[i for i in range(len(c))] for c in study_category]
recall_item = [[0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11],
[0, 4, 1, 5, 2, 3, 6, 7]]
# create merged free recall data
raw = fr.table_from_lists(subject,
study_item,
recall_item,
category=(study_category, recall_category))
data = fr.merge_free_recall(raw, list_keys=['category'])
# test ARC and LBC stats
stats = fr.category_clustering(data, 'category')
np.testing.assert_allclose(stats.loc[1, 'arc'], 0.667, rtol=0.011)
np.testing.assert_allclose(stats.loc[1, 'lbc'], 3.2, rtol=0.011)
def test_lag_crp_compound():
"""Test compound lag-CRP analysis."""
subjects = [1, 1]
study = [['absence', 'hollow', 'pupil', 'fountain'],
['tree', 'cat', 'house', 'dog']]
recall = [['fountain', 'hollow', 'absence'],
['mouse', 'cat', 'tree', 'house', 'dog']]
raw = fr.table_from_lists(subjects, study, recall)
data = fr.merge_free_recall(raw)
crp = fr.lag_crp_compound(data)
# -2, -1
# NaN, -1, +2, +1
actual = np.hstack([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
])
possible = np.hstack([
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
])
np.testing.assert_array_equal(actual, crp['actual'].to_numpy())
np.testing.assert_array_equal(possible, crp['possible'].to_numpy())
def test_pli_list_lag():
"""Test proportion of list lags for prior-list intrusions."""
subjects = [1, 1, 1, 1, 2, 2, 2, 2]
study = [
['tree', 'cat'],
['absence', 'hollow'],
['fountain', 'piano'],
['pillow', 'pupil'],
['tree', 'cat'],
['absence', 'hollow'],
['fountain', 'piano'],
['pillow', 'pupil'],
]
recall = [
['tree', 'cat'],
['hollow', 'absence'],
['fountain', 'hollow'],
['absence', 'piano', 'cat'],
['tree', 'cat'],
['absence', 'hollow'],
['fountain', 'piano'],
['pillow', 'pupil'],
]
raw = fr.table_from_lists(subjects, study, recall)
data = fr.merge_free_recall(raw)
# max lag 1 (exclude just the first list)
stat = fr.pli_list_lag(data, max_lag=1)
np.testing.assert_array_equal(stat['count'].to_numpy(), np.array([2, 0]))
np.testing.assert_array_equal(stat['per_list'].to_numpy(),
np.array([2 / 3, 0]))
np.testing.assert_array_equal(stat['prob'].to_numpy(),
np.array([0.5, np.nan]))
# max lag 2 (exclude first two lists)
stat = fr.pli_list_lag(data, max_lag=2)
np.testing.assert_array_equal(stat['count'].to_numpy(),
np.array([2, 1, 0, 0]))
np.testing.assert_array_equal(stat['per_list'].to_numpy(),
np.array([1, 0.5, 0, 0]))
np.testing.assert_array_equal(stat['prob'].to_numpy(),
np.array([0.5, 0.25, np.nan, np.nan]))
# max lag 3 (exclude first three lists)
stat = fr.pli_list_lag(data, max_lag=3)
np.testing.assert_array_equal(stat['count'].to_numpy(),
np.array([1, 1, 1, 0, 0, 0]))
np.testing.assert_array_equal(stat['per_list'].to_numpy(),
np.array([1, 1, 1, 0, 0, 0]))
np.testing.assert_array_equal(
stat['prob'].to_numpy(),
np.array([1 / 3, 1 / 3, 1 / 3, np.nan, np.nan, np.nan]))
def test_table_from_lists():
"""Test creating a data table from lists."""
subjects = [1, 1, 2, 2]
lists = [1, 2, 3, 4]
study = [
['absence', 'hollow', 'pupil'],
['fountain', 'piano', 'pillow'],
['fountain', 'piano', 'pillow'],
['absence', 'hollow', 'pupil'],
]
recall = [
['hollow', 'pupil', 'empty'],
['fountain', 'piano', 'fountain'],
['pillow', 'piano'],
['pupil'],
]
category = (
[[1, 1, 2], [2, 2, 2], [1, 1, 2], [2, 2, 2]],
[[1, 2, 1], [2, 2, 2], [2, 2], [2]],
)
task = ([[1, 2, 1], [2, 1, 2], [2, 1, 2], [1, 2, 1]], None)
# explicit labeling of list number
data = fr.table_from_lists(subjects,
study,
recall,
lists=lists,
category=category,
task=task)
# subject, list, and trial_type labels
np.testing.assert_array_equal(data['subject'].to_numpy(),
np.repeat([1, 2], [12, 9]))
np.testing.assert_array_equal(data['list'].to_numpy(),
np.repeat([1, 2, 3, 4], [6, 6, 5, 4]))
np.testing.assert_array_equal(
data['trial_type'].to_numpy(),
np.repeat(np.tile(['study', 'recall'], 4), [3, 3, 3, 3, 3, 2, 3, 1]),
)
# position
n = [len(items) for seqs in zip(study, recall) for items in seqs]
position = np.array([i for j in n for i in range(1, j + 1)])
np.testing.assert_array_equal(data['position'].to_numpy(), position)
# items
items = [
item for phase in zip(study, recall) for items in phase
for item in items
]
np.testing.assert_array_equal(data['item'].to_numpy(), np.array(items))
# category
category = [
cat for phase in zip(*category) for cats in phase for cat in cats
]
np.testing.assert_array_equal(data['category'].to_numpy(), category)
# task
task_recall = [np.tile(np.nan, n) for n in [3, 3, 2, 1]]
task = np.hstack(
[t for t_list in zip(task[0], task_recall) for t in t_list])
np.testing.assert_array_equal(data['task'].to_numpy(), task)
# implicit labeling of list number
data = fr.table_from_lists(subjects, study, recall)
np.testing.assert_array_equal(data['list'].to_numpy(),
np.repeat([1, 2, 1, 2], [6, 6, 5, 4]))