def test_psychometric_insufficient_data(self):
# the psychometric aggregate should return NaN when there is no data for a given contrast
trials, _ = self._get_trials(sess_dates=['2020-08-25', '2020-08-24', '2020-08-21'])
trials_all = train.concatenate_trials(trials)
trials_all['probability_left'] = trials_all['contrastLeft'] * 0 + 80
psych_nan = train.compute_psychometric(trials_all, block=100)
assert np.sum(np.isnan(psych_nan)) == 4
def test_concatenate_and_computations(self):
trials, _ = self._get_trials(sess_dates=['2020-08-25', '2020-08-24', '2020-08-21'])
trials_total = np.sum([len(trials[k]['contrastRight']) for k in trials.keys()])
trials_all = train.concatenate_trials(trials)
assert (len(trials_all['contrastRight']) == trials_total)
perf_easy = np.array([train.compute_performance_easy(trials[k]) for k in trials.keys()])
n_trials = np.array([train.compute_n_trials(trials[k]) for k in trials.keys()])
psych = train.compute_psychometric(trials_all)
rt = train.compute_median_reaction_time(trials_all, contrast=0)
np.testing.assert_allclose(perf_easy, [0.91489362, 0.9, 0.90853659])
np.testing.assert_array_equal(n_trials, [617, 532, 719])
np.testing.assert_allclose(psych, [4.04487042, 21.6293942, 1.91451396e-02, 1.72669957e-01],
rtol=1e-5)
assert (np.isclose(rt, 0.83655))
def load_combined_trials(sess_paths, one):
"""
Load and concatenate trials for multiple sessions. Used when we want to concatenate trials for two sessions on the same day
:param sess_paths: list of paths to sessions
:param one: ONE instance
:return:
"""
trials_dict = {}
for sess_path in sess_paths:
trials = load_trials(Path(sess_path), one)
if trials is not None:
trials_dict[Path(sess_path).stem] = load_trials(
Path(sess_path), one)
return training.concatenate_trials(trials_dict)
def test_concatenate_and_computations(self):
sess_dates = ['2020-08-25', '2020-08-24', '2020-08-21']
trials_copy = copy.deepcopy(self.trial_data)
trials = Bunch(zip(sess_dates, [trials_copy[k] for k in sess_dates]))
_ = [trials[k].pop('task_protocol') for k in trials.keys()]
trials_total = np.sum(
[len(trials[k]['contrastRight']) for k in trials.keys()])
trials_all = train.concatenate_trials(trials)
assert (len(trials_all.contrastRight) == trials_total)
perf_easy = np.array(
[train.compute_performance_easy(trials[k]) for k in trials.keys()])
n_trials = np.array(
[train.compute_n_trials(trials[k]) for k in trials.keys()])
psych = train.compute_psychometric(trials_all)
rt = train.compute_median_reaction_time(trials_all)
np.testing.assert_allclose(perf_easy, [0.91489362, 0.9, 0.90853659])
np.testing.assert_array_equal(n_trials, [617, 532, 719])
np.testing.assert_allclose(
psych, [4.04487042, 21.6293942, 1.91451396e-02, 1.72669957e-01],
rtol=1e-5)
assert (np.isclose(rt, 0.83655))