def test_ready4delay(self):
sess_dates = ['2020-09-03', '2020-09-02', '2020-08-31']
trials, task_protocol = self._get_trials(sess_dates=sess_dates)
assert (np.all(np.array(task_protocol) == 'biased'))
status, info = train.get_training_status(
trials, task_protocol, ephys_sess_dates=['2020-09-03'], n_delay=0)
assert (status == 'ready4delay')
def test_trained_1b(self):
trials, task_protocol = self._get_trials(
sess_dates=['2020-08-27', '2020-08-26', '2020-08-25'])
assert (np.all(np.array(task_protocol) == 'training'))
status, info = train.get_training_status(trials, task_protocol, ephys_sess_dates=[],
n_delay=0)
assert(status == 'trained 1b')
def test_ready4recording(self):
sess_dates = ['2020-09-01', '2020-08-31', '2020-08-28']
trials, task_protocol = self._get_trials(sess_dates=sess_dates)
assert (np.all(np.array(task_protocol) == 'biased'))
status, info = train.get_training_status(trials, task_protocol,
ephys_sess_dates=sess_dates, n_delay=1)
assert (status == 'ready4recording')
def test_training_to_bias(self):
trials, task_protocol = self._get_trials(
sess_dates=['2020-08-31', '2020-08-28', '2020-08-27'])
assert (~np.all(np.array(task_protocol) == 'training') and
np.any(np.array(task_protocol) == 'training'))
status, info = train.get_training_status(trials, task_protocol, ephys_sess_dates=[],
n_delay=0)
assert (status == 'trained 1b')
def test_ready4delay(self):
sess_dates = ['2020-09-03', '2020-09-02', '2020-08-31']
trials_copy = copy.deepcopy(self.trial_data)
trials = Bunch(zip(sess_dates, [trials_copy[k] for k in sess_dates]))
task_protocol = [trials[k].pop('task_protocol') for k in trials.keys()]
assert (np.all(np.array(task_protocol) == 'biased'))
status, info = train.get_training_status(
trials, task_protocol, ephys_sess_dates=['2020-09-03'], n_delay=0)
assert (status == 'ready4delay')
def test_trained_1b(self):
sess_dates = ['2020-08-27', '2020-08-26', '2020-08-25']
trials_copy = copy.deepcopy(self.trial_data)
trials = Bunch(zip(sess_dates, [trials_copy[k] for k in sess_dates]))
task_protocol = [trials[k].pop('task_protocol') for k in trials.keys()]
assert (np.all(np.array(task_protocol) == 'training'))
status, info = train.get_training_status(trials,
task_protocol,
ephys_sess_dates=[],
n_delay=0)
assert (status == 'trained 1b')
def compute_training_status(df, compute_date, one):
"""
Compute the training status for compute date based on training from that session and two previous days
:param df: training dataframe
:param compute_date: date to compute training on
:param one: ONE instance
:return:
"""
# compute_date = str(one.path2ref(session_path)['date'])
df_temp = df[df['date'] <= compute_date]
df_temp = df_temp.drop_duplicates('session_path')
df_temp.sort_values('date')
dates = df_temp.date.values
n_dates = np.min([3, len(dates)]).astype(int)
compute_dates = dates[(-1 * n_dates):]
assert compute_dates[-1] == compute_date
df_temp_group = df_temp.groupby('date')
trials = {}
n_delay = 0
ephys_sessions = []
protocol = []
status = []
for date in compute_dates:
df_date = df_temp_group.get_group(date)
# If habituation skip
if df_date.iloc[-1]['task_protocol'] == 'habituation':
continue
trials[df_date.iloc[-1]['date']] = load_combined_trials(
df_date.session_path.values, one)
protocol.append(df_date.iloc[-1]['task_protocol'])
status.append(df_date.iloc[-1]['training_status'])
if df_date.iloc[-1]['combined_n_delay'] >= 900: # delay of 15 mins
n_delay += 1
if df_date.iloc[-1]['location'] == 'ephys_rig':
ephys_sessions.append(df_date.iloc[-1]['date'])
n_status = np.max([-2, -1 * len(status)])
training_status, _ = training.get_training_status(trials, protocol,
ephys_sessions, n_delay)
training_status = pass_through_training_hierachy(training_status,
status[n_status])
df.loc[df['date'] == compute_date, 'training_status'] = training_status
return df
def test_in_training(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]))
task_protocol = [trials[k].pop('task_protocol') for k in trials.keys()]
assert (np.all(np.array(task_protocol) == 'training'))
status, info = train.get_training_status(trials,
task_protocol,
ephys_sess_dates=[],
n_delay=0)
assert (status == 'in training')
# also test the computations in the first test
np.testing.assert_allclose(info.perf_easy,
[0.91489362, 0.9, 0.90853659])
np.testing.assert_array_equal(info.n_trials, [617, 532, 719])
np.testing.assert_allclose(
info.psych,
[4.04487042, 21.6293942, 1.91451396e-02, 1.72669957e-01],
rtol=1e-5)
assert (np.isclose(info.rt, 0.83655))
