def get_contrastLR(session_path, save=False, data=False):
"""
Get left and right contrasts from raw datafile
**Optional:** save _ibl_trials.contrastLeft.npy and
_ibl_trials.contrastRight.npy to alf folder.
Uses signed_contrast to create left and right contrast vectors.
:param session_path: absolute path of session folder
:type session_path: str
:param save: wether to save the corresponding alf file
to the alf folder, defaults to False
:type save: bool, optional
:return: numpy.ndarray
:rtype: dtype('float64')
"""
if not data:
data = raw.load_data(session_path)
contrastLeft = np.array([t['contrast'] if np.sign(
t['position']) < 0 else np.nan for t in data])
contrastRight = np.array([t['contrast'] if np.sign(
t['position']) > 0 else np.nan for t in data])
# save if needed
check_alf_folder(session_path)
if raw.save_bool(save, '_ibl_trials.contrastLeft.npy'):
lpath = os.path.join(session_path, 'alf', '_ibl_trials.contrastLeft.npy')
np.save(lpath, contrastLeft)
if raw.save_bool(save, '_ibl_trials.contrastRight.npy'):
rpath = os.path.join(session_path, 'alf', '_ibl_trials.contrastRight.npy')
np.save(rpath, contrastRight)
return (contrastLeft, contrastRight)
def get_feedbackType(session_path, save=False, data=False):
"""
Get the feedback that was delivered to subject.
**Optional:** saves _ibl_trials.feedbackType.npy
Checks in raw datafile for error and reward state.
Will raise an error if more than one of the mutually exclusive states have
been triggered.
Sets feedbackType to -1 if error state was trigered
Sets feedbackType to -2 if correct_unreward state was triggered
Sets feedbackType to +1 if correct_reward state was triggered
Sets feedbackType to 0 if no_go state was triggered
:param session_path: absolute path of session folder
:type session_path: str
:param save: wether to save the corresponding alf file
to the alf folder, defaults to False
:type save: bool, optional
:return: numpy.ndarray
:rtype: dtype('int64')
"""
if not data:
data = raw.load_data(session_path)
feedbackType = np.empty(len(data))
feedbackType.fill(np.nan)
correct_rewarded = []
correct_unrewarded = []
error = []
no_go = []
for t in data:
correct_rewarded.append(~np.isnan(
t['behavior_data']['States timestamps']['correct_rewarded'][0][0]))
correct_unrewarded.append(
~np.isnan(t['behavior_data']['States timestamps']
['correct_unrewarded'][0][0]))
error.append(
~np.isnan(t['behavior_data']['States timestamps']['error'][0][0]))
no_go.append(
~np.isnan(t['behavior_data']['States timestamps']['no_go'][0][0]))
if not all(
np.sum([correct_rewarded, correct_unrewarded, error, no_go],
axis=0) == np.ones(len(data))):
raise ValueError
feedbackType[correct_rewarded] = 1
feedbackType[correct_unrewarded] = -2
feedbackType[error] = -1
feedbackType[no_go] = 0
feedbackType = feedbackType.astype('int64')
if raw.save_bool(save, '_ibl_trials.feedbackType.npy'):
check_alf_folder(session_path)
fpath = os.path.join(session_path, 'alf',
'_ibl_trials.feedbackType.npy')
np.save(fpath, feedbackType)
return feedbackType
def get_feedback_times(session_path, save=False, data=False):
"""
Get the times the water or error tone was delivered to the animal.
**Optional:** saves _ibl_trials.feedback_times.npy
Gets reward and error state init times vectors,
checks if theintersection of nans is empty, then
merges the 2 vectors.
:param session_path: Absolute path of session folder
:type session_path: str
:param save: wether to save the corresponding alf file
to the alf folder, defaults to False
:param save: bool, optional
:return: numpy.ndarray
:rtype: dtype('float64')
"""
if not data:
data = raw.load_data(session_path)
c_rw_times = [
tr['behavior_data']['States timestamps']['correct_rewarded'][0][0]
for tr in data
]
c_urw_times = [
tr['behavior_data']['States timestamps']['correct_unrewarded'][0][0]
for tr in data
]
err_times = [
tr['behavior_data']['States timestamps']['error'][0][0] for tr in data
]
nogo_times = [
tr['behavior_data']['States timestamps']['no_go'][0][0] for tr in data
]
assert sum(
np.isnan(c_rw_times) & np.isnan(c_urw_times) & np.isnan(err_times)
& np.isnan(nogo_times)) == 0
merge = np.array([
np.array(times)[~np.isnan(times)]
for times in zip(c_rw_times, c_urw_times, err_times, nogo_times)
]).squeeze()
if raw.save_bool(save, '_ibl_trials.feedback_times.npy'):
check_alf_folder(session_path)
fpath = os.path.join(session_path, 'alf',
'_ibl_trials.feedback_times.npy')
np.save(fpath, merge)
return np.array(merge)