|
|
@@ -50,7 +50,7 @@ from neo import Block, Segment
|
|
|
from elephant.signal_processing import butter
|
|
|
|
|
|
from reachgraspio import reachgraspio
|
|
|
-from neo_utils import add_epoch, cut_segment_by_epoch, get_events
|
|
|
+from neo.utils import add_epoch, cut_segment_by_epoch, get_events
|
|
|
|
|
|
|
|
|
# =============================================================================
|
|
|
@@ -142,10 +142,9 @@ data_block.segments[0].analogsignals.extend(filtered_anasig)
|
|
|
# annotations of the Event object).
|
|
|
start_events = get_events(
|
|
|
data_segment,
|
|
|
- properties={
|
|
|
- 'name': 'TrialEvents',
|
|
|
- 'trial_event_labels': 'TS-ON',
|
|
|
- 'performance_in_trial': session.performance_codes['correct_trial']})
|
|
|
+ name='TrialEvents',
|
|
|
+ trial_event_labels='TS-ON',
|
|
|
+ performance_in_trial=session.performance_codes['correct_trial'])
|
|
|
|
|
|
# Extract single Neo Event object containing all TS-ON triggers
|
|
|
assert len(start_events) == 1
|
|
|
@@ -163,7 +162,8 @@ epoch = add_epoch(
|
|
|
event1=start_event, event2=None,
|
|
|
pre=pre, post=post,
|
|
|
attach_result=False,
|
|
|
- name='analysis_epochs')
|
|
|
+ name='analysis_epochs',
|
|
|
+ array_annotations=start_event.array_annotations)
|
|
|
|
|
|
# Create new segments of data cut according to the analysis epochs of the
|
|
|
# 'analysis_epochs' Neo Epoch object. The time axes of all segments are aligned
|
|
|
@@ -186,7 +186,7 @@ cut_trial_block.segments = cut_segment_by_epoch(
|
|
|
# In this case this call should return one matching analysis epoch around TS-ON
|
|
|
# belonging to behavioral trial ID i. For monkey N, this is trial ID 1, for
|
|
|
# monkey L this is trial ID 2 since trial ID 1 is not a correct trial.
|
|
|
-trial_id = int(np.min(start_event.annotations['trial_id']))
|
|
|
+trial_id = int(np.min(start_event.array_annotations['trial_id']))
|
|
|
trial_segments = cut_trial_block.filter(
|
|
|
targdict={"trial_id": trial_id}, objects=Segment)
|
|
|
assert len(trial_segments) == 1
|
|
|
@@ -238,7 +238,7 @@ for event in trial_segment.events:
|
|
|
alpha=0.2,
|
|
|
linewidth=3,
|
|
|
linestyle='dashed',
|
|
|
- label='event ' + event.annotations[
|
|
|
+ label='event ' + event.array_annotations[
|
|
|
'trial_event_labels'][ev_id])
|
|
|
|
|
|
# Finishing touches on the plot
|
