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@@ -556,7 +556,7 @@ class ReachGraspIO(BlackrockIO):
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times=pq.Quantity(event_time, 'ms').flatten(),
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labels=np.array(event_name),
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name='AnalogTrialEvents',
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- description='Events extracted from analog signals')
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+ description='Events extracted from behavioural time series')
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performance_str = []
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for pit in performance_code:
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@@ -788,7 +788,7 @@ class ReachGraspIO(BlackrockIO):
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if chid not in unit_dict:
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unit_dict[chid] = {}
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if unit_id not in unit_dict[chid]:
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- group = neo.Group(name='Unit {} on channel {}'.format(unit_id, chid),
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+ group = neo.Group(name='UnitGroup-ch{}#{}'.format(chid, unit_id),
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description='Group for neuronal data related to unit {} on '
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'channel {}'.format(unit_id, chid),
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group_type='unit',
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@@ -802,6 +802,10 @@ class ReachGraspIO(BlackrockIO):
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unit_dict[chid][unit_id].add(st)
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+ # create a consistent name and description for the spike train
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+ st.name = 'SpikeTrain-ch{}#{}'.format(chid, unit_id)
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+ st.description = 'SpikeTrain of unit {} on channel {}'.format(unit_id, chid)
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+
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# if views are already created, link them to unit groups
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if view_dict:
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for chid, channel_dict in unit_dict.items():
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@@ -889,10 +893,9 @@ class ReachGraspIO(BlackrockIO):
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if not any(neural_chids):
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asig.annotate(neural_signal=False)
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- asig.name = "Behavioural Time Series"
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- asig.descriptions = "This Analogsignal object contains the continuous behavioural time series recorded in " \
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- "the experiment, including object displacements and measurements of the " \
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- "gripforce sensors."
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+ asig.name = "BehaviourTimeSeries"
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+ asig.descriptions = "Continuous behavioural time series recorded in the experiment, including " \
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+ "object displacements and measurements of the gripforce sensors"
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elif all(neural_chids):
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asig.annotate(neural_signal=True)
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@@ -934,14 +937,12 @@ class ReachGraspIO(BlackrockIO):
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))
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if asig.sampling_rate == pq.Quantity(30000 * pq.Hz):
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- asig.name = "Raw Neural Time Series"
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- asig.description = "This Analogsignal object contains the continuous raw neuronal recordings " \
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- "sampled at high resolution."
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+ asig.name = "NeuralTimeSeriesRaw"
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+ asig.description = "Continuous raw neuronal recordings sampled at high resolution"
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if asig.sampling_rate == pq.Quantity(1000 * pq.Hz):
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- asig.name = "Downsampled Neural Time Series"
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- asig.description = "This Analogsignal object contains the downsampled continuous neuronal " \
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- "recordings, where the downsampling was performed on-line by the recording " \
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- "system."
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+ asig.name = "NeuralTimeSeriesDownsampled"
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+ asig.description = "Downsampled continuous neuronal recordings, where the downsampling was " \
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+ "performed on-line by the recording system"
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self.__annotate_electrode_rejections(asig)
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