本文整理汇总了Python中neo.core.SpikeTrain.array_annotate方法的典型用法代码示例。如果您正苦于以下问题：Python SpikeTrain.array_annotate方法的具体用法？Python SpikeTrain.array_annotate怎么用？Python SpikeTrain.array_annotate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类neo.core.SpikeTrain的用法示例。
在下文中一共展示了SpikeTrain.array_annotate方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: generate_one_simple_segment
# 需要导入模块: from neo.core import SpikeTrain [as 别名]
# 或者: from neo.core.SpikeTrain import array_annotate [as 别名]
def generate_one_simple_segment(seg_name='segment 0', supported_objects=[], nb_analogsignal=4,
t_start=0. * pq.s, sampling_rate=10 * pq.kHz, duration=6. * pq.s,
nb_spiketrain=6, spikerate_range=[.5 * pq.Hz, 12 * pq.Hz],
event_types={'stim': ['a', 'b', 'c', 'd'],
'enter_zone': ['one', 'two'],
'color': ['black', 'yellow', 'green'], },
event_size_range=[5, 20],
epoch_types={'animal state': ['Sleep', 'Freeze', 'Escape'],
'light': ['dark', 'lighted']},
epoch_duration_range=[.5, 3.],
# this should be multiplied by pq.s, no?
array_annotations={'valid': np.array([True, False]),
'number': np.array(range(5))}
):
if supported_objects and Segment not in supported_objects:
raise ValueError('Segment must be in supported_objects')
seg = Segment(name=seg_name)
if AnalogSignal in supported_objects:
for a in range(nb_analogsignal):
anasig = AnalogSignal(rand(int(sampling_rate * duration)), sampling_rate=sampling_rate,
t_start=t_start, units=pq.mV, channel_index=a,
name='sig %d for segment %s' % (a, seg.name))
seg.analogsignals.append(anasig)
if SpikeTrain in supported_objects:
for s in range(nb_spiketrain):
spikerate = rand() * np.diff(spikerate_range)
spikerate += spikerate_range[0].magnitude
# spikedata = rand(int((spikerate*duration).simplified))*duration
# sptr = SpikeTrain(spikedata,
# t_start=t_start, t_stop=t_start+duration)
# #, name = 'spiketrain %d'%s)
spikes = rand(int((spikerate * duration).simplified))
spikes.sort() # spikes are supposed to be an ascending sequence
sptr = SpikeTrain(spikes * duration, t_start=t_start, t_stop=t_start + duration)
sptr.annotations['channel_index'] = s
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(len(spikes)) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
sptr.array_annotate(**arr_ann)
seg.spiketrains.append(sptr)
if Event in supported_objects:
for name, labels in event_types.items():
evt_size = rand() * np.diff(event_size_range)
evt_size += event_size_range[0]
evt_size = int(evt_size)
labels = np.array(labels, dtype='S')
labels = labels[(rand(evt_size) * len(labels)).astype('i')]
evt = Event(times=rand(evt_size) * duration, labels=labels)
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(evt_size) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
evt.array_annotate(**arr_ann)
seg.events.append(evt)
if Epoch in supported_objects:
for name, labels in epoch_types.items():
t = 0
times = []
durations = []
while t < duration:
times.append(t)
dur = rand() * (epoch_duration_range[1] - epoch_duration_range[0])
dur += epoch_duration_range[0]
durations.append(dur)
t = t + dur
labels = np.array(labels, dtype='S')
labels = labels[(rand(len(times)) * len(labels)).astype('i')]
assert len(times) == len(durations)
assert len(times) == len(labels)
epc = Epoch(times=pq.Quantity(times, units=pq.s),
durations=pq.Quantity(durations, units=pq.s),
labels=labels,)
assert epc.times.dtype == 'float'
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(len(times)) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
epc.array_annotate(**arr_ann)
seg.epochs.append(epc)
# TODO : Spike, Event
seg.create_many_to_one_relationship()
return seg