def test_spiketrain_write(self): block = Block() seg = Segment() block.segments.append(seg) spiketrain = SpikeTrain(times=[3, 4, 5] * pq.s, t_stop=10.0, name="spikes!", description="sssssspikes") seg.spiketrains.append(spiketrain) self.write_and_compare([block]) waveforms = self.rquant((3, 5, 10), pq.mV) spiketrain = SpikeTrain(times=[1, 1.1, 1.2] * pq.ms, t_stop=1.5 * pq.s, name="spikes with wf", description="spikes for waveform test", waveforms=waveforms) seg.spiketrains.append(spiketrain) self.write_and_compare([block]) spiketrain.left_sweep = np.random.random(10) * pq.ms self.write_and_compare([block]) spiketrain.left_sweep = pq.Quantity(-10, "ms") self.write_and_compare([block])
def read_spiketrain(self , # the 2 first key arguments are imposed by neo.io API lazy = False, cascade = True, segment_duration = 15., t_start = -1, channel_index = 0, ): """ With this IO SpikeTrain can e acces directly with its channel number """ # There are 2 possibles behaviour for a SpikeTrain # holding many Spike instance or directly holding spike times # we choose here the first : if not HAVE_SCIPY: raise SCIPY_ERR num_spike_by_spiketrain = 40 sr = 10000. if lazy: times = [ ] else: times = (np.random.rand(num_spike_by_spiketrain)*segment_duration + t_start) # create a spiketrain spiketr = SpikeTrain(times, t_start = t_start*pq.s, t_stop = (t_start+segment_duration)*pq.s , units = pq.s, name = 'it is a spiketrain from exampleio', ) if lazy: # we add the attribute lazy_shape with the size if loaded spiketr.lazy_shape = (num_spike_by_spiketrain,) # ours spiketrains also hold the waveforms: # 1 generate a fake spike shape (2d array if trodness >1) w1 = -stats.nct.pdf(np.arange(11,60,4), 5,20)[::-1]/3. w2 = stats.nct.pdf(np.arange(11,60,2), 5,20) w = np.r_[ w1 , w2 ] w = -w/max(w) if not lazy: # in the neo API the waveforms attr is 3 D in case tetrode # in our case it is mono electrode so dim 1 is size 1 waveforms = np.tile( w[np.newaxis,np.newaxis,:], ( num_spike_by_spiketrain ,1, 1) ) waveforms *= np.random.randn(*waveforms.shape)/6+1 spiketr.waveforms = waveforms*pq.mV spiketr.sampling_rate = sr * pq.Hz spiketr.left_sweep = 1.5* pq.s # for attributes out of neo you can annotate spiketr.annotate(channel_index = channel_index) return spiketr
def test_spiketrain_write(self): block = Block() seg = Segment() block.segments.append(seg) spiketrain = SpikeTrain(times=[3, 4, 5]*pq.s, t_stop=10.0, name="spikes!", description="sssssspikes") seg.spiketrains.append(spiketrain) self.write_and_compare([block]) waveforms = self.rquant((3, 5, 10), pq.mV) spiketrain = SpikeTrain(times=[1, 1.1, 1.2]*pq.ms, t_stop=1.5*pq.s, name="spikes with wf", description="spikes for waveform test", waveforms=waveforms) seg.spiketrains.append(spiketrain) self.write_and_compare([block]) spiketrain.left_sweep = np.random.random(10)*pq.ms self.write_and_compare([block])