Function to plot patterns from SPADE does not consider lags

[kohlerca]

The plot_patterns function apparently only works for synchronous patterns, but not when there is a lag between the spikes.

The code below illustrates that

import numpy as np
import quantities as pq
import neo
from elephant.spade import spade
from elephant.spike_train_generation import homogeneous_poisson_process
from copy import deepcopy
import viziphant

# Generate two types of pattern: one synchronous and other with lags

np.random.seed(123)
sync_spiketrains = [homogeneous_poisson_process(rate=3*pq.Hz, t_stop=1*pq.s) for _ in range(50)]
lag_spiketrains = deepcopy(sync_spiketrains)

pattern_times = pq.Quantity([212, 574, 876], units=pq.ms)

pattern_neurons = []
pattern = neo.SpikeTrain(pattern_times, t_stop=1*pq.s)
for st in sync_spiketrains[:4]:
    pattern_st = st.merge(pattern)
    pattern_neurons.append(pattern_st)
sync_spiketrains[:4] = pattern_neurons

pattern_neurons = []
for idx, st in enumerate(lag_spiketrains[:4]):
    pattern = neo.SpikeTrain(pattern_times + idx * (10 * pq.ms), t_stop=1*pq.s)
    pattern_st = st.merge(pattern)
    pattern_neurons.append(pattern_st)
lag_spiketrains[:4] = pattern_neurons

Run SPADE using the synchronous patterns and plot:

sync_patterns = spade(sync_spiketrains, bin_size=5*pq.ms, winlen=10, min_spikes=3,
    n_surr=100, dither=5*pq.ms, psr_param=[0,0,0], alpha=0.05)

print(sync_patterns['patterns'])
viziphant.patterns.plot_patterns(sync_spiketrains, sync_patterns['patterns'])

Output:

[{'itemset': (20, 0, 10, 30),
'windows_ids': (42, 114, 175),
'neurons': [2, 0, 1, 3],
'lags': array([0., 0., 0.]) * ms,
'times': array([210., 570., 875.]) * ms,
'signature': (4, 3),
'pvalue': 0.01}]

Run SPADE using the pattern with lags and plot:

lag_patterns = spade(lag_spiketrains, bin_size=5*pq.ms, winlen=10, min_spikes=3,
    n_surr=100, dither=5*pq.ms, psr_param=[0,0,0], alpha=0.05)
print(lag_patterns['patterns'])
viziphant.patterns.plot_patterns(lag_spiketrains, lag_patterns['patterns'])

Output:

[{'itemset': (24, 36, 12, 0),
'windows_ids': (42, 114, 175),
'neurons': [0, 1, 2, 3],
'lags': array([10., 20., 30.]) * ms,
'times': array([210., 570., 875.]) * ms,
'signature': (4, 3),
'pvalue': 0.0}]

Expected output

Spikes in the second plot would be shown with the lags, overlapping with the original spike trains in gray.