diff options
Diffstat (limited to 'pyspike/spikes.py')
| -rw-r--r-- | pyspike/spikes.py | 47 |
1 files changed, 29 insertions, 18 deletions
diff --git a/pyspike/spikes.py b/pyspike/spikes.py index ab6d4c4..cf47043 100644 --- a/pyspike/spikes.py +++ b/pyspike/spikes.py @@ -62,6 +62,31 @@ def load_spike_trains_from_txt(file_name, edges, return spike_trains +def import_spike_trains_from_time_series(file_name, start_time, time_bin, + separator=None, comment='#'): + """ Imports spike trains from time series consisting of 0 and 1 denoting + the absence or presence of a spike. Each line in the data file represents + one spike train. + + :param file_name: The name of the data file containing the time series. + :param edges: A pair (T_start, T_end) of values representing the + start and end time of the spike train measurement + or a single value representing the end time, the + T_start is then assuemd as 0. + :param separator: The character used to seprate the values in the text file + :param comment: Lines starting with this character are ignored. + + """ + data = np.loadtxt(file_name, comments=comment, delimiter=separator) + time_points = start_time + time_bin + np.arange(len(data[0, :]))*time_bin + spike_trains = [] + for time_series in data: + spike_trains.append(SpikeTrain(time_points[time_series > 0], + edges=[start_time, + time_points[-1]])) + return spike_trains + + ############################################################ # save_spike_trains_to_txt ############################################################ @@ -91,24 +116,10 @@ def merge_spike_trains(spike_trains): :param spike_trains: list of :class:`.SpikeTrain` :returns: spike train with the merged spike times """ - # get the lengths of the spike trains - lens = np.array([len(st.spikes) for st in spike_trains]) - merged_spikes = np.empty(np.sum(lens)) - index = 0 # the index for merged_spikes - indices = np.zeros_like(lens) # indices of the spike trains - index_list = np.arange(len(indices)) # indices of indices of spike trains - # that have not yet reached the end - # list of the possible events in the spike trains - vals = [spike_trains[i].spikes[indices[i]] for i in index_list] - while len(index_list) > 0: - i = np.argmin(vals) # the next spike is the minimum - merged_spikes[index] = vals[i] # put it to the merged spike train - i = index_list[i] - index += 1 # next index of merged spike train - indices[i] += 1 # next index for the chosen spike train - if indices[i] >= lens[i]: # remove spike train index if ended - index_list = index_list[index_list != i] - vals = [spike_trains[n].spikes[indices[n]] for n in index_list] + # concatenating and sorting with numpy is fast, it also means we can handle + # empty spike trains + merged_spikes = np.concatenate([st.spikes for st in spike_trains]) + merged_spikes.sort() return SpikeTrain(merged_spikes, [spike_trains[0].t_start, spike_trains[0].t_end]) |