Source code for bmtk.analyzer.spikes_loader

# Copyright 2017. Allen Institute. All rights reserved
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import h5py
import numpy as np
import os
import datetime




def load_spikes(file_name,trial_name=None):
    """Loads spikes from multiple file formats

    :param file_name: name of spike file
    :param trian_name: name of a trial within a spike file

    :return spikes: dict with spikes
    """

    filename, file_extension = os.path.splitext(file_name)

    if file_extension == ".nwb":
        spikes = load_spikes_from_nwb(file_name,trial_name)

    if file_extension == ".txt":
        spikes = load_spikes_from_txt(file_name)

    if file_extension == ".h5":
        spikes = load_spikes_from_h5(file_name)


    return spikes


    


def load_spikes_from_txt(file_name):
    """Load spikes from the txt file
    
    :param file_name: name of spike file
    :param trian_name: name of a trial within a spike file

    :return spikes: dict with spikes
    """
    t = os.path.getmtime(file_name)
    spk_ts, spk_gids = np.loadtxt(file_name, dtype='float32,int', unpack=True)
    spikes = {
        "time": spk_ts,
        "gid": spk_gids
    }
    print('loaded spikes from txt')
    return spikes





def load_spikes_from_h5(file_name):
    """Load spikes from the hdf5 file
    
    :param file_name: name of spike file
    :param trian_name: name of a trial within a spike file

    :return spikes: dict with spikes
    """
    t = os.path.getmtime(file_name)
    print("{} modified on: {}".format(file_name, datetime.datetime.fromtimestamp(t)))

    spikes ={}
    with h5py.File(file_name,'r') as h5:
        spikes["time"] = h5["time"][...]
        spikes["gid"] = h5["gid"][...]

    return spikes





def load_spikes_from_nwb(file_name,trial_name):
    """Load spikes from the nwb file
    
    :param file_name: name of spike file
    :param trian_name: name of a trial within a spike file

    :return spikes: dict with spikes
    """
    f5 = h5py.File(file_name, 'r')

    spike_trains_handle = f5['processing/%s/spike_train' % trial_name]  # nwb.SpikeTrain.get_processing(f5,'trial_0')
    spike_times = []
    spike_gids = []
    for gid in spike_trains_handle.keys():
        times_gid = spike_trains_handle['%d/data' %int(gid)][:]
        spike_times.extend(times_gid)
        spike_gids.extend([int(gid)]*len(times_gid))
        
    spikes = {}
    spikes["time"] = np.array(spike_times) 
    spikes["gid"] = np.array(spike_gids)

    return spikes