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""" test_load.py
Test loading of spike trains from text files
Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
from __future__ import print_function
import numpy as np
from numpy.testing import assert_equal
import pyspike as spk
import os
TEST_PATH = os.path.dirname(os.path.realpath(__file__))
TEST_DATA = os.path.join(TEST_PATH, "PySpike_testdata.txt")
TIME_SERIES_DATA = os.path.join(TEST_PATH, "time_series.txt")
TIME_SERIES_SPIKES = os.path.join(TEST_PATH, "time_series_spike_trains.txt")
def test_load_from_txt():
spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=(0, 4000))
assert len(spike_trains) == 40
# check the first spike train
spike_times = [64.886, 305.81, 696, 937.77, 1059.7, 1322.2, 1576.1,
1808.1, 2121.5, 2381.1, 2728.6, 2966.9, 3223.7, 3473.7,
3644.3, 3936.3]
assert_equal(spike_times, spike_trains[0].spikes)
# check auxiliary spikes
for spike_train in spike_trains:
assert spike_train.t_start == 0.0
assert spike_train.t_end == 4000
def test_load_time_series():
spike_trains = spk.import_spike_trains_from_time_series(TIME_SERIES_DATA,
start_time=0,
time_bin=1)
assert len(spike_trains) == 40
spike_trains_check = spk.load_spike_trains_from_txt(TIME_SERIES_SPIKES,
edges=(0, 4000))
# check spike trains
for n in range(len(spike_trains)):
assert_equal(spike_trains[n].spikes, spike_trains_check[n].spikes)
assert_equal(spike_trains[n].t_start, 0)
assert_equal(spike_trains[n].t_end, 4000)
def check_merged_spikes(merged_spikes, spike_trains):
# create a flat array with all spike events
all_spikes = np.array([])
for spike_train in spike_trains:
all_spikes = np.append(all_spikes, spike_train)
indices = np.zeros_like(all_spikes, dtype='bool')
# check if we find all the spike events in the original spike trains
for x in merged_spikes:
i = np.where(all_spikes == x)[0][0] # first axis and first entry
# change to something impossible so we dont find this event again
all_spikes[i] = -1.0
indices[i] = True
assert indices.all()
def test_merge_spike_trains():
# first load the data
spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=(0, 4000))
merged_spikes = spk.merge_spike_trains([spike_trains[0], spike_trains[1]])
# test if result is sorted
assert((merged_spikes.spikes == np.sort(merged_spikes.spikes)).all())
# check merging
check_merged_spikes(merged_spikes.spikes, [spike_trains[0].spikes,
spike_trains[1].spikes])
merged_spikes = spk.merge_spike_trains(spike_trains)
# test if result is sorted
assert((merged_spikes.spikes == np.sort(merged_spikes.spikes)).all())
# check merging
check_merged_spikes(merged_spikes.spikes,
[st.spikes for st in spike_trains])
def test_merge_empty_spike_trains():
# first load the data
spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=(0, 4000))
# take two non-empty trains, and one empty one
empty = spk.SpikeTrain([],[spike_trains[0].t_start,spike_trains[0].t_end])
merged_spikes = spk.merge_spike_trains([spike_trains[0], empty, spike_trains[1]])
# test if result is sorted
assert((merged_spikes.spikes == np.sort(merged_spikes.spikes)).all())
# we don't need to check more, that's done by test_merge_spike_trains
if __name__ == "main":
test_load_from_txt()
test_merge_spike_trains()
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