def test_multitaper_psd_against_nitime(self):
"""
This test assesses the match between this implementation of
multitaper against nitime (0.8) using a predefined time series
generated by an autoregressive model.
Please follow the link below for more details:
https://gin.g-node.org/INM-6/elephant-data/src/master/unittest/spectral/multitaper_psd
"""
data_url = r"https://web.gin.g-node.org/INM-6/elephant-data/raw/master/unittest/spectral/multitaper_psd/data" # noqa
files_to_download = [
("time_series.npy", "ff43797e2ac94613f510b20a31e2e80e"),
("psd_nitime.npy", "89d1f53957e66c786049ea425b53c0e8")
]
for filename, checksum in files_to_download:
download(url=f"{data_url}/{filename}", checksum=checksum)
time_series = np.load(ELEPHANT_TMP_DIR / 'time_series.npy')
psd_nitime = np.load(ELEPHANT_TMP_DIR / 'psd_nitime.npy')
freqs, psd_multitaper = elephant.spectral.multitaper_psd(
signal=time_series, fs=0.1, nw=4, num_tapers=8)
np.testing.assert_allclose(psd_multitaper,
psd_nitime,
rtol=0.3,
atol=0.1)
def test_Riehle_et_al_97_UE(self):
url = "http://raw.githubusercontent.com/ReScience-Archives/Rostami-" \
"Ito-Denker-Gruen-2017/master/data"
files_to_download = (("extracted_data.npy",
"c4903666ce8a8a31274d6b11238a5ac3"),
("winny131_23.gdf",
"cc2958f7b4fb14dbab71e17bba49bd10"))
for filename, checksum in files_to_download:
# The files will be downloaded to ELEPHANT_TMP_DIR
download(url=f"{url}/{filename}", checksum=checksum)
# load spike data of figure 2 of Riehle et al 1997
spiketrain = self.load_gdf2Neo(ELEPHANT_TMP_DIR / "winny131_23.gdf",
trigger='RS_4',
t_pre=1799 * pq.ms,
t_post=300 * pq.ms)
# calculating UE ...
winsize = 100 * pq.ms
bin_size = 5 * pq.ms
winstep = 5 * pq.ms
pattern_hash = [3]
t_start = spiketrain[0][0].t_start
t_stop = spiketrain[0][0].t_stop
t_winpos = ue._winpos(t_start, t_stop, winsize, winstep)
significance_level = 0.05
UE = ue.jointJ_window_analysis(spiketrain,
pattern_hash=pattern_hash,
bin_size=bin_size,
win_size=winsize,
win_step=winstep,
method='analytic_TrialAverage')
# load extracted data from figure 2 of Riehle et al 1997
extracted_data = np.load(ELEPHANT_TMP_DIR / 'extracted_data.npy',
encoding='latin1',
allow_pickle=True).item()
Js_sig = ue.jointJ(significance_level)
sig_idx_win = np.where(UE['Js'] >= Js_sig)[0]
diff_UE_rep = []
y_cnt = 0
for trial_id in range(len(spiketrain)):
trial_id_str = "trial{}".format(trial_id)
indices_unique = np.unique(UE['indices'][trial_id_str])
if len(indices_unique) > 0:
# choose only the significant coincidences
indices_unique_significant = []
for j in sig_idx_win:
significant = indices_unique[np.where(
(indices_unique * bin_size >= t_winpos[j])
& (indices_unique * bin_size < t_winpos[j] + winsize))]
indices_unique_significant.extend(significant)
x_tmp = np.unique(indices_unique_significant) * \
bin_size.magnitude
if len(x_tmp) > 0:
ue_trial = np.sort(extracted_data['ue'][y_cnt])
diff_UE_rep = np.append(diff_UE_rep, x_tmp - ue_trial)
y_cnt += +1
np.testing.assert_array_less(np.abs(diff_UE_rep), 0.3)
def test_spike_contrast_with_Izhikevich_network_auto(self):
# This test reproduces the Test data 3 (Izhikevich network), fig. 3,
# Manuel Ciba et. al, 2018.
# The data is a dictionary of simulations of different networks.
# Each simulation of a network is a dictionary with two keys:
# 'spiketrains' and the ground truth 'synchrony'.
# The default unit time is seconds. Each simulation lasted 2 seconds,
# starting from 0.
izhikevich_url = r"https://web.gin.g-node.org/INM-6/" \
r"elephant-data/raw/master/" \
r"dataset-3/Data_Izhikevich_network.zip"
filepath_zip = download(url=izhikevich_url,
checksum="70e848500c1d9c6403b66de8c741d849")
unzip(filepath_zip)
filepath = filepath_zip.replace(".zip", ".json")
with open(filepath) as read_file:
data = json.load(read_file)
# for the sake of compute time, take the first 5 networks
networks_subset = tuple(data.values())[:5]
for network_simulations in networks_subset:
for simulation in network_simulations.values():
synchrony_true = simulation['synchrony']
spiketrains = [
neo.SpikeTrain(st,
t_start=0 * second,
t_stop=2 * second,
units=second)
for st in simulation['spiketrains']
]
synchrony = spike_contrast(spiketrains)
self.assertAlmostEqual(synchrony, synchrony_true, places=2)
