def create_local_dir_if_not_exists(self):
'''
Create a local directory to store testing files and return it.
The directory path is also written to self.local_test_dir
'''
self.local_test_dir = create_local_temp_dir(self.shortname)
return self.local_test_dir
def create_local_dir_if_not_exists(self):
'''
Create a local directory to store testing files and return it.
The directory path is also written to self.local_test_dir
'''
self.local_test_dir = create_local_temp_dir(
self.shortname, directory=os.environ.get("NEO_TEST_FILE_DIR", None))
return self.local_test_dir
def test_Riehle_et_al_97_UE(self):
url = "http://raw.githubusercontent.com/ReScience-Archives/Rostami-" \
"Ito-Denker-Gruen-2017/master/data"
shortname = "unitary_event_analysis_test_data"
local_test_dir = create_local_temp_dir(shortname)
files_to_download = ["extracted_data.npy", "winny131_23.gdf"]
context = ssl._create_unverified_context()
for filename in files_to_download:
url_file = "{url}/{filename}".format(url=url, filename=filename)
dist = urlopen(url_file, context=context)
localfile = os.path.join(local_test_dir, filename)
with open(localfile, 'wb') as f:
f.write(dist.read())
# load spike data of figure 2 of Riehle et al 1997
spiketrain = self.load_gdf2Neo(os.path.join(local_test_dir,
"winny131_23.gdf"),
trigger='RS_4',
t_pre=1799 * pq.ms,
t_post=300 * pq.ms)
# calculating UE ...
winsize = 100 * pq.ms
binsize = 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,
binsize,
winsize,
winstep,
pattern_hash,
method='analytic_TrialAverage')
# load extracted data from figure 2 of Riehle et al 1997
extracted_data = np.load(os.path.join(local_test_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 * binsize >= t_winpos[j])
& (indices_unique * binsize < t_winpos[j] + winsize))]
indices_unique_significant.extend(significant)
x_tmp = np.unique(indices_unique_significant) * \
binsize.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
shutil.rmtree(local_test_dir)
np.testing.assert_array_less(np.abs(diff_UE_rep), 0.3)