def cleanup_file(self, path):
'''
Remove test files or directories safely.
'''
cleanup_test_file(self.ioclass, path, directory=self.local_test_dir)
def cleanup_file(self, path):
'''
Remove test files or directories safely.
'''
cleanup_test_file(self.ioclass, path, directory=self.local_test_dir)
def test_Riehle_et_al_97_UE(self):
from neo.rawio.tests.tools import (download_test_file,
create_local_temp_dir,
make_all_directories)
from neo.test.iotest.tools import (cleanup_test_file)
url = [
"https://raw.githubusercontent.com/ReScience-Archives/" +
"Rostami-Ito-Denker-Gruen-2017/master/data",
"https://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, os.environ.get("ELEPHANT_TEST_FILE_DIR"))
files_to_download = ["extracted_data.npy", "winny131_23.gdf"]
make_all_directories(files_to_download,
local_test_dir)
for f_cnt, f in enumerate(files_to_download):
download_test_file(f, local_test_dir, url[f_cnt])
# load spike data of figure 2 of Riehle et al 1997
sys.path.append(local_test_dir)
file_name = '/winny131_23.gdf'
trigger = 'RS_4'
t_pre = 1799 * pq.ms
t_post = 300 * pq.ms
spiketrain = self.load_gdf2Neo(local_test_dir + file_name,
trigger, t_pre, t_post)
# calculating UE ...
winsize = 100 * pq.ms
binsize = 5 * pq.ms
winstep = 5 * pq.ms
pattern_hash = [3]
method = 'analytic_TrialAverage'
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=method)
# load extracted data from figure 2 of Riehle et al 1997
try:
extracted_data = np.load(
local_test_dir + '/extracted_data.npy').item()
except UnicodeError:
extracted_data = np.load(
local_test_dir + '/extracted_data.npy', encoding='latin1').item()
Js_sig = ue.jointJ(significance_level)
sig_idx_win = np.where(UE['Js'] >= Js_sig)[0]
diff_UE_rep = []
y_cnt = 0
for tr in range(len(spiketrain)):
x_idx = np.sort(
np.unique(UE['indices']['trial' + str(tr)],
return_index=True)[1])
x = UE['indices']['trial' + str(tr)][x_idx]
if len(x) > 0:
# choose only the significant coincidences
xx = []
for j in sig_idx_win:
xx = np.append(xx, x[np.where(
(x * binsize >= t_winpos[j]) &
(x * binsize < t_winpos[j] + winsize))])
x_tmp = np.unique(xx) * 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
np.testing.assert_array_less(np.abs(diff_UE_rep), 0.3)
cleanup_test_file('dir', local_test_dir)