def download_test_files_if_not_present(self): ''' Download %s file at G-node for testing url_for_tests is global at beginning of this file. ''' % self.ioclass.__name__ if not self.use_network: raise unittest.SkipTest("Requires download of data from the web") url = url_for_tests + self.shortname try: make_all_directories(self.files_to_download, self.local_test_dir) download_test_file(self.files_to_download, self.local_test_dir, url) except IOError as exc: raise unittest.TestCase.failureException(exc)
def download_test_files_if_not_present(self): ''' Download %s file at G-node for testing url_for_tests is global at beginning of this file. ''' % self.ioclass.__name__ if not self.use_network: raise unittest.SkipTest("Requires download of data from the web") url = url_for_tests + self.shortname try: make_all_directories(self.files_to_download, self.local_test_dir) download_test_file(self.files_to_download, self.local_test_dir, url) except IOError as exc: raise unittest.SkipTest(exc)
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)