def test_ttl_frames_in_sub_multi(self):
# sub recording
start_frame = self.example_info['num_frames'] // 3
end_frame = 2 * self.example_info['num_frames'] // 3
RX_sub = se.SubRecordingExtractor(self.RX,
start_frame=start_frame,
end_frame=end_frame)
original_ttls = self.RX.get_ttl_events()[0]
ttls_in_sub = original_ttls[np.where((original_ttls >= start_frame)
& (original_ttls < end_frame))[0]]
self.assertTrue(
np.array_equal(RX_sub.get_ttl_events()[0],
ttls_in_sub - start_frame))
# multirecording
RX_multi = se.MultiRecordingTimeExtractor(
recordings=[self.RX, self.RX, self.RX])
ttls_originals = self.RX.get_ttl_events()[0]
num_ttls = len(ttls_originals)
self.assertEqual(len(RX_multi.get_ttl_events()[0]), 3 * num_ttls)
self.assertTrue(
np.array_equal(RX_multi.get_ttl_events()[0][:num_ttls],
ttls_originals))
self.assertTrue(
np.array_equal(RX_multi.get_ttl_events()[0][num_ttls:2 * num_ttls],
ttls_originals + self.RX.get_num_frames()))
self.assertTrue(
np.array_equal(RX_multi.get_ttl_events()[0][2 * num_ttls:],
ttls_originals + 2 * self.RX.get_num_frames()))
def test_multi_sub_recording_extractor(self):
RX_multi = se.MultiRecordingTimeExtractor(
recordings=[self.RX, self.RX, self.RX],
epoch_names=['A', 'B', 'C'])
RX_sub = RX_multi.get_epoch('C')
self._check_recordings_equal(self.RX, RX_sub)
self.assertEqual(4, len(RX_sub.get_channel_ids()))
RX_multi = se.MultiRecordingChannelExtractor(
recordings=[self.RX, self.RX2, self.RX3], groups=[1, 2, 3])
print(RX_multi.get_channel_groups())
RX_sub = se.SubRecordingExtractor(RX_multi,
channel_ids=[4, 5, 6, 7],
renamed_channel_ids=[0, 1, 2, 3])
self._check_recordings_equal(self.RX2, RX_sub)
self.assertEqual([2, 2, 2, 2], RX_sub.get_channel_groups())
self.assertEqual(12, len(RX_multi.get_channel_ids()))
def test_dump_load_multi_sub_extractor(self):
# generate dumpable formats
path1 = self.test_dir + '/mda'
path2 = path1 + '/firings_true.mda'
se.MdaRecordingExtractor.write_recording(self.RX, path1)
se.MdaSortingExtractor.write_sorting(self.SX, path2)
RX_mda = se.MdaRecordingExtractor(path1)
SX_mda = se.MdaSortingExtractor(path2)
RX_multi_chan = se.MultiRecordingChannelExtractor(recordings=[RX_mda, RX_mda, RX_mda])
check_dumping(RX_multi_chan)
RX_multi_time = se.MultiRecordingTimeExtractor(recordings=[RX_mda, RX_mda, RX_mda], )
check_dumping(RX_multi_time)
RX_multi_chan = se.SubRecordingExtractor(RX_mda, channel_ids=[0, 1])
check_dumping(RX_multi_chan)
SX_sub = se.SubSortingExtractor(SX_mda, unit_ids=[1, 2])
check_dumping(SX_sub)
SX_multi = se.MultiSortingExtractor(sortings=[SX_mda, SX_mda, SX_mda])
check_dumping(SX_multi)
def test_multi_sub_recording_extractor(self):
RX_multi = se.MultiRecordingTimeExtractor(
recordings=[self.RX, self.RX, self.RX],
epoch_names=['A', 'B', 'C']
)
RX_sub = RX_multi.get_epoch('C')
check_recordings_equal(self.RX, RX_sub)
check_recordings_equal(self.RX, RX_multi.recordings[0])
check_recordings_equal(self.RX, RX_multi.recordings[1])
check_recordings_equal(self.RX, RX_multi.recordings[2])
self.assertEqual(4, len(RX_sub.get_channel_ids()))
RX_multi = se.MultiRecordingChannelExtractor(
recordings=[self.RX, self.RX2, self.RX3],
groups=[1, 2, 3]
)
RX_sub = se.SubRecordingExtractor(RX_multi, channel_ids=[4, 5, 6, 7], renamed_channel_ids=[0, 1, 2, 3])
check_recordings_equal(self.RX2, RX_sub)
check_recordings_equal(self.RX, RX_multi.recordings[0])
check_recordings_equal(self.RX2, RX_multi.recordings[1])
check_recordings_equal(self.RX3, RX_multi.recordings[2])
self.assertEqual([2, 2, 2, 2], list(RX_sub.get_channel_groups()))
self.assertEqual(12, len(RX_multi.get_channel_ids()))
rx1 = self.RX
rx2 = self.RX2
rx3 = self.RX3
rx2.set_channel_property(0, "foo", 100)
rx3.set_channel_locations([11, 11], channel_ids=0)
RX_multi_c = se.MultiRecordingChannelExtractor(
recordings=[rx1, rx2, rx3],
groups=[0, 0, 1]
)
self.assertTrue(np.array_equal([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], RX_multi_c.get_channel_ids()))
self.assertTrue(np.array_equal([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], RX_multi_c.get_channel_groups()))
self.assertEqual(rx2.get_channel_property(0, "foo"), RX_multi_c.get_channel_property(4, "foo"))
self.assertTrue(np.array_equal(rx3.get_channel_locations([0])[0], RX_multi_c.get_channel_locations([8])[0]))
def test_neuroscope_extractors(self):
# NeuroscopeRecordingExtractor tests
nscope_dir = Path(self.test_dir) / 'neuroscope_rec0'
dat_file = nscope_dir / 'neuroscope_rec0.dat'
se.NeuroscopeRecordingExtractor.write_recording(self.RX, nscope_dir)
RX_ns = se.NeuroscopeRecordingExtractor(dat_file)
check_recording_return_types(RX_ns)
check_recordings_equal(self.RX, RX_ns, force_dtype='int32')
check_dumping(RX_ns)
check_recording_return_types(RX_ns)
check_recordings_equal(self.RX, RX_ns, force_dtype='int32')
check_dumping(RX_ns)
del RX_ns
# overwrite
nscope_dir = Path(self.test_dir) / 'neuroscope_rec1'
dat_file = nscope_dir / 'neuroscope_rec1.dat'
se.NeuroscopeRecordingExtractor.write_recording(recording=self.RX,
save_path=nscope_dir)
RX_ns = se.NeuroscopeRecordingExtractor(dat_file)
check_recording_return_types(RX_ns)
check_recordings_equal(self.RX, RX_ns)
check_dumping(RX_ns)
# NeuroscopeMultiRecordingTimeExtractor tests
nscope_dir = Path(self.test_dir) / "neuroscope_rec2"
dat_file = nscope_dir / "neuroscope_rec2.dat"
RX_multirecording = se.MultiRecordingTimeExtractor(
recordings=[self.RX, self.RX])
se.NeuroscopeMultiRecordingTimeExtractor.write_recording(
recording=RX_multirecording, save_path=nscope_dir)
RX_mre = se.NeuroscopeMultiRecordingTimeExtractor(
folder_path=nscope_dir)
check_recording_return_types(RX_mre)
check_recordings_equal(RX_multirecording, RX_mre)
check_dumping(RX_mre)
# NeuroscopeSortingExtractor tests
nscope_dir = Path(self.test_dir) / 'neuroscope_sort0'
sort_name = 'neuroscope_sort0'
initial_sorting_resfile = Path(
self.test_dir) / sort_name / f'{sort_name}.res'
initial_sorting_clufile = Path(
self.test_dir) / sort_name / f'{sort_name}.clu'
se.NeuroscopeSortingExtractor.write_sorting(self.SX, nscope_dir)
SX_neuroscope = se.NeuroscopeSortingExtractor(
resfile_path=initial_sorting_resfile,
clufile_path=initial_sorting_clufile)
check_sorting_return_types(SX_neuroscope)
check_sortings_equal(self.SX, SX_neuroscope)
check_dumping(SX_neuroscope)
SX_neuroscope_no_mua = se.NeuroscopeSortingExtractor(
resfile_path=initial_sorting_resfile,
clufile_path=initial_sorting_clufile,
keep_mua_units=False)
check_sorting_return_types(SX_neuroscope_no_mua)
check_dumping(SX_neuroscope_no_mua)
# Test for extra argument 'keep_mua_units' resulted in the right output
SX_neuroscope_no_mua = se.NeuroscopeSortingExtractor(
resfile_path=initial_sorting_resfile,
clufile_path=initial_sorting_clufile,
keep_mua_units=False)
check_sorting_return_types(SX_neuroscope_no_mua)
check_dumping(SX_neuroscope_no_mua)
num_original_units = len(SX_neuroscope.get_unit_ids())
self.assertEqual(list(SX_neuroscope.get_unit_ids()),
list(range(1, num_original_units + 1)))
self.assertEqual(list(SX_neuroscope_no_mua.get_unit_ids()),
list(range(1, num_original_units)))
# Tests for the auto-detection of format for NeuroscopeSortingExtractor
SX_neuroscope_from_fp = se.NeuroscopeSortingExtractor(
folder_path=nscope_dir)
check_sorting_return_types(SX_neuroscope_from_fp)
check_sortings_equal(self.SX, SX_neuroscope_from_fp)
check_dumping(SX_neuroscope_from_fp)
# Tests for the NeuroscopeMultiSortingExtractor
nscope_dir = Path(self.test_dir) / 'neuroscope_sort1'
SX_multisorting = se.MultiSortingExtractor(sortings=[self.SX, self.SX])
se.NeuroscopeMultiSortingExtractor.write_sorting(
SX_multisorting, nscope_dir)
SX_neuroscope_mse = se.NeuroscopeMultiSortingExtractor(nscope_dir)
check_sorting_return_types(SX_neuroscope_mse)
check_sortings_equal(SX_multisorting, SX_neuroscope_mse)
check_dumping(SX_neuroscope_mse)
