def test_brain_trial(self):
trial_name = 'subj01_1ksamples.wav'
trial = ingest.BrainTrial(trial_name)
self.assertEqual(trial.trial_name, trial_name.split('.')[0])
trial.load_sound(trial_name, sound_dir=self._test_dir)
brain_data_file_object = ingest.EdfBrainDataFile('sample.edf')
trial.load_brain_data(self._test_dir, brain_data_file_object)
summary = trial.summary_string()
self.assertIn('103 EEG channels', summary)
self.assertIn('with 66s of eeg data', summary)
self.assertIn('1.00006s of audio data', summary)
channels = [c for c in trial.iterate_brain_channels()]
self.assertLen(channels, 103)
self.assertIn('TRIG', [c.name for c in channels])
trial.compute_intensity()
trial.compute_spectrogram()
scalp_channel_names = (
'Fp1, Fp2, F3, F4, F7, F8, C3, C4, T7, T8, P3, P4, '
'P7, P8, O1, O2').split(', ')
trial.assemble_brain_data(scalp_channel_names)
tf_dir = tempfile.mkdtemp()
tf_file = trial.write_data_as_tfrecords(tf_dir)
count = ingest.count_tfrecords(tf_file)
print('Found tfrecords:', count)
feature_dict = ingest.discover_feature_shapes(tf_file)
print('Feature dict is:', feature_dict)
self.assertIn('spectrogram', feature_dict)
self.assertIn('loudness', feature_dict)
self.assertIn('eeg', feature_dict)
self.assertEqual(feature_dict['eeg'].shape, [
len(scalp_channel_names),
])
(count, errors) = ingest.count_tfrecords(tf_file)
print('count_tfrecords:', count, errors)
self.assertEqual(count, 64)
self.assertEqual(errors, 0)
def test_brain_memory_experiment2(self):
fs = 16000
audio_len = fs
audio_data = np.random.randn(audio_len)
frame_sr = 100
channel_one = np.arange(2 * frame_sr) # Use ints for easier debugging
channel_two = np.arange(2 * frame_sr) + 200
eeg_data = {'C1': channel_one, 'C2': channel_two}
df = ingest.MemoryBrainDataFile(eeg_data, frame_sr)
trial_two_name = 'trial_2'
experiment_dict = {
trial_two_name: [{
'audio_data': audio_data,
'audio_sr': fs
}, df],
}
experiment = ingest.BrainExperiment(experiment_dict,
self._test_dir,
self._test_dir,
frame_rate=frame_sr)
self.assertTrue(experiment)
experiment.load_all_data(self._test_dir, self._test_dir)
summary = experiment.summary()
self.assertIn('Found 1 trials', summary)
self.assertIn('Trial trial_2: 2 EEG channels with 2s of eeg data',
summary)
for trial in experiment.iterate_trials():
trial.compute_intensity()
trial.fix_eeg_offset(1.0)
trial.assemble_brain_data(list(eeg_data.keys()))
# Master copy of EEG data has moved from brain_data to audio_features dict
brain_data = trial.audio_features['eeg']
# Now the eeg size is shorter, due to fix_eeg_offset above.
self.assertEqual(brain_data.shape, (frame_sr, 2))
tmp_dir = '/tmp'
experiment.write_all_data(tmp_dir)
tf_file = os.path.join(tmp_dir, trial_two_name + '.tfrecords')
(count, error) = ingest.count_tfrecords(tf_file)
self.assertEqual(error, 0)
self.assertEqual(count, frame_sr)
file_data = ingest.read_tfrecords(tf_file)
print('Read in data and found keys:', list(file_data.keys()))
self.assertIn('eeg', file_data)
self.assertIn('loudness', file_data)
np.testing.assert_allclose(
file_data['eeg'],
np.hstack((np.reshape(channel_one[frame_sr:], (-1, 1)),
np.reshape(channel_two[frame_sr:], (-1, 1)))))
def test_brain_trial(self):
trial_name = 'meg/subj01_1ksamples.wav'
trial = ingest.BrainTrial(trial_name)
self.assertEqual(trial.trial_name, trial_name.split('.')[0])
trial.load_sound(trial_name, sound_dir=self._test_dir)
brain_data_file_object = ingest.EdfBrainDataFile('sample.edf')
trial.load_brain_data(self._test_dir, brain_data_file_object)
print(trial.summary_string)
summary = trial.summary_string()
self.assertIn('103 EEG channels', summary)
self.assertIn('with 66s of eeg data', summary)
self.assertIn('1.00006s of audio data', summary)
channels = [c for c in trial.iterate_brain_channels()]
self.assertLen(channels, 103)
self.assertIn('TRIG', [c.name for c in channels])
scalp_channel_names = (
'TRIG, Fp2, F3, F4, F7, F8, C3, C4, T7, T8, P3, P4, '
'P7, P8, O1, O2').split(', ')
self.assertNotIn('eeg', trial.model_features)
trial.assemble_brain_data(scalp_channel_names)
self.assertIn('eeg', trial.model_features)
self.assertLen(trial.model_features['eeg'][0, :],
len(scalp_channel_names))
print('trial.model_features channel 0:',
trial.model_features['eeg'][:100, 0])
print('trial.model_features channel last:',
trial.model_features['eeg'][:100, -1])
tf_dir = tempfile.mkdtemp()
tf.io.gfile.makedirs(os.path.join(tf_dir, 'meg'))
tf_file = trial.write_data_as_tfrecords(tf_dir)
feature_dict = ingest.discover_feature_shapes(tf_file)
print('Feature dict is:', feature_dict)
self.assertIn('eeg', feature_dict)
self.assertEqual(feature_dict['eeg'].shape, [
len(scalp_channel_names),
])
(_, errors) = ingest.count_tfrecords(tf_file)
self.assertEqual(errors, 0)
with self.assertRaises(ValueError):
trial.assemble_brain_data('TRIG, FOO, BAR')
with self.assertRaises(ValueError):
trial.assemble_brain_data(['TRIG', 'TRIG', 'F3'])
def test_brain_memory_experiment(self):
fs = 16000
audio_len = 2 * fs
audio_data = np.random.randn(audio_len, 1)
frame_sr = 100
eeg_len = 2 * frame_sr
channel_one = np.arange(eeg_len) # Use ints for easier debugging
channel_two = np.arange(eeg_len) + 200
eeg_data = collections.OrderedDict(
(('C1', channel_one), ('C2', channel_two)))
df = ingest.MemoryBrainDataFile(eeg_data, frame_sr)
trial_two_name = 'trial_2'
experiment_dict = {
trial_two_name: [{
'audio_data': audio_data,
'audio_sr': fs
}, df],
}
experiment = ingest.BrainExperiment(experiment_dict,
self._test_dir,
self._test_dir,
frame_rate=frame_sr)
self.assertTrue(experiment)
experiment.load_all_data(self._test_dir, self._test_dir)
summary = experiment.summary()
self.assertIn('Found 1 trials', summary)
self.assertIn('Trial trial_2: 2 EEG channels with 2s of eeg data',
summary)
for trial in experiment.iterate_trials():
trial.assemble_brain_data(list(eeg_data.keys()))
# Master copy of EEG data has moved from brain_data to model_features dict
brain_data = trial.model_features['eeg']
self.assertEqual(brain_data.shape, (eeg_len, 2))
tmp_dir = flags.FLAGS.test_tmpdir or '/tmp'
all_ingested_files = experiment.write_all_data(tmp_dir)
self.assertLen(all_ingested_files, 1)
tf_file = os.path.join(tmp_dir, trial_two_name + '.tfrecords')
(_, error) = ingest.count_tfrecords(tf_file)
self.assertEqual(error, 0)
file_data = ingest.read_tfrecords(tf_file)
self.assertIn('eeg', file_data)
np.testing.assert_allclose(
file_data['eeg'],
np.hstack((np.reshape(channel_one[:eeg_len], (-1, 1)),
np.reshape(channel_two[:eeg_len], (-1, 1)))))