def raw_dataset(request):
sample_rate = 20000
curdir = op.realpath(op.dirname(__file__))
path = op.join(curdir, 'traces/default_settings.py')
params = _read_python(path)['spikedetekt']
data_type = request.param
if data_type == 'real':
path = download_test_data('test-32ch-10s.dat')
traces = np.fromfile(path, dtype=np.int16).reshape((200000, 32))
traces = traces[:45000]
n_samples, n_channels = traces.shape
params['use_single_threshold'] = False
probe = load_probe('1x32_buzsaki')
else:
probe = {
'channel_groups': {
0: {
'channels': [0, 1, 2],
'graph': [[0, 1], [0, 2], [1, 2]],
},
1: {
'channels': [3],
'graph': [],
'geometry': {
3: [0., 0.]
},
}
}
}
if data_type == 'null':
n_samples, n_channels = 25000, 4
traces = np.zeros((n_samples, n_channels))
elif data_type == 'artificial':
n_samples, n_channels = 25000, 4
traces = artificial_traces(n_samples, n_channels)
traces[5000:5010, 1] *= 5
traces[15000:15010, 3] *= 5
n_samples_w = params['extract_s_before'] + params['extract_s_after']
yield Bunch(
n_channels=n_channels,
n_samples=n_samples,
sample_rate=sample_rate,
n_samples_waveforms=n_samples_w,
traces=traces,
params=params,
probe=probe,
)
def raw_dataset(request):
sample_rate = 20000
curdir = op.realpath(op.dirname(__file__))
path = op.join(curdir, 'traces/default_settings.py')
params = _read_python(path)['spikedetekt']
data_type = request.param
if data_type == 'real':
path = download_test_data('test-32ch-10s.dat')
traces = np.fromfile(path, dtype=np.int16).reshape((200000, 32))
traces = traces[:45000]
n_samples, n_channels = traces.shape
params['use_single_threshold'] = False
probe = load_probe('1x32_buzsaki')
else:
probe = {'channel_groups': {
0: {'channels': [0, 1, 2],
'graph': [[0, 1], [0, 2], [1, 2]],
},
1: {'channels': [3],
'graph': [],
'geometry': {3: [0., 0.]},
}
}}
if data_type == 'null':
n_samples, n_channels = 25000, 4
traces = np.zeros((n_samples, n_channels))
elif data_type == 'artificial':
n_samples, n_channels = 25000, 4
traces = artificial_traces(n_samples, n_channels)
traces[5000:5010, 1] *= 5
traces[15000:15010, 3] *= 5
n_samples_w = params['extract_s_before'] + params['extract_s_after']
yield Bunch(n_channels=n_channels,
n_samples=n_samples,
sample_rate=sample_rate,
n_samples_waveforms=n_samples_w,
traces=traces,
params=params,
probe=probe,
)
def model(tempdir):
model = Bunch()
n_spikes = 51
n_samples_w = 31
n_samples_t = 20000
n_channels = 11
n_clusters = 3
n_features = 4
model.path = op.join(tempdir, 'test')
model.n_channels = n_channels
# TODO: test with permutation and dead channels
model.channel_order = None
model.n_spikes = n_spikes
model.sample_rate = 20000.
model.duration = n_samples_t / float(model.sample_rate)
model.spike_times = artificial_spike_samples(n_spikes) * 1.
model.spike_times /= model.spike_times[-1]
model.spike_clusters = artificial_spike_clusters(n_spikes, n_clusters)
model.cluster_ids = np.unique(model.spike_clusters)
model.channel_positions = staggered_positions(n_channels)
model.all_waveforms = artificial_waveforms(n_spikes, n_samples_w,
n_channels)
model.all_masks = artificial_masks(n_spikes, n_channels)
model.all_traces = artificial_traces(n_samples_t, n_channels)
model.all_features = artificial_features(n_spikes, n_channels, n_features)
# features_masks array
f = model.all_features.reshape((n_spikes, -1))
m = np.repeat(model.all_masks, n_features, axis=1)
model.all_features_masks = np.dstack((f, m))
model.spikes_per_cluster = _spikes_per_cluster(model.spike_clusters)
model.n_features_per_channel = n_features
model.n_samples_waveforms = n_samples_w
model.cluster_groups = {c: None for c in range(n_clusters)}
return model
def model(tempdir):
model = Bunch()
n_spikes = 51
n_samples_w = 31
n_samples_t = 20000
n_channels = 11
n_clusters = 3
n_features = 4
model.path = op.join(tempdir, 'test')
model.n_channels = n_channels
# TODO: test with permutation and dead channels
model.channel_order = None
model.n_spikes = n_spikes
model.sample_rate = 20000.
model.duration = n_samples_t / float(model.sample_rate)
model.spike_times = artificial_spike_samples(n_spikes) * 1.
model.spike_times /= model.spike_times[-1]
model.spike_clusters = artificial_spike_clusters(n_spikes, n_clusters)
model.cluster_ids = np.unique(model.spike_clusters)
model.channel_positions = staggered_positions(n_channels)
model.all_waveforms = artificial_waveforms(n_spikes, n_samples_w,
n_channels)
model.all_masks = artificial_masks(n_spikes, n_channels)
model.all_traces = artificial_traces(n_samples_t, n_channels)
model.all_features = artificial_features(n_spikes, n_channels, n_features)
# features_masks array
f = model.all_features.reshape((n_spikes, -1))
m = np.repeat(model.all_masks, n_features, axis=1)
model.all_features_masks = np.dstack((f, m))
model.spikes_per_cluster = _spikes_per_cluster(model.spike_clusters)
model.n_features_per_channel = n_features
model.n_samples_waveforms = n_samples_w
model.cluster_groups = {c: None for c in range(n_clusters)}
return model
