def test_spike_detect_real_data(spikedetekt):
with TemporaryDirectory() as tempdir:
# Set the parameters.
curdir = op.dirname(op.realpath(__file__))
default_settings_path = op.join(curdir, '../default_settings.py')
settings = _read_python(default_settings_path)
sample_rate = 20000
params = settings['spikedetekt']
params['sample_rate'] = sample_rate
n_channels = 32
npc = params['n_features_per_channel']
n_samples_w = params['extract_s_before'] + params['extract_s_after']
probe = load_probe('1x32_buzsaki')
# Load the traces.
path = _download_test_data('test-32ch-10s.dat')
traces = np.fromfile(path, dtype=np.int16).reshape((200000, 32))
# Run the detection.
sd = SpikeDetekt(tempdir=tempdir, probe=probe, **params)
out = sd.run_serial(traces, interval_samples=(0, 50000))
n_spikes = out.n_spikes_total
def _concat(arrs):
return np.concatenate(arrs)
spike_samples = _concat(out.spike_samples[0])
masks = _concat(out.masks[0])
features = _concat(out.features[0])
assert spike_samples.shape == (n_spikes,)
assert masks.shape == (n_spikes, n_channels)
assert features.shape == (n_spikes, n_channels, npc)
# There should not be any spike with only masked channels.
assert np.all(masks.max(axis=1) > 0)
# Plot...
from phy.plot.traces import plot_traces
c = plot_traces(traces[:30000, :],
spike_samples=spike_samples,
masks=masks,
n_samples_per_spike=n_samples_w,
show=False)
show_test(c)
def test_spike_detect_real_data(tempdir, raw_dataset):
params = raw_dataset.params
probe = raw_dataset.probe
sample_rate = raw_dataset.sample_rate
sd = SpikeDetekt(tempdir=tempdir,
probe=probe,
sample_rate=sample_rate,
**params)
traces = raw_dataset.traces
n_samples = raw_dataset.n_samples
npc = params['n_features_per_channel']
n_samples_w = params['extract_s_before'] + params['extract_s_after']
# Run the detection.
out = sd.run_serial(traces, interval_samples=(0, n_samples))
channels = probe['channel_groups'][0]['channels']
n_channels = len(channels)
spike_samples = _concatenate(out.spike_samples[0])
masks = _concatenate(out.masks[0])
features = _concatenate(out.features[0])
n_spikes = out.n_spikes_per_group[0]
if n_spikes:
assert spike_samples.shape == (n_spikes,)
assert masks.shape == (n_spikes, n_channels)
assert features.shape == (n_spikes, n_channels, npc)
# There should not be any spike with only masked channels.
assert np.all(masks.max(axis=1) > 0)
# Plot...
from phy.plot.traces import plot_traces
c = plot_traces(traces[:30000, channels],
spike_samples=spike_samples,
masks=masks,
n_samples_per_spike=n_samples_w,
show=False)
show_test(c)
