def manual(data, *args, **kwargs):
"""Sort spikes manually by cluster cutting
Opens a new window in which you can draw cluster of arbitrary
shape.
Notes
-----
Only two first features are plotted
"""
return manual_sort._cluster(data[:, :2])
def manual(data, *args, **kwargs):
"""Sort spikes manually by cluster cutting
Opens a new window in which you can draw cluster of arbitrary
shape.
Notes
-----
Only two first features are plotted
"""
return manual_sort._cluster(data[:, :2])
def manual(data, n_spikes='all', *args, **kwargs):
"""Sort spikes manually by cluster cutting
Opens a new window in which you can draw cluster of arbitrary
shape.
Notes
-----
Only two first features are plotted
"""
if n_spikes == 'all':
return manual_sort._cluster(data[:, :2], **kwargs)
else:
idx = np.argsort(np.random.rand(data.shape[0]))[:n_spikes]
labels_subsampled = manual_sort._cluster(data[idx, :2], **kwargs)
try:
neigh = neighbors.KNeighborsClassifier(15)
except NameError:
raise NotImplementedError(
"scikits.learn must be installed to use subsampling")
neigh.fit(data[idx, :2], labels_subsampled)
return neigh.predict(data[:, :2])
def manual(data, n_spikes='all', *args, **kwargs):
"""Sort spikes manually by cluster cutting
Opens a new window in which you can draw cluster of arbitrary
shape.
Notes
-----
Only two first features are plotted
"""
if n_spikes=='all':
return manual_sort._cluster(data[:, :2], **kwargs)
else:
idx = np.argsort(np.random.rand(data.shape[0]))[:n_spikes]
labels_subsampled = manual_sort._cluster(data[idx, :2], **kwargs)
try:
neigh = neighbors.KNeighborsClassifier(15)
except NameError:
raise NotImplementedError(
"scikits.learn must be installed to use subsampling")
neigh.fit(data[idx, :2], labels_subsampled)
return neigh.predict(data[:, :2])
