def test_spikecache_1():
nspikes = 100000
nclusters = 100
nchannels = 8
spike_clusters = np.random.randint(size=nspikes, low=0, high=nclusters)
sc = SpikeCache(spike_clusters=spike_clusters,
cache_fraction=.1,
features_masks=np.zeros((nspikes, 3*nchannels, 2)),
waveforms_raw=np.zeros((nspikes, 20, nchannels)),
waveforms_filtered=np.zeros((nspikes, 20, nchannels)))
ind, fm = sc.load_features_masks(.1)
assert len(ind) == nspikes // 100
assert fm.shape[0] == nspikes // 100
ind, fm = sc.load_features_masks(clusters=[10, 20])
assert len(ind) == fm.shape[0]
assert np.allclose(ind, np.nonzero(np.in1d(spike_clusters, (10, 20)))[0])
ind, fm = sc.load_features_masks(clusters=[1000])
assert len(ind) == 0
assert len(fm) == 0
def init_cache(self):
"""Initialize the cache for the features & masks."""
self._spikecache = SpikeCache(
# TODO: handle multiple clusterings in the spike cache here
spike_clusters=self.clusters.main,
features_masks=self.features_masks,
waveforms_raw=self.waveforms_raw,
waveforms_filtered=self.waveforms_filtered,
# TODO: put this value in the parameters
cache_fraction=1.,)
def test_spikecache_2():
nspikes = 100000
nclusters = 100
nchannels = 8
spike_clusters = np.random.randint(size=nspikes, low=0, high=nclusters)
sc = SpikeCache(spike_clusters=spike_clusters,
cache_fraction=.1,
features_masks=np.zeros((nspikes, 3*nchannels, 2)),
waveforms_raw=np.zeros((nspikes, 20, nchannels)),
waveforms_filtered=np.zeros((nspikes, 20, nchannels)))
ind, waveforms = sc.load_waveforms(clusters=[10], count=10)
assert len(ind) == waveforms.shape[0]
assert len(ind) >= 10
ind, waveforms = sc.load_waveforms(clusters=[10, 20], count=10)
assert len(ind) == waveforms.shape[0]
assert len(ind) >= 20
ind, waveforms = sc.load_waveforms(clusters=[1000], count=10)
assert len(ind) == 0
