for cellid in cell_csv['id']:
node = h5f.getNode('/', cellid)
cell_path = "/".join(cellid.split('/'))
stim_path = "/".join(cellid.split('/')[:-1]+['stim'])
try:
events = node.cache.events.read()
n_events = len(events)-1
except NoSuchNodeError:
n_events = 1
try:
score = int(node.cache.score[0])
except NoSuchNodeError:
score = 0
spt = data_filter.read_spt(cell_path)
stim = data_filter.read_spt(stim_path)
n_spikes = len(spt['data'])
n_trials = len(stim['data'])
try:
psth, psth_time = patterns.CalcPSTH(spt['data'],
stim['data'],
norm=True)
psth_peak = psth.max()
except IndexError:
psth_peak = np.NaN
stim_start = 0
stim_stop = -1
records.append((cell_path, n_events, n_spikes, n_trials,
psth_peak, stim_start, stim_stop, score))
def test_read_spt(self):
filter = BakerlabFilter(self.conf_file)
sp = filter.read_spt(self.cell_node)
read_data = sp["data"]
ok_((np.abs(read_data - self.spt_data) <= 1 / 200.0).all())
features = sort.features.combine(
(sort.features.fetP2P(sp_waves, contacts=[0, 1, 2, 3]), sort.features.fetPCs(sp_waves, ncomps=1)),
normalize=True,
)
clust_idx = sort.cluster.cluster("gmm", features, 5)
features = sort.features.combine((sort.features.fetSpIdx(sp_waves), features))
spike_sort.ui.plotting.plot_features(features, clust_idx)
spike_sort.ui.plotting.figure()
spike_sort.ui.plotting.plot_spikes(sp_waves, clust_idx, n_spikes=200)
spt_cells = sort.cluster.split_cells(spt, clust_idx)
features_cells = sort.features.split_cells(features, clust_idx)
spikes_cells = sort.extract.split_cells(sp_waves, clust_idx)
stim = io_filter.read_spt(dataset)
io_filter.close()
from matplotlib.pyplot import figure, show
color_map = spike_sort.ui.plotting.label_color(np.unique(spt_cells.keys()))
for i in spt_cells.keys():
# plotPSTH(spt_cells[i]['data'], stim['data'],
# color=color_map(i),
# label="cell {0}".format(i))
figure()
dashboard.single_cell(stim, spt_cells[i], color=color_map(i))
# figure()
# spike_sort.ui.plotting.legend(spt_cells.keys(),
# color_map(spt_cells.keys()))
show()
def test_read_spt(self):
filter = BakerlabFilter(self.conf_file)
sp = filter.read_spt(self.cell_node)
read_data = sp['data']
ok_((np.abs(read_data - self.spt_data) <= 1 / 200.).all())
