(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()
# dashboard.all_cells(stim, spt_cells, color_map)
#!/usr/bin/env python
#coding=utf-8
from spike_sort.io.filters import PyTablesFilter, BakerlabFilter
in_dataset = "/Gollum/s5gollum01/el3"
out_dataset = "/SubjectA/session01/el1/raw"
in_filter = BakerlabFilter("gollum.inf")
out_filter = PyTablesFilter("tutorial.h5")
sp = in_filter.read_sp(in_dataset)
out_filter.write_sp(sp, out_dataset)
in_filter.close()
out_filter.close()
