(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()