def test_write(self): sp_dict = {'data': self.data, 'FS': self.sampfreq} spt_dict = {'data': self.spt} self.filter = PyTablesFilter("test2.h5") self.filter.write_sp(sp_dict, self.el_node + "/raw") self.filter.write_spt(spt_dict, self.cell_node) self.filter.close() exit_code = os.system('h5diff ' + self.fname + ' test2.h5') os.unlink("test2.h5") ok_(exit_code == 0)
def test_export_cells(self): n_cells = 4 self.spt_data = np.random.randint(0, 10000, (100, n_cells)) self.spt_data.sort(0) self.cells_dict = dict([(i, { "data": self.spt_data[:, i] }) for i in range(n_cells)]) fname = os.path.join(tempfile.mkdtemp(), "test.h5") filter = PyTablesFilter(fname) tmpl = "/Subject/Session/Electrode/Cell{cell_id}" export.export_cells(filter, tmpl, self.cells_dict) test = [] for i in range(n_cells): spt_dict = filter.read_spt(tmpl.format(cell_id=i)) test.append((spt_dict['data'] == self.spt_data[:, i]).all()) test = np.array(test) filter.close() os.unlink(fname) ok_(test.all())
#!/usr/bin/env python #coding=utf-8 """ Simple raw data browser. Keyboard shortcuts: +/- - zoom in/out """ import spike_sort as sort from spike_sort.io.filters import PyTablesFilter from spike_sort.ui import spike_browser import os DATAPATH = os.environ['DATAPATH'] if __name__ == "__main__": dataset = "/SubjectA/session01/el1" data_fname = os.path.join(DATAPATH, "tutorial.h5") io_filter = PyTablesFilter(data_fname) sp = io_filter.read_sp(dataset) spt = sort.extract.detect_spikes(sp, contact=3, thresh='auto') spike_browser.browse_data_tk(sp, spt, win=50)
#!/usr/bin/env python #coding=utf-8 from spike_sort.io.filters import PyTablesFilter from spike_sort import extract from spike_sort import features from spike_sort import cluster from spike_sort.ui import plotting import os dataset = '/SubjectA/session01/el1' datapath = '../../../data/tutorial.h5' io_filter = PyTablesFilter(datapath) raw = io_filter.read_sp(dataset) spt = extract.detect_spikes(raw, contact=3, thresh='auto') sp_win = [-0.2, 0.8] spt = extract.align_spikes(raw, spt, sp_win, type="max", resample=10) sp_waves = extract.extract_spikes(raw, spt, sp_win) sp_feats = features.combine( (features.fetP2P(sp_waves), features.fetPCs(sp_waves))) clust_idx = cluster.cluster("gmm", sp_feats, 4) plotting.plot_features(sp_feats, clust_idx) plotting.show() io_filter.close()
""" import os import matplotlib matplotlib.use("TkAgg") matplotlib.interactive(True) import spike_sort as sort from spike_sort.io.filters import PyTablesFilter DATAPATH = os.environ['DATAPATH'] if __name__ == "__main__": h5_fname = os.path.join(DATAPATH, "tutorial.h5") h5filter = PyTablesFilter(h5_fname, 'r') dataset = "/SubjectA/session01/el1" sp_win = [-0.2, 0.8] sp = h5filter.read_sp(dataset) spt = sort.extract.detect_spikes(sp, contact=3, thresh=300) spt = sort.extract.align_spikes(sp, spt, sp_win, type="max", resample=10) sp_waves = sort.extract.extract_spikes(sp, spt, sp_win) features = sort.features.combine( (sort.features.fetSpIdx(sp_waves), sort.features.fetP2P(sp_waves), sort.features.fetPCA(sp_waves)), norm=True) clust_idx = sort.ui.manual_sort.manual_sort(features,
def test_read_spt(self): self.filter = PyTablesFilter(self.fname) spt = self.filter.read_spt(self.cell_node) ok_((spt['data'] == self.spt).all())
def test_read_sp_attr(self): #check n_contacts attribute self.filter = PyTablesFilter(self.fname) sp = self.filter.read_sp(self.el_node) n_contacts = sp['n_contacts'] ok_(n_contacts == self.data.shape[0])
def test_read_sp(self): self.filter = PyTablesFilter(self.fname) sp = self.filter.read_sp(self.el_node) ok_((sp['data'][:] == self.data).all())
#!/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()