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()
