def test_write_sp(self):
el_node_tmp = '/Test/s32test01/el2'
sp_dict = {'data': self.data[np.newaxis, :]}
filter = BakerlabFilter(self.conf_file)
filter.write_sp(sp_dict, el_node_tmp)
files_eq = filecmp.cmp("32test011.sp", "32test012.sp", shallow=0)
os.unlink("32test012.sp")
ok_(files_eq)
def test_write_spt(self):
cell_node_tmp = '/Test/s32test01/el2/cell1'
spt_dict = {'data': self.spt_data}
filter = BakerlabFilter(self.conf_file)
filter.write_spt(spt_dict, cell_node_tmp)
files_eq = filecmp.cmp(self.spt_fname, "32test0121.spt", shallow=0)
os.unlink("32test0121.spt")
ok_(files_eq)
def test_sp_shape(self):
with open(self.conf_file, 'r+') as fid:
file_desc = json.load(fid)
file_desc['n_contacts'] = 4
fid.seek(0)
json.dump(file_desc, fid)
filter = BakerlabFilter(self.conf_file)
sp = filter.read_sp(self.el_node)
data = sp['data']
ok_(data.shape == (4, len(self.data)))
def test_write_multichan(self):
n_contacts = 4
data = np.repeat(self.data[np.newaxis, :], n_contacts, 0)
sp_dict = {'data': data}
with open(self.conf_file, 'r+') as fid:
file_desc = json.load(fid)
file_desc['n_contacts'] = 4
file_desc["fspike"] = "test{contact_id}.sp"
fid.seek(0)
json.dump(file_desc, fid)
filter = BakerlabFilter(self.conf_file)
filter.write_sp(sp_dict, self.el_node)
all_chan_files = glob.glob("test?.sp")
[os.unlink(p) for p in all_chan_files]
eq_(len(all_chan_files), n_contacts)
#!/usr/bin/env python
#coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import sys
from spike_analysis import dashboard
from spike_sort.io.filters import BakerlabFilter
if __name__ == "__main__":
cell = "/Gollum/s39gollum03/el1/cell1"
filter = BakerlabFilter("../../data/gollum_export.inf")
dashboard.show_cell(filter, cell)
plt.show()
#!/usr/bin/env python
#coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
from spike_sort.io.filters import BakerlabFilter
from spike_analysis import io_tools, xcorr
cell_pattern = "/Gollum/s4gollum*/el*/cell*"
filter = BakerlabFilter("../../data/gollum.inf")
if __name__ == "__main__":
cell_nodes = io_tools.list_cells(filter, cell_pattern)
cells = [io_tools.read_dataset(filter, node) for node in cell_nodes]
for data, name in zip(cells, cell_nodes):
data['dataset'] = name
xcorr.show_xcorr(cells)
plt.show()
#!/usr/bin/env python
#coding=utf-8
import os
import matplotlib.pyplot as plt
from spike_analysis import dashboard
from spike_sort.io.filters import BakerlabFilter
if __name__ == "__main__":
cell = "/Gollum/s39gollum03/el1/cell1"
path = os.path.join(os.pardir, os.pardir, 'data', 'gollum_export.inf')
filt = BakerlabFilter(path)
dashboard.show_cell(filt, cell)
plt.show()
def test_read_sp(self):
filter = BakerlabFilter(self.conf_file)
sp = filter.read_sp(self.el_node)
read_data = sp['data'][0, :]
print read_data.shape
ok_((np.abs(read_data - self.data) <= 1 / 200.).all())
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())
def test_writespt_overwrite_exc(self):
cell_node_tmp = '/Test/s32test01/el1/cell1'
spt_dict = {'data': self.spt_data}
filter = BakerlabFilter(self.conf_file)
filter.write_spt(spt_dict, cell_node_tmp)
#!/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()
