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_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_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_write_spt_metadata(self):
cell_node_tmp = '/Test/s32test01/el2/cell1'
spt_dict = {'data':self.spt_data,
'metadata':self.spt_metadata}
filter = BakerlabFilter(self.conf_file)
filter.write_spt(spt_dict, cell_node_tmp, overwrite=True)
files_eq = filecmp.cmp(self.spt_log_fname,"32test0121.log", shallow=0)
os.unlink("32test0121.spt")
os.unlink("32test0121.log")
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_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)
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 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 / 200.0).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)
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 __init__(self, conf_file, dataset, overwrite=False, f_filter=None):
GenericSource.__init__(self, dataset, overwrite, f_filter)
BakerlabFilter.__init__(self, conf_file)
def __init__(self, conf_file, dataset, overwrite=False, f_filter=None):
GenericSource.__init__(self, dataset, overwrite, f_filter)
BakerlabFilter.__init__(self, conf_file)
from spike_sort.io.filters import PyTablesFilter, BakerlabFilter
from spike_sort.io import export
DATAPATH = "../data"
if __name__ == "__main__":
dataset = "/Gollum/s39gollum01/el1"
cell_templ = "/Gollum/s39gollum01/el1/cell{cell_id}"
stim_node = "/".join(dataset.split("/")[:3] + ["stim"])
sp_win = [-0.2, 0.8]
start = time.time()
io_filter = BakerlabFilter("../data/gollum.inf")
export_filter = PyTablesFilter("../data/exported.h5")
sp = io_filter.read_sp(dataset, memmap="none")
spt = sort.extract.detect_spikes(sp, contact=3, thresh="auto")
spt = sort.extract.align_spikes(sp, spt, sp_win, type="max", contact=3, resample=10)
sp_waves = sort.extract.extract_spikes(sp, spt, sp_win)
features = sort.features.combine(
(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))
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())
#cell_db = '../cell_bwoser/src/cell_db.h5'
#DATAPATH= '/Volumes/PortableData/Data/dmytro/'
DATAPATH = os.environ['DATAPATH']
#output_fname = 'exported_cells.csv'
_, cell_db, output_fname = sys.argv
if __name__ == "__main__":
records = []
h5f = tables.openFile(cell_db)
cell_csv = csv2rec(DATAPATH+'cell_db.csv')
data_filter = BakerlabFilter(DATAPATH+'gollum_export.inf')
for cellid in cell_csv['id']:
node = h5f.getNode('/', cellid)
cell_path = "/".join(cellid.split('/'))
stim_path = "/".join(cellid.split('/')[:-1]+['stim'])
try:
events = node.cache.events.read()
n_events = len(events)-1
except NoSuchNodeError:
n_events = 1
try:
score = int(node.cache.score[0])
except NoSuchNodeError:
score = 0
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
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()
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 / 200.0).all())
#!/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
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()
