class TestHDF:
def setUp(self):
self.data = np.random.randint(1000, size=(4, 100))
self.spt = np.random.randint(0, 100, (10, )) / 200.
self.el_node = '/Subject/Session/Electrode'
self.fname = 'test.h5'
self.cell_node = self.el_node + '/cell'
self.h5f = tables.openFile(self.fname, 'a')
self.spt.sort()
atom = tables.Atom.from_dtype(self.data.dtype)
shape = self.data.shape
filter = tables.Filters(complevel=0, complib='zlib')
new_array = self.h5f.createCArray(self.el_node,
"raw",
atom,
shape,
filters=filter,
createparents=True)
self.sampfreq = 5.E3
new_array.attrs['sampfreq'] = self.sampfreq
new_array[:] = self.data
spt_array = self.h5f.createArray(self.el_node,
"cell",
self.spt,
title="",
createparents="True")
self.h5f.close()
def tearDown(self):
self.filter.close()
os.unlink(self.fname)
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_read_sp(self):
self.filter = PyTablesFilter(self.fname)
sp = self.filter.read_sp(self.el_node)
ok_((sp['data'][:] == self.data).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_spt(self):
self.filter = PyTablesFilter(self.fname)
spt = self.filter.read_spt(self.cell_node)
ok_((spt['data'] == self.spt).all())
class TestHDF:
def setUp(self):
self.data = np.random.randint(1000,size=(4, 100))
self.spt = np.random.randint(0,100, (10,))/200.
self.el_node = '/Subject/Session/Electrode'
self.fname = 'test.h5'
self.cell_node = self.el_node+'/cell'
self.h5f = tables.openFile(self.fname,'a')
self.spt.sort()
atom = tables.Atom.from_dtype(self.data.dtype)
shape = self.data.shape
filter = tables.Filters(complevel=0, complib='zlib')
new_array = self.h5f.createCArray(self.el_node, "raw", atom, shape,
filters=filter,
createparents=True)
self.sampfreq=5.E3
new_array.attrs['sampfreq']=self.sampfreq
new_array[:] = self.data
spt_array = self.h5f.createArray(self.el_node, "cell", self.spt,
title="",
createparents="True")
self.h5f.close()
def tearDown(self):
self.filter.close()
os.unlink(self.fname)
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_read_sp(self):
self.filter = PyTablesFilter(self.fname)
sp = self.filter.read_sp(self.el_node)
ok_((sp['data'][:]==self.data).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_spt(self):
self.filter = PyTablesFilter(self.fname)
spt = self.filter.read_spt(self.cell_node)
ok_((spt['data']==self.spt).all())
class TestHDF(object):
def setUp(self):
self.data = np.random.randint(1000, size=(4, 100))
self.spt = np.random.randint(0, 100, (10,)) / 200.0
self.el_node = "/Subject/Session/Electrode"
self.fname = "test.h5"
self.cell_node = self.el_node + "/cell"
self.h5f = tables.openFile(self.fname, "a")
self.spt.sort()
atom = tables.Atom.from_dtype(self.data.dtype)
shape = self.data.shape
filter = tables.Filters(complevel=0, complib="zlib")
new_array = self.h5f.createCArray(self.el_node, "raw", atom, shape, filters=filter, createparents=True)
self.sampfreq = 5.0e3
new_array.attrs["sampfreq"] = self.sampfreq
new_array[:] = self.data
self.h5f.createArray(self.el_node, "cell", self.spt, title="", createparents="True")
self.h5f.close()
def tearDown(self):
self.filter.close()
os.unlink(self.fname)
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_read_sp(self):
self.filter = PyTablesFilter(self.fname)
sp = self.filter.read_sp(self.el_node)
ok_((sp["data"][:] == self.data).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_spt(self):
self.filter = PyTablesFilter(self.fname)
spt = self.filter.read_spt(self.cell_node)
ok_((spt["data"] == self.spt).all())
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())
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)])
tempdir = tempfile.mkdtemp()
fname = os.path.join(tempdir, "test.h5")
ptfilter = PyTablesFilter(fname)
tmpl = "/Subject/Session/Electrode/Cell{cell_id}"
export.export_cells(ptfilter, tmpl, self.cells_dict)
test = []
for i in range(n_cells):
spt_dict = ptfilter.read_spt(tmpl.format(cell_id=i))
test.append((spt_dict["data"] == self.spt_data[:, i]).all())
test = np.array(test)
ptfilter.close()
os.unlink(fname)
os.rmdir(tempdir)
ok_(test.all())
return uni_metric, multi_metric
if __name__ == "__main__":
h5_fname = "simulated.h5"
h5filter = PyTablesFilter(h5_fname)
dataset = "/TestSubject/sSession01/el1"
sp_win = [-0.4, 0.8]
f_filter=None
thresh = 'auto'
type='max'
sp = h5filter.read_sp(dataset)
spt_orig = h5filter.read_spt(dataset+"/cell1_orig")
stim = h5filter.read_spt(dataset+"/stim")
sp = eval.filter_data(sp, f_filter)
spt, clust_idx, n_missing = eval.spike_clusters(sp, spt_orig,
stim,
thresh,
type, sp_win)
features = eval.calc_features(sp, spt, sp_win, ["P2P", "PCs"], [0,1])
single_metric, knearest_metric = calc_metrics(features, clust_idx)
n_total = len(spt_orig['data'])
print "Total n/o spikes:", n_total
