def test__int(self):
name = 'test__int'
datatype = int
targ = np.random.randint(100)
res = get_fake_value(name, datatype, seed=0)
self.assertTrue(isinstance(res, int))
self.assertEqual(targ, res)
def test__float(self):
name = 'test__float'
datatype = float
targ = 1000. * np.random.random()
res = get_fake_value(name, datatype, seed=0)
self.assertTrue(isinstance(res, float))
self.assertEqual(targ, res)
def test__str(self):
name = 'test__str'
datatype = str
targ = str(np.random.randint(100000))
res = get_fake_value(name, datatype, seed=0)
self.assertTrue(isinstance(res, str))
self.assertEqual(targ, res)
def test__sampling_rate(self):
name = 'sampling_rate'
datatype = pq.Quantity
targ = 10000.0 * pq.Hz
res = get_fake_value(name, datatype)
self.assertTrue(isinstance(res, pq.Quantity))
self.assertEqual(res.units, pq.Hz)
assert_arrays_equal(targ, res)
self.assertRaises(ValueError, get_fake_value, name, datatype, dim=1)
self.assertRaises(ValueError, get_fake_value, name, np.ndarray)
def test__t_stop(self):
name = 't_stop'
datatype = pq.Quantity
targ = 1.0 * pq.millisecond
res = get_fake_value(name, datatype)
self.assertTrue(isinstance(res, pq.Quantity))
self.assertEqual(res.units, pq.millisecond)
assert_arrays_equal(targ, res)
self.assertRaises(ValueError, get_fake_value, name, datatype, dim=1)
self.assertRaises(ValueError, get_fake_value, name, np.ndarray)
def test__ndarray(self):
name = 'test__quantity'
datatype = np.ndarray
dim = 2
size = []
for i in range(int(dim)):
size.append(np.random.randint(100) + 1)
targ = np.random.random(size) * pq.millisecond
res = get_fake_value(name, datatype, dim=dim, seed=0)
self.assertTrue(isinstance(res, np.ndarray))
assert_arrays_equal(targ, res)
def test_attr_changes(self):
""" gets an object, changes its attributes, saves it, then compares how
good the changes were saved. """
iom = NeoHdf5IO(filename=self.test_file)
for obj_type in objectnames:
obj = fake_neo(obj_type=obj_type, cascade=False)
iom.save(obj)
orig_obj = iom.get(obj.hdf5_path)
for attr in obj._all_attrs:
if hasattr(orig_obj, attr[0]):
setattr(obj, attr[0], get_fake_value(*attr))
iom.save(orig_obj)
test_obj = iom.get(orig_obj.hdf5_path)
assert_objects_equivalent(orig_obj, test_obj)
def test_attr_changes(self):
""" gets an object, changes its attributes, saves it, then compares how
good the changes were saved. """
iom = NeoHdf5IO(filename=self.test_file)
for obj_type in class_by_name.keys():
obj = fake_neo(obj_type=obj_type, cascade=False)
iom.save(obj)
orig_obj = iom.get(obj.hdf5_path)
attrs = (classes_necessary_attributes[obj_type] +
classes_recommended_attributes[obj_type])
for attr in attrs:
if hasattr(orig_obj, attr[0]):
setattr(obj, attr[0], get_fake_value(*attr))
iom.save(orig_obj)
test_obj = iom.get(orig_obj.hdf5_path)
assert_objects_equivalent(orig_obj, test_obj)
def test__datetime(self):
name = 'test__datetime'
datatype = datetime
res = get_fake_value(name, datatype)
self.assertTrue(isinstance(res, datetime))