def test_is_valid(self):
tsr = TimeSeriesReference(
0, 10,
TimeSeries(name='test' + str(0),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
self.assertTrue(tsr.isvalid())
def test_get_length1_invalid_data(self):
"""Get data from a TimeSeriesReferenceVectorData with one element and invalid data"""
temp = TimeSeriesReferenceVectorData()
value = TimeSeriesReference(
-1, -1,
TimeSeries(name='test',
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
temp.append(value)
# test index slicing
re = temp[0]
self.assertTrue(
isinstance(
re, TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE))
self.assertTupleEqual(
re, TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE)
# test array slicing and list slicing
selection = [slice(None), [
0,
]]
for s in selection:
re = temp[s]
self.assertTrue(isinstance(re, list))
self.assertTrue(len(re), 1)
self.assertTrue(
isinstance(
re[0],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE))
self.assertTupleEqual(
re[0],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE)
def test_data_property_invalid_reference(self):
tsr = TimeSeriesReference(
-1, -1,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
self.assertIsNone(tsr.data)
def test_data_property(self):
tsr = TimeSeriesReference(
5, 4,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
np.testing.assert_array_equal(tsr.data, np.array([5., 6., 7., 8.]))
def test_data_property_bad_reference(self):
tsr = TimeSeriesReference(
0, 12,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
IndexError,
"'idx_start + count' out of range for timeseries 'test0'"):
tsr.data
def test_timestamps_property(self):
# Timestamps from starting_time and rate
tsr = TimeSeriesReference(
5, 4,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
np.testing.assert_array_equal(tsr.timestamps,
np.array([5.5, 5.6, 5.7, 5.8]))
# Timestamps from timestamps directly
tsr = TimeSeriesReference(
5, 4,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
timestamps=np.arange(10).astype(float)))
np.testing.assert_array_equal(tsr.timestamps,
np.array([5., 6., 7., 8.]))
def test_get_length1_valid_data(self):
"""Get data from a TimeSeriesReferenceVectorData with one element and valid data"""
temp = TimeSeriesReferenceVectorData()
value = TimeSeriesReference(
0, 5,
TimeSeries(name='test',
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
temp.append(value)
self.assertTupleEqual(temp[0], value)
self.assertListEqual(temp[:], [
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(*value),
])
def test_get_length5_valid_data(self):
"""Get data from a TimeSeriesReferenceVectorData with 5 elements"""
temp = TimeSeriesReferenceVectorData()
num_values = 5
values = [
TimeSeriesReference(
0, 5,
TimeSeries(name='test' + str(i),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1)) for i in range(num_values)
]
for v in values:
temp.append(v)
# Test single element selection
for i in range(num_values):
# test index slicing
re = temp[i]
self.assertTupleEqual(re, values[i])
# test slicing
re = temp[i:i + 1]
self.assertTupleEqual(
re[0],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(
*values[i]))
# Test multi element selection
re = temp[0:2]
self.assertTupleEqual(
re[0],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(
*values[0]))
self.assertTupleEqual(
re[1],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(
*values[1]))
def test_is_valid_bad_index(self):
# Error: negative start_index but positive count
tsr = TimeSeriesReference(
-1, 10,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
IndexError,
"'idx_start' -1 out of range for timeseries 'test0'"):
tsr.isvalid()
# Error: start_index too large
tsr = TimeSeriesReference(
10, 0,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
IndexError,
"'idx_start' 10 out of range for timeseries 'test0'"):
tsr.isvalid()
# Error: positive start_index but negative count
tsr = TimeSeriesReference(
0, -3,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
IndexError, "'count' -3 invalid. 'count' must be positive"):
tsr.isvalid()
# Error: start_index + count too large
tsr = TimeSeriesReference(
3, 10,
TimeSeries(name='test0',
description='test0',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
IndexError,
"'idx_start + count' out of range for timeseries 'test0'"):
tsr.isvalid()
def test_check_types(self):
# invalid selection but with correct types
tsr = TimeSeriesReference(
-1, -1,
TimeSeries(name='test' + str(0),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
self.assertTrue(tsr.check_types())
# invalid types, use float instead of int for both idx_start and count
tsr = TimeSeriesReference(
1.0, 5.0,
TimeSeries(name='test' + str(0),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
TypeError, "idx_start must be an integer not <class 'float'>"):
tsr.check_types()
# invalid types, use float instead of int for idx_start only
tsr = TimeSeriesReference(
1.0, 5,
TimeSeries(name='test' + str(0),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(
TypeError, "idx_start must be an integer not <class 'float'>"):
tsr.check_types()
# invalid types, use float instead of int for count only
tsr = TimeSeriesReference(
1, 5.0,
TimeSeries(name='test' + str(0),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1))
with self.assertRaisesWith(TypeError,
"count must be an integer <class 'float'>"):
tsr.check_types()
# invalid type for TimeSeries but valid idx_start and count
tsr = TimeSeriesReference(1, 5, None)
with self.assertRaisesWith(
TypeError,
"timeseries must be of type TimeSeries. <class 'NoneType'>"):
tsr.check_types()
def test_get_length5_with_invalid_data(self):
"""Get data from a TimeSeriesReferenceVectorData with 5 elements"""
temp = TimeSeriesReferenceVectorData()
num_values = 5
values = [
TimeSeriesReference(
0, 5,
TimeSeries(name='test' + str(i + 1),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1)) for i in range(num_values - 2)
]
values = ([
TimeSeriesReference(
-1, -1,
TimeSeries(name='test' + str(0),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1)),
] + values + [
TimeSeriesReference(
-1, -1,
TimeSeries(name='test' + str(5),
description='test',
data=np.arange(10),
unit='unit',
starting_time=5.0,
rate=0.1)),
])
for v in values:
temp.append(v)
# Test single element selection
for i in range(num_values):
# test index slicing
re = temp[i]
if i in [0, 4]:
self.assertTrue(
isinstance(
re, TimeSeriesReferenceVectorData.
TIME_SERIES_REFERENCE_TUPLE))
self.assertTupleEqual(
re, TimeSeriesReferenceVectorData.
TIME_SERIES_REFERENCE_NONE_TYPE)
else:
self.assertTupleEqual(re, values[i])
# test slicing
re = temp[i:i + 1]
if i in [0, 4]:
self.assertTrue(
isinstance(
re[0], TimeSeriesReferenceVectorData.
TIME_SERIES_REFERENCE_TUPLE))
self.assertTupleEqual(
re[0], TimeSeriesReferenceVectorData.
TIME_SERIES_REFERENCE_NONE_TYPE)
else:
self.assertTupleEqual(
re[0],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(
*values[i]))
# Test multi element selection
re = temp[0:2]
self.assertTupleEqual(
re[0],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_NONE_TYPE)
self.assertTupleEqual(
re[1],
TimeSeriesReferenceVectorData.TIME_SERIES_REFERENCE_TUPLE(
*values[1]))