def test_magic_setitem_wrong_n_samples():
"""Test the setimtem for TimeData with wrong number of samples."""
time_a = TimeData([1, 0, -1], [0, .1, .3])
time_b = TimeData([2, 0, -2, 0], [0, .1, .3, .7])
with pytest.raises(ValueError):
time_a[0] = time_b
def test_data_time_setter_time():
"""Test the setter for the time data."""
data_a = [1, 0, -1]
data_b = [2, 0, -2]
times = [0, .1, .3]
time = TimeData(data_a, times)
time.time = data_b
npt.assert_allclose(time.time, np.atleast_2d(np.asarray(data_b)))
def test_magic_setitem():
"""Test the setimtem for TimeData."""
times = [0, .1, .3]
time_a = TimeData([[1, 0, -1], [1, 0, -1]], times)
time_b = TimeData([2, 0, -2], times)
time_a[0] = time_b
npt.assert_allclose(time_a.time, np.asarray([[2, 0, -2], [1, 0, -1]]))
def test___eq___notEqual():
"""Check if TimeData object is equal."""
time_data = TimeData([1, 2, 3], [0.1, 0.2, 0.3])
actual = TimeData([2, 3, 4], [0.1, 0.2, 0.3])
assert not time_data == actual
actual = TimeData([1, 2, 3], [0.2, 0.3, 0.4])
assert not time_data == actual
comment = f'{time_data.comment} A completely different thing'
actual = TimeData([1, 2, 3], [0.1, 0.2, 0.3], comment=comment)
assert not time_data == actual
def test_separation_from_signal():
"""Check if attributes from Signal are really not available."""
data = [1, 0, -1]
times = [0, .1, .3]
time = TimeData(data, times)
with pytest.raises(AttributeError):
time.sampling_rate
with pytest.raises(AttributeError):
time.domain = 'time'
def test_reshape_exceptions():
data_in = TimeData(np.random.rand(6, 256), range(256))
data_out = data_in.reshape((3, 2))
npt.assert_allclose(data_in._data.reshape(3, 2, -1), data_out._data)
# test assertion for non-tuple input
with pytest.raises(ValueError):
data_out = data_in.reshape([3, 2])
# test assertion for wrong dimension
with pytest.raises(ValueError, match='Can not reshape audio object'):
data_out = data_in.reshape((3, 4))
def test_data_time_find_nearest():
"""Test the find nearest function for a single number and list entry."""
data = [1, 0, -1]
times = [0, .1, .3]
time = TimeData(data, times)
# test for a single number
idx = time.find_nearest_time(.15)
assert idx == 1
# test for a list
idx = time.find_nearest_time([.15, .4])
npt.assert_allclose(idx, np.asarray([1, 2]))
def test_data_time_init_wrong_number_of_times():
"""Test if entering a wrong number of times raises an assertion."""
data = [1, 0, -1]
times = [0, .1]
with pytest.raises(ValueError):
TimeData(data, times)
def test_data_time_with_non_monotonously_increasing_time():
"""Test if non monotnously increasing of times raises an assertion."""
data = [1, 0, -1]
times = [0, .2, .1]
with pytest.raises(ValueError):
TimeData(data, times)
def time_data_stub(time, times):
"""Function to generate stub of pyfar TimeData class based on MagicMock.
The properties of the signal are set without any further check.
Parameters
----------
time : ndarray
Time data
times : ndarray
Times of time in second
Returns
-------
time_data
stub of pyfar TimeData class
"""
# Use MagicMock and side_effect to mock __getitem__
# See "Mocking a dictionary with MagicMock",
# https://het.as.utexas.edu/HET/Software/mock/examples.html
def getitem(slice):
time = np.atleast_2d(time_data.time[slice])
item = time_data_stub(time, time_data.times)
return item
time_data = mock.MagicMock(spec_set=TimeData(time, times))
time_data.time = np.atleast_2d(time)
time_data.times = np.atleast_1d(times)
time_data.domain = 'time'
time_data.n_samples = time_data.time.shape[-1]
time_data.cshape = time_data.time.shape[:-1]
time_data.__getitem__.side_effect = getitem
return time_data
def test_overloaded_operators_time_data():
x = TimeData([2, 1, 0], [0, 1, 2])
y = TimeData([2, 2, 2], [0, 1, 2])
# addition
z = x + y
npt.assert_allclose(z.time, np.array([4, 3, 2], ndmin=2), atol=1e-15)
# subtraction
z = x - y
npt.assert_allclose(z.time, np.array([0, -1, -2], ndmin=2), atol=1e-15)
# multiplication
z = x * y
npt.assert_allclose(z.time, np.array([4, 2, 0], ndmin=2), atol=1e-15)
# division
z = x / y
npt.assert_allclose(z.time, np.array([1, .5, 0], ndmin=2), atol=1e-15)
# power
z = x**y
npt.assert_allclose(z.time, np.array([4, 1, 0], ndmin=2), atol=1e-15)
def time_data():
"""
TimeData object with three data points.
Returns
-------
time_data TimeData
Data
"""
time_data = TimeData([1, 0, -1], [0, .1, .4])
return time_data
def test_time_data_stub_properties():
""" Test comparing properties of TimeData stub
with actual TimeData implementation.
"""
time = [1, 0, -1]
times = [0, .1, .4]
time_data_stub = stub_utils.time_data_stub(time, times)
stub_dir = dir(time_data_stub)
time_data_dir = dir(TimeData(time, times))
assert stub_dir.sort() == time_data_dir.sort()
def test_add_time_data_and_time_data():
# generate and add signals
x = TimeData([1, 0, 0], [0, .1, .5])
y = signal.add((x, x), 'time')
# check if old signal did not change
npt.assert_allclose(x.time, np.atleast_2d([1, 0, 0]), atol=1e-15)
npt.assert_allclose(x.times, np.atleast_1d([0, .1, .5]), atol=1e-15)
# check result
assert isinstance(y, TimeData)
npt.assert_allclose(y.time, np.atleast_2d([2, 0, 0]), atol=1e-15)
npt.assert_allclose(y.times, np.atleast_1d([0, .1, .5]), atol=1e-15)
def test_separation_from_data_frequency():
"""Check if attributes from DataFrequency are really not available."""
data = [1, 0, -1]
times = [0, .1, .3]
time = TimeData(data, times)
with pytest.raises(AttributeError):
time.freq
with pytest.raises(AttributeError):
time.frequencies
with pytest.raises(AttributeError):
time.n_bins
with pytest.raises(AttributeError):
time.find_nearest_frequency
def test_data_time_init_with_defaults():
"""
Test to init without optional parameters.
Test getter for domain, time, times, length, and n_samples.
"""
data = [1, 0, -1]
times = [0, .1, .3]
time = TimeData(data, times)
assert isinstance(time, TimeData)
npt.assert_allclose(time.time, np.atleast_2d(np.asarray(data)))
npt.assert_allclose(time.times, np.atleast_1d(np.asarray(times)))
assert time.signal_length == .3
assert time.n_samples == 3
assert time.domain == 'time'
def test_reshape():
# test reshape with tuple
data_in = TimeData(np.random.rand(6, 256), range(256))
data_out = data_in.reshape((3, 2))
npt.assert_allclose(data_in._data.reshape(3, 2, -1), data_out._data)
assert id(data_in) != id(data_out)
data_out = data_in.reshape((3, -1))
npt.assert_allclose(data_in._data.reshape(3, 2, -1), data_out._data)
assert id(data_in) != id(data_out)
# test reshape with int
data_in = TimeData(np.random.rand(3, 2, 256), range(256))
data_out = data_in.reshape(6)
npt.assert_allclose(data_in._data.reshape(6, -1), data_out._data)
assert id(data_in) != id(data_out)
def test_flatten():
# test 2D signal (flatten should not change anything)
x = np.random.rand(2, 256)
data_in = TimeData(x, range(256))
data_out = data_in.flatten()
npt.assert_allclose(data_in._data, data_out._data)
assert id(data_in) != id(data_out)
# test 3D signal
x = np.random.rand(3, 2, 256)
data_in = TimeData(x, range(256))
data_out = data_in.flatten()
npt.assert_allclose(data_in._data.reshape((6, -1)), data_out._data)
assert id(data_in) != id(data_out)
def test_assert_match_for_arithmetic_data_different_audio_classes():
with raises(ValueError):
signal._assert_match_for_arithmetic((Signal(1, 1), TimeData(1, 1)),
'time')
def test_add_time_data_and_number_wrong_times():
# generate and add signals
x = TimeData([1, 0, 0], [0, .1, .5])
y = TimeData([1, 0, 0], [0, .1, .4])
with raises(ValueError):
signal.add((x, y), 'time')
def test_add_time_data_and_number_wrong_domain():
# generate and add signals
x = TimeData([1, 0, 0], [0, .1, .5])
with raises(ValueError):
signal.add((x, 1), 'freq')
def test_magic_getitem_slice():
"""Test slicing operations by the magic function __getitem__."""
data = np.array([[1, 0, -1], [2, 0, -2]])
times = [0, .1, .3]
time = TimeData(data, times)
npt.assert_allclose(TimeData(data[0], times)._data, time[0]._data)
def test___eq___equal():
"""Check if copied TimeData is equal."""
time_data = TimeData([1, 2, 3], [0.1, 0.2, 0.3])
actual = time_data.copy()
assert time_data == actual