def test_getter_signal_type():
"""Test if attribute signal type is accessed correctly."""
signal = Signal([1, 2, 3], 44100, fft_norm='none')
npt.assert_string_equal(signal.signal_type, 'energy')
signal = Signal([1, 2, 3], 44100, fft_norm='rms')
npt.assert_string_equal(signal.signal_type, 'power')
def test_get_arithmetic_data_with_signal():
# all possible combinations of `domain`, `signal_type`, and `fft_norm`
meta = [['time', None], ['freq', None], ['time', 'unitary'],
['freq', 'unitary'], ['time', 'amplitude'], ['freq', 'amplitude'],
['time', 'rms'], ['freq', 'rms'], ['time', 'power'],
['freq', 'power'], ['time', 'psd'], ['freq', 'psd']]
# reference signal - _get_arithmetic_data should return the data without
# any normalization regardless of the input data
s_ref = Signal([1, 0, 0], 44100)
for m_in in meta:
# create input signal with current domain, type, and norm
s_in = Signal([1, 0, 0], 44100, fft_norm=m_in[1])
s_in.domain = m_in[0]
for domain in ['time', 'freq']:
print(f"Testing from {m_in[0]} ({m_in[1]}) to {domain}.")
# get output data
data_out = signal._get_arithmetic_data(s_in,
n_samples=3,
domain=domain)
if domain == 'time':
npt.assert_allclose(s_ref.time, data_out, atol=1e-15)
elif domain == 'freq':
npt.assert_allclose(s_ref.freq, data_out, atol=1e-15)
def test_division():
# only test one case - everything else is tested below
x = Signal([1, 0, 0], 44100)
y = Signal([2, 2, 2], 44100)
z = signal.divide((x, y), 'time')
# check result
npt.assert_allclose(z.time, np.atleast_2d([0.5, 0, 0]), atol=1e-15)
def test_power():
# only test one case - everything else is tested below
x = Signal([2, 1, 0], 44100)
y = Signal([2, 2, 2], 44100)
z = signal.power((x, y), 'time')
# check result
npt.assert_allclose(z.time, np.atleast_2d([4, 1, 0]), atol=1e-15)
def test_magic_setitem_wrong_norm(sine, sine_rms):
"""Test the magic function __setitem__."""
signal = Signal(sine.time, sine.sampling_rate, fft_norm=sine.fft_norm)
set_signal = Signal(sine_rms.time,
sine_rms.sampling_rate,
fft_norm=sine_rms.fft_norm)
with pytest.raises(ValueError, match='FFT norms do not match'):
signal[0] = set_signal
def test___eq___notEqual(sine_signal, sine):
actual = Signal(0.5 * sine.time, sine.sampling_rate, domain='time')
assert not sine_signal == actual
actual = Signal(sine.time, 2 * sine.sampling_rate, domain='time')
assert not sine_signal == actual
actual = sine_signal.copy()
actual.comment = f'{actual.comment} A completely different thing'
assert not sine_signal == actual
def test_multiplication():
# only test one case - everything else is tested below
x = Signal([1, 0, 0], 44100)
y = Signal([0, 1, 0], 44100)
z = signal.multiply((x, y), 'time')
# check result
npt.assert_allclose(z.time, np.atleast_2d([0, 0, 0]), atol=1e-15)
def test_getter_signal_type(sine, sine_rms):
"""Test if attribute signal type is accessed correctly."""
signal = Signal(sine.time, sine.sampling_rate, fft_norm=sine.fft_norm)
npt.assert_string_equal(signal.signal_type, 'energy')
signal = Signal(sine_rms.time,
sine_rms.sampling_rate,
fft_norm=sine_rms.fft_norm)
npt.assert_string_equal(signal.signal_type, 'power')
def test___eq___notEqual():
time = np.arange(2 * 3 * 4).reshape((2, 3, 4))
signal = Signal(time, 44100, domain='time')
actual = Signal(0.5 * time, 44100, domain='time')
assert not signal == actual
actual = Signal(time, 2 * 44100, domain='time')
assert not signal == actual
comment = f'{signal.comment} A completely different thing'
actual = Signal(time, 44100, domain='time', comment=comment)
assert not signal == actual
def test_setter_fft_norm():
spec_power_unitary = np.atleast_2d([1, 2, 1])
spec_power_amplitude = np.atleast_2d([1 / 4, 2 / 4, 1 / 4])
signal = Signal(spec_power_unitary,
44100,
n_samples=4,
domain='freq',
fft_norm='unitary')
# changing the fft_norm also changes the spectrum
signal.fft_norm = 'amplitude'
assert signal.fft_norm == 'amplitude'
npt.assert_allclose(signal.freq, spec_power_amplitude, atol=1e-15)
# changing the fft norm in the time domain does not change the time data
signal.domain = 'time'
time_power_amplitude = signal._data.copy()
signal.fft_norm = 'unitary'
npt.assert_allclose(signal.time, time_power_amplitude)
npt.assert_allclose(signal.freq, spec_power_unitary)
# setting an invalid fft_norm
with pytest.raises(ValueError):
signal.fft_norm = 'bullshit'
def test_signal_init_default_parameter(sine):
# using all defaults
signal = Signal(sine.time, sine.sampling_rate)
assert signal.domain == 'time'
assert signal.fft_norm == 'none'
assert signal.comment == 'none'
assert signal.fft_norm == 'none'
def test_signal_init_default_parameter():
# using all defaults
signal = Signal([1, 2, 3], 44100)
assert signal.domain == 'time'
assert signal.fft_norm == 'none'
assert signal.comment == 'none'
assert signal.fft_norm == 'none'
def read_wav(filename):
"""
Import a WAV file as signal object.
This method is based on scipy.io.wavfile.read().
Parameters
----------
filename : string or open file handle
Input wav file.
Returns
-------
signal : signal instance
An audio signal object from the pyfar Signal class
containing the audio data from the WAV file.
Notes
-----
* This function is based on scipy.io.wavfile.write().
* This function cannot read wav files with 24-bit data.
"""
sampling_rate, data = wavfile.read(filename)
signal = Signal(data.T, sampling_rate, domain='time')
return signal
def test_sti_warn_data_type_unknown():
sig = Signal(np.zeros((1, 31072)), 44100)
sig.time[0] = 1
with pytest.raises(ValueError,
match="Data_type is 'generic' but must "
"be 'electrical' or 'acoustical'."):
sti.sti(sig, data_type="generic")
def test_signal_init_val(sine):
"""Test to init Signal with complete parameters."""
signal = Signal(sine.time,
sine.sampling_rate,
domain='time',
fft_norm=sine.fft_norm)
assert isinstance(signal, Signal)
def test_iter_write():
data = np.ones((3, 1024)) * np.atleast_2d(np.arange(3)).T
sig = Signal(data, 1)
sig.domain = 'time'
for idx, s in enumerate(sig):
sig[idx] = s
def signal_stub(time, freq, sampling_rate, fft_norm):
"""Function to generate stub of pyfar Signal class based on MagicMock.
The properties of the signal are set without any further check.
Parameters
----------
time : ndarray
Time data
freq : ndarray
Frequency data
sampling_rate : float
Sampling rate
fft_norm : 'unitary', 'amplitude', 'rms', 'power', 'psd'
See documentation of pyfar.fft.normalization.
Returns
-------
signal
stub of pyfar Signal 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(signal.time[slice])
freq = np.atleast_2d(signal.freq[slice])
item = signal_stub(time, freq, signal.sampling_rate, signal.fft_norm)
return item
def find_nearest_time(times):
samples = np.zeros(len(times), dtype=int)
for idx, time in enumerate(times):
samples[idx] = np.argmin(np.abs(signal.times - time))
return samples
def find_nearest_frequency(freqs):
bin = np.zeros(len(freqs), dtype=int)
for idx, freq in enumerate(freqs):
bin[idx] = np.argmin(np.abs(signal.frequencies - freq))
return bin
signal = mock.MagicMock(
spec_set=Signal(time, sampling_rate, domain='time'))
signal.time = np.atleast_2d(time)
signal.freq = np.atleast_2d(freq)
signal.sampling_rate = sampling_rate
signal.fft_norm = fft_norm
signal.n_samples = signal.time.shape[-1]
signal.n_bins = signal.freq.shape[-1]
signal.cshape = signal.time.shape[:-1]
signal.times = np.atleast_1d(
np.arange(0, signal.n_samples) / sampling_rate)
signal.frequencies = np.atleast_1d(
np.fft.rfftfreq(signal.n_samples, 1 / sampling_rate))
signal.__getitem__.side_effect = getitem
signal.find_nearest_time = find_nearest_time
signal.find_nearest_frequency = find_nearest_frequency
return signal
def test_sti_value_error_gender():
sig = Signal(np.zeros((1, 31072)), 44100)
sig.time[0] = 1
with pytest.raises(ValueError,
match="Gender is 'generic' but must be "
"'male' or 'female'."):
sti.sti(sig, gender="generic")
def test_sti_value_error_level():
sig = Signal(np.zeros((1, 31072)), 44100)
sig.time[0] = 1
lvl = np.array([30, 30, 30, 30, 30, 30])
with pytest.raises(ValueError,
match="Level consists of wrong number of "
"components."):
sti.sti(sig, level=lvl)
def test_iter_domain_change():
data = np.ones((3, 1024)) * np.atleast_2d(np.arange(3)).T
sig = Signal(data, 1)
sig.domain = 'time'
with pytest.raises(RuntimeError, match='domain changes'):
for s in sig:
s.freq
def test_reshape():
# test reshape with tuple
signal_in = Signal(np.random.rand(6, 256), 44100)
signal_out = signal_in.reshape((3, 2))
npt.assert_allclose(signal_in._data.reshape(3, 2, -1), signal_out._data)
assert id(signal_in) != id(signal_out)
signal_out = signal_in.reshape((3, -1))
npt.assert_allclose(signal_in._data.reshape(3, 2, -1), signal_out._data)
assert id(signal_in) != id(signal_out)
# test reshape with int
signal_in = Signal(np.random.rand(3, 2, 256), 44100)
signal_out = signal_in.reshape(6)
npt.assert_allclose(signal_in._data.reshape(6, -1), signal_out._data)
assert id(signal_in) != id(signal_out)
def test_flatten():
# test 2D signal (flatten should not change anything)
x = np.random.rand(2, 256)
signal_in = Signal(x, 44100)
signal_out = signal_in.flatten()
npt.assert_allclose(signal_in._data, signal_out._data)
assert id(signal_in) != id(signal_out)
# test 3D signal
x = np.random.rand(3, 2, 256)
signal_in = Signal(x, 44100)
signal_out = signal_in.flatten()
npt.assert_allclose(signal_in._data.reshape((6, -1)), signal_out._data)
assert id(signal_in) != id(signal_out)
def test_sti_value_error_snr():
sig = Signal(np.zeros((1, 31072)), 44100)
sig.time[0] = 1
sn = np.array([30, 30, 30, 30, 30, 30])
with pytest.raises(ValueError,
match="SNR consists of wrong number of "
"components."):
sti.sti(sig, snr=sn)
def test_sti_warn_data_type_not_given():
sig = Signal(np.zeros((1, 31072)), 44100)
sig.time[0] = 1
with pytest.warns(UserWarning,
match="Data type is considered as "
"acoustical. Consideration of masking effects not valid "
"for electrically obtained signals."):
sti.sti(sig)
def test_sti_female():
expected = np.array([0.6116])
sig = Signal(np.zeros((1, 131072)), 44100)
sig.time[0, 0] = 1
sig.time[0, 1000] = 1
sig.time[0, 10000] = 1
array = sti.sti(sig, gender="female")
np.testing.assert_allclose(array, expected, rtol=1e-3, atol=0)
def test_domain_setter_time_when_freq(sine):
signal = Signal(sine.freq,
sine.sampling_rate,
domain='freq',
fft_norm=sine.fft_norm)
domain = 'time'
signal.domain = domain
assert signal.domain == domain
npt.assert_allclose(signal._data, sine.time, atol=1e-10, rtol=1e-10)
def test_sti_warn_snr_low():
sig = Signal(np.zeros((1, 31072)), 44100)
sig.time[0] = 1
lvl = np.array([70, 70, 70, 70, 70, 70, 70])
sn = np.array([10, 30, 30, 30, 30, 30, 30])
with pytest.warns(UserWarning,
match="SNR should be at least 20 dB for "
"every octave band."):
sti.sti(sig, level=lvl, snr=sn)
def test_sti_lvl_snr_1D():
expected = np.array([0.8933])
sig = Signal(np.zeros((1, 131072)), 44100)
sig.time[0, 0] = 1
sig.time[0, 1000] = 1
lvl = np.array([70, 70, 70, 70, 70, 70, 70])
sn = np.array([30, 30, 30, 30, 30, 30, 30])
array = sti.sti(sig, level=lvl, snr=sn)
np.testing.assert_allclose(array, expected, rtol=1e-3, atol=0)
def test_sti_amb_false():
expected = np.array([0.8864])
sig = Signal(np.zeros((1, 131072)), 44100)
sig.time[0, 0] = 1
sig.time[0, 1000] = 1
lvl = np.array([70, 70, 70, 70, 70, 70, 70])
sn = np.array([20, 20, 20, 20, 20, 20, 20])
array = sti.sti(sig, level=lvl, snr=sn, amb=False)
np.testing.assert_allclose(array, expected, rtol=1e-3, atol=0)
def test_sti_2D():
expected = np.ones((2, 2))
sig = Signal(np.zeros((2, 2, 131072)), 44100)
sig.time[0, 0, 0] = 1
sig.time[1, 0, 0] = 1
sig.time[0, 1, 0] = 1
sig.time[1, 1, 0] = 1
array = sti.sti(sig)
np.testing.assert_allclose(array, expected, rtol=1e-2, atol=0)
